Treatise on water science, 4-Vol. set

EDITOR-IN-CHIEF Peter Wilderer Technische Universitaet Muenchen, Institute for Advanced Study, Munich, Germany

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EDITORS Peter Rogers Harvard School for Engineering and Applied Sciences, Cambridge, MA, USA Stefan Uhlenbrook Department of Water Engineering, UNESCO-IHE, Delft, The Netherlands

Keisuke Hanaki The University of Tokyo, Tokyo, Japan Tom Vereijken European Water Partnership, Grontmij, The Netherlands

Fritz Frimmel Karlsruhe Institute of Technology, Karlsruhe, Germany

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THE IMPORTANCE OF WATER SCIENCE IN A WORLD OF RAPID CHANGE: A PREFACE TO THE TREATISE ON WATER SCIENCE The world in which we live is currently undergoing rapid changes, triggered by outstanding advances in natural sciences, medicine, and technology. As a result, the human population grows to levels never known before. Innovative communication and transportation means permit globalization of economy and urban lifestyle. Cities and city life exert an unprecedented pull. More than half of the world’s population already live in urban settings – the tendency is rising. Cities meet the expectations of immigrants, citizens, and businesses only when served by an appropriate infrastructure. Unfortunately, in many parts of the world cities grow faster than the required infrastructure can be planned, financed, and installed. In many cases, installation of water distribution networks and sewer systems, waterworks, and wastewater treatment plants is often lagging far behind schedule – be it because of the lack of financial resources or because higher priority is given to other infrastructural projects, roads, and highways, for instance. At a larger scale, the water demand of agriculture and industry is growing overproportionally with respect to population size as people shift preference to products requiring particularly high volumes of water during the growth season or during the fabrication process, respectively. Two examples underline this statement – the shift toward meat consumption and the preference of clothing made of cotton fibers. The consumers are often unaware of the water required to raise cattle, swine, and poultry, and to keep cotton fields productive particularly when such fields are located in arid regions as is the case in Uzbekistan, for instance. Although the water demand is increasing, worldwide, the capacity of local water resources is not. It is even decreasing in very many areas of the world, resulting from pollution of water bodies and soil, from over-abstraction of water, and from effects caused by climate change. Water deficits in municipal, industrial, and agricultural settings are the result. In many cases, urban and agricultural areas developed in regions where ab initio freshwater is scarce. Drought situations caused by global warming and climate change amplify the deficit between water demand and water availability. Overabstraction of groundwater to meet the local water demand is a common but unsustainable solution to the problem of water shortage. In areas close to the ocean, over-abstraction causes seawater intrusion and subsequent increase of the salinity of groundwater. Rising sea level caused by melting of shelf ice intensifies the intrusion of seawater not only in aquifers but in estuaries as well. In addition, deterioration of ground- and surface water is caused by excess usage of fertilizers and pesticides, and by uncontrolled dumping of solid and liquid wastes onto land. Aggravation of water deficits in municipal, industrial, and agricultural environments is the result. In the nineteenth and twentieth centuries, health problems and eutrophication caused by pollution of surface- and

groundwater were recognized and solved by legal frameworks and enforcement of regulations, and by investing large amounts of money in the development and implementation of infrastructural concepts and technologies. In high-income countries, design engineers and operators of water distribution and sewer systems, water works, and wastewater treatment plants are well trained, nowadays – a major prerequisite of proper functioning of technical installations. In the mediumand low-income countries, however, responsible management of water resources and effective operation and maintenance of water technology are often foreign words. In the twenty-first century, we are confronted with a comparably much larger and much more complex problem of water management compared to the years past. A new approach to water management and water technology is required in response to the rapid increase at the demand side, and rapid loss of capacity and quality at the supply side. A paradigm shift appears to be urgently necessary. The old paradigm was the answer to the conditions prevailing in the highly industrialized and water-rich regions of the world. Over the past decades, considerable time was available to develop, implement, and upgrade measures capable of solving the specific local and regional problems. This, however, is not the situation we have to deal with today and in the years to come. In future, we have to support people with effective and robust water and wastewater services even if the capacity of the local water resources is critically short. To avoid evolvement of economic and societal instabilities, we are obliged to develop techniques and management concepts which can be implemented in virtually no time. We have to serve people, industry, and agriculture alike while keeping the function of aquatic and terrestrial ecosystems preserved. We need methods which are adjustable to the changing climatic boundary conditions. We need well-educated water professionals in academia, water services, and water authorities who understand the local environmental, economic, and societal framework conditions, to draw appropriate decisions and take responsible action. We need methods which are financially affordable. These methods are to be safe with respect to public health. Moreover, they must guarantee ecosystems to exert their generic life-supporting function. The task to solve the complex issue of water-related problems caused by urbanization and lifestyle changes is challenging because of the speed of change at both the demand and the supply side, and also because of the limitations at the financial side. Business as usual is not a tolerable approach. In the course of a shifting paradigm, we should realize that sectoral approaches (as they were usually taken in the past) are to be overcome. We need to understand that the water quantity and quality issue are inextricably linked to the issue of energy and food supply, and with the issue of land management as well. What we need is a holistic approach. Measures

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are to be taken which permit solution of the energy, water, and food crisis in conjunction with measures which enable restoration of the self-regulating capacity of terrestrial and aquatic ecosystems in harmony with the human demand for land. Scientists and engineers are called to take up the task of problem solving as a challenge and as a chance. Solutions have to be found on the basis of the existing portfolio of knowledge and experience, but open minded with respect to the very local conditions in rapid transition. The Treatise of

Water Science is to be considered as a platform on which innovative research and development may proceed. It summarizes the contemporary state of knowledge in the field of water science and technology and paves the way toward a new horizon. Serving humanity with safe water while keeping the self-regulating capacity of the aquatic ecosystems intact – this has to be our common goal. Peter Wilderer

Preface – Management of Water Resources PP Rogers, Harvard University, Cambridge, MA, USA & 2011 Elsevier B.V. All rights reserved.

1 The Water Crisis 2 Why Studying Water Is So Important 3 Current Global Water Balance 4 Establishing Water Policy 5 Predicting Future Demands for Water 6 Drivers of Socioeconomic Growth 7 Transboundary Conflicts 8 River Basin Politics 9 The Contents of Volume I Acknowledgments References

The Greek philosophers gave us a physical world composed of four elements: land, water, fire, and air. Two thousand five hundred years later we are still focused upon these elements, now conventionally referred to as ecosystem (land), water (water), energy (fire), and atmosphere (air), more aware than ever that these elements are essential for all life on Earth. As human populations have multiplied 700-fold since the ancient Greeks, we are facing major crises with each of these elements. At different stages of human development, each has risen to prominence; control of fire was one of humankind’s earliest and fundamental scientific discoveries. With fire under control, land took on increased salience, humankind’s numbers soared, and we managed to inhabit the entire planet. The other two elements, air and water, were always essential; however, until recently, they were considered so abundant that we would never have to worry about depleting them. However, by the end of the nineteenth century when the globe’s land frontiers were closing and filling in, it was then that we as a species began to notice problems with having contaminated the air and water and that it was becoming difficult to find clean air to breathe safely and unpolluted water to drink. In addition, by the end of the twentieth century we discovered that our profligate use of fossil-fuel energy was in danger of changing the atmosphere in ways that were threatening the survival of our species by causing global warming. We find ourselves now on the threshold of the twenty-first century struggling to survive as a species. As a result, the two most salient global issues now facing humankind are energy and water. How do we manage our survival transition into the twenty-first century and beyond? The intertwined developing resources crises lead us to three critical questions pertaining to the global water situation: 1. Will we have enough water to grow food to feed ourselves in the twenty-first century? By far the largest quantities of freshwater are, and will be, those used in agriculture. Currently, agriculture uses about 4000 km3 of freshwater each year to feed approximately 7 billion people. Even though population growth has slowed down globally, we will still face a population of 9 billion by 2050. The demand for agricultural water is complicated by the fact that as people

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become wealthier their dietary tastes change, moving away from grains toward animal products. The same amount of water that provided food for 10 people subsisting on grains previously now only satisfies the agricultural needs of one person who has moved up the food chain toward animal products. 2. How will we provide water and sanitation for an additional 3 billion urban dwellers? Since 2007 the urban population has exceeded the rural population. This has major implications for sustaining the water and sanitation for cities. For example, China’s urban population is expected to reach 1 billion by 2030. Urbanites typically are wealthier than their rural compatriots, and have radically different water demands, more appliances, washing machines, bathtubs, showers, and flush toilets. Even though the absolute magnitude of their demands is much smaller than the demand of agriculture, water plays an important role in urban public health which cannot be ignored. This is particularly the case in the large cities of Asia and Africa where already there are huge unserved populations demanding water and sanitation services. One study estimates that as much as $22 trillion is needed by 2030 just to meet the demands for water and sanitation services. 3. How should we address the future climate uncertainties? One issue that water engineers always prided themselves on was that they could make robust forecasts of the future, at least good enough to be able to build reservoirs, dams, and embankments that would function well enough under a wide range of actual future outcomes. The very existence of the possibility of climate change seriously challenges our ability to rely upon our forecasts. The shift from using stationary time series as the basis for future forecasting is seriously undermined when faced with the possibility of nonstationarity in the time series. There is a need for creative adaptation strategies that would help avoid rapid collapse of engineered and social systems. This first volume of the Treatise on Water Science has 11 chapters dealing with how to address these questions. It is about managing our water resources and will, hence, focus on water. Without water, life, as we know it, would disappear from the

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planet. Water plays an extremely important role in maintaining a sustainable life on this planet for all species including Homo sapiens. Only those few exotic species that have managed to survive in environments which do not require potable water would survive. We fool ourselves, however, if we focus only on water and ignore the connections of water to the overall use of global resources because each of them has a critical role to play in supporting human life on planet Earth. This is the major concern of modern studies of water resources.

1 The Water Crisis Since 1900 the world’s population has tripled, but its water demand has risen sixfold (FAO, 2009). These two facts have forced the global community to focus on the management of global water resources. The major emphasis has been on making integrated water resources management (IWRM) a reality (Global Water Partnership, 2000). This is the global water crisis whose dimensions are daily beginning to manifest themselves to international agencies, national and local governments, and particularly to individual citizens. Unlike the fear of using up energy, which has occurred very rapidly, sometimes seemingly almost overnight as with the 2007 and early 2008 petroleum price rises, the water crisis is a slower crisis – but a crisis nevertheless. The situation changes imperceptibly from day to day – we do not see doubling of prices over periods of months, but like melting glaciers, it is an inexorable slow burn toward scarcity. The time frame is years rather than months, but every day there are more of us, each making demands on this global resource. Although we can find replacements for fossil fuels to power our cars and heat our homes, there is no alternative to replace water. For most important water uses, such as irrigation and drinking water, there is no substitute. Water, however, is influenced by geophysical and geochemical processes which are highly influenced by climatic change on both the supply and demand sides. On the supply side, drying up lakes and melting glaciers can reduce water availability locally, and on the demand side increased temperatures will increase demands for irrigation of food crops, air conditioning, etc. All of these changes will have to be dealt with under a fairly constant global supply of water. The great irony here is that fossil fuels are usually described as nonrenewable resources – they have a fixed amount and could be exhausted – whereas water is a renewable resource of an essentially fixed amount and is used by everybody on the globe, and cannot be used up in the sense that petroleum can be because it is a renewable resource, but access to it by growing populations overtaxes its availability.

2 Why Studying Water Is So Important Water resources have been studied for millennia. Starting even before the ancient Greek philosophers, Plato (428–348 BC), Aristotle (384–322 BC), and Archimedes (287–212 BC), the Egyptians and the Assyrians had planned, designed, and built major water resource infrastructures throughout the Middle East. The Romans took the Greek concerns about water and

public health and expanded them up to a global scale throughout the Roman Empire. The city of Rome with its 16 major aqueducts was a marvel of both engineering and water management, with a per capita water availability equivalent to current European standards. Over the succeeding centuries, we have theorized, analyzed, and prioritized water in myriad ways. The great scientists and engineers from Renaissance Europe through the end of the nineteenth century, Galileo (AD 1564–1642), da Vinci (AD 1452–1519), Torricelli (AD 1608–47), Pascal (AD 1623–62), Daniel Bernoulli (AD 1700–82), and Darcy (AD 1803–58), just to mention a few of the major contributors, made major breakthroughs which still govern management of water in all its forms today.

3 Current Global Water Balance The International Water Management Institute (IWMI), located in Sri Lanka, has adopted the blue–green water paradigm suggested by Falkenmark and Rockstro¨m (2004), in which the water accounting is done according to whether it is due to evaporation (coded green) or due to the residual surface and groundwater runoff (coded blue). Of the total annual terrestrial rainfall, called the renewable freshwater resources, of 110 000 km3, 56% evaporates by biological processes, forest products, grazing land, and biodiversity; 4.5% is evaporated from rainfed agriculture (crops and livestock); a further 0.6% of the green water is evaporated from irrigated agriculture along with 1.4% from runoff sources (these are called blue water); and an additional 1.3% is evaporated from open water storages from man-made reservoirs and lakes. Cities and industry demand only 0.1% of the total and 36% returns to the ocean. The 110 000 km3 of precipitation on the terrestrial landscape is extremely small in comparison with the total resource base and the amounts evaporated in producing food and fiber. Of course, it should be recalled that the actual withdrawal of water from the ecosystem for cities and industries could be several times larger, but that about 85% of these uses (albeit contaminated) return to the runoff account of the terrestrial system. These issues are explored in greater detail in Volume II of this treatise.

4 Establishing Water Policy It is a commonplace fact that if a resource has little or no value then it will be overused. Therefore, one of the major issues in water resources planning and management is to identify the value of water. The value of water has been pondered by scholars for millennia. Plato observed that ‘‘only what is rare is valuable, and water which is the best of all things y is also the cheapest, as quoted by Hanemann (2006) based on Bowley (1973) from Plato’s Euthydemus. Two thousand years later, in considering the difference between the market price of commodities and their economic value, the eighteenth-century economist Adam Smith compared the value of diamonds and the value of water. In his book The Wealth of Nations (1776), Smith made the distinction between value in use and value in exchange. Water, which has great value in use, often has little value in exchange, whereas diamonds, which have little value

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in use, have enormous value in exchange. Both Plato and Smith pointed out that the market price of an item did not always represent its true value. In order to predict water demand how we value and price water is very important.

5 Predicting Future Demands for Water Predicting future demands for any resource is fraught with difficulties, but the complexity of water and its singular issue of finiteness make it particularly difficult to forecast. For example, how much should we worry about climate change and global warming? Global warming – one of the great scientific debates of the twentieth century – has now the opportunity to become the political debate of the twenty-first century. A few scientists in the nineteenth century warned of the effect on the atmosphere of excessive release of carbon dioxide into the atmosphere due to the burning of fossil (carbon-based) fuels. It was not, however, until the 1950s that serious comprehensive CO2 measurements were made. Since then large amounts of research funds have been expended in the field of climate science. Ultimately, in 1990, the United Nations established the Intergovernmental Panel on Climate Change (IPCC). The IPCC has produced four assessment reports so far dealing with the effects of changing greenhouse gas concentrations in the atmosphere. The IPCC has been incredibly successful in raising the status of the scientific understanding of climate change. The action in the United Nations (UN), however, has now shifted away from scientific research toward political action which will promote mitigation and adaptation strategies that could seriously curtail the increase of CO2 in the atmosphere by the end of the twenty-first century. In its Fourth Assessment Report in 2007, the IPCC identified five key impacts of increasing global average temperature: water, ecosystems, food, coasts, and health. A closer reading of the text shows that many of the most serious impacts on the nonwater areas are, in fact, mediated via water. Therefore, for instance, impacts on food are largely due to hydrological changes; aridity has major impacts on food, ecosystems, and human health. Thinking about the relative issues involved in climate change, Mike Mueller (2007) of the Global Water Partnership (GWP) said, ‘‘if it’s mitigation then the focus is rightfully energy, and if it’s adaptation, it will be water resources!’’ By this, he implied that the bulk of the mitigation strategies deal with handling the use, and development, of new energy resources, and the adaptation strategies will be mostly driven by water concerns. Hence, we need to focus on the water-adaptation strategies, bearing in mind that adaptation for other sectors may include many of the same, or similar, strategies. The pivotal role of water impacts, and hence water’s importance to adaptation, is also stressed in the Stern Review (2006). The local and regional effects on water however are inconsistent among the climate models, often predicting large regional differences in magnitude, variability, and direction of change for the most important hydrology parameter, the precipitation. However, whichever of these models one endorses, there is still a question as to what to do in meeting the future water demands. If we are interested in adaptation to global warming and climate change, it is largely irrelevant

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which of the models we accept, because operationally there will be small differences among the adaptation strategies that one should follow, but major differences would arise if we were following a mitigation strategy. Surprisingly, even accepting the most conservative scenario leaves one in a strikingly similar situation – how to plan for the future under highly uncertain outcomes. The issue boils down to how do we deal with uncertainty in making decisions about water planning and management? Water engineers and hydrologists are supposedly expert at making such forecasts in a very uncertain world. Much of the focus has been on changes in the physical parameters, such as precipitation, stream flows, and evaporation, but rising global populations, coupled with rising incomes, and a concomitant increase in per capita consumption, will inexorably lead to serious consequences for the water resources in many areas of the globe, regardless of what happens to climate change. It is the old Malthusian population/ resources debate from the early 1960s; only now we have India and China moving into the middle classes in a big way. Keyfitz (1976) pointed out many years ago that it is the increasing middle class and their consumption patterns that were going to be the major problem for environmental sustainability. How we can adapt to meet these demands will be the major struggle for the remainder of this century. In planning for the future, we must also be aware of unintended consequences of our actions. One example of this is the current US attempt to mitigate climate change by reducing consumption of carbon-based fossil liquid fuels. During 2007–08, fueled by record crude oil prices, we rushed headlong toward a biomass-based liquid-fuel cycle. Because of their huge demand for cropland, water, and agricultural chemicals, the widespread development of biomass fuels turned out to be a disaster for the poor people of the world whose food budgets could not compete with the middle classes’ love affair with their automobiles. This means that water planners and managers need to worry a great deal about climate change. The consequences of the climate change will become apparent only if the planners work within a holistic framework to ensure that all of the consequences of climate change can manifest themselves.

6 Drivers of Socioeconomic Growth Among the earliest modern commentators on the drivers of socioeconomic growth and decline were Adam Smith, Edward Gibbon, Thomas Malthus, David Ricardo, and Karl Marx. Adam Smith, a Scottish economist, published his Wealth of Nations in 1776, which became the great classic of capitalist economic thinking. Gibbon, an English historian, combed the history of the Roman Empire for clues for these drivers in his Decline and Fall of the Roman Empire (1776–89). Malthus, an English country parson and economist, focused on the relationship between population growth and agricultural productivity in his seminal Essays on Population (1798). Ricardo, an English businessman and economist, focused on the declining economic returns from all forms of production and the increasing costs faced by industry over time. Finally, Karl Marx, a German sociologist and progenitor of Marxism, saw growth

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coming initially from capitalist accumulation and later from the labor of the proletariat. From their writings we see a concern about running out of resources as long ago as the eighteenth and early nineteenth centuries, well before the sixfold increase in population and 40-fold increase in per capita wealth arrived in the early twenty-first century. Malthus and Ricardo were particularly prescient about the roles of population, food, and energy resources. Malthus postulated a geometric rate of growth (like compound interest on a bank deposit) of population and an arithmetic growth (simple interest on a bank account) of land being brought under cultivation and, hence, arithmetic rate of growth of food production. Regardless of where they start, these curves will always intersect after a period of a couple of decades, and Malthus predicted widespread famine or violent conflicts to bring food and population into alignment with each other by ‘misery, war, pestilence, and vice’. Ricardo articulated ‘declining returns’ on investments in resources (coal and iron ore in his time; water, oil, and gas in our time) whereby the best (least-cost) resources are used first, followed by the next best, and so on. Increasing demand for the resource leads to price increases that will continue to rise until the resource becomes too expensive to use. These two nineteenth-century concepts can be used to explain our current water resources crisis and suggest pathways to end the crisis. We see these two concepts at work, for instance, in the case of New Delhi the population growth rate clearly exceeds the rate of possible increase in the water supplies (Malthus). On the other hand, in suburban Los Angeles (LA) as the cheapest sources of water are fully exploited, we see the Ricardo effect of increasing costs at work. When LA was developing in the 1930s, water was available at a reasonable cost, but as more and more people demanded more water the cost of supply was also increasing (the best projects had already been built). Without any technical breakthroughs, this means that the cost per unit of water keeps on increasing as time goes by. Of course, these constraints were also at work over previous centuries, even before they were articulated by Malthus and Ricardo, but Homo sapiens were able to avoid them by expanding our resource base through annexation and colonization, to bring in cheaper resources and food; by finding substitutes for scarce resources; and by improving our technology so that the same amounts of land and resources could be used more efficiently. Examples of these effects are seen in the British response to its nineteenth-century rapid population increases. More food was produced, not in England with its limited land and climate resources, but by Australia and other colonies such as Canada and India. This meant that the agricultural land was no longer a constraint on feeding the increasing population. So, Malthus’ limits and Ricardo’s increasing costs were avoided for the time being; however, since the globe is now pretty much filled up and most of the easiest available water is in use, there are few opportunities to expand the physical supply. The only option available to us now is improving the efficiency of water-use technology, but this is where we run into Ricardo’s increasing cost problem. The real question facing the globe at the start of the twenty-first century is whether we can keep on improving our technologies, or finding cheaper supplies or substitutes. However, just because these adaptations worked well over the past 200 years does not

mean that they will necessarily continue to work. This is the crux of the problem facing global water resources.

7 Transboundary Conflicts In historical times, control of water was the source of major conflicts among users often leading to skirmishes and minor wars. Peter Gleick (2009) tracked the history of water conflicts from 3000 BC to AD 2009. Historically, these have ranged from minor to major conflicts, but in recent times since the 1940s there has been less direct conflict and more attempts to resolve water issues by negotiation. He wrote, ‘‘There has been a lot of discussion about ‘water wars,’ a term that sounds great, but to which I do not subscribe: wars start and are fought for many reasons and while water has often been a target, tool, or objective of violence, it is certainly hard to ascribe the primary reason for any war to water alone’’ (Peter Gleick, 2009). However, the lack of availability and access to water may have been one of the conditions leading to many wars. The lack of access to water can have major impacts on the health and wealth of nations; major occupations, such as fishing and farming, cannot flourish, and the growth of cities will be limited. With the development of nation-states in the sixteenth and seventeenth centuries, the lack of access by downstream users and the control by the upstream populace was firmly established. This meant that, without a treaty, the downstream users were essentially cut off from use of the flowing river. The Industrial Revolution brought serious pollution to the rivers which also impacted the downstream users. The UN’s International Law Commission spent 26 years from 1971 to 1997 drafting the UN Convention on the Law of the Non-Navigational Uses of International Watercourses (1997). As of 2010, it has not yet been ratified by the UN General Assembly by the requisite 35 countries needed for it to come into force. The existence of such a treaty is a good indication of the international community’s intentions to improve the nature of collaboration among the riparians in international and transboundary rivers; however, the inability to ratify the Convention says a great deal about the wishes of upstream countries not to cede sovereignty to a supranational body. Despite the nonexistence of a clear set of laws and treaties, customary international laws have used many principles such as prior consultation, avoidance of significant injury, equitable apportionment, nondiscrimination and nonexclusion, and provision for settlement of disputes embedded in the UN treaty. Moreover, the fact that it has not yet come into force has not hindered the resolution of many smaller water conflicts relying on the common-sense ideas presented above. Moreover, even when ratified, the Convention lacks an effective enforcement mechanism and will thus still rely largely upon the goodwill of upstream parties, or the hegemonic strength of the downstream countries. Water conflicts seem to arise every time a river crosses a boundary. For instance, in the Colorado Basin, despite the existence of the Federal Interstate Colorado Compact, there are still serious water conflicts among the seven US basin states and Mexico. In India, we see similar conflicts regarding the Ganges River, both domestically and internationally, with Nepal and Bangladesh sharing access. Transboundary water

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conflict is one area in which the conflicts among the parties really emphasize the need for clear and transparent rules for cooperation. The present seems to be one of the periods of great interest in international rivers, as experts estimate that there are over 145 countries with at least participation in one or more of the 261 international river basins on the Earth. Over time, there have been as many as 300 river-sharing agreements in Europe since the Treaty of Versailles in 1815. However, almost all of these treaties dealt with regulating in-stream use for navigation, hydropower, fishing, and pollution disposal, all of which did not involve the large-scale diversions of water which now regularly occur with irrigation developments. Large withdrawals typically create very difficult water-allocation problems for the downstream countries and, in history, were typically resolved with violence or threats of violence. In our times, we would rather resort to negotiations than war. The previous period of great concern about transboundary river conflicts was in the 1950s and early 1960s. This period culminated in a successful treaty on the Indus Basin, brokered by the World Bank and signed by India and Pakistan. The accord fueled optimism for resolving other major water conflicts. At that time, basins such as the Ganges–Brahmaputra, the Mekong, and the Nile (and even the tiny Jordan River) were subject to detailed analysis, even to the extent of creating river basin commissions in an attempt to avoid conflict among the parties. Unfortunately, this era of concern came up short. Of these large rivers, only the Indus was eventually successfully developed. Currently, we are experiencing a resurrection of conflict, fueled by the shortage of water caused by rapid development and huge population growth, and possibly global warming. To allocate – or reallocate – the flows of a river is always a political decision. No matter how detailed the technical, economic, and social studies are, hard choices have to be made among the various users who stand to gain and lose from such accords. This is true whether the river is a national river or crosses international borders. However, transboundary rivers imply a level of political decision making that goes beyond local and national interest groups. It requires the ability to negotiate between sovereign nations. All rational planners recognize the value of cooperation on river-sharing issues, from sociocultural terms to trade and economic ones. What is not clear, however, is how to put a value on cooperation; in other words, just how valuable is cooperation?

8 River Basin Politics The problem with purely political decisions is the lack of predictive behavior on which they reside. Thus, many politically inclined decisions have led to a deviation from the scientific–technical-based analysis, which accounts for the quantitative benefits from sharing resources among the coalitions of competing groups. Political considerations are sometimes heavily influenced by noneconomic factors outside the technical analysis. They are often pursued separately and apart from economic objectives, with different personnel and rituals. Political approaches tend to be more descriptive and idiosyncratic than the analogous models in the sciences.

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Ultimately, foreign policy is the most influential determinant of a country’s position on international rivers. Linkage of the river settlement to other outstanding economic and social issues between and among countries is important, as is achieving reciprocity for one’s actions either in the linkage of issues or in sharing benefits that are only achievable through international cooperation. In addition, the climate for agreement is a prime political basis for sharing water resources; this comes about when countries have common or shared technical perception of the problems, networking and contacts at the transgovernmental levels, and the need to be seen as being collaborative as a nation. A wide range of solutions are possible in most negotiations, while the net benefits are not the only consideration; many political issues dominate in shaping the decisions on the locations of the investments which might not necessarily be in the interest of the best technical planning. Managing common property resources is a very difficult endeavor, and the added complexities of transboundary water are no exception. An interesting phenomenon is that river flows have both negative and positive externalities typically working only in one direction, that is, downstream. This pervasive unidirectional feature of water use means that resolution of basin conflicts through mutual control of external effects that work reciprocally is generally ruled out. However, downstream countries can also benefit from some positive external effects of upstream use. Aside from the water allocation problems that arise from the physical sharing of a common resource, there are also many water-quality problems that can arise downstream as an effect of upstream use. Natural processes such as floods and droughts can also cause major downstream effects and are sometimes mistaken for man-made externalities, and thus lead to further mistrust and tensions among the riparian states.

9 The Contents of Volume I In presenting a discussion on water resources, this volume has been constrained by the width of the definitions of what constitutes the field. We have presented just 11 chapters which while they cover a broad range of concerns, they do not, by any means, cover the full range of concerns. We do, however, present materials dealing with the three questions outlined at the start of this chapter: feeding the global population, providing water supply and sanitation to the ever-increasing population, and some approaches to dealing with the huge uncertainties associated with potential global climate change. The first three chapters deal with the broad frameworks of IWRM, governance, and water as an economic good. The chapters attempt to lay the groundwork for dealing with water as a fundamental resource for development. In Chapter 1, Roberto Lenton explores the history and evolution of the concept of IWRM and reports on various assessments and critiques of the concept. In particular, the critics have focused on definitions that tend to be narrowly focused such as a country having a national water policy, or a water law, or the river basin as the focus of planning, or participatory management. Lenton also provides a set of criteria by which IWRM could be viewed in practice. These are a sensible set of criteria

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and if followed would make a major difference in the sustainability of water-sector decisions. In Chapter 2, Edella Schlager takes up the issues of water governance. She emphasizes a bottom-up approach working from the water users up through multiple layers of governance. She emphasizes the fact that the modern approaches to water development and management are mostly based on methodology developed by experts for experts. She claims that the challenge now is to design and fit water governance organizations into complex multiscale and intergovernmental and watershed systems. It is no longer a game for experts controlled and run by experts. In Chapter 3, John Briscoe deals with how to value water in its different uses. His major split is between urban water and water used for irrigation, or the use of water as urban infrastructure and public health and water in its productive mode for food production. In both cases, he describes how to evaluate the direct and indirect benefits associated with water use. He argues that the indirect benefits associated with water use can be as large as, or larger than, the conventionally measured direct benefits. He concludes with an appeal to move away from the conventional formulaic applications of benefit/cost analysis and attempt to identify critical supplementary investments to use more fully the multiplier effects of large infrastructure projects. The next three chapters deal with the practical socioeconomic issues of forecasting the demand for water, the pricing of water and sanitation services, and how to know if interventions in water supply really have the benefits attributed to them. In Chapter 4, Benedykt Dziegielewski and Duanne Baumann point out that credible long-term forecasts of water demand are essential to planning for the long-lived water infrastructure. They show that such forecasts must be based on a high level of disaggregation of demand; the uses of econometric models grounded in economic theories of production and consumption, considerations of potential climate change, and must, above all, provide explicit and plausible assumptions. Dale Whittington in Chapter 5 reviews the role of economic pricing approaches to managing water and sanitation services. In this chapter, he cautions against some of the enthusiasm for investments in social overhead capital expressed by Briscoe in Chapter 4, with the potential for oversubsidization of large projects at the expense of smaller ones. Following up on this theme, Alix Zwane and Michael Kremer, in Chapter 6, examine the evidence whether community-level rural water infrastructure successfully reduces diarrheal disease and conclude that the evidence does not support it. However, from their review of the literature they found evidence that sanitation and hygiene are more important than water quality. The next set of three chapters cover the role of groundwater in providing water resources for many different types of water services, managing water for agriculture, and managing the aquatic ecosystem to provide adequate protection of the environment. In Chapter 7, Lopez-Gunn, Llamas, Garrido, and Sanz assess the development of groundwater over the past half century. They very broadly review the assessment of the total resource available, the economics of groundwater use, institutions and governance of groundwater, and the future sustainability of the resource. They conclude that groundwater may be the most important water source under the more extreme climate-change scenarios, in particular for irrigation in

low-latitude countries. They stress the need for better governance structures for groundwater management and that a much higher level of user participation will be required for sustainable use of the resource. In Chapter 8, Jorge RamirezVallejo makes a comprehensive review of all aspects of managing water for agriculture and concludes that the major challenge in this area is to reverse the serious failure of institutional arrangements at the national and local levels to deal with water correctly. The concluding chapter in this section by Max Findlayson on managing aquatic ecosystems recognizes the interdependence of people and their environment and focuses on the management of water to support the ecosystem and the environment. He concludes with a strong support for the Millennium Ecosystem Assessment as the best approach to managing wetlands and their aquatic systems. He points to the need in the coming decades to address the trade-offs among current and future uses of wetland resources, importantly inbetween agricultural production and aquatic diversity. The final two chapters return to some political and social issues of water resources management. In Chapter 10, David Moreau reviews the problems with implementing ambiguous water policy. He uses the case of the experience in the US of implementing the Clean Water Act especially under the federal system where the states are left to implement national policy. He shows how there are few ambiguities in dealing with point sources of pollution, but many in dealing with nonpoint sources which has led to the Balkanization of the implementation with the individual states essentially ignoring downstream states when setting goals for total maximum daily loads (TMDLs). Fittingly, the volume concludes with a chapter by Casey Brown on risk assessment, risk management in the context of potential climate change. He develops an approach to risk management that attempts to reconcile traditional approaches with our growing knowledge of uncertainty that mark the hydrologic records. He concludes that the water community has focused primarily on the means to reduce the uncertainty related to hydrologic events, but little effort has been devoted to reducing hydrologic risk to society or to communicate risk to promote risk-reducing behavior.

Acknowledgments The editor wishes to thank the following persons who helped in the review of the manuscripts in Volume 1: Chris Basso, John Briscoe, Robert Brumbaugh, Ximing Cai, Torkil JonchClausen, Line Gordon, Chuck Howe, Annette Huber-Lee, Roberto Lenton, Tom Maddock, Suzanne Ogden, Margaret Owens, Cliff Russell, Mike Shapiro, and Richard Vogel.

References Bowley M (1973) Studies in the History of Economic Theory Before 1870. London: Macmillan. Falkenmark M and Rockstro¨m J (2004) Balancing Water for Humans and Nature: A New Approach in Ecohydrology. London: Earthscan. FAO (2009) AQUASTAT 2009: Water and Food Security. http://www.fao.org/nr/water/ aquastat/main/index.stm (accessed September 2010). Gibbon E (1776-89/1989) The History of the Decline and Fall of the Roman Empire, vols. I-VI. New York: St. Martin’s Press.

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Gleick PH (2009) Water brief 4: Water conflict chronology. In: The World’s Water 2008-2009: The Biennial Report on Freshwater Resources, pp. 151. Washington, DC: Island Press. Global Water Partnership (2000) Integrated Water Resources Management, Technical Advisory Committee, Background Paper No. 4. Hanemann WM (2007) The economic concept of water. In: Rogers P, Llamas MR, and Martinez-Contina L (eds.) Water Crisis: Myth or Reality? ch. 4. London: Taylor and Francis. Intergovernmental Panel on Climate Change (2007) IPCC Fourth Assessment Report: Climate Change. Cambridge: Cambridge University Press. http://www.ipcc.ch/ publications_and_data/publications_and_data_reports.htm (accessed September 2010). Keyfitz N (1976) World resources and the world middle class. Scientific American 235(1): 28--35.

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Malthus TR (1798) An Essay on the Principle of Population. London: St. Paul’s Church Yard (printed for J. Johnson). Mueller M (2007) Policy Brief 5: Climate Change Adaptation and Integrated Water Resources Management – An Initial Overview, Global Water Partnership, Technical Advisory Committee. Smith A (1776/1976) An Inquiry into the Nature and Causes of The Wealth of Nations In: Cannan, E (ed.). Chicago: University of Chicago Press. Stern Sir N (2004) The Stern Review on the Economics of Climate Change. Cambridge: Cambridge University Press. United Nations (1997) Convention on the Law of the Non-navigational Uses of International Watercourses, adopted by the General Assembly on 21 May 1997. Not yet in force. http://untreaty.un.org/ilc/summaries/8_3.htm and http:// untreaty.un.org/ilc/texts/instruments/english/conventions/8_3_1997.pdf (accessed September 2010).

1.01 Integrated Water Resources Management R Lenton, The Inspection Panel, The World Bank, Washington, DC, USA & 2011 Elsevier B.V. All rights reserved.

1.01.1 1.01.2 1.01.3 1.01.4 1.01.5 1.01.6 1.01.7

Introduction IWRM at the Watershed Level: Watershed Management IWRM at the Water-Use Systems Level: Agricultural Water Management IWRM at the Water-Use Systems Level: Water Supply and Sanitation Services IWRM at the Basin Level IWRM at the National Level: Policies and Governance IWRM at the Transnational and Global Level: Information Sharing, Cooperation, and Technical and Financial Assistance 1.01.8 IWRM as a Meta-Concept 1.01.9 History and Evolution of the Concept of IWRM 1.01.10 Assessments and Critiques of the Concept of IWRM Acknowledgments References

1.01.1 Introduction This chapter is about the planning and management of water resources. It aims to provide a comprehensive look at the practices and approaches that have come to be known as integrated water-resources management (IWRM). Following GWP Technical Committee (2009), the chapter defines IWRM as the way in which water can be managed to achieve the objectives of sustainable development, and an approach that reflects the need to achieve a balance among economic efficiency, social equity, and environmental sustainability. The chapter begins with several sections that analyze integrated approaches to water-resources management at different levels (from small watersheds to basins, agricultural systems, and national and global policymaking), which have been practiced for some time and about which there is now a considerable body of knowledge. Building on these analyses, IWRM is not to be seen as a single approach but as a wide range of approaches to manage water and related resources – a meta-approach or meta-concept, as it were, which both transcends the various levels of decision making and recognizes the importance of integrating decision making at each level. The chapter then provides some historical perspective on the evolution of the concept of IWRM, concluding with a summary of recent assessments and critiques of the concept. Although the concept of IWRM is applicable to a variety of contexts, this chapter focuses on the management of water in the context of development, that is, on the management of water resources to advance sustainable development and reduce poverty. This means that the chapter examines the management of water through the lens of the major development and environment issues that are currently challenging countries across the world and which are intrinsically interconnected to water resources in one form or another. In particular, it means that the goals of water management addressed in the chapter relate to development, and the kinds of water challenges emphasized in the chapter are those most often found in the context of development – such as how to allocate more water to generate rural livelihoods and grow food in order to reduce income poverty and hunger.

9 9 10 12 13 14 16 17 18 18 19 20

The following six sections provide an overview of integrated approaches to water-resources management at different levels, from small watersheds to basins, agricultural systems, and national and global policymaking. Management at each level shares a common focus on managing water and related resources, to achieve multiple objectives, which has been practiced for some time, and has developed its own discipline, vocabulary, body of knowledge, networks of interested people, and global institutions. Each of the six sections therefore focuses on management at a specific level – explaining the main characteristics of management at this level, summarizing the literature on the subject, describing some of the networks and institutions working at this level, and providing examples of good practices. We begin with the watershed level.

1.01.2 IWRM at the Watershed Level: Watershed Management Perhaps the most salient features of management at the watershed level are (1) the crucial role of land as well as water management and (2) the strong relationships between watershed management and downstream impacts. As a result, in watershed management the need for a close integration of land and watermanagement activities and upstream/downstream considerations is imperative. Reflecting these key features, the World Bank, in Darghouth et al. (2008), has defined watershed management as ‘‘the integrated use of land, vegetation and water in a geographically discrete drainage area for the benefit of its residents, with the objective of protecting or conserving the hydrologic services which the watershed provides and of reducing or avoiding negative downstream or groundwater impacts.’’ In many developing countries, watershed management is a crucial part of rural-development efforts to generate rural livelihoods and increase incomes. The concepts and practice of watershed management have evolved in the last 30 or 40 years, in parallel with the evolution of the concept of IWRM as a whole. Importantly, from an initial emphasis on technology and engineering, driven primarily by downstream

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environmental protection considerations, they have evolved toward more integrated approaches driven by upstream poverty reduction and livelihood generation. As the above discussion suggests, there are several features of watershed management that are important in the IWRM context. First, the management of watersheds involves management of a range of resources, including soil resources, crops, forests, livestock systems, and water in the form of overland flow, streams, and soil moisture. Second, the goals of watershed management are generally economic, social, and environmental in nature, and relate both to communities in the watershed itself (principally increased productivity and incomes) and to downstream (principally flood management and drought mitigation, through more controlled water resources). Third, the instruments involved in watershed management have usually involved a combination of institutional, economic, and environmental measures. Participatory approaches have proved to be particularly valuable. The economic returns from the use of water for productive purposes within watersheds have usually proved to be a hugely important catalyst and incentive for collective environmental-preservation activities. A crucial feature of many watershed management efforts, as illustrated by the Sukhomajri experience (see Box 1), is the interplay among environmental, economic, and institutional approaches. Darghouth et al. (2008) have noted that ‘‘where communities could see the economic benefits and were empowered, they were willing to invest in long term conservation.’’ By now, there is a significant body of knowledge and experience in watershed management, which draws on both the range of watershed management experiences in many countries and a growing body of research and evaluation carried

out by institutions such as the World Bank, the International Water Management Institute (IWMI), the World Agroforestry Centre, and the Food and Agriculture Organization (FAO). These and other institutions have established some important watershed management programs and networks. The World Agroforestry Centre, for example, has established the Rewarding Upland Poor for Environmental Services (RUPES) program in Southeast Asia, which aims to develop ‘‘mechanisms for rewarding the upland poor in Asia for the environmental services they provide.’’ FAO has also been active in the subject area, in terms of both publications and networks, playing a key role, for example, in the Latin American Technical Cooperation Network on Watershed Management (RDLACH), which was created in 1980 to promote watershed management in Latin America and the Caribbean. The World Bank has been an active player in watershed management, providing finance for important initiatives and evaluating and drawing lessons from many watershed-management programs in different parts of the world (see e.g., World Bank, 2003, 2004a, 2004b, 2004c, 2005). A recent World Bank discussion paper (Darghouth et al., 2008) summarizes these experiences and lessons learned. The IWMI has carried out important research in this area, as illustrated by Sharma et al. (2005). The Comprehensive Assessment of Water Management in Agriculture (CA), which was spearheaded by IWMI, has also sponsored research on watershed management. One study under the CA (Joshi et al., 2005) carried out an in-depth analysis of the impacts of watershed management programs in India, evaluating 311 case studies of watershed programs in terms of economic efficiency, equity, and sustainability, and concluding that these programs yielded an average internal rate of return of 22%. Other

Box 1 Watershed Management in Sukhomajri, India. From Lenton R and Walkuski C (2009) A watershed in watershed management: The Sukhomajri experience. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. Sukhomajri is a small village of about 450 people on the edge of the Shivalik mountain range near Chandigarh in India. By the 1970s, over a century of heavy logging in the area and the overgrazing of cattle, sheep, and goats in open forest lands had severely degraded the area surrounding Sukhomajri. Its people were impoverished and survived primarily by raising rainfed crops and keeping goats that foraged in the denuded hills. The Sukhomajri program came about because the citizens of Chandigarh, whose Sukhna Lake had lost nearly 70% of its storage capacity due to siltation by the early 1970s, asked the nearby Central Soil and Water Conservation Research and Training Institute (CSWCRTI) for assistance in solving this problem. The Institute soon found that the lake’s siltation was caused by soil erosion in the hills in and around Sukhomajri, and developed a program to improve soil and water conservation in the watershed in consultation with the local community. While initially the people of Sukhomajri were not very interested in a project principally designed to protect Chandigarh’s lake, their attitude changed dramatically when the Institute built a small dam to control runoff and the villagers found they had a reliable source of water relatively close at hand. A cooperative effort began, focused on community participation in decision making and management, incentives for villagers to graze their animals outside of the watershed, and – crucially – an equitable system of water allocation that would benefit all villagers equally, with water rights granted to all villagers whether or not they owned land. Over time, the villagers organized and formed a water-users association and later the Hill Resource Management Society, to manage and distribute irrigation water. All these actions underscore the Sukhomajri program’s balance of economic efficiency, social equity, and environmental considerations. Over time, the impact of the Sukhomajri program has been considerable. Annual household incomes, for example, rose from around US$230 in 1979 to about US$1360 in the 2000s – more than double the per capita income of the state of Haryana, which itself is one of the highest in India. In addition, tree density in the area rose a 100-fold from 13 ha 1 to around 1300 ha 1 between 1979 and 1995, underscoring its environmental regeneration benefits. Socially, the Sukhomajri program has had important equity impacts because of the focus on landless people and equal distribution of irrigation water. More broadly, Sukhomajri’s approach to community-integrated watershed management has become a model for watershed-development programs elsewhere in India. Importantly, watershed management in Sukhomajri has undergone many changes, not all of which have been positive, since the program began. For example, the check dam led to a rise in groundwater levels, which led an increasing number of villagers to build shallow tubewells and use these tubewells rather than the check dam for irrigation water; this development lowered the incentives for limiting watershed grazing and participating in communal activities. In addition, changes in taxation led to significant declines in the income for the Hill Resource Management Society. This fall in revenues, coupled with the lower incentives for villagers to participate in watershed conservation, led to silting dams and deteriorating pipelines.

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important works on watershed management include Farrington et al. (1999), Heathcote (1998), and Sharma et al. (2005). An excellent example of watershed management in the context of IWRM is the Sukhomajri program in Northwest India, which has been extensively documented and analyzed by Seckler (1986), Agarwal and Narain (1999), Kerr (2002), Khurana (2005), CSE (1994), CSE (1998), CSE (2002), and CSE (2007). This case is summarized in Box 1.

1.01.3 IWRM at the Water-Use Systems Level: Agricultural Water Management The key feature of IWRM at the agricultural-systems level, commonly referred to as agricultural water management (AWM), is that it is defined by a particular type of water use – agriculture – which in most countries is by far the largest consumer of water. AWM thus has a common purpose, generally defined in terms of increasing and sustaining agricultural production. Management itself takes place at several levels, from a single farmer’s field to small farmer-managed systems to large publicly operated irrigation systems, usually with policy and other support at higher levels as well. Reflecting these features, AWM may be viewed as encompassing the range of structural and nonstructural measures at various levels to harness, control, and manage surface water, groundwater, and rainwater to improve and sustain agricultural production. (This definition draws on the definition used in World Bank (2006), but has been broadened to encompass both large-scale and small-scale, structural and nonstructural interventions at a variety of levels.) Structural measures include combinations of irrigation, drainage, and flood control, water conservation and storage, on-farm water management, and soil-moisture conservation, while nonstructural measures include institutions and policies to improve physical and financial sustainability, and user operation and management . AWM involves increasing access to reliable and affordable water supplies, improving management of rainwater, soil moisture, and supplemental irrigation, finding ways to gain higher yields and value from the same water amounts, and enhancing management of the resource as a whole. The interventions involved vary significantly from level to level. While at the farm level, AWM interventions might involve investments in irrigation or soil management, at the farmermanaged irrigation system level, they also include community mobilization, and at the large-system level, the operation of canals and the governance of resources. Decision making at national policy levels is also a fundamentally important aspect of AWM, as discussed later in this section. AWM, like water-resources management as a whole, is best viewed as an integrated, holistic process. Indeed, AWM is an aspect of water-resources management, and should be understood as such. Water has several characteristics that impact on its management and use in agriculture. For example, water has many competing uses outside of agriculture, and it is required in relatively large quantities to produce yield increases, but is heavy, bulky, and costly to transport in comparison to other inputs.

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While earlier it was noted that AWM is defined by the common objective of increasing and sustaining agricultural production, in fact, AWM generally has broader objectives beyond enhancing agricultural productivity, such as improvements in livelihoods and incomes, reductions in risk, and long-term sustainability of the resource. Increasing the numbers of small holders with access to reliable and affordable water provides more regular employment and livelihood opportunities to landless people as well as small holders, and enhances the prospects of ensuring access to domestic water supply and sanitation in rural areas. Efficient AWM creates opportunities for farmers to improve livelihoods, leveraging investment in other productive inputs such as improved seed and fertilizer, while also helping to ensure longterm sustainability of both surface water and groundwater resources. Finally, good AWM aims to reduce the risks that farmers and countries experience from variable rainfall, which can have a powerful impact on growth. All this reinforces the notion that efficient AWM, like IWRM as a whole, generally has a broad range of economic, social, and environmental goals. Importantly, AWM generally involves the integrated management of both blue water – water withdrawn from rivers, reservoirs, lakes, or aquifers for irrigation purposes – and green water – rainfall stored in soil moisture. While blue water is visible and its role in irrigated agriculture is clearly understood, green water and its crucial role in rainfed agriculture often goes unrecognized. Green water management measures to improve agricultural productivity can encompass soil management, crop choices and practices, and water capture. Blue water irrigation management, on the other hand, can involve water storage, lift, transportation, delivery, application, and reuse at various levels. However, average yields from rainfed agriculture using green water are much lower than those from irrigated agriculture using blue water, and, as a result, only half of the world’s food is produced under rainfed conditions practiced by the majority of the world’s farmers. Better green water management can reach relatively large numbers of farmers at relatively low cost, but the productivity gains are relatively small; improved blue water irrigation management, on the other hand, can achieve higher productivity gains, but reach relatively smaller numbers of farmers and with a relatively high cost per farmer. Within blue water, AWM generally involves the management of both groundwater and surface water. Like green water, groundwater is less visible and frequently overlooked, but enables farmers to exercise much greater control over the amount and timing of water applied to their crops than those forms of irrigation that depend on unreliable surface-water supplies, and is an effective way for small holders to improve crop production. In South Asia and North China, in particular, groundwater irrigation over the last 30 or 40 years has grown considerably, and played an ever-greater role in efforts in these regions to improve productivity, food security, livelihoods, and incomes. Indeed, according to the CA (CA, 2007), the rapid growth of groundwater irrigation in South Asia and the North China plains between 1970 and 1995 was at the heart of the agrarian boom in these two regions. Nevertheless, this boom has come at the cost of decreasing water tables that threaten its long-term sustainability (Shah, 2009).

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There are numerous examples of best practices in AWM that have adopted an integrated approach. One such example is summarized in Box 2. While the physical management of water for agriculture takes place at the agricultural-systems level, decision making at national policy levels is a fundamentally important aspect of AWM. Some of these policy issues relate specifically to irrigation, such as the balance of large-scale and small-scale irrigation, the balance of government-led and community-based interventions, and the role of the private sector. While the institutional, technological, and environmental problems that need to be tackled to improve irrigation performance are usually system specific, the use of economic instruments to increase the efficiency of irrigation is usually a matter of national policy. Other national policy issues relate to agricultural water as a whole. In many countries, for example, a major policy question is the scope for growth in food production in rainfed and irrigated agriculture, from which combination of the two should future food production lie, and what are the wateravailability implications of each of these strategies. A related topic is the extent to which trade, as against domestic food production, should be used as a strategy for sustainable food security, taking into account not only the virtual water transfers that are embodied in food trade but also the risks and uncertainties associated with trade in food supplies and the role of domestic food production in providing a source of

income and livelihoods to rural populations. A third topic is the water and energy nexus. In countries in which both energy and water resources are scarce, a key policy question is whether scarce water supplies should be allocated for biofuels or for hydropower, and if so what should be the crop choice. There is a vast literature on AWM. While much of it is narrowly focused on specific technical issues of irrigation, there is a growing literature that takes an integrated approach and addresses some of the broader policy issues identified above. A recent review commissioned by the Technical Committee of the Global Water Partnership (GWP) (GWP Technical Committee (2009)) showed the wide range of available literature on subjects such as water–food interactions, including trade in virtual water, biofuels and their implications for water, and water efficiency and productivity, in the works of Rosegrant et al. (2002); de Fraiture et al. (2008), and Hellegers et al. (2008). The most recent comprehensive review of AWM, however, is the CA, a recent multi-institutional assessment of the current state of knowledge on how to manage water resources for agriculture. This assessment is summarized in Box 3. Several global institutions have played a major role in supporting national efforts to improve AWM. The World Bank has long been a major source of financial and technical assistance in AWM, investing some US$13.2 billion in 56 countries in the 10-year period between 1994 and 2004 alone (World Bank, 2006). FAO has had an active program in AWM

Box 2 Irrigation reform in Mali. From Barry B, Namara R, and Bahri A (2009) Better rural livelihoods through improved irrigation management: Office du Niger (Mali). In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. The Office du Niger in Mali was formed in the 1930s as a centralized public enterprise to produce irrigated cotton and rice. Starting in the 1990, the Office du Niger was significantly revamped through a process involving measures such as physical rehabilitation and modernization, farming-systems intensification, improvements in land-tenure security, the creation of pro-farmer support services, the establishment of farmer organizations, and the introduction of innovations in agricultural technology, along with macroeconomic and cereal market reforms. All these have led to dramatic gains in rice production and farm incomes as well as reductions in rural poverty. The case shows that changing agricultural water management requires a supportive macro-policy environment, appropriate institutional changes, and infrastructural investments. Equally important, it shows that those reforms may need to precede improvements in water management. Moreover, in aid-dependent low-income countries, reform cannot occur unless both government and donors concur on the need for change. Finally, the case drives home that improving water management is a continuing process; gains to date in economic efficiency and (to a lesser extent) equity in the Office du Niger now need to be matched by improvements in environmental sustainability.

Box 3

The Comprehensive Assessment of Water Management in Agriculture. From http://www.iwmi.cgiar.org.

The Comprehensive Assessment of Water Management in Agriculture (CA, 2007) was a critical evaluation of the benefits, costs, and impacts of the past 50 years of water development, the water management challenges communities face today, and the solutions people have developed around the world, aimed at assessing the current state of knowledge and stimulating ideas on how to manage water resources to meet the growing needs for agricultural products, to help reduce poverty and food insecurity, and to contribute to environmental sustainability. The assessment was produced by a broad partnership of practitioners, researchers, and policymakers and organized through the CGIAR’s Systemwide Initiative on Water Management (SWIM). SWIM was convened by the International Water Management Institute (IWMI), which initiated the process and provided a secretariat to facilitate the work. The CA’s scope was water management in agriculture, including fisheries and livestock, and the full spectrum of crop production from soil tillage through supplemental irrigation and water harvesting to full irrigation in a sustainable environment context. The review covered a range of topics, from managing water in rainfed agriculture and groundwater use in agriculture to agricultural use of marginal-quality water resources and integrating water and livestock development. The assessment was originally framed by 10 questions, later expanded as interest grew, and included the overarching question: How can water in agriculture be developed and managed to help end poverty and hunger, ensure environmentally sustainable practices, and find the right balance between food and environmental security? Some of the questions addressed by the CA included: What are the options and their consequences for improving water productivity in agriculture? What are the options for better management of rainwater to support rural livelihoods, food production, and land rehabilitation in water-scarce areas? What are the options for integrated water resources management in basins and catchments? What policy and institutional frameworks are appropriate under various conditions for managing water to meet the goals of food and environmental security?

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for several decades, focusing on knowledge generation and dissemination, policy and technical advice, and preparation, investment, and implementation of projects. On the research and development side, the IWMI has played a major role in shaping thinking and action on AWM. IWMI’s evolution from a narrow blue-water focus on the management of irrigation at the systems level to its current broad mission to ‘‘improve the management of land and water resources for food, livelihoods and the environment,’’ which encompasses work at all levels, mirrors the evolution of the discipline of AWM as a whole.

1.01.4 IWRM at the Water-Use Systems Level: Water Supply and Sanitation Services While agriculture consumes the lion’s share of the world’s water resources, AWM is not the only example of a level of water management defined by a particular type of water use. Water supply and sanitation (WSS) can also be considered as a type of water management defined by its use, with a common purpose generally understood in development circles as increasing sustainable access to basic sanitation and safe water supplies for domestic purposes. As with AWM, management takes place at several levels, from the household to large cities, with policy and other support at higher levels as well. Unlike AWM, however, the amounts of water used for WSS are relatively small. The key feature of WSS is thus that the management of resources other than water – financial, human, and institutional resources in particular – is often more important than the physical management of water itself. Despite or perhaps because of this key difference, water and sanitation-services management has followed a course similar to AWM, in that it has evolved from a somewhat narrow focus on technologies to a much broader understanding of the political, institutional, and financial dimensions of improving access to water and sanitation at all levels. As a result, it is generally agreed today that increasing access to water and sanitation services by the unserved will require a broad integrated approach, at many levels, involving increased political commitment, institutional and technological innovation, and increased financial allocations, with attention not only to the supply side but also to the demand side (Lenton et al., 2005). Several global institutions have played a major role in shaping the thinking in this field as well as supporting national efforts to improve access to water and sanitation services. These include the World Bank (both through its investment program and its WSS program), the World Health Organization, and the United Nations International Children’s Education Fund (UNICEF), the Water Supply and Sanitation Collaborative Council, and several major international nongovernmental organizations, such as WaterAid.

1.01.5 IWRM at the Basin Level The key feature of water management at the basin level is that the basin is the basic unit for integrating the supply side of water-resources management, that is, for integration within the natural system. This means that the basin is well suited for

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integrated management of land and water management, green and blue water, surface and groundwater management, water quantity and water quality, spatial and temporal variability, and upstream and downstream interests. The chapter on freshwater in Agenda 21, the key document resulting from the 1992 Earth Summit (UNCED, 1992), states that ‘‘Integrated water resources management, including the integration of land- and water-related aspects, should be carried out at the level of the catchment basin or sub-basin.’’ Likewise, Article 26 of the Johannesburg Plan for Implementation, the key outcome of the 2002 World Summit for Sustainable Development in Johannesburg (WSSD, 2002), states that ‘‘the river (or water) basin should be used as the basic unit for integrating management.’’ Water management at the basin level has thus become the central focus of much of the advances in thinking about IWRM. Nevertheless, while basin boundaries provide a useful way of delimiting the supply side of the equation, they are not necessarily the best means to integrate the demand side, especially since basin boundaries usually do not coincide with political or administrative boundaries. Integrating natural and human systems therefore generally requires work at other levels beyond the basin. Basins can be characterized by the degree of pressure placed on a basin’s water resources, that is, by the relationship between the requirement for freshwater resources and its availability in the basin, taking into account both quantity and quality considerations and variability over time and space. Basins in which requirements (including environmental requirements) exceed availability and where additional water needs cannot be met without reallocating water from other users, or by improving water-use efficiency, are called closed systems. Open systems, by contrast, are those in which water availability exceeds current requirements and where there is, therefore, still room for expanding water use without environmental damage. Needless to say, management of closed or closing water basins is significantly more challenging than that of open basins. Importantly, integrated management at the basin level does not necessarily imply the need for a basin organization. An analysis by the CA, conducted together with GWP and International Network of Basin Organizations (INBO) (CA, 2008), concluded that ‘‘adaptive, multilevel, collaborative governance arrangements’’ are best able to deal with the complexities of issues that arise at the basin level. Noting that solutions need to be driven by contextual realities and that what works in one basin may not work in another, the CA emphasized that ‘‘Not all water-related problems can or should be solved at the river basin level. Some problems are best addressed at the sub-basin or local level. Others have solutions beyond the basin itself and even outside the water sector, for example in national or federal agricultural policies.’’ Numerous basin-management organizations have been established in different parts of the world. The significance of basin management is reinforced by the INBO, which was established to facilitate exchange of experience and expertise among organizations interested in river-basin management and to promote the principles and means of sound water management. Similarly, there is a vast literature that addresses water management at the water-basin level, particularly at the

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Integrated Water Resources Management

river-basin level. Perhaps the most influential and ambitious publication on the subject is Hooper (2005), which focuses on integrated approaches to river-basin management and develops an integration framework for river-basin management based on principles of natural-resources management and planning. A recent handbook on basin management has been published by GWP and INBO (2009).

There are numerous examples of good practices at the basin level. Boxes 4 and 5 illustrate the different ways in which integrated approaches have been applied at a basin level. Box 4 focuses on the Lerma–Chapala Basin in Mexico, which has faced extreme pressure on its water resources because of many factors, and can therefore be considered a closed system. The Lerma–Chapala Basin has been analyzed

Box 4 The Lerma-Chapala Basin in Mexico. From Hidalgo J and Pen˜a H (2009) Turning water stress into water management success: Experience in the Lerma–Chapala river basin. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. Mexico’s Lerma–Chapala basin is one of world’s most over-committed basins, where population growth and industrial and agricultural development have led to a serious imbalance between water availability and water use. It is a classic example of a closed basin, with no natural outflow. The Lerma–Chapala basin is part of the hydrological system formed by the rivers Lerma and Santiago and covers part of the states of Guanajuato, Jalisco, Mexico, Michoaca´n, and Queretaro in central Mexico. Six large cities with more than 1 million inhabitants compete for water in and outside of the basin, and several large industrial corridors lie in the basin and discharge a high concentration of pollutants to the river and water bodies. Over 50% of the land in the basin is used for agriculture, of which a significant amount is irrigated. Water pollution and soil degradation are serious problems, as evidenced by declining soil fertility and increasing soil erosion. The current imbalance in water use began to develop in the 1940s, driven by population growth, heavy construction of water infrastructure, significant industrialization, and large increases in irrigated agriculture. As a result of intensive water use in the middle and lower reaches of the Lerma River, water stopped flowing naturally from Lake Chapala into the Santiago River in the early 1980s. The shrinking volumes of water in Lake Chapala – which dropped 90% in the two decades from 1981 to 2001 – clearly indicate the growth of water demand in the upper basin. While drought undoubtedly contributed to this drop, the main problem appears to be that between the 1940s and 1980s when the federal water authority provided too many water concessions, which led to overexploitation of water sources and a severe water imbalance. Indeed, during the 1990s, less water flowed into Lake Chapala than flowed out through evaporation and water withdrawals, yielding an annual mean deficit of 400 million ha m. To make matters worse, untreated wastewater discharges have degraded the lake’s water quality. To address this situation, the federal government first established the Lerma–Chapala Basin Regional Management, which in the 1980s made efforts to collect more information, improve water plans, define better institutional roles, and involve basin stakeholders in decision making. This led to the first coordination agreement between the federal government and the governments of the five basin states in 1989. A Control and Evaluation Advisory Council (CEAC), a technical working group, designed new rules for surface water reallocation that were approved by consensus by the main stakeholders in 1991. At the same time, a first stage of a water-treatment program was initiated. The CEAC was transformed into the Lerma–Chapala Basin Council (LCBC) in 1993 – the first basin council in Mexico. In 2004, a revised National Water Law strengthened the basin councils, giving them more responsibilities and an organizational structure, and also transformed the river-basin regional management offices into basin organizations, recognizing them as the regional water authorities. At present, the Lerma–Chapala Basin Organization (LCBO) and the LCBC share responsibilities for basin-water governance. Within the context of these institutional changes, a range of instruments have been used over time to tackle the problems caused by water over-use in the basin, including a basin plan and a program of water-resources assessment and use. As a result, Lake Chapala is beginning to recover its natural level, and the Lerma River’s water quality is improving.

Box 5 Basin Planning in the Rio Colorado, Argentina. From Major DC and Lenton RL (1979) Applied Water Resource Systems Planning. Englewood Cliffs: Prentice Hall. The Rı´o Colorado is a relatively small river in southern Argentina that rises near Argentina’s border with Chile, and flows for some 1100 km through a largely arid and sparsely populated area to the Atlantic Ocean, with only minor tributaries. Irrigation is the principal use of water in the basin, although power generation is also important to widely dispersed rural communities. While at present the river waters are used only sparingly, principally for irrigation and hydroelectric power, over the years there have been many proposals for expansion of irrigation and hydroelectric power and for water exports to wine-growing areas north of the basin. The Colorado basin comprises five provinces – Mendoza, Rio Negro, Neuque´n, La Pampa, and Buenos Aires – with different interests in the use of the river’s waters. In the early 1970s, on the initiative of the Argentine government and the five riverine provinces, the Rio Colorado was the site of a pioneering scientific study that aimed to provide a scientific basis for reaching agreement on the allocation of water from the river. The study involved the first complete use of innovative new mathematical modeling techniques for river-basin planning, and indeed the first large-scale application of the approach outlined by the Harvard Water Program (Maass et al., 1962). The study focused on the analysis of alternative development plans, with multiple objectives, with a strong emphasis on the use of mathematical models to analyze such alternatives. As described in Major and Lenton (1979), the study presented a phased set of investment possibilities to provincial and national authorities, including an initial stage of development focused on irrigation, with a reservoir site in the lower basin that has since been developed; a second stage with complementary power; and a third stage with essentially full development of the basin. Importantly, the study formed the basis for a successful 1977 accord on water, energy, and irrigation among the five provinces and the central government of Argentina that has endured for more than 30 years. Since water availability in the Rio Colorado basin (both today and at the time of the study) significantly exceeds requirements, the study’s conclusions and thus the agreement to which it led may not be resilient in the face of possible future changes in water demand and supply. Global changes in agricultural trade, for example, may increase demands for the agricultural and livestock outputs of the Rio Colorado, and increased economic development could lead to increased demand for hydroelectric power. On the supply side, river flows could well decrease as a result of changes in rainfall patterns and the melting of the Andean glaciers. Over time, therefore, increased pressure on the basin’s water resources could force decision makers to consider the prospects for water reallocation and/or water-use efficiency improvements.

Integrated Water Resources Management

extensively, including by Hidalgo and Pen˜a (2009), Dau and Aparicio (2006), Guitro´n et al. (2003), and Wester (2008). Further information can be obtained from Comisio´n Nacional del Agua, 2001. Box 5 focuses on the Rio Colorado Basin in Argentina, an open basin where pressures on the basin’s water resources are fairly low and in which there is still room for meeting additional water needs without reallocating water from existing users.

1.01.6 IWRM at the National Level: Policies and Governance The key feature of water-resources management at the national level is that it focuses not on the physical management of water itself but on the overall governance of the resource and the policies and institutions that facilitate and support management at other levels. Management at the national level therefore deals with strengthening the enabling environment, through measures such as establishing water-resources policies and strategies, improving water legislation, enhancing institutional roles, and strengthening processes for stakeholder participation. The allocation of financial resources (e.g., for investments in water infrastructure) is also clearly a vital task of management at the national level. National-level action can also support the development and use of better instruments for water management, such as those for water-resources assessment. Importantly, national policies and strategies must facilitate and support integrated action at lower levels. While much of what might be considered natural-system integration typically takes place at the basin level, much of what might be called human-system integration takes place at the national level. This includes ensuring that governmental policies take account of water-resource implications and considering water-resource policy within national economic and sectoral policies (GWP Technical Advisory Committee, 2000). Integrating water-resource policy with national economic and sectoral policies, and ensuring that decisions of

15

economic-sector actors are water sensitive, forms another vital set of policymaking at the national level. The need for strengthening water policy as a major issue for sustainable development was among the major accomplishments of the Rio Earth Summit, whose Agenda 21 included a full chapter on freshwater resources and which called for the application of integrated approaches to the development, management, and use of water resources. Attention to national water policies and governance has increased significantly in recent years, and countries the world over have worked to find ways to strengthen their water policies to advance overall sustainable development goals. In Chile, for example, national water policy has played a major role in the country’s overall development since the 1970s. This experience, which has been well documented by Pen˜a et al. (2004), Bauer (2004), GWP Technical Committee (2006), and Pen˜a (2009), is summarized in Box 6. Chile’s example illustrates the importance of aligning water-resource planning and management strategies to overall economic development models. South Africa provides a further example of a country which made a major effort to align its water policies and strategies with national development goals, following its transition to democracy in 1994. This process has been described in Muller (2009), De Coning (2006), and DWAF (2004). In contrast to Chile, however, South Africa attempted to address social, environmental, and economic needs simultaneously, taking advantage of the opportunities to improve water management provided by the country’s broader political-change process (Muller, 2009). There has been a growing literature on the subject of policies and water governance. One landmark publication in this area is Rogers and Hall (2003), who define water governance as ‘‘the range of political, social, economic and administrative systems that are in place to develop and manage water resources, and the delivery of water services, at different levels of society.’’ Recent literature has also examined what might be termed the triggers of water-policy change, and the steps that might be taken to accelerate and sustain positive change. GWP Technical Committee (2009) emphasizes that change is a negotiated

Box 6 National water policy and development: The case of Chile. From Pen˜a H (2009) Taking it one step at a time: Chile’s sequential, adaptive approach to achieving the three Es. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. Chile stretches along a narrow strip of land 4200 km long, between the Andes and the Pacific Ocean. The Northern half of the country is arid, while in the Southern half, water is plentiful. Irrigated agriculture accounts for 85% of total consumptive water demand, while domestic uses account for 5%, and mining and industrial uses, around 10% (DGA, 1999). There is significant pressure on existing water resources in the Northern and Central regions of the country, where water requirements are high and water availability is low. At the end of the 1970s, Chile embarked on a new policy of opening up the economy to international trade, promoting the export of products in which the country was competitive. Importantly, nearly all of these export products involved significant water use as part of the production process and were located in areas where water is scarce; for example, copper production was located in the Atacama Desert. As a result, good water-resource management became a prerequisite for the success of the overall export model. Good management in turn required changes in public policies related to water. As described in Pen˜a et al. (2004), Bauer (2004), and GWP Technical Committee (2006), water policy in Chile has evolved considerably since that time, in line with the evolving overall governance structure. Initially, during Chile’s authoritarian period, a water law issued in 1981 with a strong market orientation called for private participation in water supply and sewerage services. A later irrigation law left irrigation initiatives to the private sector, with some partial financing through government subsidies. These steps led to increased efficiency in the use of water in various production processes, as well as increased private investment in Chile’s sewerage and water-supply sector. With the arrival of the democratic period, more attention was given to social, environmental, and regulatory considerations through a range of new laws. In particular, in 2005, the water law was reformed, which called for a more balanced consideration of economic, environmental, and social dimensions and reaffirmed the State’s regulatory role (Pen˜a et al., 2004).

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Integrated Water Resources Management

Table 1

Factors contributing to a conducive environment for change

Particulars

Australia

Chile

Morocco

Namibia

South Africa

Sri Lanka

Water scarcity/conflicts Financial crisis Draughts/salinity Macroeconomic reforms Political reforms Social issues Donor pressures Internal/external agreements Institutional synergy/pressures

**

*

**

**

*

*

*

**

**

***

*

***

***

–

***

*

**

–

***

**

***

–

–

***

–

***

–

***

***

*

*

–

*

**

**

–

–

*

**

*

–

***

***

–

–

*

*

–

**

***

*

*

*

*

The number of *s signifies the relative importance of the factors in the context of each country. ‘‘–’’ means the aspect in question is ‘‘not applicable’’ or ‘‘not evaluated’’. From Saleth RM and Dinar A (2005) Water institutional reforms: Theory and practice. Water Policy 7: 1–19.

political process that is informed by history, public perception, development challenges, and social and economic context; while there are no universally applicable solutions, lessons from experience can provide practical guidance to those involved in change processes. Saleth and Dinar (2005) have identified a number of factors that contribute to a conducive environment for change, as depicted in Table 1. The GWP, the IWMI, and the Stockholm International Water Institute have compiled an extensive set of literature on policy-change processes, the link to which is available in the section titled ‘Relevant websites’. There has also been increasing attention to water in the forums that foster intergovernmental discussions and agreements on sustainable development issues, including in particular the UN Commission on Sustainable Development (UNCSD), established following the Earth Summit in 1992. Recognizing the important role of national policies and strategies in fostering and enabling the development of more integrated management approaches at all levels as called for at the Earth Summit, the UNCSD paid early attention to waterpolicy issues and laid the ground for the World Summit on Sustainable Development (WSSD) in Johannesburg in 2002, which called for all countries to prepare national IWRM and water-efficiency plans by the end of 2005. Subsequently, a significant number of countries embarked on such plans, both in response to the call and as part of national efforts to meet development goals and address specific water and development challenges. Special literature on the subject also emerged at that time, such as Catalyzing Change: A Handbook for Developing Integrated Water Resources Management (IWRM) and Water Efficiency Strategies (GWP Technical Committee, 2004). A growing number of global institutions have focused on fostering better policies and governance at the national level. These include in particular the GWP, which supports countries in the sustainable management of their water resources, and the World Water Council, which promotes international dialog via periodic world water forums and other means. Within the UN system, the United Nations Development Program (UNDP) has given priority to strengthening water governance at the national level, and has established a water-governance facility in Stockholm for this purpose. United Nations Environment Program (UNEP), GWP, and others have played a role in assisting countries to prepare IWRM plans and strategies.

Importantly, the Johannesburg call for the preparation of national IWRM and water-efficiency plans reflected the significant worldwide experience in the development and implementation of national water-resource management policies and strategies. As emphasized by Lenton and Muller (2009), such an experience strongly suggested that national waterresources planning and management must be linked to a country’s overall sustainable development strategy. At the same time, it reinforced the notion that local-level management needs a supportive national-policy framework and support at higher levels.

1.01.7 IWRM at the Transnational and Global Level: Information Sharing, Cooperation, and Technical and Financial Assistance The salient feature of water resources at the transnational and global level is that it focuses on the management of water resources, or a product or resource related to water resources, which crosses national borders. Management at this level thus involves stakeholders in more than one country. The words used to describe transnational and global water management therefore tend to include information sharing, cooperation, negotiation, and technical and financial assistance. Importantly, transnational management complements, rather than replaces, decision making at national and subnational levels. Individual project decisions, for example, are usually taken at national levels, while information gathering and assessment exercises may involve shared efforts. Water resources and products and resources related to water can cross national borders or have an impact in other countries in at least five ways. This translates into at least five different forms of transnational and global water-resources management: 1. Trans-boundary river-basin management. Trans-boundary river-basin management is perhaps the most common form of transnational-level management. It is also the form most often cited in the literature and discussed in international conferences. A very large number of the world’s rivers cross national borders, and many of these involve some degree of trans-boundary management or

Integrated Water Resources Management

2.

3.

4.

5.

cooperation. The Lower Mekong Basin, for example, has witnessed several decades of cooperation in management through the Mekong River Commission and its predecessor, the Mekong Committee (Bruhl and Waters, 2009). Trans-boundary management in the Mekong involves a combination of decision making at different levels. While the Mekong River Commission facilitates information gathering and assessment, individual project decisions are taken by the member countries. The key management challenge here is therefore to manage information sharing, cooperation, negotiation, and conflictresolution activities in ways that increase benefits and/or reduce costs to all parties. Trans-boundary aquifer management. Groundwater resources frequently cross borders in the form of transnational aquifers (see IGRAC, 2009, for a world map of transboundary aquifers). However, despite the large number of transnational aquifers around the world, they have received much less attention in the literature and in international deliberations than trans-boundary river basins (Bourne, 1992). The key management challenge here is generally to ensure the sustainability of resource use by all parties, with assessment and information sharing as major tools. Exports or imports of water resources. Sometimes water resources are exported from one country to another, usually for drinking-water purposes, in pipelines or by other means. Malaysia, for example, exports water resources to Singapore, which in fact depends on imported water for most of its supplies. Since transporting water in bulk over large distances is costly, such imports and exports of water only take place in particular circumstances where other supply options are not feasible or too costly. The key management challenge here usually goes significantly beyond water itself, requiring integration with foreign policy and trade considerations. International trade. Water resources are routinely exported from one country to another via trade in virtual water, which is the term coined to denote the water used in the production of a good or service. In Chile, water-resources management has played a predominant role in the country’s export-oriented economic growth. As noted earlier, the concept of virtual water is much discussed in the literature on trade versus domestic food production. The management challenge here is to ensure that decision making on trade issues takes full account of a country’s water-resource endowments and the impacts that imports or exports of virtual water might have on national water-resource availabilities and requirements. Knowledge management and technical and financial assistance. Managing knowledge and information about waterresources management, as well as technical, institutional, and financial support for water management, generally entails a significant degree of management at regional and global levels. Indeed, much of the focus of the growing number of international forums on water management, such as the UNCSD or the World Water Forums, is on technical assistance, knowledge sharing, capacity building, and financial support. The key challenge here is for countries to coordinate global and regional efforts for

17

knowledge management and technical and financial assistance in ways that best support national policies and strategies to improve water management at all levels. Over the last couple of decades, a number of global institutions have taken on aspects of this challenge. A range of UN organizations assists countries to manage water resources through technical support and capacity building, knowledge sharing, and global monitoring and analysis. The work of these agencies has recently been reinforced by a strengthening of UN-Water, the body which brings together the water activities of all the UN systems and which also oversees the World Water Development Report. Several of the global institutions mentioned earlier are involved in knowledge and information and/or technical, institutional, and financial support for water management, such as the GWP and the World Water Council. The UN Secretary General’s Advisory Board on Water and Sanitation (UNSGAB) helps highlight and mobilize action on global issues that need urgent attention.

1.01.8 IWRM as a Meta-Concept The above-discussed six sections provided an overview of integrated approaches to water-resources management at different levels and helped to unpack the meaning of IWRM at each level. This overview of integrated approaches to waterresources management at different levels suggests that it is best to view IWRM not as a single approach but as a wide range of approaches to manage water and related resources – a metaapproach or meta-concept, as it were, that both transcend the various levels of decision making and recognizes the importance of integrating decision making at each level. Viewing IWRM in this way reinforces the need to understand integration not only in horizontal terms but also in vertical terms, that is, across the different levels of decision making. Clearly, actions at one level should seek to reinforce and complement actions at other levels, within the generally agreed principle that decision making on water resources should be taken at the lowest appropriate level. Importantly, at each level, the approach to integrated management that applies has usually been referred to as an IWRM approach and given a name specifically tailored to that level, such as watershed management or basin management. Some researchers have likewise suggested alternative names to give emphasis to particular attributes, such as adaptive water management (Pahl-Wostl, et al., 2005), which – drawing on concepts from ecosystem management – stresses the need for management approaches that increase adaptive capacity. As we have seen, the way an IWRM approach is given expression differs from level to level. The term ‘integrated’ therefore takes on many forms, depending on the level. Rather than being taken literally as requiring that everything needs to be connected to everything else, the term is best understood as an adjective used to reinforce the notion that intelligent and broad-based management is needed to achieve the goals of sustainable development. In other words, the term integrated is a symbol to describe an approach that goes well beyond integration as such and could be described using other

18

Integrated Water Resources Management

adjectives such as sound, intelligent, broad based or holistic, or systemic. In addition, integration in itself is a multidimensional concept, with the relative importance of each of these dimensions varying from level to level. For example, some aspects of integration within the natural system (e.g., green and blue water, surface and groundwater management) are more relevant at the watershed and basin levels, whereas some aspects of human-system integration (such as considering water-resource policy within national economic and sectoral policies) are more relevant at the national level. Water and development processes also take place at different spatial and temporal scales, which means that integration must also be understood as bridging different spatial and temporal scales, the latter being especially important in the context of climate change. Beyond these differences, however, there are at least four common elements that apply to sound management at all levels and that embody the essence of what an overall IWRM approach is all about: 1. The approach recognizes that water is both a social and an economic good and has multiple uses. 2. The approach seeks to balance multiple objectives that at their core relate invariably to economic efficiency, social equity, and environmental sustainability. 3. The approach entails a broad, holistic, and integrated perspective relevant to the given level of decision making. 4. The approach requires the appropriate involvement of users at the given level of decision making. The body of research and practice to date at all the different levels suggests that there are some elements common to the way in which the approach has evolved at each level as well. For example, while it is now recognized that water-resource management at all levels requires an appropriate combination of hard and soft components – that is, economically, environmentally, and socially sound infrastructure coupled with effective institutions and governance – at most levels, this was not necessarily the starting point. The early stages of watershed management, for example, emphasized engineering approaches to control soil erosion and thus downstream effects, while much of the initial literature on water management at the national level focused on governance and policy issues, at the same time neglecting the need for investments in infrastructural development. Gradually, however, a more balanced approach began to gain prominence at all levels. Similarly, while in the initial stages there was often a tendency toward packages of practices, with a more blueprint approach, in more recent years, a more pragmatic, sequenced, and contextdriven approach has prevailed. At all levels, calls for more integrated forms of management are emphasizing the need to view these as an approach rather than a formal methodology or prescription.

One force identified by Lenton and Muller (2009) was the technical and methodological advances in dealing with complex water systems that began in earnest with the Harvard Water Program carried out at Harvard University from 1955 to 1960. This large multidisciplinary program resulted in the publication of Design of Water Resources Systems: New Techniques for Relating Economic Objectives, Engineering Analysis, and Governmental Planning (Maass et al., 1962). While the program focused principally on developing planning and design methodologies for complex multipurpose water-resource systems at the river-basin level, the approach advanced by the Harvard Water Program combined economic and engineering analysis, drew on techniques of mathematical efficiency models and computer simulation for river systems, and emphasized the role of political processes in decision making. A second force identified by Lenton and Muller (2009) was the recognition of the impact of human activities on the natural environment as crystallized in the Bruntland Report (World Commission on Environment and Development, 1987), and the full expression of these ideas and concepts at the Rio Earth Summit and its far-reaching action document, Agenda 21. Indeed, Chapter 18 of Agenda 21 – the longest chapter in the document – already embodied the concept in its title, which was ‘Protection of the quality and supply of freshwater resources: Application of integrated approaches to the development, management and use of water resources.’ It made a strong case for more integrated approaches than heretofore had been the norm, stating unequivocally that ‘‘the widespread scarcity, gradual destruction and aggravated pollution of freshwater resources in many world regions, along with the progressive encroachment of incompatible activities, demand integrated water resources planning and management.’’ Viewing IWRM as a meta-concept, however, suggests a third and crucial force, which is the set of conceptual and practical advances in management at different levels that have evolved over the last 30 years. Importantly, these advances came not from purely academic centers, but rather from the world of practice and of practical policymaking, reinforced by the findings of field-based research efforts. They have included

•

•

• •

1.01.9 History and Evolution of the Concept of IWRM

• The discussion in the previous section highlights the fact that the concept of an IWRM approach has been an evolving one. Several forces have given impetus to this concept.

Advances in watershed management, led by practical efforts to manage watersheds in a variety of contexts across the globe, the documentation and evaluation of many of these initiatives by organizations such as the World Bank, and the field-based research efforts of institutions such as IWMI and the World Agroforestry Centre. Advances in basin management, led by practical efforts in a number of countries such as France that set up river-basin organizations, field-based research by initiatives such as the CA, and the knowledge-exchange efforts of INBO and others. Advances in AWM, in which the field-based research of IWMI has played a major role. Advances in WSS, fostered by the work of the World Bank’s Water and Sanitation Program and the Water Supply and Sanitation Collaborative Council. Advances in water policy and governance, spearheaded principally by innovative efforts at the national level but supported by organizations such as the GWP and its technical committee.

Integrated Water Resources Management

Importantly, while advances at each level focused on the management issues critical to that level, they all seemed to have some important common elements. In particular, the best practices advocated as appropriate at each level tended to balance multiple objectives – economic efficiency, social equity, and environmental sustainability. They tended to start from a broad, holistic, and integrated perspective relevant to the given level of decision making. In addition, they tended to find a way to involve users at the given level of decision making. Not surprisingly, these common elements are those now recognized as being fundamental to an IWRM approach. While these fairly distinct drivers can be identified, the concept and practice of IWRM evolved over time in a nonlinear way, as is so often the case. While it began with best practices, advances in analytical tools, and advances in specific areas, IWRM as such was formally adopted as a concept at the Rio Earth Summit in 1992.

1.01.10 Assessments and Critiques of the Concept of IWRM As indicated earlier, there is an extensive literature analyzing and critiquing integrated approaches to water-resources management at each of the different levels at which these approaches have evolved in the last several decades – in watershed management, for example, or AWM or basin organizations. In recent years, however, this literature has been supplemented by a significant set of publications on the concept of IWRM as a whole. Initially, much of this literature on IWRM approaches emanated from the GWP, which in 1998 began producing the GWP Technical Committee (TEC) Background Paper series, which address key conceptual issues related to water-resources management (see e.g., Falkenmark, 2003; GWP, 2003, 2008; Rees, 2002, 2006; Rees et al., 2008; and Rogers et al., 1998). Later, the GWP began producing shorter technical and policy briefs on a variety of aspects relating to IWRM, including several that were designed to support countries in their efforts to prepare IWRM and water-efficiency strategies or plans, as well as a set of publications associated with the GWP IWRM ToolBox. Importantly, a range of other authors and groups associated directly or indirectly with GWP also took on the task of explaining and further articulating the concept of IWRM. Examples of these publications include the IWRM tutorial published by CapNet (CapNet, 2008), which provides

19

a brief introductory tutorial of the basic principles of IWRM, and a recent book by Soncini-Sessa et al. (2007), which focuses on the tools available for integrated and participatory water-resources management. Particularly in the last 4 or 5 years, a growing number of researchers and practitioners have taken a harder look at the concept of IWRM, sparking a lively and continuing debate on the subject. Some of these works examine IWRM in a specific geographic context, while others analyze aspects of IWRM and its theoretical underpinnings or critique the IWRM approach and its practical application. Antao and Walkuski (2008, Literature Survey on IWRM, Global Water Partnership, unpublished document) have prepared a comprehensive bibliography on the subject. A comprehensive book edited by Warner (2007) examined multi-stakeholder platforms and their role in integrated management. One important set of publications analyzes aspects of IWRM, including its scientific and theoretical underpinnings. A special issue of the Journal of Contemporary Water Research and Education published in 2006 aimed to provide new insights into the widespread experience of IWRM (Hooper, 2006). In 2007, a special issue of The Geographical Journal focused on what the journal described as Integrated Water Management (IWM); many of the papers in this special issue emphasized that IWM is largely influenced by contextual conditions. Some recent literature (e.g., Pahl-Wostl et al., 2005; Pahl-Wostl and Sendzimir, 2005; and Timmerman et al., 2008) focuses on the concept of adaptive water-resources management, which as explained earlier emphasizes the need for more adaptive systems of management, drawing on ecosystem-management theory. Other recent literature looks at the practical aspects of IWRM. The CA (CA, 2008) notes that in developing countries, what is usually passed-off in the name of IWRM has tended to have a very narrow blueprint package focus – national water policy, a water law and regulatory framework, recognition of the river basin as the unit of planning and management, treating water as an economic good, and participatory management – that represents a big shift from current paradigms and that makes these IWRM initiatives ineffective or counterproductive. Biswas (2004), Biswas and Tortajada (2004), and Biswas (2008) see IWRM as a somewhat fixed approach and raise questions about whether such an approach is practical given the wide range of conditions under which the approach might be applied. While acknowledging some of the above concerns, Lenton and Muller (2009) examine a range of practical examples to

Box 7 Key lessons from IWRM in practice. From GWP Technical Committee (2009) Lessons from Integrated Water Resources Management in Practice, Policy Brief 9. Stockholm: Global Water Partnership. *

* * *

*

IWRM is not a one-size-fits-all prescription and cannot be applied as a checklist of actions. Pragmatic, sensibly sequenced institutional approaches that respond to contextual realities have the greatest chance of working in practice. Water-resource planning and management must be linked to a country’s overall sustainable development strategy and public administration framework. Water management must ensure that the interests of the diverse stakeholders who use and impact water resources are taken into account. Approaches to water-resources management will evolve as the pressures on the resource and social priorities change. The challenge is to support the development of institutions and infrastructure that can meet the challenges of new circumstances. While the river basin is an important and useful spatial scale at which to manage water, there are often circumstances where it is appropriate to work at smaller sub-basin scale or at a regional multi-basin level.

20

Integrated Water Resources Management

see how the principles embodied in the concept of IWRM have been applied at different scales, from very local experiences to reform at the national level and beyond, and give evidence of the positive results obtained. The lessons learned from looking at these examples have been summarized in GWP Technical Committee (2009), and reproduced in Box 7. Taken as a whole, the examples in ‘IWRM in practice’ suggest that, although IWRM has been criticized as a theory that is very difficult to put into practice, it is more like a practice around which it has been very difficult to build a good theory.

Acknowledgments The chapter draws on, and expands, the analytical framework used by the author and his colleague Mike Muller in Integrated Water Resources Management in Practice: Better Water Management for Development (Lenton and Muller, 2009), which greatly facilitated the preparation of this chapter and for which he is very grateful to Mike Muller. The author would also like to acknowledge, with thanks, the very many discussions on the IWRM approach held with colleagues on the Technical Committee of the Global Water Partnership from 2003 to 2006. These discussions, as well as the many background papers and briefs emanating from the Technical Committee during this period, have substantially contributed to the conceptual thinking and information contained in this chapter.

References Agarwal A and Narain S (1999) Community and household water management: The key to environmental regeneration and poverty alleviation. Presented at EU UNDP Conference. Brussels, February 1999. Barry B, Namara R, and Bahri A (2009) Better rural livelihoods through improved irrigation management: Office du Niger (Mali). In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development, pp. 71–87. London and Sterling, VA: Earthscan. Bauer C (2004) Siren Song: Chilean Water Law as a Model for International Reform. Washington, DC: Resources for the Future. Biswas AK (2004) Integrated water resources management: A reassessment. Water International 29: 248--256. Biswas AK (2008) Integrated water resources: Is it working? Water Resources Development 24(1): 5--22. Biswas AK and Tortajada C (eds.) (2004) Appraising the Concept of Sustainable Development: Water Management and Related Environmental Challenges. Oxford: Oxford University Press. Bourne C (1992) The International Law Commission’s draft articles on the law of international watercourses: Principles and planned measures. Colorado Journal of International Environmental Law and Policy 3: 65--92. Bruehl H and Waters M (2009) Transboundary cooperation in action for integrated water resources management and development in the Lower Mekong Basin. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development, pp. 189–204. London and Sterling, VA: Earthscan. CA (Comprehensive Assessment of Water Management in Agriculture) (2007) Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture. London: Earthscan; Colombo: International Water Management Institute. CA (Comprehensive Assessment of Water Management in Agriculture) (2008) Developing and managing river basins: The need for adaptive, multilevel, collaborative institutional arrangements, Issue Brief No. 12, International Water Management Institute and Global Water Partnership. CapNet (2008) IWRM Tutorial. http://www.archive.cap-net.org/iwrm_tutorial/ mainmenu.htm (accessed March 2010).

Comisio´n Nacional del Agua (2001) Programa Hı´drico de la cuenca Lerma-Santiago 2001–2006. Me´xico. CSE (Centre for Science and Environment) (1994) Partners in prosperity. Down to Earth, 15 February 1994. CSE (Centre for Science and Environment) (1998) Sukhomajri at the crossroads. Down to Earth, 15 December 1998. CSE (Centre for Science and Environment) (2002) Foisting failure Down to Earth, 31 August 2002. CSE (Centre for Science and Environment) (2007) Saga of two villages Down to Earth, 15 November 2007. Darghouth S, Ward C, Gambarelli G, Styger E, and Roux J (2008) Watershed Management Approaches, Policies, and Operations: Lessons for Scaling Up, Water Sector Board Discussion Paper Series, Paper No. 11. Washington, DC: The World Bank. Dau FE and Aparicio MJ (eds.) (2006) Acciones para la recuperacio´n ambiental de la cuenca Lerma-Chapala. In: Comisio´n Estatal de Agua y Saneamiento, Gobierno del Estado de Jalisco, 126 pp. Me´xico: Comisio´n Estatal de Agua y Saneamiento de Jalisco, Guadalajara. De Coning C (2006) Overview of the water policy process in South Africa. Water Policy 8: 505--528. de Fraiture C, Giordano M, and Liao Y (2008) Biofuels and implications for agricultural water use: Blue impacts of green energy. Water Policy 10(supplement 1): 67--81. Direccio´n General de Aguas (DGA) (1999) Repu´blica de Chile, Polı´tica Nacional de Recursos Hı´dricos. DWAF (2004) National Water Resource Strategy, 1st edn. Pretoria: Department of Water Affairs and Forestry. Falkenmark M (2003) Water Management and Ecosystems: Living with Change, TEC Background Papers No. 9. Stockholm: Global Water Partnership. Farrington J, Turton C, and James AJ (1999) Participatory Watershed Development: Challenges for the Twenty-First Century. New Delhi, India: Oxford University Press. Guitro´n A, Hidalgo J, Aparicio J, and Aldama A´ (2003) A water crisis management: The Lerma-Chapala basin case. In: Brebbia CA (ed.) Water Resources Management II, pp. 345--354. Southampton: WIT Press. GWP (Global Water Partnership) (2003) Integrated Water Resources Management Toolbox, Version 2. Stockholm: GWP Secretariat. GWP (Global Water Partnership) and INBO (International Network of Basin Organizations) (2009) A Handbook for Integrated Water Resource Management in Basins. GWP Technical Advisory Committee (2000) Integrated Water Resources Management, TAC Background Papers No. 4. Stockholm: Global Water Partnership. GWP Technical Committee (2004) Catalyzing Change: A Handbook for Developing Integrated Water Resources Management (IWRM) and Water Efficiency Strategies. Stockholm: Global Water Partnership. GWP Technical Committee (2006) Water and Sustainable Development: Lessons from Chile, Catalyzing Change Series Policy Brief 2. Stockholm: Global Water Partnership. GWP Technical Committee (2009) Lessons from Integrated Water Resources Management in Practice, Policy Brief 9. Stockholm: Global Water Partnership. Heathcote IW (1998) Integrated Watershed Management: Principles and Practices. p. 441. New York, NY: Wiley. Hellegers P, Zilberman D, Steduto P, and McCornick P (2008) Interactions between water, energy, food and environment: Evolving perspectives and policy issues. Water Policy 10(supplement 1): 1--10. Hidalgo J and Pen˜a H (2009) Turning water stress into water management success: Experience in the Lerma–Chapala river basin. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development, pp. 107–120. London and Sterling, VA: Earthscan. Hooper B (2005) Integrated River Basin Governance: Learning from International Experiences. London: IWA Publishing. Hooper B (2006) Integrated water resources management: Governance, best practice, and research challenges. Journal of Contemporary Water Research and Education 35: 1--7. IGRAC (International Groundwater Resources Assessment Centre) (2009) Transboundary Aquifers of the World Map – Update 2009. Joshi PK, Jha AK, Wani SP, Joshi L, and Shiyani RL (2005) Meta-analysis to assess impact of watershed programme and people’s participation. In: Comprehensive Assessment of Water Management in Agriculture, Research Report 8. Colombo: International Water Management Institute. Kerr J (2002) Sharing the benefits of watershed management in Sukhomajri, India. In: Pagiola S (ed.) Selling Forest Environmental Services: Market-Based Mechanisms for Conservation and Development. London: Earthscan. Khurana MR (2005) Common property resources, people’s participation and sustainable development: A study of Sukhomajri. Panjab University Research Journal (Arts) XXXII(18.2). April–October.

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Lenton R and Muller M (2009) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. Lenton R and Walkuski C (2009) A watershed in watershed management: The Sukhomajri experience. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development. London and Sterling, VA: Earthscan. Lenton R, Wright A, and Lewis K (2005) Health, Dignity and Development: What Will It Take?, pp. 17–28. London: Earthscan. Maass A, Hufschmidt MM, Dorfman R, Thomas HA Jr, Marglin SA, and Fair GM (1962) Design of Water-Resource Systems; New Techniques for Relating Economic Objectives, Engineering Analysis, and Governmental Planning. Cambridge: Harvard University Press. Major DC and Lenton RL (1979) Applied Water Resource Systems Planning. Englewood Cliffs: Prentice Hall. Muller M (2009) Attempting to do it all: How a New South Africa has harnessed water to address its development challenges. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development, pp. 169–185. London and Sterling, VA: Earthscan. Pahl-Wostl C, Downing T, Kabat P, et al. (2005) Transition to Adaptive Water Management: The NeWater Project. NeWater Working Paper 1, Institute of Environmental Systems Research, University of Osnabru¨ck. Pahl-Wostl C and Sendzimir J (2005) The Relationship between IWRM and Adaptive Water Management. NeWater Working Paper 3. Institute of Environmental Systems Research, University of Osnabru¨ck. Pen˜a H (2009) Taking it one step at a time: Chile’s sequential, adaptive approach to achieving the three Es. In: Lenton R and Muller M (eds.) Integrated Water Resources Management in Practice: Better Water Management for Development, pp. 153–168. London and Sterling, VA: Earthscan. Pen˜a H, Luraschi M, and Valenzuela S (2004) Water, Development and Public Policies. South American Technical Advisory Committee (SAMTAC), Economic Commission for Latin America and the Caribbean (ECLAC) and Global Water Partnership (GWP). Rees JA (2002) Risk and Integrated Water Management, TEC (formerly TAC) Background Papers No. 6. Stockholm: Global Water Partnership. Rees JA (2006) Urban Water and Sanitation Services: An IWRM Approach, TEC Background Papers No. 11. Stockholm: Global Water Partnership. Rees JA, Winpenny J, and Hall AW (2008) Water Financing and Governance, TEC Background Papers No. 12. Stockholm: Global Water Partnership. Rogers P, Bhatia R, and Huber A (1998) Water as a Social and Economic Good: How to Put the Principle into Practice, TAC Background Papers No. 2. Stockholm: Global Water Partnership. Rogers P and Hall A (2003) Effective Water Governance, TEC Background Papers No. 7. Stockholm: Global Water Partnership. Rosegrant M, Cai X, and Cline S (2002) World Water and Food to 2025: Dealing with Scarcity. Washington, DC: IFPRI. Saleth RM and Dinar A (2005) Water institutional reforms: Theory and practice. Water Policy 7: 1--19. Seckler D (1986) Institutionalism and agricultural development in India. Journal of Economic Issues XX(4). December.

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Shah T (2009) Taming the Anarchy: Groundwater Governance in South Asia. Washington, DC: Resources for the Future. Colombo: International Water Management Institute. Sharma BR, Samra JS, Scott CA, and Wani SP (eds.) (2005) Watershed Management Challenges: Improving Productivity, Resources, and Livelihoods. New Delhi, India: International Water Management Institute. Soncini-Sessa R, Cellina F, Pianosi F, and Weber E (2007) Integrated and Participatory Water Resources Management – Practice, Volume 1b. Developments in Integrated Environmental Assessment Series. Amsterdam: Elsevier. Timmerman JG, Pahl-Wostl C, and Moltgen J (2008) The Adaptiveness of IWRM: Analysing European IWRM Research. London: IWA Publishing. UNCED (United Nations Conference on Environment and Development) (1992) Agenda 21, Report of the United Nations Conference on Environment and Development. http://www.un.org/esa/sustdev/documents/agenda21 (accessed March 2010). Warner J (ed.) (2007) Multi-Stakeholder Platforms for Integrated Water Management Studies in Environmental Policy and Practice. Aldershot, UK: Ashgate Publishing. Wester P (2008) Shedding the Waters: Institutional Change and Water Control in the Lerma-Chapala Basin, Mexico. PhD Dissertation, Wageningen University, Wageningen, The Netherlands. World Bank (2003) Tunisia Northwest Mountain Areas Development Project Performance Assessment Report. Washington, DC: World Bank. World Bank (2004a) Peru Sierra Project Implementation Completion and Results Report. Washington, DC: World Bank. World Bank (2004b) Tajikistan Community Agriculture and Watershed Management Project, Project Appraisal Document. Washington, DC: World Bank. World Bank (2004c) Turkey Eastern Anatolia Project, Project Performance Assessment Report. Washington, DC: World Bank. World Bank (2005) China Loess II Project Implementation Completion and Results Report. Washington, DC: World Bank. World Bank (2006) Water Management in Agriculture: Ten Years of World Bank Assistance, 1994–2004. Washington, DC: Independent Evaluation Group (IEG)/The World Bank. World Commission on Environment and Development (1987) Our Common Future (Brundtland Report). Oxford: Oxford University Press. WSSD (World Summit on Sustainable Development) (2002) Johannesburg Plan of Implementation. http://www.un.org/esa/sustdev/documents/WSSD_POI_PD/ English/POIToc.htm (accessed March 2010).

Relevant Websites http://www.rlc.fao.org Food and Agricultural Organization of the United Nations; Regional Office for Latin America and the Caribbean; Network on Watersheds Management. http://www.gwpforum.org Global Water Partnership. http://rupes.worldagroforestry.org Rewards for, Use of and shared investment in Pro-poor Environmental Services.

1.02 Governing Water: Institutions, Property Rights, and Sustainability E Schlager and C Bauer, The University of Arizona, Tucson, AZ, USA & 2011 Elsevier B.V. All rights reserved.

1.02.1 1.02.2 1.02.3 1.02.3.1 1.02.3.2 1.02.4 1.02.5 References

Introduction International Organizations and Water Policy Debate Governing Water from the Ground Up Local Communities, Property Rights, and Water Linking Water Uses and Administration across Multiple Scales and Jurisdictions Courts: Hiding in Plain View Conclusion: Reconceptualizing Water Governance

1.02.1 Introduction What is water governance and why is it included in this treatise? Water governance is one of those elastic terms that has something for everyone – everyone agrees that it is important and many disagree about what it means (‘institutions’ is another example). In a general way, governance refers to how people make decisions and govern themselves, whether in organizations or at the larger scale of societies. Thus, governance is about social, political, and economic processes and how they interact over time. This of course includes the realm of formal governmental institutions; however, it goes beyond this realm as well to include other aspects of social and political life. Is governance any different from politics? It depends on what one thinks politics means. They are not easily distinguishable, especially since both terms tend to be used with a great deal of abstraction and generality. In much international debate about water policy, water governance has become a black box, where we put important but complicated issues that we are not sure how to think about or talk about. Some of these issues are so deeply political that they are dangerous to discuss in public, and governance is soothing and blurs the edges. Our goal in this chapter is to open the lid of the black box of water governance and look more closely at what is inside. Where is the basic political economy, the distribution of wealth, and power in society? Who gains and who loses in water governance? What are people talking about when they talk about governance? What analytical tools can we bring to bear that would allow us to flesh out and make sense of governance?

1.02.2 International Organizations and Water Policy Debate We begin by reviewing the positions on water governance of several prominent international organizations. A good place to start is Peter Rogers and Alan Hall’s paper on water governance for the Global Water Partnership, or GWP (Rogers and Hall, 2003; building on Rogers’ work for the Inter-American Development Bank (Rogers, 2002)). The GWP is an international organization that was established in 1996 to promote integrated water resources management (IWRM) around the

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world. IWRM is an international catch phrase that refers to the idea that water should be managed in a holistic, comprehensive, and multidisciplinary way – a way that does justice to the hydrologic cycle. This usually means focusing on the relationships between water quality and quantity, between surface water and groundwater, integrating across different water-using sectors at the level of river basins. Since the GWP aims to be the official, mainstream voice of IWRM around the world, its position on water governance is worth looking at (Bauer, 2004; Conca, 2006; GWP, 2000b). According to the GWP, ‘‘IWRM is a process which promotes the coordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in a equitable manner without compromising the sustainability of vital ecosystems’’ (GWP, 2000a: 22). Rogers and Hall begin by underlining the intensely political nature of allocating water, and then state that ‘‘governance is about effectively implementing politically achieved allocations’’ (Rogers and Hall, 2003: 4). Governance also ‘‘broadly embraces the formal and informal institutions by which authority is exercised’’ (Rogers and Hall, 2003: 7). They quote contemporary definitions of governance by the United Nations Development Program and the GWP that are breathtaking in their sweep: Governance is the exercise of economic, political and administrative authority to manage a country’s affairs at all levelsyit comprises the mechanisms, processes and institutions through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations and mediate their differences. (UNDP, 2001)

Water governance refers to the range of political, social, economic, and administrative systems that are in place to develop and manage water resources, and the delivery of water services, at different levels of society. (GWP, 2002)

On such a wide-open playing field, Rogers and Hall run through a series of major issues, approaches, and schools of thought in political science and political and social theory. Examples include the incentives for legislators’ behavior, the relationship between the state and civil society, and the tension between markets and centralized hierarchies. Where they focus on water governance in particular, they emphasize the legal foundations and different forms of property rights and

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Governing Water: Institutions, Property Rights, and Sustainability

institutional arrangements. They argue that water governance is affected by factors both internal and external to the water sector itself. Finally, they make the link from water governance to the need for water policy reforms and IWRM, along the lines expressed by the GWP mission. The GWP has published other documents that have shaped the international water policy debate. In its framing paper for the 2nd World Water Forum in 2000, the GWP famously declared ‘‘The water crisis is mainly a crisis of governance.’’ Governance here means conflict resolution: ‘‘The present threat to water security lies in the failure of societies to respond to the challenge of reconciling the various needs for and uses of watery. And governance lies at the center of the tension and delicate balance between different water uses and their management’’ (GWP (2000b: 23), emphasis in original; see also Cosgrove and Rijsberman (2000)). From this angle, that is, to deal with conflict, the GWP argues for a series of policy reforms to improve water governance. One key reform is to put IWRM into practice, although here too the GWP underlines the political nature of ‘‘integrating the seemingly incompatible goals, beliefs, interests, and knowledge of every water user’’ (GWP, 2000b: 25). Another key reform is to strengthen institutions and management, which will clearly require the organization and exercise of political power. Two other reforms to improve governance are to promote transparency and participation and to take a more economic approach to valuing water and designing price incentives. In short, the GWP position on water governance revolves around recognizing political conflicts and developing the capacity – that is, power – to handle them. As befits an international organization, the GWP documents are silent or vague about any specific political alliance or opposition in a given country (GWP, 2000a). In the year 2000, the other landmark event in global water governance (besides the 2nd World Water Forum) was the final report of the World Commission on Dams, or WCD (World Commission on Dams, 2000). The WCD was an unusual organization in many ways, a pioneering example of building multi-stakeholder and international consensus about complex and conflictive issues. The story and process of the WCD are as significant as the conclusions. The WCD was created through negotiations between the World Bank, several national governments, and a group of international environmental nongovernmental organizations (NGOs), and its task was to evaluate the world experience of large dams. The commissioners, once named, had to hire staff and decide how to carry out their tasks, including how to build legitimacy and authority for their results and recommendations. The Commission itself had no regulatory power and it was dissolved after disseminating its final report (the details are well delineated in Dubash et al. (2001) and Conca (2006)). The WCD report looked at water governance as a matter of social equity and justice above all: conflict is understood as driven by who wins and who loses. The report’s broad scope is announced in the second sentence: ‘‘The debate about dams is a debate about the very meaning, purpose and pathways for achieving development.’’ The WCD proposes a new framework for decision making, describing an approach based on recognizing different rights and assessing different risks involved with dams. This rights-and-risks approach aimed to broaden

the range of who counted as legitimate stakeholders in negotiating problems about dams, as well as broadening the issues on the agenda. The WCD argument is in part a moral argument. The Commission’s concrete impacts remain uncertain. Let us conclude this brief review with the World Bank. In the World Bank’s last two major documents about water policy, in 1993 and 2004, the authors use the term management instead of governance, but they are writing about the same issues. Both documents use the language of contemporary IWRM, while adding or strengthening the promarket stamp that has come with the World Bank in recent decades (e.g., World Bank (2004) adopts the GWP image of the comb to illustrate IWRM: the teeth of the comb represent different water-using sectors and the handle holds them together). However, the World Bank’s position on water politics and political economy has changed since the early 1990s, with significant implications for water governance (even if the World Bank calls it management). The World Bank’s 1993 Water Policy Paper had something for almost everyone: a great degree of emphasis on markets, privatization, and pricing, coupled with arguments in favor of strong government regulation and strengthened institutional arrangements (World Bank, 1993: 40). The paper spoke in grand generalities about IWRM and a so-called comprehensive analytical framework for resolving water problems, but the concrete meaning was vague and the political tone was muted. A decade later, in contrast, the World Bank’s key water experts were reasserting their authority and perspective after years of defending the World Bank from outside criticism. The 2004 Water Resources Sector Strategy focused on the implementation of the World Bank’s ideas and policies, which means a more down-to-earth and pragmatic approach to how reforms play out in the real world. The key is recognizing that water resources management is intensely political and that reform requires the articulation of prioritized, sequenced, practical and patient interventions. To be a more effective partner, the Bank must be prepared to back reformers and to pay more explicit attention in design and implementation to the political economy of reform. (World Bank, 2004: 3, emphasis added)

What does that mean? Later in the document the ‘‘political economy of water management and reform’’ is described as placing ‘‘particular emphasis on the distribution of benefits and costs and on the incentives that encourage or constrain more productive and sustainable resource use.’’ (World Bank, 2004: 13, emphasis added) This bare-knuckle approach means that ‘‘the World Bank will re-engage with high reward/high risk hydraulic infrastructure,’’ that is, dams (World Bank, 2004: 3). The World Bank’s maneuver is a notable dodge: having invoked political economy as the distribution of benefits and costs, the World Bank reframes the debate in terms of risks and rewards and then is silent about how the risks and rewards are distributed. If we want to know, we will have to find out for ourselves. The challenge for researchers is to identify specific reformers whom the World Bank has backed, and specific examples of water reforms, and then analyze the distribution of benefits and costs: Who gained and who lost? Also, how was that related to the reforms’ design and implementation?

Governing Water: Institutions, Property Rights, and Sustainability

The key international actors have recognized the centrality of politics in water governance, but have avoided tackling the more difficult issues of who participates in making collective decisions, the types of authority participants have to address problems, issues, and conflicts, and how benefits and burdens are distributed among people. While IWRM is often pointed to as the model for governance, it is also largely content free (Conca, 2006). Furthermore, IWRM possesses a distinct topdown bias (Kemper et al., 2007; Carlsson and Berkes, 2005). It assumes that a central government shares its power to make and enforce decisions with other, lower-level governments, and that civil society – in whatever form that takes: water user associations, nonprofit organizations, etc. – is invited into the decision-making processes. In the following section, we propose an alternative starting point, water users, and a form of policy analysis that attempts to diagnose the politics of specific water settings before prescribing institutional reforms.

1.02.3 Governing Water from the Ground Up 1.02.3.1 Local Communities, Property Rights, and Water A major research program in the social sciences focuses on the study of common pool resources, such as rivers, streams, and groundwater basins and the riparian and aquatic habitat such water sources support (Blomquist, 1992; Ostrom et al., 2002). One of the defining lessons from more than two decades of this research is the centrality of local-level resource users. Sustainable use of resources requires active participation of local water users in water governance. Fundamentally, common pool resources present two challenges to resource users and others engaged in management and governance – exclusion and use (Ostrom et al., 1994). Since it is challenging to realize and sustain exclusion, and since subtractability means resource units harvested by one user are not available for another user, common pool resources tie users together. Realizing exclusion and limiting the number of resource units subtracted from a common pool resource require the cooperation of most resource users. The benefits and costs resource users achieve in using a common resource depend on the actions of all. If sustainable use of common pool resources is to be realized, then both exclusion and subtractability must be carefully considered and managed. For instance, a common approach to address declining groundwater tables among states in the western US is to adopt well moratoria. Such a policy tool directly addresses exclusion: only those resource users with wells are allowed to access groundwater basins and no new resource users are allowed in. This policy, however, does not address problems raised by subtractability. If a well moratorium is not matched with limits on the amount of water each well may pump, a groundwater basin may be mined by existing users. Conversely, placing limits on how much water may be withdrawn from a groundwater basin, and by what means, without addressing exclusion, also exposes the basin to mining. For instance, the Arizona Groundwater Management Act, as originally written, limited water use by municipal water utilities to a specified number of gallons per person per day.

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While gallons per person per day mandates directly addressed subtractability issues, they did not address exclusion issues. In effect, any person could move to a municipality governed by the groundwater act and through the municipal water provider access the groundwater basin. No limits were placed on the number of people that water utilities could serve, thus allowing access to anyone to the groundwater basin. Until relatively recently, a common belief among policy analysts and policymakers was that local-level resource users could not adequately address exclusion and subtractability issues, that is, they could not sustainably govern their shared water resources. Rather, the expectation was that most common pool resource settings were characterized by the rule of capture and, consequently, a race to harvest as many resource units as possible before they were captured by others (Hardin, 1968; Olson, 1965; Ostrom, 1990). The result was over-harvesting at best, and severe degradation or destruction of a common pool resource at worst, and the need for external intervention to save resource users from the race to harvest. It turns out, however, that the models predicting overuse and degradation were too simple, failing to adequately capture key features of many common pool resource settings, such as the ability of resource users to communicate with one another, to share experiences and knowledge of the resource; or the capacity of resource users to develop norms of reciprocity and sharing; or the values that resource users place on the resource and on the opportunity to have their children and grandchildren use the resource; or the experience resource users have in governing other areas of their lives that they can transfer to governing a common pool resource, and so on and so forth (Ostrom, 2007). For the past two decades, hundreds of cases of resource users sustainably managing water resources have been documented and published (Digital Library of the Commons, 2009). In a number of instances, local-level resource users have developed governing arrangements that perform better than government regulation and management. For instance, in an early study comparing farmer-managed irrigation systems and government-managed irrigation systems, Tang (1994) found that among irrigation systems that performed well, rules that govern water allocation and maintenance activities are better crafted to the specific conditions of each irrigation system. High-performing systems, which were more likely to be farmer managed, were associated with multiple rules that adequately limited access to the system and that fairly allocated water among the irrigators. In other words, farmers paid careful attention to exclusion and subtractability. Poorly performing irrigation systems, which were more likely to be government managed, were characterized by a single simple rule set or by no rules at all. Access to the irrigation systems was not adequately regulated and water allocation rules often did not work well. Monitoring and enforcement systems also differ between irrigator-owned systems and government-owned systems. Government-owned systems relied on full-time, paid guards. Farmer-owned systems relied on unpaid part-time guards (Tang, 1994: 241). However, guards in farmer-owned systems were much more likely to impose sanctions on rule breakers than were guards in government-owned systems. Furthermore, rule-following behavior was much more common in

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Governing Water: Institutions, Property Rights, and Sustainability

farmer-owned systems than in government-owned systems, whether guards were present or not (Tang, 1994: 241). Farmers who participate in devising their own irrigation rules are much more likely to follow and actively monitor and enforce their rules. The means by which water users address exclusion and use issues is through devising, following, monitoring and enforcing institutional arrangements. By institutional arrangements we refer to strategies, norms, rules, and property rights systems (Ostrom et al., 1994; Ostrom, 2007). Property rights define relationships among people in relation to things, such as a common pool resource and the resource units it produces. For every right an individual holds, rules authorize or forbid specific actions in exercising the right (Schlager and Ostrom, 1992: 250). For instance, among the irrigators studied by Tang (1992, 1994), those irrigators in farmer-managed systems exercised their rights of access to and withdrawal of water from the irrigation system in substantially different ways than irrigators who were part of a government-managed system. Access rights to government-managed systems were operationalized by a single rule – ownership of land in the irrigation command area (Tang, 1994: 231). Access rights to farmer-managed systems were operationalized by a variety of rules, such as purchasing or leasing shares in the irrigation system, becoming a member in an irrigation organization, or paying fees for access to water (Tang, 1994: 231). Similarly, withdrawal rights in government-managed systems were also operationalized by a single rule, whereas farmer-managed systems were characterized by multiple rules. Furthermore, irrigators in farmer-managed systems also exercised rights of management and exclusion (Schlager and Ostrom, 1992: 251), that is, irrigators possessed the right to regulate how water would be allocated and how maintenance activities would take place (management rights), as well as to determinine who would hold rights of access and how such rights would be exercised (exclusion rights). Irrigators in government-managed systems did not exercise rights of management and exclusion; rather those rights were exercised by government officials. Rights of management and exclusion are collective-choicelevel rights (Schlager and Ostrom, 1992). Holders of such rights are authorized to develop rules that define how rights of access and withdrawal may be exercised. Who holds rights of management and exclusion and their relationship to the common pool resource affect the types of rules devised. Government officials, even as they exercise rights of management and exclusion, will not exercise rights of access and withdrawal. Since they are not directly subject to the irrigation rules they devise, they face few incentives to design rules that ensure the effective operation of irrigation systems. Instead, they may devise rules for other purposes, for instance, to increase their political support or lighten their administrative burdens. Conversely, because farmers in farmer-managed irrigation systems directly experience the consequences of their rule-making decisions, they confront incentives to craft the rules to the particular situation that they face (Tang, 1992). What common pool resource studies have demonstrated is the diversity and the multi-dimensionality of institutional arrangements devised by water users. In any given common pool resource setting, resource users are likely to hold different

bundles of property rights and exercise them in different ways, depending on the rules that specify different actions. In other words, legal pluralism, that is, multiple property rights systems, is likely to be the rule and not the exception across water settings. For instance, irrigation ditches and districts in eastern Colorado often control a portfolio of water that consists of several different types and sources, each governed by a different set of property rights and rules. Water from streams and rivers is governed differently from federal project water, and federal project water is governed differently from irrigation district reservoir water (Blomquist et al., 2004). Rivers and streams are governed by the prior appropriation doctrine, federal project water by the project’s enabling legislation, and district reservoirs by the rights and rules developed by the districts’ governing boards. In addition, the institutional arrangements devised by water users are multidimensional. Resource users give consideration to the transactions costs of implementing and administering property rights and rules, the allocation of benefits and costs realized by the rights and rules, how the rights and rules allocate risk, and whether the rights and rules are likely to dampen conflict among resource users. In other words, whether rules are effective is evaluated along multiple dimensions. A classic illustration of the multidimensionality of institutional arrangements involves the irrigation systems built and managed by farmers, located in Ilocos Norte, the Philippines, as reported by Coward (1979). Farmers gain entry into the systems by purchasing shares. A share entitles a household to a rich set of property rights – access, withdrawal, exclusion, and management. The rules the farmers have devised for exercising their property rights and engaging in irrigated farming are sophisticated. A share entitles a household to several plots of land dispersed along a canal so that all farmers have land located closer to the more desirable head of the canal and land located closer to the less desirable tail end of the canal. Not only do such rules dampen conflict that often emerges among farmers in different locations of an irrigation system, but they also allow the farmers to spread the risk of water shortages. During extremely dry periods, a portion of the irrigation system may be shut down, and farmers forbidden from irrigating plots located in the closed sections. However, farmers still have use of plots located in open sections. The shares allocate risks, benefits, and costs in a proportionate manner. Water and work obligations for maintaining the systems are allocated based on the proportion of land encompassed by shares. More land translates into not only more water but also greater work obligations. Finally, attention is paid to monitoring of water use. Water monitors are selected from among farmers and they are paid for their services by grants of land at the tail end of canals. Whether those plots receive water depends in part on how well the irrigators on the canal are monitored. The Filipino irrigation systems are just one example of many by which resource users have devised institutional arrangements that are self-reinforcing, that is, the decisions and trade-offs made along one dimension (e.g., allocating plots of land across an irrigation command area), supporting and reinforcing decisions made along another dimension (e.g., allocating the risk of water shortages), and so on. Many other such irrigation examples may be pointed to, such as Bali

Governing Water: Institutions, Property Rights, and Sustainability

(Lansing, 1991), Nepal (Lam, 1998), and Spain (Maass and Anderson, 1986). Cases such as these illustrate the institutional artisanship that resource users are capable of. As Ostrom (1990, 1999, 2007) has repeatedly noted, there is no single best set of property rights nor single rule set that support sustainable uses of water. Rather, long-enduring institutional arrangements appear to share some common features (Ostrom, 1990). According to Ostrom (1990), the most important feature is exclusion of nonowners. Exclusion is critical if water users are to commit to following a set of institutional arrangements over time and investing in modifying them as circumstances warrant. Water users must be assured that they will capture the benefits of their actions. Exclusion, however, while critical, is insufficient to ensure long-term commitment to property rights and rules. The institutional arrangements must be appropriate, crafted to the exigencies of the situation, and as the situation changes, the resource users must have the ability to modify the rules. Accountable monitors and graduated sanctioning maintain water users’ commitment to institutional arrangements. Finally, conflict-resolution mechanisms and at least a minimal recognition of the right to organize prevent these institutional arrangements from unraveling due to internal strife or invasion from external governmental authorities (Ostrom, 2007). While it is clear that water users are capable of designing relatively sophisticated and resilient institutional arrangements, there are just as many instances of water users failing to develop or sustain governing arrangements for many reasons. Resource users may not have the capacity, experience, or incentives to overcome collective action problems and devise governing arrangements that allow them to sustainably use their water resources (see Ostrom, 1999, 2000, 2007). Alternatively, certain types of resources or resource problems are extremely difficult for local resource users to address (Schlager and Blomquist, 2005; Schlager, 2005). For instance, groundwater basins present extraordinarily difficult challenges that resource users struggle with. Boundaries of basins are not easily identified, if at all, by resource users, neither is the structure of the resources. Groundwater pumpers, even if they are in close proximity of one another, may not know whether they are pumping from the same basin, or, if they are, whether they are pumping from the same aquifer within the basin. They are also unlikely to know whether and to what extent their basin is connected to other basins, or whether and in what ways their basin is connected to surface water sources. In other words, groundwater pumpers cannot easily sketch out the boundaries and dimensions of the resource or the boundaries of the resource users (Schlager and Blomquist, 2005). In addition, a groundwater basin, even if its boundaries and basic structure are well specified, is not just a bathtub filled with water. It consists of different pieces, such as layers of aquifers, connected surface water sources, recharge areas, flow paths and impediments, and so forth, structured, connected, and disconnected by varying geological formations. The pieces are characterized by different temporal and spatial scales. For instance, depending on soil characteristics, water percolating from the land surface will reach different portions of an aquifer at differing rates. Some water quality or water supply

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impacts will be felt quickly and others gradually, some throughout an aquifer and others in localized zones. The uncertainties around boundaries of the groundwater basins and the identities of groundwater pumpers combined with the complexity of the resource create substantial barriers for groundwater users to organize and develop institutional arrangements that adequately address exclusion and subtractability issues. Encouraging resource users to collectively limit their harvesting activities will be difficult if they are not assured that they will reap the benefits of conservation (Schlager and Blomquist, 2005). Finally, even if resource users do develop governing arrangements, those arrangements may rest on values or center on goals in conflict with those of the society at large. Segments of communities may be excluded from a water resource or from participating in decision-making processes based on ethnicity, gender, or class (Ilahiane, 1999). Allocation rules may favor one group of users over another (Tang, 1992), and enforcement of rules may rest on questionable practices (Rose, 2002). Each of these issues, a lack of local self-governance, the inability to tackle particular types of problems, and the pursuit of questionable values or goals, draws attention to the larger context in which local governance operates. What types of resources, authorities, and institutional ties link resource users with governments and organizations at the regional, national, or international levels that may be drawn upon to support investment in local governance capacity, or assist local resource users in addressing regional problems, or allow higherlevel officials to intervene to address particularly inequitable outcomes? These are not easy questions to address. Local knowledge and contextual information critical for designing workable rules and policies are primarily centered among resource users. As a result, workable rules and policies require the active participation of resource users in governance. Empowering them, however, is fraught with challenges. Providing aid and assistance to local communities to bolster their conservation activities is not a straightforward process. Communities are not homogeneous political, social, and economic groupings that can be treated as a single unit (Agrawal and Gibson, 1999). External interventions, if not carefully crafted to the setting, may result in tragic unintended consequences.

1.02.3.2 Linking Water Uses and Administration across Multiple Scales and Jurisdictions How to characterize and think about the ties and linkages among governments, organizations, and groups, in order to gain traction for understanding and addressing practical problems is an ongoing struggle. The chasm between the dynamics of local-level, self-governing arrangements characterized by dense social networks and webs of norms, and central governments characterized by rent seeking, interest group politics, and corruption seems too vast to bridge. Conversely, the chasm between a relatively well-operating national government capable of exercising appropriate authority and deploying necessary resources and poorly designed local governments, or resource users who are unorganized and trapped in a race to harvest and who actively resist outside

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Governing Water: Institutions, Property Rights, and Sustainability

intervention appears to be insurmountable. However, the divide must be bridged to realize the promise of watershed governance. How do watershed and river basin governing systems get assembled, and assembled in ways that allow for the balancing of different values, accounting for externalities across users and jurisdictions, and the resolution of conflict in productive ways? Carlsson and Berkes (2005) propose a productive way of engaging with complex governing systems that span multiple scales: to focus initially on the functional aspects of governance, rather than the structural design of the governing institutions and organizations. More specifically, they propose a number of steps for analyzing problem-solving settings that draw heavily on the Institutional Analysis and Development Framework (IAD) developed by Ostrom and others (Kiser and Ostrom, 1982; Ostrom et al., 1994; Ostrom, 2007). First, what types of problems do water resources exhibit, or what problems and conflicts are water users experiencing? Second, once the social–ecological system has been identified and problems specified, including conflicting conceptualizations of problems, the policy analyst should turn to identifying who the participants are, how they are organized, and how they relate to and affect the essential management tasks implicated by the problems. As Carlsson and Berkes (2005: 73) explain, ‘‘The logic is that we start from the ‘bottom’, in the activities themselves, and try to figure out how management is organized, if power is shared, if rights and duties are contracted out, and if State authorities have ‘a finger in the pie’.’’ Third, how the actors and participants are linked, the types of organizational ties and types of authority they exercise that make them relevant to others in the situation, needs to be identified. Fourth, only then can remedies be sketched out that include strengthening governing capacity as well as more specific policy changes that address identified problems. The analytic steps that Carlsson and Berkes (2005) spell out overlap and interact; they do not constitute a strict linear process, they do not promise a particular set of outcomes, nor do they result in a specific configuration of institutional arrangements. Furthermore, if engaged in carefully, they require the policy analyst to grapple with a wide range of governance levels and issues, from the constitutional choice level, where the terms and conditions of governance are specified, to the collective-choice level where decision-making authority is exercised, and implementation and monitoring occur, to the operational level where actions and activities around water resource use take place (see also Ostrom, 2007). The value of using a problem-solving approach that incorporates different levels of governance is illustrated by comparing across the states of USA and the ability of water users, local jurisdictions, and state governments to assemble workable watershed-scale governing arrangements. At a first glance, it appears that among states whose constitutions grant local jurisdictions and water users considerable autonomy and decision-making authority, the authority is used to develop a variety of new associations, organizations, and governments at multiple scales that solve different problems. In addition, in investing in new organizations and governments, resource users expand their capacity to govern at the watershed scale. For instance, Landre and Travis (1998) describe the development of new forms of governance around the Keuka Lake and

watershed in the Finger Lakes region of New York. The lake’s water quality was slowly deteriorating, threatening a highly valued resource. The lake was used for drinking water and recreation (swimming, fishing, and boating). An association of homeowners surrounding the lake spearheaded the effort to protect the lake. Drawing on expertise from a local university and watershed planning grants from the state, the association launched a research and educational campaign, and developed a forum for public officials from the dozen or so surrounding towns. It was the public officials who had the authority to develop and implement water quality regulations. New York state permits local jurisdictions to enter into memoranda of agreement (MOA) by which local governments can jointly govern a shared resource or address a common problem. The local jurisdictions used the MOA to establish common septic tank regulation that applied to all homes, especially those surrounding the lake, and a septic tank inspector, to ensure that septic tanks were properly installed and maintained, thereby protecting the lake from pollution. The state could have intervened to address the water quality problem if local jurisdictions failed to act; however, local residents and governments chose to develop their own arrangements that they are responsible for and that are accountable to them (Keuka Lake Association, 2009). Much the same type of process has emerged among a number of watersheds in southern California, another state that grants citizens and local jurisdictions considerable authority and discretion to govern their water resources. As Schlager and Blomquist (2008) describe, the San Gabriel Basin covers much of Los Angeles County, one of the most highly urbanized counties in the country. Over 100 local political jurisdictions are located in the basin. The basin ties together these multiple jurisdictions through the San Gabriel River, the Rio Hondo River, three interconnected groundwater basins, and one groundwater basin not hydrologically connected to the others (Blomquist, 1992). Several water resource management problems have arisen in the San Gabriel River watershed, owing to the combined effects of the region’s limited water supplies, its extensive agricultural and then urban development, and the hydrogeology of the watershed itself. Each of these problems has been multi-jurisdictional in scope. Water users responded to each by developing new institutional arrangements. The arrangements are fitted together through a system of interorganizational and intergovernmental relationships. For instance, one of the initial problems addressed by local jurisdictions was importing water to meet the needs of rapidly growing populations and industries. A number of jurisdictions participated in the formation of the Metropolitan Water District in the 1920s to import water from the Colorado River Basin. As additional jurisdictions sought membership in the Metropolitan Water District and access to imported water supplies, they banded together and formed water districts whose purpose was to bring imported water to their member jurisdictions. Later, these districts participated in solving groundwater overdraft problems. Districts and larger municipalities spearheaded efforts to adjudicate rights in groundwater. To ensure representation of interests not adequately covered by municipalities and districts, water associations that encompassed the major water users within each basin were

Governing Water: Institutions, Property Rights, and Sustainability

formed and participated in developing agreements for allocating groundwater. Later still, the municipalities, districts, associations, and water masters, who monitor water rights, were the foundation on which water quality issues were addressed. Initially, water masters were given the task of developing water quality monitoring systems; however, as the discovery of water quality problems began to mount, and the projects and resources needed to remediate water supplies grew, a water quality authority was created to carry out the task of remediation. Thus, problem-by-problem water users and local jurisdictions assembled a San Gabriel River Basin governance system, sometimes granting new authorities to existing governments to address new problems, and sometimes creating new associations and governments to address problems. Watershed governance in both New York and California is predicated on allowing local jurisdictions and governments the authority to devise their own institutional solutions to shared problems. However, if water users and local governments did not want to participate for a multitude of reasons – they did not believe that (1) there was a problem, or (2) they were involved in causing the problem, or (3) they should pay to resolve the problem – they could not easily opt out or free ride off of the efforts of others. For instance, New York State could have intervened and established water quality standards for Keuka Lake and imposed regulations for meeting those standards. In southern California, local water user associations and districts, and municipal water providers regularly used state courts to bring all parties to the negotiating table to address water quantity problems. The California Health Department and the US Environmental Protection Agency, in setting water quality standards and in addressing highly polluted sites through the Superfund Program, called attention to and required action in relation to water quality problems. In other words, it is not just local autonomy that is important, but local autonomy and the larger institutional environment in which it is embedded that matters. Can local jurisdictions hold one another accountable, do they have access to conflict resolution mechanisms, and can they easily avoid participating in collective action to address water quantity or quality problems that they helped create? These are important considerations in watershed governance. The experience of other states suggests that it is no easy matter to go back and fill in institutional gaps in order to encourage, support, and require local jurisdictions to account for the impacts of their actions on others. For instance, until very recently, Nebraska water law and administration separated surface water from groundwater, even if the two are hydrologically linked (Schlager and Blomquist, 2008). Surface water, grounded in the prior appropriation doctrine, is administered by a state agency. Groundwater, grounded in the beneficial use doctrine, is administered by local natural resources districts, governed by groundwater users. The state had no legal authority to require natural resources districts to manage groundwater to account for externalities or environmental values. Beginning in the 1970s, with the listing of endangered species along the Platte River in south central Nebraska, planning and development of large surface water projects ceased (Aiken, 1999). By the 1990s, existing hydropower/irrigation projects were threatened, as the Federal

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Energy Regulatory Agency required the projects to account for their impacts on endangered species habitat to gain license renewal. Part of the problem the projects faced was the effects of groundwater pumping on their surface water supplies. In addition, Kansas, a downstream neighbor of Nebraska, filed a suit before the US Supreme Court, claiming that Nebraska was failing to abide by its legal obligations to allow Kansas’ share of the Republican River to flow through to Kansas because of groundwater pumping. Finally, Nebraska surface water rights holders began filing lawsuits against groundwater pumpers to try protect surface water flows. In each instance, the state of Nebraska was helpless to respond because it had no authority to require natural resources districts to regulate groundwater pumping. After two decades of conflict, that included multiple lawsuits, several governorappointed commissions, and numerous public hearings and meetings, a law was adopted that allowed the state water agency to declare river basins overappropriated. Such a designation would immediately trigger a well moratorium and require the natural resources districts within the overappropriated basin to develop groundwater regulations sufficient to bring the basin to a fully appropriated status. This is just the first, and most critical, step in developing ties and linkages among multiple governments and jurisdictions within Nebraska that will allow the coordination of ground and surface water and, which, in turn, will allow the state to meet its water obligations to surrounding states. Nebraska state law granted groundwater users significant authority to govern groundwater supplies through natural resource districts, but provided for few accountability mechanisms. The above cases highlight the value of taking a problemsolving approach to analyzing water resources issues and taking into account the ties and relations among citizens, organizations, and governments at multiple scales. As Young (2002: 266) argues, ‘‘The extent to which specific environmental or resource regimes yield outcomes that are sustainable – much less efficient or equitable – is a function not only of the allocation of tasks between or among institutions operating at different levels of social organization but also of crossscale interactions among distinct institutional arrangements.’’ Young (2002) provides a useful set of analytic concepts to assist policy analysts in assessing the institutional ties among water actors. The three analytic concepts are competence, compatibility, and capacity. Competence refers to the political and legal authority to engage in and implement commitments. For instance, in both New York and California, local jurisdictions possess the legal authority to engage in binding agreements with each other to regulate the use of a water source. However, the state of Nebraska did not have the competence to implement its agreement to provide Kansas with a designated amount of surface water. It did not possess such competence because it did not have the authority to regulate groundwater pumping. Compatibility refers to the congruence of institutional arrangements among governments operating in a given resource setting, such as a river basin. In the San Gabriel Basin, governing arrangements for each of the linked groundwater basins are now largely compatible and in line with California water doctrines; however, it took decades for those governing arrangements to be developed, with the downstream basin,

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Governing Water: Institutions, Property Rights, and Sustainability

which experienced problems first, taking the initial steps to regulate groundwater. As groundwater users in each of the upstream basins experienced problems and came to understand their own hydrologic ties, they too developed governing arrangements. The processes were adversarial and conflictual and took several decades to complete. In the end, each basin has its own groundwater rights and rules crafted to each setting, and the institutional arrangements are aligned so that complementarities and externalities are accounted for (Blomquist, 1992; Schlager and Blomquist, 2008). Capacity refers to the material resources and social capital needed to devise, implement, administer, and monitor rules and property rights systems. For instance, one of the challenges confronting the municipalities surrounding Keuka Lake was to provide sufficient resources to facilitate joint monitoring and enforcement of the septic tank rules adopted (Landre and Travis, 1998). A facilitator brokered an agreement that the costs would be shared equally among the jurisdictions and not be proportionate to the proximity of a jurisdiction to the lake. Each jurisdiction agreed to invest in the capacity of the MOA by making adequate resources available for its implementation. Competency, compatibility, and capacity further flesh out the problem-solving analytic approach that Carlsson and Berkes (2005) propose. One of the most important tasks in assessing a complex socio-ecological setting is identifying how the actors are linked, or not, and the quality of those linkages. Paying attention to the types and qualities of linkages also highlights the delicate balance that exists among different governments, associations, and organizations that constitute river basin or watershed governance. Just as it is challenging for regional and central governments to intervene and work with local communities of resource users without creating negative unintended consequences, so also is it difficult for actors to intervene in watershed governance systems to encourage, establish, and support productive linkages. However, what Carlsson and Berkes (2005), Young (2002), and many other scholars, who have attended to the governance of complex socio-ecological systems, have stressed is the importance of using a problem-centered approach for identifying capabilities and limitations of existing governance systems, and critically examining the competency, compatibility, and capacity of governments at different scales, rather than assuming that governments at specific levels are more competent or have greater capacity than governments at other levels (Larson, 2004; Ribot et al., 2006).

1.02.4 Courts: Hiding in Plain View As water users, water managers, and public officials attempt to link (or sever) ties among governments and organizations at different scales, they often contest the competency of a participant to engage in decision making; or they question the compatibility of different courses of action; or they seek ways to build the capacity of an organization, and in so doing they often turn to courts for assistance. Certainly, each of the US cases in the previous section include courts as an important actor in providing venues, shaping issues, and structuring solutions. Yet, in spite of talk about water governance revolving

around water conflicts and how to resolve them, the specific role of courts has been widely overlooked in both national and international contexts. In some countries, water experts recognize the practical importance of judicial decisions as features in the institutional landscape, but generally without thinking further about the courts’ significance. This is an unfortunate omission, because courts play a strategic and fundamental role in many countries’ political and economic systems. This is illustrated by a large academic literature, including comparative judicial politics (Jacob et al., 1996; Shapiro, 1981) and law and economics (e.g., Mercuro and Medema, 2006). The courts’ role is especially critical in situations of market-oriented policies and institutional frameworks. Market-driven governance aims to restrict state regulation, which requires strong judicial watchdogs. We return to this later in discussing the rule of law. Here, we intend to simply highlight a few key issues involving courts in the hope that people in water governance will be moved to investigate further. In the first place, courts and judges are one of the archetypal forms of conflict resolution. The judge sits apart and hears both sides of a dispute before deciding who wins and who loses. This is what political scientist Martin Shapiro has called the ‘‘logic of the triad in conflict resolution’’ (Shapiro, 1981: 1). Making that judicial decision means applying legal rules to specific fact situations, a process that requires reasoning, analysis, and interpretation. Particularly to people outside the legal profession, the judicial process is mysterious, and indeed how it works varies widely in different national and social contexts. In a classic book comparing different countries and legal traditions, Shapiro describes the conventional prototype of courts as growing out of conflict resolution. The prototype has four elements: ‘‘(1) an independent judge applying (2) preexisting legal norms after (3) adversary proceedings in order to achieve (4) a dichotomous decision in which one of the parties was assigned the legal right and the other found wrong’’ (Shapiro, 1981). Shapiro debunks each of these elements even in the context of conflict resolution, and he argues more broadly that courts also perform two other essential social functions: social control and lawmaking. The three functions often overlap. In the contexts of social control or lawmaking, however, the courts are farther away from their ‘‘basic social logic [and] perceived legitimacy.’’ Shapiro is worth quoting at length about the logic of courts: The basic social logic, or perceived legitimacy, of courts rests on the mutual consent of two persons in conflict to refer that conflict to a third for resolution. This basic logic is threatened by the substitution of office and law for mutual consent, both because one of the two parties may perceive the third as the ally of his enemy and because a third interest, that of the regime, is introducedy. When we move from courts as conflict resolvers to courts as social controllers, their social logic and their independence is even further undercut. For in this realm, while proceeding in the guise of triadic conflict resolver, courts clearly operate to impose outside interests on the parties. Finally, in the realm of judicial lawmaking, courts move furthest from their social logic and the conventional prototype because the rules they apply in the resolution of conflicts between two parties are neither directly consented to by the parties nor ‘preexisting.’ Instead, they are created by the third in the course of the conflict resolution itself. Thus, while the triadic mode of conflict resolution is nearly universal,

Governing Water: Institutions, Property Rights, and Sustainability

courts remain problematical in the sense that considerable tension invariably exists between their fundamental claims to legitimacy and their actual operations.’’ (Shapiro, 1981: 36–37, emphasis added)

The US is probably the most extreme example of a strong judicial role in water conflicts. The courts are major actors and decision makers in water policy and water rights and routinely combine the functions of conflict resolution, lawmaking, and social control. The courts are of course not the only important arena for addressing water conflicts, but they are a distinct arena with modes of reasoning and operation that are quite different from overtly political or economic approaches. Courts have been at the heart of one of the dominant slogans in international affairs in recent years: the rule of law. The idea of the rule of law shaped international debate about political and economic development in the 1980s and 1990s, as many countries went through historic processes of reform, generally toward democratization in the political sphere and toward markets in the economic sphere (Carothers, 1998; Dezalay and Garth, 2002; Thome, 2000; Trubek, 2006). This was approximately the same time that IWRM and sustainability came to dominate international debate about water. The rule of law means, in a nutshell, that laws apply to everyone, even powerful social actors and including government officials at all levels. Different views of the rule of law have put differing emphases on political versus economic issues. For many people, the rule of law refers to protection of human rights and democratic procedures of government. For others, it refers primarily to protection of economic and property rights from excessive government regulation. What all these views have in common is the goal of placing limits on the exercise of state power (Thompson, 1975; Whitlock, 2000). Moreover, all views agree that the ultimate watchdog is an independent judicial power, capable of challenging the legislative and executive powers of government. The courts are the foundation of the rule of law. The different political and economic aspects of the rule of law mean that courts are in a complicated position in many countries, and this bears directly on water conflicts. In political terms, the courts’ legitimacy is debatable because judges are not elected by the usual democratic processes and yet they sometimes overrule decisions made by popular sovereignty. In broader terms, social and economic as well as political, the courts are a critical arena for sorting through multiple values, rights, and interests. Many values are qualitatively different from each other and belong to different categories, and therefore cannot be quantitatively compared according to a single measure. Qualitative comparison is a task for legal and political institutions, and particularly courts in the case of conflicts. Law – whether made by legislators, executives, or judges – is vital to determining economic value, by creating the rules of the game and influencing how markets determine prices (Bromley, 1982; Commons, 1924; Whiteley et al., 2008).

1.02.5 Conclusion: Reconceptualizing Water Governance In an eye-catching headline ‘‘Stationarity is Dead: Whither Water Management?’’ a group of scientists argue that climate

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change is leading to changing hydrologic cycles and no longer can engineers rely on stationarity as the foundation for managing river basins (Milly et al., 2008). Stationarity refers to ‘‘the idea that natural systems fluctuate within an unchanging envelope of variability’’ (Milly et al., 2008: 573). Instead, the scientists argue, ‘‘nonstationary, probabilistic models of relevant environmental variables’’ must be developed to optimize water management. Furthermore, considerable attention must be paid to the rapid flow of information between climate scientists and water managers to make the new modeling efforts relevant to policy and management. How to realize such a complete transformation of the foundation of the design and management of large water projects? Interestingly, the authors propose a program that reflects the spirit of the Harvard Water Program. The Harvard Water Program, begun in the 1950s, used newly emerging computer technology and large data sets to illustrate the value and the possibility of computer simulations to examine alternative choices among objectives (Reuss, 1992, 2003). It provided a tool, multi-objective planning, for explicitly incorporating and analyzing multiple values and goals in developing and managing large water projects and the river basins in which they are situated. To that point in time, engineering considerations and values largely drove the design, planning, and operations process for developing water projects (Reuss, 1992). In proposing a new Harvard Water Program, Milly et al., to their credit, realize that responding to changing hydrologic cycles is not just an engineering problem, rather it is a societal problem. How river basins should be governed in light of climate change will entail engaging many people, organizations, associations, and governments expressing and pursuing many values. What, then, would a contemporary Harvard Water Program look like in what has been called the epoch of watershed sustainability (Sabatier et al., 2006)? The epoch of watershed sustainability is characterized by features both unique to it and in contrast to earlier time periods. It is characterized by many different stakeholder groups (interest groups, government agencies, scientists, local resource users, and native peoples) actively engaged in face-to-face interactions searching for win–win solutions to complex socio-ecological problems, grounded in extensive and intensive information development processes. In contrast, the Harvard Water Program existed during a time, in the US at least, when one federal agency had primary jurisdiction in a watershed or riverbasin, ‘‘with other agencies and interest groups acting as supplicants’’ (Sabatier et al., 2006: 23). The primary agency was ‘‘principally concerned with fulfilling its statutory mandate’’ with limited consideration given to other values (Sabatier et al., 2006: 24). The revolutionary aspect of the Harvard Water Program was a new methodology developed by experts for experts that allowed for the consideration of a wider range of values in river basin and water project planning and management. For a new Harvard Water Program to be successful, it would have to develop credible science, salient for a wide range of stakeholders, in processes viewed by scientists and stakeholders alike as legitimate (Cash et al., 2003). The challenge would no longer be how to fit a broader range of values into the design and operation of models used to optimize

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Governing Water: Institutions, Property Rights, and Sustainability

water development and management, as it was five decades ago, and as Milly et al. appear to envision it. Rather, the challenge would be how to design and fit organizations charged with the development of nonstationary, probabilistic hydrologic models that would be useful for water managers, policymakers, and citizens alike into complex, multiscale, intergovernmental, and organizational watershed governance systems. In other words, it would be a program that paid as much attention to institutional design (and all the disciplines that entails) as it would to the design of decision support systems (and all the disciplines that entails). IWRM and new modeling efforts around nonstationarity share much in common. Both recognize multiple values and the conflicts and politics that are likely to emerge as people fight, argue, contest, cooperate, and compete to ensure the realization of their cherished values. Rather than embracing such disorder, IWRM and new decision support models attempt to tidy things up by providing better information through models, or by bringing many interests and stakeholders to the table in search of common ground. While both approaches are useful, they fail to provide insight and guidance around water politics and governance as it unfolds in practice. What is instead needed and what we attempted to lay out in this chapter is an analytic approach for understanding complex social and ecological systems that recognizes a wide variety of institutional arrangements (including courts) and cross-scale linkages.

References Agrawal A and Gibson C (eds.) (1999) Communities and the Environment. New Brunswick, NJ: Rutgers University Press. Aiken JD (1999) Balancing endangered species protection and irrigation water rights: The Platte River cooperative agreement. Great Plains Natural Resources Journal 3: 119--158. Bauer C (2004) Siren Song: Chilean Water Law as a Model for International Reform. Washington, DC: RFF Press. Blomquist W (1992) Dividing the Waters: Governing Groundwater in Southern California. San Francisco, CA: ICS Press. Blomquist W, Schlager E, and Heikkila T (2004) Common Waters, Diverging Streams: Linking Institutions and Water Management in Arizona, California, and Colorado. Washington, DC: Resources for the Future. Bromley D (1982) Land and water problems: An institutional perspective. American Journal of Agricultural Economics 64(5): 834--844. Carlsson L and Berkes F (2005) Co-management: Concepts and methodological implications. Journal of Environmental Management 75: 65--76. Carothers T (1998) The rule of law revival. Foreign Affairs 77(2): 95--106. Cash D, Clark W, Alcock F, et al. (2003) Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences of the United States of America 100: 8086--8091. Commons J (1924) Legal Foundations of Capitalism New York, NY: Macmillan. Conca K (2006) Governing Water: Contentious Transnational Politics and Global Institution Building. Cambridge, MA: MIT Press. Cosgrove W and Rijsberman F (2000) World Water Vision: Making Water Everybody’s Business. London: Earthscan. Coward EW (1979) Principles of social organization in an indigenous irrigation system. Human Organization 38: 28--36. Dezalay Y and Garth B (2002) Global Prescriptions: The Production, Exportation, and Importation of a New Legal Orthodoxy. Ann Arbor, MI: University of Michigan Press. Digital Library of the Commons (2009) Digital Library of the Commons Repository. http://dlc.dlib.indiana.edu/dlc (accessed April 2010). Dubash N, Dupar M, Kothari S, and Lissu T (2001) A Watershed in Governance? An Independent Assessment of the World Commission on Dams. World Resources Institute, Lokayan, and Lawyers Environmental Action Team.

GWP (Global Water Partnership) (2000a) Integrated Water Resources Management, TAC Background Paper No. 4. Stockholm: Global Water Partnership. GWP (Global Water Partnership) (2000b) Towards Water Security: A Framework for Action. Stockholm: Global Water Partnership. Hardin G (1968) The tragedy of the commons. Science 162: 1243--1248. Ilahiane H (1999) The ethnopolitics of irrigation management in the Ziz Oasis, Morocco. In: Agrawal A and Gibson C (eds.) Communities and the Environment: Ethnicity, Gender, and the State in Community-Based Conservation, pp. 89--110. New Brunswick, NJ: Rutgers University Press. Jacob H, Blankenburg E, Kritzer HM, Provine DM, and Sanders J (1996) Courts, Law, and Politics in Comparative Perspective. New Haven, CT: Yale University Press. Kemper KE, Blomquist W, and Dinar A (eds.) (2007) Integrated River Basin Management through Decentralization. Berlin: Springer. Keuka Lake Association (2009) About Keuka Lake Association. http:// www.keukalakeassoc.org/what/about_kla.php (accessed April 2010). Kiser L and Ostrom E (1982) The three worlds of action: A metatheoretical synthesis of institutional approaches. In: Ostrom E (ed.) Strategies of Political Inquiry, pp. 179--222. Beverly Hills, CA: Sage. Lam WF (1998) Governing Irrigation Systems in Nepal: Institutions, Infrastructure, and Collective Action. San Francisco, CA: ICS Press. Landre P and Travis L (1998) Collaborative watershed management in the Finger Lakes region, New York. Presented at the Conference of the International Association for the Study of Common Property. Vancouver, BC, 10–14 June 1998. http:// www.indiana.edu/˜iascp/Final/landre.pdf (accessed April 2010). Lansing JS (1991) Priests and Programmers: Technologies of Power in the Engineered Landscape of Bali. Princeton, NJ: Princeton University Press. Larson A (2004) Formal decentralization and the imperative of decentralization ‘from below’: A case study of natural resource management in Nicaragua. European Journal of Development Research 16(1): 55--70. Maass A and Anderson RL (1986) y and the Desert Shall Rejoice: Conflict, Growth, and Justice in Arid Environments. Malabar, FL: R.E. Krieger. Mercuro N and Medema S (2006) Economics and the Law, 2nd edn. Princeton, NJ: Princeton University Press. Milly PC, Betancourt J, Falkenmark M, et al. (2008) Stationarity is dead: Whither water management? Science 319: 573--574. Olson M (1965) The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA: Harvard University Press. Ostrom E (1990) Governing the Commons: The Evolution of Institutions for Collective Action. New York, NY: Cambridge University Press. Ostrom E (1999) Coping with the tragedy of the commons. Annual Review of Political Science 2: 493--535. Ostrom E (2007) Understanding Institutional Diversity. Princeton, NJ: Princeton University Press. Ostrom E, Dietz T, Dolsak N, et al. (2002) The Drama of the Commons. Washington, DC: National Academies Press. Ostrom E, Gardner R, and Walker J (1994) Rules, Games, and Common Pool Resources. Ann Arbor, MI: University of Michigan Press. Reuss M (1992) Coping with uncertainty: Social scientists, engineers, and federal water resources planning. Natural Resources Journal 32: 101--135. Reuss M (2003) Is it time to resurrect the Harvard Water Program? Journal of Water Resources Planning and Management 129(5): 357--360. Ribot J, Agrawal A, and Larson A (2006) Recentralizing while decentralizing: How national governments reappropriate forest resources. World Development 34(11): 1864--1886. Rogers P (2002) Water Governance in Latin America and the Caribbean. Washington, DC: Inter-American Development Bank. Rogers P and Hall A (2003) Effective Water Governance, TAC Background Paper No. 7. Stockholm: Global Water Partnership. Rose C (2002) Common property, regulatory property, and environmental protection: Comparing community-based management to tradable environmental allowances. In: Ostrom E, Dietz T, Dolsak N, et al. (eds.) Drama of the Commons, pp. 263--292. Washington, DC: National Academies Press. Sabatier P, Weible C, and Ficker J (2006) Eras of water management in the United States: Implications for collaborative watershed approaches. In: Sabatier P, Focht W, Lubbell M, et al. (eds.) Swimming Upstream: Collaborative Approaches to Watershed Management, pp. 23--52. Cambridge, MA: MIT Press. Schlager E (2005) Getting the relationships right in water property rights. In: Bruns BR, Ringler C, and Meinzen-Dick R (eds.) Water Rights Reform: Lessons for Institutional Design, pp. 27--54. Washington, DC: International Food Policy Research Institute. Schlager E and Blomquist W (2005) Beneath the surface: Shared attributes of fisheries and aquifers and implications for institutional design. Prepared for Presentation at

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Festschrift for Elinor Ostrom, Workshop on Political Theory and Policy Analysis, Indiana University, Bloomington, IN, 22–23 November 2005. Schlager E and Blomquist W (2008) Embracing Watershed Politics. Boulder, CO: University Press of Colorado. Schlager E and Ostrom E (1992) Common property and natural resources: A conceptual analysis. Land Economics 68: 249--252. Shapiro M (1981) Courts: A Comparative and Political Analysis. Chicago, IL: University of Chicago Press. Tang SY (1992) Institutions and Collective Action: Self-Governance in Irrigation. San Francisco, CA: ICS Press. Tang SY (1994) Institutions and performance in irrigation systems. In: Ostrom E, Gardner R, and Walker J (eds.) Rules, Games, and Common Pool Resources, pp. 225--246. Ann Arbor, MI: University of Michigan Press. Thome J (2000) Heading south but looking north: Globalization and law reform in Latin America. Wisconsin Law Review 2000: 691--712. Thompson EP (1975) Whigs and Hunters: The Origin of the Black Act. London: Allen Lane.

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Trubek D (2006) The ‘rule of law’ in development assistance: Past, present, and future. In: Trubek DM and Santos A (eds.) The New Law and Economic Development: A Critical Appraisal, pp. 74--94. New York, NY: Cambridge University Press. Whiteley J, Ingram H, and Perry R (2008) Water, Place and Equity. Cambridge, MA: MIT Press. Whitlock W (2000) The rule of law. Wisconsin Law Review 2000: 723--742. World Bank (1993) Water Resources Management: A World Bank Policy Paper. Washington, DC: World Bank. World Bank (2004) Water Resources Sector Strategy: Strategic Directions for World Bank Engagement. Washington, DC: World Bank. World Commission on Dams (2000) Dams and development: A new framework for decision-making. The Report of the WCD. London: Earthscan. Young O (2002) Institutional interplay: The environmental consequences of cross-scale interactions. In: Ostrom E, Dietz T, Dolsak N, et al. (eds.) Drama of the Commons, pp. 263--292. Washington, DC: National Academies Press.

1.03 Managing Aquatic Ecosystems CM Finlayson, Charles Sturt University, Albury, NSW, Australia & 2011 Elsevier B.V. All rights reserved.

1.03.1 1.03.2 1.03.2.1 1.03.2.2 1.03.2.3 1.03.3 1.03.3.1 1.03.3.2 1.03.3.3 1.03.3.4 1.03.4 1.03.4.1 1.03.4.2 1.03.4.3 1.03.4.4 1.03.4.5 1.03.4.6 1.03.5 1.03.5.1 1.03.5.2 1.03.5.3 1.03.5.4 1.03.6 References

Introduction Key Concepts Wise Use of Wetlands Ecological Character The Ecosystem Approach Distribution and Classification of Aquatic Ecosystems Classification of Inland Aquatic Ecosystems Extent and Distribution Loss and Degradation of Inland Aquatic Ecosystems Loss of Species from Inland Aquatic Ecosystems Drivers of Change in Inland Aquatic Ecosystems Drainage, Clearing, and Infilling Modification of Water Regimes Invasive Species Overfishing Water Pollution and Eutrophication Climate Change Management Responses Integrated Management Processes International Cooperation and Action Restoration and Wise Use of Wetlands Supporting Local Community Involvement in Management Conclusions

1.03.1 Introduction The importance of aquatic ecosystems for people has been highlighted in recent years by the Millennium Ecosystem Assessment (Finlayson et al., 2005) and subsequent assessments such as the World Water Development Report (UNESCOWWAP, 2006), the Global International Waters Assessment (UNEP, 2006), the Global Biodiversity Outlook (Secretariat of the Convention on Biological Diversity, 2006) and the Global Environment Outlook (UNEP, 2007), and the Comprehensive Assessment of Water Management in Agriculture (Molden, 2007). However, despite many aquatic ecosystems being highly important for people and biodiversity, they have been degraded over many decades and many lost (Finlayson and D’Cruz, 2005). The continued degradation and loss of these highly valued ecosystems bring into question the effectiveness of current management practices and whether or not different approaches should be developed to ensure that they are managed wisely. The scope and definition of aquatic ecosystems have been widely discussed with many different definitions and classifications being used to cover a range of inland and coastal aquatic ecosystems or wetlands (Finlayson and van der Valk, 1995; Mitsch and Gosselink, 2007; Whigham, 2009). The term wetland has often been used to define a narrow range of inland aquatic systems, such as bogs, marshes, and swamps, while at other time it has been used to define a wider range

35 35 35 37 39 42 42 43 44 45 46 47 48 50 52 53 54 55 55 56 56 56 56 57

including rivers, lakes, reservoirs, and rice fields as well (Finlayson et al., 1999). Extensive information on wetland definition and delineation is available (e.g., Finlayson and van der Valk, 1995; Mitsch et al., 1994), but the failure to consider the different definitions completely that have been used around the world has resulted in confusion and inaccurate analyses on the extent and condition of these ecosystems (Finlayson and Spiers, 1999). The term aquatic ecosystem is used here in order to illustrate that the wider range of inland wetlands is being considered. This corresponds with the approach taken in the Millennium Ecosystem Assessment and encompasses the terms inland water systems, inland waters, or inland wetlands and includes marshes, swamps, lakes, and rivers, regardless of their size or whether they are permanent or temporary or saline or fresh (Finlayson and D’Cruz, 2005). As there is no clear boundary between inland and coastal aquatic ecosystems, the differentiation between them is indicative only. As there are strong interactions between inland and coastal aquatic ecosystems, a clear differentiation is not adopted. Coastal brackish and saline marshes, estuaries, and mangroves are not considered unless there is a strong connection between them and nearby freshwater aquatic ecosystems. Inland salt lakes are included, especially as many undergo flooding, whether periodic or irregular, with freshwater. The range of ecosystems considered is shown in Figure 1.

35

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Figure 1 Inland aquatic ecosystems.

The text below outlines some of the key concepts and approaches for managing inland aquatic ecosystems with a focus on water management. Key sources of information include material derived from the Ramsar Convention on Wetlands (Ramsar Convention Secretariat, 2006a), the Millennium Ecosystem Assessment (Finlayson et al., 2005), and the Wetland Handbook (Maltby and Barker, 2009). The importance of the Ramsar Convention in promoting the conservation and wise use of wetlands globally cannot be underestimated – the text of the Convention was signed in 1971 and in many ways was a forerunner of many subsequent developments in conservation, especially the shift from species preservation to sustainable use and integrated management of wetland ecosystems (Matthews, 1993). A summary of the key features of the Convention is provided in Box 1.

1.03.2 Key Concepts A number of key concepts associated with the management of inland aquatic ecosystems are outlined in the section below. These include the concepts of wise use of wetlands and ecological character of wetlands used by the Ramsar Convention on Wetlands, and the ecosystem approach used by the

Convention on Biological Diversity and promoted by the IUCN Commission on Ecosystem Management.

1.03.2.1 Wise Use of Wetlands Since its inception in 1971, the Convention on Wetlands (Ramsar, Iran, 1971) has promoted the wise use of wetlands (Matthews, 1993). The contracting parties to the Convention have subsequently accepted a mission statement that commits them to ‘‘y the conservation and wise use of all wetlands through local, regional and national actions and international cooperation, as a contribution towards achieving sustainable development throughout the world’’ (Ramsar Strategic Plan 2009–2015). The contracting parties have also agreed to deliver this mission through three streams of activities: (1) the wise use of all wetlands, (2) the designation and management of wetlands of international importance (Ramsar sites; see Figure 2), and (3) international cooperation. Wise use of wetlands was included in the text of the Convention under article 3.1 ‘‘The Contracting Parties shall formulate and implement their planning so as to promote y as far as possible the wise use of wetlands in their territory.’’ Matthews (1993) reported that from the outset the wise use of wetlands was seen as the maintenance of their ecological character, as a basis not only for nature conservation, but for

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37

Box 1 The Ramsar Convention on Wetlands. Information from The Ramsar Convention on Wetlands, http://www.ramsar.org (accessed September 2010). The Convention on Wetlands of International Importance, called the Ramsar Convention, is an intergovernmental treaty that provides a framework for national action and international cooperation for the conservation and wise use of wetlands and their resources. Unlike the other global environmental conventions, Ramsar is not affiliated with the United Nations system of Multilateral Environmental Agreements, but it works very closely with the other biodiversity-related treaties and agreements. The Convention was negotiated through the 1960s by countries and nongovernmental organizations that were concerned at the increasing loss and degradation of wetland habitat for migratory waterbirds; the treaty was adopted in the Iranian city of Ramsar in 1971 and came into force in 1975. It is the only global environmental treaty that deals with a particular ecosystem. The Convention’s mission is ‘‘the conservation and wise use of all wetlands through local and national actions and international cooperation, as a contribution towards achieving sustainable development throughout the world.’’ It uses a broad definition of the types of wetlands covered in its mission, including lakes and rivers, swamps and marshes, wet grasslands and peatlands, oases, estuaries, deltas and tidal flats, near-shore marine areas, mangroves and coral reefs, and human-made sites such as fish ponds, rice paddies, reservoirs, and salt pans. At the center of the Convention is the wise use concept which has at its heart the conservation and sustainable use of wetlands and their resources, for the benefit of human kind. The Convention has 160 contracting parties (12 December 2009) that have agreed to four main obligations: 1. designate at least one wetland at the time of accession for inclusion in the list of wetlands of international importance (the Ramsar List) and to promote its conservation, and in addition to continue to designate suitable wetlands within its territory for the list; 2. include wetland conservation in their national land-use planning and to promote, as far as possible, the wise use of wetlands in their territory; 3. establish nature reserves in wetlands, whether or not they are included in the Ramsar List, and to promote training in the fields of wetland research, management, and wardening; and 4. consult with other contracting parties about implementation of the Convention, especially in regard to transboundary wetlands, shared water systems, and shared species.

There are currently 1891 wetland sites (information as of 23 July 2010) designated for the list of wetlands of international importance covering a total surface area of 185 464 092 ha (Figure 2). Further information on the history and development of the Convention can be obtained from Matthews (1993).

Figure 2 Wetlands of international importance listed under the Ramsar Convention on Wetlands. From http://www.ramsar.org.

sustainable development also. With this background, wise use of wetlands was later defined as ‘‘y their sustainable utilisation for the benefit of humankind in a way compatible with the maintenance of the natural properties of the ecosystem.’’ With sustainable utilization it is defined as ‘‘y human use of a wetland so that it may yield the greatest continuous benefit to present generations while maintaining its potential to meet the needs and aspirations of future generations’’ (Davis, 1993).

Guidelines for the wise use of wetlands were formally adopted by the Convention in 1990 and emphasized the link between wetlands and people, and placed wetlands in a catchment or coastal zone context (Davis, 1993; Ramsar Convention Secretariat, 2006b; Maltby, 2009). The definition and guidelines remained in place until 2005 when wise use was redefined as ‘‘y the maintenance of their ecological character, achieved through the implementation of ecosystem approaches, within the context of sustainable development.’’

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The change in definition strengthened the link between wise use and the concept of sustainable development that had been promoted by the UN World Commission on Environment and Development (Brundtland, 1987). The change in definition also reflected an expansion in the wise use guidelines with the development and adoption of an increasing range of policy and technical guidelines known as the Ramsar Toolkit of Wise Use Handbooks (Ramsar Convention Secretariat, 2006c). Table 1 provides a summary of the material covered in the handbooks. The guidelines have also been mapped onto the conceptual framework developed for the Millennium Ecosystem Assessment which is consistent with the Ramsar concept of wise use (Finlayson et al., 2005). The framework provides a guide to the Ramsar wise use guidelines, and can be used to identify gaps (Figure 3). Many of the guidelines apply directly to ecosystem

services and the links between these services and the ecological components and processes that characterize wetlands, including those for describing and assessing the condition of the wetland. Others address interventions covering the direct drivers of change to ecosystems while two sets – those covering national wetland policies and on reviewing legislative and institutional frameworks – deal with the indirect drivers of change. Some, such as those on international cooperation, communications, education, and public awareness, apply to several parts of the framework. The guidelines are under continual review by the Convention and updated at regular intervals. It is anticipated that further attention will be devoted to guidelines that address policy issues and encourage greater integration between resource users and conservation interests – themes that have been evident in recent global assessments covering biodiversity and ecosystem services.

Table 1 Guidance available through the 3rd edition of the Ramsar Wise Use Handbooks and the relevant Resolutions agreed by the Contracting Parties to the Convention Handbook no.

Title

Content

Resolutions

1.

Wise use of wetlands

IX.1

2. 3.

National wetland policies Laws and institutions

A Conceptual Framework for the wise use of wetlands and the maintenance of their ecological character Developing and implementing National Wetland Policies

4.

Wetland CEPA

5.

Participatory skills

6.

Water-related guidance River basin management Water allocation and management Managing groundwater Coastal management Inventory, assessment, and monitoring Water allocation and management Impact assessment

7. 8. 9. 10. 11.

12.

Reviewing laws and institutions to promote the conservation and wise use of wetlands The Convention’s Programme on communication, education, and public awareness (CEPA) 2003–2008 Establishing and strengthening local communities’ and indigenous people’s participation in the management of wetlands An integrated framework for the Convention’s water-related guidance Integrating wetland conservation and wise use into river basin management Guidelines for the allocation and management of water for maintaining the ecological functions of wetlands Guidelines for the management of groundwater to maintain wetland ecological character Wetland issues in Integrated Coastal Zone Management An integrated framework for wetland inventory, assessment, and monitoring

14.

Designating Ramsar sites

15.

Addressing change in ecological character Managing wetlands

Guidelines for the allocation and management of water for maintaining the ecological functions of wetlands Guidelines for incorporating biodiversity-related issues into environmental impact assessment legislation and/or processes and in strategic environmental assessment The Strategic Framework and guidelines for the future development of the List of Wetlands of International Importance Addressing change in the ecological character of Ramsar sites and other wetlands Frameworks for managing Ramsar sites and other wetlands

International cooperation

Guidelines for international cooperation under the Ramsar Convention on Wetlands

13.

16. 17.

VII.6 VII.7 VIII.31 VII.8

IX.1 VII.19, IX.1, IX.3 VIII.1 IX.1 VII.21, VIII.4 IX.1

VIII.1 VII.16, VIII.9

VIII.10

V.4, VI.1, VII.24, VIII.8, VIII.16, VIII.20, VIII.22, IX.6 V.7, VI.1, VII.10, VIII.14, VIII.18, VIII.19, IX.4 VII.19

Reproduced from The Ramsar ‘Toolkit’, 3rd edn. (2007) The Ramsar Handbooks for the Wise Use of Wetlands. http://www.ramsar.org/cda/ramsar/display/main/ main.jsp?zn=ramsar&cp=1-30^21323_4000_0 (accessed August 2010).

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39

Global Regional Local Human well-being and poverty reduction

Indirect drivers of change

• Health security • Environmental security • Economical security • Cultural security • Equity

• Demographic • Economic (e.g., trade, subsides, markets) • Sociopolitical(e.g., governance, institutional and legal framework) • Science and technology • Cultural and religious (e.g., choices about what and how much to consume)

777

171 HB17: International cooperation

HB0: Water allocation and management

HB1: Wise use

HB3:Laws and institutions

HB4: Wetland CEPA HB7: River basin Management

171

HB16: Managing wetlands HB12: Wetland inventory

Ecosystem services

HB11: Inventory, assessment, monitoring

HB14: Designating rumuer sites HB5: Participatory skills

HB10: Central management

• Provisioning (e.g., food, fresh water, fuel, genetic resources) • Regulating (e.g., climate, water, natural hazard mitigation) • Cultural (e.g., spiritual, aesthetic) • Supporting (e.g., primary production, nutrient cycling)

Life on earth: biodiversity Strategies and Interventions

HB2: National wetlands policies

Direct drivers of change

HB13: Impact assessment

HB9: Groundwater

HB8: Water allocation and management

• Changes in local land use and land cover • Species removals and/or invasive introductions • Eutrophication and pollution • Hydraulic infrastructure development • Water abstraction • Climate change HBO: Water-related guidance

HB10: Coastal management

HB7: River basin management HB15: Change in cool character

777 No specific guidance

HBxx

Dark background: Handbooks include interventions into several red bars

Figure 3 A framework for the wise use of wetlands and the application of the guidelines in the Ramsar ‘Toolkit’ of Wise Use Handbooks, 3rd edn. Reproduced from Ramsar Convention Secretariat (2006b) Wise use of wetlands: A conceptual framework for the wise use of wetlands. In: Ramsar Handbooks for the Wise Use of Wetlands, 3rd edn., vol. 1. Gland, Switzerland: Ramsar Convention Secretariat, with permission from Ramsar; updated from Finlayson CM, D’Cruz R, and Davidson NJ (2005) Ecosystem Services and Human Well-Being: Water and Wetlands Synthesis. Washington, DC: World Resources Institute.

1.03.2.2 Ecological Character The concept of ecological character was introduced in the text of the Ramsar Convention and is now seen as a basis for the wise use of wetlands globally. Contracting parties to the Ramsar Convention are required to promote the conservation of all wetlands through the maintenance of their ecological character and to do this they are expected to establish management planning and monitoring mechanisms (Ramsar Convention Secretariat, 2006a). Ecological character is now defined as ‘‘y the combination of the ecosystem components, processes and benefits/services that characterise the wetland at a given point in time’’ (Ramsar Convention Secretariat, 2007). The previous definition considered ecosystem services separately to the ecological components and processes that were seen as comprising the ecological character of a wetland (Figure 4). Ecosystem services were incorporated into the definition of ecological character after the Convention adopted the findings of the Millennium Ecosystem Assessment as they applied to water and wetlands (Finlayson et al., 2005). This widened the concept of wetland conservation and provided an overt link with the management and uses of a wetland, as expressed through ecosystem services which provide benefits to people.

The description of the ecological character of a wetland provides baseline data that establish the range of natural variation in ecological components and processes and ecosystem services at each site within a given time frame, against which change can be assessed. To describe the ecological character of a wetland and ascertain when adverse change may have occurred, the Convention has established an integrated framework for wetland inventory, assessment, and monitoring (Ramsar Convention Secretariat, 2006c; Finlayson et al., 2005). In support of this approach, the Convention also adopted the concept of ecosystem services as provided by the Millennium Ecosystem Assessment (2003) with four categories of services (Figure 5): 1. provision services – products obtained from ecosystems; 2. regulating services – benefits obtained from regulation of ecosystem processes; 3. cultural services – nonmaterial benefits obtained from ecosystems; and 4. supporting services – services necessary for the production of all other ecosystem services.

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Provisioning Biological Regulating

Chemical Physical

Cultural

Supporting

Ecosystem components and processes

Ecosystem services

Figure 4 The ecological character of a wetland showing the relationship between the ecosystem components and processes and services that comprise the wetland.

Provisioning services

Regulating services

Cultural services

Goods produced or provided by ecosystems

Benefits from regulation of ecosystem processes

Nonmaterial benefits from ecosystems

• Food • Fuel wood • Fiber • Timber

• Water partitioning • Pest regulation • Climate regulation • Pollination

• Spiritual • Recreational • Aesthetic • Educational

Support services Factors necessary for producing ecosystem services • Hydrological cycle • Soil formation • Nutrient cycling • Primary production

Figure 5 Four categories of ecosystems services as outlined in the Millennium Ecosystem Assessment (2003). Reproduced from Millennium Ecosystem Assessment (2003) Ecosystems and Human Well-Being: A Framework for Assessment. Washington, DC: Island Press.

The relative importance of these services in different types of inland aquatic ecosystems is shown in Figure 6. The information in this figure was based on expert opinion given the absence of sufficient data to support a quantitative analysis. Given this situation the collection of further information on the extent of ecosystem services provided by wetlands is encouraged by the Convention as a basis for wise use.

1.03.2.3 The Ecosystem Approach In response to widely articulated weaknesses in sectoral approaches for wetland management (e.g., Hollis, 1992, 1998), a number of ecosystem approaches have been developed to promote the conservation and sustainable and equitable use of wetlands (Brown et al., 2005). The Convention on

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41

Alpine and tundra wetlands

Springs and oases

Geothermal wetlands

Underground wetlands including caves and groundwater systems

Seasonal lakes, marshes, and swamps including floodplains Forested wetlands, marshes, and swamps, including floodplains

Permanent lakes, reservoirs

Permanent and temporary rivers and streams

Comments and examples

Services

Scale is low •, medium , to high ; not known?; blank cells indicate that the service is not considered applicable to the wetland type. The information in the table represents expert opinion for a global average pattern for wetlands; there will be local and regional differences in relative magnitudes.

?

?

?

?

?

?

?

?

?

?

Inland wetlands Provisioning Food Fresh water

Fiber and fuel Biochemical products Genetic materials

Regulating Climate regulation

Hydrological regimes

Pollution control and detoxification Erosion protection

Natural hazards Cultural Spiritual and inspirational Recreational Aesthetic Educational Supporting Biodiversity Soil formation Nutrient cycling Pollination

Production of fish, wild game, fruits, grains, and so on Storage and retention of water; provision of water for irrigation and for drinking Production of timber, fuelwood, peat, fodder, aggregates Extraction of materials from biota Medicine; genes for resistance to plant pathogens, ornamental species, and so on

? ?

?

Regulation of greenhouse gases, temperature, precipitation, and other climatic processes; chemical composition of the atmosphere Groundwater recharge and discharge; storage of water for agriculture or industry Retention recovery, and removal of excess nutrients and pollutants Retention of soils and prevention of structural change (such as coastal erosion, bank slumping, and so on) Flood control; storm protection

?

Personal feelings and well-being; religious significance Opportunities for tourism and recreational activities Appreciation of natural features Opportunities for formal and informal education and training Habitats for resident or transient species Sediment retention and accumulation of organic matter Storage, recycling, processing, and acquisition of nutrients

?

Support for pollinators

Figure 6 Ecosystem services provided by different inland aquatic ecosystems. Reproduced from Finlayson CM, D’ Cruz R, and Davidson NJ (2005) Ecosystem Services and Human Well-Being: Water and Wetlands Synthesis. Washington, DC: World Resources Institute; and Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-Being: Synthesis. Washington, DC: Island Press.

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Table 2 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12.

Principles of the ecosystem approach adopted by the Convention on Biological Diversity

The objectives of management of land, water, and living resources are a matter of societal choices Management should be decentralized to the lowest appropriate level Ecosystem managers should consider the effects (actual or potential) of their activities on adjacent and other ecosystems After recognizing potential gains from management, there is a need to understand the ecosystem in an economic context. Any ecosystem management program should: (a) reduce those market distortions that adversely affect biological diversity; (b) align incentives to promote sustainable use; and (c) internalize costs and benefits in the given ecosystem to the extent feasible Conservation of ecosystem structure and functioning, in order to maintain ecosystem services, should be a priority target of the ecosystem approach Ecosystems must be managed within the limits to their functioning The ecosystem approach should be undertaken at the appropriate spatial and temporal scales Recognizing the varying temporal scales and lag effects which characterize ecosystem processes, objectives for ecosystem management should be set for the long term Management must recognize that change is inevitable The ecosystem approach should seek the appropriate balance between, and integration of, conservation and use of biological diversity The ecosystem approach should consider all forms of relevant information, including scientific, indigenous, and local knowledge, innovations, and practices The ecosystem approach should involve all relevant sectors of society and scientific disciplines

Information derived from Ecosystem Approach, Principles, http://www.cbd.int/ecosystem/principles.shtml (accessed August 2010).

Biological Diversity has promoted the ecosystem approach by focusing on managing environmental resources and by promoting a balance between human needs and biodiversity. The wise use approach adopted by the Ramsar Convention as well as integrated coastal zone management and integrated catchment management are compatible with the ecosystem approach adopted by the Convention on Biological Diversity (Shepherd, 2004; Brown et al., 2005). The ecosystem approach adopted by the Convention on Biological Diversity was seen as a way of reaching a balance between the objectives of the Convention covering conservation, sustainable use, and the fair and equitable sharing of the benefits arising out of the utilization of genetic resource. It is based on a strategy for the integrated management of land, water, and living resources and promotes conservation and sustainable use in an equitable way (Shepherd, 2004). It places human needs at the center of biodiversity management and acknowledges that ecosystems perform multiple functions of importance to people both locally and further afield. The guiding principles and strategies adopted by the Convention are shown in Table 2. As the Convention’s ecosystem approach does not comprise a specific applicable method, it has been criticized for being too vague to be of practical value, while others have highlighted its flexibility (Brown et al., 2005). Taking note of the criticisms, Shepherd (2004) grouped the guiding principles into the following five steps, each involving a range of actions to encourage discussion, planning, and step-by-step action:

1. determine the main stakeholders, define the ecosystem area, and develop the relationship between them; 2. characterize the structure and function of the ecosystem, and set in place mechanisms to manage and monitor it; 3. identify the important economic issues that will affect the ecosystem and its inhabitants; 4. determine the likely impact of the ecosystem on adjacent ecosystems; and 5. decide long-term goals and flexible ways of reaching them.

Brown et al. (2005) have highlighted some of the constraints that are generally seen to characterize ecosystem approaches. These include: a failure to consider specific areas, resources, or species that may need a more targeted approach for their conservation; uncertainties and lack of guidance about how to balance conservation and sustainable use; and difficulties with establishing collaboration between stakeholders and negotiating trade-offs between them in a fair and equitable way. Even with these constraints the principles outlined in Table 2 have been widely accepted and, in one way or the other, now feature in many current approaches for managing aquatic ecosystems (Finlayson and D’Cruz, 2005; Maltby, 2009). The principles adopted by the Convention on Biological Diversity are generally applicable to other ecosystem approaches and have largely been addressed through the more detailed guidance for wise use developed by the Ramsar Convention (Ramsar Convention Secretariat, 2006a, 2006b). Ecosystem approaches generally provide for a broad, crosssectoral approach for managing aquatic ecosystems by addressing both direct and indirect drivers of change and considering the multiple benefits people derive from the ecosystem services that they provide.

1.03.3 Distribution and Classification of Aquatic Ecosystems The extent and distribution of inland aquatic ecosystems is poorly and unevenly known at the global and regional scales due to differences in definitions as well as difficulties in delineating and mapping ecosystems with variable boundaries due to fluctuations in water levels (Finlayson et al., 1999; Rebelo et al., 2009; Mackay et al., 2009). In many cases, comprehensive documentation of the extent and distribution of inland aquatic ecosystems at the regional or national levels also does not exist. The larger ecosystems, such as lakes and inland seas, have been mapped along with the major rivers, but for many parts of the world smaller ecosystems are not well mapped or delineated. As a consequence, assessment of

Managing Aquatic Ecosystems

the extent of and change in these ecosystems at the continental level is compromised by the inconsistency and unreliability of the data.

1.03.3.1 Classification of Inland Aquatic Ecosystems The classification of inland aquatic ecosystems or wetlands has consumed an inordinate amount of time and controversy with many systems developed and used in different countries (see summary in Finlayson and van der Valk (1995)). While a wetland inventory can be undertaken without recourse to an agreed classification, given that a standardized and logical process of data collection or collation is undertaken, it is likely to become necessary to classify them at some stage during the assessment phase of wetland management, especially where it is useful or necessary to make comparisons between different wetlands. At this stage an agreed set of terms is not only desirable but possibly mandatory to ensure conformity of comparisons and hence decisions. Thus, the importance of classification cannot be overstated, but it equally needs to be Table 3

remembered that classification is a tool within a larger set of tools that are designed to provide an adequate information base for the wise use, conservation, and management of all wetlands. The Ramsar wetland definition is supported by a classification scheme with 42 categories that is purposefully simple and global in scope, and readily compatible with other classifications that may be preferred regionally, nationally, or locally (Scott and Jones, 1995). The classification divides wetlands into three main types: marine/coastal (12 wetland types), inland (20), and human-made (10), with several categories, according to vegetation, soil/rock, inundation, water quality (freshwater to saline water), and landform, within each type (Ramsar Convention Secretariat, 2006d). The characteristics of the inland wetland types are shown in Table 3. While used in a general sense by contracting parties to the Convention, it has also been augmented by more specific classifications. The broad wetland categories included in the classification have been particularly useful when comparing between countries or regions, but less so when more specific

Characteristics of inland wetland types contained within the Ramsar wetland classification

Wetland type

Wetland characteristics

1.

Permanent rivers/streams/creeks

2. 3. 4.

Permanent inland deltas Freshwater springs; oases Seasonal/intermittent/irregular rivers/ streams/creeks

5. 6. 7. 8.

Permanent freshwater lakes Permanent freshwater marshes/pools Seasonal/intermittent freshwater lakes Seasonal/intermittent freshwater marshes/ pools on inorganic soils

Lakes and pools

9. 10. 11. 12.

Permanent freshwater marshes/pools Shrub-dominated wetlands Freshwater, tree-dominated wetlands Seasonal/intermittent freshwater marshes/ pools on inorganic soils

Marshes – inorganic soils

Permanent Permanent/seasonal/ intermittent Seasonal/intermittent

Herb dominated Shrub dominated Tree dominated Herb dominated

13. 14.

Nonforested peatlands Forested peatlands

Marshes – peat soils

Permanent

Nonforested Forested

15. 16.

Alpine wetlands Tundra wetlands

Marshes – inorganic or peat soils

High altitude Tundra

17. 18.

Permanent saline/brackish/alkaline lakes Seasonal/intermittent saline/brackish/ alkaline lakes and flats Permanent saline/brackish/alkaline marshes/pools

Lakes

Permanent Seasonal/intermittent

19.

43

20.

Seasonal/intermittent saline/brackish/ alkaline marshes/pools

21.

Geothermal wetlands

22.

Karst and other subterranean hydrological systems

Freshwater

Flowing water

Permanent

Season/intermittent

Permanent Seasonal/intermittent

Saline, brackish, or alkaline water

Rivers, streams, creeks Deltas Springs, oases Rivers, streams, creeks 48 ha o8 ha o8 ha o8 ha

Permanent Marshes and pools Fresh, saline, brackish, or alkaline water

Seasonal/intermittent

Geothermal Subterranean

Derived from Ramsar Convention Secretariat (2006c). Inventory, assessment, and monitoring: An integrated framework for wetland inventory, assessment, and monitoring. In: Ramsar Handbooks for the Wise Use of Wetlands, 3rd edn., vol. 11. Gland, Switzerland: Ramsar Convention Secretariat.

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Managing Aquatic Ecosystems

or accurate classification was required. Semeniuk and Semeniuk (1997) pointed out that the Ramsar classification for inland wetlands was not entirely systematic and promoted the adoption of a more systematic classification based on the hydro-geomorphology of the wetland. Nevertheless, except for the occasional addition of wetland types the Ramsar classification has not been substantially changed and still forms a readily available general model for many purposes.

1.03.3.2 Extent and Distribution Estimates of the global extent of inland aquatic ecosystems differ greatly and highly depend on the definition of wetlands used and on the methods for delineating wetlands (Finlayson et al., 1999; Mitsch and Gosselink, 2007; Whigham, 2009). The Global Review of Wetland Resources and Priorities for Wetland Inventory conducted on behalf of the Ramsar Convention estimated the extent of all wetlands from national inventories as approximately 1280 million hectares, which was considerably higher than previous estimates (Finlayson et al., 1999). This included inland (including lakes, rivers, swamps, and marshes), coastal (including lagoons, swamps, and estuaries), near-shore marine areas (tidal flats and marine areas to a depth of 6 m below low tide), and human-made wetlands (such as reservoirs and rice paddies). Nevertheless, this figure is considered an underestimate, especially for southern America and for certain wetland types (such as intermittently flooded inland wetlands, peatlands, and artificial wetlands) where data were incomplete or not readily accessible. The most recent attempt to ascertain the extent and distribution of inland aquatic ecosystems systems (Lehner and Doll, 2004) based on analysis of existing data sets derived largely from the Earth observation is shown in Figure 7. As with previous estimates, these data contain many inaccuracies and gaps. For example, intermittently inundated inland aquatic ecosystems are not included, and there are many inaccuracies because of the problems of scale and resolution.

Nevertheless, these data and the mapping products are considered incredibly useful even while requiring updating and refinement (Finlayson et al., 2005; Whigham, 2009). Lehner and Doll (2004) reported the extent of inland aquatic ecosystems as 917 million hectares, comprising Africa with 131, Asia 286, Europe 26, Neotropics 159, North America 287, and Oceania with 28 million hectares. Inventory and mapping of inland aquatic ecosystems have been undertaken in many parts of the world, but the level of detail varies from region to region with some regions and ecosystem types considered to be under-represented in the data given above (Finlayson and D’Cruz, 2005). The latter includes rivers, lakes and reservoirs, peatlands, and rice paddies. Inventories of major river systems are available, but there is considerable variability between areal estimates, based on the method and definitions used. Information on the estimated 5–15 million lakes distributed globally is also highly variable and dispersed (WWDR, 2003). Large lakes have been mapped reasonably well, but issues of scale occur with many smaller lakes underestimated or not recorded. Reservoirs are also widespread with the number of large dams (415 m in height) in the world increasing from approximately 5000 in 1950 to more than 45 000 with 3–6 times the standing water held by natural river channels (WCD, 2000; Vo¨ro¨smarty et al., 2005). The overall number of dams globally is uncertain with an estimated 800 000 small dams and further investment in the construction of others, large and small (Vo¨ro¨smarty et al., 2005). The total area of peatlands is estimated as approximately 400 million hectare with the majority in Canada (37%), Russia (30%), USA (13%), and Indonesia (6–7%) (Joosten and Clarke, 2002). The global area of paddies has been estimated to be 130 million hectares (Aselmann and Crutzen, 1989) with almost 90% in Asia. Information on other humanmade wetlands is variable and lacking for some countries. Groundwater systems vary in size from small-scale alluvial sediment along rivers to extensive aquifers such as the 1.2

Lake Reservoir River Freshwater marsh, floodplain Swamp forest, flooded forest Coastal wetland Pan, brackish/saline wetland Bog, fen, mire Intermittent wetland/lake 50−100% wetland 25−50% wetland Wetland complex (0−25% wetland) Figure 7 Distribution of inland aquatic ecosystems described as large lakes, reservoirs, and wetlands. Adapted from Lehner B and Doll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296: 4–22.

Managing Aquatic Ecosystems

million square kilometers of the Guarani aquifer located across parts of Argentina, Brazil, Paraguay, and Uruguay (Danielopol et al., 2003). Groundwater systems have many connections with surface waters although many of these are not well understood, although the karst systems of Slovenia cover nearly 8800 km2 and are well known for their great species biodiversity, while others are not known at all (Finlayson and D’Cruz, 2005; Vo¨ro¨smarty et al., 2005). Whigham (2009) provided a general description of the main features of inland aquatic ecosystems commonly referred to as marshes, peatlands, and swamps. Marshes are dominated by emergent herbaceous vascular plants and occur in areas that are frequently or continuously flooded and most often have mineral soils that do not accumulate peat. Dominant plant species include reeds, rushes, grasses, and sedges, although they can also contain a wide variety of plant species with many different life forms. Peatland is a generic term for inland aquatic ecosystems that have at some point accumulated partially decayed plant matter because of incomplete decomposition, usually to a depth less than 30 cm. Many terms have been used to describe peatlands, such as mires, fens, and bogs. Swamps are flooded intermittently or permanently and are dominated by trees or shrubs. They are diverse and occur from the temperate zones to the tropics with those associated with rivers typically having inorganic substrates while those with little or no connection to flowing streams may develop peat substrates. While these generic descriptions exist, there are many local variations and terms used to describe inland aquatic ecosystems.

1.03.3.3 Loss and Degradation of Inland Aquatic Ecosystems The loss and degradation of inland aquatic ecosystems have been reported from many parts of the world (Finlayson et al., 1992; Mitsch, 1998; Moser et al., 1996; Whigham, 2009), but there are few reliable estimates of the actual extent of this loss globally. Dugan (1993) speculated that about 50% of wetlands had been lost globally, but did not provide supporting evidence. As a reliable estimate of the extent of inland aquatic ecosystems, particularly peatlands and intermittently inundated wetlands in semiarid areas, is not available (Finlayson et al., 1999), it is not possible to ascertain the extent of wetland loss globally (Finlayson and D’Cruz, 2005). While there is considerable uncertainty surrounding the extent of wetland losses globally, there is a lot of information available from specific countries or regions. Some examples are given below, taken from a collation provided in the Millennium Ecosystem Assessment (Finlayson and D’Cruz, 2005). The information available on the loss of inland aquatic ecosystems is far better for North America than for many other parts of the world with systematic monitoring of wetlands, excluding lakes and rivers, in the United States showing a loss of 116 000 ha yr 1 from the mid-1970s to the mid-1980s decreasing to 23 700 ha yr 1 from 1986 to 1997 (Dahl and Johnson, 1991; Dahl, 2000). Most of this loss was from the conversion or drainage of wetlands for urban development and agricultural purposes with an estimated 42.7 million hectares of wetlands remaining out of the 89 million hectares estimated to have been present at the time of European

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colonization (Dahl, 2000). The decline in the rate of loss was attributed largely to the successful implementation of wetland policies and programs that promoted the restoration, creation, and enhancement of wetlands, as well as incentives to deter draining of wetlands. This is shown by a net gain of about 72 870 ha of wetland after 1997 and a 47 000 ha increase in the area of lakes and reservoirs (Dahl, 2000). While the data are not as illustrative for other parts of the world, Finlayson and D’Cruz (2005) concluded from the literature that much of the loss of wetlands in the northern temperate zone occurred during the first half of the twentieth century. Since the 1950s, many tropical and subtropical wetlands, particularly swamp forests, have also been lost or degraded, particularly as a consequence of agricultural expansion. The OECD (1996) estimated that by 1985, 56– 65% of available wetland had been drained for intensive agriculture in Europe and North America, 27% in Asia, 6% in South America, and 2% in Africa – a total of 26% loss to agriculture worldwide. This is still occurring, for example, in South America where peatlands linked with the Andean paramos ecosystems are being converted for agriculture, forestry, and peat mining (Hofstede et al., 2003; Blanco and de la Balze, 2004). In Southeast Asia large areas of the once-extensive tropical peat swamp forests have been degraded or lost over the last four decades mainly because of logging for timber and pulp and more recently by clear-felling and conversion to oil palm plantations (Glover and Jessup, 1999; Page et al., 1997; Rieley and Page, 1997). The most dramatic loss of peatlands to agriculture has been in northern Europe in countries, including Finland, Estonia, Denmark, the United Kingdom, and the Netherlands (once one-third peatland) which has lost virtually all of its natural peatlands (Brag et al., 2003; Joosten, 1994). Despite the absence of national data, there are many welldocumented examples of large inland aquatic ecosystems that have been degraded or lost. These include the impacts of water diversions to the Aral Sea in Central Asia (Lemly et al., 2000) and the Mesopotamian marshes in Iraq (Richardson et al., 2005), the Murray-Darling Basin in Australia (Kingsford and Johnson, 1998), the Everglades in the United States (Richardson, 2008), Donana in Spain (Bartolome and Vega, 2002), and the Hadejia-Nguru wetland complex in Nigeria (Lemly et al., 2000). The Aral Sea in Central Asia represents one of the most extreme cases in which water diversion for irrigated agriculture has caused severe environmental degradation of an inland water system with detrimental impacts on human well-being (see summary in Finlayson and D’Cruz, 2005; Box 2 and Figure 8). The extent of adverse change is so severe that the Aral Sea is considered by Falkenmark et al. (2007) as an example of human modification of an inland aquatic system having gone too far.

1.03.3.4 Loss of Species from Inland Aquatic Ecosystems The extent of species loss from inland aquatic ecosystems has been documented through several programs in recent years (Revenga et al., 2000; Finlayson and D’Cruz, 2005; Dudgeon et al., 2005; Loh et al., 2005). Information from these documents has been used to summarize the extent of species losses. The following excerpt from Revenga et al. (2005) serves as an

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Managing Aquatic Ecosystems

Box 2 The Aral Sea. Based on information derived from Finlayson CM and D’Cruz R (2005) Inland Water Systems Millennium Ecosystem Assessment, Volume 2: Conditions and Trends. Washington, DC: Island Press and Falkenmark M, Finlayson CM, and Gordon L (2007) Agriculture, water, and ecosystems: Avoiding the costs of going too far. In: Molden D (ed.) Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture, pp. 234–277. London: Earthscan. The Aral Sea is one of the most prominent examples of how unsustainable water management has led to a large-scale and possibly irreversible ecological and human disaster. Drastically reduced water flow into the sea has impaired human livelihoods and health, affected the local climate, and reduced if not decimated much of the biodiversity. Since 1960 the volume of water in the basin that surrounds the Aral Sea Basin has been reduced by 75% mainly as a consequence of the development of almost 7 million hectares of irrigation (UNESCO, 2000; Postel, 1999). It is considered unlikely that the ecological and social changes that have occurred as a consequence will be successfully restored, despite efforts to rehabilitate the northern part of the sea.

1973

1986

1999

2001

Figure 8 Changes in the Aral Sea 1973–2001. Reproduced from UNEP (2005).

introduction to the loss of species from inland aquatic ecosystems, including mention of the drivers, the relative risk of extinction compared to other ecosystems, and the inadequate level of assessment and information: Human activities have severely affected the condition of freshwater ecosystems worldwide. Physical alteration, habitat loss, water withdrawal, pollution, overexploitation and the introduction of nonnative species all contribute to the decline in freshwater species. Today, freshwater species are, in general, at higher risk of extinction than those in forests, grasslands and coastal ecosystems. For North America alone, the projected extinction rate for freshwater fauna is five times greater than that for terrestrial fauna – a rate comparable to the species loss in tropical rainforest. Because many of these extinctions go unseen, the level of assessment and knowledge of the status and trends of freshwater species are still very poor, with species going extinct before they are even taxonomically classified.

As with inland aquatic ecosystems the data on the condition and trends of freshwater species are, for the most part,

poor at the global level, although some countries have reasonable inventories. Key conclusions from Revenga and Kura’s (2003) assessment of the level of knowledge of the distribution and condition of inland water biodiversity were: fish and waterbirds were by far the best-studied groups, although with considerable regional differences; aquatic plants, insects, freshwater mollusks, and crustaceans were poorly known in most parts of the world, with fragmentary information; and that every group of organisms considered, including aquatic plants, invertebrate, and vertebrate animal species, contained examples of extinct, critically endangered, endangered, and vulnerable taxa. This contrasts with the importance of inland aquatic ecosystems which were reported by McAllister et al. (1997) to be species rich relative to other ecosystems and to support a disproportionately large number of species of some taxonomic groups, for instance, some 40% of known species of fish and about 25–30% of all vertebrate species diversity (Leveque et al., 2005). The living planet index developed by WWF and UNEPWCMC (Loh and Wackernagel, 2004) provides a measure of the trends in more than 3000 populations of 1145 vertebrate species around the world. The 2004 freshwater species population index, which took the trend data into account for 269 temperate and 54 tropical freshwater species populations (93 of which were fish, 67 amphibians, 16 reptiles, 136 birds, and 11 mammals), showed that freshwater populations declined consistently and at a faster rate than the other species groups assessed, with an average decline of 50% between 1970 and 2000 (Figure 9). Over the same period, both terrestrial and marine fauna decreased by 30% (Figure 9). A summary of the status of separate groups of species is given in Box 3. Revenga et al. (2005) reported that a review by the World Resources Institute of the status and trends of inland water biodiversity for the Convention on Biological Diversity (Revenga and Kura, 2003) drew the following conclusions:

1. freshwater fishes and waterbirds were by far the beststudied groups of species from inland aquatic ecosystems, although there were considerable regional differences; 2. aquatic plants, insects, freshwater mollusks, and crustaceans were poorly known or assessed in most parts of the world, with only fragmentary information available; and 3. in every group of organisms considered there were examples of extinct, critically endangered, endangered, and vulnerable taxa, making it clear that inland aquatic ecosystems were among the most threatened of all environments.

Managing Aquatic Ecosystems

120

100

Index 100 in 1970

Terrestrial 80 Marine 60 Freshwater 40

20

0 1970

1975

1980

1985

1990

1995

2000

Figure 9 Trends in freshwater, marine, and terrestrial living planet indices, 1970–2000. From Finlayson CM, D’Cruz R, and Davidson NJ (2005) Ecosystem Services and Human Well-Being: Water and Wetlands Synthesis. Washington, DC: World Resources Institute; and Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-Being: Synthesis. Washington, DC: Island Press.

They further concluded that in general the information on species of inland aquatic ecosystems was poor, even for economically important groups, such as fish, and pointed out that many inventories tended to be organized by taxonomic groups and not by ecosystem types which made it hard to assess the condition of aquatic ecosystems. These conclusions have been largely supported by the analyses undertaken in the Millennium Ecosystem Assessment (Finlayson and D’Cruz, 2005; Finlayson et al., 2005) and more generally by other assessments, such as the Global Environment Outlook (Arthurton et al., 2007).

1.03.4 Drivers of Change in Inland Aquatic Ecosystems Analyses over the past two decades have identified a suite of common drivers of change in inland aquatic ecosystems (e.g., Revenga and Kura, 2003; Whigham et al., 1993; Mitsch, 1994; Finlayson and D’Cruz, 2005; Dudgeon et al., 2005). Many of these previous reviews have focused primarily on biophysical pressures that directly affect the ecological condition of these ecosystems, as depicted in Figure 10. Also, the importance of addressing the indirect drivers of change has been increasingly recognized with the Millennium Ecosystem Assessment (2003) and the Global Environment Outlook (Arthurton et al., 2007) providing comprehensive overviews. The indirect drivers are derived primarily from the following: demographics such as population size, age, gender structure, spatial distribution, and migration; economic forces such as national and per capita income, macroeconomic policies, international trade, and capital flows; sociopolitical

47

processes such as governance, institutional and policy frameworks, and the roles of women and wider civil society; scientific and technological developments including rates of investments in research and development and the rates of adoption of new biotechnologies and information technologies; and cultural and religious choices individuals make about what and how much to consume and what they value. These drivers are not static – they can change rapidly and over long periods as shown by fluctuations in the global economy and the increasing global population, for example. Changes in these drivers are also expected to increase the demand for food, fiber, energy, and freshwater (Vo¨ro¨smarty et al., 2005; Molden et al., 2007). The direct drivers of change in inland aquatic ecosystems are interconnected with the indirect drivers, and include: changes in land use as a consequence of clearance, drainage, and infilling; the spread of infrastructure for urban, tourism and recreation, aquaculture, agriculture, and industrial purposes; the introduction and spread of invasive species; the regulation and fragmentation of rivers; abstraction of surfaceand groundwater; over fishing and in places unsustainable hunting; chemical pollution, salinization, and eutrophication; and more recently the impacts of global climate change. In some cases, these drivers act synergistically or cumulatively. Multiple interacting drivers can cause changes in aquatic ecosystems and their species and ecosystem services that may not be readily attributable to one or the other driver. There are many interdependencies between and among the indirect and direct drivers of change, and, in turn, changes in ecosystems can lead to feedbacks on the drivers of change. When addressing the complex scenarios of multiple drivers, it is necessary to have a clear understanding of the nature of the changes and their likely causes before implementing management responses; risk and vulnerability assessments can help ascertain the nature of change and guide management. The Millennium Ecosystem Assessment (2003) outlined the interactions between drivers and ecosystems in a framework that linked the consequences of indirect and direct drivers with changes in the biodiversity and services provided by ecosystems and the consequences for human well-being. The direct drivers of change in inland aquatic ecosystems are described below – it draws heavily on the assessment undertaken by Finlayson and D’Cruz (2005) as part of the Millennium Ecosystem Assessment and provides an update.

1.03.4.1 Drainage, Clearing, and Infilling It has been well established that clearing or drainage for agricultural expansion is the principal cause for wetland loss worldwide. Agriculture, including rangelands, now occupies roughly 40% of the world’s terrestrial surface and is a major contributor to global environmental change (Foley et al., 2005), with cropping occurring on more than 50% of the land area in many river basins in Europe and India and more than 30% in the Americas, Europe, and Asia (Millennium Ecosystem Assessment, 2005). In this respect, inland aquatic ecosystems are subject to many of the same pressures from agriculture as are other ecosystems – agriculture is recognized as a major driver of change in ecosystems globally (Millennium Ecosystem Assessment, 2005).

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Managing Aquatic Ecosystems

Box 3 Status of animals from inland aquatic ecosystems. Based on information derived from Finlayson CM and D’Cruz R (2005) Inland water systems Millennium Ecosystem Assessment, Volume 2: Conditions and Trends. Washington, DC: Island Press. Invertebrates The conservation status of most aquatic invertebrates has not been comprehensively assessed, except for regional assessments of certain taxonomic groups. Clausnitzer and Jodicke (2004) assessed the global status of dragonflies and damselflies and reported that while some 130 species were previously listed as threatened that many more were now also threatened. IUCN (2003) reported that 130 freshwater species of aquatic insects, 275 species of freshwater crustacean, and 420 freshwater mollusks were globally threatened, although no comprehensive global assessment has been made of all the species in these groups. For the United States, one of the few countries to assess freshwater mollusks and crustaceans comprehensively, 50% of known crayfish species and two-thirds of freshwater mollusks are at risk of extinction, and at least one in 10 freshwater mollusks are likely to have already gone extinct (Master et al., 1998). Freshwater fish A number of regional overviews of the status of freshwater fish are available, yet many of the existing overviews underestimate the number of species, as there are still many species to be described and assessed. There is, therefore, a high level of uncertainty about the status of fish in many inland waters with estimates of the number of freshwater fish in Latin America varying from 5000 to 8000; in tropical Asia and Africa, there are estimated 3000 species on each continent (Revenga and Kura, 2003), although these figures are almost certainly underestimates. It is estimated that in the last few decades more than 20% of the world’s 10 000 described freshwater fish species have become threatened or endangered or are listed as extinct (Moyle and Leidy, 1992). In the 20 countries for which assessments are most complete, an average of 17% of freshwater fish species are globally threatened (IUCN, 2003). Amphibians The recent Global Amphibian Assessment (IUCN et al., 2004) reported that the decline in conservation status of freshwater amphibians was worse than that of terrestrial species listed with 964 of 3908 freshwater species listed as threatened. Species associated with flowing water were found to have a higher risk of extinction than those associated with still water. Salamanders and newts have an even high level of threat (46% globally threatened or extinct) than frogs and toads (33%) and Caecilians (2%, although knowledge of these is poor, with only one-third assessed). Basins with the highest number of threatened freshwater amphibians include the Amazon, Yangtze, Niger, Parana, Mekong, Red, and Pearl in China, Krishna in India, and Balsas and Usumacinta in Central America, all of which have between 13 and 98 threatened freshwater species. Reptiles Van Dijk et al. (2000) reported that of the 200 species of freshwater turtles, 51% of the species of known status have been assessed as globally threatened, and the number of critically endangered freshwater turtles more than doubled in the four years preceding and that of the 90 species of Asian freshwater turtles and tortoises, 74% are considered globally threatened. Of the 23 species of crocodilians, which inhabit a range of wetlands including marshes, swamps, rivers, lagoons, and estuaries, four are critically endangered, three are endangered, and three are vulnerable (IUCN, 2003). There is very little information on the conservation status of aquatic snakes although IUCN (2003) reported that some semiaquatic snakes are vulnerable. Waterbirds Many waterbird species are globally threatening and the status of both inland and marine/coastal waterbirds is deteriorating faster than those in other habitats (Davidson and Stroud, 2004). Of the 35 bird families with species that are entirely or predominantly coastal/marine or inland wetland dependent, 20% of the 1058 species for which assessment data exist are currently globally threatened or extinct. Waterbirds dependent on freshwater ecosystems, especially those that also use marine and coastal ecosystems, have deteriorated in status faster than the average for all threatened species, but at similar rates for other migratory bird species. Shorebirds, many of which also use freshwater ecosystems, are declining worldwide with 48% of populations with a known trend declining. Other waterbirds in decline include cranes with 47% of populations with a known trend declining, rails (50%), skimmers (60%), darters (71%), ibis and spoonbills (48%), storks (59%), and jacanas (50%). Only gulls (18%), flamingos (18%), and cormorants (20%) appear to have a relatively healthy status. Mammals Although most mammals depend on freshwater for their survival, and many feed in rivers and lakes or live in close proximity to freshwater ecosystems, only a few are considered aquatic or semiaquatic mammals. Revenga and Kura (2003) provided an analysis of the status of aquatic and semiaquatic mammals. Some 37% of inland water-dependent mammals are globally threatened, compared with 23% of all mammals and includes otters (50% of species of known status threatened), seals (67% threatened), manatees (100% threatened), river dolphins and porpoises (100% threatened), and wetland-dependent antelopes (29% threatened) (Revenga et al., 2005).

Peatlands in particular have for centuries been converted for agriculture in many parts of the world, particularly in Europe, but also more recently in the highlands of South America and in parts of China, Southeast Asia, and Africa. In Southeast Asia, large areas of the once-extensive tropical peat swamp forests have been heavily degraded, and large extents have been lost over the last four decades as a consequence of logging for timber and pulp and conversion to oil palm plantations. The peatlands of Malaysia and Indonesia are especially threatened by drainage and forest clearing that then makes them susceptible to fire. Land clearing and subsequent uncontrolled fires in 1997 severely burned about 5 million

hectares of forest and agricultural land on the Indonesian island of Borneo (Page et al., 2002, 2009). Irrigated agriculture is another major driver of the loss and degradation of inland aquatic ecosystems with water withdrawals for irrigation worldwide resulting in major changes in river flows (Revenga et al., 2000) – flows that are essential for sustaining the ecosystem services and species that occur in inland aquatic ecosystems. The global extent of irrigated agricultural land has increased from approximately 138 million hectares in 1961 to 271 million hectares in 2000, and currently accounts for an estimated 40% of total food production even though it represents only 17% of global

Managing Aquatic Ecosystems

49

Dams

Overharvesting of wild resources,

interrupt the connectivity of river systems, disrupting fish spawning and migration. Dams with large reservoirs alter seasonal flood regimes and retain sediment needed to maintain the productivity of floodplain agriculture.

especially fish, is driven both by the subsistence needs of a growing population and by unsustainable commercial exploitation, threatening future food security and livelihoods.

River channelization and dredging for navigation reduces riverine habitat and alters flood patterns.

Large-scale irrigation and river diversions after natural flow regimes, reduce downstream water availablity for agriculture, and contribute to salinization through saltwater intrusion in the coastal zone.

Agricultural expansion is often achieved by converting natural inland water systems, reducing aquatic biodiversity and natural flood control functions, and increasing soil salinity through evaporation. When accompained by intensive use of agrochemicals, off-site pollution effects can be extensive.

Forest clearing in permanently or seasonally inundated zones, often motivated by unsustainable aquaculture production, dramatically reduces habitat for wild aquatic organisms. In the coastal zone, it also make the landscape much more susceptible to erosion.

Roads and flood-control infrastructure often interrupt wetland connectivity, disrupting aquatic habitat, reducing the function of wetlands to remove pollutants and absorb floodwaters, and potentially increasing the losses when high floods do occur.

Urban and industrial pollution, when released untreated into aquatic environments, reduces water quality, affecting the diversity and abundance of aquatic organisms as well as human health.

Figure 10 Pictorial representation of some of the direct drivers of change in inland and coastal aquatic ecosystems. Invasive species, climate change, and land conversion to urban or suburban areas affect all components of the catchment and coastal zone and are therefore not represented pictorially. From Millennium Ecosystem Assessment; adapted from Ratner BD, Ha DT, Kosal M, Nissapa A, and Chanphengxay S (2004) Undervalued and Overlooked: Sustaining Rural Livelihoods through better Governance of Wetlands, Studies and Review Series. Penang, Malaysia: World Fish Centre.

cropland area (Wiseman et al., 2003). In this respect, it seems to have a disproportionate negative impact on inland aquatic ecosystems relative to the land area involved. Possibly more significantly, around 66% of all water withdrawn for direct human use is now being used for agriculture (Scanlon et al., 2007). The problem of increasing abstraction of freshwater from aquatic ecosystems is exacerbated by the loss of much of this water from the immediate landscape – very little of the water returns as runoff to the rivers as most of it is evaporated or transpired (Falkenmark and Lannerstad, 2005). There are many well-documented examples where diversion of water for agriculture has caused a decline in the extent and degradation of inland aquatic ecosystems and their species richness (Revenga et al., 2000; Finlayson and D’Cruz, 2005; Dudgeon et al., 2005). Lake Chad provides an interesting example with major ecosystem change being due to

both human-induced and natural changes, with the loss of many species and ecosystem services as the lake shrank from about 25 000 km2 in surface area to one-twentieth of its size over 35 years at the end of the twentieth century as a consequence of a drier climate and high agricultural demands for water (Coe and Foley, 2001). This example illustrates the complexity that can arise when multiple drivers of change impact on an aquatic ecosystem, especially in areas of high climate variability.

1.03.4.2 Modification of Water Regimes Agricultural development and the diversion of water for irrigation, and increasingly for urban purposes, have modified the water regime in many inland aquatic ecosystems. Modifications include the construction of river embankments to

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Table 4

Alteration of inland freshwater systems worldwide

Alteration

Pre-1990

1900

Waterways altered for navigation (km) Canals (km) Large reservoira Number Volume (km2) Large dams (415 m high) Installed hydro-capacity (MW) Hydro-capacity under construction (MW) Water withdrawals (km3 yr 1) Wetlands drainageb (km3)

3125 8750

8750 21 250

41 14 – – – – –

581 533 – – – 578 –

1950–60 – – 1 105 1 686 5 749 o290 000 – 1 984 –

1985 4500 000 63 125 2768 5879 – 542 000 – 3200 160 000

1996–98

– 2836 6385 41 413 660 000 126 000 3800 –

a

Large reservoirs are those with a total volume of 0.1 km3 or more. This is only a subset of the world’s reservoirs. Includes available information for drainage of natural bogs and low-lying grasslands as well as disposal of excess water from irrigated fields. – Data not available. From Revenga C and Kura Y (2003) Status and Trends of Biodiversity of Inland Water Ecosystems, Technical Series No. 11. Montreal: Secretariat of the Convention on Biological Diversity. b

improve navigation, drainage of wetlands for agriculture, construction of dams and irrigation channels, and the establishment of interbasin connections and water transfers. Revenga and Kura (2003) provided data on the extent of alteration to inland freshwater systems worldwide (Table 4). These changes have had many beneficial outcomes for people through the provision of local flood control and hydropower, improved fisheries and increased agricultural output (Molden et al., 2007), but at the same time there have been many negative ecological effects on inland aquatic ecosystems (Revenga et al., 2000). Rivers have been disconnected from their floodplains and wetlands; seasonal changes in water flows have disrupted fish and bird migration and breeding; greater runoff in rivers has increased the likelihood and severity of flooding; and links with groundwater systems have been disrupted, and, in some coastal regions, enabled saline water to intrude on freshwater systems. They have also transformed many rivers through (1) the construction of large reservoirs, such as those on the Volta and Zambezi Rivers in Africa, or along the Volga River in Russia; (2) the embankment and channelization of rivers such as that along the Mississippi and Missouri rivers in the United States; or (3) significantly reduced flows to floodplains and downstream ecosystems, including deltas such as the Indus in Pakistan, or the lakes at the mouth of the Murray River in Australia. Even the large inland seas are not safe from the impacts of river regulation and diversion of water away from terminal water bodies. The Dead Sea located in the Syrina-African rift valley at the southern outlet of the Jordan River and at 417 m below sea level is the world’s saltiest large water body. It is threatened by excessive withdrawal of water from the river to support industrial, agricultural, and tourism development (ILEC and UNEP, 2003). The annual historical flow of the Jordan River to the Sea was about 1285 million cubic meters in the 1950s compared to 505 in mid-1970s, 275 in 2000s, and a projected 170 million cubic meters in the mid-2020s (Courcier et al., 2005). In addition to the reduction in water flows, the diversion of water has resulted in the development of a complex socioeconomic system (Figure 11) with undoubted economic and political ramifications associated with any proposals for

further development and regulation of water flows, including management of wastewater and irrigated agriculture (Courcier et al., 2005). The Mesopotamian marshlands also provide another example of a complex social–political scenario associated with river regulation and the restoration of an intermixed social and ecological system. The marshes have been severely affected by river regulation in recent decades with the original areas of 15 000–20 000 km2 before being reduced by drainage and dam construction along the Tigris and Euphrates rivers to less than 400 km2 (Partow, 2001). The total capacity of the reservoirs along these rivers exceeds the annual discharge of both rivers, drastically reducing the supply of flood waters that were so important for delivering sediments and nutrients to the marshlands. In addition to regulating the flows along the rivers by the construction of dams in the upstream reaches, attempts were made in the early 1990s to drain water away from the marshes through large canals. The combined effect of these moves was to reduce the extent of the marshes and threatened the culture and biodiversity that depended on the annual flooding regime. More recently, there have been partially successful but still insufficient efforts to restore parts of the marshes by breaking banks and flooding some 20% of the original area of marsh (Richardson et al., 2005). While these efforts have indicated the potential for further successful restoration, Falkenmark et al. (2007) have cautioned that attempts to return water to the central areas of the marshes upstream of the confluence of the Tigris and Euphrates could generate adverse impacts on aquatic ecosystems further downstream. That is, without an increase in the amount of water available for flooding the marshes, simply returning the water to upstream areas may not be enough to restore them and could further reduce the flow of water to downstream areas and possibly further reduce the flow to the Persian Gulf. Richardson et al. (2005) have also reported the construction of a dike along the Iraq/Iran border that could further reduce the flow of water into the marshes. The construction of large dams has doubled or tripled the residence time of river water with impacts on suspended sediment and carbon fluxes, waste processing, and aquatic

Managing Aquatic Ecosystems

1950

2000s

51

Mid-1970s

Mid-2000s

Figure 11 Changes in water resources and their allocation in the lower Jordan basin from the 1950s to the mid-2020s. Reproduced from Courcier R, Venot J-P, and Molle F (2005) Historical Transformations of the Lower Jordan River Basin (in Jordan): Changes in Water Use and Projections (1950– 2025). Colombo, Sri Lanka: International Water Management Institute.

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habitat, and has resulted in fragmentation of the river channels with 37% of 227 river basins around the world strongly affected by fragmentation and altered flows, 23% moderately affected and 40% unaffected (Revenga et al., 2000). Small dams can also have major effects on the ecological condition of inland aquatic ecosystems. The debate about the construction of dams is ongoing (WCD, 2000). The effects of modification of flow regimes on fish migrations have been reviewed by Revenga and Kura (2003) with direct impacts on diadromous fish species such as salmon being well known and increasingly recognized, whereas the indirect impacts of flow alteration, such as the reduction of floods and loss of lateral connections on floodplains, are not always as evident. In many cases, construction of dams has resulted in the disappearance of fish species adapted to river systems and the proliferation of species adapted to lakes. Changes in the fish are indicative of many changes in the biodiversity of regulated rivers and associated aquatic ecosystems, although the extent of data and information about the wider biodiversity is often inadequate or lacking (Revenga et al., 2005).

1.03.4.3 Invasive Species Despite the current concern about invasive species in inland aquatic ecosystems their importance has not always been as widely appreciated (Finlayson, 2009). This was in part because the problem of invasive species in these ecosystems was seen largely as one for developed countries, despite the paradoxical occurrence of well-documented cases of invasive species in African wetlands and lakes (e.g., Salvinia molesta and Nile perch – Lates nilotica). The reasons for this situation are not clear, although they probably included insufficient awareness and information about the impacts of these species and ways of controlling them. However, several recent assessments (such as the Millennium Ecosystem Assessment (2005)) and initiatives (such as the Global Invasive Species Programme) have demonstrated that the loss of wetland species as a consequence of invasion by invasive species is now much more of a concern globally (Revenga et al., 2005; Finlayson, 2009). The spread and establishment of non-native invasive species in inland aquatic ecosystem have caused many changes to the native biota and are likely to become even more common

Box 4

with the further development of aquaculture, interbasin transfers of water, and shipping and global commerce. There are many documented and well-known examples of plant species that have successfully invaded and established in inland aquatic ecosystems, including the pan-tropical weeds salvinia (Salvinia molesta) and water hyacinth (Eichhornia crassipes) that originated in South America but are now widely established in many countries. In many instances though, the occurrence of alien plant species may not be seen as undesirable, as shown by the establishment of the alien species Egeria densa in the Rio Cruces wetland in Chile where it was considered to be an ecological engineer and thrived and provided the mainstay to support a population of blacknecked swans (Cygnus melancoryphus) which was highly appreciated and valued by local residents (Yarrow et al., 2009). The angst in this case came not from the establishment of the alien invasive plant but from its decline when its biomass crashed suddenly in 2004 and the swans dispersed to other wetlands, leaving an acrimonious debate about the cause of the population crash (Delgado et al., 2009). The example of Canadian pond weed (Elodea canadensis) outlines many of the dilemmas raised by introduced species (Sculthorpe, 1967). It originated in North America and invaded the waterways of Europe in the late nineteenth century where it grew rapidly and spread vegetatively to reach a maximum population density within a period of a few months to 4 years. This population level was maintained for up to 5 years but then declined to levels that were not considered a nuisance. The reasons for the rapid increase and decline were not determined. Many animal species, both large and microscopic, have also invaded inland aquatic ecosystems, such as those outlined for European lakes (Box 4). The larger invasive animals include the cane toad (Bufo marinus), bullfrog (Rana catesbeiana), European domestic pig (Sus scrofa), carp (Cyprinus carpio), and zebra mussel (Dreissena polymorpha) that have become established outside of their native range and disrupted the inland water systems that they have invaded. Many fish species have been spread beyond their native ranges often in response to demands for aquaculture and aquarium species. Fish introductions have usually been done to enhance food production and recreational fisheries or to control pests such as mosquitoes and aquatic weeds. The spread of trout and salmon

Invasive species in European rivers. Based on information supplied by H. Ketelaars.

The construction of canals between rivers and other water bodies in Europe over the past two centuries has provided channels for the migration of many aquatic species, whether they migrated themselves or were carried by on the hulls of ships or in their ballast water. The Volga–Baltic Waterway, reconstructed in 1964 to connect the Caspian with the Baltic enabled the translocation of many aquatic species, including copepods, rotifers, the onychopod Bythotrephes longimanus, and several fish species to the Volga basin. The Main-Danube Canal, officially opened in 1992, is another that allowed many Ponto-Caspian invertebrate species to reach the Rhine basin and from there to disperse to other basins, mainly in ballast water. Intentional introductions of aquatic species have occurred mainly in the past two centuries. The North American amphipod Gammarus tigrinus was deliberately introduced in 1957 to Werra and Weser rivers in Germany where the local gammarid fauna had disappeared due to excessive chloride pollution. The mysid Mysis relicta was been introduced to many Scandinavian lakes to stimulate fish production. Three North American introduced crayfish species have established themselves in many European waters and with the introduced crayfish plague (Aphanomyces astaci) have almost eliminated the native crayfish (Astacus astacus). At least 76 freshwater fish species have been introduced into European fresh waters, with approximately 50 establishing self-sustained populations. When introductions between areas within Europe are also considered, the number of introduced fish species is more than 100. The numerically most important families are cyprinids and salmonids, of which grass carp (Ctenopharyngodon idella), silver carp (Hypophthalmichthys molitrix), rainbow trout (Oncorhynchus mykiss), and brook char (Salvelinus fontinalis) are now widely distributed.

Managing Aquatic Ecosystems

species for sports fishing is well known. The introduction of alien fish species has though often resulted in major ecological change, including the collapse of native fish populations. Finlayson and D’Cruz (2005) provided a summary of impacts of some invasive species, including the adverse impact of salmonids on the genetic diversity of wild stocks in many countries and the spread of tilapia species into Central and Southern America and parts of Asia. Herbivorous and omnivorous species, such as Indian, Chinese, and common carp, account for the majority of introductions in tropical Asia (Revenga and Kura, 2003). In many cases, the impact of invasive species on the native fish has not been documented. Finlayson (2009) noted that while there are some obvious examples of species that have established outside their native range, it should not be assumed that all newly established species have been transplanted by human activities or are invasive. Recent concerns over global climate change and variability provide a scenario where it may no longer be possible to attribute the occurrence of new species to natural fluctuations versus human activity.

1.03.4.4 Overfishing Inland fisheries are a major source of protein for a large proportion of the world’s population with the global production of fish and fishery products from inland waters in 2002, amounting to 32.6 million tonnes with 8.7 tonnes from wild capture and the rest from aquaculture (FAO, 2004). While inland fisheries have increased, FAO (1999) also reported that most inland fisheries that relied on natural reproduction of fish stock were overfished or being fished at their biological limit. Arthurton et al. (2007) also reported that inland fish stocks were subjected to a combination of direct pressures, including habitat alteration, and loss, altered flows, and habitat fragmentation due to dams and other infrastructure, and also faced problems from pollution, exotic species, and overfishing. With much of inland fisheries catches destined for subsistence consumption or local markets, food demand for growing populations is a major factor driving exploitation levels in inland waters. Monitoring of the extent of fishing in inland aquatic systems also seems to be underreported by a factor of 2 or 3, due to the large volume of harvest that is consumed locally, and remains unrecorded (Allan et al., 2005). Even with discrepancies in the data, it is well established that inland fisheries are extremely important in Asia and Africa and in 2002 accounted for 90% of the inland fish catch (FAO, 2004). China alone accounted for at least one-quarter of the inland catch, followed by India (9%), Bangladesh (8%), and Cambodia (4%) (FAO, 2004). The importance of aquaculture as a component of inland fish supply is shown by continued growth at an average rate of nearly 9% per year since 1970 – a much higher rate than that for capture fisheries (B1%) (FAO, 2004). Almost 58% of this came from China with an average annual increase of 11% between 1970 and 2000, compared with 7% for the rest of the world (FAO, 2004). However, many aquaculture operations, depending on their design and management, can contribute and have contributed to habitat degradation, pollution,

53

introduction of exotic species, and the spread of diseases through the introduction of pathogens (Naylor et al., 2000). There is ample evidence that overfishing is a significant factor in the decline of numerous species and fisheries, and is of global importance as a threat to inland water biodiversity (Allan et al., 2005). There are two main types of overfishing with intensive fishing of a targeted species leading to marked declines in catch per unit effort and size of individuals captured, while assemblage or ecosystem overfishing leads to sequential declines of species and depletion of individuals and species of large size, especially piscivores, and declines in the mean trophic level of the assemblage and changes in the responsiveness of populations to environmental fluctuations. The historic influence of overharvesting of fish is shown by the decline of the Murray cod of the Murray-Darling river system in Australia, some sturgeon stocks of Eurasia, the tilapiine species Oreochromis esculentus and Oreochromis variabilis of Lake Victoria, and perhaps the Pacific salmon of the Columbia River while the decline of the Mekong giant catfish and the Nile perch of Lake Victoria provide contemporary cases (Allan et al., 2005; Box 5). The consequences of eliminating fish species from inland aquatic ecosystems are likely to be numerous and of varying severity with the progressive reduction in assemblage diversity meaning that fewer species are available to perform critical functions in the ecosystem with dire consequences following the loss of a species with a disproportionately strong influence on nutrient, habitat, or assemblage dynamics (Allan et al., 2005).

1.03.4.5 Water Pollution and Eutrophication Vo¨ro¨smarty et al. (2005) noted that attempts to summarize patterns and trends in the quality of inland waters, particularly at a global scale, encompassed an array of challenges that included basic definitional problems, a lack of worldwide monitoring capacity, and an inherent complexity in the chemistry of both natural and anthropogenic pollutants. Furthermore, despite improvements in analytical methods the capacity to monitor trends in water quality is limited in terms of the spatial coverage, frequency, and duration of monitoring data. Data comparability was yet another constraint while the monitoring of groundwater was more problematic than surface water. The Millennium Ecosystem Assessment (2005) highlighted changes in the global nitrogen cycle with the loading of reactive nitrogen to the landmass having doubled from 111 million to 223 million tonnes per year with the greatest increases in North America, continental Europe, and South and East Asia (Green et al., 2004). This has caused the transport of dissolved inorganic nitrogen in rivers to increase from about 2–3 million tonnes per year from preindustrial times to about 15 million tonnes today, especially in drainage basins that are heavily populated or supporting extensive industrial agriculture. While rivers and wetlands can assimilate some nitrogen, the self-purification capacity is not unlimited and the water quality in many has deteriorated, resulting in eutrophication, harmful algal blooms, and high levels of nitrate in drinking water (Malmqvist and Rundle, 2002). Jorgensen et al. (2001) reported that eutrophication was a widespread problem in lakes and reservoirs and also one of the most

54

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Box 5 Nile Perch invasion in Lake Victoria, East Africa. Based on information from Howard (2009) Case study 1 Nile Perch invasion in Lake Victoria. In: Finlayson CM (2009) Biotic pressures and their effect on wetland functioning. In: Maltby E and Barker T (eds.) The Wetlands Handbook, pp. 674–676. Oxford: Wiley-Blackwell. The Nile perch (Lates niloticus) is a large predatory fish that can grow to 1.8 m in length and weigh as much as 200 kg. It is native to the White Nile River system in Uganda, Sudan, Ethiopia, and Egypt and was introduced into Lake Victoria in the 1950s and 1960s. At the time the lake contained around 350 species, including 300 endemic cichlids of the subfamily Haplochromiinae that occupied many niches in the lake and its wetlands. The Nile perch was introduced to Lake Victoria to boost its fishery, but was hardly seen in catches until the late 1970s with the entire lake yielding less than 25 000 tonnes in 1981 but rising to 363 000 tonnes in 1993. At the same time, the total catch of all species rose from around 100 000 tonnes in 1979 to about 500 000 tonnes in 1989 with the proportion of Nile perch rising from less than 0.1% in 1974 to more than 50% 20 years later. Over the same period the lake lost as many of 50% of its species of haplochromines and became dominated by three species, Nile perch, the introduced Tilapia nilotica, and a native cyprinid (sardine) Rastrineobola argentea. As other changes occurred in the Lake at the same time, such as the advent of the invasive alien water hyacinth (Eichhornia crassipes), it is difficult to attribute changes in diversity of the haplochromines solely to the Nile perch. There are a number of probable causes of the dramatic population increase of the Nile perch: 1. 2. 3. 4. 5.

lack of other wide-ranging predators (competitors); a fast growth rate and reproductive potential; a great range of body size during development which permits exploitation of various habitats in the lake; changes in the lake ecosystem resulting from human activities (e.g., eutrophication); and adaptability of the perch to different sources of food.

It is still not clear though whether or not the population of Nile perch and the wider fish structure in Lake Victoria has reached (or is anywhere near) stability.

difficult to abate, and cyanobacteria blooms have increased and are a major problem worldwide. The extent of water pollution from point sources is well known with an estimated 90% of wastewater in developing countries being discharged directly to rivers and streams without any waste processing treatment, and in some locations both surface- and groundwater have been so polluted that they are unfit even for industrial use (WMO, 1997). The agricultural sector also contributes a large amount, although this is usually from diffuse sources (Verhoeven et al., 2006). It is also well known that pollution from point sources such as mining has had many devastating impacts on inland waters in many parts of the world; for example, the spillage in 1998 of an estimate 5.5 million cubic meters of stored tailings (mine wastes) from the Aznalcollar mine nearly 50 km from the Don˜ana National Park in Spain spread over 46 000 km of downstream habitat with fatal consequences for much of the biota (Bartolome and Vega, 2002). The cost of removing the tailings and contaminated soil reached about 3.8 billion Euros. Meybeck (2003) provided an overview of water pollution problems for inland waters (Table 5). This showed that in industrial countries fecal contamination has been largely eliminated, while new problems, particularly from agriculture runoff, were increasing. In other countries, this was not the case and fecal contamination was still a major problem with urban and industrial pollution sources increasing faster than wastewater treatment. Contamination by pesticides has increased rapidly since the 1970s, with many different substances being involved. Vo¨ro¨smarty et al. (2005) concluded that since the 1990s the water-quality situation in most developing countries and countries in transition was likely to be worse in terms of overall water quality. In Eastern Europe, Central and South populated Americas, China, India, and populated Africa, it was probably worse for metals, pathogens, acidification, and organic matter, while there were slight improvements for the

Table 5 Major water quality issues in inland aquatic ecosystems at the global scale Issue

Rivers Lakes Reservoirs Groundwaters

Pathogens Suspended solids Decomposable organic matter Eutrophication Nitrate Salinization Trace metallic elements Organic micropollutants Acidification

XXXX XXX XXXX

XX NA XX

XX XX XXX

XXX NA XX

XX XX XX XXX

XXX X X XXX

XXXX X XX XXX

NA XXXX XXXX X

XXXX XX

XXX XX

XXX XXX

XXXX X

XXXX, severe or global deterioration observed; XXX, important deterioration; XX, occasional or regional deterioration; X, rare deterioration; NA, not applicable. Information from Meybeck M (2003) Global analysis of river systems: From Earth system controls to Anthropocene syndromes. Philosophical Transactions of the Royal Society London B 358: 1935–1955.

same issues in Western Europe, Japan, Australia, New Zealand, and North America. Nitrate though was generally still increasing everywhere, as it has since the 1950s. In the former Soviet Union there seems to have been an improvement in water quality as a consequence of the decline of industrial activities, whereas in Eastern Europe there have also been some improvements, such as those in the Danube and the Elbe basins. A few rivers, such as the Rhine, have seen a stabilization of nitrate loads after 1995. Arthurton et al. (2007) reported that water-quality degradation from human activities continued to degrade inland aquatic ecosystems and affected the health of many people. Pollutants of primary concern included microbial pathogens and excessive nutrient loads with the latter leading to eutrophication of downstream and coastal waters, and loss of

Managing Aquatic Ecosystems

55

Box 6 Waterbirds and climate change. Based on information reported in Finlayson CM, Gitay H, Bellio MG, van Dam RA, and Taylor I (2006) Climate variability and change and other pressures on wetlands and waterbirds – impacts and adaptation. In: Boere G, Gailbraith C, and Stroud D (eds.) Water Birds around the World, pp. 88–97. Edinburgh: Scottish Natural Heritage. While the general nature of the impacts of climate change on waterbirds can be described there is less certainty when it comes to identifying the extent, intensity, and time frames for such changes. It is difficult to predict with great certainty as the models used for global climate change projections are still very coarse and the ecological relationships between waterbirds and climate and aquatic ecosystems is insufficiently known. The most severe effects and those most likely to occur earliest include: 1. the loss of intertidal areas and increased salinity of coastal freshwater wetlands caused by rising sea levels; 2. a reduction in the extent of wetlands and duration of flooding in arid and semiarid areas from changes in rainfall; and 3. the loss of wetland breeding areas in the Arctic and sub-Arctic areas caused by increasing temperatures, expanding boreal forests and fires.

The extent of loss of intertidal habitats and its effects on coastal waterbirds, many of which also frequent inland aquatic ecosystems, will depend on the ability of coastal environments to migrate inland as sea level rises. The effects of rising temperatures on plant communities will be particularly strong in the Arctic with an expected expansion of the boreal forest into the tundra areas where two-thirds of all goose and 95% of all Calidrid sandpipers breed. The impacts of habitat loss could be offset to some extent by rising temperatures increasing productivity and breeding success; however, these may also be affected by an increase in loss of nests and chicks to predation as predators such as the Red Fox (Vulpes vulpes) expand their range. This example illustrates both the complexity of the changes that may occur as well as the complexity of identifying the many interactions that may occur within an ecosystem or between species. As changes in global circulation patterns will result in changes to rainfall patterns, with some areas experiencing increases and others decreases. The latter in particular may be extremely detrimental to waterbirds in areas that are already dry and subject to drought, such as parts of Australia, Asia, and Africa. As wetlands and waterbirds in these areas are already highly stressed from the impacts of agriculture, reduced water flows, pollution, and increasing salinization, they may be highly vulnerable to changes in the climate. As reduced rainfall will increase the intervals between flooding events and shorten their duration there could be reduced breeding success and recruitment of waterbird species that formerly depended on flooding events of sufficient duration to stimulate breeding and enable fledging. Reduced rainfall and flooding across large areas of arid land will particularly affect bird species that rely on a network of wetlands that are alternately or even episodically wet and fresh or drier and saline. While the exact nature of changes cannot be confirmed there will almost certainly be great regional variation, with some areas experiencing increases in waterbird populations and others, decreases. The fragmentation of rivers and wetlands or the disruption or loss of migration corridors will affect the manner in which waterbirds (and other species) will be able to respond and adapt.

beneficial human uses. Pollution from diffuse land sources such as agriculture and urban runoff was also of concern.

1.03.4.6 Climate Change It is increasingly expected that global climate change will increase the pressure on inland aquatic ecosystems in many locations both directly, especially through increased temperatures, changes in snowmelt and runoff, and in places severely decreasing rainfall, as well as indirectly by interacting with existing pressures and drivers of change (Revenga and Finlayson, 2009; Finlayson et al., 2006; Finlayson and D’Cruz, 2005). Revenga and Finlayson (2009) anticipated that most pronounced impacts from climate change will come from increased temperatures and changes in precipitation, and these will not affect all wetlands in the same way. As a consequence some aquatic ecosystems and their catchments will be drier, while others will experience more rainfall and storms, or even more intense and fewer storms. High-altitude wetlands seem to be particularly vulnerable as the annual and previously predictable glacier-melt decreases; freshwater systems near the coast are susceptible to rise in sea level and salinization. Many combinations of temperature increase and precipitation changes will affect the frequency, duration, and timing of peak floods or base-flows in rivers with subsequent impacts on aquatic species that are sensitive to changes in water flow for migration, breeding, and feeding. Further assessment is needed to ascertain the vulnerability of particular ecosystems and species. The latter has been summarized for waterbirds by

Finlayson et al. (2006) and outlined in Box 6. However, in many instances, the certainty with which we can attribute cause and effect of climate change is undermined by the extent of existing data and knowledge. There is some confidence that many inland aquatic ecosystems are vulnerable to climate change with those at high latitudes and altitudes, such as Arctic and sub-Arctic bog communities, or alpine streams and lakes being highly vulnerable (Gitay et al., 2002; Finlayson et al., 2006; Revenga and Finlayson, 2009), as well as those that are isolated or are low-lying and adjacent to coastal wetlands (Bayliss et al., 1997; Pittock et al., 2001). Danielopol et al. (2003) expected groundwater ecosystems to change as recharge of aquifers is affected by rainfall and runoff. Besides changes in waterbird populations, the warming of inland waters could affect chemical and biological processes, reduce the amount of ice cover and dissolved oxygen in deep waters, alter the mixing regimes, and affect the growth rates, reproduction, and distribution of organisms and species (Gitay et al., 2002). The rise in sea levels will affect freshwater ecosystems in low-lying coastal regions and plant species that are not tolerant to increased salinity or inundation could be eliminated. Changes in the vegetation will affect both resident and migratory animals, especially if these result in a major change in the availability of staging, feeding, or breeding grounds for particular species. The impacts on the distribution of fish species could be profound with cold-water fish being further restricted in their range, and cool- and warm-water fish potentially expanding their range, and even moving poleward. These examples illustrate the statements made at the outset of this discussion that the extent of change in inland aquatic

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ecosystems due to the climate change should not be addressed in isolation of other drivers of change, as many of the adverse effects of the above-mentioned drivers of change will be exacerbated by climate change.

• •

1.03.5 Management Responses The information on management responses is largely paraphrased from that provided by Finlayson and D’Cruz (2005) in their assessment of the condition of inland waters for the Millennium Ecosystem Assessment. As their assessment was drawn from the published literature available at the time, it provides a widespread opinion as well as various options and opportunities for sustainable use and, where necessary, rehabilitation of inland aquatic ecosystems. Particular technical responses for specific wetlands or many drivers of change outlined in the text above are not provided – these are on the whole sufficiently well known, or could be developed through the application of the processes outlined below.

1.03.5.1 Integrated Management Processes The management of inland aquatic ecosystems worldwide has often been based on sectorally based decision-making mechanisms that have not included sufficient consideration of the wider implications or outcomes of specific actions. The information provided in the text above illustrates many adverse outcomes of past sectorally based management decisions. In many instances, these decisions have not adequately considered the trade-offs between the multiple uses and values of inland aquatic ecosystems and have too often resulted in the degradation of these ecosystems. The development of more multi-sectorally based responses and decisions is strongly encouraged as way to reverse the past loss and degradation of inland aquatic ecosystems and the decline in the ecosystem services that they deliver.

1.03.5.2 International Cooperation and Action The past loss and degradation of inland aquatic ecosystems have been recognized through international conventions and treaties. The Ramsar Convention on Wetlands has provided leadership and worked collaboratively with many other organizations, both informally and through formal agreements, to develop multisectoral approaches to stop and reverse the loss and degradation of wetlands. This includes working collaboratively to reduce the rate of loss of biodiversity and restore degraded wetlands. The Mediterranean wetland (MedWet) program is an example of a collaborative initiative that has supported actions to halt and reverse the loss and degradation of wetlands. The declaration behind this initiative was made in February 1991 and contained many recommendations that are still important today. These covered:

• •

identification of priority sites for wetland restoration and rehabilitation, and the development and testing of techniques for their complete rehabilitation; evaluation of existing and proposed policies to determine how they affect wetlands;

• •

increased institutional capacity to conserve and effectively manage wetlands through vigorous education and training programs; integrated management of all activities concerning wetlands, their support systems, and the wider area surrounding them carried out by properly funded and wellstaffed multidisciplinary bodies with active participation of representatives of government, local inhabitants, and the scientific and nongovernmental communities; open consultation and free flow of information when managing wetlands; and adoption and enforcement of national and international legislation for better management.

These recommendations have since been repeated or extended in many forums and with widespread acceptance, although the sentiment behind these have not always been transferred to on-ground actions and outcomes.

1.03.5.3 Restoration and Wise Use of Wetlands The concept of replacing lost wetlands has received increasing support in recent decades and more attention is now directed toward wetland restoration worldwide. However, current rates of restoration are inadequate to offset the rate of wetland loss in many regions – more is required even as efforts are undertaken to stop further loss. In support of these efforts, the Ramsar Convention has provided a suite of guidance for the wise use of wetlands covering national wetland policies; laws and institutions; river basin management; participatory management; wetland communication, education, and public awareness; management planning; international cooperation; wetland inventory, assessment and monitoring; water allocation and management; coastal management; and management of peatlands. One of the key barriers in preventing further loss and degradation of wetlands is the seeming unwillingness of parties to the above-mentioned collaborative initiatives and declarations to undertake effective actions. Sufficient knowledge is generally now available to know what actions are required to stop further loss and degradation, although there seems to be an inadequate adoption and understanding of ecosystem approaches for managing inland aquatic ecosystems, especially when dealing with water allocations. Ongoing dialog about the allocation of water for environmental outcomes in rivers and associated wetlands is still needed – these also need to address the trade-offs that are needed to support equitable outcomes and support many services that inland aquatic ecosystems provide to wider society and the inordinate costs associated with reinstating these once they have been lost.

1.03.5.4 Supporting Local Community Involvement in Management There has been increased interest in the development of mechanisms to support the capacity of local communities to contribute to the management of inland aquatic ecosystems. This can particularly be important where local knowledge and experience can be directly applied to local management issues, but can also support wider strategic planning. Recognition of the beneficial outcomes that can occur when local people are

Managing Aquatic Ecosystems

involved in the management of inland waters and their services has long underpinned efforts by the Ramsar Convention and its partners to encourage best management practices for wetlands. Participatory management and the involvement of local communities in management planning are implicit in the guidance provided by the Convention covering policy and legal instruments, economic and social interactions, and technical tools. The challenge for the Convention and others is to ensure that such instruments and tools are used effectively and as often as possible. This can be done by adopting an adaptive management approach which incorporates active learning mechanisms, the involvement of key stakeholders, and the balancing of vested interests.

1.03.6 Conclusions In drawing the above to a close reference is again made to the Ramsar Convention on Wetlands and the Millennium Ecosystem Assessment. For over 35 years, the Convention has recognized the interdependence of people and their environment. It has promoted the wise use of wetlands as a means of maintaining their ecological character – the ecosystem components and processes that comprise the wetland and underpin the delivery of ecosystem services, such as freshwater and food – and strongly supported the Millennium Ecosystem Assessment with a set of key messages about wetlands and their importance for people. These messages are shortened and paraphrased below as a way of concluding the above review of the condition and management of inland aquatic ecosystems:

•

•

•

•

•

•

Wetlands deliver a wide range of ecosystem services that contribute to human well-being, such as fish and fiber, water supply, water purification, climate regulation, flood regulation, coastal protection, recreational opportunities, and, increasingly, tourism. A priority when making decisions that directly or indirectly influence wetlands is to ensure that information about the full range of benefits and values provided by different wetland ecosystem services is considered. The degradation and loss of wetlands is more rapid than that of other ecosystems. Similarly, the status of both freshwater and coastal wetland species is deteriorating faster than those of other ecosystems. The primary indirect drivers of degradation and loss of inland wetlands have been population growth and increasing economic development. The primary direct drivers of degradation and loss include infrastructure development, land conversion, water withdrawal, eutrophication and pollution, overharvesting and overexploitation, and the introduction of invasive alien species. Cross-sectoral and ecosystem-based approaches to wetland management – such as river (or lake or aquifer) basin-scale management – that consider the trade-offs between different wetland ecosystem services are more likely to ensure sustainable development than many existing sectoral approaches. Major policy decisions in the next decades will have to address trade-offs among current uses of wetland resources

•

•

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and between current and future uses. Particularly important trade-offs involve those between agricultural production and water quality, land use and biodiversity, water use and aquatic biodiversity, and current water use for irrigation and future agricultural production. The adverse effects of climate change will lead to a reduction in the services provided by wetlands. Removing the existing pressures on wetlands and improving their resiliency are the most effective methods of coping with the adverse effects of climate change. The Millennium Ecosystem Assessment conceptual framework for ecosystems and human well-being provides a framework that supports the promotion and delivery of the Ramsar Convention’s wise use concept. This enables the existing guidance provided by the Convention for the wise use of all wetlands to be expressed within the context of human well-being and poverty alleviation.

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Page S, Hosciło A, Woosten H, et al. (2009) Restoration ecology of lowland tropical Peatlands in Southeast Asia: Current knowledge and future research directions. Ecosystems 12: 888--905. Page SE, Siegert F, O’Reiley J, von Boehem H-D, Jaya A, and Limin S (1997) The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420: 61–65. Page SE, Siegert F, O’Reiley J, von Boehm H-D, Jaya A, and Limin S (2002) The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420: 61--65. Partow H (2001) The Mesopotamian Marshlands: Demise of an Ecosystem, 46pp. Nairobi, Kenya: UNEP. Pittock B, Wratt D, Basher R, et al. (2001) Australia and New Zealand. In: Climate Change 2001. Working Group II of the Intergovernmental Panel on Climate Change: Impacts, Adaptation and Vulnerability, ch. 12. Cambridge: Cambridge University Press. Postel S (1999) Pillar of Sand: Can the Irrigation Miracle Last? New York: WW Norton. Ramsar Convention Secretariat (2006a) The Ramsar Convention Manual: A Guide to the Convention on Wetlands (Ramsar, Iran, 1971), 4th edn. Gland, Switzerland: Ramsar Convention Secretariat. Ramsar Convention Secretariat (2006b) Wise use of wetlands: A conceptual framework for the wise use of wetlands. In: Ramsar Handbooks for the Wise Use of Wetlands, 3rd edn., vol. 1. Gland, Switzerland: Ramsar Convention Secretariat. Ramsar Convention Secretariat (2006c) Inventory, assessment, and monitoring: An integrated framework for wetland inventory, assessment, and monitoring. In: Ramsar Handbooks for the Wise Use of Wetlands, 3rd edn., vol. 11. Gland, Switzerland: Ramsar Convention Secretariat. Ramsar Convention Secretariat (2006d) Designating Ramsar sites: The Strategic Framework and guidelines for the future development of the List of Wetlands of International Importance. In: Ramsar Handbooks for the Wise Use of Wetlands, 3rd edn., vol. 1. Gland, Switzerland: Ramsar Convention Secretariat. Ramsar Strategic Plan 2009–2015. http://www.ramsar.org/pdf/key_strat_plan_2009_e.pdf (accessed August 2010). Rebelo L-M, Finlayson CM, and Nagabhatla N (2009) Remote sensing and GIS for wetland inventory, mapping and change analysis. Journal of Environmental Management 90: 2144--2153. Revenga C, Brunner J, Henninger N, Kassem K, and Payne R (2000) Pilot Analysis of Global Ecosystems: Freshwater Systems. Washington, DC: World Resources Institute. Revenga C, Campbell I, Abell R, de Villiers P, and Bryer M (2005) Prospects for monitoring freshwater ecosystems towards the 2010 targets. Philosophical Transactions of the Royal Society B 360: 397--413. Revenga C and Finlayson CM (2009) Wetlands and climate change State of the Wild. New York: World Conservation Society. Revenga C and Kura Y (2003) Status and Trends of Biodiversity of Inland Water Ecosystems. Technical Series No. 11. Montreal: Secretariat of the Convention on Biological Diversity. Richardson CJ (2008) The Everglades Experiment: Lessons for Ecosystem Restoration. New York, NY: Springer. Richardson CJ, Reiss P, Hussain NA, Alwash AJ, and Pool DJ (2005) The restoration potential of the Mesopotamian Marshes of Iraq. Science 307: 1307--1311. Rieley JO and Page SE (eds.) (1997) Biodiversity and Sustainability of Tropical Peatlands. Cardigan: Samara Publishing. Secretariat of the Convention on Biological Diversity (2006) Global Biodiversity Outlook 2. Montreal: Convention on Biological Diversity. Scanlon BR, Jolly I, Sophocleous M, and Zhang L (2007) Global impacts of conversions from natural to agricultural ecosystems on water resources: Quantity versus quality. Water Resources Research 43: W03437 (doi:10.1029/ 2006WR005486). Scott DA and Jones TA (1995) Classification and inventory of wetlands: A global overview. In: Finlayson CM and van der Valk AG (eds.) Classification and Inventory of the World’s Wetlands, Advances in Vegetation Science 16, pp. 3–16. Dordrecht: Kluwer.

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Sculthorpe CD (1967) The Biology of Aquatic Vascular Plants. London: Edward Arnold. Semeniuk V and Semeniuk CA (1997) A geomorphic approach to global classification for natural wetlands and rationalization of the system used by the Ramsar Convention – a discussion. Wetlands Ecology and Management 5: 145--158. Shepherd G (2004) The Ecosystem Approach: Five Steps to Implementation. Gland, Switzerland and Cambridge: IUCN. The Ramsar ‘Toolkit’, 3rd edn. (2007) The Ramsar Handbooks for the Wise Use of Wetlands. http://www.ramsar.org/cda/ramsar/display/main/ (accessed May 2010). UNEP (2002) Vital Water Graphics – an Overview of the State of the World’s Fresh and Marine Waters. Nairobi: United Nations Environment Programme. UNEP (2006) Challenges to International Waters – Regional Assessments in a Global Perspective. Nairobi: United Nations Environment Programme. UNEP (2007) Global Environment Outlook 4 – Environment for Development. Nairobi: United Nations Environment Programme. UNESCO (2000) Water Related Vision for the Aral Sea Basin for the Year 2025. Paris: United Nations Educational, Scientific, and Cultural Organization. UNESCO-WWAP (2006) Water: A Shared Responsibility. The United Nations World Water Development Report 2. Paris and New York: United Nations Educational, Scientific, and Cultural Organization and Berghahn Books. van Dijk PP, Stuart BL, and Rhodin AGJ (2000) Asian Turtle Trade: Proceedings of a Workshop on Conservation and Trade of Freshwater Turtles and Tortoises in Asia Chelonian Research Monographs, No. 2. Lunenburg: Chelonian Research Foundation in association with WCS, TRAFFIC,WWF, Kadoorie Farm and Botanic Gardens US Fish and Wildlife Service. Verhoeven JTA, Arheimer B, Yin C, and Hefting MM (2006) Regional and global concerns over wetlands and water quality. Trends in Ecology and Evolution 21: 96--103. Vo¨ro¨smarty CJ, Le´veˆque C, and Revenga C (2005) Fresh water. In: Hassan R, Scholes R, and Ash N (eds.) Ecosystems and Human Well-Being: Current State and Trends: Findings of the Condition and Trends Working Group. Washington, DC: Island Press. WCD (2000) Dams and development: A new framework for decision-making. The Report of the World Commission on Dams. London: Earthscan. Whigham DF (2009) Global distribution, diversity and human alterations of wetland resources. In: Maltby E and Barker T (eds.) The Wetlands Handbook. Chichester: Wiley-Blackwell. Whigham DF, Goode RE, and Kvet J (eds.) (1993) Wetland Ecology and Management – Case Studies. Dordrecht: Kluwer. Wiseman R, Taylor D, and Zingstra H (eds.) (2003) Proceedings of the Workshop on Agriculture, Wetlands and Water Resources: 17th Global Biodiversity Forum, 122pp. Valencia, Spain, November 2002. New Delhi, India: National Institute of Ecology and International Scientific Publications. WMO (1997) Comprehensive Assessment of the Freshwater Resources of the World. Stockholm: World Meteorological Organization and Stockholm Environment Institute. WWDR (2003) Water for People, Water for Life. United Nations World Water Assessment Programme. Paris, France: UNESCO/Berghahn Books. Yarrow M, Marı´n VH, Finlayson M, Tironi A, Delgado LE, and Fischer F (2009) The ecology of Egeria densa Planchon (Liliopsida: Alismatales): A wetland ecosystem engineer? Revista Chilena de Historia Natural 82: 299--313.

Relevant Websites http://www.gisp.org Global Invasive Species Programme. http://www.ramsar.org Ramsar: The Ramsar Convention on Wetlands. http://www.ramsar.org Ramsar: The Ramsar Convention on Wetlands; A brief history of the Ramsar Convention.

1.04

Water as an Economic Good: Old and New Concepts and Implications for Analysis and Implementation

J Briscoe, Harvard University, Cambridge, MA, USA & 2011 Elsevier B.V. All rights reserved.

1.04.1 1.04.2 1.04.3 1.04.3.1 1.04.3.2 1.04.3.3 1.04.3.4 1.04.4 References

Introduction Challenge One: Revisiting the Old Issue of the Indirect Effects of Investments in Major Water Projects Challenge Two: Managing Water as a Scarce Resource Issue One: The Radically Different Markets in which Irrigation and Urban Water Operate Issue Two: How Appropriate Pricing Is Understood by Economists and by Users and the Implications for Practice Issue Three: The Crucial Distinction between Financial Costs and Opportunity Costs, and the Implications for Practice Issue Four: The Political Economy of Change Conclusions

1.04.1 Introduction This chapter addresses two conceptual and operational challenges which are of major importance for the management and development of water resources. Challenge one relates to the economic impact of large water infrastructure projects and suggests, first, that conventional economic analytic tools are of little value and, second, that newly emerging tools can be of substantial practical use. Challenge two relates to management and describes both fallacies and emerging approaches for ensuring that the economic productivity of water is maximized.

1.04.2 Challenge One: Revisiting the Old Issue of the Indirect Effects of Investments in Major Water Projects During the era of rapid economic growth, the political leaders of now-rich countries invested heavily in much major water (and other) infrastructure because they believed that these investments would transform the regional economies in which the projects – such as the Tennessee Valley Authority (TVA), Hoover Dam, and Grand Coullee projects in the United States – were located. Decades after these projects were completed (and delivered these large indirect benefits), the US Office of Management and Budget in the 1950s declared that under conditions believed to be prevailing in the United States (full employment and mobile factors of production) these indirect benefits should not be taken into account in investment decisions. As described in detail elsewhere (Briscoe, 2008), this was greeted with incredulity by political leaders who asked ‘‘if we had followed this advice, what infrastructure would ever have been built?’’ (with the implicit answer ‘‘very little’’). Although this issue faded from the sight of politicians, as most of the major infrastructure in the US had already been built, it did not fade from the bible of economists and was incorporated into conventional economic wisdom and standard appraisal practices of institutions such as the World Bank.

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A recent set of detailed analyses (Bhatia et al., 2008) of the Bhakra Dam in northwest India, the Sao Francisco dams in Brazil, and Aswan Dam in Egypt confirm similar findings from earlier studies – namely that such projects have major backward linkages (for inputs into agriculture) and forward linkages (for processing of agricultural products, for instance). In all cases, not only the indirect effects were as large as the direct effects (as had been demonstrated in other analyses of the Muda projects in Malaysia and Grand Coullee in the United States) but also these projects had stimulated precisely the regional development which politicians had hoped for (and which economists now said, ‘‘don’t count’’). Equally important, where the data were available (as in the Bhakra case; Bhatia et al., 2008) it turned out that the biggest proportional beneficiaries were not the landlords but the landless, as a result of the sharp increase in the demand for labor. For decision makers in the real world, the conclusion is that these indirect impacts are large, and that such projects can, indeed, be the basis for regional development. It is true, nevertheless, that there is a serious analytic challenge and an even more serious practical challenge. The analytic challenge is that these studies are all ex post. There is no reliable ex ante method for assessing the indirect impacts. The practical challenge is that there is no established methodology for deciding on what packages of complementary public investments are needed in order to maximize the likelihood that the unquantifiable-but-very-important indirect benefits do, in fact, materialize. On the latter, there is a ray of light, from the original work of Harvard economist Ricardo (e.g., Hausmann and Klinger, 2008) on development patterns as defined by the ‘product space’. This work represents a major intellectual departure from the normative and mechanical work embodied in classic cost–benefit analysis. The approach starts not with principles, but with the collection of data and the use of network approaches to describe revealed patterns of economic development paths in hundreds of economies over time. In the case of major water projects, this would mean the following. First, planners would describe ‘‘where the region is’’ (in our case,

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after the building of the major infrastructure, the emergence of new configurations of energy generation, agriculture, industry, and transport). Second, taking account of the new regional reality, assess the opportunities this starting point affords by examining development paths which have evolved from similar endowments, thus identifying the paths that are most promising (and those which are no more than pie-in-the-sky fantasies). Armed with this X-ray (for the example of Pakistan, see Hausmann and Klinger (2008)), planners of large water infrastructure can then decide in a systematic and informed way on what complementary investments are needed to maximize the likelihood that multipliers will develop. The development of such a methodology is a high priority for developing countries (and there are many), who are in the early stages of investing in major water infrastructure, and offers an escape from what have become ritualistic and uninformative standard cost–benefit procedures.

1.04.3 Challenge Two: Managing Water as a Scarce Resource Many countries face multiple concerns regarding the growing scarcity of water, the associated conflicts among users, and ways of transferring water from low-value to high-value uses. Prominent and well-informed commentators often state that having users pay the full cost of water would solve these problems (recent examples include the CEO of Nestle (Brabeck-Letmathe, 2008) and The Economist (2008)). Experience has shown that the situation is considerably more complex and nuanced, and that it is not enough to just extol the virtues of pricing. This chapter outlines a different approach – one of principled pragmatism. Principled because economic principles such as ensuring that users take financial and resource costs into account when using water are very important; and pragmatism because solutions need to be tailored to specific, widely varying natural, cultural, economic, and political circumstances, in which the art of reform is the art of the possible. The general arguments are illustrated by focusing on two major users – farmers and cities. Here, four issues are addressed. This chapter draws on the World Bank’s Water Resources Sector Strategy (World Bank, 2003): 1. the quite different economic environments that pertain to these two sectors; 2. the crucial distinctions between the perspective of economists and the perspective of users on what constitutes appropriate pricing, and some of the implications of these distinctions for practice; 3. the critical distinction between the financial cost of providing a service and the opportunity cost of the resource itself, and the implications of this distinction; and 4. a review of some good practice developments, and the implications for a country-specific, practical, sequenced approach to dealing with these crucial issues.

1.04.3.1 Issue One: The Radically Different Markets in which Irrigation and Urban Water Operate The first, fundamental distinction is between the markets in which urban water supply and irrigation operate.

In the case of urban water supply, the product can largely be considered as a local, nontradable good. The price charged for water in Helsinki is entirely immaterial to the price charged in Timbuktu. More specifically, if Helsinki chooses to subsidize its water users, that is of no relevance to water users in Timbuktu. In the case of irrigation, where the end products are agricultural goods that trade on a global market, the situation is radically different. If the government of a developed country chooses to subsidize water (and other inputs and outputs) of its farmers, this has an impact on world prices, and thus a direct impact on producers in developing countries. As the magnitude of the agricultural subsidies from OECD countries (OECD, Organization for Economic Cooperation and Development) is huge (about $350 billion/year, to the detriment of consumers in developed countries and producers in developing countries), this has a major impact on the prices of agricultural products in developing countries and on the economic returns from farming. These distortions reinforce the demands of farmers in developing countries with regard to subsidies for water, energy, and other inputs, usually causing further harm to both the economy and the environment. This crucial fact makes the political economy of water pricing reform especially complex (in both theory and practice) for irrigation. Experience suggests that the appropriate approach is to acknowledge the need for subsidies and to document the existing levels. Then it is possible – for example, as has been done in Mexico (Gonzalez, 1997) – for the government and farmers to agree upon a subsidy-neutral transformation from a package of perverse subsidies (of fertilizers, pesticides, and water, for instance) to a package of virtuous subsidies (such as for improving land quality and for more efficient technology).

1.04.3.2 Issue Two: How Appropriate Pricing Is Understood by Economists and by Users and the Implications for Practice Economists have long had a sound theoretical basis for assessing the resource implications of pricing, namely charging users for the marginal cost of producing the next unit of input. This rule is clear and correct, because that is the signal which will cause users to take into account the cost of the next unit of production when they consider using another unit of the resource. Unfortunately, even sound theory does not always translate into rules that can easily be understood and applied in practice. The first reason for this is that ordinary users understand a price as a payment for a service rendered. When the supplier is a monopoly (and prices are set outside of the market), this means that the legitimate price in the eyes of users is that which it costs an efficient producer (usually a public utility) to produce the service. In economic terms, this means that users consider average, not marginal, cost to be legitimate. Two more questions arise from this: What is included in cost and what happens if the service provider is not efficient? Costs that users consider legitimate certainly include, in all cases, the costs of operating and maintaining the existing infrastructure. Moreover, with some explanation and communication, experience (Langford et al. (1999) describe the

Water as an Economic Good: Old and New Concepts and Implications for Analysis and Implementation

Australian case) shows that users see the costs of replacement as legitimate costs. However, even under the most advantageous of settings, users vigorously resist the notion that they should pay for sunk costs which, in their eyes, have already been paid for by taxes or other assessments. The issue of the efficiency and accountability of the service provider is critical. ‘‘Why should I pay the costs of the Water Department when it is overstaffed, corrupt, and does not maintain our systems?’’ is a frequent and legitimate complaint from consumers and farmers. An illustration of the lower bound of these inefficiencies comes from the state of Victoria in Australia. Before reform, irrigation services were provided by a government department with well-trained and wellperforming staff, and there was little corruption. When reform took place, and farmers had to pay the full costs of operation and maintenance, increased scrutiny of the supply agency led to a 40% reduction in these costs. In most developing countries, the inefficiency is much greater and the users’ resistance to paying for these services is correspondingly higher. Exhortations to increase cost recovery without addressing these fundamental accountability questions are a major part of the reason why cost recovery has been so poor in many countries. A review by the World Bank’s Operations Evaluation Department (2003) shows that, despite the fact that the World Bank has been by far the most constant and insistent advocate of cost recovery for decades, ‘‘there is no evidence of better cost recovery or of covenant compliance either.’’ The bottom line, then, is that in most urban and irrigation systems cost recovery is critical for the supply of good services. The road to cost recovery does not lie in conditionalities imposed by aid agencies, however, but in realigning the institutional arrangements so that suppliers are accountable to users, and so that charges become a principal tool used for ensuring the mutual obligations of suppliers and users.

1.04.3.3 Issue Three: The Crucial Distinction between Financial Costs and Opportunity Costs, and the Implications for Practice User payments for the financial costs of services rendered is a fundamental requirement for any financially sustainable water supply system – this is very important. However, the claims for pricing typically go beyond that of maintaining and operating infrastructure, and suggest that if ‘‘the prices are right, allocation will be optimal.’’ Proceeding from the viewpoint of users (as one must when considering political economy of reform rather than theoretical elegance), it is vital to distinguish between two radically different types of costs. First, there are the costs that any user can understand, namely the financial costs associated with pumps, treatment plants, and pipes. Second is the far more subtle concept of the opportunity cost of the resource itself. There have been many proposals for doing sophisticated calculations of this opportunity cost, and charging users for this ‘‘to ensure appropriate resource allocation.’’ This has not worked in practice for three fundamental reasons: first, because it is impossible to explain to the general public (let alone to angry farmers) why they should pay for something that does not cost anything to produce; second, because opportunity costs vary widely by place and time and could not be

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accurately calculated by even the most sophisticated of regulatory agencies; and, third, because those who have implicit or explicit rights to use of the resource (correctly) argue that they have already paid for the (implicit or explicit) rights and argue (appropriately) such proposals to be the confiscation of property. An added, and highly relevant, factor is that the ratio between financial and opportunity costs is often radically different for different sectors (Briscoe, 1996). Although everything in water (like politics) is local, there are two broad patterns. It costs a lot (per unit of water) to operate the dams, water and wastewater treatment plants, and pumps and pipes that provide households with the modest amount of water they use (and the sewage that is removed). Alongside these large financial costs, the opportunity cost of the resource itself (as measured by the value of the raw water in its next best use, often irrigation) is typically quite low. For municipal and industrial water, therefore, financial costs generally dominate opportunity costs. For irrigation, the situation is almost exactly the opposite. It costs relatively little (per unit of water) to build, operate, and maintain the usual gravity systems that provide very large quantities of water. However, the opportunity cost of the water (for cities and, increasingly, for high-value agricultural uses) is, in situations of scarcity, often much higher (typically at least an order of magnitude higher) than the financial cost of supplying the water. These numbers (remembering, of course, that every place is different) have profound implications. They mean that, from the point of view of ensuring that users take into account the cost of the resources they are using, the emphasis must be on financial costs for municipal supplies, and on opportunity costs for irrigation. (It is worth emphasizing that this does not mean that cost recovery does not matter for irrigation. Cost recovery for irrigation remains very important for infrastructure sustainability, but not for efficiency in the allocation or use of water.) The great challenge for irrigation, in light of these theoretical and practical realities, is how to have farmers take account of the opportunity cost of water. In most parts of the world where water is scarce, informal water markets have arisen, in which those who have (implicit) rights sell water to those who need it. In some cases, the practice has existed for hundreds of years and has been formalized (as in the Water Court of Valencia, Spain, which has managed transfers among users for a 1000 years). In many other cases (such as western India; Shah, 1993), these markets are extensive, sophisticated, and illegal. Throughout the arid western United States, water rights have long been legal property and, under different rules in different states, allowed for approved transfers between willing buyers and willing sellers. As other parts of the world have experienced scarcity, a number of countries facing water stress have turned toward formal, legal, managed water markets. This took place in recent decades in Chile, Australia, and Mexico. The Australian case shows the benefits – the adverse impact of water reductions on the regional economy – are reduced by two-thirds when there is both intra- and interstate water trading (Productivity Commission, 2004).

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Water as an Economic Good: Old and New Concepts and Implications for Analysis and Implementation

From the perspective of the present discussion on how to ensure that users take account of opportunity costs, these market-based arrangements have a unique virtue. Once users have clear, transferable property rights, then they automatically consider whether they wish to forego a particular use of water in exchange for compensation from another user who may place a higher value on the water. Reallocating water then becomes a matter of voluntary and mutually beneficial agreements between willing buyers and willing sellers, and not a matter of confiscation or an endless search for new sources of supply. This is not to suggest that the establishment of water markets is simple or a panacea. The operation of such systems is demanding in terms of rules for establishing initial rights (including those for the environment and informal customary rights); the plumbing required to measure and move water; the regulatory institutions that are essential to protect the rights of other water users and the environment and to ensure that the public interest is represented; and the information and management systems. Many consider these prerequisites so onerous that they cannot be made to work in most developing countries. In addition, many point to early problems that all countries have faced in making such changes. Without in any way minimizing these challenges, three observations are germane. First, the prerequisites are really prerequisites for any form of well-managed allocation system and the absence of such prerequisites is a problem for all allocation systems, including the administrative allocation systems practiced in most countries. (As with everything in water management, the choice is not between the first and the second best, but between imperfect and even more imperfect.) Second, one of the many virtues of a market-based system is that, once started, there is a strong demand for better measurement, transparency, regulation and information. Third, all such established systems are working, often after initial adjustments, reasonably well. In none of the countries that have adopted such systems is there any thought to reverting to the previous allocation procedures.

1.04.3.4 Issue Four: The Political Economy of Change The implications for practitioners are clear. First, from the point of view of financial cost recovery, the key is an institutional framework whereby service providers are accountable and efficient. When this materializes, and when users see that their payments are being used to improve the quantity and quality of services, they can and will pay. Here (as discussed earlier), watchwords are competition, regulation, transparency, benchmarking, and accountability. In the urban water supply and energy sectors, these ideas are now accepted in most parts of the world. In the irrigation sector, there is a gradual, albeit still far too slow, acceptance of these principles. Building on the historic experiences in countries such as Spain and the United States, a number of countries (including Australia, Chile, Mexico, and, more recently, the provinces of Punjab in Pakistan (Government of Punjab, Pakistan, 2008) and Maharashtra in India (Government of Maharashtra, 2003)) have moved toward systems which (1) charge irrigators for the cost incurred in providing services and (2) have clarified and made transparent water entitlements which will,

slowly and inexorably, lead to trading and the revelation of opportunity costs. In all settings, a critical element of this approach is to develop innovative mechanisms for breaking out of the typical low-level equilibrium, in which services are poor, users will not pay, service quality declines, etc. In one good example of such innovation, the World Bank helped the government of Guinea Conakry break the circle by guaranteeing a new, accountable operator a declining proportion of reasonable costs over a 5-year period (World Bank, 1993). In the first year, then, the operator had sufficient revenues (mostly from the International Development Association (IDA) credit, but some from users) to improve the operation of the system. As the level of service improved, users were informed that they would be charged for the new, improved service and that, eventually, they would pay the full costs of the service. The art of reform is less one of articulating a vision than of tracing a path for making improvements, for applying generic principles in a way that takes account of the very widely varying historical, cultural, natural, social, and economic conditions which govern water management (Briscoe, 1997). An analysis of experiences of successful reforms suggests that this means, inter alia: ‘‘picking the low-hanging fruit first,’’ for instance, by starting with temporary trading in well-defined systems where good infrastructure is in place; ‘‘not making the best the enemy of the good,’’ by having a well-defined, sequenced, prioritized, and patient approach for moving toward improvement, not seeking to attain perfection in one fell swoop; and ‘‘keeping one’s eyes peeled,’’ by understanding that it is broader reforms outside of the water sector (often relating to overall economic liberalization and fiscal and political reform) which will provide the preconditions for making the critical first steps. Recent reviews of water reforms in Pakistan and India (Briscoe and Qamar, 2007; Briscoe and Malik, 2006) describe, in considerable detail, what the application of these principles might be in practice.

1.04.4 Conclusions There is growing understanding that there are broad benefits – for the economy, users, and for the environment – if water is developed and managed as an economic good and a growing search for a new set of analytic and operational tools. In recent years, there has been a subtle but important change in discussion of economic policy. The landmark Growth Commission (Spence et al., 2008), written by several Nobel prize laureates and many eminent development practitioners, draws lessons from the history of successful growth experiences. The Commission discarded the rigid prescriptions so often advocated, and noted that there were a wide variety of different, successful, experiences. What they did conclude was that there were some common elements – for example, a disciplined examination of, and adherence to, comparative advantage – and then application of economic principles in a sequenced, nuanced manner appropriate to particular cultural and economic circumstances. Application of this less rigid approach has major implications for water.

Water as an Economic Good: Old and New Concepts and Implications for Analysis and Implementation

In terms of the development of infrastructure, it means getting away from what have become uninformative, formulaic analyses of internal rates of return, to an approach which uses new tools to identify critical supplementary investments needed to maximize the multiplier effects of major investments. In terms of management, it means moving away from the tired phrase of ‘get the prices right’ (and everything will be okay) that has been repeated for years, with little impact on the ground. It means paying much more attention to incentives and to opportunity costs, and to creating an enabling environment in which users will make much better use of limited water, or transfer the right to use that water to others who can use it more productively. Finally, it also means giving greater attention to the political economy of change. This chapter advocates (as does the 2003 World Bank Water Resources Strategy) a path of principled pragmatism – in which the principles of sound economic management are well defined and respected, but in which they are applied in a pragmatic and sequenced way which takes account of local circumstances and political economy, and in which the focus is on moving in the right direction, and on the art of the possible.

References Bhatia R, Cestti R, Scatasta M, and Malik RPS (eds.) (2008) The Indirect Economic Impact of Dams. New Delhi: The Academic Foundation. Brabeck-Letmathe P (2008) Global drying. Wall Street Journal, Asia 13 June 2008: A13. Briscoe J (1996) Water as an economic good: The idea and what it means in practice. In: Proceedings of the World Congress of the International Commission on Irrigation and Drainage. Cairo, Egypt, September 1996.

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Briscoe J (1997) Managing water as an economic good: Rules for reformers. Water Supply 15(4): 153--172. Briscoe J (2008) How theory, practice, politics and time affects views on the indirect economic impact of water infrastructure. In: Bhatia R, Cestti R, Scatasta M, and Malik RPS (eds.) The Indirect Economist Impact of Dams. New Delhi: The Academic Foundation. Briscoe J and Malik RPS (2006) India’s Water Economy: Bracing for a Turbulent Future. New Delhi: Oxford University Press. Briscoe J and Qamar U (2007) Pakistan’s Water Economy: Running Dry. Oxford: Oxford University Press. Gonzalez F (1997). Water Reforms in Mexico. Water Week, World Bank. Government of Maharashtra (2003) Maharashtra Water Regulatory Authority Bill XIX. Mumbai. Government of Punjab, Pakistan (2008) Entitlements. http://irrigation.punjab.gov.pk/ Entitlement.aspx (accessed July 2010). Hausmann R and Klinger B (2008) Structural Transformation in Pakistan. Center for International Development, Harvard University. Langford KJ, Foster CL, and Malcolm DM (1999) Towards a Financially Sustainable Irrigation System, World Bank Technical Paper 413. Washington, DC: World Bank. Operations Evaluation Department (2003) Bridging Troubled Waters: Assessing the Water Resources Strategy Since 1993. Washington, DC: World Bank. Productivity Commission (2004) Modelling Water Trade in the Southern Murray Darling Basin, Staff Working Paper, Canberra. Shah T (1993) Groundwater Markets and Irrigation Development: Political Economy and Practical Policy. Bombay: Oxford University Press. Spence M, et al. (2008) The Report of the Growth Commission: Strategies for Sustained Growth and Inclusive Development. Washington, DC: World Bank. The Economist (2008) Running dry. The Economist 18 September 2008. United States Congress (1955) Discussion of Budget Bureau Circular A-47, Hearings before the Committee on Interior and Insular Affairs, Serial no. 5. World Bank (1993) Development and Environment: The World Development Report. Washington, DC: World Bank World Bank (2003) The World Bank’s Water Sector Strategy. Washington, DC: World Bank.

1.05 Providing Clean Water: Evidence from Randomized Evaluations A Ahuja, Harvard University, Cambridge, MA, USA M Kremer, Harvard University, Cambridge, MA, USA AP Zwane, Bill and Melinda Gates Foundation, Seattle, WA, USA & 2011 Elsevier B.V. All rights reserved.

1.05.1 1.05.2 1.05.2.1 1.05.2.2 1.05.3 1.05.3.1 1.05.3.2 1.05.4 1.05.4.1 1.05.4.2 1.05.4.3 1.05.4.4 1.05.5 1.05.6 1.05.6.1 1.05.6.2 1.05.6.3 1.05.7 References

Introduction Water Quantity Health Impacts Maintenance Solutions Water Quality Health Impacts Valuation Nonprice Determinants of Clean Water Adoption Information on Water Contamination Levels Gain versus Loss Framing and Other Behavioral Marketing Communal versus Individual Persuasion Personal Contact Potentially Scalable Approaches to Improving Water Quality Methods and Theory: Contributions of Randomized Evaluations of Domestic Water Survey Effects Valuation: Revealed Preference versus Contingent Valuation Combining Randomized Evaluations with Structural Modeling Conclusion

1.05.1 Introduction Some 1.6 million children die each year from diarrhea and other gastrointestinal diseases for which contaminated drinking water is a leading cause (Wardlaw et al., 2010). This chapter critically reviews experimental work on the provision of water and improved water quality for domestic use in developing countries, discussing both policy implications and methodological lessons. Earlier work has been reviewed in research using nonrandomized approaches (Zwane and Kremer, 2007). Holla and Kremer (2008) provide a summary of the literature on randomized evaluations related to pricing and access in health and education. Cardenas (2009), Pattanayak and Pfaff (2009), and Timmins and Schlenker (2009) provide reviews on related issues. Recent calls for investment in experiments in environmental economics include Greenstone and Gayer (2009) and Bennear and Coglianese (2005). Local public good investments for services such as water for domestic use are arguably typically best prioritized by local policymakers who know the preferences of the communities they serve. However, the sole quantitative environmental target in the United Nations Millennium Development Goals is the call to ‘‘reduce by half the proportion of people without sustainable access to safe drinking water.’’ In practice, efforts to meet this goal have translated into increased donor and national government funding for building local public goods such as wells and standpipes. By increasing the number of water points, this reduces the time to collect water and makes the task more convenient.

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Health externalities that could cross jurisdictional boundaries could be an important part of the case for national or supra-national investments targeted specifically at the water sector. Diarrhea is an infectious disease. Distributional concerns provide another potential rationale for national or international policymakers to target aid to the water sector in particular. Outsiders may place more value on the consumption of child survival goods relative to consumption of other goods than the local household or other local decision maker does. One of the leading debates in the literature has been on the relative health impact of increases in water quantity versus improved water quality. Simply providing more convenient access to water, even without improving water quality, could potentially stimulate greater handwashing, which has been shown to be very important for health, and more washing of clothes and dishes. At this point, however, the limited evidence available from randomized studies does not demonstrate that increasing access to water without changing its quality improves health. In contrast, there is now abundant evidence that improved water quality reduces self-reported diarrhea. This evidence is an example of how randomized evaluations, which often yield quite different estimates of impact than nonexperimental analyses, can clarify questions that are difficult to resolve using analytic techniques that have difficulty separating causal effects of programs from selection bias. As seen below, there is some reason for external support for water quantity beyond what might be chosen by local decision makers, but much less than for water quality. Though there is

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limited evidence from randomized trials for health (as opposed to a time use or convenience) benefits of water quantity, increasing water quantity disproportionately benefits women and thus there may be a distributional case for public policy to support increased water quantity. In contrast, there is strong evidence that improving water quality improves health by reducing infectious disease and thus builds a case for subsidizing water-quality treatment based on reducing externalities. Improving water quality disproportionately also helps an even more severely under-represented group: young children. As discussed below, the evidence of low valuation for improvements in water quality, in contrast to water quantity, is consistent with the hypothesis that households put very little weight on the health costs of dirty water in the case of young children. External funders or national policymakers may put more weight on child health. A challenge with prioritizing water-quantity investments for donor funding is that maintaining the associated hardware has traditionally been difficult. When infrastructure falls into disrepair, the stream of benefits associated with the investment may be lost. Randomized impact evaluations suggest that external contracting can perform better than community-based voluntary arrangements at least in some circumstances. Evidence from India suggests that women are more likely to invest in water infrastructure but evidence from Kenya suggests little effect of efforts to encourage selection of female user committee chairs on quality of water infrastructure maintenance. Water-quality interventions face a different challenge than hardware maintenance if their biomedical benefits are to be sustained over time. In contexts where treatment does not occur at a centralized treatment plant, as in most developing countries, individuals influence the level of diffusion and adoption of treatment technologies. Randomized impact evaluations have provided evidence on the determinants of uptake in such cases, including a steep demand curve for treatment products such as chlorine or water filters. In addition to shedding light on policy debates such as the investment decision regarding quality versus quantity, experiments can help researchers come up with new solutions to hurdles such as this technology adoption decision. Randomized evaluations have demonstrated that the demand curve can be shifted outward by providing information and making treatment easy and convenient, as well as local promotion of ongoing use. Combining evidence of low valuation with this other information about influencers of adoption has allowed for new approaches to the service delivery problem to be developed. In particular, providing dilute chlorine solution free at the point of water collection, together with a local promoter, can increase takeup of water treatment from less than 10% to more than 60%. Methodologically, randomized evaluations have provided evidence that the process of collecting data through surveys can itself affect behavior and that revealed preference estimates of willingness to pay for environmental interventions in developing countries are far smaller than stated preference estimates. Recent work also marries randomized evaluations with structural modeling to provide guidance on the potential impact of alternative policies and social norms. The remainder of this chapter is structured as follows: Section 1.05.2 summarizes evidence from randomized

evaluation on the impact of infrastructure investments to increase water-quantity improvements in developing countries and on the maintenance of these investments. Section 1.05.3 argues there is considerable evidence that water-quality improvements yield health benefits, but that many households are willing to pay very little for cleaner water. Section 1.05.4 assesses alternative means of shifting the demand curve for water quality, examining information provision, communal versus individual persuasion, and local promoters. Section 1.05.5 discusses cost-effective and potentially scalable approaches to water quality drawing on the lesson of Sections 1.05.2, 1.05.3, and 1.05.4. Section 1.05.6 reviews methodological contributions from randomized evaluations of domestic water interventions. Section 1.05.7 concludes this chapter.

1.05.2 Water Quantity 1.05.2.1 Health Impacts Identifying the aspects (quantity vs. quality) of improved water supply is important for policy because different interventions affect quality and quantity asymmetrically. For example, adding chlorine to water affects quality but not quantity. Providing household connections to municipal water supplies to households that currently use standpipes is likely to have a bigger effect on the convenience of obtaining water and thus on the quantity of water consumed than on water quality. There has been considerable debate over whether increasing the quality of water or increasing the quantity of water has a greater impact on disease. Much of the most convincing nonexperimental evidence on the health impact of water and sanitation makes it difficult to separate the impact of quantity and quality (Cutler and Miller, 2005; Watson, 2006; Galiani et al., 2005; Gamper-Rabindran et al., 2010) because the interventions that are studied both reduced the cost of collection and improved quality, making it unclear which route of disease transmission mattered the most in practice. In the 1980s and 1990s, nonrandomized studies were frequently cited as evidence that water-quantity interventions were more important for health impacts than water-quality interventions (Esrey, 1996; Esrey et al., 1991). Some argued that these results could be explained because, when water supplies are rationed, increased availability of and convenience of water facilitate more frequent washing of hands, dishes, bodies, and clothes, thus reducing disease transmission (Esrey, 1996; Esrey et al., 1991; Curtis et al., 2000). However, the question remained unsettled because it was difficult to assess causality in the absence of randomized evaluations or other convincing identification. In the past 10 years, a new body of evaluations has been developed to address this question. We discuss in Section 1.05.3 the numerous randomized evaluations that have shown impacts of improved water quality on health while also confirming the importance of hand washing in reducing disease transmission. There remains limited evidence on the question of water quantity, with one recent relevant randomized evaluation. Consistent with the early claims made about the relative importance of water quantity, health benefits from hand

Providing Clean Water: Evidence from Randomized Evaluations

washing have been shown in several settings. Luby et al. (2004) reported the results of a cluster-randomized trial in a large sample of households in Karachi, Pakistan, of a hand washing promotion campaign aimed at mothers. Infants and malnourished children under age 5 living in treatment households had 39% fewer days of diarrhea compared with the control group after 1 year of intervention and observation. Two other older randomized controlled trials of hand-washing interventions with more than two communities in their samples (Khan, 1982; Han and Hlaing, 1989) each had a relatively large sample size randomly divided into treatment and control groups and measured compliance by observing or weighing provided bars of soap as well as by tracking diarrhea cases. The studies report large effects of hand washing and soap provision programs on the incidence of diarrhea. Khan (1982) reported that the provision of either soap and water-storage containers or soap alone, along with initial instructions to increase the frequency of hand washing, reduced shigella reinfection by 67% in Bangladesh. Han and Hlaing (1989) reported a 40% reduction in diarrhea incidence among children under age 2 (though there was no reduction in incidence for older children) following hand washing education and the provision of soap to a random sample of mothers in Rangoon (Yangon). Although impacts may be heterogeneous across settings, and caution is warranted in drawing general conclusions, the one available randomized evaluation found that increasing the quantity of water while maintaining unchanged quality did not lead to significant health improvements. DeVoto et al. (2009) examined provision of piped connections to homes in urban Morocco previously served by standpipes. This increased the quantity of water used by the household, but did not improve water quality, since the alternative was chlorinated water from communal taps, which was of similar quality to the water received at home. As part of a planned piped water service extension in Tangier, Morocco, the authors randomly selected half the households eligible for a first connection to receive information about and an offer of credit toward a new connection, and administrative assistance in applying for credit. Takeup was 69% (as compared to 10% in the control group). The authors compare outcomes of those who received this treatment to those for households in the control group. They find that piped water provision in this urban Moroccan context had few health benefits. There is no evidence for an impact of treatment on a subjective ranking of health of the family or on diarrhea in children under age 6 (though baseline rates were relatively low, with the average child in the control group experiencing 0.27 days of diarrhea in the past week). Households in the treatment group report increasing their frequency of baths and showers: the number of times respondents in the treatment group washed themselves (baths, showers) during the last 7 days is 25% higher than in the control group. However, hygiene practices that require less water, such as hand washing, were not affected, according to self-reports. We would not conclude that increased water quantity never yields health benefits. The benefits of increased water quantity may be context specific and require further research to fully understand. In particular, understanding when and how

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increased access to water leads to more hand washing is a research priority. Having a piped water connection had substantial private benefits, despite the lack of impacts on self-reported diarrhea, consistent with the evidence of private valuation. In particular, it saved time, which was used for leisure and social activities. Evidence of substantial willingness to pay for water quantity has been noted by other authors as well in observational studies. In Morocco, the intervention also improved measures of social integration and overall welfare for households. Consistent with this finding, households are willing to pay a substantial amount of money to gain access to a private tap at home: 1 year into the program, not only had the encouragement design resulted in high rates of takeup in the treatment group, but also, for these households, their average monthly water bill more than doubled, from 73 to 192 Moroccan dirhams (MAD), or US$9 to $24 a month (the previous cost came from households, who took water from their neighbors). The importance of a household visit in inducing adoption is something we discuss further in Section 1.05.4.4; additional evidence on personal contact has been generated in other settings as well. There is evidence from India that women particularly value water investments and that women’s involvement in investment decisions could result in improved water supply in situations where local governments set priorities among local public good investments. Women’s valuation of these goods can be part of a distributional argument in favor of additional external support for these investments as well. Chattopadhyay and Duflo (2004) found that a randomized policy change in India that increased the role of women in policy decision making led to more investment in water infrastructure. A 1993 constitutional amendment called for one-third of village council leader positions to be reserved for women. Rules ensured random assignment of the leadership reservations. Chattopadhyay and Duflo showed that village councils headed by women were significantly more likely to invest in public infrastructure for drinking water. These investments were borehole wells and other storage infrastructure that likely improve quality as well as the convenience of water collection. We takeup in the next section the question of whether women’s involvement in local decision making can also make these kinds of investments easier to maintain and keep up.

1.05.2.2 Maintenance Solutions In general, water-quantity investments, whether bundled with water-quality improvements or not, often require significant infrastructure investments. Water quality can be improved with virtually no investment in infrastructure (e.g., by leaving water in the sun or the addition of chlorine), though of course other water-quality interventions can also require hardware (as the first example in Section 1.05.3 illustrates). Along with infrastructure investments comes the challenge of maintenance, which has historically been a major problem in developing countries. The rural water sector in particular has a poor track record of maintaining infrastructure investments. For instance, a quarter of India’s water infrastructure is believed to be in need of repair (Ray, 2004). World Development Report 2004

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(World Bank, 2003) estimates that more than a third of rural water infrastructure in South Asia is not functional. Miguel and Gugerty (2005) reported that nearly 50% of borehole wells dug in a large project in western Kenya in the 1980s, and subsequently maintained using a community-based maintenance model, had fallen into disrepair by 2000. Difficulties with maintaining water infrastructure, particularly in rural areas, reduce the cost effectiveness of these interventions relative to other measures that prevent diarrhea. There are two solutions frequently mentioned as potential elements of a solution to this infrastructure challenge: empowering women to manage water resources and including communities in participatory management schemes. As discussed above, women value water investments when capital expenditure decisions are made. Randomized evidence on these solutions suggests that neither may be as effective as contracting out services via maintenance contracts, however, as we review below. Kremer et al. (2008) provided evidence from a randomized field experiment in Kenya that, at least in that context-enhanced women’s involvement in infrastructure management, did not lead to better maintenance of water supplies. This evaluation studies the impact of female affirmative action policies on actual management outcome measures relevant for protected springs (e.g., time since storm drains or drainage trenches were cleaned). When protected springs were provided to 100 communities in rural Kenya, all communities formed water-user committees. In addition, onehalf of the communities received messages encouraging women to take leadership roles in their water-user committees. This encouragement intervention did result in more women representation in the treatment communities. Communities that received the female participation intervention were twice as likely to have women in the role of water committee chair. However, this did not lead to differences in the effectiveness of the user committees’ spring management as measured by the maintenance outcome variables. Thus, the authors conclude that advocacy for female participation can increase women’s involvement without any impact (either positive or negative) on project outcomes. This has a positive interpretation: empowerment goals can be met without attended costs to project outcomes, as well as a more negative one: including women in management cannot alone solve the water infrastructure maintenance challenge, even if these investments are priorities for women. In addition to increasing women’s participation and decision-making power, another standard model for maintaining donor-funded infrastructure projects, such as water schemes, is to establish user groups responsible for maintenance and management. This approach grew out of the widespread perception that centralized government maintenance was unsuccessful. Giving communities direct control or ownership over key project decisions was intended to improve the quality of public services and increase financial sustainability. There is little convincing empirical evidence, however, that local user-committee management of local public goods such as improved drinking water sources results in better quality service than other models ongoing centralized funding from public budgets. Collective action problems may be difficult to overcome, and voluntary committees tasked with collecting user fees may be difficult to sustain or empower. In a recent

comprehensive review of community-based development projects, Mansuri and Rao (2004) noted that existing research examining successful community-based projects does not compare these projects with centralized mechanisms for service delivery or infrastructure maintenance (e.g., city or state financed). This makes it difficult to determine whether alternative project designs would have had different results. The limited empirical evidence suggests that the impact of the community-based development approach on infrastructure maintenance is mixed at best. In addition to randomly assigning the gender empowerment encouragement intervention, in the same study as described above, the nongovernmental organization (NGO) randomly assigned communities to contracted maintenance and community-based management schemes. Kremer et al. (2008) compared payments to private contractors for spring maintenance and ongoing grants to user committees, with the outcomes of a control group, in which user committees received no grants. The traditional model, user committees without grants, performed worse than either alternative across a range of maintenance outcomes. Providing grants to user committees improves a measure of overall water source maintenance quality by around 30% of one standard deviation on average, while paying contractors to maintain water source leads to an average improvement in measured maintenance quality by around 50% of one standard deviation. This difference is significant at the 10% level. This evidence from spring protection maintenance, a relatively simple technology that seems favorable to communitybased management, suggests that contracting for private maintenance service may be a promising alternative to committee-based management schemes. Nonexperimental evidence from Argentina (Galiani et al., 2005) also suggests that contracted private provision of service can expand coverage and improve health outcomes at least in certain settings in middle-income countries. Certainly, further research is needed that transparently compares the counterfactual of subsidized public service provision and community-based management schemes. In summary, the health benefits of water-quantity interventions require further investigation. Increasing availability of water, even leaving quality unchanged, brings major nonhealth benefits; yet insofar as these seem unlikely to create externalities beyond the household, let alone cross-jurisdictional externalities, local governments may be the proper institution for allocating budgets between water and other public goods. There may be a distributional case for national or supra-national water investments, as these are valued by women, however. Whatever benefits water-quantity interventions do provide can quickly be lost if infrastructure falls into disrepair or is broken. Contracting models seem to hold promise for maintaining water infrastructure.

1.05.3 Water Quality 1.05.3.1 Health Impacts A body of randomized evaluations examines interventions that affect water quality without affecting water quantity. These yield strong evidence of reductions in reported diarrhea.

Providing Clean Water: Evidence from Randomized Evaluations

One study examines source water-quality improvements. Kremer et al. (2009a), in the first randomized evaluation of the provision of improved communal water infrastructure, estimated that protecting springs reduced fecal contamination as measured by the presence of E. coli bacteria by two-thirds in water at the source, but only by 25% for water stored at home. This is likely in part due to recontamination in transport and storage within the household, as well as to the use of alternative sources. Despite the incomplete pass through of the water-quality improvement, this led to a reduction in selfreported child diarrhea of about 25%. Other epidemiological evidence on water quality, manipulated via filtration or treatment rather than infrastructure, also suggests that there may be large health gains from investments in quality. The bulk of the evidence suggests that, with takeup rates on the order of 70% (achieved via frequent visits and reminders to subjects) household water treatment reduces child diarrhea by 20–40%. (One caveat is that the outcome measure in these studies is typically mothers’ reports of child diarrhea. Studies with objective outcomes, infrequently measured, would be desirable (Schmidt and Cairncross, 2009). Nonetheless, we believe that the weight of the evidence is strong enough to believe that reductions in diarrhea are real. To the extent that reporting bias lowers estimates of diarrhea in both the treatment and comparison groups, it may in fact make it harder to pick up reductions in diarrhea. The extent of reporting bias in treatment groups would have to be very large to explain the reported reductions in diarrhea associated with cleaner water. If the reductions in diarrhea are even a fraction as large as those estimated, water treatment would still be very cost-effective.) Comprehensive reviews of this literature are provided by Waddington and Snilstveit (2009), Fewtrell et al. (2005), Arnold and Colford (2007), and Clasen et al. (2006). Because water treatment can be extremely cheap, even a 20–40% reduction in diarrhea makes water treatment very low cost per disability adjusted life years saved. To get a sense of how cheap it is to treat water, note that 1.42 gallon generic bottle of bleach with approximately 6% sodium hypochlorite concentration sold in Walmart for $2.54 as of December 2009 has enough chlorine to treat 163 400 l of water. This corresponds to a price of $0.00002 per liter of water treated. Even making generous allowances for the fact that chlorine used for water treatment should be sold at lower concentrations and has to be transported, etc., if mortality reductions are proportional to reported morbidity reductions, the cost per DALY is similar to that of childhood vaccination, at under $40 per DALY.

1.05.3.2 Valuation Despite the evidence of health benefits associated with water quality, a number of papers suggest very little willingness to pay for this class of interventions. Moreover, there is little evidence that households with young children place substantial additional value on clean water, suggesting low valuation of child health. Kremer et al. (Spring cleaning: rural water impacts, valuation, and institutions, unpublished manuscript) exploited exogenous changes in the trade-off that households face when choosing between multiple water sources, some of which are

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close but contaminated and others of which are far but clean. This variation in the distance/water quality trade-off is generated by the spring protection intervention discussed above that was randomly phased in to almost 200 communities in rural Kenya. (Spring protection reduces contamination by sealing off the eye of the spring so that it is no longer vulnerable to surface-water runoff.) The authors compare how many trips households make to protected springs and other sources, controlling for differences in the time it takes to walk to each source. The estimated mean valuation for spring protection is equivalent to 32.4 workdays. Based on household reports of trade-offs between walking time and money, this corresponds to approximately US$2.96 per household per year. Under additional assumptions, this translates into a willingness to pay $23.68 per DALY saved, which is well below the benchmark of $100–150 often assumed to be appropriate for health investments in developing countries. Kremer et al. (2009a) used randomly assigned discounts to investigate willingness to pay for dilute chlorine. They described behavior consistent with a steep demand curve for water treatment and found no evidence of higher valuation among households with vulnerable young children. In a set of impact evaluations that tested both price and nonprice interventions to increase takeup of chlorine, households were randomly assigned either to a comparison group or to treatment arms in which they received a free supply of individually packaged chlorine or coupons for half-priced chlorine that could be redeemed at local shops. Comparison households could buy WaterGuard through normal retail channels, at about $0.25 for a 1-month supply (roughly a quarter of the agricultural daily wage). Although 70–90% of households in the study region had heard of the local brand of point-of-use chlorine and roughly 70% volunteered that drinking dirty water is a cause of diarrhea, only 5–10% of households reported that their main supply of drinking water was chlorinated prior to the interventions. There were no significant differences between treatment and comparison groups at baseline. Access to free chlorine increased takeup rates to over 50%, whereas coupons for even a 50% discount hardly affected takeup relative to the comparison group. The point estimate suggests a four percentage point increase relative to the comparison group, but this is not statistically significant. This is evidence for very price elastic demand. Households with young children did not behave differently from other households (p value of 0.85 on the test of equality of means). This evidence for low valuation of child health can be part of a distributional argument for subsidizing water treatment. Preliminary results from DeVoto et al. (2009) on distribution of chlorine through clinics in Kenya and from Berry et al. (2008) on distribution of water filters in Ghana also suggest very steep demand curves for improved water quality. The authors conclude that high takeup rates can be achieved at sufficiently low prices, but that demand for chlorine among their rural samples is extremely sensitive to price even though the retail price of the product is still relatively low. Ashraf et al. (2009) used a two-stage price randomization that enables both measurement of willingness to pay for water treatment and also, under specific assumptions, allows for testing of whether higher prices induce a sunk-cost effect that

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leads households who pay more for chlorine to use it more and/or screens out households less likely to use the product. In a door-to-door marketing campaign, roughly 1000 households in the study were first asked if they wanted to purchase a bottle of dilute chlorine at a randomized offer price. If a household agreed to purchase and was able to come up with the cash needed for the transaction, they were then offered an additional randomly assigned discount which determined the transaction price. Variation in offer prices is used to test for whether households who are willing to pay a higher price are more likely to use chlorine for water treatment, controlling for the price the household ultimately did pay; variation in transaction prices is used to test for a sunk-cost effect that might lead households who actually paid more for chlorine to be more likely to use it, controlling for willingness to pay. Approximately 2 weeks after the marketing campaign, the survey team reached almost 900 of the households who received the marketing intervention to test for the presence of chlorine in stored drinking water supplies and administered a follow-up study. The authors found that willingness to pay is low, and that many more households are willing to purchase chlorine at low prices. Consistent with other evaluations, Ashraf et al. did not find that charging higher price leads to more effective targeting to those households with higher potential health gains, those households with children under age 5 or pregnant women. There are at least two possible counter-arguments to the case for subsidies in water quality: (1) a sunk-cost effect, in which paying for the product makes people more likely to use it and (2) wastage of the product when people are given it but do not use it or use it for purposes not valued by the funder. Ashraf et al. (2009) found no evidence of a sunk-cost effect, finding that the actual transaction price does not affect propensity to use, controlling for offer price. Ashraf et al. (2009) did find that when the price is lowered, the marginal households induced to buy chlorine are less likely to show chlorine residual in their water 2 weeks later. The hypothesis that they favor is that these households start using the products for other off-label uses such as cleaning clothes or toilets. They presented evidence for this hypothesis drawn from a convenience sample. However, this is somewhat puzzling since, as they note, dilute chlorine sold for water treatment is considerably more expensive per unit of chlorine than commercially available bleach. Overall, it is difficult to distinguish the hypothesis that these households are not using the product for water treatment from the alternative hypotheses that they are storing the product or giving it away for water-treatment usage (or that they tried the product but did not like the taste; this would be only a short-run loss). These hypotheses have quite different policy implications as only the first is wastage that might reduce the social value of a program that supplied the product for free. Even if diversion to alternative uses is common, because chlorine is very cheap and can have a large impact on health, high levels of diversion to alternate uses are likely to be acceptable if this occurs as a result of a process that increases use for water treatment overall. Policymakers confronted with this evidence of low valuation of water quality and child health must determine whether subsidies for water-quality interventions such as chlorination are warranted. If governments or external donors

place more value on child health relative to other consumption compared to local households, the lack of valuation for water quality and child health provides a potential rationale for subsidies. Externalities from consumption provide another potential rationale for subsidies in some cases. Although there is no direct evidence on health externalities from water treatment in any of the papers reviewed here, to the extent that consumption reduces disease incidence for the user, it is also likely to reduce disease transmission from the user to others. In this case, eliminating prices for water-quality improvements is likely to be welfare maximizing due to these externalities. In fact, given the externalities combined with the low cost of water disinfectant, negative prices may be optimal.

1.05.4 Nonprice Determinants of Clean Water Adoption In this section, we review experimental evidence on several nonprice variables that could potentially affect household behavior regarding water quality. The emphasis in this section is on identifying potential mechanisms that could increase uptake of safe water rather than also judging their cost effectiveness or scalability. Section 1.05.5 discusses potentially scalable models drawing on the lessons of this and previous sections.

1.05.4.1 Information on Water Contamination Levels Several papers suggest that providing households with information about source water quality can change behavior, but that the effects of information are small relative to price and that people are responding not as Bayesian decision makers rationally processing information. Rather, information may be important because it increases the salience of water contamination. Jalan and Somanathan (2008) randomly assigned households in their urban Indian sample to receive information on whether or not their drinking water had tested positive for fecal contamination. Among households not purifying their water initially, this information led to an 11 percentage point increase in reported water purification as measured 8 weeks after information provision; about 42% of the study population purified (meaning either filtered, boiled, purchased bottled water, or, more rarely, chemically treated) their water at baseline. They also increased water purification expenditures by about $7. Households that initially purified their water but who received information that their water was probably not contaminated (based on tests of untreated water in their household) were not statistically more likely to change their purification behavior than the control group. This finding of an asymmetric response to testing is evidence for the idea that the channel through which information campaigns work is salience of some sort rather than Bayesian learning. Bayesian learners would respond to information that their water is safer or cleaner than they thought by reducing expenditure on purification. Luoto (2009) found through a randomized controlled trial in Kenya that sharing information on fecal contamination

Providing Clean Water: Evidence from Randomized Evaluations

with Kenyan households in a context in which treatment products were provided for free increases water treatment by 8–13 percentage points (or between 12% and 23% of baseline usage rates). The study also suggests that once information on the quality of source water is provided, providing additional information on the quality of water stored in the home has no further impact on takeup. When interpreting these results, it is useful to recall that, if people had unbiased expectations about water quality initially, information provision would lead some to revise beliefs about water quality downward and others to revise them upward, with ambiguous implications for water-treatment behavior. The fairly consistent finding that information provision increases takeup suggests that these results may reflect salience as much as Bayesian learning. Further evidence consistent with this is provided by Madajewicz et al. (2007) and Tarozzi et al. (2009), who studied as to how people respond to information about water quality in an area of Bangladesh where wells are frequently contaminated with arsenic. Madajewicz et al. evaluated the effectiveness of providing coarse information about well safety by providing a random sample of household information about whether their water source has arsenic concentrations above a threshold level. Households informed that their water exceeds this threshold are 37 percentage points more likely to switch sources than control households within 1 year. They increase their walking time 15-fold (about 4 min), on average, in response to the information. These responses to arsenic contamination information are further evidence for non-Bayesian decision making when combined with more recent research from Bangladesh following the introduction of a standardized labeling system for wells in which safe sources are labeled green and unsafe sources red. Tarozzi et al. (2009) performed an evaluation in this context in which all subjects receive the coarse information about water safety for all sources around them. A random sub-sample receives additional information about relative safety along a continuous scale. The relationship between arsenic and health is likely to be continuous. Thus, if households are Bayesian decision makers, continuous information should be more useful. Households far from any nearby uncontaminated well might switch to a well that is just above the cutoff level for being colored red, for example. Similarly, households using a well that is just below the cutoff might switch to a well that is much further below the cutoff. In practice, however, receiving continuous information does not substantially affect risk perceptions or the likelihood of switching sources. In fact, providing continuous information decreases the impact of the arsenic level on the probability of switching to a new source of drinking water. People are unable to use this additional information to improve their drinking water quality more than those people armed with only coarse information. This finding that coarse information may change behavior more than finer information poses a challenge to the idea that people can be modeled as Bayesian decision makers. These findings are instead consistent with the idea that information campaigns increase salience of water contamination and that it is this salience, which changes behavior rather than the precise information content itself. Similarly, the finding of an

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asymmetric response to information, with those who find that their water is contaminated adopting safer behavior, and those with uncontaminated water not reducing their efforts to obtain clean water, is unexpected under a Bayesian model, but is consistent with the salience hypothesis.

1.05.4.2 Gain versus Loss Framing and Other Behavioral Marketing Given the evidence above that a simple Bayesian learning story is inadequate to explain behavior, we now turn to evidence on ideas from behavioral economics and psychology. Luoto (2009) provided households a variety of point-of-use watertreatment technologies for free in Kenya and then randomly assigned households to receive various promotional strategies to increase use of these products. This research generates a series of results that can inform marketing and distributional strategies. First, she examines whether emphasizing the gains from water treatment versus the losses from not treating water affected use. There are competing hypotheses in the literature for which framing should bring about the larger response. Prospect theory predicts that loss aversion will cause the lossframed message to realize a bigger effect on people’s choices and behavior (Tversky and Kahneman, 1981; Kahneman and Tversky, 1979). However, there is evidence that decisions regarding health behaviors respond more to gain-framed messages in some cases and more to loss framings in others (Rothman et al., 1999). The study compares a framing of safe water technologies as increasing health compared to one in which it is framed as both increasing health and avoiding disease. The latter approach increased usage by approximately four to six percentage points, a statistically significant difference. Luoto (2009) also tested whether a combination of commitment and a visual reminder to treat water changes behavior. A subset of the sample was assigned to make a commitment to treating their water to improving their family’s health, and also given a pictorial reminder to treat their water. This increased water treatment by five to eight percentage points, but was significant only in some specifications. A commitment to the interviewer had relatively large effects on households that showed evidence of high discount rates in responses to hypothetical questions about future payoffs.

1.05.4.3 Communal versus Individual Persuasion Kremer et al. (2009a) provided some evidence that a communal approach in which households are aware of the messages other community members receive is more effective than an individual approach in encouraging treatment of household drinking water with dilute chlorine disinfectant, although differences are limited to the case when households had to pay for the product. Their study tested three variants of a persuasion campaign in which promotional messages targeted to mother were delivered at either the household level or community level, or both. The treatment was cross-cut with providing subsidized (free) chlorine to households. The results confirm the importance of price as a key determinant of takeup. When chlorine was subsidized, community messages had no measurable impact on household

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water treatment. The point estimate of the effect of messaging is actually negative, though statistically insignificant, and very small compared to the main effect (–0.02 as compared to 0.52). Messages can influence takeup, however, when positive prices are charged. At normal retail prices, treatment of household drinking water with chlorine increased by between three and five percentage points (as measured by testing household drinking water for chlorine) for the communitybased and combined scripts in the short run. There was no measurable impact of the household script alone, but community-based messaging, a much cheaper approach to marketing, had a small but positive effect. None of the promotion scripts had any significant effect on takeup at the medium-run follow-up 3–6 months after exposure. Considering the shortrun nature of the effects and the high cost of marketing during one-on-one conversations during household visits, or even through community-level meetings, such strategies do not appear to hold much promise as cost-effective means of promoting individually packaged retail chlorine takeup at scale. It is worth noting, however, that Kremer et al. (2009a) found little evidence for peer effects in takeup of chlorine packaged for household use. Using detailed data on conversation frequency and topics collected in the second and fourth survey rounds (of the first phase of the research), they found strong evidence that the distribution of free chlorine marketed as WaterGuard promoted conversations about the product as well as about drinking water more generally and, to a lesser degree, child health. In particular, conversations about WaterGuard were roughly 3 times more likely to occur if the respondent was a member of a treatment household and slightly more than twice as likely if the other household in a relationship pair was in the treatment group. Although the distribution of free WaterGuard prompted more conversations about the product, the evidence is consistent with the hypothesis of weak social network effects on actual use, with larger impacts on social desirability bias. They found statistically and economically significant effects of peer exposure on self-reported chlorination but pointed estimates that are much smaller and not generally statistically significant using positive chlorine tests in home drinking water.

1.05.4.4 Personal Contact Kremer et al. (2009a) also tested another approach to increasing demand – hiring local community members to promote chlorine use among their neighbors. This sort of personal contact has been previously identified as important to behavior change and adoption decisions (DellaVigna and Gentzkow, 2010; Manandhar et al., 2004). In this intervention, personal persuasion was accompanied by a price discount, however. Households were also given a coupon for one free bottle of dilute chlorine solution (equivalent to 1 month’s supply). The fraction of households with residual chlorine in their water was approximately 10 times as high in communities with a local promoter and a free sample of chlorine relative to comparison households in the short-run (3 weeks), at 40% roughly versus 4%, respectively. While takeup fell to a certain extent at the medium-run (3–6 months) as households used up their free bottle, communities with promoters were nonetheless able to

sustain adoption rates between 30 and 35 percentage points higher than the comparison group takeup rate of 8%. Personal contact was also successful in achieving high levels of takeup in the evaluation of the water-quantity intervention described in Section 1.05.2.1 (DeVoto et al., 2009).

1.05.5 Potentially Scalable Approaches to Improving Water Quality Section 1.05.2 documents the strong evidence that water treatment has the potential to improve health cost-effectively. Section 1.05.3 discusses that takeup of water quality interventions is extremely sensitive to price. Section 1.05.4 indicates that personal contact, salience, and convenience, and potentially having public information about water treatment can boost takeup. This section discusses potential low-cost, scalable models for water treatment based on the findings of Sections 1.05.2, 1.05.3, and 1.05.4. Kremer et al. (2009a) and DeVoto et al. (2009) developed and tested two alternative approaches to providing clean water, both involving free distribution. DeVoto et al. (2009) provided coupons for dilute chlorine solution to mothers who bring children to vaccination clinics sufficient to cover water supplies for the 12 months until children reach approximately age 2. Mothers are told how and where to redeem coupons and urged to treat water for their children during a vulnerable stage of their life. A second approach entailed switching to free delivery of chlorine by placing a container to dispense the product at water sources. This bulk supply dramatically reduced delivery costs relative to the retail approach that requires packaging chlorine in small bottles and makes free provision more realistic. Users can treat drinking water when they collect it. The required agitation and wait-time for chlorine-treated water are at least partially accomplished automatically during the walk home from the source. The source-based dilute chlorine disinfection approach to water treatment makes this act salient, convenient, and public, in addition to making it cheaper. The dispenser provides a daily visual reminder to households to treat their water at the moment when it is most salient – as water is collected – and maximizes the potential for learning, norm formation, and social network effects by making the dispenser public. Potential users can see others, who use the dispenser, and have the opportunity to ask questions and they will also know that others will see whether they use the dispenser. Takeup of chlorine provided through dispensers dramatically exceeded takeup of chlorine provided for in-home use. When communities were randomly assigned to treatment with a promoter and a community dispenser, takeup was about 40% in the short run (3 weeks) but had climbed to over 60% by the medium term (3–6 months), representing 37 and 53 percentage point gains respectively over the control group. In contrast to the takeup levels achieved with the dispensers, the clinic-based coupon distribution approach proved initially promising, but resulted in much lower coupon redemption over time. Over 40% of households, who were given coupons, redeemed them 8 months into the program in that sample, but this fell to 20% by 12 months. This suggests that the

Providing Clean Water: Evidence from Randomized Evaluations

success of the dispenser is not due only to the zero price, but also to the reduction in the psychic cost of remembering to treat water that is achieved by source-based treatment as well as other attributes, like the visual reminders. The chlorine dispenser is also extremely cost effective, with a cost per DALY saved of less than $20. The success of the chlorine dispensers at the proof-of-concept stage described here suggests that exploring how to scale up this approach to water treatment warrants further attention in its own right, as well. An important challenge for the future will be to determine how best to handle the supply side under free provision and, in particular, scaling supply chain management.

1.05.6 Methods and Theory: Contributions of Randomized Evaluations of Domestic Water The evaluations surveyed in this chapter have provided policy guidance on several questions related to health, technology adoption, and pricing regimes. The work has also made a number of methodological contributions that are of broader interest in resource economics. We review these contributions in this section.

1.05.6.1 Survey Effects A recent randomized evaluation of a water-quality intervention provides evidence that the act of surveying can affect behavior in ways that can interfere with estimates of treatment effects, a result with broader implications. Many studies have focused on measuring reported diarrhea (by mothers of young children) through household visits as a means of assessing the health impact of domestic water interventions. Kremer et al. (2009b) provided evidence that collection of self-reported diarrhea data through repeated interviews leads to health-protective behavior change in addition to respondent fatigue and social desirability bias, which are well-known concerns in survey administration. More frequent questioning exacerbates this survey effect. As part of a larger study of the impact of spring protection, households from a rural Kenyan population were randomly assigned to be interviewed about diarrhea either every 2 weeks or every 6 months. Kremer et al. (2009b) documented that frequent data collection, as is typically used in the epidemiology literature to measure diarrhea incidence, induces behavioral change in the form of higher levels of home water treatment, verified by tests for chlorine in water. The authors also find that frequent data collection leads to lower reports of child diarrhea by mothers relative to infrequent surveying. These effects are sufficiently large so as to change the conclusions about the effectiveness of the water-quality intervention being studied, that is, spring protection. The potential for survey effects implies that researchers relying on both self-reported or otherwise subjective data and objective data to measure outcomes should consider designing data collection strategies that minimize interaction with subjects. For example, outcome data could also be collected via administrative records maintained at clinics or schools. Purchases or collection of products from central locations can also be tracked without direct interaction with subjects.

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In the particular case of the literature on water, sanitation, and hygiene, survey effect concerns imply that more research is needed that does not measure impacts via subjective reports of diarrhea. Researchers in this field should expand their datacollection strategies to emphasize other health outcomes that can be measured objectively and infrequently. This will likely require larger sample sizes to detect small treatment effects (e.g., on stunting, cognition, and ultimately, mortality) and longer study times, which funding will need to accommodate.

1.05.6.2 Valuation: Revealed Preference versus Contingent Valuation One common approach to understanding willingness to pay for environmental amenities is to use stated preference data from hypothetical situations to identify the price that households would be willing to pay. The survey-based approach for eliciting stated preference is a method known as contingent valuation (CV), the development of which is surveyed by Hanemann (1994). While data from such studies may be the most practical solution when estimating valuation of a nonrival good or one that is costly to offer in a real transaction, it is also subject to a number of pitfalls stemming from the fact that choices in hypothetical situations might not be the same as those that the respondent would make in the real world, facing real budget constraints and real benefits (Diamond and Hausman, 1994). In addition, it may be difficult for individuals to know beforehand how they will value a good and survey respondents may strategically misstate their willingness to pay (Whittington, 2002). Another approach is estimating valuation of goods not traded in markets to use discrete choice models to analyze nonexperimental survey data on households’ decisions. (There are a number of papers, including Mu et al. (1990) that estimate more general demand functions for water from various sources. We do not focus on these papers here, since they do not explicitly deal with water quality as a measurable attribute in the decision process.) While this has the advantage of evaluating real choices, and hence providing information on a part of the household preference function (McConnell and Rosado, 2000), a potential problem with this type of method is that there may be unobservable household characteristics correlated with households’ choices as well as supplier pricing decisions, which will lead to biased results. For example, suppliers may charge higher prices where demand is higher, resulting in a positive cross-sectional correlation in prices and adoption even if increasing prices would result in lower demand for each household. A third approach is to infer willingness to pay by randomizing prices or locations of new facilities, thus generating random variation in travel time. This enables analysis based on actual choices while also addressing omitted variable bias, addressing the main concerns with both CV data and results based on nonexperimental data. This method also has the potential to enable examination of the allocative role of prices in targeting populations of interest and the isolation of specific channels of causality for effects of prices on demand. The literature on domestic water that we review here has provided one of the first direct comparisons of revealed preference and stated preference valuations of an environmental

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service in developing countries. Kremer et al. (unpublished manuscript) found that the stated preference approaches generate much higher valuation estimates than the revealed preference approach, by a factor of 3, with the contingent valuation survey approach exhibiting much greater dispersion, as well as considerable sensitivity to question framing, casting doubt on the reliability of stated preference methods. A revealed preference estimate of households’ valuation of cleaner water from source water-quality improvements can be assessed using a travel-cost approach that measures the number of trips made to the improved source relative to an unimproved source at a different distance, as long as source water-quality improvements are randomly assigned. Kremer et al. (unpublished manuscript) used such a method to estimate a model of demand for clean water from source quality improvements. As previously described, they exploited exogenous changes in the trade-off that households face when choosing between multiple water sources, some of which are close but contaminated and others of which are far but clean. They then contrasted this revealed preference estimate of willingness to pay for spring protection with two different stated preference methodologies: stated ranking of alternative water sources and contingent valuation. The divergence between the valuation estimates suggests that CV studies overestimate how much users value water-quality improvements.

1.05.6.3 Combining Randomized Evaluations with Structural Modeling Several recent papers combine data from randomized experiments with structural econometric methods in development economics (e.g., Todd and Wolpin, 2006). Kremer et al. (unpublished manuscript) combined experimental results with a structural model of water infrastructure investment to explore the implications of alternative property rights institutions on social welfare and assess the welfare impacts of alternative institutions governing water property rights. Using the valuation results discussed earlier as inputs into policy simulations, the authors can compare the welfare impacts of a number of potential scenarios under alternative sets of social norms regarding property rights. A hypothetical case of pure privatization, for example, in which landowners could restrict access to the spring and charge for water, results in relatively little investment in environmental protection (i.e., spring protection) since households’ willingness to pay for cleaner water is low, but leads to large static losses since landowners can extract consumer surplus by charging for even unprotected spring water even though the marginal cost of provision is zero. They concluded that, at low income levels, common property likely yields greater social welfare than private property, but that at higher income levels private property may yield higher social welfare.

1.05.7 Conclusion As noted in the introduction, the sole quantifiable environmental goal selected by the United Nations as part of the United Nations Millennium Development Goals is to reduce by half the proportion of people without sustainable access to

safe drinking water. Standard public finance theory suggests that local governments may ordinarily be best placed to allocate funds among competing local public goods, but it also suggests that international donors and national governments could reasonably intervene to support water projects due either to disease externalities across political jurisdictions, or to different distributional preferences than local decision makers. Both factors point to an emphasis on the impact of water on communicable diseases of children. While there is currently limited evidence on the health impact of increasing access to water without improving quality, there is strong evidence that improving water quality has health benefits for young children. Since households seem more willing to pay for access to increased quantities of water than for safer water, and since investments in water treatment are extremely cost-effective relative to other health expenditures, even expenditures such as vaccination, there seems a strong case for zero prices or even negative prices for water treatment. This chapter reviews evidence from randomized evaluations that can inform this policy debate on the quality versus quantity investment decision, and strategies to drive takeup of water treatment products. One promising approach is chlorine dispensers. Takeup of chlorination via communal chlorine dispensers (Kremer et al., 2009a), combined with a local promoter, is between 60% and 70% and the authors estimate that the long-run cost of supplying a community with bulk chlorine through a dispenser is only about 1/4 to 1/3 as much as with individually packaged bottles. This amounts to a cost of about 15 cents per person per year. The feasibility of this approach depends on the ability to solve the challenge of refill servicing and efforts to increase the density of dispensers (to drive refill costs down). Additional work to understand how to combine interventions and transition to greater levels of service as incomes rise remains an important area of policy-relevant work. The methodological lessons from the research on water-treatment takeup and valuation reviewed here can inform study design on the scale-up of alternative approaches to water-treatment and other experiments in resource economics, as well.

References Arnold B and Colford J (2007) Treating water with chlorine at point-of-use to improve water quality and reduce diarrhea in developing countries: A systematic review and meta-analysis. American Journal of Tropical Medicine and Hygiene 76(2): 354--364. Ashraf N, Berry J, and Shapiro J (in press) Can higher prices stimulate product use? Evidence from a field experiment in Zambia. American Economic Review. Banerjee A, Cole S, Duflo E, and Linden L (2007) Remedying education: Evidence from two randomized experiments in India. Quarterly Journal of Economics 122(3): 1235--1264. Banerjee A and Duflo E (2009) Experimental approach to development. Annual Review of Economics 1: 151--178. Bennear LS and Coglianese C (2005) Measuring progress: Program evaluation of environmental policies. Environment: Science and Policy for Sustainable Development 47(2): 22--39. Berry J, Fischer G, and Guiteras R (2008) Willingness to pay for clean water. Presented at Workshop on Scaling up Distribution of Water Treatment Technologies in Developing Countries, Harvard University, Cambridge, MA. Cardenas JC (2009) Experiments in environment and development. Annual Review of Resource Economics 1: 157--182.

Providing Clean Water: Evidence from Randomized Evaluations

Chattopadhyay R and Duflo E (2004) Women as policy makers: Evidence from a randomized policy experiment in India. Econometrica 72(5): 1409--1443. Clasen T, Brown J, Collin S, Suntura O, and Cairncross S (2004) Reducing diarrhea through the use of household-based ceramic water filters: A randomized, controlled trial in rural Bolivia. American Journal of Tropical Medicine and Hygiene 70(6): 651--657. Clasen T, Roberts I, Rabie T, Schmidt W, and Cairncross S (2006) Interventions to improve water quality for preventing diarrhoea. Cochrane Database of Systematic Reviews 2006, Issue 3. Art. No.: CD004794. DOI: 10.1002/14651858.CD004794. pub2. Conley T and Udry C (2001) Social learning through networks: The adoption of new agricultural technologies in Ghana. American Journal of Agricultural Economics 83(3): 668--673. Curtis V, Cairncross S, and Yonli R (2000) Domestic hygiene and diarrhoea – pinpointing the problem. Tropical Medicine and International Health 5(1): 22--32. Cutler D and Miller G (2005) The role of public health improvements in health advances: The 20th century United States. Demography 42(1): 1--22. DellaVigna S and Gentzkow M (2010) Persuasion: Empirical evidence. Annual Review of Economics 2 (doi:0.1146/annurev.economics.102308.124309). DeVoto F, Duflo E, Dupas P, Pariente W, and Pons V (2009) Happiness on tap: The demand for and impact of piped water in urban Morocco. Working Paper UCLA. Diamond P and Hausman J (1994) Contingent valuation: Is some number better than no number? Journal of Economic Perspectives 8(4): 45--64. Duflo E, Dupas P, and Kremer M (2007) Peer effects, pupil–teacher ratios, and teacher incentives: Evidence from a randomized evaluation in Kenya. Cambridge: Mimeo; Harvard University. Duflo E, Glennerster R, and Kremer M (2008a) Using randomization in development economics research: A toolkit. In: Shultz TP and Strauss J (eds.) Handbook of Development Economics, 4, pp. 1--2. Amsterdam: Elsevier. Duflo E and Kremer M (2008) Use of randomization in the evaluation of development effectiveness. Evaluating Development Effectiveness 7: 93--120. Duflo E, Kremer M, and Robinson J (2009) Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. Working Paper, 15131, NBER. Eckel C and Grossman P (1998) Are women less selfish than men? Evidence from dictator experiments. Economic Journal 108(448): 726--735. Esrey SA (1996) Water, waste, and well-being: A multicountry study. American Journal of Epidemiology 143(6): 608--623. Esrey SA, Potash JB, Roberts L, and Shiff C (1991) Effects of improved water supply and sanitation on ascariasis, diarrhoea, dracunculiasis, hookworm infection, schistosomiasis, and trachoma. Bulletin of the World Health Organization 69(5): 609--621. Fewtrell L, Kaufmann RB, Kay D, Enanoria W, Haller L, and Colford JM (2005) Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: A systematic review and meta-analysis. Lancet Infectious Disease 5: 42--52. Foster A and Rosenzweig M (2001) Imperfect commitment, altruism, and the family: Evidence from transfer behavior in low-income rural areas. Review of Economics and Statistics 83(3): 389--407. Galiani S, Gertler P, and Schargrodsky E (2005) Water for life: The impact of privatization of water services on child mortality. Journal of Political Economy 113(1): 83--119. Gamper-Rabindran S, Khan S, and Timmens C (2010) The impact of piped water provision on infant mortality in Brazil: A quantile panel-data approach. Journal of Development Economics 92(2): 188–200. Garrett V, Ogutu P, Mabonga P, et al. (2008) Diarrhoea prevention in a high-risk rural Kenyan population through point-of-use chlorination, safe water storage, sanitation, and rainwater harvesting. Epidemiology and Infection 136(11): 1463--1471. Greenstone M and Gayer T (2009) Quasi-experimental and experimental approaches to environmental economics. Journal of Environmental Economics and Management 57(1): 21--44. Han AM and Hlaing T (1989) Prevention of diarrhoea and dysentery by hand washing. Transactions of the Royal Society of Tropical Medicine and Hygiene 83(1): 2128--2131. Hanemann WM (1994) Valuing the environment through contingent valuation. Journal of Economic Perspectives 8: 19--43. Hoffman V (2009) Intrahousehold allocation of free and purchased mosquito nets. American Economic Review 99(2): 236--241. Holla A and Kremer M (2008) Pricing and access: Lessons from randomized evaluation in education and health. Cambridge: Mimeo; Harvard University. Imbens G and Wooldridge JM (2009) Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47(1): 5--86.

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Jalan J and Somanathan E (2008) The importance of being informed: Experimental evidence on demand for environmental quality. Journal of Development Economics 87: 14--28. Kahneman D and Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263--291. Khan MU (1982) Interruption of shigellosis by hand washing. Transactions of the Royal Society of Tropical Medicine and Hygiene 76(2): 164--168. Kremer M, Leino J, Miguel E, and Zwane A (2008) Managing rural water infrastructure in Kenya. Working Paper. Kremer M and Miguel E (2007) The illusion of sustainability. Quarterly Journal of Economics 112(3): 1007--1065. Kremer M, Miguel E, Mullainathan S, Null C, and Zwane A (2009a) Coupons, promoters, and dispensers: Impact evaluations to increase water treatment. Working Paper. Kremer M, Miguel E, Null C, Van Dusen E, and Zwane A (2009b) Measuring diarrhea: Quantifying Hawthorne effects in frequently collected data. Working Paper, University of California, Berkeley. Laibson D (1997) Golden eggs and hyperbolic discounting. Quarterly Journal of Economics 112(2): 443--477. Lancet (2007) Science at WHO and UNICEF: The corrosion of trust. Lancet 370: 1007 (editorial). Lipscomb M and Mobarak M (2008) Decentralization and water pollution spillovers: Evidence from the re-drawing of county boundaries in Brazil. Working Paper. molly.lipscomb.googlepages.com/Brazilwater113007.pdf (accessed March 2010). Luby S, Agboatwalla M, Painter J, Altaf A, Billhimer W, and Hoekstra H (2004) Effect of intensive hand washing promotion on childhood diarrhea in high-risk communities in Pakistan: A randomized control trial. JAMA 291(21): 2547--2554. Luby S, Mendoza C, Keswick B, Chiller T, and Hoekstra R (2008) Difficulties in bringing point-of-use water treatment to scale in rural Guatemala. American Journal of Tropical Medicine and Hygiene 78(3): 382--387. Luoto J (2009) Information and persuasion: Achieving safe water behavior in Kenya. Working Paper. Madajewicz M, Pfaff A, van Geen A, et al. (2007) Can information alone change behavior? Response to arsenic contamination of groundwater in Bangladesh. Journal of Development Economics 84: 731--754. Manandhar DD, Osrin B, Prasad N, et al. (2004) Effect of a participatory intervention with women’s groups on birth outcomes in Nepal: Cluster randomized control trial. Lancet 364: 970--979. Mansuri G and Rao V (2004) Community-based and -driven development: A critical review. World Bank Research Observer 19(1): 1--39. McConnell K and Rosado M (2000) Valuing discrete improvements in drinking water quality through revealed preferences. Water Resources Research 36(6): 1575--1582. Miguel E and Gugerty M (2005) Ethnic diversity, social sanctions, and public goods in Kenya. Journal of Public Economics 89(11–12): 2325--2368. Mu X, Whittington D, and Briscoe J (1990) Modeling village water demand behavior: A discrete choice approach. Water Resources Research 26(4): 521--529. Mullainathan S, Schwartzstein J, and Shleifer A (2006) Coarse thinking and persuasion. Working Paper, W12720, NBER. Munshi K (2004) Social learning in a heterogeneous population: Technology diffusion in the Indian Green Revolution. Journal of Development Economics 73: 185--215. Muralidharan K and Sundararaman V (2009). Teaching incentives in developing countries: Experimental evidence from India. Working Paper, 15323, NBER. Nowell C and Tinkler S (1994) The influence of gender on the provision of a public good. Journal of Economic Behavior and Organization 25(1): 25--36. O’Donoghue T and Rabin M (1999) Doing it now or later. American Economic Review 89(1): 103--124. Oster SM (1995) Strategic Management for Nonprofit Organizations: Theory and Cases. Oxford: Oxford University Press. Pattanayak SK and Pfaff A (2009) Behavior, environment, and health in developing countries: Evaluation and valuation. Annual Review of Resource Economics 1: 183--217. Ray I (2004) Water for all? Peri-urban and rural water delivery options: The case of India. International Conference of Engineers for a Sustainable World, Stanford Unversity. Rothman A, Martino S, Bedell B, Detweiler J, and Salovey P (1999) The systematic influence of gain- and loss-framed messages on interest in and use of different types of health behavior. Personality and Social Psychology Bulletin 25(11): 1355--1369. Sachs JD (2005) The End of Poverty: Economic Possibilities for Our Time. New York: Penguin.

1.06 Pricing Water and Sanitation Services D Whittington, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA & 2011 Elsevier B.V. All rights reserved.

1.06.1 1.06.2 1.06.3 1.06.4 1.06.5 1.06.5.1 1.06.5.1.1 1.06.5.1.2 1.06.5.1.3 1.06.5.1.4 1.06.5.1.5 1.06.5.2 1.06.5.3 1.06.6 1.06.7 1.06.7.1 1.06.7.2 1.06.8 References

Introduction The Costs of Providing W&S Services W&S Development Paths Objectives of Tariff Design Tariff Structures – the Alternatives Single-Part Tariffs Fixed charges Volumetric charges Uniform volumetric charge Block tariffs Increasing linear tariff Two-Part Tariffs Seasonal and Zonal Water Pricing Achieving Economic Efficiency and Recovering Capital Costs: Fundamentals of Dynamic Marginal Cost Pricing in the W&S Sector Subsidizing Capital Costs: Reaching the Poor Create a Well-Run System of Public Taps as a Safety Net for the Poor Preserve Options for the Poor Concluding Remarks

1.06.1 Introduction A tariff is an important management tool that can be used to assist with efforts to improve the delivery of water and sanitation (W&S) services. The pricing of W&S services is, however, controversial, and it is important to understand why there is so little consensus on W&S tariff issues. There are four main reasons. First, there is disagreement over the objectives of water pricing and tariff design. Water pricing decisions affect several different objectives or goals of policymakers, often in conflicting ways. This means that if one person is looking solely (or mostly) at the consequences of a particular water pricing policy (or tariff design) in terms of one objective, and another person is looking at the same water pricing policy in terms of its impact on another objective, they may reach quite different conclusions about the attractiveness of the pricing policy. Second, as people do not generally know what it costs to provide W&S services, it is difficult for them to judge what is a fair or appropriate price to pay. Third, there is disagreement over what would actually happen if different water tariffs were implemented. The empirical work is often lacking that would enable someone to know with reasonable confidence how changes in water prices would affect the quantity of water that different customers would use and whether or not price changes would affect customers’ decisions to connect (or stay connected) to the water distribution system (Nauges and Whittington, 2009). Fourth, although there is some competition in the water market, there is no market test for different water tariff structures. Many tariff structures are feasible and can partially accomplish some of the competing objectives of water pricing.

79 79 82 83 84 85 85 85 85 86 87 87 87 88 91 93 94 94 94

There are typically an insufficient number of providers of piped water services for customers to reject inappropriate tariff structures. Bad ideas thus do not get weeded out of either sector practice or policy discussions. Even in different private sector participation arrangements, water tariff structures are typically set by the regulatory agency, and the private sector operator has to treat them as given and manage the system as best he can (given this constraint). The purpose of this chapter is to provide the reader with a better understanding of the main issues involved in the design of W&S tariffs. Section 1.06.2 summarizes the costs of providing piped W&S services. Obviously, these costs vary widely depending on local circumstances, but the presentation of some estimates of different components of the costs of providing such services illustrates that they are not cheap. Section 1.06.3 discusses alternative development paths for moving from low levels of W&S service (or no service at all) to modern piped services, and shows the costs associated with various incremental changes. Section 1.06.4 presents the four main objectives of tariff design. Section 1.06.5 summarizes the main tariff options. Section 1.06.6 describes the basic ideas of dynamic marginal cost pricing in the W&S sector and illustrates how a two-part tariff can be used to achieve both economic efficiency and cost recovery objectives. Section 1.06.7 discusses how subsidies can be best used in the W&S sector to reach poor households. Section 1.06.8 offers some concluding remarks.

1.06.2 The Costs of Providing W&S Services A key feature of network W&S investments is that they are very capital intensive. The majority of costs are incurred early in the

79

80

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life of the project, and the subsequent stream of benefits (and revenues) occurs over many years. Baumann and Boland (1998) estimated that the ratio of annual investment in the US water industry to gross revenues was 0.43 in 1993. (They note, ‘‘no other major industry group in the United States even approaches this ratio of annual investment to revenue.’’) Someone (e.g., private investors and government) must take a long-term perspective and put large amounts of capital at risk. If future revenues are needed to pay for the high capital costs in the early years, investors need assurance to their rights to the revenue stream. In contrast, poor households in developing countries tend to have high rates of discount (Poulos and Whittington, 2000) and thus have short planning horizons. Such households cannot easily make the long-term commitments required to pay for network W&S services. They are also uncertain about the prospects for long-term economic growth and their ability to pay in the future. The challenge of W&S tariff design in developing countries must be understood within the context of this fundamental mismatch of perspectives between investors and consumers. In urban areas, there is widespread consensus that the long-term goal of W&S service providers in developing countries should be to offer 24-h potable water supply piped into people’s homes, to remove wastewater with a piped sewerage system, and to treat this wastewater to a standard sufficient to minimize the environmental effects of its discharge to surface water bodies. (Some people in the sector do question the wisdom of pursuing the goal of piped sewerage – see, e.g., Esrey and Andersson (2000).) Even in many rural communities, households aspire to this level of service. The treatment and delivery of water to households, and the removal and treatment of the wastewater generated, cost serious money. These costs must be paid by someone, households must either pay or receive subsidies (e.g., from richer households, industries, donors, and higher levels of government). Of course, costs vary depending on local circumstances, and estimates of what it will cost to provide a certain level of service may vary widely. Also, most investments are incremental in nature. Only rarely would a community incur the costs of complete (full service) piped W&S systems at a single point in time. Nevertheless, some rough calculations may prove useful for the discussion of water pricing and tariff design. The approach here is to present some illustrative average unit costs of providing an urban household with modern W&S services. First, representative unit costs per cubic meter for different components of W&S services are looked at. Second, some typical quantities of water that different representative households use in a month are provided. Third, representative unit costs are multiplied by typical monthly household water use to obtain estimates of the monthly economic costs of providing a typical household with improved, piped W&S services. The economic costs of providing a household with modern W&S services are the sum of seven principal components: 1. opportunity costs of diverting raw water from alternative uses to the household (or resource rents); 2. storage and transmission of untreated water to the urban area;

3. treatment of raw water to drinking water standards; 4. distribution of treated water within the urban area to the household; 5. collection of wastewater from the household (sewerage collection); 6. treatment of wastewater (sewage treatment); and 7. any remaining costs or damages imposed on others by the discharge of treated wastewater (negative externalities). Table 1 presents some illustrative average unit costs for each of these seven cost components, expressed in US$ per cubic meter. The unit costs of these different cost components could vary widely in different locations. For example, in a location with abundant freshwater supplies, the opportunity cost of diverting water from existing or future users to our illustrative household (item 1) and the damages imposed by the discharge of treated wastewater (item 7) may, in fact, be very low or even zero. However, in more and more places, these opportunity costs associated with water diversion and the externalities from wastewater discharge are beginning to loom large. Some cost components are subject to significant economies of scale, particularly storage and transmission (item 2), the treatment of raw water to drinking water standards (item 3), and the treatment of sewage (item 6). This means that the larger the quantity of water or wastewater treated, the lower the per-unit cost. On the other hand, some cost components are experiencing diseconomies of scale. As large cities go farther and farther away in search of additional freshwater supplies, and good reservoir sites become harder to find, the unit cost of storing and transporting raw water to a community increases. There are also trade-offs between different cost components: one can be reduced, but only at the expense of the other. For example, wastewater can receive only primary treatment, which is much cheaper than primary and secondary treatment, but then the negative externalities associated with wastewater discharge will increase. Table 1 Cost estimates: improved water and sanitation services (assuming 6% real discount rate, see Whittington et al. (2008) for details) No.

Cost component

US$/m3

1

Opportunity cost of raw water supply Storage and transmission to treatment plant Treatment to drinking water standards Distribution of water to households (including house connections) Collection of wastewater from home and conveyance to wastewater treatment plant Wastewater treatment Damages associated with discharge of treated wastewater

0.05

3

0.10

5

0.10

5

0.60

30

0.80

40

0.30 0.05

15 3

2.00

100

2 3 4

5

6 7 Total

% of total

Pricing Water and Sanitation Services

The cost estimates in Table 1 include both capital expenses, and operation and maintenance expenses. The opportunity costs of raw water supplies (item 1) are still quite low in most places, on the order of a few cents per cubic meter. Even in places where urban water supplies are diverted from irrigated agriculture, the unit costs will rarely be above US$0.25 per cubic meter. Desalinization and wastewater reclamation costs will set an upper limit on opportunity costs of raw water in the range of US$0.50–1.00 per cubic meter for cities near the ocean, but the opportunity costs of raw water are nowhere near this level in most places. Raw water storage and transmission and subsequent treatment (items 2 and 3) will typically cost US$0.20 per cubic meter. Within a city, the water distribution network and household connections to it (item 4) comprise a major cost component, in many cases on the order of US$0.60 per cubic meter. The collection and conveyance of sewage to a wastewater treatment plant (item 5) are even more expensive than the water distribution; this will cost about US$0.80 per cubic meter, 40% of the total cost. Secondary wastewater treatment (item 6) will cost about US$0.30 per cubic meter. Damages resulting from the discharge of treated wastewater are very sitespecific, but environmentalists correctly remind us that that they can be significant, even for discharges of wastewater receiving secondary treatment. Let us assume for purposes of illustration that these costs are of the same order of magnitude as the opportunity costs of raw water supplies (US$0.05). As shown, total economic costs are about US$2.00 per cubic meter in many locations. It is emphasized that costs shown here are not intended to represent an upper bound. For example, in small communities in the arid areas of the western United States’ costs of W&S services can easily be double or triple these amounts per cubic meter. Also, note that these cost estimates assume that financing is available at competitive international market rates, and that countries do not pay a high default or risk premium. Table 2 presents a reasonable lower-bound estimate of unit costs of piped W&S services. Here, the opportunity costs of raw water supplies and the damages from wastewater discharges are assumed to be zero. Only minimal storage is included, and the only intake treatment is simple chlorination. Costs for the water distribution network assume the use of polyvinyl chloride pipes and shallow excavation. Wastewater is collected with condominial sewers, and the only wastewater treatment is provided by simple lagoons. Given all these assumptions, one can manage to reduce unit costs of piped W&S services to about US$0.80 per cubic meter. How much water does a typical household in a developing country need? The quantity of water used by a household will be a function of the price charged, household income, and other factors. Currently, most households in developing countries are facing quite low prices for piped W&S services. One can look at water use data from households in industrialized countries to see how much water one might expect a household to use for a comfortable modern lifestyle. For households with an in-house piped water connection, in many locations residential indoor water use falls in the range of 110–220 l per capita per day. For a household of six members, this would amount to about 20–40 cubic meters per month (Table 3). At the current low prices prevailing in many

81

Table 2 Cost estimates: improved water and sanitation services for low-cost option for private water and sewer connections (assuming 6% real discount rate, see Whittington et al. (2008) for details) No.

Cost component

US$/m3

1

Opportunity cost of raw water supply (steal it) Storage and transmission to treatment plant (minimal storage) Treatment of to drinking water standards (simple chlorination) Distribution of water to households (PVC pipe) Collection of wastewater from home and conveyance to wastewater treatment plant (condominial sewers) Wastewater treatment (simple lagoon) Damages associated with discharge of treated wastewater (someone else’s problem)

0.00

2 3 4 5

6 7

Total

0.07 0.04 0.24 0.30

0.15 0.00

0.80

Table 3 Range of estimates of monthly water use (in-house, private connection) Per capita daily water use (l)

Persons per household

Days per month

Monthly household water use (m3)

55 110 220

6 6 6

30 30 30

10 20 40

cities in developing countries, such levels of household water use are not uncommon. Other things being equal, households living in hot, tropical climates use more water for drinking, bathing, and washing than households in temperate or cold climates. Assuming average unit costs of US$2.00 per cubic meter, the full economic costs of providing 20–40 cubic meters of water to a household (and then dealing with the wastewater) would be US$40.00–80.00 per month (Table 4), more than most households in industrialized countries pay for the same services and far beyond the means of most households in developing countries. One would expect poor households in developing countries with in-house water connections to respond negatively to higher W&S prices: they might curtail use to as little as 50–60 l per capita per day. For a household with six members, at 55 l per capita per day, total consumption would then amount to about 10 cubic meters per month. The full economic costs of this level of W&S service at this reduced quantity of water use (assuming our unit costs of US$2.00 per cubic meter remained unchanged) would then be US$20.00 per month per household. At entirely plausible levels of water use (110 l per capita per day), the total economic cost would be about US$40.00 per month for the same household. With the unit costs of the low-cost system depicted in Table 2, the full economic cost of providing 10 cubic meters per month would be US$8.00 per household per month. This estimate should be regarded as a

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Table 4 Range of estimates of the full economic cost of providing improved W&S services (in-house, private water connection; piped sewer) Monthly household water use (m3)

Average cost ¼ US$0.80/m3 (US$)

Average cost ¼ US$2.00/m3 (US$)

10 20 40

8 16 32

20 40 80

lower bound on the full economic costs of piped W&S services in most locations. In industrialized and developing countries alike, most people are unaware of the magnitude of the true economic costs of municipal W&S services. There are several reasons why these economic costs are so poorly understood. First, the capital costs are heavily subsidized by higher levels of government (and, in developing countries, by international donors), so that households with services do not see the true capital costs reflected in the fixed charges or volumetric prices they pay. Second, in many cities tariff structures are designed so that industrial water usage subsidizes residential usage; households thus do not even see the full operation and maintenance costs in the prices they pay. Third, as many water utilities run financial deficits (in effect running down the value of their capital stock), water users in aggregate do not even see the full costs of supply. Fourth, most cities do not pay for their raw water supplies: typically, the water is simply expropriated from any existing water sources (and their users) in outlying rural areas. Fifth, wastewater externalities are typically imposed on others (downstream) without compensation. Sixth, the subsidies provided to consumers of W&S services are not only huge, but also regressive. It is often not politically desirable for the majority of people to understand that middle- and upper-income households, who generally use more water, are thus actually receiving the most benefit from subsidies. Tariff designs may in fact be made overly complicated in order to offset this reality and appear to be helping poorer households (Komives et al., 2005). Most fundamentally, poor households are often not connected to the W&S network at all and hence cannot receive the subsidized services. Even if they do have connections, the poor use less water than richer households, thus receiving lower absolute amounts of subsidies. The estimates presented here are intended merely to suggest what W&S costs are like in many developing countries. A reasonable question to ask is whether costs differ much across countries in the developing world and between industrialized and developing countries. Labor costs are obviously lower in developing countries, but because W&S projects are capital intensive, the cost component has less of an impact on total costs than for other goods and services. There are, to our knowledge, no publicly available international indices of W&S project construction costs. To illustrate the magnitude of international cost differentials for some related goods and construction costs, Table 5 compares costs of rebar, cement, and industrial construction in 11 large cities in both

Table 5 Comparison of costs of rebar, cement, and industrial facility construction in 11 cities City

Rebar (US$/ton)

Cement (US$/ton)

Industrial Construction (US$/m2)

London Boston Los Angeles Shanghai Jakarta Bangkok Hanoi New Delhi Durban Nairobi Buenos Aires

981 1100 992 435 528 482 349 600 1028 NA 765

96 85 135 43 68 63 62 64 137 NA 82

850 915 699 592 269 301 409 247 516 291 NA

From Engineering News-Record (2004) 253. 24 (December 12), 32–37.

industrialized and developing countries. Costs are indeed lower in cities such as New Delhi and Hanoi than in London and Boston, and lower costs for inputs such as cement and steel will translate into lower costs for W&S projects. It is, of course, less expensive to provide intermediate levels of W&S services (e.g., public taps and communal sanitation facilities) than the costs in Table 2 would indicate. Monthly household costs for such services are, however, often quite considerable, roughly US$5.00–10.00 per month for much smaller quantities of water and much lower levels of sanitation services. These costs are often reported to be as low as US$1.00–2.00 per household per month, but such accounts often systematically underestimate key cost components and rarely reflect the real costs of financially sustainable systems. It is also important to appreciate that intermediate services impose additional costs on households in terms of extra time spent accessing the services and increased coping costs for the inconveniences of using off-site services (Bahl et al., 2004; Pattanayak et al., 2005).

1.06.3 W&S Development Paths The high capital costs of network water and sewer systems have important implications for water prices and tariff design. Decisions on how to price network W&S services in developing countries are typically made in a dynamic, changing environment. Pricing and tariff design decisions made today should not lock households into low-level equilibrium solutions that will constrain them from improving their W&S services as economic growth occurs. In-house piped W&S services are unaffordable today in many cities in developing countries, but as economic growth occurs, there is general agreement that this goal is both desirable and achievable. It is thus important to consider carefully how pricing and tariff-design decisions influence the evolution of W&S service provision and the ability of managers and planners to upgrade services when economic growth creates the resources to make this vision a reality. There are numerous strategies or development paths for moving from a situation where households have poor or no services to

Pricing Water and Sanitation Services

modern W&S services, and it is necessary to reflect explicitly on the pros and cons of each, and how pricing and tariff design decisions push service providers and households along a particular development path, or create hurdles that must be overcome to make progress. For purposes of illustration, Table 6 compares three levels of water services and four levels of household sanitation. Let us consider a household without either improved water or sanitation services (case 1). Within the parameters given in the table, such a household might progress from this status to full modern W&S services (case 12) along any of the four principal development paths. First, some water planners would advocate for a water-first development path (case 1 - case 2 - case 3 - case 6 - case 9 - case 12); here W&S service providers concentrate on first getting piped water services into the household; only after this stage is achieved would investments go to the installation of neighborhood sewers and then to wastewater treatment. Note that the household itself has important investments to make. On the water side, in-house plumbing is required to take full advantage of the piped water connection. Similarly, the household would typically be responsible for the installation of a private water-sealed toilet, without which the installation of neighborhood sewers would be of less value. Proponents of a water-first development path argue that people want water services first and do not recognize the need for removing wastewater from the household until water has been provided and wastewater removal has become a problem. Also, as described above, sewers and wastewater treatment are very expensive, so it is easier financially to provide the less-expensive services first. From a pricing perspective, a water-first strategy has important implications. Under this strategy, revenues from water sales should not be diverted to subsidize sewers or wastewater treatment, at least until the majority of the population has high-quality water services. Also, any available subsidies from

Table 6

83

higher levels of government should be used to push households toward in-house piped service. For example, subsidies might be used to reduce the upfront connection charge for a household water hookup. Public health professionals sometimes argue in favor of a second development path: not one without the other (case 1 - case 5 - case 9 - case 12). Proponents of this approach believe that there are important public health complementaries from providing improved W&S together, and that households should not be allowed to receive in-house piped water without hooking up to a sewer line. Engineers often point out that it is cheaper to install water and sewer lines at the same time, particularly in cities where this may entail tearing up streets, sidewalks, and other infrastructure. Such bundling of W&S services has important implications for tariff design. If households are required to have sewer services when they receive piped water services, then from a household’s point of view, W&S services cannot really be charged separately. (Also, note that W&S service providers cannot practically meter the amount of water that a household receives separately from the amount of wastewater that it discharges. Thus, even if a provider claims to calculate water and wastewater charges separately, and apply a separate volumetric charge to each flow, from the household’s perspective this is simply an accounting trick. The household effectively faces a single weighted volumetric rate for the combined service.) If the service provider attempts to recover the full costs of both services, and the household is willing to pay the cost of the water services but unwilling to pay for the sanitation services, the household will reject the entire bundle. Thus, when services are bundled and tariffs are designed to recover the costs of service, tariffs can easily become a barrier to the provision of full modern network services (case 12). A third development path might be termed ‘sanitation first’ (case 1 - case 4 - case 5 - case 8 - case 9 - case 12). The rationale here is that improved sanitation is a more important

Water and sanitation development paths Unimproved water source (e.g., pond and river)

Improved water source outside the home (e.g., hand-pump and public tap)

Improved water inside the home (private water connection or yard tap)

No improved sanitation

Case 1

Case 2 (US$5/month/household)

Case 3 (US$10/month/ household)a

On-site sanitation (e.g., VIP latrine and pour flush toilet)

Case 4 (US$5/month/household)

Case 5 (US$10/month/household)

Case 6 (US$15/month/household)

Water-sealed toilet þ neighborhood wastewater collection (e.g., small-bore or conventional sewers)

Case 7 (US$15/month/ household)b

Case 8 (US$20/month/household)

Case 9 (US$25/month/household)

Water-sealed toilet þ neighborhood wastewater collection þ wastewater treatment

Case 10 (US$25/month/ household)

Case 11 (US$30/month/ household)

Case 12 (US$35/month/ household)

a

Water costs are not cumulative because having a private connection does not require a public tap or handpump. Sanitation costs are cumulative, that is, level 3 includes the costs of in-house plumbing þ neighborhood wastewater collection.

b

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Pricing Water and Sanitation Services

first step than improved water services in achieving the desired public health benefits. Thus, if resources are limited, public authorities should tackle sanitation problems before building piped water distribution networks. From a pricing perspective, if demand for improved sanitation services turns out to be low, this development path requires more initial subsidies, with revenues from water to follow when households eventually receive in-house water connections. A fourth set of development paths might be termed ‘demand-driven’ in that the paths are not selected by experts but rather by people themselves. There are numerous plausible development paths that households might choose (e.g., case 1 - case 3 - case 6 - case 9 - case 12; or case 1 - case 6 case 9 - case 12). If households’ preferences are allowed to shape the evolution of W&S services, prices and tariff design have an especially important role to play. Prices provide the signals about the real resource costs of the various steps from the status quo to full modern network services. If these signals are incorrect, households may take an unwanted or unnecessary detour on the road to case 12.

1.06.4 Objectives of Tariff Design Setting water (and sanitation) tariffs requires that one strikes a balance between four main objectives (see Boland (1993) and Whittington et al. (2002) for additional discussion of tariff objectives). Cost recovery. From the water supplier’s point of view, cost recovery is the main purpose of the tariff. (For example, the World Bank’s Operational Manual Statement No. 3.72 emphasizes the importance of the cost recovery objective and the financial autonomy of the borrower.) Cost recovery requires that, on aggregate, tariffs faced by consumers should produce revenue equal to the financial costs of supply. Moreover, the revenue stream should be relatively stable and not cause cash flow or financing difficulties for the utility. Economic efficiency. Economic efficiency requires that prices be set to ensure that customers face the avoidable costs of their decisions. In other words, prices should signal to consumers the financial, environmental, and other costs that their decisions to use water impose on the rest of the system and on the economy. In practice, this means that the volumetric charge should be set equal to the short-run marginal social cost of bringing one additional cubic meter of water into a city, delivering it to a particular customer, collecting and treating the wastewater, and discharging the treated wastewater into a receiving water body. In many cities, the cost of bringing in additional water is higher than the cost of supplying the water already on hand, as the cheapest sources tend to be developed first. The short-run marginal cost should include not only the financial cost of public works undertaken, but also the social cost of diverting water resources into public supply rather than using it for other purposes. An efficient tariff will create incentives that ensure, for a given water supply cost, that users obtain the largest possible aggregate economic benefits. Equity. The term ‘equity’ is often used to denote quite different things. Here, it is used to mean that the water tariff treats similar customers equally, and the customers in different

situations are not treated the same. This usually means that users pay monthly water bills that are proportionate to the costs they impose on the utility by their water use. Affordability. One objective of tariff design is to ensure that poor households are able to obtain adequate supplies of clean water. The terms ‘equity’, ‘fairness’, ‘poverty alleviation’, and ‘affordability’ are often used interchangeably to express this desire. It is preferred to treat affordability as an objective distinct from equity, fairness, and poverty alleviation, because a W&S tariff that is affordable may not be equitable or perceived as fair. Moreover, an affordable tariff may not pull poor households out of poverty. Many people feel that water services are a basic right and should be provided to people regardless of whether they can pay for the services. These considerations have led to recommendations that W&S tariffs should be kept low and that water should be provided free or at minimal cost, at least to the poor, through systems of subsidies. There are a number of trade-offs between these different objectives and the W&S tariffs used to calculate customers’ bills. For example, providing water free through private connections in order to achieve the objective of affordability conflicts with the objectives of cost recovery and efficient water use. Also, poor customers can sometimes be relatively expensive to serve (e.g., perhaps due to their outlying location), and hence it might not be regarded as equitable to charge them the same as, or less than, other customers. Additional objectives and considerations may be involved. For example, a tariff design should be easy to explain, understand, and implement. A tariff design should be acceptable both to the public and to the political leaders. This may require the tariff to conform to perceptions of fairness, often quite different from notions of equity. Water tariffs may be designed to discourage excessive uses of water, thus promoting water conservation, where excessive may be understood as a deviation from some notion of a fair amount. A successful W&S tariff design should not be controversial, nor should it become a focus of public criticism of the watersupply agency. Human beings are, however, acutely sensitive to situations perceived to be unfair, and fairness is often in the eye of the beholder. It can prove to be especially difficult to design a W&S tariff that is perceived to be fair when customers do not understand the true resource costs of providing modern W&S services. Consider the four cases in Table 7. If household members understand the real resource costs of supplying modern W&S services and believe the household should pay a share of these costs proportionate to its use of such services (case A), a W&S tariff that is perceived to be fair can be relatively easily designed. But if household members do not understand the real resource costs of supplying modern W&S services, it may prove to be difficult for them to believe that a tariff is fair even if they believe the household should pay a share of these costs proportionate to its use of such services (case B). For example, such a household may perceive a proposed W&S tariff to be price gouging even if it is not. On the other hand, household members may understand the real resource costs of supplying modern W&S services but not believe the household should pay a share of the costs proportionate to its use of such services (case C). This may be due to past injustices, a feeling that this household is more

Pricing Water and Sanitation Services Table 7 Households’ understanding of supply costs vs. agreement to pay a proportionate share of the costs of W&S services: four cases A household understands the real resource costs of supplying modern W& S services

A household does not understand the real resource costs of supplying modern W& S services

A household believes that it should pay a proportionate share of the costs of W& S services

Case A

Case B

A household believes that it should not pay a proportionate share of the costs of W& S services

Case C

Case D

deserving of help than others, or any number of reasons. Or household members may neither understand the real resource costs of supplying modern W&S services, nor believe the household should pay a share of the costs proportionate to its use of such services (case D). This is the most difficult situation for all stakeholders, and unfortunately it is quite common. In case C and especially in case D, the negotiation of W&S tariffs often becomes a political problem largely unrelated to the costs of service delivery.

1.06.5 Tariff Structures – the Alternatives A tariff structure is a set of procedural rules used to determine the conditions of service and the monthly bills for water users in various categories. (This section draws heavily on Whittington et al. (2002).) Table 8 presents a simple classification of the different types of water tariff structures. Two main types of tariff structures are used in the municipal water supply sector: a single-part tariff and a two-part tariff. With a singlepart tariff, a consumer’s monthly water bill is based on a single type of calculation. With a two-part tariff, a consumer’s water bill is based on the sum of two calculations. The single type of calculation used in a single-part tariff can be one of two types: a fixed charge or a water use (volumetric) charge; volumetric charges can be handled in several different ways. Figure 1 illustrates how the price of water to the consumer changes as the quantity of water used increases for some of these tariff structures. Figure 2 shows how the customer’s monthly water bill varies as the quantity of water used increases for selected tariff structures.

1.06.5.1 Single-Part Tariffs 1.06.5.1.1 Fixed charges In the absence of metering, fixed charges are the only possible tariff structure. With a fixed charge, the consumer’s monthly water bill is the same regardless of the volume used. In many countries, renters in multi-story apartment buildings have unmetered connections to their units and thus effectively pay

Table 8

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Basic types of water tariff structures

Single-part tariffs A. Fixed charge: monthly water bill is independent of the volume consumed B. Water use charge a. uniform volumetric tariff b. block tariff: unit charge is constant over a specified range of water use and then shifts as use increases (i) increasing block (ii) decreasing block c. increasing linear tariff: unit charge increases linearly as water use increases Two-part tariffs Fixed charge þ water use charge

a fixed charge for water (perhaps incorporated into their rent). Fixed charges are still quite widely used in industrialized countries, such as Canada, Norway, and the United Kingdom (and until recently in New York City), where water has historically been abundant and hence metering is not widespread. The fixed charge itself can vary across households or consumer classes depending on characteristics of the consumer. For example, historically a common way to charge differential fixed charges was to set higher fixed charges on more valuable residential properties, sometimes on the assumption that people living in higher-value dwellings tend to use more water and/or have a greater ability to pay for the water they use. It was also common to assign businesses, a different fixed charge than households, on the assumption that firms use more water than households, and notions of fairness (e.g., that firms have a greater ability to pay for water than households). Another common approach is to charge different monthly fees depending on the diameter of the pipe used by the customer to connect to the distribution system: single-family domestic connections generally require a smaller bore than connections for larger concerns (e.g., businesses, hospitals, and apartments). From the perspective of economic efficiency, the problem with a fixed-charge system is that consumers have no incentive to economize on water use, as using more water will not increase their water bill. If the short-run marginal cost of supply is very low due to excess capacity in the system, this may not be a big problem. However, from a cost recovery perspective, a fixed-charge system creates a potentially large problem for the utility (or operator) if some households still lack individual connections: customers who do have a connection can supply water to other users (e.g., unconnected households and vendors) without incurring an increase in the household water bill. Moreover, because the fixed charge offers no incentive to economize on the use of water, a fixed charge that provided sufficient revenues at one point in time will become increasingly inadequate as the economy and incomes grow and water use increases. W&S service providers will be reluctant to expand coverage because more customers may mean more financial losses. Fixed-charge tariffs are thus especially prone to locking communities into low-level equilibrium traps of few customers, low revenues, and poor service (Whittington et al., 1990).

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Pricing Water and Sanitation Services 1

Price ($/m3)

0.75

0.5

0.25

0 0

5

10

15 Quantity (m

Decreasing block

20

25

30

3)

Increasing block

Increasing linear

Uniform

Figure 1 Price of water vs. the quantity of water used for selected tariff structures.

30

Monthly bill ($)

25 20 15 10 5 0 0

5

10

15 Quantity

Decreasing block

20

25

30

(m3)

Increasing block

Increasing linear

Uniform

Figure 2 Monthly water bill vs. the quantity of water used for selected tariff structures.

1.06.5.1.2 Volumetric charges

1.06.5.1.3 Uniform volumetric charge

The second way to structure a single-part tariff is to base consumers’ water bills on the amount of water they use. In mathematical terms, the monthly water bill is thus a function of the quantity of water a consumer uses. The precise formula used for the calculation of the water bill can differ. There are three main options: (1) a uniform volumetric charge; (2) a block tariff where the unit charge is specified over a range of water use for a specific consumer, and then shifts as use increases; and (3) an increasing linear tariff whereby the unit charge increases linearly as water use increases. All volumetric charges require that the consumer has a metered connection and that this meter works reliably and is read on a periodic basis.

With a uniform volumetric charge, the household’s water bill is simply the quantity used (e.g., cubic meters) times the price per unit of water (e.g., US$ per cubic meter). This is the most common type of volumetric charge among water utilities in the United States, Australia, and a number of European countries and is also very common for industrial and commercial users throughout the world. A uniform volumetric charge has the advantage that it is easy for the consumer to understand, in part because this is how most other commodities are priced. From an economic efficiency point of view, it can be used to send a clear, unambiguous signal about the short-run marginal cost of using water.

Pricing Water and Sanitation Services 1.06.5.1.4 Block tariffs Block tariffs come in two main varieties: increasing and decreasing. They create a stepwise price structure as illustrated in Figure 1. With an increasing block tariff (IBT), consumers incur a low volumetric per-unit charge (price) up to a specified quantity (or block); for any additional water consumed, they pay a higher price up to the limit for a second block, even higher for the third, and so on. IBTs are widely used in arid areas such as Spain and parts of the Middle East, where water resources have historically been scarce. The use of IBTs is also widespread in many developing countries in Latin America and Asia. With a decreasing block tariff (DBT), on the other hand, consumers face a high volumetric charge up to the specified quantity in the first block, pay less per unit for additional water up to the limit for second block, then less still for the third, and so on. Thus, for both an IBT and a DBT structure, the water bill is calculated in the following manner: Let Q* ¼ amount of water sold to a specific consumer; Q1 ¼ maximum amount of water that can be sold in the first block at price P1; Q2 ¼ maximum amount of water that can be sold to a consumer in the second block at P2; Q3 ¼ maximum amount of water that can be sold to a consumer in the second block at P3. If Q*oQ1, then the consumer’s water bill ¼ (Q*) P1. If Q1oQ*oQ2, then the consumer’s water bill ¼ P1Q1 þ (Q* – Q1)P2. If Q1 þ Q2oQ*oQ3, then the consumer’s water bill ¼ P1Q1 þ P2Q2 þ (Q* – (Q1 þ Q2))P3. And so on for however many blocks there are in the tariff structure. The rationale commonly given for an IBT structure is that, in theory, it can achieve three objectives simultaneously. Proponents of an IBT argue that it promotes affordability by providing the poor with affordable access to a subsistence block of water (the lifeline rate). It can achieve efficiency by confronting consumers in the highest price block with the marginal cost of using water. It can raise sufficient revenues to recover costs. (Note: this argument assumes that the marginal cost of water is in fact higher than the first block price. But if a large expansion project has been recently completed, the short-run marginal cost of water may be very low.) The IBT structure has become so widely used in both industrialized and developing countries that many professionals working in the water sector assume that it must always be the most appropriate tariff structure. This is not the case. In practice, IBTs often fail to meet any of the three objectives mentioned above, in part because they tend to be poorly designed. An IBT may provide more expensive water to poorer households than to richer households, because in many cities the poor share connections, and in such cases the resulting higher volumetric use in turn results in higher prices for most of the water that those households consume (see, e.g., Whittington, 1992; Boland and Whittington, 2000; and Komives et al., 2005). Many IBTs also fail to achieve cost recovery and economic efficiency objectives, usually because the upper consumption blocks are not priced at sufficiently high levels and/or because the first subsidized consumption block is so large that almost all residential consumers never consume beyond that level. The DBT structure was designed to reflect the fact that when raw water supplies are abundant, large industrial

87

customers often impose lower average costs because they enable the utility to capture economies of scale in water-source development, transmission, and treatment. Also, large industrial users typically take their supplies from the larger trunk mains and thus do not require the expansion of neighborhood distribution networks. Although it is still used in some communities in the United States and Canada, the DBT has gradually fallen out of favor, in part because short-run marginal costs, properly defined, are now relatively high in some parts of the world, and there is thus increased interest in promoting water conservation by the largest customers. The DBT structure is also often politically unattractive because it results in high-volume users paying lower than average water prices.

1.06.5.1.5 Increasing linear tariff The increasing linear tariff structure is rarely used. It is of interest largely because it illustrates that there are many ways that water bills can be related to the quantity of water used. In this tariff structure, the price that a consumer pays per unit increases continuously (rather than in block increments) as the quantity of water used increases. (In other words, the water bill ¼ (Q*)P*, where Q* ¼ amount of water sold to a specific consumer; and P* ¼ (a1 þ a2)Q* and a1 and a2 are positive constants.) This tariff structure sends the consumer a powerful signal that increased water use is costly. Not only each additional unit of water used is sold at a higher price, but all the preceding units are sold at the last (high) price. A related but different tariff structure would require that only the last unit used would be sold at the highest price; other units would be sold at the price associated with the lower quantity. It is important to recognize, however, that an increasing linear tariff cannot send the proper economic signal to a consumer about the short-run marginal cost of additional water use. This is because the utility’s short-run marginal cost of providing water does not change appreciably as the water use of an individual household changes. An increasing linear tariff would thus be especially inappropriate if applied to large-volume industrial or commercial water users because it could drive the price they confront for increased water use far beyond the short-run marginal cost of supplying them additional water.

1.06.5.2 Two-Part Tariffs With a two-part tariff, the consumer’s water bill is based on the sum of two calculations: (1) a fixed charge and (2) a charge related to the amount of water used. There are many variations in the way these two components can be put together. The fixed charge can be either positive (a flat fee) or negative (a rebate). The water use charge can be based on any of the volumetric tariff structures described above (a uniform volumetric tariff, an increasing or decreasing block tariff, or an increasing linear tariff). In many cases, the fixed charge is kept uniform across customers and relatively low in value, and is used simply as a device for recovering the fixed administrative costs associated with meter reading and billing that are unrelated to the level of water consumption.

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Pricing Water and Sanitation Services

1.06.5.3 Seasonal and Zonal Water Pricing In some circumstances the short-run marginal costs of supplying water to customers may vary by season. For example, a community may have relatively plentiful water supplies in the rainy season, but much more limited supplies in the dry season; water storage (reservoir capacity) will also be a factor. In such cases, it makes economic sense for water tariffs to reflect the varying circumstances. By charging higher rates in the dry season and lower rates in the wet season, water tariffs can be used to signal to customers that the water supply is not constant across the seasons, and that the costs of maintaining and distributing the water supply may vary as well. The higher dry season rate also serves as a reminder that each user’s consumption of water reduces the amount available for others. Chile is one of the few developing countries that currently uses seasonal water tariffs. Similarly, it may cost the water utility more to deliver water to outlying communities due to, for example, increased pumping costs to reach higher elevations or more distant settlements. A zonal water-pricing structure charges users who live in such areas more for their water because it costs the utility more to serve them. Zonal prices can be used as an economic signal to users that living in such areas involves substantially higher water supply costs and that such information should be factored into customers’ locational and water-use decisions. However, this practice is comparatively rare, in part because it requires the water supplier to collect detailed geographically referenced accounting information. This type of special tariff is only appropriate if the costs of serving the specially zoned areas are significantly higher than for the rest of the community. In fact, costs vary among all users, and a practical tariff always reflects averaged costs to some degree.

1.06.6 Achieving Economic Efficiency and Recovering Capital Costs: Fundamentals of Dynamic Marginal Cost Pricing in the W&S Sector The high costs of the capital investments necessary to build modern network W&S systems make the two-part tariffs described in the previous section especially attractive. They offer service providers a means simultaneously to achieve economic efficiency and cost recovery objectives and also to simplify the design of subsidies to aid poor households. Economic appraisal of W&S investments requires that stakeholders first determine the optimal price to charge for services, if the services are provided, and then determine whether the benefits are greater than the costs if the optimal price is charged. For large capital projects with no constraints on raw water supply, the volumetric charge (one component of a two-part tariff) may be very low in some circumstances because short-run marginal costs can be very low. The economic logic for setting price equal to the short-run marginal cost is straightforward (see, e.g., Layard and Walters, 1978, pp. 171–176). Consider a community with an inverse demand curve for W&S services p ¼ b1  b2x, where p ¼ price of the services, x is the quantity of W&S services that can

be supplied per time period, and b1 and b2 are positive coefficients. Let C equal the fixed costs per period of the W&S system, which is by definition assumed not a function of x. The investment is able to provide an amount of water Qc per period, where b1/b2 is less than Qc. Net benefits are maximized when the optimal quantity of W&S services x* is provided:

Total benefits  costs ¼

Z

x

ðb1  b2 xÞdx  C 0

¼ b1 x  12b2 x2 dðB  CÞ=dx ¼ b1  b2 x ¼ 0 x ¼ b1 =b2 Solving for the price that will achieve this optimal quantity, we see that in this simple example, in which there are no variable costs of water production and delivery and no opportunity costs of the raw water supply, the volumetric price should be set equal to zero (the short-run marginal cost):

p ¼ b1  b2 x p ¼ b1  b2 ðb1 =b2 Þ ¼ 0 If the price is set equal to zero to ensure customers receive the optimal quantity x*, the benefits of the project exceed the costs if

Z

Total benefits4 Costs b1 =b2

ðb1  b2 xÞdx4 C 0 1 2 2b1 =b2 4

C

Such a price will result in large financial deficits unless a fixed charge for capital recovery and other fixed costs is also imposed (the other component of a two-part tariff). The principles that a W&S service provider should follow to determine the volumetric and fixed-charge components of a two-part tariff in different circumstances have not been well understood in the water resources community. The key point is that short-run marginal costs change depending on the regional water resources situation, and both the volumetric and the fixed-charge components of the two-part tariff must change in response to changes in short-run marginal costs. A simple example can illustrate this point. Consider a community without a modern W&S network system that is thinking about undertaking a new project to develop such infrastructure along with arranging a new source of raw water supply. The various stakeholders consider the benefits and costs of such an infrastructure improvement and decide that the project is desirable (benefits exceed the costs). Assume that capital for this project is not available from a higher level of government or a donor agency; the city instead borrows the necessary funds from a bond market, promising to repay the loan from new revenues available from the sale of W&S services. The citizens of the community agree to allocate the responsibility for repaying this loan among all who use the W&S services. Upon the advice of the engineering firm

Pricing Water and Sanitation Services

responsible for designing the project, the community decides to build excess capacity into their W&S system in order to accommodate future population and economic growth. The engineers’ argument is that it is cheap to build this excess capacity now due to economies of scale in the various project components. Figure 3 presents the situation after this first project is built. The community now has the capacity to supply Qc. What volumetric price should the service provider charge? As the short-run cost of supplying additional water to the existing population is now low (because the capital costs have already been incurred), the volumetric charge should be low. The economic logic is that customers should not be discouraged from using more water if such use does not impose increased or significant costs on the W&S suppliers or neighbors. If a customer derives a benefit from using more water and this use does not hurt anyone else, why not permit the additional water use? However, the loan must still be repaid, so this consumer must pay a fair share of the capital costs. How a community determines a customer’s fair share is essentially a political matter. This decision will not affect the economically efficient outcome unless it significantly affects the number of customers who decide to connect to the W&S system. If large numbers of customers decide to disconnect from the system after a project is completed and the new tariff structure is imposed, in most cases this indicates a failure of the planning process. The voices of these customers were not likely to have been heard when the decision was made to build the project. A numerical example will help to clarify this argument. Assume that this W&S project has an average cost of US$0.75 per cubic meter and a short-run marginal cost of US$0.25 per cubic meter. Suppose that if the typical household were charged US$0.25 per cubic meter, it would use 20 cubic meters per month. In this case, the volumetric charge would yield revenue of US$5.00 per month (20 cubic meters  US$0.25 per cubic meter). However, when the loan repayment is considered, the utility actually needs US$15.00 per month from this household (US$0.75 per cubic meter  20 cubic meters). This implies that the fixed charge should be set equal to US$10.00 per month so that the utility can recover its average costs.

89

In this situation, the volumetric price sends a signal that there is more water available for households at this low shortrun marginal cost if households want to use it. The demand curve in period 1 (D1) intersects the short-run marginal cost curve at a point far below capacity Qc (Figure 3). Water is relatively abundant, and households, encouraged by a low volumetric charge, use plenty of water. They pay a significant fixed charge in order to repay the capital that they collectively agreed to borrow. In a well-governed community, households would have been made fully aware of the magnitude of the volumetric and fixed charges that would be necessary when they decided (voted) to undertake the new W&S project. Assume that this two-part tariff structure stays in place and that over time the population and economy of the community grow. As shown in Figure 4, the demand for W&S services shifts out and to the right. W&S services actually become more valuable to customers, but there is no need for the service provider to increase either component of the two-part tariff until point A in Figure 4 is reached, because the loan is being repaid and revenues are sufficient to pay the average costs of service. Customers thus enjoy an increasing consumer surplus on their W&S purchases. The citizens of this community are in effect reaping the benefits of their wise decision to invest in the new W&S project, and to include excess capacity into the project design. However, the water resources professionals can see that this excess capacity is being used up as growth continues, and the day will come when the community reaches the limits of its existing water situation (Qc). They make this known to the public. The community must then decide what to do before point A is reached, because it takes time to develop a new project. Essentially, it can either make do with the amount of water that it has (Qc) or build another water project. Suppose that there is another raw water source available to the community, but a project to develop this second source is more expensive than the one included in the first investment. Assume that this second project would result in a system-wide average cost of US$1.00 per cubic meter. Assume that the short-run marginal costs of the combined system (after this second project is built) would increase as well, to US$0.50 per cubic meter. Suppose that the community decides that the new, second project is too expensive (the benefits are less than the costs). The citizens vote against a bond referendum to raise money to

$0.50/m3 $0.50/m3

$0.25/m3

A

$0.25/m3 D1 D1

D2

Qc Qc Figure 3 First period: first project is completed, excess capacity.

Figure 4 Second period: demand grows as population and economic growth proceed; system capacity is reached at point A.

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undertake the new investment. Instead, they will try to make do with the water supply they already have. In this case the magnitude of the components of the two-part tariff must change, because the short-run marginal cost of using water changes. Now the volumetric charge must be used to ration the available water supplies, as shown in Figure 5. As population and economic growth proceed, the demand curve for water continues to shift up and to the right, but in this case the total quantity of water Qc available to the community is already being used by existing consumers (Figure 6). The shortrun marginal costs must now reflect the opportunity cost associated with taking water away from some customers: if one customer increases water use, another must decrease water use. The volumetric price of water thus keeps increasing to reflect the rising opportunity cost foregone (scarcity rent) and to ensure that the available water supply is accessible to users who need it most. Suppose that the community allows this process to go on, demand keeps growing, and the volumetric price needs to

$1.00/m3 B $0.50/m3 D3

$0.25/m3

A

Qc Figure 5 Third period: water from first source must be rationed.

$0.50/m3

D4

D5

D6

$0.25/m3

Qc Figure 6 Fourth period: demand continues to grow after completion of the second water project.

increase from US$0.25 to US$1.00 per cubic meter in order to ration the available supply. Assume that if the short-run marginal cost is US$1.00 per cubic meter and the service provider charges this price, average household consumption falls from 20 cubic meters per month to 12 cubic meters per month. Households economize on their use of water because the volumetric price has quadrupled. In effect, by cutting back on water use existing customers are leaving water available for new customers and new and expanded economic activities. This should not come as a surprise to existing consumers, because they themselves voted down the bond referendum that would have provided the finance for the second water project. Assuming that average costs do not change, the W&S service provider needs US$9.00 per month in revenue from the typical household customer (US$0.75 12 cubic meters). However, the volumetric charge yields US$12.00 in revenues (US$1.00 12 cubic meters). Most people would consider it unfair for the provider to reap windfall profits from the increase in the volumetric part of the tariff. The provider does not need the increased revenue to repay the loan or to pay its financial costs of operation. The purpose of the higher volumetric price is not financial, but rather to ration water use economically. The two-part tariff can be used to resolve this fairness problem associated with water rationing. The fixed charge should be reduced as the volumetric charge increases. Instead of a positive fixed charge of US$10.00, for example, a negative charge (rebate) of US$3.00 per month will result in a typical household water bill of US$9.00 per month, precisely the amount the provider needs to cover its costs. In this example, the fixed charge is negative, but this need not be the case. If the scarcity rent is small, the volumetric charge may not be large enough to recover the service provider’s costs, a positive fixed charge may still be needed. Now suppose that the rationing of the water available from this first project becomes an increasing burden on the citizens of the community, and they finally decide that it is worthwhile to build the second water project. This new project was projected to be more expensive than the first project, but nevertheless they vote to approve a bond referendum to finance it, because now they are paying a high volumetric price for water, US$1.00 per cubic meter, due to the high scarcity rent, and an increased supply will bring greater benefits than costs. Again, the community decides to build in excess reservoir capacity, to support further population and economic growth. After the second project is finished, what should the tariff be? The principles are the same as before: the volumetric component of the two-part tariff should be set equal to the short-run marginal cost, which has now risen from US$0.25 to US$0.50 per cubic meter. When the new water project opens, the citizens in this community are relieved that the constraint on their water use has been relaxed, and the volumetric price falls from US$1.00 to US$0.50 per cubic meter. Assume that in response to this decline in volumetric price, the typical household increases its water use to 16 cubic meters per month. Note that this volumetric price is less than in the previous period, when the price was being used to ration supplies, but more than in the first period, when the

Pricing Water and Sanitation Services

community was smaller and the first water project provided cheap and abundant supplies. If the typical household now uses 16 cubic meters and the volumetric charge is US$0.50 per cubic meter, the volumetric component of the two-part tariff yields US$8.00 per month. However, this is not enough for the W&S provider to recover its average costs, which now include loan payments on the second project. The total average cost of providing services is a weighted average of the first and second projects. Recall that this is assumed to be US$1.00 per cubic meter. In this case, the provider needs US$16.00 per month from the typical household (16 cubic meters  US$1.00 per cubic meter). The fixedcharge component of the two-part tariff must then be set at a positive US$8.00 per month. This example illustrates how a two-part tariff can be used to send the correct signal to customers about the economic value of water and at the same time address the financial needs of the W&S provider. The key point is that the volumetric charge should be continually adjusted to reflect the real short-run marginal cost of using water (including any opportunity costs associated with foregone uses), and the fixed-cost component should be adjusted to meet the financial needs of the utility. It is the community’s collective decision to agree (or not) to share the capital costs of the project that ensures that the benefits of the project exceed the costs and that the allocation of costs is considered fair by most parties. Note that regulatory authorities will have an important role to play in the establishment of an optimal two-part tariff. Particularly in times of water scarcity, when a high volumetric price and possibly a negative fixed charge is warranted, a regulatory body needs to ensure that the public understands the rationale for the pricing policy adopted. Unregulated private W&S service providers cannot be expected to reduce their fixed charge as the volumetric charge increases. The major objection to using a two-part tariff in this way is the possible instability in the volumetric price for services (in the example above, the volumetric price starts low, then quadruples, and then falls again). Some water-resource professionals and utility managers feel that changing volumetric prices will confuse customers and prevent them from engaging in careful long-range planning. From this perspective, price stability is a major objective of tariff design. Households and businesses are, however, able to deal with changing prices in the telecommunications and energy sectors, so there is reason to believe that these fears are exaggerated. Two-part tariffs are widely used in telecommunications pricing, although a negative rebate is not common because short-run marginal costs have continued to fall. (Actually, a form of negative rebate does occur with some mobile phone plans that provide expensive smart phones (e.g., Apple’s iPhone) to customers with long-term contracts at prices far below the cost that the mobile phone manufacturer sells the phone to the service provider.) Also, note that in some locations the period during which the volumetric price must be used to ration water use may be quite long. As cities need to go farther and farther afield in search of new supplies, managing water use with high prices may be increasingly attractive compared to incurring the rising capital costs of new projects. If the volumetric price of water cannot change in response to changing water resources’

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circumstances, it will be increasingly difficult to develop rational W&S pricing policies. But what about poorer members of the community? How can they be provided with improved W&S services when such a two-part tariff is used?

1.06.7 Subsidizing Capital Costs: Reaching the Poor Any discussion of W&S subsidies should begin with the question ‘‘Why do many people (both those working in the water supply sector and others elsewhere) assume that it is a good idea to deliver subsidies to the poor by reducing the water bills of households with private connections?’’ What is it about a piped water distribution network that makes it a good candidate for the delivery of subsidies to the poor? It does not follow that because water itself is a basic need, a piped water distribution system provides an efficient and an effective way to deliver subsidies to the poor. After all, people also have basic needs for food, health services, and housing. The relevant question is not ‘‘How can piped water services be subsidized most effectively?’’ but ‘‘Which subsidy mechanisms reach the poor most efficiently and effectively?’’ It is also important to ask how households themselves view the importance of the good or service to be subsidized. There is strong evidence that households indeed want improved W&S services as their incomes increase; this correlation between W&S coverage and household income suggests that these services are normal goods. As economic growth occurs in developing countries, more and more people obtain improved infrastructure services. Progress is being made particularly in China and India. Figure 7 shows the percentage of households at different income levels that have four infrastructure services (piped water, sewer, electricity, and telephone); the data come from interviews with more than 55 000 households in 15 developing countries (Komives et al., 2003). What is noteworthy about these households is that at all income levels, more people have electricity than have piped water or sewer. Very few of the poorest households have piped water or sewer, yet almost a third of those households have electric service. As monthly household income increases from very low levels to US$300 per month, coverage of all of these infrastructure services increases rapidly; above US$300, coverage increases at a slower rate. The data in Figure 7 should be interpreted carefully. It could be that more households have electricity because W&S networks were not, but electricity was, available in their neighborhoods, or because electric service was less expensive than W&S service. But in fact, monthly household bills for electricity are almost always higher than for W&S service; thus, a comparatively lower cost of service does not explain the pattern seen here, where many households have obtained electricity even when they do not have piped water. Figure 8 shows the percentage of households with different infrastructure services at different income levels in Kathmandu, Nepal. All of these households had the option to connect to all three network infrastructure services: electricity, water, and sewer. The majority of the very poor chose electricity, but not water and sewer. At higher income levels the percentage of households with W&S services is also higher, but

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the percentage of households with electricity is always higher still. The important point to recognize from these examples is that although water itself is a necessity, this does not necessarily mean that people prefer piped water service to electric service. Indeed, because water is a necessity, households must already have some water source. The question is thus how much an improved source is worth to them. This will depend on many factors, but probably the most important is how poor the household’s existing water service actually is. W&S planners often present the need for improved services as a moral imperative or a basic human right, but given the

choice, many households in developing countries would appear to want electricity before an in-house piped water or sewer connection. In fact, it is unusual for a household in a developing country to have a piped water connection and not have electricity. Figure 9 shows how the prevalence of different infrastructure bundles changes as household income increases. Almost no one, at any income level, has only a piped water service. However, many people do have electricity and not water. Many households in fact have no infrastructure services at all, although that percentage declines rapidly as household income increases. These data suggest that although most households would certainly like improved W&S services,

% hhs with electricity % hhs with sewer connection

% hhs with in-house water tap % hhs with telephone

100

% hhs with service

80

60

40

20

0 200

0

400

600

800

1000

1200

Median monthly household income in 1998 (US$) Figure 7 Infrastructure coverage vs. household income. From Komives K, Whittington D, and Wu X (2003) Infrastructure coverage and the poor: A global perspective. In: Brook P and Irwin T (eds.) Infrastructure for Poor People: Public Policy for Private Provision, ch. 3, pp. 77–124. London: World Bank and Public–Private Infrastructure Advisory Facility.

Electricity

% hhs with service (among hhs with access to 3 services)

100 90

In-house tap

80 70

Sewer

60 50 40 30 20 10 0

28

45

62

74

Median monthly household income in 1998 (US$) Figure 8 Infrastructure choices vs. household income: Kathmandu, Nepal.

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93

% of hhs with bundle in home

50 40 30 20 10

e hr e

t ri el ec

d an In -h ou se

ta

p

an

ta

d

p

Al lt

ci

ile to

to d an

ty

In -h ou se

ci

ty

t

t ile

on ly ta t ri El ec

ci

ty

In -h ou se

tri El ec

p

on ly

on ly le t To i

No ne

0

Figure 9 Household infrastructure bundles vs. household income: Asia vs. rest of the world. From Whittington D and Komives K (2002) The challenge of demand assessment in pro-poor infrastructure projects. Presentation at the PPIAF/ADB Conference on Infrastructure: Providing Solutions for the Poor – The Asian Perspective. Manila, November.

this is by no means their most important development priority. Given the choice, in some locations households would probably prefer to have any available subsidies directed to other sectors (e.g., roads, power generation, and education). But suppose that a city’s public health professionals and other development experts decide that W&S services are merit goods that must be subsidized. How best can this be done? In his memoirs (Yew, 2000), the former prime minister of Singapore, Lee Kwan Yew insightfully summarizes his philosophy: subsidize investment and savings, not consumption. He succinctly states the advantage of the two-part tariff with respect to making W&S services affordable to poor households. Subsidizing consumption by selling water at low volumetric prices without an accompanying fixed charge is a never-ending distortion, a signal that continually sends customers the wrong message about how expensive the fixed costs of W&S services really are. But once the capital costs are sunk, low volumetric prices may be appropriate to let consumers know the short-run marginal cost consequences of their decisions. This logic means that any available subsidies for piped W&S services should be directed to (1) lowering connection charges and (2) reducing the recurrent fixed charge component of the monthly bill. Capital subsidies are, of course, not without problems. In theory, if the political process can ensure that only economically sound public investments are undertaken, capital subsidies can both assist poor households and foster economic growth. But capital subsidies for infrastructure investments in general, and for W&S investments in particular, require disciplined public sector decision making. Such discipline is extremely difficult when subsidies come from outside the community that is to benefit from the investment. In most circumstances, a community would be foolish to decline a capital grant for an infrastructure project with an associated stream of positive benefits. It is the high initial costs that are

typically the hurdles to W&S improvements, and if someone else volunteers to pay these costs, why not let them? In practice, it has proved almost impossible for national governments or donor agencies to conduct rigorous economic appraisals of W&S projects. Whenever it appears that a particular project might not pass a cost–benefit test, water professionals appeal to intangible benefits to argue that the investment will in fact pass the test. This is particularly the case in the evaluation of rural W&S investments in developing countries, where neither donors nor national agencies attempt serious project appraisal of W&S projects. It is not hard to provide justifications for subsidies of social overhead capital such as water, transportation, and power infrastructure; the problem is removing such subsidies when they are no longer needed. As Hirschman pointed out, ‘‘The trouble with investment in social overhead capital (e.g., water and sanitation investments) y is that it is impervious to investment criteria. y As a result, social overhead capital is largely a matter of faith in the development potential of a country or region. y Such a situation implies at least the possibility of wasteful mistakes’’ (Hirschman, 1958, p. 84, emphasis added.) This is precisely what we have witnessed in the W&S sector, where white elephants and poorly performing projects have been a standard feature of the sector landscape (Therkildsen, 1988). When higher levels of government (or donor agencies) pay the capital costs of W&S projects, numerous opportunities for rent-seeking and corruption arise (Lovei and Whittington, 1993; Olson, 2000; Davis, 2004). If subsidizing the water bills of households connected to piped networks is a bad idea, what policies can instead or additionally be put into place to protect poor households from the rise in piped water bills that will be required for effective improvements and reforms? There are in fact a number of regulations or policy initiatives that can be coupled with the tariff structure to protect poor customers. The most

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obvious is simply to identify poor households and give them cash assistance to pay their water bills. This is essentially the approach now used in Chile. But even without such means testing, two sets of appropriate pro-poor policies are available.

1.06.7.1 Create a Well-Run System of Public Taps as a Safety Net for the Poor In every locale, W&S providers and regulatory bodies planning to install or expand a piped W&S system need to look carefully at any existing system of public taps. (Here, the term ‘public taps’ refers to a system of fountains in public areas outside of people’s residences where anyone can go to collect water, perhaps for a per-bucket charge or a fixed monthly fee. These public taps do not necessarily need to be run by a public sector utility; they could be efficiently built and managed by a private operator.) In many places, public taps will become obsolete if and when piped services become available: where the majority of households have piped water connections, households without private connections will work out efficient ways of obtaining water from their neighbors at relatively low cost (Whittington et al., 1998). This solution depends on improving the piped distribution system so that connected households do not have to worry about running out of water if they give or sell water to their neighbors. (Public taps will become relatively high-cost sources of supply compared to purchasing from neighbors, because most unconnected households will have to walk farther to collect water from public taps than to obtain it from neighbors, and because the fixed costs of an attendant at the public tap will be large relative to revenues if only low volumes of water are sold.) Nevertheless, public taps may still have an important role to play because they may serve as a water source of last resort for the very poor. In some cases, it is even possible to provide water free from public taps without substantially reducing the revenues of the water utility. This can occur when the availability of free water from public taps does not reduce the number of households desiring private connections for their exclusive use, and when only a small number of households cannot afford private connections. (This is, in fact, the situation in many industrialized countries today. Water is often available free from public fountains, but the vast majority of households still demand private connections in their residences (see, World Bank Water Demand Research Team, 1990).) One source of potential revenue for financing a subsidized system of public taps is the excess revenues that are available if the volumetric price of water from private connections is higher than average costs.

1.06.7.2 Preserve Options for the Poor Poor households are hurt most when they have few options for self-help and when others have restricted their choices. In such cases, it is common to find poor households being exploited. This is as true in the W&S sector as elsewhere. One important way to protect poor households is to preserve their choices so that local mafia or other rent-seeking actors cannot exploit them. There are three main things that can and should be done.

1. Ensure that poor households (and others) can have a private water connection when they want it. Pro-poor policies should not trap poor households into always accepting a low level of off-site water service. If a poor household always has the option of choosing a private connection, when they can afford it, there are limits to the degree they can be exploited by rent seekers. 2. Legalize water vending and sale of household water to neighbors. Vendors and neighbors with private connections create options for poor households: they promote competition in local water markets, limit the reach of spatial monopolies, and drive down water prices. The poor will benefit most from these lower prices. The system of public taps described above also adds to the choices available to poor households, fosters competition, and thus protects the poor from exploitation. 3. Do not give private operators exclusive rights to provide water within a service area. Contracts with private operators should not contain exclusivity clauses. These limit competition and typically end up restricting poor households’ options. Small-scale providers can often lower the cost of providing piped water to poor households; they should be permitted to operate within the contract areas of larger private operators.

1.06.8 Concluding Remarks Two-part tariffs have an important role to play in enabling water utilities simultaneously to achieve economic efficiency and cost recovery objectives. If a large-capacity expansion project has recently been completed, the short-run marginal cost of raw water supply may be very low. Economic efficiency requires that water should be priced at short-run marginal cost. If a two-part tariff is used, however, the necessary revenues can be raised via a fixed charge, without distorting the price signal contained in the volumetric charge. However, in periods of water scarcity (e.g., just before the construction of a water supply augmentation project), the situation is reversed. In this case, pricing at short-run marginal cost implies that the volumetric charge must include the opportunity cost to the user who does not receive water due to scarcity. This scarcity rent causes the volumetric charge to be relatively high in order to ration the available water supply among competing users. Such high volumetric charges may produce revenues in excess of financial costs. This can be corrected by employing a negative fixed charge (rebate), while the volumetric charge remains high enough to send the correct signal to customers from an economic efficiency perspective. Such dynamic tariff design will require that W&S service providers, regulatory bodies, and public officials provide much more information to customers on the rationale behind sound pricing policies. As Hanemann (2005) has observed, it is extremely difficult for publicly owned W&S utilities to receive permission from political regulatory authorities for even modest rate increases, even though such increases are routinely granted to other service providers such as cable television. As water resources management becomes increasingly complicated, the public must become better informed about the challenges for tariff design posed by the high capital costs

Pricing Water and Sanitation Services

of W&S services, the long lives of the projects, and the tradeoffs between competing objectives. This degree of public understanding is unlikely to happen without increasing involvement and participation of stakeholders in the water resources planning and investment process in general and tariff design, and in rate setting in particular.

References Bahl RA, Sinha C, Poulos D, et al. (2004) Costs-of-illness of typhoid fever in Indian urban slum community: Implications for vaccination policy. Journal of Health, Population, and Nutrition 22(3): 304--310. Baumann D and Boland J. (1998) The case for managing urban water. In: Baumann D, Boland J, and Hanemann W. (eds.) Urban Water Demand Management and Planning, ch. 1, pp. 1–28. New York, NY: McGraw-Hill. Boland J (1993) Pricing urban water: Principles and compromises. Water Resources Update 92: 7--10. Boland J and Whittington D (2000) The political economy of increasing block water tariffs in developing countries. In: Dinar A (ed.) The Political Economy of Water Pricing Reforms, pp. 215--236. Oxford: Oxford University Press. Davis J (2004) Corruption in public services delivery: Experience from South Asia’s water and sanitation sector. World Development 32(1): 53--71. Davis J, Kang A, Vincent J, and Whittington D (2001) How important is improved water infrastructure to microenterprises? Evidence from Uganda. World Development 29(10): 1753--1767. Engineering News-Record (2004) 253. 24 (December 12), 32–37. Esrey SA and Andersson I (2000) Ecological sanitation – a missing link to sustainable urban development. Paper presented at the International Symposium ‘‘Urban Agriculture and Horticulture: The Linkage with Urban Planning.’’ Doma¨ne Dahlen, Berlin, Germany, 7–9 July. Hanemann WM (2005) The economic conception of water. In: Peter P, Rogers M, Llamas R, and Martinez-Cortina L (eds.) Water Crisis: Myth or Reality, pp. 61--91. London: Taylor and Francis. Hirschman A (1958) The Strategy of Economic Development. New Haven, CT: Yale University Press. Komives K, Whittington D, and Wu X (2003) Infrastructure coverage and the poor: A global perspective. In: Brook P and Irwin T (eds.) Infrastructure for Poor People: Public Policy for Private Provision, ch. 3, pp. 77–124. London: World Bank and Public–Private Infrastructure Advisory Facility. Komives KV, Halpern FJ, and Wodon Q (2005) Water, Electricity, and the Poor: Who Benefits from Utility Subsidies? Directions in Development. Washington, DC: World Bank. Layard PRG and Walters AA (1978) Microeconomic Theory. New York, NY: McGrawHill. Lovei L and Whittington D (1993) Rent-seeking in the water supply sector: A case study of Jakarta, Indonesia. Water Resources Research 29(7): 1965--1974. Middleton R, Saunders R, and Warford J (1978) The costs and benefits of water metering. Journal of the Institution of Water Engineers and Scientists 32: 11--122. Nauges C and Whittington D (2009) Estimation of water demand in developing countries: An overview. The World Bank Research Observer (forthcoming). Olson M (2000) Power and Prosperity: Outgrowing Communist And Capitalist Dictatorships. New York, NY: Basic Books.

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Pattanayak S, Yang J, Whittington D, and Kumar B (2005) Coping with unreliable public water supplies: Averting expenditures by households in Kathmandu, Nepal. Water Resources Research 41(2): W02012. Poulos C and Whittington D (2000) Individuals’ rates of time preference in developing countries: Results of a multi-country study. Environmental Science and Technology 43(8): 1445--1455. Sara J, Gross A, and Berg C (1996) Rural Water Supply and Sanitation in Bolivia: From Pilot to National Program. UNDP–World Bank Water and Sanitation Program. Therkildsen O (1988) Watering White Elephants: Lessons from Donor-Funded Planning and Implementation of Rural Water Supplies in Tanzania. Uppsala: Scandinavian Institute of African Studies. United Nations Human Settlement Programme (UN-HABITAT) (2003) Water and Sanitation in the World’s Cities: Local Action for Global Goals. London: Earthscan. United Nations Millennium Project Task Force for Water and Sanitation (2004) What will it take? Water, sanitation, and the millennium development goals. Abridged Draft Final Report. November. New York, NY: SIWI. Van Wijk-Sijbesma C (1989) What Price Water? User Participation in Paying for Community-Based Water Supply. Occasional Paper Series, No. 10. The Hague: International Reference Centre for Community Water Supply and Sanitation. Warford J (1994) A marginal opportunity approach to municipal water pricing. EEPSEA Special Paper. Singapore. White G, Bradley D, and White A (1972) In: Drawers of Water: Domestic Water Use in East Africa. chs. 4 and 5. Chicago: University of Chicago Press. Whittington D (1992) Possible adverse effects of increasing block water tariff in developing countries. Economic Development and Cultural Change 41: 75--87. Whittington D (2003) Municipal water pricing and tariff design: A reform agenda for South Asia. Water Policy 5: 61--76. Whittington D (2006) Reflections on the goal of universal access in the water and sanitation sector: Lessons from Ghana, Senegal, and Nepal. In: Liberalisation and Universal Access to Basic Services: Telecommunications, Water, Sanitation, Financial Services, and Electricity, chap. 5, pp. 135–148. OECD, The World Bank. Paris: OECD Publishing. Whittington D, Boland J, and Foster V (2002) Water Tariffs and Subsidies in South Asia: Understanding the Basics. New Delhi: Water and Sanitation Program. Whittington D, Davis J, Komives K, et al. (2009a) How well is the demand-driven, community management model for rural water supply systems doing? Evidence from Bolivia, Peru, and Ghana. Water Policy 11(6): 696--718. Whittington D, Davis J, and McClelland E (1998) Implementing a demand-driven approach to community water supply planning: A case study of Lugazi, Uganda. Water International 23: 134--145. Whittington D, Hanemann WM, Sadoff C, and Jeuland M (2008) The challenge of improving water and sanitation services in less developed counties. Foundations and Trends in Microeconomics 4(6–7): 469–609. Whittington D and Komives K (2002) The challenge of demand assessment in propoor infrastructure projects. Presentation at the PPIAF/ADB Conference on Infrastructure: Providing Solutions for the Poor – The Asian Perspective. Manila, November. Whittington D, Okorafor A, Okore A, and McPhail A (1990) Strategy for cost recovery in the rural water sector: A case study of Nsukka district, Anambra state, Nigeria. Water Resources Research 26(9): 1899--1913. World Bank Water Demand Research Team (1990) The demand for water in rural areas: Determinants and policy implications. The World Bank Research Observer 8(1): 47--70. Yew L (2000) From Third World to First – the Singapore Story: 1965–2000. Singapore: Times Publishing Group.

1.07 Groundwater Management E Lopez-Gunn, Complutense University, Madrid, Spain MR Llamas, Complutense University, Madrid, Spain A Garrido, Polytechnic University of Madrid, Madrid, Spain D Sanz, University of Barcelona, Barcelona, Spain & 2011 Elsevier B.V. All rights reserved.

1.07.1 1.07.2 1.07.2.1 1.07.2.2 1.07.2.3 1.07.2.4 1.07.2.5 1.07.2.5.1 1.07.2.5.2 1.07.2.6 1.07.3 1.07.3.1 1.07.3.2 1.07.3.3 1.07.3.4 1.07.3.5 1.07.3.6 1.07.3.7 1.07.4 1.07.4.1 1.07.4.1.1 1.07.4.2 1.07.4.3 1.07.5 1.07.5.1 1.07.5.1.1 1.07.5.1.2 1.07.5.2 1.07.5.2.1 1.07.5.2.2 1.07.6 1.07.6.1 1.07.6.1.1 1.07.6.1.2 1.07.6.1.3 1.07.6.2 1.07.6.2.1 1.07.6.2.2 1.07.6.2.3 1.07.7 References

Introduction The Global Silent Revolution of Intensive Groundwater Use Introduction The Role of Groundwater in the Global Water Cycle Location of the Main Aquifers Groundwater Uses: Past and Present The Pros and Cons of the Intensive Use of Groundwater The complex meaning of sustainability in groundwater use The ethics of pumping nonrenewable groundwater (groundwater mining) The Social Sustainability of Groundwater Management The Economics of Groundwater Use Groundwater Costs of Abstraction and Groundwater Tariffs Productivity of Groundwater Use Perverse Subsidies in Water Policy Groundwater: From Open Access to Common Pool? Optimal Groundwater Pricing Departures from Optimality: Second-Best Solutions Internalizing the Value of Environmental Services Provided by Groundwater Regulatory Frameworks for Groundwater Multilevel Governance Diversity in Groundwater Regulatory Regimes The controversy over private, public, or community groundwater rights Implementation and Enforcement of Groundwater Legislation Multilevel Regulatory Frameworks Institutional Aspects of Groundwater Management Groundwater Institutions: Mapping Groundwater Institutional Design Boundary definition The role of groundwater-user associations An Institutional Audit of Groundwater Institutions Higher-level authorities: Supporting, legitimizing, and leading Transparency and participatory groundwater management The Complex Concept of Groundwater Sustainability and Future Management Issues Groundwater Management Externalities Degradation of groundwater quality Susceptibility to subsidence Interference with surface water and ecological impacts Groundwater: Future Risks and Opportunities for Management Groundwater and climate change Future management issues Groundwater: Issues of fit and political windows of opportunity Conclusion

1.07.1 Introduction The last half century has witnessed a spectacular development in groundwater use. Its use has increased from some 100 Mm3 to almost 1000 Mm3 in the period 1950–2000 (Shah, et al., 2007). In this chapter, water volumes are referred to in million cubic meter (106 or Mm3) and billion cubic meter (109, km3 or bcm).

97 98 98 99 99 99 99 100 100 103 103 104 104 105 105 106 106 108 110 110 111 112 113 114 114 114 116 117 117 117 118 119 119 120 120 121 121 123 123 124 124

The table of conversions is as follows:

• • • •

Mm3 ¼106 m3 km3 ¼109 m3 bcm ¼ 109 m3 hm3 ¼106 m3.

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Groundwater Management

This groundwater development has been mainly used for irrigation in arid and semiarid regions. Nevertheless, the use of groundwater for rural and urban water supply is very important; in some countries, like in Italy, it represents 90% of all of the urban water supply. Although the accuracy of available water-use data is still rather illusory, it seems that today the economic value of groundwater irrigation is even greater than the corresponding value of surface-water irrigation. The main factors that have driven this spectacular development in groundwater use include (1) the availability of modern and relatively cheap rigs to drill water wells; (2) the invention and ease of use of the turbine pump that allows abstracting significant volumes of groundwater from deep water wells; and (3) the consolidation of hydrogeology as a reliable science and technology that has dispelled the mystery of groundwater (Figure 1). This extraordinary development of groundwater use has been described as a silent revolution (Forne´s et al., 2005; Llamas and Martı´nez-Santos, 2005) because it was due to the efforts of millions of farmers, with scarce planning and control by conventional water authorities. These public government bodies have been occupied for more than 50 centuries with surface-water systems, beginning in the hydraulic civilizations located in the valleys of large rivers, such as the Nile, the Ganges, and the Yellow River. Therefore, it is not surprising that most high-level water decision makers suffer from hydro-schizophrenia. This disease was described for the first time in 1973 by the American hydrologist Raymond Nace, as the mindset of those that completely separate surface water and groundwater, and usually forget the latter (Llamas, 2004).

From the dug-well to the deep borehole.

This silent revolution has produced great benefits to humankind because it has contributed significantly both to reduce malnourishment in poor countries and to provide drinking water to the rural and urban poor. Moreover, groundwater irrigation is generally a driver for positive social changes. However, the current and frequent situation of inadequate planning and control over groundwater development has also triggered problems, which are mainly related to negative ecological impacts on aquatic ecosystems and groundwater-quality degradation. These problems have been frequently exaggerated by many surface-water experts who have created the pervasive hydromyth of groundwater fragility in order to foster the traditional policy of surface-water infrastructure. In summary, our main message is that it is crucial that high-level water decision makers seriously consider the real role that groundwater is playing and can play in current and future water policy. This role is going to increase even more if the predictions by the International Panel for Climate Change (IPCC) of an increase in temperature and a decrease in precipitation in most arid and semiarid regions become true (Bates et al., 2008). Our emphasis in this chapter is on groundwater management and not on groundwater hydrology. This is, first, because one of the volumes in this treatise deals with hydrology; second, because in our view, the main current problem is not lack of knowledge about aquifer location, characteristics, and functioning, but rather about better ways to manage the aquifers as a common-pool resource. We refer the interested reader to the work of Chevalking et al. (2008) for some useful citations of websites pertaining to the various aspects of groundwater management.

From the water wheel to the pump.

Figure 1 The silent groundwater revolution: changes in global water management.

From the water-witches to hydrogeology.

Groundwater Management

Consequently, after this introduction, Section 1.07.2 is devoted to recall the value of groundwater as a strategic resource. The main specific characteristics of groundwater that require a different management style than surface water is emphasized. Data provided by the United Nations Economic Scientific and Cultural Organization (UNESCO)–International Groundwater Resources Assessment Centre (IGRAC), the International Association of Hydrogeologists and the book by Margat (2008) describe the location of the main aquifers and groundwater uses in most countries. Developing (or semi-developing or emerging) arid and semiarid regions, such as India or regions of East Asia, have experienced a spectacular development in groundwater irrigation during recent years (Shah, 2005; Shah et al., 2007). Such large regions may present a wide variety of conditions: from subsistence livelihoods to market economies and from large alluvial aquifer systems, which may sustain long-term groundwater development, to hard-rock aquifers, where small communities may rely on scarce resources and pumping may prove to be costly. Some arid regions are endowed with good aquifers: these may correspond to countries, such as Saudi Arabia or Libya, where groundwater mining is commonplace (see Section 1.07.2.5.2). Reliance on nonrenewable resources, however, does not seem to render these economies unsustainable. Contrary to the perception of some environmental organizations, a good number of authors and the UNESCO World Commission on the Ethics of Science and Technology (COMEST) consider the use of nonrenewable groundwater resources to be acceptable under certain circumstances (Selborne, 2001; Delli Priscoli et al., 2004; Llamas, 2004). In arid and semiarid regions of industrialized countries, for example, the USA (California, Texas) and Spain, intensive groundwater withdrawals for irrigation are a well-established practice. Development is essentially market driven, as the cost of obtaining groundwater generally amounts to a very small fraction of the crop value. Some authors argue that the depletion of groundwater levels results in an increase of pumping costs, and may ultimately yield these intensive uses economically unsustainable. However, empirical evidence in some areas seems to show the opposite. Farmers are not deterred from pumping despite depths in excess of 400 m (Garrido et al., 2006). This is because switching to highervalue, water-efficient crops may offset the increase of pumping costs, provided that groundwater quality does not worsen (Llamas and Martı´nez-Santos, 2005; Forne´s et al., 2005). It can also be explained by the so-called Gisser–Sanchez effect (see Koundouri (2004) for a detailed explanation), that is, ‘‘the nomanagement (competitive) dynamic solution of groundwater exploitation is almost identical (in terms of derived social welfare) to the efficient management (optimal control) solution’’ (p. 706). This is a management paradox because the serious depletion of aquifers is a major risk to many freshwater ecosystems; yet, the social benefits from managing groundwater extraction are numerically insignificant. This also has significant implication for water managers because it severely constrains the effectiveness of policy options, since implementing reduced extractions is not socially, economically, or politically costless.

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1.07.2 The Global Silent Revolution of Intensive Groundwater Use 1.07.2.1 Introduction This section summarizes the main hydrologic characteristics of groundwater occurrence, availability, and past and present uses. A detailed description of these aspects can be found in Margat (2008), in the UNESCO–IGRAC website, and is also treated in one of the volumes in this treatise. Intensive use of groundwater is a recent phenomenon, less than half a century old in most places. This situation has occurred mainly in arid and semiarid countries, in some coastal zones, and close to a few mega-cities. This groundwater development has produced great socioeconomic benefits, mainly in developing countries. It has provided cheap drinking water that has helped improve public health. The new irrigated lands have contributed to eradication, or at least mitigation, of malnourishment among those living in poverty. Millions of modest farmers with scarce public or governmental planning, assessment, financing, and control have mainly carried out this intensive groundwater development. This intensive use has really been a kind of silent revolution. In most countries, the corresponding public water or irrigation agencies have been mainly devoted to designing, building, and operating large surface-water irrigation systems. The attitude of some water decision makers who strongly separate surface and groundwater projects, usually ignoring groundwater, was described as hydro-schizophrenia by the well-known American hydrologist, Raymond Nace, in the year 1973 (Llamas, 2004). This attitude has been commonplace in India, Mexico, Spain, and many other arid and semiarid regions worldwide. As a consequence, certain adverse effects have ensued in some places. For instance, in South Asia, the current situation concerning groundwater development has been frequently described as colossal anarchy (Shah et al., 2007). Most of the problems caused by this uncontrolled groundwater development could be avoided or mitigated if the corresponding government agencies had been more active in assessing and controlling groundwater use. On the other hand, surface-water officials have frequently exaggerated such problems. This has created a pervasive hydromyth on the fragility or weakness of groundwater as a reliable resource (Custodio, 2002; Lopez-Gunn and Llamas, 2008). Due to ignorance, vested interests, or, more frequently, because of the low credibility of the official warnings of watergoverning bodies about potential threats, most farmers are not reducing their intensive groundwater abstraction. On the other hand, there are practically no documented cases where intensive groundwater abstraction from medium- or largesized aquifers has caused serious social or economic problems similar to those caused by soil water logging and salinization, or by the people displaced or ousted by the construction of large dams.

1.07.2.2 The Role of Groundwater in the Global Water Cycle The inventory and the movement of water on planet Earth is well known and acceptably quantified, for at least a half century. According to Margat (2008), groundwater storage

100

Groundwater Management

globally, that is, the volume of groundwater stored in the geological formations defined as aquifers, is huge, about 107 km3. This is about 98–99% of all the liquid freshwater in the Earth, although it is only about 1% of the total volume on the hydrosphere (including oceans). The hydrological cycle indicates that water is continuously in motion, driven mainly by solar energy and gravity. This flow of water is very important. It is estimated that every year, precipitation is in the order of 100 000 km3, river flow about 40 000 km3, and groundwater recharge in the order of 10 000 km3. This means that the storage is 1000 times greater than the annual recharge. A large part of this groundwater recharge or runoff feeds the rivers and is included in river flow. However, these global numbers give only a preliminary idea and the specific situation in each region may be quite different. In Margat (2008) and in the UNESCO–IGRAC website, details can be found by country and continent. The main idea to keep in mind is that the volume of freshwater stored in the aquifers is usually huge in comparison with their yearly recharge (usually coming from precipitation) and with discharge (usually to rivers or wetlands). The annual recharge may change from practically zero in the most arid regions to more that 1000 mm yr1 in very humid areas.

1.07.2.3 Location of the Main Aquifers Most countries have made significant efforts to characterize the geological formations defined as aquifers, including their main parameters and functioning and relation with surfacewater bodies. These data are usually synthesized in different types of maps, known as hydro-geological maps. The UNESCO–IGRAC is a center that collects these data from all over the world and makes them available to the general public. The interested reader can find a good summary in the work by Margat (2008) of the situation in most countries (Figure 2) and a brief description of the main aquifer systems, with special emphasis on those that are transboundary (Figure 3). This means that they occupy areas in two or more countries. As an international transboundary resource, it is estimated that there are nearly 240 transboundary groundwater systems or aquifers (WHYMAP, Worldwide Hydrogeological Mapping and Assessment Programme; Lopez-Gunn, 2009). Margat (2008) classifies the aquifers into four groups according to their main geological characteristics: karstic, alluvial, hard rock, and volcanic. Significant attention is devoted to the aquifers in arid and semiarid regions because in these areas, the recharge of groundwater is small and its use may be relevant.

1.07.2.4 Groundwater Uses: Past and Present The use of groundwater coming from springs is as old as humanity. Margat (2008) mentions historical data of dug wells several millennia old. In Armenia, the first infiltration galleries to drain phreatic aquifers are recorded dating to 8 BC. This technology was soon extended to the whole Mediterranean region and to Asia. Hundreds of thousand kilometers of these galleries were constructed, some of them still in operation today. Nevertheless, the spectacular increase in the use of

groundwater has been mainly driven by the improvement in drilling technology and the invention of the turbine pump in the first-third of the twentieth century. Margat (2008) estimates that groundwater abstractions in 2004 were 800 km3 yr1; Shah et al. (2007), however, estimate that this amount is more than 1000 km3 yr1 and its use is on the increase. For the sake of comparison recent studies on the water footprint and virtual water trade (Aldaya et al., 2009), the total use of green and blue water is estimated as 7000 km3 yr1; of this, probably blue water accounts for about 3000 km3.

1.07.2.5 The Pros and Cons of the Intensive Use of Groundwater As mentioned in Section 1.07.1, a good number of authors emphasize the problems related to groundwater development. In this chapter, we intend to present an objective appraisal. Groundwater development produces great economic benefits due mainly to its general resilience to drought, and thus allowing supply to meet demand in a timely fashion. The fact that most groundwater development has been done by private persons, mostly modest farmers, with no, or a small public subsidy, is the best evidence. Some externalities of groundwater development have negative impacts. These externalities are described in Section 1.07.6.1. Nevertheless, in agreement with Llamas and Custodio (2003), in general, many of the negative externalities have been exaggerated and/ or could have been corrected or mitigated by good groundwater management.

1.07.2.5.1 The complex meaning of sustainability in groundwater use Whenever adverse effects of groundwater development begin to be felt, it is common to hear about ‘overexploitation,’ a term usually equated to pumping in excess of the recharge. While this practice is often dismissed as unsustainable, the concept of overexploitation is conceptually complex. This is the reason why a significant number of authors consider it simplistic and potentially misleading (Selborne, 2001; Delli Priscoli et al., 2004; Llamas, 2004). Probably, the most complete analysis is the one by Custodio (2002). As a consequence, more and more authors are changing to the expression intensive use of groundwater instead of using groundwater overexploitation. Intensive groundwater use denotes significant changes on natural aquifer dynamics (Llamas and Custodio, 2003). In contrast with aquifer overexploitation, intensive groundwater use does not convey a positive or negative connotation. It merely refers to a change in flow patterns, groundwater quality, or interrelations with surface-water bodies. It has been stated that the frequently encountered view – that the water policy of arid countries should be developed in relation to renewable water resources – is unrealistic and fallacious. Ethics of long-term water-resources development must be considered with ever-improving technology. It has been customary – as in the Spanish 1985 Water Law – to define overexploitation as the situation when groundwater withdrawal exceeds or is close to the natural recharge of an aquifer. The observation of a trend of continuous significant decline of the levels in water wells during several years is frequently considered as a clear indication of an unsustainable

Rio de Janeiro

Cairo

Medium groundwater recharge (15 − 150 mm/a) Low groundwater recharge ( 15 mm/a)

Medium groundwater recharge (15 − 150 mm/a)

Low groundwater recharge ( 15 mm/a)

2000

3000

4000

Dhaka

5000 km

Jakarta

Seoul

Shenyang

Sydney

Selected city

Continuous ice sheet

Large saltwater lake

Large freshwater lake

Major river

Surface water and Geography

© BGR Hannover / UNESCO Paris 2006

Tokyo Osaka

Manila

Hong Kong

Shanghai

Tianjin

Beijing

Madras Bangkok

Hyderabad

Calcutta

Delhi

Bangalore

Bombay

Lahore Karachi

Area with local and shallow aquifers

1000

Tehran

Figure 2 Groundwater resources of the world. From World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP), special edition 2006.

High groundwater recharge (> 150 mm/a)

0

Istanbul

Moskva

Saint Petersburg

Kinshasa

Area with complex hydrogeological structure

Buenos Aires

Sao Paulo

Lagos

High groundwater recharge (> 150 mm/a)

Santiago

Lima

Bogota

New York

Paris

Major groundwater basin

Groundwater

Mexico City

Los Angeles

Chicago

London

Ruhr area

Figure 3 Map on transboundary aquifers of the world. From World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP), special edition 2006.

Groundwater Resources of the World

Groundwater Management

situation. This is a simplistic approach that might be a long way from the real situation. It often corresponds to a transient state of the aquifer toward a new equilibrium (Custodio, 2002). Intensive groundwater use frequently depletes the water table. Depletions of the order of 0.5 m yr1 are frequent, although rates up to 5–10 m yr1 have been reported (Llamas and Custodio, 2003; Garrido et al., 2006). Farmers are seldom concerned with this issue, except in the case of shallow aquifers. The increase in pumping costs is usually a small problem in comparison with potential groundwater-quality degradation or equity issues such as the drying up of shallow wells or khanats (infiltration galleries), owned by the less-resourceful farmers and located in the area of influence of the deep wells (Wegerich, 2006). This may cause social-equity problems in regions where many farmers cannot afford to drill new wells, or the water authorities are not able to demand just compensation in terms of water or money to poor farmers. The opposite phenomenon (rise of the water table due to surface-water over-irrigation) is also a problem, for example, in Punjab, India, and in Pakistan, or in San Joaquin Valley in California. Raising the water table often results in significant social and economic troubles due to soil waterlogging and/or salinization. It is not easy to achieve a virtuous middle way. As Collin and Margat (1993) state: ‘‘we move rapidly from one extreme to the other, and the tempting solutions put forward by zealots calling for Malthusian under-exploitation of groundwater could prove just as damaging to the development of society as certain types of excessive pumping.’’ In a given aquifer, pumping rates for irrigation may prove to be sustainable from the hydrological viewpoint provided that storage and/or average recharge are large enough. However, water table drawdown may induce degradation of valuable groundwater-dependent ecosystems, such as wetlands, which may be considered unsustainable from the ecological point of view. Would a restraint from pumping be the most sustainable course of action? The answer to this question is difficult. If farmer livelihoods rely heavily on groundwater resources, a ruthless push toward wetland restoration may not be the most sensible solution to the problem. In that case, like in many real-life situations, the social and economic aspects of sustainability come into play, and may eventually offset environmental considerations. Llamas et al. (2007) provide a succinct overview of nine different aspects of groundwater sustainability: hydrological, ecological, economic, social, legal, institutional, inter- and intra-generational, and political. Throughout the text, a distinction is often made between developed and developing regions. This is because perceptions as to what is sustainable vary across geographical boundaries, and are often rooted in cultural, political aspects, and the socioeconomic situations. In this regard, the Hydrogeology Journal theme issue of March 2006 (Llamas et al., 2006) presents the socioeconomic analyses of a number of case studies from all over the world. Therefore, any study on economic sustainability of groundwater use should take into account the specific regional settings. In developing countries where easily accessible unconfined shallow aquifers exist, devices such as the treadle pump to access shallow water tables may constitute a catalyst

103

for irrigation development, while environmental concerns are generally subordinated to human development. This is the case in many small African villages.

1.07.2.5.2 The ethics of pumping nonrenewable groundwater (groundwater mining) Some arid regions have very small amounts of renewable water resources but huge amounts of fresh groundwater reserves, for example, the existing reserves under most of the Sahara desert. In such situations, groundwater mining may be a reasonable action if various conditions are met: (1) the amount of groundwater reserves can be estimated with acceptable accuracy; (2) the rate of reserve depletion can be guaranteed for a long period, for example, from 50 to 100 years; (3) the environmental impacts of such groundwater withdrawals are properly assessed and considered clearly less significant than the socioeconomic benefits from groundwater mining; and (4) solutions are envisaged for the time when the groundwater is fully depleted. Selborne (2001), former chairman of the COMEST, agrees with this approach. In Saudi Arabia, the main aquifers (within the first 300 m of depth) contain huge amounts – a minimum of 2000 km3 – of fresh fossil water that is 10 000–30 000 years old. It is considered that these fossil aquifers can supply useful water for a minimum period of 150 years. Current abstraction seems to be around 15–20 km3 yr1. During a couple of decades, the Saudi government had pumped several km3 yr1 of nonrenewable groundwater to grow low-cost crops (mainly cereals), which were heavily subsidized. The official aim of such an activity was to help transform nomadic groups into farmers. Now, the amount of groundwater abstraction has been dramatically reduced and the farmer nomads have become high-tech farmers growing cash crops. Another example is the situation of the Nubian sandstone aquifer located below the Western Desert of Egypt, where the fresh groundwater reserves are higher than 200 km3 and the maximum pumping projected is lower than 1 km3 yr1. Probably, similar situations exist in Libya and Algeria. Other examples of mining groundwater can be found in Llamas and Custodio (2003).

1.07.2.6 The Social Sustainability of Groundwater Management As previously stated, most aquifers present a large storage volume of groundwater in relation to their renewable resources (often two or three orders of magnitude higher). A practical consequence is that the potential problems do not usually become serious in the short term (within one or two generations). By then, the farmers may have experienced a positive social transition. Groundwater irrigation has proven to be an excellent catalyst for this social transition of farmers in arid and semiarid regions worldwide (Llamas and Martı´nez-Santos, 2005; Moench, 2003, 2007). Increased revenues result in, and allow for, a greater degree of social welfare. In addition, farmers are able to provide better education for their children, who may either move on to other economic sectors (generally more productive), or return to agriculture with a more productive outlook. Therefore, this transition means a reduction of global poverty (Lopez-Gunn and Llamas, 2008).

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Groundwater Management

This social transition, triggered by groundwater together with the implementation of more efficient irrigation technologies, can often result in a sustainable use in the midterm. However, adequate groundwater management and governance remain as important challenges in areas of India, Spain, China, or the southern United States (Shah et al., 2007; Llamas and Custodio, 2003; Foster et al., 2004). Aquifers constitute an example of common-pool resources, and as in the majority of cases, all actors have direct access (legal or illegal) to groundwater. Therefore, aquifers should typically follow the widely articulated tragedy-of-the-commons pattern (Hardin, 1968). Nevertheless, after half a century of intensive groundwater use, the authors of this chapter do not know any cases of medium-sized or large good aquifers (those with a surface larger than 500 km2, and medium-tohigh transmissivity and storage-capacity values) where the tragic outcomes outlined by Hardin have taken place causing social or economic disturbances – at least not in the degree of magnitude of those caused by soil waterlogging and salinization (India, Pakistan, or California), or the serious social conflicts in relation to people displaced or ousted by the construction of large dams (Briscoe, 2005; Shah et al., 2007). The situation may be different in small or poor aquifers, where storage is not large enough to sustain development for over two or three generations. Although still uncommon, cases of small aquifers that have run out of groundwater have been some times reported verbally to the authors of this chapter. The reality is that even some poor aquifers, such as the Indian hard-rock aquifers, have played a key role in increasing food production. In India, groundwater-irrigated surface has increased by more than 40 million hectares (ha) during the last few decades (Shah et al., 2007). As a consequence, India, despite an almost 100% increase of its population in the last 50 years, has not only achieved food security in practice, but has also become an important grain exporter. However, uncontrolled aquifer development in arid and semiarid regions worldwide raises sustainability concerns, particularly whenever the natural rate of recharge is low.

It might be appropriate to point out the situation of some large aquifers that have undergone overdrafting or groundwater mining for many decades. In many such areas, pumping data are hardly reliable. Take for instance, California’s aquifers, where overdraft estimates range between 1.2 and 2.4 km3 yr1. Equally, the overdraft in California aquifers has not been adequately analyzed since the 1980s. It is perhaps the lack of willingness to monitor, rather than overdraft per se that may constitute the greatest intergenerational threat for groundwater resources.

1.07.3 The Economics of Groundwater Use It is estimated that more than two-thirds of available freshwater is groundwater, and it is currently the most extracted natural resource in the world. More than half the world’s freshwater, for uses like drinking, cooking, and hygiene, comes from groundwater; groundwater irrigates 20% of irrigated agriculture. Groundwater supplies 75–90% of drinking-water supply in European countries, and 95% of the US rural population public-water supply. Aquifers provide natural storage reservoirs with little evaporative loss at little or no cost. Equally, aquifers provide natural transmission of water from the various sources to the point of use. During periods of drought, groundwater provides reliable supplies, compared to surface water, by its use as supplementary irrigation water to surface-water supplies (Howe, 2002). Groundwater is an important economic resource for billions of people, in developed and developing countries. Ninety percent of urban supply in India, and 70% in Mexico are just examples of the socioeconomic importance of this key resource for humans. Figure 4 plots the share of agricultural groundwater use and total groundwater use in total use in 2002 in the Organization for Economic Cooperation and Development (OECD) countries. It shows the importance of both the agricultural sector with groundwater use (countries situated on left) and the percentage of groundwater use over total use (countries on the right).

Po

rtu g G al re ec e N S et pa he in rla n M ds U ni ex te d ico St at e O s C D Ko E re Tu a rk ey EU -1 Ja 5 pa Ire n D lan en d m a Fr rk an Sw ce ed Be en lg G ium er m an Sl ov Au y ak s t U ni Re ria te pu d ki blic ng do Ic m el a C ze Hu nd ch ng R ary ep ub lic

100 90 80 70 60 50 40 30 20 10 0

% share of agriculture use in the total groundwater use

% share of total groundwater use in total water use

Figure 4 Share of agricultural groundwater use in total groundwater use, and total groundwater use in total water use. From OECD (2008) Environmental performance of OECD Agriculture since 1990, Paris, France. Online at: www.oecd.org/agriculture/env/indicators

Groundwater Management

Rosegrant et al. (2002) have estimated that the sustainable yield of groundwater resources in the world would be approximately 861 km3, down from 925 km3 as evaluated in the year 1995, with half this amount abstracted in Asia, and 28% in developed countries. It is difficult to put a dollar value to the use of this resource, but considering a conservative figure of US$ 0.25 m3 (including capital, environmental, and resource costs), this generates an annual total value of US$ 231 billion as a preliminary estimation. The groundwater literature shows that in addition to the direct-use value, groundwater resources also have a significant stabilization value (Tsur and Parker, 1997) in cases where groundwater is conjunctively used with more unreliable surface waters. Estimates of the stabilization value of groundwater resources show that it can be as high as that of direct use. This is because groundwater provides reliability of supply and reduces the probability and severity of water shortages. In addition to households’ water services, groundwater resources are extensively used for food production. With few exceptions, irrigation is the result of uncoordinated efforts of small entrepreneurs and farmers all over the world. These groundwater users have sought to improve their livelihoods investing in private capital and small pumping equipment to improve farm productivity.

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1.07.3.2 Productivity of Groundwater Use Despite the illusory accuracy of global irrigation data and the variability of the existing estimates, rough calculations yield the following conclusion: groundwater-based irrigation seems to be twice as efficient as surface-water irrigation in hydrological terms (m3 ha1), a ratio that increases to between 3 and 10 times from the social and economic points of view (US$ m3 and jobs m3). Regional-scale analyses carried out in Spain seem to confirm these figures (Herna´ndez-Mora et al., 2001) (see Table 1). Thus, it appears relevant and urgent to assess the comparative hydrological and socioeconomic efficiency of surface and groundwater irrigation at a global scale, carrying out similar studies in other regions of the world. Assessing the implications of this silent revolution should constitute a valuable contribution to the debate about global irrigation needs as perceived by many water experts. The required investment to assess the value and efficiency of groundwater irrigation versus surface-water irrigation can be afforded by most governments. Many high-value crops are watered with groundwater resources or by combining ground and surface water (Llamas and Martı´nez-Santos, 2005). For instance, in Table 1, Herna´ndez-Mora et al. (2001) show that, in Andalusia, irrigated agriculture using groundwater is economically over 5 times more productive and generates almost 3 times the employment than agriculture using surface water, per unit

1.07.3.1 Groundwater Costs of Abstraction and Groundwater Tariffs Groundwater unit volume costs increase with groundwater depth, as more energy is required for pumping and deeper wells might be needed. (In our experience, these costs usually range between US$ 0.02 and US$ 0.30 m3 depending on the country and the aquifer. However, according to Shah et al. (2007) the economic cost (value) of groundwater is about US$ 0.20–0.30 m3). It would be worthwhile to study this aspect worldwide in more detail since values appear very high in comparison to the general economic situation of Southeast Asia. One possible cause is the low technology used in the drilling of the wells and the performance of pumping devices. Groundwater irrigation cost per hectare also increases with time, albeit at a lower rate. This is because farmers begin to use a more efficient technology and switch (if soil and climate allow) to less-water-consuming crops: from maize or rice to grapes or olive trees, for instance. It is estimated that groundwater irrigation cost in Spain generally ranges between US$ 20 and US$ 1000 ha1 yr1. Despite the difficulties in setting tariffs for groundwater use, a number of countries have these in place. In many cases, tariffs are accompanied by quotas and licenses. In general, developed countries have in place a fixed fee plus a volumetric fee, but these levies are generally not adapted to recharge or movements in the water table. Essentially, this indicates that tariffs on groundwater use are environmental levies but not rationing instruments to manage aquifers. This is because, to ensure sustainable management, tariffs would need to be flexible enough to change according to scarcity costs and alluse externalities – and this – assuming that perfect monitoring and information are economically feasible.

Table 1 Comparing ground and surface-water irrigation productivity: Some irrigation economic indicators in Andalusia (Spain)

Irrigated area (ha) Average water consumption (m3 ha1) Total production (106 h) Production (h ha1) Employment generated (number jobs/100 ha) EU aid to income (% of production value) Gross water productivity (h m3) Total average water price to farmer (h m3)

Groundwater

Surface water

Total

244 190 (27%)

648 009 (73%)

893 009 (100%)

3900

5000

4700

2222

2268

4480

9100

3500

5100

23.2

12.6

15.4

5.6

20.8

13.4

2.35

0.70

1.08

7.2

3.3

3.9

From Herna´ndez-Mora N, Llamas MR, and Martı´nez-Cortina L (2001) Misconceptions in aquifer over-exploitation. Implications for water policy in southern Europe. In: Dosi C (ed.) Agricultural Use of Groundwater. Towards Integration between Agricultural Policy and Water Resources Management, pp. 107–125. Dordrecht: Kluwer.

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volume of water used. This difference can be attributed to several causes: the greater control and supply guarantee that groundwater provides, which in turn allows farmers to introduce more efficient irrigation techniques and more profitable crops. Greater dynamism has normally characterized farmers who sought out their own sources of water and have had to bear the full costs of drilling, pumping, and distribution. Higher financial costs to farmers motivate them to look for more profitable crops that will allow them to maximize their return on investments. Surface and groundwater distinctions, therefore, should be taken into account in order to achieve an efficient allocation of water resources.

The hidden or open subsidies that have traditionally been a part of large hydraulic projects for surface-water irrigation are probably the main cause of the pervasive neglect of groundwater problems among water managers and decision makers. Surface water for irrigation is usually given at low or heavily subsidized costs to farmers and this often results in the wasteful use of a valuable resource. It is usual that water supply companies, farmer unions, etc., lobby the state for the construction of surface-water infrastructures that are primarily paid for through general revenues, instead of advocating a responsible use of groundwater resources. At times, this may lead to social conflicts – such as in the case of the Tagus–Segura transfer or the overruled Ebro transfer, both in Spain – between water-importing and waterexporting basins. Progressive application of the user pays or full-cost-recovery principle of the European Union (EU) Water Framework Directive (WFD) would probably make most of the large hydraulic projects economically unsound. As a result, a more comprehensive look at water planning and management would be necessary and, in turn, adequate attention to groundwater planning, control, and management would probably follow.

came earlier. In this slowness and imperceptibility lies a significant part of the difficulties of managing groundwater resources nowadays. There are a number of political and policy choices to offset the pervasiveness of open access, such as regulatory interventions, market instruments, or information and technical choices. Most often, the effectiveness of policy measures might mean the right mix of policy instruments from an existing portfolio where experience has already been gathered from groundwater management. For policymakers, there are a range of policy instruments for management (Table 2), with different strengths and weaknesses for groundwater management according to a range of criteria such as effectiveness, economic efficiency, technical efficiency, administrative feasibility, equity, and social or political acceptability (Hellegers and Van Ierland, 2003). An example of regulatory instruments for environmental protection is the case of the Edwards aquifer, in Texas (USA). The Edwards aquifer is a karstic aquifer, which means that the effects of pumping are quickly transmitted to large areas creating in effect an open-access resource since it operates under the rule of capture, that is, where under Texas groundwater law, landowners can pump without limit (Howe, 2002). Pumping for agricultural and urban purposes has represented 45–50% of the total discharge of the Edwards aquifer for the period from 1934 to 1999. Springs supported several species of fish and amphibians giving the Federal Fish and Wildlife Service the right to intervene to protect these species if the state failed to act. A lawsuit by the Sierra Club, an environmental nongovernmental organization (NGO), under the Endangered Species Act ended in a federal ruling, which meant that the Texas legislature set up the Edwards Aquifer authority in 1996 with extensive powers, including the issuing of permits to regulate groundwater withdrawals. For example, it required pumping limits to protect endangered species. A flow of 150 cubic feet per second must be maintained at the most sensitive springs.

1.07.3.4 Groundwater: From Open Access to Common Pool?

1.07.3.5 Optimal Groundwater Pricing

Economics deals with scarcity and the allocation of scarce goods. Groundwater resources until very recently, it could be argued, were not economic goods, because anyone interested in pumping water could do it ab libitum. Capital, energy, or time constraints were the only barriers for all potential users of groundwater resources. In this case, however, the economic problem, if any, was related to access to inputs, finance, or labor. Even in cases where the inputs required to pump groundwater are unlimited, deciding on how much water should be pumped may not be an economic problem. One of the most pressing institutional questions centers on inappropriate legal and administrative structures, which in effect means that groundwater rather than being an openpool resource becomes an open-access resource which can lead to excessive contemporary and inter-temporal externalities (Howe, 2002) (see Section 1.07.6). As in many other natural resources, the transition from an open access and unlimited resource to an exhaustible and rival one is gradual. It happens by the marginal adhesion of small groundwater users attracted by the benefits that are obtained by those who

An optimal groundwater price is a theoretical economic concept, and the solution to a dynamic and stochastic problem. It is dynamic because the optimal price varies with time, and is meant to ensure that pumping rates at any given moment maximize the discounted flow of benefits for an infinite time horizon. As Neher (1990) shows, there is an optimal tax that makes all users of a common pool internalize the (marginal) consequences of their pumping rate so that a socially optimal management use is achieved. Theoretical optimal rates can be obtained to account for irreversible effects – caused by excessive drawdown (Rubio and Fisher, 1997), for situations in which backstop technologies can become operative if pumping becomes too expensive (Gemma and Tsur, 2007), or when surface water and groundwater are conjunctively used (Pongkijvorasin and Roumasset, 2007). Pongkijvorasin and Roumasset’s (2007) main contribution is to combine two problems that have been addressed separately in the literature. One looks at the problem of managing large surface systems with conveyance losses (Roumasset and Chakravorty, cited by Pongkijvorasin and Roumasset, 2007),

1.07.3.3 Perverse Subsidies in Water Policy

Groundwater Management Table 2

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Portfolio of policy instruments available to manage groundwater quantity and/or quality

Portfolio of groundwater-management tools

Example

Standards and regulations

High Plains Conservancy district No 1 outside Lubbock e.g., harim rule in Islamic law (Middle East) Indonesia EU Water Framework Directive For example, sprinkling bans to reduce low value agricultural groundwater abstraction in Holland; Yemen (ban on agricultural wells); and Egypt (ban on new wells) Netherlands, France, and parts of Germany have introduced a groundwater-abstraction tax Edwards aquifer Texas, (USA)a, Italy (Perugia province) Parts of Spain China

Limit pump capacity Well spacing Groundwater permits Safe-yield criteria Ban in most critical groundwater areas

Pumping tax, for example, reflect to the individual pumper the negative externalities Caps on abstraction Metering groundwater use Metering electricity use as groundwater use proxy Market-based instruments (economic incentives)

Administrative planning and education measures

Groundwater resource fee (money raised earmarked for aquifer management), cost of right (waterresource levy) Volumetric pricing of groundwater and/or electricity supply, sliding-scale pricing strategy Electricity subsidies Groundwater farms (i.e., purchase of land for associated groundwater rights) Issuing tradable permits (leasehold) with or without cap on total abstractions Water markets (freehold), for example, from rural to urban, from small to large farmers, from low-value to high-value crops, from irrigation to environmental flows Compensation program for third-party effects, for example, tax to area of origin Full cost recovery Groundwater banks Groundwater zoning, for example, aquifer-vulnerability maps to contamination Land-use planning

Education, for example, agricultural extension service and training Political education at senior level on the value of groundwater Public awareness campaigns, civil education, for example, value of groundwater Name-and-shame list Joint regulation and monitoring Participatory groundwater monitoring Institutional (incl. information and voluntary measures)

Incentives for private entrepreneurs for example, as franchisees for billing and collecting electricity dues Self-imposed correlative rights for example, % owned above the aquifer Voluntary agreements (self-regulation), devolved groundwater management Groundwater protection codes

Indonesia for industrial water users Holland has a levy under the 1983 Groundwater Act India and China For example, Mexico 1/3 of electricity costs Arizona Spain (Parque Nacional Tablas de Daimiel) Spain, for example, for environmental flows Spain, USA, Australia, Chile Mexico (including energy pricing coupled with water rights) Contemplated in Texas EU Water Framework Directive Trialled in Texasb United Kingdom, Holland, USA, and Canada Pakistan through informal committees For example, removal of invasive species and replacement with native vegetation in South Africa to help recharge shallow aquifers, India and South Africa studies on impact of forestry (native and plantations) on evaporation and recharge High Plains District Eastern La Mancha aquifer (Spain) e-Water India UNESCO For example, China water pollution map Eastern Mancha aquifer (Spain) India and China China

For example, Water Boards in Holland (interest, payment, authority) involved in groundwater-level management UK (Continued )

108 Table 2

Groundwater Management Continued

Portfolio of groundwater-management tools

Example

Technological

e.g. Spain and Mexico

Use of Geographic Information System (GIS) for monitoring Wastewater-reuse schemes Artificial aquifer recharge enhancement and/or storage Improved irrigation technology (for example, irrigation scheduling, micro-irrigation, land leveling) Changes in crop type, for example, higher-value crops if possible (i.e., no impact on livelihoods); ban on high water-consumptive crops

Central Valley (California), Rainwater harvesting (India); well recharge movement Israel, Spain, Australia, USA, and Mexico Areas in Spain Saudi Arabia

a

A system of marketable groundwater permits to be issued to all pumpers as a proportion to historical use subject to a total pumping cap. The cap was 450 mm3 yr1 which was accepted by all users, urban, agricultural and environmental. In 1998, a groundwater trust was set up to facilitate the trading of permits. In the period 1997–2001 there have been 403 trades with an average size of 235 000 m3. b For example, under the Irrigation Suspension program, water rights were purchased from 40 farmers representing 4000 ha to supply water to San Antonio. Irrigators were paid $ 98 to $ 1850 ha1 to stop irrigating, with water savings of 20 000 acre feet at a cost of $ 2–3 million paid for by cities, counties, and water companies. For additional ideas, please see Chevalking S, Knoop L, and Van Steenbergen F (2008) Ideas for Groundwater Management. Wageningen, The Netherlands: MetaMeta and IUCN.

and the other looks at the conjunctive use of surface and groundwater. The paper’s main accomplishment is to show that, with surface pricing as the only managing instrument, farmers switch from surface to groundwater and vice versa, when groundwater-scarcity rent diminishes, with the irrigation boundary contracting or growing depending on whether groundwater-scarcity rents increase or decrease. The institutional implications of these results are worrying, unless one can think of a much more restricting context in which pumping rights could be effectively enforced, and irrigation districts had nonmobile boundaries. Yet, Rijsberman (2004) quotes work by the International Water Management Institute (IWMI) which shows that groundwater use is largely beyond the possibilities of most water institutions in the developing world for a proper monitoring. Llamas et al. (2008) and Shah et al. (2007) present a similar general situation. The large literature on theoretical groundwater pricing yields somewhat impractical policy prescriptions (e.g., Molle and Berkoff (2006)) because of the number of externalities involved in many cases of intensively exploited aquifers (see Llamas and Custodio, 2003). Brown (2000) offers very convincing arguments to explain why optimal pricing has rarely been used to allocate renewable resources such as fisheries or groundwater.

1.07.3.6 Departures from Optimality: Second-Best Solutions In economics, second-best solutions are those that cannot achieve the results of the first-best optimal solution but are more applicable in practical terms. First-best solutions may be too information demanding or based on a perfect fine-tuning to the specific circumstances. In this section, we review some of the most commonly used second-best (or even third-best) solutions to properly manage groundwater resources. These policy approaches are always implemented to solve one or the other type of the sources of economic inefficiencies. The following are the policies from the less to the more sophisticated: user rights, pumping rights, pumping quotas, water tariffs, and water markets.

Issuing user rights is the simplest way to grant access to an aquifer. The authority may or may not control the pumping capacity and the type of equipment. In intensively exploited aquifers, granting user rights may not be sufficient to deter pumping and, in many situations, the outcome may not ensure that the exploitation is adequate. However, issuing user rights is a prerequisite to consider in any of the policy menus mentioned. In principle, user rights are increasingly required to obtain legal access to tap an aquifer in virtually all contexts in which water is scarce. The next instrument in the list is granting pumping rights, whereby users are entitled to pump fixed amounts. In principle, the ownership of pumping rights can be associated with private property if those rights cannot be encumbered by new users or forfeited by a public agency. Setting up pumping quotas enables a more flexible instrument, because these can be modulated to the aquifer’s recharge. In many cases, pumping rights are combined with water tariffs, but an interesting option would be to modulate tariffs to mimic the optimal price. Perhaps, the most advanced initiatives in the area of groundwater pricing can be found in some European countries, which have seized the opportunity of the EU WFD to implement the principle of full-cost-recovery pricing. Water markets can be found in multiple formats. In India, for instance, water markets occur as informal exchanges among small farmers (Saleth, 1996). In Australia, water markets are formally established and can facilitate groundwater trading or rights’ exchanges. In Spain, the basin authorities have offered farmers permanent buy-outs of water rights, or annual pumping quotas, following public offerings (see Box 1 for examples of water trading). A prerequisite of all these policies is to have control of (at least) the number of users, and ideally of the pumping yields of all users tapping an aquifer. However, even if control and surveillance can be guaranteed, it does not imply that the aquifer will be sustainably managed. Unfettered water rights can be as damaging to aquifer management as an aquifer that is not controlled. Groundwater management is usually based on rights or licensing, but enforcing compliance with these

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Box 1 Water markets and voluntary exchange mechanisms to solve groundwater overdraft. From Garrido A and Llamas MR (2009) Water management in Spain: An example of changing paradigms. In: Dinar A and Albiac J (eds.) Policy and Strategic Behavior in Water Resource Management, pp. 125–146. London: Earthscan. Water banks or exchange centers, as these are called in Spain, received legal recognition in the 1999 Spanish Water Law reform. Not strictly a bank or agency, these centers are hosted, run, and located in the basin agencies themselves. It is widely believed that these centers are a much more efficient medium for promoting water exchanges, for a number of reasons, such as transparency, control, avoidance of third-party effects, and market activity and scope. Yet, the experience so far has been limited to the Ju´car, Segura, and Guadiana basins, since these water centers have been primarily used to tackle severe problems of groundwater intensive use. Since the enactment of the 1985 Water Law, which included special provisions to tackle the problem of overexploited aquifers, there have been at least four major initiatives to manage groundwater resources. In short, these were (1) the declaration of overexploited aquifers and the mandate to enforce regulations and implement management plans; (2) an EU agri-environmental program, only applicable to Aquifer 23 in the Guadiana Basin, with subsidies to farmers who curtail their water consumption; (3) the use of inter-basin transfers, both in the case of the southeast coastal areas and in the Upper Guadiana; and lastly, (4) the Special Upper Guadiana Plan (PEAG, Spanish acronym), and the creation of exchange centers in the Segura, Ju´car, and Guadiana basins. Clearly, the first option failed; the second one succeeded, but the financial cost was very high, and the third option failed because the second one was not sustainable. In the end, the PEAG was approved in 2007 with a total budget for 20 years of h5.5 billion (equivalent to the proposed Ebro transfer) and part of its subprograms are now operational, although under PEAG the basin would reduce to a meager 200 million m3. Underlying these initiatives, but undermining them too, was the recognition that tens of thousands of users in virtually all basins had no legal rights or concessions to the groundwater resources they had been tapping for years. Any effort to reduce total extractions in the over-drafted hydrogeological units had to be accompanied by the closure of the alegal or illegal uses. In 2005, it was clear to all managers, analysts, and users that something new had to be given a chance. The option to use buyouts of water rights, permanent or temporary, gave a rationale to the establishment of exchanges centers (centros de intercambio in Spanish). We review the different approaches taken in the Jucar and Guadiana. In the Jucar basin, the offer of public purchase (Oferta pu´blica de adquisicio´n de derechos, OPA) was targeted at farmers tapping groundwater resources near the Jucar’s headwaters. Its objective was to increase the water tables in Castille-La Mancha to ensure that the Ju´car flows to the Valencia region increase from historical lows. Farmers were given the option to lease out their rights for 1 year in return for a compensation ranging from 0.13 to 0.19 cents m3, the variation depending on the distance of the farmer’s location to associated wetlands or to the river alluvial plain. The OPA was launched in two rounds, the first with disappointing results in terms of farmers’ response, while the second had more success. The purchased waters served the unique purpose of increasing the flows, enabling more use downstream in Valencia. However, the OPA did not have any specific beneficiaries downstream, other than to increase flows. The OPAs of the Guadiana followed a completely different approach and were meant to address serious problems of overdraft in the Upper Guadiana. As stated before, the OPA formed part of the more ambitious program of aquifer recovery, the PEAG. The Guadiana’s OPA made offers to purchase permanent water rights to groundwater, paying farmers h6000–10 000 ha1 of irrigated land. Note that, since these farmers had seen their allotments reduced in preceding years, what the Guadiana basin was truly purchasing from the farmers was about 1500–2500 m3 ha1, effectively h2–4 m3. The Guadiana basin agency has the objective of purchasing the water rights of 50 000 ha1 of irrigated land, and is budgeting h500 million for the whole plan. A marked difference from the Jucar’s OPA is that the Guadiana exchange center will transfer part of these rights to other farmers (growing vegetables) and to the autonomous community of Castille-La Mancha. The Guadiana basin will grant less rights than it has purchased, allocating the difference to wetlands and to increasing the piezometric levels of the aquifers.

rights remains a major challenge for adequate groundwater management. For example, there is wide experience with water markets in the USA; according to Howe (2002), what water markets do best is to generate information on values for more rational, better-informed water allocation – for example, the sale or lease of water rights to off-site buyers such as cities. In Arizona, the government had acquired 200 000 ha of land by 1990 for the associated groundwater rights. These water farms or water ranches average about 12150 ha and are valued at US$15 million, and expected to supply 15 000 acre feet of groundwater per year for 100 years. In Colorado, Front Range water rights in 1990 sold for US$ 1000–4000 per acre foot (Colby, 1990 in Wagner, 2005). In systems where groundwater rights have been incorporated into the general water-rights system, groundwater rights can be bought and sold, transferred to other locations, or transformed into surface rights (e.g., tributary groundwater– groundwater that is intimately connected with surface water). In Texas, groundwater was purchased to secure water for urban centers such as Houston, San Antonio, and El Paso. For example, an old mining right from the Alcoa-Sandown mine was sold for US$ 688 per acre foot annually. The city of Amarillo also bought groundwater for US$ 679 ha1 for groundwater rights from 28 350 ha of lands, when the land itself sells for US$ 494 (Gillinland, 2004 in Wagner, 2005). The El Paso Water Utility purchased more than 19 000 ha of ranchland to pump 15 000 acre feet by 2010 (Texas Center for Policy Studies

in Wagner, 2005). Lucrative groundwater leases with at least four private water ranches on over 200 000 ha have been formed to sell or lease a significant amount of water to off-site users, principally cities. Another example is the Irrigation Suspension Program, where water rights were purchased from 40 farmers representing 4000 ha to supply water to San Antonio. Irrigators were paid US$ 98–1850 ha1 not to irrigate, with water savings of 20 000 acre feet at a cost of US$ 2–3 million paid for by cities, counties, and water companies (Wagner, 2005). Nevertheless, there is a range of issues that has to be considered in water marketing. Over-entitlement occurs when the sum of all legally defined rights are greater than 100% of the system’s potential. Overuse occurs when the quantity of water abstracted is greater than the system’s potential to supply. Sleeper or dozer licenses are legal entitlements that are not used at present but are legal (e.g., UK sleeper licenses). One major reason why some people might distrust markets is the fear that markets might fail to deal with issues of distributional equity, fairness, public concern, and community interest.

1.07.3.7 Internalizing the Value of Environmental Services Provided by Groundwater Water can be framed on current discussions on ecosystem functions from environmental services. Ecosystem functions refer to system properties and processes. Services represent the

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benefits that society derives, directly or indirectly, from ecosystem functions. A summary of the authors’ evaluation of annual flows of water-related ecosystems at the world scale is presented. Humans avail many types of services from water-related ecosystems in addition to water supply. Note, for example, that 1 ha of wetlands can generate almost US$ 4200 yr1 in wastetreatment services (Costanza and de Groot, 1997). While this evaluation was certainly preliminary at the time it was produced, it conveys a clear idea about the costs and damages that water scarcity can provoke. The mere recognition of many of the identified services valuable for society has huge implications for drought-policy design and implementation. Chief among this is the fact that many of these services have public-nature features, which means that they are nonrival and nonexclusive goods. As scientists have learned to identify and value them, water policy must take into account and ensure that decisions are a compromise between both productive and nonproductive services (National Research Council, 2004). The Millennium Ecosystem Assessment undertaken in 2005 classified the goods and services provided by natural resources as provisioning, regulating, supporting, and cultural services. In this context, Bergkamp and Cross (2007) discuss the high-value ecosystems supported by groundwater. The total economic value (TEV) of groundwater resources is the sum of groundwater resources: use and non-use value, based on direct values, indirect values, option, and existence values. A number of methods have been developed by environmental and ecological economists to value these goods and services using a range of environmental techniques such as contingent valuation method (willingness to pay and willingness to accept), choice modeling, production-function approaches, surrogate markets, costs-based approaches, and stated preference. These methodologies are addressed in one of the chapters of this volume. This is a way of internalizing the value of groundwater services and also the economic value of groundwater externalities. This can help decision making to evaluate the costs of action and costs of inaction. It gives a clearer signal of whether it is better or not to opt for preventative policies when the full remediation costs of polluted groundwater are accounted for, that is, measure the potential benefits (or damages) of a range of effects. In 1996, the US Department of Defense invested in 75 pump-and-treat systems to remediate contaminated sites estimated at US$ 500 000 per site. Equally, a calculation on the externality costs of overabstraction in the Queretaro aquifer (Mexico) between 1970 and 1996 included: the increased pumping costs due to drop in piezometric levels estimated at US$ 6 million; the loss of water quality for public water supply at US$ 26 million; and damage to urban infrastructure due to land subsidence at US$ 26 million (6 million as private cost and 20 million as costs to taxpayers). Moreover, it is relevant to consider impacts on other policy sectors like the costs for public health due to the mobilization of naturally occurring arsenic through deep wells in Bangladesh, which is estimated to affect 30–35 million people (WHO, 2001). Opportunity costs are the foregone benefits that could have been generated if resource was allocated to the next-best use if water is not allocated to its highest use value and, in fact,

opportunity costs may be greater than the value generated by next-best use. In this case, the economy is subjected to inefficient and suboptimal groundwater allocation, although this may be justified in equity or sociopolitical terms. Therefore, it is equally important to properly include the positive services provided by groundwater. In terms of equity, Acharya and Barbier (2000) analyze losses to farmers from reduced groundwater recharge. On average, farmers could lose US$ 413 ha1 if the groundwater benefits are not accounted for, that is, evaluating the systemwide benefits associated with groundwater use. The increased recognition of the benefits associated with groundwater use is reflected, for example, in the growing aquifer-recharge movement. In Texas, aquifer recharge through open-space protection and cooperative groundwater allocation is a new paradigm in water management (Wagner, 2005), based on valuing the products and services that a functioning system provides. In India, meanwhile, the socalled decentralized recharge movement was a spontaneous response to groundwater depletion to help water tables rebound to predevelopment levels at the end of the monsoon season in pockets of intensive use. This is an example of contrasts between popular hydrogeology and formal hydrogeology; for example, scientists argue that hard-rock areas have too little storage and advocate recharge; meanwhile, the prolific growth of recharge structures is based on the value people attach to a check-dam even if their wells provide only 1000 m3 which – although small – is crucial for life-saving irrigation in times of delayed rain. Thus, rainwater harvesting can be used to recharge groundwater via recharge ponds, based on the main sources of recharge: rain, and infiltration from riverbeds and from the floodplain. Equally, in Australia and the USA, sand dams are used to make artificial shallow aquifers in streambeds to reduce evaporation of stored waters. The growth in the number of sand dams could substantially increase to compensate for potential climate variability and change making use of groundwaters’ buffering service (see Section 1.07.6.2.1). Failing to account for the opportunity costs of groundwater and surface/groundwater linkage often result in suboptimal outcomes. Groundwater recharge is one of the most important environmental functions brought about by wetlands. For example, households in rural Nigeria rely on groundwater for drinking and cooking, and in the arid north, particularly during drought, it often has the added advantage of its higher quality (Table 3).

1.07.4 Regulatory Frameworks for Groundwater Multilevel Governance One of the most obvious examples of the Cinderella status of groundwater in global water resources is reflected in the evolution of regulatory frameworks. Due to its silent (and relatively recent) rapid growth, groundwater traditionally had little or no regulation (i.e., as exemplified in the rule of capture in Texas), part of mining law, or of private-property rights (tied to land). This lack of prominence and the lack of concern over its management and state of preservation have historically been reflected in the law. Therefore, groundwater

Groundwater Management Table 3

111

Valuing groundwater goods and services

Total economic value of groundwater

Environmental goods and services

Definition

Example

Direct value

Provisioning service

Drinking water supply

Two billion people rely on groundwater directly for drinking water More than 50% of cities with population of more than 10 million rely on or make significant use of groundwater Forty percent of world’s food relies heavily on groundwater Land irrigated from aquifers has increased 113 times between 1990 and 1990. Aquifer supplies more than half the world’s irrigated land For example, manufacturing processes and geothermal and cooling systems Groundwater stores and releases water, sustains river flows, springs, and wetlands Through microbial degradation of organic compounds and potential human pathogens, microbiological and some chemical contaminants removed, retarded, or fragmented For example, by absorbing run off Primary buffer against climate variability and spatial variability of droughts Potential innovation as future use for anthropogenic carbon sequestration in the ground For example, groundwater recharge and discharge

For example, storage and retention Agriculture

Industrial Indirect value

Regulating services

Water regulation; Water purification and waste treatment Erosion and flood control Climate regulation

Option value and existence value

Supporting services

Cultural services

Necessary for the production of all other ecosystems services Non-material benefits people obtain from ecosystem services

Spiritual enrichment, cognitive development, religious value, and symbolism

From Bergkamp G and Cross K (2007) Groundwater and ecosystem services: Options for their sustainable use. In: Ragone S, de la Hera A, Hernandez-Mora N, Bergkamp G, and McKay J (eds.) Global Importance of Groundwater in the 21st Century: The International Symposium in Groundwater Sustainability, pp. 233–246. Alicante, Spain, 24–27 January 2006. Westerville, OH: National Groundwater Association Press.

was legally structured as one more facet of the right of ownership for a specific area of land. Starting out from that premise, the various laws gave shape to the depth of that right and regulated how it would fit in with the rights held by owners of adjacent pieces of land. All of the above emerged from an eminently private perspective imbued with the wealth of duties and rights conferred by ownership rights. Groundwater doctrine in Texas is based on the rule of capture, an English common-law approach based on absolute ownership, where landowners can pump without limit, as long as water is put to beneficial use, which allows unrestricted pumping by competing groundwater users as long as it is not wasted, and whereby property rights are not defined (Wagner, 2005). This is an example of one of the few natural resources in the USA not regulated by a central agency. The activities of 88 water districts in Texas are unusual because they have been based exclusively on a voluntary approach controlling wastage of water, recharge, enhancement, and water-conservation education rather than controlling abstractions. For nearly 100 years, the rule of capture has survived attempts to regulate groundwater use. Although government oversight and technical assistance are vital, a carefully crafted free-market system based on private rights to a communal resource becomes increasingly important. A bottom-up process created the State Water Plan of 2002, which incorporated regional water plans’

gradual increase in the security of mining rights from open access to other systems.

1.07.4.1 Diversity in Groundwater Regulatory Regimes Regimes with a civil-law tradition, inherited from the principles of Roman law, are clear exponents of this and not very far from them are those grouped under the parameters of common law. Under common-law regimes, the landowners were the right holders of groundwater, flowing under their properties, which could be harnessed (Embid Irujo, 2002). From the legal viewpoint, legislation on aquifers presents two main issues of concern: first, ownership and second, transferability or flexibility with ownership rights. The first one relates to whether groundwater resources should be public or private property. Ownership of groundwater resources shows a high level of diversity from completely private (e.g., in Texas USA), groundwater from the Ogallala aquifer is mainly private (Peck, 2007), to state-owned resources, such as in the case of Mexico, to plural legal systems, as in parts of Africa, and community based, such as in Bolivia. Legal provisions may confer ownership of groundwater directly on public authorities, as part of the public domain (Morocco, Italy, Spain, Zimbabwe, Israel, some US States, Jamaica, Mexico, Argentina, Australia, the Lebanon, Jordan, and Syria), give the authorities preferential rights in groundwater (South Africa and Uganda),

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or make groundwater the common heritage of the nation and place conditions on its use (France). Particularly, in Muslim countries, the applicable legal regime is intertwined with principles related to religious beliefs which result in the classification of water as public in itself with cases of private appropriation being seen as exceptional (Caponera and Nanni, 2007). Whichever route is taken, the result is very similar: governmental authorities give themselves powers over groundwater with the aim and effect to implement public policies that they lay down. The progressive importance of groundwater in the definition of water resources in various countries has engendered the implementation of legal reforms that protect public intervention (Hodgson, 2006). When groundwater is public, the concept that is generally used by the rule makers is the permit, license, authorization, concession, or a similar instrument. This is the case of Israel, a number of states of the USA, Mexico, and many other countries. In other places, such as California, Chile, India, or Texas, groundwater is under private ownership. In all of these cases, a private party, individual, or community is granted the right to use a certain amount of groundwater. This right is subject to certain conditions relating to time or use. The right may or may not be granted according to whether it is consistent with the status of the resource and with the parameters of the planning regulations on water resources that must govern its contents. Those same premises must be used to determine the period for which the right is granted and the amount that may be extracted. It is also necessary for the law to outline the cases in which this right can be altered, restricted, or even eliminated as a result of damage to the aquifer, possible droughts, watersupply needs to the population, or similar events, or principles such as reasonable and beneficial use. A legal regime for groundwater should ideally consider some of these aspects:

• • • • • •

•

approval of compulsory legal norms for all groundwater users; determination of the legal rules and principles to be applied in the management of groundwater, including its relationship with surface water; legal parameters to define groundwater as a resource; institutional regime applicable to groundwater; specification and regime for uses of groundwater available for all citizens without being subject to specific control; determination of the rights on water (transitional rules in the event of amendments to the law; concession rules; registration processes; contents of the right – volume of water, term, conditions, and termination – transfer of rights; and dispute-resolution mechanisms); and rules on the protection of groundwater and measures to adopt if needed (control of the pollution of groundwater – rules on discharge and authorization; use restrictions; and prohibition on use).

1.07.4.1.1 The controversy over private, public, or community groundwater rights Evidence indicates that ownership per se (public, private, or common) does not guarantee or pre-empt sound management. The importance does not lie in what name is given to the legal title but rather in the contents given to that title. The

emphasis is placed on the aim to be achieved. Preferences on ownership are societal choices, which are subject to change and flux; the underpinning question is not ownership but whether management is according to some predefined a priori objectives, which in any case are themselves subject to constant negotiation and renegotiation as part of a normal political process. Some authors consider that the legal declaration of groundwater as a public domain is a conditio sine qua non to perform a sustainable or acceptable groundwater management. This assumption is far from evident. For many decades, groundwater has been a public domain in a good number of countries. Nevertheless, sustainable groundwater management continues to be a significant challenge in many of those countries. Highly centralized management of groundwater resources is not automatically the solution to promote solidarity in groundwater use as a common good because a key element is the internalization, by often thousands or hundreds of individual users, on the need for collective action. Groundwater management sometimes can successfully be devolved to stakeholders of the aquifer, in self-governance arrangements under the supervision of the corresponding water authority. Stakeholders’ participation has greater chances of success if it emerges bottom -up and is supported topdown. The practical application of a hybrid (public and private) system is exemplified by a few countries in the world where a range of systems coexist: one is the US, where states like Colorado, Arizona, Texas, and California exhibit a range of ownership rights to groundwater; and the other is Spain with a particularly interesting example of a mixed system. Wells drilled after 1 January 1986 require governmental permission, while those operational before 1986 remain private. Private groundwater may remain so for 50 years (provided the well owners reach an agreement with the government in exchange for administrative protection) or perpetually (if the owner wishes to preserve his/her rights under the 1879 Water Act). In any case, the Spanish situation is far more complex due to the lack of a reliable registry of groundwater rights. While the government is currently carrying out a series of remedial initiatives, these ignore a significant share of existing wells, and the registry or inventory is therefore incomplete. A key ingredient is the need for a strong political willingness to apply the laws. It seems clear that a reliable inventory of groundwater rights is desirable in order to ensure adequate management. The second issue refers to the way groundwater rights should be inventoried and to whether the possibility to trade with them should be allowed. This second aspect, usually equated with water markets and banks (discussed in the previous section) is perhaps subordinated to the first in terms of importance, even if significant informal markets already exist in some places (Mukherji, 2006). It cannot be ignored, however, that in other territories, the inseparable link between water, land, and private property has been maintained. The states of California and Texas or countries such as Chile and India are examples of this. These cases have maintained the private ownership of water and in many cases applied the doctrine of prior appropriation, although this is subject, depending on the territory and circumstances, to specific measures of administrative or court intervention, linked, for example, to the principle of reasonable use. It is also possible

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Box 2 Groundwater-use rights in China. By Simon Howarth, based in Gansu, under the UK Department for International Development (DFID)-funded Water Resources Demand Management Assistance Project. The Shiyang River Basin in Gansu province in Northwestern China is an area of severe water shortage, where groundwater abstraction greatly exceeds recharge. A package of measures, including greater delivery of surface water and restrictions on the use of surface water, is being introduced. A key element in this process is the allocation of water rights to individual households. These are based on land allocations and household size (to take account of domestic use and livestock as well as agricultural demands). Both land and water are owned by the state in China, but user rights have been granted to individuals. Land rights have been granted since the 1980s and further reforms are in progress. Water rights have been formalized since 2007, when individual household water-rights certificates were issued (via village committees or WUAs). The rights are calculated to be sufficient for the locally recommended cropping pattern, and are being reduced each year (e.g., from 7200 to 6615 to 6435 m3 ha1 in one typical irrigation district) as recommendations are revised and farmer skills in water savings are developed. Awareness and capacity-building programs are being run simultaneously so that farmers can protect their livelihoods while coping with less water. Wells were developed by villages and are owned by them, but the amount of water that can be pumped from them is regulated by the state through a system of permits. Well permits are now being reissued to suit the new water rights, and electronic controls (IC cards) are being installed at all wells. These will limit the amount that can be pumped from the well to the annual total permitted for that well. These cards are held by the well operator who is responsible for ensuring that each household receives water in accordance with their individual rights. These systems are new, rely on both sophisticated technology and complex administrative systems, and have been introduced rapidly (in over 10 000 wells in 1 year). Not surprisingly, some teething difficulties have been encountered, but there is a very strong political will to solve these problems. Allocation of water rights is intended to enable trading of rights, although this does not yet happen on a formal basis in this area of China. It will be subject to certain restrictions – for example, the right will be salable at a maximum of three times the water-resources fee, which is small when compared to both the value of water and the cost of pumping. There is a large and growing requirement for water for industrial development, which is a more valuable use of water, but there is a competing requirement for food security – these competing demands cannot be managed purely by market measures but will require government control as well.

to outline mechanisms for exchanging water rights in the context of what has come to be called the water market (Chile, South Africa, Mexico, Spain, the UK, Australia, and the US). The use of water-market institutions for groundwater has its detractors who point to the risks involved. In our view, however, those risks do not necessarily warrant ruling out the option completely. Bringing flexibility to the allocation of resources and allowing them to be exchanged are not in themselves misguided concepts. The usefulness of water markets is usually associated with cases of multiple supply and demand sources, with transparent exchange mechanisms and the appropriate transportation networks that make them feasible (Melgarejo and Molina, 2005). The key lies in controlling their use and making them subject parameters of sustainability and protection (Box 2).

1.07.4.2 Implementation and Enforcement of Groundwater Legislation In the context of groundwater management, rules on the ground are crucial, for example, those related to time, well location and spacing, technology, or groundwater-abstraction quotas. In addition, another factor is the interaction between formal groundwater law and its operation on the ground. More attention is being paid increasingly to the implementability of regulation, since the problem with most groundwater legislation lies in its implementation and enforceability. For example, South Africa established an implementation team with the task of anticipating what the water domestic bill would require, with close interaction between the drafting and implementation teams to identify possible implementation problems before enactment. In the case of groundwater, it would be useful to develop implementation tools such as guidelines, procedures, information systems, user manuals, and organizational arrangements. Another option is to opt for framework laws, which specify general guidelines but leave implementation to detailed regulations as used in Uruguay.

Implementation requires time, and needs political support at the highest level since strong economic and political interests are usually affected by allocating or reallocating groundwater resources. As Gardun˜o (2003) states that implementable legislation is one that the government is able to administer and enforce and water users have the ability to comply with. Experience shows the education of stakeholders and widespread presence of groundwater-user associations is crucial for an adequate participatory bottom-up management approach. One of the main problems in groundwater governance is lack of enforcement in some cases of relatively sophisticated laws, such as in Spain. As stated earlier, institutions encompass not only rules in norm but also rules in use or institutional arrangements. In effect, the implementation and enforcement of groundwater laws have to be legitimized and supported by society (social norms). The involvement of groundwater users in groundwater-management regimes is a necessary (although not sufficient) condition for successful enforcement regimes. In traditional societies, social networks were denser and therefore transaction costs lower, whereas modern societies require complex institutional structures that constrain and regulate interactions among groundwater users. Groundwater users possess detailed local knowledge on water use, and these communities can apply for sanctions unavailable through formal institutions. For example, name and shame can resolve conflicts at the local level in a manner customized to local circumstances, which reduces transaction costs, which in turn are critical for economic performance. In fact, in a study undertaken in Spain, groundwater users had a clear perception of the kind of behavior that should be penalized and how sometimes sanctions devised by farmers do not mirror sanctions designed by higher-level authorities (Lopez-Gunn, 2003). This can be rooted in different perceptions of equity and fairness. For example, farmers in an aquifer in Spain would prefer to be sanctioned in the following irrigation season with water as a penalty, in lieu for the same amount of water that farmers abstract over their quota in the previous

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irrigation season, instead of the current (formal) sanctions of a monetary penalty. Thus, groundwater users can reduce the transaction costs of enforcement and devise adequate sanctions. However, it should not be forgotten that authorities in most cases ultimately hold this legal responsibility to protect public goods. Higher-level authorities will often have to be imaginative with monitoring and sanctioning regimes. Many Asian administrations lack the capacity to perform complex tasks, for example, urban groundwater management with joint monitoring of industrial groundwater abstraction and wastewater discharges. A feasible alternative tried in Indonesia was to select a random sample and thoroughly monitor these users. In cases of noncompliance, the weight of the law should be applied and widely publicized in the media; as capacity grows, the sample could be enlarged. Limited administrative capacity is a key constraint to groundwater management and revenue-raising fees can be re-invested toward capacity programs.

1.07.4.3 Multilevel Regulatory Frameworks The section above described and discussed briefly some of the main challenges for national groundwater law. It is increasingly recognized, however, that national groundwater law is only part of the regulatory framework. Other levels (both conceptual and in terms of scale) have to be taken into account: first, international conventions currently being negotiated, for example, for transboundary aquifers or the rise in the human right to water; second, a pragmatic approach on the advantages and limitations of legislation and litigation; and third, a consideration of the legal principles that have to underpin legal norms and an evolution in our understanding of how laws will be drafted in the twenty-first century. First, in relation to international conventions, there are two conventions that are applicable to groundwater: the first relates to transboundary aquifers and the second refers to the International Convention on Human Rights (1948) and its new impetus to recognize a human right to water (or HRW). Until only a few years ago, international law did not pay too much attention to groundwater. This state of affairs has changed, aided by the Convention on the Law of the NonNavigational Uses of International Watercourses (1997) (Eckstein, 2004). This Convention, yet to be ratified, only partially covered transboundary groundwater, that is, those connected to rivers, and thus left many aquifers uncovered. As a result of this situation, in 2008, the International Law Commission delivered to the United Nations General Assembly, draft articles for the law on transboundary aquifers. After reaffirming the protective and environmental approach to the use of groundwater, they ratified the application of the principle of fair use (1997) and of sensible damage. They also outlined measures for the following: first, cooperation between states; second, the regular exchange of data and information; third, the promotion of bilateral and regional agreements; and fourth, measures for the protection and preservation of ecosystems, and the prevention, reduction, and control of pollution. Along these lines, they provided that where appropriate, a shared management mechanism will be established.

Claims for the right to water to become a fundamental right, and thus protected, are increasing. This is probably highly applicable to groundwater since in many countries public water supply (to which the HRW is addressed) is supplied largely by groundwater. This is the case, for example, in Africa, the continent lagging most behind in the Millennium Development Goals. In relation to lack of access to water and sanitation by 2015, Africa is the continent most off target where groundwater is the daily source of drinking water for more than 75% of the population The first reference in this respect is to be found in articles 11 and 12 of the International Covenant on Economic, Social, and Cultural Rights of 19 December 1966. While not expressly mentioning the right to water, its wording has led the United Nations Committee on Economic, Social, and Cultural Rights (2002) to define the HRW as one which entitles everyone to sufficient, safe, acceptable, physically accessible, and affordable water for personal and domestic uses, and even links this right to the International Bill of Human Rights (1948). This reference to the right to water has been kept in recent documents such as the Plan of Implementation of the World Summit on Sustainable Development (2002), the Charter of Water of the Senegal River (Mali et al., 2002), or the Third World Water Forum Ministerial Declaration (2003). Second, there are advantages and disadvantages to a pure regulatory approach. Recognized limitations include symptoms such as the existence of rigid overly bureaucratic administrative procedures, the large number of authorities involved in taking decisions on groundwater, scarcity of technical and human resources to enforce compliance with legislative requirements, the often-absent citizens’ participation in decision-making processes on deliberation and decision making, or the confrontation of interests among the various government departments. These are clear examples of what we could call organization sickness. Legal proceedings that are prolonged, costly, hard to enforce, or construed poorly with practical needs of water management make it problematic for courts to be able to solve groundwater conflicts. Crucial and fundamental advantages to regulatory processes remain, such as its role as leverage and recourse for aggrieved third parties in court. This is why it is crucial in the case of groundwater to facilitate legal literacy or legal empowerment improving the capacity of communities to know and use the law – training in techniques such as interest-based negotiation, mediation, and facilitation. Third, the step from regulation by rulemaking process to negotiated rulemaking can never replace the public decisionmaking process, with the participation of all interested parties, or generate inequality. A series of legal principles have to be embodied in formal groundwater regulation, leaving more freedom or flexibility in terms of the implementation and enforcement (Table 4).

1.07.5 Institutional Aspects of Groundwater Management The problem of groundwater over-use has often been portrayed by the tragedy of the commons, that is, Hardin’s seminal essay in 1968 (Hardin, 1968) which describes how the

Groundwater Management Table 4

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Legal principles applicable to groundwater legislation and its implementation

Legal principles

Rationale and justification

How

Effectiveness and efficacy

Efficacy of water management must be sought, by implementing or furthering measures

Cooperation

Participation and subsidiarity

Cooperation between authorities as fundamental (Declaration on Groundwater in the Mediterranean, 2006)a Aarhus Convention (1998)b

Sustainability and precautionary

Rio and Johannesburg Summitsc

Common responsibility

Commission on Sustainable Development, United Nations Economic and Social Council (2008)d

Adapting organization and competent authorities to conform to the natural characteristics of the resource – normally identified as a drainage basin Fostering the participation of users and interested third parties which has already been identified as a mechanism to secure acceptance and implementation of the agreed measures. Encouraging planning related to the allocation of resources or water-quality protection or restriction measures, rules on improvements and irrigation transformations, guidelines on recharge and aquifer protection. Cooperation, either through procedures or by agreeing to specific conventions, must enable more effective, allow the views of each of the players with responsibilities in the area to be known, avoid subsequent defects in the implementation of agreements and, in short, allow views to be joined to find the best solution. Environmental governance that is transparent, legitimate, and efficient. Public authorities, as the necessary guardians of correct application of the legal framework, may confer an especially important role on user associations directly involved in the management of, for example, groundwater resources. There has already been a certain amount of international experience in this area in countries such as Argentina, Colombia, Spain, the US, Indonesia, Mexico, Nepal, the Philippines, Sri Lanka, or Tunisia. Besides, it acquires greater importance in relation to groundwater as it is a way of surmounting the management difficulties caused by having multiple users. The implementation of sustainable development must pervade decisions on territorial and urban planning and the performance of specific projects, the approval of new protection rules, to end, cease or modify granted rights to groundwater and, especially, the economic development and growth initiatives in every country. Groundwater is a common good and therefore the responsibility for its protection and correct management belongs to everyone.

User and polluter pays principle

Solidarity

Levels of solidarity in groundwater management

To determine the obligation to repair and replace the resource base to their original state. In addition, it will be absolutely necessary to establish the strict liability regime in these cases, notwithstanding any potential exceptions linked to the state of technology or the grant of approvals.  Intergenerational solidarity. Future generations must be considered when adopting initiatives.  International solidarity. Not all countries have the same difficulties. Ranging from the actual exchange of water to the transmission of technology and knowledge. An example is the Johannesburg Declaration on Sustainable Development of 2002.  Regional solidarity. The areas within a state must seek points of consensus and foster instruments of cooperation in the rational and sustainable use of groundwater. It will undoubtedly be fundamental for this task to be able to plan and study the circumstances of each specific case, but it is important to take as reference the need to share and join forces in searching for the balance sought by all.

a

Ma´laga-Marrakech Declaration on Groundwater in the Mediterranean, 2006. (This Declaration is the result from two international congresses organized in 2006, AQUAinMED’06 – Ma´laga – and GIRE3D – Marrakech). b The United Nations Economic Commission for Europe Convention on access to information, public participation in decision making, and access to justice in environmental matters, Aarhus (Denmark), on 25 June 1998. c Johannesburg Declaration on Sustainable Development (World Summit on Sustainable Development, United Nations, 2002). d Commission on Sustainable Development, Report on the sixteenth session May 2007 and 2008 (Economic and Social Council, United Nations). Author: D. Sanz.

rational actions of individual actors, in our case groundwater users, lead to the demise of all, that is, aquifer over-use. This is because groundwater, which is a classic example of a common-pool resource, is defined by two characteristics: the

resource is largely rival and nonexcludable. These commonpool resources exist at different scales from transboundary to regional or small local aquifers. The works of Ostrom (1990) and other institutionalists have demonstrated that this case

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underestimated the capacity of the users to self-regulate their actions, that is, to develop rules in norm and rules in use to prevent aquifer overuse. The groundwater silent revolution described earlier in the chapter has however outpaced the capacity to develop institutions suitable for good groundwater governance in terms of resilience, while maintaining a level of flexibility and adaptability to cope with a high degree of change.

1.07.5.1 Groundwater Institutions: Mapping Groundwater Institutional Design A number of conditions have been well documented in the literature for the successful management of common-pool resources. These factors are summarized in Box 3 (Schlager and Lopez-Gunn, 2006) in relation to groundwater.

1.07.5.1.1 Boundary definition The first tenet of institutional theory refers to boundaries. This refers on the one hand to natural boundaries and on the other to institutional (property right) boundaries. In the first case, the definition of natural aquifer boundaries for management purposes has the added complication that groundwater aquifers do not necessarily coincide with surface-water systems. In addition, groundwater suffers from the same problem that surface water had traditionally experienced, lack of overlap between administrative and natural boundaries (i.e., problem of fit), that is, the boundaries of for example, regional administration do not coincide with river-basin boundaries, with the added twist that surface basins and aquifers often do not coincide, which further increases the complexity. An interesting example is currently pursued under the EU WFD, which has adopted a twin-track approach of managing water according to river basins while simultaneously mapping groundwater bodies, while setting the objective to achieve

good status for all water bodies in the EU (surface as well as groundwater) by 2015. According to Howe (2002), assigning well-defined groundwater property rights, for example, through pumping permits (discussed earlier) enhances the value of water, which creates incentives to use water more effectively or to transfer rights and/or use to third parties who are willing to pay for pumping rights. Therefore, the most complex challenge for water laws is the administration of water rights, that is, the granting of licenses, concessions, permits, and other legal deeds for the abstraction of groundwater, and for the discharge of waste water directly or indirectly into the aquifer. Groundwater, in particular, offers additional problems because of the following: first, the potentially large and often heterogeneous number of users, and second, the boom in use which has often overwhelmed administrations. In Spain, 20 years after the 1985 Water Law, the registration of groundwater rights was required (1988), but the administration has still not finished the process; or in places such as Mexico an ecological price has been effectively been paid for the process of registering 330 000 water rights by 2003, by over-allocating groundwater resources. The new 2002 Water Law in China established the need to obtain groundwater permits. Yet, the issuing of water permits in China by counties is proceeding very slowly, and there is lack of consistency between authorized abstractions via permits and groundwater-resource availability (Foster et al., 2004). Furthermore, growing experience in the process of assigning groundwater property rights has shown that it is crucial to take context into account when assigning groundwater rights, for example, in South Africa, where plural legislative frameworks (formal and customary) coexist. These plural, often dual legal systems have important implications for the registration of groundwater property rights, due to overlapping legal orders. The diversity and flexibility of customary laws, principles, and practices may be intentionally or

Box 3 Ostrom’s institutional design principles applied to groundwater institutions. Reproduced by Lopez-Gunn E from Ostrom E (1990) Governing the Commons: The Evolution of Self-Governing Irrigation Systems. Cambridge: Cambridge University Press; Schlager E and Lopez-Gunn E (2006) Collective systems for water management: Is the tragedy of the commons a myth? In: Rogers P, Llamas MR, and Martı´nez-Cortina L (eds.) Water Crisis: Myth or Reality?, pp. 43–60. London: Taylor and Francis; and Cleaver F and Franks T (2005) How Institutions Elude Design: River Basin Management and Sustainable Livelihoods. BCID research paper 12, ICID Conference, London. *

* *

* *

*

*

*

Clearly defined boundaries. Both the boundaries of the aquifer and the individuals or households with groundwater rights from the aquifer are clearly defined. This principle refers both to the physical boundary of the aquifer and a clear identification of groundwater rights (legal boundary on groundwater). Collective choice agreements. A clearly defined groundwater-user group or community should be involved in groundwater management. Appropriation rules. Operational rules in relation to time, location, technology, or groundwater-abstraction units should include the groundwater users affected by these rules and should be included in decision-making processes to modify these appropriation rules. Monitoring. Monitors who actively audit physical conditions and behavior are accountable to groundwater users and/or are groundwater users themselves. Graduated sanctions. Sanctions are devised for noncompliance with collective rules (operational rules). Groundwater users who violate operational rules are likely to receive graduated sanctions by other groundwater users, by officials accountable to these groundwater users, or by both. These sanctions have to be applied consistently, impersonally, and rapidly. Conflict-resolution mechanisms. Groundwater users and officials have access to low-cost local arenas to resolve conflict among users or between users and officials. Conflict-resolution mechanisms should be clear, accessible, and quick. Legitimacy. The legitimacy of groundwater users to organize and set up their own institutional arrangements is not challenged by external government authorities. Nested enterprises. Local groundwater institutions are nested within other levels of decision making, in multiple layers, which facilitate governance (in terms of consistent operational rules, monitoring, and enforcement and conflict resolution).

Groundwater Management

unintentionally replaced by new water laws, and uniform rigid principles and requirements. Legal frameworks empower if these recognize rights of existing water-user communities, and enable legal recourse if rights are harmed. Plural groundwater property-rights systems ideally have to be based on the principles of good governance: transparency, accountability, and the rule of law. There is also a risk in idealizing customary groundwater rights, which might not necessarily, for example, be gender neutral.

1.07.5.1.2 The role of groundwater-user associations Decentralization of groundwater-resource management is coherent with the creation of collective institutions like groundwater-user associations that can be directly involved in groundwater management. There is a range, nevertheless, on the degree of management devolution to groundwater-user groups, for example, market co-production, co-management, or regulated autonomy. In the late 1990s, decentralization was a consequence of rolling back the state, and transferring management directly to users – participatory-irrigation management (PIM; or irrigation-management transfer) since the dominant use of groundwater globally is agriculture. This is part of the wider trend in PIM (Merrey et al., 2007). In the case of groundwater, PIM has interesting twists and turns because at least two types of groundwater-irrigation systems can be identified: first, the case of collective wells which are managed as very small surface-water systems, and second, and most common, individual farmers exploiting their well for productive agriculture and/or livelihoods. The creation of wateruser groups and PIM would be similar to surface water in the first case, and would face similar limitations as those recently put forward for surface water, that is to say, that this is no panacea and it is suitable in some cases but not necessarily in all cases. These water-users associations (WUAs) are much smaller than WUAs for surface irrigation: this makes it simpler to organize them but it is also less important for them to be formal organizations. Informal groups are generally adequate for managing irrigation from individual wells, even when managed by groups of up to 50 farmers, such as in China. The second case is a true case of collective action because individual users have to be persuaded externally (top-down) or realize (internally) that the benefits of self-organization are higher than the costs, and that free riding on the collective action of others is now penalized either through formal sanctions or through informal, social norms. The objective in this case is regulation of the aquifer (i.e., the source of water) rather than equitable management of the distribution of water from the source. The case of PIM in groundwater is fascinating because there is evidence from groundwater-user associations that have been both created top-down and others which have emerged bottom-up, spontaneously. This is the case of Spain, where Comunidades de Aguas Subterraneas – which is in effect, part of the water authority and instigated by the administration, and which manages groundwater as part of the public domain – coexists with Comunidades de Usuarios de Aguas Privadas, where private groundwater-user groups have been created through user initiative. More research is needed on delivery of management outcomes; what is already evident is that the scale of

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these groundwater-user groups is large, managing aquifers which can cover areas from 7000 km2 to 300 km2, and where success is mixed in terms of sustainable aquifer management. In China, groundwater management has gradually become more decentralized, and bottom up, with increased stakeholder participation at all levels, and closer interaction between users at the local level and the responsible authority, the Water Resource Bureau, normally designated at county level for rural areas and district level for urban municipalities, while some groundwater-user associations have also been established (Foster et al., 2004). The few examples of groundwater-user associations that have become effective resource managers have two things in common: they have successfully articulated common goals and objectives, and they have established mutually accepted rules regarding resource access and use, in order to guarantee the long-term availability of groundwater to users. For example, in Mexico, in the early 1990s, due to intensive groundwater use in the central and northern part, many groups started to emerge concerned with the problem of intensive groundwater use and negative externalities: for example, the spontaneous creation of the Grupo del Agua in the Comarca Lagunera (1991) and the Grupo del Agua of Santo Domingo valley a year later (1992). Other groups appeared in other areas. Initially, there was lack of clarity on the regulatory structure of these groups and their financing, which meant there was little support from the federal level. Initially, the Mexican Federal Government did not legitimize these spontaneous water-user groups until the mid-1990s, when these groups reorganized themselves as Comites Tecnicos de Aguas Subterraneas or COTAS, starting in the Queretaro valley, and then spreading to other aquifers in the central and northern part of Mexico. In the state of Guanajuato, local authorities encouraged the formation of COTAS in all aquifers in the state, supporting them financially (Escolero and Martinez, 2007). Meanwhile in the USA, local landowner associations in Texas have been experimenting with the feasibility of selfmonitoring and regulation under local groundwater districts, which would set pumping limits and well placement based on hydrologic models, to deliver public goods such as open-space protection and aquifer recharge through cooperative landowners associations (Wagner, 2005). In India there is evidence of the spontaneous creation of WUAs, through what Shah (2005) calls swayambhoo (self-creating), involving entrepreneurial efforts, which are normally present since most groundwater users are by definition smallscale entrepreneurs. It is estimated that over a quarter of Indian irrigated areas operate through this kind of spontaneous creation of informal water markets (Shah, 2005). The challenge is when swayambhoo institutions have to be scaled up, whether motivation can shift to longer term, and collective self-interest, then, can also start to internalize externalities (Box 4).

1.07.5.2 An Institutional Audit of Groundwater Institutions Added research and experience have however highlighted new dimensions to the institutional framework analyzed above (Cleaver and Franks, 2005; Cleaver and Franks, 2008). The socalled post-institutionalist turn has added some caveats and

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Box 4 WUAs for groundwater management in China. By Simon Howarth (UK DFID-funded Water Resources Demand Management Assistance Project. Water-users associations (WUAs) are being set up in each village in the Shiyang River Basin, an arid internal river basin in Gansu Province of Northwestern China. They are being promoted by the government to assist in the management of groundwater, but with a primary focus on achieving water savings. Existing tube-well management arrangements, by local production groups (subdivisions of villages) supported by water-management stations (WMSs) (government) at township level, are believed to be effective and equitable, but insufficiently focused on reducing total water use – with the result that groundwater levels are dropping at 50– 100 cm yr1, making agriculture unsustainable. These WUAs thus have different objectives to WUAs set up elsewhere in the world, which are required to improve management of large surface canal flows and hence ensure greater equity of water distribution. Each village (or WUA) typically includes 20–50 tube-wells (each serving 5–20 ha, farmed by 10–50 households) which are managed by production groups (water-user groups). The tasks of the village-level WUA include assistance to the WMS in many of the new groundwater-management procedures, such as issuance of household water-rights certificates, enforcement of permits, and collection of fees – all of which are aimed at reducing the amount of water that farmers use. These are onerous requirements and thus the WUA are repaid part of the water-resource fees collected in order to cover a small salary for directors and vicedirectors and some administrative costs – in recognition of the role that WUAs play in water-resources management. This formal process of paying staff from part of the newly introduced water-resources fee is important for ensuring that the WUAs are effective and sustainable. In addition to these responsibilities for assisting the government, WUAs also have a small role in water management which includes improving maintenance; reducing conflicts; planning, implementing, and monitoring water distribution; monitoring groundwater levels; and ensuring effective communication between WMS, WUA, and farmers. Much of this work is done by well-established informal means by production groups, but the WUA coordinates between production groups and provides services at a higher level – such as employing a maintenance technician who is available to all groups, linking groups to governmentsponsored training programs, and assisting in contracts with crop-grower associations, seed suppliers, and markets. These WUAs are intended to be independent, autonomous, democratic, village-level organizations, but for practical reasons, the staff are often largely drawn from existing village committees (these are elected, but all candidates are required to be vetted and approved in advance). On paper, it is a strong system, but it is newly established and not yet fully effective. Many questions remain unconfirmed, including sustainability of financial arrangements, ability to deliver a positive service to farmers, and the willingness of farmers to accept the restrictions on water use. Further work on WUAs will require a combination of administrative measures at provincial, municipal, and county levels, and capacity building among WUAs. This capacity building will in turn require awareness-raising at the various levels of government, where there is typically greater faith in top-down controls (such as IC cards) or infrastructural improvements (canal lining) than there is in local institutional methods for water savings. Nevertheless, early indications are that the strong commitment to water savings by the government will ensure that WUAs will be effective, but that their role and responsibilities will be modified and simplified as they are implemented.

new dimensions to a strict application of Ostrom’s institutionalist framework. The main criticisms are that it is does not provide a causal analysis for the processes underlying these design principles. In particular, the areas that are increasingly perceived as fundamental to sound groundwater governance are: first, the key role of social capital and higher-level authorities; and second, the relevant role of political leadership and acknowledging the politics and vested interests of groundwater use, which are played out in the prioritization of groundwater use among competing users; and third, the potential problem of corruption as a symptom of a malfunctioning groundwater systems and the antidote of transparency and participatory groundwater management.

1.07.5.2.1 Higher-level authorities: Supporting, legitimizing, and leading The relevance of higher-level authorities comes to the fore as an essential supporting element for effective institutions and the development of organizational capacity since both authority structures and social norms (e.g., collective action by users) have to support and underpin the functioning of design principles. Higher-level authorities are key as facilitators for local groundwater management and for the vertical integration between the different spatial scales (farm level, aquifer, and regions, national, and international scales). For example, it appears that cross-scale linkages exist in China where there is a provision for transboundary issues across provinces, and also indirect leadership, as professional guidance from higher-level

authorities without any hierarchal subordination. County government can issue groundwater regulations within its boundaries, in agreement with provincial and national legislation (Foster et al., 2004). Higher-level authorities are increasingly perceived as a necessary condition to support local institutional arrangements. One of the most important roles for higher-level authorities is either to provide leadership or to facilitate leadership. In many cases, leadership is actually in the form of legitimizing or supporting local leaders. These local leaders in turn can drastically reduce the transaction costs of institutional change. In India, for example, the common aspect of all successful tank institutions was a leader or a leadership compact, which could sway the community and thus drastically reduce the transaction costs of ‘‘enforcing institutional arrangement that would either not work in their absence nor survive them’’ (Shah, 2005, p. 17).

1.07.5.2.2 Transparency and participatory groundwater management This chapter starts from the tenet that there is no global physical groundwater crisis; rather, there is a crisis of groundwater governance. Governance in this context is defined as the interplay of actors (public, private, and civic) to promote societal goals and the production of collective goods. One of the key basic assumptions of effective functional groundwater management is transparency and participation by all groundwater users in the decision-making process in

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line with the Aarhus Convention on Public Participation (UNECE, 1998). The problem of corruption often damages those most vulnerable, weakening the rule of law, and fostering social norms that systematically prioritize private gain over social well-being. Corruption according to Transparency International is about breaking socially established expectations of appropriate behavior (Stalgren, 2006). As stated earlier, groundwater has some inherent characteristics that should make it less prone to corruption. In the case of groundwater, the timescale and size of investment is normally smaller than in the case of surface-water projects. Evidence of corruption in the case of groundwater tends to refer to drilling concessions, bribing meter readers, distorted site selection for boreholes, for example, for those with more political or economic influence; bribery to obtain drilling permits or to cover up excessive abstraction, to obtain preferential treatment for services or repairs, and also to falsify meter readings (Transparency International, 2008). Advances are constantly made to facilitate transparency, accountability, and decentralization in groundwater management and use, for example, in the use of technology (Calera et al., 1999). Three measures are considered crucial in the case of effective groundwater management. First, reduce the complexity in regulation, licensing, and control; this is to prevent a weak and ineffective legal system which can encourage a clientelistic patronage system. Second, facilitate and incentivize so-called participatory monitoring. As stated early, transparency, monitoring, and sanctioning are part of healthy groundwater institutional arrangements. Robust groundwater institutions can benefit from advances in participatory geographical information systems, that is, use of technology jointly by groundwater users and regulators to increase transparency in water use and allocation. A good example is currently being implemented in the Mancha region in Spain, where satellite information is being used directly by farmers through an irrigation advisory service, which integrates realtime data to help farmers improve water use by different crops, while optimizing production (Calera et al., 1999). Third, encourage transparent access to data on groundwater use, licensing, and subsidies. This can be strengthened by partial decentralization to water users, to involve them in decision making, which would decrease the transaction costs of obtaining good-quality information while increasing the level of information available (Box 5). In summary, good, symmetric information equally accessible to both users and the regulator is crucial to facilitate cooperation among aquifer stakeholders. This information

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ideally has to be externally audited and contrasted, to allow for advocacy and disclosure of illicit behavior. Often, easy access to good and reliable data on abstractions, water quality, and aquifer water levels is a prerequisite to succeed in groundwater management. Current information technology can help information to be made easily and economically available to an unlimited number of users. Nevertheless, in a good number of countries, it will be necessary to change the traditional attitude of water agencies of not facilitating easy access to water data to the general public. This partly comes from a shift in mentality that strengthens accountability of public authorities downward to users and civil society.

1.07.6 The Complex Concept of Groundwater Sustainability and Future Management Issues An economic, efficient use of an aquifer would imply maximizing the present value of the resource in the case of the Ogallala, in Texas, where abstractions are much greater than natural recharge. It was discussed earlier in Section 1.07.2.5 that this exemplifies the complexities in defining what is meant by sustainable groundwater management. The exhaustible nature of the resource would raise the issue of appropriate long-term economic and demographic development of the region. The availability of open-access inexhaustible resources such as groundwater often invites gold-rush patterns of excessive fast exploitation and maladapted patterns of infrastructure and social development, that is, so-called boom towns (Howe, 2002). Economic efficiency in a renewable aquifer may imply drawing down the aquifer during droughts and allowing its recharge in periods of good surface flow. However, ecological dimension of sustainability used to equate recharge equal to abstractions is what some authors consider the renewable yield. Nevertheless, the EU WFD introduces a more complex concept, the achievement of good ecological health of aquatic ecosystems, which depends on the available yield. This new concept, not fully applied yet, may imply significantly lower amount of groundwater allowed abstraction than the renewable yield. Literature, such as Moench (2003) and Shah et al. (2007), and examples in Indonesia, China, USA (Ogallala), Spain, and Mexico, seem to indicate that de facto development (e.g., in terms of agricultural productivity) is prioritized over longerterm ecological groundwater sustainability. In Indonesia, regulation of groundwater is perceived to be at cross-purposes with industrial growth. In China, pure economic growth is

Box 5 Participatory groundwater monitoring in China. By Simon Howarth, based on Gansu under the UK DFID-funded Water Resources Demand Management Assistance Project. Simple monitoring by villages of the volumes of water abstracted and of the groundwater level is valuable for developing an understanding of groundwater. This has three classes of benefit: promoting awareness of groundwater, which is commonly less well understood than surface water; this can profitably be incorporated into school curricula so that children become aware of water issues: * * *

Enabling WUAs to manage their resource better, and understand why restrictions are being introduced. Providing data to supplement formal data-collection programs by government hydrology bureaus. Involving communities increases ownership of the concepts and reduces asymmetry of information. The information can easily be published on village notice boards.

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seen as a central part of development policy, in the policymakers’ mindset toward pure economic accumulation. In Mexico, continued overdraft in Hermosillo is driven by problems of reconciling economic efficiency and ecological sustainability (e.g., problem of saltwater intrusion), and where huge economic returns derived from groundwater in terms of income create an incentive to search for additional water resources through surface-water transfer (Escolero and Martinez, 2007). This is similar to the case in Southeast of Spain, in the Murcia and Almeria regions, where the intensive use of aquifers for highly productive agriculture started to drive national water policy to transfer water to this area, because of the political difficulty of controlling this intensive but highly profitable intensive groundwater use (Llamas and Martı´nezSantos, 2005; Llamas and Martı´nez-Cortina, 2009). The strength of negative externalities depends partly on aquifer characteristics (e.g., transmissivity storage), spacing of wells, and connection to surface water. These questions over preferred criteria as against competing uses go to the heart of the meaning of groundwater sustainability and to what extent this is feasible or, indeed, it is perceived as feasible or possible due to the institutional path dependencies of choices made in the past.

1.07.6.1 Groundwater Management Externalities In groundwater, externalities are the rule rather than the exception. The real issue is not the elimination of externalities (usually physically impossible) but rather, whether the impacts on third parties are excessive according to certain criteria. The relevant policy question is whether these externalities are considered excessive and for which criteria they are used or prioritized: economic efficiency, groundwater sustainability, or Table 5

social equity. In the EU, according to the WFD, the goal is to restore the ecosystems to a good ecological status unless the cost of this recovery is economically or socially very difficult. In this case, member states have to report in detail to the European Commission on the extenuating circumstance to ask for derogations. This is a process currently underway, but it appears that countries are anticipating the difficulty of complying with the WFD by 2015. Here, we summarize five indicators of typical problems of intensively used aquifers, but it is important to mention that these are sometimes used inadequately (Table 5).

1.07.6.1.1 Degradation of groundwater quality Groundwater abstraction can cause, directly or indirectly, changes in groundwater quality. The intrusion into a freshwater aquifer of low-quality surface water or groundwater, because of the change in the hydraulic gradient due to groundwater abstraction, is a frequent cause of quality degradation. This degradation of groundwater quality may not be related to excessive abstraction of groundwater in relation to average natural recharge. Other causes may be responsible, such as return flows from surface-water irrigation, leakage from urban sewers, infiltration ponds for wastewater, septic tanks, urban solid-waste landfills, abandoned wells, mine tailings, and many other activities not related to groundwater development (Custodio, 2002). For instance, the groundwater-quality degradation in many Central and Northern European countries is related to intensive rainfed agriculture. Saline intrusion may be an important concern for the development of aquifers adjacent to saline water bodies. This is a typical problem in many coastal regions of semiarid or arid areas. Moreover, in this case, the relevance of saline-water

Typology of groundwater externalities

Type of externality

Externality

Explanation

Environmental

Affected ecosystems

Socioeconomic

Pumping costs externality

Damage to ecosystems or surface-water features dependent on discharge from aquifers; spring-flow reduction Increase in pumping costs due to drop in aquifer levels, these costs can be fixed or marginal costs that one user imposes on another when pumping lower from a water level, external costs can be reduced by selecting a better well location Due to salinization or marine intrusion Water quality varies with depth (normally more saline with more depth) and also location specific, for example, aquifer located in coastal areas and islands (e.g., saltwater intrusion), the spread of low-quality water within an aquifer Water pollution due to intensive agriculture, for example, with nitrates and/or pesticides Decrease in pore-water pressure, related to amount of groundwater withdrawn Aquifer compaction with possible resultant reduction in aquifer storage capacity For example, when small farmers cannot adjust to the drop in aquifer levels Increased opportunity cost due to an increase in scarcity value due to intensive use Reduction to buffer value provided by groundwater against drought Diminished economic activity in the area, reduced water availability for other water-right holders, and reduced land-use options for future inhabitants

Potential loss of agricultural land Groundwater quality externalities

Land subsidence Aquifer compaction

Option and intertemporal externalities

Social externalities Increased scarcity value Buffer value of a groundwater stock Intertemporal externalities

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intrusion depends not only on the amount of the abstraction in relation to the natural groundwater recharge, but also on well-field location and design, and the geometry and hydrogeological parameters of the pumped aquifer. In most cases, the existing problems are due to uncontrolled and unplanned groundwater development and not to excessive pumping. It appears, for example, that the last half-century seawater intrusion has been well controlled in the coastal plains of Orange County (California) and Israel.

1.07.6.1.2 Susceptibility to subsidence When an aquifer is pumped, the water-pore pressure decreases and the aquifer solid matrix undergoes a greater mechanical stress. This greater stress may produce compaction of the existing fine-grained sediments (aquitards) if the stress due to the decrease in water-pore pressure is greater than the so-called preconsolidation stress. This situation has occurred in some aquifers formed by young sediments, such as those in Mexico City, Venice, and others. In Bangkok (Thailand), parts of the city were sinking at a rate of 10 cm yr1, with an increased risk of flooding and damage to roads and buildings. Caves and other types of empty spaces may exist under the water table in karstic aquifers. When the water table is naturally depleted, the mechanical stability of the roof of such empty spaces may be lost and the roof of the cave collapses. This is a natural process that gives rise to the classical dolines and poljes in karstic landscapes. When the water table depletion or oscillation increases due to groundwater abstraction, the frequency of karstic collapses can also increase. There are a number of well-known examples of land subsidence due to intensive groundwater use. In both cases, the amount of subsidence or the probability of collapses is related to the decrease in pore-water pressure, which is related to the amount of groundwater withdrawal. Nevertheless, the influence of other geotechnical factors may be more relevant than the amount of water abstracted in relation to the renewable groundwater resources of the aquifer. In Tianjin city (China), excessive abstraction of deep groundwater caused a land subsidence of up to 3.0 m (Foster et al., 2004). In Texas, Galveston and adjacent counties have experienced subsidence due to the long-term drop in aquifer levels (Wagner, 2005). Meanwhile, land surfaces in parts of central Arizona have fallen by 20 m in the last 20 years (Howe, 2002).

1.07.6.1.3 Interference with surface water and ecological impacts There are potential conflicts due to groundwater pumping and its interaction with surface waters and riparian habitat. For example, if there is no source of capture, a well will continue to withdraw water from storage until, either the aquifer is depleted, or the drawdown exceeds the well depth. The groundwater pumped from an aquifer is derived from a decrease in storage in the aquifer, a reduction in previous discharge from the aquifer, an increase in the recharge, or a combination of these changes. Capture may be defined as the increase in recharge plus the decrease in discharge. Examples of capture are: (1) an increase in groundwater recharge from losing streams (or increased infiltration); (2) a decrease in groundwater discharge to gaining streams (or interception of

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baseflow); and (3) the reduction in the component of evapotranspiration that is derived from the saturated zone. If we restrict the groundwater pumping to the capture, there will no longer be a decrease in storage. Restricting the groundwater withdrawals to what may be captured is a definition of safe yield or sustainable yield for the aquifer. Nevertheless, the definition of safe yield is controversial for some authors. Groundwater mining, that is, when water is abstracted mainly from storage (as discussed in Sections 1.07.2.5 and 1.07.2.6) may also be considered a safe yield, which can be valid under certain conditions. This is because sustainability has to consider different aspects, including economic, ecological, and social, and the real-life difficulties of implementing the concept of sustainability. Most river systems have a hydrology that is simple in concept but complex in detail. Some anthropogenic activities may have a significant impact on the catchment hydrologic cycle. For instance, the intensive use of groundwater for irrigation in the Upper Guadiana basin (Spain) has resulted in serious water-table depletion (B30–40 m). The most alarming consequences of the water-level drop were changes in the groundwater flow patterns and in the form, function, and quality of many wetlands. Areas that had received the natural discharge from the aquifer became natural recharge zones (Herna´ndez-Mora et al., 2003). This has produced a spectacular decrease in total evapotranspiration from the water table and wetlands, evaluated between 100 and 200 Mm3 yr1 (Martı´nez-Cortina, 2001). From the point of view of the water budget, there is an important increase (almost 50%) of the annual renewable resources, understood as the water that can be abstracted from the aquifer maintaining the water level as in the previous year, and calculated as the difference between aquifer recharge from precipitation and losses from evapotranspiration. This artificial depletion of the water table can also change dramatically aquifer–streams relationship, as in the previous example. Gaining rivers fed by aquifers may become dry except during storms or humid periods when they may become losing rivers, an important source of recharge to the aquifer. Nevertheless, this new water budget may present legal problems if the downstream water users have previous water rights (Llamas and Martı´nez-Cortina, 2009). The ecological impacts, mainly caused by water-table depletion as it has been showed in the Upper Guadiana basin case, are becoming an important new constraint in groundwater development in some countries, especially in the 27 countries of the EU because of the requirements of the WFD. A famous case is the Tablas de Daimiel National Park, a Ramsar site, whose main source of water used to be aquifer discharge before intensive irrigation made the area a recharge area rather than a natural outflow for the aquifer (Figures 5 and 6). Decreasing or drying up of springs and wetlands, low flow of streams, disappearance of riparian vegetation because of decreased soil moisture, alteration of natural hydraulic river regimes, and changes in microclimates because of the decrease in evapotranspiration, can all be used as indicators of ecological impact. Reliable data on the ecological consequences of these changes are not always available, and the social perception of such impacts varies in response to the cultural and economic situation of each region. The lack of adequate

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Figure 5 The Tablas de Daimiel National Park, October 2008. Photo courtesy: Pedro Zorrilla).

Figure 6 The Tablas de Daimiel National Park, October 2008. (Photo courtesy: Pedro Zorrilla).

scientific data to evaluate the impacts of groundwater abstraction on the hydrologic regime of surface water bodies makes the design of adequate restoration plans difficult. For instance, wetland-restoration programs often ignore the need to simulate the natural hydrologic regime of the wetlands, that is, restore not only its form but also its hydrological functions (Bergkamp and Cross, 2007). Similar problems result in trying to restore minimum low flows to rivers and streams. Oftentimes, minimum stream flows are determined as a percentage of average flows, without emulating natural seasonal

and year-to-year fluctuations to which native organisms are adapted (Llamas and Garrido, 2007; Garrido and Llamas, 2009).

1.07.6.2 Groundwater: Future Risks and Opportunities for Management 1.07.6.2.1 Groundwater and climate change The latest report of the IPCC by Bates et al. (2008) only marginally addresses the role groundwater can play in the

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adaptation or mitigation of the potential negative effects of climate change. The report says ‘‘There is a need to improve understanding and modeling of climate changes related to the hydrological cycle at scales relevant to decision making. Information about the water related impacts of climate change is inadequate – especially with respect to water quality, aquatic ecosystems and groundwater (added emphasis) – including their socio-economic dimensions’’ (p. 4). A detailed analysis of this topic is outside of the scope of this chapter. However, it seems that the role of groundwater development will increase significantly if the pessimistic predictions of the IPCC reports for the arid and semiarid areas become true. These pessimistic forecasts are mainly related to the increase in evaporation rates, due to the increase in temperature, and to the higher drought frequency. As it is shown in this chapter, the evaporation of groundwater from the aquifers is usually irrelevant and one important property of most groundwater reservoirs or aquifers is their resilience to dry spells. The UK Groundwater Forum, for example, studied potential scenarios for groundwater as a result of climate change, and some of these scenarios pointed to a long-term decline in aquifer storage, increased frequency, and severity of groundwater-related floods, mobilization of pollutants due to seasonally high water tables, and saline intrusion due to sealevel rise (Bergkamp and Cross, 2007). However, these predictions have to be contrasted with others across the world, with different climatic regimes. The Edwards aquifer is one of the largest freshwater aquifers in the USA with a total area of 15 640 km2, and a primary source of water (agricultural and municipal) for southern Texas (Loaiciga, 2003). It has been identified as one of the areas most vulnerable to complex, nonlinear climate feedbacks, and where potentially aquifer-exploitation strategies must be adapted to climate variability. In fact, when climate and groundwater-use changes are considered together, the role of groundwater use over climate prevails, that is, changes in groundwater use due to population growth and changes in land use or economic preferences may cause more profound aquifer impacts than those associated with global warming. For example, climate change in San Marcos springs could increase spring flow relative to the base condition by 17%, while the groundwater use alone in the year 2050 can reduce springwater flow by 22%, that is, groundwater use dominates over climate change. Therefore, the primary threat to the Edwards aquifer comes from the rise in groundwater use associated with predicted growth not from climate change. The latter in fact would increase spring flow in the study area (Loaiciga, 2003). There is also increased understanding that the vegetation response to climate change could either increase or decrease recharge. Climate change in fact could increase aquifer recharge according to recent simulation models, although highly dependent on geological settings (Green et al., 2007). In study areas characterized by sandy top soils and large interconnected aquifers, groundwater levels rose significantly. In Australia, simulations of twice the existing CO2 led to significant changes in the rate of groundwater recharge in Mediterranean and subtropical climates. Water recharged from 34% slower to 119% faster in the Mediterranean climate and from 74% to 500% faster in the subtropical climate.

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Opportunity for decrease exists but the general trend is toward increase in recharge (Green et al., 2007). In the context of groundwater and climate change, it is important to note the spatial and timescales of aquifer and climate systems. First, aquifers often operate in scales of much less than 106 km2 and in a great majority of cases, groundwater basin encloses areas of 104 km2, while global climate models (GCMs) operate on 200 km  200 km (4.104 km2) and regional climate models (RCMs) with resolutions of the order of 20 km  20 km (Loaiciga, 2003); therefore, the scales do not necessarily match up. Second, the nature of medium- and long-term climate predictions and the contrast between floodimpact studies with temporal scale from minutes to days, and drought impact studies precipitation and temperature temporal scale from days to years depending on inter- and intraseasonal variation. These uncertainties in both space and time make predictions, in terms of climate change and variability, difficult; what is clear is that in this context, aquifers have the natural capacity to act as climate regulators, that is, buffering capacity for drought and floods.

1.07.6.2.2 Future management issues There are a number of future management issues that become apparent and whose importance is increasing. One of the main issues is the joint use of surface and groundwater and the linkages between water quality and water quantity. There are good examples across the world of successful joint management of surface and groundwater that play to the strengths and weaknesses of both. For example, the case of Israel, and the case of the cities such as Barcelona (Spain) and Phoenix (Arizona), which rely on groundwater supplies as a strategic resource in times of drought. However, there are also examples on lack of joint management or in fact disjointed management, that is, when poor groundwater management leads to surface-water transfers to compensate. This is the case, for example, of lobbies pushing for surface-water transfers or the authorities pushed to find additional water supplies due to groundwater-quality problems. For example, both in London and in the coastal metropolitan area in Barcelona, there is a problem with aquifer rebound, and these polluted groundwater resources signify that it is easier to invest on large desalination plants to augment supply, since the costs of cleaning polluted groundwater are prohibitive. Meanwhile, in both Spain and Mexico, pressure builds in areas where aquifers are intensively used to bring surface-water supplies. The city of Hermosillo (Mexico) is heavily dependent on groundwater for its level of productivity and the residents of the area have been lobbying the Mexican government for a large water transfer that would bring water from the State of Sinaloa 485 km away (Plan Hidra´ulico del Noroeste). As groundwater storage continued to fall, the rising shadow (marginal price) value for groundwater rose. This shadow value can indicate what would be the optimum timing for the water transfer to occur, which would be when the values of the transferred water are lower (US$ 0.0222 m3) than the shadow groundwater value price (US$ 0.0224 m3). This highlights that the ideal time to build the project would be in the 29th year. This indicates that if the water project was built at the current costs of groundwater abstraction, it would

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have been built prematurely (Howe, 2002). This, however, is only if one applies economic-efficiency criteria. If other criteria (such as environmental sustainability) were used, this would call for a much quicker solution (e.g., due to the external costs of saltwater intrusion). In Spain, lack of groundwater management in the southern Mediterranean coastal belt, triggered in large, the conflict to divert water from the Ebro river in the north to help compensate for rapidly depleting aquifers (Llamas and Martı´nez Santos, 2005). Water agencies tend to build projects far in advance of their justifiable need on pure economic terms (Howe, 2002), and often fail to capitalize on the synergy between effective joint surface and groundwater use, which by definition implies the management of both. Restrictions on groundwater allocations are a direct loss attributed to decision makers and thus unpopular, whereas a loss of income due to over-abstraction is a probabilistic loss. Therefore, it is politically rational for decision makers to prefer users to continue pumping than to take the (unpopular) decision to cut allocations and instead opt for the politically more popular water transfers. There are very few systems of explicit conjunctive management. Until the 1940s, one main reason was the lack of understanding of hydrogeological knowledge and therefore the poorly developed model of surface and groundwater interaction. There are recent examples of regulatory innovations to deal with groundwater due to a much clearer understanding and a capacity to play to the strengths of surface and groundwater joint use – for example, in Colorado, where tributatory groundwater rights have been incorporated into the prior appropriation (priority) doctrine of water law, which in theory could preclude conjunctive management since groundwater tends to be junior rights compared to surface senior water rights (Howe, 2002).

1.07.6.2.3 Groundwater: Issues of fit and political windows of opportunity There is increasing evidence of the institutional diversity of groundwater user groups, from landowners associations, local landowner cooperatives, natural resource cooperatives, and water districts in Texas, to tube-well cooperatives in India, to agricultural transformation societies, and both public and private user communities in Spain. There is also some evidence that spikes in groundwater scarcity can trigger organization of these groundwater-user groups and in fact provide a window of opportunity for collective action, for example, drought can act as a motivator for self-organization or increased competition for groundwater resources among users. Drought intensifies conflicts and yet stimulates short-term and long-term efforts to modify rules and procedures for regulating rights. Scarcity value encourages the spontaneous creation of cooperative groups as groundwater becomes scarcer and groundwater economic value raises the cooperative model of groundwater pumping. Increasingly, it is appreciated that issues such as droughts rather than perceived as short-term crises are in fact also an opportunity for institutional change and adaptation. These are windows of opportunity for political action and social change since most stakeholders are receptive to the need for effective responses – that is, they provide opportunities for institutional innovation.

Strategically, choices can be made on the value of groundwater resources and prioritization of use, for example, for domestic water supply while at the same time providing incentives for economic transition. In addition, there is increased recognition on the importance of context and that there are no ideal aquifer-management regimes; rather governance arrangement. In particular groundwater basins are highly individualized, there is no single best-practice model for groundwater management; rather there are context-specific, multiple management scenarios which have to be negotiated. Institutional solutions that are viable in a particular context have to be framed within the inherent limitations of scientific knowledge, and centered on core objectives rather than specific groundwater parameters (Moench, 2007). That is, focus on livelihoods and environmental values rather than sustainable yield, which by itself is increasingly a contested concept (Llamas and Garrido, 2007). Responses have to be suitable for specific socioecological context rather than politically correct integrated management, which sometimes can be too rigid or overtly focused on technical ideals. Management in groundwater has to be pragmatic because timescales in the case of groundwater vary, on the one hand between the resource itself, which has an inbuilt lag, and requires a long-term perspective, and groundwater users themselves who often operate on a much shorter timeframe, normally driven by economic development. These two (long and short-term timescales) somehow have to be synchronized. The stabilization of the North China plain aquifer will be a long-term process when one considers that in 1988 the Hai river basin exceeded recharge by some 8800 Mm3 yr1, and an average recharge deficit of 40–90 mm yr1 (Foster et al., 2004). As discussed earlier in the chapter, at times it might be rational to use an aquifer intensively due to the associated socioeconomic and generational changes, which in time might decouple livelihoods from groundwater dependence, for example, through education or a more diversified economy. Issues of spatial scale and fit as raised earlier are relevant since often human institutions do not match groundwater boundaries. In the case of groundwater, in many cases, it is the individual, micro-level which drives groundwater use as the aggregate demand of thousands of groundwater users. To this, one has to add the high levels of uncertainty, which are an integral part of groundwater management, possibly magnified due to climate variability and change. For groundwater institutions, these limitations are inherent rather than situational (Moench, 2007).

1.07.7 Conclusion Suggestions or recommendations to achieve sustainable and ethical groundwater management have been presented in many conferences. The Alicante Declaration (Ragone and Llamas, 2006) is one of the recent ones. We include here what we consider relevant aspects: 1. There is no doubt that agriculture is the main blue and green water consumer. The virtual water-trade analysis

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2.

3.

4.

5.

6.

7.

seems to show that in many countries the motto ‘more crops and jobs per drop’ is changing to ‘more cash and nature per drop’. Usually hydrological and economic productivity of groundwater irrigation is significantly greater than the corresponding productivities of surface-water irrigation. Detailed analyses on this aspect in different climates and countries should be done to confirm or reject these preliminary data. The spectacular increase in groundwater use that has occurred in the last five or six decades can be classified as a silent revolution because it has taken place with scarce planning and control by governmental agencies. This is the main cause of some observed negative impacts, which could be avoided or mitigated with adequate groundwater management. It is extremely difficult to provide a general guide to good groundwater management, as complying with all the different dimensions may not be possible in most cases. Emphasis on one or another is likely to depend on economic, social, cultural, and political constraints. Groundwater management requires a higher degree of user involvement than surface-water developments. Experience shows that sustainable aquifer use cannot be solely achieved by means of top-down control-and-command measures. User participation requires a degree of hydrogeological education which is still absent in most places. Steps should be taken to make the peculiarities of groundwater resources known to all, from politicians and water decision makers to direct users and the general public. This should begin at the school level. Appropriate groundwater management requires a significant degree of trust among stakeholders. This implies that groundwater data should be transparent and widely available (e.g., via the Internet). In addition, the system should be able to punish those who act against the general interest.

References Acharya G and Barbier EB (2000) Valuing groundwater recharge through the agricultural production in the Hadejia-Nguru wetlands in northern Nigeria. Agricultural Economics 22: 247--259. Aldaya MM, Llamas MR, Varela-Oretga C, Novo P, and Rodriguez- R (2009) Challenging the conventional paradigm of water scarcity through the water footprint: The Spanish example. In: Hoekstra A (ed.) Global Water Governance. London: Earthscan. Bates BC, Kundzewicz ZW, Wu S, and Palutikof JP (2008) Climate Change and Water, Technical Paper of the Intergovernmental Panel on Climate Change, 210pp. IPCC Secretariat, Geneva. Bergkamp G and Cross K (2007) Groundwater and ecosystem services: Options for their sustainable use. In: Ragone S, de la Hera A, Hernandez-Mora N, Bergkamp G, and McKay J (eds.) Global Importance of Groundwater in the 21st Century: The International Symposium in Groundwater Sustainability, pp. 233–246. Alicante, Spain, 24–27 January 2006. Westerville, OH: National Groundwater Association Press. Briscoe J (2005) India’s Water Economy: Bracing for a Turbulent Future. Washington, DC: World Bank. Brown GM (2000) Renewable natural resource management and use without markets. Journal of Economic Literature 38(4): 876--915.

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Llamas MR and Garrido A (2007) Lessons from intensive groundwater use in Spain: Economic and social benefits and conflicts. In: Giordano M and Villholth KG (eds.) The Agricultural Groundwater Revolution: Opportunities and Threat to Development, pp. 266--295. Wallingford: CABI. Llamas MR and Martı´nez-Cortina L (2009) Specific aspects of groundwater use in water ethics. In: Llamas MR, Martı´nez-Cortina L, and Mukherji A. (eds.), pp.187– 204. Leiden: CRC Press/Balkema. (ISBN 978-0-415-47303-3). Llamas MR, Martı´nez-Santos P, and de la Hera A (2007) Dimensions of sustainability in regard to groundwater resources development: An overview. In: Ragone S, de la Hera A, Hernandez-Mora N, Bergkamp G, and McKay J (eds.) Global Importance of Groundwater in the 21st Century: The International Symposium in Groundwater Sustainability. Alicante, Spain, 24–27 January 2006. Westerville, OH: National Groundwater Association Press. Llamas MR, Martı´nez-Santos P, and de la Hera A (2008) Hydropolitics and hydroeconomics of shared groundwater resources: Experience in arid and semiarid regions. Paper presented in the conference of the NATO Advanced Study Workshop. Varna, Bulgaria, 1–12 October 2006. In: Darnault C (ed.) Overexploitation and Contamination of Shared Groundwater Resources, pp. 415– 431. Dordrecht: Springer. Llamas MR, Shah T, and Mukherji A (2006) Guest Editors’ preface. Hydrogeology Journal 14(3): 269--274. Loaiciga H (2003) Climate change and groundwater. Annals of the Association of American Geographers 93(1): 30--41. Lopez-Gunn E (2003) The role of collective action in water governance, a comparative study of groundwater user association in La Mancha aquifers, Spain. Hydrogeology Journal 28(3): 367--378. Lopez-Gunn E (2009) Governing shared groundwater: The controversy over private regulation. Geographical Journal, 175(1): 39–51. doi: 10.1111/j.14754959.2008.00313.x. Lopez-Gunn E and Llamas MR (2008) Re-thinking water scarcity: Can science and technology solve the global water crisis? Natural Resources Forum 32: 228--238. Margat J (2008) Les Eaux Souterraines dans le Monde. 178pp. Orleans: BRGM e´ditions. Martı´nez-Cortina L (2001) Estimacio´n de la recarga en grandes cuencas sedimentarias mediante modelos nume´ricos de flujo subterra´neo. Aplicacio´n a la cuenca alta del Guadiana (Recharge Estimation in Large Sedimentary Basins Using Groundwater Flow Models. The Case of the Upper Guadiana Basin), 418pp. PhD Thesis, University of Cantabria, Spain. Melgarejo J and Molina A (eds.) (2005) Los mercados del agua. Ana´lisis jurı´dicos y econo´micos de los contratos de cesio´n y bancos de agua. Madrid: Civitas. Merrey D, Meinzen-Dick R, Mollinga P, and Karar M (2007) Policy and institutional reform: The art of the possible. In: Molden D (ed.) Water for Food, Water for Life: Comprehensive Assessment of Water Management in Agriculture. London: Earthscan. Moench M (2003) Groundwater and poverty: Exploring the connections. In: Llamas MR and Custodio E (eds.) Intensive Use of Groundwater: Challenges and Opportunities, pp. 441--456. Lisse, The Netherlands: Swets and Zeitlinger. Moench M (2007) When the wells run dry but livelihoods continue: Adaptive responses to groundwater depletion and strategies for mitigating the associated impacts. In: Giordano M and Villholth KG (eds.) The Agricultural Groundwater Revolution: Opportunities and Threats for Development, pp. 173--194. Wallingford: CABI. Molle F and Berkoff J (2006) Cities versus agriculture: Revisiting intersectoral water transfers, potential gains, and conflicts. Comprehensive Assessment of Water Management in Agriculture Research Report 10. Colombo: International Water Management Institute. Mukherji A (2006) Is intensive use of groundwater a solution to world’s water crisis? In: Rogers PP, Llamas MR, and Martı´nez-Cortina L (eds.) Water Crisis: Myth or Reality? Marcelino Botin Water Forum 2004, pp. 181–193. London, UK: Balkema/ Taylor and Francis Group. National Research Council (2004) Valuing Ecosystem Services. Washington, DC: National Academies Press. Neher PA (1990) Natural Resource Economics: Conservation and Exploitation. New York: Cambridge University Press. Ostrom E (1990) Governing the Commons: The Evolution of Self-Governing Irrigation Systems. Cambridge: Cambridge University Press. Peck JC (2007) Groundwater management in the high plains aquifer in the USA: Legal problems and innovations. In: Giordano M and Villholth KG (eds.) The Agricultural Groundwater Revolution: Opportunities and Threats for Development, pp. 296--319. Wallingford: CABI. Pongkijvorasin SP and Roumasset J (2007) Optimal conjunctive use of surface and groundwater with recharge and return flows: Dynamic and spatial pattern. Review of Agricultural Economics 29(3): 531--539.

Ragone SE and Llamas MR (2006) The alicante declaration: Steps along the pathway to a sustainable future. Ground Water Reader’s Forum 44(4): 500--503. Rijsberman F (2004) Sanitation and access to water. In: Lomborg B (ed.) Global Crises, Global Solutions, pp. 498--527. New York: Cambridge University Press. Rosegrant M, Cai X, and Cline S (2002) World Water and Food to 2025. Washington, DC: IFPRI. Rubio SJ and Fisher AC (1997) Adjusting to climate change: Implications of increased variability and asymmetric adjustment costs for investment in water reserves. Journal of Environmental Economics and Management 34: 207--227. Saleth RM (1996) Water Institutions in India: Economics, Law and Policy. New Delhi: Commonwealth. Selborne J (2001) The ethics of freshwater use: A survey. Report of the Commission on the Ethics of Science and Technology (COMEST), 62pp. Paris, France: UNESCO. Schlager E and Lopez-Gunn E (2006) Collective systems for water management: Is the tragedy of the commons a myth? In: Rogers P, Llamas MR, and Martı´nez-Cortina L (eds.) Water Crisis: Myth or Reality? pp. 43--60. London: Taylor and Francis. Shah T (2005) The new institutional economics of India’s water policy. In: International Workshop on African Water Laws: Plural Legislative Frameworks for Rural Water Management in Africa0 . Johannesburg, South Africa, 26–28 January 2005. Shah T, Burke J, Villholth K et al. (2007) Groundwater: A global assessment of scale and significance. In: Molden D (ed.) Water for Food, Water for Life: Comprehensive Assessment of Water Management in Agriculture, pp. 395–423. London: Earthscan. Stalgren P (2006) Corruption in the Water Sector: Causes, Consequences and Potential Reforms, Swedish Water House Policy Brief No. 4. SIWI. Transparency International (2008) Global Corruption Report 2008: Corruption in the Water Sector. Cambridge: Cambridge University Press. Tsur Y and Parker D (1997) Decentralization and Coordination of Water Resource Management. Boston: Kluwer. UNECE (1998) Convention on access to information, public participation in decisionmaking and access to justice in environmental matters. Aarhus, Denmark, 25 June 1998. http://www.unitar.org/egp/sites/default/files/aarhus_convention.pdf (accessed March 2010). Wagner M (2005) Wildlife and Water: Collective Action and Social Capital of Selected Landowner Associations in Texas, 150pp. PhD Thesis, Texas A&M University. Wegerich K (2006) Groundwater institutions and management problems in the developing world. In: Tellam JH, Rivett MO, and Israfilov RG (eds.) Urban Groundwater Management and Sustainability. Dordrecht: Springer. WHO (World Health Organization) (2001) Arsenic in Groundwater – Factsheet. http:// www.who.int/mediacentre/factsheets/fs210/en/print.html. (accessed March 2010).

Relevant Websites http://www.acaciawater.com Acacia Water. http://www.aueas.org Asociacio´n Espan˜ola de Usuarios de Aguas Subterra´neas. http://www.whymap.org BGR: UNESCO; World-Wide Hydrogeological Mapping and Assessment Program (WHYMAP). http://www.connectedwater.gov.au Connected Water: Managing the Linkages between Surface Water and Ground Water. http://www.ecolex.org ECOLEX: The Gateway to Environmental Law. http://www.empowers.info EMPOWERS Thematic Group: Advancing participation and dialogue in local water governance in the MENA Region. http://www.epa.gov EPA: United States Environmental Protection Agency; Groundwater and Drinking Water. http://www.ewater.eu eWater Portal. http://www.water.nstl.gov.cn GWRTAC: Ground-Water Remediation Technologies Analysis Center. http://www.waterandfood.org Groundwater Governance in Asia: Water is Divine. http://www.sg-guarani.org Guarani Aquifer System.

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http://www.indiawaterportal.org India WaterPortal. http://www.igrac.nl International Groundwater Resources Assessment Centre. http://www.inpim.org International Network on Participatory Irrigation Management. http://www.waterlaw.org International Water Law Project: Addressing the future of water law and policy in the 21st century. http://www.isarm.net isarm Internationally Shared Aquifer Resources Management; Transboundary Aquifers. http://aguas.igme.es No se encuentra la pa´gina. http://www.groundwatermanagement.org Participatory Groundwater Management. http://www.ploppy.net Ploppy (educational material for children (in Spanish) on groundwater).

http://www.projectwet.org ProjectWET; Worldwide Water Education. http://www.groundwater.org The Groundwater Foundation. http://www.worldbank.org The World Bank; GW-MATE. http://www.iah.org The WorldWide Groundwater Organisation: International Association of Hydrogeologists. http://www.water-ed.org Water Education Foundation. http://www.wfdvisual.com WFDVisual: Water Framework Directive Visualisation Package. http://www.groundwateruk.org UK Groundwater Forum: Raising Awareness of Groundwater.

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1.08 Managing Agricultural Water J Ramirez-Vallejo, Universidad de los Andes, Bogota, Columbia & 2011 Elsevier B.V. All rights reserved.

1.08.1 1.08.1.1 1.08.1.2 1.08.1.3 1.08.1.3.1 1.08.1.3.2 1.08.1.3.3 1.08.1.3.4 1.08.1.3.5 1.08.1.3.6 1.08.1.3.7 1.08.1.3.8 1.08.1.4 1.08.1.4.1 1.08.1.4.2 1.08.1.4.3 1.08.1.4.4 1.08.1.4.5 1.08.2 1.08.2.1 1.08.2.2 1.08.2.2.1 1.08.2.2.2 1.08.2.2.3 1.08.2.2.4 1.08.2.3 1.08.3 1.08.3.1 1.08.3.2 1.08.3.3 1.08.4 1.08.4.1 1.08.4.2 1.08.4.3 1.08.4.3.1 1.08.4.3.2 1.08.4.4 1.08.4.4.1 1.08.5 1.08.6 1.08.7 1.08.8 1.08.9 References

Introduction and Overview Trends in Water Management for Agriculture Water Scarcity: Is It a Demand or a Supply Problem? Challenges Facing Agricultural Water Management The policy and institutional challenge The economic and financial challenge The problem of declining investment The challenge of technology and water resources to supply growing demand Poor performance of public managed irrigation systems The neglect of environmental impacts of agricultural water management Neglect of water management for rainfed agriculture The poverty and rural incomes challenge Potential/Promise of Science and Technology Advances Hydro-climatic forecast prediction Drought-tolerant crops Remote-sensing technology on the estimate of crop ET Cost-effective irrigation systems Improving irrigation water management Water Productivity in Agriculture Economic Value of Water for Agriculture Example of Estimates of the Economic Value of Water: The Case of Mexican Agriculture Indirect method Residual method Water markets Math programming method Agricultural Trade Protection Water Management and Competitiveness Framework Farmers, Water, and the Process of Economic Development Water Resource Management and the Regional Economic Strategy Water Resource Management, Institutions, and Implementation Integrated Water Resource Management Participatory Irrigation Management Lessons Learned from Participatory Management The Philippines Mexico Institutions and Water Governance Water management principles Water Management and the Environment Water for Agriculture and Poverty Reduction Water Management of Rainfed Agriculture Policy Actions for the Future Summary

1.08.1 Introduction and Overview Managing water for agriculture is a topic that covers broad dimensions in the development of countries, such as management of water for rainfed agriculture, irrigated agriculture, water-use recycling, conservation of water and land, and watershed management. It is a collection of activities that

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lies between four areas of economic development: rural development, agriculture, water supply, and environmental management. Water management has achieved an impressive record during the past century but there is a larger challenge ahead. Irrigation is responsible for approximately 75% of water demand in developing countries and low-cost waters are already

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a scarce resource. Only 17% of all cropland is irrigated, but provides 30–40% of the world’s food production, and over 60% of the world’s irrigated area is in Asia, mostly devoted to the production of rice (IUCN and WBCSD, 2008). Water needs to be more productive as an input to generate a more dynamic development of rural areas, to supply the food for the increasing demand in the world, and to leave enough water for other key uses such as domestic and industrial uses. At the same time, water use in agriculture needs to be friendly with the environment and society in general. A new context for water management in agriculture has been generated by the changing national and global trends. The high rates of population growth, the dynamic economies of countries such as China, India, and East Europe that bring each year millions of people out of poverty with increasing levels of disposable income to spend on higher value food, and the stronger desire of the world to live in an environmentally safe planet are just some of the trends that delineate the context in which water will need to be managed for agriculture. Water connects all ecosystems across the landscape and the competition for its use is becoming increasingly intense over time. In many locations, water supply is limited compared to its demand for various uses, and given that agriculture is by far the largest consumer of water, the future effective supply will depend on the productivity of water in agriculture. This water productivity is a function of the management, innovation, and governance of the water resource systems. More efficient irrigation systems, more precision agriculture, and the upgrading of rainfed systems and wastewater management will generate a better social and economic water use when agricultural production is increased. The above scenario suggests the importance of dealing with water management in agriculture as a way to optimize its use for society. This chapter first presents the context and main trends that will influence water management in agriculture. It presents the main challenges facing agricultural water management, and its impact on poverty reduction. Subsequently, two related sections present the link between water management for agriculture and competitiveness as a framework to explore water productivity in agriculture, with emphasis on the value of water for irrigation. Topics such as water management trends, water management in rainfed agriculture, and the impact of water management on the environment are also covered. Finally, governance and institutional aspects of water management are delineated.

1.08.1.1 Trends in Water Management for Agriculture According to the United Nations (2008), it is estimated that by year 2050 the world population can reach 8.9 billion, up by 47% of today’s population, and will place a significant pressure to world agriculture to satisfy food demand. Global agriculture must double in the next 30 years to sustain the population, and given the constraint on land expansion, current agriculture will need to become more efficient to meet this demand. Given this reality, the obvious question is how the world is going to use the available water to produce enough food to feed the world population in the future without compromising the environment and with a minimum

external cost for society? Also, given that it is expected that the urban population will exceed the rural population by a significant margin, how can water be more productive to deal with a shrinking rural population that needs to feed increasing urban population as well as themselves? Although the share of world’s workers who are employed in agriculture has decreased from 42% in 1996 to 36% in 2006 (International Labour Organization, 2007), this sector will remain to be an important source of occupation for many people, especially those from developing countries. Water will remain with an important role of increasing the well-being of many rural workers and their families, directly through consumption of the fruits of their labor and by increasing their income. Consumers will define the function of water management in agriculture. As income increases so does the demand for food, not only in terms of quantity but also in the composition of the traditional food intake. It has been shown that wealthier consumers demand more animal protein, such as meat and milk products, something that requires more land to be used for agriculture and livestock production. As an example, in the recent evolution of diet in China, meat consumption has more than doubled in the last 20 years and it is projected to double again in 2030 (Centre for World Food Studies, 2008). Another observed trend is the concern of the consumer about food safety where traceability becomes important and water becomes an attribute of agricultural products when consumers take the final purchasing decision. In addition, people in developed countries will demand more fruits and vegetables, a trend that will induce the production of fewer calories per hectare, reducing field crops such as cereals and producing more vegetables and fruits that require more water. Food production to satisfy a person’s daily dietary takes about 3 m3 of water, a little more than 1 l calorie1 (IUCN and WBCSD, 2008). Another trend that will transform the composition of food intake is the change in consumer taste and expectations when people place more emphasis on doing their best with the limited time at their disposal. People are eating healthier convenient food such as fresh-cut snacks, a market that has grown from 8.8 billion in 2003 to 10.5 billion by 2004, according to the International Fresh-Cut Produce Association. These trends are becoming stronger over time. Water plays then an active role in the virtuous cycle of development. Water is needed to generate high-value products and income to rural people that will demand more agricultural products. In addition, more sophisticated products will create an incentive for innovation processes. The pressure to increase water productivity in agricultural ecosystems will induce water to be used in a more friendly manner with numerous species, such as microorganisms in the soil and pests and predators that are part of the agricultural biodiversity needed to have plant stability and sustainable crop production. Water is then a necessary input to implement management systems such as integrated crop management and conservation agriculture. Water pollution is increasing and the sources of water are being affected by the augmentation in water consumption mainly for agricultural production. Irrigation water has

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generated important increases in income in many parts of the world with a clear impact on poverty but with negative effects on the environment. The problems of soil erosion, salinization, intrusion of seawater, and pollution, in general, are limiting the effective water availability for agriculture and other uses. Most of these processes are real consequences of badly managed water resources in settings with institutions that fail or are slow to change processes. Land and water productivity had raised agronomic yields from 1.4 tons per hectare to 2.7 tons per hectare over the past four decades. However, according to the IWMI (2007), the number of malnourished people is above 850 million (IWMI, International Water Management Institute), and the average daily per capita food supply in some regions of the world, such as South Asia and sub-Saharan Africa, is rising too slowly to generate a significant impact on poverty alleviation. These levels are far below from the ones observed in industrialized countries. In addition, it is also estimated that a third of what it is produced in agricultural products is lost before it is consumed. Another trend that is set to play an important role in future water management in agriculture is the world balance of energy. It is estimated that alternative fuels will provide 5% of the United States’ energy by 2020, up from 1% today, and something similar will be experienced by other developed and developing countries. Bioenergy, currently made largely from sugar cane and from corn, will increase the demand for water and generate water-associated contamination. More certification standards and enabling regulatory and policy frameworks will most likely be set to obtain sustainable practice of this economic activity. Moreover, competition for land use to provide food and fiber will affect food prices and impact lowincome consumers. There is still some debate regarding the extent to which climate change will impact agricultural productivity at the global level (World Bank, 2008). Climate change is affecting precipitation patterns and temperatures, and it is estimated that the areas of the world that are poorer will be the most adversely affected in its impact on water supplies. Groundwater levels are declining in many areas of the world, such as North Africa, North China, India, and Mexico, because of overexploitation, and there is not enough water to meet all demands of the different uses. Some estimates show that if nothing is done, climate change by the 2080s would have an impact in agricultural production capacity by about 16% if carbon fertilization is omitted and by about 3% if it is included. Some parts of the world will be more affected than others, particularly India and a number of countries in Africa will be impacted. Finally, the reconfiguration of global value chains that redefine the role of location on generating advantages to companies is a trend that will modify the geography of agricultural output and production of goods and services that are used as inputs in agriculture.

1.08.1.2 Water Scarcity: Is It a Demand or a Supply Problem? When confronting the future, the relevant question is whether the relative water scarcity is a demand or a supply challenge. Some authors are optimists and some others are pessimists. The optimists, for example, Allan (2001), the World Bank

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(2003), IFPRI (1995, 1997), Dyson (1996), Rosegrant et al. (1995), Rosegrant and Cai (2002), and Brichieri-Colombi (2004, Who Speaks for the River; PhD Thesis, University of London; unpublished) think there is sufficient freshwater and soil water in the world to meet current and future water needs if we accept the demographic predictions. On the other hand, we find pessimists such as Postel (1999) and Postel and Richter (2003) who argue that there are not enough water supplies to meet the estimated demand, given the population and income projections. For them, the prospect looks darker when considering the water-scarce Middle East and the heavily populated South Asia and China. For some, the difficulty can be described as a technology and economic problem to make water available where it is needed for distinct uses. In other words, there is plenty of water in the world – the issue is how to induce the right costefficient technology to obtain the desired quality of water available to transport it to where it is needed. Theoretically, the existing pressure on freshwater use will generate the right incentive to innovate new ways of obtaining water, such as (1) the production of freshwater by desalination of brackish or saltwater (mostly for domestic purposes), and (2) the reuse of urban or industrial wastewaters (with or without treatment), which increases the overall efficiency of use of water (extracted from primary sources), mostly in agriculture, but increasingly in the industrial and domestic sectors. This category also includes agricultural drainage water. However, these technologies are still high-cost options for most developing countries, a fact that leads to reframe the water scarcity problem as a demand problem – whether economic resources are available at the country, region, company, and farm levels to pay for the cost of these technologies. The two major cost items are treatment and transportation to users. Most likely, in a scenario with increasing water demand accompanied by scarcity of local water supply, the induced innovation hypothesis will take place (Hayami and Ruttan, 1985), in which technical and institutional changes will be induced through the responses of farmers, agribusiness entrepreneurs, scientists, and public administrators to water endowments and to changes in the supply and demand of water and other inputs. Based on this, the state of relative endowments and the accumulation of the primary resources of land, labor, and water, would be critical elements in determining the pattern of technical change that will occur in water resources and agriculture in the future. Technical change embodied in new and more productive inputs may be induced primarily to save labor, to save land, or to save water. However, this works in the right direction if the agricultural inputs are priced according to their opportunity cost (Ramirez-Vallejo and Rogers, 2004). If subsidies exist, which is the case in the majority of developing and developed countries, water will remain undervalued by society and future changes in technology will not reflect the reality of water scarcity. A trade-related concept has been proposed to solve the water-supply–water-demand correspondence. The trade-in embodied water or virtual water concept comes from the idea that water should be treated as a production factor and is equivalent to the volume of water needed to produce a commodity or service. The virtual water argument, on the other hand, shows that the importation of agricultural products

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that require significant amounts of water represents the importation of water into a water-scarce country. According to Allan (2004), ‘‘trade in commodities has already achieved a pivotal position in enabling the comparative advantages and disadvantages of regional water endowments to be balanced.’’ The concept of trade-in embodied water can be used to study the linkages that exist between trade reform and water resources development. The trade-in embodied factor or the factor content of trade approach was first employed by Leontief (1953) in his well-known test of the Heckscher– Ohlin (H–O) theorem and later formalized theoretically by Travis (1964), Melvin (1968), Deardorff (1982), and many others. However, there are only few studies in the literature that discuss trade-in embodied environmental factors. This approach has been extended to analyze the linkage between trade, food security, and water resources by using the virtual water concept and argument. The concept originates from the idea that water should be treated as a production factor and virtual water is the volume of water needed to produce a commodity or a service. In other words, countries sometimes do not have a surplus amount of water to produce water-intensive agricultural commodities such as rice, cotton, and tropical and subtropical fruits such as bananas and oranges, and use trade to compensate for this shortage. Food trade then becomes an instrument to augment water supplies on the scale needed to meet the domestic food demand. This concept has become popular since Allan (1996) used it first. An example of this concept is that it takes approximately 1500 m3 of water to grow 1 ton of grain. Equivalent figures can be estimated for virtually any traded agricultural product; however, these figures can only be used as an approximate number given that the real demand and supply of water to an individual crop depend on a large number of variables (i.e., the geographic location of the region where the crop is grown, the irrigation system used, the management of the irrigation system, soil type, climatic and socioeconomic conditions, etc.). The concept of virtual water then becomes a useful indicator of future water resources use. The virtual water argument can be also seen as an application of the well-known H–O theorem. The assumptions of this model are that factors can move without cost among industries within a country, but are completely immobile internationally, which might be the case of water as a production factor. As such, production functions for all countries (products) exhibit constant return to scale and each country has the same productive technology for each good, and consumers have the same utility functions. Under these conditions, the theorem states that a country exports products that use intensively a relatively abundant input. However, the level of protection to agricultural products via tariffs and duties and nontariff instruments by all countries, developed and developing, has distorted the virtual water movement worldwide. Ramirez-Vallejo and Rogers (2004) showed that, for example, using International food Policy Research Institute (IFPRI)’s IMPACT model results, a scenario of full liberalization of agriculture compared to a baseline scenario would have a significant net effect of virtual water flows from the relocation of meat and cereals trade. When the net effect of the meat and cereals markets are added together,

the two major contributors to the increase in virtual water trade would be the United States, which would increase its annual virtual water exports in about 86 km3, and Latin America would have a similar increase of 89 km3. These become the two water surplus regions in the world. The major changes in virtual water imports would occur in Asia in general (South Asia, Southeast Asia, and East Asia) with an increase of 112 km3, sub-Saharan Africa with an increase of almost 40 km3, and the former Soviet Union with an increase in water imports of 22 km3, mostly because of an increase in meat imports. West Asia and North Africa together, on the other hand, would decrease the level of virtual water imports to about 7 km3, but would remain as an important net importer of virtual water of about 176 km3. Ramirez-Vallejo and Rogers (2009) showed that the concept of virtual water is useful to educate public officials and society in general that water in some parts of the world is a scarce resource and that agriculture uses the great majority of water resources available on earth. The argument also has an implicit lesson underscoring the importance of running efficiently irrigation districts so that water could be allocated to other uses, including ones benefiting the environment.

1.08.1.3 Challenges Facing Agricultural Water Management Under the described trend scenarios, agricultural water management faces many challenges, especially as a result of its strong links with many global and economic issues. Agricultural management is not a unique goal by itself but it is part of a process to manage one of the most important inputs to income generation. According to the World Bank (2006), some of the challenges facing agricultural water management include the policy and institutional challenge; the economic and financial challenge; the problem of declining investment; the challenge of technology and water resources to supply growing demand; the poverty and rural incomes challenge; and the environmental dimension and sustainability imperative.

1.08.1.3.1 The policy and institutional challenge The most difficult challenge has been how to conciliate agricultural and macroeconomic and social policy. Countries want low-cost food products for the population and at the same time to improve farmer’s income levels. Low-cost products for local consumers imply low trade barriers and higher competition from international competitors, and lower support levels to agriculture for a food security policy. Generating incentives for agricultural development through water management for agriculture has its direct trade-offs with specific macroeconomic goals. The incentives for agricultural water management need to be integrated with agricultural policy and new institutional capacity needs to be built for a better water allocation and priority setting at the basin, regional, and national levels. New management skills and a better understanding of the political economy of reforms need to be developed.

1.08.1.3.2 The economic and financial challenge Use of water in agriculture generates the lowest value-add compared to other uses such as municipal, domestic, and industrial uses. When water is assigned to the higher value use, agriculture is usually ranked lower and other benefits different

Managing Agricultural Water

from direct economic and financial need to be added to justify allocation. Water scarcity increases and competition creates the incentive to improve the returns on water. The economic challenge is how to build an incentive system to encourage efficient water use and profitable agriculture of high value. A broader economic challenge deals with the generation of an incentive framework for all types of investment and to promote environmentally responsible use. On the financial side, the challenge is to achieve cost recovery in traditional agricultural systems that are characterized by high subsidies, selfsufficiency goals, and low-productivity agriculture and to create the investment environment under the existing distorted incentive frameworks.

1.08.1.3.3 The problem of declining investment The global irrigated area doubled from 1960 to 2000, developing faster at the beginning of the period and slowing down in later years (Cleaver and Gonzalez, 2003; Winpenny, 2003). The world experienced a decrease in the construction of dams for the development of surface irrigation, which has been compensated for by the growth of groundwater irrigation. According to the World Bank (2005), governments are investing less in agriculture worldwide; public investment in agriculture has dropped, and investment in irrigation, drainage, and other agricultural water management projects has also been declining worldwide. The situation for groundwater irrigation is delicate. Most of the past investment has been predominantly private and done in an unregulated environment ending up in overexploiting groundwater resources. Another area that demands significant resources for investment is drainage. Unfortunately, it is difficult to provide the right investment incentives for drainage infrastructure because usually the benefits are underestimated and the cost recovery is a difficult task.

1.08.1.3.4 The challenge of technology and water resources to supply growing demand Irrigated agriculture supplies close to 40% of the world’s food and occupies only 17% of the cultivated land. However, for the future, the Food and Agriculture Organization (FAO) estimates that by 2030, food production needs to grow at 1.4% a year, and about half of this growth would have to be generated from irrigated agriculture. Therefore, the main challenge has to do with the capacity of the agricultural sectors to meet this additional demand. Technological change has slowed down from the significant advances of the Green Revolution of the 1960s, and the water base is overexploited in many ecosystems, particularly in developing countries where institutions and regulations failed to control water exploitation. However, technology is available, but often is not disseminated and adopted. Cost incentives or profitable market opportunities generate the incentive for farmers to invest in technology. A study by the World Bank found that there is ‘‘more technology available than we know what to do with’’ (World Bank, 2005). Many innovations are available to improve productivity or conserve water but have not been adopted as it was expected, such as drip technology, for example, that has been adopted on less than 1% of irrigated lands worldwide with affordable costs.

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1.08.1.3.5 Poor performance of public managed irrigation systems Publicly managed irrigation systems account for half the irrigated area in developing countries and their performance has been generally below their technical and economic potential. Water service is poor, unreliable, and untimely. Among the causes are the decline of agricultural prices and subsidies to basic crops that have hampered diversification to high-value crops. However, perhaps the major cause comes from poor institutions with weak organization structures, bureaucratic with inefficient top-down approaches to service. Deficient management has led to lower collection rates of operation and insufficient maintenance fees, which have led to higher needs for rehabilitation intervention, which creates a never-ending pervasive cycle. Scarce farmers’ participation in the management processes has been recognized as one of the main reasons for the lack of accountability and efficiency in the public institutions.

1.08.1.3.6 The neglect of environmental impacts of agricultural water management As a result of agriculture being by far the largest user of land and water resources, there is a long list of environmental costs and risks of using water for agriculture, including land degradation, salinization, and erosion; loss of environmental water flows; pollution; destruction of natural habitats and livelihoods through drainage of wetlands and through land expansion and deforestation; and waterborne disease. Drainage was neglected in the rapid expansion of irrigation and irrigated land has become waterlogged and salinized due to the rise of the water tables and accumulation of salts, becoming a constraint to productivity. Land degradation caused by agricultural water management practices and by lack of drainage is affecting some of the world’s most fertile basins and dams, and irrigation infrastructure modifies flows, affecting their seasonality and frequency of floods. In addition, irrigated agriculture is a source of pollution, as a result of the technology package promoted with the green revolution that included the important use of chemical inputs. However, the main challenge of the management of water in agriculture is to simultaneously conserve biodiversity and diminish any external impact of water use to increase production, to secure enough production to meet the increasing demand for food, and, finally, to improve the prosperity of the rural people all around the world. Management of water in agriculture needs not only further increase in the productivity of existing farmland, but also to do it simultaneously with a support of biodiversity and ecosystem services and efficiently manage the natural resources. It needs to foster healthy populations and help them realize their development potentials and to increase prosperity in general, by generating income and improving the livelihoods of the rural communities and increase the value of the agricultural products.

1.08.1.3.7 Neglect of water management for rainfed agriculture Rainfed areas are home to most of the poor habitants of the rural areas and account for 60% of the current agricultural output. These areas are characterized by having less use of

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available technology. The technological options for improved water management in rainfed agriculture are few, mainly because the incentives have not generated the relevant research results around the world to offer higher yielding varieties for water stress and environmentally sensitive conditions. As a result, improvements in water management have been limited in these areas.

1.08.1.3.8 The poverty and rural incomes challenge Agricultural growth is central to poverty reduction. Seventy percent of the world’s poor live in rural areas, and most of them are dependent on agriculture. Typically, the rural poor live on marginal lands or on drylands, with little or no access to controlled water sources. Their technological options for improved water management are limited, and they face high risks from rainfall variations. The poor are exceptionally vulnerable to drought, floods, effluent discharge, aquifer depletion, waterlogging, salinization, and water quality deterioration. Thus, the key agricultural water challenges for the poor are food security, risk mitigation, and income growth. One of the Millennium Development Goals (MDGs) – eradicate extreme poverty and hunger – can be achieved only if agriculture grows and can provide access to food for the poorest and most vulnerable. Improved management of available water thus has a critical role to play in poverty reduction and food security. Other MDGs such as gender equality, child nutrition, and market access also depend directly or indirectly on pro-poor agricultural growth and related management of scarce water.

1.08.1.4 Potential/Promise of Science and Technology Advances At the same time, there are challenges, some of which might be more optimistic concerning agricultural water management because of the opportunities provided by advances in science and technology. Some of them are the hydroclimatic forecast and prediction, which could lead to water saving and profit gain, remote-sensing technology on the estimate of crop evapotranspiration (ET), drought-tolerant or salt-tolerant crops, improvement of cost-effective irrigation systems, and best management practices, mainly for water quality. Some of these are explained below.

1.08.1.4.2 Drought-tolerant crops Drought conditions are a well-known restriction to crop production. Research in public and private institutions is producing promising results in terms of developing varieties that demand less water while maintaining a higher level of agronomic yield. Local plant biologists are working hard to develop new crop strains that will produce abundant food even when water is scarce. Canola, in Canada, for example, is highly sensitive to water stress, which is one of the main factors that limits crop yield (Wanna et al., 2009). Development of drought-tolerant canola was then considered an important and urgent mandate for the canola industry. A breakthrough in this area should increase yield and permit expansion of canola growth regions. However, to be an effective solution, the new strain must produce normal yields under nondrought conditions and produce greater yields than conventional varieties under stress conditions. Research efforts in order to create drought-tolerant crop plants have been intensive, but the results remain incremental. Monsanto and other agricultural companies have drought-tolerant soybeans, cotton, and other crops in the pipeline. Universities are also engaged in intensive research aimed at growing more food with less water. ‘‘A more rapid growth in rainfed yield and production could compensate for reduced investments in irrigation or reduced groundwater pumping to eliminate groundwater overdraft, but that achieving the required improvements in rainfed production would be a significant challenge’’ (Rosegrant et al., 2001).

1.08.1.4.3 Remote-sensing technology on the estimate of crop ET Spatiotemporal information on actual ET helps users to better understand evaporative depletion and to establish links between land use, water allocation, and water use. Satellite-based measurements, used in association with energy-balance models, can provide the spatial distribution of ET for these linkages and optimize the use of water for irrigation (Bastiaanssen et al., 2005). Remote-sensing and hydrological models are applied to irrigation projects to estimate the water balance to support water use and productivity analyses. A case study in the Yakima River basin (Washington State) demonstrates how ET from remote sensing can be used for evaluating water-conservation projects.

1.08.1.4.4 Cost-effective irrigation systems 1.08.1.4.1 Hydro-climatic forecast prediction Expert system techniques have been used in irrigated agriculture to optimize the use of water in districts and to maximize profits by increasing agronomic yields. In the Havana Lowlands region, Illinois, USA, for example, researchers incorporated different types of weather forecasts into irrigation scheduling for corn production. The results showed that if farmers just use the real-time soil moisture information and the empirical rules set, profits might increase by 16%; and over the five testing years, it was found that the proper use of the 7-day forecast could save irrigation water and increase crop yield in dry years. The study found that farmers at the study site sometimes applied more water than necessary (Bastiaanssen et al., 2005).

Water is also saved through innovation in irrigation systems. Cost-effective irrigation systems allow for a better allocation of water resources. One way to achieve this is through the reengineering of the irrigation systems, which consists of designing the most cost-effective answer to the redefined water service within the scheme. This is a solution that demands consideration of the spatial distribution of the effective demand for the water service and the spatial distribution of the physical infrastructure characteristics. Some of the people in rural areas have developed their own water system with cost-reducing adaptations. One of these is the development of a solar system, which requires low operation cost compared to an electrical system to drive the motors or engines that finally converts the electrical energy to

Managing Agricultural Water

mechanical energy, so as to pump the water to the required destination.

1.08.1.4.5 Improving irrigation water management Best management practices are also a way to save water in irrigation systems. Improved performance in irrigation water management can usually be achieved through rehabilitation, process improvement, which consists of intervening in the process without changing the rules of the water management, and modernization. The introduction of modern techniques is a process improvement, and modernization, which is a more complex intervention implying fundamental changes in the rules governing water resource management. It may include interventions in the physical infrastructure as well as in its management.

1.08.2 Water Productivity in Agriculture Water scarcity can be seen under a multidimensional framework of physical, economic, managerial, institutional, and political water scarcity (Molle and Mollinga, 2003): physical water scarcity in which water availability is limited by natural availability; economic water scarcity when human and financial resources constrain availability of water; managerial water scarcity where availability is constrained by management limitations; institutional water scarcity where water availability is constrained by institutional shortcomings; and political water scarcity where political forces bar people from accessing available water resources. These types of scarcity can occur concomitantly, increasing both the severity and the impacts of water scarcity. Molden et al. (2003) estimated that, by 2020, approximately 75% of the world’s population will live in areas experiencing physical or economic water scarcity, precisely where most of the poor and food-insecure people live. Water productivity is a concept that has meaning and use when water is a scarce resource. Higher water productivity becomes part of the answer to the challenges described above. However, productivity is a concept used frequently by economists, engineers, and even biologists, with some degree of confusion. The concept of water productivity relates to the desire to have a higher level of output, economic value-add, or just value for the aggregate production per unit of water used. In other words, it is the way of achieving more using scarce water resources. In order to better understand the concept of water productivity, it is necessary to review the definition of efficiency. Irrigation efficiency is defined as the ratio of water consumed to water supplied. Irrigation systems in the developing world typically function at a level of irrigation efficiency from 30% to 40% (Seckler et al., 2003), showing the big difference that exists in terms of quantity between the point of water diversion in the irrigation system and the water available in the root zone of the plant. Agriculture depletes the water resources through evapotranspiration, and this signifies less water for other uses in the system. Climate plays an important role in the total amount of water that is evaporated and transpired by plants.

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Although there is not a single definition, water productivity is then the ratio of crop output to water either diverted or consumed, being expressed in either physical or monetary terms, or some combination of the two (Barker and Molden, 2003). Another related concept used by economists is the economic efficiency, which takes into account value of output, opportunity costs of inputs and externalities, and it is achieved when water is allocated and used so the net value or net returns are maximized. It is a criterion that describes the conditions that must be satisfied to achieve the largest possible net resource (Wichelns, 1999). It is a concern for economists that, in general, the true value of water is not reflected by the prices or charges for irrigation water, in which the allocation does not follow socially optimal scenarios. Management of water in agriculture should be targeted to allocate water to the highest level of water productivity as a social optimum. In theory, the allocation of water among competing uses involves an economic optimization exercise. If we were to consider only private returns to the agricultural production activity, some alternatives to improve water productivity would be (Wallace and Batchelor, 1997):

• • • •

agronomic improvements (e.g., improved crop husbandry, cropping strategies, and crop varieties); technical improvements (e.g., improved and lower-cost technologies for extracting groundwater); managerial improvements (e.g., improvements in farmlevel resource management or system operation and maintenance (O&M)); and institutional improvements (e.g., introduction of water pricing and improvement in water rights).

The first two categories are innovations or new technologies that lower cost or increase the value of output per cubic meter of water. The third category refers to an increase in technical efficiency, and the fourth relates to allocative efficiencies encouraged by the creation of market incentives (Barker and Molden, 2003). The economic value of water – economic water productivity – can be increased by different actions: (1) increasing the agricultural yield per unit of water used; (2) switching from low value to high-value crops; (3) relocating water from low to higher value water uses; (4) lowering the cost of inputs for the same water used; (5) increasing health benefits and the value of ecological services of agriculture; (6) deceasing the social, health, and environmental costs; (7) obtaining multiple benefits per unit of water; and (8) achieving more livelihood support per unit of water, such as, more jobs, nutrition, and income. However, economic theory shows that if a new practice offers net benefits and does not have any negative effects on third parties off the farm (termed externalities by economists), then the adoption of this practice is advantageous for the society as a whole, not just for the farmer. Unfortunately, there are many externalities in the agricultural sectors of developing and developed countries that make it more complicated to identify the optimal practices that should be adopted by private agents (farmers).

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Policy-induced incentives are necessary to achieve an optimal water allocation. In order to assess the optimality of the allocation of water as a scarce resource, it is necessary to value all agricultural inputs and outputs at social prices that generally diverge from market prices. The difference between these two prices is a result of distortions, subsidies, and transfers among different actors of the economy.

1.08.2.1 Economic Value of Water for Agriculture The value of water is the maximum a user is willing to pay for the resource. In other words, water has value when users are willing to pay a price instead of not having it. Water has economic value when its supply is constrained compared to the amount that is demanded, and when water as an input becomes a constraint to the economic and social development process. This concept has become important particularly after water was declared as an economic good in Dublin, 1992. Water needs to be evaluated for the benefit of humanity, and to ignore its value would lead to an overexploitation of the resource and an incorrect allocation among alternative uses. Rational decisions that support water development, its allocation, and final use, demand a measurement effort to value water for alternative uses. When defining investment priorities in a country or a region, the level and the geographical variability of the economic value of water become a key element in the decision process. It is used as a selection criterion when compared to what users are actually paying for the resource (cost recovery and operation and maintenance fees). It also becomes a good indicator of the future demand of the resource for irrigation. There exists a set of methodologies to estimate the value of water for agriculture: direct methods such as the water markets and the contingent evaluation approach. There are also indirect methods such as the estimation of demand equations and its integration, and a series of methodologies that are derived from considering water as an intermediate good. Among these are the residual value methodology, the alternative cost, and the Hedonic price methodologies. Finally, when primary information does not exist to estimate the value of water, a methodology called meta-analysis is employed to extrapolate economic values of water from other watersheds or regions to the region of interest.

1.08.2.2 Example of Estimates of the Economic Value of Water: The Case of Mexican Agriculture Mexico is an interesting country case to observe the application of alternative methodologies to estimate the value of water for irrigation. Ramirez-Vallejo and Rogers (2004) used an indirect methodology proposed by Moore (1999) to estimate the value of water for irrigation, and also applied a residual method in various irrigation districts in Mexico. Others estimated direct values from water markets and the use of optimization exercises of the producer’s behavior at the farm level. Kloezen and Garce´s-Restrepo (1998) documented rent transactions of water concessions, and Florencio-Cruz et al. (2002) applied math programming to estimate values of irrigation water. As seen below, the estimates of the economic value of water for irrigation vary significantly depending on

the methodology employed, a situation that complicates the application of water value as a criterion for allocating water.

1.08.2.2.1 Indirect method The indirect methodology employed by Ramirez-Vallejo and Rogers (2004) was justified by the fact that the water prices are divorced from the production costs in the long run. This is something that has been criticized in the past with the argument of efficiency (Ciriacy-Wantrup, 1954; Bain et al., 1996). For the case of Mexico, a revenue function was defined that related the multiproduct to fix inputs, estimated as

Rðp; x; zÞ ¼ maxfpyA Yðx; zÞ; p4 0g

ð1Þ

where p is the vector of product prices and y is the vector of product production for each product, x is the amount of water, z is the amount of the mix input, and Y(x,z) is the set of production probabilities. Chambers and Just (1989) developed the properties of the revenue function, which was estimated econometrically, and the shadow prices were derived using a quadratic normalized form suggested by Lau (1978) to estimate the willingness to pay. Applying this methodology for 14 irrigation districts using data from 1993 to 2001 resulted in the following shadow prices for irrigation water (see Table 1).

1.08.2.2.2 Residual method Using an alternative residual method, Ramirez-Vallejo and Rogers (2004) computed the value of the production factors different from water and then subtracted this value from total sale income from cultivated products in the irrigation districts. This difference was assigned to water as its economic value. Young (1996) clarified that this imputed value is valid if two Table 1

Shadow prices for irrigation water ($ m3)

Irrigation district

Average shadow price 1993–2001

Average shadow price 1997–2001

001 Pabellon, Ags. 005 Delicias, Chih. 010 Culiacan Y Humaya, SIN. 011 Alto Rio Lerma, GTO. 014 Rio Colorado, B.C. 017 Region Lagunera, DGO. Y C. 023 San Juan Del Rio, QRO. 024 Cienega De Chapala, MICH. 038 Rio Mayo, SON. 041 Rio Yaqui, SON. 075 Rio Fuerte, SIN. 076 Valle Del Carrizo, SIN. 92A R.Panuco, Tamps. ‘‘ANIMAS’’ 92B R.Panuco Pujal Coy, S.L.P.

1.160 0.844 1.455

1.568 0.929 1.557

0.630 1.649 1.572

0.888 1.132 2.050

1.856 1.691

2.000 1.578

0.810 0.695 0.190 1.341 1.679

0.677 0.907 0.987 1.737 2.637

1.073

1.329

From Ramirez-Vallejo J and Rogers P (2004). Virtual water flows and trade liberalization. Journal of Water Science and Technology 49(7): 25–32.

Managing Agricultural Water

conditions are satisfied: first, the inputs and products are in competitive markets and are not regulated, that is, the price is equal to the value marginal product. Second, the production function should behave in such a way that an increment in each of the inputs generates an equal relative increment in the product. The residual value of water for irrigation was estimated by

Residual value of water ¼ PQ 

X

Wi Ni

where P is a vector of prices of products in the district and Q is a vector of the amount produced. Wi is the quantity of input i used and Ni its price. Besides the traditional inputs (land, capital, and labor), other inputs were considered such as the administration and the expected utility of the economic activity. The shadow price was then the residual value divided by the amount of water used. The results of applying this approach to some irrigation systems in Mexico are shown in the Table 2.

1.08.2.2.3 Water markets When water markets exist, prices applied in the resource transaction become good proxy of the economic value of water. Furthermore, if the market allows observing the resource–demand curve, the value would be determined by the area under the curve. There exist some water markets in irrigation districts in Mexico where some transactions involve the water concessions and others the right to use water for specific time periods. Table 2

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Kloezen and Garce´s-Restrepo (1998) documented the rent transactions of the water concessions for the irrigation district of Alto Rio Lerma (Table 3). The observed market prices were below the economic values estimated using alternative methodologies, a situation that could be explained by the distortions of rental markets of concessions in the irrigation districts as a result of direct intervention of user associations; social and political motivations were present in the exchange process of water rights, which did not allow water markets to function properly. Other registered transaction values of concessions for the irrigation district of Lagunera varied from $0.05 to $0.1 m3. Finally, the government, in its Aquifer Program, rationalized the use of water by purchasing some of the users’ concessions. In 2004, the government, using market mechanisms, purchased back concessions at an average price of $2.5 m3, equivalent to an annual value of $0.25 m3 after applying the perpetuity formula with a discount rate of 10%.

1.08.2.2.4 Math programming method Some authors have implemented the math programming method to estimate the economic value of water, optimizing the net income subject to various constraints, among them, the budget constraint, and the water availability. The execution of these exercises for different levels of water available allows estimating the water demand equation, which, when integrated, gives the economic value of water, or willingness to pay for the resource. Florencio-Cruz et al. (2002) executed this exercise to the Alto Rio Lerma irrigation district and a significant dispersion of the estimate was found (Table 4), depending on the season considered.

Shadow price using the residual method

1.08.2.3 Agricultural Trade Protection Typology

Irrigation district

(Shadow price Pw)

Rehabilitation New New New New New

Yaqui Yaqui Angostura Carrizo Aguascalientes Queretaro

0.159 0.242 0.879 0.394 0.151 0.212

From Ramirez-Vallejo J and Rogers P (2004). Virtual water flows and trade liberalization. Journal of Water Science and Technology 49(7): 25–32.

Table 3

Agriculture is one of the most important sectors in many developing countries in terms of social and political stability, and thus it is heavily circumscribed by global trade protection, much of it originating from Organization for Economic Cooperation and Development (OECD) countries. Agricultural policies in OECD countries cost consumers and taxpayers over $280 billion every year (Anderson et al., 2006). The value of total agricultural support in OECD countries was more than 5 times higher than total spending on overseas development

Rental price in the irrigation district of Alto Rio Lerma

Season

Seller

Buyer

Volume (m3  1000)

Bought as a % of total used

Price ($m 3)

Summer 1995

Acambaro Acambaro Acambaro Acambaro Acambaro Acambaro Valle Valle Jaral

Cortazar Salvatierra Huanimaro Salvatierra Abasolo Huanimaro Salamanca Abasolo Salamanca

10 000 10 000 2000 8000 3000 2000 3500 1000 450

25 21 23 36 19 86 18 6 2

0.004 0.009 0.009 0.020 0.034 0.034 0.035 0.034 0.035

Summer 1996 Summer 1997

From Kloezen W and Garce´s-Restrepo C (1998). Assessing Irrigation Performance with Comparative Indicators: The Case of the Alto Rio Lerma Irrigation District Mexico. Colombo, Sri Lanka: IWMI.

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Table 4 Value of irrigated water for Alto Rio Lerma District using math programming method Groundwater February April Surface water January May

0.7–1.8 $ m3 1.6–2.4 $ m3

•

•

3

0.5–1.2 $ m 1.3–1.9 $ m3

From Florencio-Cruz V, Valdivia-Alcala´ R, and Scott CA (2002) Productividad del agua en el Distrito de Riego 011, Alto Rio Lerma. Agrociencia 36: 483–493.

• assistance and twice the value of agricultural exports from developing countries (OECD, 2001). Reductions in distortions to international trade of agricultural products will most likely increase real income and stimulate change in the composition and location of production and consumption of water, as a primary production factor. The impact of agricultural trade on water resource development will differ between countries depending on how protected and distorted their agricultural sectors are as well as their water resources endowment, its use, and their policy and institutional framework. That is, where trade liberalization leads to a decrease in agricultural production, there is likely to be a reduction of water use and its associated environmental impact, but where it increases production, water will be allocated to agriculture from other actual or potential uses (Ramirez-Vallejo and Rogers 2009). Therefore, a change in trade barriers and agricultural subsidies in general could impact differently water allocation, and, therefore, water policy adoption. In theory, to achieve a rational decision to allocate water within alternative uses, it is important to measure the value of water in these uses. When social and market prices are equivalent and no distortions are present, the market as a clearing mechanism yields the optimum allocation of water for a society as a whole. When this is not the case, the externalities should be taken into consideration for the analysis. However, in practice, there is often little time, money, knowledge, or will to conduct serious economic analyses of benefits and costs to consider in the water allocation disjunctives. To summarize

•

•

• •

The concept of water productivity relates to the desire have a higher level of output, economic value added, or just value for the aggregate production, per unit of water used. In other words, it is the way of achieving more using less scarce water resources. Water productivity is then the ratio of crop output to water either diverted or consumed, being expressed in either physical or monetary terms, or some combination of the two. Management of water in agriculture should be targeted to allocate water to the highest level of water productivity as a social optimum. Some alternatives to improve water productivity are: (1) agronomic improvements; (2) technical improvements; (3) managerial improvements; and (4) institutional improvements.

The value of water is the maximum a user is willing to pay for the resource. Water needs to be evaluated for the benefit of humanity, and to ignore its value would lead to an overexploitation of the resource and an incorrect allocation among alternative uses. There exists a set of methodologies to estimate the value of water for agriculture: direct methods such as the water markets, and the contingent evaluation approach; indirect methods such as the estimation of demand equations and its integration; and a series of methodologies that are derived from considering water as an intermediate good. The impact of agricultural trade on water resource development will differ between countries depending on how protected and distorted their agricultural sectors are as well as their water resources endowment, its use, and their policy and institutional framework.

1.08.3 Water Management and Competitiveness 1.08.3.1 Framework Water management is intrinsically linked to the competitiveness upgrading process. Under the current global scenario, the goal of many countries and regions is to increase the level of competitiveness to achieve a higher level of prosperity. Regions and countries are competing to offer a more competitive environment to allow agricultural firms and farmers to be more productive. The opportunity that farmers and firms have of obtaining the right amount of water at the right time to optimize the value-generation process becomes an important characteristic of the business environment. However, the link between water management and competitiveness is not well understood and most countries assign higher priority to other types of policies. Developing countries focus most on reforms to adjust their macroeconomic, political, legal, and social conditions that are necessary conditions, but far from being sufficient to increase prosperity. Wealth is created by the productivity of all production factors that a nation or region can utilize, and heavily depends on the microeconomic conditions, understood as the elements that are located outside the production unit (i.e., farms) and the conditions that take place within the boundaries of companies and farms. Unless these microeconomic capabilities improve, the growth process of prosperity will be truncated (Porter, 1998). Competitiveness is the value that firms can generate per unit of input used, that is, capital, land, labor, and so on. This real value is created by the private sector, by the farmers themselves, when transforming these inputs into products and services that consumers are willing to pay for. A farm is more productive when it generates the same amount of output but the production is sold in the market for a higher price. In addition, a farm is more productive if it generates more physical output per unit of input. True competitiveness is then synonymous with productivity, and the higher the level of productivity of the agricultural units, the higher the wages they can support, and the more important the influence on prosperity. In the agricultural context, wealth is created at the microeconomic level when farmers and firms produce valuable

Managing Agricultural Water

goods and services using water more efficiently. The wealth creation process is a result of their production activity and not a direct result of government or other societal institutions’ intervention. Water improves farm productivity in various ways. First, it creates a direct impact on the possibility of technology adoption by farmers and agricultural firms. Without water, for example, the choice of agricultural varieties would be limited. Water also allows the use of other agricultural inputs such as fertilizers, and causes a clear impact on production stability that helps to generate a higher output value. Moreover, paradoxically, water scarcity also generates the right incentive for innovation, a phenomena that explains new technology advances available for rainfed agriculture. Under a water management for agriculture framework, the foundations of productivity rest on two interrelated areas: (1) the sophistication and capabilities with which farms and agricultural companies compete, which are highly dependent on the availability of water resources and (2) the quality of the business environment in which they operate. More productive farm and firm operating practices require highly skilled people, technology and availability of water resources, better information, more efficient government processes, improved infrastructure, better suppliers, more advanced research institutions, and more intense competition, among other things. The competitiveness of a farmer and an agricultural company, then, depends on both, their internal capabilities and the characteristics of their location, which are influenced directly by water management. Water is not only a regular input of the agricultural production and transformation process, but also a facilitator of the strategic upgradation of farmers and agricultural firms. Agricultural firms and farmers must upgrade their modes of competing and capabilities in order to generate economic

development. They need to shift from competing on costs and inherited endowments to create competitive advantages from efficient and distinctive products and internal processes. Competition on cost structure or price is usually the traditional way of competition within agricultural sectors in most countries, and a characteristic of earlier stages of development. Moving to more sophisticated ways of competing depends on parallel changes in the business environment. The business environment can be understood in terms of four interrelated areas: the quality of factor (input) conditions, the context for firm strategy and rivalry, the quality of local demand conditions, and the presence of the related and supporting industries. Because of their graphical representation (see Figure 1), the four areas have collectively become referred to as the diamond (Porter, 1998). Water management impacts directly the quality of the business environment for agricultural development. First, water improves the access to high-quality agricultural inputs, such as infrastructure, fertilizers, human resources, and capital. Second, water user associations usually generate rules and incentives that affect investment and productivity, and foster or inhibit a vigorous local competition. Third, advanced water management practices induce the sophistication of local customers and needs for example, strict quality, safety, and environmental standards. Fourth, improving the availability and reliability of water supply facilitates the existence of suppliers and supporting industries of agricultural development. Improving competitiveness is a special challenge because no single strategy or action of an individual institution can improve significantly the productivity of a region. Many things matter for competitiveness. The quality of schools and roads, the financial markets penetration, the consumer sophistication, the supplier networks, the rules for competition, the quality of the private and public demand, and many more

Context for firm strategy and rivalry

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• Water user associations could generate rules and incentives that encourage investment and productivity, and foster vigorous local competition

Factor (input) Conditions

Demand conditions

• Water improves the access to highquality agricultural inputs

• Advanced water management

Related and supporting industries • Improving the availability and reliability of water supply facilitates the existence of suppliers and supporting industries of agricultural development

Figure 1 Water management and quality of the business environment for agricultural development.

practices induce the sophistication of local customers and needs –e.g., strict quality, safety, and environmental standards

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attributes act as determinants of competitiveness of the regions where agricultural products and services are produced. Water resource management is one of them and needs to be articulated with reforms in other areas to upgrade competitiveness.

1.08.3.2 Farmers, Water, and the Process of Economic Development Improving competitiveness is a collaborative process in which private sector, academia, government, institutions, and labor unions contribute in designing and executing a competitiveness strategy. Farmers, entrepreneurs, and managers of agricultural companies play a crucial role in improving competitiveness and defining regional policy. Governments play an important role in upgrading competitiveness because it affects all its determinants. Government agencies affect regulatory standards, invest in public goods, set the rules and incentives, coordinate policies, and have an effect on the quality of its purchases of goods and services. Universities and schools impact the role of water in improving competitiveness through knowledge and human resource skill formation to improve the technology. The educational system needs to be connected with farmers and government to adjust research agendas and curriculum on water resources. Farmers and business people depend on the business environment and also contribute to shaping it for their own benefit. This explains the need of implementing communication mechanisms between leaders from the private sector, government, and academia to coordinate and collaborate on public policy and specific actions to improve water management. Engaging the private sector in water resource development is key to sustaining progress in the long term. Under this scenario, changes in national and local governments would less likely impact the continuity of policies and institutions that deal with water management. To achieve an effective collaboration, it is necessary to build an organizational structure that connects all actors and fosters collective activities. Organizations for water management at the basin level should be open institutions with the participation of all stakeholders.

1.08.3.3 Water Resource Management and the Regional Economic Strategy Water contributes with economic development through backward linkages that make the agricultural input supply sectors more dynamic, and via forward linkages strengthening agro-processing industries, trade, and other activities downstream in the value chain. Some of these activities are made feasible by the available technology for water agriculture. In addition, higher agronomic yields lower unit costs and impact on cheaper food available for the populace. Water also allows the cultivation of export crops that require a more regular supply and higher technology, and has an impact on off-farm employment. In a global scenario, countries and regions compete to offer the best location based on productivity to facilitate the value generating process that finally translates in prosperity. Globalization is putting pressure on countries and regions to

improve the determinants of competitiveness and they are adopting best practices in many aspects such as water management, water technology, agricultural technology, farm operation, infrastructure development, and human resource development, among other determinants of competitiveness. This competition leads to work on ways how to make a region distinctive from other competitor regions, to differentiate and achieve a distinct role in the country and in the world. Water then becomes an interesting resource to achieve regional differentiation, allowing competition and incentivizing innovation. Water management works as a channel to build a unique mix of strengths in the business environment, to differentiate and to improve the productivity potential of farms and agricultural businesses.

1.08.4 Water Resource Management, Institutions, and Implementation 1.08.4.1 Integrated Water Resource Management During the past couple of decades, agricultural water management has been approached within the integrated water resource management (IWRM) framework defined as a systematic process for sustainable development, allocation, and monitoring of water resource use in the context of social, economic, and environmental objectives (Cap-Net, 2009). This approach recognizes that water is used in various economic sectors, it has many uses, and as such it needs to be managed with an integrated approach. It recognizes that water management needs a multidisciplinary approach to deal with water sources and demands at the basin level. It also recognizes that water is a resource that needs to be sustained over time, and it is an instrument to achieve social, economic, and environmental goals at the basin level. Recently, this framework has been broadened to incorporate participatory decision making of all stakeholders. The IWRM is considered a paradigm shift that departs from traditional approaches in three ways: first, its multiple goals and objectives are cross-cutting so that this new approach departs from the traditional sectoral approach; second, the spatial focus is the river basin instead of single water courses; and third, there is a departure from narrow professional and political boundaries and perspectives and is broadened to incorporate participatory decision making of all stakeholders. The IWRM framework deals holistically with all water, all interests, all stakeholders, all levels and all relevant disciplines, and sustainable in all senses (Jaspers, 2001). In this framework, water is considered an economic good as well as a social good, and the market is given an important role for water pricing, and to deal with water scarcity, allocation efficiency, and environmental protection. Since the Dublin principles in 1992, there has been some progress in understanding the IWRM framework and its importance. However, there are many obstacles in its implementation process as a result of different sectoral interests, cultural values, and political constraints. Negotiation situates at the center of conciliating many thematic and special interests and beliefs at the time of allocating water resources. In addition, significant institutional and legal reforms are needed to reorient the traditional water management approach to a

Managing Agricultural Water

more integrated comprehensive management. Water development professionals still have difficulties in working together to come to a unique set of principles of IWRM.

1.08.4.2 Participatory Irrigation Management One of the most applied modalities in agricultural water management has been the participatory approach. Participatory irrigation management (PIM) refers to the involvement of irrigation users in all aspects and all levels of irrigation management. Users participate in all stages of the project life cycle; they begin with the participation on the design of the new (rehabilitation) project, and continue during its construction phase, including financing. Finally, and perhaps the most decisive stage, there is the participation of the users with the operation and maintenance of the system. It has been shown that PIM in real practice helps to solve some of the most prevalent problems with irrigation management, such as the inadequate water availability at the lowest outlets; poor condition/maintenance of the system; lack of measuring devices and control structures; inadequate allocation for O&M; inequitable distribution of water; and lack of incentives for saving water and poor drainage (Merrey, 2007). Since the beginning of the 1990s, this new perspective of irrigation management has been adopted in many developed and developing countries and has helped understand why some policies work better than others, and why some irrigation systems perform better producing higher impacts in terms of productivity and prosperity for the rural inhabitants. In many countries, PIM encouraged the creation of water user associations (WUAs) that took the responsibility of management of the irrigation systems once they were constructed. Taking management responsibilities demand from organizations the capacities to represent the users and institutions endowed with an adequate governance structure to take difficult decisions and solve the multiple obstacles, particularly those experienced during periods of water scarcity and conflict resolution. Before a WUA was formed, governments usually set a package of incentives for both users and irrigation agencies to make transfer programs sustainable. Irrigation agencies in many countries built processes of organization creation and strengthening, and delegated management of irrigation systems. One of the main objectives of the new management is to make the irrigation district financially sustainable, and to limit the amount of subsidies needed from the government. To finance the O&M activities, O&M fee schemes are usually implemented. The share in O&M contribution distributed between the government and users varies significantly among districts and countries; they are particularly biased toward a higher government contribution in those systems that were built with limited user participation. Sometimes the O&M fees are financed directly or indirectly through land or other type of agricultural taxes. To estimate the level of the fees, the portfolio of services of these organizations is identified, and costs are allocated. Once this information is available, usually the government and users enter into a negotiation process in which the responsibilities are assigned between the government and the WUA.

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User participation in defining service levels is crucial for achieving financial sustainability. The higher the user involvement in defining the set of activities that is needed, the more dynamic the future demand for those services will be and the higher the willingness to pay for the corresponding fee. In Mexico, for example, following the transfer of management of irrigation districts to WUAs, irrigation plans were prepared by WUA-hired managers, taking into account cropping plans, conveyance losses, and equitable distribution. These were then negotiated with the national irrigation agency to determine the final allocation, generally based on an arranged demand pattern. Operation and maintenance costs at the level of the secondary and below were met by user fees managed by the WUAs. In addition, these associations contribute with part of O&M costs (World Bank, 2009). In Mali, joint committees were established for O&M in every region. The committees had 5–10 representatives of producers and 5–10 representatives of the agency. These committees decided on types of services, costs including procurement matters, and water service fees. They also made decisions on the use of 50% of the user fees collected for O&M. In Chile, the national federation of WUAs was consulted in the design of an irrigation project. The federation and local WUAs played an active role in project preparation especially in discussions of service options and costs. Subsequently, the project incorporated the condition of WUA-approval for investment proposals and other project components. The fees usually have a component that covers some fixed costs and another variable component as a function of the water consumed or the irrigated area. Linking the fee to the irrigated area makes it easy to estimate it and to understand it. It is recommended that the easier to understand the fee structure, the simpler its administration. On the other hand, volumetric charges that are a function of the discharge generate the right incentive for water saving but are more difficult to implement because of the necessary water-measuring devices and control mechanisms that are costly and operationally complex. Agencies often favor a combination of the two methods. In Mexico, for instance, the agency charges the WUAs volumetrically at the turnout of the secondary canal, and the WUAs base their water charges to individual members on area irrigated and type of crop. In addition to unit area and volume, WUAs have also resorted to other bases such as charges for the entire season, which clearly favor the highvolume user. In some other irrigation districts, a property tax is employed as a charge based on the increase in the land values due to water availability. The higher the land valuation, the higher is the tax level as a consequence of available water supply. However, the problem observed with this system is that it does not generate the water-saving incentives that result with other mechanisms such as the volumetric approach, and usually these taxes are collected by municipalities, which generate discontinuity between the source and use of the resources. Another system, less frequently found, is the in-kind contribution system, mainly through labor, materials or both, applied directly to O&M activities. In Vietnam, the provincial and national governments finance schemes down to 150 ha for new irrigation development. Below this level, farmers must build the channels with the government providing support for

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survey and design, as well as contributing with materials, in some instances. After schemes are completed and taken over by farmers, each member must provide up to 20 person days per year toward maintenance of the tertiary as well as secondary systems (World Bank, 2009). But perhaps the most difficult hurdle to overcome is in replacing the assets of the irrigation districts. As any system, the infrastructure that is used for storing and delivering water to users depreciates. Buildings, canals, motors, pumps, vehicles, and heavy equipment need to be replaced once they complete their life cycle. If the government and/or the WUA do not implement a saving mechanism to cover these replacement costs, the sustainability of the system was compromised. Poor management leads to short service life with higher acceleration rates that then demand an increase in fees to cover future expenses. Provisions made for collecting these asset replacement funds by the WUA are usually not accepted by farmers who adopt a free-rider behavior and wait for government agencies to intervene when major depreciations do not let the system function properly. In parts of Vietnam, the estimate of costs of services included provision for depreciation of assets. This approach demanded knowing the location and conditions of all assets within the system, and to implement a training and preparation program on the formulation of an O&M program. Linking fees to services in the irrigation districts is important, particularly when an agency collects O&M fees and these funds are then allocated later to other government agencies, or when the budget of the O&M of the irrigation district is done independently of the collection effort. In these cases, there needs to be an agreement between the WUA and the government agency on mutual rights and responsibilities. The cost of collection by WUAs is lower relative to government agents and sanctions for nonpayment by individuals are easier when enforced by WUAs. The Philippines experience has shown that fee collection is better in systems where WUAs are organized and where they have a role and incentive for collection. In the pilot projects in Maharashtra, India, WUAs were allowed to retain a proportion of collections as a bonus (World Bank, 2009).

1.08.4.3 Lessons Learned from Participatory Management Since the 1990s, there has been an important effort on transferring the management of irrigation districts to WUAs. This new trend in water management has increased net irrigated area in large public irrigation projects. Important lessons have accumulated over time on various management schemes. Some lessons from the Philippines and Mexico and documented by the World Bank are presented below that exemplify the trend toward participation (these examples are taken from the Electronic Learning Guidebook for Participatory Irrigation Management).

1.08.4.3.1 The Philippines Just as the state’s involvement – or micro-management – was reaching its peak in the 1980s, there were countervailing forces appearing. In the Philippines, the process can be traced to the mid-1970s when President Marcos ordered the National Irrigation Administration to move toward self-financing. The

agency responded by withdrawing from the small communal systems which had once been self-managed, but had grown dependent on the government. This weaning process was accomplished through intensive grass roots organizing and capacity building both among farmers and within the agency itself. Water user associations were formed to take over operations and maintenance, and to contribute to capital costs of improvements. Beginning in 1980, this organizing approach was applied to the state-run systems that had no prior history of self-management. As with the management transfer in communal systems, the goal here was cost savings to the agency, both through direct recovery of water fees and the replacement of some low-level agency operational functions by association volunteers. Until recently, this modest level of joint management was the dream of irrigation policy reformers, and the Philippines served as the model. The paradigm was one of joint management where farmers would become management partners with the agency, and decisions would be made jointly. However, the relationship is asymmetrical; the state controls the technical expertise and subsidizes maintenance and improvements even in the canals operated by farmers.

1.08.4.3.2 Mexico Independently from the trend toward joint management at the lower ends of the system, a model of irrigation management transfer was evolving in Latin America, in response to structural adjustment pressures. This model constitutes the qualitatively different paradigm where the users dominate, and the state facilitates. In the mid-1980s when Mexico was in the throes of a debt crisis, the government was bankrupt. The large irrigation districts under Federal control suffered as maintenance was deferred and the productivity of unpaid, demoralized engineering staff declined. Out of necessity, the government reorganized the state irrigation agency to create the National Water Commission (or CNA in its Spanish acronym), with a mandate to turn over the management of the irrigation districts to associations of users created specially for this purpose. In 1990, Mexico transferred the first irrigation district to the users. By 1995, more than two-thirds of the country’s 3.2 million hectare network – divided into 80 irrigation districts – had been transferred to 316 irrigation associations. The transfer program was initially in the most productive irrigation districts, which were best organized and with the most commercially oriented farmers. The most important criterion for selecting districts was the potential of the user organization to become financially self-sufficient, with users paying the fees to cover the costs of operations, maintenance, and administration. What could the government offer the farmers as an incentive to accept higher costs for their irrigation? In fact, there was a carrot as well as a stick. The carrot was management autonomy. The farmers would be free to set their own rules for when to clean the canals, and how to distribute the water. The farmers would hire their own technical staff – engineers and accountants – to run their system. The canal would be theirs on a 20-year concession, which is in practice a transfer of ownership.

Managing Agricultural Water

However, there was also a stick. If farmers refused to take over management, the government could offer no assurance that the canal network could be kept in repair. The government in effect threatened to default on its conventional understanding with farmers regarding levels of subsidy in the irrigation sector because it no longer had the financial means to do so. The government, however, also promised and provided technical, organizational, and legal assistance in realizing the transfer. Many farmers, and particularly the commercially oriented ones, could not accept the risk that the irrigation infrastructure might collapse. They preferred to take over the management, and with a few exceptions, they have not looked back. They are paying much more for their water without the government subsidy, but the reliability and responsiveness of their new management structure is well worth the price. For them it is a win situation, and for the government as well. What are farmer’s comparative advantages when they are managing for themselves? They have direct incentives to manage irrigation water in a productive and sustainable manner; they offer an on-the-ground presence that even the most dedicated off-site agency staff cannot equal, and they have an intimate knowledge about their fellow irrigators. The state’s comparative advantage is in the depth of financial and technical resources and the regulatory and administrative capacity for managing water supplies to competing interests.

exercise of economic, political, and administrative authority to manage a country’s affairs at all levels, which comprises the mechanisms, processes, and institutions through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations, and mediate their differences (United Nations Development Programme, 2001). Rogers (2002) argued that governance is not restricted to the perspective of government as the main decision-making political entity, and governance covers the manner in which allocative and regulatory politics are exercised in the management of resources (natural, economic, and social) and broadly embraces the formal and informal institutions by which authority is exercised. According to Rogers (2002), there are different models of water governance:

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• 1.08.4.4 Institutions and Water Governance Before presenting the argument of the need of institutional and organizational reform, some key concepts need to be defined. According to North (1990), institutions are the rules of the game in society. They are social arrangements that shape and regulate human behavior and have some degree of permanency and purpose transcending individual human lives and intentions. In agricultural water management, these types of institutions are user associations, rules of water allocation, market mechanisms, and property rights. Organizations refer to the group of people with shared goals and formally defined roles, such as water associations, government irrigation agencies, water companies, nongovernmental organizations, and regulation bodies. A policy is ‘‘a set of interrelated decisions taken by a political actor or group concerning the selection of goals and the means of achieving them within a specified situation where these decisions should, in principle, be within the power of those actors to achieve’’ (Howlett and Ramesh, 1995; Jenkins, 1978). On the other hand, governance is the way authority is organized and executed in society, and often includes the normative notion of the necessity of good control. The Global Water Partnership defines water governance as ‘‘the range of political, social, economic, and administrative systems that are in place to develop and manage water resources, and the delivery of water services, at different level of society’’ (Rogers and Hall, 2003). Governance is therefore a broad term that includes institutions, organizations, and policies. The World Bank broadens the definition to include the process by which those in authority are selected, monitored, and replaced, and the effectiveness of government in implementing sound policies (Jayal, 1997). The United Nations defines it as the

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The bureaucratic politics and process model. This model is based on political–bureaucratic bargaining in a federal system. Its focus is typically on the executive branch, with the elected legislature hardly in the picture. The congressional behavior model. A second federal model concentrates on the elected congress, with the view that to understand congressional behavior is to understand that congressmen are single-minded seekers of reelection. It follows from this that congressmen’s goals are to improve the welfare of their constituents in the shortest possible time frame. The interest group model. In some cases, legislators see only a few dominant interests involved with water policy. These interest groups often have overlapping concerns and overlapping memberships. It is also valuable to carry out an interest group analysis of the feasibility of pursuing specific governance goals. They examined the likelihood that the various interest groups would be powerful enough to influence the investment and management decisions in their direction. Principal–agent theory. Principal–agent models have been employed in many different academic fields, including economics, in order to explain relationships among actors in which the consumer is the principal and the producer is the agent; and in various political science subfields, in which members of the legislatures are the agents of their constituents, or bureaucrats are agents of the executive, or the governments of Third World countries are the agents of international lending institutions, and so on. Regime theory and public choice. The use of the concentration or diffusion of costs and benefits of public choices to predict what decision-making system will prevail.

It has been found that water governance for agriculture is important for the management of irrigation systems and rainfed agriculture involving different stakeholders to achieve more effective water allocation. Good governance in irrigation systems matters for achieving high levels of economic, social, and environmental performance. There are some necessary conditions for good governance: inclusiveness, accountability, participation, transparency, predictability, and responsiveness. A failure on these conditions leads to poor governance and difficulty to deal with system problems and presents a risk in terms of sustainability. Better governance will lead to higher levels of prosperity of the users

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of the agricultural systems and becomes an essential input of country and regional development. There is also a strong relationship between the level of governance of water resource systems and the income per capita, lower infant mortality, and higher literacy (Kaufmann, 2005). New institutions and governance are needed to implement water management if the goals are to achieve food security, economic growth, and poverty reduction under favorable environmental conditions. This demands institutions and organizations responsible for good agricultural water management and policymaking. There is a need to reform not only the organizations at the national and international level that deal with water management, such as the irrigation agencies and multilateral institutions, but also local and informal institutions that act at the basin level that need to be redesigned and in some cases built from scratch. The current institutional arrangement has failed to meet the challenge to respond to new technologies and rules to deal with the challenging goals of developing agriculture to reduce poverty, and generate prosperity with equitable growth under minimum environmental impacts. In the water sector, as with any sector in the economy, some institutional arrangements have shown little promise so far. On the one hand, agricultural markets in developed and developing countries present high distortions that generate strong market failures that need government intervention. On the other hand, the government itself frequently fails to make the right adjustments to align market outcomes. In the irrigation sector, arrangements for authorization, payment, and accountability impact on service provision (Huppert et al., 2001). The failure of public organizations to offer socially and environmentally optimal allocations of water resources has induced the creation of new institutional schemes. The decades of the 1990s and the 2000s have experienced an upsurge of private companies and market mechanisms not only in the construction of water systems for agriculture but also for their operation and maintenance. Some of these new organizations present a heavy involvement of the private sector. Privatization of O&M has been a component of many irrigation programs, particularly where the operation of these systems required specialized skills that individual farmers did not have. Two major reforms have been put in place in many countries to generate economic incentives for water allocation: water pricing and tradable water rights. Water pricing mechanisms are used to create incentives for water conservation but require volumetric measurement devices that are expensive and difficult to control. In addition, the strong legacy of government subsidies and public intervention in water management has generated famers’ resistance to pay for irrigation service. In addition, investment cost recovery demands such high fees that unless there exist financial markets it is difficult to obtain payments from farmers. The second privatization mechanism that has been used frequently in many countries during the past two decades is the tradable water rights. The idea behind the water rights market is that the user who has a higher value use of the resource would be willing to pay more for the right to use the water. In other words, the market allows water to move from lower to higher value uses. However, these markets are

complicated to build since specific topologies of irrigation systems to transfer water from buyer to seller are required. They also need institutional arrangements to control, facilitate, and enforce transactions, and protect against negative impacts on third parties when water is transferred (Easter et al., 1998; Rosegrant and Binswanger, 1994).

1.08.4.4.1 Water management principles In 1992, at the International Conference on Water and the Environment, convened in Dublin, Ireland, four main principles of water were adopted that have since then shaped water management. The first principle is that water is a finite and vulnerable resource, essential to sustain life, development, and the environment. This principle recognizes that water is critical to sustaining life, and it is a finite resource because of the economic and technical complications of transferring water from one place to another. This principle also suggests a holistic approach to deal with water management in which all dimensions, social, economic, and environmental are taken into account. The second principle establishes that water development and management should be based on a participatory approach, involving users, planners, and policymakers at all levels. It suggests democratization in the decision-making process when allocating water among uses, and the importance of the input of multiple stakeholders in the design, construction, and operation and maintenance of systems such as irrigation districts. The third principle is that women play a central part in the provision, management, and safeguarding of water. In many parts of the world, women are the ones who collect and safeguard water for domestic and agricultural use. However, women do not have as important a role as men in water management, and their involvement has been shown to be important for achieving higher levels of efficiency and sustainability. The fourth principle, perhaps the most controversial at that time, and the one that has created a major impact in water management, is that water has an economic value in all its competing uses and should be recognized as an economic good as well as a social good. This principle highlights the need of assigning value to water not only as a social good but also as an economic good if water is to be allocated efficiently and if water systems need to be sustainable in all dimensions. This principle has generated debate for its implicit recommendation of charging for water.

1.08.5 Water Management and the Environment Almost by definition, water management impacts the environment; changes in agriculture cause modifications in land cover and watercourses, and degrade ecosystems and impact their services for human development. In order to increase productivity, land and water are manipulated in different ways with consequences on the environment. Some of these manipulations include the following (Falkenmark et al., 2007). (1) Shifting the distribution of plants and animal, when the native vegetation is cleared and is replaced by crops and wild animals are replaced with livestock. (2) Coping with climate

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variability to secure water for crops. As water is a key material for photosynthesis, crop productivity depends intimately on securing water to ensure growth. (3) Maintaining soil fertility. The conventional way to secure enough air in the root zone is by drainage and ditching through plowing to ensure that rain water can infiltrate, a process that leads to erosion and the removal of fertile soil by strong winds and heavy rain. (4) Coping with crop nutrient needs. The nutrient supply of agricultural soils is often replenished through the application of manure or chemical fertilizers. (5) Maintaining landscape– scale interactions. When natural ecosystems are converted to agricultural systems, some ecological processes (such as species mobility and subsurface water flows) that connect parts of the landscape can be interrupted. This can have implications for agricultural systems as it can affect pest cycles, pollination, nutrient cycling, and water logging and salinization (Lansing, 1991; Cumming and Spiesman, 2006; Anderies, 2006). The Millennium Ecosystem Assessment, an international assessment by more than 1300 scientists of the state of the world’s ecosystems and their capacity to support human wellbeing, identified agricultural expansion and management as major drivers of ecosystem loss and degradation and the consequent decline in many ecosystem services and human well-being (MEA, 2009). This study showed that by year 2000, a quarter of the global land cover had been converted for cultivation, with cropland covering more than 50% of the land area in many river basins in Europe and India and more than 30% in the Americas, Europe, and Asia. It also showed that the development of water infrastructure and the regulation of rivers for many purposes, including agricultural production, fragmented rivers and generated impoundment of large amounts of water (Revenga et al., 2000; Vo¨ro¨smarty et al., 2005). Failure of agricultural water management increases the effect of natural and human-induced disasters, such as droughts and famine, on poor people. The rural poor, who are highly vulnerable, have a high dependency on the ecosystems where they farm or live and adequate water management diminishes the negative impact of low precipitation events (Silvius et al., 2000; WRI et al., 2005; Zwarts et al., 2006). Issues related to gender in management are also important in managing water for agriculture, as well as recognizing the importance of extreme events for the evolution of ecosystems. Water use for irrigation for agricultural production alters the excess supply of water for other uses, such as surface and groundwater for aquatic use downstream. The spatial water distribution changes significantly after an irrigation system is built and reduces the availability of water for downstream uses, impacting ecosystems. Irrigation infrastructure, dams, and canals alter the waterscape and impact the ecosystem, and irrigation water alters also the hydrologic cycle at the basin level. Part of the transfer water from streams or aquifers moisturizes soil and infiltrates, and the rest either is used by the plants or is evaporated. Unfortunately, increasing agronomic yields require not only water use but also the application and increased use of agrochemicals. Irrigation indirectly contributes to the application of chemicals and creates a negative impact on water quality with externalities downstream where water is also needed. The challenge is then how to increase agricultural

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production without compromising the sustainability of ecosystems needed by society in general. General recommendations are being contemplated to deal with the negative impact of water use in agriculture (Falkenmark et al., 2007). The implementation of an integrated approach to manage land and water resources and ecosystems that acknowledge the multifunctionality of agroecosystems in supporting food production and ecosystem resilience are recommended. To accomplish this, a better understanding of the services that are generated by agroecosystems and the value of biodiversity is required. Unfortunately, zero-environmental-cost agricultural water management is almost unobtainable. This obligates to understand the value of maintaining biodiversity and the interaction of ecosystems and their future water requirement to sustain ecosystem health and biodiversity and to evaluate how these benefits compare to short-term agricultural development. Given that new irrigation systems are converting land from its original ecosystems and regulating rivers are creating an impact on spatial water distribution, it is recommended to improve existing agricultural systems to obtain an increase in production as opposed to the expansion of agriculture. There is significant room for improvement in terms of technologies and management practices with techniques more environmentally safe to lower the impacts from the additional agricultural production needed to satisfy increasing food demand. There is also a need to reduce or reverse the ecosystem degradation through rehabilitation and restoration of agricultural systems. Mitigation of negative impacts on ecosystem services is achieved through an integrated management of land, water, and other dimensions of ecosystems at the watershed level. This approach requires a participatory process in which users can understand the benefits and costs of various development options, and to foster stakeholder discussion about the tradeoffs. Tools are available to assist this process, from economic evaluation to environmental assessment techniques, and should be complemented with more sociologic and human interaction methodologies. New tools need to be developed to monitor the evolution of ecosystems and to generate the required feedback to take the appropriate corrective decisions. Unfortunately, the agricultural development process and its impact on the environment are full of uncertainties that need to be dealt with when evaluating trade-offs and taking decisions. Using existing and developing new techniques to deal with uncertainty within the planning process is necessary. Tools such as scenario planning could improve the assessment and learning of the development decisions and their potential impacts on the environment. Increasing food demand requirements and minimizing its negative impact on the environment require building new institutions and organizations that could better address the different development options and their impacts on the ecological functions. To generate the best setting for decision making, it is necessary to educate all stakeholders about the ecological services of ecosystems, and the social and economic benefits of alternative policy options. A higher level of abstraction is needed so that interested people from different disciplines can internalize the relevant information for decision making.

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1.08.6 Water for Agriculture and Poverty Reduction High-income countries have shown important increases in agricultural productivity and lower-income countries have benefited less from improved varieties from the Green Revolution, which has been a major contributor to growth in staple crop production in the developing world. Production growth in sub-Saharan Africa was based almost exclusively on the extension of cultivated area, and in Asia, irrigation was part of the explanation since most of the innovations in agricultural varieties demanded supplemented water via irrigation. In developing countries, the population will remain predominantly rural until 2020 when the size of the rural population declines due to slower population growth and rapid urbanization (World Bank, 2008). Poverty is more prevalent in the rural areas and the World Bank (2008) estimates that gross domestic product (GDP) growth generated in agriculture is, on average, 4 times more effective in alleviating poverty and benefiting half of the poorest population than growth generated outside agriculture. This effect declines as the countries have higher incomes. There is a clear relationship between water management and poverty reduction. Case studies from research and international institutions show that water management is a good investment that not only contributes to poverty reduction, but also is generally cost efficient, and has the potential to generate wealth. As a result of this linkage, the global community has united to fight poverty through actions that bring different interests and organizations together in effective partnerships around the MDG agenda. Following the conceptual framework developed by the Poverty–Environment Partnership (2002), water management contributes to poverty reduction in four dimensions:

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Enhanced livelihood security. Water is considered an input to livelihood activities and a determinant of health and productivity of ecosystems. Water in agriculture positively impacts the ability of poor people to use their agricultural land and capabilities to make a living in conditions of greater security and sustainability. Ensuring continuity in water flows and minimum levels of water quality is essential for maintaining the integrity of ecosystems. Making sure that adequate and reliable water supplies are available for agricultural activities (including livestock, aquaculture, horticulture, and other types of production) is key to poverty reduction throughout the developing world. Therefore, the adequate design of water irrigation districts makes water available for activities that impact directly the livelihood of rural habitants. Reduced health risks. Water-borne and water-related vectorborne diseases, such as diarrhea and malaria, are the main killers in many parts of the developing world, and, in particular, affect children and other vulnerable groups. Some of these diseases have their origin in irrigation systems and adversely affect the most vulnerable population, women, and children, generating disabilities, poor nutritional conditions, and, eventually, death. Well-designed irrigation systems, with the right hydraulic infrastructure and with an adequate administration and operation, together with educational campaigns, is the most effective

strategy to improve health in the rural areas. It has been found that, in many cases, the economic benefit from these ameliorating activities is higher than the benefit derived from an increase in the regional economic value-added in many parts of the developing world. Integrating water management in agriculture with health systems development is one of the most effective strategies to reduce poverty through health improvement of the rural population. Every day diarrheal diseases cause nearly 5000 deaths, mostly among children under 5 years of age (United Nations, 2006). The World Health Organization estimates that the number of deaths from infectious diarrheas amounts to 1.8 million for all age groups, with a heavy toll among children under 5–1.6 million deaths. Malaria kills about 1 million people in the world every year, mainly in Africa, and about 80% of these cases are children under 5 years of age. It is estimated too that 160 million people are infected with schistosomes and 133 million suffer from high-intensity intestinal infections. These diseases have a direct impact on the welfare of the population and a long-term effect on future generations.

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Reduced vulnerability. Water in agriculture is often too much, too little, or too contaminated. It is rare to observe equilibrium of natural water supply with the right amount of water potentially demanded by the agricultural systems. This variability of water supply generates environmental, economic, and social threats as a result of sudden impact shocks and long-term trends. Droughts, floods, and highintensity rainfall have the potential to destroy livelihoods and increase poverty in the rural areas. In addition, water management in agricultural systems should compensate for the negative impacts of climate change, pollution, and solid degradation. Well-designed and managed agricultural systems reduce the resilience of the poor and help stabilize the income variability of rural landholders, and participants of the agricultural value chain. Pro-poor economic growth. Agricultural water management has the potential to generate the economic growth needed to reduce poverty in the rural sector and, in particular, to create income-generating opportunities for the poor. Water supply is a necessary input in productive activities and allows technologies adoption. Agricultural water management opens the opportunity for entrepreneurs to participate in ventures upstream and downstream of the agricultural value chain.

The potential economic dynamism generated by the induced economic activity by an improved water management will generate returns and benefits to the local economy with many multiplier effects. National and regional economies benefit with significant improvement in the management of agricultural systems, especially if these activities are complemented with measures to make input markets work, additional diversification opportunities, and investment in related infrastructure. Water management improvement should be accompanied by institutional strengthening and knowledge-generation initiatives to increase its impact on poverty reduction. Based on the previous framework, there are evident opportunities to reduce poverty through water management in

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agriculture. The UN Task Force on the MDG found four core areas on which it is necessary to work to eliminate the constraints (United Nations, 2006). First, it is necessary to work on policy, legal and regulatory reform, including issues of rights of access to water. Attention should be given to work on policies that target needs and opportunities of the poor for improved access to water for agriculture. Second, it is necessary to plan and select technology choices that are consistent with poverty reduction targets, assessing the possible impacts on the most vulnerable people and the water resources. Third, it is necessary to develop financial mechanisms such as investment incentives and cost-recovery mechanisms to have credit and financial management systems and to create a regulatory regime and climate where private investment is encouraged. Finally, institutional reform and coordination among government agencies are needed to deal with agricultural water management to support the investment and involvement from the private sector and other regional actors, and improve the management of existing systems.

1.08.7 Water Management of Rainfed Agriculture Water productivity in rainfed agriculture is low despite being the traditional practice on 80% of the world’s agricultural area, which presents an opportunity for investment to improve productivity and boost agricultural yields. In many communities in developing countries, rainfed agriculture remains as the primary source of food, particularly grain. Historically, many developing countries that practiced rainfed agriculture had chosen to increase production through brute force by simply expanding their agricultural area. Despite yields having increased by a significant amount in these countries, they still remain far from the yields found in developed countries such as the United States and Europe, which serves to demonstrate the potential for yield growth in developing areas (Rockstro¨m, 2007). On average, these developing countries have only reached approximately 30% of their achievable yields, while some countries such as Yemen and Pakistan only reaching approximately 10% of their achievable yield. Furthermore, historical evidence has demonstrated a ‘‘growing yield gap between farmers’ practices and farming systems that benefit from management advances’’ (Wani et al., 2003), which serves to show that neglecting water management practices not only prevents farms from achieving higher yields, but in fact it diminishes their yield as well. Moreover, it has been shown that improving rainfed agriculture has led to decreased poverty rates (Irz and Roe, 2000), which further emphasizes the need for investment in rainfed agriculture to heighten productivity. The key problem facing farmers that has hindered yield growth cannot simply be generalized as the amount of rainfall: depending on where in the rainfall zone spectrum an area lies, the core limiting factor varies. In arid regions, the amount of rainfall does in fact prove to be the largest problem for rainfed agriculture. However, in other regions, such as the semiarid and dry subhumid zones where the minimum crop water need is already met, the problem of yield growth becomes focused on high variability of rainfall. Challenging previous beliefs that the amount of rainfall was the main

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limiting factor for higher yields, this sporadic availability of water has helped to frame rainfed agriculture by demonstrating that basic water management strategies can double yields, on average. This variability in rainfall, characterized by seldom and high-intensity rainfall as well as frequent dry spells and high-intensity droughts, creates an insufficient water supply to meet water demand, otherwise known as water stress. Water stress has two primary divisions: one is the dichotomy between dry spells, which are 2–4-week periods during the rainy season that temporarily halts production during critical stages of growth, and droughts, which are a less common (once a decade) lack of rainfall that entirely halts crop growth, while the second division is one between man-made variability (agricultural) and natural variability (meteorological). Further exacerbating the problem of water stress is the variability in amount of rain that reaches the crops’ roots through soil moisture, which is generally only 70–80% of total rainfall, and sometimes can be as low as 40% in areas of poorly managed land. These problems incited by poor water management techniques are referred to as agricultural dry spells and droughts, and provide additional evidence that investment in water management can drastically improve yields. When rainfall is not efficiently used for plant absorption due to faulty management practices such as poor soil fertility or land degradation, the shortage of food is blamed on an agricultural drought. For example, in semiarid regions, nonplant growth rainfall, which includes drainage, nonproductive evaporation, and runoff, account for up to 85% of rainfall. Transpiration, which directly attributes to plant growth, only accounts for up to 30% of the rainfall. In certain areas with severely degraded land, only 5% of rainfall is used productively for plant growth. Rainfed agriculture yields are often limited by deficient soil fertility that comes as a result of nutrient depletion, loss of organic matter, and other forms of soil degradation. Studies in India have shown that this phenomenon is oftentimes human induced, where practices such as subsistence farming have depleted the soil of many of the necessary plant growth nutrients. Fortunately, problems concerning soil fertility can be easily rectified through investment in water management, such as injecting micronutrients into the deficient soil, which has been shown to substantially increase crop yields and rainwater productivity in India. As a result of this increased production, economic returns also increased by 50–75%. These studies further emphasize the capabilities and the need for investment in rainfed systems. The solution to the low productivity of rainfed systems is much easier said than done. A multitude of challenges stand in the way of simply investing more in rainfed agriculture and increased yields, the most fundamental of which is the adaptation and implementation of new agricultural innovations to every farm worldwide. Oftentimes, what drive innovation to be realized and adopted are social and ecological crises. However, simply waiting until the advent of the next crisis is not a viable option. Moreover, most farming households are small and marginalized and do not necessarily have access to the latest agricultural innovation. Rainfed areas also tend not to have the necessary infrastructure to implement

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innovations because throughout history, only high-potential irrigated areas have received large innovations, leaving these smaller areas in the dust. Other roadblocks include ‘‘limited information of the options available, social and economic constraints to adoption, lack of enabling environments and backup services, poor market linkages, and weak infrastructure’’ (Rockstro¨m, 2007). Developing avenues for better water management practices, such as through governance, policy, institutions, practices, and technologies, requires more attention than what has currently been devoted to it. Current strategies aim to make more efficient use of rainfall by increasing plant water availability and plant water uptake capacity through external and in-site water harvesting systems, soil and water conservation, evaporation management, and integrated soil, crop, and water management. While there are efforts to reduce water loss by increasing canopy cover, most strategies focus on securing more water produced by rainfall. In order for these strategies to be successfully implemented, they require a larger focus on small-scale water harvesting. Since small households make up a large proportion of farmers, unless these techniques are financially and structurally feasible, they simply cannot be used. The structure of rainwater harvesting is divided into three parts: ‘‘a watershed area used to produce runoff, a storage facility (soil profile, surface reservoirs, or groundwater aquifers), and a target area for beneficial use of the water (agriculture, domestic, or industry)’’ (Siegert, 1994). Another angle of attack that accounts for rainfall variability includes maintaining yields throughout dry spells and droughts. One important strategy lacking prevalence is the technique of supplemental irrigation, which collects runoff from external rainwater harvesting systems and transfers it to rainfed cropland. This technique, when compared with strictly rainfed systems, has shown substantially higher crop yields, with increases in yield ranging from approximately 30% to 400% (Oweis and Taimeh, 1996). Arguably, the most valuable quality of supplemental irrigation is its ability to supply water to a region when it is undergoing a dry spell, thus diminishing the frequency of interruptions to crop cycles. Studies have shown that 50–200 mm of supplemental irrigation is enough to combat the yield-reducing effects of dry spells (Oweis and Taimeh, 1996). Furthermore, the cost of supplemental irrigation systems is low, even for family-sized farms and small communities, in that the cost of a reduced yield due to dry spells is effectively eliminated. The technique of supplemental irrigation has already been adopted and implemented by a multitude of commercial farms in countries such as Australia, South Africa, and India; however, the benefits of such a system must be realized through simultaneous practice of other water-management techniques in order to maximize crop yield.

1.08.8 Policy Actions for the Future IWMI (2007) has completed an assessment of water management for agriculture and recommended eight policy areas, recognizing first that different actions are required for different situations, that actions for the sub-Saharan Africa will be

needed in terms of infrastructure, and where infrastructure has already being developed, as in Asia, the improvement of water availability relied on increases in productivity, relocating supplies, and rehabilitating systems. The eight policy actions are discussed below:

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Change the way we think about water and agriculture. To achieve food security, reduce poverty, and conserve the ecosystems, it is necessary to think differently about water. A broad focus should be adopted, with reformed institutions and viewing rain as the ultimate source of water that should be managed. Agriculture should be seen as a system integrated with other uses, providing services and integrating with other ecosystems. Fight poverty by improving access to agricultural water and its use. To effectively fight poverty, it is necessary to secure water access to smallholder farmers, using pro-poor technologies, and investing in roads and markets. In addition, systems that have multiple uses, such as aquaculture and agroforestry, are an ideal solution to alleviate poverty. Good governance is fundamental to any poverty-reduction strategy. Good governance means creating a fair legal, policy, and regulatory framework in which the rights of people to access water resources for agriculture are secured. It deals also with improving the effectiveness, accountability, and transparency of government agencies, ensuring the participation of the poor in decision making and enhancing the role of civil society guarantying basic security and political freedom (United Nations, 2006). Manage agriculture to enhance ecosystem services. Agricultural management could enhance other ecosystems to promote services beyond the production of traditional agricultural products. Water and land use will probably be intensified in the future but should be articulated with other services. Increase the productivity of water. More food per cubic meter of water should be the goal to increase effective water supply and reduce effective water demand. It is estimated that 35% increase in water productivity could reduce additional crop water consumption up to 80%. The poor can benefit from water productivity gains as well as largeholder farmers by introducing higher-value products. Upgrade rainfed systems – a little water can go a long way. The way to upgrade rainfed systems is by conserving soil moisture and providing supplemental irrigation where feasible. This action has a great potential to increase the income of an important number of farmers, especially in water-deficit regions such as sub-Saharan Africa and parts of Asia. Mixed crop and livestock systems have a good potential for improving the productivity of these systems. Adapt yesterday’s irrigation to tomorrow’s needs. Emphasis should be given to adapt current irrigation systems to future demands. Rehabilitation, modernization, and management techniques should be used to increase water productivity and its impact on poverty alleviation. Reform the reform process – targeting state institutions. Government institutions should be reformed if new management approaches are to be adopted and implemented. A collaborative process of all actors, private sector, and other institutions, is important to ignite a renovated process to satisfy local needs.

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Deal with trade-offs and make difficult choices. There should be good information to allocate water among conflicting uses. Informed multi-stakeholders are necessary to achieve effective negotiations and to make decisions about the use and allocation of water. Reconciling competing demands on water requires transparent sharing of information.

1.08.9 Summary Agricultural water management deals with the administration of a key input to agricultural production, and up to now it has been responsible for the majority of water use in the world, has improved nutrition, alleviated poverty, and increased production in 2.5 times the level in four decades. Agricultural water management has had the simultaneous challenge of meeting future food needs, making farms profitable and reducing poverty. New technologies are needed to intensify land and water use and to increase agronomic yields to produce the additional food to feed the world population and minimizing the impact on the environment. However, most importantly, agricultural water management needs to provide efficient solutions to improve the income levels of the farmers and rural people. These water resource challenges require better allocation of the resource using the social and economic value of the resource as distribution criteria. Agriculture has induced the degradation of many ecosystems, including those that are essential for food production. Aquifer contamination and depletion, drainage of wetlands, surface water contamination, land degradation and erosion, and aquatic ecosystems conflict are just some examples of these impacts. Therefore, water in agriculture should be managed by taking into account the array of services of the ecosystem, which are crucial for society. Adequate planning needs to take into account the trade-off between water for food production and ecosystem services, and address the social impact of poor rural people who often suffer the consequences of environmental negative externalities. Agricultural water management has the potential to become the best road to improve agricultural practices through technology adoption and diminish the environmental impact of the use of water resources. Water managers for agriculture will face many challenges while trying to accomplish multiple objectives. Sacrifices will be needed and so the appropriate institutional arrangement is to deal with these trade-offs. That is why an integrated approach to land use, water allocation, and ecosystem sustainability will be essential. In addition, understanding the tradeoffs between the conflicting goals will require an increase in the body of available knowledge to have a more accurate approximation as to the effect of irrigation to the environment and the effect of climate change on the future water supply for agriculture. The trend of irrigation investment has decreased by half during the past two decades compared to the growth experienced in the 1960s and 1970s. Constraints are larger as easy sites are already been exploited, groundwater sources are unsustainable in many places and the world is now more concerned with environmental and social issues that set a higher

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standard in agricultural water management, a situation that generates the incentive for new technology development. At the same time, nation and regional development processes have generated new uses for water that are competing with agricultural use. One of the great setbacks of agricultural water management is the severe failure of many institutional arrangements at the national and local basin levels to deal with water management. New rules and incentive mechanisms need to be put in place in order to face the future challenge to reflect our understanding of the interaction of water resources with ecosystems and human activity. Irrigation systems involve people with different interests, and therefore good governance is needed as a framework (institutional and administrative) to allow cooperation, coordination, and, most importantly, conflict resolution. Finally, water management for agriculture could become an interesting tool to achieve regional differentiation, allowing competition and incentivizing innovation. Water management could help building the strengths needed to create a differentiated business environment and therefore creating the conditions to achieving higher levels of regional and national prosperity through increments in productivity.

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Ramirez-Vallejo J and Rogers P (2004) Virtual water flows and trade liberalization. Journal of Water Science and Technology 49(7): 25--32. Ramirez-Vallejo J and Rogers P (2009) Failure of the virtual water argument: Possible explanations using the case study of Mexico and NAFTA. In: Biswas AK and Cline SA (eds.) Global Change: Implications for Water and Food Security. Washington: IFPRI. Revenga C, Brunner J, Henniger N, Kassem K, and Payner R (2000) Pilot Analysis of Global Ecosystems, Freshwater Systems. Washington, DC: World Resources Institute. Rockstro¨m J (2007) Water in rainfed agriculture. In: Molden D (ed.) Water for Food, Water for Life. A Comprehensive Assessment of Water Management in Agriculture, ch. 8. London: Earthscan, and Colombo: International Water Management Institute. Rogers P (2002) Water Governance in Latin America and the Caribbean. Washington, DC: Inter-American Development Bank, Sustainable Development Department, Environment Division. Rogers P and Hall A (2003) Effective Water Governance, GWP Technical Committee Background Paper 7. Stockholm: Global Water Partnership. Rosegrant M, Cai X, Cline S, and Nakagawa N (2001) The role of rainfed agriculture in the future of global food production (invited background research paper). In: International Freshwater Conference. Bonn, Germany. Rosegrant MW, Agcaoli M, and Perez ND (1995) Global Food Projections. Canada: Renouf Publishing. Rosegrant MW and Binswanger H (1994) Markets in tradable water rights: Potential for efficiency gains in developing country water resource allocation. World Development 22(11): 1--11. Rosegrant MW and Cai X (2002) Global water demand and supply projections: Part 2. Results and prospects to 2025. Water International 27(2): 170--182. Rosegrant MW and Cai X (2004) Implications of water development for food security. In: Lawford R, Fort D, Hartmann H, and Eden S (eds) Water Science, Policy, and Management,. Water Resources Monograph Series, vol. 16, 422pp. Washington, DC: American Geophysical Union. Seckler D, Molden D, and Sakthivadivel R (2003) The concept of efficiency in waterresources management and policy. In: Kijne JW, Barker R, and Molden DJ (eds.) Water Productivity in Agriculture: Limits and Opportunities for Improvement, Comprehensive Assessment of Water Management in Agriculture Series No. 1, pp. 37–51. Wallingford and Cambridge, MA: CABI Publishing. Siegert K (1994) Introduction to water harvesting: Some basic principles for planning, design and monitoring. In: Water Harvesting for Improved Agricultural Production, Proceedings of the FAO Expert Consultation, Water Report 3. Cairo, 21–25 November 1993. Rome: Food and Agriculture Organization. Silvius MJ, Oneka M, and Verhagen A (2000) Wetlands: Lifeline for people at the edge. Physical Chemistry of the Earth B 25(7–8): 645--652. Travis WP (1964) The Theory of Trade and Production. Cambridge: Harvard University Press. United Nations (2006) Millennium Development Goals Report. New York: United Nations. United Nations (2008) Trends in Sustainable Development. Agriculture, Rural Development, Land, Desertification and Drought. New York: United Nations United Nations Development Programme (2001) Partnerships to Fight Poverty. Annual Report. New York. United Nations Development Programme (2001) Partnerships to Fight Poverty. Annual Report. New York. Vo¨ro¨smarty CJ, Le´veˆque C, and Revenga C (2005) Fresh water. In: Hassan R, Scholes R, and Ash N (eds.) Ecosystems and Human Well-Being: Current State and Trends – Findings of the Condition and Trends Working Group. Washington, DC: Island Press. Wallace JS and Batchelor CH (1997) Managing water resources for crop production. Philosophical Transactions of the Royal Society London B 352: 937--947. Wana J, Griffithsa R, Yinga J, McCourtb P, and Huanga Y (2009) Development of drought-tolerant canola (Brassica napus L.) through genetic modulation of ABAmediated stomatal responses. Crop Science Society of America 49: 1539--1554. Wani SP, Pathak P, Sreedevi TK, Singh HP, and Singh P (2003) Efficient management of rainwater for increased crop productivity and groundwater recharge in Asia. In: Kijne JW, Barker R, and Molden DJ (eds.) Water Productivity in Agriculture: Limits and Opportunities for Improvement, Comprehensive Assessment of Water Management in Agriculture Series No. 1, pp. 199–215. Wallingford and Cambridge, MA: CABI Publishing. Wichelns D (1999) Economic efficiency and irrigation water policy with an example from Egypt. Water Resources Development 15(4): 543--560. Winpenny J (2003) Report of the World Panel on Financing Water Infrastructure. World Water Council. World Bank (2003) Managing Water as an Economic Good: Rules for Reformers, Water Resources Sector Strategy. Washington, DC: World Bank.

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1.09 Implementation of Ambiguous Water-Quality Policies DH Moreau, University of North Carolina, Chapel Hill, NC, USA & 2011 Elsevier B.V. All rights reserved.

1.09.1 1.09.2 1.09.2.1 1.09.2.2 1.09.3 1.09.3.1 1.09.3.2 1.09.4 References

Nonpoint Sources and the CWA Intrastate Cases Neuse River Nutrient Management Strategy Jordan Lake Stormwater Rules Interstate Nonpoint Management Mississippi River Basin and Hypoxia in the Gulf of Mexico Chesapeake Bay Program Summary and Conclusions

Ambiguities in water policies may create significant barriers to implementation and lead to unpredictable outcomes, especially in the United States’ federal system where the national government has made some parts of water policy unambiguous but left other parts to states with ambiguous mandates. The Clean Water Act (CWA) is a case in point. The goal of the act is to protect and enhance the quality of all waters of the United States to levels that are sufficient to support their state-designated uses, at a minimum to make them fishable and swimmable. Policies set forth in the Act establish several levels of technology-based effluent limits for point sources, publicly owned wastewater treatment plants, and a large number of categories of industrial dischargers. Enforceable permits issued to those sources contain effluent limits, monitoring protocols, and reporting requirements. The permit program also covers other sources such as urban stormwater runoff and concentrated animal feeding operations (CAFOs). There is little ambiguity if the least stringent effluent limits are sufficient to satisfy water-quality standards, but several ambiguities arise when minimal requirements covering these sources are not sufficient to satisfy water-quality standards. These ambiguities are especially important when sources of degradation are dominated by agriculture and urban stormwater runoff. In these cases, states are obligated to establish maximum allowable loads for those segments of water bodies that have been designated as being degraded. Then, states are obligated to allocate a portion of the allowable load to point sources, another portion to nonpoint sources, and reserve a third portion as a factor of safety. If more stringent effluent limits on point sources are insufficient to upgrade water quality to satisfy standards, then states are required to submit plans to control nonpoint sources to the maximum extent practicable (MEP). However, most agricultural sources are excluded from federal permits. Management of those sources is largely by voluntary participation in a variety of incentive programs. There are at least two unresolved issues. First, what is the intent of the Act? Are states expected to satisfy waterquality standards when available technologies are not sufficient to accomplish the task? Second, how do states control nonpoint sources to the MEP when those sources are largely exempt from regulation?

153 154 154 156 157 157 159 160 160

In this chapter, several nonpoint source management strategies are examined with respect to how ambiguities in the CWA either were addressed during implementation or have created as-yet insurmountable barriers to implementation. Particular attention is focused on policies for control of nonpoint sources of nutrients leading to hypoxia in receiving water and the management of urban stormwater. Cases include water-quality management for the Neuse River Basin, implementation of urban stormwater management in Jordan Lake watershed, North Carolina, control of nutrients in the Mississippi River Basin, and control of nutrients entering the Chesapeake Bay.

1.09.1 Nonpoint Sources and the CWA None of these issues were adequately addressed when fundamental changes to the federal water pollution control policy were made in 1972. That shortcoming is readily understood in the context of information about sources of pollution at the time. The Federal Water Quality Administration (FWQA) undertook the first national assessment of water quality in 1969. That assessment was largely a compilation of professional judgments by state water-quality officials. Results indicated that 33% of all stream mileage in the United States was polluted to some degree. One estimate in that assessment was that industrial sources accounted for 24% of degraded streams, municipal sources 22%, and agriculture 11% (USEPA, 1971). With that presumed factual basis, it is not surprising that Congress believed that strong regulatory limits on municipal and industrial sources would lead to substantial elimination of polluted waterways. Section 303(d)(1)(C) of Amendments to the Federal Water Pollution Control Act of 1972 (referred to as the CWA beginning in 1977) required establishment of total maximum daily loads (TMDLs) for all stream segments in which the application of technology-based effluent limits on municipal and industrial sources would be insufficient to implement the applicable water-quality standards. TMDLs had to account for seasonal variations in stream properties and a margin of safety to cover any lack of knowledge between effluent limitations and water quality. Section 302 stated that where technology-based standards were

153

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insufficient to satisfy water-quality standards, more stringent effluent limitations for point sources should be established at levels that can be reasonably expected to bring about compliance. USEPA introduced regulations to implement that portion of the act, using the following language (USEPA, 1975):

• • •

For each water quality segment, a total allocation for point sources of pollutants and a gross allotment for nonpoint sources of pollutants. A specific allowance for growth shall be included in the allocation for point sources and the gross allotment for nonpoint sources. The total of the allocation for point sources and the gross allotment for nonpoint sources shall not exceed the total maximum daily load.

The clear implication of the language in the statute and the regulations is that nonpoint sources could be addressed simply by reserving a portion of the TMDL for that purpose. The language did not address at least two possibilities: (1) that no economically reasonable available treatment technology for point sources would be sufficient to meet standards and (2) that loads from nonpoint sources on a stream segment would exceed the TMDL for that segment. Efforts to implement the policy quickly came up against realities of these two possibilities. The only option available to a state in this situation was to place a low priority on setting a TMDL for such a water body, thereby scheduling its development as far into the future as possible. When TMDL regulations were revised as of July 1987 (40 CFR 130.2), there was little change in the basic language. The loading capacity, defined as the maximum load of a pollutant on a stream segment that still satisfies related water-quality standards, is to be divided between a load allocation for nonpoint sources and background levels and a waste load allocation for point sources. These regulations went on to say that if best management practices (BMPs) to reduce nonpoint sources make more stringent load allocations for those sources practicable, then waste load allocations for point sources can be made less stringent. The regulations again failed to address the case where nonpoint source loads dominate the system and BMPs are insufficient to reduce actual loads below the TMDL. The latest version of TMDL regulations (40 CFR 130.7) states that TMDLs will be established ‘‘yat levels necessary to attain and maintain the applicable narrative and numerical water quality standardsy’’ taking into account all sources that are contributing to nonattainment of the standard. The directive to establish TMDLs does not carry with it any additional authority to control nonpoint sources. Amendments to the CWA in 1987, specifically the addition of Section 319, required states to take a number of additional steps to control nonpoint sources, including:

• •

preparation of an assessment report that identified navigable waters for which existing BMPs could not be expected to attain or maintain water-quality standards; identify BMPs and other measures to reduce to the MEP loads from each category of nonpoint sources; and

•

identify state and local programs for implementing BMPs and other measures.

States were then required to submit a management program ‘‘yto reduce pollutant loadingsy’’ from nonpoint sources ‘‘yto the maximum extent practicable.’’ Nowhere did the amendment state how much reduction was necessary. Section 319 did not require national technology standards or guidelines for nonpoint sources comparable to those for point sources in Section 302. Nor did it require any permitting, monitoring, inspection, and enforcement actions comparable to those for point sources as required under Section 402. State and USEPA officials were left to negotiate a mutually agreeable set of nonpoint source control measures, limited by whatever authorities and financial resources states and other federal agencies had to implement. Similar language was added in 1987 to Section 402(p) to address stormwater runoff from industrial activities and municipal separate storm sewer systems (MS4s). Although stormwater discharge was made subject to discharge permits, the operative provision for municipal permits is that dischargers are required to reduce pollutant loads to the MEP. That provision leaves open the question of what is the MEP. In particular, do the stormwater regulations apply to existing development as well as to new development? Portions of the stormwater provisions are rather straightforward, but, in addition to MET, other provisions are problematic. USEPA defined MS4s very broadly to include not only the conventional elements of urban stormwater management systems, but also roads with drainage systems, ditches, and man-made channels in urbanized areas that are owned by a state or any type of local government. At least two significant ambiguities have arisen from this definition and related requirements. First, urbanized areas do not necessarily coincide with boundaries of political jurisdictions to which the necessary regulatory authority has been delegated. Second, how would these provisions apply to large-scale real estate developments that convert rural areas to urbanized areas? In 1997 when EPA issued new policies for the TMDL process, it acknowledged that implementation of the program was moving at an unacceptably slow pace (USEPA, 1997). At the time, EPA regulations required each state to submit its list of impaired waters every 2 years and identify which of those would be scheduled for TMDL development over the following 2 years. The problem was that there was no schedule for all impaired segments. The revised policy urged states to develop schedules for all segments over a period of 8–13 years. Even then, implementation of nonpoint source controls was limited to the requirements in Section 319. When it issued proposed numerical nutrient criteria for the State of Florida in January 2010, EPA acknowledged that it could take many years before affected waters could be brought into compliance. Among many other provisions, the proposal asked for comments on Restoration Water Quality Standards that would allow achievement of water-quality standards in several phases over a period of up to 20 years so long as adequate progress was achieved at each stage (USEPA, 2010). This approach would allow considerable time to reach the final numerical criteria, but writing criteria does not grant additional authority to satisfy them.

Implementation of Ambiguous Water-Quality Policies

1.09.2 Intrastate Cases Two intrastate cases from North Carolina illustrate the use and limits of state authority to address problems of excessive nutrient loadings to lakes and estuaries, including urban stormwater runoff.

1.09.2.1 Neuse River Nutrient Management Strategy North Carolina’s Division of Environmental Management (DEM), the state’s water-quality management agency at the time, initiated a third-generation basinwide water-quality planning process in 1991 to coincide with 5-year renewals of the National Pollutant Discharge Elimination System (NPDES) discharge permits. The first of 17 basin plans was for the Neuse River Basin (NC Division of Environmental Management, 1993) shown in Figure 1. A supplemental classification of Nutrient Sensitive Waters had been assigned to the basin in 1983 to the Neuse River, and with that designation, all significant point sources had been required to meet a phosphorus effluent limit of 2.0 mg l 1 over the period of 1988–93. The 1993 report estimated that only 21% of the phosphorus and only 12% of nitrogen were coming from point sources. Agriculture accounted for approximately two-thirds of the balance. In compliance with USEPA policy, the report identified a wide range of state and federal programs to address nonpoint sources. It described 10 programs for agriculture, including the NC Agricultural Cost Share Program and provisions of the 1985 and 1990 Farm Bills. Four urban stormwater programs were discussed; runoff from construction was covered by the state sediment control act; and regulations on mining activities were discussed. Other programs for hydrologic modification, on-site disposal of wastewater, concentrated animal feedlots, solid waste, forestry, wetlands, and groundwater were also included in the mix.

The 1993 basinwide management plan, based on the CWA approach to nonpoint sources, failed to achieve its objective in late summer 1995 when massive fish kills occurred in the Neuse River estuary. Immediately thereafter, the North Carolina Environmental Management Commission (EMC) directed DEM to prepare a new plan to address deficiencies in the 1993 basin plan. The governor weighed in on the matter in the following March (Hunt, 1996). Draft administrative rules to implement the plan were sent to public hearing in May 1996 under the title of Neuse River Nutrient Sensitive Waters Strategy or the NSW Strategy. The state legislature provided legislative authority for a reduction of nitrogen by 30% from a 1991–95 baseline for both point and nonpoint sources (Session Law 1995 Chap. 572, ratified June 1966). This target was recommended by a special legislative committee that included knowledgeable academics and staff of DEM. Final rules were approved by the EMC in December 1997 (15A NCAC 2B.0232-0242 (Title 15, Chapter 2, Subchapter B, Sections of the North Carolina Administrative Code)), and specific state statutory authority was cited as the basis for these regulations. Several provisions of those rules established new initiatives to fill gaps in the basinwide management plan. For the first time, general agricultural operations were subject to waterquality regulations. Two options were made available to agricultural operators to satisfy the 30% reduction for nitrogen. They could act either individually to implement specified BMPs or collectively in a local (county) plan where some form of its trading among participants would be possible. A new program for certifying fertilizer applicators was established, covering all operations that applied fertilizer to 50 acres or more each year. Stormwater regulations were required for a list of municipalities and counties in the basin (they would later be covered by Phase II of USEPA regulations). Protection of existing riparian buffers was implemented in Section 233. A table of uses that are either exempt, allowable, allowable with mitigation, or prohibited applies to a

Durham

Wilson

Raleigh

Greenville

Goldsboro Kinston

Neuse River 0

Miles 10 20 30

Figure 1 Neuse River Basin, North Carolina.

40

155

New Bern

Neuse River Estuary

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50-ft-wide buffers directly adjacent to intermittent and perennial streams, lakes, ponds, and estuaries in the basin. A trading program for point sources was established in Section 02B.0235, building on experiences with the Tar RiverPamlico Sound trading program established in 1991. General powers delegated to the EMC by the state legislature were cited as the authority on which these administrative rules were adopted. EPA was in the process of formulating its policies for trading under the CWA. Unlike the Clean Air Act where national trading programs had been explicitly authorized to address acid rain, the CWA contained no mention of trading. EPA issued a policy statement on effluent trading in watersheds in January 1996, followed 4 months later by a draft framework for implementing the policy (USEPA, 1996). Basic principles for trading under the CWA were laid out in Chapter 2 of that document, but final rules were not formally proposed until proposed in the Federal Register until May 2002. Final trading rules were adopted in January 2003 (USEPA, 2003). Because many of the provisions in the NSW Strategy were new, they faced considerable obstacles and a few revisions before becoming effective. Considerable care was taken to involve stakeholders as the package of rules was being formulated, but public interest in finding a fix was very high. Not all stakeholders were enthusiastic about the rules adopted by the EMC. Under North Carolina’s Administrative Procedures Act, all administrative rules are subject to review by the Rules Review Commission (RRC), and no rule can become effective until after the next session of the legislature except in special cases where temporary rules are permitted. With very strong political support to get the rules in place, the legislature allowed all but the initial riparian buffer rule to become effective. This rule was slightly modified to meet objections by one group of stakeholders.

While the rules in Section 235 and 238 established a regulatory program on agriculture for the first time, they were limited to the installation of BMP and an annual reporting of what BMPs were in place and how much acreage was subject to BMPs. There is very limited instream monitoring to determine effectiveness of the rules, and inspections of operations subject to the rule are limited to sparse visits by agricultural agencies.

1.09.2.2 Jordan Lake Stormwater Rules A similar strategy was developed for the protection of B. Everett Jordan Lake, a critical source of public water supply in the Research Triangle of North Carolina. An important difference between the Neuse River and Jordan Lake nutrient loads is the relative importance of urban stormwater from existing development, a difference that was addressed in formulating a management plan for the lake. In the wake of actions in 1996 to address nutrient problems in the Neuse River estuary, the state legislature passed the Clean Water Responsibility Act (CWRA; also referred to as House Bill 515) in August 1997 to protect inland lakes that are designed as nutrient sensitive by the EMC. CWRA established default limits in the form of maximum concentration values for phosphorus and nitrogen in effluents from dischargers above such lakes. It also provided that alternate mass load limits could replace default values if they were based on a calibrated nutrient response model. Impacts estimated by mass loading would have to show compliance with the waterquality standard of 40 mg l 1 of chlorophyll-a. Primary targets of the CWRA were Jordan Lake in the Cape Fear Basin, shown in Figure 2, and Falls Lake in the Neuse Basin, both US Army Corps of Engineers multipurpose

Kernersville

Durham Burlington Greensboro Chapel Hill Jordan

Miles 0

5

10

15

20

Figure 2 Jordan Lake Drainage Area, Cape Fear Basin, North Carolina.

Lake

Implementation of Ambiguous Water-Quality Policies

reservoirs with a combined safe yield of about 165 million gallons per day for public water supplies in the area. Before Jordan Lake was found to be in violation of the water-quality standard in 2002, local governments in the watershed had already initiated development of a nutrient response model. A series of nutrient delivery models for point sources and nonpoint sources were developed over the period of 2001–03. A model of lake quality was delayed by errors in data collection, and a final report was not delivered until February 2005. Twenty-one stakeholder meetings were held in the process of developing the model (North Carolina Division of Water Quality, 2006). An initial draft of an NSW and TMDL strategy was submitted by the NC Division of Water Quality (DWQ) to the EMC in April of 2005, and draft rules were adopted by the Commission in November 2005. A set of technical issues were hashed out in meetings with stakeholders in 2006, before the proposed regulations were sent to formal public hearings in March 2007. The EMC approved a revised set of rules in May 2008, and rules that were passed were approved by the RRC in November 2008 in time to be considered by the 2009 session of the state legislature. Jordan Lake rules are very similar in structure to the Neuse Rive NSW Strategy with one very important exception. All prior regulations in NC directed toward control of pollutants in urban stormwater had been limited to the management of runoff from new development. However, in the process of making load allocations for Jordan Lake, DWQ could not find a feasible and equitable distribution of loads among sources without requiring reductions from existing urban development. An existing development rule was included as part of the management program. Several major urban areas in the watershed, including Burlington, Chapel Hill, Durham, and Greensboro would be affected by the proposed rule. The EMC proposed an adaptive approach to reducing loads from existing development. Municipalities and counties were given

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3 years and 6 months from the effective date of the rule to conduct feasibility studies and prepare implementation plans. Following approval by DWQ, they would have another 4 years and 6 months to initiate implementation (North Carolina Division of Water Quality, 2008). Affected municipalities and counties sought relief from the existing development rule in the 2009 session of the legislature. The legislature responded by disapproving the EMC’s existing development rule, but included much of the content of the rule in the same statute (Session Law 2009-216). The primary change was the schedule for implementation. That action concluded a 10-year process for revising protection strategies adopted in the Cape Fear Basinwide Water Quality Plan approved by the NC EMC in October 1996.

1.09.3 Interstate Nonpoint Management In the Neuse and Cape Fear River Basins, both general and explicit state authorities over nonpoint sources were used to fill gaps in the CWA in efforts to bring waters in those basins into compliance with water-quality standards. Interstate problems are much more intractable, especially in the absence of a strong federal role.

1.09.3.1 Mississippi River Basin and Hypoxia in the Gulf of Mexico An initiative to address the growing problem of hypoxia in the Northern Gulf of Mexico by reducing nutrient inputs from the Mississippi River Basin is a case in point. The areal extent of the hypoxic zone, located along the Louisiana coast as shown in Figure 3, is highly variable from 1 year to the next, but the trend is upward, and over the period 1996–2000 the average was over 14 000 square kilometers (about 5500 square miles).

Upper Mississippi

Missouri

Ohio

Arkansas Red Tennessee Lower Mississippi Atchafalaya

Hypoxic Zone Gulf of Mexico

Figure 3 Mississippi–Atchafalaya River Basin and Hypoxic Zone in the Gulf of Mexico.

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Concerns about expansion of the zone led to the formation of the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force (MR/GMWNTF) in 1997 comprised of a federal interagency working group and representatives of states and tribes. In November 1998, Congress passed the Harmful Algal Bloom and Hypoxia Research and Control Act of 1998 (HABHRCA, Title VI of P.L. 105-383), gave statutory authority to the task force, and directed it to conduct a scientific assessment of the problem and develop an action plan to control it. The Task Force was administered by the Committee on Environment and Natural Resources of the National Science and Technology Council. The first Action Plan produced in 2001 established the following goal (MR/GMWNTF, 2001): By the year 2015, subject to the availability of additional resources, reduce the 5-year running average areal extent of the Gulf of Mexico hypoxic zone to less than 5000 square kilometers through implementation of specific, practical, and cost-effective voluntary actions by all states, tribes, and all categories of sources and removals within the Mississippi/Atchafalaya River Basin to reduce the annual discharge of nitrogen into the Gulf.

An extensive body of scientific material was produced by the Task Force. Estimates of nutrient and sediment loads, their sources, and amounts delivered to the Gulf have been developed by a group of researchers at the United States Geological Survey (Alexander et al., 2008). Nitrogen and phosphorus delivered to the Gulf arrive from numerous tributary basins as shown in Table 1. Table 1 Percentage distribution of nutrients delivered to the Gulf of Mexico by Source Basin Basin

Upper Mississippi Missouri Central Mississippi Ohio and Tennessee White Arkansas Red Lower Mississippi and Atchafalaya Total

• •

•

Phosphorus

6.5 12.8 25.0 36.1 1.6 4.5 1.0 12.6

5.4 9.3 21.3 35.8 2.6 5.0 1.9 18.7

100.0

100.0

establishment of several subbasin committees; development of an integrated monitoring, modeling, and research strategy; additional monitoring of the hypoxic zone; increased assistance to agricultural producers through US Department of Agriculture programs to restore wetlands, add stream buffers, and install other best management practices; and completion of a major reassessment of the science that supports action items.

Other action items were not implemented, however, substantially impeding reductions in nutrient loads. Among the impediments cited in the report are:

• • •

failure to develop an integrated federal budget to support voluntary nutrient reduction; not only a failure to expand a long-term monitoring program, but also discontinuance of some stations; and the slow pace of development of sub-basin strategies.

Lack of progress on reducing nutrients may be attributable to several factors. First, although there may be strong desires among Gulf states and at the federal level to reduce hypoxia in the Gulf, upstream states lack a compelling interest to impose regulations on nonpoint source generators. Second, competing priorities in the Gulf states, particularly recovery from Hurricanes Katrina and Rita, could have delayed funding of the program. Third, the CWA is ambiguous on authority of

Percentage distribution of nutrients delivered to the Gulf of Mexico by type of source

Source

Nitrogen Mississippi

Urban and population-related sources Atmospheric deposition Crops Pasture/rangeland Forest Shrub and barren lands Total

• •

Percent of total Nitrogen

Table 2

Activities from which loads delivered to the Gulf are generated are shown in Table 2. Point sources account for less than 10% of the nitrogen load; they account for only 10–12% of phosphorus loads. Crops and pasture/rangelands account for approximately 70% of nitrogen and about 80% of phosphorus. The system is clearly dominated by nonpoint sources. Numerous interstate cooperative efforts among both governmental and nongovernmental agencies and organizations have been organized to address a range of water-resource issues along the Mississippi River, including water quality, navigation, and public water supply. Some of the more significant organizations are covered by a report of the National Research Council (NRC, 2008). MR/GMWNTF, with its 2001 Action Plan and its 2008 update (MR/GMWNTF, 2008) developed under authority of HABHRCA of 1998 and 2004, is the most effective action taken to date. The 2008 Action Plan reported progress on action items in the 2001 Plan, including:

Phosphorus Atchafalaya

Mississippi

Atchafalaya

9.1 16.2 65.6 5.0 4.1 0.2

8.9 18.0 62.2 5.6 5.2 0.2

12.3

10.6

43.1 37 7.5 0.4

40.0 39.4 9.2 0.97

100.0

100.0

100.0

100.0

Implementation of Ambiguous Water-Quality Policies

USEPA to take or even threaten to take regulatory action to achieve the goal. HABHRCA did not grant any additional authority to implement the 2001 Action Plan, and, as cited above, the Task Force conditioned the goal of reducing the hypoxic zone on the availability of additional resources and voluntary actions by all states. Several provisions of the CWA explicitly address interstate cooperation on nonpoint sources. These include:

• • •

Section 103(a) – Interstate Cooperation and Uniform Laws Section 319 – Nonpoint Source Programs Section 320 – National Estuarine Program.

Section 103(a) is a very brief admonition to USEPA to ‘‘yencourage cooperative activities by the Statesy’’ to manage pollution, promote uniform state laws, and encourage states to form interstate compacts when necessary to address cross-border effects. Specific authority is granted in Section 301(b) for two or more states to form compacts and agreements to control pollution. Section 319(g), part of the 1987 amendments, gives a state that does not meet water-quality standards due to nonpoint sources in other states the right to petition USEPA to convene a management conference. If USEPA finds that available information is sufficient to support the petition, tributary states are to be notified and a management conference is to be convened. USEPA is also given authority to initiate a management conference without a petition. The purpose of a conference is to develop an agreement among the participating states to improve water quality, but it is quite unclear as to what happens if the states do not reach agreement. The language is: ‘‘To the extent that the States reach agreement through such conference, the management programs of the Statesywill be revised to reflect such agreement.’’ Section 319(g)is silent on what happens in the absence of an agreement among the states. The National Estuary Program, authorized under Section 320 of the CWA, similarly directs USEPA to convene management conferences for the protection of designated national

estuaries in which water-quality standards are not being met. Estuarine management conferences are intended to assess water-quality trends, identify causes of pollution, establish relationships between pollution loads and water quality, develop comprehensive management plans, and identify federal financial assistance. The Barataria–Terrebonne Estuarine Complex along the Louisiana coast is the primary system that would be covered by this program. Section 303 of CWA – Water-Quality Standards and Implementation Plans does not mention other states in its requirements for listing of impaired waters and setting of TMDLs. Nonetheless, the Committee on the Mississippi River and the CWA (NRC, 2008) argued that USEPA has interpreted the CWA as imposing obligations on each state to protect downstream water quality in other states when setting TMDLs. A fundamental barrier to implementation of these authorities is that neither CWA nor HABHRCA grants USEPA or other federal agency any authority to set enforceable limits on nonpoint sources at levels sufficient to attain and maintain water-quality standards. In all cases, implementation depends on the will of tributary states to adopt effective nonpoint source programs, and USEPA is obligated to accept a state management plan if the state satisfies the criterion to identify BMPs and other measures to reduce to the pollution loads to the MEP.

1.09.3.2 Chesapeake Bay Program Urban growth, more intensive agricultural operations, and other factors in the watershed resulted in significant deterioration of water quality and related ecosystems in the Chesapeake Bay. Among other undesirable outcomes was widespread hypoxia due to excess nutrient loads. As shown in Figure 4, areas that drain to the Chesapeake Bay cover portions of several states. In 1983, 1987, and again in 2000, several states in the Chesapeake Bay watershed, the District of Columbia, the Chesapeake Bay Commission, and the US EPA entered into agreements to form the Chesapeake Bay

New York

Pennsylvania Basin boundary NJ

MD

West Virginia

Figure 4 Chesapeake Bay Drainage Area.

159

DE Virginia

160

Implementation of Ambiguous Water-Quality Policies

Table 3

Distribution of nutrient loads delivered to Chesapeake Bay by source

Source of nitrogen

Percent of total load

Source of phosphorus

Percent of total load

Septic Municipal and industrial wastewater Manure (agriculture) Chemical fertilizer (agriculture) Chemical fertilizer (nonagriculture) Atmospheric

4.3 18.9 17.8 15.4 10.1 33.5

Manure (agriculture) Chemical fertilizer (agriculture) Municipal and industrial wastewater Natural (wildlife and forest) Other fertilizer

27 18 22 3 30

Source: Chesapeake Bay Program, www.chesapeakebay.net/tribtools.htm#allocations.

Table 4 Direct funding provided by the federal agencies, states, and District of Columbia, fiscal years 1995 through 2004, in millions of constant 2004 dollars Federal agencies Department EPA Department Department Department

of Defense of Agriculture of the Interior of Commerce

Total – federal agencies

States 355.4 253.7 230.4 77.4 55.5

Maryland Virginia District of Columbia Pennsylvania

1862.4 752.6 41.8 28.1

972.4

Total – all states

2684.8

Source: United States Government Accountability Office, 2005.

Program (CBP) with the intent of restoring the Bay’s water quality and ecosystem. Maryland, Pennsylvania, and Virginia account for 88% of the nitrogen load and 87% of phosphorus. Distributions of sources of nitrogen and phosphorus that are delivered to the Bay are given in Table 3. As part of the 1987 agreement, the signatories committed to reduce 40% of nutrient loads that were controllable. Tributary-specific nutrient reduction strategies were adopted in 1992. The 2000 agreement asserted that where the 1992 goals had not been achieved, additional steps would be taken (CBP, 2000). Direct federal and state expenditures over the decade 1995–2004 are shown in Table 4. Federal agencies spent an average of nearly $100 million (in constant 2004 dollars) a year, and the three states and District of Columbia spent about $270 million a year (United States Government Accountability Office, 2005). Current estimates of distributions of nitrogen and phosphorus loads entering the Bay indicate that runoff from agriculture and urban areas account for large percentages of total loads. Nearly 60% of nitrogen and 80% of phosphorus loads are attributed to those sources (CBP, 2009). More recently, President Obama, citing lack of adequate progress toward restoration, issued Executive Order (EO) 13508 in May 2009, directing federal agencies to take a more active program to protect and restore water-quality and related ecosystems. Among the key challenges listed in the EQ are efforts to: (1) strengthen permit conditions for CAFOs and urban stormwater runoff and (2) enhance federal and state initiatives for conservation practices on agricultural operations (Federal Leadership Committee, 2009). USEPA also issued a notice of intent in September 2009 to establish a Bay-wide

TMDL for nutrients and sediments (USEPA, 2009). Preliminary targets for nitrogen and phosphorus loads have been established pursuant to the EO (Early, 2009). Maryland, Pennsylvania, and Virginia were allocated 88% of the nitrogen target and 89% of the phosphorus target. Even with these initiatives coming on authority of the President, it is far from clear as to how much can be achieved given ambiguities in the CWA. CAFO reductions may be more predictable because of existing permit authority, but controls on urban runoff will still be limited by the MEP criterion. All of the agricultural runoff programs cited in the EO are voluntary. Regulatory authority over urban stormwater and nonCAFO agriculture will be based on authorities of the states and the District of Columbia. In this case, the three states that are big contributors have a compelling interest and a history of strong political support for those policies, but it remains questionable as to how far that authority extends and how much political support there will be as reductions from these sources approach the limits of MEP.

1.09.4 Summary and Conclusions The several cases described in this chapter point to a conclusion that, in the absence of a compelling state interest to improving water quality, ambiguities in the CWA are likely to present strong impediments for achieving the goals of the Act. The goal of the Act is protect and enhance water quality sufficient to support designated uses of all stream segments, the lowest acceptable use being propagation of fish and aquatic ecosystems. Few ambiguities exist when it comes to managing point sources; effluent limits must be set to satisfy waterquality standards, and state boundaries do not present an insurmountable barrier. TMDLs set by one state must consider effects on waters in downstream states. However, CWA nonpoint sources require only that states employ management practices that reduce loads to the maximum extent practical, a judgment call that is left to the states. A similar requirement applies to stormwater runoff from urbanized areas. In the Neuse River, Jordan Lake, and Chesapeake cases discussed in this chapter, compelling local and regional interests have led to invocation of state authority to shore up gaps in federal legislation. It may be argued that Congress intended just that, but, in these cases, states have followed very closely the language of the CWA. States have made judgments as to what BMPs satisfy the criterion of MEP without effective

Implementation of Ambiguous Water-Quality Policies

monitoring, inspection, and enforcement procedures to insure that water-quality standards will be attained and maintained. While it may not be realistic to expect compliance with waterquality standards over short time horizons given existing land uses and agricultural and urbanization practices, ambiguities in present federal and state policies about nonpoint sources leave the public poorly informed as to what is possible and the extent to which management programs are being effective.

References Alexander RB, Smith RA, Schwarz GE, Boyer EW, Nolan JV, and Brakebill JW (2008) Differences in phosphorus and nitrogen delivery to The Gulf of Mexico from the Mississippi River Basin. Environmental Science and Technology 42(3): 822--830. CBP (Chesapeake Bay Program) (2000) ‘‘Chesapeake 2000’’, the 2000 Partnership Agreement. http://www.chesapeakebay.net/content/publications/cbp_12081.PDF (accessed April 2010). CBP (Chesapeake Bay Program) (2009) Bay Barometer: A Health and Restoration Assessment of the Chesapeake Bay and Watershed in 2008. http:// www.chesapeakebay.net/news_baybarometer08.aspx?menuitem=34917 (accessed April 2010). Early WC (2009) Acting Regional Administrator, Region III, Letter to L. Preston Bryant, Jr., Virginia Secretary of Natural Resources, 3 November 2009. http://www.epa.gov/ region3/chesapeake/bay_letter_1209.pdf (accessed April 2010). Federal Leadership Committee (2009) ‘‘Executive Summary for Draft Reports Addressing Key Challenges to Chesapeake Bay Protection and Restoration’’. Reports prepared in response to Section 202 of Executive Order 13508, 9 September 2009. Hunt JB (1996) Letter to David H. Moreau, Chairman, North Carolina Environmental Management Commission, 12 March. MR/GMWNTF (Mississippi River/Gulf of Mexico Watershed Nutrient Task Force) (2001) Action Plan for Reducing, Mitigating, and Controlling Hypoxia in the Northern Gulf of Mexico, Washington, DC. http://www.epa.gov/msbasin/pdf/ actionplan2001.pdf (accessed April 2010). MR/GMWNTF (Mississippi River/Gulf of Mexico Watershed Nutrient Task Force) (2008) Gulf Hypoxia Action Plan 2008. Washington, DC: Office of Wetlands, Oceans, and Watersheds, United States Environmental Protection Agency.

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NC Division of Environmental Management (1993) Neuse River Basinwide Water Quality Management Plan, Raleigh, NC. North Carolina Division of Water Quality (2006) Jordan water supply nutrient strategy and rules. Report to the NC Environmental Management Commission, 11 January 2006. North Carolina Division of Water Quality (2008) ‘‘Jordan Lake nutrient strategy’’. http:// portal.ncdenr.org/web/wq/ps/nps/jordanlake (accessed April 2010). NRC (National Research Council) (2008) Mississippi River Water Quality and the Clean Water Act. Washington, DC: National Academy Press. United States Government Accountability Office (2005) Chesapeake Bay program: Improved strategies are needed to better assess, report, and manage restoration progress. Report to Congress. Washington, DC: United States Government Accountability Office. USEPA (United States Environmental Protection Agency) (1971) The Cost of Clean Water, Volume I, Annual Report (4th) to Congress in Compliance with Section 26(a) of the Federal Water Pollution Control Act, Senate Document 92-23. Washington, DC: United States Government Printing Office. USEPA (United States Environmental Protection Agency) (1975) Preparation of water management plans. Federal Register 40(230): 55345--55346 (28 November 1975) USEPA (United States Environmental Protection Agency) (1996) Draft Framework for Watershed Based Trading. EPA Report No. 800 R 96 001. Washington, DC: USEPA (30 May 1996). USEPA (United States Environmental Protection Agency) (1997) ‘‘New Policies for Establishing and Implementing Total Maximum Daily Loads (TMDLs),’’ Memorandum from Robert Perciasepe, Assistant Administrator to Regional Administrators Regional Water Division Directors, 8 August 1997. http:// www.epa.gov/OWOW/tmdl/ratepace.html (accessed April 2010). USEPA (United States Environmental Protection Agency) (2003) Water Quality Trading Policy. Washington, DC: Office of Water. (13 January). USEPA (United States Environmental Protection Agency) (2009) Clean Water Act Section 303(d): Preliminary Notice of Total Maximum Daily Load (TMDL) Development for the Chesapeake Bay. Federal Register 74(179): 47792--47794 (17 September 2009). USEPA (United States Environmental Protection Agency) (2010) Water Quality Standards for the State of Florida’s Lakes and Flowing Waters: Proposed Rule. Federal Register 75(16): 4291 (26 January 2010).

1.10 Predicting Future Demands for Water B Dziegielewski and DD Baumann, Southern Illinois University Carbondale, Carbondale, IL, USA & 2011 Elsevier B.V. All rights reserved.

1.10.1 1.10.1.1 1.10.1.2 1.10.2 1.10.2.1 1.10.2.2 1.10.2.2.1 1.10.2.2.2 1.10.2.2.3 1.10.2.2.4 1.10.3 1.10.3.1 1.10.3.1.1 1.10.3.1.2 1.10.3.2 1.10.3.2.1 1.10.3.2.2 1.10.3.2.3 1.10.3.3 1.10.3.3.1 1.10.3.3.2 1.10.4 1.10.4.1 1.10.4.2 1.10.4.2.1 1.10.4.2.2 1.10.4.2.3 1.10.4.3 1.10.4.3.1 1.10.4.3.2 1.10.4.4 1.10.4.4.1 1.10.4.4.2 1.10.4.5 1.10.4.5.1 1.10.4.5.2 1.10.5 1.10.5.1 1.10.5.2 1.10.5.3 1.10.5.4 1.10.6 References

Water Supply and Demand Changing Objectives of Water-Supply Development Emergence of Water Conservation Water-Use Data Definitions and Measurement of Water Use Accessibility of Data on Water Use Public-supply sector data Industrial and commercial sector data Power generation sector data Irrigation sector data Water-Demand Relationships Theoretical Models of Water Demand Derived demand of producers Final demand of consumers Empirical Models of Water Use Configuration of data sets Functional forms and model parameters Elasticities with respect to major determinants of water use Other Water-Use Relationships Cooling water requirements Supplemental irrigation water requirements Demand Forecasting Techniques Forecasting Principles and Criteria Forecasting Models and Procedures Time trend forecasting Water requirement forecasts Demand forecasts Dealing with Forecast Uncertainty Model-dependent prediction intervals Dealing with forecast assumptions error Forecasts with Conservation End-use accounting system Baseline and restricted forecasts Forecasting Software: The IWR-MAIN Program Model structure and procedures IWR-MAIN conservation forecasts Example of a Regional Multisector Forecast Water-Use Relationships Effects of Key Forecast Assumptions Effects of Future Climate Forecast Summary Conclusion

1.10.1 Water Supply and Demand Water is an essential natural resource, which plays a vital role as input into many economic activities adding to the quality of human life and supporting the health of ecological systems. Water is also a commodity which has an economic value in all its competing uses as has been recognized by the main international conferences on water (e.g., ICWE, 1992;

163 163 163 164 164 165 165 165 166 166 166 166 167 168 169 169 170 171 172 172 173 175 175 175 175 176 177 177 178 178 178 179 179 179 179 180 181 182 182 183 185 186 187

UNCED, 1992). Despite this, the importance of water supply may not be widely appreciated by the general public because only some water resources and water uses are easily noticed while others are not. Surface water resources such as rivers and lakes are highly visible and well recognized for their cultural and amenity values as well as for their important functions in outdoor recreation and transportation. Less recognized by the general public is the portion of water

163

164

Predicting Future Demands for Water

resources – groundwater – which in the United States (the lower 48 states) actually represents three-fourths of all freshwater storage (Dziegielewski and Kiefer, 2006). Similarly, some human uses of water are easily noticed while others are not. Use of water for hydropower production or for irrigation can be easily seen and appreciated, while large water flows to urban and industrial users are usually hidden in underground pipes. For example, the network of public water supply pipes in the US carries an average flow of 67 000 cubic feet per second (cfs) – this is equivalent to the average discharge of a large river. The flow of water for thermoelectric cooling is even greater; in the US in the year 2000, it reached 302 000 cfs – more than the average annual discharge of the Ohio River. Given the overall importance of water, it is understandable that the long-term adequacy of water supply is a major national concern in many countries. However, in order to assess the future adequacy of supplies, it is necessary to determine the amount of water that is used currently and the amount that will be demanded in the future. These demands have to be compared with the future availability of water in existing and potential sources of supply. To do this, water-supply planners need appropriate tools for quantifying future water demands and assessing the effects of future climate and other factors on both water demand and water availability. Credible long-term forecasts of water demand are essential to all type of planning. Without such forecasts water planners cannot achieve an efficient allocation of water supplies among competing uses or ensure their long-term sustainability.

1.10.1.1 Changing Objectives of Water-Supply Development The traditional approach to water-supply planning has evolved during the past century, as cities, industries, and irrigation districts have expanded their water-supply infrastructure (Blake, 1956; Grigg, 1986). In urban areas, municipal water-supply agencies considered their responsibility to be on-demand delivery of sufficient quantities of drinking quality water while maintaining adequate pressure for consumption and fire protection. Even very high expenditures on the development of waterworks could be justified because adequate community water supply was considered an essential service that ensured public health and safety, economic activity, and a general community well-being. Also, accurate long-term forecasts of water demand were not very important because rapid urban growth made it possible to add large increments of water-supply capacity without the risk that it would remain unused for long periods of time. The longstanding practice of augmenting water supply by securing reliable sources, protecting water quality at the source, and developing adequate facilities for transmission, treatment, and distribution was based on several advantages of this strategy. These were summarized by Platt (1993). Specifically, somewhat distant hinterland sources provided a cheap and abundant supply of high-quality water without the need for treatment. Cities in Northeast and Northwest of the US relied on such sources prior to the adoption of water chlorination. In addition, storage reservoirs at relatively high elevations permitted gravity-flow transmission of water without the need for pumping. Also, urban governments could acquire

water rights and watershed lands for source protection at a minimum cost, often under condemnation powers, while the hinterland regions offered little political resistance to water exports. Finally, large professionally managed sources of supply offered significant economies of scale. The advantages of the hinterland sources provided little incentive for a careful matching of water supplies with water demands.

1.10.1.2 Emergence of Water Conservation Since the 1970s, the viability of the traditional supply augmentation approaches has begun to decline gradually because of several obstacles (Platt, 1993). The hinterland regions have begun to offer some political and legal resistance to water exports. Environmental legislation and interests have introduced significant barriers to a continuing expansion of offstream uses of water for urban and agricultural purposes. Major droughts over the last 40 years have also contributed to the increased competition for water supplies between urban and agricultural interests. Other factors such as the depletion and contamination of groundwater sources, difficulties in financing major construction programs, especially those sponsored by the federal government, and increasing costs of water treatment for regulated contaminants have made the traditional options of supply augmentation less viable. As a result, the range of options has expanded to include both some unconventional supply-side alternatives and the opportunities for modifying the growth in water demand. The introduction of demand management alternatives represents an important change in water-supply planning. In the early 1980s, the growing attractiveness of long-term demand management measures began to catch the attention of urban water-supply agencies (Boland et al., 1982; Dziegielewski and Baumann, 1992). Demand reduction programs allowed some agencies to balance future supply and demand at a cost that is below the economic, social, and environmental cost of new supply development and thus result in net benefits to society (Dziegielewski et al., 1983). Baumann et al. (1984) developed a practical definition of long-term water conservation as ‘‘yany beneficial reduction in water use or in water losses.’’ The authors pointed out that a water management practice constitutes conservation if it conserves a given supply of water through reduction in water use (or losses) and if it results in a net increase in social welfare where the resources used have a lesser value than those saved (p. 431). In other words, the beneficial effects of the reduction in water use (or loss) must be considered greater than the adverse effects associated with the commitment of other resources to the conservation effort. This definition provided an important guidance for long-term conservation; however, it could not be easily applied to short-term conservation measures which are usually aimed at curtailing water demand during a drought. Temporary restrictions on water use are usually undertaken in order to prevent adverse impacts of severe shortages in the future if the drought continues and their outcomes cannot be easily analyzed through benefit–cost analysis. Other marked enhancements in theory and practical knowhow for planning and evaluation of demand management alternatives have also been achieved (Dziegielewski et al., 1993). These were followed by the development of computer

Predicting Future Demands for Water

software programs for disaggregate water-use forecasting, for the analysis of demand reduction alternatives, for optimization of long-term water management plans, and for the monitoring of water demands over time (Dziegielewski and Boland, 1989; Dziegielewski, 1993; Baumann et al., 1998). This chapter provides a review of key water-demand concepts and presents analytical methods for quantifying demands by different user groups, forecasting future demands and analyzing demand management alternatives. The chapter begins with the discussion of the measurement and analysis of water use and related concepts.

1.10.2 Water-Use Data Our knowledge of water demands necessarily derives from the measurement and estimation of water use. In practice, it is impossible to know precisely all water uses – there are many different types of water users and specific purposes of use and only some uses are metered. Instead, various estimation methods are usually employed to determine the quantity of water use. Because water use depends on many factors, the analysis of water demands requires data on those factors. This chapter describes the structure of water demand and identifies the factors affecting water use as well as methods for analyzing the available water-use data.

1.10.2.1 Definitions and Measurement of Water Use From the hydrologic perspective, water use is a part of the water budget. At the most general level, water use can be defined as all water flows that are a result of human intervention within the hydrologic cycle. Accordingly, all water uses can be divided into in-stream and off-stream uses. In-stream use represents water that is used, but not withdrawn, from a natural water source for purposes such as hydroelectric power generation, navigation, water-quality improvement, fish propagation, and recreation. Off-stream use represents water withdrawn or diverted from a groundwater or surface water source for public water supply, industry, irrigation, livestock, thermoelectric power generation, and other uses (Hutson et al., 2004). The term ‘water withdrawal’ is used to designate the amount of water that is taken out from natural water sources such as lakes, rivers, or groundwater aquifers. The difference between the amount of water withdrawn and water returned to the source (also referred to as discharge) is usually taken to represent consumptive use. This is the ‘‘part of water withdrawn that is evaporated, transpired, incorporated into products or crops, consumed by humans or livestock, or otherwise removed from the immediate water environment’’ (Hutson et al., 2004). The part of amount withdrawn and returned back to the source is called nonconsumptive use. The quantity of water consumed is utilized in calculating regional annual and monthly water budgets, and represents a measure of the volume of water that is not available for repeated use. While a major portion of water withdrawn for purposes such as public water supply, power generation, and industrial use represents nonconsumptive use, these withdrawals can have significant impacts on water resources and other uses of

165

water. For example, water withdrawn from an aquifer and then returned into a surface water body may have a positive impact on streamflow or lake water levels, but a negative impact on the source of groundwater. Similarly, water withdrawn from a river for public water supply must be continuously available at the intake but not for withdrawal for other uses upstream or immediately downstream from the intake. A more restrictive definition of water use refers to water that is actually used at a specific site or for a specific purpose. Individual residential or commercial buildings, industrial facilities, and other locations can obtain water from their own sources of supply or through connections to a public or private distribution system. Individual users of water within a defined geographical area can be classified into different categories and their combined use can be summed up into broader categories, or user sectors. For example, the United States Geological Survey compiles data on water withdrawals for individual counties for eight categories of users: public supply, domestic, industrial, commercial, mining, power generation, livestock, and irrigation. Some of the categories are further subdivided based on the purpose of use. For example, irrigation use is subdivided into cropland and golf course irrigation. Similarly, public-supply use can be subdivided into residential, commercial, industrial, and public categories with each category further subdivided into two or more subcategories (e.g., residential single-family and residential multifamily). Measurement of water use can take place at the point of withdrawals or at the point of water use. Also, some measurements could be taken at the point of water treatment or along water transmission routes. Direct measurements of water volume being transmitted over a given period of time are made by meters which register the volume of flow (such as displacement meters) or by measuring and recording instantaneous flow (such as in Venturi meters). The measurements of water use are reported as water volume per unit of time. The volumetric units include cubic meters, cubic feet, gallons and liters, and their decimal multiples. In some cases, composite volumetric units (e.g., acre feet) or units of water depth (e.g., inches or centimeters of rainfall) may be used. The time periods used may include a second, minute, hour, day, month, and year. Because the annual and monthly volumes of water use generally involve large numbers, the numerical data on water use are often reported as the average daily quantities used. Two popular units are thousand cubic meters per day (1000 m3 d1) and million gallons per day (mgd). Also, in order to make the estimates of water use easy to comprehend and to make meaningful comparisons of water use for various purposes (and various users), the annual or daily quantities can be divided by some measures of size for each purpose of use. The result is an average rate of water use such as gallons per capita per day (gpcd), gallons per employee per day (ged), or other unit-use coefficients. Finally, it is important to note that the reported quantities of water use can be in the form of direct measurements obtained from water meters or they may be estimates. Estimates of water use that are derived from the measurements of water levels in storages or from pumping logs are generally more accurate than those derived from related data on the volume

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Predicting Future Demands for Water

of water-using activity. For example, the estimates of water use for hydroelectric power generation may be obtained by multiplying the amount of generated power by a water-use coefficient. In analyzing water demands, it is important to recognize the sources and nature of the data on water use. For example, when deriving statistical water-demand relationships, it makes little sense to use data which are estimates based on the volume of water-using activity or other correlates of water use. Only actual measurements of water volumes, which are withdrawn or used over time, can accurately capture the temporal and spatial variability of water demand and provide a basis for deriving econometric or other models of water use.

1.10.2.2 Accessibility of Data on Water Use The availability of data on water use depends on user sector. The best data are available for public-supply sector (also referred to as municipal and industrial use). Data for other sectors, which typically rely on self-supplied sources of water, are usually less precise and their accuracy depends on whether the withdrawals of water are regulated and are required to be metered or if only an annual reporting of the estimated quantities is required. The discussion of the available data for the different sectors is given in the following.

1.10.2.2.1 Public-supply sector data Water use in the public-supply sector can be characterized with respect to: (1) the demands of different types of customer classes (e.g., single-family residences, hotels, food-processing plants, etc.); (2) the purposes for which water is used (e.g., end-uses such as sanitary needs, lawn watering, or cooling); and (3) the seasonal variations in water use. The breakdown of total urban water use into customer groups, specific end-uses, and seasons can serve as a basis for modeling water demands and for conducting impact evaluations of water conservation programs. Generally, there are three types of water measurement records that are maintained by public water-supply utilities: (1) water production records (i.e., amounts of water pumped into the distribution system); (2) water billing records (i.e., records which detail each customer’s account activity); and (3) water sales records (i.e., summaries of total water sales or water sales by customer groups). Water production records show the amount of water pumped from treatment plants and are typically generated daily or hourly. Production data can be used for analyzing: (1) unaccounted water use (comparing production with water sales data); (2) impacts of water-use restrictions on total water demand; (3) relationships between total water demand and weather conditions; and (4) peak water use during different time intervals (e.g., peak day, peak hour, day of week, etc.). Table 1 (Dziegielewski and Chowdhury, 2008) shows an example of water production records for a sample of cities and water-supply systems in the Greater Chicago Area in Illinois. The last column shows the calculated rate of usage in gpcd. Water billing records represent the individual customer account data which are generally maintained by retail watersupply agencies. The individual record usually includes: (1) name and address of account holder, (2) type of account

Table 1 Examples of water production and purchase data by public water supply systems in selected communities in Northeastern Illinois in 2005 Community or system name

Aurora Bedford Park Belvidere Central Lake Co. JAWA Chicago Crystal Lake DeKalb DuPage Water Com. Elgin Evanston Glencoe Hammond WSS Highland Park Joliet Kankakee Aqua Illinois Lake County PWD Lake Forest Morris North Chicago Northbrook Northwest Sub. M. JAWA Oak Lawn Oswego Waukegan Wilmette Winnetka Total study area

Reported production in mgd

Estimated population served

Per capita production in gpcd

18.1 25.4 3.7 21.2 729.6 5.4 4.4 90.6 15.5 45.7 1.9 18.4 11.8 16.5 12.9 3.0 4.8 1.6 4.7 6.1 35.9

1 70 000 130 415 23 500 197 446 3 960 041 40 440 40 000 728 427 142 572 354 258 8 600 133 035 59 580 130 830 67 000 29 536 21 477 13 282 19 127 36 975 309 084

106.5 194.5 155.7 107.4 184.2 134.3 109.0 124.4 109.0 129.1 217.4 138.0 197.5 125.9 192.4 101.9 221.2 123.5 245.2 164.4 116.2

36.6 2.4 9.7 12.9 3.8 1142.3

316 389 23 000 101 919 90 391 17 600 7 164 924

115.6 102.6 94.8 142.3 217.6 159.4

mgd, million gallons per day; gpcd, gallons per capita per day. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

(single family, commercial, industrial, institutional), (3) meter size, (4) meter readings and the dates of meter readings, (5) water use between meter readings, and (6) billing information (charges incurred, dates paid, etc.). The customer billing system is usually computerized and individual customer accounts can be sorted by customer type, geographical area (e.g., pressure zone), and other characteristics. Finally, water sales records are summaries of the individual water billing records. The sales data are aggregated by the billing cycle (i.e., monthly, bimonthly, semiannually, or annually) and by customer type. They show how much water is being sold to different types of customers but they do not show for what specific purposes the water is being used.

1.10.2.2.2 Industrial and commercial sector data Data on self-supplied commercial and industrial use within a given geographical area are available in areas where annual or monthly reporting of water is practiced. The industrial subsector includes water used for ‘‘industrial purposes such as fabrication, processing, washing, and cooling, and includes

Predicting Future Demands for Water Table 2 Estimates of self-supplied industrial and commercial water demand in 11 counties in Northeastern Illinois

Table 3

Water use in a sample of large power plants in Illinois

Plant name County

Boone Cook DeKalb DuPage Grundy Kane Kankakee Kendall Lake McHenry Will Total/ave.

Self-supplied withdrawal in 2005 (mgd)

0.57 123.73 2.54 0.96 6.99 4.34 5.09 0.78 13.88 6.58 24.97 190.43

Employment in self-supplied establishments

1 200 22 364 4 025 11 024 656 6 329 157 5 229 19 495 8 515 13 727 92 721

Unit selfsupplied withdrawals per employee (gped) 475.0 5 532.6 631.1 87.1 10 655.5 685.7 32 420.4 149.2 712.0 772.8 1 819.0 2 053.8

mgd, million gallons per day; gped, gallons per employee per day. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

such industries as steel, chemical and allied products, paper and allied products, mining, and petroleum refining,’’ and the commercial subsector includes water used for ‘‘motels, hotels, restaurants, office buildings, other commercial facilities, and institutions’’ (Avery, 1999). For a given geographical area such as a county or a hydrologic basin, industrial and commercial water withdrawals will depend on the number, type, and size of water users. Table 2 shows an example of self-supplied commercial and industrial withdrawal data for 11 counties in Northeastern Illinois.

1.10.2.2.3 Power generation sector data In the US, water withdrawn by power plants is classified as thermoelectric generation water use. It represents the water applied in the production of heat-generated electric power. The main use of water at power plants is for cooling. Nearly 90% of electricity in the United States is produced with thermally driven, water-cooled generation systems which require large amounts of cooling water (Dziegielewski and Bik, 2006). The three major types of thermoelectric plants include: conventional steam, nuclear steam, and internal combustion plants. In conventional steam and nuclear steam power plants, water is used primarily for cooling and condensing steam after it leaves the turbine. In this type of generation, the use of cooling water is essential because the collapse of steam volume in the condenser creates a vacuum, which affects the rotation of the turbine. Because the level of the vacuum depends on the removal of waste heat by cooling water, the cooling system is an integral part of the power generation process. Precise estimates of thermoelectric water use are difficult to obtain. The only consistent source of thermoelectric water-use data is the annual survey of power plants by the US Energy

167

2005 Water withdrawals (mgd)

Once-through flow plants Crawford Plant 503.3 Fisk Street Plant 222.2 415.6 Dresden Nuclear Planta Waukegan Plant 758.6 Joliet 29 Plant 942.6 Joliet 9 Plant 415.3 Will County/ 917.9 Romeoville Plant Clinton Plant 810.4 Dallman Plant 328.1 Lakeside Plant 43.2 All once-through 5357.2 plants

2005 Gross generation (MWh yr1)

2005 Rate of withdrawals (gal. kWh1)

3 201 844 1 603 949 14 031 125

57.4 50.6 10.8

4 909 907 5 767 994 1 922 330 5 658 996

56.4 59.6 78.9 59.2

9 014 690 2 328 492 229 855 48 669 182

32.8 51.4 68.6 40.2

Closed-loop makeup water plants Vermilion Plant Powerton Plant Braidwood Nuclear Plant Kendall Co. Gen. Facility All closed-loop plants

2.8 25.9 49.8

702 950 10 120 133 20 390 274

1.43 0.93 0.89

2.5

1 367 008

0.67

80.9

32 580 365

0.91

a

Dresden plant uses a combination of once-through and pond recirculation system. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008 and WHPA and Dziegielewski (2008).

Information Agency (EIA). The resultant EIA-767 database consists of a series of data tables that present data on different aspects of the power plant operation (Table 3). The EIA-767 data tables are the main data sources for the analysis of thermoelectric water withdrawals (intake) and consumptive use. However, since 2006 the EIA discontinued collection of operational data from power plants; therefore, the more current data on water withdrawals have to be obtained directly from individual plants.

1.10.2.2.4 Irrigation sector data The irrigation sector includes self-supplied withdrawals of water for irrigation of cropland, turfgrass-sod farms, and golf courses. In the existing inventories of water use, the designation of irrigation water withdrawals includes ‘‘all water artificially applied to farm and horticultural crops as well as selfsupplied water withdrawal to irrigate public and private golf courses’’ (Solley et al., 1998). Irrigation water use is rarely measured and the reported data on water withdrawals are based on the inventory of the total acreage of irrigated area. The data on irrigated land are collected and reported by the US Department of Agriculture (USDA, 2009).

168

Predicting Future Demands for Water

1.10.3 Water-Demand Relationships

water. That is, in general, for k inputs,

1.10.3.1 Theoretical Models of Water Demand Guidance for empirical modeling of water demand can be derived from economic theory. From an economic perspective, water is considered to be a commodity (or an economic good) and it can be conceived as a final good to consume or as an input to the production of some other good or service. Demand for water therefore can be a final demand if the user is a consumer or a derived demand if the user is a producer whose demand is driven by the demand for other goods produced through the use of water. Theoretically, industrial and most of the commercial and public uses can be viewed as derived demands by producers, while the residential component of water demand can be viewed as a final demand by consumers (Hanemann, 1998). Different economic theories apply to these two economic categories of demand. The derived demand is described by the economic theory of production. The final demand can be described by the economic theory of consumer demand. Good examples of a complete mathematical treatment of the application of these two theories to water demand are provided by Hanemann (1998) and Renzetti (2002).

1.10.3.1.1 Derived demand of producers According to Hanemann (1998) the derived demand for water by a firm can be represented by four different types of relationships. These include both the long-run and short-run versions of conditional and unconditional demand functions. The fixed-output conditional demand function reflects optimization of the amounts of inputs (which include water) to produce a given level of output. The variable-output or unconditional demand function determines how the firm should select the level of output together with the corresponding inputs. The other two functions introduce the distinction between the short-run and long-run input demand functions. In a short run some inputs are assumed fixed, whereas in a long run all inputs are variable. The long-term conditional demand functions can be obtained by combining the production function (which represents production technology) with the firm’s behavior (i.e., cost minimization or profit maximization). The derivation of conditional or unconditional demand functions would start with an explicit formula for the production function and then solve the optimization problem. Two production functions which were frequently used in the past include the Cobb–Douglas and the constant elasticity of substitution (CES) formulas. A newer approach uses the duality relationship between a production function and the cost or profit functions. Hanemann (1998) used the duality approach to show the two demand functions – conditional and unconditional demand. The conditional function can be represented by the derivatives of the associated cost function or profit function with respect to the price of the input of interest. Accordingly, the conditional water-demand function can be written as a derivative of the cost function with respect to the price of

xk ¼ gk ðo1 ; y; on ; yÞ ¼

q Cðo1 ; y; oN ; yÞ ; k ¼ 1; y; N q ok ð1Þ

and the unconditional demand function is equal to minus the derivative of the profit function with respect to the price of water:

xk ¼ hk ðo1 ; y; oN ; pÞ ¼ 

q pðo1 ; y; oN ; pÞ ; k ¼ 1; y; N q ok ð2Þ

where C denotes the firm’s total cost of production; ok the price of the kth input xk, with k ¼ 1,y, N; y the volume of output produced per unit of time; p the profit; and p the price of output. There are many possible formulas for cost or profit functions that can be used with this approach including production functions which are less restrictive than the Cobb– Douglas or CES functions with respect to their implications for complementarity or substitution among inputs. As an example, Hanemann (1998) provides an illustration of the application of the derivatives in Equations (1) and (2) using the translog cost function:

lnC ¼ lnb0 þ by lny þ þ

X

X

ln ok þ 1=2dyy ðlnyÞ2

k

dky ln ok lny þ

k

1X dik lnoi lnok 2 ik

ð3Þ

where b‘s and d‘s are coefficients to be estimated with dik ¼ dki. The application of Equation (1) in combination with Equation (3) leads to a relatively simple share equation which represents the share of total cost devoted to each input:

ok gk ðo1 ; :::; oN ; yÞ C ¼ bk þ by ln y þ dky ln y þ dkk ln ok X þ oki ln oi

sk ðo1 ; :::; oN ; yÞ 

ð4Þ

ia k

The theoretical relationships given above provide some guidance for the development of empirical water-demand equations for industrial and commercial sectors. The main points are: (1) derived demand for water depends on the price of water (own elasticity) as well as the prices of all other inputs (cross-price elasticity), and (2) demand also depends on either the quantity or prices of the outputs. These theoretical relationships should be incorporated into econometric models of industrial water demand. However, there are some practical difficulties which limit a strict adherence to the economic theory of production. One limitation is the level of data aggregation. Water-use data are often aggregated for an entire industrial sector while the production and optimization relationships need to be estimated separately for specific industries based on the firm-level data. Other difficulties relate to (1) specification of the price of water, (2)

Predicting Future Demands for Water

availability of prices for inputs other than water, and (3) specification of the mathematical form of the demand function. Examples of empirical relationships which were obtained in studies of industrial demands can be found in the work by Renzetti (1992) and Dupond and Renzetti (1998).

1.10.3.1.2 Final demand of consumers Economic theory of consumer demand provides a basis for deriving relationships for final demand for water. The theory is based on the concept of the consumer’s utility function and the optimizing behavior of a consumer faced with a limited budget. Utility functions can take many forms; examples include the Cobb–Douglas function and its variant, the Stone– Geary function (Deaton and Muellbauer, 1999). For example, Hanemann (1998) derived a water-demand function based on the Stone–Geary function for the case of two goods (i.e., water and all other goods) of the form

x1 ¼ ð1  a1 Þg1 þ a1

y p2  a1 g2 p1 p1

ð5Þ

where x1 is the quantity of water consumed, y the consumer’s budget, g1 and g2 the minimum consumption levels (which specify the assumption that the consumer derives utility from a commodity only if the consumption exceeds gi), p1 and p2 the prices of the two goods, and a1 the exponent of x1 in the original utility function. The own price, cross-price, and income elasticities can be obtained by taking derivatives of this demand function. Generally, single-demand equations used in empirical studies of residential water demand (even if they include price of water, income, and price of other goods) are formally inconsistent with economic theory because their functional forms (i.e., model specification) cannot be derived from the maximization of the utility function (Hanemann, 1998). The correct specification of a linear demand function with these three variables would be

x1 ¼ a1  b

p1 y þg p2 p2

ð6Þ

Equation (6) indicates that demand for water depends on the relative prices and relative income and not on their absolute values which are typically used in the empirical equations which are found in the literature. Another issue in modeling final demand is related to the choice of explanatory variables to be included in the demand function along with the economic variables of price and income. Variables such as those describing the climatic conditions, demographic characteristics, or physical settings need to be included in the equation because they also affect the consumer’s utility. Different values of these variables would cause different levels of utility from the same level of consumption. Hanemann (1998) suggests that other variables should be introduced into demand functions by making one or more of the coefficients in Equation (6) a function of those variables. In summary, the theoretical relationships described above provide some guidance for the development of empirical

169

water-demand equations for residential sectors. The main points are as follows: 1. Final demand for water depends on the price of water (own elasticity) as well as the prices of all other goods (crossprice elasticity) and the level of income. 2. Demand also depends on factors other than price and income and these factors have to be incorporated into the demand function in conformity with economic theory. However, a strict adherence of water-demand studies to economic theory is rarely achieved. Equations (1)–(6) represent demand functions by individual producers or consumers for specific uses of water. Because the empirical data sets are usually aggregated over individual users and often combine different uses of water, Hanemann (1998) observes that empirical studies of existing data make some leap from a theory that applies to individual agents to more aggregate data. For example, a number of empirical studies addressed aggregate water use in the residential sector. Some notable examples include Howe and Linaweaver (1967), Foster and Beattie (1979), Howe (1982), Nieswiadomy (1992), and Epsey et al. (1997). Most of these studies use model specifications which are consistent with economic theory but the theoretical demand models are applied to aggregate data.

1.10.3.2 Empirical Models of Water Use Empirical modeling of water demand consists of the search for variables that help explain water demand and the determination of their relationships to water quantities used. The results of previous studies contain important information about the principal explanatory variables and their mathematical relationship to water demand. Because researchers have defined water demand in many different ways, numerous empirical models appear in the literature. In this section, special emphasis is placed on the models of aggregate demands, which consider total demands of (1) a group of water users who use water for a similar set of purposes or (2) various often dissimilar users within a defined geographical area. Although this section does not present a comprehensive review of the entire literature of water-demand modeling, a sufficient number of studies are included to provide a representative sample of the approaches that have been employed to explore water demand.

1.10.3.2.1 Configuration of data sets In econometric studies, data on economic activities are collected at either micro- or macro-levels. Observations on individual households, families, or firms are referred to as microdata. Regional- or national-level accounts and observations of entire industries are called macrodata. In the analysis of water use, the corresponding types of data are often referred to as disaggregate and aggregate data. Levels of data aggregation by purpose of water use range from the most disaggregate level of end-uses (e.g., toilet flushing or cooling tower makeup) to aggregated sector-wide totals, including domestic, industrial, or other uses. Water use can also be aggregated by summing it over various time periods (day, month, or year) and geographical areas (townships, cities, counties, or states).

170

Predicting Future Demands for Water

For analytical purposes, observations of water use (and of the corresponding explanatory variables) can be obtained and organized in several ways. In mathematical terms, we can describe each data configuration by designating the water use of entity i during time period t as Qit. Depending on which type of arrangement is used, the following three types of data configurations can be distinguished:

•

• •

Time series data, Qit. Recorded or estimated water use of an individual water user, group of water users, or all uses within a defined geographical area, during each time period t in a time series, where i ¼ constant and t ¼ 1, 2, y, T. Cross-sectional data, Qit. Recorded or estimated water use of each individual water user, sector, or geographical area i during time period t, where i ¼ 1, 2, y, n, and t ¼ constant. Pooled time series and cross-sectional data, Qit. Recorded or estimated water use of each individual user, sector, or geographical area i, in each time period t, where i ¼ 1, 2, y, n and t ¼ 1, 2, y, T.

In time series data, observations of all variables in the data are taken at regular time intervals (e.g., daily, weekly, monthly, or annually). In cross-sectional data, observations are taken at one time (either a point in time or time interval) but for different entities (such as households, firms, sectors of water users, cities, counties, or states). Pooled data combine both the time series and cross-sectional observations to form a single data matrix. A special case of pooled data is known as panel (or longitudinal) data, which represent repeated surveys of the same cross-sectional sample at different periods of time. The above types of data configurations form an empirical basis for developing water-use relationships. The mathematical form (i.e., linear, multiplicative, and exponential) and the selection of the right-hand side (RHS) or independent (explanatory) variables depend on the type and aggregation of water demand represented by the left-hand side (LHS) or dependent variable. Depending on the purpose for which water-use estimations are to be used, different representations of the dependent variable may be employed. For example, in hydrologic studies of surface and groundwater resources, water use is usually represented as daily, monthly, or yearly withdrawals at a point such as a river intake or a groundwater well. Because the water withdrawn is typically used (or applied) over a larger land area, an equivalent hydrologic definition of water use would be the use of water within a defined geographical area (e.g., an urban area, a township, a county, or a river basin or subbasin) which is obtained from a single point of withdrawal. While the quantities of water withdrawn in time can be precisely measured at the withdrawal points, they cannot be modeled without an appropriate consideration of the nature of water demands for which the withdrawals are made. The nature of water demand depends on the aggregation of water uses and users. The aggregation levels can range from a single user at a point location to a diverse group of users within a geographical area. For an individual user, water use can be represented as a sum of quantities of water used for specific purposes (or enduses) as

Qjt ¼

X i

qijt

ð7Þ

where Qjt is the total water use of an individual user j during time period t and qijt the water used for a specific purpose of use i, such as garden watering, washing, or cooling during time period t. At a higher level of aggregation, water use within a larger geographical area such as a city, county, or river basin can be represented as a sum of water use for several groups of users within a number of subareas:

Qt ¼

XXX j

k

Qjkgt

ð8Þ

g

where Qt is the aggregate water use of all individual users j within k user sectors within geographical subareas g and Qjkgt designates water use by individual users in the area. In the case of an urban water-supply system, j would represent individual customers, k would represent major user sectors such as the single-family residential or commercial sectors, and g would represent sections of the city or pressure zones. Generally, water use at any level of aggregation Qt can be modeled as a function of explanatory variables Xi. However, because different components of aggregate water demand may be determined by different sets of explanatory variables and different functional forms, more precise models can be obtained by disaggregating demand Qt into its components, especially sectoral demands and modeling each component separately.

1.10.3.2.2 Functional forms and model parameters The most common approach to modeling water demand relies on multiple regression techniques. Usually, the dependent variable is assumed to be a linear function of several independent variables. For example, if there are three independent variables, the model can be written as

Q ¼ a þ b1 X1 þ b2 X2 þ b3 X3 þ e

ð9Þ

where a, b1, b2, b3 are the estimated regression coefficients, X1, X2, X3 the independent variables assumed to affect independent variable q, and e the random error term. The coefficients of a and bi are estimated by finding the values that minimize the sum of the squared deviations for the observed values of the dependent variable from the values of the dependent variable predicted by the regression equation. In order for the ordinary least-squares (OLS) regression analysis to yield valid results, the error term is assumed to be normally distributed and to have zero mean, common variance across all observations and to be independent of all explanatory variables. Also, there should be no correlation between independent variables and the distribution of the dependent variable should be approximately normal. Two additional conditions are that (1) none of the independent variables can be an exact multiple (or linear) combination of any other independent variable and (2) the number of observations must exceed the number of coefficients being estimated. When the five basic assumptions of regression model are satisfied, the OLS procedure would produce unbiased estimates of the regression coefficients a and bi , which have minimum variance among all unbiased estimates.

Predicting Future Demands for Water

Alternatives to the linear model include the log–log model (which is linear in the logarithms of the dependent and independent variables) and the semi-log models (in which either only the dependent variable is or only the independent variables are transformed into logarithms). For example, the log-linear (or double log) model with k explanatory variables can be written as

Qit ¼ ea0

Y

Xit bk  eeit

X

Z¼

bk ln Xkit þ eit

ð10bÞ

k

In some empirical models, Qit is converted into per capita withdrawals, for example, public-supply withdrawals are divided by the population served in the study area. Equation (10a) can be used to represent an aggregate demand curve or a market demand curve. According to a priori (theory-based) expectations, the demand curve would be negatively sloped. The shape and position of the demand curve are determined by the values of other explanatory variables. For residential demand, these variables may include income, household size, temperature, and rainfall. According to expectations, the effect of increasing income would be to shift the curve to the right, so that the same price would result in progressively larger quantities of water being used. The effect of increasing precipitation during the growing season would shift the curve to the left. The entire demand curve does not have to be known in order to determine the effect of independent variables on water demand (Boland et al., 1984). It is usually sufficient to know how specified incremental changes in explanatory variables will affect water use. In the case of price, this information is contained in the slope of demand curve. The slope gives the incremental change in water use for an incremental change in price, at some position on the curve. Because of the units chosen for axes of the demand curve (dollar per unit of water use and units of water use), the slope of the curve has an inconvenient dimension (dollars per unit of water use squared) (Boland et al., 1984). It is customary, therefore, to use a dimensionless measure of the relationship, calculated by dividing fractional (instead of incremental) change in water use by fractional change in price. This dimensionless measure is known as elasticity. For the price variable, it is called the price elasticity of water demand. It is defined for an arc of the curve as

Q2  Q1 Q Z¼ P2  P1 P

dQ P dP Q

ð12Þ

where water use is a function of price and other variables, the ordinary derivative in Equation (12) is replaced with a partial derivative:

Z¼

where variables and coefficients are as defined in Equation (9). By taking the natural logarithm of both sides of Equation (10a), the following log-linear model is obtained:

ln Qit ¼ a0 þ

specific point on the curve as follows:

ð10aÞ

k

qQ P qP Q

ð13Þ

Both arc and point definitions give a dimensionless elasticity, which is expected to be negative (because the demand curve is negatively sloped). Price elasticity may be interpreted as the percentage change in quantity which would result from a 1% change in price. A price elasticity of  0.3, therefore, indicates that 1.0% increase in price would be expected to result in a 0.3% decrease in quantity demanded (use). Conversely, a 1.0% decrease in price would produce a 0.3% increase in quantity demanded. Depending on the magnitude of the calculated elasticity, the demand is said to be perfectly inelastic when Z ¼ 0.0; relatively inelastic when 0.0 4 Z 4 1.0; unitary elastic when Z ¼  1.0; relatively elastic when  1.0 4 Z 4 –N; and perfectly elastic when Z ¼ –N. In other words, demand is said to be relatively inelastic when quantity changes less than proportionately with price, and relatively elastic when quantity changes more than proportionately with price. Elasticity can be calculated for all other explanatory variables using the following formulas for the most common functional forms. For any variable X, the method of calculating the elasticity value from the estimated regression coefficients in four different functional forms is as follows: 1. For the linear form with no log transformation of any variables, the elasticity with respect to X is calculated in terms of the means of the variables as

Q ¼ a þ bX and Z ¼ b

X Q

ð14Þ

2. For the log–log form (also called double-log), the elasticity with respect to X is constant and equal to the regression coefficient of X:

lnQ ¼ a þ b ln X and Z ¼ b

ð15Þ

3. For the semilog form where the dependent variable is untransformed and the dependent variables are transformed into their natural logarithms, the elasticity with respect to X is

ð11Þ

where Q  ¼ ðQ1 þ Q2 Þ=2 and P  ¼ ðP1 þ P2 Þ=2. A more frequently used definition is based on the derivative of the demand function, and yields the elasticity at a

171

Q ¼ a þ b ln X and Z ¼

b X

ð16Þ

4. For a semilog form where only the dependent variable is transformed into logarithm, the elasticity with respect to X

172

Predicting Future Demands for Water Table 4 studies

is directly proportional to X:

lnQ ¼ a þ bX and Z ¼ bX

ð17Þ

To compare empirical studies of water demand, it is both simple and instructive to compare only the elasticities with respect to the explanatory variables, thus getting around the difficulty of interpreting direct comparison of regression coefficients. The following sections compare the results of past studies in terms of the elasticities of key explanatory variables.

Variables used in municipal and residential water-use

Explanatory variable

Variable definitions

Population

Number of users per account Population density Average number of residents per water meter Marginal price of the last unit of water used Average price or total water billed divided by total use Ratio of average price to marginal price Monthly income per capita per dwelling unit Residential property value Per capita income Average household income Median household income Percent of families in low income bracket Imputed rent derived from home value Number of housing units Percent of units by housing type Percent of units occupied by owners Average housing units per acre Number of rooms per dwelling Number of bathrooms House size Lawn size Outdoor irrigable area Median number of rooms Building age Number of persons per dwelling unit Age of the head of household or spouse Educational attainment of the head of household Median age of householders Percent in family households Percent of married households Summer average evapotranspiration rate Monthly effective evapotranspiration rate Monthly rainfall Average monthly rainfall between last spring freeze month and first fall freeze month Precipitation per billing period Mean annual rainfall Precipitation during growing seasons Average temperature for months between last spring freeze month and first fall freeze month Monthly average temperature Monthly average of maximum daily temperatures Number of days without significant rainfall (Z 0.04 in) times the month’s average temperature Number of retail establishments Value added in manufacturing Number of employees in all sectors Number of production workers in manufacturing

Water price

Income

1.10.3.2.3 Elasticities with respect to major determinants of water use Past empirical studies of water demand have used a broad array of possible explanatory variables. Table 4 lists variables in seven major categories and their definitions. These were found in empirical studies of municipal and residential water use. Estimated elasticities of explanatory variables can be obtained from the published empirical equations either as constant elasticities in double-log models or as calculated elasticity values at the mean values of the dependent and independent variables in linear and semilog models. The empirically derived elasticities of key explanatory are summarized in the following. Price elasticity. Economic theory assumes that consumers respond to economic incentives by adopting behaviors that maximize their well-being. In one of the earliest studies of urban water demand, Metcalf (1926) documented a relationship between water use and price that implied price elasticity of demand in the range of  0.40 to  0.65. A substantial body of literature has been published since to confirm that consumers respond to changes in the price of water (Boland et al., 1984; Epsey et al., 1997). Empirical estimates of the price response (elasticity) generally range between  0.1 and  0.9 with higher (absolute) values in industrial and agricultural uses. These values of price elasticity indicate that a 1.0% increase in price would result in a 0.1– 0.9% decrease in water use. Table 5 shows the range and most likely values of price elasticities of water demand for several types of water users. The price elasticity values were obtained from 60 empirical studies of water demands. While these elasticity coefficients indicate that demand is relatively inelastic with respect to price, significant increases in price are expected to result in major reductions in demand. During water shortages, rationing through pricing has proved to be an effective strategy for achieving significant reductions in demand. For example, during the 1988 water shortages in Santa Barbara, California, the price was raised to 27 times the normal level (from $1.09/100 cubic feet or $0.39/m3 to $29.43/100 cubic feet or $10.40/m3) to deter all but the most essential uses of water in the city (Ferguson and Whitney, 1993). As a result, the sector-wide demands were reduced by 56% in the single family, 41% in multifamily, and 20% in commercial sector. Also, average wastewater flows were reduced by 45%. Although price increases were accompanied by the implementation of a sprinkling ban and other conservation measures, the effect of the price increase on indoor

Housing

Family composition

Weather

Othera

a

Used in municipal demand models only.

use (unaffected by sprinkling restrictions) implies a short-term elasticity of  0.22. Income elasticity. Economic theory indicates that together with price, income is a key determinant of residential water demand, because the latter determines the consumer’s ability to pay for water. Consumers decide on what features and

Predicting Future Demands for Water Table 5

173

Empirical price elasticities of water demand

Demand category

No. of studies

No. of estimates

Range of price elasticitiesa

Median value

Combined urban demand Residential demand Single-family only Nonresidential demand Commercial Industrial Institutional Agricultural irrigation

25 58 24 15 6 19 3 10

93 256 94 160 53 101 54 34

– – – – – – – –

– – – – – – – –

0.11 0.18 0.22 0.27 0.24 0.33 0.24 0.24

to to to to to to to to

– – – – – – – –

0.58 0.50 0.48 0.87 0.92 0.88 0.94 0.97

0.40 0.33 0.31 0.54 0.34 0.58 0.47 0.46

a

The range shows the 25th and 75th percentile in the distribution of reported estimates.

Table 6

Empirical income elasticities of residential water demand

Demand category

No. of studies

No. of estimates

Range of income elasticitiesa

Median value

Residential demand Single-family only Multifamily only Municipal demand All sectors

37 24 2 23 86

137 82 2 38 259

0.20 0.10 0.22 0.19 0.14

0.37 0.18 0.22 0.31 0.31

to to to to to

– – – – –

0.61 0.39 0.23 0.58 0.55

a

The range shows the 25th and 75th percentile in the distribution of reported estimates.

conveniences they want in their residences, and what technology they want to employ to achieve them, considering the required investments and the price of water. Table 6 compares income elasticity estimates which were derived from 86 studies of residential (and combined municipal) water demand. For all studies, the range of 25th and 75th percentile estimates is between 0.14 and 0.55 with a median value of 0.31. Elasticities with respect to air temperature and precipitation. Weather conditions influence water demand because some uses of water such as landscape or crop irrigation are sensitive to variables such as precipitation, air temperature, or evapotranspiration. Table 7 shows elasticity estimates for air temperature and precipitation which were derived from 30 studies of residential and nonresidential water demand. For all studies, the range of 25th and 75th percentile estimates of the elasticity of temperature is between 0.43 and 2.15 with a median value of 1.15. The corresponding percentile values of the elasticities of precipitation are between  0.03 and  0.19 with a median value of  0.07. These elasticities indicate that on average demand is more than 10 times more responsive to changes in air temperature than to changes in precipitation. Elasticity with respect to production output. Output in manufacturing activities is often used as an explanatory variable in industrial water-use studies. However, different studies used different proxies for output, such as output value, number of employment hours, and number of employees. Table 8 lists 10 estimates of elasticities for output variables. All estimates are positive, and range from 0.48 to 1.94. Output elasticities 41 indicate that the use of water increases faster than output. The values in Table 8 show that this is the case for some industrial end-uses of water (i.e., cooling, processing, and steam generation) and some industrial categories. Elasticities of output for stone products, photographic

equipment, heavy industry, and paper production are o1, thus indicating that water use increases slower than output.

1.10.3.3 Other Water-Use Relationships The preceding sections described water-use relationships that derive from the economic theory of water demand. However, the limited availability of data on economic variables and the aggregate nature of data on water use often preclude the development of econometric models of water demand. A noneconomic approach is sometimes used to develop wateruse relationships which represent water requirements for different types of water users. Such approaches are explicitly or implicitly based on the assumption that the quantities of water used relate to a technical or physical requirement and are unaffected by economic choice. Nevertheless, the requirements approach remains an option for quantifying water use where econometric models cannot be developed. Two examples of the requirements models are described here.

1.10.3.3.1 Cooling water requirements In once-through cooling systems in steam-based thermoelectric power plants, theoretical water requirements are a function of the amount of waste heat that has to be removed in the process of condensing steam. According to Backus and Brown (1975), the amount of water for 1 megawatt (MW) of electric generation capacity can be calculated:

L¼

6823ð1  eÞ Te

ð18Þ

where L is the amount of water flow in gallons per minute (gpm) per MW of generating capacity; 6823 the units conversion factor; T the temperature rise of the cooling water in F;

174 Table 7

Predicting Future Demands for Water Empirical elasticities of two weather variables

Demand category

No. of studies

Air temperature Residential demand Single-family only Multifamily only Municipal demand Nonresidential All sectors Precipitation

7 6 1 5 2 21

43 11 1 16 3 74

Residential demand Single-family only Multifamily only Municipal demand Nonresidential All sectors

11 7 2 10 1 30

57 22 3 43 6 131

Range of income elasticitiesa

No. of estimates

0.44–3.58 0.88–2.00 0.35–0.35 0.53–1.58 0.02–0.81 0.43–2.15  0.04  0.01  0.04  0.05  0.01  0.03

to to to to to to

Median value

1.37 0.88 0.35 1.31 0.02 1.15

 0.24  0.09  0.12  0.21  0.15  0.19

 0.09  0.02  0.12  0.09  0.03  0.07

a

The range shows the 25th and 75th percentile in the distribution of reported estimates.

Table 8

Examples of elasticities of output variables in industrial water-use models

Study/author

Measure of output

Elasticity

Notes

De Rooy (1974)

Output value

Dziegielewski et al. (1990)

Total employment

Renzetti (1988)

Total number of employee hours

Renzetti (1993)

Output value

1.21 1.22 1.19 0.48 0.60 1.11 0.69 1.94 0.72 0.61

Cooling water use only Processing only Steam generation only SIC328 (cut stone and stone products) SIC386 (photographic equipment and supplies) SIC334 (secondary nonferrous metals) Heavy industry Light industry Self-supplied paper industry Self-supplied textile industry

and e the thermodynamic efficiency of the power plant, expressed as decimal fraction. For example, in a coal-fired plant with thermal efficiency e of 40% and the condenser temperature rise of 20F, the water flow rate obtained from Equation (18) would be 512 gpm per MW. For a typical 650 MW plant, operating at 90% of capacity, the theoretical flow rate would be nearly 300 000 gpm, or 431.3 mgd. The daily volume of cooling water is equivalent to approximately 31 gallons per 1 kWh of generation. According to Croley et al. (1975), in recirculating systems with cooling towers, theoretical makeup water requirements are determined using the following relationship:

W ¼E

1 1

c c0

ð19Þ

where c/c0 is the concentration ratio which compares the concentration of solids in makeup water to their concentration in the recirculating cooling water and E the evaporative water loss which, for a typical mean water temperature of 80 F, can be calculated as

E ¼ ð1:91145  10 6 Þ  aQ

ð20Þ

where a is the fraction of heat dissipated as latent heat of evaporation (for evaporative towers a ¼ 75  85%); 1.911 45  106 the units conversion factor; and Q the rate of heat rejection by the plant in Btu h1, which can be calculated as

Q ¼ 3414426  P 

1e e

ð21Þ

where P is the rated capacity of the plant in MW; 3 414 426 the units conversion factor; and e the thermodynamic efficiency of plant expressed as a fraction. Again, for a typical 650 MW coal-fired plant with 40% efficiency, the heat rejection would be 3329 million Btu h1 and the evaporative water loss would be 5091 gpm. At the concentration ratio c/c0 of 0.25, the makeup water flow would be 6788 gpm or 0.63 gallons per 1 kWh of generation.

1.10.3.3.2 Supplemental irrigation water requirements Water required for supplemental irrigation depends on soil moisture deficit during the growing season which can be derived based on rainfall data. The total seasonal application depth can be determined according the method developed by the Illinois State Water Survey. The method is based on weekly precipitation records for the growing season from 1 May to 31

Predicting Future Demands for Water

August (Dziegielewski and Chowdhury, 2008). Rainfall deficit is calculated by accumulating weekly deficits or surpluses over the consecutive weeks of the growing season. If more than 1.25 inches (in) of rain falls during the first week of the growing season, one-half the amount of rain exceeding 1.25 in is added to the rain amount during the following week. If o1.25 in of rain falls during the first week, the difference between the actual rainfall and 1.25 in is the rainfall deficit that is assumed to be the quantity of water (in inches) applied by irrigation that week. For each subsequent week during the growing season, one-half of the cumulative rainfall during the previous week in excess of 1.25 in is added to the rainfall amount for the week. If the cumulative rainfall amount for a week is less than 1.25 in, then the difference between the actual rainfall and 1.25 in is the rainfall deficit that is assumed to be the quantity of water (in inches) applied by irrigation that week. The rainfall deficits for each week are then added to determine the total irrigation water use during the growing season. This procedure can be expressed in mathematical terms as follows: 1. If the total rainfall in the first week r1o1.25 in, then

d1 ¼ r1  1:25

ð22Þ

2. If the total rainfall in the first week r141.25 in, then

r2

e

d1 ¼ 0 ¼ r2 þ ðr1  1:25Þ=2 d2 ¼ r2 e  1:25

ð23Þ

where r2 e is the effective rainfall in week 2. In week 2, again, the precipitation deficit will be zero if r2 e 41.25 in, and one-half of the precipitation surplus will carry to the next week. The total seasonal rainfall deficit for the 18 weeks (i.e., 4 months) which make up the irrigation season is calculated as

dt ¼

18 X

di

ð24Þ

i¼1

Here, the values of precipitation deficit represent the total depth of water application in inches during the growing season. Thus, the requirements for supplemental irrigation water can be determined using the following formula:

Qt ¼

325; 851 At  dt 12  365

ð25Þ

where Qt is the annual (seasonal) volume of irrigation water in mgd in year t; At the irrigated land area in acres in year t; dt the depth of water application in inches in year t; and the conversion factors represent: 325 851 gallons/acre-foot, 12 inches/foot, and 365 days/year.

175

1.10.4 Demand Forecasting Techniques 1.10.4.1 Forecasting Principles and Criteria A basis for forecasting future quantities of water demanded is required if any type of planning is to be undertaken. As Gardiner and Herrington (1986) simply state: ‘‘y planning, of virtually any kind, requires forecasting’’ (p. 7). In planning for sustainable future water supply, forecasts of water demand which include predictions of improvements in efficiency of water use form a basis of long-term plans for balancing water demand with supply. Other planning activities which require forecasts of water demand include expansion of the capacity of water-supply infrastructure, allocation of limited water supplies among different users, as well as short-term operational and financial planning. Boland (1998) provides an excellent exposition on the basic premises and principles of forecasting. He defines a forecast as a statement about the future. In his earlier writings, he used a more detailed definition which described a forecast as a conditional statement about the future which is likely to materialize if the forecasting assumptions are proved to be correct (Boland, 1985). Boland (1998) also distinguishes the term forecast from the related terms prediction, projection, and extrapolation in terms of implied method or procedure for preparing the statement about the future. Thus, prediction implies nothing about the method and could be an entirely subjective and judgmental statement; extrapolation represents a continuation of past trends and projection suggests a prediction which is influenced (indirectly) by past trends (Boland, 1998: 81). Recently, the International Panel on Climate Change (IPCC) considered some clarifications to this terminology and appears to prefer an alternative term scenario defined as ‘‘ya coherent, internally consistent and plausible description of a possible future state of the world. It is not a forecast; rather, each scenario is one alternative image of how the future can unfold’’ (IPCC, 2008). In a recent study of regional water demands in Northeastern Illinois (Dziegielewski and Chowdhury, 2008), the IPCC definition was adopted in describing water-demand scenarios. The adopted definition ensured that the scenarios would not represent most likely forecasts or predictions, nor would they set upper and lower bounds of future water use but instead they would only describe three alternative paths in demand growth because different assumptions or conditions could result in water demands that are within or outside of the range represented by the three scenarios. In essence, a forecast or a scenario is a translation of a set of assumptions into a future outcome (i.e., a quantity of water used at some point in time). These assumptions are the basis and the main component of any forecast. Other forecast components such as its structure (i.e., time step or level of disaggregation) or computational algorithms and empirically derived models are important but not as critical as the forecasting assumptions. Forecasting assumptions are a part of each forecasting method, regardless of the level of its sophistication. Typically, the simpler methods have only a few assumptions which are likely to be crude and difficult to verify. More elaborate methods, such as the IWR-MAIN model (to be discussed later in this chapter), rely on hundreds of assumptions in deriving a

176

Predicting Future Demands for Water

forecast. Although it is possible to document and make all such assumptions explicit, it is rarely done. Only some of the assumptions, usually those judged by the analyst to be important, are made explicit. However, because the accuracy of the forecast cannot be assessed until the forecast period has passed, its putative validity can be established only by assessing the plausibility of forecast assumptions. Boland (1998) discusses the objective and subjective components of the forecast in the context of a two-step process which consist of explanation and prediction. The explanation step involves the analyses of historical water use and represents the objective part of the forecast. The prediction step applies the factors and relationships which explained water use in the past to generate a forecast. This step necessarily reflects the subjective judgment of the analyst. This subjective judgment is present in all the forecasting assumptions mentioned above. Therefore, a forecast should attempt to make a clear and credible portrayal of the determinants and assumptions behind future water demand. In order to increase acceptability of the forecast by decision makers as well as by other analysts, while preparing the forecast it is important to ensure that: 1. historical water-use data are presented and analyzed for trends and underlying causes and relationships; 2. historical trends and causes are differentiated across user sectors and geographical parts of the study area; 3. major factors influencing water usage rates are considered (e.g., prices, income, and housing densities), and the estimated models are correctly specified; 4. all assumptions are explicit and supported by analysis of past trends or a consensus on future trends; and 5. forecasts utilize an official or a consensus forecast of population and economic growth data. The above elements of the forecast can help ensure that the forecast is understood and accepted by decision makers. The following section describes the specific analytical methods which can be used in constructing a forecast.

1.10.4.2 Forecasting Models and Procedures All forecasts attempt to predict the future value of water use, Qt, as a function of one or more explanatory variables and associated assumptions about the forecasting method and related parameters. The methods differ in terms of the number of explanatory variables and the form of the functional relationship. The forecasting methods also differ with respect to the structure of the forecast, especially in terms of separation of demands into more homogeneous categories of water use. This section describes a range of methods which can be found among the past forecasts of water demand.

In fitting trend lines, several alternatives for functional form exist, including linear, exponential, and logarithmic. When a linear trend line is selected, the future value of water use would be calculated as

Qf ¼ Qt þ bðf  tÞ

ð27Þ

where Qt is water use during the base year t (or the last year with known water demand), b the annual increment in water use Q, and f a future year of the forecast. In an exponential model where the annual (fractional) percentage change r is (b  1)100% when b40, the exponential growth of the future water use is calculated as

Qf ¼ Qt ð1 þ rÞðf tÞ

ð28Þ

This is a well-known equation for compounding interest in financial calculations. Finally, in a logarithmic (log) model, future water use would be increasing at a decreasing rate. A variant of the logarithmic function is a form linear in its logarithms. Using the multiplicative form the log-linear equation, the future water use can be calculated as

Qf ¼ Qt

 b f t

ð29Þ

where t is the time period of the forecast assuming (t – n) ¼ 1 for the first time period of the historical data series. In this trend function, the percent rate of growth is inversely proportional to time. The trend extrapolation method is often used with aggregate data. The greatest difficulty of this method is deciding on what type of function represents the best fit to the historical data. Both linear and nonlinear trends (e.g., exponential or logarithmic) can show equally good fit to the historical data, but their extrapolation into the future may produce considerably different results. Another significant problem is the main forecasting assumption behind this method: that the historical rate of growth will continue into the future and produce the same effects. Boland (1998) considers this method to be ‘‘yto simplistic for virtually any application’’ (p. 85) because of its implicit assumption that water use is explained by the passage of time and that all other variables which are known to affect water use are perfectly correlated with time or their effects cancel each other. However, the use of time trend as one of the explanatory variables in water-use models can be helpful in capturing the residual effects of unspecified variables once the effect of known factors which affect water use is accounted for. Trend extrapolation is also used in deriving future values of some explanatory variables.

1.10.4.2.1 Time trend forecasting 1.10.4.2.2 Water requirement forecasts

Future water demand can be determined by extending the historical trend in the past records of water use. This method would be termed extrapolation as defined by Boland (1998). The only analytical step in this method is finding the functional form g in the equation

A longstanding forecasting practice is to assume that water use is proportional to the size of a water-using activity or the number of water users. This proportionality can be expressed using a linear model

Qf ¼ gðQt ; Qt1 ; :::; Qtn Þ

Q ¼ a þ bN

ð26Þ

ð30Þ

Predicting Future Demands for Water 250

or a multiplicative (log-linear) model

ð31Þ

150 100 50

The assumption of strict proportionality is often criticized by the economists because it ignores other factors that can affect water use, and in its application to derived demands it treats the size variable which is often the production output or employment as exogenous to the firm’s decision on water use. However, the representation of water demands as the number of users (or a driver of water use) times average rate of usage (or intensity of water use) is a convenient and practical forecasting approach. A well-known example of this approach is the per capita requirements method, which has been widely used in forecasting urban water use. According to this method, future values of the volume of publicly supplied water for a city or municipality are often obtained by multiplying future population (which represents N in Equation (32)) with an assumed per capita rate of water use (as b in Equation (32)) This method was widely used in the past because the metric of per capita water use can be easily obtained from the production records (see Section 1.10.2.2.1) and total population served can be obtained from the population census data. However, the validity of this method depends on the constancy of the per capita use rate over time. In most urban water-supply systems, per capita use rate is not constant; it fluctuates from year to year and often exhibits long-term increasing or declining trends. For example, Figure 1 illustrates the long-term changes in per capita water use in New York City. The per capita rate had been increasing from 101 gpcd in 1915 to 208 gpcd in 1988 but had declined to 134 gpcd by 2006. A per capita forecast prepared in any year in the past would not have captured the changes in per capita rates of use. Because the per capita approach relies on the assumption that the per capita rate will remain constant throughout the forecast period, the validity of per capita forecasts is doubtful. The main problem is that it ignores the changes in the structure and composition of urban demands over time and also ignores the effects of future changes in the determinants of different component demands. However, the unit-use coefficient methods can have some validity when applied to disaggregate demands of more homogeneous sectors or categories of water use. Examples of disaggregate requirement models are given in the following. Disaggregate requirement forecasts. When total demand in a geographical area is disaggregated by sectors, demand in sector k can be represented as a product of the number of water users

50 19 60 19 70 19 80 19 90 20 00 20 10

40

19

30

19

20

19

19

19

ð32Þ

19

Q ¼ bN

10

0 00

where a, b, b, and g are treated as constants at a given level of aggregate demand. Although this simple approach is criticized by economists (Hanemann, 1998), it is widely used in forecasting water demands for industrial, commercial, and agricultural categories. Aggregate requirement forecasts. By forcing the intercept in Equation (30) to be zero or assuming that the exponent g in Equation (31) is 1, we obtain a simple requirements model in which total water use is strictly proportional to N:

200

GPCD

Q ¼ bN g

177

Year Figure 1 Historical per capita rates of water use in New York City.

and the average rate of use within the sector:

Qkt ¼ S Nkt  qkt k

ð33Þ

where Nkt represents the number of users (or other units) in sector k at time t and qkt the unit use coefficient (or average rate of water use per user) in that sector. Similarly, the total demand within the geographical area can be represented as

Qt ¼ S S S Nkgjt  qkgjt k g j

ð34Þ

where k denotes the disaggregation of water use into homogeneous sectors of water users (e.g., residential, commercial, industrial, and institutional) and g represents the spatial disaggregation of water use into various geographical subareas that are relevant for planning purposes. An example of a unituse coefficient qkgjt could be average use in single-family buildings of the residential sector in a suburban section of a city. This single-coefficient model can be extended by expressing the average rates of water use within each sector as a function of one or more explanatory variables. Usually, the dependent variable is assumed to be a linear function of more than one independent variable. For example, if there are two independent variables, the theoretical model is similar to Equation (8)and can be written as

q ¼ a þ b1 X1 þ b2 X2 þ e

ð35Þ

where a, b1, b2 are estimated regression coefficients, X1, X2 the independent variables assumed to affect independent variable q, and, e the random error term. There are also forecasting methods that are based on landuse categories. Such approaches typically represent a particular case of single coefficient requirement models. The land-usebased models display data in a way that is convenient for infrastructure planning and city master planning. For example, DCSE, a California-based software product and consulting firm, has developed a geographic information system (GIS)based water-demand forecasting procedure for estimating

178

Predicting Future Demands for Water

water demands in response to changes in land-use and related use factors. Water-demand projections are based on distribution of land-use categories and the corresponding water-use factors. Because land use in a service area can change due to conversion of land to residential, commercial, industrial, and other urban uses, the result will be a change in total water use.

1.10.4.2.3 Demand forecasts In practice, these methods require that regression models are constructed using the proper specification of the economic variables such as price and income and the level of output in the case of derived demand (see Section 1.10.3.1). It should be noted that econometric models are derived from observations which represent the points of intersection of demand and supply curves, and they usually represent reduced forms, as opposed to the structural equations which represent the true demand functions. Unfortunately, demand models are generally available only for single-family residential water use. Studies of multifamily residential sector and major industrial, commercial, and agricultural sectors are very limited in number. Nevertheless, forecasters should consider developing econometric models in order to probe the validity of estimated (or assumed) wateruse relationships.

1.10.4.3 Dealing with Forecast Uncertainty As mentioned earlier, all forecasts of future water demand are inherently uncertain. Generally, the uncertainty associated with the analytically derived future values of water demand can come from a combination of the following distinct sources: 1. Random error. The random nature of the additive error process in a linear (or log-linear) regression model which is estimated based on historical data guarantees that future estimates will deviate from true values even if the model was specified correctly and its parameter values (i.e., regression coefficients) were known with certainty. 2. Error in model parameters. The process of estimating the regression coefficients introduces error because estimated parameter values are random variables which may deviate from the true values. 3. Specification error. Errors may be introduced because the model specification may not be an accurate representation of the true underlying relationship. 4. Scenario uncertainty. No future values of any model variables can be known with certainty. Various assumptions must be introduced when projections are made for the water-demand drivers (e.g., population, employment or irrigated acreage, income, price, precipitation, and other explanatory variables). The first three sources of error can be addressed by a careful analysis of the data and model parameters. The fourth source of error – the scenario error (or assumption error) – requires an explicit evaluation of assumptions through sensitivity analysis, Monte Carlo analysis, or the use of scenario-based forecasts.

1.10.4.3.1 Model-dependent prediction intervals In econometric forecasts, each empirically derived model can be tested for specification error by using Ramsey’s specification tests (Ramsey (1969), the Breusch–Pagan–Godfrey test Breusch and Pagan (1979)), Glejser’s test (Glejser, 1969), and Harvey’s test and White’s test (White, 1980) for heteroscedasticity. The specification and heteroscedasticity tests allow the analysts to develop predictive equations which minimize the errors from misspecification of the model and biases in model parameters. Other model-dependent errors can be quantified using confidence intervals (Dziegielewski et al., 2005). For example, assuming that the errors are normally distributed in a log-linear model in which Y designates water use, it can be shown that

EðYjexplanatory variablesÞ ¼ e se

2

=2

ðe ln Y Þ

ð36Þ

Thus, in log-linear models, the predicted value denoted as Y˜ is given by 2 Y˜ ¼ e s^e =2 ðe ln YÞ

ð37Þ

^e 2 is the mean square error of the log-linear model and where s lnY^it the predicted value obtained from the log-linear models. It is straightforward to obtain the in-sample prediction confidence intervals in a linear model. However, in a log-linear model, the in-sample prediction intervals are obtained under the assumption that the errors are normally distributed. Thus, for normally distributed errors the variance of Y˜ in Equation (37) is estimated by

  ˜ ¼ exp 2lnY þ Vii s ^2e VarðYÞ "  m=2  m #   ^2e s 2^ s2e 2 ^e 1   1  exp Vii s m m ð38Þ ^e 2 is the square of the standard error of the logawhere Vii s rithmic prediction (i.e., lnYit ), m the degrees of freedom, ^e 2 the mean square error of the log-linear model. The and s standard error of Y^ is denoted as

˜ ¼ SEðYÞ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffi ˜ VarðYÞ

ð39Þ

0 Assuming that the Y˜ s are asymptotically normally distributed, the confidence interval for the prediction can be obtained as ˜ Y˜ þ za=2  SEðYÞ, ˜ ½Y˜  za=2  SEðYÞ; where za=2 is the critical value from a normal distribution for a prespecified a. However, for the out-of-sample predictions, the square of the ^e 2 ) is not standard error of the logarithmic prediction (i.e., Vii s available. To rectify this, one can use the average standard error of predictions (average over all observations in the historical data).

1.10.4.3.2 Dealing with forecast assumptions error The uncertainty caused by forecasting assumptions can be addressed by using scenario forecasts, sensitivity analysis, or probability forecasts. The scenario approach requires the

Predicting Future Demands for Water

development of scenario narratives and selection of a set of key forecasting variables and assumptions for each scenario. Usually, only a small number of scenarios are developed. The purpose of the scenarios is to capture future water demands under different sets of possible future conditions. Although the scenarios convey a sense of the range of future water use, they are not constructed to set upper and lower bounds of future values. Different sets of assumptions or conditions could result in demands that are within or outside of the range represented by the defined scenarios. Sensitivity analysis evaluates the sensitivity of the forecast values to changes in forecasting assumptions – one assumption at a time. In the case of the values of explanatory variables, alternative forecast values are generated by changing the value of the variable of interest while keeping the values of all other variables and forecasting assumptions unchanged. For key explanatory variables, the same information can be obtained by using the values of constant elasticities of water demand with respect to each variable and percent change in variable value. However, the effects of other assumptions are often less explicit and can be best determined by generating the alternative forecast values. Finally, probabilistic forecasts can be obtained by simultaneously varying two or more assumptions and generating a probability distribution of the values of water-demand forecast. Often, Monte Carlo simulation is used to generate very large numbers of outcomes based on distribution functions for the independent variables. Table 9 (IWR, 2001) shows an example of a probabilistic forecast for a water demand in public systems in a region in Virginia. The useful feature of such a forecast is the exceedance probability of the forecast values. The results in the table show that the mean forecast value for 2050 is 90.95 mgd and that there is only a 5% chance that the value of 96.94 mgd will be exceeded.

Table 9

179

Example of a probabilistic forecast

Year/water demand

2010

2050

Minimum Maximum Mean Std dev. Variance Skewness Kurtosis Distribution percentiles 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95%

62.71 71.67 67.16 1.34 1.81 0.04 2.73

80.23 105.22 90.95 3.50 12.28 0.25 3.02

64.98 65.43 65.76 66.01 66.22 66.42 66.61 66.78 66.97 67.14 67.32 67.50 67.69 67.89 68.10 68.34 68.61 68.93 69.38

85.45 86.58 87.30 87.89 88.44 88.98 89.46 89.93 90.40 90.84 91.28 91.75 92.20 92.71 93.23 93.85 94.56 95.49 96.94

From IWR (Institute for Water Resources) (2001) An evaluation of the risk of water shortages in the Lower Peninsula, Virginia. Prepared by Werick WJ, Boland JJ, Gilbert J, Dziegielewski B, Kiefer J, Massmann J, and Palmer RN. IWR Special Report. Alexandria, VA: US Army Corps of Engineers.

1.10.4.4.1 End-use accounting system

1.10.4.4 Forecasts with Conservation The increasingly important role of demand-side options in water-supply planning creates needs for methods to estimate the effects of various water conservation programs on future water demands. However, the effects of demand-side programs cannot be assessed without a detailed knowledge of water uses in a study area and without an understanding of the important factors that influence them now and will influence them in the future. Thus, the most important feature of forecasting the impacts of water conservation is a high level of disaggregation of water demands. In order to estimate the effects of water conservation measures on future demand, it is usually necessary to disaggregate water demand into the specific end-uses of water. The conservation impacts (i.e., water savings) are usually estimated as the reduction in average rates of water use for specific purposes. For example, in the residential sector average household water use can be represented as a summation of average water used for toilets, showers, kitchen faucets, washing machines, and landscape irrigation. These end-uses may each be affected by water conservation measures which would result in a lower average per household use.

Forecasting methods which focus on impacts of long- and short-term demand management measures usually employ a highly disaggregated end-use accounting approach. Dziegielewski et al. (1993) proposed the following equation for estimating water use for each end-use:

" qe ¼

X

! Mu Su

# UþK F A

ð40Þ

u

where qe is the quantity of water used by a given end-use e (gpd per unit); Mu the mechanical end-use parameter for efficiency class u (e.g., gpm and gallons per flush); Su the fraction of the sectoral end-use within each efficiency class (such as nonconserving, conserving, and ultraconserving); U the intensity of use parameters (e.g., flushes per day per unit, minutes of use per day per unit); K the mechanical parameter representing the rate of leakage; F the fraction of end-uses with leakage; and A the fraction of water-using entities in which end-use is present. Because the structure of end-use demands in a study area at any point in time will not remain constant over the forecast horizon, it is necessary to quantify the effects of various external factors on the parameters of Equation (41)(e.g., Su, U, F, A). For example, future increases in the price of water could

180

Predicting Future Demands for Water

decrease the incidence of leaks (F) in the short run and would also affect the distribution of end-uses among the efficiency classes (Su) in the long run. The other two parameters of the end-use equation (intensity U and presence A) will also be affected by changes in price. Changes in other explanatory variables will also affect the end-use parameters. For example, in the residential sector, in addition to price, the end-use parameters will be affected by variables such as income, household size, housing density, and weather.

5. nonurban; 6. user-specified sector; and 7. total residential. These categories correspond to the housing types used by the US Bureau of the Census. Average rates of water use within each residential subsector are estimated using econometric water-demand models. For the single-family sector, a log–log equation in the form of a multiplicative function of seven independent variables is used:

1.10.4.4.2 Baseline and restricted forecasts Impacts of conservation have to be assessed for different types of conservation measures, including passive, active, and emergency measures as well as future changes in water prices. It is a common forecasting practice to generate one forecast which assumes no active intervention to change future water demands. This forecast is usually referred to as a baseline or unrestricted forecast. Then one or more alternative forecasts are prepared based on assumed active efforts to reduce demands. These forecasts are referred to as restricted forecasts or forecasts with conservation. Some of the forecasting procedures and models described in this and the previous subsections have been incorporated into computer software programs, which structure the input data, provide computational algorithms, and generate forecast outputs. One example of the forecasting program is IWRMAIN.

1.10.4.5 Forecasting Software: The IWR-MAIN Program One of the first known software programs for forecasting urban water use MAIN II (Municipal And Industrial Needs) was developed by Hittman Associates Inc. (1969). The model was based on the residential and commercial water-use research projects carried on at the Johns Hopkins University (Linaweaver et al., 1966; Wolff et al., 1966; Howe and Linaweaver, 1967). During the 1980s, the Institute for Water Resources (IWR) of the US Army Corps of Engineers undertook a substantial research effort to update the model and modify it for easy access on personal computers. The product of this effort was a public-domain software package called IWR-MAIN version 5.1 (Dziegielewski and Boland, 1989) and the IWR-MAIN Water Demand Analysis Software, version 6.0 (Dziegielewski, 1993; Dziegielewski et al., 1996).

1.10.4.5.1 Model structure and procedures The model disaggregates total urban water use by customer sectors, time periods, spatial study areas, and end-use purposes. Through sectoral disaggregation, forecasts of water use can be prepared for major sectors of water users, including residential, nonresidential (constituting manufacturing, commercial, and governmental), public, and other. Demands can be further disaggregated within each sector. Within the residential sector, there are seven subsectors available for forecasting water demands. These include 1. 2. 3. 4.

single-family – 1 attached, 1 detached units; multifamily low density – 2, 3, 4 units per structure; multifamily high density – 5 or more units per structure; mobile homes;

Qr ¼ aIb1 Hb2 Lb3 T b4 Rb5 P b6 eb7 B

ð41Þ

where Qr is predicted residential water demand in gallons per housing unit per day; I the median household income in $ per year; H the average household size (persons); L the average housing density (units per acre); T the daily-maximum air temperature in farenheit; R the rainfall in inches; P the marginal price of water (including sewer) in $/1000 gallons; B the fixed charge or rate premium (i.e., Nordin’s bill difference) of the water/wastewater tariff in $ per month; a the constant; bi the constant elasticities of explanatory variables; b7 the coefficient of the rate premium (representing the water tariff structure); and e the base of the natural logarithm. The model can use generic water-use equations with default elasticities for explanatory variables in the residential sector which were derived through a meta analysis of empirical literature. Once average water use per household has been estimated by Equation (41), total water demand for a given subsector, season, and year is calculated by multiplying the average use rate Qr by the driver variable (number of households). Nonresidential water use is disaggregated by the model into the following major industry groups: (1) construction; (2) manufacturing; (3) transportation, communications, and utilities; (4) wholesale trade; (5) retail trade; (6) finance, insurance, and real estate; (7) services; and (8) public administration. These eight major industry groups are classified according to the US Department of Commerce Standard Industrial Classification (SIC) codes. Within each major industry group, SIC codes distinguish more homogeneous groups at the twodigit SIC level and even further at the three-digit SIC level. Because no generally applicable demand models exist that contain elasticities for price, labor productivity, cooling degree days, or the other variable for nonresidential water use, IWRMAIN estimates nonresidential water use by multiplying employment with water-use coefficient. Water use per employee coefficients are available for each category and were derived from a sample of about 7000 nonresidential establishments (Table 10). Some estimates in Table 10 represent relatively high values of per employee usage rates (e.g., security and commodity brokers, real estate firms, or legal services), because they represent uses of water other than indoor domestic uses. For example, in large office buildings the component end-uses may include landscape irrigation or makeup water for cooling towers.

Predicting Future Demands for Water Table 10 Water use coefficient for industrial, commercial, and institutional categories of water use Description of SIC categories

Sample size

Construction (SIC 15–17) General building contractors Heavy construction Special trade contractors Manufacturing (SIC 20–39)

246 66 30 150 2790

31 118 20 25 164

252 20 91 62 83 93 174 211 23 116

469 784 26 49 36 2614 37 267 1045 119

10 83 80 395 304 409 182 147 55 226

148 202 178 194 68 95 84 66 36 50

3 32 100 1 10 17 13 31 19 751

68 26 85 5 353 171 40 55 51 43

518 233 1044

46 87 93

Building materials and garden supplies General merchandise stores Food stores Automotive dealers and service stations Apparel and accessory stores Furniture and home furnishing stores Eating and drinking places Miscellaneous retail Finance, insurance, and real estate (SIC 60–67)

56 50 90 198 48 100 341 161 238

35 45 100 49 68 42 156 132 71

Depository institutions Nondepository institutions Security and commodity brokers Insurance carriers Insurance agents, brokers, and service Real estate Holding and other investment offices

77 36 2 9 24 84 5

Food and kindred products Textile mill products Apparel and other textile products Lumber and wood products Furniture and fixtures Paper and allied products Printing and publishing Chemicals and allied products Petroleum and coal products Rubber and miscellaneous plastics products Leather and leather products Stone, clay, and glass products Primary metal industries Fabricated metal products Industrial machinery and equipment Electronic and other electrical equipment Transportation equipment Instruments and related products Miscellaneous manufacturing industries Transportation and public utilities (SIC 40–49) Railroad transportation Local and interurban passenger transit Trucking and warehousing US postal service Water transportation Transportation by air Transportation services Communications Electric, gas, and sanitary services Wholesale trade (SIC50-51) Wholesale trade – durable goods Wholesale trade – nondurable goods Retail trade (SIC 52–59)

Table 10

Continued

Description of SIC categories

Sample size

Services (SIC 70–89)

1878

137

197 300 243 108 42 40 105 353 15 300 55 9

230 462 73 217 69 110 429 91 821 117 106 208

45 5 60 25

212 58 73 106

2 4 6 6 5 2

155 18 87 101 274 112

gped coefficient

62 361 1240 136 89 609 290 (Continued )

181

Hotels and other lodging places Personal services Business services Auto repair, services, and parking Miscellaneous repair services Motion pictures Amusement and recreation services Health services Legal services Educational services Social services Museums, botanical and zoological gardens Membership organizations Engineering and management services Services, NEC Public administration (SIC 91–97) Executive, legislative, and general Justice, public order, and safety Administration of human resources Environmental quality and housing Administration of economic programs National security and International affairs

gped coefficient

gped, gallons per employee per day. Source: Dziegielewski et al. (1996), IWR-MAIN Version 6.0.

1.10.4.5.2 IWR-MAIN conservation forecasts The conservation subroutine of IWR-MAIN 6.0 disaggregates urban water use into 20 end-uses covering residential and nonresidential water uses, both indoor and outdoor. Using Equation (40), each end-use is divided into three classes of efficiency (nonconserving, conserving, and ultraconserving). The rate of use in each of these efficiency classes is defined by the mechanical parameters (M1, M2, and M3). For example, toilets have mechanical parameters of 5.5 gallons per flush (gpf), 3.5 gpf, and 1.6 gpf for nonconserving, conserving, and ultraconserving end-uses, respectively. The percent of sector entities in each efficiency class (S1, S2, and S3) must be determined based on local information. For example, 40% of single-family residential units have toilets with 5.5 gpf, 50% use toilets with 3.5 gpf, and 10% use toilets with 1.6 gpf. The intensity (U) for each fixture defines how frequently a particular end-use occurs on a per household, per employee, or other basis. In the residential sector, an event or flow rate that is defined on a per person basis is multiplied by the average number of persons per household to determine the intensity of the end-use at the household level. For example, a toilet is flushed an average of 5.0 times per person per day. This value, when multiplied by the average number of persons per household in the study area, gives the site-specific intensity value for the toilet end-use. The presence of a particular end-use may vary by forecast year and represents the fraction of units (housing units, employees, or other measures of size) in a water use sector that

182

Predicting Future Demands for Water

have that particular end-use. The ability to adjust the presence factor by forecast year allows the forecaster to account for potential changes in the presence of an end-use. For example, the presence of dishwashers for the single-family sector in the base year may be 76% but could be expected to increase to 84% in 10 years. The conservation savings procedures distinguish between active and passive demand management programs. Active programs include interventions by water providers or other entities. Passive demand reduction is a result of natural shifts toward higher-efficiency classes (e.g., from the standard 3.5 gallon per flush toilet to the ultraconserving 1.6 gallon per flush toilet). The shifts of end-uses toward higher classes of efficiency are brought about primarily by plumbing codes that require increased efficiency in water-using fixtures which affect new construction, remodeling, and customer-initiated retrofitting. Shifts from less efficient to more efficient pools of end-uses are also expected to occur naturally over time as technology continually improves. The natural shifts toward higher efficiency pools (often accelerated by plumbing or efficiency codes) are determined using appropriate rates of movement. These movements predict the form in which these shifts take place and additionally show the rate at which they occur. Conservation measures may be defined as long-term, shortterm, or emergency (restricted use) programs. Long-term conservation programs permanently shift the number of entities into higher efficiency classes. These include all plumbing codes (passive programs) as well as active programs. An example of an active program that would be considered a longterm conservation program is a retrofit campaign in which low-flow showerheads are installed. Showerheads usually last about 15 years, after which it is assumed that they will be replaced with units that are at least as efficient, if not more efficient. Short-term conservation measures are implemented during periods of water shortages. These measures target the end-use fixtures by temporarily shifting them into a higher efficiency class. An example would be the distribution of toilet dams. These devices would temporarily shift users into a higher efficiency level but are unlikely to remain in the toilets for a long period of time. Although the short-term conservation measures focus on increasing the efficiency of end-uses, the emergency or restricted use measures focus on temporarily altering the behavior of water-using entities. Like the short-term programs, the restricted use programs evaluate potential reductions in demand which could be enacted during periods of water shortages. A restricted use conservation program might entail, for example, restricting the days when the residents of a community can wash their cars. Restrictive programs target specific end-uses and achieve water savings by using alternate (restrictive) values for presence factors, leakage percents, and intensities.

1.10.5 Example of a Regional Multisector Forecast This example taken from Dziegielewski and Chowdhury (2008) illustrates the development of future water-demand

scenarios for geographical areas that encompass the 11-county regional planning area of Northeastern Illinois, including the counties of Boone, Cook, DeKalb, DuPage, Kane, Kankakee, Kendall, Grundy, Lake, McHenry, and Will (Figure 2). In 2005, total resident population of the area was estimated at 8 743 900 persons and a total of 4 355 200 persons were employed in the local economy. Nearly 96% of the population was served by about 530 public water-supply systems and nearly 400 000 residents relied on private wells. Other water users included 12 large power plants, 352 golf courses, and about 30 000 acres of irrigated cropland. The study generated three water-demand scenarios by major user sectors and geographical service areas within the region that were extended to the year 2050. The scenarios were formulated to represent growth assumptions under current trends (CTs or baseline scenario) as well as under less resource-intensive (LRI or low-growth scenario) and more resource-intensive (MRI or high-growth scenario).

1.10.5.1 Water-Use Relationships The historical data on water withdrawals were used to estimate water-demand relationships that expressed water demand as a function of relevant explanatory variables. Table 11 lists the demand drivers and estimated elasticities of the explanatory variables for each demand sector. Because the dependent and, in most cases, independent variables were converted to natural logarithms, the coefficients represent constant elasticities of water demand. Accordingly, the elasticity of marginal price in the public-supply sector was estimated to be  0.1458, indicating that a 1.0% increase in price is expected to result in a 0.145 8% decrease in demand. The three forecast scenarios were defined by different sets of assumed conditions regarding the future values of demand drivers and explanatory variables. Table 12 compares several key assumptions that were used in constructing the three scenarios. Table 13 provides a summary of the future scenarios of average day water withdrawals for six categories of users within the four major sectors. For 2005, both the reported values and weather-adjusted values are shown. The lower panel of Table 13 shows the sum of total withdrawals with and without the once-through flow for power generation and gross per capita withdrawals without once-through flows. This distinction is made because the very high volumes of water withdrawals for once-through cooling are not directly comparable to withdrawals by other sectors. The three future water-demand scenarios show that total water withdrawals in the 11-county area of Northeastern Illinois will continue to increase to meet the demands of growing population and the concomitant growth in the economy of the region. However, the growth in total water demand could be faster or slower depending on which assumptions and expectations about the future conditions will prevail. Under the baseline (CT) scenario, by 2050, total water withdrawals (excluding water withdrawn for once-through cooling in electric power plants) would increase above the 2005 level by 35.8%, or 530 mgd. During the same period of time, total population is projected to increase by nearly 3 370 000, or 38.5%. This implies that water demand would grow slightly

Predicting Future Demands for Water

183

W I S C O N S I N BOONE

MCHENRY

LAKE

I

L

L

Chicago

DUPAGE

KANE

I

N

O

I

S

e

r

DEKALB

Lake Michigan

Fox

Riv

COOK KENDALL

WILL Ka nk ak

N

*Groundwater is also used within these areas in some cases.

Ri

GRUNDY

ee

10 MILES

INDIANA

ve r

KANKAKEE

Source: Chicago Metropolitan Agency for Planning

Figure 2 Regional forecast area in Northeastern Illinois.

slower than the region’s population. Gross per capita water withdrawals (i.e., total withdrawals by all sectors divided by total population) during the dry year of 2005 were estimated at 182.8 gpcd. Under normal weather conditions, the 2005 demands would have been 169.3 gpcd. In the baseline (CT) scenario, the gross per capita usage would decrease to 166.0 gpcd by 2050. This relatively unchanged per capita rate is a result of assumptions about gradual increases in water prices and a continuation of the historical trend in water conservation. Under the key assumptions of the high-growth (MRI) scenario, future water demands would grow faster than total population if income grows at a somewhat higher rate than under the baseline scenario, if future prices of water do not grow faster than inflation, if no additional gains in water conservation are achieved, and if more population growth takes place in the collar counties of Kane, Kendall, and McHenry in single-family housing. Under these conditions, by 2050, total water withdrawals would increase above the 2005 level by 64.1%, or 949.1 mgd. The growth of water demand would exceed the rate of population growth because of the increasing gross per capita usage rate. By 2050, it would increase to about 200.6 gpcd, as compared to the 2005

weather-normalized rate of 169.3 gpcd. In a sense, the high-growth scenario could be viewed as a warning that there is a possibility of a large increase of water demands in the future. Under the key assumptions of the low-growth (LRI) scenario, future demands would grow significantly slower than population if income grows at a somewhat slower rate than under the baseline scenario, if future prices of water grow significantly faster than inflation, if additional gains in water conservation are achieved, and if more population growth takes place in the urbanized counties of Cook and DuPage in multifamily housing. Under these conditions, by 2050, total water withdrawals would increase above the 2005 level by about 7.2%, or 107.2 mgd. The growth of water demand would be much slower than the rate of population growth because of the decreasing gross per capita usage rate. By 2050, it would decrease to about 131.1 gpcd, as compared to the 2005 weather-normalized rate of 169.3 gpcd. This scenario could be interpreted as a future outcome which would require an intervention in order to maintain a slower growth of demand. This intervention would likely require monitoring and management of water demand and making investments in long-term efficiency of water use.

184

Predicting Future Demands for Water

Table 11

Drivers of water demand and estimated elasticities of explanatory variables

Demand sector

Demand driver

Explanatory variables

Elasticity/coefficient

Public supply

Population served

Power generation

Gross electric generation

Air temperature Precipitation (growing season) Employment fraction Marginal price of water Median household income Conservation trend Unit-use coefficients

Industrial and commercial

Employment

Agricultural and irrigation

Irrigated acres Livestock counts Population

1.0951 –0.0949 0.0931a –0.1458 0.2845 –0.0593 0.67–0.89b 10.8–78.9c 0.3298 –0.0896 0.0279a –0.1077a 0.0032a –0.0074a 1.000 0.03–35.0d 1.6238 –0.2186 0.3499 –0.0325

Domestic self-supplied

Cooling degree days Precipitation (growing season) Manufacturing employment (%) Transportation employment (%) Fraction of self-supplied (%) Conservation trend (linear) Rainfall deficit Unit-use coefficients Air temperature Precipitation (growing season) Median household income Conservation trend

a

Dependent variables are in linear form. All other coefficient represent constant elasticities. The values represent unit withdrawal coefficients in gallons per kilowatt-hour of gross generation in plants with closed-loop cooling systems. c The values represent unit withdrawal coefficients in plants with open-loop once through cooling systems. d The values represent unit use coefficient per animal type. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008. b

Table 12

Scenario assumptions for factors affecting future water demands in the 11-county area of Northeastern Illinois

Factor

CT (baseline)

LRI scenario

MRI scenario

Distribution of population of growth Mix of commercial/industrial activities Median household income Demand for electricity

Official projections

Shift to Cook and DuPage counties Decrease in water-intensive activities Growth of 0.7% yr1 9.61 MWh per capita per year þ no growth No new power plants, 3 units retired, 2 plants convert to closed-loop cooling 50% higher rate than historical trend Growth of 2.5% yr1 Decreasing cropland þ no new golf courses

Shift to Kane, Kendall, and McHenry counties Increase in water-intensive activities Growth of 1.0% yr1 9.61 MWh per capita per year þ 0.56% per year growth Two new power plants in study area with closed-loop cooling

Power generation

Current trends Growth of 0.5% yr1 9.61 MWh per capita per year þ 0.56% per year growth No new plants within study area, 3 units retired

Water conservation

Historical trend

Future water prices Irrigated land

Growth of 0.9% yr1 Constant cropland þ new golf courses: 10 per decade

No extension of historical trend Constant in 2005$ Constant cropland þ new golf courses: 20 per decade

CT, current trends; LRI, less resource-intensive; MRI, more resource-intensive. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

1.10.5.2 Effects of Key Forecast Assumptions The plausibility of the scenario forecasts depends on how reasonable the assumptions about future changes in the explanatory variables and the size of their effects on water demand are. In order to verify the forecasts of total or sectoral water use in the future, it is helpful to re-analyze the resultant

forecasts in terms of the underlying effects of demand drivers and the unit usage rates. The total change in demand is the result of the change in the value of the driver (e.g., population served, employment, and irrigated acreage) and the change in unit usage rate. Table 14 compares the resultant unit usage rates for the major demand sectors for each scenario. The changes in future values of these rates are a result of changes in

Predicting Future Demands for Water Table 13

185

Summary of water withdrawal scenarios for Northeastern Illinois (in mgd)

Sector

2005-R

2005-Na

2050-CT

2050-LRI

2050-MRI

Public supply Self-supplied I&C Self-supplied domestic Irrigation and agriculture Power plants (makeup) Power plants (through flow) Total – all sectors Total w/o through-flow power

1255.7 191.6 36.8 62.0 52.3 4207.2 5805.6 1598.4

1189.2 162.4 31.8 44.6 52.3 4207.2 5687.5 1480.3

1570.2 291.6 41.2 55.4 52.3 3830.2 5840.9 2010.7

1217.9 222.1 37.3 43.8 66.4 2472.3 4059.8 1587.5

1837.2 391.4 49.3 60.7 90.8 3830.2 6259.6 2429.4

a

For comparison with future values, the 2005 withdrawals were adjusted by the model to represent normal weather conditions. R, reported; N, normal weather; CT, current trends; LRI, less resource-intensive; MRI, more resource-intensive; mgd, gallons per capita per day; 1.0 mgd, 3784.4 m3. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

Table 14

Summary of unit usage rates by sector

User sector

2005-R

2005-Na

2050-CT

2050-LRI

2050-MRI

Public supply (gpcd) Self-supplied I&C (gped) Self-supplied domestic (gpcd) Irrigation and agriculture (in yr1) Power plants (makeup)(gal. kWh1) Power plants (through-flow)(gal. kWh1) Gross per capita rates (gpcd)

150.4 130.6 93.6 15.3 0.86 41.4 182.8

142.1 109.3 81.1 11.0 0.86 41.4 169.3

134.9 120.1 86.4 11.5 0.86 40.2 166.0

104.7 90.6 78.3 9.1 0.81 33.3 131.1

157.9 155.2 103.5 12.5 0.77 40.2 200.6

a

For comparison with future values, the 2005 withdrawals were adjusted by the model to represent normal weather conditions. gpcd, gallons per capita per day; gped, gallons per employee per day; in yr1, irrigation water application depth in inches per year; gal. kWh1, water use for thermoelectric cooling in gallons per kilowatt-hour. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

Table 15

Effects of driver and unit rate changes on future water demand (CT scenario)

Sector

Public supply Self-supplied I&C Self-supplied domestic Irrigation and agriculture Power plants (makeup) Power plants (through flow) Total (w/o through flow)

2005 demand (mgd)

1189.2 162.4 31.8 44.6 52.3 4207.2 1480.3

2005–2050 increase in demand

Effect of change in unit rates

Effect of change in driver count

mgd

%

mgd

%

mgd

%

381.0 129.2 9.4 10.8 0.0 –377.0 530.4

32.0 79.5 29.6 24.2 0.0 –9.0 35.8

–83.5 37.5 2.6 2.2 0.0 –109.8 –41.1

–7.0 23.1 8.2 4.9 0.0 –2.6 –2.8

464.5 91.7 6.8 8.6 0.0 –267.2 571.5

39.0 56.4 21.4 19.3 0.0 –6.4 38.6

CT, current trends; mgd, million gallons per day. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

key explanatory variables such as income, price, or conservation trend. The effects of weather conditions are shown by comparing the observed and weather-normalized usage rates for 2005. Only normal-weather predictions are shown for the future years. Table 15 compares the effects of growth (or change) in demand drivers, which are separated from the effects of

projected changes in unit usage rates. The 2005–50 increments in demand are decomposed into the effect of growth (or change) of demand driver and the effect of projected changes in unit usage rates. For example, for the public-supply sector, the 32.0% increase in demand is a net result of a 39% increase in population served and a 7% decrease in per capita rate of water use.

186 Table 16

Predicting Future Demands for Water Effects of key assumptions on per capita rates in public supply sector

Forecast assumption

2005-Na

Public supply (gpcd) Total 2005–2050 effect Effects of individual variables

142.1

Retail price of water Median household income Conservation trend Employment/population ratio Differential growth

2050-CT

2050-LRI

2050-MRI

134.9 –7.2

104.7 –37.4

157.9 þ 15.8

 8.2 þ 11.5 –11.2 þ 1.2 –0.5

–18.4 þ 6.5 –23.3 þ 1.0 –3.2

0.0 þ 18.1 0.0 þ 1.4  2.7

a

For comparison with future values, the 2005 withdrawals were adjusted by the model to represent normal weather conditions. CT, current trends; LRI, less resource-intensive; MRI, more resource-intensive; N, normal weather; gpcd, gallons per capita per day. From Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planning, Chicago, IL, USA, 15 June 2008.

The change in unit usage rates is a result of changes in the future values of explanatory variables. Table 16 shows the effects of future changes in the values of four variables on per capita rates in public-supply sector. For the low-growth scenario, the per capita rate of usage is projected to decrease by 37.4 gpcd. This value represents a net effect of the increase in marginal price, increase in median household income, effect of conservation, and effect of the changing ratio of employment to resident population. One additional component of the change in gpcd is the effect of differential growth in population across the 37 geographical areas used in the forecast. For the baseline scenario, this effect is  0.5 gpcd; it would be zero if population served in all areas was projected to grow at the same rate. Making the effects of these different factors explicit allows the stakeholders who participate in the planning process to assess the reasonableness of the forecast and to decide which scenarios should be taken into account when formulating water-supply plans.

1.10.5.3 Effects of Future Climate Climate models indicate that by 2050 in Illinois there may be an average annual normal temperature departure of up to þ 6 F, and a possible departure from normal annual precipitation in a range of  5 to þ 5 in yr1 from the 1971–2000 long-term normal values (ISWS, 2007). Due to the nature of climate scenarios, no probabilities can be placed on the possible ranges of future air temperature and precipitation. The changes in normal annual temperature and precipitation would also result in average-weather changes during the growing season. The temperature increase of 6 F will also apply to the summer growing season. The distribution of precipitation changes is expected to range from þ 2.5 to  3.5 in during the growing season. The effects of these changes on future demands will vary by user sector, depending on each sector’s sensitivity of water withdrawals to air temperature and precipitation. The results show that future demands in all sectors are likely to be higher if future annual average air temperature increases and/or annual precipitation decreases. If, by 2050, temperature increases by 6 F, total withdrawals would increase

by 178.0 mgd (9.1%) above the baseline (CT) scenario values. The largest increase in total withdrawals above the baseline scenario would be 229.5 mgd (or 11.7%) by 2050, resulting from the combined effect of the temperature increase and a decrease in summer precipitation.

1.10.5.4 Forecast Summary The scenario forecasts of future water demands in the 11county planning area in Northeastern Illinois revealed the possibility of potentially large increases in total water withdrawals by 2050. The baseline (CT) and high-growth (MRI) scenarios when viewed in the context of regional supply limitations make a compelling case for the need to manage regional water demands. Total withdrawals (excluding oncethrough flows in power plants) in 2050 under baseline scenario would increase by 530.4 mgd and the increase could reach as high as 949.1 mgd under high-growth scenario. Meeting these additional demands would require large capital outlays for water infrastructure and would likely have significant impacts on some of the regional sources of water supply, especially groundwater aquifers and local rivers, which would create increased demands on water from Lake Michigan.

1.10.6 Conclusion The prospect of climate change, which could alter meteorological and hydrological regimes and possibly result in lower water availability and higher demands, is challenging water managers to search for effective ways to satisfy future demands without jeopardizing the long-term sustainability of current water resources systems. Credible long-term forecasts of water demand can help water planners to achieve an efficient allocation of future water supplies among competing uses. When predicting future demand for water, it is important to recognize that water is an economic good and, therefore, its future use will be responsive to changes in future economic conditions. The economic considerations should help water planners to achieve efficient and equitable use of water supplies while encouraging conservation and protection of water resources.

Predicting Future Demands for Water

This chapter described data and methods for developing a clear and credible forecast of future water demand which would enhance its acceptability by decision makers. The main characteristics of such a forecast include a high level of disaggregation of demand, use of econometric water-use models which conform to the economic theories of production and consumption, and provision of explicit and plausible forecasting assumptions. A credible forecast cannot be developed without first examining the historical data on water use. Obtaining valid data on water use for a sufficiently long period of time (usually 15–20 years) is usually the most time-consuming task of a forecasting project. The data must allow for disaggregation of total water use into different categories of demand and also have appropriate geographical and temporal resolution to permit the development of water-use relationships which would capture the effects of variables which affect water demand and operate at different scales of space and time. Additional large effort is required in obtaining data on explanatory variables. Once appropriate data are assembled, the next challenge is the development of water-use relationships, usually by applying statistical methods such as multiple regression. The criteria for deriving a empirical model which is useful for forecasting are somewhat different than those in a typical econometric studies where researcher wishes to know which variables are statistically significant and if the resultant model conforms to economic theory. A useful forecasting model requires not only an appropriate model specification but also accurate estimates of the regression coefficients (or elasticities) for the explanatory variables. The forecaster must develop fairly strong expectations about the size of the regression coefficients for key explanatory variables such as price, income, air temperature, and precipitation. Expectations about the sign (positive or negative) of the explanatory variables come directly from economic theory and the underlying physical relationships. The next step in developing a credible forecast involves the development of forecasting assumptions which are the basis and the main component of the forecast. The most critical are the assumptions about the future values of explanatory variables. Where possible, forecasts of water demand should be based on official or consensus forecast of population and economic growth in the study area. The future values of other explanatory variables (e.g., price of water, income, or climate) should be derived based on explicit and plausible assumptions. The uncertainty caused by forecasting assumptions can be addressed by using forecast scenario, sensitivity analysis, or developing probability forecasts. Finally, once the forecasted quantities of future water demand are prepared, the forecaster should provide a postforecast analysis of results. In essence, such an analysis should identify and quantify the effects and relative contributions of individual forecasting assumptions on the final results. For example, the change in water demand during the forecast period can be separated into the effects of population growth, changes in the price of water, income, climate, and other explanatory variables. The post-forecast analysis can help ensure that the forecast is understood and accepted by decision makers.

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References Avery C (1999) Estimated water withdrawals and use in Illinois, 1992. Open-File Report 99–97. Urbana, IL: US Geological Survey. Backus CE and Brown ML (1975) Water requirements for solar energy. In: Gloyna EF, Woodson HH, and Howard Drew R (eds.) Water Management for the Electric Power Industry, Water Resources Symposium #8, pp. 270–279. Austin, TX: Center for Research in Water Resources, The University of Texas at Austin. Baumann D, Boland JJ, and Hanemann WM (1998) Urban Water Demand Management and Planning. New York: McGraw-Hill. Baumann DD, Boland JJ, and Sims JH (1984) Water conservation: The struggle over definition. Water Resources Research 20(4): 428--434. Boland J (1998) Forecasting urban water use: Theory and principles. In: Baumann DD, Boland JJ, and Hanemann M (eds.) Urban Water Demand Management and Planning, ch. 3, pp. 77–94. New York: McGraw-Hill. Boland JJ (1985) Forecasting water use: A tutorial. In: Torno HC (ed.) Computer Applications in Water Resources, pp. 907--916. New York, NY: The Society. Boland JJ, Dziegielewski B, Baumann DD, and Opitz EM (1984) Influence of price and rate structures on municipal and industrial water use. IWR Report 84-C-2. Fort Belvoir, VA: US Army Engineer Institute for Water Resources. Boland JJ, Dziegielewski B, Baumann DD, and Turner C (1982) Analytical bibliography for water supply and conservation techniques. IWR Report 82-C-07. Fort Belvoir, VA: US Army Engineer Institute for Water Resources. Blake NM (1956) Water for the Cities: A History of the Urban Water Supply Problem in the United States. Syracuse, NY: Syracuse University Press. Breusch TS and Pagan AR (1979) A simple test for Heteroskedasticity and random coefficient variation. Econometrica 47: 1287--1294. Croley TE II, Patel VC, and Cheng MS (1975) The Water and Total Optimization of Wet and Dry–Wet Cooling Towers for Electric Power Plants. Iowa City, IA: Iowa Institute of Hydraulic Research, University of Iowa. Deaton A and Muellbauer J (1999) Economics and Consumer Behavior. Cambridge: Cambridge University Press. De Rooy J (1974) Price responsiveness of the industrial demand for water. Water Resources Research 10(3): 403--406. Dupond D and Renzetti S (1998) Water use in the Canadian food processing industry. Canadian Journal of Agricultural Economics 46: 1--10. Dziegielewski B (1993) IWR-MAIN 6.0: A tool for demand management and planning. Journal of American Water Works Association (Aqualink Department) 85(8): 24. Dziegielewski B and Baumann DD (1992) Benefits of managing urban water demands. Environment 34(9): 6--11. 35–41. Dziegielewski B, Baumann DD, and Boland JJ (1983) Prototypal application of a drought management optimization procedure to an urban water supply system. IWR Report 83-C-4. Fort Belvoir, VA: US Army Engineer Institute for Water Resources. http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html &identifier=ADA138473 (accessed April 2010). Dziegielewski B and Bik T (2006) Water use benchmarks for thermoelectric power generation. Prepared for the United States Geological Survey. Southern Illinois University at Carbondale, 15 August 2006. Dziegielewski B and Boland JJ (1989) Forecasting urban water use: The IWR-MAIN model. Water Resources Bulletin 25(1): 101--109. Dziegielewski B and Chowdhury FJ (2008) Regional water demand scenarios for Northeastern Illinois: 2005–2050. Project Completion Report. Prepared for the Chicago Metropolitan Agency for Planing, Chicago, IL, USA, 15 June 2008. Dziegielewski B and Kiefer JC (2006) U.S. water demand, supply and allocation: Trends and outlook. IWR Report 2007-R-3. A white paper prepared for the U.S. Army Corps of Engineers Institute for Water Resources, Alexandria, VA, USA, 22 December 2006. Dziegielewski B, Opitz EM, Kiefer JC, and Baumann DD (1993) Evaluation of Urban Water Conservation Programs: A Procedures Manual, xxi þ 274pp., Book No. 0-89867-676-2. Prepared for California Urban Water Agencies, Sacramento, CA. Denver, CO: American Water Works Association. Dziegielewski B, Opitz EM et al. (1996) IWR-MAIN Water Demand Analysis Software. Users Manual and System Description. Carbondale, IL: Planning and Management Consultants. Dziegielewski B, Rodrigo D, and Opitz E (1990) Commercial and Industrial Water Use in Southern California. Carbondale, IL: Planning and Management Consultants. Dziegielewski B, Sharma SC, and Margono H (2005) Models for long-term forecasts of public supply water use. In: Proceedings of the XIIth IWRA World Congress on Water Resources. New Delhi, India, 22–25 November 2005. New Delhi, India: Central Board of Irrigation and Power.

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Dziegielewski, B, Strus CA, and Hinckley RC (1993) End-use approach to estimating water conservation savings. In: CONSERV93: The New Water Agenda. American Water Works Association, Denver, Colorado. Grigg NS (1986) Urban Water Infrastructure: Planning, Management, and Operations. New York: Wiley. (original from the University of Michigan, MI). Epsey M, Epsey J, and Shaw WD (1997) Price elasticity of residential demand for water. Water Resources Research 33(6): 1369--1374. Ferguson B and Whitney A (1993) Demand reduction in response to drought: The city of Santa Barbara experience. In: CONSERV93 Proceedings, ASCE, AWWA, AWRA, pp. 429–437. Foster HS Jr and Beattie BR (1979) Urban residential demand for water in the United States. Land Economics 55(1): 43--58. Gardiner V and Herrington P (1986) Water Demand Forecasting: Proceedings of a Workshop. Exeter: Short Run Press. Glejser H (1969) A new test for heteroscedasticity. Journal of the American Statistical Association 64: 316--323. Hanemann WM (1998) Determinants of urban water use. In: Baumann DD, Boland JJ, and Hanemann M (eds.) Urban Water Demand Management and Planning, ch. 2, pp. 31–75. New York: McGraw-Hill. Hittman Associates, Inc. (1969) Forecasting Municipal Water Requirements, Volume 1: The MAIN II System, PB 190275. Columbia, MD: Hittman Associates, Inc. Howe CW (1982) The impact of price on residential water demand: Some new insights. Water Resources Research 18(4): 713--716. Howe CW and Linaweaver FP Jr (1967) The impact of price on residential water demand and its relation to system design and price structure. Water Resources Research 3(1): 13--32. Hutson SS, Barber NL, Kenny JF, Linsey KS, Lumia DS, and Maupin MA (2004) Estimated use of water in the United States in 2000. US Geological Survey. USGS Circular 1268. http://water.usgs.gov/pubs/circ/2004/circ1268/ (accessed April 2010). ICWE (International Conference on Water and the Environment) (1992) The Dublin statement on water and sustainable development. http://www.un-documents.net/ h2o-dub.htm (accessed April 2010). IPCC (2008) Definition of terms used with the DDC pages. http://www.ipcc-data.org/ ddc_definitions.html (accessed July 2010). ISWS (Illinois State Water Survey) (2007) Tomorrow’s climate – future scenarios: CO2 concentrations. http://www.sws.uiuc.edu/wsp/climate/ ClimateTom_scenarios_co2.asp (accessed April 2010).

IWR (Institute for Water Resources) (2001) An evaluation of the risk of water shortages in the Lower Peninsula, Virginia. Prepared by Werick WJ, Boland JJ, Gilbert J, Dziegielewski B, Kiefer J, Massmann J, and Palmer RN. IWR Special Report. Alexandria, VA: US Army Corps of Engineers. Linaweaver FP, Jr., Beebe JC, and Skrivan FA (1966) Data Report of the Residential Water Use Research Project, Johns Hopkins University, Department of Environmental Engineering Science, Baltimore, MD. Metcalf L (1926) Effects of water rates and growth in population upon per capita consumption. Journal of the American Water Works Association 15(1): 1--20. Nieswiadomy ML (1992) Estimating urban residential water demand: Effects of price structure, conservation, and education. Water Resources Research 28(3): 609--615. Platt RH (1993) Water demand management. Commentary. Environment 35(3): 2--3. Ramsey JB (1969) Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society Series B 31: 350--371. Renzetti S (1988) An econometric study of industrial water demands in British Columbia, Canada. Water Resources Research 24(10): 1569--1575. Renzetti S (1992) Estimating the structure of industrial water demands: The case of Canadian manufacturing. Land Economics 69(2): 181--188. Renzetti S (1993) Examining the difference in self- and publicly supplied firms’ water demands. Land Economics 69(2): 181--188. Renzetti S (2002) The Economics of Water Demands. Norwell, MA: Kluwer. Solley WB, Pierce RR, and Perlman HA (1998) Estimated Use of Water in the United States in 1995, U.S. Geological Survey Circular 1200. Denver, CO: US Geological Survey. UNCED (United Nations Conference on Environment and Development) (1992) Agenda 21, Protection of the Quality and Supply of Freshwater Resources: Application of Integrated Approaches to the Development, Management and Use of Water Resources, ch. 18. Brazil: Rio de Janeiro. USDA (United States Department of Agriculture) (2009) 2007 Census of Agriculture, Summary and State Data Volume 1, Geographic Area Series Part 51. Washington, DC: National Agricultural Statistics Service. White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48: 817--838. WHPA and Dziegielewski B (2008) Water Demand Scenarios for the East-Central Illinois Planning Region. Bloomington, IN: Wittman Hydro Planning Associates. Wolff JB, Linaweaver FP, Jr., and Geyer JC (1966) Water Use in Selected Commercial and Institutional Establishments in the Baltimore Metropolitan Area. Johns Hopkins University.

1.11 Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change C Brown, University of Massachusetts, Amherst, MA, USA KM Baroang, International Research Institute for Climate and Society, Palisades, NY, USA & 2011 Elsevier B.V. All rights reserved.

1.11.1 1.11.2 1.11.2.1 1.11.2.2 1.11.2.3 1.11.3 1.11.4 1.11.4.1 1.11.4.2 1.11.4.2.1 1.11.4.2.2 1.11.4.2.3 1.11.5 References

Introduction Background on Risk Assessment and Management Hazard Characterization Risk Assessment Risk Management Risk Management versus Consequence Management: The Upside of Risk Climate Risk Risk and Nonstationarity: Uncertain Information and Unreliable Probability Estimates Climate Consequence Management with Decision Scaling: An Approach Designed for Uncertain Information Step 1: Vulnerability and uncertainty identification Step 2: Consequence assessment Step 3: Consequence and uncertainty management Conclusion

1.11.1 Introduction The subject of risk is a familiar one for the water resources engineer and the water planner. When a harmful event is possible but not certain, there is risk. In the world of water resources harmful events are largely, but not solely, associated with precipitation: too much, too little, and too variable. Other aspects of the hydrologic cycle modulate this basic signal as do various human activities, but we largely depend on precipitation and it is uncertain. Risk is inherent to the profession. Yet there does not seem to be a predominant method for addressing risk in the profession. This fact gains prominence due to climate change, nonstationarity of the hydrologic record, and the potential for changing and growing risks in the future. In the field of water resources, research has largely proceeded from a scenario-based rather than a riskbased framework of analysis. The primary direction of analysis has been simulation of a few possible future scenarios and assessment of impacts corresponding to these scenarios. Unfortunately, the uncertainty of these scenarios in comparison to other possible futures makes this approach unhelpful for risk assessment. It may be that the approach to risk is so institutionalized that all remains is discourse on the probabilities of extreme events and how to produce better estimates of them. The cost of this institutionalization is an accompanying ossification of the way in which risk is addressed. Climate change implies that our tried and true methods to reduce uncertainties to produce estimates of risk may no longer serve us well. For this reason, it is an opportune moment to review the treatment of risk in water resources, from assessment to management and finally to communication. Interestingly, the rise in importance of risk management as a framework to plan for a changing climate is accompanied by a questioning as to

189 190 190 191 191 193 193 194 195 195 196 197 198 199

whether the prevailing approaches to risk management are still valid. As we shall see, standard approaches to risk assessment and risk management as employed in the water sector utilize estimates of probability density functions (PDFs) associated with hydrologic variables. There is reasonable concern as to whether we have the ability to assign such probabilities with any confidence given to the small sample size associated with extreme events, which are the events of interest. There is further concern as to whether use of the historic record to estimate hydrologic design variables is appropriate for designs prepared for the future climate. In order to explore these issues, we require an understanding of what risk assessment and management mean and then explore why climate change may necessitate a refinement of this approach. In this chapter, we will review basic risk concepts, some history of its development in water resources engineering and planning, visit some emerging approaches to risk associated with deep uncertainties such as those associated with climate change, and finally propose a process for addressing climate risks to water resources systems. In doing so, we find that a process for climate risk is a special application for the general risk assessment and management framework as presented here. Because the terminology associated with risk is used to mean different things in different communities, it is useful to begin with some basic definitions. The definitions listed here are generally consistent with usage in water resources and hydrology and to some extent with the natural hazards community (Plate, 2002; UNDRO Office of the United Nations Disaster Relief Coordinator, 1991): Hazard. The probability of an event that causes failure or a negative effect on a community or system. Risk. The product of the probability of a hazardous event occurring and the impact or consequence of that event; risk

189

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can increase if either the probability increases or the consequences of a hazard become more severe. Vulnerability. The characteristics of a community or system that cause them to be susceptible to adverse outcomes when exposed to a particular event. Resilience. The capacity of a community or system to recover from an adverse outcome due to a hazard and return to an acceptable level of functioning. Careful consideration of the definition of risk raises a topic of interest that is often not addressed in water resources. This is the idea that there are opportunities presented by uncertain events that have positive consequences. Our typical focus is on the tail of the event probability distribution that is associated with negative consequences, such as droughts or floods. However, there can also be beneficial opportunities associated with the tails of probability distributions. Consider the case of a farmer engaged in rain-fed agriculture planning the crop pattern for the season ahead. If that farmer plans a pattern optimal for the average seasonal rainfall total but not ideal for conditions that are below normal, then there is a risk associated with below-normal rainfall. If the rainfall is below normal then the crop yield will be reduced. The farmer could reduce his risk exposure to drought by choosing a crop pattern that is well suited for below-normal rainfall. However, the farmer would now forgo the opportunities associated with above-average rainfall, say a bumper crop of a water-intensive, high yielding cash crop. The relationship between risk and opportunity will be discussed later in this chapter.

yielding events, when they occur. Note that the consequences of an event are contingent on the risk management steps taken or not taken, and so C(x) can be specified as C(x|D), where D represents a decision. For example, the consequences associated with a flood of stage x is a function of the decision, D, to build a levee of some level or none at all. In the typical approach, the estimation of risk then requires the estimation of two functions, the probability of occurrence of the events of interest, f(x), and the estimation of the consequences of that event for a given decision, C(x|D). The identification of hazards and the estimation of f(x) associated with those hazards is the first step of addressing risks, which is called hazard characterization. This process is discussed in more detail below. While f(x) is typically thought of as acts of nature, in the case of hydrology human action can influence the characteristics of stream flow. For example, significant development of natural land surfaces to conditions of partially or completely impervious surface would likely change the frequency of high flow events. Decisions to preserve land surfaces in natural states or to develop them with low-impactdevelopment designs could similarly change the frequency of high flow events. Thus, one could consider a PDF of stream flow that is conditional on decisions, such as land use, that are made that affect stream flow characteristics, f(x|d). The next step is risk assessment which consists of the estimation of the expected value of the consequences, C(x|D). This is accomplished by integrating over the PDF of x:

 ¼ RðxÞ

Z

N

CðxjDÞf ðxÞdx

ð1Þ

0

1.11.2 Background on Risk Assessment and Management There are a variety of processes with different steps that have been proposed for risk assessment and management. Some place risk assessment as a single step within a larger risk management framework. Here, we describe the process of three general steps which cover the various processes that are consistent with the themes in most approaches. We also highlight particular topics not typically covered in depth, namely opportunities, residual risk, and surprise. The three general steps can be described as a hazard characterization step, a risk assessment step, and a risk management step. The mathematical description presented here draws from previous formalizations of risk, for example, Plate (2004) and Lund (2002). Risk consists of impacts or consequences that result from an event that may occur with some probability. It can be summarized in the following equation:

RðxÞ ¼

Z

where R is the expected risk for a given decision, D. The process of risk assessment produces a risk associated with a hazard or range of hazards. In the final step of risk management, one can then attempt to make a decision or decisions that reduce the unwanted risks. One can then evaluate the expected risks for the set of possible decisions, Di, i ¼ 1,y, N, for N possible decisions and select the risk-minimizing decision:

min Z ¼

Z

D

N

CðxjDÞf ðxÞdx

ð2Þ

0

Considering decisions related to changes to stream flow characteristics and thus the PDF of the stream flow, the equation can be more generally specified as

min Z ¼ D;d

Z

N

CðxjDÞf ðxjdÞdx

ð3Þ

0

N

CðxÞf ðxÞdx

0

where f(x) is the PDF of the event and C(x) is the consequences associated with that event on the system of interest. For the hydrologic risks, f(x) is the PDF of a hydrologic event such as a flood of the stage (height), x. The consequence function yields the costs or damages associated with a flood of stage x. We use the term consequence function in place of the more common damage function to highlight the potential for benefits that may arise from opportunities, uncertain benefit

This is one possible mathematical specification of the quantification of risks. One could consider this as an optimization, either formally with an optimization model that can be used to evaluate risk management alternatives or informally through a decision process to achieve a similar objective. The actual processes involved in hazard characterization, risk assessment, and risk management are more difficult. While there is much focus within the research community on the description of f(x) in terms of the selection of the distribution type and methods employed for the fitting of

Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change

parameters, the importance of correctly characterizing CðxjDÞ and of developing a wide range of options for managing risks, Di, are perhaps of greater importance given to our growing awareness of the limitation in correctly specifying f(x).

1.11.2.1 Hazard Characterization The first step is one of identifying and characterizing hazards. This is an exploratory phase in which the performance of the system of interest is reviewed and its past and possible vulnerabilities are identified. Often past experiences with extreme hydrologic events offer the strongest evidence of the hazards that should be addressed. Each water-resource system is unique and has exposure to events of particular characteristics. For example, in a general way Vogel et al. (1997) classified reservoir systems into over-year or under-year storage categories based on their vulnerability to drought durations of months but less than years. Such systems would be interested in very different definitions of drought. It is also useful to consider the past experiences of other water-resource systems, especially those with similar size, similar climate, or similar operating constraints such as those posed by multiuse water systems and environmental requirements. Once the events of concern have been identified, the next part of this step is the characterization of the hazard through the estimation of probabilities associated with it. For waterresource systems, the events of interest are typically related to stream flow and so a hydrologic record that relates to the water system is genrally used to estimate these probabilities. Thus, a hazard is an event of interest (typically due to its potential to cause danger) with an associated probability of occurrence. Here, we focus on hydrologic hazards but the methodology is consistent with other hazards such as earthquakes or terrorist attacks. Note that the methodology also applies to events with positive consequences. These events opportunities are termed an event of interest (with potential for benefit) and its associated probability of occurrence. In the case of floods, the process is one of estimating the exceedance probability of certain high-flow events that were deemed to be dangerous. Similarly, the exceedance probability of droughts of specific duration and intensity (e.g., 20% reduction of inflows over a 6-month period) can be estimated. Traditionally, these probabilities were estimated using the historical record. For example, for floods, the annual maximum time series for the location of interest was used to estimate the parameters of an extreme value probability distribution. This distribution was then used to calculate the estimated exceedance probability for a flood of a given magnitude. The techniques for calculating parameters, choosing distributions, and using the distributions have been the focus of much attention and are fairly mature. A useful summary is found in Stedinger et al. (1993). Methods are also available for using data from other locations in a region when the location of interest has a limited historical record. For droughts, the process is similar, with the added dimension of the duration as well as the intensity of the event. Engineers have long been aware of the uncertainties that accompany the estimation of rare events such as flood events with low exceedance probabilities. These are addressed through risk management and communication. Nonetheless,

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a great deal of effort is expended in reducing the uncertainty of hazard estimates. This has yielded better understanding of the physical causes of hydrologic extremes and in some cases identified temporal structure in their occurrence. Our growing knowledge of the physical causes and temporal structure of the frequency domain of hydrologic extremes has important implications for the estimation of hazard probabilities. This leads to significant implications for our management of risks as well and perhaps the need for significant reconsideration of how climate related risk in water-resource systems is addressed. The most prominent example is the case of the El Nino/ Southern Oscillation (ENSO). ENSO is a coupling of seasurface temperature anomalies, winds, and atmospheric pressure in the equatorial Pacific that influences temperature and precipitation patterns around the world through teleconnections, with the largest effect in the tropics. The ENSO phenomenon has two phases, one warm (El Nino) and one cold (La Nina), each with partly but not entirely opposite directions of influence (warmer vs. colder; dryer vs. wetter). ENSO is described as quasi-periodic and these events occur on an irregular cycle of about 3–5 years. Where the teleconnections are strong, an ENSO event implies that the hazard associated with a particular hydrologic event may be elevated in some locations and reduced in others, relative to the longterm mean rate of occurrence. For example, the drought hazard in the Philippines is elevated during an El Nino, while it is reduced during a La Nina. The presence of such temporal structure has implications for risk assessment and management, since foreknowledge of year-to-year changes in risk was not previously considered.

1.11.2.2 Risk Assessment In this step the concept of risk is quantified in terms of the consequences of an event and its probability of occurrence, in accordance with Equation (1). Here the description of consequences and the specification of the consequence function are central. A challenge in calculating the consequences is that they are not necessarily measured in commensurate units. Many consequences are fairly straightforward to quantify in economic terms, and methods for doing so are mature. In the field of water resources, however, in many cases damages may be more difficult to describe in monetary terms. For example, flood events often involve the potential loss of life. Floods and droughts may also have substantial impact on the environment through the destruction of habitat or insufficient stream flows for aquatic ecosystems. This issue is more prominent in the risk management step, where the alternatives for managing hydrologic risks are evaluated in terms of their costs and the benefits provided and it is addressed further below. As before, the involvement of stakeholders is a critical aspect of risk assessment. Stakeholders are knowledgeable about the consequences of historical events and may be the best source of conjecture on the impact of events not yet seen. Their opinions will also be valuable in the ranking of risks that cannot be easily quantified, such as specific environmental

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damages or the relative value of structural damages in comparison with lost life.

1.11.2.3 Risk Management The final step in this process entails making decisions and systematically exploring the consequences of those decisions as well as the uncertainties that will accompany any risk assessment process. There are a variety of approaches that have been proposed for risk management. Here, we present risk management as a systematic process that can be described in three parts: (1) systematic evaluation of the risks previously quantified and the cost of measures that address those risks; (2) development of a plan to address those risks and accompanying uncertainty; and (3) consideration and planning for residual risks and performance of the system when failure occurs. The systematic evaluation of risks begins with a review of the risks quantified during hazard characterization and risk assessment. These risks can then be evaluated in terms of the expected losses that they represent. As described above, the expected losses may be expressed in economic terms, such as monetary units of damage to structures, or in units of human injuries and deaths, or in terms of damage to the environment. Since these expected losses are compared with the economic costs of the measures considered for mitigating the risks, expression in terms of economic units simplifies the development of a risk management plan. Methods are available for calculating the economic costs of injuries, deaths, and environmental damage, although none are without controversy. Environmental impacts are similarly difficult to quantify in economic terms, although methods have been proposed. At the least these potential impacts must be tracked and incorporated informally into the decision process. Next, the candidate measures for reducing risk are evaluated in terms of their cost and their effectiveness. Often, engineers limit themselves to consideration of structural approaches to managing hydrologic risks. For example, dikes and levees are typically prime candidates for reducing flood risk, whereas increasing the storage capacity of a water-supply system is the main method for addressing drought concerns. However, there exist a suite of nonstructural measures that should be considered as alternatives or complements for structural measures. For droughts, examples include economic mechanisms such as water markets, option contracts, and even insurance (Characklis et al., 2006; Brown and Carriquiry, 2007). For flood risk reduction, examples include designated flooding areas, improved flood warning systems, and land-use planning. An appealing aspect of some nonstructural measures is that they may be used as options, meaning that they are not executed or fully paid for until they are needed. For example, a levee can be heightened through the addition of sand bags which are not filled and put into place until they are needed (Lund, 2002). The option approach becomes more advantageous as the uncertainty related to the risk analysis increases, such as due to possible changing climate conditions. Also, the advantage of the option approach increases with improved forecasts that can result in better decisions related to

execution of the options. Unfortunately, our experience with these measures is limited as is the number from which to choose in comparison to traditional structural approaches. It is an area of needed research. When risks and the costs of risk reduction measures are well characterized, optimization and decision analysis can be an effective means for developing and evaluating the risk management plan. The objective function for a plan that considers permanent measures and option measures can be specified as follows:

min Z ¼

X þ

Z

permanent measures  cost ðoption measures  costs þ damagesÞf ðxÞdx ð4Þ

An example of the use of optimization using a form of this equation to develop a flood risk reduction plan is described in Lund (2002). Optimization techniques such as used in that analysis allow a quantitative approach to the development of a risk management plan. However, the result of that approach, say the optimal plan, is only as useful as the quality of the data that was used to develop the plan. In the case of estimating probabilities of rare events or equivalently the magnitude of events with very low exceedance probabilities, the quality of these estimations may not be very reliable. Furthermore, according to the engineer, the process of going from the optimal plan to a plan that is actually implemented by a public agency with diverse and conflicted stakeholders is quite difficult and not well understood. The latter subject may be the most important to solve in order to achieve efficient reduction of hydrologic risks to society. The subject is explored in Fiering and Matalas (1993). The uncertainty related to the estimation of magnitudes of rare events can be addressed in several ways during the development of the risk management plan. The first approach is simply the application of decision analysis and should be a standard part of the risk management plan development. Using a decision analysis approach, it might be determined that the decision is not sensitive to specification of these very rare events. For example, it may become clear that a particular flood risk reduction candidate measure dominates other measures regardless of the magnitude of floods with exceedance probabilities of 103 or 104. Or the decision to provide protection for a structure may be decided based upon what a society is willing to pay rather than the estimated return period of an event that is protected against (Bondi (1985) cited in Plate (2004)). In other cases, the choice of a given plan and its subsequent performance is sensitive to these estimations. In such cases where uncertainties are significant and influential, a risk management plan should strive to achieve aspects of robustness and flexibility (de Neufville, 2004). While it can be defined in a variety of ways, here we define as robust the quality of a system that can continue to operate effectively under unexpected conditions. Redundancy is a key component of achieving robustness. For example, two pumps should allow water to be moved even if one unexpectedly fails. A groundwater well field allows a water-supply system to

Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change

continue to operate if the reservoir runs dry. The quality of flexibility is a quality of a water system that provides the engineer or manager with options to react to changing conditions as they evolve. For example, a regulation plan for a reservoir could allow different release rules based on the prevailing climate conditions in place of the standard static regulation plan that is unchanging. Brown et al. described the development of a dynamic regulation plan for the management of outflows from Lake Superior that demonstrates how instilling flexibility in normally static plans is an adaptation strategy for climate change. Flexibility also allows the water manager to make use of forecast information. With only static operating procedures, foresight regarding future water supplies or demand is of little value since there are no changes that can be made in response to the new information. Once a risk reduction design has been formulated, the final step consists of addressing the full spectrum of possible risks that a system faces. There are two aspects to this. The first aspect is the consideration of residual risk. Residual risk is the risk that remains once a particular decision has been made. The concept applies to operational or planning timescales. For example, a planned flood risk reduction design may include the construction of a levee that is designed to withstand a flood magnitude with an estimated 500 year return interval. The residual risk consists of the risk that is not addressed by the design, in this case floods that are greater than the 500year flood. Such floods are certainly possible and must be planned for even if they are not part of the structural design considerations. On an operational timescale, it is the risk that remains once a specific operational design has been made under uncertainty. The residual risk for decision i may be specified as

RRi ¼

Z

N

CðxjDi Þf ðxÞdx

ð5Þ

0

The residual risk may be addressed in a variety of ways. It may be considered insignificant and no further action is taken. A decision analysis approach could be used. Using this method, the costs of addressing the residual risk could be estimated and compared with the risk. In most cases the residual risk estimate will be highly uncertain since by nature it involves the tails of distributions. The decision will be largely a subjective one and may or may not be influenced by the precautionary principle. Since in most cases it will not be economically acceptable to address all residual risks, there will be risks that are known but are not addressed. There will also be risks to the system that have been underestimated. The uncertainty associated with climate change, land-use change, and technological change compromises our ability to estimate future probabilities with confidence. As a result, we should not be surprised by underestimation and overestimation of risk. We should be surprised by risks that are unknown and cannot be reasonably anticipated. These are surprises or even black swans as popularized by Taleb (2007). From the water engineer standpoint, these three considerations make clear that any system design that incorporated risk reduction planning may still fail. This brings us to the second aspect of addressing the full spectrum of risk. Although risk reduction plans may

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not cover all risks, we can plan for how our system operates during an inevitable failure. This may seem to fall outside the traditional realm of engineering. But since the engineer is ultimately trying to reduce impacts of hydrologic events on society (or rather reduce negative impacts and increase positive impacts), this represents more options for doing so successfully. For a water-supply system, a plan can be developed for rationing water, purchasing tanker water deliveries, and other measures if a drought were to shut down the reservoir supply. Similarly, emergency response, evacuation planning, designated flood areas, and secondary protections beyond primary levees should be incorporated into a flood risk management plan. The key is for managing surprises such that although they lead to failure of the system, they do not lead to catastrophe.

1.11.3 Risk Management versus Consequence Management: The Upside of Risk Risk management focuses on uncertainties related to events that have the potential to cause damages or harm. These are typically events that differ from the average conditions. Therefore, droughts are brought on abnormally by low water supply availability and floods by water that exceeds normal conditions. In some cases or for some stakeholders, these relatively rare events represent opportunities instead of risks. For example, excess water represents the opportunity to generate additional energy for a hydroelectricity generator or to provide high flow for ecosystems that require it. Droughts may provide opportunities for offline inspection and maintenance work. The concept of planning for the opportunities as well as for the risks associated with uncertainty has been described as uncertainty management (de Neufville, 2004). Here, the term consequence management is preferred as this is a neutral term of risk (positive or negative) and is distinct from uncertainty which derives from a variety of sources and does not necessarily have consequences. With consequence management, we include the positive consequences of possible hydrologic conditions in addition to the negative consequences (risks). Management plans are developed and evaluated for all consequences in the same way as done for risks alone. Interestingly, the management of risk (negative consequences) and the ability to exploit opportunities (positive consequences) are connected in many cases. The reason is that if risks are not being effectively managed, it is more difficult to capitalize on the opportunities associated with favorable side of the distribution. As a result, there can be substantial lost opportunities that result from ineffective risk management. Examples from the use of seasonal climate forecasts demonstrate this issue. With the forecast for above-average reservoir inflows, a multi-use reservoir can consider making additional releases for hydroelectricity production while still expecting to meet its storage requirement for water-supply purposes. However, the typical water manager would prefer not to make the additional releases because of the concern that belowaverage inflows may occur even if the probability is very low. As a result, the excess water and the opportunity it represents are lost as spills from a full reservoir. This decision is perfectly rational if the consequences associated with a shortage of

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water in the reservoir are disastrous even if the odds are low. Without an effective plan for managing low water conditions the risk associated with additional reservoir releases outweigh the benefit of extra hydroelectricity production. If there is an effective way to manage the downside risk, then the upside consequences could be better captured. So a system that has a backup water supply or the means to buy-out major water users (such as agriculture) is in a good position to take advantage of such an opportunity. These issues are explored in a case study based on the multipurpose reservoir system that supplies water to the city of Manila, the Philippines (Brown et al., 2008; Brown and Carriquiry, 2007).

1.11.4 Climate Risk The subject of climate change and nonstationarity has stirred interest in risk-based approaches within water resources planning and management. Specifically, there is an emerging question as to whether our traditional approaches to assessing and managing risk are appropriate with the recognition of nonstationarity. While there is naturally strong interest in improving our ability to estimate the return periods or exceedance probabilities of hazardous events, there is also a cause to reconsider more generally how we assess and manage risk. Initially, the sense of the water community was that there was too much uncertainty to take much action in response to climate change. The resulting focus emphasized the use of output from general circulation models (GCSMs) to reduce that uncertainty. This style of analysis typically utilized a scenario approach whereby a few simulations of possible future conditions were generated from a particular GCM run representing a possible future or scenario. The approach generally consisted of the following steps (see, e.g., Brekke et al., 2009): (1) choose a GCM or a few GCMs from which to derive the climate change model projection; (2) downscale the coarse-scale GCM output to the scale of a higher-resolution hydrologic model; (3) use the hydrologic model to produce estimates of stream flow for the climate change scenario; and (4) use the stream flow estimates to estimate a water resources impact of interest, such as reliability of a reservoir system, change in production of hydroelectricity, and implications for low stream flows. While seemingly straightforward, there are a number of drawbacks to this approach when used for planning and decision making. There are a variety of difficulties associated with the use of the GCM information itself. Typically mean conditions from the GCM are utilized. As a result, climate change is depicted as a shift in mean conditions. However, the water resources sector is usually more concerned in variability and extremes. For the water-supply sector, changes in the serial correlation of rainfall or stream flow are of concern, since several months or years of continuous below-normal conditions are the greatest risks. For risk reduction, extreme hydrologic events are more important than the mean conditions. The scenario approach does not attempt to address changes in variability or in the distribution of extremes. A more fundamental critique of this approach is that it is not risk based. The approach produces possible consequences that result from a particular scenario of climate change. There

are no probabilities associated with the particular scenarios or the consequences. As a result, there was no way to quantify the risk that climate change posed. The focus of research efforts turned to attempts to improve the ability to simulate future climate conditions. Emphasis was placed on increasing the resolution of the climate models and incorporating more and better understanding of the processes that comprise the Earth’s climate system. While this emphasis continues, the difficulty in reducing the uncertainty significantly due to the complexity of the Earth’s climate system and predicting the evolution of civilization over the next 100 years has led to dissatisfaction with this emphasis for use in water resources planning. On the topic of climate change, until recently there has been more focus on risk-based approaches within the adaptation segment of the climate change community generally than within water. This is reflected in the Intergovernmental Panel on Climate Change (IPCC) reports and broadly in the literature. While there are a wide variety of approaches, one might place them in one of two general categories. The first is the top-down approach. In this approach, the analysis begins with some attempt to predict future conditions and those future conditions are used to estimate impacts on society, ecosystems, hydrologic systems, etc. This approach does not typically broach the subject of probabilities. In some sense they may not be truly risk based for that reason. The notion of top down refers to the direction of analysis beginning from climate change impacts through various physical processes and finally considering the potentially impacted systems. The second category is characterized by the opposite direction of analysis and, for that reason, is referred to as a bottom-up or vulnerability-based approach. The analysis begins with the vulnerability, that is, consideration of how a system, such as a community, is susceptible to harmful effects from changes in climate. Thresholds can be set where systems are particularly vulnerable or where significant impacts begin to set in that are meant to be avoided. Since we define risk as a product of consequences and probability of those consequences, the estimation of probabilities of consequences is ostensibly a primary concern of risk management. We are severely limited in our ability to estimate the probability of future events. For many extreme events, this is true whether one considers climate change or not. We have very few observations of very rare events that the magnitude of particular percentile events is a function of the data which consists primarily of much more common events (of lower magnitude) and the choice of the extreme value distribution used to model them. A review of the methodologies explored in the estimation of probabilities of extreme events such as floods is beyond the scope of this chapter. The reader is referred to Stedinger et al. (1993). Here, it is noted that the prospects of getting the answer correct seem to be slim. From a risk perspective, a risk that must be managed is the very real risk that we are unable to accurately estimate the magnitude of a design flood or drought.

1.11.4.1 Risk and Nonstationarity: Uncertain Information and Unreliable Probability Estimates The prospect of climate change has raised to new prominence the specter that our estimates of probabilities associated with

Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change

hydrologic events based on historical records may not be reliable in the future. Simultaneously, the projections of future climate conditions from GCMs are not reliable enough to be used to replace the historical record (Brown et al., 2009). As a result, water engineers face a future which has more uncertainty than they have previously been aware. Risk management is an applicable framework for addressing these issues. The methodology described here still relies on estimates of probabilities that are most likely to come from the historical record. Due to uncertainties related to climate change, among others, the final step of risk management, especially consideration of residual risk, surprise, and operation in failure mode, is essential. In addition, methods for decision making under so-called deep uncertainty may be explored. Robust decision making is representative of these methodologies (Lempert and Collins, 2003). The approach begins from a traditional statistical decision analysis approach but then evaluates decisions free of probability estimation. Instead, a candidate decision is evaluated for the conditions of the state variables under which that decision is compromised or performs poorly. Cluster analysis is used to identify the major areas of concern. Trade-off analysis is then used to evaluate alternatives to the original candidate that perform in these areas of concern. The approach appears computationally intensive in practice but the authors report good results in a few case studies (Lempert et al., 2003). Robust decision making is useful when little can be said of future conditions. However, there is some insight available regarding climate change. It may be possible to utilize the decision analysis if the process is tailored for the specific issues related to the use of uncertain information from GCM and historical data. Such an approach is presented here. The approach is called climate consequence management to highlight the possible opportunities that climate variability and change may present.

1.11.4.2 Climate Consequence Management with Decision Scaling: An Approach Designed for Uncertain Information Climate consequence management is a version of risk management that has been designed for the specific case of uncertainties associated with climate variability and change. The fundamental breakthrough is that a decision-analytic approach is employed in a manner that allows the use of climate information in a bottom-up or vulnerability-based way. It emphasizes identification of the specific climate information required that a decision hinges on. This allows the tailoring of the climate information to fulfill decision needs. The premise is that climate information can be useful in certain conditions by setting the bar fairly low in terms of what is needed. In some cases, for example, where climate information is deemed fairly reliable and projections are consistent in direction, this allows for probabilistic estimates of risk and risk-weighted decision making. There will be other cases where projections are contradictory and the process enables the identification of climate sensitivities and provides a framework for addressing them. In other cases, it will be found that some decisions are not sensitive to the projections of

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climate change due to limited options or limited effects. The term ‘consequence’ is used to highlight the concept that variations and changes in climate bring opportunities as well as risks. The process consists of three steps that are analogous to the three-step process described above. In the first step, vulnerability and uncertainty identification, the hazards, and opportunities associated with climate are identified. Next, consequence assessment is the process of estimating probable consequences from the hazards and opportunities identified in step 1 and tailored climate information produced through the process of decision scaling to assign probabilities to the hazards and opportunities. In the final step, consequence and uncertainty management, a strategy for addressing probable consequences and key uncertainties, is being developed using a decision analytic framework. Decision scaling is again employed to tailor climate information to aid in the analysis of different decision options. Finally, residual risk and surprise are addressed through the incorporation of robust and resilient design, which may in some cases include adaptive management.

1.11.4.2.1 Step 1: Vulnerability and uncertainty identification The first step of climate consequence management is to assess the impacts of changes in climate across all timescales on water resources. This necessitates knowledge of both historical climate information and the resulting local consequences. The historical record is a useful starting point for identifying how climate has impacted the system in the past and the particular climate episodes that are challenging. A general overview of climate change information for the region being studied is accessed to prompt consideration of potential climate impacts that have not been observed in the historical record. At present, the IPCC reports on regional impacts are logical starting point for a summary of literature. The brainstorming process of identifying future potential impacts is not limited to these projections since they are not certain to describe the full range of future climate possibilities. It can also be very useful to identify thresholds in the system performance that when exceeded signify the need for adaptive actions. These thresholds are used in the consequence assessment and consequence management steps that follow to determine whether action is necessary in response to projected risks. Developing the appropriate knowledge requires a dialog with the stakeholders affected by or engaged in the water sector. Engaging stakeholders can both ensure that relevant impacts are considered and keep stakeholders aware of the process. A dialog with climate scientists and meteorological agencies can help supplement and interpret relevant climate information. By gaining a more robust understanding of these hazards and impacts, one can begin to determine the hydroclimatic risk and opportunity for a given system. While the focus naturally turns to the negative climate events in the historical record, it is important to consider also the effect that uncertainty in general has on the system. For example, preparations for a rare negative event may cause considerable lost opportunities in the many years the event does not occur. This can represent a significant opportunity cost due to

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uncertainty. Better management of the vulnerability to the rare event may allow the exploitation of more opportunities. While this process focuses on climate risks, it is important to recognize that climate is one of many factors affecting the system. When projecting future risk scenarios for a given system, possible changes in population growth, water demand, land use, and even values should also be considered and integrated into any comprehensive risk assessment. The following questions provide a general guideline for what to consider when performing vulnerability and uncertainty identification. What key climate-related challenges does the system currently face? These challenges might include moderate or severe droughts, flood events, variable flows, or others that are particularly disruptive to the system. This assessment is based on historical climate and current system characteristics, such as land use, population, and economic factors. It is important to identify the hazards historically associated with climate variability for the system while understanding also that the same type of climate event might have a more or less severe impact based on evolving nonclimate characteristics of the system. What damages occur as functions of these events? Once one has identified the climate-related hazards, one should assess the local impacts on the system. This includes an analysis of the spatial distribution of impacts and a determination of whether there are distributional effects from these events. Distributional effects cause some populations, such as those at lower economic status, to be more vulnerable to hazards (Rees, 2002). One should also determine impacts on both the human and environmental systems and seek to assess their vulnerabilities. The method of valuing consequences may differ. For example, economic valuation of consequences (e.g., foregone profits, direct costs associated with switching to another water source) is appropriate in some cases. However, in the case of severe consequences (e.g., famine), economic valuation alone may not be sufficient, as the social consequences may far outweigh direct economic costs. It may be important to determine local thresholds that determine the extent of climate-related consequences. While some water users can easily adapt to small reductions in water supply with little or no adverse effects, others may face significant damages from even the smallest supply variations. This may also be true for floods. The vulnerability across different users might lead to an aggregate threshold level and expected reliability for the system. Are there potential opportunities due to climate variability and change? Although the emphasis is generally on the possible negative impacts from climate variability and long-term change, changes in the system might also bring benefits. For example, a shift in phase in multi-decadal variability within a system could lead to improved average climate conditions for some sectors. Consider the apparent upswing in West African rainfall recently. It is important to remember interactions between climate variability and the possible impact of long-term climate change. The latter might also offer some opportunities (e.g., increased average precipitation in arid regions). Assessments should take into account the varying opportunities and risks across sectors and across or within regions, along with their uncertainties.

Are there opportunity losses due to decisions made to avoid current climate risks? Water-resources managers are typically quite risk averse, meaning that they would prefer an option with less uncertainty but possibly a lower net benefit over an option with greater uncertainty but a higher possible net benefit. Thus, decisions that minimize climate risks may also decrease the potential benefit and result in lost opportunities. Identifying these lost opportunities reveals increased possible benefits from improved climate consequence management. Have the occurrences of hazard events over the historical record followed identifiable patterns? The initial step is to determine recurrence periods for relevant climate events over the historical record. For example, the analysis might reveal how frequently the system has experienced severe droughts. In some cases, there is spatial or temporal structure (i.e., a pattern) in the historical hazard occurrence. This might include variability across various timescales (such as inter-annual variability due to ENSO) or longer-term trends. The main purpose at this point is to understand variability in the climate system and how it has affected hazard probabilities in the past. One is not yet making forecasts or projections about future scenarios. This analysis reveals the probabilities that have determined system risk up to the current period. The understanding of historical climate variability at different timescales also suggests the key components to consider in developing projections in future steps. This may include identifying appropriate predictors that can help one make simple forecasts of possible shifts in the probability distribution of supply in the system (e.g., shifts due to ENSO phases). Can thresholds be identified that represent changes in system performance that require action? Hydro-climatic conditions affect a water system’s ability to meet performance objectives. Climate variability and change have a significant impact on whether the system fails or is able to meet stakeholder needs. Given the uncertainty related to climate change, it is useful for decision purposes to identify thresholds that signal where a system performance is no longer acceptable and adaptive action is necessary. For example, a reservoir may be designed for a long-term reliability of water delivery of no less than 95%. A threshold of 95% reliability would then be appropriate, for if the reliability fell below this level action would need to be taken. Analysis and answers to the previous questions in this section provide data on historical climate variability and probabilities associated with various climate outcomes. If climate conditions and the historical variability were expected to continue into the future without any changes, one could model the expected reliability based on past experiences. However, this assumes that one is aware of all forms of variability in the past and has the ability to model the future with a high degree of accuracy. If the historical record is too short to capture the full range of climate variability (and this is not uncommon), the results of the analysis can be significantly biased due to sampling variability. In addition, this does not take into account the possible nonstationarity of the system. In order to address these concerns and appropriately assess the sensitivity of the system, it is best to model system performance based on both historical data and scenarios of

Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change

possible future climate conditions. A procedure for doing so, decision scaling, is described below.

1.11.4.2.2 Step 2: Consequence assessment This step corresponds to the risk assessment described earlier. In the general sense, this is the product of the probability of some event and the consequences of the event. The process described here is designed for the special case of using climate information for the estimation of probable consequences. This process is termed decision scaling and described below. Decision scaling: tailoring climate information for risk assessment. Decision scaling is the process of tailoring climate information to aid in decision making related to climate risks and opportunities. It uses a decision analytic framework to identify the needed climate information, which is then produced through tailoring climate information from all possible, reliable sources. In general, this means that first the decision must be identified and next the points at which the decision changes as a function of climate information are specified. By doing so, the specific climate information can be tailored from various sources, including GCMs and paleoclimatological data. For example, if it is known that a reservoir system is vulnerable to multi-year droughts and inter-annual variability, but not vulnerable to within year variability, then the effort to produce climate information focuses on trends and temporal structure in inter-annual variability. The premise of this approach is that there are significant and irreducible uncertainties associated with projections of future climate and the resulting hydrologic conditions. Therefore, the emphasis should shift away from attempting to predict the future or provide a few examples of possible future climates. The usual process of analysis starts with GCMs and all their uncertainties, a few scenarios are generated and then those are input to a system model to review the impacts. In decision scaling, the process begins with the system or decision model which is perturbed with parametrically varied climate to generate a climate response function, that is, a representation of the systems’ sensitivity to climate changes. Then the GCM or other climate data are used to estimate the subjective probability of the climates of interest – these climate changes that cause significant impacts, exceed thresholds identified in step 1, or are related to a point where a decision changes. The most effective way to do this is through the use of multiple run, multi model ensembles to utilize the fullest representation of the model projection uncertainty. Decision scaling is used during the consequence assessment to describe probable consequences or the probability of exceeded adaptation action thresholds. It is used again during Step 3: Consequence and uncertainty management to aid in the decision making process. Also, the focus of recent interest in risk assessment and management is largely related to climate change implications. However, the process described here is appropriate for climate variability as well. Some particular considerations related to climate variability are described below. Consider uncertainty in climate forecasts. Based on location and climate characteristics, there may be significant variation in the ability to make climate predictions. For the same system, forecast skill might vary significantly across time scales.

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It is critical to be aware of the predictive capacity for the given system and the uncertainty associated with any predictions. The probabilistic nature of climate forecasts reinforces the idea that they are neither guaranteed nor absolute. This uncertainty plays a significant role when integrating the climate information into decision making and one should explicitly assess the uncertainty of any forecasts you consult. The approach to assessing the forecast uncertainty depends on the techniques used to create the forecast and the projected time scale. For example, if a seasonal forecast has been developed using a statistical model, a cross-validation technique can be used to understand and quantify the uncertainty in the model. With complex dynamical and GCM-based models and projections over longer timescales, it is best to consult climate professionals to determine the uncertainty and errors present in the model. Some of the key discussion points regarding longer-term climate projections that include the effects of increasing greenhouse gases and other anthropogenic influences include 1. the climate model’s ability to reproduce climatology in the region; 2. whether the model captures the observed regional trend in twentieth century climate; 3. the extent to which there is a well-established physical basis for the model’s forecasts; 4. the degree of agreement between different models; and 5. the extent to which natural multi-decadal variability impacts the region. Asking these questions and validating forecast models can show where the model made errors and help understand that possible model weaknesses. This demonstrates the remaining uncertainty that must be addressed through management options, as discussed in the next step. In the case of climate change, uncertainty may be considered too significant to assign probabilities to specific climate events. Even when probabilities are assigned, they must be considered fairly broad estimations and due consideration given to surprise, resilient design, and performance in failure mode.

1.11.4.2.3 Step 3: Consequence and uncertainty management The final step consists of developing a plan to manage probable consequences and uncertainty that have been identified and described in the previous steps of this process. The process proceeds in the three parts described in Section 1.11.2. Decision scaling is used to provide climate information that is needed in the decision analytic framework used to evaluate alternatives. Here, decision scaling provides the probabilities of the scenarios that cause a particular option to be preferred over another. The estimated hydro-climatic risk determined in the previous two steps serves as the foundation for developing a portfolio of options to mitigate the risk and take advantage of possible opportunities. It is critical to realize that climate information provides information about the probability of particular climate events (such as droughts), but anything can still happen, even if it is very unlikely. For this reason, we again highlight critical aspects of consequence and uncertainty

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management that are vital to managing risks and opportunities associated with climate variability and change. These are residual risk, surprise, and performance of the system when failure occurs. In the context of climate uncertainty, systematically evaluating risks and opportunities due to climate and comparisons with costs of various actions will benefit from a decision analytic framework. Decision scaling facilitates the production of relevant climate information for use in such a framework. For example, decision analysis might determine that decision A is optimal for a future in which mean annual stream flow decreases by 10% or more, while decision B is optimal in all other cases. Decision scaling is used to estimate the probability of decision A being the better choice using the full range of climate data available. While there is uncertainty related to that probability, if it is a large number relative to the probability associated with the optimality of B, say more than 60%, then we have some confidence that A is the right choice even with the uncertainty. Through such analyses, a plan is developed for addressing risk, opportunities, and uncertainty. The degree of uncertainty associated with future climate variability and change may favor delay in decisions where appropriate. It may also favor the methods for managing the impacts of climate events that do not necessitate new investments in infrastructure. The reasoning is as follows: if an event is not very likely to occur, it is typically not worth making major investments to manage the impact. However, given the possibility that we have underestimated a risk, we should consider ways to avoid the negative impacts of that event, if possible. Finding solutions that can be called upon only when needed is an efficient way to manage the impacts of unlikely events. Residual risk and surprise may also be addressed through redundancy. If a water supply system consists of a single source, any impact on that source leaves the system vulnerable. While it may not be economically efficient to build new infrastructure to tap new sources, other opportunities may exist. The suite of risk management options might include economic instruments (such as insurance or water banks), infrastructure modifications, or integrating seasonal forecasts into decision making, among many others. Together, these approaches are termed portfolio of options because they consist not of a single solution, but rather a basket of possibilities – each of which may be the best choice in a particular circumstance. The final consideration is the ability of the system to perform when the primary risk management approaches fail. For a water-supply system this situation may occur due to the loss of the supply due to drought. For flood protection this is the overtopping of the levees. While the primary system has failed this does not mean the plan has failed and should not mean catastrophe. Through residual risk management and redundancies as described above, the system may be able to continue performing. Water-supply systems may use boil water orders or may contract with neighbor utilities for trucked water. In floods, the planning of evacuations and local emergency response can ensure that an extreme hydrologic event does not have extreme human impact.

Below are some additional considerations when developing the portfolio and determining the most appropriate solutions. Consider planning and operational approaches. The risk management solutions available depend partly on the time frame for action. Near-term operational options will most likely assume fixed infrastructure and some level of sunk costs (those that have already been allocated and cannot be recovered). Possible planning solutions, on the other hand, can include decisions regarding infrastructure and system design. Climate information should be integrated into decision making at the appropriate time scale to inform options most effectively. Projections of long-term climate change may have little value at the operational level for current practices. However, such projections might inform planning decisions as well as the framework under which operational decisions are made in the future (i.e., whether expected climate changes necessitate more flexible operational policies). Assess possible trade-offs. Limited human, financial, and natural resources lead to trade-offs in almost all decisions in water resources management. Water managers must seek to understand and assess possible benefits or consequences of their decisions within the context of these resource constraints. Uncertainty makes such assessment even more difficult, but can also increase the importance of decision outcomes. There is often a trade-off between increasing expected reliability for a system and increasing possible benefits from water allocation. Improved climate information and projections of likely futures may help shift the reliability scenarios. While this does not eliminate the necessary tradeoff, it can improve the possibility of achieving positive outcomes. Integrating thresholds of acceptable costs into decision making can help water managers balance trade-offs. Consider the impact of uncertainty. It is necessary to understand the uncertain nature of probabilistic forecasts in order to assess the suite of options appropriately. Rather than planning for a specific outcome, the most appropriate approach often requires planning for a set of scenarios. While the likelihood of a specific outcome might be higher than the likelihood of another, both are possible and should be considered in decision making. This uncertainty may lead to more flexible approaches and policies, with less emphasis on rigid options that leave little room for alternative outcomes. A flexible, adaptive plan might also increase the capacity to take advantage of possible opportunities from better than expected outcomes. Of particular importance is to consider the effects of low-probability but high-impact events on the system when actions are taken based on a forecast. For example, if the forecast leads one to expect more water, are there ways to mitigate the effects of an unlikely severe drought? This is important to consider because sometimes the anticipatory actions based on a forecast may leave a system more exposed to the down-side risk, or the risk associated with the less likely, but still possible, climate extreme.

1.11.5 Conclusion In this chapter an approach to risk assessment, management, and communication is presented that attempts to reconcile

Risk Assessment, Risk Management, and Communication: Methods for Climate Variability and Change

traditional approaches with our growing knowledge of uncertainty and nonstationarity that mark the hydrologic record. In describing these steps, it becomes clear to the authors at least that the research emphasis within the water community has focused primarily on the means to reduce the uncertainty related to hydrologic events and better prescribe the distributions used to estimate their probabilities. Relatively little effort has been devoted to develop innovative means of reducing hydrologic risk to society or of communicating risk in order to promote risk-reducing behavior. The effects on the hydrologic record of climate change and land-use change calls this research orientation into question. Irreducible uncertainties that hamper our ability to estimate hydrologic design variables imply that greater effort is needed for the development of designs and strategies that perform well under a range of possible future conditions. By developing a wider range of options, water engineers and planners will have greater opportunity to design systems that are resilient as conditions evolve into the future. To develop these options, new ideas related to the use of information technology, emergency planning, and economic incentives should be explored. The challenge of managing risks which cannot be effectively constrained with our traditional approaches means we must be willing to expand beyond our comfortable set of tools. If we are not willing to manage the full range of hydrologic risks, who do we expect will?

References Bondi H (1985) Risk in perspective. In: Cooper, MG (ed.) Risk: Man-Made Hazards to Man. New York: Oxford University Press. Brekke LD, Kiang JE, Olsen JR, et al. (2009) Climate Change and Water Resources Management – a Federal Perspective, U.S. Geological Survey Circular 1331, 65p (ISBN 978-1-4113-2325-4). http://pubs.usgs.gov/circ/1331 (accessed April 2010). Brown C and Carriquiry M (2007) Managing hydroclimatological risk to water supply with option contracts and reservoir index insurance. Water Resource Research 43: W11423 (doi:10.1029/2007WR006093).

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Brown C, Conrad E, Sankarasubramanian A, and Someshwar S (2009) The use of seasonal climate forecasts within a shared reservoir system: The case of Angat reservoir, Philippines. In: Ludwig F, Kabat P, van Schaik H, and van der Valk M (eds.) Climate Change Adaptation in the Water Sector, 274pp. London: Earthscan. Brown C, Greene A, Block P, and Giannini A (2008) Review of downscaling methodologies for Africa applications. IRI Technical Report 08-05, 31 pp. Characklis GW, Kirsch BR, Ramsey J, Dillard KEM, and Kelley CT (2006) Developing portfolios of water supply transfers. Water Resources Research 42: W05403 (doi:10.1029/2005WR004424). de Neufville R (2004) Uncertainty Management for Engineering Systems Planning and Design, Engineering Systems Monograph. p. 18. Cambridge, MA: MIT Press. Dessai S, Hulme M, Lempert R, and Pielke R, Jr. (2009) Do we need better predictions to adapt to a changing climate? EOS Transactions AGU 90(13) (doi:10.1029/ 2009EO130003). Fiering MB and Matalas NC (1990) Decision-making under uncertainty. In: Waggoner PE (ed.) Climate Change and U.S. Water Resources, pp. 75–83. New York: John Wiley & Sons. International Research Institute for Climate and Society (IRI) (2006) A gap analysis for the implementation of the Global Climate Observing System Programme in Africa. IRI Technical Report Number IRI-TR/06/1. New York: IRI. Lempert R and Collins M (2007) Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis 27(4): 1009–1026. Lund JR (2002) Floodplain planning with risk-based optimization. Journal of Water Resources Planning and Management 3: 202–207. Plate EJ (2004) Risk management for hydraulic systems under hydrological loads. In: Bogardi JJ and Kundzewicz ZW (eds.) Risk, Reliability, Uncertainty and Robustness of Water Resources Systems, 220pp. New York, NY: UNESCO and Cambridge University Press. Plate EJ (2002) Risk management for hydraulic systems under hydrological loads. In: Bogardi JJ and Kundzewicz ZW (eds.) Risk, Reliability, Uncertainty and Robustness of Water Resources Systems, chap. 23, pp. 209–220. Cambridge, UK: Cambridge University Press. Rees J (2002) Risk and Integrated Water Resources Management. Global Water Partnership TEC Background Paper No. 6. Stedinger JR, Vogel RM, and Foufoula-Georgiou E (1993) Frequency analysis of extreme events In: Maidment DR (ed.) Handbook of Hydrology, chap. 18. New York: McGraw-Hill. Taleb NN (2007) The black swan: The impact of the highly improbable. New York: Random House, 400pp. UNDRO Office of the United Nations Disaster Relief Coordinator (1991) Mitigating Natural Disasters: Phenomena, Effects and Options. A Manual for Policy Makers and Planners. New York: United Nations. van Aalst M, Hellmuth M, and Ponzi D (2007) Come rain or shine: Integrating climate risk management into African development bank operations, Working Paper No. 89. Tunis: African Development Bank.

Preface – The Science of Hydrology S Uhlenbrook, Department of Water Engineering, DA Delft, The Netherlands & 2011 Elsevier B.V. All rights reserved.

The world is changing and it seems that the speed of changes is accelerating. In the overall introduction of the Treatise on Water Sciences, the editor-in-chief Peter Wilderer (The Importance of Water Science in a World of Rapid Change: A Preface to the Treatise on Water Science) discusses the prevailing changes, its drivers, and possible impacts on different water disciplines. A major challenge is that all changes and their various impacts are interacting with each other, although how and to what extent is often poorly understood. For scientists and practitioners, this makes the problem identification and the development of sustainable solutions for water problems a very difficult task. Therefore, it is very timely to summarize the contemporary state of the knowledge in the different fields of water sciences and technology, and to provide a platform for innovative research and development. I am pleased to conclude that this volume on hydrology is an important piece of the complex puzzle. What is hydrology? The International Association of Hydrological Sciences (IAHS) in collaboration with UNESCO defined hydrology as the ‘‘science that deals with the water of the earth, their occurrence, circulation and distribution, their chemical and physical properties, and their reaction with their environment, including their relation to living beings.’’ In addition, it states that hydrology is the ‘‘science that deals with the processes governing the depletion and replenishment of the water resources of the land areas of the earth, and various phases of the hydrological cycle.’’ This is indeed a very wide definition. Many aspects of the chemical properties and interactions with the environment are part of Volume 3 of this treatise. Topics that are directly related to the management of the water resources are part of (Preface – Management of Water Resources) of this treatise. However, this volume (The Science of Hydrology) deals with all major components of the water cycle and key water-quality aspects. It also discusses the linkages to closely related disciplines. The aims of the science of hydrology were well summarized by the Dutch Foresight Committee on Hydrological Science (KNAW, 2005) as follows:

1. to understand the mechanisms and underlying processes of the hydrological cycle and its interactions with the lithosphere, atmosphere, and biosphere; 2. to enhance our knowledge of interactions between the hydrosphere and atmosphere, the hydrosphere and lithosphere, and the hydrosphere and biosphere, thereby increasing our understanding of the role that water plays in the Earth system; 3. to quantify human impact on the past, present, and future conditions of hydrological systems; and 4. to develop strategies for sustainable use and protection of water resources, hydrological systems, and the associated environmental conditions.

The science of hydrology is special, as it holds a place, on the one hand, in the field of Earth System Sciences, where it is directly linked to earth science disciplines, such as atmospheric sciences, geomorphology, geology, soil sciences, geobiology, and ecology. On the other hand, hydrology is an applied science and, as such, a part of engineering. This makes the discipline highly relevant to the management and development of the water resources and the prediction and mitigation of water-related natural hazards (floods, droughts, landslides, etc.) to finally support life, civilization, and sustainable development. These complementary aspects of hydrology (Earth System Sciences and the basis for water management/engineering) make it an exciting and very relevant discipline. It is quite a dynamic discipline given the significant developments of the past decades; many of them are reviewed in this volume. The volume starts with a comprehensive overview of global hydrology and the spatio-temporal variability of hydrological fluxes and water resources on a large scale. It continues with several chapters on the main variables of the water balance, such as precipitation, evaporation and interception, and stream discharge; then it goes on to discuss the storage components of groundwater, soil water, lakes, and reservoirs. Unfortunately, a chapter on snow and ice, the globally largest and regionally/locally often very important water storage component, was withdrawn at a late stage and could not be replaced in time. The volume continues with several chapters discussing the state of the art and the possible future developments of observation methods for ground-based techniques (i.e., fieldbased methods, tracer techniques, and hydrogeophysics) and remote-sensing techniques. Key data analysis and modeling techniques as well as theoretical considerations are reviewed in four, mainly theoretical, chapters on scaling and regionalization, statistical methods, hydrological modeling, and uncertainty estimation techniques. The linkages between hydrology and aquatic ecology and biogeochemistry are discussed in two comprehensive chapters. Two chapters are related to the processes and issues of erosion and sedimentation as well as surface water–groundwater interactions. The inclusion of all these topics results in a sizable volume with 20 chapters, exceeding 500 pages. However, several hydrology-related topics are not or could be only partly covered (e.g., urban hydrology, snow and ice, coastal hydrological systems, landscape evolution, and hydrogeomorphology). Perhaps this can be seen as an invitation to redo the exercise in a few years from now, and to review the latest developments in this dynamic field and strive for more completeness.

References KNAW (2005) Turning the Water Wheel Inside Out. Foresight Study on Hydrological Science in the Netherlands. Amsterdam: Royal Academy of Arts and Sciences.

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2.01 Global Hydrology T Oki, The University of Tokyo, Tokyo, Japan & 2011 Elsevier B.V. All rights reserved.

2.01.1 Introduction 2.01.1.1 The Earth System and Water 2.01.1.2 Water Reserves, Fluxes, and Residence Time 2.01.2 Global Water Cycle 2.01.2.1 Existence of Water on Earth 2.01.2.2 Water Cycle on the Earth 2.01.3 Global Water-Balance Requirements 2.01.3.1 Water Balance at Land Surface 2.01.3.2 Water Balance in the Atmosphere 2.01.3.3 Combined Atmosphere–River Basin Water Balance 2.01.3.3.1 Estimation of large-scale evapotranspiration 2.01.3.3.2 Estimation of total water storage in a river basin 2.01.3.3.3 Estimation of zonally averaged net transport of freshwater 2.01.3.4 Bottom Line of Global Water Balance 2.01.4 Global Water Balance 2.01.4.1 Uncertainties in Global Water-Balance Estimates 2.01.4.2 Water Balance and Climate 2.01.4.3 Annual Water Balance in Climatic Regions 2.01.5 Challenges in the Global Hydrology and Research Gaps 2.01.5.1 Macroscale Hydrological Modeling 2.01.5.2 Global Changes and Global Hydrology 2.01.5.3 Global Trace of Water Cycles 2.01.5.4 Interactions of Global- and Local-Scale Hydrology 2.01.5.5 Research Opportunities in Global Hydrology 2.01.5.6 Research Gaps in Global Hydrology Acknowledgments References

2.01.1 Introduction 2.01.1.1 The Earth System and Water The Earth system is unique in that water exists in all three phases, that is, water vapor, liquid water, and solid ice, when compared with the forms of water on other planets. The transport of water vapor is regarded as energy transport because of the large amount of latent-heat exchange that occurs during its phase change to liquid water (approximately 2.5 106 J kg1); therefore, the water cycle is closely linked to the energy cycle. Even though the energy cycle on the Earth is an open system driven by solar radiation, the amount of water on the Earth does not change during shorter than geological timescales (Oki, 1999), and the water cycle itself is a closed system. On a global scale, hydrologic cycles are associated with atmospheric circulation, which is driven by the unequal heating of the Earth’s surface and atmosphere in latitude (Peixo¨to and Oort, 1992). Annual mean absorbed solar energy at the top of the atmosphere is highest near the equator with approximately 300 W m2, and decreases rapidly at higher latitudes, and is

3 3 3 4 4 5 8 9 9 11 11 11 12 12 12 12 14 15 16 16 19 19 20 22 22 24 24

approximately 60 W m2 at the Arctic and Antarctic regions. Emitted terrestrial radiative energy from the Earth at the top of the atmosphere is approximately 250 W m2 for the areas between 201 N and 201 S, gradually decreases at higher latitudes, and is approximately 175 W m2 at the Arctic region and 150 W m2 at the Antarctic region. As a consequence, the net annual energy balance is positive (absorbing) over tropical and subtropical regions between 301 N and 301 S, and negative in higher latitudes (Dingman, 2002). Without atmospheric and oceanic circulations on the Earth, temperature differences on the Earth would have been more drastic. Temperatures in the equatorial zone would have been much higher such that the outgoing terrestrial radiation balances the absorbed solar energy, and the temperatures in the polar regions would have been much lower as well. Both the atmosphere and the ocean carry much energy from the equatorial regions toward the polar regions. In the case of atmosphere, the energy transport consists of sensible heat and latent-heat fluxes (Masuda, 1988). The global water circulation includes the latent-heat transport in which water vapor plays an active role in the atmospheric circulation. Water vapor is not a passive component of the atmosphere system;

3

4

Global Hydrology

rather, it affects atmospheric circulation by both radiative transfer and latent-heat release of phase change.

2.01.1.2 Water Reserves, Fluxes, and Residence Time The total volume of water on the Earth is estimated as approximately 1.4 1018 m3, and it corresponds to a mass of 1.4 1021 kg (Figure 1, revised from Oki and Kanae (2006)). Compared with the total mass of the Earth (5.974 1024 kg), the mass of water constitutes only 0.02% of the planet, but it is critical for the survival of life on the Earth, and the Earth is called the Blue Planet and the Living Planet. There are various forms of water on the Earth’s surface. Approximately 70% of its surface is covered with salty water, the oceans. Some of the remaining areas (continents) are covered by freshwater (lakes and rivers), solid water (ice and snow), and vegetation (which implies the existence of water). Even though the water content of the atmosphere is comparatively small (approximately 0.3% by mass and 0.5% by volume of the atmosphere), approximately 60% of the area of the Earth is always covered by cloud (Rossow et al., 1993). The Earth’s surface is dominated by the various phases of water. Water on the Earth is stored in various reserves, and various water flows transport water from one to another. Water flow

Water vapor over sea 10

Evaporation over ocean 436.5

(mass or volume) per unit time is also called water flux. The mean residence time in each reserve can be simply estimated from total storage volume in the reserve and the mean flux rate to and from the reserve:

Tm ¼ V=F

ð1Þ

where Tm, V, and F are mean residence time, total storage, and the mean flux rate, respectively. We can also represent the distribution of flux rate of water flow that comes in and goes out from the storage Chapman, 1972). The last column of Table 1 (simplified from the table in Korzun (1978)) presents global values of the mean residence time of water. Evidently, the water cycle on the Earth is a stiff differential system with variability on many timescales, from a few weeks to thousands of years. The mean residence time is also important when considering water-quality deterioration and restoration, since it can be an index of how much water is turned over. Apparently, river water or surface water is more vulnerable to pollution than groundwater; however, any measure to increase waterquality recovery tends to be more efficient for river water than groundwater, and, as suggested from Table 1, the mean

Total terrestrial precipitation 111 Snowfall Rainfall 12.5 98.5

Net water-vapor flux transport 45.5

Water vapor over land 3

Glaciers and snow 24 064

Total terrestrial evapotranspiration 65.5 21 Precipitation over ocean 391

6.4 11.7 Others (29.3)

7.6 11.6 Cropland (12.6) 2.66

Unirrigated

0.38 Domestic Sea 1 338 000

0.77

Irrigated

45.5

River 2

Industry

54

Forest (40.1)

0.2 0.3 Wetland (0.2) Wetland 11

31

Grassland (48.9)

29

Biological Permafrost water 300 1 Surface runoff 15.3

1.3 2.4 Lake (2.7) Soil moisture 17

Subsurface runoff 30.2

Lake 176

Groundwater 23 400 Flux, 103 km3 yr−1 Storage, 103 km3

The terrestrial water balance does not include Antarctica

( )

Area 106 km2

Figure 1 Global hydrological fluxes (1000 km3 yr1) and storages (1000 km3) with natural and anthropogenic cycles are synthesized from various sources. Big vertical arrows show total annual precipitation and evapotranspiration over land and ocean (1000 km3 yr1), which include annual precipitation and evapotranspiration in major landscapes (1000 km3 yr1) presented by small vertical arrows; parentheses indicate area (million km2). The direct groundwater discharge, which is estimated to be about 10% of the total river discharge globally, is included in river discharge. The values of area sizes for cropland and others are corrected from original ones. From Oki T, Nishimura T, and Dirmeyer P (1999) Assessment of annual runoff from land surface models using total runoff integrating pathways (TRIP). Journal of the Meteorological Society of Japan 77: 235–255 and Ok T and Kanae S (2006) Global hydrological cycles and world water resources. Science 313(5790): 1068–1072.

Global Hydrology Table 1

5

World water reservesa

Form of water

Covering area (km2)

Total volume (km3)

Mean depth (m)

World ocean Glaciers and permanent snow cover Ground water Ground ice in zones of permafrost strata Water in lakes Soil moisture Atmospheric water Marsh water Water in rivers Biological water Artificial reservoirs Total water reserves

361 300 000 16 227 500 134 800 000 21 000 000 2 058 700 82 000 000 510 000 000 2 682 600 148 800 000 510 000 000

1 338 000 000 24 064 100 23 400 000 300 000 176 400 16 500 12 900 11 470 2120 1120 8000 1 385 984 610

3700 1463 174 14 85.7 0.2 0.025 4.28 0.014 0.002

510 000 000

2718

Share (%) 96.539 1.736 1.688 0.0216 0.0127 0.0012 0.0009 0.0008 0.0002 0.0001

Mean residence time 2500 years 1600 years 1400 years 10 000 years 17 years 1 year 8 days 5 years 16 days A few hours 72 days

100.00

a

Simplified from Table 9 of ‘‘World water balance and water resources of the earth’’ by UNESCO Korzun, 1978. The last column, mean residence time, is from Table 34 of the report.

residence time of river water is shorter than that of groundwater. Since the major interests of hydrologists have been the assessment of volume, inflow, outflow, and the chemical and isotopic composition of water, the estimation of mean residence time of a certain domain has been one of the major targets of hydrology. It should be recalled that the residence time estimated with isotope tracers often differs from the hydrological residence time derived from Equation (1) (Uhlenbrook et al., 2002, 2004). This is due to the fact that in the subsurface system, the diffusive exchange processes between mobile and immobile parts make the residence time usually much longer. This process is particularly important for hydrochemical processes.

2.01.2 Global Water Cycle 2.01.2.1 Existence of Water on Earth Table 1 and Figure 1 denote the quantity of water stored in each of the reserves on the Earth. Most of the storage values given in Table 1 are taken from Korzun (1978), except for water vapor in the atmosphere which is calculated from atmospheric data (Oki et al., 1995). The various reserves of water on the Earth are discussed in the following: The proportion of water in the ocean is large (96.5%). Even though in classical hydrology, ocean processes are traditionally excluded, the global hydrological cycle is never closed without including them. The ocean circulations carry large amounts of energy and water. The surface ocean currents are driven by surface wind stress, and the atmosphere itself is sensitive to the sea-surface temperature. Temperature and salinity determine the density of ocean water, and both factors contribute to the overturning and deep-ocean general circulation Other major reserves are solid waters on the continent (glaciers and permanent snow cover) and groundwater. Glaciers are the accumulation of ice originated from the atmosphere, and they generally move slowly on land over a long period. Glaciers form a discriminative U-shaped valley over land, and remain moraine when they retreat. If a glacier flows into an ocean, its terminated end often forms an iceberg. Glaciers react in comparatively longer timescales against

climatic change, and they also induce isostatic responses of continental-scale upheavals or subsidence in even longer timescales. Even though it is predicted that the thermal expansion of oceanic water dominates the anticipated sea-level rise due to global warming, glaciers over land are also a major concern as the cause for sea-level rise associated with global warming. Groundwater is the subsurface water occupying the saturated zone. It contributes to runoff in the low-flow regime between storm events. Deep groundwater may also reflect the long-term climatological situation. Groundwater in Table 1 includes both gravitational and capillary water, but groundwater in the Antarctica (roughly estimated as 2 106 km3) is excluded. Gravitational water is the water in the unsaturated zone (vadose zone) which moves under the influence of gravity. Capillary water is water found in the soil above the water table by capillary diffusion, a phenomenon associated with the surface tension of water in soils acting as porous media. In terms of groundwater recharge, Do¨ll and Fiedler (2008) estimated the global groundwater recharge flux to be 12 666 km3 yr1 and approximately 1000 mm yr1 in the Amazon region. They assumed the recharge flux is a fraction of the total runoff. Koirala (2010) estimated groundwater recharge flux by coupling a land-surface scheme, namely Minimal Advanced Treatments of Surface Interaction and RunOff (MATSIRO; see Takata et al., 2003), with a macro-scale groundwater representation Yeh et al., 2005). The global distribution of model-simulated groundwater recharge is illustrated in Figure 2(a). Total groundwater recharge flux is estimated as 31 789 km3 yr1 and the value is close to the flux of subsurface runoff in Figure 1 (30 200 km3 yr1). Soil moisture is the water that is held above the groundwater table. It influences the energy balance at the land surface, such that a lack of available moisture suppresses evapotranspiration (which consists of soil evaporation, plant transpiration, and interception loss), and changes surface albedo. Soil moisture also alters the fraction of precipitation partitioned into direct runoff and infiltration. The precipitation water becoming direct runoff cannot be evaporated from the same place, while the water infiltrated into soil may be taken up by hydraulic suction and evaporated back into the atmosphere. The global distribution of model-estimated mean

6

Global Hydrology 90° N

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Figure 2 (a) Global map of long-term mean net groundwater recharge (mm yr ) estimated by an LSM coupled with macroscale groundwater model (Yeh and Eltahir, 2005; S. Koirala, 2010); (b) global distribution of annual mean soil wetness index estimated by 13 LSMs averaged for 1986–95 through the second phase of the Global Soil Wetness Project; (c) same as (b) but for annual precipitation (mm yr1) used in the GSWP2 based on observation; (d) same as (b) but for estimated annual runoff (mm yr1) by LSMs; (e) same as (b) but for mean river discharge (106 m3 yr1); (f) annual vapor-flux convergence (mm year1) for 1989–92 (Oki et al., 1995). (g) Annual mean evapotranspiration (mm yr1) estimated as a residual of (f) and precipitation corresponding to the period; (h) same as (b) but for annual mean evapotranspiration (mm yr1) by LSMs for 1986–95. Data of (a) from Takata K, Emori S, and Watanabe T (2003) Development of minimal advanced treatments of surface interaction and runoff. Global and Planetary Change 38: 209–222 and Koirala S (2010) Explicit Representation of Groundwater Process in a Global-Scale Land Surface Model to Improve Hydrological Predictions. PhD Thesis, The University of Tokyo; (b) from Dirmeyer PA, Gao XA, Zhao M, Guo ZC, Oki T, and Hanasaki N (2006) GSWP-2 multimodel anlysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society 87: 1381–1397; and (f) from Oki T, Musiake K, Matsuyama H, and Masuda K (1995) Global atmospheric water balance and runoff from large river basins. Hydrological Processes 9: 655–678.

Global Hydrology

7

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Figure 2 Continued.

soil wetness index is shown in Figure 2(b). Generally, the distribution is correlated with precipitation distribution (Figure 2(c)) as well as with runoff distribution (Figure 2(d)), but the global distribution of river discharge (Figure 2(e)) accumulates total runoff generated in the upper watershed, and the shape of the river channels can be seen in the distribution. The atmosphere carries water vapor, which influences the heat budget via latent-heat-exchange processes. Condensation of water vapor releases latent heat, which warms the atmosphere and affects the atmospheric general circulation. Liquid water (droplets, clouds, etc.) in the atmosphere is another result of condensation. Clouds significantly change the radiation in the atmosphere and at the Earth’s surface. However, the volume of liquid (and solid) water contained in the atmosphere is relatively small, as most of the water in the atmosphere exists as water vapor. Water vapor is also the major absorber in the atmosphere of both short-wave and long-wave radiation. Precipitable water is the total water vapor in the atmospheric column integrated from land surface to the top of the atmosphere. Vertically integrated water-vapor flux convergence is a useful tool to diagnose global water balance (see Figure 2(f) for its global distribution). The amount of water stored in rivers (Figure 2(e)) is rather tiny compared to other reserves at any time; however, the recycling speed, which can be estimated as the inverse of the mean residence time (Equation (1)), of river water (river discharge) is relatively high, and it is important because most societal applications ultimately depend on river water as a renewable and sustainable resource. The amount of water stored transiently in a soil layer, in the atmosphere, and in the river channels is relatively minute,

and the time spent through these subsystems is relatively short. However, they play a dominant role in the global hydrological cycle.

2.01.2.2 Water Cycle on the Earth The water cycle plays many important roles in the climate system, and Figure 1 schematically illustrates various flow paths of water in the global hydrologic system (Oki and Kanae, 2006). Precipitation is calculated from global estimates based on observations from the forcing data of the Global Soil Wetness Project (GSWP2, the second phase the project, see the discussion in Section 2.01.4) over land, and data from Climate Modeling Analysis and Prediction (CMAP; Xie and Arkin, 1996) over ocean. Land-surface fluxes, such as evapotranspiration and surface and subsurface runoff, are the estimated results from GSWP2. Differentiation of precipitation between snow and rain over land is also either estimated by land-surface models that participated in the GSWP2, or given by individual forcing determined by temperature. Values on human water withdrawals for irrigation, industry, and households are taken from Shiklomanov (1997). Water-vapor transport and its convergence are estimated using the European Centre for Medium-Range Weather Forecast (ECMWF) objective analyses, obtained as the 4-year mean from 1989 to 1992 (Oki et al., 1995). The roles of these water fluxes in the global hydrologic system are now briefly reviewed:

•

Precipitation is the water flux from atmosphere to land or ocean surface. It drives the hydrological cycle over the land surface, also changes surface salinity (and temperature) over the ocean, and affects its thermohaline circulation. Rainfall

8

Global Hydrology

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Water scarcity index

Figure 2 Continued.

•

refers to the liquid phase of precipitation. A part of it is intercepted by canopy over vegetated areas, and the remaining part reaches the Earth‘s surface as through-fall. The highly variable, intermittent, and concentrated behavior of precipitation in time and space domain compared to other major hydrological fluxes mentioned below makes the observation of this quantity and the aggregation of the process complex and difficult. Global distribution of precipitation is presented in Figure 2(c). Currently, satellitebased estimates merged with in situ observational data have been produced and revealed to the public (e.g., Kubota et al., 2007). Snow has special characteristics compared with rainfall. Snow may be accumulated and the surface temperature will not rise above 0 1C until the completion of snowmelt. The albedo of snow is quite high (as high as clouds). Consequently, the existence of snow changes the surface energy and water budget enormously. A snow surface typically

•

reduces the aerodynamic roughness, and therefore may also have a dynamical effect on the atmospheric circulation and hydrologic cycle. Evaporation is the return flow of water from the surface to the atmosphere and the latent-heat flux from the surface. The amount of evaporation is determined by both atmospheric and hydrological conditions. From the atmospheric point of view, the partition of incoming solar energy to the surface between latent and sensible heat flux is important. Wetness at the surface influences this partition significantly because the ratio of actual evapotranspiration to the potential evaporation is reduced due to drying stress. The stress is sometimes formulated as a resistance under which evaporation is classified as hydrology driven (soil controlled). If the land surface is wet enough compared to available energy for evaporation, the condition is classified as radiation driven (atmosphere controlled).

Global Hydrology

9

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•

•

Transpiration is the evaporation of water through stomata of leaves. It has two special characteristics different from evaporation from soil surfaces. One is that the resistance of stomata is related not only to the dryness of soil moisture but also to the physiological conditions of vegetation, through the opening and closing of stomata. Another feature is that roots of plants can uptake water from deeper soil layers and transpirate the water, compared to the case of evaporation from bare soil without plants. Vegetation also modifies the surface energy and water balance by altering surface albedo and by intercepting some precipitation and evaporating this rainwater. The global distribution of total evapotranspiration is shown in Figure 2(g), which is estimated using the atmospheric water-balance computation (Equation (7) in Section 2.01.3.3.1), and in Figure 2(h), estimated by land-surface models (in Section 2.01.4.1). Runoff returns water from the land to the ocean, which may otherwise be transported in vapor phase by evaporation and atmospheric advection. The runoff into the ocean is also important for the freshwater balance and the salinity of the ocean. Rivers carry not only water mass but also sediment, chemicals, and various nutritional matters from continents to seas. Without rivers, global hydrologic cycles on the Earth are not closed. Runoff at hillslope scale is a nonlinear and complex process. Surface runoff can be generated when the intensity of rainfall or snowmelt exceeds the infiltration rate of the soil, or when precipitation falls on the saturated land surface. Saturation at land surface can be formed mostly by topographic-concentration mechanism along the hillslopes. Infiltrated water in the upper part of the hillslope flows down the slope and discharges at the bottom of the hillslope. Due to the highly

variable heterogeneity of topography, soil properties (such as hydraulic conductivity and porosity) and precipitation, basic equations such as Richard’s equation, which are fairly valid at a point scale or hillslope scale, cannot be directly applied in the macroscale because of the nonlinearity involved. The global distributions of runoff and river discharge are illustrated in Figures 2(d) and 2(e). The global water cycle integrates these components, which consist of the state variables (precipitable water, soil moisture, etc.) and the fluxes (precipitation, evaporation, etc.).

2.01.3 Global Water-Balance Requirements The conservation law of water mass in any arbitrary control volume indicates the water balance. In this section, the water balance over land, for an atmospheric column, and its combination is presented (Oki, 1999). Some applications of these water-balance equations for estimating some of the waterbalance components are introduced as well.

2.01.3.1 Water Balance at Land Surface In the field of hydrology, river basins have commonly been selected for study, and water balance has been estimated using ground observations, such as precipitation, runoff, and storage in lakes and/or groundwater. The water balance over the land is described as

q S=q t ¼ P  E  Ro  Ru

ð2Þ

where S represents the water storage within the area, t is the time, (q S/q t) is the change of total water storage with time,

10

Global Hydrology Annual evapotranspiration (mm yr−1) 0

60° E

120° E

180°

120° W

60° W

0

90° N

60° N

30° N

EQ.

30° S

60° S

90° S

(g)

0.00

300

900

600

1200

1500

1800

2100

2400

2700

3000

90°

60°

30°

0°

−30°

−60°

−90° −180°

(h)

−200

−150°

0

−120°

20

−90°

50

−60

100

−30°

150

0°

200

30°

300

500

60°

800

90°

1400

120°

1800

150°

180°

2400

3000

Figure 2 Continued.

P is the precipitation, E is the evapotranspiration, Ro is the surface runoff, and Ru is the groundwater movement (all fluxes above are given in the unit volume per time step). S includes snow accumulation in addition to soil moisture, groundwater, and surface-water storage including retention

water within the control volume. The control volume is defined by the area of interest over the land with its bottom generally at the impermeable bedrock. These terms are shown in Figure 3(a). Equation (2) implies that water storage over land is increased by precipitation, and decreased

Global Hydrology

by evapotranspiration, surface runoff, and groundwater movement. If the considered area of water balance is set within an arbitrary boundary, Ro represents the net outflow of water from this area (i.e., the total outflow minus total inflow from surrounding areas). Although, in general, it is not easy to estimate groundwater movement Ru, the net flux per unit area within a large area is expected to be comparatively small. If all groundwater movement is considered to be that observed at the gauging point of a river (Ru ¼ 0), then Equation (2) becomes

q S=q t ¼ P  E  Ro

2.01.3.2 Water Balance in the Atmosphere Atmospheric water-vapor flux convergence contains waterbalance information that can complement the traditional hydrological elements such as precipitation, evapotranspiration, and discharge. The basic concepts as well as the application of atmospheric data to estimate terrestrial water balance were first presented by Starr and Peixo¨to (1958). The atmospheric water balance for a column of atmosphere from the bottom at land surface to the top of the atmosphere is described by

q W=q t ¼ Q þ ðE  PÞ

2.01.3.3 Combined Atmosphere–River Basin Water Balance Since there are common terms in Equations (3) and (4), they can be combined as

q W=q t þ Q ¼ ðP  EÞ ¼ q S=q t þ Ro

• •

Annual change of atmospheric water-vapor storage is negligible ((q W/q t) ¼ 0). Annual change of water storage at the land is negligible ((q S/q t) ¼ 0).

With these assumptions, Equation (5) simplifies into

Q ¼ ðP  EÞ ¼ Ro

(a) Water balance

in the basin

Groundwater movement

ð6Þ

If a river basin is considered as the water-balance region, Ro is simply the discharge from the basin. The simplified Equation (6) demands that the water-vapor convergence, that is, precipitation minus evaporation and net runoff should balance over the annual period when the temporal change of all storage terms can be neglected.

Precipitable water

Vapor flux

Precipitable water

Runoff Basin storage

ð5Þ

Figure 3(c) illustrates the balance in Equation (5), and shows that the difference of precipitation and evapotranspiration is equal to the sum of the decrease of atmospheric water-vapor storage and lateral (horizontal) convergence, and also to the sum of the increase of water storage over the land and runoff. Theoretically, Equation (5) can be applied for any control volume of land area combined with the atmosphere above, even though the practical applicability depends on the accuracy and availability of atmospheric and hydrologic information. The following further assumptions are often employed in annual water-balance computations:

ð4Þ

where W represents the precipitable water (i.e., column storage of water vapor) and Q is the convergence of water-vapor flux in the atmosphere (all fluxes given in the unit volume per time step). Since the atmospheric water content in both solid and liquid phases are generally small, only the water vapor is considered in Equation (4). The balance is schematically illustrated in Figure 3(b), which describes that the water storage

Precipitation Evapotranspiration

in an atmospheric column is increased by the lateral convergence of water vapor and evapotranspiration through the bottom of the column (i.e., land surface), and decreases by the precipitation falling out from the bottom of the atmosphere column to the land.

ð3Þ

This assumption is generally valid at the outlet of a catchment. In most cases, surface runoff Ro becomes river discharge through the transport of river-channel network. The river discharge is an integrated quantity over the whole catchment and can be observed at a downstream point in contrast to other fluxes, such as P and E, which have to be spatially measured.

11

Vapor flux

Runoff

Precipitation

Basin storage

Evapotranspiration (b) Water balance in the atmosphere

Groundwater movement

(c) Combined water balance

Figure 3 (a) Terrestrial water balance, (b) atmospheric water balance, and (c) combined atmosphere–land surface water balance. (a), (b), and (c) correspond to Equations (2), (4), and (5), respectively.

12

Global Hydrology

2.01.3.3.1 Estimation of large-scale evapotranspiration Generally, it is not an easy task to obtain large-scale evapotranspiration E based on observations except for the annual timescale in which E can be estimated as the residual of P and Ro. However, the combined water balance can help estimate E at a shorter timescale, for example, monthly. Note that Equation (4) can be rewritten as

E ¼ q W=q t  Q þ P

ð7Þ

which can be applicable over a period shorter than a year, unlike the assumption in Equation (6). If atmospheric and precipitation data are available over a short timescale such as a month or a day, evapotranspiration can be estimated at the corresponding timescales; however, it is also subject to severe limitations imposed by the data accuracy. The region over which the evapotranspiration is estimated is not limited to a river basin; rather, it depends on the scale and the associated accuracy of the available atmospheric and precipitation data. The global distributions of precipitation P, integrated water-vapor convergence Q, and evapotranspiration E estimated by Equation (7) are presented in Figures 2(c), 2(f), and 2(g), respectively. The zonal mean precipitation P, integrated water-vapor convergence Q, and evapotranspiration E estimated by Equation (7) are presented in Figures 4(a)–4(c). As can be seen from the zonal mean precipitation along the midlatitude, storm tracks over the North Pacific and Atlantic oceans are stronger in December–January–February (DJF) than in June–July–August (JJA). The Intertropical Convergence Zone (ITCZ) is enhanced in JJA, when the southeastern part of the Asian continent is covered by the southwest monsoon rainfall. The distribution of E is less dependent on the latitude and has smaller seasonal changes compared to P (see also Trenberth and Guillemot, 1998). The mean evapotranspiration in tropical areas is approximately 4 mm d1.

2.01.3.3.2 Estimation of total water storage in a river basin

2.01.3.4 Bottom Line of Global Water Balance Water balance on the global scale with consideration of land and ocean areas separately can be expressed as

Total terrestrial water storage, as the sum of surface water (such as river water, snow water, and water in lakes), soil moisture, and groundwater, is generally difficult to estimate on the global scale. Combining Equations (3) and (4) yields

q S=q t ¼ q W=q t þ Q  Ro

the cycles of energy and water are closely related. Wijffels et al. (1992) used values of atmospheric water-vapor convergence Q from Bryan and Oort (1984) and discharge data from Baumgartner and Reichel (1975) to estimate the freshwater transport by oceans and atmosphere, but their results seem to have large uncertainties and they did not present the freshwater transport by rivers. The annual freshwater transport in the meridional (north– south) direction can be estimated from Q and river discharge with geographical information such as the location of river mouths and basin boundaries (Oki et al., 1995). The estimated result is shown in Figure 5, in which it shows that in the case of oceans net transport is the residual of northward and southward freshwater flux by all ocean currents globally, and it cannot be compared directly with individual ocean currents such as the Kuroshio and the Gulf Stream. Transports by the atmosphere and by the ocean have almost the same absolute values for most of the latitudes, but with a different sign. The transport by rivers is about 10% of these other fluxes globally (there may be an underestimation because average Q tends to be smaller than average river discharge observed at the global land surface). The negative (southward) peak by rivers at 301 S is mainly due to the Parana River in South America, and the peaks at the equator and 101 N are due to rivers in South America, such as the Magdalena and Orinoco. Large Russian rivers, such as the Ob, Yenisey, and Lena, carry freshwater toward the north between 50 and 701 N. These results indicate that the hydrological processes over land play non-negligible roles in the climate system, not only by the exchange of energy and water at the land surface, but also through the transport of freshwater by rivers which affects water balance of the oceans and forms a part of the hydrological circulation on the Earth among the atmosphere, land, and oceans.

ð8Þ

which indicates that the change of water storage in the control volume over the land can in principle be estimated from the atmospheric and runoff data. Although an initial value is required to obtain the absolute value of storage, the atmospheric water balance can be useful in estimating the seasonal change of total water storages in large river basins.

2.01.3.3.3 Estimation of zonally averaged net transport of freshwater The meridional (north–south direction) distribution of the zonally averaged annual energy transports by the atmosphere and the ocean has been evaluated, even though there are quantitative problems in estimating such values (Trenberth and Solomon, 1994). However, the corresponding distribution of water transport has not often been studied, although

P1  E1  q S1 =q t ¼ R ¼ ðPo  Eo Þ þ q So =q t

ð9Þ

where P, E, S, and R represent precipitation, evapotranspiration, total water storage, and continental runoff, respectively, with the subscript l indicating values for land and o for ocean. For the steady state, the temporal changes of Sl and So can be neglected and Equation (9) becomes

P1  E1 ¼ R ¼ ðPo  Eo Þ

ð10Þ

which indicates that continental runoff can be estimated as a residual of total evapotranspiration (Eo) and precipitation (Po) over the ocean. It could be an effective method to estimate continental runoff since a macroscale estimation of precipitation and evaporation is relatively easier over ocean than over the land. If precise estimates of the long-term trend of global mean precipitation and evapotranspiration over both land and ocean are available, there is a potential to infer the trend of the water stored over the land or ocean as suggested by Equation (9).

Global Hydrology

13

2.01.4 Global Water Balance

From Equation (10)

Pe ¼ Ee

ð11Þ

can be derived which states that precipitation all over the Earth Pe ¼ Pl þ Po and evapotranspiration all over the Earth Ee ¼ Pl þ Po should be identical under the conditions when the temporal changes of water storage over land and ocean are negligible.

The values quoted in Table 1 and Figure 1 are estimated based on various observations with some assumptions in order to obtain global perspectives. These values are sometimes different in other references probably because the source of observed data, methodology of estimation, and assumptions are different. In some cases, global water balances are estimated using empirical relationship of evapotranspiration to precipitation in each latitude belt (Baumgartner and Reichel, 1975).

Zonal mean precipitation (overall, land and sea) 10.0 Annual DJF JJA

(mm d−1)

7.5

5

2.5

0 60° S (a)

40° S

20° S

90° S

EQ.

20° N

40° N

60° N

Latitude

90° N

Vapor-flux convergence (overall, land and sea) 6 Annual DJF JJA

(mm d−1)

3

0

−3

−6 (b)

60° S 90° S

40° S

20° S

EQ. Latitude

20° N

40° N

60° N 90° N

Figure 4 (a) Meridional distribution of precipitation (P ) for mean over land and sea; (b) same as (a) but for vapor-flux convergence (Q ); (c) same as (a) but for evapotranspiration (E ) calculated as a residual of P and Q. Data of (a) from Xie P and Arkin PA (1996) Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. Journal of Climate 9: 840–858 and (b) from Oki T, Musiake K, Matsuyama H, and Masuda K (1995) Global atmospheric water balance and runoff from large river basins. Hydrological Processes 9: 655–678.

14

Global Hydrology Zonal mean evaporation (overall, land and sea) 9 Annual DJF JJA

(mm d−1)

6

3

0

−3 (c)

60° S 90° S

40° S

20° S

EQ. Latitude

20° N

40° N

60° N 90° N

Figure 4 Continued.

Under an international research project, the land-surface models (LSMs) were used to estimate global water and energy balances for 1986–95 in order to obtain global distribution of surface soil moisture, which is not easy to obtain but relevant for understanding the land–atmosphere interactions (Dirmeyer et al., 2006). The project was called the second phase of GSWP2 and its goal was to produce state-of-the-art global data sets of land-surface fluxes, state variables, and related hydrologic quantities.

2.01.4.1 Uncertainties in Global Water-Balance Estimates In GSWP2, meteorological forcing data are hybrid products of the National Center for Environmental Prediction (NCEP)/ Department of Energy (DOE) reanalysis data and observational data based on in-situ and satellite monitoring, provided at a 3-hourly time step for a period of 13.5 years from July 1982 to December 1995. The first 3.5 years’ data are used for spin up. The land-surface parameters are specified from the Earth Resources Observation and Science Data Center (EDC) for the land-cover data and the International Geosphere–Biosphere Programme Data Information System (IGBP-DIS) for the soil data. Both land-surface parameters and meteorological forcing are at 11 resolution for all land grids excluding Antarctica. Figure 6(a) illustrates the model-derived global water balance over the global land excluding ice, glacier, and lake. The numeric in the box corresponds to the 10-year mean annual value of eight LSMs participated in the GSWP2 project (Oki et al., 2005). All the simulations were performed using identical forcing data given by 11 11 longitudinal and latitudinal grid boxes, and typical time steps of the calculations are 5 min to 3 h. The vertical ranges shown above and below the boxes indicate the maximum and minimum values in the interannual variation of mean annual value among the eight LSMs. The horizontal ranges shown left and right to the

boxes indicate the maximum and minimum values of the intermodel variation of the 10-year mean value of eight LSMs. Generally, intermodel variation exceeds interannual variation, which suggests that the uncertainty associated with model selection is larger than the sampling error of estimating global water balance. In the case of rainfall, intermodel variation is small because identical precipitation forcing was given to LSMs so that the differences among LSM estimates were merely caused by the rain/snow judgment made by each modeling group. The advantage of using models to estimate global water balance is the capability to have more detailed insights than using observations. For example, snow over the land excluding ice and glacier areas is approximately 10% of total precipitation, and the ratio of surface runoff and subsurface runoff is approximately 2:3 in Figure 6(a), but the latter is approximately 1:2 in Figure 1. This is because the model results used for calculating average value are different for Figures 1 and 6(a). In some LSMs, neither surface nor subsurface runoff process is considered, which is the reason why the minimum values are zero. Even though at present it is difficult to assess the validity of these breakdowns, due to the lack of observations on the partitions between snow and rain or between surface and subsurface runoff, such model-based estimates will stimulate scientific interest to collect and compile global information on these important hydrological quantities in the future. Further, evapotranspiration was estimated separately by bare-soil evaporation (Es), evaporation from intercepted water on leaves (Ei), evaporation from open water (Ew), and transpiration from vegetation (Et), as shown in Figure 6(b), even though the intermodel variations are quite large partially because some LSMs do not consider all of these components of evapotranspiration. Even though the values in Figure 6(b) are not definitive, it is interesting to see that bare-soil evaporation

Global Hydrology

15

Toward north (mean 1989−92) 40

100

Atmosphere Continent

30

Ocean

10

0

50

Land/sea (%)

Water flux (1012 m3 yr−1)

20

−10 −20 −30 −40

0 60° S 90° S

30° S

EQ. Latitude

30° N

60° N 90° N

Figure 5 The annual freshwater transport in the meridional (north–south) direction by atmosphere, ocean, and rivers (land). Water-vapor flux transport of 20  1012 m3 yr1 corresponds to approximately 1.6  1015 W of latent-heat transport. Shaded bars behind the lines indicate the fraction of land at each latitudinal belt.

and transpiration from vegetation are closely comparative, and interception loss is approximately 10% of the total evapotranspiration. It would be interesting if these estimates can be revised and validated by certain observation-based measures, and intermodel discrepancies can therefore be reduced.

2.01.4.2 Water Balance and Climate Based on observed precipitation (Xie and Arkin, 1996) and river-discharge records archived at the Global Runoff Data Centre, annual water balance for most river basins worldwide was estimated (Oki et al., 1999), and this is presented in Figure 7. The ordinate in Figure 7 is the residual of long-term mean annual precipitation and runoff, and this annual loss should correspond to long-term mean annual evapotranspiration. Different symbols are used for plotting: red stars indicate water balance of the river basins where gauging stations are located between 201 S and 201 N. The plus symbols indicate the river basins where gauging stations are located between 201 and 401 in both hemispheres, and the blue circles are 401 or higher. The line connects the mean precipitation and annual loss for each 51 latitudinal belt. As seen, even though the scatter is large, approximately 70% of precipitation is evapotranspirated in high-latitude river basins. On the other hand, mean evapotranspiration in tropical river basins is approximately 1000 mm yr1 with less dependency on annual precipitation. Such analyses on the relationship between P and E have long been used for estimation of global water balance, for example, in Baumgartner and Reichel (1975). It is also

clear from Figure 7 that river basins with annual precipitation less than 800 mm yr1 have marginal amounts of river runoff since most precipitation is used for evapotranspiration. In these river basins, evapotranspiration is mainly controlled by the availability of water (water controlled), and this is in contrast to tropical river basins with precipitation higher than 1000 mm yr1 where annual evapotranspiration is limited by the available energy (energy- or radiation controlled). Budyko (1974) proposed an equation

E=P ¼ ½zðtanh 1=zÞð1  cosh z þ sinh zÞ ð1=2Þ

ð12Þ

where E and P are annual evapotranspiration and precipitation respectively, and the Budyko’s dryness index is defined as

z ¼ Rn =lP

ð13Þ

where Rn and l are net radiation and the coefficient of latent heat, respectively. This equation is derived by considering that the E/P should be asymptotic to 1.0 for dry regions (large z) since E should be less than P, and E/P should be asymptotic to Rn/lP for wet regions (small z) since E should be less than Rn/l. Budyko’s equation (12) is conceptual, but it can provide a realistic water balance as shown in Figure 8. Mean water balance averaged for each 0.2z( ¼ Rn/lP) bin estimated by an LSM corresponds fairly well with the curve according to the Budyko’s equation (12), even though large scatters are found in the plots of each 11 longitudinal and latitudinal grid box. Yang et al.

16

Global Hydrology

Global terrestrial water budget Unit: mm yr−1 91 58

86

793

102

80

Legend

759

742

Snow

508

414

726

742

574

499

Rainfall

0

Intermodel range

142

302

133

Surface runoff

432

338 308

Total discharge

138 60

303

150

Soil-water storage

149

* Except lake and ice/glacier, 14 409 grids or 1.302 59e + 8 km2

Average of eight models (1986−95)

364 270

152

Subsurface runoff

788

726

302

196

0

759

ET 151

214

Interannual range

793

524

788

Balance = −1 mm

(a)

Global composition of evapotranspiration Unit: mm yr−1

Legend

574

793

Interannual range

524 414

508 499

742

ET Es 0

65

62

137

0

229

544

Average of eight models (1986−95)

248 0 7

238

Intermodel range

Et

59

11

788

726

Ew

Ei

759

6

47

210

531

235

1

223

(b)

Figure 6 (a) Global terrestrial water balance averaged for 1986–95 estimated by eight LSMs in boxes. Interannual variation range (vertical) for 1986–95 and intermodel discrepancies (horizontal) among eight models are presented for the annual mean estimates; (b) same as (a) but for global composition of evapotranspiration.

(2009) analyzed annual water balance in 99 river basins in China and concluded that this scatter can at least partially be explained by the vegetation cover in the river basin.

2.01.4.3 Annual Water Balance in Climatic Regions Annual water balances estimated by GSWP2 were analyzed and each 11 11 grid box was classified into one of the

following six climatic regions according to the Budyko’s dryness index z and annual precipitation:

• • • • •

arid region: z44.0; semiarid region: 4.04z4 ¼ 2.0; semi-humid region: 2.04z4 ¼1.2; humid region: 1.24z4 ¼ 0.7; tropical humid region: 0.74 ¼ z and annual precipitation larger than 2000 mm yr1; and

Global Hydrology

17

Precipitation and annual loss over the world 2000 Low latitude (20°S−20°N) Mid-latitude (20°S−40°S, 20°N−40°N) High latitude (40°S−90°S, 40°N−90°N)

Annual loss (mm yr−1)

1600

1200 5 15 20 25

800

10

0

30 40 45 35 50 55 60 65

400 70 0 0

400

800

1200

1600

Precipitation (mm

2000

2400

2800

yr−1)

Figure 7 Annual water balance in major river basins. Annual loss is estimated as a residual of annual precipitation and observed runoff in catchments of 250 gauging stations of river discharge. From Oki T, Nishimura T, and Dirmeyer P (1999) Assessment of annual runoff from land surface models using total runoff integrating pathways (TRIP). Journal of the Meteorological Society of Japan 77: 235–255.

•

very humid region: 0.74 ¼ z and annual precipitation less than 2000 mm yr1.

The six classified regions are illustrated in Figure 9 along with the ice-covered region. The differentiation is difficult between tropical humid region and very humid region only by using the Budyko’s dryness index z. Therefore, annual precipitation is considered in the classification. It is interesting to see that z is similar in both tropical and high-latitude regions in addition to the Asian monsoon region. Perhaps, it is also necessary to consider the seasonal change of major waterbalance terms for better differentiation of these regions. Long-term mean water balance for each climatic region classified by z is presented in Figure 10(a). Separation of mean annual precipitation into evapotranspiration, surface runoff, and subsurface runoff is also illustrated. The sum of these corresponds to annual precipitation, and the ratio of annual evapotranspiration to precipitation is close to the mean z of each region. Slightly negative evapotranspiration in the ice region indicates net sublimation in the region, that is, the land surface obtains energy from the atmosphere through sublimation. Evapotranspiration is also divided into four components: bare-soil evaporation, transpiration, evaporation from intercepted water, and evaporation from open water, as presented in Figure 10(b). Note that not all the LSMs that participated in GSWP2 have considered all of these four components, and some of these values could be underestimated. Transpiration

and evaporation from intercepted water are proportional to the vegetation biomass in each region, and it is interesting to note that the magnitude of bare-soil evaporation is relatively uniform globally than other components, except for arid, very humid, and ice regions.

2.01.5 Challenges in the Global Hydrology and Research Gaps 2.01.5.1 Macroscale Hydrological Modeling The development of macroscale hydrological models was a serious topic of discussion among Japanese scientists researching land–atmosphere interaction studies in the early 1990s when Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment (GAME) was under preparation. Two approaches were identified: one to expand a conventional microscale rainfall-runoff hydrological model into a macroscale model, which can run on the continental scale with a detailed energy balance and vegetation representation, and the other to enhance hydrological processes in LSMs and couple them with horizontal water-flow processes, particularly with river flow. A river-routing scheme was hence developed with a globalflow direction map, and named as the total runoff integrating pathways (TRIPs) (Oki and Sud, 1998). Such a river-routing scheme can be coupled with any LSM, and can also be used as a post-processor integrating the runoff estimated by LSMs into

18

Global Hydrology Budyko’s diagram for GSFC (Mosaic) (1987)

E/P; evapotranspiration/precipitation

1.0

0.8

0.6

0.4

0.2

0.0 0.0

0.5

1.0

1.5 2.0 2.5 3.0 3.5 4.0 Rn/lP; net radiation/(latent heat * precipitation)

4.5

5.0

5.5

6.0

Figure 8 Annual energy and water balance of 11 longitude and latitudinal grids calculated by an LSM for 1987 (Koster et al., 1999). Plots indicate the energy and water balances in each grid box and green line presents the mean E/P for each 0.2 z ¼ Rn/lP bin. The red curve indicates Equation (12). From Budyko MI (1974) Climate and Life, Miller DH (trans.). San Diego, CA: Academic Press.

Arid Semiarid Semi-humid Humid Tropical humid Very humid Iee cover Figure 9 Classification of energy and water-balance regime using Budyko’s dryness index z ¼ Rn/lP.

Mean water balance in climatic regions (1986−95)

l ba

e

G lo

Ic

id m

id Ve

ry

hu

um

id

lh

Tr o

pi

Se

m

ca

i- h

H um

r id m

ia

Ar Se

(a)

id

Rsub Rsurf ET

um

3000 2500 2000 1500 1000 500 0 −500

id

(mm y−1)

Global Hydrology

1200 Ew Ei Et Es

1000 800 600

19

seasonal pattern of observed TWS variation by Gravity Recovery and Climate Experiment (GRACE; see Tapley et al., 2004) without an appropriate representation of river-storage component. The dominant role of river storage was already indicated in a pilot study which compared total TWS changes estimated by the atmospheric water-balance method and a GCM simulation coupled with TRIP in the Amazon river basin (Oki et al., 1996). However, the message was not undoubtedly convincing until recent years when satellite-observed GRACE data became available. Using a geodesy approach, Han et al. (2009) employed a fixed-velocity version of TRIP in the Amazon river basin and its vicinity, and compared the model simulations to the residual of GRACE raw measurements derived from removing all the gravity-influencing factors except for the horizontally moving water. They demonstrated that the optimal flow velocity of TRIP in the Amazon varies between rising and falling water levels.

2.01.5.2 Global Changes and Global Hydrology

400 200 l G

lo

ba

e Ic

id hu

m

id ry

ic op

Ve

al

H

hu

um

m

id

id

Tr

(b)

um i-h

m Se

Se

m

ia

Ar

rid

id

0

Figure 10 Components of (a) the surface hydrologic balance, and (b) total evapotranspiration estimated under the second phase of the Global Soil Wetness project. From Dirmeyer PA, Gao XA, Zhao M, Guo ZC, Oki T, and Hanasaki N (2006) GSWP-2 multimodel anlysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society 87: 1381–1397.

river discharge (Oki et al., 1999). The first version of TRIP adopted a primitive fixed-velocity scheme (Miller et al., 1994), while the variable-velocity version was also developed (NgoDuc et al., 2007). TRIP was coupled in some general circulation model (GCM) projections used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) to identify the impact of climate change on hydrological cycles (Falloon and Betts, 2006), and there have been some studies of future assessment on the world water resources and global flood disasters utilizing the TRIP model, as well (Oki and Kanae, 2006; Hirabayashi and Kanae, 2009). Further, Kim et al. (2009) underscored the importance of river component in terrestrial water storage (TWS) variation over global river basins. To reduce simulation uncertainty, ensemble simulations were performed with multiple precipitation data, and a localized Bayesian model averaging technique was applied to TRIP simulation. Figure 11 shows that river storage not only explains different portions of total TWS variations, but also plays different roles in different climatic regions. It is the most dominant water-storage component in wet basins (e.g., the Amazon) in terms of amplitude, and it acts as a buffer which smoothes the seasonal variation of total TWS especially in snow-dominated basins (e.g., the Amur). It signifies that model simulation of TWS may not be able to properly reproduce the amplitude and

Macroscale hydrological models have also been developed in response to the societal expectations for solving current and future world water issues. There has been a concrete demand for the information on how much water resources are available now and what kinds of changes are projected for the future. Conventionally, available freshwater resources are commonly defined as annual runoff estimated by historical river-discharge data or water-balance calculation (Lvovitch, 1973; Baumgartner and Reichel, 1975; Korzun, 1978). Such an approach has been used to provide valuable information on the annual freshwater resources in many countries. Atmospheric water balance using the water-vapor flux-convergence data could be alternatively used to estimate global distribution of runoff owing to the advent of atmospheric reanalysis and data-assimilation system (Oki et al., 1995). Simple analytical water-balance models have been widely used to estimate global-scale available freshwater resources in the world since the beginning of this century (Alcamo et al., 2000; Vo¨ro¨smarty et al., 2000; Do¨ll et al., 2003; Rockstro¨m et al., 2009). Later, LSMs were used to simulate global water cycles (Oki et al., 2001; Dirmeyer et al., 2006). Changes of hydrological cycles during the twenty-first century associated with anticipated climate change are projected (Milly et al., 2005; Nohara et al., 2006), and their impacts on the demands and supplies of global water resources are estimated assuming future climatic and social-change scenarios (Arnell, 2004; Alcamo et al., 2007; Shen et al., 2008). Some of those estimations were calibrated by multiplying an empirical factor for the river basins, where observed river-discharge data are available. However, recent model simulations with advanced climate forcing data can estimate global runoff distribution with adequate accuracy without the need of calibration (Hanasaki et al., 2008a). Changes in extreme river discharge are also of interest now (Hirabayashi et al., 2008). Several recently developed macroscale hydrological models for water-resource assessment also include a reservoir-operation scheme (Haddeland et al., 2006; Hanasaki et al., 2006) in order to simulate the real hydrological cycles, which are significantly influenced by anthropogenic activities and modified from natural hydrological cycles even on the global

20

Global Hydrology (b) Storage anomaly (mm per month) (c)

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Figure 11 (a) Seasonal variations of gauged discharge (black solid line), discharge routed by TRIP (red solid line) and runoff without routing (gray dashed line). (b) Seasonal variations of GRACE observed TWSA (black solid line), simulated TWSA with river storage (red solid line), simulated TWSA without river storage (gray dashed line), and the major water storage components in TWS. Gray crosses ( þ ), green circles (K), and blue triangles (m) represent the individual storage component of snow water, soil moisture, and river storage, respectively. (c) Interannual variations of relative TWS: the GRACE observation (black dot), simulation with river storage (red solid line), and simulation without river storage (gray dashed line). Each area shaded by blue, gray, and green indicates the portion of river storage, snow water, and soil moisture in the simulated relative TWS, respectively. From Kim H, Yeh P, Oki T, and Kanae S (2009) The role of river storage in the seasonal variation of terrestrial water storage over global river basins. Geophysical Research Letters 36: L17402 (doi:10.1029/2009GL039006).

scale in the Anthropocene (Crutzen, 2002). An integrated water-resources model is further coupled with a crop-growth submodel, which can simulate the timing and quantity of irrigation requirement, and a submodel, which can estimate environmental flow requirement (Hanasaki et al., 2008a). Such an approach is able to assess the balances of water demand and supply on a daily timescale, and a gap in the subannual distribution of water availability and water use can be detected in the Sahel, the Asian monsoon region, and southern Africa, where conventional water-scarcity indices such as the ratio of annual water withdrawal to water availability and available annual water resources per capita (Falkenmark and Rockstro¨m, 2004) cannot properly detect the stringent balance between demand and supply (Hanasaki et al., 2008b).

2.01.5.3 Global Trace of Water Cycles Numerical models can be associated with a scheme tracing the origin and flow path as if tracing the isotopic ratio of water (Yoshimura et al., 2004; Fekete et al., 2006). Such a flow-tracing function of water in the integrated water-resources model (Hanasaki et al., 2008a) considering the sources of water withdrawal from stream flow, medium-size reservoirs, and nonrenewable groundwater, in addition to precipitation to croplands, enabled the assessment of the origin of water producing major crops (Hanasaki et al., 2010). Figure 12(a) illustrates the ratio of blue water to total evapotranspiration during cropping period in irrigated croplands. Here, the blue water is defined as that part of evapotranspiration originating from irrigation, whereas the green water is from precipitation (see Falkenmark and Rockstro¨m, 2004). Figure 12(a) shows a distinctive geographical distribution in the dependence on blue water. In addition, the ratios of the source of blue water for stream flow including the influence of large reservoirs,

medium-size reservoirs, and nonrenewable and nonlocal blue water are shown in Figures 12(b)–12(d). Areas highly dependent on nonrenewable and nonlocal blue water were detected in Pakistan, Bangladesh, western part of India, north and western parts of China, some regions in the Arabian Peninsula, and the western part of the United States through Mexico. Cumulative nonrenewable and nonlocal blue-water withdrawals estimated by the model correspond fairly well with the country statistics of total groundwater withdrawals (Hanasaki, 2009, personal communication), and such an integrated model has the ability to quantify the global virtual water flow (Allan, 1998; Oki and Kanae, 2004) or water footprint Hoekstra and Chapagain, 2007) through major crop consumption (Hanasaki et al., 2010). It is apparent that these achievements illustrate how the framework of global off-line simulation of LSMs, coupled with lateral river-flow model and/or anthropogenic activities, driven by the best-available meteorological forcing data, such as precipitation and downward radiation, is relevant for estimating global energy and water cycles, validating the estimates and sometimes the quality of forcing data with independent observations, and improving the models themselves. There are attempts to utilize this framework for assessing the impacts of climate change on future hydrological cycles which would demand adaptation measures in waterresources management, flood management, and food production. For such purposes, it is necessary to develop reliable forcing data for the future, based on GCM projections probably with bias corrections and spatial and temporal downscaling, as well as developing best estimates for the future boundary conditions for hydrological simulations such as vegetation type and land use/land cover. Figure 13 summarizes the concept of how the forcing data have a large impact on the accuracy of the output from theories, equations, and numerical models. Certainly, the spatial

Blue/(blue+green)

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Figure 12 (a) The ratio of blue water to the total evapotranspiration during a cropping period from irrigated cropland (the total of green and blue water). The ratios of (b) streamflow, (c) medium-size reservoirs, and (d) nonrenewable and nonlocal blue-water withdrawals to blue water. From Hanasaki N, Inuzuka T, Kanae S, and Oki T (2010) An estimation of global virtual water flow and sources of water withdrawal for major crops and livestock products using a global hydrological model. Journal of Hydrology 384: 232–244.

22

Global Hydrology

and temporal boundary conditions as well as the field information characterizing the target region, such as land cover and land use, are critically important to obtain reasonable estimates. Therefore, it is recommended to examine the quality of forcing data and boundary conditions along with revising the core theory, principle equations, and model code, or tuning model parameters, for hydrological modeling. It is particularly important for global-scale studies since uncertainties in the forcing data and field information are relatively large compared to those at the local scale. It should also be recalled that the applicability and accuracy of the model are highly dependent on the specific temporal and spatial scales.

Owing to recent advancements in global earth-observation technology and macroscale modeling capacity, global hydrology can now provide basic information on the regional hydrological cycle which may support the decision-making process in the integrated water-resources management. It should also be examined to what extent such a framework of off-line simulation of LSMs can be applied to finer spatial and temporal scales, such as 1-km grid spacing and hourly time interval. For such research efforts, observational data from regional studies can provide significant information, and efforts to integrate data sets from various regional studies should be promoted.

2.01.5.5 Research Opportunities in Global Hydrology 2.01.5.4 Interactions of Global- and Local-Scale Hydrology As described in Oki et al. (2006), water, as one of the major components of the global climate system, is one of the major cross-cutting axes in the Earth system science. The water cycle transports various materials, such as sediments and nutrients, from land to the oceans. Water resources are closely related to energy, industry, and agricultural production. Of course, water is indispensable for life and supports health. Water issues are related to poverty, and providing access to safe drinking water is one of the key necessities for sustainable development. In the past, water issues remained local issues; however, due to the increase in international trade and mutual interdependence among countries, water issues now often need to be dealt with on the global scale, and require information on global hydrology for their solutions. Sharing hydrological information relating to the transboundary river basins and shared aquifers will help reduce conflict between relevant countries, and quantitative estimates of recharge amounts or potentially available water resources will assist in implementing sustainable water use. Global hydrology is not merely concerned with global monitoring, modeling, and world water-resources assessment.

There are still challenging scientific issues to be resolved in global hydrology. For instance, separation of rain and snow on the global scale, as illustrated in Figure 1, is of interest. However, this is based on numerical-model estimates and it is quite uncertain about the accuracy of the numbers or even the ratio between rain and snow (here, approximately 8:1). The situation is similar for the ratio between surface and sub-surface runoff (here, approximately 1:2). It is reasonable to infer direct groundwater discharge from land to oceans to be a residual between total runoff estimated by numerical models subtracted by observation-based total discharge; however, the accuracy of such an estimate is yet to be determined. As described in Section 2.01.1, it is believed that total amount of the water on the Earth is conserved on a timescale shorter than geological timescales; however, do we have any reliable observational evidence for it? Our knowledge seems to be incomplete on the water exchange between Earth’s surface and mantle, although a recent report suggested that the lower mantle may store 5 times more water than the ocean (Murakami et al., 2002). The direct groundwater discharge to the ocean, estimated to be about 10% of total river discharge globally (Church, 1996),

Sources of uncertainties • Quantity • Runoff, ET, … • Quality • Solutes • Isotopic ratio • Sediments, …

Environmental info. Precip., radiation, temp., humidity, wind,… (atmospheric forcing)

Regional info. soil, vegetation, topography, basin area, … (parameters)

Theory, equation, or model

Initial/boundary conditions Applicability and accuracy would differ by temporal and spatial scales, and the characteristics of the target region.

Output with various temporal scales • Annual mean • Daily, hourly, … • Duration curve, … • Extremes, …

Figure 13 Sources and causes of uncertainties and errors in the hydrological simulations and estimations.

Global Hydrology

is included in the river discharge plotted in Figure 1. According to the model estimates by Koirala (2010), groundwater recharge in the grid boxes of 11 11 in longitude and latitude near the coastal line is totally 5890 km3 yr1, and it is approximately 13% of the annual total runoff from the continents to oceans of 45 500 km3 yr1. Even though the total amount of groundwater recharge depends on the grid size defining the coastal region, is it just a coincidence or is it that groundwater recharge in the coastal areas provides a good proxy of direct groundwater runoff from continents to oceans? Scientific interest in global hydrology has increased since the 1980s as public awareness of global environmental issues and process interactions, such as El Nin˜o events and anthropogenic climate change, has risen. Further work is required for the detection and attribution of the present-day hydrological changes – in particular, changes in the intra-seasonal water availability and in the frequency and magnitude of extreme events. Uncertainties in the future projections of hydrological quantities and water qualities should be reduced and quantitative accuracy should be enhanced, particularly for runoff regime changes. It is further anticipated that feedbacks of mitigation and adaptation measures for the concerned climate change on water sectors will be assessed for proper policymaking.

23

In addition to anthropogenic climate change, it is of interest in global hydrology to assess the impact of land-use change, such as deforestation and urbanization, human activities, such as reservoir construction and water withdrawals for irrigation, industry, and domestic water uses, and emission of air pollutants which would have been suppressing weak rainfall and modulate precipitation occurrence weekly.

2.01.5.6 Research Gaps in Global Hydrology Hydro-meteorological monitoring networks need to be maintained and further expanded to enable the analysis of hydro-climatic trends at the local level and the improvement in the accuracy of predictions, forecasts, and early warnings. As clearly illustrated in Figure 14 (from Oki et al., 1999), global hydrological simulations are relatively poor in areas with little in-situ observations. Basic observational networks on the ground are critically indispensable for proper monitoring and modeling of global hydrology; however, it is also required to utilize remotely sensed information in order to fill the gaps of in-situ observations. One of the current trends in the utilization of remote sensing technique is the so-called data

Runoff estimation error (mean 11 LSMs) and the density of raingauges in each river Basin Y1987 600

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Runof (model observation) (mm yr−1) Figure 14 Comparisons between the density of rain gauge (/106 km2) used in preparing the forcing precipitation and the mean bias error (mm yr1) of 11 LSMs for 150 major river basins in the world in 1987 and 1988. From Oki T, Nishimura T, and Dirmeyer P (1999) Assessment of annual runoff from land surface models using total runoff integrating pathways (TRIP). Journal of the Meteorological Society of Japan 77: 235–255.

24

Global Hydrology

assimilation, which optimally merges observations (not limited to remote sensing) with numerical-model estimates. Reliable observational data are essentially necessary not only as the forcing data for global hydrological modeling, but also for the validation of model estimates. River discharge and soil-moisture data are critically important for global hydrological studies. However, contributions from the operational agencies in the world are not yet well established and need to be enhanced. Isotopic ratio of rainwater is collected and the data set is available through the Global Network of Isotopes in Precipitation (GNIP) by the International Atomic Energy Agency (IAEA). These observational data can be used to investigate the routes and mechanisms of how the evaporated waters from the ocean surface are transported and precipitated at particular locations, which can be estimated by water-vapor transfer models with the consideration of isotopic processes (Yoshimura et al., 2008). The information on the isotopic ratio of river waters is not well organized and cannot be used easily on the global scale. Current global hydrological modeling has not yet integrated most of the latest achievements in process understanding and regional- or local-scale modeling studies. Global simulation of solutes and sediments are emerging. Both natural and anthropogenic sources should be considered, as for nutrients, and probably such models should be coupled with agricultural models which simulate crop growth. Moreover, it is rather difficult that river ice jams can be simulated properly by current hydrologic models. For both problems of water quality and ice jams, a proper simulation of water temperature in rivers and lakes are requisite. Moreover, the representation of groundwater has been rather simple in global hydrological modeling. Some of the above issues have not been emphasized in the current global hydrological modeling due to their relatively minor impact on the climatic feedbacks from the land surface to the atmosphere. It is meaningful to recall that global hydrology has been developed in cooperation with global climate modeling; however, it is time to develop global hydrological models primarily for responding to the demands of understanding the hydrological cycle on the land surface and for supporting better water-resources management. From this point of view, integrated hydrological and water-resource models, which consider natural and anthropogenic water cycles and are coupled with crop models and reservoir operation models in order to provide a more realistic impact assessment and support the design of practical adaptation measures, should be developed and implemented.

Acknowledgments This study was funded by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (19106008 4), the Global Environment Research Fund of the Ministry of Environment (S-5), and the Innovation Program of Climate Change Projection for 21st Century of the Ministry of Education, Culture, Sports, Science, and Technology of Japan.

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2.02 Precipitation D Koutsoyiannis, National Technical University of Athens, Athens, Greece A Langousis, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA & 2011 Elsevier B.V. All rights reserved.

2.02.1 2.02.1.1 2.02.1.2 2.02.1.3 2.02.1.4 2.02.1.5 2.02.1.5.1 2.02.1.5.2 2.02.1.5.3 2.02.1.5.4 2.02.1.5.5 2.02.2 2.02.2.1 2.02.2.2 2.02.2.3 2.02.2.3.1 2.02.2.3.2 2.02.2.4 2.02.2.4.1 2.02.2.4.2 2.02.2.5 2.02.2.5.1 2.02.2.5.2 2.02.2.5.3 2.02.2.5.4 2.02.2.5.5 2.02.2.5.6 2.02.2.5.7 2.02.3 2.02.3.1 2.02.3.1.1 2.02.3.1.2 2.02.3.1.3 2.02.3.2 2.02.3.2.1 2.02.3.2.2 2.02.3.3 2.02.3.3.1 2.02.3.3.2 2.02.3.3.3 2.02.4 2.02.4.1 2.02.4.2 2.02.4.3 2.02.4.4 2.02.4.5 2.02.5 2.02.5.1 2.02.5.2 2.02.5.3 References

Introduction The Entrancement of Precipitation Forms of Precipitation Precipitation Metrics The Enormous Variability of Precipitation Probability and Stochastic Processes as Tools for Understanding and Modeling Precipitation Basic concepts of probability Stochastic processes Stationarity Ergodicity Some characteristic stochastic properties of precipitation Physical and Meteorological Framework Basics of Moist Air Thermodynamics Formation and Growth of Precipitation Particles Properties of Precipitation Particles Terminal velocity Size distribution Clouds and Precipitation Types Cumulus cloud systems Stratus cloud systems Precipitation-Generating Weather Systems Fronts Mechanical lifting and orographic precipitation Extratropical cyclones Isolated extratropical convective storms Extratropical squall lines and rainbands Monsoons Tropical cyclones Precipitation Observation and Measurement Point Measurement of Precipitation Measuring devices Typical processing of rain gauge data Interpolation and integration of rainfall fields Radar Estimates of Precipitation Basics of radar observation and measurement Radar observation of distributed targets and the estimation of precipitation Spaceborne Estimates of Precipitation The IR signature of cloud tops The visible reflectivity of clouds The microwave signature of precipitation Precipitation modeling Rainfall Occurrence Rainfall Quantity Space–Time Models Rainfall Disaggregation and Downscaling Multifractal Models Precipitation and Engineering Design Probabilistic versus Deterministic Design Tools Extreme Rainfall Distribution Ombrian Relationships

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2.02.1 Introduction 2.02.1.1 The Entrancement of Precipitation Precipitation and its related phenomena, such as cloud formation and movement, thunders, and rainbow, are spectacular (Figure 1) due to their huge diversity and complexity. This complexity makes them difficult to comprehend, model, and predict. Hence, it is understandable that ancient civilizations explained these phenomena in a hyperphysical manner, assuming that deities were responsible for their creation. For example, in Greek mythology, some of the phenomena were deified (e.g., Iris is the name of a goddess as well as of the rainbow), whereas the most impressive among them, thunders in particular, were attributed to the action of the King of the Gods, Zeus (Jupiter in Roman mythology; similar deities are Indra in Hinduism, Thor in Norse, etc.). Demystification of these processes and formation of the physical concept of the hydrological cycle was closely related to the birth of science, by the turn of the seventh century BC. While the hydrological cycle was founded as a concept in the sixth century BC by

Anaximander, Anaximenes, and Xenophanes, and was later advanced by Aristotle (Koutsoyiannis et al., 2007), certain aspects related to precipitation can be understood only within the frame of modern science. The fact that a solid or liquid hydrometeor resists gravity and remains suspended in the atmosphere in a cloud is counterintuitive, and needs advanced knowledge of physics, fluid dynamics, and statistical thermodynamics to be understood and modeled. The complexity of the processes involved in precipitation and their enormous sensitivity to the initial conditions (where tiny initial differences produce great differences in the final phenomena), retain, to this day, some of the ancient mythical and magical magnificence of the societal perception of precipitation. People still believe in hyperphysical interventions in matters concerning precipitation. As put by Poincare´ (1908), father of the notion of chaos: Why do the rains, the tempests themselves seem to us to come by chance, so that many persons find it quite natural to pray for rain or shine, when they would think it ridiculous to pray for an eclipse?

Figure 1 Precipitation and related phenomena (from upper-left to lower-right): Monsoon rainfall (Pune, India, September 2009; photo by D Koutsoyiannis); snowy mountainous landscape (Mesounta, Greece, December 2008; from http://www.mesounta.gr/mesounta/ist_eik1/ 07_xion_03.htm); thunder (Athens, Greece, November 2005; from the photo gallery of Kostas Mafounis); rainbow (Mystras, Greece, April 2008, from laspistasteria.wordpress.com/2008/04/08/rainbow-3/).

Precipitation

Amazingly, however, and at the very same time, there is little disbelief in some climate modelers’ prophecies (or outputs of global circulation models (GCMs)) of the precipitation regimes over the globe in the next 100 years or more. This indicates an interesting conflict between perceptions of precipitation – that it is so unstable, uncertain, and unpredictable that prayers are needed to invoke precipitation, and that for some scientists, the future evolution of precipitation on Earth is still predictable in the long term. The latter belief concerns not only the general public, but also the scientific community. For example, a Google Scholar search with either of the keywords ‘precipitation’ or ‘rainfall’, plus the keywords ‘climate change’ and ‘GCM’, locates 21 700 publications (as of August 2009), of which about 200 have been cited 100 times or more. This huge list of results appears despite the fact that climate modelers themselves admit to the performance of their models being low, as far as precipitation is concerned (Randall et al., 2007). An independent study by Koutsoyiannis et al. (2008), which compares model results for the twentieth century with historical time series, has shown that the models are not credible at local scales and do not provide any basis for assessment of future conditions. These findings demonstrate that, even today, the perception of precipitation, not only by the general public, but even by scientists specialized in the study of precipitation, meteorologists, climatologists, and hydrologists, continues to be contradictory, problematic, and, in some sense, mysterious.

2.02.1.2 Forms of Precipitation Precipitation occurs in a number of forms, either liquid or solid, or even mixed (sleet). Liquid precipitation includes rainfall and drizzle, where the former is the most common and most significant, and the latter is characterized by much smaller drop sizes and lighter intensity. Dew is another liquid form, formed by condensation of water vapor (mostly at night) on cold surfaces (e.g., on tree leaves). Most important among the solid forms of precipitation are snow and hail. At high latitudes or at high altitudes, snow is the predominant form of precipitation. Snowfall may occur when the temperature is low and snow accumulates on the ground until the temperature rises sufficiently for it to melt. On the other hand, hail may fall in relatively high temperature and usually melts rapidly. While hailstones are amorphous and usually large (one to several centimeters in diameter), snowflakes are symmetrical and visually appealing with a tremendous variety of shapes, so that no two snowflakes are the same. Occult precipitation is induced when clouds or fog is formed in forested areas, and it includes liquid (fog drip) and solid (rime) forms. Fog drip occurs when water droplets are deposited on vegetative surfaces, and the water drips to the ground. Rime is formed when supercooled air masses encounter exposed objects, such as trees, that provide nucleation sites (see Sections 2.02.2.1 and 2.02.2.2) for formation and buildup of ice, much of which may fall to the ground in solid or liquid form. In some places (e.g., in humid forested areas), precipitation of this type may reach significant amounts; for example, rime constitutes about 30% of the annual precipitation in a Douglas fir forest in Oregon (Harr, 1982; Dingman,

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1994) and about 30% of total precipitation in fir-forested mountainous areas of Greece (Baloutsos et al., 2005), and it is the sole precipitation type on the rainless coast of Peru (Lull, 1964; Dingman, 1994).

2.02.1.3 Precipitation Metrics The principal metric of precipitation is the rainfall depth h (commonly expressed in millimeters) that falls at a specified point in a specified period of time t; this can be easily perceived and measured by a bucket exposed to precipitation. A derivative quantity is the precipitation intensity

i :¼

dh dt

ð1Þ

with units of length divided by time (typically mm h1, mm d1, and mm yr1). Since it cannot be measured directly (at an instantaneous time basis), it is typically approximated as

i¼

Dh Dt

ð2Þ

where Dh is the change of the depth in a finite time interval Dt. The intensity derived from Equation (2) is a time-averaged value – but at a point basis. Spatial averaging at various scales is always very useful as can be seen in Section 2.02.1.4. This averaging needs precipitation measurement at several points, followed by appropriate numerical integration methods (see Section 2.02.3.1). While traditional precipitation-measurement networks are sparse, thus making the estimation of areal precipitation uncertain, in recent decades, new measurement techniques have been developed implementing radar and satellite technologies (Sections 2.02.3.2 and 2.02.3.3). These provide a detailed description of the spatial distribution of precipitation, thus enabling a more accurate estimation. The latter techniques inherently involve the study of other metrics of precipitation such as the distribution of the size, velocity, and kinetic energy of the precipitation particles, and the socalled radar reflectivity (Section 2.02.3.2). Furthermore, the quantitative description of the processes related to the fall, accumulation, and melting of snow involves a number of additional metrics, such as the snowfall depth (new snow falling), the snowcover depth or snowpack depth (the depth of snow accumulated at a certain point at a particular time), the snow density rs, and the water equivalent of snowfall or of snowpack, defined as h ¼ h0 rs/rw, where h0 is the snowfall or the snowcover depth, and rw ¼ 1000 kg m3 is the liquid water density. Typical values of rs for snowfall range between 0.07 and 0.15rw (e.g., Dingman, 1994) but a commonly used value is rs ¼ 0.1rw ¼ 100 kg m3. For this value, a snowfall depth of say, 10 cm, corresponds to a precipitation water equivalent of 10 mm. The density of snowpack is generally larger than 0.1rw (because of compaction due to gravity and other mechanisms) and depends on the elapsed time and snowpack depth. After a few days, it is about 0.2rw, whereas after some months it may become about 0.4rw.

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2.02.1.4 The Enormous Variability of Precipitation The different phases and forms of precipitation, and the different shapes of precipitation particles (drops, flakes, and hailstones) are just a first indicator of the great diversity of the precipitation phenomena. At a macroscopic level and in quantitative terms, this diversity is expressed by the enormous variability of the precipitation process, in space and time, at all spatial and temporal scales. Intermittency is one of the aspects of variability, but even in areas or time periods in which precipitation is nonzero, the precipitation depth or average intensity is highly variable. Figure 2 shows the spatial variability of precipitation over the globe in mm d1 at the climatic scale (average for the 30year period 1979–2008) and at an annual scale (average for the year 2006), based mostly on satellite data (see Figure 2 caption and Section 2.02.3.3). While the average precipitation rate over the globe and over the specified 30-year period is 2.67 mm d1 or 977 mm yr1, we observe huge differences in different areas of the globe. In some areas, mostly in tropical seas and in equatorial areas of South America and Indonesia, this rate exceeds 10 mm d1 or 3.65 m yr1. On the other hand, in large areas in the subtropics, where climate is dominated by semi-permanent anticyclones, precipitation is lower than 1 mm d1 or 365 mm yr1. Significant portions of these areas in Africa, Australia, and America are deserts, where the average precipitation is much lower than 1 mm d1. In addition, in polar regions, where the available atmospheric moisture content is very low due to low temperature (see Section 2.02.2.1 and Figure 14), the amounts of precipitation are very small or even zero. For example, it is believed that certain dry valleys in the interior of Antarctica have not received any precipitation during the last 2 million years (Uijlenhoet, 2008). Figure 3 depicts the zonal precipitation profile and shows that the climatic precipitation rate at an annual basis is highest at a latitude of 51 N, reaching almost 2000 mm yr1 and has a second peak of about 1500 mm yr1 at 51 S. Around the Tropics of Cancer and Capricorn, at 23.41 N and S, respectively, the rainfall rate displays troughs of about 600 mm yr1, whereas at mid-latitudes, between 351 and 601 both N and S, rainfall increases again and remains fairly constant, close to the global average of 977 mm yr1. Then, toward the poles, it decreases to about 100 mm in Antarctica and slightly more, to 150 mm yr1, in the Arctic. Figure 3 also shows monthly climatic profiles for the months of January and July. It can be seen that the rainfall conditions for the 2 months are quite different, with the largest differences appearing at about 151 N and S and the smallest at about 301 N and S. Below 301 in the Northern Hemisphere, as well as above the Arctic Circle (66.61), rainfall is higher during summer (July) than during winter (January), but at mid-latitudes, this relationship is reversed. Similar conditions are met in the Southern Hemisphere (where January and July are summer and winter months, respectively). In both Figures 2 and 3, apart from climatic averages, the specific values for a certain year, namely 2006, are also shown. We observe that there are differences in the climatic values, manifesting temporal variability over the different years. This variability seems to be lower in comparison to the spatial

variability over the globe, as well as to the seasonal variability reflected in the profiles of different months. However, while the spatial variability over the globe and the seasonal variability are well comprehended and roughly explainable in terms of basic physical and astronomical knowledge (i.e., solar radiation, relationship of temperature and atmospheric moisture content, and motion of Earth), in other words, they are regular, the interannual variability is irregular, and difficult or even impossible to predict. Such irregular variability appears at finer timescales as well as at finer spatial scales. In fact, as easily understood from elementary statistics, as the spatial and/or temporal scale becomes finer, the variability increases. Figure 4 demonstrates how the variability of the spatial distribution of rainfall at a monthly temporal scale (January 2006) increases when the spatial scale decreases from 2.51  2.51 (upper panel) to 0.251  0.251 (lower panel). Clearly, the areas of equal rainfall amount (including areas of negligible rainfall, i.e., o1 mm d1E0.04 mm h1), which are smooth in the upper panel become rough and erratic in the lower panel. Moreover, the maximum observed rainfall is 21 mm d1 (monthly 651 mm) in the upper panel and 1.2 mm h1 (monthly 893 mm) in the lower panel. Figure 5 demonstrates the increasing variability with the decreasing timescale. Specifically, it depicts how the image of the rainfall distribution changes at a daily scale (9 January 2006) and at a sub-daily scale, at 3-hourly intervals of the same day. The differences between Figure 5 and Figure 4 are prominent. Especially at the 3-hourly scale, a vast part of the globe receives no rainfall, and the part that receives rainfall, it is irregularly distributed, yet not showing a totally random pattern. The maximum observed rate during this 3-hourly interval is 22 mm h1, about 18 times higher than the maximum rate at the monthly scale shown in Figure 4. The lowest panels of Figure 5 provide a zoom-in over the area lying between 91 N–51 S and 78–921 E, which is located in the Indian Ocean, south-east of Sri Lanka. This area received a large amount of rainfall on this particular day, with a rate that is nonuniform in space and time. Figures 6–8 focus on the temporal variability of precipitation. Figure 6 depicts the monthly and annual variation of the average precipitation over the globe. We can see that at both scales, the variability is remarkable. Thus, the annual precipitation in the last 30 years has varied between 957 and 996 mm and obviously much higher variation should have occurred in the past – but data of this type covering the entire globe do not exist for earlier periods. However, we can get an idea of earlier variation using rain gauge data (see Section 2.02.3.1) at certain locations. Perhaps, the oldest systematic observations of rainfall quantity in the world were made in Korea, in the fifteenth century. Rainfall records for the city of Seoul (37.571 N, 126.971 E, and 85 m) exist for the period since 1770, and are considered to be reliable (Arakawa, 1956; Wang et al., 2006, 2007). The recorded annual rainfall in Seoul is plotted in Figure 7 along with the running climatic averages at 10-year and 30-year timescales. The data are now available at a monthly scale from the climatic database of the Dutch Royal Netherlands Meteorological Institute (KNMI), while the monthly data for 1770–1907 appear also in Arakawa (1956).

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Figure 2 Precipitation distribution over the globe in mm d , (upper) at a climatic scale (average for the 30-year period 1979–2008) and (lower) at an annual scale (average for year 2006). Data and image generation due to the Global Precipitation Climatology Project (GPCP) made available by NASA at http://disc2.nascom.nasa.gov/Giovanni/tovas/rain.GPCP.2.shtml; resolution 2.51  2.51.

Comparisons show that the two time series are generally consistent, but not identical. The more modern data series has a few missing values, which generally correspond to high values of the older version (and it has been common practice in hydrometeorological data processing to delete very high

values or outliers, which are regarded suspect, see Section 2.02.3.1.2). In the time series plotted in Figure 7, these gaps have been filled in using the values of the older time series, and a few other missing values have been filled in with the average of the four nearest monthly values of the same month

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Precipitation (mm d−1) Figure 3 Zonal precipitation profile: precipitation averaged over all longitudes for different latitudes varying from 901N to 901S (–901N); data from GPCP.

(see justification in Section 2.02.3.1.3). The plot shows that during the 238 years of record, the annual rainfall varied between 634 and 3057 mm and the climatic 30-year average varied between 1139 and 1775 mm. These figures indicate a huge variability: the maximum observed annual rainfall is almost 5 times greater than the minimum and the maximum 30-year climatic rainfall is 55% higher than the minimum. Such observed changes underscore the ever-changing character of climate, and render future changes of precipitation predicted by climate modelers (which typically vary within 10– 20%; compare Fig. 10.12, upper left panel, in Meehl et al., 2007, with Figure 2 herein) to be unrealistically low and too unsafe to support planning. Figure 7 also includes a plot of another long time series, for Charleston City, USA (32.791 N, 79.941 W, and 3 m); the record begins in 1835. This time series is also available at the KNMI database, and a few missing monthly values have been filled in by the average of the four nearest monthly values of the same month. Here, the annual rainfall varied between 602 and 1992 mm (3.3 times higher than minimum) and the climatic 30-year average varied between 1135 and 1425 mm (25% higher than minimum). Finally, Figure 8 depicts the time series of a storm measured at unusually high temporal resolution, that is, 10 s. This storm, with duration 96 790 s or about 27 h starting at 199002-12T17:03:39, is one of several storms that were measured at the University of Iowa using devices that support high

sampling rates (Georgakakos et al., 1994). Figure 8 also includes plots at 5-min and hourly timescales. The minimum intensity was virtually zero at all three scales, whereas the maximum rainfall intensity was 118.7, 38.9, and 18.1 mm h1 at timescales of 10 s, 5 min, and 1 h, respectively. As the mean intensity during the storm is 3.89 mm h1, these maximum values are 30, 10, and 4.6 times higher than the mean. This example highlights the spectacular variability of rainfall, particularly at fine timescales (see also Uijlenhoet and SempereTorres, 2006). As the total rainfall amount of this storm event only slightly exceeds 100 mm, it could be thought of as a rather modest event. Storms with amounts much higher than this are often recorded even in semi-dry climates and, obviously, the variability of rainfall intensity during such storms is even higher.

2.02.1.5 Probability and Stochastic Processes as Tools for Understanding and Modeling Precipitation The high variability and the rough and irregular patterns in observed fields and time series are much more prominent in precipitation than in other meteorological variables such as atmospheric pressure or temperature. High variability implies high uncertainty and, unavoidably, this affects predictability in deterministic terms. Considering weather prediction as an example, it is well known that the forecasts of atmospheric pressure and temperature are much more reliable than those

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Figure 4 Monthly rainfall distribution over the globe in January 2006 in mm h : (upper) data with resolution 2.51  2.51 from GPCP; (lower) data with resolution 0.251  0.251 from the Tropical Rainfall Measuring Mission (TRMM) and Other Rainfall Estimate (3B42 V6) archive, made available by NASA at http://disc2.nascom.nasa.gov/Giovanni/tovas/TRMM_V6.3B42.shtml.

of precipitation. Numerical weather prediction (NWP) uses current weather conditions as an input to mathematical models of the atmosphere, which solve the flow (Navier– Stokes) equations, the thermodynamic energy equation, the state equation of gases, and the equation for conservation of water vapor, over a grid covering the entire atmosphere. The processes related to cloud formation and precipitation (see Section 2.02.2.2) are less accurately represented in these models. While the continuous improvement of NWP models resulted in a considerable reduction of forecast errors on pressure and temperature, the improvement in the so-called quantitative precipitation forecast (QPF) has been slower (Olson et al., 1995). Further, although the advances in computing infrastructure permitted the increase in model resolution that leads generally to an improvement of precipitation forecasts, recently, many authors have highlighted the limitations of such an approach (e.g., Mass et al., 2002; Lagouvardos et al., 2003; Kotroni and Lagouvardos, 2004). The major advancement in QPF in the last decades was the abandonment of the pure deterministic approach, which seeks a unique prediction, and the adoption of a more probabilistic approach to precipitation forecast, based on earlier ideas of Epstein (1969) and Leith (1974). In this approach, known as ensemble forecasting, the same model produces many

forecasts. To produce different forecasts, perturbations are introduced, for example, in the initial conditions, and, because of the nonlinear dynamics with sensitive dependence on the initial conditions (e.g., Lorenz, 1963), these perturbations are magnified in time, thus giving very different precipitation amounts in a lead time of 1 or more days. The different model outputs can then be treated in a probabilistic manner, thus assigning probabilities to rainfall occurrence as well as to the exceedance of a specified rainfall threshold. In this manner, although the model uses deterministic dynamics, the entire framework is of the Monte Carlo or stochastic type. This method is satisfactory for a time horizon of forecast of a few days. In hydrology, this time horizon is relevant in realtime flood forecasting. However, in hydrological design, horizons as long as 50 or 100 years (the lifetimes of engineering constructions) are typically used. For such long horizons, the use of deterministic dynamics and of the related laborious models would not be of any help. However, a probabilistic approach is still meaningful – in fact the only effective approach – and, in this case, it can be formulated irrespective of the dynamics. Rather, the probabilistic approach should be based, in this case, on historical records of precipitation, such as those displayed in Figures 7 and 8. The reasoning behind neglecting the deterministic dynamics is

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Figure 5 Spatial rainfall distribution at daily and sub-daily scale: (upper) daily rainfall over the zone between 501N and 501S on 9 January 2006; (middle) 3-hourly rainfall at 09:00 on the same day; (lower left) zoom-in of the upper panel for daily rainfall in the Indian Ocean south-east of Sri Lanka (shown in figure); (lower right) zoom-in of the middle panel for 3-hourly rainfall for the same area. Data in mm h1 with resolution 0.251  0.251 from the TRMM 3B42 V6 archive.

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that, beyond a certain time horizon (which in precipitation is of the order of several days), even the simplest nonlinear systems tend to a statistical equilibrium state. In this state, the probability distribution of the system properties, conditioned on the initial state, is practically equal to the marginal (i.e., unconditional) probability distribution of the same properties (Koutsoyiannis, 2009). This equilibrium, which is different from the typical thermodynamic equilibrium, corresponds to the maximization of the entropy of the vector of random variables defining the system state.

2.02.1.5.1 Basic concepts of probability Probability is thus not only a mathematical tool to model precipitation uncertainty, but also a concept for understanding the behavior of precipitation. Probabilistic thinking provides insights into phenomena and their mathematical descriptions, which may not be achievable in deterministic terms. It should be recalled that, according to the Kolmogorov (1933) system, probability is a normalized measure, that is, a function P that maps sets (areas where unknown quantities lie) to real numbers (in the interval [0, 1]). Furthermore, a random variable x is a single-valued function of the set of all elementary events (so that to each event, it maps a real number) and is associated with a probability distribution function. The latter is defined as

Fx ðxÞ :¼ Pfx r xg

ð3Þ

where x is any real number, which should be distinguished from the random variable x. (Distinction of random variables from their values is usually done by denoting them with upper case and lower case letters, respectively. This convention has several problems – e.g., the Latin x and the Greek w, if put in

upper case, are the same symbol X – other texts do not distinguish the two at all, thus creating another type of ambiguity. Here, we follow a different convention, in which random variables are underscored and their values are not.) Fx ðxÞ is a nondecreasing function of x with the obvious properties Fx ðNÞ ¼ 0 and Fx ðþNÞ ¼ 1. For continuous random variables (as is, for instance, the representation of a nonzero rainfall depth), the probability that a random variable x would take any particular value x is Pfx ¼ xg ¼ 0. Thus, the question of whether one particular value (say x1 ¼10 mm, assuming that x denotes daily rainfall at a location) is more probable than another value (say x2 ¼10 m, which intuitively seems extremely improbable) cannot be answered in terms of the probability function P, as all particular values have probability equal to zero. The derivative of F, that is,

f x ðxÞ :¼

dFx ðxÞ dx

ð4Þ

termed the probability density function, can provide this answer, as the quantity f x ðxÞ dx is the probability that rainfall will lie in an interval of length dx around x. Apparently, then the ratio f x ðx1 Þ=f x ðx2 Þ equals the ratio of the probabilities at points x1 and x2. These rather simple notions allow quantification of uncertainty and enable the production of different type of predictions, which offer a concrete foundation of rational decisions for the design and management of water-resourses projects. This quantification is sometimes (mostly in Bayesian statistics) referred to as ‘probabilization of uncertainty’ that is meant to be the axiomatic reduction from the notion of unknown to the notion of a random variable (Robert, 2007).

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Figure 7 Annual precipitation time series in two of the stations with the longest records worldwide: (upper) Seoul, Korea; (lower) Charleston City, USA. Data from the database of the Dutch Royal Netherlands Meteorological Institute (KNMI; http://climexp.knmi.nl) and additional information as shown in text.

2.02.1.5.2 Stochastic processes In the study of rainfall variation in time, the notion and the theory of stochastic processes provide the necessary theoretical framework. A stochastic process is defined as an arbitrarily (usually infinitely) large family of random variables xðtÞ (Papoulis, 1991). In most hydrological applications, time is discretized using an appropriate time step d; for integer i, the average of the continuous time process xðtÞ from t ¼ (i  1)d to t ¼ i d, is usually denoted xi and forms a discrete time stochastic process. The index set of the stochastic process (i.e., the set from which the index t or i takes its values) can also be a vector space, rather than the real line or the set of integers. This is the case, for instance, when we assign a random variable (e.g., rainfall depth) to each geographical location (a two-dimensional (2D) vector space) or to each location and time instance (a 3D vector space). Stochastic processes with a multidimensional index set are also known as random fields.

A realization x(t) (or xi) of a stochastic process xðtÞ (or xi ), which is a regular (numerical) function of the time t (or a numerical sequence in time i), is known as a sample function. Typically, a realization is observed at countable time instances (and not in continuous time, even if the process is of continuous-time type). This sequence of observations is also referred to as a time series. Clearly then, a time series is a sequence of numbers, whereas a stochastic process is a family of random variables. (Unfortunately, a large body of literature does not make this distinction and confuses stochastic processes with time series.) The distribution and the density functions of the random variable xi, that is,

Fi ðxÞ :¼ Pfxi r xg;

f i ðxÞ :¼

dFi ðxÞ dx

ð5Þ

Precipitation 3

6

9

12

18

181

21

24

37

27 h

120 10-s scale 5-min scale

100 Rainfall intensity (mm h−1)

Hourly scale 80

60

40

20

0 0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

Time (s) Figure 8 Time series of a storm in Iowa, USA, measured at the University of Iowa with temporal resolution of 10 s; time zero corresponds to 199002-12T17:03:39. From Georgakakos KP, Carsteanu AA, Sturdevant PL, and Cramer JA (1994) Observation and analysis of Midwestern rain rates. Journal of Applied Meteorology 33: 1433–1444.

are called, respectively ‘first-order distribution function’ and ‘first-order density function’ of the process. Likewise, the second-order distribution function is Fi1 i2 ðx1 ; x2 Þ ¼ Pfxi1 r x1 ; xi2 r x2 g and this can be generalized to define the nth-order distribution function. It should be recalled that the expected value of a function g of one, two, or more random variables is the integral of g multiplied by the density f, that is,

E½gðxi Þ :¼

ZN

gðxÞf i ðxÞ dx

N

E½gðxi1 ; xi2 Þ ¼

ZN ZN

gðx1 ; x2 Þf i1 i2 ðx1 ; x2 Þ dx1 dx2

2.02.1.5.3 Stationarity As implied by the above notation, in the general setting, the statistics of a stochastic process, such as the mean and autocovariance, depend on time i and thus vary with time. However, the case where these statistical properties remain constant in time is most interesting. A process with this property is termed a ‘stationary’ process. More precisely, a process is called ‘strict-sense’ stationary, if all its statistical properties are invariant with a shift in the time origin. That is, the distribution function of any order of xiþj is identical to that of xi . A process is called ‘wide-sense stationary’ if its mean is constant and its autocovariance depends only on time differences (lags), that is,

E½Xi  ¼ m;

N N

ð6Þ The use of square brackets in E[ ] and the random variables xi rather than their values x signifies the fact that the expected value is not a function of the real number x; rather, it depends solely on the distribution function associated with the random variable xi. Of particular interest are the cases where gðxi Þ ¼ xi, where E½xi  ¼: mi is the mean value of xi , and gðxi1 ; xi2 Þ ¼ ðxi1  mi1 Þðxi2  mi2 Þ, where E½ðxi1  mi1 Þðxi2  mi2 Þ ¼: Ci1 i2 is the process autocovariance, that is, the covariance of the random variables xi1 and xi2 . The process variance (the variance of the variable xi ), is a special case of the latter, that is, Var½xi  ¼ Cii , whereas the standard devipffiffiffiffiffiffi ation is the square root of the latter, that is, si :¼ Cii . Consequently, the process autocorrelation (the correlation coefficient of the random variables xi1 and xi2 ) is ri1i2: ¼ Ci1i2/ (si1 si2).

E½ðXiþj  mÞðXi  mÞ ¼ Cj

ð7Þ

Evidently, the standard deviation is constant too, that is, si ¼ s, and the autocorrelation is a function of the time lag only, that is, riþj, i ¼ rj. A strict-sense stationary process is also wide-sense stationary, but the reverse is not true. A process that is not stationary is called nonstationary. In a nonstationary process, one or more statistical properties depend on time. A typical case of a nonstationary process is the cumulative rainfall depth whose mean obviously increases with time. For instance, let us assume that the instantaneous rainfall intensity iðtÞ at a geographical location and period of the year is a stationary process, with a mean m. Let us further denote by hðtÞ, the rainfall depth collected in a large container (a cumulative rain gauge) at time t, and assume that at the time origin, t ¼ 0, the container is empty. Clearly E½hðtÞ ¼ mt. Thus hðtÞ is a nonstationary process. It should be stressed that stationarity and nonstationarity are properties of a stochastic process, not of a sample function

38

Precipitation

or time series. There is some confusion in the literature about this, as there are several studies that refer to a time series as stationary or nonstationary. As a general rule, to characterize a process as nonstationary, it suffices to show that some statistical property is a deterministic function of time (as in the above example of the cumulative rainfall), but this cannot be directly inferred merely from a time series. To understand this, let us consider the time series of annual rainfall in Seoul, plotted in the upper panel of Figure 7. Misled by the changing regime of precipitation at the climatic scale, as manifest in the plot of the 30-year average, it would be tempting to note (1) an increasing trend in the period 1770–90; (2) a constant climate with high precipitation during 1790–1870; (3) a decreasing trend between 1870 and 1900; and (4) a constant climate with low precipitation thereafter. It is then a matter of applying a fitting algorithm to determine, say, a broken-line type of function to the time series, which would be called a deterministic function of time. The conclusion would then be that the time series is nonstationary. However, this is a wrong ex-post argument, which interprets the long-term variability of the processes as a deterministic function. Had the function been indeed deterministic, it would also apply to future times, which obviously is not the case. Comparison with the previous example (cumulative rainfall), where the deterministic function E½xðtÞ ¼ mt was obtained by theoretical reasoning (deduction) rather than by inspection of the data, demonstrates the real basis of nonstationarity. Koutsoyiannis (2006b) has provided a more detailed study of this issue. Stochastic processes describing periodic phenomena, such as those affected by the annual cycle of the Earth, are clearly nonstationary. For instance, the daily rainfall at a mid-latitude location cannot be regarded as a stationary process. Rather, a special type of a nonstationary process, whose properties depend on time in a periodical manner (are periodic functions of time), should be used. Such processes are called ‘cyclostationary’ processes.

implies that the random variable has zero variance. This is precisely the condition that makes a process ergodic, a condition that does not hold true for every stochastic process.

2.02.1.5.4 Ergodicity

However, this law hardly holds in geophysical time series, including rainfall time series, whatever the scale is. This can be verified based on the examples presented in Section 2.02.1.4. A more plausible law is expressed by the elementary scaling (power-law) property

The concept of ergodicity (from the Greek words ergon, work; and odos, path) is central to the problem of determining the distribution function of a process from a single sample function (time series). A stationary stochastic process is ergodic if any statistical property can be determined from a sample function. Given that, in practice, the statistical properties are determined as time averages of time series, the above statement can be formulated alternatively – a stationary stochastic process is ergodic if time averages equal ensemble averages (i.e., expected values). For example, a stationary stochastic process is mean ergodic if

E½xi  :¼ limN-N

N 1X xi N i¼1

ð8Þ

The left-hand side in the above equation represents the ensemble average, whereas the right-hand side represents the time average, for the limiting case of infinite time. While the left-hand side is a parameter, rather than a random variable, the right-hand side is a random variable (as a sum of random variables). Equating a parameter with a random variable

2.02.1.5.5 Some characteristic stochastic properties of precipitation It has been widely accepted that rainfall exhibits some autocorrelation (or time dependence) if the timescale of study is daily or sub-daily, but this dependence vanishes at larger timescales, such as monthly or yearly. Thus, for timescales monthly and above, rainfall data series have been traditionally treated as independent samples. Mathematically, such a perception corresponds to a Markovian dependence at fine timescales, in which the autocorrelation decreases rapidly with time lag in an exponential manner, that is,

rj ¼ rj

ð9Þ

where r: ¼ r1. Then for a large lag j, or for a large scale of aggregation and even for the smallest lag (one), the autocorrelation is virtually zero (e.g., Koutsoyiannis, 2002). If xi denotes the stochastic process at an initial timescale, which is designated as scale 1, then the averaged process at an aggregated timescale k ¼ 2, 3, y, is ðkÞ

xi :¼

xði1Þkþ1 þ y þ xi k

ð10Þ

ð1Þ

(with xi  xi ). Let s(k) be the standard deviation at scale k. In processes xi independent of time, s(k) decreases with scale according to the well-known classical statistical law of inverse square-root, that is,

s s ðkÞ ¼ pffiffiffi k

s ðkÞ ¼

s k 1H

ð11Þ

ð12Þ

where H is the so-called Hurst exponent, named after Hurst (1951) who first studied this type of behavior in geophysical time series. Earlier, Kolmogorov (1940), when studying turbulence, had proposed a mathematical model to describe this behavior. This behavior has been known by several names, including the Hurst phenomenon, long-term persistence, and long-range dependence, and a simple stochastic model that reproduces it is known as a simple scaling stochastic model or fractional Gaussian noise (due to Mandelbrot and van Ness, 1968). Here, the behavior is referred to as the Hurst–Kolmogorov (HK) behavior or HK (stochastic) dynamics and the model, as the HK model. This behavior implies that the autocorrelation decreases slowly, that is, according to a power-type function,

Precipitation

with lag j: ðkÞ

ri ¼ rj ¼ ð1=2Þ½ðjj þ 1jÞ2H þ ðjj  1jÞ2H   jjj2H E Hð2H  1Þj2H2

ð13Þ

so that independence virtually never holds, unless H ¼ 0.5, a value which reinstates classical statistics including the law in Equation (11). Most often, natural processes including rainfall are positively correlated and H varies in the range (0.5, 1). The above framework is rather simple and allows easy exploration of data to detect whether they indicate consistence with classical statistics or with the HK behavior. A simple exploration tool is a double logarithmic plot of the estimates of standard deviation s(k) versus scale k, which is known as a ‘climacogram’. (ClimacogramoGreek Klımako´grammao(climax ´ (klımax) ¼ scale) þ (gramma (gramma) ¼ written).) In such a plot, the classical law and the HK law are manifested by a linear arrangement of points with slopes –0.5 and H  1, respectively. We must bear in mind, however, that a consequence of the HK law in Equation (12) is that the classical estimator of the variance

s2 ¼

n 1 X ðxi  xÞ2 n  1 i¼1

ð14Þ

ðnÞ

where n is the sample size, x  x1 is the estimator of the mean, and s the estimator of standard deviation, implies negative bias if there is temporal dependence. The bias becomes very high for HK processes with H approaching 1. Apparently then, s could be a highly biased estimator of s; an approximately unbiased estimator is (Koutsoyiannis, 2003a; Koutsoyiannis and Montanari, 2007):

rffiffiffiffiffiffiffiffiffiffiffiffiffi n0 s :¼ s n0  1

E

ð15Þ

39

where n0 is the equivalent (or effective) sample size, that is, the sample size that in the framework of classical statistics would ðnÞ lead to the same uncertainty (in the estimation of m by x  x1 ) as an HK series yields with sample size n. For an HK process, n0 is related to n by

n0 ¼ n 2ð1HÞ

ð16Þ

It can be seen that n0 can be very small even for high n if H pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi is high, and thus the correcting factor n0 =ðn0  1Þ in Equation (15) can be very large (see Koutsoyiannis and Montanari, 2007). Returning to the time series of globally averaged monthly precipitation in the 30-year period 1979–2008, which has been discussed earlier and is displayed in Figure 6, we may now study its statistical properties for several timescales. As the precipitation amounts are averaged over the entire globe, the effect of seasonality is diminished and the time series can be modeled using a stationary process rather than a cyclostationary one. Figure 9 depicts the climacogram, that is, a logarithmic plot of standard deviation versus scale. Empirical estimates of standard deviations have been calculated using both the classical estimator in Equation (14) and the HK estimator in Equation (15). Theoretical curves resulting from the classical statistical model (assuming independence), the Markovian model, and the HK model have also been plotted. For the Markovian model, the lag one autocorrelation coefficient, estimated from the monthly data, is r ¼ 0.256 and for the HK model, the estimate of the Hurst coefficient is H ¼ 0.70. This can be obtained readily from the slope of the straight line fitted to the group of empirical points in Figure 9, which should be H  1. Here, a slightly modified algorithm from Koutsoyiannis (2003a) has been used for the estimation of H. Overall, Figure 9 clearly demonstrates that the empirical

−1.2 Empirical, classical estimate Empirical, HK estimate Classical statistical model Markov model HK model

Log(standard deviation in mm d−1)

−1.3

−1.4

−1.5

−1.6

−1.7

−1.8 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Log(scale in months) Figure 9 Climacogram of the time series of globally averaged monthly precipitation in the 30-year period 1979–2008 shown in Figure 6. The estimate of the Hurst coefficient for the HK model is H ¼ 0.70.

40

Precipitation

points are inconsistent with the classical and Markovian models and justify an assumption of HK behavior. Similar plots have been constructed, and are shown in Figure 10, for the annual precipitation time series from Seoul, Korea, and Charleston City, USA, displayed in Figure 7. Again, the empirical evidence from data precludes the applicability of the classical statistical model and favors the HK statistics. An additional plot for the 10-s precipitation time series in Iowa, USA, displayed in Figure 8, is depicted in Figure 11. Here, the Hurst coefficient is very high, H ¼ 0.96. The difference between the empirical points based on classical statistics on the

one hand and the HK statistics on the other hand is quite distinctive. Apparently, the classical model is completely inappropriate for the rainfall process. The HK stochastic processes can be readily extended in a 2D setting (or even a multidimensional one). The 2D version of Equation (12) is

s ðkÞ ¼

s k 22H

ð17Þ

This can be obtained by substituting k2 for k in Equation (12). Equations (15) and (16) still hold, provided that n is the

2.7

Empirical, classical estimate Empirical, HK estimate Classical statistical model

Log(standard deviation in mm)

2.6

HK model 2.5

2.4

2.3

2.2

2.1 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Log(scale in years)

2.5 Empirical, classical estimate Empirical, HK estimate

Log(standard deviation in mm)

2.4

Classical statistical model HK model

2.3

2.2

2.1

2

1.9

1.8 0

0.2

0.4

0.6

0.8

1

1.2

1.4

Log(scale in years) Figure 10 Climacogram of the annual precipitation time series at: (upper) Seoul, Korea and (lower) Charleston City, USA, which are shown in Figure 7; the estimated Hurst coefficients are 0.76 and 0.74, respectively.

Precipitation

41

0.9

Log(standard deviation in mm h−1)

0.85 0.8 0.75 0.7 0.65 0.6 0.55 Empirical, classical estimate 0.5

Empirical, HK estimate Classical statistical model

0.45

HK model 0.4 0

0.5

1

1.5

2

2.5

3

Log(scale in 10-s intervals) Figure 11 Climacogram of the 10-s precipitation time series in Iowa, USA, displayed in Figure 8; the estimated Hurst coefficient is 0.96.

1.5

Log(standard deviation in mm h−1)

1.4

1.3

1.2

Empirical, classical estimate

1.1

Empirical, HK estimate Classical statistical model HK model

1 0

0.2

0.4

0.6

0.8

1

Log(scale in 0.25° of latitute and longitude) Figure 12 Climacogram of the spatial daily rainfall over the area 91 N–51 S and 78–921 E (Indian Ocean south-east of Sri Lanka) on 9 January 2006, as shown in the lower-left panel of Figure 5; the estimated Hurst coefficient is 0.94.

number of points, which is inversely proportional to k2. Figure 12 demonstrates this behavior by means of a climacogram for the spatial daily rainfall over the area 91 N–51 S and 78–921 E (Indian ocean south-east of Sri Lanka) on 9 January 2006, displayed in the lower-left panel of Figure 5. Here, the estimated Hurst coefficient is again very high,

H ¼ 0.94. As in all previous cases, the classical model is completely inappropriate, while the HK model seems reasonable for scales X4, which correspond to a resolution of 11 11 and beyond. Thus, the evidence presented using several examples of different spatial and temporal scales indicates that HK

42

Precipitation

dynamics is consistent with the nature of rainfall. These dynamics appear as scaling behavior, either in time or in space, which is either full, applicable to the entire range of scales, or asymptotic, applicable to large scales. Both these scaling behaviors are manifested as power laws of standard deviation versus temporal or spatial scale and of autocorrelation versus lag. There exists another type of scaling behavior in precipitation, the scaling in state, which is sometimes confused with the other two scaling behaviors, but is fundamentally different. Scaling in state is a property of the marginal distribution function of rainfall (it has no relation to the dependence structure of the process unlike other types of scaling) and is expressed by power laws of the tails of (1) the probability density function f(x), (2) the survival function (or exceedance probability) F  ðxÞ :¼ Pfx4 xg ¼ 1  FðxÞ, and (3) the return period T ¼ d/F*(x) where d is the length of the timescale examined. These scaling properties are expressed as

xp T k ;

F  ðxÞp x 1=k ;

f ðxÞp x 11=k

ð18Þ

and are equivalent to each other. All these are asymptotic, that is, they hold only for large values of x or, in other words, for the distribution tails. Such tails are known by several names, such as long, heavy, strong, power-type, overexponential, algebraic, or Pareto tails. The latter name comes from the Pareto distribution, which in its simplest form is given in Equation (18), although its generalized form is applicable to rainfall (see Section 2.02.5.2). As this is an asymptotic behavior, long records are needed to observe it. Figure 13 shows a logarithmic plot of the empirical distribution (expressed in terms of return period T) of a large data set of daily rainfall. This data set was created using records of 168 stations worldwide, each of which contained data of 100 years or more (Koutsoyiannis, 2004b). For each station with n years of record, n annual maximum values of daily rainfall were extracted. These values

were standardized by their mean and merged into one sample of length 17 922 station-years. From the theoretical distributions, also plotted in Figure 13, it is observed that the Pareto distribution (whose right tail appears as a straight line in the logarithmic plot; see Section 2.02.5.2) with k ¼ 0.15 provides the best fit, thus confirming the applicability of asymptotic scaling in state and the inappropriateness of the exponential-type tail. This has severe consequences, particularly in hydrological design, as distributions with exponential tails have been most common in hydrological practice, whereas it is apparent that the power-type tails are more consistent with reality. As shown in Figure 13, the difference between the two types can be substantial. Koutsoyiannis (2005a, 2005b) produced the aforesaid different types of scaling from the principle of maximum entropy. As entropy is a measure of uncertainty, the applicability of the principle of maximum entropy and its consistence with observed natural behaviors characterizing the precipitation process underscores the dominance of uncertainty in precipitation.

2.02.2 Physical and Meteorological Framework Atmospheric air is a heterogeneous mixture of gases, also containing suspended particles in liquid and solid phase. The most abundant gases are nitrogen (N2) and oxygen (O2) that account for about 78% and 21%, respectively, by volume of the atmospheric permanent gases, followed by argon (Ar) and traces of other noble gases. Their concentrations are almost constant worldwide and up to an altitude of about 90 km. Water vapor (H2O) appears in relatively low concentrations, which are highly variable. However, water vapor is very important for energy exchange on Earth (it accounts for 65% of the radiative transfer of energy in the atmosphere; Hemond

10 Empirical Pareto/least squares Pareto/L-moments Exponential/L-moments

Rescaled rainfall depth

5

2

1

0.5 1

2

5 10

100

1000

10 000

100 000

Return period Figure 13 Logarithmic plot of rescaled daily rainfall depth vs. return period: empirical estimates from a unified sample over threshold, formed using rainfall data from 168 stations worldwide (17 922 station-years). The unified sample was rescaled by the mean of each station, and fitted using a Pareto and an exponential distribution model. Adapted from Koutsoyiannis D (2004b) Statistics of extremes and estimation of extreme rainfall: 2. Empirical investigation of long rainfall records. Hydrological Sciences Journal 49(4): 591–610.

Precipitation

and Fechner-Levy, 2000), as well as for mass transfer processes in the hydrological cycle. Under certain conditions (i.e., pressure and temperature), water vapor can transform into droplets or ice crystals with subsequent release of latent heat (see Sections 2.02.2.1 and 2.02.2.2). More generally, the water-vapor content of atmospheric air affects its density, and it is of central importance in atmospheric thermodynamics (Section 2.02.2.1). The varying content and importance of water vapor in precipitation processes and thermodynamics has led to the study of atmospheric air as a mixture of two (ideal) gases: dry air and water vapor. This mixture is usually referred to as ‘moist air’ and has thermodynamic properties determined by its constituents (e.g., Rogers and Yau, 1996; Cotton and Anthes, 1989). The particles of solid and liquid material suspended in air are called ‘aerosols’. Common examples of aerosols are water droplets and ice crystals (called ‘hydrometeors’), smoke, sea salt (NaCl), dust, and pollen. The size distribution of solid aerosols depends strongly on their location. For example, the size spectrum of aerosols over land is narrow with high concentrations of small particles (e.g., kaolinite, dust, and pollen), whereas the size spectrum of aerosols over sea is wider with small concentrations of larger particles (e.g., sea salt; Ryan et al., 1972). Existence of aerosols in the atmosphere is of major importance, since a select group of aerosols called ‘hydroscopic nuclei’, is crucial for the nucleation of liquid water and initiation of rain (e.g., Brock, 1972, and Section 2.02.2.2). When moist air is cooled (i.e., below its dew point; see Section 2.02.2.1), an amount of water vapor condenses and a cloud forms, but precipitation may or may not occur. Initiation of rain requires the formation of hydrometeors (i.e., water droplets and ice crystals) of precipitable size (e.g., Gunn and Kinzer, 1949; Twomey, 1964, 1966; Brock, 1972). Formation and growth of these particles are governed by processes that take place at scales comparable to their size (micrometers to millimeters). The latter processes form the core of cloud microphysics, whereas large-scale processes, related to thermodynamics of moist air and motion of air masses, form the core of cloud dynamics. Importantly,

precipitation is the combined effect of both large- and microscale processes, and both processes are equally important and necessary for precipitation to occur.

2.02.2.1 Basics of Moist Air Thermodynamics In a parcel of moist air at temperature T with volume V and mass M ¼ Md þ Mv, with the two components denoting mass of dry air and water vapor, respectively, the density is r ¼ M/V and the concentration of water vapor, known as specific humidity, is q: ¼ Mv/M. The quantity r ¼ Mv/Md: ¼ q/(1  q) is usually referred to as the mixing ratio. The total pressure of the moist air in the parcel, p (the atmospheric pressure), equals the sum of the partial pressures of dry air pd and that of water vapor e (i.e., p ¼ pd þ e). Specific humidity and vapor pressure are interrelated through

q¼

ee p  ð1  eÞe

ð19Þ

where e ¼ 0.622 is the ratio of molar masses of water vapor and dry air. Air cannot hold an arbitrarily high quantity of vapor. Rather, there is an upper limit of the vapor pressure e*, called the saturation vapor pressure, which depends on the temperature T and is given by the Clausius–Clapeyron equation. A useful approximation to this equation is

e  ðTÞ ¼ 6:11 exp



 17:67T T þ 237:3

Saturation specific humidity, q* (g kg−1)

10

1

−30

−20

−10

0

10

Temperature, T (°C) Figure 14 Saturation specific humidity as a function of air temperature.

ð20Þ

where e* is in hPa and T is in 1C. Consequently, from Equations (19) and (20) we can calculate the saturation specific humidity q*, which is a function of T, and expresses the water vapor holding capacity of air. As shown in Figure 14, this capacity changes drastically, almost exponentially, with temperature, so that a change of temperature from  40 to 40 1C increases this capacity by 2.5 orders of magnitude. The ratio of the actual to saturation vapor pressure, that is, e/e* ¼ : U, called the relative humidity, is normally lesser than 1. When an air parcel cools, while e remains constant, e*

100

0.1 −40

43

20

30

40

44

Precipitation

decreases and hence U increases, up to the saturation value 1 or 100%. The temperature Td at which saturation occurs is called the dew point temperature and is calculated using Equation (20) by setting e*(Td) ¼ e. Therefore, cooling of the air parcel below the dew point temperature results in condensation, or transformation of the excess water vapor into liquid water in the form of droplets. During this change of phase, the relative humidity remains 100%. Condensation releases heat at a fairly constant rate (LE2.5 MJ kg1); this rate equals that of evaporation of water at a constant temperature and is thus called latent heat. For an air parcel to ascend and expand spontaneously, so that condensation and cloud formation can occur, the ambient (atmospheric) temperature gradient g : ¼  dT/dz, where z denotes altitude, also known as lapse rate, must be high (otherwise, an uplifted air parcel will sink again). While the parcel ascends and expands adiabatically (i.e., in a way that no heat transfer takes place between the air parcel and its ambient air), its own lapse rate is gd ¼ 9.8 1C km1 if the expansion is dry adiabatic (i.e., if it takes place without condensation of water vapor) and somewhat smaller, g*, if the expansion is moist adiabatic (i.e., if the temperature has fallen below dew point, so that some of the water vapor in the parcel condenses to liquid form). The gradient g* is not constant but varies with temperature T and air pressure p so that g* ¼ 4 1C km1 for T ¼ 25 1C and p ¼ 1000 hPa, whereas g* ¼ 9 1C km1 for T ¼  25 1C and p ¼ 1000 hPa; an average value is g* ¼ 6.5 1C km1 (Koutsoyiannis, 2000b; see also Wallace and Hobbs, 1977). When the ambient lapse rate g is smaller than g*, the atmosphere is stable, and no spontaneous lift occurs and no clouds are formed. When g4gd, the atmosphere is unstable and favors air lift and formation of clouds. The case g*ogogd is known as conditional instability and it serves as an important mechanism for mesoscale precipitation processes (see Sections 2.02.2.4 and 2.02.2.5).

2.02.2.2 Formation and Growth of Precipitation Particles The Clausius–Clapeyron equation describes the equilibrium condition of a thermodynamic system consisting of bulk water and vapor. A state out of the equilibrium, in which e4e* (U41) is possible, but is thermodynamically unstable, and is called supersaturation. Detailed study of the transition of water vapor to liquid or ice at or above saturation is associated with certain free-energy barriers. An example of such an energy barrier is the dynamic energy associated with the surface tension, s, of a water droplet. For a spherical droplet, s is proportional to the pressure of water within the droplet p and inversely proportional to its radius r (i.e., s ¼ p/2r). This means that a high vapor pressure is needed for a very small droplet to be maintained and not evaporate. In essence, the free-energy barrier of surface tension makes droplet formation solely by condensation of water vapor (a process usually referred to as ‘homogeneous nucleation’), almost impossible in nature. However, if the surface-tension barrier is bypassed, common supersaturations of the order of 1–2% (i.e., U ¼ 1.01–1.02) are sufficient for water vapor to diffuse toward the surface of the droplet. The rate of diffusional growth is proportional to the supersaturation U  1 of the ambient air,

and inversely proportional to the radius r of the droplet: that is, dr/dtp(U  1)/r (Mason, 1971; Rogers and Yau, 1996). While homogeneous nucleation requires large supersaturations, formation of droplets is drastically facilitated by particulated matter of the size of micrometers or lower, the aerosols, some of which, called condensation nuclei, are hydrophilic and serve as centers for droplet condensation (Brock, 1972; Slinn, 1975; Hobbs et al., 1985). This process is usually referred to as ‘heterogeneous nucleation’ and it is almost exclusively the process that governs water vapor condensation in the atmosphere (Houze, 1993). When the temperature in the cloud drops below the freezing point, water droplets are said to be supercooled, and they may or may not freeze. For pure water droplets, homogeneous freezing does not occur until the temperature drops below  40 1C (Rogers and Yau, 1996). However, the presence of certain condensation nuclei, called ice nuclei, may allow freezing of water droplets at temperatures a few degrees below 0 1C. These nuclei are particles of the size of micrometers, or lower, which form strong bonds with water and closely match the crystallic structure of ice. Different particles serve as condensation nuclei at different subfreezing temperatures. For example, silver iodide (AgI) serves as an ice nucleator at  4 1C and kaolinite at  9 1C (e.g., Houghton, 1985). Evidently, a cloud is an assembly of tiny droplets with usually met concentrations of several hundreds per cubic centimeter, and radii of several micrometers. This structure is very stable and the only dominant process is vapor diffusion, which accounts for the size-growth evolution of the whole droplet population (Telford and Chai, 1980; Telford and Wagner, 1981). Precipitation develops when the cloud population becomes unstable and some droplets grow faster relative to others. In general, two main mechanisms account for the cloud microstructure becoming unstable. The first mechanism is the collision and coalescence (i.e., sticking) of larger (and fastermoving) collector drops with smaller (and slower-moving) collected droplets. This mechanism is particularly important for precipitation development in warm clouds (i.e., at temperatures in excess of 0 1C; see, e.g., Houze, 1993) and, for a long time, it has formed an active research area in cloud and precipitation physics (e.g., Langmuir, 1948; Bowen, 1950; Telford, 1955; Scott, 1968, 1972; Long, 1971; Drake, 1972a, 1972b; Gillespie, 1972, 1975; Robertson, 1974; Berry and Reinhardt, 1974a, 1974b; Vohl et al., 1999; Pinsky et al., 1999, 2000; Pinsky and Khain, 2004; and review in Testik and Barros (2007)). Its significance for precipitation processes depends considerably on the droplet-size spectra, with larger effectiveness for wider spectra with small concentrations of larger particles (Berry and Reinhardt, 1974a, 1974b). The second mechanism is related to interaction between water droplets and ice crystals, and is limited to clouds with tops that extend to subfreezing temperatures (i.e., cold clouds). In particular, when an ice crystal develops in the presence of a large number of supercooled droplets, the situation becomes immediately unstable and the ice crystal grows due to diffusion of water vapor from the droplets toward the crystal. This is due to the fact that the equilibrium vapor pressure over ice is less than that over water at the same subfreezing temperature. Thus, the ice crystal grows by

Precipitation

diffusion of water vapor and the supercooled droplets evaporate to compensate for this. The transfer rate of water vapor depends on the difference between the equilibrium vapor pressure of water and ice, a quantity that becomes sufficiently large at about  15 1C (Uijlenhoet, 2008). The latter process is called the Bergeron–Findeisen mechanism, named after the scientists who first studied it (Bergeron, 1935; Findeisen, 1938). Once the ice crystals have grown by vapor diffusion to sizes sufficiently large for gravitational settling to dominate, they start falling and colliding with their ambient droplets and ice crystals, a process usually referred to as ‘accretional growth’. In the first case (i.e., when ice crystals collide with droplets), graupel or hail may form, whereas in the second case, snowflakes are likely to form. As the frozen particles fall, it is possible to enter layers with temperatures higher than 0 1C and start melting. If the particles have relatively small terminal velocities (or equivalently small size; see Section 2.02.2.3), they may reach the ground as raindrops indistinguishable from those formed by coalescence. Alternatively, in cold weather, or when large hailstones are formed, the precipitation particles may reach the ground unmelted. Additional discussion on the mechanisms of formation and growth of precipitation particles, and the potential human intervention on the mechanisms by technological means are discussed in Chapter 4.05 Abstraction of Atmospheric Humidity.

2.02.2.3 Properties of Precipitation Particles 2.02.2.3.1 Terminal velocity The terminal velocity UX(D) of a precipitable particle of type X ¼ R (rain), H (hail), S (snow), and effective diameter D is the maximum velocity this particle may develop under gravitational settling relative to its ambient air. In theory, UX(D) can be obtained by balancing the weight of the particle with the sum of the static and dynamic buoyancy (i.e., drag forces) on the particle. For a rigid spherical raindrop, one obtains pffiffiffiffi UR ðDÞp D (e.g., Rogers and Yau, 1996). Theoretical calculation of UX(D) becomes more complicated when the dynamical characteristics of the falling particles depend on their linear size D and the ambient temperature T. For example, droplets with diameters D smaller than about 0.35 mm are approximately spherical, drops with diameters in the range 0.35–1 mm tend to deform by the aerodynamic shear receiving a more elliptical shape, whereas larger drops frequently break down into smaller droplets due to excessive elongation or surface vibrations (e.g., Testik and Barros, 2007; Uijlenhoet, 2008). Moreover, the crystallic structure, shape, size, and, hence, the aerodynamic properties of snowflakes depend on the ambient temperature T (Fletcher, 1962; Locatelli and Hobbs, 1974; Houghton, 1985; Rogers and Yau, 1996). In the absence of exact theoretical solutions for the terminal velocity UX(D) of precipitation particles under complex atmospheric conditions, several empirical formulae have been developed (e.g., Gunn and Kinzer, 1949; Liu and Orville, 1969; Wisner et al., 1972; Locatelli and Hobbs, 1974; Atlas and Ulbrich, 1977; Lin et al., 1983). According to Liu and

45

Orville (1969), who performed a least squares analysis of Gunn and Kinzer’s (1949) data, the terminal velocity of raindrops of diameter D can be approximated by a power-law type relationship:

UR ðDÞ ¼ a Db

ð21Þ

where a ¼ 2115 cm1b s1 and b ¼ 0.8 are empirical constants. For raindrops with diameters in the range 0.5pDp5 mm, Atlas and Ulbrich (1977 (see also Uijlenhoet, 2008) suggest the use of Equation (21) with parameters a ¼ 1767 cm1b s1 and b ¼ 0.67. For hail, Wisner et al. (1972) suggest

UH ðDÞ ¼ D1=2



4grH 3CD r

1=2 ð22Þ

where g ¼ 9.81 m s2 is the acceleration of gravity, rE1.2 kg m3 is the density of air, rH ¼ 800–900 kg m3 is the density of the hailstone, and CD ¼ 0.6 is a drag coefficient. For graupel-like snow of hexagonal type, Locatelli and Hobbs (1974) suggest:

US ðDÞ ¼ c Dd

ð23Þ

where c ¼ 153 cm1d s1 and d ¼ 0.25 are empirical constants that, in general, depend on the shape of the snowflakes (e.g., Stoelinga et al., 2005). UX(D) relationships other than power laws have also been suggested (e.g., Beard (1976) and review by Testik and Barros (2007)). However, the power-law form in Equations (21)–(23) is the only functional form that is consistent with the powerlaw relations between the radar reflectivity factor Z (see Section 2.02.3.2) and the rainfall intensity i (Uijlenhoet, 1999, 2008).

2.02.2.3.2 Size distribution A commonly used parametrization for the size distributions of precipitation particles is that introduced by Marshall and Palmer (1948). According to this parametrization, precipitation particles have exponential size distributions of the type

nX ðDÞ ¼ n0X expðbX DÞ;

X ¼ R; H; S

ð24Þ

where the subscript X denotes the type of the particle: rain (R), hail (H), or snow (S); D is the effective diameter of the particle; bX is a distribution scale parameter with units of (length1) (see below); and n0X is an intercept parameter that depends on the type of the particle with units of (length4): that is, number of particles per unit diameter and per unit volume of air (see below). To determine the parameters n0R and bR in Equation (24) for rainfall, Marshall and Palmer (1948) used observations from summer storms in Canada. The study reported a constant value of the intercept parameter n0R ¼ 8 102 cm4, whereas the scale parameter bR was found to vary with the rainfall intensity i at ground level as: bR ¼ 41 i0.21 cm1, where i is in millimeters per hour. Clearly, the mean raindrop size 1/bR increases with increasing rainfall intensity i.

46

Precipitation

Gunn and Marshall (1958) used snowfall observations from Canada to determine the parameters n0S and bS for snow. The study concluded that both n0S and bS depend on the precipitation rate as:

n0S ¼ 0:038 i0:87 cm4 ;

bS ¼ 25:5 i0:48 cm1

2.02.2.4 Clouds and Precipitation Types ð25Þ

where i is the water equivalent (in millimeters per hour) of the accumulated snow at ground level. Similar to the mean raindrop size, the mean snowflake size 1/bS increases with increasing i. A modification to the distribution model of Gunn and Marshall (1958) has been proposed by Houze et al. (1979) and Ryan (1996). According to these authors, the intercept parameter for snow, n0S, is better approximated as a decreasing function of the temperature T of the ambient air. The latter is responsible for the properties and structures of ice crystals (see Section 2.02.2.2). Federer and Waldvogel (1975) used observations from a multicell hailstorm in Switzerland to determine the parameters n0H and bH for hail. The study showed pronounced variability of the intercept parameter n0H ¼ 15 106 to 5.2 104 cm4, moderate variability of the scale parameter bH ¼ 3.3–6.4 cm1, and concluded showing an exponential mean size distribution for hailstones with constant parameters: n0HE1.2 104 cm4 and bHE4.2 cm1. Alternative models, where the size distributions of precipitation particles are taken to be either gamma or lognormal, have also been suggested (e.g., Ulbrich, 1983; Feingold and Levin, 1986; Joss and Waldvogel, 1990). However, the exponential distribution model introduced by Marshall and Palmer (1948) has been empirically validated by a number of studies (see, e.g., Kessler, 1969; Federer and Waldvogel, 1975; Joss and Gori, 1978; Houze et al., 1979; Ryan, 1996; Ulbrich and Atlas, 1998; Hong et al., 2004), and has found the widest application by being used in the cloud-resolving schemes of many state-of-the-art NWP models (e.g., Cotton et al., 1994; Grell et al., 1995; Reisner et al., 1998; Thompson et al., 2004; Skamarock et al., 2005). A more general formulation for the size distribution of precipitation particles, which includes the exponential model of Marshall and Palmer (1948), and the gamma and lognormal models as special cases, was suggested by SempereTorres et al. (1994, 1998). According to their formulation, the size distribution of precipitation particles can be parametrized as

nX ðDÞp if gðDX =iz Þ;

X ¼ R; H; S

ð26Þ

where f and z are constant exponents, i is the precipitation rate, and g(x) is a scalar function with parameter vector a. For a certain form of g, the functional dependence of the parameters f, z, and a is obtained by satisfying the equation for the theoretical precipitation rate originating from particles with size distribution nX(D) (Sempere-Torres et al., 1994, 1998; Uijlenhoet, 2008):

i¼

p 6

ZN 0

nX ðDÞUX ðDÞD3 dD;

X ¼ R; H; S

where UX(D) is given by Equations (21)–(23). Note, however, that the units of nX depend on those used for D and i and, of course, the functional form of g(x).

ð27Þ

Clouds owe their existence to the process of condensation, which occurs in response to several dynamical processes associated with motions of air masses, such as orographic or frontal lifting (see Section 2.02.2.5), convection, and mixing. At the same time, clouds and the resulting precipitation influence the dynamical and thermodynamical processes in the atmosphere. For example, clouds affect air motions through physical processes, such as the redistribution of atmospheric water and water vapor, the release of latent heat by condensation, and the modulation of the transfer of solar and infrared (IR) radiation in the atmosphere. A cloud system is formed by a number of recognizable isolated cloud elements that are identifiable by their shape and size (e.g., Scorer and Wexler, 1963; Austin and Houze, 1972; Orlanski, 1975). On the lowest extreme, cloud systems with a scale of about 1 km or less are classified as microscale systems. On the highest extreme, atmospheric phenomena of linear extent of 1000 km and upward are classified in the synoptic scale and include the cloud systems associated with baroclinic instabilities, and extratropical cyclones (i.e., lowpressure centers). In between these two extreme scales, atmospheric phenomena with linear extent between a few kilometers and several hundred kilometers are the so-called mesoscale phenomena. These phenomena are more likely associated with atmospheric instabilities, as well as frontal and topographic lifting. Mesoscale phenomena include many types of clouds and cloud systems that are usually classified into two main categories: stratiform and convective (cumulus) cloud systems. In general, stratiform cloud systems have the shape of a flat appearing layer and produce widespread precipitation associated with large-scale ascent, produced by frontal or topographic lifting, or large-scale horizontal convergence. By contrast, convective cloud systems have large vertical development, produce localized showery precipitation, and are associated with cumulus-scale convection in unstable air. Next, we focus on the structure of these systems and the forms of precipitation they produce.

2.02.2.4.1 Cumulus cloud systems Cumulus clouds are formed by small thermals (upwardmoving air parcels heated by contact to the warm ground) where condensation occurs and they grow to extend vertically throughout the troposphere. Their vertical extent is controlled by the depth of the unstable layer, while their horizontal extent is comparable to their vertical extent. A typical linear dimension of a cumulus cloud is 3–10 km, with updraft velocities of a few meters per second (Rogers and Yau, 1996). Observations performed by Byers and Braham (1949; see also Weisman and Klemp, 1986) revealed that convective storms are formed by a number of cells, each one of which passes through a characteristic cycle of stages (Figure 15). The cumulus stage of a cell is characterized by an updraft throughout most of the cell. At this stage, which lasts approximately 10–20 min, the cell develops and expands

Precipitation

47

z ≈ 10−12 km

Rain

Rain z ≈ 6 km

New cell development Updraft

Downdrafting air

Updraft

z = 0 km 6−8 km

8−16 km

6−11 km

Cumulus stage 10−20 min

Mature stage 15−30 min

Dissipating stage ≈ 30 min

Figure 15 Stages of development of convective cells. Adapted from Weisman ML and Klemp JB (1986) Characteristics of isolated convective storms. In: Ray PS (ed.) Mesoscale Meteorology and Forecasting, ch. 15, pp. 331–358. Boston, MA: American Meteorological Society.

vertically while the air becomes saturated and hydrometeors grow due to vapor condensation and turbulent coalescence (see Section 2.02.2.2). Some ice and water particles grow large enough to fall relative to the ambient updraft and initiate a downdraft within the cell. The downdraft is initially in saturated condition, but as it moves toward the lower troposphere and mixes with subsaturated air, evaporational cooling occurs, which introduces negative buoyancy and accelerates the downdraft. This is the start of the mature stage of the cell, which lasts for approximately 15–30 min. The air of the downdraft reaches the ground, as a cold core, and changes the surface wind pattern. This change may initiate a new thermal at a neighboring location, which might grow to a new cell. The downdraft interferes with the updraft at the lower levels of the cloud and finally cuts off the updraft from its source region. At this point, the cell enters its dissipating stage. At this stage, which lasts for about 30 min, the updraft decays and consequently, the precipitation source is eliminated.

whereas thick stratus clouds (i.e., 1–2 km vertical extent) are capable of producing substantial widespread rain or snow. Although the classification of cloud systems in stratiform and convective is useful for observation purposes, it cannot be considered sharp (Harrold and Austin, 1974). Observations from radars or rain gauges show that widespread precipitation has a fine-scale structure with intense precipitation regions confined to elements with size of a few kilometers, while rainfall features of convective origin (e.g., cells) can grow and/ or cluster over a large region producing continuous precipitation similar to that of stratiform formations. In general, convective rainfall patterns are nonuniform and are associated with locally intense rainfall regions ranging in size from 3 to 10 km. The latter evolve rapidly in time and are separated by areas free of precipitation. By contrast, stratiform patterns are associated with less-pronounced small-scale structures and a wider overall extent that persists in time.

2.02.2.5 Precipitation-Generating Weather Systems 2.02.2.5.1 Fronts

2.02.2.4.2 Stratus cloud systems Stratus clouds are associated with mesoscale, or even synoptic, vertical air motions that arise from large-scale horizontal convergence and frontal or orographic lifting of moist air masses. The ascending motion of air is weak (i.e., a few tens of centimeters per second) relative to cumulus convection, but it extends over large areas and durations to produce widespread rain or snow. The lifetime of a stratus formation is of the order of days, and its size may extend over hundreds of kilometers horizontally. The ascended air masses, having the form of a flat appearing layer, remain convectively stable even after they are lifted to higher altitudes. Since atmospheric turbulence is not intense, initiation of rain is mainly dominated by the ice particle growth due to vapor deposition (the Bergeron–Findeisen mechanism; Section 2.02.2.2), when the ascended air masses are thick enough to reach subfreezing temperatures. In general, thin stratus clouds are usually nonprecipitating,

Atmospheric circulation is formed by advecting air masses with fairly uniform characteristics. Depending on their source of origin, different air masses may have different temperatures and moisture contents. For example, continental air masses are drier and their temperatures vary in a wider range relative to maritime air masses. The interface of two opposing air masses with different temperatures and moisture contents is usually referred to as a front. Along this interface, the warmer and lighter air rises above the colder and denser air. The vertical lifting causes the warmer air to cool adiabatically, the water vapor to condense, and, hence, precipitation to form. A cold front occurs when advancing cold air wedges itself under warmer air and lifts it (Figure 16(a)), whereas a warm front develops when faster-moving warm air overrides a colder and denser air mass (Figure 16(b); Koutsoyiannis and Xanthopoulos, 1999). An occluded front forms when warm air is trapped between two colder and denser air masses. An example of an occluded front is shown in Figure 16(c), where a cold front catches up a slower-moving warm front.

48

Precipitation

Upper-level winds

Cold front Warm air

Advancing cold air (a)

Warm front

Advancing warm air

Retreated cold air Cold air

(b)

Warm air Warm front Cold front Cold air Advancing cold air

Occluded front

(c)

Figure 16 Schematic illustration of different types of fronts: (a) cold front, (b) warm front, and (c) occluded front. Adapted from Koutsoyiannis D and Xanthopoulos T (1999) Engineering Hydrology, 3rd edn., p. 418. Athens: National Technical University of Athens (in Greek).

Fronts may extend over hundreds of kilometers in the horizontal direction and are associated with vertical wind speeds of the order of a few tens of centimeters per second. This range of values is in accordance with vertical motions caused by the horizontal wind convergence of synoptic-scale low-level flow. Hence, frontal precipitation is mostly stratiform with widespread rain or snow over large areas and durations. Note, however, that embedded within the areas of frontal precipitation there are mesoscale regions that exhibit cellular activity.

2.02.2.5.2 Mechanical lifting and orographic precipitation Orographic precipitation occurs when horizontally moving warm and humid air meets a barrier such as a mountain range. In this case, the barrier causes uplift of the incoming air. As the moist air moves upslope, it cools adiabatically, water vapor condenses to liquid water or ice (depending on the altitude where the dew point temperature occurs), and precipitation is likely to form (e.g., Smith, 1993; Hemond and Fechner-Levy, 2000). In general, orographic precipitation (unless combined with other mechanisms such as cyclonic activity and fronts) is narrow banded since it occurs in association with water-vapor condensation by mechanical lifting, a process that becomes

effective at a certain elevation along the topography. After surpassing the top of the mountain range, on the lee side, the air moves downward and this causes adiabatic warming, which tends to dissipate the clouds and stop the precipitation, thus producing a rain shadow.

2.02.2.5.3 Extratropical cyclones Extratropical cyclones are synoptic scale low-pressure systems that occur in the middle latitudes (i.e., pole-ward of about 301 latitude) and have length scales of the order of 500–2500 km (e.g., Hakim, 2003). They usually form when two air masses with different temperatures and moisture contents that flow in parallel, or are stationary, become coupled by a preexisting upper-level disturbance (usually a low-pressure center) near their interface. An example is the formation of extratropical cyclones along the interface of mid-latitude westerlies (i.e., winds that flow from West to East; e.g., Lutgens and Tarbuck, 1992), with the equator-ward-moving polar, and thus colder, air masses (i.e., polar easterlies). As shown in Figure 17, which refers to the Northern Hemisphere, the motion of both warm and cold air masses is caused by pressure gradients and their direction is south–north and north–south, respectively (Koutsoyiannis

Precipitation

49

er lie s

High ld

air

la r

ea

st

Co

Low

Po

Cold air

Warm

t

on d fr

`

Col

rli es

Warm air

te

r

.w es M id la t

(a)

front

m ar

ai

W

(b)

High

Cold air Low

Low

Co

ld

m

ar

air

W

Occluded front air

Warm front

(c)

(d)

m

ar

air

W

Wid stra espre a tifo rm d reg io

n

ar m W

Co l

d

air

ai

r

Great Britain

Band of precipitation

France

(e) Figure 17 (a)–(d) Schematic illustration of the evolution of an extratropical cyclone at the interface of mid-latitude westerlies and the equator-wardmoving polar easterlies; and (e) extra-tropical cyclone over the British Isles on 17 January 2009: motion of air masses, fronts, and characteristic precipitation regions. (a)–(d), Adapted from Koutsoyiannis D and Xanthopoulos T (1999) Engineering Hydrology, 3rd edn., p. 418. Athens: National Technical University of Athens (in Greek); and (e) from http://www.ncdc.noaa.gov/sotc/index.php?report ¼ hazards&year ¼ 2009&month ¼ jan).

50

Precipitation

Over shooting top

Storm

motion

12−16 km Upper-level winds anvil vau lt

9 km

Rain and hail

3−6 km Mid-level winds

Forward flank downdraft

Rear flank downdraft Tornado

Gust front Figure 18 Schematic illustration of the wind circulation in a super-cell storm. Adapted from http://www.nssl.noaa.gov/primer/tornado/.

and Xanthopoulos, 1999). However, these directions are diverted to the right (in the Northern Hemisphere) by Coriolis forces. The initial disturbance formed by the shear along the interface of the two air masses (Figure 17(a)) grows as the warmer and lighter air rises above the colder air and starts rotating in an emerging spiral called the cyclone (Figure 17(b)). As the cyclone evolves, the cold front approaches the slower-moving warm front (Figure 17(c)) and then catches up with it forming an occluded front (Figure 17(d)). Finally, mixing between the two air masses causes the fronts to lose their identities and the cyclone to dissipate. The adiabatic cooling of the warm and moist air results in a widespread region of stratiform precipitation that propagates with the upper-level flow far beyond the fronts (Figure 17(e)).

2.02.2.5.4 Isolated extratropical convective storms

The super-cell storm is the most intense of all isolated convective storms. It has a lifetime of several hours, it exhibits large vertical development, and produces strong winds, heavy rainfall, or hail, and long-lived tornadoes, that is, intense vortices with diameters of the order of 100–500 m (e.g., Browning and Ludlam, 1962; Rotunno, 1986; Weisman and Klemp, 1986; Bluestein, 2003), where the updrafts and downdrafts are displaced horizontally and interact mutually to sustain a long-lived circulation (Figure 18). The updraft enters at low levels and ascends in a region called the vault, which might penetrate into the stratosphere. Super-cell storms usually evolve from multicell formations when the magnitude of the vertical wind shear, defined as the difference between the density-weighted mean wind over the lowest 6 km and a representative surface layer wind (e.g., 500 m mean wind), suffices to produce a long-lived rotating updraft that mutually interacts with the downdraft (e.g., Weisman and Klemp, 1982, 1984, 1986).

A short-lived single-cell is the simplest storm of convective origin. Single cells have horizontal cross sections of the order of 10–100 km2 and move with the mean environmental flow over the lowest 5–7 km of the troposphere. The stages of development of a single-cell storm were discussed in Section 2.02.2.4. The multicell storm is a cluster of short-lived single cells with cold outflows (i.e., downdrafts) that combine to form a large gust front (Weisman and Klemp, 1986). The convergence along the leading edge of the front triggers new updraft development and subsequent formation of new cells. Because of the new cell development, multicell storms may last several days and span over large areas with linear extents of hundreds of kilometers.

Intense rainfall events are usually organized in lines (i.e., squall lines) and bands (i.e., rainbands) with characteristic scales of hundreds of kilometers. According to Hane (1986), rainbands are sufficiently elongated rainfall areas that are nonconvective or weakly convective, and squall lines include all linear convective structures stronger than rainbands. These large-scale features are considered to be manifestations of the large mesoscale horizontal circulation, in association with spatial fluctuations of the surface temperature and moisture content of atmospheric air masses.

2.02.2.5.5 Extratropical squall lines and rainbands

Precipitation

The conditions for squall line formation are (1) a convectively unstable near-surface environment (i.e., moist and warm near-surface air with relatively cold air aloft) to maintain the development of convective cells, (2) a layer of dry air directly above the near-surface moist air to enhance development of an intense and wide cold downflow by evaporative cooling (i.e., the dry middle-level air causes precipitation particles to evaporate and a negatively buoyant cold front to form), and (3) a triggering mechanism for release of the convective instability (e.g., frontal or orographic lifting). Once the squall line has formed, it feeds itself through convergence along the cold gust front. This convergence produces strong ascent and forms new cells ahead of the storm. Rainbands in extratropical regions occur primarily in association with well-organized extratropical cyclones (Hane, 1986). In this case, precipitation is maintained by the ascent resulting from the warm advection of the advancing cyclone, with subsequent formation of a widespread region of stratiform precipitation (Section 2.02.2.5.3). Extratropical rainbands can also be formed in synoptic-scale environments other than those associated with cyclonic circulation. An example is the environment associated with the development of symmetric instabilities (e.g., Bennetts and Sharp, 1982; Seltzer et al., 1985).

2.02.2.5.6 Monsoons The term monsoon generally applies to climates that exhibit long, distinct, and remarkably regular rainy and dry periods associated with the spatial distribution of solar heating during summer and winter. According to a definition proposed by Ramage (1971), a monsoon climate is characterized by (1) prevailing wind directions that shift by at least 1201 between January and July, (2) prevailing wind direction that persists at least 40% of the time in January and July, (3) mean wind speeds that exceed 3 m s1 in either January or July, and (4) fewer than one cyclone–anticyclone alternation every 2 years in either January or July in a 51 latitude–longitude rectangle. In essence, Ramage’s (1971) criteria exclude most extratropical regions with prevailing synoptic-scale cyclonic and anticyclonic circulations and, in addition, require the mean wind direction to be driven and sustained exclusively by the seasonally varying temperature contrast between continental and oceanic masses. Under these constraints, only India, SouthEastern Asia, Northern Australia, and West and central Africa have monsoon climates (Slingo, 2003). For example, in India, about 80% of the mean annual rainfall accumulation (about 2 m) occurs during the months of June, July, and August (Smith, 1993). The main driving mechanism for monsoons is the temperature contrast between continental and oceanic masses due to the seasonal cycle of solar heating. More precisely, the lower thermal inertia of continental masses relative to oceans causes the former to heat up more rapidly during spring and summer by the solar radiation. This results in a sharp temperature gradient, which causes a humid flow of oceanic near-surface air to move toward the land (something similar to a massive sea breeze). As it reaches the land, the humid air warms up and rises, water vapor condenses to liquid water, and rain falls. A similar process occurs during winter, when the continental

51

air masses cool up more rapidly than the surrounding ocean water, with subsequent formation of a cold and dry massive low-level flux toward the ocean. An important factor that determines the intensity of monsoon rainfall is the geographical orientation of continents and oceans relative to the equator (Slingo, 2003). For example, the north–south orientation of the South-Eastern Asian and Northern Australian monsoon system allows the dry outflow from the winter continent to warm up and load moisture from the ocean, flow across the equator toward the summer hemisphere, and, eventually, feed the monsoon rains over the summer continent. This is also the reason why the largest rainfall accumulations for durations larger than 24 h are associated with the Asian–Australian monsoon system (Smith, 1993).

2.02.2.5.7 Tropical cyclones Tropical cyclones form a particular class of synoptic-scale lowpressure rotating systems that develop over tropical or subtropical waters (Anthes, 1982; Landsea, 2000). These systems have linear extent of the order of 300–500 km and are characterized by well-organized convection and cyclonic (counterclockwise in the Northern Hemisphere) surface wind circulation around a relatively calm low-pressure region, called the eye of the storm (Figures 19 and 20). Tropical cyclones with sustained wind speeds in the range 17–32 m s1 are called tropical storms whereas stronger tropical cyclones are usually referred to as hurricanes (i.e., when observed in the North Atlantic Ocean, in the Northeast Pacific Ocean east of the dateline, and in the South Pacific Ocean east of 1601 E) or typhoons (i.e., when observed in the Northwest Pacific Ocean west of the dateline). Note, however, that extreme rainfall accumulations for durations of the order of a day, or higher, are usually produced by moderate or even low-intensity tropical cyclones (Langousis and Veneziano, 2009b). An example is the tropical storm Allison in 2001, which looped over the Houston area causing rainfall accumulations in excess of 850 mm. According to the US National Oceanic and Atmospheric Administration (NOAA; Stewart, 2002), Allison (2001) ranks as the costliest and deadliest tropical storm in the history of the US with 41 people killed, 27 of who were drowned, and more than $6.4 billion (2007 USD) in damages. The genesis and development of tropical cyclones require the following conditions to be maintained (e.g., Gray, 1968, 1979): (1) warm ocean waters (surface temperature T427 1C); (2) a conditionally unstable atmosphere where the air temperature decreases fast with height; (3) a relatively moist midtroposphere to allow the development of widespread thunderstorm activity; (4) a minimum distance of about 500 km from the equator in order for the Coriolis force to be sufficiently large to maintain cyclonic circulation; (5) a nearsurface disturbance with sufficient vorticity and low-level convergence to trigger and maintain cyclonic motion; and (6) low magnitude of vertical wind shear (less than 10 m s1), defined as the difference between the 200- and 850-hPa horizontal wind velocities in the annular region between 200 and 800 km from the tropical cyclone center (Chen et al., 2006). The latter condition is important for the maintenance of the deep convection around the center of the cyclone.

52

Precipitation Tropopause, Z ≈ 15 km Main vortex top Z ≈ 10 km

Divergence region

Outflow Eye

Cloud

Rain Main vortex

Vertical wind

Boundary layer top, H ≈ 1−2 km Surface boundary, Z=0

Inflow R = 0 R ≈ 15−40 km

Boundary layer

R ≈ 150−200 km

Figure 19 Schematic representation of the structure of a mature hurricane.

30

d

an

b ain r-r

Storm motion

te

Ou

29

≥18 16

Inner-rainbands

14

Latitude (deg)

28

12 10 27

8 6 4

26

25

Eyewall

2 0 (mm h−1)

Out

er-r ainb

and Katrina (2005)

24 −92

−91

−90

−89

−88

−87

−86

Longitude (deg) Figure 20 TRMM microwave imager (TMI) rainfall retrievals for Hurricane Katrina on 28 August (2005) at 21:00 UTC (frame 44373): different types of rainbands and their locations relative to the center of the storm.

At a first approximation, a tropical cyclone can be seen as a heat engine fueled by the buoyant motion of warm and saturated (hence convectively highly unstable) air masses that lie directly above the warm tropical and subtropical ocean waters (e.g., Emanuel, 1986, 1989; Renno and Ingersoll, 1996; Marks, 2003). By contrast, extratropical cyclones obtain their energy

from the horizontal temperature gradients in the atmosphere (Section 2.02.2.5.3). During its mature stage, a tropical cyclone includes four distinct flow regions (Yanai, 1964; Smith, 1968; Frank, 1977; Willoughby, 1990; Smith, 2000), as depicted in Figure 19:

Precipitation

1. Away from the surface boundary (in the altitude range from 2–3 km to about 10 km), frictional stresses are negligible and the horizontal winds are in approximate gradient balance (e.g., La Seur and Hawkins, 1963; Hawkins and Rubsam, 1968; Holland, 1980; Willoughby, 1990, 1991; Vickery et al., 2000). In this region, usually referred to as the main vortex, the radial inflow is negligible, whereas the tangential flow is maintained by the balance between the inward-directed pressure gradient force and the sum of the outward-directed centrifugal and Coriolis forces. 2. Within the boundary layer (in the altitude range below 1– 2 km), frictional stresses decelerate the tangential flow, reduce the magnitude of the Coriolis and centrifugal forces, and result in an inward net force that drives low-level convergence. Calculations performed by Smith (1968, 2003), Kepert (2001), Kepert and Wang (2001), and Langousis et al. (2008) show that the radial inflow in the boundary layer turns upward before it reaches the tropical cyclone center causing vertical fluxes of moisture. Langousis and Veneziano (2009a) showed that these fluxes can be used to obtain accurate estimates for the large-scale mean rainfall intensity field in tropical cyclones as a function of the tropical cyclone characteristics. 3. At altitudes in excess of about 10 km, the curved isobars, which are responsible for the tropical cyclone formation and maintenance, start to flatten. As a consequence, the inward-directed pressure gradient force that maintains the cyclonic circulation decreases with increasing height leading to an outward-directed net force that drives high-level divergence. 4. Finally, there is a core flow region, called the eye of the tropical cyclone, with diameters of the order of 15–40 km. This region is free of cloud with light tangential winds and a downflow close to the axis. The condensation of water vapor caused by the ascending motion of humid near-surface air leads to the formation of cloud systems. These systems, which are usually precipitating, are organized around the cyclone center into long quasi-circular formations usually referred to as rainbands. Despite variations of rainband characteristics from one storm to another, and during the evolution of a single storm (e.g., Miller, 1958; Barnes et al., 1983; Marks, 1985; Molinari et al., 1999), a number of studies (Willoughby et al., 1984; Powell, 1990; Molinari et al., 1994, 1999, among others) have shown that rainbands, depending on their location relative to the storm center, share similar structural characteristics and can be organized into three distinct classes: eyewall, inner-rainbands, and outer-rainbands (Figure 20): 1. The eyewall is a well-developed convective band that surrounds the eye of the tropical cyclone. This band has a width of approximately 10–15 km with upward-directed quasi-steady velocities in the range of 0.5–3 m s1 or more, with the larger values being associated with more intense systems. The quasi-steady updrafts mostly reflect the radial convergence of horizontal fluxes, which become maximum close to the eye of the tropical cyclone (Smith, 1968; Shapiro, 1983; Kepert, 2001). The eyewall almost always

53

has the highest cloud tops (Jorgensen, 1984a), contains the largest annular mean rainfall intensity (Marks, 1985; Houze et al., 1992), and exhibits weak cellular structure as evidenced by radar observations (e.g., Jorgensen, 1984b; Marks, 1985). 2. The inner-rainbands (Molinari et al., 1994, 1999) are a group of spiral bands located outside the eyewall at radial distances smaller than approximately 120 km, and are also referred to as a stationary band complex (Willoughby et al., 1984). This group moves slowly, if at all, and maintains a rather fixed position relative to the vortex. Rainfall inside the inner-rainband region is mostly stratiform, with active convection covering 5–10% of the total rainfall area and contributing 40–50% of the total rainfall volume (e.g., Marks, 1985; Marks and Houze, 1987; Marks et al., 1992). 3. Outer-rainbands typically occur at radial distances larger than approximately 150 km from the tropical cyclone center (e.g., Powell, 1990; Molinari et al., 1994). They develop by the increased convergence at the boundary of the vortex envelope, where the convectively unstable environmental air flows around the storm and gives rise to formation of convective cells (e.g., Beer and Giannini, 1980; Ooyama, 1982; Molinari et al., 1994). Consequently, outerrainbands have more cellular structure than inner ones, which develop in a less-unstable atmosphere.

2.02.3 Precipitation Observation and Measurement 2.02.3.1 Point Measurement of Precipitation 2.02.3.1.1 Measuring devices The measurement of precipitation at a point is as easy as placing a bucket at the point of observation and periodically measuring the quantity of water it collects. The collected volume divided by the area of the opening is the precipitation depth. Due to this simplicity, such gauges have been used systematically since many centuries, and must have been discovered independently in different times, perhaps even in the antiquity, and in different places in the world, such as in ancient Greece and ancient India (Kosambi, 2005). However, their records have not survived; so the oldest available records now are those in Seoul, Korea, already presented in Section 2.02.1.3 and Figure 7 (upper), which go back to 1770, even though measurements must have been taken in much earlier periods since 1441 (Arakawa, 1956). The traditional device for rainfall measurement, known as rain gauge or pluviometer, is still in use today and, in fact, remains the most accurate device also providing the calibration basis for new measurement devices and techniques. It is a simple cylinder whose opening has an area (e.g., 200– 500 cm2 according to World Meteorological Organization (1983)) larger than (e.g., 10-fold) the cross section of the cylinder, which allows a greater sensitivity of the reading of the rainfall depth in a millimetric ruler attached to the cylinder. In another type of instrument, known as cumulative gauge, which is placed in inaccessible areas, the diameter of the cylinder may be larger than that of the opening, to enable the storage of a large volume of precipitation between the times of two visits to the place.

54

Precipitation

In an autographic (or recording) rain gauge, also known as a pluviograph, the water depth in the cylinder is recorded with the help of a mechanism involving a floating device. Another type of recording gauge, known as a tipping-bucket gauge, introduces the rainwater to one of a pair of vessels with a known small capacity (typically equivalent to 0.2 mm of rainfall) that is balanced on a fulcrum; when one vessel is filled, it tips and empties, while the time of this event is recorded, and the other vessel is brought into position for filling. In traditional autographic devices, these recordings are done on a paper tape attached to a revolving cylinder driven by a clockwork motor that is manually wound. In modern instruments, this device is often replaced by electronic systems, which provide digital recordings on a data logger and/or a computer connected by a cable or radio link. A rain gauge does not include all precipitation forms, snow in particular, except in light snowfalls when the temperature is not very low and the snow melts quickly. Generally, accurate measurement of snow precipitation (the water equivalent) needs specific instruments, equipped with a heating device to cause melting of snow. If such an instrument is not available, the snow precipitation is estimated as 1/10 of the snowfall depth (see justification in Section 2.02.1.3).

2.02.3.1.2 Typical processing of rain gauge data Measurement of precipitation in rain gauges is followed by several consistency checks to locate measurement errors and inconsistencies. Errors are caused due to numerous reasons, including human lapses and instrument faults, which may be systematic in case of inappropriate maintenance. Inconsistencies are caused by changes of installed instruments, changes in the environmental conditions (e.g., growing of a tree or building of a house near the rain gauge), or movement of the gauge to a new location. When errors are detected, corrections of the measurements are attempted. The standard meteorological practices include checks of outliers (a measured value is rejected if it is out of preset limits), internal consistency (checks are made whether different variables, e.g., precipitation and incoming solar radiation are compatible with each other), temporal consistency (the consistency of consecutive measurements is checked), and spatial consistency (the consistency of simultaneous measurements in neighboring stations is checked). Such checks are done in the timescale of measurement (e.g., daily for pluviometers or hourly for pluviographs) but systematic errors can only be located at aggregated (e.g., annual) timescales. The most popular method applied at an aggregated timescale for consistency check and correction of inconsistent precipitation data is that of the double-mass curve, which is illustrated in Figure 21. The method has a rather weak statistical background and is rather empirical and graphical (but there is a more statistically sound version in the method by Worsley (1983)). The double-mass curve is a plot of the successive cumulative annual precipitation Syi at the gauge that is checked versus the successive cumulative annual precipitation Sxi for the same period of a control gauge (or the average of several gauges in the same region). If the stations are close to each other and lie in a climatically homogeneous region, the annual values should correlate to each other. A fortiori, if the

two series are consistent with each other, the cumulated values Syi and Sxi are expected to follow a proportionality relationship. A departure from this proportionality can be interpreted as a systematic error or inconsistency, which should be corrected. Such a departure is usually reflected in a change in the slope of the trend of the plotted points. The aggregation of annual values xi and yi to calculate Sxi and Syi is typically done from the latest to the oldest year. Figure 21 (upper) shows the double-mass curve for 50 pairs of values representing annual precipitation at two points, whose cross-correlation (between xi and yi) is 0.82. The newest 25 points form a slope of m ¼ 0.70, whereas the oldest 25 form a much greater slope, m0 ¼ 0.95. Assuming that the newest points are the correct ones (with the optimistic outlook that things are better now than they were some years before), we can correct the older 25 annual yi by multiplying them with the ratio of slopes, l ¼ m/ m0 : ¼ 0.737. A second double-mass curve, constructed from the corrected measurements, that is, from y0i :¼ yi for ip25 (the newest years) and y0i :¼ lyi for i425 (the oldest years) is also shown in Figure 21 (upper). In fact, the data values used in Figure 21 are not real rainfall data but rather are generated from a stochastic model (Koutsoyiannis, 2000a, 2002) so that both stations have equal mean and standard deviation (1000 and 250 mm, respectively), be correlated to each other (with correlation coefficient 0.71) and, most importantly, exhibit HK behavior (with H ¼ 0.75, compatible with the values found in the real-world examples of Section 2.02.1.5). Hence, evidently, all values are correct, consistent, and homogeneous, because they were produced by the same model assuming no change in its parameters. Thus, the example illustrates that the method can be dangerous, as it can modify measurements, seemingly inconsistent, which however are correct. While this risk inheres even in time-independent series, it is largely magnified in the presence of HK behavior. Figure 21 (lower) provides a normal probability plot of the departure of the ratio l from unity (where the horizontal axis z is the standard normal distribution quantile and the distributions were calculated by the Monte Carlo method) for two cases: assuming independence in time and assuming HK behavior with H ¼ 0.75 as in the above example. The plots clearly show that, for the same probability, the departure of l from unity in the HK case is twice as high as in the classical independence case. For the HK case, departures of 70.25 from unity appear to be quite normal for 25-year trends and even more so for finer timescales, that is, 70.35 to 70.40 for 10-year to 5-year consecutive trends (not shown in Figure 21). Note that the method is typically applied even for corrections of as short as 5-year trends (Dingman, 1994), and so its application most probably results in distortion rather than correction of rainfall records. Apparently, the correction of the series using the doublemass curve method removes these trends that appear in one of the two time series. Removal of trends results in reduction of the estimated Hurst coefficient or even elimination of the exhibited HK behavior (Koutsoyiannis, 2003a, 2006b). Thus, if we hypothesize that the HK behavior is common in precipitation, application of methods such as the double-mass curves may have a net effect of distortion of correct data, based on a vicious circle logic: (1) we assume time independence of

Precipitation

55

50 000 Data Fitted broken line Adjusted data Adjusted straight line

40 000

Generation model Σyi (mm)

30 000

20 000

10 000

0 0

10 000

20 000 30 000 Σ xi (mm)

40 000

50 000

0.4 H = 0.5 (classical independence case) H = 0.75 (HK case)

Departure of slope ratio from 1, λ−1

0.3

0.2

0.1

0

−0.1

−0.2

−0.3 −3

−2

−1

0

1

2

3

Standard normal variate, z Figure 21 Illustration of the double-mass curve method and the associated risks in applying it. (Upper): Typical double-mass curve for 50 pairs of points, where the first 25 (newest) and the last 25 (oldest) form slopes m ¼ 0.7 and m0 ¼ 0.95, respectively; the adjusted points with l ¼ m/m0 ¼ 0.737 are also shown. (Lower): Comparison of probability distributions of the departure of the ratio l from unity for series independent in time or with HK behavior with H ¼ 0.75; the distributions were calculated using the Monte Carlo method based on synthetic series with a total size of 1000.

the rainfall process; (2) we interpret manifestation of dependence (the HK behavior in particular) as incorrectness of data; (3) we modify the data so as to remove the influence of dependence; and (d) we obtain a series that is much closer to our faulty assumption of independence. The widespread use of the double-mass curve method in routine processing of

precipitation time series may thus have caused enormous distortion of the real history of precipitation at numerous stations worldwide, in addition to masking HK behavior. The above discourse aims to issue a warning against unjustified use of consistency check and correction methods that could eliminate the extreme values (see, e.g., the note about

56

Precipitation

the Seoul station in Section 2.02.1.3) and the long-term variability implied by the HK behavior; the effect of both these mistreatments of data is a serious underestimation of design precipitation and flow in engineering constructions and management decisions. As a general advice for their correct application, we can stress that all methods of this type should never be applied blindly. An inspection of local conditions (environment of the rain gauge station and practices followed by the observer) as well as of the station’s archive history is necessary before any action is taken toward altering the data. Unless information on local conditions and archive history justify that inconsistencies or errors exist, corrections of data should be avoided.

2.02.3.1.3 Interpolation and integration of rainfall fields The interpolation problem, that is, the estimation of an unmeasured precipitation amount y from related precipitation quantities xi (i ¼ 1, y, n) is encountered very often in routine hydrologic tasks, such as the infilling of missing values of recorded precipitation at a station or the estimation of precipitation at an ungauged location. The integration problem refers to the estimation of an average quantity y over a specified area (or time period) based on measurements xi (i ¼ 1, y, n) of the same quantity and the same time period at different points (or respectively, at different time periods at the same point). The literature provides a huge diversity of methods, most of which, however, could be reduced to a linear statistical relationship applicable to both the interpolation and the integration problem:

y ¼ w1 x1 þ y þ wn xn þ e

ð28Þ

where wi denotes a numerical coefficient (weighting factor) and e denotes the estimation error. The same could be written in vector form:

y ¼ wTx þ e

ð29Þ

with w :¼ [w1, y, wn]T and x :¼ ½x1 ; ? ; xn T , and the superscript T denotes the transpose of a vector (or a matrix). The notation in Equations (28) and (29) suggests that x, y, and e are treated as random variables, even though this may not be necessary in some of the existing methods. All interpolation techniques provide a means for estimating the numerical coefficients wi, either conceptually or statistically, whereas the statistical methods provide, in addition, information about the error. Most commonly, the latter information includes the expected value me :¼ E½e and its standard deviation se :¼ ðVar½eÞ1=2 . A statistical estimation in which E½e ¼ 0 is called unbiased, and one in which the mean square error MSE :¼ E½e 2  ¼ s2e þ m2e , is the smallest possible, is called best; if both these happen, the estimation is called best linear unbiased estimation (BLUE). While the BLUE solution is in principle quite simple (see below), the estimation of its weighting factors is not always straightforward. Hence, several simplified statistical methods as well as empirical conceptual methods are in common use. Another reason that explains why such a diversity of methods has emerged is the different type of objects that each of the elements of the vector x represents. For instance, in temporal interpolation, these

elements can be observed values at times before and after the time of interpolation. In spatial interpolation these can be simultaneously observed values for stations lying in the neighborhood of the point of interpolation. Simultaneous temporal and spatial interpolation, although unusual, may be very useful. For example, an optimal way to infill a missing value in a time series at a specific time would be to include in x measurements taken in neighboring gauges at this specific time, as well as measurements taken at the point of interest at preceding and subsequent times. Let us first examine the different methods in which the estimation of y is based on a single observation x  xi at one neighboring (in space or time) point only. Here is a list of options, in which the following notation has been used: mx :¼ E½x and my :¼ E½y are the expected values of x and y, respectively; s2x :¼ E½ðx  mx Þ2  and a2y :¼ E½ðy  my Þ2  are the variances of x and y, respectively; sxy :¼ E½ðx  mx Þðy  my Þ is the covariance of x and y; and rxy :¼ sxy/(sx sy) is the correlation of x and y. 1. Equality: y ¼ x. The single point of observation considered in this naive type of interpolation is the station i nearest to the interpolation point, with x  xi. As discussed below, this simple interpolation forms the background of the Thiessen method of spatial integration. It is generally biased, with bias me ¼ my  mx and its MSE is s2y þ s2x  2sxy þ m2e . However, if the precipitation field is stationary (so that the means and variances at all points are equal to global parameters m and s2, respectively), it becomes unbiased, with MSE ¼ 2s2 (1  rxy). Evidently, for rxyo0.5, the method results in MSE 4 s2 and therefore there is no meaning in adopting it for low correlation coefficients (an estimate x ¼ m would be more effective). 2. Normal ratio: y ¼ w x with w ¼ my/mx. This is a better alternative to the equality case, but it requires a sample of measurements to be available for y in order to estimate the average my. This estimation is unbiased (me ¼ 0) but not best (MSE ¼ sy2 þ sx2 my2/mx2  2 sxy my/mx). 3. Homogenous linear regression: y ¼ wx with w ¼ E½y x= E½x 2  ¼ ðsxy þ mx my Þ=ðs2x þ m2x Þ. This is a biased estimation (me ¼ my  w mx) albeit best ðMSE ¼ s2y þ ðm2y s2x  2mx my sxy  s2xy Þ=ðm2x þ s2x ÞÞ. 4. Linear regression: y ¼ w x þ b with w ¼ Cov½y x=Var½x ¼ sxy =s2x and b ¼ my  w mx. This can be derived from Equation (28) by adding an auxiliary variable whose values are always 1 (i.e., y ¼ w x þ b 1). It has the properties of being both unbiased and best, with MSE ¼ s2y ð1  r2xy Þ. However, it has the deficiency of potentially resulting in negative values, if bo0, or of excluding values between 0 and b if b 4 0. Another drawback emerges when many values of y are estimated in an attempt to extend a record of y based on a longer record of x. In this case, the resulting extended record has negatively biased variance, because the method does not preserve variance. To remedy this, a random error e should be added (using the probability distribution of e), which however is not determined in a unique manner and makes the method no longer best. 5. Organic correlation: y ¼ wx þ b with w ¼ sign [rxy] sy/sx and b ¼ my  w mx. This preserves both mean (i.e., it is unbiased) and variance, but it is not best ðMSE ¼ 2s2y ð1  jrxy jÞ.

Precipitation Evidently, for |rxy|o0.5, the method results in MSE 4 s2y and therefore adopting it is pointless for low correlation coefficients. Similar to the standard linear regression, the organic correlation retains the deficiency of producing negative values or excluding some positive values. Coming to the interpolation based on multiple xi, in the simplest case, all weights wi are assumed equal for all i, that is, wi ¼ 1/n so that y is none other than the average of xi (the arithmetic mean method). This simple version is used very often to fill in sparse missing values of rain gauge records. The quantities xi could be simultaneous measurements at neighboring points (say, within a radius of 100 km), or at neighboring times, or both. Here, neighboring times should not necessarily be interpreted in the literal meaning, but with an emphasis on similarity of states. For example, a missing value of monthly precipitation in April 2000 could be estimated by, say, the average of the precipitation of the April months of 1998, 1999, 2001, and 2002. In another version, the average of all April months with available data are used, but a local average (as we have already discussed in Section 2.02.1.4) is preferable over an overall average, assuming that precipitation behaves like an HK process rather than a purely random one; this is similar to taking the average of points within a certain distance rather than a global average in spatial interpolation. This is not only intuitive but it can have a theoretical justification (D. Koutsoyiannis, personal notes), according to which for an HK process with H ¼ 0.7, a local average based on 3 time steps before and 3 after the interpolation time is optimal (produces lowest MSE); the optimal number of points becomes 2 þ 2 and 1 þ1 for H ¼ 0.75 and HX0.8, respectively. This simple method does not impose any requirement for calculation of statistical quantities for its application. Another method of this type, which takes account of the geographical locations and, in particular, the distances di between the interpolated stations, is the method of inverse distance weighting (IDW). In each of the basis stations, it assigns weights as

db wi ¼ Pk i b j¼1 dj

ð30Þ

where the constant b is typically assumed to be 2. Among methods whose application requires statistical quantities to be known, the simplest is a direct extension of the normal ratio method, in which wi ¼ ð1=nÞðmy =mxi Þ. The BLUE method itself belongs to this type. Initially, we can observe that a simple but biased solution for w in Equation (29) can be easily obtained as

w ¼ C 1 g;

me ¼ my  wT lx ;

s2e ¼ s2y  gT C1 g ¼ s2y  wT g

ð31Þ

where g :¼ Cov½y; x is the vector whose elements are the covariances of y with x (see Section 2.02.1.5) and C :¼ Cov½x; x is the positive definite symmetric matrix whose elements are the covariances of the vector x with itself. One way to make it unbiased is to add an auxiliary variable xnþ1 whose values are always 1. This is the multivariate extension of the typical linear regression described in point 4 of the previous list, and thus it retains the deficiency of potentially

57

producing negative values or excluding some positive values. A better way to make it unbiased is to add a constraint my ¼ wT lx (the bivariate analog of this is the equality case, described in point 1 of the list). In the latter case, the MSE becomes

MSE ¼ s2e ¼ s2y þ m2y þ wT ðC þ lx lTx Þw  2wT ðg þ my lx Þ

ð32Þ

Minimization of the MSE with the above constraint using a Lagrange multiplier –2l results in the system of equations

lTx w ¼ my

Cw þ lx l ¼ g;

ð33Þ

whose solution for the n þ 1 unknowns w1, y, wn, l is 0

w0 ¼ C 1 g0

ð34Þ

where

" 0

w :¼

# " w C 0 ; C :¼ l lTx

# " # lx g 0 ; g :¼ my 0

ð35Þ

The value of the error is then calculated as

MSE ¼ s2e ¼ s2y þ wT Cw  2wT g

ð36Þ

As seen in Equations (31) and (34), the application of the method requires a number of covariances to be estimated (specifically, this number is (n2 þ 3n)/2, given that C is symmetric). Not only does this restrict the method’s application to points where measurements exist, in order to estimate the covariances, but, when n is large, it is infeasible to reliably estimate so many parameters from data and to derive a positive definite C. The viable alternative is to assume a parametric stochastic model for the precipitation field. In the simplest case, the field could be assumed stationary and isotropic, where mxi ¼ my ¼ m; sxi ¼ sy ¼ s, and the covariance among any two points i, j is a function f of the geographical distance dij between these points, that is, sij : ¼ Cov[xi , xj ] ¼ f(dij). In this case, Equation (35) simplifies to

" 0

C :¼

C 1T

" # # " # 1 w g 0 0 ; w :¼ 0 ; g :¼ l 1 0

ð37Þ

where l0 ¼ l m and 1 is a vector with all its elements equal to 1. The last solution is widely known as ‘kriging’ (although kriging is sometimes formulated not in terms of covariance as in here, but in terms of the so-called semivariogram, a notion that is not appropriate for processes with HK behavior). We can observe from Equation (37) that the solution is now independent of m, as is also the error, which is still calculated from Equation (36). It only depends on the covariance function f(d). A function f(d) compatible with the HK behavior of precipitation, as discussed in Section 2.02.1.5, is of the form

f ðdÞ ¼ minðc; a d 4

H4

Þ

ð38Þ

where H is the Hurst coefficient and cb0 and a are parameters; in particular, c violates theoretical consistency but has

58

Precipitation

been introduced to avoid problems related to the infinite covariance for distance tending to zero. It can be observed that if the point of interpolation coincides with any one of the basis points i, then g is identical to one of the columns of C and g0 is identical to one of the columns of C0 . Thus, given the symmetry of C and C0 , from Equation (31) or (34), we obtain that w is a unit vector, that is, all elements are zero except one, which will be equal to 1. This shows the consistency of the method, that is, its property to reproduce the measurements at gauged points with zero error. All of the above methods that can interpolate at an arbitrary point (rather than only at a gauged one) provide a basis for numerical integration to find the average precipitation over a specific area A. Eventually, these methods result again in Equation (28) or (29), where now y is the areal average precipitation. In particular, in the arithmetic mean and the normal ratio methods, because they do not make any assumption about the position of the point to which interpolation refers, the estimate y is an interpolation at any point and a spatial average as well. The equality method works as follows: the geographical area of interest is divided into polygons, the so-called Thiessen polygons, each of which contains the points nearest to each of the stations. All points belonging to a specified polygon are regarded to have received a precipitation amount equal to that of the station corresponding to this polygon. Thus, in the integration, we use either Equation (28) or (29), where all gauged xi in the area are considered with weights wi ¼ Ai/A, whereas Ai and A are the areas of the polygon corresponding to xi. The remaining methods (IDW and BLUE) can be explicitly put in the form of Equation (29), but this is rather tedious if done analytically. A simpler alternative is to make interpolations to many points, for example, on a dense square grid. In turn, the gridded interpolations could be used for integration using equal weights for all grid points (i.e., arithmetic mean).

2.02.3.2 Radar Estimates of Precipitation Radio detection and ranging (radar) was developed at the beginning of World War II as a remote-sensing technique to measure the range and bearing of distant objects (such as ships and airplanes) by means of radio echoes (e.g., Battan, 1973). Since the early 1970s, radar techniques have also been used for the identification (i.e., shape, size, motion, and thermodynamic properties) of precipitation particles. The latter are weather-related distributed targets, which in contrast to ships and airplanes, have characteristics that evolve in time and depend on the atmospheric conditions. Because of their ability to provide estimates of areal precipitation quickly (i.e., at time intervals of about 5–15 min), at high resolutions (i.e., down to spatial scales of about 1 km) and over wide areas (i.e., with an effective range of about 200–400 km), radars have found wide application in atmospheric research, weather observation, and forecasting (e.g., Atlas et al., 1984; Doviak and Zrnic, 1993; Uijlenhoet, 1999, 2008; Bringi and Chandrasekar, 2001; Krajewski and Smith, 2002; Testik and Barros, 2007). An example is the next generation weather radar (NEXRAD) network with 159

operational weather surveillance radar 88 Doppler (WSR88D) units (as of February 2009), deployed throughout the continental United States and at selected locations overseas. According to NOAA’s weather service (US National Oceanic and Atmospheric Administration, 2009), since its establishment in 1988, the NEXRAD project has provided significant improvements in severe weather and flash flood warnings, air traffic control, and management of natural resources.

2.02.3.2.1 Basics of radar observation and measurement A typical weather radar has three main components (Battan, 1973): (1) the transmitter, which generates short pulses of energy in the microwave frequency portion of the electromagnetic spectrum, (2) the antenna, which focuses the transmitted energy into a narrow beam, and (3) the receiver, which receives the backscattered radiation from distant targets that intercept the transmitted pulses. Some important parameters, and their range of values, that characterize the radar equipment are (Rogers and Yau, 1996): (1) the instantaneous power of the pulse PtE10–103 kW (also referred to as peak power), (2) the duration of the pulse tE0.1–5 ms, (3) the frequency of the signal nE3–30 GHz, (4) the pulse repetition frequency (PRF) frE200–2000 Hz, defined as the reciprocal of the time interval tmax that separates two distinct pulses (i.e., tmax ¼ f 1 r E 0:5  5 ms), and (5) the beamwidth of the antenna yE11, defined as the angular separation between points where the power of the transmitted signal is reduced to half of its maximum value (or equivalently 3 dB below the maximum). The latter is attained at the beam axis. The wavelength l of the signal is defined as the distance between two sequential crests (or troughs) of the electromagnetic wave and it is related to its frequency as

ln ¼ c

ð39Þ

where c ¼ 3 108 m s1 is the velocity of light in vacuum. It follows from Equation (39) that typical frequencies n ¼ 3–30 GHz correspond to wavelengths l between 10 and 1 cm, but most weather radars operate at wavelengths l ¼ 3–10 cm (X-, C-, and S-band; see, e.g., Uijlenhoet and Berne, 2008). Shorter wavelengths are more effectively attenuated by atmospheric hydrometeors and precipitation particles (hence the transmitted signal has a small effective range), whereas for longer wavelengths the backscattered radiation from the precipitation particles does not have sufficient power to be detected by the receiver without noise induced by ground targets (e.g., Uijlenhoet, 2008). When conducting radar observations and measurements, the direction of the target is obtained from the azimuth and elevation of the antenna when the returning echo is received. The range r of the target is calculated from the relation

r ¼ c t=2

ð40Þ

where t is the time interval between the transmission of the pulse and the reception of the echo. If the target is moving, the radial velocity ur of the target (i.e., in the radar-pointing direction) can be obtained from the frequency shift Dn of the

Precipitation

received relative to the transmitted signal. The frequency shift is caused by the Doppler effect and it is related to ur as:

Dn ¼ 2ur =l

ð41Þ

with positive Dv being associated with targets that move toward the radar. If t (the time interval between transmission and reception) is larger than tmax (the reciprocal of the pulse repetition frequency, fr), the echo from the target will reach the receiver after a new pulse has been transmitted. Hence, targets that return enough energy to be detected by the receiver (see below) and are located at distances r4rmax ¼ c/(2fr), will appear unrealistically close to the antenna. Thus, rmax is the maximum range within which targets are indicated correctly on the radar screen and it is usually referred to as the unambiguous range (Battan, 1973; Rogers and Yau, 1996). The visibility of a target by the radar depends on whether the returning signal has sufficient power Pr to be detected by the receiver. As an example, we consider a point target (i.e., a target with linear dimension smaller than about 10% of l) with cross section At located at distance r from the radar. We suppose that the radar transmits pulses with peak power Pt that propagate isotropically in space (i.e., in a 3D sphere). It follows from simple geometric considerations that the power Pi intercepted by the target is

Pi ¼

Pt At 4pr 2

ð42Þ

where 4pr2 is the surface area of a sphere with radius r. If the transmitted signal is focused in a narrow beam by the antenna (as is commonly the case), Equation (42) becomes

Pt At Pi ¼ G 2 4pr

ð43Þ

2

where G ¼ (4p Ae)/l is a dimensionless constant called the antenna axial gain that depends on the characteristics (i.e., the wavelength l) of the signal and the aperture Ae of the antenna. Assuming that the target scatters the intercepted signal isotropically in space, the power Pr that reaches the radar is

Pr ¼

Pi Ae Pt At Ae l 2G 2 ¼G 2 ¼ Pt At ð4pÞ 3 r 4 2 4pr 2 ð4pr Þ

ð44Þ

If the power Pr is large enough to be detected by the receiver without unwanted echoes (e.g., noise from ground targets), the target is visible to the radar and it is indicated on the radar screen. For nonisotropic scatterers, the cross section of the target At should be replaced by the backscatter cross section s of the target. For spherical particles with diameter Dol/10, usually referred to as Rayleigh scatterers, s can be calculated from the relation (Battan, 1973)

s¼

p 5 jKj 2 D 6 l4

ð45Þ

59

where |K| is the amplitude of the complex refraction index (|K|2E0.93 for liquid water and 0.21 for ice), which characterizes the absorptive and refractive properties of the spherical scatterer. Due to the much higher value of |K|2 for liquid water relative to ice (about 4.5 times higher), the melting layer of ice particles in precipitation-generating weather systems appears on the radar screen as a bright band of high reflectivity.

2.02.3.2.2 Radar observation of distributed targets and the estimation of precipitation For a typical weather radar that operates in the C-band portion of the electromagnetic spectrum (l ¼ 3.75–7.5 cm), raindrops and snowflakes (i.e., particles with effective diameters Do5– 6 mm) can be approximated as Rayleigh spherical scatterers with backscatter cross section s given by Equation (45). However, there are reasons why atmospheric hydrometeors should not be treated as isolated point targets. One reason is that the pulse transmitted by the radar illuminates simultaneously, numerous precipitation particles that are included in a certain volume of air V, referred to as the resolution volume of the radar. Hence, the returned signal contains spatially averaged information from the whole population of raindrops and snowflakes in V. For parabolic antennas, where the beam pattern is approximately the same in all directions, an accurate estimate of V can be obtained by assuming that the resolution volume is a cylinder with effective height equal to half of the pulse length l ¼ c t and diameter dV ¼ r y, that is, the separation distance between points where the power of the transmitted signal is reduced to half of its maximum value. This gives

 2 ry ct V¼p 2 2

ð46Þ

where y is in radians. Equation (46) assumes that all energy in the radar transmitted pulse is contained within the half-power beamwidth; assuming a Gaussian shape of the beam pattern, the denominator of (46) (and, likewise, that of (49) below) should be multiplied by a factor 2 ln 2 (Probert-Jones, 1962). Another reason why raindrops and snowflakes cannot be treated as isolated point targets is that their turbulent motion that causes the power Pr of the returned signal to fluctuate in time. To this extent, an accurate approximation of the timeaveraged power Pr (over a sufficiently long interval of about 102 s), which accounts also for multiple backscattering cross sections, is given by (Rogers and Yau, 1996)

l 2G 2 X Pr ¼ Pt s ð4pÞ 3 r 4 V

ð47Þ

where r is the time-averaged range of the resolution volume V, and the summation is taken over all s in V. For Rayleigh scatterers, Equations (45) and (47) are combined to give

p 2 G 2 jKj 2 X 6 D Pr ¼ Pt 64r 4 l 2 V

ð48Þ

60

Precipitation

Assuming homogeneity of the population of hydrometeors in V, Equation (48) can be written as

Z N p 2 G 2 jKj 2 V nðDÞD6 dD Pr ¼ Pt 64r 4 l 2 0 p 3 G 2 jKj 2 y 2 ct jKj 2 Z Z¼C 2 ¼ Pt 2 2 512r l r

ð49Þ

where n(D) is the size distribution of precipitation particles in V (i.e., number of particles per unit diameter and per unit volume of air), C is the so-called radar constant that depends solely on the characteristics of the system under consideration, and

ZN Z :¼

nðDÞD 6 dD

ð50Þ

0

is the reflectivity factor with units (length3) that depend solely on the size distribution of the precipitation particles. For the Marshall and Palmer (1948) parametrization described by Equation (24), Equation (50) takes the form

Z ¼ 720 n0 b7

ð51Þ

where n0 and b are the intercept and scale parameters of the exponential size distribution. For the expressions given in Section 2.02.2.3.2, for rain and snow we obtain

ðaÞ Z ¼ 296 i 1:47 ðrainÞ ðbÞ Z ¼ 3902 i 2:49 ðsnowÞ

ð52Þ

where Z has units of mm6 m3 and i is the rainfall intensity (or the water equivalent of the accumulated snow at ground level) in millimeters per hour. For rain, Equation (52a) is very close to the empirical Z  i relationships (usually referred to as Z  R relationships, where Ri denotes the rainfall intensity) found in the literature (e.g., Marshall et al., 1955; Battan, 1973; Uijlenhoet, 1999, 2001, 2008), whereas for snow there is more variability and Equation (52b) should be seen only as an approximation. When combined, Equations (40), (49), and (52) allow conversion of radar measurements (i.e., Pr , t and r) to precipitation intensity i.

2.02.3.3 Spaceborne Estimates of Precipitation The history of observation of Earth from space started on 4 October 1957, when the Soviet Union successfully launched Sputnik-I, the first artificial satellite. Sputnik-I provided information on the density of the highest layers of the atmosphere and on the radio-signal distribution in the ionosphere. The first launch was immediately followed by the launch of Sputnik-II by the Soviet Union on 3 November 1957 and the launches of Explorer-I (1 February 1958), Vanguard-I (17 March 1958), Vanguard-II (17 February 1959), and TIROS-I (1 April 1960) by the United States of America. The success of TIROS-I in surveying atmospheric conditions (in particular, the cloud coverage of Earth) opened a new era for

meteorological research and development using spaceborne observations. Since the 1970s, meteorological satellites have become essential in studying the development and evolution of weatherrelated phenomena over the 71% of the Earth’s surface covered by sea, where other types of measurements are unavailable. For example, the Tropical Rainfall Measuring Mission (TRMM; Simpson et al., 1988; Kummerow et al., 1998), which started in November 1997 by the National Aeronautics and Space Administration (NASA) of the United States and the National Space Development Agency (NASDA) of Japan, has provided vast amounts of rainfall and energy estimates in tropical and subtropical regions and advanced the understanding and modeling of extreme rainfall events caused by tropical cyclones (e.g., Lonfat et al., 2004, 2007; Chen et al., 2006, 2007; Langousis and Veneziano, 2009a, 2009b). TRMM data have also been used to improve the accuracy of high-resolution weather forecasts produced by limited-area models (e.g., Lagouvardos and Kotroni, 2005) and to investigate the relationship between lighting activity, microwave brightness temperatures (see below), and spaceborne radar-reflectivity profiles (Katsanos et al., 2007). We can distinguish two types of sensing by satellites, passive and active. Passive sensing is based on measuring the radiative intensity emitted or reflected by particles in the atmosphere, such as cloud droplets and hydrometeors of precipitable size. Active sensing is conducted using radar equipment carried by the satellite. Next, we discuss some basic principles of passive remote sensing in the visible (V, lE0.39– 0.77 mm), IR (wavelengths lE0.77 mm–0.1 mm), and microwave (MW, lE0.1 mm–10 cm) portions (channels) of the electromagnetic spectrum. The basic principles of operation of active sensors are similar to those of radars, reviewed in Section 2.02.3.2. For a more detailed review on the principles and techniques of remote sensing, the reader is referred to Barrett and Martin (1981), Elachi (1987), Stephens (1994), and Kidder and Vonder Haar (1995).

2.02.3.3.1 The IR signature of cloud tops The high absorptivity of cloud droplets in the IR spectral range causes clouds to appear opaque in the IR channel. Hence, the IR radiation received by the satellite’s radiometer originates mostly from the cloud tops, which can be approximated with sufficient accuracy as black bodies, that is, as objects that absorb all incident radiation and emit it at a rate that depends solely on their temperature. In this case, we can use Stefan– Boltzman’s law of radiation (e.g., Barrett and Martin, 1981) to calculate the temperature Tb of the cloud tops from the intensity J of the received IR radiation:

Tb ¼ ðJ=sSB Þ1=4

ð53Þ

where sSB ¼ 5.7 108 W m2 K4 is the Stefan–Boltzman constant and Tb is in kelvins. Tb is usually referred to as brightness temperature (e.g., Smith, 1993) and, for a given atmospheric lapse rate g (see Section 2.02.2.1), it can be used to calculate cloud top heights. Evidently, lower brightness temperatures Tb correspond to clouds with higher tops and larger probabilities of rain.

Precipitation

Hence, we can develop regression equations to relate brightness temperatures to observed surface rainfall rates (e.g., Griffith et al., 1978; Stout et al., 1979; Arkin, 1979; Richards and Arkin, 1981; Arkin and Meisner, 1987; Adler and Negri, 1988). Two important limitations apply (Richards and Arkin, 1981; Liu, 2003): (1) due to the statistical character of the regressed quantities, the accuracy of the rainfall-retrieval algorithm increases with increasing scale of spatial or temporal averaging, and (2) the parameters of the regression depend on the climatology of the region and, therefore, cannot be used at regions with different climatic characteristics. An example of surface rainfall estimation from IR images is the temperature threshold method developed by Arkin (1979), Richards and Arkin (1981) and Arkin and Meisner (1987). Arkin (1979) used IR imagery from the Synchronous Meteorological Satellite-1 (SMS-1) and radar data from Global Atmosphere Research Program (GARP) Atlantic Tropical Experiment (GATE) to investigate the correlation between radarestimated precipitation rates and the fraction of areas with brightness temperature Tb below a certain threshold Tmin. The study found a maximum correlation (around 0.85) for a brightness temperature threshold TminE235 K (  38 1C). Richards and Arkin (1981) showed that a linear relationship is sufficient to describe the dependence between spatially averaged surface rainfall and the fraction of areas with Tbo235 K, with error variance that increases with decreasing scale of spatial averaging. Based on these results, Arkin and Meisner (1987) suggested the use of the Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI) to calculate spatial rainfall averages in the tropics:

GPI ¼ 3ðmm=hÞFc H

ð54Þ

where GPI is the spatially averaged rainfall accumulation in a grid box of 2.51 latitude  2.51 longitude, Fc is the mean fraction (a dimensionless quantity between 0 and 1) of the grid box covered by brightness temperatures Tbo235 K, and H is the length of the observation period in hours. The temperature threshold method of Arkin (1979), Richards and Arkin (1981), and Arkin and Meisner (1987) produces accurate estimates of the spatially averaged rainfall in the tropical belt (301 S to 301 N), at grid scales larger than 2.51(E275 km) (Arkin and Meisner, 1987) and for averaging durations greater than about a month (Ba and Nicholson, 1998). The error increases significantly as we move to midlatitudes, especially during cold seasons (e.g., Liu, 2003). Extensions of the method include the use of the upper tropospheric humidity (UTH) in the vicinity of convective clouds as an additional predictive variable (Turpeinen et al., 1987), and the combination of IR and visible imagery (i.e., bi-spectral methods; see below) to exclude nonprecipitating clouds with high tops.

2.02.3.3.2 The visible reflectivity of clouds The signature of Earth in the visible (V) channel is due to the reflection of the sunlight by clouds and, when the sky is clear, the surface features. Consequently, visible imagery is available only during daylight hours. Due to its shorter wavelength, visible radiation can penetrate deeper into clouds than the

61

infrared portion of the electromagnetic spectrum, but similar to the IR channel, it still represents the upper portion of clouds and serves as an indirect signature of surface rainfall. However, visible reflectivity can complement the IR brightness temperatures to allow better classification of clouds and qualitative assessment of the probability of precipitation. This is the basis of the well-known bi-spectral methods (e.g., Lovejoy and Austin, 1979; Bellon et al., 1980; Tsonis and Isaac, 1985; Tsonis, 1987; O’Sullivan et al., 1990; Cheng et al., 1993; Cheng and Brown, 1995; King et al., 1995; Liu, 2003). The visible reflectivity of clouds increases fast with the increasing liquid water path, that is, the vertically integrated liquid water in the atmospheric column. Hence, we can use IR brightness temperatures to calculate the altitude of the cloud tops and visible reflectivities to obtain a qualitative estimate of the vertically averaged liquid water of the cloud, which is indicative of the rainfall potential. For example, low brightness temperatures (i.e., cold cloud tops) and high visible reflectivities (i.e., thick clouds) indicate cumulonimbus formations with high probability of precipitation (see Section 2.02.2.4.1), warm cloud tops and high visible reflectivities indicate stratiform rainfall (see Section 2.02.2.4.2), whereas cold cloud tops and low visible reflectivies indicate cirrus clouds, which are usually nonprecipitating. An example of bi-spectral methods is the RAINSAT technique developed by Lovejoy and Austin (1979) and Bellon et al. (1980). This technique uses visible reflectivities to reduce the number of false alarms obtained from the IR channel and more accurately estimate surface rainfall rates. The RAINSAT method was developed using GOES infrared and visible imagery and radar data from tropical (i.e., GATE) and mid-latitude (i.e., McGill weather radar, Quebec, Canada) locations as ground truth. The method was optimized by Cheng et al. (1993) and Cheng and Brown (1995) for the area of the UK, using IR and visible imagery from the European geostationary satellites Meteosat-2, Meteosat-3, and Meteosat-4 and rainfall retrievals from nine weather radars located in the United Kingdom and Ireland. A similar cloud classification technique has been proposed by Tsonis and Isaac (1985) and Tsonis (1987). This technique is based on cluster analysis of pixels with different brightness temperatures and visible reflectivities and has been developed using GOES satellite data and rainfall retrievals from the Woodbridge weather radar in Ontario, Canada.

2.02.3.3.3 The microwave signature of precipitation Contrary to the IR and visible spectral ranges, microwave radiation can effectively penetrate through cloud and rain layers and provide the signature of the integrated contribution of precipitation particles in the atmospheric column. Hence, brightness temperatures obtained from the MW channel are better linked to surface rainfall rates than the visible reflectivities and IR brightness temperatures. The type and size of the precipitation particles detected by the microwave radiometer depends on the frequency of the upwelling radiation. Above 80 GHz (i.e., wavelengths lo3.75 mm), ice crystals scatter the upwelling MW radiation and fade the signature of raindrops. Hence, above 80 GHz, the radiometer senses only ice, where lower brightness

62

Precipitation

temperatures are associated with more scattering, larger ice particles, and higher precipitation intensities at ground level. Below about 20 GHz (i.e., l41.5 cm), the radiative intensity of raindrops dominates the microwave signature of hydrometeors in the atmospheric column, whereas ice particles are virtually transparent. Thus, below 20 GHz, the microwave radiometer detects the vertically integrated signature of rainwater, where higher brightness temperatures are associated with more intense rainfall at ground level. Lowfrequency microwave imagery is especially useful when calculating surface rainfall rates over oceans, where the almost constant sea surface temperature and emissivity allow translation of the spatial and temporal variations of brightness temperatures to variations of sea-level rainfall rates (e.g., Liu, 2003). The same is not true over land, where the surface features cause the ground temperature and emissivity to vary significantly in space and time. Another limitation of lowfrequency microwave images is the saturation of the microwave channel at high rainfall rates, which causes negative biases of the obtained rainfall intensity (e.g., Liu, 2003; Viltard et al., 2006). Between 20 GHz and 80 GHz, scattering and emission by raindrops and ice particles occur simultaneously and the microwave radiation undergoes multiple transformations. Hence, the microwave radiometer detects different rain paths at different microwave frequency ranges. Combining brightness temperatures from different MW channels to more accurately assess surface rainfall rates is an open research problem and it has driven the development of many rainfall-estimation algorithms (Grody, 1991; Spencer et al., 1989; Alishouse et al., 1990; Berg and Chase, 1992; Hinton et al., 1992; Liu and Curry, 1992, 1993; Ferriday and Avery, 1994; Petty, 1994a, 1994b, 2001a, 2001b; Kummerow and Giglio, 1994a, 1994b; Ferraro and Marks, 1995; Kummerow et al., 1996, 2001; Berg et al., 1998; Aonashi and Liu, 2000; Levizzani et al., 2002). For a review of microwave methods of estimation over ocean and land, and their advantages and limitations, the reader is referred to Wilheit et al. (1994), Petty (1995), and Kidd et al. (1998) respectively.

2.02.4 Precipitation modeling As already clarified in Section 2.02.1.5, modeling of precipitation is not possible without using any type of a stochastic approach. Even the deterministic numerical weather forecast models, which determine the state and motion in the atmosphere by solving differential equations, to model precipitation, use parametrization schemes. These schemes, instead of describing the detailed dynamics of the precipitation process, establish and use equations of statistical type to quantify the output of the dynamical system. In addition, as mentioned in Section 2.02.1.5, the modern framework for predicting precipitation particularly as input to hydrological models (the ensemble forecasting), is of the Monte Carlo or stochastic type. The description of these stochastic techniques belongs to the sphere of weather forecasting and is not within the scope of this chapter. In more engineering-oriented applications, precipitation is typically modeled as an autonomous process, without particular reference to the atmospheric

dynamics. Next, we outline some of the most widespread modeling practices for precipitation but without details and mathematical formulations, which the interested reader can find in the listed references.

2.02.4.1 Rainfall Occurrence From the early stages of the analysis of precipitation intermittency, it was recognized that rainfall occurrences are not purely random. In other words, rainfall occurrence cannot be modeled (effectively) as a Bernoulli process in discrete time or, equivalently, as a Poisson process in continuous time. It should be recalled that in a Bernoulli process, an event (rainfall/wet state) occurs with a probability p (and does not occur with probability 1  p) constant in time, and each event is independent of all preceding and subsequent events. In a Poisson process, the times of occurrence of events (i.e., the starting times of rainfalls) are random points in time. In this process, the time differences between consecutive occurrences are independent identically distributed (IID) with exponential distribution. Both discrete time and continuous time representations of the rainfall occurrence process, which in fact are closely related (e.g., Foufoula-Georgiou and Lettenmaier, 1986; Small and Morgan, 1986), have been investigated. The most typical tool of the category of discrete time representations is the Markov chain model (Gabriel and Neumann, 1962; Feyerherm and Bark, 1964; Hershfield, 1970; Todorovic and Woolhiser, 1975; Haan et al., 1976; Chin, 1977; Katz, 1977a, 1977b; Kottegoda and Horder, 1980; Roldan and Woolhiser, 1982). In this model, any time interval (e.g., day) can be in one of two states, dry or wet, and it is assumed that the state in a time interval depends on the state in the previous interval. It was observed, however, that Markov chain models yield unsatisfactory results for rainfall occurrences, especially for dry intervals (De Bruin, 1980). Moreover, the interannual variance of monthly (or seasonal) total precipitation is greater than that predicted by Markov chain models, an effect usually referred to as overdispersion (Katz and Parlange, 1998). Extended versions of the binary state Markov chains using a higher number of past states may improve performance. Additional states in such model versions have been defined based on a combination of states of two consecutive periods (Hutchinson, 1990) or on accounting for the rainfall depth of each interval (Haan et al., 1976). A more effective enhancement is to use transition probabilities taking into account more than one previous interval, which leads to stochastic binary chains of order higher than one (Pegram, 1980; Katz and Parlange, 1998; Clarke, 1998). In more recent developments, to account for a long number of previous time intervals and simultaneously avoid an extremely high number of transition probabilities, it was proposed that, instead of the sequence of individual states of these intervals, one could use conditional probabilities based on aggregation of states of previous intervals (Sharma and O’Neill, 2002). Similarly, one could use a discrete wetness index based on the number of previous wet intervals (Harrold et al., 2003). An extension of the Markov chain approach to multiple sites has been studied by Pegram and Seed (1998).

Precipitation

In a more recent study, Koutsoyiannis (2006a) used the principle of maximum entropy, interpreted as maximum uncertainty, to explain the observed dependence properties of the rainfall-occurrence process, including the overdispersion or clustering behavior and persistence. He quantified intermittency by the probability p(1) that a time interval of length 1 h is dry, and dependence by the probability that two consecutive intervals are dry, that is by p(2), where in general p(k) denotes the probability that an interval of length k is dry. Using these two probabilities and a multiscale entropy-maximization framework, he was able to determine any conditional or unconditional probability of any sequence of dry and wet intervals at any timescale. Thus, he described the rainfall occurrence process including its dependence structure at all scales using only two parameters. The dependence structure appeared to be non-Markovian, yet not over-exponential. Application of this theoretical framework to the rainfall data set of Athens indicated good agreement of theoretical predictions and empirical data at the entire range of scales for which probabilities dry and wet can be estimated (from 1 h to several months). An illustration is given in Figure 22. In the continuous time representation of the rainfall occurrence process, the dominant tools are the cluster-based point processes (Waymire and Gupta, 1981a, 1981b, 1981c). These are essentially based on the prototype of the spatial distribution of galaxies devised by Neyman and Scott (1952) to describe their property of clustering relative to the Poisson process. With reference to storms, if they were regarded as instantaneous pulses positioned at random points in time, the logarithm of probability that the interarrival time exceeds a value x, or the log survival function, would be proportional to

63

x. However, empirical evidence suggests that the log survival function is a nonlinear concave function of x, which indicates a tendency for clustering of rainfall events relative to the Poisson model (Foufoula-Georgiou and Lettenmaier, 1986). This clustering has been modeled by a cascade of two Poisson processes, corresponding to two characteristic timescales of arrivals of storms and storm cells. The Neyman–Scott process with instantaneous pulses was the first one applied to rainfall occurrence (Kavvas and Delleur, 1981; Rodriguez-Iturbe et al., 1984), later succeeded by the Neyman–Scott rectangular pulses and the very similar Bartlett–Lewis rectangular pulse models (Rodriguez-Iturbe et al., 1987). The Bartlett–Lewis rectangular pulse model, which is the most typical and successfully applied model of this type, assumes that rainfall occurs in the form of storms of certain durations and that each storm is a cluster of random cells. The general assumptions of the rainfall occurrence process are: 1. Storm origins ti occur according to a Poisson process with rate l. 2. Origins tij of cells of each storm i arrive according to a Poisson process with rate b. 3. Arrivals of each storm i terminate after a time vi exponentially distributed with parameter g. 4. Each cell has a duration wij exponentially distributed with parameter Z. In the original version of the model, all model parameters are assumed constant. In a modified version, the parameter Z is randomly varied from storm to storm with a gamma distribution with shape parameter a and scale parameter n.

p(k)

1

0.1

0.01 1

10

100 k

1000

10 000

Figure 22 Probability dry p(k) vs. scale k (in h), as estimated from a hourly rainfall data set in Athens, Greece, and predicted by the maximum entropy model in Koutsoyiannis (2006a) for the entire year (circles and red full line) and the dry season (June–September; diamonds and blue full line). The model was fitted using two data points in each case (marked in full in the plot), that is, the probability dry for 1 h, pp(1), and 2 h, p(2), which are respectively 0.9440 and 0.9335 for the entire year and 0.9888 and 0.9860 for the dry season. The final model is expressed as 1=Z Z p ðk Þ ¼ p ½1þðx 1Þðk 1Þ , where the parameters are respectively Z ¼ 0.63 and x ¼ 0.816 for the entire year and Z ¼ 0.83 and x ¼ 0.801 for the dry season. For comparison, lines resulting from the Markov chain model are also plotted (dashed lines). From Koutsoyiannis D (2006a) An entropicstochastic representation of rainfall intermittency: The origin of clustering and persistence. Water Resources Research 42(1): W01401 (doi:10.1029/ 2005WR004175).

64

Precipitation Storms

Intensity (mm h−1)

4

Storm 1 Storm 2 Storm 3 Storm 4

3

2

1

0 2

1 Time (days)

Figure 23 Simulated realization of a series of four storms from the Bartlett–Lewis rectangular pulse model (modified version with randomly varying Z) occurring within three days (notice the overlap of storms 1 and 2, which is allowed by the model), implemented by the Hyetos software (see Section 2.02.4.4). The model parameters are l ¼ 0.94 d1, k ¼ b/Z ¼ 1.06, j ¼ g/Z ¼ 0.059, a ¼ 2.70, n ¼ 0.0068 d1, and mx ¼ sx ¼ 24.3 mm d1.

Subsequently, parameters b and g also vary so that the ratios k: ¼ b/Z and j: ¼ g/Z are constant. A major problem of these models was their inability to reproduce the probability of zero rainfall at multiple timescales (Velghe et al., 1994). In this respect, Foufoula-Georgiou and Guttorp (1986) noted that the Neyman–Scott model parameters are scale dependent and thus cannot be attributed a physical meaning. To ameliorate this, modifications of both the Neyman–Scott model (Entekhabi et al., 1989) and the Bartlett–Lewis model (Rodriguez-Iturbe et al., 1988; Onof and Wheater, 1993, 1994) were proposed. These are in fact based on the randomization of the mean interarrival time of one of the two Poisson processes. Evaluation and comparison of several cluster-based rectangular pulse models for rainfall were done by Velghe et al. (1994) and Verhoest et al. (1997), whereas a comprehensive review of Poisson-cluster models has been provided by Onof et al. (2000). An extension of the concept introducing a third Poisson process was proposed by Cowpertwait et al. (2007).

2.02.4.2 Rainfall Quantity In the discrete time representations of rainfall occurrence, the rainfall quantity in each wet interval is modeled separately from the occurrence process, usually based on statistical analysis of the observed record. In the point-process representations, the storms and cells are abstract quantities that do not fully correspond to real-world objects. Therefore, they cannot be identified in the recorded time series. An assumption is typically made that each cell has a uniform intensity xij with a specified distribution, and based on all assumptions, the statistical characteristics of the rainfall process at one or more timescales are derived analytically (Rodriguez-Iturbe et al., 1987, 1988). These statistical characteristics are compared to the empirically derived statistics, and, by minimizing the departures of the two, the model parameters are estimated. The distribution of the uniform intensity xij is typically assumed to be exponential with parameter 1/mx. Alternatively, one can choose a two-parameter gamma distribution with mean mx

and standard deviation sx. In this manner, the point-process models describe the entire rainfall process, including occurrence and quantity. A demonstration of the model is shown in Figure 23. However, in some cases (e.g., Gyasi-Agyei and Willgoose, 1997), point processes have been used to simulate merely rainfall occurrences and then have been combined with other models that simulate rainfall depths. Other modeling approaches for the rainfall process (including its intermittency) are reviewed in Srikanthan and McMahon (2001). With their typical assumptions, including those of the exponential or gamma distribution for rain-cell amount, the point-process models, despite providing satisfactory representation of the process at a specific timescale or a small range of timescales, cannot really perform satisfactorily over a wide range of scales and also lead to exponential distribution tails, whereas it has been recently recognized that the tails must be of power type (see Sections 2.02.1.5 and 2.02.5.2). Generally, the distribution function of rainfall varies among different timescales. At very fine scales, the density is J-shaped, that is, with a mode at zero, and perhaps with density tending to infinity as the rainfall depth or the intensity tends to zero. At coarse timescales such as monthly (for wet months) and annual, the distribution becomes bell-shaped and tends to become normal as the scale increases. However, its tail always departs from the exponential tail of the normal distribution. In fact, for theoretical reasons, if at the right tail, the survival function is a power function of the rainfall depth or intensity x, with exponent 1/k, that is, F*(x)px1/k (see Equation (18)), then it will be of the same type and will have precisely the same exponent 1/k at any timescale (the proof is omitted). This behavior of the tail is perhaps the only invariant distributional property across all scales, whereas the shape of the body of the distribution varies significantly across different scales. However, even this variation must have a simple and unique explanation, which is the principle of maximum entropy. Specifically, Koutsoyiannis (2005a) has shown that all diverse shapes of the distribution across different scales can be derived from the principle of maximum entropy constrained on known mean and variance.

Precipitation

Papalexiou and Koutsoyiannis (2008) proposed a single distribution (a power-transformed beta prime distribution, also known as generalized beta of second kind; see also Koutsoyiannis, 2005a) with four parameters, which provides good fits for rainfall intensity at timescales from hourly to annual. Only one of the four parameters (corresponding to the exponent of the tail) is invariant across scales. If the range of scales of interest is smaller, then specific special cases of this distribution can be used as good approximations. For example, the three-parameter Burr type VII distribution, which has the advantage of providing a closed form of the quantile function, can be used effectively for timescales from a few minutes to a couple of months (Papalexiou and Koutsoyiannis, 2009).

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Rainfall cells

2.02.4.3 Space–Time Models Space–time modeling of precipitation is one of the most demanding tasks of stochastic modeling in hydrology and geophysics. Rainfall intermittency should be modeled in both space and time, along with the motion of rainfall fields, the rainfall quantity, and its temporal and spatial structure. One of the relatively simple solutions has been provided by the extension of point-process models used for the rainfall process at a single site. This extension introduces a description of rainfall cells in space, in addition to that in time, and a motion of the cells. As an example, we summarize here the Gaussian displacement spatial–temporal rainfall model (GDSTM; Northrop, 1996, 1998). This model, is a spatial analog of a point-process model having a temporal structure similar to that of the Bartlett-Lewis rectangular pulse model described above and a spatial structure known as the Gaussian displacement structure, introduced by Cox and Isham (1988). Similar to its single-site analog, GDSTM assumes that rainfall is realized as a sequence of storms, each consisting of a number of cells. Both storms and cells are characterized by their centers, durations, and areal extents (see sketch in Figure 24) and, in addition, cells have certain uniform rainfall intensity. Specifically, the following assumptions characterize storms and cells. Storm centers arrive according to a homogeneous Poisson process of rate l in 2D space (denoted by x, y) and time (denoted by t) and move with a uniform velocity (Vx, Vy). Each storm has a finite duration L (assumed exponentially distributed with parameter b ¼ 1/mL) and an infinite areal extent, represented by an elliptical geometry with eccentricity E and orientation y, and incorporates a certain number of rainfall cells. However, a storm can be assigned a finite storm area, the area that contains a certain percentage of rainfall cells. The storm area varies randomly and in each storm, it is determined in terms of the realization of a random variable w, which determines uniquely (for the specific storm) a set of parameters s2x ; s2y , and r that determine the displacement of cell centers from the storm center. Specifically, w is Gammadistributed with shape and scale parameters determined in terms of the eccentricity e and the mean storm area ms. At the same time, the parameter r is determined in terms of the eccentricity e and the storm orientation, y. Following the generation of w, the parameters s2x and s2y are determined

Storm center Figure 24 Sketch of the spatial structure of the Gaussian displacement spatial–temporal rainfall model.

in terms of the eccentricity e, the storm orientation y, and the value of w. Each rainfall cell is assigned a center ðxc ; yc ; tc Þ. The time origin tc follows a Poisson process starting at the time ordinate of the storm origin t0 (with the first cell being located at this point) and ending at t0 þ L. The expected number of cells within that time interval is mc ¼ 1 þ b/g, where g is the cellgeneration Poisson process parameter. The spatial displacements from the storm center are random variables jointly normally distributed with zero means, variances s2x and s2y , and correlation r. Given these parameters, the displacement Dx of each cell is generated as a normal variate (0, sx) and the displacement Dy as a normal variate (my|x, sy|x). Furthermore, each cell has a finite duration D (assumed exponentially distributed with parameter 1/mD) and an elliptical area with major axis a, forming an angle y with the x-axis (west–east), ffi pffiffiffiffiffiffiffiffiffiffiffiffiffi and minor axis b ¼ 1  e 2 a. It is assumed that a is a random variable gamma distributed with shape and scale parameters depending on the mean storm area mA and the eccentricity e, respectively. Finally, each cell has an intensity x independent of any other variable, exponentially distributed with parameter 1/mx. The model is defined in terms of 11 independent parameters, namely: (1) the rate of storm arrival (number of storms per area per time), l; (2) the mean cell duration, mD; (3) the mean storm duration, mL; (4) the mean cell area, mA; (5) the mean storm area, ms; (6) the mean number of cells per storm, mc; (7) the mean cell intensity, mx; (8 and 9) the components of the cell and storm velocity in the x direction (east), Vx, and in the y direction (north), Vy; (10) the cell and storm eccentricity, e; and (11) the cell and storm orientation, y. Similar to its single-point analog, the entities of the spatial point-process model are abstract. To make the model outputs comparable to reality, integration from continuous time over a specific timescale and/or spatial scale is needed, from which the first- and second-order rainfall statistics are

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calculated. The latter serve as the basis for parameter estimation using either rain gauge or radar data. Due to model complexity, the calculation of the statistics can be done only numerically; hence, the entire model application (and the parameter estimation in particular, which needs numerical optimization, e.g., using the generalized reduced-gradient method) is laborious.

2.02.4.4 Rainfall Disaggregation and Downscaling Both disaggregation and downscaling refer to the generation of a precipitation field at a specific temporal and/or spatial scale given a known precipitation field (measured or simulated) at a certain larger temporal and/or spatial scale (lower resolution). Disaggregation and downscaling are very useful procedures and have several applications, such as in the following cases: 1. Global-scale weather-prediction models provide rainfall forecasts at a low resolution, for example, grid size of 50 km. Hydrologic models require the description of the precipitation field at a much higher resolution, with grid size of the order of 1 km. 2. Satellite precipitation estimates are available at a spatial scale X0.251 (latitude and longitude), or about 28 km at the equator, and a temporal scale of 3 h. Again, hydrologic applications require higher resolutions. 3. The majority of historical point rainfall records come from daily rain gauges, which have often been operational for several decades. The number of rain gauges providing hourly or sub-hourly resolution data is smaller by about an order of magnitude. However, hydrologic applications, especially flood studies, usually need hourly or even subhourly data. 4. In complex problems of stochastic generation of precipitation time series or precipitation fields, it is difficult to reproduce simultaneously, the long-term and the shortterm stochastic structure of precipitation using a single model. A better approach is to couple several models, starting from a large-scale model to represent the long-term behavior. The outputs of the latter are then disaggregated into finer scales. Note, however, that in a recent study Langousis and Koutsoyiannis (2006) developed a stochastic framework capable of reproducing simultaneously the long-term and the short-term stochastic structure of hydrological processes, avoiding the use of disaggregation. While disaggregation and downscaling are similar in nature, they also have a difference that distinguishes them. Downscaling aims at solely producing a precipitation field y with the required statistics at the scale of interest, being statistically consistent with the given field x at the finer scale. Disaggregation demands full and precise consistency, which introduces an equality constraint in the problem of the form

Cy¼x

ð55Þ

where C is a matrix of coefficients. For example, assuming that x is an annual amount of precipitation at a station and y is the vector consisting of the 12 monthly precipitation values at the

same station, C will be a row vector with all its elements equal to 1, so that Equation (55) represents the requirement that the sum of all monthly precipitation amounts must equal the annual amount. Task 1 could be accomplished by running a second meteorological model at the limited area of interest. Such models, known as limited-area models, can have much higher resolution than global models. The description of this type of downscaling, known as dynamical downscaling, because it is based on the atmospheric dynamics, is not within the scope of this chapter. In contrast, a stochastic procedure need not refer to the dynamics, and is generic and appropriate for both downscaling and disaggregation and for all above tasks 1–4. This generic procedure resembles the interpolation procedure described in Section 2.02.3.1.3, but there are two important differences. First, it is necessary to include the error terms in the generation procedure (recall that in interpolation, which is a point estimation, knowing only the mean and variance of the error was sufficient). Second, the generated values y at the different points should be statistically consistent to each other. This precludes the separate application of an algorithm at each point of interest and demands simultaneous generation at all points. In turn, this demands that the error terms in different points should be correlated to each other. All these requirements could be summarized in the linear generation scheme

y ¼A xþB v

ð56Þ

where A and B are matrixes of coefficients and v is a vector of independent random variables, so that the term Bv ¼: e corresponds to the error term in interpolation (cf. Equation (29)). In disaggregation, Equation (56) should be considered simultaneously with Equation (55). For Gaussian random fields without intermittency, the application of Equations (55) and (56) is rather trivial. However, the intermittency of the rainfall processes and the much-skewed distributions at fine timescales are severe obstacles for rainfall disaggregation. To overcome such obstacles, several researchers have developed a plethora of rather ad hoc disaggregation models (see review by Koutsoyiannis (2003b)). However, the application of the above theoretically consistent scheme is still possible, if combined with a stochastic model, accounting for intermittency (e.g., a Bartlett– Lewis model), and if an appropriate strategy is used to implement Equation (55). Such a strategy includes recursive application of Equation (56) until the error in Equation (55) becomes relatively low, and is followed by correction of the error of the accepted final iteration by appropriate adjusting procedures, which should not alter the covariance structure of the precipitation field. The general strategy of stochastic disaggregation is described in Koutsoyiannis (2001) and two implementations for temporal rainfall disaggregation at a fine (hourly) scale at a single site and at multiple sites are described in Koutsoyiannis and Onof (2001) and Koutsoyiannis et al. (2003), respectively. The models described in the latter two papers, named Hyetos and MuDRain, respectively, are available online, and have been used in several applications worldwide. Typical results of the two models are shown in Figures 25 and 26, respectively.

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Figure 25 Typical screens produced by the Hyetos software during disaggregation of daily to hourly rainfall data, where plots in green and red refer to disaggregated and original data respectively. Upper-left panel shows typical hyetographs, where the green (disaggregated) plot is the result of the storms shown in Figure 23 converted to a hyetograph at an hourly scale. Notice that while daily totals match, the temporal distribution of rainfall differs in the disaggregated and original hyetographs. However, in the statistical sense, the disaggregated series resembles the original, as shown in the other panels comparing statistics of disaggregated and original series.

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Figure 26 While, as shown in Figure 25 (upper-left panel), in single-variate disaggregation, the produced hyetographs resemble the actual ones only in a statistical sense, multivariate disaggregation reproduces the actual shapes of hyetographs provided that fine-scale (e.g., hourly) data exist in at least one of the stations. The two panels show a comparison of historical (marked H) and simulated (by the MuDRain disaggregation model; marked S) hyetographs on a day with relatively high rainfall (B16 mm) at two rain gauges (2 and 5) in the Brue catchment located in South-Western England. From Koutsoyiannis D, Onof C, and Wheater HS (2003) Multivariate rainfall disaggregation at a fine timescale. Water Resources Research 39(7): 1173 (doi:10.1029/2002WR001600).

2.02.4.5 Multifractal Models Rainfall models of multifractal type have for a long time been known to accurately reproduce several statistical properties of actual rainfall fields in finite but practically important ranges

of scales: typically from below 1 h to several days in time and from below 10 km to more than 100 km in space (Schertzer and Lovejoy, 1987, 1989; Tessier et al., 1993; Fraedrich and Larnder, 1993; Olsson, 1995; Lovejoy and Schertzer, 1995; Over and Gupta, 1996; Carvalho et al., 2002; Nykanen and

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Harris, 2003; Kundu and Bell, 2003; Deidda et al., 2004, 2006; Gebremichael and Krajewski, 2004; Calenda et al., 2005; Gebremichael et al., 2006; Veneziano and Langousis, 2005; Garcı´a-Marı´n et al., 2007; Langousis and Veneziano, 2007). These properties include the scaling of the moments of different orders (Schertzer and Lovejoy, 1987; Menabde et al., 1997; Deidda et al., 1999; Deidda, 2000), the power law behavior of spatial and temporal spectral densities (Olsson, 1995; Tessier et al., 1996; Deidda et al., 2004, 2006), the alteration of wet and dry intervals (Over and Gupta, 1996; Schmitt et al., 1998; Olsson, 1998; Gu¨ntner et al., 2001; Langousis and Veneziano, 2007), and the distribution of extremes (Hubert et al., 1998; Veneziano and Furcolo, 2002; Veneziano and Langousis, 2005; Langousis and Veneziano, 2007; Langousis et al., 2007; Veneziano et al., 2009). Significant deviations of rainfall from multifractal scale invariance have also been pointed out. These deviations include breaks in the power-law behavior of the spectral density (Fraedrich and Larnder, 1993; Olsson, 1995; Menabde et al., 1997), lack of scaling of the non-rainy intervals in time series (Schmitt et al., 1998), differences in scaling during the intense and moderate phases of rainstorms (Venugopal et al., 2006), the power deficit at high frequencies relative to multifractal models (Perica and Foufoula-Georgiou, 1996a, 1996b; Menabde et al., 1997; Menabde and Sivapalan, 2000), and more complex deviations as described in Veneziano et al. (2006a). Next, we review some basic properties of stationary multifractal processes and discuss a simple procedure to construct discrete multifractal fields based on the concept of multiplicative cascades. For a detailed review on the generation of multifractal processes and their applications in hydrological modeling and forecasting, the reader is referred to Veneziano and Langousis (2010). Let i ðdÞ ðtÞ be the average rainfall intensity averaged over timescale d at time t. The stochastic process i ðdÞ ðtÞ is said to be stationary multifractal if, for any timescale d, its statistics remain unchanged when the time axis is contracted by a factor r41 and the intensity is multiplied by a random variable ar , that is, d

i ðd=rÞ ðtÞ ¼ ar iðdÞ ðtÞ d

has zero mean, it can be viewed as a special case of the multifractal process in which the random variable ar is replaced by a deterministic power function of resolution r. A property of stationary multifractal processes, which has been used to verify multifractality, is that the spectral density s(o) behaves like ob where o is the frequency, and bo1 is a constant (e.g., Fraedrich and Larnder, 1993; Olsson, 1995, Deidda et al., 2004; Hsu et al., 2006). More comprehensive checks of multifractality involve the dependence of statistical moments of different orders on scale. In particular, under perfect multifractality E½ði ðdÞ Þ q p E½ðar Þq p dKðqÞ p r KðqÞ, where K(q) is a convex function, usually referred to as moment-scaling function (Gupta and Waymire, 1990; Veneziano, 1999). All concepts and methods are readily extended to space–time rainfall (Veneziano et al., 2006b). A simple procedure to construct discrete stationary multifractal fields is based on iterative application of Equation (57) starting from a large timescale dpdmax and gradually decreasing the timescale (i.e., at resolutions rpmn, where m 4 1 and nX1 are integers). The contraction by the same factor r ¼ m at each step simplifies generation, since only the distribution of ar  am is needed. This forms the concept of socalled isotropic discrete multiplicative cascade. Its construction in the D-dimensional cube SD starts at level 0 with a single tile O01  SD with constant unit intensity inside O01 . At level n ¼ 1, 2, y (or equivalently at resolutions r ¼ mD, m2D, y) each tile at the previous level n  1 is partitioned into mD tiles where m 4 1 is the integer multiplicity of the cascade. The intensity inside each cascade tile Oni (i ¼ 1, y, mnD) is obtained by multiplying that of the parent tile at level n  1 by an independent copy yi of a unit-mean random variable y, called the generator of the cascade. Clearly, for r ¼ mnD, ar ¼ y1 y2 ? yn . For illustration, Figure 27 shows a simulated realization of a 2D binary (i.e., m ¼ 2) discrete multiplicative cascade developed to level n ¼ 8.

2.02.5 Precipitation and Engineering Design 2.02.5.1 Probabilistic versus Deterministic Design Tools

ð57Þ

where ¼ denotes equality in (any finite-dimensional) distribution. The notation implies that the distribution of ar depends only on r and not on time t or the intensity i ðdÞ. Obviously, the mean of ar is 1 and furthermore ar is assumed to be stochastically independent of i ðdÞ at the higher scale d. The distribution of ar characterizes the scaling properties as well as many other characteristics of the rainfall process including the marginal distribution, intermittency, distribution of extremes, etc. Equation (57) need not apply for arbitrarily large timescales but rather applies up to a maximum scale d ¼ dmax. In rainfall, dmax seems to be of the order of several days and it is representative of the mean interarrival time of rainfall events (Langousis and Veneziano, 2007; Langousis et al., 2007; Veneziano et al., 2007). We note for comparison that the related equation in the simple scaling (HK) repred sentation of Section 2.02.1.5. is ði ðd=rÞ  mÞ ¼ r 1H ði ðdÞ  mÞ or 1H 1H ðdÞ ðd=rÞ d i ¼ mð1  r Þþr i , so that, when the HK process

The design and management of flood protection works and measures require reliable estimation of flood probability and risk. A solid empirical basis for this estimation can be offered by flow-observation records with an appropriate length, sufficient to include a sample of representative floods. In practice, however, flow measurements are never enough to support flood modeling. The obvious alternative is the use of hydrologic models with rainfall input data to generate streamflow. Notably, even when flow records exist, rainfall probability still has a major role in hydrological practice; for instance, in major hydraulic structures, the design floods are estimated from appropriately synthesized design storms (e.g., US Department of the Interior, Bureau of Reclamation, 1977, 1987; Sutcliffe, 1978). The need to use rainfall data as the basis of hydrologic design becomes even more evident in the study of engineering structures and urban water-management systems that modify the natural environment, so that past flood records, even if they exist, are no longer representative of the future modified system.

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Figure 27 Simulated realization of a 2D stationary multifractal field. The random variable y is taken to be lognormal with unit mean value and logvariance ðslny Þ2 ¼ 0:2lnð2Þ.

Hydrologic design does not necessarily require full modeling of the rainfall process, of the type discussed in Section 2.02.4. Usually, in design studies, the focus is on extreme rainfall, which, notably, may not be represented well in such models, which are better for the average behavior of rainfall. However, historically, the perception of intense rainfall and the methodologies devised to model it have suffered from several fallacies spanning from philosophical to practical issues, which we describe next to cast a warning against their acceptance and use. The first fallacy is of a rather philosophical type. As discussed in Section 2.02.1.5, the modeling of the rainfall process in pure deterministic terms has been proven to be problematic. However, deterministic thinking in science is strong enough, so that after the failure in providing full descriptions, it was headed to determining physical bounds to precipitation in an attempt to design risk-free constructions or practices. The resulting concept of probable maximum precipitation (PMP), that is, an upper bound of precipitation that is physically feasible (World Meteorological Organization, 1986), is perhaps one of the biggest failures in hydrology. Using elementary logic, we easily understand that even the terminology is self-contradictory, and thus not scientific. Namely, the word probable contradicts the existence of a deterministic limit. Several methods to determine PMP exist in literature and are described in World Meteorological Organization (1986). However, examination in depth of each of the specific methods separately will reveal that they are all affected by logical inconsistencies. While they are all based on the assumption of the existence of a deterministic upper limit, they determine this limit statistically. This is obvious in the so-called statistical approach by Hershfield (1961, 1965), who used 95 000 station-years of annual maximum daily rainfall belonging to 2645 stations, standardized each record, and found the maximum over the 95 000 standardized values. Naturally, one of the 95 000 standardized values would be the greatest of all others, but this is not a deterministic limit to call PMP (Koutsoyiannis, 1999). If one examined 95 000 additional measurements, one might have found an even higher value. Thus, the logical problem here is the incorrect interpretation that an observed maximum in precipitation is a physical upper limit.

The situation is perhaps even worse with the so-called moisture maximization approach of PMP estimation (World Meteorological Organization, 1986), which seemingly is more physically (hydrometeorologically) based than the statistical approach of Hershfield. In fact, however, it suffers twice by the incorrect interpretation that an observed maximum is a physical upper limit. It uses a record of observed dew point temperatures to determine an upper limit, which is the maximum observed value. Then it uses this limit for the so-called maximization of an observed sample of storms, and asserts the largest value among them as PMP. Clearly, this is a questionable statistical approach, because (1) it does not assign any probability to the value determined and (2) it is based only on one observed value (known in statistics as the highest-order statistic), rather than on the whole sample, and thus it is enormously sensitive to one particular observation of the entire sample (Papalexiou and Koutsoyiannis, 2006; Koutsoyiannis, 2007). Thus, not only does the determination of PMP use a statistical approach (rather than deterministic physics), but it uses bad statistics. The arbitrary assumptions of the approach extend beyond the confusion of maximum observed quantities with physical limits. For example, the logic of moisture maximization at a particular location is unsupported given that a large storm at this location depends on the convergence of atmospheric moisture from much greater areas. Rational thinking and fundamental philosophical and scientific principles can help identify and dispel such fallacies. In particular, the Aristotelian notions of potentia (Greek, dynamis) and of potential infinite (Greek, apeiron; Aristotle, Physics, 3.7, 206b16) that ‘‘exists in no other way, but ... potentially or by reduction’’ (and is different from mathematical complete infinite) would help us to avoid the PMP concept. In fact, this does not need a great deal of philosophical penetration. The same thing is more practically expressed as ‘‘conceptually, we can always imagine that a few more molecules of water could fall beyond any specified limit’’ (Dingman, 1994). Yet, the linkage to the Aristotelian notions of potentia and potential infinity may make us more sensitive in seeing the logical inconsistencies (see also Koutsoyiannis, 2007). According to Popper (1982) the extension of the Aristotelian idea of potentia in modern terms is the notion of

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probability. Indeed, probability provides a different way to perceive the intense rainfall and flood and to assign to each value a certain probability of exceedance (see next session) avoiding the delusion of an upper bound of precipitation and the fooling of decision makers that they can build risk-free constructions. In this respect, the criticism of the PMP and the probable maximum flood (PMF) involves logical, technical, philosophical, and ethical issues (e.g., Benson, 1973). One typical argument against the use of probabilistic approaches, in favor of PMP, which is very old yet popular even today, has been stated by Horton (1931; from Klemes, 2000), ‘‘It is, however, important to recognize the nature of the physical processes involved and their limitations in connection with the use of statistical methods. y Rock Creek cannot produce a Mississippi River flood any more than a barnyard fowl can lay an ostrich egg.’’ However, this argument reveals an incorrect perception of probability and statistics. In a probability theoretic context, there is not a logical inconsistency. Assuming, for example, that the annual peak flood of the Mississippi river (xM) is on the average (mM), a million times larger than the average (mC) flood of a certain small creek (xC), and assuming that both xM and xC have a lognormal distribution with standard deviation slnx of logarithms of about 0.3 (which is roughly equal to the coefficient of variation of the annual flood peaks, assumed equal in the two streams), one can readily find that the probability that the flood in the creek xC in some year exceeds the mean annual flood mM of Mississippi is F*(z): ¼ 1  FG(z) where FG is the standard normal distribution function and z ¼ ln(mM/mC)/slnx or z ¼ ln(106)/0.3 ¼ 46. For large z, the approximation ln F*(z) ¼  (1/2)[ln(2pz2) þ z2] holds (e.g., Abramowitz and Stegun, 1965); hence ln F*(z) ¼  1062.75, so that the probability of exceedance is F*(z) ¼ 10462. That is, according to the probabilistic approach, the return period of the event that the small creek flood matches or exceeds the mean annual flood of the Mississippi is 10462 years. Assuming that the age of the universe is of the order of 1010 years, one would wait, on the average, 10452 times the age of the universe to see this event happen – if one foolishly hoped that the creek, the Mississippi, and the Earth would exist for such a long time. Evidently, such a low probability could be regarded as synonymous to impossibility, which shows that the probabilistic approach does not regard the floods of Mississippi equivalent to those of a small creek (see also an example about the age of a person by Feller (1950)).

2.02.5.2 Extreme Rainfall Distribution Having been exempted from the concept of an upper limit to precipitation and having adopted a probabilistic approach, the real problem is how the rainfall intensity grows as the probability of exceedance decreases. Clearly, as the probability of exceedance tends to zero, the intensity tends to infinity. There exists a mathematically proven lower limit to the rate of this growth, which is represented by an exponential decay of the probability of exceedance with intensity. The alternative is a power-low decay and, as already mentioned in Section 2.02.1.5, the two options may lead to substantial differences in design quantities for high return periods. In this respect, the

most important questions, which have not received definite answers yet, are again related to the notion of infinity. Accordingly, the distribution tails are important to know in engineering design. However, the study of the tails is difficult and uncertain because the tails refer to infrequent events that require very long records to appear. Traditionally, rainfall records are analyzed in two ways. The most frequent is to choose the highest of all recorded precipitation intensities (for a given averaging timescale) at each year and form a statistical sample with size equal to the number of years of the record. The other is to form a sample with all recorded intensities over a certain threshold irrespective of the year they occurred. Usually, the threshold is chosen high enough, so that the sample size is again equal to the number of years of the record. This however is not necessary: it can well be set equal to zero, so that all recorded intensities are included in the sample. However, the threshold simplifies the study and helps focus the attention on the distribution tail. If x1 ; x2 ; y; xn are random variables representing the recorded average intensities within a year at nonoverlapping time periods equal to a chosen timescale d, then the maximum among them y :¼ maxðx1 ; x2 ; y; xn Þ has a distribution function Hn(y) fully dependent on the joint distribution function of xi . Assuming that xi are IID with common distribution function F(x), then Hn(x) ¼ [F(x)]n. If n is not constant, but rather can be regarded as a realization of a random variable (corresponding to the fact that the number of rainfall events is not the same in each year) with Poisson distribution with mean n, then the distribution function H becomes (e.g., Todorovic and Zelenhasic, 1970; Rossi et al., 1984)

HðxÞ ¼ expfn½1  FðxÞg

ð58Þ

In particular, if the threshold has been chosen with the above rule (to make the sample size equal to the number of years of the record) then obviously n ¼ 1. Equation (58) expresses in a satisfactory approximation, the relationship between the above two methodologies and the respective distributions F and H. The two options discussed above are then represented as follows: 1. Exponential tail.

FðxÞ ¼ 1  expðx=l þ cÞ; HðxÞ ¼ exp½expðx=l þ cÞ; x  lc

ð59Þ

where l 4 0 and c 4 0 are parameters, so that lc represents the specified threshold. Here F is the exponential distribution and H is the Gumbel distribution, also known as extreme value type I (EV1) distribution. 2. Power tail.

x  FðxÞ ¼ 1½1 þ k  c 1=k ;  h l  i1=k  x ; HðxÞ ¼ exp  1 þ k  c l

x  lc

ð60Þ

where l 4 0, c 4 0 and k 4 0 are parameters, and lc represents the specified threshold. Here F is the generalized Pareto distribution (a generalized form of Equation (18)) and H is the generalized extreme value (GEV) distribution.

Precipitation

In the case k 4 0 considered here, GEV is also called the extreme value type II (EV2) distribution. The case ko0 is mathematically possible and is called the extreme value type III (EV3) distribution. However, this is inappropriate for rainfall as it puts an upper bound (lc) for x, which is inconsistent. The case k ¼ 0, corresponds precisely to the exponential tail (exponential and Gumbel distributions). For years, the exponential tail and the Gumbel distribution have been the prevailing models for rainfall extremes, despite the fact that they yield unsafe (the smallest possible) design rainfall values. Recently, however, their appropriateness for rainfall has been questioned. Koutsoyiannis (2004a, 2005a, 2007) discussed several theoretical reasons that favor the power/EV2 over the exponential/EV1 case. As already mentioned (Section 2.02.1.5.5), Koutsoyiannis (2004b, 2005a) compiled an ensemble of annual maximum daily rainfall series from 169 stations in the Northern Hemisphere (28 from Europe and 141 from the USA) roughly belonging to six major climatic zones and all having lengths from 100–154 years. The analysis provides sufficient support for the general applicability of the EV2 distribution model worldwide. Furthermore, the ensemble of all samples was analyzed in combination and it was found that several dimensionless statistics are virtually constant worldwide, except for an error that can be attributed to a pure statistical sampling effect. This enabled the formation of a compound series of annual maxima, after standardization by the mean, for all stations (see Figure 13, which shows the distribution of a compound sample over threshold of all stations, except one in which only annual maxima existed). The findings support the estimation of a unique k for all stations, which was found to be 0.15. Additional empirical evidence with the same conclusions is provided by the Hershfield’s (1961) data set, which was the basis of the formulation of Hershfield’s PMP method. Koutsoyiannis (1999) showed that this data set does not support the hypothesis of an upper bound in precipitation, that is, PMP. Rather, it is consistent with the EV2 distribution with k ¼ 0.13, while the value k ¼ 0.15 can be acceptable for that data set too (Koutsoyiannis, 2004b). This enhances the trust that an EV2 distribution with k ¼ 0.15 can be regarded as a generalized model appropriate for mid-latitude areas of the Northern Hemisphere. In a recent study, Veneziano et al. (2009) used multifractal analysis to show that the annual rainfall maximum for timescale d can be approximated by a GEV distribution and that typical values of k lie in the range 0.09–0.15 with the larger values being associated with more arid climates. This range of values agrees well with the findings of Koutsoyiannis (1999, 2004b, 2005a). Similar results were provided by Chaouche (2001) and Chaouche et al. (2002). Chaouche (2001) explored a database of 200 rainfall series of various time steps (month, day, hour, and minute) from the five continents, each including more than 100 years of data. Using multifractal analyses, it was found that (1) an EV2/Pareto type law describes the rainfall amounts for large return periods; (2) the exponent of this law is scale invariant over scales greater than an hour (as stated in Section 2.02.4.2, it cannot be otherwise

71

because this is dictated by theoretical reasons); and (3) this exponent is almost space invariant. Other studies have also expressed skepticism for the appropriateness of the Gumbel distribution for the case of rainfall extremes and suggested hyper-exponential tail behavior. Coles et al. (2003) and Coles and Pericchi (2003) concluded that inference based on the Gumbel model for annual maxima may result in unrealistically high return periods for certain observed events and suggested a number of modifications to standard methods, among which is the replacement of the Gumbel model with the GEV model. Mora et al. (2005) confirmed that rainfall in Marseille (a rain gauge included in the study by Koutsoyiannis (2004b)) shows hyper-exponential tail behavior. They also provided two regional studies in the Languedoc-Roussillon region (south of France) with 15 and 23 gauges, for which they found that a similar distribution with hyper-exponential tail could be fitted. This finding, when compared to previous estimations, leads to a significant increase in the depth of rare rainfall. On the same lines, Bacro and Chaouche (2006) showed that the distribution of extreme daily rainfall at Marseille is not in the Gumbel-law domain. Sisson et al. (2006) highlighted the fact that standard Gumbel analyses routinely assign near-zero probability to subsequently observed disasters, and that for San Juan, Puerto Rico, standard 100-year predicted rainfall estimates may be routinely underestimated by a factor of two. Schaefer et al. (2006) using the methodology by Hosking and Wallis (1997) for regional precipitation-frequency analysis and spatial mapping for 24-h and 2-h durations for the Washington State, USA, found that the distribution of rainfall maxima in this State generally follows the EV2 distribution type.

2.02.5.3 Ombrian Relationships One of the major tools in hydrologic design is the ombrian relationship, more widely known by the misnomer rainfall intensity-duration-frequency (IDF) curve. An ombrian relationship (from the Greek ombros, rainfall) is a mathematical relationship estimating the average rainfall intensity i over a given timescale d (sometimes incorrectly referred to as duration) for a given return period T (also commonly referred to as frequency, although frequency is generally understood as reciprocal to period). Several forms of ombrian relationships are found in the literature, most of which have been empirically derived and validated by the long use in hydrologic practice. Attempts to give them a theoretical basis have often used inappropriate assumptions and resulted in oversimplified relationships that are not good for engineering studies. In fact, an ombrian relationship is none other than a family of distribution functions of rainfall intensity for multiple timescales. This is because, the return period is tied to the distribution function, that is, T ¼ d/[1  F(x)], where d is the mean interarrival time of an event that is represented by the variable x, typically 1 year. Thus, a distribution function such as one of those described in Section 2.02.4.2, is at the same time an ombrian relationship. This has been made clear in Koutsoyiannis et al. (1998) who showed that the empirical considerations usually involved in the construction of

72

Precipitation

ombrian curves are not necessary at all, and create difficulties and confusion. However, the direct use in engineering design of a fully consistent multiscale distribution function may be too complicated. Simplifications are possible to provide satisfactory approximations, given that only the distribution tail is of interest and that the range of scales of interest in engineering studies is relatively narrow. Such simplifications, which were tested recently and were found to be reasonable (Papalexiou and Koutsoyiannis, 2009) are 1. The separability assumption, according to which the influences of return period and timescale are separable (Koutsoyiannis et al., 1998), that is,

iðd; TÞ ¼

aðTÞ bðdÞ

ð61Þ

where a(T) and b(d) are mathematical expressions to be determined. 2. The use of the Pareto distribution for the rainfall intensity over some threshold at any timescale, as discussed in Section 2.02.5.2; this readily provides a simple expression for a(T). 3. The expression of b(d) in the simple form

bðdÞ ¼ ð1 þ d=yÞ Z

ð62Þ

where y40 and Z40 are parameters. A justification of this relationship, which is a satisfactory approximation for timescales up to a few days, can be found in Koutsoyiannis (2006a). Based on assumptions 1–3, we easily deduce that the final form of the ombrian relationship is

iðd; TÞ ¼ l0

ðT=dÞ k  c0 ð1 þ d=yÞ Z

ð63Þ

where c0 40, l0 40 and k40 are parameters. In particular, as discussed in Section 2.02.5.2, k is the tail-determining parameter and unless a long record exists, which could support a different value, it should be assumed k ¼ 0.15. Equation (63) is dimensionally consistent, if y has units of time (as well as d), l0 has units of intensity, and k and c are dimensionless. The numerator of Equation (63) differs from a pure power law that has been commonly used in engineering practice, as well as in some multifractal analyses. Consistent parameter-estimation techniques for ombrian relationships have been discussed in Koutsoyiannis et al. (1998) as well as in Chapter 2.18 Statistical Hydrology.

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2.03 Evaporation in the Global Hydrological Cycle AJ Dolman and JH Gash, VU University Amsterdam, Amsterdam, The Netherlands & 2011 Elsevier B.V. All rights reserved.

2.03.1 2.03.2 2.03.2.1 2.03.2.2 2.03.2.3 2.03.3 2.03.4 2.03.5 References

Introduction General Theory of Evaporation Vegetated Surfaces Bare Soil Open Water and Lakes Regional and Equilibrium Evaporation Trends and Variability in Global Evaporation Summary and Conclusions

2.03.1 Introduction Evaporation is the transfer of moisture from a particular surface to the overlying atmosphere. The physical process of evaporation consists of the exchange of water molecules between a free water surface and the air. The surface can be any among the following nonexhaustive list: a lake, the inside of plant leaves, the water surface adhering to a soil conglomerate, or the surface of a soil or canopy during or just after rain. The evaporation rate is expressed as the quantity of water evaporated per unit area per unit time from a (water) surface under existing atmospheric conditions. This chapter describes the progress in understanding evaporation at the local scale, both from an observational and a conceptual perspective. It then moves on to the global scale, through a discussion of regional scale feedbacks. The main conclusion is that while we have gained considerable understanding in local scale evaporation, the data sets required to study the impact of evaporation on the global water cycle are still lacking. Evaporation at the Earth’s surface is constrained both by the energy available to convert liquid water into vapor and by the capability of the surrounding air to transfer moisture away from the saturated surface. At the surface, these constraints are best expressed through the energy balance equation and the transfer equations of latent and sensible heat:

Rg ð1  zÞ þ Lk  Lm ¼ lE þ H þ G þ dS

ð1Þ

lE ¼ rlKv

qq qz

ð2Þ

H ¼ rcp Kh

qT qz

ð3Þ

b¼

H lE

ð4Þ

with Rg the incoming short-wave radiation (W m2), z the short-wave albedo, Lk and Lm the incoming and outgoing long-wave radiation (W m2), lE the evaporation (or latent heat flux, W m2) (with l the latent heat of vaporization and E the mass flux), H the sensible heat flux (W m2), G the soil heat flux (W m2), dS the change in heat storage in the

79 80 81 82 82 83 83 85 85

biomass and atmosphere below a reference height above the surface (W m2), Kv and Kh the transfer coefficients for water vapor and heat, respectively, q q/q z the vertical gradient in specific humidity (g kg1), and similarly q T/q z is the vertical gradient in temperature (K), r is the density of air (kg m3), and cp the specific heat of air (J kg1 K1). b is the Bowen ratio and a useful indicator of the dryness of the surface because it shows how the available energy is partitioned into sensible and latent heat. Factors influencing the rate of evaporation can easily be determined from the above equations: radiation as the limit of available energy, the gradient in moisture, or the specific humidity deficit and factors that influence the transfer coefficients such as wind speed and roughness of the surface, and, crucially, when dealing with evaporation from leaves (transpiration), water availability (soil moisture). Thus, despite the complexity of the interaction between a partially wet surface and the atmosphere, it may be possible to simplify this interaction and to approximate evaporation by considering only two factors, available energy (radiation) and water availability. Available energy determines the maximum evaporation possible for the given climatic conditions and unlimited water availability, that is, potential evaporation (see also Allen et al., 1998). Water availability can be characterized by the amount of precipitation. Under dry conditions, potential evaporation exceeds precipitation, and the actual evaporation from an area will approach the amount of precipitation received. Conversely, under wet conditions, water availability exceeds potential evaporation and actual evaporation will approach asymptotically potential evaporation. This relationship was first used by Budyko (1974) to set a constraint on global evaporation. Later, Milly and Dunne (2002) suggested that the long-term water balance of a catchment is determined by the interaction of supply (precipitation) and demand (potential evaporation), mediated by soil moisture storage. At longer timescales, the change in soil moisture can be neglected. Figure 1 shows results of Zhang et al. (2001), from a simple model based on the above considerations for grass and forested catchments, using catchment discharge and precipitation data, that allow evaporation to be calculated as the residual of these two. These results suggest that at the scale of catchments such a simple approach works remarkably well, with a linear (1:1) relation between rainfall and evaporation for catchments that receive up to 500 mm yr1, and an

79

80

Evaporation in the Global Hydrological Cycle 1600

Annual evapotranspiration (mm)

Forest Mixed veg. Pasture 1200

800

400

0

0

500

1000

1500

2000

2500

3000

3500

Annual rainf all (mm) Figure 1 Relationship between annual precipitation and evaporation. The dashed line represents the best fit of a theoretical model (Zhang et al., 2001) for grass-only catchments, the solid line the best fit for forests. From Zhang et al. (2001). (Note that we use the word evapotranspiration here in this graph consistent with its origin. This rather loosely defined term refers to the total of (wet canopy and bare soil) evaporation and transpiration. Throughout the text, we prefer to use the word evaporation as the physical term denoting the process of transforming liquid water into its vapor form.)

asymptotic relation (driven by available energy and not by water availability) for catchments receiving more than 500 mm yr1. The difference between forest and grassland is a result of the high rate of evaporation of intercepted rainfall from forest; interception loss creates a considerable additional evaporative loss for forest (see also Chapter 2.04 Interception). Total evaporation from forest saturates at about 1400 mm yr1, while for grasslands this value is around 900 mm yr1. Direct observations of evaporation have been made since the mid-1990s using the eddy-covariance technique in a global network, Fluxnet (see Aubinet et al., 2001). While there are other techniques based on scintillometry available, these have not been used extensively in networks such as Fluxnet. Evaporation measured by micrometeorological techniques usually refers to dry-canopy evaporation only (transpiration) and contains little information on evaporation during wet-canopy conditions (interception). Consequently, total evaporation values such as those given by Law et al. (2002) should be treated with caution, when interception losses are not explicitly treated. With this caveat in mind, the annual evaporation from Fluxnet data for coniferous forests is 397 (731) mm yr1; for mixed evergreen and deciduous forest, 386 (718) mm yr1; for deciduous broadleaf, 512 (769) mm yr1; for grassland, 494 (7104) mm yr1; and for crops, 666 (767) mm yr1. Grassland and crops have higher dry-canopy evaporation than forest, because they are less strongly coupled to the atmosphere and thus show less stomatal control (e.g., Shuttleworth and Calder, 1979). This also gives forest the possibility to survive occasional drought that would kill off annual grassland species. The Fluxnet data can be used to identify the main controls on evaporation. Wilson et al. (2002) showed how the

partitioning of energy, as expressed by the ratio of sensible heat to latent heat, the Bowen ratio (b), can be used to classify different vegetation types in climate space. Figure 2 shows the position of the individual sites with respect to the magnitude of the latent heat (evaporation) and sensible heat fluxes. In contrast to the annual rates discussed earlier, these data refer to the growing season only. Also shown are lines of constant available energy (the sum of latent and sensible heat) and lines of constant b. Moving from the lower left corner of the diagram to the upper right, the available energy increases, whereas moving from the lower right to the upper left, the value of b increases. Low evaporation rates are found in Sitka spruce, tundra, and boreal forest, and high evaporation rates in deciduous forests and agriculture. Most of the forests have high b’s, implying that much of the energy received is transmitted back to the atmosphere as sensible heat. Deciduous forests tend to have lower b’s with higher evaporation rates than coniferous forests. The average b at tundra sites appears to be close to 1.

2.03.2 General Theory of Evaporation Penman (1948) was among the first to achieve the crucial combination of the energy balance equation (1) with the transfer Equations (2) and (3) to derive an expression for actual evaporation from vegetation well supplied with water. Although the vertical gradients in Equations (2) and (3) could be derived from the differences between air and surface values, measurements of surface temperature are difficult and not made routinely. Penman overcame this problem by introducing the slope of the saturated vapor pressure versus temperature curve, D, approximated as a linear function and

Evaporation in the Global Hydrological Cycle 15

81

=3 T1

=2

T2

12

M

Daily sensible heat flux (MJ m−2 d−1)

ed

ite

rra

ne

an

m

S1

9

=1

cli

at

es

Boreal Canadian

S2 N4 R 2 R1 31 Y2 NN

I1

6 Sitka spruce

Q3

 = 0.5

M1 P1 T3

O1

N2

K1 G 2

K 2 F3 G1Y1F1 Q1 H1 A8 L1 F2 Tundra A7A1 M2 I2 D1 L2 A1 A4 X1 A5 B B1 A2 H3 V1V 2 B4 G3 A6 B5 B3 U 3 G2 A3 J1 Z1 B2 Conifers U2 B6 C1 O2

Q2

HW1 2

X2

3

E2

 = 0.25

Agriculture E1

Deciduous forests

0 0

3

6

9

Daily latent heat flux (MJ

m−2

12

15

d−1)

Figure 2 The daily cumulative sensible heat flux vs. the daily cumulative latent heat flux between days 165 and 235 for the Fluxnet sites analyzed by Wilson et al. (2002). The letter and number codes refer to the sites as given by Wilson et al. (2002). Also shown are lines of constant Bowen ratio (dashed lines) and lines of constant total turbulent energy fluxes (solid diagonal lines). Enclosed areas denote subjective delineations between different vegetation types and climates. From Wilson et al. (2002).

evaluated at air temperature:

D¼

e  ðTs Þ  e ðTa Þ Ts  Ta

ð5Þ

where e* (Ts) is the saturated vapor pressure at surface temperature Ts (K), and e* (Ta) is the saturated vapor pressure at air temperature Ta. The evaporation, E, in mm d1 is given by

E¼

DQ þ gEa Dþg

ð6aÞ

where Q is water equivalent of the net radiation (from the lefthand side of Equation (1)), g the psychrometric constant and

Ea ¼ f ðUÞðe ðTa Þ  ea Þ

ð6bÞ

Ea is the aerodynamic or demand term for evaporation, with f(U) a wind function, and (e* (Ta)  ea) the vapor pressure deficit. The original Penman equation contains an empirical wind function that replaces the transfer coefficients in Equations (2) and (3). This wind function was difficult to generalize, and subsequently Thom and Oliver (1977) provided a more

physical basis including considerations of aerodynamic transfer over rough surfaces by introducing an explicit aerodynamic resistance. Penman applied his equation to bare soil evaporation, vegetated or cropped surfaces, and open water bodies. The equation is still widely used to calculate evaporation from well-watered, short vegetation.

2.03.2.1 Vegetated Surfaces Monteith (1965) (see also Gash and Shuttleworth, 2007) introduced the control of vegetation on evaporation by including a canopy scale resistance rs. This resistance represents the restriction on the transfer of water from the collective saturated surfaces inside the plant stomatal cavities to the air outside the leaves. The resulting equation, now carrying both an aerodynamic (to replace the Penman wind function) and a canopy resistance, is known as the Penman–Monteith (PM) equation and is arguably still the most elegant yet advanced resistance model of evaporation used in hydrological practice today (Shuttleworth, 1993). For a mathematically precise and exact definition and brief historical overview of its development, the reader is referred to Raupach (2001). Most commonly, the PM equation is expressed in specific humidity units (rather than vapor pressure deficit as in Equation (6)) with

82

Evaporation in the Global Hydrological Cycle

evaporation expressed in energy units (W m2) and reads as

lE ¼

rcp ðq * ðTa Þ  qa Þ ra   cp rs 1þ Dþ l ra

DðQ *  GÞ þ

ð7Þ

with D the slope of the saturated specific humidity versus temperature curve and Q* the net radiation (W m2). q* (Ta) is the saturated specific humidity of the air (g kg1) at the reference level and qa the specific humidity at the same level. Two important variables appear in this equation that replace the transfer coefficients of the transfer equations of heat and moisture: the aerodynamic and surface resistance, ra and rs (s m1). (Only in the case of a full canopy cover, can the surface resistance be equal to the canopy resistance. For the derivation of the big-leaf version of the PM model as presented here, this is not important. When the canopy cover is not full, and there is bare ground directly in contact with the overlying atmosphere, the surface resistance is not equal to the canopy resistance and approaches the reciprocal sum of the canopy and an assumed soil resistance.) The PM equation assumes that evaporation and sensible heat originate from the same source in the canopy. The main advantage of the PM equation is that the meteorological driving variables, wind speed, specific humidity deficit, and temperature are required only at a single level above the surface, removing the need for the notoriously difficult observation of surface values. The main obstacle for the practical application of this equation is the estimation of the values for aerodynamic and surface resistance. When there is unlimited supply of water, the PM equation can be used to calculate potential evaporation. It can be shown then to collapse to the Penman equation with an aerodynamic resistance rather than a wind function. The PM equation is now the preferred method of estimating crop water requirements as reference crop evaporation (Allen et al., 1998). When the canopy is wet, the surface resistance equals zero and the PM equation can be used to calculate evaporation of intercepted rainfall (see also Chapter 2.04 Interception). Although there is a wide range of empirical evaporation equations of which some are particular cases of the PM equation (see Brutsaert, 1982; Shuttleworth, 1993), the use of the PM equation is widespread because of its clear physical interpretation.

2.03.2.2 Bare Soil Evaporation from bare soil can be a significant component of the water balance, particularly in semi-arid environments (Wallace and Holwill, 1997). Soil evaporation can be described as a two-stage process. The first stage occurs when the available soil moisture is sufficient to meet the atmospheric demand. This occurs immediately after rainfall or irrigation events. Soil evaporation under these conditions equals potential evaporation. Typically, this stage lasts 1–2 days, although in some cases when evaporative demand is low and the soil contains a high amount of clay, this stage may last for up to 5 days. In the second stage, the amount of soil moisture has dropped and soil evaporation is no longer only restricted by evaporative demand but also by availability of moisture. In

these conditions, the change of soil moisture with time can be described as a desorption process with evaporation proportional to the square root of the time since the start of the process:

lEs ¼

1 aðt  t0 Þ1=2 2

ð8Þ

with lEs the soil evaporation, t the time and t0 the time since the start of second stage drying, Ds is a desorptivity (in units of W m2 d1/2 when evaporation is expressed as a heat flux, or in mm d1/2 when evaporation is expressed as water flux). The desorptivity is assumed constant for a particular soil type. It varies from a value of 2.1 for sandy loam with gravel, to a value of 5 mm d1 for a clay loam soil (Kustas et al., 2002). Although the two-stage process describes soil evaporation at diurnal timescales, extension of the theory to (sub) hourly timescales is straightforward (Brutsaert and Chen, 1996; Porte´Agel et al., 2000). The determination of the desorptivity coefficient can be problematic, as can the identification of the switch from stage 1 to 2 (Kustas et al., 2002). The observed dependence of soil evaporation on available soil moisture suggests the feasibility of a resistance approach that incorporates a dependence of soil surface resistance on soil moisture. Mahfouf and Noilhan (1991) review several such formulations. These approaches can be divided into socalled a- and bs-approaches. In the a-approach, the saturated humidity in the soil pore space is adjusted by a factor a that may be related to soil matrix potential and takes into account that, averaged over a certain depth, the evaporation takes place from a nonsaturated surface. In the bs-approach, the humidity in the pore space at the evaporation front is assumed to be saturated, and bs is the ratio of an aerodynamic resistance to the sum of the aerodynamic and soil surface resistance.

2.03.2.3 Open Water and Lakes The key process controlling evaporation from large lakes (or reservoirs) is the absorption of solar energy. This energy is not absorbed at the surface; because water is a semitransparent medium, the solar radiation is absorbed over depth. The rate of change of absorption with depth depends on the turbidity, but may be significant down to several meters below the surface. Solar energy heats the water up during the spring and summer, and this energy is not available for evaporation; but energy released as the water cools in autumn and winter is available and enhances the evaporation. This creates a phase lag between lake evaporation and the annual radiation cycle. While in the tropics this lag will be small, at high latitudes the phase lag may be as much as 5 or 6 months (Blanken et al., 2000) with the rate of change in storage, dS/dt, being the dominant source of energy for evaporation. The energy available for evaporation is given by

A ¼ Q þ

dS þ Aq dt

ð9Þ

where Aq is the rate of net energy advection due to inflow and outflow of water. Net radiation, Q* , is given by Equation (1)

Evaporation in the Global Hydrological Cycle

with long-wave radiation emitted by the lake as

Lm ¼ esT4w

ð10Þ

e is emissivity, s the Stefan–Boltzmann constant, and Tw the surface temperature of the water (K). Q* over the lake can be estimated from measurements over land, but must take account of the different albedo and different surface temperature of the lake. Like the ocean, lake albedo varies strongly with solar elevation (see Finch and Gash, 2002; Finch and Hall, 2005; Payne, 1972). The Penman (1948) equation (Equation (6)) is often used to estimate lake evaporation, as it appears to remove the need for surface temperature measurement; however, this is not the case as water temperature is still needed to calculate the emitted long-wave radiation. Nevertheless, it should give good results if used with the available energy calculated from Equation (9) and measurements made over the lake (see Linacre, 1993). Working in the tropics where the annual cycle in water temperature is small and energy storage can be neglected, Sene et al. (1991) found good agreement between daily estimates made with the Penman equation and eddy-covariance measurements of evaporation. To overcome the lack of water temperature measurements, Finch and Gash (2002) applied a simple numerical, finite difference scheme to calculate a running balance of lake energy storage. A new value of the water temperature required to force energy closure was calculated at each time step. For a well-mixed lake of known depth, the evaporation could then be calculated from land-based, daily meteorological observations of sunshine hours, relative humidity, wind run, and average air temperature. The model gave good agreement with mass-balance measurements of the water loss from a reservoir with no inflow or outflow.

2.03.3 Regional and Equilibrium Evaporation

D Dþ

cp A l

the parameter a. a can be shown to be unity only when the specific humidity deficit in the PM equation is zero, in other words, when advection is negligible. That this is hardly ever the case proves the fact that most empirical values of a for short crops are of the order 1.2–1.3 (e.g., Brutsaert, 1982). For tall crops, Shuttleworth and Calder (1979) showed convincingly that the equilibrium approach is not appropriate because the physiological control of the forest transpiration reduces a below a value of 1 in dry canopy conditions, while in wet canopy conditions large-scale advection and negative sensible heat fluxes (Stewart, 1977) may form an additional supply of energy and force a to be well above the value for short crops. This emphasizes the important point that for tall crops in particular, it is important to estimate dry and wet canopy evaporation separately (see also Figure 1). Thus, at larger scale, atmospheric conditions can override the surface control by exerting a strong feedback on evaporation through the humidity and temperature of the atmospheric boundary layer. A number of concepts have been derived that use the feedback power of the atmosphere to estimate regional-scale evaporation (e.g., Bouchet, 1963; Morton, 1983). Although McNaughton and Jarvis (1991) show that at larger scale the feedback of the atmospheric boundary layer dampens the effects of surface controls – and thus makes precise estimation of the surface resistance less important – the physical basis of the Bouchet and Morton schemes remains doubtful (see de Bruin, 1983; McNaughton and Spriggs, 1989). Nevertheless, de Bruin showed that the feedback of the increasing atmospheric boundary-layer humidity during the day causes the regional surface conductance to vary less than if the feedback were neglected. The relatively large confidence of hydrologists in using potential evaporation formulas probably finds its physical explanation in this feedback.

2.03.4 Trends and Variability in Global Evaporation

Priestley and Taylor (1972) showed that the Bowen ratio would approach a constant value defined by b ¼ s/l when air moves over a moist surface, and gradients of temperature and specific humidity with height are small or become saturated with respect to moisture. Combining this insight with the PM equation, and setting the second term above the nominator to zero as well as the surface or canopy resistance (rs ¼ 0) as would be appropriate for a moist surface, yields the equilibrium evaporation (see also Brutsaert, 1982; Raupach, 2001):

lE ¼

83

ð11Þ

Equilibrium evaporation as defined by these authors refers to the lower limit of evaporation from a moist surface where the specific humidity deficit of the second term in the nominator of the PM equation has become zero as a result of contact of air with a moist surface over a very long fetch. It can easily be shown that the second term in the nominator of the PM equation (Equation (7)) now represents the departure from this equilibrium. Priestley and Taylor (1972) represented this departure from equilibrium evaporation by

Globally, evaporation from the land surface to the atmosphere amounts to 71 103 km3 yr1 (Baumgartner and Reichel, 1975). It is the key return flow in the hydrological cycle from the surface on which the precipitation falls, back to the atmosphere. Evaporation from the oceans is a far larger component of the hydrological cycle at an estimated 428 103 km3 yr1 (Baumgartner and Reichel, 1975). A recent multi-model ensemble of 11 state-of-the-art land surface models (Dirmeyer et al., 2006) estimated annual evaporation over a range of 58 103 to 85 103 km3, indicating the uncertainty in our ability to model evaporation from land. Oki and Kanae (2006) estimated total terrestrial evaporation at 66 103 km3 yr1. Thus, although it is an important component of the hydrological cycle, the exact magnitude and variability, both spatially and temporally, of evaporation from land remains highly uncertain. Figure 3 shows one of the few available estimates of the latitudinal distribution of evaporation in mm yr1 for both ocean and land. This estimate (Baumgartner and Reichel, 1975) is based on the balance between precipitation and runoff on land and a variety of other methods (Peixoto and Oort, 1996). Note the relatively large contribution of Southern

84

Evaporation in the Global Hydrological Cycle

Latittude

0 85 75 65 55 45 35 25 15 5 −5 −15 −25 −35 −45 −55 −65 −75 −85

375

750

1125

1500

Evaporation (mm yr−1)

Figure 3 Global evaporation according to Baumgartner and Reichel (1975) for different 101 latitude bands.

ρRg,E

−1 +1

ρP,E 0

+1

0

−1 Figure 4 Multi-model analysis of controls on yearly evaporation. Correlation between yearly evaporation and global radiation (rRg,E), and precipitation (rP,E), for the period 1986–95. Each color corresponds to a unique combination of rRg,E and rP,E. The gray lines (legend) show the global frequency distribution (Teuling et al., 2009).

Hemisphere latitude bands 20–401 S compared to their northern equivalents. This difference is largely due to the greater area of land in the Northern Hemisphere, which evaporates at a significantly lower rate than the ocean. The largest evaporative flux is found in the humid tropics, mainly as a result of large amounts of precipitation and high solar radiation. Compared to the latitudinal distribution of precipitation, the evaporation is more smoothly distributed with a general tendency of decreasing evaporation when moving poleward. Decreases in radiation, global dimming, have caused a debate about an observed decline in pan measured evaporation (e.g., Roderick and Farquhar, 2002) that would be contrary to expectations for a warming climate. Peterson et al. (1995), using data from a network of pan evaporimeters in the US and the former Soviet Union, found a decrease in pan evaporation between 1950 and 1990. However, Fu et al. (2009) in a more comprehensive analysis suggested that, although many observations across the world indicate a general trend of pan evaporation decreasing over the last 50 years, this trend is not universal. A decrease in evaporation presents a

paradox, as with global warming one would expect an increase due to the larger water holding capacity of the atmosphere through the Clausius–Clapeyron equation. An increase in evaporation would also match an increase in precipitation, although this is regionally very variable. Roderick and Farquhar (2002) explained the paradox by relating the decrease in evaporation to a decrease in solar radiation (global dimming). However, the dimming trend has recently reversed into a brightening trend and thus cannot singularly be held responsible for the decrease in pan evaporation. Teuling et al. (2009) presented an analysis of the major controls on evaporation using an ensemble of land surface models forced off line with meteorological data. They investigate the control of two key drivers on evaporation, incoming solar radiation and soil moisture. Figure 4 shows that Europe, North Africa, and North America are characterized by two evaporation regimes: a humid regime with high correlation with radiation expressed through the correlation coefficient rRg,E, but low correlation with precipitation, P(rP,E); and a more arid regime with high rP,E, but low rRg,E. Because radiation and precipitation tend to be negatively correlated,

Evaporation in the Global Hydrological Cycle

Teuling et al. (2009) concluded that yearly variations in evaporation reflect either variations in Rg or P, but not in both. Central Europe is among the regions with the highest rRg,E correlation, while in more arid regions such as the US Midwest and the Sahara, evaporation correlates only with precipitation. Using data from direct observations of evaporation (Fluxnet; see Baldocchi et al., 2001), Teuling et al. were able to reproduce and validate these modeled patterns quite well. Teuling et al. (2009) made a strong argument for a regional approach to explain some of the evaporation trends. Based on the different sensitivities of the various drivers of evaporation (see Figure 4) and the conclusion of other work that a dimming trend has been reversed, they concluded that scenarios of both decreasing actual evaporation with decreasing pan evaporation in regions with ample supply of water (e.g., central Europe), and of increasing evaporation with decreasing pan evaporation (e.g., the US Midwest) are consistent. Using basin-scale discharge data and precipitation they found that evaporation decreased over Europe during the dimming period and increased later, consistent with the high sensitivity of European evaporation to radiation rather than precipitation. During the dimming period, the positive trend in runoff is induced by reduced evaporation, rather than increased precipitation. After 1983, evaporation derived as the residual of precipitation minus runoff, increased in all central European basins during the brightening phase. These results suggest that evaporation trends follow radiation trends in central Europe. In contrast, in the US Midwest the upward trends in evaporation derived as a catchment residual before 1983 are followed by decreasing trends. These may be explained by trends in precipitation combined with high correlation between solar radiation and evaporation as inferred from Figure 2. Next to the analysis of Teuling et al. (2009) that attribute changes in evaporation to either radiation or water availability (precipitation), recent studies of changes in pan evaporation in Australia attribute most of the reduction in pan evaporation to reduced wind speed (Roderick et al., 2007; Rayner, 2007; McVicar et al., 2008). The cause of such a reduction in regional wind speed is not certain, although wind speed reductions have been widely reported in mid-latitudes in both hemispheres (see also Shuttleworth, 2009).

2.03.5 Summary and Conclusions Evaporation is an important, but regionally a still poorly quantified term in the global water balance. At global scale its determination as a residual of the continental scale water balance hinges on adequate estimation of precipitation and river discharge. Estimates obtained by direct bottom-up modeling vary considerably. This is in some contrast to our understanding of the basic physics of evaporation, that is well known, as for instance is shown by the Penman and PM equations. Application of these equations for use at local scales, for instance irrigation practice, is widespread (e.g., Allen et al., 1998), and to a large extent very successful. The importance of evaporation in the global hydrological cycle critically encompasses two related aspects: its direct role as a term in the water budget, and its potential to impact

85

weather and climate processes, by changing aspects of the surface energy balance and boundary layer. Both these roles depend on the balance between the controlling forces of evaporation, surface moisture and available energy. Where only water availability is limiting and radiation plentiful, such as in the (semi)-arid tropics, evaporation may add moisture to the overlying air that can then tip the balance to produce precipitation. These semi-arid areas have been found (e.g., Koster et al., 2002) to be sensitive to land surface precipitation feedbacks. In these feedbacks the role of evaporation is critical. On the other hand, in areas where both precipitation and radiation are not limiting, large-scale evaporation appears constrained by the available energy. Under these conditions, equations such as the Priestly–Taylor equation that do not explicitly take account of water availability on average perform well. Thus, to be able to predict the effect of, for instance, landuse change on evaporation (and resulting catchment discharge), one would need to determine first which process, if any, is limiting. In cases where water availability is limiting, atmospheric feedbacks may also become important and simple bottom-up estimates of the change in evaporation may be wrong. In cases where neither water nor energy is limiting, to first order a bottom-up estimate based on energy constraints would be appropriate. Changes in the pattern of largescale moisture recycling, such as found in the Amazon (e.g., Meesters et al., 2009), may however be important to estimate changes in the resulting precipitation climate. The debate whether and where evaporation is increasing or decreasing is fundamental to our understanding of the role of evaporation in the global water cycle and climate. Improved, high-quality data sets are needed to provide benchmarks for climate models and to increase our process understanding. Currently, such data sets unfortunately do not exist. It is worth noting that the role of evaporation in climate is not related only to its direct effects on the hydrological cycle. Through the influence evaporation exerts on the partitioning of the energy balance, the effects on climate are also seen in surface temperatures. High evaporation keeps surfaces cool; low evaporation makes them hot. Seneviratne et al. (2006) and Fischer et al. (2007) used regional and global climate modeling to investigate the role of land surface atmosphere feedbacks on temperature in a changing climate. They concluded that soil moisture through its effect in reducing evaporation has a major impact on the variability and mean and maxima of surface temperatures in Europe. Fischer et al. (2007) concluded that land–atmosphere interactions over drought regions account for typically 50–80% of the number of hot days in a Northern Hemisphere summer. This is mainly due to local effects through the limitation of evaporation (and increased sensible heat flux) due to drought conditions. Drought conditions may also have remote effects on areas around or outside the actual drought region, through changes in atmospheric circulation and advection of air masses. These mechanisms can enhance an existing anticyclonic circulation over, or slightly downstream of, a drought anomaly. Evaporation plays a key role in the global water cycle and hence in the global climate system. There have, however, been very few attempts to produce robust estimates of evaporation based on a global approach. Yet such data are urgently needed

86

Evaporation in the Global Hydrological Cycle

to validate and constrain current climate models. Thus, despite that at the practical level, considerable advances have been made in our ability to estimate and observe evaporation at local level, our understanding of evaporation in the global climate system (e.g., Kleidon and Schymanski, 2008) still shows significant gaps. The challenge for the next decade in evaporation research is to fill these gaps.

References Allen RG, Pereira LS, Raes D, and Smith M (1998) Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements, p. 300. Rome: Irrigation and Drainage, FAO. Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, and Laitat E (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agricultural and Forest Meteorology 108: 293--315. Baldocchi D, Falge E, Gu L, et al. (2001) FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 82: 2415--2434. Baumgartner A and Reichel E (1975) Die Weltwasserbilanz, 179pp. Mu¨nchen: Oldenbourg Verlag. Blanken PD, Rouse WR, Culf AD, et al. (2000) Eddy covariance measurements of evaporation from Great Slave Lake, Northwest Territories, Canada. Water Resources Research 36: 1069--1077. Bouchet RJ (1963) Evapotranspiration re´elle et potentielle, signification climatique, Int. Assoc. Sci. Hydrol., Proc. Berkeley, Calif. Symp., Publ. 62: 134–142. Brutsaert W (1982) Evaporation into the Atmosphere. Dordrecht: Kluwer. Brutsaert W and Chen D (1996) Diurnal variation of surface fluxes during thorough drying (or severe drought) of natural prairie. Water Resources Research 32: 2013--2019. Budyko MI (1974) Climate and Life, 508p. New York: Academic Press. De Bruin HAR (1983) A model of the Priestley–Taylor parameter, a. Journal of the Applied Meteorology 22: 572--578. Dirmeyer PA, Gao X, Zhao M, Guo Z, Oki T, and Hanasaki N (2006) GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society 87: 1381--1397. Finch JW and Gash JHC (2002) Application of a simple finite difference model for estimating evaporation from open water. Journal of Hydrology 255: 253--259. Finch JW and Hall RL (2005) Evaporation from lakes. In: Anderson MG (ed.) Encyclopedia of Hydrological Sciences, pp. 635--646. Chichester: Wiley. Fischer EM, Seneviratne S, Luethi M, and Schaer C (2007) Contribution of land– atmosphere coupling to recent European summer heat waves. Geophysical Research Letters 34: L06707 (doi:10.1029/2006GL029068). Fu G, Charles SP, and Yu J (2009) A critical overview of pan evaporation trends over the last 50 years. Climatic Change 97(1–2): 193--214. Gash JHC and Shuttleworth WJ (2007) Evaporation. Wallingford: IAHS Press. Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, and Stott PA (2006) Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439: 835--838. Kleidon A and Schymanski S (2008) Thermodynamics and optimality of the water budget on land: A review. Geophysical Research Letters 35: L20404. Koster RD, Dirmeyer PA, Guo Z, et al. (2004) Regions of strong coupling between soil moisture and precipitation. Science 305: 1138–1140. Kustas WP, Prueger JH, and Hipps LE (2002) Impact of using different time-averaged inputs for estimating sensible heat flux of riparian vegetation using radiometric surface temperature. Journal of Applied Meteorology 41: 319--332. Law BE, Falge E, Gu L, et al. (2002) Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agricultural and Forest Meteorology 113: 97--120. Linacre ET (1993) Data-sparse estimation of lake evaporation, using a simplified Penman equation. Agricultural and Forest Meteorology 64: 237--256. Mahfouf JF and Noilhan J (1991) Comparative study of various formulations of evaporation from bare soil using in situ data. Journal of Applied Meteorology 30: 1354--1365. McNaughton KG and Jarvis PG (1991) Effects of spatial scale on stomatal control of transpiration. Agricultural and Forest Meteorology 54: 279--302. McNaughton KG and Spriggs TW (1989) An evaluation of the Priestley–Taylor equation. In: Black TA, Spittlehouse DL, Novak MD, and Price DT (eds.) Estimation

of Areal Evaporation, IAHS Publication No. 177, pp. 89–104. Wallingford: IAHS Press. McVicar TR, Van Niel TG, Li LT, et al. (2008) Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters 35: L20403 (doi:10.1029/2008GL035627) Meesters AGCA, Dolman AJ, and Bruijnzeel LA (2009) Comment on ‘‘Biotic pump of atmospheric moisture as driver of the hydrological cycle on land’’ by AM Makarieva and VG Gorshkov, Hydrol. Earth Syst. Sci., 11, 1013–1033, 2007. Hydrology and Earth System Sciences 13: 1299--1305. Milly PCD and Dunne KA (2002) Macroscale water fluxes, 2, Water and energy supply control of their interannual variability. Water Resources Research 38(10): 1206 (doi:10.1029/2001WR000760) Monteith JL (1965) Evaporation and environment. In: The State and Movement of Water in Living Organisms. Proceedings of the 19th Symposium Society for Experimental Biology, pp. 205--234. Swansea: Cambridge University Press. Morton FI (1983) Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. Journal of Hydrology 66: 1–76. Oki T and Kanae S (2006) Global hydrological cycles and world water resources. Science 313: 1068--1072. Payne RE (1972) Albedo of the sea surface. Journal of the Atmospheric Sciences 29: 959--970. Peixoto JP and Oort AH (1996) The climatology of relative humidity in the atmosphere. Journal of Climate 9: 3443--3463. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London, Series A: Mathematical and Physical Sciences 193: 120--145. Peterson TC, Golubev VS, and Groisman PY (1995) Evaporation losing its strength. Nature 377: 687--688. Piao S, Friedlingstein P, Ciais P, De Noblet-Ducoudre N, Labat D, and Zaehle S (2007) Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proceedings of the National Academy of Sciences of the United States of America 104: 15242--15247. Porte´-Agel F, Parlange MB, Cahill AT, and Gruber A (2000) Mixture of time scales in evaporation: Desorption and self-similarity of energy fluxes. Agronomy Journal 92: 832--836. Priestley CHB and Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100: 81--92. Raupach MR (2001) Combination theory and equilibrium evaporation. Quarterly Journal of the Royal Meteorological Society 127: 1149--1181. Rayner DP (2007) Wind run changes are the dominant factor affecting pan evaporation trends in Australia. Journal of Climate 20: 3379--3394. Roderick ML and Farquhar GD (2002) The cause of decreased pan evaporation over the past 50 years. Science 298: 1410--1411. Roderick ML, Rotstayn LD, Farquhar GD, and Hobbins MT (2007) On the attribution of changing pan evaporation. Geophysical Research Letters 34: L17403 (doi:10.1029/ 2007GL031166). Sene KJ, Gash JHC, and McNeil DD (1991) Evaporation from a tropical lake: Comparison of theory with direct measurements. Journal of Hydrology 127: 193--217. Seneviratne SI, Luethi D, Litschi M, and Schaer C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443, doi:10.1038/nature05095. Shuttleworth WJ (1993) Evaporation. In: Maidment DR (ed.) Handbook of Hydrology, pp. 4.1--4.53. New York: McGraw-Hill. Shuttleworth WJ (2009) On the theory relating changes in area-average and pan evaporation. Quarterly Journal of the Royal Meteorological Society 135: 1230--1247. Shuttleworth WJ and Calder IR (1979) Has the Priestley–Taylor equation any relevance to forest evaporation? Journal of Applied Meteorology 18: 639--646. Stewart JB (1977) Evaporation from the wet canopy of a pine forest. Water Resources Research 13: 915–921. Sweers HE (1976) A nomogram to estimate the heat-exchange coefficient at the air– water interface as a function of wind speed and temperature: A critical survey of some literature. Journal of Hydrology 30: 375--401. Teuling AJ, Hirschi M, Ohmura A, et al. (2009) A regional perspective on trends in continental evaporation. Geophysical Research Letters 36(2): L02404. Thom AS and Oliver HR (1977) On Penman’s equation for estimating regional evaporation. Quarterly Journal of the Royal Meteorological Society 103: 345--357. Valentini R, Mateucci G, Dolman AJ, et al. (2000) Respiration as the main determinant of carbon balance in European forests. Nature 404: 861--865.

Evaporation in the Global Hydrological Cycle van der Molen MK, Dolman AJ, Waterloo MJ, and Bruijnzeel LA (2006) Climate is affected more by maritime than by continental land use change: A multiple scale analysis. Global and Planetary Change 54: 128--149. Wallace JS and Holwill CJ (1997) Soil evaporation from tiger-bush in south-west Niger. Journal of Hydrology 188–189: 426--442. Willett KM, Gillett NP, Jones PD, and Thorne PW (2007) Attribution of observed surface humidity changes to human influence. Nature 449, doi:10.1038/ nature06207.

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Wilson KB, Baldocchi DD, Aubinet M, et al. (2002) Energy partitioning between latent and sensible heat flux during the warm season at FLUXNET sites. Water Resources Research 38(12): 1294 (doi:10.1029/2001WR000989). Zhang L, Dawes WR, and Walker GR (2001) Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research 37: 701--708.

2.04 Interception AMJ Gerrits and HHG Savenije, Delft University of Technology, Delft, The Netherlands & 2011 Elsevier B.V. All rights reserved.

2.04.1 2.04.2 2.04.3 2.04.3.1 2.04.3.2 2.04.3.3 2.04.3.4 2.04.3.5 2.04.4 2.04.4.1 2.04.4.2 2.04.5 2.04.5.1 2.04.5.2 2.04.5.3 2.04.5.3.1 2.04.5.3.2 2.04.5.3.3 2.04.6 2.04.7 References

Introduction Importance of Interception Types of Interception Canopy Interception Forest Floor Interception Fog Interception Snow Interception Urban Interception Methods to Measure Interception Canopy Forest Floor Interception Models Conceptual Rutter Model Analytical Gash Model Stochastic Interception Models Poisson distribution Markov chains Gamma probability density function and transfer functions Consequences of Underestimating Interception for Hydrological Modeling and Water Resource Assessment Outlook

Nomenclature b Br c cp D Di E Ei Ei,c E li;c E ti;c Ei,f El Ep H I L m n nr,d nm p

constant in Rutter (1971) model (L1) Bowen ratio (–) canopy coverage (–) specific heat (L M T3 K1) drainage rate from the canopy (L T1) interception threshold (L T1) actual evaporation (L T1) interception evaporation (L T1) interception evaporation from canopy (L T1) evaporation from leaves (without trunk) (L T1) evaporation from the trunk (L T1) interception evaporation from forest floor (L T1) evaporation from lower basin (L T1) potential evaporation (L T1) sensible heat flux (M T3) interception process (L T1) number of elemental surface areas per unit ground (L2) mean number of raindrops striking an element (–) mean number of drops retained per element (–) number of rain days per month (–) days within a month ( ¼30.5) (–) throughfall coefficient (–)

pt P Pg P0g P00g q

r Sc Slc Stc Sf Si Sl Su Tf Ts t z b e k q h

89 90 90 90 91 91 91 92 93 93 94 94 94 95 95 95 96 98 98 99 99

trunk fraction coefficient (–) precipitation (L T1) gross precipitation (L T1) gross precipitation necessary for canopy saturation (L T1) gross precipitation necessary for trunk saturation (L T1) maximum amount of rain drops on element (–); specific humidity (M T1L1) amount of rain drops on element (–) storage of canopy (L) storage of leaves (without trunk) (L) storage of trunk (L) storage of forest floor (L) interception storage (L) storage of the lower basin (L) storage of the upper basin (L) throughfall (L T1) stemflow (L T1) mean volume of raindrops (L3) height (L) scaling factor (L T1) constant in Rutter (1971) model (–) latent heat of vaporization coefficient (L2 T2) density of water (M L3) potential temperature (K)

89

90

Interception

2.04.1 Introduction When it rains the entire surface becomes wet: trees, shrubs, grass, forest floor, footpaths, etc. Also in urban areas, roads and roofs become wet, sometimes forming pools of stagnant water. After rainfall has ceased these surfaces soon become dry again. This process is called ‘interception’. It is the part of the rainfall that is captured by surface storage (i.e., vegetation, roofs, etc.) before it can run off or infiltrate into the soil. The intercepted water generally evaporates during the event and shortly after the rainfall ceased, so that it can repeat its function during the next rainfall event. In the literature, interception is defined in different ways: sometimes as a stock, sometimes as a flux, or, more appropriately, as the entire interception process (Savenije, 2005). If only interception storage (Si [L]) is considered, interception is defined as the amount of rainfall which is temporarily stored on the Earth’s surface. Actually, this is the interception capacity or water-holding capacity. If interception is defined as a flux, then it is the intercepted water which evaporated over a certain time [L T1] during and after the event. When interception is considered as a process (I [L T1]), it is defined as the part of the rainfall flux which is intercepted on the wetted surface after which it is fed back to the atmosphere. The interception process equals the sum of the change of interception storage (Si) and the evaporation from this stock (Ei):

I¼

dSi þ Ei dt

ð1Þ

The timescale of the interception process is in the order of 1 day. After 1 day, it is fair to assume for most climates that the first term on the right-hand side in Equation (1) approaches zero, and I ¼ Ei. Of course, in the case of snow under cold climates, this may take longer. How much of the precipitation is intercepted depends on several factors, which can be divided into three groups:

•

Vegetation characteristics. Large vegetation types, such as trees, have a high aerodynamic roughness, causing high potential evaporation rates. Grasses, crops, or bushes, on the other hand, have a much lower roughness and thus do not have as high potential evaporation rates. The storage capacity also depends on the vegetation type. The shape of the leaves, the thickness, the density (leaf area index), and the configuration of the branches determine how much water can be stored. For example, the capacity of a coniferous or a deciduous tree is different (e.g., Rutter et al., 1975; Baird and Wilby, 1999; Bryant et al., 2005; Toba and Ohta, 2005). Although intuitively one might think that a deciduous tree can hold more water in its bucket-like leaves, a coniferous tree can hold much more water by adhesion. Furthermore, it is also important to take the seasonality into account. Deciduous trees lose their leaves in the dormant season, causing a large reduction in the canopy storage capacity. Vegetation also determines the amount of understorey growth and forest floor. The forest floor of different vegetation types can have significantly

•

•

different interception behavior (e.g., a thick needle layer or a thin leaf litter layer). Rainfall characteristics. Rainfall has a large influence on the interception process. The rainfall frequency is a major determining factor. It makes a big difference if rainfall occurs as one continuous storm or as a sequence of several small events with dry spells in between. Even if the total rainfall depth is the same, the last scenario intercepts much more rainwater, because between the events the storage can be (partly) emptied by evaporation and thus more storage is available. Second, the rainfall intensity is important, although there is no consensus in literature. Horton (1919) and Wang et al. (2007) concluded that the interception capacity is lower at higher intensity because high rainfall intensities cause splashing and shaking of leaves. On the other hand, Aston (1979) and Keim et al. (2006b) noted the opposite: high rainfall intensities coincide with high storage capacities, due to dynamic storage. Evaporative demand. If the potential evaporation (i.e., open water evaporation) is high, the intercepted water can evaporate more easily during and after the event. Wind plays an important role in removing moisture from the surface providing a higher vapor deficit, particularly in the canopy. Moreover, the roughness of the vegetation increases the evaporative power, by causing turbulence which makes it easier to take up the intercepted water. However, wind can also reduce the amount of interception by reducing the storage. Horton (1919), Klaassen et al. (1996), and Ho¨rmann et al. (1996) noted that with increasing wind speed the measured storage capacity is less, due to the fact that the wind shakes the rainwater off the leaves.

Of the above three factors, the rainfall characteristics are most dominant for evaporation from interception. Although both the storage capacity (mainly vegetation characteristic) and the available energy form a constraint to the evaporation flux per event, the number of events is a more important factor. This is confirmed by the sensitivity analysis of Gerrits et al. (2009c).

2.04.2 Importance of Interception Although most surfaces can store only a few millimeters of rainfall, which is often not much in comparison to other stocks in the water balance, interception is generally a significant process. The impact becomes evident at longer timescales. Although interception storage is generally small, the number of times that the storage is filled and depleted can be so large that the interception flux is generally of the same order of magnitude as the transpiration flux. In addition, the interception process smooths the rain intensities, causing more gradual infiltration. Interception redistributes the rainfall as well. Some parts of a field receive less water due to interception, whereas other parts receive more due to funneling of the vegetation (e.g., Germer et al., 2006; Gerrits et al., 2009b). Subsequently, this has an influence on the soil moisture patterns, and this is again important for flood generation (Roberts and Klingeman, 1970). Besides the hydrological effects, there are influences on the nutrient cycle of a forest, and on agricultural applications.

Interception

For example, interception affects the efficiency of insecticides and fertilizers (Aston, 1979). Besides, fire retardants are more effective if they are stored by vegetation. Finally, interception may reduce soil erosion by preventing rain drops to directly hit and erode the soil layer (Walsh and Voigt, 1977), although in the case of canopy interception the opposite can be true due to the formation of larger rain drops with a higher impact on the forest floor.

2.04.3 Types of Interception As already stated in Section 2.04.1, it is possible to define an infinite number of interception types. In principle, every surface that can store water can be considered as an interception type. In this chapter, we focus on the major types, mainly occurring in a natural environment plus some special mechanisms. However, more often than not, it is a combination of mechanisms. For example, in a forest, it is likely that a part of the rainfall is intercepted by the canopy of a tree, while the remaining part can be intercepted by epiphytes on the branches and/or bark, and, finally, the understorey and forest floor intercept the throughfall before infiltration starts.

2.04.3.1 Canopy Interception Canopy interception is the rainwater that is stored on the leaves and branches of a tree which is subsequently evaporated. This interception can be calculated by measuring rainfall above the trees or measured in an open area nearby (gross

Precipitation

Canopy interception

91

rainfall Pg) and subtracting the throughfall (Tf) and stemflow (Ts) (Figure 1):

Ei;c þ

dSc ¼ Pg  Tf  Ts dt

ð2Þ

Many research studies have been carried out on canopy interception. In Table 1 an overview is given. We can see in the table and also in tables in Kittredge (1948), Zinke (1967), and Breuer et al. (2003) that there is a large difference in the canopy interception by deciduous and coniferous trees (e.g., Kittredge, 1948; Bryant et al., 2005; Toba and Ohta, 2005). Not only because deciduous trees lose their leaves, but also because the leaf area of coniferous trees is much larger than of deciduous trees; coniferous trees can store much more water. Furthermore, leaves may swing over when they become too heavy, causing a (sudden) decrease of the storage capacity. However, Herbst et al. (2008) found counterintuitive results, where higher evaporation rates were found in deciduous trees in winter caused by rougher aerodynamics of the bare canopy and deeper penetration of the wind. In most cases, the storage of water on the branches is small; however, in some environments, the branches can be overgrown by epiphytes. Pypker et al. (2006) showed that in a Douglas fir forest the canopy water storage can potentially be increased by 41.3 mm and Ho¨lscher et al. (2004) found that epiphytes can account for 50% of the storage capacity. However, this large increase in storage capacity is not necessarily resulting in high interception values (storage þ evaporation), because the water uptake and release by the epiphytes is delayed. It takes a while to saturate the epiphytes, and already before saturation, runoff generation can take place. Successively, after wetting, the drying of the epiphytes takes much longer than drying of the canopy, causing less storage to be available. Another special type of canopy interception is interception by agricultural crops. In essence, there is no difference between crops and other vegetation types. They both can store water up to a certain threshold and then drain water to the floor as throughfall. However, whereas vegetation has a gradual seasonal pattern (summer vs. winter), crops have a phenological growth cycle (seeding to harvesting) which is therefore more abrupt. Hence, when modeling crop interception the appropriate description of the variation in the storage capacity is important.

2.04.3.2 Forest Floor Interception

Throughfall Stemflow Forest floor interception Infiltration Figure 1 Two major interception types in the natural environment.

Forest floor interception is the part of the throughfall that is temporarily stored in the top layer of the forest floor and successively evaporated within a few hours or days during and after the rainfall event. The forest floor can consist of short vegetation (like grasses, mosses, bushes, and creeping vegetation), litter as described by Hoover and Lunt (1952) as the litter and fermentation (L and F) layer (i.e., leaves, twigs, and small branches), or bare soil. Although the latter seems to have an overlap with soil evaporation, we distinguish them by the fact that soil evaporation refers to the water that is stored in the root zone (Groen and Savenije, 2006).

92

Interception

Table 1 Canopy interception values in literature, with Sc,max the water storage capacity and Ei,c the interception evaporation as percentage of gross precipitation Source

Specie

Location

Sc,max (mm)

Rutter et al. (1975)

Corsian pine (Pinus nigra) Douglas fir (Pseudotsuga menziesii) Norway spruce (Picea abies) Hornbeam (Carpinus betulus) Oak (Quercus robur)

United United United United United

Gash and Morton (1978) Gash et al. (1980)

Scots pine (Pinus sylvestris) Sitka spruce (Picea sitchensis) Scots pine (Pinus sylvestris) Beech (Nothofagus) Acacia auriculiformis

United Kingdom United Kingdom United Kingdom New Zealand Indonesia

1.05 1.2 1.5 1.0 (leafy) 0.65 (leafless) 0.875 (leafy) 0.275 (leafless) 0.8 0.75–1.2 1.02 1.5 (leafy) 1.2 (leafless) 0.5–0.6

Norway spruce (Picea abies) Beech (Asperulo-fagetum) Pinus pinaster Eucalyptus globulus Tamaulipan thornscrub Loblolly (Pinus taeda) & shortleaf pine (Pinus echinata) Longleaf pine (Pinus palustris) Scrub oak (Quercus berberidifolia) White oak (Quercus alba) & shortleaf pine (Pinus echinata) & loblolly pine (Pinus palustris) Hardwood Larc (Larix cajanderi) Red pine (Pinus sylvester) Red pine (Pinus densiflora) Sawtooth oak (Quercus acutissima) Oak (Quercus serrata) Rain forest

France Germany Portugal Portugal Mexico USA (GA)

1.97

34.2 18 10.8 17.1 18.9 22.3

USA (GA) USA (GA) USA (GA)

1.70 1.40 1.58

17.6 17.4 18.6

USA (GA) Siberia Siberia Japan Japan Japan Brazil

0.98

17.7 29 36 13–17 24 18 13–22

Rowe (1983) Bruijnzeel and Wiersum (1987) Viville et al. (1993) Ho¨rmann et al. (1996) Valente et al. (1997) Navar et al. (1999) Bryant et al. (2005)

Toba and Ohta (2005)

Cuartas et al. (2007)

Kingdom Kingdom Kingdom Kingdom Kingdom

1.28 (leafy) 0.84 (leafless) 0.41 0.21

1.0

Ei,c (%) 35 39 48 36 18

27–32 42 35 (leafy), 22 (leafless) 11–18

See also tables in Kittredge (1948), Zinke (1967), and Breuer et al. (2003).

In Table 2 some results are presented of previous work on forest floor interception.

moisture. These instruments suffer from various limitations. An overview of fog collectors can be found in Bruijnzeel et al. (2005).

2.04.3.3 Fog Interception A special type of interception is fog interception or cloud interception. Vegetation can intercept not only rain, but also moisture (in the form of small water droplets) from the air. Fog can occur due to different processes. Bruijnzeel et al. (2005) distinguished nine types: radiation fog, sea fog, stream fog, advection fog, ice fog, coastal fog, valley fog, urban fog, and mountain fog. Fog interception is mainly important in tropical montane environments (table in Bruijnzeel (2005): 6–53% of rainfall), and can also play a significant role in semi-arid regions near the coast (e.g., Hursh and Pereira, 1953; Hutley et al., 1997; Hildebrandt et al., 2007). In both environments, the main problem with fog interception studies is to measure precipitation and throughfall (Equation (2)), which is especially important because fog deposition can be twice as high as normal rainfall. Since conventional rain gauges are not suitable to measure fog deposition, special fog collectors have been developed with often wire meshes to intercept the

2.04.3.4 Snow Interception Snowfall is also intercepted by trees. Especially, coniferous trees can store so much snow, that they collapse under its weight. As an example, Storck et al. (2002) found in a Douglas-fir-dominated forest that up to 60% of the snowfall was intercepted, equaling 40 mm of snow water equivalent (swe). The storage of snow on the canopy is different from rain. For rainfall interception the storage capacity is mainly a function of the leaf surface area, whereas for snow interception the branch strength and canopy shape are more important (Ward and Trimble, 2004). Furthermore, the snow storage is also dependent on the temperature. If snow falls with temperatures close to freezing point, the cohesion of snow is higher causing more snow to be accumulated on the canopy (Ward and Trimble, 2004). Another difference between rainfall interception and snow interception is the way in which interception storage is depleted. Rainfall interception is a real threshold process,

Interception

93

Table 2 Forest floor interception values in literature, with the water storage capacity Sf,max and the interception evaporation Ei,f as percentage of net precipitation (i.e., throughfall) Source

Forest floor type

Location

Haynes (1940) Kittredge (1948) Beard (1956) Helvey (1964) Brechtel (1969)

Kentucky bluegrass (Poa pratensis) Californian grass (Avena, Stipa, Lolium, Bromus) Themeda and Cymbopogon Poplar Scot’s pine Norway spruce Beech Oak Shorea robusta and Mallotus philippensis Pinus roxburghii and Quercus glauca Pinus roxburghii Quercus leucotrichophora and Pinus roxburghii Quercus floribunda and Quercus leucotrichophora Quercus lanuginosa and Quercus floribunda Blue stem Andropogon gerardi Vitman Pine (Pinus sylvestris) Beech (Fagus sylvaticus) Bracken litter (Pteridium aquiliunum) Norway spruce Sitka spruce Beech (Asperulo-Fagetum) Pinus radiata Eucalyptus Douglas fir Peble mulch (5–9 cm) Peble mulch (2–6 cm) Cryptomeria japonica Lithocarpus edulis Grass (Aristida divaricata) Woodchips (Pinus) Poplar leaves (Populus nigra)

? USA (CA) South Africa USA (NC) USA (NY) USA (NY) USA (NY) USA (NY) India India India India India India USA (TX) United Kingdom United Kingdom United Kingdom Scotland Scotland Germany Australia Australia Netherlands China China Japan Japan Mexico Mexico Mexico

Pathak et al. (1985)

Clark (1940) in Thurow et al. (1987) Walsh and Voigt (1977) Pitman (1989) Miller et al. (1990) Thamm and Widmoser (1995) Putuhena and Cordery (1996) Schaap and Bouten (1997) Li et al. (2000) Sato et al. (2004) Guevara-Escobar et al. (2007)

a

Sf,max (mm)

Ei,f (%) 56a 26a 13a 34 21 16 16 11 11.8 7.8 9.6 10.6 11.0 11.3 57–84

0.6–1.7 0.9–2.8 1.67

2.5–3.0 2.78 1.70 0.281 0.526 0.27–1.72 0.67–3.05 2.5 8 2.3

18a 16a 12–28

0.23 mm d1 11.5a 17.4a

% of gross precipitation instead of net precipitation.

whereby throughfall starts when the storage capacity is exceeded. The storage capacity is then emptied by evaporation. Snow, on the other hand, can only be removed from the canopy by three ways: sublimation, mechanical removal (sliding leading to mass release), and melt water drip (Miller, 1966).

Island is mainly caused by the (relatively warm) buildings that block the cold night sky. Furthermore, the thermal properties of a city are different: concrete and asphalt have much higher heat capacities than forests and also the surface radiative properties differ (e.g., albedo and emissivity). The lack of vegetation in urban areas, which reduces cooling by transpiration, also causes a difference in the energy balance.

2.04.3.5 Urban Interception Most hydrological studies focus on natural environments and not on urbanized areas, which is also the case for interception studies. However, recently, with the increasing interest for alternative sources of water for nonpotable domestic use (socalled ‘gray water’), water balance studies on (interception) evaporation in urban areas increased (Grimmond and Oke, 1991; Ragab et al., 2003; Gash et al., 2008; Nakayoshi et al., 2009). The difference between urban and rural interception is not only that the typical storage capacities of buildings, roads, etc., are unknown, but also that the entire energy balance is different in a city. Oke (1982) discovered the so-called ‘Urban Heat Island’, that is, higher temperatures in urban areas compared to the surrounding rural areas. The Urban Heat

2.04.4 Methods to Measure Interception 2.04.4.1 Canopy There exist already many methods to measure canopy interception. The most-often used method is by measuring rainfall above the canopy and subtract throughfall and stemflow (e.g., Helvey and Patric, 1965). However, the problem with this method is that the canopy is not homogeneous, which causes it to be difficult to obtain representative throughfall data. Using multiple rain gauges under the canopy (Helvey and Patric, 1965; Keim et al., 2005; Gerrits et al., 2009b) reduces this problem. Sometimes the collectors are moved to achieve a better representation of throughfall (e.g., Lloyd and Marques, 1988; Tobo´n-Marin et al., 2000; Manfroi et al., 2006; Ziegler

94

Interception

et al., 2009). Another method to avoid the problem with the spatial distribution of the canopy was introduced by Calder and Rosier (1976) and applied by, for example, Shuttleworth et al. (1984), Calder et al. (1986), and Calder (1990). They covered the forest floor with plastic sheets and collected the throughfall. The disadvantage of this method is that for long periods irrigation is required, because otherwise, in the end, the trees will dry out and may even die due to water shortage. The method by Hancock and Crowther (1979) avoided these problems, by making use of the cantilever effect of branches. If leaves on a branch hold water, it becomes more heavy and will bend. By measuring the displacement, it is possible to determine the amount of intercepted water. Huang et al. (2005) refined this method by making use of strain gauges. However, the disadvantages of these methods are that only information about one single branch is obtained and it is quite laborious to measure an entire tree. Edwards (1986), Fritschen and Kinerson (1973), and Storck et al. (2002) made use of weighing lysimeters with trees. Although interception of a whole tree is measured with this method, the big disadvantage of this method is that it is expensive and destructive. Friesen et al. (2008) developed a nondestructive method to measure canopy interception of a whole tree. With mechanical displacement sensors, Friesen et al. (2008) measured the stem compression due to interception water, which is an integration of the whole canopy. However, although this method looks promising, it is still under development. A totally different way of measuring canopy interception of a forest plot is to make use of ray attenuation. Calder and Wright (1986) used the attenuation of gamma rays. They transmitted from a tower gamma-rays through the canopy at different heights and measured the gamma-ray density at a receiving tower. The ratio between transmitted and received gamma-ray density during dry conditions is successively compared to this ratio during a rainfall event. This gives an estimate of the amount of water stored on the canopy over time. Although the method gives interception estimated of an entire forest, the method becomes inaccurate under windy conditions. Furthermore, safety standards inhibits unattended use of this method. Bouten et al. (1991) overcame this problem by making use of microwave attenuation. It appears to be a suitable method to measure canopy wetness, although it is an expensive method. Evaporation can also be measured by flux measurements. By measuring temperature (y) and specific humidity (q) at several heights (z) above the canopy, one can calculate the Bowen ratio (Br), which is the sensible heat flux, H, divided by the latent heat flux (lE):

Br ¼

cp dy=dz H ¼ rlE ldq=dz

ð3Þ

Combined with the energy balance, evaporation can be calculated (Gash and Stewart, 1975). The main difficulty with the Bowen ratio method is to measure the humidity gradient more accurately (Stewart, 1977). Another method is the eddy covariance technique, where the net upward or downward flux is determined by fast-response three-dimentional (3D) wind speed measurements combined with a concentration

measurement. This concentration can be humidity, temperature, or CO2 concentrations (Amiro, 2009).

2.04.4.2 Forest Floor In the literature, little can be found on forest floor interception, although some researchers have tried to quantify the interception amounts. Generally, these methods can be divided into two categories (Helvey and Patric, 1965): 1. lab methods, whereby field samples are taken to the lab and successively the wetting and drying curves are determined by measuring the moisture content and 2. field methods, whereby the forest floor is captured into trays or where sheets are placed underneath the forest floor. An example of the first category is that of Helvey (1964), who performed a drainage experiment on the forest floor after it was saturated. During drainage, the samples were covered, and after drainage had stopped (24 h), the samples were taken to the lab, where the samples were weighed and successively dried until a constant weight was reached. By knowing the oven dry weight of the litter per unit area and the drying curve, the evaporation from interception could be calculated. In this way, they found that about 3% of the annual rainfall evaporated from the litter. Similar work was done by Bernard (1963), Walsh and Voigt (1977), and Sato et al. (2004). However, what they all measured was not the flux, but the storage capacity. Another example of lab experiments was carried out by Putuhena and Cordery (1996). First, field measurements were carried out to determine the spatial variation of the different forest floor types. Second, storage capacities of the different forest floor types were measured in the lab using a rainfall simulator. Finally, the lab experiments were extrapolated to the mapping step. In this way, Putuhena and Cordery (1996) found average storage capacities of 2.8 mm for pine and 1.7 mm for eucalyptus forest floors. Moreover, GuevaraEscobar et al. (2007) made use of a rainfall simulator. Examples of the second category have been, for example, carried out by Pathak et al. (1985), who measured the weight of a sample tray before and after a rainfall event. They found litter interception values of 8–12% of the net precipitation. In addition, here, they measured the storage capacity, rather than the flux. Schaap and Bouten (1997) measured the interception flux by the use of a lysimeter and found that 0.23 mm d1 evaporated from a dense Douglas fir stand in early spring and summer. Also, Brechtel (1969) and Thamm and Widmoser (1995) made use of lysimeters. Brechtel (1969) manually measured the infiltrated water and Thamm and Widmoser (1995) developed an automatic and more sophisticated method, whereby the suction under the forest floor is controlled by a tensiometer. Gerrits et al. (2007) developed a method whereby both the forest floor interception and the infiltrated water are continuously weighed in suspended trays with strain gauges. In Figure 2 the schematic setup is shown. Measurements with sheets were done, for example, by Li et al. (2000), who found that pebble mulch intercepts 17% of the gross precipitation. Miller et al. (1990) found comparable results (16–18%) for a mature coniferous plantation in Scotland.

Interception

95

three parts:

Eint

Precipitaion

1. free throughfall, that is, throughfall, which did not touch the canopy at all (pPg), 2. trunk input (ptPg), and 3. canopy input ((1  p  pt)Pg). Litter Su Geotextile El

Weighing device

Infiltration

The rain that falls on the canopy can drain to the ground (i.e., canopy drainage, D), or evaporate ðEli;c Þ, or it can be stored on the canopy ðSlc Þ:

ð1  p  pt Þ

Z

Pg dt ¼

Z

Ddt þ

Z

Eli;c dt þ

Z

dSlc

ð4Þ

Sl

The rain that falls on the trunk can evaporate from the trunk ðEti;c Þ, or drain in the form of stemflow (Ts), or it can be stored on the trunk ðStc Þ:

Valve

Z Figure 2 Forest floor interception device by Gerrits AMJ, Savenije HHG, Hoffmann L, and Pfister L (2007) New technique to measure forest floor interception – an application in a beech forest in Luxembourg. Hydrology and Earth System Sciences 11: 695–701.

2.04.5 Interception Models In literature, several models have been developed to simulate forest interception. Almost all of these models are concentrated on canopy interception, sometimes including stem interception (Table 3). In principle, these models can be expanded to include forest floor or any surface interception as well. The most often used interception models are the conceptual model of Rutter et al. (1971) (Section 2.04.5.1) and the analytical model of Gash (1979) (Section 2.04.5.2) or revisions of these models. Furthermore, there exist some stochastic models, which will be described in Section 2.04.5.3. In Table 3 an overview and summary of the models are given. A more detailed overview and comparison can be found in Muzylo et al. (2009).

2.04.5.1 Conceptual Rutter Model The conceptual framework of the original Rutter model is depicted in Figure 3. As can be seen the rainfall is divided into Table 3

Characteristics of interception models

Main author

Model type

Rutter Gash C alder De Groen

Conceptual Analytical Stochastic Concept./ stoch. Concept./ stoch.

Keim

Interception element: canopy

stem

x x x x

x x

x

x

Timescale

forest floor

x

rhourly event rhourly monthly 6-hourly

pt

Pg dt ¼

Z

Ts dt þ

Z

Eti;c dt þ

Z

dStc

ð5Þ

with Ei;c ¼ Eli;c þ Eti;c and Sc ¼ Slc þ Stc for the total canopy interception. The evaporation from the wet canopy is calculated with the Penman equation (Penman, 1948). Because the canopy is not always completely wet ðSlc o Slc;max Þ, the actual evaporation rate can be calculated by the fraction of the potential evaporation: Ep Slc =Slc;max . The same concept is applied for the trunks. However, for the determination of the potential evaporation of the trunks, the potential evaporation of the canopy is multiplied with and extra constant E. Stemflow is modeled as a threshold process, whereby no stemflow is generated when Stc o Stc;max , and when the threshold is exceeded stemflow equals the difference between Stc and Stc;max . Canopy drainage is modeled in a similar way; however, when the threshold Slc;max is exceeded, drainage is defined as

h i D ¼ Ds exp bðSlc  Slc;max Þ

ð6Þ

with Ds being the rate of drainage when the canopy is saturated and b [L1] an empirical coefficient. Valente et al. (1997) revised the original Rutter model, to model interception in a more realistic way for sparse canopies. The main drawbacks of the original model were the partitioning of free throughfall and canopy input, and the conceptual error that evaporation from interception can theoretically be higher than potential evaporation (Valente et al., 1997). Therefore, they divided the conceptual model into two areas: a covered area (c) and an uncovered area (1  c). Second, in the revised Rutter model, only water can reach the trunk after it has flowed through the canopy as a part of the canopy drainage. Water which is not drained by the trunk is directly dripping to the ground. The final change was made that evaporation from the saturated canopy is not equal to the potential evaporation, but is reduced by a factor 1  E (0oEo1). The remaining energy ((E)Ep) is then available for evaporating water from the saturated trunk (Figure 4).

96

Interception

Canopy evaporation S lc ⎧ ,S lc< S lc,max E ⎪ E li,c = ⎨ p S lc,max ⎪ ,S lc ≥ S lc,max Ep ⎩

Canopy input ( 1−p−pt )Pg

Scl

Gross rainfall Pg

Free throughfall p Pg

Trunk evaporation S tc ⎧ ,S tc < S tc,max ⎪εEp t t E i,c = ⎨ S c,max ⎪ ,S tc ≥ S tc,max ⎩ εEp

Trunk input p t Pg

Sct

Scl, max

Sct,max

Drainage D = Ds exp[b (Scl- Scl,max]

Throughfall, Tf

Stemflow, Ts

Figure 3 Conceptual framework of the Rutter model. Modified from Valente F, David JS, and Gash JHC (1997) Modelling interception loss for two sparse eucalypt and pine forest in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology 190: 141–162.

2.04.5.2 Analytical Gash Model The original Gash model is conceptually the same as the Rutter model (see Section 2.04.5.1); however, it does not require meteorological data of high temporal resolution (hourly) and requires less computation time. The main assumption of the Gash model is that it is possible to represent the real rainfall pattern by different discrete rainfall events, each consisting of three phases: 1. wetting phase, 2. saturation phase, and 3. drying phase (long enough to dry the entire canopy). Similar to the Rutter model, rainfall is divided into canopy input (1  p  pt), free throughfall (p), and trunk input (pt). The Gash model makes a distinction between storms which are not large enough to saturate the canopy ðPg o P0g : m stormsÞ and storms which are large enough to saturate the canopy ðPg  P0g : n stormsÞ. The amount of gross rainfall necessary to saturate the canopy is P0g (see Table 4). Interception evaporation is then calculated for the canopy and the trunk. Although the original Gash model appears to work fine for several types of forests, it contains some weaknesses for modeling sparse forests, similar to the Rutter model. Hence, Gash et al. (1995) revised their existing model according to the revised Rutter model (Rutter et al., 1975). An overview of the formulas of the revised Gash model can be found in Table 4.

2.04.5.3 Stochastic Interception Models

areas, which all have the same probability to be struck by raindrops. The Poisson probability of an element to be struck by r drops equals

Pr ¼

m r m e r!

ð7Þ

with m the mean number of raindrops striking an element per storm. If an element can hold q raindrops, the mean number of drops per element (n) can be expressed as

n¼

q X

r  Pr þ q  Pðr 4 qÞ

ð8Þ

r¼0

¼qþ

q X

Pr  ðr  qÞ

ð9Þ

r¼0

with P (r4q) the probability of elements being struck by more P than q drops and is equal to 1  qr¼ o Pr. To upscale from elemental area to canopy area, the number of elemental surface areas per unit ground (L) is required and the mean volume of raindrops (u):

Sc ¼ nuL

ð10Þ

Sc;max ¼ quL

ð11Þ

Pg ¼ muL

ð12Þ

Evaporation is then obtained by (with dScdEi,c ¼  1)

2.04.5.3.1 Poisson distribution Calder (1986) developed a stochastic interception model, where he assumes that a tree consists of several elemental

dn dSc dn 1 ¼  ¼ dEi;c dEi;c dSc uL

ð13Þ

Interception Gross rainfall Pg

97

Interception from evaporation: Ei,c = c·( E li,c + E ti,c ) Covered area input Pg

Uncovered area input Pg

Uncovered area (1−c)

Covered area c

S lc

⎧ ⎪ (1−ε)Ep l S lc,max E i,c = ⎨ ⎪ (1 − ε) Ep ⎩

Free throughfall Pg

Trunk evaporation:

Canopy evaporation: ,S lc < S lc,max ,S lc ≥ S lc,max

S tc ⎧ ⎪ εEp t S c,max i,c = ⎨ ⎪ εEp ⎩

Et

,S tc,< S tc,max ,S lc, ≥ S tc,max

Scl Scl, max

Drainage Dc = d(S lc−S lc,max)/dt

Drip D i,c =( 1−pd ) Dc

Trunk input pd Dd

Sct Sct, max

Trunk drainage Dt,c = d(Sct- Sct, max)/dt

Throughfall, Tf (1−c)Pg+c Di,c

Stemflow, Ts c Dt,c

Figure 4 Conceptual framework of the revised Rutter model. Modified from Valente F, David JS, and Gash JHC (1997) Modelling interception loss for two sparse eucalypt and pine forest in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology 190: 141–162.

The Calder model is very simple and describes the threshold behavior of interception very well; however, it is difficult to upscale from drop size scale to forest size scale. This hinders the applicability of the model.

2.04.5.3.2 Markov chains Groen and Savenije (2006) developed a monthly interception model based on a daily interception model and the daily rainfall characteristics. They assumed interception on a daily

98 Table 4

Interception Components of interception of the original Gash (1979) model and the revised Gash et al. (1995) model for sparse canopies Original Gash (1979)

Amount of gross rainfall necessary to saturate the canopy ðP 0g Þ and trunk ðP00g Þ

Revised (sparse canopy) Gash et al. (1995)

"

P 0g

 g Sc;max p P E ¼ g  p ln 1  ð1  p  pt ÞP E

P 00g ¼ Stc;max =pt Evaporation from canopy interception ðE li;c Þ: 1. for m storms ðPg o P 0g Þ 2. for n storms ðP g  P 0g Þ Evaporation from trunk interception ðE ti;c Þ: 1. for q storms ðP g  P 00g Þ 2. for m þ n  q storms ðPg o P 00g Þ

# P 0g

" # g p P Sc;max ð1  EÞE ¼  p c ln 1  g ð1  EÞE P

g Stc;max P 0 P 00g ¼   p pt c þ P g P g  ð1  EÞE

Pg;j  p Pn E 0 ðP g;j  P 0g Þ nð1  p  pt ÞP g þ  P g j¼1

Pm P g;j " j¼1 #  p Pn ð1  EÞE 0 0 c nP g þ j¼1 ðP g;j  P g Þ g P

qStc P pt mþnq Pg;j j¼1

qStc "

ð1  p  pt Þ

Pm

j¼1

c

pt c 1 

#  p Pn ð1  EÞE 0 ðP  P Þ g g;j j¼1 g P

Modified from Valente F, David JS, and Gash JHC (1997). Modelling interception loss for two sparse eucalypt and pine forest in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology 190:141–162.

scale as (Savenije, 1997, 2004)

Markov probabilities to model monthly interception based on daily information.

Ei;d ¼ minðDi;d ; Pg;d Þ

ð14Þ

The probability distribution of rainfall on a rain day can be described as

  Pg;d 1 f i;d ðPg;d Þ ¼ exp  b b

ð15Þ

with b being the scaling factor, equal to the expected rainfall on a rain day, which can be expressed as

b¼

Pg;m Eðnr;d =nm Þ

ð16Þ

with Pg,m being the monthly rainfall and nr,d and nm the number of rain days per month and amount of days per month, respectively. The number of rain days per month can be expressed by the use of Markov properties. Being p01 the Markov properties of the transition from a dry day to a rain day, and p11 the probability of a rain day after a rain day:

nr;d ¼ nm

p01 1  p11 þ p01

ð17Þ

Multiplying Equations (14) and (15) and successively integrating results in monthly evaporation from interception:

Ei;m ¼ Eðnr;d jnm Þ

ZN

Ei;d  f i;d ðPd Þd Pd

ð18Þ

2.04.5.3.3 Gamma probability density function and transfer functions Keim et al. (2004) developed a stochastic model to obtain from 6-hourly rainfall to 6-hourly throughfall for extreme events. They made use of the gamma probability density function (PDF; for 6 hourly rainfall): Pg =y Pa1 Tf g e  100% ¼ Pg GðaÞya

ð20Þ

The parameters a and y can be estimated by dividing the 6-hourly rainfall in ranges and find the best-fit sets. After downscaling the rainfall and throughfall data, rainfall is transferred through the canopy by a linear system convolution to obtain high-resolution throughfall data, which allows one to investigate the effect of intensity smoothing:

Tf ðtÞ ¼

Zt

PðtÞgðt  tÞdt

ð21Þ

0

with the transfer function g(t  t). Keim et al. (2004) found that the transfer function can be best described with the exponential distribution:

0

   Di;d ¼ Pm 1  exp b

ð19Þ

Hence, the model of Groen and Savenije (2006) is a parsimonious model with only one measurable parameter, and

gðtÞ ¼ a e at

ð22Þ

By coupling the stochastic model with the the intensity smoothing transfer function, effects of forest canopies on extreme rainfall events can be investigated.

Interception

2.04.6 Consequences of Underestimating Interception for Hydrological Modeling and Water Resource Assessment Hydrologists often consider precipitation as the start of the hydrological cycle. After a rainfall event, the first separation point in the cycle is on the Earth surface. Part of the rainwater is intercepted by the vegetation or ground surface and the remainder infiltrates into the unsaturated zone or runs off. The part of the rainfall that is intercepted successively evaporates from the temporary storage. This first separation point in the hydrological cycle is not always considered a significant process. This is partly due to the technical difficulties that are inherent to interception measurements (Lundberg et al., 1997; Llorens and Gallart, 2000), but it is also generally considered a minor flux, although previous studies tell us that interception can amount to 10–50% of the precipitation depending on the vegetation type (Klaassen et al., 1998). Even then, these studies mostly refer to canopy interception only. If forest floor interception is taken into account as well, the percentage is substantially higher. Furthermore, it is often stated that interception is particularly not important for the generation of floods. This is not true. Interception strongly influences the antecedent soil moisture conditions, which are very important for the generation of floods (Roberts and Klingeman, 1970). Still, interception is regularly (partly) disregarded in hydrological models, or taken as a fixed percentage of the precipitation. As a result, after model calibration, interception is generally compensated by other processes such as transpiration, soil evaporation, or even recharge (Savenije, 2004). Zhang and Savenije (2005) showed that the hydrograph at the outlet of the Geer basin in Belgium improved significantly when interception was included in a rainfall–runoff model using the representative elementary watershed (REW) approach. Both the Nash–Sutcliffe efficiency and the percentage bias improved. They also showed that, in calibration, the soil moisture storage capacity compensated for the neglect of the interception process. Keim et al. (2006a) investigated the effects of (canopy) interception. They looked at the influence on the subsurface stormflow generation and concluded that interception caused a delay in the onset of subsurface stormflow, lowered and delayed stormflow peaks, and decreased total flow and the runoff ratio. They also found that simply reducing the rainfall by a constant factor did not result in a satisfactory peak flow response. Fenicia et al. (2008) looked at the change in the movement of the Pareto front when stepwise new processes were included in a variable model structure. They concluded that when interception was included and especially, when spatially distributed interception was included, the Pareto front moved significantly to the origin. Hence, their conclusion was that interception is an important process and should therefore be included in hydrological models.

2.04.7 Outlook More than 2000 articles have been published on interception studies (source: Scopus and ISI Web of KnowledgeSM) and still

99

new articles are being published. Most of these articles focus on canopy interception and describe in detail the process for different tree species in different climates, resulting in long reference tables as, for example, presented in Table 1 and by Breuer et al. (2003). Although this information is of high value for modeling purposes, it would have been more logical if these tables had also been available for the other types of interception, such as described in Section 2.04.3. Especially, since generally more than one mechanism occur, these mechanisms interact (see, e.g., between canopy and forest floor interception (Gerrits et al., 2009a). It would really be a way forward, if a broader scope was systematically considered in interception studies. Although a more balanced database on interception values will help, it is not the complete solution for hydrological modeling. Often, experimental results are site and time specific. Therefore, it is difficult to upscale literature values on interception for catchment modeling. This problem may be solved by considering the energy balance. If we would know how the available energy is partitioned over the different fluxes and compartments, we would be able to determine interception evaporation as well. However, this would require intensive field experiments where both the energy fluxes and the evaporation processes are measured simultaneously. Remote sensing could provide the necessary spatial and temporal information on energy partitioning. Through a combination of methods, interception could be more adequately incorporated in hydrological models.

References Amiro B (2009) Measuring boreal forest evapotranspiration using the energy balance residual. Journal of Hydrology 366(1–4): 112--118. Aston A (1979) Rainfall interception by eight small trees. Journal of Hydrology 42 (3–4): 383--396. Baird AJ and Wilby RL (eds.) (1999) Eco-Hydrology-Plants and Water in Terrestrial and Aquatic Environments. London: Routledge. Beard JS (1956) Results of the mountain home rainfall interception and infiltration project on black wattle, 1953–1954. Journal of South African Forestry 27: 72--85. Bernard JM (1963) Forest floor moisture capacity of the New Jersey pine barrens. Ecology 44(3): 574--576. Bouten W, Swart PJF, and De Water E (1991) Microwave transmission, a new tool in forest hydrological research. Journal of Hydrology 124(1–2): 119--130. Brechtel HM (1969) Wald und Abfluss-Methoden zur Erforschung der Bedeutung des Waldes fur das Wasserdargebot. Deutsche Gewasserkundliche Mitteilungen 8: 24--31. Breuer L, Eckhardt K, and Frede H-G (2003) Plant parameter values for models in temperate climates. Ecological Modelling 169(2–3): 237--293. Bruijnzeel LA (2005) Tropical montane cloud forest: A unique hydrological case. In: Bonell M and Bruijnzeel LA (eds.) Forests, Water and People in the Humid Tropics, pp. 462--483. Cambridge: Cambridge University Press. Bruijnzeel LA, Eugster W, and Burkard R (2005) Fog as a hydrologic input. In: Anderson MG (ed.) Encyclopedia of Hydrological Sciences, pp. 559--582. Chichester: Wiley. Bruijnzeel LA and Wiersum KF (1987) Rainfall interception by a young Acacia Auriculiformis (a. cunn) plantation forest in West Java, Indonesia: Application of Gash’s analytical model. Hydrological Processes 1: 309--319. Bryant ML, Bhat S, and Jacobs JM (2005) Measurements and modeling of throughfall variability for five forest communities in the southeastern US. Journal of Hydrology 312: 95--108. Calder IR (1986) A stochastic model of rainfall interception. Journal of Hydrology 89: 65--71. Calder IR (1990) Evaporation in the Uplands. Chichester: Wiley. Calder IR and Rosier PTW (1976) The design of large plastic-sheet net-rainfall gauges. Journal of Hydrology 30(4): 403--405.

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Calder IR and Wright IR (1986) Gamma ray attenuation studies of interception from Sitka Spruce: Some evidence for an additional transport mechanism. Water Resources Research 22: 409--417. Calder IR, Wright IR, and Murdiyarso D (1986) A study of evaporation from tropical rain forest–West Java. Journal of Hydrology 89: 13--31. Clark OR (1940) Interception of rainfall by prairie grasses, weeds and certain crop plants. Ecological Monographs 10: 243--277. Cuartas LA, Tomasella J, Nobre AD, Hodnett MG, Waterloo MJ, and Mnera JC (2007) Interception water-partitioning dynamics for a pristine rainforest in Central Amazonia: Marked differences between normal and dry years. Agricultural and Forest Meteorology 145(1–2): 69--83. de Groen MM and Savenije HHG (2006) A monthly interception equation based on the statistical characteristics of daily rainfall. Water Resources Research 42: W12417. Edwards WRN (1986) Precision weighing lysimetry for trees, using a simplified taredbalance design. Tree Physiology 1: 127--144. Fenicia F, Savenije HHG, Matgen P, and Pfister L (2008) Understanding catchment behavior through stepwise model concept improvement. Water Resources Research 44: 1--13. Friesen J, Beek C van, Selker J, Savenije HHG, and Giesen N van de (2008) Tree rainfall interception measured by stem compression. Water Resources Research 44: W00D15. Fritschen LJ, Cox L, and Kinerson R (1973) A 28-meter Douglas-fir in a weighing lysimeter. Forest Science 19: 256--261. Gash JHC (1979) An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society 105: 43--55. Gash JHC, Lloyd CR, and Lauchaud G (1995) Estimation sparse forest rainfall interception with an analytical model. Journal of Hydrology 170: 79--86. Gash JHC and Morton AJ (1978) An application of the rutter model to the estimation of the interception loss from Thetford Forest. Journal of Hydrology 38(1–2): 49--58. Gash JHC, Rosier PTW, and Ragab R (2008) A note on estimating urban roof runoff with a forest evaporation model. Hydrological Processes 22(8): 1230--1233. Gash JHC and Stewart JB (1975) The average surface resistance of a pine forest derived from Bowen ratio measurements. Boundary-Layer Meteorology 8: 453--464. Gash JHC, Wright IR, and Lloyd CR (1980) Comparative estimates of interception loss from three coniferous forests in Great Britain. Journal of Hydrology 48(1–2): 89--105. Germer S, Elsenbeer H, and Moraes JM (2006) Throughfall and temporal trends of rainfall redistribution in an open tropical rainforest, South-Western Amazonia (Rondonia, Brazil). Hydrology and Earth System Sciences 10: 383--393. Gerrits AMJ, Pfister L, and Savenije HHG (2009a) Spatial and temporal variability of canopy and forest floor interception in a beech forest. Hydrological Processes, doi: 10.1002/hyp. 7712, published online, 7 june 2010. Gerrits AMJ, Savenije HHG, Hoffmann L, and Pfister L (2007) New technique to measure forest floor interception–an application in a beech forest in Luxembourg. Hydrology and Earth System Sciences 11: 695--701. Gerrits AMJ, Savenije HHG, and Pfister L (2009b) Canopy and forest floor interception and transpiration measurements in a mountainous beech forest in Luxembourg. IAHS Redbook 326: 18--24. Gerrits AMJ, Savenije HHG, Veling EJM, and Pfister L (2009c) Analytical derivation of the Budyko curve based on rainfall characteristics and a simple evaporation model. Water Resources Research 45: W04403. Grimmond CSB and Oke TR (1991) An evapotranspiration-interception model for urban areas. Water Resources Research 27: 1739--1755. Guevara-Escobar A, Gonzalez-Sosa E, Ramos-Salinas M, and Hernandez-Delgado GD (2007) Experimental analysis of drainage and water storage of litter layers. Hydrology and Earth System Sciences 11(5): 1703--1716. Hancock NH and Crowther JM (1979) A technique for the direct measurement of water storage on a forest canopy. Journal of Hydrology 41: 105--122. Haynes JL (1940) Ground rainfall under vegetation canopy of crops. Journal of the American Society of Agronomy 32: 176--184. Helvey JD (1964) Rainfall interception by hardwood forest litter in the southern Appalachians U.S. Forest Service Research Paper SE, vol. 8, pp. 1--8. Asherille, NC: Department of Agriculture, Forest Science, Southeastern Forest Experiment station. Helvey JD and Patric JH (1965) Canopy and litter interception of rainfall by Hardwoods of Eastern United States. Water Resources Research 1(2): 193--206. Herbst M, Rosier PT, McNeil DD, Harding RJ, and Gowing DJ (2008) Seasonal variability of interception evaporation from the canopy of a mixed deciduous forest. Agricultural and Forest Meteorology 148(11): 1655--1667.

Hildebrandt A, Al Aufi M, Amerjeed M, Shammas M, and Eltahir EAB (2007) Ecohydrology of a seasonal cloud forest in Dhofar: 1. Field experiment. Water Resources Research 43: W10411. Ho¨lscher D, Ko¨hler L, Dijk AIJM van, and Bruijnzeel LAS (2004) The importance of epiphytes to total rainfall interception by a tropical montane rain forest in Costa Rica. Journal of Hydrology 292(1–4): 308--322. Hoover MD and Lunt HA (1952) A key for the classification of forest humus types. Soil Science Society Proceedings 16: 368--371. Ho¨rmann G, Branding A, Clemen T, Herbst M, Hinrichs A, and Thamm F (1996) Calculation and simulation of wind controlled canopy interception of a beech forest in Northern Germany. Agricultural and Forest Meteorology 79(3): 131--148. Horton RE (1919) Rainfall interception. Monthly Weather Review 47(9): 603--623. Huang YS, Chen SS, and Lin TP (2005) Continuous monitoring of water loading of trees and canopy rainfall interception using the strain gauge method. Journal of Hydrology 311: 1--7. Hursh CR and Pereira HC (1953) Field moisture balance in the Shimba Hills, Kenya. East African Agricultural Journal 18: 139--148. Hutley LB, Doley D, Yates DJ, and Boonsaner A (1997) Water balance of an Australian subtropical rainforest at altitude: The ecological and physiological significance of intercepted cloud and fog. Australian Journal of Botany 45: 311--329. Keim R, Skaugset A, Link T, and Iroum A (2004) A stochastic model of throughfall for extreme events. Hydrology and Earth System Sciences 8(1): 23--34. Keim RF, Meerveld HJT van, and McDonnell JJ (2006a) A virtual experiment on the effects of evaporation and intensity smoothing by canopy interception on subsurface stormflow generation. Journal of Hydrology 327: 352--364. Keim RF, Skaugset AE, and Weiler M (2005) Temporal persistence of spatial patterns in throughfall. Journal of Hydrology 314: 263--274. Keim RF, Skaugset AE, and Weiler M (2006b) Storage of water on vergetation under simulated rainfall of varying intensity. Advances in Water Resources 29: 974--986. Kittredge J (ed.) (1948) Forest Influences. New York: McGraw-Hill. Klaassen W, Bosveld F, and de Water E (1998) Water storage and evaporation as constituents of rainfall interception. Journal of Hydrology 212–213: 36--50. Klaassen W, Lankreijer HJM, and Veen AWL (1996) Rainfall interception near a forest edge. Journal of Hydrology 185(1–4): 349--361. Li XY, Gong JD, Gao QZ, and Wei XH (2000) Rainfall interception loss by pebble mulch in the semi arid region of China. Journal of Hydrology 228: 165--173. Llorens P and Gallart F (2000) A simplified method for forest water storage capacity measurement. Journal of Hydrology 240: 131--144. Lloyd CR and Marques ADO (1988) Spatial variability of throughfall and stemflow measurements in amazonian rainforest. Agricultural and Forest Meteorology 42(1): 63--73. Lundberg A, Eriksson M, Halldin S, Kellner E, and Seibert J (1997) New approach to the measurement of interception evaporation. Journal of Atmospheric and Oceanic Technology 14: 1023--1035. Manfroi OJ, Kuraji K, Suzuki M, et al. (2006) Comparison of conventionally observed interception evaporation in a 100-m2 subplot with that estimated in a 4-ha area of the same Bornean Lowland tropical forest. Journal of Hydrology 329(1–2): 329--349. Miller HD (1966) Transport of intercepted snow from trees during snowstorms US Forest Service–Research Paper, vol. 33, pp. 1--30. Berkeley, CA: US department of Agriculture, Forest Service, Pacific Southwest Forest & Range Experiment Station. Miller JD, Anderson HA, Ferrier RC, and Walker TAB (1990) Comparison of the hydrological budgets and detailed hydrological responses in two forested catchments. Forestry 63(3): 251--269. Muzylo A, Llorens P, Valente F, Keizer J, Domingo F, and Gash J (2009) A review of rainfall interception modelling. Journal of Hydrology 370(1–4): 191--206. Nakayoshi M, Moriwaki R, Kawai T, and Kanda M (2009) Experimental study on rainfall interception over an outdoor urban-scale model. Water Resources Research 45: W04415. Navar J, Charles F, and Jurado E (1999) Spatial variations of interception loss components by Tamaulipan thornscrub in Northeastern Mexico. Forest Ecology and Management 24: 231--239. Oke TR (1982) The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society 108(455): 1--24. Pathak PC, Pandey AN, and Singh JS (1985) Apportionment of rainfall in central Himalayan forests (India). Journal of Hydrology 76: 319--332. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London 193: 120--146. Pitman JI (1989) Rainfall interception by bracken litter–relationship between biomass, storage and drainage rate. Journal of Hydrology 111: 281--291. Putuhena W and Cordery I (1996) Estimation of interception capacity of the forest floor. Journal of Hydrology 180: 283--299.

Interception Pypker TG, Unsworth MH, and Bond BJ (2006) The role of epiphytes in rainfall interception by forests in the Pacific Northwest. I. Laboratory measurements of water storage. Canadian Journal of Forest Research 36: 808--818. Ragab R, Bromley J, Rosier P, Cooper JD, and Gash JHC (2003) Experimental study of water fluxes in a residential area: 1. Rainfall, roof runoff and evaporation: The effect of slope and aspect. Hydrological Processes 17(12): 2409--2422. Roberts MC and Klingeman PC (1970) The influence of landform and precipitation parameters on flood hydrograph. Journal of Hydrology 11: 393--411. Rowe L (1983) Rainfall interception by an evergreen beech forest, Nelson, New Zealand. Journal of Hydrology 66(1–4): 143--158. Rutter AJ, Kershaw KA, Robins PC, and Morton AJ (1971) A predictive model of rainfall interception in forests. I. Derivation of the model and comparison with observations in a plantation of Corsican pine. Agricultural Meteorology 9: 367--384. Rutter AJ, Morton AJ, and Robins PC (1975) A predictive model of rainfall interception in forests. II. Generalization of the model and comparison with observations in some coniferous and hardwood stands. Journal of Applied Ecology 12: 367--380. Sato Y, Kumagai T, Kume A, Otsuki K, and Ogawa S (2004) Experimental analysis of moisture dynamics of litter layers – the effect of rainfall conditions and leaf shapes. Hydrological Processes 18: 3007--3018. Savenije HHG (1997) Determination of evaporation from a catchment water balance at a monthly time scale. Hydrology and Earth System Sciences 1: 93--100. Savenije HHG (2004) The importance of interception and why we should delete the term evapotranspiration from our vocabulary. Hydrological Processes 18: 1507--1511. Savenije HHG (2005) Interception. In: Lehr JH and Keeley J (eds.) Water Encyclopedia: Surface and Agricultural Water. Hoboken, NJ: Wiley Publishers. Schaap MG and Bouten W (1997) Forest floor evaporation in a dense Douglas fir stand. Journal of Hydrology 193: 97--113. Shuttleworth WJ, Gash JHC, Lloyd CR, Moore CJ, Roberts JM, et al. (1984) Eddy correlation measurements of energy partition for Amazonian forest. Quarterly Journal of the Royal Meteorological Society 110: 1143--1162. Stewart JB (1977) Evaporation from the wet canopy of a pine forest. Water Resources Research 13(6): 915--921.

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Storck P, Lettenmaier DP, and Bolton SM (2002) Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States. Water Resources Research 38: 1223. Thamm F and Widmoser P (1995) Zur hydrologischen Bedeutung der organischen Auflage im Wald: Untersuchungsmethoden und erste Ergebnisse. Zeitschrift fur Pflanzenernahrung und Bodenkunde 158: 287--292. Thurow TL, Blackburn WH, Warren SD, and Taylor CA Jr. (1987) Rainfall interception by midgrass, shortgrass, and live oak mottes. Journal of Range Management 40(5): 455--460. Toba T and Ohta T (2005) An observational study of the factors that influence interception loss in boreal and temperate forests. Journal of Hydrology 313: 208--220. Tobo´n-Marin C, Bouten IW, and Dekker S (2000) Gross rainfall and its partitioning into throughfall, stemflow and evaportation of intercepted water in four forest ecosystems in Western Amazonia. Journal of Hydrology 237: 40--57. Valente F, David JS, and Gash JHC (1997) Modelling interception loss for two sparse eucalypt and pine forest in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology 190: 141--162. Viville D, Biron P, Granier A, Dambrine E, and Probst A (1993) Interception in a mountainous declining spruce stand in the Strengbach catchment (Vosges, France). Journal of Hydrology 144: 273--282. Walsh RPD and Voigt PJ (1977) Vegetation litter: An underestimated variable in hydrology and geomorphology. Journal of Biogeography 4: 253--274. Wang A, Diao Y, Pei T, Jin C, and Zhu J (2007) A semi-theoretical model of canopy rainfall interception for a broad-leaved tree. Hydrological Processes 21(18): 2458--2463. Ward AD and Trimble SW (2004) Environmental hydrology, 2nd edn. Boca Raton, FL: CRC Press. Zhang GP and Savenije HHG (2005) Rainfall-runoff modelling in a catchment with a complex groundwater flow system: Application of the Representative Elementary Watershed (REW) approach. Hydrology and Earth System Sciences 9: 243--261. Ziegler AD, Giambelluca TW, Nullet MA, et al. (2009) Throughfall in an evergreendominated forest stand in Northern Thailand: Comparison of mobile and stationary methods. Agricultural and Forest Meteorology 149(2): 373--384. Zinke PJ (1967) Forest interception studies in the United States. In: Sopper WE and Lull HW (eds.) Forest Hydrology, pp. 137--161. Oxford: Pergamon.

2.05 Infiltration and Unsaturated Zone JW Hopmans, University of California, Davis, CA, USA & 2011 Elsevier B.V. All rights reserved.

2.05.1 Introduction 2.05.2 Soil Properties and Unsaturated Water Flow 2.05.2.1 Soil Water Retention 2.05.2.2 Unsaturated Hydraulic Conductivity 2.05.2.3 Modeling of Unsaturated Water Flow and Transport 2.05.2.4 Infiltration Processes 2.05.3 Infiltration Equations 2.05.3.1 Philip Infiltration Equation 2.05.3.2 Parlange et al. Model 2.05.3.3 Swartzendruber Model 2.05.3.4 Empirical Infiltration Equations 2.05.4 Measurements 2.05.4.1 Infiltration 2.05.4.2 Unsaturated Water Flow 2.05.5 Scaling and Spatial Variability Considerations 2.05.6 Summary and Conclusions Acknowledgments References

2.05.1 Introduction As soils make up the upper part of the unsaturated zone, they are subjected to fluctuations in water and chemical content by infiltration and leaching, water uptake by plant roots, and evaporation from the soil surface. It is the most dynamic region of the subsurface, as changes occur at increasingly smaller time and spatial scales when moving from the groundwater toward the soil surface. Environmental scientists are becoming increasingly aware that soils make up a critically important component of the earth’s biosphere, because of their food production and ecological functions, and the soil’s important role in controlling water quality. For example, prevention or remediation of soil and groundwater contamination starts with proper management of the unsaturated zone. Water entry into the soil by infiltration is among the most important soil hydrological processes, as it controls the partitioning between runoff and soil water storage. Runoff water determines surface water quantity and quality, whereas infiltrated water determines plant available water, evapotranspiration, groundwater recharge, and groundwater quality. Also through exfiltration, infiltrated water affects water quality in waterways and associated riparian zones. Despite its relevance and our reliable physical understanding of infiltration, we have generally many difficulties predicting infiltration at any scale. Mostly, this is so because the infiltration rate is a time-varying parameter of which its magnitude is largely controlled by spatially variable soil properties, in both vertical and horizontal directions of a hydrologic basin. Moreover, infiltration rate and runoff are affected by vegetation cover, as it protects the soil surface from the energy impacts of falling raindrops or intercepting rainfall, serving as temporary water storage. The kinetic energy of rainfall causes soil degradation, leading to soil surface sealing and decreasing infiltration.

103 103 104 105 105 106 108 108 109 109 110 110 110 111 111 112 113 113

Historically, solutions to infiltration problems have been presented by way of analytical solutions or empirically. Analytical solutions provide values of infiltration rate or cumulative infiltration as a function of time, making simplifying assumptions of soil depth variations of water content, before and during infiltration. Instead, we now often use powerful computers to conduct numerical simulations of unsaturated water flow to solve for water content and water fluxes throughout the unsaturated soil domain in a single vertical direction or in multiple spatial dimensions, allowing complex initial and boundary conditions. However, although the modeling of multidimensional unsaturated water flow is extremely useful for many vadose zone applications, it does not necessarily improve the soil surface infiltration rate prediction, in light of the large uncertainty of the soil physical properties and initial and boundary conditions that control infiltration. In contrast, empirical infiltration models serve primarily to fit model parameters to measured infiltration, but have limited power as a predictive tool.

2.05.2 Soil Properties and Unsaturated Water Flow The soil consists of a complex arrangement of mostly connected solid, liquid, and gaseous phases, with the spatial distribution and geometrical arrangement of each phase, and the partitioning of solutes between phases, controlled by physical, chemical, and biological processes. The unsaturated zone is bounded by the soil surface and merges with the groundwater in the capillary fringe. Water in the unsaturated soil matrix is held by capillary and adsorptive forces. Water is a primary factor leading to soil formation from the weathering of parent material such as rock or transported deposits, with additional factors of climate, vegetation, topography, and parent material determining soil physical properties.

103

104

Infiltration and Unsaturated Zone

Defining the soil’s dry bulk density by rb (M L3), soil porosity, e (L3 L3), is defined by

e¼1

rb rs

ð1Þ

with rs being the soil’s particle density (M L3). Equation (1) shows that soil porosity has lower values as bulk soil density is increased such as by compaction. Unsaturated water flow is largely controlled by the physical arrangement of soil particles in relation to the water and air phases within the soil’s pore space, as determined by pore-size distribution and water-filled porosity or volumetric water content, y (L3 water/L3 bulk soil). The volumetric water content y expresses the volume of water present per unit bulk soil as

y¼

wrb rw

ð2Þ

where w is defined as the mass water content (M of water/M dry soil) and we take rw ¼ 1000 kg m3. Alternatively, the soil water content can be described by the degree of saturation S (–) and the equivalent depth of stored water De (L), or

S¼

y e

and

De ¼ yDsoil

ð3Þ

so that y can also be defined by the equivalent depth of water per unit depth of bulk soil, Dsoil (L). The volumetric water content ranges between 0.0 (dry soil) and the saturated water content, ys, which is equal to the porosity if the soil were completely saturated. The degree of saturation varies between 0.0 (completely dry) and 1.0 (all pores completely waterfilled). When considering water flow, the porosity term is replaced by the saturated water content, ys, and both terms in Equation (3) are corrected by subtracting the so-called residual water content, yr (soil water content for which water is considered immobile), so that the effective saturation, Se, is defined as

Se ¼

y  yr ys  yr

weight of water, leading to soil water potential expressed by the equivalent height of a column of water (L). The resulting pressure head equivalent of the combined adsorptive and capillary forces in soils is defined as the matric pressure head, h. When expressed relative to the reference potential of free water, the water potential in unsaturated soils is negative (the soil water potential is less than the water potential of water at atmospheric pressure). Hence, the matric potential decreases or is more negative as the soil water content decreases. In using head units for water potential, the total water potential (H) is defined as the sum of matric potential (h), gravitational potential (z), hydrostatic pressure potential (p), and osmotic potential (p). For most hydrological applications, the contribution of the osmotic potential can be ignored, so that for unsaturated water flow (p ¼ 0) the total soil water potential can be written as

H ¼hþz

ð5Þ

The measurement of the soil water matric potential in situ is difficult and is usually done by tensiometers in the range of matric head values larger (less negative) than  6.0 m. A tensiometer consists of a porous cup, usually ceramic, connected to a water-filled tube (Young and Sisson, 2002). The suction forces of the unsaturated soil draw water from the tensiometer into the soil until the water pressure inside the cup (at pressure smaller than atmospheric pressure) is equal to the pressure equivalent of the soil water matric potential just outside the cup. The water pressure in the tensiometer is usually measured by a vacuum gauge or pressure transducer. Other devices that are used to indirectly measure the soil water matric potential include buried porous units (Scanlon et al., 2002), for which either the electrical resistance or the thermal conductivity is measured in situ, after coming into hydraulic equilibrium with the surrounding soil (h in sensor and soil are equal). Although widely used, these types of sensors require laboratory calibration, before field installation.

2.05.2.1 Soil Water Retention ð4Þ

In addition to the traditional thermogravimetric method to determine soil water content, many other measurement techniques are available, including neutron thermalization, electrical conductivity, dielectric, and heat pulse methods. A recent review on soil moisture measurement methods was presented by Robinson et al. (2008), focusing on measurement constraints between the many available methods across spatial scales. In soils, the driving force for water to flow is the gradient in total water potential. The total potential of bulk soil water can be written as the sum of all possible component potentials, so that the total water potential (ct) is equal to the sum of osmotic, matric, gravitational, and hydrostatic pressure potential. Whereas in physical chemistry the chemical potential of water is usually defined on a molar or mass basis, soil water potential is usually expressed with respect to a unit volume of water, thereby attaining units of pressure (Pa); or per unit

The soil water retention function determines the relation between the volume of water retained by the soil, expressed by y, and the governing soil matric, or suction forces (Dane and Hopmans, 2002). These suction forces are typically expressed by the soil water matric head (strictly negative) or soil suction (strictly positive). These suction forces increase as the size of the water-filled pores decreases, as may occur by drainage, water uptake by plant roots, or soil evaporation. Also known as the soil water release or soil water characteristic function, this soil hydraulic property describes the increase of y and the size of the water-filled pores with an increase in matric potential, as occurs by infiltration. Since the matric forces are controlled by pore-size distribution, specific surface area, and type of physico-chemical interactions at the solid–liquid interfaces, the soil water retention curve is very soil specific and highly nonlinear. It provides an estimate of the soil’s capacity to hold water after irrigation and free drainage (field capacity), minimum soil water content available to the plant (wilting point), and root zone water availability for plants.

Infiltration and Unsaturated Zone

The soil water retention curve exhibits hysteresis, that is, the y value is different for wetting (infiltration) and drying (drainage). By way of the unique relationship between soil water matric head and the radius of curvature of the air–water interface in the soil pores, and using the analogy between capillary tubes and the irregular pores in porous media, a relationship can be derived between soil water matric head (h) and effective pore radius, re, or

rgh ¼

2s cos a re

ð6Þ

where s and a are defined as the surface tension and wetting angle of wetting fluid with soil particle surface (typically values for s and a are 0.072 N m1 and 01, respectively ), r is the density of water, and g is the acceleration due to gravity (9.8 m s2). Because of capillary equation, the effective pore-size distribution can be determined from the soil water retention curve in the region where matric forces dominate. Laboratory and field techniques to measure the soil water retention curve, and functional models to fit the measured soil water retention data, such as the van Genuchten (1980) and Brooks and Corey (1964) models, are described by Kosugi et al. (2002). Alternatively, knowledge of the particle size distribution may provide information on the shape of the soil water retention curve, as presented by Nasta et al. (2009). An example of measured and fitted soil water retention data for two different soils is presented in Figure 1 (Tuli and Hopmans, 2004).

2.05.2.2 Unsaturated Hydraulic Conductivity The relation between the soil’s unsaturated hydraulic conductivity, K, and volumetric water content, y, is the second essential fundamental soil hydraulic property needed to

Soil matric potential head (cm)

describe unsaturated soil water flow. K is a function of the water and soil matrix properties, and controls water infiltration and drainage rates, and is strongly affected by water content and possibly by hysteresis. It is defined by the Darcy– Buckingham equation, which relates the soil water flux density to the total driving force for flow, with K being the proportionality factor. Except for special circumstances, the total driving force for water flow in soils is determined by the sum of the matric and gravitational forces, expressed by the total water potential head gradient, DH/L (L L1), where DH denotes the change in total water potential head over the distance L. For vertical flow, the application of Darcy’s law yields the magnitude of water flux from

 q ¼ KðyÞ

Measured Oso Flaco sand Optimized Oso Flaco sand Measured Columbia sandy loam Optimized Columbia sandy loam

 dh þ1 dz

ð7Þ

where q is the Darcy water flux density (L3 water L2 soil surface T1) and z defines the vertical position (z40, upwards, L). A soil system is usually defined by the bulk soil, without consideration of the size and geometry of the individual flow channels or pores. Therefore, the hydraulic conductivity (K) describes the ability of the bulk soil to transmit water, and is expressed by volume of water flowing per unit area of bulk soil per unit time (L T1). Functional models for unsaturated hydraulic conductivity are based on pore-size distribution, pore geometry, and connectivity, and require integration of soil water retention functions to obtain analytical expressions for the unsaturated hydraulic conductivity. The resulting expressions relate the relative hydraulic conductivity, Kr, defined as the ratio of the unsaturated hydraulic conductivity, K, and the saturated hydraulic conductivity, Ks, to the effective saturation, Se, and can be written in the following generalized form (Kosugi et al., 2002):

2Z

10 000

105

Se

3g

jhj Z dSe 7 6 7 6 Kr ðSe Þ ¼ Sle 6 Z0 S 7 5 4 Z jhj dSe

ð8Þ

0

1000

100

10

1 0

0.2

0.4

0.6

0.8

Sew Figure 1 Measured (symbols) and fitted (lines) soil water retention data. From Tuli AM and JW Hopmans (2004) Effect of degree of saturation on transport coefficients in disturbed soils. European Journal of Soil Science 55: 147–164.

where l and Z are parameters related to the tortuosity and connectivity of the soil pores, and the value of the parameter g is determined by the method of evaluating the effective pore radii. For values of l ¼ 0.5, Z ¼ 1.0, and g ¼ 2.0, Equation (8) reduces to the so-called Mualem (1976) model, that is routinely combined with the van Genuchten (1980) soil water retention model to yield a closed-form expression for the unsaturated hydraulic conductivity function. The moisture dependency is highly nonlinear, with a change in K of five or more orders of magnitude across field-representative changes in unsaturated soil water content. Methods to measure the saturation dependency of the hydraulic conductivity are involved and time consuming. A variety of methods are described in Dane and Topp (2002) and Dirksen (2001). Measurement errors are generally large due to (1) the difficulty of flow measurements in the low water content range and (2) the dominant effect of large pores (macropores), cracks, and fissures in the high water content range. An example of the unsaturated hydraulic conductivity for water, relative to its

Infiltration and Unsaturated Zone 1.0

1.0

0.8

0.8

0.6

0.6 K ra

K rw

106

0.4

0.4

0.2

0.2

0 (a)

0 0

0.2

0.4

0.6

0.8

1.0

(b)

0

0.2

0.4

0.6

0.8

1.0

Volumetric water content Oso Flaco fine sand

Columbia sandy loam

Measured Krw

Measured Krw

Measured Kra

Measured Kra

Measured Drg

Measured Drg

Measured ECra

Measured ECra

Figure 2 (a) Measured relative hydraulic conductivity for water (Krw) and (b) air conductivity (Kra) as a function of degree of water (Sew) and air (Sea) saturation. From Tuli AM and JW Hopmans (2004) Effect of degree of saturation on transport coefficients in disturbed soils. European Journal of Soil Science 55: 147–164.

saturated values (Krw), is presented in Figure 2(a), for the same two soils as in Figure 1. We note that ys in the vadose zone is typically about 85% of the porosity, so that a saturated soil (e.g., as the result of ponded infiltration) is really a satiated soil due to entrapped air, with a saturated hydraulic conductivity that is significantly smaller than the true Ks. The unsaturated hydraulic conductivity is related to the intrinsic soil permeability, k (L2), by

K¼

rgk m

ð9Þ

where m denotes the dynamic viscosity of water (F T L2). The usage of permeability instead of conductivity allows application of the flow equation to liquids other than water with different density and viscosity values. In addition to unsaturated hydraulic conductivity, Figure 2 also includes data for the saturation dependency of the relative air conductivity (Kra), as might be important for water infiltration in soil, when the soil gas phase is trapped and increasing in pressure, so that water infiltration is partly controlled by soil air permeability (Latifi et al., 1994).

2.05.2.3 Modeling of Unsaturated Water Flow and Transport Numerous studies have been published addressing different issues in the numerical modeling of unsaturated water flow using the Richards’ equation. In short, the dynamic water flow equation is a combination of the Darcy expression and a mass balance formulation. Using various solution algorithms, the soil region of interest is discretized in finite-size elements, i, that can be one, two, or three dimensional, to solve for temporal changes in h, y, or water flux, q, for each element or voxel i at any time t.

Most multidimensional soil water flow models use a finiteelement, Picard time-iterative numerical scheme (Sˇimunek et al., 2008) to solve the Richards equation. For isotropic conditions and one-dimensional vertical flow, the general water flow equation simplifies to

   qy q qh ¼ KðhÞ þ 1  Sðz; tÞ qt qz qz

ð10Þ

where S (L3 L3 T1) is the sink term, accounting for root water uptake. Boundary and initial conditions must be included to allow for specified soil water potentials or fluxes at all boundaries of the soil domain. Richards’ equation is a highly nonlinear partial differential equation, and is therefore extremely difficult to solve numerically because of the largely nonlinear dependencies of both water content and unsaturated hydraulic conductivity on the soil water matric head. Both the soil water retention and unsaturated hydraulic conductivity relationships must be known a priori to solve the unsaturated water flow equation. Specifically, it will need the slope of the soil water retention curve, or water capacity C(h), defined as CðhÞ ¼ dy=dh. As dissolved solutes move through the soils with the water, various physical, chemical, and biological soil properties control their fate. In addition to diffusion and dispersion, fate and transport of chemicals in the subsurface are influenced by sorption to the solid phase and biological transformations. Both diffusion and dispersion of the transported chemical are a function of pore-size distribution and water content. Mechanical or hydrodynamic dispersion is the result of water mixing within and between pores as a result of variations in pore water velocity. Increasing dispersivity values cause greater spreading of the chemical, thereby decreasing peak

Infiltration and Unsaturated Zone

2.05.2.4 Infiltration Processes For one-dimensional infiltration, the infiltration rate (L T1), i(t), can be defined by Equation (7) at the soil surface (subscript surf), or

 iðtÞ ¼ KðyÞ

 qh þ1 qz surf

ð11aÞ

Cumulative infiltration I(t), expressed as volume of water per unit soil surface area (L), is defined by

IðtÞ ¼

Z

t

iðtÞdt

ð11bÞ

0

Analytical solutions of infiltration generally assume that the wetted soil profile is homogeneous in texture with uniform initial water content. They also make distinction between ponded (h4 0 or p) and nonponded soil surface (unsaturated, ho0) infiltration. The infiltration capacity of the soil is defined by ic(t), the maximum rate at which a soil can absorb water for ponded soil surface conditions. Its maximal value is at time zero, and decreases with time to its minimum value approaching the soil’s saturated hydraulic conductivity, Ks, as the total water potential gradient decreases, and tends to unity, with the downward moving wetting front. As defined by Equation (11b), the soil’s cumulative infiltration capacity, Ic(t), is defined by the area under the capacity curve. It represents the maximum amount of water that the soil can absorb at any time. Typically, at the onset of infiltration (t ¼ 0), the rainfall rate, r(t), will be lower than ic(t), so that the infiltration rate is equal to the rainfall rate (i.e., r(t)oic(t) for hsurfo0). If at any point in time, the rainfall rate becomes larger than the infiltration capacity, ponding will occur (hsurf40), resulting in runoff. The time at which ponding occurs is defined as tp (time to ponding). Thus, the actual infiltration rate will depend on the rainfall rate and its temporal changes. This makes prediction of infiltration and runoff much more difficult for realistic time-variable rainfall patterns. Therefore, infiltration rate prediction is often described as a function of the cumulative infiltration, I, or i(I), independent of the time domain, and with i(I) curves that are independent of rainfall rate (Skaggs, 1982). An example of such a

time-invariant approach is the IDA or infiltrability-depth approximation (Smith et al., 2002). The main IDA assumption is that time periods between small rainfall events are sufficiently small so that soil water redistribution and evaporation between events do not affect infiltration rate. IDA implies that the infiltration rate at any given time depends only on the cumulative infiltration volume, regardless of the previous rainfall history. Following this approach, tp is defined as the time during a storm event when I becomes equal to Ic(tp), or

R¼

Z

tp

rðtÞdt ¼ Ic ðtp Þ

t¼0

whereas i(t) ¼ r(t) for totp. The time invariance of i(I) holds true also when a layered/sealed soil profile is considered (Mualem and Assouline, 1989). For illustration purposes, we present a hypothetical storm event with time-varying r(t) in Figure 3 (from Hopmans et al., 2007) in combination with an assumed soil-specific infiltration capacity curve, ic(t). At what time will ponding occur? It will not be at t ¼ 7, when r(t) exceeds ic for the first time. In order to approximate tp, we plot both Ic and R for the storm in Figure 4(a), as a function of time and determine tp as the time at which both curves intersect (tp ¼ 13, for R ¼ Ic ¼ 110), since at that time, the cumulative infiltration of the storm is identical to the soil’s infiltration capacity. The final corresponding i(I) for this soil and storm event is presented in Figure 4(b), showing that the soil infiltration rate is equal to r(t) until I ¼ R(t) ¼ Ic(tp) ¼ 110, after which the infiltration rate is soilcontrolled and determined by Ic(t). More accurate approximations to the time-invariant approach can be found in Sivapalan and Milly (1989) and Brutsaert (2005), using the time compression or time condensation approximation that more accurately estimates infiltration prior to surface ponding. In addition to whether the soil is ponded or not, solutions of infiltration distinguish between cases with and without gravity effects, as different analytical solutions apply. As Equation (11a) shows, infiltration rate i(t) is determined by both the soil water matric potential gradient, dh=dz, and gravity. However, at the early stage of infiltration into a relatively dry soil, infiltration rate is dominated by the matric potential gradient so that the gravity effects on infiltration can 15 Infiltration rate, i

concentration. Sorbed chemicals move through the vadose zone slower than noninteracting chemicals, and the degree of sorption will largely depend on mineral type, specific surface area of the solid phase, and organic matter fraction. In addition, biogeochemical processes and radioactive decay affect contaminant concentration, such as by cation exchange, mineral precipitation and dissolution, complexation, oxidation–reduction reactions, and by microbial biodegradation and transformations. However, all these mechanisms depend on soil environmental conditions, such as temperature, pH, water saturation, and redox status, and their soil spatial variations. The solute transport equation is generally referred to as the convection–dispersion equation (CDE), and includes the relevant transport mechanisms to simulate and predict temporal changes in soil solute concentration within the simulation domain (Sˇimunek et al., 2008).

107

r (t ) 10

5 ic 1 0

5

10

15

20

Time, t Figure 3 Hypothetical rainfall event, r(t), and soil infiltration capacity, ic(t). The rainfall event starts at t ¼ 0. From Hopmans JW, Assouline S, and Parlange J-Y (2007) Soil infiltration. In: Delleur JW (ed.) The Handbook of Groundwater Engineering, pp. 7.1–7.18. Boca Raton, FL: CRC Press.

108

Infiltration and Unsaturated Zone

Cumulative infiltration

160

Rainfall R (t ) Ic (t )

120 80 40

tp

0 0

5

10

20

Time

(a)

Infiltration rate, i

15

16 14 12 10 8 6 4 2 0

t p = 13

 I ¼ Ic ¼ K1 t þ ðhsurf  hf ÞDy ln 1 þ

ic (I )

0

50

100

150

Cumulative infiltration, I

(b)

Figure 4 (a) Cumulative infiltration corresponding with infiltration capacity, Ic(t), and cumulative rainfall, R(t) and (b) actual infiltration rate vs. I. Ponding starts only after tp ¼ 13, or I ¼ 110. From Hopmans et al. (2007).

be ignored. Gravity becomes important in the later stages of infiltration, when the wetting front has moved further down. For gravity-free drainage, a simple analytical solution can be found, after transforming Equation (11a) into a y-based form by defining the diffusivity DðyÞ ¼ KðyÞdh=dy, so that

iðtÞ ¼ DðyÞ

  qy q z surf

ð12Þ

Using the Boltzmann transformation for a constant head boundary condition (Bruce and Klute, 1956), and defining the scaling variable j ¼ z=t 1=2, combination of Equations (10) without gravity and sink term and (12) resulted in a unique solution of y as a function of j, from which the wetting profile can be computed for any time t (Kirkham and Powers, 1972). Defining y1 and y0 as the surface water content during infiltration and the initial uniform profile water content, respectively, cumulative infiltration, I, is computed from

I¼

Z

y1

z dy ¼ t 1=2

y0

Z

y1

j dy

ð13aÞ

y0

and results in the simple infiltration equation I ¼ St1/2, where the sorptivity S (L T1/2) is defined as

Sðy1 Þ ¼

Z

y1

j dy

horizontal infiltration, I is a linear function of t1/2, with S being defined as the slope of this line. Hence, for saturated soil conditions where y1 ¼ ys, the infiltration capacity is computed from ic(t) ¼ 12St1/2. Incidentally, this also leads to Ic ¼ S2/2ic. A relatively simple analytical solution without and with gravity effects was suggested by Green and Ampt (1911) for a ponded soil surface, with ysurf ¼ y1. The assumptions are that the wetting front can be approximated as a step function with a constant effective water potential, hf, at the wetting front, a wetting zone hydraulic conductivity of K(y1) ¼ K1 ¼ Ks, and a constant soil water profile of Dy ¼ y1  y0 . Using this so-called delta-function assumption of a D(y) with a Dirac-delta function form, both solutions for horizontal and vertical infiltration can be relatively easily obtained (Jury et al., 1991; Haverkamp et al., 2007). Assuming that K0 at the initial water content, y0, is negligible, the Green and Ampt (GA) solution of vertical infiltration for ponded conditions is (h ¼ hsurf40):

ð13bÞ

y0

Equation (13a) states that for gravity-free infiltration during the early times of vertical infiltration, and at all times for

ðhsurf

 I  hf ÞDy

ð14Þ

which can be solved iteratively for I. This simple, yet physically based, solution appears to work best for dry coarse-textured soils. A theoretical expression for the wetting front potential head,R hf, was defined by Mein and Farrell (1974), to yield that hf ¼ h00 Kr ðhÞdh; where the relative conductivity Kr ¼ K(h)/Ks. The so-called S-form of the GA equation can be obtained by comparing the gravity-free solution of GA with the Boltzmann solution, to yield S20 ¼ 2K1 hf Dy:

S21 ðy1 Þ ¼ 2K1 Dyðhsurf  hf Þ ¼ S20 þ 2K1 hsurf Dy

ð15aÞ

so that

  S20 I I ¼ K1 t þ hsurf Dy þ ln 1 þ hsurf Dy þ S20 =2K1 2K1

ð15bÞ

In reality, the wetting front is not a step function, but will consist of a time-dependent transition zone where water content changes from y1 to y0. The shape of this transition zone will be a function of time and is controlled by soil type. The step function assumption is better for uniform coarsetextured soils that have a Dirac-like D(y), for which there is a sharp decline in K with a decrease in water content near saturation. The wetting front is generally much more diffuse for finer-textured soils that have a wide pore-size distribution. By now, it must be clear that infiltration and its temporal changes are a function of many different soil factors. In addition to rainfall intensity and duration and the soil physical factors, such as soil water retention and hydraulic conductivity, infiltration is controlled by the initial water content, surface sealing and crusting, soil layering, and the ionic composition of the infiltrated water (Kutilek and Nielsen, 1994; Assouline, 2004). For example, Vandervaere et al. (1998) applied the GA model to sealed soil profiles, by assuming that the wetting front potential decreases suddenly as it leaves the seal and enters the soil. This results in a discontinuous drop in the infiltration rate. Many relatively simple infiltration equations have been proposed and are successfully used to

Infiltration and Unsaturated Zone

characterize infiltration. This has been achieved despite that these equations apply for homogeneous soils only, in theory.

2.05.3 Infiltration Equations In addition to the solutions in Section 2.05.2, other physically based analytical solutions have been presented, using different assumptions allowing for a closed-form solution. These can potentially be used to predict infiltration from known soil hydraulic properties of homogeneous soils. However, in practice, this is difficult as soil physical characteristics near the soil surface are time dependent because of soil structural changes and their high spatial variability. Alternatively, various empirical infiltration models have been proposed that are very useful for describing measured infiltration data. A parameter sensitivity analysis of many of the presented infiltration models, analyzing the effects of measurement error, was given by Clausnitzer et al. (1998). This section presents the most frequently used infiltration models in both categories.

2.05.3.1 Philip Infiltration Equation Philip (1957a) presented an analytical infinite-series solution to the water-content-based form of Richards’ equation for the case of vertical infiltration:

  qy q qy ¼ DðyÞ þ KðyÞ qt qz qz

ð16Þ

For the boundary condition of hsurf ¼ 0 and y1 ¼ ys , the Philip (1957a) solution converged to the true solution for small and intermediate times, but failed for large times. In this case, an alternative solution was presented (Philip, 1957b). With additional assumptions regarding the physical nature of soil water properties, Philip (1987) proposed joining solutions that are applicable for all times. Philip (1957c) introduced a truncation of the small-time series solution that is a simple two-parameter model equation (PH model):

Ic ¼ At þ St 1=2

ð17aÞ

which should be accurate for all but very large t, and suitable for applied hydrological studies. The sorptivity S depends on several soil physical properties, including initial water content y0, and the hydraulic conductivity and soil water retention functions. S is equal to the expression defined in Equation (13b). Philip (1969) showed that A may take values between 0.38Ks and 0.66Ks. The physical interpretation of A is not straightforward; however, for long times when gravity is dominant and hsurf ¼ 0, one would expect A to be equal to Ks. Differentiation of Equation (17a) yields the infiltration rate, or

ic ¼ 1=2St 0:5 þ A

ð17bÞ

Using (17b) to express t as a function of ic and substituting in Equation (17a) yields I(i), or

I¼

S 2 ði  A=2Þ 2ði  AÞ 2

ð17cÞ

109

For positive pressure heads (hsurf), the correction of Equation (15a) to S can be applied. In many cases, values of S and A are obtained from curve fitting. We note that for gravity-free flow, the pH solution without the gravity term corresponds with the Boltzmann solution for horizontal flow in Equation (13).

2.05.3.2 Parlange et al. Model Parlange et al. (1982) proposed the following universal model (Parlange et al., model, PA model):

  3 2dK1 I exp þ d  1 7 62K1 S2 S2 7 6 t¼ 2 I  ln 5 4 2 d 2K1 ð1  dÞ S 2

ð18aÞ

assuming that K0 is small so that the DK in Parlange et al. (1982) is equal to K1. The value of the parameter d can be chosen to approach various closed-form solutions. For example, Equation (18a) reduces to the GA solution for d equal to zero. Its value is a function of K(y), and is defined by (Parlange et al., 1985):

d¼

1 ys  y0

Z

ys y0

Ks  KðyÞ dy Ks

ð18bÞ

An approximate value of d ¼ 0.85 was suggested by Parlange et al. (1982) for a range of soil types. After taking the time derivative of I, the following i(I)-relationship can be derived (Espinoza, 1999):

  1 2IdK1 i ¼ K1 þ dK1 1  exp S2

ð18cÞ

Because Equation (18) is based on integration of the watercontent-based form of Richards’ equation, its theoretical scope is limited to nonponded conditions. A generalization of Equation (18) to include ponded conditions without affecting the value of S was introduced by Parlange et al. (1985). Haverkamp et al. (1990) presented a modification of their model to include upward water flow by capillary rise. The resulting infiltration model contained six physical parameters, in addition to the interpolation parameter d (Haverkamp et al., 1990). Both the PA and the Haverkamp et al. (1990) model require an iterative procedure to predict I(t). Barry et al. (1995) presented an explicit approximation to the Haverkamp et al. (1990) model, retaining all six physical parameters (BA model):

S 2 þ 2K1 hsurf Dy I ¼ K1 t þ 2DK    6ð2t  Þ 0:5 2t     t þ 1  g  exp 6 þ ð2t  Þ 0:5 3     g 2t 8 2:5 exp  ½1  ð1  gÞ t  þ 3 1 þ t   t þð2g þ t  Þln 1 þ g

ð19aÞ

110

Infiltration and Unsaturated Zone

where

t ¼

Another simple empirical infiltration equation is the Kostiakov (1932) model (KO):

2tðDKÞ 2 ; S 2 þ 2K1 hsurf Dy

g¼

2K1 ðhsurf þ ha ÞDy S2 þ 2K1 hsurf Dy

ð19bÞ

and ha denotes the absolute value of the soil water pressure head at which the air phase becomes discontinuous upon wetting. By defining

B1 ¼ ðhsurf þ ha ÞDy and B2 ¼

2 S 2 þ 2K1 hsurf Dy

ð19cÞ

Equation (19a) can be expressed by only four fitting parameters K0, K1, B1, and B2. The Clausnitzer et al. (1998) study concluded that both the PA and BA models described infiltration equally well; however, the BA model, while most advanced, was not as well suited to serve as a fitting model due to nonuniqueness problems caused by the larger number of fitting parameters.

i ¼ at b

ð22Þ

Clearly, this equation will not fit infiltration data at long times, as it predicts zero infiltration rate as t-N. The value of a should be equal to the infiltration rate at t ¼ 1, and 0obo1. Mezencev (1948) proposed another infiltration model, and modified the KO model by including a linear term with a coefficient b1, so that b1-K1 for t-N provided 0ob3o1 and b240 (ME model):

I ¼ b1 t þ

b2 tð1  b3 Þ 1  b3

ð23Þ

Other models include the Soil Conservation Service (1972) method and the Holtan solution (Kutilek and Nielsen, 1994; Espinoza, 1999).

2.05.3.3 Swartzendruber Model Swartzendruber (1987) proposed an alternative series solution that is applicable and exact for all infiltration times, and also allows for surface ponding. Its starting point is similar to the GA approach; however, its derivation does not require a step function for the wetted soil profile. Its simplified form is a three-parameter infiltration equation (SW model):

I ¼ K1 t þ

S 1  expðA0 t 1=2 Þ A0

ð20Þ

where A0 is a fitting parameter of which its value depends on the surface water content, y1. As A0-0, it reduces to a form of the Philip (1957b) model with K1 as the coefficient of the linear term, and for which dI/dt approaches K1 as t-N. As for the GA model, the S-term can be corrected using Equation (15a) to account for ponded conditions.

2.05.3.4 Empirical Infiltration Equations For most of these types of infiltration equations, the fitting parameters do not have a physical meaning and are evaluated by fitting to experimental data only. However, in many cases, the specific form of the infiltration equation is physically intuitive. For example, the empirical infiltration equation by Horton (1940) is one the most widely used empirical infiltration equations. It considers infiltration as a natural exhaustion process, during which infiltration rate decreases exponentially with time from a finite initial value, ic|t ¼ 0 ¼ (a1 þ a2), to a final value, a1 ¼ K1. Accordingly, cumulative infiltration I (L) is predicted as a function of time t (HO model):

I ¼ a1 t þ

a2 ½1  expða3 tÞ a3

ð21Þ

with the soil parameter a340, representing the decay of infiltration rate with time. In Equation (21), a1 can be associated with the hydraulic conductivity (LT1) of the wetted soil portion, K1, for t-N.

2.05.4 Measurements 2.05.4.1 Infiltration Infiltration measurements can serve various purposes. In addition to characterizing infiltration, for example, to compare infiltration between different soil types, or to quantify macropore flow, it is often measured to estimate the relevant soil hydraulic parameters from the fitting of the infiltration data to a specific physically based infiltration model. This is generally known as inverse modeling. Infiltration is generally measured using one of three different methods: a sprinkler method, a ring infiltration method, or a permeameter method. The sprinkler method is mostly applied to determine time of ponding for different water application rates, whereas the ring infiltrometer method is used when the infiltration capacity is needed. The permeameter method provides a way to measure infiltration across a small range of h-values p0. A general review of all three methods was recently presented by Smettem and Smith (Smith et al., 2002), whereas a comparison of different infiltration devices using seven criteria was presented by Clothier (2001). Rainfall sprinklers or rainfall simulators are also sprinkler infiltrometers, but they are typically used to study runoff and soil erosion (e.g., Morin et al., 1967). They mimic the rainfall characteristics (e.g., kinetic energy) of natural storms, specifically the rainfall rate, rainfall droplet size distribution, and drop velocity. Most of these devices measure infiltration by subtracting runoff from applied water. Using a range of water application rates, infiltration measurements can be used to determine the i(I) curve for a specific soil type, with specific soil hydraulic properties such as Ks or S. Various design parameters for many developed rainfall simulators, specifically nozzle systems, were presented by Peterson and Bubenzer (1986). A portable and inexpensive simulator for infiltration measurements along hillslopes was developed by Battany and Grismer (2000). This low-pressure system used a hypodermic syringe needle system to form uniform droplets at rainfall intensities ranging from 20 to 90 mm h1.

Infiltration and Unsaturated Zone

Ring infiltrometers have historically been used to characterize soil infiltration by determining the infiltration capacity, ic. A ring is carefully inserted in the soil so that water can be ponded over a known area. Since a constant head is required, a constant water level is maintained either by manually adding water and using a measuring stick to maintain a constant depth of ponded water, by using a Mariotte system, or by a valve connected to a float that closes at a predetermined water level. Measurements are usually continued until the infiltration rate is essentially constant. Water seepage around the infiltrometer is prevented by compaction of the soil around and outside of the infiltrometer. Multidimensional water flow under the ring is minimized by pushing the ring deeper into the soil, or by including an outer buffer ring. In the latter case, the soil between the two concentric rings is ponded at the same depth as the inner ring, to minimize lateral flow directed radially outward. The deviation from the assumed one-dimensionality depends on ring insertion depth, ring diameter, measurement time and soil properties such as its hydraulic conductivity, and the presence of restricting soil layers. A sensitivity analysis on diverging flow of infiltrometers was presented by Bouwer (1986) and Wu et al. (1997). Permeameters are generally smaller than infiltrometers and allow easy control of the soil water pressure head at the soil surface. Generally, multidimensionality of flow must be taken into account, using Wooding’s (1968) equation for steady flow (QN, L3 T1) from a shallow, circular surface pond of free water, or

 QN ¼ Ks

pr20

4r0 þ a

 ð24aÞ

The first and second terms in parentheses denote the gravitational and capillary components of infiltration and a denotes the parameter in Gardner’s (1958) unsaturated hydraulic conductivity function:

KðhÞ ¼ Ks expðahÞ

ð24bÞ

In this model of the so-called Gardner soil, the macroscopic capillary length, lc, is equivalent to 1/a. The basic analysis for most permeameter methods relies on Wooding’s solution. An extensive review of the use of permeameters was presented by Clothier (2001), including the tension infiltrometers and disk permeameters, by which the soil water pressure at water entry is controlled by a bubble tower. Their use is relatively simple, and based on analytical solutions of steady-state water flow. The permeameter method is economical in water use and portable. The soil hydraulic properties (S and K), in an inverse way, can be inferred from measurements using (1) both shortand long-time observations, (2) disks with various radii, or (3) using multiple water pressure heads. Transient solutions of infiltration may be preferable, as it allows analysis of shorter infiltration times, so that the method is faster and likely will better satisfy the homogeneous soil assumption. Differences between one- and three-dimensional solutions for transient infiltration were analyzed by Haverkamp et al. (1994), Vandervaere et al. (2000), and Smith et al. (2002) from multidimensional numerical modeling analysis. These effects were reported to be small if gravity effects were included.

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Nowadays, permeameters are most often applied to estimate the soil’s hydraulic characteristics in an inverse way, by fitting infiltration data to analytical solutions. In many cases, auxiliary water content or matric potential data are required to yield unique solutions.

2.05.4.2 Unsaturated Water Flow Whereas infiltration measures are typically conducted along the soil surface only, measurement of unsaturated water flow requires installation of instruments and sensors below ground, thereby largely complicating measurement procedures and analysis. The simplest expression for unsaturated water flow estimation is the Darcy equation (7), but still requires the measurement of soil water content (y) or soil water matric potential (h) at various soil depths, and knowledge of the unsaturated hydraulic function, K(y), as expressed by Equation (8). Installation of soil moisture or potential sensors requires extreme care, because of issues of soil disturbance, inadequate soil sensor contact, and inherent soil heterogeneities. In addition, it is not always straightforward to determine installation depth of sensors, as it will depend on a priori knowledge of soil horizon differentiation. Inherently problematic is the fact that no soil water flux meters are available to accurately measure the unsaturated soil water flux q in Equation (7). A review by Gee et al. (2003) provides possible direct and indirect methods, but none of them are adequate because of problems with divergence of water flow near the flux measurement device. Recently, the heat pulse probe was developed (Kamai et al., 2008) for indirect measurement of soil water flux, but is limited to fluxes of 6 mm d1 or higher. Finally, very few routine measurements are available to determine the K(y) relationship. In fact, the lack of the unsaturated conductivity information is the most limiting factor of in situ application of the Darcy equation. Most promising is the application of inverse modeling for parameter estimation of the soil hydraulic functions, using both laboratory and field techniques (Hopmans et al., 2002b), which can be used in conjunction with in situ water content and soil water potential measurements to estimate temporal changes in depth distribution of soil water flux. Selected steady-state solutions are provided in Jury et al. (1991), but are only of limited use for real field conditions since soil water content and matric potential values change continuously. Most realistically, one must apply the transient unsaturated water flow (Equation (10)) that arises from combination of the Darcy equation with mass conservation. However, its solution also requires a priori knowledge of the soil water capacity, C, as determined from the slope of the soil water retention curve, and time measurements of y and h, at the various soil horizon interfaces and at the boundaries of the soil domain of interest, including at the soil profile bottom. Although certainly possible, relatively few of such field experiments are conducted routinely because they are time consuming and wrought with complications. However, in combination with inverse modeling, such field experiments can provide a wealth of information, including plant root water uptake dynamics, plant transpiration, and drainage rates (Vrugt et al., 2001). Therefore, large lysimeters with selected

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water content and soil water potential measurements may be very useful.

2.05.5 Scaling and Spatial Variability Considerations Soil hydrologists need to apply locally measured soil physical data to characterize flow and transport processes at large-scale heterogeneous vadose zones. For example, prediction of soil water dynamics, such as infiltration at the field scale, is usually derived from the measurement of soil hydraulic properties from laboratory cores, as collected from a limited number of sampling sites across large spatial extents. Soil parameters obtained from these small-scale measurements are subsequently included in numerical models with a grid or element size many times larger, with the numerical results extrapolated to predict large-scale flow and transport behavior. Because of the typical nonlinearity of soil physical properties, their use across spatial scales is inherently problematic. Specifically, the averaging of processes determined from discrete small-scale samples may not describe the true soil behavior involving larger spatial structures. Moreover, the dominant physical flow processes may vary between spatial scales. Considering that soil physical, chemical, and biological measurements are typically conducted for small measurement volumes and that the natural variability of soils is enormous, the main question asked is how small-scale measurements can provide information about large-scale flow and transport behavior. In their treatise of scale issues of vadose zone modeling, Hopmans et al. (2002a) offer a conceptual solution, considering the control of small-scale processes on larger-scale flow behavior. Hence, vadose zone properties are nonunique and scale dependent, resulting in effective properties that vary across spatial scales and merely serve as calibration parameters in simulation models. Therefore, their accurate prediction in heterogeneous materials can only be accomplished using scale-appropriate measurements, including those that measure at the landscape scale. In addition, infiltration measurements are typically conducted at measurement scales in the range of 0.2–1.0 m. This is relevant for irrigation purposes, especially for micro-irrigation applications. Yet, infiltration information is often needed for much larger spatial scales, at the pedon scale, hillslope scale, and watershed scale. Very little work has been done relating infiltration process to measurement or support scale. Exceptions are the studies by Sisson and Wierenga (1981) and Haws et al. (2004), who measured steady-state infiltration at three spatial scales, ranging from 5 to 127- cm-diameter infiltrometer rings. Their results showed that much of the larger-scale infiltration occurs through smaller-scale regions, and that the spatial variability of infiltration decreased as the measurement scale increased. Thus, in general, we find that the process of infiltration might vary with spatial scale, and that larger spatial scales are required to estimate representative infiltration characteristics across a typical landscape. Many field studies have dealt with the significant areal heterogeneity of soil hydraulic properties, and particularly that of the saturated hydraulic conductivity, Ks (Nielsen et al., 1973). The heterogeneity in Ks is recognized to have a major effect on unsaturated flow, leading to significant variation in

local infiltration. In general, accounting for areal heterogeneity leads to shorter ponding times and to a more gradual decrease of the infiltration flux with time (Smith and Hebbert, 1979; Sivapalan and Wood, 1986). To characterize spatial variable infiltration rates, Sharma et al. (1980) measured infiltration with a double-ring infiltrometer at 26 sites in a 9.6-ha watershed. The infiltration data were fitted to the PH infiltration Equation (17a), and fitting parameters S and A were scaled to express their spatial variability and to describe the ensembleaverage or composite infiltration curve of the watershed. A simpler but similar scaling technique for infiltration data was presented by Hopmans (1989), who measured transient infiltration at 50 sites along a 100-m transect. Data were fitted to both the PH and a modified KO model that includes an additional constant c as a second term in Equation (23). This paper showed that spatial variability of infiltration can be easily described by the probability density function of a single scaling parameter, to be used for applications in Monte Carlo simulation of watershed hydrology, as suggested for the first time by Peck et al. (1977). For application at the field scale, the so-called one-point method was presented by Shepard et al. (1993) to estimate furrow-average infiltration parameters of PH Equation (17a), across a furrow-irrigated agricultural field. They used the volume-balance principle from furrow advance time across the field, water inflow rate, and flow area measurements. For modeling surface hydrology, by subtracting the infiltration rate, i(t), from the rainfall rate, r(t), it is possible to estimate spatial and temporal distributions of rainfall excess or runoff. The influence of spatial heterogeneity in rainfall and soil variability on runoff production was studied by Sivapalan and Wood (1986) from an analytical solution of infiltration and making use of the IDA approximation. Statistical characteristics of ponding time and infiltration rate were presented for two cases, one with a spatially variable soil with a lognormal Ks distribution and uniform rainfall, and the other for a homogeneous soil with spatially variable rainfall. Among the various results, this study concluded that the ensemble infiltration approach is biased for spatially variable soils. Their results also showed that the cumulative distribution of ponding times or proportion of ponded area is an excellent way of analyzing mean areal infiltration. Moreover, the spatial correction of infiltration rate is time dependent and varies depending on the correlation lengths of rainfall and soil Ks. This study neglected the effects of surface water run-on, as caused by accumulated water upstream, running on to neighboring areas, thereby contributing locally to infiltration. A quantitative analysis of soil variability effects on watershed hydraulic response that included surface water interactions, such as run-on, was presented by Smith and Hebbert (1979), through analysis of the effects of deterministic changes of infiltration properties in the direction of surface water flow, using a kinematic watershed model. In a subsequent study by Woolhiser et al. (1996), it was clearly demonstrated that runoff hydrographs along a hillslope are significantly affected by spatial trends in the soil’s saturated hydraulic conductivity. We expect that important new information can be collected by linking this interactive modeling approach with remote sensing and geographical information system (GIS) tools. A detailed analysis and review of the control of spatially

Infiltration and Unsaturated Zone

variable hydrologic properties on overland flow are presented by Govindaraju et al. (2007). Yet another concern regarding nonideal infiltration, causing spatially variable infiltration at small spatial scales, comes from the presence of water-repellent or hydrophobic soils. Since the 1980s much new research and findings have been presented, improving the understanding of the underlying physical processes and its relevance to soil water flow and water infiltration (DeBano, 2000; Wang et al., 2000). Infiltration may be controlled by soil surface crust-forming dynamics, which is another complex phenomenon dominated by a wide variety of factors involving soil properties, rainfall characteristics, and local water flow conditions. Two types of rainfall-induced soil seals can be identified: (1) structural seals that are directly related to rainfall through the impact of raindrops and sudden wetting and (2) depositional or sedimentary seals that are indirectly related to rainfall as it results from the settling of fine particles carried in suspension by runoff in soil depressions. A recent review on concepts and modeling of rainfall-induced soil surface sealing was presented by Assouline (2004).

2.05.6 Summary and Conclusions Although important and seemingly simple, infiltration is a complicated process that is a function of many different soil properties, rainfall, land use, and vegetation characteristics. In addition to rainfall intensity and duration as well as the soil physical factors, such as soil water retention and hydraulic conductivity, infiltration is controlled by the initial water content, surface sealing and crusting, hydrophobicity, soil layering, and the ionic composition of the infiltrated water. Many relatively simple infiltration equations have been proposed historically, and are successfully used to characterize infiltration. Other physically based analytical solutions have been presented that can potentially be used to predict infiltration. However, in practice, this is difficult as soil physical characteristics near the soil surface show naturally high soil spatial variability and are often time dependent because of soil structural changes. Alternatively, infiltration is often measured to estimate the relevant soil hydraulic parameters from the fitting of the infiltration data to a specific infiltration model by inverse modeling, such as by using permeameters. Whereas most infiltration measurement techniques and infiltration models apply to relatively small spatial scales, infiltration information is often needed at the watershed and hillslope scales. Yet, it has been shown that much of the largerscale infiltration occurs through smaller-scale regions, for example, because infiltration is largely controlled by spatial variations of the soil’s physical characteristics at the land surface, vegetation cover, and topography. In general, we expect that the process of infiltration varies with spatial scale, and that measurements at larger spatial scales are needed to estimate representative infiltration characteristics across hillslope and larger spatial scales. For that purpose, improved solutions to infiltration across scales from the field to basin scale are needed, such as may become available using rapidly developing techniques including remote sensing, GIS, and new measurement devices.

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Acknowledgments This chapter is partly based on the paper by Hopmans et al. (2007), and includes edited sections of that paper. The author acknowledges the significant input received by Drs. J.-Y Parlange and S. Assouline in writing the 2007 paper.

References Assouline S (2004) Rainfall-induced soil surface sealing: A critical review of observations, conceptual models and solutions. Vadose Zone Journal 3: 570--591. Barry DA, Parlange J-Y, Haverkamp R, and Ross PJ (1995) Infiltration under ponded conditions: 4. An explicit predictive infiltration formula. Soil Science 160: 8--17. Battany MC and Grismer ME (2000) Development of a portable field rainfall simulator for use in hillside vineyard runoff and erosion studies. Hydrological Processes 14: 1119--1129. Bouwer H (1986). Intake rate: Cylinder infiltrometer, In: Klute A, (ed.) Methods of Soil Analysis, Part 1. Number 9 in the Series Agronomy, pp. 825–844. Madison, WI: American Society of Agronomy. Brooks RH and Corey AT (1964) Hydraulic Properties of Porous Media, Hydrology Paper No. 3. Fort Collins, CO: Colorado State University. Bruce RR and Klute A (1956) The measurement of soil moisture diffusivity. Soil Science Society American Proceedings 20: 458--462. Brutsaert W (2005) Hydrology – An Introduction. New York, NY: Cambridge University Press. Clausnitzer V, Hopmans JW, and Starr JL (1998) Parameter uncertainty analysis of common infiltration models. Soil Science Society of America Journal 62: 1477--1487. Clothier BE (2001) Infiltration. In: Smith KA and Mullins CE (eds.) Soil and Environmental Analysis, Physical Methods, 2nd edn., Revised and Expanded, pp. 239–280. New York: Dekker. Dane JH and Hopmans JW (2002) Soil water retention and storage – introduction. In: Dane JH and Topp GC (eds.) Methods of Soil Analysis. Part 4. Physical Methods, pp. 671--674. Madison, WI: Soil Science Society of America. Dane JH and Topp GC (eds.) (2002) Methods of Soil Analysis. Part 4. Physical Methods, vol. 5, Madison, WI: Soil Science Society of America. DeBano LF (2000) Water repellency in soils: A historical overview. Journal of Hydrology 231: 4--32. Dirksen C (2001) Hydraulic conductivity. In: Smith KA and Mullins CE (eds.) Soil and Environmental Analysis, pp. 141--238. New York: Dekker. Espinoza RD (1999) Infiltration. In: Delleur JW (ed.) The Handbook of Groundwater Engineering, pp. 7.1--7.18. Boca Raton, FL: CRC Press. Gardner WR (1958) Some steady state solutions of unsaturated moisture flow equations with application to evaporation from a water table. Soil Science 85: 228--232. Gee GW, Zhang F, and Ward AL (2003) A modified vadose zone fluxmeter with solution collection capability. Vadose Zone Journal 2: 627--632. Govindaraju RS, Nahar N, Corradini C, and Morbidelli R (2007) Infiltration and run-on under spatially-variable hydrologic properties. In: Delleur JW (ed.) The Handbook of Groundwater Engineering, pp. 8.1--8.15. Boca Raton, FL: CRC Press. Green WA and Ampt GA (1911) Studies on soils physics: 1. The flow of air and water through soils. Journal of Agricultural Science 4: 1--24. Haverkamp R, Debionne S, Viallet P, Angulo-Jaramillo R, and de Condappa D (2007) Soil properties and moisture movement in the unsaturated zone. In: Delleur JW (ed.) The Handbook of Groundwater Engineering, pp. 6.1--6.59. Boca Raton, FL: CRC Press. Haverkamp R, Parlange J-Y, Starr JL, Schmitz G, and Fuentes C (1990) Infiltration under ponded conditions: 3. A predictive equation based on physical parameters. Soil Science 149: 292--300. Haverkamp R, Ross PJ, Smettem KRJ, and Parlange J-Y (1994) Three-dimensional analysis of infiltration from the disc infiltrometer. 2. Physically based infiltration equation. Water Resources Research 30: 2931--2935. Haws NW, Boast CW, Rao PSC, Kladivko EJ, and Franzmeier DP (2004) Spatial variability and measurement scale of infiltration rate on an agricultural landscape. Soil Science Society of America Journal 68: 1818--1826. Hopmans JW (1989) Stochastic description of field-measured infiltration data. Transactions of the American Society of Agricultural Engineers 32: 1987-1993.

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Hopmans JW, Assouline S, and Parlange J-Y (2007) Soil infiltration. In: Delleur JW (ed.) The Handbook of Groundwater Engineering, pp. 7.1--7.18. Boca Raton, FL: CRC Press. Hopmans JW, Nielsen DR, and Bristow KL (2002a) How useful are small-scale soil hydraulic property measurements for large-scale vadose zone modeling. In: Smiles D, Raats PAC, and Warrick A (eds.) Heat and Mass Transfer in the Natural Environment, the Philip Volume. Geophysical Monograph Series No. 129, pp. 247–258. Washington, DC: American Geophysical Union. Hopmans JW, Sˇimunek J, Romano N, and Durner W (2002b) Inverse methods. In: Dane JH and Topp GC (eds.) Methods of Soil Analysis. Part 4. Physical Methods, pp. 963--1008. Madison, WI: Soil Science Society of America. Horton RE (1940) An approach towards a physical interpretation of infiltration capacity. Soil Science Society American Proceedings 5: 399--417. Jury WA, Gardner WR, and Gardner WH (1991) Soil Physics. New York: Wiley. Kamai T, Tuli A, Kluitenberg GJ, and Hopmans JW (2008) Soil water flux density measurements near 1 cm/day using an improved heat pulse probe. Water Resources Research 44: doi: 10.1029/2008WR007036. Kirkham D and Powers WL (1972) Advanced Soil Physics. New York: Wiley. Kostiakov AN (1932) On the dynamics of the coefficient of water percolation in soils and on the necessity of studying it from a dynamic point of view for purposes of amelioration. In: Transactions of the Sixth Commission of the International Society of Soil Science A, pp. 17–21. Kosugi K, Hopmans JW, and Dane JH (2002) Water retention and storage – parametric models. In: Dane JH and Topp GC (eds.) Methods of Soil Analysis. Part 4. Physical Methods, pp. 739--758. Madison, WI: Soil Science Society of America. Kutilek M and Nielsen DR (1994) Soil Hydrology. GeoEcology Textbook. CremlingenDestedt. Germany: Catena Verlag. Latifi H, Prasad SN, and Helweg OJ (1994) Air entrapment and water infiltration in two-layered soil column. Journal of Irrigation and Drainage Engineering 120: 871--891. Mein RG and Farrell DA (1974) Determination of wetting front suction in the Green– Ampt equation. Soil Science Society of America Proceedings 38: 872--876. Mezencev VJ (1948) Theory of formation of the surface runoff (Russian). Meteorologia i Gidrologia 3: 33--40. Morin J, Goldberg D, and Seginer I (1967) A rainfall simulator with a rotating disc. Transactions of the American Society of Agricultural Engineers 10: 74--77. Mualem Y (1976) A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research 12: 513--522. Mualem Y and Assouline S (1989) Modeling soil seal as a non-uniform Layer. Water Resources Research 25: 2101--2108. Nasta P, Kamai T, Chirico GB, Hopmans JW, and Romano N (2009) Scaling soil water retention functions using particle-size distribution. Journal of Hydrology 374: 223–234. Nielsen DR, Biggar JB, and Ehr KT (1973) Spatial variability of field measured soil water properties. Hilgardia 42: 215--260. Parlange J-Y, Haverkamp R, and Touma J (1985) Infiltration under ponded conditions: 1. Optimal analytical solution and comparison with experimental observations. Soil Science 139: 305--311. Parlange J-Y, Lisle I, Braddock RD, and Smith RE (1982) The three-parameter infiltration equation. Soil Science 133: 337--341. Peck AJ, Luxmoore RJ, and Stolzy JL (1977) Effects of spatial variability of soil hydraulic properties in water budget modeling. Water Resources Research 13: 348--354. Peterson AE and Bubenzer GD (1986). Intake rate: Sprinkler infiltrometer, In: Klute A, (ed.) Methods of Soil Analysis, Part 1. Number 9 in the series Agronomy, pp. 45–870. Madison, WI: American Society of Agronomy. Philip JR (1957a) The theory of infiltration: 1. The infiltration equation and its solution. Soil Science 83: 345--357. Philip JR (1957b) The theory of infiltration: 2. The profile at infinity. Soil Science 83: 435--448. Philip JR (1957c) The theory of infiltration: 4. Sorptivity and algebraic infiltration equations. Soil Science 84: 257--264. Philip JR (1969) Theory of infiltration. In: Chow VT (ed.) Advances in Hydroscience, vol. 5, pp. 215--296. New York, NY: Academic Press. Philip JR (1987) The infiltration joining problem. Water Resources Research 12: 2239--2245. Robinson DA, Campbell CS, Hopmans JW, et al. (2008) Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone Journal 7: 358--389. Scanlon BR, Andraski BJ, and Bilskie J (2002) Miscellaneous methods for measuring matric or water potential. In: Dane JH and Topp GC (eds.) Methods of Soil Analysis. Part 4. Physical Methods, pp. 643--670. Madison, WI: Soil Science Society of America.

Sharma ML, Gander GA, and Hunt CG (1980) Spatial variability of infiltration in a watershed. Journal of Hydrology 45: 101--122. Shepard JS, Wallender WW, and Hopmans JW (1993) One-point method for estimating furrow infiltration. Transactions of American Society of Agricultural Engineers 36: 395--404. Sˇimunek J, Van Genuchten MTh, and Sejna M (2008) Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone Journal 7: 587--600. Sisson JB and Wierenga PJ (1981) Spatial variability of steady-state infiltration rates as a stochastic process. Soil Science Society of America Journal 45: 699--704. Sivapalan M and Milly PCD (1989) On the relationship between the time condensation approximation and the flux-concentration relation. Journal of Hydrology 105: 357--367. Sivapalan M and Wood EF (1986) Spatial heterogeneity and scale in the infiltration response of catchments. In: Gupta VK, Rodriguez-Iturbe I, and Wood EF (eds.) Scale Problems in Hydrology, pp. 81--106. Hingham, MA: Reidel. Skaggs RW (1982) Infiltration, In: Haan CT, Johnson HP, and Brakensiek DL, (eds.) Hydrologic Modeling of Small Watersheds, ASAE Monograph No. 5, 121–166. St. Joseph, MI: ASAE. Smith RE and Hebbert RHB (1979) A Monte-Carlo analysis of the hydrologic effects of spatial variability of infiltration. Water Resources Research 15: 419--429. Smith RE, Smettem KRJ, Broadbridge P, and Woolhiser DA (2002) Infiltration Theory for Hydrologic Applications. Water Resources Monograph 15, Washington, DC: American Geophysical Union. Soil Conservation Service (1972) Estimation of direct runoff from storm rainfall National Engineering Handbook, Section 4: Hydrology, pp. 10.1--10.24. Washington, DC: USDA. Swartzendruber D (1987) A quasi-solution of Richards’ equation for the downward infiltration of water into soil. Water Resources Research 23: 809--817. Tuli AM and Hopmans JW (2004) Effect of degree of saturation on transport coefficients in disturbed soils. European Journal of Soil Science 55: 147--164. Vandervaere J-P, Vauclin M, and Elrick DE (2000) Transient flow from tension infiltrometers: I. The two-parameter equation. Soil Science Society of America Journal 64: 1263--1272. Vandervaere J-P, Vauclin M, Haverkamp R, Peugeot C, Thony J-L, and Gilfedder M (1998) Prediction of crust-induced surface runoff with disc infiltrometer data. Soil Science 163: 9--21. Van Genuchten MTh (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44: 892--898. Vrugt JA, Hopmans JW, and Sˇimunek J (2001) Calibration of a two-dimensional root water uptake model. Soil Science Society of America Journal 65: 1027-1037. Wang Z, Wu QJ, Wu L, Ritsema CJ, Dekker LW, and Feyen J (2000) Effects of soil water repellency on infiltration rate and flow instability. Journal of Hydrology 231: 265--276. Wooding RA (1968) Steady infiltration from a shallow circular pond. Water Resources Research 4: 1259--1273. Woolhiser DA, Smith RE, and Giraldez J-V (1996) Effects of spatial variability of saturated hydraulic conductivity on Hortonian overland flow. Water Resources Research 32: 671--678. Wu L, Pan L, Robertson MJ, and Shouse PJ (1997) Numerical evaluation of ring-infiltrometers under various soil conditions. Soil Science 162: 771--777. Young MH and Sisson JB (2002) Tensiometry. In: Dane JH and Topp GC (eds.) Methods of Soil Analysis, Part 4: Physical Methods, pp. 575--606. Madison, WI: Soil Science Society of America.

Relevant Websites http://www.decagon.com Decagon Devices, Mini-Disk Infiltrometer. http://hopmans.lawr.ucdavis.edu Jan W. Hopmans, Vadose Zone Hydrology. http://www.pc-progress.com PC-Progress: Engineering Software Developer; HYDRUS 2D/3 D for Windows, Version 1.xx. http://ag.arizona.edu/sssa-s1 SSSA Soil Physics Division S-1. http://en.wikipedia.org Wikipedia, Infiltration (Hydrology).

2.06 Mechanics of Groundwater Flow M Bakker, Delft University of Technology, Delft, The Netherlands EI Anderson, WHPA, Bloomington, IN, USA & 2011 Elsevier B.V. All rights reserved.

2.06.1 Introduction 2.06.2 Brief History 2.06.3 Hydraulic Head 2.06.4 Darcy’s Law 2.06.5 Steady Conservation of Mass 2.06.6 Flow Types 2.06.6.1 Spatial Dimension 2.06.6.2 Time Dependence 2.06.6.3 Geologic Setting 2.06.7 The Dupuit Approximation 2.06.8 Potential Flow and the Discharge Vector 2.06.9 One-Dimensional Flow 2.06.9.1 Confined Flow between Two Rivers 2.06.9.2 Combined Flow between Two Rivers 2.06.9.3 Unconfined Flow in a River Valley 2.06.10 One-Dimensional Radial Flow 2.06.10.1 Flow to a Well at the Center of a Circular Island without Recharge 2.06.10.2 Recharge on a Circular Island 2.06.10.3 Well at the Center of a Circular Island with Recharge 2.06.11 The Principle of Superposition 2.06.11.1 A Well in Uniform Flow 2.06.11.2 The Method of Images 2.06.11.3 Flow to a Pumping Well in an Alluvial Valley 2.06.12 The Stream Function and the Complex Potential 2.06.12.1 Evaluation of the Capture Zone Envelope Using the Complex Potential 2.06.13 Transient Flow 2.06.13.1 One-Dimensional Periodic Flow 2.06.13.2 Transient Wells 2.06.13.3 Convolution 2.06.14 Computer Models 2.06.15 Discussion Acknowledgments References

2.06.1 Introduction Groundwater is the most important resource of freshwater on earth. It moves very slowly through the top part of the earth’s crust from areas of recharge (often originating from rainfall) to discharge in springs, wells, rivers, lakes, and oceans. The baseflow of rivers, the flow between rainfall or snowmelt events, is caused predominantly by inflow from groundwater. In many parts of the world, groundwater is the only source for drinking water or irrigation. Groundwater resources are threatened by over-exploitation and contamination. Major problems include a rapid decline of the groundwater table caused by pumping of groundwater for irrigated agriculture, salinization of groundwater resources due to heavy pumping in coastal areas, and contamination of groundwater by leakage of toxic chemicals.

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Accurate tools are needed to predict whether the current and proposed uses of groundwater resources are sustainable and safe. The field of groundwater flow, also called hydrogeology, is large and only the basic physical principles of groundwater flow through porous media are discussed in this chapter. Detailed textbooks include Verruijt (1970), Bear (1972), Strack (1989), and Fitts (2002). This chapter focuses on groundwater flow through porous materials such as sand, silt, or clay. Significant amounts of groundwater may flow through fractured rock formations. The concepts outlined in this chapter apply to such formations when the fractured rock may be represented by an equivalent porous medium. Compared to other areas of hydrology, the governing equations for groundwater flow are relatively well known. Exact solutions can be obtained for many important flow

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systems. These exact solutions provide important insights into the flow of groundwater and groundwater interactions with the accessible environment. This chapter begins with a description of the governing equations. Flow principles are explained through discussion of a set of steady and transient flow problems. This chapter concludes with a brief discussion of available modeling tools for solving more complicated problems.

2.06.2 Brief History The foundation of the quantitative description of groundwater flow was laid by Henry Darcy and Jules Dupuit. Darcy (1856) peformed column experiments that led to what is now called Darcy’s law for groundwater flow. Jules Dupuit was a classmate of Darcy and his replacement as director of Water and Bridges in Paris in the 1850s. Dupuit (1863) recognized that in many cases the vertical variation of the horizontal components of flow may be neglected, in essence reducing the mathematical description of groundwater flow by one dimension; one of his examples was a formula for flow to a well. At the end of the nineteenth century, Forchheimer (1886) combined Darcy’s law with the continuity equation to show that steady groundwater flow through piecewise homogeneous aquifers is governed by Laplace’s equation. This opened the door to many existing solutions that were derived for other problems governed by the same equation. Equations that describe transient groundwater flow take into account that the aquifer can store water. Phreatic storage, storage through movement of the groundwater table, was included by Boussinesq (1904). The process of elastic storage was conceptualized by Meinzer (1928), Theis (1935), and Jacob (1940), and led to the definition of storativity.

2.06.3 Hydraulic Head The mechanical energy per unit weight in an incompressible fluid is given at a point by the following sum:

p V2 þZþ 2g g

ð1Þ

H A

z L x

Figure 1 Measurement of the hydraulic head at a point in an aquifer using a piezometer. Saturated zone is darker gray and is bounded on top by the groundwater table.

The hydraulic head provides a good estimate of the available energy per unit weight at a point in a groundwater flow field, and is fairly easy to measure. Figure 1 shows a piezometer set into the saturated portion of an aquifer. A piezometer is a hollow tube, open to the aquifer only at the bottom (point A in the figure). The hydraulic head at point A is the sum of the pressure head and elevation head. The fluid that rises into the piezometer is hydrostatic and the pressure at point A is

pA ¼ gH

ð3Þ

or the pressure head at point A is H. The elevation head at point A, measured with respect to the datum shown in the figure, is L. The hydraulic head at point A is given by L þ H and is equal to the height above the datum to which water rises in the piezometer. The hydraulic head may be measured at a point in a groundwater flow field once a piezometer has been placed. Hydraulic head data at surface water features such as lakes and streams, which form the natural boundaries of many aquifers, are often available as time series. Head data are the most abundant information a groundwater engineer has at his disposal. The importance of the hydraulic head in groundwater calculations will become clear in the remainder of this chapter.

where p [ML1T2] is the pressure, g [MT2L2] is the specific weight of the fluid, Z [L] is the elevation above a fixed datum, V [LT1] is the speed of the fluid, and g [LT2] is the acceleration due to gravity. The first term, referred to as pressure head, reflects the pressure energy of the fluid. The second term reflects the potential energy of the system and is called the elevation head. The third term, known as the velocity head, reflects the kinetic energy of the fluid. In groundwater applications, typical fluid speeds within the porous medium are so small that the velocity head is negligible. The combination of pressure head and elevation head is known as the hydraulic head, also called piezometric head or simply head. The dimension of head h is length [L]:

In 1856, Henry Darcy performed experiments from which he concluded that the flow of groundwater is proportional to the head gradient (Darcy, 1856). The general setup of the experiment is simple. A cylinder is filled with aquifer material. The ends of the cylinder are attached to two reservoirs with different levels (Figure 2). Water flows through the aquifer material from the higher reservoir to the lower reservoir. In this fashion, Darcy showed that the discharge Q [L3T1] through the soil column is proportional to the head difference h1  h2 [L] and the cross-sectional area A [L2] of the column, and inversely proportional to the length of the soil column L [L]:

p h¼ þZ g

h1  h2 Q ¼ kA L

ð2Þ

2.06.4 Darcy’s Law

ð4Þ

Mechanics of Groundwater Flow

interconnectedness of the pores of the aquifer. For example, the hydraulic conductivity is smaller for oil than for water when flowing through the same aquifer, as oil is more viscous than water. Similarly, warmer water results in a larger hydraulic conductivity than colder water, as the viscosity of water decreases with temperature. The hydraulic conductivity may be written as

h1 − h2

k¼

L

Figure 2 The experiment of Darcy. Darcy used a vertical column, but the flow is independent of the angle of the soil column.

The proportionality constant is k [L/T] and is called the hydraulic conductivity. Equation (4) is adequate to describe flow through a soil column, but not flow through an aquifer. Flow through an aquifer is expressed in terms of the specific discharge vector ~ q½LT 1 , the discharge per unit area of aquifer normal to the direction of flow:

~ q ¼ krh

ð5Þ

The components of ~ q in the Cartesian x, y, and z directions may be written as

qh qx ¼ k ; qx

qh qy ¼ k ; qy

qh qz ¼ k qz

ð6Þ

Equation (5) is known as Darcy’s law, although it is an empirical formula relating the head gradient to the specific discharge vector. Note the equivalence between Darcy’s law and other physical laws such as Fourier’s law for heat flux, Ohm’s law for current density, and Fick’s law for diffusive flux. The hydraulic conductivity of aquifers may be anisotropic. Sedimentary aquifers often consist of a sequence of thin layers of slightly coarser or slightly finer material. In such aquifers, the average vertical hydraulic conductivity is smaller than the average horizontal hydraulic conductivity. Hence, the hydraulic conductivity is anisotropic and is written as a tensor K so that Darcy’s law becomes

~ q ¼ Krh

ð7Þ

When the principal directions of the hydraulic conductivity tensor coincide with the horizontal and vertical directions of sedimentary aquifers, the Cartesian components of Darcy’s law become

qx ¼ kh

qh ; qx

qy ¼ kh

qh ; qy

qz ¼ kv

117

qh qz

ð8Þ

where kh is the horizontal hydraulic conductivity and kv the vertical hydraulic conductivity. The hydraulic conductivity is a function of the fluid that flows through the aquifer and of the shapes, sizes, and

krg m

ð9Þ

where k [L2] is called the intrinsic permeability of the aquifer and is a characteristic of the pore-size distribution and tortuosity of the porous medium, r [ML3] is the density of the fluid, g [LT2] is the acceleration of gravity, and m [MLT1] is the dynamic viscosity. In this way, the property of the porous material (k) is separated from the properties of the fluid (r and m). Variations of r and m may play a role in coastal aquifers because of changes in salinity, or in cases where the temperature of the groundwater varies significantly, for example, in river bank filtration projects or systems for aquifer thermal energy storage. The hydraulic conductivity of an aquifer may be measured with a Darcy experiment. This requires, however, that undisturbed and representative samples are taken from an aquifer. As this is a difficult, if not impossible, task, field measurements are more likely to give accurate results. Representative values for the hydraulic conductivity are given in Table 1 for water flowing through different aquifer materials. The hydraulic conductivity generally varies spatially throughout an aquifer. The heterogeneity is rarely known well. The head and flow in the aquifer may often be simulated accurately by treating the aquifer properties as piecewise homogeneous. Travel times are, however, strongly affected by heterogeneity of the aquifer (e.g., Moore and Doherty, 2006). The specific discharge vector has the same units as a velocity and is sometimes called the Darcy flux or the Darcy velocity. It is important to note, however, that a water particle that flows through the aquifer does not flow with an average velocity equal to the specific discharge. The specific discharge is the discharge through a unit area of aquifer. Only part of this unit area consists of pores while the larger part consists of solid particles. The ratio of the volume of pores to the volume of aquifer is called the porosity n. Water can only flow through the pores, so that the average velocity vector ~ u may be obtained Table 1 Representative values of hydraulic conductivity for various aquifer materials Material

k (m d1)

Clay Sandy clays Peat Silt Very fine sands Fine sands Coarse sands Sands with gravel Gravels

o0.0001 0.0001–0.001 0.0001–0.01 0.001–0.01 0.1–1 1–10 10–100 100–1000 41000

Modified from Verruijt A (1970) Theory of Groundwater Flow. New York: MacMillan.

118

Mechanics of Groundwater Flow

2.06.6 Flow Types

from the specific discharge as

~ u ¼~ q=n

ð10Þ

This is called an average velocity. The velocity through a larger pore, or through the center of a pore is likely to be larger than the velocity through a smaller pore or along the edge of a soil particle. The velocity of groundwater is generally very small. The head may drop 1 or 2 m every 1000 m. A gradient of 0.002 in a sand with a hydraulic conductivity of 10 m d1 and a porosity of 0.2 gives an average velocity of only 0.1 m d1. Larger velocities occur in very specific cases only, such as near pumping wells.

2.06.5 Steady Conservation of Mass Darcy’s law provides three scalar equations for the four unknowns: qx, qy, qz, and h. The fourth equation for solving the system is obtained from conservation of mass. A derivation of the differential statement of conservation of mass for a flowing fluid is given in any standard fluid mechanics text (i.e., Munson et al., 2002). The result states that the divergence of the mass flow rate and the rate of accumulation of fluid mass are in balance at every point in the flow field:

Groundwater flow may be classified according to the spatial dimensions of the flow field, the dependence of the flow on time, and the aquifer setting in which the flow occurs. The focus in this chapter is on one- and two-dimensional, steady and transient flow in single aquifer systems with isotropic and homogeneous properties. These flow types and others are described in the following.

2.06.6.1 Spatial Dimension Flow in an aquifer may be one, two, or three dimensional depending on the boundary conditions associated with the flow. Most aquifers are relatively thin in comparison to their areal extent. In these settings, which are referred to as shallow aquifers, one- and two-dimensional analyses are often adequate. In shallow aquifers the vertical variations in the hydraulic head are negligibly small when compared to horizontal variations in head. For problems where three-dimensional flow is important, near local features such as partially penetrating or horizontal wells, or near partially penetrating streams, the effects of concentrated vertical flow can be incorporated approximately into two-dimensional models.

2.06.6.2 Time Dependence

qr r  ðr~ uÞ þ ¼0 qt

ð11Þ

where ~ u is the fluild velocity and r the fluid density. By analogy, a statement for conservation of mass made for groundwater flowing through a porous material of porosity n is

r  ðr~ qÞ þ

q ðrnÞ ¼0 qt

ð12Þ

The fluid density is multiplied by the porosity in the second term as the fluid mass occurs only in the pore spaces. If the porous media is rigid (qn/qt ¼ 0) and the fluid density is constant in time, or if the flow is steady, (12) reduces to

Groundwater flow is either steady or transient. In steady flow, there are no changes in flow or hydraulic head in time. Analyses of steady flow are used to reflect long-term, average conditions in an aquifer, for example, the dewatering of an aquifer for a large construction project, or delineation of wellhead protection areas for municipal water supply wells. Transient flow occurs when aquifer boundary conditions change in time, for example, changing aquifer recharge, changing river levels, and varying pumping rates of wells. A specific application of a transient flow analysis is the evaluation of aquifer properties by field tests, such as pumping tests, when it is not practical to run the test until steady conditions are reached.

2.06.6.3 Geologic Setting r  ðr~ qÞ ¼ 0

ð13Þ

If, in addition, the fluid density is constant in space the simplest form of conservation of mass emerges,

r ~ q¼0

ð14Þ

Equation (14) is also known as the continuity of flow equation; when the density is constant, conservation of mass is equivalent to continuity of flow. Conservation of mass (14) may be combined with Dary’s law (5) to obtain a single differential equation governing three-dimensional groundwater flow through a homogeneous aquifer:

r 2h ¼ 0 This result was first obtained by Forcheimer (1886).

ð15Þ

The geologic setting of an aquifer may be used to further define the flow type as confined, unconfined, combined, or multiaquifer flow. The subsoil may be divided in more permeable and less permeable layers. The permeable layers may transmit significant amounts of water. They are called aquifers and can be used as the source for drinking water or irrigation. The less permeable layers transmit little or no water and cannot be used for water supply; they are commonly called aquicludes, confining layers, aquitards, or leaky layers. An aquifer is confined when it is bounded on the top and bottom by impermeable layers, or layers with significantly lower permeability than the aquifer. In contrast, an unconfined aquifer is not bounded on top by an impermeable layer. Flow in an aquifer is called confined when the head in the aquifer is above the impermeable top of the aquifer (Figure 3(a)). For unconfined flow, the saturated part of the aquifer is bounded on top by the groundwater table, also called the

Mechanics of Groundwater Flow

119

Confining layer

Phreatic surface Confined flow

Unconfined flow

Impermeable base (a)

Areal recharge

Phreatic surface

(b)

Impermeable base

Figure 3 Definition of aquifer types and flow types: (a) combined confined and unconfined flow and (b) unconfined flow with recharge.

phreatic surface (Figure 3(b)). The concept of a groundwater table seems simple: when one digs a deep enough hole, it will fill up with water to the level of the groundwater table. Upon closer examination, the concept is less clear, however. When digging down, the soil gets wetter and wetter until the groundwater table is reached. In the section from the surface to the groundwater table, the pores of the soil are filled with both water and air; this section is called the unsaturated zone and is described in detail in Chapter 2.05 Infiltration and Unsaturated Zone. The saturated zone starts at the phreatic surface, which is defined as the depth where the pressure in the water is equal to atmospheric. The phreatic surface is curved when there is flow in the aquifer. The surface goes down in the direction of flow; thus, the velocity of a water particle always has a downward component; in most cases, this component is relatively small. In unconfined flow, the saturated thickness varies with the elevation of the water table. Flow in a confined aquifer becomes unconfined when the head falls below the impermeable top of the aquifer. In a confined aquifer, the flow may consist of both regions where the head is above the confining layer and regions where the head is below the confining layer. This is referred to as combined confined and unconfined flow, or simply combined flow. Combined flow in a confined aquifer is illustrated in Figure 3(a). Often, aquifers are stratified with alternating layers of relatively permeable material separated by layers of less permeable materials. The flow in these systems may move from one aquifer through a leaky layer to another aquifer, and is referred to as multiaquifer flow.

2.06.7 The Dupuit Approximation The basic idea behind the Dupuit approximation (also called the Dupuit–Forchheimer approximation) is to approximate groundwater flow in an aquifer as two-dimensional flow in a horizontal plane. The approximation allows many problems to be solved in simple form that otherwise could not be solved. Conditions of the Dupuit approximation are commonly stated as (e.g., Bear, 1972): 1. the flow is horizontal (qz ¼ 0); 2. the hydraulic head is constant in the vertical (h ¼ h(x,y)); and 3. the hydraulic gradient is equal to the slope of the water table. There are various interpretations of the physical meaning of the Dupuit approximation. Bear (1972) shows that the head predicted with a Dupuit model in a single aquifer represents the average head over the depth of the aquifer. PolubarinovaKochina (1962) shows that Dupuit models are exact for anisotropic aquifers with infinite vertical hydraulic conductivity. This idea was explored further by Kirkham (1967). Strack (1984) showed that conditions 2 and 3 listed above are consequences of neglecting the resistance to vertical flow in an aquifer, and that condition 1 is unnecessary. Strack’s interpretation allows for the calculation of nonzero vertical flow components (qz) and three-dimensional pathlines in two-dimensional Dupuit models of single- and multiaquifer flow. Also, this interpretation clearly identifies where errors may be

120

Mechanics of Groundwater Flow

introduced by making the Dupuit approximation. Strack’s interpretation is adopted here and the Dupuit approximation is defined as neglecting the resistance to vertical flow in an aquifer. As stated, the major advantage of the Dupuit approximation is a two-dimensional head field (h ¼ h(x,y)) with two-dimensional horizontal flow components (qx ¼ qx(x,y), and qy ¼ qy(x,y)), while the vertical flow remains a function of all three coordinates (qz = qz(x,y,z)).

2.06.8 Potential Flow and the Discharge Vector ~ 2 =T is defined as the depth-inteThe discharge vector Q½L grated specific discharge vector. The x-component of the discharge vector is obtained as

Qx ¼

ZZt

qx ðx; y; zÞdz

ð16Þ

Zb

where Zb and Zt are the bottom and top elevations of the saturated portion of the aquifer, respectively. Upon making the Dupuit approximation in a shallow aquifer (16) becomes

Qx ¼ qx ðx; yÞ

ZZt

dz ¼ qx ðZt  Zb Þ

ð17Þ

Zb

The term within parentheses is the saturated thickness of the aquifer. For confined flow, the saturated thickness equals the aquifer thickness H. For unconfined flow, it is equal to h  Zb. In this chapter, the datum for h is chosen at the bottom of the aquifer (Zb ¼ 0), so that the saturated aquifer thickness is h. Substituting Darcy’s law into (17) and applying the appropriate saturated thicknesses gives

8 qh > > > < qx H ¼ kH q x ; confined flow Qx ¼ > > qh > : qx h ¼ kh ; unconfined flow qx

ð18Þ

flow, the product kH in (20) is referred to as the transmissivity T[L2/T] of the aquifer. Groundwater flow may be written as potential flow when the base of the aquifer is horizontal and the aquifer properties are piecewise constant. Writing groundwater flow as potential flow simplifies the formulation of confined and unconfined flow and allows the use of the many potential flow solutions that exist in other fields. The definition of potential flow is that the flow is equal to the gradient of the potential. For groundwater flow, the definition is modified by adding a minus sign (19). The discharge potential has no useful physical meaning, but is merely a convenient quantity in mathematical modeling. Using the discharge potential, Darcy’s law has been rewritten in terms of the discharge vector (19). The differential statement of conservation of mass may also be written in terms of the discharge vector. This may be done either by writing a flow balance on an elementary volume of aquifer (e.g., Strack, 1989), or by integrating the continuity equation over the depth of the aquifer (e.g., Bear, 1972). The result is

~¼N rQ

ð21Þ

where N [L/T] is the steady areal recharge rate, or the rate at which water infiltrates through the unsaturated zone into the saturated portion of the aquifer. If the aquifer is confined, or there is no recharge to the aquifer, N ¼ 0. Combining (19) and (21) results in Poisson’s equation

r 2 F ¼ N

ð22Þ

where the Laplacian is now understood to mean differentiation in the horizontal plane only. Confined and unconfined flow are handled in the same way in terms of the discharge vector. Boundary conditions are written in terms of F using ~ or a combination of F and (20), in terms of components of Q, ~ The resulting boundary-value problem is solved for F and Q. the results translated to heads, using the inverse of (20).

2.06.9 One-Dimensional Flow The y-component of the discharge vector is obtained in a similar manner. Equation (18) suggests the existence of a discharge potential, F [L3/T], from which the discharge vector may be calculated:

~ ¼ rF Q

ð19Þ

The following function satisfies both (19) and (18) and is the discharge potential (e.g., Strack, 1989):

( F¼

kHh  12kH 2 ; confined flow 1 2 unconfined flow 2kh ;

ð20Þ

When hZH, flow is confined, otherwise (or in the absence of a confining layer) it is unconfined. Equation (20) represents a single potential for combined flow; the potential is continuous across the interface where flow changes from confined to unconfined and the head in the aquifer is equal to the aquifer thickness (h ¼ H). For confined

The governing differential equation for steady one-dimensional, confined, unconfined, or combined flow in a shallow aquifer is (see (22))

d 2F ¼ N dx 2

ð23Þ

where F is related to hydraulic head, h, by (20). When the recharge rate N is constant, the general solution to this differential equation is

F ¼ 12Nx 2 þ Ax þ B

ð24Þ

where A and B are constants that must be evaluated from boundary conditions. If the flow is confined, or there is no areal recharge, N ¼ 0. Three examples of one-dimensional flow that demonstrate various boundary conditions to evaluate the constants are presented in the following.

Mechanics of Groundwater Flow 2.06.9.1 Confined Flow between Two Rivers

Application of the boundary conditions to the general solution with N ¼ 0 results in the following discharge potential:

The simplest case of confined flow is one-dimensional flow between two fixed-head boundaries, for example, two fully penetrating rivers as illustrated in Figure 4(a). When the aquifer is homogeneous, the solution shows that the head varies linearly between the head on the left and the head on the right. The head at the river to the left is equal to hL (hLZH), and the head at the river to the right is hR (hRZH). The boundary conditions must be written in terms of the discharge potential. From (20),

F¼

  FL  FR x þ FL L

ð26Þ

The discharge vector is obtained by differentiating (19):

Qx ¼ 

dF FL  FR ¼ L dx

ð27Þ

where Qx is constant throughout the aquifer. Alternatively, the discharge potential may be written as

F ¼ Qx x þ FL

FL ¼ Fðx ¼ 0Þ ¼ kHhL  12kH2 FR ¼ Fðx ¼ LÞ ¼ kHhR  12kH2

121

ð28Þ

ð25Þ 2.06.9.2 Combined Flow between Two Rivers If the head at the river on the right is below the aquifer confining unit (hRoH), the flow in the aquifer will be combined flow. In this case, FR is computed as (see (20))

FR ¼ 12 kh2R hL

z

H

k

The head at the left remains above the confining unit, and therefore FL is defined as before (25). The solution, written in terms of the discharge potential, (26), is still valid, as well as the expression for Qx (27). The location where the flow changes from confined to unconfined flow, as shown in Figure 4(b), is found by setting the discharge potential equal to kH2/2 and solving for the x-coordinate

hR

x L (a)

xc ¼ hL

FL  kH2 =2 Qx

ð30Þ

k hR

(b)

ð29Þ

2.06.9.3 Unconfined Flow in a River Valley Consider unconfined flow in a buried bedrock valley to a river of constant head, illustrated in Figure 5. There is no confining unit on the aquifer. The discharge potential is related to head by (20). The right aquifer boundary (x ¼ L) is the impermeable valley wall and the left boundary (x ¼ 0) is the river. The

xc

Figure 4 One-dimensional flow between two rivers: (a) confined flow and (b) combined confined and unconfined flow.

N

hL

k

z x

L Figure 5 One-dimensional unconfined flow in a river valley with recharge.

122

Mechanics of Groundwater Flow

corresponding boundary conditions are

Qx ðx ¼ LÞ ¼ 0

ð31Þ

Fðx ¼ 0Þ ¼ 12kh2L ¼ FL

ð32Þ

Application of the boundary conditions (31) and (32) to the general solution (24) results in

x  F ¼ Nx  L þ FL 2

ð33Þ

Radial flow in an aquifer is another case of one-dimensional flow. The governing equation for one-dimensional, radial, potential flow, written in radial coordinates r, is

ð34Þ

ð35Þ

where A and B are constants to be evaluated from boundary conditions. The discharge vector is obtained, as before, as minus the gradient of the discharge potential. In polar coordinates, the radial component of the discharge vector is Qr ¼ dF=dr. Radial flow problems and solutions are important in groundwater engineering because they represent the local flow field around pumping wells. Solutions to three example problems of radial flow are provided below.

2.06.10.1 Flow to a Well at the Center of a Circular Island without Recharge The discharge potential that is the solution to this problem is referred to as F1. The following boundary conditions fix the head at the perimeter of the island (r ¼ R) and the perimeter of the well (r ¼ rw), respectively:

F1 ðr ¼ RÞ ¼ FR

Q lnðr=RÞ þ FR 2p

ð36Þ

dF Q ¼ dr 2pr

ð37Þ

Application of the boundary conditions to the general solution, using N ¼ 0, results in the following discharge potential:

F1 ¼

FR  Fw lnðr=RÞ þ FR lnðR=rw Þ

ð38Þ

The discharge rate of the well, Q [L3/T], may be obtained by evaluating the discharge vector Qr(r ¼ rw) and multiplying by the perimeter of the well:

FR  Fw Q ¼ 2prw Qr ðr ¼ rw Þ ¼ 2p lnðR=rw Þ

ð39Þ

ð41Þ

In many two-dimensional problems or problems with recharge, the condition at the well is approximated as

Q ¼ lim 2prw Qr ðr ¼ rw Þ

ð42Þ

Condition (42) produces accurate results as the radius of the well is often much smaller than the horizontal scale of the groundwater problem being considered. However, only in simple radial flow cases are conditions (42) and (39) equivalent.

2.06.10.2 Recharge on a Circular Island The solution for recharge on a circular island is referred to as F2. Once again, the head is fixed at the perimeter of the island

F2 ðr ¼ RÞ ¼ FR

ð43Þ

By considering symmetry, the second boundary condition may be written as

Qr ðr ¼ 0Þ ¼ 0

ð44Þ

Application of the boundary conditions (43) and (44) to the general solution (35) results in the following discharge potential:

  F2 ¼ 14N r 2  R 2 þ FR

ð45Þ

Note that, by continuity of flow, the total groundwater discharge at the perimeter of the island is

Qr ðr ¼ RÞ2pR ¼ NpR2 F1 ðr ¼ rw Þ ¼ Fw

ð40Þ

Equation (40) is known as the Thiem equation. The radial component of the discharge vector for a steady well is

rw -0

The general solution to this differential equation is

F ¼ 14Nr 2 þ Alnr þ B

F1 ¼

Qr ¼ 

2.06.10 One-Dimensional Radial Flow

d 2 F 1dF r 2F ¼ þ ¼ N dr 2 r dr

Equation (39) is useful to compute the discharge for a desired head at the well. In practice, it is more common to know the discharge of a well. If the discharge of the well is known, the solution may be written as (combine (38) and (39))

ð46Þ

which may also be derived by taking the derivative of (45).

2.06.10.3 Well at the Center of a Circular Island with Recharge This problem contains both the features of the first two examples: recharge and a pumping well. Here the boundary conditions are specified as

F3 ðr ¼ RÞ ¼ FR

ð47Þ

Q ¼ lim 2prw Qr ðr ¼ rw Þ

ð48Þ

rw -0

Mechanics of Groundwater Flow

Application of the boundary conditions to the general solution yields the following discharge potential:

 Q 1  F3 ¼  N r 2  R 2 þ lnðr=RÞ þ FR 4 2p

ð49Þ

The head as a function of radial distance from the well is shown in Figure 6. The solid line represents the case for which the well pumps half the total areal recharge entering the aquifer, and the dashed line represents the case for which the well pumps exactly all the recharge on the island. Note that for the latter case, the flow at the perimeter of the island is zero and thus the phreatic surface is horizontal there; furthermore, the drawdown at the well is much larger than for the former case. It is emphasized that it is easy to create a case for which the well cannot pump all the infiltrated water. Theoretically, the maximum discharge is reached when the water level at the well is at the bottom of the aquifer; the practical limit is much less, of course. When the specified discharge in formula (49) is not possible, the potential at the well will be negative, and thus a head cannot be computed with the inverse of formula (20) for unconfined flow.

2.06.11 The Principle of Superposition Comparison of the solutions to the three example problems above reveals that the third solution is the sum of the first two solutions with the additive constant modified. Addition of multiple solutions to obtain another solution is an example of the principle of superposition, which is applicable to all linear differential equations, including the equations of Laplace and Poisson. The sum of the potentials of the first two problems in the previous section satisfies the differential equation of the third problem:

Similarly, the boundary condition at the well (48) is satisfied by the sum of the two potentials. Finally, the value of the sum of the two potentials at r ¼ R is a constant

F3 ðr ¼ RÞ ¼ F1 ðr ¼ RÞ þ F2 ðr ¼ RÞ ¼ 2FR

ð51Þ

This is not the value specified in the boundary condition (47), but this is easily corrected by modification of the additive constant. In this example of superposition, two radial solutions are added such that the resulting solution is also radial, one-dimensional flow. In general, however, superposition of radial flow solutions results in two-dimensional flow. As an example, consider two pumping wells of strengths Q1 and Q2 at locations (x1, y1) and (x2, y2) in an infinite aquifer. By superposition, the discharge potential is

F¼

Q1 Q2 lnr1 þ lnr2 þ A 2p 2p

ð52Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi where r1 ¼ ðx  x1 Þ2 þðy  y1 Þ2 ; r2 ¼ ðx  x2 Þ2 þðy  y2 Þ2, and A is a constant that may be determined from a reference point of known potential. The resulting contours of head are presented in Figure 7 with dotted lines and indicate that the superposition of the two one-dimensional solutions results in a truly two-dimensional flow field. The solid lines in Figure 7 are streamlines.

2.06.11.1 A Well in Uniform Flow The case of confined flow to a well in an otherwise uniform flow field is another example of the principle of superposition. The potential for a uniform flow field with a head gradient G in the positive x-direction is given by

F ¼ TGx

ð53Þ

ð50Þ

Head

r 2 F3 ¼ r2 ðF1 þ F2 Þ ¼ r2 F1 þ r2 F2 ¼ 0  N ¼ N

123

R

R/2

0

R/2

R

Radial distance Figure 6 Head as function of radial distance for well at center of circular island with recharge: half the recharge is pumped by the well (solid), and all the recharge is pumped by the well (dashed).

Figure 7 Flow net for two wells with Q1 (left) larger than Q2: head contours (dotted) and streamlines (solid).

124

Mechanics of Groundwater Flow

such that Qx ¼ TG, where T is the transmissivity. The potential for a well in uniform flow is obtained through superposition of the potential for uniform flow (53) and a steady well located at the origin (40) plus an arbitrary constant F0:

F ¼ TGx þ

Q lnðr Þ þ F0 2p

ð54Þ

An example of head contours obtained from this solution is shown in Figure 8. The heavy line in Figure 8 is part of two streamlines that separate the groundwater flowing to the well from the groundwater that flows past the well. This dividing streamline forms the capture zone envelope of the well. It is important to protect drinking water wells from contamination and many countries have guidelines for the delineation and protection of capture zones for water supply wells. Guidelines commonly require protection of the zone of groundwater around the well that will be captured by the well within a certain period of time, for example, 5 years or 20 years. These capture zones and capture zones for other time periods all lie within the capture zone envelope. The dashed lines in Figure 8 represent the 5- and 20- year capture zones for this case. In most cases, capture zones are actually threedimensional parts of the aquifer, but they are commonly approximated as two-dimensional zones on a map. The width W of the capture zone envelope far upstream of the well in Figure 8 may be computed from continuity as

W ¼ Q=ðGT Þ

ð55Þ

At the well (x ¼ 0), the width of the capture zone is reduced to W/2. Special attention is paid to the point on the capture zone envelope farthest downstream of the well. This is a stagnation point, as the discharge vector is, theoretically, zero there. At the stagnation point, the effect of the well is exactly balanced by the hydraulic gradient of the uniform flow. The capture zone boundaries for large times approach the stagnation point, but only the boundary of the capture zone envelope passes through it. For this simple problem, the capture zones for any time period may be evaluated analytically (Bear and Jacobs, 1965). It is more common, however, to evaluate the capture zone boundaries by particle tracking methods (e.g., Strack, 1989; Bakker and Strack, 1996).

2.06.11.2 The Method of Images The method of images is an application of the superposition principle. Wells or other singularities are placed outside of the problem domain using symmetry to satisfy conditions specified along a boundary. For example, if the two wells in (52) have the same discharge (Q1 ¼ Q2 ¼ Q), and are placed symmetrically about the y-axis (x2 ¼  x1, y1 ¼ y2 ¼ 0), the discharge potential becomes

F¼

Q lnðr1 r2 Þ þ A 2p

ð56Þ

Figure 8 Head contours for a well in uniform flow (dotted). Capture zone envelope (heavy solid line), 5-year capture zone (small dashed contour), 20-year capture zone (large dashed contour).

Mechanics of Groundwater Flow

(a)

125

(b)

Figure 9 The method of images. Equipotentials (dotted) and streamlines (solid) for a well pumping near (a) an impermeable boundary and (b) a boundary of constant potential. The dots to the right of the flow field indicate the locations of the image wells.

Investigation of the behavior of this solution along the line passing midway between the wells (x ¼ 0) shows that the xcomponent of the discharge vector is zero. This potential is the solution to the problem of a well pumping next to an infinitely long impermeable boundary in a semi-infinite aquifer. As the problem domain lies to the left of the impermeable line, the well operating at ( þ x1, 0) is referred to as the image well. Contours of the discharge potential are shown in Figure 9(a). Another solution is obtained when the image well at ( þ x1, 0) is given the opposite discharge of the pumping well:

F¼

Q r1 ln þ A 2p r2

ð57Þ

Investigation of the behavior of this discharge potential shows that the potential is constant and equal to A along the line x ¼ 0. This discharge potential is the solution to the problem of a well pumping near a large lake or fully penetrating stream of constant potential A whose boundary lies along x ¼ 0. Again, the image wells lie outside the problem domain. Contours of the discharge potential are shown in Figure 9(b). Superposition and the method of images are two of the primary tools available to hydrologists and engineers for developing analytical solutions to steady and transient groundwater flow problems. Many analytical solutions to problems with wells and equipotential and/or impermeable boundaries may be obtained by the method of images. The method is also applicable to heterogeneity boundaries (Maxwell, 1873; Muskat, 1933) and leaky (Cauchy-type) boundaries (Keller, 1953; Anderson, 2000). The solution to a more complex and practical problem of groundwater flow is developed below.

2.06.11.3 Flow to a Pumping Well in an Alluvial Valley The problem of groundwater flowing in an alluvial aquifer in a bedrock valley is considered. The aquifer is unconfined and

receives areal recharge at a rate N; the governing differential equation is Poisson’s equation (22). The aquifer is bounded below by impermeable bedrock, and to the right by the bedrock wall of the buried valley as illustrated in Figure 10. The condition specified at the valley wall is

Qx ðx ¼ LÞ ¼ 0

ð58Þ

To the left the aquifer is bounded by a flowing river; the sloping head at the river is approximated with the condition

Fð0; yÞ ¼ Ay þ F0

ð59Þ

where F0 is the potential of the river at x ¼ 0 and A is approximately the slope of the water surface of the river. The condition at the pumping well is

Q ¼ lim 2prQr r-0

ð60Þ

where r2 ¼ (x  xw)2 þ (y  yw)2, and (xw,yw) are the coordinates of the pumping well. A solution is obtained by considering three simpler problems, each representing a particular feature of the whole problem, and applying superposition to the results. First, the effects of the recharge are considered. The problem of onedimensional flow along the x-axis from the valley wall (x ¼ L) to a boundary of zero constant potential at the river (F1(0, y) ¼ 0) was solved previously. Substitution of 0 for FL in (33) gives

x  F1 ¼ Nx  L 2

ð61Þ

Second, the effects of the well are considered. Laplace’s equation is solved subject to (58), (60), and F2(0, y) ¼ 0. The solution to this problem is obtained by repetitive use of the method of images about the two aquifer boundaries, using the elementary solutions (57) and (56), which results in an

126

Mechanics of Groundwater Flow

x

(xw ,yw )

Valley wall

Flowing river

y

Q

z x

L Figure 10 Definition sketch: flow to a well in an alluvial valley.

infinite sum of image wells:

F2 ¼

Q ðx  xw Þ2 þðy  yw Þ2 ln 4p ðx þ xw Þ2 þðy  yw Þ2 N QX þ ð1Þn ln 4p n¼1

(

"(

ðx  xw  2nLÞ2 þðy  yw Þ2 ðx þ xw  2nLÞ2 þðy  yw Þ2

ðx  xw þ 2nLÞ2 þðy  yw Þ2 ðx þ xw þ 2nLÞ2 þðy  yw Þ2

)#

)

ð62Þ

Third, the effect of the sloping stream is included. This solution satisfies Laplace’s equation and represents one-dimensional flow along the y-axis:

F3 ¼ Ay þ F0

ð63Þ

As this solution produces no flow in the x-direction, condition (58) is satisfied. By comparison of (63) and (59), it is seen that this boundary condition is also satisfied. The full solution is the sum of the three potentials (61), (62), and (63):

F ¼ F1 þ F2 þ F3

ð64Þ

A careful check of the solution shows that the correct differential equation is satisfied (Poisson’s equation), and that the boundary conditions (58) through (60) are satisfied exactly. Finally, as the flow is unconfined, the discharge potential is related to the head through equation (20). A threedimensional depiction of the groundwater table is shown for this example in Figure 11. The distance between the river and valley wall is L ¼ 500 m, and the distance between the well and the river is 100 m. The bottom of the aquifer is at z ¼ 0 m. In addition, contours are shown on the bottom of the figure.

2.06.12 The Stream Function and the Complex Potential Head contours have been defined previously as curves of constant head which allow us to visualize the variation of mechanical energy within a flow field. In homogeneous aquifers, head contours are equal to potential contours, called equipotentials. Streamlines allow for the visualization of the average paths of groundwater flow. Streamlines are defined as lines that are everywhere tangent to the discharge vector. Using this definition, a differential equation may be written for a

Mechanics of Groundwater Flow

127

40.5

40.0 Vall ey

wall

39.5

39.0 200

400 100 Riv er

300 0

200

−100 −200

100

Figure 11 A three-dimensional depiction of the groundwater table for a well pumping in an alluvial valley as shown in Figure 10. Contours of the head are shown on the bottom of the figure.

streamline

Qy cosa dy=ds ¼ ¼ Qx sina dx=ds

ð65Þ

where s is the position along the streamline and a is the angle between the s and x axes. Substituting in Darcy’s law the equation becomes



q F dx q F dy þ ¼0 q y ds q x ds

ð66Þ

By Darcy’s law, the discharge vector is everywhere normal to equipotentials. Examples are provided in Figure 9 which shows streamlines and equipotentials for flow to a well near an impermeable boundary and near an equipotential boundary. Note that the two sets of lines cross everywhere at right angles. Equipotentials and streamlines may both be drawn for problems of steady groundwater flow governed by the equations of Laplace and Poisson. However, in the case of Laplace’s equation, a special function exists – the stream function, or C(x, y) – whose contours represent streamlines. The stream function exists for a steady, two-dimensional, divergence-free groundwater flow. As the value of the stream function does not change along a streamline,

dC q C dx q C dy ¼ þ ¼0 q x ds q y ds ds

ð67Þ

By comparing (66) with (67), the following relationships are obtained between derivatives of the discharge potential and the stream function:

qF qC ¼ qx qy

ð68Þ

qF qC ¼ qy qx

ð69Þ

Equations (68) and (69) are known as the Cauchy–Riemann equations (e.g., Strack, 1989). It may also be shown that the stream function is single-valued, and harmonic (r2C ¼ 0). These properties of the stream function indicate that it is the harmonic conjugate of the discharge potential. Given a discharge potential that satisfies Laplace’s equation, the corresponding stream function may be evaluated from (68) and (69). The properties of the discharge potential and the stream function suggest the use of complex variables to solve groundwater flow problems governed by Laplace’s equation. There are many texts on complex variables including their use in solving groundwater flow problems (e.g., PolubarinovaKochina, 1962; Verruijt, 1970; Bear, 1972; Strack, 1989). The topic is only briefly discussed here. For groundwater problems governed by Laplace’s equation, a complex potential O exists which is an analytic function of the complex coordinate z ¼ x þ iy. The real part of the complex potential is the discharge potential and the imaginary part is

128

Mechanics of Groundwater Flow

2.06.13 Transient Flow

the stream function:

OðzÞ ¼ Fðx; yÞ þ iCðx; yÞ

ð70Þ

The negative derivative of the complex potential is the complex discharge

WðzÞ ¼ 

dO ¼ Qx  iQy dz

ð71Þ

Introduction of complex variables allows for the use of more sophisticated tools, including conformal mapping, to solve many groundwater flow problems. In particular, using complex variables allows for the simultaneous solution of the discharge potential and the stream function. An example demonstrating the utility of the stream function and the complex potential is presented in the following.

2.06.12.1 Evaluation of the Capture Zone Envelope Using the Complex Potential The complex potential for a well in an otherwise uniform flow field is

O ¼ TGz þ

Q lnz þ F0 2p

ð72Þ

Separation into real and imaginary parts shows that the discharge potential is the same as obtained previously (54):

F ¼ TGx þ

Q lnr þ F0 2p

ð73Þ

Q y 2p

ð74Þ

C ¼ TGy þ

where (r, y) are polar coordinates. The location of the stagnation point, zs, is evaluated as

Wðz ¼ zs Þ ¼ 0

ð75Þ

which gives

zs ¼

Q 2pTG

ð76Þ

The value of the complex potential at the stagnation point is

Oðz ¼ zs Þ ¼ 

 

Q Q 1  ln þ F0 2p 2pTG

ð77Þ

which is a purely real number. Therefore, the value of the stream function at the stagnation point is zero. The contour C ¼ 0 ¼ Cs defines the capture zone envelope:

TGy þ

Q y¼0 2p

ð78Þ

The equation for the capture zone envelope is obtained in polar coordinates using y ¼ r sin y and solving (78) for r:

r¼

Q y 2p TGsiny

ð79Þ

In the previous sections, steady-state flow was treated: the head was only a function of the spatial coordinates. In reality, the head is often also a function of time. When the head increases, more water is stored in the aquifer, and when the head decreases, less water is stored in the aquifer. For steady flow, continuity of flow states that the divergence of the discharge vector (21) is equal to the areal recharge rate N. When groundwater flow is transient, the divergence of the discharge vector is equal to the areal recharge plus the decrease in storage of water in the aquifer. The physics of the storage process is different for unconfined aquifers than for confined aquifers, but with suitable approximations, both lead to the same governing differential equation. The derivation of the governing equation for transient flow from the general statement of conservation of mass includes many approximations which are not discussed here. Rigorous derivations stating all necessary approximations are provided by Verruijt (1969) and Brutsaert (2005). First, consider a column of an unconfined aquifer with constant surface area A. When the head in the column is increased by an amount dh (i.e., the phreatic surface is raised dh), the volume of water in the column increases by an amount

dV ¼ SdhA

ð80Þ

were S [–] is the storativity of the unconfined aquifer. When the aquifer material above the phreatic surface is dry, the storativity of the unconfined aquifer is equal to the porosity. In practice, the storativity is always smaller than the porosity, as there is water present in the pores above the phreatic surface. The storativity of an unconfined aquifer is also called the specific yield. Next, consider a column of a confined aquifer with constant surface area A. When the head is now increased by dh, the volume of water still increases by an amount dV (80), but the storage coefficient is much smaller. Additional water can only be stored in the column through compression of the water and expansion of the aquifer. For most unconsolidated aquifers, the ability of the aquifer to expand is significantly larger than the ability of the water to compress, so that the compression of the water may be neglected. The storage coefficient of a confined aquifer is a function of the aquifer thickness: an aquifer of the same material but twice the thickness has a storage coefficient that is twice as large. The storage coefficient of a confined aquifer may be written as

S ¼ Ss H

ð81Þ

where Ss [L1] is the specific storage of the aquifer. Typical values for the specific storage of sand are between Ss ¼ 103 m1 and Ss ¼ 105 m1. Inclusion of the storage term in the divergence of the discharge vector (21) gives

~ ¼ S rQ

qh þN qt

ð82Þ

Mechanics of Groundwater Flow

1.0

where the areal recharge N may now vary with time. Using the potential for confined flow, this equation may be converted to

1 qF N D qt

0.8

ð83Þ

where the aquifer diffusivity D is defined as

D ¼ T=S

ð84Þ

and T is the transmissivity. The governing differential equation reduces to the diffusion equation when the areal recharge equals zero:

r 2F ¼

1 qF D qt

S qF N kh q t

ð86Þ

This nonlinear differential equation for transient unconfined flow is called the Boussinesq equation (Boussinesq, 1904). A common way to linearize the equation is to replace the head h in front of the time derivative on the right-hand side by an average head h (Strack, 1989), so that the diffusivity of an unconfined aquifer becomes D ¼ S=ðkhÞ: Note that after linearization, unconfined flow is also described by the diffusion equation (in absence of areal infiltration). Another way to linearize the differential equation for transient unconfined flow is to use the differential equation for transient confined flow, to approximate the transmissivity by T E kh and to use the storage coefficient for unconfined flow. The latter approach is used in this chapter. The solution of combined transient confined and transient unconfined flow is not as easy as it was for steady flow, because the storage coefficients differ between confined and unconfined flow. Exact solutions for transient groundwater flow are, not surprisingly, more difficult to obtain than those for steady flow. Common mathematical approaches include separation of variables, Fourier series, and Laplace or other transforms (e.g., Bruggeman, 1999). In this chapter solutions are presented, without derivation, for one-dimensional flow. These solutions are valid for both confined and for unconfined flow as long as the linearization of the differential equation for unconfined flow is reasonable.

2.06.13.1 One-Dimensional Periodic Flow Consider one-dimensional transient flow where the boundary condition varies periodically through time. The aquifer is semi-infinite and is bounded by open water at x ¼ 0; there is no areal infiltration and no flow at infinity. The water table at the boundary varies sinusoidally:

hð0; tÞ ¼ h0 þ Acosð2pt=tÞ

0.6

0.4

0.2

0.0

ð85Þ

The diffusion equation governs the transient behavior of many other physical processes. Using the potential for unconfined flow, the continuity equation (82) may be written as

r 2F ¼

Damping

r 2F ¼

129

ð87Þ

0

2

4

6

8

10

x/ or r/ Figure 12 Damping of the amplitude with distance for one-dimensional periodic flow. Damping of the head when head at boundary varies as (87) (solid) and damping of the radial flow when discharge of a well varies as (90) (dashed).

where A is the amplitude of the fluctuation and t is the time period of the fluctuation. The sinusoidal fluctuation in the surface water (87) may be caused, for example, by tides, by the periodic operation of hydroelectric dams, or by seasonal fluctuations of the surface water level. Solutions to problems of periodic flow may be obtained by separtation of variables. The solution to this problem is

pffi F ¼ Th0 þ AT< expðx i=l þ 2pit=tÞ

ð88Þ

where < stands for taking the real part of the complex function, and l is a characteristic length defined as

l¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi tD=2p

ð89Þ

The amplitude A dampens away from the open water as pffiffiffi exp½x=ðl 2Þ; and is shown in Figure 12. At a distance of 3l, the amplitude has damped to less than 5% of the amplitude at x ¼ 0, and at a distance of 6l, the amplitude has damped to less than 0.25% of the amplitude at x ¼ 0. This result may be used as a rule of thumb to assess whether fluctuations in surface water levels need to be taken into account when considering the head and flow in an aquifer. If the area of interest is farther away from a surface water body than 6l, periodic fluctuations of the surface water level with a period of t may be neglected. Note that l is a function of the period t: the longer the period t, the larger the characteristic length l. Fluctuations with different periods and amplitudes may be superimposed in time. An arbitrary fluctuation of the water level may be approximated by a Fourier series. A similar analysis may be carried out for a well with an average discharge of Q0 and a sinusoidal discharge with an amplitude of Q0:

QðtÞ ¼ Q0 þ Q0 cosð2pt=tÞ

ð90Þ

At a certain distance from the well, the sinusoidal fluctuation of the discharge is unnoticeable and it seems that the well pumps with a steady discharge Q0. This distance depends

130

Mechanics of Groundwater Flow

again on the characteristic length l (89). The discharge vector for a well with a constant discharge Q0 is given by Equation (41). The relative difference between the radial flow caused by the well with sinusoidal discharge (90) and the flow caused by a well with constant discharge Q0 is 4.6% at a distance of 6l, reducing to 0.3% at 10l (see Figure 12). Hence, a well with a periodic discharge (90) varying between 0 and 2Q0 may be represented by a well with steady discharge Q0 beyond a distance of 10l from the well.

water source closer than infinity, and if that source is included in the solution, the transient solution will approach a steady solution for large time. For example, consider a well at (  x1, y1) near a large lake with a constant potential A along y ¼ 0; the steady solution was obtained with the method of images and is given in Equation (57). A transient solution may also be obtained with the method of images as

F¼

   

Q Sr21 Sr22  E1 þA E1 4Tðt  t0 Þ 4Tðt  t0 Þ 4p

ð96Þ

2.06.13.2 Transient Wells In Equation (40) the solution was presented for steady flow to a well with discharge Q. Here, the transient equivalent is discussed. At time t ¼ t0 the head in the aquifer is constant and equal to h0 everywhere and a well starts pumping with discharge Q. The head h0, and thus the corresponding potential F0, at infinity remains constant throughout time:

FðN; tÞ ¼ F0

ð91Þ

This problem may be solved as a similarity solution or by Laplace transforms. The potential as a function of time and the radial distance from the well is



F ¼ F0 þ

Q Sr 2 E1 4p 4Tðt  t0 Þ

 ;

t  t0

ð92Þ

where E1 is the exponential integral defined as

E1 ðuÞ ¼

ZN

expðsÞ ds s

ð93Þ

u

Solution (92) is known as the Theis solution (Theis, 1935). The head is a function of only one dimensionless parameter, u

u¼

Sr 2 4Tðt  t0 Þ

ð94Þ

Hence, if a certain drawdown h0  h(r1, t1) is reached at a distance r1 at time t1, the same drawdown is reached at a distance 2r1 at time 4t1. A common approximation for E1 is the series

E1 ðuÞ ¼ g  ln u 

N X ðuÞ n n¼1

nðn!Þ

ð95Þ

where g ¼ 0.5772y is Euler’s constant. The infinite series in (95) converges quickly (when uo1), so that in practice only a small number of terms needs to be used. One might expect that if the well is pumped for a longenough period of time, the head will approach a steady-state position. This is not the case: the Theis solution (92) does not approach the Thiem solution (40) for large time. For the Thiem solution, the head approaches infinity when r approaches infinity, because the source of water for the Thiem solution lies at infinity. The Theis solution approaches h0 when r approaches infinity according to (91), and all the pumped water comes from storage. In reality, there is always a

When time approaches infinity, u approaches zero, and E1 may be represented with the first two terms of (95). Substitution of these terms for E1 in (96) leads to the steady solution (57). Even though the head of the Theis solution by itself does not approach the steady–state head of the Thiem solution, the discharge vector does approach the steady-solution. The radial flow Qr of the Theis solution may be obtained through differentiation of (92) to give

Qr ¼ 

Q expðuÞ 2pr

ð97Þ

It is seen from this equation that when time approaches infinity, and u approaches zero, Qr approaches the steady discharge vector (41). The consequence is that head gradients in the Theis solution approach the steady head gradients obtained with the Thiem solution, even though the head values themselves do not. The Theis solution is very useful to determine aquifer parameters from a pumping test. During a pumping test, a well is turned on and the drawdown is measured in a nearby observation well. The Theis solution may be fit to observed head data to determine the transmissivity T and the storage coefficient S in the neighborhood of the well. Tansient solutions may be superimposed in time as well as in space. For example, consider a well with a discharge Q operating from t ¼ t0 to t ¼ t1 and with zero discharge after t1. For the period t 4 t1, the potential may be represented by two Theis wells, one with a discharge Q starting at t ¼ t0 and one with a discharge –Q starting at t ¼ t1:

   

Q Sr 2 Sr 2 F¼ E1  E1 ; 4p 4Tðt  t0 Þ 4Tðt  t1 Þ

t  t1

ð98Þ

This is called a pulse solution, where the pulse lasts from t0 until t1.

2.06.13.3 Convolution In the last example of the previous section, a solution was presented for a well that pumped with a discharge Q from t0 to t1. When the pumping period is 1 day, this solution may be used to compute the head variation caused by a well for which daily discharge records are available. This requires the repeated superposition through time of solution (98), called convolution. This solution approach is an example of a standard technique to solve differential equations. More formally, the approach is based on the determination of the solution for a unit impulse, in this case a discharge of unit volume over a short period, theoretically an infinitely short period. The

Mechanics of Groundwater Flow

131

defined as

Fj ðx; y; tÞ ¼ Yðx; y; t; tj Þ  Yðx; y; t; tjþ1 Þ;

t  tjþ1

ð103Þ

Response

where tj ¼ jDt. An example of a pulse response is given in Figure 13. Consider the case for which the applied stress is known over periods of equal length Dt, tn is defined as tn ¼ nDt, and Qn is the stress from t ¼ tn until t ¼ tnþ1. The potential at time tn may be computed with the convolution sum:

Fðx; y; tn Þ ¼

n1 X

Qj Fj ðx; y; tn Þ;

n1

ð104Þ

j¼0

t0

t0 + Δt

Time

Figure 13 Examples at one specific point for an impulse response (solid), step response (dashed), and pulse response (dash-dotted) for a well near a long straight river; the pulse response is identical to the step response for the period of the pulse Dt.

response due to a unit impulse is called the impulse response function (e.g., Figure 13). For a well, the impulse response function y of the potential is

yðr; tÞ ¼

1 expðuÞ 4p t

ð99Þ

The potential for a time-varying discharge Q(t) is obtained with the convolution integral (Duhamel’s principle):

Fðr; tÞ ¼

Zt

Qðt 0 Þyðr; t  t 0 Þdt 0

ð100Þ

N

The Theis equation (92) may be obtained with the convolution integral by specifying the discharge as Q(t0 ) ¼ Q for t0 Z t0 in (99), and using that

dt 0 du ¼ t  t 0 uðr; t  t 0 Þ

ð101Þ

The Theis equation is an example of a step response (e.g., Figure 13): at time t0, the discharge changes from 0 to Q. In general, the unit step response Y is obtained from the impulse response through integration

Yðx; y; t; t0 Þ ¼

Zt

yðx; y; t  t 0 Þdt 0

ð102Þ

t0

where the step occurs at time t0. For practical application, the convolution integral is often written as a sum of pulse response functions. An example of a pulse response was given by the last example in the previous section, where a well was pumped at a constant discharge for a finite period. In general, the pulse response Fj for a pulse of length Dt starting at t ¼ tj is

The convolution approach assumes that the system is linear. Nonlinear behavior may occur, for example, in the summer time when pumping is at its peak and there is little rainfall. During such periods, ditches or streams may go dry, which means that the hydrological system and thus the impulse response function change. In such cases it is not possible to simulate the head variation with a straightforward convolution. In practice, when a system is sufficiently linear, the convolution approach works very well. The pulse response is different for different stresses (areal recharge, pumping, lake-level changes) and needs to take into account all nearby boundary conditions. Once the pulse response for a stress is known at a point, the head variation may be simulated using the convolution approach. Consider again the problem of unconfined flow in a buried bedrock valley to a river of constant head, as illustrated in Figure 5. The solution for a constant recharge rate is given in Equation (33). Instead of a constant recharge rate, the recharge now varies daily as shown in Figure 14(a) for a period of 7 years. Note that the recharge is negative for days without rainfall due to evaporation. The step response for this problem may be obtained with the Laplace transform technique and is given in Bruggeman (1999, Eq. 133.16). Alternatively, the pulse response may be obtained with a computer model; computer models are discussed in the next section. Convolution of the recharge with the step response gives the head variation. The head variation at the valley wall (x ¼ L) and near the river at x ¼ 0.1L are shown in Figure 14(b). Note that the total head variation at the valley wall (B1.8 m) is much larger than near the head boundary (B0.4 m). The head variation at the valley wall has a long memory: the head value depends on the recharge that fell almost 2 years ago. In other words, the pulse response approaches zero after approximately 2 years. The head variation is not shown for the first 2 years in Figure 14(b) as it would require recharge information prior to the record shown in Figure 14(a). When heads are measured, they always show the effect of recharge, as shown in Figure 14. Most head measurements also show the effect of barometric variations and earth tides. The latter are often undesirable and need to be removed; a computer program to remove these variations is called BETCO, which is available for download from the Internet. Time series of head observations always show the effect of the different time-varying stresses that act on the groundwater system. A stochastic approach called ‘time series analysis’ may

132

Mechanics of Groundwater Flow 35

Recharge (mm)

30 25 20 15 10 5 0 −5 2001

2002

2003

(a)

2004 2005 2006 Time, beginning of the year

2007

2008

2004 2005 2006 Time, beginning of the year

2007

2008

2.0

Head (m)

1.5 1.0 x=L

0.5

x = 0.1L

0.0 −0.5 2001 (b)

2002

2003

Figure 14 Daily recharge rate (a) and head variation (b) at valley wall (x ¼ L, large fluctuation), and near specified-head boundary (x ¼ 0.1L, small fluctuation) for the system shown in Figure 10.

be used to unravel the series and compute the head variations due to the individual stresses. The traditional method for time series analysis is the Box–Jenkins method (Box and Jenkins, 1970). Recently, the PIRFICT method was developed for time series analysis of hydrological data (Von Asmuth et al., 2008). The PIRFICT method uses predefined, parameterized shapes for the impulse response functions and allows for irregular time series, and time series with missing data.

2.06.14 Computer Models The relatively simple solutions presented in this chapter may be used to solve real problems. They may be used for first estimates, to verify more complicated models, and to gain insight in the flow problem. In many cases, however, the setting is more complicated than, for example, a well near a long, straight lake boundary. To obtain solutions for more complicated problems, general solution approaches for the governing differential equations are implemented in computer programs. These computer programs may be applied to simulate groundwater flow in domains with more complicated boundary shapes, with a variety of boundary conditions, as well as flow in aquifers that are not homogeneous. The resulting computer models remain an approximation of reality and the modeler must decide what details to put into the model based on the purpose of the model. Most existing computer programs for modeling groundwater flow are based on one of three methods to solve the mathematical problem: the analytic element method, the finite difference method, or

the finite element method. Characteristics of these three methods are discussed here briefly and some references to free software are given. The analytic element method is based on the superposition of analytic functions (Strack, 1989; Haitjema, 1995; Fitts, 2002). In this respect, it is an extension of many of the solutions presented in this chapter. Each analytic function represents a hydrogeologic feature in the aquifer, such as a well, the section of a stream, or the boundary between two geologic formations. Each analytic element has at least one free parameter. The free parameter of an element may be specified or a condition may be specified so that the computer program can compute the value of the free parameter. For example, the free parameter of a well is its discharge. The discharge may be specified, or the modeler may specify the desired head (or drawdown) at the well, and the computer program computes the corresponding discharge (often simultaneously with the other free parameters in the model). An advantage of the analytic element method is that the head and flow can be computed analytically at any point in the aquifer. The model extends, theoretically, to infinity, but has no practical significance beyond the area where sufficient analytic elements are defined to simulate the flow. Many analytic elements exist, including wells, stream segments with and without leaky bottoms, boundaries between zones with different aquifer properties, leaky and impermeable walls, areal recharge, and lakes. Most currently available analytic element programs are restricted to steady flow in piece-wise homogeneous aquifer systems. Analytic element approaches have been developed for

Mechanics of Groundwater Flow

transient flow through piecewise homogeneous aquifers (e.g., Bakker, 2004; Kuhlman and Neuman, 2009) and for flow through aquifers with continuously varying properties (e.g., Craig, 2009). Analytic elements are ideally suited for implementation in an object-oriented computer code; a simple design is presented in Bakker and Kelson (2009). Several analytic element programs are available for modeling steady flow. Single aquifer codes include WhAEM, which contains a graphical interface, and Split. An approach for steady multiaquifer flow was developed by Bakker and Strack (2003) and is implemented in the program TimML. A graphical user interface for both Split and TimML is VisualAEM. Commercial programs are available as well, but are not discussed here. The most popular computer program for modeling groundwater flow is MODFLOW (Harbaugh et al., 2000), which is based on the finite difference method and is available by the download form the internet. MODFLOW model domains are discretized in a grid of rectangles, called cells. Heads are computed at cell centers and flows are computed between cell centers. Hydrogeologic features need to be simplified to fit the chosen grid. The governing differential equation is solved approximately by replacing the derivatives in the differential equation by difference equations. Transient solutions are obtained by stepping through time. The cell size and time step need to be chosen sufficiently small to obtain an accurate solution. A condition must be specified along the entire model boundary. This is in contrast to analytic element models, which do not have a formal boundary. The model boundary should be chosen, where possible, along real hydrogeologic boundaries, such as rivers or impermeable rock outcrops. This is never possible everywhere, however, especially in deeper aquifers. Hence, along parts of the boundary artificial boundary conditions need to be specified based on the modeler’s expert knowledge. The finite difference method is relatively easy to implement in computer codes and allows for continuously varying aquifer properties. Many specialized packages exist for MODFLOW to model a variety of features (e.g., wells, lakes, and drains) and flow types (e.g., unsaturated flow). Seawater intrusion may be simulated with the SWI package or SEAWAT. Creation of input files for MODFLOW is cumbersome. Many powerful graphical interfaces are available commercially. A free graphical interface is version 5 of PMWIN. Python scripts to run MODFLOW are available by the download from the Internet. The finite element method also requires a discretization of the model area, although the common choice is a discretization in triangles. Heads are computed at the corners of the triangles (called nodes) and flows are computed between them. The mathematics behind the finite element method is based on a minimization principle, which is more complex than the finite difference method. Grids of triangles are more flexible than grids of rectangles as it is much easier to represent shapes of hydrogeologic features with triangles, and small triangles can be used where needed. Sophisticated grid builders are available to construct complicated grids of triangles. Other practical advantages and disadvantages of the finite element method are similar to the finite difference method. It is more complicated to implement the finite element method in a computer code than the finite difference method. A commonly used free finite element code is SUTRA, which can also be used to model unsaturated flow and variable density flow.

133

2.06.15 Discussion The basic principles of groundwater flow were discussed in this chapter. Essential principles, such as Darcy’s law, the Dupuit approximation, and the derivation of Laplace’s equation, originate from the nineteenth century. Many analytic solutions were developed in the twentieth century, while numerical methods and computer models became important in the last few decades. The size of numerical models has grown over the years with the available computational power. Evaluation in other fields shows that the size of modeling grids follows Moore’s law and doubles approximately every 1.5 years, as does the computational power (Voller and Porte´ Agel, 2003). Analytic solutions play an important role in the evaluation of numerical model results. Analytic formulas may be used to assess whether the results of a numerical model are approximately correct. Such a comparison often shows that application of a simple analytic model provides a very reasonable solution. A number of discussions have been published on the future of hydrogeology in the first decade of the twenty-first century (e.g., Voss, 2005; Miller and Gray, 2008). Progress is being made in the modeling of heterogeneous domains using advanced calibration tools and assessment of the predictive ability of groundwater models (e.g., Doherty, 2008) and stochastic modeling (e.g., Zhang and Zhang, 2004). Another active area of research is the linkage of groundwater models with unsaturated zone models, surface water models, and ultimately atmospheric models. Such linkages run into serious issues of differences in temporal and spatial scales that have yet to be resolved satisfactorily. As in other areas of hydrology, some groundwater models try to include details because they exist, not because they matter (Haitjema, 1995). The inability to capture the full complexity of systems and processes makes the search for accurate simplifications a continuing endeavor.

Acknowledgments We gratefully acknowledge Tanja Euser for creating Figures 1– 5 and 32.10.

References Anderson EI (2000) The method of images for leaky boundaries. Advances in Water Resources 23: 461--474. Bakker M (2004) Transient analytic elements for periodic Dupuit–Forchheimer flow. Advances in Water Resources 27(1): 3--12. Bakker M and Kelson VA (2009) Writing analytic element programs in Python. Ground Water 47(6): 828--834. Bakker M and Strack ODL (1996) Capture zone delineation in two-dimensional groundwater flow models. Water Resources Research 32(5): 1309--1315. Bakker M and Strack ODL (2003) Analytic elements for multiaquifer flow. Journal of Hydrology 271(1–4): 119--129. Bear J (1972) Dynamics of Fluids in Porous Media. New York: Dover Publications. Bear J and Jacobs M (1965) On the movement of water bodies injected into aquifers. Journal of Hydrology 3: 37--57. Boussinesq J (1904) Recherches the´oriques sur le coulement des nappes d’eau infiltre´es dans le sol. Journal de Mathe´matiques Pures et Applique´es 10: 5--78. Box GEP and Jenkins GM (1970) Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden-Day.

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Mechanics of Groundwater Flow

Bruggeman GA (1999) Analytical Solutions of Geohydrological Problems, Developments in Water Science, 46, Amsterdam: Elsevier. Brutsaert W (2005) Hydrology – An Introduction. New York: Cambridge University Press. Craig JR (2009) Analytic elements for flow in harmonically heterogeneous aquifers. Water Resources Research 45: W06422 (doi:10.1029/2009WR007800). Darcy H (1856) Les Fourntaines Publiques de la Vlle de Dijon. Paris: Dalmont. Doherty J (2008) Manual and Addendum for PEST: Model Independent Parameter Estimation. Brisbane, Australia: Watermark Numerical Computing. Dupuit J (1863) E´tudes The´oriques et Practiques sur le Mouvement des Eaux dans les Canaux Decouverts et a´ Travers les Terrains Perme´ables, 2nd ed. Dunod: Paris. Fitts CR (2002) Groundwater Science. New York: Academic Press. Forchheimer P (1886) Ueber die Ergiebigkeit von Brunnenanlagen und Sickerschlitzen. Z. Architekt. Ing. Ver. Hannover 32: 539--563. Haitjema HM (1995) Analytic Element Modeling of Groundwater Flow. San Diego, CA: Academic Press. Harbaugh AW, Banta ER, Hill MC, and McDonald MG (2000) MODFLOW-2000, the US Geological Survey modular ground-water model-user guide to modularization concepts and the ground-water flow process. USGS Open-File Report 00–92. Jacob CE (1940) The flow of water in an elastic artesian aquifer. Transactions, American Geophysical Union 21: 574--586. Keller JB (1953) The scope of the image method. Communications on Pure and Applied Mathematics VI: 505--512. Kirkham D (1967) Explanation of paradoxes in Dupuit–Forchheimer seepage theory. Water Resources Research 3(2): 609--622. Kuhlman KL and Neuman SP (2009) Laplace-transform analytic-element method for transient porous-media flow. Journal of Engineering Mathematics 64(2). 113–13. Maxwell JC (1873) A Treatise on Electricity and Magnetism, vol. 1, Oxford: Clarendon Press. Meinzer OE (1928) Compressibility and elasticity of artesian aquifers. Economic Geology 23: 263--291. Miller CT and Gray WG (2008) Hydrogeological research, education, and practice: A path to future contributions. Journal of Hydrologic Engineering 13(7): 7--12. Moore C and Doherty J (2006) The cost of uniqueness in groundwater model calibration. Advances in Water Resources 29: 605623. Munson BR, Young DF, and Okiishi TH (2002) Fundamentals of Fluid Mechanics, 4th edn. New York: John Wiley and Sons. Muskat M (1933) Potential distribution about an electrode on the surface of the Earth. Physics 4(4): 129--147. Polubarinova-Kochina PY (1962) Theory of Groundwater Movement. De Wiest JMR. (trans.). Princeton, NJ: Princeton University Press.

Strack ODL (1984) Three-dimensional streamlines in Dupuit-Forchheimer models. Water Resources Research 20(7): 812--822. Strack ODL (1989) Groundwater Mechanics. Englewood Cliffs, NJ: Prentice Hall. Theis CV (1935) The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. Transactions, American Geophysical Union 16: 519524. Verruijt A (1969) Elastic stage of aquifers. In: de Wiest RJM (ed.) Flow Through Porous Media, pp. 331--376. New York: Academic press. Verruijt A (1970) Theory of Groundwater Flow. New York: MacMillan. Voller VR and Porte´ Agel (2003)) Moore’s law and numerical modeling. Journal of Computational Physics 172(2): 698--703. Von Asmuth JR, Maas K, Bakker M, and Petersen J (2008) Modeling time series of ground water head fluctuations subjected to multiple stresses. Ground Water 46(1): 30--40. Voss CI (2005). The future of hydrogeology. Hydrogeology Journal 13(1): 1–6. Zhang Y-K and Zhang D (2004) Forum: The state of stochastic hydrology. Journal of Stochastic Environmental Research and Risk Assessment 18(4): 265.

Relevant Websites http://www.civil.uwaterloo.ca Civil and Environmental Engineering, University of Waterloo; James R. Craig, Visual AEM. http://www,epa.gov EPA: United States Environmental Protection Agency; Ecosystems Research Division, WLAEM2000. http://code.google.com Google.Wigaem; timml; flopy. http://www.groundwater.buffalo.edu Groundwater Research Group, UB Groundwater Group Software. http://www.hydrology.uga.edu Hydrology@University of Georgia; BETCO: Barometric and Earth tide Correction. http://bakkerhydro.org Mark Bakker, SWI package. http://www.pmwin.net PMWiN.NET by Wen-Hsing Chiang, PMWIN Version 5.3. http://water.usgs.gov USGS: U.S. Geological Survey. SEAWAT; SUTRA Version 2.1; MODFLOW-2000 version 1.18.01.

2.07 The Hydrodynamics and Morphodynamics of Rivers N Wright, University of Leeds, Leeds, UK A Crosato, UNESCO-IHE, Delft, The Netherlands & 2011 Elsevier B.V. All rights reserved.

2.07.1 2.07.2 2.07.2.1 2.07.2.1.1 2.07.2.1.2 2.07.2.1.3 2.07.2.2 2.07.2.2.1 2.07.2.3 2.07.2.4 2.07.2.5 2.07.2.6 2.07.2.7 2.07.2.7.1 2.07.2.7.2 2.07.2.8 2.07.2.9 2.07.2.10 2.07.2.10.1 2.07.2.10.2 References

Early History of Hydrodynamics and Morphodynamics in Rivers and Channels State of the Art in Hydrodynamics and Morphodynamics Fluid Flow Mass Momentum Energy Numerical Solution Boundary conditions Depth and Process Scales Cross-Section Scale River Reach Scale Spatial Scales in River Morphodynamics Geomorphological Forms in Alluvial River Beds Ripples and dunes Bars River Planimetric Changes Bed Resistance and Vegetation Discussion of Current Research and Future Directions Incremental changes Step changes

2.07.1 Early History of Hydrodynamics and Morphodynamics in Rivers and Channels The study of flow in open channels and their shape is inextricably linked to the study of fluid dynamics more generally, and hydrodynamics can perhaps be best defined as the application of the theory of fluid dynamics to flows in open channels. Early work on the general properties of fluids was carried out by the ancient Greeks. They studied many fluid phenomena, and the work of Archimedes on hydrostatics is well known. However, it was the Romans who demonstrated a more practical knowledge of fluid flow and open-channel flow in particular. They constructed advanced water-supply systems including aqueducts and water wheels. Archaeological evidence confirms their use of sophisticated siphon systems that required advanced techniques to seal the pipes in order to maintain the necessary pressures and this is likely to have required an understanding of pressure and fluid potential energy. Unfortunately, there is no documentary evidence of the knowledge that they had, as it was a practical skill. In Islamic civilizations around the ninth century, engineers and physicists studied fluid flow and made use of hydraulics through water wheels in order to process grain and carry out other mechanical tasks. They also engineered channels for irrigation and developed the systems of qanats for irrigation. Chinese engineers also harnessed energy by using water wheels to power furnaces.

135 137 138 138 138 138 139 139 139 140 141 141 143 143 145 146 148 151 152 152 152

Despite its widespread use and study the theory of open channel flow did not advance, and by the beginning of the nineteenth century the study of flow in pipes was probably more advanced, particularly in its mathematical description. This reflects the intrinsic difficulty of open-channel flow that is often not fully appreciated by a cursory examination. Under more detailed examination, it becomes clear that we do not know a priori what the depth will be in a channel as opposed to full pipe flow where the cross-sectional area is known: that is, the relationship between depth (m), discharge (m3 s1), and cross-sectional geometry cannot be expressed in a simple formula. In essence, this is the fundamental question to be answered by both theoreticians and practitioners. The situation is further complicated by the high variation in bed and bank material. Due to this complexity, early studies were empirical. The first step to a more mathematics- and physics-based approach had been taken by Leonardo da Vinci (1452–1519). His book entitled Del moto e misura dell’acqua (Water Motion and Measurement), written in around 1500 and published in 1649 after his death, is a treatise of nine individual books, of which the first four deal with open-channel flows (Graf, 1984). In this book, da Vinci made an early attempt to formulate the law of continuity linking the water flow to channel width, depth, slope, and roughness. Nevertheless, the founder of river hydraulics has been traditionally viewed as Benedetto Castelli (1577–1644), a pupil of Galileo Galilei, who wrote

135

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the book entitled Della misura delle acque correnti (Measurement of Water Flows) (1628), in which he explained the law of continuity in more precise terms. It is perhaps worth noting that Castelli was also engaged by the Pope as a consultant on the management of rivers in the Papal States, reflecting the combination of theoretical and practical approaches. Sir Isaac Newton (1642–1727) discussed fluid statics and dynamics at length in his Principia Mathematica (1687) (Anderson, 2005). He proposed his law of viscosity stating that shear stress was proportion to the velocity gradient with the constant of proportionality being the viscosity. Newton’s work informed later studies and Prandtl used the shear stress relationship to create an analogy for turbulent flow. In the eighteenth century, work on the fundamental mathematical description of fluid mechanics was advanced by Daniel Bernoulli (1700–82), Jen le Rond d’Alembert (1717–83), and Leonhard Euler (1707–83). The latter used momentum and mass conservation to derive the Euler equations for fluid flow and these were not surpassed until the Navier–Stokes equations were derived with their treatment of viscous shear stress. These were derived independently by Claude-Louis Navier in 1822 and George Stokes in 1845 (Anderson, 2005). The Navier–Stokes equations were of a general nature. In terms of open-channel flow, it was realized that the key parameters were discharge (m3 s1), depth (m), cross-section geometry, longitudinal bed slope, and the nature of the bed and banks. The cross-section geometry is clearly infinitely variable and difficult to encapsulate in a formula and so key geometric properties were chosen to represent it. These are wet area (A (m2)) and wetted perimeter (P (m)), and these are often used to derive the hydraulic radius, R ( ¼ A/P (m)). Based on this theory, Che´zy (1717–98) developed his theory of open-channel flow as balance of the frictional and gravitational force. He proposed the formula

pffiffiffiffiffiffi V ¼ C RS

ð1Þ

where C is the Che´zy coefficient (m1/2 s1), R the hydraulic radius (m), and S the longitudinal bed slope (m m1). Although C is often assumed to be constant for a given channel, it has dimensions and does vary with the water depth. Later Manning proposed an alternative formula based on his measurements and this has been widely adopted in the English-speaking world:

1 V ¼ R 2=3 S 1=2 n

ð2Þ

where n is Manning’s coefficient. Again, this is dimensional (m1/3 s1) and varies with water depth. The formulations by Che´zy and Manning are valid for flows that are steady state and uniform. These assumptions clearly do not apply in many cases, particularly in natural rivers. In a treatise published in 1828, Be´langer put forward an equation for a backwater in steady, one-dimensional (1-D) gradually varied flows, that is, flows with constant discharge, but gradually varied depth (Chanson, 2009). This equation can be used to qualitatively assess the flow profile in a section of a river and further allows for the analysis of the profile across a series of different reaches with different characteristics

(Chanson, 1999). It still uses Che´zy or Manning to calculate a friction slope, but it must be borne in mind that this takes these equations beyond their validity. A full solution of the backwater equation is not possible with a closed or continuous solution, but it is possible to use discrete, stepping methods to calculate solutions as a set of points moving away from a control section. This is one of the early examples of numerical solution. Be´langer used the direct step method to calculate the longitudinal distance taken for a given depth change, and other methods such as the standard step method, Euler method, and predictor–corrector methods have subsequently been developed. Be´langer also recognized the importance of the Froude number, which is the ratio of momentum to gravitational effects in an open channel and which governs whether information can flow upstream, in a similar way to its analogy, the Mach number, in compressible gas dynamics. Be´langer also identified that there were singular points in the solution of the backwater equations where the flow was critical and where the Froude number has the value of 1. The ability to calculate gradually varied flow allowed for the calculation of water profiles between control points and critical points, but it is not applicable at the control points themselves. These control points include structures such as weirs, sluices, and bridges which were increasingly being used in the nineteenth century as a result of the industrial development in Europe. Be´langer paid much attention to the phenomenon of the hydraulic jump. This is observed when the water flow changes from a shallow, fast flow with a Froude number greater than 1 to a flow that is deep and slow with a Froude number less than 1. This transition cannot occur smoothly and is therefore highly turbulent and complex. Be´langer used the momentum concept to derive an equation relating the depths upstream and downstream of the jump (the conjugate depths). After a first attempt, he presented his complete theory in 1841 (Chanson, 2009) and the equation bearing his name is still in use today. Be´langer also went on to examine other control structures such as the broad-crested weir. This formed the basis of the study of rapidly varied flows using the concept of specific energy to obtain insight into the phenomena. Further progression in 1-D open-channel flow led to the development of the full shallow water equations by Barre´ de Saint Venant (1871) but these are discussed in the next section in view of their continued widespread use in modern river modeling software. The next major development of relevance to open-channel flow came in the more general field of boundary layer theory. The boundary where the main flow in a channel meets the bed and banks is of crucial importance particularly in steady flows where there is a balance between gravity and the friction generated at the interface. The contribution of Ludwig Prandtl (1875–1953) to fluid dynamics was significant and comprehensive (Anderson, 2005), but the most significant contribution was to identify the concept of the boundary layer. He postulated that the flow at a surface was zero and that the effect of friction was experienced in a narrow layer adjacent to the surface: away from this boundary layer, the flow was inviscid and could be studied with simpler techniques such as those of Euler. Prandtl then used his theory to derive

The Hydrodynamics and Morphodynamics of Rivers

equations for the velocity profile and consequent shear stresses in the boundary layer. These concepts are particularly relevant to open-channel flows as they demonstrate that the friction effects are confined to a narrow region adjacent to the bed and banks; they also provide a theoretical framework for studying these. Nikuradse used these concepts to study the effect of roughness in pipes and this led to his seminal work that produced the concept of sand grain roughness in pipes. He used the latter to derive friction factors for pipes and much of this theory was later transferred to the study of resistance due to friction in open channels. In the above, we can see that there has been a move from empiricism to a more physical and mathematical basis for the equations used in open-channel flow. However, a completely nonempirical formulation is still not available and is arguably impossible to achieve. This distinction should always be borne in mind and it is vital to remember that although we can find accurate solutions to the equations, these solutions represent models of reality and whoever is conducting the analysis must also use their knowledge and judgment in drawing conclusions. So far, this brief history has focused on hydrodynamics, but in addition to the movement of water, an understanding of rivers needs a sound understanding of the movement of sediment and changes in the shape and location of the river channel. The balance between entrainment and deposition of sediment by water flow is the fundamental process governing the geomorphological changes of alluvial rivers at all spatial and temporal scales. The water flow over a mobile bed generates spatial and temporal variations of the sediment transport capacity, causing either net entrainment or net deposition of sediment. Subtractions and additions of sediment are the cause of local bed level changes that in turn alter the original flow field. The discipline of river morphodynamics deals with the interaction between water flow and sediment, which is controlled by the bed shape evolution. Morphodynamic studies use the fundamental techniques of fluid mechanics and applied mathematics to describe these changes and to treat related problems, such as local scour formation, bank erosion, river incision, and river planimetric changes (Parker’s e-book). River morphodynamics became a science with Leonardo da Vinci, who annotated and sketched several morphodynamic phenomena (Manuscript I, 1497), such as bed erosion and deposit formation generated by flow disturbances due to obstacles, channel constrictions, and river bends. Leonardo reported two possible experiments, one on bed excavation by water flow and another on near-bank scour (Marinoni, 1987; Macagno, 1989). Initiation of sediment motion was first described by Albert Brahams (1692–1758), who wrote the two-part book Anfangsgru¨nde der Deich und Wasserbaukunst (Principles of Dike and Hydraulic Engineering) between 1754 and 1757. Brahams suggested that initiation of sediment motion takes place if the near-bed velocity is proportional to the submerged bed material weight to the one-sixth power, using an empirically based proportionality coefficient. Later Shields (1936) proposed a general relationship for initiation of sediment motion based on the analysis of data gathered in numerous experiments. He provided an implicit relation between shear velocity, u*(m s1), and critical shear stress, tc (Pa), at the

137

point of initiation of motion. His relationship is still the one most used for issues dealing with sediment transport. Although sediment transport is the basic process leading to geomorphological changes in rivers, it is the balance between the volume of sediment entrained by the water flow and the volume of deposited sediment that governs the shape of river beds. Pierre Louis George Du Buat (1734–1809), in his Principes d’hydraulique (Du Buat, 1779), realized the importance of bed material for the river cross-sectional shape and conducted experiments to study the cross-section formation in channels excavated in different soil materials ranging from clay to cobbles. However, the first attempt to treat a morphodynamic problem in quantitative terms was made only about one century and a half later by the Austrian Exner (1925), who is consequently considered the founder of morphodynamics. Exner was interested in describing the formation of dunes in river beds, for which he derived one of the existing versions of the conservation laws of bed sediment that are now known as Exner equations. His equation, however, does not describe dune generation, but the evolution of existing dunes:

q zb q qs ð1  pÞ ¼ qt qx

ð3Þ

where p is the soil porosity (–); zb the bed level (positive upward) (m); t the time (s); qs the sediment transport rate per unit of channel width (m2 s1); and x the longitudinal direction (m). By substituting the sediment transport rate, qs, with a monotonic function of flow velocity in Equation (3), the obtained relation reads

  q zb dqs q u ¼ with qs ¼ qs ðuÞ qt du q x

ð4Þ

where u is the flow velocity (m s1). The amount of transported sediment qs increases when the velocity increases, which means that the term

dqs du

ð5Þ

in Equation (4) is always positive. The result is that erosion occurs in areas of accelerating flow, whereas sedimentation occurs in areas of decelerating flow. This could explain why dunes move downstream. Exner had assumed sediment transport capacity to be simply proportional to the flow velocity, whereas in reality sediment transport capacity is related to the flow velocity to the power three or more (Graf, 1971). The combination of Exner’s relation (Equation (3)) to a relation for sediment transport and to the continuity and momentum equations for water flow leads to a fully integrated 1-D morphodynamic model. Several models of this type have been developed after Exner and it is not easy to establish who was the first to do this. Already in 1947, van Bendegom developed a mathematical model describing the geomorphological changes of curved channels in two dimensions (2-D). The model consisted in coupling the 2-D (depth-averaged) momentum and continuity equations for shallow water with the sediment balance equation (Exner’s equation in two dimensions) and a relation describing the sediment transport

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The Hydrodynamics and Morphodynamics of Rivers

capacity of the flow. He corrected the sediment transport direction to take into account the effects of spiral flow and channel bed slope. van Bendegom carried out the first simulation of 2-D morphological changes of a river bend with fixed banks by hand, since computers were not available then. Bank erosion was finally introduced in 1-D morphodynamic models in the 1980s (Ikeda et al., 1981) and in 2-D models about 10 years later (Mosselman, 1992). Only in recent decades it has been realized that river morphology may be strongly influenced by the presence of aquatic plants and animals, as well as by floodplain vegetation (Tsujimoto, 1999). For a long period, vegetation in open channels was only considered as an additional static flow resistance factor to bed roughness, although already at the end of the nineteenth century some pioneer concepts suggested links between the river geomorphology and plants (Davis, 1899). Over the past few decades the move from empiricism to a more theoretical description of hydrodynamics and morphodynamics has been followed by a move from the expression of theory in equations to computer-based methods. Initially, the latter involved numerical solution of the theoretical equations, but more recently it has been developed with machinelearning techniques for extracting information from measured data which can be seen as a return to empiricism but with vast computing resources compared with past centuries.

2.07.2 State of the Art in Hydrodynamics and Morphodynamics Rivers convey water and sediment through the catchment to the sea. Moving water and sediment are subjected to forces such as gravity, friction, viscosity, turbulence, and momentum. In order to quantify the system we consider physical variables, such as velocity, depth, discharge, sediment concentration, and channel shape. Hydrodynamics and morphodynamics seek to relate these variables to the forces using the concepts of momentum and energy.

•

Process scale (local). This is the spatial scale at which processes, such as sediment entrainment, deposition, and turbulence, occur.

Whatever scale is being considered, the fundamental principles used in fluid dynamics are conservation of mass, momentum (Newton’s second law), and energy. These may need to be simplified according to the scale under consideration, the data available, and the level of detail required in the analysis, but they cannot be violated.

2.07.2.1.1 Mass Conservation of mass is based on the fact that mass can be neither created nor destroyed; therefore, within a general control volume the accumulation of mass is equivalent to the difference between the input and the output. For a definitive derivation the reader is referred to Batchelor (1967) and for a more accessible derivation to Versteeg and Malalasekera (2007). Expressed in partial differential form, conservation of mass is governed by

q q q q ðrÞ þ ðr  uÞ þ ðr  vÞ þ ðr  wÞ ¼ 0 qt qx qy qz

where r is the water density (kg m3); x the longitudinal distance (m); y the transversal distance (m); z the vertical distance (m); t the time (s); u the flow velocity component in longitudinal direction (m s1); v the flow velocity component in transversal direction (m s1); and w the flow velocity component in vertical direction (m s1). Equation (6) states that the change in density r with respect to time within a volume element plus the change in mass flow ðr  uÞ in x-direction plus the change in mass flow ðr  vÞ in y-direction plus the change in mass flow ðr  wÞ in z-direction is equal to zero. In comparison, the equation for the conservation of mass in integral form for an arbitrary volume is

q qt

Z Z Z

r  dV þ

V

2.07.2.1 Fluid Flow The concept of scale, both spatial and temporal, is vital to any study of hydrodynamics or morphodynamics and so in the discussions below we consider the following spatial scales:

•

•

•

Reach scale (entire river reach). A river reach is a large part of the river, which can reasonably be considered as uniform. River reach studies focus on the longitudinal variations of flow field, water depth, and other variables, such as sediment concentration. Often, one value of the variable per river cross section is enough. Cross-section scale (main channel cross section). This is the spatial scale of studies for which the transverse variations of flow field, water depth, roughness, etc., are relevant. In this case it is often sufficient to derive the depth-averaged value of the variable and its variation in transverse direction. Depth scale (water depth). This is the spatial scale of those studies for which the vertical variations of flow field are relevant.

ð6Þ

Z Z

r  u  dS ¼ 0

ð7Þ

S

where the change in density r with respect to time within the control volume plus the change in mass flow r  u over the surface S of the control volume is zero. More compactly, the equation in divergent form is

q ðrÞ þ =  ðr  uÞ ¼ 0 qt

ð8Þ

with the velocity vector u ¼ u  i þ v  j þ w  k in the three directions i, j, k in space.

2.07.2.1.2 Momentum Newton’s second law states that the rate of change of momentum of a body is equal to the force applied. In the case of a fluid, this principle is applied to the general control volume and the net momentum flux (inflow less outflow) is equated to the forces. The forces considered depend on the situation under consideration, but the main ones are gravity, shear stress, and pressure. Again the reader is referred to other

The Hydrodynamics and Morphodynamics of Rivers

texts for detailed derivation (Batchelor, 1967; Versteeg and Malalasekera, 2007).

    Du qp q qu q qu qv r ¼ þ 2m þ l div u þ m þ Dt qy qy qx qx qx qx    q qu qw m þ þ Fx ð9aÞ þ qz qz qx      Dv qp q qu qv q qv þ m þ þ 2m þ l div u r ¼ qy qx qy qx qy Dt qy    q qn qw þ m þ þ Fy ð9bÞ qz qz qy       Dw qp q qu qw q qv qw þ m þ þ m þ r ¼ qz qx qz qx qz qy Dt qy   q qw ð9cÞ þ 2m þ l div u þ Fz qz qz

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functions as solutions, to a set of algebraic equations that connect values at various discrete points that can be manipulated by a computer. This process is called discretization. Various methods are used for this and the main three are finite difference, finite element, and finite volume. More details can be found elsewhere (Wright, 2005).

2.07.2.2.1 Boundary conditions Whether seeking an analytical or numerical solution, it is necessary to specify boundary conditions for any problem. In open-channel flow, these are specific and tend to be different from those encountered in other fields. In most cases the flow in a reach of river or channel is controlled by a specified discharge at the upstream and downstream boundary, a condition that specifies the depth. The latter includes a fixed depth, a time-varying depth, a critical flow condition, or a depth-discharge relationship.

2.07.2.3 Depth and Process Scales where u, v, and w are the components of velocity in the x, y, and z directions respectively; r the density; p the pressure; m the dynamic viscosity; l the second viscosity; and Fx, Fy, and Fz are the components of body force. Using the divergent form again gives the Navier–Stokes equations as

Du qp r ¼  þ r  ðmruÞ þ Fx qx Dt

ð10Þ

2.07.2.1.3 Energy Conservation of energy comes from the first law of thermodynamics

dE ˙ þ Q˙ ¼W dt

ð11Þ

which states that the change in the total energy E in the vol˙ plus the heat flux Q˙ in the ume element equals the power W volume element. Its application is dependent on the exact situation in which it is applied, and given the large variation in situations it will not be considered in detail here.

2.07.2.2 Numerical Solution It is possible to solve Equations (6)–(11) analytically in a few, simplified cases, and pioneers such as Prandtl were able to obtain significant insight through doing this. However, the full equations are not amenable to closed solutions and only with the advent of digital computing it has become possible to obtain solutions, albeit approximated ones. To derive a form that is suitable for computer solution, the continuous partial derivatives are converted to difference equations for discrete, point values. There are many ways of doing this and specific cases are discussed below in the relevant context. However, numerical techniques for partial differential equations fall into three main categories: finite differences, finite volumes, and finite elements. The initial task, as mentioned above, is to convert the differential equations, which have continuously defined

Viewed at a local scale, the flow is complex and 3-D. It has a predominant downstream flow direction, but the flow can be separated into a boundary layer, where the effects of the boundary and its nature are predominantly felt, and the free stream flow. Within the latter, there are relatively low gradients as the speed of the water increases toward the free surface. The maximum speed is achieved just below the free surface and there is a slight reduction at the surface due to the effects of air resistance and the attenuation of turbulence toward the surface. At channel bends, a particular flow structure is observed. The water higher in the column travels faster than that at a lower position and therefore does not change its direction in as short a distance. This leads to an increase in the water surface elevation at the outer, concave bank, which in turn drives fluid down and along the bed toward the inner, convex bank. In this way, we observe a super-elevation at the outer bend and a secondary circulation. Further counter-rotating circulations may be induced by the main secondary circulation if the bend is sharp (Blanckaert, 2002). The particular configuration of the flow inside river bends should be taken into account for the modeling of sediment transport and river morphodynamics. The complete description of fluid flow, based on the continuum hypothesis which ignores the molecular nature of a fluid, is given by the Navier–Stokes equations described above. For a laminar flow, these equations can be discretized to give a highly accurate representation of the real fluid flow. However, laminar flow rarely occurs in open-channel flows so we must address one of the fundamental phenomena of fluid dynamics: turbulence. As the Reynolds number (Reynolds number is defined by Re ¼ ruL/m, where r is the density, u the velocity, L the representative length scale, and m the viscosity) of a flow increases, random motions are generated that are not suppressed by viscous forces as in laminar flows. The resulting turbulence consists of a hierarchy of eddies of differing sizes. They form an energy cascade which extracts energy from the mean flow into large eddies and in turn smaller eddies extract energy from these which are ultimately dissipated via viscous forces.

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In straight prismatic channels, secondary circulations are present just as in curved ones, but at a much smaller magnitude. Although the main flow is in the downstream direction with no deviation, the effect of the walls on turbulence causes secondary circulations of the order of 1–2% of the main flow (Beaman et al., 2007). Turbulence is perhaps the most important remaining challenge for fluid dynamics generally. In theory, it is possible to predict all the eddy structures from the large ones down to the smallest. This is known as direct numerical simulation (DNS). However, for practical flows this requires computing power that is not available at present and may not be available for many years. A first level of approximation can be made through the use of large eddy simulations (LESs). These use a length scale to differentiate between larger and smaller eddies. The larger eddies are predicted directly through the use of an appropriately fine grid that allows them to be resolved. The smaller eddies are not directly predicted, but are accounted for through what is known as a subgrid scale model (Smagorinsky, 1963). This methodology can be justified physically through the argument that large eddies account for most of the effect on the mean flow and are highly anisotropic whereas the smaller eddies are less important and mostly isotropic. Care is needed in applying these methods as an inappropriate filter or grid size and low accuracy spatio-temporal discretization can produce spurious results. If this is not done, LES is not much more than an inaccurate laminar flow simulation. Although less computationally demanding than DNS, LES still requires fine grids and consequently significant computing resources that still mean it is not a viable, practical solution. In view of the demands of DNS and LES, most turbulence modeling still relies on the concept of Reynolds averaging where the turbulent fluctuations are averaged out and included as additional modeled terms in the Navier–Stokes equations. The most popular option is the k–e model, which is usually the default option in Computational Fluid Dynamics (CFD) software, where k represents the kinetic energy in the turbulent fluctuations and e represents the rate of dissipation of k. Interested readers are referred to CFD texts (Versteeg and Malalasekera, 2007) for further details. Given the complexities and computational demands of 3-D modeling in rivers, it has largely remained a research tool. Notable work has been done by Rastogi and Rodi (1978), Olsen and Stokseth (1995), Hodskinson and Ferguson (1998), and Morvan et al. (2002), and a more comprehensive review is given by Wright (2001).

2.07.2.4 Cross-Section Scale The fully 3-D equations while being a complete representation are computationally expensive to solve and in many situations unnecessarily complex. It is therefore necessary to simplify them and this is often done in the case of open-channel flow. The assumption is made that the flow situation being considered is shallow, that is to say, the lateral length scale is much greater than the vertical one (note: in this regard the Pacific Ocean is shallow in that it is much wider than it is deep!). Once we have assumed shallow water, we can further assume that streamlines are parallel and that there is no

acceleration in the vertical leading to the vertical momentum equation being replaced by an equation for hydrostatic pressure. In turn, once we have assumed that there is no vertical velocity, we can depth-integrate the two horizontal velocities, resulting in three equations: one for conservation of mass and two for momentum in the horizontal. These equations can be derived rigorously by either considering the physical situation or applying the assumptions to the Navier–Stokes equations. These 2-D equations are less time consuming to solve than the Navier–Stokes equations and there is a significant body of research devoted to this. This has culminated in a number of computer codes that are available both commercially and as research codes. These can be classified into those based on the finite difference, finite element, or finite volume methodology. In the present context one significant difference is relevant. The finite element method minimizes the error in the solution to the underlying mathematical equations in a global sense while finite volume minimizes it in a local sense. This means that a finite volume method will always conserve mass at each time step and throughout a simulation. The finite element and finite difference methods will only have true mass conservation once the grid is refined to a level where further refinement makes no further change to the solution. A number of codes based on the finite difference method have been developed and used in practice. Details of each can be found on the developers’ websites. Examples are ISIS2 D (Halcrow), MIKE21 (DHI), TUFLOW (WBM), and Sobek & Delft3d (Deltares). Codes using the finite element method are less common in river applications, but have been popular for flows in estuaries and coastal areas where the geometries can be complex. Examples are TELEMAC-2 D (EDF) (Bates, 1996), SMS produced by Brigham Young University based on codes from the USACE such as RMA2 D (King, 1978), and CCHE2 D produced by NCCHE, University of Mississippi (Wang et al., 1989). Codes using the finite volume method have been developed more recently as their strength in mass conservation and their ability to correctly model transitions have been realized. The latter is based on the use of Godunov-based methods (Sleigh et al., 1998; Alcrudo and Garcia-Navarro, 1993; Bradford and Sanders, 2002) or on the use of total variation diminishing (TVD) schemes (Garcia-Navarro and Saviron, 1992). In recent decades, there has been significant development of unstructured finite volume codes (Anastasiou and Chan, 1997; Sleigh et al., 1998; Olsen, 2000). These can be considered as a combination of finite element and finite volume approaches. They use the same unstructured grids as finite element and solve the mathematical equations in a finite volume manner that ensures conservation. In this way, they ensure physical realism and ease of application. The issue of wetting and drying is a perennially difficult one for 2-D models (Bates and Horritt, 2005). As water levels drop, areas of the domain may become dry and the calculation procedure must remove these from the computation in a way that does not compromise mass conservation or computational stability. Most available codes can deal with this phenomenon, but they all compromise between accuracy and stability. This issue must be carefully examined in results from any 2-D simulation where wetting and drying are

The Hydrodynamics and Morphodynamics of Rivers

significant. There is active research in this area with a number of recent contributions that may well improve matters (Liang, 2008; Lee and Wright, 2009). In assuming a depth-averaged velocity, 2-D models neglect vertical accelerations and make no prediction of vertical velocities. This, in turn, means that they do not predict or model the effects of the secondary circulations described above. The neglect of secondary circulations can lead to inappropriate model predictions for velocity and depth and in turn this can cause inaccuracies in morphological studies where the secondary circulations are a significant contribution to bed/bank erosion. There are a number of amendments to 2-D models to take an account of this phenomenon. The simplest calculates a measure of helical flow from an analysis of the velocity and acceleration vector at a point. This, in turn, is used to calculate a vertical velocity profile and vertical velocities. This approach is adopted in different forms in MIKE21C (DHI 1998), CCHE3D (NCCHE, University of Mississippi; Kodama, 1996), and CH3D (USACE; Engel et al., 1995) among others. A more accurate but computationally expensive method is the layered model (TELEMAC-3D, EDF; Delft3D, Deltares; TRIVAST; Falconer and Lin, 1997). This establishes a number of vertical layers and solves equations for the horizontal velocities in each layer. Subsequently, equations are solved for a vertical velocity based on an analysis of the interactions between each layer and the water depth is calculated appropriately. This is mainly suitable for wide bodies of water with significant vertical variations of velocity, temperature, salinity, or other variables in the vertical such as estuaries, lakes, and coastal zones. Nex and Samuels (1999) applied TELEMAC-3 D to the River Severn. They reported some success and qualitative agreement with measurements. A further development of this technique is to include the treatment of nonhydrostatic pressure variations (Stansby and Zhou, 1998; Casulli and Stelling, 1998). A 2-D model of a river and its floodplains require information about the channel bed topography and the terrain heights of the surrounding floodplain. In the past this required a mixture of time-consuming measurements and interpolation from published, paper-based maps. A significant advance over the past 10–15 years has been the use of remotely sensed data, which offer both increased accuracy and density of data along with reduced collection times. This comes at some expense, but the cost continues to come down. Current techniques such as light detection and ranging (LiDAR) can provide data every 25 cm at accuracies down to 10 cm. More experimental techniques can also be used to measure through the water surface to give detailed and accurate bed topography. Besides providing accurate data for model construction, remote sensing can also provide data on flood extents for use in validation. These procedures are now in regular use in commercial work and continue to be an area of active research. More details can be found in the literature (Horritt et al., 2001; Wright et al., 2008). Remotely sensed data need to be used with careful consideration of accuracy and the level of detail required in specific areas. For example, in modeling the interaction of a main channel with a floodplain it is necessary to have accurate data along the embankments of the main channel, and commonly used LiDAR data can miss these features through the use of a regular rectangular grid.

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In this case, the LiDAR may need to be supplemented by other techniques such as Global Positioning System (GPS) (Wright et al., 2008)

2.07.2.5 River Reach Scale When considering long river reaches even a 2-D model can become cumbersome. In such cases, the length of the river is of several orders of magnitude greater than the width. It is therefore assumed that lateral variations in velocity and free surface height can be neglected and that the flow direction is entirely along stream. Under these assumptions the equations first formulated by Jean-Claude Barre´ de Saint Venant apply and these have formed the basis for the most widely used commercial river modeling packages. Each of these conceptualizes the river as a series of cross sections. At each the velocity is assumed perpendicular to the cross section. The resistance due to the bed and banks is based on one of the steady-state formulations for normal flow such as Mannings, Che´zy, or Colebrook-White (Chanson, 1999). Early numerical methods for solving this system of equations were pioneered by Abbott and Ionescu (1967) and Preissmann (1961). Both of these methods are essentially parabolic in nature, while the equations are hyperbolic. In view of this more recent methods have drawn on the body of research from compressible gas dynamics which has a similar set of equations. This has produced algorithms that are more robust and able to correctly represent transitions (GarciaNavarro et al., 1999; Crossley et al., 2003), but which are not so straightforward to implement particularly with regard to the incorporation of hydraulic structures such as weirs and sluices. Another recent development that is proving popular in some countries is the linking of 1-D and 2-D models. The former offers efficiency and lower data requirements while the latter can give better results on floodplains. A number of techniques have been proposed for linking these models (Dhonda and Stelling, 2003; Wright et al., 2008), but which one is the most reliable or successful is not yet clear. In fact, there is evidence to suggest that there are considerable differences among the different formulations and even among the different users of the same software package (Kharat, 2009). Although the 1-D approach is based on an analysis of the situation at a cross section, it can be applied to rivers of significant lengths up to hundreds if not thousands of kilometers. Further through the incorporation of junction equations relating flows and depths at confluences and difluences, it can be used to model complex networks of rivers and channels. Over the past three decades, several commercial packages have been developed based on the 1-D shallow water equations (InfoWorks, ISIS, MIKE11, and Sobek, among others). In the US, the USACE Hydrologic Engineering Center has also developed the HEC-RAS software that is freely available. These software packages combine the basic numerical solution with sophisticated tools for data input and graphical output. They are designed to make use of remotely sensed data and to provide 2-D and 3-D output in both steady and animated formats.

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The Hydrodynamics and Morphodynamics of Rivers

2.07.2.6 Spatial Scales in River Morphodynamics River morphodynamics deals with the shape and, in a wider sense, composition of the river bed. The shape of alluvial rivers is made up by the combination of many geomorphological forms, which can be recognized at specific spatial scales, from small ripples to large bars and meanders. The development of geomorphological forms is related to the balance between entrainment and deposition of sediment over different control volumes and times. In modeling, every factor influencing sediment motion has to be taken into account, but in different ways depending on the spatial and temporal scale of the study (Schumm and Lichty, 1965; Phillips, 1995). In particular, processes that operate at smaller scales are parametrized to take into account their effects at larger spatial and temporal scales. Processes that operate at larger scales may be represented as boundary conditions for the studies focusing on smaller scales. At the largest spatial scale, the one of the entire river basin or single sub-basins, we can recognize the entire river network. Typical river basin-scale issues involve soil erosion, reservoir or lake sedimentation, as well as solid and water discharge formation. Basin-scale studies are characterized by the description of the entire river drainage network or large parts of it, such as the delta or a sub-basin. Geographic information systems, 0-D and 1-D morphodynamic models, as well as 1-D or 2-D runon–runoff models, are the typical tools used. The river basin scale is not further treated here, since its issues generally fall under the other related disciplines of hydrology and physical geography. Lowering the observation point and zooming in on the river system, different river reaches, each one characterized by planform style and sinuosity, are highlighted. A single river reach is characterized by one value of the water discharge, but

changing with time. Depending on the reach characteristics, the typical temporal variations range from hours to days for the discharge; from years to several tens of years for the longitudinal bed slope. A river reach in morphodynamic equilibrium is characterized by a longitudinal bed slope that can be considered constant at a chosen temporal scale (de Vries, 1975). Reach-scale issues mainly deal with the assessment of the environmental impact of human interventions, such as river training, and with the natural river evolution on the long term. For this, morphodynamic studies need to determine bed aggradation and degradation, along the river reach, changes in sinuosity and planform style. The typical tools are 0-D reachaveraged formula (e.g., Che´zy, 1776; Lane, 1955), describing the water flow at reach-scale morphodynamic equilibrium, as well as 1-D cross-sectionally averaged models. Commercial 1-D codes updating the riverbed elevation are: MIKE11 (DHI) and SOBEK-RE (Deltares). By further zooming in on the river, the attention moves to the river corridor, or river belt, the area including main river channel and floodplains. Specific morphological features recognizable at this spatial scale are scroll bars inside river bends (Figure 1), a sign of past bend grow. Corridor-scale studies mainly deal with flood risk, river rehabilitation projects, as well as river planimetric changes. The typical tools are 2-D, depth-averaged, or a combination of 1-D (cross-sectionally averaged) and 2-D (depth-averaged) morphodynamic models. These models often have to include formulations for bank retreat and advance and for the effects of (partly) submerged vegetation on water levels, sediment transport, and deposition. Commercial codes developed for the study of the river morphological changes at this and smaller spatial scales are (among others): MIKE21 (DHI), Delft3 D (Deltares), and SOBEK-1 D-2 D (Deltares). Examples of free 2-D codes are: FaSTMECH (Geomorphology and Sediment Transport

Figure 1 Aerial view of a tributary of the Ob River (Russia). Scroll bars on floodplains and point bars inside river bends are clearly visible. Courtesy of Saskia van Vuren.

The Hydrodynamics and Morphodynamics of Rivers

Laboratory of USGS) and RIC-Nays (Hokkaido University). These two models adopt the user interface IRIC, developed in the Geomorphology and Sediment Transport Laboratory of USGS (USA). Central and multiple bars, either migrating or static, are the characteristic geomorphological features to be studied at the cross-section scale (Figure 2). Typical engineering issues are river navigation and the design of hydraulic works, such as trains of groynes, bridges, and offtakes. Typical tools are 2-D, depth-averaged, models, formulated for curved flow (van Bendegom, 1947), often including bank retreat and advance (Mosselman, 1998). Modeling often regards bar formation, bar migration, and channel widening and narrowing as the natural development or as the effects of human interventions. If the observation point moves from a point above the river to a point inside the river channel, the vertical contour of the river cross section becomes visible (Figure 3). Water-depth variations in transverse direction, due to the presence of local deposits and scours, as well as water-depth variations in longitudinal direction, due to the presence of dunes, are the major morphological features observable at this spatial scale. Typical depth-scale studies deal with scour formation around structures, bank erosion, bank accretion, as well as dune development and migration. Typical tools are either 3-D or 2-D and 1-D vertical morphodynamic models, often focusing on local bed level changes or on vertical variations, of, for instance, salinity, suspended solid concentration, soil stratification, and bank slope. The smallest spatial scale that is relevant for the river morphodynamics is called the process scale. This is the scale of fundamental studies describing processes such as sediment entrainment and deposition, for which phenomenon such as turbulence plays a major role. The typical geomorphological

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forms to be studied at this small spatial scale are ripples (Figures 4–6). The typical tools are detailed morphodynamics models in one, two, and three dimensions. In morphodynamics, temporal and spatial scales are strongly linked. Phenomena with small spatial scales also have small temporal scales, and phenomena with large spatial scales have large temporal scales (de Vriend, 1991, 1998; Blo¨schl and Sivapalan, 1995). The linkage between spatial and temporal scales is formed by sediment transport. For the development or migration of a small bedform, only a small amount of sediment needs to be displaced, whereas large amounts of sediment are needed for the development of large geomorphological forms, such as bars. Phenomena interact dynamically when they occur more or less on the same scale. Small-scale phenomena, such as ripples, appear as noise in the interactions with phenomena on larger scales, such as bar migration, but they can produce residual effects, such as changes of bed roughness (Figure 5). Their effect on larger scales can be accounted for by parametrization procedures (upscaling). Phenomena operating on much larger spatial and temporal scales can be treated as slowly varying or constant conditions. They define scenarios, described in terms of boundary conditions, when studying their effects on much smaller scales. Thus, basin-scale studies are essential for the generation of the input (boundary conditions) for the morphodynamic studies on smaller spatial scales.

2.07.2.7 Geomorphological Forms in Alluvial River Beds Geomorphological forms in rivers can be caused by the presence of geological forcing, human interventions, and man-made structures, but they also arise as a natural instability of the interface between the flowing water and

Figure 2 Multiple bars in the braided Hii River (Japan). Courtesy of Takashi Hosoda.

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The Hydrodynamics and Morphodynamics of Rivers Floodplain Floodplain

38

36 (m a.s.l.)

Excavated area

34 Initial bed level 2007 2017

32

2027 30

200

300

400

500 (m)

600

700

Figure 3 River Meuse (the Netherlands): temporal bed level changes during the period 2007–27. On the vertical, the bed elevation in meters above sea level (Villada Arroyave and Crosato, 2010).

Figure 4 Ripples in a straight experimental flume with a sandy bed (the bar shows centimeters). Laboratory of Fluid Mechanics of Delft University of Technology.

sediment. In analogy with the interaction between air moving above water (wind), the instability of the water–sediment interface produces waves of different sizes, which can coexist and interact with each other. Ripples are the smallest ones, originating from the instability of the viscous sublayer near the river bed (Figure 4).

Dunes are the main source of hydraulic resistance of a river and hence a key factor in raising water levels during floods (Figure 7). They are also the first parts of the river bed that need to be dredged to improve navigation. Dune formation and propagation is so intimately linked to sediment transport, that the latter cannot be modeled properly without

The Hydrodynamics and Morphodynamics of Rivers

accounting for dunes (ASCE Task Committee on Flow and Transport over Dunes, 2002). Bars are the largest waves in the river bed; they can be scaled with the channel cross section (Figure 2).

Figure 5 The presence of 3-D ripples acts as noise for the study of alternate bars in this laboratory experiment carried out at the Laboratory for Fluid Mechanics of Delft University of Technology.

Figure 6 2-D ripples in the Het Swin Estuary (the Netherlands).

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2.07.2.7.1 Ripples and dunes For increasing Froude numbers the river bed is first plane and then covered by ripples and dunes. The flow regime close to the critical Froude number (Fr E 1) is again characterized by plane bed. If the Froude number increases further (supercritical flow), antidunes begin to form with upstream breaking waves over the crest (Simons and Richardson, 1961). Southard and Boguchwal (1990) provided the most extensive bedform phase diagrams showing the possible occurrence of ripples, dunes, antidunes, or plane bed under different sediment size and flow conditions. Bedforms may have either a 2-D or a 3-D pattern. 2-D ripples and dunes have fairly regular spacing, heights, and lengths. Their crest lines tend to be straight or slightly sinuous, and are oriented perpendicular to the mean flow lines (Figure 6). In contrast, 3-D features have irregular spacing, heights, and lengths with highly sinuous or discontinuous crest lines (Ashley, 1990), as in Figures 4 and 5. In general, ripples scale with the sediment diameter while dunes scale with the water depth (Bridge, 2003), but there is no clear distinction between ripples and dunes for limited water depths, as for instance, in flume experiments. Extensive data compilations by Allen (1968) and Flemming (1988) demonstrated that there is a break in the continuum of observed bedforms discriminating ripples from dunes. For instance, ripples are only present for fine sediment with Do1 mm. However, there are no generally valid techniques to divide ripple from dune regimes and some authors choose to make no distinction at all. The first theoretical study of dune instability was carried out by Kennedy (1969). Spectacular progress in knowledge of dune dynamics is linked to the increasing sophistication of numerical modeling (Nelson et al., 1993). Recent models produce detailed simulations of the instantaneous structure of flow over a dune-covered bed. Giri and Shimizu (2006)

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Figure 7 Dunes in the Waal River and Pannerdense Canal (the Netherlands) on 4 November 1998. Flow from right to left. Courtesy of Rijkswaterstaat. Upstream of bifurcation: discharge 9600 m3 s1, water depth 10.7 m, mean grain size 3.3 mm, flow velocity 2.1 m s1, dune height 1.0 m, and dune length 22 m. Analysis by Wilbers, Department of Physical Geography, Utrecht University, Utrecht, The Netherlands.

developed a 2-D model for the prediction of dunes under unsteady flow regime. Nabi et al. (2009) provided the first detailed 3-D model of dune formation.

2.07.2.7.2 Bars Bars are shallow parts of river bed topographies that become visible at low flows and can be either migrating or steady. Bars occur in more or less regular, periodic patterns as a result of interactions between flowing water and sediment. In a river channel one or more parallel rows of bars may be present; the number of rows present is called bar mode m. Alternate bars have mode m equal to 1 (Figure 8); multiple bars have mode larger than 2. Migrating bars as well as steady bars (Crosato and Desta, 2009) develop spontaneously as a result of morphodynamic instability and for this they are often referred to as free bars. Confined sediment deposits caused by local changes of the channel geometry, such as point bars inside river bends (Figure 1), should therefore be distinguished from free bars. The stability analyses performed by, among others, Hansen (1967), Callander (1969), and Engelund (1970) define the conditions that govern the development of bars in alluvial river channels. The width-to-depth ratio of the river channel is the dominating parameter for free bar formation: the larger the width-to-depth ratio, the larger the bar mode. This means that multiple bars form at larger width-to-depth ratios than alternate bars. Moreover, no bars can form for width-to-depth ratios that are smaller than a certain critical value. Parker (1976) and Fredsøe (1978) related the presence or absence of free bars to the channel planform, that is, meandering or braided. By persistently enhancing opposite bank erosion, steady alternate bars (Figure 8, left) are seen as a

m=1

m=2

Figure 8 Left: alternate bars (m ¼ 1). Right: central bars (m ¼ 2).

key ingredient for the evolution of straight water courses into meandering water courses (Olesen, 1984). Multiple bars are a characteristic of braided rivers. The linear theory by Seminara and Tubino (1989) defines marginal stability curves separating the conditions in which a certain number of bars per cross section grows from the conditions in which the same bar mode decays. The river is supposed to select the bar mode with the fastest growth rate, which is a function of the width-to-depth ratio, the Shields parameter, the sediment grain size, and the particle Reynolds number. A single physics-based formula was recently derived by Crosato and Mosselman (2009) from a stability analysis. The formula allows one to compute directly the mode of free bars that develop in an alluvial channel, but it is limited to rivers having width-to-depth ratio smaller than 100. By assuming that meandering rivers are characterized by the

The Hydrodynamics and Morphodynamics of Rivers

presence of alternate bars and braided rivers by multiple bars, the same formula can also be used to determine the type of planform that can be expected to develop after widening or narrowing of a river channel.

2.07.2.8 River Planimetric Changes The study of the river planimetric changes requires the assessment of both bank erosion and bank accretion rates and for braided-anabranched rivers also to the assessment of the stability of channel bifurcations. Meandering rivers have single-thread channels with high sinuosity and almost constant width (Figure 1). They could be regarded as a particular type of braided rivers (Murray and Paola, 1994), those having bar mode equal to 1. River meandering is governed by the interaction between bank accretion, bank erosion, and alluvial bed changes (Figure 9). Bank erosion causes channel widening and enhances opposite bank accretion. Conversely, bank accretion causes river narrowing and enhances opposite bank erosion. The two processes of bank erosion and accretion do not occur contemporarily, and for this reason the river width is subject to continuous fluctuations. However, generally a stable time-averaged width is achieved in the long term. Understanding the process of bank accretion and width formation is therefore a fundamental prerequisite for the modeling of meandering river processes and, more in general, for the modeling of the river morphology. All existing meander migration models (Ikeda et al., 1981; Johannesson and Parker, 1989; Crosato, 1989; Sun et al., 1996; Zolezzi, 1999; Abad and Garcia, 2005; Coulthard and van de Wiel, 2006) assume the rate of bank retreat to be the same as the rate of opposite bank advance. This means that the lateral migration rate of the river channel can be assumed to be equal to the retreat rate of the eroding bank. This is in turn assumed to be proportional to the near-bank flow velocity excess with respect to the normal flow condition, following the approach by Ikeda et al. (1981). Some meander migration models take also into account the effects of the near-bank water depth excess on the bank retreat rate (e.g., Crosato, 1990). The proportionality coefficients in the channel migration formula are supposed to weigh the bank erosion rates.

These coefficients should be a function of the bank characteristics only, but are in fact bulk parameters incorporating the effects of opposite bank advance and some numerical features (Crosato, 2007). Existing theories on river meandering focus on the assessment of bank retreat rates without defining the conditions for the opposite bank to advance with the same speed. However, it is just the balance between the rate of bank advance and the rate of opposite bank retreat that makes the difference between braiding and meandering (Figure 10). A meandering river requires that, in the long term, the bank retreat rate is counterbalanced by the bank advance rate at the other side. If bank retreat exceeds bank advance, the river widens and, by forming central bars or by cutting through the point bar, assumes a multi-thread (braided) pattern. If bank advance exceeds bank retreat, the river narrows and silts up. So far, most research has focused on the processes of bank erosion (e.g., Partheniades, 1962, 1965; Krone, 1962; Thorne, 1988, 1990; Osman and Thorne, 1988; Darby and Thorne, 1996; Rinaldi and Casagli, 1999; Dapporto et al., 2003; Rinaldi et al., 2004) and bed development, whereas the equally important bank accretion has received little attention

(a)

(b)

Figure 10 A sinuous water flow is not sufficient for meandering. (a) straight river planform with bank retreat, but without bank advance. (b) meandering river planform in which bank advance counterbalances bank retreat.

Bank accretion

Bank erosion Bed level changes

Figure 9 Morphological processes shaping the river cross section.

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The Hydrodynamics and Morphodynamics of Rivers

(Parker, 1978a, 1978b; Tsujimoto, 1999; Mosselman et al., 2000). As a result, there are no comprehensive physics-based river width predictors. Bank accretion is governed by the dynamic interaction between riparian vegetation, flow distribution, frequency as well as intensity of low and high flow stages, local sedimentation, soil strengthening and by the interaction between opposite (eroding and accreting) banks. Bank erosion and accretion strongly depend on climate (Crosato, 2008). Climate changes can therefore alter the river cross section and the river pattern. Present knowledge on river morphological processes is insufficient to fully assess these effects. A number of existing 2-D and 3-D morphological models, such as Delft3 D, treat bank accretion as near-bank bed aggradation and bank erosion as near-bank bed degradation. These models are suitable for the prediction of width changes of channels without vegetation and with mildly sloping banks, but fail to predict the morphodynamics of meandering rivers, which are characterized by cohesive banks and riparian vegetation. A few 2-D morphological models simulate bank erosion, but not bank accretion. One example is the model RIPA, which was developed at Delft University of Technology by Mosselman (1992) and further extended by the University of Southampton (Darby et al., 2002). In large valleys or near the sea, the river can split into several channels. In anabranched rivers each anabranch is a distinct, rather permanent, channel with bank lines (Figure 11). The river bed is mainly constituted by loose sediment, such as sand and gravel, whereas silt prevails at the inner parts of bends and in general where the water is calm. Anabranches are commonly formed within deposits of fine material. Vegetation and soil cohesiveness stabilize the river banks and the islands separating the anabranches, so that the planimetric changes are slow if compared to the river bed changes. Studying the morphological changes of this type of rivers requires the assessment of the stability of bifurcations

(Wang et al., 1995; Kleinhans et al., 2008). Experimental and theoretical research started almost 100 years ago (Bulle, 1926; Riad, 1961) and are still going on (Bolla Pittaluga et al., 2003; De Heer and Mosselman, 2004; Ten Brinke, 2005; Bertoldi et al., 2005; Kleinhans et al., 2008). The major difficulty rises in the assessment of sediment distribution between the two branches of the bifurcating channel, which is a function of water discharge distribution, sediment characteristics, channel curvature at the bifurcation point, and presence of bars.

2.07.2.9 Bed Resistance and Vegetation In any study of a river, whether it is experimental, full-scale measurement or numerical in 1-D, 2-D, or 3-D, the irregular geometry of the boundary (bed and banks) cannot be directly represented. Even with full-scale measurements, the boundary cannot be accurately mapped at the scale of the bed material. In all cases, a conceptual representation of the effect of the boundary on the flow is used to account for momentum and energy dissipation. There is much misunderstanding of the nature of these resistance or roughness laws and inconsistencies in their application (Morvan et al., 2008). Clearly, given the importance of boundary resistance determining flow and depth it is necessary to have a clear understanding of the various methods and any limitations on their applicability. If it were possible to solve the Navier–Stokes equations on a grid that was fine enough to resolve the smallest scale of turbulence (the Kolmogorov scale), then there would be no need for a turbulence model or a model of the effect of the boundary. However, this is not yet generally possible and even in 3-D solutions there is a need to simplify the equations. In 3-D a turbulence model is used as well as the resistance model, but in 2-D and 1-D models both these phenomena tend to be included in a resistance term. This in turn can lead to uncertainty in the definition and a lack of rigor in its application. This uncertainty together with lack of rigor

Figure 11 Anabranched planform: the Amazon River near Iquitos, Peru. Courtesy of Erik Mosselman.

The Hydrodynamics and Morphodynamics of Rivers

increases as the dimensionality decreases. Another consequence of the difference between 3-D, 2-D, and 1-D modeling is that the value of a parameter such as roughness height will vary between each dimensionality even if the physical situation under consideration is identical due to the fact that the resistance model incorporates different physical phenomena in each case. This can be seen in Figure 12 from Morvan et al. (2009). The results from Manning’s equation differ from those of the 3-D model as ks is varied indicating that the two are quite different. In fact, the Manning’s equation results are more sensitive to changes in the roughness which is due to the 3-D model representing phenomena such as turbulence and secondary circulations directly rather than in the resistance parametrization. In view of its significance there has been much work in this area over the last century and the reader is referred to Davies and White (1925), Ackers (1958), ASCE (1963), Rouse (1965), Yen (1991, 2002), and Dawson and Fisher (2004). Specific types of roughness are considered by Sayre and Albertson (1963) and ESDU (1979). Reynolds and Schlicting have written useful textbooks on the wider subject (Reynolds, 1974; Schlichting et al., 2004). A good review of the topic in the context of modeling is given in the paper by Morvan et al. (2008). Early work on roughness was performed in pipes by Nikuradase, mentioned above as building on the work of Prandtl. The Darcy–Weisbach equations for pipe flow uses a friction factor that is based on geometry (diameter in the case of pipes), mean velocity, and surface characteristics based on a relative roughness defined by a quantity known as the Nikuradse equivalent sand grain roughness nondimensionalized by the diameter of the pipe. In 1-D openchannel studies the geometric parameter of diameter is replaced by hydraulic radius (area divided by wetted

35

149

perimeter) which leads to discontinuities when the flow moves onto flood plains as the wetted perimeter increases abruptly while the cross-sectional area does not. It is clear that using the theory from pipe flow in open channels raises difficult issues with complex cross sections and using the hydraulic radius to capture geometrical effects is problematic. In practice, many people use hydraulic radius and then adjust the value of the Nikuradse roughness or equivalent to ensure that the frictional head loss per unit channel length matches the bed slope. This demonstrates that the roughness parameter is often related to energy loss in the model as much as any physical measurement of the nature of the surface. In fact, the parameter is a function of local bed geometry, flow regime, cross-section geometry, and turbulence. Given the wide range of effects, it is clear that the parametrization depends on the model used for the overall fluid flow. It is worth considering in a little more detail the nature of the forces acting on the fluid due to presence of the bed. Morvan et al. described these as

• • •

skin drag (e.g., roughness due to surface texture, grain roughness); form drag (e.g., roughness due to surface geometry, bedforms, dunes, separation, etc.); and shape drag (e.g., roughness due to overall channel shape, meanders, bends, etc.).

Skin and form drag can be considered to occur on a plane, but shape drag is due to larger-scale 3-D patterns. Again, it is clear that the way each of these is represented depends on the sort of model used. A resistance parameter such as Manning’s coefficient n or Che´zy used at a reach scale is based on the concept of bed resistance, although in practice it is also calibrated to account for shape drag. In many representations, roughness is characterized by a roughness height. It is often not appreciated that although this quantity has the units of length, it is not a measure of the height of the roughness elements. It is rather a parameter in an analytical model of flow at the wall (i.e., in 3-D):

Q (1-D)

ut 1 þ ¼ lnðEðkþ s Þy Þ u k

Q (3-D)

Mass flow rate

30

Experiment

25

20

15

10 0

0.2

0.4

0.6

0.8

1

ks Figure 12 Variation of the mass flow rate in 1-D model and 3-D model for a trapezoidal channel compared against the measured value (Morvan et al., 2009).

ð12Þ

where Eðkþ s Þ is a function of the nondimensional roughness height, kþ s ¼ ks u =n, in which ks is the roughness height, k the von Karman’s constant usually taken equal to 0.41, and n is the kinematic viscosity. It seems attractive to base our estimates for roughness heights on work such as Nikuradse’s on relatively zsmooth experimental channels. This has led to formulations such as ks ¼ 3:5  D84 or ks ¼ 6:8  D50, where DXX stands for the grain diameter for which xx% of the particles are finer, reported in Clifford et al. (1992). The latter paper makes interesting reading and shows that the grain–roughness relationship is inadequate. This is because there are several momentum loss mechanisms in these flows and they are not represented by such a simple equation. A further complication is that in some 3-D simulations values of the roughness height are derived from these formulas that are in fact greater than the size of the grid perpendicular to the wall. This could suggest that the grid resolves flows at a scale less than the size

150

The Hydrodynamics and Morphodynamics of Rivers

of the roughness which contradicts the fact that the roughness features have been removed to give a smooth planar surface. The above discussion has focused mainly on 3-D models, but the situation when we consider 2-D and 1-D models is even less clear. Continuing the approach of considering surface roughness as the parameter governing resistance, various formulas have been proposed to connect the roughness height with a parameter such as Manning’s n for 1-D models: HR Wallingford tables (Ackers, 1958):

• •

ks ðmmÞ ¼ ðn=0:038Þ6

ð13Þ

•

Massey (Massey 1995):

 ks ðSIÞ ¼ 14:86R=exp10

0:0564R 1=6 n

 ð14Þ

Chow (1959):

 ks ðSIÞ ¼ 12:20R=exp10

It is clear from this discussion that parametrizing resistance in open-channel flows is not straightforward and needs knowledge and experience from numerical and physical modeling. A number of conclusions can be drawn (based on those in Morvan et al. (2009)):

0:0457R 1=6 n

 ð15Þ

Strickler (1923):

ks ðftÞ ¼ ðn=0:0342Þ6

ð16Þ

These differ not only in the numerical values used, but also in the functional form. They also give large ranges for roughness height for small variations in Manning’s n. This indicates the uncertainties in this process, which have led authors to seek better means of characterizing the geometry and surface characteristics in order to approximate resistance. In some cases, particularly with large cross section covering a main channel and floodplains, there are zones with quite different resistances within the cross section. In such cases divided channel method (DCM) can be used where the cross section is divided into panels, and a conveyance is calculated in each one before being combined into a composite value (Knight, 2005). This has been shown to be successful and is incorporated in most commercial software. All these methods assume quasi-straight river reaches, and do not include lateral momentum transfer effects. Thus, they cannot predict accurately either the water level in compound river channels or the proportion of flow between the main channel and floodplains. More recent developments include the effect of flow structure, through the adoption of improved methods (Knight, 2005). These may be grouped under the headings: the DCM, the coherence method (COHM), the Shiono and Knight method (SKM), and the lateral division method (LDM). Several authors have presented examples of these methods applied to fluvial problems (Knight et al., 1989; Knight, 2005). The SKM, for example, uses three parameters rather than just the one used by approaches such as Manning’s or Che´zy. In fully 2-D shallow water models the flow is considered in separate vertical water columns and the variables are depth and two perpendicular velocities or discharges per unit width. In this case, the resistance is applied only to the surface at the base of the water column (the bed) and the roughness height will be different even from a 1-D model and for the same bed material.

•

roughness varies between models, which represent different dimensions and therefore reach-scale roughness is a different concept from local roughness; using roughness to represent features other than sand-grain roughness lessens the validity of the underlying theory and is questionable; models of roughness in 1-D hydraulic models are valid and will continue to be useful when based on sound analysis and calibrated appropriately; and 1-D modelers should focus more on estimating conveyance than establishing one sole value of Manning’s n or Che´zy’s C for a channel.

This shows that the representation of resistance in real rivers is a complex task. It could therefore lead to the conclusions that hydraulic modeling is fraught with difficulty and that it is of little benefit. This is not the case and when used with care they are extremely useful (Knight et al., 2009). If the representation of the resistance due to the nonuniform surface of the bed and banks presents a significant challenge to modelers, the representation of the effects of vegetation is perhaps an even greater one. Further, the need to represent vegetation is becoming greater with the design of more natural channels and the need to model inundation flows across vegetated floodplains. Besides being nonuniform, vegetation experiences changes in its resistance as it deforms as the velocity of the water increases. The effects of vegetation on river processes are many, complex, and difficult to quantify (Fisher and Dawson, 2003; Rinaldi and Darby, 2005; Gurnell et al., 2006). The ability of vegetation to stabilize river banks (Ott, 2000) partly depends upon scale, with both size of vegetation relative to the watercourse and absolute size of vegetation being important (Abernethy and Rutherfurd, 1998). Vegetation stabilization is most effective along small watercourses. On relatively large rivers, fluvial processes tend to dominate (Thorne, 1982; Pizzuto, 1984; Nanson and Hickin, 1986). The effect of vegetation on the conveyance of a channel depends on a number of factors such as density, type, height, and distribution of plants and their development stage (Allmendiger et al., 2005; Dijkstra, 2003). At the local scale, single plants act as roughness elements. Isolated trees and relative small clusters of plants increase turbulence around them leading to local scour, just as bridge piers do. Dense vegetation, instead, reduces the flow velocity between and above plants and sediment transport, enhancing local siltation. In this way, riparian vegetation increases the development of natural levees during floods as well as bank accretion. Rooted plants reduce local soil erosion by binding the soil with the roots (Figure 13) and by covering it. In this way, riparian vegetation decreases bank erosion. Heavy trees, however, can enhance gravitational bank failure by increasing the load on the bank (Ott, 2000). Finally, vegetation causes local accumulation of organic material (falling leaves,

The Hydrodynamics and Morphodynamics of Rivers

Figure 13 Roots protecting the river bank against erosion. Geul River (The Netherlands). Courtesy of Eva Miguel.

branches, and dead plants), which further reinforces the soil cohesion and strength (Baptist, 2005; Baptist and De Jong, 2005; Baptist et al., 2005). At the cross-section scale vegetation affects the river morphodynamics by acting on (Crosato, 2008) (1) river bed degradation/aggradation, (2) bank erosion, and (3) bank accretion by:

•

• • •

Deflecting the water flow. Aquatic and riparian vegetation increase the local hydraulic roughness and for this reason, the flow concentrates where vegetation is absent (Tsujimoto, 1999; Pirim et al., 2000; Rodrigues et al., 2006). This lowers the flow velocity within the plants, where sedimentation increases, and causes bed degradation in the nonvegetated area of the channel, where the flow velocity becomes higher. By deflecting the flow toward the opposite bank, riparian vegetation enhances opposite bank erosion (Dijkstra, 2003). Protecting the vegetated parts of the riverbed and bank against erosion (Figure 13). Accelerating the vertical growth of accreting banks and bars. Raising water levels. By increasing the hydraulic roughness, aquatic vegetation increases the water levels.

At the river-reach scale vegetation affects the water levels as well as the river planform formation (e.g., Murray and Paola, 2003; Jang and Shimizu, 2007; Samir Saleh and Crosato, 2008; Crosato and Samir Saleh, 2010). Murray and Paola

151

studied the effects of soil strengthening by floodplain vegetation on the river planform, whereas Jang and Shimizu and Samir Saleh and Crosato studied the effects of increased hydraulic roughness. All works demonstrated that vegetation decreases the degree of braiding of river systems and might even transform a braiding into a meandering system. Early studies considered the effects of vegetation on flow qualitatively (Powell, 1978; Dawson and Robinson, 1984) and demonstrated that the effects of vegetation varied over the seasons and that the relationship between resistance and vegetation varied greatly with depth. Later, semiquantitive relationships (Stephens et al., 1963; Shih and Rahi, 1982; Pitlo, 1982) were studied and demonstrated that if Manning’s n is used to represent the resistance in a vegetated channel, values of up to 20 times the nonvegetated value can be found, but that such changes were more pronounced in smaller channels. These semiquantitative approaches of increasing the amount of numerical resistance by changing the resistance parameter are still widely used by many practitioners. This is, however, based on the flawed concept resistance due to vegetation, whether emergent or submerged, stems from a boundary layer phenomenon while it is actually a mixing layer phenomenon (Ghisalberti and Nepf, 2002). This implies that the resistance from vegetation depends on depth and can therefore never be fully accounted for by a resistance parameter that is based on a surface representation rather than extending through the water column. These limitations have led to the proposal of more quantitative methods and a number of these were given by Fisher and Dawson (Table 1). The work in Table 1 and that of others (Larsen et al., 1990; Bakry, 1992; Salama and Bakry, 1992; Watson, 1997) indicate that while there may be a relationship between resistance and vegetation, it is complex and there is, as yet, no ideal equation for this relationship. The limitations of this approach have led a number of authors to propose more sophisticated representations based on analyzing the drag coefficient of vegetation. Most work (Wu et al., 1999; Fischer-Antze et al., 2001; Ghisalberti and Nepf, 2002, 2004; Wilson et al., 2003) has focused on laboratory channels which is vital to reduce the uncertainties in full-scale cases and to allow for well-founded fundamental conclusions to be drawn. However, work that has been carried out on real rivers is scarce (Stoesser et al., 2003; Nicholas and McLelland, 2004), which has had little or no measured data for comparison. Stoesser et al. (2003) applied a 3-D model for vegetative resistance on the Restrhein and Nicholas and McLelland (2004) used a 3-D model on the floodplains of a natural river. The drag coefficient is often based on that for a nonflexible cylinder, but this is clearly not the case with vegetation. More recent work has studied the effect of flexibility (Kouwen, 1988; Querner, 1994; Rahmeyer et al., 1996; Fathi-Maghadam and Kouwen, 1997). Further fundamental understanding has been advanced by Japanese researchers and are reviewed by Hasegawa et al. (1999). The reduction-factor approach outlined in Baptist (2005) and Baptist et al. (2007) quantifies the hydraulic effect that vegetation can exert on the flow by considering the distribution of shear stress within the water column rather than

152 Table 1

The Hydrodynamics and Morphodynamics of Rivers Different methods to derive the Manning’s roughness coefficient of vegetated channels (Fisher and Dawson, 2003)

Authors

VRa range (m2 s1)

Discharge (m3 s1)

Areab (m2)

Marshall and Westlake (1990)

0.24–1.3

0.2

Pepper (1970 )

0.58–8.46

2.4

Wessex Scientific Environmental Unit (1987)

0.24–1.3

15

43

Wessex Scientific Environmental Unit (1987)

0.15–1.1

15

43

Wessex Scientific Environmental Unit (1987)

0.15–1.1

15

43

Larsen et al. (1990)

0.025–0.15

0.1

0.7

HR Wallingford (1992)

0.04–0.11

4

3.5

1

Equationc,d K va n ¼ 0:1 þ 0:153 VR K va n ¼ 0:06 þ 0:17 VR K va n ¼ 0:032 þ 0:027 Vd K va n ¼ 0:041 þ 0:022 Vd K va n ¼ 0:029 þ 0:022 Vd K va n ¼ 0:057 þ 0:0036 VR K va n ¼ 0:035 þ 0:0239 VR

a

VR, product of the flow velocity V (m s1) and the hydraulic radius R (m). A, channel cross-sectional area (m2). c Kva, vegetation coverage coefficient. d d, water depth (m). b

considering the forces on individual vegetation stands. In order to include this approach in 2-D and 3-D models, an equivalent value of Che´zy’s roughness coefficient is calculated based on characteristics of the vegetation such as drag and density. Unlike the standard approach, this value changes with vegetation density and depth as the simulation progresses. As observed by Baptist (2005), other 3-D models for the resistance due to vegetation have been developed. The models mentioned earlier by Stoesser et al. (2003) and Nicholas and McLelland (2004) did not add any further source terms to the turbulence model, because they were not certain that this would improve the simulation results. Baptist’s model includes the effects of vegetation in the turbulence closure. This has been shown by Uittenbogaard (2003) to fit laboratory measurements of mean flow, eddy viscosity, Reynolds stress, and turbulence intensity well.

2.07.2.10.1 Incremental changes Incremental changes are as follows:

• • •

2.07.2.10.2 Step changes Step changes are as follows:

• 2.07.2.10 Discussion of Current Research and Future Directions Any discussion of future directions quickly becomes dated and in view of this the authors restrict themselves to outlining the areas where new developments are anticipated or required. As a precursor the overall context for river studies should be mentioned and a significant challenge that is already being addressed is how to position river science and engineering within the overall framework of modern river management which entails full recognition of environmental, societal, and economic issues. Overall the major issue in rivers, as in all studies of the natural environment, is how to account for physical features and phenomena that are not directly incorporated into the models (whether conceptual or numerical). In rivers this means, amongst others, bed resistance, vegetation, turbulence, each of which is a significant challenge in its own right. It is perhaps best to consider future directions as progressing by either increments or step changes.

improvements in the estimation of the parameters for bed resistance and better end-user tools that acknowledge uncertainty and encourage a rigorous approach to calibration; improvements in our understanding of flow through vegetation and the ways in which this can be parameterized; and increased understanding of which models to use in which circumstance which should take account of spatial and temporal scales, uncertainty, and levels of acceptable risk; this includes more knowledge of the role of reduced complexity modeling (Hunter et al., 2007).

• • •

new methods of representing resistance parameterization based on improved encapsulation of knowledge from experimental and full-scale measurement; development of fundamental understanding and models for bank accretion to bring this to the level of current work on bank erosion; development of new paradigms to explicitly acknowledge all sources of uncertainty in modeling; and development of a scientific basis for an understanding of the generation, movement, and impact of floating debris.

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Thorne CR (1982) Processes and mechanisms of river bank erosion. In: Hey RD, Bathurst JC and Thorne CR (eds.) Gravel-Bed Rivers, pp. 227--259. Chichester: Wiley. Thorne CR (1988) Riverbank stability analysis. II: Applications. Journal of Hydraulic Engineering 114(2): 151--172. Thorne CR (1990) Effects of vegetation on riverbank erosion and stability. In: Thornes JB (ed.) Vegetation and Erosion, pp. 125--144. Chichester: Wiley. Tsujimoto T (1999) Fluvial processes in streams with vegetation. Journal of Hydraulic Research 37(6): 789--803. Uittenbogaard R (2003) Points of view and perspectives of horizontal large-eddy simulation at Delft, CERI, Sapporo, http://www.wldelft.nl/rnd/publ/docs/ Ui_CE_2003.pdf. van Bendegom L (1947) Enige beschouwingen over riviermorfologie en rivierverbetering. De Ingenieur B. Bouw- en Waterbouwkunde 1 59(4): 1–11 (in Dutch). (Some considerations on river morphology and river improvement. English translation, Natural Resources Council Canada, 1963, Technical Translation No. 1054.) Versteeg HK and Malalasekera W (2007) Introduction to Computational Fluid Dynamics: The Finite Volume Method, 503pp. Harlow: Pearson. Villada Arroyave JA and Crosato A (2010) Effects of river floodplain lowering and vegetation cover. In: Proceedings of the Institution of Civil Engineers, Water Management, vol. 163, pp. 1–11 (doi:10.1680/wama2010.163.1.1). Wallingford (1992) The hydraulic roughness of vegetated channels. Report No. SR 305, March 1992. HR Wallingford. Wang SSY, Alonso VV, Brebbia CA, Gray WG, and Pinder GF (1989) Finite elements in water resources. Third International Conference, Finite Elements in Water Resources, Mississippi, USA. Wang ZB, Fokkink RJ, De Vries M, and Langerak A (1995) Stability of river bifurcations in 1D morphodynamic models. Journal of Hydraulic Research 33(6): 739--750. Watson D (1987) Hydraulic effects of aquatic weeds in UK rivers. Regulated Rivers: Research and Management 1: 211--227. Wessex Scientific Environmental Unit (1987) The Effect of Aquatic Macrophytes on the Hydraulic Roughness of a Lowland Chalk River. Wright NG (2001) Conveyance implications for 2D and 3D modelling. Scoping Study for Reducing Uncertainty in River Flood Conveyance. Environment Agency (UK). Wright NG (2005) Introduction to numerical methods for fluid flow. In: Bates P, Ferguson R, and Lane SN (eds.) Computational Fluid Dynamics: Applications in Environmental Hydraulics. Chichester: Wiley.

Wright NG, Villanueva I, Bates PD, et al. (2008) A case study of the use of remotelysensed data for modelling flood inundation on the River Severn, UK. Journal of Hydraulic Engineering 134(5): 533--540. Wu FC, Shen HW, and Chou YJ (1999) Variation of roughness coefficients for unsubmerged and submerged vegetation. Journal of Hydraulic Engineering 125(9): 934--942 (doi:10.1061/(ASCE)0733-9429(1999)). Yen BC (1991) Channel Flow Resistance: Centennial of Manning’s Formula. Colorado, USA: Water Resources Publications. Yen BC (2002) Open channel flow resistance. Journal of Hydraulic Engineering 128(1): 20--39. Zienkiewicz OZ and Cheung YK (1965) Finite elements in the solution of field problems. Engineer 507--510. Zolezzi G (1999) River Meandering Morphodynamics. PhD Thesis, 180pp. Department of Environmental Engineering, University of Genoa.

Relevant Websites http://delftsoftware.wldelft.nl Deltares; Delft Hydraulics Software: SOBEK and Delft3D. http://www.halcrow.com Halcrow; ISIS Software. http://www.hec.usace.army.mil Hydrologic Engineering Center; HEC-RAS Software. http://www.mikebydhi.com MIKE by DHI. http://www.river-conveyance.net Reducing Uncertainty in Estimation of Flood Levels; Conveyance and Afflux Estimation System (CES/AES). http://wwwbrr.cr.usgs.gov US Geological Survey Central Region Research; Geomorphology and Sediment Transport Laboratory of USGS. http://vtchl.uiuc.edu Ven Te Chow Hydrosystems Laboratory; Gary Parker’s e-book. http://www.wallingfordsoftware.com Wallingford Software; InfoWorks Software.

2.08 Lakes and Reservoirs D Uhlmann and L Paul, University of Technology, Dresden, Germany M Hupfer, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany R Fischer, Consulting Engineers for Water and Soil Limited, Possendorf, Germany & 2011 Elsevier B.V. All rights reserved.

2.08.1 2.08.1.1 2.08.1.1.1 2.08.1.1.2 2.08.1.1.3 2.08.1.2 2.08.1.3 2.08.1.4 2.08.1.4.1 2.08.1.4.2 2.08.1.5 2.08.1.6 2.08.1.6.1 2.08.1.6.2 2.08.1.6.3 2.08.1.6.4 2.08.1.6.5 2.08.1.6.6 2.08.1.6.7 2.08.1.7 2.08.1.8 2.08.1.9 2.08.2 2.08.2.1 2.08.2.2 2.08.2.3 2.08.3 2.08.3.1 2.08.3.2 2.08.3.3 2.08.3.3.1 2.08.3.3.2 2.08.4 2.08.4.1 2.08.4.2 2.08.4.3 2.08.4.4 References

Morphometry, Hydrodynamics, Chemistry, and Biology of Lakes Origin and Development of Lakes Origin of lakes Lake development with a large contribution of photosynthesis Development of reservoirs Structure and Functioning of Drainage Basins of Natural and Man-Made Lakes Lake and Reservoir Morphometry Influx and Vertical Distribution of Solar Energy Underwater light conditions Heat budget and thermal structure Water Movement Basic Chemistry Systematics of lakes with respect to water quality Ionic balance Inorganic compounds and buffer properties Sequence of microbially mediated redox processes Iron, manganese, and sulfur compounds Nutrients (nitrogen and phosphorus) and trace substances Organic carbon – humic compounds Biotic Structure Photosynthesis: Generation and Consumption of Dissolved Oxygen Oxygen Stratification: Circulation/Quality Types of Lakes Fundamental Properties of Reservoirs Functions of Reservoirs Characteristic Differences between Natural Lakes and Reservoirs Environmental Impacts of Reservoirs Management, Protection, and Rehabilitation of Lakes and Reservoirs Main Water-Quality Problems General Management Strategies Measures for Eutrophication Control External measures Internal measures Current Knowledge Gaps and Future Research Needs Lakes and Reservoirs as Constituents of Their Catchment Areas Responses of Lakes and Reservoirs to Climate Change Biodiversity and Its Role in the Functioning of Lake and Reservoir Ecosystems Integrated Management of Lakes and Reservoirs

2.08.1 Morphometry, Hydrodynamics, Chemistry, and Biology of Lakes 2.08.1.1 Origin and Development of Lakes

157 157 157 158 159 159 160 163 163 166 170 173 173 176 176 177 179 181 184 185 189 192 196 196 199 203 204 204 204 205 205 205 208 209 210 210 210 211

useful in understanding some of the general characteristics of the lake and drawing comparisons with other lakes of similar origins. In general, the forces forming a lake are

2.08.1.1.1 Origin of lakes Lakes are hollows which are filled, at least partially, with water. The nature of the physico-chemical and biological events taking place in a lake is related to its shape and size, as well as to the characteristics of the drainage basin. These characteristics are in turn largely determined by the mode of origin of the lake. Thus, ascertaining the mode of origin of a lake is

1. catastrophic, or sudden in geological terms; 2. regional in nature, often giving rise to several similar lakes forming a lake district; and 3. caused by erosion (of the outlet) and sedimentation of the basin so that lakes become temporary features of the landscape.

157

158

Lakes and Reservoirs

From a geological point of view, not only reservoirs but also lakes are short-lived (with a few exceptions such as Lake Baikal and Lake Tanganyika, the first of which is more than 56 million years old). Lakes are formed in two ways: (1) by the filling with water into a natural depression or (2) by impoundment behind a natural dam. A depression may be formed by a geological deformation (tectonic or volcanic), by elutriation, by ice or water erosion, or by the slow melting of dead ice blocks in the subsoil. Lake pans may also have been deflated by wind action. Natural dams may be formed by glacial moraines, by landslides, or by biogenic CaCO3 deposition. Most of the existing lakes have been formed by glacial activity during the last ice age. The following statements on the genesis of lakes are essentially based upon Wetzel (2001), Hutchinson (1957), and Keller (1962): 1. Lakes formed by glacial ice movement are more in number than lakes formed by other processes. With the retreat of the large Pleistocene glaciers, an immense number of lakes were formed, creating a large variety of glacially formed lake types. Glacial ice-scour lakes occur in extended rock areas where loosened rock materials have been removed by glaciers. Examples are the upland peneplains of Scandinavia, the United Kingdom, and the great Canadian Shield. Due to scouring of preexisting valleys to great depths, the Great Bear Lake and the Great Slave Lake were formed. The most impressive examples of lakes on the North American continent produced by ice erosion of rocks, however, are the Laurentian Great Lakes. 2. In glaciated valleys, chains of smaller lakes could also have evolved by scouring. Morainal damming of pre-glacial valleys created many lakes in the Northern Hemisphere. Dams of moraine material may, in high mountains, occasionally attain a height of more than 120 m (Keller, 1962). Cirque lakes are frequently arranged along a valley in stairways. Many lakes also emerged from cavities in tillite, a metamorphosed old bedrock. The closely related Kettle lakes originated from the melting of ice blocks which had previously been buried for up to several hundred years, in moraine material. 3. In the flat regions of Siberia and in northern America, millions of small, shallow cryogenic lakes, which evolved from the thawing of local permafrost soils, were formed. Biogenic formation of CaCO3 dams occurs when due to biological (photosynthesis) or mechanical (very turbulent flow) removal of (carbonate-balanced) CO2 from Ca (HCO3)2-rich water (cf. Section 2.08.1.6.3), calcareous crusts may be generated with a growth of 1 cm yr1 or more. In this way, barriers which are able to impound a river can be formed. In front of these dams, there are usually waterfalls and behind them there are lakes (Figure 1). One of the organisms which may cause this growth of high-calc-sinter dams is the filamentous cyanobacterium, Schizothrix. 4. The lake basins of tectonic lakes are depressions which have been formed by movements of comparatively deep portions of the Earth’s crust. Most of these lakes are a result

Figure 1 Some of the Plitvice Lakes, Croatia, the dams of which have been formed by biogenic precipitation of CaCO3. The brown color is caused by Fe3þ. Courtesy of Dr. Anita Belanovic.

Figure 2 Diagram of a tectonic lake basin: a depressed fault-block between two upheaved fault-blocks. In the foreground, situation after a considerable period of erosion and deposition. From Wetzel RG (2001) Limnology. Lake and River Ecosystems, 3rd edn. San Diego, CA: Academic Press.

of faulting with single-fault displacements, or exist in downfaulted troughs (Figure 2; Wetzel, 2001). The latter type is called a ‘graben’. Well-known examples are Lake Baikal in Siberia and Lake Tanganyika in equatorial Africa. These lakes have maximum depths of 1620 m and 1435 m, respectively. Both lakes contain a large number of plant and animal species which are endemic, that is, they occur only in these particular water bodies. Both lakes were already in existence in the Mesozoic period. Another

Lakes and Reservoirs

5.

6.

7.

8.

9.

well-known example of a graben lake is Lake Tahoe (California/Nevada). From a moderate uplifting of the seabed connected to tectonic movements, the Caspian Sea and the Aral Sea in Western Asia were formed in the Miocene period. Upwarping of the Earth’s crust also resulted in the formation of other large lakes such as Lake Okeechobee, Florida, and Lake Victoria, Central Africa. Volcanic lakes are formed when depressions that may be formed due to volcanic activity are undrained, and usually are filled with water. The basins and their drainage areas often have a basaltic nature and thus a low concentration of dissolved solids, inclusive of nutrients. Volcanic crater lakes are often circular and are called ‘maar lakes’ if they have small diameters (up to 2000 m) and ‘calderas’ when of a larger size. A well-known example of a maar lake is Crater Lake, Oregon, with a depth of 608 m. Lava streams may flow into a preexisting river valley and form a dam wall. Behind this dam, a lake may be created. Lake origins are also formed by landslides when large quantities of unconsolidated material suddenly move into the floors of valley streams to create dams and lakes (Figure 3; Wetzel, 2001). Such landslides occur frequently in glaciated mountains. The landslides are usually brought about by abnormal events such as excessive rain acting on unstable slopes, or by earthquake activity. Disastrous floods may be caused downstream if such dams break. Solution lakes have been created by the dissolution of carbonate, also of sulfate or other soluble rock. They are mostly connected, similar to many other lake types, with the groundwater. Among several lake types formed by river activity, floodplain lakes are the best known. Oxbow lakes were created from truncated meanders. Deflation lakes result from the erosive effect of wind, mostly in arid areas. They are often ephemeral. The fine structure of sediments/soils in very large dry depressions in North and South Africa, in many cases, reflect climatic changes over the last millennia.

Figure 3 Lake formed by a large landslide into a steep-sided streameroded canyon. From Wetzel RG (2001) Limnology. Lake and River Ecosystems, 3rd edn. San Diego, CA: Academic Press.

159

2.08.1.1.2 Lake development with a large contribution of photosynthesis As soon as in a shallow lake the higher emergent vegetation (see Figure 33; Uhlmann, 1979) starts to predominate, the accumulation of biomass residues, mostly, cellulose and lignin mud, considerably increases. The thickness of the organic sediment layer may be a multiple of the water depth in the senescent stage of a lake (see Figure 41; Kusnezow, 1959). The accelerated sedimentation and resulting shallowness favor both the further spreading of emergent vegetation and the increase in water losses by evapotranspiration. This often results in the final disappearance of the water body. The biomass residues may also originate from floating mats of Sphagnum moss. These peat-forming mosses initially colonize the outer margins of the water body. The drainage patterns of lakes in the flood plains of tropical rivers can be largely altered by massive growths, not only of emergent, but also of floating-leaved vegetation. This can probably also go along with increased sediment accumulation. In the early stages of development of clear-water lakes formed by glaciation, the biotic productivity is limited by the lack of nutrients, the long winter period, and a high removal rate of dissolved organic materials by photolysis. During the long intermediate stage of lake ontogeny, phytoplankton production governs sediment accumulation, with a small contribution of macrophyte biomass. Organic sedimentation here is largely balanced by microbial decomposition. In the later and terminal stages, the proportion of emergent and wetland vegetation and thus the biogenic silting-up rapidly increases, due to the great amount of lignified and cellulosic residues which now accumulate. This also applies to lakes which are shallow initially. There are also other terminal stages in the development of lakes in temperate climates, for example, various types of persistent mire ecosystems and shallow lakes which are durable due to a permanent high inflow and level of groundwater.

2.08.1.1.3 Development of reservoirs Compared with natural lakes, reservoirs are extremely shortlived. They are designed for a lifetime of at least 50 years, but this is, in many cases, only realistic if the deposited silt is collected in pre-reservoirs and mechanically removed at intervals of several decades. Without such countermeasures, a reservoir may be completely filled with sediment within a few years in areas with very heavy erosion. On the other hand, reservoirs with an estimated lifetime of several centuries also exist (Nilsson, 2009). In the first phase of impoundment, the plankton benefit from the release of nutrients, due to the degradation of the submerged terrestrial vegetation. This may even increase the fish yields. The second phase is characterized by an oligotrophication if the inflowing water is poor in nutrients, or by a eutrophication if the water is fertilized by domestic animals or agricultural effluent. The final phase is an advanced deposition of silt whereby conditions for biogenic siltation are substantially improved. In tropical reservoirs, luxuriant growths of floating-leaved plants become possible under conditions of comparatively small water-level fluctuations.

160

Lakes and Reservoirs

2.08.1.2 Structure and Functioning of Drainage Basins of Natural and Man-Made Lakes The drainage basin clearly regulates the characteristics of lakes and reservoirs, which include soil, ionic composition, slope, and, in combination with the climate, vegetation cover (Wetzel, 2001). Soil and vegetation not only influence the runoff, but also the composition and quantity of organic matter that enters the tributaries. The area of the drainage basin as related to the area of the water body is normally o10:1 in the case of lakes, but it is 410:1 for reservoir catchments. The drainage basin of a reservoir is usually large enough to fill the man-made lake within a period of 1 year or less. The influence of the structure and the uses of the drainage basin upon the water quality are therefore quite substantial. Reservoirs are also subjected much more to the stochastics in the relationship between atmospheric precipitation and runoff, than is the case in Pleistocene lakes, which are largely supplied by groundwater. Lakes are often close to the center of their drainage basin, whereas reservoirs are located at the margin. This is predetermined by the morphometry of the territory. Densely forested drainage basins provide a good water quality in reservoirs because of their anti-erosion function. Furthermore, a forest cover promotes the infiltration of rainwater as a pre-treatment step in the context of drinking-water supply. The potable water supply of big cities such as New York, Tokyo, Beijing, Rio de Janeiro, and Los Angeles is completely, or largely, based upon water from forested drainage basins (K. H. Feger, personal communication). From agricultural areas, mainly soil particles, nitrogen and phosphorus from fertilizers, as well as herbicides and pesticides are lost by surface runoff, due to heavy rains. Pastures may not only be sources of N- and P-compounds, but also release cysts of parasitic protists from manure depositions. The nitrate concentration in a reservoir or lake is often an indicator of the state of the environment. In the Saidenbach Reservoir (Germany), the increase in agricultural production, and particularly the application of liquid manure to the catchment, has caused a threefold increase of nitrate concentration in 17 years (rise from 10 mg l1 in 1962 to 30 mg l1 in 1979, W. Horn in Uhlmann and Horn, 2001). Densely populated drainage basins are generally not compatible with the safe operation of drinking-water reservoirs. The introduction of purified domestic effluent (with advanced treatment) into drinking-water reservoirs is extremely problematic not only due to the loads of potentially harmful microorganisms, but also due to unacceptable N and P concentrations in storm-water outlets, subsequent to heavy rains. The allowable P concentrations for purified effluent in flowing waters are normally set at a level which does not affect the ecosystem. However, the very low P concentrations of around 10 mg l1, which are required for drinking-water reservoirs, may not be achieved downstream of wastewatertreatment plants even if these are operated in full accordance with internationally accepted regulations. In temperate climates, drinking-water reservoirs are situated mostly in hilly areas with igneous rock as the mineral subsoil. Consequently, they often have water of low hardness or they can even be weakly acidic. This facilitates the binding

of phosphate to Al- and Fe3þ-complexes (also in the colloidalsize class) and favors an oligotrophic state of the water body. In the past decades, many sites in Europe and North America have become subject to an acidification of soil and water due to atmospheric depositions. Liming of the forests in the drainage basins has been used as a counteractive measure, but it simultaneously increases the trophic state (i.e., phytoplankton production) as is well known from fishponds. In former decades, bogs in the catchments of drinkingwater reservoirs were often drained. If clearing of the drainage ditches is not done on a regular basis, the resulting waterlogging leads not only to an increased leaching of (coniferous) soils, but also to the growth of bogs. This may result in an increased concentration of humic substances/dissolved organic carbon in reservoir waters (Sudbrack et al., 2005). Thus, the costs for water treatment may largely increase. Sometimes reservoir systems are interconnected. Downstream water bodies generally receive better quality water which is improved due to the retention in the upstream water bodies, and they serve as pre-reservoirs for water treatment. Thus, the concentration of imported suspended solids is generally much lower downstream. In many cases, the trophic state and phytoplankton production (Sections 2.08.1.7 and 34.1.8) likewise decrease. The quality may also be improved by introducing water from reservoirs which are situated in a bypass.

2.08.1.3 Lake and Reservoir Morphometry The morphology of lakes, their size and shape, is often related to their origin and age. Lake morphometry is the quantification of characteristic morphological dimensions whose fundamental limnological importance was emphasized by Kalff (2002): Regardless of how lakes are formed, their surface shape, surface area, underwater form, depth and the irregularity of their shorelines have a major impact on turbulence, lake stratification, sedimentation and resuspension, and the extent of littoral-zone wetlands that determine lake functioning.

The determination of morphometric measures requires a bathymetric map of the lake with a scaled outline of the shoreline and submerged contour lines in several depths below the surface. In the past, the depth development of a lake had to be determined by lowering a plumb line from a boat or the frozen surface at many stations. Nowadays, precise bathymetric maps of water bodies are created using digital sonars coupled with a global positioning system (GPS)-receiver and data evaluation using geographic information system GIS-software (Figure 4; Sytsma et al., 2004). The interaction of a lake with the atmosphere (e.g., radiative energy balance, gas exchange, and direct matter import by precipitation) and the impact of driving meteorological forces (particularly the effect of wind on mixing and stratification, surface waves, and water movements) depend primarily on its surface area A0 and form described by the maximum length lmax and width bmax measured perpendicular to lmax. The maximum length is the distance between the two most distant points of the lake surface. It is often measured as a straight line that may cross islands or promontories. Sometimes, it is

Elevation, m

Lakes and Reservoirs

161

1660 1650 1640 1630 1620 1610 1600 1590 1580 1570 1560 1550 1540 1530 1520 0

300

600

900 1200 1500 1800 2100 2400 2700 3000 Surface area, ha

Depth (m) N

0

Elevation, m

Waldo Lake Bathymetric Map

130 Kilometers 0

0.5

1

2

1660 1650 1640 1630 1620 1610 1600 1590 1580 1570 1560 1550 1540 1530 1520 0

(a)

(b)

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

(c)

Volume, cubic hectometers

Figure 4 Example of a bathymetric study at the Waldo Lake (Oregon, USA): (a) data-collection cruise paths; (b) bathymetric map interpolated from data collected; (c) resulting hypsographic curve Az (above) and volume–depth distribution Vz (below). From Sytsma M, Rueter J, Petersen R, et al. (2004) Waldo Lake Research in 2003. Center for Lakes and Reservoirs, Department of Environmental Sciences and Resources, Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon 97201–0751. http://www.clr.pdx.edu/docs/2003report.pdf (accessed April 2010).

Wind fetch (km) 0

5

10

15

20

25

30

0 A0

5 Zmix (m)

VBL b max l max

b eff

l eff

Temperate zone

10

Arai (1981) Patalas (1984) Kling (1988)

15

Hanna (1990)

20 25

Africa

30 500 m Figure 5 Morphometric characteristics of the Neunzehnhain II reservoir (Germany, 501 42.60 N, 131 09.130 E; VBL: pre-dam). Reservoir and circle have identical areas (A0 ¼ 28.9 ha). The shoreline development DL of the reservoir is 2.0 and that of the circle is 1.0.

measured along the thalweg of the lake. In contrast with bmax, the mean width bmean is given as the quotient of A0 and lmax. However, most important are the effective length leff and width beff (perpendicular to leff) which represent the longest distances from shore to shore, not interrupted by land (Figure 5). The effective lake axis deff is the average of leff and beff. Surface dimensions are used to quantify the wind effect on a lake. The parameter leff is usually called the maximum ‘effective wind fetch’ F (Ha˚kanson, 1981; Kling, 1988; Hanna, pffiffiffiffiffi 1990). Other authors define F as A0 (Arai, 1981) or deff (Patalas, 1984). No matter how F is defined, it was frequently

Figure 6 Empirical relationships between wind fetch and mixing depth zmix established for lakes in Japan, Europe and North America, and in tropical Africa. Data for lakes in Japan from Arai T (1981) Climatic and geomorphological influences on lake temperature. Verhandlungen Internationale Vereinigung fu¨r Theoretische und Angewandte Limnologie 21: 130–134; for lakes in Europe and North America from Patalas K (1984) Mid-summer mixing depths of lakes of different latitudes. Verhandlungen Internationale Vereinigung fu¨r Theoretische und Angewandte Limnologie 22: 97–102 and Hanna M (1990) Evaluation of models predicting mixing depth. Canadian Journal of Fisheries and Aquatic Sciences 47: 940–947; and for lakes in tropical Africa from Kling GW (1988) Comparative transparency, depth of mixing and stability of stratification in lakes of Cameroon, West Africa. Limnology and Oceanography 33: 27–40.

found to correlate significantly with mixing depth zmix (Figure 6; Arai, 1981; Patalas, 1984; Kling, 1988; Hanna, 1990). Furthermore, the maximum height of surface waves and thus, their erosive impact on the shores, sediment resuspension,

162

Lakes and Reservoirs

and transport into deeper regions of the lake are related to F (see Section 2.08.1.5). The length of the shoreline L0 and the dimensionless shoreline development DL with

L0 DL ¼ pffiffiffiffiffiffiffiffi 2 pA0

light conditions, nutrients, oxygen, primary productivity, sediment resuspension, and many others. Thienemann (1927) has already stated that shallow lakes generally tend toward a higher eutrophy than deep lakes. He defined the boundary between eutrophy and oligotrophy at z ¼ 18 m for German lakes. Kalff (2002) named z probably the most useful single morphometric feature available. The shallowness of a lake can be characterized by its relative depth zrel (%), which is the ratio between zmax and the diameter of a circle with area A0:

ð1Þ

characterize the land–water and littoral–pelagial interactions of a lake. DL relates L0 to the circumference of a circle with an area identical to the lake’s surface A0 (Figure 5). Thus, the minimum of DL is 1, and the more the lake’s surface differs from a circular shape, the higher is the DL, indicating stronger linkage of the lake to the drainage basin and more extended shallow littoral zones. Many reservoirs exhibit high DL due to their dendritic surface shape. The depth distribution of a lake is described by the hypsographic curve. The areas Az are dependent on depths z. Az is calculated from determinations of the areas enclosed by k contour lines in different depths, from surface down to the maximum depth zmax, drawn on a bathymetric map. The volume Vi  Viþ1 of the layer between neighboring contour lines at depths zi and ziþ1 can be estimated as follows:

Vi  Viþ1 E 13ðAi þ Aiþ1 þ

pffiffiffiffiffiffiffiffiffiffiffiffiffi Ai Aiþ1 Þðziþ1  zi Þ

zrel ¼ 50zmax

V0 A0

ð4Þ

Characteristic values of zrel lie between 1% (large and shallow lakes) and about 4% (deep lakes). Calderas, maars, fjords, or solution basins may have zrel410%. The record zrel ¼ 374% is held by the Hawaiian volcanic crater lake, Kauhako (A0 ¼ 0.35 ha, zmax ¼ 250 m) (Cole, 1994). The extension of the littoral zone, the potential development of submerged macrophytes, the near-shore sediment transport and quality (water and organic content, particle size; e.g., Ha˚kanson and Boulion, 2002), and the colonization of littoral sediments with benthic organisms, depend on the slope s (%) of the shore. The slope si between two contour lines at depths zi and ziþ1 is calculated as follows (lengths and depths in m, areas in m2; see Figure 7)

ð2Þ

with 0rirk – 1, zk ¼ zmax, Ak ¼ 0, and Vk ¼ 0. The sum of the volumes of the layers below zi is the volume Vi and consequently, the sum of the volumes of all layers is the total volume V0 of the lake. Finally, the volume–depth development Vz for 0rzrzmax can be constructed. It is clear that the accuracy of the curves Az and Vz increases with the increasing number k of contour lines. Maximum depth zmax and average depth z with

z ¼

rffiffiffiffiffi p A0

si ¼ 100

ðLi þ Liþ1 Þðziþ1  zi Þ 2ðAi  Aiþ1 Þ

ð5Þ

The average basin slope s is

s ¼ 50

ð3Þ

k1 zmax X ðLi þ Liþ1 Þ nA0 i¼0

ð6Þ

Lakes of identical A0 and zmax may have different volumes due to different volume–depth distributions. In order to classify lake types, the index DV called ‘volume development’ was defined. DV is the ratio between the lake’s real volume

are very important parameters influencing the vertical distribution and zonation of, for example, temperature, underwater

d

L1

di

d

Am

Am

L2

L

(L1 + L2)/2 d ≈

2A m

=

L1 + L2

2(A1 – A2) L 1 + L2

di tan() =

Z2 – Z1 di

tan() =

Z2 – Z1

 di

Z2 – Z1 d

=

(L1 + L2) (Z2 – Z1) 2(A1 – A2 )

Figure 7 Schematic illustration on how to determine the slope between two contour lines L1 and L2 at depths z1 and z2 (L1, L2, z1, and z2 in m, areas A1 and A2 enclosed by L1 and L2 given in m2, Am ¼ A1  A2, di – distance between L1 and L2 at a certain position, d – average distance).

Lakes and Reservoirs V0 ¼ A0 z (Equation (3)) and the volume 13A0 zmax of an inverted cone with base area equal to the lake’s surface A0 and height coincident with the lake’s zmax:

DV ¼

z 3A0 z ¼3 A0 zmax zmax

0

n ¼ 2DV  3 ðJunge s shape indexÞ

consequences:

•

ð7Þ

A total of 202 out of 243 lakes evaluated by Carpenter (1983) came under the range 1 (V-shaped or cone) rDVr2 (U-shaped or ellipsoid). The U-shaped basins of many old natural lakes are the result of lake aging, that is, the deposition and focusing of large quantities of allochthonous and autochthonous sediments in the deepest parts, over a long time. Relatively young reservoirs, however, often have V-shaped basins. Morphometric models were developed that describe the geometric shape of lakes as quadric surfaces and sinusoids based on DV. Junge (1966) introduced the transformation

•

•

ð8Þ

and derived the following formulas for the relative area Ax and volume Vx using the normalized depth x ¼ z=zmax (z downward positive):

Az ¼ 1  nx2  ð1  nÞx A0

ð9Þ

Vz 6x  3ð1  nÞx 2  2nx 3 ¼1 V0 3þn

ð10Þ

Ax ¼

Vx ¼

•

Junge (1966) found satisfactory agreement between measured and calculated volume–depth distributions for most lakes and ponds considered in the survey. Significant deviations are characteristic of lakes with singular deep pits (DVo1) or large flat-bottom areas (DV42). Based on Junge’s model, lake types can be classified by principal geometric characteristics (Table 1; Junge, 1966). In order to elucidate the most important differences between the basic geometric lake types, circular basins with identical A0 and zmax are assumed. The areas and volumes of the upper (epilimnetic) water layers decrease much faster in the cone than in the ellipsoid (Figure 8; Junge, 1966). This has many very important Table 1

163

•

•

The epilimnion of V-shaped basins is shallower. Thus, the impact of sediment-related processes, such as the extension of the littoral area and its colonization with submerged macrophytes, as well as its role as a habitat for fishes, the influence of benthic organisms, sediment resuspension, and nutrient remobilization at the sediment–water interface on the epilimnetic matter turnover, is potentially highest in V-shaped water bodies. The area of the threshold between epilimnion and hypolimnion is smallest in V-shaped and largest in U-shaped basins. Therefore, the probability that particles and algae settle on the epilimnetic sediment area is highest in Vshaped lakes. The dilution of nutrients released from epilimnetic sediments is greatest in those lakes. Both features were found to influence the primary productivity of lakes (Fee, 1979). The ratio rV between the epilimnion and hypolimnion volumes is highest and increases faster in the cone and thus, the hypolimnetic oxygen balance is more critical in Vshaped basins. Thienemann (1927) had already postulated that lakes with rV 4 1 tend toward a eutrophic state while those with rVo1 are more oligotrophic. Sedimentation from epilimnion into hypolimnion is an important loss factor for phytoplankton, primarily for fast-settling diatoms. The probability of algae settling into the hypolimnion is much higher in U-shaped than in V-shaped lakes. The average epilimnetic light intensity is higher in V-shaped systems, due to the lower volume in the deeper zones compared with U-shaped ones. Light limitation of phytoplankton growth may be more significant in U-shaped water bodies. The transport of sediments into deeper regions of the lake, the so-called sediment focusing, depends on the average basin slope (Blais and Kalff, 1995), which is highest in Ushaped lakes. The total volume V0 of the ellipsoid is twice the volume of the cone and 4/3V0 of the paraboloid (Table 1). Consequently, the cone has a much lower heat capacity. Furthermore, its thermal stability is much lower due to the low depth of the gravity center, if identical vertical temperature distributions are assumed. Both facts may influence the timing of the periods of full turnover and stratification and the beginning of ice covering.

Parameters describing the shape of basic geometric lake types Basic geometric lake type

Parameter

Symbol

Cone

Paraboloid

Ellipsoid

Volume development Junge’s shape index Normalized area Normalized volume Depth of gravity center of the completely mixed lake Total volume (m3)

DV n Ax Vx xgc V0

1 1 (1–x)2 (1–x)3 1/4 A0zmax/3

3/2 0 1–x (1–x)2 1/3 A0zmax/2

2 1 1–x2 1–x(3–x2)/2 3/8 2A0zmax/3

From the morphometry model of Junge CO (1966) Depth distributions for quadric surfaces and other configurations. In: Hrbacek J (ed.) Hydrobiological Studies, Academia Publishing House of the Czechoslovak Academy of Sciences, pp. 257–265. Prague.

164

Lakes and Reservoirs

Cone (V-shaped)

Paraboloid

Ellipsoid (U-shaped)

Ax 0.2

0.4

0.6

0.8

1

0

0

0

0.2

0.2

0.4 0.6 0.8 1

Normalized depth x

Normalized depth x

0

Vx 0.2

0.4

0.6

0.8

1

0.4 0.6 0.8 1

Figure 8 (Top) Bathymetric maps of idealized (identical circular surface and maximum depth) lake types based on the morphometry model. Contour lines are drawn for the normalized depths x ¼ 0, 0.2, 0.4, y, 1. Attention should be paid to the different portions of the gray (epilimnion) and white (hypolimnion) areas (assumed a mixing depth of xmix ¼ 0.4). (Bottom) Normalized hypsographic curves Ax ¼ f(x) (left) and volume–depth distributions cone, paraboloid, and Ellipsoid). The dashed lines mark the assumed mixing depth of Vx ¼ f(x) (right) for idealized lake types ( xmix ¼ 0.4. Bathymetric maps based on the morphometry model of Junge CO (1966) Depth distributions for quadric surfaces and other configurations. In: Hrbacek J (ed.) Hydrobiological Studies, Academia Publishing House of the Czechoslovak Academy of Sciences, pp. 257–265. Prague.

•

V-shaped lakes have a shorter theoretical residence time t ¼ V0 =Qa (a), where Qa (m3 a1) is the mean annual discharge. The dilution of inflowing water-carrying nutrients, suspended matter, and other substances is low in V-shaped lakes, but much higher in U-shaped ones. Hence, the resistance of U-shaped basins against changing external loading is greater. The delay of an aggravation of the trophic state, in the case of increasing nutrient imports, is longer in those lakes. V-shaped water bodies may respond faster to reduced external loading.

It can be concluded that not only the size, but also the shape of lakes considerably influences their physical, chemical, and biological structure and functioning.

2.08.1.4 Influx and Vertical Distribution of Solar Energy Solar radiation is the Earth’s most important natural energy source and has a prominent ecological role. The global solar irradiance IG (W m2) is the solar radiation measured on a horizontal plane at the Earth’s surface and spans wavelengths l between about 200 and 3000 nm. The spectrum is divided into the ranges of ultraviolet (UV) radiation (lo380 nm; may be harmful to organisms), visible light (380 nmrlr750 nm), and infrared radiation (l4750 nm; thermal radiation). IG is

the sum of direct sun radiation and diffuse sky radiation (measured at full cloud cover, radiation reflected from clouds, water and dust particles, and other aerosols suspended in the atmosphere). It depends on latitude (Figure 9; Stras ’kraba, 1980), altitude (thickness of the atmosphere, higher IG at higher altitudes), and penetrability of the atmosphere (higher IG in dry regions compared with wet regions). If measured values of the local global radiation are not available, they can be approximately calculated from daily integrals of the radiation reaching the Earth’s surface on totally cloudless days, observations of sunshine duration and day length (Strasˇkraba, 1980).

2.08.1.4.1 Underwater light conditions The ratio of the irradiance reflected at surfaces to the incident flux is called ‘albedo’ r (in parts of 1). The reflection at water surfaces is mostly mirror like (specular reflection), with respect to the surface normal (angle of incidence a equals angle of reflection). Albedo r of direct sun radiation is a function of a and, therefore, the daily average varies geographically. However, r decreases quickly with decreasing a (ro0.13 if ao701, and ro0.03 if ao451). The reflection of diffuse sky radiation is lower at high a and higher at low a, than that of direct sun radiation. Surface waves reduce r at a 4701. For Central

Lakes and Reservoirs 3000

the changing spectral composition of light, with increasing water depth (Figure 10; Vollenweider, 1961; Uhlmann and Horn, 2001), due to the wavelength-specific transmission

2500 2000

3.4° 10°

1500

20°

Global radiation (J cm–2 d–1)

30° 1000 40°

70°

500

50°

Northern Hemisphere

60°

3000 Southern Hemisphere 2500 0° 2000

10° 20°

1500

30° 1000 40° 500

50°

70°

Tlz ¼ 100

J

F

M A M

J

Ilz ð%Þ Il0

Ilz ¼ Il0 expðkl zÞ J

A

S

O N

ð13Þ

D

Figure 9 Annual variations of daily integrals of the radiation reaching the Earth’s surface on totally cloudless days calculated for selected latitudes (atmospheric transmission factor 0.6). Modified from Strasˇkraba M (1980) The effects of physical variables on freshwater production: Analysis based on models. In: le Cren ED and McConnell RH (eds.) The Functioning of Freshwater Ecosystems, IBP 22, pp. 13–84. Cambridge University Press.

Europe, a daily average of rE0.1 can be assumed for water surfaces. Fresh snow reflects about 80–90%, old snow about 40–70%, and ice c. 25–35% of the irradiation. Radiation entering the water surface changes its direction due to the higher density and lower velocity of propagation in water than in air. This phenomenon is called refraction. The angle of refraction b is lower than a. The opposite applies to radiation, which is scattered from particles in the water back into the air. The radiation is angled away from the surface normal. If the angle of incidence of the backscattered radiation is greater than 491, it is completely reflected at the water– air interface. The photosynthetically active radiation (PAR) spanning 400–700 nm, within the range of visible light, is potentially a growth-limiting factor with regard to plants. Considering the intensity I0þ of PAR above the water surface, the approximation I0þE0.46IG is widely accepted and with an average daily albedo r ¼ 0.1, the intensity I0– of PAR just below the water surface can be expressed as

I0 ¼ 0:414IG

ð12Þ

with the wavelength l and the light intensities Il0– and Ilz just below the surface and at depth z, respectively. While the transmission of pure water is high in the blue–green part of the spectrum (l ¼ 450–500 nm), the range of the most penetrating light component shifts toward green–yellow (l ¼ 530– 580 nm) or even yellow–red (l ¼ 580–650 nm), depending on the concentration of suspended particles and dissolved substances (e.g., humic acids). Thus, the visual images of lakes change correspondingly: clear lakes appear blue–green, and eutrophicated lakes or those influenced by colored dissolved organic substances look greenish or brownish, due to the predominant backscattering of the respective range of the light spectrum. Although some phytoplankton species were found to react specifically to changing light quality (chromatic adaptation), the decrease of absolute light intensity with increasing water depth is far more important for photosynthesis. The light attenuation is described by the Lambert–Beer’s law:

60° 0

165

ð11Þ

Divers realize that colored objects become much paler in greater water depths. This phenomenon is the consequence of

The attenuation coefficient kl (m1) is a measure for the combined effect of absorption (i.e., transformation of radiation energy into heat or biochemical energy) and scattering (i.e., change of propagation direction caused by particles or water-density inhomogeneities) on the intensity of the light of the wavelength l. Although Equation (13) is, strictly speaking, only valid for parallel monochromatic light beams, it can also be applied to relatively narrow spectral bands such as PAR. The average PAR attenuation coefficient kPAR is derived from the spectral attenuation coefficients kl

1 kPAR ¼ 300

7Z00

kl dlE 13ðk450 þ k550 þ k650 Þ

ð14Þ

400

or, nowadays, directly calculated from underwater light measurements using spherical quantum sensors whose spectral response is adapted to the PAR range. The value kPAR is the sum of kW (pure water), kS (dissolved or colloidal matter), kP (phytoplankton), and kD (nonliving particles)

kPAR ¼ kW þ kS þ kP þ kD ¼ kW þ eS CS þ eP CP þ eD CD

ð15Þ

where the ei (l m1 mg1) are the substance-specific attenuation coefficients, and the Ci (mg l1) the substance concentrations, respectively. The mean extinction of the light flux directed downward is a suitable index for evaluating its spectral distribution (Vollenweider, 1961; see Figure 10). The property of substances to preferably absorb and reflect light in specific wavelength ranges is utilized in the remote sensing of waterquality criteria (e.g., the distribution of chlorophyll and water temperature) by air or satellite-borne reflectance measurements.

166

Lakes and Reservoirs 100 Max

Min

Mean

f(kPAR)

f(zSD)

Std

3

D1 80 Transmission (%)

Attenuation coefficient k, (m–1)

4

2

1

D10

60

S1 D100

40 S2 20

0 350 400 450 500 550 600 650 700 750 Wavelength  (nm)

0 400

S5

S3

S10 450

500 550 600 Wavelength  (nm)

650

700

Figure 10 (a) Mean (7standard deviation) and range of variation (min, max) of spectral attenuation coefficients (measured bi-weekly during the icefree seasons from 1975 to 1985) as well as approximations derived from the average attenuation coefficient of PAR (f (kPAR)) and the average Secchi disk transparency (f (zSD)) of the Saidenbach Reservoir (Germany) compared with the spectral standard distribution (Std); (b) average spectral underwater light transmission in the Saidenbach Reservoir (S1: 1m, S2: 2m, y, S10: 10m) and of distilled water (thickness of water layer D1: 1m, D10: 10m, D100: 100m). (a) Std from Vollenweider RA (1961) Photometric studies in inland waters: I. Relations existing in the spectral extinction of light in water. Memorie dell’Istituto Italiano di Idrobiologia 13: 87–113. (b) Curves D1, D10, and D100 from Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer; and data of the Saidenbach Reservoir from Paul L (1989) Interrelationships between optical parameters. Acta Hydrophysica, Berlin 33: 41–63.

The transparency or Secchi depth zSD provides a clear impression of the optical properties of standing waters. It is easily measured using a Secchi disk, a white disk, usually 25 cm in diameter, which is lowered on the shady side of a boat down to the depth of its visual disappearance. Relationships such as kPAR zSD ¼ a have been frequently published, since the early work of Poole and Atkins (1929). The value of a must be considered as water-body specific and, is consequently found to be widely scattering between about 1.1 and 4.6. This is quite understandable, because of the fact that the Secchi disk visually disappears, not only because of light attenuation but primarily because the contrast between disk and background becomes imperceptible. This is strongly influenced by both the concentration and size of the suspended particles in the water column above the disk. Thus, the percentage of transmission at zSD must be higher in a turbid lake. Nevertheless, the Secchi depth can be used as a predictor for spectral underwater light distribution. This was shown by Paul (1989), who found highly significant correlations of the type

ki ¼ ai þ

bi zSD

ð16Þ

where i stands for both l and PAR, respectively (Figure 10). The value ai can be considered as a first-order approximation for the lake-specific attenuation coefficients of water without suspended particles. The light requirements of phototrophic organisms are quite different and, moreover, depend on their physiological state. Water plants adapted to low-light intensities are very sensitive to small enhancements of illumination or, on the other hand, their photosynthetic response is inhibited if the light intensity increases sharply. Thus, it is impossible to define a general minimum-compensation light intensity Icomp (W m2) that is necessary to compensate for the oxygen consumption at night (respiration), with the respective photosynthetic oxygen

production in the daytime. Therefore, the underwater light situation is characterized by the depth zeu of the euphotic zone, which is the water layer expanding from surface down to the depth, where the PAR intensity Izeu is 1% of I0:

zeu ¼

4:6 kPAR

ð17Þ

In the euphotic zone (phototrophic layer), the light intensity is considered to be sufficiently high to allow photosynthesis. In the layers below zeu (tropholytic zone), respiration exceeds production and the phytoplankton development is light limited. It has to be borne in mind that zeu is only an approximate guiding principle. It is related to full daylight and sufficient day length. The light intensity Izeu in medium or high latitudes is much lower in winter than in summer and thus, zeu may substantially overestimate the depth of the compensation point in winter, and vice versa in summer. Phytoplankton cells are vertically transported by winddriven and/or convective currents throughout the mixed layer bounded by the mixing depth zmix and are exposed to an average light intensity

Izmix ¼ I0 zmix ¼

zZ mix

expðkPAR zÞdz 0

I0 ð1  expðkPAR zmix ÞÞ kPAR zmix

ð18Þ

or, if kPARzmix 4 3 and Equations (11) and (17) are considered

Izmix E

I0 zeu I0 zeu ¼ E 0:09IG kPAR zmix 4:6zmix zmix

ð19Þ

Hence, if the ratio between mixing depth zmix and euphotic depth zeu exceeds a critical value, phytoplankton development

Lakes and Reservoirs

• • • • •

•

the absorption of short-wave solar radiation, the net exchange of long-wave radiation between lake surface and atmosphere, the conductive exchange of heat at the water surface depending on the temperature difference between water and air, the evaporative heat loss, the heat import and export by inflow and outflow (only important in lakes or reservoirs with short retention times or in lakes significantly fed by the meltwater of glaciers), and the heat exchange at the lake bottom (only important in instances of geothermal activities).

Heat is vertically transported by convective and advective currents and turbulent eddy-diffusion. Currents generated by wind have a particular impact, and heat conduction is of minor importance. Therefore, the thermal structure of a lake greatly depends on the size and shape of the surface and its exposure to wind. The amount of heat stored by a lake, the heat content y (kJ), is calculated as follows:

y ¼ cp r

zZ max

 Az Wz dzE 4186 * V0 W

ð20Þ

0

with area Az (m2) and temperature Wz (1C) at depth z (m), maximum depth zmax, specific heat of water cp ¼ 1 kcal kg1 K1 ¼ 4.1855 kJ kg1 K1, density of water r (kg m3), lake  (1C). The annual volume V0 (m3), and average temperature W heat budget, the quantity of heat gained during warming and released during autumnal cooling, is the difference between the annual maximum and minimum heat content. HalbfaX (1921) described the immense magnitude of a lake’s annual heat turnover, comparing it with the length of a wagon train loaded with an equivalent amount of coal for heating. For the relatively small Klingenberg reservoir (near Dresden, Germany; V0 E 16 * 106 m3 and zmaxE33 m), he calculated the annual heat budget equivalent to the combustion heat of a coal train, 17 km in length. Thus, large lakes may significantly influence local climate. The vertical temperature distribution represents density stratification. Generally, the water density r depends on salinity s (%), pressure P (bar), and temperature W. A set of formulas to precisely calculate r(W,s,P), was provided by Chen and Millero (1986). However, the influence of salinity (normally so1 %) and pressure P with PE0.1z is negligible in freshwater and r is primarily determined by W (Figure 11, see also Figure 12; Chen and Millero, 1986). The unique quality of water, the decrease of density at temperatures o4 1C (anomaly of water), and the fact that the density of ice only is

( )

1000

0.30

999

0.25

998

0.20

997

0.15

996

0.10 |( )–( +1)|

995

0.05

994

Density diff. |(  )–( +1)| (kg m–3)

2.08.1.4.2 Heat budget and thermal structure The heat budget of a lake is determined by seasonal variations of

0.35

1001

Density ( ) (kg m–3)

is severely restricted, due to a very low average light intensity in the mixed water column. This principle is utilized in the artificial destratification of lakes, to control mass development of nuisance algae (see Section 2.08.3.3).

167

0.00 0

5

10 15 20 25 Temperature  (°C)

30

35

Figure 11 Density of water r(W) and density difference Dr ¼ |r(W)  r(W þ 1)| vs. temperature W at sea level and normal air pressure.

about 92% of the density of water at 0 1C, ensures the survival of aquatic organisms even in cold winters. Ice develops and floats at the lake surface and the water temperature in deep layers is not much lower than 4 1C. Therefore, aquatic organisms need to resist a much smaller annual temperature variation than terrestrial organisms. The density difference Dr increases considerably with increasing temperature. For instance, Dr between water temperatures of 19 1C and 4 1C is almost identical with Dr between 28 1C and 22 1C. Thus, relatively small temperature differences in tropical lakes may represent stronger density gradients than in temperate lakes. At the beginning of the warmer seasons, the heat gain in surface water is higher than wind-driven currents that can distribute vertically, and a temperature stratification is established in deep or even shallower, but wind-sheltered lakes, that usually consists of three characteristic layers (Figure 12):

• • •

Epilimnion – the upper warm, less dense, and turbulently mixed layer of almost homogenous temperature and density. Metalimnion – the intermediate stratum with strongly decreasing temperature and increasing density. Hypolimnion – the cold, more dense, and relatively quiescent bottom layer with low temperature and density gradients.

The static stability of temperature stratification can be characterized by density gradients, for example, by the buoyancy or Brunt–Va¨isa¨la¨ frequency N (s1) with

N2 ¼

g dr 2 * 9:81 * ðrzþDz  rz Þ E r dz rzþDz þ rz

ð21Þ

(g (m s2) the local acceleration due to gravity and Dz ¼ 1 m), or by the relative thermal resistance to mixing R (Wetzel and Likens, 1991) with

R¼

rzþDz  rz 10 3 E ðrzþDz  rz Þ rð41 CÞ  rð51 CÞ 8

ð22Þ

168

Lakes and Reservoirs

Temperature  (°C) 5

0

10

Density  (kg m–3)

15

20

998.5

0

Depth z (m)

5

999

999.5

1000

1000.5

0.3

0.4

 ( ,s,P ) Epilimnion

10

Metalimnion

15

Hypolimnion

20



()

25

20

30

Δ()

35 40 0.2 (a)

0.205

0.21

0.215

0.22

Conductivity 20 (mS cm–1)

0 (b)

0.1

0.2

Density diff. Δ (kg

m–3)

Figure 12 Characteristic vertical temperature and density stratification for lakes of the transient zone (about 401–601 N or S) in summer. (a) Vertical profiles of water temperature W and conductivity k20 (mS cm2, related to W ¼ 20 1C) measured in the Saidenbach Reservoir on 10 July 2007. (b) Respective distributions of water density r(W) as a function of W alone, density r(W,s,P) depending on W, salinity sE0.5k20, and pressure PE0.1z, and density difference Dr(W) ¼ r(Wzþ1) – r(Wz). The maximum deviation of r(W) from r(W,s,P) is less than 0.04%, which clearly shows the primary impact of temperature on density in freshwaters. (b) Curve r(W,s,P) from Chen C-TA and Millero FJ (1986) Precise thermodynamic properties of natural waters covering only the limnological range. Limnology and Oceanography 31: 657–662.

Schmidt (1915) defined thermal stability S0 (Nm m2) as the required energy per square meter of the lake surface, to completely mix the stratified water body, without change in its heat content

S0 ¼

¼

g A0 g A0

zZ max

Az ðzfc  zÞðrfc  rz Þdz

0 zZ max 0

Az rz ðz  zfc Þdz ¼

gM ðzst  zfc Þ A0

ð23Þ

with area Az and density rz at depth z, acceleration due to gravity g ¼ 9.81 m s2, surface area A0, depth zfc of the gravity center, and density rfc during the full circulation period. Thus, S0A0 corresponds to the work (to be accomplished by the wind) that is required to lift the total mass M of the lake by the distance zst  zfc, which is the difference between the depths zst and zfc of the gravity center of the stratified, and the completely circulating lake. In the case of a stable stratification (lighter, less dense above heavier water layers with higher density), it is zst4zfc and S040. The stability characterizes the degree of separation of the hypolimnetic water layers from the epilimnetic ones. Deep lakes have a higher S0 than shallow water bodies. The difference zst  zfc usually amounts to only a few millimeters. However, the energy needed for mixing a stably stratified lake is huge, due to the enormous mass to be raised. For instance, the mass of the relatively small Klingenberg reservoir, mentioned above, is about 1600 times the mass of the Eiffel tower in Paris. Thermal stability is an important parameter that has to be considered in planning artificial lake destratification, as a measure to prevent hypolimnetic oxygen depletion and/or the mass development of noxious

phytoplankton by light limitation, resulting from a too high ratio of zmix and zeu. The metalimnion is usually defined as the stratum where the temperature gradient exceeds a certain limit (e.g., 1 K m1). However, considering lakes at different latitudes with quite different temperature ranges, the upper and the lower threshold of the metalimnion should be related to density gradients. In temperate zones, the beginning of a stable summer stratification is often observed when the surface temperature exceeds 10 1C. Accordingly, the depth of the 10 1C-isotherm is a good predictor for the threshold between meta- and hypolimnion. Consequently, the metalimnion could more generally be defined as the layer with density gradients greater than 0.08 kg m4 (see Figure 12). This limit corresponds approximately to the water-density difference between 9 1C and 10 1C and, thus, to N 2 E 0:0008 s 2 (Equation (21)) or to RE 10 (Equation (22)). The level of the maximum density gradient is called ‘thermocline’. Talling (1971) determined the mixing depth zmix of the upper mixed layer as the depth with a temperature 0.5 K below the temperature at a depth of 2 m. In this manner, superficial thermal gradients, which may develop during transitional calm weather periods, are largely excluded. Referring to this principle, but transferred to density gradients, zmix can be defined as the depth with a density 0.08 kg m3 higher than that at the depth of 2 m. Thus, for a given temperature W247.2 1C in z ¼ 2 m, the temperature Wzmix (1C) at the depth zmix amounts to

Wzmix ¼ W2  0:28  3031200 expð2W2 Þ  5:2 expð0:2W2 Þ

ð24Þ

Latitude and altitude determine a lake’s seasonal temperature range and mixing scheme, depending on the regional

Lakes and Reservoirs

•

A sequence of typical vertical temperature profiles of a dimictic water body (Lake Stechlin, Germany) is shown in Figure 15.

) co ve r en

ti

ce

POLYMICTIC

1000 0 90

80

70

ic ictic

om

T

Dim

ic

2000

ict

tic

(p

er

m

an

3000

Warm monomictic

•

4000

Am

•

5000

on

•

6000

m

Oligomictic lakes. They are mostly very deep tropical lakes, with high heat capacities, that are rarely and irregularly mixed (usually under extreme weather situations, e.g., tropical storms). Polymictic lakes. They refer to shallower lakes with low vertical-density gradients that mix frequently, sometimes daily. Monomictic lakes. They are lakes with one mixing period, either in winter at water temperatures Z4 1C (warm monomictic subtropical lakes, or large and deep lakes, in the temperate zone with high heat capacity, that do not freeze), or in summer at water temperatures r4 1C (cold monomictic lakes at high latitudes, where ice cover melts only in summer). Dimictic lakes. They are sufficiently deep lakes of the temperate zone that circulate in spring and autumn and are ice covered in winter. Meromictic lakes. They refer to partially mixed lakes with a deep-water layer enriched by dissolved salts, or are sufficiently wind sheltered, small but deep lakes.

ld

•

Co

1. Amictic lakes. They are permanently frozen lakes at high latitudes and/or altitudes that never overturn; and 2. Holomictic lakes. They refer to lakes that mix at least once per year, further specified as:

An inverse temperature distribution (colder above warmer water) is observed during the winter stagnation when the lake is ice covered (e.g., 15 February 2006 in Figure 15). Vertical mixing is strongly reduced due to the cutoff of wind action by the ice. Some convective mixing just below the ice is possible on sunny days, if the ice is clear and irradiance heats the uppermost water layers. After the disappearance of the ice cover, complete mixing of the entire water body is likely. As long as the water temperature is lower than 4 1C, warming at the surface provokes an increase in the density and mixing is induced, even in dead calm. After the water has reached the temperature of maximum density (profile from 7 April 2006 in Figure 15), further heating produces less-dense water at the surface and mixing requires sufficient wind energy. Thus, mixing becomes more and more episodic and depends on the actual weather

Altitude (m)

air temperature range (Figure 13; Stras ’kraba 1980). For latitudes up to about 401 N or S, the bottom temperature WB of deep lakes corresponds approximately to the minimum annual water temperature and decreases from very high values in the tropics, to 4 1C. At higher latitudes, WB remains constant at the temperature of the density maximum. The resulting mixing type depends on the absolute temperature range and the seasonal surface temperature variation. Increasing distance from the equator and increasing altitude have the same effect (Figure 14; Hutchinson and Lo‘ ffler, 1956). The following thermal lake types are distinguished depending on the principal mixing behavior:

169

T

OLIGOMICTIC

T

60 50 40 30 Latitude (°N or °S)

20

10

0

Figure 14 Scheme of the distribution of thermal lake types depending on latitude and altitude. Modified from Hutchinson GE and Lo¨ffler H (1956) The thermal classification of lakes. Proceedings of the National Academy of Sciences of the United States of America 42: 84–86. T, transitional regions.

Water temperature (°C)

30

20

5° N 35° N 50° N 72° N

10

0 J

F

M

A

M

J

J

A

S

O

N

D

Figure 13 Trends of seasonal variations of surface (open symbols) and corresponding bottom temperatures (filled symbols) of medium-sized lakes at low elevations calculated for selected northern latitudes from empirical equations provided by Strasˇkraba M (1980) The effects of physical variables on freshwater production: Analysis based on models. In: le Cren ED and McConnell RH (eds.) The Functioning of Freshwater Ecosystems, IBP 22, pp. 13–84. Cambridge University Press.

170

Lakes and Reservoirs Temperature (°C) 0

5

10

15

20

25

0 5

Depth z (m)

10 15 20 25

15.02.06 07.04.06 11.05.06

30

01.08.06 15.11.06

35

19.12.06

40 Figure 15 Sequence of temperature profiles characterizing the seasonal change between mixing and stratification typical for a dimictic lake (upper 40 m of Lake Stechlin, 131 020 E, 531 090 N, Germany; maximum depth of the lake is 69 m). Courtesy of Dr. P. Kasprzak, IGB Berlin.

situation. Inconsistent and relatively cold weather (typical April-weather) may prolong the period of spring full circulation and foster the warming of the whole water column (increase in temperature of the deep-water layers to more than 5 1C). Conversely, warm and calm weather immediately after the temperature homogeneity at 4 1C, may quickly form density gradients at the surface, which even strong winds cannot equalize any further. Thus, the spring full circulation period is short and the deep-water layers remain relatively cold (as was observed on 11 May 2006, Figure 15). Once a stable thermocline is established and the summer stagnation has started, further increasing air and, consequently, surface water temperatures, strengthen the temperature and density differences (1 August 2006 in Figure 15) and, thus, the thermal, hydrodynamic, chemical, and biological decoupling between the illuminated, warm, flushed, wind-mixed epilimnion and the usually dark, cold, quiescent hypolimnion takes place. The thermal structure fundamentally influences the temporal development and spatial distribution of biological and chemical food-web components. The water column is subdivided into two reaction spaces with completely different physical, chemical, and biological properties. Therefore, Ruttner (1962) characterized thermics as the pivotal point of lake limnology. After midsummer, irradiation and air-temperature decline and successive cooling and mixing increase the depth of the epilimnion, and the metalimnion slowly propagates downward. The metalimnetic density gradients decrease, and heat and matter exchange by eddy-diffusion, between epi- and hypolimnion, increases. For instance, the maximum density gradients of the temperature profile from 15 August 2006 in Lake Stechlin (Figure 15) were much smaller than

0.08 kg m4 and thus, by definition, the stratification could no longer be considered as stable. Eventually, the stratification disappeared (19 December 2006) and the lake went into the phase of autumn full circulation. Further cooling favors convective overturn, until the water temperature reaches 4 1C. From then on, the ice cover on an entire lake may be established in a single, calm, and frosty night and winter stagnation will be initiated. If this happens early, the deep-water temperature remains relatively high (B4 1C) all through the winter. Paradoxically, the temperature of the water column may decrease much more in a mild winter with a late ice-up. Climate change is expected to significantly influence seasonal temperature development, the duration of the mixing and stagnation periods, the solubility of gases, and the exchange of heat and matter between water and sediment (Blenckner et al., 2002). Milder winters result in later freeze-up and earlier ice break-up and, in extreme cases, ice cover and winter stagnation do not even develop. Thus, formerly dimictic lakes may become monomictic. Recent model simulations predict opposite effects of climate change in some regions of the temperate latitudes, for example, in Northern Atlantic regions (Hansen et al., 2004). Decreasing temperatures are forecast, due to changes in the thermohaline circulation of the ocean. Therefore, climate change will influence the duration of summer stagnation, the epilimnetic temperatures and density gradients, and, thus, the hypolimnetic oxygen budget of stagnant water bodies. It will also likely affect the phytoplankton species composition, succession, and abundance.

2.08.1.5 Water Movement Unlike rivers, lakes are identified as stagnant or standing water bodies. However, natural waters are never completely quiescent. Horizontal and vertical water movements of quite different spatial and temporal scales transport dissolved and particulate materials and heat. They influence the gas exchange with the atmosphere and affect the basin morphology, due to erosion and deposition of sediments. Therefore, knowledge about the hydrodynamic structure is important for the understanding of the matter turnover of lakes. Water flows in lakes and reservoirs are largely turbulent, that is, chaotic, swirling, multidirectional, and disordered. Unidirectional and smooth laminar flows can only be observed at very low flow velocities, for example, in thin boundary layers between water and sediment in deep, stratified lakes or in the metalimnion of wind-sheltered, small basins during calm weather. Wind, solar radiation, and in- and outflows are the most important forces generating water movement. In large lakes, air-pressure differences along the surface, the Coriolis force, resulting from the Earth’s rotation, and the gravitational attraction of the sun and moon, may also cause or influence water movement. The spatial and temporal variations in wind force are of the greatest importance for the formation of nonperiodic currents. Wind acts at the water surface and, thus, the size and shape of the lake and its orientation to the prevailing wind direction are decisive factors. The wind exposure of a lake is described as

Lakes and Reservoirs

a wind fetch, defined as the unobstructed distance that wind can travel over water in a constant direction. The kinetic energy of the wind is proportional to u3, where u (m s1) is the wind velocity, normally measured 10 m above the surface. The velocity of wind-driven currents is about 0.02u and is independent of the height of surface waves. In the open water of large and deep lakes, the Coriolis force causes a deflection of the wind drift to the prevailing wind direction of about 451 to the right in the Northern hemisphere, and to the left in the Southern hemisphere, respectively. This deflection increases with water depth and therefore the currents in the deepest water layers may flow opposite to the wind direction. This phenomenon is called the ‘Ekman spiral’. In smaller lakes, the water feels the shore and the bottom, and boundary effects influence the flow-field. Currents parallel to the shores prevail. A downwind drift of water masses, unavoidably causes the leeward drift of a corresponding amount of water and, consequently, large-scale horizontal and, for example, in the mixed epilimnion, vertical circular motions are formed (Figure 16; Hutter K (1983)). Such gyres may produce inhomogeneous (patchy) distributions of chemical or biological constituents (e.g., patchiness of phytoplankton or waterquality parameters). The circulation patterns are strongly influenced by lake-basin irregularities (e.g., islands and bays). Attentive observers may occasionally notice streaks of foam or debris (windrows) at uniform distances from one another, at the surface of lakes on windy days, that are deflected 51–151 to the right of the wind direction (in the Northern Hemisphere). This appearance is an indication of the Langmuir circulation, a wind-driven helical circulation system, rotating clockwise and counterclockwise alternatively, that is initiated at wind speeds of more than about 3 m s1 (Figure 17). Air bubbles, produced by breaking waves and floating materials, flow from the upwelling range (divergence zone) to the range of the downwelling motion (convergence zone) and concentrate at the surface. The distances between the windrows increase with increasing wind speed. The diameter of the vortices is about half of the distance between the streaks, but never larger than the depth of the epilimnion. Langmuir cells may significantly affect the development of the phytoplankton, which are passively transported vertically, through the underwater light field, within short time intervals (Vincent, 1980). Patchiness of zooplankton may also be caused by Langmuir circulation (Malone and McQueen, 1983). Propagating surface-gravity waves imply a horizontal transport of water. However, wind waves only cause surface water particles to move in circular orbits, with almost no drift of water. Wind waves are characterized by their height H from trough to crest, length L from crest to crest, and period of oscillation (Figure 18). Wave formations and their dimensions, depend on wind speed, wind fetch F, wind duration, and water depth. In the open water, the maximum height Hmax of thepwaves can be estimated from fetch F as ffiffiffi Hmax ¼ 0:105 F. Hmax is identical to the diameter of the orbital motion at the surface. The diameters Dz of the orbits shrink with increasing depth z and no vertical displacement of water parcels, attributed to surface waves is found below zE0.5 L (Figure 18). Waves approaching the shore regions or in shallow lakes with zmaxo0.5 L feel the bottom, and the shape of the orbital motions close to the bottom becomes

171

Figure 16 Qualitative distribution of the mean steady state transport in Lake Zu¨rich for spatially uniform constant winds blowing from N, S, W, E, and SE. Modified from Hutter K (1983) Stro¨mungsdynamische Untersuchungen im Zu¨rich- und im Luganersee – Ein Vergleich von Feldmessungen mit Resultaten theoretischer Modelle. Schweizerische Zeitschrift fur Hydrologie 45: 101–144.

more and more elliptical. Lightweight particles are resuspended and washed downward into the deeper regions of the basin. With further decreasing water depth, the waves at the surface become higher and steeper, the wavelengths shorter, and their erosive impact increases. Finally, if zo0.05 L, the waves break and strong erosion of the shore may be observed. Thus, wind waves strongly affect the development of the shorelines and the littoral zones of lakes. Strong wind, persistently blowing from a constant direction, pushes the upper warm water masses of a stratified lake to the downwind side and generates a tilt of the whole surface and thus, an unstable position. Due to the restoring force of gravity, the water flows back and, due to inertia, a swinging, oscillating motion of the surface is caused, which is called a surface or ‘external seiche’. Periods of external seiches are

172

Lakes and Reservoirs

Wind

Top view

Windrow

Wind

Windrow

Surface Cross section Downwelling (fast)

Upwelling Downwelling (fast) (slow)

Figure 17 Schematic representation of Langmuir circulation cells. Air bubbles and debris are flowing from the divergence (upwelling) zone to the convergence (downwelling) zone and create streaks (windrows) of almost constant distance at the surface that are nearly parallel to the wind direction.

Period of oscillation Time 0

Depth

H/2

L/2 Figure 18 Circular motion of water particles in five layers from surface down to the depth L/2 at five moments during one period of oscillation of a wind wave. L, wave length in cm; H, wave height in cm.

rather short – seconds in small lakes and minutes or a few hours in large basins. The amplitudes vary between a few centimeters in small water bodies and about 2 m in large lakes. However, internal seiches, that is, the periodic up- and downwelling of water layers of different density and depth, forming standing waves of much larger amplitudes and longer periods of oscillation, are more important in terms of matter transport and impact on phytoplankton development. This phenomenon is not visible to observers at the surface, but becomes evident from considerable periodic temperature variations in the depths of the metalimnion and below. The example shown in Figure 19 indicates superimposed, onenodal, internal seiches of different periods of oscillation (about 24 h and 8 h) in a reservoir. The temperature variations at the West and East stations almost mirror each other, while they are comparably low at the central station, which is apparently close to the position of the wave node of the oscillation. Large horizontal, but low vertical water movements are observed in the nodal areas. The opposite applies to the crest regions, where up- and downwelling prevails. However, these vertical movements generate highly turbulent currents along the sloped bottom and create so-called internal surges, similar to breaking surface waves (Mortimer and Horn, 1982). The

temperature stratification at the sediment surface periodically varies from stable (warm water over colder sediment; situation 1 in Figure 19) to unstable (colder water over warmer sediment that fosters the release of interstitial water; situation 2 in Figure 19). The velocities of the vertical movements are usually in the range of several millimeters per second, while those of the horizontal current components may be up to a hundred times higher. The vertical displacements of the layers from their stable positions depend on the size of the lake basin, density gradient, depth of and vertical distance to the thermocline, and can be higher than 10 m. Sudden changes of wind direction or periodic (e.g., diurnal) fluctuations of wind speed may cause phase shifts of the oscillations in different depths, resulting in the interference of waves with several lakespecific periods of oscillation (Figure 20). Internal seiches may be observed almost permanently in stratified lakes during the summer stagnation (Figure 21). Even in calm weather, the oscillations continue for a long time (days or even weeks) with, however, decreasing amplitudes after their excitement has ceased. Wave structures rotating around large lake basins, may occur under the influence of the Coriolis force and wind-driven, horizontal large-scale circulations and, finally, highly complex current patterns result.

Lakes and Reservoirs West

Centre

Temperature (°C)

East

1

6.5

173

2

6.0

5.5

5.0

(a)

4.5 060513

060514

West

060515

060517 060516 Date (yymmdd)

Centre

1

060518

060519

East

2

(b)

Figure 19 (a) Periodic temperature variations measured in the Saidenbach reservoir (Germany) at three stations (West about 50 m in front of the dam, Center B1 km, and East about 2 km apart from the West station) in time intervals of 30 min in May 2006. (b) Diagram of the position of a water interface at the two moments marked in the graph above. Arrows qualitatively indicate prevailing water movements.

Internal seiches have a great impact on the turbulent vertical exchange of heat and the transport of materials. They resuspend sediment particles, accelerate their dislocation into the deepest regions of the lake, and enhance the release of dissolved substances from the sediment. The periodical transport of phytoplankton cells, throughout the vertical light field in the crest regions of the internal waves, substantially increases the photosynthesis rate in water layers at the base of the euphotic zone (Paul, 1987). Discharge-related currents, especially floods, may generate basin-wide water movements, particularly in lakes with short retention times. As the import of nutrients, allochthonous particulate matter and other substances by the tributaries, is most important to the materials budget, knowledge about the seasonal variability of the depth of inflow and the propagation of the inflowing water is crucial for the understanding of the trophic situation and the availability of nutrients in the euphotic zone. In reservoirs, the balance between inflow and outflow from different depths, decisively determines the development of the fill-level and the volume of the hypolimnion during summer stratification. The withdrawal of water from the deep-water layers of reservoirs causes currents in the hypolimnion, which is more or less quiescent in natural lakes. The entrainment depth to which the inflowing water plunges characteristically varies seasonally, depending on the

temperature (density) distribution in the lake and the temperature (density) of the tributaries (Figure 22; Carmack et al., 1979). Surface inflow is observed when the density of the river water is lower than that of the lake; underflow occurs in the reverse instance. Interflows are typical in situations in which the river density is between that of the lake’s surface and the bottom of the lake. Hydraulic short-circuiting, that is, the longitudinal distribution of river water from the mouth of the tributary to the dam in a relatively thin metalimnetic layer, within a very short time (a few hours), has frequently been observed in reservoirs. Such events are critical in the case of drinking-water reservoirs, because harmful substances (e.g., turbidity and microbial pollution) in high concentrations may contaminate the raw water (Clasen and Bernhardt, 1983). Intrusion far below the depth of the respective lake temperature can be observed, if the density of the inflowing water is considerably enhanced due to very high flood-induced turbidity, caused by suspended mineral particles. Such turbidity currents may import oxygen into the hypolimnion of seldom fully circulating (e.g., deep pre-alpine) lakes (Lambert et al., 1984; DeCesare et al., 2006). Turbidity currents are a special form of density currents. Density currents are, in general, water movement, caused by density differences. They can also result from water-temperature differences, as a consequence of differential heating or

174

Lakes and Reservoirs Time (h) 0

48

24

72

96

120

0

Depth (m)

<5

5

5...7 7...9

10

1

9...11 11...13

15

13...15 > 15

20

3

2

1

3

2

Figure 20 Vertical and temporal temperature variability caused by superimposed internal waves of different periods of oscillation and phase shifts in the upper 20 m water layer of the West station c. 50 m in front of the dam (zmax ¼ 45 m) of the Saidenbach reservoir observed from 18 May 2005 0 a.m. to 22 May 2005 12 p.m. (top left). For the marked points in time, wave modes and principal water movements are schematically shown.

30 25 Temperature (°C)

W_1 W_3

20

W_5 W_7.5

15

W_10 W_15 W_20

10

W_30

5 0 060501

060531

060630

060731

060830

060930

061030

Date (yymmdd) Figure 21 Results of short-term temperature records (measuring interval 30 min) at different depths (m, indicated by the numbers given in the legend) at station West (B50 m in front of the dam) of the Saidenbach reservoir (Germany) during the summer stratification in 2006 (dates are given in the yymmdd-format). The permanent temperature fluctuations at depths below the epilimnion show the ubiquitary nature of internal seiches. Those at the surface may also result from short-term changes of irradiation and air temperature.

cooling in lake segments. For instance, shallow bays on the margins of lakes, may heat up during the day and cool down at night, more rapidly than the open water. The resulting density differences generate convective exchange of water and of dissolved materials between littoral and pelagic zones (Wells and Sherman, 2001). Strongly increased conductivity (salinity), for example, due to thaw salt from roads in winter, may also generate density currents and vertical temperature inversions. Convective currents are generally upwelling movements of less dense, lighter water (e.g., plumes of heated waste water), or downwelling movements of denser, heavier

water (resulting from surface cooling in summer or warming in spring when the deeper water layers have temperatures lower than 4 1C). As mentioned above, water movement in lakes is mostly turbulent. Turbulence results from friction between water layers moving with different velocities (Baumert et al., 2005). Shear forces produce vortices and, if they collapse, they dissipate the energy of motion and cause mixing of water. The spatial dimensions of these vortices, that is, the intensity of eddy-diffusion, decrease with increasing density gradient and/ or reduction of velocity differences between adjacent water

Lakes and Reservoirs

Summer

Winter

TS > TR

TR < TL

TR > TB (a)

TB > 4 °C

175

TL < 4 °C (e)

Early autumn

Early spring

T S > TR TR > TB

TL < 4 °C

TB > 4 °C (b)

TR ~ 4 °C (f)

Middle autumn

Middle spring

TL > 4 °C

TL < 4 °C

TR ~ 4 °C (c)

TR > 4 °C (g)

Late autumn

Late spring

TL > 4 °C TR < 4 °C

TR < TL TL > 4 ° C

(d)

(h)

Figure 22 Schematic representations of seasonal riverine circulation patterns of the Kamloops Lake, British Columbia. Stippled areas denote river water; dashed areas denote lake and river water mixtures involved in cabbeling process (mixing of water of identical density but slightly different temperature and salinity). From Carmack EC, Gray CBJ, Pharo CH, and Daley RJ (1979) Importance of lake–river interaction on seasonal patterns in the general circulation of Kamloops Lake, British Columbia. Limnology and Oceanography 24: 634–644. TR, river temperature; TL, lake water temperature; TS, lake surface temperature; and TB lake bottom temperature.

strata. Turbulence transports heat, dissolved substances, and gases vertically between the epilimnion and the hypolimnion. This can be observed by small-scale vertical temperature inversions in temporally and spatially highly resolved, thermal microstructure measurements (Wu¨est et al., 2000).

2.08.1.6 Basic Chemistry 2.08.1.6.1 Systematics of lakes with respect to water quality There are approximately 8 million natural lakes with surface areas of 41 ha, on the Earth (Ryanzhin et al., 2001). The majority of them are freshwater lakes, which are of vital importance for humankind, animals, and plants. A global model based on the Pareto distribution, shows that the global extent of natural lakes Z0.1 ha is about 304 million lakes (Downing et al., 2006).

Some of the different types of lakes classified on the basis of their water quality are as follows: 1. Soft- and hard-water lakes. As many lakes are connected with the groundwater, their water chemistry is influenced by the geological substrate of the watershed. Hard-water lakes dominate when the catchment is rich in calcium. Soft-water lakes are characterized by the low content of the hardness components, calcium and magnesium. Lakes with a calcium deficit are normally fed by rainwater without soil contact, or exist in Ca-deficient, sandy, outwash plains. In calcareous, oligotrophic (i.e., clear water) lakes that are mainly fed by groundwater, such as Lake Stechlin, Northern Germany, submerged plants use HCO 3 /CO2 as a C source for photosynthesis þ CO2  H2 O3 HCO 3 þH

ð25Þ

176

Lakes and Reservoirs 2 þ HCO 3 3 CO3 þ H

ð26Þ

Suspensions or deposits of hardly soluble CaCO3 (biogenic decalcification) may be formed by CO2 uptake and Hþ consumption (pH increase)

Ca 2þ þ CO2 3 3 CaCO3 ðsÞ

ð27Þ

Another example of hard-water lakes are the acidic hard-water lakes. Here, CaCO3 formation does not occur and sulfate is the dominating anion. These lakes are, in many cases, impacted or man made (mining lakes). Acid mine drainage (AMD), a result of the mining and milling of sulfur-bearing coal and ores, plays a dominant role in surface-water chemistry and pollution, in many areas of the world. Oxidation of disulfide minerals (e.g., pyrite or marcasite (FeS2)) occurs mostly from the reactions of tailing and mining wastes with oxygen and water in underground workings, tailings, open pits, and waste rock dumps. This produces acidic water, rich in metals, commonly referred to as AMD. The most noticeable environmental change is the pollution of flowing water with severe impacts on aquatic life. The following reactions result from acidified runoff on regulated mine sites. Representative species of bacteria engaged in these processes are also mentioned 1.1. Iron disulfide (pyrite and marcasite) is oxidized to sulfate by oxygen with a very high energy yield for bacteria (Thiobacillus ferrooxydans and Thiobacillus thiooxydans)

2FeS2 ðsÞ þ 7O2 þ 2H2 O þ ) 2Fe2þ þ 4SO2 4 þ 4H

ð28Þ

1.2. Ferrous iron is oxidized to ferric iron

14Fe 2þ þ 3:5O2 þ 14Hþ ) 14Fe3þ þ 7H2 O ðslow under acidic conditionsÞ ð29Þ 1.3. Sulfur/sulfide is oxidized with ferric ions to sulfate

FeS2 ðsÞ þ 14Fe3þ þ 8H2 O þ ) 15Fe2þ þ 2SO2 4 þ 16H

ð30Þ

1.4. Ferric iron is hydrolyzed to ferric hydroxide

Fe 3þ þ 3H2 O ) FeðOHÞ3 ðsÞ þ 3Hþ

ð31Þ

In the presence of nitrate, the FeS2 oxidation proceeds by autotrophic denitrification (Thiobacillus denitrificans) þ 5FeS2 ðsÞ þ 14NO 3 þ 4H

) 5Fe2þ þ 10SO2 4 þ 7N2 þ 2H2 O

ð32Þ

whereas Fe(II) is further oxidized to Fe(III) by species such as Gallionella ferruginea

10Fe 2þ þ 2N 3 þ 14H2 O ) 10FeOOHðsÞ þ N2 þ 18Hþ

ð33Þ

Reactions (28), (30), (31), and (33) lead to an enormous production of acid. Sulfur concentrations in many surface waters have increased greatly as a result of acid mine run off and SO2 emissions. 2. Saline lakes. In semiarid and arid climates, lakes may show a high concentration of dissolved solids, due to the surplus of evapotranspiration above the runoff into the lakes. In relation to their main ingredients, salt lakes may be subdivided into soda, chloride, and sulfate lakes. The majority of saline lakes, in terms of area, are chloride lakes. Historically, they are remnants of isolated seawater bodies in continental locations. 3. Soda lakes. Lakes with a very high alkalinity level, mainly due to soda (Na2CO3), occur in southeast Europe and are common in the East African rift valley. The water in soda lakes becomes alkaline with a pH of approximately 10, due to the alkalis, carbonate, and bicarbonate. The water tastes bitter and feels oily. These salts accumulate in lakes without discharge, if the subsoil consists of carbonate or volcanic rock, and whose water budget is characterized by high evaporation rates. Therefore, soda lakes are usually found in semi-deserts and steppe areas. Mono Lake (California) contains about 280 million tons of dissolved solids and, depending on its seasonally fluctuating water level, is 2–3 times more salty than the ocean. It is also rich in borate and potassium. Periodic eruptions of volcanic ash have also considerably contributed to Lake Mono’s chemical mix. Soda lakes are often rich in biomass, provided they are not too deep. Due to the high pH values and salt concentration, alkaliphilic/alkalitolerant and simultaneously, halophilic organisms, are characteristic. The limited biodiversity essentially comprises specialized bacteria (among others: cyanobacteria such as Spirulina and Archaea) and algae. They may appear in great abundance and reduce the Secchi disk transparency to a few centimeters. Soda lakes thus rank among the most productive ecosystems. Special protophytes (flagellates) are characterized by accessory-colored pigments (carotenoids, phycobiline, and rhodopsin). They are responsible for the conspicuous coloration of numerous soda lakes. Many sodium carbonate lakes are utilized for the production of natural soda. 4. Bog lakes. These lakes are normally poor in electrolytes. Bog lakes are found in all geographic latitudes of the humid climate zones, from the wetlands in the hills to the plains, to the marshes adjacent to large rivers. They are among the aquatic systems with high species diversity. In bogs and bog lakes, the production of organic C compounds is greater than microbial mineralization. The slow and incomplete decomposition of vegetation residues, under continuous water surplus from rainfall or soil water, is accompanied by a high oxygen deficit, resulting in peat deposition and siltation (see Section 2.08.1.6). Dystrophic lakes are poor in nutrients and calcium as well as phytoplankton, and are mostly strongly acidic and rich in dissolved humic materials. They are clear, but mostly brownish. Their watershed is often small; therefore, it is not remarkable that some species typical of bog lakes are also found in acidic mine lakes.

Lakes and Reservoirs

5. Crater lakes or volcanic lakes. Crater lakes covering active (fumarolic) volcanic vents are sometimes termed volcanic lakes – a cap of meteoric water over the vent of an active volcano. The chemistry of the water may be dominated by high-temperature volcanic gas components or by a lower temperature fluid that has interacted extensively with volcanic rock. Precipitation of minerals such as gypsum (CaSO4* H2O) and silica (SiO2) can determine the concentration of Ca and Si (Kusakabe, 1994). The water of these lakes may be extremely acidic (e.g., pH B 0.3). Lakes located in dormant or extinct volcanoes tend to contain freshwater, and the water clarity in such lakes may be exceptionally high due to the lack of inflowing streams and sediments. Crater lakes form as incoming precipitation fills the depression. The lake deepens until equilibrium is reached between water inflow, losses due to evaporation, subsurface drainage, and possibly also surface outflow, if the lake fills the crater up to the lowest point of its rim. Surface outflow can erode the deposits damming the lake, lowering its level. If the dam erodes rapidly, a breakout flood can be produced.

2.08.1.6.2 Ionic balance

balance of water

NaAlSi3 O8 ðsÞ þ5:5H2 O ) Naþ þ OH ðalbiteÞ

þ 2H4 SiO4 þ 12Al2 Si2 O5 ðOHÞ4 ðsÞ

Main inorganic compounds and buffering properties present in lakes and reservoirs are as follows: 1. Alkalines. The alkali ions Naþ and Kþ are mostly discharged in the K- and Na-feldspar weathering processes and represent an important part of the ion

NaAlSi3 O8 ðsÞ þ 4:5H2 O þ CO2  H2 O 1 ) Naþ þ HCO 3 þ 2H4 SiO4 þ 2Al2 Si2 O5 ðOHÞ4 ð35Þ 3KAlSi3 O8 þ2CO2  H2 O þ 12H2 O ) 2Kþ ðK-feldsparÞ þ 2HCO 3 þ 6H4 SiO4 þ KAl3 Si3 O10 ðOHÞ2 ðmica-illiteÞ

 CaCO3 þH2 O ) Ca2þ þ HCO 3 þ OH

ð37Þ

CaCO3 þ CO2 H2 O ) Ca2þ þ 2HCO 3

ð38Þ

ðcalciteÞ

CaSO4 ðanhydriteÞ

) Ca 2þ þ SO2 4

CaMgðCO3 Þ2 þ2H2 O ) Ca2þ þ Mg2þ ðdolomiteÞ  þ 2HCO 3 þ 2OH

4% 15%

17%

73%

(a)

Figure 23 Major anions (a) and cations (b) in freshwater systems.

ð40Þ

3. Carbonate species (CO2/HCO3–/CO32). The reactive inorganic forms of environmental carbon are carbon dioxide (CO2*H2O), carbonic acid (H2CO3), bicarbonate (HCO 3) and carbonate (CO2 3 ). Carbon dioxide plays a fundamental role in determining the pH in lakes. An important element in acid–base chemistry is the bicarbonate ion, HCO 3 , which may act as either an acid or a base. Aqueous

HCO3– SO42– Cl – Other

–

ð39Þ

The predominant source of magnesium is dolomite:

<1%

16%

ð36Þ

The weathering of aluminosilicates is accompanied by a release of cations and of silic acid. As a result of these reactions, alkalinity is released. Minerals of the kaolinite group are the main metabolites of feldspar weathering. 2. Alkaline earths. The hardness components Ca2þ and Mg2þ are quantitatively the most important cations in freshwater lakes. Calcium usually enters the water as either calcium carbonate (CaCO3), or calcium sulfate (CaSO4):

< 1%

10%

ð34Þ

ðkaoliniteÞ

Apart from living organisms, lakes contain a wide array of ions, molecules, and complexes from the weathering of soils and bedrock in the watershed, the atmosphere, and the sediments. Therefore, the chemical composition of a lake is fundamentally a function of its climate, which affects its hydrology and its basin geology. An ion balance based on equivalents for typical freshwater is presented in Figure 23. These ions (bicarbonate, sulfate, chloride, calcium, magnesium, sodium, and potassium) are usually present in concentrations of mg l1 (ppm), whereas other ions such as phosphate, nitrate, ammonium, and heavy metals are present at mg l1 (ppb) levels.

2.08.1.6.3 Inorganic compounds and buffer properties

177

(b)

+

63%

Ca2+ Mg2+ Na2+ K+ Other

178

Lakes and Reservoirs

oxygen (the oxidation reactions can be observed in the bottom half of Figure 25).

CO2 100

Fraction (%)

H2O + CO2

50

CO2– 3

HCO3–

H2CO3

CaCO3 0 5

6

7

8 pH

8

10

11

12

Figure 24 States of inorganic carbon depending on water pH (CO2-system). From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

CO2 solutions react acidically by forming carbonic acid. Carbonic acid can subsequently dissociate in two steps to release protons:

CO2 þ H2 O3 H2 CO3 þ H2 CO3 3 HCO 3 þH 2 þ HCO 3 3 CO3 þ H

pK ¼ 2:8

ð41Þ

pKs1 ¼ 6:35

ð42Þ

pKs2 ¼ 10:33

ð43Þ

2 The pH dependence of the CO2/HCO 3 /CO3 -system is shown in Figure 24 (Uhlmann and Horn, 2001). 4. Buffer intensity, base-neutralizing capacity, acid-neutralizing capacity. Most lake waters are pH-buffered, because of the existence of the carbonate buffer system. Buffer solutions are necessary to maintain the optimal pH for the enzyme systems of plants and animals. The buffer intensity is a measure of the ability of water to compensate for the addition of strong acid or base without appreciable pH change.

2.08.1.6.4 Sequence of microbially mediated redox processes These processes become noticeable at the sediment/water interface and from there may extend into the hypolimnion. Many chemical changes that take place during the early diagenesis of sediments, depend on the redox environment in the interstitial water. The redox environment is determined by the degree to which organic compounds are preserved or undergo microbial decomposition. Important redox processes are based on

•

•

the surplus of organic matter being oxidized by oxygen or by an oxidized category of nitrogen (NO 3 ), iron (Fe(III), sulfur (SO2 4 ), or by organic carbon itself in methane fermentation (the reduction reactions in the upper half of Figure 25; Stumm and Morgan, 1981), or  reduced species of iron (Fe(II)), nitrogen (NHþ 4 , NO2 ), 2  sulfur (HS , S, and S2O3 ), or methane being oxidized by

The two types of reactions do not occur in the same place or under the same redox conditions, but may very well occur at different depths in the same stratified lake. For example, it is likely that nitrate (NO 3 might be reduced in the hypolimnion of a lake with a clinograde oxygen curve, producing ammonia (NH3). Should the ammonia subsequently be transported into the oxygenic epilimnion, the ammonia will be oxidized back to nitrate. In general, the first type of reaction (upper half of Figure 25) will take place under more reducing conditions, and the second type (lower half of Figure 25) will occur under more oxidizing conditions and in the presence of oxygen. The reactions occur as a hierarchy. In general, nitrate and ferric iron will be reduced before any reduction of sulfate occurs. Similarly, sulfate reduction will proceed to nearly total consumption of sulfate, before methane fermentation occurs. Thus, the pH and redox circumstances in which each of the reactions may be expected to occur can be anticipated, based on the hierarchy of reactions. In detail, the following oxidation processes concerning organic matter, take place: 1. Aerobic microorganisms use oxygen (O2) as the electron acceptor for the microbial degradation of organic compounds (aerobic respiration)

½CH2 O þO2 ðorganic matterÞ

) CO2 þ H2 O

ð44Þ

Bacteriological oxygen demand (BOD) refers to the amount of oxygen needed by aerobic organisms in a given water volume. 2. In case of O2 depletion, nitrate (NO 3 ) is the next electron acceptor to be used by microorganisms for the oxidation of organic carbon. Nitrate is then converted to gaseous nitrogen, under anoxic conditions, by denitrifying bacteria (denitrification). The reduction product is a nitrogencontaining gas mixture, usually molecular nitrogen (see Equation (45)), but nitrogen oxides may also be produced: þ 5½CH2 O þ 4NO 3 þ 4H ) 2N2 ðgÞ þ 5CO2 þ 7H2 O

ð45Þ

3. Before Fe(III) reduction, the reduction of Mn(IV)-compounds (in most cases as MnO2) will take place, which will also be released through the interstitial water of the sediment into the deeper hypolimnion (Davison, 1993):

½CH2 O þ 2MnO2 ðsÞ þ 4Hþ ) 2Mn2þ þ CO2 þ 3H2 O

ð46Þ

4. Fe(III) (e.g., FeOOH) in the sediment or in the hypolimnion is reduced to Fe(II) by chemo-autotrophic, facultative/obligatory anaerobic microorganisms of the genera Geobacter sp., Geovibrio, and Shewanella sp. (Jones et al., 1983):

½CH2 O þ 4FeOOHðsÞ þ 8Hþ ) 4Fe2þ þ CO2 þ 7H2 O

ð47Þ

Lakes and Reservoirs

179

Redox potential EH (V) 0

–0.5 –10

+0.5

0

–5

+5

+1.0

+10

+15

O2 Reduction

+20

pε

A

Reductions Denitrification

B C

Mn (IV) Reduction NO3– Reduction D

Combination pH = 7 Fe (III) Reduction

E

Examples

kcal / eq.

Aerobic respiration

A + L –29.9

Denitrification

B + L –28.4

G

Nitrate reduction

D + L –19.6

CH4– Fermentation H

Fermenatation Sulfate reduction

F+L G+L

–6.4 –5.9

Red. org. matter F SO42– Reduction

N2 Fixation

I

H2 Formation K L

Oxid. org. matter M

Methane fermentation

H+L

–5.6

N-Fixation

J+L

–4.8

Sulfide oxidation

A + M –23.8

Nitrification

A + O –10.3

Ferreous oxidation

A + N –21.0

Mn (II) oxidation

A+P

HS–

Oxidation

N

Fe (II) Oxidation

–7.2

O Fe2+ Oxidation Nitrification

P Oxidations

N2 → NO3–

Q R

–10

0

–5 0

5

10

+5

+10

O2 Formation

+15

+20

pε

20 Free enthalpy

kcal / equivalent Figure 25 Microbially mediated redox processes. From Stumm W and Morgan JJ (1981) Aquatic Chemistry: An Introduction Emphasizing Chemical Equilibria in Natural Waters, 2nd edn., 780pp. New York: Wiley.

As FeOOH occurs in the sediment, the Fe(II) formed will first appear in the interstitial water, with the highest concentrations in the top layer, from where it diffuses into the overlying water. The presence of NO 3 suppresses the FeOOH-reducing activity and will therefore keep the P-adsorbing capacity of the sediment intact. Davison (1992)showed that Fe(II) does not form complexes with organic compounds, but may fix phosphate as vivianite (Fe3(PO4)2* 8H2O).

5. The next electron acceptor to be discussed is sulfate (SO2 4 ). It will be reduced to H2S, mostly by obligatory anaerobic microorganisms of the genera Desulfovibrio or Desulfotomaculum: þ  2½CH2 O þ SO2 4 þ H ) 2CO2 þ HS þ H2 O

ð48Þ

6. In the complete absence of oxygen and other electron acceptors (anaerobic conditions), microorganisms

180

Lakes and Reservoirs

decompose organic material to CO2 and CH4 in various steps (methane fermentation): 6.1. Hydrolysis. – extracellular splitting of macromolecules:

• • • •

fats ) fatty acids, glycerine by lipolytic bacteria (Bacillus, Alcaligenes, and Pseudomonas); proteins ) amino acids by proteolytic bacteria (Peptococcus, Staphylococcus, and Clostridiun); cellulose ) glucose, acetic acid, alcohol, H2, and CO2 by anaerobic cellulolytic bacteria. There are many species of cellulolytic bacteria and fungi; starch ) glucose by amylolytic bacteria (Micrococcus and Clostridium).

6.2. Anaerobic fermentation – performed in sequencing steps:

• • •

Acidogenic step. C6H12O6 ) C2H5COO þ CH3COO þ CO2 þ H2 þ 2Hþ DRG ¼  286 kJ mol1 Acetogenic step. C2H5COO þ 2H2O ) CH3COO þ CO2 þ 3H2 DRG ¼  81.9 kJ mol1 Methanogenic step. Acetate decarboxylization/methane formation CH3COOH ) CH4 þ CO2 DRG ¼  56 kJ mol1 CO2 þ 4H2 ) CH4 þ 2H2O DRG ¼ –139 kJ mol1.

In summation

2ðnCH2 OÞ ) nCO2 ðgÞ þ nCH4 ðgÞ

ð49Þ

The methane-producing bacteria consist of four major genera: Methanbacterium, Methanobacillus, Methanococcus, and Methanosarcina.

2.08.1.6.5 Iron, manganese, and sulfur compounds The following list explains the presence of compounds of iron, manganese, and sulfur: 1. Iron. The primary oxidation states of iron in water are Fe(II) and Fe(III). In most aerobic surface water, Fe(III) predominates and is nearly insoluble at neutral pH values:

Fe 3þ þ 3OH  ) FeðOHÞ3 ðsÞ

ð50Þ

Fe(II), on the other hand, is soluble and dominates under anaerobic conditions. The oxidation–reduction cycle controls the fate of iron in most lakes, but it varies seasonally, particularly in lakes that develop an anoxic hypolimnion during the stagnation period. Oxygen concentration at the water–sediment interface often approaches zero. This causes the reduction of Fe(III) to soluble Fe(II), which is then transported upward into the water column. The oxygenated water produces re-oxidation into the insoluble Fe(III), which settles at the bottom to repeat the cycle.

An oxidation–reduction cycle also controls the fate of manganese; however, Fe(II) is oxidized to particulate Fe(III) much more rapidly than the corresponding species of Mn. In addition, Fe(III) is reduced at a lower redox potential than Mn(IV). Many elements, including P (as phosphate), are scavenged by iron through adsorption onto particles when Fe(III) is formed as part of the cycle. In the presence of phosphate, a basic iron phosphate (Fe2(OH)3PO4) is formed with a Fe:P ¼ 2:1 stoichiometry:   2FeðOHÞþ 2 þ H2 PO4 þ OH

) Fe2 ðOHÞ3 PO4 ðsÞ þ 2H2 O

ð51Þ

Another possible reaction occurs when phosphate is adsorbed on to the hydrolyzed sediment surface (Me ¼ Al, Fe, and Mn):

¼ Me  OH þ H2 PO 4 3 ¼ Me2H2 PO4 þ OH ðadsorptionÞ

ð52Þ

Fe(III) reduction takes place as explained next. In surface sediments, iron-bound phosphate is solubilized as follows:

Fe2 ðOHÞ3 PO4 ðsÞ þ 12½CH2 O þ 3Hþ 1 5 3 2Fe2þ þ H2 PO 4 þ 2CO2 þ 2H2 O

ð53Þ

Iron is predominately associated with sulfides. Sulfides are an important sink for trace metals in reduced sediments. The proportion of heavy metals bound by Fe- and Mn-hydrous oxides is highly variable and depends on water depth and redox conditions. As iron plays such an important role in the fate of trace metals and nutrients, a breach in the iron redox cycle may also lead to the mobilization of toxic elements into the environment, for example, redistribution of sulfidic sediments during dredging. 2. Manganese. Total Mn concentration in freshwater is extremely variable, ranging from 0.002 to 44 mg l1, whereas particulate Mn accounts for 490%, and often 495% of the total waterborne residue. Mn is of little direct toxicological significance, but may limit the growth of algae. Mn exists in the oxidation states from þ 2 to þ 7, mostly as Mn(II)-(manganous) and Mn(IV)-(manganic). The oxidation–reduction cycle is important in controlling the fate of Mn in most lake waters. Oxygen concentration at the sediment/water interface often approaches zero. This causes the reduction of Mn(IV) to soluble Mn(II), which is then transported upward into the water column. The oxygenated water causes re-oxidation to insoluble Mn(IV) which settles at the bottom to repeat the cycle. The Mn(II) oxidation is autocatalytic and may be represented as follows (Stumm and Morgan, 1981):

Mn 2þ þ 12O2 þ H2 O ) MnO2 ðsÞ þ 2Hþ

ð54Þ

Mn 2þ þ MnO2 ðsÞ ) Mn2þ MnO2 ðsÞ

ð55Þ

Lakes and Reservoirs

0.8

At circum-neutral pH, oxidation leads to considerable sorption of Mn2þ to MnO2. The rate of Mn oxidation increases through the presence of manganese-oxidizing bacteria. Mn(IV) is reduced to dissolved Mn(II) at higher redox potentials than Fe(III) oxides. Oxidation of Mn(II) to Mn(IV) proceeds much more slowly than the oxidation of Fe(II) to Fe(III). Mn(II) and Mn(IV) follow essentially the same cycle as iron in lakes, where O2 may be in short supply during one or more seasons. 3. Sulfur compounds. Anthropogenic emission to the atmosphere dominates the S cycle in many parts of the world. In fact, 80% of the global SO2 emissions and 445% of the total river-borne sulfate-sulfur comes from man-made sources. In addition, sulfur occurs in surface waters as a result of natural weathering and due to emission from volcanoes, sea-salt aerosols, forest fires, and microbial decomposition of organic material. The main components produced by microbial decomposition are dimethyl disulfide ((CH3)2S2), hydrogen sulfide (H2S), carbon disulfide (CS2), dimethyl sulfide ((CH3)2S), and methane thiol (CH3SH). The dominant S species, under normal pH and Eh conditions in lake waters, are sulfate (SO2 4 ), sulfide (H2S, HS), and elemental sulfur (Figure 26; Zehnder and Zinder, 1980). S( þ VI) and S(-II) are the dominant stable oxidation states, but under reducing conditions thionates, thiosulfates, polysulfides, and sulfites may also be present. Sulfur has an environmental significance because it 3.1. forms complexes with many toxic agents, organic materials, and hydrogen in many surface waters and 3.2. is the primary agent of acidification in many lakes and reservoirs (see Section 2.08.3.1). The S cycle is shown in Figure 27. Sulfide is often present as dissolved anion HS, especially in hot springs. It is a common product of microbial processes in wetlands and eutrophic lakes. There are two important sources of H2S in the environment: 1. the anaerobic decomposition of organic matter containing sulfur and 2. the reduction of sulfates and sulfites to sulfide. Both mechanisms require reducing, anaerobic conditions and are strongly accelerated by the presence of sulfur-reducing bacteria. Some possibilities include 2.1. Microbial sulfate and sulfur reduction. The sulfur-reducing bacteria are strictly anaerobic. Desulfovibrio, Desulfotomaculum, Desulfomonas, and Desulfolobus re duce SO2 4 to HS : þ 4CH3 COCOOH þ SO2 4 þ 2H ) H2 S þ 4CH3 COO þ 4Hþ þ 4CO2

ð57Þ

whereas e-donors are lactate, acetate, ethanol, malate, formiate, and fatty acids. Dissimilatoric S reducers use fatty acids, lactate, benzoate, and succinate to reduce

HSO4–

ð56Þ 0.6

O

2

H

2O

SO42–

0.4 0.2 S Eh (V)

Mn 2þ MnO2 ðsÞ þ 12O2 þ H2 O ) 2MnO2 ðsÞ þ 2Hþ

181

H

2S

0

(aq

)

–0.2

H

2O

H

2

–0.4

HS –

(aq

)

–0.6 S2– –0.8 0

2

4

6

8

10

12

14

pH Figure 26 Equilibrium distribution of total dissolved sulfur species in the presence of iron. From Zehnder AJB and Zinder SH (1980) The sulfur cycle. In: Hutzinger O (ed.) The Handbook of Environmental Chemistry, vol. 1A, pp. 105–145. Berlin: Springer.

S to H2S. H2S may have two stages of dissociation under reducing conditions depending on pH:

H2 SðaqÞ þ H2 O3 HS þ H3 Oþ

pKs1 ¼ 7:02

ð58Þ

HS  þ H2 O3 S2 þ H3 Oþ

pKs2 ¼ 13:9

ð59Þ

2.2. Aerobic sulfide oxidation. It is catalyzed by bacteria such as Achromatium oxaliforum (sometimes with calcium carbonate crystals) and Beggiatoa. These oxidize HS to SO2 4 þ HS  þ 2O2 ) SO2 4 þH

ð60Þ

The genera Thioploca, Thiothrix, and Lamprocystis oxidize HS with nitrate anoxically (chemolithotrophic). The Csource is CO2. The microorganisms form carpets at the bottom. Thiothrix can store sulfur in its cells just as Beggiatoa sp. does. The obligate aerobic, gram-negative Acidithiobacillus uses the e-donors sulfide, sulfur, or thiosulfate for the chemolithoautotrophic metabolism, whereas adenosine triphosphate (ATP) is obtained from the respiration chain:

H2 S þ 12O2 ) S þ H2 O DGR ¼ 209 kJ mol1

ð61Þ

H2 S þ 2O2 ) 2Hþ þ SO2 4 DGR ¼ 798 kJ mol1

ð62Þ

S þ 32O2 þ H2 O ) 2Hþ þ SO2 4 DGR ¼ 587 kJ mol1

ð63Þ

182

Lakes and Reservoirs Terrestrial sources (weathering, plant debris, ...)

Atmospheric deposition (volcanic ash, H2SO4, SO2, ...)

Agriculture and industry Gas exchange

H2S

Photo-autotrophic S-Oxidation*

Assimilation

SO42–

S Org.

Trophogenic zone

Decomposition

Groundwater

Chemo-autotrophic S-Oxidation*

H2S

S0 Assimilation

SO42–

S Org.

Tropholytic zone

Putrefaction

Desulfurication

H2S

Assimilation

SO42–

Putrefaction

S Org.

Sediment

Fe2+ FeS2 Figure 27 Vertical zonation of main reactions of the sulfur cycle in lakes and reservoirs. S Org, sulfur in cells of organisms (e.g., amino acids and gluthione); S0, elementary sulfur; *photo-autotropic and chemoautotrophic sulfurication in aerobic/anaerobic interfaces.

•

2 þ S2 O2 3 þ H2 O þ 2O2 ) 2SO4 þ 2H

DGR ¼ 818 kJ mol1 :

ð64Þ

Acidithiobacillus ferrooxydans can oxidize sulfides and ferrous ions. 3. Anaerobic phototrophic sulfide oxidation. This is performed by green sulfur bacteria (Chlorobium) that can release elementary sulfur into the water, and by red (purple) sulfur bacteria (Chromatium) that can oxidize sulfur to sulfate. Both can store sulfur in their cells hn

2H2 S þ CO2 ) ½CH2 O þ H2 O þ 2S

ð65Þ

Further reactions of S to thiosulfate (S2O2 3 ) and/or sulfate SO2 4 are possible. Chemical and microbial sulfide oxidation produces large amounts of Hþ, as illustrated by the following equation: þ HS  þ 32O2 ) SO2 3 þH

ð66Þ

Sulfur has following effects on the chemistry of surface water:

• •

increase in the production of Hþ, change in redox potential through several reactions, including:

HS  3 S 0 þ H þ þ 2e

•

ð67Þ

decrease in alkalinity through the oxidation of H2S, and

mobilization of metals and phosphate from sediments, due to changes in pH and Eh (also noted for natural and man-made derived radionuclides).

All these effects have been noted in response to atmospheric deposition of sulfur and discharge of acid mine runoff.

2.08.1.6.6 Nutrients (nitrogen and phosphorus) and trace substances In temperate climate regions, essential nutrients for the growth of aquatic organisms, such as bioavailable phosphorus and nitrogen, typically increase in spring due to snowmelt runoff and due to the mixing of accumulated nutrients into the water column, from the bottom, during spring turnover. In lessproductive systems, significant amounts of nitrogen compounds may be deposited during rainfall or snowfall events (wet deposition), and during the less-obvious deposition of aerosols and dust particles (dry deposition). Nitrogen and phosphorus, in dry fallout and wet precipitation, may also originate from fertilizers in agricultural areas. The two prin cipal nutrients P and N exist in the anionic form (NO 3 /NO2  and HPO2 /H PO ) and are not subject to retention by cat4 2 4 ion-exchange processes. However, the ammonium cation (NHþ 4 ), which is formed mainly in the sediment, is fixed by cation exchange. The nitrate anion, unlike phosphate, does not form insoluble compounds with metals and is therefore readily leached from the soil. It dissolves easily into surface water and groundwater. Phosphorus tends to be a minor element in natural waters because most inorganic P compounds have low solubility. Dissolved P concentration is generally in the range of 0.01– 0.1 mg l1 and rarely exceeds 0.2 mg l1. The dissolved

Lakes and Reservoirs Fertilizer and sewage runoff

183

Natural terrestrial sources

Dry and wet deposition

Fishes

Adsorption Zooplankton

DIP

PIP Colloid.P

Bacteria

Desorption

PIP Adsorption

TDP

DOP

Groundwater

Trophogenic zone

Phytoplankton Detritus

DIP Mineralization DOP Bacterial

Tropholytic zone

POP

Iron dissolution

PIP

TDP

uptake

Adsorption

Ca, Fe Mineral precipitation P minerals

DIP Decomposition DOP

POP

Sediment

Bacterial uptake

Refractory POP

Figure 28 Main processes of the phosphorus cycle in lakes and reservoirs. DIP, dissolved inorganic P; DOP, dissolved organic P; TDP, total dissolved P; PIP, particulate inorganic P; POP, particulate organic P.

phosphorus in lakes is most often the principal growth-limiting nutrient for the development of phytoplankton, planktonic bacteria, and aquatic plants. The critical level of inorganic phosphorus for algal-growth blooms can be as low as 0.01–0.005 mg l1 in summer, but is more frequently around 0.05 mg l1. Phytoplankton is able to use phosphorus 2  only from ortho-phosphate (PO3 4 /HPO4 /H2PO4 ), and not, for example, the polyphosphate form for growth. The concentration of dissolved polyphosphates in lakes is normally negligible. The excess of phosphorus in domestic wastewater (synthetic detergents and human waste, including about 1.5 g d1 per person in urine), effluent from agriculture (liquid manure, mineral fertilizers, and several insecticides), and in corrosioncontrol agents in water supply and industrial cooling water systems, is frequently the main cause of algal blooms and other symptoms of lake eutrophication (see overview in Figure 28). In lakes, phosphorus is cycled between organically and inorganically bound forms. Phosphate anions are largely immobilized, both in the soil and in oxidized sediment layers, by the formation of insoluble Fe, Ca, and Al phosphates, or by adsorption to soil/sediment particles. The two major steps of the phosphorus cycle, the conversion of organic P into inorganic P and back to organic P, are both microbially mediated. The conversion of insoluble P forms, such as Ca3(PO4)2 into soluble HPO2 4 is also carried out by microorganisms (Figure 28). Organic P in the tissue of dead plants and animals is also converted, microbially, to ortho-phosphate. Phosphorus mobility increases under anoxic conditions, because solid ferric iron, into which phosphate is strongly

adsorbed, is reduced to soluble ferrous iron, thereby releasing adsorbed phosphate:  2þ  FeðOHÞ3 ? HPO2 þ HPO2 4 þ e 2Fe 4 þ 3OH

ð68Þ

Aluminum and iron phosphates precipitate in acidic sediments. In calcite-rich sediments, Ca-phosphate is deposited. The immobilization of phosphorus in sediments is therefore controlled by properties such as pH, redox potential, texture, cation-exchange capacity, the amount of Ca, Al, and Fe-oxides present, and uptake by microorganisms and rooted plants. Emergent aquatic plants often obtain large quantities of phosphorus from the sediment and can release large amounts into the water. When the ortho-phosphate concentration in the water is low, which is usually the case, phytoplankton excretes extracellular enzymes, alkaline phosphatases, which decompose the organic P (e.g., phosphate esters) that is excreted by higher aquatic plants. The released ortho-phosphate is then available for the phytoplankton. Phosphate (in contrast to nitrate) is strongly adsorbed to soil particles and does not move freely within the groundwater in the majority of cases. High inputs of total phosphorus are due to particle erosion from steep slopes with easily erodible soils. In deep stratified lakes, transport of mobilized phosphate into the upper water layers is limited and the availability of dissolved phosphate in late winter may predetermine the phytoplankton growth in spring and summer. Direct P supply of the euphotic zone from the sediment is important in the littoral and shallow lakes during the whole season. Nitrogen compounds that are of the greatest interest to water-quality management are those that are biologically available as nutrients, or are toxic to humans or aquatic life.

184

Lakes and Reservoirs

Fertilzer and sewage runoff

Atmospheric deposition

N2

Terrestrial sources

Gas exchange

N 2 fixation

PON

DON Groundwater N2

N 2 fixation

PON

Assimilation

NO3–

Ass imil atio Dec n omp osit ion n atio n imil sitio Ass o omp Dec

NO3–

NO2–

NO2– Nitrification

Trophogenic zone

NH4+

Am Nit ra m on te ific at io n

N2

Tropholytic zone

Denitrification

NH4+

on

siti

DON PON

o mp

co

De

Sediment

Figure 29 Main processes of nitrogen transformation in lakes and reservoirs. PON, particulate organic N; DON, dissolved organic N.

Atmospheric nitrogen (N2) is the primary source of all nitrogen species, but it is not directly available to plants, because it is normally not bioavailable. The conversion of atmospheric nitrogen into other chemical forms is called nitrogen fixation and is accomplished by a few types of heterotrophic bacteria. Some autotrophs, namely several cyanobacteria, also have the ability to fix nitrogen from the air in heterocysts (special thick-walled cells that assure strict anaerobic conditions) by means of the enzyme nitrogenase. The N cycle is illustrated in Figure 29. When nitrogen is cycled, it undergoes a series of oxidation– reduction reactions that convert it from N-containing organic molecules, such as proteins, to ammonia (NH3). This is called ‘desamination/ammonification’

RNH2 þ H2 O ) NH3 þ ROH

ð69Þ

ð70Þ

In oligotrophic lakes, high oxygen concentrations permit metabolism of ammonia to nitrate, resulting in low levels of nitrite and ammonia and comparatively high levels of nitrate in the hypolimnion (nitrification). Nitrification is the oxidation of nitrogen (III) to nitrogen (V). Two steps are indicated: 1. Ammonia oxidation by Nitrosomonas (obligatory autotrophic, CO2 as C source), Nitrosococcus, Nitrosospira, and Nitrosolobus:

ð71Þ

2. Nitrite oxidation by Nitrobacter (facultatively autotrophic, CO2 and organic C as C source), Nitrococcus, and Nitrospira:  2NO 2 þ O2 ) 2NO3

DR G ¼ 5:2 kJ mol1

þ 0:00125HPO2 4 ) 0:00125C106 H263 O110 N16 P þ þ 1:9775H þ 0:98H2 O þ 0:98NO 3

ð72Þ

ð73Þ

Denitrification is the biological reduction of nitrate to niþ trogen (by-products/intermediates NO 2 , NH4 , NO, and N2O) 1 under anoxic conditions (O2o1 mg l ) in the pH range 7–8. It is typically restricted to the sediment and/or occurs in a fully or almost deoxygenated hypolimnion. The nitrite concentration in lakes is usually very low: I

II

III

IV

ð74Þ

with the enzymes: I ¼ nitrate reductase – enzyme associated with the respiration chain; II ¼ nitrite reductase; III ¼ NO reductase; and IV ¼ N2O reductase. Denitrification is performed by heterotrophic bacteria, such as Paracoccus denitrificans, and by autotrophic denitrifiers (e.g., Thiobacillus denitrificans). þ Another reaction pathway is the reduction of NO 3 to NH4 (nitrate ammonification): þ þ NO 3 þ 2H þ 8ðHÞ ) NH4 þ 3H2 O

 2NHþ 4 ðaqÞ þ 3O2 ) 2NO2 ðaqÞ þ 2H2 O

þ 4Hþ DR G1 ¼ 272 kJ mol1 :

 NHþ 4 þ 0:1325ðCO2 H2 OÞ þ 1:8275O2

 NO 3 ) NO2 ) NO ) N2 O ) N2

followed by the acid–base-reaction  NH3 þ H2 O ) NHþ 4 þ OH

Considering the formation of bacterial biomass, it follows that:

ð75Þ

Nitrate ammonification is carried out by several groups of microorganisms, especially of the Enterbacteriaceae (Escherichia coli and Enterobacter aerogenes). These are all facultative anaerobes which are able to work under anaerobic conditions. This process does not lead to the liberation of molecular nitrogen.

Lakes and Reservoirs

In eutrophic lakes, anoxia results in increased levels of ammonia with increasing sediment depth, and also in the hypolimnion.

2.08.1.6.7 Organic carbon – humic compounds Organic matter in lakes, seas, and oceans originates predominantly from photosynthesis. The fate of organic matter from plants, animals, and microbes is extremely complex because many organisms and many compounds are involved. The detritus is composed of a broad spectrum of substrates. Structural polymers of plant cell walls are most abundant. These include cellulose, hemicelluloses, pectins, and lignin. The biodegradation of organic matter in the aquatic system by microorganisms occurs by way of a number of stepwise, microbially catalyzed processes (e.g., oxidation, reduction, dehalogenation, and others). Biodegradation is the natural way of recycling waste, or breaking down organic matter into nutrients that can be used by other organisms. Degradation means decay and the bio prefix means that the decay is carried out by a huge assortment of bacteria, fungi, insects, worms, and other organisms. Primary biotic substances and decomposition products are summarized in Table 2 (Sigg and Stumm, 1991). Next, we discuss the carbon cycle. Many organic compounds (see Figure 30; Thurman, 1995) and their decomposition products interact with both suspended material and sediments in water bodies. Colloids can also play a Table 2

185

significant role in the transport of organic pollutants in surface waters. Settling of suspended material, containing adsorbed organic matter, carries organic compounds into the sediment. Some organics are transported into the sediments by particulate remains of organisms, or by fecal pellets from zooplankton. Suspended particulate matter affects the mobility of organic compounds adsorbed to particles. Furthermore, this organic matter undergoes biodegradation and chemical degradation, by different pathways and at different rates, when compared with organic matter in solution. The most common types of sediments considered for their organic binding abilities are clays, organic (humic) substances, and complexes of clays and humic materials. Both clays and humic substances act as cation exchangers. Therefore, these materials adsorb cationic, organic compounds through ion exchange. Since most sediments lack strong anion-exchange sites, negatively charged organics do not adhere strongly. Thus, these compounds are relatively mobile and biodegradable in water, despite the presence of solids. Microorganisms are strongly involved in the carbon cycle, mediating crucial biochemical reactions. Photosynthetically, active organisms are the predominant C-fixing components in water, as they assimilate CO2. The pH of the water is raised enabling precipitation of CaCO3 and CaMg(CO3)2. Humic substances are refractory, irregularly built-on substances of high molecular weight (structure proposal, see Figure 31; Stottmeister, 2008) that occur in soil and water. These complex compounds are formed by microbial

Primary biotic substances and decomposition products

Primary biotic substances

Decomposition products

Intermediates and end products that appear in natural freshwater systems

Proteins

Polypeptides ) Amino acids ) RCOOH RCH2OHCOOH RCH2OH RCH3 RCH2NH2

 NHþ 4 , CO2, HS , CH4, peptides, amino acids, urea, phenols, indols, fatty acids, mercaptans

Lipids Fat Waxes Oils Hydrocarbons

Fatty acids þ Glycerin ) RCH2OH, RCOOH, RCH3, RH

Aliphatic acids, acetic -, lactic-, citric-, glycolic-, maleic-, stearic-, oleic acids, carbohydrate, hydrocarbons

Hydrocarbons Cellulose Starch Hemicellulose Lignin

Monosaccharide ) Hexogene Oligosaccharide Pentogene Chitin Glucosamine

Glucose, laevulose, galactose, arabinose, ribose, xylose

Porphyrin and plant pigments Chlorophyll Hemin

Chlorine ) Pheophytin ) Hydrocarbons

Phytan, pristan, carotinoide, isoprenoid, alcohol, ketone, porphyrin

Carotene Xanthophylls Polynucleotides

Nucleotides ) Purines and pyrimidine bases

Complex substances, formed from intermediate products

Phenols, quinoides, amino acids and decomposition products of hydrocarbons

From Sigg L and Stumm W (1991) Aquatische Chemie. Stuttgart: B.G. Teubner Verlag.

Melanin, humic substances, humic-, fulvic acids, tannines

186

Lakes and Reservoirs Fraction of total DOC (%) 0

5

10

15

20

25

30

40

35

Fulvic acids Humic substances Humic acids Hydrophilic acids Carbohydrates Simple compounds

Carbonic acids Amino acids Hydrocarbons

Figure 30 Distribution of dissolved organic carbon in typically freshwater with DOC ¼ 5 mg l1. From Thurman EM (1995) Organic Geochemistry of Natural Waters, 497pp. Boston, MA: Martinus Nijhoff/Dr. W. Junk Publishers.

HO O HO

O (CH3)0–3

O

OH

O

O OH O

OH

OH HO O O

O O

OH O HO

OH

OH (CH3)0–2

O

O OH O

HO OH

N H

HO HO O OH HO

O

O

OH O

OH

HO

O

O

OH O

OH O O

OH

OH

OH

N

OH O

O

HO

(CH3)0–2

O

O

OH

OH

O

O OH O OH O O OH OH OH

O

HO OH

O

C OH O (CH3)0–4 OH

N

N H

(CH3)0–2

(CH3O)0–3 O OH

(CH3)0–4 (CH3)0–2

OCH3 HO O

O

HO

OH

O

O OH

N

OH

O

(CH3)0–5

C

O

OH

CH2OH

(CH3)0–5

Figure 31 Structural proposal of a humic molecule. After Stottmeister U (2008) Altlastensanierung mit Huminstoffsystemen. Prinzipien der Natur in der Umwelttechnologie. Chemie in unserer Zeit, vol. 42, pp. 24–41. Weinheim: Wiley-VCH Verlag GmbH.

decomposition and/or partial new syntheses of vegetable and animal material. Humic acids, a fraction of the humic substances, exist at neutral pH values, in dissolved form. However, under strongly acidic conditions, they are insoluble. The fraction of humic acids that are soluble at pH ¼ 1 are termed fulvic acids. They have smaller, average molar masses than humic acids. Humic and fulvic acids both have a cyclic

aromatic basic structure (Figure 31), but fulvic acids are more soluble in water because they have more number of hydrophilic functional groups than humic acids. From soil leaching, terrestrial detritus input, shallow groundwater, and overland flow, organic compounds (e.g., humics) can generally be discharged into lakes. The greater the area of the drainage basin, relative to the surface of the lake,

Lakes and Reservoirs

the higher the input of humic acids from the soil, as against extraction and dilution by rain. Humic acids undergo slow degradation by sunlight and biodegradation. As the retention time of water in the lake increases, the color of the lake reduces because the humic acids are exposed to sunlight and the attack by microorganisms continues for a longer period. When the slope of a watershed is steep, surface runoff moves rapidly to the lake, allowing less time for contact with the soil during which humic acids may be picked up. Steep watersheds also tend to have less organic content in their soils and therefore yield less humic acid. Relatively flat watersheds allow rainwater to penetrate the soil for longer periods. They also have larger accumulations of organic material in the soil and larger areas of wetland, which contribute color to runoff. Calcium is known to precipitate humic acids. Lakes with high Ca2þ concentrations and/or with CaCO3 as sedimentary rock are lighter in color than those in hard, igneous rock catchments because of the precipitation of humic acids.

2.08.1.7 Biotic Structure The biotic structure of a lake is governed by vertical gradients in light intensity and temperature. In temperate climates, thermal gradients occur in the water column, right down to the lake bottom. The organisms which are characteristically found in these layers are described later in the chapter. The livelihood of the biota in lakes and reservoirs is provided mainly by photosynthesis. The photosynthetic organisms, that is, cyanobacteria, algae, and macrophytes, are called ‘primary producers’.

187

Their biomass is eaten by animals, that is, by primary consumers. These consumers comprise a very broad range of organisms, from aquatic protozoa to many species of freshwater fish. There are usually differing trophic levels of consumers, with the carnivores occupying the highest level (Figure 32; Ligvoet and Witte, 1991). There are also secondary and tertiary consumers. The biological communities of the water body, that is, in the pelagic zone, are closely linked to benthic communities, which are localized at the bottom, or in the sediment. This also applies to nutrients in the water column, which are linked to nutrients in the sediment through sedimentation– resuspension and diffusion. Suspended nonliving particles with a high content of organic materials (e.g., cellulose) and associated bacteria, called ‘detritus’, are an important source of biochemical energy for microorganisms and animals, especially those with a filtering apparatus such as several types of zooplankton. Very little of the organic material produced by photosynthesis is utilized directly in the consumer food chain (Wetzel, 2001). Instead, it enters the detritus pool. After deposition as organic bottom sediment, the detritus is processed by bacteria, fungi, and bottom-dwelling invertebrate animals such as oligochaete worms, mussels, and, in particular, insect larvae. The quantity of this particulate organic material is huge, but its (mainly microbial) metabolism is slower than that of biomass because not enough oxygen is available in the sediment. The principal controlling factor in the supply of biochemical energy is solar radiation and its penetration depth. For the photosynthetic production of organic matter in a lake ecosystem, not only the open water (pelagic zone) with its

1 3

2

4

5

Figure 32 The current food web of Lake Victoria. The main food chains are: 1. via Caridina (freshwater prawn) to Lates (Nile perch, top predator); 2. to juvenile Lates, 3. via benthonic insect larvae to juvenile Lates, 4. via zooplankton to Rastrineobola (Silver Cyprinid) and Lates, 5. from micro-algae to Oreochromis (Tilapia). From Ligvoet W and Witte F (1991) Perturbation through predator introduction: Effects on the food web and fish yields in Lake Victoria (East Africa). In: Ravera O (ed.) Terrestrial and Aquatic Ecosystems. Perturbation and Recovery, pp. 263–268. New York: Ellis Horwood.

188

Lakes and Reservoirs Phytoplankton

Zooplankton

m 0

Large sedges

Reeds, rushes

Emergent flora

Waterlilies, Nymphaea Pondweeds and Nuphar

Floatingleaved plants

illuminated

HQ NQ

5

Submersed plants

Tubifex worms (Tubificidae)

dark

10

15 Figure 33 Littoral profile of a lake. HQ and NQ are the limits of the mean water level. The dots in the water body should represent phytoplankton and zooplankton, respectively. From Uhlmann D (1979) Hydrobiology. A Text for Engineers and Scientists. Chichester: Wiley.

phytoplankton, but also the riparian and the (illuminated) bottom zones containing macrophytes and algae, are responsible (Figure 33). Among the aquatic macrophytes, there are emergent plants such as reeds, submerged vegetation such as milfoil, and floating-leaved species such as water lilies. Some of the algae are filamentous; others are unicellular or grow in colonies. These sessile, phototrophic organisms are called the ‘phytobenthos’. The riparian area is called the ‘littoral zone’. The phytobenthos also includes microscopic algae, which grow on solid surfaces (living and nonliving) together with bacteria, as biofilms (Aufwuchs). These biofilms cohere by extracellular polymeric substances (EPSs) which consist mainly of polysaccharides and proteins. Biofilms in which microscopic algae dominate are also called ‘periphyton’. Invertebrate animals living in free water are called zooplankton (small crustaceans, some of which are effective grazers of phytoplankton and bacteria, and rotifers). Many of these animals are filter feeders and some of them are able to feed on cells with diameters smaller than 1 mm. In the free water, there are, in addition, rapidly swimming water insects (larvae) and fish. Fish larvae may also rank among the zooplankton. Several species of fish (such as some species of whitefish, Coregonus) and fish larvae eat mainly zooplankton, while others feed on smaller fish or on zoobenthos, that is,

invertebrate animals living at the bottom of the lake. Several types of zoobenthos are mobile, for example, insect larvae, snails, worms, and protozoa. Others are sessile or slow moving, for example, sponges, different types of worms, certain insect larvae, and mussels. Only a small number of animal species in lakes are omnivorous. We now discuss the seasonal variations in lakes. In many lakes and reservoirs of the temperate zones, rapid growth of phytoplankton dominates early in spring, because the nutrient supply (from the winter) is still high, and the activity of filtering zooplankton is, on account of the low temperatures, still far below the maximum values found in summer. Therefore, during this season, the vertical flux of nutrients from the lower water layer, rich in dissolved N and P, is still not restricted by the thermal stratification of the water body. At the height of the summer season, mass growth of particular phytoplankton species (water blooms of cyanobacteria) may become a nuisance, because of toxic metabolites or of odors. This, however, is mostly to be expected if the nutrient load from outside is too high. Solar radiation not only controls the photosynthesis of organic materials, but also controls the generation of molecular oxygen. In many lakes, this process is even more relevant to oxygenation than the diffuse introduction of oxygen from the atmosphere. The light-penetration depth separates an upper water layer (euphotic zone, normally

Lakes and Reservoirs

located within the epilimnion), and this layer is warmed in the summer season, from a lower water layer (aphotic zone) in the hypolimnion, which is dark and remains cold. The latter is called the ‘profundal zone’ as far as it concerns the lake bottom. The phototrophic biota is inseparably connected to the dynamics of several chemical elements, in particular inorganic C, N, and P. In the drainage basin, the duration of contact of water with soil and associated microorganisms influences the content of dissolved salts, all the above-mentioned nutrients, and organic materials such as humic acids (Wetzel, 2001). A lake ecosystem consists of the lake and its entire drainage basin. Lake ecosystems require a continual input of organic matter, produced mainly by photosynthesis (in part outside the water body). This organic matter is used by animals, who are the consumers, or degraded by microorganisms. Biochemical energy and inorganic nutrients must continually be replenished, basically by import from the drainage basin. A usually small proportion of the produced biomass sometimes becomes unavailable due to sedimentation. The average, elementary composition of biomass is approximately in accordance with the formula

C106 H263 O110 N16 P

ð76Þ

Thus, the organic substance of the organisms comprises, as related to the dry mass, about 40% carbon. The nitrogen content is usually comparatively constant, because of the irreplaceable amino and nucleic acids, whereas phosphate may be stored by many microorganisms and algae as polyphosphate, and is thus highly variable. The biotic communities in lakes and reservoirs are selfsustaining. This is also true for single components such as the phytoplankton. Nearly all species are free floating and find the best conditions for propagation in a level not too deep (because of the lack of light energy), but also not at the surface because of potential damage by UV radiation. Nearly all species aim at neutral buoyancy and avoid losses by sinking toward the lake bottom, through storage of substances low in weight, such as oil droplets in the case of diatoms. The zooplankton, on the other hand, is actively motile and prefers depths with abundant food, but with protection from predation by fish. The entity of fluxes of energy/organic materials, which are necessary to supply/maintain the structural and functional stability of a lake ecosystem, or of its compartments, is called the food web (Figure 32). There are unidirectional transport pathways of biochemical energy through the ecosystem from the primary producers to the top carnivores. At each trophic level, up to 90% of the collected energy is lost through respiration and defecation, and thus released as heat energy or as waste material. The sum of energy losses increases with the number of trophic levels within a food web. This also implies that, in principle, for fisheries/human nutrition, the use of plant-eating fish is more economical than the production of fish biomass on the uppermost trophic level, because the number of trophic levels between producers and consumers is less. The flux of biochemical energy within the lake ecosystem may be controlled not only bottom-up, for example, by

189

increasing or decreasing the supply of nutrients to the phototrophic organisms, but also, alternatively, top-down, for example, by a higher or lower grazing pressure from herbivorous consumers, or by fish species feeding on phytoplankton. Thus, mass growth of zooplankton may cause clear water, whereas a very dense stock of zooplankton-eating fish may sustain a high turbidity, induced by phytoplankton. The bulk of the biochemical energy flow is usually carried by a comparatively low number of dominant species. Most of the many thousands of different species, in a large lake ecosystem, probably play an ancillary role in terms of biochemical energy flux. The names and the portion of most species of bacteria and fungi in the community metabolism of lakes and reservoirs, is still unknown. Their metabolic rate is often higher than that of algae, higher plants, and animals. Shallow lakes, rich in nutrients, may exhibit alternative equilibria (Uhlmann 1980; Scheffer et al., 1993): (1) status 1 – if there is abundant submerged vegetation, the water is clear and light can penetrate to the lake bottom. The vegetation also operates as a screen, which eliminates particles from water moved by wind currents. This is a self-preserving mechanism; (2) status 2 – the alternative is characterized by dense and long-lasting growth of phytoplankton, so that the submerged vegetation cannot become relevant. The shift from phytoplankton turbidity to clear water, is mostly caused by the disappearance of zooplankton-eating fish, for example, by ice and snow cover, which may cause oxygen deficiency and winterkill of fish. The overall species diversity is specifically high in aquatic systems which are not subject to anthropogenic impacts such as acidification, eutrophication, or organic pollution. Thus, the biotic diversity is (with some notable exceptions) a measure of organization and integrity within the lake ecosystems (Wetzel, 2001).

2.08.1.8 Photosynthesis: Generation and Consumption of Dissolved Oxygen Apart from temperature, oxygen is the most significant parameter in water bodies, because it is essential for the metabolism of the bulk of the biota. The dynamics of oxygen distribution are basic to the understanding of the growth and distribution of aquatic organisms. The dissolved oxygen in water bodies originates from two sources: 1. entry from the atmosphere and 2. photosynthesis. Air contains approximately 21% oxygen (by volume) and the remainder is mainly nitrogen. The amount of molecular oxygen, which can be dissolved in a water body in equilibrium with the atmosphere, is low and increases markedly at low temperatures (Figure 34). Thus, at the end of the vernal circulation, the O2 concentration is 13.1 mg l1 in lakes of the temperate climate belt at sea level, but substantially lower in tropical, lowland lakes, with their higher temperatures. With increased salinity, this concentration becomes even lower. The higher the barometric (atmospheric) pressure, the higher the oxygen saturation concentration.

190

Lakes and Reservoirs 16

Oxygen (mg l–1)

14

12

10

8

6 0

10

20 Temperature (°C)

30

40

Figure 34 Relationship between water temperature and O2 concentration (at saturation) under normal air pressure for pure water.

Photosynthetically active organisms normally produce oxygen, as well as organic materials, from inorganic substances, using sunlight as the sole energy source. In the process, they reduce carbon dioxide and in part cleave the water molecule:

6CO2 þ 12H2 O þ 2:872 kJ water light energy carbon dioxide

)

C6 H12 O6 þ 6H2 O þ 6O2 glucose

water

oxygen

ð77Þ

The synthesis of algal biomass may be (simplified) described as follows: 2 þ 106CO2 þ 16NO 3 þ HPO4 þ 122H2 O þ 18H þ 3 C106 H263 O110 N16 P þ 138O2 ð78Þ

[H] denotes biochemically bound hydrogen. From this equation, it is evident how important nitrogen and phosphorus are as sources for the synthesis of biomass. When no free carbon dioxide is available, phototrophic organisms precipitate carbonate in accordance with Equation (79):

Ca 2þ þ 2HCO 3 3 CaCO3 þ H2 O þ CO2

ð79Þ

Thus, over long periods of time, large depositions of limestone may evolve by biogenic decalcification. With photosynthetic activity, deep lakes and reservoirs are vertically subdivided into two levels: the upper, fully illuminated level which is called ‘euphotic’, that is, with generation of O2 and of organic material, and a lower one, called ‘aphotic’, that is, very dark with consumption of both O2 and organic reserves and formation of CO2. In clear water bodies, photosynthesis is inhibited by UV radiation immediately below the surface; thus, the light intensity is optimal at depth zopt, which may be measured using a series of vertically oriented light and dark bottles, filled with ambient water and phytoplankton. The maximum possible photosynthesis is regulated by the upper limit of the quantum yield. It amounts to 30–40 g m2

d1 C in some tropical water bodies with a very high density of phototrophic biomass (Baumert and Uhlmann, 1983). In many clearwater lakes with a very low concentration of nutrients, not even the annual primary production attains such a high level. As many species of phytoplankton are able to grow at low temperatures, the photosynthetic production may reach high supersaturation, even under a cover of clear ice. Instead of solar energy, several species of bacteria (and only bacteria) are capable of utilizing the energy obtained by the oxidation of energy-rich inorganic compounds, such as sulfide (oxidation to sulfate) and ammonia (oxidation to nitrite/nitrate). This ability is known as chemosynthesis (as opposed to photosynthesis). As an intermediate reservoir of biochemical energy, obtained by photosynthesis or chemosynthesis, all organisms use an organic phosphate, ATP (Figure 35; Uhlmann and Horn, 2001) as bioenergetic currency. Moreover, many species of microorganisms are able to store energy-rich polyphosphates. For the process of photosynthesis, only visible sunlight can be utilized, which represents about 46% of the total irradiation. From glucose (cf. Equation (77)), many organic materials and biomass (for cell propagation) are produced inside the cells of the photosynthetically or chemosynthetically active organisms. According to their energy source, autotrophic organisms, which depend on solar energy or on the oxidation energy of inorganic compounds such as H2S, can be distinguished from heterotrophic organisms, which need an organic source of biochemical energy. All organisms require biochemical energy (ATP) for the maintenance of life functions and for growth/reproduction. Energy is released mainly from stored carbohydrates, by biochemical combustion with oxygen:

C6 H12 O6 þ 6O2 ) 6CO2 þ 6H2 O þ 2:872 kJ

ð80Þ

This equation is the reverse of Equation (77) and an expression of respiration, another principal metabolic process. Nearly all organisms require oxygen for energy generation. O2 is also needed by bacteria and fungi for the decomposition of organic material. In polluted water bodies, microbial decomposition may require a high amount of dissolved oxygen, sometimes up to total depletion. If in a water body, the concentration of nutrients (beside CO2, mainly N and P compounds) for the growth of phototrophic biomass is very high, the concentration of produced molecular oxygen may substantially exceed the O2-saturation level, which results from the equilibrium between atmospheric and aquatic oxygen (Figure 36; Uhlmann and Horn, 2001). On the other hand, during the night, when respiration is not counterbalanced by photosynthesis, the oxygen concentration may, in extreme cases, decrease to zero. For a natural water body, this situation is completely undesirable. Thus, a basic principle of water-quality management is to prevent too-high diurnal and seasonal amplitudes in oxygen concentration, caused by photosynthesis and respiration, as far as possible. There should only be a low supersaturation level, if any, and a decline down to about 3 mg l1 dissolved oxygen in the water at the most. In a lake, this concentration level has to be maintained in the hypolimnion up to the autumnal circulation, if salmonid fish/whitefish are

Lakes and Reservoirs

191

Adenine NH2 N

N

Phosphoric acid O

N

N

CH2

H

H

OH

OH

Adenosine

O

P O–

H

O O ~P O–

O O ~P

OH

O–

H

O Ribose

Adenosinmonophosphate (AMP)

Adenosindiphosphate (ADP)

Adenosintriphosphate (ATP)

Figure 35 Structure of adenosine triphosphate, ATP, the principal biochemical energy store. From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

Supersaturation

A

300%

O2 (mg l–1)

O2 deficit 20

200%

B

10

100%

0 6

12 Day

18

24

6

12

18

24 h

Night

Figure 36 Diurnal variations of the oxygen concentration in the upper water layer of a wastewater-treatment lagoon, very rich in phytoplankton. Diagrammatically presented after empirical data of I. Ro¨ske. From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

to survive. In populated catchments, such a water-quality level needs to be maintained, principally by the advanced treatment of wastewater and by the control of nutrient fluxes from fertilized agricultural areas. In the upper water layers of lakes, O2-depleting processes are not usually noticeable, because these are fully compensated for by photosynthesis and by atmospheric aeration.

Thus, the oxygen concentration here does not deviate much from the saturation level. Generally, a comparison of the rate of oxygen consumption, in relation to the photosynthesis rate, permits an approximate evaluation of the metabolism in a lake or a reservoir. Diffusion of oxygen in water is a very slow process and its vertical transport requires external energy for turbulent mixing. However, oxygen produced by photosynthesis at depths greater than 1–4 m can remain dissolved there, even if the turbulent mixing of the water body is very low. In lakes and reservoirs of the temperate climate belt, atmospheric oxygen is introduced immediately after the disappearance of the ice cover in early spring. At 4 1C, the O2 concentration is near 100% saturation, that is, at an altitude around sea level r13 mg l1. As shown in Figure 37 (Uhlmann and Horn, 2001), thermal stratification of the water body is usually associated with oxygen stratification. However, at very low nutrient levels, the phytoplankton production remains low. Such a lake is described as oligotrophic. Accordingly, oxygen saturation in the upper layers barely exceeds 100% and the O2 deficit in the deeper strata remains low. At the end of the summer stagnation, the oxygen concentration in the hypolimnion is still approximately as high as in the epilimnion, despite the much lower temperature. In most of the lakes in populated areas, however, the amount of organic matter (plankton biomass) that arrives in the hypolimnion by sinking from the upper, illuminated (i.e., euphotic) water layer is high. Therefore, the oxygen concentration in the hypolimnion is progressively reduced. This results in an O2 deficit. Lakes with increased nutrient content and increased production are called ‘eutrophic’.

192

Lakes and Reservoirs 10

0

20

°C

E

H T

Phytoplankton Living

O2

0

5

0

10

Saturation

O2

10 mg O2 l–1

Dead E

20

°C

O2 T

H 0

5

O2 10 mg O2

Saturation l–1

Figure 37 The vertical distribution of dissolved oxygen in thermally stratified lakes. It is presumed that the phytoplankton cells, after sinking down into the dark hypolimnion, die and are microbially decomposed due to O2 consumption. Situation at the end of the summer stagnation period, O2, actual concentration; T, water temperature. In the top portion of the figure, the lake has a large, and in the lower one a comparatively small, initial O2 store. Based on Thienemann, from Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

The loss of oxygen in the hypolimnion and, in particular, at the sediment–water interface, primarily results from microbial decomposition of plankton residues and other organic particles. Oxygen consumption by bacterial respiration is intensive in all water layers, but in the epilimnion is offset by turbulent mixing and photosynthesis. It is most intensive at the sediment/water interface, which may rapidly become anaerobic. The smaller the hypolimnion volume is, compared with the volume of the epilimnion, the higher is the probability that the oxygen pool is not large enough to prevent O2 depletion down to a very low, or even a zero level (Figure 37). According to the O2 stratification, increased hypolimnic concentrations of redox-sensitive components may result, which are also associated with the decreased oxygen concen2þ 4þ 3þ 2þ þ tration. This relates to NO 3 /NH4 , Mn /Mn , Fe /Fe , 2 and SO4 /H2S. These first develop within the sediment, but then diffuse into the water. Most species of fish cannot survive, even at low temperatures, at oxygen concentrations of less than 2 mg l1. The development of the O2 deficit in the hypolimnion from the beginning of the stratification period quantifies the metabolic relationship between the illuminated, that is, trophogenic (euphotic) zone, and the underlying tropholytic (aphotic) layer, which is dark. In tropical or subtropical zones, the hypolimnion temperature will never be as low as 4 1C, and the microbial degradation processes are thus substantially enhanced. If the bottom of a future reservoir in the humid tropics is not cleared, the submerged terrestrial vegetation will give rise to rapid oxygen depletion and microbial sulfate reduction. Thus, the personnel in the powerhouse of a newly constructed reservoir in humid Latin America had to wear gas masks for several years, because of the hydrogen sulfide that escaped

from the hypolimnetic water used (to obtain a sufficiently high power head) for the operation of the turbines. Figure 38 (Kusnezow, 1959) depicts the H2S accumulation in an eastern European lake. If dissolved humic materials are present, chemical oxidation due to photochemical reactions caused by UV radiation near the lake surface may be relevant. The biochemical, largely inert, humic acid molecules (Figure 31) are split into smaller fragments which are accessible to microbial degradation and may thus introduce biochemical energy into the food web. With the initiation of the autumnal circulation, oxygenrich epilimnetic water disperses deeper and deeper into the hypolimnion. When circulation is complete, the oxygen concentration is high, right down to the bottom. An ice cover prevents O2 exchange with the atmosphere. An ice cover is usually also covered by snow and thus the photosynthetic oxygen production is low, or zero, due to the strongly reduced underwater light intensity. In the warm season, the oxygen concentration in the metalimnion may be higher than above (metalimnetic O2 maximum), if there is still enough light for photosynthesis and a lower temperature (i.e., higher O2 solubility) than in the overlying epilimnion (Figure 39; Uhlmann and Horn, 2001). In the metalimnion, the vertical diffusion is lower than both in epi- and hypolimnion. Conversely, absence of light and decreased vertical turbulence may cause a metalimnetic O2 minimum. This can, inter alia, be caused by the microbial oxidation of methane ascending from the bottom sediment into an upper water layer (under conditions of stratification), the temperature of which is higher than that below. According to the respective O2 stratification, gradients of redox-sensitive components of nitrogen, manganese, iron (Figure 40; Uhlmann and Horn, 2001), and sulfur (SO3 4 / H2S) also may develop here.

Lakes and Reservoirs

193

T (°C), O2 (mg l –1), H2S (mg l –1) 0

5

10

15

20

25

30

0

5

Depth (m)

T O2

10

H2S Chr

15

20

25 0

200

400

600 Chr

(103

cells

800

1000

1200

l –1)

Figure 38 The vertical distribution of temperature (T), oxygen (O2), hydrogen sulfide (H2S), and the bacterium Chromatium (Chr) at the end of July in Lake Belowod, Russia. Note also the metalimnetic O2 maximum. Redrawn from Kusnezow SI (1959) Die Rolle der Mikroorganismen im Stoffkreislauf der Seen, 301pp. Berlin: Deutscher Verlag der Wissenschaften.

This outflow of interstitial water also concerns nonreducing substances with an increased density, such as CaHCO3 in hard-water lakes. Such a layer with increased CaHCO3 concentration may then act as a physical barrier against the vertical (turbulent) flow of dissolved materials within the hypolimnion. The volume loading of the hypolimnion with oxidizable matter, that is, mainly oxygen-depleting residues of phytoplankton, is high if the hypolimnion volume is small and vice versa (Figure 39). Unfortunately, many lakes in lowland areas and also many reservoirs have small hypolimnion volumes and are therefore often very sensitive to increased phytoplankton production caused by human impacts (eutrophication by sewage-borne nutrients or by runoff from fertilized agricultural areas). The organic material deposited in the bottom sediment is largely unavailable for rapid biotic conversion, due to the lack of oxygen. Exceptions are the microbial utilization of nitrate and sulfate as electron acceptors, and the anaerobic digestion of organic materials, with methane (CH4) as an organic, but volatile end product. Over long periods, mud deposition may raise the bed of a lake to such an extent (Figure 41) that light can penetrate everywhere, right down to the bottom. Thus, macrophytes may spread over the whole area if very dense phytoplankton blooms are absent. Consequently, an already shallow lake may be converted into a reed swamp. In periods of high transpiration of emergent plants, a drop in water level may be caused. While some lakes progress through this sequence, it is not always the rule. Other types of wetlands, in succession stages, may similarly be formed. In many cases, the biogenic fine structure of the sediment layers (with pollen grains and spores) reflects former climatic conditions.

2.08.1.9 Oxygen Stratification: Circulation/Quality Types of Lakes The combination of thermal stratification and biological activity causes characteristic patterns in water chemistry. Typical seasonal changes in dissolved oxygen and temperature are shown in Figure 42 (Wetzel, 1975). In spring and autumn, both oligotrophic and eutrophic lakes tend to have uniform, well-mixed conditions throughout the water column. During summer and winter stratification, the conditions diverge. The O2 concentration in the epilimnion remains high throughout the summer. However, conditions in the hypolimnion vary with trophic status. In eutrophic (i.e., phytoplankton-rich) lakes, hypolimnetic O2 concentration declines during the summer because it is cut off from all sources of oxygen, while organisms continue to consume oxygen. The bottom layer of the lake and even the entire hypolimnion may eventually become anoxic. Epilimnetic oxygen concentrations vary on a daily basis in eutrophic lakes. Fluctuations between O2 supersaturation and deficit are typical. In oligotrophic lakes with thermal stratification in summer, the oxygen content of the epilimnion decreases as the water temperature increases. The O2 content of the hypolimnion is initially higher than that of the epilimnion, because the saturated colder water contains more oxygen (from spring turnover). This oxygen distribution is known as an ‘orthograde oxygen profile’ (Figure 42; Wetzel, 1975). In oligotrophic lakes, low phytoplankton biomass favors deeper light penetration and less decomposition. The O2 concentration may therefore increase with depth below the thermocline, where colder water with higher O2 concentration occurs (oxygen is more

194

Lakes and Reservoirs

mg l –1 Light zeu < zmix

0

5

10

15

O2

20 °C

T

E M

H

mg l –1 Light zeu < zmix E

0

5 O2

10

15

20 T

°C

M

H

Figure 39 Differing relationships between light-penetration depth zeu and epilimnion depth zmix. If zeuZzmix, planktonic photosynthesis is possible in the metalimnion (sometimes even in the hypolimnion). This results in a metalimnetic O2-maximum. If zeurzmix, the result is a metalimnetic O2minimum. From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

soluble in colder water). In eutrophic lakes, on the other hand, the oxygen profile is clinograde because of the sharp decline of O2 concentration in the hypolimnion (Figure 42), after only a few weeks of summer stratification. Later, the hypolimnion is often anaerobic. These differences between eutrophic and oligotrophic lakes tend to disappear with autumn turnover. In winter, oligotrophic lakes generally have uniform O2 conditions along the vertical axis. Ice-covered eutrophic lakes however, may develop a winter stratification of dissolved oxygen. If there is only limited, or no snow cover to block sunlight, phytoplankton and several macrophytes may continue to photosynthesize, resulting in an increase in O2 content just below the ice. However, microorganisms continue to decompose organic material in the water column and in the sediment. No oxygen inflow from the air occurs because of the ice cover, and if snow covers the ice, it becomes too dark for photosynthesis. This condition can cause fish mortality during the winter (winterkill). When the dissolved oxygen level drops below 1 mg l O2, biochemical processes at the

sediment–water interface accelerate the release of phosphate and ammonium from the sediment as a basis for increased phytoplankton growth. The dynamics of oxygen distribution in inland waters are essential to the dynamics of the biota, because many animal species cannot survive/propagate at low (r3 mg l1) O2 concentrations. For lakes and reservoirs in the temperate climate regions, the following seasonality in temperature, with direct impacts on the oxygen balance, is representative:

1. Spring. Complete overturn subsequent to the disappearance of the ice cover. Atmospheric oxygen is absorbed and evenly distributed throughout the water body. Its concentration corresponds with the saturation level. If the lake is not too deep, and the average light intensity is high enough, a phytoplankton spring bloom may even result in remarkable oxygen supersaturation within the entire water column.

Lakes and Reservoirs

O2

0

NO –3 –N

CO2

9.3

NH+4 –N

PO43– –P

Fe2+

Mn2+

0.47

18.2

6.9

195

0.16

(m)

10 5.4 0.8

20 22

0.21

14.0

30 54.8

40

4.5

Figure 40 Chemical stratification in an Austrian lake due to hypolimnetic O2 depletion. The numbers in the graphs correspond to the cubic roots of the concentrations (mg l1). From Ruttner From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

Lake Ostrovno

Lake Pijavočnoje

Depth (m)

0

5

10

Water

Sediment

15 Figure 41 Profiles of two lakes in different stages of silting-up (biogenic sediment accumulation). From Kusnezow SI (1959) Die Rolle der Mikroorganismen im Stoffkreislauf der Seen, 301pp. Berlin: Deutscher Verlag der Wissenschaften.

Spring

Summer 0

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4

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4

Autumn

T (°C) 0

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Figure 42 Temperature (T) and O2 concentration (DO) in dependence on depth and season in a dimictic oligotrophic (above) and eutrophic (below) lake. Modified after Wetzel RG (1975) Limnology. Philadephia, PA: WB Saunders.

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Lakes and Reservoirs

2. Summer. Heating of the upper water layers with formation of the epi-, meta-, and hypolimnion. In eutrophic lakes, oxygen at saturation level is present only in the epilimnion, but a noticeable deficit exists in the hypolimnion. The hypolimnetic concentration further decreases toward the end of the summer. 3. Autumn. Convective mixing of the epilimnion due to the cooling down of the overlying air. This turbulence goes far into the depth. At the end of the season, a homothermal state is achieved with 4C and near O2 saturation. 4. Winter. During calm weather and frost, the water surface may freeze. The upper water layers are now not only colder, but also lighter than the bulk of the water body. Such an inverse thermal stratification is maintained as long as the ice cover exists. On the other hand, if the ice is clear, a thin surface layer of water may even attain a temperature of up to 10 1C.

The O2 concentration below a cover of clear ice corresponds, at least, with the saturation level, because respiration is usually low in winter and because photosynthetic oxygenation is possible. In shallow lakes however, below snow-covered ice, the accumulation of sediment-borne methane and/or hydrogen sulfide may cause oxygen depletion and fish kills. (Below clear ice, photosynthetic O2 production may be high enough to counteract depletion.) Lakes with two overturn periods per year are called ‘dimictic’ and they undergo at least two phases of atmospheric oxygenation. In lakes, the average water temperature correlates with the mean air temperature. The hypolimnion temperature shows an inverse, nearly linear relationship with the altitude above sea level (Figure 43; Uhlmann and Horn, 2001). The seasonal change between overturn and stratification periods is governed by the seasonality in air temperature. As is evident from Figure 44 (Uhlmann and Horn, 2001), this has drastic consequences for oxygenation. In the humid tropics, the variations in air temperature are often so slight and wind action so low that overturn in oligomictic lakes does not occur and oxygen depletion is high. This is all the more valid as hypolimnion temperatures are also high and thus accelerate rapid microbial degradation. The formation of H2S by microbial sulfate reduction is not uncommon. If, on the other hand, the wind is violent and nocturnal cooling likewise strong, a lake may be subjected to a frequent overturn, sometimes at diurnal intervals. Such a lake is called ‘polymictic’ and is situated either in an arid/semiarid climate or at high elevations in the tropic belt. A diurnal, deep overturn introduces so much atmospheric oxygen that no marked deficit can evolve. As in tropical/subtropical lakes in the lowlands, no ice cover can form, the stratification period is interrupted by a single long-lasting circulation period in the cool season. Such a lake is ‘warm monomictic’. The dissolved oxygen here may diminish after a prolonged stratification period. Conversely, lakes at very high altitudes, or in the Arctic, often have an ice cover for more than 9 months of the year. An overturn occurs only in the summer. As long as ice still remains, the water temperature is not higher than 4 1C. This type

Elevation above sea level (m)

4000

L. Naivasha

L. Kiloles

2000

L. Nakuru

L. Tana

L. Abaya L. Victoria

L. George

0 0

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30

Temperature (°C) Figure 43 Influence of the elevation above sea level upon the hypolimnetic temperature of East African lakes. The names of some well-investigated lakes are also mentioned. From Talling and Lemoalle, modified from Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

of lake is called ‘cold monomictic’. Such lakes are usually well oxygenated. In reservoirs, the spatial and temporal variability in oxygen is even higher than in lakes (Cole and Hannan, 1990). We now discuss the trophic level of lakes and reservoirs. Lakes are generally classified according to their potential primary production, based on nutrient supply: 1. Oligotrophic water bodies have a low chlorophyll concentration (o4 mg m3), which is based upon a low nutrient supply (up to 2.5 mg m3 P). The water is very clear, usually with a low Ca concentration and is circum-neutral to low acidic. The bedrock is mostly siliceous. The water body is deep and exhibits only a low O2 hypolimnetic deficit. The light penetration depth often exceeds 20 m. 2. Mesotrophic lakes are, according to climatic conditions and the geological substrate, the most frequent type of non-shallow water bodies in Eurasia and Northern America. They are clear, have a P content o12 mg m3, and a light penetration depth of approximately 8 m. If their bedrock is poor in Ca, the pH is slightly o7.0. There are also mesotrophic lakes with higher alkalinity levels, that are well buffered against acidification. The submerged vegetation in which Characeae often dominate, may be covered by CaCO3 crusts. This is often the case if inflows from groundwater are relevant. The planktonic chlorophyll a concentration is about 4–7 mg m3 and the Secchi transparency is more than 2 m. The oxygen deficit in the hypolimnion at the end of the summer stagnation is still

Lakes and Reservoirs

Climate

Cold

Type of overturn

Geographical position

Amictic

Arctic Antarctic

Monomictic

Arctic, High mountains

Polymictic

Tropical High mountains

Pleomictic Temperate

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dimictic

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Autumn

Winter

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197

O2

O2

O2

O2

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O2

O2

Humid tropics

Ice O2 O2 Overturn by wind

Convection

≥25

20–25 15–20 10–15 4–10 0–4 °C

Oxygenation by overturn

Figure 44 Influence of different geographic locations on the thermal stratification and overturn periods in lakes. From Uhlmann D and Horn W (2001) Hydrobiologie der Binnengewa¨sser. Ein Grundriss fu¨r Ingenieure und Naturwissenschaftler. Stuttgart: Eugen Ulmer.

modest. If mesotrophic lakes are poor in Ca, they are sensitive to an increased P load, that is, to eutrophication. 3. Eutrophic water bodies are mostly rich in Ca and subject to a P concentration of o30 mg m3. They have a Chl a concentration of up to 1 mg l1 and high phytoplankton production. Light penetration depth is often not more than 2 m. Although oxygen concentration in the epilimnion may achieve supersaturation, it may go down to zero in the hypolimnion at the end of the summer stagnation. Polytrophic/hypertrophic water bodies may have a total P concentration of more than 100 mg m3. They are characterized by long-lasting phytoplankton growth in the warm seasons, which is often caused by Cyanobacteria. These may produce liver-toxic metabolites. The water bodies are subject to heavy variations in O2 concentration with supersaturation during the day and depletion during the night (Figure 36), or long-lasting depletion in the hypolimnion, due to the high phytoplankton

concentration. The quality status of such water bodies in temperate climates is exclusively man made (mostly by the introduction of untreated domestic effluent).

2.08.2 Fundamental Properties of Reservoirs 2.08.2.1 Functions of Reservoirs A reservoir is an artificial lake in which water is impounded. The storage in a reservoir increases the availability of water for various purposes. Reservoirs also provide the most effective control of floods, if they are properly designed (enough retention capacity) and operated. The degree of regulation expresses the proportion of the mean annual discharge of a river, which can be stored in a reservoir. Most reservoirs have a degree of regulation below 100%, but Lake Volta, Ghana, holds the world record of 428%. This implies that more than 4

198

Lakes and Reservoirs

years of average discharge could be stored in the reservoir without releasing any water downstream (Nilsson, 2009). Reservoirs are usually constructed in areas in which the groundwater resources are not sufficient to satisfy the demand for drinking or irrigation water, or where the discharge of a river and the morphology of the site favor the installation of a man-made lake to supply power. Reservoirs increase the hydraulic head. They provide 19% of the world’s total electricity supply. Often, the sites of reservoir construction are in areas where natural lakes seldom occur. The most common purpose of reservoirs is for irrigation. There are nearly 50 000 dams in the world with heights above 15 m, which store more than 15% of the global runoff (Nilsson, 2009). There are innumerable smaller dams. Reservoirs were already built about 5000 years ago in China, Mesopotamia, and Egypt. In view of the anticipated global warming, reservoirs are expected to become even more important in the future for water management during dry periods. In this context, even the storage and final purification of (preferably treated) domestic effluent in reservoirs, for the irrigation of crops, become increasingly important in dry climates, in an anticipated dramatic depletion of groundwater resources. In this way, not only will wastewater effluent be reused, but in addition, nitrogen and phosphorus compounds, which otherwise generate excessive growths of photosynthetically active organisms in water bodies (eutrophication), may serve as fertilizers for crop production and contribute to an increased production of food. With the construction of a reservoir, a single, high-priority use is usually crucial, but most reservoirs are subject to multiple uses. It is impossible to operate each function at its optimum level. Some of the uses are conflicting, such as hydropower and irrigation, or drinking-water supply and commercial fisheries. Secondary uses of drinking-water reservoirs in central Europe are for flood protection, power generation, and recreation (which is not necessarily water bound). In dry climates, the supply of irrigation water is often the principal function of reservoirs. For example, the agricultural productivity of Egypt doubled after the completion of the Aswan Dam (Lake Nasser/Nubia). Fisheries in reservoirs are very important as they provide fish, which are a source of protein in warm regions. However, the drawdown of the water level may lead to the temporary loss of important habitats for propagation and shelter of certain fish species (unlike the situation in natural lakes). Drawdown also has an adverse effect on water-based recreation and the development of macrophytes. In temperate and in warm, semi-humid climates, the primary function of many of the large dams is power generation. For example, more than 70% of the energy production in Brazil is provided by dams (Tundisi and Strasˇkraba, 1999). Due to the construction of such dams, the residents are provided with a steady source of electricity. Many hydroelectric power stations in Europe and North America generate peak current, or compensate for variations in power consumption by means of pumped-storage systems. Most reservoirs, including many drinking-water reservoirs, have a considerable flood-retention capacity. Nearly all

reservoirs operate as hydraulic buffer systems and thus bring about, not only equalization of the flow, but also of the concentrations of dissolved materials, for example, of nitrate, in the case of water supply. Other water uses, such as power generation and navigation, are also dependent on a more equalized flow. For several reservoirs, low-flow augmentation of rivers is a principal function. For this reason, maximum water-level fluctuations of 125 m and 140 m, respectively, are legislated for two reservoirs in Norway (Nilsson, 2009). For the different uses of reservoirs, there are different standards required with regard to water quality. The highest demands have to be met in the case of a drinking-water supply. Many reservoirs are situated in attractive, scenic, hilly areas and may thus become appealing for tourism and recreational uses such as sport fishing, bathing, diving, rowing, sailing, surfing, hiking, rafting, excursions on motorboats, and camping. Several reservoirs in Northwest Germany have an occupancy rate of four or more sailing boats and surfers per hectare (LAWA, 2001). With regard to drinking-water supply, there are different views on the clearing of vegetation and soil beneath an envisaged reservoir. In several countries, clearing is ordered to remove sources of low redox potential, which might induce the release of dissolved manganese and iron from the sediment. In subtropical and tropical conditions, hypolimnion temperatures may exceed 15 or even 20 1C. At such a high level, microbial degradation processes are accelerated, which facilitate not only oxygen depletion, but also microbial sulfate reduction, with the accumulation of hydrogen sulfide in the hypolimnion. For this very reason, in the Brokopondo reservoir, Suriname, turbines were damaged by the microbial generation of sulfuric acid, when atmospheric oxygen entered the turbines. In lakes having a high priority for fisheries, oxygen depletion and H2S generation are unwanted, but the losses of dissolved nitrogen and phosphorus compounds by the hypolimnetic outflow are likewise undesirable. These substances are mainly released by the microbial breakdown of vegetation at the bottom of the water body and by leaching from the soil. This so-called ‘trophic upsurge’ is absolutely undesirable in drinking-water reservoirs, but in the case of reservoirs such as Lake Kariba (Zambia), fishermen were disappointed because the plankton and fish productivity stabilized at a very low (oligotrophic) level. A reservoir normally operates as a biochemical reactor. In the longitudinal direction, the concentration of microbially degradable substances, introduced by the inflow, is reduced. The same applies to the reduction in the number of hygienically relevant microorganisms. In the high-temperature range, a water residence time of at least 20 days is considered to be appropriate in reducing the number of potentially pathogenic microorganisms by 99% (Thornton et al., 1996). In this regard, the residence time of the microbial cells in the surface layer exposed to a high intensity of UV radiation, and in some cases, grazing by zooplankton, seem to be among the most relevant mechanisms for pathogen elimination. In former decades, reservoirs were used for the biological polishing of wastewater effluent, often in Europe and North

Lakes and Reservoirs

Figure 45 Retention of highly turbid inflowing water (from right) after a summer flood, by a submerged flexible curtain in the Saidenbach Reservoir (Germany). Courtesy of LTV, Saxony.

America. Nowadays, many reservoirs are still used as advanced wastewater-treatment plants in nearly all the other areas of the world (with exceptions such as Australia and Japan). Worldwide, reservoirs are used for the storage and quality amelioration of river water, as a source of raw water for drinking-water supply. This applies to, among others, the densely populated areas around the lower courses of large rivers such as the Rhine and the Meuse. When river water with a very high content of dissolved phosphorus and nitrogen compounds is impounded, phytoplankton growth may become excessive; therefore, water quality in terms of particulate and dissolved organic carbon may substantially deteriorate. Thus, costly measures may become necessary to control phytoplankton growth. The situation is even worse in warm climates, where equipment for nutrient elimination from wastewater effluent is often lacking and where the growth of cyanobacteria is favored, which produces not only unsightly water blooms, but also hepatotoxic metabolites. With further progress in wastewater treatment, reclaimed domestic effluent may even become one of the sources of raw water for drinking-water reservoirs in semiarid climates. There are also reservoirs that serve to improve navigation on rivers. One of the unintended functions of multipurpose reservoirs is the retention of sediments. Sedimentation reduces the lifetime of the water body, sometimes to an intolerable level. An effective countermeasure, in addition to sediment-bypassing and sediment-flushing, is the construction of pre-reservoirs (Paul and Pu¨tz, 2008), which may more easily be mechanically de-silted than the bottom of the main dam. The application of a floating underflow baffle, for the retention of turbidity currents near the surface, is a low-cost solution when compared with a concrete structure. Such a plastic curtain may, however, be operated in combination with an already-existing underwater wall (Paul et al., 1998) (Figure 45).

2.08.2.2 Characteristic Differences between Natural Lakes and Reservoirs Reservoirs are frequently regarded as man-made lakes. However, there are remarkable morphological and hydraulic

199

differences between natural lakes and reservoirs that are primarily related to their origin, use, and management practices. Reservoirs are generally not older than 100 years and are created by damming a river, while natural lakes are usually much older and were formed by natural geologic processes. The ratio between length and width, the shoreline length and the shoreline development (see Section 2.08.1.3) of dendritic river-type reservoirs, as a rule, is much larger than that of lakes with comparable surface areas. The retention time of reservoirs depends on their main usage. Although almost all reservoirs today are multifunctional, those that are primarily used for hydropower generation and/or flood control have mostly short residence times of a few days, up to some weeks. Drinking-water dams largely exhibit theoretical retention times of about 1 yr. The flushing of natural lakes, however, is much lower (retention times of many years, frequently even of several decades). The consequence of the larger shoreline development and the much shorter theoretical retention time of reservoirs is their stronger coupling to the drainage basins. Thus, reservoirs respond faster and are more sensitive to changes in the tributary water quality, caused by alterations of the catchment usage. Extended droughts or floods have stronger and more immediate impacts on reservoirs. The quality of the sediment is also affected. While in lakes the sediments are primarily of an autochthonous (internally produced) organic nature, the distribution of allochthonous (imported) mineral material is higher in reservoirs. Siltation as a consequence of erosion and, hence, faster aging, is a serious problem with reservoirs in catchment areas with small areas of forested regions. The import of suspended particles and nutrients in reservoirs can be reduced by pre-impoundments, located upstream of the tributaries’ mouths (Paul, 2003; DWA, 2005; Paul and Pu¨tz, 2008). As a result of aging (concentration of sediments in the deepest parts of a lake basin), the majority of natural lakes exhibit U-shaped basins, unlike the comparatively young, predominantly V-shaped reservoirs in mountainous regions. The deepest areas of lakes are more or less in their centers, while those of reservoirs are, normally, not far from the dam walls (Figure 46; Strasˇkraba and Gnauck, 1983). Hence, reservoirs are characterized by longitudinally differing morphometric structures:

• • •

strongly flushed riverine, shallow and narrow regions below the tributaries’ mouths; transitional region of moderate depth; and lesser drained lacustrine and deep basin near the dam.

This hydro-morphological structuring and the high flushing of reservoirs cause considerably stronger longitudinal differences in the physical, chemical, and biological characteristics of the water quality (Figure 47; UNEP, 2000) than in natural lakes, which are predominantly vertically structured (Figure 33). The shape of comparable shallow-bounded reservoirs situated in the lowlands and filled with water pumped from an adjacent river is more like a natural lake. Thienemann (1913) emphasized that the principal difference between a man-made reservoir and a natural lake lies in the flow-through conditions. While a normal lake exhibits

200

Lakes and Reservoirs

th

ng

Le

l

Wi

Width b

dth

l

b

z max

z max

th

ng

Le

Figure 46 Diagrammatic comparison of principal basin-shape characteristics of reservoirs (left) and lakes (right). Modified from Strasˇkraba M and Gnauck A (1983) Aquatische O¨kosysteme – Modellierung und Simulation, 279pp. Fischer Jena.

Riverine zone

Transitional zone

Lacustrine zone Width, depth

Flow rate Suspended solids, turbidity Underwater light availability Advective nutrient supply Internal nutrient recycling Light limitation of primary production Nutrient limitation of primary production Cell losses by sedimentation Cell losses by grazing Allochthonous organic matter Autochthonous organic matter Degree of eutrophy Figure 47 Diagram of the longitudinal zonation of the hydro-morphology and water quality in large reservoirs. From Kimmel and Groeger modified from UNEP (2000) Lakes and reservoirs – similarities, differences and importance. IETC Short Report 1 http://www.unep.or.jp/ietc/Publications/ Short_Series/LakeReservoirs-1 (accessed April 2010).

surface outflow, most of the water of a reservoir is withdrawn from deep-water layers and overflow seldom occurs. Consequently, Thienemann characterized three main points that distinguish lakes from reservoirs: 1. The absence of a shallow shore-bank in reservoirs, usually formed by long-term wave action at a more or less constant water level in natural lakes.

2. The fast growth of vegetation on dry shores during periods of water level drawdown. Thienemann considers the emerging vegetation as positive for the productivity of reservoirs. The organic substances produced during dry periods can be utilized by zoobenthos when the shores are flooded again. 3. The influence of the deep-water release on the thermal structure of reservoirs. Thienemann describes the farreaching consequences of the accelerated downward displacement of

Lakes and Reservoirs

the metalimnion, resulting from the permanent withdrawal of cold hypolimnion water during the summer stratification. The main purpose of most reservoirs is the storage of water during seasons with high discharge and the release of water during dry periods. Thus, considerable seasonal ups and downs of the surface level are normal in reservoirs. A reduced fill level generally has potentially negative impacts on the water quality of reservoirs. All hydrographic parameters affecting the trophic state deteriorate: (1) average and maximum depth, retention time, and ratio between lake area A0 and catchment area, decrease; (2) ratio between epilimnion volume Vepi and hypolimnion volume Vhypo, and nutrient load per unit surface area, increase. The impact of extreme nutrient-, suspended matter, and microbial loads resulting from floods is much higher at reduced fill level, due to diminished dilution in the reservoir. During the drawdown of the water level, not only fine substrate but also coarse sediments are successively washed into deeper regions of the basin and the slopes of the shores remain steep. Therefore, extended belts of macrophytes and the rich fauna of the warmed, highly productive littoral regions of lakes are almost completely missing and the pelagicmatter turnover obtains higher importance in reservoirs. Recent investigations (Kahl et al., 2008) have shown the strong influence of water-level fluctuations on the reproduction of fish species spawning in the shallow littoral. With regard to drinking-water reservoirs, in particular, the resuspension of sediments during the drawdown of the water level, may cause substantial turbidity that complicates the raw water treatment in waterworks. Conversely, the terrestrial vegetation growing quickly on the dry shores reduces sediment resuspension when the fill level rises again. However, the flooded terrestrial plants have mostly negative effects on the water quality. Their microbial degradation may cause oxygen depletion, remobilization of nutrients, and the development of substances producing odor and flavor (Scharf, 2002). Furthermore, they represent an excellent habitat for planktivorous fish feeding on zooplankton. As with lakes, the development of the thermocline in reservoirs at the beginning of the summer, depends primarily on the morphological and meteorological conditions (above all, wind exposure) and occurs annually in a relatively narrow time span. At that time, a high level of filling of a reservoir guarantees the formation of a large hypolimnion volume with high oxygen content. The lower the ratio Vepi/Vhypo at the onset of the summer stagnation, the lower the risk of critical oxygen deficits in the deep-water layers, at the end of the summer. During the summer stratification, the thermal structure of the epilimnion, both in lakes and reservoirs, is primarily meteorologically determined. The depth propagation of the metalimnion in lakes is controlled by heat transport and mixing processes. In deep reservoirs with stable stratification, however, the increase of the ratio Vepi/Vhypo and the temperature distribution in the hypolimnion depends mainly on the balance of water inflow and outflow. The inlet temperatures are in the range of metalimnetic or even epilimnetic temperatures in summer. Thus, inflow from the inlets into the

201

hypolimnion can usually be excluded. Therefore, the downward movement of the threshold between meta- and hypolimnion, characterized in stagnant water bodies of the temperate zone by the depth of the 10 1C contour line can be estimated with sufficient accuracy only from the reduction of the hypolimnion volume caused by the deep-water release (Figure 48). The depth of a reservoir’s epilimnion (i.e., mixing depth zmix), increases much faster than in a natural lake, which in turn affects the summer phytoplankton species composition and abundance. The significance of the underwater light intensity as a potential growth-limiting factor may be higher in dams during the summer stagnation. Phytoplankton circulates vertically through the deeper epilimnion, as it receives lower average light intensity. The enlargement of zmix is also associated with resuspension and transport of sediments into the deeper parts of the dam, due to the higher turbulence in the epilimnion. The hypolimnetic water temperatures in summer and, thus, the temperatures of the entire water body at the beginning of the autumn circulation, are higher in reservoirs than in natural lakes of corresponding depth. If the quantity of water withdrawn from a reservoir’s hypolimnion during the summer exceeds its volume at the beginning of the summer stagnation, the autumn full circulation is initiated much earlier than in a lake of comparable size and depth (as was the case in the Saidenbach reservoir in 1987, see Figure 48). The hypolimnetic warming and the continuous increase of the ratio Vepi/ Vhypo are critical, because the higher specific loading of the smaller hypolimnion with suspended organic material settling down from the epilimnion, and the higher temperatures, accelerate the oxygen consumption in the bottom waters. Conversely, the temperature of the deep-water layers in winter is lower in reservoirs than in lakes. The facts described above seem to suggest that the release of water from the hypolimnion of reservoirs is generally negative. However, other factors should be considered. First, there is no alternative, because economic drinking-water production from reservoirs during the summer is only possible from the cold and comparatively clear hypolimnetic water. Second, partial renewal of the deepest water layers is highly recommendable. The export of oxygen-free water enriched with nutrients, dissolved manganese, and other substances, remobilized from the sediment, has an oligotrophication effect and cause a continuous oxygenation of the deep water, due to the downward displacement of water from above with higher oxygen concentrations (Figure 49). Therefore, this principle is utilized in the artificial deepwater withdrawal from lakes as a restoration measure (Section 2.08.3.3). Lakes and reservoirs influence the downstream river in quite different ways. Except in lakes of the arid or semiarid zones, the hydrological coupling between the tributaries and the downstream river is, although damped, intact. Reservoirs, however, noticeably isolate the upper river reaches from the lower ones. The seasonal discharge variation of the downstream river is totally different from that of the tributaries. The often-missing, or changed, temporal sequence of floods and droughts completely alters the processes of river-bed formation and ecological structuring below a reservoir. During summer, the effluent of a lake is warm and exhibits high

202

Lakes and Reservoirs

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45

Figure 48 Development of the water temperature (ranges given in 1C) of the Saidenbach Reservoir vs. depth and time (DOY – day of the year), over 2 years with different hypolimnetic raw water release (1987: B109 000 m3 d1, 2003: B62 500 m3 d1). 1987 was a wet year and 2003, an extremely dry year. The black dots mark the sampling dates and the surface-level development, white dots the course of the 10 1C contour line calculated from only the volume balance of the hypolimnion, and the double-arrows the duration of ice covering.

O2 (mg l–1)

Depth (m)

Depth (m)

O2 (mg l–1) 0 12 02.11.92 12 0 19.10.92 12.11.01 05.10.92 29.10.01 21.09.92 15.10.01 07.09.92 01.10.01 24.08.92 11.09.01 10.08.92 29.08.01 27.07.92 06.08.01 12.07.92 23.07.01 29.06.92 09.07.01 17.06.92 25.06.01 01.06.92 11.06.01 0 28.05.01 0

45 45

Figure 49 Influence of different raw water-intake levels on the development of the oxygen concentration above the bottom of the Saidenbach reservoir. The raw water was released from an intake at a depth of 30 m in 1992 and thus, anaerobic conditions resulted in the deep layers of the reservoir at the end of the summer stagnation (left). The oxygen concentration above the bottom remained higher than 5 mg l1 until the end of the summer stratification in 2001, when the raw water was withdrawn via the bottom outlet of the reservoir (right).

Lakes and Reservoirs

203

Spillway Flood control volume Dam Epilimnion Inflow Metalimnion Raw water intakes

Hypolimnion

Out 1

Out 2

Bottom outlets

Figure 50 Characteristic vertical segmentation of a drinking-water reservoir’s volume during the summer stagnation and assembly of outlet structures for the regulation of outflows. Out 1: raw water to the water-treatment plant, Out 2: discharge into the downstream river.

concentrations of oxygen, but is low in dissolved nutrients. Mineral turbidity is low as well, but the export of phytoplankton may be high. The water released from the hypolimnion of a dam, however, is cold, contains almost no particles, and may have low oxygen, but high dissolved nutrient, iron, and manganese concentrations. Even substances potentially harmful to organisms, such as nitrite, ammonia, and redox-sensitive dissolved heavy metals (e.g., arsenic) may be released. In winter, the outflow of a dam is warmer, compared with a lake. Thus, the structures of the biotic community beneath a lake or reservoir may be quite different. Finally and importantly, most reservoirs are impassable barriers for ascending organisms and may cause genetic degradation in upstream rivers. As mentioned earlier, the discharge management of reservoirs, including the proportioning of the amounts of water released through outlets in different depths at a certain time of the year, has a strong impact on the materials turnover and water quality in the reservoir. This is particularly important in drinking-water reservoirs that are also used for flood control (Figure 50). In general, raw water supply for drinking-water production is optimal if the reservoir is entirely filled and allowed to overflow in cases of strong inflows. Under these conditions, the largest possible hypolimnion volume is formed at the beginning of the summer stagnation and its decrease is low during summer. However, flood control works best if the flood-control storage is large and kept permanently empty, except in cases of disastrous, uncontrollable discharge, which is therefore in direct conflict with the drinking-water supply. Flood control implies that overflow is avoided and, consequently, the water inflow has to be released through outlets below the surface, after the water surface has reached the full supply level (lower boundary of the flood control storage). In many (especially older) dams, the raw water intakes cannot be used for discharge into the downstream river or their capacity is too small. Thus, there is no alternative for

the release of discharge beyond the demand for raw water through the bottom outlets (see Figure 50). Consequently, turbid, nutrient-enriched, warm inflowing water is stored in the reservoir and valuable clear, cold hypolimnetic water has to be released into the underlying stream. Hence, the risk of serious problems with raw water treatment on the basis of a too-small or prematurely depleted hypolimnion with all the consequences described above is high in rainy summers, especially in a reservoir with a theoretical retention time of much less than a year. It is, in a certain way, paradoxical that not dry, but wet summers, are especially critical for drinkingwater reservoirs.

2.08.2.3 Environmental Impacts of Reservoirs A reservoir imposes serious impacts on the natural environment. Even more adverse is the cumulative effect of multiple dams built along the length of a river. These impacts concern not only the downstream reaches of the flowing water, but also the sites which once were fluviatile or terrestrial, inclusive of the biota. Due to the construction of reservoirs, large areas of wetland, with a high biotic diversity, have been lost. In a temperate climate region, a storage reservoir may reverse the hydrological conditions during summer: early lowwater and a later flood. Dams impede the migration of river fish species into the upstream or the downstream reaches. From this ecological fragmentation (Nilsson, 2009) of the river continuum, many species have lost their propagation sites and have become extinct. In the Columbia River (Washington State), which has been blocked by a series of dams, migrating salmon populations, along its total length, have dropped by as much as 95%. Along the Yangtse River, many species of fish have become extinct, due to the construction of the Three Gorges Dam, which otherwise protects the lives of hundreds of

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thousands of people in the densely populated downstream plains of the river. To ensure public water supplies in semiarid climates, dams are, in many instances, the only alternative. In many cases, the economic benefits of dams are so high that all environmental impacts have been neglected. Turbines, which produce more than 10% of the electric energy in the USA, and even more in Brazil, cause very high mortality in river fish. There are many cases in which the environmental impacts of dam construction are higher than the economic benefits. For this reason, the operation licenses for several dams in North America have not been renewed. The number of people who have been forced to migrate because of the construction of dams is estimated to be 40–80 million (Nilsson, 2009). Among the most important impacts of reservoirs in warm climates is the excessive growth of submerged or floatingleaved vegetation. This, in turn, provides a favorable habitat for water snails, which host trematode worms, causing Schistosomiasis (bilharzia), a very serious disease of the inner organs (bladder and liver). The larval vectors may rapidly penetrate the human skin after a brief contact with the reservoir water. About 200 million people are affected worldwide. In sheltered bays, the floating-leaved vegetation hosts myriads of larvae of biting midges. Schistosomiasis and other waterborne diseases, such as malaria, substantially reduce the capability of a person to do physical work. Thus, the construction of reservoirs in warm climates has, in many cases, had dramatic socioeconomical impacts. One of many examples is the area surrounding the Volta reservoir, Ghana (Thornton et al., 1996). Intensive public discussion on the impacts of very large dams underpinned the foundation of the World Commission on Dams in 1997. This institution, mandated by the World Conservation Union and the World Bank, evaluates existing reservoirs, develops guidelines for the design, construction, and sustainable operation of dams, and even requests a potential decommissioning, if deemed necessary.

2.08.3 Management, Protection, and Rehabilitation of Lakes and Reservoirs 2.08.3.1 Main Water-Quality Problems Lakes and reservoirs are exposed to numerous natural and anthropogenic stress factors. The impact of human activities on the aquatic environment has increased during the past centuries, due to an exponentially increasing population and industrialization. Intensified agriculture, increasing industrial and sewage discharge, and the direct use of water resources, for example, for drinking-water supply, energy production, or fisheries, has resulted in the degradation of many aquatic ecosystems and a dramatic loss of biodiversity. Multiple pressures are caused mainly by eutrophication, acidification, salinization, and contamination by hazardous substances, which are all accompanied by human-induced climate change and by the invasion of neobiota (Hupfer and Kleeberg, 2007). Eutrophication is the most common problem in lakes and reservoirs. It is defined as the increasing intensity of primary production (trophy) due to enhanced availability and uptake

of nutrients. About 30–40% of lakes and reservoirs worldwide are affected by unnaturally high nutrient concentrations. The current changes in the trophic state are termed cultural eutrophication, which can clearly be separated from the natural eutrophication that occurs during the aging of a lake over thousands of years. Obvious indications of eutrophication are high turbidity caused by algal blooms, dense macrophyte growths, food-web changes, mass development of toxin-producing cyanobacteria (blue-green algae), reduced species diversity, oxygen depletion, enrichment of reductive substances (e.g., toxic hydrogen sulfide), fish kills, and odors. Consequently, eutrophication has a strong influence on anthropogenic water uses such as drinking-water supply, fisheries, and recreation. Eutrophication is one of the major global environmental problems, because of its importance in health and food production in densely populated areas (Smith, 2003). In most lakes and reservoirs, phosphorus (P) is the minimum factor controlling the trophic state. The P input originates from point sources (e.g., wastewater-treatment plants and industrial wastewater) or nonpoint sources (e.g., erosion, atmospheric deposition, surface runoff, and groundwater). Recent studies suggest that nitrogen (N) is apparently of greater importance as a limiting nutrient, than previously assumed (Sterner, 2008). Acidification appears, after eutrophication, as the most important threat and stress factor for lakes and reservoirs. Acidification is the additional input of acids into water, decreasing the pH value and usually eliminating the carbonate buffer system. The decrease in pH may cause an extreme loss of biodiversity, as many invertebrates, fish, and other vertebrates cannot survive or reproduce in acidic environments. Biota is also influenced by indirect consequences of acidification, such as the increased release of potentially toxic metal ions (in particular aluminum, copper, cadmium, zinc, and lead) from soils and sediments. Acid deposition has changed the natural water chemistry and, thus, the biological structure in 50 000–100 000 lakes and watercourses in Europe and North America. This adverse situation prohibits the use of the impacted water for irrigation, fishery, and aquaculture as well as its use as drinking water. Many lakes and reservoirs are atmospherically acidified due to emissions of sulfur and nitrogen oxides mainly from the burning of fossil fuels or from agriculture, as well as from natural sources (e.g., volcanic eruptions and emissions from soils and wetlands due to oxidation of sulfur-containing minerals). Declining groundwater levels, enhanced nitrate concentrations in the groundwater, the artificial drainage of wetlands, or long-lasting droughts in soils due to global warming and mining activities can lead to the oxidation of reduced sulfur minerals (pyrite and marcasite), whereby the acid input into surface waters is geogenically increased (see Section 2.08.1.6). Salinization also leads to marked changes in biotic communities, since freshwater organisms usually have only a limited tolerance for enhanced Cl concentrations. Salinization is caused by changes in the hydrological regime, which may be due to enhanced evaporation or discharge of salt-rich water from mining, oil production, and agriculture (irrigation). Hazardous substances comprise a variety of organic and inorganic substances, such as toxic metals, pesticides, organic surfactants, pharmaceuticals, and mineral oils. Metals and organic compounds, for example, polychlorinated

Lakes and Reservoirs

biphenyls (PCBs), are accumulated in sediments or in food chains and can build up toxic concentrations. Finally, aquatic ecosystems are very vulnerable to climate change. Climate influences the physical, chemical, and biological structure of temperate lakes and reservoirs due to fluctuating water levels, increase of water temperature, shorter ice-cover periods, invasion of nonindigenous animal and plant species from tropical and subtropical regions, and structural changes in catchment areas (e.g., soil erosion and land use), including boundaries between terrestrial and aquatic systems. Most scenarios show that the risk of eutrophication problems, such as critical oxygen conditions and mass development of toxic blue–green algae, will increase due to a warmer climate.

2.08.3.2 General Management Strategies The different kinds and causes of water-quality problems have resulted in the development of numerous strategies to restore the functioning of degraded aquatic ecosystems. Surface waters are closely connected to their terrestrial and atmospheric environment and therefore, preventive protection of lakes and reservoirs begins in the catchment areas. External measures are aimed at the reduction of pollutant sources in the catchment area as primary reason for water-quality problems. These inputs become restricted when the terrestrial matter cycles are mostly closed. These measures include the construction of sewage treatment plants for the purification of municipal and industrial wastewater, extensification measures in agriculture, the recycling of industrial waste, and the establishment of buffer systems such as ponds, pre-dams, or constructed wetlands. Critical load models are helpful for achieving qualitative aims of aquatic ecosystem management (e.g., Vollenweider, 1976). In this way, it is possible to assess a necessary limit of nutrients and harmful substances not only as concentrations at the location of emissions, but also with respect to their effects, depending on the characteristics of the lakes or reservoirs. This implies that potential control strategies may include the optimization of the structure and processes within the waters, so that negative symptoms of excessive nutrient loadings can be minimized (Benndorf, 2008). Ecological engineering, also called ecotechnology, involves several ecological approaches for optimizing the ecosystem structure or to support specific ecosystem functions in a way such that the management targets are being maximally supported. Integrative concepts of lake and reservoir management consider the control of multiple external factors as well as internal measures. As eutrophication is still the major water-quality problem with far-reaching ecological and economical consequences, it is not surprising that a broad spectrum of lake and reservoir rehabilitation/restoration methods has been developed to combat eutrophication and its symptoms. The following section describes management options in more detail using eutrophied lakes as an example.

2.08.3.3 Measures for Eutrophication Control Measures for eutrophication control are focused on decreasing phosphorus availability in water and minimizing the

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symptoms of eutrophication. Depending on the location, water managers can select both external and internal measures.

2.08.3.3.1 External measures The reduction of excessive nutrient loading as the cause of eutrophication is the key to sustainable effects. Phosphorus load can be reduced by (1) decreasing P emissions, (2) increasing the P-retention capacity in the catchment, or (3) purification of inlet water immediately before it enters the lake. Decreasing P emissions includes measures such as building treatment plants for municipal and industrial effluent, extensifying agricultural land use (e.g., reducing fertilizers and lowering stocking densities), and using phosphate-free detergents. Diversion of wastewater outside the watershed has been often used when the reduction of nutrients in wastewater-treatment systems is not sufficient. Alternatively, the nutrient reduction can be achieved by so-called ring canalization, collecting sewage and storm water for treatment in a central plant downstream of the protected lake. Thereby, the storm water with its high P load is kept away from the lake in the case of combined sewage systems. Land-management procedures, generally known as 0 best management practice’, are the primary methods for protecting surface waters from nonpoint sources. Increasing P-retention capacity in the landscape is a strategy that takes advantage of the ability of structural landscape elements to retain P by reestablishing effective former sinks (e.g., re-wetting of fens) or by constructing equivalent systems (constructed wetlands). Purification of inlet water includes buffer systems between highly productive agricultural areas and the water, such as pre-dams, macrophyte belts, and ponds. A P-elimination plant (PEP) at the main inflow of a lake is the costlier technical alternative. Despite external load reduction, lakes and reservoirs have often not shown the expected improvement of water quality in an acceptable time. The resistance is explained by the following main delay mechanisms (Hupfer and Hilt, 2008): 1. Long hydraulic water-retention time prevents a fast response to decreased P loading. 2. The biological response of a lake to changes in the nutrient level is nonlinear. One example is the top-down control of phytoplankton growth by fish. High abundances of planktivorous fish established during the eutrophic period prevent both the appearance of large herbivorous zooplankton and thus a higher grazing pressure on phytoplankton (Figure 51; Hupfer and Hilt, 2008). A second example is the occurrence of two alternative stable states in shallow lakes (Scheffer et al., 1993). As a consequence of top-down and bottom-up processes, the threshold P concentration for a shift from clear (macrophyte-dominated) to turbid (plankton-dominated) state (eutrophication) is higher than the P concentration necessary to shift the system backward from turbid to clear-water stage (re-oligotrophication). 3. The P concentrations remain high due to release of P from the pool accumulated in the sediment during high external P loading. Examples in the literature show that this process can continue over a time span of more than 10 years before the surplus P pool is released or permanently buried

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Biomanipulation

Piscivorous fish High biomass

Low biomass

Zooplanktivorous fish Low biomass

High biomass

Zooplankton High biomass large species

Low biomass small species Algae

Low biomass large species

High biomass small species

Figure 51 Schematic view of top-down control of phytoplankton abundance in eutrophic lake. Effect of manipulation of zooplanktivorous fish biomass (left), compared with an unmanipulated food web. From Hupfer M and Hilt S (2008) Lake restoration. In: Jorgensen SE and Fath BD (eds.) Encyclopedia of Ecology, pp. 2080–2093. Oxford: Elsevier.

(Søndergaard et al., 2005). If the problem of eutrophication is not solved by reducing external nutrient input alone, additional internal measures can shorten the adaptation time to reach the desired water quality. This can support the regime shift according to management targets. Additionally, some internal measures are able to compensate for a too-high residual external loading.

2.08.3.3.2 Internal measures Physical, chemical, and biological measures aim at nutrient control or at changes of biological structure to reinforce the recovery. Table 3 summarizes the main internal methods for controlling excessive phytoplankton growth due to eutrophication, as well as for reducing other undesired symptoms of a high trophic state. The lake P concentration can be influenced by increasing P export and by increased P retention in the sediment. The addition of chemicals is aimed at supplying new sorption sites for phosphate, leading to P removal from the water and a subsequent sedimentation to the bottom. High doses of chemicals not only remove P from the water, but also increase the P-binding capacity in the sediment, so that P release from sediments is decreased for a longer period. For inactivation, salts of aluminum, iron, and calcium have been widely used. A newly developed product is Phoslocks – a bentonite (95%)

artificially enriched with lanthanum (5%). Depending on the lake size and chemicals, the treatment is realized by piping, by distribution on the ice cover, by airplane or boats, and by aeration devices. In-lake P inactivation was successfully performed in many stratified and nonstratified lakes in North America and Europe, but only short-term effects were observed in cases of continued external loading that quickly replaced the eliminated P. Hypolimnetic withdrawal increases the P export, since nutrient-rich hypolimnetic water instead of P-poor epilimnetic water is removed from the lake or reservoir. Coincidentally, the retention time in the hypolimnion is shortened and the risk for enrichment of reduced substances is reduced. Typically, a withdrawal pipe is installed near the deepest point of the lake with an outlet below the water level so that it acts as a siphon. In reservoirs, hypolimnetic withdrawal can be achieved with a variable deep-water outlet in the dam. To prevent hydrological imbalances, the hypolimnetic withdrawal of P-rich water can be combined with an external Pelimination plant or constructed wetland with returning of the water to the lake or reservoir after treatment. The P export is also increased by flushing of the lake water with water low in nutrients, or by destratification (increase of P at water surface and in outflow). Permanent or intermittent destratification destroys or prevents density gradients within the water column, thus

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Table 3 Physical, chemical, and biological measures for decreasing the primary production and for abatement of negative symptoms of eutrophication in lakes and reservoirs Principle

Main control variable

Further effects

In-lake P inactivation by metal hydroxides, addition to water surface or into the hypolimnion

PLakek

Mobile sedimentary Pk

Phoslocks

Addition of lanthanum containing bentonite

PLakek

Mobile sedimentary Pk

Nitrate addition

Application of nitrate into the deep water or sediment

Redox potentialm, stabilizing of iron-bound phosphorus at the sediment surface

Mineralization of organic matterm Reduced substancesk P releasek

External P elimination

Treatment of P-rich (hypolimnetic) water by Al or Fe salts outside the lake

PLakek

Hypolimnetic oxygenm Reduced substancesk

Aeration/oxygenation

Introducing of air or molecular oxygen into the deep water

Hypolimnetic oxygenm

Reduced substancesk P releasek

Hypolimnetic withdrawal

Natural discharge of P-poor surface waters is (partly) replaced by P-rich deep water

P exportm

Hypolimnetic oxygenm Reduced substancesk

Destratification

Temporary or intermittent increasing of mixing layer by aerators, jet-stream pumps, or by inducing a thermal convection flux

Light for algae growthk

P exportm

Sediment dredging

Partial or complete removal of surface sediments

Water depthm

Reductive potentialk P releasek Toxic substancesk

Dilution

Transfer of P-poor water from outside the catchment

Water residence timek

P exportm

Food-web manipulation

Increasing of grazing pressure on phytoplankton by (1) removal of zooplanktivorous and benthivorous fish or (2) protection or stocking of piscivorous fishes

Algae biomassk

PLakek

Macrophyte removal

Mechanical harvesting methods, water level drawdown, sediment covers, and surface shading

Macrophytesk

Phytoplanktonm Prevention of siltation

Macrophyte transplantation

Planting or seeding of submerged plants

Macrophytesm

Zooplanktonm Allelopathic depression of phytoplankton Sedimentationm Sediment resuspensionm

Chemical methods Al-/Fe-salts

Physical methods

Zooplanktonm CO2 inhibition of blue–green algae

Biological methods

increasing the oxygen supply of deeper water layers. Increasing of mixing layers can lead to light-limitation of algae growth. Additionally, cyanobacteria are outcompeted by green algae or diatoms when the carbon dioxide concentration in the euphotic zone is increased (Shapiro, 1984). The destratification is often realized by introduction of compressed air, jetstream pumps, or by inducing a convection flux by introducing warmer surface water into the hypolimnion.

Aeration and oxygenation (introduction of oxygen as air or liquid oxygen) are applied to prevent acute oxygen depletion. Thus, the enrichment of dissolved iron, manganese, ammonium, hydrogen sulfide, and free carbonic acid in the hypolimnetic water is prevented, and internal P loading may be reduced. Additionally, oxygen availability improves the conditions for cold-water fish and invertebrates in the hypolimnion. Analogously to molecular oxygen, nitrate is used for

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in situ oxidation of reduced substances and of biodegradable organic matter. The advantage of nitrate instead of oxygen is that much higher oxidation equivalents can be added, because nitrate is more soluble than molecular oxygen. Thus, nitrate penetrates deeper into the sediment than oxygen. On the other hand, oxygen is introduced from the atmosphere during mixing, whereas nitrate must be applied repeatedly. Nitrate is added as solution of calcium nitrate directly into the deep water or by injection into the sediment. In some cases, nitrate is added in granular form. Nitrate and oxygen enhance P retention in sediments only under special geochemical circumstances (mobility or surplus of iron). In many cases, oxidation has not shown expected results (Liboriussen et al., 2009). The dredging of sediments is the partial or complete removal of sediment layers rich in nutrients and organic matter. Dredging aims at (1) deepening of lakes, (2) elimination of toxic substances, and (3) reducing the P release rate. Additionally, dredging serves to sustain several technical functions of lakes and reservoirs (pre-dams, flood protection, and shipping lanes). In some cases, dredging did not successfully lower eutrophication by P control because the mobile P pool (temporary P) in the sediment was small, or the internal P cycle was controlled by the newly settled P (Annadotter et al., 1999). Similar reasons can minimize the effects of sediment capping for eutrophication control (Hupfer et al., 2000). During this measure, an artificial barrier is inserted between sediment and water. The barrier minimizes the transport of nutrients and other harmful substances from the sediment (or groundwater) into the lake. The material (e.g., foil of polyethylene, clay, calcite, sand, and zeolites) for capping can act as physical or chemical barriers. Another use of sediment capping is the prevention of excessive growth of rooted macrophytes. Biomanipulation influences the biological structure within a lake/reservoir to improve the water quality. The main applications for lake restoration include (1) food-web manipulation and (2) macrophyte biomass control. Food-web manipulations in lakes are man-made alterations of the lake biota and their habitats to facilitate reduction of algal (phytoplankton) biomass. In most cases, food-web manipulation refers to the reduction of zooplanktivorous fish-biomass that leads to a reduction of phytoplankton (Figure 51) and clearer water. Food webs are regulated either by resources (bottom-up) or by predation (top-down). A reduction of the biomass of zooplanktivorous and benthivorous fish can be achieved by stocking with piscivorous fish, such as pike (Esox lucius L.), pike-perch (Sander lucioperca L.), or perch (Perca fluviatilis L.). A direct reduction can also be achieved by poisoning, removal by conventional fishery techniques, or a temporary drainage of the lake. Stocking of piscivorous fish has often been less successful than fish removal. A marked reduction of the biomass of zooplanktivorous fish such as roach (Rutilus rutilus L.) is often followed by an increase in the abundance and size of zooplankton (predominantly Daphnia species). This increases the grazing pressure on phytoplankton and potentially leads to the top-down control of phytoplankton biomass, in which case, the water becomes clear and extreme values of oxygen and pH are avoided. The success of food-web manipulation may also be triggered by bottom-up forces. Benthivorous fish, such as bream (Abramis brama L.) or

common carp (Cyprinus carpio L.), exert bottom-up effects on water quality as they increase sediment resuspension, water turbidity, and internal nutrient loading. The removal of benthivorous fish may therefore also strongly determine the success of a food-web manipulation. Top-down control of phytoplankton biomass was found to occur in shallow lakes and in deep lakes of slightly eutrophic or mesotrophic state. It is unlikely to be effective in eutrophic or hypertrophic deep lakes. The substantially higher success rates of food-web manipulations in shallow lakes can be attributed to positive feedback mechanisms, triggered by the recovery of submerged macrophytes. Experience has shown that lake water quality can only be improved by food-web manipulation if annual loading is lower than 0.5–1.0 g of total P per square meter of lake surface (see Benndorf, 2008), or if the in-lake P concentration is lower than 50 mg l1 in shallow lakes (see Jeppesen et al., 2009). Macrophyte biomass control includes measures to restore aquatic plant communities in order to take advantage of the beneficial aspects of plants in lakes, as well as measures to control excessive growth that results in conflict with certain lake uses, or to eradicate nonindigenous species. Submerged macrophytes are of crucial importance in shallow lakes, due to the vegetation-turbidity feedback. They stabilize the clear, vegetation-dominated state due to the reduction of nutrient and light availability to phytoplankton, enhancement of topdown control of algae by providing refuge for zooplankton, suppression of phytoplankton by the excretion of allelopathic substances, facilitation of phytoplankton sedimentation, and prevention of sediment resuspension. In shallow lakes, the successful establishment of submerged macrophytes is therefore a prerequisite for the long-term success of other rehabilitation measures such as food-web manipulations. Submerged vegetation will develop naturally in most cases when light and sediment conditions in the lake are favorable, for example, after the application of other internal measures to reduce phytoplankton development. Artificial support by planting or seeding of submerged plants might be useful if viable propagules are lacking in the sediment and no macrophyte stands are present in the vicinity of the lake, if a rehabilitation method applied only decreased turbidity for a period too short for natural re-colonization, if submerged macrophytes are immediately needed for the successful development of introduced pike, or if the promotion of specific (e.g., low growing) macrophyte species in particular areas of the lake is required to enable recreational use (Hilt et al., 2006). Excessive macrophyte growth can be a result of eutrophication, or of increasing water transparency after the application of rehabilitation measures, as well as of invasion of neophytes. Measures against macrophyte growth are only needed when macrophytes hinder certain lake uses (e.g., recreation and navigation). Methods to control or eradicate aquatic macrophytes include water-level drawdown for a period sufficient to kill the plants and their reproductive structures, mechanical harvesting methods, sediment covering and surface shading, aquatic herbicides, and biological controls, such as phytophagous insects, plant-feeding fish, and plant pathogens. In shallow lakes, macrophyte-control measures should be applied with caution, due to the risk of a return to the turbid, phytoplankton-dominated state.

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Planting

Biomanipulation

Resuspension (–)

Licht (+)

Submersed macrophytes (+)

Zooplankton (+)

Algae (–)

Chemical inactivation P (–) Hypolimnetic withdrawal

Destratification

Figure 52 Positive feedback mechanisms (simplified) induced by different restoration strategies. Positive spiral extended from Hansson LA and Bro¨nmark C (2009) Biomanipulation of aquatic ecosystems. In: Likens G (ed.) Encyclopedia of Inland Waters, pp. 242–248. Elsevier.

Figure 52 (Hansson and Bro¨nmark, 2009) provides an overview of processes triggered by biomanipulation and other internal measures, which can lead to self-stabilization of the system. The positive spiral of reduced algal biomass, improved light conditions, macrophyte growth, and P lowering can be enforced by technical measures at several starting points. Experiences with long-term efficiency of internal measures to control eutrophication are summarized in several publications (e.g., Cooke et al., 2005; Søndergaard et al., 2007; Gulati et al., 2008).

2.08.4 Current Knowledge Gaps and Future Research Needs Worldwide, lakes and reservoirs are of tremendous importance for diverse human usage. One of the urgent global problems is the supply of freshwater of adequate quality, for use as drinking water and for food production. The demands and impacts on freshwater ecosystems not only influence the human population directly, but also increasingly threaten their natural environment, to varying spatial scales. For example, exported pollutants degrade coastal waters (Behrendt and Dannowski, 2005; Feistel et al., 2008) and modifications of freshwater ecosystems may even alter the carbon cycle on a global scale (Tranvik et al., 2009). Lakes and reservoirs are also impacted upon by far-off matter transport via atmosphere, surface water, and groundwater. Long-term changes in climate are expected to exacerbate usage problems and modify the linkages between terrestrial environment and freshwater systems, in a complex way. The impacts on freshwater ecosystems have drastically decreased their biodiversity (Dudgeon et al., 2006; Weijters et al., 2009), although the role of biodiversity for resilience and functioning of lakes and reservoirs and for adjacent systems is not well known. The complexity of mechanisms determining the response, cannot be adequately predicted with the existent understanding of processes and the available modeling tools. Quantification of changes and better

understanding of cause–effect relationships are necessary for new concepts of protection and adaptive management. The challenges for scientists in the field of lake and reservoir research are manifold and enormous. Therefore, the following sections are restricted to selected topics, which are under intense discussion.

2.08.4.1 Lakes and Reservoirs as Constituents of Their Catchment Areas Lakes and reservoirs are linked with terrestrial environments and connect different types of surface waters in complex landscapes. Surface waters are depressions in watersheds, integrating different influences from the surrounding area. The accumulation of organic matter and other substances in lakes and reservoirs can intensify the elemental cycles. Therefore, lakes and reservoirs can be considered as hot spots for biogeochemical processes, with disproportionately large effects on mass flows in the landscape. On a smaller scale, boundary layers, for example, interfaces between groundwater and surface water, the sediment and water interface, and interfaces between separate water bodies, are places with high turnover rates. Ecological boundaries are characterized by steep gradients, which stimulate intensive reactions that determine their sink or source function (Cadenasso et al., 2003). The biogeochemical reaction rates and their temporal dynamics are influenced mainly by the hydrological regimes and transport processes. The understanding of these processes is essential to estimate regional or global budgets of nutrients, carbon, and greenhouse gases namely CO2, CH4, and N2O (Tranvik et al., 2009). The emission of these greenhouse gases is considered the most important impact of global warming. It is not yet possible to rate the contribution of lakes and reservoirs to the global greenhouse gas balance. It is assumed that particularly CH4 and possibly N2O emissions from inland waters, primarily in the tropics, might have a significant impact. Furthermore, increasing temperatures may stimulate greenhousegas production, not only in wetlands, but also in lakes and

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reservoirs. The greenhouse-gas production could also be fostered by an enhanced input of carbon. Observations of increasing concentrations of dissolved humic acids have recently been reported from many regions worldwide. The reasons for this phenomenon are not yet definitely established. It is probably a consequence of an intensified microbial turnover of organic soil substances, due to higher temperatures and increasing soil pH, resulting from rapidly declining anthropogenic acidification in large regions in Europe and North America. From investigations into the drainage basins of drinking-water reservoirs of the German federal state, Saxony, it was concluded that the re-wetting of formerly drained, swampy, coniferous forest sites (revitalization of fens) may have considerably enforced the leaching of dissolved humic substances and caused higher costs for water treatment (Sudbrack et al., 2005). On the other hand, the re-wetting of fens as management options to use the natural P retention in the catchment, is of great importance to decrease diffusive P loading into surface waters. Research in the following decades should be aimed at (1) the determination of precise rates of carbon and nutrient cycling and storage in lakes and reservoirs, and to quantify the regional and global budget of nutrients, carbon, and greenhouse gases and (2) the quantification of substance fluxes in the watershed to the lake/reservoir due to land-use changes and climate variations. These approaches need (1) a better implementation of single studies to a global level, (2) interdisciplinary science and effective collaboration among aquatic and terrestrial scientists, (3) more process studies concerning the interplay between physical, biological, and chemical processes in highly reactive boundary layers, and (4) the development of models integrating landscape, climate change, and aquatic systems stimulating the generation of hypotheses and new empirical studies.

2.08.4.2 Responses of Lakes and Reservoirs to Climate Change Worldwide temperature increases of 1–5 K within the next 50, years caused by climate change, have been predicted (IPCC, 2007). Responses of lakes and reservoirs to climate change are estimated to include (1) warming of waters with impacts on duration of ice covering, on timing and intensity of mixing and stratification, on the gas exchange between atmosphere and water as well as the solubility of gases in water (e.g., oxygen saturation), and on heat and matter exchange at the sediment–water interface; (2) alterations of flow through quantity and dynamics, import of suspended matter and dissolved substances such as nutrients and dissolved organic matter and water-residence time; (3) water-level fluctuations with implications on sediment transport, resuspension, and accumulation, matter exchange between sediment and water, hypolimnetic oxygen depletion, gas-exchange rate, and drying and re-wetting of littoral areas; and (4) changes of irradiation and underwater light intensity and possibly increasing effects of UV radiation on organisms (climate-driven effects; see e.g., Blenckner et al., 2002; Keller et al., 2008; Wantzen et al., 2008; Boulding et al. 2008; Livingstone and Adrian, 2009; Vincent, 2009).

However, the effects of global-change phenomena on a specific water body may differ considerably, depending on geographic location (latitude and altitude) and the morphology of surface waters, and can also be masked by many other impacts. The local effects of global change are difficult to predict and remain largely uncertain. Little is currently known about the changes in the amount and timing of seasonal runoff, derived from rainfall and snowmelt and the frequency of wind-induced disturbances on the vertical structure in the water bodies. Further research is needed (1) to improve understanding of the sensitivity of different types of lakes/reservoirs pertaining to changes of climate drivers, (2) on effects of climate change on biodiversity, and (3) on improvements of precise weather forecasts as precondition for reservoir management, the optimization of flood control and water-quality requirements, and the development of reservoir-safety plans and risk assessment. To fill these gaps, the following actions are necessary: (1) exploration of long-term data sets (e.g., long-time series of phytoplankton succession) and their integration into ecological sub-models for the water-quality management of standing waters; (2) combination of climate models with lake/ reservoir models; (3) merging of paleolimnological records with hydrological and hydrochemical modeling, with changes in land uses, and application of improved methods such as DNA techniques to recognize changes in biota and isotope methods; (4) improvement of models for hydrological forecasting, especially of discharge fluctuations in the tributaries; and (5) collaborative research among climatologists, hydrologists, and limnologists to understand critical transitional events.

2.08.4.3 Biodiversity and Its Role in the Functioning of Lake and Reservoir Ecosystems Stability and service functions of lakes and reservoirs are closely linked with the conservation of biological diversity. Although freshwater ecosystems cover only 0.8% of the Earth’s surface, it is estimated that more than 10% of all animals and 35% of all vertebrates live (at least during one growth stage) in inland waters. Related to area, inland waters have a 10-times higher biodiversity than other ecosystems. Therefore, inland waters are biodiversity hotspots in the landscape with huge importance for distribution and speciation. One explanation of this concentration of diversity is the high potential for isolation of inland waters. Compared to species in marine and terrestrial ecosystems, freshwater species had 4–6-times higher extinction rates in the recent past. Anthropogenic changes of biotopes (e.g., construction of dams, pollution, draining of wetlands, and climate change) and invasion of exotic species are the main causes of losses in biodiversity. The complexity of current changes is, however, not fully understood. Biodiversity plays a crucial role in the resilience (elasticity) and dynamics of aquatic ecosystems. The role of biodiversity and the role of single species for different ecological functions are the subject of an increasing number of theoretical and empirical studies. The species composition of bacteria and fungi is still largely unknown, although these microorganisms are responsible for the flow and cycling of elements in reservoirs and lakes. The identification of the aquatic microbial species, which in most

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cases are noncultivable, became possible in the last decade by the broad availability of molecular probes, which specifically bind to the DNA of taxonomic units and species. Thus, future research should be focused on (1) the monitoring and documentation of biodiversity, (2) the improved understanding of ecosystem functioning with respect to evolutionary processes, (3) the consequences of biodiversity on biogeochemical cycles, (4) the relationships between different aspects of biodiversity and anthropogenic disturbances, and (5) the effects of invading species on the structure, function, and achievements of the aquatic ecosystems. Further tasks include the development of a global database for freshwater systems, the quantification and assessment of biodiversity for ecosystem services, including the identification of keystone species, and the development of a preventive management of biodiversity.

2.08.4.4 Integrated Management of Lakes and Reservoirs Measures for protection and remediation of damaged aquatic ecosystems are mainly focused on the control of external loading of nutrients, toxic substances, organic matter, and other pollutants. Currently, the reduction of the external loading is oriented to prescriptive concentration limits for the ambient water or to the minimization of emissions according to the best available technology (e.g., the four purification steps in wastewater-treatment plants). Critical load concepts such as the Vollenweider model are helpful for achieving qualitative aims of lake and reservoir management (Vollenweider, 1976). These models enable the user to predict qualitative states depending on the characteristics of lakes and reservoirs. Consequently, the water quality is not only the result of external pollution, but also a function of the physical, chemical, and biological structures of lakes and reservoirs. This means that a potential control strategy may include changes of internal structures within the reservoirs/lakes. Optimized ecosystem structures may tolerate higher emissions and mitigate negative consequences of pollution, which means that integrated control strategies, including both remediation measures in the catchments (reduction of loading) and lake-internal measures (optimization of ecosystem structure), are most promising and effective (Benndorf, 2008). In the last few decades, numerous techniques for lake and reservoir management were developed to optimize the structure of lake/reservoir ecosystems and their catchments, to achieve specific targets. The application of ecological approaches is referred to as ecological engineering or ecotechnology mainly used to control eutrophication and acidification. Many case studies have demonstrated that internal measures are successful when they are combined with a sufficient external load reduction. On the other hand, actual effects of internal measures have often not achieved the expected results. Although limnologists and hydrologists have focused for a long time on the causes and consequences of eutrophication, it remains a burning problem (primarily in tropical regions) and requires further research. A great deal is known about the role of macronutrients, particularly those that are consumed in large quantities, especially carbon, phosphorus, and nitrogen, their sources, import and cycling, as well as their passage through the food web. Far less,

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however, is known about the impact of micronutrients, which are required in relatively small quantities, such as iron, manganese, some trace elements, and vitamins. Furthermore, the influence of the huge variety of xenobiotics (e.g., antibiotics, drogues, herbicides, pesticides, insecticides, and fungicides) and their degradation products on aquatic ecosystems and (upon re-entering the human food chain) on human health, is currently largely unknown. The imperative for an integrative management, which includes social, ecological, and economic aspects, can be illustrated through reservoir management: The drinking-water supply from reservoirs is of great and growing importance in many countries. It is of decisive social and economic interest to have a strict control on the costs for production, processing, and distribution of drinking water. Competing uses of drinking-water reservoir (e.g., flood control, irrigation, delivery of water to the downstream river, hydropower production, and if necessary, tourist and leisure usage), as well as ecological aspects have to be taken into account. Therefore, a far-sighted and scientifically substantiated and integrated dam-management approach, considering all processes and factors influencing water quantity and quality in a complex manner, becomes increasingly important. Reservoir management must include ecological aspects of the entire river system from the origins of the tributaries to the downstream reaches. Medium-term and long-term concepts have to incorporate forecasts of development of water resources and the drinking-water consumption, as well as changing water quality in the reservoirs and downstream, resulting from possible structural alterations in the catchment area, as well as local climate trends. The following future tasks have been identified: (1) improvement of the theoretical basis for ecotechnological principles for lakes/reservoirs and their catchment areas through a better understanding of the complexity of influenced processes and functions; (2) development of a consistent concept for the implementation of internal measures (definition of target concentration for initiating in-lake measures, time sequence of external and internal measures, determination of the longterm stability of restoration methods, and cost–benefit calculations); (3) improvement of decision support systems (DSSs) with implementation of predictive models; (4) development of an adaptive management to mitigate changes in climate and land usage; and (5) definition of critical threshold concentrations of pollutants in different climate regions, for moving toward the good ecological state of surface waters.

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2.09 Tracer Hydrology C Leibundgut, University of Freiburg, Freiburg, Germany J Seibert, University of Zurich, Zurich, Switzerland & 2011 Elsevier B.V. All rights reserved.

2.09.1 2.09.1.1 2.09.1.2 2.09.2 2.09.2.1 2.09.2.2 2.09.2.3 2.09.3 2.09.3.1 2.09.3.2 2.09.3.2.1 2.09.3.2.2 2.09.3.3 2.09.3.3.1 2.09.3.3.2 2.09.3.3.3 2.09.4 2.09.4.1 2.09.4.2 2.09.4.3 2.09.4.4 2.09.4.5 2.09.4.6 2.09.4.7 2.09.5 2.09.5.1 2.09.5.2 References

Introduction Patterns of Development Questions that Tracer Hydrology Helps to Answer Principal Conception and Approaches of Tracer Hydrology Hydrological System Approach Mathematical Models Design of Tracer Hydrology Studies Fundamentals of Environmental and Artificial Tracers Different Types of Tracers Environmental Tracers Isotope tracers 18O and 2H Other environmental tracers Artificial Tracers Characteristics of artificial tracers Fluorescent tracers Nonfluorescent artificial tracers Tracer Hydrology Applications Hydrograph Separation Catchment Transit Time Estimation Analysis of Sources of Nitrogen in Streams Advection–Dispersion Modeling Discharge Measurement Chloride-Based Groundwater-Recharge Estimation Tracer Experiment in a Porous Aquifer Concluding Remarks Guidance on Further Reading Reflections and Future Research

2.09.1 Introduction Tracers are substances which can be detected in the water at very low concentrations and allow following, or tracing, the flow of water. The ability to trace the flow of water is crucial for understanding the complex processes in hydrological systems. This understanding is important in many respects such as predicting water quality, which is often controlled by different water sources, flow pathways, and transit times, or the impacts of climate or land-use/land-cover changes on catchment’s hydrological response. Tracer hydrology helps to address questions such as how runoff is generated, which flow pathways the water takes, how long the water is in the catchment, where runoff comes from, or where water from a pollution source will flow. Tracer methods are of special importance in soil- and groundwater systems, because of the limited availability of other observation methods for subsurface waters. In this chapter, the use of tracer methods in hydrology is presented. In addition to the description of different tracers and methods, tracer hydrology is also presented as an advanced way to holistically study the characteristics of hydrological systems. We begin with describing the development and the concept of tracer hydrology and a discussion of the hydrological

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questions which can be treated by tracer techniques. The different tracers used in hydrology and the methods used to interpret tracer experiments are then described. Finally, selected tracer studies are reviewed as examples of how tracer hydrology has contributed to the advancement of hydrology. This chapter presents a short and informative overview of the current state of tracer hydrology. We are aware that there are many aspects of tracer hydrology, which cannot be addressed within this short chapter. For more detailed information several textbooks are available and guidance on further reading is provided in Section 2.09.5.1. This chapter largely builds on the recent textbook by Leibundgut et al. (2009). Further information on many issues, which are briefly discussed in this chapter, can be found in the textbook even if there is not always an explicit reference to the textbook.

2.09.1.1 Patterns of Development First tracer experiments were conducted about 150 years ago (Ka¨ss, 1998). It followed a slow but fascinating development of tracer hydrology, before, in the 1960s, tracer techniques began to be developed and used more widely in hydrology. This development was possible in particular because of the

215

216

Tracer Hydrology

progress made in measurement techniques and by the digitalization of data processing. Almost simultaneously, the computer era began and opened up new possibilities for environmental modeling. During this fascinating phase of development of natural sciences came the evolution of holistic approaches. In the case of tracer hydrology, this implied an increased focus on using tracers, often in combination with other investigation methods, to characterize hydrological system behavior. Most of the fundamental principles of tracer hydrology have been developed during this phase. Environmental isotopes have provided major inputs to the study of hydrological processes such as runoff generation, runoff component separation, transit times, recharge, and groundwater flow, and are still central for developing perceptual understanding and conceptual models of hydrological processes. The application of artificial tracers moved from being a measurement technique to a powerful tool for the development, parametrization, and validation of models for solute transport in ground- and surface waters. Besides many other factors, three fortunate milestones marked this development. A powerful framework for realizing numerous ideas was created through the founding and activities of the Association of Tracer Hydrology (ATH). The ATH promoted the use of tracer techniques in Europe between the late 1950s and the end of the twentieth century in many ways. The second milestone was the establishment of the Isotope Hydrology Laboratory at the International Atomic Energy Agency (IAEA) in Vienna in 1961. It propelled the rapid development of isotope techniques, beginning with environmental tritium, as a research tool for investigating the hydrological cycle worldwide. The XXth General Assembly of the International Union of Geodesy and Geophysics in 1991 in Vienna can be considered to be the third milestone. The International Commission on Tracers (ICT), within the International Association of Hydrological Sciences (IAHS), was established. Its aim was, among others, to bring together experimental hydrologists with modelers for the integrated investigation of the hydrological system. This event is significant since at the that time there was a much more uncritical belief in the potential of (pure) modeling in hydrology than today and, thus, the establishment of a clearly experimentally oriented commission within the IAHS was not without criticism. The following years showed an increasing integration of the tracer methods into hydrological research and applied hydrology by the international community, which validated this structural development. Experimental hydrology, in particular the strongly emerging catchment hydrology, increasingly used tracer methods in order to assess hydrological processes and system functions. In particular, the calibration and validation of mathematical models were based increasingly on tracer hydrological research.

2.09.1.2 Questions that Tracer Hydrology Helps to Answer The objective of tracer hydrology is the investigation of water in all its various phases, behaviors, and characteristics within the different media and substrates represented in the water

cycle. The use of tracers in hydrology, therefore, defines the scientific field that aims at understanding the hydrological system by making use of environmental and artificial tracers and modeling. Tracing of water provides unique methods for a direct insight into the dynamics of surface and subsurface water bodies. The large degree of complexity often found in hydrological systems is one of the main reasons why tracer techniques are needed both in hydrological research and in applied hydrology to characterize these systems. Tracers provide empirical data of real and often unexpected flow patterns, whereas models provide tools for flow and transport predictions. There is a fruitful connection between empirical tracer-based observations of flow and transport processes and the theoretical formulation of these processes, which has resulted in beneficial combinations and co-developments of both approaches to characterize hydrological systems. How does water flow through a hillslope or a glacier? How much runoff in rivers during an event originates directly from the event rainstorm? How much water is stored in an aquifer? Where and when water entered an aquifer as recharge? Phase changes, such as evaporation, condensation, and sublimation, can be identified and quantified using tracers. Tracers provide information on the origin of pollution and, thus, assist in deciding on the appropriate remediation approaches. Finally, tracer techniques are useful tools in understanding and quantifying transport processes. Thus, tracer techniques are applicable in all general fields of hydrology and experience can be gained in all the components of the water cycle. The possibilities of tracer techniques are vast, as described above. Tracer hydrology provides an integrative system approach for hydrological studies and other water-related research. The system approach is based on the determination of a function characterizing the system based on time series of known (measured) inputs to a system and known (measured) outputs. A more extensive review of publications in the field of tracer hydrology can be found in Leibundgut et al. (2009). There are two basic groups of tracers: (1) artificial tracers are actively brought into the hydrological system, so we refer to their application and (2) environmental tracers are defined as specific components of the water cycle, thus we discuss their utilization. Artificial tracers are defined as substances which are added intentionally to hydrological systems in planned experiments. The scales of application of artificial tracers are limited in both time and space. In general, artificial tracers are used in systems, which have a residence time of less than 1 year. On the other hand, artificial tracers allow labeling specific parts of a hydrological system. Typical fields of applications of artificial tracers include

• • • • • • • •

the detection of hydrological connections, flow paths and flow directions in catchments and aquifers, delineation of catchments and aquifers (qualitative), determination of flow velocities and further aquifer flow parameters based on the tracer breakthrough curves (TBCs), hydrodynamic dispersion, runoff separation, residence time, infiltration and runoff generation processes,

Tracer Hydrology Table 1

Relevant hydrological tracers

Environmental tracers

Artificial tracers

Stable isotopes Deuterium (2H) Oxygen-18 (18O) Carbon-13 (13C) Nitrogen-15 (15N) Sulfur-34 (34S)

Solute tracers Fluorescent dyes Naphthionate Pyranine Uranine Eosine Rhodamines

Radioactive isotopes Tritium (3H) (and helium-3 (3He)) Carbon-14 (14C) Argon-39 (39Ar) Krypton-85 (85Kr) Radon-222 (222Rn) Radium-226 (226Ra) Silicium-32 (32Si) Chlorine-36 (36Cl)

Nonfluorescent dyes e.g., Brilliant blue Salts Sodium/potassium chloride Sodium/potassium bromide Lithium chloride Potassium iodide Sodium borate (borax)

Noble gases

Fluorobenzoic acids

Anthropogenic trace gases Chlorofluorocarbons (CFCs) Sulfur hexafluoride (SF6) Geochemical compounds e.g., silicate, chloride, DOC, heavy metals

Deuterated Water (2H) Radionuclides e.g., Tritium (3H) Chrome-51 (51Cr) Bromide-82 (82Br) Indium-131 (131I)

Physio-chemical parameters e.g., Electrical conductivity, temperature

• • • •

217

labeling of unsaturated zone water movement, convection–diffusion processes in surface water, simulation of contaminant transport, and discharge measurement applying dilution methods.

A major characteristic of environmental tracers is that the input, or the injection, of the tracer to the hydrological system is provided by nature (Kendall and McDonnell, 1998). This can be both an advantage and a disadvantage. The major advantage is that catchment-wide injections are possible. Therefore, environmental tracers can be used at different scales such as catchment scale studies and even at the global scale. On the other hand, it is usually impossible to trace the contributions from single locations with environmental tracers. Another advantage of environmental tracers is that they have been injected for a long period of time. Therefore, these tracers allow systems with long transit times to be characterized. Substances such as sulfur hexafluoride (SF6) or cesium (anthropogenic tracers) brought to the hydrological cycle by accidents or as pollution may in some way be classified as artificial tracers but are often used in similar ways as environmental tracers (Table 1). Environmental tracers have been used in studies on all components of the water cycle. Thus, the utilization of environmental tracers provides methods for the investigation of some major components of the hydrological systems such as precipitation processes and origin assignment, open water evaporation, transpiration and stem flow, soil water dynamics, groundwater flow and recharge studies, subsurface flow mechanisms, and runoff components. Major applications of

Activatable radionuclides Dissolved gas tracers Helium Neon Stable isotopes of krypton Sulfur hexafluoride (SF6)

Particulate tracers Lycopodium spores Bacteria Viruses Phages DNA Synthetic microspheres Phytoplankton

environmental tracers include

• •

• • •

hydrological process studies: direct or indirect recharge mechanisms, identification of runoff components, and subsurface flow mechanisms; origin of water and water constituents: for instance, the discrimination of summer or winter recharge, the assignment of recharge altitude or the detection of origin of nitrate or dissolved inorganic carbon; determination of residence times: age dating or analysis of the amplitude of the variation of stable isotopes of water; quantitative determination of flow components: estimation of evaporation from open water surface, hydrograph separation; and paleohydrological studies.

2.09.2 Principal Conception and Approaches of Tracer Hydrology 2.09.2.1 Hydrological System Approach Artificial and environmental tracer experiments provide an improved understanding of hydrological systems such as catchments, streams, and aquifers. Environmental and artificial tracer methods have both been developed into important tools for various aspects of hydrology. These methods are used to estimate water resources to both reconstruct past hydrological conditions and investigate runoff generation processes at scales from plots to catchments. Without tracer methods many hydrological processes would not be possible to observe. In other words, tracer hydrology provides tools for

218

Tracer Hydrology

understanding and characterizing complex flow processes through soils, on surfaces, in channels, through and along hillslopes, in aquifers, or in artificial systems. Environmental and artificial tracer approaches have both advantages and limitations; both approaches might, thus, also complement each other in experimental studies. When trying to understand hydrological systems, hydrometric data, such as runoff or groundwater levels, are often not sufficient on their own, but tracer information provides important information on system characteristics needed to derive a perceptual model of the investigated system. Tracer hydrology aims to develop, test, and validate such representations of hydrological systems that are in sound agreement with the available data by making use of environmental and artificial tracer experiments and modeling. Describing hydrological systems such as catchments, hillslopes, soil columns, or stream segments is generally based on 1. measured or otherwise known inputs (water fluxes, constituent loads, and energy) as a function of time and space; 2. a characteristic functioning of the system, which can be described by a set of equations representing, for instance, the flow in surface water bodies, in the subsurface, or at the soil–vegetation–atmosphere interface; and 3. a known or measured output of the investigated variables, again as a function of space and time (Figure 1). In the case of tracer hydrology approaches, the input is usually the concentration or load of a particular tracer, for instance, in groundwater recharge or precipitation (for environmental tracers) or the injected mass for artificial tracers. The output from a system is usually the tracer load leaving the system, being controlled by runoff volume or well yield and the tracer concentrations. Interpreting the information contained in the input–output relationship of specific variables of a system is, thus, a key in tracer hydrology. Transfer functions between input and output are identified from tracer data and can be used for system characterization and, subsequently, predictions of system behavior. Mathematical modeling is an important tool for the interpretation of both environmental tracers and artificial tracer experiments. The application of the convergence approach in tracer hydrology can be used to derive concepts of hydrological systems (Leibundgut, 1987; Leibundgut et al., 2009). The convergence approach utilizes the fact of flow path converge toward a spring or a branch of a river. At the spring, the information represented by the tracers and the hydrograph respectively can be deciphered and combined by adequate techniques. These concepts and the corresponding mathematical models can be varying in complexity, ranging from simple to more complex

2.09.2.2 Mathematical Models For the interpretation of a tracer experiment, it is in most cases necessary to use some kind of a mathematical model. Such a model has to be based on adequate concepts of tracer transport and tracer behavior in the system. The conclusions which might be derived from a tracer hydrology study might largely depend on the model used for interpretation as already discussed by Eriksson (1958). Therefore, it is important to be clear about the underpinning assumptions. A perceptual model is a qualitative description of a system and its most important characteristics, such as geometry, hydraulic connections, parameters, and initial and boundary conditions related to the intended use of the model. In practice, the perceptual model demonstrates the principal idea of Output

Input Q(t), C(t), E(t) Effective precipitation Environmental tracers Artificial tracers

model structures, representing the principal functioning of the investigated hydrological system. Tracer experiments and mathematical modeling can be performed in an iterative way, where predictions derived from a system model can be used for an improved design of further experiments (e.g., better planning of the observation network). Using several independent methods and techniques simultaneously when investigating a hydrological question is generally beneficial. In tracer hydrology, such an approach is common, for instance, when using different tracer techniques (environmental and artificial tracers) in combination with other hydrological methods (such as hydrometric or geophysical methods). The combination of different tracers, if possible, ensures that any specific limitation of a single tracer does not bias the characterization of the hydrological system. Often, different artificial tracers are combined and there are also many studies where different environmental tracers are used. The combination of environmental and artificial tracers, however, might often be the most promising approach. Moreover, it is always valuable to combine or even integrate tracer methods with other experimental hydrological investigation methods (e.g., hydrometry, geophysics, hydrochemistry, and remote sensing). The advantage of such an integrated approach is the added value of the combination of results obtained by different, often independent, methods. If different methods provide consistent results, more general conclusions can be drawn. Uncertainties of individual methods can be reduced if different methods point in the same direction. Disagreements between different methods, on the other hand, can be even more valuable as they provide impetus for an improved experimental design or research aiming at resolving the contradiction.

System

Q(t), C(t), E(t) Runoff Concentration/properties of environmental and artificial tracers

Figure 1 Hydrological system approach adapted to tracer hydrology by the convergence approach. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

Tracer Hydrology

water circulation in the system. In tracer hydrology, the term ‘conceptual model’ has also been used for this qualitative description, but here the term ‘perceptual model’ is used to avoid confusion with the use of the terms in catchment runoff modeling (Beven, 2001). Based on a perceptual model, a mathematical model can be derived. Here, the hydrological, physical, and/or hydrochemical system is represented by mathematical functions, including model parameters. Typically, features such as geometry, hydraulic connections, and initial and boundary conditions need to be specified in some way. Mathematical models in tracer hydrology typically describe the storage and movement of both water and tracer mass. In certain cases, analytical solutions of the mathematical formulation exist for given boundary conditions. Depending on the system being described, the mathematical model can more or less be based on physical concepts such as Richard’s equation or on a system approach such as transit-time distribution functions. Calibration and validation are important steps in any model application. During calibration, model parameters are varied until a good fit between model simulations and experimental observations is obtained. Sometimes, model structures are also varied during calibration. Trial-and-error calibration refers to the procedure where model parameters are changed by hand based on, often subjective, decisions by the model user. The advantage is that prior knowledge can be considered; the disadvantages are the subjectivity and the large time demand. Automatic optimization procedures based on some objective functions are often used as a more objective and time-efficient way to calibrate model parameters. This approach, on the other hand, has been criticized as advanced, but still simplistic curve fitting. The calibration of a mathematical model to experimental data can also be interpreted as inverse modeling of parameter values for a certain system. Depending on the experimental data and the model used, it might not be possible to find a unique solution through optimization due to the problem of equifinality (Beven, 2001). Tracer methods are often used to describe poorly known systems. Therefore, mathematical models should be as simple as possible to allow determination of parameters through calibration. The aim of validation is to increase confidence that a model is a suitable representation of the system for which it is applied. One way of validation is comparing the calibrated model parameters with independent measurements of the parameters (e.g., filter velocity). Applying the model to make predictions using data, which is independent from those used in calibration, is another way of model validation. While this independent data are often data of the same type but from a different time period, different types of data might also be used. In catchment modeling, for instance, models are usually calibrated using runoff data, but tracers might be used to falsify or confirm certain model structures (Seibert and McDonnell, 2002; Seibert et al. 2003; Uhlenbrook and Sieber, 2005). In addition, regional circulation models might be tested based on isotope data (Sturm et al., 2005). It must be noted, however, that the simulation of isotopes requires model extensions. Therefore, parameter uncertainty often is not reduced despite additional validation information. However, including tracer data in the model testing will lead to internally more consistent models. The role of isotopes in the

219

validation of global and regional atmospheric circulation models and their coupling with hydrological models as well as the response of ecosystems to climate change has not been fully investigated yet. For modeling of environmental tracers, usually, the injection of tracer, which occurs naturally over an area and continues over a longer time period, has to be considered. This injection can be either by precipitation or by solution of minerals from earth substrate. Generally, system approaches such as the convolution integral for transit time analyses are useful in these cases. The tracer injection for artificial tracer, on the other hand, is typically concentrated to a single point or line. In this case, mathematical models based on dispersion theory are generally used. Analytical solutions for advection– dispersion processes in all dimensions and different boundary conditions are available and further described in Section 2.09.4.4. For heterogeneous systems and complex boundary conditions, transport equations usually have to be solved numerically. Hydrological systems, such as an aquifer or a catchment, can be considered as systems, for which the characteristic behavior can be evaluated if both input and output time series of water and tracer concentrations are known. Usually, some forms of mean or effective parameters are used to describe the system in such approaches. Examples of parameters include volumes of water, transit times, and flow rates through the system. The tracer transport through the system, that is, between input and output, can then be described by a lumpedparameter approach (cf. Figure 1). The transit-time distribution function, or its first moment, the mean transit-time (MTT), is an especially important system characteristic, which is often derived for investigated systems (Maloszewski and Zuber, 1982). When stagnant water can be neglected, the MTT of water in the system can be further used to estimate the volume of water stored in a system. In more complex systems with mobile and stagnant water (e.g., fissured aquifers), it is important to distinguish between the MTT of tracer and the MTT of water, as these will usually not be the same in these cases. The transit time of a tracer in such systems is controlled by both the flow of the mobile water component and the diffusive exchange of tracer between mobile and stagnant water (see also Section 2.09.4.4).

2.09.2.3 Design of Tracer Hydrology Studies Tracer studies and experiments are always carried out as part of a hydrological or a water resource issue. The success of an integrated tracer study as well as an artificial tracer experiment very much depends on careful planning. Figure 2 structures the process of planning and executing an experiment using a flowchart. A clear idea of the aims of the experiment and of the influencing factors is fundamental. A major tracer study program (master plan) must be established in advance, in order to minimize problems and inconsistencies. For instance, it is important to estimate an appropriate mass for tracer injections or to plan sampling at a proper temporal (and/or spatial) resolution. Guidelines for carrying out practical experiments are provided by Leibundgut et al. (2009). When using environmental tracers it is important to ensure that it is possible to clearly distinguish different sources or flow pathways by

220

Tracer Hydrology

Problem and aim of a tracer study

Tracer analysis

Artificial tracers

Planning and preparation

Hydrological data collection and evaluation

Hydrology of system investigated

Environmental tracers

Sampling and measurement Laboratory analysis

Data processing

Analysis and interpretation

Conclusions and recommendations Figure 2 Flowchart illustrating the planning and execution of a tracer study and a tracer experiment. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

their tracer fingerprint. This means that differences must be larger than the analytical accuracy and the natural variation within certain sources or pathways. Practical aspects of tracer applications are further discussed in Section 2.09.4, together with the discussion of specific types of tracer studies, such as hydrograph separation or discharge measurement.

2.09.3 Fundamentals of Environmental and Artificial Tracers 2.09.3.1 Different Types of Tracers There are basically two groups of hydrological tracers. On the one hand, there are tracers which are added in defined amounts to hydrological systems for planned experiments. On the other hand, there are the environmental tracers which are naturally occurring in hydrological systems. Here, concentration differences, which exist for some reason between different parts of the investigated hydrological system, are used to trace water fluxes. Isotopes and geochemical compounds are examples of this latter group of tracers, which are usually called environmental or natural tracers. Examples of tracers which can be artificially added to hydrological systems (so called artificial tracers) are fluorescent or salt tracers. In both groups, there are many different possible tracers, which all have their advantages and disadvantages and are more or less

suitable for particular applications (Table 1). Pollutants, or the so-called pollution tracers, can be seen as a group of tracers in between environmental and artificial tracers. While they are not naturally occurring, they are not added to the hydrological system in well-defined ways either. Environmental tracers are defined as properties or constituents of water that are occurring naturally and have not been added within an intended experiment, providing qualitative or quantitative information about a hydrological system. Naturally occurring stable and radioactive isotopes are the most commonly used natural tracers, especially the isotopes found in the water molecule (18O and 2H; see Section 2.09.3.2.1). Some of the environmental tracers result from anthropogenic releases to the atmosphere or to the hydrological cycle. Constituents such as krypton-85, chlorofluorocarbons (CFCs), and SF6, for instance, have been released to the atmosphere as a result of industrial activities, and tritium (3H) has been released due to atomic bomb tests. While their injections were not planned and definitely not aimed at the purpose of providing age dating methods to hydrologists, these natural tracers can be used in similar ways to naturally occurring constituents for tracer studies. Different pollution tracers, such as nitrate, organic pollutants, or remnants of past mining activities, can also provide information about hydrological processes. Environmental tracers allow major components of the hydrological cycle to be studied. These tracers have, for

Tracer Hydrology

instance, been used in studies on precipitation processes, open water evaporation, transpiration and stem flow, soil water dynamics, groundwater recharge, subsurface flow mechanisms, and runoff generation. Natural tracers enter hydrological systems by diffuse and continuous processes via precipitation, the outflow of certain reservoirs or the solution from minerals. The fact that the input function or the injection of tracer to the hydrological system is provided by nature is a major advantage of natural tracers. This enables investigations on a large scale with respect to both space and time, and natural tracers are, thus, particularly useful for integrated approaches at the catchment scale and water balance studies. Environmental tracers can also be used for very long timescales, including paleohydrological studies such as for the analysis of rainfall origin or recharge in the Holocene or for relict groundwater formation in arid areas, if the past input of an environmental tracer can be reconstructed from data or from physical principles. In other words, environmental tracers allow information from experiments to be obtained which have been started long before a particular research project actually started. A disadvantage is that often the input function is difficult to define. In addition, the input and output signals might be relatively weak. Artificial tracers are added to a hydrological system in planned experiments by well-defined injections. The boundary conditions for such experiments are generally much easier to define than in the case for natural tracers. The input signal can also be much more pronounced and it is possible to label a specific component of the investigated system such as an inflow to a lake. On the other hand, the scales in time and space for application are limited, and it might only be possible to gain insights into a part of the system during the time of the experiment. For example, artificial tracers cannot usually be used for systems having transit times larger than 1 year for practical reasons. Besides, catchment-wide injections are usually not possible.

2.09.3.2 Environmental Tracers 2.09.3.2.1 Isotope tracers

18

O and 2H

Isotopes are different types of atoms of a certain element having a different atomic mass due to a different number of neutrons. Some isotopes are stable, whereas others are radioactive. The stable isotopes oxygen-18 (18O) and deuterium (2H) are the most commonly used environmental tracers. Since both isotopes are naturally occurring as part of the water molecule, they provide almost perfect tracers for the flow of water. The relative occurrence of these isotopes compared to 16 O and 1H can be expressed as abundance ratio. Usually, the isotopic content of a water sample is expressed by d-values. These are computed based on the isotope fractions in a certain sample (Rsample) compared to the ratio of a standard (Rstandard). Generally, the internationally accepted standard, Vienna Standard Mean Ocean Water (VSMOW), is used. In this water, the average ratio of 18O compared to 16O is 2.005 2 10–3 and the ratio for 2H compared to 1H is 1.557 5 10–4:

Rsample  Rstandard d¼  1000ð% Þ Rstandard

ð1Þ

221

The values of d18O or d2H are expressed as % difference from the standard being used. Positive d-values indicate an increased concentration of 18O or 2H compared to the standard being used, whereas negative d-values denote a decrease of heavier isotopes in the sample. When VSMOW is used as standard (as is usually done), ocean water by definition has a d18O value close to 0%. In most parts of the hydrological cycle, 18O occurs less frequently than in ocean water, which means that d-values are usually negative with most negative values found in ice samples from cold, arctic regions (about – 50 to –25%). While d-values do not directly express concentrations, they can be used as concentrations in most cases. The d-notation can be confusing because there are different ways to compare the d of two water samples: lower or higher, more or less negative, lighter or heavier, and depleted or enriched (Table 2). Lower, that is, more negative d-values imply a lower concentration of the heavier isotopes and, thus, lighter water (e.g., water from glaciers). A sample with higher, that is, less negative d-values, on the other hand, can also be described as heavier water as it contains more 18O isotopes (e.g., ocean water or rainwater during the summer season). Sometimes a sample is also described to be enriched or depleted; here, it is important to be clear about what isotope is enriched or depleted in the sample relative to another. Fractionation processes. These processes cause changes of the isotopic composition through an exchange of isotopes due to physical or chemical processes. In the case of 18O/16O and 2 H/1H, such processes by which the isotopic concentrations are changed are mainly related to phase changes of the water. Until equilibrium is reached, the exchange continues. Note that an equilibrium state does not imply that the phases have the same isotope concentrations, but that the concentrations no longer change over time. Evaporation is of special importance for the fractionation of 18O/16O and 2H/1H. Since the light isotopic species of water (1H16 2 O) has a higher vapor 1 2 16 pressure than heavier species (1H18 2 O or H H O), the latter are less likely to be evaporated than the light water species (Figure 3). In other words, when water is evaporating, the evaporated water will have lower d-values (more negative) than the remaining water. Consequently, the effect can be used to determine evaporation rates of lakes (Gibson et al., 1996). Larger increases in the d-values (i.e., values becoming less negative or even positive) of the lake water over time correspond to higher evaporation rates. Variation in precipitation. As a result of the fractionation processes, clear geographic and seasonal patterns of isotopic precipitation composition can be observed (Rozanski et al., 1992). These patterns form the basis for many hydrological studies using isotopes. The two major factors controlling the

Table 2 Different expressions used to compare the isotopic composition of two samples d18O ¼  8%

d18O ¼  15%

Higher d-value Less negative d-value Higher concentration of Enriched in 18O

Lower d-value More negative d-value Lower concentration of Depleted in 18O

18

O

18

O

222

Tracer Hydrology

isotopic composition of precipitation are temperature, which controls fractionation, and the amount of the original water vapor, which has not become precipitation earlier. As a result of these two factors, the following effects can be observed:

•

•

•

•

Latitude effect. The concentration of heavier isotopes is lower at higher latitudes. The explanation is that a larger part of the vapor has already been precipitated and that fractionation during phase transitions between water and vapor is more pronounced at low temperatures. Elevation effect. Precipitation is increasingly depleted in heavier isotopes at higher altitudes. This is the combined result of increased fractionation due to lower temperatures and moisture depletion by adiabatic cooling during orographic precipitation. Equilibrium fractionation increases with lower temperatures, making fractionation more efficient at higher altitudes. Repeated rainout during the uplift of air masses further causes a decreasing concentration of heavier isotopes. Continental effect. Precipitation becomes more depleted in heavier isotopes with increasing distance from the source of the water vapor, which is usually the ocean. On its way over a continent cloud water in the concentration of heavier isotopes will decrease with each rainfall event along the trajectory. Seasonal effect. Typical seasonal variations of stable isotope compositions are observed for many regions. This is a result of different sources and trajectories of the air masses

Vapor Vapor pressures Water 1

H218O

1

H216O

Figure 3 Differences in the vapor pressure for the two isotope species 1 18 H2 O and 1H16 2 O during equilibrium exchange with water vapor. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

•

reaching the region and varying fractionation processes in the source area of atmospheric moisture as well as along its way to the region. Amount effect. Rainwater during small events usually has higher concentrations of heavier isotopes than rainwater collected from large rainfall events. This effect is caused by evaporation of rain on its way toward the ground and is mainly observed for light rains or early rains during an event. Note that this effect does not apply to snowfall.

The above effects can be seen in long-term average data. For individual precipitation events, there can be a large variability both between and within events due to air masses of different origin and history. There is usually a clear relation between d18O and d2H in precipitation (Figure 4(a)). At a global scale, d18O and d2H in precipitation is characterized by the equation d2H ¼ 8d18O þ 10 (Craig, 1961). The equation is also called the global meteoric water line (GMWL). Based on the data of the IAEA global network of isotopes in precipitation (GNIP), a revised version of the GMWL with a slope of 8.1770.07% and an intercept of 11.2770.65% has been proposed (Rozanski et al., 1992). Deviations from this global correlation exist at a regional scale and, for several regions, specific meteoric water lines have been determined. Deviations from the GMWL are especially significant in coastal areas, on islands, and in tropical mountainous regions with typical slopes and intercepts. Some regional meteoric water lines should be interpreted with care as local meteoric water lines might be influenced by deficiencies of the sampling network or less cautious procedures than those used by IAEA. When water is affected by evaporation from free water surfaces, the increase in heavier isotopes in the remaining water differs for 18O and 2H. As a result of this, the slope of the d18O– d 2H relationship differs from the slope of the meteoric water line in the d 18O-d 2H diagram with typical evaporation slopes varying between 4 and 5.5 (Figure 4(b)). This allows determining, for instance, whether a lake contributes to groundwater recharge. Sampling and measuring. The accuracy of stable isotope measurements depends on sampling procedures and the analytical technique used. The most important issue when

18O (‰ VSMOW) −15

−20

−10

18O (‰ VSMOW) −5

−8

0

−120

−4

−2

0

−20 Meteoric water line

Evaporation line

−40

2H (% VSMOW)

−80

2H (‰ VSMOW)

−40

−6

−60 −160 (a)

(b) 18

2

Figure 4 d O–d H diagrams/global meteoric water line (GMWL). (a) GMWL and isotopic composition of samples (1998–2008) from two precipitation stations in the Black Forest, Southern Germany (Katzensteig and Schweizerhof). (b) GMWL and samples of surface water (triangles) influenced by evaporation. Modified from Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

Tracer Hydrology

taking water samples for isotope measurements is to avoid evaporation because of the associated artificial fractionation processes. Using double inlet mass spectrometry, the analytical error amounts to about 70.1% for d18O and 71.0 to 1.5% for d2H. As an alternative, isotope concentrations can be measured using tunable diode laser spectroscopy, which is an innovative technique for stable isotope measurements and has gained importance recently. This new technique is used by an increasing number of research groups and allows isotopes to be analyzed at considerably lower costs with a precision of about 0.3% for d18O and of about 1.0% for d2H. Another advantage is the smaller sample volume needed for the analysis. To analyze both d18O and d2H by traditional mass spectrometry normally requires a minimum sample volume of about 15 ml, while 2 ml are sufficient when using tunable diode laser spectroscopy. This represents important progress, for instance, for hydrological isotope studies investigating sap flow and soil water. However, for practical reasons and to minimize sources of uncertainties, it is usually recommendable to take for hydrological purposes samples not o50 ml in the field.

2.09.3.2.2 Other environmental tracers There are several other environmental tracers which can provide useful information. First, there are additional isotopes. The 13C/12C ratio can be used to trace sources of carbon dioxide and the 15N/14N ratio can be used to trace sources of nitrogen. Radioactive isotopes, such as tritium or 14C, have been used for age dating of runoff and groundwater. Geochemical compounds, such as silicate, chloride, and dissolved organic carbon (DOC), can also be used as tracer. However, here it has to be considered that these tracers might not be conservative on their way through a hydrological system. Temperature and electrical conductivity provide simple-tomeasure tracers, the latter being a lumped measure of ions in the water. Some tracers, such as krypton-85, CFCs, and SF6, have been released to the atmosphere as a result of industrial activities. They can nevertheless be considered as environmental tracers because they can now be utilized in a similar way to naturally occurring tracers. In particular, they are applied over large areas in nonplanned experiments, a characteristic they have in common with tracers such as 18O or 2H. They are in particular used for estimating the residence of water in groundwater.

223

Table 3 Required properties of artificial tracers in general and in the example of fluorescent tracers in particular Properties to be considered

1. 2. 3. 4. 5. 6. 7. 8. 9.

Solubility in water Fluorescence intensity Detection limit pH dependency Temperature dependency Photolytic stability Sorption processes Chemical and biological stability Toxicity and related environmental effects 10. Costs and other practical aspects

Requirements of an ideal (conservative) tracer High High Low Low Low High Negligible High None or minimal Low or moderate

The properties in italic are the main characteristics of conservative tracers. From Leibundgut Ch, De Carvalho-Dill A, Maloszewski P, Mu¨ller I, and Schneider J (1992) Investigation of solute transport in the porous aquifer of the test site Wilerwald (Switzerland). Steirische Beitra¨ge zur Hydrogeologie 43: 229–250.

(nonreactive in natural water) and not sorptive. Particularly in classical quantitative tracer investigations, it is essential to use conservative tracers to allow correct determination of hydrodynamic system properties and flow and transport parameters. Besides conservative behavior, further characteristics are needed to make a substance an appropriate tracer in hydrological field experiments from a more practical point of view. These properties are listed in Table 3. If these requirements are met completely, we refer to the tracer as ideal, albeit only nearly ideal artificial tracers occur in reality. In principle, when choosing an artificial tracer for application in (field) experiments, the guideline followed is that an ideal tracer represents the water flow, but nonideal tracers can also be useful for special applications. In any case, a sound knowledge of the characteristics of the tracer substances and the respective measurement techniques is required in order to perform experiments successfully both in the field and in the laboratory. In the following, the principles of (ideal) tracer properties are discussed using fluorescent tracers as an example, but are valid principally for all other groups of artificial tracers. Thereafter, a short summary of the other artificial tracer groups is given.

2.09.3.3 Artificial Tracers 2.09.3.3.1 Characteristics of artificial tracers

2.09.3.3.2 Fluorescent tracers

Classically, four main groups of suitable artificial tracers are distinguished based on their chemical appearance: fluorescent, salt, radioactive, and particulate tracers. In recent years, additional substances, such as dissolved gases, nonfluorescent dyes, fluorobenzoic acids (FBAs), or deuterium, were also applied successfully (and increasingly) in hydrological tracer studies. Fluorescent tracers, however, are still the most important and most applied tracers (cf. Table 1). For the primary purpose of tracing the flow of water, the substance needs to be conservative in the aquatic environment under the conditions of the hydrological system. A tracer is considered conservative if it is physico-biochemically stable

Fluorescence is a luminescence that occurs where energy is emitted as visible light after being absorbed as electromagnetic radiation of a different wavelength. The substances used for tracing purposes are situated within the small range of visible light between the higher ultraviolet and the infrared wavelengths (B350–750 nm). They are characterized by specific fluorescence spectra corresponding to their wavelengths of excitation and emission. The emission takes just as long as the activation is driven by an excitation energy source, causing only transient fluorescence effects compared to longer-lived phosphorescence. The intensity of fluorescent emission follows a linear dependency involving the intensity of incident

224

Tracer Hydrology

light and the tracer concentration over a wide range. At very high concentrations, the fluorescence intensity is reduced, because a self-shadowing effect of the molecules, called ‘quenching’, occurs. However, as the expression tracer implies, only very low concentrations are used as far as possible. The spectral fluorometer, which features a light source that excites the sample and a detector that measures the emitted light spectrum, is the most common laboratory device for fluorescent tracer analysis today. In the future, advanced multicoupled analytical techniques, such as high-performance thin-layer chromatography with automated multiple development (HPTL/AMD), nano-chip liquid chromatography/ quadruple time-of-flight mass spectrometry (Nano-Chip-LC/ QTOF-MS), and laser spectroscopy, might become important instruments. On-site measurements to monitor the tracer breakthroughs are often desirable. For fluorescent tracers, different types of in situ devices are available (fiberoptic-, flowthrough-, pocket-, and Xe-flashlight fluorometers). A completely different sampling approach is placing active charcoal bags (probe, fluocapteur) in the system under investigation, with the subsequent tracer extraction in the lab, which provides only the cumulative tracer amount. The technique is applied to ensure that no tracer passes unobserved at remote sampling points or where site access is difficult (e.g., in karst caves or glaciers) during long-term experiments or at places where no tracer propagation is expected. All fluorescent tracers in use are organic dyes. Relevant characteristics of fluorescent tracers are listed in Table 4. In the following, the different characteristics are briefly summarized and, as already mentioned, are exemplarily valid for the other artificial tracers. The detection limit plays a key role for a substance’s applicability as artificial tracer because it is strongly linked to rather practical criteria of experiment realization, which include questions about required tracer mass, possibility of adequate execution of tracer injection, costs, and, not least, the degree of interference to the aquatic environment of an experiment. The level of detection limits exhibited by the whole group of fluorescents is quite appropriate for hydrological tracing purposes. The detection limit depends, on the one hand, on the fluorescence intensity to which it is positively Table 4

correlated, and, on the other hand, on the background fluorescence of the sample. Relative fluorescence intensities of some commonly applied fluorescent tracers are shown in Table 4. Uranine plays a dominant role among the fluorescent tracers, due to its much higher fluorescence intensity. The solubility of tracers in water is a crucial requirement of tracers used to investigate water flows in the hydrological cycle, as the tracer should be as close to the characteristics of water as possible. Pyranine, uranine, eosine, and naphthionate are characterized by good-to-very-good solubility. By contrast, the solubility of the rhodamines is considerably lower. The temperature dependency of the fluorescent tracers is usually unproblematic. A changing pH value has the potential to change the electrical charge of the molecule from negative, through neutral to a positive value, and vice versa, which again affects the excitation and emission spectra of the fluorescent tracers. If the medium reaches a certain degree of acidity, the compounds partially lose their fluorescence. The fluorescent tracers’ sensitivities to pH vary, in the pH range of natural waters uranine and pyranine react most sensitively. This needs to be considered especially for in situ measurements. Problems occur principally in peat-bog and swamp regions, and generally in acid soils and crystalline geological settings, whereas in laboratory analysis of samples, the problem can be managed quite simply as the pH dependency of dye tracers is reversible by means of adequate buffering prior to fluorometric analysis. The exposure to light, called ‘photolytic dependency’, has an irreversible effect on fluorescence. The different sensitivities of tracers to light should be considered when planning the use of fluorescent tracers in experiments. In principle, the two standard tracers, uranine and eosine, are clearly not suitable for surface water experiments during daylight, except for experiments of short duration. The sorption behavior is the most important criterion relevant to the use of artificial tracers generally, and fluorescent tracers in particular, because sorptivity is strongly linked to the question of conservativity. Sorption is a highly complex but crucial process in the performance of experiments in the unsaturated and saturated zones. Depending on their molecular makeup, fluorescent tracers exhibit widely contrasting

Summary of the relevant characteristics of the fluorescent tracers

Tracer

Ex/Em (nm)

Rel. fluorescence yield (%)

Detection limit (mg m3)

Toxicity (–)

Solubility (20 1 C) (g l1)

Naphthionate Pyranine Uranine Eosine Amidorhodamine G Rhodamine B Rhodamine WT Sulforhodamine B

325/420 455/510 491/516 515/540 530/555

18 18 100 11.4 32

0.2 0.06 0.001 0.01 0.005

Harmless Harmless Harmless Harmless Sufficient

240 350 300 300 3

555/575 561/586 564/583

9.5 10 7

0.02 0.02 0.03

Toxic Toxic Sufficient

3–20 3–20 10 (10 1C)

Light sensitivity (–)

Absorption behavior (–)

High High High Very high Low

Very good Good Very good Good Sufficient

Low Very low Low

Insufficient Insufficient Insufficient

Ex/Em: excitation and emission wavelength respectively. Relative fluorescence yield with respect to the fluorescence intensity of uranine, which is among all fluorescent tracers the most sensitive one. From Leibundgut Ch, De Carvalho-Dill A, Maloszewski P, Mu¨ller I, and Schneider J (1992) Investigation of solute transport in the porous aquifer of the test site Wilerwald (Switzerland). Steirische Beitra¨ge zur Hydrogeologie 43: 229–250.

Tracer Hydrology

reactions upon contact with different substrates. Cationic tracers usually interact more strongly with the substrates than anionic ones; however, both groups usually react in a way that is referred to as reversible sorption. As was mentioned above, changing pH values have the potential to change the electrical charge of the molecule. Thus, the sorption affinity of fluorescent tracers also depends on the pH. The two parameters, Kd [L3 M1] (distribution coefficient) and Rd [–] (retardation coefficient), characterizing sorption processes are commonly used in tracer studies. Kd represents the mobility of the tracers in a certain medium, describing the thermodynamic equilibrium of the tracer between substrate and solution. Describing the partitioning between liquids and solids using Kd is only valid if the involved reactions are fast and reversible, and if the isotherm is linear. Commonly Kd is determined by means of batch experiments (Equation (2)) and depends on the ionic composition of the exchanger and the solution used:

Kd ¼

V ci  cs m cs

ð2Þ

where V is the volume of the solution [L3], m the mass of the dry substrate [M], ci the initial tracer concentration [M L3], and cs is the dissolved tracer concentration (equilibrium solution) [M L3]. Rd [–] describes the retardation of average tracer transport velocity vt [L T1] compared to the average flow velocity of water v [L T1] (Equation (3)):

Rd ¼

v vt

ð3Þ

Rd can also be expressed as a function of Kd, porosity n [–], and the bulk mass density rb [M L–3] (Equation (4)):

Rd ¼ 1 þ

rb Kd n

225

indicating fluorescents to remain rather stable in unpolluted aquifers. However, it is strongly recommended to use brown glass bottles for sampling and to execute analysis as quickly as possible to minimize these problems. Dealing with less pure water, one must be aware of the potential effects of degradation or chemical reactions on the experimental results. Toxicity and related environmental effects have to be considered. Each injection of artificial tracer in a hydrological system is in a sense a contamination of the water body in question. Therefore, it is a strong requirement to users to apply tracer masses as low as possible. Carefully planned and correctly prepared tracer experiments generally involve only minimal quantities of fluorescent tracer substances. Thus, the contamination is usually tolerable. Summarizing the results of studies related to human and eco-toxicological aspects, it can be stated that uranine is harmless and eosine, pyranine, and naphthionate appear to be harmless. It is suspected that the rhodamine group as a whole is toxic, except amidorhodamine G and sulforhodamine B, which are less problematic. When preparing a tracer experiment, it is important that national regulations pertaining to tracer experiments are consulted. Due to their relatively easy handling, the high sensitivity of the analysis, the low detection limit, and, consequently, the small quantity of tracer mass needed in field experiments fluorescent tracers are popular, in general, among tracer hydrologists. They are also attractive because of the linearity of the calibration curve in the measuring scale, and comparable low toxicity levels. For a better evaluation in the evaluation of tracer substances, a summary assessment of the relevant properties of the commonly used fluorescent tracers is provided in Table 4. For further information the reader is referred to Leibundgut et al. (2009), where a comprehensive presentation and discussion of the fundamental methodological basics concerning the hydrological application of fluorescent tracers are given.

ð4Þ

Both parameters assume a linear sorption isotherm with instantaneous equilibrium, which is applicable only to a limited extent under the natural conditions of experiments since most sorption and desorption processes follow kinetic reactions in reality. Direct transfer of reference values of Kd and Rd obtained from laboratory experiments to field conditions is not possible; nevertheless, they provide a useful guideline for the estimation of the sorption loss of a given tracer and, consequently, the estimation of the tracer injection mass required. Sorption tests have been performed and distribution and retardation coefficients can be found in Leibundgut et al. (2009). The chemical and biological stability of fluorescent tracers may readily decompose as a consequence of oxidation and other chemical changes. However, oxidative processes affect the dye tracers, as they are organic compounds, to different degrees. In addition, microbial degradation of fluorescent tracers is known to occur both under natural conditions during experiments and in stored samples. High salinity may decrease fluorescence intensity but generally to a much lesser degree than either pH effects or exposure to light. Further processes that may cause problems, especially in the case of long-term experiments, were reported, but there are examples

2.09.3.3.3 Nonfluorescent artificial tracers Salts are inorganic compounds which break up into cations and anions when dissolved in water. Various salts have been used as tracers such as sodium chloride (NaCl), potassium chloride (KCl), sodium bromide (NaBr), lithium chloride (LiCl), borax (Na2B4O7), and potassium iodide (KI). The advantages of salt tracers are their common availability, relative low costs, simple handling, and the potential for continuous measurements. The disadvantages of salt tracers include high natural background concentrations in many cases, relatively high detection limits, and issues related to sorption and ion exchange. The first two points often result in the need for a large mass injection, which in turn might cause transport problems and environmental concerns. While many salt tracer experiments have been performed in the past, these disadvantages limit the usability of salts as tracers. Salt tracers are mainly useful in small-scale experiments such as soil column tests or investigations in small surface water bodies. It should be noted that sodium chloride is widely used as a tracer for discharge measurements using the dilution method (see Section 2.09.4.5). Of the several anions tested as hydrological tracers, bromide (Br–) might be the most suitable. Bromide is often used for tracer experiments in the vadose zone, and as a

226

Tracer Hydrology

reference tracer for comparison purposes (e.g., Onodera and Kobayashi, 1995; Sambale et al., 2000; Parsons et al., 2004; Einsiedl, 2005; Hangen et al., 2005). The advantages of bromide compared to other anions are lower background concentrations in natural waters (B300 times lower than Cl–), low toxicity, and low sorption to soil particles. Drifting particles, such as spores, phytoplankton, bacteria, viruses, phages, DNA tracers, and microspheres, constitute another group of artificial tracers. Their most characteristic feature is that they are not in solution. Obviously, they do not correspond to the properties of a conservative (ideal) tracer. They particularly have considerable potential as tracers for special applications, as in the investigation of the filtration capacity of the unsaturated zone and aquifers. They are being used to study and investigate the flow behavior of microorganisms and particles in saturated, unsaturated, and surface water systems with respect to the infiltration of contaminants in sewage or irrigation water, and for all applications with hygiene implications, and impacts on water supply installations and water protection zones (Sabir et al., 1999; Auckenthaler et al., 2002; Zvikelsky and Weisbrod, 2006). The potential health and environmental risks posed by radioactive tracers mean that the use of human applied radioactive substances is very limited nowadays. The particular suitability of radioactive tracers is due to the very high sensitivity and the possibility of a selective detection, the disappearance of the tracer from the system due to decay, and the ability to follow the flow path of water and tracer using a Geiger counter. One of the most important methods when using radioactive tracers are the single-well technique and groundwater recharge estimation (Moser and Rauert, 1980; IAEA, 1983). A variety of FBAs have been used as tracers, especially for vadose zone hydrology because of the lack of background concentrations and the relatively good mobility properties. Their use in hydrological applications has received considerable attention over the past 20 years. They appear to be useful tracers that behave under most conditions found in soils and aquifers nearly conservative. FBA tracers are applied in studies of both porous and fractured aquifers as well as in water flow and solute transport in the unsaturated zone. Most FBAs are quite expensive. The greatest potential use of FBAs would appear to be in multitracer tests due to the wide variety of available isomers in this tracer family displaying comparable characteristics (Dahan and Ronen, 2001; Hu and Moran, 2005). The stable isotopes 18O and 2H are used as environmental tracers, but the isotopes can also be used as artificial tracers when water with a specified isotope composition (usually with enriched concentrations of 18O or 2H) is injected into a system. In laboratory tests, lysimeter and groundwater studies, and for investigations of solute transport in the unsaturated zone and water flow in the soil–vegetation system, deuterated water has been used successfully (Garcia Gutie´rrez et al., 1997; Himmelsbach et al., 1998; Becker and Coplen, 2001; Stamm et al., 2002; Mali et al., 2007). An advanced application of deuterated water is the estimation of tree transpiration and the investigation of plant water uptake in the xylem flow (Calder, 1992). Deuterium can be purchased in concentrated form (i.e., water with a high portion of 2H2O molecules), but only

applications involving relatively small volumes of water are feasible because of the high costs. Despite this restriction, deuterium is an attractive artificial tracer, particularly for investigations in the unsaturated zone and plant water transport (Ko¨niger et al., 2010). Due to even higher costs, 18O is hardly used as artificial tracer. The use of dissolved gas tracers as environmental tracers in paleohydrological studies was already proposed 50 years ago. The widespread use as tracers was inhibited by technical difficulties related to the injection, sampling, and analysis of the gases. Many of these problems have been overcome in recent times and, since the 1990s, their application as artificial tracers in hydrology has increased. Usually, applied gas tracers include helium (He), neon (Ne), stable isotopes of krypton (Kr), and SF6 (Wilson and McKay, 1996; Solomon et al., 1998; Divine et al., 2003). The advantages are inert and nontoxic behavior in hydrological systems as well as low background concentrations compared to the concentrations of the injected dissolved gas. This means that dissolved gas tracers can be used for tracer experiments, including large volumes of water. The volatile nature of these tracers makes them obviously different from other tracers and demands specific considerations for tracer injection, sampling, and analysis to prevent unwanted degassing. The volatile nature of gas tracers can, however, also be used as advantage when degassing is used on purpose. This approach has been suggested, for instance, to delineate unsaturated zones or to detect pools and residual zones of nonaqueous phase liquids (NAPLs) in the subsurface using dissolved gas as a partitioning tracer. Nonfluorescent dye tracers applied in staining techniques have attracted remarkable interest as a tool to demonstrate and study the occurrence of preferential flow in soils (Flury and Wai, 2003; see also Chapter 2.13 Field-Based Observation of Hydrological Processes). The visualization staining experiments in vadose zone hydrology rely on the otherwise problematic sorption effect of the tracer, ensuring its distinct visibility along its flow pathways. This is in contrast to the classical idea of tracer applications, as the tracer remaining along the flow pathway is of interest rather than the tracer outflow.

2.09.4 Tracer Hydrology Applications Tracer methods allow numerous hydrological questions to be studied. In this section, some of these applications are discussed. The applications used as examples here help to answer the following questions: Where does storm runoff come from (Section 2.09.4.1)? How long does water travel in a catchment from rainfall to runoff (Section 2.09.4.2)? What are the main sources for nitrogen in streams (Section 2.09.4.3)? How can the transport in hydrological systems be described mathematically (Section 2.09.4.4)? How much water is flowing in a stream (Section 2.09.4.5)? How can groundwater recharge be estimated based on chloride concentrations (Section 2.09.4.6)? Also, how can flow in a complex porous aquifer be described (Section 2.09.4.7)? While the tracer approaches listed here include some of the most important applications of tracer hydrological methods, this section is of course by no

Tracer Hydrology

means a complete review of all approaches by which tracer methods make important contributions to hydrology (see also Chapter 2.13 Field-Based Observation of Hydrological Processes and Chapter 2.20 Stream–Groundwater Interactions).

2.09.4.1 Hydrograph Separation One of the most significant contributions of tracer methods to hydrological science is the result of isotopic hydrograph separation studies. The idea is that one can estimate the contribution of groundwater to stream runoff during events based on the mixing ratio of precipitation and groundwater in the stream. Starting with the influential works by Pinder and Jones (1969) and Sklash and Farvolden (1979), many studies have used this approach to estimate the amount of old water in stream flow events based on the isotopic composition of the different components. This old water is usually interpreted as groundwater already being stored in the catchment before the event compared to precipitation as new water. Isotopic hydrograph separation has also been used for studying snowmelt runoff (Rodhe, 1981, 1987). Results, especially in humid catchments, have repeatedly shown that this old water is a surprisingly large portion of the total event runoff for both rainfall and snowmelt events (Genereux and Hooper, 1998). These results dramatically changed the view on runoff generation from theories involving significant amounts of overland flow to theories where groundwater plays a more prominent role. The tracer approach to hydrograph separation requires the concentration of isotopes or other tracers in the different sources of stream water (e.g., precipitation and groundwater) to be significantly different. Hydrograph separation is then the computation of the relative contributions of these sources based on end-member-mixing analysis (EMMA; Hooper et al., 1990). The basic idea is that different flow components can be separated based on mass balance calculations for both water and tracer(s) based on known end-member compositions. Details on the method are given by Genereux and Hooper (1998) and Buttle (2005). In principle, end-member analysis can be used to differentiate any number of flow components by using the same number of end-members. In practice, however, hydrograph separation is mainly applied for two or three components, as it is difficult to find more suitable, distinctly independent tracers and also error propagation becomes an increasingly problematic issue when more endmembers are used. In its simplest version, the end-member analysis is used to distinguish two components which are often called new and old water or event and pre-event water. Old, or pre-event, water is the water that is already in the catchment before the event starts, whereas new, or event, water is the water that enters the catchment as rainfall or snowmelt. Combining the mass balance equations for water (Equation (5)) and tracer (Equation (6)), the fraction of old water, X [–], in the event stream flow can then be computed (Equation (7)). Q [L3 T1] is the flow of water, C [M L3] is the concentration of the tracer and the subscripts T, P, and E refer to total, pre-event, and event, respectively. While the d-values are, strictly speaking, no concentrations, the d-values can be generally used directly as

227

concentrations in these calculations:

QT ¼ Qp þ QE

ð5Þ

cT QT ¼ cp Qp þ cE QE

ð6Þ

Qp CT  CE ¼ QT Cp  CE

ð7Þ

X¼

The isotope concentrations of both event water and stream flow are measured during the event, whereas the isotopic composition of the pre-event water is usually assumed to equal the composition of stream flow before the event. This seemingly simple method of hydrograph separation is complicated by the fact that the isotopic composition of both event and pre-event water might vary in time and space. The problem of defining input concentrations is of special concern when performing hydrograph separations for snowmelt events, because the isotopic composition of the event ( ¼ melt) water may, in this case, vary considerably with time. Laudon et al. (2002) suggested a method to consider these temporal variations of the event water concentration. Their socalled runCE method considers the timing and amount of melt water entering the soil water reservoir and the portion of previously melted water which already left the catchment (i.e., runoff of event water). By assuming full mixing of the soil reservoir, the event-water isotopic composition is computed based on the cumulative snowmelt (including rain water) from the snow lysimeters and the cumulative volume of melt water that has drained from the snow pack but has not yet left the catchment as stream flow during the event. In theory, using 18 O and 2H for hydrograph separation should provide the same results. Lyon et al. (2009), however, demonstrate that results might differ for the two isotopes and attribute this to spatial and temporal isotopic variability. Most often, hydrograph separations have been performed in single catchments. There is great value in comparative studies with simultaneous measurements for hydrograph separation in nearby catchments as demonstrated for springflood hydrograph separations for 15 nested catchments in Northern Sweden (Laudon et al., 2007). There was a good correlation between the portion of new water (i.e., snowmelt) in the streams and wetland percentage in the catchments, which was interpreted as an effect of surficial runoff on top of frozen wetlands. Only by using several catchments could these differences in runoff generation mechanisms be inferred.

2.09.4.2 Catchment Transit Time Estimation Water spends different amounts of time in a catchment from entering the catchment as precipitation until leaving the catchment as runoff due to a variety of possible flow pathways. Transit time distributions can be used to describe this variation and the mean of these distributions, the MTT, is an important characteristic of a catchment (McGuire and McDonnell, 2006). In the literature, often, the terms transit (or travel) time and residence time are used with no distinction. However, in most systems, there is a difference between the time water parcels need to travel from input to output and the time a water parcel has been in the system. If

228

Tracer Hydrology

the simple case of a piston flow system is considered (i.e., each water parcel travels the same flow path and there is no dispersion), the MTT will be twice the mean residence time (McGuire and McDonnell, 2006). The transit time distribution g(t) can be thought of as the tracer concentration response in runoff at the outlet to an instantaneous, conservative tracer addition over the entire catchment area (for simplicity, a zero background concentration is assumed; Maloszewski and Zuber, 1982; McGuire and McDonnell, 2006). The transit time distribution can be described by

gðtÞ ¼ Z

cðtÞ

¼

N

cðtÞ dt

cðtÞQ M

ð8Þ

irrigated water differed clearly from that of the natural precipitation due to fractionation during lake evaporation. This resulted in a step change of input water isotope concentrations, which could be used to estimate the transit time distribution directly. In most cases, however, there is no instantaneous input (or change of input) of a tracer to the catchment, but transit time distributions are derived from time series of the input and output of a conservative tracer such as 18O. In this case, the transit time distribution g(t) can be derived by inverse modeling using the convolution integral approach. This means that the concentration at the outlet at time t, cout(t), can be expressed as a function of g(t) and the input concentrations, cin, during the preceding period (Equation (14)):

0

c(t) [M L–3] is the tracer concentration caused by an injection of a certain mass of tracer (M [M]) at t ¼ 0 and Q [L3 T–1] is the discharge. The transit time distribution g(t) describes how much of the tracer at a given time is leaving the catchment. The MTT of the tracer (tM) is then the average arrival time of the tracer at the catchment outlet (Equation (9)):

Z

N

t cðtÞ dt tM ¼ Z0

¼

N

cðtÞ dt

Z

N

ð9Þ

t gðtÞ dt 0

0

Commonly used transit time distribution functions, g(t), include the piston flow model (no mixing, Equation (10)), the exponential model (complete mixing, Equation (11)), the combined exponential piston flow model (Equation (12)), or the advection–dispersion model (Equation (13)) (see also Section 2.09.4.4):

gðtÞ ¼ dðt  tM Þ

ð10Þ

where d(t) denotes the Dirac delta function:

  1 t gðtÞ ¼ exp tM tM

ð11Þ

  Z Zt gðtÞ ¼ exp  þ Z  1 for t4 ðZ  1ÞtM =Z tM tM gðtÞ ¼ 0 for tr ðZ  1Þ tM =Z 1 ð1  t=tM Þ2 gðtÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp  4 PD t=tM t 4pPD t=tM

ð12Þ

! ð13Þ

PD [–] is the dispersion parameter and Z [–] is the ratio of the total water volume in the system to the volume of that part of the water characterized by the exponential transit time distribution. The roof project at Ga˚rdsjo¨n, Sweden, provided a special opportunity for transit time estimations (Rodhe et al., 1996). In this catchment scale experiment, a transparent plastic roof was constructed below the tree crowns to intercept the acid deposition. Beneath the roof, water input to the catchment was simulated by an irrigation system using water from a nearby lake for irrigation. The isotopic composition of the

cout ðtÞ ¼

Z

N

gðtÞ cin ðt  tÞdt

ð14Þ

0

In Equation (14), steady state is assumed, that is, flow does not change over time. This can be relaxed if t and t are expressed as flow time instead of clock time. Equation (14) also assumes that the flow pathways do not change significantly over time and that the transit time distribution, thus, is timeinvariant. In reality, g(t) might vary with time, because, for instance, different, faster flow pathways are activated during wetter conditions. These issues make transit time estimates challenging (McDonnell et al., 2010). As for the hydrograph, MTT estimates are of special interest when they can be compared for different catchments. McGuire et al. (2005) computed MTT estimates for seven catchments in Oregon and found that these values could be related to a topography-based travel time estimate. Tetzlaff et al. (2009) extended this approach to 55 catchments in five different regions and found that the relations found in Oregon hold in some regions, whereas soil controls on transit times caused reversed relations between the topography-based travel time estimate and MTT in other regions.

2.09.4.3 Analysis of Sources of Nitrogen in Streams Tracer methods can also be used to trace the sources of nutrients and pollutants and, thus, support sustainable watershed management by providing information about origins and flow paths of nutrients and pollutants. The basic idea is similar to hydrograph separation, namely that different sources can be identified by their specific chemical composition. The different composition of water chemistry can thereby be used as a fingerprint to identify different sources of pollutions. This approach can be mathematically formalized as EMMA. In the case of nitrogen, the isotope 15N can be used to identify sources of nitrogen in the stream flow, especially when combined with 18O measurements, since different sources of nitrogen are characterized by different isotopic compositions (Figure 5 Kendall, 1998). Several recent studies, mainly in North America, have highlighted the potential of using the combination of the two isotopes to determine the primary sources, flows, and fates of nitrogen at the landscape scale. While there is a great potential of using isotopes for the identification of sources, several studies also point out as-yet unsolved problems with these approaches (Bedard-Haughn

Tracer Hydrology

229

60.0 −

NO3 in rainfall

50.0

δ18O-NO3 (‰)

40.0 Denitrification

30.0 −

NO3 Mineral fertilizer

20.0

10.0

0.0 −10.0 −10.0

NH4+ fertilizer −5.0

Soil N

0.0

5.0

Sewage/manure

10.0 δ15N-NO3 (‰)

15.0

20.0

25.0

30.0

Figure 5 Results of nitrate isotope analysis and isotopic composition of various sources of nitrate. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

et al., 2003). Fractionation processes of NO–3, for instance, might alter the d15N source signature. Wide ranges of d18O values for different nitrogen sources can make a quantitative source apportionment difficult. Furthermore, there are issues related to the sample collection of nitrate and the analysis of nitrogen and oxygen isotope ratios, as discussed by Silva et al. (2000). Despite all these difficulties, the combination of N and O isotope information may provide a valuable tracer approach for distinguishing among potential sources of nitrate. Battaglin et al. (2001), for instance, concluded based on isotope data, that ‘‘in-stream N assimilation and not denitrification accounts for most of the N loss in the lower Mississippi River during the spring and early summer months.’’

2.09.4.4 Advection–Dispersion Modeling The advection–dispersion equations (ADEs), also called transport equations, are often used to describe the solute transport in systems such as aquifers or stream reaches. Advection refers to the transport of dissolved substances with the bulk water flow, whereas dispersion is caused by mechanisms such as velocity variations, mixing of different flow pathways, and molecular diffusion. Together with tracer experiments, ADE can be used to infer system parameters by inverse modeling. Obviously, it is important that the mathematical description of tracer transport and tracer behavior in the studied system is adequate and, in many situations, the simple ADE might not be appropriate because of processes such as chemical reactions along the flow pathways or an exchange of mobile and immobile water. In a porous media, saturated flow can be described by a three-dimensional (3D) ADE when there is a steady, Darcian flow, no exchange with mobile water, and nonreactive solutes

(ideal tracers). In most cases, the solution of such an ADE can only be found by applying numerical techniques. In cases where the flow lines in the medium can be assumed to be always parallel to the x-axis and where the tracer is already well mixed through the whole vertical distance of a homogeneous system, for example, by injection into an aquifer through a fully penetrating well, the 3D ADE can be simplified to the 2D form (Equation (15)), which describes tracer transport in the horizontal plane along the flow direction:

DL

q 2C q 2C qC qC þ D v ¼ T q x2 qy2 qx qt

ð15Þ

DL [L2 T1] and DT [L2 T1] are the longitudinal and transverse dispersion coefficients, v [L T1] is the mean water velocity, t [T] is time, and x and y [L] are the Cartesian coordinates in a horizontal plane. Equation (15) can only be solved numerically in most cases. One special case for which there is an analytical solution is an input which can be mathematically described by the Dirac function. This means that an instantaneous injection of the tracer can be assumed, as is adequate for many practical experiments. Equation (16) provides the analytical solution of the 2D ADE for the case of a homogenous aquifer where a tracer of mass M [M] is instantaneously injected over the entire depth at the origin of the coordinate system (i.e., x ¼ y ¼ 0) at time t ¼ 0 (Lenda and Zuber, 1970):

Cðx; y; tÞ ¼

  M x ðx  vtÞ 2 y2 p ffiffiffiffiffiffiffiffiffiffiffi  ð16Þ exp  4DL t 4DT t nH 4pvt 2 DL DT

where H [L] is the mean thickness of the aquifer and n [–] is the effective porosity. The velocity and dispersion coefficients (v, DL, DT) can then be estimated based on a tracer experiment

230

Tracer Hydrology

where observation wells are situated perpendicular to the flow direction. Transverse dispersion is usually much smaller than the longitudinal dispersion. In some cases, transverse dispersion can be neglected; examples are transport through soil columns when the tracer is injected throughout the whole cross section of the column perpendicular to the flow direction, or transport in streams, when the tracer is injected throughout the whole cross section of the stream. If the x-axis is defined to be parallel to the flow direction, the transport equation can then be reduced to its 1D form (Equation (17)), which can be solved analytically for an instantaneous injection (Equation (18); Lenda and Zuber, 1970; Kreft and Zuber, 1978):

q 2C qC qC ¼ v qx2 qx qt   M x ðx  vtÞ 2 Cðx; tÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi3ffi exp  4DL t Q 4pDL t DL

ð17Þ ð18Þ

Q [L3 T1] is the volumetric flow of water (flow rate through the column, river discharge or pumping rate in a combined pumping-tracer experiment). The two unknown parameters (v, DL) can be calculated from the observed concentration (tracer breakthrough) curve. Both the MTT of water, t0 ¼ x/v [T], and the dispersion parameter, PD ¼ DL/(vx) [–] can be derived from tracer experiments. The latter is a measure of system heterogeneity and affects the shape of the theoretical tracer concentration curve. With increasing PD values, the time to peak concentrations decreases. The solution of the inverse problem (i.e., the estimation of the transport parameters) can be obtained in an automatic fitting procedure that combines the least squares method with Taylor series approximation of both solutions (Maloszewski, 1981). The assumption is that the inverse problem (best fit) is solved when the values of parameters (t0 and PD) are chosen so that the sum of squared differences between the theoretical and observed concentrations is minimal. The parameters can also be obtained by manually fitting the appropriate theoretical solutions to experimental concentrations using a trialand-error procedure. Often, parameter estimation is also carried out using approximate solutions of the ADE combined with the method of moments or the cumulative curve method. However, the usefulness of these methods in practice is limited because their application is possible only under very restricted conditions. Experimental TBCs are observed which deviate from the idealized tracer concentration curve described theoretically by the dispersion equation, for instance, TBCs with multiple peaks or featuring a strong tailing effect. To address the underlying processes, advanced models for tracer experiments in complex systems, such as multiflow and double-porosity systems, have been developed. In cases where mass transfer between mobile and immobile zones or states by physical or chemical processes is of importance, the late tailing of TBCs might be heavily influenced by these processes. Haggerty et al. (2004) analyzed results from 316 tracer experiments compiled from literature

and found evidence of multiple timescales of mass transfer in aquifers and soils. They also concluded that mass transfer parameters were related to experimental duration, which implies that single-rate mass transfer model predictions over longer (or shorter) timescales than the duration of the experiment should be interpreted with care. In streams, there are two types of transient storage, in-channel dead zones or out-of-channel hyporheic zones. While these storages are associated with different biogeochemical processes, it is difficult to distinguish between these two types of transient storage based on stream tracer experiments. The two storages might also be characterized by different residence time distributions (Gooseff et al., 2005). Similar to aquifers, the late tailing in TBCs is also in streams heavily influenced by the mass transfer between mobile and immobile water (transient storage), which implies that both results are very sensitive to the method used to derive mass transfer parameters and to the length scale of the experiment (Wo¨rman and Wachniew, 2007).

2.09.4.5 Discharge Measurement The tracer dilution method is widely used to determine discharge in small catchments, in headwaters, and especially in poorly accessible regions. It is the only adequate technique to measure discharge in strongly turbulent rivers and springs with bouldery cross sections where other techniques are not suitable. The approach of discharge measurement using artificial tracers (tracer dilution gauging) is based on the principle that the dilution of a known mass of tracer injected into the flow system is in proportion to discharge, if complete mixing of the tracer is ensured. After complete mixing of the injected artificial tracer into the surface water stream, the concentration is measured downstream. The dilution is in proportion to the discharge. An indispensable prerequisite when applying the dilution method is a uniform, complete (3D) mixing of the tracer over the entire body of water at the observed cross section of the watercourse. Downstream of the full mixing point a measurement is possible at any point of the cross section. The distance needed to achieve uniform mixing can differ considerably depending on the channel roughness. The estimation of an appropriate distance requires some experience. In general, increasing roughness results in increased mixing and, thus, shorter distances are needed in streams with a high roughness. Sampling for background concentrations upstream of the injection site is required. For the slug injection method, a known mass of tracer (M [M]) as concentrated solution is poured in bulk into the system as a (quasi) instant impulse (Dirac impulse). The tracer pulse spreads due to vertical, lateral, and longitudinal dispersion as well as turbulent mixing. The longitudinal dispersion causes the typical TBC with a relatively steep increase from the background concentration to the peak, and a gradual decline (tailing) back to the background (Figure 6). Measurements and/or sampling are needed at the measuring cross section during the entire passage of the tracer cloud downstream. Assuming 100% recovery of tracer at the sampling point, provided there are no losses of the tracer mass, and the TBC is measured until it reaches the background level (cb), the

Tracer Hydrology background concentration, cb [g l1] (Equation (20)):

discharge (Q) can then be calculated as follows:

Q¼Z

M N

231

ð19Þ

ðcðtÞ  cb Þ dt

Q ¼ qin

cin  cb cp  cb

ð20Þ

0

M [g] is the injected tracer mass, c(t) [g l1] is the measured concentration at time t, and cb [g l1] is the background concentration. For the constant rate injection method, the tracer solution is injected into the system at a constant rate over an extended period of time. The flux rate of the injected solution and the tracer concentration in the solution are required to be constant over time. The resulting TBC measured downstream typically rises from the background concentration to a constant value, also called plateau concentration (Figure 7). At this point, a sample is taken to determine this constant plateau value at the end of the mixing section. In order to obtain the constant plateau concentration, the duration of the injection has to be sufficiently long. The discharge can then be calculated based on the tracer solution inflow rate, qin [l s1], the tracer solution concentration, cin [g l1], the measured sustained plateau concentration, cp [g l1], and the

Duration of injection

Concentration

Plateau Plateau samples

Background concentration

Time

Figure 6 Schematic presentation of the tracer breakthrough curve after slug injection. At the downstream sampling point, after the mixing length, the tracer breakthrough curve is recorded. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

Concentration

Period of measuring

Upstream slug injection

Background concentration

Time

Figure 7 Schematic presentation of the tracer breakthrough after constant rate injection (cf. via Mariotte bottle). At the downstream sampling point, the tracer concentration rises to a plateau value. From Leibundgut Ch, Malozewski P, and Ku¨lls Ch (2009) Tracers in Hydrology. Chichester: Wiley.

Both the slug injection and the constant injection approaches have their advantages as well as problematic issues. A main advantage of the slug injection is that the injection is experimentally rather simple and does not require any special instruments. On the other hand, continuous registration of the TBC is required. The measurement is also directly sensitive to any error in the value used for the injected tracer mass, M. Errors might be caused by incomplete dissolution of salt when preparing the injection solution. The constant rate injection approach requires accurate concentration measurements only at two points in time, but the injection requires special arrangements to ensure a constant rate of tracer injection over a long period. Although any good soluble chemical is a potential tracer for the dilution method, only the salt tracers (NaCl) and fluorescent tracers are routinely used for operational purposes. When choosing a tracer for the dilution discharge measurement, the following requirements should be considered. A tracer which does not undergo photolytic decay is absolutely required, since the solution to be measured is more or less completely exposed to daylight due to the turbulences in rivers. Since all fluorescent tracers are at least slightly affected by the photolytic decay, the tracer experiment should be short (usually unproblematic) or scheduled for a night test using this type of tracer. Furthermore, sorption onto streambed materials should be negligibly low for the tracer used. Sorption leads to retention of the tracer cloud, thus the measurement is incorrect. Finally, pH independence, low detection limit, and toxicological harmlessness are required. Among others, the fluorescent tracer amidorhodamine G (sulforhodamine G) is the most applied fluorescent tracer in dilution gauging. However, due to the strong sorption effects of the rhodamines group, uranine is increasingly used. It may be used especially in tests conducted at night or for short tests in order to avoid photolytic decay. Background concentrations of fluorescent tracers in most rivers are negligible. Hence, only small amounts of tracer are required even for higher discharge. This is a major advantage compared to salt tracers. For salt tracers, the measurement is based on the relation between salt concentration and electrolytic conductivity, as there is a linear correlation between these quantities. The natural electrolytic background conductivity, due to the mineralization of the stream water, has to be considered. Calibration of the measuring instruments with stream water is essential to estimate the specific correlation between salt concentration and the electrolytic conductivity of the water to be measured. Site-specific calibration is needed because the specific chemical composition of the stream water may influence the relation between salt concentration and electrolytic conductivity. For slug injection measurements, the amount of salts required are usually relatively high in order to achieve a peak concentration which has an electrolytic conductivity of more than 100 mS cm1 above the background concentration. As a general rule, about 1 kg of salt (NaCl) should be used per

232

Tracer Hydrology

100 l s1 estimated discharge, depending on the background concentration. With fluorescent tracers, the required injection mass is much smaller because of their lower detection limits (see Section 2.09.3.3.2). Using electrolytic conductivity as proxy, the continuous measurement of salt concentrations has become very convenient, especially with the advancement of hand-held field loggers for conductivity measurements. Overall, the slug injection method is usually much easier to use than the constant rate injection method. More detailed instructions for salt dilution measurements can also be found in Leibundgut et al. (2009) and in a series of papers by Moore (2004a, 2004b, 2004c, 2005). Today, fluorescent tracer concentrations can also be easily measured continuously in the stream. Robust portable fluorescent fiber optical fluorometers for in situ measurement are useful and reliable. These instruments operate using a light conductor sensor. Through the light conductor, the excitation signal enters the river water in the same way that the emission signal arrives at the measuring instrument. The evaluated quantity is the intensity of the emission signal. As there is a linear correlation between fluorescence and the dye concentration, the fluorescence is used as a measure for the dye concentrations. When using fluorescent tracers measured by in situ fluorometers, calibration of the instruments with the river water to be measured is required. The background concentration, as well as the whole calibration curve, needs to be specified correctly for any water to be measured. There is a general linearity of the dependency between tracer concentration and fluorescent intensity of the water to be measured. Stream water is filled into a measuring cup, and the tracer concentration is gradually increased while the fluorescence intensity is measured.

2.09.4.6 Chloride-Based Groundwater-Recharge Estimation Chloride concentration measurements can be used for estimating recharge fluxes in the saturated or unsaturated zone in arid and semi-arid regions using the chloride mass-balance (CMB) method (Eriksson and Khunakasem, 1969; Allison and Hughes, 1978; Bazuhaira and Wood, 1996). The method is based on the mass balance of chloride considering long-term input (precipitation) and groundwater (or soil water) concentrations. Due to evaporation, the chloride concentration in the soil increases and the relationship between chloride concentrations in the precipitation and in the soil water or groundwater is thus a measure of evaporation and recharge. The recharge R [mm a1] can be estimated based on the precipitation amount P [mm a1], the chloride concentration in the groundwater (or soil water), cCl,G [mg l1], and the precipitation-weighted mean chloride concentration in the input, cCl,P [mg l1] (Equation (21)):

R¼

P cCl;P cCl;G

ð21Þ

It is important to note that the CMB only considers vertical flow processes and is limited in its applicability by a set of assumptions which have to be fulfilled. These assumptions include only direct recharge can take place (i.e., recharge

resulting from direct infiltration of precipitation), no additional internal source or lateral inflow of chloride exists, chloride concentration in groundwater or soil water experiences no increase or decrease by dissolution, plant uptake or other sink terms, and precipitation amount and chloride concentration in precipitation are not correlated. If these assumptions are fulfilled, in general, the method provides more accurate estimates with increasing evaporation rates.

2.09.4.7 Tracer Experiment in a Porous Aquifer A study performed at a test site situated in Quaternary sediments in the Swiss Central Plateau is presented here as an example of tracer experiments in porous aquifers (Leibundgut et al., 1992). Postglacial Holocene sandy gravel aquifers in the proximity of the Alps are generally known to be strongly heterogeneous. This heterogeneity was also confirmed for the test site through geophysical prospecting and tracer tests. However, in the range of the tracer tests, the aquifer was assumed to be relatively homogenous with a fairly regular thickness of approximately 15 m and a hydraulic gradient uniformly decreasing in the longitudinal axe. In this section, the depth to the groundwater table was found to be o5 m which provided good accessibility. The water table contour lines indicated a convergent flow pattern (Figure 8). The chemical composition of the groundwater indicated no problems caused by potential interactions between the groundwater and the applied tracers. According to preliminary hydrogeological and geophysical tests, the aquifer was found to consist of three distinct layers with different hydraulic conductivities. This multilayered structure of the aquifer required a multilevel sampling within the three layers, which was realized using a well sampling system with packers and sampling at depths of 5, 8, and 11 m. Uranine (1 kg) and naphtionate (20 kg) dissolved in water (90 l) were injected evenly as mixed tracer solution over the whole depth of the aquifer over a period of 1 h (Dirac impulse). The observation wells were sampled for a period of nearly 400 hs using automatic sampling devices. The topmost layer (5 m) and the bottom layer (11 m) were sampled in 4-hr intervals and the middle layer (8 m) in 1-h intervals. Here, only data from one sampling site (D5) located 100 m from the injection well are presented (see Figure 8). TBCs were derived from these observations for uranine and naphtionate, respectively. The evaluation of the breakthrough curves of the three layers (or flow domains) took place under the assumption of constant transport parameters. The resulting dispersivities were far larger than values observed for similar formations and, thus, a reinterpretation of the data was performed. The superposition of three tracer concentration curves shows a multipeak shape (Figure 9). This can be interpreted as a result of flow through parallel layers. The following mean values of aquifer parameters have been assumed for modeling: mean thickness of 10 m and average saturated porosity of 12–17%. The modeling was performed using the transport model described in Section 2.09.4.4 (Maloszewski and Zuber, 1993) and, in particular, the modeling in multilayered systems (Maloszewski et al., 2006).

Tracer Hydrology

0

233

50 m 200m F

4

N

461.0

150m

461.1 E 4

461.4 100m D 5 Observation wells galleries

1.5

46 461.6

7

1.

46

25.9.1.1986

50m

461.1 = m a.s.l

C 5

0m

46 46

1.

Bern SWITZERLAND

9

ZH TestfieldO WILERWALD

1.8

100 km

0

Injection well EP 1

GE

Figure 8 Test site with location of wells and water table contours. From Leibundgut Ch, De Carvalho-Dill A, Maloszewski P, Mu¨ller I, and Schneider J (1992) Investigation of solute transport in the porous aquifer of the test site Wilerwald (Switzerland). Steirische Beitra¨ge zur Hydrogeologie 43: 229–250.

The transport parameters found for the D5 sampling site, for all of three layers and both applied tracers, allowed to reproduce the measured breakthrough curve based on modeled partial curves for the three layers (Figure 9). The partial curves are calculated from the fitted concentration-time curve (breakthrough curve). Both the obtained transport parameters and the aquifer parameters are listed in Table 5. The aquifer parameters were calculated based on the modeling of a naphtionate breakthrough curve, so should be considered as an approximation of the real parameters. The results show a strong heterogeneity of the aquifer in both the vertical and the horizontal direction. In the original paper, the complex issue of a tracer test in porous multilayered aquifer and its modeling is discussed in further detail (Leibundgut et al., 1992).

2.09.5 Concluding Remarks 2.09.5.1 Guidance on Further Reading The following standard textbooks are suitable as further information on tracer methods.

Tracers in Hydrology (Leibundgut et al., 2009) provides a comprehensive, up-to-date presentation of the use of tracer methods in hydrology including modeling and the presentation of selected case studies illustrating the theory. The book Isotope Tracers in Catchment Hydrology (Kendall and McDonnell, 1998) consists of 22 chapters treating the use of isotope tracers in catchment hydrology from different perspectives. This book is a valuable source of information on fundamentals of catchment hydrology, principles of isotope geochemistry, and the isotope variability in the hydrological cycle. Many case studies using isotope tracer methods are described and illustrate the opportunities for using isotope techniques for a wide range of investigations. Environmental Isotopes in the Hydrological Cycle (Mook, 2000), published by IHP and IAEA, is a series of six volumes which give a comprehensive review of theoretical concepts and practical applications in isotope tracer hydrology. The first volume provides a general overview of theory and methods followed by three volumes dealing with atmospheric water, surface water, as well as water in the saturated and unsaturated zone. The fifth volume focuses on human impact on groundwater and the final volume on modeling.

Tracer Hydrology

Uranine (mg m−3)

40

Observed

30

Observed

40

Fitted

Fitted

Partial curve 1

Partial curve 1

Partial curve 2 Partial curve 3 20

10

Naphtionate (mg m−3)

234

Partial curve 2

30

Partial curve 3 20

10

0

0 200

0

400

600

800

1000

0

200

Time (hours)

400

600

800

1000

Time (hours)

Figure 9 Observed tracer concentration and calculated (best fit) curves (in well D 5) for uranine (right) and naphtionate (left). From Leibundgut Ch, De Carvalho-Dill A, Maloszewski P, Mu¨ller I, and Schneider J (1992) Investigation of solute transport in the porous aquifer of the test site Wilerwald (Switzerland). Steirische Beitra¨ge zur Hydrogeologie 43: 229–250.

Table 5 Parameters obtained as a result of modeling of uranine and naphtionate concentration curves (in well D 5); where toj are mean transit time, vj mean water velocity, aLj longitudinal dispersivity each for the respective layer j, Rj relative contribution of partial curve j to the total concentration curve and obtained from those the hydrogeological parameters: to and k are mean values for all layers of the mean transit time and the hydraulic conductivity respectively, kj is the hydraulic conductivity, and hj the thickness of single layers Curve/layer j

1 2 3

Uranine

Naphtionate 1

toj (days)

vj (m d )

aLj (m)

toj (days)

vj (m d1)

aLj (m)

Rj (–)

to (days)

k (m s1)

kj (m s1)

hj (m s1)

6.5 12.1 22.3

15.4 8.3 4.5

7.7 3.7 4.0

5.4 9.3 16.3

18.4 10.8 6.1

6.8 2.6 3.4

0.45 0.32 0.23

9.2

4.7  103

8.0 x103 4.8 x103 2.7 x103

2.7 3.2 4.1

From Leibundgut Ch, De Carvalho-Dill A, Maloszewski P, Mu¨ller I, and Schneider J (1992) Investigation of solute transport in the porous aquifer of the test site Wilerwald (Switzerland). Steirische Beitra¨ge zur Hydrogeologie 43: 229–250.

Isotopes in the Water Cycle: Past, Present and Future of a Developing Science (Aggarwal et al., 2005) presents the history of isotope hydrology, state-of-the-art applications, and new developments. The applications described in this book include quantification of groundwater resources, water balance studies, and investigations of past and present global environmental and climate changes. The books by Clark and Fritz (1997) and Mazor (2004) focus on the use of environmental tracers in groundwater systems, the first in particular on hydrogeology and environmental tracers, the latter in particular on environmental tracers, noble gases, and chemistry. The book Tracing Technique in Geohydrology by Ka¨ss (1998) describes the various tracing techniques. Specific aspects of tracer hydrology have also been discussed in review papers on topics such as transit times (McGuire and McDonnell, 2006) or dye tracers in the vadose zone (Flury and Wai, 2003).

2.09.5.2 Reflections and Future Research Tracer hydrology will continue to play an important role for the advancement in understanding of hydrological systems.

Developments of analytical techniques provide new opportunities. The reduction of costs per sample allows more samples to be analyzed. Decreasing the required sample volume allows samples to be analyzed from sources where only limited amounts of water can be sampled. Reduced detection limits allow tracer signals at higher dilutions to be measured. New measurement techniques also increased the interest in using temperature recently, because fiber optic distributed temperature sensors allow measurements in high spatial and temporal resolution (Selker et al., 2006). Finally, there are new natural and artificial substances that could be used as tracers such as DNA (Smith et al., 2008) and diatoms (Pfister et al., 2009). The integration of tracer data into the development and testing of hydrological and environmental models is still not fully explored. Tracer approaches can provide important additional information for the evaluation of models. On the other hand, how to use this information is often not straightforward and additional model parameters or routines might be needed to allow comparison of model simulations with experimental tracer data. How to best integrate experimental approaches, mathematical methods for their interpretation, and hydrological models, such as catchment and groundwater models or soil–water–atmosphere transfer

Tracer Hydrology

(SVAT) schemes, will continue to be important research questions in tracer hydrology.

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2.10 Hydrology and Ecology of River Systems A Gurnell, Queen Mary, University of London, London, UK G Petts, University of Westminster, London, UK & 2011 Elsevier B.V. All rights reserved.

2.10.1 2.10.2 2.10.2.1 2.10.2.2 2.10.3 2.10.3.1 2.10.3.2 2.10.3.3 2.10.3.4 2.10.3.5 2.10.4 2.10.4.1 2.10.4.1.1 2.10.4.1.2 2.10.4.2 2.10.4.2.1 2.10.4.2.2 2.10.4.2.3 2.10.5 References

Introduction Key Hydrological Characteristics of River Networks Flow Indices and Regimes Hydrological Connectivity River-Corridor Dynamics River Regimes and River Styles Changing River Styles The River-Corridor Habitat Mosaic Distribution and Connectivity of Physical Habitats Habitat Dynamics and the Role of Plants as Ecosystem Engineers Aquatic Ecosystems Instream Flows and Flow Regimes The significance of multidimensional variations in flow characteristics Adaptations of biota to running water Ecohydraulics and Mesohabitats Hydraulic stream ecology and the mesohabitat template Habitat-suitability criteria Models of biological responses to changing flows Managing River Flows to Protect Riverine Ecosystems

2.10.1 Introduction Scientists have long explored relationships between hydrological processes and river ecosystems and many notable concepts, frameworks, and hypotheses have been proposed and empirically tested as the field has advanced over the last 50 years. These advances in scientific understanding (summarized in Table 1) demonstrate the fundamental and complex controls that hydrological processes impose on aquatic and riparian ecosystems. In many cases, these hydrological controls are direct, for example, specific properties of river flows are related to the dynamics and life cycles of many organisms (e.g., the flood pulse concept (FPC), Junk et al., 1989). In other cases, the controls are indirect. Of particular significance here is the influence of hydrological processes on river-channel forms and dynamics that in turn determine the mosaic of habitat patches available for organisms (e.g., the impact of flood disturbance on the succession of riparian plant communities (De´camps and Tabacchi, 1994), patch dynamics, and the shifting-habitat mosaic within river systems (Pringle et al., 1988; Stanford et al., 2005; Latterell et al., 2006). Most recently, research has started to explore how some organisms found in aquatic and riparian environments, particularly certain plant species, not only respond to but also control hydrological and geomorphological processes (Corenblit et al., 2007), supporting interactions that increase both the physical- and bio-complexity of river landscapes (e.g., Gurnell et al., 2005). In this chapter, we explore the ways in which flows, directly and indirectly, drive riverine ecosystems. We investigate three

237 237 237 240 242 242 245 246 248 249 252 252 252 254 255 255 256 258 258 263

main themes, which progressively move focus from the catchment (river-flow dynamics) to the river corridor (rivercorridor dynamics and the riparian zone) and then to the river (in-river dynamics and the aquatic ecosystem). In Section 2.10.2, we explore the key spatial and temporal properties of river flows that have been shown to have significance for rivercorridor (riparian and aquatic) ecosystems. In Section 2.10.3, we consider the impact of hydrological processes on river corridors, ranging from their impact on gross corridor morphology to their influence on the character and turnover of the mosaic of physical habitats contained within the corridor, placing particular emphasis on the riparian zone and the style of river that it encloses. In Section 2.10.4, we focus on the importance of hydrological processes for aquatic ecosystems and organisms, first, emphasizing the importance of the river’s flow regime and then on the finer spatial scale of mesohabitats and hydraulic stream ecology. The chapter concludes by considering how river flows can be managed to protect riverine ecosystems. In providing an overview of relationships between hydrology and ecology within river corridors, there is inevitable overlap with the themes of other chapters in this volume. We deliberately exclude discussion of biogeochemical processes, since these are the theme of Chapter 38. However, we include some topics that are fundamental to our theme, while being the focus of other chapters. Flow hydraulics (Chapter 2.07 The Hydrodynamics and Morphodynamics of Rivers), erosion and deposition of sediment (Chapter 2.12 Catchment Erosion, Sediment Delivery, and Sediment Quality and Chapter 2.20 Stream–Groundwater Interactions), and

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238 Table 1

Hydrology and Ecology of River Systems Some river ecosystem concepts, frameworks, and hypotheses that explicitly incorporate hydrological processes

Concept/framework/hypothesis

Dimensions emphasized

Source

Stream order within river networks Hydraulic geometry approach: linking channel form to discharge Zonation of fish communities along rivers Concept of different river-flow regimes Slope-discharge control of river-channel patterns The hyphoreic zone Dynamic equilibrium Zonation of macroinvertebrates and fish along rivers Fluvial process-form dynamics The ecology of running waters Drainage basin form and process Nutrient recycling Instream flow needs Structure and process-form dynamics of the fluvial system River continuum concept Resource spiraling Ecosystem perspective of riparian zones Groundwater and stream ecology Serial discontinuity concept Tributaries modify the river continuum concept Hydraulic stream ecology Nested-hierarchical framework for stream habitat classification River–floodplain connectivity Patch dynamics in lotic systems Role of disturbance in stream ecology Flood pulse concept Four-dimensional nature of lotic systems Aquatic–terrestrial ecotone Hyporheic corridor concept Flood-disturbance regime and succession of riparian plant communities Hydraulic food-chain models Fluvial hydrosystem approach Indicators of hydrologic alteration The natural-flow regime Process domains and the river continuum Flow pulse concept Geomorphic thresholds in riverine landscapes

Longitudinal Longitudinal

Horton (1945) Leopold and Maddock (1953)

Longitudinal Temporal Longitudinal Vertical Temporal Longitudinal Four dimensions Longitudinal Four dimensions Longitudinal Longitudinal Four dimensions Longitudinal Longitudinal Four dimensions Vertical Longitudinal Longitudinal Longitudinal Four dimensions

Huet (1959) Parde´ (1955) Leopold and Wolman (1957), Lane (1957) Orghidan (1959) Hack (1960) Illies and Botosaneanu (1963) Leopold et al. (1964) Hynes (1970, 1975) Gregory and Walling (1973) Webster et al. (1975) Orsborn and Allman (eds., 1976) Schumm (1977) Vannote et al. (1980) Newbold et al. (1981, 1982), Elwood et al. (1983) Gregory et al. (1991) Hynes (1983) Ward and Stanford (1983a) Bruns et al. (1984) Statzner and Higler (1986), Statzner et al. (1988) Frissell et al. (1986)

Lateral Temporal Temporal Temporal and lateral Four dimensions Lateral Vertical Temporal

Amoros and Roux (1988) Pringle et al. (1988) Resh et al. (1988) Junk et al. (1989) Ward (1989) Naiman and De´camps (1990) Stanford and Ward (1993) De´camps and Tabacchi (1994)

Temporal Four dimensions Temporal Temporal Longitudinal Temporal and lateral Temporal, longitudinal, lateral Four dimensions Longitudinal Temporal Longitudinal Temporal Longitudinal, lateral Four dimensions

Power et al. (1995) Petts and Amoros (1996) Richter et al. (1996) Poff et al. (1997) Montgomery (1999) Tockner et al. (2000) Church (2002)

Flow-sediment–biota relations Processes and downstream linkages of headwater streams Ecological effects of drought perturbation Network dynamics hypothesis Effective discharge for ecological processes River styles framework The riverine ecosystem synthesis Fish environmental guilds Vegetation as a driver of physical- and bio-complexity in fluvial corridors Hydrologic spirals Ecological limits of hydrologic alteration

Four dimensions

Osmundson et al. (2002) Gomi et al. (2002) Lake (2003) Benda et al. (2004) Doyle et al. (2005) Brierley and Fryirs (2005) Thorp et al. (2006) Welcomme et al. (2006) Gurnell et al. (2005), Corenblit et al. (2007)

Longitudinal and vertical Temporal

Poole et al. (2008) Poff et al. (2009)

interactions between surface and groundwater all contribute to the definition of river-corridor habitats. These processes also drive the geomorphological features (landforms and sedimentary structures) of river corridors that are the

foundation of their habitat mosaic. We integrate aspects of all these themes to provide a robust context for our discussions of the relationships between hydrology and the river corridor.

Hydrology and Ecology of River Systems

2.10.2 Key Hydrological Characteristics of River Networks 2.10.2.1 Flow Indices and Regimes A fundamental premise is that the characteristic community of species that comprise the riverine ecosystem is adapted to the natural-flow regime (Naiman et al., 2002). Despite incomplete understanding of precisely how hydrological processes support river ecosystems, it is accepted that the ecological integrity of river systems depends upon their dynamic character (Poff et al., 1997). As a result, a great deal of research has been devoted to (1) extracting key parameters of river flows that appear to be of ecological importance (e.g., Olden and Poff, 2003; Doyle et al., 2005); (2) identifying direct human modifications of river flows (e.g., Vo¨ro¨smarty et al., 1997; Vo¨ro¨smarty and Sahagian, 2000) and indirect human-induced hydrological changes attributable to catchment land-use change (e.g., Allan, 2004; Gurnell et al., 2007) as well as climate change (e.g., Huntington, 2006); and (3) understanding how to manipulate impacted river flows to reinstate those crucial elements of the flow regime that sustain river ecosystems (e.g., Petts, 2009; Poff et al., 2009). A fundamental but simple method of describing flow conditions at a site is to construct a flow-duration curve, which graphically displays the proportion of time that any particular river flow is exceeded at a site. Flow-duration curves provide a summary of both the central tendency and dispersal of flows and so support comparison of flow characteristics across space and time as well as permit extraction of summary flow indices such as flow percentiles (e.g., Patel, 2007). Their simplicity and high information content have made flow-duration curves an important tool in water resource and river-flow management. Various flow percentiles, particularly the 95th percentile, have been associated with the maintenance of biological and chemical quality in surface waters (e.g., Dakova et al., 2000), and have been used to prescribe flows for regulated systems, such as the minimum acceptable flow (MAF) that has been required for river systems in England and Wales since the 1963 Water Resources Act (Petts, 1996). Similar proposals based on specific proportions of median (50th percentile) or mean annual flow have been adopted in many parts of the world (Arthington et al., 2006). Moreover, as pressures on water resources have increased over the last 50 years, the flow-duration curve has been used to underpin operational rules for delivering ecologically acceptable flow regimes that balance the requirement for water abstractions with the hydrological needs of the river ecosystem (e.g., Petts et al., 1999). A more sophisticated approach to characterizing river flows, which incorporates not only flow magnitude but also timing, is to define the (mean) annual flow regime. This describes the typical annual sequence of flows based on average monthly or weekly discharges over a period of years (typically 20 years of records). Parde´ (1955) was the first to recognize that strong spatial variations in the annual flow regime existed, whereas the ecological importance of the annual flow regime was encapsulated in the flood pulse concept of Junk et al. (1989). Haines et al. (1988) undertook a global analysis of flow regimes, defining 15 classes from a cluster analysis of 32 000 station years of data from 969 stations (Figure 1(a)). A more recent global analysis of monthly streamflows from

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1345 sites by Dettinger and Diaz (2000) generated similar results, recognizing 10 broad classes of annual regime (Figure 1(b)) and showing wide regional contrasts in the timing of monthly flow extremes and the amplitude of variations in flow between months (i.e., regime seasonality) as well as variability in regime between years. At the regional level, Harris et al. (2000) applied principal components analysis (PCA) to 25 years of monthly river flow and air-temperature data from four sites in the UK to classify subregional shifts in both temperature and river-flow-regime magnitude and timing (Table 2). Since both the temperature regime (Caissie, 2006) and flow regime (Junk and Wantzen, 2004) are recognized as major controls on river ecology, such joint analyses may prove a profitable line of research to further advance understanding of river-ecosystem dynamics. For example, the flow and temperature regime classification approach described by Harris et al. (2000) was combined with analysis of 6 years of macroinvertebrate sample data for a groundwater-fed river in South East England by Wood et al. (2001). The analysis revealed a significant difference in macroinvertebrate community abundance in relation to flow-regime class and its component timing and magnitude classes across all scales of analysis (entire river, upstream and downstream sectors, and habitat type), and the influence of air-temperature regime, used as a surrogate for water temperature, varied significantly between riffle sites. Characterization of flow regime as a context for ecological studies has more frequently been based on a series of indices that represent magnitude, frequency, duration, and timing of flow properties rather than an integration of the entire annual regime. Richter et al. (1996) devised a method for calculating indicators of hydrologic alteration to support aquatic ecosystem management. This method compares ecologically relevant hydrological indices extracted from pre- and post-impact (or reference and impacted system) data series, so that contrasts in the properties of the index frequency distributions can be used to define key hydrological changes within impacted systems. Olden and Poff (2003) investigated 171 published hydrological indices to identify a reduced index set capable of explaining the major part of the statistical variation encompassed in the full set. By subjecting a data set containing estimates of all of the indices for 420 US gauging sites to PCA, they illustrated considerable redundancy between many of the indices. They interpreted the results of their analysis to aid researchers’ choice of a subset of indices suited to the hydroclimatic region and ecological question being addressed. More recently, Monk et al. (2007) presented a similar PCA-based analysis of hydroecological data for 83 rivers across England and Wales. Three recent studies illustrate how index-based characterizations of flow regimes differ according to geographical variations in controlling factors such as climate, land cover, and catchment characteristics. Poff et al. (2006) analyzed 10 indices describing properties of peak flows, low flows, flow duration, and variability for 158 catchments from four hydroregions of the United States. They showed how regional flow regimes are modified by land cover and the presence of dams. Snelder et al. (2005) demonstrated the effectiveness with which an a priori, map-based, classification of New Zealand river systems and river sectors (the River Environment Classification (REC)) discriminated between sites with

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Hydrology and Ecology of River Systems 0.4

0.3

0.2

Proportion of annual flow

0.1

0.0 (a)

2

4

6

8

10

12

2

4

6

8

10

12

0.4

0.3

0.2

0.1

0.0 Month (b)

Figure 1 Classifications of global river flow regimes by (a) Haines AT, Finlayson BL, and McMahon TA (1988) The global classification of flow regimes. Applied Geography 8: 255–272 and (b) Dettinger MD and Diaz HF (2000) Global characteristics of stream flow seasonality and variability. Journal of Hydrometeorology 1: 289–310.

different flow regimes. The REC is based on a hierarchy of climate and topographic factors that are assumed to influence the physical and biological characteristics of rivers. Following Richter et al. (1996) and Poff et al. (1997), Snelder et al. (2005) selected flow variables that characterized five ecologically relevant properties of the intra-annual flow regime: magnitude, frequency, duration, and timing of high and low flows and measures of rate of change of flow. Finally, McMahon and Finlayson (2003) derived a variety of hydrological indices to explore the low-flow hydrology of Australia. They investigated styles, trends, and cycles of low-flow sequences within reference rivers located in different Australian flow-regime zones and considered their implications for indigenous aquatic biota. Given the acknowledged importance of flow regimes for river ecosystems, it is important to consider the degree to which river-flow regimes may respond to climate change, with or without direct human impacts on land use and regulation of river flows. While broad overviews of projected changes to global water resources can be found in Arthurton et al. (2007) and Kundzewicz et al. (2007), the underlying science specific to adjustments in river-flow regimes is briefly outlined here. Allen and Ingram (2002) explained the scientific challenges involved in making predictions of future adjustments in the hydrological cycle, but Huntington (2006) reviewed the

hydroclimatological evidence and suggested that, despite substantial uncertainties, the global water cycle has been intensifying during much of the twentieth century. These observations give support to predictions of future increases in precipitation and evaporation under global warming, with hydrological feedbacks involving more atmospheric heat trapping associated with increased atmospheric water vapor; changes in cloud properties and extent, which impact on surface warming; and changes in snow and ice melt that influence albedo and thus surface reflection – absorption of radiation (Huntington, 2006). Arora and Boer (2001) modeled the impacts of such increases in global temperature, precipitation, and evapotranspiration on the annual flow regime for rivers in different parts of the world. In particular, they predicted a decrease in the amplitude and earlier highand low-flow periods in mid- and high-latitude rivers as a result of a decrease in the proportion of precipitation falling as snow and the earlier annual occurrence of snowmelt. They also predicted a less-marked reduction in the amplitude of the flow regime for low-latitude rivers. Arnell (2003) drew similar conclusions regarding flow-regime changes across the globe and also suggested that inter-annual variability in runoff is likely to increase in most catchments. Increasing runoff variability was also emphasized by Milly et al. (2002), who suggested a notable increase in the occurrence of great floods (i.e.,

Hydrology and Ecology of River Systems Table 2

241

The 14 tenets of the river-ecosystem synthesis

Tenet set/ number

Description

A

Factors influencing species distributions/the composition of the species pool

1

Species distributions are associated primarily with the distribution of small to large spatial patches formed by hydrogeomorphological forces and modified by climate and vegetation Distributions of species and ecotypes and community diversity from headwaters to river mouth primarily reflect the nature of the functional-process zone rather than the position along the the river network Species diversity is maximum at ecological nodes/transitions between hydrogeomorphological patches or areas of marked habitat convergence/divergence within functional process zones Throughout river networks, species diversity and density vary significantly with flow velocity and, in large rivers, are positively correlated with hydrological retention, except where other abiotic environmental conditions (e.g., oxygen, temperature, and substrate type) restrict taxa

2 3 4

B

Factors controlling species diversity and abundance in the context of the assemblage of species potentially present

5

The most important environmental feature regulating community composition is the hierarchical habitat template, primarily determined by interactions between landforms and flow characteristics Deterministic and stochastic factors contribute significantly to community regulation. Their relative importance is scale- and habitatdependent, although stochastic factors are more important overall A quasi-equilibrium is maintained by a dynamic patch mosaic (a) Classical (facilitative) succession is primarily limited to terrestrial elements of river landscapes (riparian and floodplain habitats including on islands) and occurs in response to hydrogeomorphological processes; (b) the relative importance of simple seasonal species replacement vs. true, non-facilitative succession (e.g., a blend of inhibition and tolerance succession) within wetted portions of the riverscape varies directly with stream size and inversely with hydrological variability

6 7 8

C

Processes at the ecosystem and landscape levels

9

(a) Annually, through an algal-grazer food web pathway, autochthonous autotrophy provides for most metazoan productivity across the river network, but allochthonous organic matter may be more important for some species, in some seasons and in shallow, heavily canopied headwaters; (b) A collateral, weakly linked, decomposer food pathway (the microbialviral loop) is primarily responsible (sometimes with algal respiration) for a river’s heterotrophic state (P/Ro1) Algal production is the primary source of organic energy fueling aquatic metazoan food webs in the floodplains of most river systems during over-bank floods especially in rivers with seasonal, warm-weather floods Average current velocity and nutrient spiral length are positively correlated with river discharge; both decrease in functional process zones with extensive lateral components Naturally dynamic hydrological patterns are necessary to maintain the evolved biocomplexity in river networks The frequency of flood-linked life-history characteristics increases directly with seasonal predictability of floods and their concurrence with periods of maximum system primary productivity. Biocomplexity generally peaks at intermediate levels of connectivity between the main channel and lateral aquatic habitats of the river landscape, but the relationship varies among the types of connectivity, evolutionary adaptation of taxa to flowing water, and functional processes examined

10 11 12 13 14

Adapted from Thorpe et al. (2003).

discharges exceeding 100-year levels from basins larger than 200 000 km2). Overall, river-flow regimes are highly sensitive to changes in climate, particularly where temperature increases are experienced in snow-fed catchments (e.g., Krasovskaia, 1996; Krasovskaia and Saelthun, 1997). Such changes, even in the absence of direct human manipulations of catchments and river systems, are likely to have profound ecological consequences. However, across many areas of the globe, changes are superimposed on extensive human modifications of flow regimes. For example, Vo¨ro¨smarty et al. (1997) estimated that in the mid-1980s, the maximum water storage behind 746 of the world’s largest dams (generally over 15 m high with maximum storage capacities X0.5 km3) was equivalent to 20% of the global mean annual runoff. Crucially, for downstream flow regimes as well as the physical and chemical properties of river water, the median water-residence time behind these impoundments was 0.40 years.

2.10.2.2 Hydrological Connectivity A second fundamental principle is that the connectivity between habitats along a river and between a river and its floodplain is essential to the viability of populations of many riverine species (Bunn and Arthington, 2002). Pringle (2001) defined hydrological connectivity in a river-ecosystem context as ‘‘water-mediated transfer of matter, energy, or organisms within or between elements of the hydrologic cycle’’ (p. 981). A brief summary of the concept can be found in Pringle (2003). The idea of hydrological connectivity is built on concepts of transfers across the three spatial dimensions of fluvial systems (longitudinal, lateral, and vertical) and along the fourth dimension – time (Stanford and Ward, 1988; Ward, 1989). Specifically, the hydrological dynamics of a river’s natural-flow regime (Section 2.10.2.1) lead to spatially and temporally complex inundation dynamics; surface and subsurface flow pathways; organism movement pathways; and

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Hydrology and Ecology of River Systems Longitudinal connectivity Lateral connectivity Vertical connectivity Hillslope dominated Tributary dominated Floodplain dominated Floodplain and groundwater dominated

Figure 2 Schematic representation of longitudinal, lateral, and vertical connectivity along river systems.

mobilization, transport, and deposition of matter throughout the three-dimensional (3-D) corridors defined by the river network (Figure 2). The ecological significance of longitudinal (upstream to downstream) connectivity was encapsulated in the river continuum concept (RCC, Vannote et al., 1980). The RCC considers inputs, transport, and processing of organic matter down river systems from headwater to lower reaches (the river continuum). High allochthonous inputs of organic matter from riparian vegetation are conceptualized to dominate narrow, shaded headwater streams. These allochthonous inputs are gradually replaced by autochthonous production as the river widens and receives more light in its middle reaches. However, autochthonous production may decrease toward lower river reaches as water depth and turbidity increase. These downstream changes in the nature and quantity of both locally produced organic matter and the products of organicmatter processing from upstream are associated with progressive downstream shifts in the species composition of macroinvertebrate communities. Ward and Stanford (1983a) extended the RCC to regulated rivers in their serial discontinuity concept (SDC), which considered the impacts of impoundments located in the headwaters, middle, and lower reaches of river systems on ecosystem character and processes. In parallel with the development of the RCC and SDC, the concept of nutrient spiraling (Newbold et al., 1981, 1982; Elwood et al., 1983) described how nutrient cycles are stretched into spirals by downstream transport processes within stream ecosystems. The crucial role of geomorphology in supporting physically distinct segments along river systems with distinct assemblages of physical habitats, rather than a smooth downstream continuum, is embedded in the sector level of the nested, hierarchical approach to river characterization and classification proposed by Frissell et al. (1986) (Figure 3). The definition of physically distinct river stretches and the role of fluvial processes in supporting such distinct stretches and defining thresholds between them in alluvial systems (Church, 2002) are examples of the way in which river systems can be conceptualized as forming a discontinuum (Poole, 2002) or a series of beads on a string (Ward et al., 2002, Figure 4(a)), where geomorphologically different stretches exhibit different

responses to flow disturbances (Resh et al., 1988) and different dynamic, mosaics of habitats (Pringle et al., 1988; Townsend, 1989). Indeed, Montgomery (1999) introduced the geomorphologically based process domain concept as an alternative to the RCC to encompass how ‘‘spatial variability in geomorphological processes governs temporal patterns of disturbances that influence ecosystem structure and dynamics’’ (p. 397) (Figure 4(b)). Following the proposal of the RCC, the importance of lateral (river to floodplain) connectivity was soon recognized, particularly along large floodplain rivers (Welcomme, 1979; Sedell et al., 1989). This formed the core of the FPC (Junk et al., 1989), which emphasized the crucial importance of the connection and disconnection of the floodplain from the river during seasonal flood events. The FPC highlights the importance of inputs of dissolved and suspended sediments from the river into the floodplain, which contribute to nutrient cycles, production, and decomposition processes on the floodplain, and also the role of flood disturbances in resetting floodplain community development. While the river provides a refuge and dispersal route for aquatic organisms during lower flows, much of the primary and secondary production of the river system occurs within the floodplain. More recently, building on the role of the relatively predictable (usually annual) flood pulse, emphasis has been placed on the ecological importance of all river-flow events, from high-frequency, within-channel pulses to low-frequency, high-magnitude floods. The role of this spectrum of events was formalized in the FPC (Tockner et al., 2000), and its importance has been demonstrated empirically in relation to connecting and disconnecting water bodies (Arscott et al., 2002; Malard et al., 2006), and sustaining a shifting mosaic of habitats (Gurnell et al., 2005; Stanford et al., 2005) and organisms (Arscott et al., 2003) within complex, near-natural river landscapes. In the vertical dimension, the hyporheic zone (the zone of surface water–groundwater interactions below the river bed) was first recognized as a distinct biotope by Orghidan (1959). Subsequent research has succeeded in demonstrating the remarkable spatial extent and temporal dynamics of the hyporheic corridor (Stanford and Ward, 1993), its high vertical connectivity with surface water and groundwater bodies (e.g., Ward et al., 1999), the importance of geomorphological controls for its structure and functioning (Poole et al., 2006), as well as its role in supporting diverse subsurface and surface aquatic faunal communities (e.g., Danielopol, 1989; Malard et al., 2003) often at considerable distances from surface river channels (e.g., Stanford and Ward, 1988). Brunke and Gonser (1997) provided an exhaustive review of the ecological significance of exchange processes between rivers and groundwater across the hyporheic corridor. More recently, Poole et al. (2008) focused on surface–groundwater connections to develop the concept of hydrologic spirals, which describes streams ‘‘as a collection of hierarchically organized, individual flow paths that spiral across ecotones within streams and knit together stream ecosystems.’’ This concept provides a very dynamic perspective on longitudinal, lateral, and vertical connectivity that incorporates biogeochemical as well as hydrological spiraling. It links tightly with the concepts of patch mosaics and their dynamics, longitudinal (dis)continua, and the interactions between hydrology and geomorphology

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Sector or segment containing reaches

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Wooded hillslopes Wooded floodplain Deposited sediment Abandoned channel Water

Network subdivided into segments or sectors

Deeper water

Assemblage of (meso) habitats

Channel sectors or segments

Riffle

Containing microhabitats

Wood jam

Bench Reach containing assemblages of (meso) habitats

Pool

Microhabitats e.g., Submerged wood Exposed wood Dead leaves Submerged gravel Exposed silt

Figure 3 The nested, hierarchical structure of river systems. Adapted from Frissell CA, Liss WJ, Warren CE, and Hurley MD (1986) A hierarchical framework for stream habitat classification: Viewing streams in a watershed context. Environmental Management 10: 199–214.

that are increasingly being recognized as generating hierarchies of landscape filters onto which species map according to their traits.

2.10.3 River-Corridor Dynamics 2.10.3.1 River Regimes and River Styles The character of river corridors depends upon the flow regime and the longitudinal, lateral, and vertical fluxes of water, sediment, and organic matter that the flow regime drives. If maintained in their natural state, these fluxes support heterogeneous, biodiverse landscapes (e.g., Nilsson and Svedmark, 2002) and give rise to a wide spectrum of dynamic river planform styles (e.g., Gurnell et al., 2009) that provide a diversity of physical habitats. Interactions between the flow regime and the form of river channels and their floodplains are the central theme of fluvial geomorphology, and understanding of these interactions has advanced rapidly over the last 50 years (e.g., see overviews by Leopold et al. (1963), Schumm (1977), Knighton (1998), and Brierley and Fryirs (2005)). The FPC (Junk et al., 1989) was the first ecosystemfocused concept to link the flow regime with river–floodplain connectivity, encapsulating both the core multidimensional

processes that produce the landforms and dynamism of river landscapes and also the adaptations of aquatic and riparian organisms to these dynamics. Recent ecologically based reviews have highlighted this crucial role of flow–form–organism interactions. For example, Bunn and Arthington (2002) noted that flow largely determines physical habitat in streams and that physical habitat is a significant control on biotic composition, as the first of their four key principles linking hydrology and aquatic biodiversity. Changes in climate and the direct and indirect activities of humans have led to major changes in flow regimes and thus fluxes of water and sediment, which have resulted in dramatic changes to rivercorridor characteristics worldwide (e.g., Tockner and Stanford, 2002; Petts and Gurnell, 2005). The potential morphological complexity that fluvial processes can confer on river corridors has been revealed through major advances in scientific understanding over the last 50 years. In reviewing progress in the classification and scientific understanding of river planform styles over this period, Gurnell et al. (2009) identified four main research phases:

•

The earliest phase yielded simple classifications that discriminated between straight, meandering, and braided planform patterns and defined threshold conditions of

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Hydrology and Ecology of River Systems

Plan view Hydrological exchange pathways Profile of impermeable bedrock and permeable alluvial segments (a) Valley segment

Disturbance processes

Habitat characteristics

Hillslopes

Wind, fire intense precipitation

Relatively stable hyporheic habitat occasional extreme disturbance of entire stream corridor

Colluvial channels

Avalanches, debris flows channel scour

Confined channels

Debris flows, scour, deposition flood stress, erosion, deposition

Frequent bed mobility, little hyporheic and off-channel habitat

Floodplain channels

Flood stress, erosion, deposition channel migration, avulsion

Shifting channel position, abundant and complex hyporheic and off-channel habitat

(b) Figure 4 Representation of the river discontinuum as (a) a series of three-dimensional, hydrologically connected beads linked by a string of confined reaches and (b) a sequence of process domains reflecting spatially variable geomorphological responses to temporal patterns of disturbances. (a) Adapted from Ward JV, Tockner K, Arscott DB, and Claret C (2002) Riverine landscape diversity. Freshwater Biology 47(4): 517–539. (b) Adapted from Montgomery DR (1999) Process domains and the river continuum. Journal of the American Water Resources Association 35: 397–410.

•

•

channel forming (e.g., bankfull, mean, or median annual flood) discharge (Q) and slope (S) at which rivers might switch from one form to another (e.g., Lane, 1957; Leopold and Wolman, 1957). A second phase, demonstrated that different Q–S threshold conditions existed in different river environments and that bed-sediment caliber could be added to Q and S to improve the identification of thresholds or more gradual transitions between different river planform styles (e.g., Osterkamp, 1978; Begin, 1981; Bray 1982; Carson, 1984a, 1984b; Ferguson, 1987; van den Berg, 1995; Bledsoe and Watson, 2001). A third phase recognized an increasing number of channel planform styles, including a transitional wandering style between braided and single-thread planforms (Desloges and Church, 1989), and also the importance of sediment supply as well as sediment caliber in influencing river planform styles and blurring the transitions between them. Schumm (1985) described three groups of alluvial river according to their dominant caliber and mode of sediment transport (bedload, mixed load, and suspended load). He noted that suspended load channels tended to be fairly narrow with a predominantly single thread, stable planform, whereas bedload channels were wider and tended to have more unstable, multithread planforms. Mixed-load channels possessed intermediate characteristics between the other two groups. Building on Schumm’s (1985) and

•

Mollard’s (1973) work on river planform styles, Church (1992) defined sequences of river planform styles along gradients in stream power (a combination of Q and S), sediment caliber, and sediment supply, whereas Nanson and Knighton (1996) derived a detailed subdivision of multithread (anabranching) river styles discriminated by specific stream power, bed, and bank-material caliber. There was also, within this third phase, increasing recognition that river planform styles produced distinct river corridor and floodplain styles. In particular, Nanson and Croke (1992) identified 15 floodplain styles associated with particular styles of river planform and reflecting gradients in specific stream power, sediment caliber, and valley confinement. The fourth phase had its origins in the work of Nanson and Knighton (1996) who explicitly mentioned the role of living and/or dead vegetation in association with virtually all of the anabranching styles that they identified. At the same time, Gurnell (1995) also reviewed research on vegetation along river corridors in relation to different styles of river channel and floodplain. It is increasingly evident that plants not only form a crucial contribution to the biodiversity of river corridors but that there are also many different ways in which vegetation exerts a direct influence on river planforms. For example, within very low energy anastomosing systems of the upper Narew River, Poland, Gradzinski et al. (2003) stressed the overwhelming impact

Hydrology and Ecology of River Systems

of vegetation, which produces an erosion-resistant peat layer that stabilizes channel banks, stimulates aggradation of channel beds, and induces avulsions to maintain the multithread network. Within the low-energy, arid environment of the Northern Plains, Central Australia, channels adopt a multithread form separated by long sinuous ridges and islands that are developed, maintained, and reinforced by the action of trees on flow patterns, sediment transport, retention, and reinforcement (Tooth and Nanson, 1999, 2000). Huang and Nanson (2006) suggested that this development of multiple vegetated ridges can be an important mechanism for achieving stability in anabranching systems, particularly where the flow regime and sediment supply are highly irregular. The ridges reduce channel width allowing an increase in flow and sediment-transport efficiency without any adjustment in channel slope. Gurnell et al. (2001, 2005) and Gurnell and Petts (2006), working on the Tagliamento River, Italy, established the importance of vegetation for channel planform style in highenergy multithread systems. A model of island development within braided reaches of this system explains their transition from bar-braided to island-braided styles. Island formation depends upon supply and rapid sprouting of large numbers of uprooted trees on gravel bar surfaces and the ability of these sprouting deposited trees to trap, grow through, and reinforce fine sediment. In this way, tree-reinforced patches aggrade and coalesce to form mature, elevated, vegetated islands and extensions to the wooded floodplain in periods between large destructive floods. Other researchers have made similar observations of the impact of vegetation on multithread systems in both field and flume studies (Gran and Paola, 2001; Tal et al., 2004; Coulthard, 2005). Recently, Gurnell et al. (2009) reviewed evidence for direct and indirect impacts of vegetation on European multithread river-channel styles. Indirect influences operate at the catchment scale, where changing vegetation cover influences the drainage basin hydrological cycle and, as a consequence, both runoff and sediment delivery to the river network. These changes then combine with direct influences of vegetation at the channel margins (e.g., Zanoni et al., 2008), including changes in the rate of vegetation growth with fluctuations in moisture supply from the alluvial aquifer (e.g., Gurnell and Petts, 2006). In summary, the flood magnitude and frequency required to maintain a particular channel planform style increase with the rate of riparian vegetation growth particularly tree growth, and the vegetationgrowth stage. Vegetation-growth performance in turn reflects local climate and the subsurface hydrological regime. Strong rates of riparian vegetation (particularly tree) colonization and growth can shift the threshold conditions at which planform change can occur, despite no change in the flow and sediment regimes (Gurnell et al., 2009, Figure 5).

2.10.3.2 Changing River Styles Human impacts on flow and sediment regimes are manifest in changing river styles, and are further moderated by changes in vegetation colonization, growth, and management. For example, Petts and Gurnell (2005) reviewed scientific progress in understanding the impact of dam construction and associated

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flow regulation on downstream river-channel characteristics. Reductions in flood disturbance combined with increasing moisture availability attributable to raised baseflows provide improved vegetation survival and growth, particularly where dams are constructed in semiarid areas (e.g., Williams and Wolman, 1984). As a result of the moist, less-disturbed environment downstream of dams, vegetation encroaches across river margins and bar surfaces, inducing channel narrowing (Johnson, 1997, 2000; Merritt and Cooper, 2000). This process is usually underpinned by construction of marginal bench-type landforms through sediment trapping in the encroaching vegetation (e.g., Sherrard and Erskine, 1991). Petts and Gurnell (2005) conceptualized the spectrum of channel responses to reduced discharge and sediment delivery in river channels downstream of dams (Figure 6). The impact of a reduced sediment load on downstream river channels (Figure 6(a)) is mediated by the resistance of the channel margins to erosion. Erosion resistance results from the caliber and cohesiveness of bed and bank sediment plus any additional protective cover and cohesion provided by vegetation. These responses are usually confined to a relatively short reach below a dam, although significant responses may be expressed in downstream reaches whose susceptibility to erosion is high. Accommodation (no observable channel change) may occur in reaches where the regulated flows are not large enough to be competent to erode and transport sediment. Elsewhere, channel-bed incision and bank erosion will progress, unless limited by bed armoring, or the exposure of resistant banks, or until the channel slope is reduced sufficiently to reduce stream energy below competent levels. In reaches receiving sediment from upstream erosion or tributary inputs, bed incision and channel narrowing, enhanced by riparian vegetation encroachment, may occur simultaneously. Moreover, desynchronization of sediment delivery from upstream reaches and tributaries can create highly unstable phases of scour and fill. Channel narrowing following the removal of flood flows (Figure 6(b)) can also occur in an unstable manner and at different rates but it is accelerated by riparian vegetation encroachment onto sediment deposits. However, where sediment sources are limited or vegetation establishment and growth is slow, flows are accommodated within the existing channel form. In the vicinity of unregulated tributary confluences (Figure 6(c)), bed aggradation, lateral berm construction, and channel migration can all occur and extend downstream, with vegetation encroachment again reinforcing the development of depositional landforms. Illustrations of the impact of human manipulation of vegetation on channel planform can be drawn from case studies where vegetation biomass has been reduced or removed. Brooks and Brierley (2002) and Brooks et al. (2003) demonstrated the massive influence of riparian vegetation and large wood removal from alluvial rivers in southeast Australia, by reconstructing changes in the heavily impacted Cann River over 150 years of European settlement in comparison with the relatively unimpacted Thurra River. They showed that their study reach on the Cann River has experienced a 3.6-fold increase in channel depth, 2.4-fold increase in channel slope, 7-fold increase in channel capacity, and 150-fold increase in its lateral migration rate, since European settlement. In extreme cases, reduction or removal of riparian vegetation can

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Hydrology and Ecology of River Systems

Rapid growth

M ur at e es

Tree growth

tre

es s tre g on ) iti re un nd atu Yo co er th mp ow te gr / g re in tu bs ov is ru p r mo Sh Im oil (s

Intermediate growth

Slow growth

Time Time periods between uprooting floods under rapid growth

Braided − occasional islands Occasional islands − islands braided Island braided − wandering

Time periods between uprooting floods under slow growth

Braided − occasional islands Occasional islands − island braided Island braided − wandering

Example tree growth trajectories under different growth conditions and in the absence of uprooting floods Growth stages along the growth trajectories Range in time periods between uprooting floods across the rapid and slow growth trajectories to prevent transition from the first to second channel planform style Figure 5 Conceptual model of associations between tree-growth performance and flood magnitude/frequency in relation to the maintenance of different channel styles. Three trajectories of riparian tree growth (green lines) pass through tree-growth stages (black dashed lines) at different rates according to different growing conditions (blue arrow). The impact of these different trajectories on transitions between channel styles vary with different growing conditions (the growth trajectories) and the maximum time period between floods capable of uprooting trees across the three trajectories (horizontal green arrows below the main graph), which prevent transitions between the given river planform styles (green text associated with the horizontal green arrows). Sediment supply is assumed to be sufficient to support the different channel styles. Adapted from Gurnell AM, Surian N, and Zanoni L (2009) Multi-thread river channels: A perspective on changing European alpine river systems. Aquatic Sciences (71(3): 253–265.).

lead to floodplain unraveling and transition from single thread to braided channel styles, as was observed by Griffin and Smith (2004) on the Plum Creek, Colorado, USA, in response to the coincidence of overgrazing of floodplain shrubs with a high-energy flow event. Conversely, Smith (2004) attributes the maintenance of a single thread channel following a 300-year flood on the Clark Fork of the Columbia River, USA, to the presence of a high riparian shrub biomass. Changes in flow regime can allow alien species to invade (Bunn and Arthington, 2002). Within riparian zones, alien invaders can result in significant changes to vegetation cover and biomass, leading to major adjustments in channel planform style. The invasion of many semiarid systems in the USA by Tamarix species (e.g., Lite and Stromberg, 2005; Birken and Cooper, 2006; Stromberg et al., 2007) is particularly well documented. These species have proved to be particularly invasive where natural perennial flow regimes that support native willow and poplar species have been replaced by intermittent flow regimes with changed peak-flow timing, depressed alluvial water tables, and often a more-saline water

quality (Glenn and Nagler, 2005). In some areas, invasion by Tamarix species has produced a dense cover of shrubs across the river corridor, confining channel widths and simplifying channel patterns (e.g., Graf, 1978). In summary, river styles and their dynamics reflect the interaction of three key ingredients: (1) hydrological processes (catchment hydrological cycle, river-flow regime including flow extremes, and alluvial groundwater dynamics); (2) sediment supply, transport, and caliber; and (3) vegetation performance. Rivers of different style support different types and patterns of physical habitats and different rates of habitat turnover. They are also maintained by, as well as act as, controls on hydrological connectivity. Thus, river styles are heavily influenced by hydrological processes and they also integrate all of the key physical as well as many of the chemical factors that control the potential biotic composition of river corridors (Ward et al., 2002). As planform styles reflect dynamic interactions among river flows, sediments, and vegetation in different topographic settings, they can be highly dynamic in space as well as time, with downstream sequences of river

Hydrology and Ecology of River Systems

High Unstable fill and scour

Accommodation

Sediment delivery

Fast rate of narrowing

Low Accommodation

High Unstable sedimentation reset by floods

Slow

Low

High

Long

Relaxation time

Slow progressive change

Rapid incision and narrowing

Rapid degradation

Fast

Riparian vegetation growth

Low

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Bed and bank resistance

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(a)

(b)

Low

Accommodation

Bed degradation High

Long (e.g., western European streams)

Relaxation time

Short (e.g., semiarid streams)

Bank resistance Bed aggradation bank erosion and berm construction downstream Widening widening upstream downstream Sediment delivery Low High

(c)

Figure 6 Domains of channel change in response to changes of discharge and sediment load. Responses to a dominant reduction in sediment load (a) are compared with those to a dominant reduction in floods (b); and the special case of channel change below a tributary confluence in a river dominated by flood reduction (c). Adapted from Petts GE and Gurnell AM (2005) Dams and geomorphology: Research progress and future directions. Geomorphology 71: 27–47.

styles expanding, contracting, and switching in response to changes in climate, catchment land use and management, and river management. These spatial and temporal controls and responses are embedded in the spatially hierarchical riverstyles framework, proposed by Brierley and Fryirs (2005), which provides a geoecological structure within which to explore river-environment functioning and develop management and restoration strategies.

2.10.3.3 The River-Corridor Habitat Mosaic Naturally functioning river corridors support a variety of river and floodplain styles (Section 2.10.3.1) and therefore are characterized by a diverse array of both vegetated and unvegetated landforms that are subject to continuously varying patterns of water inundation (that connect and disconnect water bodies), moisture content (from precipitation, surface water, and alluvial groundwater dynamics), vegetation development (colonization, growth and establishment, and uprooting and breakage), and morphological change (sediment deposition, sorting, and erosion). Indeed, Tockner and Stanford (2002) identified the maintenance of a high shoreline (water edge) length across the annual range of river flows as a simple but powerful index of habitat quality, noting that dynamic, morphologically complex river corridors maintain shoreline lengths well above the minimum 2 km km1. For example, in some island-braided reaches, the Tagliamento River, Italy, sustains shoreline lengths of up to 25 km km1 across all but the lowest flows. Furthermore, the length of the

edge of patches of riparian vegetation, which is in part a product of inundation dynamics, also displays very high values in naturally functioning multithread rivers (Bertoldi et al., 2009). Typical vegetation-edge lengths within the active tract of island-braided reaches of the Tagliamento River have been found to be 5–6 km per river km (Zanoni et al., 2008). Such naturally functioning open systems are in a state of continual biophysical change where individual landscape elements turnover rapidly as a result of interplay between fluvial disturbances and ecological succession, but their relative abundance tends to remain fairly constant, and therefore predictable (Ward et al., 2002; Zanoni et al., 2008). This phenomenon has been described as the ‘shifting habitat mosaic’ (Stanford et al., 2005) that drives bio-complexity across spatial and temporal scales (Thorp et al., 2006). Shifting refers specifically to the fact that, although the overall abundance of various landscape elements may remain approximately constant, the individual landscape elements change their location, size, and configuration over time. As water, sediment, and vegetation dynamics can be viewed across all spatial and temporal scales, research on the rivercorridor habitat mosaic is usually based on a spatial, hierarchical, typology of landscape units. This places the units or habitats within segments or sectors of the river channel and floodplain system and then positions each river channel and floodplain sector within the river network or catchment (Figure 3). Frissell et al. (1986) were the first to formalize a spatial, hierarchical approach to river-habitat classification, whereby the catchment river network was subdivided into

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segments (sometimes called sectors), usually terminating at tributary junctions, which were in turn subdivided into river reaches, and then into landforms such as pool-riffle couplets, bars, wood jams, and vegetated patches or islands (habitats or mesohabitats), which in turn supported microhabitats (or patches) of, for example, different sediment caliber or vegetation cover. Since 1986, researchers have reviewed and proposed hierarchical schemes (e.g., Naiman et al., 1992) to structure description, investigation, and management of river systems. These schemes place landscape elements and habitats into their reach and network context (e.g., Montgomery, 1999; Poole, 2002; Snelder and Biggs, 2002; Benda et al., 2004; Thorp et al., 2006). Thorp et al. (2006) proposed a river ecosystem synthesis (RES) of preexisting models, which links the hierarchical structure of river systems and their functioning. The RES is based on 14 tenets relating to distribution of species, community regulation, and ecosystem and river-landscape processes (Table 2). The fundamental role of hydrology and fluvial geomorphology is apparent in virtually all of these tenets, with widespread reference to the importance of hydrological/hydraulic and landform/physical habitat and the functional process zones in which they are located. Three broad properties of these physical habitats have ecological significance: their spatial distribution, their hydrological connectivity, and their dynamics or turnover. These properties are considered in the next two sections.

2.10.3.4 Distribution and Connectivity of Physical Habitats In naturally functioning river corridors, the pattern of physical habitats reflects interactions between fluvial disturbance and ecological succession. Hydrological connectivity and inundation are the key discriminators of the aggregate and timevarying properties of different habitat types and also of the species and life-cycle stages that the habitats support. Within the riparian zone, habitats can be defined according to their sediment caliber and vegetation cover and composition but, in addition, they may or may not support surface water during low river flows, and therefore, riparian-habitat characteristics under low-flow conditions reflect the form and water-retention characteristics of the landforms on which they are superimposed. For example, Ward et al. (2002) identified the following spatial elements of river landscapes that underpin the habitat mosaic: (1) geomorphological features or landforms such as channels, floodplains, terraces, levees, bars and islands, and ridges and swales; (2) a spectrum of standing to running surface water bodies; (3) subsurface water bodies showing a spectrum of surface-water influences; and (4) vegetation communities of wetlands, meadows, and alluvial forests. As river dynamics construct floodplains, the nature and proportional mix of riparian habitats reflect river planform style and valley confinement (Nanson and Croke, 1992). This association was supported by Gurnell et al. (2000), who used topographic map data to show how the extent of some easily defined riparian habitats (exposed sediments, vegetated islands, and surface water bodies), varied along the Tagliamento River, in association with changes in lateral confinement, total stream power (5-year flood magnitude and downstream valley-floor slope), and river planform style (single thread, single thread with backwaters, bar braided, bar

braided with occasional islands, and island braided) (Figure 7). Increases in river stage progressively extend the river corridor’s aquatic zone into the riparian zone, leading to an increase in the extent of inundated habitats and a decrease in exposed sediments and vegetated areas. In complex multithread reaches, inundation causes major changes in the degree of connection between surface water bodies, with ponds and backwaters being integrated into bodies of flowing water (e.g., Bertoldi et al., 2009, Figure 8(a)) and also in the level of the water table in the alluvial aquifer, and thus water availability to habitats such as islands that may not have been inundated. Contrasts in hydrological connectivity between subsurface and surface water and between adjacent surface water bodies, as well as the degree of shading by vegetation, strongly affect surface-water quality and temperature. For example, the average daily summer-water temperature in isolated surface water bodies (parafluvial ponds) along the reach of the Tagliamento River depicted in Figure 8(b) can range from 13 to 22 1C and the daily (24 h) range in water temperature within a single pond can vary from less than 1 to more than 12 1C (Karaus et al., 2005). Even connected water bodies display strongly varying temperature regimes and, regardless of water-body type, a more stable temperature regime indicates strong connectivity with subsurface waters. In gravel-bed systems, such vertical connectivity can be high, leading to distinct habitats associated with the surface and subsurface in areas of downwelling and upwelling as well as with the degree of connection between surface water bodies (Arscott et al., 2001). Indeed, Arscott et al. (2001) found that in summer and autumn, thermal variation between lowland floodplain aquatic habitats within the same reach exceeded thermal variation observed in the main channel of the Tagliamento River along the entire studied river corridor (c. 120 km length and c. 1100 m relative relief). The thermal regime is almost as important as the flow regime for aquatic biota (Section 2.10.4). The impacts of surface water–groundwater interactions extend from the aquatic into the riparian zone, with riparian-vegetation growth rates varying between downwelling and upwelling reaches (Harner and Stanford, 2003). The aquatic and terrestrial sides of the dynamic surfacewater shoreline also provide important habitats for many organisms. For example, Paetzold et al. (2005) found that on exposed sediments within 2 m of the water’s edge of the Tagliamento River, there was a particularly high abundance of ground beetles and spiders, and gut-content analysis revealed that the diet of these terrestrial animals was predominately aquatic insects. Hydrological connectivity also involves the transfer of many other materials apart from water between river landscape units, controlling the spatial distribution and character of habitats as well as their connectivity. Mineral and organic (coarse particulate organic matter (CPOM) and fine particulate organic matter (FPOM)) sediment particles as well as dissolved chemicals, particularly nutrients, are transferred within and between land and water bodies, driving the construction and turnover of physical habitats and complex biogeochemical processes that are reviewed in Chapter 2.11 Hydrology and Biogeochemistry Linkages (see also Craig et al. (2008), who relate nitrogen dynamics to the

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morphological complexity and connectivity of the river environment). Viable propagules (e.g., seeds, spores, rhizomes, and eggs), as well as mineral and dead organic matter, are also transported and redistributed by water, supporting colonization of habitats constructed by the erosion, transport, and deposition of mineral sediment. The process of seed dispersal by water, which is known as hydrochory, has been shown to play a major role not only in transporting and depositing freshly produced seeds along river corridors (e.g., Boedeltje et al., 2003; Vogt et al., 2004; Truscott et al., 2006), but also in remobilizing seeds (Pettit and Froend, 2001; Goodson et al., 2003; Gurnell et al., 2008), and structuring riparian plant communities (Nilsson et al., 1991; Johansson et al., 1996; Andersson et al., 2000; Goodson et al., 2002). Large floods may transport mineral and organic particles long distances, but transport of seeds of many species is further facilitated by properties such as their low density or the presence of air-filled seed casing or appendages (Murray, 1986) that can maintain buoyancy and thus transport for prolonged periods, even when river levels and flow velocities are low (Danvind and Nilsson, 1997; Nilsson et al., 2002; Boedeltje et al., 2004).

2.10.3.5 Habitat Dynamics and the Role of Plants as Ecosystem Engineers In Section 2.10.3.2, it was noted that river styles and their dynamics reflect the interaction of three key ingredients: hydrological processes; sediment supply, transport, and caliber; and vegetation performance. Hydrological and sediment transfer processes provide the broad environmental controls on river styles and their physical habitats, but dead and living vegetation provide key local controls on the type, density, and rate of development of physical habitats. Sediments are redistributed by hydrological processes, causing vertical accretion of river-corridor surfaces (e.g., Asselman and Middelkoop, 1995; Gomez et al., 1998; Benedetti, 2003) and also avulsion, lateral erosion, and lateral/oblique accretion of flow pathways within the river’s active tract (e.g., Hickin and Nanson, 1984; Brewer and Lewin, 1998; Mack and Leeder, 1998; Hooke, 2003; Page et al., 2003). However, dead and living vegetation and propagules, which are also transferred and redistributed by hydrological processes, drive much of the detail and complexity of habitats along river corridors (e.g., Gurnell et al., 2001, 2005).

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Large wood is transported and deposited to form a habitat in its own right as well as a food source (Gurnell et al., 2002). Wood habitats vary from individual wood pieces to large accumulations of pieces that provide storage reservoirs for leaves and fine sediment particles filtered from flowing water. Wood can snag on landform surfaces such as bars during flood recessions, but retention of large quantities of wood usually depends on the presence of preexisting retention structures such as rocks and trees around which it becomes snagged. Thus, hydrological connection with retention structures is a crucial factor in the distribution of wood across river channels and floodplains, particularly because wood from the majority of tree species floats (Gurnell, 2003). Once wood becomes retained within a river’s active tract, its complex hydraulic resistance causes the development of other landforms. In small rivers, large wood can snag easily in many different locations (e.g., between the channel banks, behind trees, or against other large wood pieces) causing the development of downstream plunge pools, upstream dammed pools and bars, and many other sediment and flow-related habitats. In larger rivers, where the wood pieces are significantly smaller than the channel width, snagging depends on floodwaters interacting with hydraulically rough surfaces and objects (Gurnell et al., 2002). Gurnell et al. (2000) estimated that relatively small quantities of wood were stored on smooth open-gravel surfaces along the large Tagliamento River despite their frequent inundation (estimates ranged from 1 to 21 t ha1);

intermediate quantities were associated with established islands (24–186 t ha1), which although morphologically rough (vegetated surfaces and topographic complexity) were located at high elevations where inundation was relatively infrequent; but the largest quantities of wood were associated with small vegetated patches or pioneer islands (293– 1664 t ha1) that were both morphologically rough and at intermediate elevations subject to relatively frequently inundation (Francis, 2007). These observations support the view that although wood can engineer ecosystems in narrower channels, where it can form a significant hydraulic obstruction, its potential to create physical habitats in larger systems is dependent upon the presence of other roughness elements, particularly rooted, living vegetation. In larger river systems, interactions among trees, wood, river flows, and sediments produce a wide variety of structures, which are fully reviewed by Gurnell (2010). In brief, in forested landscapes characterized by large trees with low decomposition rates that are delivered to the river in large quantities, accumulations of dead wood can block sizable river channels and persist as extensive hard points within alluvial sediments for prolonged periods (e.g., Brooks and Brierley, 2002; Montgomery and Abbe, 2006; Arsenault et al., 2007). In such landscapes, a complex mosaic of habitats develops, each associated with particular soil, vegetation, and large wood assemblages and turned over at characteristic rates to form a shifting mosaic through biophysical feedbacks

Hydrology and Ecology of River Systems

between geomorphological processes, and large wood and living vegetation (e.g., the Queets River, USA, Latterell et al., 2006). In such systems, low wood-decomposition rates, large piece sizes, and the high rates of recruitment allow wood to act as a river-ecosystem engineer driving a fluvial-biogeomorphic succession (Corenblit et al., 2007). Wood interacts with river flows and fluvial sediments to construct landforms; trap seeds, and shelter them while they germinate and grow; and in the longer term, it protects these sheltered forest patches from erosion. In large river systems where trees are relatively smaller, often have high rates of decay, or are not supplied to the river system in large quantities, fluvial ecosystem engineering becomes heavily dependent upon living wood – wood pieces or entire shrubs and trees capable of developing root systems and sprouting a canopy following erosion, transport, and deposition. Uprooted trees become snagged on river bars during the falling limb of flood events, typically with their root wad oriented upstream and with smaller wood pieces braced or snagged against the root wad. As in dead wood systems, the hydraulic impact of such trees creates a suite of habitats on the bar surface that is characteristic of bar apex jams (Abbe and Montgomery, 2003). Landforms include deep scour hollows where flows diverge around the root wad of the tree, often

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exposing coarse lag deposits, and bars of finer sediment in the shelter of the root wad and along the stem and canopy (Figure 9(a)). In living wood systems under suitable environmental conditions (Gurnell and Petts, 2006), these features are fundamental to the development of vegetated patches and subsequent islands or floodplain extensions. Many species of riparian willows and poplars, characteristic of river margins across much of the Northern Hemisphere, reproduce vegetatively (Karrenberg et al., 2002). Uprooted trees and fragments produce roots that can extend at rates 4 2.5 cm d1 to track the falling water table following seasonal high flows (Barsoum and Hughes, 1998; Francis et al., 2005). Shoots also grow rapidly (typically 5–10 mm d1 for deposited specimens of Populus nigra and Salix eleagnos during the summer on the Tagliamento River, Francis et al., 2006), producing a canopy that increases flow resistance above that of an equivalent dead wood deposit, and thereby enhance trapping of sediment from both inundations and wind storms (Gurnell et al., 2008). As a consequence, if the deposited tree survives early uprooting floods and continues to grow, the area of hydraulically induced scour, sedimentation, and growing vegetation enlarges to form a pioneer island (Edwards et al., 1999; Figure 9(b)). Pioneer islands usually support a diverse vegetation cover produced by sprouting of trapped wood pieces

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Figure 9 Island development from uprooted, deposited, and resprouting riparian trees: (a) a deposited tree inducing the development of a scour hole and pool and an accumulation of fine sediment in the lee of the root bole; (b) a tree sprouting to produce a line of young shrubs and further scour, deposition of fine sediment, and trapping of wood pieces to form a pioneer island; and (c) an island complex with deposited trees, pioneer islands, and established islands produced by coalescence of smaller islands, distributed across an extensive gravel bar surface. Adapted from Gurnell AM, Tockner K, Edwards PJ, and Petts GE (2005) Effects of deposited wood on biocomplexity of river corridors. Frontiers in Ecology and Environment 3(7): 377–382.

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and germination of seeds rafted in on the root bole or deposited with mineral and dead organic particles from water or wind transport (Kollmann et al., 1999; Francis et al., 2008). If they survive flood events, pioneer islands continue to trap sediment and plant propagules, and grow and coalesce, forming wooded islands or floodplain extensions (Figure 9(c) Gurnell et al., 2001) that support the same suite of habitats (scour hollows and ponds, coarse lag deposits, and aggraded islands of finer sediment), which form the template for biocomplex river corridors (Gurnell et al., 2005). Vegetated patches also facilitate germination and establishment of tree seedlings on open bar surfaces in their lee (Moggridge and Gurnell, 2009), increasing the potential for woodland to extend rapidly across areas of exposed riverine sediments and to link isolated vegetated patches. On the Tagliamento River, these processes occur rapidly. Figure 10 shows the same area of bar surface between 1999 and 2008, with loss of islands and widespread deposition of wood apparent following a major flood in 2001, and subsequent development and coalescence of pioneer islands to support the establishment of an extensive area of new woodland by 2008. Ongoing research reveals that there are many plant species or groups of species that are capable of acting as river ecosystem engineers. To date, research has largely focused on riparian trees (e.g., Gurnell et al., 2005; Corenblit et al., 2007), but there is increasing evidence that riparian herbs and grasses (e.g., Corenblit et al., submitted) and aquatic macrophytes (e.g., Gurnell et al., submitted) can induce river-channel adjustments and thereby create or facilitate new habitats that strongly affect the character of the river-habitat mosaic. These engineering plant species not only have a direct effect on the rate and style of physical habitat construction, but also, through their flow resistance, they are very effective in trapping

hydrochorously dispersed plant propagules (Gurnell et al., 2008), which in turn can germinate to support diverse plant assemblages on the evolving landforms as well as additional vegetation roughness.

2.10.4 Aquatic Ecosystems 2.10.4.1 Instream Flows and Flow Regimes In Section 2.10.2, we described some key hydrological characteristics of river networks and briefly illustrated some of the contexts in which these have been shown to have ecological importance. In this section, we revisit these properties, emphasizing how their importance has been revealed in research on aquatic ecosystems.

2.10.4.1.1 The significance of multidimensional variations in flow characteristics Flowing water along rivers has a number of advantages over still water because it is constantly mixed by turbulence providing nutrients, exchange of respiratory gases, and removal of wastes, and is vital for both downstream movement and upstream migrations of species throughout a drainage network. The communities of animals and plants at any point along a river reflect the species pool of that bioclimatic region modified by location (altitude and distance downstream) within each stream network and the historical sequence of flows, especially the incidence of unpredictable floods and droughts, which disturb biological populations. The spatial distribution of species with shorter life cycles, such as the benthic macroinvertebrates, typically reflect flow conditions at each site along a river (Figure 11(a)) and many species are associated with specific conditions of flow velocity and water depth

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(Figure 11(b)). Fundamentally, the ecological integrity of riverine ecosystems depends on their natural dynamic character (Petts and Amoros, 1996; Poff et al., 1997); flow variability maintains habitat complexity and promotes species diversity by providing recruitment opportunities and refuge from competition. Studies concerned with temporal variability in river flows and, in particular, the impact of fluctuations in the river-flow regime on a river’s health have often focused on benthic (riverbed dwelling) macroinvertebrates, because their short life cycles make them sensitive to habitat changes. The impacts of flow-regime properties and change are illustrated by many

studies on benthic macroinvertebrate communities, undertaken in many different environments. For example, in a study of summer benthic macroinvertebrate communities within the temperate maritime climate region of England and Wales, Monk et al. (2006) illuminated the significance of monthly flows and the magnitude and duration of annual extreme flows through the analysis of a 20-year data set from 83 rivers. Wood et al. (2001) demonstrated the particular significance of late winter/spring high flows in a groundwater-dominated chalk stream, especially the absence of these high flows in drought years, which were associated with low invertebrate community abundance. Similarly, for 856 monitoring sites in

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New Jersey, USA, Kennen et al. (2008) isolated the importance of disturbance regime, indexed by the average number of annual storms and flashiness of the hydrological regime, in driving the aquatic-invertebrate assemblage. Changes in hydrological regime reflecting land-use changes were associated with the retention of highly tolerant aquatic species and the loss of more sensitive species. Rivers show a characteristic zonation of biota and the RCC (Vannote et al., 1980; see Section 2.10.2.2) describes progressive changes of stream conditions. The fauna of relatively cool, leaf-litter-dominated headwater streams shaded by trees along the river banks contrasts with that of the relatively wide and shallow midsectors where light and nutrients favor algal production on the channel bed, and the lower river where food chains are based on high levels of fine particulate organic matter from upstream and from floodplain inputs. In forested headwater streams, large wood plays an important role in sustaining the diversity of habitats, in regulating flood flows, and in controlling sediment movement (Maser and Sedell, 1994), whereas in downstream floodplain reaches, the main channel food web can be significantly influenced by dissolved organic carbon and detritus (wood, leaves, seeds, etc.) delivered by the recession limb of floods. In the middle and downstream reaches of larger rivers, temporal variations in river flows, particularly the seasonal high flows of the annual flow regime, enable lateral movements of plants and animals from main channels into floodplain lakes and backwaters (Petts and Amoros, 1996; Ward and Stanford, 1995; Welcomme, 1979). Furthermore, vertical exchanges of water between the river and the alluvial aquifer and especially the oxygenated subsurface flows where rivers have significant gravel-fills (known as the hyporheos) can also provide important habitats for many species (Stanford and Ward, 1988; Gibert et al., 1990). Indeed, especially along once-glaciated valleys, sequences of alluvial basins separated by rock steps create the sequential downwelling and upwelling of river water into and out of the deep valley-fill sediments. These lateral and vertical flow paths are juxtaposed with longitudinal changes from headwaters to mouth, forming three-dimensional river corridors (Stanford and Ward, 1993; Figure 4a). Some streams may be adequately described as linear, longitudinal systems but many, especially larger river systems, are more appropriately seen as three-dimensional systems modified by biological hotspots and embedded within a branched network. Thus, the actual spatial pattern of habitats and biota deviates from the idealistic RCC to reflect a number of factors such as history over the Holocene, including glacial-valley deepening and channel incision by sea-level lowering and then valley aggradation, leading to significant vertical fluxes, hydraulic transition zones, and the character of the drainage network. The resilience in regional production of riverine species can be enhanced by the existence of core populations that can buffer metapopulations against environmental change. Metapopulation theory proposes that regional populations have core–satellite structures and the core populations – large populations occupying high-quality habitat – are critical for the persistence of the metapopulation, providing stable sources of dispersers to recolonize peripheral habitats following a major disturbance. Metapopulation linkages allow for local extinction of populations, which reestablish via

colonization from adjacent populations. Alluvial zones with active lateral and vertical connections may be particularly important biological hotspots (Stanford et al., 1996). Other hotspots are hydraulic transition zones and major tributary confluences. The manner in which water velocity, water depth, and channel substratum interact influences the distribution of aquatic biota both along the length of a river and within any given reach (Statzner and Higler, 1986; Statzner et al., 1988). Along the river profile, changes in slope, channel shape, and substratum create hydraulic transition zones between reaches of low and high hydraulic stress, and these zones tend to be associated with high diversity of biota because of the wide variety of hydraulic conditions. The disruption of a simple downstream continuum and its importance for aquatic organisms have been associated with hydraulic discontinuities (Statzner and Higler, 1986), often induced by hydrological and sediment discontinuities at stream network junctions (e.g., Gomi et al., 2002; Benda et al., 2004), which in turn are related to contrasts in the geomorphological characteristics of reaches. Thus, Statzner and Higler (1986) found high benthic invertebrate diversity at transitional zones between those of low and high hydraulic stress for 14 streams worldwide. The branching and hierarchical drainage network imposes a spatial and temporal organization on river systems (Benda et al., 2004). The influence of the drainage network is seen particularly clearly along recently deglaciated valleys where a diverse community of invertebrates, for example, inhabitat snowmelt or groundwater-fed first- and second-order tributaries flowing through wooded slopes and with stable channels. These tributaries provide sources for rapid colonization of the main channel following further ice retreat or physical disturbance by either downstream migration or drift, or aerial oviposition (Petts and Bickerton, 1994). Tributary junctions represent locations in a network where channel and valley morphology can change and where habitat heterogeneity (in space and time) can be enhanced, potentially leading to increased species richness. Thus, Scarnecchia and Roper (2000) identified tributary mouths as thermal refugia for fish and Rice et al. (2001) found changes in abundance and composition of macroinvertebrate species at confluences. Enhanced channel dynamics at tributary confluences leads to increased width of the active tract and the creation of low-energy backwaters for specialized aquatic species or life stages including fish-rearing habitat. Furthermore, aggradation at confluences can lead to enhanced hyporheic flow with warmer and more nutrient-rich water emerging and supporting increased primary production and microhabitat benefits for some fish (Baxter and Hauer, 2000).

2.10.4.1.2 Adaptations of biota to running water Adaptations to the flow regime include behaviors to avoid floods, and life-history strategies synchronized with long-term, predictable flow patterns. However, rivers also present hostile environments to biota; natural disturbances, including floods and droughts, and recovery mechanisms contribute to regulating population sizes and species diversity (Milner, 1994; Lake, 2007). Recovery is the natural process by which an ecosystem returns to a condition that closely resembles unstressed rivers in the same region following disturbance. The

Hydrology and Ecology of River Systems

changes of the biological community that occur at a site following disturbance is known as ‘succession’ (Fisher, 1990). Except in the severest of floods and droughts, organisms can find refuge locally in the hyporheic zone, pools, and backwaters of the three-dimensional corridor, and these act as colonizing sources to drive succession and recovery along with other sources, especially hotspots, across the drainage network via aquatic and aerial pathways. The viability of biotic populations links to recruitment and survival rates during early life stages that are determined by external forcing mechanisms, including changes in the flow regime and feedbacks among system components that may also depend on flow (Anderson et al., 2006). Discharge variations drive habitat conditions but large fluctuations in population abundance may be decoupled from long-term availability of usable habitat. Most river fauna are ectotherms, where growth and reproduction are vitally influenced by river temperature, and thus temperature is a critical habitat attribute (Ward, 1985). However, day length also appears to influence the life cycles of many riverine species. Thus, temperature and day length appear to synchronize hatching, maturation of larvae, emergence, and mating of adult aquatic insects (Hynes, 1970; Ward and Stanford, 1982). For aquatic plants of the river food web, availability of light and nutrients is critical. Rivers that flood frequently maintain different food webs than rivers that have more stable flow regimes. Furthermore, biotic interactions (e.g., competition, predation, and parasitism) that occur continually in all habitats are particularly important in spring brooks and lake-outlet streams with naturally stable flow regimes (Ward and Stanford, 1983b). In other rivers, biological interactions tend to become more important with time since the last major flood or drought disturbance. Over evolutionary time, floods and droughts exert primary selective pressure for adaptation and many organisms have evolved traits that enable them to survive, exploit, and in some cases depend upon, disturbances. Thus, Lytle and Poff (2004) identified three modes of adaptation that plants and animals use to survive floods and droughts. These relate to different hydrological phenomena: the timing of events (calendar day possibly linked to temperature or day length) is important for many life-history adaptations; predictability (the strength and regularity of the seasonal flow cycle) influences behavioral adaptations which may be triggered by linked environmental signals such as rainfall events, seasonal temperature extremes, or sudden changes in flow; and the magnitude and frequency of events of relatively short duration are associated with morphological adaptations. Lytle and Poff (2004) reviewed life-history adaptations that include the timing of reproduction in fish, and diapause and emergence into an aerial adult stage in aquatic invertebrates. The timing of reproduction to coincide with optimal conditions enhances the fitness of offspring and this adaptation appears particularly common in rivers with predictable flow regimes such as those associated with spring/early summer snowmelt or the tropical monsoon. Although unpredictable floods can cause significant disturbance to river communities, a regular, annual flood can be an advantage to aquatic systems. This is particularly so along large tropical floodplain rivers (Junk et al., 1989) where many species are adapted to the annual flood pulse which connects

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the main channel to floodplain backwaters and food resources. Agostinho et al. (2004) demonstrate that the flood regime is the primary factor influencing biological processes in neotropical rivers. In the Upper Parana River, the annual variation in the hydrograph affects species with distinct lifehistory strategies differently, and influences the composition and structure of fish assemblages. Large migratory fish species that spawn in the upper stretches of the basin and use flooded areas as nurseries were favored by annual floods at the beginning of summer that lasted more than 75 days, with longer floods yielding larger populations. Aquatic organisms colonize the floodplain at rising and high water levels because of the breeding and feeding opportunities that arise. Wetland species also benefit from floods. Kingsford and Auld (2005) used 25 years of breeding data for 10 species of colonial waterbirds in the Macquarie Marshes, Australia, to show that the number of waterbird nests was positively related to flow prior to breeding and area inundated, and that breeding was triggered by a threshold flow. In rivers with unpredictable flows, flood benefits are less obvious (Bayley, 1991). Nevertheless, in these rivers, Lytle and Poff (2004) suggest that bet-hedging strategies might evolve where a parent produces diverse offspring types which correspond to different possible environmental states. Behavioral adaptations often involve reaction on an individual-event basis to a hydrological (change in water level) or hydraulic (change in velocity) cue, often also influenced by a seasonal factor such as temperature. This has been observed in many fish species in seeking refuge from flood flows and in spawning, for example, stimulated by high-flow pulses in warm months or with the onset of declining temperatures. Thus, Schramm and Eggleton (2006), with reference to catfish growth in the lower Mississippi, USA, demonstrated that the FPC applies more strongly to temperate floodplain–river ecosystems when thermal aspects are considered.

2.10.4.2 Ecohydraulics and Mesohabitats 2.10.4.2.1 Hydraulic stream ecology and the mesohabitat template Statzner et al. (1988) introduced the concept of hydraulic stream ecology to focus on the energy budget of an organism and particularly the relative difference in speed between an organism and the medium in which it lives. Current velocity is significant for lotic organisms influencing respiration and other measures of metabolism, feeding biology, and behavioral characteristics including rheotaxis, locomotory activity, schooling, and territoriality. A common assumption is that biological communities have evolved to exploit the full range of mesohabitats available along rivers (e.g., the structures defined at the habitat scale of Figure 3), the variability of flows determining when and for how long mesohabitats are available to different species at different locations throughout the stream network. Considerable research effort has been devoted to the categorization of mesohabitats within river channels. These reflect the habitat scale of the hierarchical structure illustrated in Figure 3 and therefore are essentially channel geomorphic units (CGUs, Hawkins et al., 1993) with particular structural and hydraulic properties. These classifications have been

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developed by some researchers from multivariate statistical associations between the biota and properties of the physical environment; for example, the functional habitats of Harper et al. (1992, 1995), and the mesohabitats of Pardo and Armitage (1997) and Tickner et al. (2000). These classifications have tended to discriminate habitats on the basis of substrate caliber and structural elements of dead and living plants rather than explicitly incorporate topographic features/landforms found on the riverbed and margins. For example, Harper et al. (1995) recognized the following functional habitats: exposed rock, cobbles, gravel, sand, silt, emergent macrophytes, floating-leaved macrophytes, submerged broadleaved macrophytes, submerged fine-leaved macrophytes, mosses, macroalgae, marginal plants, leaf litter, wood debris, and tree roots. An alternative approach develops hydraulically based habitat classifications that reflect flow structures that are influenced by bed roughness (mainly sediment caliber) and morphology (landforms) such as the physical biotopes of Padmore (1997) and the hydraulic biotopes of Wadeson (1994). Newson and Newson (2000) describe physical biotopes based on a simple visual assessment of water-surface flow types. They combined eight flow types (free fall, chute, broken standing waves, unbroken standing waves, rippled, upwelling, smooth boundary turbulent, and scarcely perceptible flow) with other, largely sedimentary, evidence to identify 10 physical biotopes (waterfall, spill, cascade, rapid, riffle, run, boil, glide, pool, and marginal dead water). Harvey et al. (2008) explored the interdependence of these groupings using the UK national database of River Habitat Surveys (Raven et al., 1997), illustrating how vegetation and sediment caliber functional habitats map onto biotopes (Figure 12).

Sand Silt Emergent macrophytes Submerged broadleaved macrophytes Floating-leaved macrophytes

This linkage was also developed by Kemp et al. (2000), who found strong associations between flow depth, flow velocity, and the occurrence of functional habitats. The same linkage was also demonstrated at a national scale by Gurnell et al. (2010), who showed discrimination between bed-sediment caliber classes (Figure 13(a)) and the presence and abundance of macrophyte morphotypes (Figure 13(b)) when they were superimposed on Q (median annual flood) – S (channel gradient) plots for a sample of 467 British river reaches. The flooding regime leads to a particular configuration of aquatic and riparian habitats but the process of habitat creation and destruction results from the balance between rejuvenating flooding events and habitat stabilization and decay. Habitat turnover may be high along natural river corridors but at the sector scale (a geomorphologically distinctive river segment often of c. 10 km in length, Figure 3), the composition and configuration of habitats remain relatively stable (Arscott et al., 2002), providing a continuity of habitat associations that are available to sustain biotic populations. However, there has been much debate about the identification and parameterization of physical habitat at the mesoscale. A major problem is the dynamic relationship between hydraulic parameters and discharge with changes in number and arrangement of mesohabitats (Emery et al., 2003). Thus, attempts to argue the biological significance of mesoscale hydraulic habitat surveys appear premature (Petts, 2009), although the practicality of the mesohabitat approach, and prohibitive cost of microscale surveys, makes it attractive for managers (Newson et al., 1998). Hydrological change leads to changes of channel morphology and the array of physical habitats available for biota.

Smooth boundary turbulent

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Figure 12 Venn diagram to illustrate the intersection (represented by the yellow-shaded overlap areas) of flow biotopes (no perceptible flow, smooth boundary turbulent flow, rippled flow, and unbroken standing waves) associated with assemblages of functional habitats (listed in the yellow text boxes), based on an analysis of data from the UK Environment Agency’s River Habitat Survey. Adapted from Harvey GL, Clifford NJ, and Gurnell AM (2008) Towards an ecologically meaningful classification of the flow biotope for river inventory, rehabilitation, design and appraisal purposes. Journal of Environmental Management 88(4): 638–650.

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Figure 13 Mean bed-sediment size (a) and the assemblage of macrophyte morphotypes (b) found on a sample of 467 British river reaches in relation to valley gradient (slope in m m1), the median annual flood (in m3 s1), and thresholds of channel style identified by Church M (2002) Geomorphic thresholds in riverine landscapes. Freshwater Biology 47: 541–557. Adapted from Gurnell AM, O’Hare JM, O’Hare MT, Dunbar MJ, and Scarlett PD (2010) Associations between assemblages of aquatic plant morphotypes and channel geomorphological properties within British rivers. Geomorphology 116: 135–144.

Thus, channel changes to flow regulation below dams involves the complex interaction of sediment-transport processes and riparian-vegetation growth (e.g., Petts and Gurnell, 2005, Section 2.10.3.2). One major concern is the impact of flow regulation on channel sedimentation at salmon-spawning grounds, which could impact upon the intra-gravel environment for egg development over winter and for fry emergence in late winter (Milhous, 1982, 1998; Reiser et al., 1989). In particular, deoxygenated conditions in spawning gravels can cause poor egg survival (Malcolm et al., 2005). Consequently,

there have been considerable efforts to quantify the volume, magnitude, duration, and timing of sediment-maintenance flows that flush fines without eroding the important underlying gravels (Wu and Chou, 2004).

2.10.4.2.2 Habitat-suitability criteria Medium and low flows, together experienced for about 90% of the time along most rivers, sustain a diversity of hydraulic habitats. The complex channel morphology of natural rivers

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creates a heterogeneous flow environment, described most simply by patterns of velocity and depth. These hydraulic habitats map onto the mesohabitats defined above, but represent the varying velocity-depth conditions within different parts of the river channel to which mobile species can respond as discharge varies. Different species of animals have been observed to have preferred habitats or tolerate different habitats in terms of velocities and depths, to which the caliber of the substratum has been added as a third key habitat characteristic in defining habitat suitability (Bovee, 1978; Gore, 1978). Thus, these associations between the three habitat properties and the preferences of biota have been developed into habitat-suitability criteria (Figure 11(b)), which define the probability of use of habitats by a specific life stage or a particular species. The underlying premise behind the concept of habitatsuitability criteria is that populations, and thus biodiversity in rivers, are limited by habitat events (Stalnaker et al., 1996). Habitat-suitability criteria describe how individuals of a species select the most favorable conditions in a stream but will also use less favorable conditions, with the preference for use decreasing where conditions are less favorable. Simple indices are based on the frequency of occurrence of actual habitat conditions used by a target organism in a particular reach. The ratio of the proportion of habitat utilized to available habitat area within the reach defines the habitat preference. More complex, composite indices may be defined (e.g., Vadas and Orth, 2001; Ahmadi-Nedushan et al., 2006) but these involve several assumptions (Bovee, 1986) not least that all physical variables are equally important and independent. This habitat-suitability-criteria concept has been challenged over the past 30 years because of the lack of concordance between changes in suitable habitat and fish populations, its simplified approach to hydraulic habitat characterization (e.g., Gore and Nestler, 1988), and lack of biological realism (e.g., Orth, 1987), but it remains central to biological-response models that seek to explain and predict spatial and temporal distributions of instream biotic populations.

2.10.4.2.3 Models of biological responses to changing flows A very widely used model called Physical HABitat SIMulation (PHABSIM, Tharme, 2003) integrates the changing hydraulic conditions associated with variations in discharge with the habitat preferences of one or more selected species (Figure 11(b)). The method relies on three principles (Stalnaker, 1994): the chosen species exhibits preferences within a range of habitat conditions that it can tolerate; these ranges can be defined for each species; and the area of stream providing these conditions can be quantified as a function of discharge and channel structure. The primary approach uses a simple 1-D hydraulic model, but this fails to predict spatial patterns of velocity in natural rivers, although it is useful for determining average velocity variations with changing discharge. This weakness has been overcome by the increasing use of 2-D hydraulic models that can describe the spatial and temporal heterogeneity of hydraulic conditions and provide a link to mesohabitat patterns (Bovee, 1996; Hardy, 1998; Stewart et al., 2005; Crowder and Diplas, 2006).

Considerable efforts have been spent on attempts to assess the ecological credibility of PHABSIM by demonstrating the biological significance of carrying capacity as a limiting factor of population size (Lamouroux et al., 1999; Kondolf et al., 2000). However, validation of the approach in biological terms has proved difficult not least in establishing discrete relationships between biological populations and the weighted usable area (WUA) from empirically derived habitat-suitability curves. From a practical perspective, there is no doubt that the accumulated experience of using PHABSIM means that its strengths and weaknesses are well understood. Parasiewicz (2003) advanced a PHABSIM derivative, MesoHABSIM. By mapping mesohabitats at different flows along extensive sections of a river and establishing the suitability of each mesohabitat for the dominant members of the fish community, it is possible to derive rating curves to describe changes in relative areas of suitable habitat in response to flow. MesoHABSIM focuses on mesoscale approaches to build on strengths of PHABSIM protocols while providing options for addressing large spatial scales appropriate for water-resource planning (Jacobson, 2008). A rational framework for modeling fish-community response to changing habitat conditions developed by Bain and Meixler (2008) is appropriate for integrating with physical-habitat modeling (Parasiewicz, 2008). The fish-collection survey is the most effort-intensive component of MesoHABSIM, but literature-based evidence and expert opinion can be used, and a regional approach allows transfer of habitat-use models among rivers of similar type (Parasiewicz, 2007). However, the challenge to relate habitat use to changing flows remains elusive (Petts, 2009). The temporal dynamics of habitat quantity may be a major factor determining fish-population responses in riverine environments (Stalnaker et al., 1996), but there is limited evidence that this is manifest by different patterns of habitat use and a large number of empirical case studies have been unable to develop general relationships (Poff and Zimmerman, 2009). The biomass of a species or a particular life stage within a community can vary because of biological processes such as reproduction, energetics, and mortality that may be influenced by one or more unspecified environmental factors, which undoubtedly blur any simple relationships between species abundance and habitat criteria. Considering trout, for example, recruitment has been shown to be strongly influenced by winter flows (Cattane´o et al., 2002; Lobon-Cervia, 2003; Mitro et al., 2003), but Sabaton et al. (1997) and Gouraud et al. (2001) demonstrated the impact of summer low flows that limit adult-trout biomass and spring flows that limit young-ofthe-year numbers between emergence and their first summer, supporting the findings of Capra et al. (2003) that postemergence high flows have a major impact on the density of 0 þ fishes. For unionid mussels, Morales et al. (2006) predicted community development as a function of individual growth and reproduction, biotic interaction involving host fish and intra- and inter-species food competition, and habitat criteria (substrate stability), and demonstrated that for lowdensity species, even a small level of habitat modification could have a substantial impact on population survival. For common floodplain fishes, Halls and Welcomme (2004) advanced an age-structured population-dynamics model

Hydrology and Ecology of River Systems

incorporating density-dependent growth, mortality, and recruitment to show the importance of high flood duration, large area of inundation, and a slow rate of flood recession. Thus, biological interactions and flow variability, especially the length of time since the last major flood or drought, may confound attempts to demonstrate simple relationships between habitat availability and fish stocks (Sabaton et al., 2008) and provide a challenge to the future development of predictive tools.

2.10.5 Managing River Flows to Protect Riverine Ecosystems In this chapter, we have developed three interdependent themes. In Section 2.10.2, we identified those hydrological characteristics of river networks that have been found to be of high ecological importance, primarily focusing on flow regimes, flow extremes, and hydrological connectivity. In Section 2.10.3, we demonstrated that these hydrological characteristics form the main control on the geomorphological style of river corridors and thus their shifting habitat mosaic. Complex interactions occur among river flows, fluvial sediments, and vegetation within naturally adjusting corridors, providing resilient, bio-complex river environments. In Section 2.10.4, we showed how the flow regime and the style and dynamics of the river corridor and river system define the ecohydraulic and mesohabitat complexity of the aquatic ecosystem and its three-dimensional connectivity. Throughout, we have touched on the effects of human interventions. In this section, we conclude our discussion of hydrology and ecology by considering how river flows can be managed to protect riverine ecosystems as well as support human needs, an area that is attracting enormous attention from researchers, managers, and policymakers as the world’s rivers come under increasing human pressure (e.g., Annear et al., 2004; Naiman et al., 2002). Many rivers today have flow regimes that differ markedly from the climate-driven regime because of impoundments; the magnitude, frequency, and timing of floods have altered with land-use change; and moderate- to low-flow percentiles have been changed in various ways along the length of a river as a consequence of both abstractions and discharges from wastewater-treatment works (Figure 14). River-flow regulation to control flooding and provide water for human use has had deep-seated impacts on river-corridor ecosystems (e.g., Ward and Stanford, 1979; Petts 1984; Tockner and Stanford, 2002). Direct surface-water abstractions and structural flood-alleviation measures, the construction of all types of surface reservoirs, and development of groundwater resources, including the conjunctive management of surface and groundwater, change the river flow regime and thus induce changes in river-channel characteristics (size, form, and style) and the river corridor and channel-habitat mosaic. The importance of managing flows to sustain riverine ecosystems and especially populations of native species has been demonstrated by the impacts of flow regulation below dams upon river-channel characteristics (Petts and Gurnell, 2005, Section 2.10.3.2) and biota (Petts, 1984, 2007). The deep impact of flow regulation is supported by the observation that regulated rivers regain normative attributes with sufficient distance

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below a dam and that depressed populations of native species can recover if the natural magnitudes and variations in flows are reestablished. Thus, Armitage’s (2006) long-term study of the impacts of Cow Green Dam on the River Tees in Northern England revealed that a narrower range of environmental conditions and increased flow stability led to a dynamically fragile community (indicated by observed changes in community diversity and abundance) which is very susceptible to perturbations because it has developed in their absence. Periphyton and reservoir plankton play an important role in structuring the faunal composition by creating an environment where biotic interactions are more likely. This does not require restoration to some pristine state, but the recovery of some large portion of the lost capacity to sustain native biodiversity and bioproduction is possible by management of processes that maintain normative habitat conditions (Stanford et al., 1996). Naiman et al. (2002) summarized the fundamental ecological principles for understanding hydrology–ecology relationships along rivers, focusing on the climatically driven variability of flows at least from season to season and from year to year. The two linked general principles are 1. that the natural-flow regime shapes the evolution of aquatic biota and ecological processes; and 2. that every river has a characteristic flow regime and an associated biotic community. Four further principles were elaborated by Bunn and Arthington (2002): 1. Flow is a major determinant of physical habitat in rivers, which in turn is a major determinant of biotic composition. 2. Maintenance of the natural patterns of connectivity between habitats (a) along a river and (b) between a river and its riparian zone and floodplain is essential to the viability of populations of many riverine species. 3. Aquatic species have evolved life-history strategies primarily in response to the natural-flow regime and the habitats that are available at different times of the year and in both wet and dry years. 4. The invasion and success of exotic and introduced species along river corridors is facilitated by regulation of the flow regime, especially with the loss of natural wet–dry cycles. These principles underpin three elements of regulated river management: the determination of (1) benchmark flows, (2) ecologically acceptable hydrographs, and (3) ecologically acceptable flow-duration curves (Figure 15). These three elements inform short-term and local operational rules; seasonal and short series of annual flow management; and long-term water-resource planning, respectively. The science and application of environmental flows has attracted considerable attention and Tharme (2003) identified over 200 approaches that have been described for advising on environmental flows in 44 countries. These range from reconnaissance-level assessments relying on ecologically informed hydrological methodologies to approaches using complex hydrodynamic habitat modeling. In some areas, such as Australia and southern Africa, a lack of ecological data and

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Figure 14 Residual flow diagrams representing the river condition under dry weather flow (DWF) (the average of the annual series of the minimum weekly/7 consecutive days) for a relatively natural (River Dove) and heavily influenced river (River Tame) within the River Trent catchment, UK. The diagrams show downstream flow increments from tributaries (natural flow component) and water-reclamation works and abstractions (artificial flow component). The total discharge at any point along the river is the combination of the natural and artificial flow components. Adapted from Pirt J (1983) The Estimation of Riverflows in Ungauged Catchments. Unpublished Doctoral Thesis, 2 volumes, Loughborough University of Technology.

process models, and political pressure to deliver environmental flow recommendations in short timeframes, often less than 1 year, has led to the use of scientific panels to set environmental flows (Cottingham et al., 2002). For more than 40 years, tools have been advanced defining benchmark flows to allocate flow to meet in-river needs (Petts and Maddock, 1994) of which PHABSIM has been most widely used. In the 1960s and 1970s, early attempts to set instream flows for rivers focused on the annual minimum flow expressed as a hydrological statistic, commonly as either a flow-duration statistic (such as the 95th percentile flow) or as a fixed percentage of the average daily flow (ADF), with several studies proposing 20% ADF to protect aquatic habitat in streams (e.g., Tennant, 1976). However, recognition of the threat to fisheries of confining flow management to annual minimum flows led to more complex and hydrologically rational approaches (Stalnaker 1979, 1994; Stalnaker et al., 1996). By the early 1990s, the science and management of regulated rivers had expanded from the determination of instream flows to environmental flows and many schemes applied more complex flow-habitat models to address wider

issues than the instream needs (the hydraulic habitats) of a single species. Three general approaches to the allocation of flows to support river-ecosystem needs are being advanced and have achieved some success (Arthington et al., 2003): hydrological methods, hydraulic models, and holistic approaches. These approaches address the sustainability of communities and ecosystems, the access of aquatic biota to seasonal floodplain and riparian habitats as well as the need for high flows to sustain the geomorphological dynamics of the river corridor and floodplain habitats (RRA, 2003). They enabled advancement of an ecologically acceptable flow regime concept (Figure 15; Petts, 1996; Petts et al., 1999). This recognized that different life stages and different species benefit from different flows at different times of the year, and in different years. Rivers must be protected in wet years as well as drought years because high flows provide optimum conditions for some species and are also responsible for sustaining the quality and diversity of in-channel and riparian habitats. Societal demands for river-ecosystem protection have accelerated the development of innovative, locally applicable

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Figure 15 A general procedure for deriving an ecologically acceptable flow regime represented as one or more (e.g., wet and dry years) hydrographs for defining operational rules and as a flow-duration curve for assessing abstractable volumes. The procedure allows the evaluation of alternatives including physical-habitat improvements as part of the decision-making process. Adapted from Petts GE and Amoros C (eds.) (1996) Fluvial Hydrosystems, 308pp. London: Chapman and Hall.

methods and tools especially within regions having limited databases. However, there are also many examples where sophisticated, science-based models are being applied to specific problems. For example, Grand et al. (2006) used a cell-based model of backwater geometry, a pond-based temperature model, and a model of invertebrate production to investigate the effects of within-day flow fluctuations caused by hydropower operations on nursery habitats for larval and juvenile Colorado pike minnow (Ptychocheilus lucius) along the Green River below Flaming Gorge dam, Utah, USA. As noted by Parasiewicz (2001), if community structure reflects habitat structure, then securing habitat for the most common species might preserve the most profound characteristics of the ecosystem and provide survival conditions for the majority of the aquatic community. Progress in developing models that link physical-habitat dynamics and population biology of large organisms such as fish may have been constrained by the

difficulty in merging the space- and timescales appropriate to both physical and biological sciences (Petts et al., 2006). In the hydrological approaches, flow is considered as a simple proxy for a number of related parameters that may have a key influence on the range of aquatic, wetland, and riparian habitats along the river corridor. Thus, Extence et al. (1999) developed a scoring system as an indicator of hydrological stress based upon surveys of macroinvertebrate fauna that has been shown to be sensitive to particular hydrological indices (Wood et al., 2001a; Monk et al., 2008). A range of hydrologic parameters for each year of flow record can be used to characterize inter-annual variation before (reference period) and after flow regulation/abstraction (Richter et al., 1996, 1997). Of the hydrological approaches, White et al. (2005) used wavelet analysis to assess dam operations in reconstructing desired flow characteristics. This method provides an easy-to-interpret approach for investigating hydrological

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change when the management history is uncertain and timescales of important cycles are unknown. White et al. (2005) used wavelet analysis to detect the hydrological implications of management practices over a range of timescales and suggested that the method could provide a powerful data-mining technique for assessing hydrological changes. The statistical characterization of ecologically relevant hydrograph parameters could be used to define the variability of the dimensions of the flow regime within which artificial influences should be contained (Richter et al., 1997). To date, such approaches have been used in water resources and environmental management in the USA (e.g., Richter et al., 1998, 2006; Mathews and Richter, 2007) and elsewhere (e.g., Shiau and Wu, 2004, 2006). Thus, Galat and Lipkin (2000) recommended changes in reservoir management to return regulated flows to within the pattern of natural variability, thereby simulating a natural riverine ecosystem. They argued that naturalization of the flow regime would benefit not only the ecological system but also the economic value of the Missouri River, once the products of agriculture, electric-power generation, and transportation are integrated with the socioecological benefits of a naturalized flow regime. In a follow-up study, Jacobson and Galat (2008) focused on developing a flow regime to support the endangered pallid sturgeon (Scaphirhynchus albus). Specific hydrograph requirements for pallid sturgeon reproduction were unknown; so much of the design process was based on hydrological parameters extracted from the reference natural-flow regime. Three issues often hinder the apparently simple and reasonable application of such hydrological approaches. First, standards need to be set to apply an appropriate record length. At least 12 years data are required for statistical integrity and longer records are needed to incorporate variable weather patterns over decadal timescales and to provide for actual scales of variability in the magnitude and timing of flows and the natural frequencies of these flows. The flow regime is a complex concept. Flow regimes typical of each hydro-climatic region across the globe represent average conditions created by combining a small number of flow-regime types, particular to each hydro-climatic region. Variations of the flow regime from year to year within the British temperate maritime hydro-climatic zone based upon analysis of 80 station-years of flow data (1977–97) from four major rivers across the United Kingdom showed that the typical flow regime with a high-flow season from December to February and a low-flow season from June to August occurred in only 51% of the years (Harris et al., 2000). Of the other flow years, three variants of the typical flow regime were differentiated:

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Subtype A. Twenty-eight percent had a dominant peak in November often with a secondary peak in April. Subtype B. Sixteen percent had a dominant peak in March with a secondary peak in December. Subtype C. Six percent were characterized by winter drought with no dominant peak and typically a very dry January.

Second is the issue of naturalizing the gauged flow regime. In many areas, the pristine catchment has no relevance to the modern day. The hydrology of catchments characterized by long-term human interference – such as urban conurbations

and intensive agriculture – bears little resemblance to the hydrologic character of unmodified catchments in a given hydro-climatic region. The concept for such catchments may be to produce functionally diverse, self-regulating ecological systems (Petts et al., 2000). In reality, this requires determination of the flow regime that would be sustained under current or future catchment conditions in the absence of existing dams, reservoirs, diversions, and abstractions. Third, the linkages between flow regime and ecological health are complex in both time and space. The natural dynamic character relates not only to flow variability but also to water quality, especially temperature variations; sediment dynamics and channel dynamics (that are also influenced by patterns of woody vegetation growth); changes in food/energy supply; and interactions between biological populations. Across the UK, most regulated rivers are supported in summer by compensation flows that maintain minimum flows or they may even experience enhanced flows during dry summers where the river supports abstractions from the lower river. Under one scenario, the main ecological impact of flow regulation below reservoirs would not be during a summer drought but during the late summer, autumn, and early winter following a subtype C flow year when the need to fill reservoir storage could eliminate high flows along the main river. Under such circumstances, there would be inadequate river flows to stimulate up-river fish migrations and spawning grounds could be impacted by siltation caused by fine-sediment loaded tributary spates. Water temperature is a particularly important parameter and a river’s thermal regime is a key component of environmental flows. Harris et al. (2000) and Olden and Naiman (2009) have encouraged ecologists and water managers to broaden their perspective on environmental flows to include both flow and thermal regimes in assessing e-flow needs. Assessments should include the comprehensive characterization of seasonality and variability in stream temperatures in the face of artificial influences on flow and potential impacts of climate change. From a scientific perspective, we need to more clearly elucidate the relative roles of altered flow and temperature in shaping ecological patterns and processes in riverine ecosystems. In the absence of universal relationships between flows and biotic responses, King et al. (2003) advanced a value-based system, Downstream Response to Imposed Flow Transformation (DRIFT). This provided a data-management tool for many types and sources of information, predictive models, theoretical principles, and expert knowledge of a panel of scientists. The approach was developed to link the productivity of large floodplain rivers to their flow characteristics in countries or river basins where data scarcity constrains prediction of ecological responses to flow regulation. It was also produced in a developing region with severe water shortage and uncertainties about river-linked ecological processes and where riparian subsistence populations are important in the decision-making process. DRIFT supports the scientific-panel approach to recommend environmental flows within an adaptive management framework. It is based around four modules: (1) the biophysical module describes the present nature and functioning of the ecosystem; (2) the sociological module identifies subsistence users at risk from flow

Hydrology and Ecology of River Systems

abstraction or regulation; (3) a module that combines the outputs from the first two to develop biophysical and subsistence scenarios; and (4) a module to address mitigation and compensation costs. Arthington et al. (2003) used DRIFT to establish the environmental flow requirements of rivers in Lesotho and contended that the methodology can provide a best-practice framework for conducting scientific-panel studies. Linking environmental variables to dam-release rules has been shown to achieve significant water savings (Harman and Stewardson, 2005). However, there is no single best method or approach (IUCN, 2003). Given the variety of water resource contexts, the range of environmental settings and variety of species concerned, there has been an increasing tendency to use evidence-based expert judgment. In reality, all environmental flow assessments provide the evidence available to larger or smaller, expert panels, – the decision-making process that evaluates tradeoffs between water users. From a flowmanagement perspective, these tradeoffs include those between magnitude, frequency, duration, timing, and rate of change (Poff and Ward, 1989), and the evidence is often hierarchically structured to include three orders (Petts, 1980) or levels (Young et al., 2004), broadly linking primary processes, habitat structure, and biota. Moreover, Jacobson and Galat’s (2008) experience with flow-regime design using a hydrological approach demonstrated lack of confidence by stakeholders in the value of the natural-flow regime as a measure of ecosystem benefit. The lack of confidence resulted from the lack of fundamental scientific documentation, as might have been provided by a more complex hydrological– hydraulic–biological model. Stakeholders desired proof of ecological benefits commensurate with certainty of economic losses. This conflict between demands for more biologically accountable models and political actions to set environmental flows has also been highlighted by Arthington et al. (2006). Despite considerable progress in understanding how flow variability sustains river ecosystems, there is a growing temptation to ignore natural-system complexity in favor of simplistic, static, environmental flow rules to resolve pressing river-management issues. Arthington et al. (2006) argue that such approaches are misguided and will ultimately contribute to further degradation of river ecosystems. In the absence of detailed empirical information of environmental flow requirements for rivers, they proposed a generic approach that incorporates essential aspects of natural-flow variability shared across particular classes of rivers that can be validated with empirical biological data and other information in a calibration process. They further argue that this approach can bridge the gap between simple hydrological rules of thumb and more comprehensive environmental flow assessments and experimental flow-restoration projects. Thus, in the USA, Poff et al. (2009) achieved a consensus view from a panel of international scientists on a framework for assessing environmental flow needs that combines a regional hydrological approach and ecological response relationships for each river type based initially on the literature, existing data, and expert knowledge. Stakeholders and decision makers then explicitly evaluate acceptable risk as a balance between perceived value of the ecological goals, the economic costs involved, and the scientific uncertainties. The main risk is a perceived lack of

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incentive for what could be considered to be costly monitoring and longer-term research to develop evidence of biota– flow relationships for supporting adaptive management. In conclusion, it is impossible to return regulated rivers to an unimpacted state or even to define what such an unimpacted state might be, given the long history of human intervention across the Earth’s surface superimposed on a background of global environmental change. However, it is possible to combine scientific understanding and expert judgment to establish river flows that can support river ecosystems, where the river channel and riparian zone are also managed. At least in a set of reaches distributed across the river network, river flows, sediment, and vegetation need to interact relatively freely to provide refugia for species and resilient sites from which other areas of the river network can be recolonized. Sustaining river ecosystems needs to be based on the maintenance of appropriate river flows coupled with restoration of the potential for reaches within the river network to adjust to those flows.

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2.11 Hydrology and Biogeochemistry Linkages NE Peters, US Geological Survey, Atlanta, GA, USA JK Bo¨hlke, US Geological Survey, Reston, VA, USA PD Brooks, University of Arizona, Tucson, AZ, USA TP Burt, Durham University, Durham, UK MN Gooseff, Pennsylvania State University, University Park, PA, USA DP Hamilton, University of Waikato, Hamilton, New Zealand PJ Mulholland, Oak Ridge National Laboratory, Oak Ridge, TN, USA NT Roulet, McGill University, Montreal, QC, Canada JV Turner, CSIRO Land and Water, Wembley, WA, Australia & 2011 Elsevier B.V. All rights reserved.

2.11.1 2.11.2 2.11.3 2.11.3.1 2.11.3.1.1 2.11.3.2 2.11.3.3 2.11.3.4 2.11.3.4.1 2.11.3.4.2 2.11.3.5 2.11.4 2.11.5 2.11.6 2.11.7 2.11.8 2.11.9 2.11.10 References

Introduction Hydrological Pathways on Drainage Basin Slopes Mountain Environments Precipitation Snow Change in Storage Evaporation and Transpiration Stream Flow Nitrate isotopes in stream water Transit time and residence time Groundwater Recharge Within-River Processes Wetland Processes Lakes Groundwater Acidic Atmospheric Deposition – Acid Rain Summary and Future Considerations Additional Reading

2.11.1 Introduction Biota depend on water, energy, and nutrient transfers within and between ecosystems, which result in complex interactions between hydrology and biogeochemistry. The hydrological cycle and the variation in rates and magnitudes of water transfers along pathways in turn affect biogeochemical interactions. Nutrient uptake by biota varies markedly depending on availability of water, the pathways by which it moves through ecosystems, and the ecosystem type (aquatic, terrestrial), climate, and many geomorphological factors, such as slope and soil type. Variations in flow rates along pathways, reaction rates, composition (mineralogy, chemistry, and biology) and characteristics of interacting materials, chemical composition of the water, and temperature are major factors affecting biogeochemical processes. Most chemical cycles either affect or are affected by biological activity. Research since the early 1990s has revealed that even mobile and conservative elements, such as chlorine, are ¨ berg et al., 1996; White and actively cycled by biota (O ¨ berg, 2003; Lovett et al., 2005; O ¨ berg Broadley, 2001; O and Sande´n, 2005). However, the cycles of nutrients, carbon, and other biogeochemical components are intricately linked

271 272 274 275 275 276 276 276 278 278 280 282 285 287 291 293 294 295 295

to hydrology and biogeochemistry, and are the foci for most of the discussion in this chapter. Scientists have been challenged by the task of determining and quantifying the background processes that affect hydrological and biogeochemical linkages because human activities that accompany population growth and the associated requirements of obtaining and consuming natural resources have accelerated landscape changes (Peters and Meybeck, 2000; Meybeck, 2001; Peters et al., 2005). Human impacts on ecosystems are evident everywhere on Earth. For example, deforestation, channelization, dams and river regulation, land drainage, agriculture, energy generation, and urbanization and management of these activities have had major impacts on hydrology and biogeochemistry (Poff et al., 1997; Friedman et al., 1998; Peters and Meybeck, 2000; Meybeck 2001; Paul and Meyer, 2001; Meyer et al., 2005; Peters et al., 2005; Poff et al., 2006; Palmer et al., 2008; Peters, 2008). Human activities and resource management have evolved and it has been widely recognized that while point-source pollution has become more manageable, diffuse or nonpoint sources of pollutants are dominating contamination of ecosystems, and are much more difficult to identify, quantify, and control (Novotny, 2003; Campbell and Novotny, 2004; Loague and

271

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Corwin, 2005). Freshwaters are experiencing declines in biodiversity far greater than those in the most affected terrestrial ecosystems and human threats to global freshwater biodiversity and ecosystem services include overexploitation, water pollution, flow modification, destruction or degradation of habitat, and invasion by exotic species; the combined and interacting influences have resulted in population declines and range reduction of freshwater biodiversity worldwide (Dudgeon et al., 2005). Ecosystem processes, including water, nitrogen, carbon, and phosphorus cycling, changed more rapidly in the second half of the twentieth century than at any time in recorded human history. Human modifications of ecosystems have changed not only the structure of the systems (such as what habitats or species are present in a particular location), but their processes and functioning as well (Millenium Ecosystem Assessment, 2005). The capacity of ecosystems to provide services derives directly from the operation of natural biogeochemical cycles, which in some cases have been modified substantially. Ecosystem services are the human benefits provided by ecosystems. Furthermore, invasive and exotic species continue to affect ecosystems (Mooney and Hobbs, 2000; Mooney et al., 2005), and the increasing presence of recalcitrant endocrine disruptors and pharmaceuticals in stream water has the potential to alter ecosystems and change biogeochemical cycles (McMaster, 2001; Boyd et al., 2003; Stackelberg et al., 2004). Although our understanding of how invasive species affect ecosystem processes is not well understood (Gordon, 1998; Levine et al., 2003), researchers have reported a wide range of effects on hydrology and biogeochemistry, such as by invasive earthworms in northern temperate forests (Bohlen et al., 2004), invasive vegetation in riparian zones (De´camps et al., 2004) caused by damming and river regulation (Nilsson and Berggren, 2000), and invasive species in grasslands (Scott et al., 2001; Hook et al., 2004). However, it is beyond the scope of this chapter to provide detailed information about hydrological and biogeochemical linkages for these and the myriad of other human activities that affect the landscape. The objective of this chapter is to provide an overview of the linkages between hydrology and biogeochemistry in terrestrial and aquatic systems by tracking water flow from headwaters to rivers in larger drainage basins, including groundwater, wetlands, and lakes. The selection of foci was arbitrary and determined largely by the expertise of the co-authors, but provides continuity from a hydrological-cycle perspective and with a bias toward a northern temperate hydroclimate, a geographic region with some of the most detailed process research. To focus the discussion, the chapter is sectioned topically and these topics include hydrological pathways on drainage basin slopes, mountain environments, within-river (or in-stream) processes, wetlands, lakes, and groundwater (and groundwater–surface water interactions). In particular, this chapter provides a view of the linkages among the hydrosphere, biosphere, lithosphere, and chemosphere of processes that affect nutrient cycles, particularly nitrogen and carbon. In addition to the general discussion of nutrient cycling, an example is given of the effects of human activities on these linkages through the widespread impacts of acidic atmospheric deposition. Topics discussed in Chapters (see also Chapter 1.10 Predicting Future Demands for

Water and Chapter 1.09 Implementation of Ambiguous Water-Quality Policies) overlap with some of the material in this chapter, hence will not be discussed here in detail.

2.11.2 Hydrological Pathways on Drainage Basin Slopes

Although the river and the hill-side waste sheet do not resemble each other at first sight, they are only the extreme members of a continuous series, and when this generalization is appreciated, one may fairly extend the ‘river’ all over its basin and up to its very divides. Ordinarily treated, the river is like the veins of a leaf; broadly viewed, it is like the entire leaf (Davis, 1899).

The drainage basin (also known as watershed in the USA and catchment elsewhere) has long been recognized as the fundamental unit of analysis for the sciences of hydrology and geomorphology. It is usually a clearly defined and unambiguous topographic unit, which acts as an open system for inputs of precipitation and outputs of river discharge and evaporation (Chorley, 1971). The topographic, hydraulic, and hydrological unity of the drainage basin provided the basis of the morphometric stream ordering system of Horton (1945), as elaborated by Strahler (1964). Schumm (1977) had generalized sediment transport within the drainage basin into three zones: source area, transfer zone, and sediment sink or area of deposition. This is also a convenient subdivision for analyzing solute transport through the drainage basin, including in-stream cycling, and thus closely accords with the concepts of river continuum (Vannote et al., 1980) and nutrient spiraling (Webster and Patten, 1979). The source zone includes low-order headwater basins, which comprise most of the basin area, and where stream biogeochemical dynamics are primarily controlled by flushing of solutes and organic matter into the stream. Further downstream, in higher-order reaches, the channel becomes increasingly isolated from the surrounding land. Although concentrated flow may occasionally occur on hillslopes in rills and gullies and in the subsurface through cracks and pipes, there is generally a clear division between diffuse flow on slopes and concentrated flow in the river channel. The nature of these diffuse flows has important implications for the residence time of water in the catchment and the transit time of water moving to the stream channel. In headwater basins, the occurrence of runoff leads to sediment and solute removal from hillslopes. Therefore, detailing these processes, source areas, and pathways is relevant. Stream flow may be divided into base flow, that is, stream discharge that is not attributable to direct runoff from precipitation or melting snow and generally is maintained by groundwater discharge during rain- and snowmelt-free periods, and runoff (rainstorm and snowmelt) events. The physical characteristics of the soil and bedrock determine the pathways by which hillslope runoff will reach the channel. The paths taken by water (Figure 1) determine many of the characteristics of the landscape, human uses of the land, and strategies required for wise land-use management (Dunne and Leopold, 1978). There are essentially two models of storm

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273

Infiltration-excess overland flow

rm

flo w

Direct runoff from saturated soil

flo w

su Su b

rn tu Re flow

Bedrock

rfa

ce

sto

Soil

r te wa d n Grou

Figure 1 Schematic of the hydrologic pathways and connections between uplands and streams in headwater catchments.

Storm-runoff mechanisms

Partial-area model

Variable-source area model

Infiltration-excess or Saturation-excess overland flow Hortonian overland flow

Rapid throughflow of new water via macropores or pipes

Subsurface stormflow

Displacement of old water through the soil matrix

Figure 2 Relations among hydrological pathways/mechanisms delivering stormflow from hillslopes to streams.

runoff generation (Figure 2): the partial area and variablesource area models. Horton (1933, 1945) argued that storm runoff is mainly produced by infiltration excess-overland flow. When rainfall intensity exceeds the infiltration capacity of the soil, the excess begins to fill up surface depressions; once these are full, water overflows downslope and surface runoff begins. Horton claimed that infiltration-excess overland flow would occur uniformly across the catchment area, but Betson (1964) showed that this is not necessarily widespread even within a small basin. Betson proposed the partial area model of overland flow generation in which surface runoff is produced only from small areas of the basin during any given storm event. Despite its localized extent, infiltration-excess overland flow can generate large flood peaks and is often associated with soil erosion (Herwitz, 1986). Overland flow moves rapidly and

because its residence time is short, it is usually characterized as new water. Hewlett (1961) proposed the variable-source area model to describe runoff production in areas of permeable soil where subsurface runoff can account for much if not all the storm runoff leaving a basin. The production of significant quantities of subsurface storm flow (otherwise called ‘throughflow’ or ‘interflow’) requires the development of saturated conditions within the soil profile, in effect an ephemeral perched water table. Subsurface storm flow may be generated by movement of water through the soil matrix, by flow in macropores (large diameter conduits in the soil, created by agents such as plant roots, soil cracks, or soil fauna), or by a mixture of both (Beven and Germann, 1982). Given its longer residence time within the soil matrix, throughflow usually has a high solute content and therefore appears relatively old compared to precipitation, whereas macropore flow can be sufficiently rapid to retain the characteristics of new water as evidenced from laboratory (Wildenschild et al., 1994; McIntosh et al., 1999) and field studies (Richard and Steenhuis, 1988; Everts et al., 1989; Jardine et al., 1990; Luxmoore et al., 1990; Peters and Ratcliffe, 1998). If the soil profile becomes completely saturated, saturation-excess overland flow (Dunne and Black, 1970a) will be produced, consisting of a mixture of return flow (exfiltrating old soil water) and direct runoff (new rainfall unable to infiltrate the saturated surface). As the extent of the zone of saturation varies seasonally and during storms, Hewlett (1961) coined the phrase variable-source area to describe his model of storm flow generation. Subsurface storm flow dominates the storm hydrograph where deep-permeable soils overlie less-permeable soil or bedrock, and where steep hillslopes abut the stream. Soils may also contain an impeding layer with can cause a perched water table. Soil saturation is

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more likely to occur in soils of moderate hydraulic conductivity, in areas of reduced soil-moisture storage, and on lower slope angles. The role of topography is particularly important in determining where soil saturation occurs and is favored in hillslope hollows, in places where the soil profile is shallow, and at the foot of slopes especially where the slope becomes less steep. Considerable erosion can occur in these areas where groundwater discharges to the surface at the base of a hillslope, referred to as groundwater sapping (Higgins, 1984). Flow in stream channels reflects different source waters. In large seasonally snow-covered basins, such as the Fraser River in western Canada or rivers draining the Rocky Mountains, the Himalayas, or the Alps, snowmelt provides the main annualdischarge response. Depending on the nature of the aquifer, peak baseflow discharge may significantly lag behind precipitation inputs. Groundwater provides the prolonged recession flow associated with the falling limb of the annual hydrograph. Buttle (1994) reviewed processes responsible for conveying pre-event (old) water rapidly to the channel during storm events: groundwater ridging, translatory flow (pistonflow displacement of pre-storm groundwater), macropore flow, saturation-excess overland flow, kinematic waves, and release of water from surface storage. Not all processes occur in all catchments, of course, but whichever predominate, the focus seems inevitably on near-stream zones (Cirmo and McDonnell, 1997; Burt and Pinay, 2005), including upwelling and discharge of deeper groundwater (Winter, 1999; Cle´ment et al., 2003; Lischeid et al., 2007). Bishop et al. (2004) argued that the chemistry of water moving downslope is modified at any given point by soil chemistry, and riparian soils have a particularly important influence on stream-water chemistry because they are the last soils to contact the water before it discharges (Hooper et al., 1998; Buttle, 2005). Riparian zones are not chemically inert and, therefore, whatever the nature and sources of water flowing into the near-stream zone, it should rapidly acquire a chemical signature determined by the nature of the riparian zone (Cirmo and McDonnell, 1997) or mix with upwelling deeper groundwater (Cle´ment et al., 2003). Dilute (new) event water may reach the stream channel quickly as infiltration-excess overland flow, as direct runoff from saturated areas, or where macropores discharge at the channel bank. The extent to which the chemical composition of flow lines is reset within the near-stream zone depends on the residence time of water there, the size of the riparian aquifer storage, and the amount of new water moving through and mixing with the riparian-zone water. In addition, riparianzone vegetation can alter the hydrologic cycle (Figure 3), including water partitioning through plant physiology (uptake). In addition, the vegetation growth function and structure are affected by water quality (Cirmo and McDonnell, 1997; Tabacchi et al., 2000; De´camps et al., 2004). Because of variable contributions of water from various pathways, differences exist in the delivery of carbon and nutrients to streams during base flow compared to storm flow (Buffam et al., 2001). Much contemporary research is concerned with the ability of riparian zones to buffer rivers from upslope pollution inputs, nitrate in particular. Biological processes tend to affect nitrate more than most other solutes. Nitrate may be produced in the near-stream zone by

8 7 1

2

3 4

5 6 Figure 3 The main physical impacts of riparian vegetation on water cycling: 1, interaction with over-bank flow with stems, branches, and leaves (turbulence); 2, flow diversion by log jams; 3, change in the infiltration rate of flood waters and rainfall by litter; 4, increase of turbulence as a consequence of root exposure; 5, increase of substrate macroporosity by roots; 6, increase of the capillary fringe by fine roots; 7, stem flow (the concentration of rainfall by leaves, branches, and stems); and 8, condensation of atmospheric water and interception of dew by leaves. From Tabacchi E, Lambs L, Guilloy H, PlantyTabacchi AM, Muller E, and Decamps H (2000) Impacts of riparian vegetation on hydrological processes. Hydrological Processes 14(16–17): 2959–2976.

mineralization, and rising water tables can then flush this nitrate into the stream (Triska et al., 1989). However, nitrate may also be removed, temporarily by uptake and immobilization, or permanently by denitrification. For forests, stand age affects N-uptake rates and thus N transformation and leaching rates in soils (Stevens et al., 1994; Emmett et al., 1993). Note, however, that channel flow remains a mixture of different source waters, and if significant amounts of water bypass saturated riparian soils, either by flowing across the surface or through permeable strata below the floodplain alluvium, the riparian zone will be ineffective in removing nitrogen (Burt and Pinay, 2005). Contrasts in stream water nitrate response to similar inputs in seemingly comparable watersheds over various timescales also provide insight into the importance of coupling biogeochemical reactions and hydrological pathways (Reynolds et al., 1992; Christopher et al., 2008). Given the several potential mechanisms for stream flow generation, all of these mechanisms point to the fact that streams and groundwater are intricately linked (Winter et al., 1999).

2.11.3 Mountain Environments The wide ranges of elevation and aspect that characterize mountain environments result in tremendous variability in

Hydrology and Biogeochemistry Linkages

how hydrology and biogeochemical cycles are linked in space and time. Topographical complexity affects the amount of precipitation, solar radiation, temperature, and the lateral redistribution of water, resulting in highly heterogeneous biogeochemical processes. Energy and water balance at the land surface are intimately related to ecosystem productivity directly through transpiration, growth, acclimation, and assembly (McDowell et al., 2008), and indirectly through changes in surface physical characteristics, such as albedo and roughness (Bonan and Levis, 2006). Together, these factors affect both in situ biogeochemical reactions and the transport of biogeochemical solutes through the landscape and into surface water. The spatial variability in elevation, aspect, vegetation, soils, and precipitation, including snow cover, associated with mountain systems results in a high degree of both spatial and temporal variability in the coupling of hydrology and biogeochemistry. The coupling of hydrology and biogeochemistry in mountain environments can be observed by addressing a fundamental question, ‘‘What happens to precipitation?’’ (Penman, 1961), which can be evaluated by assessing the components of the water balance equation:

P ¼ DS þ E þ T þ Q þ R

ð1Þ

where P is the input of hydrometeors mainly precipitation, and also includes fog, rim ice, and cloud water, DS is the change in near-surface water storage, E is the evaporation/ sublimation, T is the transpiration, Q is the runoff, and R is the groundwater recharge. The units used for each waterbalance component are typically given in depth per unit time, such as millimeter per day or year, for a drainage/catchment/ watershed area. These terms are implicitly linked to biogeochemical cycling and suggest mechanisms for explicitly linking hydrology and biogeochemistry in mountain catchments. The remainder of the discussion on mountain environments is organized around these components of the water balance.

2.11.3.1 Precipitation Precipitation (P) typically increases with elevation, while temperature decreases (Barros and Lettenmaier, 1994; GarciaMartino et al., 1996). In mountain catchments, soils are wetter for longer at higher elevations for similar landscape positions (Band et al., 2001), for example,, riparian zones, stream channels, and hillslopes, and soil types as at lower elevations; but soils generally are thinner at higher elevations. Both increased soil-water availability and decreased temperature at high elevations reduce water stress on vegetation while simultaneously slowing soil heterotrophic activity, resulting in a smaller percentage of fixed carbon being respired from soils (Schlesinger, 1997). Thicker soils and higher temperatures at low elevations favor higher carbon storage and respiration. Fog and cloud water deposition may be a large percentage of annual water input to some forests in coastal ecosystems (Dawson, 1998; Klemm et al., 2005; Scholl et al., 2007) and to high-elevation forests (Lovett, 1984; Lovett and Kinsman, 1990; Clark et al., 1998; Heath and Huebert, 1999; Herckes et al., 2002; Scholl et al., 2007). Fog and cloud water typically have higher solute concentrations than precipitation (Lovett,

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1984; Asbury et al., 1994; Reynolds et al., 1996; Weathers and Likens, 1997; Clark et al., 1998; Oyarzu´n et al., 2004) and therefore have higher solute-deposition per unit volume of water. Furthermore, fog and cloud water deposition at forest edges is notably higher than within the forest (Weathers et al., 1995; Ewing et al., 2009). The presence of fog or cloud water affects the plant physiology and biogeochemistry (Joslin and Wolfe, 1992; Bruijnzeel and Veneklaas, 1998; Dawson, 1998; Burgess and Dawson, 2004). Atmospheric washout (scavenging of aerosols and gases by hydrometeors including rain, snow, sleet, freezing rain, and hail (Pruppacher et al., 1983)) affects solute concentrations temporally during rainstorms with concentrations typically decreasing in time. Rainfall also washes off solutes concentrated by evaporation and dry atmospheric deposition that accumulate on vegetation producing much higher solute concentrations in throughfall and stemflow at the onset of rainstorms and decreasing thereafter (Peters and Ratcliffe, 1998).

2.11.3.1.1 Snow Snow is a special case of precipitation that has important implications for biogeochemistry in mountain environments. Seasonal snow cover has been shown to be an important hydrological control on biogeochemistry in many mountain catchments. Both dry and wet deposition are stored in winter snowpacks and released to soil and surface water in the spring (Jeffries, 1989; Peters and Driscoll, 1989; Bales et al., 1993; Williams et al., 1995). Snow cover also insulates soils from low air temperatures during winter (Peters, 1984; Brooks et al., 1996, 2005), providing an environment where soil microorganisms actively cycle carbon and nutrients during winter. Microbial biomass has been shown to reach annual maximum values under snow cover (Brooks et al., 1997, 1998), and these maximum values are also associated with changes in species composition (Lipson et al., 1999). Overwinter soil respiration may return 20–50% of the carbon fixed during the previous growing season to the atmosphere as CO2 (Brooks et al., 2005). Variability in the amount of winter CO2 loss is associated with the timing and amount of snow cover, soil frost, and labile-carbon availability (Brooks and Williams, 1999; Brooks et al., 1999a; Groffman et al., 1999; Grogan and Chapin, 1999; Groffman et al., 2001b). Similarly, overwinter nitrogen mineralization and immobilization in microbial biomass has been shown to be an important source of plant N at the initiation of the growing season. As with CO2 efflux, the magnitude of overwinter N cycling is related to the timing of snow cover and soil frost (Brooks et al., 1995; Groffman et al., 2001a). Consequently, natural variations in seasonal snow cover and climate change can have major impacts on soil processes (Edwards et al., 2007). In many forested mountain catchments, snow–vegetation– energy interactions define snow amount and timing of water availability to terrestrial ecosystems during the growing season (Liston, 2004; Molotch and Bales, 2005; Liston and Elder, 2006; Molotch and Bales, 2006; Veatch et al., 2009). Net ecosystem carbon uptake is dominated by fixation during the snowmelt season when water is not limiting (Monson et al., 2002), and soil respiration and N cycling are also strongly

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controlled by the timing of snow cover (Brooks et al., 1997; Grogan and Chapin, 1999; Groffman et al., 2001 b; Grogan and Jonasson, 2003). Finally, snowmelt transports large, but variable amounts of nutrients, organic matter, and inorganic carbon out of the terrestrial ecosystem (Rascher et al., 1987; Hornberger et al., 1994; Campbell et al., 1995; Boyer et al., 1997; Brooks and Williams, 1999; Brooks et al., 1999b; Heuer et al., 1999; Campbell and Law, 2000). Solute concentrations vary markedly in meltwater and preferentially elute during snowmelt (Johannessen et al., 1975; Johannessen and Henriksen, 1978; Tranter et al., 1986; Berg, 1992). As observed for rainfall and throughfall, early meltwater is high in dissolved solutes that have accumulated in the snowpack and are concentrated in brines around snow crystals during snow metamorphism (Tranter and Jones, 2001). The combination of active abiotic and biotic processes under the snowpack and elution of solutes in meltwater may result in high stream concentrations during snowmelt (Peters and Leavesley, 1995; Mitchell, 2001; Driscoll et al., 2005). The snowpack is biologically active (Hoham and Duval, 2001), particularly when liquid water is present and temperatures and temperature ranges are optimum (Jones, 1999; Hoham and Duval, 2001). Light is also an important constraint to the distribution and reproduction of some biological components such as snow algae (Hoham et al., 1998, 2000), and these algae in turn affect the timing and magnitude of snowmelt (Hoham and Duval, 2001). In addition to snowpack biological activity, physical characteristics, such as atmospheric conditions (wind, vapor pressure, and temperature), can affect gaseous transfers of nutrients (CO2 and NOx) to and from snowpacks (Pomeroy et al., 1999; Tranter and Jones, 2001).

2.11.3.2 Change in Storage The first-order controls on near-surface storage of water (DS) are soil and slope. Soil characteristics and landform are major controls on the partitioning of precipitation into infiltration (Philip, 1967), storage (Beven, 1982), recharge (Gee and Hillel, 1988; Phillips et al., 2004), runoff (see Section 2.11.1), and stream flow (Dunne and Black, 1970b). By affecting the amount and timing of water availability to vegetation and soil microbes, these characteristics interact with climate to affect both potential productivity (carbon fixation) and soil microbial processes. Soils are typically shallower, less weathered, and coarser on ridge lines and higher elevations, and deeper and finer in depressions. Fine-textured soils retain water, reduce diffusion, and result in an environment where anaerobic or facultatively anaerobic processes dominate biogeochemical cycling (Pusch et al., 1998; Hill and Cardaci, 2004). Similarly, topographic depressions and areas of hydrological convergence have higher soil moisture, which may result in either episodic or continuous anaerobic conditions. As aerobic respiration decreases so does carbon mineralization, which can result in increases in denitrification and methanogenesis (Jones et al., 1995; Baker et al., 1999). Several factors affect denitrification across time and space as shown schematically in Figure 4 and discussed with respect to each of the environments presented in this chapter.

2.11.3.3 Evaporation and Transpiration Although the absolute magnitude of evaporation and transpiration (E and T) is controlled by climate, the partitioning between E and T is controlled by vegetation water use, and thus is directly linked to carbon fixation and input into the ecosystem. Vegetation serves both as the carbon pump, bringing organic matter into ecosystems, and as the water pump, removing water from ecosystems, and thus controls the amount of chemical energy in organic matter and the amount of water in the environment. Organic matter and water in turn are the primary controls on soil biogeochemical processes, affecting carbon balance, nutrient cycling, and mineral weathering (Amundson et al., 2007). Both the type and amount of vegetation within an ecosystem vary predictably with elevation and latitude (Holland and Steyn, 1975), as the effect of increasing elevation can generally be equated to that of increasing latitude. Similarly, vegetation varies with aspect in relation to both temperature and water availability (Grace, 1989). For example, the tree line often is limited by temperature and typically extends to a higher elevation on south-facing slopes than north-facing slopes in the Northern Hemisphere (Treml and Banas, 2008). In seasonally water-stressed environments, north-facing and topographically shaded slopes may have more dense vegetation cover and different species assemblages than southfacing or nonshaded slopes (Zhang et al., 2009). These differences arise from the interaction between energy and water. By changing the structure and productivity of the land surface, which control the input of carbon to soil and plant N demand from soil, the potential magnitude of soil biogeochemical processes is affected. Moreover, the root zone and rhizosphere in the soil are particularly active biologically and biogeochemically and the presence of roots affects hydrology. The rhizosphere, which is the dynamic interface among plant roots, soil microbes and fauna, and the soil, is attributed to the evolution of soil, that is, the alteration of primary and secondary soil minerals (Cardon and Whitbeck, 2007). The redistribution of soil moisture by plants is not typically considered in biogeochemical-cycling research or in typical hydrological-process assessment (Burgess et al., 1998; Caldwell et al., 1998; Jackson et al., 2000; Meinzer et al., 2001). Plant physiologists with the aid of stable isotopes have made major advances in understanding how plants use water (Ehleringer and Dawson, 1992; Dawson, 1993; Emerman and Dawson, 1996; Dawson and Ehleringer, 1998; Moreira et al., 2000). A general pattern is that roots transport water from deep moist horizons to shallow drier surface-soil horizons, particularly at night, by a process called hydraulic lift (Caldwell et al., 1998). It is not surprising that plant redistribution of water would predominate in dry land, as observed in arid areas, for example, for phreatophytes (Hultine et al., 2003) and sage brush (Richards and Caldwell, 1987; Caldwell and Richards, 1989). However, plant redistribution of water has also been documented for sugar maples in northern temperate forests (Dawson, 1996; Emerman and Dawson, 1996), for three species representing each of three canopy niches in Amazonia (Oliveira et al., 2005), and for blue oaks in the foothills of the Sierra Nevada (Millikin Ishikawa and Bledsoe, 2000). Roots not only move water from depth to surface soils, but can

Space mm

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1000 km

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stratified aquatic systems

Low-O2 river reaches

d Time

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yr Periodically anoxic soils cent Groundwater

Marine oxygen-minimum zones OMZs

(a)

Denitrification system types Group A. Diffusion dominated

Group B. Advection dominated

Group C. Periodic anoxia

Distinct nitrification and denitrification layers dictated by O2 concentrations

Stable suboxic/anoxic water or sediment mass into which nitrate is advected

Periodic anoxia caused by soil moisture changes or water stratification

mm-cm scale (Meters for aquatic systems)

Time O2 Nitrification Denitrification

Low-O2 region

Advective input NO3

Dry soils or well-mixed water High O2 nitrification

Low O2

Water-saturated soils or stratified water Low O2 denitrification

High O2

• Water saturated soils and sediments (lakes, wetlands, rivers, estuaries, and continental shelf) • Soil microsites • Anoxic bottom water (lakes, enclosed seas, and fjords)

• OMZs, groundwater, and river reaches

• Terrestrial soils • Periodically stratified lakes, estuaries, and continental shelf

(b)

Figure 4 (a) Classification of systems according to the magnitude of temporal and spatial separation between nitrification and denitrification. Diffusion-dominated systems are indicated in gray, advection-dominated systems are indicated with heavy outlines, and systems with periodic anoxia are indicated by dashed lines. (b) Schematic groupings of systems according to mechanism of nitrate delivery to denitrification zone. Vertical profiles of oxygen concentrations are indicated. Adapted from Seitzinger S, Harrison JA, Bo¨hlke JK, et al. (2006) Denitrification across landscapes and waterscapes: A synthesis. Ecological Applications 16(5): 2064–2090.

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also move water from surface to depth as documented for perennial grasses in northwestern South Africa (Schulze et al., 1998); inverse hydraulic lift may be an important mechanism to facilitate root growth in dry desert soil layers below the upper soil zone to which precipitation penetrates. In addition, meteorological conditions through the effect of shade and clouds on soil water potential can affect hydraulic lift (Williams et al., 1993). Furthermore, water hydraulically lifted by large trees may be used by smaller trees and shrubs as shown by isotope tracers (Dawson, 1996); the authors concluded that small trees and shrubs use soil water and large trees use groundwater. Clearly, these plant–water relations should have major implications for biogeochemistry. However, there is a lack of hard evidence linking biogeochemical cycles to water redistribution by plants, although some of these studies cited above discuss the implications of water redistribution on nutrient cycling.

2.11.3.4 Stream Flow Stream water integrates the myriad of catchment hydrological and biogeochemical processes. The catchment is a natural spatial domain to study the coupling of hydrological and biogeochemical cycles and simultaneous measurements of catchment discharge (Q) and hydrochemistry capture the integrated catchment hydrological and biogeochemical behavior. Hydrological and biogeochemical processes are tightly coupled at the Earth’s surface and hydrological fluxes within catchments provide an opportunity to balance material fluxes at defined spatial and temporal scales. Catchments link the atmosphere, plants, soil surface, subsurface, groundwater, and streams through the convergence and interaction of material and energy flows. Quantifying the variability in the contributions of different water sources to stream flow allows inferences to be drawn about hydrological pathways and biogeochemical processes in these source areas (Peters and Driscoll, 1987b; Hooper et al., 1990; McDonnell et al., 1991). For N export, the magnitude of individual responses to a runoff event appears to be related to soil microbial processes, while seasonal to decadal trends are related to vegetation demand (Bormann and Likens, 1994; Likens and Bormann, 1995). A common observation across mountain catchments is that large fractions of both C and N are exported in stream flow in response to rainstorms and snowmelt runoff (Hood et al., 2006), presumably because of changes in routing through the catchment (McGlynn et al., 2003; Bishop et al., 2004; McGuire and McDonnell, 2006). Shallower soils, higher hydraulic conductivity, and proximity to the stream channel increase the likelihood of increased export of biogeochemically active solutes during runoff events.

2.11.3.4.1 Nitrate isotopes in stream water The ability to differentiate sources of nitrate in streams has advanced with the application of multiple isotope techniques for d15N, d18O, and d17O in nitrate (Durka et al., 1994; Kendall, 1998; Burns and Kendall, 2002; Mayer et al., 2002; Fukada et al., 2003; Michalski et al., 2004; Kendall et al., 2007). Nitrate isotopic variations can be related to sources

of nitrate, and they also are modified by subsequent reactions such as denitrification and assimilation. Isotope studies emphasizing either source variations or cycling processes may be complicated by these overlapping effects, but they can be useful in some situations. For example, temporal isotopic studies have provided important constraints on the transmission of atmospheric nitrate through watersheds. Oxygenisotope data indicate that atmospheric nitrate, commonly, is only a minor component of stream nitrate during a range of flow conditions, including snowmelt events, highlighting the importance of nitrification sources in runoff (Burns and Kendall, 2002; Campbell et al., 2002; Buda and DeWalle, 2009). Exceptions include peak flows during storm events, especially in watersheds containing impervious ground cover such as urban areas, where atmospheric nitrate can be a substantial component of stream nitrate (Kendall et al., 2007). Spatially distributed isotopic data in stream networks can provide supporting evidence for varying nitrate sources in different subwatersheds (Mayer et al., 2002; Lindsey et al., 2003). For example, during moderate base-flow conditions in the predominantly agricultural Mahantango WE-38 watershed in Pennsylvania, USA (Lindsey et al., 2003), small streams with low nitrate concentrations and low d15N values from forested upland watersheds joined other streams with higher nitrate concentrations and higher d15N values draining cropland, whereas a few streams with exceptionally high nitrate concentrations and high d15N values drained areas with animal feedlots or pastures. Nitrate apparently was transmitted through the watershed without major isotopic modification after infiltrating through soils. In contrast to the above, it has been suggested that isotopic indicators could be incorporated into conceptual, analytic, or numerical models of flow, transport, and denitrification to evaluate nitrogen losses within watersheds (Sebilo et al., 2003). This approach requires information or assumptions about the upscaling properties of isotope-fractionation effects from micro- to diffuse scales (scale at which mixing of water and solutes is relatively complete). In principle, the regionalscale status of diffuse denitrification could be evaluated by monitoring d 15 NNO3 and d 18 ONO3 in stream flow to determine the catchment-scale status and dynamics of denitrification. A hypothetical example of such an approach is illustrated in Figure 5, which shows predictions of the d 15 NNO3 isotopic behavior of nitrate in stream flow as distributed within an idealized model framework. The stable isotope predictions are based on the riparian nitrate model (RNM), which operates as a filter (plug-in) module within a node-link catchment-scale model (E2); E2 is capable of simulating the hydrological behavior of catchments (Rassam et al., 2006, 2008). The RNM– E2 modeling framework has been augmented with Rayleigh fractionation algorithms at each node within the model domain to track the isotope shifts because of denitrification. The modeling example considers a simple homogeneous riparianzone soil with decreasing available carbon and related microbial activity with depth and denitrification only occurring in the riparian-zone soils. As denitrification proceeds in the groundwater prior to discharge, the residual nitrate becomes relatively enriched in 15N as nitrate concentration decreases (Marriotti et al., 1988; Kendall, 1998). Figure 5 illustrates the RNM–E2 simulated dynamics of nitrate concentrations and

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Figure 5 Linkages between stream nitrogen (N) loading, variation of d15N of nitrate (d15N–NO3) corresponding to varying amounts of dentrification, and hydrology of the Maroochy catchment southeast Queensland, Australia during the wet season 1982; (a) temporal variations in hydrologic response through quick and baseflow components, and N loading; and (b) concurrent predictions of the streamwater d15N–NO3. From Rassam DW, Knight JH, Turner J, and Pagendam D (2006) Groundwater surface water interactions: Modelling denitrification and d15NNO3–d18ONO3 fractionation during bank storage. In: Institution of Engineers Australia (ed.) Proceedings of the 30th Hydrology and Water Resources Symposium, pp. 157–161. Launceston, TAS, Australia, 4–7 December 2006. Sandy Bay, TAS: Conference Design.

d15N as the stream flow source switches between base flow and rapid delivery of new water during storm flow. The d15N of the stream nitrate increases as the extent of denitrification in the catchment increases. Similar results could be generated for d18ONO3 depending on the denitrification fractionation factors used (Bo¨ttcher et al., 1990; Fukada et al., 2003; Granger et al., 2008). In these models, isotopic variations in nitrate sources are assumed to be negligible.

2.11.3.4.2 Transit time and residence time The distributed hydrological response to precipitation encompasses the spatial and temporal variations of water fluxes in landscapes, and, therefore, is directly related to the variability in biogeochemical cycling described earlier. The hydrological response in a mountainous catchment is controlled largely by the near-surface landscape properties (landform and soil characteristics) that function as hydrological filters (Meybeck and

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Vo¨ro¨smarty, 2005); yet, variations are often nonlinear and difficult to represent (Beven and Germann, 1982; Troch et al., 2003). Mean hydrological transit time, or the mean age of water discharging to a stream channel, is an important hydrological descriptor, but its relation to storage, flow pathways, mixing, and sources of water is complex and model dependent (Maloszewski et al., 1983; McGuire et al., 2005; McGuire and McDonnell, 2006; Soulsby et al., 2006). Feedbacks between vegetation structure, soils, biogeochemistry, and landform development result in a range of transit-time distributions throughout a catchment, that is, rates of water movement from the catchment to the channel at various locations along the channel (McGuire and McDonnell, 2006; McDonnell et al., 2007). For example, based on a simple agedistribution model (Tetzlaff et al., 2007), slope was found to be the dominant control of mean transit time in steep Scottish catchments, but soil permeability was a more important control in flat lowland catchments (Tetzlaff et al., 2009). In another study, aspect appeared to be a dominant control on mean transit time, with north-facing slopes having longer transit times than south-facing slopes (Broxton et al., 2009). From a biogeochemical perspective, the residence time, that is, length of time that water remains in a catchment

Soil column: cross section

before it becomes stream flow, has a pronounced effect on potential solute concentrations and export. As water moves from upland areas in the catchment, downslope interactions with other water sources may occur in areas of convergent flow (Anderson and Burt, 1978), promoting physical mixing of waters, exchange of solutes, and increased rates of oxidation– reduction (redox) reactions or hot spots (Fisher et al., 1998; McClain et al., 2003). These hot spots are where high rates of nutrient modifications occur, for example, in riparian zones (Burt and Pinay, 2005), the hyporheic zone, and wetlands (Figure 6) with associated chemical transformations (Figure 7). Episodic transport during hydrological events, such as snowmelt or heavy rain, can reduce the importance of hot spots by reducing residence time, resulting in increased nutrient flux (Boyer et al., 1997; Stanley et al., 1997). Even after water has entered a stream channel, its residence time may be greater than that predicted by stream velocity because of hyporheic exchange, that is, the movement of stream water into the subsurface and back to the stream at a location downstream. The typical rough texture of headwater mountain-basin geomorphology (e.g., pool-riffle sequences) often drives exchange of water through the bed and riparian sediments (Harvey and Bencala, 1993; Kasahara and

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Figure 6 Hot spots of denitrification occur at multiple spatial scales. (a) Hot spots in a meter of soil may occur along root channels where moisture and organic-matter content are high. (b) Topographic depressions that accumulate organic matter and retain moisture may be hot spots within a catena. (c) Along a toposequence from upland to river, the soil–stream interface may represent a hot spot where high-nitrate groundwater intercepts organic-rich soils. (d) At the scale of sub-basins, the occurrence of hot spots may be dictated by the spatial configuration of upland–wetland or upland–river contact zones. (e) The percentage of land occupied by wetlands determines denitrification hot spots at the scale of large river basins. Adapted from McClain ME, Boyer EW, Dent CL, et al. (2003) Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6(4): 301–312.

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Figure 7 A schematic diagram of some common oxidation and reduction reactions in riparian zones and their relative redox potentials at pH 7. Reaction sequences in waters recharging from, or discharging to, a stream depend on the origin (initial redox status) of the water, the composition of the substrate, and the interaction with biota (bacteria, roots, etc.). Adapted from Dahm CN, Grimm NB, Marmonier P, Valett HM, and Vervier P (1998) Nutrient dynamics at the interface between surface waters and groundwaters. Freshwater Biology 40(3): 427–451; and Appelo CAJ and Postma D (2007) Geochemistry, Groundwater, and Pollution, 2nd edn., 649pp. Rotterdam: AA Balkema.

Wondzell, 2003). The changing morphology along a river network exerts a strong control on gradients that drive exchange, and therefore, the amount of water that flows through the hyporheic zone (Kasahara and Wondzell, 2003). This exchange not only increases stream water residence time in the basin, but also moves nutrients and other solutes into the

subsurface: (1) fueling biogeochemical processes in the shallow subsurface around streams, (2) providing a subsurface habitat that is a mix of surface and groundwater conditions, and (3) generating patches of varying conditions on the streambed in downwelling (where stream water enters the bed) and upwelling (where hyporheic water returns to the

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Catchment-scale gradients

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Figure 8 Lateral diagrammatic view of the hyporheic zone (HZ) at three spatial scales. At the catchment scale (a), the hyporheic corridor concept predicts gradients in relative size of the HZ, hydrologic retention, and sediment size (126). At the reach scale (b), upwelling and downwelling zones alternate, generating gradients in nutrients, dissolved gases, and subsurface fauna. At the sediment scale (c), microbial and chemical processes occur on particle surfaces, creating microscale gradients. Arrows indicate water flow paths. From Boulton AJ, Findlay S, Marmonier P, Stanley EH, and Valett HM (1998) The functional significance of the hyporheic zone in streams and rivers. Annual Review Ecology and Systematics 29: 59–81.

channel) locations (Figure 8). Thus, hyporheic exchange is an important hydrological process that directly contributes to many biological functions such as macroinvertebrate population dynamics (Boulton et al., 1998; Malard et al., 2002) and salmonid spawning (Baxter and Hauer, 2000), and biogeochemically important reactions (Figure 5) such as denitrification and retention of dissolved organic carbon (DOC; Baker et al., 1999), water-temperature buffering (Arrigoni et al., 2008), and buffering of heavy metal transport (Fuller and Harvey, 2000).

2.11.3.5 Groundwater Recharge Groundwater recharge (R) is directly linked to mineral weathering rates through the delivery of water, DOC, and CO2 to the subsurface; the export of weathering products to surface water; and CO2 release to the atmosphere (Richey et al., 2002). Consequently, groundwaters with longer transit times typically

contain lower DOC concentrations because of microbial degradation, and higher concentrations of conservative alkalinity-associated DIC (Szramek and Walter, 2004), base cations, and silica (Rademacher et al., 2001). Climate affects water–rock interactions. For example, increasing temperature and high rainfall tend to increase rates of chemical weathering (White and Blum, 1995; White and Brantley, 2003). In addition, weathering is controlled by lithology (Meybeck, 1987; Bluth and Kump, 1994; Meybeck and Vo¨ro¨smarty, 2005). In the long term, linkages or feedbacks between biota and earth materials have modified the near-surface environment of Earth or ‘critical zone’ (Brantley et al., 2007), and, in turn, the chemistry of streams and rivers shows the evidence of biological processes (Amundson et al., 2007). Groundwater, including recharge and discharge, is an important topic and is presented in a separate section, and is also discussed in each of the following sections where it interfaces with the main topic of the section.

Hydrology and Biogeochemistry Linkages

2.11.4 Within-River Processes Biogeochemical processes within streams and rivers can result in high rates of nutrient uptake, chemical transformation, and release of dissolved materials to water. These biogeochemical processes are largely the result of organisms, such as bacteria and fungi, algae, and higher plants, attached to hard surfaces or to organic and inorganic sediments on the streambed. In larger rivers, biogeochemical processes associated with suspended algae and microbes attached to suspended particles can be important. These processes can significantly alter the flux and chemical form of several biologically active solutes, particularly carbon, nitrogen, and phosphorus. Most of the organisms responsible for biogeochemical processes in streams and rivers are stationary, associated with the streambed, yet solutes taken up or released to water are under strong advective forces of downstream water flow. Thus, nutrient cycling has a distinctive spatial or longitudinal component along the axis of stream flow. Biological communities generally change from dominance of shredders, associated with leaf litter and woody debris accumulations in turbulent, higher-velocity streams in headwaters, to collector–gatherers in larger more quiescent streams with lower gradients downstream. Nutrient spirals tend to lengthen from upstream where streambed-surface:water-volume ratios tend to be high to downstream where surface:volume ratios are lower (Figure 9). The concept of nutrient spiraling was proposed as a framework to study nutrient cycling in streams and explicitly considers the simultaneous processes of biological uptake, transformation, or remineralization and hydrological transport downstream (Webster and Patten, 1979; Newbold et al., 1983). As an example of the nature of these reactions and interactions, a schematic of inorganic nitrogen transformations and interactions with streambed biota and hyporheic zone is shown in Figure 10 (Peterson et al., 2001). Several metrics quantifying nutrient spiraling have been defined, including uptake length – the average distance traveled by a nutrient atom in water from where it enters the stream to where it is taken up by biota. Methods for field measurement of uptake length have been developed, including the experimental addition of isotopic nutrient tracers, such as 15N for studies of nitrate and ammonium uptake and denitrification (Newbold et al., 1981; Stream Solute Workshop, 1990; Mulholland et al., 2000; Peterson et al., 2001; Bo¨hlke et al., 2004; Mulholland et al., 2004; Bo¨hlke et al., 2009). Distinctive temporal patterns characterize within-river biogeochemical processes. These patterns are related to seasonality in biological processes and in hydrology. Biological seasonality is largely controlled by the regulation of inputs of light and organic matter by terrestrial ecosystems bordering streams and rivers. In streams draining deciduous forests, nutrient-uptake rates often have two peaks each year: (1) early spring prior to leaf-out when uptake and growth rates of attached algae are high because of high light levels reaching the stream and (2) autumn after leaf-fall when uptake rates by bacteria and fungi associated with leaf decomposition are high (Roberts and Mulholland, 2007). In small, heavily shaded streams, summer is often a period of relatively low rates of nutrient uptake, despite high water temperatures, because algal growth is severely limited by low light levels and activity

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of heterotrophic microbes is limited by the lack of easily decomposable organic matter in the streambed. However, streams that do not have dense forest canopies (e.g., those in more arid climates and agricultural landscapes) often have late spring or summer peaks in nutrient uptake and cycling because light levels are high and stream flows are more stable (Arango et al., 2008). Late spring and summer peaks in nutrient uptake may also be common in larger rivers because of high light levels and longer water-residence times under lower flows that permit development of larger communities of algae in the water column; however, little is known about rates and temporal dynamics of nutrient uptake in large rivers, that is, with drainage areas greater than 250 000 km2. There is considerable spatial variation in within-river biogeochemical processes, largely controlled by the hydrological regime, channel morphology, catchment land use, and characteristics of stream-bank (riparian) vegetation (Tabacchi et al., 2000). For example, desert streams in monsoonal climates can develop very high rates of nutrient uptake and cycling as algal communities develop during long periods of low, stable stream flow after the monsoon season ends (Grimm et al., 2005). In addition, within-river seasonal variations in macrophyte growth and related geomorphology can affect sediment respiration, nitrification, and denitrification rates (Duff et al., 2002). Furthermore, Duff et al. (2002) showed that as the rooted aquatic macrophyte communities matured, pore water became chemically reduced and nutrient levels increased by one to two orders of magnitude above background in the root zone. These levels were significantly higher than those found in either groundwater or surface water, indicating that streambeds can serve as a nutrient reservoir. Streams with very flashy hydrographs tend to have lower rates of biogeochemical processes because attached organisms are frequently scoured from the streambed during high flows. Cross-site studies have been particularly valuable for identifying broad-scale controls on nutrient uptake and cycling. Stream discharge is often the strongest predictor of nutrient-uptake length in streams, with longer uptake lengths (lower rates of uptake relative to downstream transport) under higher discharge (Peterson et al., 2001; Marti et al., 2004). In relatively undisturbed catchments, land use and riparian vegetation are also important determinants of nutrient uptake, but indirectly via their effects on light regime and stream primary productivity and nutrient inputs (Tabacchi et al., 2000; Hall et al., 2009). Stream algae and microbes are able to increase rates of uptake with increasing nutrient concentrations, although they become somewhat less efficient at removing nutrients from water as concentrations increase (Dodds et al., 2002). As discussed previously, subsurface (hyporheic) zones within streambed sediment accumulations are important sites for biogeochemical processes (Dahm et al., 1998). Water exchange between surface and subsurface zones and between the main channel and backwaters is an important mechanism for increasing rates of nutrient cycling (Triska et al., 1989; Jones and Holmes, 1996), and particularly for rates of denitrification (Duff and Triska, 1990; McMahon and Bo¨hlke, 1996; Mulholland et al., 2009). In-stream processes can be important for the retention of nutrients within river networks and landscapes, thus reducing the potential for eutrophication and harmful algal blooms in

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12 Relative channel width Figure 9 Nutrient spiraling along the river continuum. In general, nutrient spirals are produced by the simultaneous processes of nutrient cycling (uptake from water by biota and subsequent release back to water) and downstream transport. Adapted from Vannote RL, Minshall GW, Cummins KW, Sedell JR, and Cushing CE (1980) The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37(1): 130–137.

Hydrology and Biogeochemistry Linkages

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Figure 10 Simplified schematic diagram of selected processes affecting inorganic N cycling in streams. Biota includes bacteria distributed within the sediments. Although not shown, N species can transfer between stream and hyporheic zone without interacting with biota. Modified from Peterson BJ, Wollheim WM, Mulholland PJ, et al. (2001) Control of nitrogen export from watersheds by headwater streams. Science 292(5514): 86–90.

estuaries and coastal waters. Streams receiving relatively low nutrient inputs can retain more than half of their inputs over a 1-km stream length (Peterson et al., 2001). In addition, increasing nitrogen retention by in-stream processes in landscapes recovering from past disturbances has been reported (Bernhardt et al., 2005). Nutrient cycling is a serial process in streams and rivers, and large cumulative stream length results in long residence times and potentially high rates of nutrient retention in river networks (Wollheim et al., 2006; Alexander et al., 2007; Mulholland et al., 2008). Although small, shallow streams in the network can have particularly high rates of nutrient uptake because of high surface:volume ratios (Alexander et al., 2000), nutrient uptake can also be high in larger streams and rivers (Ensign and Doyle, 2006; Alexander et al., 2007; Tank et al., 2008). However, high nutrient inputs can overwhelm the capacity of biological-uptake processes within streams, resulting in low retention efficiency and large losses to downstream ecosystems (Mulholland et al., 2008; Bo¨hlke et al., 2009). Humans have had large impacts on within-river biogeochemistry. Agriculture and urbanization have been among the most widespread effects on streams. Agriculture often results in stream channelization and other modifications that reduce geomorphologic and hydrodynamic complexity and organic-matter storage and thus the rates of biogeochemical processes; in effect, farmland streams become more of a conduit and less of a barrier to runoff (Burt and Pinay, 2005) and this loss of landscape structure may account, for example, for persistently high concentrations of nitrate in river water (Burt et al., 2008). However, during extended periods of low stable flow in summer, nutrient uptake rates can be high because of high level of light and nutrient concentrations (Bernot et al., 2006). Urbanization can also result in substantial changes to in-stream biogeochemical processes because of many of the same morphological, hydrodynamic, and organic-matter impacts as agriculture, although greater extent of impervious surfaces can result in much flashier

urban-stream hydrographs (Boyd et al., 1993; Finkenbine et al., 2000; Rose and Peters, 2001). Rapid runoff or flashiness generally reduces nutrient-uptake rates in urban streams (Paul and Meyer, 2001; Groffman et al., 2004; Meyer et al., 2005; Walsh et al., 2005) and riparian zones (Groffman et al., 2002). In contrast, modifications to stream networks in heavily urbanized areas, such as detention basins and artificial lakes, can enhance nutrient uptake and retention (Grimm et al., 2005).

2.11.5 Wetland Processes Wetlands are defined differently by country and agency of use, but all definitions recognize the persistence of near-saturated conditions at or above the mineral sediments, hydric soils, and the presence of plants adapted for generally saturated conditions (Mitsch and Gosselink, 2007). Wetlands occur in all climatic and geographic regions of the world but they are more prominent in regions where precipitation exceeds potential evaporation and topographically flat areas where drainage rates are slow. However, given a supply of water, for example, rivers, streams, and groundwater, they can occur even in the most arid regions. The dominant physical factor of wetlands is the presence and persistence of near-saturated conditions. Whether the wetland is in the tropics or the high Arctic, waterlogged conditions are a function of the hydrology of the catchments in which the wetland is located, but the presence of a wetland also alters the hydrology of a catchment. Some wetlands provide temporary storage attenuating flood peaks and sustaining flows during drier periods (Mitsch and Gosselink, 2007); however, wetlands that also accumulate partially decomposed plants, for example, peat, have a poor ability to attenuate runoff because of their hydraulic properties (Bay, 1969; Holden and Burt, 2003; Holden et al., 2006). The role of wetlands in the hydrology and biogeochemistry of catchments is determined, in part, by the position of the

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Hydrology and Biogeochemistry Linkages

wetland in the landscape. Given topographic depressions and an excess of precipitation over evapotranspiration, wetlands can exist in the headwaters of catchments, in valley floors, or on plateaus adjacent to the drainage divides. While these wetlands might seem isolated from the catchment, their outflow provides water downstream, particularly during periods of storm runoff (Holden and Burt, 2003). The hydrological interaction between streams and wetlands can be very complex, particularly for wetlands adjacent to streams in higherorder drainage basins with low stream gradients. In particular, groundwater can become important in creating saturated conditions that are needed to sustain wetlands (Winter and Woo, 1990). The isolated wetland’s hydrology is mainly a function of precipitation and antecedent water storage, making runoff strongly event related (Verry et al., 1988; Evans et al., 1999). In contrast, wetlands that are well connected to groundwater can have a less variable storage dynamic on the short term, for example, wetlands located at break points on hillslopes where they receive groundwater discharge (Winter, 1999). The magnitude and rate of water input to a wetland relative to the wetland’s storage capacity control residence time, and residence time plus the mixture of biological and chemical reactants determine the biogeochemical dynamics. For example, valley-bottom riparian wetlands can receive groundwater discharge that far exceeds precipitation input and this leads to relatively constant levels of water storage and rapid turnover of stored water (Roulet, 1990). Residence time also can be greatly increased by near-shore processes that effectively cycle wetland water from surface water to groundwater and back again. This is because of evapotranspiration (ET) drawing the water table below wetland stage, reversing hydraulic gradients, and allowing wetland water to flow into groundwater troughs that ring the wetlands, a common characteristic of prairie wetlands (Rosenberry and Winter, 1997). In contrast, water exchange in some wetlands, such as raised peat bogs, is confined to a thin hydrologically active layer near the surface (Ingram, 1978) and leads to a twocompartment system – one compartment with a short residence time (hours to days) and the other compartment a deeper groundwater with long residence times (hundreds of years) (Fraser et al., 2001). Water-exchange rates are an important factor affecting wetland biogeochemistry because they affect the magnitude and supply rate of chemical inputs  (e.g., DO, SO2 4 , and NO3 ) relative to the supply of reactants (e.g., Fe2þ, Mn2þ, NH4þ, organic matter, and microbes) in the wetland. For example, headwater and isolated wetlands receive their chemical inputs from precipitation alone; hence, they tend to be nutrient-poor systems, and if they accumulate peat, the surface vegetation can become isolated from the source of minerals in the underlying substrate and result in acidic conditions (Damman, 1986; Wilcox et al., 1986). In contrast, wetlands that receive water that has contacted other land covers and soils, such as marshes, valley-bottom swamps, or prairie pot holes, can receive a large influx of nutrients and cations, which can result in mineral and nutrient-rich productive ecosystems (Bedford et al., 1999). In wetlands, the temporal and spatial variations in chemical composition and biogeochemical transformations can be evaluated using chemical thermodynamics with a particular

emphasis on redox conditions (Hedin et al., 1998). Because of saturated conditions, oxygen is limited in most wetlands. The diffusion of oxygen in water is 10 000 times slower than that in air. Consequently, if there are processes that consume oxygen, such as decomposition of organic matter, the wetland substrate becomes progressively more reduced. In wetlands with short water-residence times, that is, the water is renewed regularly, and wetlands where the surface is flooded, oxic conditions prevail, at least near the surface; biogeochemistry of these wetlands is dominated by oxygen, nitrate, and iron reduction. However, when residence times are longer and oxygen consumption exceeds supply, wetlands become progressively more reduced, and sulfate reduction and eventually methanogenesis become common (Reddy and DeLaune, 2008). In addition to the importance of hydrologic fluxes of oxygenated waters for oxygen input to wetlands, wetland plants oxygenate their roots, and differences in the ability of wetland plants to aerate their submerged tissues under different flooding regimes play a major role in controlling plant distribution (Sorrell et al., 2000; Pezeshki, 2001). Temporal and spatial variations in saturation are important in controlling temporal and spatial dynamics of wetland biogeochemistry. Spatial and temporal dynamics of the inputs and the distribution of plants are also important controls on wetland biogeochemistry. The wetland setting is based on hydrology, the wetland salinity is based on climate, and biogeochemical response is based on a combination of the two. Recently, biogeochemists have begun to refer to locations on the landscape that show steep redox gradients, which have high biogeochemical transformation rates, as hot spots and the times when redox conditions change quickly at a location as hot moments (McClain et al., 2003). This conceptualization works well across many scales in wetland settings. For example, in unsaturated wetland sediments, the interface between saturated pores and adjacent air-filled pores could be a hot spot for chemical transformations. At a larger scale, transformations from oxidizing conditions upgradient of a riparian wetland to reducing conditions within a wetland are also likely hot spots. A hot moment may occur when the wetland water table rises rapidly during a hydrological event resulting in a large decrease in oxygen availability. Wetland ecosystems can also be viewed as a wetland continuum (Euliss et al., 2004), similar to the stream-continuum concept (Vannote et al., 1980). The concept places wetlands in two dimensions (Figure 11); one in relation to groundwater interaction, that is, recharge and discharge, and the other with respect to climate condition, that is, atmospheric water, from dry or drought conditions to wet or flood/deluge conditions. The wetland continuum provides a framework for organizing and interpreting biological data by incorporating the dynamic changes these systems undergo as a result of normal climatic variation. There are many examples of specific linkages between biogeochemistry and hydrology in wetlands. In almost all settings, carbon availability is the main driver of wetland biogeochemistry. This is reflected in the large accumulation of organic matter commonly observed in wetland soils, and in the importance of wetlands as a major source of DOC downstream (Hinton et al., 1998; Freeman et al., 2001; Worrall et al., 2004; Roulet et al., 2007; Nilsson et al., 2008) and

Hydrology and Biogeochemistry Linkages

287

Drought

Hydrologic relation to atmospheric water

Deluge

The wetland continuum

Recharge

Hydrologic relation to groundwater

Discharge

Terrestrial perennials

Wetland annuals

Robust wetland perennials

Terrestrial annuals

Early-season wetland perennials

Submersed wetland perennials

Figure 11 The wetland continuum, a wetland classification based on hydrology with respect to groundwater interactions from recharge to discharge and climate conditions from drought to deluge. Potential plant communities in wetlands at four discrete points along this axis are depicted. Adapted from Euliss NH, Jr., LaBaugh JW, Fredrickson LH, et al. (2004) The wetland continuum: A conceptual framework for interpreting biological studies. Wetlands 24: 448–458.

emissions of CO2, methane (CH4), and N gases (N2 and N2O). Wetland CH4 and N2O fluxes are extremely high compared to those from other landscapes (Bowden, 1986; Robertson, 2001; Svensson et al., 2001; Rosenberry et al., 2006). Wetland CH4 fluxes have been linked to water-table depths and temperature (MacDonald et al., 1998), which is the basis for a one-dimensional methane flux model (Figure 12) for natural wetlands (Walter and Heimann, 2000; Walter et al., 2001). Wetlands can accumulate carbon in the form of peat deposits and export water with high DOC concentrations (Moore, 2003); therefore, area of wetlands is often strongly correlated with rates of DOC export (Dillon and Molot, 1997; Xenopoulos et al., 2003). However, this correlation is not universal even in landscapes with significant wetland cover (Frost et al., 2006). Even in catchments where wetlands are only a small fraction of the catchment area, they can still have a profound effect on catchment biogeochemistry producing relatively high DOC concentrations in drainage waters, because they are often the last point of contact before the water enters a stream or river (Bishop et al., 2004). In this case, the role of the wetland can be quite dynamic depending on temporal variations in the hydrological connection of the

wetland with the adjacent hillslope (McGlynn et al., 2003; Burt and Pinay, 2005). Wetlands can be more effective in removing nutrients from circulating waters than lakes or rivers (Saunders and Kalff, 2001), and retaining constituents from atmospheric deposition, but removal is not a universal conclusion. Riparian wetlands, in particular, can be quite effective in removing and retaining nutrients and nitrogen in particular (Jansson et al., 1994), but the dynamics and hydrological setting play a critical role in the effectiveness of nutrient removal (Cirmo and McDonnell, 1997). Nutrients retained under one condition, such as periods when the water table is receding, can be rapidly mobilized upon rewetting, and the retention can be much less throughout the year based on an event or on a seasonal basis (Devito et al., 1989). Wetlands, through dynamic coupling with uplands and the atmosphere, can sometimes act as biogeochemical hot spot sources instead of sinks. For example, wetlands are sources of methyl-mercury (St. Louis et al., 1996; Babiarz et al., 1998), and the hydrological coupling of wetlands to upland sources of water (Branfireun et al., 1996; St. Louis et al., 1996; Galloway and Branfireun, 2004) and sources of sulfur, either through hydrological input or by atmospheric deposition

Hydrology and Biogeochemistry Linkages

CH4 emission

Climate

Soil surface

Rooting depth

CH4 production

NPP

Diffusion

CH4 concentration

Soil temperature

Oxic soil

Ebullition

CH4 oxidation

Water table

Atmosphere

Vegetation

Plant-mediated transport

288

Anoxic soil

Relative pore space

Soil depth Figure 12 Schematic of the one-dimensional methane model. The processes leading to methane emission to the atmosphere occur in the soil between soil depth and soil surface. Methane production takes place in the anoxic soil below the water table; the methane production rate depends on soil temperature and net primary productivity (NPP). Methane oxidation occurs in the oxic soil above the water table and depends on temperature. The model calculates methane concentrations in each (1 cm thick) soil layer. Transport occurs by diffusion through water/air-filled soil pores, ebullition to the water table, and plant-mediated transport from layers above the rooting depth. From Walter BP, Heimann M, and Matthews E (2001) Modeling modern methane emissions from natural wetlands, 1. Model description and results. Geophysical Research Letters 106(D24): 34189–34206.

(Branfireun et al., 1999), is a critical factor. In addition, the reducing conditions in wetlands are active areas for redox reactions involving iron, aluminum, and manganese. In summary, the key link between hydrology and biogeochemistry in wetlands originates from the dynamics of water storage and related residence time on the supply of oxygen and chemical reactants.

2.11.6 Lakes Figure 13 shows the major sources, transformations, and sinks of nutrients in lakes. There are three major initial points of entry of nutrients into lakes: surface-water inflows, groundwater inflows, and atmospheric inputs, including wet and dry deposition. Surface-water inflows may include both point sources and nonpoint sources, while atmospheric inputs and most groundwater contributions are nonpoint sources. Occasionally, other nonpoint sources of nutrients, such as guano from abundant bird populations (O’Sullivan, 1995), direct wastewater or pollutant inputs, or salmon-spawning migrations, may make significant contributions to the total lakeinput nutrient load (Naiman et al., 2002). In the latter case, the loss of sockeye salmon cadavers from some oligotrophic lakes in British Columbia, Canada, as a result of human effects on spawning runs, was determined to have significantly affected the total load of phosphorus (Stockner and MacIsaac,

1996). An active lake-fertilization program was used in several of these lakes to stimulate primary productivity and initiate a trophic cascade that would ultimately provide improved growth conditions for juvenile salmon (Stockner and MacIsaac, 1996). While reduced levels of primary production in lakes – sometimes termed (re) oligotrophication – may occasionally occur in highland regions or in response to major nutrient load-reduction strategies, the recent history of human influences on lakes has been characterized by nutrient concentrations that are substantially higher than natural or background concentrations. These high nutrient concentrations have led to eutrophication and some of its undesirable consequences such as harmful algal blooms, oxygen depletion of bottom waters, and, occasionally, fish kills. The species and transformations of nutrients around the point of entry of an inflow to a lake may be highly important in determining the biogeochemical effects of the inflow. Nutrients in groundwater entering a lake may pass through alternating zones of oxidizing and reducing conditions – the latter are often prevalent in the organic-rich sediments deposited in sheltered areas of the lake bed (Schuster et al., 2003). This heterogeneous environment can stimulate a diverse microbial flora associated with the rich array of microenvironments and redox conditions. Reducing conditions can (1) stimulate denitrification of nitrate when this nutrient is present, (2) lead to a buildup of ammonium as nitrification is inhibited, (3) result in dissolution of phosphate

Hydrology and Biogeochemistry Linkages

289

Groundwater inflows

Stream inflow Stream inflow

Point source

Dry atmospheric deposition Wet atmospheric deposition N fixation Denitrification

Periphyton

Trophic cascade

Macrophytes Sedimentation

Recycling

Sedimentation

Figure 13 Sources, sinks, and transformations of nutrients in a lake (not including outflows).

in association with reduction of oxidized forms of iron and manganese, and, (4) under strongly reducing conditions, may lead to sulfate reduction and methanogenesis (Reeburgh, 1983). Thus, the rich microbial consortia associated with the land–water transition can strongly influence the availability and species of nutrients, including carbon, nitrogen, and phosphorus. Processes in the littoral zone of lakes also can profoundly alter the composition of surface inflows. This zone may support a rich benthos and can include emergent and submerged macrophytes and periphyton that may have a large demand for nutrients. Dense beds of submerged macrophytes can also create quiescent conditions suitable for sedimentation of particulate forms of nutrients and associated inorganic sediments (Madsen et al., 2001). The effects of large surface inflows may be strongly dependent on their water-column insertion depths, that is, the depth at which a surface inflow enters the water column of the lake because of density differences, which in turn are regulated by the relative temperature of the inflow and water column, or occasionally also by the salinity or sediment concentration of the inflow (see Chapter 1.08 Managing Agricultural Water). In the case of a thermally stratified lake, an inflow that is warmer than lake water into which it intrudes will promote a surface overflow in which inorganic nutrients in the inflow will be available in the pelagic zone to the resident microscopic suspended plants (i.e., phytoplankton). An inflow that is cooler and therefore denser than water throughout the entire water column of the lake is likely to create an underflow, in which nutrients may not be immediately available to the phytoplankton resident in

the photic zone. When these inflows are large relative to the lake volume, they can have important secondary effects such as oxygenation of bottom waters (Hamilton et al., 1995). Many lake inflows have a temperature intermediate between those of the surface and bottom of the water column, and therefore create an interflow that can propagate horizontally at discrete depths in stratified lakes. Thus, nutrients, suspended sediments, contaminants, and microbes (notably pathogens) that are often present at much higher concentrations in storm flow, may be rapidly dispersed across a stratified lake in an interflow (Chung et al., 2009). In very simple terms, a box-type model can be used to describe steady-state concentrations of nutrients in a lake in which losses are because of sedimentation and outflows. This type of model has been widely used to describe lake-water concentrations of phosphorus (TPlake), but is not commonly used for nitrogen because atmospheric transformations (i.e., N fixation and denitrification) and related gaseous transfers between the lake and the atmosphere are more difficult to quantify. Vollenweider (1969) was first to apply the model in the form

TPlake ¼

L zðr þ sÞ

ð2Þ

where L is the areal loading rate of TP, z is the mean lake depth, r is the hydraulic flushing rate given by the inflow discharge divided by the lake volume (i.e., r ¼ 1/tw) where tw is the residence time, and s is a first-order decay rate for TP to account for sedimentation losses. Calculations are typically based on annualized values. Various extensions and

290

Hydrology and Biogeochemistry Linkages

simplifications of this model have been made, for example, s can be approximated by 10 ðmÞ=z, and Vollenweider and Kerekes (1982) produced the following modified model:

TPin TPlake ¼ 1:55 pffiffiffiffiffi0:82 1  tw

ð3Þ

where TPin is the inflow total phosphorus concentration. Equation (3) is based on data for 87 lakes, which show hydraulic flushing rate (or residence time) to be the primary factor driving differences in total phosphorus concentrations between the inflows and the lake. Many, mostly empirical, relationships in turn have been used to derive annual mean and peak concentrations of phytoplankton chlorophyll a and primary production as well as Secchi disk transparency from TPlake (Vollenweider and Kerekes, 1982). The focus on predictive models for total phosphorus reflects a long-held paradigm that phosphorus generally limits productivity in freshwater ecosystems (Likens, 1972; Schindler et al., 2008) and that increases in this nutrient can contribute to lake eutrophication (Lean, 1973). An argument has been made that a shortfall in nitrogen supply compared with the requirements for balanced growth – the Redfield ratio (Sterner and Elser, 2002) – can be compensated for by heterocystous cyanobacteria (blue-green algae) that will fix atmospheric nitrogen (i.e., N2 dissolved in water) when this nutrient becomes limiting. Earlier work on this subject (Lean, 1973; Smith, 1983) has remained topical but has recently been reignited by Schindler et al. (2008) who declared that controlling nitrogen inputs alone could exacerbate eutrophication by increasing the dominance of N-fixing cyanobacteria and the probability of harmful algal blooms. In contrast, recent research suggests that human activities have changed biogeochemical dynamics (Bergstro¨m and Jansson, 2006; Elser et al., 2009a, 2009b) and that phytoplankton biomass yield in most of the lakes in the Northern Hemisphere was limited by N in their natural state. Furthermore, there are advocates for control on both nitrogen and phosphorus loads (Lewis and Wurtsbaugh, 2008) on the basis that N fixation often fails to compensate sufficiently for N limitation in lake phytoplankton, that experimental systems manipulated with additional nutrients have often been found to be similarly controlled by N and P, and that high background loads of P that saturate demand necessarily dictate that another nutrient will be limiting (Lewis et al., 2008). Debate about the relative merits of N versus P control will surely continue into the foreseeable future, but considerations should also be given to the connectivity of inland waters to estuarine and coastal waters for which N limitation is the norm. In some cases, silica has been reported as a limiting nutrient for diatom production (Tilman et al., 1982). In contrast, only rarely is inorganic carbon supply limiting to primary production. These cases may arise for lakes that are poorly buffered and where high rates of photosynthesis remove carbon dioxide and raise pH to a level where bicarbonate (pHE7–9) or even carbonate (pHEZ10) predominate; the latter form is not available to plants, while only some plants can take up bicarbonate (Wetzel, 2001). The models given by Equations (2) and (3) do not reveal the mechanisms by which nutrients are regenerated and transformed within lakes. As a result of surface wave action,

internal waves, or unidirectional currents, the lake bed may be subject to water motions that can disturb pore water and resuspend particulate material, thus increasing concentrations of sediments and nutrients in the overlying water column (Hamilton and Mitchell, 1997). Dissolved nutrients may also be recycled from bottom sediments to the water column as a result of concentration gradients between these two media. These gradients may be enhanced by reducing conditions in the bottom sediments and sometimes in bottom waters, which result in dissolution of iron- or manganese-bound forms of phosphorus and a buildup of ammonium as nitrification shuts down. The pioneering work of Mortimer (1941, 1942) pointed to the key role of dissolved oxygen (DO) and redox potential in waters overlying the sediments. Thus, conditions that stimulate sediment nutrient releases through the benthos also control the benthic macroinvertebrate communities, which burrow deeply into layered sediments and accelerate nutrient cycling through bioturbation and fecal production (Covich et al., 1999). DO concentrations in bottom waters of deep lakes are closely linked to the availability of labile organic matter, the duration of density stratification, and the pool of DO in the bottom waters. Lakes may thermally stratify for periods of minutes to years, creating vertical density gradients that persist only temporarily in shallow lakes, seasonally (monomictic or dimictic lakes) in deeper systems, and not at all in permanently ice-covered lakes or at high altitude (amictic lakes) (Lampert and Sommer, 2007). These mixing patterns, of which there are several variants, dictate the renewal periods of oxygen to bottom waters and therefore play a key role in determining nutrient-release rates from bottom sediments based on their oxidation status. A high rate of supply of organic matter to the bottom waters (hypolimnion) of a stratified lake can completely remove DO from this layer and occurs in deep, eutrophic lakes. In contrast, in oligotrophic lakes that mix seasonally, DO generally remains present in bottom waters between periods of mixing when oxygen is renewed to levels approximating saturation, and nutrientregeneration rates from bottom sediments are markedly lower than in deep eutrophic lakes. Eutrophic lakes in which the hypolimnion becomes anoxic may support high rates of denitrification and can also emit considerable quantities of both methane and nitrous oxide (Seitzinger et al., 2006). Another situation relevant to eutrophication is when iron and manganese sequester phosphorus under oxic conditions, removing more P to the sediments than in lakes without high iron and manganese concentrations (Dean et al., 2003). Relatively high groundwater discharge to the lake from lithologies containing high amounts of iron and manganese can produce high concentrations of iron and manganese in the lake, that is, a combined lithology and hydrological control on P removal. Redox-sensitive species transformations of iron and manganese mean, however, that under anoxic conditions, P may be released with dissolution of iron and manganese from inorganic sediments. A key driver of the fluxes of organic matter to the deeper waters and sediments of lakes is the phytoplankton productivity of surface waters, as organic material synthesized in the photic zone eventually falls into bottom waters and sediments if it is not oxidized during settling or removed via outflows. In stratified lakes, most production of phytoplankton biomass

Hydrology and Biogeochemistry Linkages

Climatic dominance

occurs in the surface mixed layer (epilimnion) although it can occur at greater depths in oligotrophic lakes with high water clarity. Most nutrients in the epilimnion occur either as organic forms within the biomass, including not only phytoplankton, but also other microorganisms (e.g., bacteria and fungi) and higher trophic levels (e.g., zooplankton or fish), as well as in dead organic matter (detritus) and dissolved nutrient species. Occasionally, in very turbid waters with high concentrations of inorganic suspended solids, concentrations of phosphorus in particulate inorganic form may be the dominant component of the total phosphorus concentration (Grobblelaar and House, 1995). Heterotrophic microorganisms (mostly bacteria and fungi) recycle organic nutrients into forms that can be taken up again by autotrophs. Generally, only a very small fraction of the total nutrients is in a bioavailable inorganic form. The concentration and nature of nonliving particulate organic matter critically influence rates of primary production by controlling rates of regeneration of inorganic nutrients. In large and/or eutrophic lakes, much of this organic matter is generated within the lake itself (autochthonous production), while in smaller lakes, organic matter within the lake may be heavily subsidized by external inputs from the catchment (allochthonous production). Thus, the species and concentrations of nutrients in a lake vary from the interplay of many complex processes, including loading rates, mixing and stratification, redox-associated transformations, and uptake and partitioning of nutrients through the biota.

Dissolution

Biogenic dominance

Concentration

Lithologic dominance

Groundwater can have various meanings depending on the context and timescale of interest. This section is mainly concerned with the part of the natural hydrological cycle beginning at the top of the saturated zone where groundwater recharge occurs and ending in a discharge area where groundwater becomes surface water (Figure 14). Timescales of groundwater movement from recharge to discharge range from minutes (e.g., near-stream response to a rainstorm) to millions of years (e.g., fossil groundwater beneath an arid landscape). The biogeochemistry of groundwater is driven by abiotic and microbially mediated reactions that in part result from the physical transport of aqueous reactants into contact with subsurface materials with which they are not in equilibrium. In this way, groundwater movement and biogeochemistry affect the development and distribution of microbial communities in the subsurface. In turn, groundwater is an important route for delivery of water and solutes, including nutrients and toxins, from the land surface to streams with a range of residence times and chemical compositions that are different from those of surface runoff. The relative proportions of runoff, and shallow and deep groundwater discharge, can change at various time scales, causing marked changes in stream chemistry as a function of flow. Groundwater controls on stream chemistry may be at least as important as in-stream biogeochemical controls in many situations.

Evapotranspiration

Water reuse Fertilizers

Closed system relative to soil gases

2.11.7 Groundwater

Atmospheric gases

Airborne constituents

Soluble minerals and soil gases Insoluble residues Open system relative to soil gases

291

• Dissolution • Precipitation

Runoff infiltration Soil reactions

• Ion exchange • Redox reactions • Sorption Water table

Springs Lakes and rivers

Short residence time

• Dissolution • Precipitation • Ion exchange • Sorption • Redox reactions Long residence time • Gas generation and consumption

Brackish seeps

Ocean

e

ing

n zo

Seawater

ix

Volcanic or magmatic CO2

M

High temperature and pressure Figure 14 (A) schematic of the hydrochemical cycle. From Back W, Baedecker MJ, and Wood WW (1993) Scales in chemical hydrogeology: A historical perspective. In: Alley WM (ed.) Regional Ground-Water Quality, pp. 111–129. New York: van Nostrand Reinhold.

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Hydrology and Biogeochemistry Linkages

Groundwater recharge beneath an unsaturated zone typically contains oxidants (electron acceptors, e.g., nitrate, sulfate, and dissolved atmospheric oxygen (O2)), whereas recharge beneath a surface-water body is more likely to contain higher concentrations of reduced species (electron donors, e.g., ammonium, methane, and dissolved organic matter (DOM)). In some cases, electron acceptors and electron donors are carried into the saturated zone together during recharge (e.g., O2 and DOM), in which case they may react with each other until one or the other is consumed. In many cases, however, solid phases in the aquifer are the predominant sources of electron donors (e.g., organic matter, ferrous iron minerals, and sulfide minerals) or electron acceptors (e.g., ferric iron minerals and sulfate minerals), and the rates and progress of biogeochemical reactions affecting solutes in recharge are largely controlled by the geology of the subsurface (abundance and reactivity of solid phases) and water–rock contact (McMahon and Chapelle, 2008). Many of these reactions are catalyzed by bacteria taking advantage of chemical potential energy caused by the juxtaposition of chemical species that are not in equilibrium (Appelo and Postma, 2007; Stumm and Morgan, 1996). Reaeration of groundwater is limited by downward advection and loss of communication with overlying air in recharge areas. Where groundwater flow is largely unidirectional (advection7longitudinal dispersion), redox zones generally tend to follow relatively simple progressions with age, for example, if starting with oxic recharge, 4þ 2þ 2 reduction of O2, NO 3 Mn , Fe , SO4 , and CO2 (Figure 7), but the progression through this sequence may be more or less complete depending on groundwater flow time, element availability, and aquifer reactivity (Edmunds et al., 1984). Reduction of O2 and NO 3 typically progress more rapidly in organic-rich mudstones and unweathered glacial deposits containing reactive rock fragments, whereas these reactions commonly are slow in highly evolved siliciclastic sediments and some carbonate rocks. Thus, discharging groundwater can be oxic or highly reduced depending on the hydrogeologic setting. Starting with anoxic recharge, other types of reactions may be important and redox processes affecting aqueous 2þ species (e.g., oxidation of CH4, H2S, NHþ 4 , and Fe ) may be reversed. Reversed redox sequences along groundwater flowpaths are well documented in anthropogenic contaminant plumes, for example, near landfills and organic spill sites (Baedecker et al., 1993; Christensen et al., 2001), and they also occur in aquifers underlying recharge areas in wetlands and lakes containing organic-rich bed sediments (e.g., Katz et al., 1995). Other reactions involving dissolution and precipitation of inorganic solid phases cause concentration gradients in non-redox-sensitive constituents such as SiO2, Na, Mg, Ca, Sr, and Mg. For unconsolidated water-table aquifers with typical recharge rates in humid to semiarid environments, groundwater is commonly stratified, being youngest near the water table and progressively older downward. Groundwater chemistry therefore also may be stratified in recharge areas as a result of changing conditions in recharge composition (e.g., changing composition of atmospheric deposition or addition of anthropogenic contaminants) and biogeochemical reactions in the aquifer (Back et al., 1993; Bo¨hlke, 2002). In discharge areas, and where groundwater flowpaths are confined between

impermeable units, these patterns can change. In discharge areas, preexisting gradients of groundwater age and chemistry may become horizontal or overturned as flow vectors turn upward (Bo¨hlke et al., 2002). These spatial patterns may be confused with locally generated biogeochemical gradients in the absence of detailed information. Aquifer heterogeneity can result in complex reaction zones including bidirectional transport of reactants and products across aquifer–aquitard contacts (McMahon, 2001). In karst and fractured rock aquifers, groundwater flowpaths and biogeochemical mass transfers may be especially complex because of coexisting high-permeability conduits and massive low-permeability units (Bakalowicz, 2005). Complex patterns of solute transport and redox progression are typical near shallow water tables (Scholl et al., 2006) and near sediment-surface water interfaces, such as hyporheic zones, lake beds, and wetlands, where flow reversals and(or) diffusion are important, for example, beneath forested wetlands (Alewell et al., 2006). Biogeochemical processes affecting groundwater chemistry operate over a large range of timescales. For example, measured rates of oxygen reduction and denitrification range over at least 8–10 orders of magnitude. Because of this, and because these reactions commonly are limited by the distributions of reactive solid phases, groundwater chemical gradients may be either sharp boundaries (flux-controlled) or gradual transitions (kinetically controlled). Rates of biogeochemical reactions in aquifers have been measured by various techniques, in part reflecting the range of timescales involved. Laboratory experiments with groundwater and aquifer material can be used to study reaction potential on short timescales. In situ tracer injection experiments including isotopically labeled reactants can be used for intermediate timescales. Examples include single-well or push–pull tests and natural-gradient tracer breakthrough experiments (Istok et al., 1997; Smith et al., 2004; Kellogg et al., 2005; Bo¨hlke et al., 2006). Laboratory experiments and single-well injections commonly indicate higher reaction rates than in situ natural gradient measurements, presumably in part because of physical disruption or other forms of biogeochemical stimulation (Smith et al., 1996, 2006). Groundwater dating of reaction-zone chemical gradients may be the only practical empirical method of measurement at longer timescales. Examples include the use of modern atmospheric environmental tracers, for example, tritium and chlorofluorocarbons for reaction zones on 0–60year timescales (Bo¨hlke et al., 2002; Green et al., 2008) and 14 C for reaction zones on 103–104-year timescales (Vogel et al., 1981; Plummer et al., 1990). Because of aquifer heterogeneity, groundwater ages and reaction rates are evaluated most reliably from field data using solute transport models that account for dispersion and sample mixing (Scholl, 2000; Weissmann et al., 2002; Green et al., 2010). In discharge areas, groundwater can interact with sediments and plants in riparian wetlands, streambeds, and estuaries, where organic matter and other reactants may be more abundant than elsewhere in the saturated zone. Reactions in these areas are strongly dependent on groundwater flowpaths. Near-stream geomorphology and vertical components of groundwater flow largely determine whether groundwater interacts with shallow riparian soils and plants or bypasses

Hydrology and Biogeochemistry Linkages

those potential reaction sites before discharging to streams. Slow diffuse flow through reactive material may cause important changes in groundwater chemistry just prior to discharge, whereas rapid flow through permeable layers and macropores may avoid such reactions (Burt et al., 1999; Angier et al., 2005). The relative importance of these flowpaths and reactions for overall mass balance in discharge areas is difficult to assess in watershed-process studies. Direct discharge of groundwater to estuaries is a potential source of land-derived water and nutrients to coastal waters, but is difficult to quantify and may exhibit complex patterns of physical and chemical interaction with salty pore water (Manheim et al., 2004; Andersen et al., 2005). Discharge also may be affected or enhanced by bioirrigation, the augmentation of flow across the sediment–water interface by filter feeders living in estuaries (Martin et al., 2006; Meysman et al., 2006). Groundwater discharge is an important component of stream flow and solute loads, especially in low-order streams (Alexander et al., 2007). Total stream flow typically is dominated by groundwater discharge except briefly during intense runoff events. Even during flood peaks, the fraction of stream flow delivered from the land surface to the stream without moving into the subsurface typically is small (Buttle, 1994; Buttle and Peters, 1997; Bishop et al., 2004; Burt and Pinay, 2005). As groundwater is stratified in age and chemistry, the age and chemistry of groundwater contributing to stream flow can be quite variable and complex. Old groundwater (decades to millennia) may discharge upward from below the streambed, while younger groundwater (months to years) discharges from shallower flowpaths. Because of variations in subsurface hydraulic properties, age distributions in discharge may be difficult to define and mean age alone may be a poor indicator of the assemblage of watershed transit times. Changing conditions throughout a drainage basin can cause changes in the proportions of different groundwater types and the proportions of groundwater and runoff, contributing to stream flow over timescales ranging from hours to days (storms and snowmelt events) to months (seasonal effects of precipitation and evapotranspiration) to years (interannual and longer climate variation or land-use changes). For example, seasonal variation of nitrate and sulfate concentrations with stream flow may depend on production, consumption, and storage at different timescales in the unsaturated and saturated zones (Lynch and Corbett, 1989; Shanley and Peters, 1993; Huntington et al., 1994; Peters, 1994; Bo¨hlke et al., 2007). At interannual and decadal timescales, responses of streams to changes in loadings of nonpoint-source contaminants can be complex, subdued, or delayed because of changing inputs, groundwater residence times, and water–rock interactions (Bo¨hlke and Denver, 1995; Burt et al., 2008). Surficial aquifers in unconsolidated sediments, such as coastal plains, alluvial valleys, and glacial outwash deposits, commonly have mean groundwater residence times of the order of decades. As changes in land use, agricultural-fertilizer use, and atmospheric deposition commonly occur on decadal timescales, many aquifers contain transient records of anthropogenic nonpoint-source contaminants. This means that the mass flux of a constituent in annual recharge may be different from the mass flux in annual discharge even where the constituent

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behaves conservatively and the water balance is in steady state. This is a common feature of agricultural drainage basins and an important limitation on watershed nitrogen balances and export predictions (Bo¨hlke, 2002). Commonly, it is difficult to resolve the effects of temporal changes in recharge chemistry from progressive biogeochemical reactions in aquifers without detailed study.

2.11.8 Acidic Atmospheric Deposition – Acid Rain Many linkages between hydrology and biogeochemistry were revealed from research conducted to understand the effects of acid rain on terrestrial and aquatic ecosystems beginning in the 1970s. Some of the most import linkages were demonstrated through studies of deleterious effects on biota, particularly forests and fish. Decreases in pH and increases in dissolved inorganic aluminum concentrations have diminished species diversity and abundance of plankton, invertebrates, and fish in acid-impacted surface waters (Schindler, 1988). Acid rain effects on ecosystems include forest decline (Pitelka, 1994; DeHayes et al., 1999), bird population declines and changes (Graveland, 1998), and aquaticbiota declines including algae, macroinvertebrates, and fish (Schindler, 1988). Extremely high deposition of N species (wet and dry deposition) has had a range of effects on forests from fertilization to changes in N mineralization and increased N leaching through soils to surface water (Vitousek et al., 1982; Aber, 1992; Aber et al., 1995, 1998; Emmett et al., 1998a, 1998b; Emmett, 1999; Mitchell, 2001). Vegetation filters atmospheric contaminants and dry deposition can be a major input to ecosystems (Reynolds et al., 1994; Peters et al., 1998). Acidification causes base cations and metals, particularly inorganic aluminum, to be mobilized, which in turn, has deleterious effects on aquatic biota, such as fish (Driscoll et al., 1980). For example, aluminum precipitates on fish gills ultimately affecting blood pH and decreasing the capacity of hemoglobin to transfer oxygen (Fromm, 1980). The loss of nutrient base cations, such as calcium, from soils affects forest growth and health (DeHayes et al., 1999), and subsequent decreases in receiving waters affect aquatic biota (Holt and Yan, 2003; Keller et al., 2003; Jeziorski and Yan, 2006; Jeziorski et al., 2008; Cairns and Yan, 2009). A knock-on effect of the S emissions and deposition associated with acid rain is that increased inputs of sulfate decrease methane production in wetlands (Schimel, 2004). Hydrology is a major driver that delivers acid rain through terrestrial vegetation, soils, and groundwater to streams and lakes. Acid-neutralizing capacity (or alkalinity) is generated by mineral weathering, but base-poor lithologies for which weathering rates are relatively low, including quartzites, sandstones, and granitoid metamorphic and igneous rocks, are particular susceptible to the addition of acids (Schnoor and Stumm, 1986; Reynolds et al., 2001). Glaciated terrain on these lithologies is susceptible to acidic deposition, particularly where glacial deposits are thin and relatively impermeable. For example, US lakes and streams with comparable-sized drainage basins in the west-central Adirondack Mountains, NY, receiving similar acidic deposition,

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responded differently in neutralizing the acidity simply because of differences in the thickness and distribution of surficial material (Peters and Murdoch, 1985; Peters and Driscoll, 1987a). The residence time of water combined with the weathering rate of surficial materials determines the amount of alkalinity in the water. Longer residence time associated with long hydrological pathways results in higher alkalinities compared to those of short hydrological pathways. For moderate to high acidic-deposition rates, streams become chronically acidic when residence times are short and similarly for lakes where the drainage basin:lake area is high. However, even drainage basins with large deposits of thick till or stratified drift and generally long residence time may experience episodic acidification during snowmelt, large rainstorms, or sea-salt episodes (Wright et al., 1988; Heath et al., 1992; Hindar et al., 1995; Larssen and Holme, 2005). Through cleaner fuel technologies and emission-control systems, acid rain generally has decreased throughout North America and Europe since the, 1970s, but acidity remains higher than the inferred pre-industrial conditions (Stoddard et al., 1999; Jeffries et al., 2003; Wright et al., 2005). In addition, liming of lakes and watersheds can restore pH values, but other changes resulting from acid deposition, such as those in soil chemistry and related biota, are not reversible on short timescales (Schindler, 1999). For example, weathering is a primary source for soil base cations and the process of restoring the original soil cation-exchange complex may take several hundred years before full recovery occurs (Cosby et al., 1985; Driscoll et al., 2001). The persistence of surfacewater acidification even with reductions in acid deposition has been attributed to losses of exchangeable base cations in the soil (Lawrence et al., 1999; Lawrence, 2002), which is reflected in many soil organisms and other biota, such as birds (Graveland, 1998; Hamburg et al., 2003). A multitracer assessment of red spruce, a species showing recent growth reductions and decreases in plant-available calcium in the northeastern US, suggests a progressive shallowing of effective depth of base-cation uptake by fine roots (Bullen and Bailey, 2005). Forest harvesting also decreases exchangeable basecation pools (Federer et al., 1989; Watmough et al., 2003). The reductions in atmospheric emissions have targeted S and although substantial – for example, greater than 40% in the northeastern United States and eastern Canada and greater than 60% in Norway – have not been sufficient for surface waters to recover chemically and biologically (Stoddard et al., 1999; Aherne et al., 2003; Jeffries et al., 2003; Skjelkva˚le et al., 2003; Watmough and Dillon, 2003; Larssen, 2005; Wright et al., 2005) and the biological recovery may be hampered by other environmental factors such as drought and increasing water temperatures (Arnott and Yan, 2002; Ashforth and Yan, 2008). Remediation has resulted in restoration of some aquatic biota, such as fish, but restoration of surfacewater chemistry to pre-industrial conditions may not be possible and the trajectory of biogeochemical and species evolution has likewise changed (Schindler, 1999). The restoration may also be exacerbated by other environmental factors such as climate-change affects on the frequency and severity of sea-salt episodes and drought, turnover of organic carbon, and mineralization of nitrogen (Skjelkva˚le et al., 2003; Wright et al., 2006).

2.11.9 Summary and Future Considerations The hydrosphere, biosphere, lithosphere, and chemosphere are intricately linked through a wide range of spatial and temporal scales. For a comprehensive understanding of biogeochemical cycling, an understanding of the hydrologicalprocesses is required. In addition to the wealth of information linking hydrology and biogeochemistry across different aspects of the hydrological cycle, there is a wealth of information on in-stream hydrological variability and biogeochemical processing in streams and rivers. Section 2.11.2 provided an overview of hydrological processes in headwaters with respect to stream flow generation. Mechanisms delivering water from hillslopes to stream channels were presented and discussed with respect to the relative contributions of old water, that is, water stored in the basin soils and groundwater, and new water, that is, associated with precipitation and snowmelt. The relative importance of biogeochemical processes along hydrological pathways was highlighted with a particular focus on the importance of nearstream (riparian) saturated zones in resetting the chemical signature of water flowing into the riparian zone. The riparian zone in many basins effectively buffers upslope nutrient inputs, but may also alter nutrient concentrations and fluxes through N cycling processes, such as mineralization, denitrification, and uptake by riparian vegetation. Section 2.11.3 discussed processes affecting the components of the water budget, snow formation, and ablation processes, and those in the soil below snow-cover overwinter and during snowmelt. Microbes remain active in soils under the snowpack where water is not limited. The coupling of these nutrient transformations and snow-meltwater fluxes can result in delivery of large quantities of nutrients, organic matter, and carbon export from terrestrial ecosystems. Furthermore, solutes in snowpacks preferentially elute during melting, which in turn, can stimulate biological activity within the snowpack, for example, snow algae. The presence and rate of water movement combined with the organic-matter composition and temperature of soils largely determine the nature of the biogeochemical reactions, for example, aerobic versus anaerobic. Vegetation and solar radiation control soil water content through evaporation and transpiration and the vegetation is in part controlled by soil type and thickness, aspect, and elevation. Tree roots can redistribute water in soils affecting nutrient uptake. Plant–soil relations are intricately linked to biogeochemical cycling through the rhizosphere. Downstream mixing affects water and solute transit times, which are intricately linked to hydrological pathways through soils and groundwater and in streams with riparian and hyporheic zones. These pathway contributions, in turn, are controlled by the magnitude and intensity of rainfall and snowmelt. Section 2.11.4 presented the concept of nutrient spiraling including the concept of nutrient-uptake length and the importance of temperature and stream flow variability on biogeochemistry. The effects of stream–groundwater interactions through hyporheic and riparian zones were also discussed. Hyporheic zone processes tend to have a larger effect per unit area on the water column in shallow upper reaches, but continuing losses through large river networks can have

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large cumulative effects.. Field studies involving isotopes (including isotopically labeled compounds) have elucidated the within-river transformations of nitrogen species including how these are affected by seasonality, stream flow, light penetration, and terrestrial organic matter and nutrient inputs from near-stream ecosystems. Spatial variations in within-river processes are also controlled by hydrology, channel morphology, catchment land use, and riparian vegetation. Section 2.11.5 contrasted important processes in hydrologically isolated wetlands with those temporally connected to streams and rivers. The exchange of water, sediments, and nutrients in wetlands with adjacent catchment areas, groundwater, and streams has a major effect on biogeochemical processes. Residence time is a key driver of biogeochemical dynamics ranging from rapid turnover rates in valley-bottom riparian wetlands with high groundwater discharge to extremely slow turnover rates in a thin active layer at the surface of raised peat bogs. The near-saturated conditions of wetlands with typically high organic contents control the redox potential, which drives the biogeochemical processes. Oxygen typically limits degradation rates in wetlands and carbon is the main driver of wetland biogeochemistry. Furthermore, the temporal and spatial variability of residence time and related turnover rates therefore dictate the biogeochemical processes. Section 2.11.6 discussed atmospheric, stream, and groundwater nutrient inputs, stratification and within-lake processes, interactions with sediments, and limiting nutrients. The nutrients associated with groundwater discharge to lakes are affected by the composition of sediments, which may alternate from oxidized to reduced conditions. Differences in sediment composition control redox conditions and, subsequently, aerobic or anaerobic reactions that affect nutrient transformations and species. Plants in littoral zones, such as emergent and submerged macrophytes and periphyton, can also alter lake nutrient composition by trapping particulates and through nutrient uptake (growth) and release (decay). Phosphorus generally limits productivity in freshwater ecosystems, but with excess phosphorus, nitrogen may be limiting; however, nitrogen can be supplemented by blooms of N-fixing blue-green algae. Although recent research suggests that surface waters were N limited prior to industrialization, the science is still contentious about the relative importance (or limitations) of N and P in controlling biological productivity of freshwaters. Lake stratification controls mixing of top and bottom waters, thus affecting biogeochemical processes. The nutrient status and productivity of surface waters determines light penetration and subsequent supply of organic matter and nutrients to bottom waters. Section 2.11.7 presented information about typical reactions controlled by hydrological pathways, lithology (mineralogy) and biota, the importance of residence time in biogeochemical evolution, and linkages between groundwater and surface water. Biogeochemistry of groundwater is largely related to microbially mediated redox reactions that result from physical transport of aqueous reactants into contact with subsurface materials with which they are not in equilibrium, where microbial communities develop to catalyze reactions in exchange for energy. Redox conditions in groundwater vary depending on landscape position, with oxidizing conditions prevailing in headwaters and beneath the unsaturated zone

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and more reducing conditions occurring in lowlands and under streams and lakes. Redox conditions may also be affected by lithology. Consequently, discharging groundwater can be oxic or highly reduced depending on the hydrogeologic setting. Biogeochemical processes affecting groundwater chemistry operate over a large range of timescales (e.g., 8–10 orders of magnitude for oxygen reduction and denitrification). Stream flow is typically dominated by groundwater discharge, even during floods. But because groundwater can vary markedly in age and chemistry, the discharging mixture of groundwater contributed from a wide range of hydrological pathways can cause stream water composition and delivery of nutrients to aquatic ecosystems to likewise vary markedly in time and space. Examples are given of the effects of human activities on hydrology and biogeochemistry linkages in each of the sections and in a separate section on acidic atmospheric deposition. Although much research has been conducted in assessing the linkages between hydrology and biogeochemistry, many challenges remain, particularly in linking observations across a wide range of temporal and spatial scales. Vegetation, soils, hydrology, and biogeochemistry develop and respond together; yet, our efforts to study these linkages are often narrowly focused, resulting in high levels of site-specific knowledge, but slower progress in extrapolating to larger spatial scales and in developing meaningful generalizations. The need for morecomprehensive interdisciplinary studies is warranted to link terrestrial vegetation and soils in headwaters through riparian zones/floodplains to streams. These interdisciplinary studies would incorporate in-stream processes including interactions with the hyporheic zone, across scales and hydroclimatic zones. Understanding hydrological and biogeochemical processes also requires knowledge of the biological components and their functioning within these studies. Advances in technology continue to provide smaller and more robust sensors, smaller data-acquisition packages with innovative data-transmission capabilities, and better analytical instrumentation for accurate and precise measurement of low elemental and solute concentrations on small samples. In addition, new tools are evolving in the areas of nanotechnology, remote sensing, and biosensor technology, which are providing new and innovative ways to evaluate processes linking hydrology and biogeochemistry. In addition, computer-technology advances and new visualization software with much higher computation and processing speeds provide a platform for innovative designs in data analysis and modeling. Interdisciplinary research incorporating some of these new technologies for data collection and processing coupled with the computer processing and visualization may provide new ways of data mining and testing of hydrological, biological, and biogeochemical process interactions.

2.11.10 Additional Reading The literature is comprehensive with information about hydrology and biogeochemistry linkages. For some additional details about general water-quality characteristics, see Meybeck et al. (2005) and Peters et al. (2005); for nitrogen cycling,

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see Burt et al. (1993), Heathwaite et al. (1996), Cirmo and McDonnell (1997), Edwards and Wetzel (2005), Goulding et al. (1998), Lohse et al. (2009), Mitchell (2001), Vollenweider and Kerekes (1982), and Wetzel (2001); and for stream– groundwater interactions, see Burt and Pinay (2005), Dahm et al. (1998), Jones and Holmes (1996), Jones and Mulholland (2000), Rosenberry and Labaugh (2008), Winter (1999), Winter and Woo (1990), and Winter et al. (1999). Finally, Lohse et al. (2009) provide an overview of linkages between hydrology and biogeochemistry, and Belnap et al. (2005) discuss hydrology and microbial linkages in arid and semiarid watersheds.

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2.12 Catchment Erosion, Sediment Delivery, and Sediment Quality DE Walling, University of Exeter, Exeter, UK SN Wilkinson, CSIRO Land and Water, Townsville, QLD, Australia AJ Horowitz, US Geological Survey, Atlanta, GA, USA & 2011 Elsevier B.V. All rights reserved.

2.12.1 2.12.2 2.12.2.1 2.12.2.2 2.12.2.3 2.12.3 2.12.3.1 2.12.3.2 2.12.3.3 2.12.3.4 2.12.4 2.12.4.1 2.12.4.2 2.12.4.2.1 2.12.4.2.2 2.12.4.2.3 2.12.4.2.4 2.12.4.3 2.12.4.3.1 2.12.4.3.2 2.12.4.4 2.12.4.4.1 2.12.4.4.2 2.12.4.4.3 2.12.5 2.12.5.1 2.12.5.2 2.12.5.3 2.12.5.3.1 2.12.5.3.2 2.12.5.3.3 2.12.5.3.4 2.12.5.4 References

A Changing Context Sediment Budgets The Sediment Budget as an Integrating Concept The Functioning of the Sediment Budget The Global Sediment Budget Documenting Catchment Sediment Budgets The Background The Use of Fallout Radionuclides Sediment Source Fingerprinting The Future Modeling the Catchment Sediment Budget The Requirement Model Development Modeling approaches and model complexity Empirical modeling of catchment sediment yield Conceptual process modeling of catchment sediment budgets Mechanistic, physically based modeling of hillslope processes SedNet – A Sediment Budget Model for River Networks Model outline Management applications Current Status and Future Directions Modeling across scales for planning and management Directions in modeling erosion and deposition processes Model uncertainty considerations The Quality Dimension Introduction Basic Sediment Geochemistry Major Issues Associated with Sediment Quality Background/baseline sediment-associated constituent concentrations The collection of representative sediment samples and the issues of spatial and temporal variability The chemical analysis of suspended and bed sediments Bioavailability and toxicity Future Directions

2.12.1 A Changing Context Although it has attracted the interest of fluvial geomorphologists, geologists, sedimentologists, and hydrologists, the study of erosion and sediment transport by rivers has traditionally been largely the preserve of the agricultural engineer and the hydraulic or civil engineer (e.g., ASCE, 1975; Schwab et al., 1981; Lal, 1994; Julien, 1995, 2002; Morgan, 1995; Yang, 1996; US Department of Agriculture, 1997; Chien and Wan, 1999; Fangmeier et al., 2006; US Bureau of Reclamation, 2006; Garcia, 2008). In the case of erosion, attention focused largely on soil erosion on agricultural land and emphasized the assessment of rates of soil loss and their implications for crop productivity and the sustainability of land use practices, as well as the design of soil conservation measures.

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The well-known Universal Soil Loss Equation (USLE) developed in the USA (see Wischmeier and Smith, 1978) and modified for application elsewhere (e.g., Schwertmann et al., 1990; Larionov, 1993) was a product of this interest in erosion processes, providing a basis for predicting the spatial variation of rates of soil loss in response to their control by rainfall, topography, and land use practices, and for assessing the potential impact of improved management and cropping practices. Hydraulic engineers directed attention to the study of sediment transport by rivers and related problems associated with the management of river channels for navigation and flood control and reservoir sedimentation, as well as to the design of hydraulic structures able to cope with high sediment loads. Such work commonly emphasized the coarser fractions of the sediment load, as this was most important in terms of

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river morphology. It was also more readily predicted from a knowledge of sediment properties and flow conditions, than the finer washload, which was generally a noncapacity load and therefore a supply-controlled load. The washload was frequently viewed as being of limited importance, since it was readily transported through a river system and had limited morphological impact. The emphasis on the transport of coarser sediment either as bedload or as suspended bed material is well demonstrated by the large number of sediment transport formulas that were developed in the middle years of the twentieth century for predicting these components of the sediment load (see ASCE, 1975). As a noncapacity or supplycontrolled load, the washload of a river was seen as something that was not easy to predict using sediment transport formulas and, if it was of interest, it therefore needed to be measured. Against this background, attention traditionally focused on soil loss from upstream areas, with emphasis on on-site problems of soil degradation and loss of productivity and on downstream sediment transport and sediment yield. There was often only limited contact between those working on these two aspects. Stated very simply, soil loss from fields was lost from the farm and its ultimate fate was of limited interest to the agricultural engineer. Equally, eroded soil generally contributed primarily to the finer washload of a river, which was seen as being of limited importance in terms of river morphology and hydraulics, although it was an important cause of reservoir sedimentation and some knowledge of downstream suspended sediment yields was therefore needed. In general, the degree of attention given to the study of erosion and sediment transport was broadly proportional to the magnitude of erosion rates and sediment loads. In countries such as the USA, there was considerable activity in these areas, whereas in countries such as the UK, where erosion rates were low and soil erosion was not perceived to be a problem, and sediment yields were also low and rivers relatively small, activity and interest were limited. A major change in the above situation occurred in the latter part of the twentieth century. Changing perspectives on erosion and sediment transport promoted increased interest in this general field and emphasized the need for a more multidisciplinary perspective and a more integrated approach that directed attention to the functioning of the entire catchment system. Greater emphasis was therefore placed on the hydrological context. Several key drivers of these changes can be identified. One was the recognition of the importance of fine sediment, both as a key water-quality parameter in terms of its physical presence and also as an important control on river water quality more generally. Many pollutants and contaminants, including heavy metals, pesticides and other organic contaminants, as well as nutrients such as phosphorus, are transported primarily in association with sediment and interactions between the solid (sediment) and liquid (water) phases exert an important influence on water quality (e.g., Golterman, 1977; Shear and Watson, 1977; UNESCO, 1978; Allan, 1979; Fo¨rstner and Wittmann, 1981; Hart, 1982; Salomons and Fo¨rstner, 1984; Horowitz, 1991; Ongley et al., 1992; Santiago et al., 1994; US Environmental Protection Agency, 1997; House and Warwick, 1999; Warren et al., 2003). In addition to considering the amount of sediment transported by a river, there was also an increasing need to consider the

quality of that sediment. Another important driver was the increasing evidence of the detrimental effects of fine sediment in degrading aquatic habitats and ecosystems, through, for example, the siltation of fish spawning gravels, the smothering of aquatic vegetation and increased nutrient inputs to floodplains, riparian areas, and other water bodies, through sediment deposition (see Ritchie, 1972; Clark, 1985; Clark et al., 1985; Waters, 1995; Newcombe and Jensen, 1996; Wood and Armitage, 1997; Soulsby et al., 2001; Suttle et al., 2004; Cavalcanti and Lockaby, 2005). Both in terms of its physical presence and its quality, fine sediment is frequently an important cause of environmental degradation and it has been widely referred to as the world’s number one pollutant. Diffuse source pollution was increasingly recognized as an important cause of water pollution, and sediment, which can be mobilized from throughout a river basin, is a major component of such pollution. Around the Great Lakes of North America, for example, concern for the eutrophication and pollution of these water bodies and particularly Lake Erie, directed attention to the need to control diffuse source pollution and sediment assumed a central role in the Pollution from Land Use Activities Reference Group (PLUARG) program developed by the International Joint Commission on the Great Lakes (Coote et al., 1982; Ongley, 1982). In many ways, this program was ahead of its time in recognizing the importance of sediment and the role of land use in influencing sediment mobilization and transfer to water bodies. Sediment has also assumed considerable importance in the recent EU Water Framework and Habitats Directives (Fo¨rstner, 2002, 2003) aimed at improving land management practices, protecting aquatic habitats, and maintaining conditions of good ecological status in rivers. In addition, increasing interest in the functioning of the Earth’s system has highlighted the important role of land–ocean sediment transfer in global geochemical cycling, and particularly the carbon cycle (Ludwig et al., 1996; Lyons et al., 2002; Beusen et al., 2005; Seitzinger et al., 2005; Gislason et al., 2006; Van Oost et al., 2007; Saenger et al., 2008). River sediment loads have been shown to be very sensitive to the various drivers of global change (e.g., Walling and Fang, 2003; Walling, 2008) and to exert an important influence on the health of receiving waters in the coastal zone. Within the International Geosphere Biosphere Programme (IGBP), launched in 1987 by the International Council for Scientific Unions (ICSU) particular attention was directed to land–ocean sediment and material transfers through its Land–Ocean Interactions in the Coastal Zone (LOICZ) core project. Significant outcomes of the evolution of these new perspectives on erosion and sediment transport include the following. First there has been an increasing emphasis on fine sediment (see Owens et al., 2005). This is the most significant fraction of the sediment load in terms of pollution and sediment-associated transport of nutrients and contaminants, since contaminants are in most cases preferentially associated with the finer (o63 mm) particles (Horowitz, 1991). Equally, it is generally fine sediment which is of greatest importance in terms of the degradation of aquatic ecosystems and habitats. Since the fine sediment load of a river is commonly a noncapacity load and supply-limited, interest in fine sediment transport has necessarily shifted the emphasis of sediment

Catchment Erosion, Sediment Delivery, and Sediment Quality

transport studies. It has moved away from a hydraulic approach, that emphasized hydraulic controls and the transport conditions in the channel toward developing an improved understanding of sediment mobilization and transfer within the entire catchment and thus the supply of sediment to the river system. Clearly, this demands a hydrological approach. Second, with greater emphasis being directed to sediment quality, and increasing recognition that sediment quality is in most instances closely related to sediment source, there has been a need to develop an improved understanding of potential sediment sources and transfer pathways. This was also a key requirement for any attempt to develop sediment management or control programs and to implement mitigation measures. Resources need to be targeted to those sources or parts of a catchment that provide the main source of the sediment transported by a river and which need to be controlled. Again, therefore, a distributed hydrological approach for understanding and modeling the sediment dynamics of a catchment or river system has been increasingly required. Third, the shift of emphasis away from concern for sediment problems linked primarily to the amount of sediment and thus the magnitude of erosion rates and sediment yields to the more wide ranging environmental significance of fine sediment has broadened the relevance of the study of erosion and sediment transport to include most areas of the world. Paradoxically, it is often areas with low erosion rates and low sediment yields where the environmental impacts of sediment are potentially greatest and the need to develop an improved understanding of the processes of sediment mobilization and transfer is therefore strongest. Finally, as indicated above, these new perspectives and requirements have created the need to integrate studies of erosion and sediment transport. The offsite problems of soil erosion, which relate to the onward transfer of the mobilized sediment through a drainage basin and the impacts of this sediment, are frequently seen as being equally, if not more, important than the on-site problems of soil loss. Equally, the increased emphasis on fine, rather than coarse, sediment transport has focused attention on sediment supply to river channels and the need to look beyond the river channel and to consider the processes of sediment mobilization and transfer within the entire upstream catchment area. A hydrological perspective is a key requirement for the new perspectives on erosion and sediment transport outlined above. This has in turn strengthened the position of the study of erosion and sediment transport as an important branch of hydrology. This position has long been recognized by the International Association of Hydrological Sciences (IAHS), through the activities of its Commission on Continental Erosion which was established in the middle years of the twentieth century. Equally, the Hydrology section of the American Geophysical Union and the Hydrological Sciences division of the European Geosciences Union both include groups devoted to the study of erosion and sedimentation. The need for a broader multidisciplinary perspective is also demonstrated by the emergence of specialist groups focusing on sediment studies, such as the International Association for Sediment Water Science (IASWS), which was established in 1984, and the World Association for Sedimentation and Erosion Research (WASER), which was founded in 2004.

307

This contribution reviews some of the key developments associated with the changing focus of studies of erosion and sediment yield outlined above. Attention is directed first to the sediment budget concept, which provides a valuable framework for studying, modeling, and managing erosion and sediment yield in catchments. Second, approaches to modeling catchment sediment budgets are considered. Finally, several of the key contemporary issues associated with sediment quality are discussed.

2.12.2 Sediment Budgets 2.12.2.1 The Sediment Budget as an Integrating Concept Although there is undoubtedly still a place for a reductionist approach, which focuses attention on the dynamics of a particular process associated with erosion and sediment transport, recognition of the wide ranging environmental significance of fine sediment and the need to link information on sediment output from drainage basins with information on sediment sources and sediment mobilization, transfer, and storage has resulted in a general acceptance of the sediment budget as a central integrating concept for the study of erosion and sediment yield. In addition to integrating the various components of sediment mobilization, transfer, storage, and output and providing a valuable scientific framework for research investigations, the sediment budget concept also provides an essential management tool (Walling and Collins, 2008). It identifies the key sediment sources and transfer pathways within a catchment, which are likely to represent the focus of any management strategy. Furthermore, it emphasizes the sensitivity of the sediment response of a catchment to environmental change and the potentially complex links between changing erosion rates and changes in sediment yield, which must be recognized when planning and implementing sediment management and control strategies. Figure 1, which is based on the classic work of Trimble (1983) in the 360 km2 catchment of Coon Creek, Wisconsin, USA, provides a useful demonstration of the catchment sediment budget concept and the way in which it integrates consideration of sources, sinks, and output and thus sediment mobilization, transport, deposition and storage, as well as the dynamic interaction of these components. In the Coon Creek study, two separate budgets were developed. The first was for the period of poorly managed agriculture and severe erosion that followed land clearance and the expansion of agriculture in the latter half of the nineteenth century and the early part of the twentieth century. The second was for the subsequent period, when soil conservation measures were introduced to control erosion and soil degradation. An important feature of the budgets for both periods is that only a relatively small proportion of the total mass of sediment mobilized within the basin by erosion reaches the basin outlet (i.e., B5–7%). This emphasizes that information on the sediment yield at a basin outlet may provide a poor indication of the overall amount of sediment mobilized and moved through a basin and emphasizes that the key to understanding the system frequently lies in identifying and quantifying the sediment sinks or stores. Whereas attention has traditionally focused on erosion processes and sediment transport, the sinks and stores can

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Catchment Erosion, Sediment Delivery, and Sediment Quality

dominate the functioning of a catchment sediment budget, with much of the sediment mobilized in catchments being deposited on hillslopes, in riverbeds, on floodplains, and in reservoirs, rather than contributing to catchment export (e.g., Dunne et al., 1998; Trimble and Crosson, 2000). Figure 1 demonstrates that during both periods, large amounts of sediment were being stored in the colluvial deposits associated with the hillslopes within the upland areas and in alluvial sinks within both the tributary valleys and the main valley of Coon Creek. Comparison of the sediment budgets for the two periods shows that although the implementation of soil conservation measures after 1938 greatly reduced upland erosion rates, producing a substantial (i.e., B25%) reduction in sediment mobilization from the slopes, the sediment yield at the basin outlet changed very little, due to the increased efficiency of sediment transfer through the channel system (i.e., reduced deposition) and the remobilization of sediment that had accumulated within the middle valley during the preceding period of accelerated erosion. From a management perspective, a sediment budget, such as that presented in Figure 1, provides valuable information for use in developing effective catchment-based sediment management and control strategies. It identifies the most important sediment sources that would need to be targeted in any attempt to reduce downstream sediment fluxes and thus facilitates the optimum use of the resources available for implementing sediment control measures. Equally, it also

Table 1

emphasizes that reduction of upstream erosion may not necessarily result in a significant reduction of the downstream sediment yield. Much of the sediment generated upstream may have previously been deposited and stored before reaching the catchment outlet, and reduction in upstream sediment mobilization could be offset by remobilization of sediment from intervening stores. An understanding of the sediment budget of a drainage basin is clearly also important for predicting the likely impact of future climate change on downstream sediment response. This could change significantly, if hydrological changes resulted in the remobilization of sediment from existing sediment sinks, for example, through changing channel morphology and increased channel migration and erosion.

2.12.2.2 The Functioning of the Sediment Budget As indicated above, the sediment budget concept provides a valuable integrating framework for studying the various processes of sediment mobilization and delivery operating within a catchment. Table 1 lists most of the key processes involved and some of the recent research aimed at developing an improved understanding of these processes. Although, as shown in Figure 1, the emphasis is commonly placed on the magnitude of the fluxes and stores, and thus on the quantities of sediment involved, it is also important to recognize that the properties of the sediment associated with different

Examples of recent research on the component processes of the sediment budget

Process Sediment mobilization Interrill or sheet erosion

References

Abrahams et al. (2001), Gime´nez and Govers (2002), Prosser and Rustomji (2000), Valmis et al. (2005), Wei et al. (2009), Zhang et al. (2009a, 2009b).

Rill erosion

Cerdan et al. (2006), Govers et al. (2007), Lei et al. (2006), Merten et al. (2001), Schiettecatte et al. (2008).

Gully erosion

Gomez et al. (2003), Gordon et al. (2008), Poesen et al. (2003), Rustomji (2006), Valentin et al. (2005).

Mass movements

Brayshaw and Hassan (2009), Chappell et al. (2004), Hassan et al. (2005), Heimsath et al. (2002), Lavigne and Suwa (2003),Wemple et al. (2001), Schuerch et al. (2006).

Channel bank erosion

Atkinson et al. (2003), Florsheim et al. (2008), Fox et al. (2007), Rinaldi et al., (2008), Hupp et al. (2009), Jeffries et al. (2003), Laubel et al. (2003), Wynn and Mostaghimi (2006).

Sediment transfer or delivery Slope to channel Rustomji and Prosser (2001), Croke et al. (2005), Deasy et al. (2009), Haygarth et al. (2006), Preston and Schmidt (2003), Smith and Dragovich (2008). Channel

Droppo et al. (2001, 2004), Forbes and Lamoreux (2005), Malmon et al. (2005), Petticrew et al. (2007), Rubin and Topping (2001), Simon et al. (2004), Stone et al. (2008). Sediment deposition and storage Colluvial Brardinoni et al. (2009), Cochrane and Flanagan (2006), Croke et al. (1999), de Moor and Verstraeten (2008), Macaire et al. (2002), Rommens et al. (2006).

Channel

Collins and Walling (2007), Hart (2002), Macnab et al. (2006), Petticrew et al. (2007), Smith et al. (2003), Steiger et al. (2003).

Alluvial fans

Field (2001), Harvey (2002), Harvey et al. (2005), Leeder and Mack (2001), Ritter et al. (2000), Staley et al. (2006).

Floodplain

Aalto et al. (2003, 2008), Hughes et al. (2009), Kronvang et al. (2009), Lauer and Parker (2008), Sweet et al. (2003), Swanson et al. (2008), Jeffries et al. (2003), Thonon (2006), Thonon et al. (2007).

Sediment yield Ali and de Boer (2008), Evans and Slaymaker (2004), Haregeweyn et al. (2008), Steegen et al. (2001), Syvitski and Milliman (2007), Molina et al. (2007), Tamene et al. (2006), Verstraeten and Poesen (2002), Verstraeten et al. (2003).

Catchment Erosion, Sediment Delivery, and Sediment Quality Coon Creek 1853−1938 Upland sheet and Sources (t × 103) rill erosion 630 Upland gullies Tributaries 80 46 Sediment discharge at mouth 42

Lower Middle Tributary valley valley 230 Upland valleys 78 valleys 96 Hillslopes 269 42 Sinks and stores (t × 103) Coon Creek 1938−1975 Upland sheet and rill erosion Sources (t × 103) 456 Upland gullies Tributaries Middle 71 39 valley 30 Sediment discharge at mouth 40

Lower Middle valley valley Upland 153 30 valleys 42 Hillslopes 332 Sinks and stores (t × 103)

Figure 1 The sediment budgets for Coon Creek, Wisconsin, USA for the periods 1853–1938 and 1938–1975 produced by Trimble (1983). The fluxes shown are mean annual values. From Trimble (1983) A sediment budget for Coon Creek basin in the Driftless Area, Wisconsin, 1853–1977. American Journal of Science 283: 454–474.

components of the budget may change, as sediment is transferred from source to sink. Mobilization, transfer, and deposition processes will frequently involve selectivity related to both particle size and particle density, resulting in contrasts between the composition of sources and sediment associated with different components of the budget (e.g., Fontaine et al., 2000; Stone and Droppo, 1996). In the case of variations in particle size, the contrast between the effective or in situ grain size and the ultimate or absolute grain size of the sediment, which reflects the existence of composite particles (i.e., aggregates and flocs), can introduce further complexity and exert an important influence on the properties of sediment associated with individual components of the sediment budget (see Stone and Saunderson, 1992; Stone and Walling, 1997; Walling et al., 2000; Blake et al., 2005; Woodward and Walling, 2007). In the case of sinks, for example, coarser particles may be preferentially deposited, but if these coarser particles comprise aggregates or flocs, they may contain a significant proportion of fine particles, the deposition of which might otherwise be unexpected (Nicholas and Walling, 1996). Droppo (2001) has called for a rethinking of conventional approaches to investigating suspended sediment dynamics to

309

reflect the existence and importance of such composite particles. Equally, sediment mobilized from different sources may be characterized by different properties, and the sediment associated with different components of the budget may change according to its source or the relative contribution from different sources. In this context, contrasts in the properties of sediment derived from different sources may reflect both the source type (e.g., sheet and rill erosion vs. gully and channel bank erosion) and the spatial variability of source material properties caused by variations in geology, soil type, or land use across a catchment. Key characteristics of the functioning of a sediment budget include its connectivity and thus the extent to which the slopes or upstream parts of a catchment are linked to the channel system or the catchment outlet. The connectivity of a system will depend on the incidence and efficiency of the transfer pathways and the magnitude of the stores. The connectivity of the system is clearly of fundamental importance when investigating and attempting to control sediment-induced diffuse source pollution. Detailed assessment of connectivity necessitates consideration of the transfer pathways involved and their efficiency in transferring sediment through the sediment delivery system. By providing a clearer representation of the links between erosion or sediment mobilization and sediment yield, the catchment sediment budget represents an important advance over the sediment delivery ratio concept. The latter simple blackbox concept (e.g., Roehl, 1962; Walling, 1983) recognized that the sediment output was likely to be less than the gross sediment mobilization and represented the ratio of the former to the latter. The magnitude of this ratio was in turn linked to the size of the catchment, with its magnitude commonly decreasing as the scale of the catchment increased. Many limitations of the sediment delivery ratio concept have been widely debated (e.g., Walling, 1983; Parsons et al., 2006; de Vente et al., 2007) and Beven et al. (2005) provide a useful overview of the problems to be faced in conceptualizing sediment delivery to stream channels. The precise form taken by the sediment budget of a catchment will reflect a wide range of controls, including the local topography and the hydrological regime, as well as the size of the catchment. Figure 2 provides an indication of the potential nature and extent of such variability by indicating the key characteristics of the sediment budgets of four small drainage basins on the Russian Plain documented by Golosov et al. (1992). These are all relatively small basins, heavily impacted by agricultural land use and associated soil erosion. The investigation aimed to establish the proportion of the sediment mobilized within the catchments by different erosion processes that reached the basin outlets. In this environment, three key sediment mobilization processes were identified. These comprised sheet erosion (i.e., widespread erosion of the surface by surface wash), rill erosion (i.e., erosion by concentrated flow in micro-channels developed on the slopes), and gully erosion (i.e., erosion within deeper ephemeral channels that dissect the landscape and where sediment is mobilized by mass movements on the gully sides as well as by the flow through the gully). In this environment, sheet and rill erosion are generally more important than gully erosion as a sediment source and there is little evidence of

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sediment storage on the lower parts of the slopes. Slopes are frequently convex, terminating at the margins of balkas (flat floored, gully-like features), that dissect the landscape. Even within this relatively homogeneous area, the proportion of the sediment mobilized by erosion within the individual catchments that reaches the basin outlet ranges from 0% to 89%. In most of the catchments, both the balka bottoms and the river floodplains constitute major sinks for sediment moving through the system and as with Coon Creek (see Figure 1), the sinks represent a very important component of their sediment budgets. As the scale of the drainage basin increases, deposition of sediment on the river floodplains in the lower parts of the basin will commonly assume increasing importance. Work within the catchments of the Rivers Ouse (3315 km2) and Wharfe (818 km2) in Yorkshire, UK, reported by Walling et al. (1998) has, for example, shown that as much as 30–40% of the sediment delivered to the main channel system is deposited on the adjacent floodplains during overbank flood events and does not reach the basin outlet. At a larger scale, Bobrovitskaya et al. (1996) provided information on the sediment budget of the lower River Ob which drains a vast catchment of 2 950 000 km2 in Siberia to the Arctic Ocean. The available information on the suspended load of this river provided by two gauging stations on its lower reaches separated by an 870-km reach indicates that in its lower reaches approximately 50% of its suspended sediment load is deposited on the well-developed floodplain bordering the river and fails to reach the lowest measuring station. At this larger scale, tectonic subsidence within the interior of a river basin can also promote the development of major sediment sinks which reduce the downstream sediment flux. This is well illustrated by the Rio Madeira, a major tributary of the Amazon in Bolivia and information reported by Baby et al. (2009). The upper basin of this river, which extends to B170 000 km2, drains the Andean Cordillera where erosion rates are high and consequently transports a very high annual suspended sediment load of B500–600 Mt. However, on leaving the Andes, the downstream course of the Rio Madeira passes through a subsiding foreland basin where of the order of 270 Mt of sediment is deposited each year. As a result, only about 45% of the upstream load of the Rio Madeira is transported downstream into the main Amazon river system. The operation of sediment budgets, such as those depicted in Figures 1–3, can be considered over several different timescales. Several recent sediment budget investigations that have taken a longer-term perspective have emphasized the importance of sediment storage, with the majority of the sediment mobilized being stored within the catchment over long periods. For example, Rommens et al. (2005) reported a Holocene sediment budget for a small 103 ha agricultural catchment in the Belgian loess belt that shows that 58–80% of the sediment mobilized within the catchment had been stored near its source and not delivered to downstream rivers. Similarly, Prosser et al. (2001a) estimated that as much as 80% of the sediment eroded from large coastal catchments in Eastern Australia in historical times remains stored in their channels and floodplains. Many of the sediment sinks associated with a sediment budget are likely to be long-term sinks. For example, the sediment deposited on the lower parts of a slope will

Veduga Creek (86.9 km2)

Sheet 51%

Sheet 87%

Gully 49%

Balka Rolzavets (181.5 km2)

Rill 6.5% Gully 6.5% Balkas 55% Floodplain 45%

Balkas 91% 9% Output

Little Kolysheley River (181.5 km2) Sheet 72.5% Rill 5% Gully 22.5%

Kijuchi Creek Slope 42% Rill 4.5% Gully 53.5%

(8 km2)

Balkas 11%

Slope18% Floodplain 54% 28% Output

89% Output

Figure 2 The sediment budgets for four small drainage basins on the Russian Plain, established by Golosov et al. (1992). From Golosov et al. (1992) Sediment budgets of river catchments and river channel aggradation on the Russian plain. Geomorphology (Moscow) 4: 62–71 (in Russian).

commonly remain in near-permanent storage, unless there is a significant change in the pattern of erosion. River floodplains will frequently also represent longer-term sinks, with bank erosion perhaps causing some loss, which is balanced by point bar formation and deposition elsewhere. Floodplain sinks could, however, be rapidly remobilized by changes in channel pattern and increased channel migration associated with changes in the flow regime caused by human activity in the upstream catchment or climate change. Some sinks will, however, operate as shorter-term stores. This was the case with the middle valley sink within the Coon Creek catchment depicted in Figure 1. Furthermore, at the annual timescale, sediment deposited within the channel system may accumulate during one period of the year, only to be remobilized and flushed out during a subsequent period (e.g., Collins and Walling, 2007). In this situation, storage is clearly temporary and in their study of three groundwater-dominated lowland catchments in the UK, Collins and Walling (2007) demonstrated that fine sediment accumulated within the channel during the winter period, when most sediment was transported through the system, and was subsequently remobilized during the summer period. Estimates of the average mass of sediment stored in the channel systems of the three catchments during the 2-year study period demonstrated that this was equivalent to between 21% and 38% of the mean annual

Catchment Erosion, Sediment Delivery, and Sediment Quality

311

Soil redistribution rate (t ha−1 year−1) 5 0 −5 −10

30

t (m)

h Heig

−15 20

10

350

30

He

igh

20

250

t (m )

300

10

ce

tan

Dis

200

0

25

)

(m

150 0

20

100

0

15 0 10

50

ce

n ista

)

(m

D

50 Figure 3 The pattern of soil redistribution within a 7.5 ha field at Butsford Barton near Colebrooke, Devon, UK established by Walling and his coworkers using 137Cs measurements. More than 200 bulk cores were collected from the field at the intersections of a 20 m grid. The soil redistribution rates depicted represent mean annual values for a B40 year period prior to the mid-1990s.

suspended sediment export from the three catchments. By documenting changes in storage through time, it was also possible to estimate the total amount of sediment entering and leaving channel storage within the three catchments over the study period. The amounts of sediment entering and leaving channel storage within the three catchments were equivalent to between B20% and 75% and between 25% and 70%, respectively, of the mean annual sediment yield, demonstrating that a significant proportion of the sediment flux passed through this short-term sink.

2.12.2.3 The Global Sediment Budget Although the application is somewhat different, in terms of both scale and the nature of the budget, a sediment budget approach has also been used to assess the impact of recent changes in the sediment loads of the world’s rivers on the global land–ocean sediment transfer. Such changes have important implications for global geochemical cycling. In this case the emphasis has been on the total land–ocean sediment flux and the magnitude of the changes in this flux that have

occurred as a result of human activity. Although reliable information on sediment loads is unavailable for many world rivers, there is a general consensus that the contemporary land–ocean sediment flux is of the order of 15 Gt yr1, and a recent study reported by Syvitski et al. (2005) suggests that the value may be somewhat lower around 12.6 Gt yr1. However, it is known that this flux is changing as a result of human impact (Walling, 2006a; Syvitski and Milliman, 2007). In some rivers it is increasing, due to land clearance and the expansion of agricultural land use and associated increases in erosion, whereas in others it is declining due to the trapping of sediment by dams. In some river basins both drivers may be operating and the net effect will depend on their relative importance. A key issue is the extent to which the global sediment budget has been perturbed by human influence. This involves establishing the likely natural land–ocean sediment flux and then assessing the extent to which it has been increased and reduced by land disturbance and dam construction respectively. Syvitski et al. (2005) have used a regression model incorporating the main controls on natural river loads to

312

Catchment Erosion, Sediment Delivery, and Sediment Quality

estimate the pre-human sediment loads of the world’s major rivers as being B14 Gt yr1. This is 1.4 Gt yr1 greater than their estimate of the contemporary land–ocean sediment flux (12.6 Gt yr1), which will have been influenced by both increases and decreases relative to the pre-human flux, as a result of land disturbance and sediment trapping by dams, respectively. Lack of sediment load data for many rivers in the developing world, where sediment loads are likely to have increased as a result of population growth, makes it difficult to estimate the magnitude of any increase, but more information is available on the impact of dams in reducing sediment fluxes. Although the impact of dams in reducing the sediment loads of the world’s rivers is widely recognized (see Milliman et al., 1984; Vo¨ro¨smarty et al., 1997, 2003; Walling and Fang, 2003; Walling, 2006a), there is currently considerable uncertainty associated with existing estimates of the likely amount of sediment sequestered behind dams on the world’s rivers and the resulting reduction in the global land–ocean sediment flux. Vo¨ro¨smarty et al. (2003) estimated that more than 40% of the global river discharge is currently intercepted by large (Z0.5 km3 maximum storage capacity) reservoirs, and by coupling this information with estimates of reservoir trap efficiency they estimated that reservoirs are currently sequestering B4–5 Gt yr1 of sediment, with the potential for this value to be considerably higher if the large number of smaller reservoirs are also taken into account. Using a similar approach, Syvitski et al. (2005) suggested that the contemporary land–ocean sediment flux is being reduced by B3.6 Gt yr1 as a result of sediment trapping by dams. These values are, however, an order of magnitude lower than the estimate of the current sedimentation behind the world’s major dams provided by a recent study involving the B33 000 dams included in the ICOLD World Register of Dams (ICOLD, 2006), undertaken by the ICOLD Reservoir Sedimentation Committee and reported by Basson (2008). The data provided by this study suggest that sedimentation behind the world’s major dams is currently equivalent to an annual sequestration of B60 Gt yr1 (see Walling, 2008). It is, however, important to recognize that the estimate of the current rate of sediment sequestration in the world’s reservoirs of B60 Gt yr1 presented above represents the mass of sediment sequestered behind the dams and this does not equate to the associated reduction in the land–ocean sediment flux. Much of this sediment would previously not have reached the oceans, due to deposition and storage within the river system, and particularly on river floodplains. The conveyance loss associated with sediment movement through a river system can clearly be expected to vary according to the magnitude of the sediment flux, the sediment transport and flood regime of the river, and the morphology of the channel system, and is likely to decrease in heavily managed channels, where the flow is constricted and flood inundation restricted. It is therefore difficult to propose a typical value for the conveyance loss likely to be associated with the B60 Gt yr1 of sediment currently being sequestered behind the dams constructed on the world’s rivers. However, Walling, (2008) has suggested a value of 60% as a first-order estimate. Use of this value would mean that 40% of the total B60 Gt yr1 might be expected to have previously reached the oceans and that dam construction is currently reducing the global land–ocean

Table 2 A comparison of the estimates of the major components of the global sediment budget and their modification by human activity provided by Syvitski et al. (2005) with those generated by Walling (2008), using a different estimate of the reduction in the contemporary sediment flux caused by sediment trapping Component

Syvitski et al. (2005)

Pre-human land–ocean flux (Gt yr1) Contemporary land–ocean sediment Flux (Gt yr1) Reduction in flux associated with reservoir trapping (Gt yr1) Contemporary flux in the absence of reservoir trapping (Gt yr1) Increase over pre-human flux due to human activity (%) Reduction in contemporary gross flux due to reservoir trapping (%)

14.0 12.6 3.6 16.2

Walling (2008) 14.0 12.6 24 36.6

22

160

16

66

sediment flux by about 24 Gt yr1, a value that is considerably in excess of the likely contemporary global land–ocean sediment flux. This value of 24 Gt yr1 is approaching an order of magnitude greater than the values of 3.6 Gt yr1 suggested by Syvitski et al. (2005) as representing the reduction in the contemporary global annual land–ocean flux resulting from sediment trapping by reservoirs. Taking the above information on the likely magnitude of the contemporary and pre-human land–ocean sediment flux and the potential impact of sediment trapping by dams, it is possible to speculate further on the possible nature of the global sediment budget and the extent to which it has been perturbed by human activity (see Table 2). If the contemporary land–ocean sediment flux is taken to be 12.6 Gt yr1, but it is assumed that this has been reduced by 3.6 Gt yr1 as a result of reservoir trapping, the contemporary flux in the absence of reservoir trapping would be 16.2 Gt yr1. This represents a B16% increase over the pre-human flux, with this contemporary flux being reduced by B22%. As such, the perturbation associated with human activity is fairly limited. If, however, the same value is used for the contemporary land–ocean flux (12.6 Gt yr1), but it is assumed that this has been reduced by 24 Gt yr1 as a result of reservoir trapping, the contemporary flux in the absence of reservoir trapping would be 36.6 Gt yr1. This represents a B169% increase over the pre-human flux and this has, in turn, been reduced by B66% as a result of reservoir trapping. Under this scenario, human activity must be seen to have had a major influence on the global sediment budget. Further research is clearly required to confirm the magnitude of human impact on the global sediment budget.

2.12.3 Documenting Catchment Sediment Budgets 2.12.3.1 The Background Traditionally, fine sediment monitoring programs in river basins have focused on measuring the sediment load at the outlet of the catchment or river basin under investigation. This

Catchment Erosion, Sediment Delivery, and Sediment Quality enables the sediment yield (t yr1) and specific sediment yield (t km2 yr1) to be quantified. Such traditional measuring programs are commonly based on manual suspended sediment sampling, using samplers designed to collect representative suspended sediment samples from the measuring cross section (e.g., Guy and Norman, 1970; Gray et al., 2008). These samples are subsequently filtered to determine the suspended sediment concentration (mg l1). The suspended sediment flux at the time of sampling is computed as the product of the water discharge and the sediment concentration, taking account of the variation of both sediment concentration and flow velocity in the cross section. If frequent samples are collected, it is possible to interpolate the record of sediment flux between the sampling times, to compute the total load for the study period. Where, as in many situations, fewer samples are collected, rating curves representing relationships between suspended sediment concentration or discharge and water discharge are established using the available samples and the rating curve is used in conjunction with the record of water discharge to estimate the load for the period of interest. The use of rating curves introduces the potential for significant errors in the estimate of sediment flux (see Walling, 1977; Ferguson, 1986; Walling and Webb, 1988). Recent technological advances have greatly expanded the potential for obtaining more reliable estimates of suspended sediment load. Programmable automatic samplers can be used to increase the sampling frequency and to ensure that samples are collected at key times during a flood event (e.g., Lewis and Eads, 2001; Alexandrov et al., 2003). In situ sensors can also be deployed to record surrogate information that can be used to provide a continuous or near continuous record of suspended sediment concentration. Turbidity measurements obtained using both optical backscatter and transmission sensors have proved to be particularly valuable for this purpose (e.g., Gippel, 1995; Glysson and Gray, 2002; Schoellhamer and Wright, 2003), although their use is generally limited to relatively low levels of suspended sediment concentration. Other principles, involving lasers and ultrasonic sensors, have also been successfully used to collect information on the variation of both suspended sediment concentration and its grain size composition through time (e.g., Melis and Topping, 2003; Thonon et al., 2005; Topping et al., 2005). The requirement for information on the overall sediment budget of a catchment, rather than simply an estimate of the sediment load at the catchment outlet, has, however, necessarily introduced a need to develop new approaches capable of documenting rates of sediment mobilization, quantifying the storage elements of sediment budgets and obtaining information on sediment sources and transfer pathways. Traditional techniques provide some scope for assembling such information, but in a recent paper Walling (2006b) suggested that there was a need for a new paradigm which focused on tracing rather than monitoring. Monitoring was seen as continuing to be important, and indeed to be an essential component of any comprehensive measurement program, but the use of tracing techniques was viewed as representing the only effective means of assembling much of the information required to establish a sediment budget. Two key advances in the application of tracer techniques for investigating catchment sediment budgets can usefully be

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highlighted. The first is the use of fallout radionuclides to obtain information on soil and sediment redistribution rates within a catchment and the second is the use of sediment source fingerprinting techniques to provide information on the relative contribution of a range of potential sources to the sediment output from a catchment. Both applications are briefly considered below.

2.12.3.2 The Use of Fallout Radionuclides The use of fallout radionuclides to obtain information on soil and sediment redistribution rates within a catchment is founded on the existence of a number of natural and manmade radionuclides that reach the land surface as fallout, primarily as wet fallout in association with rainfall, and are rapidly and strongly fixed by the surface soil or sediment. The subsequent redistribution of these radionuclides within a catchment or river system is a direct reflection of the movement of the soil or sediment particles to which the radionuclides are attached. By studying the post-fallout redistribution and fate of the selected fallout radionuclide, it is possible to obtain information on soil and sediment redistribution and, therefore, on erosion and deposition rates. The fallout radionuclide most widely used for this purpose is cesium-137 (137Cs) (see Ritchie and Ritchie, 2008). Cesium137 is a man-made radionuclide that was produced by the testing of thermonuclear weapons in the 1950s and early 1960s. The 137Cs released by these bomb tests was carried up into the stratosphere and transported around the globe. Significant fallout occurred in most areas of the world during the period extending from the mid-1950s through to the 1970s, although the depositional fluxes were much greater in the northern than the southern hemisphere. In the absence of further bomb tests after the Nuclear Test Ban Treaty in 1963, fallout effectively ceased in the mid-1970s. However, in some areas of the world a further fallout input occurred in 1986 as a result of the Chernobyl accident. Fallout from that accident was short-lived, but in some neighboring regions the total fallout associated with the Chernobyl accident exceeded the earlier bomb fallout. Cesium-137 has a half-life of 30.2 years and much of the original fallout still remains within the upper horizons of the soils and sediments of a catchment. By investigating the current distribution of the radionuclide in the landscape, it is possible to obtain information on the net effect of soil and sediment redistribution processes operating over the past B50 years (see Zapata, 2002). When sampling the soils and sediments in a catchment, attention is usually directed to both the inventory or the total amount of 137Cs contained in the soil or sediment (Bq m2) and its depth distribution. However, emphasis is frequently placed on the collection of bulk soil cores and their use to determine the inventory at the sampling point, since the sectioning of a core to determine the depth distribution necessitates the analysis of a much larger number of samples, which can prove to be costly and time-consuming. Samples are analyzed by gamma spectrometry and count times of 12–24 h may be required when activities are low. Mean soil redistribution rates over the past B50 years, since the main period of fallout, are established by comparing the inventories measured at individual sampling points with the reference inventory for the study site.

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The latter is commonly based on cores collected from an adjacent undisturbed area with minimum slope that can be expected to have experienced neither erosion nor deposition over the past B50 years. Points with inventories less than the reference inventory are indicative of eroding areas, whereas those with inventories in excess of the reference value indicate deposition. A range of conversion models have been developed for use in estimating erosion and deposition rates, based on the degree of departure of the measured inventory from the reference inventory (e.g., Walling and He, 1999a; Walling et al., 2002; Li et al., 2009). Using a similar approach, 137 Cs measurements have also been successfully used to estimate deposition rates on river floodplains over the past B50 years (Walling and He, 1993, 1997; Terry et al., 2002; Ritchie et al., 2004). Cesium-137 has now been successfully used in many areas of the world to obtain hitherto essentially unavailable information on medium-term rates of soil and sediment redistribution (see Figures 3 and 4, Table 3; Ritchie and Ritchie, 2008) and its value as a tracer has been strongly promoted by the International Atomic Energy Agency (IAEA) (see Zapata, 2002). Most applications have involved relatively small areas, since this permits the collection of sufficient samples to obtain representative information on the spatial patterns of soil and sediment redistribution involved. There is, nevertheless, a need for further work to establish procedures for using the approach in a reconnaissance mode, in order to obtain information from larger areas without a major increase in the number of samples that need to be collected and analyzed. Key advantages of the approach include the ability to obtain retrospective information on medium-term soil redistribution rates, the need for only a single sampling campaign, the provision of spatially distributed information relating to the individual sampling points, and the ability to collect information from the natural landscape, without the need to install plots or to otherwise constrain the location of the measuring points. Although most studies employing fallout radionuclides have been based on 137 Cs, both excess lead-210 (210Pbex) and beryllium-7 (7Be) have also been used in a similar manner (see Mabit et al., 2008). Lead-210 is a natural geogenic radionuclide produced as a product of the uranium decay series. Radium-226 (226Ra) is found in most soils and rocks and this decays to produce gaseous radon-222 (222Rn), which in turn decays to 210Pb. Some of the 222Rn diffuses upward through the soil and escapes into the atmosphere where it decays to 210Pb and is deposited as fallout. As with 137Cs, the 210 Pb fallout reaching the land surface is rapidly fixed by the soil and its subsequent redistribution is governed by the movement of soil and sediment particles. The fallout 210Pb is termed excess or unsupported 210Pb, to distinguish it from the 210 Pb produced by in situ decay, which will be in equilibrium with, or supported by, the parent 226Ra. The use of 210Pbex to document soil and sediment redistribution within the landscape employs similar assumptions and procedures to those used with 137Cs. Walling and He (1999b) discuss its use in soil erosion studies and He and Walling (1996) provide examples of its application for estimating rates of overbank sedimentation on river floodplains. The half-life of 210Pb is 22.3 years and therefore similar to that of 137Cs. However, because 210Pb

is a natural geogenic radionuclide, the fallout has been essentially constant through time and the activity in the soil will reflect fallout receipt and subsequent decay over the past B100 years. In the case of soil redistribution on slopes, the influence of past erosion on the present inventory will increase toward the present. Measurements of 210Pbex activity can therefore provide information on longer-term soil and sediment redistribution rates over the past B100 years and use of both 137Cs and 210Pbex in combination can provide additional information on the erosional or depositional behavior of a study area (He and Walling, 1996; Walling et al., 2003a) (see also Figure 4). Beryllium-7 is a natural cosmogenic radionuclide formed in the upper atmosphere by its bombardment with cosmic rays. In contrast to 137Cs and 210Pb, 7Be has a very short half-life of only 53 days and, because of this, it can be used to provide information on soil and sediment redistribution rates associated with individual events or short periods of heavy rainfall extending over a few weeks (e.g., Walling et al., 1999; Blake et al., 2002; Wilson et al., 2003; Schuller et al., 2006; Sepulveda et al., 2007). The principles involved in applying 7Be measurements are similar to those for 137Cs and 210 Pbex, but for most approaches it is important to ensure that the period of interest conforms to a number of requirements, to avoid carry-over effects from previous periods of heavy rain, which could influence the magnitude and spatial distribution of 7Be inventories across the study area. Walling et al. (2009) have recently described a refined procedure for employing 7Be measurements, which largely overcomes this constraint and makes the approach more generally applicable.

2.12.3.3 Sediment Source Fingerprinting Sediment source fingerprinting techniques can provide important information on the source of the suspended sediment transported by a stream. In simple terms, the techniques attempt to match the properties of the sediment to those of potential sources within the catchment and to establish the relative contribution of those sources to a given sediment sample. Source can be interpreted in terms of both spatial sources, representing different parts of the catchment, perhaps different tributaries or areas underlain by different rock types, and source types, representing sediment mobilized by different processes or from areas with different land use. A set of potential source types could, for example, include surface erosion from cultivated areas and areas of permanent pasture or range, gully erosion, and channel erosion. A wide of sediment properties including color (e.g., Krein et al., 2003), geochemistry (e.g., Collins and Walling, 2002), mineral magnetic properties (e.g., Caitcheon, 1993; Hatfield and Maher, 2009), radionuclide content (e.g., Wallbrink et al., 1998; Matisoff et al., 2002), and stable isotopes (e.g., Fox and Papanicolaou, 2007) have been used to fingerprint potential sources and in most cases a composite fingerprint incorporating several properties is required to discriminate between potential sediment sources. A mixing (or unmixing) model is used to estimate the relative contribution of the potential sediment sources to the sediment sample under consideration. Walling (2005) provides an overview of the development of source tracing techniques and their potential, emphasizing many complexities that need to be taken into account in order

Catchment Erosion, Sediment Delivery, and Sediment Quality

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Figure 4 The patterns of overbank deposition of fine sediment on a portion of the floodplain of the River Severn near Buildwas, Shropshire, UK established by Walling and his co-workers using 137Cs and 210Pbex measurements. 124 bulk cores were collected from the floodplain at the intersections of a 25-m grid. The estimates of mean annual sedimentation rate estimated using the 137Cs measurements relate to the past B40 years, whereas those based on the 210Pbex measurements relate to the past B100 years.

to obtain meaningful and reliable results. Of particular importance are the need to verify statistically the discriminatory power of the fingerprints employed (e.g., Collins et al., 1997a), to recognize many sources of uncertainty incorporated into the approach (e.g., Rowan et al., 2000) and to express and interpret the results accordingly, and to take account of possible differences between source material and sediment samples in terms of grain size composition and organic matter content (e.g., Collins et al., 1997b). The success of source fingerprinting techniques depends heavily on identifying a range of elements and/or isotopes that are capable of

discriminating potential sources with a high degree of reliability. Fallout radionuclides have frequently been successfully incorporated into fingerprints used to discriminate between surface sources under different land use (e.g., cultivation, pasture, and forest) and channel/subsurface sources (e.g., Walling et al., 2008) but a new generation of fingerprint properties based on compound specific stable isotopes (CSSIs) associated with the fatty acids produced by plants appears to offer the potential to discriminate between source areas supporting different vegetation covers. Gibbs (2008) reports a study undertaken in North Island New Zealand where CSSIs

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were used to establish the relative importance of areas under sheep pasture, indigenous forest, and exotic forest plantations as sources of the sediment deposited in a downstream estuary. Most existing fingerprinting studies focus on establishing the source of the suspended sediment transported by a river. In early work this commonly involved collecting individual samples of sediment, but more recent studies have frequently made use of time-integrating sediment traps (e.g., Phillips et al., 2000), in order to provide a single sample representative of the sediment transported during the period of sample

Table 3 A spatially integrated assessment of soil redistribution within the 7.5 ha field at Butsford Barton near Colebrooke, Devon, UK, based on the estimates of soil redistribution rates provided by 137Cs measurements presented in Figure 3 Parameter

Value

Percentage area with erosion (%) Percentage area with deposition (%) Mean erosion rate for eroding area (t ha1 yr1) Mean deposition rate for deposition zones (t ha1 yr1) Net erosion rate for the field (t ha1 yr1) Sediment delivery ratio for the field (%)

79 20 10 7.5 6.5 81

collection. In addition to suspended sediment, the fingerprinting approach has also been applied to overbank sediment deposits on floodplains (e.g., Bottrill et al., 2000) and fine sediment recovered from salmon spawning gravels (e.g., Walling et al., 2003c) and it is possible to generate a temporal perspective and to investigate changes in sediment source through time by applying the same approach to a sediment core collected from a lake or floodplain and interpreting downcore changes in sediment properties in terms of source fingerprints (e.g., Collins et al., 1997b; Walling et al., 2003b; Pittam et al., 2009). Taken together and combined with more traditional monitoring techniques for obtaining information on the sediment flux at the catchment outlet, these two sets of sediment tracing techniques afford a valuable means of obtaining much of the information required to establish a catchment sediment budget (e.g., Walling et al., 2001, 2006). Thus, for example, it is possible to link information on the source of the sediment load at the catchment outlet provided by source fingerprinting techniques with information on rates of sediment redistribution within those source areas and rates of accretion in sediment sinks such as river floodplains provided by fallout radionuclides, to quantify the key sources and sinks within the sediment budget of a catchment. Figure 5 depicts

Pang Cultivated fields

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Figure 5 The sediment budgets established for the Pang ( B166 km ) and Lambourn (B234 km2) catchments in southern England by Walling et al. (2006). 2

Catchment Erosion, Sediment Delivery, and Sediment Quality the sediment budgets of the Pang (B166 km2) and Lambourn (B234 km2) catchments (located on the chalk of southern England), which were established using this approach. In this case, the highly permeable strata underlying the catchments and the resulting dominance of groundwater in the runoff from the catchment mean that storm runoff is limited and that little sediment leaves the catchment. However, there is evidence of relatively high rates of sediment mobilization and redistribution within the catchments, and in this environment the functioning of their sediment budgets is dominated by the internal sediment sinks.

2.12.3.4 The Future Recent years have seen important advances in the development of improved methods for characterizing and establishing catchment sediment budgets. The methods now available are able to provide information and understanding to support the development of sediment management programs. However, further advances are required to meet future information requirements, which are likely to place increasing emphasis on the targeting of sediment control strategies, in order to maximize the benefits achieved by investment in such strategies. The use of new sediment source fingerprints, such as CSSIs, can be expected to provide significant improvements in tracing sediment from specific sources and assessing the importance of those sources. In the case of fallout radionuclide applications, most existing work has focused on small areas and there is an important need to upscale their use to larger areas. Because of the limitations on sample numbers commonly associated with sample counting facilities, this upscaling cannot be achieved by simply increasing the number of samples collected. Attention needs to be directed to the development of reconnaissance sampling strategies capable of maximizing the information supplied by a small number of samples. In turn, there is a need to integrate the use of fallout radionuclide techniques with numerical modeling and geographical information systems (GISs), in order to optimize the spatial extrapolation of the resulting information. Advances in sensor technology will undoubtedly bring new and improved methods for monitoring sediment fluxes at catchment outlets that increase the temporal resolution of the records obtained and extend the scope of the data obtained. Scope undoubtedly exists to obtain valuable information on the grain size composition of the sediment load, as well as its magnitude. Such developments are likely to prove to be important in compensating for the progressive reduction in field staff dedicated to sediment sampling and other related monitoring activities that have occurred in many countries in recent years.

2.12.4 Modeling the Catchment Sediment Budget 2.12.4.1 The Requirement Supporting national and international programs that aim to reduce the threat soil erosion poses to agricultural production and downstream aquatic environments require systematic assessments of sediment sources and their connectivity to downstream impacts (Phillips, 1986). Assessments can provide a technical basis to assist targeting of limited resources to

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maximize benefits (NLWRA, 2001; Bohn and Kershner, 2002). At river basin scale, managers are often faced with a paucity of erosion data or uneven distribution of measurements across the assessment area. Measurements will have been made at different times and various scales for a range of purposes. Modeling can enable systematic assessment of erosion severity over much larger areas than can be practically covered by measurement alone (Reid and Dunne, 2003). Modeling can also enable assessment over longer time periods with a wider range of climatic conditions, including potential future climates, which is critical for long-term planning, given the high temporal variability of erosion and sediment delivery. Modeling periods of decades may be required to represent adequately the aggregate effects of climatic variability. Several requirements for modeling erosion and sediment delivery can be identified from a management perspective: 1. Models should explicitly represent the primary erosion processes occurring, so that priorities can be developed, effective interventions identified, and the effect of alternative management scenarios simulated. 2. Spatial patterns in erosion rates should be identified by representing the primary environmental drivers of erosion and sediment delivery. 3. The connectivity of upstream erosion sources to downstream sediment loads should be represented, which requires consideration of sediment sinks as well as sources, and the potential for source connectivity to vary spatially depending on the location of sediment sinks. 4. Assessing erosion and sediment delivery at national or continental scales requires models with modest data requirements. 5. Where sediment-associated pollutants, such as phosphorus and agro-chemicals, are a focus of management, then sediment particle size fractions should be explicitly represented. Pollutants preferentially attach to fine sediment fractions (Section 2.12.5.2), and erosion and transport process behavior will differ between fine and coarse fractions. These requirements, particularly the first three, suggest that a sediment budget is a suitable framework for modeling, because it accounts for the sources, transport and sinks of material, with a river basin being the confining domain (Trimble, 1993; Reid and Dunne, 2003). The following subsections describe three aspects of sediment budget modeling: (1) the evolution and development of models, (2) an example of integrated sediment budget modeling, and (3) the current status and future directions of model development and application.

2.12.4.2 Model Development 2.12.4.2.1 Modeling approaches and model complexity Erosion and sediment delivery modeling can be based upon interpolation and synthesis of measurements, or upon physical reasoning and identification of the environmental factors that control the key processes. Typically, a combination is used (DeRoo, 1996), with process models providing spatial resolution between measurement points and helping to identify the upstream erosion and delivery processes, and

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measurement points being used to constrain model predictions. Three approaches to erosion and sediment delivery modeling can be identified, representing different weightings between measurement and mathematical process description (Beck, 1987; Merritt et al., 2003): 1. Empirical modeling, usually based on a small number of causal variables, often spatially and temporally lumped, and calibrated to measurements. These include catchmentspecific relationships between catchment area and sediment yield for example. 2. Conceptual process modeling, which represents generation, routing and storage processes within landscape units or catchments using simple representation of their controlling parameters, and without process interactions. They may be semi-lumped into units or subcatchments with time-steps of days to decades (see Merritt et al., 2003). 3. Mechanistic physical-process modeling provides detailed representation of runoff-generation processes, and usually for application at point, field, or small watershed scale, at finer temporal and spatial resolution than employed in conceptual modeling.

2.12.4.2.2 Empirical modeling of catchment sediment yield

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Empirical modeling of catchment sediment yield emphasizes available data rather than process representations, and this feature can represent both a strength and weakness, depending on the problem being addressed. On the one hand, sensitivity to data constrains model output and enhances the opportunity for new system understanding, making empirical modeling particularly useful where system understanding is weak. Model empiricism is also vital for investigating or calibrating models of sediment yield processes for which the fundamental physical constraints are not sufficiently well known or described. Empirical models are generally relatively simple, and consequently have modest data requirements.

Model unable to exploit data

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Model complexity generally increases from empirical through conceptual to mechanistic approaches, as the level of process representation and the level of spatial and temporal resolution required increase. There is interdependence between process complexity and the temporal and spatial resolution of a model; finer spatial and temporal resolution requires more complexity in process representation, and vice versa. For example, conceptual rainfall-runoff models require more storage terms and model parameters for predicting daily or monthly runoff than for long-term average runoff (Jothityangkoon et al., 2001). The interdependence between process complexity and model resolution scale means that most empirical models focus on lumped process representations of catchment sediment yield or individual erosion processes, and can be implemented over large areas and long time periods.

Most mechanistic models are spatially distributed and focus on hillslopes or small watersheds for individual events. Conceptual models are often semi-lumped, and focus on simple representation of catchment erosion and deposition processes. The most appropriate model design considers each component of the erosion and sediment system with a level of complexity that is appropriate for the problem at hand and for the data available (Reid and Dunne, 2003). This is important because, for a given data availability and process knowledge, there is a maximum model complexity which fully exploits the information provided by input data and above which predictive capacity is reduced (Grayson and Blo¨schl, 2000; Figure 6). Above this level of complexity, it also becomes difficult to identify appropriate parameter values (Beck, 1987). The optimization of model complexity to maximize predictive capacity has contributed to the number of models which have been developed in recent decades. The evolution of each modeling approach is briefly described below, where some of the more widely known models are used as examples and model-specific reviews are cited.

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Figure 6 Schematic diagram of the relationship between hydrologic model complexity, data availability and predictive performance. Reproduced from Grayson R and Blo¨schl G (2000) Spatial modelling of catchment dynamics. In: Grayson R and Blo¨schl G (eds.) Spatial Patterns in Catchment Hydrology, ch. 3, pp. 51–81. Cambridge: Cambridge University Press.

Catchment Erosion, Sediment Delivery, and Sediment Quality

Examples of empirical sediment yield modeling include:

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Modeling the sediment load at a river station, using discharge and sediment concentration data. Commonly, frequent discharge measurements (L3 T1) are available, with occasional measurements of suspended sediment concentration (M L3). There is considerable short-term temporal variability in sediment concentration (Nistor and Church, 2005). The key modeling challenge is to explain and predict the variation in sediment concentrations between observations, with sediment load in a given time period (M T1) then being the product of discharge and concentration. A common approach is to model concentration using functions of discharge, or sediment rating curves (Asselman, 2000). Rating curves assume steady-state behavior, although records can be divided into multiple time windows to represent system changes. More recently, neural network and other modeling techniques have been employed, which consider the influence of antecedent as well as present discharge and concentration on sediment load (Kisi, 2005). Lumped models of sediment yield, based on upstream catchment area (Wasson, 1994), include other basin metrics such as relief, runoff, climate zone, lithology, and anthropogenic factors (Syvitski and Milliman, 2007).

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constraints on process behavior. Thus, conceptual models evolve as the process understanding improves. Process component models are usually somewhat empirical in nature, requiring some calibration to match observed erosion and deposition rates, but providing predictive capacity where measurements are not available. There are several types of conceptual process models of erosion and sediment budgets, which employ different types of input data:

2.12.4.2.3 Conceptual process modeling of catchment sediment budgets

1. A catchment sediment budget developed from a combination of air-photo interpretation, mapping of geomorphic units or zones, field erosion measurements, and dating of sediment deposits (Trimble, 1983; Wasson et al., 1998; Curtis et al., 2005). As the amount of data increase, these budget models become increasingly complex in terms of the sources and sinks represented, and the spatial and temporal resolutions and extents. These models can help guide rehabilitation efforts (Trimble, 1993). Prediction outside the study area is limited where measurements dominate model inputs. 2. A reach sediment budget generated from load estimates based on sediment monitoring data, which can be used to identify reaches of net erosion or deposition (Singer and Dunne, 2001). 3. A source fingerprinting analysis based on sediment tracer properties, which assesses the relative contribution of erosion processes and/or source areas to river sediment without requiring direct measurement of erosion or deposition rates (Walling, 2005; Walling and Collins, 2008; Davis and Fox, 2009). 4. Semi-lumped spatial models of sediment budgets which commonly use GIS functions to divide river basins into subcatchments or watersheds, each draining to a river link, and route sediment through river networks (Benda and Dunne, 1997; Prosser et al., 2001b; Wilkinson et al., 2006, 2009). 5. Distributed spatial models which compute hillslope runoff, erosion, and sediment delivery at the resolution of input data sets, with sediment routed according to topography. Examples include AGNPS (Agricultural Nonpoint Source model; Young et al., 1989) and SEDEM (SEdiment DElivery Model; Van Rompaey et al., 2001). More precisely, these models should be seen as partially distributed, with some lumped elements remaining.

Conceptual process modeling generally represents sediment routing through a catchment, using a semi-lumped structure of subcatchments or management units. Source (erosion) and sink (deposition) processes are commonly represented for each defined spatial unit, although not all models calculate complete sediment budgets. Feedbacks between processes will not usually feature. Models that identify sediment sources and delivery through the river network are more consistent with field-based evidence of sediment fluxes (Trimble and Crosson, 2000), and are more suitable than lumped models of sediment yield for the modeling requirements identified in Section 2.12.4.1 (Phillips, 1986). Individual erosion and deposition components of conceptual process models are based on prior field measurements and studies, which provide the basis to identify and mathematically formulate the environmental controls and physical

Spatial lumping of model domains into morphological units or subcatchments, and application of lumped parameter values across units and timescales is common in conceptual models. However, the scale of lumping can strongly influence the model predictions. For example, spatial patterns of land use within units may be far from random, which can affect sediment delivery. For practical reasons related to the time and effort involved, more complex semi-lumped and distributed models were originally limited to small hillslope applications. By the late 1990s, the wide availability of GIS software and techniques enabled more complex models with spatially varying input data to be easily applied to larger areas. Today, data requirements and the ability to realistically describe processes are the dominant limitations on modeling.

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Weaknesses of empirical modeling include: (1) the spatial and temporal resolution and extent are limited by the data available; (2) their lack of explicit process representations can limit predictive capacity outside the study area or measured range of environmental characteristics; (3) the heterogeneity of catchment characteristics such as rainfall, topography, lithology, and land use is not usually represented in spatially lumped models; this reduces predictive capacity, given the significant spatial correlations, and nonlinear dependencies, between slope gradient, runoff, and other driving variables of erosion (Van Rompaey et al., 2001); (4) the absence of source and sink process representations in empirical sediment yield models can limit the number of different types of data which can be meaningfully assembled. Data sets that may enhance the model may instead be used to interpret model results (e.g., Singer and Dunne, 2001).

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2.12.4.2.4 Mechanistic, physically based modeling of hillslope processes Available mechanistic models span a range of complexity in the process representations used to model runoff and erosion. Some models are more physically based, representing soil infiltration and runoff routing analytically, for example, using kinematic waves, while others use empirical approaches such as runoff curve numbers. Surface erosion is modeled using USLE-based detachment equations, or more complex shear stress and stream power functions (DeRoo et al., 1996; Srivastava et al., 2007). Some models predict net soil loss considering both detachment and deposition (Nearing et al., 1989). Indicating their focus toward hillslope or small watershed scales, mechanistic models do not commonly include gully and riverbank erosion and channel deposition processes. Several stand-alone models predict ephemeral gully erosion using flow shear stress and sediment transport capacity, although their predictive capacity has received little testing (Poesen et al., 2003). Several mechanistic models of permanent gullies have also been developed, describing the evolution of morphology during the early stages of gully development and the final morphological characteristics (Poesen et al., 2003). Again, however, their applicability has not been widely tested. Available physically based models cover a wide variety of spatial and temporal scales, with the former ranging from individual hillslopes to fields. Some models are pixel-based, and accommodate GIS data to facilitate modeling small watersheds (DeRoo et al., 1996; Srivastava et al., 2007). The majority of physically based models are designed to simulate individual events rather than long time periods (Aksoy and Kavvas, 2005). Many mechanistic and distributed models have input data requirements that are technically or financially unattainable over large river basins or continents or multiyear time periods (Van Rompaey et al., 2001). The distributed or hillslope unit structure of mechanistic models addresses problems associated with spatial lumping, such as spatial correlation between inputs. However, considerable spatial variability in surface roughness, slope gradient, and other variables often exist even within model spatial units. Distributed models are sensitive to errors in surface slope and topography. Consequently, it is common for mechanistic physics-based models to require calibration to reproduce observed behavior, which potentially introduces error in process modules.

2.12.4.3 SedNet – A Sediment Budget Model for River Networks 2.12.4.3.1 Model outline To illustrate considerations in design and application of sediment budget models, this section describes the SedNet (Sediment budget river Network) model. SedNet constructs budgets of the primary sources and sinks of sediment for each link in a river network (Prosser et al., 2001b; Wilkinson et al., 2004, 2009). This model structure may be considered generic to all semi-lumped sediment budget models. The structure enables spatial representation of the connectivity of

upstream sources to downstream yields, including the role of floodplains and impoundments within catchments (Prosser et al., 2001c). Predicted suspended sediment yield is supply limited in the long term, which is consistent with observations. The river network is defined from a digital elevation model (DEM). Separate budgets are constructed for sand and gravel bed material (Wilkinson et al., 2006), for suspended sediment (Wilkinson et al., 2009), and for particulate and dissolved phosphorus and nitrogen (Wilkinson et al., 2004). The sediment yield from the downstream end of each link accounts for material sourced from hillslope and gully erosion in the subcatchment which drains directly to the link, bank erosion along the link, and from upstream tributaries. Deposition is accounted for on floodplains, in impoundments or reservoirs, and accumulation of bed material in the river channel. The processes of land-sliding, debris flow, and hillslope soil creep are not significant sediment sources in the Australian environment (for which SedNet was developed), although they could be added for model application elsewhere. The net change in channel storage of suspended sediment over decades is assumed to be negligible relative to other terms, and so in-channel deposition and re-entrainment of suspended sediment is ignored. The budget is reported as mean annual values for a set of conditions. The effects of temporal variability in climate and hydrology on each source and sink are modeled by regionalizing statistics of daily discharge. The process representations are generally conceptual in nature, designed to show the primary physical controls to provide predictive capacity in low-data environments. For example, hillslope erosion is represented by the Revised Universal Soil Loss Equation (RUSLE; Renard et al., 1997), with a sediment delivery ratio accounting for deposition within hillslopes. Gully sediment yield is constrained by the estimated volume of gully networks and their period of development. Spatial variation in riverbank erosion is estimated as a function of stream power, and the extent of erodible soil and riparian vegetation (Wilkinson et al., 2009). Parameter values for a given environment are specified based on the knowledge of erosion and deposition processes developed through field measurement (Bartley et al., 2007), reconstruction of erosion histories (Wasson et al., 1998), sediment tracing (Wallbrink et al., 1998), and independent sediment yield estimates (Rustomji et al., 2008).

2.12.4.3.2 Management applications SedNet was developed for the Australian National Land and Water Resources Audit, which investigated the spatial patterns in erosion processes, and the offsite impacts of agriculture across the Australian continent (NLWRA, 2001). The modeling indicated marked differences in sediment supply between regions, with gully and river bank erosion dominating sediment supply in temperate regions, and hillslope erosion dominating in tropical regions, due to the higher rainfall intensity. Only 25% of fine sediment delivered to streams was predicted to be delivered to estuaries overall (Prosser et al., 2001a).

Catchment Erosion, Sediment Delivery, and Sediment Quality

hotspots. This approach provides improved predictive capacity over spatially lumped models (Wilkinson et al., 2006). River links predicted to have bed material accumulation have impaired biological health, with lower abundance Suspended sediment export (kt yr−1)

SedNet has since been applied at regional scale using higher-resolution datasets, to better support catchment planning. For example, increased riverine sediment exports from the catchments draining to the Great Barrier Reef (GBR) threaten to degrade near-shore coral reef and benthic ecosystems (De’ath and Fabricius, 2010). Modeling predicted that 70% of sediment export comes from just 20% of the total catchment area (Figure 7). The spatial pattern of contribution to export was highest in near-coastal areas with high rainfall intensity, steep slopes, and more intensive land management (McKergow et al., 2005). The model has also been used to compare scenarios of future management. Figure 8 demonstrates that targeting erosion control to areas and erosion sources in descending order of their contribution to sediment export can achieve reductions in export several times larger than would be achieved by spatial random changes in land management. Evaluation of SedNet against yield estimates from suspended sediment rating curves, and against sediment tracer data, indicates that the model can reliably differentiate between the areas contributing most and least to basin yield, provided that input data are of good quality (Wilkinson, 2008; Wilkinson et al., 2009). SedNet has also been used to assess the location and extent of accumulations of sand and gravel bed material within river networks, indicating that up to 25% of the river network is affected in some river basins, particularly downstream of gully and riverbank erosion

321

620 Targeted riparian revegetation

600

Random riparian revegetation

580 560 540 520 500 0

200 400 600 800 Riparian revegetation (km)

1000

Figure 8 Simulated reductions in suspended sediment export from the Murrumbidgee River catchment, showing that spatially-targeted control of gully and river bank erosion can achieve larger reductions in export than spatially-random control measures. Reproduced from Wilkinson SN, Prosser IP, Olley JM, and Read A (2005) Using sediment budgets to prioritise erosion control in large river systems. In: Batalla RJ and Garcia C (eds.) Geomorphological Processes and Human Impacts in River Basins, IAHS Publication 299, pp. 56–64. Solsona, Catalonia: IAHS Press, with permission from IAHS press.

Ratio of hillslope to channel Specific suspended Contribution of suspended Current minus natural (gully and bank) erosion sediment load (t ha−1 yr−1) sediment to the coast (t ha−1 yr−1) contribution of suspended sediment < 0.01 < 0.2 < 0.5 to the coast (t ha−1 yr−1) 0.2−0.5 0.5−2 0.01−0.05 < 0.01 0.05−0.1 0.5−1.4 >2 (a) (b) (d) (c) 0.01−0.05 > 1.4 0.1−05 0.05−0.1 0.5−1 0.1−0.5 >1 0.5−1 >1 N

0

200

400 km

Figure 7 SedNet results for the catchments draining to the Great Barrier Reef (a) estimated ratio of hillslope erosion to channel erosion (gully plus riverbank) in each subcatchment, (b) predicted specific suspended sediment load, (c) predicted contribution of suspended sediment to the coast under current conditions, and (d) the difference between estimated current and natural contribution to suspended sediment export. Reproduced from McKergow et al. (2005) Sources of sediment to the Great Barrier Reef World Heritage Area. Marine Pollution Bulletin 51: 200–211, with permission from Elsevier.

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Catchment Erosion, Sediment Delivery, and Sediment Quality

of habitat-sensitive macro-invertebrate taxa (Harrison et al., 2008).

2.12.4.4 Current Status and Future Directions 2.12.4.4.1 Modeling across scales for planning and management There is an increasing demand for land management and environmental stewardship to be underpinned by technical assessments. Land management and planning is increasingly driven by, and administered through, national and international programs, rather than locally. The challenge for erosion and sediment modeling is to provide robust assessments of the effects of local and dynamic changes in land management on erosion and sediment yield outcomes over large areas (100–100 000 km2) and long time periods (10–100 years). Modelling and measurement are becoming more integrated in the study of catchment erosion and sediment delivery. Remote sensing has provided a rapid increase in the quality of spatial data of topography, soil, and vegetation cover (Vrieling, 2006). A range of data types are now being used to constrain the modeled sediment budget, including independent load estimates and sediment tracing (Rustomji et al., 2008; Wilkinson et al., 2009), and dating of sediment deposits (de Moor and Verstraeten, 2008). There are no models that simultaneously operate at all scales from point land management practices to basin sediment yields, and such a model would require large increases in input data resolution (Srivastava et al., 2007). The present response to the need for information at multiple scales is to apply erosion and sediment delivery models of different scales in a more closely coupled and integrated fashion. For example, conceptual river basin models can be used to identify priority source areas, where mechanistic models are applied at the field scale to optimize management practices, the outputs of which are then represented in the basin-scale model. Given the considerable resources and expertise required to operate models and improve data inputs, having a clear strategy for model integration will help achieve the most effective outcomes. The current status of empirical, conceptual, and mechanistic modeling approaches is summarized below. Empirical models of sediment yield can help to identify areas or catchments with more intense erosion or deposition (Singer and Dunne, 2001). Because they do not usually identify sediment source processes, their ability to simulate climate or land management scenarios is limited. Empirical sediment yield models are often used to validate conceptual and mechanistic sediment budget process models (Takken et al., 1999; Wilkinson et al., 2009). In this context, methods to quantify the uncertainty associated with empirical sediment load estimates are important (Rustomji and Wilkinson, 2008). Conceptual and mechanistic models provide frameworks for routing material from sources to sinks and downstream environments, to identify erosion hotspots and to estimate the effects of practice changes. The benefit of conceptual and mechanistic modeling approaches are realized only when all of the important erosion and deposition processes are represented. Many models provide in-depth treatment of surface erosion but omit gully and riverbank erosion and channel

deposition processes, despite their importance at basin scale (de Vente and Poesen, 2005). Inappropriate or omitted process representations can lead to a model predicting the right river basin sediment yield for the wrong reasons, jeopardizing investment priorities (Boomer et al., 2008). The application of mechanistic models across the large areas and long time-periods of interest for land management is often constrained by limited data with which to specify parameter values, and simplified versions of mechanistic models have emerged (Van Rompaey et al., 2001; Borah and Bera, 2002; Brasington and Richards, 2007). Stochastic or probabilistic descriptions of hydrology and soil properties have also been proposed (Aksoy and Kavvas, 2005). Spatial interfaces have been developed to facilitate application of hillslope profile models to broader areas with complex topography (e.g., Ascough et al., 1997; Renschler, 2003). Simulating the effects of global warming-induced climate change on erosion and sediment yield is likely to become more common, but requires careful consideration of all model inputs, including changes in variability and mean condition.

2.12.4.4.2 Directions in modeling erosion and deposition processes A fundamental principle guiding further developments in modeling erosion and sediment delivery processes is that the complexity and the spatial and temporal scales should be appropriate to the depth of process knowledge for the study area, the input data available, and the modeling objective. Four less-well developed areas can be identified as foci for further development of process modeling: Overland sediment transport capacity. It is now well recognized that models of catchment sediment delivery should separate surface erosion and overland sediment transport from erosion and deposition processes occurring at larger spatial scales, such as gully erosion and floodplain deposition (Trimble and Crosson, 2000; de Vente et al., 2007). Predicting the spatial variations in overland transport capacity is challenging due to local variability in terrain, soil properties, and vegetation cover, and consequently predictions are usually calibrated to match observed hillslope sediment yields (Verstraeten et al., 2007). Recent approaches include: (1) pixelbased sediment transport capacity estimation as a function of the erosion rate (Van Rompaey et al., 2001), (2) functions of stream power (Young et al., 1989; Prosser and Rustomji, 2000), and (3) subcatchment delivery ratio estimation using functions of storm duration and runoff travel time, considering the distance to stream, surface slope, and roughness (Ferro and Minacapilli, 1995; Lu et al., 2005). Hillslope mass failures are an important sediment source in many mountainous and hilly areas. Modeling the spatial controls on hillslope mass failure has received less attention to date than sediment mobilization processes that are more common in lowland areas. Rainfall intensity and duration, slope gradient, and soil properties are important determining factors (Chang and Chiang, 2009). Predicting the occurrence of mass failure at given locations is difficult over broad areas, and the temporal patterns of sediment delivery to streams can be described stochastically (Benda and Dunne, 1997).

Catchment Erosion, Sediment Delivery, and Sediment Quality

Channel network erosion. Available models of the extent and erosion rate of gully networks are empirical, which is an appropriate given the strong random component to the upstream extent of incised channel networks (Shreve, 1966). Modeling gully extent across large river basins can be based on manual mapping of sample areas. Soil type, slope, and climate variability provide useful, but not powerful, explanatory variables (Hughes et al., 2001; Kuhnert et al., 2010). Where gully extent estimates are available, gully volume and age provide constraints on long-term gully sediment yield (Prosser et al., 2001b; Wilkinson et al., 2006). Gully sediment yield also declines with gully age (Prosser and Winchester, 1996). There are no widely validated models available for assessing gully sediment yield dynamics over shorter time periods, although runoff, land use, drainage area, slope gradient, and soil properties are factors commonly applied to explain local variability in measured rates (Poesen et al., 2003; Valentin et al., 2005). The most common drivers used to predict spatial variation in bank erosion rates are bankfull discharge (Rutherfurd, 2000), stream power (Finlayson and Montgomery, 2003), riparian vegetation, and bank erodibility (Wilkinson et al., 2009). Recent work is improving ability to represent the mechanisms driving bank erosion, including subaerial weathering, scour, mass failure, and channel meandering (e.g., Lawler, 1995; Sun et al., 1996; Abernethy and Rutherfurd, 1998; Langendoen and Simon, 2008). Achieving robust predictions at finer temporal resolution is a particular challenge, but is not always required for management purposes. There have been limited data on the extent and rates of the above erosion processes, which have constrained model development. However, high-resolution DEMs from laser altimetry are now providing data over much larger spatial extents on gully dimensions (Ritchie, 1996) and on bank erosion rates (Notebaert et al., 2009). Pixel resolution is a key factor influencing the utility of laser altimetry DEMs for mapping erosion features and quantifying erosion rates (Notebaert et al., 2009). River channel and floodplain deposition. Representing deposition remains essential to modeling sediment delivery at river basin scale in most environments. However, hydraulic controls on sediment deposition cannot generally be resolved in basin-scale modeling (Nicholas et al., 2006). Conceptually, the primary controls on floodplain deposition are the sediment delivery to floodplains, controlled by the overbank discharge and concentration, and the residence time of overbank flow determined by floodplain size (Prosser et al., 2001c). More complex models consider shallow-water hydraulics more explicitly, and the uncertainty associated with model parameters (Nicholas et al., 2006). Dating of floodplain sediments is useful to verify model predictions (Nicholas et al., 2006; de Moor and Verstraeten, 2008). Long-term fine sediment deposition within river channels, such as lateral point-bar accretion, is controlled by similar fluid mechanics principles to vertical floodplain deposition. Accounting for temporary sediment storage within river channels, which is remobilized in subsequent flow events, can be important for modeling fine sediment yield in shorter time periods, and accounting for the progression of bed material through river networks (Viney and Sivapalan, 1999; Wilkinson et al., 2006).

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However, few river basin models currently represent temporary channel storage, and further development is warranted. Analytic representations of channel storage and sediment wave routing are under active development, but data availability has prevented application or verification at basin scale (Cui et al., 2005; Lauer and Parker, 2008).

2.12.4.4.3 Model uncertainty considerations Uncertainty analysis methods are covered in the Chapter Chapter 2.17 Uncertainty of Hydrological Predictions. Many of these methods are applied in erosion and sediment modeling, especially with more complex models where parameter values may be less well defined. The importance of quantifying model uncertainty, relative to generating best estimates of catchment function, depends on whether the modeling purpose is to formulate hypotheses for further investigation, or for practical application such as developing and justifying investment priorities (Sivapalan, 2009). Understanding the relative contributions of sources of uncertainty is useful for guiding efforts to improve in model performance. This is determined by model sensitivity to changes in parameter values, but also to the levels of uncertainty in each parameter (Reid and Dunne, 2003). Calibration data on erosion and deposition rates are sparser than data on rainfall and runoff, and there is potential for calibration of erosion and sediment models to distort predicted spatial patterns and sediment source contributions. The method of model calibration should ideally align with the modeling purpose. For example, sediment yield data are useful for calibrating sediment yield predictions, but if the modeling purpose is to predict relative contributions of erosion processes to yield then sediment tracing data may be more useful. The requirement for evaluating model predictions is especially important when modeling environments in which the erosion and deposition processes are less well understood.

2.12.5 The Quality Dimension 2.12.5.1 Introduction Although, as indicated in Section 2.12.1, fluvial/lacustrine suspended and bed sediments have traditionally been treated as a physical issue (e.g., reservoir sedimentation, channel and harbor silting, bridge scour, and soil erosion and loss), they also can pose a significant chemical/toxicological (waterquality) problem (Vanoni, 1977; Walling, 1977; Baker, 1980; Fo¨rstner and Wittmann, 1981; Salomons and Fo¨rstner, 1984; Ferguson, 1986; Horowitz, 1991, 1995; de Vries and Klavers, 1994; Stumm and Morgan, 1996; US Environmental Protection Agency, 1997; Horowitz et al., 2001; Walling et al., 2003d; Blum et al., 2004; Cinque et al., 2004; Reed et al., 2004; de Vente and Poesen, 2005; Radaone and Radaone, 2005; Walling, 2005; de Arau´jo et al., 2006; Black et al., 2007; Domenici et al., 2007; Horowitz and Stephens, 2008). Chemical constituents that primarily are sediment-associated fall into a general class called hydrophobes or hydrophobic compounds (e.g., Fo¨rstner and Wittmann, 1981; Luthy et al., 1997; Warren et al., 2003). This group includes heavy metals/trace elements (e.g., Cu, Pb, Zn, As, and Hg), nutrients (e.g., N, P, Si, and C), and persistent organic compounds such

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Catchment Erosion, Sediment Delivery, and Sediment Quality

as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dioxin, kepone, and chlorinated pesticides (e.g., Aldrin, Chlordane, Mirex, DDT and its breakdown products DDD and DDE; e.g., US Environmental Protection Agency, 1997; Simpson et al., 2005). Even in relatively pristine environments, barely detectable dissolved-constituent concentrations occurring in the water column can simultaneously be detected at levels 3–5 orders of magnitude higher in association with naturally occurring suspended and bed sediments (Fo¨rstner and Wittmann, 1981; Fo¨rstner, 1989; Horowitz, 1991; Chapman, 1992; Foster and Charlesworth, 1996; Horowitz and Stephens, 2008). Further, bed sediments can make substantial chemical contributions to interstitial water, usually as the result of changing redox conditions that trigger post-depositional mineralogical changes and subsequent chemical remobilization, often into a more bioavailable form. Numerous studies have demonstrated that sedimentassociated chemical constituents can affect aquatic organisms. The organisms can range from small zooplankton (near the base of the food chain), through benthic organisms that live in intimate contact with bed sediment and its surrounding interstitial water, to humans, who may be affected ultimately as constituent levels increase and bioaccumulate up the food chain (Fo¨rstner and Wittmann, 1981; Salomons and Fo¨rstner, 1984; Chapman, 1992; Fo¨rstner and Heise, 2006). Ever since the publication of the Hawkes and Webb (1962) treatise on geocemical exploration, as well as the subsequent publication of numerous geochemical atlases (e.g., Webb et al., 1978; Fauth et al., 1985; Ottesen et al., 2000), there is a widely accepted perception that suspended and bed sediments reflect local environmental inputs. This is the result of both physical (e.g., grain size, surface area) and chemical (e.g., unbalanced surface charges, presence of oxyhydroxide or organic coatings) factors that make aquatic sediments akin to chemical sponges (Fo¨rstner and Wittmann, 1981; Horowitz, 1991; US Environmental Protection Agency, 1997). Hence, sediment-associated chemical levels can increase or decrease in response to natural environmental processes (e.g., changes in Eh, pH, and grain-size distribution) and interactions (e.g., changes in local geology, volcanic activity). For similar reasons, sediment chemistry also tends to reflect anthropogenically derived contributions, for example, due to land use changes, from both point and nonpoint sources (e.g., Reimann and Garrett, 2005; Horowitz and Stephens, 2008).

2.12.5.2 Basic Sediment Geochemistry The majority of sediment-associated chemical constituents are found on or near the surface of sediment particles, and usually are held by sorption or complexation as a result of unbalanced surface charges (e.g., Fo¨rstner and Wittmann, 1981; Salomons and Fo¨rstner, 1984; Horowitz, 1991). As such, grain size and particle surface area play a significant role in controlling sediment chemical-associated concentrations (e.g., Horowitz, 1991). Generally, as particle size decreases, total surface area increases, as do the chemical levels; hence, elevated concentrations are more likely to be found associated with silt- and/

or clay-sized particles (r63 mm) than coarser sand-sized material (Z63 mm). Although some contaminants can attach directly to the surfaces of sediment particles (e.g., clay minerals), it is more typical to find them associated with particle coatings that are composed of either organic matter or Fe and/ or Mn oxides and oxyhydroxides (e.g., Fo¨rstner and Wittmann, 1981; Fo¨rstner, 1989; Horowitz, 1991; Foster and Charlesworth, 1996). Authigenic minerals that form in situ, and which exist as separate particles, may entrain chemical constituents within their crystalline or cryptocrystalline structure as a result of either chemical bonding or physical trapping (e.g., Fo¨stner and Wittmann, 1981; Horowitz, 1991). As with surface coatings, sorption/desorption processes may increase or reduce associated chemical levels depending on changing physicochemical conditions. Less commonly, substantial concentrations of inorganic constituents may be held within mineral lattices. This is most likely to occur in association with mining or mining related and some industrial activities, and occurs as a result of the discharge of ore minerals and/or mining/industrial waste (typically sulfides such as pyrite (Fe), arsenopyrite (As), galena (Pb), sphalerite (Zn), etc.), through the physical erosion of exposed mine tailings (e.g., Horowitz et al., 1988, 1993; Pope, 2005), or industrial discharges.

2.12.5.3 Major Issues Associated with Sediment Quality Despite the potential environmental impacts of sedimentassociated chemical constituents, only a very limited number of countries currently (e.g., Canada, The Netherlands, Australia, New Zealand, and Germany) have established sediment-chemical regulatory limits; however, many have established guidelines (e.g., Persaud et al., 1993; US Environmental Protection Agency, 2005; Simpson et al., 2005). This situation reflects a number of long-standing arguments associated with sediment chemical quality that have yet to be fully resolved. Four of the most significant ones are: (1) what are the background/baseline concentrations for a variety of sediment-associated constituents; (2) how best to collect representative suspended and bed sediment samples for subsequent chemical analysis; (3) how best to determine the concentrations of sediment-associated constituents; and (4) how to estimate/determine bioavailability?

2.12.5.3.1 Background/baseline sediment-associated constituent concentrations Unlike the vast majority of sediment-associated synthetic organic compounds that have no natural source(s), unless they are manufactured copies of natural substances, sedimentassociated inorganic constituents typically do occur in the environment. As a result, a background/baseline level must be established to determine the presence of contamination and/ or the impact of variations in land use (e.g., Goldschmidt, 1958; Hawkes and Webb, 1962; Plant et al., 1997; Reimann and Garrett, 2005). Background and baseline are concepts that tend to be used interchangeably, and often are qualified by terms such as geochemical, natural, or ambient. However, background concentrations usually refer to chemical levels that imply the exclusion of anthropogenic influence whereas baseline concentrations are typically determined at a

Catchment Erosion, Sediment Delivery, and Sediment Quality

particular point in space and/or time; albeit, it may imply limited anthropogenic effects (e.g., Gough, 1993; Reimann and Garrett, 2005). The occurrence of natural geological phenomena (e.g., volcanic eruptions, unworked ore deposits) and changing local geology, combined with the advent of the industrial revolution, and the concomitant eolian and fluvial distribution/redistribution of a variety of materials, and their associated chemical constituents means that it is unlikely that background concentrations can be determined from any current surficial material. It also means that background/baseline concentrations can change spatially and temporally. This leads to a major issue: how to define the natural inorganic chemical composition of sediments, and has led many geochemists/ environmental chemists to accept two precepts: (1) background chemical composition is neither spatially nor temporally static, and should be viewed as a range rather than as a single value; and (2) chemical changes induced by natural processes should not be viewed as contamination, even when the source may be an unmined mineralized zone. Hence, only sediment-chemical enhancements derived from anthropogenic activities/sources should be viewed as contamination. On the other hand, regulatory agencies tend to take a broader view, and define contaminated sediment as that which ‘‘contains chemical substances in excess of appropriate geochemical, toxicological, or sediment quality criteria or measures, or is otherwise considered to pose a threat to human health or the environment’’ (US Environmental Protection Agency, 1997). For many years, geochemists attempted to provide a single set of chemical values in an effort to define the natural background or baseline for the inorganic composition of sediments, and used average crustal chemical abundances, or constructed values for a so-called average shale for that purpose (e.g., Clarke and Washington, 1924; Poldervaart, 1955; Turekian and Wedepohl, 1961; Taylor, 1964; Krauskopf, 1967; Bowen, 1979; Wedepohl, 1995). In turn, these background/ baseline concentrations were then used to determine the presence and extent of sediment-chemical contamination. A recent US Geological Survey (USGS) National Water Quality Assessment (NAWQA) Program study has provided a fairly comprehensive and up-to-date (1990–2000) continental-scale assessment of baseline values for a wide variety of sedimentassociated inorganic constituents (Horowitz and Stephens, 2008). The results are based on the chemical analyses of the r63-mm fraction of nearly 450 bed-sediment samples obtained from undeveloped or agricultural sites within the conterminous US (Table 4). The baseline values associated with these samples do not appear markedly different from those generated from other studies performed in the US and globally (Bowen, 1979; Shacklette and Boerngen, 1984; Horowitz, et al., 1991; Gustavsson, et al., 2001; Manheim and Hayes, 2002). Hence, they probably represent a useful benchmark for identifying anthropogenically enhanced (contaminated) sediment-associated constituents and levels. The same study also indicates that with the exception of mining, urbanization, and population density (Horowitz and Stephens, 2008), land use does not exercise substantive controls on sediment-associated inorganic chemical concentrations (Table 4).

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2.12.5.3.2 The collection of representative sediment samples and the issues of spatial and temporal variability It should be accepted as given in any sediment-associated chemical study that no amount of high-quality analytical work can overcome poor and/or nonrepresentative sampling (e.g., Horowitz, 1997). Consequently, the number, location, and density of sampling locations need to be carefully evaluated within the context of study objectives, acceptable levels of associated error, and some knowledge of the expected levels of chemical variation. In other words, what is the minimum level of acceptable error that still permits sound management decisions and or data interpretations (e.g., Keith, 1988; Mudroch and MacKnight, 1991; Horowitz, 2008). Both bed and suspended sediments have unique characteristics and distribution patterns that must be understood prior to designing an adequate sampling and analysis program (e.g., Keith, 1988; Horowitz, 1991, 2008; Mudroch and MacKnight, 1991). Studies have provided a clear picture regarding the distribution of suspended sediment in fluvial cross sections, as well as the impacts of those distributions on sediment-associated chemical concentrations (Horowitz et al., 1989, 1992; Horowitz, 1991, 1995, 2008). Suspended sediment and suspended sediment-associated chemical concentrations can exhibit marked short-term spatial and temporal variability (e.g., Horowitz, 1991, 1995, 2008). These distributions tend to support the use of equal-width increment (EWI) or equal-discharge increment (EDI), depth- and width-integrated isokinetic sampling. In other words, collecting a dip sample at a single location in a normal fluvial cross section is unlikely to generate a representative sample of suspended sediment or sediment-associated chemical constituents (Horowitz et al., 1989, 1990, 1992; Horowitz, 1991, 1995, 2008). There are exceptions, such as during low flow (baseflow) conditions when suspended sediment concentrations are very low (r10 mg l1), or at elevated energy levels in relatively narrow channels when suspended sediment concentrations can be very high, but such cases require careful sampling and subsequent analysis to justify the use of localized (e.g., a dip sample, an autosampler) rather than depth- and widthintegrated sampling (e.g., Horowitz, 2008). When both sand- (463 mm) and silt/clay-sized (o63 mm) particles are present in a stream, the concentration of suspended sediment tends to increase with increasing distance from the riverbanks (Figure 9). This results from increasing stream velocity (discharge) due to decreasing frictional resistance away from the riverbanks and the riverbed (in shallow water) (e.g., Vanoni, 1977). Note that the typical cause of an increase in suspended sediment concentration is an increase in the amount of sand-sized (463 mm) material (Figure 9). These concentration changes can occur over relatively short distances (e.g., o3 m). There is a concomitant decrease in the concentrations of most sediment-associated chemical constituents with increasing distance from the riverbanks (Figure 9). This decrease occurs as a direct result of the increase in sand-sized (463 mm) particles because the coarser material typically contains markedly lower chemical concentrations (e.g., trace elements, nutrients) than the finer silt/clay-sized material (Fo¨rstner and Wittmann, 1981; Salomons and Fo¨rstner, 1984; Horowitz, 1991). Note that Al does not follow

Table 4 A continental scale assessment of baseline sediment chemistry for the conterminous US. The table presents information on minimum, maximum, mean, median, and median absolute deviations (MAD) chemical concentrations for background, as well as various land-use categories Al (%)

Sb (mg kg1)

Be (mg kg1)

Cd (mg kg1)

Ca (%)

Ce (mg kg1)

Cr (mg kg1)

Co (mg kg1)

Cu (mg kg1)

Fe (%)

La (mg kg1)

Pb (mg kg1)

Li (mg kg1)

Mg (%)

Background chemical concentrations – all data Count 448 446 447 447 448 Min 0.2 0.1 0.1 7.0 0.1 Max 13.0 3.7 60 1300 7.0 Mean 6.0 0.8 8.1 470 1.6 Median 5.9 0.7 6.6 490 1.8 MAD 1.0 0.2 2.2 110 0.8

445 0.1 2.8 0.5 0.4 0.2

447 0.1 28 3.0 1.8 1.3

447 12.0 360 79 69 15

447 6.3 270 66 58 13

448 0.5 78 14 12 4.0

448 1.0 150 24 20 6.0

448 0.2 10 3.3 2.9 0.7

447 6.3 190 42 39 8.0

448 2.0 200 24 20 6.0

448 3.0 97 33 30 10

447 0.04 4.3 1.0 0.9 0.4

Agricultural sites (Z50%) Count 237 237 237 Min 3.2 0.1 2.4 Max 10.5 3.0 60 Mean 6.0 0.8 8.8 Median 5.8 0.7 7.2 MAD 0.9 0.2 1.9 Forest sites (Z50%) Count 286 284 Min 1.4 0.1 Max 13.0 3.7 Mean 6.4 0.8 Median 6.5 0.7 MAD 0.9 0.2 Rangeland Count Min Max Mean Median MAD

sites (Z50%) 59 59 2.1 0.1 11.0 24 5.9 1.1 5.7 0.6 0.6 0.1

As (mg kg1)

285 0.1 41 7.9 6.8 2.0

Ba (mg kg1)

Mn (mg kg1)

Hg (mg kg1)

Mo (mg kg1)

Ni (mg kg1)

P (%)

K (%)

Se (mg kg1)

Ag (mg kg1)

Na (%)

448 15 9000 1100 840 360

448 0.01 3.1 0.08 0.04 0.02

448 0.3 13 1.1 1.0 0.0

447 1.0 160 28 23 7.0

447 0.02 0.47 0.11 0.10 0.02

447 0.03 3.1 1.4 1.5 0.3

447 0.1 5.6 0.8 0.7 0.2

445 0.1 4.3 0.3 0.2 0.1

447 0.02 2.2 0.7 0.6 0.3

S (%)

Sn (mg kg1)

V (mg kg1)

448 17 970 160 150 60

439 0.03 1.5 0.12 0.08 0.04

433 1.2 54 2.8 2.5 o0.1

448 5.1 380 92 83 21

Zn (mg kg1)

Ti (%)

OC (%)

TC (%)

448 5.2 430 100 91 20

444 0.04 1.9 0.38 0.33 0.08

425 0.01 25 3.7 2.4 1.1

426 0.7 25 4.5 3.3 1.6

237 9.0 860 490 500 70

237 0.5 6.0 1.4 1.0 0.3

237 0.1 2.8 0.4 0.4 0.1

237 0.1 12.0 3.3 2.6 1.8

237 35 360 74 66 12

237 34 200 65 58 11

237 5.0 78 14 12 3.0

237 6.0 86 24 22 6.0

237 1.4 10.0 3.3 2.9 0.6

237 19 150 40 36 6.0

237 6.0 310 24 20 5.0

237 20 110 33 30 8.0

237 0.1 4.4 1.2 1.0 0.4

237 190 8400 1200 870 260

237 0.01 1.0 0.06 0.04 0.02

237 0.3 6.5 1.2 1.0 0.1

237 12 160 30 25 5.0

237 0.04 0.31 0.11 0.10 0.02

237 0.1 3.1 1.4 1.5 0.3

237 0.1 4.8 0.8 0.7 0.2

237 0.1 1.0 0.2 0.2 0.1

237 0.03 1.8 0.6 0.6 0.2

237 24 470 160 140 40

236 0.03 0.75 0.10 0.07 0.04

222 1.4 5.0 2.7 2.5 0.0

237 47 260 96 88 21

237 30 190 99 93 19

234 0.14 1.1 0.37 0.31 0.06

220 0.02 18 2.7 2.2 0.8

220 0.8 18 3.6 3.2 1.4

285 7.0 1300 420 420 80

286 0.5 7.0 1.9 2.0 0.2

283 0.1 4.2 0.6 0.4 0.1

285 0.1 26 1.9 1.0 1.0

285 13 350 89 81 14

285 13.0 270 71 65 12

286 2.0 64 18 17 4.0

286 1.0 250 29 24 8.0

286 0.7 8.5 3.7 3.6 0.8

285 9.0 190 47 43 7.0

286 2.0 200 34 28 7.0

286 6.0 97 38 39 14

285 0.1 4.2 0.8 0.6 0.2

286 20 20 000 1400 1000 400

286 0.01 3.1 0.12 0.07 0.02

286 0.3 13 1.2 1.0 0.0

285 6.0 140 30 29 6.5

285 0.02 0.39 0.13 0.11 0.04

285 0.1 2.7 1.3 1.4 0.3

285 0.1 8.6 0.9 0.7 0.2

283 0.1 4.3 0.3 0.2 0.1

285 0.0 2.2 0.6 0.4 0.2

286 17 660 130 100 38

278 0.03 1.5 0.13 0.09 0.03

275 1.2 54 3.1 2.5 0.0

286 14 380 91 86 21

285 21 440 130 110 31

282 0.05 1.9 0.43 0.41 0.06

269 0.01 25 4.8 3.3 0.9

270 0.7 25 5.2 3.6 1.0

59 10 80 33 30 8.0

59 0.4 3.8 1.2 1.1 0.3

59 170 3200 660 490 120

59 0.01 4.7 0.12 0.03 0.02

59 0.3 1.4 0.9 1.0 0.0

58 7.0 160 24 19 5.0

59 0.04 0.19 0.11 0.10 0.02

59 0.5 2.1 1.7 1.7 0.2

59 0.2 2.6 0.8 0.6 0.2

59 0.1 1.0 0.3 0.2 0.1

59 0.1 1.7 0.8 0.8 0.3

59 110 970 290 250 50

59 0.03 0.48 0.12 0.08 0.04

59 1.4 5.0 2.5 2.5 0.0

59 28 200 82 72 15

59 38 150 85 84 17

59 0.14 0.72 0.32 0.30 0.04

59 0.5 3.7 1.6 1.4 0.6

59 0.7 9.9 2.9 2.6 1.2

94 0.04 2.0 0.7 0.5 0.3

94 39 1000 180 120 40

94 0.03 1.0 0.20 0.15 0.08

88 2.0 69 7.4 2.5 0.1

94 52 180 99 94 18

94 45 1700 330 270 120

93 0.18 0.85 0.42 0.40 0.12

88 0.01 16 4.4 3.3 1.4

88 0.9 19 5.5 4.9 1.7

505 31 1600 160 120 40

504 0.03 1.7 0.16 0.11 0.06

471 1.1 92 5 2.5 0

505 24 240 91 86 16

504 2 1700 200 150 51

492 0.1 1.1 0.4 0.36 0.1

468 0.01 29 3.9 2.9 1.1

467 0.7 29 4.8 3.9 1.5

59 1.9 36 6.9 5.2 1.4

58 10 1100 590 590 85

59 0.5 3.0 1.5 1.6 0.6

59 0.1 1.6 0.4 0.3 0.1

59 1.0 25 5.0 3.7 2.0

59 28 130 69 69 10

58 18 220 59 48 11

59 4.0 32 11 9.0 2.0

59 4.0 79 24 20 5.0

59 1.0 6.2 2.7 2.4 0.4

59 17 100 40 40 5.0

59 6.0 330 24 18 4.0

94 1.3 140 13 9.1 3.0

93 33 920 430 450 70

94 0.5 4.3 1.8 1.7 0.4

94 0.2 7.3 1.4 1.0 0.5

94 0.2 19.0 2.9 1.7 1.0

94 27 270 85 78 22

94 45 700 97 81 18

94 5.0 64 18 16 4.0

94 9.0 420 76 53 24

94 1.6 11.0 4.2 3.9 0.8

94 12 120 45 41 12

94 8.0 590 110 76 35

94 10 100 33 30 10

94 0.2 4.7 1.2 0.9 0.4

94 130 12 000 1600 1100 570

94 0.03 2.2 0.25 0.13 0.07

94 0.9 11 2.2 1.0 0.0

94 11 130 39 36 7.0

94 0.04 1.3 0.20 0.14 0.04

94 0.2 2.5 1.4 1.4 0.3

94 0.2 4.1 1.0 0.7 0.3

94 0.1 17 1.1 0.5 0.3

Population density sites (Z50 Percentile; Z27 p km2) Count 505 504 504 501 505 503 Min 1.7 0.1 1.2 6 0.5 0.1 Max 14 24 160 920 12 18 Mean 6.4 1.2 10 460 1.8 0.9 Median 6 0.9 7.9 460 2 0.5 MAD 1 0.2 2.3 80 0.5 0.2

504 0.1 20 3 1.7 1.2

505 15 270 83 74 20

504 11 700 79 69 15

505 0.9 21 3.8 3.6 0.8

505 10 130 45 40 11

505 8 590 64 39 16

505 6 110 37 34 6

505 0.1 4.7 1.1 0.9 0.4

505 74 12 000 1400 1000 400

504 0.01 14.5 0.22 0.09 0.04

505 0.3 34 1.6 1 0

504 4.2 170 34 30 7

505 0.03 1.8 0.15 0.13 0.04

505 0.1 2.8 1.5 1.5 0.3

504 0.1 13 0.9 0.7 0.2

503 0.1 17 0.7 0.4 0.2

Urban sites (Z50%) Count 94 94 Min 3.9 0.2 Max 13.0 10 Mean 6.6 1.6 Median 5.8 1.1 MAD 0.8 0.4

Sr (mg kg1)

505 3 170 17 15 5

505 6 620 51 36 13

505 0 2.6 0.6 0.5 0.3

Catchment Erosion, Sediment Delivery, and Sediment Quality Arkansas River, CO: 11 May 1987

327

Cowlitz River, WA: 20 April 1987 600

Concentration (mg l−1)

800 600

400

400 200 200

0

0 D-6.1

D-12.2

D-15.2

D-22.9

D-27.4

< 63 µm Fraction

D-22.9

D-39.6

% Concentration (mg kg−1)

4

0

0

D-99.1

Al

Zn

Cu

200

20

0 40

mg kg−1

Fe

2

40

D-70.1

>63 µm Fraction

Arkansas River, CO: 11 May 1987 8

4

D-57.9

100

0 Pb

12

Co

8

20

4

0

0 D-6.1 D-12.6 D-15.2 D-22.9 D-27.4

D-6.1 D-12.6 D-15.2 D-22.9 D-27.4

Figure 9 Horizontal cross sectional changes in suspended sediment concentration for the Arkansas and Cowlitz rivers based on isokinetic depthintegrated vertical samples. The numbers following the D are distances, in meters, from the left bank (upper). Horizontal cross sectional variations in selected suspended sediment-associated trace element in depth-integrated isokinetic vertical samples from the Arkansas River on May 11, 1987. The numbers following the D are distances, in meters, from the left bank of the river (lower).

this pattern, because the majority of this element is lattice-held rather than sorbed to mineral surfaces, and both fractions contain substantial quantities of aluminosilicates (Horowitz, 2008). Vertical concentrations of suspended sediment in fluvial systems tend to increase with increasing depth; this also is due to an increase in sand-sized material (Figure 10). This occurs because the velocity (discharge) in most rivers, under normal

flow conditions, is insufficient to homogeneously distribute the coarser material. The majority of sand-sized particles tend to be transported on or near the riverbed. The increase in sand-sized particle concentration, from top to bottom, also leads to a concomitant decrease in sediment-associated chemical concentrations (Figure 10). As with the horizontal variations noted previously, these changes can occur over relatively short distances (o1.5 m).

328

Catchment Erosion, Sediment Delivery, and Sediment Quality Arkansas River, CO: 11 May 1987

Depth (%)

6.45 m from left bank

17.4 m from left bank

20

20

40

40

60

60

80

80 0

200 400 600 Concentration (mg l−1)

0

<63 µm fraction

200 400 600 800 1000 Concentration (mg l−1)

>63 µm fraction

Arkansas River, CO: 11 May 1987 6.45 m from left bank

17.4 m from left bank

20 40 Fe (%) 60 80 0

2

4

0

2

4

20 40

Cu (mg kg−1)

Depth (%)

60 80 0

10

20

30

0

20

40

20 40

Zn (mg kg−1)

60 80 0

100

200

0

100

200

300

20 40

Pb (mg kg−1)

60 80 0

10

20 30 Concentration

40

0

20 40 Concentration

60

Figure 10 Vertical cross sectional changes in suspended sediment and selected sediment-associated trace element concentrations for the Arkansas River on May 11, 1987, based on isokinetic point samples collected at 20%, 40%, 60%, and 80% of depth. One vertical was 6.45 m and the other was 17.4 m from the left bank.

Sediment chemistry, especially suspended sediment chemistry is markedly affected by hydrology and can display substantial changes in concentration over relatively short as well as relatively longer timescales (e.g., Horowitz, 1995, 2008; Horowitz et al., 2008). This accrues for two reasons, and

means that in order to delimit the range of sediment-associated constituent concentrations at any particular location, samples must be collected over a range of flow conditions and temporal scales. The hydrologic linkage can be both direct and indirect. The direct linkage results from the changes in

Catchment Erosion, Sediment Delivery, and Sediment Quality

suspended sediment grain-size distribution mentioned earlier. As velocity (discharge) increases, the median grain size of suspended sediment tends to increase that normally produces a concomitant decrease in sediment-associated constituent concentrations (e.g., Horowitz, 1991, 1995, 2008; Horowitz, et al., 2008). Although sediment-associated chemical concentrations decline as discharge increases, the fluxes (loads) of these same constituents usually increase. This occurs because the increase in discharge, in conjunction with increasing amounts of suspended sediment, although coarser, typically more than compensates for the decline in actual chemical concentrations (e.g., Horowitz, 1995, 2008; Old et al., 2003, 2006; Lawler et al., 2006; Horowitz et al., 2008). The indirect linkage between hydrology and sedimentassociated chemical concentrations occurs as a result of changing sediment sources. Although it is something of an over-simplification, there is a generally accepted perception that under baseflow, sediment chemistry tends to be dominated by point sources, whereas during high flow (stormflow), sediment chemistry tends to be dominated by nonpoint (diffuse) sources (e.g., Horowitz, 1995, 2008; Old, et al., 2003, 2006; Horowitz, et al., 2008). Sediment from nonpoint sources in urban (e.g., trace elements, nutrients, PAHs, and pesticides), mining (e.g., trace/major elements), and agricultural areas (e.g., nutrients, agricultural chemicals) is particularly enriched in a wide variety of both organic and inorganic constituents (e.g., Horowitz, 1995; Horowitz, et al., 2008). At least in the US, baseflow point-source sedimentassociated chemical effects tend to be limited in terms of both amount and chemical concentration by controlling end-of-pipe discharges through permitting processes such as National Pollutant Discharge Elimination System (NPDES) under the Clean Water Act of 1972. On the other hand, nonpoint-source discharges are much harder to limit/control, and entail the application of a variety of best-management practices (BMPs) such as increasing the width of riparian zones, procurement of additional green space, reforestation, low or no tillage in agricultural areas, installation of highway runoff settling ponds, etc. As a result, of all the foregoing, accurate assessments of suspended sediment-associated chemical concentrations may require sampling over several different temporal scales (e.g., sampling over the course of a storm to determine concentration changes associated with the rising limb, the peak, and the falling limb of the hydrograph, sampling between storms to determine the impact of different lengths of antecedent dry conditions, and seasonal sampling to deal with the application of various agricultural chemicals (e.g., fertilizers, pesticides), or to evaluate the impact of, for example, deicing salts). On the other hand, such factors as changing demographics (e.g., population density) and land use factors (e.g., urbanization) can affect sediment-associated chemical concentrations over longer temporal scales, of the order of years or decades, depending on the size of the hydrologic system. Bed sediments can exhibit marked spatial variability, but rarely short-term temporal variability (e.g., Horowitz, 1991, 1995). In addition, unlike suspended sediment samples, bed sediments rarely pose a problem relative to collecting sufficient masses to meet any requisite analytical needs. As such, it usually is far easier to collect representative bed sediment

329

rather than suspended sediment samples. As long as the goal of a study is not intended to determine relative levels of localized spatial variability, the sampling issues associated with bed sediments usually can be addressed through the production of composite samples generated by combining a sufficient number of spatially separated equal-volume aliquots. The number of requisite aliquots typically is predicated on the level of local spatial variability in conjunction with acceptable levels of chemical variance. Normally, the more subsamples that are combined, the more representative the composite, and the smaller the level of associated chemical variance (e.g., Hakanson, 1984; Garner et al., 1988; Horowitz, 1991; Mudroch and MacKnight, 1991).

2.12.5.3.3 The chemical analysis of suspended and bed sediments As noted previously, sediment-associated constituent concentrations typically occur at levels three to five orders of magnitude higher than found in solution (e.g., Horowitz, 1991, 1995, 2008). As such, analytical sensitivity normally is not an issue except when samples masses are very small; this rarely is the case with bed sediment, but can be an issue with suspended sediment. However, even in the case of suspended sediment, there are a number of techniques available, such as flow-through centrifugation or filtration that permit the collection/concentration of sufficient amounts of suspended matter for subsequent chemical analyses (e.g., Horowitz, 1995, 2008). Although there are a number of direct techniques for the chemical analysis of solid-phase materials, the majority, with limited exceptions, are performed on either liquid extracts (organic constituents) or liquid digests (inorganic constituents). In the past, the method of choice for the solubilization of sediment-associated organic constituents was the soxhlet extraction (e.g., Amalric and Mouvet, 1997). However, modern production laboratories have begun to switch to either microwave extraction (e.g., Morozova et al., 2008; Forster et al., 2009) or accelerated solvent extraction (especially useful for the determination of hydrophobic persistent organic pollutants; e.g., Jacobsen et al., 2004; Silvia Diaz-Cruz et al., 2006; Berrada et al., 2008). The latter two procedures are markedly faster than the soxhlet extraction, and can be modified to be compound and/or compound-class specific through the selection of an appropriate solvent, and by programming the temperature and/or pressure, and the length of time for the extraction (e.g., Raynie, 2004, 2006). The analytical instruments of choice currently in use are: (1) gas chromatography using various detectors; (2) gas chromatography-mass spectroscopy; and (3) liquid chromatography-mass spectroscopy. Extraction efficiencies are normally determined by the concomitant analysis of reference materials, and analytical recoveries are typically determined by spiking extracts with known amounts of the analyte(s) of interest. The analytical instrumentation of choice for a wide variety of inorganic constituents including trace elements (e.g., Cu, Zn, Cd, and Pb), major elements (e.g., Fe, Al, Na, and K), and some nutrients (e.g., P, S) is some type of inductively coupled plasma (ICP)-based system. The two most common devices are ICP-atomic emission spectroscopy (ICP-AES), sometimes

330

Catchment Erosion, Sediment Delivery, and Sediment Quality

called ICP-optical emission spectroscopy (ICP-OES), and ICPmass spectroscopy (ICP-MS). The former is a purely optical system that measures emitted light at a specific (constituentspecific) wavelength whereas the latter is based on determining the concentrations of specific individual stable isotopes, and converting that value to a total concentration in the digestate based on fixed isotopic percentages. Both systems require sample solubilization before quantitation. Several common nutrient determinations (e.g., total carbon, total organic carbon, total nitrogen, and total sulfur) are determined directly on dried sediment by combusting the sample at high temperatures, in the presence of oxygen, and quantitating the evolved gases using a variety of different detectors. There are numerous choices for the solubilization of various sediment-associated inorganic constituents for subsequent chemical analysis (e.g., Johnson and Maxwell, 1981; van Loon, 1985; Batley, 1989; ASTM, 2008). Essentially, these methods fall into one of the three categories: total analyses, total recoverable analyses, or selective extractions (e.g., Horowitz, 1995, 2008). Geochemists normally define a total analysis as one in which Z95% of the analyte of concern is quantified. This approach usually entails the complete breakdown of mineral lattices, and requires a mixture of concentrated mineral acids and relatively high temperatures, and/or some type of fusion with various fluxes (e.g., sodium carbonate, lithium tetraborate/metaborate) at elevated temperatures, with the solubilization of the resulting bead (e.g., Johnson and Maxwell, 1981). Analytical precision and bias are normally determined by the concomitant analysis of appropriate reference materials. Analyses of this type are unambiguous because they are independent of sediment-associated mineralogical/ petrological variation; hence, they are comparable across spatial and/or temporal scales, and represent a known endmember because of the level of recovery (Z95%). Total recoverable digestions, typically favored by regulatory agencies at least in the US, are nonspecific partial extractions usually employing mineral acids and some level of heating. The levels of constituent solubilization and subsequent quantitation are highly dependent on variations in mineralogy/ petrology, as such, concentrations determined using this procedure may not be comparable across spatial and/or temporal scales, and can be very difficult to interpret. It sometimes has been claimed that this type of digestion produces a measure of bio- and/or environmental-availability but that interrelation has never been demonstrated (e.g., Horowitz, 1991, 2008). Another difficulty associated with this approach is a lack of certified reference materials, so while it is possible to determine analytical precision (reproducibility) by replicate analyses of the same material, it is difficult to evaluate analytical bias as well as percent recoveries. Spiking digestates is not a typical sediment-associated inorganic analytical procedure because the major source of analytical variance does not result from the quantitation step, but from the solubilization step. Selective extractions represent a special category of partial and/or recoverable digestions that are intended to provide specific information about sediment-chemical partitioning (e.g., Kersten and Fo¨rstner, 1987; Horowitz, 1991). Sedimentchemical partitioning entails all the various procedures that can be used to determine how (mechanistic approach; e.g.,

adsorption, complexation; within mineral lattices) and/or where (phase approach; e.g., iron oxides, manganese oxides, organic matter; carbonates) various chemical constituents are associated with sediment particles (e.g., Chao, 1984; Bately, 1989; Horowitz, 1991; Hall and Pelchat, 1999). Unfortunately, all these procedures can be categorized as operational definitions and there are numerous procedures that purport to provide similar information (e.g., bound to iron oxides), but which do not provide equivalent analytical results (e.g., Horowitz, 1991). These procedures usually also have additional limitations such as they can only be used on sediment collected in oxidized environments (e.g., Tessier et al., 1979).

2.12.5.3.4 Bioavailability and toxicity By far, the single biggest barrier to the widespread development and acceptance of sediment quality guidelines/ regulatory limits is the continuing controversy regarding the bioavailability, and potential toxicity of sediment-associated chemical constituents. Unlike dissolved constituents, where the total concentration is presumed to be bioavailable, it has always been assumed that only limited portions of sedimentassociated constituents (e.g., non-lattice held) fall into the same category (e.g., Allen et al., 1993; Hansen et al., 2005; Simpson et al., 2005). Additional concerns stem from disagreements over the interpretation of toxicity tests in terms of exposure rates, selection of appropriate test organisms, and relative measures of lethality, as well as an understanding of sediment-chemical partitioning and potential bioaccumulation (e.g., Lahr et al., 2003; Hansen et al., 2005). Current sediment quality guidelines (SQGs) are typically provided at two concentrations. The first level is the lower of the two, and normally reflects concentrations at which little or no biological/ecological effects are expected. This level has been given a variety of names such as lowest effect level (Persaud et al., 1993), threshold-effects concentration (TEC; MacDonald et al., 2000), and threshold-effects level (TEL; US Environmental Protection Agency, 1997). The second level is the higher of the two, and normally reflects concentrations at which biological/ecological effects are expected. This level has also been given a variety of names such as severe effect level (Persaud et al., 1993), probable-effects concentration (PEC; MacDonald, et al., 2000), and probable-effects level (PEL; US Environmental Protection Agency, 1997). Although these various concentrations tend to overlap, there currently appears to be no consensus on a single set of values for either category or for various sediment-associated constituents (e.g., Persaud et al., 1993; US Environmental Protection Agency, 1997; MacDonald et al., 2000; Hansen et al., 2005; Simpson et al., 2005). To further complicate this issue, other terms have been used to indicate the potential effects of sediment-associated chemical constituents. For example, in 1997, the US EPA evaluated sediment chemical data from over 21000 locations in the US and found that 26% had a higher probability and 49% had an intermediate probability of adverse effects on aquatic life and human health. The chemical constituents most often associated with these increased probabilities were PCBs, Hg, DDT, Cu, Ni, and Pb (US Environmental Protection Agency, 1997).

Catchment Erosion, Sediment Delivery, and Sediment Quality

Other than the results from various toxicity tests, there appear to be two basic approaches associated with establishing SQGs; one is based on equilibrium partitioning models whereas the other is based on some type of sediment-chemical partitioning (e.g., Hansen et al., 2005; Simpson et al., 2005). In either case, evaluations have to be made on a site-specific basis (e.g., Simpson et al., 2005). The equilibrium partitioning approach requires measuring chemical concentrations in interstitial water; the underlying assumption being that whatever concentrations in the porewater represent the bioavailable component of the equivalent sediment-associated constituent (e.g., Simpson et al., 2005). The actual guideline concentrations are then based on those established for dissolved constituents. There are three major issues with this approach. The first is that it obviously does not apply to suspended sediment. The second issue is associated with those organisms that actually ingest sediment. The physicochemical conditions in the gut of an organism are obviously not the same as those that exist in the interstitial water (e.g., they are likely to be more acidic and less oxygen will be present); hence, bioavailable concentrations derived from interstitial water may be inappropriate. The third issue is associated with specific size fractions from a bulk sediment sample. Examination of the gut contents of various aquatic organisms indicates that they tend to limit their intake to specific grain-size ranges. As such, determining equilibrium partitioning concentrations based on interstitial water derived from a bulk sediment sample may be inappropriate. The sediment partitioning approach is predicated on the view that some form of chemical extraction/digestion can be found that functions as a measure of, or surrogate for bioavailability (e.g., Di Toro et al., 1992; Allen et al., 1993). One such procedure that has received a good deal of attention is acid volatile sulfides-simultaneously extracted metals (AVSSEM). This extraction employs cold dilute HCl; those metals that exceed the concentration of available sulfide, on a molar basis, are considered bioavailable (Di Toro et al., 1992; Allen et al., 1993; Hansen et al., 2005; Simpson et al., 2005). However, a number of studies have indicated that the AVSSEM method can over- or under-predict bioavailability when compared to other approaches, and may not be appropriate for all environments and/or organisms (e.g., Chen and Mayer, 1999; Lahr et al., 2003; Meador et al., 2005; Simpson and Batley, 2007; Prica et al., 2008).

2.12.5.4 Future Directions While the foregoing summary, covering the current status of sediment quality, clearly indicates that a great deal is known about the subject, there still remains much to do with respect to achieving scientific consensus in a variety of areas. While the scientific community has begun to reach some level of consensus relative to background/baseline values for sedimentassociated constituents, more refinement is needed. It would also be appropriate to begin to delineate those constituents that are particularly sensitive to, or indicative of various types of land use or source material. The general lack of broad scale agreement on appropriate sampling and analytical procedures is likely to continue to make transboundary/multinational studies difficult as a result of potential data incompatibilities.

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Lastly, it appears that a great deal of more work needs to be done in the areas of sediment-associated constituent bioavailability and toxicity before there can even be a modicum of consensus that could lead to fairly universal sediment-quality guidelines.

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Relevant Websites http://www.loicz.org Land Ocean Interactions in the Coastal Zone.

2.13 Field-Based Observation of Hydrological Processes M Weiler, Albert-Ludwigs University of Freiburg, Freiburg, Germany & 2011 Elsevier B.V. All rights reserved.

2.13.1 2.13.1.1 2.13.1.2 2.13.1.3 2.13.2 2.13.2.1 2.13.2.2 2.13.2.3 2.13.3 References

Runoff Generation Processes Early Research Defining the Pathways of Storm Runoff Current Directions Quantifying the Processes Field-Based Observations Quantifying the Processes: Hydrometric Observations Quantifying the Processes: Tracers Conclusion

2.13.1 Runoff Generation Processes 2.13.1.1 Early Research Infiltration was the first process recognized as being significant to runoff generation during a precipitation event. In the early part of the twentieth century, Robert Elymer Horton first described quantitatively the process of infiltration into the soil surface and introduced terminology still used by hydrologists today (Horton, 1933). Following Horton, others recognized that surface runoff was often not the dominant process responsible for increased stream discharge observed during precipitation events. Beginning with Hursh and Brater’s (1941) work at Coweeta (North Carolina, USA), subsurface flow became recognized as a potentially important component of storm flow. Later, studies identified the concepts of variable runoff source areas and the importance of subsurface flow as a contributor to event stream flow response (Betson, 1964; Hewlett and Hibbert, 1963, 1967). Shortly after these developments, old water (pre-event water stored in the watershed as soil water or/and groundwater) was identified as being a significant contributor to runoff (e.g., Pinder and Jones, 1969; Sklash and Farvolden, 1979). Indeed, it is now widely accepted that old water constitutes the majority of stormflow in humid watersheds (e.g., Pearce et al., 1986). However, new water may still be an important contribution to storm runoff in urbanized watersheds or many arid and mountainous watersheds. Horton (1933) defined infiltration as a result of the need to describe the physical process by which water moves into the soil, distinct from other terms sometimes used such as percolation or absorption. Horton defined infiltration capacity as ‘‘the maximum rate at which rain can be absorbed by a given soil at a given condition’’ (Horton, 1933: 453). Horton attributed surface runoff to rainfall intensities that exceeded the infiltration capacity of the soil. This is widely known as Hortonian overland flow or infiltration excess overland flow. However, Horton was not working in forested environments and therefore probably concluded incorrectly that runoff for an individual storm event was mainly or wholly surface runoff. The storm hydrograph response in a forested watershed was shown to consist of subsurface flow and channel

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precipitation by Hursh and Brater (1941). Engler (1919) already recognized the importance of subsurface stormflow after making detailed measurements of infiltration and physical properties of soil, including porosity, water content, soil texture, and hydraulic conductivity. Subsequently, soil depth, topography, and hydrologic characteristics associated with different elevations were shown to influence peak discharge (Hoover and Hursh, 1943). Hewlett and Hibbert (1963) first recognized the importance of unsaturated flow and concluded that unsaturated flow could not be ignored in hydrograph analysis. Utilizing a concrete trough to observe unsaturated flow at the Coweeta experimental watershed, they coined the term ‘translatory flow’ to describe unsaturated flow and attributed it to the thickening of water films surrounding soil particles, which results in a pulse of water. Substantial amounts of runoff can be generated on areas which have been saturated with water (Dunne and Black, 1970). Furthermore, not only water quantity but also water chemistry and quality are affected by runoff from saturated soils (Mole´nat et al., 2002). Cappus (1960) recognized that saturated areas often occur at specific locations in a watershed which led to the development of the partialcontribution area concept (Betson, 1964). Runoff-generating areas are frequently located in valley floors and on particularly shaped slopes (Amerman, 1965). Even though extent and location of runoff generation areas can vary notably, it has been demonstrated that generally only a small part of a watershed contributes to storm runoff from saturated areas (Ragan, 1968). Cappus (1960) characterized a catchment in terms of its runoff-generating areas. His research showed that it is parts of the watershed, not the whole area, that contribute to runoff (partial contribution area concept). Concerning the involved parts of the catchment, he differentiated between infiltration excess (roads and compacted soil) and saturation areas (valley bottoms). The variable source area concept was developed in the early 1960s and is largely attributed to Betson (1964). He found out that contributions made by different parts of the catchment depend on the precipitation intensity, but the variability is so small that the contributing areas remain almost constant for the duration of one event in his study area. Betson (1964) demonstrated that contributing areas were

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almost constant during heavy rainfalls. For such events, infiltration excess overland flow was observed. Research by Ragan (1968) supports the partial contribution area concept, when he states that only a small part of the basin, less than 3% of the total watershed, contributes an appreciable amount to the storm hydrograph. Amerman (1965) found out that these areas are often located on ridge tops, in valley floors, and on valley slopes. Dunne and Black (1970) collected subsurface flow and saturation overland flow (SOF) with a large trench and could demonstrate that the partial area concept can be extended from infiltration excess overland flow (Betson, 1964) to saturation excess overland flow. Furthermore, Weyman (1970) described that the concept of partial contributing areas, approved by his experiments, can be extended to subsurface flow. He observed that subsurface throughflow and saturation excess surface runoff mainly occurred in specific parts of his watershed. Subsurface stormflow was finally recognized as being an important contributor to event-based stream discharge. In addition, it was previously observed that preferential subsurface stormflow could occur in forest soils (i.e., water moving faster than the soil matrix should allow, typically through some form of soil pipe) (Whipkey, 1965). Whipkey

was the father of trench studies, in which trenches are commonly excavated along the base of a hillslope down to the impermeable layer and flow from the soil horizons is collected and measured. Figure 1 shows an example of how permanent trenches can be built to collect runoff at the soil surface and in the subsurface.

2.13.1.2 Defining the Pathways of Storm Runoff Development of runoff theory proceeded rapidly in the 1960s and 1970s and the studies conducted by Dunne and Black (1970) set a precedent that was rarely exceeded during the next two decades. Dunne and Black used intensive instrumentation across various hillslope types to observe subsurface processes in a wet, mountainous area of Vermont, USA. Three hillslopes consisting of well-drained sandy loams over glacial till, with convex, concave, and straight contours, were instrumented with wells and piezometers to measure water-table elevation and pressure potential, and a nuclear depth probe was used to measure soil moisture along a transect up the middle of each slope. A trench was excavated along the base to the hillslopes to measure runoff at various levels and weirs were installed above and below the reach of river channel running at the

Cross section

Geomembrane (AMERDRAIN 200)

Pipes (runoff collection)

PVC-foil (0.5 mm)

Drainage pipe PVC (perforated)

Geotextile Backfill

Collection sheet (steel) Concrete

Undisturbed soil Figure 1 Cross section and the actual picture (trench is in construction and refilled to the upper observation depth) of a permanent trench for measuring surface runoff and subsurface runoff in two different depths.

Field-Based Observation of Hydrological Processes

base of the study site. Subsurface stormflow was found to occur only during large events and SOF occurred in significant quantities only on the concave (hollow) hillslope. Overland flow occurring on the concave hillslope during large precipitation events was the only flow measured in large enough quantities to account for the measured stream flow. Other important contributions during this decade include Weyman’s (1973) study, which advocated the theory of a saturated wedge developing from riparian margins and moving upslope with increasing precipitation. Groundwater hydrologists, such as Alan Freeze, were developing theories on regional groundwater flow in the early 1970s (e.g., Freeze and Witherspoon, 1967). Freeze et al. (1972) suggested that the majority of event hydrograph response could be attributed to subsurface stormflow. Near the end of the 1970s, a series of studies focused on searching for the mechanism that could explain this process (e.g., Sklash and Farvolden, 1979). Until this time, subsurface flow was considered to be a function of measurable physical properties of soil, namely hydraulic conductivity. However, measurement of soil hydraulic conductivity could not account for the rates of flow necessary to deliver water, via the subsurface, to the stream channel in order to affect the observed stream response. This quandary is resolved by separately considering the flow in the soil matrix described by Darcy’s law (where flow is dependent on soil hydraulic conductivity) and preferential flow pathways via soil pipes and macropores (e.g., Harr, 1977). Studies such as Mosley (1979) showed that rates of subsurface flow could be large enough to account for the observed hydrograph response in a steep headwater catchment with very moist conditions (M8 catchment, Maimai, New Zealand). Large peak flow rates observed at concentrated locations of soil pit faces were found to coincide with stream hydrograph peaks and dye-tracing experiments were used to quantify the rate of water movement through the profile. Mosley’s dye experiments led him to conclude that the majority of the stream flow response was from the contribution of event or new water (water contributed by the current precipitation event). Significant debate over the source of water that generates the storm hydrograph response followed Mosley’s (1979) published work. Mosley believed that the new water was entering macropores and the soil surface, and flowing by lateral macropores without interacting with the soil matrix. Around the time Mosley was working in Maimai, a forested headwater catchment in New Zealand, a new method of examining the source of stormflow was conceived. Pinder and Jones (1969) were the first to use the two-component mixing model to separate event water on the basis of chemical signatures by measuring various ions in rainwater, storm discharge, and stream baseflow. However, it would be almost 20 years before hydrochemical observations were combined with hydrometric observations. Pinder and Jones (1969) concluded that up to 42% of event stream flow might be old water in the Nova Scotia catchment studies. Later, Sklash and Farvolden (1979) measured tritium, oxygen-18 (d18O), and deuterium isotope ratios across various watersheds and concluded that groundwater was the main contributor to the event hydrograph. The process responsible for the transfer of old water to the stream in sufficient quantities to explain their observations was attributed to groundwater ridging near the riparian

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margins via the rapid conversion of the tension saturated capillary fringe to phreatic water (i.e., saturation occurred soon after the commencement of an event). Gillham (1984) studied these groundwater ridging processes and further realized the importance for stream flow generation in watersheds with extending riparian zones. Following Mosley (1979), Pearce et al. (1986) and Sklash et al. (1986) published the results of studies in which they examined the relative concentrations of chloride, deuterium, and d18O in addition to the electrical conductivity of samples of rainfall, streamflow, and soil water flowing from pit faces in the Maimai catchment, New Zealand. Generally, old (preevent) water and new (event) water were thought to be mixing in the soil profile and then discharging to the stream in a fairly uniform mixture in terms of isotopic and chemical composition (Pearce et al., 1986, Sklash et al., 1986). Groundwater ridging and saturated wedge development from the rapid conversion of tension-saturated zones to positive potentials were cited as the mechanisms responsible for the delivery of stormflow, although hydrometric data were not available to augment these findings. If conversion of tension-saturated zones to positive potentials was occurring, rapid transmission of new water was not needed to explain stormflow. The majority of stormflow would be contributed by old water already stored in the soil and only a small amount of new water would be needed as input (Pearce et al., 1986; Sklash et al., 1986). To solve the old-water new-water dichotomy, a unification of hydrochemical and hydrometric measurements was necessary and McDonnell (1990) did just that in the same catchment (Maimai-M8) as studied by Mosley (1979), Pearce et al. (1986), and Sklash et al. (1986). Using the same soil pits excavated in the previous studies, a combination of isotope and chemical tracing, and an extensive tensiometer network, McDonnell (1990) observed that water tables arising at the soil bedrock interface were not maintained but correlated well with throughflow rates. Soil piping (connection of macropores) was suggested to explain the rapid dissipation of the water table and pore water pressures (McDonnell, 1990). To explain his observations, McDonnell (1990) suggested that rapidly infiltrating new water perched at the impermeable layer and mixed with larger volumes of old water and subsequently drained as the saturated areas in the hillslope expanded creating continuous saturated areas thus affecting rapid stormflow. The formation of these saturated areas, then, largely depends on topography. McGlynn et al. (2002) provided a thorough review of the experiments to date at the Maimai research area. Another explanation of the old-water dominance was provided by the transmissivity feedback mechanism (Rodhe, 1989; Seibert et al., 2003). The process, which was mainly observed in glacial till soils in Scandinavia and Canada, describes the rapid rise of the water table into the more transmissive (permeable) topsoil and the resulting higher subsurface flow. As water is stored below field capacity in the soils, the new water mixes with the old water and the resulting runoff is characterized by dominance in old water (Laudon et al., 2004). Beven (2006) compiled many relevant original papers about runoff generation that are also introduced in this chapter and provided the historical context in detail.

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2.13.1.3 Current Directions A range of mechanisms facilitates subsurface stormflow and it is useful to separate these into various subareas in order to examine the controls on subsurface stormflow. From the above discussion, we know that increases in subsurface flow are a result of increasing hydraulic gradient, cross-sectional area, rise in the water table into more transmissive soil layers, and the linking of isolated saturated areas across the hillslope (variable source area). To what degree each of these phenomena influences subsurface stormflow appears to depend on various conditions such as antecedent moisture conditions and the morphology of the defined basin or hillslope. Subsurface stormflow initiation and the mechanisms for preferential flow are still debatable issues. Topographic control is being examined in greater detail as it becomes apparent how complex the influence of morphology is on subsurface flow. The aim to better understand the subsurface structures focused on using different geophysical methods. It is also recognized that there are thresholds to the occurrence of subsurface storm flow. Again, these thresholds seem to vary with individual site conditions and it is recognized that the response is nonlinear (Weiler et al., 2005). Research in the last decade focuses on any one or on a number of these issues, either directly or indirectly. Preferential flow has been shown to be important for both flow initiation and rapid lateral transport of water downslope (Mosley, 1979; McDonnell, 1990). In the first case, preferential infiltration has been identified as being significant enough for rapid development of saturated areas and water tables in more permeable soils (e.g., De Vries and Chow, 1978; McDonnell, 1990; Weiler and Naef 2003). In order for rapid stream flow response to be facilitated by subsurface storm flow, water must infiltrate and move down slope at rates greater than the estimates based on soil matrix properties would predict. Preferential flow can occur via macropores, cracks and soil pipes, and in areas of higher permeability in the soil, including highly permeable layers (Bonell, 1998; McGlynn et al., 2002). It has also been recognized that the permeability of macropore and crack walls may be lower than that of the soil matrix, which would allow for rapid unimpeded flow once water fills these conduits (Calver and Cammeraat, 1993; McDonnell, 1990). Rates of pipe flow are largely determined by their diameter, and it has been recognized that there are certain precipitation thresholds that must be exceeded before pipe flow will dominate the subsurface flow (Weiler and McDonnell, 2007). In Bonell’s (1998) review of runoff generation, he states ‘‘reconciling their [soil pipes] hydrochemistry coupled with the need for more sophisticated hydrometric studies to address the pipeflow issue, stands out as one of the principle research challenges connected with storm hydrograph separations.’’ We are still lacking the knowledge to address these pipe flow issues; however, there have been several attempts to perform more sophisticated tracer-based and hydrometric studies (Anderson et al. 2009a, 2009b; Anderson et al., 2010) or to use other approaches to understand the flow pathways along the soil bedrock interface (Graham, 2009). Geophysical methods offer the opportunity to rapidly collect subsurface information in a noninvasive or minimally

invasive manner, which may be a key information to see flow pathways and hence understand runoff generation processes. These techniques are sensitive to different physical properties (e.g., magnetic, elastic, and electrical properties) of subsurface materials. In near-surface environments, techniques such as ground-penetrating radar (GPR), electrical resistivity tomography (ERT), electromagnetic induction surveying, or different seismic methods have been proven to provide valuable data for a variety of applications (Butler, 2005). Much of the related work and progress made in the field of hydrology within the past decade is documented in Rubin and Hubbard (2005) and Vereecken et al. (2006). Especially, GPR and seismic methods may provide structural information to characterize the subsurface. In sandy sediments, GPR allows imaging of subsurface geometries up to depths of B10 m with a resolution at the dm-to-m scale (Beres et al., 1999). However, translation of geophysical observations into the relevant subsurface state and properties, such as moisture content, or hydraulic conductivity, remains a challenging task. The relations between geophysical and the hydrological target variables are usually complex, nonunique, and site specific (Scho¨n, 1998). Uncertainty in analyzing and interpreting geophysical data may be reduced by multimethod approaches. For example, time-lapse imaging of subsurface flow processes is possible by combining geophysical techniques with artificial tracers, as for instance, salt tracers in the unsaturated zone imaged by ERT or cross-hole radar attenuation tomography (Johnson et al., 2007). Kienzler (2007) developed a nice example of the potential combining artificial salt tracer injection to visualize preferential lateral flow pathways (Figure 2). Another interesting idea relies on extracting structural information such as correlation lengths from geophysical images. The basic assumption is that the statistical properties of geophysical structures and parameter variations, respectively, can be used as a proxy for the statistical characteristics of the target hydrological parameter field. Until now, this idea has primarily been used with GPR reflection data (e.g., Knight et al., 2007); more experience, using realistic synthetic scenarios and other geophysical data, is clearly needed. Nevertheless, such concepts can be extremely useful to investigate and characterize geophysical data and hydrological systems to understand runoff generation mechanisms. Until today, experimental studies to understand runoff generation processes and flow pathways have been conducted basically worldwide in various climatic and geological settings, leading to an advanced and refined perception of rainfall runoff processes (Graham, 2009; Kienzler, 2007; Scherrer et al., 2006). Despite the large variety of observed flow processes, one commonality is the strong nonlinearity of hillslope response to rainfall (Tromp-van Meerveld and Weiler, 2008). One possibility to explain this behavior is the sudden connection of different areas in the watershed that are locally generating runoff (either at the soil surface or as subsurface runoff) by different flow pathways (macropores, pipes, and channels). Tromp-van Meerveld and McDonnell (2006) proposed the so-called fill-and-spill mechanisms to explain the threshold behavior at the Panola watershed experiment, USA. Bachmair and Weiler (2010) extended this concept to the connect-and-react hypothesis to generally describe the sudden connection of runoff generation processes in the watershed

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Tracer line source 15% 5

15

m

15 m

25 m m

2.5 m

30

m

10 m

40 m Figure 2 Subsurface flow paths detected with ground-penetrating radar (GPR) at the Koblenz experimental slope, Switzerland. The profiles have been taken before as well as 4 h after the start of a salt tracer injection. Displayed are the differences between these two measurements, large differences (yellow–red colors) indicate flow path locations. From Kienzler P (2007) Experimental Study of Subsurface Stormflow Formation. Combining Tracer, Hydrometric and Geophysical Techniques. Diss. ETH Nr. 17330, Eidgeno¨ssische Technische Hochschule (ETH), Zu¨rich, Switzerland.

and the resulting nonlinearity or threshold behavior in the runoff response. However, explaining and predicating the threshold behavior continue to be a challenging task (Zehe and Sivapalan, 2009).

2.13.2 Quantifying the Processes 2.13.2.1 Field-Based Observations Many different techniques and methods have been developed in the last 100 years to directly or indirectly observe runoff generation processes in the field. Many of these methods require only simple instruments or observations, but their power can be increased if enough spatial and temporal explicit observations (e.g., Trubilowicz et al., 2009) are being taken to understand the complex spatial–temporal processes during storm runoff generation. In Table 1, measurement methods are listed together with the spatial scale and the processes which can be observed. Many of these methods are explained in more detail in the following chapter by showing the potential and issues within sample application and past studies. The references listed in Table 1 are only possible sources of information.

2.13.2.2 Quantifying the Processes: Hydrometric Observations The contribution of SOF to storm runoff has been repeatedly studied since the first work of Dunne and Black (1970). A central aspect of the SOF estimation is the delineation of the contributing saturated areas. Soil saturation can be detected with remote sensing (e.g., Mohanty and Skaggs, 2001). However, saturation patterns cannot be captured efficiently with remote sensing in dense forests (e.g., Kite and Pietroniro, 1996). Soil saturation under forests was therefore mapped

based on soil and vegetation characteristics in order to evaluate the models for saturation predictions (Gu¨ntner et al., 2004). However, only few studies have evaluated the mapping criteria with direct saturation measurements (Rosin et al., 2009). Moreover, saturated areas have been mapped and modeled in a single climate (e.g., Blazkova et al., 2002), but only few investigations have been made to compare mapping and modeling in different climates, mostly using different data sources (Me´rot et al., 2003). It still seems to be important to improve our methods to better monitor the spatial dynamics and connectivity of saturated areas in particular in watersheds that are dominated by SOF. The observations in the Miniflet catchment in Sweden (Myrabo, 1997) are a nice example of the space–time variations of water-table response and saturation areas due to subsurface flow and flow accumulation (Figure 3). Observing subsurface runoff is still a challenge. Woods and Rowe (1996) excavated a trench in the Maimai M8 catchment and measured flow rate and quantity in a series of troughs along a trench face coupled with tensiometer and piezometer measurements. They found that bedrock topography was responsible for flow routing as saturated areas developed in hollows and converged as ribbons of concentrated flow. Flow volumes were highly variable and were not well predicted by the surface topography and flow accumulation. In a follow-up study, Woods et al. (1997) concluded that variability in runoff depends on both topography and soil moisture conditions. Freer et al. (1997) excavated another similar trench in the Panola watershed close to Atlanta, USA. They determined that bedrock topography improved predictions as subsurface flow routing is dependent on the morphology of the impeding layer or bedrock. Freer et al. (1997) also noted that antecedent soil moisture conditions were a significant control on the occurrence of saturation.

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Table 1

Measurement methods to observe runoff generation processes at different scales

Measurement

Spatial scales

Processess

Issues

References

Infiltrometer Sprinkling experiments

Plot, hillslope Plot, hillslope

Infiltration Infiltration, overland, and subsurface flow

Mertens et al. (2002) Weiler and Naef (2003)

Soil moisture measurement

Point

Percolation, saturation, evapotranspiration

Lysimeter

Plot

Soil water potential

Point

Percolation, evapotranspiration, groundwater recharge Flow direction, saturation

Boundary conditions Drop size distribution and intensity. Trench is necessary for subsurface runoff Point measurement, high spatial, and temporal variability High costs, disturbed soil monolith

Anderson and Burt (1978)

Dye staining experiments

Plot, hillslope

Flow pathways, preferential flow

Artificial tracer experiments (1-D)

Plot, hillslope

Flow velocity, preferential flow

Artificial tracer experiments (2-D)

Plot

Trenching

Hillslope

Flow velocity, flow direction, preferential flow Subsurface flow

Point measurement, high spatial, and temporal variability Destructive but very visually informative, artificial experiment Sampling method influences results (destructive, flow, suction cups, etc.) Stationary conditions

Woods and Rowe (1996)

Chemical hydrograph separation Mapping of saturated areas Saturation collectors

Hillslope, catchment

Interruption of low pathways, artificial drainage Concentration of end members Depends on climate Point observations

Hillslope, catchment Plot, hillslopes

Water sources (spatially or temporally) Potential saturation Saturation

Utilizing smaller troughs to collect discharge from the soil profile of a hillslope and a network of piezometers, Hutchinson and Moore (2000) examined how throughflow is related to surface topography and basal till/confining layer. The troughs were oriented such that the hillslope was divided up into units so they could be compared. They found that at the lowest flow, the subsurface flow distribution was correlated well with upslope contributing area as calculated from the basal till layer topography. However, at the highest flows, subsurface flow was more closely related to the contributing area of the surface topography. In other words, the saturated area, or water table, shifted from being parallel to the confining layer to being parallel to the surface. Moreover, they observed macropores which can deliver enough discharge to negate the topography as a control on subsurface flow. It was suggested that macropores can route water laterally which questions the validity of models which use topographic controls to predict subsurface flow (Hutchinson and Moore, 2000). Finally, it is of great relevance to this study that topographic models usually assume a quasi-steady-state for throughflow, which Hutchinson and Moore (2000) did not find appropriate at their site. Scherrer et al. (2006) surpassed, in number of trenches, all other experimental trench studies to understand runoff generation processes. They performed sprinkling experiments at 60 m2 hillslopes and also measured, in addition to surface runoff, subsurface runoff in a 6-m-wide trench (Figure 4).

Jost et al. (2005)

Scanlon et al. (2002), Aboukhaled et al. (1982)

Weiler and Naef (2003), Anderson et al. (2005) Weiler et al. (1998), McGuire et al. (2005)

Roth et al. (1991)

Hooper and Shoemaker (1986) Merot et al. (1995) Rosin et al. (2009)

In order to quantify the internal processes, they also instrumented the slope with many time-domain reflectometer (TDR) probes, tensiometers, and piezometers. This combined hydrometric observation setup allowed a detailed description of flow processes within the hillslope, resulting in runoff generation processes. Tromp-van Meerveld and Weiler (2008) also instrumented a hillslope in detail, similar as Scherrer et al. (2006) did, but with the focus of a longer-term observation to explore the wet and dry season and its processes. In particular, the combination of soil moisture measurements, maximum water-table observations, and sap-flow measurements enabled them to observe the spatial–temporal patterns of flow processes in the soil and transpiration processes. Jost et al. (2005) presented another approach, focusing on soil moisture measurements with TDRs to observe the spatial– temporal patterns of transpiration, recharge, and soil moisture storage. They measured soil moisture dynamic at 198 locations in a 0.5-ha forest site and were able to use these observations together with geostatistical methods to predict the patterns of water fluxes at the soil vegetation atmosphere. In future, the potential to use a large number of sensors to better observe the spatial–temporal patterns of fluxes and storage changes in the saturated and unsaturated zone will increase when using wireless sensor techniques. The wireless sensors will only provide a more efficient way to collect the data, but the development of cost-effective sensors to observe the relevant fluxes states the need to go hand in hand. Trubilowicz

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Q = 0.54 mm h−1

Q = 0.61 mm h−1

Q = 4.9 mm h−1

Q = 6.8 mm h−1 cm −10 0 10 20 30 40 50 60 m 0

50

100

Figure 3 Water table variations and saturation (blue areas) at the Minifelt watershed in Sweden for different stream runoff conditions. From Myrabo S (1997) Temporal and spatial scale of response area and groundwater variation in till. Hydrological Processes 11: 1861–1880.

et al. (2009) tested the potential of low-cost, low-power wireless sensor networks (mote networks) for monitoring throughfall, temperature, humidity, soil water content, and water-table dynamics using 41 motes in a small forested watershed (Figure 5). They found that while motes gave the ability to monitor a catchment at resolution levels that were previously impossible, they still need to evolve into an easierto-use, more reliable platform before they can replace traditional data collection methods.

2.13.2.3 Quantifying the Processes: Tracers Hydrologists have used tracers to study water movement for several decades and there are a number of different tracers available some being better suited to specific applications (see also Chapter 2.09 Tracer Hydrology). There are two basic types of tracers, the one considered natural and the other

artificial. Natural tracers are ones that can be found in the natural environment such as oxygen isotope 18, tritium, or weathered materials like silicates. These can be measured from water samples in the soil, precipitation, groundwater, and the stream. Artificial tracers are applied to the system; this includes various types of dyes and anions (e.g., chloride). Of course, chloride is naturally occurring but it is often applied in much larger quantities so that it overrides the natural background concentrations. Natural and artificial tracers both have advantages and disadvantages and neither are necessarily better, even of these two types, one tracer may be completely inappropriate where another is very useful. For example, Rhodamine WT is not a very useful soil–water tracer in lab application, whereas Lissamine FF is (Trudgill, 1987). The first tracers to be used in runoff generation studies were naturally occurring. By measuring the relative concentrations in the different sources (soil water, precipitation, and

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Tensiometer

Sprinkler

15

m

TDR−probes (installed vertically) Humus

Piezometer A

Collector tray for overland flows Collection of subsurface flow via ditch

Mineral soil

B1 B2/Cv Trench

TDR probes (installed horizontly)

Rock

α

Slope angle: 15−55%

Figure 4 Experimental setup to study runoff generation processes at the hillslope scale. From Scherrer S (1997) Abflussbildung bei Starkniederschla¨gen, Identifikationvon Abflussprozessen mittels ku¨nstlicher Niederschla¨ge. Versuchsanstalt fu¨rWasserbau, Hydrologie und Glaziologie der ETH Zu¨rich, Zu¨rich, 147pp.

stream), researchers could separate the storm hydrograph into different component sources (e.g., Pinder and Jones, 1969). For example, McGuire et al. (2005) used d18O to measure the residence time of water falling on eight different catchments in the HJ Andrews experimental forest, Coastal Range Oregon. They measured d18O in the various input sources and in the stream water to determine the source of the stormflow and how long it has been in the catchment. By accounting for variations in isotope ratios with changes in elevation and also values for snowpack melt water, they were able to determine residence times for the catchments and compare how it varied across scale. Interestingly, residence time was not dependent on scale but was more closely related to simple topographic measures such as median flow path length and gradient. Other types of natural tracing include measurements of silica content and alkalinity. For example, Soulsby et al. (2004) used alkalinity and silica content measurements to determine the main sources of runoff in the Scottish highlands. Natural tracers lend themselves well to catchment scale and larger studies while artificial tracers are convenient for hillslope and plot scale applications. The main drawback of natural tracers is the uncertainty associated with characterizing the sources (e.g., Didszun and Uhlenbrook, 2008). We know that chemical signatures are variable in both space and time. The chemical signature of soil moisture varies spatially and temporally depending on the length of time that moisture has resided in the soil. In turn, residence time of soil water is dependent on the length of time since the last precipitation event and the size of that event. This dependence makes it difficult to account for the soil water signature. In addition, the influence of interception and the

spatial variation in chemical composition of precipitation at the catchment scale has received little attention to date. Most hydrochemical studies have focused on the hillslope scale and often use only a single rain gauge to determine the chemical signature of rainwater. As McGuire et al. (2005) pointed out, the isotope signature of rainfall varies with elevation. In addition, deposition of minerals and soil physical properties can vary over small spatial scales, which will alter the chemical signature of water flowing through various areas. It would be nearly impossible, or at least very labor intensive, to account for such variations. Nonetheless, naturally occurring tracers continue to be used and do provide certain advantages over artificial tracers, mainly that they can be used on a larger scale and are ubiquitous. Artificial tracers overcome the uncertainty associated with characterizing the naturally occurring tracers, in that we can control when, where, and how much to apply. Artificial tracers have been used around for some time as early as the late 1960s (e.g., radioactive tracers used by Pilgrim (1966)). Exploration of the utility of tracers in hydrological study continued through the 1970s (e.g., Pilgrim and Huff, 1978; Smart and Laidlaw, 1977). Pilgrim and Huff (1978) demonstrated the usefulness of artificial tracers for monitoring water movement in the subsurface and observed irregular patterns of movement despite a uniform surface. More recently, dye experiments have been used to examine infiltration in greater detail (Weiler and Flu¨hler, 2004; Weiler and Naef, 2003). Weiler and Flu¨hler (2004) used simulated rainfall with Brilliant Blue dye followed by soil pit excavation and image analysis to examine infiltration (example images in Figure 6). Extended vertically stained sections of macropore

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2 1

PE

3

6 5

4

7

Figure 5 Wireless sensor network with measurement station ready for deployment: (1) pressure transducer; (2) mote with power supply; (3) tipping bucket; (4) soil moisture probe; (5) air temperature/humidity probe with radiation shield; (6) ground temperature thermistor; (7) overland flow weir. From Trubilowicz J, Cai K, and Weiler M (2009) Viability of motes for hydrological measurement. Water Resources Research 45: W00D22 (doi:10.1029/2008WR007046).

flow were observed which initiated close to the soil surface. This is in accordance with the conclusions of McDonnell (1990). Weiler and Naef (2003) concluded that although macropores make up a much smaller fraction of the total porosity (o1%), they account for the majority of saturated flow and preclude the use of Darcy’s law or the Richards’ equation to predict flow rates. However, dye-staining experiments at the hillslope scale seem to be possible. Anderson et al. (2009a) were able to reconstruct lateral preferential flow networks by staining a 30-m-long hillslope and excavating the pathways. The experiment revealed that larger contributing areas coincided with highly developed and hydraulically connected preferential flow paths that had flow with little interaction with the surrounding soil matrix. They found evidence of subsurface erosion and deposition of soil and organic material laterally and vertically within the soil (see detailed information about the experimental setup, results, and interpretation in the electronic supplements). These dyestaining results are important because they add to the understanding of the runoff generation, infiltration, solute transport, and slope stability of preferential flow-dominated soils. Artificial tracers have also been used at the hillslope scale often injected through piezometers at specific depth (e.g., Talamba et al. (2000) or Weiler et al. (1998)) or done as a line application at the top of a hillslope or hillslope plot (Weiler et al., 1998). It seems that the time has come for artificial tracers to be tested at the small catchment scale. Application is

the biggest limitation with artificial tracers. It is either labor intensive or expensive in the case of sprinkler systems. Rodhe et al. (1996) and Lange et al. (1996) conducted studies in catchment which have been covered below the canopy so that chemical signature of the input water could be controlled, an impressive undertaking. Rodhe et al. (1996) used d18O ratios while Lange et al. (1996) used LiBr as their tracers. Of greatest interest are the results of Lange et al. (1996) who had low recovery and concluded that residence time was long enough to permit equilibrium exchange between the soil water and soil matrix. They believed that hydrochemical processes related to catchment runoff are underestimated because they are often based on soil column studies that do not account for lateral movement. While artificial tracers have been used in hydrology for some time, their usefulness as a tool for studying runoff generation has not been extensively explored. It could be possible in future to use artificial tracer more extensively if instrumentation techniques to detect the tracers are becoming better and smaller amounts need to be applied to observe the movement of tracers in the watershed.

2.13.3 Conclusion In the 1960s and 1970s, the focus was on observing hydrological processes in the field. There have been many groundbreaking studies and experiments observing the

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Figure 6 Examples of four dye patterns in forest soils after sprinkling 60 mm dyed water in 3 h. The soil types range from sandy soils to loamy soils; however, macropores, root channels, and hydrophobicity at the soil surface are more relevant for generating different infiltration patterns than soil type.

spatial–temporal dynamics of water and solute fluxes on the plot, hillslope, and catchment to understand the interplay of different hydrological processes. Unfortunately, the focus on hydrology shifted toward modeling and computer simulations in the last 20 years. Field-based observations are demanding and time consuming and the rewards are often not as pronounced compared to developing or applying a new hydrological model at the watershed or even at the continental scale. However, we have forgotten many important lessons that we have learned about the functioning of watersheds. Our current hydrological models are all very similar and most of them do not incorporate the hydrological processes and flow pathways that have been observed in the field. As pleaded, for example, by Weiler and McDonnell (2004), a new area of more connections and discussions between field hydrologists and

hydrological modeler is needed to overcome the current deficit in hydrological model development. It is also believed that new techniques and new sensors need to be developed, tested, and implemented into field-based observation to enhance the possibility and understanding of processes, in particular flow processes in the subsurface and surface–groundwater interaction.

References Aboukhaled A, Alfaro A, and Smith M (1982) Lysimeters, Irrigation and Drainage, Paper 39, 68pp. Rome: FAO. Amerman CR (1965) The use of unit-source watershed data for runoff prediction. Water Resources Research 1(4): 499--507.

Field-Based Observation of Hydrological Processes Anderson AE, Weiler M, Alila Y, and Hudson RO (2009a) Dye staining and excavation of a lateral preferential flow network. Hydrology and Earth System Sciences 13: 935--944. Anderson AE, Weiler M, Alila Y, and Hudson RO (2009b) Subsurface flow velocities in a hillslope with lateral preferential flow. Water Resources Research 45: W11407 (doi:10.1029/2008WR007121). Anderson AE, Weiler M, Alila Y, and Hudson RO (2010) Water table response in zones of a watershed with lateral preferential flow as a first order control on subsurface flow. Hydrological Processes (in press). Anderson MG and Burt TP (1978) The role of topography in controlling throughflow generation. Earth Surfaces Processes and Landforms 3: 331--334. Bachmair S and Weiler M (2010) New dimensions of hillslope hydrology. In: Levia D, Carlyle-Moses D, and Tanaka T (eds.) Forest Hydrology and Biogeochemistry: Synthesis of Research and Future Directions. New York, NY: Springer. Beres M, Huggenberger P, Green AG, and Horstmeyer H (1999) A study of glaciofluvial architectures using two- and three-dimensional georadar methods. Sedimentary Geology 129: 1--24. Betson RP (1964) What is watershed runoff? Journal of Geophysical Research 69(8): 1541--1551. Beven K (2006) Streamflow generation processes. In: McDonnell JJ (ed.) IAHS Benchmark Papers in Hydrology Series, 432pp. Wallingford: IAHS. Blazkova S, Beven KJ, and Kulasova A (2002) On constraining TOPMODEL hydrograph simulations using partial saturated area information. Hydrological Processes 16(2): 441--458. Bonell M (1998) Selected challenges in runoff generation research in forests from the hillslope to headwater drainage basin scale. Journal of the American Water Resources Association 34(4): 765--786. Butler DK (ed.) (2005) Near-Surface Geophysics. Tulsa, OK: Society of Exploration Geophysicists. Calver A and Cammeraat LH (1993) Testing a physically based runoff model against field observations on a Luxembourg hillslope. Catena 20: 273--288. Cappus P (1960) Etude des lois de l’eAˆ coulement, application au calcul et a la prevision des de bits. La Houille Blanche A 493--520. De Vries J and Chow TL (1978) Hydrologic behavior in a forested mountain soil in coastal British Columbia. Water Resources Research 14(5): 935--942. Didszun J and Uhlenbrook S (2008) Scaling of dominant runoff generation processes: Nested catchments approach using multiple tracers. Water Resources Research 44: W02410 (doi:101029/2006WR005242). Dunne T and Black RD (1970) An experimental investigation of runoff production in permeable soils. Water Resources Research 6(2): 478--490. Engler A (1919) Untersuchungen u¨ber den Einfluss des Waldes auf den Stand der Gewa¨sser. Mitteilung der Schweizerischen Anstalt fu¨r fortsliches Versuchswesen 12: 1--626. Freer J, McDonnell JJ, Beven KJ, et al. (1997) Topographic controls on subsurface storm flow at the hillslope scale for two hydrologically distinct small catchments. Hydrological Processes 11(9): 1347--1352. Freeze AR, McDonnell JJ, Beven KJ, et al. (1972) Role of subsurface flow in generating surface runoff: 2 Upstream source areas. Water Resources Research 8(5): 1272--1283. Freeze AR and Witherspoon PA (1967) Theoretical analysis of regional groundwater flow: 2. Effect of water-table configuration and subsurface permeability variation. Water Resources Research 3: 623--634. Gillham RW (1984) The capillary fringe and its effect on water-table response. Journal of Hydrology 67: 307--324. Graham C (2009) A Macroscale Measurement and Modeling Approach to Improve Understanding of the Hydrology of Steep, Forested Hillslopes. PhD Thesis, Oregon State University, USA. Gu¨ntner A, Seibert J, and Uhlenbrook S (2004) Modeling spatial patterns of saturated areas: An evaluation of different terrain indices. Water Resources Research 40: W05114 (doi:10.1029/2003wr002864). Harr RD (1977) Water flux in soil and subsoil on a steep forested slope. Journal of Hydrology 33: 37--58. Hewlett JD and Hibbert AR (1963) Moisture and energy conditions within a sloping soil mass during drainage. Journal of Geophysical Research 68: 1081--1087. Hewlett JD and Hibbert AR (1967) Factors affecting the response of small watersheds to precipitation in humid areas. In: Sopper WE and Lull HW (eds.) Forest Hydrology, pp. 275--291. New York, NY: Pergamon. Hooper RP and Shoemaker CA (1986) A comparison of chemical and isotopic hydrograph separation. Water Resources Research 22(10): 1444--1454. Hoover MD and Hursh CR (1943) Influence of topography and soil depth on runoff from forest land. Transactions of the American Geophysical Union 2: 693--698.

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Horton RE (1933) The role of infiltration in the hydrological cycle. In: Transactions of the American Geophysical Union, Fourteenth Annual Meeting, pp. 445–460. Washington, DC. Hursh CR and Brater EF (1941) Separating storm-hydrographs from small drainageareas into surface- and subsurface-flow. Transactions of the American Geophysical Union 3: 863--871. Hutchinson DG and Moore RD (2000) Throughflow variability on a forested slope underlain by compacted glacial till. Hydrological Processes 14(10): 1751--1766. Johnson TC, Routh PS, Barrash W, and Knoll MD (2007) A field comparison of Fresnel zone and ray-based GPR attenuation-difference tomography for time-lapse imaging of electrically anomalous tracer or contaminant plumes. Geophysics 72: G21--G29. Jost G, Heuvelink GBM, and Papritz A (2005) Analysing the space-time distribution of soil water storage of a forest ecosystem using spatio-temporal kriging. Geoderma 128(3–4): 258--273. Kienzler P (2007) Experimental Study of Subsurface Stormflow Formation. Combining Tracer, Hydrometric and Geophysical Techniques. Diss. ETH Nr. 17330, Eidgeno¨ssische Technische Hochschule (ETH), Zu¨rich, Switzerland. Kite GW and Pietroniro A (1996) Remote sensing applications in hydrological modelling. Hydrological Sciences 563--591. Knight R, Irving J, Tercier P, Freeman G, Murray C, and Rockhold M (2007) A comparison of the use of radar images and neutron probe data to determine the horizontal correlation length of water content. In: Hyndman DW, Day-Lewis FD, and Singha K (eds.) Subsurface Hydrology: Data Integration for Properties and Processes, Geophysical Monograph Series, vol. 171, pp. 31–44. Washington, DC: AGU. Lange H, Lischeid G, Hoch R, and Hauhs M (1996) Water flow paths and residence times in a small headwater catchment in Ga˚rdsjo¨n, Sweden, during steady state storm flow conditions. Water Resources Research 32: 1689--1698. Laudon H, Seibert J, Kohler S, and Bishop K (2004) Hydrological flow paths during snowmelt: Congruence between hydrometric measurements and oxygen 18 in meltwater, soil water, and runoff. Water Resources Research 40: W03102 (doi:10.1029/2003WR002455). McDonnell JJ (1990) A rationale for old water discharge through macropores in a steep, humid catchment. Water Resources Research 26(11): 2821--2832. McGlynn BL, McDonnell JJ, and Brammer DD (2002) A review of the evolving perceptual model of hillslope flowpaths at the Maimai catchments, New Zealand. Journal of Hydrology 257: 1--26. McGuire KJ, McDonnell M, Weiler M, et al. (2005) The role of topography on catchment-scale water residence time. Water Resources Research 41: W05002. Merot Ph, Ezzehar B, Walter C, and Aurousseau P (1995) Mapping waterlogging of soils using digital terrain models. Hydological Processes 9: 27--34. Me´rot P, Squividant H, Aurousseau P, et al. (2003) Testing a climato-topographic index for predicting wetlands distribution along an European climate gradient. Ecological Modelling 163(1–2): 51--71. Mertens J, Jacques D, Vanderborght J, and Feyen J (2002) Characterisation of the field-saturated hydraulic conductivity on a hillslope: In situ single ring pressure infiltrometer measurements. Journal of Hydrology 263(1–4): 217--229. Mohanty BP and Skaggs TH (2001) Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation. Advances in Water Resources 24(9–10): 1051--1067. Mole´nat J, Durand P, Gascuel-Odoux C, Davy P, and Gruau G (2002) Mechanisms of nitrate transfer from soil to stream in an agricultural watershed of French Brittany. Water, Air, and Soil Pollution 133(1–4): 161--183. Mosley MP (1979) Streamflow generation in a forested watershed. Water Resources Research 15: 795--806. Myrabo S (1997) Temporal and spatial scale of response area and groundwater variation in till. Hydrological Processes 11: 1861--1880. Pearce AJ, Stewart MK, and Sklash MG (1986) Storm runoff generation in humid headwater catchments: 1. Where does the water come from? Water Resources Research 22: 1263--1272. Pilgrim DH (1966) Radioactive tracing of storm runoff on a small catchment. Journal of Hydrology 4: 289--326. Pilgrim DH and Huff DD (1978) A field evaluation of subsurface and surface runoff: I. Tracer studies. Journal of Hydrology 38: 299--318. Pinder GF and Jones JF (1969) Determination of the groundwater component of peak discharge for the chemistry of total runoff. Water Resources Research 5(2): 438--445. Ragan RM (1968) An experimental investigation of partial area contributions. In: Proceedings of the Berne Symposium, IAHS Publ. 76, pp. 241–249. Rodhe A (1989) On the generation of stream runoff in till soils. Nordic Hydrology 20: 1--8.

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Rodhe A, Nyberg L, and Bishop K (1996) Transit times for water in a small till catchment from a step shift in the oxygen 18 content of the water input. Water Resources Research 32: 3497--3511. Rosin K, Weiler M, and Smith R (2009) Evaluating soil saturation models in forests in different climates. Journal of Hydrology (in review). Roth K, Jury WA, Flu¨hler H, and Attinger W (1991) Transport of chloride through an unsaturated field soil. Water Resources Research 27(10): 2533--2541. Rubin Y and Hubbard SS (2005) Hydrogeophysics. Dordrecht: Springer. Scanlon BR, Healy RW, and Cook PG (2002) Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeology Journal 10(1): 18--39. Scherrer S (1997) Abflussbildung bei Starkniederschla¨gen, Identifikationvon Abflussprozessen mittels ku¨nstlicher Niederschla¨ge. Versuchsanstalt fu¨rWasserbau, Hydrologie und Glaziologie der ETH Zu¨rich, Zu¨rich, 147pp. Scherrer S, Naef F, Faeh AO, and Cordery I (2006) Formation of runoff at the hill-slope scale during intense precipitation. Hydrology and Earth System Sciences 11(2): 907--922. Scho¨n JH (1998) Physical Properties of Rocks: Fundamentals and Principles of Petrophysics. Oxford: Pergamon. Seibert J, Bishop K, Rodhe A, and McDonnell JJ (2003) Groundwater dynamics along a hillslope: A test of the steady state hypothesis. Water Resources Research 39(1). 2-1–2-9 (doi:1029/2002WR001404 2003). Sklash MG, Beven KJ, Gilman K, and Darling WG (1996) Isotope studies of pipeflow at Plynlimon, Wales, UK. Hydrological Processes 10(7): 921--944. Sklash MG and Farvolden RN (1979) The role of groundwater in storm runoff. Journal of Hydrology 43: 45--65. Sklash MG, Stewart MK, and Pearce AJ (1986) Storm runoff generation in humid headwater catchments: 2. A case study of hillslope and low-order stream response. Water Resources Research 22(8): 1273--1282. Smart PL and Laidlaw IMS (1977) An evaluation of some fluorescent dyes for water tracing. Water Resources Research 13: 15--33. Soulsby C, Rodgers PJ, Petry J, Hannah DM, Malcolm IA, and Dunn SM (2004) Using tracers to upscale flow path understanding in mesoscale mountainous catchments: Two examples from Scotland. Journal of Hydrology 291: 174--196. Talamba D, Joerin C, and Musy A (2000) Study of subsurface flow using environmental and artificial tracers: The Haute-Mentue case, Switzerland. In: Tracers and Modelling in Hydrogeology, IAHS Publication No. 262, pp. 559–264. Tromp-van Meerveld HJ and McDonnell JJ (2006) Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resources Research 42: W02411 (doi:10.1029/2004WR003800).

Tromp-van Meerveld I and Weiler M (2008) Hillslope dynamics modeled with increasing complexity. Journal of Hydrology 361(1–2): 24--40. Trubilowicz J, Cai K, and Weiler M (2009) Viability of motes for hydrological measurement. Water Resources Research 45: W00D22 (doi:10.1029/ 2008WR007046). Trudgill ST (1987) Soil water dye tracing, with special reference to the use of rhodamine WT, Lissamine FF and amino G acid. Hydrological Processes 1: 149--170. Vereecken H, Binley A, Cassiani G, Revil A, and Titov K (2006) Applied Hydrogeophysics. Dordrecht: Springer. Weiler M and Flu¨hler H (2004) Inferring flow types from dye patterns in macroporous soils. Geoderma 120(1–2): 137--153. Weiler M and McDonnell J (2007) Conceptualizing lateral preferential flow and flow networks and simulating the effects on gauged and ungauged hillslopes. Water Resources Research 43: W03403 (doi:10.1029/2006WR004867). Weiler M, McDonnell J, Tromp-van Meerveld HJ, and Uchida T (2005) Subsurface stormflow. In: Anderson MG and Jeffrey JJ (eds.) Encyclopedia of Hydrological Sciences, vol. 3, ch. 112, pp. 1719–1732. Chichester: Wiley. Weiler M and Naef F (2003) An experimental tracer study of the role of macropores in infiltration in grassland soils. Hydrological Processes 17(2): 477--493. Weiler M, Naef F, Leibundgut C (1998) Study of runoff generation on hillslopes using tracer experiments and physically based numerical model. IAHS Publication No. 248, pp. 353–360 Weyman DR (1970) Throughfall on hillslopes and its relation to the streamhydrograph. Bulletin of the International Association of the Scientific Hydrology 15: 23--25. Weyman DR (1973) Measurements of the downslope flow of water in a soil. Journal of Hydrology 20: 267--288. Whipkey RZ (1965) Subsurface storm flow from forested slopes. Bulletin of the International Association of the Scientific Hydrology 2: 74--85. Woods R and Rowe L (1996) The changing spatial variability of subsurface flow across a hillside. Journal of Hydrology (NZ) 35(1): 51--86. Woods RA, Sivapalan M, and Robinson JS (1997) Modelling the spatial variability of subsurface runoff using a topographic index. Water Resources Research 31: 2097--2110. Zehe E and Sivapalan M (2009) Threshold behavior in hydrological systems as (human) geo-ecosystems: Manifestations, controls, implications. Hydrology and Earth System Sciences 13: 1273--1297.

2.14 Observation of Hydrological Processes Using Remote Sensing Z Su, University of Twente, Enschede, The Netherlands RA Roebeling, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands J Schulz, Deutscher Wetterdienst, Offenbach, Germany I Holleman, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands V Levizzani, ISAC-CNR, Bologna, Italy WJ Timmermans, University of Twente, Enschede, The Netherlands H Rott, University of Innsbruck, Innsbruck, Austria N Mognard-Campbell, OMP/LEGOS, Toulouse, France R de Jeu, VU University Amsterdam, Amsterdam, The Netherlands W Wagner, Vienna University of Technology, Vienna, Austria M Rodell, NASA/GSFC, Greenbelt, MD, USA MS Salama, GN Parodi, and L Wang, University of Twente, Enschede, The Netherlands & 2011 Elsevier B.V. All rights reserved.

2.14.1 2.14.1.1 2.14.1.2 2.14.1.3 2.14.2 2.14.2.1 2.14.2.2 2.14.2.2.1 2.14.2.2.2 2.14.2.3 2.14.2.3.1 2.14.2.3.2 2.14.2.3.3 2.14.2.3.4 2.14.2.4 2.14.2.4.1 2.14.2.5 2.14.2.5.1 2.14.2.5.2 2.14.2.6 2.14.2.6.1 2.14.2.6.2 2.14.3 2.14.3.1 2.14.3.2 2.14.3.3 2.14.3.3.1 2.14.3.3.2 2.14.3.4 2.14.3.4.1 2.14.3.4.2 2.14.3.5 2.14.4 2.14.4.1 2.14.4.2 2.14.4.2.1 2.14.4.2.2 2.14.4.2.3 2.14.4.3 2.14.4.3.1 2.14.4.3.2 2.14.4.3.3 2.14.5

General introduction Water Cycle and Water Resources Management Water and Energy Balance of the Earth From Radiometric Observations to Object Properties Water in the Atmosphere: Clouds and Water Vapor Introduction Satellite RS Observing water vapor Observing clouds Retrieval Algorithms Water vapor Total column water vapor Water vapor profiles Upper tropospheric humidity Cloud Detection Cloud property retrievals Validation Water vapor Cloud properties Data Sets Water vapor products Cloud products Water from the Atmosphere: Precipitation Introduction Precipitation Measurements from Weather Radars Precipitation Measurements from Satellite Retrievals from VIS–IR sensors Retrievals from passive MW sensors Validation Weather radar retrievals Satellite retrievals Applications Water to the Atmosphere – Evaporation Introduction and Historic Development Current State of Science Statistical approaches Variability approaches Physical approaches Future Research Needs Scaling Feedbacks Validation Water on the Land – Snow and Ice

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2.14.5.1 Introduction 2.14.5.2 Techniques for Retrieval of Extent and Physical Properties of Snow and Ice 2.14.5.3 Examples of Products and Applications 2.14.5.4 Future Research Needs 2.14.6 Water on the Land – Surface Water, River Flows, and Wetlands (Altimetry) 2.14.6.1 Introduction 2.14.6.2 In Situ Measurements 2.14.6.3 RS Techniques 2.14.6.4 Validation and Synergy of RS Techniques 2.14.6.5 Availability of the Satellite Data Sets 2.14.6.6 SWOT: The Future Satellite Mission Dedicated to Surface Hydrology 2.14.7 Water in the Ground – Soil Moisture 2.14.7.1 Introduction 2.14.7.2 State of the Art 2.14.7.3 Data Sets BBB 2.14.7.3.1 Active MW data sets 2.14.7.3.2 Passive MW data sets 2.14.7.4 Validation 2.14.8 Water in the Ground – Groundwater (Gravity Observations) 2.14.8.1 Introduction 2.14.8.2 GRACE Data Processing 2.14.8.3 Retrievals of Groundwater Storage with GRACE Data 2.14.8.4 GRACE Data Access 2.14.8.5 Concluding Remarks and Future Perspective 2.14.9 Optical RS of Water Quality in Inland and Coastal Waters 2.14.9.1 Introduction 2.14.9.2 Atmospheric Correction 2.14.9.3 Retrieval Algorithms 2.14.9.4 Uncertainty Estimates 2.14.9.5 Concluding Remarks and Future Perspective 2.14.10 Water Use in Agro- and Ecosystems 2.14.10.1 Introduction 2.14.10.2 Continuous Evaluation of Crop Water Use with Support from RS 2.14.10.3 Drought Indices and Soil Moisture Monitoring 2.14.10.4 Algorithm Retrievals and Operability 2.14.10.5 SEBS Algorithm 2.14.10.6 Evaluation Example Acknowledgment References

2.14.1 General introduction 2.14.1.1 Water Cycle and Water Resources Management The United Nations (UN) Millennium Declaration called on all members ‘‘to stop the unsustainable exploitation of water resources by developing water management strategies at the regional, national and local levels which promote both equitable access and adequate supplies.’’ Improving water management can make a significant contribution to achieving most of the Millennium Development Goals established by the UN General Assembly in 2000, especially those related to poverty, hunger, and major diseases. The World Summit on Sustainable Development (WSSD) in 2002 recognized this need. Water and sanitation in particular received great attention from the Summit. The Johannesburg Plan of Implementation recommended to improve water resources management and scientific understanding of the water cycle through joint cooperation and research. For this purpose, it is

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recommended to promote knowledge sharing, provide capacity building, and facilitate the transfer of technology including remote-sensing (RS) and satellite technologies, especially to developing countries and countries with economies in transition, and to support these countries in their efforts to monitor and assess the quantity and quality of water resources, for example, by establishing and/or further developing national monitoring networks and water resources databases and by developing relevant national indicators. The Johannesburg Plan also adopted integrated water resources management as the overarching concept in addressing and solving water-related issues. As a result of the commitments made in the Johannesburg Plan of Implementation, several global and regional initiatives have emerged. Current international initiatives such as the Global Monitoring for Environment and Security (GMES) program of the European Commission and the European Space Agency (ESA), and the Global Earth Observation System of Systems (GEOSS)

Observation of Hydrological Processes Using Remote Sensing

10-Year Implementation Plan (GEO, 2005), have all identified Earth observation (EO) of the water cycle as the key in helping to solve the world’s water problems. More specifically, the 10-year implementation plan states that ‘‘Enhanced prediction of the global water cycle variation based on improved understanding of hydrological processes and its close linkage with the energy cycle and its sustained monitoring capability is a key contribution to mitigation of water-related damages and sustainable human development. Improved monitoring and forecast information, whether of national or global origin, if used intelligently, can provide large benefits in terms of reduced human suffering, improved economic productivity, and the protection of life and property. In many cases, the combination of space-based data and high-resolution in-situ data provides a powerful combination for effectively addressing water management issues. Information on water quantity and quality and their variation is urgently needed for national policies and management strategies, as well as for UN conventions on climate and sustainable development, and the achievement of the Millennium Goals’’ (GEO, 2005). The availability of spatial information on water quantity and quality will also enable closure of the water budget at river basin and continental scales to the point where effective water management is essential (e.g., as requested by the European Union’s Water Framework Directive (WFD), as well as national policies). Geo-information science and EO are vital in achieving a better understanding of the water cycle and better monitoring, analysis, prediction, and management of the world’s water resources. Subject to climate change, the security of freshwater resources has emerged as one of the key societal problems. According to a report prepared under the auspices of the Intergovernmental Panel on Climate Change (IPCC, 2008), ‘‘Observational records and climate projections provide abundant evidence that freshwater resources are vulnerable and have the potential to be strongly impacted by climate change, with wide-ranging consequences on human societies and ecosystems.’’ Floods, droughts, water scarcity, water usage, water quality, water and ecosystem interactions, and water and climate interactions are all issues of direct importance to our human society. The only key to safeguard the security of water resources is better water resources management. This in turn requires better understanding of the water cycle, water climate interactions, and water ecosystem interactions in the Earth’s climate system. To achieve such an understanding, it is essential to be able to measure hydroclimatic variables at different spatial and temporal scales, such as radiation, precipitation, evaporation and transpiration (or evapotranspiration (ET)), soil moisture, clouds, water vapor, surface water and runoff, vegetation state, albedo and surface temperature, etc. The major components of the water cycle of the Earth system and their possible observations are presented in Figure 1. Such observations are essential to understand the global water cycle and its variability, both spatially and temporally, and can only be achieved consistently by means of EOs. Additionally, such observations are essential to advance our understanding of coupling between the terrestrial, atmospheric, and oceanic branches of the water cycle, and how this coupling may influence climate variability and predictability. Figure 1 also shows the proportion of the water-cycle flux

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components in the ocean (including evaporation of the ocean water into the atmosphere and condensation of the water vapor falling as precipitation into the ocean again), the proportion of the terrestrial water-cycle components (including precipitation as a consequence of condensation of water vapor generated by evaporation and transpiration from land and water vapor transported from the ocean, the river discharge, and groundwater discharge returning water into the ocean), water and ocean ice sheets in the ocean, permafrost and snow, soil moisture and groundwater on land, and atmospheric water vapor. Water resources management directly interferes with the natural water cycle in the forms of building dams, reservoirs, water transfer systems, and irrigation systems that divert and redistribute part of the water storages and fluxes on land. The water cycle is mainly driven and coupled to the energy cycle in terms of phase changes of water (changes among liquid, water vapor, and solid phases) and transport of water by winds in addition to gravity and diffusion processes. The water-cycle components can be observed with in situ sensors as well as airborne and satellite sensors in terms of radiative quantities. Processing and conversion of these radiative signals are necessary to retrieve the water-cycle components. To enhance prediction of the global water-cycle variation, based on improved understanding of hydrological processes and its close linkage with the energy cycle and its sustained monitoring capability, is a key contribution to mitigation of water-related damages and sustainable human development. In many cases, the combination of space-based data and high-resolution in situ data in a modeling system using data assimilation provides a powerful tool for effectively addressing water management issues.

2.14.1.2 Water and Energy Balance of the Earth The Sun is the primary source of energy of Earth’s climate system and its five major components: the atmosphere, the biosphere, the cryosphere, the hydrosphere, and the land surface (ESA, 2006). In Earth’s energy balance, the shortwave (solar) radiation is redistributed by different radiative climate forcing components. In the long term, the amount of incoming solar radiation absorbed by land, ocean, and atmosphere is balanced by releasing the same amount of outgoing longwave (terrestrial) radiation from Earth to space. About half of the incoming solar radiation is absorbed by the Earth’s surface. This energy is transferred to the atmosphere by warming the air in contact with the surface (thermals), by ET and by longwave radiation that is absorbed by clouds and greenhouse gases. The atmosphere in turn radiates longwave radiation back to the Earth’s surface as well as out to space. Changes in greenhouse gas concentrations cause altering the longwave radiation from the Earth out to space. The climate system will respond directly to such changes, as well as indirectly, through a variety of feedback mechanisms. For example, an increased concentration of water vapor enhances the amount of thermal radiation absorbed by the atmosphere and consequently leads to an increase of the surface temperature, but will also likely lead to an increase of cloud amount and precipitation. Simplified schematic representations of the annual mean energy flux budgets for the Earth,

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Radiation

Vapor transport 10%

Precipitation (27%) Permafrost storage (23 k km3) (0.043 m)

Infiltration

River and lakes storage (178 k km3) (0.349 m) Evaporation/ transpiration (17%)

Soil moisture storage (122 k km3) (0.239 m)

River and groundwater discharge (10%) Groundwater storage (15 300 k km3) (29.996 m)

Atmosphere water storage (12.7 k km3) (0.025 m)

Condensation (90%)

Evaporation (100%, 413 k km3 yr−1)

Ocean ice storage (26 350 k km3) (51.659 m)

Ocean water storage (1 335 040 k km3) (2627 m)

Figure 1 Global water cycle of the Earth system and their possible observations with in situ, airborne instruments (low altitude and high altitude), and satellites. The flux components (condensation, water vapor transport, precipitation, evaporation and transpiration, and river and groundwater discharge) are normalized with the total ocean evaporation of 413 000 km3yr1 (100%). The storage components are also converted to water depth using the total surface area of earth 510 072 000 km2. Data from Trenberth et al. (2007).

land, and ocean are presented in Figure 2, using data reported by Trenberth et al. (2009). For the Earth energy budget, the incoming solar radiation at the top of atmosphere (TOA) is 341.3 W m2, equivalent to one-quarter of the solar constant 1365.2 W m2, of which 101.9 W m2 is reflected (79 W m2 by clouds and 23 Wm2 by the Earth’s surface) to space resulting in a planetary albedo (or TOA albedo) of 29.8%. The surface albedo 14.3% (at the bottom of atmosphere, BOA) is the ratio of the reflected solar radiation (23 W m2) to the absorbed solar radiation (161 W m2). In addition, 78 W m2 of the incoming solar radiation is absorbed by the atmosphere. The atmosphere emits 333 W m2 downward to the surface, while the surface emits 396 W m2 upward to the atmosphere, resulting in a net upward surface longwave of 93 W m2. Part of the emitted surface longwave radiation passing through the atmospheric window (40 W m2), together with the upward longwave radiation from the atmosphere (169 W m2) and that from clouds (30 W m2), makes up the outgoing longwave radiation to space (238.5 W m2). The sum of the net radiation at the surface 98 W m2 (net downward solar radiation 161 W m2 less net surface upward longwave radiation 63 W m2) is balanced by the thermals (i.e., sensible heat flux 17 W m2) and latent heat flux for evaporation/transpiration (80 W m2), with 0.9 W m2 absorbed by the surface. At the TOA, the radiation balance is

also 0.9 W m2 (incoming solar radiation 341.3 W m2 less reflected solar radiation 101.9 W m2 and outgoing longwave radiation 238.5 W m2), indicating a net gain of 0.9 W m2 in energy, which may be conceived as a possible warming of the Earth system. However, this quantity is derived only for the Clouds and the Earth’s Radiant Energy System (CERES) (Wielicki et al., 1996) period from March 2000 to May 2004 and cannot be taken as long-term evidence. Similar explanations can be made for the energy budgets for land and ocean separately. The differences in land and ocean energy budget components are caused mainly by different albedo over land and water as well as the different thermodynamic properties of land and water. EO of water cycle primarily uses information in the optical, thermal, and microwave (MW) regions of the electromagnetic spectrum to retrieve water-cycle components, though other measurement using, for example, gravity measurement has also shown great promise for monitoring mass changes. One example of EO of water cycle is the Water Cycle Multi-mission Observation Strategy (WACMOS) project initiated by the European Space Agency (ESA) and the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research Programme (WCRP). The WACMOS project aims to develop and validate novel and improved multimission-based global water-cycle data sets using multimission satellite data.

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Global energy budget, TOA albedo 29.8% 102

Reflected solar radiation 101.9 W m−2

Incoming solar radiation 341.3 W m−2

341

Reflected by clouds and atmosphere 79

Outgoing longwave radiation 238.5 W m−2

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79 Emitted by atmosphere

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Figure 2 Schematic representation of the mean annual energy budgets for the earth, land, and ocean. The surface emissivity is assumed to be 1.0. Scheme and primary data from Trenberth KE, Fasullo JT, and Kiehl J (2009) Earth’s global energy balance. Bulletin of the American Meteorological Society 311–323: doi:10.1175/2008BAMS2634.1.

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Figure 2 Continued.

2.14.1.3 From Radiometric Observations to Object Properties The scientific challenge in EO of water cycle is to determine turbulent, thermodynamic, and fluid dynamic properties of the whole water cycle by using radiometric observations. As illustrated in Figure 3, a sensor with a certain response function measuring radiation reflected or emitted from an object can have different geometric arrangements with respect to the object, each with always atmosphere between the sensor and the object. In order to retrieve the properties of the object in concern using data in terms of its range to the sensor, its combined temperature and emissivity (or reflectivity) at different times, at different spatial resolution, at different wavelengths, at different direction, and at different polarization, detailed data processing is needed (see Section 2.14.10 for a detailed example). In terms of the sensor response, we need to ask two types of questions: (A) How much radiation is detected at the sensor? (B) When and how does it arrive? If the answers are only relevant to question (A), then we have a passive sensor system, otherwise if the answers are relevant to both questions (A) and (B), then we have an active sensor system. Many excellent textbooks exist that deal with the theoretical aspects of the sensor–object relationships and the practical issues related to retrievals of object properties (e.g., Rees, 2001; and Liang, 2004). Applications of RS in hydrology and climate studies can be found in related chapters in Anderson and McDonnell (2005) and in Bolle (2003); the current chapter is a continuation of these earlier efforts on RS in hydrology and water resources management. Many Internet

sites also provide very useful resources, data, and examples of EO of water-cycle variables, some of the most relevant ones are provided in Table 1. There have been excellent field campaigns in hydrology – HAPEX Sahel, HAPEX Mobily (Goutorbe et al., 1997), International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE; Sellers et al., 1992), MONSOON’90 (Kustas and Goodrich, 1994), the Southern Great Plains Hydrology Experiment (SGP), and the Soil Moisture Experiment (SMEX; Jackson et al., 1999) – to study the complex hydrological processes and land–atmosphere interactions at local to regional scales. The FIFE project was a large-scale climatology project set in the prairies of central Kansas from 1987 to 1989. This project was designed to improve understanding of carbon and water cycles; to coordinate data collected by satellites, aircraft, and ground instruments; and to use satellites to measure these cycles. More information on FIFE can be found on the Internet. The MONSOON’90 large-scale interdisciplinary field experiment was conducted in the summer of 1990 in southeastern Arizona to investigate the utility of RS coupled with energy and water-balance modeling for providing large-area estimates of fluxes in semiarid rangelands. Large-scale field experiments related to soil moisture are the series of the SGP and the SMEX series, focusing on validation of retrieval algorithms and demonstration of technological feasibilities of RS of soil moisture. Some examples of both sensor systems and the retrievals of the relevant geo-biophysical parameters can be found in some recent large-scale field experiments, the SPAR 2004 and SEN2FLEX 2005 campaigns (Sobrino et al., 2008, 2009;

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The fundamental of Earth observations (link sensor response to object properties in a thermodynamic environment)

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Its range, its combined temperature, and emissivity (or reflectivity) at different times, or different spatial resolution, at different wavelengths, at different direction, at different polarization.

A : A passive sensor system A + B: an active senor system

(e.g., SEN2FLEX campaign) Figure 3 Schematic representation of links between radiometric observations and object properties.

Su et al., 2008) as well as the EAGLE 2006 campaign (Su et al., 2009). The data collected from these field experiments are open to the scientific community for collaborative investigations and are accessible at the European Space Agency’s Principle Investigators portal or by contacting the authors directly. As an example, the spectra of bright sand and of a young pine tree collected during the EAGLE 2006 campaign are shown in Figure 4, indicating the sensor responses to different properties of the object. Some most relevant web links to data portals, software tools, and training courses related to water and energy balance of the Earth are given Table 1. In the following sections, we discuss details of the different components of the water cycles from the perspectives of EO.

2.14.2 Water in the Atmosphere: Clouds and Water Vapor 2.14.2.1 Introduction Accurate information on the distribution of water vapor and clouds in the atmosphere is essential for water and energy balance studies. The atmosphere acts as a medium for the transport of water around the globe. Water vapor is brought into the atmosphere through evaporation from liquid water bodies (B90%) and transpiration from plants (B10%). Clouds are formed in lifting air parcels, in which water vapor condensates into cloud particles due to the cooler temperatures. Once in the atmosphere, clouds are moved around the globe by strong winds and either evaporate back into water vapor or disappear as precipitation to replenish the earthbound parts of the water cycle. The presence of water vapor and clouds in the atmosphere warms the Earth’s troposphere and surface, and acts as a partial blanket for the longwave radiation coming from the surface. Water vapor and clouds absorb and emit infrared (IR)

radiation and thus contribute to warming the Earth’s surface. For clouds, this effect is counterbalanced by the reflection of visible (VIS) radiation, which reduces the amount of shortwave (solar) incoming radiation at the Earth’s surface and has a cooling effect on the climate system. The net average effect of the Earth’s cloud cover in the present climate is a slight cooling because the reflection of radiation more than compensates for the blanketing effect of clouds. Information on the distribution of water vapor and clouds in the atmosphere is also relevant for studying the hydrological cycle. The shortwave and longwave radiation that reach the Earth’s surface directly affect the evaporation (latent) and sensible heat fluxes. The part of the radiation that is used to evaporate soil moisture (evaporation) or crop moisture (transpiration) is released to the atmosphere as water vapor. The evaporated water vapor, in turn, is carried upward where it condenses into cloud droplets, ice crystals, or precipitation. Ground-based measurements are inadequate to observe the large spatial and temporal variations in water vapor and cloud properties (Rossow and Schiffer, 1999). The advent of satellite RS has changed this situation. Satellites can provide the required information at adequate temporal and spatial scales. Satellite observations can be used to retrieve integrated water vapor amounts and cloud physical properties from passive MW radiometers or spectral radiances, respectively. The accuracies and precisions of these satellite retrievals have been well determined within various validation studies.

2.14.2.2 Satellite RS Since the 1960s, many satellites have been providing continuous observations of the state of the atmosphere over very large regions or even for the entire globe. The satellite instruments of most interest for observing water vapor and clouds are MW radiometers that measure emitted MW radiation of the Earth’s surface, atmosphere, and clouds, and the

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Table 1 cycle

Some resources for Earth observation program, satellites and data, software, and training courses related to earth observation of water

Organization/program

Web links

Comments

Group on Earth Observations (GEO)

http://www.earthobservations.org/

GEO portal

http://www.geoportal.org/

GEO applications

http://www.earthobservations.org/ documents/the_full_picture.pdf http://www.esa.int/esaeo/ http://www.esa.int/esalp/

GEO coordinates international efforts to build a Global Earth Observation System of Systems (GEOSS). In its water societal benefit area it aims at improving water resource management through better understanding of the water cycle. The GEO portal provides an entry point to access remote sensing, geospatial static, and in situ data, information and services. Water is one of the nine societal benefit areas. ‘The Full Picture’ provides an overview of the progresses in applications of GEOSS in the nine societal benefit areas till 2007. ESA’s Earth observation programs include the Global Monitoring for Environment and Security (GMES) and the Living Planet Programme. Observation of the hydrosphere is one of the foci of this program. The ESA-MOST (Ministry of Science and Technology, China) Dragon program includes a dedicated training program to provide training in data processing, algorithm and product development from ESA Earth Observation (EO) data in land, ocean, and atmospheric applications. NASA’s Hydrological Sciences focuses on the interpretation of remotely sensed data and land surface hydrological, meteorological, and climate modeling. GLDAS generates optimal fields of land surface states and fluxes (Rodell et al., 2004) by assimilating satellite- and ground-based observational data products into advanced land surface models. The high-quality, global land surface fields provided by GLDAS support several current and proposed weather and climate prediction, water resources applications, and water-cycle investigations. GLDAS has resulted in a massive archive of modeled and observed, global, surface meteorological data, parameter maps, and output which include 11 and 0.251 resolution 1979–present simulations of the Noah, CLM, and Mosaic land surface models. The main purpose of the Land SAF is to increase the benefits from Meteosat Second Generation (MSG) and European Polar Satellite (EPS) data related to land, land–atmosphere interactions and biophysical applications by developing techniques, products and algorithms that will allow a more effective use of data from the two satellites of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The Earth Observation Handbook presents the main capabilities of satellite Earth observations, their applications, and a systematic overview of present and planned CEOS agency Earth observation satellite missions and their instruments. It also explores society’s increasing need for information on our planet.

European space agency (esa)

European Space Agency (ESA) Dragon programme

http://earth.esa.int/dragon/

National Aeronautics and Space Administration (NASA)

http://neptune.gsfc.nasa.gov/hsb/

Global land data assimilation system (gldas)

http://ldas.gsfc.nasa.gov/

Land surface analysis satellite applications facility (lsa saf)

http://landsaf.meteo.pt/

Committee on Earth Observation Satellites (CEOS)

http://www.eohandbook.com

passive imagers that measure VIS, near-IR, and IR radiances. The importance of satellite observations is determined by the spatial and temporal sampling resolution of their instruments. Frequent sampling is especially required for parameters that are highly variable in space and time, such as clouds.

2.14.2.2.1 Observing water vapor The strong variations of water vapor in space and time lead to the necessity of monitoring this quantity globally from satellites. Absorption lines of water vapor are present in almost every part of the electromagnetic spectrum. A great variety of space-borne sensors are used to retrieve atmospheric profiles of humidity or the column amount, even if they were not designed for it. These sensors observe the interaction of radiation with water vapor in the different parts of the spectrum

(MW, IR, optical, and ultraviolet (UV)). The number of available instruments is further increased due to the need for measurements at different observation geometries (nadir view, limb scanning, occultation, and day or night) and at different orbit orientations. Here, only some of the observation systems for tropospheric water vapor are described. MW radiometers observe the radiation close to the 22-GHz water vapor absorption line that is closely related to the total column content of water vapor. These observations can be used over oceans in clear sky and cloudy conditions. The conically scanning special sensor microwave/imager (SSM/I) on the DMSP satellites is available since 1987 and is continued with the special sensor microwave imager sounder (SSMIS) instrument into the future. Among others, this type of radiometers is flown on the US TRMM satellite (TRMM Microwave Imager (TMI)) and on the US Aqua mission

Observation of Hydrological Processes Using Remote Sensing Sand − location #7

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Figure 4 Example spectra of bright sand (upper panels) and of a young pine tree (lower panels). The frame of the photos represents an area of 1  1 m2. On the graphs, the gray lines show the measured spectra, the thick black line is the spectrum accepted as the characteristic spectrum of the site under consideration (Su et al., 2009).

(Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)). In addition, the advanced microwave sounding unit (AMSU) makes its observations at this frequency but is a cross-track scanner. Imaging spectrometers, such as ESA’s medium-resolution imaging spectrometer (MERIS), are used to retrieve the total column content of water vapor at a very high spatial resolution (B300 m) from near-IR observations during daytime (Rast et al., 1999). MERIS is especially useful over land surfaces. Such observations are also available from the moderateresolution imaging spectroradiometer (MODIS) flown on the NASA Aqua and Terra satellites (NASA, National Aeronautics and Space Administration). Since 1977, the Meteosat Visible and Infrared Radiation Imager (MVIRI) and Spinning-Enhanced Visible and Infrared Imager (SEVIRI) instruments in geostationary orbit observe radiation at 6.3 and 7.2 mm (only SEVIRI), and allow the retrieval of upper tropospheric humidity (UTH) with a very high temporal resolution (up to 15 min) that allows for studies of atmospheric dynamics (Schmetz et al., 2002). Also in geostationary orbit, humidity sounders similar to the HIRS instrument are flown on the US Geostationary Operational Environmental Satellites (GOES).

UV/VIS spectrometers, such as the Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (Sciamachy), are used for the retrieval of total column water vapor over land and ocean surfaces with approximately the same accuracy as the SSM/I but only under daylight and clear sky conditions at much coarser spatial resolution (Burrows et al., 1999). Since 1978, the observations of the Advanced Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) on NOAA and MetOp satellites, with its IR spectrometer (High resolution Infrared Radiation Sounder (HIRS)), and MW radiometers (AMSU-A/B and Microwave Humidity Sounder (MHS)), have been combined to derive atmospheric temperature and humidity profiles. Since 2007, EUMETSATs MetOp satellite has been carrying the Infrared Atmospheric Sounding Interferometer (IASI) instrument. This new generation of IR sounding instruments is capable of observing about 15 independent pieces of information on the vertical profile by performing observations over a large part of the IR spectrum (4–50 mm) (Simeoni et al., 1997). A similar instrument, called Atmospheric Infrared Sounder (AIRS), is flown since 2002 on NASAs Aqua mission (Aumann et al., 2003).

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Finally, temperature and humidity profiles can also be retrieved from Radio Occultation measurements, performed by, for example, the GRAS (Loiselet et al., 2000) instrument onboard the MetOp or the COSMIC fleet (Anthes et al., 2008).

2.14.2.2.2 Observing clouds During the last 25 years, observations from passive imaging satellites have been successfully used for the retrieval of cloud cover and cloud physical properties. Rossow and Garder (1993) used observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA series of polar-orbiting satellites to derive global cloud climatology since 1982. Recently, several more sophisticated instruments for EOs have been launched. These include the instruments that are flown onboard the NASA Earth Observing System (EOS) geosynchronous orbiting satellites, which were launched in 1999 (Terra) and in 2002 (Aqua). The MODIS instruments on both satellites operate the required spectral channels for the retrieval of cloud properties at high spatial resolutions (better than 1 1 km2) globally, but at low temporal resolutions (revisit time 1 day or more). The unprecedented sampling frequency of geostationary satellites (better than 30 min) allows for monitoring the diurnal variations in cloud properties. The SEVIRI instrument on board METEOSAT-8, which was launched in 2002, is the first instrument that can be used for the retrieval of these properties from a geostationary orbit (Figure 5). SEVIRI constitutes a valuable source of data for water and energy balance studies. Another types of instruments for cloud observations are passive MW radiometers. These instruments measure emitted MW radiances from the Earth surface and the overlaying atmosphere. Greenwald et al. (1993) showed that the radiances observed by these instruments can be used for a simultaneous retrieval of atmospheric water vapor and cloud liquid water.

Figure 5 METEOSAT-8/SEVIRI image of the visible channel (0.6 mm) for the SEVIRI field of view for 17 January 2006 at 11:45 UTC.

Recently, even more advanced satellite systems are available for observing clouds. The most advanced cloud measurements are provided by the radar on the Cloudsat satellites and lidar on the Calipso satellites, which were launched in 2006 and fly in the A-train constellation. These instruments measure vertical profiles of cloud reflectivity of large particles (radar) and small particles (lidar).

2.14.2.3 Retrieval Algorithms 2.14.2.3.1 Water vapor Retrieval methods have to correspond to the instrument spectral range and observation geometry. Processes in the atmosphere complicate the retrieval task, for example, the coexistence of the three thermodynamic phases of water on the Earth, interaction with aerosols, and varying surface emissivity. The number of retrieval algorithms is much larger than the number of sensors. Retrieval methods generally depend on a priori information, which could be the coefficients of a regression, the constraints for retrieval based on inversion, or the training set of a neural network. The quality of a retrieval scheme depends on the applicability of the a priori information to the prevailing environmental conditions, that is, surface properties, clouds, etc.

2.14.2.3.2 Total column water vapor Major instruments utilized for the retrieval of total column water vapor are MW radiometers (SSM/I), UV/VIS spectrometers (GOME), and VIS and near-IR imaging spectrometers (MERIS). Retrieval schemes for MW radiometer can be distinguished in semiphysical and physical schemes. In both cases, observations of the instrument are simulated using a radiative transfer model. Input to the model is the atmospheric state vector and instrument parameters. The semiphysical schemes then retrieve the water vapor content by applying a statistical scheme (linear regression or neural networks) based on the training data (Schlu¨ssel and Emery, 1990). The physical schemes mostly use a first guess, often coming from a numerical weather forecast model (NWP), as the basis for the forward computation and then vary the first guess until the used set of observed radiances is best matched (e.g., Wentz, 1997). The latter requires much more computer power but has generally replaced statistical methods in the past 10 years. The basic principle in retrieval applied to GOME is the Differential Optical Absorption Spectroscopy (DOAS) method to calculate the difference between the Sun normalized measured Earthshine radiance and absorption cross sections at wavelengths where water vapor absorbs radiation and relate this absorption depth to the water vapor column concentration (e.g., Noel et al., 1999). The DOAS method provides a global (land and ocean) completely independent data set, because it does not rely on any additional external information. Near-IR MERIS algorithms are based on radiative transfer simulations, where the radiance ratio between the MERIS channels 15 (900 nm) and 14 (885 nm) are used in an inversion procedure based on regression (Bennartz and Fischer, 2001). Near-IR and IR algorithms were also developed for the MODIS instrument by Huang et al. (2004).

Observation of Hydrological Processes Using Remote Sensing

2.14.2.3.4 Upper tropospheric humidity The relative humidity (RH) of the upper troposphere has a strong influence on the amount of outgoing longwave radiation. It is often derived employing IR and MW instruments as HIRS and MVIRI/SEVIRI as well as AMSU-B/MHS. The brightness temperature of one channel, 6.3 mm for IR and 183 GHz for MW, is related to the RH of the upper troposphere. A typical physical retrieval method for Meteosat is described in Schmetz and Turpeinen (1988). The retrieval is confined to areas with neither medium- nor high-level clouds. Similar schemes, using Jacobian vertical weighting, have been developed by Buehler and John (2005) for AMSU-B, Brogniez et al. (2007) for Meteosat, and Jackson and Bates (2001) for HIRS.

2.14.2.4 Cloud Detection In general, cloud detection methods are based on the fact that clouds have a higher reflectance and a lower temperature than the underlying Earth surface. In addition, cloudy scenes have a higher spatial and temporal variability than clear sky scenes. However, difficulties in cloud detection appear when the contrast between the cloud and underlying surface is small. At VIS wavelengths, it is difficult to detect clouds over high reflecting surfaces such as snow or desert. At IR wavelengths, it is difficult to discriminate low clouds from clear sky land surfaces during the night, when surface temperatures may drop below cloud-top temperatures. In these cases, testing the spatial coherence in IR radiances in cloudy and clear skies is an effective manner to identify cloudy areas (Coakley and Bretherton, 1982). Moreover, cloud edges are difficult to detect, as the satellite pixels at these edges are only partly cloudy. Part of the difficulties touched on above may be alleviated by the combined use of the multi-spectral observations from satellite (Saunders and Kriebel, 1988; Ackerman et al., 1998).

is based on the principle that the reflectance of clouds at a nonabsorbing wavelength in the VIS region is strongly related to the optical thickness and has very little dependence on particle size, whereas the reflectance of clouds at an absorbing wavelength in the near-IR region is primarily related to particle size (Nakajima and King, 1990; Han et al., 1995). The example simulated TOA solar reflectance spectra presented in Figure 6 shows the substantial differences in the absorption properties of water and ice in the near-IR solar region (0.7 mmol o4 mm). Especially at 1.6, 2.2, and 3.9 mm, ice exhibits stronger absorption than water. Due to differences in cloud optical thickness, the ice cloud is somewhat brighter than the water cloud in the VIS region (lo0.7 mm). An inversion procedure is used to relate observed radiances to cloud thermodynamic phase, optical thickness, and particle size. A radiative transfer model is used to prepare lookup tables of simulated reflectances for clouds with different optical thicknesses, thermodynamic phases, and particle sizes for a wide variety of solar and satellite viewing geometries. Liquid and ice water path are computed from retrieved cloud optical thickness and particle size. Note that the retrieval of particle size from near-IR reflectances is weighted toward the upper part of the cloud (Platnick, 2001). At thermal IR wavelengths, the retrieval of cloud microand macro-physical properties is based on the interpretation spectral variations in emitted radiances at the cloud top. Absorption and emission dominate cloud radiative transfer at these wavelengths, because cloud particles have a low single scattering albedo and a large asymmetry parameter. For clouds with an optical thickness smaller than 4, the amount of observed upwelling radiance at the top of the atmosphere will be affected by cloud properties, such as optical thickness, particle size, and thermodynamic phase (Baum et al., 1994). By selecting two (or more) appropriate wavelengths, it is feasible to infer the emissivity and temperature at the cloud top, and deduce information on cloud optical thickness, particle size, and thermodynamic phase. A major drawback of IR retrievals techniques is that cloud emissivities saturate at relatively low optical thicknesses. Passive MW radiometers measure radiances, expressed as brightness temperatures, at various frequencies between 10 and 100 GHz. These radiances have distinct atmospheric

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2.14.2.3.3 Water vapor profiles In general, the so-called 1 D-VAR technique is employed for water profile retrievals. 1 D-VAR schemes use the variational principle to solve the retrieval problem (Eyre, 1989), and invert the radiances to simultaneously retrieve the temperature and humidity profile, the surface temperature and MW emissivity, as well as cloud amount and cloud-top pressure. It employs an iterative method, which finds the maximum probability solution to a nonlinear retrieval/analysis problem. Li et al. (2000) applied a 1 D-VAR scheme to TOVS/ATOVS observations. Moreover, 1 D-VAR schemes are also applied to atmospheric sounders, such as AIRS and IASI and to Radio Occultation instruments. Semiphysical schemes, such as neural networks, can also be used to simultaneously retrieve temperature and water vapor profiles (e.g., Kuligowski and Barros, 2001).

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absorption characteristics, and can be used for a simultaneous retrieval of atmospheric water vapor and cloud liquid water (Greenwald et al., 1993). The liquid water path (LWP) retrievals from MW radiometers provide a measurement of the integrated LWP, and only represent the liquid droplets volume in the cloud. Numerous algorithms have been developed to estimate the incoming shortwave radiation from satellite radiances (Pinker et al., 1995). Some methods calculate the incoming shortwave radiance by directly interpreting the TOA albedo in terms of atmospheric transmission (Mueller et al., 2009), while others calculate the transmission for a clear and cloudy atmosphere separately, using atmospheric water vapor and cloud microphysical properties (Deneke et al., 2008).

2.14.2.5 Validation Validation is prerequisite to generate accurate data sets of water vapor and cloud properties for water and energy balance studies.

2.14.2.5.1 Water vapor The validation of atmospheric water vapor retrieval schemes is very difficult because classical observations are only sparsely available; for example, radiosondes are mostly available over land surfaces and their observation time does not match overpass times of polar orbiting satellites. Ground-based global positioning system (GPS) observations are available more often over land surfaces, but they are only suitable to validate total column water vapor estimates. As aircraft observations are only available along major flight paths, and the accuracy of their instruments often insufficient for validation, the upper troposphere and lower stratosphere are hard to validate. Instead, satellite systems are compared among themselves or to atmospheric reanalysis. Such comparison can also help to uncover specific instrumental and retrieval problems. For instance, the comparison of the passive MW AMSR-E and IR AIRS estimates of total water vapor content revealed some systematic differences due to the treatment of clouds in the AIRS retrievals (Fetzer et al., 2006). The most comprehensive comparison of SSM/I-based retrievals among themselves and to radiosondes, performed by Sohn and Smith (2003), revealed that differences in statistical retrievals are mostly caused by differences in the training data that were used. It was also found that statistical algorithms outperform physical ones because of simplifying assumptions on tangential factors, such as near-surface wind speed, sea-surface temperature, and residual cloud liquid water. On a seasonal scale (3 months means), the differences between satellite and radiosondes are B1 kg m2 bias and B2.5 kg m2 rms. Sensitivity of instruments influences the satellite comparisons. Fetzer et al. (2008) compared AIRS UTH with the Microwave Limb Sounder (MLS) data. The mean values agree well within 10% and standard deviations of their differences are 30% or less. Differences in wet and dry regimes were found to be caused by different sensitivities of the two instruments. Milz et al. (2009) compared monthly mean distributions of UTH products from AMSU-B, Humidity Sounder Brazil (HSB), and AIRS for January 2003. The UTH, based on simulated AMSU-B brightness temperatures from AIRS profiles, has a

slight moist bias of up to 4% in RH. This bias is small compared to the differences in UTH observations from radiosondes and nadir-looking IR sounders, which were between 10% and 15%, depending on the type of radiosondes (Soden and Lanzante, 1996). It is also small compared to the large differences in UTH between different climate models (John and Soden, 2007). Thus, most of the existing UTH data sets are suitable as benchmark for improving climate model representations of humidity. Li et al. (2000) reported for temperature profile retrievals from ATOVS, an accuracy of 2 K for temperatures at 1-km resolution and 3–6 K for dew-point temperatures. IASI profile retrievals have recently been evaluated by Pougatchev et al. (2009). Besides the very much improved temperature retrieval, they found that the instantaneous RH retrievals have a bias of about 710%, and a standard error lower than 10% in the 800–300-hPa range.

2.14.2.5.2 Cloud properties The validation data of cloud properties are obtained from flight measurements or special observatory sites. During flight measurement campaigns, heavily instrumented aircrafts collect very detailed measurements of cloud micro- and macrophysical properties over a limited period of time, providing valuable information to obtain a better understanding of cloud microphysics (e.g., EUCAARI over Europe, AMMA over Africa, and RICO over the Caribbean). Special observatory sites aim to measure the physical state of the (cloudy) atmosphere over longer periods of time (years). These sites are equipped with a suite of RS instruments to measure radiation, water vapor, and cloud properties. The number of these sites is limited, and comprises the three America Atmospheric Radiation Measurement (ARM) sites and the four Cloudnet sites in Northern Europe. The measurements of the above-described observatory sites play a key role in the continuous validation of cloud properties. Recently, measurements from Cloudsat (radar) and Calipso (lidar) can be used for validation as well. The combined use of radar and lidar observations allows the retrieval of vertical profiles of cloud optical thickness, cloud phase, particle size, and cloud water content (Delanoe¨ and Hogan, 2008). These retrievals are of great value for the validation of cloud property retrievals or for deriving global cloud climatology. Validation studies confirmed that LWP can be retrieved with high accuracy from both AVHRR (Han et al., 1995; Jolivet and Feijt, 2005) and SEVIRI (Roebeling et al., 2006). Although some retrieval algorithms use the 0.6-, 3.8-, and 10.5-mm radiances (Han et al., 1995), while others use the 0.6- and 1.6mm radiances (Jolivet and Feijt 2005; Roebeling et al. 2008), similar accuracies (B15 g m2) and precisions (B30 g m2 for thin clouds and up to 100 g m2 for thick clouds) were found. The above-mentioned accuracies suggest that LWP retrievals from satellite could be an appropriate source of information for the evaluation of climate-model-predicted LWP values. For nonprecipitating water clouds, Van Meijgaard and Crewell (2005) found differences up to 50 g m2 between climatemodel-predicted and MWR-inferred LWP values. During the FIRE Arctic cloud experiment, Curry et al. (2000) compared large-scale model LWP values to MWR-inferred LWP values.

Observation of Hydrological Processes Using Remote Sensing

They found that all models underestimate the mean LWP by 20–30 g m2, which corresponded to a relative accuracy worse than 60%.

2.14.2.6 Data Sets 2.14.2.6.1 Water vapor products In the framework of the GEWEX Water Vapor Project, the NVAP total column water vapor product (Randel et al., 1996) was derived from a combination of SSM/I, TOVS, and radiosonde data for the years 1988–2001. This product was partly renewed by the additional use of AMSU and TRMM data, but this covers only the years 2000–01. Over ocean, the total column water vapor derived from SSM/I (Figure 7) is available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF) (Schulz et al., 2009) and from RS systems (Wentz, 1997). These data sets have been successfully used for climate analysis, the evaluation of climate models, model-based reanalysis, trend studies (Trenberth et al., 2005), and investigations of the human impact on the water vapor distribution (Santer et al., 2007). Moreover, GOME/SCIAMACHY data sets have also been used to compute trends of total column water vapor (Mieruch et al., 2008). High-quality data sets of atmospheric profiles for climate studies, based on TOVS, have been derived by Scott et al. (1999). These profiles are highly correlated to corresponding ATOVS profiles. Operationally processed data from ATOVS, AIRS, and IASI exist at various places, such as NOAA, NASA, EUMETSAT, and the CM-SAF. UTH data sets are derived from AMSU-B and described in Buehler et al. (2008). UTH data sets derived from geostationary satellites have been used for the evaluation of climate models (Brogniez et al., 2005).

2.14.2.6.2 Cloud products The International Satellite Cloud Climatology Project (ISCCP) provided the first global climatology of cloud cover at an acceptable spatial resolution of 30  30 km (Rossow and Garder, 1993). For a limited area, Karlsson (2003) presented a cloud climatology from AVHRR observations for Scandinavia. With more advanced retrieval algorithms, MODIS continues the survey of cloud cover (Ackerman et al., 1998).

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The ISCCP data have been successfully used to derive information on cloud physical properties, such as cloud phase, cloud optical depth, or cloud particle size (Rossow and Schiffer, 1999). In turn, these properties have been applied to derive parameters, such as the shortwave radiation budget (Gupta et al., 1999). Other global cloud climatologies are derived from AVHRR, such as the PATMOS climatology (Jacobowitz et al., 2003), or MODIS (Minnis et al., 2003; Platnick et al., 2003).

2.14.3 Water from the Atmosphere: Precipitation 2.14.3.1 Introduction Precipitation can be considered the most crucial link between the atmosphere and the surface in weather and climate processes. Quantitative precipitation estimates (QPEs) on high spatial and temporal resolutions are of increasing importance for water resources management, for improving the precipitation prediction scores in numerical weather prediction (NWP) models, and for monitoring seasonal to interannual climate variability. Although operational networks of weather radars are expanding over Europe and North America, large areas remain where information on the occurrence and intensity of rainfall is missing. Rain rate estimates from passive MW or VIS and IR imaging sensors on polar and/or geostationary orbiting satellites may bridge this gap, and provide quasi-global information on the spatial extent and intensity of rain.

2.14.3.2 Precipitation Measurements from Weather Radars Weather radars employ scattering of radio-frequency waves (5.6 GHz for C-band) to measure precipitation and other particles in the atmosphere (Rinehart, 2004). The intensity of the atmospheric echoes is converted to the so-called radar reflectivity factor Z using the Rayleigh scattering approximation (Probert-Jones, 1962):

Z¼

X

D6i

ð1Þ

i

where Di is the diameter of raindrop i and the summation is over all drops in a unit volume. Marshall and Palmer (1948) proposed a simple exponential form of the drop size distribution N(D) which is widely accepted:

60

NðDÞ ¼ N0 expðLDÞ

ð2Þ

WVPA (kg m−2)

50 40 30 20 10 0 Figure 7 EUMETSAT CM-SAF SSM/I-derived total column water vapor.

where the drop density N0 ¼ 8 103 mm1 m3 and L ¼ 4.1R0.21 mm1 depends on the rain rate R in mm h1. The radar reflectivity factor can be estimated from the sixth moment of the drop size distribution:

Z¼

Z

NðDÞD 6 dD ¼ 720 N0 =L7 ¼ 296R1:47

ð3Þ

with Z in mm6 m3. Many different Z–R power laws are used as the appropriate power law depends on climatic and actual meteorological circumstances (e.g., stratiform vs. convective precipitation). Apart from variations in the drop size

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distribution, several other factors impact the quality of radarbased QPE (Rossa et al., 2005). The vertical profile of reflectivity (VPR) is, especially at higher latitudes, the major source of error in QPE deduced from weather radar observations (Joss and Waldvogel, 1990; Koistinen, 1991). At longer ranges, the height of observation will increase and in the presence of a significant gradient in the VPR this will typically generate an underestimation of the accumulated precipitation. Many different techniques have been developed to estimate the VPR and to subsequently correct the radar QPEs for this profile. The VPR can be estimated from weather radar data using climatological profiles, mean reflectivity profiles, or local profiles obtained at short ranges (Vignal and Krajewski, 2001). On the other hand, gauge adjustment techniques have been developed which correct the radar precipitation estimates using a second-order polynomial in range (Michelson et al., 2000). The radio frequency radiation transmitted and received by weather radar is scattered by precipitation. During very intense precipitation, scattering can become so strong that the radar beam is attenuated causing underestimation of precipitation intensity or even disappearance of the rain cells behind very strong cells. The observed radar reflectivity may be corrected for the attenuation when the one-way attenuation due to rainfall is approximated by a power law. However, the correction algorithm for attenuation is potentially highly unstable (Hitschfeld and Bordan, 1954). For the (near) future, dual-polarization weather radars offer promising new possibilities to correct for attenuation during intense rainfall (Bringi and Chandrasekar, 2001).

2.14.3.3 Precipitation Measurements from Satellite The reader can find an up-to-date review of satellite rainfall retrieval methods in Kidd et al. (2009) and Levizzani et al. (2007).

2.14.3.3.1 Retrievals from VIS–IR sensors Over the past decades, several rain rate retrieval methods based on observations from VIS and IR sensors were developed. The methods based on geostationary (GEO) satellites often use thermal IR observations and relate daily minimum cloud-top temperatures (Adler and Negri, 1988; Anagnostou et al., 1999) or cold cloud durations (CCD) to rain rates (Todd et al., 1995). These methods tend to perform reasonably well over areas where rainfall is governed by deep convection, but are less effective at higher latitudes, where precipitation originates from both convective and stratiform systems. A major limitation of the CCD methods is that rain rates are proportional to cloud duration, which is an assumption that fails in case high rain intensities occur over a short time period (Alemseged and Rientjes, 2007). Several methods have been developed that relate cloud physical properties, retrieved from passive imaging sensors, to precipitation. The GOES Multi-Spectral Rainfall Algorithm (GMSRA; Ba and Gruber, 2001) utilizes data from five channels, covering the VIS, near IR, water vapor, and two thermal channels, to extract information on the cloud and rain extent. Nauss and Kokhanovsky (2007) showed that cloud LWP retrievals from MODIS daytime observations are directly

proportional to the probability of rainfall. On the other hand, Rosenfeld and Gutman (1994) and Rosenfeld and Lensky (1998) found that clouds require droplets with effective radii 414 mm for the onset of precipitation. This is consistent with the findings of Twomey (1977), who reported that the precipitation efficiency of a given cloud depends on the size of the cloud droplets and the amount of aerosols in the air. Roebeling and Holleman (2009) present a novel approach, which uses information on cloud condensed water path, particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on condensed water path and cloud top height is used to estimate rain rates. The fact that their approach can be applied to GEO observations from the SEVIRI potentially allows for the provision of precipitation observations over large parts of the globe every 15 min. Figure 8 shows the effect of increasing threshold condensed water path and droplet effective radius values on the spatial extent of precipitation over the Netherlands as retrieved from SEVIRI.

2.14.3.3.2 Retrievals from passive MW sensors A more direct measurement of precipitation from satellite is made possible by the use of the MW frequencies as in this part of the spectrum precipitation-sized particles are the main source of atmospheric attenuation. Over ocean, the signal is mainly due to the increased emission of radiation from rain droplets so that rain areas appear warmer over the radiometrically ‘cold’ water background. Over land, rainfall is associated with scattering of the upwelling surface radiation by precipitation-related ice particles. The main problem of the passive-MW-based techniques is that the instruments are currently only available on low-Earth orbiting (LEO) satellites, and thus observations are available only twice per day per satellite (at best). Moreover, the resolutions of the measurements are for ocean rainfall products of the order of 50  50 km2, while over land they are typically no better than 10 10 km2. MW-based estimation techniques belong to two main groups: empirical techniques that calibrate the observations with surface data sets and physical techniques that minimize the difference between a modeled atmospheric rainfall event and the observation. An example of the physical techniques is the Goddard profiling (GPROF) technique (Kummerow et al., 2001) that uses a database of model-generated atmospheric profiles to which the observed satellite measurements are compared, and the best profile match is selected. The advantage of such a technique, first conceived for the TRMM TMI, is that it provides more information about the precipitation system than techniques that just provide information on surface rainfall. With the launch of sensors such as the Advanced Microwave Sounding Unit-B (AMSU-B, cross-track scanner) or the Special Sensor Microwave Imager/Sounder (SSMIS, conical scanner), higher frequency channels in the strong water vapor absorption lines at 183 GHz became available. The response functions of these channels peak at altitudes higher than 2 km and thus are much less influenced by ground emissivity features that represent a large portion of the errors in precipitation estimation over land. Several

Observation of Hydrological Processes Using Remote Sensing

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30

% Precipitation

% Precipitation

15

r eT : 15.0 (µm)

25 20 15 10

Radar SEVIRI (PP)

5 0

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400 CWPT (g

600

800

CWPT : 160.0 (gm−2)

10

5

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Radar SEVIRI (PP)

0

6

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m−2)

18

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r eT (µm)

Figure 8 Relationship between spatial extents of precipitation and threshold condensed water path values (CWPT) for clouds with particle sizes larger than 15 mm (left), and threshold particle sizes (reT) for clouds with condensed water path values larger than 160 g m2 (right). The horizontal gray line indicates the spatial extent of precipitation derived from weather radar observations that were collocated and synchronized with the SEVIRI retrievals. Note that the optimum thresholds for the detection of precipitation are 160 gm2 for CWP and 15 mm for re.

56 52 48

52 48

0

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−4

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−8

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0

Figure 9 22 November 2008. Precipitation retrieval (in mm h1) over NW France, the Channel and the UK using high-frequency AMSU-B MW channels for a mixed-type precipitating system (right) and radar retrieval from the NIMROD network (left). The circle delimits the area where both radar and satellite sense snowfall. Image courtesy of S. Laviola, ISAC-CNR.

algorithms are now available for operational applications, including detection of cloud droplets, snowfall, and snow on the ground (e.g., Ferraro et al., 2005; Laviola and Levizzani, 2008; Surussavadee and Staelin, 2008; Weng et al., 2003). An example of mixed-phase precipitation retrieval is shown in Figure 9.

2.14.3.4 Validation 2.14.3.4.1 Weather radar retrievals Surface networks of rain gauges can be used for both reduction of the gross errors and validation of quantitative precipitation estimates. Wilson (1970) pioneered with the integration of radar and rain gauge data and showed that this can improve the area rainfall measurements. A real-time calibration of radar-based surface rainfall estimates by telemetering rain

gauges was performed by Collier (1983) and an improved accuracy was seen on most locations. Nowadays, mean-field bias adjustment of radar-based quantitative precipitation estimates is widely used. At the Royal Netherlands Meteorological Institute (KNMI), mean-field bias adjustment with gauges is used operationally for an hourly updated QPE product (Holleman, 2007). An extensive spatial and temporal verification of the bias-adjusted radar composites over a 6-year period (2000–05) using dependent and independent gauge data is performed. It is found that the real-time adjustment scheme effectively removes the mean-field bias from the raw accumulations over a large area and that it substantially reduces the daily standard deviation. The adjustment method cannot correct for a rangedependent bias and it is recommended to use a simple VPR adjustment procedure for that.

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2.14.3.4.2 Satellite retrievals Weather radar observation can be used to validate retrievals of the occurrence and intensity of precipitation from passive imaging satellites. Roebeling and Holleman (2009) compared 15-min SEVIRI retrievals of spatial extent of precipitation and rain rates against weather radar observations (Figure 10). The instantaneous rain rates from SEVIRI are retrieved with a high accuracy of about 0.1 mm h1, and a satisfactory precision of about 0.8 mm h1. SEVIRI is very accurate in detecting percentages of precipitation over larger domains (the Netherlands), which is shown by the high correlation of about 0.90 between spatial extents of precipitation from SEVIRI and weather radar. Similarly, the rain rates retrievals from SEVIRI correlate reasonably well with the weather radar observations (corr. ¼ 0.63). An international effort is being conducted by the International Precipitation Working Group (IPWG) to obtain reasonably homogeneous validation figures for the various satellite rainfall estimation algorithms over the various continents. Ebert et al. (2007) argued that the results of such a validation exercise so far confirm that the performance of satellite precipitation estimates is highly dependent on the rainfall regime and generally opposed to those of the NWP model Quantitative Precipitation Forecasts (QPF). Satellite estimates of rainfall occurrence and amount are most accurate during summer and at lower latitudes, whereas the NWP models show greatest skill during winter and at higher latitudes. In general, the more the precipitation regime tends toward deep convection, the more (less) accurate the satellite (model) estimates are.

2.14.3.5 Applications The third phase of the Network of European Meteorological Services (EUMETNET) Operational Program on the Exchange of Weather Radar Information (OPERA) is a joint effort of 30 European countries, which runs from 2007 till 2011, and is managed by KNMI. OPERA-3 is designed to firmly establish the Program as the host of the European Weather Radar Network. Currently, OPERA’s operational network consists of

more than 175 weather radars, of which roughly 100 systems have Doppler processing and about 15 systems have dualpolarization capability. In the coming years, the number of dual-polarization systems is expected to increase dramatically, thus offering new opportunities for quantitative precipitation estimation (Bringi and Chandrasekar, 2001). During this program phase, an OPERA Data Center (ODC) for the weather radar network should be specified, developed, and operated. This data center is crucial for reaching the main objective of OPERA-3, that is, establishing the weather radar networking as a solid element of the European infrastructure. The ODC will enhance and monitor availability of radar data, facilitate quality control of single-site radar data, stimulate exchange of volume radar data and quality information, and produce a homogeneous European radar composite. Furthermore, the ODC will deliver radar data to users inside and outside the National Meteorological Services. In May 2009, the EUMETNET Council has approved further development of the ODC. Start of operation of the ODC is planned for early 2011. More information on OPERA can be found in Holleman et al. (2008) and on the website. Satellite rainfall products, in spite of their intrinsic problems and not completely defined quality figures, are characterized by a global perspective that no other measurement method has. Because of this, their applications are numerous and cover very different fields such as meteorology, hydrology, civil protection, and climate. We will only mention a few examples without pretending of being complete. As is the case of radar precipitation measurement, the first field of application is in nowcasting, when a larger coverage than the one ensured by the radar is necessary. The satellite, in fact, ensures a mesoscale perspective, which becomes synoptic when LEO and GEO orbits are used. Another very important meteorological application is in data assimilation for NWP. Several physical (nudging) and variational methods have been developed in time at all scales. It is generally accepted that precipitation assimilation (e.g., Davolio and Buzzi, 2003) is more suited at the mesoscale, where current models start to incorporate the appropriate cloud parametrizations that general circulation models often lack.

Figure 10 9 June 2009, 12:30 UTC. An example of Opera rain rate composite (left) and MSG-SEVIRI rain rate retrievals (right) for Europe, presented in the projection of MSG.

Observation of Hydrological Processes Using Remote Sensing

Hydrological applications of satellite rainfall products span from the assimilation into hydrological models for basin management to global hydrological predictions. In all cases, uncertainty definition is the key to successfully use satellite data in this field (e.g., Voisin et al., 2008). Another important problem of current global products is the effect of orography on the retrieval (Adam et al., 2006). An upcoming very interesting application that merges a hydrological and a civil protection perspective is the one that uses satellite global rainfall data for landslide prediction (Hong and Adler, 2008); their methodology identifies landslide-prone areas on the basis of morphological information and rainfall for providing a hazard map. The number of climatological applications is expected to increase substantially over the next few years, given the global character of satellite data. The Global Precipitation Climatology Project (GPCP; Adler et al., 2003) has gathered global satellite rainfall estimations since 1979. GPCP products are now used to evaluate models and verify scenarios on the impacts of the various phenomena (ENSO, volcanic eruptions, etc.) on climate. The most important scenario is to verify whether global warming produces an acceleration of the global water cycle with more extremes (droughts on one side and extreme floods on the other) or not (e.g., Curtis et al., 2007). Finally, regional studies are conducted to examine the structure of propagating convective episodes in the warm season for their better forecasting and their modification in a climate perspective (e.g., Carbone and Tuttle, 2008; Laing et al., 2008).

2.14.4 Water to the Atmosphere – Evaporation 2.14.4.1 Introduction and Historic Development In the middle of the last century, ET from well-watered land surfaces was thought to be controlled by meteorological conditions, and only in the 1970s it was recognized that spatially and temporally dynamic feedback mechanisms between ET and land surface (e.g., albedo, rooting depth, and temperature) play an important role. This invoked the first applications of RS-based approaches, which mainly made use of airborne scanners (Bartholic et al., 1972; Idso et al., 1975; Jackson et al., 1977; Stone and Horton, 1974). It was only in the following decade that the first use of thermal data obtained from satellites to estimate ET was seen (Price, 1982; Seguin and Itier, 1983). They comprised of statistical approaches using linear relationships between daily totals of ET and net radiation and the difference between near-midday observations of radiant temperature and near-surface air temperature. Naturally, these linear relations needed local calibration, and the influences such as wind velocity, thermal stratification, and surface roughness were incorporated in later work (Riou et al., 1988), as such trying to bridge the gap already recognized by Seguin and Itier (1983) between sophisticated models useful for understanding basic processes and for performing informative simulations on the one hand and real estimation of ET on the other. These developments led to the general acceptance of the idea that spatial variability in ET is important, which in turn stimulated the development of effective methods for determining landscape-scale ET. However, still a need was noticed

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for models that can realistically simulate the distributed nature of land surface processes and for techniques capable of upscaling estimates that are based on point-scale observations (Shuttleworth, 1988; Kalma and Calder, 1994), mainly used for validation. As observed by Kalma et al. (2008), this viewpoint developed more or less simultaneously with the use of airborne eddy correlation measurements (Schuepp et al., 1992; Mann and Lenschow, 1994), the development of scintillometry (de Bruin et al., 1995; Green et al., 2001), and an increased use of RS techniques. The main attraction of the last technique probably is the possibility of integration over a heterogeneous area at different resolutions and of routinely generating operational ET estimates. From 1990 onward until now, a vast amount of models have been developed and tested in a large number of multidisciplinary large-scale field experiments (Kustas and Goodrich, 1994; Shuttleworth et al., 1989; Kabat et al., 1997; Hollinger and Daughtry, 1999; Su et al., 2008, 2009). The increased understanding of the observed processes resulted in a number of excellent overview papers on both these processes and their typical impediments (Moran and Jackson, 1991; Becker and Li, 1995) as well as on the methodologies to estimate ET themselves by Kustas and Norman (1996) and Quattrochi and Luvall (1999) and more recently by Kalma et al. (2008). The models that have evolved mainly differ in type or purpose of the application which basically determines the type of RS data used and to which extent ancillary data are needed. What they all have in common is that the main input originating from RS is thermal information. It is obvious that no method or algorithm will outperform all other methods under all conditions and that a selection has to be based on the scale and purpose of the application as well as on the availability of the required data.

2.14.4.2 Current State of Science There are currently several methods being used, which can roughly be divided into three categories: they are either based on statistics and empirics, on spatial variability using either within image hydrological contrasts or some kind of index, or they are physically based, more specific on the energy balance at the Earth’s surface. As this chapter deals with the observation of hydrological processes, we will focus on the last category. For the sake of completeness, first, we briefly discuss the statistical and spatial variability methodologies followed by a description of current frequently used physical, or analytical, approaches.

2.14.4.2.1 Statistical approaches The methods using mainly statistical and empirical relations, also the first that were developed, make use of quasi-linear relationships between difference in daily amounts of ET and net radiation on one side and observed instantaneous differences between radiometric temperature and near-surface air temperature on the other. A prerequisite here is the use of near-midday temperature differences on clear days as these are representative of the entire day due to the regular course of climatic parameters during cloud-free days. These methods all originate from the work of Jackson et al. (1977), who derived a single statistical regression constant for the relation between

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Observation of Hydrological Processes Using Remote Sensing

inverting an energy balance model by Boegh et al. (2002) and McVicar and Jupp (2002) to overcome this shortcoming.

the instantaneous temperature differences and daily ET and net radiation. Although later work incorporated aerodynamic surface properties on atmospheric stability effects (Seguin and Itier, 1983; Riou et al., 1988) and, as such, moved into the direction of a physically based energy balance approach (Nieuwenhuis et al., 1985; Soer, 1980; Lagouarde and McAneney, 1992), these types of approaches still require local calibration. Therefore, they are currently more often used in combination with scaled indices derived from scatterplots of midday temperature versus normalized difference vegetation index (NDVI; Carlson et al., 1995) as such reducing the need for local calibration and ancillary data input, making them more suitable for operational monitoring of ET, reaching accuracies of around 1 mm on a daily basis (Kustas and Norman, 1996).

2.14.4.2.3 Physical approaches This brings us to the physically based RS algorithms to derive ET estimates. They are all based on the idea that ET is a change of the liquid state of water to the gaseous state, hereby using available energy in the environment for vaporization. The available energy is the net radiation, which is the budget of all shortwave and longwave incoming and outgoing radiation at the Earth–atmosphere interface, less the heat used for heating up that interface, that is, the Earth’s surface, commonly referred to as the soil heat. The available energy is then thought to be used either for heating up the atmosphere, the so-called sensible heat, or for changing the state of water, the latent heat. Soil heat is generally considered a fraction of net radiation (Su, 2002; Norman et al., 1995; Anderson et al., 1997) depending on vegetation characteristics, and several studies have indicated that net radiation can be accurately determined from RS data (Timmermans et al., 2007; Boegh et al., 1999; Kustas and Norman, 1999; Su et al., 2001); the main remaining task is the division of the available energy between sensible and latent heat. The most widespread approach, also commonly used in land-surface modeling (Overgaard et al., 2006), is to consider the Earth–atmosphere interface, the Earth’s surface, as an electrical analog. Basically, this means that the rate of exchange (i.e., flux) of a quantity (e.g., temperature or vapor pressure) between two media (e.g., the Earth and the atmosphere) is driven by a difference in potential of that quantity, and controlled by a number of resistances that depend on the local climate as well as on the internal properties of the two media. The remote determination of vapor pressure is not feasible with the current state of technology. Therefore, the approach is to determine the rate of exchange of temperature between the Earth and the atmosphere, that is, the sensible heat flux, and determine the latent heat flux as a rest term. Dividing the latent heat flux by the latent heat of vaporization then yields ET. This means that current research efforts aim at the proper determination of the sensible heat flux. The different approaches to this problem are sketched in Figure 11. Basically, they differ in whether or not they discriminate between soil and canopy components. In Figure 11(a), the

2.14.4.2.2 Variability approaches

Soil

Canopy

Atmosphere

This brings us to the methods using the spatial variability within the image. They use either two-dimensional scatterplots of surface radiant temperature versus NDVI, the so-called triangle methods (Nemani and Running, 1989; Price, 1990) or the within-image relation between surface temperature and surface reflection (Bastiaanssen et al., 1998a; Roerink et al., 2000; Su et al., 1999), both based on the original work of Menenti and Choudhury (1993) to determine the hydrological wet and dry extremes. In the triangle approaches basically the observed position of a pixel within the scatterplot determines the ratio between actual and potential ET. The methods using temperature and reflective properties use a scaling between the observed wet and dry edges along the surface temperature, using either solely temperature such as in the S-SEBI approach (Roerink et al., 2000) or in combination with the use of a local surface roughness estimate as in the SEBAL approach (Bastiaanssen et al., 1998a). Whereas these methods do not need very accurate atmospheric correction techniques nor detailed meteorological inputs, they are limited by the fact that hydrological contrast needs to be present within the observed scene. In the case of the triangle methods, this is circumvented by comparison with a theoretically derived scatter triangle (Jiang and Islam, 2001; Carlson et al., 1994; Gillies et al., 1997; Venturini et al., 2004), whereas a temperature scaling was coupled to surface resistance by

(a)

Figure 11 Sketch of different resistance schemes.

(b)

(c)

Observation of Hydrological Processes Using Remote Sensing

sensible heat flux is driven by the difference between the aerodynamic surface temperature at the canopy source/sink height and the near-surface air temperature and controlled by a single aerodynamic resistance to sensible heat transfer between the canopy source/sink height and the air/atmosphere at a reference height above the canopy. The aerodynamic resistance is generally calculated from local wind speed, surface roughness length, and atmospheric stability (Brutsaert, 1982, 1992). Although these single-source models are known to give good results under a variety of conditions and environments (Kustas, 1990; Kustas et al., 1996; Bastiaanssen et al., 1998; Su, 2002; Jia et al., 2003), their main problem is that the necessary aerodynamic surface temperature at the mean canopy air stream is different from the radiometric surface temperature obtained from RS observations. This is usually corrected for by introducing an extra resistance that mainly depends on the inverse Stanton number, a dimensionless heat transfer coefficient that originates from the difference in source/sink heights for momentum and for heat transport. Although robust models exist to estimate this parameter (Massman, 1999; Su et al., 2001), it is known to vary widely, especially over sparse vegetation. This has led to the so-called dual-source models that treat the soil and canopy separately. Two different approaches are noticed. When the surface, or pixel, is divided into different fractions of bare soil and vegetation, the soil and vegetation components do not interact. These models, first introduced by Avissar and Pielke (1989), are known as patch models, or parallel resistance network as they are more frequently named in the RS community (see Figure 11(b)). However, when sparse vegetation is present, the soil and vegetation components are known to interact and a so-called series resistance network is more appropriate. In this case, the canopy consists of a semitransparent layer located above the soil surface such that heat and moisture have to enter or leave the surface layer through the canopy layer, whereby the component fluxes are allowed to interact (see Figure 11(c)). The structure proposed by Shuttleworth and Wallace (1985) is most widely used and incorporates a bulk stomata resistance for the vegetation as well as a resistance controlling the soil fluxes. It is assumed that aerodynamic mixing within the canopy invokes a mean canopy-airflow where fluxes from the components are allowed to interact after which they are exchanged with the atmosphere, controlled by a third aerodynamic resistance. Both structures require component temperatures, whereas a remote sensor only observes the effective radiometric surface temperature, which is a combination of the component temperatures, depending on viewing angle and fractional vegetation cover. To derive the component temperatures from the effective temperature, additional information is required. Several methods have been developed, ranging from empirical relationships (Lhomme et al., 1994), via coupling to a crop growth model (Chehbouni et al., 1996; Chehbouni et al., 1997) and the NDVI–surface temperature relationship (Boegh et al., 1999), to dual viewing angle approaches (Francois et al., 1997; Kustas and Norman, 1997; Merlin and Chehbouni, 2004). A different approach was developed by Norman et al. (1995) where transpiration initially is estimated through the Priestley–Taylor equation, whereby they were able to relate the canopy temperature to air temperature. This allowed initial

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guesses of component temperatures, which were then used in an iterative procedure to derive soil evaporation and canopy transpiration that satisfied the energy balance. In their original paper, Norman et al. (1995) described an operational procedure both for the series as well as for the parallel approach that, in different improved versions (Kustas et al., 2001; Anderson et al., 1997, 2005; Kustas and Norman 1999, 2000), is nowadays widely used (Sanchez et al., 2008; French et al., 2002; Li et al., 2005). By now it may be clear that using RS observations to derive latent heat fluxes, or ET, requires a certain amount of assumptions depending on the model and data used as well as on the purpose of the application. As such, it is no surprise that different techniques lead to deviating estimates (Zhan et al., 1996; French et al., 2005; Timmermans et al., 2007) and works still need to be undertaken to minimize those deviations.

2.14.4.3 Future Research Needs From the previous some residual challenges and thus future directions of research follow. They relate to spatial and temporal scaling issues, coupling and feedback issues, and, last but not least, validation issues. Although they may be categorized, they are discussed here in a coherent manner, as most of them are interrelated.

2.14.4.3.1 Scaling Depending on the application purpose, models describing the land–atmosphere interaction assume that both processes and variables are scale invariant (Menenti et al., 2004), which means that it is assumed that the relation of observations with model variables is the same at all spatial scales. Intermodel variability of predicted fluxes is therefore often large and causes are difficult to pinpoint (Menenti et al., 2004), which is probably the reason why only a few pixel-by-pixel flux comparisons (Timmermans et al., 2007; French et al., 2005; Boegh et al., 2004; Timmermans et al., 2009; De Lathauwer et al., 2009) are made (Overgaard et al., 2006). An in-depth analysis is needed of the nature of feasible observations in the soil– vegetation–atmosphere system at different (Tol et al., 2009; Timmermans et al., 2009) and multiple scales (McCabe et al., 2006) to detect and understand inconsistencies in model variables and parametrizations. There is also a need for improved temporal scaling procedures to extrapolate instantaneous estimates of ET derived from RS platforms to hourly, daily or longer periods (Kalma et al., 2008). Concepts most widely used so far (Shuttleworth et al., 1989; Batra et al., 2006) to extrapolate to daily values yield unsatisfying results, especially over drying surfaces (Chehbouni et al., 2008; Gentine et al., 2007). Given the fact that cloudy conditions hamper the frequent remote observation of ET, alternative approaches have to be explored, especially on timescales longer than 1 day.

2.14.4.3.2 Feedbacks Describing transfer of energy into the atmosphere using the energy balance methods generally invokes assuming homogeneous atmospheric properties. This requires neglecting fast changes in air temperature and humidity, and thus in fluxes

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thereof, due to turbulence. These changes occur at small spatial scales implying that spatial variability of the atmospheric properties at a given time is significant. A more realistic description of the structure and dynamics of the atmosphere is obtained by large eddy simulation (Albertson, 1996; Albertson et al., 2001). Opportunities to improve current RS-based ET estimates therefore include testing the spatial validity of the meteorological data used (Gowda et al., 2007). In addition, there is a critical need to understand the feedbacks between the land surface and the atmosphere at various scales (Wood, 1998). Feedback between land and atmosphere arises from the fact that the fluxes of heat and water from the land surface to the atmosphere will change the properties of the atmosphere, which in turn will change the fluxes. Therefore, more work is required in the line of Bertoldi et al. (2007) and Timmermans et al. (2008), who examined feedback effects at multiple scales using an RS-based energy balance model dynamically coupled to a large eddy simulation model.

2.14.4.3.3 Validation Apart from the ongoing discussion on the mismatch between available energy observations and turbulent flux measurements from eddy correlation (Foken, 2008) resulting in uncertainties of up to 30% in validation data, there is also considerable doubt on the applicability of scintillometry over very heterogeneous terrain (Timmermans et al., 2009; Ezzahar et al., 2007). Moreover, RS-based energy balance models tend to be validated versus a handful of tower-based measurements, which does not ensure a reliable performance over the broader landscape. To address this uncertainty, intercomparisons of flux model output need to be performed as reported by Timmermans et al. (2007) and French et al. (2005). In addition, a dynamic coupling of distributed hydrological and atmospheric models through an RS-based surface energy balance model, such as Timmermans et al. (2008), Velde et al. (2009), and Bertoldi et al. (2007), is vital for future applications and probably improves possibilities for making a more spatially detailed evaluation (Overgaard et al., 2006). To summarize, advances in improving parametrization and validation of physically based ET models will rely heavily on the understanding of physical processes at different scales as well as on the ability to obtain distributed physical information. In order to achieve this, satellite EO will prove to be of paramount importance in the future.

2.14.5 Water on the Land – Snow and Ice 2.14.5.1 Introduction The seasonal and perennial snow and ice masses (the cryosphere) cover a major part of the land surfaces. They are essential or dominating elements of the hydrological cycle in mid- and high latitudes, as well as in many mountain areas. The terrestrial cryosphere comprises the seasonal snow cover, lake and river ice, permafrost, seasonally frozen ground, glaciers, ice caps, and the large ice sheets of Greenland and Antarctica. Of these, seasonal snow cover and frozen ground on land dominate in spatial extent and temporal variability, covering at maximum about 50% of the land area in the Northern Hemisphere. Due to feedbacks with the atmosphere

and other elements of the hydrosphere, the cryosphere responds very sensitively to climate warming, as reports on past and ongoing changes of the snow and ice masses confirm (Lemke et al., 2007). Due to the large spatial extent and temporal variability of snow and ice coverage, RS techniques provide the only feasible means for timely and comprehensive observation of these elements of the Earth system. The potential of RS for monitoring snow and ice has been recognized already in the 1960s, applying optical imaging sensors of the NOAA satellites to mapping the global snow cover on a weekly basis (Robinson et al., 1993). Thanks to advancements in sensor technology, the 1970s brought in a big step forward in satellite-borne RS, including observations of the cryosphere. Optical sensors of improved spatial and spectral resolution and new active and passive MW sensors opened up the opportunity to monitor all the individual elements of the global cryosphere. Already at that time, RS became an indispensable tool for snow and ice monitoring and research that further evolved over the years, thanks to advancements in sensor technology and data processing (Key et al. 2007). Airborne sensors play an important role in the development of techniques for data processing and analysis, as well as in local to regional surveys of snow and ice. However, due to the near-global coverage and the regular repeat capabilities, satellite-borne sensors are the main tool for snow and ice monitoring. Therefore, in this chapter, we focus on applications of satellite sensors.

2.14.5.2 Techniques for Retrieval of Extent and Physical Properties of Snow and Ice Sensors in the VIS, infrared, and MW part of the electromagnetic spectrum are employed to monitor the extent and physical properties of snow and ice. In order to explain the information content of the various sensor types, the main features affecting the radiance reflected or emitted by snow and ice are summarized below. Electromagnetic waves, incident on a snow or ice medium, are subject to scattering at volume inhomogeneities (snow grains and air bubbles in ice) and absorption along the propagation path. In the case of melting snow, water adds as a third component of the mixture. The absorption and scattering characteristics are determined by the dielectric and structural properties of the medium and the sensor wavelength. At VIS wavelengths, the dielectric losses of ice and water are small, but increase considerably in the near- and mid-IR. In the thermal IR, snow is almost a black body (emissivity 0.99). Consequently, clean fresh snow has a high reflectance in the visible part of the spectrum (0.9oRVISo0.99), dropping to Ro0.1 in the shortwave IR at wavelengths X1.5 mm. The spectral reflectance in the visible decreases significantly with aging of snow due to pollution. In the near IR, between 0.9 and 1.3 mm, the reflectance decreases with increasing size of the snow grains, which is used to estimate this parameter from satellite measurements (Dozier and Painter, 2004). The direct effect of liquid water in a snow pack on near-IR reflectance is small, although the reflectivity decreases because melt metamorphosis causes snow grains to grow.

Observation of Hydrological Processes Using Remote Sensing

The decrease of reflectance in the IR is employed by the normalized difference snow index (NDSI) for discriminating snow cover and snow-free surfaces:

NDSI ¼

RVIS  RSWIR RVIS þ RSWIR

ð4Þ

The automated MODIS snow-mapping algorithm uses at satellite reflectances in MODIS bands 4 (0.545–0.565 mm) and 6 (1.628–1.652 mm) to calculate the NDSI (Hall et al., 2002). Different thresholds of the NSDI are used to detect snow in forested areas and open land (Salminen et al., 2009). For excluding cloud-covered pixels, the quality flag from the MODIS cloud-masking algorithm is applied which uses visible, SWIR, and thermal IR channels to detect clouds (Ackerman et al., 1998). In the case of patchy snow cover, the binary classification shows a trend of overestimating the total snow area. To account for these effects, spectral unmixing techniques, using VIS and near-IR channels to map snow cover fraction at subpixel scale, are applied (Vikhamar and Solberg, 2003; Dozier and Painter, 2004; Sirguey et al., 2009). Active MW sensors (synthetic aperture radar, SAR) and passive MW sensors (radiometers) are widely applied for mapping the extent and physical properties of the snow cover. For interpreting and analyzing MW measurements of snow, it is essential to consider the layers contributing to the observed signal. The penetration depth, dp, can be computed from the complex permittivity (e ¼ e0 – ie00 ) by

dp ¼

pffiffiffiffi l e0 2p e00

ð5Þ

The imaginary part of the permittivity of snow, e00 , and, therefore, also dp, shows a strong dependence on the liquid water content (Ma¨tzler, 1987). The dielectric losses in dry snow are small, and the penetration depth is of the order of several hundred wavelengths (e.g., about 20 m for C-band SAR with l ¼ 5.6 cm). On the other hand, the penetration depth in wet snow is only about one wavelength or less due to the high dielectric losses of water. In the C-band, for example, the penetration dp in snow with 5% by volume of liquid water is only 3 cm. This has an important impact on the signal observed by MW sensors. For wet snow, the MW signal reflected or emitted from a melting snow pack originates from a thin top snow layer and the snow surface, whereas for dry snow both the snow volume and the medium below the snow pack contribute to the observed signal (Rott, 1997). These properties cause distinct differences in the information provided by the various MW sensors. Imaging radars for snow mapping typically operate in the C- and X-band (l ¼ 5.6 cm, l ¼ 3 cm). At these wavelengths, the scattering contribution of a dry winter snow pack is small, and the backscatter contribution of the ground surface dominates. On the other hand, due to the high absorption losses, the radar reflectivity of melting snow is rather low. These characteristics enable to map melting snow areas by means of C- and X-band SAR, applying a change detection algorithm using SAR image time series (Nagler and Rott, 2000). Combining optical and SAR sensors for snow area mapping helps to overcome the cloud handicap of optical sensors which is a particular

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problem for updating snow extent in the ephemeral melting snow zones (Solberg et al., 2008). For observing snow water equivalent (SWE), a critical parameter for snow hydrology, shorter MW wavelengths need to be employed to obtain a distinct signal of the snow volume. The radiance emitted by the ground is attenuated in the dry snow pack by scattering at the snow grains. The attenuation due to volume scattering increases inversely to the third power of the wavelength (Bl3) (Hallikainen et al., 1987). The scattering losses depend on snow depth, density, and grain size. Currently, no satellite-borne imaging radar systems are available at short wavelengths, but MW radiometers are applied to map the depth and water equivalent of the snow pack. Retrieval of SWE from passive MW data is conventionally based on empirically determined relationships between SWE and emitted brightness temperature (TB). Standard procedures apply the difference in TB at 37 GHz (l ¼ 0.8 cm) and 19 GHz (l ¼ 1.6 cm) to estimate SWE (Foster et al., 2005). In order to compensate for effects of grain size, the parameters of the retrieval algorithms need to be tuned to regional snow conditions (Derksen et al., 2003). Another option for compensating grain size effects is the assimilation of in situ snow measurements in the SWE processing line (Pulliainen, 2006). Due to the coarse resolution of the sensors and the saturation of the signal in deep snow, radiometric SWE retrievals are subject to major errors in mountain areas and forests. Satellite-borne RS is widely applied for mapping the extent, surface topography, and motion of glaciers. For glacier mapping, spectral ratios in optical imagery are applied, similar to the techniques for snow mapping (Kargel et al., 2005; Paul et al., 2002). Manual post-processing is required to correct for debris-covered glacier surfaces. Stereo-optical satellite imagery (ASTER, SPOT-5) is applied to map surface topography, but the limited radiometric contrast reduces the accuracy in the snow areas (Berthier and Toutin, 2008). This problem can be overcome by radar interferometry (InSAR). Single-pass interferometry with two antennas on a platform, as on the Shuttle Radar Topography Mission (SRTM), avoids the problem of temporal decorrelation of the radar signal. The SRTM data set, acquired in February 2000, is the basis of a freely available DEM (90 m grid) covering the land surfaces between 601 N and 561 S (Rodriguez et al., 2005). Repeat-pass SAR images enable the mapping of ice motion at high accuracy by means of differential processing techniques. Differential InSAR processing techniques are applied to separate the phase contributions of surface motion and topography (Hanssen, 2001). However, decorrelation of the radar phase due to snowfall, wind drift, or melt in the time interval between the image acquisitions severely limits the application of repeat-pass interferometry over snow and ice. A unique InSAR data set for glacier studies, less affected by decorrelation, was acquired during the concurrent (‘tandem’) operation of the satellites ERS-1 and ERS-2 in the years 1995– 99 (Weydahl, 2001). During the tandem phase, the two satellites imaged the same swath on the Earth’s surface at a time difference of 24 h. If stable features are apparent on a glacier surface, image correlation techniques can be applied to map glacier motion from repeat-pass images of high-resolution optical sensors and SAR. This technique is less sensitive to motion than InSAR, but

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does not require phase coherence (Strozzi et al., 2002). The new very high resolution SAR systems of TerraSAR-X and COSMO-SkyMed are very useful for this application (Floricioiu et al., 2008).

2.14.5.3 Examples of Products and Applications Satellite-derived products on snow and land ice have been widely used in research and are increasingly applied also for operational applications in hydrology and water management. Table 2 provides an overview on some key snow and land ice products, including a few links to sample data sets. Medium-resolution optical sensors (e.g., AVHRR on NOAA and MODIS on the Terra and Aqua platforms) are the main data sources for snow mapping at national to global scales. These data are widely applied for studies in climate research and hydrology of snow-covered regions (e.g., Brown et al., 2008; Pu et al., 2007; Rodell and Houser, 2004; Shamir and Georgakakos, 2006). An example for a snow map derived ¨ tztal basin from MODIS data is shown in Figure 12 for the O in the Austrian Alps. The inset shows the area-altitude distribution of the snow cover, which is used as input to a semidistributed model for simulating and forecasting snowmelt runoff (Nagler et al., 2008). Daily snow maps are often rather fragmentary due to cloud cover, so that for some applications (e.g., climate studies) weekly composites are preferred. MODIS daily snow maps, 8-day composites, and monthly fractional snow cover can be found on the Internet. Sensors at higher spatial and/or spectral resolution are used for regional studies of snow physical properties and snow distribution (Dozier and Painter, 2004; Molotoch, 2009), but usually lack the temporal sequence required for real-time runoff forecasting applications. SAR data are used for regional snow mapping, with emphasis on snow depletion during the melt period, exploiting the sensitivity of the sensors for detecting melting snow. Preferably, SAR data of the wide swath mode (ScanSAR) are Table 2

used, providing a swath width of 400 km (Envisat ASAR) and 500 km (Radarsat) (Luojus et al., 2007; Nagler and Rott, 2005). SAR-derived snow maps are applied for snowmelt runoff modeling and forecasting (Nagler et al., 2008), for snow cover modeling linked to regional meteorological models (Longe´pe´ et al., 2009), and for climate studies. Global maps of snow depth and water equivalent, derived from satellite-borne multichannel MW radiometer data reaching back to 1979, are available for climate studies (Foster and Chang, 1993). However, in many regions, the data show systematic differences to in situ measurements, requiring further improvement of retrieval algorithms (Foster et al., 2005). In western Canada, weekly SWE maps retrieved from satellite MW radiometer data are produced on an operational basis since the 1980s (Derksen et al., 2003). An example of such a product for the Canadian Prairies is shown in Figure 13. Because the retrieval parameters are tuned for regional snow morphology, these SWE maps provide better accuracy than the global products. EO satellite data are widely applied for compiling and updating glacier inventories and provide key input data for models of glacier mass balance, hydrology, and ice dynamics. The main satellite products for glacier research and monitoring applications are maps of glacier area, topography, surface velocity, diagenetic facies, and albedo. The Global Land Ice Measurements from Space (GLIMS) initiative is aimed at compiling a global data base of glacier outlines in digital format from optical satellite data (Raup et al., 2007). The database, available to the public, includes satellite image glacier maps, vector outlines and related metadata, and, optionally, also snow lines, center flow lines, hypsometry data, and surface velocity fields. Observations of the temporal evolution of the snowline during the ablation period are used as input for modeling glacier mass balance and runoff (Rott et al., 2008). Satellite observations of changes in ice surface elevation and ice fluxes are also very relevant to mass balance studies (Bamber and Rivera, 2007). SAR interferometry is an

Overview on selected snow and land ice products derived from satellite observations

Product type

Sensor type

Spatial resolution (typical range)

Sensors (examples)

Selected data sets

Snow area (total)

Multispectral optical imager SAR, scatterometer Multispectral optical imager Imaging microwave radiometer

30 m–1 km

Modis, avhrr, landsat

Global snow area: http://modis-snowice.gsfc.nasa.gov/

30–100 m 250 m–1 km

Asar, radarsat Modis, meris

25 km

Ssm/i, amsr

Multispectral optical imager, SAR

30–250 m

Modis, asar, radarsat

Multispectral optical imager Interferometric SAR, optical stereo imager SAR, optical imager

5–30 m

Spot hrv, aster, landsat

10–100 m

Spot hrv, aster, srtm, asar ASAR, radarsat, terrasar-X, optical

Snow area (melting) Snow albedo Snow water equivalent

Lake and river ice extent and concentration Glacier outlines Glacier surface topography Glacier motion

3–30 m

http://www-modis.bu.edu/brdf/userguide/ albedo.html Amsr-e/aqua daily l3 global snow water equivalent: http://www.nsidc.org/data/ ae_dysno.html http://www.polarview.org/services/lim.htm http://www.polarview.org/services/rim.htm Glims glacier data base http://nsidc.org/glims/ SRTM data products: http://www2.jpl.nasa.gov/ srtm/cbanddataproducts.html

Observation of Hydrological Processes Using Remote Sensing

373

Figure 12 Snow map of the O¨tzal Alps, Austria, derived from MODIS image data, 25 April 2007. Superimposed to Google Earth. (Inset) Area-altitude distribution of snow-covered and snow-free surfaces in the sub-basin Vent.

important tool for studying glacier hydraulics (Magnu´sson et al., 2007) and provides detailed maps of ice flow, which can be used for estimating the ice export of calving glaciers (Stuefer et al., 2007).

2.14.5.4 Future Research Needs Currently available satellite missions and sensors are providing important information on the extent and physical properties of snow and ice from local to regional and global scales. This potential has been utilized so far mainly for dedicated research studies in the fields of water balance and hydrology, surface energy fluxes, land surface processes, and Earth surface/ atmosphere interactions. However, the potential of remotely sensed cryosphere data for process modeling has so far been rarely exploited. Fostering the use of spatially distributed snow data requires further advancements of data assimilation techniques, a topic that has gained in importance over the last years (e.g., Clark et al., 2006; Kolberg et al., 2006; Nagler et al., 2008; Rodell and Houser, 2004; Slater and Clark, 2006). Regarding sensors and satellites, a large variety of imaging sensors in the optical and MW spectral range is available, many of which can be employed for snow and ice observations. However, many sensors lack continuity, which represents an obstacle for operational use in hydrology and water management. New initiatives will provide better continuity of observations, such as the Sentinel satellites within the GMES initiative of the ESA and the European Union. A major observational deficit is the lack of a sensor for spatially detailed

observations of the snow mass (SWE), a key parameter of the water balance. The feasibility of a satellite mission for SWE mapping with dual frequency (X-band and Ku-band) SAR is presently studied by ESA (Kern et al., 2008).

2.14.6 Water on the Land – Surface Water, River Flows, and Wetlands (Altimetry) 2.14.6.1 Introduction Terrestrial surface water is absolutely essential to life, economies, environment, climate, and weather. Both national and local economies rely on flowing rivers to transport storm waters, sewage, and other effluents away from cities besides offering major shipping lanes to inland areas. The ecologies of wetlands and floodplains depend on surface water flows to deliver nutrients and to exchange carbon and sediments. Surface waters play a role in global climate through energy and water mass exchange with the lower atmosphere. Moreover, local weather is strongly affected by the surface area of nearby water bodies. Runoff is a strong indicator of accumulated precipitation throughout a watershed, and large, periodically flooded wetlands provide vast surfaces for evaporation as well as water storage. Earth’s 6 billion people critically rely upon surface water availability for domestic use, agriculture, and industry, while human health is impacted by waterborne diseases (e.g., disease-vector-related malaria). National defense issues are related to surface water, particularly via politically charged water impoundment projects. The global

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Observation of Hydrological Processes Using Remote Sensing

Snow water equivalent (mm) 0

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MSC Climate Research Branch SMC Climate processes and earth observation division

Figure 13 Map of snow water equivalent (color coded) over the Canadian Prairies, derived from satellite microwave radiometer measurements. The numbers refer to SWE measured at snow stations. Courtesy: Meteorological Service of Canada.

water issues will have large effects on many of the world’s major decisions in the next decades and will require operational monitoring tools to support water policies. The following sections provide a review of the surface-water measurements available on the ground, onboard satellites, and a description of the future satellite mission Surface Water and Ocean Topography (SWOT), first satellite mission dedicated to the hydrology of continental surface water.

2.14.6.2 In Situ Measurements In situ gauging networks have been installed for several decades in many river basins, distributed nonuniformly throughout the world. In situ measurements provide time series of water levels and discharge rates, which are used for studies of regional climate variability as well as for socioeconomic applications (e.g., water resources allocation, navigation, land use, infrastructures, hydroelectric energy, and flood hazards) and environmental studies (rivers, lakes, wetlands, and floodplain ecohydrology). In situ methods are essentially a one-dimensional, point-based sampling of the water surface that relies on well-defined channel boundaries to confine the flow. Yet, water flow and storage changes across wetlands and floodplains are spatially complex with both vast

diffusive flows and narrow confined hydraulics. This complexity is fundamentally a three-dimensional process varying in space and time, which cannot be adequately sampled with one-dimensional approaches. In addition, gauging stations are scarce or even absent in parts of large river basins due to geographical, political, or economic limitations. For example, over 20% of the freshwater discharge to the Arctic Ocean is ungauged and surface water across much of Africa and portions of the Arctic either is not measured or has experienced the loss of over two-thirds of the gauges (Stokstad, 1999). Therefore, our ability to measure, monitor, and forecast global supplies of freshwater using in situ methods is essentially impossible because of (1) the decline in the numbers of gauges worldwide (Vo¨ro¨smarty et al., 2001), (2) the poor economic and infrastructure problems that exist for nonindustrialized nations, and (3) the physics of water flow across vast lowlands.

2.14.6.3 RS Techniques During the past decade, RS techniques (satellite altimetry, radar and optical imagery, active and passive MW techniques, InSAR, and space gravimetry) have been used to monitor some components of the water cycle in large river basins (Cazenave et al., 2004). Radar altimetry, in particular, has been used

Observation of Hydrological Processes Using Remote Sensing

2.14.6.4 Validation and Synergy of RS Techniques Surface water levels estimated from conventional nadir altimetry have been compared to those obtained from in situ

gauges located along the satellite tracks and in the proximity of the altimeter swath over many of the largest river basins. The rms differences between in situ and altimetry-derived water levels have been computed and are usually in the order of a few to several tens of centimeters (Kouraev et al., 2004). Combining nadir altimetry-derived water levels with satellite imagery provides a new method for remotely measuring surface water volumes over large floodplains. Figure 15 shows an example of the interannual surface water volume signal variability obtained with a combination of altimetry and NDVI data from the SPOT-4/Vegetation instrument over the lower Mekong River Basin compared with the GRACE signal (black) that integrates surface and underground water

Mekong Basin 40 Water volume (km3 month−1)

extensively in the recent years to monitor water levels of lakes, rivers, inland seas, floodplains, and wetlands (e.g., Birkett, 1995, 1998; Birkett et al., 2002; Mercier et al., 2002, Maheu et al., 2003; Kouraev et al., 2004). A few examples of altimetry-derived water level time series over rivers are presented in Figure 14. Nadir-viewing altimetry has a number of limitations over land because radar waveforms (e.g., raw radar altimetry echoes after reflection on the land surface) are complex and multipeaked due to interfering reflections from water, vegetation canopy, and rough topography. These effects result in less valid data than over oceans. Systematic reprocessing of raw radar waveforms with optimized algorithms provides decade-long time series of terrestrial water levels, at least over large (41 km width) rivers. Repeat-pass SAR interferometry has been shown to offer important information about floodplains in measuring small water-level changes (Alsdorf et al., 2000). Poor temporal resolutions are associated with repeat-pass interferometric SAR. Off-nadir single-pass interferometric SAR does not work over open water; instead, it requires special hydrogeomorphologies of flooded vegetation (Alsdorf et al., 2000; Lu et al., 2005; Kim et al., 2005). Optical sensors are used to provide estimates of surface water extent under favorable conditions when there are few or no clouds. The GRACE gravimetry mission provides estimates of water volume but its resolution, on the order of 400 km, is poor (Tapley et al., 2004). Although the Shuttle Radar Topography Mission (SRTM) produced a high spatial resolution image of heights, the errors over water surfaces are quite large and the mission was active for a sampling period of only 11 days in February 2000, preventing temporal change studies (e.g., 75.5 m; LeFavour and Alsdorf, 2005).

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30 20 10 0 −10 −20 −30 −40 1998

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Year Figure 15 Variation of surface-water volume from ERS-2/ENVISAT altimetry and SPOT-VGT imagery (green) and total land water volume from GRACE (black) (Frappart et al., 2006).

Figure 14 Water-level time series over the Niger (upper panel; left), Yangtze (upper panel; right), Indus (lower panel; left), and Danube (lower panel; right) based on Topex–Poseidon altimetry. From http://www.legos.obs-mip.fr/en/soa/hydrologie/hydroweb/.

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Observation of Hydrological Processes Using Remote Sensing

(Frappart et al., 2006). In the near future, when GRACE observations improve in terms of geographical resolution, it will be possible to estimate change in water volumes stored in soil and underground reservoirs by using in synergy GRACE, altimetry, and imagery data.

2.14.6.5 Availability of the Satellite Data Sets A recently developed water-level database for major rivers, lakes, and wetlands using altimetry measurements from Topex/ Poseidon, Jason-1, ERS-2, ENVISAT, and GFO satellites can be accessed through the Internet. The database includes water levels for over 130 lakes and man-made reservoirs, 250 virtual stations on rivers, and about 100 sites on flooded areas. The time series are regularly updated and the number of sites increases regularly. Users have access to associated errors. For optical sensors, several databases are available through the web. For instance, the SPOT VEGETATION products can be found on the website where the NDVI products are available; for the MERIS instrument, the data can be found on the Internet.

2.14.6.6 SWOT: The Future Satellite Mission Dedicated to Surface Hydrology The currently operating radar altimeters built to sample the surface of the open ocean miss numerous water bodies

between orbital tracks. Optical sensors cannot penetrate the canopy of inundated vegetation and typically fail to image water surfaces when clouds or smoke is present (e.g., Smith, 1997). The prevalent vegetation and atmospheric conditions in the tropics lead to much reduced performances for technologies operating in and near the optical spectrum. Hydraulic measurements with repetitive global coverage of the continental surface water are needed to accurately model the water cycle and to guide water management (Alsdorf et al., 2003; Alsdorf and Lettenmaier, 2003). The future satellite mission SWOT dedicated to continental surface hydrology in cooperation between NASA and CNES will contribute to a fundamental understanding of the global water cycle by providing for the first time global measurements of terrestrial surface water storage changes and discharge, which are critical for present and future climate modeling (Mognard and Alsdorf, 2006; Alsdorf et al., 2007; Mognard et al., 2007; Fu et al., 2009). The Ka-band Radar Interferometer (KaRIN) (Figure 16) is the technology capable of supplying the required imaging capability of water level (h) with global coverage at least twice every 21 days. KaRIN has two Ka-band SAR antennae at opposite ends of a 10-m boom with both antennae transmitting and receiving the emitted radar pulses along both sides of the orbital track. Look angles are limited to less than 4.51 providing a 120-km-wide swath. The 200-MHz bandwidth achieves cross-track ground

ay r arr

Sola

Interferometer antenna 1

Interferomete antenna 2

10 m baseline

950 km

Nadir altimeter

Surface water topography

Ocean topography H-pol interometer swath 10−70 km

Nadir altimeter path

V-pol inteferometer swath 10−70 km

Figure 16 Artistic view of the satellite SWOT with the Ka-band Radar Interferometer (KaRIN) instrument.

Intrinsic resolution from 2 m × 70 m to 2 m × 10 m

Observation of Hydrological Processes Using Remote Sensing

resolutions varying from about 10 m in the far swath to about 60 m in the near swath. A resolution of about 2 m in the along track direction is derived by means of synthetic aperture processing. SWOT will contribute to a fundamental understanding of the global water cycle by providing global measurements of terrestrial surface-water storage changes and discharge, which are critical for present and future climate modeling. SWOT will facilitate societal needs by (1) improving our understanding of flood hazards by measuring flood waves and water elevations, which are critical for hydrodynamic models; (2) freely providing water volume information to countries that critically rely on rivers that cross political borders; and (3) mapping the variations in water bodies that contribute to disease vectors (e.g., malaria).

2.14.7 Water in the Ground – Soil Moisture 2.14.7.1 Introduction Soil moisture is defined as the amount of water in the rooting zone, or any other depth in the unsaturated zone and is usually expressed in volumetric percentage (Hillel, 1998). It is a variable that has always been required in many disciplinary and cross-cutting scientific and operational applications such as numerical weather prediction, ecology, biogeochemical cycles, flood forecasting, etc. (Jackson et al., 1999). With increasing evidence of climate change, it becomes even more urgent to be able to elucidate the critical role of soil moisture. Unfortunately, soil moisture is notoriously difficult to observe at large (landscape to global) scale due to its high spatial and temporal variability. Most of our limited understanding of the role of soil moisture in meteorology, hydrology, ecology, and biogeochemistry has been developed from point to field-scale studies, where the emphasis was typically on the variation of soil moisture with depth. Our failure to translate this smallscale understanding to natural landscapes can be attributed largely to our lack of understanding of soil moisture variability at larger spatial scales. As a parallel consequence, most models have been designed around the available point data and do not reflect spatial variability (Leese et al., 2000). The potential to use MW RS for measuring soil moisture has been recognized early (Eagleman and Ulaby, 1975). The theoretical basis for measuring soil moisture at MW frequencies lies in the large contrast between the dielectric properties of liquid water and dry soil material. The large dielectric constant of water is the result of the water molecule’s alignment of its permanent electric dipole in response to an applied electromagnetic field. Therefore, when water is added to the soil matrix, the effective dielectric constant of the soil increases strongly (Hipp, 1974). As the emission and scattering properties of the soil are strongly influenced by the soil dielectric constant, both active and passive MW measurements are highly sensitive to soil moisture (Ulaby, 1976; Schmugge et al., 1974). Methodological problems, lack of validation, and limitations in computing have frequently delayed the research process to retrieve soil moisture from space observations (Wagner et al., 2007). But research in these fields evolved, resulting in several global-and continental-scale soil moisture

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data sets (e.g., (Wagner et al., 2003; Owe et al., 2008; Njoku et al., 2003). This section gives a brief overview of the state of science on satellite soil moisture.

2.14.7.2 State of the Art Since the early days, satellite RS was seen as a potential tool to provide spatial and temporal continuous soil moisture measurements (Engman and Chaunhan, 1995). In particular, MW sensors are attractive because they can acquire imagery day and night unimpeded by cloud cover. However, even more important is the fact that many MW sensors are operated at frequencies below the relaxation frequency of water (9–17 GHz, depending on temperature) where the dielectric constant of soil changes strongly with the soil moisture content. For example, at 1.4 GHz, the dielectric constant of dry soil is around 3, while it is around 20–25 for a wet soil depending on soil texture (Wang and Schmugge, 1980). Given the strong effect of the soil dielectric properties on the emission and scattering of electromagnetic waves, both passive and active MW sensors provide a relatively direct means for assessing soil moisture when the soil is not frozen or snow covered. Further, sensors operating in the VIS and IR parts of the electromagnetic spectrum have been used for mapping soil moisture (Verstraeten et al., 2008). These methods use remotely sensed surface variables such as surface temperature or vegetation to constrain the surface energy and water balances to indirectly infer soil moisture. These methods essentially belong to the group of methods used for estimating evaporation and are hence discussed elsewhere. Active MW sensors used for soil moisture retrieval include synthetic aperture radars (SARs) for local- to regional-scale mapping and scatterometers for global monitoring (GM). These instruments transmit an electromagnetic pulse and measure the energy scattered back from the Earth’s surface. On the other hand, passive MW sensors (radiometers) merely record the radiation emitted by the Earth surface itself, which is related to the physical temperature of the emitting layer and the emissivity of the surface (Ulaby et al., 1981). Even though one might expect that active and passive sensors observe very different surface properties due to their different measurements principles, several land surface parameters, such as soil moisture, surface roughness, or vegetation biomass, have a comparable impact on both active and passive measurements. The fundamental reason for this is Kirchhoff’s law which, applied to the problem of RS of the Earth’s surface, states that the emissivity is one minus the hemisphere integrated reflectivity (Schanda, 1986). Therefore, soil moisture observed by active or passive sensors can be directly compared, particularly when the sensors are operated at the same frequency. The basic challenge for both active and passive soil moisture retrieval methods is that other surface variables, such as vegetation water content, vegetation structure, and surface roughness, also have a strong impact on the MW signal. Therefore, successful retrieval methods must be able to account for all these confounding land surface parameters. This might suggest that one should use models that describe the interaction of the MWs with the Earth’s surface in as much details as possible. Yet, such models become very complex and it is in general not possible to invert them. Even more

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Observation of Hydrological Processes Using Remote Sensing

problematic is that one generally does not have enough experimental observations to falsify complex models, simply because different model structures and parameter sets may explain the observations equally well (Wagner et al., 2009). This so-called equifinality problem (Beven, 2001) is possibly the major reason why it is often not possible to transfer algorithms calibrated over one region to another. These considerations show that it is essential to develop models that capture the main physical phenomena, yet be simple enough to allow falsification and inversion. This implies that models may differ depending on the spatial resolution of the satellite system, because the dominant processes often change with scale. Equally important as the retrieval algorithm is the selection of MW instruments. A suitable sensor exhibits a high sensitivity to soil moisture while minimizing instrument noise and the perturbing impacts of other surface variables on the measured signal (Wagner et al., 2007a). Many RS studies conducted in the 1970s, 1980s, and 1990s indicated that lowfrequency MW radiometers should offer the best performance because of the minimal influence of surface roughness and vegetation on these measurements (Jackson et al., 1999). Therefore, the first satellite mission dedicated to measuring soil moisture on a global scale uses an MW radiometer operated at a frequency of 1.4 GHz (L-band), that is, the Soil Moisture and Ocean Salinity (SMOS) launched on November 2009 (Figure 17). To improve its spatial resolution, SMOS uses a passive interferometric design inspired from the very large baseline antenna concept in radio astronomy (Kerr et al., 2001). Yet, its spatial resolution will only be in the order of about 40 km, which limits its use to large-scale applications such as numerical weather prediction or climate change. To enlarge the number of potential applications, the Soil Moisture Active/Passive (SMAP) mission foreseen for launch in 2014 uses both active radar and passive radiometer instruments at L-band. It will use a 6-m large rotating mesh antenna shared by the radar and radiometer to cover a 1000-km-wide swath (Figure 17). Thus, SMAP will offer a 40-km soil moisture product derived from its passive observations and a 10km product derived from the combined active and passive observations (Entekhabi et al., 2010).

SMOS and SMAP employ novel measurement concepts with the goal to measure soil moisture with unprecedented accuracy, and also existing MW sensors operated at frequencies below about 10 GHz can provide valuable soil moisture information. Particularly, in recent years, several soil moisture data sets derived from both active and passive MW sensors have become freely available, which demonstrate the advances made in algorithmic research. Wagner et al. (2007) suggested that this initially less visible revolution became possible, thanks to the increasing availability of computer power, disk space, and powerful programming languages at affordable costs. This has allowed more students and researchers to develop and test algorithms on regional to global scales, which lead to a greater diversity of methods and, consequently, to more successful algorithms.

2.14.7.3 Data Sets BBB The soil moisture data sets described in this section are all available for user download via file transfer protocol (FTP) or web portals. Table 3 gives an overview of the different products. Most of these data sets have a rather coarse spatial resolution in the order of 20–50 km because they are derived from MW radiometers or scatterometers. In addition, a first continental-scale 1-km soil moisture data set derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) operated in GM mode has recently been published.

2.14.7.3.1 Active MW data sets Investigations into the potential of active MW sensors for soil moisture retrieval began already in the 1960s and gained momentum in the 1990s due to the launch of several satellites that carried a synthetic aperture radar (SAR) on board. Unfortunately, there is still no widely accepted method that delivers SAR-derived soil moisture data at fine spatial scales (10–100 m). This is to some extent surprising given that a large number of backscatter models and retrieval approaches were proposed and successfully applied within pilot studies (Dubois et al., 1995; Zribi et al., 2005). Unfortunately, independent testing and transferring of the methods to other

Figure 17 Artist impressions of ESAs Soil Moisture and Ocean Salinity Mission (left) and NASAs Soil Moisture Active Passive Mission (right). Both satellites will be used for global soil moisture mapping. (Left) Image courtesy: ESA. (Right) image courtesy NASA.

Observation of Hydrological Processes Using Remote Sensing Table 3

379

A short list of accessible satellite derived soil moisture products using active and passive microwave instruments

Product name

Satellite

Spatial resolution

Temporal resolution

Period

Url

Reference

NSIDC L3 soil moisture LPRM soil moisture

Amsr-e

0.251

Sub daily

2002–now

http://nsidc.org/

Amsr-e, trmm-tmi, ssm/i, smmr Windsat

0.251

Sub daily

1978–now

http://geoservices.falw.vu.nl

Njoku et al. (2003) Owe et al. (2008)

0.251

Daily

2003–now

http://www.nrl.navy.mil/windsat/

Li et al. (2009)

Ers-1, ers-2

50 km

B 6 days

1991–now

http://www.ipf.tuwien.ac.at/radar/

Ascat

Metop

25/50 km

Daily

2006–now

http://www.eumetsat.int/

Asar

Envisat

1–5 km

Weekly

2005–now

http://www.ipf.tuwien.ac.at/radar/

Wagner et al. (2003) Bartalis et al. (2007) Pathe et al. (2009)

Windsat soil moisture Scat

regions or data sets often did not yield the hoped-for results (Walker et al., 2004). The major problem appears to be the failure to accurately model surface roughness and vegetation effects at fine spatial scales (Verhoest et al., 2008), besides the technical characteristics of most SARs (revisit time, frequency, etc.) are not well suited for the task of soil moisture monitoring. Parallel to the work on SAR, some research groups started to investigate the potential of the ERS scatterometer for land applications in the 1990s (Pulliainen et al., 1998; Woodhouse and Hoekman, 2000). Despite scatterometers were designed for monitoring winds over the oceans, these studies quickly demonstrated the potential of the ERS scatterometer for soil moisture monitoring at a 50-km scale (Wagner et al., 1999; Wen and Su, 2003). From an algorithmic point of view, the advantage of working at a scale to 50 km is that surface roughness and land cover can reasonably be assumed to be constant. The major technical benefits of the ERS scatterometer are its short revisit time and its high radiometric accuracy. In addition, its three antennas acquire three quasiinstantaneous backscatter measurements from different azimuth and incidence angles, which is important for separating vegetation and soil moisture effects on the signal. The first global soil moisture data set was derived from ERS scatterometer data using a change detection algorithm (Wagner et al., 2003). It was released in 2003 and has since then been used in several validation and application studies (e.g. Scipal et al., 2008). Using the same algorithm, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has developed the first operational, near-real-time soil moisture monitoring system based upon the Advanced Scatterometer (ASCAT) flown on board of the Meteorological Operational (METOP) satellite series. ASCAT is the successor instrument of the ERS scatterometer and offers a twofold improved temporal and spatial resolution (Bartalis et al., 2007). The change-detection algorithm developed for the scatterometer has been adapted to 1-km GM mode data as acquired by the Advanced Synthetic Aperture (ASAR) on board of ENVISAT (Pathe et al., 2009). This particular SAR mode has a rather poor radiometric resolution, but requires less energy as high-resolution SAR modes. Thus, it offers a good temporal coverage suitable for studying soil moisture dynamics.

2.14.7.3.2 Passive MW data sets In the passive domain, soil moisture research already started in the 1970s and one of the first soil moisture retrieval algorithms was developed by Njoku and Kong (1977). This algorithm used a simple regression technique on multifrequency MW observations to obtain soil moisture from a controlled bare soil site. In time, this modeling approach started to become more complex with the addition of a surface roughness module (Choudhury et al., 1979; Wang and Choudhury, 1981; Wigneron et al., 2001), a vegetation module (Kirdiashev et al., 1979; Meesters et al., 2005), and a dielectric mixing module to convert the soil dielectric properties to soil moisture (Wang and Schmugge, 1980; Dobson et al., 1985; Mironov et al., 2004). On a later stage, an atmosphere module (Pellarin et al., 2003; Liebe, 2004) and snow module (Pulliainen et al., 1999) were introduced to obtain a better description of the MW emission as measured by the satellite. Most of the global soil moisture data sets from passive MW observations are based on a selection of the given modules and the differences between the different products vary on the choice of modules. In this section, we describe the two most commonly used global soil moisture data sets. The first global soil moisture product was developed by Njoku et al. (2003) and uses X-band AMSR-E MW observations to retrieve soil moisture. This model uses a multichannel iterative forward-model optimization method to solve simultaneously for surface temperature, soil moisture, and vegetation water content (Njoku et al., 2003). In the forward mode, the retrieval algorithm iteratively adjusts values of the retrieval parameters using Fresnel relations adjusted for surface roughness and attenuation by vegetation cover using time-invariant parameters based on land cover type (Njoku and Chan, 2006). The modeled brightness temperature is then compared to the observed at-sensor brightness temperature until an iterative least-squared minimized solution is obtained. Polarization ratios are used instead of absolute brightness temperature because these minimize the effects of surface temperature (Sahoo et al., 2008). The model uses the X-band frequency to minimize the effects of radio-frequency interference (RFI) on the at-sensor brightness temperature (Njoku et al., 2005). The final soil moisture data set is screened to remove data over large water bodies, dense vegetation, snow, and permanent ice. This product is distributed by the

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Observation of Hydrological Processes Using Remote Sensing

National Snow and Ice Data Center (NSIDC) in EASE–GRID format with a nominal grid spacing of 25 km. The second global soil moisture product was obtained from different satellites sensors, including Nimbus SMMR, TRMM TMI, SSM/I, and AMSR-E (Owe et al., 2008). It used the Land Parameter Retrieval Model (LPRM) to retrieve soil moisture from passive MW observations. The soil moisture retrievals from LPRM were based on the solution of an MW radiative transfer model and solved simultaneously for the surface soil moisture and vegetation optical depth without a priori information of land surface characteristics (Meesters et al., 2005). The flexible approach created the possibility to retrieve soil moisture from a variety of frequencies. LPRM produced volumetric (approximately in m3 m3) soil moisture of approximately the first 12 cm for C-band MW observations. For X band, the penetration depth is a bit smaller, resulting in soil moisture values of the first centimeter. The data are distributed in a rectangular grid with a pixel spacing of 0.251

2.14.7.4 Validation Soil moisture products from passive and active MW satellite observations were extensively validated. In the absence of a homogeneous global soil moisture station network, the data sets were validated over regional networks (Ceballos et al., 2005; Wagner et al., 2007; Draper et al., 2009), intercompared (De Jeu et al., 2008; Rudiger et al., 2009; Mladenova et al., 2009), and evaluated against model data (Wagner et al., 2003). These studies found high correlations with in situ observations in semi-arid regions and somewhat lower correlations in agricultural areas. On average, the current active and passive MW soil moisture products have an accuracy of about 0.06 m3 m3 for sparse-tomoderate vegetated regions (De Jeu et al., 2008). For denser vegetation classes such as forests, the soil moisture retrievals start to become less accurate and at an LAI of about 4, no reliable soil moisture can be retrieved from the current passive MW sensors (De Jeu, 2003). Nevertheless, recent assimilation studies have demonstrated the potential use of these existing data sets for the regions where they can obtain reliable soil moisture. The assimilation of soil moisture observations from operational satellite systems was found to improve the model performance in agro-meteorology (de Wit and van Diepen, 2007), hydrology (Parajka et al., 2006), meteorology (Drusch et al., 2009; Zhao et al., 2006; Scipal et al., 2008), and climate (Liu et al., 2007; Loew et al., 2009). With the anticipated launch of the new satellites with more innovative sensors and the continuous scientific movement in algorithm development, an improvement on the quality of satellite soil moisture is expected. Furthermore, the use of satellite soil moisture in environmental research is not yet fully exploited, and further research is necessary to fully demonstrate the potential of these new data sets.

2.14.8 Water in the Ground – Groundwater (Gravity Observations) 2.14.8.1 Introduction Groundwater is vital for meeting agricultural, domestic, and industrial water needs, particularly in parts of the world where

the climate or topography does not allow for a reliable supply of surface water. It is also by far the most abundant form of fresh, unfrozen water on the Earth (Shiklomanov, 1993). Groundwater storage does not vary as rapidly as soil moisture or surface water, but it does exhibit significant seasonal and interannual variability (Rodell and Famiglietti, 2001) and it is susceptible to overexploitation (Alley et al., 2002). The slow process of groundwater recharge acts like a low-pass filter on transient weather conditions, so that multiannual water-table fluctuations in a natural setting may be a useful indicator of climate variations. Hence, groundwater storage observations are valuable for both practical and scientific applications. As with other water-cycle variables, monitoring groundwater storage at regional scales using in situ measurements is expensive and problematic, and at the global scale it is simply not feasible. RS has propelled global hydrology forward in the past 30 years, but because groundwater is hidden deep beneath the surface, it was the last component of the terrestrial water cycle to benefit from the technology. Near-surface stocks and fluxes of the water cycle can be inferred based on electromagnetic radiation (various wavelengths of light) emitted or reflected from the land surface and atmosphere. Satellites can only sense groundwater by the effect it has on Earth’s time-varying gravity field. Redistributions of water and other forms of mass cause changes in gravitational potential, which is imperceptible to human beings yet strong enough to perturb satellite orbits. This is the concept behind one of the most innovative Earth-observing satellite systems yet launched, the Gravity Recovery and Climate Experiment (GRACE).

2.14.8.2 GRACE Data Processing The primary goal of GRACE is to map the static and timevarying components of the Earth’s gravity field with better spatial resolution and accuracy than ever before (Tapley et al., 2004). GRACE comprises two satellites in a tandem, nearpolar orbit, approximately 200 km apart and 500 km above the Earth. As they orbit, a K-band MW ranging system continuously measures the distance between the two satellites, which is affected by heterogeneities in the Earth’s gravity field. These measurements, along with precise location information, can be used to construct a mathematical model of the shape of the gravity field, nominally on a monthly basis. Each gravity field solution is delivered as a set of spherical harmonic coefficients, rather than a gridded map. Wahr et al. (1998) and Rowlands et al. (2005) described two of the techniques available for converting the GRACE gravity data to mass anomalies (deviations from the long-term mean field). Further, in order to isolate changes in terrestrial water storage mass (groundwater, soil moisture, snow and ice, surface water, and biomass) one must remove the effects of atmosphere and ocean circulations using atmospheric analysis and ocean model outputs. Glacial isostatic adjustment must also be considered in certain regions, and a major earthquake can produce a gravitational anomaly, but the timescales of most solid earth processes are too long to be an issue (Dickey et al., 1997). Because of the nature of the measurements, GRACE has no ‘footprint’ or pixel resolution. Rather, there is a tradeoff between resolution and accuracy, so that the effective limit of resolution for estimating changes in terrestrial water storage is

Observation of Hydrological Processes Using Remote Sensing approximately 160 000 km2 (Rodell and Famiglietti, 1999; Rowlands et al., 2005; Swenson et al., 2006).

2.14.8.3 Retrievals of Groundwater Storage with GRACE Data Despite its origins in the field of geodesy, GRACE’s greatest contributions have been in the cryospheric and hydrologic sciences. GRACE has monitored the melting of the Greenland and Antarctic ice sheets as never before possible (Luthcke et al., 2006; Velicogna and Wahr, 2006) and quantified glacier melt in the Gulf of Alaska (Chen et al., 2006). GRACE terrestrial water-storage data have been used to constrain regional ET rates (Rodell et al., 2004), river discharge (Syed et al., 2005), soil moisture variations (Swenson et al., 2008), and surface-water-storage variations (Han et al., 2009), and to describe intercontinental teleconnections (Crowley et al., 2006). GRACE is also the first satellite system to observe regional scale variations in aquifer storage. Isolating groundwater from GRACE-derived terrestrial water-storage data requires knowledge of the other waterstorage variables, because gravimeters provide no indication of the sources or stratification of the mass changes affecting the time-variable gravity field. In polar and alpine regions, terrestrial water-storage variability is often dominated by changes in snow and ice (Niu et al., 2007). In humid tropical regions, such as the Amazon, surface water can be the major variable (Han et al., 2009). In the rest of the world, soil water typically exhibits the largest fluctuations on daily-to-seasonal timescales, whereas groundwater storage amplitudes can be as large or larger on seasonal and longer timescales (Rodell and Famiglietti, 2001). Biomass variations are near or below GRACE’s limit of detectability (Rodell et al., 2005). Following the approach suggested by Rodell and Famiglietti (2001), Yeh et al. (2006) and Rodell et al. (2007) demonstrated that groundwater storage variations can be isolated from GRACE terrestrial water-storage data using in situ root zone soil moisture observations or numerically modeled soil moisture fields. They verified their results using data from groundwater monitoring networks in Illinois and the Mississippi River Basin. Strassberg et al. (2007) achieved good results using the model-supported technique to estimate groundwater storage changes in the High Plains aquifer, likewise verified by monitoring well observations. Rodell et al. (2009) applied the technique to determine that groundwater beneath the Indian states of Rajasthan, Punjab, and Haryana (including Delhi) is being depleted at a rate of 17.7 km3 yr1 due to withdrawals for irrigation. Zaitchik et al. (2008) presented a more sophisticated approach for disaggregating GRACE-derived terrestrial water storage into its components, whereby an ensemble Kalman smoother is used to assimilate the GRACE data into a numerical land surface model. This approach has several advantages. First, physical equations of hydrologic and energetic processes, integrated within the model, provide a basis for synthesizing GRACE and other relevant observations such as precipitation in a physically consistent manner. Second, the model fills spatial and temporal data gaps, while observations anchor the results in reality. Third, in addition to separating groundwater, soil moisture, and other component

381

contributions, the assimilated output has much higher spatial and temporal resolutions than the original GRACE data. Zaitchik et al. validated the technique in the Mississippi River Basin using groundwater data from a network of wells, and showed significant improvement in both the timing and amplitude of modeled groundwater variations.

2.14.8.4 GRACE Data Access GRACE gravity data are produced and distributed by three centers that support the mission: the University of Texas Center for Space Research, NASA’s Jet Propulsion Laboratory (JPL), and the German Research Centre for Geosciences (GFZ). GRACE terrestrial water-storage products have been developed by many groups. Visualization and data portals include those provided by NASA/JPL, NASA/Goddard Space Flight Center (GSFC) and Stinger Ghaffarian Technologies, and the University of Colorado.

2.14.8.5 Concluding Remarks and Future Perspective Although other RS data can provide clues as to the location and characteristics of aquifers (Becker, 2006), satellite gravimetry is the only technology currently available for measuring regional-scale groundwater storage changes from space. In addition to GRACE, two other advanced gravity-monitoring satellites have been launched: GFZ’s Challenging Minisatellite Payload (CHAMP) in 2000 and the European Space Agency’s Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) in 2009. CHAMP was a major advance in gravimetry at the time of launch, but it was not accurate enough to infer water-storage changes, and it was quickly made obsolete by GRACE. GOCE will map the static gravity field with significantly higher spatial resolution than GRACE, but it is not well suited for monitoring the time-variable gravity field and inferring changes in groundwater storage (Han and Ditmar, 2008). GRACE is in its extended mission phase, beyond its initial 5-year goal. It could potentially continue through 2012. NASA, ESA, and many independent reports (e.g., NRC, 2007) have recognized the importance of the data provided by GRACE and the need for a follow-on mission to enable continued monitoring of terrestrial water and ice as only satellite gravimetry can. Technology upgrades, such as a laser ranging system, a lower Earth orbit with drag-free propulsion, or more satellites and different orbital configurations, could increase the accuracy and spatial resolution of the products. However, at the time of writing, a next-generation time-variable gravity mission had not yet been approved.

2.14.9 Optical RS of Water Quality in Inland and Coastal Waters 2.14.9.1 Introduction Inland and coastal waters are important natural resources yet they are seriously threatened by eutrophication, salinization, and heavy metal contamination. Excessive concentrations of suspended particulate matter (SPM) influence the productivity and thermodynamic stability of inland and coastal waters (Muller-Krager, 2005: 348). Traditional measurements of

382

Observation of Hydrological Processes Using Remote Sensing

water quality are costly, time consuming, and are limited in their spatial and temporal coverage. EO data, on the other hand, provide rapid and repeated information over large and often inaccessible areas. EO, in conjunction with modeling and strategic in situ sampling, can play a crucial role in determining the current status of water-quality conditions and helps anticipate, mitigate, and even avoid future water catastrophes (DiGiacomo et al., 2007). The primary measurement of EO data over water is the visible light leaving the water column, hereafter called the water-leaving reflectance. In inland and coastal waters, this water-leaving reflectance is strongly affected by different materials, for example, terrigenous particulate and dissolved materials, resuspended sediment, or highly concentrated phytoplankton bloom. The majority of inland and coastal waters can therefore be classified as case 2 waters (Gordon and Morel, 1983). In case 2 waters, the constituents are independent of each other and do not covary with chlorophyll a as in case 1 waters. RS of inland and coastal waters is quite challenging due to the complicated signals from turbid water, substrate reflectance, and adjacent land surfaces (Figure 18). Consistent EO estimates of water-quality parameters in inland and coastal waters require three components: (1) a reliable atmospheric correction method; (2) an accurate retrieval algorithm; and (3) an objective method to estimate the uncertainty budget based on their sources. Because of limitation in length, the scope of this chapter has been narrowed to confine some of the recent developments in

each of the above-mentioned areas. Knowledge of the basic concepts of aquatic optics is assumed available.

2.14.9.2 Atmospheric Correction Most of the atmospheric correction procedures fail over inland and coastal waters, that is, case 2 waters. The failure of atmospheric correction might be attributed to the complexity of the recorded reflectance. The standard approach by Gordon and Wang (1994), for example, assumes a zero water-leaving reflectance in the near-infrared (NIR). In case 2 waters, this water-leaving reflectance has distinctive values at the NIR part of the spectrum (Siegel et al., 2000). The non-negligible value of water-leaving reflectance at the NIR was accounted by many authors (Carder et al., 1999; Gould et al., 1999; Ruddick et al., 2000; Hu et al., 2000; Salama et al., 2004). Coupled approaches are increasingly used to retrieve the optical properties of both water and atmosphere simultaneously. For each atmosphere–water setup, a TOA reflectance is simulated at variable viewing-illumination conditions. The parameters that define each media are tuned until the best convergence to the recorded reflectance is found (Chomko et al., 2003; Stamnes et al., 2003; Gordon et al., 1997; Zhao and Nakajima, 1997). However, most of these algorithms were developed for case 1 waters, that is, assuming known and spatially homogeneous water-leaving reflectance at the NIR. Newly developed algorithms are emerging for case 2 waters (Kuchinke et al., 2009a, 2009b). The spectral optimization method (Kuchinke et al.,

Scattering of direct and diffuse incident light

Observed reflectance by the sensor

Direct and diffuse incident sun light

ce e rfa Su ctanc le ref

Land

Water

Scattering, absorption and remittance by water constituents

A ref djac lec en tan t ce

Reflectance from mixed land water pixel

Bidirectional substrate reflectance Figure 18 Schematic diagram of the different processes that contribute to the observed remote-sensing reflectance at a pixel size in inland and coastal waters.

Observation of Hydrological Processes Using Remote Sensing 2009b) was constrained to 0.1 m m1 as a maximum value of backscattering coefficient of SPM at 0.443 mm. This value of backscattering is equivalent to 12 g m3 concentration of suspended particles using the specific backscattering coefficient of Albert and Gege (2006). On the other hand, artificial neural network techniques (Doerffer and Schiller, 2007) are usually limited to the range of their training sets. Most of estuarine and coastal waters have high loads of SPM, exceeding 12 g m3. For instance, the Yangtze estuarine water is extremely turbid with SPM concentration ranging between 80 and 500 g m3 (Shen et al., 2010). The spatial variabilities of the aerosol and water signals at the NIR part of the spectrum are characteristic features of turbid inland and coastal waters. These variabilities can be attributed to the different aerosol types that may coexist in this transaction zone as well as to the distinctive shape of the water-leaving reflectance. Salama and Shen (2009) proposed an analytical approach to consider and quantify this variability (Figure 19). Their method was validated with in situ measurements and successfully applied on data obtained from orbital ocean color and geostationary sensors. Deriving water-quality parameters from geostationary satellite in open coastal areas (Salama and Shen, 2009; Neukermans et al., 2008) is of unprecedented benefit. It will facilitate resolving the temporal dynamic of marine bio-geophysical parameters and overcome cloud covers.

2.14.9.3 Retrieval Algorithms Most developed algorithms for water-quality retrievals in inland and coastal waters are empirical in nature. This empiricism limits their application to a specific range of concentrations, area, and season. Kallio et al. (2001) studied different algorithms to estimate chlorophyll a in lakes. These algorithms were empirical and estimated one variable using band ratio of approximately 0.675 and 0.705 mm (Dekker et al., 1992; Gitelson et al., 1993). A generalized retrieval algorithm is, however, hindered by the large natural variability of inland waters. Significant efforts on improving the accuracy of air- and space-bornederived water-quality parameters are therefore required for inland and near-coastal waters. Many studies have used semi-

1.3

0.1 116°9′E

116°9′E

Aerosol reflectance 865 nm 0 (a)

29°31′N

29°31′N

116°9′E

29°31′N

29°31′N

116°9′E

0

b b(spm)(550) m−1

(b)

Figure 19 (a) Derived aerosol reflectance above the Poyang Lake. (b) Derived SPM concentration in the Poyang Lake. Notice that they are totally uncorrelated.

383

analytical models to derive water-quality parameters in lakes (Hoogenboom et al., 1998; Gons et al., 2002). Derived waterquality parameters from multivariable inversion methods are ambiguous and not unique (Sydor et al., 2004). Other promising methods are used when the inversion employs lookup tables (Van Der Woerd and Pasterkamp, 2008). Salama et al. (2009) showed that the inversion is very sensitive to the spectral shape parameters of SPM backscattering and absorption of dissolved organic matter. Including these two parameters has enhanced the retrieval in inland waters (Figure 20).

2.14.9.4 Uncertainty Estimates Reliable methods for uncertainty quantification of waterquality EO products are important for sensor and algorithm validation, assessment, and operational monitoring. High accuracy in both observations and algorithms may reduce considerable ranges of errors. EO-derived water-quality parameters have, however, an inherent stochastic component. This is due to the dynamic nature of water, intrinsic fluctuations, model approximations, correction schemes, and inversion methods. Quantitative measures of uncertainty support water quality and ocean-color product validation, especially with the introduction of the new AERONET-OC network (Zibordi, 2006). Due to stochasticity of the measurements, as well as model approximations and inversion ambiguity, the retrieved inherent optical properties (IOPs) are not the only possible set that caused the observed spectrum (Duarte et al., 2003; Sydor et al., 2004). Instead, many other sets of IOPs may be derived. Each of these sets has an unknown probability of being the derived ocean-color product. The probability distribution of the estimated IOPs provides, therefore, all the necessary information about the variability and uncertainties of derived water-quality parameters. Several efforts have been carried out to resolve the uncertainty of the derived IOPs. Duarte et al. (2003) analyzed the sensitivity of the observed RS reflectance due to variable concentrations of water constituents. Salama (2003) proposed a stochastic technique to quantify and separate the source of errors of IOPs derived from hyperspectral airborne measurements. Maritorena and Siegel (2005) employed a nonlinear regression technique for consistent merging of different ocean-color-derived products. Wang et al. (2005) performed a detailed study on the uncertainties of ocean-color model inversion related to fluctuations in each of the IOPs and their spectral shape. In general, these studies used the method of Bates and Watts (1988) to construct the confidence interval around the derived IOPs following different approaches, however. It is adequate as long as model inversion has a wellconditioned Jacobian matrix of the minimum cost function. Recently, Salama and Stein (2009) developed a generic method to quantify the uncertainties in the derived waterquality products based on their sources, namely, model approximations, measurement noise, and atmosphere correction. The method was evaluated and validated using oceancolor data sets (Figure 21). The method has promising applications for inland and coastal water. Moreover, it provides vital input to SPM assimilation models (Eleveld et al., 2008) and EO product merging (Pottier et al., 2006).

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Observation of Hydrological Processes Using Remote Sensing

Chlorophyll a mg m−3

Poyang lake RMSE = 0.03 g m−3 2 R = 0.95 30

Wolderwijd and Veluemeer −3 RMSE = 0.32 mg m 2 R = 0.78

Wolderwijd and Veluemeer RMSE = 0.342 g m−3 R 2 = 0.95

−3

8

Poyang lake RMSE = 0.06 mg m−3 2 R = 0.94

Derived SPM g m

Derived chlorophyll a mg m−3

10

SPM g m−3

40

6

4

20

10 2

Poyang Lake 1:1 line Wolderwijd and Veluemeer

Poyang lake 1:1 line Wolderwijd and Veluemeer

0 0

2 4 6 8 Measured chlorophyll a mg m−3

0 0

10

10

20 30 Measured SPM g m−3

40

Figure 20 Results from remote-sensing inversion in inland waters. The spectral shape parameters were also derived from the inversion. (a) Derived chlorophyll a and (b) derived suspended particulate matter.

Total absorption

Scattering; SPM

10

15 r 2 = 0.88 2 = 0.005 r reg f = 0.93

1:1 line Regression

5

0

-5 -5

(a)

This work Derived error of a total(440), m−1

Derived error of b spm(550), m−1

15

0

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Known RMSE of bspm(550), m−1

10

r 2 = 0.67 2 = 0.001 r reg f = 0.86

5

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-5 -5

(b)

0

5

10

15

Known RMSE of a total(440), m−1

Figure 21 Estimated total error (dot symbols) on IOPs derived from SeaWiFS spectra of the Nomad data set. Nonlinear regression results are superimposed as plus symbols; r2 and r2reg are the correlation coefficients for dot and plus symbols respectively; f is the fraction of successful retrievals. (a) Errors of scattering coefficient; (b) error of total absorption coefficient. From Salama MS and Stein A (2009) Error decomposition and estimation of inherent optical properties. Applied Optics 48: 4947–4962.

2.14.9.5 Concluding Remarks and Future Perspective We have summarized the three requirements for reliable retrievals of water-quality parameters from RS data in inland and coastal waters. Although EO operational products are still under development, there are few issues that need extra attention in the future: 1. The red, NIR, and even shortwave IR bands, with sufficient signal-to-noise ratio, are necessary for RS of inland waters. They improve the accuracy of derived IOPs. 2. Improved parametrization of IOPs is needed for inland waters. The improvement should (a) account for different phytoplankton species and (b) deconvolve the overlapped absorptions at the blue.

3. Reliable methods to account for absorbing aerosol and adjacency effect in inland waters. 4. Studying the effects of climate change on water quality and a better understanding of the role of water quality of large inland lakes on the radiative energy budget on a subcatchment scale.

2.14.10 Water Use in Agro- and Ecosystems 2.14.10.1 Introduction A comprehensive review of reflective (and partly thermal) RS techniques applied to agro-hydrology and ecological systems was given by Dorigo et al. (2007). Understanding the

Observation of Hydrological Processes Using Remote Sensing

opportunistic nature of RS acquisitions, the traditional approach selected in most cases for the evaluation of RS-derived biophysical variables consists of the statistical comparison between field ancillary data and a corresponding RS data subset. The analysis ends by evaluating the strength of the correlation between these data sets. Actual water use, transpired by the crops and evaporated from the soil, is the main output of a vast number of soil– vegetation–atmosphere transfer (SVAT) algorithms and surface energy balance (SEB) models. In the SVAT and SEB sequence, a great number of submodels are required to retrieve land properties from reflective optical measurable from RS instruments. As such, crop-water requirements from RS (AET-RS) processes are heavily demanding in terms of data input and modeling. Along with the image process and analysis, a dedicated number of intermediate products are elaborated which are simultaneously essential for many other ecohydrological applications. We consider that a good overview of the use of water in agro- and ecosystem can be tackled by reviewing SVAT and SEB models. Efforts focused on the use of AET-RS and its pre- and postelaborated products are not only to improve water management in irrigated lands, but also associated with this, in irrigation planning, and irrigation monitoring, leading to performance indicators (Bos et al., 2005), water competition and water strategy at basin level (Bos et al., 2009), soil moisture retrievals (Wang and Qu, 2009), and several other categories of hydrological modeling benefiting from this approach. AET-RS estimation opens the essential spatial dimension to a diversity of agro-ecological models on the one hand, and welcomes continuous model output to cover the typical temporal gaps in RS imagery. Increasingly, data assimilation techniques are used to integrate RS information into continuous modeling with success (Loew, 2007). In this section, the topic of AET-RS products and the suggested link to FAO crop factors and irrigation (FAO, Food and Agriculture Organization; Allen et al., 1998) is selected as an example of a variety of opportunities that congregates much of the knowledge of RS in agro- and ecosystem.

2.14.10.2 Continuous Evaluation of Crop Water Use with Support from RS Despite the intensive research in the field of RS to evaluate energy fluxes on an instantaneous basis, setting an operational scheme for practical agronomical purposes proves more troublesome. The techniques to elaborate energy flux maps from RS are temporally intermittent, as ephemeral as the image opportunity. They depend on the availability of the image that is affected by spatial resolution (nadir and off-nadir views), revisiting time, and, mainly, cloudiness. High-resolution thermal sensors are less available today (2009) than it was in the past, probably after the failure in establishing operational sequences for use in the market. As such, low-resolution thermal sensors are the only available RS source with adequate temporal revisiting time. The spatial resolution is, at the same time, the main limitation of these sensors for applications at field scale. The elaboration of a single flux map requires the highest level of expertise and continuous upgrading. Updated preprocessing including sensor calibration, atmospheric correction,

385

narrow-to-broadband conversions, emissivity retrievals, angulargeometrical effects, and instantaneous-to-daily integration are some of the necessary steps to be performed. The processing time is much slower than the dynamics of the ET process, such that this might be the cause of the few operational efforts beyond the framework of international projects. The selected criteria for classification of the approaches to evaluate AET-RS regarding the FAO Kc (crop factor) link is based on the relative weight that the RS and modeling components have on the final product. In view of the extensive bibliography available, only a few main references are indicated here. Continuous monitoring of the crop-water requirements using RS images exclusively can be attempted in areas where both clear days and image coverage are frequent. Daily AET is estimated from SEB models (see Section 2.14.4) on the available cloud-free images. Auxiliary ground data are collected from meteorological stations on daily (or shorter) basis to complement the database required for the SEB. The net radiation ‘Rn’ is calculated on a daily basis from a combination of RS and meteorological stations (Hurtado and Sobrino, 2001) or from ground stations when images are not available (Allen et al., 1998; Irmak et al., 2003). For the days when images are available, surface albedo (Liang et al., 2003), surface temperature (sensor dependent) (Coll et al., 1994, 2005; Gillespie et al., 1998), emissivity (Valor and Caselles, 1996), and fractional vegetation coverage (Su, 2002) can be evaluated. Considering the dynamic behavior of these parameters in the diurnal cycle, the intention is to describe a continuous pixel-based temporal evolution of them from day to day, as good as possible. If the daily values of each parameter are known on a daily basis, a ‘potential image’ of these parameters is obtained. After that, the evaporative fraction ‘EFi’ can be evaluated (Su, 2002). This method also considers the evaporative fraction being conservative during the daily course (Brutsaert and Sugita, 1992), providing a method to scale instantaneous evaporative fraction to daily evaporative fraction. However, several authors have shown the variability of the evaporative fraction during the day (Chehbouni et al., 2008; Crago, 1996) to the point that the original hypothesis stated by the one-time AET-RS must be reviewed for the particular situation considered. The soil heat flux averaged over the day is usually assumed zero (Brutsaert, 2008), so the AET for any pixel at any day ‘i’ is calculated as: Rni * EFi. As AET and potential evapotranspiration (PET) are obtained on a daily basis, direct application to irrigation becomes possible. The approach is suited for irrigated lands in areas of data scarcity where clear skies are common as shown in a case study in Morocco (Jacobs et al., 2008). A second approach estimates AET replacing the Penman– Monteith method with the Priesley and Taylor (PT) method that has proved to be reasonable for wet irrigated areas (de Bruin, 1987; McNaughton and Spriggs, 1989). Under these circumstances, the evaluation of Kc * Ks ¼ AET/ET0 can be resolved from the balance of radiative fluxes at the surface only (Mekonnen and Bastiaanssen, 2000). If the area is under irrigation, then the water availability factor Ks ¼ 1, and a locally adjusted value of Kc can be evaluated directly from RS measurements.

386

Observation of Hydrological Processes Using Remote Sensing

This method of evaluating a Kc becomes also suitable for heterogeneous cropping patterns in the same land, typical of many agricultures in Asia and Africa. The fractional cover of each crop can be evaluated and the average Rn, G, and H used in the evaluation of a ‘composite Kc’ can be obtained from the homogeneous covers by using their fractions as weights. As AET is estimated from PT, the values could be verified by using an SEB method from RS, which allows monitoring. The reference ET0 in the above method is obtained with the PT method for grasslands. This approach is used in irrigated areas with data scarcity in order to improve the estimations of Kc and reduce the need of images. As Kc varies in terms of weeks, this approach is suitable to build adjusted Kc factors for irrigated crops after some seasons of analysis. After the Kc is evaluated, it can be applied for crop specifics without the requirement of images. The main deficiency of the purely RS-based models is the uncertainty of clear image acquisition in the right time window. As such, major attention is turned to dual approaches where a model does the calculations on crop-water requirements and RS images are used to feed the model. Difference in these methods is centered on how the images are used as model input. The first group of approaches using SVAT models such as SWAT (Arnold and Fohrer, 2005; Arnold et al., 1998), CRIWAR (Bos et al., 2009), SWATRE (Belmans, 1983), or SIMGRO (Querner and van Bakel, 1989) is applied independently or combined with ground data to make the actual evaluation of the crop-water requirements. As the models are based on water balance, soil moisture and AET are estimated at every time step and for each land-use location. Due to the balance agreement between soil moisture and evaporation, a good calibration of the soil moisture ensures a good estimate of the ET from the model, mainly in the case of irrigation (surplus). After the model is calibrated, AET is a product that can be used on daily basis. In well-managed irrigation schemes, the assessment of water efficiency and uniformity of water distribution is preferred over the strict use of the Kc factor for cropwater requirements estimates. These two items are considered essential in the evaluation of irrigation performances (Bos et al., 2005). In this type of appraisal model, the concept of Kc is replaced for the ‘relative ET’ defined as RE ¼ AET/PET, where PET can be obtained from standard meteorological measurements (Doorenbos and Pruitt, 1977). Irrigation efficiency in the command area is then evaluated as the combination of three measurable properties:

•

• •

Optimal water requirement ensures that no stress occurs. In general, when AET/PET X0.75, this condition prevails. The value of 0.75 is generally adopted, although it might be adjusted locally. Water efficiency is defined as the ratio of AET to irrigation water depth given to each unit of parcel or crop. Uniformity refers to the homogeneity of the distribution of water in space and time inside the command area. As AET and RE are obtained on a daily and pixel basis, the coefficient of variation of these variables is used to evaluate it. A threshold is set to evaluate uniformity.

As presented here, the approach does not use AET derived from imagery as part of the procedure. Then, images obtained on clear days are used to derive AET establishing a strong basis of comparison with the information produced by the model. AET estimated by the image is used to evaluate the three indicators mentioned earlier, allowing real-time monitoring and the detection of flaws in the model that then can be corrected. This approach was used very successfully by Roerink et al. (1997) in the irrigation fields in Mendoza, Argentina, with irrigation performance methods developed by Bos et al. (1994). For operational purposes, the most accepted approach is to allow a continuous SVAT model to perform the calculations of AET and crop-water requirement. The model is calibrated mainly against soil moisture and groundwater table, both key variables in the process that can also be easily monitored at point scale. No SEB-RS model is used in this case. In this sense, this approach is similar to the previous method. The difference occurs on the use of the imagery as input to the model. RS imagery is used only to evaluate crop patterns and land parameters or properties that are required for the model. From a sequence of high-resolution images, a selected vegetation index evolution in time is estimated at pixel level (usually the Normalized Difference Vegetation index (NDVI) is used in many SEB). From the individual points in time, smoothing, filtering, or mainly harmonic techniques are normally adopted to achieve a time-continuous evolution (Verhoef, 1996). The NDVI time series and up-to-date land-use maps suffice to evaluate basis Kc (Kcb) described in Allen et al. (1998) and the fractional cover (fc), both needed to input in the model (Bausch and Neale, 1987; Bausch, 1995; Gonzalez Piqueras, 2006; Heilman et al., 1982; Ray and Dadhwal, 2001; Valor and Caselles, 1996). The model ‘HidroMORE’ was the result of the application of Irrigation Advisory Services (IAS) in Mediterranean areas, and validation of the results are given by Rubio et al. (2003). This approach was successfully used in a demonstration European project called ‘Demeter’ (Jochum and Calera, 2006) and in worldwide international projects such as ‘Pleiades’. In general, AET derived from SEB algorithms using RS imagery needs strict field (on-site) validation and local finetuning. On wet conditions on typically well-covered irrigated fields, the actual evaporation approaches to the potential one and in this situation most of the SEB methods work well (Bastiaanssen et al., 1998; Norman et al., 1995b; Su, 2002). Kite and Droogers (2000) reviewed some models and RS retrieval under the same conditions and warned about the great variability in the results between FAO-24 and satellite methods. There is a general agreement in the circle of experts that some high degree of expertise is required for the application of AET-RS SEB models, a warning that must not be overlooked.

2.14.10.3 Drought Indices and Soil Moisture Monitoring The partition of available energy at the Earth surface is largely controlled by the available soil moisture. The relation between soil moisture and ET is very tight, to the point that irrigation supply (moisture) can be estimated directly from accumulated ET as well as from monitoring soil moisture changes. However, from the water management point of view these two processes

Observation of Hydrological Processes Using Remote Sensing

are very different in the sense that ET can be monitored by RS but cannot be controlled easily and the soil moisture can be controlled but it is not directly observable in the rooting depth. As we can only adjust the water allocated to crops, the soil moisture becomes the most relevant of all parameters in the water cycle for agro-systems. In watershed management, there are situations requiring continuous monitoring of soil moisture as during droughts, which is the most devastating form of agricultural deficiency. The evaluation of droughts is done through methods that account for the endurance of low soil moisture values through time on a pixel basis. RS in the visible and thermal wavelengths is unable to directly measure soil moisture on the ground. As such, indirect methods were designed to approach it. There are three approaches to monitor droughts purely from standard passive RS. The initial methods were based on the fact that during drought conditions a sudden change in soil moisture would be followed by a distinctive jump in the spectral reflectance of the observed pixel. In a soil, more humidity implies less reflectance. Bowers and Hanks (1965) and Bowers and Smith (1972) linearly related the soil moisture and the spectral bands where soil moisture is an energy absorber. The property that bodies oppose to temperature changes is called ‘thermal inertia’. It can be evaluated from multitemporal imagery (day and night) from the visible and thermal bands. Pratt and Ellyet (1979) presented a modification of the model to map soil moisture, and later Price (1985) used the energybalance concept to add certainty to the model. All these approaches were successful under controlled conditions in areas of sparse vegetation, as soil contrast needs to be only affected by moisture. More appropriate was the attempt to monitor soil moisture under vegetated conditions. Moisture depletion affects plant physiology and, in particular, the reflective properties of the vegetation. In the absence of vegetation water as incoming radiation needs dissipation, vegetation reacts by increasing both the reflectivity of the leaves (albedo) and the sensible heat (surface temperature). The accounting of the difference between the surface and air temperature in time leads to the design of the crop-water stress index (Idso et al., 1981, 1975; Jackson et al., 1981), which was suitable for evaluating stress for full cover situations. Moran et al. (1994) developed a water deficit index (WDI) using vegetation indices to account for partially vegetation covered areas, using a composite of the surface temperature for vegetation and land, an approach that was later on extended to SEB models. As air temperature was needed in this type of method and the availability of ground meteorological measurements was restrictive, the use of the variability of the canopy surface temperature allowed a pure RS approach, especially applicable to nonfully covered areas (Gonzalez Dugo et al., 2005). Vegetation indices and surface temperature were also used in the development of indices dedicated to drought monitoring (Carlson et al., 1991, 1994; Kogan, 1990; Sandholt et al., 2002). Indices are rarely absolute, as they might not directly compare to a similar degree of drought severity, which leads to the designs of regional indices that can be compared.

387

RS-derived SEB subproducts were earlier involved in the calculation of drought indices. Using air and surface temperature and its variation, albedo, and net radiation in a consistent framework allowed quantifying droughts spatially. It was only after AET-SEB product became operational, for example, SEBI (Menenti and Choudhury, 1993), S-SEBI (Roerink et al., 2000), SEBAL (Bastiaanssen, 1995), TSEB (Kustas and Norman, 1999; Kustas et al., 2004; Norman et al., 1995a), ALEXI (Anderson et al., 1997; Mecikalski et al., 1999), and Dis-ALEXI (Norman et al., 1995a), the derived energy fluxes, directly conditioned by soil moisture, could be used for the evaluation of drought indices. Su et al. (2003) used outputs of SEBS (Su, 2002) to propose a fully RS-derived Drought Severity Index (DSI) for the North China Plain for low-resolution imagery. The results showed that the relative evaporation (actual latent heat flux over potential latent heat flux) can be used to predict soil moisture within one standard deviation, but the effects of cloudcontaminated pixels highly condition the applicability of the approach. Soil moisture derived from MW RS techniques can also be used for drought monitoring; however, the applicability is limited to continental scale as the most available products have rather coarse spatial resolution (tens of kilometers) and hence not applicable for most agricultural applications. More details on MW-derived soil moisture can be found in Section 2.14.7. More recently, it has been shown that time series of soil moisture derived from high-resolution MW sensors (tens of meters), such as ASAR (Van der Velde and Su, 2009). However, at the time of writing, such data are restricted to selected experimental areas where access of data is guaranteed.

2.14.10.4 Algorithm Retrievals and Operability AET estimates from RS are now available from a number of empirical, physically based, and mixed-approach methods. The scientific community agrees that there is no one single approach that best suits all cases, but some methods are preferred over others according to specific land-cover patterns and wetness characteristics of the site. In order to make RS information operational, continuous modeling, testing, and validation are required. The Demeter and Pleiades projects are examples of European efforts in using the latest observation and communication technologies to narrow the gap between research and operational needs. The reader is referred to the cited literature for detailed information on the different retrieval algorithms; we will only briefly describe the practical issues used in the SEBS algorithm for practical applications.

2.14.10.5 SEBS Algorithm The SEBS algorithm (Su, 2002) is a single-source physical model for the evaluation of the energy-balance fluxes from RS imagery. A full description of the model can be found in Su (2002, 2005) and the corresponding open-source software SEBS4ILWIS from the International Institute for Geo-information Science and Earth Observation. SEBS is a column model, which means that information on adjacent pixels is not affecting the pixel where the calculations

388

Observation of Hydrological Processes Using Remote Sensing

are done. This means that, in the ideal case, the information required as input needs to be measured independently at the site. However, in practice, this is never the case and interpolation techniques are always required. The examples presented at the end of the section were evaluated at measuring points. In the following, the values between brackets are examples taken from one point during this analysis. In the case that SEBS applied to imagery, the input indicated with (*) should desirably be a map. SEBS for ILWIS Open Source software also provides a set of routine for bio-geophysical parameter extraction. It uses satellite EO data, in combination with meteorological information as inputs, to produce the evaporative fraction, net radiation, and soil heat flux parameters. The main steps using the MODIS data as an example are as follows (SEBS core is theoretically independent of the sensor used): 1. Reprojecting and converting MODIS level-1 B data with the ModisSwathTool software. 2. Importing images into ILWIS. 3. Preprocessing for SEBS:

• • • • • •

raw data to radiance/reflectance conversion; brightness temperature computation; SMAC for atmospheric correction; land surface albedo computation; land surface emissivity, NDVI, vegetation proportion, and emissivity difference computation; and land surface temperature computation.

4. SEBS core model for bio-geophysical parameter extraction. Inputs in SEBS. Meteorological information from meteorological stations or atmospheric model out fields is used at the time of the satellite pass:

• • • • • • • •

Reference height (zref): height from the ground where measurements of temperature, wind, pressure, and specific humidity are made [m]. Specific humidity: [kg kg1]. Wind speed (uref): [m s1]. Air temperature at reference height (Ta): [1C]. Air pressure at reference height: [Pa]. Air pressure at land surface: [Pa]. PBL height (hi): height of the planetary boundary layer (PBL) in [m] that can be estimated by radiosounding or using atmospheric model outputs. (default hi ¼ 1000 m). Incoming global solar radiation: [W m2] (636 W m2).

Input normally derived after image preprocessing:

• • •

Atmospherically corrected broadband albedo (a) [–] (0.18). Surface emissivity (e0) (0.98). Atmospherically corrected surface temperature [K] (296 K).

Input derived from land-cover properties. Land properties affect roughness, the proven most sensitive information in all SEBSVAT methods. A good estimation of aerodynamic roughness is the key for success:

•

The percentage of fractional vegetation cover (fc) – it controls the partition of energy fluxes between vegetation and bare soil.

• •

The land use that contains roughness classes (zom) and displacement zero heights (d0), all in (m), associated with vegetation height values. The leaf area index is included in the deduction of the aerodynamic roughness for heat transport (zoh).

Outputs of SEBS. After the successful completion of the SEBS operation in ILWIS, following raster maps are generated:

• • • • • • • • •

sebs_ evap: evaporative fraction [–] sebs_daily_evap: daily evaporation [mm d1] sebs_evap_relative: relative evaporation [–] sebs_G0: soil heat flux [W m2] sebs_H_dry: sensible heat flux at the dry limit [W m2] sebs_H_i: sensible heat flux [W m2] sebs_H_wet: sensible heat flux at the wet limit [W m2] sebs_Rn: net radiation [W m2] sebs_LE: latent heat flux [W m2]

More details on the operation of the software SEBS4ILWIS can also be obtained from the online help.

2.14.10.6 Evaluation Example A simple application example is presented for part of the Guaren˜as catchment in the Duero Basin, Spain, which is being monitored with the Network of Soil Moisture Measurement Stations of the University of Salamanca (REMEDHUS). Measurements started from June 1999 to the present. The network consists of a series of 23 soil moisture stations, three meteorological stations, and discharge gages. First, RS-derived soil moisture estimates were compared to ground-truth data. The RS soil moisture retrievals were obtained indirectly from the SEBS RS model. AET retrievals using SEBS (Su, 2002) were calculated using the software SEBS4ILWIS (ITC, 2008; Wang et al., 2008). The top soil moisture values of the 23 stations were recorded simultaneously for 13 clear-day MODIS images taken during 2007. All VIS and NIR bands were radiometrically and atmospherically corrected using the SMAC (Rahman and Dedieu, 1994) version implemented in the Integrated Land and Water Information System (ILWIS, a GIS system). Surface temperature was obtained using a split window technique (Sobrino and Raissouni, 2003) and albedo estimates following (Liang, 2001). Due to the very low resolution (B1000 m in the thermal band) of the imagery, the comparison between the ground measurement and the RS soil moisture derived at the pixel where each ground station was located was futile, because the soil moisture at the spot did not represent the one of the pixel. However, a more representative and realistic approach was to make use of the simultaneous information of all 23 stations and the corresponding RS-derived soil moisture at the pixels where the stations were located. The RS-derived soil moisture average was obtained for the network. To estimate RS soil moisture, it was considered that the relative soil moisture (the ratio of the actual soil moisture to the soil moisture at the limiting wet case) was equal to the relative ET that could be evaluated after SEBS (Su et al., 2003). The soil moisture at limiting wet case was evaluated after laboratory analysis. The results illustrate that there is a good

Observation of Hydrological Processes Using Remote Sensing

389

Field average soil moisture

Ground measured (%)

20

16

12

8 Y = 0.85 + 0.01 R 2 = 0.85

4

0 0

4

8

12

16

20

SEBS derived (%) Figure 22 SEBS-derived vs. ground-measured soil moisture for the REMEDHUS project. Data courtesy: The University of Salamanca.

Table 4

Comparison between FAO 56 and remote-sensing-derived Kc factors after SEBS analysis

Crop Stage

Image date

Daily ETact (mm d1)

Average daily ETact (mm d1)

Ten-day average ET0 (mm d1)

Kc average calculated ETact/ET0

Kc FAO guide lines

Initial Crop development

(14 Nov. 2007) 16 Dec. 2007) (8 Mar. 2007) (27 Apr. 2008) (1 May. 2008) (18 Jun. 2008)

1.09 0.99 2.17 4.47 3.65 4.98

1.09 0.99 2.17 4.06

1.57 0.83 2.18 4.23

0.70 1.19 1.00 1.19

0.70 0.7–1.15 0.7–1.15 1.15

4.98

5.75

0.87

1.15–0.25

Mid-season Late season

agreement between SEBS-estimated soil moisture values and ground-measured values on the field scale level with a strong correlation. Figure 22 shows the results of the comparison. The general dry condition for the soils in the region was captured by the model. This information suffices for modeling purposes of the surface water that was done with the HVB model (Bergstro¨m, 1995). As a second example, SEBS was used to calculate AET from the available imagery. Then, RS-derived Kc factor for four stages of wheat development was derived and compared with the tabulated values of the FAO guidelines. The single crop coefficient (Allen et al., 1998) is used to calculate crop ET. With the available imageries, the Kc was computed for four stages of wheat development, namely, the initial, the crop development, the mid-season, and the late season and then compared with the tabulated values of the FAO guidelines. In the study area, the sowing dates for winter wheat vary from October to November and the harvesting dates vary from June to July. The subset was selected in a way of having a clear wheat zone close to meteorological stations. The nearest two meteorological stations, Canizal and VA_02, were selected to calculate a 10-day average crop reference ET. The 10-day average ET0 was calculated based on the estimations of 5 consecutive days before and after the imagery date. Table 4 shows the results of the comparison. The RS values of Kc are in agreement with the values given in the FAO guidelines; however, the procedure allows local fitting of the

Kc. The procedure is universally applicable mainly in irrigated areas, as it is recommended for spatial irrigation performance studies (Bos et al., 2005).

Acknowledgment This work was partially funded by EUMETSAT Satellite Application Facility on Climate Monitoring.

References Ackerman SA, Strabala KI, Menzel WP, Frey RA, Moeller CC, and Gumley LE (1998) Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research 103(D24): 32141--32157. Adam JC, Clark EA, Lettenmaier DP, and Wood EF (2006) Correction of global precipitation products for orographic effects. Journal of Climate 19: 15--38. Adler RF, Huffman GJ, Chang A, et al. (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology 4: 1147--1167. Adler RF and Negri AJ (1988) A satellite IR technique to estimate tropical convective and stratiform rainfall. Journal of Applied Meteorology 27: 30--51. Albert A and Gege P (2006) Inversion of irradiance and remote sensing reflectance in shallow water between 400 and 800 nm for calculations of water and bottom properties. Applied Optics 45: 2331--2343. Albertson JD (1996) Large Eddy Simulation of Land–Atmosphere Interaction. PhD Thesis, University of California, Davis, 185pp.

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van Meijgaard E and Crewell S (2005) Comparison of model predicted liquid water path with ground-based measurements during CLIWA-NET. Atmospheric Research, Special Issue: CLIWA-NET: Observation and Modelling of Liquid Water Clouds 75(3): 201--226 (doi:10.1016/j.atmosres.2004.12.006). Velde R, Su Z, Ek M, Rodell M, and Ma Y (2009) Influence of thermodynamic soil and vegetation parameterizations on the simulation of soil temperature states and surface fluxes by the Noah LSm over a Tibetan Plateau site. Hydrology and Earth System Sciences 13: 759--777. Velicogna I and Wahr J (2006) Measurements of time-variable gravity show mass loss in Antarctica. Science 311: 1754--1756. Venturini V, Bisht G, Islam S, and Jiang L (2004) Comparison of evaporative fractions estimated from AVHRR and MODIS sensors over South Florida. Remote Sensing of Environment 93: 77--86. Verhoef W (1996) Application of Harmonic Analysis of NDVI Time Series, Report No. 108. Wageningen, NL: DLO Winnand Staring Centre. Verhoest NEC, Lievens H, Wagner W, A´lvarez-Mozos J, Moran MS, and Mattia F (2008) On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar. Sensors 8: 4213--4248. Verstraeten W, Veroustraete F, and Feyen J (2008) Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors 8: 70--117. Vignal B and Krajewski W (2001) Large-sample evaluation of two methods to correct range-dependent error for WSR-88 D rainfall estimates. Journal of Hydrometeorology 2: 490--504. Vikhamar D and Solberg R (2003) Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Remote Sensing of Environment 88(3): 309  323. Voisin N, Wood AW, and Lettenmaier DP (2008) Evaluation of precipitation products for global hydrological prediction. Journal of Hydrometeorology 9: 388--407. Vo¨ro¨smarty CJ, Askew A, Barry R, et al. (2001) Global water data: A newly endangered species. EOS, Transactions, American Geophysical Union 82(5): 54--58. Wagner W, et al. (2003) Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. Journal of Geophysical Research – Atmospheres 108(D19): 4611. Wagner W, et al. (2007a) Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nordic Hydrology 38(1): 1--20. Wagner W, Lemoine G, and Rott H (1999) A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment 70: 191--207. Wagner W, Naeimi V, Scipal K, De Jeu R, and Martinez-Fernandez J (2007b) Soil moisture from operational meteorological satellites. Hydrogeology Journal 15(1): 121--131. Wagner W, Verhoest NEC, Ludwig R, and Tedesco M (2009) Editorial, Remote sensing in hydrological sciences. Hydrology and Earth System Sciences 13: 813--817. Wahr J, Molenaar M, and Bryan F (1998) Time-variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research 103(30): 205--230. Walker JP, Houser PR, and Willgoose GR (2004) Active microwave remote sensing for soil moisture measurement: A field evaluation using ERS-2. Hydrological Processes 18: 1975--1997. Wang JR and Choudhury BJ (1981) Remote sensing of soil moisture content over bare field at 1. GHz frequency. Journal of Geophysical Research 4(86): 5277--5287. Wang JR and Schmugge TJ (1980) An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Transactions on Geoscience and Remote Sensing 18: 288--295. Wang L, Parodi GN, and Su Z (2008) SEBS module BEAMS: A practical tool for surface energy balance estimates from remote sensing data. In: Agency ES (ed.) Proceedings of the 2nd MERIS/(A)ATSR Workshop, ESA SP-666, November 2008, Tool and Services, 8pp. Frascati, Italy, 22–26 September 2008. Noordwijk: ESA Communication Production Office ESTEC. Wang L and Qu JJ (2009) Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China 3(2): 237--247. Wang P, Boss E, and Roesler C (2005) Uncertainties of inherent optical properties obtained from semianalytical inversions of ocean color. Applied Optics 44: 4074--4084. Wen J and Su Z (2003) A time series based method for estimating relative soil moisture with ERS wind scatterometer data. Geophysical Research Letters 30(7): 1397 (doi:10.1029/ 2002GL016557). Weng F, Zhao L, Ferraro RR, Poe G, Li X, and Grody NC (2003) Advanced microwave sounding unit cloud and precipitation algorithms. Radio Science 38: 8068 (doi:10.1029/2002RS002679). Wentz FJ (1997) A well-calibrated ocean algorithm for special sensor microwave/ imager. Journal of Geophysical Research 102: 8703--8718.

Weydahl DJ (2001) Analysis of ERS tandem coherence from glaciers, valleys and fjord ice in Svalbard. IEEE Transactions on Geoscience and Remote Sensing 39: 2029--2039. Wielicki BA, Barkstrom BR, Harrison EF, Lee RB, Smith GL, and Cooper JE (1996) Clouds and the Earth’s radiant energy system (CERES): An earth observing system experiment. Bulletin of the American Meteorological Society 77: 853--868. Wigneron JP, Laguerre L, and Kerr Y (2001) A simple parmeterization of the L-band microwave emission from rough agricultural soils. IEEE Transactions on Geoscience and Remote Sensing 39: 1697--1707. Wilson JW (1970) Integration of radar and raingage data for improved rainfall measurement. Journal of Applied Meteorology 9: 489--497. Wood EF (1998) Hydrologic measurements and observations: An assessment of needs. In: Hydrologic Sciences: Taking Stock and Looking Ahead, pp. 67--86. Washington, DC: National Academies Press. Woodhouse IH and Hoekman DH (2000) A model-based determination of soil moisture trends in Spain with the ERS-scatterometer. IEEE Transactions on Geoscience and Remote Sensing 38(4): 1783--1793. Yeh PJ-F, Swenson SC, Famiglietti JS, and Rodell M (2006) Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE). Water Resources Research 42: doi:10.1029/2006WR005374. Zaitchik BF, Rodell M, and Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River Basin. Journal of Hydrometeorology 9(3): 535--548 (doi:10.1175/2007JHM951.1). Zhan X, Kustas WP, and Humes KS (1996) An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature. Remote Sensing of Environment 58: 242--256. Zhao D, Su ZB, and Zhao M (2006) Soil moisture retrieval from satellite images and its application to heavy rainfall simulation in eastern China. Advances in Atmospheric Sciences 23(2): 299--316. Zhao F and Nakajima T (1997) Simultaneous determination of water-leaving reflectance and aerosol optical thickness from Coastal Zone Color Scanner measurements. Applied Optics 36: 6949--6956. Zibordi G (2006) A network for standardized ocean color validation measurements. EOS, Transactions, American Geophysical Union 87: 293--297. Zribi M, Baghdadi N, Holah N, and Fafin O (2005) New methodology for soil moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sensing of Environment 96: 485--496.

Relevant Websites http://grace.sgt-inc.com Access Program; Mass Anomalies. http://www.cloud-net.org Cloudnet. http://www.demeter-ec.net DEMETER. http://earth.esa.int ESA Earthnet. http://www.eumetnet.eu.org EUMETNET, the Network of European Meteorological Services. http://www.knmi.nl EUMETNET, the Network of European Meteorological Services; Opera. http://ec.europa.eu European Commission; GMES. http://geoid.colorado.edu Fedora Core Test Page; Grace. http://www-app2.gfz-potsdam.de GFZ Potsdam, Department 1: The Grace Mission. http://www.glims.org GLIMS: Global Land Ice Measurements from Space. http://www.gewex.org Global Energy and Water Cycle Experiment: GEWEX. http://www.isac.cnr.it ISAC; CGMS, IPWG; IPWG-5, Hamburg, Germany. http://www.itc.nl ITC, Faculty of Geo-Information Science and Earth Observation. http://www.legos.obs-mip.fr LEGOS; Hydrology from Space. http://grace.jpl.nasa.gov NASA; Grace Tellus.

Observation of Hydrological Processes Using Remote Sensing

http://daac.ornl.gov ORNL, DAAC; The First ISLSCP Field Experiment (FIFE). http://www.pleiades.es PLEIADES. http://postel.mediasfrance.org Postel, December 2008: Globcover Validation Report and New Regional Land Cover Products Available. http://modis-snow-ice.gsfc.nasa.gov The Modis Snow/Ice Global Mapping Project.

http://www.ars.usda.gov United States Department of Agriculture; Agricultural Research Service; Monsoon’90; Soil Moisture Experiments. http://www.csr.utexas.edu University of Texas at Austin, Center for Space Research. http://free.vgt.vito.be Vegetation, Free Vegetation Products. http://www.wacmos.org WACMOS.

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2.15 Hydrogeophysics SS Hubbard, Lawrence Berkeley National Laboratory, Berkeley, CA, USA N Linde, University of Lausanne, Lausanne, Switzerland & 2011 Elsevier B.V. All rights reserved.

2.15.1 Introduction to Hydrogeophysics 2.15.2 Geophysical Methods 2.15.2.1 Electrical Resistivity Methods 2.15.2.2 IP Methods 2.15.2.3 SP Methods 2.15.2.4 Controlled-Source Inductive EM Methods 2.15.2.5 GPR Methods 2.15.2.6 Seismic Methods 2.15.2.7 Surface Nuclear Magnetic Resonance 2.15.2.8 Gravity 2.15.2.9 Magnetics 2.15.2.10 Well Logging 2.15.3 Petrophysical Models 2.15.3.1 Electrical Conductivity 2.15.3.1.1 Archie’s law 2.15.3.1.2 Waxman–Smits law 2.15.3.1.3 The Johnson, Koplik, and Schwartz model 2.15.3.1.4 Self-similar models 2.15.3.2 Dielectric Permittivity 2.15.3.2.1 Volume averaging 2.15.3.2.2 Topp’s equations 2.15.3.3 Complex Conductivity 2.15.3.3.1 Cole–Cole model 2.15.4 Parameter Estimation/Integration Methods 2.15.4.1 Key Components, Constraints, Metrics, and Steps in Parameter Estimation 2.15.4.1.1 Model space and initial model 2.15.4.1.2 Objective function (systems of equations) 2.15.4.1.3 Inversion step or model proposal 2.15.4.1.4 Geophysical model or model population 2.15.4.2 Example Parameter Estimation Approaches 2.15.4.2.1 Direct mapping approaches 2.15.4.2.2 Integration approaches (geostatistical, Bayesian) 2.15.4.2.3 Joint inversion or fully coupled hydrogeophysical inversion 2.15.5 Case Studies 2.15.5.1 Subsurface Architecture Delineation 2.15.5.1.1 3D resistivity mapping of a Galapagos volcano aquifer 2.15.5.1.2 High-resolution GPR imaging of alluvial deposits 2.15.5.1.3 Subsurface flow architecture delineation using seismic methods 2.15.5.1.4 Fracture zonation characterization using azimuthal electrical methods 2.15.5.2 Delineation of Anomalous Fluid Bodies 2.15.5.2.1 Electrical resistivity to delineate high-ionic-strength plume boundaries 2.15.5.2.2 SP imaging of redox potentials associated with contaminated plumes 2.15.5.3 Hydrological Process Monitoring 2.15.5.3.1 Soil moisture monitoring 2.15.5.3.2 Saline tracer monitoring in fractured rock using time-lapse GPR methods 2.15.5.3.3 Seasonal changes in regional saltwater dynamics using time-lapse EM methods 2.15.5.4 Hydrogeological Parameter or Zonation Estimation for Improving Flow Predictions 2.15.5.4.1 Hydraulic conductivity and zonation estimation using GPR and seismic methods 2.15.5.4.2 Joint modeling to estimate temporal changes in moisture content using GPR 2.15.6 Summary and Outlook Acknowledgments References

401 403 403 404 404 405 405 406 406 407 407 407 407 408 408 408 408 409 409 409 409 410 410 410 410 410 410 413 413 413 413 414 414 414 414 415 415 416 417 417 417 419 420 420 422 423 424 424 425 427 430 430

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2.15.1 Introduction to Hydrogeophysics

(e.g., Gelhar, 1993). For example, the distributions of microfractures and geological formations both influence the hydraulic conductivity and thus subsurface flow, albeit over dramatically different spatial scales. Similarly, different hydrological processes may exert varying degrees of control on subsurface flow and transport as a function of the scale: the overall system response of the particular problem may be dominated by seasonal precipitation patterns or by surface– groundwater interactions at the catchment scale; by the influence of groundwater pumping wells, gradients, and heterogeneity-induced mixing at the local scale; and by microbe–mineral interactions and diffusion at the grain scale (Figure 1). The level of subsurface characterization required for a particular problem depends therefore on many factors,

The shallow subsurface of the Earth is an extremely important geological zone, one that yields our water resources, supports our agriculture and ecosystems, influences our climate, and serves as the repository for our contaminants. The need to develop sustainable water resources for increasing population, agriculture, and energy needs and the threat of climate and land-use change on ecosystems contribute to an urgency associated with improving our understanding of flow and transport processes in the shallow subsurface. Developing a predictive understanding of subsurface flow and transport is complicated by the disparity of scales across which controlling hydrological properties and processes span

Evapotranspiration Precipitation

Coupling of hydrological system with ecosystem and climate

O2

Coupling of groundwater, vadose zone, and surface waters

O2

River stage fluctuation

Preferential transport and mixing induced by heterogeneity

U(VI) Qtz

Interaction between minerals, pore water, and microbes

Qtz Biofilm Fe(OH)3

Qtz

Figure 1 Subsurface flow and transport is impacted by coupled processes and properties that preferentially exert influence over a wide range of spatial scales, rendering characterization based on borehole data only challenging. Modified from US DOE (2010) Complex Systems Science for Subsurface Fate and Transport, Report from the August 2009 Workshop, DOE/SC-0123, U.S. Department of Energy Office of Science (www.science.doe.gov/ober/BER_workshops.html).

Hydrogeophysics

including: the level of heterogeneity relative to the characterization objective, the spatial and temporal scales of interest, and regulatory or risk drivers. In some cases, reconnaissance efforts that delineate major characteristics of the study site may be sufficient, while other investigations may require a much more intensive effort. Conventional techniques for characterizing or monitoring the hydrogeological properties that control flow and transport typically rely on borehole access to the subsurface. For example, established hydrological characterization methods (such as pumping, slug, and flowmeter tests) are commonly used to measure hydraulic conductivity in the vicinity of the wellbore (e.g., Freeze and Cherry, 1979; Butler, 2005; Molz et al., 1994), and wellbore fluid samples are often used for water-quality assessment (e.g., Chapelle, 2001). Unfortunately, data obtained using borehole methods may not capture sufficient information away from the wellbore to describe the key controls on subsurface flow. The inability to characterize controlling properties at a high-enough spatial resolution and over a large-enough volume for understanding and predicting flow and transport processes using borehole methods often hinders our ability to predict and optimally manage associated resources. The field of hydrogeophysics has developed in recent years to explore the potential that geophysical methods have for characterization of subsurface properties and processes relevant for hydrological investigations. Because geophysical data can be collected from many different platforms (such as from satellites and aircrafts, at the ground surface of the Earth, and within and between wellbores), integration of geophysical data with direct hydrogeological or geochemical measurements can provide characterization information over a variety of spatial scales and resolutions. The main advantage of using geophysical data over conventional measurements is that geophysical methods can provide spatially extensive information about the subsurface in a minimally invasive manner at a comparatively high resolution. The greatest disadvantage is that the geophysical methods only provide indirect proxy information about subsurface hydrological properties or processes relevant to subsurface flow and transport. Hydrogeophysical investigations strive to provide information that can be used to (1) develop insights about complex hydrological processes, (2) serve as input data to construct flow and transport models, and (3) guide the management of subsurface water resources and contaminants. The field of hydrogeophysics builds on many decades of experience associated with the mining and petroleum industries, which have relied heavily on geophysical methods to guide the exploration of ore and hydrocarbons, respectively. Because geophysics has been used as a tool in these industries for so long, there is a relatively good understanding about methods and optimal data acquisition approaches for given problems, as well as about petrophysical relationships associated with the consolidated, high-pressure, and hightemperature subsurface environments common to those industries. However, such subsurface conditions are quite different from the shallow, low-temperature, low-pressure, and weakly consolidated environments that typify most hydrogeological investigation sites. The parameter values and the functional form of the petrophysical relationships that link

403

geophysical properties to subsurface parameters, as well as the geophysical response itself, can vary dramatically between different types of environments. In the last decade, many advances have been made that facilitate the use of geophysical data for shallow subsurface hydrogeological characterization. These advances include those associated with instrument development, interpretation procedures, petrophysical relationships relevant to near subsurface environments, integration or joint inversion approaches for combining multiple data sets, and coupled hydrological and geophysical modeling. Simultaneously, the number of publications related to hydrogeophysics has dramatically increased and are now common contributions to hydrological and geophysical journals such as Water Resources Research, Journal of Hydrology, Vadose Zone Journal, and Geophysics. Most hydrological and Earth science professional meetings (such as American Geophysical Union, Geological Society of America, and European Geosciences Union) now commonly host one or more hydrogeophysical special sessions at their annual meetings. These meetings have created an active environment where geophysicists and hydrologists can interact to learn about each other’s methods and challenges. Many groundbreaking hydrogeophysical studies have now been published by researchers with a formal hydrological training and it is becoming more common for geophysicists to strive to gain hydrological insights in addition to focusing primarily on advancing geophysical instrumentation and methodology. Some of the fairly recent hydrogeophysical advances are summarized in two edited books Hydrogeophysics (Rubin and Hubbard, 2005) and Applied Hydrogeophysics (Vereecken et al., 2006), as well as by numerous individual publications. Generally, hydrogeophysical characterization and monitoring objectives can often be categorized into the following three categories: 1. hydrological mapping of subsurface architecture or features (such as interfaces between key geological units, water table, or contaminant plume boundaries); 2. estimating subsurface properties or state variables that influence flow and transport (such as hydraulic conductivity or soil moisture); and 3. monitoring subsurface processes associated with natural or engineered in situ perturbations (such as infiltration through the vadose zone and tracer migration). There are several components that are common to most hydrogeophysical studies. First and foremost, it is critical to collect high-quality geophysical data sets using the geophysical method or methods that are most likely to provide data that can help to resolve the hydrogeological characterization or monitoring objective and that work well in the given environment. Although the corresponding geophysical properties (such as electrical conductivity/resistivity from electrical and electromagnetic (EM) methods or dielectric constant from ground penetrating radar (GPR) methods) can be used to infer hydrogeological properties or structures, petrophysical relationships must be developed and invoked at some stage to link the geophysical properties or data with the property or variable of interest (such as hydraulic conductivity or water

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content). Integration or joint inversion methodologies are used to systematically integrate or fuse disparate data sets (geophysical and hydrogeological) to obtain a meaningful interpretation that honors all data and physical laws. The ultimate step is the use of the integrated hydrogeophysical property or state model to interpret complex subsurface system processes or to guide the optimal management of subsurface water resources and contaminants. The key objectives of this chapter are to familiarize hydrogeologists and water-resource professionals with the state of the art as well as the existing challenges associated with hydrogeophysics. We provide a review of the key components of many hydrogeophysical studies as well as example case studies that are relevant to understanding of hydrological behavior at the field scale. The remainder of this chapter is organized as follows. A brief description of some of the key geophysical methods that are used in hydrogeophysics is provided in Section 2.15.2. Descriptions of theoretical and empirical petrophysical relationships that can be used to link the geophysical attributes to the hydrogeological property of interest are discussed in Section 2.15.3. Section 2.15.4 reviews parameter estimation and integration methods that are used to combine disparate data sets for a consistent interpretation of critical flow and transport properties. Finally, in Section 2.15.5 we present various case studies that illustrate the use of geophysical data sets, petrophysics, and estimation methods to investigate near subsurface systems, with a particular emphasis on case studies that are conducted over field scales relevant to water resources and contaminant remediation.

2.15.2 Geophysical Methods The purpose of this section is to introduce some of the geophysical techniques that are most commonly used for hydrogeological studies, including electrical resistivity tomography (ERT), induced polarization (IP), electromagnetic induction (EMI), self-potential (SP), GPR, seismic, surface nuclear magnetic resonance (SNMR), gravity, magnetics, and wellbore logging techniques. For each method, we provide a brief description of the underlying physical principles and instrumentation, common acquisition strategies, and general data reduction and interpretation methods. We restrict our discussions to practical use and limitations of common geophysical methods; geophysical theory (e.g., Telford et al., 1990) is beyond the scope of this discussion. For detailed information, references are given for each geophysical method. This discussion of classical geophysical methods is envisioned to compliment existing literature on what are typically considered to be hydrological sensors or measurement approaches, even though they rely on geophysical mechanisms; examples include soil moisture probes (time domain reflectometer and capacitance probes), EM wellbore flowmeters, and various remote-sensing sensors deployed from airborne or space-borne platforms. Reviews of these methods are provided by Vereecken et al. (2008) and Butler (2005). The discussion of geophysical methods provided here is augmented by Section 2.15.3, where several petrophysical relationships are provided that may permit the transfer of

geophysical measurements and models into estimates of hydrological parameters.

2.15.2.1 Electrical Resistivity Methods For groundwater studies, electrical resistivity methods have perhaps been more frequently used than any other geophysical method. Resistivity is a measure of the ability to resist electrical current flow through materials; it is the inverse of electrical conductivity and is an intrinsic property of the material. In electrical resistivity methods, a typically lowfrequency (o1 Hz) current is injected into the ground between two current electrodes, while one or more pairs of potential electrodes are used to measure electrical potential differences. At the low frequencies measured, energy loss via ionic and electronic conduction dominates. Ionic conduction results from the electrolyte filling the interconnected pore space (Archie, 1942) as well as from surface conduction via the formation of an electrical double layer at the grain-fluid interface (e.g., Revil and Glover, 1997, 1998). Electronic conduction resulting from the formation of continuous conductive pathways by metallic minerals is typically not important for most environmental applications. The current distribution can be visualized by equipotential surfaces, with current flow lines running perpendicular to these surfaces. The fraction of total current flow that penetrates to a particular depth is a function of the current electrode spacing and location, the electrical resistivity distribution of the subsurface materials, and the topography. Most resistivity surveys utilize a four-electrode measurement approach. To obtain a value for subsurface resistivity, two potential electrodes are placed at some distance from the current electrodes, and the difference in electrical potential or voltage is measured. This measurement, together with the injected current and the geometric factor which is a function of the particular electrode configuration and spacing, can be used to calculate resistivity for uniform subsurface conditions following Ohm’s law. Common electrode configurations include the Wenner, the Schlumberger, and the dipole–dipole arrays. In real heterogeneous (nonuniform) subsurface environments, the more general term ‘apparent resistivity’ is used, which refers to the resistivity of an equivalent uniform media. There are several modes of acquiring electrical data. Profiling is undertaken by moving the entire array laterally along the ground surface by a fixed distance after each reading to obtain apparent resistivity measurements over a relatively constant depth as a function of distance. As profiles give lateral variations in electrical conductivity but not information about vertical distribution, the interpretation of profile data is generally qualitative, and the primary value of the data is to delineate sharp lateral contrasts associated with vertical/near vertical contacts. Vertical electrical sounding (VES) curves give information about the vertical variations in electrical conductivity at a single ground surface location assuming an idealized one-dimensional (1 D) resistivity structure. For example, soundings with the Wenner array are obtained by expanding the array along a straight line so that the spacing between the individual electrodes remains equal for each measurement, but increases after each measurement.

Hydrogeophysics

The depth of investigation for a given measurement is a function of the electrode spacing as well as the subsurface resistivity contrasts; as the electrode spacing is increased, the data are increasingly sensitive to deeper structures. Modern multichannel geoelectrical equipment now includes multiplexing capabilities and automatic and autonomous computer acquisition, which greatly facilitate data acquisition within acceptable timeframes. Such surface imaging, now commonly called electrical resistivity tomography or ERT, allows the electrodes (tens to hundreds) to be used alternatively as both current and potential electrodes to obtain 2D or 3D electrical resistivity models (e.g., Gu¨nther et al., 2006). In fact, when performing ERT, it is limiting to restrict the measurement sequence to a given configuration type, since optimal data sets often consist of a combination of traditional and nontraditional configuration types (Stummer et al., 2004; Wilkinson et al., 2006). With the development of advanced and automated acquisition systems, robust inversion routines, and the capability of recording tens of thousands of measurements per hour, ERT has proved to be useful for dynamic process monitoring using electrodes placed at the ground surface or in wellbores. A review of surface and crosshole ERT methods for hydrogeological applications is given by Binley and Kemna (2005), and discussion of petrophysical relationships that link the electrical properties with hydrological properties of interest is described in Section 2.15.3.1.

2.15.2.2 IP Methods IP methods measure both the resistive and capacitive properties of subsurface materials. IP measurements can be acquired using the same four-electrode geometry that is conventionally used for electrical resistivity surveys, although IP surveys typically employ nonpolarizing electrodes. Surveys can be conducted in the time domain as well as in the frequency domain. In the time domain, the current is injected and the decay of the voltage over time is measured. Frequency-domain methods measure the impedance magnitude and phase shift of the voltage relative to an injected alternating current. Spectral induced polarization (SIP) methods measure the polarization relaxation over many frequencies (typically over the range of 0.1–1000 Hz). The voltage decay (in the time domain) and spectral response (in the frequency domain) are caused by polarization of ions in the electrical double layer at the mineral–fluid interface, by accumulation of electrical charges at pore space constrictions (e.g., pore throats), and by conduction in the pore fluid and along the fluid-grain boundaries. More information about IP methods is provided by Binley and Kemna (2005) and Leroy and Revil (2009). The linkage between IP attributes, granulometric properties, and interfacial phenomena suggests that it also holds significant potential for exploring hydrogeological properties (e.g., Slater and Lesmes, 2002) as well as complex biogeochemical processes associated with contaminant remediation (e.g., Williams et al., 2005, 2009; Slater et al., 2007). Section 2.15.3.3 provides discussion of petrophysical models associated with SIP data sets.

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2.15.2.3 SP Methods SP is a passive method where naturally occurring electric fields (voltage gradients) are measured at the ground surface or in wellbores using nonpolarizable electrodes and a high-impedance voltmeter. Electrical potentials measured with the SP method obey a Poisson’s equation with a source term given by the divergence of an electrical source current density (e.g., Minsley et al., 2007). The source current density has several possible contributors, including those associated with ground water flow, redox phenomena, and ionic diffusion. The electrokinetic contribution associated with the flow of ground water in a porous medium (or more precisely, with the drag of charges contained in the diffuse layer that surrounds mineral surfaces) has been recognized for many decades and has been used to qualitatively interpret SP signals in terms of seepage beneath dams or to map groundwater flow (e.g., Poldini, 1938). However, only more recently have such data sets been used to quantify hydrological properties by coupling equations that represent volumetric fluid flux and volume current density, which are linked by a coupling coefficient (e.g., Sill, 1983; Revil et al., 2003). The underlying physics of the redox and ionic diffusion contributions are now better understood and current research is advancing our ability to use SP for quantitative hydrogeochemical characterization (such as for characterizing field-scale redox gradients; refer to case study provided in Section 2.15.5.2). The SP method is the only geophysical method that is directly sensitive to hydrological fluxes (e.g., Sill, 1983). Even if several alternative formulations exist to describe electrokinetic phenomena, we consider here the case where the SP sources are expressed in terms of Qv, where Qv is expressed as excess charge in the diffuse double layer per saturated pore volume. The relative movement of an electrolyte with respect to mineral grains with a charged surface area results in socalled streaming currents (e.g., Sill, 1983). These currents are intimately linked to the Darcy velocity U and an excess charge Qv along the hydrological flow paths. A practical formulation of the streaming currents that corresponds to this parametrization is (e.g., Revil and Linde, 2006)

Js ¼ Qv U

ð1Þ

This equation is only strictly valid when the size of the double layer is comparable to the size of the pores (see Revil and Linde (2006) for a description of chemico-electromechanical coupling under such conditions) and when internal permeability variations within the averaged volume are small. Equation (1) can be used in heterogeneous media or in coarse sediments when we replace Qv with an effective Qeff v that is scaled with the relative contributions to permeability of all flow paths in the media (Linde, 2009). It is straightforward to deduce Qeff v of aquifer materials in the laboratory using the relationship (Revil and Leroy, 2004)

Csat ¼

Qeff v k mw s

ð2Þ

where k is the permeability and mw is the dynamic water viscosity. The voltage coupling Csat can at moderate to high

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permeabilities be obtained using a simple experimental setup, for example, using the type of column experiment presented by Suski et al. (2006). The dependence of Qeff v;sat with water content depends on the geological media considered, but it is to a first order inversely related to water content (Linde et al., 2007; Linde, 2009). The source current that is responsible for observed electrical potential signals associated with these processes is given by the divergence of Equation (1) (e.g., Linde et al., 2007).

2.15.2.4 Controlled-Source Inductive EM Methods Controlled-source inductive EM methods use a transmitter to pass a time- or frequency-varying current through a coil or dipole placed on the Earth’s surface, in boreholes, mounted on an aircraft or towed behind a ship. Governed by Maxwell’s equations and typically operating in the 1–15 kHz range, this alternating current produces a time-varying primary magnetic field, which in turn interacts with the conductive subsurface to induce time-varying eddy currents. These eddy currents give rise to a secondary EM field. Attributes of this secondary magnetic field, such as amplitude, orientation, and/ or phase shift, can be measured by a receiver coil. By comparing these attributes with those of the primary field, information about the presence of subsurface electrical conductors or the subsurface electrical conductivity distribution can be inferred. Because a conductive subsurface environment or target is required to set up the secondary field measured with inductive EM methods, EM methods are best suited for use when attempting to detect the presence of highconductivity subsurface targets, such as saltwater saturated sediments or clay layers. However, because coils do not require contact with the ground, EM methods are often more successful on electrically resistive or paved ground than the classical DC resistivity method, which requires electrode contact. As with ERT and SIP data, EM induction data can often be collected in profile or sounding mode. The mode of acquisition and the resolution and depth penetration of the data are dictated by the electrical conductivity distribution of the subsurface and the coil spacing and source configuration. For frequency domain systems, high transmitter frequencies permit high-resolution investigation of subsurface conductors at shallow depths, while lower transmitter frequencies permit deeper observations but at a loss in resolution. Time domain systems measure the secondary magnetic field as a function of time, and early-time measurements yield information about the near surface, while later-time measurements are increasingly influenced by the electrical properties at larger depths. The depth of penetration and resolution are also governed by coil configuration; the measurements from larger coil separations are influenced by electrical properties at greater depths, while smaller coil spacings sample from the near surface. A review and discussion of the use of controlled-source EM methods for hydrogeological investigations is given by Everett and Meju (2005). It should be noted that it is also possible to use civilian and military radio transmitters, operating in the 10–250 kHz frequency range, as the source signal. These are the signals used in the popular

very low frequency (VLF) (e.g., Pedersen et al., 1994) and radio magnetotelluric (RMT) (e.g., Linde and Pedersen, 2004) techniques.

2.15.2.5 GPR Methods GPR methods use EM energy at frequencies of B10 MHz to 1 GHz to probe the subsurface. At these frequencies, the separation (polarization) of opposite electric charges within a material that has been subjected to an external electric field dominates the electrical response. GPR systems consist of an impulse generator which repeatedly sends a particular voltage and frequency source to a transmitting antenna. When the source antenna is placed on or above the ground surface, waves are radiated downward into the soil. In general, GPR performs better in unsaturated coarse or moderately coarse textured soils; GPR signal strength is strongly attenuated in electrically conductive environments (such as systems dominated by the presence of clays or high ionic strength pore fluids). Together, the electrical properties of the host material and the frequency of the GPR signal primarily control the resolution and the depth of penetration of the signal. Increasing the frequency increases the resolution but decreases the depth of penetration. GPR data sets can be collected in the time or in the frequency domain. Time-domain systems are most commonly used in near-surface investigations. Generally, one chooses a radar center frequency that yields both sufficient penetration and resolution; for field applications this is often between 50 and 250 MHz. However, significant advances have been made in the development of frequency domain systems. Lambot et al. (2004a) describe a stepped-frequency continuous-wave radar deployed using an off-ground horn antenna over the frequency range of 0.8–3.4 GHz. The wide bandwidth and off-ground configuration permits more accurate modeling of the radar signal, thus potentially leading to improved estimates of subsurface parameters (Lambot et al., 2004b, 2006). The most common ground surface GPR acquisition mode is surface common-offset reflection, in which one (stacked) trace is collected from a transmitter–receiver antenna pair pulled along the ground surface. With this acquisition mode, GPR antennas can be pulled along or above the ground surface at walking speed. When the EM waves in the ground reach a contrast in dielectric constants, part of the energy is reflected and part is transmitted deeper into the ground. The reflected energy is displayed as 2D profiles that indicate the travel time and amplitude of the reflected arrivals; such profiles can be displayed in real time during data collection and can be stored digitally for subsequent data processing. An example of the use of GPR profiles for interpreting subsurface stratigraphy is provided in Section 2.15.5.1. The velocity of the GPR signal can be obtained by measuring the travel time of the signal over a known distance between the transmitter and the receiver. The propagation phase velocity (V) and signal attenuation are controlled by the dielectric constant (k) and the electrical conductivity of the subsurface material through which the wave travels. At the high-frequency range used in GPR, the velocity in a low electrical conductivity material can be related to the dielectric

Hydrogeophysics

constant, also known as the dielectric permittivity, as (Davis and Annan, 1989)

407

where c is the propagation velocity of EM waves in free space 8 (3 10 m s1). Approaches that facilitate EM velocity analysis include surface common-midpoint (CMP), crosshole tomography acquisition, as well as analysis of the groundwave arrival recorded using common-offset geometries. Full-waveform inversion approaches have recently been developed (e.g., Ernst et al., 2007; Sassen and Everett, 2009) that offer potential for improved subsurface property characterization over methods based on travel times alone. Discussion of petrophysical relationships that link dielectric permittivity with hydrological properties of interest is described in Section 2.15.3.2. A review of GPR methods applied to hydrogeological applications is given by Annan (2005).

distribution using many first arrival travel times corresponding to refracted energy for many combinations of transmitter and receiver locations. Refraction techniques are most appropriate when there are only a few shallow (o50 m) targets of interest, or where one is interested in identifying gross lateral velocity variations or changes in interface dip. Seismic refraction methods yield much lower resolution than seismic reflection and crosshole methods. However, because refraction methods are inexpensive and acquisition may be more successful in unsaturated and unconsolidated environments, they are often chosen over reflection methods for applications such as determining the depth to the water table and to the top of bedrock, the gross velocity structure, or for locating significant faults. With crosshole seismic tomographic data, the multiple sampling of the inter-wellbore area via raypaths that emanate from instruments lowered down boreholes permits very detailed estimation of the velocity structure that can be used to estimate hydrogeological properties. A review of shallow seismic acquisition and processing techniques is given by Steeples (2005).

2.15.2.6 Seismic Methods

2.15.2.7 Surface Nuclear Magnetic Resonance

Seismic methods common to hydrological investigations use high-frequency (B100–5000 Hz) pulses of acoustic energy to probe the subsurface. These pulses are generally artificially produced (using weight drop, hammers, explosives, piezoelectric transducers, etc.) and propagate outward as a series of wavefronts. The passage of the wavefront creates a motion that can be detected by a sensitive geophone or hydrophone. According to the theory of elasticity upon which seismic wave propagation is based, several different waves are produced by a disturbance; these waves travel with different propagation velocities that are governed by the elastic constants and density of the material. The P-wave energy is transmitted by a back-and-forth particle movement in the direction of the propagating wave. Transverse waves, also called S (secondary or shear)-waves, have lower velocities than the P-wave and thus arrive later in the recording. P-wave arrivals are the easiest to detect and most commonly used arrival; we focus here exclusively on information available from P-waves. The principles of seismic reflection, refraction, and tomographic methods are briefly described below. The surface reflection technique is based on the return of reflected P-waves from boundaries where velocity and density (or seismic impedance) contrasts exist. Processing of seismic reflection data generally produces a wiggle-trace profile that resembles a geologic cross section. However, due to the lack of well-defined velocity contrasts and strong signal interference in shallow unconsolidated and unsaturated materials, seismic reflection approaches to image near subsurface architecture can be challenging. With refraction methods, the incident ray is refracted along the target boundary before returning to the surface. The refracted energy arrival times are displayed as a function of distance from the source, and interpretation of this energy can be accomplished by using simple software or forward modeling techniques. As with GPR methods, the arrival times and distances can be used to obtain velocity information directly. More advanced applications include multi-dimensional inversion for the subsurface velocity

SNMR is a geophysical method that takes advantage of the NMR response of hydrogen protons, which are components of water molecules, to estimate water content. This method involves the use of a transmitting and a receiving loop to induce and record responses to an EM excitation induced at the resonance frequency of protons (the Larmor frequency). Under equilibrium conditions, the protons of water molecule hydrogen atoms have a magnetic moment that is aligned with the Earth’s local magnetic field. Upon excitation, the axis of the precession is modified. When the external field is removed, relaxation occurs as a function of the spatial distribution, amount, and mobility of water; this relaxation manifests itself as an EM signal that decays over time. Through use and analysis of different excitation intensities, initial amplitudes, and decay time, approaches have been proposed to estimate the density distribution of hydrogen atoms as well as associated pore and grain size and water content. Although SNMR holds significant potential for directly investigating subsurface hydrological properties, it is still in an early stage of development and its resolving power is rather limited. As described by Yaramanci et al. (2005), advances are needed to overcome induction effects and inversion errors associated with multi-dimensional heterogeneities and regularization. A further problem with this method is that it is very sensitive to cultural EM noise and that the measured signals are often weak. Hertrich (2008) provides a review of SNMR for groundwater applications, and describes recent algorithm and method development.

kE

 c 2 V

ð3Þ

2.15.2.8 Gravity Measurements of changes in gravitational acceleration can be used to obtain information about subsurface density variations that can in turn be related to variations in lithology or moisture content. The common measuring device for this potential field method is a gravimeter, an instrument which is portable and easy to use. An extremely sensitive spring balance

408

Hydrogeophysics

inside the gravimeter measures differences in the weight of a small internal object from location to location; the weight differences are attributed to changes in the acceleration of gravity due to lateral variations in subsurface density. Measurements can be collected at a regional or local scale depending on the station spacing, which is usually less than half of the depth of interest. The theoretical response to the gravitational field due to factors such as the datum, latitude, terrain, drift, and regional gradient is typically compensated for prior to interpretation of the remaining gravity anomaly. Qualitative interpretation usually consists of constraining a profile or contoured anomaly map with other known geologic information to delineate, for example, the boundary of a sedimentary basin that overlies denser bedrock. A general review of the gravity technique and applications to environmental studies is given by Hinze (1990). More recently, microgravity studies have recently been performed in an attempt to quantify changes in water storage associated with hydrological processes (e.g., Krause et al., 2009) and to characterize cavities in karstic terrains (Styles et al., 2005).

2.15.2.9 Magnetics Magnetic methods obtain information related to the direction, gradient, or intensity of the Earth’s magnetic field. The intensity of the magnetic field at the Earth’s surface is a function of the location of the observation point in the primary earth magnetic field as well as from contributions from local or regional variations of magnetic material such as magnetite, the most common magnetic mineral. After correcting for the effects of the Earth’s natural magnetic field, magnetic data can be presented as total intensity, relative intensity, and vertical or horizontal gradient anomaly profiles or contour maps. Interpretation of magnetic surveys generally involves forward modeling or mapping of the anomalies correlating them with other known geologic information. As magnetic signatures depend to a large extent on magnetic mineral content, which is low in most sediments that comprise aquifers, magnetics is not commonly employed for hydrological investigations, but it can be a very powerful technique to locate lateral boundaries of landfills. Exceptions include mapping subsurface structures (basement topography, faults, and paleochannels), provided that a sufficient magnetic signature or contrast exists. A review of magnetic methods as applied to environmental problems is given by Hinze (1990).

volume of investigation of the borehole measurement is related to the log type, source–detector spacing, the borehole design, and the subsurface material. The well log measurements can be compared with each other and with direct measurements (such as from core samples) to develop sitespecific petrophysical relationships. Log data are also useful to tie hydrological and geological data collected at the wellbore location with geophysical signatures of property variations collected using surface or crosshole geophysical data. References for borehole geophysics applied to hydrogeologic investigations are given by Keys (1989) and Kobr et al. (2005).

2.15.3 Petrophysical Models To be useful in hydrology, geophysical data and hence the corresponding geophysical properties need to be sensitive to hydrological primary (e.g., total and effective porosity, and permeability) or state variables (e.g., salinity, water content, and pressure gradients). In this section, we introduce different petrophysical models that link hydrological and geophysical properties. We focus on models related to electrical properties, since they dominate hydrogeophysical applications through methods such as ERT, SIP, EM, and GPR (see Section 2.15.2). Pertinent models related to gravity, seismics, and borehole geophysical data are not considered here for brevity, but can be found in references such as Mavko et al. (1998), Scho¨n (1996), Gue´guen and Palciauskas (1994), and Carcione et al. (2007). A wealth of models for electrical properties in porous media has been proposed and only main results are summarized below; the reader is referred to Lesmes and Friedman (2005), Keller (1987), and Slater (2007) for more information. The petrophysical models discussed below were chosen because they are fairly general, and also because most of them share a similar parametrization. Purely mathematical models are useful to define bounds on properties, such as the classical Hashin–Shtrikman bounds (Hashin and Shtrikman, 1962). More common in hydrogeophysical studies is the use of semi-empirical models that partly incorporate geometrical and physical properties of the components that comprise the porous media. Examples of such models are Archie’s law (Archie, 1942) or the complex refractive index model (Birchak et al., 1974). In many cases, purely empirical relationships are obtained by fitting polynomial functions (e.g., Topp et al., 1980). Below, we briefly review petrophysical models associated with electrical conductivity, dielectric permittivity, complex conductivity, and electrokinetics.

2.15.2.10 Well Logging

2.15.3.1 Electrical Conductivity

Well logging refers to the process of recording and analyzing measurements collected discretely or continually within wellbores. Borehole measurements are made by lowering a probe into the borehole on the end of an electric cable. The probe, generally 2.5–10.0 cm in diameter and 0.5–10.0 m in length, typically encloses sources, sensors, and the electronics necessary for transmitting and recording signals. A variety of different types of wellbore probes are available; perhaps the most common for hydrological studies include: SP, electrical, EM, gamma–gamma, natural gamma, acoustic, temperature, flowmeter, neutron–neutron, televiewer, and caliper logs. The

The conductive and capacitive properties of an isotropic and homogeneous media can be represented by a complex conductivity (s*), a complex resistivity (r*), or a complex permittivity (e*):

s * ðoÞ ¼

1 ¼ ioe*ðoÞ r*ðoÞ

ð4Þ

pffiffiffiffiffiffiffi where o is the angular frequency and i ¼ 1. It is common practice to refer to the real-valued component of s * ðoÞ ¼ s0 ðoÞ þ is00 ðoÞ at low frequencies (say 0–250 kHz) as s and

Hydrogeophysics

the real–valued relative permittivity at high frequencies (10– 1000 MHz) as k ¼ e0 /e0, where e0 is the effective permittivity of the media and e0 is the permittivity of vacuum. It is important to note that in these frequency ranges both properties, s and k, have a weak frequency dependency (e.g., see Figure 4.1 in Lesmes and Friedman (2005)) that needs to be taken into account for quantitative comparisons. Low-frequency polarization s00 ðoÞ is discussed in Section 2.15.3.3.

409

2.15.3.1.1 Archie’s law

dominates over the surface conductivity term (e.g., Waxman and Smits, 1968; Johnson et al., 1986; Sen et al., 1988). One problem with Waxman and Smits’ model is that it uses an average Qv determined by titration while only the excess charge located along the conducting paths in the pore space will contribute to electrical flow. Another problem arises when surface conductivity becomes more important, since the electrical conduction paths change and can no longer be expressed by F (see Equation (6)) only (Johnson et al., 1986; Revil et al., 1998).

The aggregated empirical Archie’s first and second law (Archie, 1942), expressed here in terms of electrical conductivity, is probably the most commonly used model to interpret electrical conductivity in hydrological studies:

A fundamental length-scale parameter L was introduced by Johnson et al. (1986) as

s ¼ sw Snw fm ¼ sw Snw F 1

ð5Þ

where s is the bulk electrical conductivity of the media, sw is the electrical conductivity of the pore fluid, Sw is the water saturation, n is the water saturation exponent, f is the porosity, and m is the cementation exponent. The electrical formation factor F is defined in the absence of surface conductivity ss as (e.g., Revil et al., 1998)

  1 s ¼ fm  lim F ss -0 sw

ð6Þ

The attraction of Archie’s law in hydrological applications is obvious since it includes key properties, namely the electrical conductivity of the pore fluid related to salinity and the inverse of the electrical formation factor, which can be thought of as an effective interconnected porosity (Revil and Cathles, 1999). Archie’s law not only explains a lot of experimental data, but also is physically justified when surface conduction is negligible (Sen et al., 1981). In the vadose zone, the water saturation exponent may display significant hysteresis (Knight, 1991). Archie’s law is only valid for a continuous water phase, which might break down in dry areas where evaporation is significant (e.g., Shokri et al., 2009). Another more serious problem with this model is that surface conduction, which plays a role when significant clay and silt fractions are present in the media, is ignored.

2.15.3.1.2 Waxman–Smits law A number of models have been proposed to incorporate surface conduction. One of the most commonly used models that includes surface conduction in saturated media is the model of Waxman and Smits (1968):

1 s ¼ ðsw þ BQv Þ F

ð7Þ

where B is the equivalent conductance per ion and Qv is the density of counter ions per unit pore volume. Electrical conduction is here modeled as being composed of an electrical path in the pore volume and another parallel path at the mineral–water interface. This equation has been extensively used in the oil industry and it provides normally a good fit to experimental data when the electrolytic conductivity term

2.15.3.1.3 The Johnson, Koplik, and Schwartz model

Z 2 ¼Z L

jrc0 ðrÞj2 dS ð8Þ jrc0 ðrÞj2 dVp

where rc0 ðrÞ is the electrical potential gradient at position r in the absence of surface conductivity from a current source imposed from the sides and where the integration is performed over the mineral–water interface (S) and the pore volume (Vp), respectively. It follows that 2/L is an effective surface-to-pore-volume ratio weighted by the local strength of the electric field. This weighting eliminates contributions from dead-end pores (Johnson et al., 1986). Johnson et al. (1986) use a perturbation technique to derive the following equation:

s¼

  1 2Ss sw þ þ OðS2s Þ F L

ð9Þ

where the specific surface conductivity is given by (e.g., Schwartz et al., 1989)

Ss ¼

ZN

½sðeÞ  sw de

ð10Þ

0

where e measures the distance along a normal directed into the pore space from the grain boundary. The contributions to Ss become insignificant for values much larger than the Debye screening length that is at most some 100 A˚. The Ss is fairly well-known and is much less variable than L (Leroy and Revil, 2004). Neglecting second-order terms in Equation (9), OðS2s Þ, is only valid in the vicinity of the high-salinity limit. Schwartz et al. (1989) extended the theory of Johnson et al. (1986) to the low-salinity limit in which the electrical flow paths are determined by regions with significant surface conduction. They showed that Pade´ approximants (a ratio of two polynomials) are effective to interpolate between the high- and low-salinity limits. Johnson et al. (1986) also show that L can be used to predict permeability k with a high predictive power using the relation (see also Bernabe´ and Revil, 1995)

kE

L2 4F

ð11Þ

410

Hydrogeophysics

2.15.3.1.4 Self-similar models Another approach to model electrical conductivity is based on self-similar models with electrolytic conduction only (Sen et al., 1981) or with surface conductivity included (Bussian, 1983). Revil et al. (1998) extended the model of Bussian (1983) to explicitly model the different conduction paths taken by anions and cations. Tortuosity affecting the migration of the anions is given by Ff, but the dominant conduction paths for the cations shift toward the conduction paths defined by the distribution of Qv at the mineral–water interfaces as the salinity decreases. The ubiquitous presence of surface conductivity in geological porous media makes models of electrical conductivity alone uncertain tools in hydrological studies, since a moderately high electrical conductivity can be explained by either a fairly high sw with a well-connected pore space (i.e., low F) without any clay particles, or a low sw and a poorly connected pore space (i.e., high F) with a moderate clay fraction. The hydrological behaviors of these two types of media are fundamentally different and electrical conductivity data alone may not offer even qualitative information about the dominant hydrological properties (e.g., Purvance and Andricevic, 2000). To make quantitative predictions, it is therefore often necessary to have access to other types of geophysical (such as IP) or geological data or to perform time-lapse experiments, where temporal variations in the geophysical data are recorded (e.g., Binley et al., 2002).

2.15.3.2.1 Volume averaging Variations of electrical polarization at the frequencies used in ground-penetrating radar (GPR; 10–1000 MHz) are mainly determined by water content and less by mineralogy, even if polarizations of mineral grains need to be considered. Due to the need for complimentary data in many hydrogeophysical applications, it is common to use estimates of both electrical conductivity and the relative permittivity (e.g., Binley et al., 2002; Linde et al., 2006a). When explaining relative permittivity data, it can therefore be useful to use a relative permittivity model that shares a similar parametrization of the pore geometry as the one used to explain electrical conductivity. Such an approach was presented by Pride (1994) who used a volume-averaging approach to derive the following equation for relative permittivity



1 ðkw  ks Þ þ ks F

 ð12Þ

where kw is the relative permittivity of water (kw E 80) and ks is the relative permittivity of the solid (ks ¼ 3–8). This equation was extended by Linde et al. (2006a) to incorporate partial saturations as

k¼

ka ¼

n X

fi kai

ð14Þ

i¼1

where the subscript i indicates the contribution of each phase (e.g., rock matrix, water, and air). Equation (14) with a ¼ 0.5 is referred to as the complex refractive index model (Birchak et al., 1974)

pffiffiffi pffiffiffiffiffiffi pffiffiffiffiffi pffiffiffiffi k ¼ y kw þ ðf  yÞ ka þ ð1  fÞ ks

ð15Þ

where y is water content. Brovelli and Cassiani (2008) showed that this commonly used model is only valid when the cementation exponent m B 2 and when the dielectric contrast between phases are large. This means that Equation (15) is based on an implicit assumption about the connectedness of the pore space that in reality varies (e.g., m is typically B1.5 in unconsolidated aquifer materials; Lesmes and Friedman, 2005). Recently, Brovelli and Cassiani (2010) showed convincingly that an appropriately weighted combination of the lower and upper Hashin–Shtrikman bounds using the cementation factor could predict permittivity measurements very well.

2.15.3.2.2 Topp’s equations

2.15.3.2 Dielectric Permittivity

k¼

One of the most common petrophysical models used to estimate water content from relative permittivity data is the so-called Lichteneker–Rother model (e.g., Gue´guen and Palciauskas, 1994):

1 n S kw þ ð1  Snw Þka þ ðF  1Þks F w

ð13Þ

where ka is the relative permittivity of air (ka ¼ 1) (see Linde et al. (2006a) for a corresponding model for electrical conductivity with surface conductivity included).

A set of models that are purely empirical but have high predictive power in soils are the so-called Topp’s equations that were derived at high frequencies for different soil types. The general Topp equation (Topp et al., 1980) when the soil type is unknown is

k ¼ 3:03 þ 9:3y þ 146y 2  76:7y 3

ð16Þ

2.15.3.3 Complex Conductivity 2.15.3.3.1 Cole–Cole model We now focus on the frequency behavior of the imaginary component of the complex electrical conductivity (Equation (4)) s00 ðoÞ at low frequencies. Recent experiments suggest that the electrochemical polarization of a grain is dominated by the mineral/water interface of the Stern layer and Maxwell– Wagner effects associated with accumulation of electrical charges at pore throats (Leroy et al., 2008). SIP data (also referred to as complex conductivity; Kemna, et al., 2000) have been identified as the most promising method to develop robust inferences of polarization processes (Ghorbani et al., 2007) and potentially permeability in hydrological studies (Slater and Lesmes, 2002; Binley et al., 2005). The most common petrophysical model used in SIP is the phenomenological Cole–Cole model (Cole and Cole, 1941) or combinations of several Cole–Cole models. The Cole–Cole model can be expressed as

  s *ðoÞ ¼ s0 1 þ m

ðiotÞ c 1þðiotÞ c ð1  mÞ

 ð17Þ

Hydrogeophysics

where s0 is the conductivity at the DC limit, t is the mean relaxation time, c is an exponent that typically takes values in the range of 0.1–0.6, and m is the chargeability (m ¼ 1 – s0/ sN, where sN is the electrical conductivity at high frequency). Parameters of this model might be sensitive to specific surface area (Bo¨rner and Scho¨n, 1991; Slater et al., 2006), dominant pore-throat sizes (Scott and Barker, 2003), or effective grain sizes (Slater and Lesmes, 2002). Laboratory measurements on sandstone suggest a strong correlation between the relaxation time and the permeability (r2 ¼ 0.78) (Binley et al., 2005) and promising results have been reported from field applications (Ho¨rdt et al., 2007). It is likely that new physical models based on a more physical parametrization of the pore space that is consistent with the ones developed for other electrical properties (e.g., Leroy et al., 2008; Leroy and Revil, 2009) will help to gain a better understanding of the low-frequency polarization response and its sensitivity to hydrological parameters. In particular, it is important to develop a theory that holds at any frequency and that takes the characteristics of the electrical double layer and the surface chemistry into account.

2.15.4 Parameter Estimation/Integration Methods This section addresses how geophysical data and models can be used together with hydrological data and models to improve the imaging of hydrological properties or monitoring of hydrological processes. The approaches that have been presented in the literature differ mainly in how they represent the model parameter space; what importance and representation is given to a priori information; at what stage different data types are coupled; how uncertainties in the observations, the forward models, and the petrophysical models are treated. Figure 2 provides a schematic view of how geophysical and hydrological data and models can be integrated at different stages in the inversion process. The figure can also be seen as a general representation of how joint inversion can be carried out for the case of two data types. For an in-depth treatment of inversion theory, the reader is referred to Menke (1984), Parker (1994), McLaughlin and Townley (1996), and Tarantola (2005). Study objectives and the available budget will determine many of the choices made throughout the inversion process. These aspects are not incorporated in Figure 2, since it mainly serves to illustrate where interactions between geophysical and hydrological components of the inversion process might take place. In a given hydrogeophysical inversion method only a fraction of the links between the geophysical and hydrological compartments in Figure 2 is likely to be used.

2.15.4.1 Key Components, Constraints, Metrics, and Steps in Parameter Estimation 2.15.4.1.1 Model space and initial model A key choice in any inversion is to decide on the model parametrization used to represent the subsurface, the permissible ranges of model parameters, and the initial model (see box ‘Model space and initial model’). These choices will mainly be based upon prior knowledge (see box ‘Prior knowledge’). Prior knowledge is information about

411

characteristics of the system that we have obtained from other sources of information than the actual geophysical or hydrogeological data that we try to invert. Prior knowledge might, in this case, be related to information about the geological setting and previous exploratory or detailed studies. The link from box ‘Geophysical (or hydrological) system property data’ to box ‘Prior knowledge’ indicates also estimates of system properties that have been made outside the parameter estimation procedure (e.g., sonic log data transformed to P-wave velocities, neutron–neutron data transformed into porosity, and EM flowmeter data translated into relative variations in permeability) and we assume that these properties are known with an associated uncertainty.

2.15.4.1.2 Objective function (systems of equations) The number of independent parameters that can be inferred from hydrological and geophysical data depend on the data type, the experimental design, the number of data available, the data quality, and the forward model used. There exists however an upper limit of how many parameters one can independently estimate from a given data set. For this reason, we must find ways to constrain model space in order to obtain meaningful results, in addition to simply decrease computing time and memory use. In practice, it is necessary to explicitly constrain the parameter space by solving either an overdetermined problem with few model parameters or an underdetermined problem where a unique solution defined as the model that fits the data with the least model structure as defined by the regularization constraints used to stabilize the inverse solution. The zonation approach to model parametrization is to assume that the subsurface can be represented by a number of zones with similar physical properties, where the boundaries are either assumed to be known or updated during the inversion process. Possible applications where a zonation approach could be justified are the delineation of sand from interbedded clay layers or sediments from the underlying bedrock. The advantage of the zonation approach is that the number of model parameters can be kept relatively small and smoothness constraints across boundaries in the inversion may thus be avoided. The geostatistical approach is based on the assumption that the parameter field can be explained by a known or estimated spatial random variable with a certain correlation structure and deterministic trend. This parametrization is probably preferable when the parameters of interest vary in more or less random fashion and there is no clearly defined structure (see further discussion in Mclaughlin and Townley (1996)). Geophysical inversion is typically performed using a very fine model discretization where the aim of the inversion is to fit the data to a certain error level while minimizing deviations from an assumed prior model or spatial variability between neighboring cells. Regardless of the parametrization used, it is clear that prior knowledge should affect the objective function as indicated in Figure 2 (see arrow to box ‘Objective function (systems of equations)’). After defining the model parametrization, it is necessary to define a metric that defines what constitutes a good model and an algorithm that can be used to find such models. There are two main groups of inversion strategies: (1) deterministic

Geophysics

No

Hydrology

Model space and initial model

Prior knowledge

Prior knowledge

Model space and initial model

Inversion step or model proposal

Objective function (Systems of equations)

Objective function (Systems of equations)

Inversion step or model proposal

Forward modeling (Jacobian matrix)

Forward modeling (Jacobian matrix)

Geophysical state data

Hydrological state data

Geophysical system property data

Hydrological system property data

Proposed model acceptable or improved?

Yes

No

Yes Save model

Save model

Data misfit > target or stochastic

Proposed model acceptable or improved?

Petrophysical model

Data misfit > target or stochastic End inversion?

End inversion? Data and model integration Data misfit < target or sufficient number of realizations Geophysical model or model population

Final model or population of models

Data misfit < target or sufficient number of realizations Hydrological model or model population

Figure 2 This flowchart illustrates possible ways that geophysics and hydrology can be integrated in hydrogeophysical studies. Recent hydrogeophysical research indicates that it is important to tightly couple hydrology and geophysics throughout the inversion and modeling process; see possible connections from blue hydrological boxes to green geophysics boxes, and vice versa. Please refer to text for details.

Hydrogeophysics

inversion where one unique model is sought that describes the subsurface in some average sense (Menke, 1984; Parker, 1994) and (2) stochastic inversion where a probabilistic description of the model space is used and where a large population of possible models are sampled without specifying which is the best model, only how likely they are to explain the available data and any prior knowledge (Tarantola, 2005). Regardless of the inversion approach, the definition of the objective function will, to a large degree, determine the type of models that will be obtained for a given data set. Objective functions, at least in deterministic inversions, often include two different terms: (1) one data misfit term that characterizes how well a model explains the observed data and (2) one model misfit term that defines how a model corresponds with prior knowledge or any assumptions about how the model is likely to vary spatially. The most common approach is to quantify these two terms by using a least-squares formulation, where a weighted sum of the two squared misfit terms is penalized simultaneously, which typically works well when system properties are expected to vary smoothly and when data errors have an approximately Gaussian distribution. In this case, the data misfit is expressed as

w2d ¼ ðd  F½mÞT C1 d ðd  F½mÞ

ð18Þ

where d is an N  1 data vector (e.g., electrical resistances or observed drawdown at a pumping well); F[m] is a forward model operator response for a given model vector m of size M  1; superscript T indicates transposition; C1 d is the inverse of the data covariance matrix. It is commonly assumed that data errors are uncorrelated, rendering C1 d a diagonal matrix that contains the inverses of the estimated variances of the data errors; thus, more reliable data carry larger weight when evaluating the data fit. The corresponding model norm is

w2m ¼ ðm  m0 ÞT C1 m ðm  m0 Þ

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where m0 is a reference model of size M  1; C1 m is the inverse of the model covariance matrix, which characterizes the expected variability and correlation of model parameters (Maurer et al., 1998; Linde et al., 2006a). It should be noted that it is often common to neglect the term m0 and replace C1 m with a regularization term that approximates the square of a first or second derivative of the model. The objective function for a classical geophysical deterministic inversion is in the general least-squares case:

Wl ðmÞ ¼ ðm  m0 ÞT C1 m ðm  m0 Þ  1 ðd  F½mÞ T C1 þl d ðd  F½mÞ

ð20Þ

where l1 acts as a trade-off parameter between the smooth well-conditioned problem defined by a heavy penalty on deviations from the predefined model behavior (i.e., l is large) and the ill-conditioned problem defined by the data misfit term (i.e., l is small). In order to obtain models that display sharper contrasts between geological units or when data noise has a nonGaussian distribution, it is possible to use a method called iteratively reweighted least-squares based on the Ekblom

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lp-norm (Farquharson, 2008), thereby approaching a formulation of the inverse problem where only absolute differences in misfit are penalized, while maintaining the numerical advantages of least-squares formulations. A number of alternative data and model norms have been proposed to obtain models that provide closer representations of the expected model behavior; these methods are all based on iterative reweighting of the model misfit terms (Zhdanov, 2009; Ajo-Franklin et al., 2007; Minsley et al., 2007). In order to evaluate the performance of a proposed model for a given objective function, it is necessary to have access to a forward model (see box ‘Forward modeling (Jacobian matrix)’), which is the model that numerically solves the governing partial differential equation for a given model, boundary conditions, and excitation (e.g., current injection, detonation of explosives, or water injection in a wellbore). The accuracy of the forward model is of key importance in any inversion scheme. In deterministic inversions, it is also important to have access to the Jacobian or sensistivity matrix that defines how sensitive the modeled data are to a given small perturbation of each model parameter. The objective function offers many opportunities to couple different data types to perform joint inversion by simply augmenting d and m with new data and model types, respectively. In order to perform joint inversion, it is necessary to define some sort of constraint such that the different data types and models interact in a meaningful way. These constraints can be of many types, such as structural constraints that penalize dissimilarity between two types of models (see arrows from box ‘Save model’). One possible approach to structural joint inversion is to assume that the gradients in two models should be parallel or anti-parallel, thereby providing models that are structurally similar (e.g., Gallardo and Meju, 2003, 2004; Linde et al., 2006a, 2008). When performing joint inversion, only one objective function is used. To decrease the number of model unknowns when solving the corresponding system of equations, it is also possible to use an iterative sequential approach where two different objective functions are used as indicated in Figure 2. Another approach is to use the final model from one method to define spatial statistics that can be used to constrain the other model (Saunders et al., 2005), or the models can be constrained by using system property data from another method (Dafflon et al., 2009) or key interfaces can be incorporated, such as the depth to bedrock determined by seismic refraction in hydrogeological modeling of hillslope processes. Such information enters the objective function through the box ‘Prior knowledge’. Another approach when an accurate petrophysical relation is known to exist is to couple different model or data types by directly assuming that a given petrophysical model (known or with a given functional form with unknown parameter values) exists (see box ‘Petrophysical models’). In this way, a geophysical model can be defined by a number of hydrological properties and state variables, and the geophysical data can thereby be directly incorporated into the hydrological inversion without the need to construct a geophysical model. In this case, only a geophysical forward model and a petrophysical model is needed to interpret the geophysical data within a hydrological inverse framework. These types of inversion methods are often referred to as fully coupled

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hydrogeophysical inversion (Kowalsky et al., 2005; Pollock and Cirpka, 2010). One problem with such an approach is that not only the parameter values used in petrophysical models might change within the study area, but also their functional form if they are too simplified.

2.15.4.1.3 Inversion step or model proposal The next step corresponds to the box ‘Inversion step or model proposal’. For the deterministic case, the system of equations are solved for a given trade-off l of the different data and model misfit terms of the objective function. A large amount of numerical methods are available to solve this problem and a review of the most common methods is outside the scope of this chapter, but a good starting point is Golub and van Loan (1996). In a typical deterministic inversion, the inversion process continues until w2d E w2 , where w2 is a predefined * * target data misfit. A new inversion step using the model obtained in the previous model is carried out if w2d 4 w2 , where * typically also the value of l is decreased. If w2d o w2 , it is cus* tomary to repeat the previous inversion step with a larger l until w2d E w2 (see boxes ‘End inversion?’). In cases where no * convergence is obtained, one needs to change the inversion settings. In stochastic inversions, a proposed model is evaluated based on prior knowledge and the so-called likelihood function, which is closely related to the data misfit term. Bayesian theory offers a consistent and general framework to sequentially condition models to different data types. The posterior distribution of the model parameters m given data d is given by Bayes’ theorem

pðmjdÞ ¼ CpðdjmÞpðmÞ

ð21Þ

where C is a normalizing coefficient, pðdjmÞ is the likelihood function, and pðmÞ is the prior distribution of the model parameters (permissible range and the distribution within the range for each parameter). The likelihood functions provide information about how likely it is that a given model realization is responsible for the observed data. A main attraction of Bayesian sampling methods is that virtually any formulation of the likelihood function and the prior model can be used and it can differ between data and model types if performing joint inversion. The functional form of the petrophysical relationship can also be chosen in a flexible manner. The aim of Bayesian methods is generally to explore pðdjmÞ and this is often done by using Monte Carlo Markov Chain (MCMC) methods (e.g., Hastings, 1970; Mosegaard and Tarantola, 1995; Chen et al., 2006; Vrugt et al., 2009).

2.15.4.1.4 Geophysical model or model population Assessment of the uncertainty in the final inversion images obtained from deterministic inversion is often limited to classical linear uncertainty estimates based on the posterior model covariance matrix and resolution measures based on the resolution matrix. These estimates bear a strong imprint of the regularization used to create a stable solution (Alumbaugh and Newman, 2000). The estimated uncertainty of individual model parameters is therefore often vastly underestimated.

Different approaches have been proposed in the literature to address the variance and resolution properties of deterministic inversion models. One popular approach is simply to perform several inversions where the regularization operators or the initial model vary. This approach provides a qualitative assessment of parameters that are well resolved by the geophysical data (Oldenburg and Li, 1999). Another approach is to perform a most-squares inversion (Jackson, 1976), where the bounds within which a model parameter can vary are sought for a given small increase in data misfit. Kalscheuer and Pedersen (2007) present a nonlinear variance and resolution analysis that investigate for a given variance of a model parameter, the resulting resolution properties of this estimate. The advantage compared with classical resolution analysis (e.g., Alumbaugh and Newman, 2000; Friedel, 2003) is that resolution properties are calculated for the same model variance and that regularization operators do not influence resolution estimates. Nonlinearity is partly handled by introducing nonlinear semi-axis that takes nonlinearity in the model eigenvectors into account. Even if these methods provide a qualitative assessment of model resolution and parameter uncertainty, they provide limited insight with respect to the probability distribution of the underlying model parameters and their multi-dimensional cross-correlations.

2.15.4.2 Example Parameter Estimation Approaches 2.15.4.2.1 Direct mapping approaches The simplest application of geophysical data in quantitative hydrology is direct mapping (Linde et al., 2006b). In its simplest case, all boxes and arrows related to hydrology in Figure 2 are removed and the inversion is performed using a standard geophysical inversion method. It is assumed that a known petrophysical model exists and that it can be used to map the final geophysical model into a hydrological model. Such transformations can be useful, but it is important to understand that geophysical models are only smoothed descriptions of the real property distribution and that the estimates might be biased. Day-Lewis and Lane (2004) and Day-Lewis et al. (2005) have developed a framework to describe how resolution in geophysical images degrade as a function of experimental design and data errors for linear and linearized nonlinear problems. They also show how it is possible to establish apparent petrophysical models from a known intrinsic petrophysical model that take this smoothing into account and thereby transform the geophysical model into a more realistic hydrological model. Direct mapping approaches can be made more effective when defining the model space and initial model, as well as the objective function, using prior knowledge related to the hydrology.

2.15.4.2.2 Integration approaches (geostatistical, Bayesian) A more advanced approach is to combine site-specific hydrological system property data with geophysical models. We refer to this group of models as integration approaches and they are often based on concepts from geostatistics (Linde et al., 2006b). In this case, the geophysical inversion is performed in the same way as for direct mapping, but the petrophysical model and the model integration differ. One example of this

Hydrogeophysics

approach would be to update a model of hydraulic conductivity observed at observation wells with geophysical models that are partly sensitive to hydraulic conductivity (e.g., Chen et al., 2001). Such models can incorporate some of the uncertainty in the geophysical and petrophysical relationships in the resulting hydrological models, but they are bound to use either petrophysical models with parameters determined from laboratory measurements (which are often unsuitable in this context; Moysey et al., 2005) or empirical field-specific relationships (which may be invalid away from calibration points; Linde et al., 2006c). Direct mapping and integration approaches are useful routine tools, but they share several main limitations: (1) laboratory-based or theoretical petrophysical models often cannot be used directly, (2) the estimation of site-specific parameter values of the petrophysical models are not included within the inversion process, (3) there is no informationsharing between different data types during the inversion, (4) resulting uncertainty estimates are qualitative at best, and (5) they often provide physically impossible models (e.g., mass is not conserved when performing tracer tests, e.g., Singha and Gorelick, 2005).

2.15.4.2.3 Joint inversion or fully coupled hydrogeophysical inversion The hydrogeophysical research community has in the recent years developed approaches that do not suffer from some of the limitations of direct mapping and integration methods by using both hydrological and geophysical state data during the inversion process and by coupling the hydrological and geophysical models during the inversion. We refer to such approaches as joint inversion (Linde et al., 2006b) or, alternatively, as fully coupled hydrogeophysical inversion. These approaches often include one or more of the following: (1) hydrological flow and transport modeling form together with geophysical forward modeling an integral part of the parameter estimation process; (2) petrophysical relationships are inferred during the inversion process; (3) nonuniqueness is explicitly recognized and a number of equally possible models are evaluated. This type of approach has at least four main advantages: (1) mass conservation can be assured in time-lapse studies and physically impossible flow fields are avoided when incorporating flow and transport simulations within the inversion framework; (2) data sharing during the inversion makes it often possible to obtain more realistic models with a higher resolution; (3) unknown parameters of petrophysical models can be estimated during the inversion process; (4) and physically implausible model conceptualization might make it impossible to fit the data to a realistic error level. This last point is important, since it makes joint inversion well suited to distinguish not only between possible realistic models and inconsistent parameter distributions, but also between competing conceptual models. Joint inversion comes at a price since it is necessary to develop new inversion codes that are suitable to the available data and model objectives; recent hydrogeophysical joint or fully coupled inversion methodologies include Kowalsky et al. (2005, 2006); Chen et al. (2006, 2010), Linde et al. (2006a, 2008),Lambot et al. (2009), Pollock and Cirpka (2009), and Huisman et al. (2010). Such developments

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can in practice be greatly facilitated by incorporating freely available forward codes or by using commercial multiphysics modeling packages. Despite this, the amount of work involved is typically more significant compared with direct mapping and data integration approaches.

2.15.5 Case Studies Several case studies are presented to illustrate the use of geophysical methods for delineating subsurface architecture (Section 2.15.5.1), delineating anomalous subsurface fluid bodies (Section 2.15.5.2), monitoring hydrological processes (Section 2.15.5.3), and estimating hydrological properties (Section 2.15.5.4). The examples are based primarily on published hydrogeophysical studies that were conducted to gain insights about field-scale system behavior, improve flow and transport predictions, or to provide input to water resources or contaminant remediation management decisions. Examples were chosen to illustrate the utility for a variety of different characterization objectives, geophysical methods, and hydrogeophysical estimation approaches. Each example provides a brief background of the study as well as references for readers interested in more information.

2.15.5.1 Subsurface Architecture Delineation Because geophysical properties are often sensitive to contrasts in physical and geochemical properties, geophysical methods can be useful for mapping subsurface architecture, defined here as a distribution of hydrogeologically distinct units. Using geophysical methods for subsurface mapping is perhaps the most well-developed application in hydrogeophysics, and it is often commonly performed using surface-based geophysical techniques. Examples of common mapping objectives in hydrogeological applications include the mapping of stratigraphy or the depth to bedrock or the water table. The ability to distinguish hydrogeologically meaningful boundaries using geophysical data depends on their sensitivity to subsurface physical properties, contrasts in these properties, and the resolution of the geophysical method at the characterization target depth. In this section, we describe the use of geophysical data for mapping subsurface architecture or features by presenting several case studies that differ in their choice of geophysical method, the scales involved, characterization objective, and interpretation or integration approach. These examples include the use of airborne EM data to map lithofacies relevant for water resources at an island; high-resolution GPR imaging of braided river deposits; seismic methods to estimate subsurface architecture in contaminated environments; and fracture characterization using azimuthal SP measurements.

2.15.5.1.1 3D resistivity mapping of a Galapagos volcano aquifer A fundamental limitation of most geophysical methods used in hydrogeophysics is that they have a limited ability to cover areas larger than B1 km2 within a reasonable time and budget at a high resolution. If funding permits, airborne geophysics can be very useful for gaining an overall view of the geological

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structure at the watershed scale. Most airborne data that are collected by geological surveys around the world provide only a limited depth resolution (e.g., Pedersen et al., 1994), whereas systems developed by the mining industry are designed for deeper targets and for more pronounced anomalies such that data quality demands are lower than in hydrogeological applications. An exception is the helicopter-borne SkyTEM system that was purposefully developed for mapping of geological structures in the near surface for groundwater and environmental applications (Sørensen and Auken, 2004). This is a transient electromagnetic (TEM) system that uses a strong current flowing in the transmitter coil to induce weak secondary subsurface currents whose resulting magnetic fields are subsequently measured with a receiver coil. SkyTEM measurements provide similar data quality and resolution as ground-based TEM systems, but with the advantage that measurements are carried out at speeds exceeding 15 km h1 corresponding to a typical station spacing of 35–45 m. This system operates normally at altitudes of 15–20 m with the helicopter located at an altitude of 50 m. The system is a standalone system that can be attached to the cargo hook of any helicopter. It uses a four-turn 12.5 12.5 m2 transmitter loop with a low moment using one turn only and a high moment using all four turns. The receiver coil (0.5  0.5 m2) is located 1.5 m above a corner of the quadratic and rigidly fixed transmitter loop. D’Ozouville et al. (2008) illustrate the tremendous amount of information that airborne EM can provide in hydrological

studies in remote areas where only limited prior geological and geophysical work have previously been carried out. This study was motivated by the need to better understand the groundwater resources on the volcanic island of Santa Cruz in the Galapagos island, which is experiencing challenges in meeting the water demands of the island’s population and its many visitors. In order to provide an overall view of potential groundwater resources at the island, a SkyTEM survey of 900 km covering 190 km2 was carried out to obtain a detailed view of the island’s internal 3D electrical resistivity structure. Figure 3 shows the resulting electrical resistivity models from two profiles that cross the island. 3D inversion of TEM data is computationally infeasible for large data sets, and the inversions were performed using 1 D forward modeling. The strong lateral continuity of these models is the result of lateral model constraints that are imposed during the inversion (Viezzoli et al., 2008). Four hydrogeological units were interpreted and they are indicated as I–IV in Figure 3. Unit I represents unsaturated fractured basalts with resistivities above 800 O m; unit II is the other resistivity end-member with resistivities smaller than 10 O m representing fractured basalt invaded by seawater. Unit III is a near-surface unit and unit IV a buried unit with resistivity values that range between 50 and 200 O m. These units might correspond to weathered zones or fractured basalts saturated with freshwater. The top of unit II images the saltwater wedge to distances approximately 9 km inland and its slope is in perfect agreement with predictions based on the hydraulic gradient observed in one borehole and the density contrast between Profile A

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Hydrogeophysics

salt- and freshwater. Of significant hydrological interest is unit IV that displays electrical resistivities similar to those of freshwater-saturated basalts on other islands. It forms an internal low-resistivity zone that is present only in the upper section of the southern side of the volcano. This wedge-shaped unit covers 50 km2 and it has a thickness that varies between 10 and 80 m. It is quasi-parallel to the topography and coincides with the area of maximum precipitation. D’Ozouville et al. (2008) interpret unit IV as being composed of a similar basalt as unit I, but underlain by an impermeable layer that prohibits further downward percolation.

2.15.5.1.2 High-resolution GPR imaging of alluvial deposits When surface conductivity is insignificant and pore water salinity is reasonably low, one of the primary tools in nearsurface (up to tens of meters or so) hydrogeophysical studies is ground-penetrating radar (Davis and Annan, 1989). This method can be used to image interfaces of the 3 D water content distribution and can therefore be very useful to gain information about variations in water saturation in the vadose zone (Irving et al., 2009) and porosity in the saturated zone (Beres and Haeni, 1991). GPR can also be used to image fractures (Grasmueck, 1996) or investigate the depositional setting (van Overmeeren, 1998; Beres et al., 1999). The widespread use of GPR is mainly due to its superior vertical resolution (in the order of 0.1–1.0 m depending on the antenna frequency and the velocity of the subsurface) and the very fast data acquisition, which makes it possible to routinely obtain high-quality data at close to walking speed. Gravelly, braided river deposits form many aquifers and hydrocarbon reservoirs. These deposits typically display a hierarchical architecture where permeability varies over a multitude of scales (e.g., Ritzi et al., 2004), since permeability is linked to the sediment texture, geometry, and spatial distribution of sedimentary stata. Detailed characterization of such systems is difficult, but at least their statistical properties need to be known prior to attempting reservoir or aquifer management. In order to improve the understanding of such systems, Lunt et al. (2004) developed a 3 D depositional model of the gravelly braided Sagavanirktok River in northern Alaska. The data used to construct this model were obtained from cores, wireline logs, trenches, and some 90 km of GPR profiles using different antenna frequencies (110, 225, 450, and 900 MHz) with corresponding depths of penetration varying from 7 to 1.5 m. The 17 boreholes only provided limited sampling, and the 1.3 km of destructive trenches provided information down to the water table only. The GPR data provide continuous coverage over the whole thickness of the deposits and provide information about the depositional setting both across stream and along stream over the whole channel-belt width of 2.4 km. The GPR data made it possible to locate channel fill, unit bars, side bar deposits, confluence scour, compound braid bar deposits, and other depositional features that are of importance to understand the depositional setting. Figure 4 displays a comparison between a sedimentary log and a collocated GPR profile. Not only reflections corresponding to large-scale compound bar

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boundaries, but also certain unit bar boundaries are clearly imaged.

2.15.5.1.3 Subsurface flow architecture delineation using seismic methods The use of both GPR and seismic data sets typically entails the processing of the geophysical measurements into estimates of geophysical properties, such as reflectivity or velocity, followed by a comparison of the attributes with direct measurements often available from wellbores (e.g., lithological boundaries). Figure 4 illustrated a comparison between GPR reflectivity and wellbore lithological information. Although this two-step method often provides useful information and takes advantage of expert knowledge, the qualitative approach can limit our ability to quantify errors associated with the interpretation and it can lead to dramatically different interpretations of subsurface heterogeneity depending on the interpreter and the processing steps employed. To circumvent these limitations, Chen et al. (2010) developed a joint inversion method that simultaneously considers surface seismic refraction travel times and wellbore data sets for delineating watershed architecture that may exert an influence on contaminant plume mobility at the Oak Ridge National Laboratory site in Tennessee. The groundwater at this site includes uranium, nitrate, and other contaminants that emanated from a seepage basin (S-3 ponds, Figure 5). Underlying the seepage basin is weathered and fractured saprolite that overlies bedrock. Flow is expected to preferentially occur through the more intensely fractured and weathered zones. It is impossible to image individual fractures on a 100-m scale. However, because the fractures occur in discrete zones at this site and because the P-wave velocity in weathered and fractured zones should be lower than the surrounding more competent rock (e.g., Mair and Green, 1981, Chen et al., 2006, Juhlin and Stephens, 2006), seismic methods hold potential for aiding in the delineation of preferential flow zones. A Bayesian joint inversion approach was developed and tested at two locations within the watershed to delineate architecture that may be important for controlling plume scale transport. Within the developed framework, the seismic firstarrival times and wellbore information about key interfaces were considered as input. A staggered-grid finite-difference method was used to forward model the full seismic waveform in 2 D with subsequent automated travel-time picking. Seismic slowness and indicator variables of key interfaces are considered as unknown variables in the framework. By conditioning to the seismic travel times and wellbore information, Chen et al. (2010) estimated the probability of encountering key interfaces (i.e., between fill, saprolite, weathered low velocity zone, and consolidated bedrock) as a function of location and depth within the watershed. An example of the results obtained from two surface seismic data sets collected along the watershed reveals the presence of a distinct low velocity zone that is coincident with the trend of the plume axis (Figure 5). This example illustrates how the joint inversion approach can explicitly incorporate wellbore data into the inversion of the seismic travel time data in the estimation of aquifer architecture. Although not shown,

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Figure 5 Bottom: Examples of seismic velocity models and subsurface architecture obtained through joint stochastic inversion of wellbore and surface-based seismic refraction data sets, which reveal distinct low velocity zones that are laterally persistent along the plume axis. Top: Superposition of low velocity zone region (shown in purple) on top of plume distribution (shown in pink), suggesting the control of the low velocity zone on plume migration. Modified from Chen J, Hubbard S, Gaines D, Korneev V, Baker G, and Watson D (2010) Stochastic estimation of aquifer geometry using seismic refraction data with borehole depth constraints. Water Resources Research (in press).

the approach also provides estimates of uncertainty about the location of the interfaces.

2.15.5.1.4 Fracture zonation characterization using azimuthal electrical methods A significant body of literature has developed on using azimuthal electrical resistivity soundings to determine anisotropic electrical properties in fractured media (e.g., Taylor and Fleming, 1988; Lane et al., 1995). Electrical anisotropy in such systems is due to preferential fracture orientations, variable aperture distributions with azimuth, or clay-filled fractures. Field data suggest that directions of electrical anisotropy can, under certain conditions, be linked to anisotropy in hydraulic transmissivity (e.g., Taylor and Fleming, 1988), and it could therefore serve as an important data source in hydrogeological applications in fractured rock systems. Watson and Barker (1999) show that many of the electrode configurations that have been employed in past azimuthal resistivity surveys cannot discriminate between electrical anisotropy and heterogeneity. This problem can be avoided by using certain specialized electrode configurations. Unfortunately, data collection is very slow and no inversion for anisotropic parameters using such surveys has been performed to date. Linde and Pedersen (2004) demonstrate how these problems can be avoided by employing a frequency-domain EM method,

namely RMT. Despite these methodological developments to estimate azimuthal electrical anisotropy, it is not guaranteed that electrical anisotropy coincides with preferred hydrological flow directions and any such relationship is likely to be site specific. Wishart et al. (2006, 2008) introduced the azimuthal selfpotential gradient (ASPG) method, which provides data that may be sensitive to dominant hydrological flow directions in fractured media. In ASPG, one electrode is successively moved in steps on the order of 101 on the perimeter of an inner circle while the reference electrode moves with steps of the same size on the perimeter of an outer circle with the same midpoint as the inner circle. If the underground is predominantly anisotropic, the data will display a 1801 symmetry except for data errors, while a 3601 symmetry appears for measurements where lateral heterogeneity dominates. Wishart et al. (2008) applied this technique to four different fractured rock field sites in the New Jersey Highlands and found that three sites showed ASPG responses that compared well with observed fracture patterns at the sites. Figure 6 shows an example from one of the sites where the ASPG data display a significant 1801 symmetry indicating that large-scale fracture anisotropy is responsible for the observed ASPG data. It is also seen that the direction of the anomalies corresponds well with the mapped fracture directions at outcrops within 100 m of the measurement locations. The data from an azimuthal resistivity survey

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Figure 6 Azimuthal self-potential (SP) and resistivity data superimposed on rose diagrams of fracture strike sets mapped at the Wawayanda State Park, New Jersey. From Figure 4 in Wishart DN, Slater LD, and Gates A (2008) Fracture anisotropy characterization in crystalline bedrock using fieldscale azimuthal self potential gradient. Journal of Hydrology 358: 35–45.

using an asymmetric arrow type array (Bolshakov et al., 1997) do not seem to correspond to either of the dominant fracture directions; the data are strongly nonsymmetric, indicating that lateral variations in the electrical conductivity structure dominate. It is reasonable to assume that the positive lobes of the ASPG data indicate groundwater flow directions, but no detailed field evidence is available to confirm this even if regional flow considerations point in this direction. The magnitudes of the SP signals presented by Wishart et al. (2008) are rather large (e.g., up to 300 mV over distances of 36 m) and the variations of ASPG signals with offset are sharp. The large magnitudes can partly be explained by the shallow water table (B0.5 m) and only a few meters of till overlying the highly resistive fractured rock mass. Applications in locations with thicker and more conductive overburden and with a deeper location of the source current will likely result in much smaller magnitude and a less clear-cut interpretation even for identical flow and fracture conditions. The usefulness of this technique in other field-settings needs to be assessed, but it appears that the ASPG technique can be a very rapid method to nonintrusively map preferential flow paths in fractured rock at shallow depths where overburden thickness is thin.

2.15.5.2 Delineation of Anomalous Fluid Bodies Geophysical methods, particularly those collected from the ground surface or from aircrafts (e.g., Paine, 2003), have been successfully used to identify anomalous subsurface fluid bodies, such as contaminant plume boundaries and regions impacted by saltwater intrusion. Here, we illustrate the use of surface electrical approaches for delineating high ionic strength plumes and for characterizing redox gradients associated with contaminant plumes.

fluid, electrical methods are commonly used to delineate subsurface plumes having high ionic strength (e.g., Watson et al., 2005; Adepelumi et al., 2005; Titov et al., 2005). As shown in Equation (5), electrical resistivity responds to porosity and surface conduction (often linked to lithology) as well as to saturation and pore fluid ionic strength. As described by Atekwana et al. (2004), activity of the natural microbial population can also impact the electrical resistivity through facilitating processes such as mineral etching, which appear to be more prevalent at the fringes of organic plumes. If the contrast between the concentration of the groundwater and the plume is great enough so that other contributions are considered to be negligible, electrical methods can be used, at least in the absence of significant clay units, to indicate contrasts in pore water electrical conductivity, or to delineate approximate boundaries of high-ionic-strength plumes. An example of the use of inverted surface electrical resistivity data to delineate a deep (B50 m) nitrate plume at the contaminated Department of Energy (DOE) Hanford Reservation in Washington is given by Rucker and Fink (2007). They collected six ERT transects (each at least 200 m long), inverted the data to estimate the electrical resistivity distribution in the contaminated region, and compared their results with wellbore borehole measurements of pore water electrical conductivity and contaminant concentrations. They found a strong, negative correlation between electrical resistivity and nitrate concentration above a threshold value, which was used with the electrical models to delineate the plume. Figure 7 shows several of the inverted transects as well as the correlation between electrical resistivity and nitrate concentration obtained from co-located electrical and wellbore measurements.

2.15.5.2.1 Electrical resistivity to delineate high-ionicstrength plume boundaries

2.15.5.2.2 SP imaging of redox potentials associated with contaminated plumes

Because of the strong positive correlation between total dissolved solids (TDSs) and the electrical conductivity of the pore

The traditional application of the SP method has been in mineral exploration where large negative SP anomalies are

Hydrogeophysics

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Figure 7 (a) Inverted electrical resistivity profiles at the BC crib area of the contaminated Hanford, Washington Reservation, where the low electrical resistivity (high electrical conductivity) regions were interpreted as the plume. (b) Observed relationship between electrical conductivity and nitrate concentration at a single wellbore location. Modified from Rucker DF and Fink JB (2007) Inorganic plume delineation using surface high resolution electrical resistivity at the BC Cribs and Trenches Site, Hanford. Vadose Zone Journal 6: 946–958.

typically associated with mineral veins (Fox, 1830). As an extreme example, Goldie (2002) presents a peak anomaly of  10.2 V associated with highly resistive high-sulfidation oxide gold deposits. The main contribution of such anomalies is thought to be related to electrochemical half-reactions (Sato and Mooney, 1960; Bigalke and Grabner, 1997), even if it has been suggested that some field data contradict this model (Corry, 1985). Naudet et al. (2003, 2004) observed large negative SP anomalies over the Entressen domestic landfill outside Marseille, France. Redox potential, or Eh, indicates the tendency for oxidation–reduction reactions to occur. Strong redox gradients often become established adjacent to contaminant plumes. They found that the residual SP data (Figure 8(a)), where the effects of streaming currents had been filtered out, were strongly correlated with the difference in redox potential between groundwater samples in the contaminated landfill and uncontaminated areas. They invoked an explanation in analogy with the models of Sato and Mooney (1960) and Bigalke and Grabner (1997). To remotely map variations in redox potential, Linde and Revil (2007) developed a linear inversion model where the difference in redox potential is retrieved from the residual SP data assuming a known 1 D electrical resistivity model and a known depth at which electrochemical reactions take place. They created a simplified representation of the electrical conductivity structure of the Entressen landfill based on ERT models and they assumed that source currents are located at the water table. Figure 8(b) shows a comparison between the

simulated and observed residual SP data of Naudet et al. (2003, 2004). Figure 8(c) displays the retrieved redox potentials assuming a known background value outside of the contaminated area. By comparing these estimates and measured redox potentials in the wells (Figure 8(d)), they found that the inversion results can retrieve the measured redox potentials quite well given the simplifying assumptions involved. It should be noted that equally good data fits between the simulated and observed SP data could have been achieved by shifting the depth at which the sources are imposed or by assuming that the vertical dipole sources are distributed over a volume and not over an area (Blakely, 1996). The interpretation of SP data must therefore be treated with caution, and significant prior constraints must be imposed. Nevertheless, it appears that SP mapping and monitoring may provide a cheap and reliable method for monitoring field scale distribution of redox potential at contaminated sites. It is necessary that this approach is tested at other research sites before its applicability can be properly assessed.

2.15.5.3 Hydrological Process Monitoring A particularly powerful component in hydrogeophysics is the use of a suite of geophysical data sets, collected at the same locations as a function of time, to monitor hydrological processes. Such repeated studies are often referred to as time-lapse geophysics, and their advantages compared to static images are

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Figure 8 (a) Residual SP map at the Entressen landfill, in which the black lines indicate the SP profiles (2417 SP measurements). (b) Comparison of simulated SP with the residual SP estimated from the measured SP data. The response of the inverted model fits the residual SP to the estimated standard deviation. (c) Inverted redox potential in the aquifer at Entressen. (d) Comparison of inverted redox potentials in the aquifer with in situ measurements from Entressen (the correlation coefficient is 0.94). Modified from Linde N and Revil A (2007) Inverting self-potential data for redox potentials of contaminant plumes. Geophysical Research Letters 34: L14302 (doi: 10.1029/2007GL030084).

significant for process monitoring. First of all, changes in welldesigned time-lapse inversions are most often primarily related to changes in state variables only (e.g., temperature, pressure, partial saturations of different phases, and the electrical conductivity of the pore fluid) and not to characteristics of the rock matrix itself. Time-lapse imaging has also the advantage that errors in the forward model tend to cancel (e.g., LaBrecque and Yang, 2001; Lien and Mannseth, 2008). It should be noted that subsurface engineered manipulations, such as those associated with environmental remediation, aquifer storage and recovery, and carbon sequestration can indeed alter the physical properties of the material. For example, remediation treatments can induce biogeochemical transformations that in turn alter the pore geometry and ultimately the fluid flow characteristics (Englert et al., 2009; Li et al., 2009). The geophysical responses to such processes are currently under intense study in the research area of biogeophysics (Atekwana et al., 2006; Williams et al., 2005; Chen et al., 2009; Slater et al., 2009), but are not covered in this chapter. In what follows, we present several examples that illustrate the use of time-lapse geophysics, including: GPR to monitor the spatiotemporal distribution of soil water content in agricultural and hillslope settings; the use of GPR to monitor the distribution of saline tracers in fractured rock; and the use of EM methods to monitor seasonal changes in freshwater– seawater dynamics.

2.15.5.3.1 Soil moisture monitoring The vadose zone mediates many of the processes in the hydrological cycle, such as the partitioning of precipitation into infiltration and runoff, groundwater recharge, contaminant transport, plant growth, evaporation, and sensible and latent energy exchanges between the Earth’s surface and its atmosphere. As an example, in catchment hydrology, the readiness of an area to generate surface runoff during storm rainfall is related to its surface storage capacity. Given the predominant effects of soil moisture on the production of crops, soil salinization, carbon cycling, and climate feedback, development of methods for monitoring moisture content over field-relevant scales is desirable (e.g., Vereecken et al., 2008). Equations (5) and (13) indicate that both the dielectric constant and electrical conductivity are sensitive to water content. Because of this sensitivity, geophysical methods that are sensitive to these properties (e.g., GPR and ERT) have been used fairly extensively to monitor the spatiotemporal distribution of soil moisture. As described by Huisman et al. (2003) and Lambot et al. (2008), GPR is commonly used in hydrogeophysical studies to estimate water content. Various GPR waveform components and configurations have been used to estimate water content, including: crosshole radar velocity (Hubbard et al., 1997; Binley et al., 2002), surface ground wave velocity (Grote et al., 2003), subsurface reflection (Greaves et al., 1996; Lunt et al., 2005), and air-launched ground-surface reflection approaches

Hydrogeophysics

(Lambot et al., 2006). An example of the use of time-lapse surface reflection GPR coupled with a Bayesian method to estimate seasonal changes in water content in the root zone of an agricultural site is given by Hubbard et al. (2006). Within a 90 m  220 m section of this agricultural site, a thin (B0.1 m), low-permeability clay layer was identified from borehole samples and logs at a depth of 0.8–1.3 m below ground surface. GPR data were collected several times during the growing season using 100 MHz surface antennas; these data revealed that the thin clay layer was associated with a subsurface channel. Following equations (3) and (15), as the bulk water content in the unit above the GPR reflector increased, the dielectric constant increased, which lowered the velocity and lengthened the two-way travel time to the reflector. As a result, the GPR reflections revealed seasonal changes in the travel time to the clay layer as a function of average root zone moisture content. At the wellbore locations, a site-specific relationship between the dielectric constant and volumetric water content was used with the radar travel times to the clay reflector to estimate the depth-averaged volumetric water content of the soils above the reflector. Compared to average water content measurements from calibrated neutron probe logs collected over the same depth interval, the estimates obtained from GPR reflections at the borehole locations had an average error of 1.8% (Lunt et al., 2005). To assess seasonal variations in the root zone water content between the wellbores, the travel time picks associated with all GPR data sets, the wellbore information about the depths to the clay layer, and the site-specific petrophysical relationship were used within a Bayesian procedure (Hubbard et al., 2006). Figure 9 illustrates the estimated volumetric water content for the zone located above the reflecting clay layer at different times during the year. The figure indicates seasonal variations in mean water content and also that the channel-shaped feature influences

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water content distribution: within this area the soils are consistently wetter than the surrounding soils. The soil moisture variations appeared to play a significant role in the crop performance: crops located within the channel region had consistently higher crop weight relative to the surrounding regions. These results suggest that the two-way GPR reflection travel times can be used to obtain estimates of average soil layer water content when GPR reflectors are present and when sufficient borehole control is available. Several studies have also explored the use of surface ERT data sets for characterizing moisture infiltration and redistribution at the hillslope and watershed scales. For example, Berthold et al. (2004) compared electrical conductivity estimates from surface ERT images with groundwater electrical conductivity measurements to evaluate the roles of wetlands and ponds on depression-focused groundwater recharge within a Canadian wildlife region. The surface electrical data revealed a complex pattern of salt distribution that would have been difficult to understand given point measurements alone. Koch et al. (2009) collected surface electrical profiles over time along 18 transects within a German hillslope environment, and used the images together with conventional measurements to interpret flow pathways and source areas of runoff.

2.15.5.3.2 Saline tracer monitoring in fractured rock using time-lapse GPR methods Hydrogeophysical applications in fractured media are challenging because of the large and discrete variations between the physical properties of the intact rock mass and the fractures (NRC, 1996). Time-lapse imaging of geophysically detectable tracers has been used in recent years to improve the understanding of fracture distribution and connectivity. The best adapted geophysical technique to image individual

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Figure 9 Plan-view map of average volumetric water content of the top soil layer (o1.5 m below ground surface) at the agricultural study site, estimated using 100 MHz GPR reflection travel-time data and borehole neutron probe data within a Bayesian estimation approach. Color key at right indicates relative volumetric water content, from red (drier) to blue (wetter). Modified from Hubbard S, Lunt I, Grote K, and Rubin Y (2006) Vineyard soil water content: mapping small scale variability using ground penetrating radar. In: Macqueen RW and Meinert LD (eds.) Fine Wine and Terroir – The Geoscience Perspective. Geoscience Canada Reprint Series Number 9 (ISBN 1-897095-21-X; ISSN 0821-381X). St. John’s, NL: Geological Association of Canada.

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fractures away from boreholes, up to few tens of meters away from the boreholes, is probably single-hole radar reflection measurements. This method has been shown to be a useful tool in site characterization efforts to determine possible orientations and lengths of fractures in nuclear waste repository laboratories (Olsson et al., 1992) and in characterizing unstable rock masses (Spillmann et al., 2007). By stimulating individual fractures by adding a saline tracer, it is possible to image tracer movement from the surface (Tsoflias et al., 2001; Talley et al., 2005; Tsoflias and Becker, 2008) and in-between boreholes (Day-Lewis et al., 2003, 2006) by investigating how the amplitude of GPR signals varies over time for a given transmitter– receiver geometry while the saline tracer migrates in the rock fractures. One problem with such studies is that the data acquisition time is often comparable to the timescale of the hydrological flow processes in the fractures where fluid flow velocities might be very high, creating large inversion artifacts if the data acquisition time is ignored in the inversion process. Day-Lewis et al. (2002, 2003) present an innovative inversion method for difference-attenuation crosshole GPR data where the data acquisition time is included within the inversion. Synthetic (Day-Lewis et al., 2002) and field-based (Day-Lewis et al., 2003, 2006) inversion results show significant improvements compared with classical time-lapse inversion algorithms. The research of Day-Lewis et al. (2003, 2006) was carried out at the Forest Service East (FSE) well field at the US Geological Survey (USGS) Fractured Rock Hydrology Research Site located near Mirror Lake, New Hampshire. This well field consists of 14 boreholes distributed over an area of 120  80 m2. Saline injection tests were carried out at 45 m depth where four boreholes, with side lengths of approximately 10 m located in a square-like shape seen from above, are hydraulically connected (Hsieh and Shapiro, 1996). These tracer tests were performed using weak-doublet tracer tests, where fluid was pumped out of one borehole at a rate of 3.8 l min1 and water was injected in another borehole at 1.9 l min1. After achieving steady-state flow, the injection fluid was changed from freshwater to a sodium chloride (NaCl) concentration of 50 g l1 NaCl. Injection of freshwater was resumed after 10 min. The electrical conductivity ratio of these two fluids was estimated to be close to 170. A conventional packer system was used in the pumping well, whereas a special PVC packer system that allowed measurements while preventing vertical flow and the saline solution from entering the boreholes was used in the injection well and in two neighboring wells where GPR measurements were also conducted. The energy of a GPR signal that arrives at the receiving antenna depends to a large degree on the electrical conductance of the media in between the transmitting and receiving antenna. It is expected that the magnitude of the signal at the receiving antenna decreases significantly when a saline tracer passes the ray path. Difference-attenuation inversion is a linear problem since electrical conductivity has no significant effect on the actual ray path. Figure 10 displays variations in the ray energy that arrives in the receiver antennas normalized by the ray length for different transmitter and receiver separations during the tracer experiment of Day-Lewis et al. (2003) for a borehole plane roughly perpendicular to the injection and pumping borehole. Figure 12 also shows the corresponding chloride concentration in the pumping well. The geophysical

difference-attenuation data and the chloride data seem to agree qualitatively and a quicker breakthrough in the GPR data is observed because they were acquired over an area halfway between the injection and pumping boreholes. This type of data was later inverted by Day-Lewis et al. (2003) and they showed that it was possible to remotely monitor the tracer movement relatively well given that only three 2 D slices through the 3 D volume could be imaged. It appears that difference-attenuation data might provide the resolution needed to study fluid flow in fractured rock with only limited hydrological point sampling.

2.15.5.3.3 Seasonal changes in regional saltwater dynamics using time-lapse EM methods Falga`s et al. (2009) present one of few published time-lapse hydrogeophysical studies at the km scale (see Ogilvy et al. (2009) and Nguyen et al. (2009) for seawater intrusion studies using ERT). They used a frequency-domain EM method, namely Controlled-Source Audiomagnetotellurics (CSAMT) (Zonge and Hughes, 1991), to monitor freshwater–seawater interface dynamics in the deltaic zone of the Tordera River in northeastern Spain. Monitoring of saltwater intrusion in coastal aquifers is important due to population growth and since most of the World’s population is concentrated along coastal areas. The CSAMT data were collected over an ancient paleochannel that controls seawater intrusion in a part of the delta. During 2 years, a profile of seven soundings was acquired along a 1700-m N–S trending line. Due to agricultural activity, they could not recover previous site locations with accuracy higher than 100 m when performing the repeated measurements. The resulting individually inverted resistivity models are shown in Figure 11 together with a weighted root-meansquare (RMS) data misfit calculated with an assumed error level of 5%. To better distinguish temporal changes, the inversions used the inversion results of the first survey as initial model for the subsequent inversions. The changes of the electrical resistivity models over time clearly indicate saltwater encroachment in the low-resistivity layer at approximately 50 m depth. These dynamic processes are best imaged in the northern part of the profile where the seawater wedge retreated toward the sea from April 2004 until December 2004, followed by progression until August 2005, and finally followed by a new retreat until May 2006. Multilevel sampling of a piezometer (W06) in April 2004 displayed a saltwater content of approximately 8% at a 50-m depth. Additional evidence to support the interpretation of the geoelectrical models in terms of seawater intrusion is offered by the piezometric levels that were the lowest in August 2005 when the seawater intrusion was interpreted to be at its maximum. Another zone displaying seawater intrusion dynamics is shown in a shallow aquifer located in the upper tenths of meters close to the sea located to the South. Even if the study of Falga`s et al. (2009) had certain limitations, namely rather few stations, long periods between measurements, and not identical measurement locations between surveys, it still shows the potential of EM methods to monitor seawater intrusion processes on a scale that is relevant for water-resource planning.

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EM methods have a higher sensitivity to conductors (e.g., the seawater plume) than the more commonly used ERT method, even if they have a poor resolving power in defining the lower boundary of conductors. This limitation can partly be resolved by combining this type of geophysical data with other types of geophysical data, such as seismic refraction data during the inversion (Gallardo and Meju, 2007).

2.15.5.4 Hydrogeological Parameter or Zonation Estimation for Improving Flow Predictions Developing a predictive understanding of subsurface flow is complicated by the inaccessibility of the subsurface, the disparity of scales across which controlling processes dominate (e.g., Gelhar, 1993), and the sampling bias associated with

different types of measurements (e.g., Scheibe and Chien, 2003). In this section, we describe the use of geophysical methods to improve flow predictions, through improved parametrization of flow and transport models as well as through fully coupled hydrogeophysical inversion. Although the examples provided here have been conducted at the local scale, joint or fully coupled hydrogeophysical inversion at the watershed scale is a research area that we expect to become more advanced in the coming years.

2.15.5.4.1 Hydraulic conductivity and zonation estimation using GPR and seismic methods Several studies have described the use of geophysical data for estimating hydraulic conductivity (e.g., Cassiani et al., 1998; Hyndman et al., 2000; Hubbard et al., 2001; Chen et al., 2001;

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Gloaguen et al., 2001; Slater, 2007; Linde et al., 2008). A few studies have also illustrated the value of geophysically obtained information for improving flow and transport predictions (Scheibe and Chien, 2003; Bowling et al., 2006; Scheibe et al., 2006). One such example is provided by the linked hydrogeophysical-groundwater modeling study performed at the DOE Oyster Site in Virginia. At this site, tomographic data were used together with borehole flowmeter

logs to develop a site-specific petrophysical relationship that linked radar and seismic velocity with hydraulic conductivity. Using a Bayesian approach, a prior probability of hydraulic conductivity was first obtained through geostatistical interpolation (i.e., kriging) of the hydraulic conductivity values obtained at the wellbore location using the flowmeter logs. Within the Bayesian framework, these estimates were then updated using the developed petrophysical relationship and

Hydrogeophysics

estimates of radar and seismic velocity were obtained along the tomographic transects (Figure 12). The method yielded posterior estimates of hydraulic conductivity (and their uncertainties) along the geophysical transects that honored the wellbore measurements (Chen et al., 2001; Hubbard et al., 2001). Examples of mean values of the geophysically obtained hydraulic conductivity estimates are shown in Figure 12, where the transects are oriented parallel and perpendicular to geological strike. The estimates were obtained at the spatial resolution of the geophysical model, which had pixel dimensions of 0.25 m  0.25 m. The geophysically obtained estimates were then used to develop a synthetic aquifer model (Scheibe and Chien, 2003). Other types of data sets were also used to develop other aquifer models, including interpolated core hydraulic conductivity measurements and interpolated flowmeter data. The breakthrough of a bromide tracer through these different aquifer models was simulated and subsequently compared with the breakthrough of the bromide tracer measured at the Oyster site itself (Scheibe and Chien, 2003). Even though this site was fairly homogeneous (the hydraulic conductivity varied over one order of magnitude) and had extensive borehole control (i.e., wellbores every few meters), it was difficult to capture the variability of hydraulic conductivity using borehole data alone with sufficient accuracy to ensure reliable transport predictions. Scheibe and Chien (2003) found that ‘‘conditioning to geophysical interpretations with larger spatial support significantly improved the accuracy and precision of model predictions’’ relative to wellbore-based data sets. This study suggested that the geophysically based methods provided information at a reasonable scale and resolution for understanding field-scale processes. This is an important point, because it is often difficult to take information gained at the laboratory scale or even from discrete wellbore samples and apply it at the field scale. The level of detail shown in the hydraulic conductivity estimates of Figure 12 may not always be necessary to adequately describe the controls on transport; in some cases, defining only contrasts between hydraulic units (Hill, 2006) or the hydraulic zonation may be sufficient to improve flow predictions. Several studies have illustrated the utility of tomographic methods for mapping zonation of lithofacies or hydrologically important parameters. Hyndman and Gorelick (1996) jointly used tracer and seismic tomographic data to map hydrological zonation within an alluvial aquifer. Linde et al. (2006c) used tomographic zonation constraints in the inversion of tracer test data and found that the constraints improved hydrogeological site characterization. Hubbard et al. (2008) used a discriminant analysis approach to estimate hydraulic conductivity zonation at the contaminated Hanford 100 H site, and found that the identified heterogeneity controlled the distribution of remedial amendments injected into the subsurface for bioremediation purposes as well as the subsequent biogeochemical transformations.

2.15.5.4.2 Joint modeling to estimate temporal changes in moisture content using GPR In this example, we illustrate the value of the joint inversion approach for taking advantage of the complementary nature

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of geophysical and hydrological data and for circumventing some of the obstacles commonly encountered in other types of integration approaches (see Section 2.15.4.2). As was previously discussed, the use of GPR methods for mapping water content distributions in the subsurface is now well established. However, in general, GPR measurements cannot be directly related to the soil hydraulic parameters needed to make hydrological predictions in the vadose zone (such as the permeability and the parameters describing the relative permeability and capillary pressure functions). On the other hand, time-lapse GPR data often contain information that can be indirectly related to the soil hydraulic properties, since these soil hydraulic properties influence the time- and spacevarying changes in water distribution, which in turn affect GPR data. Kowalsky et al. (2004, 2005) illustrated an approach for incorporating time-lapse GPR and hydrological measurements into a hydrological–geophysical joint inversion framework for estimating soil hydraulic parameter distributions. Coupling between the hydrological and GPR simulators was accomplished within the framework of an inverse model (iTOUGH2, Finsterle, 1999). The inversion was performed using a maximum a posteriori (MAP) approach that utilized concepts from the pilot point method. One of the benefits of this approach was that it directly used the GPR travel times rather than radar velocity tomograms, which circumvented some of the problems that were discussed in Section 2.15.4.2. The approach also accounted for uncertainty in the petrophysical function that related water content and dielectric permittivity. The approach was applied to data collected at the 200 East Area of the US Department of Energy (DOE) Hanford site in Washington. The Hanford subsurface is contaminated with significant quantities of metals, radionuclide, and organics; contaminants are located in the saturated as well as in a thick vadose zone. Gaining an understanding of vadose zone hydraulic parameters, such as permeability, is critical for estimating plume infiltration at the site and the ultimate interception with groundwater and the nearby Columbia River. To gain information about the vadose zone hydraulic parameters, an infiltration test was performed by ponding water on the ground surface and subsequently measuring the subsurface moisture distribution over time using neutron probe data collected within wellbores and radar tomographic data collected between boreholes (Figures 13(a) and 13(b)). Because water infiltration behavior is a function of the permeability distribution, the joint inversion procedure could be used with the time-lapse moisture data to estimate log permeability. The inversion procedure was also used to estimate other parameters of the petrophysical relationship, porosity, and the injection rate, none of which were measured precisely at the site. Figure 13(c) illustrates the permeability values estimated from the joint inversion procedure, which have been conditioned to GPR travel times and to the measured hydrological properties. The obtained permeability values were then used to predict fluid flow at future times. The accuracy of predictions for future times was evaluated through comparison with data collected at later times but not used in the inversion. In the first case, inversion was performed using only neutron probe data collected in two wells at three different times. In the second case, inversion was performed

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Estimates of hydraulic conductivity Figure 12 Example of Bayesian approach for integrating disparate data sets for the estimation of hydraulic conductivity distributions, where the mean value of the estimated hydraulic conductivity distributions is shown on the bottom right. Modified from Hubbard S, Chen J, Peterson J, et al. (2001) Hydrogeological characterization of the DOE bacterial transport site in Oyster, Virginia, using geophysical data. Water Resources Research 37(10): 2431–2456.

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Figure 13 Time-lapse data sets collected during water injection at Hanford site, including (a) interpolated water content inferred from dense neutronprobe measurements and (b) ground-penetrating radar acquisition geometry. Estimates of log permeability (c) obtained using the coupled inversion approach. Modified from Kowalsky et al. (2006).

using GPR data collected at two times in addition to the neutron probe data used in the first case. Compared to predictions made through inversion of only neutron probe data, inclusion of GPR data in the joint inversion resulted in more accurate estimates of water content at later times.

2.15.6 Summary and Outlook This chapter has reviewed several case studies that illustrated how hydrogeophysical methods can be used to: map subsurface architecture, estimate subsurface hydrological properties or state variables, and monitor subsurface processes associated with natural or engineered in situ perturbations to the subsurface system. These and many other studies have now demonstrated that hydrogeophysical approaches can successfully be used to gain insight about subsurface hydrological processes, provide input that improves flow and transport predictions, and provide information over spatial scales that are relevant to the management of water resources and contaminant remediation. Critical to the success of hydrogeophysical studies are several factors: (1) the acquisition of high-quality geophysical data; (2) the availability of

petrophysical relationships that can link geophysical properties to the parameters or processes relevant for the hydrological study; and (3) the use of inversion approaches that allow for reliable and robust estimation of hydrological parameters of interest. Here, we briefly comment on each of these important factors and associated research needs. Section 2.15.2 reviewed many of the geophysical methods that are common or are being increasingly employed in hydrogeophysical studies, including: electrical resistivity, IP, controlled-source inductive EM, SP, GPR, seismic, SNMR, gravity, magnetics, and well logging methods. We stressed that acquisition of high-quality data is critical to a successful hydrogeophysical study. The choice of which geophysical data to invoke for a particular investigation must be made based on the expected sensitivity of the geophysical attribute to the properties associated with the characterization objective (or the contrast of the target properties with the surrounding sediments or rocks). Different geophysical methods perform optimally in different environments and have different resolving capabilities. It is thus necessary, when deciding on which geophysical method to use to consider the general geological setting and the size/depth/contrast magnitude of the characterization target. Although these characteristics

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should be considered prior to choosing a method (and ideally considered through synthetic modeling), commonly the performance of a geophysical method cannot be truly assessed until it is tested at a particular field site. This is because factors that influence its performance (such as clay content, depth of particular target, contrast in characterization target properties with surrounding material, and presence of cultural features such as underground pipes) are often not known with sufficient certainty prior to field testing. For this reason, geophysical campaigns are ideally performed in an iterative manner, starting first with reconnaissance campaigns that involve testing the geophysical responses of a few different methods prior to choosing the method for further highresolution investigation. Section 2.15.3 described common petrophysical models that link electrical conductivity, dielectric permittivity, complex conductivity, and SP measurements to hydrological variables, which are commonly based on theoretical considerations or on laboratory- or field-based experiments. Unfortunately, all of these petrophysical model types pose challenges for hydrogeophysical studies. Theoretical models often invoke assumptions or simplifications that deviate from heterogeneous, in situ conditions. Problems with laboratorybased measurements (e.g., Ferre´ et al., 2005) are that it is very difficult to (1) acquire undisturbed samples that adequately represent conditions in the near subsurface and (2) upscale developed relationships from the laboratory to the field scale (Moysey et al., 2005). Application of field-scale relationships (e.g., using co-located hydrogeophysical wellbore data sets; Hubbard et al., 2001) can also be problematic if the petrophysical relationship differs at locations away from the wellbore (Linde et al., 2006c). Finally, because most geophysical attributes are sensitive to more than one property that typically varies substantially in the subsurface, methods must be developed to handle nonuniqueness in geophysical responses to property variations (Hubbard and Rubin, 1997). The development and testing of petrophysical relationships that describe the linkages between field-scale geophysical responses to variably saturated, semi- to unconsolidated, low-pressure materials that typify many of our shallow subsurface environments continues to be a need in hydrogeophysics. Embedded in that need is the development of methods that can adequately handle scale effects, nonuniqueness, and uncertainties associated with petrophysical relationships. The importance of parameter estimation/integration methods that honor available hydrogeological and geophysical data in the interpretation procedure was described in Section 2.15.4. We defined three different parameter estimation processes, namely: (1) direct mapping; (2) integration approaches (geostatistical and Bayesian); and (3) joint inversion or fully coupled hydrogeophysical inversion. Each of these has advantages and limitations, and the decision about which approach to use is a function of the data available, the characterization objective and project budget, and the experience of the interpreter with the different methods. Clearly, the motivation exists to take advantage of the complimentary nature of hydrological and geophysical data and modeling to improve experimental design and interpretation while recognizing that each of these approaches has associated uncertainty. We thus believe that one of the most important

developments in hydrogeophysical research in the coming years will arise from data integration schemes that provide a flexible way to couple different hydrological and geophysical data and model types in a framework that explicitly assesses uncertainty in the final model or model predictions. An important challenge will be the development of methods that disregard models that are inconsistent with our available data and a priori conceptions while retaining a representative subset of models that are consistent with available data. It is expected that joint inversion approaches can provide more significant improvements compared to other approaches, especially when working with time-lapse data and when the hydrological dynamics of the geophysical and hydrological forward responses display strong nonlinearities. Although inversion approaches have been developed to meet some of these criteria, for the most part they have been tested in conjunction with specific research projects and are not generally accessible for use by nonspecialists or flexible enough to be applied to other problems and data sets. An existing need is thus the development of software that will facilitate the transfer of the state-of-the-art inversion algorithms, which allow joint consideration of geophysical and hydrological measurements and phenomena and that provide meaningful assessments of uncertainty, into practice. Related to all three key factors in hydrogeophysical studies (high-quality geophysical data sets, petrophysics, and integration methods) is the need to better advance our capabilities to improve the characterization of subsurface hydrological parameters and processes at the larger watershed scale. The majority of the hydrogeophysical studies that have focused on quantitative hydrological parameter estimation or model coupling have been performed at the local scale (typically with length scales o10 m), where the disparity in measurement support scale between wellbore (direct) measurements and geophysical measurements is smaller and where stationarity of petrophysical relationships can often be reasonably assumed. Although these studies have illustrated the power of hydrogeophysical methods for improving the resolution and understanding of subsurface properties or processes at the local scale, they are often still limited in their ability to inform about behavior that may be most relevant at the larger scales where water resources or environmental contaminants are managed. As described in Section 2.15.4, although a handful of case studies have now illustrated the potential that geophysical methods hold for providing quantitative information over large spatial scales, additional effort is needed to continue to advance this area of watershed hydrogeophysics (Robinson et al., 2008). In particular, there is a great need to develop petrophysical models and integration schemes that permit the coupling of different hydrological and geophysical data and model types within a framework that explicitly assesses uncertainty in the final model or model predictions over watershed- or plume-relevant scales.

Acknowledgments Support for Susan Hubbard was provided by the US Department of Energy, Biological and Environmental Research Program as part of the Oak Ridge Integrated Field Research

Hydrogeophysics

Center (ORIFRC) project and through DOE Contract DEAC0205CH11231 to the LBNL Sustainable Systems Scientific Focus Area. We thank Lee Slater and Se´bastien Lambot whose constructive reviews helped to substantially improve the text.

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2.16 Hydrological Modeling DP Solomatine, UNESCO-IHE Institute for Water Education and Delft University of Technology, Delft, The Netherlands T Wagener, The Pennsylvania State University, University Park, PA, USA & 2011 Elsevier B.V. All rights reserved.

2.16.1 2.16.1.1 2.16.1.2 2.16.1.3 2.16.2 2.16.2.1 2.16.3 2.16.4 2.16.5 2.16.6 2.16.6.1 2.16.6.2 2.16.6.2.1 2.16.6.2.2 2.16.6.2.3 2.16.6.3 2.16.6.4 2.16.7 2.16.7.1 2.16.7.2 2.16.7.3 2.16.7.4 2.16.7.5 2.16.8 2.16.8.1 2.16.8.2 2.16.8.2.1 2.16.8.2.2 2.16.9 References

Introduction What Is a Model History of Hydrological Modeling The Modeling Process Classification of Hydrological Models Main types of Hydrological Models Conceptual Models Physically Based Models Parameter Estimation Data-Driven Models Introduction Technology of DDM Definitions Specifics of data partitioning in DDM Choice of the model variables Methods and Typical Applications DDM: Current Trends and Conclusions Analysis of Uncertainty in Hydrological Modeling Notion of Uncertainty Sources of Uncertainty Uncertainty Representation View at Uncertainty in Data-Driven and Statistical Modeling Uncertainty Analysis Methods Integration of Models Integration of Meteorological and Hydrological Models Integration of Physically Based and Data-Driven Models Error prediction models Integration of hydrological knowledge into DDM Future Issues in Hydrological Modeling

2.16.1 Introduction Hydrological models are simplified representations of the terrestrial hydrological cycle, and play an important role in many areas of hydrology, such as flood warning and management, agriculture, design of dams, climate change impact studies, etc. Hydrological models generally have one of two purposes: (1) to enable reasoning, that is, to formalize our scientific understanding of a hydrological system and/or (2) to provide (testable) predictions (usually outside our range of observations, short term vs. long term, or to simulate additional variables). For example, catchments are complex systems whose unique combinations of physical characteristics create specific hydrological response characteristics for each location (Beven, 2000). The ability to predict the hydrological response of such systems, especially stream flow, is fundamental for many research and operational studies. In this chapter, the main principles of and approaches to hydrological modeling are covered, both for simulation (process) models that are based on physical principles (conceptual

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and physically based), and for data-driven models. Our intention is to provide a broad overview and to show current trends in hydrological modeling. The methods used in data-driven modeling (DDM) are covered in greater depth since they are probably less widely known to hydrological audiences.

2.16.1.1 What Is a Model A model can be defined as a simplified representation of a phenomenon or a process. It is typically characterized by a set of variables and by equations that describe the relationship between these variables. In the case of hydrology, a model represents the part of the terrestrial environmental system that controls the movement and storage of water. In general terms, a system can be defined as a collection of components or elements that are connected to facilitate the flow of information, matter, or energy. An example of a typical system considered in hydrological modeling is the watershed or catchment. The extent of the system is usually defined by the control volume or modeling domain, and the overall

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X0

Inputs Outputs State variables

I

X

O Model structure Initial state

X2 = F (X1, ,I1,) O2 = G (X1, ,I1,)

Parameters

Figure 1 Schematic of the main components of a dynamic mathematical model. I, inputs; O, outputs; X, state variables; X0, initial states; and y, parameters.

modeling objective in hydrology is generally to simulate the fluxes of energy, moisture, or other matter across the system boundaries (i.e., the system inputs and outputs). Variables or state variables are time varying and (space/time) averaged quantities of mass/energy/information stored in the system. An example would be soil moisture content or the discharge in a stream [L3/T]. Parameters describe (usually time invariant) properties of the specific system under study inside the model equations. Examples of parameters are hydraulic conductivity [L/T] or soil storage capacity [L]. A dynamic mathematical model has certain typical elements that are discussed here briefly for consistency in language (Figure 1). Main components include one or more inputs I (e.g., precipitation and temperature), one or more state variables X (e.g., soil moisture or groundwater content), and one or more model outputs O (e.g., stream flow or actual evapotranspiration). In addition, a model typically requires the definitions of initial states X0 (e.g., is the catchment wet or dry at the beginning of the simulation) and/or the model parameters y (e.g., soil hydraulic conductivity, surface roughness, and soil moisture storage capacity). Hydrological models (and most environmental models in general) are typically based on certain assumptions that make them different from other types of models. Typical assumptions that we make in the context of hydrological modeling include the assumption of universality (i.e., a model can represent different but similar systems) and the assumption of physical realism (i.e., state variables and parameters of the model have a real meaning in the physical world; Wagener and Gupta, 2005). The fact that we are dealing with real-world environmental systems also carries certain problems with it when we are building models. Following Beven (2009), these problems include the fact that it is often difficult to (1) make measurements at the scale at which we want to model; (2) define the boundary conditions for time-dependent processes; (3) define the initial conditions; and (4) define the physical, chemical, and biological characteristics of the modeling domain.

2.16.1.2 History of Hydrological Modeling Hydrological models applied at the catchment scale originated as simple mathematical representations of the input-response behavior of catchment-scale environmental systems through

parsimonious models such as the unit hydrograph (for flow routing) (e.g., Dooge, 1959) and the rational formula (for excess rainfall calculation) (e.g., Dooge, 1957) as part of engineering hydrology. Such single-purpose event-scale models are still widely used to estimate design variables or to predict floods. These early approaches formed a basis for the generation of more complete, but spatially lumped, representations of the terrestrial hydrological cycle, such as the Stanford Watershed model in the 1960s (which formed the basis for the currently widely used Sacramento model (Burnash, 1995)). This advancement enabled the continuous time representation of the rainfall–runoff relationship, and models of this type are still at the heart of many operational forecasting systems throughout the world. While the general equations of models (e.g., the Sacramento model) are based on conceptualizing plot (or smaller) scale hydrological processes, their spatially lumped application at the catchment scale means that parameters have to be calibrated using observations of rainfall– runoff behavior of the system under study. Interest in predicting land-use change leads to the development of more spatially explicit representations of the physics (to the best of our understanding) underlying the hydrological system in form of the Systeme Hydrologique Europeen (SHE) model in the 1980s (Abbott et al., 1986). The latter is an example of a group of highly complex process-based models whose development was driven by the hope that their parameters could be directly estimated from observable physical watershed characteristics without the need for model calibration on observed stream flow data, thus enabling the assessment of land cover change impacts (Ewen and Parkin, 1996; Dunn and Ferrier, 1999). At that time, these models were severely constrained by our lack of computational power – a constraint that decreases in its severity with increases in computational resources with each passing year. Increasingly available high-performance computing enables us to explore the behavior of highly complex models in new ways (Tang et al., 2007; van Werkhoven et al., 2008). This advancement in computer power went hand in hand with new strategies for process-based models, for example, the use of triangular irregular networks (TINs) to vary the spatial resolution throughout the model domain, that have been put forward in recent years; however, more testing is required to assess whether previous limitations of physically based models have yet been overcome (e.g., the lack of full coupling of processes or their calibration needs) (e.g., Reggiani et al., 1998, 1999, 2000, 2001; Panday and Huyakorn, 2004; Qu and Duffy, 2007; Kollet and Maxwell, 2006, 2008).

2.16.1.3 The Modeling Process The modeling process, that is, how we build and use models is discussed in this section. For ease of discussion, the process is divided into two components. The first component is the model-building process (i.e., how does a model come about), whereas the second component focuses on the modeling protocol (i.e., a procedure to use the model for both operational and research studies). The model-building process requires (at least implicitly) that the modeler considers four different stages of the model

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(see also Beven, 2000). The first stage is the perceptual model. This model is based on the understanding of the system in the modeler’s head due to both the interaction with the system and the modeler’s experience. It will, generally, not be formalized on paper or in any other way. This perceptual model forms the basis of the conceptual model. This conceptual model is a formalization of the perceptual model through the definition of system boundaries, inputs–states–outputs, connections of system components, etc. It is not to be mistaken with the conceptual type of models discussed later. Once a suitable conceptual model has been derived, it has to be translated into mathematical form. The mathematical model formulates the conceptual model in the form of input (–state)–output equations. Finally, the mathematical model has to be implemented as computer code so that the equation can be solved in a computational model. Once a suitable model has been built or selected from existing computer codes, a modeling protocol is used to apply this model (Wagener and McIntyre, 2007). Modeling protocols can vary widely, but generally contain some or most of the elements discussed below (Figure 2). A modeling protocol – at its simplest level – can be divided into model identification and model evaluation parts. The model identification part mainly focuses on identifying appropriate parameters (one set or many parameter sets if uncertainty in the identification process is considered), while the latter focuses on understanding the behavior and performance of the model. The starting point of the model identification part should be a detailed analysis of the data available. Beven (2000) provided suggestions on how to assess the quality of data in

the context of hydrological modeling. This is followed by the model selection or building process. The model-building process has already been outlined previously. In many cases, it is likely that an existing model will be selected though, either because the modeler has extensive experience with a particular model or because he/she has applied a model to a similar hydrological system with success in the past. The universality of models, as discussed above, implies that a typical hydrological model can be applied to a range of systems as long as the basic physical processes of the system are represented within the model. Model choice might also vary with the intended modeling purpose, which often defines the required spatio-temporal resolution and thus the degree of detail with which the system has to be modeled. Once a model structure has been selected, parameter estimation has to be performed. Parameters, as defined above, reflect the local physical characteristics of the system. Parameters are generally derived either through a process of calibration or by using a priori information, for example, of soil or vegetation characteristics. For calibration, it is necessary to assess how closely simulated and observed (if available) output time series match. This is usually done by the use of an objective function (sometimes also called loss function or cost function), that is, a measure based on the aggregated differences between observed and simulated variables (called residuals). The choice of objective function is generally closely coupled with the intended purpose of the modeling study. Sometimes this problem is posed as a multiobjective optimization problem. Methods for calibration (parameter estimation) are covered later in Section 2.16.5. Further, the model

Model identification Data analysis Model selection/building Boundary conditions Objective function defination Revise model

Parameter estimation Order can vary

Validation/verification Sensitivity analysis Further (diagnostic) evaluation Uncertainty analysis Model prediction Model evaluation

Figure 2 Schematic representation of a typical modeling protocol.

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should be evaluated with respect to whether it provides the right result for the right reason. Parameter estimation (calibration) is followed by the model evaluation, including validation (checking model performance on an unseen data set, thus imitating model operation), sensitivity, and uncertainty analysis. A comprehensive framework for model evaluation (termed diagnostic evaluation) is proposed by Gupta et al. (2008). One tool often used in such an evaluation is sensitivity analysis, which is the study of how variability or uncertainty in different factors (including parameters, inputs, and initial states) impacts the model output. Such an analysis is generally used either to assess the relative importance of model parameters in controlling the model output or to understand the relative distributions of uncertainty from the different factors. It can therefore be part of the model identification as well as the model evaluation component of the modeling protocol. The subsequent step of uncertainty analysis – the quantification of the uncertainty present in the model – is increasingly popular. It usually includes the propagation of the uncertainty into the model output so that it can be considered in subsequent decision making (see Section 2.16.7). When a model is put into operation, the data progressively collected can be used to update (improve) the model parameters, state variables, and/or model predictions (outputs), and this process is referred to as data assimilation. One aspect needs mentioning here. Due to the lack of information about the modeled process, a modeler may decide not to try to build unique (the most accurate) model, but rather consider many equally acceptable model parametrizations. Such reasoning has led to a Monte-Carlo-like method of uncertainty analysis called Generalised Likelihood Uncertainty Estimator (GLUE) (Beven and Binley, 1992), and to research into the development of the (weighted) ensemble of models, or multimodels (see e.g., Georgakakos et al., 2004).

2.16.2 Classification of Hydrological Models 2.16.2.1 Main types of Hydrological Models A vast number of hydrological model structures has been developed and implemented in computer code over the last few decades (see, e.g., Todini (1988) for a historical review of rainfall–runoff modeling). It is therefore helpful to classify these structures for an easier understanding of the discussion. Many authors present classification schemes for hydrological models (see, e.g., Clarke, 1973; Todini, 1988; Chow et al., 1988; Wheater et al., 1993; Singh, 1995b; and Refsgaard, 1996). The classification schemes are generally based on the following criteria: (1) the extent of physical principles that are applied in the model structure and (2) the treatment of the model inputs and parameters as a function of space and time. According to the first criterion (i.e., physical process description), a rainfall–runoff model can be attributed to two categories: deterministic and stochastic (see Figure 3). A deterministic model does not consider randomness; a given input always produces the same output. A stochastic model has outputs that are at least partially random. Deterministic models can be classified based on whether the model represents a lumped or distributed description of the considered catchment area (i.e., second criterion) and whether the description of the hydrological processes is empirical, conceptual, or more physically based (Refsgaard, 1996). With respect to deterministic models, we will distinguish three classes: (1) data-driven (also called data-based, metric, empirical, or black box models), (2) conceptual (also called parametric, explicit soil moisture accounting or gray box models), and (3) physically based (also called physics-based, mechanistic, or white box models) models. The two latter classes are sometimes referred to as simulation (or process) models. Figure 4 provides some guidelines on estimation of structure and parameters for various types of deterministic models. Note that the distinction between deterministic and stochastic models is not clear-cut. In many modeling studies, it is

Rainfall−runoff model

Deterministic

Randomness

Stochastic

Simulation models Data-driven

Conceptual

Physically based Physical insight

Data requirement Figure 3 Classification of hydrological models based on physical processes. Adapted from Refsgaard JC (1996) Terminology, modelling protocol and classification of hydrological model codes. In: Abbott MB and Refsgaard JC (eds.) Distributed Hydrological Modelling, pp. 17–39. Dordrecht: Kluwer.

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Basis for estimation of Classification

Data

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Structure

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Data

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Modeling strategy

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White box Figure 4 Estimation of structure and parameters for various types of deterministic models.

assumed that the modeled variables are not deterministic, but still more developed apparatus of deterministic modeling is used. To account for stochasticity, additional uncertainty analysis is conducted assuming probability distributions for at least some of the variables and parameters involved.

2.16.3 Conceptual Models Conceptual modeling uses simplified descriptions of hydrological processes. Such models use storage elements as the main building component. These stores are filled through fluxes such as rainfall, infiltration, or percolation, and emptied through processes such as evapotranspiration, runoff, and drainage. Conceptual models generally have a structure that is specified a priori by the modeler, that is, it is not derived from the observed rainfall–runoff data. In contrast to empirical models, the structure is defined by the modeler’s understanding of the hydrological system. However, conceptual models still rely on observed time series of system output, typically stream flow, to derive the values of their parameters during the calibration process. The parameters describe aspects such as the size of storage elements or the distribution of flow between them. A number of real-world processes are usually aggregated (in space and time) into a single parameter, which means that this parameter can therefore often not be derived directly from field measurements. Conceptual models make up the vast majority of models used in practical applications. Most conceptual models consider the catchment as a single homogeneous unit. However, one common approach to consider spatial variability is the segmentation of the catchment into smaller subcatchments, the so-called semidistributed approach.

One typical example of a conceptual model – Hydrologiska Byra˚ns Vattenbalansavdelning (HBV) – (Bergstro¨m, 1976) as rainfall–runoff model is given below. The HBV model was developed at the Swedish Meteorological and Hydrological Institute (Hydrological Bureau Water balance section). The model was originally developed for Scandinavian catchments, but has been applied in more than 30 countries all over the world (Lindstro¨m et al., 1997). A schematic diagram of the HBV model (Lindstro¨m et al., 1997) is shown in Figure 5. The model of one catchment comprises subroutines for snow accumulation and melt, soil moisture accounting procedure, routines for runoff generation, and a simple routing procedure. The soil moisture accounting routine computes the proportion of snowmelt or rainfall P (mm h1 or mm d1) that reaches the soil surface, which is ultimately converted to runoff. If the soil is dry (i.e., small value of SM/CF), the recharge R, which subsequently becomes runoff, is small as a major portion of the effective precipitation P is used to increase the soil moisture. Whereas if the soil is wet, the major portion of P is available to increase the storage in the upper zone. The runoff generation routine transforms excess water R from the soil moisture zone to runoff. The routine consists of two conceptual reservoirs. The upper reservoir is a nonlinear reservoir whose outflow simulates the direct runoff component from the upper soil zone, while the lower one is a linear reservoir whose outflow simulates the base flow component of the runoff. The total runoff Q is computed as the sum of the outflows from the upper and the lower reservoirs. The total runoff is then smoothed using a triangular transformation function. Input data are observations of precipitation and air temperature, and estimates of potential evapotranspiration. The

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SF − snow RF − rain EA − evapotranspiration SP − snow cover

SF

P − effective precipitation

RF

R − recharge

EA

SM − soil moisture CF − capillary transport

SP

UZ − storage in upper reservoir PERC − percolation

SM

LZ − storage in lower reservoir R

Q0 − fast runoff component

CF Q0

Q1 − slow runoff component Q − total runoff

UZ PERC LZ

Q1

Q = Q0 + Q1 Transform function

Figure 5 Schematic representation of the HBV-96 model with routines for snow, soil, and runoff response. Modified from Lindstro¨m G, Johansson B, Persson M, Gardelin M, and Bergstro¨m S (1997) Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201: 272– 228.

time step is usually 1 day, but it is possible to use shorter time steps. The evaporation values used are normally monthly averages, although it is possible to use the daily values. Air temperature data are used for calculations of snow accumulation and melt. It can also be used to adjust potential evaporation when the temperature deviates from normal values, or to calculate potential evaporation. Note that the software IHMS-HBV allows for linking several lumped models and thus making it possible to build separate models for sub-basins, which are integrated, so that the overall model is the semi-distributed model. The HBV model is an example of a typical lumped conceptual model. Other examples of such models differ in the details of describing the catchment hydrology. The following examples can be mentioned: Sugawara’s tank model (Sugawara, 1995), Sacramento model (Burnash, 1995), Xinanjiang model (Zhao and Liu, 1995), and Tracer Aided Catchment (TAC) model (Uhlenbrook and Leibundgut, 2002).

2.16.4 Physically Based Models Physically based models (e.g., Freeze and Harlan, 1969; Beven, 1996, 1989, 2002; Abbott et al., 1986; Calver, 1988) use much more detailed and rigorous representations of physical processes and are based on the laws of conservation of mass, momentum, and energy. They became practically applicable in 1980s, as a result of improvements in computer power. The hope was that the degree of physical realism on which these models are based would be sufficient to relate their parameters, such as soil moisture characteristic and unsaturated zone hydraulic conductivity functions for subsurface flow or

friction coefficients for surface flow, to physical characteristics of the catchment (Todini, 1988), thus eliminating the need for model calibration. However, mechanistic models suffer from high data demand, scale-related problems (e.g., the measurement scales differ from the simulation model (parameter) scales), and from over-parametrization (Beven, 1989). One consequence of the problems of scale is that (at least not all of) the model parameters cannot be derived through measurements; physically based models structures, therefore, still require calibration, usually of a few key parameters (Calver, 1988; Refsgaard, 1997; Madsen and Jacobsen, 2001). The expectation that these models could be applied to ungauged catchments has, therefore, not yet been fulfilled (Parkin et al., 1996; Refsgaard and Knudsen, 1996). They are typically rather applied in a way that is similar to conceptual models (Beven, 1989), thus demanding continued research into new approaches to merge these models with data. Physically based models often use spatial discretizations based on grids, triangular irregular networks, or some type of hydrologic response unit (e.g., Uhlenbrook et al., 2004). A typical model of this kind is, for example, a physically based model based on triangular irregular networks – the Penn State Integrated Hydrologic Model (PIHM) (Qu and Duffy, 2007); its simplified structure is presented in Figure 6. Such models are therefore particularly appropriate when a high level of spatial detail is important, for example, to estimate local levels of soil erosion or the extent of inundated areas (Refsgaard and Abbott, 1996). However, if the main interest simply lies in the estimation of stream flow at the catchment scale, then simpler conceptual or data-driven models often perform well and the high complexity of physically based models is not required (e.g., Loague and Freeze, 1985; Refsgaard and Knudsen, 1996). Regarding the results of

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Solar radiation Interception

Precipitation

Transpiration Overland flow

Capillary lift

Infiltration Recharge

Groundwater flow

Bedrock Saturated Unsaturated zone zone

Evaporation Precipitation

Evaporation

Overland weir flow Lateral flow Downstream flow

Infiltration

Unsaturated zone

Recharge

Saturated zone Groundwater flow Bedrock Figure 6 Schematic representation of the PIHM model, an example of a TIN-based physically based hydrological model (Qu and Duffy, 2004).

a comprehensive experiment to compare lumped and distributed models, the reader is referred to Reed et al. (2004). This experiment has shown that due to difficulties in calibrating distributed models, in many cases, conceptual models are in fact more accurate in reproducing the resulting catchment stream flow than the distributed ones.

2.16.5 Parameter Estimation Many, if not most, rainfall–runoff model structures currently used to simulate the continuous hydrological response can be classified as conceptual, if this classification is based on two criteria (Wheater et al., 1993): (1) the model structure is specified prior to any modeling being undertaken and (2) (at least some of) the model parameters do not have a direct

physical interpretation, in the sense of being independently measurable, and have to be estimated through calibration against observed data. Calibration is a process of parameter adjustment (automatic or manual), until catchment and model behavior show a sufficiently (to be specified by the hydrologist) high degree of similarity. The similarity is usually judged by one or more objective functions accompanied by visual inspection of observed and calculated hydrographs (Gupta et al., 2005). The choice of such objective functions has itself been the subject of extensive research over many years. Traditionally, measures based on the mean squared error (MSE) criterion were used, for example, root mean squared error (RMSE) or Nash–Sutcliff efficiency (NSE). Appearance of the squared errors in many formulations is the result of an assumption of the normality (Gaussian distribution) of model errors, and

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using the principle of maximum likelihood to derive the error function. Calibration includes the process of finding a set of parameters providing the minimum of RMSE or the maximum value of NSE. For more information on the formulations of these and other error function, the reader is referred to, for example, Gupta et al. (1998). In a recent paper, Gupta et al. (2009) showed certain deficiencies of MSE-based objective functions and suggested possible remedies. Often a single measure may not be enough to capture all the aspects of the system response that the model is supposed to reproduce, and several criteria (objective functions) have to be considered simultaneously so that multiobjective optimization algorithms have to be used. Examples of such multiple objectives include the RMSE calculated separately on low and high flows, timing errors, and error in reproducing the water balance. The models constituting the Pareto in criteria space should be seen as the best models, since there are no models better than these on all criteria. If a single model is to be selected from this set, it is done either by a decision maker (who would use some additional criteria that are difficult to formalize), or by measuring the distance of the models to the ideal point in criteria space, or by using the (weighted) sum of objective functions values. This section covers single-objective optimization; for the use of multiobjective methods, the reader is directed to the papers by Gupta et al. (1998), Khu and Madsen (2005), and Tang et al. (2006) (with the subsequent discussion). Hydrological model structures of the continuous watershed response (mainly stream flow) became feasible in the 1960s. They were usually relatively simple lumped, conceptual mathematical representations of the (perceived to be important) hydrological processes, with little (if any) consideration of issues such as identifiability of the parameters or information content of the watershed response observations. It became quickly apparent that the parameters of such models could not be directly estimated through measurements in the field, and that some sort of adjustment (fine-tuning) of the parameters was required to match simulated system responses with observations (e.g., Dawdy and O’Donnell 1965). Adjustment approaches were initially based on manual perturbation of the parameter values and visual inspection of the similarity between simulated and observed time series. Over the years, a variety of manual calibration procedures have been developed, some having reached very high levels of sophistication allowing hydrologists to achieve very good performing and hydrologically realistic model parameters and predictions, that is, a well-calibrated model (Harlin, 1991; Burnash, 1995). This hydrological realism is still a problem for most automated procedures as discussed in van Werkhoven et al. (2008). Necessary conditions for a hydrological model to be well calibrated are that it exhibits (at least) the following three characteristics (Wagener et al. 2003; Gupta et al. 2005):

1. the input–state–output behavior of the model is consistent with the measurements of watershed behavior; 2. the model predictions are accurate (i.e., they have negligible bias) and precise (i.e., the prediction uncertainty is relatively small); and

3. the model structure and behavior are consistent with a current hydrological understanding of reality. This last characteristic is often ignored in operational settings, where the focus is generally on useful rather than realistic models. This will be an adequate approach in many cases, but will eventually lead to limitations of potential model uses. This problem is exemplified in the current attempts to modeling watershed residence times and flow paths (McDonnell, 2003). This aspect of the hydrologic system, though often not crucial for reliable quantitative flow predictions, is however relevant for many of today’s environmental problems, but cannot be simulated by many of the currently available models. The high number of nonlinearly interacting parameters present in most hydrological models makes manual calibration a very labor-intensive and a difficult process, requiring considerable experience. This experience is time consuming to acquire and cannot be easily transferred from one hydrologist to the next. In addition, manual calibration does not formally incorporate an analysis of uncertainty, as is required in a modern decision-making context. The obvious advantages of computer-based automatic calibration procedures began to spark interest in such approaches as soon as computers became more easily available for research. In automatic calibration, the ability of a parameter set to reproduce the observed system response is measured (summarized) by means of an objective function (also sometimes called loss or cost function). As discussed above, this objective function is an aggregated measure of the residuals, that is, the differences between observed and simulated responses at each time step. An important early example of automatic calibration is the dissertation work by Ibbitt (1970) in which a variety of automated approaches were applied to several watershed models of varying complexity (see also Ibbitt and O’Donnell, 1971). The approaches were mainly based on local-search optimization techniques, that is, the methods that start from a selected initial point in the parameter space and then walk through it, following some predefined rule system, to iteratively search for parameter sets that yield progressively better objective function values. Ibbitt (1970) found that it is difficult to conclude when the best parameter set has been found, because the result depends both on the chosen method and on the initial starting parameter set. The application of local-search calibration approaches to all but the most simple watershed models has been largely unsuccessful. In reflection of this, Johnston and Pilgrim (1976) reported the failure of their 2-year quest to find an optimal parameter set for a typical conceptual rainfall–runoff (RR) model. Their honesty in reporting this failure ultimately led to a paradigm shift as researchers started to look closely at the possible reasons for this lack of success. The difficulty of the task at hand, in fact, only became clear in the early 1990s when Duan et al. (1992) conducted a detailed study of the characteristics of the response surface that any search algorithm has to explore. Their studies showed that the specific characteristics of the response surface, that is, the (n þ 1)-dimensional space of n model parameters and an objective function, of hydrological models give rise to conditions that make it extremely difficult for local optimization strategies to be successful. They listed the following characteristics commonly associated with the response surface of a

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typical hydrological model:

• • • • •

it contains more than one main region of attraction; each region of attraction contains many local optima; it is rough with discontinuous derivatives; it is flat in many regions, particularly in the vicinity of the optimum, with significantly different parameter sensitivities; and its shape includes long and curved ridges.

Concluding that optimization strategies need to be powerful enough to overcome the search difficulties presented by these response surface characteristics, Duan et al. (1992) developed the shuffled complex evolution (SCE-UA) global optimization method (UA, University of Arizona). The SCE-UA algorithm has since been proved to be highly reliable in locating the optimum (where one exists) on the response surfaces of typical hydrological models. However, in a follow-up paper, Sorooshian et al. (1993) used SCE-UA to show that several different parameter combinations of the relatively complex Sacramento model (13 free parameters) could be found which produced essentially identical objective function values, thereby indicating that not all of the parameter uncertainty can be resolved through an efficient global optimizer (see discussion in Wagener and Gupta (2005)). Similar observations of multiple parameter combinations producing similar performances have also been made by others (e.g., Binley and Beven, 1991; Beven and Binley, 1992; Spear, 1995; Young et al., 1998; Wagener et al., 2003). Part of this problem had been attributed to overly complex models for the information content of the system response data available, usually stream flow (e.g., Young, 1992, 1998). It is worth mentioning that practically any direct search optimization algorithm can be used for model calibration. The reason of using direct search (i.e., the search based purely on calculation of the objective function values for different points in the search space) is that for most calibration problems computation of the objective function gradients is not possible, so the efficient gradient-based search cannot be used. (Another name for this class of algorithms is global optimization algorithms since they are focused on finding the global minimum rather than a local one.) For example, in many studies a popular genetic algorithm (GA) is used. If a model is simple and fast running, then it is really not important how efficient the optimization algorithm is. Here, efficiency is measured by the number of the model runs needed by an optimization algorithm to find a more-or-less accurate estimate of the parameter vector leading to the minimum value of the model error. However, if a model is computationally complex, as is the case for physically based and distributed models, efficiency of the optimization algorithm used becomes an issue. With this in mind, the so-called adaptive cluster-covering algorithm (ACCO) was developed (Solomatine, 1995; Solomatine et al., 1999, 2001), and it was shown that on a number of calibration problems it is more efficient than GA and several other algorithms. A large number of other algorithms has been applied to hydrological models, including a multialgorithm genetically adaptive method (AMALGAM) (Vrugt and Robinson, 2007) and epsilon-NSGA-II (NSGA, nondominated sorting genetic

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algorithm; Tang et al., 2006), and different algorithms have come out as most effective or most efficient depending on the study. A range of algorithms for model calibration can be obtained from the Hydroarchive website. As mentioned above (Figure 2), once parameters are estimated, the model has to be validated, that is, the degree to which a model is an accurate representation of the modeled process has to be determined. Case of ungauged basins. A different problem has to be solved in the case of the so-called ungauged basins, that is, watersheds for which none or insufficiently long observations of the hydrological response variable of interest (usually stream flow) are available. The above-discussed strategy of model calibration cannot be used under those conditions. Early attempts to model ungauged catchments simply used the parameter values derived for neighboring catchments where stream flow data were available, that is, a geographical proximity approach (e.g., Mosley, 1981; Vandewiele and Elias, 1995). However, this seems to be insufficient since nearby catchments can even be very different with respect to their hydrological behavior (Post et al., 1998; Beven, 2000). Others propose the use of parameter estimates directly derived from, among others, soil properties such as porosity, field capacity, and wilting point (to derive model storage capacity parameters); percentage forest cover (evapotranspiration parameters); or hydraulic conductivities and channel densities (time constants) (e.g., Koren et al., 2000; Duan et al., 2001; Atkinson et al., 2002). The main problem here is that the scale at which the measurements are made (often from small soil samples) is different from the scale at which the model equations are derived (often laboratory scale) and at which the model is usually applied (catchment scale). The conceptual model parameters represent the effective characteristics of the integrated (heterogeneous) catchment system (e.g., including preferential flow), which are unlikely to be easily captured using small-scale measurements since there is generally no theory that allows the estimation of the effective values within different parts of a heterogeneous flow domain from a limited number of small-scale or laboratory measurements (Beven, 2000). It seems unlikely that conceptual model parameters, which describe an integrated catchment response, usually aggregating significant heterogeneity (including the effect of preferential flow paths, different soil and vegetation types, etc.), can be derived from catchment properties that do not consider all influences on water flow through the catchment. Further fine-tuning of these estimates using locally observed flow data is needed because the physical information available to estimate a priori parameters is not adequate to define local physical properties of individual basins for accurate hydrological forecasts (Duan et al., 2001). However, useful initial values might be derived in this way (Koren et al., 2000). The advantages of this approach are that the assumed physical basis of the parameters is preserved and (physical) parameter dependence can be accounted for, as shown by Koren et al. (2000). Probably, the most common apprnoach to ungauged modeling is to relate model parameters and catchment characteristics in a statistical manner (e.g., Jakeman et al., 1992; Sefton et al., 1995; Post et al., 1998; Sefton and Howarth, 1998; Abdullah and Lettenmaier, 1997; Wagener et al., 2004;

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Merz and Bloschl, 2004; Lamb and Kay, 2004; Seibert, 1999; Lamb et al., 2000; Post and Jakeman, 1996; Fernandez et al., 2000), assuming that the uniqueness of each catchment can be captured in a distinctive combination of catchment characteristics. The basic methodology is to calibrate a specific model structure, here called the local model structure, to as large a number of (gauged) catchments as possible and derive statistical (regression) relationships between (local) model parameters and catchment characteristics. These statistical relationships, here called regional models, and the measurable properties of the ungauged catchment can then be used to derive estimates of the (local) model parameters. This procedure is usually referred to as regionalization or spatial generalization (e.g., Lamb and Calver, 2002). While this approach has been widely applied, it still does not constrain existing uncertainty sufficiently in many cases (Wagener and Wheater, 2006). Recent approaches also used regionalized information about stream flow characteristics to further reduce this uncertainty (Yadav et al., 2007; Zhang et al., 2008). It seems as if the most promising strategies for the future lie in combining as much information as possible to reduce predictive uncertainty, rather than relying on a single approach.

2.16.6 Data-Driven Models 2.16.6.1 Introduction Along with the physically based and conceptual models, the empirical models based on observations (experience) are also popular. Such models involve mathematical equations that have been assessed not from the physical process in the catchment but from analysis of data – concurrent input and output time series. Typical examples here are the unit hydrograph method and various statistical models – for example, linear regression, multilinear, ARIMA, etc. During the last decade, the area of empirical modeling received an important boost due to developments in the area of machine learning (ML). It can be said that it now entered a new phase and deserves a special name – DDM. DDM is based on the analysis of all the data characterizing the system under study. A model can then be defined based on connections between the system state variables (input, internal and output variables) with only a limited number of assumptions about the physical behavior of the system. The methods used nowadays can go much further than the ones used in conventional empirical modeling: they allow for solving prediction problems, reconstructing highly nonlinear functions, performing classification, grouping of data, and building rule-based systems. It is worth mentioning that among some hydrologists there is still a certain skepticism about the use of DDM. In their opinion, such models do not relate to physical principles and mathematical reasoning, and view building models from data sets as a purely computational exercise. This is true, and indeed DDM cannot be a replacement of process-based modeling, but should be used in situations where data-driven models are capable of generating improved forecasts of hydrological variables. There are cases where the traditional statistical models (typically linear regression or ARIMA-class models) are

accurate enough, and there is no need of using sophisticated methods of ML. Some of the concerns of this nature are discussed, for example, by Gaume and Gosset (2003), See et al. (2007), Han et al. (2007), and Abrahart et al., 2008. Abrahart and See (2007) also addressed some of these problems, however, demonstrated that the existing nonlinear hydrological relationships, which are so important when building flow forecasting models, are effectively captured by a neural network, the most widely used DDM method. In this respect, positioning of data-driven models is important: they should be seen as complementary to process-based simulation models; they cannot explain reality but could be effective predictive tools.

2.16.6.2 Technology of DDM 2.16.6.2.1 Definitions One may identify several fields that contribute to DDM: statistical methods, ML, soft computing (SC), computational intelligence (CI), data mining (DM), and knowledge discovery in databases (KDDs). ML is the area concentrating on the theoretical foundations of learning from data and it can be said that it is the major supplier of methods for DDM. SC is emerging from fuzzy logic, but many authors attribute to it many other techniques as well. CI incorporates two areas of ML (neural networks and fuzzy systems), and, additionally, evolutionary computing that, however, can be better attributed to the field of optimization than to ML. DM and KDDs used, in fact, the methods of ML and are focused typically at large databases being associated with banking, financial services, and customer resources management. DDM can thus be considered as an approach to modeling that focuses on using the ML methods in building models that would complement or replace the physically based models. The term modeling stresses the fact that this activity is close in its objectives to traditional approaches to modeling, and follows the steps traditionally accepted in (hydrological) modeling. Examples of the most common methods used in data-driven hydrological modeling are linear regression, ARIMA, artificial neural networks (ANNs), and fuzzy rulebased systems (FRBSs). Such positioning of DDM links to learning which incorporates determining the so far unknown mappings (or dependencies) between a system’s inputs and its outputs from the available data (Mitchell, 1997). By data, we understand the known samples (data vectors) that are combinations of inputs and corresponding outputs. As such, a dependency (mapping or model) is discovered (induced), which can be used to predict (or effectively deduce) the future system’s outputs from the known input values. By data, we usually understand a set K of examples (or instances) represented by duple /xk, ykS, where k ¼ 1,y, K, vector xk ¼ {x1,y, xn}k, vector yk ¼ {y1,y, ym}k, n ¼ number of inputs, and m ¼ number of outputs. The process of building a function (or mapping, or model) y ¼ f (x) is called training. If only one output is considered, then m ¼ 1. (In relation to hydrological and hydraulic models, training can be seen as calibration.) In the context of hydrological modeling, the inputs and outputs are typically real numbers (xk, ykAZRn), so the main

Hydrological Modeling

learning problem solved in hydrological modeling is numerical prediction (regression). Note that the problems of clustering and classification are rare but there are examples of it as well (see, e.g., Hall and Minns, 1999; Hannah et al., 2000; Harris et al., 2000). As already mentioned, the process of building a data-driven model follows general principles adopted in modeling: study the problem, collect data, select model structure, build the model, test the model, and (possibly) iterate. There is, however, a difference with physically based modeling: in DDM not only the model parameters but also the model structure are often subject to optimization. Typically, simple (or parsimonious) models are valued (as simple as possible, but no simpler). An example of such parsimonious model could be a linear regression model versus a nonlinear one, or a neural network with the small number of hidden nodes. Such models would automatically emerge if the so-called regularization is used: the objective function representing the overall model performance includes not only the model error term, but also a term that increases in value with the increase of model complexity represented, for example, by the number of terms in the equation, or the number of hidden nodes in a neural network. If there is a need to build a simple replica of a sophisticated physically based hydrological model, DDM can be used as well: such models are called surrogate, emulation, or metamodels (see, e.g., Solomatine and Torres, 1996; Khu et al., 2004). They can be used as fast-working approximations of complex models when speed is important, for example, in solving the optimization or calibration problems.

2.16.6.2.2 Specifics of data partitioning in DDM Obviously, data analysis and preparation play an important role in DDM. These steps are considered standard by the experts in ML but are not always given proper attention by hydrologists building or using such models. Three data sets for training, cross-validation, and testing. Once the model is trained (but before it is put into operation), it has to be tested (or verified) by calculating the model error (e.g., RMSE) using the test (or verification) data set. However, during training often there is a need to conduct tests of the model that is being built, so yet another data set is needed – the crossvalidation set. This set serves as the representative of the test set. As a model gradually improves as a result of the training process, the error on the training data will be gradually decreasing. The cross-validation error will also be first decreasing, but as the model starts to reproduce the training data set better and better, this error will start to increase (effect of over fitting). This typically means that the training should be stopped when the error on cross-validation data set starts to increase. If these principles are respected, then there is a hope that the model will generalize well, that is, its prediction error on unseen data will be small. (Note that the test data should be used only to test the final model, but not to improve (optimize) the model.) One may see that this procedure is more complex than the standard procedure of the hydrological model calibration – when no data are allocated for cross-validation, and, worse, often the whole data set is used to calibrate the model.

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Note that in an important class of ML models – support vector machines (SVMs) – a different approach is taken: it is to build the model that would have the best generalization ability possible without relying explicitly on the cross-validation set (Vapnik, 1998). In connection to the issues covered above, there are two common pitfalls, especially characteristic of DDM applications where time series are involved, that are worth mentioning here. The desired properties of the three data sets. It is desired that the three sets are statistically similar. Ideally, this could be automatically ensured by the fact that data sets are sufficiently large and sampled from the same distribution (typical assumption in machine and statistical learning). However, in reality of hydrological modeling, such situations are rare, so normally a modeler should try to ensure at least some similarity in the distributions, or, at least, similar ranges, mean and variance. Statistical similarity can be achieved by careful selection of examples for each data set, by random sampling data from the whole data set, or employing an optimization procedure resulting in the sets with predefined properties (Bowden et al., 2002). One of the approaches is to use the 10-fold validation method when a model is built 10 times, trained each time on 9/10th of the whole set of available data and validated on 1/10th (number of runs is not necessarily 10). A version of this method is the leave-one-out method when K models are built using K  1 examples and not using one (every time different). The modeler is left with 10 or K trained models, so the resulting model to be used is either one of these models, or an ensemble of all the built models, possibly with the weighted outputs. Strictly speaking, for generation of the statistically similar training data sets for building a series of similar but different models, one should typically rely on the well-developed statistical (re)sampling methods such as bootstrap originated by Tibshirani in the 1970s (see Efron and Tibshirani, 1993) where (in its basic form) K data are randomly selected from K original data. For many hydrologists, there could be a visualization (or even a psychological) problem. If one of these procedures is followed, the data will not be always contiguous: it would not be possible to visualize a hydrograph when the model is fed with the test set. There is nothing wrong with such a model if the time structure of all the data sets is preserved. Such models, however, may be rejected by practitioners, since they are so different from the traditional physically based models that always generate contiguous time series. A possible solution here is to consider the hydrological events (i.e., contiguous blocks of data), to group the data accordingly, and to try to ensure the presence of statistically similar events in all the three data sets. This is all possible of course, if there is enough data. In the situations when the data set is not large enough to allow for building all three sets of substantial size, modelers could be forced not to build cross-validation set at all with the hope that the model trained on training set would perform well on the test set as well. An alternative could be performing 10-fold cross-validation but it is somehow rarely used.

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2.16.6.2.3 Choice of the model variables Apart from dividing the data into several subsets, data preparation also includes the selection of proper variables to represent the modeled process, and, possibly, their transformation (Pyle, 1999). A study on the influence of different data transformation methods (linear, logarithmic, and seasonal transformations, histogram equalization, and a transformation to normality) was undertaken by Bowden et al. (2003). On a (limited) case study (forecasting salinity in a river in Australia 14 days ahead), they found that the model using the linear transformation resulted in the lowest RMSE and more complex transformations did not improve the model. Our own experience shows that it is sometimes also useful to apply the smoothing filters to reduce the noise in the hydrological time series. Choice of variables is an important issue, and it has to be based on taking the physics of the underling processes into account. State variables of data-driven models have nothing to do with the physics, but their inputs and outputs do have. In DDM, the physics of the process is introduced mainly via the justified and physically based choice of the relevant input variables. One may use visualization to identify the variables relevant for predicting the output value. There are also formal methods that help in making this choice more justified, and the reader can be directed to the paper by Bowden et al. (2005) for an overview of these. Mutual information which is based on Shannon’s entropy (Shannon, 1948) is used to investigate linear and nonlinear dependencies and lag effects (in time series data) between the variables. It is the measure of information available from one set of data having knowledge of another set of data. The average mutual information (AMI) between two variables X and Y is given by

AMI ¼

X i;j

 PXY ðxi ; yj Þlog2

PXY ðxi ; yj Þ PX ðxi ÞPY ðyj Þ

 ð1Þ

where PX(x) and PY(y) are the marginal probability density functions (PDFs) of X and Y, respectively, and PXY(x,y) the joint PDFs of X and Y. If there is no dependence between X and Y, then by definition the joint probability density PXY(x,y) would be equal to the product of the marginal densities (PX(x) PY(y)). In this case, AMI would be zero (the ratio of the joint and marginal densities in Equation (1) being 1, giving the logarithm a value of 0). A high value of AMI would indicate a strong dependence between two variables. Accurate estimate of the AMI depends on the accuracy of estimation of the marginal and joint probabilities density in Equation (1) from a finite set of examples. The most widely used approach is estimation of the probability densities by histogram with the fixed bin width. More stable, efficient, and robust probability density estimator is based on the use of kernel density estimation techniques (Sharma, 2000). It is our hope that the adequate data preparation and the rational and formalized choice of variables will become a standard part of any hydrological modeling study.

2.16.6.3 Methods and Typical Applications Most hydrological modeling problems are formulated as simulation of forecasting of real-valued variables. In

terminology of machine (statistical) learning, this is a regression problem. A number of linear and (sometimes) nonlinear regression methods have been used in the past. Most of the methods of ML can also be seen as sophisticated nonlinear regression methods. Many of them, instead of using very complex functions, use combinations of many simple functions. During training, the number of these functions and the values of their parameters are optimized, given the functions’ class. Note that ML methods typically do not assume any special kind of distribution of data, and do not require the knowledge of such distribution. Multilayer perceptron (MLP) is a device (mathematical model) that was originally referred to as an ANN (Haykin, 1999). Later ANN became a term encompassing other connectionist models as well. MLP consists of several layers of mutually interconnected nodes (neurons), each of which receives several inputs, calculates the weighted sum of them, and then passes the result to a nonlinear squashing function. In this way, the inputs to an MLP model are subjected to a multiparameter nonlinear transformation so that the resulting model is able to approximate complex input–output relationships. Training of MLP is in fact solving the problem of minimizing the model error (typically, MSE) by determining the optimal set of weights. MLP ANNs are known to have several dozens of successful applications in hydrology. The most popular application was building rainfall–runoff models: Hsu et al. (1995), Minns and Hall (1996), Dawson and Wilby (1998), Dibike et al. (1999), Abrahart and See (2000), Govindaraju and Rao (2001), Coulibaly et al. (2000), Hu et al. (2007), and Abrahart et al. (2007b). They were also used to model river stage–discharge relationships (Sudheer and Jain, 2003; Bhattacharya and Solomatine, 2005). ANNs were also used to build surrogate (emulation, meta-) models for replicating the behavior of hydrological and hydrodynamic models: in model-based optimal control of a reservoir (Solomatine and Torres, 1996), calibration of a rainfall–runoff model (Khu et al., 2004), and in multiobjective decision support model for watershed management (Muleta and Nicklow, 2004). Most theoretical problems related to MLP have been solved, and it should be seen as a quite reliable and wellunderstood method. Radial basis functions (RBFs) could be seen as a sensible alternative to the use of complex polynomials. The idea is to approximate some function y ¼ f(x) by a superposition of J functions F(x, s), where s is a parameter characterizing the span or width of the function in the input space. Functions F are typically bell shaped (e.g., a Gaussian function) so that they are defined in the proximity to some representative locations (centers) wj in n-dimensional input space and their values are close to zero far from these centers. The aim of learning here is to find the positions of centers wj and the parameters of the functions f(x). This can be accomplished by building an RBF neural network; its training allows the identification of these unknown parameters. The centers wj of the RBFs can be chosen using a clustering algorithm, the parameters of the Gaussian can be found based on the spread (variance) of data in each cluster, and it can be shown that the weights can be found by solving a system of linear equations. This is done for a certain number of RBFs, with the

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exhaustive optimization run across the number of RBFs in a certain range. The areas of RBF networks applications are the same as those of MLPs. Sudheer and Jain (2003) used RBF ANNs for modeling river stage–discharge relationships and found out that on the considered case study RBF ANNs were superior to MLPs; Moradkhani et al. (2004) used RBF ANNs for predicting hourly stream flow hydrograph for the daily flow for a river in USA as a case study, and demonstrated their accuracy if compared to other numerical prediction models. In this study, RBF was combined with the self-organizing feature maps used to identify the clusters of data. Nor et al. (2007) used RBF ANN for the same purpose, however, for the hourly flow and considering only storm events in the two catchments in Malaysia as case studies. Regression trees and M5 model trees. These models can be attributed simultaneously to (piece-wise) linear regression models, and to modular (multi)models. They use the following idea: progressively split the parameter space into areas and build in each of them a separate regression model of zero or first order (Figure 7). In M5 trees models in leaves are first order (linear). The Boolean tests ai at nodes have the form xioC and are used to progressively split the data set. The index of the input variable i and value C are chosen to minimize the standard deviation in the subsets resulting from the split. Mn are models built for subsets filtered down to a given tree leaf. The resulting model can be seen as a set of linear models being specialized on the certain subsets of the training set – belonging to different regions of the input space. M5 algorithm to build such model trees was proposed by Quinlan (1992).

Training data set

a1 New instance

a2

M3

a4

M1

a3

M2

M4

M5

Output

Figure 7 Building a tree-like modular model (M5 model tree). Boolean tests ai have the form xioC and split data set during training. Mn are linear regression models built on data subsets, and applied to a new instance input vector in operation.

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Combination of linear models was already used in dynamic hydrology in the 1970s (e.g., multilinear models by Becker and Kundzewicz (1987)). Application of the M5 algorithm to build such models adds rigor to this approach and makes it possible to build the models automatically and to generate a range of models of different complexity and accuracy. MTs are often almost as accurate as ANNs, but have some important advantages: training of MTs is much faster than ANNs, it always converges, and the results can be easily understood by decision makers. An early (if not the first) application of M5 model trees in river flow forecasting was reported by Kompare et al. (1997). Solomatine and Dulal (2003) used M5 model tree in rainfall– runoff modeling of a catchment in Italy. Stravs and Brilly (2007) used M5 trees in modeling the precipitation interception in the context of the Dragonja river basin case study. Genetic programming (GP) and evolutionary regression. GP is a symbolic regression method in which the specific model structure is not chosen a priori, but is a result of the search (optimization) process. Various elementary mathematical functions, constants, and arithmetic operations are combined in one function and the algorithm tries to construct a model recombining these building blocks in one formula. The function structure is represented as a tree and since the resulting function is highly nonlinear, often nondifferentiable, it is optimized by a randomized search method – usually a GA. Babovic and Keijzer (2005) gave an overview of GP applications in hydrology. Laucelli et al. (2007) presented an application of GP to the problem of forecasting the groundwater heads in the aquifer in Italy; in this study, the authors also employed averaging of several models built on the data subsets generated by bootstrap. One may limit the class of possible formulas (regression equations), allowing for a limited class of formulas that would a priori be reasonable. In evolutionary regression (Giustolisi and Savic, 2006), a method similar to GP, the elementary functions are chosen from a limited set and the structure of the overall function is fixed. Typically, a polynomial regression equation is used, and the coefficients are found by GA. This method overcomes some shortcomings of GP, such as the computational requirements – the number of parameters to tune and the complexity of the resulting symbolic models. It was used, for example, for modeling groundwater level (Giustolisi et al., 2007a) and river temperature (Giustolisi et al., 2007b), and the high accuracy and transparency of the resulting models were reported. FRBSs. Probability is not the only way to describe uncertainty. In his seminal paper, Lotfi Zadeh (1965) introduced yet another way of dealing with uncertainty – fuzzy logic, and since then it found multiple successful applications, especially in application to control problems. Fuzzy logic can be used in combining various models, as done previously, for example, by See and Openshaw (2000) and Xiong et al. (2001), building the so-called fuzzy committees of models (Solomatine, 2006), and also the instrumentarium of fuzzy logic can be used for building the so-called FRBSs which are effectively regression models. FRBS can be built by interviewing human experts, or by processing historical data and thus forming a data-driven model. These

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rules are patches of local models overlapped throughout the parameter space, using a sort of interpolation at a lower level to represent patterns in complex nonlinear relationships. The basics of the data-driven approach and its use in a number of water-related applications can be found in Ba´rdossy and Duckstein (1995). Typically, the following rules are considered:

IF x1 is A1;r ANDyAND xn is An;y THEN y is B where {x1,y, xn} ¼ x is the input vector; Aim the fuzzy set; r the index of the rule, r ¼ 1,y,R. Fuzzy sets Air (defined as membership functions with the values ranging from 0 to 1) are used to partition the input space into overlapping regions (for each input these are intervals). The structure of B in the consequent could be either a fuzzy set (then such model is called a Mamdani model) or a function y ¼ f (x), often linear, and then the model is referred to as Takagi–Sugeno–Kang (TSK) model. The model output is calculated as a weighted combination of the R rules’ responses. Output of the Mamdani model is fuzzy (a membership function of irregular shape), so the crisp output has to be calculated by the so-called defuzzification operator. Note that in TSK model, each of the r rules can be interpreted as local models valid for certain regions in the input space defined by the antecedent and overlapping fuzzy sets Air. Resemblance to the RBF ANN is obvious. FRBSs were effectively used for drought assessment (Pesti et al., 1996); reconstruction of the missing precipitation data by a Mamdani-type system (Abebe et al., 2000b); control of water levels in polder areas (Lobbrecht and Solomatine et al., 1999); and modeling rainfall–discharge dynamics (Vernieuwe et al., 2005; Nayak et al., 2005). Casper et al. (2007) presented an interesting study where TSK type of FRBS has been developed using soil moisture and rainfall as input variables to predict the discharge at the outlet of a small catchment, with the special attention to the peak discharge. One of the limitations of FRBS is that the demand for data grows exponentially with an increase in the number of input variables. SVMs. This ML method is based on the extension of the idea of identifying a hyperplane that separates two classes in classification. It is closely linked to the statistical learning theory initiated by V. Vapnik in the 1970s at the Institute of Control Sciences of the Russian Academy of Science (Vapnik, 1998). Originally developed for classification, it was extended to solving prediction problems, and, in this capacity, was used in hydrology-related tasks. Dibike et al. (2001) and Liong and Sivapragasam (2002) reported using SVMs for forecasting the river water flows and stages. Bray and Han (2004) addressed the issue of tuning SVMs for rainfall–runoff modeling. In all reported cases, SVM-based predictors have shown good results, in many cases superseding other methods in accuracy. Instance-based learning (IBL). This method allows for classification or numeric prediction directly by combining some instances from the training data set. A typical representative of IBL is the k-nearest neighbor (k-NN) method. For a new input vector xq (query point), the output value is calculated as the mean value of the k-nearest neighboring examples, possibly weighted according to their distance to xq. Further extensions are known as locally weighted regression (LWR) when the regression model is built on k nearest instances: the training instances are assigned

weights according to their distance to xq and the regression equations are generated on the weighted data. In fact, IBL methods construct a local approximation to the modeled function that applies well in the immediate neighborhood of the new query instance encountered. Thus, it describes a very complex target function as a collection of less complex local approximations, and often demonstrates competitive performance when compared, for example, to ANNs. Karlsson and Yakowitz (1987) introduced this method in the context of water, focusing however only on (single-variate) time-series forecasts. Galeati (1990) demonstrated the applicability of the k-NN method (with the vectors composed of the lagged rainfall and flow values) for daily discharge forecasting and favorably compared it to the statistical autoregressive model with exogenous input (ARX) model, and used the k-NN method for adjusting the parameters of the linear perturbation model for river flow forecasting. Toth et al. (2000) compared the k-NN approach to other time-series prediction methods in a problem of short-term rainfall forecasting. Solomatine et al. (2007) explored a number of IBL methods, tested their applicability in rainfall–runoff modeling, and compared their performance to other ML methods. To conclude the coverage of the popular data-driven methods, it can be mentioned that all of them are developed in the ML and CI community. The main challenges for the researchers in hydrology and hydroinformatics are in testing various combinations of these methods for particular waterrelated problems, combining them with the optimization techniques, developing the robust modeling procedures able to work with the noisy data, and in developing the methods providing the model uncertainty estimates.

2.16.6.4 DDM: Current Trends and Conclusions There are a number of challenges in DDM: development of the optimal model architectures, making models more robust, understandable, and ready for inclusion into existing decision support systems. Models should adequately reflect reality, which is uncertain, and in this respect developing the methods of dealing with the data and model uncertainty is currently an important issue. One of the interesting questions that arise in case of using a data-driven model is the following one: to what extent such models could or should incorporate the expert knowledge into the modeling process. One may say that a typical ML algorithm minimizes the training (cross validation) error seeing it as the ultimate indicator of the algorithms performance, so is purely data-driven – and this is what is expected from such models. Hydrologists, however, may have other consideration when assessing the usefulness of a model, and typically wish to have a certain input to building a model rightfully hoping that the direct participation of an expert may increase the model accuracy and trust in the modeling results. Some of the examples of merging the hydrological knowledge and the concepts of process-based modeling with those of DDM are mentioned in Section 2.16.8.2. Data-driven models are seen by many hydrologists as tools complementary to process-based models. More and more practitioners are agreeing to that, but many are still to be convinced. Research is now oriented toward development of

Hydrological Modeling

the optimal model architectures and avenues for making datadriven models more robust, understandable, and very useful for practical applications. The main challenge is in the inclusion of DDM into the existing decision-making frameworks, while taking into consideration both the system’s physics, expert judgment, and the data availability. For example, in operational hydrological forecasting, many practitioners are trained in using process-based models (mainly conceptual ones) that serve them reasonably well, and adoption of another modeling paradigm with inevitable changes in their everyday practice could be a painful process. Making models capable of dealing with the data and model uncertainty is currently an important issue as well. It is sensible to use DDM if (1) there is a considerable amount of observations available; (2) there were no considerable changes to the system during the period covered by the model; and (3) it is difficult to build adequate processbased simulation models due to the lack of understanding and/or to the ability to satisfactorily construct a mathematical model of the underlying processes. Data-driven models can also be useful when there is a necessity to validate the simulation results of physically based models. It can be said that it is practically impossible to recommend one particular type of a data-driven model for a given problem. Hydrological data are noisy and often of poor quality; therefore, it is advisable to apply various types of techniques and compare and/or combine the results.

2.16.7 Analysis of Uncertainty in Hydrological Modeling 2.16.7.1 Notion of Uncertainty Webster’s Dictionary (1998) defines uncertain as follows: not surely or certainly known, questionable, not sure or certain in knowledge, doubtful, not definite or determined, vague, liable to vary or change, not steady or constant, varying. The noun uncertainty results from the above concepts and can be summarized as the state of being uncertain. However, in the context of hydrological modeling, uncertainty has a specific meaning, and it seems that there is no consensus about the very term of uncertainty, which is conceived with differing degrees of generality (Kundzewicz, 1995). Often uncertainty is defined with respect to certainty. For example, Zimmermann (1997) defined certainty as ‘‘certainty implies that a person has quantitatively and qualitatively the appropriate information to describe, prescribe or predict deterministically and numerically a system, its behaviour or other phenomena.’’ Situations that are not described by the above definition shall be called uncertainty. A similar definition has been given by Gouldby and Samuels (2005): ‘‘a general concept that reflects our lack of sureness about someone or something, ranging from just short of complete sureness to an almost complete lack of conviction about an outcome.’’ In the context of modeling, uncertainty is defined as a state that reflects our lack of sureness about the outcome of a physical processes or system of interest, and gives rise to potential difference between assessment of the outcome and its true value. More precisely, uncertainty of a model output is the

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state or condition that the output cannot be assessed uniquely. Uncertainty stems from incompleteness or imperfect knowledge or information concerning the process or system in addition to the random nature of the occurrence of the events. Uncertainty resulting from insufficient information may be reduced if more information is available.

2.16.7.2 Sources of Uncertainty Uncertainties that can affect the model predictions stem from a variety of sources (e.g., Melching, 1995; Gupta et al., 2005), and are related to our understanding and measurement capabilities regarding the real-world system under study: 1. Perceptual model uncertainty, that is, the conceptual representation of the watershed that is subsequently translated into mathematical (numerical) form in the model. The perceptual model (Beven, 2001) is based on our understanding of the real-world watershed system, that is, flowpaths, number and location of state variables, runoff production mechanisms, etc. This understanding might be poor, particularly for aspects relating to subsurface system characteristics, and therefore our perceptual model might be highly uncertain (Neuman, 2003). 2. Data uncertainty, that is, uncertainty caused by errors in the measurement of input (including forcing) and output data, or by data processing. Additional uncertainty is introduced if long-term predictions are made, for instance, in the case of climate change scenarios for which as per definition no observations are available. A hydrological model might also be applied in integrated systems, for example, connected to a socioeconomic model, to assess, for example, impacts of water resources changes on economic behavior. Data to constrain these integrated models are rarely available (e.g., Letcher et al., 2004). An element of data processing, that is, uncertainty, is introduced when a model is required to interpret the actual measurement. A typical example is the use of radar rainfall measurements. These are measurements of reflectivity that have to be transformed to rainfall estimates using a (empirical) model with a chosen functional relationship and calibrated parameters, both of which can be highly uncertain. 3. Parameter estimation uncertainty, that is, the inability to uniquely locate a best parameter set (model, i.e., a model structure parameter set combination) based on the available information. The lack of correlation often found between conceptual model parameters and physical watershed characteristics will commonly result in significant prediction uncertainty if the model is extrapolated to predict the system behavior under changed conditions (e.g., land-use change or urbanization) or to simulate the behavior of a similar but geographically different watersheds for which no observations of the variable of interest are available (i.e., the ungauged case). Changes in the represented system have to be considered through adjustments of the model parameters (or even the model structure), and the degree of adjustment has so far been difficult to determine without measurements of the changed system response. 4. Model structural uncertainty introduced through simplifications, inadequacies, and/or ambiguity in the description

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of real-world processes. There will be some initial uncertainty in the model state(s) at the beginning of the modeled time period. This type of uncertainty can usually be taken care of through the use of a warm-up (spin-up) period or by optimizing the initial state(s) to fit the beginning of the observed time series. Errors in the model (structure and parameters) and in the observations will also commonly cause the states to deviate from the actual state of the system in subsequent time periods. This problem is often reduced using data assimilation techniques as discussed later. Figure 8 presents how different sources of the uncertainty might vary with model complexity. As the model complexity (and the detailed representation of the physical process) increases, structural uncertainty decreases. However, with the increasing complexity of model, the number of inputs and parameters also increases and consequently there is a good chance that input and parameter uncertainty will increase. Due to the inherent trade-off between model structure uncertainty and input/parameter uncertainty, for every model there is the optimal level of model complexity where the total uncertainty is minimum.

prediction intervals consist of the upper and lower limits between which a future uncertain value of the quantity is expected to lie with a prescribed probability. The endpoints of a prediction interval are known as the prediction limits. The width of the prediction interval gives us some idea about how uncertain we are about the uncertain entity. Although useful and successful in many applications, probability theory is, in fact, appropriate for dealing with only a very special type of uncertainty, namely random (Klir and Folger, 1988). However, not all uncertainty is random. Some forms of uncertainty are due to vagueness or imprecision, and cannot be treated with probabilistic approaches. Fuzzy set theory and fuzzy measures (Zadeh, 1965, 1978) provide a nonprobabilistic approach for modeling the kind of uncertainty associated with vagueness and imprecision. Information theory is also used for representing uncertainty. Shannon’s (1948) entropy is a measure of uncertainty and information formulated in terms of probability theory. Another broad theory of uncertainty representation is the evidence theory introduced by Shafer (1976). Evidence theory, also known as Dempster–Shafer theory of evidence, is based on both the probability and possibility theory. In hydrological modeling, the primary tool for handling uncertainty is still probability theory, and, to some extent, fuzzy logic.

2.16.7.3 Uncertainty Representation For many years, probability theory has been the primary tool for representing uncertainty in mathematical models. Different methods can be used to describe the degree of uncertainty. The most widely adopted methods use PDFs of the quantity, subject to the uncertainty. However, in many practical problems the exact form of this probability function cannot be derived or found precisely. When it is difficult to derive or find PDF, it may still be possible to quantify the level of uncertainty by the calculated statistical moments such as the variance, standard deviation, and coefficient of variation. Another measure of the uncertainty of a quantity relates to the possibility to express it in terms of the two quantiles or prediction intervals. The Minimum uncertainty

Uncertainty

Total uncertainty

Structure uncertainty

Input and parameter uncertainty

2.16.7.4 View at Uncertainty in Data-Driven and Statistical Modeling In DDM, the sources of uncertainty are similar to those for other hydrological models, but there is an additional focus on data partitioning used for model training and verification. Often data are split in a nonoptimal way. A standard procedure for evaluating the performance of a model would be to split the data into training set, cross-validation set, and test set. This approach is, however, very sensitive to the specific sample splitting (LeBaron and Weigend, 1994). In principle, all these splitting data sets should have identical distributions, but we do not know the true distribution. This causes uncertainty in prediction as well. The prediction error of any regression model can be decomposed into the following three sources (Geman et al., 1992): (1) model bias, (2) model variance, and (3) noise. Model bias and variance may be further decomposed into the contributions from data and training process. Furthermore, noise can also be decomposed into target noise and input noise. Estimating these components of prediction error (which is however not always possible) helps to compute the predictive uncertainty. The terms bias and variance come from a well-known decomposition of prediction error. Given N data points and M models, the decomposition is based on the following equality: N X M N 1 X 1X ðti  yij Þ2 ¼ ðti  yi Þ2 NM i¼1 j¼1 N i¼1

þ Model complexity Figure 8 Dependency of various sources of uncertainty on the model complexity.

N X M 1 X ð yi  yij Þ2 NM i¼1 j¼1

ð2Þ

where ti is the ith target, yij the ith output of the jth model, and P  yi ¼ 1=M M j¼1 yij the average model output calculated for input i.

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The left-hand-side term of Equation (2) is the well-known MSE. The first term on the right-hand side is the square of bias and the last term is the variance. The bias of the prediction errors measures the tendency of over- or under-prediction by a model, and is the difference between the target value and the model output. From Equation (2), it is clear that the variance does not depend on the target, and measures the variability in the predictions by different models.

2.16.7.5 Uncertainty Analysis Methods Once the uncertainty in a model is acknowledged, it should be analyzed and quantified with the ultimate aim to reduce the impact of uncertainty. There is a large number of uncertainty analysis methods published in the academic literature. Pappenberger et al. (2006) provided a decision tree to help in choosing an appropriate method for a given situation. Uncertainty analysis process in hydrological models varies mainly in the following: (1) type of hydrological models used; (2) source of uncertainty to be treated; (3) the representation of uncertainty; (4) purpose of the uncertainty analysis; and (4) availability of resources. Uncertainty analysis has comparatively a long history in physically based and conceptual modeling (see, e.g., Beven and Binley, 1992; Gupta et al., 2005). Uncertainty analysis methods in all the above cases should involve: (1) identification and quantification of the sources of uncertainty, (2) reduction of uncertainty, (3) propagation of uncertainty through the model, (4) quantification of uncertainty in the model outputs, and (6) application of the uncertain information in decision-making process. A number of methods have been proposed in the literature to estimate model uncertainty in rainfall–runoff modeling. Reviews of various methods of uncertainty analysis on hydrological models can be found in, for example, Melching (1995), Gupta et al. (2005), Montanari (2007), Moradkhani and Sorooshian (2008), and Shrestha and Solomatine (2008). These methods are broadly classified into several categories (most of them result in probabilistic estimates): 1. analytical methods (see, e.g., Tung, 1996); 2. approximation methods, for example, first-order second moment method (Melching, 1992); 3. simulation and sampling-based (Monte Carlo) methods leading to probabilistic estimates that may also use Bayesian reasoning (Kuczera and Parent, 1998; Beven and Binley, 1992); 4. methods based on the analysis of the past model errors and either using distribution transforms (Montanari and Brath, 2004) or building a predictive ML of uncertainty (Shrestha and Solomatine, 2008; Solomatine and Shrestha, 2009); and 5. methods based on fuzzy set theory (e.g., Abebe et al., 2000a; Maskey et al., 2004). Analytical and approximation methods can hardly be applicable in case of using complex computer-based models. Here, we will present only the most widely used probabilistic methods based on random sampling – Monte Carlo simulation method. The GLUE method (Beven and Binley, 1992), widely used in hydrology, can be seen as a particular case of

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MC approach. MC simulation is a flexible and robust method capable of solving a great variety of problems. In fact, it may be the only method that can estimate the complete probability distribution of the model output for cases with highly nonlinear and/or complex system relationship (Melching, 1995). It has been used extensively and also as a standard means of comparison against other methods for uncertainty assessment. In MC simulation, random values of each of uncertain variables are generated according to their respective probability distributions and the model is run for each of the realizations of uncertain variables. Since we have multiple realizations of outputs from the model, standard statistical technique can be used to estimate the statistical properties (mean, standard deviation, etc.) and empirical probability distribution of the model output. MC simulation method involves the following steps: 1. randomly sample uncertain variables Xi from their joint probability distributions; 2. run the model y ¼ g(xi) with the set of random variables xi; 3. repeat the steps 1 and 2 s times, storing the realizations of the outputs y1, y2,y, ys; and 4. from the realizations y1, y2,y, ys, derive the cdf and other statistical properties (e.g., mean and standard deviation) of Y. When MC sampling is used, the error in estimating PDF is inversely proportional to the square root of the number of runs s and, therefore, decreases gradually with s. As such, the method is computationally expensive, but can reach an arbitrarily level of accuracy. The MC method is generic, invokes fewer assumptions, and requires less user input than other uncertainty analysis methods. However, the MC method suffers from two major practical limitations: (1) it is difficult to sample the uncertain variables from their joint distribution unless the distribution is well approximated by a multinormal distribution (Kuczera and Parent, 1998) and (2) it is computationally expensive for complex models. Markov chain Monte Carlo (MCMC) methods such as Metropolis and Hastings (MH) algorithm (Metropolis et al., 1953; Hastings, 1970) have been used to sample parameter from its posterior distribution. In order to reduce the number of samples (model simulations) necessary in MC sampling , more efficient Latin Hypercube sampling has been introduced (McKay et al., 1979). Further, the following methods in this row can be mentioned: Kalman filter and its extensions (Kitanidis and Bras, 1980), the DYNIA approach (Wagener et al., 2003), the BaRE approach (Thiemann et al., 2001), the SCEM-UA algorithm (Vrugt et al., 2003), and the DREAM algorithm (Vrugt et al., 2008b), a version of the MCMC scheme. Most of the probabilistic techniques for uncertainty analysis treat only one source of uncertainty (i.e., parameter uncertainty). Recently, attention has been given to other sources of uncertainty, such as input uncertainty or structure uncertainty, as well as integrated approach to combine different sources of uncertainty. The research shows that input or structure uncertainty is more dominant than the parameter uncertainty. For example, Kavetski et al. (2006) and Vrugt et al. (2008a), among others, treat input uncertainty in hydrological

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modeling using Bayesian approach. Butts et al. (2004) analyzed impact of the model structure on hydrological modeling uncertainty for stream flow simulation. Recently, new schemes have emerged to estimate the combined uncertainties in rainfall–runoff predictions associated with input, parameter, and structure uncertainty. For instance, Ajami et al. (2007) used an integrated Bayesian multimodel approach to combine input, parameter, and model structure uncertainty. Liu and Gupta (2007) suggested an integrated data assimilation approach to treat all sources of uncertainty. Regarding the sources of uncertainty, Monte-Carlo-type methods are widely used for parameter uncertainty, Bayesian methods and/or data assimilation can be used for input uncertainty and Bayesian model averaging method is suitable for structure uncertainty. The appropriate uncertainty analysis method also depends on whether the uncertainty is represented as randomness or fuzziness. Similarly, uncertainty analysis methods for real-time forecasting purposes would be different from those used for design purposes (e.g., when estimating design discharge hydraulic structure design). It should be noted that the practice of uncertainty analysis and the use of the results of such analysis in decision making are not yet widely spread. Some possible misconceptions are stated by Pappenberger and Beven (2006): a) uncertainty analysis is not necessary given physically realistic models, b) uncertainty analysis is not useful in understanding hydrological and hydraulic processes, c) uncertainty (probability) distributions cannot be understood by policy makers and the public, d) uncertainty analysis cannot be incorporated into the decisionmaking process, e) uncertainty analysis is too subjective, f) uncertainty analysis is too difficult to perform and g) uncertainty does not really matter in making the final decision.

Some of these misconceptions however have explainable reasons, so the fact remains that more has to be done in bringing the reasonably well-developed apparatus of uncertainty analysis and prediction to decision-making practice.

2.16.8 Integration of Models 2.16.8.1 Integration of Meteorological and Hydrological Models Water managers demand much longer lead times in the hydrological forecasts. Forecasting horizon of hydrological models can be extended if along with the (almost) real-time measurements of precipitation (radar and satellite images, gauges), their forecasts are used. The forecasts can come only from the numerical weather prediction (NWP; meteorological) models. Linking of meteorological and hydrological models is currently an adopted practice in many countries. One of the examples of such an integrated approach is the European flood forecasting system (EFFS), in which development started in the framework of EU-funded project in the beginning of the 2000s. Currently, this initiative is known as the European flood alert system (EFAS), which is being developed by the EC Joint Research Centre (JRC) in close collaboration with several European institutions. EFAS aims at developing a 4–10-day inadvance EFFS employing the currently available medium-range

weather forecasts. The framework of the system allows for incorporation of both detailed models for specific basins as well as a broad scale for entire Europe. This platform is not supposed to replace the national systems but to complement them. The resolution of the existing NWP models dictates to a certain extent the resolution of the hydrological models. LISFLOOD model (Bates and De Roo, 2000) and its extension module for inundation modeling LISFLOOD-FP have been adopted as the major hydrological response model in EFAS. This is a rasterized version of a process-based model used for flood forecasting in large river basins. LISFLOOD is also suitable for hydrological simulations at the continental scale, as it uses topographic and land-use maps with a spatial resolutions up to 5 km. It should be mentioned that useful distributed hydrological models that are able to forecast floods at meso-scales have grid sizes from dozens of meters to several kilometers. At the same time, the currently used meteorological models, providing the quantitative precipitation forecasts, have mesh sizes from several kilometers and higher. This creates an obvious inconsistency and does not allow to realize the potential of the NWP outputs for flood forecasting – see, for example, Bartholmes and Todini (2005). The problem can partly be resolved by using downscaling (Salathe, 2005; Cannon, 2008), which however may bring additional errors. As NWP models use more and more detailed grids, this problem will be becoming less and less acute. One of the recent successful software implementations of allowing for flexible combination of various types of models from different suppliers (using XML-based open interfaces) and linking to the real-time feeds of the NWP model outputs is the Delft-FEWS (FEWS, flood early warning system) platform of Deltares (Werner, 2008). Currently, this platform is being accepted as the integrating tool for the purpose of operational hydrological forecasting and warning in a number of European countries and in USA. The other two widely used modeling systems (albeit less open in the software sense) that are also able to integrate meteorological inputs are (1) the MIKE FLOOD by DHI Water and Environment, based on the hydraulic/hydrologic modeling system MIKE 11 and (2) FloodWorks by Wallingford Software.

2.16.8.2 Integration of Physically Based and Data-Driven Models 2.16.8.2.1 Error prediction models Consider a model simulating or predicting certain water-related variable (referred to as a primary model). This model’s outputs are compared to the recorded data and the errors are calculated. Another model, a data-driven model, is trained on the recorded errors of the primary model, and can be used to correct errors of the primary model. In the context of river modeling, this primary model would be typically a physically based model, but can be a data-driven model as well. Such an approach was employed in a number of studies. Shamseldin and O’Connor (2001) used ANNs to update runoff forecasts: the simulated flows from a model and the current and previously observed flows were used as input, and the corresponding observed flow as the target output. Updates of daily flow forecasts for a lead time of up to 4 days were

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made, and the ANN models gave more accurate improvements than autoregressive models. Lekkas et al. (2001) showed that error prediction improves real-time flow forecasting, especially when the forecasting model is poor. Abebe and Price (2004) used ANN to correct the errors of a routing model of the River Wye in UK. Solomatine et al. (2006) built an ANN-based rainfall–runoff model whose outputs were corrected by an IBL model.

2.16.8.2.2 Integration of hydrological knowledge into DDM An expert can contribute to building a DDM by bringing in the knowledge about the expected relationships between the system variables, in performing advanced correlation and mutual information analysis to select the most relevant variables, determining the model structure based on hydrological knowledge (allowed, e.g., by the M5flex algorithm by Solomatine and Siek (2004)), and in deciding what data should be used and how it should be structured (as it is done by most modelers). It is possible to mention a number of studies where an attempt is made to include a human expert in the process of building a data-driven model. For solving a flow forecasting problem, See and Openshaw (2000) built not a single overall ANN model but different models for different classes of hydrological events. Solomatine and Xue (2004) introduced a human expert to determine a set of rules to identify various hydrological conditions for each of which a separate specialized data-driven model (ANN or M5 tree) was built. Jain and Srinivasulu (2006) and Corzo and Solomatine (2007) also applied decomposition of the flow hydrograph by a threshold value and then built the separate ANNs for low and high flow regimes. In addition, Corzo and Solomatine (2007) were building two separate models related to base and excess flow which were identified by the Ekhardt’s (2005) method, and used overall optimization of the resulting model structure. All these studies demonstrated the higher accuracy of the resulting models where the hydrological knowledge and, wherever possible, models were directly used in building data-driven models.

2.16.9 Future Issues in Hydrological Modeling Natural and anthropogenic changes constantly impact the environment surrounding us. Available moisture and energy change due to variability and shifts in climate, and the separation of precipitation into different pathways on the land surface are altered due to wildfires, beetle infestations, urbanization, deforestation, invasive plant species, etc. Many of these changes can have a significant impact on the hydrological regime of the watershed in which they occur (e.g., Milly et al., 2005; Poff et al., 2006; Oki and Kanae, 2006; Weiskel et al., 2007). Such changes to water pathways, storage, and subsequent release (the blue and green water idea of Falkenmark and Rockstroem (2004)) are predicted to have significant negative impacts on water security for large population groups as well as for ecosystems in many regions of the world (e.g., Sachs, 2004). The growing imbalances among freshwater supply, its consumption, and human population will only increase the problem (Vo¨ro¨smarty et al.,

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2000). A major task for hydrologic science lies in providing predictive models based on sound scientific theory to support water resource management decisions for different possible future environmental, population, and institutional scenarios. But can we provide credible predictions of yet unobserved hydrological responses of natural systems? Credible modeling of environmental change impact requires that we demonstrate a significant correlation between model parameters and watershed characteristics, since calibration data are, by definition, unavailable. Currently, such a priori or regionalized parameters estimates are not very accurate and will likely lead to very uncertain prior distributions for model parameters in changed watersheds, leading to very uncertain predictions. Much work is to be done to solve this and to provide the hydrological simulations with the credibility necessary to support sustainable management of water resources in a changing world. The issue of model validation has to be given much more attention. Even if calibration and validation data are available, the historical practice of validating the model based on calculation of the Nash–Sutcliffe coefficient or some other squared error measure outside the calibration period is inadequate. Often low or high values of these criteria cannot clearly indicate whether or not the model under question has descriptive or predictive power. The discussion on validation has to move on to use more informative signatures of model behavior, which allow for the detection of how consistent the model is with system at hand (Gupta et al., 2008). This is particularly crucial when it comes to the assessment of climate and land-use change impacts, that is, when future predictions will lie outside the range of observed variability of the system response. Another development is expected with respect to modeling technologies, mainly in the more effective merging of data into models. One of the aspects here is the optimal use of data for model calibration and evaluation. In this respect, more rigorous approach adopted in DDM (e.g., use of crossvalidation and optimal data splitting) could be useful. Modern technology allows for accurate measurements of hydraulic and hydrologic parameters, and for more and more accurate precipitation forecasts coming from NWP models. Many of these come in real time, and this permits for a wider use of data-driven models with their combination with the physically based models, and for wider use of updating and data assimilation schemes. With more data being collected and constantly increasing processing power, one may also expect a wider use of distributed models. It is expected that the way the modeling results are delivered to the decision makers and public will also undergo changes. Half of the global population already owns mobile phones with powerful operating systems, many of which are connected to widearea networks, so the Information and communication technology (ICT) for the quick dissemination of modeling results, for example, in the form of the flood alerts, is already in place. Hydrological models will be becoming more and more integrated into hydroinformatics systems that support full information cycle, from data gathering to the interpretation and use of modeling results by decision makers and the public.

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Relevant Websites http://www.deltares.nl Deltares. http://www.dhigroup.com DHI; DHI software. http://efas.jrc.ec.europa.eu European Commission Joint Research Centre. http://www.sahra.arizona.edu SAHRA; Hydroarchive.

2.17 Uncertainty of Hydrological Predictions A Montanari, University of Bologna, Bologna, Italy & 2011 Elsevier B.V. All rights reserved.

2.17.1 Introduction 2.17.2 Definitions and Terminology 2.17.2.1 Probability 2.17.2.2 Randomness 2.17.2.3 Random Variable 2.17.2.4 Stochastic Process 2.17.2.5 Stationarity 2.17.2.6 Ergodicity 2.17.2.7 Uncertainty 2.17.2.8 Global Uncertainty and Individual Uncertainties 2.17.2.9 Uncertainty Assessment 2.17.2.10 Probabilistic Estimate/Estimation/Assessment of Uncertainty (Probabilistic Uncertainty) 2.17.2.11 Nonprobabilistic Estimate/Estimation/Assessment of Uncertainty (Nonprobabilistic Uncertainty) 2.17.2.12 Confidence Band 2.17.2.13 Equifinality 2.17.2.14 Behavioral Model 2.17.3 Classification of Uncertainty and Reasons for the Presence of Uncertainty in Hydrology 2.17.3.1 Inherent Randomness 2.17.3.2 Model Structural Uncertainty 2.17.3.3 Model Parameter Uncertainty 2.17.3.4 Data Uncertainty 2.17.3.5 Operation Uncertainty 2.17.4 Uncertainty Assessment 2.17.5 Classification of Approaches to Uncertainty Assessment 2.17.5.1 Research Questions about Uncertainty in Hydrology 2.17.5.2 An Attempt of Classification 2.17.6 Assessment of the Global Uncertainty of the Model Output 2.17.6.1 Analytical Methods 2.17.6.2 The Generalized Likelihood Uncertainty Estimation 2.17.6.3 The Bayesian Forecasting System 2.17.6.4 Techniques Based on the Statistical Analysis of the Model Error 2.17.6.5 Bayesian Model Averaging 2.17.6.6 Machine Learning Techniques 2.17.7 Assessment of Data Uncertainty 2.17.7.1 Precipitation Uncertainty 2.17.7.2 River Discharge Uncertainty 2.17.8 Assessment of Parameter Uncertainty 2.17.8.1 The MOSCEM-UA Method 2.17.8.2 The AMALGAM Method 2.17.9 Assessment of Model Structural Uncertainty 2.17.10 Uncertainty Assessment as a Learning Process 2.17.11 Conclusions Acknowledgments References

2.17.1 Introduction Hydrological modeling is receiving increasing attention from researchers and practitioners. The increasing availability of mathematical tools and computing power together with an improved understanding of the dynamics of hydrological processes has favored the continuous development of new

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modeling approaches in the past few decades (see Chapter 2.16 Hydrological Modeling). Hydrological modeling is an attractive option today for solving many practical problems of environmental engineering, flood protection, water resource management, and applied hydrology in general. Setting up a hydrological model in order to solve a practical problem requires the application of proper procedures of

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model identification, parameter calibration, hypothesis testing, model testing (also called model validation), and uncertainty assessment. The above procedures are often strictly related and are the subject of an increasing research activity by hydrologists. In particular, uncertainty estimation is very much related with parameter calibration and model validation. It consists of a verification of the hydrological model appropriateness and performances finalized to providing a quantitative assessment of its reliability. As a matter of fact, uncertainty estimation in hydrological surface and subsurface modeling is today one of the most important subfields of hydrology, according to the numerous contributions in recent scientific literature. Uncertainty reduction is also one of the main goals of the Prediction in Ungauged Basins (PUB) initiative promoted by the International Association of Hydrological Sciences. While quantitative uncertainty assessment in hydrology is often considered a relatively new topic, it is worth noting that hydrologists were aware of uncertainty and used to deal with it because the first hydrological studies and applications were carried out. In particular, empirical techniques were used to compensate for the lack of information about model reliability. For instance, hydrologists are well used to adopt safety factors or allowance for freeboard, which are usually set basing on consensus, expert opinion, and empirical evidence. These safety factors were the first and very useful tools to take into account inherent uncertainty and imperfect knowledge of hydrological processes in hydrological design. However, expert knowledge is by itself subjective and referred to specific contexts and situations. The call for a generalized and systematic approach to uncertainty estimation in hydrology is the motivation for the renewed interest in the past few years. One of the reasons why uncertainty assessment in hydrology was not much investigated on theoretical basis until the recent past is that hydrological modeling itself is a relatively young discipline. In fact, the first hydrological models were the rational formula proposed by Kuichling (1889) (although the principles of the method were introduced by Mulvaney (1851)), and the unit hydrograph model proposed by Sherman (1932). Most of the hydrological models that are used today were proposed after the 1960s. The interest in new techniques for uncertainty assessment was stimulated by Spear and Hornberger (1980), who introduced the generalized sensitivity analysis methodology, also known as regional sensitivity analysis. Their work inspired the development of the generalized likelihood uncertainty estimation (GLUE; Beven and Binley, 1992; see Section 2.17.6.2), which works under the hypothesis that different sets of model parameters/ structures may be equally likely as simulators of the real system. In the 1990s the emerging need for reliable techniques for uncertainty estimation, for the multitude of modeling situations and approaches that are experienced in hydrology, stimulated the development of many methods (for a long, though still incomplete, list one can refer to Liu and Gupta (2007) and Matott et al. (2009)). Another reason limiting the use of uncertainty assessment methods is that the transfer of the know-how about uncertainty in hydrology from scientists to end-users was and still is, difficult, notwithstanding the relevant research activity mentioned above. Pappenberger and Beven (2006) provided

an extensive analysis of this issue. A relevant problem today is that uncertainty assessment in hydrology suffers from the lack of a coherent terminology and a systematic approach. The result of this situation is that it is extremely difficult (if not impossible) to obtain a coherent picture of the available methods. This lack of clarity is an example of linguistic uncertainty (Regan et al., 2003). Therefore, much is still to be done to reach a coherent treatment of the topic. Quantitative uncertainty assessment in conditions of data scarcity is a very difficult task, if not impossible, in some cases. Usually, uncertainty estimation in applied scientific modeling is dealt with by comparing the model output with observed data, by borrowing concepts from statistics. According to this procedure, model reliability is quantified in a probabilistic framework. However, statistical testing becomes not as reliable in situations of data scarcity and therefore the use of statistical concepts for uncertainty assessment in hydrology sometimes may not be appropriate. This is one of the reasons why hydrologists are looking for different procedures that can be complementary or alternative to statistics. Moreover, uncertainty in hydrology might arise from limited knowledge (epistemic uncertainty, see Section 2.17.3) or from natural variability. In the former case, we deal with uncertainties that might not be aleatory in nature. They can be treated with statistical methods (e.g., the BATEA method, see Section 2.17.7.1), but many authors question the validity of statistics in this case and prefer nonstatistical approaches. These procedures are generally conceived in order to allow incorporation of expert knowledge in a theoretically based framework. They are characterized by a certain degree of subjectivity, which needs to be reduced as much as possible in order to allow their application in situations where knowledge is lacking. Therefore, different philosophies and approaches for quantifying the reliability of hydrological models were recently proposed. As a result, an active debate recently began about the relative advantages of each of them. Such debate in many cases assumed a philosophical behavior, because the philosophy underlying each method is one of the main subjects of the discussion. On the one hand, such a debate stimulated additional developments and insights in itself; on the other, it is still not clear which approach is most appropriate given the needs of the user. For this reason, the hydrologic scientific community still calls for more pragmatism in uncertainty estimation. On the one hand, hydrology is a science where uncertainty is very significant. Progress in monitoring techniques, process understanding, and modeling will certainly reduce uncertainty in the future but will never eliminate it. On the other hand, hydrologists are in charge of providing design variables that play a fundamental role in water engineering, civil protection, and water resource management. Therefore, it is clear that the efficient real world use of an uncertain design variable should necessarily be based on uncertainty assessment. This chapter aims at presenting a comprehensive introduction to the subject of uncertainty assessment in hydrology. After presenting a brief glossary and a discussion about the reasons for the presence of uncertainty in hydrology, a review of the most-used approaches to uncertainty assessment is presented.

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2.17.2 Definitions and Terminology There is currently a linguistic uncertainty affecting the topic of uncertainty assessment in hydrology (Regan et al., 2003; Beven, 2009), meaning that an agreed terminology is still lacking. Some basic definitions are provided in the following.

2.17.2.1 Probability Probability can be defined in different ways. In fact, probability is currently interpreted according to two broad and distinguished views. The classical frequentist view of probability defines the probability of an event occurring in a particular trial as the frequency with which it occurs in a long sequence of similar trials. In a Bayesian or subjectivist view, the probability of an event is dependent upon the state of information available and this information can include expert opinion. Probability theory forms the basis of classical statistics, which has estimators based on a likelihood function that represents how likely an observed data sample is for a given model and parameter set.

2.17.2.2 Randomness Randomness is a term that is used within science with different meanings. In statistics, and hydrology as well, a random process is such that its outcome cannot be predicted deterministically. Randomness does not imply lack of knowledge about the process dynamics or impossibility to set up a deterministic model for it. However, if a deterministic model can be set up for a process, randomness implies that such a model cannot perfectly predict the process outcome. For instance, in the case of a roulette wheel, if the geometric and dynamic behaviors of the system are perfectly known, then the number on which the ball will stop would be a certainty. However, one is fully aware that even a small imperfection in the description of the geometry of the system and/or its initial conditions makes the outcome of the experiment unpredictable. A probabilistic description can thus be more useful than a deterministic one for analyzing the pattern of outcomes of repeated rolls of a roulette wheel. Physicists face the same situation in kinetic theory of gases, where the system, while deterministic in principle, is very complex so that only a statistical description of its properties is feasible. Another example is the experiment of dropping balls into a spiked sieve. Here, the geometry of the system is perfectly known as well as the initial and boundary conditions. However, once a ball is dropped in the sieve, it is impossible to predict deterministically its trajectory, because no one can predict which way the ball will follow after hitting a spike. However, the distribution of the balls at the bottom of the sieve is well known to be Gaussian. In this case, the full comprehension of the geometry and dynamics of the system does not allow one to set up a deterministic description, while a stochastic description can provide a satisfactory model. Actually, one cannot exactly predict the number of balls in each bar, but the probabilistic prediction will have a small uncertainty.

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An important discovery of the twentieth-century physics was the random character of all physical processes that occur at subatomic scales and are governed by the laws of quantum mechanics. This means that probability theory is required to describe nature. This type of interpretation was questioned by many scientists, as the famous quote by Albert Einstein, from a letter to Max Born, clearly testifies: ‘‘I am convinced that He does not play dice.’’ A similar controversy currently occurs in hydrology (for an interesting discussion, see Koutsoyiannis et al. (2009)). The trend toward the so-called physically based models induced in the last few decades the inspiration to pursue a completely deterministic description of hydrological systems, through a better understanding of the internal dynamics of hydrological processes. However, such deterministic description is so complicated that only a probabilistic treatment is possible. This does not mean that knowledge is unuseful. On the contrary, it allows one to set up a plausible probabilistic description of the random outcome.

2.17.2.3 Random Variable A random variable maps all possible outcomes from a random event into the real numbers. As such, it is affected by uncertainty and cannot be deterministically predicted. Random variables can assume discrete and continuous values.

2.17.2.4 Stochastic Process A stochastic process can be defined as a collection of random variables. For instance, if we assume that the river flow at time t is a random variable, then the time series of river flow observations during an assigned observation period is a realization of a stochastic process. While a deterministic process gives only one possible value of its output under assigned initial and boundary conditions (as it is the case, e.g., for the solution of an ordinary differential equation), the output of a stochastic process is affected by some uncertainty that is described by the corresponding probability distributions. This means that there are many possible paths for the evolution of the process, with some of them being more likely and others less. A stochastic process can assume discrete or continuous values. Although the random variables of a stochastic process may be independent, in most commonly considered situations in hydrology, they exhibit statistical correlations. A stochastic process can include a deterministic representation but always includes a random component which makes its output uncertain.

2.17.2.5 Stationarity A stochastic process is strictly stationary when the joint probability distribution of an arbitrary number of its random variables does not change when shifted in time or space. As a result, parameters such as the statistics of the process also do not change over time or position. Stationarity is a property of the mathematical representation of the system, or an ensemble of outcomes from a repeatable experiment, and therefore does not constitute an actual property of the natural process itself. This latter follows just one trajectory and therefore its outcome is unique, because nature and life do not

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enable repeatability. Stationarity is a property that is used in statistics in order to make inference about the physical process and therefore does not imply any assumption on the natural process itself. It is interesting to mention that the opposite of stationarity is nonstationarity, which implies that the above statistics change accordingly to deterministic functions of time, where deterministic means that the above-mentioned functions should be known independently of the data and should apply to any time, past, present, and future (Papolulis, 1991). Conversely, if the above functions are random (i.e., realizations of stationary stochastic processes), then the process is stationary. The concept of stationarity is a way to find invariant properties in complex natural systems. In view of what was anticipated above, it is important to note that stationarity does not imply that the statistics of a realization of a process are constant in time. Actually, such statistics are affected by sampling variability and therefore they certainly change after a time shift. The crucial issue is to detect if such a change exists in the process and can be expressed through a deterministic function of time. Recently, the scientific literature presented contributions stating that stationarity is dead because of hydrological change and climate change. Actually, stationarity is an assumtpion and therefore can hardly be dead.

2.17.2.6 Ergodicity A stochastic process is said to be ergodic if its statistical properties can be deduced from a single, sufficiently long sample (realization) of the process.

2.17.2.7 Uncertainty Uncertainty can be defined as an attribute of information (Zadeh, 2005; Montanari, 2007). In the context of hydrology, uncertainty is generally meant to be a quantitative indication of reliability for a given hydrological quantity, either observed or inferred by using models. The indication of reliability can be provided by estimating the error affecting the quantity or the expected range of variability (due to uncertainty) for the quantity itself. Uncertainty can be broadly grouped into two major categories, namely, aleatory and epistemic uncertainty (see Section 2.17.3), and can be inferred by using probabilistic or nonprobabilistic methods.

2.17.2.8 Global Uncertainty and Individual Uncertainties Global uncertainty can be defined as the discrepancy between the model output and the true value of the corresponding variable. Different uncertainties can compensate each other in the formation of the global uncertainty; for instance, parameter errors can compensate, at least in part, for data errors and model structural errors. These different uncertainties are termed individual uncertainties and are specifically referred to with a terminology which recall their causal origin, such as parameter uncertainty and model structural uncertainty (see Section 2.17.3.2 for an extended description). The terms above are not formally defined and therefore some linguistic uncertainty is present. For instance, the terms parameter

uncertainty, input uncertainty, and model structural uncertainty should be used to indicate the uncertainty affecting the model parameters, input, and structure, respectively. Hereafter this is the meaning that will be used in this chapter. However, these terms are sometimes used to indicate the part of uncertainty in the model output that is caused by imperfect parameters, input, and model structure, respectively. While global uncertainty is relatively easy to estimate a posteriori, for instance, by computing the difference between the model output and the corresponding observed variable (under the assumption that this latter is correct), the identification of the contribution of individual uncertainties above is impossible, unless assumptions are introduced or independent observations are available (see Section 2.17.4). This means that it is usually difficult, if not impossible, to assess whether the model performance is affected by, say, a parameter error rather than a model structural error.

2.17.2.9 Uncertainty Assessment In what follows, we refer to uncertainty assessment to mean a quantitative evaluation of uncertainty affecting a hydrological variable, parameter, or model. Uncertainty estimation and uncertainty quantification will be considered synonymous with uncertainty assessment, which is different from uncertainty analysis and uncertainty modeling. The former is a preliminary step of uncertainty assessment aimed at identifying the reasons for the presence of uncertainty and the nature of uncertainty itself, while the latter term refers to the tools that are used for uncertainty assessment.

2.17.2.10 Probabilistic Estimate/Estimation/Assessment of Uncertainty (Probabilistic Uncertainty) We will use the term probabilistic estimate of uncertainty to mean that uncertainty estimation for a given hydrological quantity has been carried out consistently with formal probability theory. In the probabilistic approach, uncertainties are characterized by the probabilities associated with events. Therefore, if one refers to the output of a hydrological model, the related probability distribution should actually provide an estimate of the frequency with which the true values fall within a given range.

2.17.2.11 Nonprobabilistic Estimate/Estimation/ Assessment of Uncertainty (Nonprobabilistic Uncertainty) Nonprobabilistic methods to uncertainty estimation in hydrology are frequently applied. Nonprobabilistic methods are various generalizations of probability theory that have emerged since the 1950s, including random set theory, evidence theory, fuzzy set theory, and possibility theory (Jacquin and Shamseldin, 2007). In particular, fuzzy set theory and possibility theory have received considerable attention from hydrologists, because much human reasoning about hydrological systems is possibilistic rather than strictly probabilistic. We reason about whether a given scenario could happen, without necessarily endeavoring to attach probabilities to the likelihood of it happening, particularly in situations of very scarce information.

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2.17.2.12 Confidence Band A range around an estimated quantity that encompasses the true value with a probability 1  a, where a is the significance level and 1  a is the confidence level. It is worth pointing out that the terminology is sometimes ambiguous. Some authors use the term confidence band or confidence interval when referring to the distribution of estimates that cannot be observed (e.g., a model parameter), while the term prediction interval is used when referring to the distribution of future values. Moreover, some authors indicate with the term tolerance interval a range in the observations that encompasses a 1  a proportion of the population of the related random variable. For more details, the reader is reffered to Hahn and Meeker (1991). Figure 1 shows an example of confidence bands computed with the meta-Gaussian approach (Montanari and Brath, 2004; see Section 2.17.6.2) for river flow simulations referred to the Samoggia River at Calcara (Italy). It is interesting to note that the shape of the confidence bands themselves provides indications about the goodness of the fit provided by the model. Moreover, the skew in the prediction distribution results indicates that a systematic error is likely to be present.

2.17.2.13 Equifinality

River flow (m3 s−1)

Equifinality implies that in a system interacting with its environment a given end state can be reached by more than one potential mean. The term is due to von Bertalanffy (1968), the founder of general systems theory. The idea of equifinality suggests that similar results may be achieved with different initial conditions, different model parameters, and different model structures. In hydrology the concept of equifinality was introduced by Beven (1993) as an unavoidable effect of the presence of uncertainty. For an extended discussion, see Beven

Observed Simulated 95% confidence bands

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(2006a). Equifinality leads to the idea of multimodeling solutions in hydrology (see Section 2.17.6.5).

2.17.2.14 Behavioral Model Within the context of equifinality, a behavioral model is one that provides an acceptable simulation of observed natural processes. In a multimodel approach, the collection of behavioral models provides a means for assessing the uncertainty of their output (see Section 2.17.6.5).

2.17.3 Classification of Uncertainty and Reasons for the Presence of Uncertainty in Hydrology There have been many attempts presented by the literature to classify uncertainty in hydrology. The proposed solutions were not always in agreement because, given the uncertain nature of hydrological processes, it is sometimes impossible to unambiguously decipher the reason for the presence of errors. It is generally agreed that uncertainties can be grouped into two major categories: (1) natural variability (also called structural uncertainty, aleatory, external, objective, inherent, random, irreducible, or stochastic uncertainty) and (2) knowledge uncertainty (also called epistemic, functional, internal, reducible, or subjective uncertainty (Table 1 in NRC, 2000; Koutsoyiannis et al., 2009; Hall and Solomatine, 2008). These two categories have different ramifications. In fact, the global uncertainty of a given model or variable may be characterized in three ways: purely structural, partly epistemic and partly structural, and purely epistemic (Cullen and Frey, 1999). When evaluating model performances and when possible, the different types of uncertainty should be separated (Cullen and Frey, 1999; Hoffman and Hammonds, 1994; Nauta, 2000; Sonich-Mullin, 2001). However, this is not always possible and therefore epistemic uncertainty and natural variability are often dealt with in an integrated fashion. Other classifications were proposed. According to the causes for the presence of uncertainty in hydrology (which nevertheless are not always identifiable), one may identify the following categories: (1) inherent randomness (the geometry 200

Observed river flow (m3 s−1)

In a more general context, we will refer to nonprobabilistic uncertainty when the estimation is carried out by using other approaches than formal probabilistic ones. This category includes probabilistic methods where some of the underlying assumptions are relaxed.

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Figure 1 (a) Example of confidence bands computed with the meta-Gaussian approach (Montanari and Brath (2004); see Section 2.17.6.2) for a flood event occurred in the Samoggia River at Calcara (Italy) in 1995. (b) Example of confidence bands computed with the meta-Gaussian approach (Montanari and Brath (2004); see Section 2.17.6.2) drawn on a scatterplot of observed versus simulated hourly river flows for the Samoggia River at Calcara (Italy) during the years 1995–97.

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Table 1

Uncertainty assessment methods in hydrology, along with their classification (see Section 2.17.5) and purpose (see Sections 45.6–45.10)

Assessment method

Classification

Type of uncertainty estimated

AMALGAM BATEA BFS BMA DYNIA GLUE

Nonprobabilistic, parameter estimation Probabilistic, parameter estimation, uncertainty assessment, sensitivity analysis Probabilistic, Bayesian Probabilistic, multimodel Nonprobabilistic, identifiability analysis Nonprobabilistic (when an informal likelihood is used), parameter estimation, uncertainty assessment, sensitivity analysis Probabilistic, parameter estimation, uncertainty assessment, sensitivity analysis

Parameter Precipitation induced Global Global Parameter Global, parameter, data, structural

IBUNE Machine learning Meta-Gaussian MOSCEM-UA SCE-UA

Nonprobabilistic

Global, precipitation induced, model structure induced Usually global, in principle all

Probabilistic, data analysis Nonprobabilistic, parameter estimation, sensitivity analysis Probabilistic, parameter estimation

Global Parameter Parameter

Classification is ambiguous in some cases; it distinguishes between probabilistic and nonprobabilistic methods, as well as among the seven categories introduced by Matott et al. (2009) (see Section 2.17.5.2). AMALGAM, a multialgorithm genetically adaptive method for multiobjective optimization; BATEA, Bayesian total error analysis; BFS, Bayesian forecasting system; BMA, Bayesian multimodel analysis; DYNIA, dynamic identifiability analysis; GLUE, generalized likelihood uncertainty estimation; IBUNE, integrated Baysian uncertainty estimator; MOSCEM-UA, multiobjective shuffled complex evolution University of Arizona; SCE-UA, shuffled complex evolution university of Arizona. References for the methods are in the text.

of the control volumes, the weather, etc.); (2) model structural uncertainty that reflects the inability of a model to represent precisely the true behavior of the system; (3) model parameter uncertainty; and (4) data uncertainty. When using models to make engineering or management decisions about hydrologic systems we also have to deal with (5) operation uncertainties (associated with construction and maintenance; Loucks and Van Beek, 2005). The above sources of uncertainty are briefly discussed in the following. It is generally agreed that uncertainty in hydrology cannot be eliminated, no matter if it is epistemic in nature or induced by inherent randomness. For instance, rainfall inputs to a catchment might be highly structured, with different structures in different events that lead to nonrandom errors in estimates of areal rainfall. This type of error can be reduced with new measurement techniques but cannot be fully removed.

2.17.3.1 Inherent Randomness Inherent randomness is one of the main reasons for the presence of uncertainty and is a intrinsic behavior of hydrological processes. For instance, a deterministic description of subsurface flow paths is impossible. Different soils and rocks, irregular macropores, faults and cracks with their heterogeneous patterns in both space and time, combined with two phase flows, varying wetting fronts, form a extremely complex system, for which a deductive description is impossible (Koutsoyiannis et al., 2009). Inherent randomness emerges also from meteorology, variability of the surface flow paths, and so on. It is not only related to a coarse description of the system (that also induces uncertainty, which is nevertheless epistemic at least in part; in fact, it could be reduced by an increased capability to monitor the processes at finer spatial and temporal scales) but rather related to the effective impossibility to describe deterministically the inherent variability of the process. Inherent randomness has been long

discussed by the hydrologic community in the recent past (Koutsoyiannis et al., 2009; Koutsoyiannis, 2009). It has been argued that dynamical systems theory has well shown that uncertainty can emerge even from an insignificant perturbation of the initial conditions of a pure, simple, and fully known deterministic (chaotic) dynamics.

2.17.3.2 Model Structural Uncertainty In the ideal situation in which perfect input data are available and model parameters are perfect, model structural uncertainty is defined as the uncertainty in the model output induced by the inhability of the hydrological model to perfectly reproduce the dynamics of hydrological systems. This means that the model output would still be uncertain even in the ideal situation in which no other uncertainties are present. Model structural uncertainty can be induced by imperfect model structure or lack of computational power. If the reason for the presence of uncertainty is an incorrect selection of the model or the computational tools, then model structural uncertainty is epistemic; on the other hand, the effective impossibility to describe the system with a mathematical model induces the presence of irreducible uncertainty. Given that model structural uncertainty is epistemic at least in part, the search for improved modeling tools has been the main focus of the hydrologic scientific community in the last few decades.

2.17.3.3 Model Parameter Uncertainty Model parameter uncertainty is the result of the lack of a sufficiently extended database of good quality, or the inefficiency of the optimization algorithm and/or the related objective function, which induce parameter estimates to be significantly uncertain even if a perfect model and a perfect knowledge of the system were available. This is a relevant

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problem in all the hydrological applications and motivates the intense efforts that were dedicated to parameter estimation and related uncertainty assessment (e.g., Ibbitt and O’Donnell, 1974; Alley, 1984; Kleisenn et al., 1990; Gan and Burges, 1990; Duan et al., 1992; Brath et al., 2004; Vrugt et al., 2003a; 2003b; Vrugt and Robinson, 2007). Parameter estimation can be coupled with sensitivity analysis and model diagnostic to identify the most sensitive parameters in periods of model failures, thus gaining insights into the reasons for model inadequacy (Sieber and Uhlenbrook, 2005). To this end, Wagener et al. (2003) proposed the dynamic identifiability analysis (DYNIA). Usually, an objective function is used to calibrate the model parameters to observed data. Independently of the objective function and the tools employed to optimize the model parameters, most hydrological models suffer from the existence of multiple optima of the objective function itself and the presence of high interaction or correlation among the parameters. These problems make parameter calibration uncertain even when a relatively large database is at disposal (Kuczera and Mroczkowski, 1998). Parameter uncertainty also arises when the parameters are not calibrated but rather estimated on the basis of field surveys or expert knowledge, for instance, while defining land-cover parameters. Parameter uncertainty can be epistemic at least in part.

2.17.3.4 Data Uncertainty Data uncertainty is an emerging problem that is gaining renewed attention by hydrologists in the recent past (see, e.g., Di Baldassarre and Montanari, 2009; Dottori et al., 2009; Koussis, 2009; Petersen-Øverleir and Reitan, 2009). In fact, even modern technologies cannot avoid the presence of a significant approximation in observations of, say, rainfall, river flows (for both low and high flows), and so forth. Data uncertainty emerges from limitation of the monitoring techniques (instrumentation error, rating curve approximations, etc.) or variability of the spatial and temporal distribution of the observed hydrological variables (spatial variability of rainfall, time variability of streamflow, etc.). It follows that hydrological models are optimized against imperfect data and therefore an error is induced in hydrological simulations. Data uncertainty has both epistemic and aleatory components and therefore it is particularly important how observation errors are treated. Some authors claim that treating data error with purely statistical approaches may induce overconditioning in hydrological modeling (Beven, 2006a).

2.17.3.5 Operation Uncertainty Operation uncertainty arises when hydrological models are used in the real world. In fact, it is well known that in realtime applications uncertainties of different nature are present that do not affect off-line exercises. Often the data and the initial and boundary conditions cannot be preliminary checked, the computational time might become a relevant constraint, the end-users operate under stress and therefore the human error becomes more likely, there is a weak ability to identify decision criteria, communication becomes difficult, and so forth. Operation uncertainty is difficult to assess, is

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rarely considered by researchers, and represents an emerging awareness among hydrological modelers and end-users. As a matter of fact, the identification of the most suitable model should be carried out in view of operation uncertainty as well. Data assimilation can be used to constrain uncertainty during model application.

2.17.4 Uncertainty Assessment It is well known that uncertainty assessment in hydrology is a topical issue. Already in 1905, W.E. Cooke, who was issuing daily weather forecasts in Australia, stated: ‘‘It seems to me that the condition of confidence or otherwise form a very important part of the prediction, and ought to find expression.’’ Uncertainty assessment in hydrology involves the analysis of multiple sources of error, the main ones being outlined in Section 2.17.3. The contribution of these latter to the formation of the global uncertainty cannot be quantified independently, unless (1) one is willing to introduce subjective assumptions about the nature of the individual error components or (2) independent observations are available for estimating each source of error. As an example for the latter solution, the reader is referred to Winsemius et al. (2006, 2008) where gravity and evaporation measurements are used to constrain the water balance and the land surface parameters, respectively, for a rainfall–runoff model. However, in some hydrological applications it is not necessary to separate different sources of error. For this reason in many cases, uncertainty is assessed in an aggregated solution, therefore quantifying global uncertainty.

2.17.5 Classification of Approaches to Uncertainty Assessment This section aims to propose a classification of uncertainty assessment methods in hydrology. Classifying the methods is useful to clarify their behavior and operational purpose. However, it should be premised that such a classification might be subjective, because some methods lend themselves to different interpretations of their nature and scope.

2.17.5.1 Research Questions about Uncertainty in Hydrology The uncertain nature of hydrology has pushed hydrologists to raise many questions related to uncertainty assessment. The most urgent ones are those related to quantifying the reliability of the output variables of hydrological models (forecasts, simulations, etc.). Hydrological simulation is often used in real-time prediction systems for natural hazards or for assessing long-term effects of climate change or for assessing the reliability of proposed water resource management strategies. In these cases, quantifying the uncertainty of the hydrological model response is extremely important from a societal point of view. Uncertainty assessment in hydrology includes additional research issues. Among them, there is the call for assessing the uncertainty of observed data, model parameters, and model structure. These issues are also significant for gaining further

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insight into the dynamics of hydrological processes. Indeed, to identify the most appropriate model is a means to provide support to hydrological theory. Therefore, uncertainty assessment became strongly related to parameter estimation, multiobjective optimization, model identification, model building, model diagnostics, model averaging, data collection, and information theory in general. All topics in this list have gained the attention of researchers in recent years and are often allocated under the one umbrella of uncertainty assessment in hydrology. Indeed, it would be helpful for end-users to formally identify such subtopics and the related research questions.

2.17.5.2 An Attempt of Classification The traditional way of dealing with uncertainty in science is through statistics and probability (see, e.g., Montanari et al., 2009) but, as mentioned above, nonprobabilistic approaches to uncertainty analysis are also popular in hydrology. In some cases, it is not easy to classify an approach as either probabilistic or not. In fact, there are some methods that are based on probability theory, but in real-word applications simplifying assumptions are often introduced which finally lead to a nonprobabilistic estimation of the likelihood of a given scenario. Such assumptions are introduced in order to overcome operational problems, for instance, due to lack of enough data to support a statistical application. The decision to use probabilistic or nonprobabilistic methods is currently the most controversial issue in hydrologic uncertainty analysis. This debate has raised the very relevant question about the capability of probabilistic and nonprobabilistic methods to correctly infer the frequency properties of hydrological simulations and predictions (see, e.g., Beven, 2006a; Montanari, 2005, 2007; Mantovan and Todini, 2006; Beven et al., 2007, 2008). Criticism about probabilistic methods is focused on the concern that for many data sets it is not clear if the assumptions of classical statistics (e.g., stationarity) can be justified. The main reason for criticism of nonprobabilistic methods is that they are subjective and not necessarily coherent from a statistical point of view (see, e.g., the criticism of Mantovan and Todini (2006) with respect to GLUE). Moreover, on known problems for which the data do support the necessary probabilistic assumptions, probabilistic and nonprobabilistic methods provide different answers (e.g., Stedinger et al., 2008). The suitability of probabilistic versus nonprobabilistic methods and the difference in their response are dictated by the knowledge that the user has about the structure of the error model. Using a correctly based inference should lead to similar results in uncertainty assessment. Conversely, some authors claim that with unknown error structure it is dangerous to rely on statistical methods based on simple assumptions about the nature of the errors themselves. There is an increasing consensus about the opportunity to use probabilistic approaches, as a way to efficiently summarize the information content of the data, when sufficient information is available to support statistical hypotheses with appropriate statistical tests (Montanari et al., 2009). Conversely, data scarcity calls for expert knowledge to support uncertainty assessment. Above all, data scarcity calls for the

integration of different types of information, within a framework that is unavoidably subjective, given that the information itself is often soft. Besides the above, additional classifications were recently proposed for uncertainty assessment methods. For instance, Matott et al. (2009) identified seven categories of models: (1) Data analysis methods, including analytical and statistical procedures for evaluating the accuracy of data. These include also parametrization of probability distributions. (2) Identifiability analysis, aiming at detecting data inadequacy and suggesting model improvements. (3) Parameter estimation methods, quantifying uncertain model parameters. (4) Uncertainty analysis techniques, meaning methods to propagate sources of uncertainty through the model to generate probability distributions for the model output. These methods include approximation and sampling methods. (5) Sensitivity analyses, investigating to what extent different sources of variation in the input of a mathematical model affect the variation of the output. Sensitivity analysis aims at identifying what source of uncertainty weights more on the model output (see, e.g., Van Griensven et al. (2006); Go¨tzinger and Ba´rdossy, 2008). Sensitivity analysis and uncertainty estimation are well distinguished. Their results can be comparable, because a probability distribution of model outputs corresponding to different inputs can be similar to the analogous distribution derived through the analysis of probabilistic uncertainty. This similarity of results has originated a confusion of terms in some applications. (6) Multimodel analysis, consisting of generating multiple possible outputs accordingly to different models, parameters, and boundary conditions. (7) Bayesian methods, which were previously defined (this category could be joined with category 4 above). The seven categories above are not strictly separated, meaning that a method can belong to more than one of them. Another classification for uncertainty assessment methods for the model output was recently proposed by Shrestha and Solomatine (2008) who consider the following categories: (1) analytical methods, using derived distribution methods to compute the probability distribution function of the model output; (2) approximation methods, providing only the moments of the distribution of the uncertain output variable; (3) simulation and sampling–based methods, estimating the full distribution of the model output via simulation; (4) Bayesian methods, which combine Bayes’ theorem and various simulation approaches to either estimate or update the probability distribution function of the parameters of the model and consequently estimate the uncertainty of the model output; (5) methods based on the analysis of the model errors, such as the meta-Gaussian approach described in Section 2.17.6.4; and (6) fuzzy-theory-based methods, providing a nonprobabilistic approach for modeling the kind of uncertainty associated with vagueness and imprecision. Whatever approach is chosen to uncertainty assessment, the end-user should be made fully aware of the assumptions and drawbacks of the method that is being used. The presence of subjectivity should be clearly stated and the limitations of the underlying hypotheses, both in the probabilistic and nonprobabilistic approaches, clearly described and discussed. An appropriate terminology should also be used to make the meaning of the provided confidence bands clear. Whenever a

Uncertainty of Hydrological Predictions

subjective method is adopted, the user should be made aware that the uncertainty bands reflect user belief instead of providing a frequentist assessment of the probability of the true value to fall between them. Appropriate use of the methods being proposed by the scientific community, depending on the user needs and data availability, would allow us to successfully reach a better communication between scientists and end-users. It is as important to communicate uncertainty as communicate the assumptions on which an assessment has been based.

2.17.6 Assessment of the Global Uncertainty of the Model Output Assessment of the global uncertainty for the model output is by far the application that is most frequently presented by the hydrological literature, as a means for quantifying model reliability and providing end-users with operational indications. Several methods are available to this end, ranging from statistically based to subjective approaches.

2.17.6.1 Analytical Methods The most direct method to assess the uncertainty of a system output is to derive its statistics from a knowledge of the statistical properties of the system itself and the input data (Langley, 2000). However, this approach may be limited by two main problems: first, the derivation of the statistics of the output can imply significant mathematical and numerical difficulties; and, second, the statistical properties of the system and the input may not be known in detail. The first difficulty has stimulated the development of a first type of uncertainty assessment technique, namely, the approximate analytical methods. An example is the asymptotic reliability analysis, like the first-order reliability method (FORM) and second-order reliability method (SORM). Examples of applications in hydrology are given by Melching (1992) and Vrugt and Bouten (2002). Point estimate methods are an interesting option too, in view of their computational efficiency (Tsai and Franceschini, 2005). The second problem mentioned above may be even more difficult to deal with. For instance, the definition of the statistics of the system is a delicate step of the uncertainty assessment method recently proposed by Huard and Mailhot (2006) in a hydrological context.

2.17.6.2 The Generalized Likelihood Uncertainty Estimation GLUE was introduced by Beven and Binley (1992), who were inspired by the generalized sensitivity analysis methodology proposed by Spear and Hornberger (1980). GLUE rejects the concept of an optimum model and parameter set and assumes that, prior to input of data into a model, all models and parameter sets have an equal likelihood of being acceptable. The acceptance of the existence of multiple likely models has been called equifinality (Beven, 1993) to suggest that this should be accepted as a generic problem in hydrological modeling rather than simply

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reflecting the problem of identifying the true model in the face of uncertainty. GLUE is performed by first selecting different modeling options (different hydrological models and different parameters). In order to reduce the computational requirements of the procedure, it might be necessary to limit the dimension of the sample space of the parameters and models. Then, a high number N of simulation is generated by sampling the model and parameter spaces accordingly to a prior probability distribution. In the absence of prior knowledge, uniform sampling can be used. By increasing N one increases the probability of trying all of the most relevant solutions. The different models are then run for each of the parameter sets and the model output is then compared to a record of observed data (e.g., for observed hydrographs or annual maximum peak flows, see Cameron et al. (1999); another interesting example is given by Blazkova and Beven (2009)). The performance of each trial is assessed via likelihood measures, either formal or informal. This includes rejecting some parameter sets as nonbehavioral. For instance, the Nash and Sutcliffe (1970) efficiency can be used as informal likelihood measure of the simulation of a continuous hydrograph. All parameter sets that lead to obtaining an efficiency above a subjective threshold are retained. Finally, likelihood weighted uncertainty bounds are calculated depending on the likelihood (Freer et al., 1996). For instance, the calculated likelihoods can be rescaled to produce a cumulative sum of 1.0, thereby obtaining informal weights. A cumulative distribution function of simulated discharges is then constructed using the rescaled weights. Linear interpolation is used to extract the discharge estimates corresponding to cumulative probabilities of a/2, 0.5, and 1.0  a/2. This allows 100(1  a)% uncertainty bounds to be derived, in addition to a median simulation. If either (or both) the likelihood measure or the procedure for computing the rescaled weights is informal, the probabilities computed with GLUE do not possess the classical frequentist meaning. Therefore, strictly speaking, it is inappropriate to refer to them with the term probability and many authors classify GLUE as a nonprobabilistic approach. Conversely, if formal statistical procedures are used, GLUE assumes the behavior of a probabilistic methodology. For extended discussions, the reader is referred to Beven et al. (2008) and Stedinger et al. (2008). GLUE could be applied in principle even in the absence of observed historical data, in those real-world applications in which the likelihood measure is estimated on the basis of expert knowledge. GLUE is highly computationally demanding, especially if the number of significant model parameters is high. This problem may prevent the application of GLUE when dealing with complex models. Beven (2006a) formally introduced a different procedure for the identification of behavioral models, by following previous practical experiments by Pappenberger and Beven (2004) and Page et al. (2007). A recent interesting application is presented by Liu et al. (2009). In this approach, limits of acceptability are preliminarily identified for the model output or selected performance measures. All the models that meet the limits of acceptability are retained so that an envelope of behavioral model simulations can be identified. Finally, a likelihood weighted cumulative density function for the

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hydrological model and aims at estimating the global uncertainty of the forecast, which is considered to be caused by: (1) precipitation uncertainty, which is dominant and quantified by the probability distribution of the future rainfall specified by the PQPF and (2) hydrologic uncertainty, which is the aggregate of all uncertainties arising from sources other than precipitation uncertainty. In particular, it aggregates the model uncertainty and parameter uncertainty. The BFS has three structural components: the precipitation uncertainty processor (PUP; Kelly and Krzysztofowicz, 2000), the hydrologic uncertainty processor (HUP; Krzysztofowicz and Kelly, 2000), and the integrator (INT; Krzysztofowicz, 2001b). Figure 2 reports a sketch of the BFS structure adapted from Krzysztofowicz (2002). The PUP has the purpose of mapping precipitation uncertainty to output uncertainty under the hypothesis that there is no hydrologic uncertainty. This involves running the hydrological model for a set of specified quantiles of the probability distribution of the future rainfall. The HUP quantifies hydrologic uncertainty under the hypothesis that there is no precipitation uncertainty. Finally, the INT integrates the two uncertainties in order to produce a PRSF. For extended details on the PUP and INT, the interested reader is invited to refer to Kelly and Krzysztofowicz (2000) and Krzysztofowicz (2001b, 2002). Next, we provide a brief description of the HUP for the purpose of illustrating the meta-Gaussian approach adopted by BFS. Let hn denote the true river stage on day n, counting from day n ¼ 0 when the forecast is issued. At the forecast time the actual river stage on day n is unknown and thus uncertain.

model output can be computed as previously in GLUE so that simulation quantiles can be estimated (see also Blazkova and Beven, 2009). There are many other variants of GLUE; for example, Tolson and Shoemaker (2008) and Mugunthan and Shoemaker (2006) combined optimization methods with a nonprobabilistic GLUE-like approach to increase computational efficiency of nonprobabilistic uncertainty analysis. The hydrological literature presented many applications to GLUE to numerous hydrological problems, including rainfall– runoff modeling (Cameron et al., 1999), groundwater modeling (Christensen, 2003), inundation modeling (Aronica et al., 1998, 2002), and urban water-quality modeling (Freni et al., 2008, 2009).

2.17.6.3 The Bayesian Forecasting System The Bayesian Forecasting System (BFS) was proposed by Krzysztofowicz (1999, 2001a, 2002), Krzysztofowicz and Kelly (2000), and Krzysztofowicz and Herr (2001). The purpose is to produce a probabilistic river stage forecast (PRSF) based on a probabilistic quantitative precipitation forecasting (PQPF) as an input to a hydrological model that is in charge of simulating the response of a river basin to precipitation. It can be adapted to produce a probabilistic river discharge forecast. The BFS assumes that the dominant source of uncertainty derives from the imperfect knowledge of the future precipitation, so that it can be assumed that all other sources of uncertainty play a minor role. The system can work with any

PQPF

Deterministic inputs

Deterministic hydrologic model

Precipitation amounts Model river stages

Precipitation uncertainty processor (PUP)

Hydrologic uncertainty processor (HUP) Marginal distributions

Output distribution parameters Posterior distribution parameters Observed river stage Integrator INT Real-time processing

PRSF

Prior simulation Figure 2 Sketch of the Bayesian forecasting system. PQPF and PRSF are probabilistic quantitative precipitation forecasting and probabilistic river stage forecasting, respectively. Adapted from Krzysztofowicz R (2002) Bayesian system for probabilistic river stage forecasting. Journal of Hydrology 268: 16–40.

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Therefore, it is treated as a random variable which we refer to with the symbol Hn. Let sn be an estimate of Hn from the hydrologic model based on all the input variables and the true precipitation amount. Estimate sn is treated as a realization of the random variable Sn. One would observe hn ¼ sn if there were no hydrologic uncertainty (let us remark that HUP is developed under the assumptions that there is no precipitation uncertainty). The presence of hydrologic uncertainty gives rise to a probability distribution of the actual river stage Hn, conditional on a realization of the model river stage Sn ¼ sn. Therefore, we can treat Hn as a random variable whose probability distribution is conditioned on the corresponding realization of the model river stage Sn ¼ sn. The purpose of the HUP is to provide an estimate for such probability distribution. This is achieved by applying a Bayesian technique. First of all, Hn and Sn are transformed to the Gaussian variables Wn and Xn respectively, by applying the standard normal quantile transform (NQT; see Kelly and Krzysztofowicz, 1997) and assuming that all conditional and joint densities are Gaussian. Then, it is assumed that the actual river stage process is well represented by a Markov stochastic process of order 1 and strictly stationary. This allows one to derive the prior probability distribution gn(wn|w0) of wn conditional on W0 ¼ w0. The prior density is derived under the assumption that the following normal linear equation applies in the transformed domain:

Wn ¼ rWn1 þ Yn

ð1Þ

where r is a parameter and Yn is a random variable stochastically independent of Wn1 and normally distributed with mean zero and variance s2(Yn). Therefore, the probability distribution of wn is Gaussian with mean equal to rwn1 and variance s2(Yn). The recursive application of the above derivation allows one to estimate gn(wn|w0). Subsequently, a probability distribution of the normalized model river stage xn conditioned on wn and w0 is built, and is denoted as fn(xn|wn, w0). This is derived under the hypothesis that the stochastic dependence between the transformed variables is governed by the following normal linear equation:

Xn ¼ an Wn þ dn W0 þ bn þ Fn

ð2Þ

in which an, bn, and dn are parameters and Fn is a variate stochastically independent of (Wn,W0) and normally distributed with mean zero and variance s2(Fn). It follows that the probability distribution fn(xn|wn, w0) is Gaussian with mean and variance (see Krzysztofowicz and Kelly, 2000):

EðXn jWn ¼ wn ; W0 ¼ w0 Þ ¼ an wn þ dn w0 þ bn

ð3Þ

VarðXn jWn ¼ wn ; W0 ¼ w0 Þ ¼ sn 2 ðFÞ

ð4Þ

The coefficients r, an, bn, and dn are derived by running the hydrological model for an extended record of the model input with a perfect forecast of precipitation amount, thus obtaining joint realizations of the model-actual river stage process that are transformed to the Gaussian probability distribution through the NQT. These joint realizations are used to estimate

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the parameters of the above regressions (1) and (2). This design of the analysis assures that there is no precipitation uncertainty, but only hydrologic uncertainty. Once gn(wn|w0) and fn(xn|wn, w0) are known, the total probability law allows one to derive the distribution kn(xn|w0) of the transformed model river stage conditioned on w0, while the Bayes theorem allows one to derive the posterior density of wn conditioned on xn and w0, namely:

rðwn jxn ; w0 Þ ¼

f n ðxn jwn ; w0 Þgn ðwn ; w0 Þ kn ðxn jw0 Þ

ð5Þ

where

kðxn jw0 Þ ¼

Z

N

f n ðxn jwn ; w0 Þgn ðwn jw0 Þdwn

ð6Þ

N

Finally, the inverse of the NQT allows one to derive the posterior density of hn conditioned on sn and h0. Such distribution allows one to quantify the hydrologic uncertainty. More details are provided by Krzysztofowicz and Kelly (2000). Despite a theoretical development that may appear complicated, the BFS has the advantage of being easy to apply and allowing rapid implementation in real time. However, it was conceived for estimating the uncertainty of forecasted variables only.

2.17.6.4 Techniques Based on the Statistical Analysis of the Model Error Several methods for uncertainty assessment were proposed based on the statistical analysis of the model error. Accordingly, the model error is treated as a stochastic process for which realizations are obtained by performing off-line simulations which are matched with the corresponding observations. Of course, observed data are themselves uncertain and therefore the model reliability analysis could not be correct in absolute terms (in the ideal situation of a perfect model, if we compared its response with uncertain output observations, that we assumed to be correct, we would wrongly conclude that the model is uncertain). However, in any case, from a practical point of view, the difference between the model response and what we measure in the field gives an important information for the sake of inferring reality based on the model output (Refsgaard et al., 2006). A technique for global uncertainty assessment based on the analysis of the model error is the meta-Gaussian approach proposed by Montanari and Brath (2004) for the case of hydrological simulations and extended by Montanari and Grossi (2008) for hydrological forecasting. Next, the latter methodology is presented, therefore making reference to realtime flood forecasting systems. The meta-Gaussian approach is probabilistic. In order to estimate the uncertainty of a hydrological forecast, it is assumed that the forecast error is a stationary and ergodic stochastic process, denoted with the symbol E(t). Its statistical properties are inferred by analyzing a past realization eobs(t) ¼ Qobs(t)  Qpred(t) that it is assumed to be available, where Qobs(t) and Qpred(t) are true and forecasted river flows, respectively. The use of a meta-Gaussian model is

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then proposed to derive the time-varying probability distribution of the forecast error. In particular, the probability distribution of E(t) is inferred on the basis of its dependence on M selected explanatory random variables. The statistical inference is performed in the Gaussian domain, by preliminarily transforming E(t) and the explanatory variables to the Gaussian probability distribution. The above transformation is operated through the NQT. The probabilistic model for E(t) is built as follows. First of all, it is assumed that positive and negative errors come from two different statistical populations E(þ)(t) and E()(t). Therefore, the probability model for E(t) is given by a mixture of two probability distributions, one for E(þ)(t) and one for E()(t). The mixture is composed such that the area of the probability distribution of E(þ)(t) is equal to the percentage, P(þ), of positive errors over the total sample size of the available past realization eobs(t) of the forecast error. The two realizations e ðþÞobs ðtÞ and e ðÞobs ðtÞ are transformed through the NQT, therefore obtaining the normalized realizations Ne ðþÞobs ðtÞ and Ne ðÞobs ðtÞ. Then, M explanatory variables, X(i)(t) with i ¼ 1,y, M (which should be readily available at the forecast time), are selected in order to explain the variability in time of the marginal statistics of E(þ)(t) and E()(t). The values of such explanatory variables for the realizations e ðþÞobs ðtÞ and e ðÞobs ðtÞ above are estimated and then transformed by using the NQT, therefore obtaining the normalized explanatory variables Nx ðiÞobs ðtÞ with i ¼ 1,y, M. In the Gaussian domain, it is assumed that the forecast error can be expressed as a linear combination of the selected explanatory variables. Let us focus on the positive error. The linear combination can be expressed through the following relationship: ðþÞ

ðþÞ

Ne ðþÞ ðtj Þ ¼ C1 Nxð1Þ ðtj Þ þ C2 Nxð2Þ ðtj Þ ðþÞ

þ ? þ CM NxðMÞ ðtj Þ þ eðþÞ ðtj Þ

ð7Þ

where e ðþÞ ðtj Þ is an outcome of a homoscedastic and Gaussian random variable and tj is an assigned time step. An analogous relationship holds for Ne()(t). It is assumed that positive and negative errors are conditioned by the same explanatory variables, but the fit of the linear regression (7) leads to a different set of coefficient values. Such coefficients are estimated by inserting in (7) the past realizations of transformed (i) forecast error, Ne(þ) obs (t), and explanatory variables, Nxobs(t), and then by identifying the coefficient values that lead to the best fit (for instance by minimizing the sum of the squares of e ðþÞ ðtj Þ). The goodness of the fit provided by (7) can be verified by drawing a normal probability plot and a residual plot for e ðþÞ ðtj Þ as in Montanari and Brath (2004). Once the linear regression (7) has been calibrated, for positive and negative errors, the probability distribution of the transformed positive forecast error can be easily derived for real-time and real-world applications. Such distribution is Gaussian and is expressed by the following relationship:

Ne ðþÞ ðtj ÞB G½m½NeðþÞ ðtj Þ; s½NeðþÞ ðtj Þ

ð8Þ

where ‘B’ means equality in probability distribution and G indicates the Gaussian distribution whose parameters

are given by ðþÞ

ðþÞ

m½Ne ðþÞ ðtj Þ ¼ C1 Nxð1Þ ðtj Þ þ C2 Nxð2Þ ðtj Þ ðþÞ

þ ? þ CM NxðMÞ ðtj Þ s½Ne ðþÞ ðtj Þ ¼ s½eðþÞ ðtj Þ

ð9Þ ð10Þ

Analogous relationships (from (8) to (10)) hold for the negative error. Therefore, the confidence bands for the transformed forecast and an assigned significance level can be straightforwardly derived. In detail, the upper confidence band of the transformed forecast at the a significance level is given by the 1  aX(2  P(þ)) quantile of the Gaussian distribution given by (8), (9), and (10). Given that P(þ) can be arbitrarily close to 0, in the technical computation one may obtain values greater than 1 of aX(2  P(þ)). This means that the probability of getting a positive forecast error is small enough to make equal to 0 the width of the upper confidence band at the a significance level. For instance, if P(þ) ¼ 0.5 and a ¼ 10%, the transformed upper confidence band is given by the well-known relationship: ðþÞ

Ne90% ðtj Þ ¼ m½NeðþÞ ðtj Þ þ 1:96s½NeðþÞ ðtj Þ

ð11Þ

Finally, by applying back the NQT one obtains the confidence bands for the assigned significance level in the untransformed domain. The reason why positive and negative errors are treated separately is that a good fit is frequently not achieved through the linear regression (7) when the errors are pooled together. In fact, in this case, it appears that the NQT is not effective in making the errors homoscedastic and therefore the assumption of linearity does not hold. The reason for this result is that the NQT is not efficient in assuring homoscedasticity if the mean of the model error is not significantly changing across the range of the error itself, as it often happens when dealing with hydrological models. By treating positive and negative errors separately, the problem disappears and the assumptions of the linear regression are met. Finally, it is important to note that the only assumption made about the sign of the future forecast error is that it has a probability equal to P(þ) to be positive. Therefore, no inference is made on the sign of the forecast error on the basis of the explanatory variables. Figure 3 shows the confidence bands computed with the meta-Gaussian approach for the forecast with 1-h lead time of two flood events occurred on the Toce River at Candoglia, in Italy.

2.17.6.5 Bayesian Model Averaging Bayesian model averaging (BMA, Hoeting et al., 1999) is a statistical way of postprocessing model output ensembles to derive predictive probability density functions for hydrological variables. It represents the predictive probability distribution as a weighted average of the individual predictive probabilities of each model, where the weights are posterior probabilities of the models themselves and reflect the models’ relative contributions to predictive skill over a training period. The combination of multiple models is an important component of

Uncertainty of Hydrological Predictions

Observed Simulated 95% confidence bands

Observed Simulated 95% confidence bands

1200

2400 River discharge (m3 s−1)

River discharge (m3 s−1)

471

1000 800 600 400 200 0

2000 1600 1200 800 400 0

0

20

40

60

80

100

120

140

Hours from 0.00 of 3 November, 1994

0

10

20

30

40

50

60

70

Hours from 14.00 of 19 September, 1999

Figure 3 95% confidence bands computed with the meta-Gaussian approach for the forecast with 1-h lead time of two flood events occurred on the Toce River at Candoglia, on 3 November 1994 (left) and 19 September 1999 (right). From Montanari A and Grossi G (2008) Estimating the uncertainty of hydrological forecasts: A statistical approach. Water Resources Research 44: W00B08 (doi:10.1029/2008WR006897).

model validation (Burnham and Anderson, 2002). Multimodeling solutions are often applied in real time forecasting (ensemble forecasting). See, for instance, the activity carried out in the framework of the HEPEX Project (Franz et al., 2005; Zappa et al., 2008). BMA is applied in a Bayesian framework. Let M ¼ {Mi; i ¼ 1,2,y, N} be a set of N hydrological models for obtaining the vector zˆ of hydrological variables. Given a set of data, D, the posterior probability Pr(zˆ|D) of zˆ is obtained through the BMA according to the law of total probability:

PrðˆzjDÞ ¼ EM ½PrðˆzjMi ; DÞ ¼

N X

PrðˆzjMi ; DÞPrðMi jDÞ ð12Þ

i¼1

where Prðˆz; DÞ is the posterior probability of zˆ for the given data set D, PrðˆzjMi ; DÞ is the posterior probability of zˆ for given data set D and model Mi, PrðMi ; DÞ is the posterior model probability for model Mi, and EM is the expectation operator over simulation models. Essentially, Equation (12) says that the probability distribution given by the model ensemble for the output variable is a weighted mixture of the individual distributions given by each model, where the weights are the posterior model probabilities. Therefore, Equation (12) presupposes that individual probability distributions for the output from each model, conditioned on the model itself and the available data set, are available. According to Bayes’ rule, the posterior model weight is

PrðDjMi ÞPrðMi Þ PrðMi ; DÞ ¼ PN i¼1 PrðDjMi ÞPrðMi Þ

ð13Þ

where PrðDjMi Þ is the marginal model likelihood function for model Mi, Pr(Mi) is the prior model probability for model Mi, P and p PrðMi Þ ¼ 1. A uniform distribution can be assumed for the priors if better information is not available. Equation P (13) implies the total model weight p PrðMi jDÞ ¼ 1. The marginal model likelihood function PrðDjMi Þ plays an important role in the determination of the degree of importance for each model, given the same data set. For noninformative

model priors, higher posterior model weights reflect better agreement between results and observed data. According to Equation (12), the law of total expectation allows one to obtain the means of the predicted zˆ over the models for given data D:

EðˆzjDÞ ¼ EM ½EjˆzjMðpÞ ; D ¼

X

E½ˆzjMðpÞ ; DPrðMðpÞ jDÞ ð14Þ

p

where E is the expectation operation over zˆ. Analogous relationships allow one to obtain the covariance matrix of the predicted zˆ , therefore allowing a quantification of uncertainty. For more details, and an application that refers to the prediction of groundwater heads, the reader is referred to Li and Tsai (2009). There are plenty of applications of BMA in hydrology (see, for instance, Ajami et al. (2006), Duan et al. (2007), Zhang et al. (2009), Reggiani et al. (2009), and Li and Tsai (2009)). Figure 4 shows confidence bands computed with BMA for simulations of river flows obtained with the soil and water assessment tool (SWAT) model in the Yellow River Headwater Basin (from Zhang et al., 2009) BMA tends to be computationally demanding and relies heavily on prior information about models. Neuman (2003) proposed a maximum likelihood version (MLBMA) of BMA to render it computationally feasible and to allow dealing with cases where reliable prior information is lacking (Ye et al., 2004). BMA is also used within the Integrated Bayesian Uncertainty Estimator (IBUNE) proposed by Ajami et al. (2007).

2.17.6.6 Machine Learning Techniques In the recent past, there has been an increased interest about machine learning technique for global uncertainty assessment (see, for instance, Shrestha et al., 2009; Solomatine and Shrestha, 2009; Hall and Solomatine, 2008). These methods are frequently used as a mean to approximate complex models for uncertainty assessment, therefore obtaining a less computationally intensive approach.

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Uncertainty of Hydrological Predictions 3000

66.7% interval Observed data

2000

Streamflow (cm s)

1000 0 3000 2000

5

10 15 20 25 30 35 40 45 50 55 60 90% interval Observed data

1000 0

5 10 15 20 25 30 35 40 45 50 55 60 Monthly flow from January 1986 to December 1990

Figure 4 Confidence bands computed with BMA for simulations of river flows obtained with the SWAT model in the Yellow River Headwater Basin. From Zhang X, Srinivasan R, and Bosch D (2009) Calibration and uncertainty analysis of the SWAT model using genetic algorithms and Bayesian model averaging. Journal of Hydrology 374: 307–317.

Machine learning is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Machine learning techniques include, among others, approaches that have been widely used in hydrology, such as neural networks, nearest-neighbor methods, and statistical methods.

2.17.7 Assessment of Data Uncertainty Among the sources of uncertainty in hydrology, data uncertainty is often believed to play a marginal role. Accordingly, only a few attempts have been made to quantify the effects of uncertainty in observations on hydrological modeling (see, for instance, Clarke, 1999). Different types of observations are currently used in hydrological modeling. The most common applications usually refer to precipitation as input and river flows as output data, although very often solar radiation, temperature, wind speed, soil moisture, groundwater levels, geomorphological features, land use, and others are also employed. Some of the above variables are affected by a limited uncertainty with respect to the others. In particular, uncertainty in precipitation and river flow is often considered to be dominant, because of the spatial variability of rainfall and snowfall on the one hand, and the errors in the determination of the rating curve on the other. The presence of uncertainty in input and output data induces two types of problems to hydrologists: the first is related to its estimation (to what extent the observed data are uncertain?), whereas the second is connected to accounting for such uncertainty in hydrological modeling.

2.17.7.1 Precipitation Uncertainty Hydrologists are well aware that a multitude of problems and research issues are related to precipitation uncertainty, which

are connected to precipitation monitoring and prediction (Chua and Bras, 1982; Gottschalk and Jutman, 1982). Precipitation monitoring is carried out through direct measurements (raingauges and snowgauges) or remote sensing (satellite, radar, and microsensors). The uncertainty of gauge measurements is typically limited and therefore the estimation error of the precipitation field is mainly induced by spatial variability. When remote-sensed data are used, spatial variability is generally better estimated but the uncertainty in point measurements is relevant. There is a large body of literature about uncertainty assessment for precipitation, starting from the pioneeristic work of Thiessen (1911). Uncertainty of gauge measurements of precipitation has been the subject of numerous case studies (see, for instance, Morrissey et al., 1995; Brath et al., 2004). These studies proved that the estimation error of mean areal precipitation significantly depends on the climatic conditions, the spatial structure of the precipitation itself, the morphology of the catchment, and the gauging network. The task of quantifying remotely sensed precipitation uncertainty has proved to be difficult. A fundamental problem is the lack of a term of comparison (Habib and Krajewski, 2002). Numerous studies compared remotely sensed and gauged data and showed significant disagreement. For instance, Austin (1987) found that for individual storms, radar and raingauge measurements can differ of a factor of 2 or more. In a more recent investigation, Brandes et al. (1999) found that radar-to-gauge ratios of storm totals were in the range of about 0.7–1.9. These differences become even more significant when satellite versus raingauge comparisons are carried out. In general, estimating precipitation uncertainty is a difficult task and no general rule exists. Integrating different monitoring techniques is certainly a potentially valuable solution. Turning to the purpose of accounting for precipitation uncertainty in hydrological modeling, different methods were proposed by the literature. The BFS described in Section 2.17.6.3 is a relevant example. GLUE can be applied as well, by introducing an input error model and then generating many different realizations of the input data themselves, with which one can derive a likelihood weighted model output. Kavetski et al. (2002, 2006a, 2006b) introduced the Bayesian total error analysis (BATEA), that is, a method for explicitly accounting for sampling and measurement uncertainty in both input and output data. In view of the inability to build a formal and sufficiently representative input error model in many real-world applications, BATEA is based on the use of vague error models, with the awareness that such an approach can cause a degeneration of the reliability of the inference equations. The basic working hypothesis of BATEA consists of assuming that the input uncertainty is multiplicative Gaussian and independent of each storm, even though its general framework allows alternative uncertainty models. The multiplier approach assumes that the storm depth is the only quantity in error, whereas the rainfall pattern is correct up to a multiplicative constant mi, such that d0i ¼ mi di , where d0i and di are the observed and true precipitation depths for the ith storm. Accordingly, the parameter vector of the hydrologic model is extended to include the parameters of the uncertainty

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models. Parameter inference is then carried out within a Bayesian framework, which requires indentifying a prior distribution of the model parameters that is subsuquently updated by using Bayes’ theorem in view of the available observations. The above treatment of input uncertainty implies that the dimensionality of the parameter vector is increased with latent variables, whose number depends on the sample size of the observed data (and the type of error model assumed). Moreover, if both the input and the output data are observed with large uncertainty, the utility of any parameter estimation methodology becomes questionable. Finally, one might be concerned that rainfall multipliers can possibly interact with other sources of error and therefore separation of errors in BATEA is conditional on other sources of uncertainty. For instance, classic underprediction by a hydrological model after a long dry period can be compensated by increasing the rainfall multiplier. A similar treatment for precipitation uncertainty is used in IBUNE (Ajami et al., 2007).

2.17.7.2 River Discharge Uncertainty Pelletier (1987) reviewed 140 publications dealing with uncertainty in the determination of the river discharge, thereby providing an extensive summary. He referred to the case where the river discharge at a given cross section is measured by using the velocity–area method, that is,

Q obs ðtÞ ¼ AðtÞ  vðtÞ

ð15Þ

where t is the sampling time, Q obs ðtÞ the measured river discharge, A(t) the cross-sectional area of the river, and v(t) the velocity of the river flow averaged over the cross section. Errors in Q obs ðtÞ are originated by uncertainties in both A(t) and v(t), which in turn are originated by uncertainty in the current meter, variability of the river flow velocity over the cross section, and uncertainty in the estimation of the cross-section geometry. Pelletier (1987) highlighted that the overall uncertainty in a single determination of river discharge, at the 95% confidence level, can vary in the range (8–20%), mainly depending on the exposure time of the current meter, the number of sampling points where the velocity is measured, and the value of v(t). Another interesting contribution was provided by the European ISO EN Rule 748 (1997) that quantified the expected errors in the determination of the river discharge with the velocity–area method. The conclusions were similar to those of Pelletier (1987). In some cases, including the usual practice in many countries of Europe, river discharge values are estimated by using the rating curve method, which is very easy to apply. According to the rating curve method, observations of river stage are converted into river discharge by means of a rating curve, which is preliminarily estimated by using observations collected using the velocity–area method. Hence, an additional error is induced by imperfect estimation of the rating curve. Di Baldassarre and Montanari (2009) proposed a model for estimating the error affecting river flow observations derived by the rating curve method. The model aims at taking into account the main sources of uncertainty within a

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simplified approach. The most important assumptions underlying the model are as follows. (1) The uncertainty induced by imperfect observation of the river stage is negligible. This is consistent with the fact that these errors are usually very limited (around 1–2 cm; e.g., Schmidt, 2002; Pappenberger et al., 2006) and therefore of the same order of magnitude as standard topographic errors. (2) The geometry of the river is assumed to be invariant, which means that the rating curve changes in time only because of seasonal variation of roughness (see below). This assumption has been made because the uncertainty induced by possible variations of the river geometry is heavily dependent on the considered case study and no general rule can be suggested. However, it is worth noting that, using this assumption, the study neglects one of the most relevant sources of uncertainty that may affect river discharge observations where relevant sediment transport and erosion processes are present. (3) Uncertainty in the rating curve derives from the following causes: errors in the river discharge measurements that are used to calibrate the rating curve itself; interpolation and extrapolation error of the rating curve; unsteady flow conditions; and seasonal changes of roughness. The uncertainty affecting the river discharge measurements was estimated by Di Baldassarre and Montanari (2009) according to the guidelines reported by the European ISO EN Rule 748 (1997), which lead to an estimate of about 5–6% when the measurements are collected in ideal conditions. This outcome matches the indications reported in Leonard et al. (2000) and Schmidt (2002). The remaining sources of uncertainty were evaluated by Di Baldassarre and Montanari (2009) by developing a numerical simulation study for a 330-km reach of the Po River (Italy). The study focused on 17 cross sections and found that the estimation of river discharge using the rating curve method is affected by an increasing error for increasing river discharge values. At the 95% confidence level, the error ranges from 6.2% to 42.8% of the observation, with an average value of 25.6%. Furthermore, the uncertainty induced by the extrapolation of the rating curve is dominating the other errors in high flow conditions. In fact, previous contributions in hydrology (e.g., Rantz et al., 1982) do not recommend extrapolating rating curves beyond a certain range. Nevertheless, several hydrological applications are unavoidably based on flood flow observations (e.g., calibration and validation of rainfall–runoff models, flood frequency analysis, and boundary conditions of flood inundation models) and therefore one needs to extrapolate the rating curve beyond the measurement range (Pappenberger et al., 2006). The above analysis proved that river flow uncertainty can indeed be very significant and therefore should be accounted for in practical applications. An interesting opportunity is offered by the application of GLUE according to the limits of acceptability concept (Blazkova and Beven, 2009). Once the uncertainty in the river flows is estimated, it is possible to fix limits of acceptability for the observed river flows, and the models that do not respect them can be rejected as nonbehavioral. The collection of the behavioral outputs allows the user to obtain an envelope of likely model simulations. The above approach is nonprobabilistic.

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2.17.8 Assessment of Parameter Uncertainty Calibration in hydrology is increasingly done automatically, while manual calibration (through trial and error procedures) is used only when dealing with complex models requiring high computational costs. Parameter calibration techniques lead to either a single solution or multiple solutions (i.e., parameter sets). The approaches leading to a single solution are basically optimization problems, while techniques leading to multiple likely solutions can serve as tools for uncertainty assessment. Many search algorithms have been successfully devised and applied to automatically find the optimal parameter set for hydrological models, which can be subdivided into local, global, and hybrid search techniques (Duan et al., 1992; Mugunthan and Shoemaker, 2006; Tolson and Shoemaker, 2008; Thyer et al., 2009; Tonkin and Doherty, 2009). Approaches for multiple-solution parameter estimation can be broadly divided into two categories: importance sampling and Markov chain Monte Carlo (MCMC) sampling (Kuczera and Parent, 1998). With this approach full parameter distributions rather than simple point estimates can be obtained. Methods based on importance sampling aim to identify a set of behavioral model parameter configurations according to a selected objective function. Then, parameter distributions are estimated using a weighted combination of the behavioral parameter sets. GLUE is perhaps the most-used method based on importance sampling. MCMC parameter estimation incorporates importance sampling into a procedure for evaluating conditional probability distributions. Prior parameter distributions are selected (for instance, by assigning a uniform distribution or a distribution derived through expert knowledge) and the sampler evolves them into posterior distributions that are estimated by using the observed data. Thus, multiple-solution approaches can be used to assess parameter uncertainty. A relevant example within this respect is the SCEM-UA algorithm by Vrugt et al. (2003a). Once the uncertainty in the parameters is known, simulation approaches can be applied to estimate the related uncertainty induced in the model output. An example is given by Thorsen et al. (2001) who assessed the uncertainty in simulations of nitrate leaching induced by using model parameters obtained from databases at the European level. End-users frequently experience the case where multiple or competing objectives need to be optimized. According to this need, numerous multiobjective optimization algorithms have been devised, with numerous developments in the recent past (see, for instance, Zhang et al., 2008). Relevant examples are the MOSCEM-UA and AMALGAM methods (Vrugt et al., 2003b; Vrugt and Robinson, 2007). These two methods are briefly described in the following.

2.17.8.1 The MOSCEM-UA Method Multiobjective calibration problems can be dealt with by defining more than one optimization criteria (objective functions) that correspond to different performance measures of the selected model. Then, a multicriteria optimization method can be used to identify the set of nondominated, efficient, or Pareto optimal solutions (Gupta et al., 1998). The

Pareto solutions represent tradeoffs among the different performance measures that are often conflicting. As such, moving from one solution to another results in the improvement of one objective and deterioration in one or more others. A simple way to deal with multiobjective calibration is to weigh the different criteria into a single objective function and to run a large number of independent single-criteria optimization runs using different values for the weights (Madsen, 2000). This method is simple to implement, but has the drawback that a complete single-objective optimization is to be solved to obtain each discrete Pareto solution. Moreover, maintaining the independence of the various criteria will allow the user to analyze the tradeoffs among the different criteria, therefore enabling an improved understanding of the limitations of the model structure. MOSCEM-UA (Vrugt et al., 2003b) is an effective and efficient MCMC sampler, which is capable of generating a fairly uniform approximation of the Pareto frontier within a single optimization run. The algorithm is closely related to the SCEM-UA algorithm (Vrugt et al., 2003a). In addition, MOSCEM-UA uses a newly developed, improved concept of Pareto dominance, thereby also containing the single-criteria solutions at the extremes of the Pareto solution set. For more details, the interested reader is invited to refer to Vrugt et al. (2003b). The ensemble of the models lying on the Pareto frontier allows the user to identify an envelope of model outputs corresponding to the nondominated solutions.

2.17.8.2 The AMALGAM Method AMALGAM (Vrugt and Robinson, 2007) is a follow-up of MOSCEM-UA and is specifically designed to take full advantage of the power of distributed computer networks. AMALGAM runs multiple different search strategies simultaneously for population evolution and adaptively updates the weights of these individual methods based on their reproductive success. This ensures a fast, reliable, and computationally efficient solution to multiobjective optimization problems.

2.17.9 Assessment of Model Structural Uncertainty Model structural uncertainty is induced by inadequateness of the hydrological model to represent the hydrological system. This situation is also characterized by reduced model identifiability, because the imperfectness of the modeling solutions makes many of them potentially suboptimal, regardless of the different values of the selected performance measure. In the presence of model, structural uncertainty a performance measure becomes less effective and therefore the highest of its value does not necessarily identify the best model. For instance, a performance measure that lays emphasis on floods may be biased toward a (imperfect) model that could not be as reliable in reproducing the low flows. This is the reason why multiobjective calibration is frequently applied in hydrology. A statistical and rigorous evaluation of model structural uncertainty is not possible in practical hydrological applications, at least because it should necessarily be performed with perfect data. The literature proposed approximate

Uncertainty of Hydrological Predictions

techniques for estimating the uncertainty in the model output induced by model structural uncertainty. The most popular of them are based on multimodel analysis. In fact, the variability of the response provided by different models, if other uncertainty sources are negligible, provides indications on the uncertainty induced by a wrong model structure. Multimodel analysis is based on the use of many different plausible models that may consider, for instance, alternative processes and alternative simplified approximations. An example of model combination is the BMA presented in Section 2.17.6.5. Another quantitative approach for performing multimodel application was presented by Burnham and Anderson (2002) and Ye et al. (2008). It is implemented by assigning performance scores and importance weights to each candidate model with which the ensemble of model outputs can be constructed basing on the importance of each model. Multimodel applications can be performed also by applying GLUE, with which different models can be considered and evaluated according to a single likelihood measure or one or more likelihood measures. An example of application of GLUE with different modeling solutions is provided by Rojas et al. (2009). From a practical point of view, the above techniques are often applied for assessing the global uncertainty in the model output instead of model structural uncertainty only, because it is impossible to carry out such techniques in the absence of data and parameter uncertainty. As such, the combination of different models with uncertain parameters and uncertain data bases does not allow one to separate the above sources of uncertainty, unless one makes heavy assumptions (see, e.g., the IBUNE method; Ajami et al. (2007); see also Clark et al. (2008)).

2.17.10 Uncertainty Assessment as a Learning Process Uncertainty assessment is an effective mean to quantitatively assess model reliability and therefore perform model diagnostic and evaluation. These latter, in turn, provide indications about the model ability to simulate hydrology at a given place and therefore about the correctness of our understanding of the hydrological processes at that place. Thus, uncertainty assessment plays a fundamental role in the learning process. In the past, the learning process was mainly linked to parameter estimation for a given model. The optimal parameter values, actually, provide information about the conditions of the system. Treating modeling more explicitly as a learning process allows one to follow a new approach to this problem based on a methodology that will link models, databases, and parameters with the areas of interest, thereby providing information on the dominant hydrological processes (see Beven (2007); applications are presented in Montanari et al. (2006), Fenicia et al. (2008), and Schoups et al. (2008)). This is part of the downward modeling approach that recently gained increased attention within the context of PUB. One of the most exciting future perspectives is the possibility to implement many different models as a process of

475

learning about specific places (Beven, 2007). The representation will be uncertain so that this learning process should be implemented within a framework of uncertainty estimation. Indeed, uncertainty estimation, providing quantitative information about model reliability, if coupled with a multimodel approach, could provide indications about the dominant hydrological processes and their dynamics. Within this framework it is also necessary to set up a mechanism for model rejection (e.g., the model providing the simulations presented in Figure 1 could be rejected because it is too biased). There is the potential problem that model rejection is not by default embedded in uncertainty assessment methods. In particular, it is not embedded in statistical approaches, which in many cases do not assess the motivation for the presence of uncertainty. Model rejection is often based on expert knowledge, which is subjective but indeed necessary in the context of a learning process (see, for instance, Merz and Blo¨schl (2008a, 2008b)). This implies that the use of statistical methods for uncertainty assessment in a learning process should be based on including in the statistical representation the available information about the underlying physical process (for an extended discussion, see Koutsoyiannis (2009)).

2.17.11 Conclusions Uncertainty assessment in hydrology is a relevant practical problem and still a research challenge. The limited extension of hydrological databases and the complexity of hydrological processes, whose dynamics and domains are to a great extent nonobservable, make the interpretation of the results of hydrological modeling studies not easy. The intense research activity recently done on uncertainty resulted in the development of many new techniques for uncertainty assessment, which differ in behavior and scope. It is essential to formally define a terminology and make clear the prerogatives of each method in order to make clear to end-users the meaning of uncertainty in hydrology and convey them a useful information. In order to provide a contribution to this end, we provide in Table 1 a brief summary of the most-used uncertainty assessment methods, including those presented here, by also providing an attempt of classification and by specifying their purpose. Uncertainty assessment in hydrology will represent a research challenge for a long time to come. Uncertainty is an inherent property of hydrological processes which in principle will not prevent gaining a much better understanding of how water flows downstream. Uncertainty in hydrology should not be viewed as a limitation to be eliminated but rather as a intrinsic feature that needs to be properly and objectively quantified, whenever possible, with scientific method, that is, through the collection of data by means of observation and experimentation, and the formulation and testing of hypotheses. Communicating uncertainty to end-users should not undermine their confidence in models (Beven, 2006b; Pappenberger and Beven, 2006; Faulkner et al., 2007), but rather increase it through an improved perception of the underlying natural processes and an increased awareness of model

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reliability. Uncertainty does not mean lack of knowledge or lack of modeling capability but that the predicted value of a hydrological variable is uncertain. A proper estimation of uncertainty is the way forward to a reliable hydrological design and therefore a proper management of the environment and water resources.

Acknowledgments The author is grateful to Demetris Koutsoyiannis, Jasper Vrugt, Keith Beven, Simone Castiglioni, an anonymous referee and the Editor Stefan Uhlenbrook for providing very useful comments on the text. The support of the Italian Government, through the National Research Project ‘‘Uncertainty estimation for precipitation and river discharge data. Effects on water resources planning and flood risk management’’ is also acknowledged.

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Relevant Websites http://education.mit.edu Gaussian Distribution. http://www.itia.ntua.gr Presentation: Hurst-Kolmogorov dynamics and uncertainity. http://www.agu.org Special issue on uncertainty.

2.18 Statistical Hydrology S Grimaldi, Universita` degli Studi della Tuscia, Viterbo, Italy S-C Kao, Oak Ridge National Laboratory, Oak Ridge, TN, USA A Castellarin, Universita` degli Studi di Bologna, Bologna, Italy S-M Papalexiou, National Technical University of Athens, Zographou, Greece A Viglione, Technische Universita¨t Wien, Vienna, Austria F Laio, Politecnico di Torino, Torino, Italy H Aksoy and A Gedikli, Istanbul Technical University, Istanbul, Turkey & 2011 Elsevier B.V. All rights reserved.

2.18.1 2.18.2 2.18.2.1 2.18.2.1.1 2.18.2.1.2 2.18.2.1.3 2.18.2.1.4 2.18.2.1.5 2.18.2.1.6 2.18.2.1.7 2.18.2.1.8 2.18.2.2 2.18.3 2.18.3.1 2.18.3.1.1 2.18.3.1.2 2.18.3.1.3 2.18.3.1.4 2.18.3.1.5 2.18.3.1.6 2.18.3.1.7 2.18.3.1.8 2.18.3.1.9 2.18.3.2 2.18.3.2.1 2.18.3.2.2 2.18.3.2.3 2.18.3.3 2.18.4 2.18.4.1 2.18.4.2 2.18.4.2.1 2.18.4.2.2 2.18.4.3 2.18.4.3.1 2.18.4.3.2 2.18.5 2.18.5.1 2.18.5.2 2.18.5.3 2.18.6 2.18.6.1 2.18.6.2 2.18.6.2.1 2.18.6.2.2 2.18.6.2.3 2.18.6.2.4 2.18.6.2.5

Introduction Analysis and Detection of Nonstationarity in Hydrological Time Series The Common Nonstationarity Analysis Methods Randomness test Detection of trend Simple regression on time Mann–Kendall test Spearman rank order correlation test Detection of shifts (segmentation) t-Test Mann–Whitney test A New Method of Segmentation Extreme Value Analysis: Distribution Functions and Statistical Inference Probability Distributions for Extreme Events Normal distribution Lognormal distribution Exponential distribution Gamma distribution Pearson type 3 distribution Log-Pearson type 3 distribution Extreme value distributions Generalized Pareto distribution Generalized logistic distribution Parameter Estimation Methods Method of moments Method of L-moments Method of the maximum-likelihood and Bayesian methods Model Verification: Goodness-of-Fit Tests IDF Curves Definition of IDF Curves and Clarifications Empirical Methods Parameter estimation Application in a real-world data set Theoretically Consistent Methods Parameter estimation Application in a real-world data set Copula Function for Hydrological Application Concepts of Dependence Structure and Copulas Copulas in Hydrologic Applications Remarks on Copulas and Future Research Regional Frequency Analysis Index-Flood Procedure, Extensions and Evolutions Classical Regionalization Approach Estimation of the index flood Estimation of the regional dimensionless quantile Homogeneity testing Choice of a frequency distribution Estimation of the regional frequency distribution

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2.18.6.2.6 2.18.6.3 References

Validation of the regional model Open Problems and New Advances

2.18.1 Introduction Hydrological phenomena such as precipitation, floods, and droughts are inherently random by nature. Due to the complexity of the hydrologic system, these physical processes are not fully understood and reliable deterministic mathematical models are still to be developed. Therefore, in order to provide useful analyses for designing hydraulic facilities and infrastructures, statistical approaches have been commonly adopted. In literature and in the practical hydrological applications, many statistical methods are considered with different aims. Simulation, forecasting, uncertainty analysis, spatial interpolation, and risk analysis are some of the most important ones. The use of statistical analyses is strongly related to the data availability and to the quality of observations. Particular emphasis is given to the case of ungauged area where the statistical approach is particularly important to develop hydrological analyses without direct observations (the relevance of this issue is well documented by the Decade on Prediction in Ungauged Basins (PUB) promoted by the International Association of Hydrological Sciences (IAHS, Sivapalan et al., 2003). This chapter describes some statistical topics widely used in hydrology. Among the large number of subjects available in literature, the attention is focalized on some of them particularly useful either for innovative hydrological analyses or for an appropriate application of common procedures. Many statistical methods are strongly affected by specific conditions to be verified on the available data set. Indeed, for instance, complex procedures, used for different important applications, usually need a very common and simple hypothesis: the stationarity. This condition, simple to define but very difficult to verify, probably is the most important in statistical hydrology. For this reason, the first section of this chapter provides a short review of this topic and a detailed description of the segmentation method that is a promising procedure for time series trend detection. Another primary topic, described here, is the univariate extreme value (EV) analysis. The EV approach is the widest used in hydrology (i.e., for the derivation of return levels for extreme rainfall and flood estimates) and it should be carefully and correctly applied in order to avoid dangerous underor overestimation of the analyzed design variables (rainfall, runoff). With this aim in the second section, a detailed distribution functions used with hydrological variables are described; moreover, the approaches to develop the parameter estimation and the goodness-fit-test steps are reviewed. Since rainfall is the most-observed hydrological phenomenon, a peculiar section is included in this chapter providing an update description of EV-IDF (intensity–duration–frequency) procedure. IDF curves are an invaluable tool in hydrology having a crucial role in the safe and efficient design of major

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or minor infrastructures (e.g., water dams, urban hydraulic works, flood design, etc.) that affect human lives. IDF curves are in use almost for a century, and the many different forms and methods proposed and studied through the years underline their importance. During all those years, IDF curves have evolved from purely empirical forms to theoretically more consistent, while today, their study still remains an active field of research. In this text, some of the most commonly used forms and techniques have been presented and applied in a real world data set. The search of the literature and the application presented here reveals that some commonly used techniques and forms of IDF curves may result in underestimating the rainfall intensity, especially for large return periods, and thus should be used with caution. More advanced forms and estimation procedures are described and compared to the most commonly used ones in practical applications. Until now the efforts of hydrologists were primarily devoted to analyze single parameters (flood peak, rainfall intensity, etc.), not because it is not important to consider other variables (i.e., flood duration and flood volume, or rainfall duration and volume, etc.) but because of the absence of a flexible approach to jointly analyze these different but useful variables. However, this is now finally possible, thanks to the relatively recent introduction of copula function. This statistical and mathematical method is quickly evolving and numerous applications are described in literature. Since this approach is promising and it could change and improve many hydrological procedures, a specific section on copula function is considered in this chapter, providing an updated review useful for hydrological applications. As mentioned at the beginning of this section, the ungauged basin is a sensitive problem. Most of the little basins (o150 km2) are characterized by poor hydrological observations (usually few raingauges are available) that stimulated an intense research on statistical methods for regional frequency analysis. Therefore, in the last section, it is essential to include a review and a specific description of this important topic. This chapter is written by a group of researcher members of the Statistics in Hydrology – STAHY Working Group recently launched by the International Association of Hydrological Sciences – IAHS with the purpose of sharing knowledge and stimulating research activities on statistical hydrology.

2.18.2 Analysis and Detection of Nonstationarity in Hydrological Time Series Hydrological time series used in water resources planning studies are very often supposed to meet the stationary hypothesis. Under steady-state natural conditions, time series exhibit regular fluctuations around a mean value; however,

Statistical Hydrology

2.18.2.1 The Common Nonstationarity Analysis Methods

when the natural conditions change markedly, they may form trends or exhibit jumps. Hydrological data series frequently show this type of significant nonstationarity due to several reasons (human activities, climate change, etc.). A random process is an indexed family (xt)tAI of random variables, which may be discrete time if I is a set of integers. A discrete random time process x ¼ (x1,x2, y , xn) is said to be stationary if, for every k and n, the distribution of xkþ1,xkþ2, y , xkþn is the same as the distribution of x1,x2, y , xn (Baseville and Nikiforov, 1993). In other words, a random process or variable is said to be strictly stationary if its statistical properties do not vary with time, and hence independent of changes of time origin. Trends, jumps/shifts, seasonality/periodicity, or nonrandomness in a hydrological time series can be referred to as components of the time series. Presence of these components makes the time series nonstationary. Indeed, nonstationarity is under the effect of persistency and scaling issues (Koutsoyiannis, 2006). Hydrological time series frequently exhibit nonstationary behavior, for example; flow and precipitation or rainfall stay below or above the mean long-term average (Rao and Yu, 1986), although they are generally assumed to be stationary at annual scale. When the time interval used is shorter than a year (month, week, or day), the stationarity assumption in the hydrological time series then becomes nonvalid simply because of the annual cycle of the Earth around the Sun. Trends in a time series can result from gradual natural and human-induced disruptive and evolutionary changes in the environment, whereas a jump may result from sudden catastrophic natural events (Haan, 2002). Any change in the time series is most reliable if it is detected by statistical tests and also has physical and historical evidences (Salas et al., 1980). Therefore, it is considered an important issue to identify (detect), describe (test), and remove these components.

A number of parametric and nonparametric tests have been suggested in literature for the detection of trend and jumps, and for checking randomness. These tests are considered to be important for scientific purposes as well as for practicing hydrologists. In what follows, a combination of the above-mentioned tests has been briefly described.

2.18.2.1.1 Randomness test An adapted version of a simple nonparametric run test, reported by Adeloye and Montaseri (2002), is given below. The test consists of the following steps (Figure 1): 1. The median of the observation is determined. 2. Each data item is examined to find out if it exceeds the median. If a data item exceeds the median, this is defined as a case of success, S, if not, this is defined as a case of failure, F. Cases that are exactly equal to the median are excluded. 3. Successes and failures are counted and denoted by n1 and n2, respectively. 4. The total number of runs (R) in the data set is determined. A run is a continuous sequence of successes until it is interrupted by a failure or vice versa. 5. The test statistics is computed by

  2n1 n2 R 1 n1 þ n2 z ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2n1 n2 ð2n1 n2  n1  n2 Þ ðn1 þ n2 Þ2 ðn1 þ n2  1Þ

t = t +1

R

xt = x50

R

N

z=

N F

xt > x50

Y

n2 = n2 + 1

S

−1

2n1n2 (2n1n2 − n1 − n2) (n1 + n2)2 (n1 + n2 −1)

N

N R Figure 1 Randomness test.

2n1n2 n1 + n2

n1 = n1 + 1

t
ð1Þ

where z has a standard normal distribution under the null hypothesis, H0, that the sequence of successes and failures is random.

x50

Y

481

Y Accept H0

z < −z/2 z > z/2

Y

Reject H0

H0: The sequence of Ss and Fs is random.

482

Statistical Hydrology

6. Critical values of the standard normal distribution are obtained for the chosen significance level, a, and denoted by 7za/2. 7. Computed statistics z is compared to the critical values 7za/2. H0 is rejected if zo  za/2 or z4za/2.

2.18.2.1.4 Mann–Kendall test The Mann–Kendall test checks the existence of a trend without specifying if the trend is linear or nonlinear. It is widely reported as in Libiseller and Grimwall (2002). The univariate statistics for monotone trend in a time series xt (t ¼ 1, 2, y , n) is defined as

2.18.2.1.2 Detection of trend A number of parametric and nonparametric trend detection tests are available in the literature (Berryman et al., 1988; Cluis et al., 1989; Helsel and Hirsch, 1992; Salas, 1993; Fanta et al., 2001; Yue et al., 2002; Burn and Elnur, 2002; Adeloye and Montaseri, 2002; Xiong and Guo, 2004; Koutsoyiannis, 2006). Among these, one parametric and two nonparametric tests are supplied below.

2.18.2.1.3 Simple regression on time The simple linear trend line between the variable (x) and time (t) can be written as

xt ¼ a þ bt

S¼

X

sgnðxi  xj Þ

ð4Þ

jo i

where

8 > < 1; sgnðxÞ ¼ 0; > : 1;

if x4 0 if x ¼ 0 if xo 0

ð5Þ

If no ties are present and the values of x1, x2, y , xn are randomly ordered, the test statistics has expectation zero and variance

ð2Þ

VðSÞ ¼

nðn  1Þð2n þ 5Þ 18

ð6Þ

where a and b are parameters of the regression model. A linear trend exists when the null hypothesis that b ¼ 0 is rejected. The null hypothesis is rejected if the test statistics, Tc, satisfies

In the case of presence of tied groups, equations are modified (Salas, 1993).

 pffiffiffiffiffiffiffiffiffiffiffiffi   n2  Tc ¼  pffiffiffiffiffiffiffiffiffiffiffiffiffiffi4 T1a=2; v r 1  r2

The Spearman rank order correlation nonparametric test is used to investigate the existence of a trend that might be found in the time series. The step-by-step explanation of the test for a time series xt (t ¼ 1, y , n) observed in time t (Figure 2) is as follows:

ð3Þ

where r is the cross-correlation coefficient between the variable x (x1, x2, y , xn) and time t ¼ 1, 2, y , n, and T1a/2, v is the 1  a/2 quantile of the Student t distribution with v ¼ n  2 degrees of freedom.

t=1

2.18.2.1.5 Spearman rank order correlation test

1. Ranks, Rxt, are assigned to xt, such that the rank 1 is assigned to the largest xt and the rank n to the least xt. Where there are ties in the xt, then a rank equal to the average of

x1< x2<...< xt<...< xn 1 2 Rt n

dt = Rt − t

t=t+1

T = rs

n−2 1 − rs2

N t
N n

Y

dt2

1−6 rs =

T > T/2, n − 2 T < −T/2, n − 2

t=1

n(n 2 − 1)

Accept H0

Reject H0

H0: The time series has no trend.

Figure 2 Spearman rank order correlation coefficient test.

Statistical Hydrology

the ranks which would have been used had there been no ties is assigned to each of the ties. 2. The difference

dt ¼ Rxt  t

x 1 , x2 n1, n2

ð7Þ

is computed. 3. The coefficient of trend, rs, is computed by

rs ¼

P 1  6 nt¼1 d2t nðn2  1Þ

n1 (x i =1 i

s =

− x1)2

n2 j=1

(xj − x2)2

n1 + n2 − 2

ð8Þ

Under the null hypothesis that the time series has no trend, the variable

sffiffiffiffiffiffiffiffiffiffiffiffiffi n2 T ¼ rs 1  r2s

483

x1 − x2

T=

n1 − n2 n1n2

s

ð9Þ

has a Student’s t-distribution with n  2 degrees of freedom. 4. The critical values of the t-distribution for the chosen significance level, a, and n  2 degrees of freedom are obtained. For a two-tailed test, the critical values are denoted by 7Ta/2, n2. 5. The values of T are compared to the critical values. H0 is rejected if T4Ta/2, n2 or To  Ta/2, n2.

N Accept H0

T > T1 −  / 2, n1 + n2 − 2

Reject H0

H0: The shift in the mean is insignificant. Figure 3 Detection of shift: t-test.

using the parametric t-test for which details are given below (Figure 3).

2.18.2.1.6 Detection of shifts (segmentation) Segmentation of a time series is the first step of jump analysis also called change point detection problem (or detection of shifts) for which statistical tests such as the Pettitt (1979) and Alexandersson (1986) tests are available in the literature. The simplest case is the segmentation with regression by constant in which it is aimed to determine the change points or boundaries where the average of the current segment is statistically different than the average of the next segment as well as that of the previous one. This shift or jump may be either positive or negative. By using a proper algorithm, the time series is first divided into segments with different mean values. Then the significance of the difference in the mean is tested. A number of tests are available in the literature to test the significance, that is to detect whether the time series is consistent. The tests are either parametric or nonparametric as in the trend detection tests (Hirsch et al., 1993; Chen and Rao, 2002; Fanta et al., 2001; Xiong and Guo, 2004; Wong et al., 2006). Here, the t-test and Mann–Whitney test are described.

1. The time series is divided into several segments by a segmentation algorithm. 2. The average of two consecutive segments ( x1 and x2 ) is calculated and the length of the segments (n1 and n2) is determined. 3. The t-statistics is calculated by

jx1  x2 j T ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n1 þ n2 s n1 n2

ð10Þ

with n1 þ n2  2 degrees of freedom. s in Equation (10) is the pooled variance given by

s¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn1 P2 1 Þ2 þ nj¼1 ðxj  x2 Þ2 i¼1 ðxi  x n1 þ n2  2

ð11Þ

2.18.2.1.7 t-Test

4. The null hypothesis that shift in the average value is insignificant is rejected if the sample T statistics in Equation (10) is greater than the critical value of Student’s t-distribution T1a=2; n1 þn2 2 with n1 þ n2  2 degrees of freedom.

A segmentation algorithm can be used for splitting the sample into segments with significantly different means. The segmentation algorithm divides the time series into as many segments as possible. Then, if two or more segments are identified, the starting year of the last segment is chosen as the first year for splitting the time series. The comparison is made between the segments before and after the chosen year. Once segmentation is completed, the jump analysis is performed by

The Mann–Whitney test is used when a time series xt (t ¼ 1, 2, y , n) can be divided into two segments x1 ; x2 ; y; xn1 and xn1 þ1 ; xn1 þ2 ; y; xn such that n2 ¼ n  n1. This is a widely reported test and briefly given below as reported in Salas (1993): a new series zt (t ¼ 1, 2, y , n) is defined by rearranging the original data xt at increasing order of magnitude. The

2.18.2.1.8 Mann–Whitney test

484

Statistical Hydrology

hypothesis that the mean of the first segment is equal to the mean of the second segment is tested by

Pn1 uc ¼

t¼1

Rðxt Þ  n1 ðn1 þ n2 þ 1Þ=2

½n1 n2 ðn1 þ n2 Þ=12

1=2

depends on x. The segmentation cost J(t) is defined by

J ðt Þ ¼ ð12Þ

where R(xt) is the rank of the observation xt in ordered series zt. The hypothesis of equal means is rejected at the significance level a when |uc|4u1a/2 where u1a/2 is the 1  a/2 quantile of the standard normal distribution.

dtk1 þ1;tk

ð13Þ

k¼1

where ds,t (for 0rsotrT) is the segment error corresponding to segment [s,t]. The segment error depends on the data vector fxs ; xsþ1 ; y; xt g. A variety of ds,t functions can be used. In this study,

ds;t ¼

t X ðxt  ms;t Þ2

ð14Þ

t¼s

2.18.2.2 A New Method of Segmentation In addition to the classical tests, various segmentation algorithms have been developed to determine stationary segments and estimate parameters characterizing each segment. The usual criterion to decide if a change point exists is based on the segmentation cost defined as the sum of squared deviation of the data from the means of their respective segments. The number of segments has the lowest limit of 1 and the highest n, the length of the time series, and determines the order of segmentation, that is, a fifth-order segmentation, for instance, means the time series is divided into five segments. The segmentation procedure of Hubert et al. (1989) and Hubert (2000) used also by Cluis and Laberge (2001), Fortin et al. (2004), Aksoy (2006), Dahamsheh and Aksoy (2007), and Aksoy et al. (2008b), among others, is available in the literature. Some earlier examples developed to determine stationary segments and estimate the parameters characterizing each segment can be found in Appel and Brandt (1983) and Imberger and Ivey (1991). Kehagias (2004) and Kehagias et al. (2006) developed segmentation algorithms based on dynamic programming (DP) and hidden Markov model (HMM). Gedikli et al. (2008) made the segmentation algorithm – denoted as AUG – available. Gedikli et al. (2010b) modified the DP algorithm (mDP). The HMM, DP, AUG, and mDP algorithms are all motivated from the segmentation algorithm of Hubert (2000). In the following subsection, the AUG algorithm is briefed with the aim of piecewise-stationarity analysis of hydrological time series. Following definitions are required to explain the formulation behind the segmentation algorithm. For details, the reader is referred to Gedikli et al. (2008) and Aksoy et al. (2008a). Assume that a time series x ¼ (x1, x2, y , xn) is given. Segmentation of such a series can be described by a sequence t ¼ (t0, t1, y , tK) to satisfy 0 ¼ t0ot1o y otklotk ¼ n. The intervals of integers ½t0 þ 1; t1 ½t1 þ 1; t2 y; ½tK1 þ 1; y; tK  are called segments, the times t0 ; t1 ; y; tK are called segment boundaries and K, the number of segments, is called the order of the segmentation. In other words, the time points where changes take place are called change points; the interval included between two change points is a segment (of the time series); and the procedure by which the segments of a time series are determined is called time series segmentation. The set of all segmentations of {1, 2, y , n} is denoted by N and the set of all segmentations of order K by NK. Clearly, N ¼ ,nK¼1 NK : The number of all possible segmentations of {1, 2, y , n] is 2n1. This can be formulated as an optimization problem. In other words, the optimal segmentation

K X

is used where the segment mean is given by

Pt

ms;t ¼

t¼s xt tsþ1

ð15Þ

The optimal segmentation, denoted by tˆ ¼ ð^t0 ; ^t1 ; y; ^tK Þ, is defined as tˆ ¼ arg mintAN JðtÞ and the optimal segmentation ðKÞ ðKÞ ðKÞ of order K, denoted by tˆ ðKÞ ¼ð^t0 ; ^t1 ; y ; ^tK Þ, is defined as ðKÞ tˆ ¼ arg mintANK JðtÞ. The optimal segmentation can be found by exhaustive enumeration of all possible segmentations (and computation of the corresponding ds,t). In computational sense, this is an infeasible way as the total number of segmentations increases exponentially with T. In order to obtain fast algorithms, a fast method for computing the costs ds,t is first required. For this aim, the recursive formulation of

ds;tþ1 ¼ ds;t þ ðt  s þ 1Þðms;t  ms;tþ1 Þ2 þ ðxtþ1  ms;tþ1 Þ2

ð16Þ

is easily proved where

ms;tþ1 ¼

ðt  s þ 1Þms;t þ xtþ1 tsþ2

ð17Þ

The segmentation algorithm is based on the branch-andbound-type technique. The branches are the possible segments of the kth-order segmentation. As suggested by Hubert (2000), the upper bound, u, of the kth segment in the Kth-order segmentation can trivially be given as

tk r u ¼ n  K þ k

ð18Þ

In the segmentation algorithm, the term ‘upper bound’ is the possible maximum value that tk can take. The basic idea of the algorithm is to enumerate (branch into) the possible solutions of the segmentation problem but, at the same time, to avoid exhaustive enumeration by eliminating clearly suboptimal solutions (bounds). It is possible to eliminate segmentations by reducing the upper bound of the segments as defined in Equation (18). It is also easy to check that

  cktþ1  ckt  ckþ1 and ckþ1 t tþ1

ð19Þ

is valid for t ¼ 2, y , N  1 and k ¼ 1,2, y , t. Equation (19) is rather obvious; a detailed derivation of it can be found in Gedikli et al. (2008). In order to reduce the upper bound, u, the remaining cost concept is defined as K k RK;k n;t ¼ cn  ct

ð20Þ

Statistical Hydrology

As stressed in Stedinger et al. (1993), ‘‘frequency analysis is an information problem.’’ If one had a sufficiently long record of flood flows, rainfall, low flows, etc., then a frequency distribution for a site could be precisely determined, so long as change over time due to urbanization or natural processes did not alter the relationships of concern. However in most situations, available data are not enough to precisely define the risk of large floods, rainfall, or low flows. This forces hydrologists to use practical knowledge of the processes involved, and efficient and robust statistical techniques, to develop the best estimates of risk that they can. These techniques are generally restricted, with 10–100 sample observations, to estimate events exceeded with a chance of at least 1 in 100, corresponding to exceedance probabilities of 1% or more. In some cases, they are used to estimate the rainfall exceeded with a chance of 1 in 1000 (the rainfall with return period of 1000 years), and even the flood flows for spillway design exceeded with a chance of 1 in 10 000 (the 10 000 years flood). In essence, the extreme value analysis consists of fitting distribution functions to ordered sequences of observed data and extrapolating the tails of the distribution to low exceedance probabilities. The immediate problem pertains to the way in which the probabilities are estimated and what level of accuracy is associated with such probabilities. The hydrologist should be aware that in practice the true probability distributions of the phenomena in question are not known. Even if they were, their functional representation would likely have too many parameters to be of much practical use. The practical issues are: how to select a reasonable and simple distribution to describe the phenomenon of interest, finding the correct trade-off between estimation bias and variance, that respectively decreases and increases as the number of model parameters increases; to estimate the distribution’s parameters;

2.18.3 Extreme Value Analysis: Distribution Functions and Statistical Inference

Table 1

1200 Flow (m3 s−1)

1000 800 600 400 200

Year Figure 4 The fifth-order segmentation of the Senegal River annual streamflow data.

Change points of annual streamflow data of Senegal River data (1903–86)

Segmentation order

Change points

2 3 4 5

1902 1902 1902 1902

1967 1949 1938 1921

1990

1980

1970

1960

1950

1940

1930

1920

0 1900

The study of the statistics of extreme events is the first step for most of the hydrological studies. In many situations, historical records containing observations from the past are the only reliable source of information. In the flood contest, the analysis of extreme events was introduced at the beginning of the twentieth century (e.g., Fuller, 1914) to replace the earlier design flood procedures, such as envelope curves and empirical formulas, by more objective estimation methods. When longer flood records became available by the middle of the twentieth century and with further theoretical developments such as extreme value theory of Gumbel (1958), the method rapidly became what Klemesˇ (1993) termed ‘the standard approach to frequency analysis’.

1400

1910

where krK and trn. This is a unique concept developed to make the algorithm fast. The segmentation algorithm computes a sequence of optimal segmentations ˆt1 ; tˆ2 ; y ; tˆk , where ˆtk is the kth-order optimal segmentation. For a given segmentation (tˆk for instance), the hypothesis that the means of consecutive segments are significantly different is tested. Determining the optimal order of segmentation, that is, selecting the number of segments, is a subsequent step in the segmentation procedure to be performed for which the Scheffe (1959) test is employed. The test is run on the optimal segmentations tˆ ð1Þ ; tˆ ð2Þ ; y; tˆ ðKÞ . Hubert (2000) accepts tˆ ðkÞ as the optimal segmentation when tˆ ðkþ1Þ is the first lowest order segmentation which is rejected by the Scheffe test (i.e., the first segmentation for which at least two consecutive segments do not show a statistically significant difference in their means). In the AUG algorithm, however, not the first lowest but the highest order segmentation which is accepted by the Scheffe test is considered instead. The application of the segmentation algorithm was performed by using a previously used data set: the annual mean streamflow data of Senegal River originating from Hubert (2000) and used by Kehagias (2004), Kehagias et al. (2006), and Gedikli et al. (2008). The data set is available on the Internet. A user-friendly software (the AUG-Segmenter version – 1.1) based on the above algorithm is now available (Gedikli et al., 2010a). The software is able to segment time series efficiently and fast. Using this software the Senegal River annual mean streamflow data set is segmented. The length of the data is 84 years for the period 1903–86. The fifth-order segmentation is found to be optimal after the execution of the algorithm (Table 1 and Figure 4).

485

1986 1967 1949 1936

1986 1967 1949

1986 1967

1986

486

Statistical Hydrology

and thus to obtain risk estimates of satisfactory accuracy for the problem at hand.

2.18.3.1 Probability Distributions for Extreme Events In this section, several distributions commonly used in hydrology are briefly described. Tables 2 and 3 provide a summary of the probability density functions (PDFs) or cumulative distribution functions (CDFs) of these probability distributions. The moments and L-moments for these distributions are reported in Tables 4 and 5 (see Section 2.18.3.2 for more details).

2.18.3.1.1 Normal distribution The normal distribution (N) arises from the central limit theorem, which states that if a sequence of random variables Xi are independently and identically distributed, then the distribution of the sum of n such random variables tends toward the normal distribution as n becomes large. The important point is that this is true no matter what the probability distribution function of X is. Hydrologic variables, such as annual precipitation, calculated as the sum of the effects of many independent events tend to follow the normal Table 2

distribution. The main limitations of the normal distribution for describing hydrological variables are that it varies over a continuous range (  N, þ N), while most hydrologic variables are non-negative, and that it is symmetric about the mean, while hydrologic data tend to be skewed. Because of its definition, the normal distribution is not suitable for extreme value analysis.

2.18.3.1.2 Lognormal distribution If the random variable Y ¼ log(X) is normally distributed, then X is said to be lognormally distributed (LN). This distribution is applicable to hydrologic variables formed as the products of other variables, because of the central limit theorem, provided that these are independent and identically distributed (see, e.g., Sangal and Biswas (1970), Martins and Stedinger (2001), and Kroll and Vogel (2002) for applications in hydrology). The lognormal distribution has the advantages over the normal distribution that it is bounded (X40) and that the log transformation tends to reduce the positive skewness commonly found in hydrologic data (especially in extremes), because taking logarithms reduces large numbers proportionately more than small numbers. Some limitations of the lognormal distribution are that it has only two parameters

Commonly used frequency distributions in hydrology

Distribution Normal (N)

PDF, fx(x), CDF, Fx(x), and quantile function, xF "

  # 1 1 x  y1 2 f X ðx Þ  pffiffiffiffiffiffi exp  2 y2 y2 2p x F ¼ y1 þ y2 F1 ðF Þ

Lognormal (LN) f X ðx Þ ¼

"   # 1 1 logðxÞ  y1 2 pffiffiffiffiffiffi exp  y2 2 x y2 2p

x F ¼ exp ½y1 þ y2 F1 ðF Þ 3-par Lognormal (LN3) f X ðx Þ ¼

"   # 1 logðx  y1 Þ  y2 2 pffiffiffiffiffiffi exp  2 y3 ðx  y1 Þy3 2p 1

x F ¼ y1 þ exp ½y2 þ y3 F1 ðF Þ Exponential (E)

 1 x  y1 exp  y2 y2  x  y1 F X ðx Þ ¼ 1  exp  y2 f X ðx Þ ¼

Range No x o N

y2 4 0 x40

y2 4 0 x 4 y1

y3 4 0 x 4 y1 for y2 4 0

x F ¼ y1  y2 ln ð1  F Þ Gamma (G)

Pearson type 3 (P3)

f X ðx Þ ¼

 y2 1  1 x x exp  y1 jy1 jGðy2 Þ y1

f X ðx Þ ¼

   1 x  y1 y3 1 x  y1 exp  jy2 jGðy3 Þ y2 y2

x 0

y1 o x o N if y2 4 0 No x o y1 if y2 o 0 y3 4 0

Log-Pearson type 3 (LP3)

   1 logðx Þ  y1 y3 1 logðx Þ  y1 exp  f X ðx Þ ¼ y2 y2 x jy2 jGðy3 Þ

y1, y2, and y3 are distribution parameters, F is the standard normal CDF, and G is the gamma function.

expðy1 Þo x o N if y2 4 0 0o x o expðy1 Þ if y2 o 0 y3 4 0

Statistical Hydrology Table 3

487

Commonly used frequency distributions in hydrology

Distribution Gumbel (EVl)

PDF, fx(x), CDF, Fx(x), and quantile function, xF

  1 x  y1 x  y1 f X ðx Þ ¼ exp   exp  y2 y2 y2

  x  y1 F X ðxÞ ¼ exp exp  y2

Range No x o N

x F ¼ y1  y2 ln½lnðF Þ Fre´chet (EV2)

"   #   y2 y1 y2 þ1 y1 y2 exp  y1 x x "   # y2 y1 F X ðxÞ ¼ exp  x f X ðx Þ ¼

x 4 0; y1 ; y2 4 0

x F ¼ y1 ½lnðF Þ1=y2 Weibull (EV3)

"   #   y2 x y2 1 x y2 exp  y1 y1 y1 "   # y2 x F X ðxÞ ¼ 1  exp  y1 f X ðx Þ ¼

GEV

x F ¼ y1 ½lnð1  F Þ1=y2 (  F X ðxÞ ¼ exp  1  y3 x F ¼ y1 þ

Generalized Pareto (GP)

) ðx  y1 Þ 1=y3 y2

y2 ½1  ðlnðF Þy3 Þ y3

 1 ðx  y1 Þ 1=y3 1 1  y3 y2 y2  ðx  y1 Þ 1=y3 F X ðxÞ ¼ 1  1  y3 y2

y2 1  ð1  F Þ y3 x F ¼ y1 þ y3

f X ðx Þ ¼

Generalized Logistic (GL)

1  1=y3 y3 1 þ 1  ðx  y1 Þ y2 "   # y2 1  F y3 1 x F ¼ y1 þ y3 F F X ðxÞ ¼

x 4 0; y1 ; y2 4 0

  y2 x o y1 þ if y3 4 0 y3   y2 if y3 o 0 x 4 y1 þ y3 y1 r x o N if y3 o 0

y1 r x r y1 þ

y2 if y3 4 0 y3

  y2 if y3 o 0 x 4 y1 þ y3   y2 x o y1 þ if y3 4 0 y3

y1, y2, and y3 are distribution parameters, F is the standard normal CDF, G is the gamma function.

and that it requires the logarithms of the data to be symmetric about their mean. Moreover, the lognormal distribution cannot be used when dealing with variables that can assume null values (e.g., discharge in ephemeral rivers). The three-parameter lognormal distribution (LN3) differs from the LN2 distribution by the introduction of a lower bound (indicated as y1 in Table 2) so that if X follows the LN3 distribution, log(X  y1) is normally distributed.

2.18.3.1.3 Exponential distribution Some sequences of hydrologic events, such as the occurrence of precipitation, may be considered Poisson processes, in which events occur instantaneously and independently on a time horizon, or along a line. The time between such events,

or interarrival time, is described by the exponential distribution (E) whose parameter y2 is the mean rate of occurrence of the events. The exponential distribution is used to describe the interarrival times of random shocks to hydrologic systems, such as slugs of polluted runoff entering streams as rainfall washes the pollutants off the land surface. The advantage of the exponential distribution is that it is easy to estimate y2 from observed data and the exponential distribution lends itself well to theoretical studies, such as a probability model for the linear reservoir (y2 ¼ l/k, where k is the storage constant in the linear reservoir). Its disadvantage is that it requires the occurrence of each event to be completely independent of its neighbors, which may not be a valid assumption for the process under study (e.g., the arrival of a front may generate many showers of rain) and this has led investigators to study

488

Statistical Hydrology

Table 4

Moments and L-moments of commonly used frequency distributions in hydrology

Distribution

Moments

L-moments

Normal (N)

m ¼ y1 ; s ¼ y2 g ¼ 0; k ¼ 3   m ¼ exp y1 þ y22 =2 s 2 ¼ ½expðy22 Þ  1 expð2y1 þ y22 Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g ¼ ½expðy22 Þ þ 2 expðy22 Þ  1

l1 ¼ y1 ; l2 ¼ p1=2 y2 t3 ¼ 0; t4 ¼ 0:1226   l1 ¼ exp y1 þ y22 =2

Lognormal (LN)

2

3-par Lognormal (LN3)

2

2

pffiffiffi l2 ¼ expðy1 þ y22 =2Þ ½2Fðy2 = 2Þ  1 t3 : NA; see HW; eq: ðA72Þ

k ¼ e 4y2 þ 2e 3y2 þ 3e 2y2  3

t4 : NA; see HW; eq: ðA73Þ

m ¼ y1 þ expðy2 þ y23 =2Þ

s 2 ¼ expðy23 Þ  1 expð2y2 þ y23 Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

g ¼ expðy23 Þ þ 2 expðy23 Þ  1

l1 ¼ y1 þ expðy2 þ y23 =2Þ pffiffiffi l2 ¼ expðy2 þ y23 =2Þ ½2Fðy3 = 2Þ  1 t3 : NA; see HW; eq: ðA72Þ

2

2

2

t4 : NA; see HW; eq: ðA73Þ

k ¼ e 4y3 þ 2e 3y3 þ 3e 2y3  3 2

y22

Exponential (E)

m ¼ y1 þ y2 ; s ¼ g ¼ 2; k ¼ 9

Gamma (G)

m ¼ y1 y2 s 2 ¼ y2 y21 pffiffiffiffiffi g ¼ 2 signðy1 Þ= y2 k ¼ 6=y2 þ 3

l1 ¼ y1 y2 l2 ¼ p1=2 y1 Gðy2 þ 1=2Þ=Gðy2 Þ t3 : NA; see HW; eq: ðA86Þ and ðA88Þ t4 : NA; see HW; eq: ðA87Þ and ðA89Þ

Pearson type 3 (P3)

m ¼ y1 þ y3 y2 s 2 ¼ y3 y22 pffiffiffiffiffi g ¼ 2 signðy2 Þ= y3 k ¼ 6=y3 þ 3

l1 ¼ y1 þ y2 y3 l2 ¼ p1=2 y2 Gðy3 þ 1=2Þ=Gðy3 Þ t3 : NA; see HW; eq: ðA86Þ and ðA88Þ t4 : NA; see HW; eq: ðA87Þ and ðA89Þ

Log-Pearson type 3 (LP3)

m ¼ e y1 ½ð1  y2 Þy3 h i s 2 ¼ e 2y1 ð1  2y2 Þy3  ð1  y2 Þ2y3

l1 ¼ ey1 ½ð1  y2 Þy3 l2 : NA

g : NA; see ST; page 18:21 k: NA; see ST; page 18:21

t3 : NA t4 : NA

l1 ¼ y1 þ y2 ; l2 ¼ y2 =2 t3 ¼ 1=3; t4 ¼ 1=6

y1, y2, and y3 are distribution parameters, F is the standard normal CDF, and G is the gamma function. NA indicates that the moment or L-moment is very complicated or not available in analytical form, with reference to Hosking and Wallis (1997) (HW in the table) or Stedinger et al. (1993) (ST in the table) when formulas or approximations are available.

various forms of compound Poisson processes, in which y2 is considered a random variable instead of a constant. The exponential distribution has been used in extreme value analysis as a simple model of the flood or rainfall exceedances over high thresholds in peak over threshold analyses (see, e.g., Todorovic, 1978).

2.18.3.1.4 Gamma distribution The time taken for a number of events, n, to occur in a Poisson process is described by the gamma distribution (G), which is the distribution of a sum of n independent and identical exponentially distributed random variables. The gamma distribution has a smoothly varying form and is useful for describing skewed hydrologic variables without the need for log transformation. It has been applied, for example, to describe the distribution of depth of precipitation in storms (see, e.g., Sivapalan et al., 2005; Viglione and Blo¨schl, 2009). The two-parameter gamma distribution has a lower bound at zero, which is a disadvantage for application to hydrologic variables that have a lower bound larger than zero.

2.18.3.1.5 Pearson type 3 distribution The Pearson type 3 distribution (P3), also called the threeparameter gamma distribution, introduces a third parameter,

the lower bound. This is a very flexible distribution, assuming a number of different shapes as the parameters vary. The normal distribution is a special case of the Pearson type 3 distribution, describing a nonskewed variable. The Pearson type 3 distribution was first applied in hydrology by Foster (1924) to describe the probability distribution of annual maximum flood peaks. When the data are very positively skewed, a log transformation is used to reduce the skewness. Examples of use of the Pearson type 3 distribution in extreme value analysis are Matalas and Wallis (1973), Bobe´e and Rasmussen (1995), and Kroll and Vogel (2002) among others.

2.18.3.1.6 Log-Pearson type 3 distribution If log(X) follows a Pearson type 3 distribution, then X is said to follow a log-Pearson type 3 distribution (LP3). This distribution is the standard distribution for frequency analysis of annual maximum floods in the United States (Benson, 1968; Stedinger and Griffis, 2008). As a special case, when log(X) is symmetric about its mean, the log-Pearson type 3 distribution reduces to the lognormal distribution. The location of the bound y1 in the log-Pearson type 3 distribution depends on the skewness of the data. If the data are positively skewed, then log(X)4y1 and y1 is a lower bound, whereas if the data are negatively skewed, log(X)4y1 and y1 is an upper bound.

Statistical Hydrology Table 5

489

Moments and L-moments of commonly used frequency distributions in hydrology

Distribution

Moments

L-moments

Gumbel (EV1)

m ¼ y1 þ 0:5772 y2 ; s2 ¼ p2 y22 =6 g ¼ 1:1396; k ¼ 5 þ 2=5

l1 ¼ y1 þ 0:5772 y2 ; l2 ¼ y2 lnð2Þ t3 ¼ 0:1699; t4 ¼ 0:1504

Fre´chet (EV2)

m ¼ y1 Gð1  1=y2 Þ

s 2 ¼ y21 Gð1  2=y2 Þ  G2 ð1  1=y2 Þ

l1 ¼ y1 Gð1  1=y2 Þ l2 ¼ y1 Gð1  1=y2 Þ ð21=y2  1Þ

Weibull (EV3)

m ¼ y1 Gð1 þ 1=y2 Þ

s 2 ¼ y21 Gð1 þ 2=y2 Þ  G2 ð1 þ 1=y2 Þ

l1 ¼ y1 Gð1 þ 1=y2 Þ l2 ¼ y1 Gð1 þ 1=y2 Þ ð21=y2  1Þ

GEV

m ¼ y1 þ y2 ½1  Gð1 þ y3 Þ=y3  2

y2 s2 ¼ Gð1 þ 2y3 Þ  G2 ð1 þ y3 Þ y3 g : NA; see ST; eq: ð18:2:19Þ k : NA

l1 ¼ y1 þ y2 ½1  Gð1 þ y3 Þ=y3 l2 ¼ y2 ð1  2y3 ÞGð1 þ y3 Þ=y3

Generalized Pareto (GP)

Generalized Logistic (GL)

m ¼ y1 þ y2 =ð1 þ y3 Þ h i s 2 ¼ y22 = ð1 þ y3 Þ2 ð1 þ 2y3 Þ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g ¼ 2 1 þ 2y3 ð1  y3 Þ=ð1 þ 3y3 Þ 3ð1 þ 2y3 Þ ð3  y3 þ 2y23 Þ k¼ ð1 þ 3y3 Þ ð1 þ 4y3 Þ

t3 ¼ 2ð1  3y3 Þ=ð1  2y3 Þ  3 5 ð1  4 y3 Þ  10 ð1  3 y3 Þ þ 6 ð1  2 y3 Þ t4 ¼ 1  2 y3 l1 ¼ y1 þ y2 =ð1 þ y3 Þ l2 ¼ y2 =½ð1 þ y3 Þ ð2 þ y3 Þ t3 ¼ ð1  y3 Þ=ð3 þ y3 Þ t4 ¼ ð1  y3 Þ ð2  y3 Þ=½ð3 þ y3 Þ ð4 þ y3 Þ

m ¼ y1 þ y2 ð1=y3  p=sin ðpy3 ÞÞ   2 p  s 2 ¼ py22 2 y3 sin ð2py3 Þ sin ðpy3 Þ

l2 ¼ y2 y3 p=sinðpy3 Þ

g : NA; see JO; eq: ð23:71Þ k: NA; see JO; eq: ð23:71Þ

t3 ¼ y3 t4 ¼ ð1 þ 5y23 Þ=6

l1 ¼ y1 þ y2 ð1=y3  p=sin ðpy3 ÞÞ

y1, y2, and y3 are distribution parameters, F is the standard normal CDF, and G is the gamma function. NA indicates that the moment or L-moment is very complicated or not available in analytical form, with reference to Hosking and Wallis (1997) (HW in the table) or Stedinger et al. (1993) (ST in the table) or Johnson et al. (1994) (JO in the table) when formulas or approximations are available.

The log transformation reduces the skewness of the transformed data and may produce transformed data which are negatively skewed from original data which are positively skewed. In this case, the application of the log-Pearson type 3 distribution would impose an artificial upper bound on the data. Depending on the values of the parameters, the log-Pearson type 3 distribution can assume many different shapes. Its use is justified by the fact that it has been found to yield good results in many applications, particularly for flood peak data (e.g., Bobe´e, 1975).

2.18.3.1.7 Extreme value distributions Extreme values are selected maximum or minimum values of sets of data. For example, the annual maximum discharge at a given location is the largest recorded discharge value during a year, and the annual maximum discharge values for each year of historical record make up a set of extreme values that can be analyzed statistically. Distributions of the extreme values selected from sets of samples of any probability distribution have been shown by Fisher and Tippett (1928) to converge to one of three forms of extreme value distributions, called types I, II, and III, respectively, when the number of selected extreme values is large. Unfortunately, for many hydrologic variables this convergence is too slow for this argument alone to justify adoption of an extreme value distribution as a model of

annual maxima and minima. The properties of the three limiting forms were further developed by Gumbel (1941) for the extreme value type I (EV1) distribution, Fre´chet (1927) for the extreme value type II (EV2), and Weibull (1939) for the extreme value type III (EV3). The three limiting forms were shown by Jenkinson (1955) to be special cases of a single distribution called the generalized extreme value (GEV) distribution. The three limiting cases are: (1) for y3 ¼ 0, the EV1 distribution for which x is unbounded; (2) for y3o0, the EV2 distribution for which x is bounded from below by y1 þ y2/y3; (3) for y340, the EV3 distribution for which x is bounded from above by y1 þ y2/y3. The EV1 and EV2 distributions are also known as the Gumbel and Fre´chet distributions, respectively. Note that the Gumbel and Fre´chet distributions are mutually related through the logarithmic transformation, that is, if X is a Fre´chet-distributed variable, then log(X) is distributed as a Gumbel. If a variable x is described by the EV3 distribution, then  x is said to have a Weibull distribution. The Gumbel model is widely applied, often gives satisfactorily results, and is parsimonious (two parameters), but may underestimate the design rainfall depth or discharge for large return periods (e.g., Koutsoyiannis, 2004a, 2004b). By contrast, the GEV model encounters difficulties when its parameters are estimated using small to medium-size samples, due to the large estimation variance of the shape parameter y3.

490

Statistical Hydrology

Examples of using the extreme value distribution in extreme value analysis are of course very numerous; an extensive list of references would be out of the scope of this chapter.

2.18.3.1.8 Generalized Pareto distribution The generalized Pareto (GP) distribution is a simple distribution useful for describing events which exceed a specified lower bound, such as all floods above a threshold or daily flows above zero. The GP distribution allows a continuous range of possible shapes that includes both the exponential and Pareto distributions as special cases. The GP distribution is commonly used in the peaks over threshold (POT) approach. Examples of use of the generalized Pareto in extreme value analysis are Hosking and Wallis (1987), Rosbjerg et al. (1992), Madsen et al. (1997a, 1997b), Lang et al. (1999), and Claps and Laio (2003).

The second moment about the mean is the variance defined as s 2 ¼ E½ðX  mÞ 2 . The standard deviation s is the square root of the variance and describes the width or scale of a distribution. These are examples of product moments because they depend upon powers of X. A dimensionless measure of the variability in X, appropriate for use with positive random variables XZ0, is the coefficient of variation, defined as s/m. The relative asymmetry of a distribution is described by the coefficient of skewness y ¼ E½ðX  mÞ 3 =s 3 while the coefficient of kurtosis k ¼ E½ðX  mÞ 4 =s 4 describes the thickness of distribution’s tails. These four moments are tabled for different distributions in Tables 4 and 5. From a set of observations (X1, y , Xn), the unbiased estimators of the mean, variance, and coefficient of skewness are

^ ¼ X ¼ m

n 1X Xi n i¼1

ð22Þ

2.18.3.1.9 Generalized logistic distribution The generalized logistic distribution (GL) has been used extensively for maximum rainfall modeling, and in the UK and elsewhere is used in hydrological risk analysis as the standard model for flood frequency estimation (Institute of Hydrology, 1999; Atiem and Harmancioglu, 2006).

^2 ¼ S2 ¼ s

2.18.3.2 Parameter Estimation Methods Fitting a distribution to data sets provides a compact and smoothed representation of the frequency distribution revealed by the available data, and leads to a systematic procedure for extrapolation to frequencies beyond the range of the data set. When flood flows, low flows, rainfall, or waterquality variables are well described by some family of distributions, a task for the hydrologist is to estimate the parameters Y of that distribution so that required quantiles and expectations can be calculated with the fitted model. Appropriate choices for distribution functions can be based on examination of the data using probability plots, moment ratios and goodness-of-fit tests (discussed in Section 2.18.3.3), the physical origins of the data, previous experience, and administrative guidelines. Several approaches are available for estimating the parameters of a distribution. Some commonly used approaches are described in the following subsections.

^g ¼

n X n 3 ð Xi  XÞ ðn  1Þðn  2ÞS 3 i¼1

m ¼ E½ X  ¼

Z

N

N

xfX ðxÞdx

ð21Þ

ð23Þ

ð24Þ

The method of moments consists of inverting the equations in Tables 4 and 5 so as to express the parameters of the distributions in terms of their moments and then using the sample moments to estimate the distribution moments.

2.18.3.2.2 Method of L-moments L-moments are another way to summarize the statistical properties of hydrologic data. L-moments were introduced by Sillitto (1969) and formalized by Hosking (1990) and are linear combinations of the probability-weighted moments defined by Greenwood et al. (1979). The first L-moment estimator is again the mean l1 ¼ E[X]. Let Xi:n be the ith smallest observation in a sample of size n (i ¼ 1 corresponds to the smallest). Then, for any distribution, the second L-moment is a description or scale based on the expected difference between two randomly selected observations:

2.18.3.2.1 Method of moments The method of moments was first developed by Karl Pearson in 1902. He considered that good estimates of the parameters of a probability distribution are those for which moments of the PDF about the origin are equal to the corresponding moments of the sample data. Pearson originally considered only moments about the origin, but later it became customary to use the variance as the second central moment and the coefficient of skewness as the standardized third central moment, to determine second and third parameters of the distribution if required. Given a distribution function fX(x), the mean is defined as

n 1 X 2 ð Xi  XÞ n  1 i¼1

l2 ¼ 12E½ X2:2  X1:2 

ð25Þ

Similarly, the third and fourth L-moments are

l3 ¼ 13E½ X3:3  2X2:3 þ X1:3 

ð26Þ

l4 ¼ 14E½ X4:4  3X3:4 þ 3X2:4  X1:4 

ð27Þ

and in general

lr ¼ r

1

r1 X ð1Þj j¼0

r1 j

!



E Xrj:r

ð28Þ

Statistical Hydrology

The coefficient of L-variation (L-CV) is defined by the ratio of two L-moments as t ¼ l2/l1. Other L-moment ratios are t3 ¼ l3/l2 and t4 ¼ l4/l2 that measure the skewness and the kurtosis of the distributions. Analogously to the method of moments, the method of L-moments consists of inverting the equations of Table 2 so as to express the parameters of the distributions in terms of their L-moments and to use the sample L-moments as estimators of distribution L-moments. Sample L-moments are defined as

lr ¼

r1 X

pr1;k bk

ð29Þ

k¼0

where the coefficients

pr;k ¼ ð1Þrk

r

!

k

rþk

! ð30Þ

k

are those of the ‘shifted Legendre polynomials’ (see Hosking and Wallis, 1997) and bk are the sample probability-weighted moments. These are computed from the ordered statistics X1:n, X2:n, y , Xn:n, that is, the data values arranged in increasing order, as

bk ¼ n1

n1 k

!1

n X j¼kþ1

poorly when the distribution of the observations deviates in significant ways from the distribution being fitted. MLE methods provide a computationally convenient way to fit frequency distributions by using different sources of information. In flood frequency analysis, for example, systematic records can be combined with historical events through a proper formulation of the likelihood function in which also uncertainties (particularly measurement errors) are taken into account (see, e.g., Stedinger and Cohn, 1986; O’Connell et al., 2002; O’Connell, 2005). The best parametrization of the assumed flood frequency distribution can then be obtained maximizing the likelihood function. The Bayesian inference, in addition to the maximum likelihood method, combines prior information (or, for example, regional hydrologic information) with the likelihood function in a posterior probability model that quantifies the belief in the hypothesis (i.e., the flood frequency distribution with a given parameter set) after evidence (the flood data) has been observed. Another advantage of the Bayesian method over the method of maximum likelihood is that it allows the explicit modeling of uncertainty in the parameters of the frequency distribution, which can be used to assign confidence bounds to the estimated flood quantiles.

2.18.3.3 Model Verification: Goodness-of-Fit Tests

!

j1 Xj:n k

491

ð31Þ

where n is the sample length and k the order of the probability-weighted moment. Since L-moment estimators are linear functions of the sample values, they should be virtually unbiased and have relatively small sampling variance. The sample L-CV is defined by the ratio t ¼ l2/l1, where l1 is the sample mean and l2 a measure of the dispersion around the mean value. Other sample L-moment ratios are t3 ¼ l3/l2 and t4 ¼ l4/l2 that measure the skewness and the kurtosis of data. According to Hosking (1990), also the L-moment ratio estimators are asymptotically normally distributed and have small bias and variance, especially if compared with the classical coefficients of variation, skewness, and kurtosis (Hosking and Wallis, 1997). In many hydrological applications an occasional event may be several times larger than other values; when product moments are used, such values can mask the information provided by the other observations, while product moments of the logarithms of sample values can overemphasize small values. In a wide range of hydrologic applications, L-moments provide simple and reasonably efficient estimators of the characteristics of hydrologic data and of a distribution’s parameters.

2.18.3.2.3 Method of the maximum-likelihood and Bayesian methods Still another method that has strong statistical motivation is the maximum-likelihood method. Maximum-likelihood estimators (MLEs) have very good statistical properties in large samples, and experience has shown that they generally do well with records available in hydrology. MLEs sometimes perform

Goodness-to-fit criteria are useful for gaining an appreciation for whether the lack of fit is likely to be due to sample-tosample variability, or whether a particular departure of the data from a model is statistically significant. Model testing and verification are basic steps of statistical inference, and several testing techniques, borrowed from applied statistics, have been applied in the hydrologic field. However, none of these tests has reached a broad consensus in the hydrologic community, possibly due to some complications that inevitably arise when the parameters of the hypothetical distribution are unknown. In order to evaluate which is the best distribution for a specific sample, a first simple step can be the graphical representation of the functions. The tail behavior of the distributions described in the previous paragraphs is analyzed in Figure 5, which shows the quantile function versus the return period for threeparameters distributions with equal L-moments of order 1, 2, and 3. It is evident that the distributions follow a similar behavior for return periods up to 50 years, but they tend to diverge for larger return periods. This highlights the necessity to consider methods for the choice of the distribution function from a set of candidate distributions. Graphical procedures form a useful visual method of verifying whether a theoretical distribution fits an empirical distribution (a data sample). Among these procedures, probability plots are commonly used in hydrology (Powell, 1943). In most cases, probability plots are constructed to suit the CDF of a particular distribution. Thus, when the distribution function is plotted against the variate, a linear relationship is obtained if the observations are from the hypothetical distribution. Figure 5 does not refer to one particular distribution but is simply a convenient probability plot that highlights the shape of different distributions for very low exceedance probabilities (very high return periods). If one plots a data sample (which,

492

Statistical Hydrology

250

0.8

0.6

200

LN3 P3 LP3 GEV GP GL Lower bound

4

xF

0.4 150 LN3 P3 LP3 GEV GP GL

100

10

20

50

100 200 500 1 (years) T= 1 − FX (x )

0.2

E EV1 N

0.0

1000 0.0

0.2

0.4 3

0.6

0.8

Figure 5 Frequency plot of the quantile function xF vs. the return period T for some three-parameter distributions with l1 ¼ 40, t ¼ 0.3, and t3 ¼ 0.3.

Figure 6 Probability distributions in the L-kurtosis vs. L-skewness diagram.

for example, has sample L-moments l1 ¼ 40, t ¼ 0.3 and t3 ¼ 0.3) using a plotting position on such a graph, one can have an idea of which distribution is better suited to the data. Since the method is subjective, it should always be supplemented by objective goodness-of-fit tests and/or by the application of model selection techniques. Objective procedures for the probabilistic model selection can be found in the specific literature. The subject was first proposed in the Akaike (1973) work, where the principle of maximum entropy was introduced as the theoretical basis for model selection, and by Schwartz (1978) who, by developing a similar idea in a Bayesian context, proposed the Bayesian information criterion for model selection. Applications of objective model selection techniques in statistical hydrology can be found in Strupczewski et al. (2002), Mitosek et al. (2006), Di Baldassarre et al. (2008), and Laio et al. (2009). Perhaps the most common approach to the choice of the probabilistic model in hydrology is based on the use of L-moments plots, which are used to determine the probability distribution closer to the available sample of data (see, e.g., Hosking, 1990; Chowdhury et al., 1991; Stedinger et al., 1993; Hosking and Wallis, 1997; Peel et al., 2001). Figure 6 represents the distributions treated in this chapter on the L-moment ratio diagram t3–t4. Two-parameter distributions are shown as points while three-parameter distributions are represented as curves. Because the LP3 distribution has two shape parameters, the L-moment ratio diagram covers a two-dimensional area (see Griffis and Stedinger, 2007). The diagram is convenient for plotting sample at-site or regional average L-moments ratios for comparison with the population values. Also this approach, however, is not fully objective, because the goodness-of-fit of a distribution to the data is often based only upon graphic judgment. Again, the choice of the distribution should be based on goodness-of-fit test. In the following some goodness-of-fit tests are described. It will be referred to as ‘case p’, in analogy to the Stephens (1986) use of ‘case 0’ for the case when the parameters are

fully specified a priori. In case p, the distribution test statistics depend on the so-called null hypothesis H0, that is, on the probability distribution that is being tested (e.g., Stephens, 1986). This means that the percentage points, that is, the 100(1  a) percentiles of the distributions of the test statistics (a is the significance level of the test), have to be recalculated for each H0. The method of parameter estimation, the presence of a shape parameter, and the sample size also have an influence on percentage points, and this further complicates the analysis. There is thus the necessity to have a different table for each distribution, and tables of percentage values for some tests and distributional families are still lacking. Some testing techniques commonly used in the hydrologic field are listed in the following, with reference to available tables of percentage points when applicable. 1. Tests of chi-squared type. The use of the classical Pearson test requires that the range of x is partitioned in classes; a convenient procedure to avoid arbitrariness and maximize the power of the test entails the choice of k equiprobable classes under the hypothesized distribution, with k ¼ 2n0.4 (Moore, 1986). The test statistic distribution in case 0 is the chi-squared distribution with k  1 degrees of freedom. In case p the distribution is not completely known, since there is a partial recovery of degrees of freedom of the chi-squared distribution with respect to the commonly recommended value of k  p  1 (p is the number of model parameters), when efficient estimators are used (e.g., Kendall and Stuart, 1977: 455; Moore, 1986). When using maximum likelihood to estimate model parameters, the critical points fall between those of w2(k  1) and those of w2(k  p  1), and not even this can be said when moments or L-moments estimators are employed. 2. Tests using the linearity of the probability plot for measuring the goodness of fit. A probability plot is a graph of the ranked observations x(i) versus an approximation of their expected value, F1(1  qi), where qi is the plotting position, which

Statistical Hydrology

2.18.4 IDF Curves

can be written as

qi ¼

ia n þ 1  2a

where ao0.5 is a coefficient (see Stedinger et al., 1993, table 18.3.1 for standard a values). Appropriate critical values for probability plot tests for the EV1 and normal distributions are tabled by Stedinger et al. (1993). No such tables exist for the GEV with the three parameters estimated from the sample. For the P3 distribution, the testing procedure is described by Vogel and McMartin (1991). 3. Tests based on the comparison of empirical and hypothetical L-moments ratios. The appropriate testing procedure and percentage points are found in Fill and Stedinger (1995) for the EV1 distribution, in Stedinger et al. (1993) for the normal distribution, and in Wang (1998) for the GEV distribution. The test is not available for the P3 distribution. 4. Tests based in the empirical distribution function (EDF). EDF tests are based on the comparison between the hypothetical and empirical distribution function, Fn(x), a cumulative probability distribution function that concentrates probability 1/n at each of the n values in a sample. The discrepancy between the two distributions can be measured either with statistics of the form maxjFn ðxÞ  (KS) test), or using quadratic FX ðxÞj (Kolmogorov–Smirov R statistics, Q 2 ¼ n all x ½Fn ðxÞ  FX ðxÞ2 CðxÞ dx, where C(x) is a weight function. When C(x) ¼ 1, one has the Cramer– von Mises statistic, usually called W2, which is a measure of the mean square difference between the empirical and hypothetical CDF; when CðxÞ ¼ ½FX ðxÞð1  FX ðxÞÞ1 , the tails of the distribution are weighted more than the central part, and one has the Anderson–Darling statistic, called A2. W2 and A2 are estimated in practice as (e.g., Stephens, 1986)

W2 ¼

 n  X  ð2i  1Þ 2 1 FX xðiÞ  þ 12n i¼1 2n

ð32Þ

and

A2 ¼  n 

493

n 1X fð2i  1Þ lnðFX ðxðiÞ ÞÞ n i¼1

þ ð2n þ 1  2iÞ lnð1  ðFX ðxðiÞ ÞÞg

ð33Þ

respectively, where x(i) represents the ith element in the ordered sample. Suitable tables of percentage points for the KS test in the p-case are found in Stephens (1986) for the EV1 and normal distributions. For the GEV and GAM distributions with all the parameters estimated, the appropriate percentage points are instead not known. Percentage points in the p-case for EV1, NORM, GAM, and GEV distributions can be calculated following the procedure described by Laio (2004) for the Cramer–von Mises and Anderson–Darling tests.

IDF curves are probably one of the most commonly used tools in engineering practice. IDF curves are simple functions between the rainfall intensity i, the timescale k at which the rainfall process is studied, and the return period T (see Section 2.18.4.1 for definitions). Nevertheless, the concept of IDF curves is often misinterpreted, mainly because of the imprecise terms used in its definition. It will be apparent in the following analysis that both terms, ‘duration’ and ‘frequency’, are misleading. Specifically, the term ‘duration’ is often misinterpreted as the actual time duration of a rainfall episode, while the term is meant for the time interval k, or else the timescale k, over which the rainfall process is averaged. For example, the actual duration of a rainfall episode may be only a fraction of the time interval k, or may be equal to many time intervals k. In addition, the term ‘frequency’ traditionally is meant for the number of occurrences of a periodic event during a time unit, and is reciprocal to the period which is defined as the exact time between two successive occurrences of the event. Thus, this may falsely lead to the belief that the rainfall intensity value assigned to a return period T will occur once every T years. Of course, this is wrong, as the correct interpretation is that the rainfall intensity value i, assigned to a return period T, will on average be exceeded once every T years. This means that during a particular period of T years the value i may not be exceeded at all, or exceeded several times, and only on average it will be once every T years. Thus, frequency in the IDF curves, it could be said, is referred to an ‘average frequency’. Given the above, a more correct term for IDF curves would be intensity–timescale–return period curves. Recently, the term ‘ombrian curves’ has been coined (Papalexiou and Koutsoyiannis, 2008) based on the ancient Greek word ‘o´mbroB’ (pronounced o´mbros) meaning rain (send by the Olympian god Zeus). Nevertheless, as the prevailing term at the moment is IDF curves, this term will be used in the rest of the text. It seems that Kuichling (1889) was the first, who studied the rainfall in relation with timescale; however, IDF curves, on a basis that is still in use, were established by Bernard (1932). Since then, the importance of IDF curves in engineering necessitated the study and the construction of IDF curves in several parts of world. For example, in the USA, the US Weather Bureau created a rainfall frequency Atlas (Hershfield, 1961); in addition, the NOOA developed maps for the Western US (Miller, 1973) and the Eastern and Central US (Frederick, 1977). Similar maps were constructed for Sri Lanka (Baghirathan, 1978), Namibia (Pitman, 1980), areas of Brazil (Brasil Vieira and Zink de Souza, 1985), Australia (Canterford, 1986), Pennsylvania (Gert, 1987), India (Kothyari, 1992), and many more. More recently, IDF relationships have been studied for Southeast Asia (Dairaku et al., 2004), Quebec in Canada (Mailhot et al., 2007), the Netherlands (Overeem et al., 2008), and for Denmark (Madsen et al., 2009). IDF curves have a great variety of applications, as they are a very convenient and useful tool used in the hydraulic design of flood protection infrastructures and in flood risk management in general. They provide the basic input in models that convert

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Statistical Hydrology

the rainfall to flood discharge, for example, they provide the rainfall rate for the so-called rational method. Essentially, their usefulness is in predicting the average rainfall intensity value, for a given timescale that depends on the infrastructure’s characteristics, and for a given return period that depends on the infrastructure’s importance and the aimed safety.

2.18.4.1 Definition of IDF Curves and Clarifications IDF curves are mathematical formulas that relate the rainfall intensity i with the timescale k and the return period T, that is, formulas that establish a one-to-one correspondence among rainfall intensity i, timescale k, and return period T. To clarify, the rainfall intensity is, in general, a continuous time stochastic process, that is, at every time instant t, the rainfall intensity has a value i(t) that can be either zero or positive. Of course, the instantaneous rainfall intensity i(t) cannot be known, however; the rainfall depth can be measured at consecutive time intervals of duration k each, and thus, dividing the rainfall depth by the time duration k results in the average rainfall intensity time series. The time duration k over which the rainfall intensity is averaged is called the timescale k. Obviously, as in reality only the average rainfall intensity is used, and for the sake of brevity, instead of the term average rainfall intensity the term rainfall intensity will be used. The rainfall intensity at timescale k can be regarded as a random variable (r.v.), denoted as I(k), following a probability distribution FI(k)(i), whereas the timescale k is a specified quantity and not an r.v. Moreover, it is well known that the return period T assigned to a value of an r.v. is defined as the average time needed for this value to be exceeded, and in a discrete time process is explicitly related to the probability distribution F of the r.v. by

T¼

1 1F

ð34Þ

It is noted that T is expressed in the same time units as the timescale k of the discrete time process, for example, if the timescale k is 1 h then T is expressed in hours. Therefore, if the probability distribution FI(k)(i) is known, the rainfall intensity at timescale k and for a return period T can be estimated, given Equation (34), by the quantile function QI(k)(T) of the distribution, FI(k)(i):

  1 iðk; T Þ ¼ FI1 1  ¼ QIðkÞ ðT Þ ðkÞ T

ð35Þ

Nevertheless, the estimation of rainfall intensity for a given return period T and for an arbitrary timescale k within a desired interval – as it is often the demand in engineering practice – would require knowledge of the distribution FI(k)(i) for every timescale k within this interval. Undoubtedly, this is hard to accomplish, if not impossible, as in reality, the distribution FI(k)(i) can only be estimated for a few discrete timescales. In fact, the construction of IDF curves remedies this problem by using the few estimated distributions FI(k)(i) to establish a function that assigns a rainfall intensity value to any given timescale k and any return period T.

In the literature, there are several different techniques for constructing IDF curves that vary significantly. Regarding the starting series, or the historical samples used for the construction of IDF curves, some methods use annual maxima series (AMS) of rainfall intensity, that is, the annual maximum values of every timescale, and others use partial duration series (PDS), that is, the series of values above a threshold (for comparison see e.g., Langbein, 1949; Cunnane, 1973; Takeuchi, 1984; Buishand, 1989; Madsen, 1997a, 1997b; Begueria, 2005; Ben-Zvi, 2009). Nevertheless, the use of AMS is by far more popular as the AMS are usually readily available, or can be easily prepared. In addition, the use of AMS offers computational simplicity (it will be apparent in the next sections), as it can be assumed that the probability distribution of the annual maximum rainfall intensity at each timescale kj belongs to the same family of distributions, that is, the extreme value distributions. Methodologies for constructing IDF curves do not only vary in the samples used, but also may be based on different approaches. For example, the classical empirical forms are presented in Chow et al. (1988: 459); a more general approach applied in United States has been proposed by Chen (1983); general forms consistent with the probability theory are given by Koutsoyiannis et al. (1998); forms in relation with L-moments by Hosking and Wallis (2005); approaches in relation with multifractals are given by Bendjoudi et al. (1997) and Veneziano et al. (2007); and in relation with copula functions by Singh and Zhang (2007). Nevertheless, most of forms of IDF curves can be combined in the following general expression:

iðk; TÞ ¼

gðTÞ hðkÞ

  i in mm h 1 ; k in h; T in years

ð36Þ

where g(T) is a function of the return period T and h(k) is a function of the timescale k. Clearly, this expression implies the separable function dependence of the rainfall intensity i on the return period T and on the timescale k, and even though the theoretical consistency of this assumption has been recently disputed, for moderate and large return periods provides a close approximation sufficient for practical purposes (Papalexiou and Koutsoyiannis, 2008). Of course, as it is obvious form Equation (36) that the rainfall intensity is a monotonically increasing function of the return period T, and a monotonically decreasing function of the timescale k.

2.18.4.2 Empirical Methods Empirical forms of IDF curves, due to their long history, as they date back to 1932 (Bernard, 1932), are those mostly studied and used in practice, while are still the most popular forms covered in existing text books (e.g., Chow et al., 1988: 459; Wanielista, 1990: 61; Shaw and Shaw, 1998: 228; Mays, 2004: 219). In general, compared to other forms of IDF curves, their expressions are characterized by simplicity, while their parameters are easy to estimate, at least for the most simple forms among them. The most commonly used empirical expression for the return period function is g(T) ¼ aTb, while others have also been used. In addition, the timescale function can be found in many variations that, however, can all be

Statistical Hydrology combined to the general expression h(k) ¼ (kg þ d)e (for a comparison see, e.g., Garcı´a-Bartual and Schneider, 2001; Di Baldassarre et al., 2006a). For convenience, the different variations of the return period function g(T) and the timescale function h(k) used in this text are distinctly named:

gðTÞ : hðkÞ :

g1 ðTÞ ¼ aT b ; g2 ðTÞ ¼ a þ b ln T

ð37Þ

h1 ðkÞ ¼ kg ; h2 ðkÞ ¼ kg þ d; h3 ðkÞ ¼ ðk þ dÞe ; h4 ðkÞ ¼ ðk g þ dÞe

ð38Þ

where a, b, g, d, and e are the parameters to be estimated. Of course, all the different variations of the g(T) and the h(k) functions may be used, thus resulting in several different empirical forms of IDF curves.

2.18.4.2.1 Parameter estimation The typical estimation procedure of IDF curves’ parameters, based on AMS (e.g., Chow et al., 1988: 459), can be summarized in three steps. Step 1: In the first step, a suitable probability distribution is selected and fitted to each maximum rainfall intensity data set that comprises values of the same timescale kj, where j ¼ 1, y , m, with m denoting the total number of different timescales that data are available. Clearly, the many distribution choices and the many available distribution fitting methods (e.g., the method of moments and L-moments, or the maximum likelihood and the least-squares error methods) may significantly affect the estimated parameters of the IDF curves. Nevertheless, a natural choice for the probability distribution to be fitted, given that the groups of rainfall intensity values are annual maximum values, is one of the two maximum extreme value distributions, that is, the Gumbel distribution, given in Equation (39), or the GEV distribution, given in Equation (40) (with parameter y3o0 in order to be unbounded form above), and with the latter comprising the Gumbel distribution as a special case for y3 ¼ 0:

   i  y1 FIðkj Þ ðiÞ ¼ exp exp  y2

ðy1 AR; y2 4 0Þ

ð39Þ

"   # i  y1 1=y3 FIðkj Þ ðiÞ ¼ exp  1  y3 y2 ðy1 AR; y2 4 0; y3 4 0Þ

ð40Þ

where the symbol I(kj) stands for the annual maximum rainfall intensity at timescale kj. Yet, it should be noted that apart from the Gumbel and GEV distributions, other distributions have also been used to describe annual maxima, for example, the log-Pearson III and lognormal distributions. Although the Gumbel distribution has been the traditional choice for describing maxima, as it is a parsimonious model and often gives good results, new evidence suggests (Gellens, 2002; Ramesh and Davison, 2002; Koutsoyiannis, 2004a, 2004b; Salvadori and De Michele, 2006) that the Gumbel distribution may seriously underestimate the rainfall intensity for large return periods, and thus its use should be avoided. Alternatively, the GEV distribution, which has gained

495

popularity the last decade, can be used taking special care in the estimation of the parameter y3. In particular, as the typical rainfall samples are usually small, the estimation of the parameter y3 may be highly uncertain. In order to remedy this, Koutsoyiannis (2004a, 2004b) proposed to adopt a global value for y3, that is, y3 ¼  0.15, as this value resulted from studying many rainfall samples from stations all over the world. Step 2: In the second step, a set of p characteristic return period values {T1, y , Tl, y , Tp} is defined (e.g., {2, 5, 10, 20, 50, y , Tp}) and the m fitted distributions form step 1 are used to evaluate the rainfall intensity for the selected return periods and for each of the m timescales kj. This procedure will result in a set comprising m  p points of the form (ij,l,kj,Tl). The evaluation of the maximum rainfall intensities ij,l can be accomplished using the quantile functions QIðkj Þ ðTl Þ of the fitted distributions expressed in relation with the return period T. For the Gumbel and GEV distributions, the quantile functions are given, respectively:

   1 ij;l ¼ QIðki Þ ðTl Þ ¼ y1  y2 ln ln 1  Tl (    ) y2 1 y3 1  ln 1  ij;l ¼ QIðkj Þ ðTl Þ ¼ y1 þ y3 Tl

ð41aÞ

ð41bÞ

Step 3: In this final step the parameters of the selected form of IDF curves are estimated. The parameter estimation of the most simple and one of the most commonly used empirical forms of IDF curves, that is, i(k,T) ¼ g1(T)/h1(k), can be done analytically using the method of multiple linear regression. Clearly, logarithmizing this simple form results in

ln iðk; TÞ ¼ ln a þ b ln T  g ln k

ð42Þ

which is for the form y ¼ x0 þ x1x1 þ x2x2, and consequently, the parameters a, b, and g can be estimated by performing a multiple linear regression using the set of (ln ij,l, ln kj, ln Tl) points, evaluated in step 2. Obviously, the rainfall intensity logarithm ln i is considered as the dependent variable y, whereas the timescale logarithm ln k and the return period logarithm ln T as the independent variables x1 and x2, respectively. The parameters a, b, and g will straightforwardly result from the estimated multiple linear regression coefficients x0, x1, and x2, that is, a ¼ exp(x0), b ¼ x1, and g ¼  x2. This technique, however, is not directly applicable in the case of more general forms of IDF curves. Specifically, logarithmizing the i(k,T) ¼ aTb/(kg þ d)e results in

ln iðk; TÞ ¼ b lnT  e ln ðk g þ dÞ þ ln a

ð43Þ

which is not of the form y ¼ x0 þ x1x1 þ x2x2. Nevertheless, inspection of Equation (43) suggests that if the timescale function h(k) ¼ h3(k), then the term ln(kg þ d) in the equation becomes ln(k þ d), and thus, multiple regression can be performed by assuming given values of d. The estimated parameters a, b, and g should be those for that d minimizes a proper norm between the values of the set of points (ln ij,l, ln kj, ln Tl) and the estimated ones by the selected form of IDF

496

Statistical Hydrology

curves. Obviously, if the return period function g(T) ¼ g2(T), this methodology is not applicable. In addition, one global way of fitting a function, linear or nonlinear, to a given data set of values, and thus applicable in the parameter estimation of IDF curves, is to minimize the mean square error (MSE) between the values of the given data set and the corresponding values calculated from the function to be fitted. Alternatively, in cases where there are large differences between the values of the given data set, instead of minimizing the MSE, it may be more suitable to minimize the logarithmic SE (log SE). In the case studied here, the MSE and the log SE are given, respectively, by m Xh i2 1 X iðkj; Tl Þ  QIðkj Þ ðTl Þ mp j¼1 l¼1 p

MSE ¼

log SE ¼

p m X X

log 2

j¼1 l¼1

iðkj ; Tl Þ QIðkj Þ ðTl Þ

ð44Þ

ð45Þ

where i(kj, Tl) is the rainfall intensity values calculated from the selected form of IDF curves, for example, one of the forms resulted from the combinations given in Equations (37) and (38) and QI(kj)(Tl) is the quantile functions of the distributions fitted in step 1, for the rainfall intensity of timescale kj and of return period Tl.

2.18.4.2.2 Application in a real-world data set In this section, the methodologies described in Section 2.18.4.2.1 will be applied in a real-world data set of recorded rainfall intensities form 1987 to 2004, in the station Ardeemore in UK. The data set was originally available, by the British Atmospheric Data Centre (BACD), as tipping bucket measurements that were first converted in the 5-min temporal resolution, second, aggregated over several timescales and third, the annual maximum values of each timescale were extracted (British Atmospheric Data Centre, 2006). The summary statistics of the resulted data set of maximum rainfall intensities in several different timescales are presented in Table 6. As described in Section 2.18.4.2.1, the typical parameter estimation procedure of IDF curves begins with selecting and fitting a theoretical probability distribution to the same

Table 6

timescale data. In this application, for demonstration and comparison, both the Gumbel and the GEV distributions are fitted to the data. In addition, the L-moments method (Hosking, 1990) was selected as a fitting method, as it is robust and easy to apply – especially for the Gumbel and the GEV distributions, it results in analytical equations. The results are presented in Table 7. The fitted distributions to the empirical data, and the empirical distribution functions according to Weibull plotting position, are depicted in Figure 7. Clearly, both distributions perform very well up to return period values approximately equal to 20 years. Of course, as it was expected, the estimated rainfall intensity difference between the two distributions increases monotonically with the return period, and for return period values higher than 100 years, the difference gets significant, with the GEV distribution resulting in higher rainfall intensity estimates. Both the Gumbel and the GEV distributions fit equally well to the empirical points (see Figure 7); however, the Gumbel distribution may underestimate the rainfall intensity for high return periods (see step 1 of Section 2.18.4.2.1), and thus, the GEV distribution is preferred to generate the set of (ij,l, kj, Tl) points described in step 2 of Section 2.18.4.2.1. In addition, this argument is fortified by noticing in Figure 7 that the empirical return period of the higher historical value in the smaller timescales is disproportionally small compared to the one resulted by the Gumbel distribution. For example, the empirical return period of the largest value in the 5 min timescale is 19 years, and while the GEV distribution assigns a theoretical return period to that value approximately equal to 80 years, the corresponding value by the Gumbel distribution is about 180 years. The selected characteristic return periods Tl are {2, 5, 10, 20, 50, 100, 200, 500, 1000} years, a total of p ¼ 9 values. It is noted that due to the small recorded sample (18 years), the rainfall intensity estimates in high return periods will be uncertain. Furthermore, the number of the selected timescales kj is m ¼ 9 (see Table 6), and therefore, a set of m  p ¼ 99 points of the form (ij,l, kj, Tl) is generated. The rainfall intensity values ij,l are calculated using the quantile function of the GEV distribution, given in Equation (41), for the characteristics return periods Tl, with the estimated parameters that correspond to the timescale kj (see Table 7). The set of generated points is depicted in Figure 8.

Summary statistics of maximum rainfall intensity data (mm h1) at several timescales observed at Ardeemore

Timescale kj

Sample size

Mean

Standard deviation

Variation coefficient

Skewness coefficient

Minimum

Maximum

5 min 10 min 20 min 30 min 60 min 2h 3h 6h 12 h 24 h 48 h

18 18 18 18 18 18 18 18 18 18 18

44.03 33.07 23.00 17.49 11.23 7.89 5.99 4.22 2.85 1.82 1.25

20.77 19.14 10.03 6.62 3.64 3.26 2.05 0.91 0.70 0.40 0.37

0.47 0.58 0.44 0.38 0.32 0.41 0.34 0.22 0.25 0.22 0.30

2.04 2.86 1.52 1.14 0.61 1.86 2.04 0.58 0.03 –0.09 0.94

20.30 16.20 11.35 8.27 4.90 4.19 3.13 2.70 1.50 1.00 0.72

112.83 103.22 52.97 35.50 20.40 18.10 12.60 6.30 4.07 2.61 2.17

Statistical Hydrology

497

Table 7 Estimated sample L-moments of the maximum rainfall intensity in 11 different timescales and the corresponding estimated parameters of the fitted Gumbel and GEV distributions Timescale

Sample L-moments

5 min 10 min 20 min 30 min 60 min 2h 3h 6h 12 h 24 h 48 h

GEV parametersa

Gumbel parameters

l1

l2

y1

y2

y1

y2

44.03 33.07 23.00 17.49 11.23 7.89 5.99 4.22 2.85 1.82 1.25

10.29 8.25 5.30 3.62 2.06 1.63 0.97 0.51 0.41 0.23 0.21

35.47 26.21 18.59 14.47 9.51 6.53 5.18 3.79 2.51 1.63 1.08

14.84 11.90 7.65 5.23 2.97 2.35 1.40 0.74 0.59 0.33 0.30

34.54 25.46 18.11 14.14 9.32 6.38 5.09 3.75 2.47 1.61 1.06

12.66 10.15 6.53 4.46 2.54 2.01 1.19 0.63 0.50 0.28 0.25

a

Parameters estimated setting a priori y3 ¼  0.15 in all timescales.

200 Rainfall intensity, i (mm h−1)

Rainfall intensity, i (mm h−1)

102

101

i (k,T ) = 100 70 50

10.12 T 0.212 k 0.615

30 20 10 7 5

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

3 2 1 5

100 100

101

102

103

Return period, T (years) Figure 7 Empirical distribution functions (dots) according to the Weibull plotting position, and fitted by the method of L-moments, Gumbel (dashed lines) and GEV (solid lines) distributions for the data in the timescales given in Table 8, ranging form 5 min to 48 h (from above to below).

Finally, the parameters of the most simple form of IDF curves (i.e., i(k,T) ¼ aTb/kg) are estimated (see step 3 in Section 2.18.4.2.1) by performing a multiple linear regression to the set of (ln ij,l, ln kj, ln Tl) points. The estimated multiple linear regression coefficients are x0 ¼ 2.315, x1 ¼0.211, and x2 ¼  0.615, and consequently, the resulted parameters of the IDF curves are a ¼ 10.12, b ¼ 0.211, and g ¼ 0.615. The resulted IDF curves are depicted in Figure 8. In addition to the previous simple form of IDF curves, some more complicated empirical forms – and in order to demonstrate and compare their performance – were constructed for the Ardeemore station data set, by numerically minimizing the log SE given in Equation (45) (see step 3 in Section 2.18.4.2.1). The log SE minimization was performed using one of the many software packages that include numerical minimization routines. It is worth noting that

10 20 30 Minutes

60

2 3 4 6 8 12 Hours

24

48

Timescale, k Figure 8 IDF curves for the Ardeemore station in UK constructed using the typical parameter estimation procedure, and the set of rainfall intensity points generated form the fitted GEV distribution to the empirical data.

estimating the parameters of the simple i(k,T) ¼ aTb/kg by minimizing the log SE is actually the same as performing the multilinear regression method. The fitted IDF curves the estimated parameters, and the resulted log SE are presented in Table 8. As expected, the additional parameters result in smaller log SE, and thus, in a better fit. Nevertheless, especially in the particular case studied here, the difference in log SE among the different forms of IDF curves is not substantial, and therefore, according to the principle of parsimony, a more parsimonious form should be preferred than the most general five-parameter case. The last argument is also fortified by Figure 9, where the more general IDF curves of Table 9 are depicted. Clearly, the IDF curves with return period function g1(T) and the threeparameter timescale function h4(k), compared with the ones with the two-parameter functions h2(k) and h3(k), are not significantly different.

Table 8

Statistical Hydrology Five different empirical forms of IDF curves fitted by minimizing the logarithmic square error

IDF curves i(k,T)

Estimated parameters

aT b =k g aT b =ðk g þ dÞ aT b =ðk þ dÞe aT b =ðk g þ dÞe ða þ b lnT Þ=ðk g þ dÞe

Rainfall intensity, i (mm h−1)

200

Log SE

a

b

g

10.12 10.47 10.42 10.39 7.33

0.212 0.212 0.212 0.212 4.66

0.615 0.627

i (k,T ) =

100 70 50

10.47 T 0.212 k

0.627 +

0.02

T=1000 yr T=500 yr T=200 yr

10 7 5 3 2

200

1

Rainfall intensity, i (mm h−1)

200

10 20 30 Minutes

i(k,T ) =

60

2 3 4 6 8 12 Hours Timescale, k 10.39 T 0.212

(k1.81 +

100 70 50

0.005)0.347

30 20

24

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

10 7 5 3 2

0.626 0.347 0.192

i (k,T ) =

10.42 T 0.212 (k +

100 70 50

0.015)0.626

30 20

0.99 0.97 0.95 0.91 0.92

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

10 7 5 3 2 1

48

5

200 Rainfall intensity, i (mm h−1)

5

e

0.020 0.015 0.005 0.0003

1.810 3.258

T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

30 20

d

Rainfall intensity, i (mm h−1)

498

10 20 30 Minutes

i (k,T ) =

60

2 3 4 6 8 12 Hours Timescale, k 7.33 + 4.66 ln T

(k3.258 + 0.005)0.192

100 70 50 30 20

24

48

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

10 7 5 3 2 1

1 5

10 20 30 Minutes

60

2 3 4 6 8 12 Hours Timescale, k

24

48

5

10 20 30 Minutes

60

2 3 4 6 8 12 Hours Timescale, k

24

48

Figure 9 Four different empirical forms of IDF curves constructed for the Ardeemore station data set using the typical parameter estimation procedure.

Moreover, it is important to note that all IDF curves shown in Figure 9, compared to the simple form depicted in Figure 8, exhibit a slight curvature in small timescales – which is more apparent in the IDF curves with the timescale function h4(k). This is obviously the effect of the parameter d, a very

important parameter that allows a better fit of the IDF curves in small timescales, or equally in the high rainfall intensities. Although in this particular case the resulted curvature is very slight, and thus not important, this is not the general rule, as for a different data set this curvature may be very strong and

Statistical Hydrology Table 9

499

Theoretically consistent forms of IDF curves fitted by two different methods for the Ardeemore station data set

Method

Parameters

Two-step robust estimation

y1 y2 y3 g d e KKW

One-step log SE minimization

y1 y2 y3 g d e log SE

IDF curves i(k,T) QGEV (T ) a (k þ d)e

QGEV (T ) (k g þ d)e

QGEV (T ) (k þ d) e

9.92 2.82 0.09 2.298 0.002 0.255 12.08

10.02 2.88 0.09

9.95 2.67 0.15

0.017 0.593 13.38

0.017 0.593 13.38

9.83 3.07 0.16

9.85 3.10 0.15

0.022 0.577 2.16

0.022 0.577 2.16

9.65 3.02 0.16 3.089 0.0004 0.185 2.13

QGumb (T ) (k g þ d)e

QGumb (T ) (k þ d)e

10.04 3.09

10.14 3.14

2.298 0.002 0.255 12.08

0.017 0.593 13.38

9.85 3.28

10.04 3.36

3.089 0.0004 0.185 2.26

0.022 0.577 2.29

a

The parameters were estimated by setting a priori y3 ¼  0.15.

thus essential in engineering practice. Consequently, it is proposed that the selected form of IDF curves should include this parameter.

iðk; TÞ ¼

QGEV ðTÞ ¼ h4 ðkÞ

(    ) y2 1 y3 y1 þ 1  ln 1  Tl y3 ðkg þ dÞe

ð48Þ

2.18.4.3 Theoretically Consistent Methods Apart from the classical empirical forms of IDF curves, described in Section 2.18.4.2, there are forms of IDF curves, based on the general Equation (36), that are theoretically more consistent. Koutsoyiannis et al. (1998) proposed that empirically derived return period functions g(T) are unnecessary, as the g(T) can be determined from the probability distribution function of the maximum rainfall intensity I(k). Specifically, this method is based on the fact that the probability distribution FI(i) of the r.v. I ¼ I(k)h(k) is just a scaled version of the distribution of the r.v. I(k), as the function h(k) for a certain timescale k is just a real number. Thus, instead of estimating the probability distribution FI(k)(i) of the r.v. I(k) for several timescales, only the distribution of the r.v. I should be estimated. Consequently, the form of IDF curves would be

iðk; TÞ ¼

F1 QI ðTÞ I ð1  1=TÞ ¼ hðkÞ hðkÞ

ð46Þ

where QI(T) is the quantile function of the r.v. I, and not some empirically proposed function. Using the extreme value distributions in this framework, as they are the natural choice for describing maxima – although many other distributions have been used – the forms of IDF curves, according to Equation (46) and for the general three-parameter timescale function h4(k), for the Gumbel and GEV distributions, respectively, become

   1 y1  y2 ln ln 1  QGumb ðTÞ Tl iðk; TÞ ¼ ¼ ðkg þ dÞe h4 ðkÞ

ð47Þ

where the symbols QGumb(T) and QGEV(T), obviously, denote the quantile functions of the GEV and Gumbel distributions, respectively. Nevertheless, in this theoretical framework, the return period functions in the empirical forms of IDF curves actually correspond to theoretical probability distributions. Specifically, the return period function g1(T) ¼ aTb corresponds to

 b  1=b 1 i aT b ¼ QI ðTÞ3 a ¼ i3 FI ðiÞ ¼ 1  1  FI ðiÞ ab ð49Þ which is the celebrated two-parameter Pareto distribution with parameters a40, b40 and support iA[ab,N). In addition, the return period function g2(T) ¼ a þ b ln T corresponds to

1 1  FI ðiÞ   ia ¼ i3 FI ðiÞ ¼ 1  exp  b

a þ b ln T ¼ QI ðTÞ3 a þ b ln

ð50Þ

which is the celebrated two-parameter exponential distribution with parameters aAR, b40, and support iA[a,N).

2.18.4.3.1 Parameter estimation Two-step robust estimation method. This method (Koutsoyiannis et al., 1998) estimates the parameters of the IDF curves in two steps. First, it estimates the parameters of the scale function h(k), and second, the parameters of the return period function g(T), which, in this framework, is the quantile

500

Statistical Hydrology

function of a probability distribution. The method is based on the fact that, the r.v.’s Ij ¼ I(kj)h(kj) should be distributed identically. Given the above, in the first step, multiplying the values of each timescale kj group, denoted fij;1 ; y ; ij;nj g by the value h(kj), should result in groups from the same population. Apparently, the function of h(k) is not a priori known. Consequently, the method assumes a set of values for the h(k) parameters, and consecutively uses an appropriate statistic to check that indeed the resulted different timescale groups (i.e., fhðkj Þ  ij;1 ; y; hðkj Þ  ij;nj g) belong to same population. This naturally leads to the Kruskal–Wallis test (Kruskal and Wallis, 1952), which is a nonparametric test applied to infer whether or not different groups of values belong to the same population. The test static KKW is given by

KKW ¼

  nj m X 12 Nþ1 2 1X nj rj  ; rj ¼ rj;l NðN þ 1Þ j¼1 2 nj i¼1

ð51Þ

where m is the total number of timescales, nj is the sample size of the timescale kj group, N is the total sample size across all groups, rj the average rank of the timescale kj group, and rj,l the rank (among all data) of the lth data value of the timescale kj group. Clearly, different groups from the same population would result in a small value of the KKW statistic. Therefore, the estimated parameters of the scale function h(k) are those that minimize the KKW statistic. Essentially, minimizing the KKW statistic results in forcing the different groups of data to belong to the same population. Unfortunately, this minimization can only be accomplished numerically, but numerical optimization can now be routinely performed with widely spread software packages. Once the parameters of the timescale function h(k) are estimated, it is straightforward to estimate the parameters of the return period function g(T). Specifically, the values of all the resulted groups fhðkj Þ  ij;1 ; y; hðkj Þ  ij;nj g are unified in one sample – at least theoretically should belong to the same population – and the probability distribution that corresponds to the return period function g(T) of the selected form of IDF curve is just fitted to this unified sample. The estimated parameters of the fitted distribution are, evidently, the parameters of the return period function g(T). One-step least-squares estimation method. The basic difference of this method compared to the least-squares method presented in Section 2.18.4.2.1 is that it uses historical data (Koutsoyiannis et al., 1998). As a result, first, it avoids the procedure of fitting distributions to each timescale kj group and generates values using a set of characteristic return periods, and second, it does not depend on the range of the characteristic return period set, as it uses the empirical return periods resulting from the historical data. In particular, to every rainfall intensity value of every timescale kj group of historical data, an empirical return period can be assigned. Specifically, sorting in decreasing order the values of every timescale kj group, for example, ij;ð1Þ 4 y4 ij;ðlÞ 4 y4 ij;ðnj Þ , the empirical return period Tj,l of the lth largest rainfall intensity value, denoted by ij,l, of the timescale kj group, is given according, to the Weibull plotting

position, by

Tj;l ¼

nj þ 1 ; l

l ¼ 1; y; nj

ð52Þ

where nj is the sample size of the timescale kj group. Consequently, the MSE and log SE given in Equations (44) and (45), respectively, are modified to n

MSE ¼

j m X

2 1 X iðkj ; Tj;l Þ  ij;l mn j¼1 l¼1

log SE ¼

nj m X X j¼1 l¼1

log 2

iðkj ; Tj;l Þ ij;l

ð53Þ

ð54Þ

where i(kj,Tj,l) is the rainfall intensity calculated from the selected form of IDF curves for the timescale kj and the empirical return period Tj,l of the historical rainfall intensity value ij,l as defined above. Therefore, the one-step least-squares error method consists of selecting a form of IDF curves, for example, one of the theoretically consistent forms given in Equations (47) or (48), and numerically minimizes the resulted MSE or log SE between the selected form of IDF curves and the historical data. Again, as noted in Section 2.18.4.2.1 the log SE may be more suitable due to the large differences in the rainfall intensity values in small and large timescales. Evidently, the estimated parameters are the ones that minimize the MSE or the log SE. Alternatively, this method can also be used with the empirical forms of IDF curves or with any other form.

2.18.4.3.2 Application in a real-world data set This section demonstrates the applicability of the aforementioned methodologies and presents the consistent forms of IDF curves constructed for the Ardeemore station data set used in Section 2.18.4.2.2. Among the several forms of IDF curves that could emerge by combining different return period functions g(T) and timescale functions h(k), the ones presented in Equations (47) and (48) are used for the two- and three-parameter timescale functions h2(k) and h4(k), respectively. Each form of IDF curves, for comparison and demonstration, was fitted using both methods described in Section 2.18.4.3.1, that is, the two-step robust estimation method, and the one-step least-squares estimation method. The results are presented in Table 9. It seems that the one-step least-squares error method is the most straightforward method to apply. Simply, the desired form of IDF curves is selected for an arbitrary set of parameters, and is used to estimate the rainfall intensity values i(kj ,Tj,l) that correspond to the empirical return periods Tj,l given by Equation (52). The numerical minimization of the MSE or of the log SE, given in Equations (53) and (54), respectively, between the historical data and the ones predicted by the selected form of IDF curves, results in the estimated parameters. The selected forms that were fitted by minimizing the log SE as the estimated parameters are presented in Table 9. Among the several variations of fitted IDF curves presented in Table 9, Figure 10 depicts the QGEV(T)/h2(k) for

Statistical Hydrology

Rainfall intensity, i (mm h−1)

100 70 50 30 20 10 7 5 3 2 1 20 30

60

2 3 4 6 8 12

24

30 20 10 7 5 3 2

48

Timescale, k

(a)

5

Hours

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

200 100 70 50 30 20 10 7 5

10 20 30 Minutes

(b)

3 2

60

2 3 4 6 8 12 Hours Timescale, k

1

24

48

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

200 Rainfall intensity, i (mm h−1)

10

Minutes

Rainfall intensity, i (mm h−1)

100 70 50

1 5

100 70 50 30 20 10 7 5 3 2 1

5 (c)

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

200 Rainfall intensity, i (mm h−1)

T=1000 yr T=500 yr T=200 yr T=100 yr T=50 yr T=20 yr T=10 yr T=5 yr T=2 yr

200

501

10 20 30 Minutes

60

2 3 4 6 8 12 Hours

24

48

Timescale, k

5 (d)

10 20 30 Minutes

60

2 3 4 6 8 12 Hours

24

48

Timescale, k

Figure 10 IDF curves constructed for the Ardeemore station data set. Graphs (a) and (b) depict the QGEV ðT Þ=h2 ðk Þ for y3 ¼  0.15 and the Q Gumb ðT Þ=h2 ðk Þ, respectively, fitted with the robust estimation method, while graphs (c) and (d) depict the same IDF curves fitted with the one-step LSE method. The parameters for each case are given in Table 9.

y3 ¼  0.15, and the QGumb(T)/h2(k), fitted with robust estimation and by minimizing the log SE. The comparison of the same form of IDF curves fitted by different methods reveals that there are small, albeit noticeable, differences between them. Specifically, the IDF curves fitted with the one-step LSE method, especially for large return periods, are slightly more conservative, that is, the predicted rainfall intensity is higher compared to the one predicted by the other method. In addition, the one-step LSE method results in stronger curvature in the area of small timescales. This behavior can be explained by the presence of a very large value in the small timescales of the historical data set (see Figure 7), and it is well known that the LSE methods, in general, are sensitive to outliers. Of course, as Figure 10 demonstrates, the major difference is between the two forms of IDF curves: the form that uses the quantile of the Gumbel distribution as a return period function, especially for the large return periods, predicts significantly smaller rainfall intensity values compared to one that uses the quantile of the

GEV distribution. Obviously, from a mathematical point of view this was expected, but given that the log SE is smaller in the form QGEV(T)/h2(k) than in the QGmnb(T)/h2(k), this may suggest that adoption of the QGumb(T)/h2(k) for the design purposes may be a dangerous choice.

2.18.5 Copula Function for Hydrological Application Since most of the hydrologic phenomena involved multiple variables across various temporal and spatial scales with significant inter-dependencies and non-Gaussian-like behaviors, univariate approaches with the assumption of normality or independence among variables may cause significant over-simplification. In order to address the interwoven dependencies between hydrologic variables, multivariate joint probability distribution needs to be properly modeled. In the past there were attempts focusing on preserving the correct

502

Statistical Hydrology

correlation relationship (e.g., Goel et al., 2000; Singh and Singh, 1991; Yue, 2001), but they usually required more assumptions and case-specific restrictions (types of marginal distributions, variables, and selected fixed-form joint distributions) may apply. Thus, though the univariate approach may be less realistic, it is sometimes a necessary trade-off between complexity and applicability. With the need to characterize multidimensional randomness in nature, a flexible approach with general applicability is of desire. Such a method should be able to model different types of probability distributions for hydrologic variables governed by various physical mechanisms (e.g., rainfall intensity, flood peak, and drought severity), while also being able to faithfully describe their dependence structures. Delightfully, these challenges can now be addressed by using a novel statistical tool – copulas. Copulas got the name as functions that couples arbitrary univariate distributions to form the multivariate joint distribution. In order words, they are the mathematical formulations of the entire dependence space rather than a single correlation or dependence measure (e.g., Pearson’s linear correlation coefficient). Since all multivariate probability functions (such as multivariate Gaussian and bivariate exponential distributions) can be re-expressed into the combinations of their marginal distributions and the corresponding copulas, the use of copulas does not conflict with the existing multivariate techniques, but endow them with more possibility. In practice, conceptually similar to the selection of a most appropriate PDF for each individual variable, the most suitable dependence structure between variables of interest can be identified by testing various candidate copula functions. Associated with the identified marginal distributions, together a general joint distribution can be formed. Though the core theorem supporting copulas was proposed by Sklar early in 1959, the growing number of copula applications was not found until recently, mostly in the field of finance (see Cherubini et al., 2004). In the hydrologic community, it is still a relatively new concept. Nevertheless, copulas were soon found useful in various types of water resources problems due to their great feasibility in modeling multivariate dependence structure. Generally speaking, there are several advantages that make copulas an appealing method for hydrologic topics: (1) it can model non-Gaussian-like variables; (2) the assumption of statistical independence is not a prerequisite; (3) it proceeds in a parallel fashion and all the existing univariate techniques hold; (4) it is less mathematical challenging compared to the conventional multivariate statistical approach; and (5) it helps generate sets of random vectors with prescribed marginal distributions and dependence levels conveniently. Expect that copulas will gradually play a more important role in the future hydrologic study; this section aims to provide the general hydrologic audience with the introduction, state-of-the-art applications, limitations, and future research needs of the copula techniques.

2.18.5.1 Concepts of Dependence Structure and Copulas As Gaussian distribution has been the most commonly used statistical model for probability distributions and uncertainties, to some engineers and hydrologists, the term correlation

(or dependence) refers directly to Pearson’s linear correlation coefficient r. For random variables, X and Y with means as x and  y, r is defined as E½ðX  xÞðY  yÞ=Std½XStd½Y, in which E[  ] and Std[  ] are the operators of expectation and standard deviation. Though r is widely adopted, its limitations are less emphasized: (1) r tends to be highly affected by outliers and hence is not suitable for extreme value analysis; (2) the value of r may change if X and/or Y are transformed monotonically (such as exponentiation) while their rank correlations remain the same; (3) most important of all, r is only adequate for Gaussian (or elliptical) distributions (Nelsen, 2006). An example is illustrated in Figure 11, where two bivariate distributions and the corresponding realizations are presented. Figure 11(a) shows the bivariate Gaussian distribution with r ¼ 0.8. The two-dimensional surface represents the joint PDF hXY(x,y). When integrating either one of the variables over the entire domain (  N,N), the marginals fx(x) and fY(y) can be obtained, which are plotted on the two sides. In Figure 11(a), both marginals are the typical bell-shape Gaussian densities. In the other case, the joint distribution shown in Figure 11(b) is clearly not bivariate Gaussian, as hXY(x,y) has a different shape and the realizations reveal dissimilar patterns. However, the marginals shown in Figure 11(b) can still be univariate Gaussian (identical to Figure 11(a)), and the correlation coefficient r is again the same as 0.8. This example indicates that: (1) the joint distribution cannot be determined only by known marginals and (2) the correlation coefficient r is not a sufficient measurement of dependence for non-Gaussian distributions. As a matter of fact, a single dependence measure (e.g., besides r, Kendall’s concordance measure t and Spearman’s rank correlation r) may not be sufficient to describe the entire dependence space, just as the statistical moments are only the summary of a univariate PDF. Hence, it motivates the use of copulas. The first usage of copula is attributed to Sklar (1959) in a theorem describing how one-dimensional distribution functions can be combined to form multivariate distributions. For d-dimensional continuous random variables {Xl,y,Xd} with marginal CDFs uj ¼ Fxj ðxj Þ; j ¼ 1; y; d, Sklar showed that there exists one unique d-copula CU1 ;y;Ud such that

CU1 ;y;Ud ðu1 ; y; ud Þ ¼ HX1 ;y;Xd ðx1 ; y; xd Þ

ð55Þ

where uj is the jth marginal and HX1 ;y;Xd is the joint CDF of {X1, y , Xd}. Copulas CU1 ;y;Ud can be regarded as a transformation of HX1 ;y;Xd from [  N, N]d to [0,1]d. The consequence of this transformation is that the marginal distributions are segregated from HX1 ;y; Xd . Hence, CU1 ;y; Ud becomes only relevant to the association between variables, and it gives a complete description of the entire dependence structure. In other words, the characterization of joint distributions can be performed separately for the marginal distributions and for the dependence structure (described by copulas), and therefore the dependence between variables can be clearly revealed. Among various types of copula function, one-parameter Archimedean copulas have attracted the most attention owing to their several convenient properties. For an Archimedean copula, there exists a generator j such that the following

Statistical Hydrology ∞

Marginals fx (x ) =

503

3

∫−∞ hXY (x, y ) dy

fY (y ) =

2

∞

0.4

∫−∞ hXY (x, y ) dx

1 y

0.2

0 −1

0 2 0

Joint PDF hXY (x, y )

−2

y

−2

−2

2

0

−3

x

−3

−2

−1

(a)

0 x

1

2

3

3 2 1 y

0.4 0.2

−1 −2

0 2

0 y

(b)

0

−2

−2

2

0 x

−3 −3

−2

−1

0 x

1

2

3

Figure 11 Illustration of bivariate joint distributions: (a) bivariate Gaussian distribution with r ¼ 0.8 and the corresponding samples and (b) joint distribution with Gaussian marginals and Clayton copulas and the corresponding samples (r ¼ 0.8).

relationship holds:

jðCðu; vÞÞ ¼ jðuÞ þ jðvÞ

ð56Þ

where the generator j is a continuous, strictly decreasing function defined in [0,1], and j(1) ¼ 0. When the generator j(t) ¼  ln t, the copula in (56) is C(u,v) ¼ uv, which is the special case when the variables are independent. Some commonly used families of one-parameter Archimedean copulas are listed in Table 10, in which y is the dependence parameter. It should be noted that not every family of Archimedean copulas can accommodate the entire range of dependencies (from perfectly positive dependence to perfectly negative dependence). The choice of copulas depends on the range of dependence levels they can describe. For instance, Gumbel-Hougaard can only be applied for positive dependence, Ali-Mikhail-Haq is only suitable for weaker dependence (  0.1807oto0.3333), while Clayton, Frank, and Genest-Ghoudi are suitable for both positive and negative dependencies. Figure 12 shows an example of using Frank family of copulas in computing random samples with various levels of dependence. Archimedean copulas find wide applications because they are easy to construct and possess several nice features. For example, several statistical properties can be simply expressed in terms of j, such as the distribution function KC of copulas (i.e. KC(t) ¼ P[C(U,V)rt]):

jðtÞ KC ðtÞ ¼ t  0 ; j ðtÞ

tA½0; 1

ð57Þ

The distribution function KC offers a cumulative probability measure for the set {(u,v)A[0,1]2|C(u,v)rt}, and it can help

project multivariate information onto a single axis. The quantity KC was also used by Salvadori and De Michele (2004b) for defining secondary return period for bivariate copulas. Another statistic that can be related to j is the concordance measure Kendall’s t, which is defined as t ¼ P[(X1  X2)(Y1  Y2)40]  P[(X1  X2)(Y1  Y2)o0], where (X1,Y1) and (X2,Y2) are independent and identically distributed random vectors with the same joint CDF HXY(x,y). Kendall’s t can be interpreted as the difference between probability of concordance P[(X1  X2)(Y1  Y2)40] (for positive dependence) and probability of discordance P[(X1  X2)(Y1  Y2)o0] (for negative dependence). The value of Kendall’s t falls in [  1,1], where 1 represents total concordance,  1 represents total discordance, and 0 represents concordance. To obtain the sample estimator of Kendall’s t, let (x1, y1) and (x2, y2) be two observations from a size-n sample space, and then ^t can be estimated by

^t ¼

ðc  dÞ ! n 2

ð58Þ

where c denotes concordant pairs ((x2  x1)(y2  y1)40), and d denotes disconcordant pairs ((x2  x1)(y2  y1)o0). By using generator j, the theoretical Kendall’s t can be expressed as

t¼1þ4

Z

0

1

jðtÞ dt j0 ðtÞ

ð59Þ

This useful property leads to the nonparametric procedure of estimating dependence parameter y by equating ^t ¼ t. This nonparametric estimator does not rely on prior information of

504 Table 10

Statistical Hydrology Some commonly used one-parameter Archimedean copulas j(t)a

Family Ali-Mikhail-Haq

ln

Range of ya

1  yð1  tÞ t

½1; 1Þ

1 y ðt  1Þ y

Clayton Frank

ln

KC(t)b tð1  y þ ytÞ 1  y þ yt ln 1y t   1 t yþ1  t 1þ y y

tþ

½1; 0Þ,ð0; NÞ

e yt  1 e y  1

t(t)b,c

ð N; 0Þ,ð0; NÞ

tþ

1

y yþ2

e yt  1 e y  1 ln yt 1 y e

Genest-Ghoudi

ð1  t 1=y Þ y

½1; NÞ

t 11=y

Gumbel-Hougaard

ðlntÞ y

½1; NÞ

  lnt t 1 y

  2 2 1 2  1 lnð1  yÞc 3y 3 y

4 1 þ ½D1 ðyÞ  1d y 2y  3 2y  1 y1 y

a

Column j(t), range of y adapted from Nelsen (2006). Column KC(t) and (t) of the Genest-Ghoudi family adopted from Kao and Govindaraju (2007b). c t(t) of the Ali-Makhail-Haq, Frank, and Gumbel-Hougaard families adopted from Zhang and Singh (2007a), t(t) of the Clayton family adopted from Grimaldi and Serinaldi (2006b). R d D1 is the Debye function of order 1, D1 ðyÞ ¼ y0 ðt =yðet  1ÞÞ dt . b

Frank family,  = 10

Frank family,  = 10

Frank family,  = −10

1

1

1

0.5

0.5

0.5

0 1

1

0.5 v

0 1 0.5

0.5

0 0

1

v

u

Frank family,  = 10

0.5

0 0

0 1

1 0.5 v

u

Frank family,  = 0.01 0.8

0.8

0.6

0.6

0.6 v

0.8

v

1

v

1

0.4

0.4

0.4

0.2

0.2

0.2

0

0.2 0.4

0.6 0.8

1

0 0

Frank family,  = −10

1

0

0.5 u

0 0

0.2 0.4

u

0.6 0.8

1

0 0

0.2 0.4

0.6 0.8

1

u

u

Figure 12 Frank family of Archimedean copulas with various levels of dependencies.

marginal distributions, and provides a more objective measure of dependence structure. Therefore, the existence of outliers, while affecting the estimation of marginal distributions, would not affect the determination of copulas. This aspect of being able to determine the dependence structure independent of marginals can be an advantage. Further information on statistical interference, detailed theoretical background, and descriptions of copulas can be found in Genest and Rivest (1993), Genest et al. (1995), Joe (1997), Nelsen (2006), and Salvadori et al. (2007).

To examine the appropriateness of a selected copula, one can construct the empirical copula (i.e., the observed probabilities) and apply it to perform goodness-of-fit tests. Similar to the concept of plotting position formula used in univariate statistical analysis (e.g. Weibull formula), empirical copulas are rank-based empirically joint cumulative probability measures. For sample size n, the d-dimensional empirical copula Cn is

Cn

  k1 k2 kd a ; ; y; ¼ n n n n

ð60Þ

Statistical Hydrology where a is the number of samples {x1,y,xd} with x1 r x1ðk1 Þ ; y; xd r xdðkd Þ ; and x1ðk1 Þ ; y; xdðkd Þ with 1rk1,y,kdrn are the order statistics from the sample. In an analogous fashion, the empirical distribution function Kcn can be expressed as

KCn

  l b ¼ n n

ð61Þ

where b is the number of samples {x1, y , xd} with Cn(k1/ n, y , kd/n)rl/n. Empirical copulas Cn and empirical distribution function Kcn are mostly applied for model verification and are treated as the observed (real) dependence structure. Currently, the development of goodness-of-fit tests for copulas remains a major interest. Several applicable tests include: multidimensional KS test (Saunders and Laud, 1980), tests based on the probability integral transformation (Breymann et al., 2003; Genest et al., 2006; Dobric and Schmid, 2007; Kojadinovic, in press), kernel-based smoothing techniques (Fermanian, 2005; Panchenko, 2005; Scaillet, 2007), and cross-product ratio model (Wallace and Clayton, 2003).

2.18.5.2 Copulas in Hydrologic Applications Though being a relatively new method in statistical hydrology, the flexibility offered by copulas for constructing joint distributions is now evident from many applications. Copulas were firstly applied in hydrologic studies by De Michele and Salvadori (2003), and Salvadori and De Michele (2004a) for rainfall frequency analysis. Hourly precipitation data from two raingauges at La Presa (Italy) for 7 years (from 1990 to 1996) were utilized to construct a bivariate model for regular storms, in which the generalized Pareto distribution was selected to describe the marginals, and the Frank family of Archimedean copulas was adopted to construct the dependence structure between rainfall duration and average intensity. This study was further extended to the trivariate level in Salvadori and De Michele (2006), where the dry period between rainfall events was added as the third variable, and again the generalized Pareto distribution and Frank families of Archimedean copulas were adopted. For studying extreme rainfall behavior, Grimaldi and Serinaldi (2006b) and Serinaldi et al. (2005) discussed the relationship between design rainfall depth (critical depth, obtained from IDF curves by specifying design duration and return period) and the actual features of extreme rainfall events. Half-hourly rainfall data from 10 raingauges at Umbria (Italy) from 1995 to 2001 were combined with the assumption of regional homogeneity to form a 70-year annual maximum series for analysis. The trivariate model containing critical depth, actual total depth, and peak intensity was constructed via copulas. By providing critical depth, it was expected that important features of extreme rainfall could be obtained. Zhang and Singh (2007a, 2007b) performed multivariate analysis for extreme rainfall events via copulas. Hourly precipitation data from three raingauges at Amite River basin in Louisiana (US) for 42 years were analyzed. Bivariate rainfall models between total depth (volume), duration, and average intensity were constructed. Several types of conditional and joint return periods were illustrated in their study. Kao and Govindaraju (2007b, 2008) adopted 53 hourly

505

precipitation stations in Indiana, USA with a minimum recording of 50 years. Extreme rainfall events were analyzed using both bivariate Archimedean copulas and trivariate Plackett copulas. The most appropriate definition for the selection of extreme rainfall samples was suggested. Joint distributions of extreme rainfall events were constructed and used to compute design rainfall estimates. Comparisons between the conventional and copula-based rainfall estimates showed that the traditional univariate analysis provides reasonable estimates of rainfall depths for durations greater than 10 h but fails to capture the peak features of rainfall. Further applications of copulas in rainfall analysis can be found in Kuhn et al. (2007), Singh and Zhang (2007), Evin and Favre (2008), Laux et al. (2009), and Serinaldi (2009). Copulas were adopted in flood related problems as well. Favre et al. (2004) applied copulas for multivariate flood frequency analysis for two watersheds in Quebec, Canada. The combined flooding risk of multiple catchments and the joint distribution of peak flows and volume were discussed. Two families of Archimedean copulas (Frank, Clayton), independent and Farlie-Gumbel-Morgenstern copulas, were investigated. They showed that conditional probability of flood volumes is quite different when compared to the univariate result. Therefore, return periods of design floods would be different when the joint behavior is taken into account. De Michele et al. (2005) used the Gumbel family of Archimedean copulas to model the dependence between flood peaks and flood volumes. These two margins were analyzed by the generalized extreme value distribution. A bivariate model was constructed to calculate the flood hydrographs for a given return period, and was combined with the linear reservoir model to assess the adequacy of dam spillway of Ceppo Modrelli dam in Northern Italy. Zhang and Singh (2006, 2007c) investigated the dependence structure between flood peak, volume, and duration by testing four different families of Archimedean copulas: Gumbel-Hougaard, Ali-MikhailHaq, Frank, and Cook-Johnson. The margins were analyzed by using the extreme value type I and log-Pearson type III distributions. They found positive correlations between flood peak and volume, and flood volume and duration, and concluded that the Gumbel-Hougaard family was most appropriate to characterize the dependence structure. They also applied the copula model in calculating conditional return period. Other applications of copulas in flood frequency analysis can be found in Grimaldi and Serinaldi (2006a), Shiau et al. (2006), Genest et al. (2007), Renard and Lang (2007), and Serinaldi and Grimaldi (2007). Besides rainfall and flood frequency analyses, copulas were adopted in other hydrologic topics as well. Salvadori and De Michele (2004b) discussed the use of copulas to assess the return period of hydrological events using bivariate models and defined the secondary return period. Kao and Govindaraju (2007a) quantified the effect of dependence between rainfall duration and average intensity on surface runoff and demonstrated that the use of copulas could result in simpler, more elegant mathematical treatment of zero runoff probabilities. Copulas were also applied in estimating groundwater parameters using a copula-based geostatistics approach (Ba´rdossy, 2006; Ba´rdossy and Li, 2008), drought analysis (Beersma and Buishand, 2004; Shiau, 2006; Shiau et al., 2007; Serinaldi

506

Statistical Hydrology

et al., 2009), multivariate L-moment homogeneity test (Chebana and Ouarda, 2007), tail dependence in hydrologic data (de Waal et al., 2007; Poulin et al., 2007), uncertainty quantification in remote-sensed data (Gebremichael and Krajewski, 2007; Pan et al., 2008; Villarini et al., 2008), rainfall IDF curves (Singh and Zhang, 2007), sea storm and wave height analysis (Wist et al., 2004; de Waal and van Gelder, 2005; De Michele et al., 2007), and atmospheric and climatologic studies (Vrac et al., 2005; Maity and Kumar, 2008; Norris et al., 2008). A review of copulas in Genest and Favre (2007) indicated that application of copulas in hydrology is still in its nascent stages, and their full potential for analyzing hydrologic problems is yet to be realized. More detailed theoretical background and descriptions for the use of copulas in problems related to water resources can be found in Dupuis (2007), Salvadori et al. (2007), and Salvadori and De Michele (2007).

2.18.5.3 Remarks on Copulas and Future Research While copulas can help advance hydrologic analysis at multivariate levels and provide broad potential applications, it is important to bear in mind also their limitations. One should always be aware that the reliability of copulas is founded upon the sufficiency and quality of observations. Copulas, like any other statistical methods, can only elucidate the information embedded in the samples. In order to characterize multivariate joint distributions, a much larger sample size is needed. As such, data processing and quality control will play equally important roles. The assumption of stationarity should also be examined. It is particularly necessary since the changing climate may cause fundamental changes in the past climate pattern and invalidate the predictions based on historic observations. Another major limitation, which is essential for more extensive usage in hydrologic applications, is the curse of dimensionality. Though Sklar’s theorem was proposed for a general dimension, most of the current copula functions are valid only on the bivariate level. Choices are limited especially on a higher dimension (44). It is a major disadvantage particularly while modeling natural phenomenon with a complicated dependence structure such as droughts. Moreover, the mathematical compatibility among various marginals and lower-level dependencies complicates the issue and it remains as an open problem (see Kao and Govindaraju, 2008). Further researches are deemed necessary to explore more choices of copulas, for the expanding possibility in modeling complex natural dependence structures.

2.18.6 Regional Frequency Analysis Regional frequency analysis is widely employed for estimating design variables in ungauged sites or when dealing with data record lengths that are short as compared to the recurrence interval of interest (see, e.g., Stedinger et al., 1993). Regionalization procedures embody the first principles proposed by the NRC-US (1988) for hydro-meteorological modeling: ‘substitute time for space’ by using hydrologic information

collected at different locations to compensate for the limited (or absence of) information at the site of interest. The literature proposes several approaches to regionalization (traditional approaches are illustrated for instance in Stedinger et al. (1993), Hosking and Wallis (1997), Pandey and Nguyen (1999), and FEH (1999)) and presents applications to different hydrological problems and contexts such as the T-year flood estimation (see, e.g., Dalrymple, 1960; Burn, 1990; Gabriele and Arnell, 1991; Castellarin et al., 2001; Merz and Blo¨schl, 2005), frequency analysis of low flows (see, e.g., Smakhtin, 2001; Castellarin et al., 2004; Laaha and Blo¨schl, 2006; Vogel and Kroll, 1992; Furey and Gupta, 2000), and rainfall extremes (Schaefer, 1990; Alila, 1999; Faulkner, 1999; Brath et al., 2003; Di Baldassarre et al., 2006b). This section makes a direct and explicit reference to regional flood frequency analysis (RFFA); nevertheless, concepts and algorithms presented herein are suitable for regionalization of other hydrological extremes (e.g., low flows and rainstorms). More in general, the main features of a regionalization procedure, which are summarized in the remainder of this section, can be easily extended to broader hydrologic problems such as the prediction of within-year variability of streamflow regime in ungauged basins. Examples are reported in the literature that describe how to regionalize annual and long-term flow–duration curves (see, e.g., Fennessey and Vogel, 1990; Smakhtin et al., 1997; Castellarin et al., 2004, 2007) or to predict the streamflow regime in ungauged catchments via simulation by using rainfall-runoff models with regional parameters (see, e.g., Parajka et al., 2007).

2.18.6.1 Index-Flood Procedure, Extensions and Evolutions A traditional approach to regional frequency analysis is the index-flood procedure (Dalrymple, 1960). The approach has a long history in hydrology and is based on the identification of homogeneous groups of sites (homogeneous regions) for which the frequency distribution of floods (or other hydrological extremes) is the same except for a scale parameter, called index flood (or in general index term, e.g., index storm for the regionalization of rainfall extremes), which reflects the local hydrological conditions. According to the index-flood procedure, the T-year flood at a given site, QT, can be expressed as the product of two terms: the index-flood, mQ, and the dimensionless growth factor qT, which describes the relationship between the dimensionless flood and the recurrence interval, T (the so-called growth curve):

QT ¼ mQ  qT

ð62Þ

The index flood mQ in (62) is generally a measure of central tendency of the at-site frequency distribution. It is common to refer to the mean of the distribution (see, e.g., Brath et al., 2001), but the literature points out that the median is a valid alternative (see, e.g., Robson and Reed, 1999). The procedure allows the estimation of more reliable growth curves due to the exploitation of information from the entire homogenous region. The index-flood approach can be applied with respect to AMS or PDS (also known as POT; see, e.g., Madsen et al., 1997a, 1997b). The remainder of this section refers directly to AMS, which are generally more common and easier to obtain,

Statistical Hydrology

(a)

Region 1

Region 1

Ungauged target site

Region 2

Region 2

Neighboring station

Region 3

Region 3

Region 4

(b)

Region 4

507

Non-neighboring station (c)

Figure 13 Approaches for the delineation of homogeneous regions: (a) geographically continuous regions; (b) noncontiguous homogeneous regions; and (c) hydrologic neighborhoods. From Ouarda TBMJ, Girard C, Cavadias GS and Bobee B (2001) Regional flood frequency estimation with canonical correlation analysis. Journal of Hydrology 254(1–4): 157–173, Fig. 1.

even though the concepts and procedures described can be easily extended for PDS (or POT) series. The classical implementation of the index-flood procedure is based on the most restrictive fundamental hypothesis of existence of homogeneous regions within which the statistical properties of dimensionless flood flows do not vary with location (e.g., dimensionless statistical moments such as the coefficients of variation and skewness are constant). Nevertheless, after proposing the original procedure the literature reported several extensions and evolutions, which partly relax the above fundamental hypothesis. The hierarchical application of the index-flood procedure, for instance, assumes that statistics of increasing order are constant within a set of nested regions; the larger the order of the statistics, the larger the region (see, e.g., Gabriele and Arnell, 1991). Another relevant example of evolution of the original hypothesis is or region of influence (RoI) approach (see, e.g., Burn, 1990), which replaces the original concept of fixed and contiguous regions with homogeneous pooling groups of sites. The pooling groups are identified in order to maximize the hydrological affinity with the site of interest (focused pooling), and the regionalization procedure enables one to weight the regional hydrological information according to the similarities with the site of interest and to use the at-site information in a very efficient way (see, e.g., Zrinji and Burn, 1996; Castellarin et al., 2001). Although the techniques for delineating homogeneous pooling group of sites were definitely enhanced and improved since the first studies on regionalization, moving toward more objective and process-related approaches, the definition of homogeneous pooling-group of sites is still a hot topic of regional frequency analysis, highly debated among the scientific community. During the evolution of the regionalization techniques, the definition of boundaries between poolinggroup of sites evolved steadily, from administrative boundaries to physiographic and meteo-climatic boundaries (panel a) in Figure 13), from geographically identifiable boundaries (panels a) and b) in Figure 11) to boundaries associated with the particular site of interest (panel c) in Figure 13; RoI, see, e.g., Burn, 1990; Reed et al., 1999; Ouarda et al., 2001.

The latest advances tend to remove completely the concept of boundaries from the definition of homogeneous regions (pooling group of sites). Examples in this direction are the models in which regional parameters vary continuously with geomorfoclimatic indices (see, for instance, Alila, 1999; Di Baldassarre et al., 2006b) or the adaptation of geostatistical interpolation techniques to the problem of regionalization of hydrological information.

2.18.6.2 Classical Regionalization Approach A few main steps characterize any regionalization procedure. If the index flood is selected as the regionalization scheme and one is interested in estimating the flood quantile at a given site, these steps can be summarized as follows (see, e.g., Hosking and Wallis, 1997): (1) estimation of the index flood, mQ; (2) estimation of the regional quantile, qT, that is, (2a) identification of homogeneous pooling group of sites, (2b) choice of a frequency distribution, and (2c) estimation of the regional frequency distribution; and (3) validation of the regional model.

2.18.6.2.1 Estimation of the index flood Without lack of generality, let us consider the case in which mQ is assumed to be the mean of the distribution. The estimation of the index flood is straightforward when the site of interest is gauged and the record length is sufficiently long. In this case mQ can be obtained directly by calculating the arithmetic mean of the available observations. Indirect methods have to be used in ungauged sites, instead. Multiregression models are probably the most common indirect methods (see, e.g., Brath et al., 2001; Castellarin et al., 2007). They link mQ to an appropriate set of morphological and climatic descriptors of the basin through statistical relations that, for instance, may read as An A3 ^Q ¼ A0 oA2 m 1 o2 yoi þ W

ð63Þ

^Q is the index-flood estimate, oi, i ¼ 1,2, y , n, are the where m explanatory variables of the model (i.e., a suitable set of

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geomorphologic and climatic indexes), Ai, i ¼ 0,1, y , n, are parameters, and y is the residual of the model. The structure of (63), that is, selection of the smallest and most efficient set of catchment descriptors, and the values of the parameters can be identified by multivariate stepwise regression analysis (e.g., Wiesberg, 1985; Brath et al., 2001). Instead of stepwise regression analysis, alternative multivariate procedures can also be adopted for this task, such as artificial neural network, principal component or canonical correlation analysis (see, e.g., Shu and Burn, 2004; Ouarda et al., 2001; Chokmani and Ouarda, 2004). The literature indicates that statistical indirect models such as (63) are generally more accurate than conceptual indirect models for predicting mQ in ungauged basins (see, e.g., Brath et al., 2001). The latter models attempt to interpret the dynamics of rainfall–runoff transformation and are characterized by a more rigid structure. As a consequence, conceptual models reduce the influence on model parametrization of the specific information which arrives from any gauged station and therefore are typically more robust than statistical models. The literature also reports that direct estimation may be a preferable alternative to indirect methods when 2–5 years of data are available, especially for basins with physiographic and climatic characteristics that differ significantly from the average characteristics of the set of basins considered for the identification of the indirect estimation models.

The literature reports on different approaches for delineating pooling groups of sites, as well as selecting and estimating the regional frequency distribution (see, e.g., GREHYS, 1996; FEH, 1999). Hosking and Wallis (1997) proposed an integrated approach completely based on the use of L-moments. The approach summarizes the frequency regime of the pooling group of sites through the regional L-moment ratios. Regional L-moment ratios can be defined as follows: R X

ni tðiÞ r =

i¼1

R X

ni

PR V1 ¼

ð64Þ

i¼1

where tr is the regional L-moment ratio of order r (e.g., the L-coefficients of variation, skewness, and kurtosis, L-Cv, L-Cs, ðiÞ and L-Ck, correspond to r ¼ 2, 3, and 4 in this order), tr is the sample L-moment ratio of order r for site i that can be computed as described in Section 2.18.3, ni is the record length for site i, while R is the number of sites in the pooling group. Numerous applications in different contexts and to different regionalization problems (not necessarily confined to the estimation of the design flood) proved the validity and reliability of the approach, whose main steps are briefly illustrated in this section.

2.18.6.2.3 Homogeneity testing Once a pooling group of sites has been delineated, its homogeneity degree has to be tested. The homogeneity of the group of sites is a fundamental requirement in order to perform an effective estimation of the T-year quantile (e.g., Lettenmaier et al., 1987; Stedinger and Lu, 1995: see Viglione

i¼1

 2 ðiÞ ni t2  t2 PR i¼1 ni

ð65Þ

A measure of dispersion for both the L-Cv and the L-Cs coefficients in the L-Cv–L-Cs space

PR

i¼1 ni



V2 ¼

2  2 1=2 ðiÞ ðiÞ t2  t2 þ t3  t3 PR i¼1 ni

ð66Þ

A measure of dispersion for both the L-Cs and the L-Ck coefficients in the L-Cs–L-Ck space

PR

i¼1 ni

V3 ¼

2.18.6.2.2 Estimation of the regional dimensionless quantile

tr ¼

et al. (2007) for a comparison of some homogeneity tests proposed by the literature). Hosking and Wallis (1997) defined a heterogeneity measure that is a standardized measure of the intersite variability of L-moment ratios. This measure is routinely used by hydrologists to test regional homogeneity. The Hosking and Wallis (1997) heterogeneity measure assesses the homogeneity of a group of basins at three different levels by focusing on three measures of dispersion for different orders of the sample L-moment ratios. A measure of dispersion for the L-Cv



2  2 1=2 ðiÞ ðiÞ t3  t3 þ t4  t4 PR i¼1 ni

ð67Þ

where t2, t3 , and t4 are the regional L-Cv, L-Cs, and L-Ck reðiÞ ðiÞ ðiÞ spectively; t2 ; t3 ; t4 , and ni are the values of L-Cv, L-Cs, L-Ck, and the sample size for site i; and R is the number of sites in the pooling group. The underlying concept of the test is to measure the sample variability of the L-moment ratios and compare it to the variation that would be expected in a homogeneous group. The expected mean value and standard deviation of these dispersion measures for a homogeneous group, namely mVk and sVk , are assessed through repeated simulations, by generating homogeneous groups of basins having the same record lengths as those of the observed data. To avoid any unduly commitment to a particular three-parameter distribution, the authors recommend the four-parameter kappa distribution to generate the synthetic groups of flood sequences. The kappa distribution includes, as special cases, several well-known twoand three-parameter distributions (see, e.g., Hosking and Wallis, 1997; Castellarin et al., 2007). The heterogeneity measures are then evaluated using the following expression:

Hk ¼

V k  mVk ; sVk

for k ¼ 1; 2; 3

ð68Þ

Hosking and Wallis suggest that a group of sites may be regarded as ‘acceptably homogeneous’ if Hko1, ‘possibly heterogeneous’ if 1rHko2, and ‘definitely heterogeneous’ if Hk42. According to the authors, these reference values are guidelines. For instance, the amount H ¼ 1 can be regarded as the borderline of whether a redefinition of the region may lead to a meaningful increase in the accuracy of the regional quantile estimate.

Statistical Hydrology

509

Pareto (GPA), lognormal (LN3), Pearson type III (PE3), and GEV (see Section 2.18.3). The measure defined for selecting the three-parameter distribution quantifies how well the L-Cs and the L-Ck of the of the fitted distribution match the regional average L-Cs and L-Ck. The fit can be geometrically interpreted on a diagram that reports the values of L-Cs and L-Ck on the x- and y-axis (L-moment ratio diagram) as the vertical distance between the point corresponding to the regional average and the curve representing the theoretical relationship between L-Cs and L-Ck for the considered distribution (see Figure 14). Hosking and Wallis (1997), Vogel and Fennessey (1993), and Peel et al. (2001), among others, recommended using the L-moment ratio diagrams to guide the selection of the most suitable parent distribution.

H1 is the most selective heterogeneity measure. H2 and H3 tend to identify larger homogeneous pooling groups of sites; therefore, the utilization of all three measures well suits the application of a hierarchical regionalization approach (Gabriele and Arnell, 1991; Castellarin et al., 2001).

2.18.6.2.4 Choice of a frequency distribution Hosking and Wallis suggest to base the selection of the parent distribution (the frequency distribution for all sites in the pooling-group) on the value of regional L-moments. The authors define a goodness-of-fit measure to be used for selecting the candidate distribution among a family of possible three-parameter distributions. The authors consider as possible candidates the generalized logistic (GLO), generalized

At-site data Mean of sample data Gumbel

U-uniform, E-exponential, G-gumbel, L-logistic, N-normal Generalized pareto Generalized extreme value Generalized logistic

Generalized extreme value Generalized pareto Generalized logistic Regional sample L-moments

0.7

0.25

0.5 L-kurtosis

L-kurtosis

0.20 0.3 L G

N

0.1

E

6h 3h 12 h 24 h 1h

0.15

U −0.1 0

0.2

0.4

0.6

L-skewness

(a)

0.10 0.10

0.8

1.0

L-kurt

0.6 0.4

0.20

0.25

0.30

L-skewness 0.5

GEV 0.4

GUM Data L-kurtosis

0.8

0.15

(b)

Mean

0.2

0.3 0.2 0.1

GEV Gumbel Mean

0.0 −0.2 −0.2 (c)

0.0 0.2 L-skew

0.6

1.0

−0.2 (d)

0.0

0.2 L-skew

0.4

0.6

Figure 14 (a) L-moment ratio diagrams: application to AMS of flood flows; (b) rainfall depths with different duration; (c) global data set of earthquake magnitudes; and (d) extreme wind speeds at 129 stations in the contiguous United States. (a) From Castellarin A, Burn DH and Brath A (2001) Assessing the effectiveness of hydrological similarity measures for flood frequency analysis. Journal of Hydrology 241: 270–285, Fig. 2. (b) From Brath A, Castellarin A and Montanari A (2003) Assessing the reliability of regional depth-durationfrequency equations for gaged and ungaged sites. Water Resources Research 39(12): 1367–1379, Fig. 3. (c) From Thompson EM, Baise LG and Vogel RM (2007) A global index earthquake approach to probabilistic assessment of extremes. Journal of Geophysical Research 112: B06314, Fig. 1. (d) Personal communication, Eugene Morgan, Tufts University, Deparment of Civil and Environmental Engineering, 2009.

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For the details concerning the goodness-of-fit measure the interested reader is referred to Hosking and Wallis (1997). The literature documents that the GEV distribution is conceptually appropriate and technically suitable for accurately reproducing the sample frequency distribution of geophysical extremes observed in different geographical contexts around the world (see, e.g., Stedinger et al., 1993; Vogel and Wilson, 1996; Robson and Reed, 1999; Castellarin et al., 2001; Thompson et al., 2007). For instance, Figure 14 shows by means of L-moment ratios the appropriateness of the GEV distribution for flood flows, rainfall extremes, earthquake magnitudes, and wind speed extremes.

2.18.6.2.5 Estimation of the regional frequency distribution The estimation of the regional distribution can be performed through the method of L-moments. This method is analogous to the method of moments, which is probably the oldest and widely understood technique for fitting frequency distributions to observed data (Vogel and Fennessey, 1993). The method equates regional L-moment estimates with the theoretical L-moments of the distribution, resulting in a system of nonlinear equations whose variables are the parameters to be estimated. The use of L-moments instead of conventional moments offers several advantages, for instance, the possibility to characterize a wider range of distributions, smaller bias and higher robustness of the estimators when applied to short samples (see, e.g., Hosking and Wallis, 1997). For the case of the GEV distribution, Hosking and Wallis (1997) proposed the following system of equations for the application of the method of L-moments:

k^R E 7:8590c þ 2:9554c 2 ;

^a R ¼

t2 k^R ð1  2k^ R ÞGð1 þ k^R Þ

;

c¼

2 ln 2  3 þ t3 ln 3

obtain reasonably accurate estimates of the T-year quantile avoiding undue extrapolations.

2.18.6.2.6 Validation of the regional model Regional flood frequency models are generally applied for predicting the flood frequency regime in ungauged basin. Therefore, it is fundamental to quantify the accuracy of the models and the uncertainty of regional estimates when no observation is available at the site of interest. A powerful and easy-to-implement cross-validation technique that can be used for this purpose is the jack-knife resampling procedure (see, e.g., Shao and Tu, 1995; Castellarin, 2007; Castellarin et al., 2007). Regardless of the regionalization approach or structure of the regional model being considered, the jack-knife procedure is a leave-one-out cross-validation technique that can be described as follows: 1. one gauging station (site i) is removed from the set of R stations; 2. the regional model is constructed for site i pooling group by neglecting site i data; 3. the quantity of interest (i.e., T-year flood QT) is estimated at site i through the regional model identified at step 2 (jackknife estimate); and 4. steps 1–3 are repeated R  1 times for each one of the remaining gauges. The R jack-knife estimates are then compared with the corresponding reference values (i.e., regional estimates that do consider the data observed at site i, or at-site estimate if viable), for instance in terms of relative BIAS, MSE, and Nash and Sutcliffe efficiency measure (NSE).

BIAS ¼

jk R 1X x^i  xi MSE ¼ xi R i¼1

^x R ¼ 1  ^a R ½1  Gð1 þ k^R Þk^R ð69bÞ

^ R, a ^ R , and ^ where k x R indicates the regional estimates of the GEV t2 and t3 are the regional L-Cv and L-Cs, GðxÞ ¼ parameters, R x x1 t e dt is the gamma function, and the regional GEV parent 0t is assumed to have unit mean (i.e., index flood coincides with the mean of the distribution). The empirical polynomial equation in c has accuracy better than 9 104 for typical L-Cs values. Once the regional parameters are estimated, the regional dimensionless quantiles can be computed as follows:

(    ^ R ) ^R 1 k a 1  ln 1  q^T ¼ x þ T k^R ^R

    1 ^R ln ln 1  q^T ¼ ^xR  a T

for ka0

for ka0

ð70aÞ

ð70bÞ

To improve the accuracy of the regional quantile estimates, the target pooling-group size can be determined according to the 5T guideline (Jakob et al., 1999), which suggests that a pooling group should contain at least 5T station-years of data so as to

R 1X x^i  xi R i¼1 xi jk

ð69aÞ

!2

jk R X x^i  xi NSE ¼ 1  xi  x i¼1 jk

!2 ð71Þ

where xi and x^i are respectively the reference and jack-knife estimates for site i; R is the number of sites in the pooling group; and x is the average of the R reference values. NSE varies between  N and 1, where NSE ¼ 1 indicates the perfect fit, and NSE ¼ 0 stands for a model that performs as efficiently as a mean regional value. The general structure of the jack-knife procedure can be applied for the cross-validation of any regional model. The actual implementation of step 2 depends on the particular regional model being considered. For example, if a regional multiregression model for the estimation of the index flood is considered, step 2 will involve the calibration of the statistical coefficients of the model (i.e., coefficients Aj, with j ¼ 1,2, y , n in Equation (63)). If the estimation of QT is considered instead, step 2 will include the whole regionalization process. Figure 15 reports an example of cross-validation for a regional model adopting the index-flood scheme (see also Castellarin, 2007).

Statistical Hydrology

511

2 Subregions

Relative error

1.5

53 45 43 47 46 5 50 44 48 49

1 0.5

Tyrrhenian sea

Region W

54

56 5762 55 64 66 69 61 59 63 68 70 71 58 60

0

72 74 73

Adriatic sea

75

−0.5 −1

Region C Region E

0 QT

qT

Q

50

100

78 79 80 81

km

Figure 15 Box plots summarizing the relative error distributions in terms of 25th, 50th, and 75th percentiles, maximum and minimum values and outliers (circles) for cross-validated flood quantiles of given recurrence interval T, QT, dimensionless flood quantiles, qT, and index flood, mQ for the three homogeneous regions depicted in the right panel. From Castellarin A, Camorani G and Brath A (2007) Predicting annual and long-term flowduration curves in ungauged basins. Advances in Water Resources 30(4): 937–953, Fig. 6.

2.18.6.3 Open Problems and New Advances RFFA has been a research sector for more than five decades now, yet the scientific community is still very active on this topic. This results from the existence of open problems, as it is discussed later, but it also can be ascribed to the potentiality of statistical regionalisation for solving a very common problem in hydrology, that is prediction in ungauged basins (see, e.g., Sivapalan et al., 2003). For instance, probabilistic interpretation and regionalization of classical deterministic hydrological tools (e.g., flow duration curves, FDC, regional envelope curves of flood flows, REC, etc.) renewed the scientific appeal of these simple methods, further promoting RFFA among hydrological research topics (see, e.g., Castellarin et al. (2004, 2007) for FDC and Castellarin et al. (2005) and Castellarin (2007) for REC). Some issues and aspects associated with RFFA may perhaps be considered to be well studied and the margin of improvement in the accuracy of regional estimates associated with them is probably rather limited. Examples are the choice and estimation of the regional parent distribution or the statistical homogeneity testing (Castellarin and Laio, 2006). Some other issues are still critical, instead, and further analyses may significantly improve the accuracy of regional predictions in ungauged sites. One of these issues is certainly the estimation of the index flood in ungauged basins. Figure 15 eloquently shows for a given case study, but this is a widespread condition (see, e.g., Kjeldsen and Jones, 2007) that the largest amount of uncertainty is associated with this step of the regionalization procedure. Investigators are still dedicating a great deal of effort to the improvement of existing methodologies (see, e.g., Shu and Burn, 2004; Kjeldsen and Jones, 2007) and to the definition of guidelines for the identification of the most reliable and suitable ones depending on the problem at hand (see, e.g., Bocchiola et al., 2003). Also, classical studies document that intersite correlation among flood flows observed at different sites is typically not

negligible (see, e.g., Matalas and Langbein, 1962; Stedinger, 1983) and leads to increases in the variance of regional flood statistics (see, for instance, Hosking and Wallis, 1988). Nevertheless, the analysis of the impacts of cross-correlation on regional estimates is still poorly understood. Recent studies have pointed out that cross-correlation may significantly reduce the regional information content in practical applications, quantified in terms of equivalent number of independent observation (see, e.g., Troutman and Karlinger, 2003, Castellarin et al., 2005; Castellarin, 2007). This reduction has an impact on the reliability of regional quantiles, as it increases the variance of regional estimators, and can also severely affect the power of statistical tests for assessing the regional homogeneity degree (Castellarin et al., 2008). The delineation of homogeneous pooling group of sites, or catchment classification, is still an open and highly debated problem, on which the scientific community is very active (see, e.g., McDonnell and Woods, 2004). Concerning this issue, the main research activities focus on: (1) the identification of the most descriptive and informative physiographic and climatic catchment descriptors to be used as proxies for the flood frequency regime (see, e.g., Castellarin et al., 2001; Merz and Blo¨schl, 2005) and (2) the development of pooling procedures as objective and nonsupervised as possible. Several objective approaches have been proposed by the scientific literature, such as cluster analysis (Burn, 1989) or unsupervised artificial neural networks (ANNs) (see, e.g., Hall and Minns, 1999; Toth, 2009). Furthermore, the scientific community is dedicating an increasing attention to the possibilities offered by the application of geostatistical techniques to the problem of statistical regionalisation. These techniques have been showed to have a significant potential for regionalization and, for this reason, will be briefly discussed and presented in this section. Geostatistical procedures were originally developed for the spatial interpolation of point data (see, e.g., kriging: Kitanidis, 1997). The literature proposes two different ways to apply geostatistics to the problem of regionalisation of hydrological

Statistical Hydrology

2 1 0 −1 −2 −3 −4 −5 2

Standardised Q100

512

0.30 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.80 1.00 2.00

1 0

−1

y (a)

−2

−3 −2

−1

1

0

2

3

x

(b) 3 1

2

Figure 16 (a) Topkriging: 100-year flood per unit area (color codes in m s km ) for a portion of the Mur region (Austria): Topkriging estimates along the stream network and empirical values as circles. From Skøien JO, Merz R and Bloschl G (2006) Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences 10(2): 277–287, Fig. 7. (b) PSBI: 3D representation of standardised value of 100-year flood over the physiographic space identified for a set of basins in northern central Italy (gauged basins are represented as dots).

information. The first technique is called physiographic spacebased interpolation (PSBI) and performs the spatial interpolation of the desired hydrometric variable (e.g., T-year flood, but also annual streamflow, peak flow with a certain return period, low flows, etc.) in the bidimensional space of geomorphoclimatic descriptors (Chokmani and Ouarda, 2004; Castiglioni et al., 2009). The x and y orthogonal coordinates of the bidimensional space are derived from an adequate set of n41 geomorphologic and climatic descriptors of the river basin (such as drainage area, main channel length, mean annual precipitation, and indicators of seasonality; see Castellarin et al., 2001) through the application of multivariate techniques, such as the principal components or canonical correlation analysis (Shu and Ouarda, 2007). The second technique, named Topological kriging or Topkriging, is a spatial estimation method for streamflow-related variables. It interpolates the streamflow value of interest (i.e., T-year flood, low-flow indices, etc.) along the stream network by taking the area and the nested nature of catchments into account (Skøien et al., 2006; Skøien and Blo¨schl, 2007). The philosophy behind these innovative approaches to regionalization is rather interesting because they enable one to regionalize hydrometric variables dispensing with the definition or identification of homogeneous regions or pooling groups of sites (see Figure 13). The approaches are particularly appealing for predictions in ungauged basins as they provide a continuous representation of the quantity of interest (e.g., T-year flood) along the stream network (Topkriging) or in the physiographic space (PSBI), providing the user with an estimate of the uncertainty associated with the interpolated value. In particular, the final output for Topkriging is the estimation of the measure of interest (with uncertainty) along the stream network (see Figure 16). A little less intuitive is the output of PSBI. With this technique any given basin (gauged or ungauged) can be represented as a point in the x–y space described above; in the same way the set of gauged basins of the study area can be represented by a cloud of points in this space. The empirical values of the quantity of interest (e.g., T-year flood) can be represented along the third dimension z for

each gauged catchment, and can then be spatially interpolated (with uncertainty) by applying a standard interpolation algorithm (e.g., ordinary or universal kriging). The spatial interpolation enables one to represent the quantity of interest over the entire portion of the x–y space containing empirical data, and therefore to estimate it at ungauged sites lying within the same portion of the space (see Figure 16).

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Relevant Websites http://www.stahy.org STAHY- WG, Statistics in Hydrology Working Group. http://www.iash.info What you need, when you need it.

2.19 Scaling and Regionalization in Hydrology G Blo¨schl, Vienna University of Technology, Vienna, Austria & 2011 Elsevier B.V. All rights reserved.

2.19.1 Introduction 2.19.2 The Linear Statistical Approach 2.19.2.1 Geostatistics 2.19.2.2 Scaling: Variance Reduction of Aggregation 2.19.2.3 Regionalization: The Top-Kriging Method 2.19.3 Scaling in Hydrology 2.19.3.1 Self-Similarity and Fractals 2.19.3.2 Effective Parameters 2.19.3.3 Groundwater Models 2.19.3.4 Runoff Models 2.19.3.5 Global Circulation Models 2.19.4 Regionalization in Hydrology 2.19.4.1 Similarity Measures 2.19.4.2 Floods 2.19.4.3 Low Flows 2.19.4.4 Runoff Model Parameters 2.19.5 Concluding Remarks Acknowledgment References

2.19.1 Introduction Hydrological processes exhibit an astounding variability in both space and time. The purpose of the hydrological sciences is to understand this variability, understand where and when the water flows, how much and in what quality. In fact, there is astounding variability at all scales, in both space and time. At the smallest scales of interest in hydrology, water fluxes and composition may vary between individual pores of the soil, and climate and hydrological processes vary over continental scales as well. Infiltration may vary over seconds and groundwater tables may vary over decades and more. Within these limits, variability abounds (Sivapalan, 2003a; Blo¨schl and Zehe, 2005). Virtually, any quantitative approach to this problem requires the selection of a limited set of spatial and temporal scales. Any particular choice of time and space scales has a major influence on which aspects of this hydrological variability are perceived. Measurements of hydrological quantities are rarely available at the right scales. For example, soil samples, typically, are less than 1 dm3 is size and yet, in modeling the rainfall–runoff processes of catchments, one attempts to represent water flow in the soils at the scale of square kilometers. One is therefore left with the problem of relating the small-scale measurements to larger-scale model descriptions. The process of doing this is usually termed scaling, either upscaling when going from small to large scales or downscaling, when going from large to small scales. Similarly, runoff measurements are never available at all locations of a stream. When estimating runoff characteristics at locations without measurements, procedures usually referred to as regionalization are used, which is a summary term for spatial analysis and estimation in hydrology.

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Various aspects of scale issues in hydrology have been reviewed by Blo¨schl and Sivapalan (1995) and Blo¨schl (1999, 2005a). A recent concerted effort has been devoted to improving regionalization methods of predicting hydrological variables in ungauged catchments, which has been singled out as one of the major research issues in the hydrological sciences (Sivapalan, 2003b). The International Association of Hydrological Sciences has announced a decade of research on the socalled prediction in ungauged basin (PUB) problem, which is often addressed by regionalization methods (Sivapalan et al., 2003). The purpose of this chapter is to summarize the main concepts of scaling and regionalization in hydrology and assess their applicability to real-world problems. Before dealing with the individual methods in detail, it is useful to understand the concept of scale as used in hydrology. Natural variability can be characterized by characteristic lengths (Skøien et al., 2003) such as the average distance over which, say, soil properties are correlated. A sampling exercise will rarely reveal the underlying natural variability in full detail because of instrument error and because the spatial and temporal dimensions of the instruments or measurement setup will always be finite. Blo¨schl and Sivapalan (1995) proposed the concept of a sampling scale triplet to represent the spatial dimensions of measurements. The scale triplet consists of the spacing, extent, and support of the data (Figure 1). In dedicated studies, soil hydraulic conductivity measurements, for example, may have spacings of decimeters, while rain gauges in a region are typically spaced at tens of kilometers. The extent, which is the overall size of the domain sampled, may range from meters to hundreds of kilometers in hydrological applications. The support is the integration volume or area of the samples ranging from, say, 1 dm2 in the

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Extent

Support

Space

Space

Space

Figure 1 Scale triplet to represent the spatial dimensions of measurements or a model. From Blo¨schl G and Sivapalan M (1995) Scale issues in hydrological modelling – a review. Hydrological Processes 9: 251–290.

case of time domain reflectometry in situ probes, to hectares in the case of groundwater pumping tests (Anderson, 1997) or micrometeorological studies of the atmosphere (Schmid, 2002), and square kilometers in the case of remotely sensed data (Western et al., 2002). In hydrology, the catchment area can also be thought of as a support scale (Bierkens et al., 2000). For the case of models, the notion of a scale triplet is similar. For example, for a spatially distributed hydrologic model, the scale triplet may have typical values of, say, 25-m spacing (i.e., the grid spacing), 1-km extent (i.e., the size of the catchment or aquifer to be modeled), and 25-m support (the cell size). Analog scales apply to the temporal domain. The important point in the context of upscaling and downscaling is that the sampling scale triplet will have some bearing on the data and the modeling scale triplet will have some bearing on the predictions. Generally, if the spacing of the data is large, the small-scale components of the natural variability will not be captured by the measurements. If the extent of the data is small, the large-scale variability will not be captured and will translate into a trend in the data. If the support is large, most of the variability will be smoothed out and the data will appear very smooth. These sampling scale effects can be thought of as a kind of filtering in that the true patterns are filtered by the properties of the measurements (Skøien and Blo¨schl, 2006c; Blo¨schl and Grayson, 2000). In the case of models, the scale effects are similar.

Change of spacing

Singling out

Interpolation

The different types of upscaling and downscaling are illustrated in Figure 2. Downscaling in terms of spacing (i.e., decreasing the spacing) is usually referred to as interpolation; the opposite is singling out. Upscaling in terms of extent (i.e., increasing the extent) is usually referred to as extrapolation, the opposite is, again, singling out. Upscaling and downscaling in terms of the support is referred to as aggregation and disaggregation, respectively, particularly if the spacing is changing at the same time as the support. The scheme in Figure 2 can relate to both the sampling step (upscaling/downscaling from the underlying distribution to the data) and the modeling step (upscaling/downscaling from the data to the model predictions). These two steps are conceptually similar.

2.19.2 The Linear Statistical Approach 2.19.2.1 Geostatistics When one attempts to represent spatial hydrological variability (both in the context of scaling and regionalization), one can choose a geostatistical approach with a number of simplifying assumptions:

•

The hydrological quantity can be represented by a spatial random variable.

Change of extent

Singling out

Extrapolation

Change of support

Aggregation

Disaggregation

Figure 2 Schematic of upscaling and downscaling by changing the scale triplet. The top row represents the underlying natural variability. The bottom row shows the actual information reflected in the samples (or the model).

Scaling and Regionalization in Hydrology

•

The distribution function and the spatial correlation (represented by the variogram) fully represent the variability of that quantity, and the variogram does not change with location (something termed second-order stationarity).

This then allows us to:

•

characterize the spatial structure of hydrological quantities by the variogram.

For example, the variogram is used to characterize rainfall fields and the heterogeneity of aquifers (Skøien et al., 2003; Blo¨schl, 2005a). The variogram is defined as half the spatial variance of the variable Z at two points with a spacing of h, plotted against that spacing, that is,

gðhÞ ¼ 12Ef½Zðx þ hÞ  ZðxÞ 2 g

ð1Þ

where x is the location and E denotes the expected value. The variogram contains equivalent information to the spatial correlation function. There are a number of parametric variogram models that are fitted to the data such as an

521

exponential variogram:

gðhÞ ¼ s 2  ð1  expðh=lÞÞ

ð2Þ

where l is the correlation length, s2 is the variance, and h is the distance between two points in the random field as mentioned above. This type of variogram implies that g(0) ¼ 0, that is, it is assumed that, for very small distances, the variable Z does not vary because there is no microscale variability and no measurement error. The variogram embodies the type of correlation, that is, how similar adjacent measurements are. If l is large, the spatial field will appear smooth. If s2 is large, there are big differences between the values in the field. Figure 3 illustrates the relationship between the variogram and the underlying pattern of the variable of interest. There are additional useful assumptions for an estimation method: the estimation method

• • •

is linear, is unbiased (i.e., the expected values of the estimator and the underlying random variable are identical), and minimizes the expected estimation error.

Figure 3 Spatial fields of a random variable (such as rainfall, soil moisture, or hydraulic conductivity) with different variances and correlation lengths and associated variograms (blue lines): (a) large variance, small correlation length; (b) large variance and large correlation length; (c) small variance, small correlation length; and (d) small variance and large correlation length. g is half the spatial variance of a variable at two points with a spacing of h.

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This then allows us to:

•

support scale is related to the same variable at a different support scale. The most obvious assumption is that:

estimate the quantity of interest at locations without measurements, using geostatistical regionalization methods such as kriging.

The linearity assumption above implies that the unknown value ^zðx0 Þ of the hydrological variable at position x0 can be estimated as a weighted average of the variable measured in the neighborhood:

^zðx0 Þ ¼

n X

•

This then allows us to:

• li zðxi Þ

ð3Þ

the hydrological variable aggregates linearly or, in other words, simple arithmetic averaging applies. For example, the arithmetic average of soil moisture samples in a catchment will give the total moisture volume in that catchment.

scale the distributions for that variable from small to large scales or conversely (upscaling and downscaling).

i¼1

where li is the interpolation weight of the measurement at position xi and n is the number of neighboring measurements used for interpolation. The weights li should not be confused with the correlation lengths l in Equation (2). The similar notation is coincidence and has been chosen here because of their usage in the literature. The weights li can be found by solving the kriging system (which derives from the other two assumptions):

Pn

j¼1 lj gij

 lj s2i þ m ¼ g0i ; Pn i¼1 li ¼ 0

i ¼ 1; y; n

ð4Þ

The gij’s are the expected semivariance between two measurements i and j, as found from a semivariogram model such as Equation (2). m is the Lagrange parameter. s2i represents the measurement error or uncertainty of measurement i. The use of measurement errors in the kriging equations is termed kriging with uncertain data (KUD) (de Marsily, 1986, p. 300; Merz and Blo¨schl, 2005). There is an abundant literature on geostatistics (e.g., Journel and Huijbregts, 1978; Isaaks and Srivastava, 1989; Webster and Oliver, 2001) and numerous software packages exist that facilitate application of the method (e.g., GSLIB (Deutsch and Journel, 1997), SURFER (Golden Software, 2009), and the R software environment for statistical computing (R-software, 2009)).

2.19.2.2 Scaling: Variance Reduction of Aggregation

Variance

In order to use geostatistical theory for the scaling problem, an additional assumption is needed of how the variable at one

Note that linear aggregation does not always apply. In Darcy’s law, for example, the average hydraulic conductivity over an area does not give the average flux over the same area. Similarly, the average hydraulic roughness of a surface does not give the average flow on that surface. We will now illustrate the effects of the scale triplet (as in Figures 1 and 2) on samples and model results for the simplest case of linear upscaling and downscaling based on the geostatistical assumptions above. We will also assume that the spatial correlation can be represented by the exponential variogram of Equation (2). Figure 4 shows the results from a Monte Carlo analysis where hypothetical samples were drawn from two-dimensional random fields. From the samples, the sample variance was calculated. The results indicate (and this is, of course, consistent with theory) that large spacings (relative to the correlation length of the underlying variability) do not bias the sample variance, but small extents do and will lead to an underestimation of the variance. The latter is because not the entire variability is sampled (see Figure 2). Large supports will reduce the variance and this is related to the smoothing effect of the support mentioned above. It is clear that spacing, extent, and support are all scales but they have a different role in upscaling and downscaling. For example, the variance of, say, precipitation tends to increase with scale (if scale is defined as extent) but decreases with scale (if scale is defined as support) because of the filtering involved. Geostatistical methods allow us to estimate these sampling biases in a consistent manner. For example, the lines in Figure 4 are taken from Western and Blo¨schl (1999) and there is a rich geostatistical literature on methods for estimating these scale effects, in particular those on the support (e.g., Journel and Huijbregts, 1978; Isaaks and Srivastava, 1989; Goovaerts, 1997; Chile`s and Delfiner, 1999).

1

1

1

0.1

0.1

0.1

0.01

0.01

0.01

1

10 Spacing / λ

0.1

1 Extent / λ

10

0.1

1 Support / λ

10

Figure 4 Effect of the sampling scale triplet on the sample variance for the case of a two-dimensional stationary random field. l is the correlation length of the random field (see Figure 3). The circles show the ensemble mean and the error bars represent the standard deviation around the ensemble mean for 100 gridded samples from a Monte Carlo study. The solid lines show the predictions for the ensemble mean. From Skøien JO and Blo¨schl G (2006a) Sampling scale effects in random fields and implications for environmental monitoring. Environmental Monitoring and Assessment 114(1–3): 521–552.

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This literature also gives methods for estimating the effects of the scales on the sample variogram. Monte Carlo simulations of these scale effects can be used to assess the uncertainty in the variogram that is introduced by the sampling scales (Skøien and Blo¨schl, 2006a, 2006b). The reduction in variance as a result of increasing support is widely used for linear aggregation methods and may also give some guidance for nonlinear cases such as extreme value analyses of rainfall and model parameter aggregation (see, e.g., Sivapalan and Blo¨schl, 1998).

2.19.2.3 Regionalization: The Top-Kriging Method In the regionalization problem, we are interested in estimating streamflow-related variables at locations where no measurements are available. The main advantage of using geostatistical methods for this purpose is that they are best linear unbiased estimators as noted above and they can provide estimates of the uncertainty as well. Geostatistical methods have evolved in the mining industry where the main problem consisted of estimating the expected ore grade (and its uncertainty) of a block using point samples of the ore grade in the area (Journel and Huijbregts, 1978). However, the problem in catchment hydrology is quite different in that catchments are organized into nested subcatchments. Water follows a stream network. It is therefore clear that upstream and downstream catchments would have to be treated differently from neighboring catchments that do not share a subcatchment. In order to take the stream network topology into account, a method termed ‘topkriging’ has been developed (Skøien et al., 2006). The main idea behind the method is to combine two groups of hydrological variability. The first group consists of

523

variables that are continuous in space such as rainfall, evapotranspiration, and soil characteristics, which are related to local runoff generation. In top-kriging, the variability of these continuous processes in space is represented by the variogram. The second group of variables, such as runoff, is related to routing in the stream network. These variables are only defined for points on the stream network. In top-kriging, the aggregation effects that lead to these variables are represented by the catchment boundaries associated with each point on the stream network. In the method, the distance between two catchments is measured by the geostatistical distance of the two catchment areas rather than by the Euclidian distance of the stream gauges. The geostatistical distance is the average distance of all pairs of points in the two catchment areas, thus taking the catchment shapes and the pattern of the relative locations of the catchments into account. The Euclidian distance can either be chosen as the distance between the stream gauges or the distance between the centers of gravity of the catchment areas and, hence, contains much less information than the geostatistical distance. The variogram is then aggregated over the catchment areas (i.e., the support as in Figures 1 and 4) and kriging is performed to estimate the variables at the ungauged locations. Variables that can be estimated include the mean annual runoff, flood characteristics, low-flow characteristics, concentrations, turbidity, and stream temperature. To illustrate the approach, Figure 5 shows an example of using top-kriging to regionalize the specific 100-year flood in southeastern Austria. Some of the stream gauges are situated directly at the main stream, others at the tributaries. The topkriging estimates at the main stream are similar to the measurements at the main stream and they do not change much

10 km 0.6

1.0 m

km−2

M

ai

n

st

re

am

0.3

3 s−1

Streamflow measurements 10 km

Top-kriging

Ordinary kriging

Figure 5 Estimates of the normalized specific 100-year flood Q100N from top-kriging and ordinary kriging color-coded on the stream network of the Mur region. The measurements (i.e., values at the stream gauges) are shown as circles. Units are in m3 s1 km2. From Skøien J, Merz R, and Blo¨schl G (2006) Top-kriging – geostatistics on stream networks. Hydrology and Earth System Sciences 10: 277–287.

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along the reach as would be expected. The estimates at the northern tributaries are much smaller than those at the main stream, which is consistent with the measurements at the same tributaries. For comparison, the results of using ‘ordinary kriging’ are also shown. Although the gauge in the center of the panel has measurements of 0.4 (red color), most of the ordinary kriging estimates along this tributary are around 0.6 (yellow to green colors). This is because the estimates along this tributary are too much influenced by the measurements along the main river while they should be mainly influenced by the downstream gauge as is the case in top-kriging. On the other hand, the estimates at the main stream are somewhat underestimated by ordinary kriging as they are too much affected by the measurements at the tributaries. Top-kriging takes both the area and the nested nature of catchments into account while ordinary kriging cannot separate the main stream and the tributaries since Euclidian distances are used. Because of the minimum number of assumptions, topkriging is the most natural method of estimating streamflowrelated variables on stream networks. It is currently being used for spatially interpolating a range of variables, including runoff time series, flood characteristics, low flow characteristics, and stream temperatures (Skøien and Blo¨schl, 2007; Merz et al., 2008; Laaha, 2008).

2.19.3 Scaling in Hydrology 2.19.3.1 Self-Similarity and Fractals The concept of self-similarity and fractals revolves around the idea that the small-scale properties of a variable are similar to the large-scale properties of the same variable:

˜ ¼ v b  jðv  xÞ ˜ jðxÞ

ð5Þ

where j is a property of the variable; x˜ is space or time scale; n ˜ and b is is the ratio of the large scale v  x˜ to the small scale x; the scaling exponent. The space or time scale x˜ is usually taken as the spacing between two points (in space or time) but, alternatively, it can be taken as the support or the extent (Figure 1). The idea of fractals was first conceived by Richardson (1961) in the context of estimating the length of the borderline of states to understand whether it is related to the likelihood of armed conflicts. In hydrology, this self-similarity concept is mainly used because it is often able to represent the variability of hydrological variables over many orders of magnitudes of scales – from millimeter to kilometer and from minutes to centuries. Depending on what property, j, is considered, different types of scaling relationships are used in hydrology:

•

•

Perimeter–area relationships: These are, for example, relationships between the perimeter and the area of clouds (Lovejoy, 1982) or similar relationships for catchments (Hack, 1957). Probability density functions (pdf): In this type of fractals, the property, j, that is self-similar is the pdf and, in essence ,suggests that the pdf is a power law. Examples include the distribution of contributing area and the product of area and slope in river basins (Rodrı´guez-Iturbe et al., 1992).

•

Variogram: Here, the variogram is a power law, which is different from the exponential variogram in Equation (2).

gðhÞ ¼ a  h b

ð6Þ

where b is the scaling exponent and a is a constant. A power law variogram suggests that there is variability at all scales, that is, there is no scale where the spatial field becomes stationary. This type of variogram is typical of many hydrologic processes where it is possible to fit a straight line to the variogram in a log–log plot. However, whether this type of variogram should be preferred over a nested variogram is not always clear (see Blo¨schl, 1999). All the fractal relationships listed above are represented by power laws. This is because a power law is consistent with selfsimilarity as indicated by Equation (5). From a practical hydrological perspective, the fractal variograms are probably the most relevant scaling relationships. They are scaling relationships as they relate the variance at the small scale (small h) to the variance at the large scale (large h). Fractal variograms can be used to represent the spatial variability of many variables in hydrology such as rainfall, soil characteristics, and aquifer hydraulic conductivity (Skøien et al., 2003). Based on this characterization, one can, for example, derive aggregate parameters for groundwater models (see Section 2.19.3.2), and interpolate variables such as soil parameters with different sampling supports. One can also generate two- or three-dimensional fields similar to those in Figure 3, which can then be used as inputs (e.g., rainfall) or parameters (e.g., hydraulic conductivity) of models. In the time domain, the self-similar temporal characteristics of stream flow have been strongly disputed in hydrology under the topic ‘Hurst phenomenon’ (Hurst, 1951). In essence, the idea is that variability occurs at all scales – from hours to centuries, which implies long-term persistence. The latter has been usually interpreted as implying the existence of an infinite memory of the hydrologic system, which is difficult to understand from a process perspective as the storage capacity of catchments is always finite. Klemesˇ (1974), however, indicated that long-term persistence is not necessarily a consequence of infinite memory and may be related to nonstationarities in the data. There are also issues with the way the data are usually analyzed (Kirchner, 1993). Notwithstanding this discussion, the presence of hydrological variability at all time scales is widely accepted in the literature (see, e.g., Koutsoyiannis, 2002). There are more complicated types of fractals than those discussed here and a rich literature is available (e.g., Feder, 1988). Multifractals (Sreenivasan, 1991) are one type of more complicated fractals that are used in hydrology. They allow for multiple scaling exponents and are able to represent the intermittency of rainfall well. They are hence mostly used in rainfall models in hydrology (Gupta and Waymire, 1993; Menabde and Sivapalan, 2001; Sivapalan et al., 2005).

2.19.3.2 Effective Parameters As mentioned above, the sample size in hydrology is, often, much smaller than the model element size. Typical examples

Scaling and Regionalization in Hydrology

are groundwater models. Samples of hydraulic conductivities taken from boreholes have support scales of a decimeter, while the size of the elements of groundwater models ranges from tens of meters to kilometers, depending on the application. Similarly, model equations such as Darcy’s law apply at the sample scale of a decimeter as this is the scale at which laboratory tests are made. If the subsurface were homogeneous, these samples could be directly used to specify the parameters (in this case, hydraulic conductivity) of the model and the equations could be directly used as well. However, aquifers and catchments are usually very heterogeneous; therefore, one needs an upscaling procedure to obtain the parameters and the underlying equations at the right scale. One way of addressing this issue is to assume that the parameters are uniform within each (large scale) model element and that the small-scale equations apply to the whole element. The small-scale parameters are then replaced by effective parameters that are applicable at the (large) scale of the model elements. This gives rise to two questions: (a) Can the small-scale equations be used to describe processes at the large scale (or, in other words, do effective parameters exist)? (b) If so, what is the aggregation rule (or scaling rule) to obtain the large-scale model parameters from the detailed pattern or the distribution of the small-scale parameters. Methods that address (a) and (b) make use of the definition of effective parameters by matching the outputs of the uniform and the heterogeneous systems. If an adequate match can be obtained, an effective parameter exists. The aggregation rule is then derived by relating the effective parameter to the underlying heterogeneous distribution. This is shown schematically in Figure 6. The answer to (a) obviously depends on the type of problem. For groundwater flow, effective parameters exist for many cases (see Section 2.19.3.3). For unsaturated flow in porous media, generally, there exists no effective conductivity that is a property of the medium only (Russo, 1992) and approximate effective parameters may exhibit hysteresis as a

Input

Identical

Heterogeneous medium

Output

Identical

Scaling rule

Input

Uniform effective medium

Output

Figure 6 Schematic definition of effective parameters by matching outputs of the uniform and the heterogeneous systems. The aggregation rule (or scaling rule) as indicated by the double arrow is derived by relating the effective parameter to the heterogeneous distribution. From Blo¨schl G (1996) Scale and scaling in hydrology (Habilitationsschrift). Wiener Mitteilungen, Wasser-Abwasser-Gewa¨sser, Band 132, Institut fu¨r Hydraulik, TU Wien, 346pp.

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result of the soil spatial variability (Mantoglou and Gelhar, 1987). For land–atmosphere interactions, again, the existence of effective parameters depends on the degree of nonlinearity (Raupach and Finnigan, 1995; Lhomme et al., 1996). The upscaling methods for obtaining effective parameters work well if the media is disordered, that is, conductivities look like the patterns in Figure 3. In reality, the media are often spatially organized and preferential flow paths may exist. In this case, most upscaling methods based on stochastic theory do not work well as the main assumptions are violated (e.g., the assumption that conductivity is a random variable and can be represented by the variance as in Section 2.19.2.2). The variogram simply cannot capture preferential flow and alternative concepts are needed to represent the connectivity of the flow paths at a range of scales (see, e.g., Western et al., 1998, 2001; Trinchero et al., 2008, Schaap et al., 2008). Effective parameters are often used inadvertently (and without scaling rules) when assuming uniform model parameters within an element and calibrating the model output to field data. While, from a practical perspective, this is a useful approach for both groundwater and rainfall–runoff modeling, it is important to realize that effective parameters will depend not only on the media characteristics (such as the hydraulic conductivity) but also on the flow pattern. If hydraulic conductivity is anisotropic (depends on direction), changed flow patterns will result in a changed effective conductivity; therefore, the calibrated parameters do not necessarily apply to scenarios with changed flow patterns.

2.19.3.3 Groundwater Models As mentioned above, scaling in relation to groundwater models is a well-studied research issue (see, e.g., Dagan, 1989; Gelhar, 1993; Wen and Go´mez-Herna´ndez, 1996; Paleologos et al., 1996; Rubin, 2003). Here, we will only give a very brief summary of effective parameters that are of interest from a practical perspective. Consider uniform (parallel flow lines) two-dimensional steady saturated flow through a block of porous medium made up of smaller blocks of different conductivities. For an arrangement of blocks in series, the effective conductivity equals the harmonic mean of the block values while, for an arrangement of blocks in parallel, the effective conductivity equals the arithmetic mean. This scaling rule derives directly from Darcy’s law and illustrates the dependence of effective parameters on flow patterns mentioned above. If one assumes that the porous medium is a random field, there are comprehensive theories of how to estimate the effective parameters. Whatever the spatial correlation and distribution function of conductivity and whatever the number of dimensions in space, the average conductivity always ranges between the harmonic mean and the arithmetic mean of the local conductivities (Matheron, 1967; de Marsily, 1986). This is a useful result if the variability of conductivities in the domain is small; but if the variability is large, this provides a very wide margin that can span various orders of magnitude. For more restrictive assumptions on the nature of variability, more precise results have been found in the literature based on a number of methods (see, e.g., Gelhar, 1993; Wen and Go´mez-Herna´ndez, 1996). If the probability density

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function of the conductivity is lognormal (and for any isotropic spatial correlation) Matheron (1967) and Gelhar (1986) showed that for the two-dimensional case, the effective conductivity equals the geometric mean of the local conductivities. Using the geometric mean is a scaling rule in the sense of Figure 6. For the three-dimensional case, the scaling relationships are more complicated and involve the variance and the spatial correlation of the media (e.g., Dykaar and Kitanidis, 1992). For transient conditions, in general, no effective conductivities independent of time may be defined (El-Kadi and Brutsaert, 1985) and for flow systems involving well discharges, the effective conductivity is dependent on pumping rates and boundary conditions (Go´mez-Herna´ndez and Gorelick, 1989; Ababou and Wood, 1990; Neuman and Orr, 1993). It has been observed by a number of authors that effective hydraulic conductivities tend to increase with scale (see, e.g., Ababou and Gelhar, 1990; Clauser, 1992). Sa´nchez-Vila et al. (1996) suggested that this effect may be related to the increasing importance of channelized flow with increasing scale. Blo¨schl (1996) noted that this scale effect may be related to the dimensionality of the problem. The higher the dimension, the more degrees of freedom are available from which flow can choose the path of lowest resistance (which is consistent with Sa´nchez-Vila et al. (1996)). Hence, for a higher dimension, flow is more likely to encounter a low-resistance path, which tends to increase the effective conductivity. If the dimensionality of the flow problem increases with scale (from essentially one-dimensional flow in the laboratory experiment to a more-dimensional field case), the dimensionality effect translates into increasing effective conductivities with increasing scale (Ababou and Wood, 1990; Indelman et al., 1996). An alternative to using effective model parameters is to generate two- or three-dimensional fields of the media characteristics (e.g., conductivities) and use them for high-resolution groundwater modeling. Numerous methods for media generation exist, some of which assume fractal media characteristics (e.g., Bellin and Rubin, 1996). Koltermann and Gorelick (1996) and Anderson (1997) provide overviews of media-generation methods.

2.19.3.4 Runoff Models There are four main issues related to scale and scaling in rainfall–runoff modeling: (a) what is an ideal model grid scale; (b) how do the model parameters change with catchment scale; (c) how does model performance change with catchment scale for a given model structure; and (d) how to best address the scale mismatch between measurements and model elements. Issue (a) can be addressed by examining how the average values of some property over an area change when increasing the size of that area. This idea was first conceived by Hubbert (1956) in the context of discussing the continuum assumption in groundwater flow theory, which lead to the notion of a representative elementary volume (REV) as the order of magnitude where ‘f (porosity) approaches smoothly a limiting value’ (i.e., varies only smoothly with changing volume). In analogy, Wood et al. (1988) introduced the representative

elementary area (REA) in catchment hydrology. The analysis method is to plot peak flow of an event (or many events) versus subcatchment size for a set of nested catchments, and where the variability levels out, one finds the REA. The concept has been re-examined a number of times (e.g., Famiglietti, 1992; Blo¨schl et al., 1995; Fan and Bras, 1995) suggesting that the REA so obtained very much depends on the catchment scales considered. Blo¨schl et al. (1995) hence suggested that an arbitrary elementary area (AEA) of any size can be used, so choice of element size should depend on the computational resources and the amount of information that is available. More information would justify finer elements. This is also the concept used in sister disciplines such as hydrodynamics and atmospheric sciences. For addressing issue (b) and (c), results from a recent study are presented that is typical of modeling studies with large data sets. Merz et al. (2009) simulated the water balance dynamics of 269 catchments in Austria ranging in size from 10 to 130 000 km2 using a semi-distributed conceptual model with 11 parameters based on a daily time step. They found that both calibration and verification model efficiencies increase over the scale range of 10 and 10 000 km2. The authors explained this by the larger number of rainfall stations in each catchment. Indeed, in the very small catchments, it is likely that no raingauge exists, while in the large catchments, there are always a number of raingauges providing for more reliable inputs to the model. The study also showed that the scatter of the model performances decreases with catchment scale, particularly the volume errors as illustrated in Figure 7. This result implies that the model simulates the long-term water balance more reliably as one goes up in scale, which is again related to the number of raingauges. Merz et al. (2009) also examined how the calibrated model parameters change with catchment scale and found a trend with catchment area of the upper and lower envelope curves of some parameters. Although Merz et al. (2009) carefully checked the robustness of the estimated parameters, there may still be issues with parameter uncertainty (Savenije, 2001; Montanari, 2007). From a practical perspective, it seems to be clear that for the case of runoff modeling, data availability should be the main criterion for selecting model grid size and it is also the main driver of scale dependencies in model performance. The scale mismatch between measurements and model elements (d) has also attracted considerable interest in hydrology (e.g., Grayson et al., 1993). It is increasingly becoming clear that, in catchment hydrological modeling, finer is not necessarily better (Schoups et al., 2008; Savenije, 2009). Stephenson and Freeze (1974) were one of the first to recognize this fact and there is a long track record of studies demonstrating the difficulties in model identification and calibration once the model becomes too complex (Naef, 1981; Loague and Freeze, 1985; Beven, 1989, 2001). This is mainly because the media properties (both soil and vegetation) are highly heterogeneous and essentially always unknown or at least poorly known (Zehe and Blo¨schl, 2004; Zehe et al., 2007). There will, hence, always be some degree of calibration needed for any model to accurately represent the hydrological processes in a particular case (Blo¨schl and Grayson, 2002). This also suggests that model element size should mainly depend

Scaling and Regionalization in Hydrology

527

VE (calibration)

0.4 0.2 0 −0.2

(a) −0.4

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0.4 0.2 0 −0.2

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Cal. 91-05

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0.08 0.04 0 10

100

(d)

1000

10 000

100 000

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Figure 7 Volume errors, VE, plotted vs. catchment area: (a) calibration; (b) verification; (c) differences of verification and calibration efficiencies; and (d) standard deviation of volume errors for catchment scale classes. Solid lines in ((a)–(c)) show mean efficiencies for a scale range of 3–30 km2, 30–300 km2, etc. Black and gray dots represent the model performances of catchments calibrated to 1976–90 and 1991–2005, respectively. From Merz R, Parajka J, and Blo¨schl G (2009) Scale effects in conceptual hydrological modelling. Water Resources Research 45: W09405 (doi:10.1029/ 2009WR007872).

on the amount of relevant hydrological information available, as mentioned above. Grayson et al. (2002) pointed out that the scale mismatch between small-scale samples and largerscale model elements can be effectively addressed by using spatial patterns of hydrological response variables. Such patterns include snow-cover patterns to assess the performance of spatially distributed energy- and water-balance models (Blo¨schl et al., 1991; Wigmosta et al., 1994), patterns of dominant hydrological processes (Peschke et al., 1999), and soil moisture patterns (Western and Grayson, 2000).

Summaries of several recent case studies of how patterns can be used to address the scale mismatch are reported in Grayson and Blo¨schl (2000).

2.19.3.5 Global Circulation Models Outputs of climate simulations from general circulations models (GCMs) cannot be directly used for hydrological impact studies of climate change because of the scale mismatch.

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The spatial grid resolution (i.e., support) of GCMs in use today is on the order of several tens of thousands of square kilometers. The useful grid box size is even larger as GCMs are inaccurate at the scale of a single grid box. In contrast, the spatial scale at which inputs to hydrologic impact models are needed is on the order of tens or hundreds of square kilometers. Because of the scale mismatch, the statistical characteristics of the GCM output may be vastly different from those of the local (surface) variable that shares the same name. For example, the maximum rainfall intensities simulated by GCMs tend to be much smaller than those at the point scale. There will also be local effects induced by topography, land cover, etc., which are not captured in the GCM. Downscaling methods can be used to transfer the large-scale GCM output to small-scale variables and to account for local effects. There are two approaches to downscaling GCM output (IPCC, 2007; Yarnal et al., 2001). The first is dynamic downscaling where deterministic regional climate models are nested into global circulation models. This means the initial conditions and boundary conditions to drive the regional climate model are taken from the GCMs. Dynamic downscaling is discussed, for example, in von Storch (2005). The second approach is empirical or statistical downscaling, which will be briefly reviewed here in a hydrological context. In empirical or statistical downscaling, explicit relationships between the large-scale GCM output and the observed small-scale or local station data, such as precipitation, are used. Unlike dynamic downscaling, statistical downscaling methods are computationally inexpensive. They can thus be used to generate a large number of realizations to assess the uncertainty of predictions. They can also use climate data from individual stations directly, so that local information can be accounted for in an efficient way. The method, however, hinges on the assumption that the statistical relationships developed for the present-day climate will also hold under the different forcing conditions of possible future climates. This assumption is essentially unverifiable. The relationships can indeed be unstable for many reasons as short-term relationships can be conditional on longer term variations in the climate system (Charles et al., 2004). The application of the method consists of four steps (Yarnal et al., 2001; Blo¨schl, 2005a):

• •

•

•

Selection of the local variable such as precipitation. The corresponding data are usually collected at individual climate stations in the area. Selection of the large-scale GCM-derived variable or variables (termed predictors) such as sea-level pressure or geopotential heights (Wilby and Wigley, 2000; Wilby et al., 2002). Deriving relationships between the observed small-scale or local station data and the large-scale GCM-derived variable: the relationships (i.e., the downscaling models) are regression techniques, stochastic models, or the analog method. The latter consists of identifying, from a pool of historical circulation patterns, the one that is most similar to the circulation pattern on the day of interest (Zorita and von Storch, 1999). As a final step in the downscaling procedure, the relationships derived above are applied to the GCM output for

changed climate scenarios to estimate the local variables for a changed climate. These local variables (mainly precipitation and air temperature) can then be used to drive hydrological models in an impact assessment (IPCC, 2007). The downscaled data (e.g., precipitation) typically exhibit larger variability than the GCM-derived predictor variables. This is consistent with the effect of support scale on the variance shown in Figure 4. A realistic variability of precipitation is extremely important when using precipitation as an input to rainfall–runoff models, as an underestimate in precipitation variability will translate into significant underestimates in runoff because of the nonlinearity of the rainfall–runoff relationship (Komma et al., 2007).

2.19.4 Regionalization in Hydrology 2.19.4.1 Similarity Measures While the problem of scaling consists of relating variables at different scales, the problem of regionalization consists of relating variables at different locations. A concept that is essential in regionalization is hydrologic similarity (Blo¨schl, 2001; Wagener et al., 2007; Harman and Sivapalan, 2009). Similarity of hydrological processes can be defined in various ways. Ideally, one would like to relate catchments that are similar in terms of their driving processes. Dunne (1978) suggested that runoff processes are mainly controlled by physioclimatic controls and identified three main types: (1) infiltration of excess runoff, which is generated from partial areas where surface hydraulic conductivities are low; (2) saturation excess runoff, which is generated in areas with shallow water tables or near-channel wetlands; and (3) subsurface storm flow, which is likely to be active and dominant on steep, humid forested hill slopes with very permeable surface soils. In two similar catchments, the relative role of each of these processes would be similar. The characteristics of these processes in natural catchments are never known in full detail so a number of similarity concepts have been proposed in the literature that attempt to represent these processes to various degrees. Spatial proximity. In the first concept, catchments that are close to each other are assumed to behave hydrologically similarly. The rationale of this concept is that the controls on the rainfall–runoff relationship are likely to vary smoothly in space, or are uniform in predefined regions; therefore, one can expect spatial proximity to be a good indicator of the similarity of catchment response. However, if adjacent catchments are very hydrologically different (see, e.g., the example in Blo¨schl, 2005b, Figure 5), proximity is not a good similarity measure. Geostatistical methods and various methods that are based on homogeneous regions are based on this similarity measure. Similar catchment attributes. The second similarity concept consists of using measurable catchment attributes as indicators of hydrological similarity such as catchment size, mean areal rainfall, and geological characteristics of the catchments. There are a range of methods that differ in the way the attributes are used in the spatial transfer of information including

Scaling and Regionalization in Hydrology

regression, cluster analysis, classification trees, and the region of influence approach. Similarity indices. Similarity indices are based on some understanding of the structure of hydrological processes and are usually defined as a dimensionless number. For example, similarity in climate can be quantified by the aridity index of Budyko (1974), which is the ratio of long-term potential evaporation to precipitation. Similarity in runoff generation can be quantified by the topographic wetness index of Beven and Kirkby (1979), which is a function of the area drained per unit contour length at a given point and the local slope gradient (Chirico et al., 2005). These similarity concepts are the basis of regionalizing floods, low flows, and runoff model parameters in order to obtain estimates for ungauged catchments. The regionalization methods are discussed below. In all cases, the need for regionalization stems from the fact that no local runoff data are available from which floods, low flows, or runoff model parameters could be estimated; therefore, they need to be obtained by regionalization.

2.19.4.2 Floods A range of methods are available for estimating flood peaks in ungauged catchments (Cunnane, 1988; Bobe´e and Rasmussen, 1995; Hosking and Wallis, 1997). In the index flood approach (Dalrymple, 1960), the domain is subdivided into regions. Within each region, the flood frequency response is assumed to be similar apart from a scaling factor. The scaling factor, termed index flood, can be the mean annual flood or the median annual flood. This means that the flood frequency curve scaled by the index flood (termed the growth curve) is identical in each region. For an ungauged site, the flood of a given return period is then estimated as the product of a regional growth curve and the local index flood estimated from catchment attributes. The regions are spatially contiguous. They are usually delineated by expert judgment but there exist objective methods such as cluster analyses and methods based on the seasonality of floods (Piock-Ellena et al., 2000). The latter approach is appealing as it allows some process interpretation for each region, such as convective rainfall and glacier runoff as likely flood causes. However, seasonality is only one of the important fingerprints of flood mechanisms. Merz and Blo¨schl (2003) extended the concept to analyze more complex flood processes at the regional scale. The region of influence (ROI) approach can be thought of as an extension to the index flood approach. It assumes that every catchment for which flood probabilities are to be estimated is associated with a different homogeneous ROI (Burn, 1990; IH, 2000). Similarity measures are usually based on catchment attributes and seasonality measures. This is the most flexible approach, but it is not straightforward to specify appropriate similarity measures. An alternative method that uses catchment attributes is the quantile regression method. The term ‘quantile’ relates to the flood peak discharge of a given return period (e.g., the 100year flood). The quantile regression approach assumes that the spatial variability is mainly related to catchment attributes such as catchment area and mean annual rainfall. Cunnane (1988), however, noted that the quantile regression method

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has a number of disadvantages over other methods. Most importantly, methods should be preferred where the first statistical moment (the mean or the median) of the flood distribution is estimated from catchment attributes while the second and the third moments (or, equivalently the shape of the flood frequency curve) should be estimated by pooling regional flood data. This is because of the larger uncertainty of the higher moments. The geostatistical approach (i.e., some variants of kriging) assumes that the spatial variability is random and only depends on spatial proximity (see Section 2.19.2.1). The method proposed by Merz and Blo¨schl (2005) regionalizes the three flood moments (mean, coefficient of variation, and skewness) separately. The main advantage of the method is that it can take the different uncertainties of the three moments into account, in line with the note of Cunnane (1988). There are also combinations of the geostatistical approach with regressions, for example, using mean annual precipitation as a catchment attribute (Merz and Blo¨schl, 2005). This combined approach is termed georegression. Merz and Blo¨schl (2005) compared the various types of approaches based on a jack-knifing comparison of locally estimated and regionalized flood quantiles for 575 Austrian catchments. Figure 8 shows an example of their results. The biases of the methods (in terms of the normalized mean errors of specific discharges) range between 3% and 12%, depending on the method. The random errors (in terms of the normalized standard deviations of the errors) range from 30% and 50% depending on both the method and the return period of the flood peaks to be estimated. Georegression yields the best predictive performance. The methods that only use catchment attributes (the ROI approach and multiple regressions) perform more poorly than the methods based on spatial proximity (kriging and georegression). In engineering practice, flood frequency regionalization is often supported by expert judgment (e.g., IH, 1999), which was not represented in the analyses of Merz and Blo¨schl (2005) in order to obtain an objective comparison. Merz et al. (2008) did take local expert judgment into account to obtain regionalized floods in Austria.

2.19.4.3 Low Flows Similar to the case of floods, a range of methods are available for estimating low flows in ungauged catchments (Smakhtin, 2001). The methods are usually based on some sort of regression between the low-flow characteristic and catchment attributes. A common low-flow characteristic is the Q95 low flow, that is, the discharge that is exceeded on 95% of all days. The catchment attributes include rainfall and geological attributes. If the study domain is large or very heterogeneous in terms of the low-flow processes, it is useful to split the domain into regions and apply a regression relationship to each of the regions independently. This is termed the regional regression approach (Gustard and Irving, 1994). Finding suitable regions is the most critical step in low flow regionalization. The following methods (termed ‘grouping methods’) exist:

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and catchment attributes are plotted, from which geographically contiguous regions are obtained by manual generalization on a map (e.g., Hayes, 1992; Aschwanden and Kan, 1999). Cluster analyses where, usually, both low-flow data and catchment attributes are used in a statistical analysis that obtains regions (or clusters) by maximizing the homogeneity within each region and maximizing the heterogeneity between the regions (Nathan and McMahon, 1990). Classification and regression tree (CART) models (Breiman et al., 1984; Laaha and Blo¨schl, 2006a), where the independent variables in the regression trees are the catchment attributes and the dependent variables are the low flows. Seasonality of low flows where the rationale is that differences in the occurrence of low flows within a year are a reflection of differences in the hydrologic processes, such as winter low flows due to snow and freezing processes, and summer low flows due to evaporation (Laaha and Blo¨schl, 2006b).

Laaha and Blo¨schl (2006a) performed an analysis similar to that of Merz and Blo¨schl (2005) referred to in the previous section. They compared four catchment grouping methods in terms of their performance in predicting q95 specific low-flow discharges. The grouping methods were the residual pattern approach, weighted cluster analysis, regression trees, and an approach based on seasonality regions. For each group, a regression model between catchment attributes and q95 was fitted independently. The performance of the methods was assessed by a jack-knifing comparison of locally estimated and regionalized flood quantiles for 325 catchments in Austria. Their results indicate that the grouping based on seasonality regions performs best and explains 70% of the spatial variance

of q95. The favorable performance of this grouping method is likely related to the striking differences in seasonal low-flow processes in the study domain. Winter low flows are associated with the retention of solid precipitation in the seasonal snow pack while summer low flows are related to the relatively large moisture deficits in the lowland catchments during summer. The regression tree grouping performs second best (explained variance of 64%) and the performance of the residual pattern approach is similar (explained variance of 63%). The weighted cluster analysis only explains 59% of the spatial variance of q95, which is only a minor improvement over the global regression model, that is, without using any grouping (explained variance of 57%). Laaha and Blo¨schl (2007) then applied the seasonality method to obtain regionalized low flows in Austria. Figure 9 shows the results of the regionalization for illustration. The main spatial patterns are the large low flow values at the northern rim of the Alps (West–East band in the center of the country), which are a result of the above average precipitation in the area. The finer scale patterns are mainly due to geological heterogeneities.

2.19.4.4 Runoff Model Parameters Again similar to the floods and low-flow cases, a range of methods are available for estimating runoff model parameters in ungauged catchments (Blo¨schl, 2005b). There are a number of engineering procedures for estimating event-scale runoff model parameters. Lag time parameters have been listed as tabulated functions of catchment attributes such as topographic slope, stream slope, and flow length (e.g., USACE, 1994). Similarly, loss parameters (what percentage of rainfall infiltrates) have been listed as tabulated functions of land cover, soil type, and antecedent soil moisture (such as in the

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Figure 9 Top: Specific low-flow discharge q95 (ls1 km2) from runoff data observed in 325 subcatchments in Austria. Alpine catchments show higher values and a larger variability. Bottom: Regionalized q95 based on the seasonality method. From Laaha G and Blo¨schl G (2007) A national low flow estimation procedure for Austria. Hydrological Sciences Journal 52(4): 625–644.

Curve Number method of the US Soil conservation service, SCS, 1973; Mishra and Singh, 2003). From the perspective of water science, soil moisture accounting schemes or continuous rainfall–runoff models are of more interest. The most common methods of estimating the parameters of these models are based on relating the calibrated model parameters to catchment attributes. This involves the following main steps:

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Estimating the model parameters for one or more gauged catchments, termed donor catchments, by manual or automatic calibration of the runoff model on observed runoff data. Relating each rainfall–runoff model parameter to a set of catchment attributes, for example, by multiple linear regressions, possibly within homogeneous subregions of the entire domain. Identifying homogeneous regions is similar to the low-flow case in Section 2.19.4.3. Estimating each parameter of the rainfall–runoff model for the ungauged catchment from the regression model. Simulating runoff for the ungauged catchment of interest by applying the runoff model using the regionally transposed model parameters.

A number of authors have tested the relationships between model parameters and catchment attributes and found the correlations to range between 0.5 and 0.8 (Sefton and Howarth, 1998; Seibert, 1999; Beldring et al., 2002). In a recent study, Parajka et al. (2005) examined the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. They calibrated 11 parameters of a semi-distributed conceptual rainfall–runoff model to daily runoff and snow cover data of 320 Austrian catchments and then evaluated the predictive accuracy of the regionalization methods by jack-knife cross-validation against daily runoff data. As an example of the results, Figure 10 shows the cumulative distribution functions of the model efficiencies of daily runoff (ME, left) and volume errors of runoff (VE, right). As would be expected, the at-site model calibrations (blue lines) show the best model performances (large ME, and VE closest to zero). The regionalization methods show lower performances. The differences between the methods are not large but there is a trend that two methods perform better than the others to a certain extent. The first is a kriging approach (green line) where the model parameters are regionalized independently from each other, based on their spatial correlation. The second is a similarity

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approach (red line) where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its catchment attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils, and geology). One of the difficulties with this type of parameter regionalization is that it hinges on a robust calibration of the model parameters. This is similar to the problems with scaling of runoff models discussed in Section 2.19.3.4. As the calibration period increases in length, the parameters can be estimated more robustly; but there is a limit to the degree to which the parameters can be identified from the runoff data (see Figure 10 in Merz et al., 2009). One possibility of obtaining more robust parameters is multiple objective calibration against, say, runoff and snow data (Parajka et al., 2007b). An

alternative is regional calibration where the model parameters of a number of catchments are calibrated simultaneously (Parajka et al., 2007a). Still another alternative is based on socalled signatures of runoff such as the mean annual runoff (Zhang et al., 2008). In this approach, the mean annual runoff is regionalized to the ungauged catchment in a first step. In a second step, the model parameters for the ungauged catchment are jointly estimated from the catchment attributes and the mean annual runoff. Another option are methods that optimize a transfer function relating model parameters to catchment characteristics (Hundecha and Ba´rdossy, 2004). The transfer function assures that similar catchments are mapped to similar parameter sets. There are also difficulties with finding suitable catchment characteristics that are hydrologically relevant (see, e.g., Merz

Scaling and Regionalization in Hydrology

and Blo¨schl, 2009); and the spatial arrangements of soils and land use in the catchment may be important but are not captured by the methods mentioned above. A useful strategy hence is to infer the model parameters from field observations in the ungauged catchment. Although the scale difference between point measurements and model elements may be a problem (Blo¨schl, 2005b), there are frameworks for using local field information to assist in parameter estimation (e.g., Blo¨schl et al., 2008).

2.19.5 Concluding Remarks This review has summarized current methods of scaling and regionalizing hydrological variables. What is common to scaling and regionalization is that there is no single best method that should be used in all cases. Rather, the method of choice should depend on the purpose of the study, data availability, and the nature of the underlying hydrological variability. The predictive accuracy of these methods will depend on the same factors. The accuracy will also depend on the degree of nonlinearity present in the system as pointed out by Blo¨schl and Zehe (2005) and Blo¨schl (2006). As the degree of nonlinearity increases, predictability tends to decrease. One of the exciting research fields in hydrological scaling and regionalization in the next years will hence be to learn how to separate the predictable and the unpredictable. This will help better understand just how well one can estimate hydrological quantities across space and scale.

Acknowledgment The author would like to thank FWF project P18993-N10 for financial support.

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2.20 Stream–Groundwater Interactions KE Bencala, US Geological Survey, Menlo Park, CA, USA Published by Elsevier B.V.

2.20.1 2.20.1.1 2.20.1.2 2.20.2 2.20.2.1 2.20.2.2 2.20.2.3 2.20.2.4 2.20.3 2.20.3.1 2.20.3.1.1 2.20.3.1.2 2.20.3.2 2.20.3.2.1 2.20.3.2.2 2.20.4 2.20.5 References

Introduction The Stream Connected to Its Surroundings – The Stream Is Not a Pipe Unidirectional Dominance, but Not Preclusion Hydrology – Range of Interactions Perspective Hyporheic Stream–Catchment River–Aquifer Chemical and Ecological Significance Solute Chemistry Non-nutrients Nutrients Ecosystem Temperature Biota Field Study Methods and Models Summary and Future Challenges

2.20.1 Introduction 2.20.1.1 The Stream Connected to Its Surroundings – The Stream Is Not a Pipe Streams exist in connection to their surroundings, specifically to their catchment and the subsurface flows of the catchment. This statement appears to be obvious, particularly if we are envisioning the typical gaining stream increasing in water flow and acquiring solute load as the stream channel proceeds down-valley. A dated view of a stream can lead to an (unstated) assumption that a stream acts predominantly as a pipe, in effect, open only in certain areas to receive water and draining the catchment at the stream’s base. A stream is, however, not a pipe (Bencala, 1993). It is now well recognized that surface waters are connected to groundwater systems in a variety of hydrologic settings and at a variety of scales (Winter et al., 1998; Figure 1). This chapter identifies the diversity and dynamic nature of these connections.

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riparian areas. Further, while the breadth of the interactions can be discussed in a primarily stream-transport-centric viewpoint (e.g., Bencala, 2006; Figure 2), a larger physical scale view explores the interactions from the hydrogeologic perspective of the groundwater system (Woessner, 2000; Sophocleous, 2002; Figure 3). At the hydrologic scales of extensive aquifers and rivers, issues of water volumes transferred between the subsurface and surface systems seasonally to inter-annually are of importance (e.g., Konikow and Bredehoeft, 1974; Alley et al., 2002). With this chapter focusing on streams and catchment groundwater, the importance of solute exchanges (Runkel et al., 2003), nutrient cycling (Jones and Mulholland, 2000), and functions in aquatic ecosystems such as connectivity and maintenance of habitat structure (Stanford and Ward, 1988) are accentuated.

2.20.2 Hydrology – Range of Interactions 2.20.1.2 Unidirectional Dominance, but Not Preclusion

2.20.2.1 Perspective

As represented by two recent journal issues that focused on stream–groundwater interactions (Krause et al., 2009; Borchardt and Pusch, 2009), the topic is of active current hydrologic research and environmental interest. In working to understand the functional significance of stream–groundwater interactions, it is important to recognize, and accept, that these interactions are typically not the dominant process in the stream. Unidirectional, down-stream, down-valley surface and subsurface water flow constitute the context in which stream– groundwater interactions occur. However, the predominant direction of flow in a catchment does not preclude the importance of other flow paths including those of water into (and then back out of) the streambed and into subsurface

A variety of perspectives on surface-water–groundwater interactions can be taken, even when focusing on the issues of water and solute exchanges in streams. Conceptually, different possible perspectives can be arrived at according to where you stand (Packman and Bencala, 2000). Are the interactions viewed from the stream, from the streambed interface, or from the subsurface? From each of these perspectives, interactions occur conceptually within a boundary layer (Triska et al., 1989) or across an ecotone (Boulton et al., 1998). Additionally, there are surface-water–groundwater interactions that can be viewed (1) from the interface being essentially a reactive membrane which water and solutes traverse (Schindler and Krabbenhoft, 1998) or (2) from the hydraulic connection

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Stream–Groundwater Interactions Recharge area

Stream

Discharge area

Pumped well

s ay

Ye ars

D

Unconfined aquifer

s ar Ye

Da ys

Water table

Confining bed Confined aquifer

Centuries

Confining bed Confined aquifer

Millennia

Figure 1 Groundwater flow paths greatly vary in length, depth, and travel time from points of recharge to points of discharge in the groundwater system. Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

(a)

Pool and riffle stream Flow in hyporheic zone

(b)

Meandering stream Flow in hyporheic zone

Figure 2 Surface-water exchange with groundwater in the hyporheic zone is associated with abrupt changes in streambed slope (a) and with stream meanders (b). Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

causing water to flow from the stream into bank storage (Sharp, 1977) as stream stage rises and from the banks and streambed into the stream as the stage falls. Conceptually, these interactions are quite specific to a given stream. Wo¨rman et al. (2006, 2007) have used spectral analyses to significantly generalize solute retention times and effluxes from the channel form to the continents in terms of power law scaling that reflects the fractal nature of surface topography across these scales. The hydrology of surface- and groundwater interactions is summarized at three scales in Sections 2.20.2.2–2.20.2.4 (hyporheic, stream–catchment, and river–aquifer). Section 2.20.3 addresses the central topic of chemical and ecological significance of surface- and groundwater interactions.

2.20.2.2 Hyporheic In the hyporheic zone, stream flow infiltrates into the shallow subsurface material forming the channel bed and banks, flows following the general down-valley gradient, and then returns to stream. Although there are no rigorous criteria, hyporheic flow paths are commonly thought of as being tens of meters in length with residence times on the order of hours to days. Variation in stream and catchment characteristics such as hydraulic conductivity, alluvial volume, streambed slope, and turbulence help drive hyporheic flows (Tonina and Buffington, 2009). Hyporheic flow is routinely observed in various stream settings including gravel-bed steep mountain streams (Bencala, 1984) and naturally ephemeral, wastewatertreatment-dominated, sand-bed streams (Cox et al., 2003). At

Stream–Groundwater Interactions

539

Regional upland Riverine valley Water table

Flood levels

Direction of local flow

Direction of regional flow

Figure 3 In broad river valleys, small local groundwater flow systems associated with terraces overlie more regional groundwater flow systems. Recharge from floodwaters superimposed on these groundwater flow systems further complicates the hydrology of river. Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

the stream channel scale, stepped-channel morphology (Harvey and Bencala, 1993) can set up local hydraulic gradients that define hyporheic flow paths originating at the downstream end of stream pools and returning water to the stream at the base of subsequent steep riffle sections. Largerscale features of the catchment such as geologic setting and alluvial characteristics control substantial variability observed in hyporheic zone extent and functioning (Morrice et al., 1997). Typically, the water in a hyporheic flow path is a mixture of stream water and local groundwater. Hyporheic flows exist in both surface-water and groundwater-dominated sites (Malcolm et al., 2009). Variation in stream discharge also influences mixture, pathways, and flux of hyporheic flow (Arntzen et al., 2006; Wondzell and Swanson, 1996, 1999).

(a)

Flow direction

Unsaturated

zone

Water table Shallow aquifer

(b)

Water table

Flow direction

Unsaturated zone

2.20.2.3 Stream–Catchment At larger scales, the hyporheic zone represents the boundary across which streams and their catchment exchange water and solutes. Typically, streams are envisioned gaining water from catchment runoff. However, a stream may be both gaining and losing water (Figure 4). The relationship between stream discharge and groundwater flow may shift across the mountain to alluvial valley transition (Anderson et al., 1992; Covino and McGlynn, 2007). Fluvial processes and geomorphic features also result in episodic to seasonal modification (Malard et al., 2002). The stream–catchment system may be visualized as settings in a three-dimensional mosaic of multiple scale patches. These patches exhibit cycles of expansion and contraction resulting in differences among patches in their hydrologic exchange rate with the stream and thus the supply of organic matter. Localized interactions are influenced by (1) groundwaterdominated hydrology, (2) surface-water-dominated hydrology, and (3) transient water tables (Malcolm et al., 2005). These interactions are within the context of valley geomorphology (constrictions and channel confinement), which influences the spatial variation of groundwater movement to the stream. Geomorphic structure affects the bed sediments and flow pathways that govern exchange and mixing with

Figure 4 (a) Gaining streams receive water from the groundwater system. (b) Losing streams lose water to the groundwater system. Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

floodplains and riparian areas (Huggenberger et al., 1998). Connectivity with the main channel water source varies within alluvial flood plains (Ward et al., 1999) and in turn influences the exchange processes in a specific channel (Figure 5). Stream–groundwater connections do occur in systems losing water and in which flow is not maintained throughout the year. In arid regions, stream–catchment connections may center on losing streams with water spreading into the parafluvial area and later returning to the stream (Dent et al., 2007). Similar connections of streams and groundwater are induced in irrigated areas of the western United States (Fernald and Guldan, 2006; Konrad, 2006). After irrigation flooding, seepage water in the shallow subsurface may return to the river.

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Stream–Groundwater Interactions

(a)

Flow direction

Unsaturated zone

Water table

(b)

Flow direction Water table at high stage

High stage

Water table during base flow

Bank storage

Figure 5 (a) Disconnected streams are separated from the groundwater system by an unsaturated zone. (b) If stream levels rise higher than adjacent groundwater levels, stream water moves into the stream banks as bank storage. Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

In rivers of the Northwestern United States, exchanges with aquifers can be a significant aspect of the water budget (Konrad, 2006). The gaining and losing zones along the river are concentrated in a small fraction of the river’s length, associated with specific geomorphic and geologic forms (lithologic contacts, tributaries, and thinning of alluvial deposits). Groundwater flow was often longitudinal along the valley axis, rather than directly lateral into the river channel. Irrigation can influence river–aquifer interaction with alterations to natural flow (see also Chapter 1.01 Integrated Water Resources Management), leading to both groundwater discharge and surface-water infiltration. In an irrigated system, flow patterns differed at transect locations 100 m apart along the river (Wildman et al., 2009). Hypothetical simulations demonstrated variation in river depletion between settings with confined versus leaky aquifers, which can result from groundwater pumping (Butler et al., 2007). In large-scale interactions between surface water and groundwater in the Everglades, Florida, USA, the observed fluxes of water occur on a decadal timescale (Harvey et al., 2006); water may be resident in the subsurface for years prior to reemergence to the surface.

2.20.3 Chemical and Ecological Significance 2.20.3.1 Solute Chemistry 2.20.3.1.1 Non-nutrients

Losing and gaining reaches have been identified in streams in the Midwestern US, not solely in the arid zones (Silliman and Booth, 1993). Streambed infiltration at a site varies diurnally (Constantz, 1998). Stream–catchment connections are not necessarily permanent. The permanence of subsurface hydrologic connections between non-navigable streams and nearby wetlands is a legal as well as hydrologic issue (Leibowitz et al., 2008; see also Chapter 1.03 Managing Aquatic Ecosystems).

2.20.2.4 River–Aquifer A common assumption of river–aquifer interaction is of subsurface flow to the river. In some cases, down-valley flow, or underflow may dominate. Geomorphic characteristics of channel slope, river sinuosity, incision through the alluvium, width-to-depth ratio, and the fluvial depositional system determine settings in which these flows may be important (Larkin and Sharp, 1992). Aquifer heterogeneity at the scale of 100 m influences spatial distribution of seepage between the river and the aquifer. The arrangement of hydrofacies impacts the connectivity between the river and the aquifer (Fleckenstein et al., 2006). Secondary river channels create zones of hyporheic interaction on the scale of kilometers. Water exchanges in backwaters are related to local variations in streambed porosity. In the Upper Rhone, Germany, groundwater dominates within the exchange flows (Dole-Olivier, 1998).

In natural systems, hyporheic exchange can modulate stream major ion chemistry. In an extreme example of a minimally impacted environment, an experiment using the injection of tracer cations and anions into an Antarctica Dry Valley stream demonstrated the role of hydrologic exchange in increasing ion sorption from the stream water (Gooseff et al., 2004a). Stream–groundwater interactions also regulate water quality in ways that are significant for human impacts on hydrologic systems and human uses of water. Natural attenuation processes in hyporheic flows may limit the movement and availability of mining-derived pollutants at the stream– groundwater interface (Gandy et al., 2007). In Silver Bow Creek, Montana, depletion of mine drainage constituents indicates precipitation, or adsorption occurs along hyporheic flow paths (Benner et al., 1995). Reactive uptake of several dissolved metals by manganese oxide was observed in Pinal Creek, Arizona (Fuller and Harvey, 2000). In Pinal Creek, reactive uptake resulting from hyporheic exchange accounted for removal of approximately 20% of the dissolved manganese flowing out of the drainage basin (Harvey and Fuller, 1998). The role of the hyporheic zone in the transport of arsenic varies spatially in a Virginia mine-influenced stream–aquifer system. Arsenic is retained in hyporheic sediments along segments of the stream, while in other segments arsenic was delivered to the stream by hyporheic flow through mine tailings (Brown et al., 2007). Bank filtration is a widely utilized scheme in European water supply systems (see also Chapter 3.04 Emerging Contaminants). Knowledge of the processes determining geochemical transport is important for human health but remains largely site specific (Hiscock and Grischek, 2002). Infiltration of river water to groundwater is an important water

Stream–Groundwater Interactions

variation in hydraulic conductivity influences the retention and transformation of nutrients. This coupling of nutrient dynamics to physical processes illustrates the importance of understanding streams as parts of their catchment system (Valett et al., 1996). The geology of a stream’s catchment establishes alluvial hydrologic properties that determine the nature of surface-water–groundwater interactions influencing nutrient retention (Valett et al., 1997). Hydrologic processes contribute to stream–catchment nutrient dynamics. Denitrification observed in shallow hyporheic sediments is controlled by hydrologic exchange (Duff and Triska, 1990). Dissolved oxygen supplied by hyporheic exchange with the stream is reduced through respiration of groundwater-derived dissolved organic carbon (DOC), enabling redox conditions conducive for denitrification. In extreme stream environments in the McMurdo Dry Valleys, Antarctica, nitrate is removed through exchange with both microbial mats as well as denitrification in the hyporheic zones (Gooseff et al., 2004b). Short-term hydrologic events that drive stream flow into the hyporheic zone, for example, the passage of a flood wave, may alter the flow dynamics of the surface-water–groundwater interface creating the opportunity for denitrification to occur in the sediments (Gu et al., 2008). Multiple flow paths may exist within the stream. In a given groundwater–riparian–stream system, for some constituents, concentrations entering the stream may be controlled by the groundwater flowing through the riparian area. For other constituents, transformation may occur with the near-stream zone (Hill, 1990). Hyporheic zone biogeochemistry varies among stream ecosystems. In streams with high nitrate concentrations, nutrient removal may occur at the stream– streambed interface, leaving an unused potential for nitrogen

supply component in the River Glatt, Switzerland. With river water transporting anthropogenic ligands, the remobilization of trace metals along the subsurface flow path from the river is shown to be possible (Nowack et al., 1997). Seasonal cycles in the mobilization and precipitation of manganese occurred as river water entered the aquifer adjacent to the River Glatt, Switzerland (Von Gunten et al., 1991). In a study reach of the Cedar River, Iowa, although tributaries aggregate most of the agricultural tile-drain flows, the alluvial aquifer, through bank storage retention and release, contributes a significant portion of the pesticides atrazine and deethylatrazine to the river (Squillace et al., 1993).

2.20.3.1.2 Nutrients

of stream

flow

High ox

ygen

Aerobic microb processe ial s

Very lo wo no oxyg r en

Nitrate

n ctio Dire

gr of

nd ou

Ferric iro n

Ammoniu

flo w

Anaerob ic micro processe bial s

wa te r

Groundwater

Hyporheic

Stream

In their textbook, Stream Ecology: Structure and Function of Running Waters, Allan and Castillo (2007) introduced the coupling between nutrient dynamics and the physical movement of water (Figure 6). The transport of water through a stream–catchment system influences the residence time of conservative solutes and nutrients alike. Dahm et al. (1998), in their review, further discussed the stream–catchment geomorphology establishing the framework in which the surface-water–groundwater interface influences the transport of nutrients. Study of nutrient dynamics at scales from a few meters to several kilometers demonstrates both the significance and the inherent patch-nature of subsurface process influence on stream nutrient dynamics (Dent et al., 2001). Chloride tracer and nitrate injected into the stream, but observed in hyporheic flow paths, demonstrate that hyporheic flows are an integral component of fluvial structure and function (Triska et al., 1989). The lithology of the material comprising the subsurface hydrologic system as expressed in

Direction

541

Common ly lo dependin w in oxygen g on geo land use logy, , and pre organic c sence of arbon

m

Ferrous iron

Inches to feet

Feet to miles

Figure 6 Microbial activity and chemical transformations commonly are enhanced in the hyporheic zone compared with those that take place in groundwater and surface water. This diagram illustrates some of the processes and chemical transformations that may take place in the hyporheic zone. Actual chemical interactions depend on numerous factors including aquifer mineralogy, shape of the aquifer, types of organic matter in surface water and groundwater, and nearby land use. Reproduced from Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey.

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Stream–Groundwater Interactions

depletion in the hyporheic zone at depth (Hill et al., 1998). In addition to the coupling of hydrology and nitrate supply, nitrate removal in stream ecosystems may be dependent on the supply of organic carbon (Hill et al., 2000). In the South Platte River alluvial aquifer, Colorado, denitrification and mixing of river and groundwaters may substantially decrease the nitrate load transported in the river (McMahon and Bohlke, 1996). Phosphorus uptake in forested streams may also be increased with the occurrence of hyporheic zones (Mulholland et al., 1997). The importance of hydrologic mixing in the subsurface must be accounted for in the nutrient budget of the hydrologic system (Pinay et al., 1998). In floodplain of the River Garonne, France, mixing of river water and groundwater leads to subsurface nitrate concentrations lower than in the groundwater discharging to the river. Also in the floodplain on the River Garonne, France, denitrification rates were higher during snowmelt when subsurface mixing with river water was greatest than during lowflow periods (Baker and Vervier, 2004). Both catchment flow path and in-stream processes work to control stream water nutrient and DOC concentrations. The dominance of each set of processes may shift over the annual hydrologic cycle (Mulholland and Hill, 1997). DOC transported by groundwater can be immobilized in the streambed. This DOC is then available to the stream trophic system (Fiebig and Lock, 1991). Fluctuations in river stage, sufficient to alter the direction of groundwater–river-water exchange, can establish conditions in which the residence time of solutes in the streambed is increased (McMahon et al., 1995). This leads to an enhanced uptake of oxygen from the river water. In low-gradient streams, nutrient-rich stream water may be retained in the streambed, possibly for days (Puckett et al., 2008). High-flow events can alter the groundwater–stream water exchange and increase the movement of stream water into the streambed. Although a stream may, over its length, be predominately losing water to the subsurface, exchange between the stream and streambed can be an ongoing process. The amount of denitrification in the stream is strongly influenced by the physical transport exchange (Ruehl et al., 2007).

2.20.3.2 Ecosystem The location of and fluxes across the river–groundwater interface are variable in space and dynamic in time. The geomorphic components can be considered as being linked in a hydrologic continuum (Brunke and Gonser, 1997). The interactions that establish ecological conditions (e.g., nutrient availability, water temperature, and dissolved oxygen) are functionally part of both the fluvial and the groundwater systems. The interface and the physicochemical gradients within it are the result of characteristics and processes occurring across spatial scales. The interface can function as a source and/or a sink for solutes. Hyporheic zone processes and the resulting ecosystem functions occur at the catchment scale, the reach scale, and at the sediment scale (Boulton et al., 1998; see also Chapter 2.10 Hydrology and Ecology of River Systems). The continuing challenge is to link the extensive knowledge at each scale to the whole. Groundwater–surface water connections have a role in the restoration of stream ecosystems. Adding small dams in

restoration projects may promote the formation of hot spots of biogeochemical activity in the streambed (Lautz and Fanelli, 2008). The groundwater–surface-water interface may be significantly degraded as a consequence of human activities. To restore streams to their natural ecosystem function, the interface needs to be considered in addition to the surfacewater channel form (Hester and Gooseff, 2010). Wood in the stream channel has a role in stream ecosystem function and consequently in restoration. The dynamics of the hyporheic zone are sensitive to the wood load in the channel. Initially, following wood removal, the streambed may be scoured, thus diminishing hyporheic exchange. As the stream adjusts to a reset wood load, sediment accretion may establish new hyporheic zones (Wondzell et al., 2009b).

2.20.3.2.1 Temperature Due to groundwater–surface water connections, the temperature patterns at the scale of riffles may vary seasonally. Successive riffles along a stream may exhibit different patterns between them. The influence of both the immediate riffle structure and the larger-scale groundwater flows can be observed in temperature patterns (Hannah et al., 2009). Hyporheic-zone temperature patterns may be buffered and lagged relative to the diel cycle of the main channel water (Arrigoni et al., 2008). On a seasonal basis, water returning to the main channel may be either warmer or cooler than the upstream water originally in the channel. The variety of flow-path lengths and residence times point to the need to study streams in the context of their catchments. In the Clackamas River, Oregon, the influence of hyporheic flow on the temperature of the main river channel is small; however, the existence of hyporheic exchange contributes locally to the presence of patches of cooler water (Burkholder et al., 2008). Such patches of cool water may be ecologically important in providing thermal refugia for fish and other aquatic organisms (Torgersen et al., 1999).

2.20.3.2.2 Biota Aquatic communities develop in response to chemical, geomorphic, and hydraulic conditions (Power et al., 1999). Within a river’s catchment connectivity, variation contributes to the creation of the habitat mosaic sustaining biodiversity (Brunke et al., 2003). The incubation of salmonid embryos depends on a supply of oxygen within riverbed gravels (Greig et al., 2007). The flux of oxygen is determined by several factors including bed topography, bed permeability, and surface roughness. Thus, the details and complexities of river- and groundwater exchange are important for establishing this habitat. The variation in groundwater–surface-water interactions contributes to the survival rates of salmonids. In a river system in Scotland, ova survival was reduced in stream reaches in which the influence of groundwater was most substantial (Malcolm et al., 2003). The direction of dominant flow (groundwater discharge vs. stream-water infiltration) was variable with season as well as during hydrologic events. Hyporheic zones may provide advantages of enriched oxygenated water and moderated temperature for salmon redd sites and spawning habitats. In identifying river characteristics favorable for fish habitat, gravel-bed vertical hydraulic

Stream–Groundwater Interactions

gradient may be a significant factor (Geist and Dauble, 1998). Groundwater–stream-water interactions are significant for the quality of fish habitat at catchment, valley segment, reach, and pool-riffle scales. The presence of groundwater inflows appears significant at the larger scales, while strong intergravel flows and zones of downwelling are characteristic of the location of redd sites (Baxter and Hauer, 2000).

2.20.4 Field Study Methods and Models Standard methods for the study of groundwater flow can be applied to the range of scales of stream–groundwater interactions. Dahm et al. (2007) review these applications for measuring vertical hydraulic gradients, and hydraulic conductivity. Modern geophysical methods, for example, groundpenetrating radar (Brosten et al., 2009), are increasingly deployed to provide the detailed three-dimensional geometry of the subsurface zone of interaction (see also Chapter 2.13 Field-Based Observation of Hydrological Processes and Chapter 2.15 Hydrogeophysics). At the scale of streams and hyporheic exchange, injected tracers (Zellweger et al., 1989), combined with standard physical flow metering, are used to estimate the flow of water in gravel zones adjacent to the open channel. Hyporheic scale exchange can occur along stream reaches which overall are either gaining or losing water. In a stream system with significant, sustained flow loss, physical differential gauging is used to estimate net flow loss. Combined with tracer injections (Ruehl et al., 2006), exchange can also be estimated separately from the loss. Schemes of multiple injections have been devised (Zellweger, 1994; Payn et al., 2009) for estimating the loss. Tracer injections (Castro and Hornberger, 1991) are used to demonstrate the exchange of water and storage solutes from the stream with both short-term areas in the gravel beds and longer-term areas in deeper alluvium. Naturally occurring radon activities are used to estimate groundwater discharge to surface waters. In a river with hyporheic exchanges, the use of injected volatile tracer was used (Cook et al., 2006) to make the corrections for the apparent losses of radon into the hyporheic zone. For identifying connections at the river scale to the regional groundwater, isotopic analyses (Hinkle et al., 2001) are employed (see also Chapter 2.09 Tracer Hydrology). Often tracers of flow path are specifically sought for their conservative, minimally reactive properties. An innovative extension is the use of smart tracers (Haggerty et al., 2008) which convert to an alternative chemical form when flow is into microbiological active zones under mildly reducing conditions. In a manner analogous to the use of chemical tracers, heat is becoming widely used as a tracer in the determination of stream–groundwater exchanges. Constantz (2008) reviews many of these techniques, for example, the use of temperature time-series phase and amplitude analysis (Hatch et al., 2006) to determine seepage, augmentation of temperature data with regulator-mandated water-quality data (Cox et al., 2007), and mapping by Essaid et al. (2008) of spatial temperature profiles to infer near-stream stratigraphic layering. Modern geophysical methods are also deployed to explicitly use information in the temperature signal. Westhoff et al. (2007)

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describe one of several recent uses of a distributed temperature-sensing fiber-optic cable system to identify sources of subsurface lateral inflow to a stream. Remotely sensed thermal spatial distributions in a stream can be determined using hand-held infrared (IR) imaging cameras (Cardenas et al., 2008a). As with the standard physical methods for the study of groundwater flow, the standard modeling tools are also applied to stream–groundwater interactions. Applications of modular finite-difference flow (MODFLOW) model (see, e.g., Harbaugh et al., 2000) have been at the hyporheic scale (Lautz and Siegel, 2006) as well as for complex hydraulic analysis of intermittent and ephemeral streams (Niswonger et al., 2008). Wondzell et al. (2009a) examined the issue of validation of groundwater flow models of hyporheic interactions. The transient storage model (TSM) is a tool used primarily at the hyporheic zone scale of stream–groundwater interactions in the analysis of solute dynamics. The concepts of the model, along with presentation of methods for application with field tracer studies, are given in Webster and Valett (2007). A widely used version of the TSM is available as the one-dimensional transport with inflow and storage (OTIS) code (Runkel, 1998). Runkel (2002) developed a metric based on the parameters of the TSM that provides an indication of the significance of exchange processes to solute dynamics of a stream. Harvey et al. (1996) and Harvey and Wagner (2000) provide quantitative guidance for interpreting the degree of confidence with which the TSM parameters can be determined. The importance of estimation of the lateral inflows in the interpretations of TSM parametrizations was demonstrated by Scott et al. (2003). Although the TSM is most widely used as a tool for parametrization of stream–hyproheic exchange with conservative tracer studies, it has been extended for use with Michaelis–Menten kinetics (Kim et al., 1992; Claessens and Tague, 2009) and geochemical equilibrium (Runkel, 2010).

2.20.5 Summary and Future Challenges Stream–groundwater interactions are now recognized as significant components of stream aquatic ecosystems. In simple terms, the hydrology underlying this significance may be summarized as ‘‘The stream is not a pipe.’’ and ‘‘Ground water and surface water: A single resource.’’ This recognition has been built upon decades of field studies demonstrating the ongoing connections between streams and their catchments. Considerable opportunities remain for new and challenging hydrologic work. Flume studies of environmental fluid mechanics (Marion et al., 2008) with sophisticated simulations (Cardenas et al., 2008b) have demonstrated that streambed forms, substrate distributions, and flow conditions can be set up to drive exchange of water and solutes between free-flowing surface and subsurface zones. An actual stream, however, is highly variable in these characteristics. It is the very heterogeneity of the subsurface media and the patchy biogeochemical conditions that create the environments in which the aquatic ecosystem influences are manifest. Stream– groundwater interactions are, in general, secondary processes compared with the bulk water flow or solute transport. Thus, a sequence of challenges is present in first applying mechanistic

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understanding to measurement of characteristics and processes that vary at relatively fine scales in the field and then in this information to interpret the effects at the scale of the stream–catchment system. A recent series of review articles makes clear that the study of stream–groundwater interactions is truly a multidisciplinary challenge requiring an understanding of the hydrogeomorphology (Poole, 2010), solute dynamics (Mulholland and Webster, 2010), and implications for stream ecology and management (Boulton et al., 2010).

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Cardenas MB, Harvey JW, Packman AI, and Scott DT (2008a) Ground-based thermography of fluvial systems at low and high discharge reveals potential complex thermal heterogeneity driven by flow variation and bioroughness. Hydrological Processes 22: 980--986. Cardenas MB, Wilson JL, and Haggerty R (2008b) Residence time of bedform-driven hyporheic exchange. Advances in Water Resources 31: 1382--1386. Castro NM and Hornberger GM (1991) Surface–subsurface water interactions in an alluviated mountain stream channel. Water Resources Research 27: 1613--1621. Claessens L and Tague CL (2009) Transport-based method for estimating in-stream nitrogen uptake at ambient concentration from nutrient addition experiments. Limnology and Oceanography Methods 7: 811--822. Constantz J (1998) Interaction between stream temperature, streamflow, and groundwater exchanges in alpine streams. Water Resources Research 34: 1609--1615. Constantz J (2008) Heat as a tracer to determine streambed water exchanges. Water Resources Research 44: W00D10 (doi:10.1029/2008WR006996). Cook PG, Lamontagne S, Berhane D, and Clark JF (2006) Quantifying groundwater discharge to Cockburn River, southeastern Australia, using dissolved gas tracers 222Rn and SF6. Water Resources Research 42: W10411. Covino TP and McGlynn BL (2007) Stream gains and losses across a mountain-tovalley transition: Impacts on watershed hydrology and stream water chemistry. Water Resources Research 43: W10431. Cox MH, Mendez GO, Kratzer CR, and Reichard EG (2003) Evaluation of tracer tests on the Upper Santa Clara River, Los Angeles and Ventura Counties, California, during October 1999 and May 2000. US Geological Survey Water-Resources Investigation Report 03-4277. http://pubs.er.usgs.gov/usgspubs/wri/wri034277 (accessed April 2010). Cox MH, Su GW, and Constantz J (2007) Heat, chloride, and specific conductance as ground water tracers near streams. Ground Water 45: 187--195. Dahm CN, Grimm NB, Marmonier P, Valett HM, and Vervier P (1998) Nutrient dynamics at the interface between surface waters and groundwaters. Freshwater Biology (3): 427--451. Dahm CN, Valett H, Baxter CV, and Woessner WW (2007) Hyporheic zones. In: Hauer FR and Lamberti GA (eds.) Methods in Stream Ecology, 2nd edn., pp. 119--142. London: Elsevier. Dent CL, Grimm NB, and Fisher SG (2001) Multiscale effects of surface–subsurface exchange on stream water nutrient concentrations. Journal of the North American Benthological Society 20: 162--181. Dent CL, Grimm NB, Martı´ E, Edmonds JW, Henry JC, and Welter JR (2007) Variability in surface–subsurface hydrologic interactions and implications for nutrient retention in an arid-land stream. Journal of Geophysical Research G: Biogeosciences 112: G04004. Dole-Olivier MJ (1998) Surface water–groundwater exchanges in three dimensions on a backwater of the Rhone River. Freshwater Biology 40: 93--109. Duff JH and Triska FJ (1990) Denitrification in sediments from the hyporheic zone adjacent to a small forested stream. Canadian Journal of Fisheries and Aquatic Sciences 47: 1140--1147. Essaid H, Zamora C, McCarthyu KA, Vogel JR, and Wilson JT (2008) Using heat to characterize streambed water flux variability in four stream reaches. Journal of Environmental Quality 37: 1010--1023. Fernald AG and Guldan SJ (2006) Surface water–groundwater interactions between irrigation ditches, alluvial aquifers, and streams. Reviews in Fisheries Science 14: 79--89. Fiebig DM and Lock MA (1991) Immobilization of dissolved organic matter from groundwater discharging through the stream bed. Freshwater Biology 26: 45--55. Fleckenstein JH, Niswonger RG, and Fogg GE (2006) River–aquifer interactions, geologic heterogeneity, and low-flow management. Ground Water 44: 837--852. Fuller CC and Harvey JW (2000) Reactive uptake of trace metals in the hyporheic zone of a mining-contaminated stream, Pinal Creek, Arizona. Environmental Science and Technology 34: 1150--1155. Gandy CJ, Smith JWN, and Jarvis AP (2007) Attenuation of mining-derived pollutants in the hyporheic zone: A review. Science of the Total Environment 373: 435--446. Geist DR and Dauble DD (1998) Redd site selection and spawning habitat use by fall Chinook Salmon: The importance of geomorphic features in large rivers. Environmental Management 40: 655--669. Gooseff MN, McKnight DM, and Runkel RL (2004a) Reach-scale cation exchange controls on major ion chemistry of an Antarctic glacial meltwater stream. Aquatic Geochemistry 10: 221--238. Gooseff MN, McKnight DM, Runkel RL, and Duff JH (2004b) Denitrification and hydrologic transient storage in a glacial meltwater stream, McMurdo Dry Valleys, Antarctica. Limnology and Oceanography 49: 1884--1895.

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Ruehl C, Fisher AT, Hatch C, Los Huertos M, Stemler G, and Shennan C (2006) Differential gauging and tracer tests resolve seepage fluxes in a strongly-losing stream. Journal of the North American Benthological Society 26: 191--206. Ruehl CR, Fisher AT, Los Huertos M, et al. (2007) Nitrate dynamics within the Pajaro River, a nutrient-rich, losing stream. Journal of the North American Benthological Society 26: 191--206. Runkel RL, McKnight DM, and Rajaram H (2003) Modeling hyporheic zone processes. Advances in Water Resources 26: 901--905. Schindler JE and Krabbenhoft DP (1998) The hyporheic zone as a source of dissolved organic carbon and carbon gases to a temperate forested stream. Biogeochemistry 43: 157--174. Scott DT, Gooseff MN, Bencala KE, and Runkel RL (2003) Automated calibration of a stream solute transport model: Implications for interpretation of biogeochemical parameters. Journal of the North American Benthological Society 22: 492--510. Sharp JM, Jr. (1977) Limitations of bank-storage model assumptions. Journal of Hydrology 35: 31--47. Silliman SE and Booth DF (1993) Analysis of time-series measurements of sediment temperature for identification of gaining vs. losing portions of Juday Creek, Indiana. Journal of Hydrology 146: 131--148. Sophocleous M (2002) Interactions between groundwater and surface water: The state of the science. Hydrogeology Journal 10: 52--67. Squillace PJ, Thurman EM, and Furlong ET (1993) Groundwater as a nonpoint source of atrazine and deethylatrazine in a river during base flow conditions. Water Resources Research 29: 1719--1729. Stanford JA and Ward JV (1988) The hyporheic habitat of river ecosystems. Nature 335: 64--66. Tonina D and Buffington JM (2009) Hyporheic exchange in mountain rivers. I: Mechanics and environmental effects. Geography Compass 3: 1063--1086. Torgersen CE, Price DM, Li HW, and McIntosh BA (1999) Multiscale thermal refugia and stream habitat associations of Chinook salmon in northeastern Oregon. Ecological Applications 9: 301--319. Triska FJ, Kennedy VC, Avanzino RJ, Zellweger GW, and Bencala KB (1989) Retention and transport of nutrients in a third-order stream in northwestern California: Hyporheic processes. Ecology 70: 1893--1905. Valett HM, Dahm CN, Campana ME, Morrice JA, Baker MA, and Fellows CS (1997) Hydrologic influences on groundwater–surface water ecotones: Heterogeneity in nutrient composition and retention. Journal of the North American Benthological Society 16: 239--247. Valett HM, Morrice JA, Dahm CN, and Campana ME (1996) Parent lithology, surface– groundwater exchange, and nitrate retention in headwater streams. Limnology and Oceanography 41: 333–345. Von Gunten HR, Karametaxas G, Kra¨henbu¨hl U, et al. (1991) Seasonal biogeochemical cycles in riverborne groundwater. Geochimica et Cosmochimica Acta 55: 3597--3609. Ward JV, Malard F, Tockner K, and Uehlinger U (1999) Influence of ground water on surface water conditions in a glacial flood plain of the Swiss Alps. Hydrological Processes 13(3): 277--293. Webster JR and Valett HM (2007) Solute dynamics. In: Hauer FR and Lamberti GA (eds.) Methods in Stream Ecology, 2nd edn., pp. 169--185. London: Elsevier.

Westhoff MC, Savenije HHG, Luxemburg WMJ, et al. (2007) A distributed stream temperature model using high resolution temperature observations. Hydrology and Earth System Sciences 11: 1469--1480. Wildman RA, Jr., Domagalski JL, and Hering JG (2009) Hydrologic and biogeochemical controls of river subsurface solutes under agriculturally enhanced ground water flow. Journal of Environmental Quality 38: 1830--1840. Winter TC, Harvey JW, Franke OL, and Alley WM (1998) Ground Water and Surface Water: A Single Resource, US Geological Survey Circular 1139. Denver, CO: US Geological Survey. Woessner WW (2000) Stream and fluvial plain ground water interactions: Rescaling hydrogeologic thought. Ground Water 38: 423--429. Wondzell SM, LaNier J, and Haggerty R (2009a) Evaluation of alternative groundwater flow models for simulating hyporheic exchange in a small mountain stream. Journal of Hydrology 364: 142--151. Wondzell SM, LaNier J, Haggerty R, Woodsmith RD, and Edwards RT (2009b) Changes in hyporheic exchange flow following experimental wood removal in a small, low-gradient stream. Water Resources Research 45: W05406. Wondzell SM and Swanson FJ (1996) Seasonal and storm dynamics of the hyporheic zone of a 4th-order mountain stream. I: Hydrologic processes. Journal of the North American Benthological Society 15: 3--19. Wondzell SM and Swanson FJ (1999) Floods, channel change, and the hyporheic zone. Water Resources Research 35: 555--567. Wo¨rman A, Packman AI, Marklund L, Harvey JW, and Stone SH (2006) Exact threedimensional spectral solution to surface–groundwater interactions with arbitrary surface topography. Geophysical Research Letters 33: L07402 (doi:10.1029/ 2006GL025747). Wo¨rman A, Packman AI, Marklund L, Harvey JW, and Stone SH (2007) Fractal topography and subsurface water flows from fluvial bedforms to the continental shield. Geophysical Research Letters 34: L07402 (doi:10.1029/2007GL029426). Zellweger GW (1994) Testing and comparison of four ionic tracers to measure stream flow loss by multiple tracer injection. Hydrological Processes 8: 155--165. Zellweger GW, Avanzino RJ, and Bencala KE (1989) Comparison of tracer dilution and current-meter discharge measurements in a small gravel-bed streams, Little Lost Man Creek, California. US Geological Survey Water-Resources Investigation Report 89-4150. http://pubs.er.usgs.gov/usgspubs/wri/wri894150 (accessed April 2010).

Relevant Websites http://water.usgs.gov MODFLOW and Related Programs, US Geological Survey (USGS). http://co.water.usgs.gov OTIS: One-Dimensional Transport with Inflow and Storage, US Geological Survey (USGS). http://water.usgs.gov USGS Water Resources Applications Software: OTEQ.

Preface – Aquatic Chemistry and Biology FH Frimmel, Karlsruhe Institute of Technology, Karlsruhe, Germany & 2011 Elsevier B.V. All rights reserved.

The World of Aquatic Chemistry and Microbiology Aquatic chemistry and microbiology do not belong to the classical subjects taught in universities. Nevertheless, they are part of many curricula in natural sciences and engineering. It is beyond doubt that the fascination of the molecular dimension of water itself and all its constituents, which goes like a red threat through all the aspects of structure, transport, and reactions of and in aquatic systems, attracts so many people. Due to the broad and fundamental importance of water for life, including the humans, the molecular water sciences (MoWaS) have to be transdisciplinary. The discipline includes not only physics, chemistry, biology, and geology, but also mathematics, engineering, and economics and even parts of social sciences. As a consequence, several subjects have developed based on fundamental ones but focusing on the special aspects of water, examples of which include limnology, oceanography, hydrogeology, hydrology, groundwater dynamics, drinking water treatment, municipal water management, industrial water usage, wastewater treatment, and hydrothermal usage. Many of them either are cross-linked or bridge the gap to the fields of quantitative water management. The big challenge when dealing with MoWaS can be deduced from the nano- and microscale of the substances involved and their low concentrations. The related bio-response can range from subtle to acute toxic effects. Methods to obtain reliable results are still scarce, especially for applications in natural environment. Here, the influences of matrices and the synergetic or antagonistic effects in multicomponent samples are often unclear. It is well accepted that water is the fundamental basis for our known life and in its unique function cannot be replaced by anything else. The physical properties of liquid water are reflected in its properties as transport medium, reaction phase, and mediator for higher molecular structures. One of the most impressive properties of the water molecules is the ability to form intermolecular hydrogen (H)-bonds. Linus Pauling once said, ‘‘y the hydrogen bond is especially suited to play a part in reactions occurring at normal temperatures, and I believe that it will be found that the significance of the hydrogen bond for physiology is greater than of any other single structural feature.’’ In other words, the formation and breaking of H-bonds in the energy band of our common environmental situation deliver the key for understanding life and its supporting element – water. It is also obvious that all major changes in water quality and temperature, for example, as a result of climate change, must have an influence on the dynamics of reactions and on the material balances involved. This again will influence the water cycle and hence the aquatic resources. Here, water management comes into the focus. Different kinds of water use with different influences on water quality in small- or large scale must be considered. Industrial development and population growth have led to one of the biggest

challenges to supply sufficient and hygienically safe water for human consumption and food production. Severe water shortage and necessary water quality are issues that have arisen regionally and are predicted to intensify drastically during the following decades. Concepts for multiple water use and water reuse need to be developed, taking advantage of the specific hydrological, climatic, and ecological situations. In addition, the special demands of social communities such as mega cities or developing countries have to be considered. Wherever possible, the ecological functions of regions must be protected for it is most reasonable to use nature as a self-sustained system also for water cleaning. The protective function of soils and their capability to degrade and eliminate aquatic pollutants make it attractive to use groundwater as a resource for drinking water supply, especially when protective zones and assisting technical measures are established. Toxicity and hygiene reflecting criteria are, besides the technical aspects such as corrosivity, most important for the use of water. A meaningful assessment of the use-oriented water quality has also to include parameters which quantify, for example, biota friendliness, potential for bacterial growth, eutrophication, and disinfection by-product formation. Occurrence of pathogenic microorganisms and waterborne epidemic episodes belong to the most serious events often with peaks in wars, natural disasters, and badly managed camps, homes, and companies. Quite often, shortcuts between the systems for drinking water supply and wastewater discharge have been identified as reason. Economic aspects are one of the master drivers for use of water and its management. On the one hand, the availability of enough water of suitable quality has been discussed as an issue of human rights. On the other hand, water has become a trade good, which is sold directly in bottles or through pipes or as virtual water in the manifold forms of industrial products. No matter how much profit might be involved in this business, the availability of reasonable resources and economically feasible treatment technologies will play a fundamental role. The application of cheap energy sources such as sun light and the use of homogeneous and heterogeneous catalysis, including biocatalysis, lead to most promising watertreatment concepts. Intelligent combination and an optimized sequence of treatment steps can further improve the economy of water plants. Hybrid systems are suited for highly efficient water treatment in fast working small reactors with the advantage of decentralized application. Keeping these aspects in mind, it becomes obvious that understanding the details of the properties of living and nonliving water constituents, their reactivities, and transport behavior will help to tailor powerful methods for waterquality assessment and to derive efficient concepts for timely water-treatment processes. The water cycle is an ideal case study not only for its different stages and hot spots, but also as

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Preface – Aquatic Chemistry and Biology

a whole which can teach us the systematic approach to complex systems and to the solutions of the related man-made problems. It also shows the necessity of transdisciplinary thinking in the sense of lifelong learning. Starting in the early days of childhood, we need to lay the foundation for a responsible care for water as a basis for our life and culture. Furthermore, we need to invest in the tools for a sustainable water management by developing measures to save the water cycle in its proper ecological function. This calls for the classical components of teaching and research and beyond that for innovative concepts to serve the daily needs of water usage in an economically affordable and socially acceptable way. To serve this aim, a comprehensive treatise on water is presented. Volume 3 of this work includes the chemistry and microbiology of MoWaS. The analytical aspects cover water-specific sum parameters, methods for the determination of trace metals and metalloids, as well as radioactive substances, and the characterization of natural organic matter (NOM). Emerging contaminants, colloids, and engineered nanoparticles are presented and data handling is described. The identification of bacteria and parasites helps to characterize the hygienic status of water. Online monitoring,

screening of estrogen activities, and enzyme-linked immunotests show the way to modern concepts for continuous quality control and bioeffect-related assessment. The development and application of standardized methods supply tools to obtain reproducible and well-comparable results. For the special needs of water treatment and distribution, it is most useful to quantify biodegradability and toxic effects. Reaction mechanisms of oxidation and disinfection processes as well as bioremediation are important not only to understand the pathways of technical transformations and natural attenuation, but also to optimize treatment strategies. All these topics are addressed by leading experts in the field. They all intend to supply for the interdisciplinary water community the molecular facts for a meaningful diagnosis of the status of aquatic systems and for efficient technical processes within the water cycle. As the editor of this volume, I would like to thank all the authors for their valuable contributions. Furthermore, I am grateful to U. Bilitewski, T. Bu¨nger, G. Donnevert, G. Gauglitz, H. Geckeis, B. Hambsch, T. Hofmann, H. Horn, T. P. Knepper, D. Knopp, V. Neitzel, R. NieXner, B. Nowack, F. Petry, H.-J. Pluta, M. Spiteller, and M. Weller for their input by peerreview.

3.01 Sum Parameters: Potential and Limitations FH Frimmel and G Abbt-Braun, Karlsruhe Institute of Technology, Karlsruhe, Germany & 2011 Elsevier B.V. All rights reserved.

3.01.1 3.01.2 3.01.3 3.01.3.1 3.01.3.1.1 3.01.3.2 3.01.3.2.1 3.01.3.3 3.01.3.4 3.01.3.5 3.01.3.5.1 3.01.3.5.2 3.01.3.5.3 3.01.3.6 3.01.4 3.01.4.1 3.01.4.2 3.01.4.2.1 3.01.4.2.2 3.01.4.2.3 3.01.4.2.4 3.01.4.3 3.01.4.3.1 3.01.4.3.2 3.01.4.3.3 3.01.4.3.4 3.01.4.4 3.01.4.4.1 3.01.4.4.2 3.01.4.4.3 3.01.4.4.4 3.01.4.4.5 3.01.4.5 3.01.5 3.01.5.1 3.01.5.2 3.01.5.3 3.01.5.4 3.01.6 3.01.6.1 3.01.6.2 3.01.6.3 3.01.6.4 3.01.7 3.01.7.1 3.01.7.2 3.01.7.2.1 3.01.7.2.2 3.01.7.2.3 3.01.7.2.4 3.01.7.3 References

Introduction General Considerations and Scope DOC and TOC Background Relevance Analytical Procedure Method variations Interferences Advanced TOC (DOC) Characterization Applications Hydrosphere Surface water Water treatment Surrogate Parameters Oxygen Demand Parameters Introduction Chemical Oxygen Demand Background Analytical procedure Interferences Applications PMC and Permanganate Index (IMn) Background Analytical procedure Interferences Applications Biochemical Oxygen Demand Background Analytical procedure Interferences Applications Related parameters (AOC) Interdependences UVA and Visible Range Absorbance Background Analytical Procedure Interferences Applications Organically Bound Halogens Adsorbable on Activated Carbon (AOX) Background Analytical Procedure Applications Related Parameters Additional Sum Parameters Background Examples for Emerging Parameters Humic substances NPs and colloids Luminescence Bioeffect quantification View

3 3 3 3 4 4 5 5 5 7 7 7 7 8 8 8 9 9 9 10 12 12 12 13 13 13 13 13 13 14 14 14 15 15 15 15 16 16 18 18 18 19 19 19 19 20 20 21 22 22 23 23

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Sum Parameters: Potential and Limitations

3.01.1 Introduction The general assessment of the quality of aquatic systems and the judgment of the efficiency of a water-treatment facility quite often relies on the application of sum parameters. Sum parameters are normally based on an integrative quantification of a specific group of compounds. However, the results obtained are mostly operationally defined and are often prone to misinterpretation. Therefore, it is essential to understand the power and at the same time the limitations of the parameters applied. Like signposts, they can give first information on assessment strategies and the necessity of singlecompound analysis. They are also suited for a total balance even in the presence of compounds with unknown structure (Frimmel and Abbt-Braun, 2009; Abbt-Braun and Frimmel, 2010). All these advantages have led to a prosperous development of water-specific sum parameters and their application in legislation, in technical rules, and in environmental recommendations. It is beyond doubt that research in and development of sum parameters have been significantly influenced by the practical aspects of water quality and vice versa. The applicability of the corresponding methods has also stimulated the development of specific instrumentation (see also Chapter 3.10 Online Monitoring Sensors). Some of the instruments are well suited for continuous measurements and can be used as online detectors. This opens the door for the resolution of mixtures by chromatography or by other fractionation methods. As a consequence, the sum parameter-based detector systems have an important bridging function between unresolved sum parameter quantification and single substance determination. All these aspects have led on the one hand to a tremendous increase of valuable information, but on the other hand to often uncritical interpretation of the results. The aim of this chapter is to focus on some well established sum parameters and to highlight their characteristics such as 1. 2. 3. 4. 5. 6.

background, principle of the method, interferences and limitations, advanced method, application, and related parameters.

3.01.2 General Considerations and Scope Sum parameters such as single-compound determination have to fulfill task-specific minimum requirements concerning exactness. It has to be decided whether the principle ‘as exact as possible’ or the approach ‘as exact as necessary’ meets best the requirements of the specific task. Often the desire for a specific and sensitive measurement finds its limitation in the needs of a high throughput of samples and/or a low economic investment. A reasonable compromise can normally be reached by a sound problem analysis prior to the determination itself. In general, classical spectroscopic and electrochemical methods cover the concentration range in aqueous samples from mg down to ng l1 (Skoog et al., 2003; Standard

Methods, 2005). This puts the application of sum parameters right into the center of a comprehensive assessment concept which is open for a dynamic back bonding of the results with the selection of further analytical steps. As a consequence, sum parameters have found their way into legislation and assessment of environmental protection with all the demands of data quality acceptance in court cases. In this chapter, we discuss in depth the parameters: dissolved organic carbon (DOC) and total organic carbon (TOC), chemical oxygen demand (COD), permanganate consumption (PMC), biochemical oxygen demand (BOD) and assimilable organic carbon (AOC), the color and ultraviolet (UV) absorbance (UVA), and on activated carbon adsorbable organically bound halogens (AOX). Most of these parameters refer to the dissolved state of the matter to be determined. Filtration through membranes with nominal pore size of 0.45 mm is widely used as analytical operation even though there might be pitfalls from pore blocking, fouling layer formation, or scaling. Quite often, the water samples are analyzed without pretreatment. This has to be clearly stated in the protocol and is normally assigned as total concentration value, for example, TOC. To close the gap between the dissolved state and particulate matter, a method for the determination of the particle-size distribution in the nanometer (nm) range is presented.

3.01.3 DOC and TOC 3.01.3.1 Background The basis for the parameters DOC and TOC is the chemical definition of organic compounds. They can be of biogeogenic or anthropogenic origin. Most natural organic substances in water are the left overs of biological activities and products of a huge variety of naturally occurring physical, chemical, and biochemical reactions in air, soil, and water. The endless number of possible substances involved in these processes makes the identification tedious and, from the quantitative point of view, impossible. Therefore, the terms natural organic matter (NOM) or humic substances (HSs) as the refractory part of it are often used for an integrative description, and the parameters DOC or TOC for quantification (Thurman, 1985; Frimmel and Christman, 1988; Perdue and Gjessing, 1990; Frimmel et al., 2002). Organic compounds of anthropogenic origin can find their way into the aquatic systems from effluents of wastewater treatment plants and industrial activities, from chemical wastes and landfills, by accidents during storage and transport of organic chemicals, and from combustion and by deposition from the air (Kolpin et al., 2002; Frimmel and Mu¨ller, 2006; Reemtsma and Jekel, 2006; Ku¨mmerer, 2008). In the current industrialized environment, it is quite idle to distinguish strictly between the purely natural components and the anthropogenic ones in many cases. Concerning the quantification for C, this might be irrelevant anyhow. Table 1 gives an overview of the different C species defined according to their character and/or to the pretreatment of the sample prior to elemental C determination. In practical work, the definitions often are only semi-quantitatively accurate.

Sum Parameters: Potential and Limitations Table 1

Common terms for property-related TOC fractions

POC) is retained together with the sorbed substances in/on the filter and the volatile compounds (volatile organic carbon, VOC) are normally lost:

Synonym

Meaning, definition

AOC BOC

Assimilable OC (see Section 3.01.4.4.5) Biodegradable OC (by microorganisms) (see Section 3.01.4.4) Chromatographable OC (by LC, GC, etc.) (see Section 3.01.3.4) Dissolved OC (o0.45 mm) Dissolved OM (E50% DOC) Natural OM (geogenic) Particulate OC (40.45 mm) Persistent organic pollutants Refractory OM (poorly biodegradable) Volatile OC (e.g., boiling point (substances)o80 1C)

COC DOC DOM NOM POC POP ROM VOC

OC, organic carbon; OM, organic matter.

3.01.3.1.1 Relevance The relevance of TOC can be deduced from its character as universal parameter. Other parameters reflecting specific properties of organic matter (OM) such as DOC or AOC or surrogate parameters such as UVA or COD can preferably be related to the TOC value to provide the basis for an especially meaningful comparison of water samples. However, it has to be kept in mind that TOC as sum parameter always remains limited in the information it can supply on the chemical structure of the matter it reflects. The instrumental tools suited for continuous TOC determination can be used as detection system for chromatographic TOC fractionation and by this it can help to overcome the limitation of structural information to some extent. The total carbon (TC) includes all C in inorganic and organic form (Equation (1)). The total inorganic carbon (TIC) reflects mainly the carbonate system (CO2, HCO3  , and CO3 2 ), and by definition also the traces of CO, CN, OCN, and SCN, which might be of relevance in specific wastewaters:

TC ¼ TIC þ TOC

5

ð1Þ

TOC comprises all the C atoms which are covalently bound in organic molecules and even particulate matter like carbon black. In natural aquatic systems and water technology, the carbonate system is considered to be most relevant due to its high mass concentrations. TIC can be quantified as CO2 after acidification (pHo2) and purging with an inert gas of high purity such as N2 or Ar. The purging step would also transfer other volatile substances such as HCN or small organic molecules such as methane (CH4), methanol (CH3OH), and C1or C2-halogen compounds from the aqueous phase to the gas phase. Due to the low concentrations of such volatile compounds in most waters, this is often neglected in the mass balances, but it can be important in the assessment of specific situations such as the occurrence of toxic substances. The TOC includes the organic carbon (OC) in dissolved and particulate matter. These two types of C can be distinguished by filtration through a 0.45-mm pore-size membrane, leading to the DOC in the filtrate. The particulate part (particulate organic carbon,

TOC ¼ DOC þ POCk þ VOCm

ð2Þ

This method has been widely accepted, even though there are still controversial debates on the influence of the mostly poorly defined filter cakes, on the results and whether the pore size of the filter should be chosen to be 0.1 mm or even below that to better reflect the dissolved state. A well acceptable way out of these problems can be seen in a detailed description of the experimental protocol of the method applied. The occurrence of TOC is a consequence of life on the Earth. The ubiquity of TOC in aquatic systems has been demonstrated in many investigations (Table 1). It is mostly refractory, that is, the biologically stable part of dissolved organic matter (DOC) which leads to a kind of steady-state TOC concentration in the different aqueous phases. DOC is often used synonymously with TOC, and other properties of the OM are reflected in specific parameters (Table 1). It is obvious that some terms and their definitions must remain vague. This means that the concerned parameter values can have a considerable span of uncertainty. Keeping this in mind, it seems to be acceptable to use Equation (3) as an approximation based on many elemental analyses. Unfortunately in literature, the databases are quite often unclear. Therefore, experimental data and procedures need to be described in detail and unambiguously to be useful:

rðDOMÞE 2rðDOCÞ

ð3Þ

3.01.3.2 Analytical Procedure Due to the high importance of TOC and DOC values in water assessment, there are standardized international methods for their determination (DIN EN 1484, 1997; Standard Methods 5310 B, 5310 C, 5310 D, 2005; see also Chapter 3.11 Standardized Methods for Water-Quality Assessment). They are mostly based on a quantitative oxidation of the organic molecules to CO2 which can be determined with a very low limit of determination around 10 mg l1. Oxidation is done either by high-temperature (up to 950 1C) combustion in the presence of a catalyst (e.g., platinum-group metals, cobalt oxide, or barium chromate) and oxygen or at ambient temperature in solution using UV irradiation and/or chemical oxidants such as H2O2 or persulfate. Inorganic C (IC) has to be removed within a pretreatment step, for example, by acidification with H3PO4 and purging as CO2. This separation step is most important for reliable TOC results, because TOC is quantified also as CO2 and this value is much smaller than the IC concentration in most waters. The CO2 produced from the inorganic carbonate system and from the organic water constituents can be (a) quantified in the gas phase after drying and transfer to a nondispersive infrared (IR) detector or (b) trapped in alkaline aqueous solution with coulometric titration. The principle of a system based on continuous flow injection of the sample is shown in Figure 1. Calibration can be done with defined aqueous potassium biphthalate (C8H5O4K) solutions for OC and with sodium

6

Sum Parameters: Potential and Limitations

CO2 analyzer

CO2 analyzer

Inorganic CO2

Organic CO2

Purger

UV reactor

Aqueous sample

H3PO4

Data system

Liquid waste

K2S2O8

CO2-free air

CO2-free N2

Figure 1 System for continuous-flow TIC/TOC analysis.

carbonate (Na2CO3) solutions for IC. The different methods operate in the concentration range 10 mg l1or(C)o1 g l1.

3.01.3.2.1 Method variations There is another procedure for continuous-flow injection of the aqueous sample (Figure 2). After acidification and persulfate addition, the sample is split: one sample flow passes through the UV reactor, whereas the other one passes to a delay coil. The CO2 from each branch is separated by CO2selective membranes into high-purity water. There the increase in the electrical conductivity can be directly related to the CO2 concentration. The CO2 from the non-UV-irradiated branch represents the TIC, whereas the CO2 from the irradiated branch represents the TC. TOC results from the difference. Samples with relatively high levels of TOC (r(C)4mg l1) and/or suspended OC can be well determined by the hightemperature combustion method. This method is suited for online measurement. The inorganic carbon can be converted to CO2 by acidification (pHo2) and removed by purging or it can be quantified, for example, in a nondispersive IR detector. In the purged sample, OC can be quantified after high-temperature catalytic oxidation as CO2 (Figure 3). A variation of the method determines the TC of the sample after its direct injection into the combustion chamber which is kept at temperatures above 950 1C to decompose all carbonates. TIC and TOC or other carbon fractions can be deduced from the respective differences.

3.01.3.3 Interferences Special care has to be taken with TOC determination of suspensions. Often the analytical homogeneity and hence the representative character of a sample are endangered by sedimentation and its kinetics. A way out of that dilemma is the

Aqueous sample Acid Oxidant (K2S2O8)

Delay coil 6 min

UV reactor 6 min

Membrane module

Membrane module

CO2 detector (TIC)

CO2 detector (TC)

Data treatment TC − TIC = TOC Figure 2 Membrane-based procedure for the continuous-flow TIC/TOC analysis.

separate quantification of the concentration of particulate matter and that of the dissolved matter, for example, after applying filtration through a membrane with defined pore size. However, it has to be kept in mind that the filtration can be influenced by the type of membrane and its bleeding (Khan

Sum Parameters: Potential and Limitations

7

Inorganic CO2 (TIC) H2SO4 or H3PO4

Sample

Catalytic combustion chamber

Purging unit

Gas (air) CO2 free

O2 CO2 free

Organic CO2 (OC) (+ H2O, HCl …)

Cooler CO2 analyzer (non-disp. IR)

Figure 3 Experimental setup for the catalytic combustion method for TIC/TOC determination in aqueous solutions.

and Pillai, 2007), its surface tension, and age and state of equilibration. In addition, undefined pore blocking and sorption processes have to be considered. The DOC concentration at the beginning of a filtration experiment can be quite different to the DOC concentration at the end. For samples with high turbidity, filtration through a set of filters with decreasing pore size and determination of the fractions obtained can be a reasonable though time-consuming option. Another possibility is the determination of the colloidal index (CI, also known as silt density index SI or fouling index FI) for OC characterization (ASTM Standard D4189-07, 2007). The principle of the method is to relate the specific filtrate volumes with the time needed to obtain them as the filtration process proceeds. In case short-wavelength UV lamps are used to increase the amount of OH radicals for oxidation, care has to be taken as the intensity of the UV light may be reduced by highly turbid samples or by aging of the light source resulting in incomplete oxidation. Problems can also arise by chloride concentrations above 0.05 wt.%, due to preferential oxidation of chloride. At relatively low DOC concentrations as present in marine systems, special care has to be taken to guarantee correct results. Dafner and Wangersky (2002) showed that special attention toward the cleanness of the sampling facilities and procedure is crucial. Sample storage should be short (o2 days) at low temperature (o4 1C) and in the dark. Examples for field procedures to collect and preserve freshwater samples for DOC analysis were shown by Kaplan (1994), and Zsolnay (2003) addressed some basic problems and artifacts such as flock formation and agglomeration in sampling and preserving DOM from soil seepage water (see also Chapter 3.06 Sampling and Conservation, Chapter 3.07 Measurement Quality in Water Analysis). Blank samples should be run to determine background values of equipment, used chemicals, gases, and filters (in the case of DOC determination).

3.01.3.4 Advanced TOC (DOC) Characterization The great relevance of TOC and DOC parameters for the assessment of aquatic systems together with the available

powerful instrumentation for quantification paved the way for their advanced analysis. In addition to all, the intelligently designed experiments which are controlled by a suite of TOC or DOC measurements, a liquid chromatographic (LC) system with online DOC detection has been developed (Huber and Frimmel, 1994) for advanced OC characterization. Especially the principle of size-exclusion chromatography (SEC; e.g., TSK HW-50S or -40S turned out to be useful for the assessment of DOM and its behavior in water-treatment processes (Her et al., 2002b). The principle of the method is given in Figure 4. To reach high chromatographic resolution and low detection limits, special care has to be taken for low background levels of OC. This means that the phosphate buffer as mobile phase, the N2 carrier gas, and the phosphoric acid as acidifier for the CO2 purging of the inorganic carbonates have to be free of organic contamination. The sample can be injected to either pass the column or bypass it. This leads to the possibility of determining the amount of chromatographable OC and the TOC. Between the column and the spinning thin film photoreactor, noninvasive online UV/visible (Vis) and fluorescence detectors can be installed to give multi-dimensionally detected chromatograms. In principle, the retention times (or elution volumes) obtained for SEC columns are reversely correlated with the molecular size and in good approximation with the molecular weight of the eluted substances. The column elution can be calibrated with polyethylene glycols and/or polystyrene sulfonates. The exclusion volume (V0) and the permeation volumes (VP) can be determined by dextrane blue and methanol, respectively. However, the molecular size calibration bears some problems because aquatic TOC contains many unknown substances and hence calibration with authentic molecules is impossible (Lankes et al., 2009). Most common errors come from interfering adsorption and ion-exchange effects of the eluted substances in the stationary phase of the columns. A typical chromatogram of the OM in tap water obtained by UV (l ¼ 254 nm), fluorescence (lex ¼ 254 nm, lem ¼ 450 nm), and OC detection is given in Figure 5. The OC trace of the chromatograms of the injected water with a TOC concentration of 0.5 mg l1 shows a dominance of

8

Sum Parameters: Potential and Limitations

Aqueous sample

Data logging processing Column (e.g., SEC)

Eluent (P-buffer)

Piston pump

Injection port

UV/Vis detector

Phosphoric acid

Carrier gas (N2)

Fluorescence detector

pH 2 Piston pump

Peltier condenser

UV thin film reactor

IR detector

Inorganic CO2

IR detector

Organic CO2

Liquid waste

Relative OC-, UV (254)-, fluorescence (450)-signal

Figure 4 Experimental setup for the size exclusion chromatographic characterization of aquatic OC.

Vp

V0

OC Fluorescence UV

1.0

0.5

0 20

40

60

80

Elution volume, Ve (ml) Figure 5 Multi-dimensional size exclusion chromatograms for tap water (Karlsruhe, sampling date 07.07.09; r(DOC) ¼ 0.5 mg l1; resin: TSK HW-50 S; eluent: phosphate buffer, 26.8 mmol l1; injection volume 2.5 ml).

high molecular substances between 40 and 50 ml of elution volume followed by a less large fraction. This material has a relatively strong UVA and does fluoresce. It is attractive to assign these fractions to refractory HSs of higher and lower molecular size. The relatively sharp chromatographic peak reflects small organic acids as reported by Brinkmann et al. (2003a, 2003b) and is followed by gradually eluting unidentified OC. There are some detector-specific differences in the fractions and in their relative intensities. In general, however, the main fractions look quite similar. As a consequence, the easy-tomeasure UVA is often used as surrogate parameter for OC determination (Her et al., 2002a, 2003). In the case of very low background values, fractions of a few tens of ng l1 OC can be quantified.

3.01.3.5 Applications The DOC methods and their combination with fractionation methods (e.g., SEC-UV/OC method) are well suited for the advanced characterization and semi-quantitative assessment of environmental processes such as nutrient cycling and pollutant transport as well as technical water-treatment processes. (see also Chapter 3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter).

3.01.3.5.1 Hydrosphere Typical ranges for TOC/DOC concentrations of aquatic systems are given in Table 2.

Sum Parameters: Potential and Limitations Table 2

9

DOC in different aquatic systems

Aquatic systems

DOC concentration (mg l 1) Average

References

Range

Ocean

0.5

0.3–2.0 in 0–300 m; 0.2–0.8 in 4300 m

Williams (1971); Duursma and Dawson (1981)

Freshwater Ice and snow Rivers Lakes

0.5 7 2.2

0.1–5.0 5–9 Oligotrophic 2–3 Eutrophic 9–16 10–50

Laird et al. (1988); Frimmel et al. (2002) Malcolm (1985); Sontheimer et al. (1986) Steinberg (2003); McKnight and Aiken (1998) Aitkenhead-Peterson et al. (2003); Bertilsson and Jones (2003) Thurman (1985); Frimmel and Abbt-Braun (1999)

19–31 B0.5 up to 10

Abbt-Braun (1992); Frimmel (1992) Dinar et al. (2006); Graber and Rudich (2006) Matthess et al. (1992); Wedepohl (1969)

Brown water Soil seepage water Rain Groundwater, CaCO3 aquifer

12 25 B1 0.7

3.01.3.5.2 Surface water

3.01.3.5.3 Water treatment

The SEC-UV/OC method finds a broad application in characterizing the OM of rivers. In Figure 6, typical chromatograms for (a) the river Rhine (Germany) and (b) the river Moskva (Russia) are shown. Although the DOC concentrations are significantly different, the main fractions of the OC for both rivers are quite similar, but the small-sized substances are more abundant in the case of the river Rhine. Both rivers show a small but significant OC fraction around the exclusion volume without any UVA. It could be shown that these substances are of high molecular carbohydrate type. For comparison, the chromatograms for water from a brown water lake (c) and for wastewater (d) are shown. The brown water is dominated by a single fraction and it is attractive to assign it to plant-derived matter of humic structure. In the case of the wastewater, there are obviously plenty of low-molecular-weight organic substances (acids) which get eliminated by biological treatment. As a result of biotreatment, a large organic fraction with low UVA is generated. Based on the assignment to matter with carbohydrate structures, this fraction around the exclusion volume of the SEC column can be used for a rough estimation of the allochthonous and autochthonous part of aquatic refractory OC. In large molecular size fractions, there was a predominance of polysaccharide material. N-Acetylated polysaccharides derived from microbial leftovers. Lignin and tannin derivatives were most abundant in the intermediate size fraction (Lankes et al., 2008). However, detailed interpretation has to rely on advanced spectroscopic information on molecular structure. For a critical evaluation of OC assignment, see, for example, AbbtBraun et al. (2004), Lankes et al. (2008), Reemtsma et al. (2008), and Kunenkov et al. (2009). Also, it has to be kept in mind that photochemical OC detection often does not work quantitatively, for example, up to 70% of certain OC compounds were not detected with the organic carbon detection system in systematic investigations (Lankes et al., 2009). Assuming that the majority of refractory OM components do absorb UV radiation, UVA values are a valuable supplement for OC detection.

The SEC-OC system can also be used to follow technical separation processes such as flocculation, membrane filtration, or adsorption. Figure 7 shows the example of bog lake (brown water) OC as it decreases after (a) addition of ferric chloride (flocculation with FeCl3) and (b) equilibration with increasing amounts of powdered activated carbon (PAC; adsorption). It is obvious that in flocculation most of the OM (87%) gets eliminated. Especially, the high-molecular-size substances get better eliminated than the small ones. Interesting to note is the high elimination yield of UV-absorbing matter and the relatively, poor elimination yield of AOX forming precursors. In the case of PAC adsorption, the rest OC which remains in solution is strongly dependent on the amount of PAC added as expected but it is mainly higher molecular matter which remains in solution. These findings can be explained by the limited availability of pores with larger size. All information that can be derived from advanced OC characterization does not only supply the basis for a better understanding of the mechanisms which rule the OC distribution, but it also opens the door for the development of technically relevant elimination processes and their optimization (see also Chapter 3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter, Chapter 3.16 Chemical Basis for Water Technology).

3.01.3.6 Surrogate Parameters There are a number of sum parameters for the determination of OM which have been developed independently or supplementary to the DOC/TOC methods. Most of them work simpler and therefore find a broad application as surrogate parameters for OC. They focus on a specific character of the present organic substances and can add valuable information for the assessment of water quality. Their specific information can be related to the mass unit of OC as a universal parameter and which can supply the basis for a sound assessment and comparison of different aquatic sources or for following a

10

Sum Parameters: Potential and Limitations 4 V0

OC UV

1.5 River water Rhine (Wörth) (OC) = 1.7 mg l−1

1.0

0.5

OC UV River water Moskva (Kolomna) (OC) = 8.5 mg l−1

3

2

1

0

0.0 20

40

60

2.5

20

80

Elution volume, Ve (ml)

(a)

30

40

50

60

70

80

Elution volume, Ve (ml)

(b)

Vp

V0

V0

OC UV Brown water HO23 (OC) = 27.7 mg l−1 dilution: 1:10

2.0

Vp

4

1.5

1.0

Relative OC-, UV (254)-signal

Relative OC-, UV (254)-signal

Vp

V0

Vp Relative OC-, UV (254)-signal

Relative OC-, UV (254)-signal

2.0

Wastewater (OC) (OC) = 24.9 mg l−1, dilution: 1:3

3

Wastewater effluent after biological treatment (OC) (OC) = 9.9 mg l−1

2

a: OC b: UV

1

a b

0

0.5 20 (c)

40

60

20

80

Elution volume, Ve (ml)

(d)

40

60

80

Elution volume, Ve (ml)

Figure 6 Size exclusion chromatogram detected by OC- and UV (l ¼ 254)-detection of river water ((a) river Rhine, (b) river Moskva), brown water ((c) Hohlohsee, HO23), and wastewater (dilution 1:3) and wastewater treatment plant effluent (d) (resin: TSK HW-50S; eluent: phosphate buffer, 26.8 mmol l1).

complete treatment pathway. Most common surrogate parameters and complementary parameters for OC are given in Table 3. They are discussed in the following sections in more detail.

3.01.4 Oxygen Demand Parameters 3.01.4.1 Introduction Despite the broad distribution, the stability of the nonradioactive elements leads to their quite constant total amounts on earth. However, their appearance in different compounds and phases called speciation makes them distinguishable according to the chemical bonds in which they are engaged (Pauling, 1960). The corresponding oxidation state of the atoms in their chemical appearance is a typical guide for their reactivity. Carbon is one of the elements which covers all the range of eight oxidation state levels from the lowest one of  IV in CH4 up to the highest of one of þ IV in CO2. The elemental form is represented by the graphite and diamond structure. CO2 is the common end product of all

biochemical degradation reactions of C-compounds and chemical combustions if sufficient O2 is available. According to the high importance of the load of organic substances in water, their oxidative transformation into CO2 has become the basis for the development of sum parameters for quality assessment (Wagner, 1973). Most of them are based on the quantification of the oxygen necessary for a more or less quantitative oxidation of all organic compounds. There are purely chemical methods and there are biochemical methods, using a mixed bacterial population.

3.01.4.2 Chemical Oxygen Demand 3.01.4.2.1 Background The aim of the COD is to obtain a complete oxidation of all organic compounds of an aqueous sample to CO2. This is best reached by wet oxidation with potassium dichromate (K2Cr2O7) in hot acid solution. Problems can arise from other water constituents, for example, inorganic ones, which also get oxidized under the reaction conditions. These disturbances can be tackled by elimination of the substances concerned or

Sum Parameters: Potential and Limitations

11

Elimination in % SAK254

AOX-FP

87

96

54

Relative OC-signal

Relative UV (254)-signal

DOC

20

40 60 80 Retention time, t (min)

100

After flocculation (FeCl3) Original

20

40

60

80

100

Retention time, t (min)

(a)

Brown water

Relative OC-signal

Remaining DOC + 50 mg l−1 PAC + 500 mg l−1 PAC + 1000 mg l−1 PAC

0 (b)

10

20

30

40

50

60

Retention time, t (min)

Figure 7 Size exclusion chromatogram obtained by OC detection (a, b) and UV detection ((a), inset) of diluted brown water and the remaining DOC after flocculation (a) and after adsorption on PAC (b) (TSK HW-50 S; eluent: phosphate buffer, 26.8 mmol l1; AOX-FP, AOX-formation potential; SAK, spectral absorption coefficient).

12

Sum Parameters: Potential and Limitations

by masking them such that they do not react. The oxidation reaction is given in Equation (4) and has a standard potential of E1 ¼ 1.36 V:

Cr2 O7 2 þ 6e þ 14H3 Oþ -2Cr3þ þ 21H2 O

ð4Þ

3.01.4.2.2 Analytical procedure The redox reactions with K2Cr2O7 work best under fairly concentrated H2SO4 conditions and at the elevated boiling temperature of the sample/acid mixture. The oxidative power of the defined amount of added dichromate is partly consumed by the known volume of the aqueous sample to be analyzed. The remaining gets quantified by reductive back titration with ferrous sulfate (Equation (5)). The color change from orange-yellow (Cr2 O7 2 ) to pale green (Cr3þ) is used as indicator for the equivalence point and for the final calculation of the result: Cr2 O7 2 þ 6Fe2þ þ 14Hþ -2Cr3þ þ 6Fe3þ þ 7H2 O

ð5Þ

The final result is calculated from the amount of consumed K2Cr2O7 converted into O2 equivalents according to

6:13  rðK2 Cr2 O7 Þ in mg l1  rðO2 Þ in mg l1

ð6Þ

The whole laboratory procedure is outlined in Figure 8. Table 3

Common surrogate parameters for OC in aquatic samples

Surrogate parameter

Acronym

Quantification as

Chemical oxygen demand Permanganate consumption Spectral UV and visible absorbance Biochemical oxygen demand Adsorbable organic halogens Various other sum parameters

COD PMC SUVA SVIA BOD AOX See Section 3.01.7 OC

O2 O2 A(254 nm) A(436 nm) O2 Cl

Organic carbon

CO2

HgSO4 1g Aqueous sample 50 ml

Ag2SO4 (50 mg) in H2SO4 (conc.) 5 ml

The method is broadly used in wastewater characterization. It works best in the concentration range 50 mg l1or(O2) o900 mg l1. Concentrations steps or dilution with organic free water are recommended if the COD concentrations are below 50 mg l1 or higher than 900 mg l1. In case ferrous ammonium sulfate (FAS) titrant and ferroin indicator are used, the color changes from blue-green to reddish brown. For COD determination, several standard methods have become available (DIN 38409-41, 1980; DIN 38409-43, 1981; DIN 38409-44, 1992; DIN ISO 15705, 2003; Standard Methods 5220 B, 5220 C, 5220 D, 2005); (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment). Method variations. In addition to the described open reflux method, there is the possibility to use a so-called closed reflux method which uses borosilicate culture tube-like digestion vessels of 10 ml or more capacity and 10–25 mm diameter with polytetrafluoroethylene lined tightly fitting caps. Alternatively, borosilicate ampules can be used. The tubes or ampules filled with sample and chemicals are inserted in a block digestor at 150 1C for 120 min reflux. After cooling to room temperature, the digested solutions are titrated with ferroin indicator and FAS titrant. Alternatively, the change of C2 O7 2 to Cr3þ can be quantified spectrophotometrically at l ¼ 600 nm. The first method is mostly used for COD concentrations r(O2)o90 mg l1 whereas the l ¼ 600 nm absorption turned out to be better suited for higher concentrations. Experimental kits for these methods are commercially available. Calibration of all versions of the COD method is preferably done by potassium hydrogen phthalate (C8H5O4K) standard solutions with concentrations within the concentration range concerned. The whole procedure and equipment should be the same as for the determination of the samples.

3.01.4.2.3 Interferences The COD method based on the oxidative Cr2 O7 2 reaction at boiling conditions leads to parameter values with a coefficient of variation o8%. Use of especially cleaned glassware (e.g., H2SO4 rinsing), at least duplicate determinations and the subtraction of the blank COD of reagents and dilution water in the applied procedure can improve the data. The relevance

Calculated result

Distilled water

Flask with reflux condenser 120 min boiling

K2Cr2O7 (42 mmol l−1) 25 ml

Titrator

Ferroin indicator FAS (*) (0.25 mol l−1)

Figure 8 Laboratory procedure for the determination of the COD with dichromate (*FAS, ferrous ammonium sulfate).

Waste

Sum Parameters: Potential and Limitations

of the determination of background values was pointed out by Wagner (1973). Samples below the normal concentration range ask for even more care. In these cases, a higher volume of sample and diluted K2Cr2O7 standard solution (0.004 M) together with the appropriate amount of reagents are used and all are concentrated under boiling conditions to a volume of 150 ml. Titration is done with standardized 0.025 M FAS. Substances which are prone to poor or incomplete digestion in all described versions of the COD method are pyridine, its derivatives, and straight-chain aliphatic compounds. The latter ones can be more effectively oxidized in the presence of silver sulfate as catalyst. In the open reflux methods, volatile organic compounds can also get lost. The most common interferences are the halides, bromide and iodide, and especially chloride ions. They can form insoluble silver halides and by this inactivate the catalytic effect of Agþ. In addition, under the strong oxidative conditions of the K2Cr2O7 reaction, they can be transferred to the elements and beyond that to halo-oxoacids and their ions and by this false positive results are produced. Due to the complex reactions and the undefined mixture of resulting products, a correction of the results based on simple theoretical considerations is not possible. The addition of mercury sulfate (HgSO4) before boiling which leads to a close to complete complexation of the halides can eliminate the problem to a great extent. However, in the case of halide concentrations r(X)42 g l1, the method fails. Saline water samples can be pretreated by evaporation of the hydrogen-halide acids at reduced pressure. The hydrogen-halide acids are produced by addition of concentrated sulfuric acid to the sample under rigorous agitation:

2 X  þ 2 H þ þ SO4 2 2SO4 2 k þ 2HXm

ð7Þ

Nitrite (NO2  ) exerts about 1 mg O2 per mg NO2  – N. Due to the low NO2  concentrations in most waters, this can mostly be ignored.

3.01.4.2.4 Applications The COD is well suited for the characterization of fairly polluted waters. Municipal wastewater consumes O2 in the range from 300 to 1000 mg l1. After biological treatment, the COD (O2) drops to 20–1000 mg l1. Landfill leachate can reach up to r(O2) of 3000 mg l1. The COD of surface water normally ranges from 5 to 20 mg l1 .The COD as standardized method has found its way into wastewater legislation. In Germany, for example, 50 kg is the COD unit for payment of fees (1 unit B36 h) for the direct discharge of wastewater into the aquatic environment, and the threshold amount of discharge is 20 mg l1 of 250 kg yr1. The environmental hazards of Agþ, Cr(VI), and Hg2þ used in the COD determination ask for methods which work with smaller volumes or for alternative clean methods. From this point of view, TOC (DOC) is a promising parameter for replacing COD. COD for quantitative determination of oxidizable OC does not necessarily lead to the equivalent result as TOC (DOC) measurements, even though in both cases the end product of the reactions is CO2. The simple approximation that one mass unit of COD(O2) equals one mass unit of TOC or DOC is not precise enough in most cases due to the different oxidation

13

states of the averaged carbon (DOC, TOC) in the organic load which consequently leads to different consumptions of oxidant and hence COD values. (see also Chapter 3.16 Chemical Basis for Water Technology). The O/C atomic ratios for moderately polluted rivers (e.g., Rhine, Main, Danube, and Elbe) are around 2 ranging from 1.3 to 2.7. For wastewater, the ratios are similar or a bit higher (Zanke and Ho¨pner, 1982).

3.01.4.3 PMC and Permanganate Index (IMn) 3.01.4.3.1 Background Potassium permanganate (KMnO4) is a fairly strong oxidizing agent. Therefore, it has been used as analytical tool to characterize dissolved organic water constituents. The oxidation method has been established as fairly simple wet chemical procedure since the early days of water-quality assessment. The method should be used as operationally defined determination of the oxidizability of relatively clean water samples. The results mostly do not allow a clear correlation with the OC content of the samples. The PMC is defined to be the amount of permanganate that reacts with the sample under defined conditions. The oxidative function of permanganate in acid medium (sulfuric acid) is given in Equation (8) and shows a standard potential of E0 ¼1.52 V:

MnO4  þ 5e þ 8H3 Oþ -Mn2þ þ 12H2 O

ð8Þ

3.01.4.3.2 Analytical procedure The redox reaction partners are the oxidizable organic substances which are mostly the aim of quantification. However, inorganic water constituents (e.g., Fe2þ, Mn2þ, Cl, or NH4 þ ) which can be oxidized have to be considered. According to the protocol (DIN EN ISO 8467, 1995; (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment)), the sample (defined volume) is mixed with sulfuric acid and the well-defined potassium permanganate solution, and the mix is heated for 10 min. Then a defined amount of sodium oxalate (Na2C2O4) is added in excess for reduction of the unreacted MnO4  , and the remaining oxalate is quantified (Figure 9). From all this, the amount of MnO4  consumed by the sample can be calculated, and from that the resulting oxygen demand is deduced:

rðKMnO4 Þ in mg l1  3:95 rðO2 Þ in mg l1

ð9Þ

The IMn is calculated according to

IMn ¼ f ðV1  V0 Þ=V2 M ¼ 16ðV1  V0 Þ=ðV2 Þ

ð10Þ

where V1 is the volume (in ml) of consumed permanganate solution of the sample; V0 the volume (in ml) of consumed permanganate standard solution of the blank solution; V2 the volume (in ml) of the consumed permanganate standard solution of the blank solution after addition of oxalic acid; and f (16 mg mmol1) the equivalence coefficient for the conversion into oxygen. The method is applicable to samples with r(O2)41 mg l1.

14

Sum Parameters: Potential and Limitations

KMnO4 (2 mmol l−1)

H2SO4 (2 mol l−1) 5 ml Aqueous sample 25 ml

Stirring heating in boiling water bath

Hot titration Waste 30 min pale rose

10 min reaction

Na2C2O4 (5 mmol l−1) 5 ml

KMnO4 (2 mmol l−1) 5 ml

Calculated result

Figure 9 Laboratory procedure for the determination of the permanganate consumption.

3.01.4.3.3 Interferences As far as PMC is used as surrogate parameter for OC, pitfalls resulting from the presence of oxidizable inorganic water constituents have to be considered. Halide concentrations, for example, r(Cl) 4300 mg l1, can cause significant errors leading to higher values due to the complex redox reactions of the halogen species. Fe2þ can also lead to positive false results which, however, can be corrected according to

rðFe 2þ Þ ¼ 1 mg l 1  rðKMnO4 Þ ¼ 0:57 mg l1

ð11Þ

In addition, there is a risk that aqueous KMnO4 solutions can decompose, especially at elevated temperatures. Recalibration and determination of blanks are, therefore, crucial. Another important aspect is the limited oxidation potential of KMnO4 solutions which results in only partial oxidation of OM, mostly of up to 40%.

3.01.4.3.4 Applications The IMn is mainly used for the assessment of drinking water, surface water, groundwater, and bottled water. Wastewater and other polluted waters need dilution before determination. The range for application is 0.5 mg l1or(O2)o10 mg l1. It is also suited for waters with r(Cl)o300 mg l1. Many pitfalls, the often poor yields in the oxidation of OM, and the often lacking reproducibility of the results have brought the parameter to a questionable reputation, and therefore it practically plays no major role in modern water assessment. Due to the large amount of available data of PMC from the old days, however, there might be some interest in comparison to longterm trends in water quality reflected in the oxidizability. The range of application reaches from around 1 mg l1 (as O2 equivalent) to several hundreds of mg l1. The EU directive for drinking water states 5 mg l1 as maximum parameter for oxidizability and recommends 2 mg l1. (see also Chapter 3.16 Chemical Basis for Water Technology).

3.01.4.4 Biochemical Oxygen Demand 3.01.4.4.1 Background Sustainable water management includes treatment of used water. Technical wastewater-treatment systems have been

developed for this purpose. From an economical and ecological point of view, it is most attractive to use microbiological methods (Wagner, 1979). Their application can be optimized with the help of parameters suited for the assessment of wastewater and for the control of the performance of the treatment units and the secondary effluents. Closely connected to the task of quantifying biodegradability, there is the aspect of the time frame, for example, the question: how long does it take to degrade a specific amount of OM? The time window may reach from several hours and a few days (poorly biodegradable  refractory) to even several years (practically nonbiodegradable). This poor precision asks for a pragmatically defined approach to reach meaningful results. Even though the ordinary oxygen from the air has only a standard potential of þ 0.82 V and hence is a relatively weak oxdidant (Equation (12)) at ambient temperature, with the help of biocatalysis the BOD is turned out to be a powerful parameter to serve the needs of a valuable assessment. Several standardized laboratory procedures have been developed on the basis of the O2 consumption of OM and inorganic compounds such as Fe2þ, sulfides, or reduced nitrogen compounds during a specified period of incubation with a mixed microbial population. Mostly, the procedure is focused on the organic load (carbonaceous BOD, OM):

O 2 + 4H 3 O + + 4e− OM + O2

Bacteria

pH = 7

6H 2 O

CO 2 + H2 O + biomass

ð12Þ ð13Þ

3.01.4.4.2 Analytical procedure Standard methods are available for the determination of BOD (DIN EN 1899-1, 1998; ISO 5815:1989; Standard Methods 5210 B, 5210 C, 5210 D, 2005; (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment)). The BOD is mostly determined for an incubation period of 5 days (BOD5), but other incubation periods (1–50 days) can also be applied. The principle of the procedure is given in Figure 10. The air-saturated sample (if necessary seeded and/or diluted) is filled to overflow in a then airtight corked bottle of specified volume. Dissolved oxygen (DO) is measured immediately and after incubation of 5 days at 2073 1C. BOD5 is

Sum Parameters: Potential and Limitations

15

Defined dilution water

Glass bottle (e.g., 300 ml)

Aqueous sample (air saturated)

At start time After n days

O2 determination

Waste

7.0 < pH < 7.2 20 ± 3 °C

Seed suspension if needed

Nitrification inhibitor if needed

Figure 10 Experimental procedure for the determination of the BOD of aqueous samples.

calculated as concentration difference of the initial DO and the end DO. In case the oxygen consumption should exclude the demand of reduced nitrogen compounds (nitrogenous demand; e.g., ammonia and organic nitrogen), a nitrification inhibitor (e.g., 2-chloro-6-(trichloromethyl)pyrodine, TCMP, or allylthiourea (ATU)) has to be added. The DO determination is done either iodometrically (azide modification) or electrochemically by a membrane O2-electrode.

3.01.4.4.3 Interferences At the end, a proper BOD determination needs a residual concentration of oxygen of at least 2 mg l1. Water samples with high loads of OM can be measured after dilution. In case the water to be determined has a poor bacterial population, seeding is necessary. For that 0.5 ml sedimented municipal wastewater, B2 ml of biodegraded wastewater, or 5–10 ml river water are suited. The BOD of these additions has to be considered as blank. In case plankton is present, elevated BOD values have to be expected. The same applies for other O2-consuming water constituents such as Fe2þ, SO3 2 , and/or H2S/HS. A major problem for the BOD determination is the presence of poisonous or inhibiting substances (CN, CrO4 2 , Cu2þ, Hg(0, I, II), etc.) which might be overcome by dilution. In wastewater, nitrification may also lead to interferences. In order to avoid this, the addition of N-ATU to concentrations of 2–5 mg l1 is recommended. However, in this case O2 determination using the Winkler method becomes questionable. Many of the possible pitfalls can be diagnosed by online determination of O2 over the whole observation period (Figure 11).

3.01.4.4.4 Applications The method is well suited for the characterization of samples from rivers, lakes, estuaries, and wastewaters, and for their treatment efficiency in plants and effluents. BOD5 values normally range from 5 mg l1or(O2)o 250 mg l1. Other variations with shorter or longer incubation times than 5 days exist to measure rates of oxygen uptake. In special cases, incubation times of up to 90 days are used to determine the socalled ultimate BOD. Continuous oxygen monitoring (e.g., by

O2 electrodes) allow the characterization of different phases of biodegradation over time. The domain of BOD determinations consists of the wastewater and samples from its biological treatment. Typical municipal wastewater BOD5 lies around 60 g per capita equivalent. An average daily water use of 150–200 l per capita results in BOD5 concentrations of o25 and 300–350 mg l1 in treated and untreated wastewater, respectively. With respect to the changing biodegradability of wastewater constituents, it is interesting to relate the chemical oxidizability to the biochemical one, that is, to use the COD/BOD ratio for assessment (Leithe, 1971) (Table 4).

3.01.4.4.5 Related parameters (AOC) Another approach to quantify the biodegradability of OM uses the growth effect of a mixed population. After sterile filtration through a 0.2-mm nucleopore membrane, 275 ml of the water sample together with 25 ml of a sterile filtered merely inorganic nutrient solution is filled into a cuvette. The mixed population of bacteria retained by the 0.2-mm membrane filter is washed by NaCl solution and added to the mixed solution in the cuvette to reach a turbidity of 0.03 ppm SiO2 equivalents. The turbidity is measured as 121 forward scattering of a visual light beam in 30 min intervals for 60 h. The function of the relative turbidity over time gives the growth curve. Based on the assumption that the turbidity reflects the growth of the microbial population which is caused by the nutritious effect of the sample’s OM, it is attractive to relate the change of turbidity to the amount of assimilable carbon. The function of the relative turbidity over time gives the socalled growth curve (Figure 12). For the evaluation of the growth curve, the growth rate (GR; Equation (14)) and the growth factor (GF; Equation (15)) can be determined (Hambsch et al., 1992):

GR ¼

d lnðturbÞ at t ¼ tw dt

ð14Þ

turbðmaxÞ turbðstartÞ

ð15Þ

GF ¼

16

Sum Parameters: Potential and Limitations

O2-consumption (mg l−1)

35 30

d

25

c b

20 15 10 5 0

a

−5 0

2

4

6

8

10

12

14

16

Time, t (days) Figure 11 Typical O2-concentration curves for BOD determination (a: no biological degradation; b: biological degradation with lag phase; c, d: biological degradation without lag phase).

Table 4 COD/BOD ratios for the assessment of the removal efficiency of organic compounds by biochemical degradation COD/BOD

Assessment

o1.7

Organic substances show high biodegradation and mineralization Chemical degradation is insufficient due to – slow adaption of the bacteria – high amount of persistent compounds – inhibition of the reaction because of toxic substances No or practically no chemical degradation due to – persistent substances – inhibition of the reaction by highly toxic substances

1.7–10

410

where GR is the slope(s) at the inflection point of the curve for the exponential growth phase (tw). The steeper s the better assimilable the organic substances, for example, the GR gives information on the quality of the assimilable carbon. The ratio of the maximal turbidity and the initial turbidity (GF) is related to the quantity of assimilable carbon. From the shape of the curves inhibition and retardation, for example, by toxic water constituents, of the assimilation, for example, in the presence of recalcitrant fractions, can also be deduced. Figure 13 shows the growth curves for a lake water sample without and after treatment with different amounts of hydrogen peroxide (H2O2). It is obvious that in the original water sample, assimilation starts at the earliest. After about 15 h there is still a significant but slowly increasing turbidity possibly due to refractory OM. Oxidation with H2O2 (initial concentration r0(H2O2) ¼ 0.2 mg l1) leads to an increased lag phase in the assimilation of about 10 h followed by a steep exponential growth phase and finally, after 25 h, to a quite constant maximum turbidity; this reflects the higher amount

of assimilable carbon after chemical oxidation compared to the matter in the original sample. Tenfold initial H2O2 concentration leads to a further increased lag phase possibly due to the toxic effect of H2O2. The exponential growth phase shows a similar GR as the original lake water, and after 30 h a gradual increase of turbidity occurs up to 50 h which was the end of turbidity monitoring. From the gradual increase, an ongoing oxidative degradation of the organic substances to better assimilable ones can be deduced.

3.01.4.5 Interdependences There is a large amount of data on the load of OM in aquatic samples (Table 5). Despite the prosperous situation of available data, there are not too many reliable correlations between the different parameters. It might be relatively simple for defined model compounds but the complex mixture of realistic aquatic systems is difficult to assess, even though there are some reliable data for municipal wastewater (Table 6), and for drinking water and surface water (Leithe, 1971).

3.01.5 UVA and Visible Range Absorbance 3.01.5.1 Background Aquatic systems with high concentrations of OM, for example, bog lakes and organically rich aquifers, show a typical yellow to brown color. The absorption spectra for NOM samples in the UV (UVA) and visible range (VIA) are poorly resolved with a characteristic strong increase of the absorbance to lower wavelengths. This is typical for complex mixtures of substances with significant amounts of unsaturated bonds, lone pair electrons, and/or aromatic structures (Langhals et al., 2000). In addition, strong intermolecular interactions can add to

Sum Parameters: Potential and Limitations

17

Sample preparation Water sample

Sterile filtration

Cuvette

Inoculum

0.2 µm Nucleopore

275 ml of the sterile filtered sample

Mixed population of bacteria, washed from the sterile filters by NaCl solution

25 ml of a sterile filtered nutrient salt solution Registration of the growth curve Cuvette

Turbidity

Addition of inoculum until turbidity is 0.03 ppm SiO2

Measurement

Additional measures

(12° forward scattering) 60 h, every 30 min

Dissolved organic carbon (DOC) Total cell number (TCN) at the start and at the end

Evaluation of the growth curve Growth rate

Rel. turb.

Growth factor

(in the exponential phase tw) tW

GR = d ln(turb) dt

Time (h)

t = tw

GF =

turb(max) turb(start)

Figure 12 Procedure for the turbidimetric quantification of the AOC.

Turbity (12° forward scattering)

1.2 1 0.8 0.6 Original sample

0.4

Addition of (H2O2) = 0.2 mg l−1 0.2

Addition of (H2O2) = 2 mg l−1

0 0

10

20

30

40

50

Time, t (h) Figure 13 Typical growth curves for the organic carbon in lake water without and after addition of hydrogenperoxide (H2O2).

UV–Vis absorbance. Figure 14 shows two examples of typical UVA and VIA spectra of aquatic samples. This is the basis for using UV–Vis range information as surrogate parameter for a rough estimation of the dissolved OC concentration. It is quite common to use l ¼ 254 nm of the UV range and l ¼ 436 nm of the visible range for quantification. Around l ¼ 254 nm often a weak shoulder in the spectra is obvious which is assigned to chromophores with conjugated CQC and CQO double bonds.

According to Lambert–Beer’s law spectral absorbance is proportional to the concentration of the analyte:

AðlÞ ¼ kðlÞcd

ð16Þ

AðlÞ ¼ SAKðlÞd

ð17Þ

where A(l) is the absorbance at wavelength l; k(l) the molar absorption coefficient, in l (mol m)1 or l (g m)1; c the

18

Sum Parameters: Potential and Limitations

Table 5

Typical ranges for the content of organic matter in aquatic systems as reflected in sum parameters

Type of water

DOC, r(C) (mg l1)

Drinking water Groundwater Surface water Mesotrophic Eutrophic Municipal Wastewater Treated Landfill leachate

o2 0.5–4

COD, r(O2) (mg l1)

KMnO4, r(O2) (mg l1)

BOD5, r(O2) (mg l1)

o5 3–8 5–20

2–5 4–10

20–35 100–150

200 o25 4500

300–1000 20–100 o3000

AOC, r(Ac-C)a (mg l1)

AOX, r(Cl) (mg l1)

9–20 o80

30 50–80

6

250 20 200–13 000

o500 4500

a

Acetate-C calibrated.

Table 6 Transfer factors (A:B) for the values of sum parameters in wastewater assessment (Koppe and Stozek, 1990) Parameter B

KMnO4 COD BOD5 TOC

Parameter (A) KMnO4

COD

BOD5

TOC

1.0 0.6 1.4 2.0

1.6 1.0 2.2 3.1

0.7 0.5 1.0 1.5

0.5 0.3 0.7 1.0

concentration in mol l1 or g l1; d the path length, for example, of cuvette in m; and SAK(l) the spectral absorption coefficient in m1.

3.01.5.2 Analytical Procedure According to standard methods color (VIA) is determined by visual comparison of the sample with known concentrations of colored solutions (Standard Methods 2120 B, 2005) and as spectrophotometric method using l ¼ 436 nm (Standard Methods 2120 C, 2005; DIN EN ISO 7887 C1, 1994; (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment). The measurement of the color is either performed in Nessler tubes by looking vertically downward through the tubes (Standard Methods 2120 B, 2005) or by spectrophotometric determination at a wavelength between l ¼ 450 and 465 nm (Standard Methods 2120 C, 2005). The color unit (CU) of 500 is related to a mixture of 1.246 g potassium chloroplatinate and 1 g cobaltous chloride in 100 ml HCl conc. and diluted to 1000 ml. As a consequence, the unit of color equals 1 mg l1 platinum (in the form of chloroplatinate ions). Calibrated glass color disks are also used for comparison. The platinum–cobalt method is applicable to natural water, drinking water, and wastewater. A special advantage of the determination of samples in Nessler tubes is the relatively long optical pathway in the tubes which leads according to Lambert–Beer’s law to low limits of determination. Besides this method, the spectral absorption at l ¼ 436 nm can be used to determine the color (DIN EN ISO 7887 C1, 1994). Here, results are given as absorption coefficient in m1 (Standard Methods 5910 B, 2005; DIN 38404-3, 2005).

Based on the gradual decrease of absorbance with increasing wavelength, the value of l ¼ 254 nm is often used as fairly sensitive characteristic information on the content of UV-absorbing organic constituents. The results are given as absorption coefficients in m1. In addition to the described quite simple methods, more sophisticated methods for the determination of color have been standardized as well. There are the multi-wavelength method (Standard Methods 2120 D, 2005) and the tristimulus spectrophotometic method (Standard Methods 2120 E, 2005). Samples have to be filtered through 0.45-mm pore-size membranes to remove turbidity as the apparent color can be higher than the true color of the solution itself.

3.01.5.3 Interferences Interferences may arise from inorganic constituents, for example, ferrous iron, nitrate, nitrite, bromide, and from certain oxidants and reducing agents (e.g., ozone, chlorate, chlorite, and thiosulfate). An absorption scan between l ¼ 200 and 400 nm can be used to determine the presence of interferences. In addition, turbidity adds to the molecular spectrometric absorption in a complex way by absorption and light scattering. Reproducible results are obtained after a separation step is clear (o0.45 mm) solutions.

3.01.5.4 Applications Typical values for color determined at l ¼ 436 nm (SAK436) of different water samples are shown in Table 7. UV absorption is often used to monitor industrial wastewater effluents, and to evaluate the DOC removal during water-treatment processes. (see also Chapter 3.16 Chemical Basis for Water Technology, Chapter 3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter, Chapter 3.10 Online Monitoring Sensors). The spectral absorption exhibits a dependence on pH values with decreasing specific absorbance as solution pH decreases (Langhals et al., 2000). This reflects the different acid– base forms of the chromophores within the molecules or as suggested by Chen et al. (1977), an increase in molecular size due to macromolecular associations. The color caused by NOM also changes with the chemical characteristics of the

Sum Parameters: Potential and Limitations

19

3.5 Brown water (HO20) Wastewater effluent (Alb5)

3.0

Extinction

2.5 2.0 1.5

λ (254 nm)

1.0 λ (436 nm)

0.5 0.0 200

300

400

500

600

Wavelength,  (nm) Figure 14 UV and visible range spectra of a brown water lake sample (Hohlohsee, HO20) and a sample from the effluent of a biological wastewater treatment plant.

Table 7

SAK436 (VIA 436) values of different types of water

Type of water

SAK436 in (m1)

Tap water River water (moderately polluted) Brown water Wastewater Visual verification limit

o0.5 (recommended) 41 2–5 45 2

water. For example, NOM–metal complexes can be formed in the presence of Ca and Fe ions, and this affects the type and the intensity of the color. The relatively simple method of the determination at l ¼ 254 nm has led to its broad application as surrogate parameter for the more complicated instrumental determination of DOC. The correlation of the two parameters depends on the origin of the water sample but is quite constant for the individual aquatic systems which is demonstrated by the experimental data for the river Rhine (Figure 15). The values allow a rough characterization of the OM according to its genesis. The high sensitivity and consequently the small sample volume demand of the method is a significant advantage. This has led to a broad application of the method for the characterization of original samples from soils or water without major pretreatment and concentration procedures. Beyond that specific SAKs (SSAKs), for example, SAK values for r(DOC) ¼ 1 mg l1, can be used for a more detailed characterization and sound comparison of aquatic OM (Table 8). Many publications have become available on the UV and visible spectroscopic characterization, including luminescence of OM from natural origin (NOM) (e.g., MacCarthy and Rice, 1985; Cabaniss and Shuman, 1987; Bloom and Leenheer, 1989; Senesi et al., 1989; Hautala et al., 2000). They all prove

the comparability power and relevance of the SSAK in waterquality assessment.

3.01.6 Organically Bound Halogens Adsorbable on Activated Carbon (AOX) 3.01.6.1 Background Halogen-containing organic compounds are widely used in industrialized countries. Due to the resulting high amounts of production and the broad application as solvent and in many products, these anthropogenic halo-compounds have found their way into aquatic systems. Many of the compounds are of toxicological relevance for men and environment. Therefore, distribution and fate of the halo-compounds in nature and technical systems such as wastewater treatment plants are of major interest. A well-suited assessment parameter is a sum parameter reflecting all organically bound halogens which adsorb on activated carbon (AOX), where X ¼ Cl, Br, and I (Ku¨hn and Sontheimer, 1973). Similar parameters are dissolved organic halogens (DOX) or total organic halogens (TOX) which are often used synonymously to AOX.

3.01.6.2 Analytical Procedure The AOX procedure is based on the equilibration of PAC with the sample solution in batch mode (Ku¨hn and Sontheimer, 1973). Unwanted adsorption of common inorganic halides on PAC is reversed by competitive displacement by nitrate ions. After filtration of the loaded PAC, it is introduced into a furnace that pyrolyzes PAC and OC to CO2 and the bound halogens to hydrogen halides (HX). A carrier gas stream (mostly O2) transports the HX to a micro-coulometric titration cell. There the halides are quantified by measuring the current produced by silver-ion precipitation of the halides. In the cell, a constant silver-ion concentration is maintained from a solid silver electrode. The current for that is

20

Sum Parameters: Potential and Limitations 9.5

SAK (254 nm) (m−1)

8.5

Ludwigshafen km 421.4 l Mainz km 500.6 l

7.5

Koblenz km 588.3 l 6.5

Köln km 684.5 l

5.5

Düsseldorf km 732.1 r Wittlaer km 757.9 r

4.5

Basel-Birsfleden

3.5

Karlsruhe

2.5 1.5

2

2.5

3

Dissolved organic carbon (DOC) (g m−3) Figure 15 UVA and DOC concentrations of the river Rhine water (monthly composite samples from the river Rhine at different sampling places (stream km); l ¼ left, r ¼ right side; ARW, 2004; AWBR, 2006).

Table 8

Spectroscopic characteristics of selected original water and fulvic acid (FA) samples (Frimmel et al., 2002)

Water sample

Brown water lake Brunnsee Hohlohsee Brown water lake, fulvic acid HO14 FA (pH ¼ 2) HO14 FA (pH ¼ 7) HO14 FA (pH ¼ 11) River water (Rhine) Lake water (Lake Constance) Groundwater (high load of humic substances, Fuhrberg) Secondary effluent, Neureut Soil seepage water

proportional to the number of moles of halogens introduced by the carrier gas. Figure 16 gives an outline of the AOXdetermination procedure. The standardized version of the parameter method has also made its way into wastewater legislation in Germany (e.g., DIN EN ISO 9562, 2005; Standard Methods 5320 B, 2005). The determination of AOX in waters with high salt content can be preferably done with the help of solid-phase extraction (SPE), for example, with styrene–divinylbenzene copolymer. After rinsing the polymer phase with sodium nitrate solution, the AOX compounds are eluted with methanol, the methanol extract is diluted with water, and the ordinary AOX procedure using activated carbon is applied to the solution (DIN 3840922, 2001). Calibration can be done, for example, by 2,4,6-trichlorophenol. Forensic analysis may aim for a differentiation of the halogens. The appearance of iodinated X-ray contrast media in aquatic systems (Putschew et al., 2000) or the formation of organically bound bromine in the course of oxidative water treatment (Tercero and Frimmel, 2008) are prominent examples where halogen-specific AOX can help to determine the distribution and to investigate the fate of the

SAK254/DOC (l (mg m)1)

SAK436/DOC (l (mg m)1)

SAK254 /SAK436

4.50 5.09

0.42 0.30

10.6 13.2

4.80 4.98 5.20 2.21 2.92 2.92 1.44 3.13

0.30 0.38 0.58 0.12 0.09 0.15 0.10 0.18

15.9 13.1 8.8 17.6 31.2 19.7 13.7 16.7

specific group of compounds. The analytical approach traps the AOX combustion gases containing the different HX molecules in an alkaline solution which then is analyzed for Cl, Br, and I by ion chromatography (Oleksy-Frenzel et al., 1995, 2000) or by atomic emission spectroscopy (OES-ICP) (Abbt-Braun et al., 2006).

3.01.6.3 Applications The standard method is applicable for samples with AOX concentrations of r(Cl)45 mg l1. Special care has to be taken for Cl-free PAC and highly pure chemicals. Blank determinations are mandatory. The Cl content of virgin PAC should not exceed r(Cl)o20 mg g1. According to the relatively low detection limit, the method can be applied to a broad range of aquatic samples, including drinking water, process water, wastewater, and water from different stages of water treatment and from the entire aquatic environment. Due to its broad applicability and ecological relevance, the parameter has found its way into water legislation and into many assessment protocols for wastewater, treatment plant effluents, and rivers and lakes. In addition to the classical AOX determination

Sum Parameters: Potential and Limitations

Aqueous sample (100 ml)

21

Discard filtrate

Adsorption batch (50 mg PAC)

Equilibration

Filtration (0.4 μm polycarbonate filter)

Washing (nitrate solution)

h

ICP-AES ICP-MS Cl−, Br−, I− determination

Microcoulometric titration of Σ(Cl−, Br−, J−)

Ionchromatographic Cl−, Br−, I− determination or

PAC combustion (in O2 gas 950 °C)

HXabsorption (75% acidic acid; 20 mmol l−1 (NH4)2CO3)

Alternatively Figure 16 Experimental procedure for the determination of adsorbable organic halogen and the amounts of the different halogen species.

which includes most of the organic halogen compounds, there is the possibility to quantify specific fractions. (see also Chapter 3.16 Chemical Basis for Water Technology, Chapter 3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter). Recently on several occasions, elevated iodinated AOX concentrations were found in aquatic systems. The family of poorly biodegradable iodinated X-ray contrast media which has been broadly applied in medical diagnosis is the reason for that (Ternes and Hirsch, 2000; Putschew et al., 2000; AbbtBraun et al., 2006; Putschew and Jekel, 2006; Wolf et al., 2006). Data from sand column experiments (Table 9) run with contaminated wastewater reveal a partial elimination and/or degradation of the iodinated contrast media.

3.01.6.4 Related Parameters There is another approach for an integral determination and characterization of organic compounds containing halogens. The method is based on repeated liquid/liquid extraction of the aqueous sample with pentane, hexane, or heptane at a volume ratio of 20:1 (EOX, extractable organically bound halogens). The extract is dried and incinerated in a hydrogen oxygen flame. The mineralized products in the condensate are quantified with the help of volumetric precipitation analysis based on silver nitrate. In comparison to the AOX determination which can be found in wastewater legislation, the quantification of EOX is of lower importance possibly due to less reliable results (Sontheimer and Schnitzler, 1982; DIN 38409-8, 1984).

3.01.7 Additional Sum Parameters 3.01.7.1 Background There are several other sum parameters for water-quality assessment. They focus on either inorganic species such as pH,

Table 9

Iodine balance of column experiments (numbers in mg1)

I Species

Total iodine (ICP-MS) Iodine of X-ray compounds (HPLC) Inorganic iodide (IC-ICP-MS) AOI (iodinated organic metabolites) AOI-iodine of X-ray compounds

Influent aerobic

Effluent column 1 unsaturated

Effluent column 2 saturated

2.3470.04

2.4870.23

2.4370.14

2.3570.01

0.5570.06

1.5370.30

0.02070.003 1.6470.22

0.03770.012

2.2570.10

1.3870.09

2.2270.04

–0.1170.16

0.8270.13

0.6970.13

Glass columns (l ¼ 1.2 m, d ¼ 20 cm) filled with medium grain quartz sand. Feed: wastewater from the inflow of a municipal sewage treatment plant spiked with X-ray contrast media (Neureut/Karlsruhe, r(DOC) ¼ 30  90 mg l1). Conditions: waterunsaturated and water-saturated conditions (V E 2 l d1). Despite the complex and highly dynamic wastewater matrix, the example demonstrates clearly the valuable information on interdependences of the different organic and inorganic iodine species.

electrical conductivity, radioactivity or metals, (see also Chapter 3.03 Sources, Risks, and Mitigation of Radioactivity in Water, Chapter 3.02 Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species) and they quantify organically bound hetero-elements or represent special types of organic compounds. Some of them reflect specific structural features, others are based on defined operations used for isolation or identification. A selection is given in Table 10. (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment).

22

Sum Parameters: Potential and Limitations

Table 10

Selection of additional sum parameters (see also Chapter 3.11 Standardized Methods for Water-Quality Assessment)

Targets (concentration range)

Method principle

Reference

Lignin and tannin Volatile organic acids (up to C6)

Folin phenol reagent, blue colour (a) Adsorption on silicic acid, elution with chloroform-butanol, titration with NaOH (b) Distillation, titration with NaOH (c) GC-FID 4-aminoantipyrine (a) After distillation (b) Chloroform extraction (c) Flow analysis

Box (1983) Westerhold (1963)

Phenols (1–250 mg l1)

Surfactants (0.2–2 mg l1 non-ionic) (40.1 mg l1 non-ionic) (40.01–0.2 mg l1 anionic) Aquatic humic substances Hydrocarbons, oil, and grease (o10 mg l1)

Nano- and microparticles Metal complexation capacity

Cholinesterase

Sublation (N2, ethylacetate) Methylene blue (MBAS) (in chloroform) Dragendorff reagent Cobalt thiocyanate (CTAS) (in methylene chloride) Adsorption/desorption on/from XAD (a) Gravimetric n-hexane/MTBE (extraction) (b) Extraction trichlorotrifluoroethane IR (c) Soxhlet extraction (for sediments) (d) Gravimetric (solid phase extraction) Flow-field-flow-fractionation (F4) (a) Polarographic Cu2þ titration (b) Metal selective electrode titration (c) Fluorescence Photometry

To differentiate between sum parameters and group parameters as it was suggested in the past (e.g., Sontheimer et al., 1986) seems to be idle since sum parameters in the modern sense do all allow an integrative view on well-defined aquatic water constituents without single compound (or even particle) identification. Of course, the precise definition of the individual sum parameter is most important to avoid misinterpretation of the results. As valuable tools for assessment and orientation, they are challenged by naturally occurring matter and emerging pollutants as well.

3.01.7.2 Examples for Emerging Parameters Four examples are given to briefly demonstrate the power of actual sum parameters’ development and their application in the assessment of aquatic samples. The given approaches are selected as typical examples of modern needs for information on the aquatic environment and its sustainable use and management. The first example focuses on the vital reservoir of refractory OM called humic substances (HSs), the second one addresses the colloids and the young world of nanoparticles (NPs) (see also Chapter 3.05 Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems), the third one shows a typical impact of the availability of advanced instrumentation on the gross characterization of aquatic samples, and the fourth one helps to answer fundamental questions such as: What does it all matter? Is the determined amount dangerous? Which kind of bioeffect has to be watched?

3.01.7.2.1 Humic substances Aquatic humic substances (AHSs) consist of refractory OM ubiquitous in aquatic systems and can be isolated according to

Heukelekian and Kaplovsky (1949) Pavan et al. (2000) DIN 38409 (1984) Neufeld and Paladino (1985) Standard Methods 6420 B (2005) DIN EN ISO 14402 (1999) Schwuger (1996) Kunkel et al. (1977) Osburn (1986); DIN 38409 (1980) ISO 7875-2 (1984) Tabak and Bunch (1981) Frimmel et al. (2002) US-EPA (1998) Gruenfeld (1973) Ullmann and Sanderson (1959) US-EPA (1999) Von der Kammer and Fo¨rstner (1998); Delay et al. (2010) Lund et al. (1990) Tuschall and Brezonik (1983) Ryan and Weber (1982) Herzsprung et al. (1989)

standardized procedures recommended by the International Humic Substances Society (IHSS) (Thurman and Malcolm, 1981; Malcolm and MacCarthy, 1992; Leenheer and Croue´, 2003; IHSS, 2010). The principle of the methods is given in Figure 17 and allows to differentiate between fulvic acids (FAs) and humic acids (HAs) according to their pH-dependent solubility and adsorption/desorption on polymer resins. The procedure has opened the door for a better understanding of the occurrence and structure of AHS and their contribution to the DOM in the hydrosphere (Frimmel et al., 2002; Senesi et al., 2009). They are left over from organisms and the reservoir for new life. Their genesis and fate can now be compared for different regions and climatic zones. Based on OC concentrations, reliable balances can be made even on global scale. In addition, the multimethod approach with analytical instrumentation helps to elucidate the aquatic function and fate of operationally defined fractions.

3.01.7.2.2 NPs and colloids Colloids and particles (NPs) of the micro-scale have been recognized in the aquatic environment for quite some time (e.g., Frimmel et al., 2007). Recently, the broad application of engineered NPs (ENPs) in daily life has led to the concern of their role in the water cycle (Frimmel and Delay, 2010; (see also Chapter 3.05 Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems)). For the assessment of the resulting heterogeneous systems, powerful methods are needed. Figure 18 shows an experimental setup which is suited to gain information, for example, on particle size distribution and the carrier function of particulate matter in aquatic samples (von der Kammer and Fo¨rstner, 1998;

Sum Parameters: Potential and Limitations

23

Aquatic sample (TOC/DOC 100%)

Filtration

<0.45 μm

Suspended sediment Variable 30% ±

>0.45 μm

HCl pH ≤ 2 XAD-8 Dissolved nonhumic substances (NHS) (NHS; 30−50%) and inorganic ions

Penetration Adsorption NaOH 0.1 molar

Elution

Humic substances (HS; 20−60 %) HCl pH = 2 Precipitation Filtration >0.45 μm

Humic acids (HA; 5−20%)

<0.45 μm

Fulvic acids (FA; 10−40%) Figure 17 XAD method for the isolation of FAs and HAs from aqueous solutions.

Frimmel et al., 2007; Wilkinson and Lead, 2007; Frimmel and NieXner, 2010). Depending on the applied detection systems, summary of the element-specific data and/or spectrometric information can be obtained. An example of the carrier function of NP for heavy metals is given in Figure 19. It has to be noted that the particle-size distribution is pH dependent. At lower pH, there is agglomeration. The example of copper shows the vehicle function of the particles which also works for many other metal ions and even organic substances when they are present in the water matrix. The consequence for the transport of pollutants in heterogeneous or porous systems is their quite different distribution in comparison to merely dissolved systems. Powerful detection modes (e.g., ICP-MS, UVA, and laser-induced breakdown detection) and the advantage of aquatic sample application without major pretreatment open the door for a broad and urgently needed investigation of environmental samples (Ko¨ster et al., 2007; Siepmann et al., 2007).

3.01.7.2.3 Luminescence Luminescence (short-lived fluorescence and longer-lived phosphorescence) belongs to the most powerful spectrometric methods combining relatively simple applicability with high sensitivity. The principle of luminescence is based on excitation and retarded relaxation of electronic systems, including interactions and redox reactions. With the help of powerful instrumentation, luminescence images of a variety of aquatic substances can be obtained and a sort of mapping of structural features is possible. Figure 20 shows the excitation emission matrix for the luminescence of algae-derived substances from an aqueous algae extract (Ziegmann et al., 2010). Even though fluorescence has a quite long tradition in water-quality assessment (Christman and Ghassemi, 1966), the systematic assessment of water samples with advanced fluorescence/phosphorescence methods is still not very common (Chen et al., 2003; Cumberland and Baker, 2007). However, the availability of advanced instrumentation which leads to high sensitivity and resolutions will definitely

24

Sum Parameters: Potential and Limitations Computer ICP-MS Internal standard (20 μg l−1 Rh, In, Ir)

Eluent

Injection valve

Degazer

6 5

1 4

2 3

FD

Injection pump Waste UV

Laminar flow pump Sample injection

Separation channel

Syringe pump Cross flow Waste

8 Laponite (Mg signal) Laponite (Si signal) Laponite (UV signal)

0.0006 0.0004

4 2

0.0002

0

0.0000 101

6

102

103

15 10 5

28Si/103Rh

0.0008

24Mg/103Rh

UV absorbance (AU)

Figure 18 Experimental setup (example for coupling of A4 with different detectors) for the characterization of particulate water constituents (suspensions, colloidal systems, etc., according to Delay et al. (2010)).

0

104

63Cu/103Rh

Hydrodynamic particle diameter, dh (nm) 0.30 0.25 0.20 0.15 0.10 0.05 0.00 101

Cu (with laponite)

102

103

104

Hydrodynamic particle diameter, dh (nm) Figure 19 Typical flow-field-flow-fractionation (F4) derived particle size distribution for an aquatic suspension of laponite and its carrier function for copper (according to Metreveli and Frimmel (2007)).

stimulate a broad application of the method to fingerprint aquatic samples and follow bulk reactions (Kumke et al., 1998; Kumke and Frimmel, 2002). Due to the limited understanding of relaxation mechanisms in complex aquatic systems and the manifold influences of water constituents (e.g., light scattering and quenching), the structure-related

interpretation must remain uncertain so far and further research is urgently needed.

3.01.7.2.4 Bioeffect quantification According to the most interesting question on the bioeffects of water constituents, a new generation of biochemical tests and

Sum Parameters: Potential and Limitations

25

0

700

1.0E5 Excitation wavelength (nm)

600

2.0E5 3.0E5

500 4.0E5

400

300

300

400

500

600

700

Emission wavelength (nm−1) Figure 20 Excitation emission matrix for the luminescence of algae-derived substances from an aqueous algae extract (Microcystis aeruginosa after 24 days of growth and after ultrasonic decomposition, tentative assignment left to right: proteins, HSs, and pigments).

16000 100% Effect 14000

Peak area in AU (mm)

12000 10000 8000 6000 4000 2000 0% Effect 0 1

10

100

1000

Mass (pg) Figure 21 Typical dose/response curve for integrative quantification of endocrine activity (17b-estradiol on diol).

quantification methods has been developed. In addition to the basic determination of biodegradation (see Section 3.01.4.4), there are mainly toxic effects which need quantification (Tschmelak et al., 2005; Fent et al., 2006; Hock et al., 2006). A whole world of toxicities has gained attention reaching from acute toxicity genotoxicity, mutagenicity, etc. (Kokan et al., 1985; McDaniels et al., 1990; Hansen, 1992; McKelvey-Martin et al., 1993; Metcalfe et al., 2001) to the more subtle effects such as endocrine disruption (Routledge et al., 1998; Ternes et al., 1999; Kolpin et al., 2002; (see also Chapter 3.09 Bioassays for Estrogenic and Androgenic Effects of Water Constituents, Chapter 3.14 Drinking Water Toxicology in Its Regulatory Framework, Chapter 3.04 Emerging Contaminants)). Many

biochemical methods developed for medical application have been adapted to aquatic environmental application. Many of them are based on cell tests (Yasunaga et al., 2004) and/or use biochemical methods (see also Chapter 3.08 Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization, Chapter 3.09 Bioassays for Estrogenic and Androgenic Effects of Water Constituents). Figure 21 shows a typical concentration/response curve mostly used for quantification. The point of 50% response is often used for comparative characterization and quantification. Since the individual compounds responsible for the effect are often unknown, reference substances are used for calibration. Detectable

26

Sum Parameters: Potential and Limitations

Aquatic sample

Determination of abiotic Σ-parameters

Determination of bioeffects (bio Σ-parameters)

Assessment (a) maximum allowed values (b) adverse effects

Application of treatment methods

Determination of treatment related Σ-parameters

Identification and quantification of single compounds

Figure 22 Role of sum parameters in the use related assessment of water quality.

concentrations as they can be reached with the help of, for example, fluorescence indicators lie in the range of nanomole. It is beyond doubt that these bioeffect-related parameters supply highly relevant information on the function and possible dysfunction of aquatic systems and on their usability for mankind as well.

3.01.7.3 View The application of chemical and biochemical sum parameters has been and will be a vital part of water-quality assessment. There is a unique task to lead from the first glance to the final detailed characterization and information. Especially from an economic point of view on determinations, the principle has to hold: as integrated as possible and as detailed as necessary. In addition, reliable material balances can only be achieved by applying suitable sum parameters. Since the hydrological cycle and its compartments are highly dynamic, this also reflects on the sum parameters. They face emerging tasks for a comprehensive assessment. It is not only the demand of more powerful methods with higher precision and accuracy and hence lower limits of determination for classical parameters, it also includes the parameter itself which has to supply the information for a timely investigation of new water quality challenges such as determination of emerging classes of compounds and quantification of different bioeffects. Another important task of sum parameters is to identify domains of so far not recognized compounds and to lead the way to their molecular identification. This applies to aquatic systems in nature as well as to the technical steps of their use and by this to the entire hydrosphere (Figure 22). The well-understood key role of sum parameters in waterquality assessment is no contradiction or alternative to single compound identification. Instead, both approaches are highly complementary and urgently need one another for profound results.

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27

modified), In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), 43, Lieferung 1999. Weinheim: Wiley-VCH; Berlin: Beuth. DIN EN ISO 8467 (1995) Wasserbeschaffenheit – Bestimmung des Permanganat-Index (German version EN ISO 8467:1995; water quality – determination of permanganate index (ISO 8467:1993)), In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), 34, Lieferung 1996. Weinheim: Wiley-VCH; Berlin: Beuth. DIN EN ISO 9562 (2005) Bestimmung adsorbierbarer organisch gebundener Halogene (AOX). (German version EN ISO 9562:2004; determination of adsorbable organically bound halogens (AOX) (ISO 9562:2004)). In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), Verfahren H14, 63, Lieferung 2006. Weinheim: Wiley-VCH; Berlin: Beuth. DIN EN ISO 14402 (1999) Bestimmung des Phenolindex mit der FlieXanalytik (FIA und CFA) (German version EN ISO 14402:1999; water quality – determination of phenol index by flow analysis (FIA and CFA) (ISO 14402:1999)). In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), Verfahren H37, 49, Lieferung 2001. Weinheim: Wiley-VCH; Berlin: Beuth. DIN ISO 15705 (2003) Ku¨vettentest (ISO 15705:2002; determination of the chemical oxygen demand index (ST-COD) – small-scale sealed tube method). In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), Verfahren H45, 57, Lieferung 2003. Weinheim: Wiley-VCH; Berlin: Beuth. DIN EN ISO 7887 C1 (1994) Wasserbeschaffenheit – Untersuchung und Bestimmung der Fa¨rbung (German version EN ISO 7887:1994; water quality – examination and determination of colour (ISO 7887:1994)). In: Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung (German Standard Methods for the Examination of Water, Waste Water and Sludge), Verfahren C1, 32, Lieferung 1995. Weinheim: Wiley-VCH; Berlin: Beuth. Dinar E, Taraniuk I, Graber ER, et al. (2006) Cloud condensation nuclei properties of model and atmospheric HULIS. Atmospheric Chemistry and Physics 6: 2465--2482. Duursma EK and Dawson R (1981) Marine Organic Chemistry. Amsterdam: Elsevier. Fent K, Weston AA, and Caminada D (2006) Ecotoxicology of human pharmaceuticals. Aquatic Toxicology 76: 122--159. Frimmel FH (1992) Isolation and general characterization of organic acids from pore water. In: Matthess G, Frimmel FH, Hirsch P, Schulz HD, and Usdowski HE (eds.) Progress in Hydrogeochemistry, pp. 9--13. Berlin: Springer. Frimmel FH and Abbt-Braun G (1999) Basic characterization of reference NOM from central Europe – similarities and differences. Environment International 25: 191--207. Frimmel FH and Abbt-Braun G (2009) Dissolved organic matter (DOM) in natural environments. In: Senesi N, Xing B, and Huang PM (eds.) Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems, pp. 367--406. Hoboken, NJ: Wiley. Frimmel FH, Abbt-Braun G, Heumann KG, Hock B, Lu¨demann HD, and Spiteller M (eds.) (2002) Refractory Organic Substances (ROS) in the Environment. New York: Wiley. Frimmel FH and Christman RF (1988) Humic Substances and Their Role in the Environment. Chichester: Wiley. Frimmel FH, Delay M (2010) Introducing the ‘‘nano-world’’, In: Frimmel FH NieXner R Nanoparticles in the Water Cycle, pp. 1–12. Berlin: Springer. Frimmel FH and Mu¨ller MB (eds.) (2006) Heil-Lasten. Heidelberg: Springer. Frimmel FH and NieXner R (eds.) (2010) Nanoparticles in the Water Cycle. Berlin: Springer. Frimmel FH, von der Kammer F, and Flemming H-C (eds.) (2007) Colloidal Transport in Porous Media. Berlin: Springer. Graber ER and Rudich Y (2006) Atmospheric HULIS: How humic-like are they? A comprehensive and critical review. Atmospheric Chemistry A: Physics 6: 729--753. Gruenfeld M (1973) Extraction of dispersed oils from water for quantitative analysis by infrared spectrophotometry. Environmental Science and Technology 7: 636--639. Hambsch B, Werner P, and Frimmel FH (1992) Bacterial growth measurements in treated waters from different origin. Acta Hydrochimica et Hydrobiologica 20: 9--14. Hansen PD (1992) Phagocytosis in Mytilus edulis, a system for understanding the sublethal effects of anthropogenic pollutants and the use of AOX as an integrating parameter for the study of equilibria between chlorinated organics in Dreissena polymopha following long term exposures. In: Neumann D and Jenner HA (eds.) The Zebra Mussel Dreissena Polymorpha, pp. 171--184. Jena: Gustav Fischer Verlag.

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Leithe W (1971) Comparison of COD, BOD and PMC in drinking water and surface waters. Vom Wasser 38: 119–127 (in German). Lee W and Westerhoff P (2006) Dissolved organic nitrogen removal during water treatment by aluminium sulfate and cationic polymer coagulation. Water Research 40(20): 3767--3774. Lund W, Helbak IA, and Seip HM (1990) Studies of the complexation properties of aquatic humic material by differential pulse polarography. Science of the Total Environment 92: 269--281. MacCarthy P and Rice JA (1985) Spectroscopic methods (other than NMR) for determining functionality in humic substances. In: Aiken GR, McKnight DM, Wershaw RL, and MacCarthy P (eds.) Humic Substances in Soil, Sediment and Water, pp. 527--559. New York: Wiley. Malcolm RL (1985) Geochemistry of stream fulvic and humic substances. In: Aiken GR, McKnight DM, Wershaw RL, and MacCarthy P (eds.) Humic Substances in Soil, Sediment and Water, pp. 181--209. New York: Wiley. Malcolm RL and MacCarthy P (1992) Quantitative evaluation of XAD 8 and XAD 4 resins used in tandem for removing organic solutes from water. Environment International 18: 597--607. Matthess G, Frimmel FH, Hirsch P, Schulz HD, and Usdowski HE (eds.) (1992) Progress in Hydrogeochemistry: Organics – Carbonate Systems – Silicate Systems – Microbiology – Models Springer Berlin. McDaniels AE, Reyes AL, Wymer LJ, Rankin CC, and Stelma GN, Jr. (1990) Comparison of the Salmonella (Ames) test, umu test and the SOS chromotests for detecting genotoxins. Environmental and Molecular Mutagenesis 16: 204--215. McKelvey-Martin VJ, Green MH, Schmezer P, Pool-Zobel BL, De Me´o MP, and Collins A. The single cell gel electrophoresis assay (comet assay): A European review. Mutation Research 288: 47–63. McKnight DM and Aiken GR (1998) Sources and age of aquatic humus. In: Hessen DO and Tranvik LJ (eds.) Aquatic Humic Substances, pp. 9--39. Berlin: Springer. Metcalfe CD, Metcalfe TL, Kiparissis Y, et al. (2001) Estrogenic potency of chemical detected in sewage treatment plant effluents as determined by in vivo assays with Japanese medaka (Oryzias latipes). Environmental Toxicological Chemistry 20: 297--308. Metreveli G and Frimmel FH (2007) Influence of Na-bentoite colloids on the transport of heavy metals in porous media. In: Frimmel FH, von der Kammer F, and Flemming H-C (eds.) Colloidal Transport in Porous Media, pp. 29--53. Berlin: Springer. Neufeld RD and Paladino SB (1985) Comparison of 4-aminoantipyrine and gas–liquid chromatography techniques for analysis of phenolic compounds. Journal of the Water Pollution Control Federation 57: 1040--1044. Oleksy-Frenzel J, Wischnack S, and Jekel M (1995) Bestimmung der organischen Gruppenparameter AOCl, AOBr und AOI in Kommunalabwasser. Vom Wasser 85: 59--67. Oleksy-Frenzel J, Wischnack S, and Jekel M (2000) Determination of organic group parameters AOCl, AOBr and AOI in municipal wastewater. Journal of Analytical Chemistry 366: 89--94. Osburn QW (1986) Analytical methodology for LAS in waters and wastes. Journal of the American Oil Chemists’ Society 63: 257--263. Pauling (1960) The Nature of the Chemical Bond and the Structure of Molecules and Crystals: An Introduction to Modern Structural Chemistry, 3rd edn. New York: Cornell University Press. Pavan P, Battistoni P, Cecchi F, and Mata-Alvarez J (2000) Two-phase anaerobic digestion of source sorted OFMSW (organic fraction of municipal solid waste): Performance and kinetic study. Water Science and Technology 41: 111--118. Perdue EM and Gjessing ET (1990) Organic Acids in Aquatic Ecosystems. Chichester: Wiley. Putschew A and Jekel M (2006) Iodinated X-ray contrast media. In: Reemtsma T and Jekel M (eds.) Organic Pollutants in the Water Cycle, pp. 87--98. Weinheim: WileyVCH. Putschew A, Wischnack S, and Jekel M (2000) Occurrence of tri-iodinated X-ray contrast agents in the aquatic environment. Science of the Total Environment 255: 129--134. Reemtsma T and Jekel M (2006) Organic Pollutants in the Water Cycle. Weinheim: Wiley-VCH. Reemtsma T, These A, Springer A, and Linscheid M (2008) Differences in the molecular composition of fulvic acid size fractions detected by size-exclusion chromatography–on line Fourier transform ion cyclotron resonance (FTICR)–mass spectrometry. Water Research 42: 63--72. Routledge EJ, Sheahan D, Desbrow C, Brightly GC, Waldock M, and Sumpter JP (1998) Identification of estrogenic chemicals in STW effluent. II: In vivo responses in trout and roach. Environmental Science and Technology 32: 1559–1565.

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Ryan DK and Weber JH (1982) Flourescence quenching titration for determination of complexing capacities and stability constants of fulvic acids. Analytical Chemistry 54: 986--990. Schwuger MJ (1996) Detergents in the Environment. Surfactant Science Series, vol. 65. New York: Dekker. Senesi N, Miano TM, Provenzano MR, and Brunetti G (1989) Spectroscopic and compositional comparative characterization of IHSS reference and standard fulvic and humic acids of various origin. Science of the Total Environment 81/82: 143--156. Senesi N, Xing B, and Huang PM (eds.) (2009) Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems. Hoboken, NJ: Wiley. Siepmann R, Von der Kammer F, and Fo¨rstner U (2007) Transport of colloids in filter columns: Laboratory and field experiments. In: Frimmel FH, von der Kammer F, and Flemming H-C (eds.) Colloidal Transport in Porous Media, pp. 87--115. Berlin: Springer. Skoog DA, West DM, Holler FJ, and Crouch SR (2003) Fundamentals of Analytical Chemistry, 8th edn. Belmont, CA: Brooks Cole. Sontheimer H and Schnitzler M (1982) EOX oder AOX? Zur Anwendung von Anreicherungsverfahren bei der analytischen Bestimmung von chemischen Gruppenparametern. Vom Wasser 59: 169--179. Sontheimer H, Spindler P, and Rohmann U (1986) Wasserchemie fu¨r Ingenieure. Frankfurt/Main: ZfGW-Verlag GmbH. Standard Methods (2005) Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 2120 B (2005) Color – visual comparison method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 2120 C (2005) Color – spectrophotometric – single-wavelength method (proposed). In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 2120 D (2005) Color – spectrophotometric – multi-wavelength method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 2120 E (2005) Color – tristimulus spectrophotometric method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5210 B (2005) Biochemical oxygen demand – 5-day BOD test. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5210 C (2005) Biochemical oxygen demand – ultimate BOD test. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5210 D (2005) Biochemical oxygen demand – respirometric method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5220 B (2005) Chemical oxygen demand (COD) – open reflux method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5220 C (2005) Chemical oxygen demand (COD) – closed reflux, titrimetric method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5220 D (2005) Chemical oxygen demand (COD) – closed reflux, colorimetric method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5310 B (2005) Total organic carbon (TOC) – high-temperature combustion method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5310 C (2005) Total organic carbon (TOC) – persulfate–ultraviolet or heated-persulfate oxidation method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5310 D (2005) Total organic carbon (TOC) – wet-oxidation method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association. Standard Methods 5320 B (2005) Dissolved organic halogen – adsorption–pyrolysis– titrimetric method. In: Standard Methods for the Examination of Water and Wastewater, 21st edn. Washington, DC: American Public Health Association.

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3.02

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

AV Hirner and J Hippler, University of Duisburg-Essen, Essen, Germany & 2011 Elsevier B.V. All rights reserved.

3.02.1 Introduction 3.02.2 Natural Waters and Anthropogenic Influence 3.02.2.1 Metal(loid) Concentration 3.02.2.2 Metal(loid) Species 3.02.3 Selected Elements 3.02.3.1 Arsenic and Antimony 3.02.3.1.1 Arsenic: Introduction and overview 3.02.3.1.2 Arsenic in drinking water 3.02.3.1.3 Methylated As species 3.02.3.1.4 Thiolated As species 3.02.3.1.5 Antimony 3.02.3.2 Mercury 3.02.3.2.1 Introduction and overview 3.02.3.2.2 Impact of mining, Minamata, and Florida Everglades 3.02.3.2.3 Essentials of biomethylation 3.02.3.2.4 Biomethylation within parameter gradients 3.02.3.2.5 Abiotic methylation 3.02.3.2.6 Global concern 3.02.3.3 Other Metals (Cd, Cu, Pb, and Zn) 3.02.3.4 Platinum Group Elements 3.02.4 Conclusions Acknowledgments References

3.02.1 Introduction Common instrumental analytical techniques such as spectrometry (e.g., graphite furnace atomic absorption spectrometry (GFAAS), hydride-generation atomic absorption spectrometry (HG-AAS), hydride-generation atomic fluorescence spectrometry (HG-AFS), inductively coupled plasma-atomic emission spectroscopy (ICP-AES), and instrumental neutron activation analysis (INAA)), mass spectrometry (mostly inductively coupled plasma-mass spectrometry (ICP-MS)), or electro-analytical techniques are used for reliable determination of trace elements in waters (e.g., Kellner et al., 1998); with respect to speciation, hyphenated combinations of chromatographic separation (gas chromatography (GC), highperformance liquid chromatography (HPLC)) with online elemental (ICP-MS) as well as molecular detection (MS of mass fragments) are needed (Ko¨sters 2003; Krupp et al., 2008), and have to be carefully applied to avoid artifacts leading to misinterpretations (e.g., Hirner, 2006; Francesconi and Sperling, 2005). However, only routine application of these methods will not provide adequate detection limits for the determination of ultra-trace concentrations such as Sb or Pb in uncontaminated groundwater. At extremely low concentrations, serious challenges are extreme analytical sensitivity (e.g., requiring a sectorfield ICP-MS) and associated lowest blank levels (e.g., clean bench class 100 conditions, low-density polyethylene (LDPE) bottles; Shotyk et al., 2005; Boutron and Go¨rlach, 1990).

31 32 32 33 36 36 36 37 40 41 41 42 42 43 45 46 47 48 48 49 50 50 50

Parallel to improvements in instrumental analytical techniques, the quality of analytical data received also increases, in particular enabling lower detection limits, and thereby facilitating the measurement of hitherto unavailable ultra-trace concentrations. As a consequence of these analytical efforts, new analytes may come into focus, especially if they are of interest to environmental research. Metal(loid)s resemble the most important group of inorganic contaminants in environmental chemistry, and have become an environmental-quality target worldwide: for example, in the US, they are of concern to almost all programs of the US Environmental Protection Agency (EPA); e.g., ambient water-quality criteria for the protection of human health and aquatic life against the potential toxic effects of these elements are developed (Clean Water Act). Together with the metal(loid)s mentioned in this chapter, the EPA priority list of elements of concern also includes Al, Ba, Be, Cr, Co, Mn, Mo, Ni, Se, Ag, Sr, Tl, and V. Among the emerging metal(loid) pollutants being presently discussed in pertinent water research (Richardson, 2009, 2008, 2007) there are some included in this chapter, such as arsenic and antimony (see Section 3.02.3.1), while others, such as (1) tungsten, (2) uranium, and (3) tributyltin (TBT), are only briefly introduced here: 1. Although tungsten (W) is widely used in consumer products because of its physical–chemical properties, in the

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Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

industry and in the military (e.g., currently in green bullets as an environment-friendly (?) alternative to conventional lead-based ammunition), its (potential) mobility in environmental compartments, biogeochemistry, and toxicity is largely unknown yet (Koutsospyros et al., 2006; Strigul et al., 2005). 2. Besides arsenic (As), uranium (U) has also been identified as drinking-water contaminant: for example, while in the groundwater of western Bangladesh, concentrations of As exceeding World Health Organization (WHO) drinkingwater guidelines were found in 33% of the sampled wells, in 48% of the wells, elevated levels of U were detected (Frisbie et al., 2009). Although in Germany, o0.6% of all households are estimated to receive drinking water exceeding the threshold level of 10 mg l1 U, up to 75 mg l1 have been measured in Bavaria (Friedmann and Lindenthal, 2009). 3. Triorganotin biocides amount to about one-quarter of organometallic tin compounds constituting 7% of total tin (Cima et al., 2003). In particular, because of its high stability as well as mobility and toxicity, TBT is considered to represent a high-priority pollutant in aquatic systems requiring very sensitive analytical detection (Cima et al., 2003; Dopp et al., 2007; Gremm and Frimmel, 1992): for example, the present European Union (EU) environmental quality standard (Water Framework Directive) for TBT in water will be lowered to 0.2 ng l1 requiring a lower limit of quantification of 0.06 ng l1 (H. Emons, pers. commun). The main goal of this chapter is to address the importance of speciation to evaluate metal(loid) transport behavior, bioconcentration, and toxicity in aquatic systems. While association of most metal(loid)s with inorganic and macromolecular organic ligands such as dissolved organic carbon DOC is roughly known (especially for economically important metals such as Cd, Cu, Pb, and Zn), specific low-molecular organic species have been found for As (and partly for Sb) as well as Hg exhibiting remarkable toxic properties. However, toxicity is still not sufficiently understood in the case of the platinum group elements (PGEs) due to ultra-trace concentration levels posing extreme problems in their lack of their speciation. Notably, with the exception of PGEs because of their environmental importance, all elements mentioned earlier are listed as metal(loid)s of primary interest by EPA commissions, in particular those concerned with drinking-water standards and Ambient Water Quality Criteria (AWQC) (Fairbrother et al., 2007). Generally, the following text focuses on the elements listed in the title as well as on the water phase; therefore, other elements, sediment/soil chemistry, and remediation of polluted sites are not discussed here. For understanding basics of water chemistry as well as geochemistry, the reader is referred to relevant standard references such as Merian et al. (2004), Merian (1984), Stumm and Morgan (1996), Morgan and Stumm (1991), Hitchon et al. (1999), Frimmel (1999), Salomons and Fo¨rstner (1984), or Fo¨rstner and Wittmann (1981).

3.02.2 Natural Waters and Anthropogenic Influence 3.02.2.1 Metal(loid) Concentration Complex physical–chemical and biological, equilibrium and kinetic processes control the distribution of trace elements in

groundwater imported by a variety of natural and anthropogenic sources. For reasons that may be unrelated to human activity, many surface and groundwaters contain natural concentrations of metals exceeding common drinking-water standards (Runnells et al., 1992). On the other hand, when a regulatory decision is to be made to restore affected waters to a presumed earlier state, it is obviously unrealistic to assign clean-up goals being below preexistent metal levels. Thus, metal amounts that were naturally present and amounts added as a result of human activities must be distinguished from each other. In other words, for most environmental studies (at the local, regional, and global scales) it is of fundamental importance to know the (local, regional, and global, respectively) ‘background concentrations’, that is, the borderline between concentrations of a species that naturally occur in groundwater to be compared with those concentrations in the case when anthropogenic activities are involved. It is already well known that the latter may contain extreme metal loads such as Cd, Pb, or Zn in fertilizers in the g kg1 range with significant solubility (Frimmel, 1999). However, due to progress in environmental measures and technology, water pollution in developed Western countries will decrease, making the differentiation against natural background values even more critical. As an appropriate example, in Germany, heavy-metal emissions in aqueous systems have significantly reduced from 1985 to 2000 (94% of As, 72% of Hg, 76% of Cd and Cu, and 80% of Zn; SRU, 2008) and are not anymore mainly derived from industry but from diffuse sources. A closer look, however, shows that the environmental background mentioned above is not identical to the geochemical background well defined by (exploration) geochemists to differentiate between the analyte concentration within a rock matrix devoid of enrichments and those rock parts showing positive anomalies such as in fissures or veins (Matschullat et al., 2000). Thus, environmentalists must explain if they are explicitly including natural positive anomalies in the term natural background. It should also be noted that – because of significant geogenic variations due to different underlying rocks (Frimmel, 1999; White et al., 1963) – while in principle it is almost impossible to quantify a single true background value, it should be possible to define upper limits for the background with a defined statistical reliability (Matschullat et al., 2000). Generally, when comparing traceelement concentrations in waters as given earlier by Bowen (1979) as reference, with those of more recent publications (e.g., Bruland, 1983; Salbu and Steinnes, 1995; Reimann and De Caritat, 1998), it is apparent that the latter concentrations are lower, maybe because of their higher analytical quality (improvement of techniques and avoidance of artifacts and contaminations). Taking extraordinary precautions needed to measure artifact-free trace elements in the ng l1 range, Shotyk and Krachler (2009) tried to find something such as baseline or background concentrations of trace elements in natural water. They investigated ancient layers of ice from Devon Island, Nunavut (Canada), considered to be the cleanest water on earth. They found As, Cd, Cu, Pb, and Sb at the low ng l1 range; other elements such as Ag, Bi, Mo, Sb, Sc, Tl, and U were present even at concentrations below 1 ng l1. Similar concentrations for Cd, Cu, and Pb could be found in

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

groundwater from two artesian flows in Simcoe County (Canada) and for Cd and Cu in Onyx River water (Wright Valley, Antarctica) (Green et al., 2005). Shotyk and Krachler (2009) came to the conclusion that in natural waters, many trace-element concentrations can be extremely low, in the range of a few ng l1 or even lower. Although some chalcophile elements may be highly enriched in rain and snow because of atmospheric contamination, some of these, such as Cd, Cu, Pb, or Sb, may be efficiently removed by soils, leading to very low concentrations being left in groundwater. Shotyk et al. (2010) used the element scandium to relate measured metal concentrations to natural elemental abundances: as there are effectively no industrial uses of Sc and therefore no anthropogenic emissions, and Sc behaves conservatively during chemical weathering in soil, this element can be used as a reference element for quantifying the extent of anthropogenic trace-element enrichment. Thus, it was found that chalcophile elements (e.g., As, Cd, Cu, Pb, Sb, and Zn), relative to their natural abundance in rocks, are highly enriched in snow with enrichment factors of 2–3 magnitudes. The removal of these elements from water is presumably due to processes such as retention by organic and mineral soil components; for Pb, these removal processes are so effective that apparently natural Pb/Sc ratios are found in groundwater. When drinking water is filled into bottles, from the latter, trace metals may be leached from the container walls, thus increasing the metal concentration in the water. Although some constituents in bottled waters may reflect their composition in the groundwater prior to packing, others exhibit contamination from the packing: polyethylene terephthalate (PET) plastic releases Sb, while glass releases Pb and Zn. While investigating 23 elements in 32 brands of bottled water from 28 countries, Krachler and Shotyk (2009) found trace-metal

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levels of most bottled waters being below guideline levels, but some elements such as Li, Al, Be, Mn, or U exceeded the threshold limits for drinking water. In particular, coated aluminum and stainless-steel bottles are harmless with respect to leaching of trace metals into drinking water, but pocket flasks should be selected with great care to avoid contamination of beverages with harmful amounts of potentially toxic trace metals such as Sb. Representative data for trace-element composition of groundwater throughout the USA were retrieved from the EPA’s public domain Storage and Retreival (STORET) database by Newcomb and Rimstidt (2002) using robust data-analysis techniques described by Helsel (1990) and Helsel and Hirsch (1992). In Figure 1, their pertinent results with respect to elements discussed in this chapter are illustrated. The observed trace-element concentrations are approximately log-normally distributed. Median values for o90% of the censored sample populations range from 0.2 to 35 mg l1, minimum to maximum concentrations ranging as high as seven orders of magnitude for elements such as Zn. With respect to drinking-water quality, numerous guidelines have been published internationally, for example, in 1996 and 1998 by the WHO. When discussing about human health risk assessment with respect to drinking water, it has to be mentioned that distribution systems within homes (pipes, storage containers, etc.) can contribute significant amounts of metals such as Pb or Cu to the drinking water (Graziano et al., 1996).

3.02.2.2 Metal(loid) Species For evaluating behavior and toxicity of metal(loid)s in the environments, besides total-element concentrations, it is

1010 109

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4)

5)

2) 4)

1,5) 3)

1)

3)

3)

1 ng l−1

10−1 Antimony

Arsenic

Cadmium

Copper

Lead

Mercury

Zinc

Figure 1 Concentration ranges of elements in water, as discussed in this study (see text) (1) Newcomb and Rimstidt, 2002; 2) US EPA, 2002; 3) Shotyk and Krachler, 2009; 4) Frimmel, 1999; 5) Merian et al., 2004).

34

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

absolutely necessary to know about elemental speciation. Chemically speaking (according to International Union of Pure and Applied Chemistry (IUPAC)), as speciation is the process yielding evidence of the atomic or molecular form of an analyte (Irgolic, 1990), a metal(loid) species is a specific form of an element defined with regard to isotopic composition, electronic or oxidation state, complex or molecular structure, and phase (Templeton et al., 2000). These forms can be free metal ions or metal complexes dissolved in solution and sorbed on solid surfaces. In the light of this definition, a relevant example is also that of Cr speciation based on redox conditions, that is, the determination of the Cr(VI)/Cr(III) ratio (e.g., Dyk et al., 1990). To explain the strength of metal complexing, the concept of hard and soft acids and bases (HSAB) is useful (Pearson, 1973). In this concept, metal cations are Lewis acids and ligands are Lewis bases, with the metal cation and ligand in a complex acting as electron acceptor and donor, respectively. Hard acids (typical respective cations such as Co3þ, Cr3þ, Fe3þ, Mn2þ, or U4þ) are not discussed in this chapter, and also ((CH3)nAs(3n)þ) build up strong, chiefly ionic bonds with hard bases, whereas soft acids (such as Cd2þ, Cuþ, Hg2þ, Hgþ, and CH3Hgþ) and soft bases (e.g., alkylated arsenic) form strong, chiefly covalent bonds; Cu2þ, Fe2þ, Pb2þ, and Zn2þ or (CH3)nPb(4n)þ and (CH3)nSb(3n)þ are classified as borderline (meaning between hard and soft) acids (Langmuir, 1997). Soft and borderline metals generate bonds with soft ligands (including halogenated and organic species) of decreasing strength in the order Pb2þ 4 Cu2þ 4 Cd2þ 4 Zn2þ (Pickering, 1986). Arsenic species of the type HnAsOn3 4 and HnAsOn3 are classified as hard and borderline bases, 3 respectively. The HSAB concept can also be applied to toxicology: For example, the amino acids cysteine and methionine, present at active sites of some enzymes contain S functional groups representing soft ligands, and thus form strong covalent bonds with Hg, Cd, Cu, and Pb, resulting in breakdown of normal enzyme function (Manahan, 1994). With respect to inorganic speciation of natural waters for elements of interest in this chapter, forming typical cations such as Cd, Cu, and Zn (Cd2þ, Cuþ, Cu2þ, and Zn2þ), the most abundant binding partners are hydroxide, (hydrogen)carbonate, (hydrogen)sulfide, sulfate, and halogen (Cl, F, I, Br) ions, while for a typical oxoanion, such as As, just (mostly negatively charged) compounds with hydroxides and fluoride are important (Frimmel, 1999); note that the physical form of these species can be dissolved, colloidal, and particulate (crystalline and amorphous) covering a size range from macroscopic dimensions down to just a few nanometers (Merian, 1984). As a consequence, there exists the problem of the colloid and part of the particulate fraction being able to pass the conventionally used 0.45 mm filters; so these two fractions could really exceed the dissolved metal concentration by an order of magnitude of one or even more (Kennedy et al., 1974; Bergseth, 1983). While for Cd, Cu, and Zn, ions are the dominant inorganic species in water – followed by CdCO31, CuCO31, and ZnCO31 (Stumm and Morgan, 1996) – for Hg, Pb, and Sb, hydroxides are dominant (for Hg, HgCl21 is also dominant); and for arsenic, AsO3 and AsO3 are dominant. To find out the 4 3 specific inorganic-element speciation of a certain natural water

sample, the first step is to obtain information about the master parameters Eh and pH of these waters, which differ significantly among ocean, rain/stream, bog, and groundwaters (Baas-Becking et al., 1960). Species stability under thermodynamic equilibrium can then be found in respective Eh–pHstability diagrams (Stumm and Morgan, 1996; Brookins, 1988; Drever, 1997); for example, at pH 8, the heavy metals Hg, Cu, Pb, and Zn should be chiefly present as complexes. While, for cadmium, copper, lead, and zinc, Cd2þ, Cu2þ, 2þ Pb , and Zn2þ are the most toxic forms, for inorganic arsenic, it is AsO3 3 . In inter-element comparisons, the toxicity (in decreasing sequence) for algae, flowering plants, fungi, and freshwater phytoplankton was found to be Hg 4 Cu 4 Cd 4 Zn (Sposito, 1989). As mentioned earlier, colloids need special attention because of their small size (nm-range). Separations at this scale can be performed by sedimentation field-flow fractionation (SdFFF) or flow field-flow fractionation (FlFFF) coupled to an ICP-MS (Schmitt et al., 2002; Lyven et al., 2003). For example, the latter authors used these instruments to determine the chemical composition of colloids from a freshwater sample, and found colloidal components of organic carbon (size 1–1.5 nm) and iron (size up to 5 nm) as most abundant. In another study (Schmitt et al., 2002) it was shown that the presence of natural organic matter (NOM) decreased the adsorption of metals onto clay particles (size 0.1–1 mm). The water-soluble fraction of NOM is the major form of organic matter (OM) in water, and is usually called dissolved organic matter (DOM) or dissolved organic carbon (DOC), and it exhibits a high capacity in binding heavy metals (Buffle, 1990). While complexes of trace elements with fulvic acid (FA) are water soluble (Frimmel, 1990), those with humic acid (HA) form colloids (Lund, 1990); metals may also be complexed by saccharides (Geraldes and Castro, 1990). After centrifugation, (5000 rpm) DOM in prefiltered samples (0.45 mm polycarbonate) can be characterized by sizeexclusion chromatography (SEC) with ultraviolet (UV) detection at 254 nm (Chen et al., 2006). Pokrovsky et al. (2006) filtered water samples from pristine boreal rivers (Central Siberia) in the field through progressively decreasing pore size (5 mm 4 220 nm 4 25 nm 4 10 kDa 4 1 kDa) using cascade frontal filtration technique, and found Cu and Zn associated to small organic complexes (size o1 to 10 kDa). Even though Fe-(hydr)oxide reduction is an important factor controlling metal mobility in aqueous systems, the dominant mechanism for this process seems to be organic matter (DOM respectively DOC) release (Grybos et al., 2009) because of negatively charged functional groups, organic matter have a high capacity for cation absorption (Laveuf and Cornu, 2009). While complexation with organic ligands was found to be important for Cu and Zn (Schro¨der et al., 2008; Du Laing et al., 2009; Koretsky et al., 2007), no or low correlation has also been reported for Zn and Cd (Beesley et al., 2010; Kalbitz and Wennrich, 1998). A special group of metal(loid) species are organometallic compounds characterized by a metal(loid)–carbon bond; these bonds are generally covalent and occur between soft acid metals and soft ligands. Organometallic compounds are formed by interactions of metal(loid)s with other chemicals and biota in the environment. As, Hg, Pb, and Sb can be

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

biotically/abiotically methylated/demethylated and can form stable methylated species in the environment. These processes are influenced by several environmental parameters such as salinity or the presence of sulfides/sulfates and DOC; while high temperatures and anoxic conditions often favor methylation, the opposite is the case for demethylation (i.e., low temperatures and oxic conditions). Maximum methylation rates typically occur at the redox boundary, which varies seasonally and frequently coincides with the sediment–water interface (Ullrich et al., 2001). In higher organisms, because of their volatility and amphiphilicity, methylated metal(loid)s show high mobility, possibly leading to critical effects in metabolism and toxicity; for several metal(loid)s (As, Bi, Se, and probably Te as well) in situ biomethylation in mammals had already been demonstrated (Dopp et al., 2004; Hirner and Rettenmeier, 2010). While previously methylation of As species was regarded as a detoxification mechanism, it is now clear that methylation of any metal(loid) actually increases its toxicity, the only possible detoxification process being volatilization (e.g., as hydride or peralkylated species, for instance, for mercury in the form of Hg1, CH3HgH, or (CH3)2Hg). Evidence for the possible occurrence of up to 17 metal(loid)s in the natural environment can be found in literature (Feldmann, 2003). While most of them have been identified in air, only 10 have been detected in water so far (Table 1). Environmental concentrations along with physical–chemical properties of these species such as thermodynamic and kinetic stability or stability against water together with mechanisms of biomethylation can be found in Craig (2003). While neutral, fully alkylated (i.e., peralkylated) species are relatively volatile and present in aqueous systems in extremely low concentrations, charged, partly alkylated species are found dissolved in water, and because of their amphiphilic properties they are enriched along the food chain. Higher alkylated compounds of As, Hg, Mn, Pb, and Sn are synthesized by the chemical industry, and have been mainly used as gasoline additives

Table 1 Metal(loid)

As Bi Cd Ge Hg Mn Mo Ni Pb Sb Se Sn Te Tl W

35

(alkylated Pb) and biocides (phenylated As/Hg and butyltin). Carbonyls of Mn, Mo, Ni, and W have not yet been reported in natural waters; there are also some indications that polonium may react with methylcobalamin to form Me2Po (Feldmann, 2003). Biomethylation potential could be demonstrated in laboratory experiments (also including sterile controls) for As, Bi, Hg, Sb, Se, Te, and is probably, but not yet validated for Cd, and Ge, but it still remains questionable for Pb, Sn, and Tl. With respect to the latter three elements, due to large anthropogenic emissions, it is actually impossible to trace low geogenic background levels: for example, for Pb, in natural waters, not only Me4Pb and Et4Pb (primary gasoline additives), but also all mixed forms MeEt3Pb, Me2Et2Pb, and Me3EtPb (secondary transformation/degradation products) can be detected in the pg l1 range; given enough time, stepwise dealkylation degrades these leadalkyls eventually into inorganic Pb (Yoshinaga, 2003). Of course, Co is also to be added to the list of metals exhibiting biomethylation potential, because it certainly can be methylated biologically as proven by the existence of the natural product methylcobalamin (MeCoB12). As mentioned earlier, biomethylation in the natural environment occurs at the water–sediment interface at the bottom of water bodies or within sediment pore water. With respect to potential hot spots emitting high concentrations of methylated metal(loid)s into the environment, contaminated sediments are most interesting. However, at highly polluted spots, bacterial life is impossible. Therefore, following the decreasing metal(loid) concentration with increasing distance from the contamination spot, bacterial populations increase along with the methylation rate; however, the latter decreases again when moving further because of metal(loid) concentrations becoming too low then. Altogether, the spatial distribution occurs as indicated in Figure 2, showing that maximum metal(loid) and methylmetal(loid) concentrations do not coincide (halo hypothesis).

Organometal(loid) species found in the environment Methylated species found In Air

In Water

X X X

X X X X X

X X X X X X X X X

X X X X X

X

Biomethylation (lab. exp.)

Higher alkyls

X X (X) (X) X

X

? X X ? X ?

X X

Carbonyls

X X X

X

X

X

Modified from Feldmann J (2003) Other organometallic compounds in the environment. In: Craig P (ed.) Organometallic Compounds in the Environment, 2nd edn., pp. 353–389. New York, NY: Wiley.

36

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

Metal hot spot

B A

Rel. amount

A

(a)

B C

C

Distance from hot spot

Metal(loid) concentration Organometal(loid) concentration Biological activity

Methylated species (b)

Figure 2 Spatial distribution of metal(loid) species at contaminated sites. (a) Metal-organic species distribution independent of metal concentration, and (b) halos of methylated species around metal hot-spot contaminations.

As a consequence of the described features, it should be borne in mind that due to their behavior in the environment, wherein organometallic forms have different characteristics from inorganic metal(loid) compounds, for risk assessment, the same general principles and approaches do not apply (Fairbrother et al., 2007). While in theory, speciation targets the determination of all chemical species present in the sample, in practice, only scientifically lower goals, resulting in operationally defined fractions, may be reached: this depends on the experimental (e.g., elution tests to simulate acid-rain leachable or plantavailable fractions) or measurement technique applied (e.g., methods determining size or redox state of the analyte). A relevant empirical method, which is often used, is sequential extraction where fractions according to element mobility are isolated. For example, Singh et al. (2005) received the mobile element fraction in sediments by applying the classical fractionation scheme of Tessier et al. (1979); sequential-extraction schemes have also be optimized for individual elements such as As (Oliveira et al., 2006; Hudson-Edwards et al., 2004; Keon et al., 2001; Wenzel et al., 2001). Perhaps, the simplest and most standardized sequential-extraction method was the one proposed by the European Community Bureau of Reference (BCR), which was improved upon in later studies (Rauret et al., 1999; Sahuquillo et al., 1999). As there exist severe relevant objections from analytical chemistry (Hirner, 1996, 2000; Sulkowski and Hirner, 2006), and since sediments are not the focus of this chapter, this speciation technique is not further discussed here. When addressing questions on metal(loid) bioaccessibility (compounds arriving at the organism membrane) and bioavailability (compounds having passed the organism membrane), information on the presence of the most important elemental species is mandatory. Here, the mechanistic-based approach of the biotic ligand model (BLM), which is designed to predict acute toxicity to aquatic organisms on the basis of physical–chemical factors affecting speciation (Di Toro et al., 2001), has to be mentioned; the BLM has been most extensively developed for copper (Santore et al., 2001). It was used by Balistrieri and Blank (2008) to compute the speciation of Cd, Cu, Pb, and Zn in aquatic systems, together with the

diffuse gradients in thin films (DGT) as another approach to evaluate speciation; the latter in situ method determines concentrations of labile metal by diffusion of ions through a hydrogel to a binding agent like Chelex-100 resin (Davison and Zhang, 1994).

3.02.3 Selected Elements 3.02.3.1 Arsenic and Antimony 3.02.3.1.1 Arsenic: Introduction and overview Arsenic (As) is found in virtually every part of the geosphere (see review by Matschullat (2000)). The element dissolves relatively well in water leading to As concentrations of 1–2 mg l1 in seawater and 1–100 mg l1 in freshwater systems (Francesconi, 2005), and in groundwater 0.5–0.9 mg l1 (Allard, 1995). Concerning the latter, mineral and thermal waters are enriched by up to and more than three magnitudes (Bissen and Frimmel, 2003a): for example, Landrum et al. (2009) claim to report the highest natural As (up to 44.9 mg l1) and Sb (up to 4.3 mg l1) concentrations ever found in natural surface water (El Tatio Geyser Field, Chile). The background concentration of dissolved As is mentioned in different sources to be 0.1 mg l1 for freshwaters, although this value is still under discussion (Matschullat, 2000). In the aqueous environment, the inorganic arsenic species, arsenite (As(III)) and arsenate (As(V)), are the most abundant species. The mobility of these compounds is influenced by pH, redox potential, and the presence of adsorbents such as oxides and hydroxides of Fe(III), Al(III), Mn(III/IV), humic substances, and clay minerals (Bissen and Frimmel, 2003a). In seawater, arsenate is the dominant dissolved species (average concentration 1.7 mg l1), taken up in the photic zone by phytoplankton and reduced to arsenite and (up to 10%) transformed to methyl- and dimethylarsenate (Matschullat, 2000). Based on research in Arctic ice cores, it can be seen that significant anthropogenic contamination of the environment with arsenic began nearly 3000 years ago: while Pb enrichments increased threefold above the natural background levels during Greek/Phoenician, Roman, and Medieval times, As is

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

elevated by a factor of 5 (Krachler et al., 2009). The most massive anthropogenic import of arsenic is associated with mining: for example, during the three centuries of gold mining in the Iron Quadrangle (Brazil), at least 390 kt of As was discharged into the drainage system, leading to up to 1960 and 60 mg l1 total and trivalent As, respectively, in mine effluents; as a consequence, in surface water in the vicinity of mining areas, up to 300 mg l1 As was found (Borba et al., 2003). Quite a different kind of environmental contamination by As was its use in biocides (e.g., Pb, Na, Ca, and Zn (methyl)arsenates) before the introduction of DDT in 1947 (banned in Germany since 1974). However, Na and Zn arsenates as wood preservatives are still used and sold (Bissen and Frimmel, 2003a). In the USA, about 40% of the total As consumption is in wood preservation (Peters et al., 1999). Arsenic is ranked first in the list of hazardous substances by US EPA (2002), and has been identified as a human carcinogen leading to skin, bladder, lung, and other kinds of cancer as well as having cardiovascular effects (ATSDR, 2005; Hughes, 2002). The main problem with this element is that arsenic ingestion associated with drinking water and diet cannot be avoided. While groundwater and surface waters used for drinking contain As usually in the low mg l1 range, in some regions, such as Southeast Asia, these concentrations often exceed the recommended international health standard for drinking water, of 10 mg l1, or even the local maximal permissible value of 50 mg l1 (WHO, 2001; Smedley and Kinniburgh, 2002; Mandal et al., 1996). It is to be noted, however, that when compared to other known or suspected human carcinogens generally set to risk levels of 1:106, for As, the correspondent risks are 1:104 (for 10 mg l1) to 1:400 (for 50 mg l1) (Morales et al., 2000); an earlier evaluation by US EPA proposed only 2 mg l1 as an acceptable 1:104 risk (NRC, 1999). However, it is to be mentioned that the kind of toxicological evaluation presented earlier has to be rated to be very approximate (if not to completely wrong) if As speciation has not been considered: thus, it is well known that arsenite can be up to two magnitudes more toxic for human cells when compared to arsenate (Petrick et al., 2000; Sakurai et al., 1998), making the problem of As in drinking water strongly dependent on the environmental redox conditions (see Section 3.02.3.1.2); as a consequence, arsenite oxidation is generally considered to represent a detoxification process. The known mechanisms for arsenite toxicity are, for example, its affinity to sulfhydryl groups leading to enzyme inactivation or interferences with DNA repair (Basu et al., 2001; Gebel, 2001). As(V) competes with phosphate in cell reactions, and can decouple oxidate phosphorylation (Squibb and Fowler, 1993). Moreover, methylated and thiolated As compounds exhibit particular toxicity, and are discussed later. In order to reduce As mobilization in the environment, redox potential should be high and pH not in the alkaline range (Bissen and Frimmel, 2003a). Under reducing conditions, As bound to Mn and Fe (hydr)oxides is mobilized because Fe(III) is reduced to Fe(II) and Mn(III/IV) to Mn(II); these reductions start at a redox potential of þ 200 mV under neutral and acidic conditions. As(III) and As(V) complexation may occur in natural waters in the presence of NOM, FA, and

37

HA (Thanabalasingam and Pickering, 1986; Xu et al., 1988; Redman et al., 2002). In general, besides the environment, organisms contain many organoarsenic compounds up to 4100 mg g1 wet mass (Francesconi, 2005). Among the latter are arsenosugars and lipids (e.g., in algae), arsenobetaine and -choline (e.g., in marine animals); all of these food-arsenic species seem to be of no significant toxicity. For an overview, the mentioned environmentally important arsenic species are compiled in Figure 3.

3.02.3.1.2 Arsenic in drinking water The most important natural sources of arsenic in the environment are volcanic emissions, geothermal fluids, and sulfide-rich mineralization. However, the most problematic challenge of current water research is dealing with elevated arsenic concentrations in drinking water (NRC, 1999; Smedley and Kinniburgh, 2002). Arsenic concentrations in groundwater can vary to a great extent; for example, in groundwater from active volcanic areas in Italy, As concentrations range from 0.1 to 7000 mg l1, and are highest where active hydrothermal circulation occurs at shallow depths (Aiuppa et al., 2003). Here, intermediate redox environments, where neither sulfides nor Fe hydroxides are stable, lead to maximal As mobility. As release can also be related to bacterial reduction of Fe(III) to Fe(II), for example, leading to high-As waters in the deepest aquifer in Murshidabad, West Bengal (Stueben et al., 2003). However, in West Bengal aquifers, the spatial distribution pattern of As is patchy, with areas containing groundwater that is high in As (4200 mg l1) found in close vicinity to low As (o50 g l1) groundwater; there is no relationship between high groundwater As concentration and high groundwater abstraction (Nath et al., 2008b). Fe-oxyhydroxide is considered to act as a potential sink for As, and organic matter controls microbially mediated redox reactions (Nath et al., 2008a). Arsenopyrite (FeAsS) being the most common As-bearing sulfide mineral, upon oxidation acids of As and S are released into drainage waters with high concentrations of dissolved As. Corkhill and Vaughan (2009) showed that the oxidation of arsenopyrite in acid is more rapid than in air, water, or in alkaline solutions. It could also be demonstrated that, when this oxidation is bacterially mediated, for example, by acidophilic Fe- and S-oxidizing bacteria, such as Acidithiobacillus ferrooxidans and Acidithiobacillus caldus, it is more extensive than abiotic oxidation. The resulting oxidized forms such as Fe arsenates can again be reduced by a variety of dissimilatory Fe-reducing bacteria such as Swanella sp. strain ANA-3, thus again releasing aqueous As(III) (Babechuk et al., 2009). Elevated groundwater As concentrations in a fractured silicate bedrock aquifer in central New Hampshire are related to the presence of pegmatites bordering granites and intruding metasedimentary rocks (Peters and Blum, 2003). As concentrations in the pegmatites averaged 9.6 mg kg1, which is much higher than that in the associated granites (0.24 mg kg1) and in the metasedimentary rocks (0.8 mg kg1); As concentrations in these pegmatites were due to the partial melting of calcareous metapelites and subsequent

38

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species Some main structures of environmental arsenicals Arsenic acids/methylated arsenic acids (main structures): OH

OH HO

H 3C

As

OH Arsenous acid

HO

OH

As

H3C

As

OH

CH3

O

O

As

CH3

CH3

As

CH3

CH3 Trimethylarsine oxide (TMAsO)

CH3

OH CH3

CH3 O Dimethylarsinoyl acetic acid

As

H3C

O

As

H3C

CH3 Tetramethyl arsonium cation (TetraMAsC)

CH3 Trimethylarsonio propionate

CH3 As

As

Trimethylarsine (TMAs III)

OH Dimethylarsinic acid (DMAs V) CH3

O

As

CH3

As

OH Dimethylarsinous acid (DMAs III)

H3C

OH Monomethylarsonic acid (MMAs V)

O

CH3

H3C

O

OH Arsenic acid

CH3

As

OH Methylarsonous acid (MMAs III)

O

CH3

CH3

CH3 CH2

CH3 Arsenobetaine (AsB)

COO

CH3

As

CH2

CH2

OH

CH3 Arsenocholine (AsC)

Figure 3 Structures of important arsenic species in the environment. Modified from Craig PJ (2003) Organometallic Compounds in the Environment, 2nd ed., 415pp. New York, NY: Wiley; Feldmann J (2003) Other organometallic compounds in the environment. In: Craig P (ed.) Organometallic Compounds in the Environment, 2nd ed., pp. 353–389; and Raml R, Goessler W, and Francesconi KA (2006) Improved chromatographic separation of thio-arsenic compounds by reversed-phase high performance liquid chromatography-inductively coupled plasma mass spectrometry. Journal of Chromatography A 1128: 164–170.

recrystallization as granites with low As concentrations and pegmatites with high As concentrations. In some areas of Bangladesh and West Bengal, concentrations of As in groundwater out of the sedimentary aquifer of the Bengal Delta Plain exceed internationally and nationally set guideline concentrations (10–50 mg l1), eventually reaching levels in the mg l1 range (Nickson et al., 2000). The mobilized As is considered to have been derived from reductive dissolution of Fe oxyhydroxide covering sedimentary grains, releasing sorbed As. When observed more closely, As was found to have been mobilized only after orange Fe(III) oxyhydroxides were reduced to gray or black solid phases of Fe(II) or Fe(II/III). According to Horneman et al. (2004) and Van Geen et al. (2004), much of the As concentrated in relatively labile phases can be biotically mobilized perhaps

without the need for extensive Fe dissolution. However, since FeOOH is formed only in the secondary phase and sediments of the lower part of the Holocene aquifer (where most domestic wells are installed) contain As mostly fixed in biotite and organic matter, Seddique et al. (2008) argue for biotite being the primary source of As. The same authors interpret the patchy distribution seen of the As-enriched groundwater reflecting an uneven distribution of biotite-rich sediments, and the rate of chemical biotite weathering at the depths of the wells installed (20–50 m); this hypothesis is still under scientific discussion (Anawar and Mihaljevic, 2009; Seddique et al., 2009). Itai et al. (2010) suggest that the concentration of dissolved As is controlled by an adsorption–desorption equilibrium between sediment and groundwater.

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

39

Arsenosugar/riboses(main structures): Dimethylarsinoribosides O CH3

O

CH2

As

R

R: O

CH3

H3C

OH

OH

R:

R:

O

R: CH2

CH2

CH2

CH2 CH

SO3H

CH

O

OH

R:

O

CH

OH

OH

O

CH2

CH2

OSO3H

CH2

C

O

OH

CH

OH

OH R:

R:

R: OH

O

CH2

CH2 O

R:

SO3H

CH

CH2

C O

NH2

OH

OH

N

C

NH

O

OH

N

R:

))

R: O

OH

N

OH P

P O

O

N

OH

O

O

NH2

OH

O

OH

O

O

OH

O

OCO(CH2)nCH3

OH

OH

OCO(CH2)nCH3

Trimethylarsinoribosides CH3 CH3

As

O

CH2

R CH2

CH3

O OH

OH

R:

CH2 CH OH

CH2 OH

O R:

CH2 CH

OSO3H

OH

Figure 3 (Continued).

Deeper aquifers of the Holocene and the Pleistocene both exhibit significantly lower As contents (possibly because of the more available FeOOH as sorbent), and could possibly be used for domestic water supply as long as withdrawals do not exceed recharge rates (Zheng et al., 2004; Swartz et al., 2004); however, future effects on As mobility cannot be excluded (e.g., reductive FeOOH dissolution). In this respect, it should be borne in mind that 100 years of pumping deep groundwater for the public water supply of Hanoi (Vietnam) has

likely promoted hazardous As enrichment of the deep Pleistocene aquifer (Berg et al., 2008). When comparing the Bengal with the Huhhot basin (Inner Mongolia), As in both the basins appears to be mobilized by microbially mediated reductive dissolution of metal (hydr)oxides (Mukherjee et al., 2009); the sources of the As-bearing sediments are speculated to be in the Himalayas (Bengal) and the Da Qing mountain belt (Huhhot), respectively. In Holocene aquifers of the Red River flood plain

40

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

Arsenolipids:

CH 2OCOR' CH 3 O CH3

CHOCOR"

As O

P

O

CH 2

CH3 O

Phosphatidyl arsenocholine

CH 2OCOR'

CH3

As

CHOCOR"

O

O O

P

O

CH 2

O

CH3

Phosphatidyl dimethylarsenic acid

CH 2OCOR'

O

OH O

H 3C

As

CHOCOR"

O

O

O

P

CH 3

O

CH 2

O HO

OH

Phosphatidyl arsenosugar Figure 3 (Continued).

(Vietnam), the groundwater was found to contain up to 550 mg l1 As (Postma et al., 2007); as As correlates well with ammonia, the main mechanism for As mobilization is considered to be the reduction of iron oxide by decomposition of sedimentary organic matter. Quite a different role of the latter was hypothesized by Bauer and Blodau (2009), on observing As being completely sorbed and sedimented with FeOOH precipitates at neutral pH only in the absence of DOM. In the presence of the latter, however, the precipitation and sedimentation of FeOOH and associated As was impeded by the formation of aqueous Fe complexes and inhibition of Fe colloid growth. Therefore, the authors concluded that As mobility increased in the presence of DOM due to (1) competition between As and organic molecules for sorption sites on Fe particles; and (2) due to a higher amount of As bound to more abundant Fe colloids. Scanlon et al. (2009) described high groundwater As concentrations in semiarid oxidizing systems which were explained by As adsorption onto hydrous metal (Al, Fe, or Mn) oxides and subsequent mobilization with increased pH.

Similar relevant scenarios, that is, (1) the critical species being arsenate As(V) adsorbing strongly to mineral surfaces, and the more mobile and toxic being arsenite As(III) and (2) the occurrence of chemical and biochemical reduction of As(V) (Oremland et al., 2000), are often described in literature, for example, in the USA and Argentina (Peters and Burkert, 2008; Smedley et al., 2002). Generally, a more detailed discussion of As in the groundwater of sedimentary aquifers can be found in Bhattacharya et al. (2004), and with special emphasis on Cambodia and Vietnam in Polya et al. (2008). Of course, groundwater can also be contaminated with As by anthropogenic actions such as mining activity (e.g., Gemici et al., 2008). Moreover, at the abandoned As mine in Nishinomaki (Japan), discharged water from the mining and wastedump area is acidic and rich in As. However, the As concentration in the drainage has reduced to below the maximum contaminant level of 10 mg l1 without any artificial treatments, thus resembling natural attenuation by newly formed

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

schwertmannite (Fukushi et al., 2003). In another example, Fitzmaurice et al. (2009) examined the fate and transport of As at an industrial site where groundwater contamination leading to concentrations up to 1200 mg l1 was derived from the application of As2O3 as a herbicide. Currently, as many as 140 million people worldwide may have been exposed to drinking water with concentrations of As 410 mg l1, simpler and quicker analysis techniques rather than common instrumental methods in the chemical laboratory are needed. In this respect, a novel development, worth mentioning, is based on gold nanoparticles in a simple colorimetric and dynamic light-scattering assay, requiring only 10 min for analysis, and exhibiting detection limits of 3 ng l1 (Kalluri et al., 2009). Environmental technologists know that removal of As(III) is more difficult than the that of As(V). The reason for this is that oxidation in the presence of only air or even pure oxygen is a slow process, which may be accelerated by the presence of ozone, hypochlorite, chlorine (dioxide), H2O2, or oxidecoated sands (Bissen and Frimmel, 2003b). Respective removal techniques are coprecipitation with Fe(OH)3 and MnO2, fixed-bed filters with FeOOH, activated alumina, activated carbon or zeolites as adsorbents, anion exchange, and electrocoagulation as well as membrane filtration (ultra- and nanofiltration and reverse osmosis).

3.02.3.1.3 Methylated As species The basic mechanism of biomethylation can be described as follows: (1) As(V) species are reduced by glutathione (GSH); (2) the resulting As(III) can then accept a methyl group from S-adenosylmethione (SAM) to produce the methyl-arsenic(V) species in an oxidative–addition reaction (Cullen and Reimer, 1989; Challenger, 1951); and (3) the end products of repeated cycling are trimethylarsine oxide or trimethylarsine for fungi, tetramethylarsonium ion for clams, and dimethylarsinic acid for humans (Cullen and Reimer, 1989; Cullen et al., 1994). Previously, methylation of arsenic had been considered to be a detoxification process because pentavalent mono- and dimethylated As species (MMAs(V) and DMAs(V)) were found to be less toxic than inorganic As by more than one magnitude (NRC, 1999); 400–500 mg d1 can be excreted by humans via DMAs(V). However, studies have shown that trivalent organic analogs (MMAs(III) and DMAs(III)), which are also formed in As-methylation pathways (Challenger, 1945; Hayakawa et al., 2005), are as toxic or even more toxic than the inorganic species (Styblo et al., 1997; Lin et al., 1999; Petrick et al., 2000; Thomas et al., 2001; Dopp et al., 2005, 2006b, 2006c, 2008). MMAs(III) and DMAs(III) were shown to nick DNA at very low concentrations without chemical or enzymatic activation (Mass et al., 2001), and MMAs(III) was found to be a more potent inhibitor of thioredoxin reductase by two orders of magnitude when compared to arsenite (Lin et al., 1999). Furthermore, neurotoxic effects (blockade/enhancement of glutamate receptor responses and influence on synaptic transmission) can also be induced by methylated arsenic species (Kru¨ger et al., 2006, 2009). In groundwater impacted by methylated As pesticides, indications of the existence of the trivalent arsenic species (CH3)AsO2 ¨ ger and London (2008). 2 were found by Wallschla

41

3.02.3.1.4 Thiolated As species With respect to their role in microbial redox transformations, arsenic behaves similar to the commonly coexisting, more abundant sulfur leading to interlinked environmental cycling of both elements. For example, experimental evidence shows that sulfur-oxidizing bacteria use free or arsenic-bound sulfur as a growth substrate, and directly or indirectly transform arsenite and thioarsenates to arsenate during growth (Fisher et al., 2008). In general, sulfides seem to facilitate complete As(V)/As(III) redox cycling by direct coupling of sulfide oxidation to arsenate reduction and by stimulating arsenite oxidation (Fisher et al., 2008). Thus, more than 50% of total As in sulfidic waters are As–S compounds (Stauder et al., 2005; Planer-Friedrich et al., 2007); the latter are found in alkaline (Mono Lake, CA) (Hollibaugh et al., 2005) as well as acidic waters (Yellowstone NP) (Langner et al., 2001). Abundant field and laboratory evidence demonstrates that in sulfidic waters, As thioanions replace arsenates and arsenites (Beak et al., 2008; Planer-Friedrich et al., 2007). Contrary to most published hypotheses that As thioanions are either As(III) or As(V) species (Beak et al., 2008; Bostick et al., 2005; Stauder et al., 2005), other authors (e.g. Suess et al., 2009) argue that both kinds of thioanions are likely, even in the same solutions. However, preservation of the latter species is very critical, and can be currently established best by flashfreezing of the samples (Planer-Friedrich et al., 2007), while simple acidification as a common procedure in water analysis leads to As loss by precipitation of As–S phases (Smieja and Wilkin, 2003; Planer-Friedrich and Wallschla¨ger, 2009; Suess et al., 2009). Moreover, numerous novel thioarsenic species have been found in the environment (Hansen et al., 2004; Schmeisser et al., 2004; Raml et al., 2005; Kahn et al., 2005; Nischwitz et al., 2006; Nischwitz and Pergantis, 2006), some of them even synthesized in the laboratory (Planer-Friedrich et al., 2007; Wallschla¨ger and London, 2008). In particular, in the geothermal waters of the Yellowstone National Park, besides arsenite/arsenate and mono-, di-, tri-, and tetrathioarsenate, methylated arsenic oxy- and thioanions were also detected (Planer-Friedrich et al., 2007). These thioarsenates occurred over a pH range of 2–9; while they dominated under alkaline conditions (up to 83% of total As), they were also found in acidic waters (up to 34%). Based on these observations, the authors suggested three separate reaction pathways: transformation of trithioarsenate to arsenite, stepwise ligand exchange from tri- via di- and monothioarsenate to arsenate, and oxidation of arsenite to arsenate (after thioarsenates disappeared). In groundwater impacted by methylated As pesticides, several thiolated methylspecies could also be identified; their lifetimes were up to 6 months before oxidation to pentavalent oxyspecies (Wallschla¨ger and London, 2008). However, not much is known yet regarding the toxicity of inorganic and organic thioarsenic species: while formation of thioarsenic species was found to reduce acute arsenic toxicity for the bacterium Vibrio fischeri (Rader et al., 2004), PlanerFriedrich et al. (2008) observed increasing toxicity with increasing number of thiol groups for this bacterium. While dimethyl species of arsenoacetate and arsenoethanol exhibited no toxicity against human hepatocarcinoma cells (Raml et al., 2005), in the same system, thio-DMAs(V) proved to be

42

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

10 times as toxic as DMAs(V) (Raml et al., 2007). Thio-DMAs were found to be more toxic than dithio-DMAs (Ochi et al., 2008), and in the human bladder EJ-1 cells, dimethylmonothioarsenate was more toxic than arsenite (Naranmandura et al., 2009).

3.02.3.1.5 Antimony Due to its low natural abundance, no special interest for the industry and the public, and because it has no known function in living organisms, antimony (Sb) has not been in the focus of environmental scientific research during the last decades. Together with arsenic, Sb first gained public attention in mid1990s when both elements were discussed with respect to being involved in sudden infant death syndrome (SIDS); however, there is no scientifically sound argument found in this regard, yet (Cullen, 2008). Average continental crust abundance of the element antimony is 0.2 ppm; nonpolluted filtered surface waters exhibit typical concentrations of less than 1 ppb (Filella et al., 2002b; Rudnick and Gao, 2003). Hydrothermal-volcanic ore deposits, however, may contain up to hundreds of ppm in the form of sulfide (stibnite Sb2S3), Cu–Ag–Pb–As sulfosalts, or in minerals such as galena, pyrite, arsenopyrite, chalcopyrite, or sphalerite; associated hydrothermal fluids are dominated by Sb–OH–Cl complexes, in particular Sb(OH)3 (Pokrovski et al., 2006). Wilson and Webster-Brown (2009) described the behavior of such geothermally derived Sb down the large lowland Waikato River system (New Zealand). Removal of Sb from the stream was observed to occur by its adsorption onto suspended particulate material enhanced at low pH (o5), and in the anoxic base of stratified lakes. In most natural systems, antimony mainly exists in the penta- and trivalent oxidation stages (in oxic systems Sb(III) mostly o10%), both species being subjected to strong hydrolysis in aqueous solutions by forming dominantly uncharged and negatively charged hydroxide compounds at low pH (Filella and May, 2003; Filella et al., 2009). Although this strong affinity of Sb to hydroxyl groups in solution significantly can limit Sb complexing with other inorganic and organic ligands, up to 85% of total Sb in lake and pore waters was found to be associated with NOM (Deng et al., 2001; Chen et al., 2003); moderate binding of trivalent Sb with humic compounds has been demonstrated experimentally (Buschmann and Sigg, 2004; Steely et al., 2007). In particular, calculations showed that up to 35% of dissolved trivalent Sb may be bound to HAs in the form of bidendate complexes in continental waters containing 5 mg l1 of DOC (Tella and Pokrovski, 2009). The latter authors could also demonstrate that stable complexes form between uncharged Sb(OH) and oxalic, citric, lactic acids, and catechol, whereas no complexing was detected with acetic, malonic, and adipic acids in the pH range of natural waters; trivalent Sb may also be bound to trace thiol-bearing moieties in HAs. More details regarding the environmental chemistry of antimony can be found in reviews by Filella et al. (2002a, 2002b, 2007, 2009). Due to the recent widespread use of antimony in industry (e.g., flame retardant, fireworks, pigments, ceramics, plastics, glass, brake linings, batteries, semiconductors, diodes, bactericides, polycondensation catalyst in PET production)

nowadays it is difficult to find anthropogenically not-influenced samples. Based on modern analytical equipment and clean laboratory conditions, on observing pristine groundwater of 34 samples from Springwater Township (Ontario, Canada), Sb concentrations of 2.271.2 ng l1 were found (maximum concentration 5 ng l1), reflecting reactions between percolating fluids and calcite/dolomite in depths of more than 100 m (Shotyk et al., 2005). The same water filled in polypropylene and PET bottles shows significantly higher concentrations (around 8 and 160 ng l1, respectively), providing evidence for Sb leaching from the containers (Shotyk et al., 2006). In another study, elevated Sb concentrations in bottled waters (even at or above the maximum allowable Sb concentration of 2 mg l1 for drinking water in Japan) are from the Sb2O3 used as catalyst in manufacturing PET (Shotyk and Krachler, 2007a). Dated ice cores (1842–1996) and a snow pit (1994–2004) in the Canadian High Arctic showed Sb concentrations ranging from 0.07 to 108 ng l1 (Krachler et al., 2005), while another core dated between 1300 and 10 590 BP, averaged 0.0870.03 ng l1 (Krachler et al., 2008), indicating that anthropogenic emissions have dominated throughout the entire period, amounting today to approximately 99.8% of the Sb deposited in the Arctic, thus clearly exceeding that of Pb (Krachler et al., 2005, 2008). Mono- to trimethylated Sb compounds along with the corresponding As species could be identified in geothermal waters from New Zealand (Hirner et al., 1998), and in pore waters of sediments (Duester et al., 2008); maximum observed methylation rates were about 1%. However, antimony speciation still remains a challenging task (Filella et al., 2009). Even fundamental questions such as the behavior of Sb(III)/Sb(V) during sample storage and preparation are not known systematically. Thus, currently, many key aspects of Sb environmental chemistry (e.g., biogeochemical cycles) and toxicology (e.g., ecotoxicology) remain largely unknown yet; first-cell experiments could demonstrate genotoxic effects caused by trimethylantimony dichloride (Dopp et al., 2006a).

3.02.3.2 Mercury 3.02.3.2.1 Introduction and overview Mercury is present in natural waters in very low concentrations: maximum contents may be estimated in oceans to be 1 ng l1, and in surface freshwaters 20 ng l1 (Merian et al., 2004). While the dominant forms of Hg in seawater are HgCl4 2 and HgCl 3 (along with some methylmercury chloride), in freshwater habitats, binding to humic substances is more common (Stumm and Morgan, 1996). Hg distribution in aquatic environments is characterized by high stability of compounds with sulfur and carbon, and a strong affinity to particles, colloids, and organic matter; only a small part of the element is in the completely dissolved form (Merian et al., 2004). Dimethyl mercury has been only reported in deep ocean waters, but it is lost by evaporation and photolytic degradation, and is not considered to be available for aquatic organisms. In environmental chemistry, mercury is considered a global-priority pollutant that is distributed around the earth via the atmosphere (Gustin et al., 2008; Jiang et al., 2006). In the atmosphere, the fairly unreactive elemental Hg is the

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

predominant form with an average residence time of about 1 year (Wiener et al., 2003). Thereafter, elemental Hg may be directly deposited onto soils and vegetation or it may be transformed into oxidized species, which are readily deposited and more bioavailable (especially for biomethylationgenerated methylmercury). Due to various reduction processes occurring in the soil and the water bodies, oxidized fractions may again be transformed back into volatile elemental Hg reemitted into the atmosphere. Mercury speciation is thus mostly focused on the fractions of elemental (Hg1) and monomethyl mercury (MeHg); when the sum of these two fractions (also named dissolved gaseous mercury (DGHg)) is subtracted from the total Hg, the resulting residual fraction should be mostly Hg2þ (also named reactive mercury (RHg)) in water (Horvat et al., 2003). Applying this fractionation scheme, for example, average concentrations for total, methyl, and elemental Hg measured in the water column of a remote lake (Big West Dam, Canada) were 5 ng l1, 96 and 20 pg l1, respectively (Ethier et al., 2008). As for other elements mentioned in Section 3.02.2.2, mercury can be biomethylated in the environment; therefore, dimethyl and monomethyl mercury are found in the atmoand hydrosphere, respectively (Dopp et al., 2004; Hirner et al., 1998). For example, hot springs (pH about 3.0) in Yellowstone National Park contain MeHg in microbial mats in concentrations ranging from 1 to 10 mg kg1 (King et al., 2006). While inside the mats, the methylation rate is 5–10%, in hot spring water it is o0.1% and thus comparable to other aquatic systems. The concentration of MeHg was 2–5 times higher in larval tissue than mat biomass, indicating that MeHg biomagnification occurred between primary producer and primary consumer trophic levels (Boyd et al., 2009). Particulate transport for Hg is more important in particlerich fresh and coastal waters than in the open sea. Particulate Hg consists of Hg bound to inorganic particles and particulate organic matter, as well as biogenic particles such as bacteria, algae, and phytoplankton (Ullrich et al., 2001). Inorganic Hg tends to bind more strongly to mineral particles and detrital organic matter, whereas MeHg is more strongly associated with biogenic particles. Therefore, oxyhydroxides and organic matter are among the main vectors controlling Hg mobility and transport in aquatic systems. In particular, due to the high stability of Hg–humic complexes, a high percentage of Hg in natural waters is present in organically complexed form, and Hg concentrations in lake or pore water are often significantly correlated to DOM. Anthropogenic enrichment to environmental Hg cycling is estimated to be a factor between 3 and 6 when compared to natural fluxes (Torky and Foth, 2007). In particular, environmental waters are significantly affected by technical applications of this element (e.g., as biocide in agriculture or antiseptic in medicine; Tchounwou et al., 2003); more critical industrial branches are, for example, chloroalkali electrolysis or paper production (Fitzgerald and Clarkson, 1991). Another globally important contribution is the use of elemental mercury in amalgamation techniques in gold mining, contaminating, for example, Amazon sediments by 130 t Hg yearly (Cleary, 1990); despite a few national bans, these techniques are increasingly used in countries such as China or Brazil (SRU, 2008). Generally, anthropogenic sediment pollution

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has been demonstrated in many rivers, for example, up to 120 mg l1 elemental Hg and 130 mg l1 methylmercury have been reported for the Elbe River in Germany (Dopp et al., 2004). Compared to background values, Hg concentrations in forest soil were found to be enriched by factors ranging from 2 (Arctic) to over 4 (S Sweden) to 10 (Czech Republic) (Barrega˚rd, 2005). The respective critical concentration of 0.5 mg kg1 (Meili et al., 2003) is apparently exceeded by most countries in Middle Europe (SRU, 2008). Dated sediment cores from remote Californian lakes revealed that modern (1970–2004) lake sediment concentrations of Hg have increased by an average factor of 5 times more than historic (pre-1850) Hg concentrations (Sanders et al., 2008). Fitzgerald et al. (1998), however, estimated global atmospheric Hg deposition as inferred from lake sediments to have increased by a factor of only 2 relative to natural levels prior to the industrial revolution; but lakes in closer proximity to industrial point sources showed greater enrichments. A discussion of marine biogeochemical Hg cycling, biomagnification, and global flux models can be found in Fitzgerald et al. (2007). While biomonitoring of mussels and fishes in the Atlantic revealed no clear trends, relatively high Hg concentrations were found in inland lakes in Northern Europe explained by former Hg inputs via fungicides (Nixon et al., 2003). In Germany, apparently uncontaminated aquatic systems usually exhibit Hg concentrations o0.02 mg l1, and thus concentrations in drinking water above the limit of 1 mg l1 are rare (Bundesgesetzblatt, 2001). The present EU environmental quality standard (Water Framework Directive) for mercury and its compounds in water (after 0.45 mm filtration) is planned to be lowered from 50 to 15 ng l1.

3.02.3.2.2 Impact of mining, Minamata, and Florida Everglades When high concentrations of (inorganic) mercury are present in the environment (e.g., outcropping ore deposits, and mining districts), mercury speciation may be of special public interest. As the process of Hg recovery involves roasting (calcinations), the mine waste generated is referred to as calcine or mine-waste calcine (Gray et al., 2006). However, retorting of Hg-bearing ore is an inefficient and incomplete process; thus mine wastes often contain unconverted cinnabar, Hg1, ionic Hg compounds, and Hg chlorides and sulfates – and, of course, MeHg. In this respect, detailed studies centered around the Idrija Mine (Slovenia), the second largest Hg mine in the world (ceased operation 1995), are a good example (Hines et al., 2006; Covelli et al., 2008). Hg emissions from the mine (mainly in the form of particulate cinnabar) are transported by the Idrija and Isonzo River into the northern Adriatic Sea, 100 km away. At the Gulf of Trieste, sediments of the river mouth and in the bay are capable of producing MeHg (Hines et al., 2006). In addition, high MeHg concentrations (up to 22 mg kg1) were found in the adjacent Grado Lagoon (Covelli et al., 2008). It could be demonstrated that sediments of this lagoon release MeHg into overlying water until sulfide inhibition occurs and the methylation zone is restricted to the sediment surface (exactly the first few centimeters just below

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Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

the sediment–water interface). In the latter, MeHg in porewater ranged between 0.1 and 15% of the dissolved Hg, producing diffusive benthic MeHg fluxes similar to those of the Gulf of Trieste; however, the methylation rate seems to be higher in the lagoon. However, only a very small quantity of Hg is exported from abandoned Hg mines in SW Texas, primarily due to the arid climate and lack of precipitation resulting in no runoff in this region (Gray et al., 2006). The latter is not able to reach Rio Grande, the largest ecosystem in this region being conducive to Hg methylation. The minewaste material itself was found to contain up to 1.5 mg kg1 MeHg (among the highest concentrations reported); the latter correlated positively with Hg2þ, organic C, and total S. Conversion and transfer of MeHg from active and inactive Hg mines to surrounding ecosystems have also been reported for other cases, and often is a potential concern worldwide (Gray et al., 2004, 2003). Hg methylation is generally more efficient in humid climates where temperatures and precipitation are high, mine wastes are water saturated, and methylating bacteria are more active (Rytuba, 2000; Gray et al., 2003). Nonferrous metal smelters in China are another significant anthropogenic Hg source contributing to approximately 50% of Hg emissions to the atmosphere (Wu et al., 2006). Rivers, the estuary, and the bay along the NW Bohai Sea coast (NE China) have also been heavily contaminated by Hg due to long-term Zn-smelting activity. Hg concentrations in coastal sediments were 0.5–64 mg kg1 and in water, 39–2700 ng l1, up to three orders of magnitude higher when compared to background levels (Wang et al., 2009). These values are about one magnitude higher than those in the Hudson River, USA (Heyes et al., 2004), and MeHg in the water samples is also comparable to that in the Wanshan mining area (Qiu et al., 2005). Highest concentrations of MeHg in sediment and water reached 35 mg kg1 and 3 ng l1, respectively – the latter exceeding the Chinese drinking water guideline (1.0 ng l1); MeHg concentrations in both water and sediment pore water fairly good correlated with MeHg sediment concentration (r2 ¼ 0.74, po0.001). In freshwater hydrophytes, 5–100 mg kg1 Hg and 0.1–12 mg kg1 MeHg were found (Wang et al., 2009), indicating that MeHg accumulation in plants has a strong effect on food chains (Pickhardt and Fisher, 2007); also, humans and mollusks in and around Jinzhou Bay showed high Hg and MeHg accumulation (Liang et al., 2003; Fu et al., 1992). The most notorious MeHg poisoning case via the food chain occurred in the 1950s in Minamata Bay in Japan (Harada, 1995). As much as 380–455 t of Hg was used as a catalyst for acetaldehyde and vinyl compound production, and about 250 t was deposited in the Minamata Bay from 1932 to 1968; however, the main tragedy began in 1952 when Mn dioxide was replaced by Fe sulfide as a cocatalyzer. The industrial Hg waste was discharged into the bay sediments where it was biomethylated, releasing MeHg into the overlying waters. This was directly demonstrated by Tomiyasu et al. (2008) when investigating the Hg concentration in the water column: the concentration was highest near the sediment and decreased gradually. Up to 4 ng l1 MeHg was found in the turbid layer of water at the bottom, and the respective methylation rates were 24–54% (Tomiyasu et al., 2008). As a consequence, because of its lipophilicity, MeHg was enriched in

the aquatic food chain resulting in Hg loads in fish and shellfish being more than two orders of magnitude higher when compared to uncontaminated fish (Ullrich et al., 2001). Although high Hg concentrations were found in sediments from the Minamata Bay and its vicinity, the levels decreased gradually with distance from the bay arguing against significant movement of Hg out of the bay into the Northern Yatsushiro Sea; these sediments also have high natural and anthropogenic loads of Cd, Cu, Pb, and Zn, but distributed differently when compared to Hg (Nakata et al., 2008). Although as early as 1953 local inhabitants observed that cats were dying from eating cramps (dancing disease), the first cases of Minamata disease were reported not earlier than 1956. However, the evidence became overwhelming by 1960, that MeHg was indeed the cause of this disease (Grandjean and Choi, 2008), although the major case concerning compensation to Minamata disease patients was resolved not earlier than 2005! Characteristic of Minamata disease are special clinical signs and symptoms (impairment of speech and bilateral constriction of the visual fields) known in MeHg poisoning (previously known as Hunter–Russell triad), and eventually (in human autopsy cases) pathological changes in the nervous system, especially in the cerebral cortices, and peripheral sensory nerves (Eto, 2000). Following Minamata, in Japan, a second epidemic occurred in 1965 along the Agano River. By November 1999, 2953 cases of Minamata disease were identified and 1706 patients died, both catastrophes taken together (Eto, 2000). Sixty-four infant cases with cerebral palsy in villages where adult cases had occurred, were established as having congenital Minamata disease; the developing brains of the unborn had been affected by MeHg through transplacental exposure and even by breastfeeding (Kondo, 2000). Summarizing about MeHg-caused health effects as observed by Minamata tragedy, we learned that long-term exposure to this species (1) has a strong adverse impact on neurologic signs among residents in a local community (Yorifuji et al., 2008), (2) will increase the rate of hypertension in the affected population (Yorifuji et al., 2010), and (3) will generally lead to a greater variety of complaints in polluted when compared to nonpolluted areas (Futatsuka et al., 2000). Finally, and importantly, it should be mentioned that similar to Minamata, there are many coastal zones with industrial activities worldwide (examples follow). In addition, problems with MeHg poisoning among fish-eating populations are reported from elsewhere, for example, from the Amazon (Gochfeld, 2003). While Minamata is a first milestone in global mercury biomethylation research, the Florida Everglades is a second one. The latter representing a subtropical freshwater wetland ecosystem in the Everglades National Park (ENP) at the southern end. Freshwater flow is from north to south, transporting nutrients (N and P), sulfate (for soil amendment), and DOC from the northern Everglades Agricultural Area (EAA) in a decreasing gradient from the N to ENP. Via sulfate discharge from the EAA, the Everglades ecosystem faces the problem of Hg biomethylation, posing health risks to fish-eating birds, reptiles, and mammals (e.g., Florida panther), including humans; thus, at present, the Everglades still constitute the

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

largest continuous area in Florida where fish consumption is banned or limited because of mercury contamination. As total Hg concentrations in Everglades surface water (o10 ng l1) and soil (o500 mg kg1) are typically within background levels, biogeochemical controls make Hg available for bioaccumulation, rather than high Hg loading (Gilmour et al., 1998). The high MeHg production rate in Everglades soil (i.e., at the water–sediment interface) is related to elevated Hg levels in wildlife (Gilmour et al., 1998). Relatively high MeHg concentrations were also observed in floc and periphyton (Liu et al., 2008b); from the latter, MeHg can enter higher trophic level zooplankton and fish (Cleckner et al., 1999; Loftus, 2000). Due to the spatial variability in ecological conditions (e.g., organic matter in Everglades soil ranges from o1% up to 97%), in addition to MeHg production, biogeochemical controls make this species available to aquatic organisms (e.g., DOC, soil, and floc properties), and are important in Hg bioaccumulation (Liu et al., 2009). For the latter, the wet season is more favorable, and higher levels of Hg in mosquitofish, higher bioaccumulation, as well as biomagnification factors from periphyton to mosquitofish were observed compared to the dry season (Liu et al., 2008a). It could be shown that Hg level in mosquitofish is positively correlated with periphyton MeHg and DOC-normalized water; after uptake from water, Hg bioaccumulation occurs mainly through the food web (Liu et al., 2008b). Driven by concerns about deteriorating conditions affecting the Everglades ecosystem, in the early 1970s, the public, together with federal and state governments, initiated relevant projects to improve water quality, in particular, of the now hypereutrophic Lake Okeechobee (in the N) providing the headwaters for the Everglades (Perry, 2008). As a consequence, the Florida Everglades have been diminished by over 50% of their former extent (Perry, 2008), and dramatic declines in Hg levels in Everglades’ fish, birds, and alligators have been reported (Rumbold et al., 2002; Frederick et al., 2002). However, MeHg hot spots still remain: relevant risk assessment of MeHg exposure to three piscivorous wildlife species resulted in near 100% probability that these birds would experience exposures above the acceptable dose when foraging in northern ENP (Rumbold et al., 2008). Summarizing biota in ENP currently, they still contain the highest MeHg levels in southern Florida, being similar to or greater than other known MeHg hot spots in the USA. There is also no evidence of any significant Hg decline in Florida Bay fish still averaging over 1 mg kg1 Hg (Rumbold et al., 2008).

3.02.3.2.3 Essentials of biomethylation In water bodies, elemental Hg becomes water soluble by oxidation to (mono- and) divalent Hg ions which can be transformed into organic compounds with time. Here, the most relevant process is biomethylation by microorganisms just below the oxic/anoxic interface being often near the sediment surface in aquatic systems, leading to increase in Hg toxicity by bioaccumulation in crustaceans and fish (CHA, 2001; Clarkson, 1997); consequently, long-living big fishes and carnivores reach high enrichment factors (Davidson et al., 2004). Suitable conditions for effective biomethylation are the

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presence of mobilizable elements as well as anaerobic environments such as wetlands or sewage/sludge/wastedepositing or (biological) treatment facilities; however, several (at least macroscopically) aerobic environments also show biomethylation potential (Dopp et al., 2004). It has also been suggested that inorganic Hg in fish tissues (liver) may be methylated endogeneously (Woshner et al., 2002). As a consequence of resembling the highest members of the aquatic food chain, humans are exposed to health risks (mainly neurotoxic effects) by ingestion of methylmercury. The mobile amphiphilic species methylmercury forms water-soluble complexes in blood plasma, and binds preferentially to sulfhydryl groups of peptides and proteins (Castoldi et al., 2001; CHA, 2001). When complexed to L-cysteine, because of its structural–chemical similarity to the essential neutral amino acid L-methionine (molecular mimicry), it can cross the blood/brain barrier via the amino acid transporter channel LAT1 (Kerper et al., 1992; Bridges and Zalups, 2005); in a similar manner, methylmercury can cross the placenta and thus reach the fetus. Therefore, methylmercury is a proven neurotoxic agent for humans with highest sensitivity when exposed prenatally. While in animal experiments, methylmercury chloride generates cancer, this is not known for humans; also, there is not enough evidence yet for genotoxic effects in humans (Torky and Foth, 2007). Se and Zn can retard the toxic effects of mercury and methylmercury by forming complexes with/without involvement of GSH; vitamins C, B, and E were also discussed in this respect (Torky and Foth, 2007). Eventually, intracellularly, methylmercury may be stored as a selenide (WHO, 1990). Finally, with respect to mercury species toxicity, it should be mentioned that Hg(II) salts are not known to be mutagens in bacterial systems, and are only weak mutagens in mammalian cells (Beyersmann and Hartwig, 1994; Hartwig, 1995). The environmental behavior of the toxic biomethylation product, MeHg, is also of great interest (i.e., coordination, geometry, and binding strength), and can meanwhile adequately be studied by X-ray fine structure methods (extended X-ray absorption fine structures (EXAFS)/X-ray absorption near edge structures (XANES); Yoon et al., 2005; Qian et al., 2002). Highly reduced organic S groups seem to be the most important binding partners for MeHg in soil and sediment. While at low loading, with MeHg, the Hg atom is associated with one S atom in the first coordination shell, at higher loading (contaminated soils/sediments) O (and/or N) atoms were found in the first coordination shell of Hg. Therefore, it is concluded, that after saturation of MeHg with thiol ligands, MeHg complexation by carboxyl ligands becomes significant; no sulfide/disulfide and polysulfide complexes were found yet. Humic sulfur ligands also bind Hg2þ (Xia et al., 1999; Hesterberg et al., 2001). In humic solutions, however, Amirbahman et al. (2002) observed only a small fraction of reduced sulfur complexing MeHg. Of course, because of the described dependency of the biomethylation process on environmental parameters, the resulting contamination of fish is not uniform, but it is dependent on local factors: for example, while in freshwater fish from St. Clair Lake, (Canada) 1935 mean Hg concentrations of 0.07 mg kg1 and 1970 higher values of 0.2 mg kg1 were detected, for seawater tuna 1970 mean Hg concentrations

46

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

of 0.13 and 0.25 mg kg1 were reported (Dales et al., 1971; Hammond, 1971). Generally, seawater fish from northern seas is more highly contaminated than freshwater fish from great inland lakes; fishes from the Mediterranean Sea have higher contamination than those from the Atlantic; long-living species such as shark or tuna are more highly contaminated than short-living ones such as salmon; and for big tuna fish in certain areas, Hg concentrations of up to 10 mg kg1 have been reported (Torky and Foth, 2007) – this would mean that according to the above-cited German drinking-water guidelines, focusing on 1 mg l1 corresponding to 2 mg intake daily, only 0.2 g of this fish will be allowed per day (or 180 g yr1)! The average real daily methylmercury dose for individuals living in the Northern Hemisphere is estimated roughly to range between 0.2 and 14 mg (NRC, 2000). From the conditions described, it is important to know not only the amount of mercury present and available for methylation, but also more about the master variables deciding about the effectiveness of the biomethylation process, that is, those environmental parameters that have the greatest influence on the methylation rate (concentration ratio of methylated to total Hg), also known as mercury methylation rate (MMR), which in nature may range from just below a few 0/00 (biogeochemical background) to a high percentage range (e.g., in the Florida Everglades), up to about 30% in freshwater lakes and rivers, and 37% in the anoxic bottom waters of a stratified pristine lake (Ullrich et al., 2001); up to 65% has been found by Potgeter (1998), and in pore water, the proportion of MeHg can reach up to 85% (Merian et al., 2004). While methylation rates in riverine and coastal surface waters are 1–5%, ocean waters and ocean rain show about 1% or less (Mason et al., 1998). Biomethylation rates appear to increase under anaerobic conditions, high temperatures, and low pH, and are usually lower in marine compared to freshwater environments because of salinity effects and sulfide/chloride interferences (Merian et al., 2004). Although high MMRs produced by biofilms at liquid/ solid interfaces may appear impressive (and are decisive in environmental health evaluations), from a global geochemical point of view, water column methylation may be potentially more important, simply because the volume of water is much larger than the volume of surficial sediments. Furthermore, when attempting to establish a global MeHg cycle, other potent sources, such as the root zones of floating macrophytes in tropical systems, also have to be considered (Ullrich et al., 2001). While not being the only possible methylators (Pak and Bartha, 1998; Warner et al., 2003; Kerin et al., 2006), sulfatereducing bacteria (SRB; populations such as Desulfovibrio, Desulfobacteriaceae, or Desulfobacter) are the main acting microorganisms methylating Hg (methanogens playing only a minor role); thus their growth conditions (e.g., nutrient availability) represent an important biomethylation parameter (Compeau and Bartha, 1985; King et al., 1999, 2000). The ability to methylate Hg is not confined to one phylogenetic group of SRB, but is scattered throughout the phylogenetic tree of sulfate-reducing eubacteria. Different organisms clearly have different rates of Hg methylation, even among the SRB, and not all SRB methylate Hg (Benoit et al., 2003). A small number of Fe-reducing bacteria, that are phylogenetically

similar to methylating SRB, have been shown capable of methylating Hg in pure culture. However, bacteria can also degrade MeHg via oxidation or reduction pathways; for example, reduction of MeHg to Hg1 is achieved by microorganisms using the mer A and/or mer B genes (Marvin-DiPasquale and Oremland, 1998). Therefore, note that reporting biomethylation data from natural systems is actually related to the difference between methylation and demethylation (i.e., net or gross methylation rates). Another key parameter strongly affecting MeHg concentrations is the presence of organic material. Generally, DOM interacts very strongly with Hg (binding to reduced S), affecting its speciation, solubility, mobility, and toxicity in the aquatic environment; DOM competes with sulfide for Hg binding (Ravichandran, 2004). However, complexation with DOC generally limits the amount of inorganic Hg available for uptake by methylating bacteria, because DOC molecules are too large to cross bacterial cell membranes. At low pH, DOC is less negatively charged, and therefore less likely to complex Hg, making it more available to methylating bacteria. In sulfate-limiting environments, where microbes may utilize organic matter as an energy source, DOC may have a stimulating effect on microbial growth and thus enhance methylation rates in the water column. Maximal methylation is often observed in surface sediments, where microbial activity is greatest due to the input of fresh organic matter. As a result, systems with high levels of organic-matter production, such as wetlands, recently flooded reservoirs, or periodically flooded river plains, may exhibit extremely high rates of MeHg production. In addition, estuaries define transitional environments for active biogeochemical transformations such as biomethylation. Chemical and physical gradients characterizing these continent–ocean interfaces affect the cycles of many metals (Mota et al., 2005). The creation of new reservoirs and enlargement of lakes significantly increase MeHg production, leading to elevated Hg concentrations in fish that remain high for several decades; flooding may provide large amounts of organic matter and nutrients, thereby stimulating microbial methylation activity (reviewed by Ullrich et al. (2001)). Kelly et al. (1997) found that MeHg production increased by almost 40 times following the experimental flooding of a boreal forest wetland. Elevated Hg concentrations in fish (Big Dam West, Canada) were explained by Ethier et al. (2008) by low pH and high DOC concentrations in lake water reflecting the poor buffering capacity of peat. Artificial reservoirs such as Petit-Saut reservoir (French Guiana) can be seen as large manmade reactors exhibiting extensively altered Hg speciation in favor of methylating species: MeHg constituted 8%, 40%, and 18% of the total Hg in the dissolved phase, the particulate suspended matter, and in the unfiltered samples, respectively (Muresan et al., 2008b). It is known for long that high lake DOC concentrations and/or low pH (Hg2S more bioavailable for methylation by SRBs, via diffusive uptake) promote methylation within sediments (Bjornberg et al., 1988; Benoit et al., 2001). Typically, in coastal and marine sediments, organic matter has been shown to be the main factor regulating MeHg concentrations (Hammerschmidt et al., 2004; Lambertsson and Nilsson, 2006). Therefore, in areas dominated by wetlands, organic

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

3.02.3.2.4 Biomethylation within parameter gradients Essentially, the complex interplay of the individual parameters or environmental factors affecting mercury biomethylation (potential master variables: microbial activity; concentrations of bioavailable Hg, organic matter, and sulfide; and redox potential, pH, salinity, and temperature), described in the last section decides about the intensity of biomethylation in a given scenario (Ullrich et al., 2001). Although it often may appear that enhanced rates of MeHg production are linked in particular with low pH, low salinity, and the presence of decomposable organic matter in reducing environments (Ullrich et al., 2001), it is not possible for any real scenario to follow up the kinetics of relevant biomethylation processes with all their complex synergistic and antagonistic variables in detail (e.g., Merritt and Amirbahman, 2009). Therefore, it is interesting to observe just a few or even one gradient parameter; in this chapter, we focus on the redox potential and the presence of inorganic sulfur (acid volatile sulfide (AVS)/sulfate). Muresan et al. (2008a) stated that it is not only the redox potential that is important, but also important is the existence of redox transition zones. As a striking relevant example, these authors studied Hg speciation along the redox gradient of an anthropogenically perturbed tropical estuary, the 70-km long Sinnamary Estuary in French Guiana, characterized by an anoxic freshwater (hypolimnetic discharges from the artificial reservoir of Petit-Saut) and a saline end-member (Amazon Plume). The upper part of the estuary producing 0.5–3.5 kg MeHg per year, exceeding the amount of Hg volatilized into the atmosphere by one order of magnitude (Muresan et al., 2008a). Maximal MMRs were observed for dissolved Hg in slightly acidic and sulfidic milieus, whereas for particulate Hg, this was the case for the most acidic and oxidized waters. Trapping of dissolved MeHg (e.g., by precipitation of Fe oxyhydroxides in the lower estuary) and mobilization of particulate MeHg (e.g., accompanying AVSs dissolution in the vicinity of the reservoir) constitute dynamic processes simultaneously occurring in the Sinnamary Estuary (Muresan et al., 2008a). In part, similar observations have already been described earlier (Bloom et al., 1999; Gill et al., 1999): MeHg mobility in estuary surface sediments was found to be linked to the Fe redox cycle, while Hg mobility was controlled by the formation of soluble polysulfide and organic complexes. Under anoxic conditions, oxyhydroxides dissolve and release any associated Hg, which could be one reason for the frequently observed Hg and MeHg enrichment in anoxic waters; seasonal and diurnal trends in MeHg concentrations in sediment pore waters may also be linked with redox effects (Ullrich et al., 2001). Taking into account both the redox potential and the presence of AVS, Ouddane et al. (2008) compared Hg

methylation rates in highly industrialized salt marsh/mudflat systems from two macrotidal estuaries: the Seine (France) and the Medway (UK); Hg concentrations were higher than uncontaminated sediments by factors up to 50 (background concentrations about 0.03 mg kg1). While Medway mudflat is characterized by stable anoxic redox conditions (about  200 mV), Seine mudflat is more oxidized (about þ 100 mV); consequently, MeHg concentrations of Medway samples were fourfold higher than those of Seine samples in spite of similar total Hg concentrations. MeHg variability was associated with the activity of SRBs and the presence of AVSs. A strong correlation was observed between MeHg and AVSs in sediments from these mudflats, possibly as consequence of the common origin of AVSs and MeHg, both being produced by microorganism activity. For estuarine environments, it was also reported that sulfide enrichment varies with organic-matter enrichment resulting in increased Hg methylation at higher sulfide concentration (Sunderland et al., 2006). As SRB are thought to be the key methylating agents (Jensen and Jernelov, 1969; King et al., 1999; Benoit et al., 2003), sulfur geochemistry plays a dominant role in Hg methylation, contrary to normally methanogen-induced biomethylation as already described in Section 3.02.2.2. In particular, while sulfate stimulating both sulfate reduction rate (SRR) and MMRs at low sulfate concentrations, the buildup of dissolved sulfide at high sulfate concentrations inhibits Hg methylation (Figure 4). Figure 4 is based on literature descriptions and data (Gilmour and Henry, 1991; Ullrich et al., 2001), and in particular on the hypothesis offered by Benoit et al. (2003) implying that inorganic Hg uptake by SRB occurs by passive diffusion of neutral Hg complexes (in anoxic waters such as HgS1, Hg(SH)21, polysulfides HgSn1, or Hg(SH)(OH)1) across the cell membrane. While this happens at low sulfide levels, at high sulfide concentrations, methylation is inhibited because of the formation of charged disulfide complexes which are likely to be less bioavailable. The hypothesis is based on field (especially in the Everglades) and laboratory studies demonstrating that the balance between sulfate availability (controlling SRB activity) and sulfide production and accumulation (controlling Hg bioavailability) are the main factors for Hg biomethylation (Benoit et al., 2003). Sulfide inhibition SRR MMR

MMR/SRR

soils, and humic surface waters, high concentrations of MeHg are formed and accumulate in higher biota (Hurley et al., 1995; St. Louis et al., 1996). This has led authorities to discourage people to regularly eat fish from the 40 000 lakes in Sweden (Hakansson, 1996). Coastal wetlands along the Gulf Coast are key sites for MeHg production and may be a principal source of MeHg to foodwebs in the Gulf of Mexico (Hall et al., 2008).

47

SRBmethanogens competition 0

10

20

30

40

50

60

[SO42−] mg l−1 Figure 4 Mercury methylation rate (MMR) as a function of sulfate supply.

48

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

Correlations between SRR and MMR have often been reported in literature (reviewed in Merritt and Amirbahman (2009)), for example, in sediment assays or core-depth profiles (MartinDoimeadios et al., 2004; Choi and Bartha, 1994; King et al., 1999). From Figure 4, it can be understood that anthropogenic sulfate from the EAA can be transported with the water flow to the south toward ENP, leading to methylation rates there rising up to the high-percentage range. Field studies in the Florida Everglades covering a large gradient in sulfate and sulfide showed that the highest MMRs were at sites of intermediate SRR and sulfide concentrations. Sites with highest MMRs are those that also have the highest fish MeHg concentrations, confirming the direct link between the extent of Hg methylation and fish MeHg levels. Although the results of the investigation of Hg methylation in a tidal estuary in French Guiana (Muresan et al., 2008a) also conformed to the neutral complex theory of Benoit et al. (2003), in other scenarios, the application of this model in its present form may be more critical (Merritt and Amirbahman, 2009; Goulet et al., 2007).

3.02.3.2.5 Abiotic methylation Mercury methylation by organisms may be enzymatic or nonenzymatic. While enzymatic methylation requires the presence of actively metabolizing organisms, nonenzymatic methylation requires only the methylated products of active metabolism; thus metabolically produced methylcobalamin (resembling the most likely environmental methyl donor) can spontaneously methylate ionic Hg in aqueous solution (Ullrich et al., 2001). However, it should also be mentioned that purely abiotic pathways can methylate inorganic mercury in the environment, when suitable chemicals such as watersoluble methylsilicon compounds or humic substances are present, or methyl groups can be transferred from other Hg, Pb, Sn, or As alkyl species. Relevant hot spots may be humusrich soils, possibly because humic substances are good methyl donors for Hg2þ (Weber, 1993). DOC in water, however, seems to suppress Hg methylation (Miskimmin et al., 1992). Both Hg2þ and MeHg are known to have strong affinities for organic matter in terrestrial and aquatic environments (Hintelmann et al., 1997; Wallschla¨ger et al., 1998) – according to Schlu¨ter (1997), Hg2þ even more than MeHg. Another pathway leading to abiotic methylation is photoproduction of MeHg: Siciliano et al. (2005) observed in aerobic surface water of lakes increasing MeHg concentrations during sunshine. This generation of MeHg was also dependent on DOM concentrations and type (o5 kDa or 30–300 kDa). Furthermore, especially in coastal waters, it appeared that Hg oxidation and reduction were photochemically mediated also affecting Hg1 and DGM cycling (Whalin et al., 2007; Zhang and Dill, 2008). However, Benoit et al. (2003) came to the conclusion that abiotic transformations (in particular, methylation/demethylation) are possible but not important in most ecosystems. Last but not the least, it should be mentioned in this respect, that research in biotic/abiotic methylation/demethylation of Hg can be successfully performed since the application of isotopic spiking techniques is possible (e.g., Martin-Doimeadios et al., 2004).

3.02.3.2.6 Global concern Summarizing, although mercury emissions via rivers and air into the Atlantic Ocean and the North Sea have decreased between 1987 and 1995 by 50%, the mercury already present in the environment is because of the extreme mobility of this element (especially, its methylated variety generated in ecosystems) actually posing increasing troubles for the only chance to export Hg from the cycling mercury-pool within a 10–15-year cycle time (SRU, 2008) by precipitation as insoluble mineral (HgS) and keeping it under oxygen-free conditions (Torky and Foth, 2007). In February 2007, at the 24th meeting in Nairobi, the councilors of the United Nations Environment Programme (UNEP) passed the resolution that serious efforts have to be undertaken globally to reduce the mercury load upon our environment. Moreover, the EU expressed it will to contribute substantially to reduce the global mercury load, for example, by avoidance of Hg-dependent technologies (SRU, 2008).

3.02.3.3 Other Metals (Cd, Cu, Pb, and Zn) In contrast to the already discussed As, Sb, and Hg, these four metals are of much higher interest in the metal industry for manufacturing of alloys and other special applications; for example, Cd (in plating (Cd-coated steel), batteries, or as stabilizer in polyvinyl chloride (PVC)), Cu (in electroplating, pigments, catalysts, and biocides), or Pb (batteries, paints, or alkyllead). Thus, in accordance with economic growth, mining and processing of these metals have markedly increased during the last decades. However, from a chemical point of view, compared to the elements discussed before, not much is known concerning the speciation of these elements, just information about ordinary inorganic (complexes with major anions such as hydroxide, carbonate, sulfide, and sulfate) and organic ligands (complexes by humic materials) can be found (Merian et al., 2004). In contrast to all the elements discussed in this chapter, only Zn is not regarded as toxic. Despite this fact, the WHO has set guideline levels of 5 mg l1 for this element in drinking water, but mainly because the aesthetic quality of drinking water will be impaired at higher concentrations; such cases, however, are not often found as Zn levels in drinking water are usually below 0.2 mg l1 (Merian et al., 2004). The history of anthropogenic use of heavy metals can be best illustrated by taking lead as an example: in dated ice cores from Devon Island Ice Cap (Canada), Pb concentrations were proportional to those of Sc (as a geogenic reference) until 3100 BP, consistent with the hypothesis that soil dust particles derived from physical and chemical weathering dominate the Pb inputs to the atmosphere, with the magnitude of these sources being climate dependent (Zheng et al., 2007). With respect to estimating the Pb content of pristine groundwater, Shotyk and Krachler (2007b) reported a mean concentration of 5.1 ng l1 for six artesian flows in southern Ontario (Canada), Hirao and Patterson (1974) reported a mean concentration of 15 ng l1 for remote stream waters of the Sierra Nevada watershed, and Field and Sherrell (2003) found in 0.3–8 ng l1 water samples from Lake Superior. Notably, filling such waters in glass bottles will increase the Pb concentrations by factors ranging from 26 to 57 (Shotyk and

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

Krachler, 2007b); however, the maximum allowable concentration of 10 mg l1 in drinking water established by the EU, Health Canada, and the WHO, will usually not be attained by this effect. For Cd, mean concentrations in natural waters are reported to range from 0.01 to 0.1 mg l1 for seawater and 3 mg l1 for freshwater, respectively; Antarctic ice shows 0.3 mg l1, and surface ocean waters o5 ng l1 (Merian et al., 2004). Although there exist natural low-pH environments unaffected by human activity (Eppinger and Fuge, 2009), acid waters are often anthropogenically generated, in particular, when draining mining areas are heavily affected by runoff from mining operations. These waters are typically acid mine drainage (AMD), eroded material from mine-tailing deposits, and waste from ore-processing operations (Salomons, 1995). Acidity and high metal concentrations of AMD are the result of pyrite oxidation during weathering or industrial processes (Banks et al., 1997). At pHo4.6, pyrite oxidation progresses slowly, and direct bacterial sulfide oxidation (e.g., by Thiobacillus thiooxidans) is the dominating process (Kirby and Elder Brady, 1998). A good example is the Iberian Pyrite Belt, an important metal-rich sulfide deposit (Braungardt et al., 2003). The rivers Rio Tinto and Rio Odiel draining this area are highly acidic (pH 2.2–3.6) and show milli-molar sulfate and iron concentrations as well as micro-molar Cu and Zn contents. Dissolved metal concentrations are at a maximum during autumn and early winter (e.g., Rio Tinto 29–54 mg l1 Cu, while 8–11 mg l1 Cu during the residual year). This variability is interpreted by the production of AMD during periods of enhanced microbial activity at higher temperatures in summer, and a subsequent runoff into the rivers with the first rain in autumn (Braungardt et al., 2003). Oxidation of sulfide-rich rocks in leftover debris from Cu mining in the 1920s contributed to metal contamination of local coastal environments in Prince William Sound (Alaska), leading to inverse correlation between pH and metal concentration in pore waters of sediments showing pH down to 3 and base metal concentrations up to 25 mg l1 (Koski et al., 2008). Adverse heavy metal impacts were also reported for the aquatic ecosystem of the Lean River in S China (Liu et al., 2003): while acidic drainage from Dexing Cu mine contains large amounts of Cu, high concentrations of Pb and Zn are found in the effluents released from many smelters and mining/panning activities. Astro¨m (2001) reports about the medium-sized stream Munsala (Finland) draining areas covered with acid sulfate soils developed on sulfide-bearing marine sediments; during high flows in autumn, while there was a strong downstream increase in Cd, Cu, and Zn concentrations related to extensive acid soil leaching, other elements such as As, Sb, and Pb were not leached as much from other soils/sediments within the catchment. Note that in the headwater of Munsala stream, trace-element concentrations were similar to local background concentrations. Elevated metal concentrations (average values given in mg kg1) of 8.6 (Cd), 481 (Cu), 4450 (Pb), and 753 (Zn) were found in waste tailings of Daduk mine, Korea (Lee et al., 2001), and similar ones of 9.4 (Cd), 229 (Cu), 6160 (Pb), and 1640 (Zn) in Imcheon mine, Korea (Jung, 2001). Water samples from the latter site were very acidic (pH down to 2.2) and contained up to 0.3 (Cd), 1.9 (Cu), 2.8 (Pb), and 53 (Zn)

49

(average values given in mg l1); concentrations of metals (and anions such as sulfate or fluoride) decreased exponentially with increasing distance from the mine. In favorable circumstances, the buffer capacity of the natural environment neutralizes the AMD (natural attenuation): For example, while at the Pecos mine (New Mexico), drainage near the waste rock pile is acidic (pH between 3 and 5) and carries high loads of Cu, Pb, and Zn, because of reacting with limestone-containing bedrock, drainage flow downstream toward the Pecos river shows increasing pH and decreasing metal contents (Berger et al., 2000). Lower concentrations (ranges given in mg l1) were reported for surface water of Udden pit lake (associated with open pit mining) in N Sweden (Ramstedt et al., 2003): Cd 0.02–0.08, Cu 0.03–0.17, Pb 0.006–0.06, and Zn 2–81. However, even these concentrations are significantly higher than background concentrations and guideline concentrations for lake and surface water in Sweden (Ramstedt et al., 2003), and are explained by weathering of ore sulfide minerals. Comparison with Hall Lake shows similar behavior for Cu, As, and Zn (Balistrieri et al., 1994); Hall lake is a natural lake and most metals in this lake precipitate below the anoxic border where sulfide is generated. However, compared to other pit lakes as described by Miller et al. (1996), the water quality of Udden pit lake is relatively good in terms of pH and heavy metal content; note that the latter two parameters correlate in that pit lakes with higher pH tend to have lower metal concentrations. Mine wastes may be discharged and dispersed into nearby agricultural soils, food crops, and stream sediments, eventually posing a potential health risk to residents in the vicinity of mining areas. For example, enriched concentrations of heavy metals were found in various plants grown in the vicinity of Daduk mine in Korea, and were correlated to those in soils (Lee et al., 2001); in particular, relatively high concentrations were detected in rice leaves and stalks grown under oxidizing rather than reducing conditions. In Mexico, large quantities of waste are derived from mining and abandoned mines pollute the aqueous systems (Armienta et al., 2003). For example, for the mine-impacted tropical river, Taxco, it could be shown that in case of Pb pollution, anthropogenic sources have contributed significantly, while natural sources contributed only small amounts (Arcega-Cabrera et al., 2009). Bioavailable Pb in riverbed sediments was 450% in 80% of the sampling stations indicating a high potential environmental risk. Highest Pb concentrations were found close to tailings during the rainy and post-rainy seasons; during dry and post-rainy seasons, Pb chemistry was mainly controlled by organic matter and carbonate content. Due to recent advances in mass spectrometry, isotope scientists are now able to precisely determine stable isotope variations even in metallic elements (Bullen and Eisenhauer, 2009); with respect to the elements covered in this chapter, already a few relevant application studies exist in literature: Peel et al. (2009) determined the ratio 66Zn:64Zn in settling particles in the hypolimnion of the eutropic Lake Greifen (Switzerland). Enrichment of the light isotope was observed during the productive summer period from June to September, when the Zn in the settling particles was predominantly

50

Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species

associated with organic material, and when Zn concentration in the epilimnion was the lowest. By investigating stream waters draining historical mining districts in USA and Europe, Borrok et al. (2008) concluded that besides Zn, Cu isotopes also may be powerful tools for probing biogeochemical processes in surface waters.

3.02.3.4 Platinum Group Elements The PGEs (Ru, Rh, Pd, Os, Ir, and Pt) are found in the earth’s crust in very low concentrations of about 0.05–0.1 ng g1 (Wedepohl, 1995). These elements occur as sulfides (e.g., RuS2, OsS2, and IrAsS) or alloys (Os–Ir–Ru and Pt–Fe) in the form of micrometer-sized mineral inclusions or millimetersized nuggets in placer deposits associated with mafic/ultramafic complexes; while the former may be formed at 700– 1100 1C, the latter are considered to recrystallize at lower temperatures (Petrou and Economou-Eliopoulos, 2009). PGEs are considered to be very valuable in society (e.g., jewellery, coins/bars), and there is an increasing demand for them in the automotive, chemical, dental, medical/biomedical, and petroleum industries (Ek et al., 2004; Johnson Matthey, 1999). At the end of their usage or lifetime, these products become part of our environment along with contributions by emissions during mineral processing and fossil-fuel burning (Crocket and Teruta, 1976; Chyi, 1982); in particular, important PGE sources in the environment are sewage systems and automobile catalytic converters (Lottermoser and Morteani, 1993; Helmers, 1997; Jarvis et al., 2001). Eventually, significant quantities of PGEs will enter fluvial, estuarine, and coastal sediments (e.g., de Vos et al., 2002; Jarvis et al., 2001; Ravizza and Bothner, 1996; Wei and Morrison, 1994; Esser and Turekian, 1993). For example, up to 6 ng g1 PGE have been found in sediments of the Kentish Stour (de Vos et al., 2002). Along streets with heavy traffic, platinum concentrations up to the lower mg g1 range have been detected, corresponding to anthropogenic enrichment factors of 105–106 (Hoppstock and Sures, 2004); ash from sewage sludge burning and street dust contain up to 0.5 mg g1 palladium (Leopold et al., 2008). Via road runoff, these metals are also introduced into aquatic habitats. For example, while the natural background concentration of Pt in rainwater is o0.2 ng l1, in surface-water drainage along highways, Pt levels up to 80 ng l1 are reported (Merian et al., 2004). The water solubility of PGE decreases in the order Pd 4 Pt 4 Rh, resembling their biological availability for plants and mussels; solubility is increased by aging of catalysts and the presence of humic substances (Merian et al., 2004). Thus, the most significant health risk is posed by soluble PGE contents, because via mobilization by waters, a considerable amount of PGE emitted from cars is able to enter different environmental compartments. Although adsorption and surface complexation onto soil and sediment particles lead to PGE immobilization (e.g., Sako et al., 2009), there are indications for transformation effects into more reactive/bioavailable and thus mobile species (Moldovan et al., 2001; Rauch and Morrison, 1999; Lustig et al., 1996; Alt et al., 1994). In this respect, palladium seems to be more critical than platinum and rhodium (Zimmermann and Sures, 2004): While Pd was found in soil depths of 12–16 cm, in the same soil profile, Pt and Rh have been found

mainly concentrated on the surface (depths below 8 cm). Palladium is significantly incorporated in cells, plants, and animals, and exhibits stronger biological effects similar to Pt and Rh or other heavy metals (Battke et al., 2008; Frank et al., 2008; Singer et al., 2005; Sures et al., 2006; Hoppstock and Sures, 2004). Pd forms very stable complexes not only with soft donors such as sulfur (e.g., in cysteine, methione, proteins, or enzymes), but also with N and O donors (e.g., in DNA, RNA, or peptides). Compared to Pt, Pd shows reaction rates higher by three to five orders of magnitude (Kozlowski and Pettit, 1991; Al-Bazi and Chow, 1984).

3.02.4 Conclusions The most important points raised in this chapter are summarized as follows:

•

•

•

•

•

Currently, the most serious problem globally is the intoxication of millions of people with drinking water containing too much arsenic. As the latter is of geogenic origin and the world population (and therefore demand for drinking water) is still growing, this problem will become even more pressing in the future. There exists an apparent lack in arsenic toxicology even now because speciation is not accounted for: only when the total arsenic content is known, it will make an enormous difference if the arsenic in question is in the form of arseno-betaine or arsenite (or even trivalent organic species) because of the different toxicity of the As-species. Maximal methylmercury production does not usually coincide with the site of maximal mercury pollution (see Figures 2 and 4), because MMR is a product of many variables to be accounted for. Relevant examples have been discussed in the chapter (e.g., Gulf of Trieste and Everglades National Park). Officials should be aware that flooding of large areas (e.g., wetlands or reservoirs) promote mercury methylation, thus creating the risk of methylmercury enrichment along the food chain, eventually reaching mercury concentrations in fish even in the mg kg1 range (i.e., too contaminated to be eaten). As PGEs are increasingly used, there will be an urgent demand in our society to develop relevant speciation methods to evaluate the environmental and toxicological impact of these elements.

Acknowledgments The authors are grateful to the Deutsche Forschungsgemeinschaft for generously sponsoring speciation research (grant FOR 415), and to Dr. Roland A. Diaz-Bone for providing them with a computer model of the halo hypothesis (Figure 2(a)).

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3.03 Sources, Risks, and Mitigation of Radioactivity in Water D Crawford-Brown, University of Cambridge, Cambridge, UK & 2011 Elsevier B.V. All rights reserved.

3.03.1 3.03.2 3.03.3 3.03.4 3.03.5 3.03.6 3.03.7 References

Introduction Establishing Limits on the Risk from Radionuclides Specific Radionuclides of Interest Mitigation Methods Geographic Areas of Special Concern Measuring Radioactivity in Water Conclusions

3.03.1 Introduction Radionuclides occur in all water sources, whether surface water or aquifers. This ubiquity stems from their multiple origins, which include the natural composition of the Earth, with long-lived radionuclides such as elements of uranium left over from the initial creation of the Earth, and production by cosmic rays that continuously bombard the atmosphere. Add the emissions from nuclear power plants, the manufacture of nuclear fuel and weapons, and the treatment and disposal of radionuclides from uses such as medical equipment, and one finds radionuclides in essentially all materials with which water might come into contact (although naturally occurring radionuclides remain by far the largest contributor). A distinction is useful here before exploring the various aspects of radionuclides in water to be discussed in this chapter. There is a distinction between radioactivity, radionuclides, and radiation. Radioactivity refers to the ability of an atom to undergo radioactive decay, generally to a more energetically stable atom. The result of this decay is some form of radiation emerging from the atom, either as an electron (beta radiation), photon (gamma or X-ray, with gammas emerging from the nucleus of an atom and X-rays from the shells of electrons), or two neutrons and two protons bound together (an alpha particle). A radionuclide is one of the nuclides of a particular element (such as uranium) that is radioactive and, hence, emits radiation. All radioactive atoms transform eventually into a stable isotope of either the original or a different element. The unit of measure for radionuclides refers to the rate at which radioactive decays occur in a sample. This does not necessarily equal the rate at which radiations are being emitted, since more than one radiation can emerge from an atom undergoing decay. The historical unit of radioactivity is the curie or Ci, with 1 Ci being equal to 3.7 1010 disintegrations per second (the rate of radioactive decay in approximately 1 g of the radium with which Marie and Pierre Curie worked). More recently, the Ci was replaced with the Becquerel or Bq, equal to 1 disintegration per second. Note that 1 Ci is therefore equal to 3.7 1010 Bq. Neither the Ci nor the Bq depends on the type of radiation emitted; these depend only on the rate of radioactive decay underlying the emission. For radionuclides in water, the relevant measure of contamination is Becquerels per liter or Bq l1, although for historical reasons

59 59 62 63 64 65 67 67

some radionuclides such as radon continue to be reported in units of pCi l1(picocuries per liter, with 1 pCi being 1 1012 Ci or 0.037 Bq) (Table 1). This chapter focuses on radionuclides as a concern in water supplies, although that concern is for the most part related to the radiation emitted rather than to the radionuclide itself. While a radionuclide is also an element, and can therefore produce toxicity quite apart from its radioactive properties, regulatory controls are almost all based on the risk from radiation emitted by the radionuclide (uranium is toxic to organs such as the kidneys quite apart from its radiotoxicity, although it is the only regulated element where chemical toxicity, rather than radiation risk, dominates). In the case of either chemical or radiological toxicity, the dose is still the relevant metric for degree of toxicity. There is no difference in methodology for estimating risks from radiation received from radionuclides in water, food, soil, or air, or even from radiation received by procedures such as X-rays. As a result, it is necessary to consider terms and methods that may be unfamiliar to readers who have dealt primarily with chemical or biological contaminants in water.

3.03.2 Establishing Limits on the Risk from Radionuclides As mentioned, the risk from a radionuclide may be either from its action as an element, in which case all nuclides of that element when in purely elemental form produce the same effect because their shells of electrons are the same, or from Table 1 world

Radionuclides generally of greatest concern around the

Beta, gamma, and X-ray emitters

Alpha emitters

K-40 H-3 C-14 Ru-87

Ra-226 Ra-228 Po-210 Isotopes of U Isotopes of Th Rn-220 Rn-222

From De Zuane J (1997) Handbook of Drinking Water Quality. New York, NY: Wiley.

59

60

Sources, Risks, and Mitigation of Radioactivity in Water

exposure to the radiation emitted during radioactive decay. The exception is when the different nuclides of an element are found in different chemical forms. The first category of effects can be set aside for this discussion because they tend to occur at concentrations much higher than any found in water supplies. The exception is uranium in water, which produces toxicity in the kidneys due to its chemical properties. The second category contains two effects from the radiation, both of which result from damage to tissue as the radiation passes through the tissue. Deterministic effects are ones for which the severity of the tissue damage caused is proportional to the amount of radiation received (this amount is called the dose, which has a specialized meaning in radiation dosimetry as discussed later) and for which a threshold dose exists below which the effect does not occur. The severity of deterministic effects such as killing of the cells that line the gastrointestinal tract increases with the dose, and also depends on other factors, including the type, energy and dose rate of the radiation, and the sensitivity of the irradiated organ. Radiation protection for deterministic effects is built around the identification of a threshold dose, and maintaining water concentrations at levels low enough to remain below this threshold dose. As mentioned previously, uranium is the only radionuclide for which these deterministic effects are significant in regulatory decisions at environmental levels of exposure. The more common effect of radionuclides in water, and the one that drives regulatory decisions, is related to the ability of radiation to damage and alter the DNA and other structures such as membranes of cells. The result is primarily leukemia and other forms of cancer. These effects are called stochastic because radiation increases the probability of the effect (cancer), but not necessarily the severity of that effect. As a result, essentially all regulatory limits on exposure to radionuclides are intended to reduce the probability of cancer down to some level judged acceptable (the concept of acceptable risk in regulatory law). The stochastic effects of radiation are related to the ability of this radiation to cause ionization in cells, including in the DNA. This ionization breaks chemical bonds, such as the bonds holding base pairs together, which in turn can lead to mutations or re-arrangements of the DNA. The bonds are broken either directly by the radiation or through the production of chemical entities such as free radicals in the water of cells, which then act chemically on the bonds. Since the number of ionizations is related to the energy deposited in a cell by radiation, the dose of radiation is defined as the density of energy deposited in tissue as the radiation passes into or through the cells. The modern or SI unit of dose is the gray or Gy, and is equal to radiation that deposits an average of 1 J of energy per kilogram of tissue, or 1 J kg1. The historical unit was the rad, equal to 0.01 J kg1 or 0.01 Gy. While energy density is important in determining the probability of effect, studies in radiobiology show that this probability is also related to the spatial pattern of the ionizations produced. More densely packed ionizations (thus with shorter distances between each individual ionization) are more effective in damaging DNA and killing cells. Since gammas, X-rays, betas, and alphas have different spatial densities, measured by the quantity linear energy transfer, they also have different probabilities of producing an effect even if

they deliver the same dose in Gy. Each radiation is therefore assigned a quality factor Q, normalized to gamma radiations with a Q of 1. The relevant values of Q at present are 1 for X-rays, gamma rays, positrons, and electrons; 3 for neutrons below 10 keV and 10 for energies above; 10 for protons and singly charged particles of unspecified energy; and 20 for alpha and other multiply charged particles. The product of the dose (in Gy) and this QF (unitless) is the dose equivalent or effective dose, characterized as the rem in historical units (the product of dose in rads and the QF) or sievert (Sv) in modern units (the product of the dose in Gy and the QF). Choosing a limit for the allowed concentration of a radionuclide in water requires first establishing an allowed probability of effect – here of cancer. This allowed probability varies from country to country according to their internal laws governing environmental risks and the ways in which the precautionary principle is applied (Wiener, 2002), but as an example, consider the US where the limiting probability generally is 1 104 excess lifetime risk or probability of cancer. The International Commission on Radiological Protection (ICRP) (ICRP, 2007) recommends a limit on effective dose of 1 mSv yr1 from all combinations of radiation apart from natural background radiation and medical or therapeutic exposures (bearing in mind that many of these radionuclides are also part of the natural background). Using the conservative assumption of linearity of risk with dose equivalent, they estimate that the excess lifetime risk of cancer from a single exposure to 1 mSv is 7.3 105 (ICRP, 2007). A rate of dose equivalent equal to 1 mSv yr1 over a lifetime of 75 years would therefore produce a lifetime excess risk of cancer equal to about 6 103 (60 times the allowed risk in the US). Again, it has to be borne in mind that this risk estimate is produced through adoption of the conservative assumption of a linear no-threshold model of radiation induced cancer; the actual risk may be significantly lower. Restricting the lifetime excess risk of cancer to 1 104 as mentioned previously indicates that the maximum allowed rate of dose equivalent from radionuclides in water alone would be 1/60 mSv or 0.017 mSv yr1 (17 mSv yr1). Regulations on radionuclides in water are not, however, specified as limits on dose equivalent (mSv yr1). They are instead specified as limits on the concentration of the radionuclide in water (Bq l1), since that is what is mitigated in treatment of the water. It is necessary, therefore, to determine for each radionuclide the concentration that will produce the limit on dose equivalent. It is necessary here to distinguish between two broad categories of ways in which a radionuclide can produce a dose equivalent in the body when humans are exposed to water bearing that radionuclide.

•

External exposures take place when the radionuclide is present in the water and a person is exposed to the radiation either when standing near the water or when immersed in the water. In these cases, the radionuclide does not enter the body but the radiation it emits passes into the body and irradiates the tissue. The dose is then related to the concentration of the radionuclide in the water, the distance a person stands from the water, and the presence and nature of intervening materials. External exposures are generally not significant for regulatory decisions except in

Sources, Risks, and Mitigation of Radioactivity in Water

•

rare cases such as exposure to the core or holding pool of a nuclear reactor. Internal exposures take place when the radionuclide enters the body and irradiates the tissue by radiations emitted while the radionuclide is still inside. Primary exposure routes are then ingestion of water (almost always dominant in regulatory decisions on waterborne contaminants), ingestion of water used in cooking, or dermal absorption. A prominent exception to these general routes of exposure is radon, an inert gas whose risks from water are caused largely by the radon emanating into the air of a building during a shower, cooking, heating water, etc., followed by inhalation of the radon and its radioactive decay products. Internal exposures represent by far the most significant risk pathway for radionuclides in water at environmental levels.

Since internal exposures are more important in setting regulatory limits, methods are needed to estimate the dose equivalent produced by taking the radionuclide into the body (via ingestion in most cases, or inhalation in the case of radon). Both the ICRP and the National Radiological Protection Board (NRPB) have calculated dose conversion factors (DCFs) for radionuclides (see, e.g., NRPB, 1991; ICRP, 1996). These factors take into account the processes that move a radionuclide into and through the body (metabolic or pharmacokinetic models), and the processes that cause radioactive decay and hence irradiation of the tissues (dosimetric models). Each DCF is an estimate of the lifetime dose equivalent resulting from a single intake of 1 Bq of a given radionuclide, from which one can calculate the dose equivalent delivered over a lifetime from continuous intakes of 1 Bq yr1. In some cases, there are data on the movement of a particular radionuclide through the body, and these metabolic and dosimetric models can be based on these radionuclidespecific data. In many cases, however, such data do not exist and it is necessary to rely on data for the movement of other elements. Radionuclides that are in soluble form and chemically analogous to essential nutrients will tend to follow pathways through the body in a fashion similar to these analogs. For example, radionuclides of strontium (Sr), barium (Ba), radium (Ra), and calcium (Ca) all are bone-seekers similar to ionic calcium and so tend to exert their effect through irradiation of bone tissue such as the endosteal cells or marrow. Radioisotopes of cesium (Cs) and potassium (K) follow the general movement of ionic potassium and distribute to tissues throughout the body, irradiating the tissues uniformly. Radioisotopes of iodine (I) accumulate in the thyroid, leading to concerns for thyroid cancer. The lifetime excess probability of cancer, P, from water containing a radionuclide at a concentration of C (Bq l1) may be calculated as

P ¼ C  IR  DCF  SF  365  75

ð1Þ

where IR is the intake rate of water (l d1); DCF is the dose conversion factor mentioned previously (mSv Bq1); SF is the slope factor (probability of cancer per mSv, this is 7.5 105 mSv1 as mentioned previously based on the ICRP recommendations); 365 is the number of days per year and 75

61

is the mean number of years of life. The allowed concentration of the radionuclide in water may then be found by rearranging Equation (1) to yield

C ¼ P=ðIR  DCF  SF  365  75Þ

ð2Þ

As an example of a regulatory calculation of allowed concentration in water (generally called the maximum allowed concentration (MAC)), assume the allowed lifetime excess probability of cancer is 1 104; the intake rate of water is 2 l d1 (regulatory calculations use this value because it is conservative or protective of health, being in the upper few percent of the probability density function for rates of water intake); the DCF is 1 105 mSv Bq1; and the value of the slope factor SF is 7.5 105 mSv1. Using Equation (2),

MAC ¼ 10 4 =ð2  1  10 5  7:5  10 5  365  75Þ ¼ 2:4 Bq l 1

ð3Þ

Note also that this radionuclide produces an annual dose equivalent of

DE ¼ 2:5  10 5  2  1  365 ¼ 1:8  10 2 mSv yr 1

ð4Þ

or an excess lifetime probability of cancer of 1 104. By way of comparison, the sum of all natural sources of radiation worldwide is approximately 2.4 mSv yr1 (UNSCEAR, 2000). About one-third of this total dose equivalent is due to external radiation (terrestrial plus cosmic); the other two-thirds are due to the inhalation and ingestion of radionuclides in air, water, and food. Clearly, the regulatory limits are designed to produce dose equivalents that are a small fraction of that from natural background radiation. There are a few assumptions underlying the calculations above, the one of most interest here being an assumption that the probability of cancer is directly proportional to the dose equivalent down to the lowest values of dose equivalent. The main epidemiological studies on which radiation risks have been based are in populations with high levels of dose, the most important being survivors of the Hiroshima and Nagasaki bombs, patients receiving high dose equivalents for medical or therapeutic reasons and occupationally exposed workers such as uranium miners, radium-dial painters, and radiologists (National Research Council, 2005). The risk at low levels of dose equivalent is extrapolated from these epidemiological studies using a linear, no-threshold model as a conservative assumption to provide a margin of safety in regulations. These same epidemiological populations received their radiation over a short period of time, and so their dose rates were high. It is known that higher dose rates are generally more effective than low dose rates in producing cancer, the possible exception being for alpha particles emitted by radionuclides such as radium, and this dose–rate effect is not reflected in regulatory calculations of risk. This is again a conservative and health-protective approach to deal with the uncertainty in extrapolating to the conditions found in environmental exposures to radionuclides.

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Finally, regulators must also determine how to treat the fact that water may not be the only route by which a given radionuclide irradiates the body. The same radionuclide may be present in air, food, soil, etc. To account for this, many regulatory bodies estimate the fraction of total dose equivalent contributed by water, and reduce the allowed concentration accordingly. In the previous example, the allowed concentration was 2.5 105 Bq l1. If studies show that exposure to water accounts only for half of the total annual dose equivalent from this radionuclide, the regulator might reduce the allowed concentration by half to 1.2 105 Bq l1. The result is a total risk (water plus other routes combined) that is below the allowed or acceptable risk level. This relative source contribution calculation is not, however, uniformly applied across regulatory bodies around the world, or even across offices of the same regulatory body (it is not, e.g., applied in decisions on radionuclides in air in the US Environmental Protection Agency).

3.03.3 Specific Radionuclides of Interest There are hundreds of radioisotopes of elements. Only a handful, however, are found routinely in water supplies at a concentration sufficient to produce significant risks, and hence are of regulatory interest. The major ones are described here. To broaden the discussion beyond the US regulatory system (used in Section 3.03.2), consider the limits on specific waterborne radionuclides for drinking water, published by the World Health Organization in chapter 9 of their Guidelines for Drinking Water Quality (volume 1, 2008; see table 9.3) (Table 2). In addition, many countries have established limits on exposure to mixtures of radionuclides that act on the same tissue by the same mechanism, or because measurement methods do not distinguish easily between the sources of a given kind of radiation (such as alpha particles). In the US, some of the more important mixtures are shown below. In these cases, the regulatory limit is a maximum contaminant level (MCL), essentially the same as an MAC.

•

•

Combined radium-226 and radium-228 has been assigned a total MCL of 5 pCi l1 (or 0.19 Bq l1). This combination of radionuclides is found in most water supplies that come into contact with rocks containing radium from the uranium and thorium decay chains. The limit of 5 pCi l1 (or 0.19 Bq l1) was selected because the maximally allowed level of risk implied by regulatory limits would be attained if the mixture actually contained pure Ra-226 or Ra-228. Gross alpha has been assigned a total MCL of 15 pCi l1 (or 0.6 Bq l1) after subtracting out radon and isotopes of uranium. While this MCL does not exclude radionuclidespecific MCLs, measurement methods are simple for total alpha emissions but more complex for specific alpha emitters, generally requiring chemical separation. Note that even if a sample passes this gross alpha test, it is still necessary for it to pass the combined Ra-226 and Ra-228 test because water containing either of these radionuclides at a

Table 2 The MAC values for the most significant radionuclides as determined by the World Health Organization (2008) Americium-241 Barium-140 Bismuth-210 Bromine-82 Calcium-45 Calcium-47 Carbon-14 Cesium-131 Cesium-134 Cesium-136 Cesium-137 Chromium-51 Cobalt-57 Cobalt-58 Cobalt-60 Iodine-125 Iodine-129 Iodine-131 Iron-55 Iron-59 Lead-210 Manganese-54 Mercury-197 Mercury-203 Neptunium-239 Phosphorus-32 Plutonium-238 Plutonium-239 Plutonium-240 Plutonium-241 Polonium-210 Radium-224 Radium-226 Radium-228 Ruthenium-103 Ruthenium-106 Sodium-22 Strontium-85 Strontium-89 Strontium-90 Technecium-99 Thorium-228 Thorium-230 Thorium-232 Thorium-234 Tritium Uranium-234 Uranium-235 Uranium-238 Zinc-65 Zirconium-95

•

1 100 100 100 100 100 100 1000 10 100 10 10 000 1000 100 100 10 1 10 1000 100 0.1 100 1000 100 100 100 1 1 1 10 0.1 1 1 0.1 100 10 100 100 100 10 100 1 1 1 100 10 000 1 1 10 100 100

Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1 Bq l1

concentration of 15 pCi l1 (or 0.6 Bq l1) would produce an unacceptable level of risk. Combined uranium isotopes have been assigned an MCL of 30 mg l1 based on chemical toxicity (primarily to the kidneys). Note that the MCL is unit of mass rather than radioactivity (with 1 mg l1 being equal to approximately 12.3 mBq l1), reflecting the chemical toxicity rather than radiological risk as the primary cause of concern. It

Sources, Risks, and Mitigation of Radioactivity in Water

does not matter which mixture of isotopes of uranium is present in the sample because all have the same chemical toxicity. Radon (specifically Rn-222) presents a special challenge throughout the world, because the risks are generally much higher than for other radionuclides in water and also because the route of exposure is dominated by emanation into the air of a building rather than through direct ingestion. Empirical studies and the results of modeling of indoor air exchange indicate that the ratio of the concentration of radon in air (Bq l1) over that in water (Bq l1) due to the waterborne radon alone is approximately 1 104 (i.e., 10 000 Bq l1 in water produces 1 Bq l1 in air). The risk from the radon emanated out of the water and into the air during activities in the home (showers, washing clothes, etc.) is almost a factor of 50 higher than for direct ingestion of the radon in the water (National Research Council, 1999). As a result, regulatory limits on radon in water are based on the risks imposed by this emanation and the subsequent inhalation of radon and its radioactive decay products. Mitigation is accomplished either by removing the radon from the water before it enters the home air, or by removing the radon from the air once it has been emanated. Regulatory control is complicated further by the fact that the relative source contribution for radon in water is quite small. Radon enters the air of a building by many routes, only one of which emanates from water. The risk from radon emanated from water is only a few percent of the risk from radon that enters the air from rock underlying a building or from the building materials themselves. This leaves regulators in the position of finding that radon in water poses a level of risk much higher than for most of the other substances, including radionuclides and chemicals, currently considered in regulations. Not controlling radon in water would appear to weaken the argument for controlling these other contaminants. Removing radon from water, however, will reduce the risk to health from radon in the home by only a percent or two, and hence is not a cost-effective way to protect the public health against the total risk of environmental radon. This difficulty has been at the heart of problems in developing a regulation for radon in water in the United States, where the regulatory process has been stalled for decades as this comparative risk issue is debated. How can this challenge be resolved? The approach of the US Environmental Protection Agency, still under development, has been to propose two limits (USEPA, 2009). A regulatory limit (MCL) of 300 pCi l1 (or 11 Bq l1) is likely to become the primary standard. Even this concentration will produce a lifetime excess cancer risk above the value of 1 104 mentioned previously, being closer to 2–3 104, although through rounding one could argue that the risk is right at 1 104. If a state chooses, however, it can tackle the problem of radon primarily through a program of control of radon in air, such as increased ventilation in buildings or sealing of the ground, so radon cannot enter the building from nonwater sources. If such a program were put in place, the regulatory limit on radon in water becomes 4000 pCi l1 (150 Bq l1) in recognition of the low value of the relative source contribution for radon in water compared to other routes. Therefore,

63

state level regulators are being encouraged to adopt the most cost-effective mitigation strategy across multiple exposure pathways, the first such regulatory approach in the US. As a comparison, consider the standard being developed by the European Commission EC Recommendation 2001/928/ Euratom: For public water supplies, if the radon concentration exceeds 1000 Bq l1, remediation is considered justified. Where the radon level is above 100 Bq l1 but below 1000 Bq l1, the local authority must consider whether this poses a risk to human health. If it is concluded that such a risk exists, then remedial action should be considered. For private water supplies, where water is found to have levels of radon in excess of 1000 Bq l1 remediation of the supply should be considered.

Note that the proposed US standard is far below – or more stringent than – that being considered in the EC, in large measure because the US standard is based primarily on the calculation of excess lifetime risk where the EC standard is related more directly to the feasibility of mitigation. The risks from radionuclides in drinking water are dominated by the alpha emitters, especially Ra-226, Ra-228, natural U (a mixture of uranium isotopes with primordial origins), and Rn-222. The relative risks from these four radionuclides can be seen from data produced in the US (Milvy and Cothern, 1990) based on a national survey of ground- and surface-water supplies; similar results would be obtained in most other countries. Data are summarized in Table 3. From these data, it can be seen that radon contributes almost 98% of the total risk from these four radionuclides in the case of groundwater supplies; these supplies are examined here because the concentration in groundwater is generally quite a bit higher than in surface water. Note also that the cost of mitigation, measured as millions of dollars spent per cancer averted through mitigation varies considerably, from a low of $5 M for radon to a high in excess of $10 000 M for uranium (this is largely because U is mitigated chiefly due to its toxic effect on kidneys rather for its carcinogenicity). Also, bear in mind that the predicted savings in life – through cancer cases averted – are purely the results of the theoretical application of the linear nonthreshold model. While the costs of treatment are well established, the benefits through reduced risk remain speculative at present.

Table 3 Comparison of mean concentration in groundwater, lifetime risk, and cost per cancer case averted for isotopes of radium, uranium, and radon Radionuclide

Ra-226 þ Ra228 Natural uranium Rn-222

Mean concentration (pCi l1)

Lifetime risk

$M per cancer averted

1.1

1  105

20

1.2

2  106

10 000

4  104

5

600

64

Sources, Risks, and Mitigation of Radioactivity in Water

3.03.4 Mitigation Methods Many of the methods to treat water for other elements are effective at reducing radionuclides as well – and hence reducing the risks posed by these radionuclides. In fact, there is no difference in methods used to treat water for the radioactive or stable form of the same element. As a result, the treatment methods listed below will be familiar to anyone who has been engaged in removing chemicals and turbidity from water. To ensure confidence in removal of radionuclides, methods should conform to the recommendations of the NSF International American National Standards Institute (ANSI).

•

•

•

•

•

•

Filtration involves passing the water through a filter with suitable pore size. It has been used routinely to treat for radium, with more than 50% of the radium removed if it is in colloidal form. A special case is the use of activated carbon as the filter medium, which can remove more than 90% of radon in water, assuming the filter medium is periodically cleaned. Nanofiltration membranes have been reported to achieve significantly higher removal efficiencies in laboratory testing (Annanma¨ki, 2000), although their expense has kept them from wide application to date. Ion exchange, where the water flows through resin granules that act as a softener. With maintenance of the system, including regeneration of the resins, 90% of radionuclides can be removed. This is the approach often found in small water systems as the system is cost effective even at this scale. Lime softening, in which lime is added to water and isotopes such as radium settle out. This approach can remove up to 90% of the radium. This method has also been effective in removing isotopes of iodine, strontium, and uranium. An advantage is that such method is often employed anyway to treat hard water. Preformed hydrous manganese oxide filtration is slightly less effective than some of the methods above. The addition of potassium permanganate and manganese sulfate to water before it is filtered increases the removal of radium from the value of 50% noted in the first bullet to up to 80%. This can, however, be an inexpensive approach if filters are already in place. Reverse osmosis, in which water is passed under pressure through a semi-permeable membrane. Up to 98% of many radionuclides (including radium and uranium) can be removed by this approach. Isotopes of cesium, strontium, and iodine are also removed, although with slightly lesser efficiency. This approach, however, removes 20–30% of water from the system, which can be unacceptable in areas of a country already facing a shortage of water. Aeration is used primarily for radon. Since radon is an inert gas, it is stripped easily from the water during aeration, with a well-maintained system capable of removing more than 90% of the radon. Note that the effectiveness of aeration is related to two other aspects of radon risk: (1) radon concentrations in surface water are generally low due to the ability of the radon to diffuse to the surface of these supplies and escape to the air, so only groundwater supplies are of interest and (2) radon is readily emanated from water into the air of homes, with more than 90% emanated

during cooking and more than 50% during a shower or pouring water from a tap. Since isotopes of radium have received particular attention (second only to radon), a large number of specialized methods to remove it from water have been assessed (Clifford, 1990). The more effective methods have proven to be

• • • • • • • •

combining radium with iron removal, which has efficiencies of slightly less than 40%; lime softening, with removal efficiencies of up to 95%; sodium ion exchange softening, with removal efficiencies of up to 98%; weak acid cation exchange, with efficiencies of up to 95%; reverse osmosis hyperfiltration, with removal efficiencies of up to 96%; manganese dioxide filters, with removal efficiencies of up to 97%; absorption onto a radium selective complexer, with removal efficiencies of up to 99%; and absorption onto barium sulfate impregnated alumina, with removal efficiencies of up to 95%.

As can be seen, some of the methods above are generic and provide radium removal as a side benefit to treatment for other contaminants, whereas others are tailored to removal of radium. An issue to consider with all of the methods aside from aeration is the disposal of the removal medium. The reduction in radioactivity in the water is accompanied by a similar increase in the radioactivity in the medium. This medium may, therefore, be subject in some cases to regulations on transport and disposal of radioactive materials. It should not be assumed that these products can be disposed of at landfills intended for municipal waste. Smaller water supplies in particular face issues of the cost of treatment of the system itself, which can add significant costs to the water supplied to consumers. In these areas, it has become common to consider point-of-use technologies as an alternative to system treatment. The most common method by far is activated carbon filtration placed on the tap or at the point where the pipe enters a building. These systems can in fact be more cost effective than treatment at the source, again for smaller water systems. It is, however, at times less effective as a public health measure because the owners of buildings forget to replace the filters on a regular schedule, allowing the buildup of materials, including the radionuclide, the eventual breakthrough of the radionuclide, and the reduction of filter efficiency, as well as the possibility of microbial growth in the filters. Efficiencies for point-of-use and point-of-entry treatment technologies are similar to those found in municipal-scale treatment. One such system applied routinely – second only to granular activated carbon – is water softening, involving ion-exchange technology with a rejuvenating solution (the particular solution depends on the contaminant, which can be problematic if the home owner does not know which rejuvenator to use with which contaminant). For example, uranium removal requires a strongly basic solution, while radium requires an acid-rejuvenating solution. In addition, softeners rejuvenated with common salt (sodium chloride)

Sources, Risks, and Mitigation of Radioactivity in Water

could add a significant amount of sodium into the water. As in many cases of water treatment, therefore, there is a risk-risk trade-off to be considered

3.03.5 Geographic Areas of Special Concern As mentioned in the case of radon, highest concentrations of naturally occurring radionuclides are found in groundwater, especially in deep wells drilled into aquifers with elevated mineralization of radionuclides. Although these minerals are dissolved slowly, groundwater can be in contact with the surrounding rock for hundreds or thousands of years. Concentrations will, however, be highly variable in space, albeit less so in time, although seasonal changes in groundwater flow can affect the radionuclide concentration because it affects the contact time with rock, and radon can be affected by seasonal variations in pressure (in these cases, concentration can vary by a factor of 2–3 throughout the seasons). Concentrations are not necessarily correlated with surface geology, although radon in water does correlate well with the presence of aquifers within granitic rocks. Concentrations of radionuclides are affected by the composition of the underlying bedrock, and by the particular physical and chemical conditions in the aquifer. The result can be radionuclide concentrations that vary significantly in two wells located just a few meters apart, requiring careful consideration of the location and number of samples drawn from an aquifer in getting an accurate estimate of average concentration to which a population might be exposed. An additional complication with respect to radionuclides in the natural radioactive decay chains such as that of uranium (U-238) is the degree of secular equilibrium reached. In a decay chain, all radionuclides in a sample eventually reach the same level of activity (Bq) so long as there is no process removing any of the radionuclides from the sample. However, this equilibrium can take thousands or millions of years to achieve depending on the half-lives of the radionuclides (the half-life is the time required for half of a given radionuclide to decay). Since a long-lived radionuclide such as isotopes of radium or uranium leached from the rock and into the water may be withdrawn for use before equilibrium is reached, the residence time of water in an aquifer can be a strong determinant of total radioactivity present from the decay products of the original, leached radionuclide. This issue is not significant for radon, however, because it has a very short halflife of less than 4 days, well below the residence time in essentially any aquifer. Still, radon cannot be assumed to be in equilibrium with the radium in the water, since radon is an inert gas and can enter water from the surrounding rock above and beyond the radon produced by the decay of radium in the water. As a result, there can be wide variations in the relative concentrations of radionuclides in a given water sample (Figure 1). Even natural radionuclides can enter water through human activities that perturb the environment. This is particularly true of uranium mining and milling operations, which move these radionuclides from the subsurface to the surface, where they can contaminate surface waters at levels much higher than would be expected from natural processes. Areas that contain

65

such mining operations will, therefore, generally show waterborne radionuclides at elevated levels, especially isotopes of radium and uranium. The presence of nuclear facilities may increase the level of radionuclides in water supplies. This is due to the operation of the facility, which potentially releases fission products of uranium, tritium, and activated corrosion products (often metals irradiated by neutrons of the facility) through the cooling water, although at low concentrations; due to the generation of low-level waste discharged to landfills and subsequently leached to surface- and groundwater; and due to the radioactive products at the backend of the fuel cycle, including high-level radioactive waste stored in repositories in geological structures. For a more complete review, the reader is referred to UNSCEAR (2000). Fallout from nuclear weapons testing contributes to waterborne radionuclide concentrations today, despite a ban on atmospheric testing for several decades. The radionuclides of most interest in monitoring programs have been H-3 (tritium), C-14, Sr-90, and Cs-137. Since these radionuclides were released initially to the air, they tend to be found almost entirely in surface waters, and especially waters where the radionuclides settled in to the sediment and can be re-entrained during storms. Fallout radionuclides percolating into soils or sediments will tend to bind to grains in the soil and not reach groundwater supplies. These radionuclides are now ubiquitous, but the concentrations in water are highest near the points of testing. A potentially challenging feature of the geographic distribution of radionuclides in water is illustrated by radon. Drawing on the most recent large-scale assessment of the occurrence of radionuclides in US water supplies, Longtin (1990) divided the supplies into those serving 1000 or more people and those serving fewer than 1000 people. The national average concentration for the larger supplies was 8.9 Bq l1, while that for the smaller supplies was 28.9 pCi l1. The difference is largely due to the rural nature of populations served by small supplies, where groundwater is a more common source. This difference means that the percentage of supplies requiring mitigation, and the degree of mitigation required, are significantly higher for smaller supplies than larger ones. Since there is economy of scale in mitigation, and since smaller supplies tend to serve poorer populations, these data demonstrate that the costs associated with mitigation of radionuclides, as measured by increased water bills, are higher for the poorer populations served by small supplies.

3.03.6 Measuring Radioactivity in Water Methods for measuring radioactivity in water samples generally rely on detection of the radiations (for a good review of methods, see Knoll (2000)). For photon emitters, the sample may be placed on a scintillation counter (NaI is common) or GeLi system and the spectrum is obtained. Each radionuclide has a unique signature gamma or X-ray, and the spectrum from the detector can be used to identify the specific radionuclides by their signature peaks in the spectrum. The amount of the specific radionuclide in the sample is then proportional to the area under the peak. Such an approach requires

206Pb

(stable)

(22.6 y)

(5.01 d)

210Pb

(19.7 m) (26.9 m)

214Pb

210Bi

(138.4 d)

(1.6E-4 s)

214Bi

(3.04 m)

210Po

214Po

212Po

215Po

207TI

(4.77 m)

(3.05 m)

(36.1 m)

211Pb 208TI

200Pb

(stable)

212Pb

(10.6 h)

(stable)

207Pb

211Bi

(2.14 m)

(1.78 ms) 212Bi

(3E-7 s)

Beta decay

Alpha decay

219Rn

(3.96 s)

(1.01 h)

(0.15 s)

216Po

(55.6 s)

(3.823 d)

Radionuclide (half-life)

223Ra

(11.4 d)

224Ra

220Rn

218Po

227Th

(18.7 d)

(3.66 d)

222Rn

228Ra

(5.75 a)

226Ra

(1.6E3 a)

227Ac

(21.8 a)

220Ac

(24.1 d)

231Th

(1.06 d)

(6.15 h)

220Th

(1.91 a)

232Th

(1.4E10 a)

230Th

(7.5E4 a)

234Th

231Pa

(3.3E4 a)

234P

(7E8 a)

235U

235-Uranium decay series

(6.69 h)

234U

(2.45E5 a)

238U

(4.47E9 a)

232-Thorium decay series

Figure 1 The three primary natural radioactive decay chains found in water supplies. Reproduced from the public domain USGS website at http://gulfsci.usgs.gov/tampabay/data/2_biogeochemical_cycles/ radionuclides.html.

Tl

Pb

Bi

Po

Rn

Ra

Ac

Th

Pa

U

238-Uranium decay series

Sources, Risks, and Mitigation of Radioactivity in Water

a detector, a multichannel analyzer, and calibration samples of known activity to establish the efficiency of detection. The calibration samples are placed on the detection system both to ensure the peaks appear in anticipated channels of the multichannel analyzer, and to develop the ratio of peak area to activity. For beta emitters, the most common method is scintillation counting. The water is dissolved into, or at least mixed into, a scintillation cocktail in a vial. Radiations emerging from the radionuclides excite the molecules of the scintillation cocktail, causing cascades of photons. These photons are detected by a scintillation counter, with the number of pulses being related to the activity in the sample. The systems require use of quench curves to account for absorption of scintillations by materials present in the vials, such as turbidity. The method may also be applied to radon in water; in fact, because radon is an inert gas, scintillation counting is usually the preferred method of analysis. Detection of most alpha emitters is more complex due to sample preparation. Most of the methods involve concentration of the radionuclides through precipitation, often with a carrier agent such as barium or Fe(III) salts followed by solvent extraction. The resulting material can then be plated and the alpha emissions measured in a solid-state detector. In most cases, the extracted and plated sample is allowed to stand for a period of time to allow re-growth of the daughter products back into equilibrium, increasing the detection efficiency and accuracy while significantly improving the detection limit. Uranium may be measured through alpha spectrometry described above, but it is more common to measure it through fluorometry, and more recently by inductively coupled plasma (ICP) mass spectrometry. The former is either traditional fluorometry or a laser kinetic phosphorescence method. Neither of the methods can determine the specific isotopes of uranium, but do provide measurements of the total uranium present in the sample. This is not as significant a limitation as it would be for the isotopes of radium, since the health effects and regulatory limits for uranium are down primarily to the chemical toxicity for which separation into the contributions from different isotopes is not important. If for any reason it is important to distinguish between these isotopes, alpha spectroscopy or ICP mass spectrometry is required.

3.03.7 Conclusions Radioactivity is found essentially in any water sample, whether of groundwater or surface water. This is because the sources are numerous, not least of which is the primordial composition of the earth underlying or surrounding the body of water. Wellestablished procedures have been developed, however, to assess the concentrations of most radionuclides in water samples, to calculate the health risks, and to reduce this risk to acceptable levels through mitigation. In many cases, these mitigation methods may be adjuncts to measures that must be taken to reduce other water problems such as turbidity. From the perspective of public health, radon is by far the most significant contaminant of water supplies, especially those drawn from groundwater aquifers. The health risk of radon in water alone is larger than the sum of the risks from

67

the large majority of other waterborne contaminants. Therefore, there has been significant regulatory attention directed toward radon in the world of water science and regulation. While radioactivity is ubiquitous, there remain geographic areas known to be associated with higher levels of radioactivity in water. In regards to naturally occurring radionuclides, attention is directed primarily to groundwater withdrawn from aquifers surrounded by granitic rock, with slow water movement and hence large contact times. In regards to artificial radionuclides, or to artificial enhancement of natural radioactivity, attention is directed to uranium mines and mills; facilities involved in the nuclear fuel cycle; and to poorly managed landfills with low-level radioactive waste – especially where there is disposal of radionuclides from medicine or experiments. Despite the potential health risks posed by radionuclides in water, this is the area of water science in which more is known about the quantification and control of risks than almost any other contaminant. Through careful application of the principles discussed here, risks from waterborne radionuclides can be kept below acceptable levels.

References Annanma¨ki M (ed.) (2000) Treatment techniques for removing natural radionuclides from drinking water. Final Report of the TENAWA Project, STU K-A16 9. Helsinki: STUK. Clifford DA (1990) Removal of radium from drinking water. In: Cothern CR and Rebers PA (eds.) Radon, Radium and Uranium in Drinking Water, pp. 225--247. Chelsea, MI: Lewis. De Zuane J (1997) Handbook of Drinking Water Quality. New York, NY: Wiley. Health Canada (1995) Radiological characteristics. http://www.hc-sc.gc.ca/ewh-semt/ pubs/water-eau/radiological_characteristics/index-eng.php (accessed April 2010). ICRP (International Commission on Radiological Protection) (1996) Age-Dependent Doses to Members of the Public from Intake of Radionuclides: Part 5. Compilation of Ingestion and Inhalation Dose Coefficients, Annals of the ICRP, vol. 26(1–3), ICRP Publication 72. Oxford: Pergamon. ICRP (International Commission on Radiological Protection) (2007) Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Oxford: Pergamon. Knoll G (2000) Radiation Detection and Measurement, 3rd edn. New York, NY: Wiley. Longtin J (1990) Occurrence of radionuclides in drinking water, a national study. In: Cothern CR and Rebers PA (eds.) Radon, Radium and Uranium in Drinking Water, pp. 97--140. Chelsea, MI: Lewis. Milvy P and Cothern CR (1990) Scientific background for the development of regulations for radionuclides in drinking water. In: Cothern CR and Rebers PA (eds.) Radon, Radium and Uranium in Drinking Water, pp. 1--16. Chelsea, MI: Lewis. National Research Council (1999) Biological Effects of Ionizing Radiation VI: The Health Effects of Exposure to Indoor Radon. Washington, DC: National Academies Press. National Research Council (2005) Biological Effects of Ionizing Radiation VII. Washington, DC: National Academies Press. NRPB (National Radiological Protection Board) (1991) Committed Equivalent Organ Doses and Committed Effective Doses from Intakes of Radionuclides. Chilton, NRPB R-245. London: HMSO. UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation) (2000) Sources, Effects and Risks of Ionizing Radiation. New York, NY: United Nations. USEPA (US Environmental Protection Agency) (2009) Fact Sheet on Radon and Drinking Water. http://www.epa.gov/safewater/radon/qa1.html (accessed April 2010). Wiener J (2002) Precaution in a multi-risk world. In: Paustenbach (ed.) Human and Ecological Risk Assessment, pp. 1509–1532. New York, NY: Wiley. World Health Organization (2008) Guidelines for Drinking Water Quality, vol. 1, ch. 9. Geneva: WHO.

3.04 Emerging Contaminants K Ku¨mmerer, Leuphana University, Lu¨neburg, Germany & 2011 Elsevier B.V. All rights reserved.

3.04.1 3.04.2 3.04.3 3.04.4 3.04.5 3.04.6 3.04.6.1 3.04.6.2 3.04.6.2.1 3.04.6.2.2 3.04.6.3 3.04.7 3.04.8 3.04.9 3.04.10 3.04.11 3.04.12 3.04.13 3.04.14 3.04.15 3.04.15.1 3.04.15.2 3.04.15.3 3.04.16 3.04.17 3.04.18 References

Introduction General Aspects: What Are the Emerging Contaminants and Micro-Pollutants? Parent Compounds, Metabolites, and Transformation Products A High Diversity of Chemicals Is Present in the Aquatic Environment Sources and Fate Examples of Individual Groups Aryl Sulfonates Flame Retardants Organobromine compounds Organophosphorus compounds Pesticides Endocrine Disrupting Chemicals Anticorrosive Additives – BT and TT Gasoline Additives – Methyl tert-Butyl Ether Perfluorinated Surfactants – PFOS and PFOA Personal-Care Products Fragrances and Odorants Disinfectants UV Filters Pharmaceuticals Active Pharmaceutical Ingredients Illicit Drugs Metabolites Engineered Nanoparticles Artificial Sweeteners Cyanotoxins

3.04.1 Introduction The history of chemistry and the pharmaceutical sciences is an impressive success story. The products of the chemical and pharmaceutical industries are ubiquitous in everyday life. They help us to define the modern way of living. They contribute to our health and high living standards. The production of chemicals and pharmaceuticals, their usage, and application was associated over a long period with heavy pollution of the environment and serious health effects. During the second half of the last century, tremendous progress was made to prevent the pollution of environment and to reduce the impact of such pollution on health. Nowadays, proper and effective treatment and the prevention of emissions into air, water, and soil is in place in developed countries and will spread woldwide. However, it has also been learned since the end of the last century that products of the chemical and pharmaceutical industries themselves such as medicines, disinfectants, contrast media, personal care products, laundry detergents, surfactants, pesticides, dyes, paints, preservatives food additives, and personal care products, to name a few, also constitute a new type of environmental pollution and a possible health risk for the consumer.

69 69 70 71 72 73 73 74 74 74 75 75 75 76 77 77 77 78 78 78 78 80 81 81 82 82 83

Population growth and climate change will place great pressure on water resources in the future. Even now, several regions of this planet suffer surface water shortage as an everyday challenge, because water is necessary for agricultural, industrial, recreational, laundry, personal care, and drinking purposes. Therefore, the quality and quantity of water have to be carefully surveyed and managed. Artificial recharge can contribute to groundwater resources. However, quality control of the waters to be used must ensure that the groundwater is not contaminated by the water used for charging, so the recharge water must itself meet high standards. The presence of organic pollutants such as medicines, disinfectants, contrast media, personal care products, laundry detergents, surfactants, pesticides, dyes, paints, preservatives and food additives, and their metabolites and transformation products is of growing interest in this context. Chemicals, like many of the xenobiotic organic compounds, are of increasing concern in urban water management, because water supply, urban drainage, and wastewater-treatment systems were expressly designed originally to solve other problems (supply of potable water, flooding prevention, and sanitation). Thus, there is a need to understand, in an integrated manner, the sources, flow paths, fates, and effects of hazardous chemicals on both humans and

69

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Emerging Contaminants

ecosystems. In this chapter, a very brief overview is given of our present knowledge about the presence of emerging pollutants in the aquatic environment. With respect to the vast amount of literature available, only a rough overview for some groups of emerging contaminants will be given here for the sake of demonstration rather than claiming an exhaustive elaboration and full overview of the topic. Instead, some typical examples and results are presented to demonstrate general principles and important issues and the diversity of chemicals involved. Therefore, after some general considerations referring to the term ‘emerging contaminants’, some selected examples will be presented in the following. The purpose of this chapter is not, and cannot be, to review all the available results as the literature is vast. Instead, typical and illustrative examples and facts will be presented to demonstrate the underlying issues. The reader interested in more detailed data and findings will find help in the numerous books and reviews that have already been published. As for analysis, for example, only recently two extensive reviews were published (Richardson, 2009; Giger, 2009). Therefore, analytical issues are not addressed here. Instead, the interested reader is advised to seek help in these articles and the ones cited in this chapter. Phthalates, polychlorinated dioxins, polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons, the older pesticides such as the chlordienes, dichlorodiphenyltrichloroethane, and others, surfactants such as linear alkylsulfonates and nonylphenol and nonylphenol ethoxylates, their transformation products, and other compounds have long been known to be present in the aquatic environment. Hence, they will not be included here.

3.04.2 General Aspects: What Are the Emerging Contaminants and Micro-Pollutants? The presence of certain chemicals at the lower mg l1 level in the aquatic environment has become evident with the improvement of analytical techniques (Reemtsma and Jekel, 2006; Barcelo´ and Petrovic, 2008; Ku¨mmerer, 2008a). The broader availability of liquid chromatography–mass spectrometry (LC–MS) and LC–MS/MS, in particular, permits the detection of polar compounds such as most pharmaceuticals, metabolites, and transformation products that have not previously been amenable to analysis. Some will never be detected. This is one reason why these chemicals are often called emerging contaminants. However, there is neither a general definition nor a complete list of compounds available that are in general included by the term ‘emerging contaminant’. The environment is contaminated by myriads of ‘merging contaminants’, that is, chemicals present in the aquatic environment in the mg l1 range and below. Because of this concentration range, they are often called organic micropollutants too. They are released from urban, industrial, agricultural, and other anthropogenic activities. Many of these have been and are currently undetected. As most of the chemicals applied by consumers will end up in sewage, ‘emerging contaminants’ is a term that describes nowadays often the pollution of the aquatic environment. Within the groups of emerging contaminants and/or

micro-pollutants, chemicals with similar structures can be found. However, often groups of chemicals with very different structures and properties belong to the same category in terms of application and usage. Pharmaceuticals are often classified according to their purpose and biological activity. The same hold for other groups such as insecticides, biocides, dyers, plasticizers, antibiotics, analgesics, and anti-neoplastics. Classification according to chemical structure is often used within subgroups of chemicals, for example, within the group of antibiotics or the subgroups within the antibiotics such as b-lactams, cephalosporins, penicillins, or quinolones. Other classifications refer to the mode of action (MOA), for example, anti-metabolites or alkylating agents within the group of cytotoxics/anti-neoplastics. In the case of classification according to MOA, the structures of molecules within the same group can be very different and, hence, so can their environmental fates. Sometimes authors summarize a certain group of chemicals according to their chemical structure (e.g., brominated diphenyl ethers) and sometimes according to their use (e.g., pharmaceuticals and flame retardants) or both at the same time. As some chemicals with a certain chemical structure such as the nyphtylsulfonates are applied for different purposes, such chemicals are often referred to in terms of their chemical structure only. In general, there is no clear differentiation, and overlaps of different classification schemes are common. Depending on the class of compounds, the emerging contaminant is the chemical itself or it may only be the transformation products or metabolites (e.g., pesticides or some surfactants). It is the transformation products, for others such as pharmaceuticals it is the parent compounds and, since only recently, the transformation products too. Often authors used the term ‘emerging contaminant’ to emphasize the novelty of the detection of a certain chemical in the (aquatic) environment. However, how ‘first’ is first enough is judged differently by different authors and readers. Therefore, the expression ‘emerging contaminant’ is not only loosely defined and used. The chemicals included may change from one author to another and within time. Furthermore, generally, it does not mean that these compounds have only recently appeared in the environment; sometimes, it does not even mean that there have been no earlier reports on their presence, because in some cases older literature has not been searched for or cited. In some other cases, these substances may have been introduced into the environment long ago but had not been detected because they had not been searched for or their concentration is so low (mg l1 and below) that until recently they were undetectable. In cases where findings had been reported earlier but not noticed by the researches and the compounds are of interst now (again), the term ‘emerging contaminant’ is applied by some authors despite this earlier findings. Emerging contaminant is a more or less loosely defined subgroup of the micro-pollutants. The list given here reveals that the emerging contaminants are not a homogeneous group of chemicals. On the contrary, they are very different and they can be grouped according to chemical structure, properties, purpose of application, or effects, respectively. The composition of the types of compounds constituting the whole bunch of emerging contaminants undergoes subtle change with time, as compounds that have been named emerging contaminant years ago may not be in

Emerging Contaminants

the focus of the researches anymore after some time for several reasons. Instead, other groups of chemicals may be included. The presence of such pollutants in the aquatic environment, however, is one of the big challenges for a sustainable water future in any case (DFG, 2003; Schwarzenbach et al., 2006; Ledin and Patureau, 2008; Fatta-Kassinos et al., 2010). Summarizing, there is no clear definition of emerging pollutants. In contrast, the term ‘micro-pollutants’ refers clearly to a nonambiguous criterion, that is, to such compounds that are detected in the environment in the mg l1 range and below, independently of their chemical structure, usage, or MOA, whereas the term emerging contaminant seems to be most helpful in the press room, not in science. Therefore, the author recommends to abandon the term ‘emerging contaminant’ and to stick with ‘micro-pollutant’.

3.04.3 Parent Compounds, Metabolites, and Transformation Products Many pharmaceuticals undergo a structural change in the bodies of humans and animals. The results of such processes are metabolites. After their excretion and release into the environment, both parent compounds and metabolites can undergo structural changes by a variety of biotic and nonbiotic processes, including photolysis, hydrolysis, and biotransformation. Pharmaceuticals and other chemicals are often incompletely transformed, that is, they are not fully mineralized by organisms such as bacteria and fungi in the environment (Haiß and Ku¨mmerer, 2006; Gro¨ning et al., 2007; Trautwein et al., 2008) as well as by light and other nonbiotic chemical processes. Structural transformations of chemicals may also be a result of technical processes such as effluent treatment by oxidation and photolysis (Ravina et al.,

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2002; Zu¨hlke et al., 2004; Li et al., 2008a; Me´ndez-Arriaga et al., 2008). The resulting substances are often referred to as metabolites by authors. However, this is confusing as metabolism is linked to living organisms. Chemicals resulting from nonbiotic transformation in the environment should therefore be referred to as transformation products. When considering pharmaceuticals and other chemicals in the environment that are transformed by living organisms, it is advisable to be even more restrictive. One should only refer to those substances as metabolites that have been altered in their chemical structure within a target organism (La¨ngin et al., 2008; Figure 1). All other compounds should be referred to as transformation products. Their origin can be indicated in designation by adding the process of formation, for example, phototransformation product. Generally, such a structural change results in new chemical entities with new properties. Normally, it is assumed that metabolism and transformation of pharmaceuticals and other chemicals lead to decreased toxicity. In some cases, however, metabolism (e.g., in the case of pro-drugs) and transformation lead to more active compounds. The same has been found for phototransformation and other oxidizing processes. Nowadays, several European regulations require the inclusion of transformation products in environmental risk assessment and monitoring (e.g., Drinking Water Directive, 1998; European Commission, 2003). The exposure to transformation products can be relevant as has been indicated, for example, for pesticides in groundwater (Boxall et al., 2004; Kolpin et al., 1997, 2004; Hanke et al., 2007). In these studies, several pesticide metabolites (e.g., well known from the extensively applied pesticide metolachlor) were found in higher concentrations in groundwater than the parent compounds (see also the case of tolylfluanid described in this chapter).

Active pharmaceutical (parent compound)

Metabolites

Nonbiological metabolism (hydrolysis in the stomach) Human metabolism Microbial metabolism (liver, mucosa, etc.) (skin and gut)

Transformation products Biological transformation (organisms)

Nonbiological transformation (light, oxidation, hydrolysis, etc.)

Technical transformation (ozonolysis, photolysis, chlorination, etc.)

Humans

Sewage Water Soil Manure (air)

(Water) treatment

Figure 1 Metabolites and transformation products of pharmaceuticals. For other compounds only transformation products are of interest. From Ku¨mmerer K (ed.) (2008a) Pharmaceuticals in the Environment: Sources, Fate, Effects and Risks, 3rd edn. Berlin: Springer.

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3.04.4 A High Diversity of Chemicals Is Present in the Aquatic Environment In a study, more than 100 individual water samples from over 100 European rivers from 27 European Countries were analyzed for 35 selected compounds, comprising pharmaceuticals, pesticides, fluorinated surfactants, benzotriazoles (BTs), hormones, and endocrine disrupters. Around 40 laboratories participated in this sampling exercise. The compounds most frequently detected and at the highest concentration levels were BT, caffeine, carbamazepine, tolyltriazole (TT), and nonylphenoxyacetic acid (Loos et al., 2009a, 2009b). Only about 10% of the river water samples analyzed could be classified as very clean in terms of chemical pollution. Rodil et al. (2009), for example, described a method for the simultaneous determination of 53 multiclass compounds, which they call emerging organic pollutants (e.g., acidic herbicides, ultraviolet (UV) filters, insect repellents, organophosphorus flame retardants, bactericides, pharmaceuticals, and metabolites). The authors found 31 pollutants in wastewater with concentrations up to 10 mg l1 in the case of the active pharmaceutical ingredient (API) ibuprofen. In total, 13 compounds were detected in tap water with concentrations up to 0.13 mg l1 for tri(chloropropyl)phosphate. The five most frequently detected chemicals in surface water in a study performed in the USA were cholesterol (59%, natural sterol), metolachlor (53%, herbicide), cotinine (51%, nicotine metabolite), beta-sitosterol (37%, natural plant sterol), and 1,7-dimethylxanthine (27%, caffeine metabolite), whereas, in groundwater, the most frequently detected were tetrachloroethylene (24%, solvent), carbamazepine (20%, pharmaceutical), bisphenol A (BPA; 20%, plasticizer),

1,7-dimethylxanthine (16%, caffeine metabolite), and tri-(2chloroethyl)phosphate (12%, fire retardant). A median of four compounds was detected per site, indicating that chemicals generally occur in mixtures in the aquatic environment and are likely to originate from a variety of natural origin, animal and human uses, and waste sources. In another study, four wells down-gradient from a landfill were investigated for the presence of waste-indicator and pharmaceutical compounds in groundwater affected by landfill leachate. The compounds identified included detergent degradation products, plasticizers (ethanol-2-butoxy-phosphate and diethyl phthalate), BPA, triclosan, an antioxidant (5-methyl-1H-benzotriazole), fire-retardant compounds, and several pharmaceuticals and their metabolites (Buszka et al., 2009).

3.04.5 Sources and Fate Numerous studies have shown that a variety of organic compounds such as pharmaceuticals, steroids, surfactants, flame retardants, fragrances, plasticizers, and other chemicals often associated with wastewaters have been detected in the vicinity of municipal wastewater discharges and agricultural livestock facilities, as these are an important source for the introduction of chemicals into the aquatic environment. There they may undergo different distribution and transformation processes (Figure 2). More detailed information on the (incomplete) removal of some groups of emerging contaminants by different effluent treatment technologies was addressed by Barcelo´ and Petrovic (2008). The presence of the micro-pollutants in the aquatic environment demonstrates that technical emission treatment is

Municipal or industrial sewage discharge Volatilization to atmosphere

Photolysis Hydrolysis

Dilution and diffusion

Deposit

ion

Pa r ti

cl

e

p ns tra

por t rans ed t v l o s Biodegradation Dis and Sorption transformation onto sediments or t

Biocencentration Deposition and resuspension Deposition and accumulation

Figure 2 The fate of pollutants in the aquatic environment. From US Geological Survey, http://toxics.usgs.gov/regional/emc/transport_fate.html.

Emerging Contaminants

not sufficiently effective. The presence of micro-pollutants in water is currently leading to much research and development effort being directed toward advances in municipal wastewater treatment. The advanced treatment of effluents has been investigated using (photochemical) oxidation processes (e.g., Qiting and Xiheng, 1988; Zwiener and Frimmel, 2004; Ravina et al., 2002; Kiffmeyer, 2003; Ternes and Joss, 2006; Watkinson et al., 2007; Isidori et al., 2007; Putschew et al., 2007; Lee et al., 2007), filtration (Schro¨der, 2002; Drewes et al., 2002; Heberer and Feldmann, 2008), application of powdered activated charcoal (Metzger et al., 2005; Nowotny et al., 2007), and constructed wetlands (Matamoros and Bayona, 2006). Reviews are available describing the advantages and disadvantages of the different technologies (Schulte-Oehlmann et al., 2007; Jones et al., 2007; Wenzel et al., 2008; Ternes and Joss, 2006). However, the approach of effluent treatment has some limitations in principle and may not in the end be a sustainable solution. Some of these limitations may also apply for the treatment of solid waste and the chemical treatment of exhaust air, some not in the case of the application of biofilters for the treatment of exhausts:

• • • •

•

• • • • • •

Efficiency may depend strongly on the type of compound to be removed. None of the technologies can remove all of the compounds (Ravina et al., 2002; Schro¨der, 2002; Wenzel et al., 2008). Will the now so-called advanced treatment technology work for new compounds in the future? Reaction products of (photo)oxidation processes have been found themselves to possess mutagenic and toxic properties (Isidori et al., 2005, 2007; Lee et al., 2007; Wei-Hsiang and Young, 2008). Prolongation of the hydraulic retention time in sewage treatment plants (STPs) results in only a little improvement of the elimination rates. It may, however, cause high costs because of the necessity to enlarge the STPs. Resistance in bio-membrane reactors: Is the enrichment of antibiotics and resistant bacteria causing increasing resistance? (No information is available on this topic.) Resistant material will not fully be retained by membranes. Combined sewer overflow: no treatment of storm water. Sewage from leaking from drains is not treated since it soaks into the ground before it reaches the STP. The advanced treatment processes depend on a high energy input and a minimum water flow. Therefore, they are often not possible/affordable in less developed countries. Costs are not clear and whether they are affordable is not known. As for the costs, different authors present different data depending on the assumptions made. It is questionable whether the additional costs are acceptable (Jones et al., 2007).

In principle, the advanced treatment processes are not compatible with a sustainable development as they are end-of-thepipe technologies, not affordable and/or applicable in all countries, and costly for manufacturers and the public. Energy demand causes high emissions of CO2 (Jones et al., 2007) and other green house gases cause additional costs for consumers and companies, as well as for the general public in the future. (See, for example, the Kyoto-Protocol and the European

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Union Emission Trading System (EU ETS; EU, 2003). In January 2008, the European Commission proposed a number of changes to the scheme, including the inclusion of other greenhouse gases, such as nitrous oxide and perfluorocarbons. It is also under consideration whether to extend the EU ETS to other industries. Clark (2006) has described other drivers for green chemistry.) Ozonation, for example, can consume an equivalent amount of energy to that needed to run a municipal STP. Wenzel et al. (2008) investigated the advantages and disadvantages of advanced wastewater treatment for micropollutants using environmental life-cycle assessment (LCA) and a literature review of advanced treatment performance. The LCA evaluation involved sand filtration, ozonation, and membrane bioreactors, and assessed the effect of extending existing tertiary treatment with these technologies on a variety of micro-pollutants (heavy metals, endocrine disruptors, polycyclic aromatic hydrocarbons (PAH), phthalates, and detergents, flame retardants, and others). The authors assessed the environmental break-even point where the removal of micro-pollutants and reduction in (eco-)toxicity will outweigh the increased resource and energy consumption. It was found, in some of the scenarios considered, that more environmental impact may be induced than removed by the advanced treatment. Furthermore, advanced treatment of effluent and exhausts is not available and affordable worldwide. Therefore, other approaches are necessary. (Green and sustainable are sometimes used synonymously by authors; however, they are not synonymous. Green chemistry is focused on the product/ chemical/pharmaceutical itself, whereas sustainable includes all aspects of a product related to sustainability, e.g., the shareholders, the stakeholders, and the people applying and using the compounds when seeking for solutions that will work. (First International Conference on Sustainable Pharmacy, Osnabru¨ck, 24/25 April 2008).) It has been learned that the source of micro-pollutants is often not a focus source. These molecules end up in the environment not because of their improper use, but rather as a result of their proper use. Furthermore, one has to be aware that especially in the case of consumer products it is not only a single chemical compound that is involved. Most often, it is a complex mixture of compounds that constitute a product such as a shampoo, a medicine, a disinfectant, a pesticide, a cleaning agent, or facade paint. Often, it is not feasible to assess the substance flow of compounds that are ingredients of such products as not all of the ingredients have to be declared in terms of quantity. Some are not even declared at all. If the chemicals and pharmaceuticals, their metabolites, and transformation products are not eliminated during sewage treatment, they enter the aquatic environment and eventually reach drinking water. Going back to the beginning of the pipe, the products of chemical industries – the molecules themselves – come into focus. Therefore, it is reasonable to think about emission management directly related to the properties of the molecules themselves and to focus on the whole life cycle of products and substances. This is addressed by the concepts of green and sustainable chemistry (Anastas and Warner, 1998; Clark and Smith, 2005; Clark, 2006) and, more recently, by the concept of green and sustainable pharmacy (Ku¨mmerer, 2009a). Green and sustainable are sometimes used synonymously by

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Emerging Contaminants

authors. However, they are not synonymous. Green chemistry is focused on the product/chemical/pharmaceutical itself, whereas sustainable includes all aspects of a product related to sustainability, e.g., the shareholders, the stakeholders, and the people applying and using the compounds when looking for solutions that will work (Ku¨mmerer and Hempel, 2010). A key point is the benign-by-design concept (Daughton, 2003; Boethling et al., 2007; Ku¨mmerer, 2007, 2008b).

flame retardants, for example, for textiles. Synthetic materials, usually halocarbons, include organochlorine compounds such as PCBs, chlorendic acid derivates (most often dibutyl chlorendate and dimethyl chlorendate), and chlorinated paraffins. Nowadays, compounds such as organobromine compounds and organophosphates are also of interest.

3.04.6.2.1 Organobromine compounds

3.04.6 Examples of Individual Groups 3.04.6.1 Aryl Sulfonates Aryl sulfonates are used as precursors for sulfonated azo dyes, wetting agents, leveling agents, dispersants, optical brighteners, pesticides, ion-exchange resins, pharmaceuticals, and concrete plasticizers (Knepper et al., 1999; Reemtsma, 1999; Arslan-Alaton et al., 2010). Sulfonated azo dye formulations used in the tannery and textile industry are one major source of sulfonated aromatic amines in water. Dye production today takes place mainly in Asian countries such as China and India. It is estimated that during the dyeing process, up to 10–30% of sulfonated dyes end up in the exhausted dye bath (ArslanAlaton et al., 2010). Hence, aromatic sulfonates can be present at high concentrations in industrial wastewater in these countries. Recently, the discharge into receiving water bodies has led to serious environmental pollution problems and the production of aryl sulfonates, such as H-acid, has been restricted in some countries. Sulfonates are strong organic acids; hence, they are anions over a wide range of pH and cannot be effectively trapped by conventional adsorbents such as RP 18 materials. In the aquatic environment, they do not significantly sorb on biosludge or sediments (Zerbinati et al., 1997). Sulfonated aromatic amines can be formed during the reduction of sulfonated azo dyes under anaerobic/anoxic conditions (Knepper et al., 1999). These are potentially toxic and/ or carcinogenic (Oh et al., 1997). The fate of aryl sulfonates and their degradation products in the aquatic ecosystem and in biological treatment facilities is still not very clear, since until now only limited attention has been paid to their occurrence and degradability in the natural environment and in engineered systems (Jandera et al., 2001). Laboratory testing of organic sulfonates has revealed them to be nonbiodegradable (Su¨tterlin et al., 2008). Depending on their molecular structure or other physical–chemical properties, aryl sulfonates may be biodegradable in engineered biological treatment systems at very slow rates and after acclimation (O’Neill et al., 1999; Rieger et al., 2002). Tan et al. (2005) reported concentrations in the ng l1–mg l1 levels in European rivers and they are detectable at ng l1–mg l1 levels in surface waters. However, their concentrations in industrial wastewater-treatment plants (WWTPs) can be in the mg l1–mg l1 range (Tan et al., 2005).

3.04.6.2 Flame Retardants Minerals such as asbestos, compounds such as aluminum hydroxide, magnesium hydroxide, antimony trioxide, various hydrates, red phosphorus, and boron compounds, mostly borates are applied as flame retardants. In addition to these inorganic compounds, organic compounds are also used as

Organobromine compounds such as polybrominated diphenyl ethers (PBDEs) are one of the important groups of organic chemicals used as flame retardants. PBDEs are used as flame retardants in polymeric materials such as furnishing foam, rigid plastics, and textiles. PBDEs are technical, that is, loosely defined mixtures. Many of these chemicals are considered harmful, having been linked to liver, thyroid, reproductive/ developmental, and neurological effects. The EU has included the PBDEs on a list of chemicals to be phased out of use in electrical and electronic equipment (e.g., personal computers and mobile phones). The main source of the organobromine compounds in the aquatic environment is textiles. The compounds are washed out during the washing cycles. The manufacture of textiles may be another important source for their introduction into the environment. PBDEs are included in Annex X of the Water Framework Directive (EU, 2000) and pentabromodiphenyl ether (pentaBDE) is a priority hazardous substance. The EU risk assessment of tetraBDE, sometimes also called tetrabromobisphenol A (TBBPA), suggests the classification ‘‘Very toxic to aquatic organisms, may cause long-term adverse effects in the aquatic environment.’’ In the USA, decaBDE manufacturers have successfully petitioned for an exemption for decaBDE from this ban in the USA (Illinois EPA, 2007). A different chemical structural class of flame retradand is hexabromocyclododecane (HBCD). The EU risk assessment of HBCD (ECB, 2007) recommends that HBCD be considered a persistent, bio-accumulative, and toxic (PBT) substance, although there is no official classification yet. Amounts used may be different in different countries. Important subgroups of PBDEs are pentaBDE, octaBDE, and decaBDE. tetraBDE, sometimes also called TBBPA, has the biggest share (Thuresson, 2006) in most countries. Over 40% of the use of decaBDE occurs in North America. The US has historically led the world production of these chemicals (50% of the total global demand in 2001). High pentaBDE, also called tetrabromobisphenol (A), PBDE (A), levels in the US marine environment reflect that over 90% of the pentaBDE global production has been utilized in the USA (Yogui and Sericano, 2009). PBDEs are ubiquitous in all compartments, including water, sediment, and biota. Contamination is higher in urbanized regions. The organobromine compounds are lipophilic, bioaccumulative (log Kow often six and above), and adsorptive. Therefore, only a minor proportion will pass STPs and reach the surface water. It was found that the mean concentrations of decaBDE, pentaBDE, HBCD, and TBBPA in STP sludge were 0.12, 0.11, 0.045, and 0.040 mg kg1 dry weight (d.w.), respectively (Nylund et al., 2002). This confirms the significance of elimination by sorption. Another emerging brominated flame retardant, marketed as a replacement for decaBDE, is decabromo-diphenyl ethane

Emerging Contaminants

(deBDethane). DeBDethane has a chemical structure similar to decaBDE and would accordingly have similar properties concerning bioaccumulation and persistence. Recently, a survey was conducted on decaBDE and deBDethane in sludge from 42 WWTPs in 12 different countries around the world (Ricklund et al., 2008a, 2008b). The authors found decaBDE in concentrations from 0.003 mg kg1 dry matter (d.m.) to a maximum of 19 mg kg1d.m. in all samples. DeBDethane was present in all samples but two, in levels from 0.001 to 0.22 mg kg1 d.m. The highest deBDethane/decaBDE ratios were found in Germany and neighboring countries, whereas the lowest ratios were found in the USA and the UK. The data reflect the use patterns of the substances, that is, the known high imports of deBDethane into Germany and the largest market demands for decaBDE in the USA and the UK. Chemically applied nonhalogen decaBDE substitutes are available for natural cellulose fibers such as cotton, wool, rayon, and linen. They include:

• • •

dimethylphosphono (N-methylol)propionamide; phosphonic acids such as (3-{[hydroxymethyl]amino}3oxopropyl)-dimethyl ester; and tetrakis(hydroxymethyl)phosphonium urea ammonium salt.

The future will show whether these will be part of the nextgeneration emerging contaminants.

3.04.6.2.2 Organophosphorus compounds Another important group of flame retardants already heavily applied is organophosphates in the form of halogenated phosphorus compounds such as phosphates and phosphonium salts, for example, tri-o-cresyl phosphate, tris(2,3dibromopropyl)phosphate (TRIS), bis(2,3-dibromopropyl)phosphate, tris(1-aziridinyl)-phosphine oxide, tris(2-chloro-1methylethyl)phosphate (TCPP), tris(2-chloroethyl)phosphate (TCEP), and tris(2-chloro-1-chloromethyl-ethyl)phosphate (TDCP). The main source of these organophosphates is probably construction materials (Bester et al., 2009). The phosphates are slightly less polar (log Kow 4 and below) than the organobromine compounds. Organophosphates have been found in the effluents of WWTPs (Marklund et al., 2005). Potential risks to groundwater have been reported for tetrakis(hydroxymethyl) phosphonium chloride (Illinois EPA, 2007). The concentrations of TCPP and related compounds in five WWTPs in the Rhine/Ruhr region ranged from a few hundred to 410 000 ng l1, depending on the respective activities within the catchment area (Bester et al., 2009). Because these compounds are not usually eliminated in wastewater treatment, the effluent concentrations are identical to the inflow concentrations (Marklund et al., 2005; Bester, 2007). Several wastewater-related organic micro-pollutants such as chlorinated and nonchlorinated organophosphates have been detected in a spring. TCPP was found in concentrations up to 0.13 mg l1 in tap water in the United States (Rodil et al., 2009).

3.04.6.3 Pesticides Some pesticides have been detectable in the aquatic environment for decades. Hence, they cannot be called emerging

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pollutants. However, transformation products formed from the applied parent compounds in the environment (e.g., soil) came into focus in a broader range only recently. Nowadays, data on the fate of the pesticide itself and the possible products of biotransformation (often called ‘metabolites’) in soil and sediments have to be presented and assessed by the applicant before pesticides are authorized. Hence, these compounds are the subject of a great deal of study. The following is a typical example. The transformation product chloridazon-methyldesphenyl is formed from chloridazon (5-amino-4-chloro-2phenylpyridazin-3(2H)-one) in soil by biotransformation (Roberts and Hutson, 2002), which is further transformed to chloridazon-methyl-desphenyl. The transformation product chloridazon-methyl-desphenyl was detected at concentrations up to several hundreds of nanograms per liter in many of the groundwater samples investigated. Weber et al. (2007) detected this compound in surface-ground-, and drinking water in Germany. The transformation product was often found in higher concentrations than the parent compound. Knowledge on the fate of the transformations product, for example, in the aquifer or drinking-water treatment is little. Transformation products may give rise to further concerns. This can be illustrated by the case of the pesticide tolylfluanid. Application and microbial degradation of the fungicide tolylfluanid in soil result in a transformation product, N,N-dimethylsulfamide (DMS). DMS was found in groundwater and surface water with typical concentrations in the range of 100–1000 and 50–90 ng l1, respectively. DMS itself is not of toxicological concern. However, it exhibits high mobility in soils and water. Hence, it can enter the drinking-water-treatment process. Laboratory-scale and field investigations concerning its fate during drinking-water treatment revealed that DMS cannot be removed via riverbank filtration, activated carbon filtration, flocculation, and oxidation or disinfection procedures based on hydrogen peroxide, potassium permanganate, chlorine dioxide, or UV irradiation (Schmidt and Brauch, 2008). Even nanofiltration does not provide adequate removal efficiency. Disinfection with hypochlorous acid converts DMS to so far unknown degradation products but not to N-nitrosodimethylamine (NDMA) or 1,1-dimethylhydrazine. However, most important, during ozonation about 30–50% of DMS is converted into the carcinogenic NDMA (Figure 3). Wei-Hsiang and Young (2008) have described the NDMA formation during chlorination and chloramination of aqueous diuron solutions. NDMA is biodegradable and can be at least partially removed by subsequent biologically active drinking-water-treatment steps, including sand or activated carbon filtration.

3.04.7 Endocrine Disrupting Chemicals Endocrine-disrupting chemicals (EDCs) are chemicals of natural or synthetic origin that may interfere with the endocrine system. They may have estrogenic, anti-estrogenic, androgenic, anti-androgenic, and other hormone-like effects. For example, the egg yolk protein vitellogenin, only produced under normal circumstances in mature female fish (Sumpter and Jobling, 1995) was found in male fish that have been exposed to such substances. The same phenomenon has been observed downstream in rivers receiving STP effluent (Purdom et al., 1994;

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O

O S

N

N

S

Cl F

O

O O

S H2N

N

N

N

Cl

Tolylfluanid

N,N -Dimethylsuklfamid (DMS)

NDMA

Figure 3 Formation of NDMA by ozonation of the transformation product (DMS) of the pesticide tolyfluanid by ozonation in drinking-water treatment.

Vethaak et al., 2006; Sumpter et al., 2006). Recently, an aquatic predicted no-effect concentration (PNEC) for ethinylestradiol (EE2) has been derived based on a large number of studies on the effects of EE2 on aquatic organisms. Caldwell et al. (2008) recommended the use of a PNEC of 0.35 ng EE2 l1 in order to adequately protect organisms in surface waters. Evidence that environmentally relevant concentrations of estrogens in surface waters can impact the sustainability of wild fish populations has been presented in the study by Kidd et al. (2007). In the last 15 years, the discovery of the estrogenic nature of STP effluents (Purdom et al., 1994) has resulted in a vast public concern and a huge amount of scientific literature in order to better understand and identify EDCs, their occurrence, fate, and their biological effects on wildlife. The naturally occurring estrogenic hormones (i.e., 17b-estradiol (E2) and estrone (E1)) have been studied extensively (Desbrow et al., 1998; Routledge et al., 1998) in the 1990s. Together with EE2, the synthetic active ingredient in many contraceptive pills, they were identified as the main contributors to estrogenicity of effluents of STPs. As fish feminization was one of the most striking of their effects, they were also referred to as gender benders. In the years that followed, an increasing number of chemicals were classified as EDCs. Compounds or compound classes that had been detected in the environment in the last two decades such as BPA, phthalates, alkylphenols and their ethoxylates, some pesticides, dioxins and PCBs, tributyltin compounds, and others were shown to be endocrine active (e.g., Jobling et al., 2006; Blair et al., 2000; Silva et al., 2002). Brominated organic flame retardants are also on the list now. In the last decade, a great deal of research has been dedicated to the technological improvement of the various stages of sewage treatment in terms of removal efficiency of micropollutants, and especially estrogenic chemicals, in municipal STPs. Removal of EE2 in particular is often nowhere near complete, as demonstrated by the findings of Kanda and Churchley (2008), Esperanza et al. (2007), Vethaak et al. (2006), and Lamoree et al. (2009).

TT is a mixture of 4- and 5-methyl isomers. The BT and the TT possess good water solubility, low vapor pressure, and low octanol water distribution coefficients (log Kow 1.23 and 1.89, respectively). BT is toxic and not biodegradable (Hem et al., 2003), and it can be degraded by UV irradiation at pH values below 7 in laboratory testing. Approximately 65% reduction in the BT concentration was achieved at a dose of 320 mW s cm2, and almost 90% reduction was achieved at 1070 mW s cm2 (Hem et al., 2003). BT is transformed into several compounds instead of full mineralization. Aniline and phenazine were identified as main compounds. The authors report that the transformation products show toxic effects, but that they are not as toxic as BT itself; hence, UV irradiation brings about a general decrease in toxicity (Hem et al., 2003). However, aniline itself is a confirmed animal carcinogen with unknown relevance to humans (IARC, 1987). The overall summary evaluation of carcinogenic risk to humans refers to group 3: the agent is not classifiable as to its carcinogenicity to humans (HSDB, 2009). Weiss et al. (2006) noted 2.1 mg l1 of BT and 13 mg l1 of TTs in untreated municipal wastewater. BT and TT were found in the lower mg l1 range in the samples of primary and secondary effluents STPs (Voutsa et al., 2006; Weiss et al., 2006). The elimination rate of BT in WWTP is only 30–40% (Weiss et al., 2006) and lower for TTs. Accordingly, these compounds are found in receiving waters. Concentrations of TT in receiving rivers were lower than 80 mg l1. In an EU-wide reconnaissance of the occurrence of polar organic persistent pollutants in European river waters, more than 100 individual water samples from over 100 European rivers from 27 European countries were analyzed for 35 selected compounds, including BTs among other substances. BT and TT were among the most frequently detected compounds and also had the highest concentration levels (Loos et al., 2009b). Because of extensive de-icing activities, BT was found in the groundwater below de-icing platforms at airports (Cancilla et al., 2003a, 2003b) and in the subsurface waters at airports at concentrations up to 126 mg l1 for BT and 198 mg l1 for total TT.

3.04.8 Anticorrosive Additives – BT and TT 3.04.9 Gasoline Additives – Methyl tert-Butyl Ether BT and TT are widely used as anticorrosive additives. They are also components of cooling and hydraulic fluids, antifreezing products, aircraft de-icer, and anti-icing fluid. The main input of BT into the aquatic environment stems from their use as dishwasher detergent additives where they are used for silver protection. From there, they are discharged in municipal wastewaters (Ort et al., 2005).

Methyl tert-butyl ether (MTBE) is a common gasoline additive often found in drinking water. On account of its physicochemical properties, it can contaminate large water volumes. It is to be phased out. The MTBE situation in the USA differs significantly from the one in Europe where the concentrations measured in river water and drinking water are approximately

Emerging Contaminants

two to three orders of magnitude lower than the USA, where MTBE was detected first. This example demonstrates the impact of use patterns on the presence of contaminants. The detection limits of analytical instruments and the volumes of samples used and the location of sampling determine when and where a specific compound or a group of compounds ‘emerges’ as a contaminant. MTBE concentrations in German river water, for example, show a tendency toward increasing concentrations since 1999 (Achten et al., 2002). It is not clear when the phasing out of these compounds will result in decreasing concentrations in drinking water because the residence time of contaminants in groundwater is often not known. Kolb and Pu¨ttmann analyzed 83 finished drinkingwater samples from 50 cities in Germany for MTBE. The detection frequency was 46% at a detection limit of 10 ng l1. The concentrations ranged from 17 to 712 ng l1 (100– 200 ng l1 range in rivers for example). MTBE was detected at concentrations lower than 100 ng l1 in bank-filtered drinking water from the rivers Rhine and Main and at 43–110 ng l1 in drinking water (Achten et al., 2002). All the MTBE concentrations measured were below the proposed limit values for drinking water (Achten et al., 2002). The occurrence of MTBE and gasoline hydrocarbons was examined in three types of studies of groundwater in the USA conducted by Moran et al. (2005). The detection frequency of MTBE was highest on monitoring wells located in urban areas and in public supply wells. Only 13 groundwater samples from all study types, or 0.3%, had concentrations of MTBE that exceeded the lower limit of the US EPA’s Drinking-Water Advisory Board. The probability of detecting MTBE in groundwater was strongly associated with population density, use of MTBE in gasoline, and recharge. Groundwater in areas with high population density, in areas where MTBE is used as a gasoline oxygenate, and in areas with high recharge rates had a greater probability of the presence of MTBE. In addition, groundwater from public supply wells and shallow groundwater underlying urban land-use areas had a greater probability of MTBE occurrence than groundwater from domestic wells and groundwater underlying rural land-use areas. In a review on the MTBE production, use, properties, and its behavior in the environment and occurrence in groundwater, surface water, drinking water, and wastewater, it was concluded that the conventional methods to remove volatile organic compounds from drinking water – air stripping and adsorption on granular active carbon – are not effective in the case of MTBE with concentrations of 100 mg l1 or more. Peerreviewed laboratory data on the application of nine different advanced oxidation processes (AOPs) to the degradation and mineralization of MTBE in water revealed that the most promising seem to be UV/H2O2 and O3/H2O2 processes, from both the technical and economic point of view (Siminiceanu, 2007). However, data on possibly formed transformation products were not given.

3.04.10 Perfluorinated Surfactants – PFOS and PFOA Perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) are perfluorinated surfactants used to produce polymers and telomers whose carbon chains can be of various

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lengths. They are also applied in the electroplating industry to prevent volatilization of chemicals from the electroplating bath. PFOA and PFOS are discussed as end products of many fluorochemical compounds in the natural environment (Nakayama et al., 2005). In a study, more than 100 individual water samples from over 100 European rivers from 27 European countries were analyzed for 35 selected compounds, comprising pharmaceuticals, pesticides, PFOS, PFOA, BTs, hormones, and endocrine disrupters. The rivers responsible for the major aqueous emissions of PFOS and PFOA from the European continent could be identified (Loos et al., 2008). Nakayama et al. (2005) gave a review on the distribution of PFOA and PFOS in Japan and their toxicities. PFOA and PFOS had a low order of toxicity in an acute toxicity study in rodents; however, they exhibited versatile toxicities in primates. Both chemicals are carcinogenic in rodents, causing reproductive toxicity, neurotoxicity, and hepatotoxicity (Nakayama et al., 2005). Nakayama et al. (2005) reported an epidemiological study conducted by a manufacturer that revealed an increase in prostate cancer mortality among workers exposed to PFOA. Another study conducted by the same manufacturer showed an increase in bladder cancer mortality among workers exposed to PFOS. In addition, peroxisome proliferation and calcium channel modulation are demonstrated effects. There are large interspecies differences in toxicokinetics. Perfluorinated surfactants (e.g., PFOS and PFOA) have shown different potentials for reproduction toxicity and carcinogenity in animal experiments as well as partly long half-lives in humans (Guruge et al., 2006; Zhang et al., 2010). They possess compound-dependent persistence in the environment. As they are nonpolar compounds, they tend to bioaccumulate in animals and humans (Houde et al., 2006). Accordingly, Nakayama et al. reported that the concentrations of PFOA and PFOS in the sera of Japanese people may reach 57.7 mg l1. Many studies in recent years have reported the ubiquitous distribution of this group of perfluorinated substances, especially PFOS and PFOA in surface and drinking waters (Skutlarek et al., 2006; Hoelzer et al., 2008). When measurements were made on samples from the Rhine, Ruhr, Moehne, and some of their tributaries, the Rhine-Herne Canal and the Wesel-Datteln Canal, the concentrations (sum of seven components usually detected) measured in the river Rhine and its main tributaries (at the confluences) were below 100 ng l1. The highest concentration (94 ng l1) was detected in the river Ruhr (tributary of the Rhine). The pattern here was different, PFOA was the major component in contrast to the other tributaries and the river Rhine. This may indicate a specific source. Remarkably, high concentrations of PS were found in the upper catchment of the river Ruhr (up to 446 ng l1) and the river Moehne (tributary of the Ruhr, up to 4385 ng l1). Illegal waste dumping was discussed as a possible source. Dilution effects were held responsible for decreasing downstream concentrations of PS in surface waters of the rivers Moehne and Ruhr. C-7–C-11 perfluorinated carboxylates and PFOS have been analyzed in selected stretches of the river Po and its major tributaries. Concentrations of about 1.3 mg l1 of PFOA were detected in the Tanaro River close to the city Alessandria. Below this tributary, levels between 60 and 337 ng l1 were measured in the Po River on several occasions. A mass load of

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Emerging Contaminants

about 2.6 tons per year is discharged into the Adriatic Sea according to these data. PFOS concentration levels in the Po River at Ferrara were around 10 ng l1 (Loos et al., 2008). Concentrations of 0.4–123 ng l1 for PFOS and 4.2– 2600 ng l1 for PFOA were measured in the Yodo River basin (Japan). The highest concentrations were found in effluents of some STPs and tributary streams. The PFOA concentration was often higher than that of PFOS, but their profiles in the basin were somewhat similar. Load estimates revealed that the main sources of the contaminants in Katsura River were three STP effluents, whereas the sources in Uji River could be from Lake Biwa. It is suggested that wastewaters other than domestic wastewater contributed significant loads of PFOS and PFOA in highly contaminated STP effluents (Lien et al., 2008). In another study from Japan, PFOA and PFOS concentrations in surface water were in the ranges 0.1–67 000 and 0.1– 526 ng l1, respectively. Although the origin of PFOS in surface water in Japan remains unknown, PFOA present in surface water is very likely to have been released from a few industries according to the authors. In the study performed for the river Ruhr area, the major component PFOA was determined in many drinking-water samples as well as in the surface water of the same area. There the water supplies are mainly based on bank filtration and artificial recharge. Maximum concentrations in drinking-water samples were 598 ng l1 (major component PFOA: 519 ng l1).

3.04.11 Personal-Care Products Personal care involves products as diverse as deodorant, eye liner, facial tissue, lipstick, lotion, makeup, mouthwash, pomade, perfumes, personal lubricant, shampoo, shaving cream, skin cream, and toothpaste, to give a few examples. Some personal-care products such as shampoos and washing lotions can include up to 10–20 different ingredients such as surfactants, preservatives, dyes, fragrances and odorants, and others. During or after their use, a more or less big share of it is washed off from the skin or hair into sewage.

being aware of it, humans are also affected by human infochemicals (Bhutta, 2007). Every substance which can be smelled by the human nose can be used as a scent. It is assumed that humans can distinguish up to 10 000 different scents. Of these 10 000 compounds, more than 2100 substances representing 22 chemical groups are listed as scents in the Research Institute for Fragrance Materials (RIFM) database (Salvito et al., 2002; Salvito et al., 2004). As different as they are with regard to their chemical structure, they also differ in their physical and chemical properties such as vapor pressure, water solubility, and partition coefficient between octanol and water (log Pow). Fragrances such as the musk fragrances HHCB (galaxolide), AHTN (tonalide), OTNE (tso-E-Super), and others are used in washing processes, especially in softeners, in cosmetics, and in perfumery (Reiner and Kannan, 2006). According to their use, most of these compounds end up in wastewater. The musk fragrances are often lipophilic, as they are tailored to sorb onto fabric. Hence, they can bioaccumulate (Schmid et al., 2007). Some of the polycyclic musks are under discussion, because they are assumed to act as endocrine disruptors (Seinen et al., 1999; Bitsch et al., 2001). Influent concentrations in STPs are in the lower mg l1 range (Simonich et al., 2000, 2002; Bester, 2005). Effluent concentrations are only little lower indicating little elimination on sewage treatment. The elimination of HHCB and AHTN is mainly due to sorption. Because of their persistence in the aquatic environment, HHCB and AHTN have been used as markers for wastewater discharge into surface waters. Other mechanisms might also be relevant in the case of OTNE (Ternes and Joss, 2006; Andresen et al., 2007). Other fragrances such as musk xylene and metabolites (amines) as well as the musk ketone are found in the range 10–100 ng l1 in the inflow of the WWTPs in European countries (Bester et al., 2009). Newer fragrances such as the macrocyclic musks (e.g., habanolide, cyclopentadecanolide, and ethylenebrassylate) have not been detected in wastewater yet – this may be because of low usage, low dosage, or ready degradability (Gautschi et al., 2001). Terpenoid chemicals such as linalool are also used as fragrances and scents. Knowledge on their significance is still scarce, especially as there are natural source of their origin and as they may act as infochemicals (Bolek and Ku¨mmerer, 2010).

3.04.12 Fragrances and Odorants Humans depend very much on visual and acoustic stimuli to get along in their everyday life, whereas the role of scents is considered to be of secondary importance. For fish, insects, and other organisms, however, they are much more important. As they transport information for living organisms, such chemicals are also called infochemicals (Klaschka, 2008). Today, we distinguish between several classes of different infochemicals. Pheromones transfer information in which the sender and the receiver are both from the same species, whereas allelochemicals contain interspecific information. Allelochemicals are further divided into allomones (only the sender takes advantage of the information), kairomones (only the receiver takes advantage of the information), and synomones (sender and receiver take advantage of the information) (Dicke and Sabelis, 1988). The relationship of the different infochemicals can be seen in Figure 3. Although not

3.04.13 Disinfectants Compounds that kill micro-organisms (bacteriocides) or at least prevent their growth (bactriostatics) are employed to meet hygienic standards in medicine and food processing as well as for the preservation of certain chemical products such as paints or glues (Russell et al., 1992). Some of these compounds such as alcohols (e.g., ethanol and isopropyl alcohol) are readily biodegradable preservatives. Others such as trichlosan form stable transformation products (methyltrichlosan). Others are less biodegradable and they are not fully removed in sewage treatment, including some quaternary ammonium compounds (QACs, e.g., benzalkonium chloride). QACs are cationic microbiocidal substances. They are used as surfactants, anti-electrostatics, and phase-transfer catalysts. They are also important ingredients in disinfectants. They are

Emerging Contaminants

surface active compounds consisting of a hydrophobic alkyl chain and a hydrophilic group carrying a positively charged quaternary nitrogen atom. QACs are emitted via effluents from hospitals, households, and industries and finally end up in municipal sewage where they can reach surface water (Ku¨mmerer et al., 1997; Martı´nez-Carballo et al., 2007a; Su¨tterlin et al., 2008). Because of their positive charge, QACs adsorb strongly to the negatively charged surfaces of sludge, soil, and sediments (Ferrer and Furlong, 2002; Sun et al., 2003). Their widespread use and sorption behavior imply that they are expected to be present in many environmental compartments (Martı´nez-Carballo et al., 2007b). Only recently, it has been found that QACs are weakly mutagenic (Ferk et al., 2007).

3.04.14 UV Filters Typical compounds used in sun screens are benzophenone (BP), benzhydrol, 4-hydroxybenzophenone, 2-hydroxy-4methoxybenzophenone (HMB), 2,4-dihydroxybenzophenone (DHB), 2,20 -dihydroxy-4-methoxybenzophenone, and 2,3,4trihydroxylbenzophenone. Some of these compounds have endocrine effects (Schlumpf et al., 2008). UV filters applied in sun screens are directly emitted into surface water after application when people go bathing and swimming. Accordingly, they have been detected in water samples (Giokas et al., 2004, 2005). Analysis of wastewater in Spain revealed the systematic presence of HMB (BP 3) and DHB (BP 1) in raw samples with maximum concentrations close to 500 and 250 ng l1 (Negreira et al., 2009). Analysis of seven BP-type UV filters in water in Korea (Jeon et al., 2006) revealed concentrations of 500–18 380 ng kg1 in soil samples of and of 27– 204 ng l1 in water samples.

3.04.15 Pharmaceuticals 3.04.15.1 Active Pharmaceutical Ingredients The presence of pharmaceuticals for human use in the environment has been a topic for several years now. Though the study of pharmaceuticals in the environment is still a fairly recent, a vast amount of literature has already been published, making it impossible to cover all topics and issues in this chapter.

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Pharmaceuticals and disinfectants can be classified according to their purpose and biological activity (e.g., antibiotics, analgesics, anti-neoplastics, anti-inflammatory substances, antibiotics, antihistamines, X-ray contrast media, and surface disinfectants). The classification of small molecule APIs by their chemical structure is used mainly for the active substances within subgroups of medicines, for example, within the group of antibiotics, or subgroups within the antibiotics such as b-lactams, cephalosporins, penicillins, or quinolones. In this case, one might expect that the compounds could be treated as groups with respect to chemical behavior. However, even small changes in the chemical structure may have a significant impact on solubility and polarity as well as other properties that govern their environmental fate to some extent (Cunningham 2008, Figure 4). Other classifications refer to the MOA, for example, antimetabolites or alkylating agents within the group of cytotoxics/anti-neoplastics. In the case of classification according to MOA, chemical structures of molecules within the same group can be very different and, hence, their environmental fate can also differ. Therefore, these compounds cannot be treated as a group with respect to environmental issues. Compared with most bulk chemicals, pharmaceutically active compounds are often complex molecules with specific properties, for example, dependence of the octanol–water partition coefficient (Kow) on pH (Cunningham, 2008; Ku¨mmerer, 2009a). Some medicines contain molecules based on protein (biopharmaceuticals). Biopharmaceuticals may be defined as medical drugs produced using biotechnology by means other than direct extraction from a native (i.e., nonengineered) biological source. Examples include proteins (including antibodies) and nucleic acids. The first and best-known example was recombinant human insulin. Biopharmaceuticals are not typically regarded as biopharmaceuticals by the industry. Probably, not all naturally occurring compounds that are used as drugs are biopharmaceuticals. For example, estrogen is not regarded as a biopharmaceutical. The environmental relevance of biopharmaceuticals is not yet clear and they are not the focus of environmental research and risk management. One view is that they are not relevant because they are closely related to natural products and are therefore expected to be quickly biodegraded or are denatured, that is, inactivated in the environment. The other view is that naturally occurring

HN N

N OH

F O

O

Figure 4 Zwitterionic character of ciprofloxacin. Depending on pH additional different chemical species can be formed by internal protonation, i.e., the shift of protons between the basic amino functions and acidic carboxyl groups. Calculator Plugins were used for structure property prediction and calculation, Marvin 4.1.1, 2006, ChemAxon (http://www.chemaxon.com).

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Emerging Contaminants

compounds are not always easily biodegraded, and modified natural compounds even less so. Structurally related compounds such as plasmids have been found in the environment (Schlu¨ter et al., 2007; Ku¨mmerer, 2009a). Furthermore, it is known that the protein structures known as prions are very stable. A prion (proteinaceous infectious particle, -on by analogy to virion) is an infectious agent composed only of protein.) They cause a number of diseases in a variety of animals and Creutzfeldt–Jakob disease in humans. Prions are believed to infect and propagate by refolding abnormally into a structure which is able to convert normal molecules of the protein into the abnormally structured form. This altered structure renders them quite resistant to denaturation by chemical treatments and physical agents (proteases, heat, radiation, and formalin) making disposal and containment of these particles difficult. Prions can be denatured by subjecting them to a temperature of 134 1C in a pressurized steam autoclave. Besides the active substances, formulations may also incorporate excipients and, in some instances, pigments and dyes. They are often of minor importance for the environment. Some medicines contain EDCs as excipients. For data and findings that are more detailed, the reader is advised to seek help from the numerous books and reviews that have already been published (e.g. Ku¨mmerer, 2001a, 2001b, 2004, 2008b, 2009a, 2009b, 2009c; Heberer, 2002; Williams, 2005; Ternes and Joss, 2006). Pharmaceutically active compounds (sometimes called active pharmaceutical ingredients or APIs) are complex molecules with various functionalities and physico-chemical and biological properties. They are developed and used because of their more or less specific biological activity on human and/or organisms that may cause illness such as bacteria and other microorganisms. Therefore, they are of special scientific and public interest. Most pharmaceutically active compounds are polar compounds in order to make them orally available. Their molecular weights range typically from 200 to 500/1000 Da. Such APIs are called small molecules. These are the ones currently being researched and detected in the environment. They are among the compounds called micro-pollutants, because they are often found in the mg l1 or ng l1 range in the aquatic environment. They are often complex molecules with different functionalities and physico-chemical and biological properties. There is no data available about the total worldwide use of pharmaceuticals. The consumption and application of pharmaceuticals may vary considerably from country to country (Verbrugh and de Neeling, 2003; Goossens et al., 2005, 2007; Schuster et al., 2008). If there are legislative changes imposed on the health system, it may happen that some compounds are not used any more or others gain more importance, for example, for economical reasons. According to United Nations’ figures, 2.3% of Japanese women of reproductive age take a contraceptive pill containing EE2 as the main active compound, compared to 16% in North America and up to 59% in Europe (United Nations, 2004). Some pharmaceuticals are sold over the counter without prescription in some countries, whereas in others they are only available by prescription. Some antibiotics such as streptomycins are used in the growing of fruit (pomology) whereas others are used in bee-keeping. Again, the situation may

vary from country to country. Antimicrobials are among the most widely used pharmaceutical compounds in animals (Boxall et al., 2003a, 2003b; Sarmah et al., 2006). These drugs are used in animal husbandry for veterinary purposes, or as growth promoters (particularly in large-scale animal farming and intensive livestock treatment). The active compounds as well as excipients may enter the environment by different routes via several different nonpoint sources such as effluents of STPs, waste, and landfill effluent or treatment of animals. Because of good manufacturing practice (GMP) regulations (required for the manufacturing of pharmaceuticals) and the frequent high economic value of the active substances, the amount of emissions occurring during manufacturing had been thought to be negligible. Indeed, such emissions are assumed to be low in Europe and the North Americas. However, manufacturers have not yet published data. It has only recently been found that in Asian countries, concentrations for single compounds up to several mg l1 can be found in effluents (Larsson et al., 2007; Li et al., 2008a, 2008b). However, even in Norway the input from a local manufacturer was high (Thomas, 2008). As expected, pharmaceuticals are present in hospital wastewater (Brown et al., 2006; Steger-Hartmann et al., 1996; Ku¨mmerer and Helmers, 1997; Hartmann et al., 1999; Ku¨mmerer, 2001a, 2001b; Ha¨drich, 2006; Go´mez et al., 2006; Seifrtova´ et al., 2008; Ku¨mmerer, 2008a). The concentrations of pharmaceuticals in hospital wastewater are higher (up to 100 mg l1 in some cases) than in municipal sewage (often in the range of a few up to 20 mg l1). However, the total substance flow is much lower because of the much lower share of effluent from hospitals in municipal effluent in developed countries. The dilution of hospital wastewater by municipal wastewater is by much more than a factor of 100 (Ku¨mmerer and Helmers, 1997, 2000). In terms of volume, the public (households) is the main source for pharmaceuticals in households (Ku¨mmerer, 2009a). Concentrations in municipal wastewater are typically in the lower mg l1 range. Another, but minor, source is effluents from landfills as date-expired medicaments are often disposed of with household waste (Eckel et al., 1993; Holm et al., 1995; Ahel and Jelicˇic, 2001; Metzger, 2004). Systematic studies of the occurrence of pharmaceuticals in the environment are now available for several countries. Meanwhile, there is evidence of the occurrence of some 180 different drugs in STP effluent, surface water, and groundwater. Probably, there are many more, however, validated methods not yet available for the analysis of most of the APIs in the environment. The concentrations of pharmaceuticals in surface waters have been shown to lie most often in the ng l1 range, in rare cases in the low mg l1 range. The findings of recent years have been confirmed for various countries and different environmental compartments (Ku¨mmerer, 2009a). Compared to the free water phase, the analysis of APIs is difficult in biosolids and sewage sludge, despite the fact that information about pharmaceuticals in sewage sludge and biosolids is necessary for a proper understanding of fate and for risk assessment (Jones-Lepp and Stevens, 2007). APIs have also been detected in the arctic environment (Kallenborn et al., 2008). Some pharmaceuticals are used by hydrologists as tracers for anthropogenic impact on waters (Mo¨ller et al.,

Emerging Contaminants

2000, 2002; Elbaz-Poulichet et al., 2002; Verplanck et al., 2005; Buerge et al., 2006). Some APIs have even been detected in drinking water (Heberer, 2002; Ku¨mmerer, 2008a).

3.04.15.2 Illicit Drugs Around 200 million people in the world are estimated to have used illicit drugs at least once during the last year (United Nations Office of Drug and Crime, 2010). They are of special concern because of their psychoactive properties. Cannabis is the one most consumed, involving far more than 4% of the global population aged between 15 and 64 years old. Next come opiates (especially heroin) and cocaine, which are the two second most consumed illicit drugs on the global level (United Nations Office of Drug and Crime, 2010). Recently, psycho-active and illicit drugs such as amphetamine, cocaine and its metabolite benzoylecgonine, morphine, 6-acetylmorphine, 11-nor-9-carboxy-delta-9-tetrahydrocannabinol, methadone and its main metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine, and amphetamines have been detected in surface water and wastewater. The high consumption rates reported for these compounds explain their relatively high concentration levels in the aquifer. In some studies, the measured concentrations were employed to estimate the use of these drugs. Seasonal variations have been found in several studies (Kasprzyk-Hordern et al., 2008). Maximum levels of cocaine, ecstasy, and methamphetamine values were found in winter but high loads were also detected in summer. There was a significant increase in the concentrations of these compounds during the last days of December and the first days of January, corresponding to the Christmas and New Year holidays. Up to now, drugs of abuse have been detected in wastewaters and surface waters in the USA, Italy, Germany, UK, and Spain. One goal of several studies was to estimate the levels abused drugs discharged. Most of the main illicit drugs consumed are excreted unaltered or as slightly transformed metabolites, which reach the sewage system. The detection of these drugs shows that the parent compound or conjugates can pass STPs and reach receiving waters that may be used for drinking-water production. Daily and seasonal variability was examined and revealed fluctuations in the concentrations of nicotine, paraxanthine, amphetamine, cocaine, and ecstasy during the week. Estimations of consumption were made using the total concentrations found in wastewater (Zuccato et al., 2008a, 2008b; Hummel et al., 2006; Boleda et al., 2009; Huerta-Fontela et al., 2007, 2008, 2009; Jones-Lepp et al., 2004). Studies are available for Italy (Zuccato et al., 2005; Castiglioni et al., 2006), Germany (Hummel et al. 2006), USA (Jones-Lepp et al., 2004), Ireland (Bones et al., 2007), Belgium (Gheorghe et al., 2008; van Nuijs et al., 2008), UK (Kasprzyk-Hordern et al., 2007; Zuccato et al., 2008a, 2008b), Poland (Kasprzyk-Hordern et al., 2008), and in Spain (Boleda et al., 2009). Concentrations of methamphetamine were 0.8 and 0.5 ng l1 for MDMA in effluent samples from three WWTPs in the USA. The presence of cocaine (42–120 ng l1) and its major metabolite, benzoylecgonine (390–750 ng l1), in wastewaters and in the Po River (2 and 25 ng l1, respectively) was demonstrated. Cocaine and its metabolite were detected in Spanish

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wastewaters at concentrations ranging from 4 to 4700 ng l1 and from 9 to 7500 ng l1, respectively (Huerta-Fontela et al., 2008). Compounds of the amphetamine type, morphine, or methadone were detected in the influents and effluents of two Italian WWTPs and in several Spanish ones (2–688 ng l1). Removal rates for cocaine and benzoylecgonine were higher than 88%, whereas those for amphetamine type compounds varied, ranging from 40% to more than 99%. Cannabis (THC) has been detected as its main carboxylic metabolite (THCCOOH) at concentrations from 0.5 to 7 ng l1 in Italian rivers and at 1 ng l1 in one UK River. Huerta-Fontela et al. (2009) described the removal of illicit drugs through drinking-water treatment. Drinking-water treatments achieved the complete removal of all the illicit drugs or metabolites detected in the raw water except for benzoylecgonine, a cocaine metabolite, methadone, and a methadone metabolite. Amphetamine-type stimulants (except MDMA) were completely removed during prechlorination, flocculation, and sand filtration steps. Granulated activated carbon filtration removed cocaine (100%), MDMA (88%), and benzoylecgonine (72%). Post-chlorination resulted in the complete elimination of MDMA. However, no information was reported for reaction by-products.

3.04.15.3 Metabolites In general, little is known about the occurrence, fate, and activity of metabolites of pharmaceuticals. An important question to be addressed is whether the glucoronides, methylates, glycinates, acetylates, and sulfates are still active. It has been found that some compounds (e.g., conjugates of sulfamethoxazole and ethinlyestradiol) can be cleaved back by bacteria during sewage treatment (D’Ascenzo et al., 2003; Go¨bel et al., 2005). This results in the active compound being set free again. Bendz et al. (2005) detected human ibuprofen metabolites not only in the WWTP but also in the receiving river, and carbamazepine metabolites were found in WWTP effluent and even in drinking water (Hummel et al., 2006; Miao et al., 2005). Other types of metabolites are excreted too and can be detected in wastewater (Miao et al., 2005). Their effects on environmental organisms may be less than that of the parent. However, in the case of pro-drugs the situation is probably different as it may also be for the metabolites of several other pharmaceuticals as has been shown, for example, for norfluoxetin.

3.04.16 Engineered Nanoparticles Nanomaterials are of increasing technological and economical importance, and, in turn, contribute to higher standards of living. Important properties include size, shape, surface properties, aggregation state, solubility, structure, and chemical composition. According to chemical composition and shape, for example, there are several classes of engineered nanoparticles (ENPs) such as inorganic particles (e.g., TiO2, SiO2, and ZnO), organic ones such as fullerens, and multiand single-walled carbon nanotubes. Some of them are chemically modified, that is, they carry organic functionalities on their surface. Insoluble material such as fullerens may

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become soluble by such a modification. As a result of their widespread use, their exposure to the human body is increasing, for example, by ingestion or application onto skin (e.g., use of sun screens), or through the use or handling of materials or products containing them. Some of them can also reach the environment via production processes (emissions into air and wastewater) and even more likely as a result of the routine use of products containing nanoparticles (e.g., sun screens). Others are the result of wash off, for example, from facades of buildings (Kaegi et al., 2008). These authors found evidence that synthetic nanoparticles are released from urban applications into the aquatic environment. They investigated TiO2 particles as these particles are used in large quantities in exterior paints as whitening pigments and are, to some extent, also present in the nano-size range. Analytical electron microscopy revealed that TiO2 particles are detached from new and aged facade paints by natural weather conditions and are then transported by facade runoff and are discharged into natural, receiving waters. Some of the ENPs are used for the remediation of environmental pollution too such as the treatment of effluents and wastewater (e.g., photocatalysis by TiO2). In such cases, the introduction of nanomaterials is intended; however, the focus of an LCA of such materials is indispensable for the sustainable use of these nanoparticles. There is an increasing concern over the safety of ENPs to humans and the environment and it is likely that the environmental risks of these particles will have to be tested under regulatory schemes such as REACH. However, to date, little is known about the occurrence, fate, and toxicity of ENPs. Knowledge on toxicological issues of nanomaterials is almost nonexistent (Albrecht et al., 2006; Landsiedel et al., 2009) and still less as far as the environment is concerned. Frequently, genotoxicity seems to be associated with nanomaterials (Landsiedel et al., 2009). Predicted environmental concentrations (PECs) of ENPs in water arising form their use in consumer products range from o0.1 ng l1 (CeO2) to 310 ng l1 for fullerenes up to 76 mg l1 for ZnO (Tiede et al., 2009). Gottschalk et al. (2009) calculated PECs based on a probabilistic material flow analysis from a life-cycle perspective of ENP-containing products. The simulated modes (most frequent values) range from 0.003 ng l1 (fullerenes) to 21 ng l1 (TiO2) for surface waters and from 4 ng l1 (fullerenes) to 4 mg l1 (TiO2) for sewage treatment effluents. These data demonstrate that there is still only knowledge on usage and introduction of ENPs into the environment. Gottschalk et al. (2009) concluded that risks to aquatic organisms may currently emanate from nano-Ag, nano-TiO2, and nano-ZnO in sewage treatment effluents for all considered regions and for nano-Ag in surface waters and that for the other environmental compartments for which ecotoxicological data were available, no risks to organisms are presently expected. However, the aggregation of ENPs plays an important role in the environmental fate and effects because the size and shape of nanoparticles will determine the magnitude of any potentially toxic effect. Aggregation is affected by pH, ionic strength, and ionic identity (inorganic and organic) of aqueous suspensions (Sharma, 2009). In Europe, the Water Framework Directive (WFD) is responsible for maintaining a good chemical and ecological

status of surface waters. In this context, ‘priority substances’ are set up. Braun et al. (2009) concluded that it is impossible to set limit values for ENPs in surface waters now and in the foreseeable future. This is not only due to the extensive lack of knowledge in relation to unknown toxic effects, degradability, and bioaccumulation of ENPs in the aquatic environment, but also due to the questionable validity of test systems and methods to establish environmental quality standards (EQS). The limitations in our knowledge are partly due to the lack of methodology for the detection and characterization of engineered nanoparticles in complex matrices, that is, water, soil, or food (Tiede et al., 2008; Perez et al., 2009; Frimmel and Nießner, 2010). Methods have been developed for the detection of natural or engineered nanomaterials in simple matrices, which could be optimized to provide the necessary information, including microscopy, chromatography, spectroscopy, centrifugation, as well as filtration and related techniques. A combination of these is often required. A number of challenges will arise when analyzing environmental materials, including extraction challenges, the presence of analytical artifacts caused by sample preparation, problems of distinction between natural and engineered nanoparticles, and lack of reference materials.

3.04.17 Artificial Sweeteners Artificial low-calorie sweeteners are consumed in considerable quantities with food and beverages. After ingestion, some sweeteners pass through the human metabolism largely unaffected, are quantitatively excreted via urine and feces, and thus reach the environment associated with domestic wastewater. Typical sweeteners are sucralose, acesulfame, cyclamate (banned in the US), and saccarin. Their presence in the aquatic environment came into focus only recently and knowledge is still little. Concentrations in two influents of German STPs were up to 190 mg l1 for cyclamate, about 40 mg l1 for acesulfame and saccharin, and less than 1 mg l1 for sucralose (Scheurer et al., 2009). Buerge et al. (2006) detected acesulfame consistently in untreated and treated wastewater (12–46 mg l1). Removal in the STPs was limited for acesulfame and sucralose and 494% for saccharin and cyclamate (Scheurer et al., 2009). In German surface waters, acesulfame was the predominant artificial sweetener with concentrations exceeding 2 mg l1. Other sweeteners were detected up to several hundred ng l1 in the order saccharin approximate to cyclamate higher than sucralose (Scheurer et al., 2009). The analysis of 120 river surface water samples from 27 European countries showed that sucralose, which is in use in Europe since the beginning of 2005, can be found in the aquatic environment, at concentrations up to 1 mg l1. Sucralose was predominately found in samples from the UK, Belgium, the Netherlands, France, Switzerland, Spain, Italy, Norway, and Sweden, suggesting an increased use of the substance in Western Europe (Loos et al., 2009a). In North American coastal and open ocean waters, the concentration of sucralose varied over several orders of magnitude in these environmental samples with the greatest abundance in a WWTP effluent (120 mg l1).

Emerging Contaminants

The concentration decreased in receiving waters where surface water concentrations at the mouth of the estuary were 374 ng l1. Sucralose was also detected in the oligotrophic waters of the Gulf Stream (33 28.61 N–76 48.21 W) where it ranged in concentration from below detection limit to 68 ng l1 (Mead et al., 2009). In the Northern and Middle Florida Keys, values were 147 and 394 ng l1, respectively. Acesulfame was measured by Buerge et al. (2006) in groundwater samples, and even in several tap water samples (up to 2.6 mg l1) from Switzerland. Up to 4.7 mg l1 were detected in groundwater that received considerable infiltration of river water, where the infiltrating water received considerable discharges from WWTPs. Concentrations increased with population in the catchment area and decreased. with water throughflow. The persistence of some artificial sweeteners during soil aquifer treatment was demonstrated by Scheurer et al. (2009). These data show that these artificial sweeteners were not eliminated in WWTPs and were quite persistent in surface waters. Like some pharmaceuticals and dadolium contract media, the artificial sweeteners are regarded as ideal marker for the detection of domestic wastewater in natural waters, particularly groundwater (Buerge et al., 2006; Scheurer et al., 2009).

3.04.18 Cyanotoxins Cyanobacteria are ubiquitous organisms found in all types of aquatic environments. During favorable environmental conditions, cyanobacteria form dense growth referred to as algal bloom or scum. Cyanobacterial blooms occur globally as a result of eutrophication – may it be for natural reasons or as a result of human activities. Eutrophication favors the growth of toxin-producing cyanobacteria, which are algae and not bacteria in the strict sense. There seems to be a worldwide increase in the occurrence of cyanobacterial harmful algal blooms in natural and man-made water reservoirs. Cyanotoxins consist of several classes such as microcystins anatoxins, saxitoxins, and cylindrospermopsins (Pelaez et al., 2009). Cyanotoxins may exert effects on humans and wildlife. In addition to the presence of toxins, water-supply problems associated with cyanobacteria include an unpleasant taste and odor imparted to the water (Watson et al., 2008). Brittain et al. (2000) reported microcystin levels as high as 3.4 mg l1 in Lake Erie. The same concentration level was found later again for microcystin and other cyanotoxins in other Great lakes (Boyer, 2008; Makarewicz et al., 2006). Concentrations up to 97 mg l1 have been measured in Florida and even higher ones in other places in the USA (Pelaeza et al., 2009). Cyanobacteria blooms have been reported for water bodies in Australia, Europe (Bla´hova´ et al., 2008; Boaru et al., 2006; Carrasco et al., 2006; Chorus, 2002; Gkelis et al., 2005; Karlsson et al., 2005), as well as in South America (Ame´ et al., 2003; Deblois et al., 2008), Asia (Li et al., 2008a, 2008b) and in Africa (Nasri et al., 2008; Oudra et al., 2008) – all more or less in the same concentration range (Pelaez et al., 2009). A median of 0.2 mg l1 has been recorded in samples from drinking-water supplies (in both 2004 and 2005) with a maximum of 17 mg l1.

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3.05 Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems M Baalousha and JR Lead, University of Birmingham, Birmingham, UK Y Ju-Nam, University of Sheffield, Sheffield, UK & 2011 Elsevier B.V. All rights reserved.

3.05.1 3.05.2 3.05.2.1 3.05.2.2 3.05.3 3.05.3.1 3.05.3.2 3.05.4 3.05.4.1 3.05.4.1.1 3.05.4.1.2 3.05.4.2 3.05.4.2.1 3.05.4.2.2 3.05.4.2.3 3.05.4.2.4 3.05.4.3 3.05.4.3.1 3.05.4.3.2 3.05.4.4 3.05.5 3.05.5.1 3.05.5.2 3.05.5.3 3.05.5.4 3.05.5.5 3.05.5.6 3.05.6 3.05.6.1 3.05.6.2 3.05.6.3 3.05.7 3.05.7.1 3.05.7.2 3.05.7.3 3.05.7.4 3.05.8 3.05.8.1 3.05.8.2 3.05.8.3 3.05.8.4 3.05.9 3.05.9.1 3.05.9.2 3.05.9.3 3.05.9.4 3.05.9.5 3.05.9.6 References

Introduction Definitions Colloids Nanoparticles Major Types of Natural Colloids Inorganic Colloids Organic Macromolecules Major Types of Manufactured NPs Carbon-Based NPs Fullerenes Carbon nanotubes Metal Oxide NPs Iron oxide NPs Zinc oxide NPs Titania NPs Ceria NPs Metal NPs Gold and silver NPs Zero-valent iron NPs Quantum Dots Important Physico-Chemical Properties of Natural Colloid Size Shape and Morphology Surface Coating Surface Charge Pollutant Binding and Behavior Interaction Forces Intrinsic Properties of Manufactured NPs Size Shape and Morphology Surface Properties Environmental Fate and Behavior of Natural Colloids Aggregation/Disaggregation Aggregate Structure and Fractal Dimension Transport and Sedimentation in Aquatic Media Transport in Porous Media Environmental Fate and Behavior of Nanomaterials Exposure/Release of NPs Fate in Water Fate in Wastewater Fate in Soil Conclusions and Recommendations Environmental Fate and Behavior Need for New Metrology and Analysis Tools Understanding Complexity on the Nanoscale Knowledge of Uptake and Toxicity of NPs Knowledge of Structure–Activity Relationships Next Generation NPs

89 90 90 91 91 93 93 94 94 94 94 97 97 97 98 98 99 99 100 100 102 102 103 104 104 106 107 109 109 109 110 111 111 112 113 114 115 115 117 117 118 118 118 119 119 119 120 120 121

89

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3.05.1 Introduction All aquatic and terrestrial environmental systems contain small particles, covering the size range from 1 nm to several micrometers. Colloids (1–1000 nm) have different properties and behavior compared to dissolved species (o1 nm) and particles (41000 nm), (see discussion in Section 3.05.2.1). The small size of colloids has two important consequences: (1) colloids have enormous surface areas and surface energies, and are able to interact strongly with contaminant species (Wilkinson and Lead, 2007) and (2) the properties of the smallest fraction of colloids (nanoparticles (NPs), 1–100 nm) may be different to those of the larger particles (4100 nm) or those of the corresponding atoms or molecules forming the colloids (Wigginton et al., 2007), based on size alone. The concentration, composition, size distribution, and other properties of environmental particles, including colloids, depend on their origin, the catchment geochemistry, rainfall, industrial waste, and other factors (Table 1). They are characterized by properties such as size, shape, surface charge, conformational properties, and interaction forces as they approach each other (see detailed discussion in Sections 3.05.5.1–3.05.5.6). Some of these particles are small enough to be transported with the water current, and others are large enough to exhibit sedimentation. The behavior of colloids and particles is dominated by processes such as aggregation/disaggregation and sedimentation (see Sections 3.05.7.1 and 3.05.7.3) in aquatic systems (Buffle et al., 1998) and attachment (see Section 3.05.7.4) to surfaces in soils and aquifers (McDowellBoyer et al., 1986). Aggregation of colloids results in the formation of large structures (see Section 3.05.7.2), often porous and fractal (Chen and Eisma, 1995), which tend to sediment in the water body or attach to surfaces in soils which results in their losses together with any adsorbed chemicals from a water body in a process called colloidal pumping (Honeyman and Santschi, 1992). Aggregation/disaggregation and the structure of the formed aggregates are highly influenced by solution physico-chemistry (e.g., pH, cation types, and concentrations) and sorption of natural organic molecules (Wilkinson et al., 1997a, 1997b), and may change during transport as physico-chemical conditions change. Such variation in structure and conformation of colloids and their aggregates may result in sorption–desorption of chemical compounds (e.g., pollutants and nutrients), and possibly a permanent retention within the structure of colloids/aggregates (Kan et al., 1994). Further, the fractal nature of colloidal

aggregates influences their sedimentation behavior (see Section 3.05.7.3). Environmental colloids have important environmental functions in aquatic and terrestrial systems. They can control pollutant (trace elements and organic contaminants) and nutrient chemistry and behavior (see Section 3.05.5.5), that is, pollutant speciation, transport, and bioavailability (Lead et al., 1999; Doucet et al., 2006). Despite decades of research, the precise role of colloids and NPs in these processes is still poorly understood in a quantitative manner and much work is required to fully elucidate their role. Importantly, the analytical and modeling capabilities are increasingly available to study such complex systems. The ongoing use of manufactured NPs (deliberately manmade materials in the size range 1–100 nm, see Section 3.05.2.2) and their consequent environmental release (either accidentally or deliberately) (Nowack and Bucheli, 2007; Luoma, 2008; Blaser et al., 2008) are likely to increase in the short term. The ability of man to manipulate materials in the nanoscale level has become the core of an exciting new research area named nanotechnology. Researchers have been strongly attracted to the idea of using NPs due to their unique and tunable optical, magnetic, and electrical properties controlled by their size, morphology, and chemistry, and therefore their potential applications in a wide range of fields such as medicine, engineering, the environment and pharmaceuticals (Klaine et al., 2008; Poole and Owens, 2003; Rotello, 2004; Schmid, 2004). Although the developments in the area of nanotechnology are recent, NPs have been fabricated in the past. Glasses with colloidal gold such as the famous Lycurgus cup and church stained glass windows are examples which have been used since ancient Greek times (Daniel and Astruc, 2004). However, the design and control of novel NPs have been the basis of many advances in this technology over the last decade. Studies of manufactured NP fate and impact in the environment are becoming important due to the discharges already occurring to the environment, the likely increase in discharges as the industry grows dramatically, the known toxicity of NPs and the immense gaps in our knowledge leading to difficulties in risk assessment and management (Handy et al., 2008a). Despite some recently acquired knowledge on the effects of NPs on human toxicology and to a lesser extent in ecotoxicology, very little is known about mechanisms of biological uptake and toxicity modes of action, about transport in and between environmental and biological compartments and their chemical behavior in the

Table 1 Major types of environmental particles, their concentrations and size distribution in different environmental compartments (Buffle and Leeuwen, 1993) Groundwater

River

Marine

Atmosphere

Major types

Alumino-silicates, metal oxyhydroxides, natural organic matter

Alumino-silicates, natural organic matter, biogenic colloids

Carbonates, aluminosilicates, biogenic colloids

Salt, silicates, ash, pollen, soot

Concentration

0.001–1 mg l1

1–1000 mg l1

0.01–0.05 mg l1

0.1–1000 ng m3

Size

o10 mm

o300 mm

o100 mm

o30 mm

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

environment. Exposure studies are rare (Mueller and Nowack, 2008; Valsami-Jones et al., 2008), but increasing rapidly (Handy et al., 2008a; Klaine et al., 2008). Recent concern has been focused on the asbestos-like behavior of carbon nanotubes (CNTs) (Poland et al., 2008) and the environmental toxicity and wide exposure of nanosilver. Next generation and active NPs such as drug delivery devices used in nanomedicine will be of concern in the next few years. An important consideration to environmental studies is that NPs are not one class of potential pollutant. As there are many trace metals and many trace organic pollutants, NPs contain a wide range of different materials with different physical, chemical, and toxicological properties. NPs should not be considered as a single homogeneous group. If we take into account different surface chemistries (including from capping agents, surfactants, or co-solvents), sizes, and other properties, then it is easy to see that the range of possible nanomaterials is vast and they have many different properties which will impact on their environmental behavior substantially (Klaine, 2009). NPs can be classified as inorganic and carbon based (see Section 3.05.3). Inorganic NPs can be further divided into metal oxides, metals, and quantum dots (QDs), while carbon-based NPs can be divided into fullerenes and CNTs. Clearly, other factors such as size and surface chemistry will change the properties of materials within this class substantially (see Section 3.05.6.3). For instance, 2 nm polyvinylpyrrolidone (PVP) stabilized gold NPs will have different surface reactivity and aggregation behavior than 50 nm citrate stabilized gold. Clearly, the role of natural colloids and the environmental behavior of NPs are interlinked (see Sections 3.05.7 and 3.05.8). First, the technological developments, which have underpinned the development of nanoscience and nanotechnology, including electron and force microscopy, are fundamental to understanding the environmental structure and interactions of colloids and NPs, and NP fate. Second, there is a long history of research into the role aquatic and terrestrial colloids play in environmental processes, transport, and biouptake (Lead and Wilkinson, 2006). With the caveat that this literature is not directly transferable, it is a potentially valuable tool to help understand the environmental behavior and impacts of manufactured NPs. Third, there is a direct interaction in the environment between NPs and colloids (Hyung et al., 2007) with consequent modification of NP fate and behavior.

3.05.2 Definitions Both colloids and NPs are solid materials in the nanoscale range and are defined more specifically below. The nanometer is a metric unit of length, and represents one-billionth of a meter or 109 m. Many existing materials (natural or manufactured) are structures on the nanometer scale (nanomaterials). Figure 1 shows the nanoscale dimension in comparison to the known dimensional scale of the universe (Hochella, 2002). At the smallest end of the scale (Figure 1(a)), fundamental particles, such as electrons and quarks, are smaller than 1018 m. At the larger end of the scale are the size of the Earth (107 m in diameter) and the Sun (109 m in diameter).

91

The nanoscale with other related objects is described in Figure 1(b) and is in the range 1–100 nm. A nanometer is a billionth of a meter (i.e., 109 m). The size of a single atom is of the order of several angstroms (0.1 nm). The size of a bacterium is about 1 mm (1000 nm), approximately the limit of visibility in light microscopes. The size of a virus is approximately equal to 100 nm (the upper size range of NPs), which cannot be detected through standard light microscopes, because they are smaller than wavelengths of light (approximately 400–700 nm). They can be observed only with higher-resolution microscopes such as scanning electron microscope (SEM, resolution of the order of 10 nm) and transmission electron microscope (TEM and atomic force microscope (AFM), both have a resolution which can be down to 0.1 nm).

3.05.2.1 Colloids The formal definitions of colloids and NPs are given by international organizations such as the International Union of Pure and Applied Chemistry (IUPAC), the International Organization for Standardization (ISO), and the Organization for Economic and Co-operation and Development (OECD). Colloid dispersions can be regarded as a particular state of matter between true solutions and suspensions. In such a colloidal phase, one substance is dispersed in another phase; in principle, the dispersed phase and the phase in which it is dispersed can be solid, liquid, or gas. However, for this chapter and with some simplifications, colloidal phases can be thought of as solids dispersed in aqueous phases, generally shortened to colloids. Colloids are generally in the submicrometer size range. Suspensions may then be defined as a heterogeneous mixture containing particles large enough (usually 41 mm) to sediment. According to the IUPAC definition, natural aquatic colloids can be defined as materials with at least one dimension between 1 nm and 1 mm (see Figure 2(a)), while particles are larger than 1 mm (Hofmann et al., 2003; Lead and Wilkinson, 2007). Alternatively, colloids can be defined as organic or inorganic entities small enough to be dominated by aggregation and to remain in the water column due to Brownian motion (diffusion) over reasonable timescales (4hours to days), but large enough to have supramolecular structure and properties, for example, electrical surface charge and possibility of conformational changes (Lead and Wilkinson, 2007). Particles are large enough (41 mm) to be dominated by sedimentation, rather than aggregation (Buffle and Leppard, 1995a) and will be removed from the water column rapidly. This definition was developed and somewhat extended by Gustafsson and Gschwend (1997) where aquatic colloids can be defined as any constituent that: (1) provides a molecular milieu into and onto which chemicals can partition which has different properties to the aqueous phase (e.g., dielectric constant) and (2) its vertical movement (in water) is not significantly affected by gravitational settling over a reasonable timescale. In practice and in much of the literature, colloids are defined as materials that permeate a filter (pore size between 0.1 and 1.0 mm) while being retained by an ultrafilter (1–100 kDa, nominal pore size). The upper size limit is often chosen to reduce sample complexity and

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Electrons and quarks Scale of the Earth sciences

10–20 10–16 10–12 10–8

10–4

100

104

108

1012

1016

1020 1024 1028 m

(a)

Nanoscale m 10–10

10–8

10–6

10–4

10–2

100

102

104

106

108

1010

(b)

Figure 1 (a) The known dimensional scale of the universe. On the small end, fundamental particles such as electrons and quarks are smaller than 1018 m, and may approach 1030 m in size or smaller, but such dimensions are not physically measurable at least at this time. Other stops depicted along this dimensional journey include: the scale of the solid Earth sciences, from atoms to the Earth (1010–107 m); the Sun (109 m in diameter) as seen from the Extreme UV Imaging Telescope on the SOHO satellite; expanding gas rings (1016 m in diameter) from supernova SN1987a as observed by the Hubble Space Telescope; infrared image of the inner portion of our own galaxy (the Milky Way is nearly 1021 m in diameter); and distant galaxies (the most distant are 1026 m away). (b) The dimensional scale of the Earth sciences. Stops depicted along this dimensional journey include: scanning tunneling microscope image of lead and sulfur atoms on a galena surface (atomic size 1010 m); crystallization nucleus of calcite (109–108 m); bacterial cells (106 m in length); a single crystal of quartz (102 m); a typical open pit mine (the Carlin Mine in Nevada, USA, 102–103 m); Mt. Fuji, Japan, a composite volcano (104 m); the Red Sea from space (105 m wide and 106 m long); Earth (107 m); the Earth–Moon system as seen from Apollo 11 (4  108 m). From Hochella MF (2002) There’s plenty of room at the bottom: Nanoscience in geochemistry. Geochimica et Cosmochimica Acta 66(5): 735–743.

partially sterilize the colloidal fraction. In addition, other filter cut-offs have been used to identify different colloidal fractions (e.g., ultrafine, fine, and coarse) (Guo and Santschi, 2007). It is clear that the formal, mechanistic, and practical definitions do not entirely mesh and some work on standardization is required. Within this colloidal fraction, it is becoming useful to define a nanoscale fraction of natural particles (Lead and

Wilkinson, 2006; Wigginton et al., 2007), which may be thought of as between 1 and 100 nm, as with manufactured NPs (see Section 3.05.2.2). However, the Bo10–25 nm may be the size range in which environmental properties such as metal binding, zeta potential, and redox properties change radically compared with the bulk or larger-sized phases (Lyven et al., 2003; Madden and Hochella, 2005; Madden et al., 2006).

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

Colloids or macromolecules

Organic compounds

Solutes –10

–9

–8

1A

1 nm

93

Particles Log (size / m)

–7

–6

–5

1 μm

0.45 μm

Cellular debris

Amino acids

Polysaccharides Peptides

Peptidoglycans Proteins

Fulvics

Humic aggregates Viruses

Humics

Bacteria

Algae

(a)

Inorganic compounds

Organic compounds absorbed ‘Simple’ hydrated ions (e.g., OH–, Cl, SO42–, HS– Na+, Ca2+, etc.)

on inorganic particles Clays (aluminosilicates) Fe oxyhydroxides Mn oxides Metal sulfides Carbonates, phosphates Amorphous SiO2 Filtration

Analytical techniques

Ultrafiltration

(b)

Electron microscopy Atomic force microscopy Flow-FFF

Confocal microscopy Optical microscopy

Sedimentation-FFF

FCS Light scattering X-Ray, neutron scattering

X-Ray, absorption

LIBD

(c)

Figure 2 (a) Typical example of natural colloids and aggregates (Rhine River), scale bar corresponds to 1 mm and (b) natural heteroaggregate of colloids and particles from Lake Bret, Switzerland, as shown by transmission electron microscopy, scale bar corresponds to 250 nm. From Buffle J, Wilkinson KJ, Stoll S, Filella M, and Zhang J (1998) A generalized description of aquatic colloidal interactions: The three-colloidal component approach. Environmental Science and Technology 32(19): 2887–2899. (c) Schematic representation, by size distributions, of the major environmental colloidal and particulate components. From Lead JR and Wilkinson KJ (2006) Aquatic colloids and nanoparticles: Current knowledge and future trends. Environmental Chemistry 3: 159–171.

3.05.2.2 Nanoparticles In the literature and websites related to the nanotechnology, numerous definitions of NPs can be found and many standard bodies such as ISO, SCENIHR, OECD, the US National Nanotechnology Initiative (NNI), British Standard Institution (BSI), and the American Society for Testing Materials (ASTM) are investigating definitions for nanoscience, nanotechnology, and NPs (Klaine et al., 2008). Nanoscience is generally defined as the scientific study of materials on the nanoscale (Borm et al., 2006). Nanotechnology, as defined by US-NNI, is ‘‘the research and technology development at the atomic, molecular or macromolecular levels, in the length scale approximately 1–100 nm; the creation, and use of structures, devices and systems that have novel properties and functions because

of their small size; and ability to be controlled or manipulated on the atomic scale’’ (NNI, 2004). The Royal Society and the Royal Academy of Engineering defines nanotechnology as ‘‘the design, characterization, production and application of structures, devices and systems by controlling shape and size at the nanometre scale’’ (Royal Society and Royal Academy, 2004). Nanomaterials are the major component of nanotechnology and can be defined as materials that have one or more dimensions in the range of 1–100 nm (Lead and Wilkinson, 2006). A recent attempt to develop a more structured approach has been published by the BSI (BSI, 2007). In their terminology for nanomaterials, they define nanoscale as ‘‘size range from approximately 1 nm to 100 nm’’, a nanoobject as ‘‘discrete piece of material with one or more external dimensions in the nanoscale,’’ and an NP as a ‘‘nano-object

94

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

with all three external dimensions in the nanoscale.’’ A nanomaterial is a ‘‘material having one or more external dimensions in the nanoscale or which is nanostructured,’’ with nanostructured being defined as ‘‘possessing a structure comprising continuous elements with one or more dimension in the nanoscale.’’ These definitions are based on particle size and imply that there is a size range (1–100 nm) between that of molecules and bulk materials, where particles have unique properties different than those of molecules or bulk material (Tratnyek and Johnson, 2006). This overlaps with a definition based on novel properties, that is, an NP has novel properties based on size alone. Some of these properties arise only for particles smaller than approximately 10 nm or so, where particle size approaches the length scale of certain molecular properties (Klabunde et al., 1996). For instance, below 10 nm, particlespecific surface area increases exponentially and similar trends apply to related properties such as the ratio of surface/bulk atoms. Another reason for changes is the issue of spatial confinement such as quantum confinement, which arises because the band gap of semiconducting materials increases as particle size decreases (Klabunde et al., 1996). For instance, the decrease in hematite particle size (from 37 to 7.3 nm) greatly promotes the oxidation of aqueous Mn(II) in the presence of molecular oxygen (Madden and Hochella, 2005), quite separate from the effect of specific surface area. Small magnetite NPs (9 nm) exhibit greater reactivity toward carbon tetrachloride (CCl4) relative to larger NPs (80 nm), both on mass and surface area normalized bases (Vikesland et al., 2007). The decrease in size of ceria NP alters the oxidation state of the NPs with an increase in the fraction of Ce3þ at sizes Bo15 nm with complete reduction of ceria particles to Ce3þ at sizes Bo3 nm (Wu et al., 2004). Size-dependent inhibition of nitrifying bacteria has been observed and the inhibition was correlated to the fraction Bo5 nm in the suspension (Choi and Hu, 2008).

3.05.3 Major Types of Natural Colloids Aquatic and terrestrial colloids cover a wide range of materials, including organic, inorganic, or biota (Table 1) with proportions dependent on the nature of the inputs, outputs, and within-media processes (Bertsch and Seaman, 1999; Zimmermann-Timm, 2002). They are highly heterogeneous in size, shape, structure, chemical composition, and other properties (Figures 2(a) and 2(b)). Figures 2(a) and 2(b) show typical examples of surface freshwater colloidal material of various sizes, as observed by transmission electron microscopy. Figure 2(c) summarizes the different types of environmental colloids together with the size range they cover and the analytical tools that can be used to characterize them. Environmental colloids have been simplified and modeled in terms of three major colloidal components, namely (1) inorganic colloids, (2) humic substances (HSs), and (3) biopolymers (Buffle et al., 1998). HSs and biopolymers are both organic colloids and therefore are presented together. Their formation, composition, and properties have been described in detail in Filella (2006) and Baalousha et al. (2009), and are summarized in Tables 2 and 3.

3.05.3.1 Inorganic Colloids There are two main types of inorganic colloidal particles in oxygenated terrestrial and aquatic environments, which are aluminum phyllosilicates (e.g., clay, mica, and chlorite) and oxides and hydrous oxides of iron (e.g., hematite and magnetite), manganese (e.g., pyrolusite), and silicon (e.g., silicates) (see Table 2 for a summary of their characteristics). Other inorganic colloids can also be found, but they are usually minor components (e.g., other groups of silicates), or are primarily present in anoxic waters (e.g., FeS, FeS2, and MnS). Calcium carbonate can be found in significant amount in freshwaters but is more likely in a particulate form (Sigg, 1994).

3.05.3.2 Organic Macromolecules Natural organic matter (NOM) in the aquatic or terrestrial environment can be divided into two classes of compounds: (1) HSs including humic and fulvic acids and (2) nonhumic materials (e.g., proteins, polysaccharides, nucleic acids, and small molecules such as sugars and amino acids). HSs and extracellular polymeric substances (EPSs, e.g., polysaccharides and proteins, usually present as fibrillar material) represent the major constituents of NOM and play a significant role in determining the fate and behavior of colloids and particles (Buffle et al., 1998). Other compounds are limited by their rapid turnover or low production rates and hence low concentrations in different environmental systems (Fabiano and Pusceddu, 1998; Mannino and Harvey, 2000). The formation, properties, and characteristics of HSs and EPS are summarized in Table 3. Although this separation of compounds into relatively simple classes of colloids is widespread and of significant value, inferring behavior based on extracted phases or laboratory synthesized surrogates should be performed with caution for several reasons. First, the surface nature of natural colloids is different when compared with such pure phases or homologous series (Schulthess and Sparks, 1989; Schulthess and Huang, 1990) containing complex mixtures of organic and other material. Second, the use of particles larger than colloids in many of these studies (Aldahan et al., 1999; Arnold et al., 2001) means they may be unrepresentative. Finally, extensive particle pretreatment, such as drying, grinding, and saturation with sodium ions, often leads to selective removal of or change to phases and coatings (Mukhopadhyay and Walther, 2001).

3.05.4 Major Types of Manufactured NPs NPs be classified according to their core composition, morphology, surface chemistry, or aggregation state (Buzea et al., 2007; Ju-Nam and Lead, 2008; Rotello, 2004). NP classification based on core composition is the one used in this chapter. Among these categories, metal, metal oxide, QDs, and carbon-based NPs are the most relevant. They can be composed by a single constituent material or can be a composite of two or more materials.

Permanent and variable, that is, pH-dependent

Covers colloids and particulate range

Irregular

Mobilization of particles in soil and subsurface water, or sediment resuspension at river bed

Are the most abundant inorganic colloids in aquatic and terrestrial systems Crystalline with different crystalline structures

Surface charge

Size range

Shape

Formation

Abundance

Structure

Examples

Mainly Al and Si and less concentrations of Na, K,Ca, and Mg Contains trace concentrations of Fe and Ti Clays, mica, chlorite

Aluminum phyllosilicates

Inorganic colloids

Composition

Table 2

Exist under different crystalline and/or amorphous forms such as hematite (a-Fe2O3), goethite (a-FeOOH), lepidocrocite (g-FeOOH), maghemite (g-Fe2O3), magnetite (Fe3O4), and ferrihydrite (amorphous Fe(III) phase)

Abundant in aquatic and terrestrial systems

Particulate Fe(III) oxyhydroxides are formed by oxidation of Fe(II) and hydrolysis by subsurface aeration at oxic/ anoxic boundaries in groundwater, freshwater lakes, and coastal marine water. Biogenic processes

Amorphous or crystalline

Abundant in aquatic and terrestrial systems

Bacteria-mediated oxidation of Mn(II) to Mn(III, IV)

Irregular

B0.05–0.5 mm

B0.05–0.5 mm Spherical or rod like

Variable, largely neagtive

MnS in anoxic water Variable, largely negative

Biogenic silica formed after death of some plankton (diatoms) In all natural waters, in particular at diatom blooms Crystalline

Silica colloids can be released during the diagenesis of amorphous silica

Irregular

Covers colloids and particulate range

Quartz or its polymorphs

Si and O

Silica

MnO2 in oxic water

Mn and O

Oxides and hydrous oxides of manganese

Hematite (a-Fe2O3), goethite (a-FeOOH), lepidocrocite (g-FeOOH), maghemite (g-Fe2O3), magnetite (Fe3O4), and ferrihydrite (amorphous Fe(III) phase) FeS, FeS2 in anoxic water Variable, pzc at near neutral pH

Fe(III) stable in the presence of oxygen at neutral pH then hydrolysis to form oxides

Oxides and hydrous oxides of iron

Baalousha et al. (2009), Davison and De Vitre (1992)

Baalousha et al. (2009), Buffle et al. (1998), Filella (2007)

Chapnick et al. (1982), Helgeson et al. (1984), Murphy and Helgeson (1987), Nelson et al. (1995), Sunda and Huntsman (1987), Wolthoorn et al. (2004a, 2004b)

Buffle et al. (1998)

Lienemann et al. (1999), Tipping et al. (1981)

Buffle et al. (1998), Buffle (2006), Buffle and Leppard (1995a), Buffle and Leppard (1995b)

Berner and Holdren (1977), Davison and De Vitre (1992), Filella (2007), Helgeson et al. (1984), Murphy and Helgeson (1987)

Berner and Holdren (1977), Sigg (1994), Wolthoorn et al. (2004a, 2004b)

References

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Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

Table 3

Organic colloids

Composition

Types/examples

Surface charge

Humic substances

Extracellular polysaccharides

References

C, H, O, N, and minor component S Fresh water humic substances have a high C/N ratio (40–50) Marine humic substances have a low C/N ratio (15–20) C/N ratio of fresh microbial material is between 5 and 10 to 1 Due to different sources Fulvic acids: soluble in water under all pHs Humic acids: soluble at pH 4 2 Humins: insoluble at all pHs pH dependent charge 10 meq g1 over pH range

C, O, N, and occasionally S

Hedges et al. (1997), Thurman (1985)

Organism-specific EPS examples areyalginic acid etc.

Thurman and Malcolm (1981)

pH-dependent charge 0 to  0.8 mequiv g1, alginates represent an exception and might have a maximum surface charge of  6 mequiv g1 Few nanometers thick with a length which can be greater than 1 mm

Buffle (1988), Filella (2007)

Release from phytoplankton cells and bacteria during all stages of growth, in particular during phytoplankton blooms and may comprise 80–90% of the total extracellular release EPS represent the most abundant organic compounds in the biosphere and constitute the largest fraction of cells (concentrate on noncellular organics, minor fraction B35% at most in eutrophic waters) They may represent a significant proportion of NOM in freshwater, varying seasonally B5–30% in surface waters of lakes and likely accounts for higher proportion (up to 80%) of NOM in marine systems Polysaccharides can be rigid due to the large quantity of strongly bound hydration water (up to 80%), their association into double or triple helices that may be stabilized by hydrogen or calcium bridges or helices aggregation. Flexible conformations also possible adopt variable conformation as a function of pH and ionic strength

Leenheer and Croue´ (2003), Myklestad (1995), Strycek et al. (1992), Thurman and Malcolm (1981)

Size range

Primarly particles 1–2 nm, globules B10–20 nm and aggregates of several micrometers, surface coatings

Formation

Degradation of higher plants in terrestrial and freshwater environments. Degradation of plankton in marine systems

Abundance

Humic acids are generally terrestrial, while aquatic HS are dominated by FA. Make up most of organic fraction

Structure

Different models depending on the technique used, including macromolecules, supramolecular association of small molecules, micelles, and semipermeable spheres

3.05.4.1 Carbon-Based NPs 3.05.4.1.1 Fullerenes Fullerenes are molecules with 60 atoms of carbon, commonly denoted as C60 or, less commonly, with a larger number of carbon atoms, for example, C70, C76, C78, and C80 (Kikuchi

Baalousha et al. (2005), Buffle et al. (1998), Leppard et al. (1990), Santschi (1998), Thurman et al. (1982)

Aluwihare et al. (1997), Leenheer and Croue´ (2003), Myklestad (1995), Santschi (1998), Thurman and Malcolm (1981), Wilkinson et al. (1997)

Duval et al. (2005), Leppard et al. (1990), Leppard and Arsenault (2003), Leppard (1997), Morris et al. (1980), Piccolo (2001), Rees (1981), Swift (1989), Wershaw (1999)

et al., 1992). However, the most widely studied is the C60 molecule. C60 possesses a spherical molecular structure where the carbon atoms are positioned at the vertices of a regular truncated icosahedron structure (Kroto et al., 1985). There are also higher mass fullerenes with different geometric structures.

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

A large number of applications of fullerenes in optics (Singh and Roy, 2004), electronics (Diederich, 2005; Otsubo et al., 2005), and biomedicine (Yang et al., 2007) make this molecule important and promising in the nanotechnology field; the most popular technique for nC60 production reported in the literature is based on a physical method that involves the use of an arc discharge between graphite electrodes in 200 torr of He gas (Kratschmer et al., 1990). However, the direct use of carbon-based fullerenes in high-impact applications, for instance, in biological ones, is restricted by their poor solubility in aqueous media (Kadish and Ruoff, 2000) where they aggregate and are often termed nC60. C60 is soluble in a small range of organic solvents but the insolubility in water limits utilization in biological applications. In order to overcome this problem, two different strategies to increase their solubility have been reported in the literature: (1) noncovalent encapsulation of fullerene molecules into soluble polymeric or host molecules (Atwood et al., 1994; Yamakoshi et al., 1994) and (2) covalent functionalization of fullerenes by introduction of hydrophilic groups by chemical modification (Chiang et al., 1996). The second strategy has attracted more interest as these novel building blocks can be used for further molecular constructions. Yang and co-workers have reported the syntheses and characterization of a series of fullerene-derivatized amino acids, [60]fullerene-substituted phenylalanine, and lysine derivatives, suggesting that the incorporation of fullerene-based amino acids into proteins, peptides, or antibodies could lead to new applications in medicinal chemistry (Yang et al., 2007). In water, unmodified fullerenes are largely insoluble forming large aggregates of tens to hundreds of nanometers (or larger). Dispersion in water as NPs has been achieved in two ways: solvent exchange (Deguchi et al., 2001) and ultrasonication and stirring in water (Brant et al., 2005; Deguchi et al., 2006) in order to promote the formation of smaller clusters. The mechanism of this process is not currently clear. However, it has been assumed that the self-assembly of the fullerene molecules occurs during the formation of the NPs, and aggregation of C60 has been observed in different solvents. In addition, measured zeta potentials of these fullerenes show a negative charge, although differing mechanisms have been suggested about how a negative potential is acquired (Brant et al., 2005). Interesting differences in nC60 between the cosolvent and water-stirred varieties have been observed, generally including greater toxicities in bacteria, fish, and invertebrates (Kashiwada, 2006; Lyon et al., 2006, 2005) when dissolved with co-solvent. Again, the mechanism needs to be clarified, although the direct impact of the co-solvent or its contaminants on toxicity has been suggested (Henry et al., 2007; Oberdo¨rster, 2004). In addition, in most studies physico-chemical characteristics have not been measured fully and until this is performed alongside toxicity studies, our lack of understanding of C60 toxicity will persist.

3.05.4.1.2 Carbon nanotubes CNTs have been used in multiple applications in the development of novel materials, due to their structural robustness and synthetic versatility. Ijima and co-workers, pioneers in the synthesis of CNTs in 1991 (Iijima, 1991), reported the

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formation of these nanotubes by using a carbon cathode and the arc discharge technique. However, the discovery of the structure of these nanomaterials took place a few years after their first synthesis (Bethune et al., 1993; Iijima and Ichihashi, 1993). There are two main forms of manufactured CNTs, the single-walled CNT (SWCNT) and multi-walled CNT (MWCNT). In terms of structure, the SWCNT is a single-layer graphene sheet rolled up as cylindrical shapes, with a diameter of approximately 1 nm and a length of several micrometers, whereas the MWCNT contains two or more concentric layers with various lengths and diameters (Gao, 2004) and larger diameters. CNTs are generated by arc evaporation (Ebbesen, 1994), laser ablation (Paradise and Goswami, 2007), and pyrolysis (Endo et al., 1993). Ebbensen and co-workers demonstrated that CNTs can be generated in bulk amounts by varying arcevaporation conditions (Ebbesen and Ajayan, 1992). Inherent properties of CNTs have been widely investigated and SWCNTs possess important mechanical, thermal, photochemical, and electrical properties (Arepalli et al., 2001) which are industrially useful. These nanomaterials are robust and stiff but flexible, and they have been reported as the strongest of all the synthetic fibers (Arepalli et al., 2001), although practical experience suggests that batch to batch (and even within batch) variability is significant. Materials containing CNTs have been suggested as being strong enough to build spacecrafts, space elevators, artificial muscles, and land and sea vehicles (Kumar, 2006), although such claims are as yet unvalidated. SWCNTs can conduct twice the electricity of copper, making these materials excellent electrical conductors, and may also be used to improve rechargeable batteries and fuel cell production, for instance (Kumar, 2006). They also have a distinctive electron-transport property, and commonly in a manufactured material bulk sample B30% of the SWCNTs are conductors and 70% are semiconductors (Watts et al., 2002). In general, CNT products contain substantial amounts of metal impurities. Clearly, the presence of such metal impurities might lead to a variety of adverse biological endpoints. The similarity in structure with asbestos suggests that CNTs may behave as these fibers do when considering their toxicity. Recent evidence (Poland et al., 2008), where a variety of CNT morphologies were examined in animal studies, has suggested long, high aspect ratio, and biopersistent CNTs cause pathologies similar to asbestosis. A second-order effect appeared to be due to metal contamination, although insufficient metal data were provided to draw firm conclusions. CNTs appeared to be more hazardous than asbestos, but in these studies producing CNT aerosols appeared to be difficult, so their risk to human health may be low. Their hydrophobicity suggests that lipids and organic materials may be an ultimate sink, for example, sediments. Functionalized CNTs which are likely to be more hydrophilic may behave differently. In aquatic systems, Smith and co-workers obtained evidence of oxidative injury in rainbow trout (Oncorhynchus mykiss) exposed to SWCNTs (Smith et al., 2007). They concluded that CNTs acted as respiratory toxicants in rainbow trout and also caused brain injury, reminiscent of a stroke. A significant concern of investigations of CNT toxicity is inadequate assessment of purity, for example, the presence of metal catalysts used in

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production, and their physico-chemical form (Handy et al., 2008a, 2008b; Kagan et al., 2006; Pulskamp et al., 2007). For instance, toxicity in zebrafish embryos exposed to SWCNTs has been attributed to the presence of Co and Ni catalysts used in the preparation of the SWCNTs (Cheng et al., 2007), although this remains to be confirmed.

3.05.4.2 Metal Oxide NPs This class of NPs is extensively used in a considerable number of applications in food, material, chemical, and biological sciences (Aitken et al., 2006). It is well known that bulk materials based on TiO2, SiO2, and aluminum and iron oxides have been massively produced for many years. More recently, nanoparticulate versions of these metal oxides and others have been manufactured and introduced in commercial products such as cosmetics and sunscreens (TiO2, Fe2O3, and ZnO) (Nowack and Bucheli, 2007), fillers in dental fillings (SiO2) (Balamurugan et al., 2006), in catalysis (TiO2) (Aitken et al., 2006), and as diesel additives (CeO2) (Laosiripojana et al., 2005). Several of the most commercially important NPs are discussed below.

3.05.4.2.1 Iron oxide NPs Iron oxide NPs have been extensively used for biological and medical applications such as magnetic resonance imaging (MRI) and manufacturing pigments (Cornell and Schwertmann, 1996). NPs in the form of Fe3O4 and Fe2O3 have been synthesized with a number of methods involving different compositions and phases (Neveu et al., 2002). During the last few years, many publications describing efficient synthetic methods to obtain shape-controlled, highly stable, and monodisperse magnetic NPs have been produced. Co-precipitation (Willis et al., 2005), thermal decomposition (Park et al., 2004), and hydrothermal synthesis (Wang et al., 2005) techniques are among the most used methods and are also easily scalable with high synthetic yields. Steric and electrostatic repulsion are the interactions involved in the stability of the colloidal iron oxide NPs manufactured by the previously mentioned techniques. Their stability depends on the stabilizers, such as fatty acids or amines, and the polarity of the solvent used. In the case of the synthesis of the iron oxide NPs in an aqueous medium through the coprecipitation method, NPs are stabilized by repulsive electrostatic forces due to positive charge on particles (Lu et al., 2007), dependent on pH and ionic strength (Baalousha et al., 2008), whereas NPs obtained by the thermal decomposition technique are often sterically stabilized in an organic solvent by fatty acids or surfactant (Sun et al., 2004). Despite their high usage, it is unlikely that these materials present significant environmental problems. In fact, iron oxide NPs could be beneficial to some extent. The addition of NPs to oceanic waters, where primary productivity is limited by low Fe concentrations, may increase oceanic productivity and drawdown sufficient CO2 to some extent mitigating climate change (Raiswell et al., 2006).

3.05.4.2.2 Zinc oxide NPs Zinc oxide (ZnO) is a direct band-gap semiconductor with band-gap energy of 3.36 eV at room temperature, high exciton

binding energy of 60 meV, and high dielectric constant (Singh et al., 2007). Therefore, the luminescent properties of ZnO have attracted considerable attention due to its potential application in ultraviolet (UV) light-emitting devices. This band-gap semiconductor has numerous potential applications, particularly in the form of thin films, nanowires, nanorods, or NPs (Starowicz and Stypua, 2008), and can be introduced to optoelectronic and electronic devices. They can also be used in the production of chemical sensors and solar cells (Singh et al., 2007). One of the most widely used commercial applications is their use in the production of sunscreens and cosmetics, due to their property of blocking broad UV-A and UV-B rays (Huang et al., 2008) but being transparent. This is thus a potentially important diffuse source of NP contamination, due to wash-off from individuals into the environment. ZnO NPs are believed to be a nontoxic, biosafe, and biocompatible nanomaterial (Zhou et al., 2006), although a few reports have shown some toxicological activity of ZnO NPs, for example, in algae (Adams et al., 2006) and in vitro studies of human lung cells. It has been shown that they cause membrane damage in the Escherichia coli, possibly due to oxidative stress mechanisms (Zhang et al., 2007). More recently, Huang et al. (2008) have investigated the possible interactions that govern the bactericidal activity of 60–100 nm polyvinyl alcohol (PVA)–ZnO NPs against Streptococcus agalactiae and Staphylococcus aureus. Results showed that low concentrations of ZnO NPs did not induce any cellular damage, as previously demonstrated by other research groups (Feldmann, 2003). However, they observed cellular damage when the PVA-coated ZnO NP concentrations are higher than 0.016 M in the ethylene glycol (EG) medium containing the cells. After contact with the cells, there was a significant change in the ZnO NP crystal structure (Huang et al., 2008). Heinlaan and co-workers investigated the toxicity of ZnO NPs to bacteria Vibrio fischeri, and crustaceans Daphnia magna and Thamnocephalus platyurus, and discerned the toxic effects of metal oxides and soluble metal ions (Heinlaan et al., 2008). They showed that the NPs did not necessarily have to enter the cells to cause damage in the cell membrane. In fact, the contact between the particle and the bacterial cell wall (or other cell wall/membrane) may cause changes in the vicinity of organism–particle contact area and may also increase the solubilization of metals (Heinlaan et al., 2008). It has also been demonstrated that the release of metal ions from the ZnO NP, that is, from the NP dissolution, was responsible for toxicity in lung cell lines (Brunner et al., 2006), while under realistic environmental conditions, a similar mechanism was reported (Franklin et al., 2007). As a general point, toxicity due to NP dissolution needs to be considered for all inorganic NPs and this appears to be particularly so for the highly soluble ZnO.

3.05.4.2.3 Titania NPs Titanium oxide (TiO2) exists in three main crystallographic structures, for example, anatase, rutile, and brookite (Arami et al., 2007) of which the first two are usually considered the most important in the environment. Each of these forms presents different properties and therefore different applications and environmental impacts. The phase-specific

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environmental effects have not been much studied due to the difficulties of producing phase-pure NPs (Valsami-Jones et al., 2008). It is also well known that it is easier to obtain TiO2 NPs with good crystallinity and high specific surface area from the anatase crystalline form. This evidence can be attributed to the thermodynamic stability which is size dependent. Indeed, anatase is more stable than rutile at particle diameters below 14 nm (Zhang and Banfield, 2000). Apart from the applications of TiO2 as a catalyst support (Djenadic et al., 2007), semiconductor photocatalyst (Gra¨tzel, 2001), and sensors (Ruiz et al., 2003), there is also a great interest in the development of synthetic methods to obtain TiO2 NPs with high morphological specificity such as nanofibers, nanowires, nanorods, and nanotubes (Wu et al., 2006) due to their potential applications in solar energy conversion, photocatalysis, photovoltaic devices, and carrier for metallic NPs (Zhu et al., 2005). TiO2 NPs are, like zinc oxide NPs, high-band-gap semiconductors due to thier large energy gap (Eg ¼ 3.2 eV). This nanostructured material requires the use of near UV light in order to be photoactivated (Bellardita et al., 2007; Reijnders, 2008). Consequently, photocatalysis using TiO2 NPs has recently become very important. The use of TiO2 NPs has improved the photodegradation process and the complete mineralization of toxic organic pollutants. In fact, TiO2 NPs have been successfully used in environmental technology for the treatment of wastewater and groundwater, the removal of benzothiophene from diesel fuel, and the degradation of air pollutants, specifically nitrogen oxide, sulfur oxides, and volatile organic compounds (Toma et al., 2006; Yu et al., 2006). Although TiO2 NPs possess large specific surface area, commercial applications have not been developed rapidly due to their tendency to aggregate and coalesce very easily forming larger particles. This size increase has an undesirable effect on the catalyst efficiency and also the difficulties in the separation and recovery of TiO2 particles from the reactant mixture (Yu et al., 2002). Nevertheless, TiO2 is used widely, as with ZnO, in sunscreens, because of its ability to absorb UV radiation. However, its photoactivity, via reactive oxygen species (ROS) production, might result in harm to skin tissue, although certain crystal phases (rutile) are less photoactive than others (anatase) (Sayes et al., 2006), while doping of titania with Mn has resulted in reduced photoactivity (Kim et al., 2007). Microorganisms in the presence of light (Oberdo¨rster et al., 2007) are adversely affected by TiO2 NPs due to the production of ROS (Hirano et al., 2005). This experimental evidence suggests that these NPs can produce oxidative stress in aquatic organisms. This has been confirmed in rainbow trout, where inflammatory injury and respiratory distress were observed after the exposure to TiO2 NPs (Federici et al., 2007; Reijnders, 2008). TiO2 particles of different sizes (10, 30, and 300 nm, as described by the manufacturer) were shown to inhibit algal growth, but the physiological mode of action is not yet understood (Hartmann et al., 2009). Authors suggested that mechanism for this toxic effect is other than the generation of ROS, and possible mechanisms include adhesion of TiO2 to algal cells and physical disruption of the cell membranes; however, it is not possible to make a general conclusion on the factors determining the algal toxicity of TiO2 at the current state of knowledge. TiO2 NPs were also shown to

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inhibit bacteria growth in the presence and absence of light. The presence of light was shown to be an important factor presumably due to its role in promoting the generation of ROS; however, the inhibition of bacterial growth in the absence of light suggests that undetermined mechanisms in addition to production of ROS were responsible for toxicity (Adams et al., 2006).

3.05.4.2.4 Ceria NPs Cerium oxide NPs (CeO2–x, where x is between 0 and 0.5) of range 1–10 nm have been the focus of most of the advances involving the use of these materials, and also it is well known that the distinct properties are strongly size dependent and would show significant quantum size effects (Xu et al., 2008). Therefore, the development of methodologies for the synthesis of monodispersed CeO2 NPs of different and well-controlled sizes is the ultimate aim in this field. These NPs have been produced using preparation routes similar to the ones used for TiO2 and ZnO NPs. Cerium dioxide, also named ceria (CeO2–x), is a cubic fluorite-type oxide which has high thermodynamic affinity for oxygen and sulfur (Bumajdad et al., 2009). Cerium is especially interesting among rare earths because of its ability to easily change its valance state from Ce(III) to Ce(IV) (Hailstone et al., 2009), and the ratio of the two oxidation states appears to be size dependent, with increasing Ce(III) at the lower size. CeO2 has been used as an oxygen sensor (Robinson et al., 2002) and as a diesel additive, increasing fuel efficiency. The latter use, with its potential to distribute NPs widely in the environment from diesel exhausts, is of particular concern. CeO2 NPs are one of the most rapidly growing nanomaterials due to its broad use in this and other areas such as polishing and computer chip manufacturing (Rothen-Rutishauser et al., 2009). More recently, CeO2–x NPs have been used to reduce oxidative stress in biological systems as a free radical scavenger (Niu et al., 2007), although they might also cause oxidative stress under different conditions. The evidence suggests that toxicity is low in humans (Park et al., 2008) in very short term exposure studies and that positive effects occur, such as reduction in the particle number concentration of ultrafine particles form diesel combustion, but further work is required given the likely high exposures from this use. Negative effects of CeO2 on E. coli have been reported (Thill et al., 2006), where the NPs can be adsorbed on the outer membrane of E. coli as shown in the TEM images of Figure 3. Ceria also has been demonstrated to show Fenton-like chemistry (Heckert et al., 2008). It has also been shown that CeO2 NPs can be reduced in biological media. This reduction has been observed during the contact between CeO2 NPs and human dermal fibroblasts in vitro (Auffan et al., 2009) with oxidative stress and strong genotoxic effects for very low doses (40.06 mg l1). The suggested mechanism of action via oxidative stress is not fully proved and has not been elucidated in algae and invertebrates (Van Hoecke et al., 2009), although close spatial association of NPs and cells was required for toxicity. Ceria NPs have been shown to inhibit algal growth more effectively than the bulk counterparts with 72-h IC50 vlaues of 10.371.7 and 66722 mg l1 respectively, and the toxicity was related to cell-membrane damage (Rogers et al., 2010).

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3.05.4.3 Metal NPs Metal NPs have attracted special attention in the fields of biological and medical sciences, catalysis, and sensing (Murphy et al., 2008; Tao et al., 2008) due to their optical properties. The applications based on these materials can benefit from their sensitive and tunable electromagnetic responses. These NPs have been introduced in many commercially available products such as clothing, cosmetics, footwear, and plastic containers.

3.05.4.3.1 Gold and silver NPs Metal systems are expected to form the basis of new diagnostic biosensor technologies and novel therapeutic agents, and the most widely studied systems are based on gold and silver (Langer and Tirrell, 2004; Liu and Lin, 2007). Silver NPs (AgNPs) have been widely used in commercial applications as bactericides in fabrics, cosmetics, and other consumer products (Jeon et al., 2003; Kim et al., 2007). Risk from these NPs is potentially high for these reasons, that is, high inherent toxicity and large use in consumer products leading to high environmental exposure (Eckelman and Graedel, 2007). These NPs, especially Au, have interesting properties such as their stability, inertness (45 nm), and size-related and tunable electronic, magnetic, and optical properties. Furthermore, metal NPs have the advantages of easy preparation and the possibility of chemical modification of the surface (Haick, 2007) by a variety of capping agents. Metal NPs exhibit the so-called surface plasmon resonance (SPR), which is caused by the interaction with the incident light and the free electrons in the materials (Noguez, 2007).

The strong extinctions of conductive metal NPs arise from an electrodynamic phenomenon known as surface plasmons. These are generated by the collective excitation of free electrons in response to a characteristic electromagnetic frequency (Daniel and Astruc, 2004). The plasmonic coupling of metal NPs with light enhances a broad range of useful optical phenomena, such as resonant light scattering (RLS), SPR, and surface-enhanced Raman scattering (SERS), all of which have tremendous potential for ultrasensitive chemical and biomolecular detection and analysis (Rotello, 2004). The resonance effect depends on a number of properties such as shape, surface chemistry, aggregation state, and size. In general, the aqueous solutions of gold NPs stabilized with citrate are red colloidal solutions which present a surface plasmon band (SPB) at approximately 550 nm (Grabar et al., 1997), whereas the corresponding silver NPs have an SPB at approximately 420 nm (Doty et al., 2005); this varies though. SPR is an important analytical tool and is potentially important in environmental applications and environmental detection, although only at the most sensitive methodologies available. The surface plasmon has been used to quantify the changes in gold NP properties upon interaction with natural organic macromolecules, such as HSs, which absorb maximally at B254 nm (Diegoli et al., 2008). Diegoli and co-workers monitored aging and aggregation behavior of acrylate- and citrate-stabilized gold NPs in the presence and absence of HS by UV–visible absorption spectroscopy and TEM (Figure 4), at different pHs. AgNPs (and also dissolved silver) are known to have significant antibacterial properties, and are used in fabrics and cosmetic and have medical uses. Ag is used in dental resin composites (Herrera et al., 2001), in synthetic zeolites (Faghihian and Kamali, 2003), and in coatings of medical equipments such as catheters, infusion systems, and medical textiles (Markarian, 2006). The antibacterial activity of silver ion (Agþ) and related silver species and, to a lesser extent, AgNPs has been studied (Sondi and Salopek-Sondi, 2004; Morones et al., 2005; Pal et al., 2007; Fabrega et al., 2009a), although the exact mode of action is not fully known. The catalytic oxidation by metallic silver and reaction with dissolved monovalent silver ion likely contribute to its bactericidal effect. The antibacterial mechanism of silver-containing products possibly ends in a long-term release of silver ions (Agþ) (see Figure 5) by oxidation of zero-valent metallic silver Ag0 in contact with water (Kumar and Mu¨nstedt, 2005) and it has been shown, for instance, that the Agþ ion inhibits the enzymes for the P, S, and N cycles of nitrifying bacteria (Kumar et al., 2005). In addition, Agþ can block DNA transcription, interrupt bacterial respiration and adenosine triphosphate (ATP) production, and react with proteins by combining the –SH groups of enzymes which leads to the inactivation of the proteins (Jeon et al., 2003). When this metal is in a nanostructured form, higher antimicrobial activity is expected due to its larger specific surface area (Yoon et al., 2007) at least in comparison to the bulk. A number of detailed studies have investigated Ag toxicity to bacteria (Elechiguerra et al., 2005; Pal et al., 2007; Shahverdi et al., 2007; Sondi and Salopek-Sondi, 2004). AgNPs have been shown to increase antibacterial activity of antibiotics such as vancomycin and amoxicillin when used on S. aureus and

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E. coli (Shahverdi et al., 2007). Both size and shape (Morones et al., 2005; Pal et al., 2007) have been shown to affect antibacterial properties, with smaller-sized NPs having more effect. In addition, triangular plates had improved antibacterial efficiency compared with spherical and rod-shaped NPs. Along with Agþ (i.e., AgNO3), triangular nanoplates show the strongest biocidal action. A number of these studies also observed aggregation of silver NPs and larger effects on bacteria on agar plates compared with bacteria grown in a culture medium. This effect was most likely due to the increased dose and certain experimental conditions such as temperature, pH, and mixing speed (Pal et al., 2007). The work of Fabrega et al. (2009a, 2009b) indicates that dissolved silver has higher antibacterial effects compared to AgNPs (20–50 nm, citrate stabilized), but that the NPs have a different toxicity mechanism compared to dissolved ions, a finding backed up by recent work on CuNPs in fish (Griffitt et al., 2007). Navarro and co-workers performed a study comparing the toxicity of dissolved silver ion (Agþ) and AgNPs in freshwater algae (Navarro et al., 2008a). They examined the short-term toxicity of Agþ and AgNPs to photosynthesis in Chlamydomonas reinhardtii using fluorometry. Their results indicate that the interaction of these particles with algae influences the toxicity of AgNPs, which is mediated by Agþ. NPs contributed to the toxicity as a source of Agþ which was formed in the presence of algae. They also showed that abiotic factors influence or affect the dissolution rates, such as size and surface area, or the chemical conditions of the environment. They also suggested that biotic interactions should be considered to assess the risks caused by NPs in natural aquatic systems (Navarro et al., 2008a). The exact role of size, surface properties, and dissolution has yet to be fully elucidated in relevant organisms.

3.05.4.3.2 Zero-valent iron NPs The zero-valent metal NPs have received great attention due to their potential applications in the remediation of

contaminated groundwater (Elliott and Zhang, 2001; Quinn et al., 2005). Several studies have shown that these iron NPs possess the capacity of transforming (nZVI, nano-zero valent iron) or sorbing (surface oxide layer) a wide range of common environmental contaminants, including chlorinated organic solvents (Nutt et al., 2005), organic dyes (Liu et al., 2005), various inorganic compounds (Alowitz and Scherer, 2002; Cao et al., 2005), and metals (Kanel et al., 2005; Xu et al., 2005). Despite the apparent lack of risk from iron itself (see argument above related to iron oxide NPs), zero-valent iron provides one of the few examples of an adverse environmental or human impact of NPs. Zhang (2003) showed that these NPs reduced high concentrations of solvents to nearly zero within days, but at the same time oxygen levels were reduced making the groundwater anoxic and pH levels changed significantly (Zhang, 2003). No further analyses were performed in this study, although presumably there were at least substantial short-term effects on groundwater ecology. In the absence of further applications, it is also likely that conditions returned to their previous state within a relatively short period of time, but continued application may have had a prolonged effect.

3.05.4.4 Quantum Dots The semiconductor NPs have attracted a special interest for their promising applications in molecular biology, medicine, and information technology (Chen, 2008; Gao, 2004), although given their composition (e.g., Cd), they are likely to be inherently highly toxic and large-scale use needs to be viewed with caution. Capping agents are likely to reduce dissolution and passivate, at least in the short term. Semiconductors are key components of devices used in computers, light-emitting diodes, sensors, etc. (Wang et al., 2007). Semiconductors are a unique class of materials, in that they can assume characteristic properties of both metals and isolators, depending on conditions that determine the electronic nature of valence and

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Figure 6 Proposed mechanism of QD-induced cell death involving Fas, lipid peroxidation and mitochondrial impairment. Cells exposed to cadmium telluride quantum dots (unmodified and NAC-modified) induce ROS which causes Fas upregulation and plasma membrane lipid peroxidation. Apoptotic cell death is induced by activation of Fas and its downstream effectors. Lipid peroxidation also occurs at the mitochondrial membranes, degrading cardiolipin, changing the mitochondrial membrane potential, eventually leading to the release of cytochrome c, and promoting apoptotic cascades. NAC bound to the QD surface, modifies the extent of QD internalization, which is correlated with cell death, upregulation of Fas, and ROS-induced lipid peroxidation. NAC treatment (2–5 mM) abolishes oxidative stress, induces antioxidant enzymes and attenuates mitochondrial impairment. From Choi AO, Cho SJ, Desbarats J, Lovric J, and Maysinger D (2007) Quantum dot-induced cell death involves Fas upregulation and lipid peroxidation in human neuroblastoma cells. Journal of Nanotechnology 5(1): doi:10.1186/1477-3155-5-1.

conduction bands. In the ground state, the valance band is completely filled and separated from the conduction band by a narrow band gap (Eg) (Rotello, 2004; Schmid, 2004). Semiconductor nanocrystals, or QDs, have been reported from a variety of compositions, including CdSe, CdS, Si, GaAs, and PbSe (Rotello, 2004). The ecotoxicity of QDs has only recently gained interest (Moore, 2006). For instance, the toxicity of CdTe may be linked to the leaching of toxic heavy metals from the colloidal form, and derived from the intrinsic properties of the size and surface chemistry of the CdTe QDs (Clapp et al., 2004). In theory, they could transfer energy to nearby oxygen molecules and lead to the formation of ROS, which may lead to cell inflammation, damage, and death. In fact, Choi and co-workers showed that QDs can induce cell death in human neuroblastoma cells, and lipid peroxidation and mitochondrial impairment were proposed as possible mechanisms (see Figure 6) (Choi et al., 2007). More recently, the ecotoxicological effects of CdTe QDs to freshwater mussel (Elliptio complanata) have been reported showing that these NPs are immunotoxic to freshwater mussels and can cause oxidative stress in gills and DNA damage (Gagne´ et al., 2008).

3.05.5 Important Physico-Chemical Properties of Natural Colloid The behavior, fate, and environmental functions of natural colloids and manufactured NPs are strongly influenced by their physico-chemical properties such as size, shape, surface

charge, surface coating, and others. These properties are rarely, if ever, uniform. This implies the need to understand these properties, factors influencing them, the extent of their variations, and some of the experimental methods that are available to measure these properties.

3.05.5.1 Size As discussed above, size is the primary means of defining colloids (see Section 3.05.2.1) in natural systems and manufactured NPs (see Section 3.05.2.2). Size is a useful parameter that can potentially help understanding the behavior of colloid in the environment and its role in the biogeochemical cycling of trace contaminants and nutrients, since other important physical and chemical parameters relevant to colloidal behavior, for example, specific surface area, surface reactivity, diffusion coefficient (Lead et al., 2000a, 2000b), pollutant binding and speciation (Lyven et al., 2003; Stolpe and Hassellov, 2007), bioavailability and biouptake of pollutants (Guo et al., 2002; Pan and Wang, 2002), sedimentation, and transport, are influenced greatly by colloid size. Smaller particles, in general, have a larger surface area, a higher adsorption capacity, diffuse more rapidly, and travel for longer distances in the environment. In addition, small particles reduce the bioavailability of chemicals by reducing the free fraction (Pan and Wang, 2002), though they might be themselves bioavailable. Natural colloids are usually characterized by a wide range of size distribution. Monodisperse colloids have never been reported in natural systems, although laboratory studies usually use monodisperse colloids. The size distribution of

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Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

natural colloids depends on the source and nature of particles, physical, chemical, and biological processes such as erosion, degradation, aggregation, disaggregation, and aging and the physico-chemical parameters of the system such as pH, ionic strength, and redox potential. Figures 2(a) and 2(b), correspondingly, show typical TEM images of freshwater natural colloidal particles (Buffle et al., 1998). Figure 2(a) shows that the materials with different sizes from (1) 1–2 nm, presumably correspond to HSs; (2) several hundreds of nanometers with clear defined angular edges, presumably correspond to inorganic colloids (possibly clays); (3) several hundred nanometers of globular shape, presumably correspond to iron oxides; and (4) fibrillar materials of several nanometer thickness and several hundreds of nanometer length, presumably correspond to biopolymers. Figure 2(b) shows mainly roughly spherical silica particle (gray at center) aggregated with smaller iron hydroxide particles (black spheroids). A simplified classification of environmental particles together with their approximate size range is given in Figure 2(c). Several observations can be made. For instance, the size of each class spreads over several orders of magnitude and the boundary between different colloid types is somewhat artificial and rarely found in natural systems. More realistically, colloids are often found as components of heteroaggregates and sorbed to larger particles (Figure 2(b)). Although polydisperse, natural colloids can be fractionated either by physical (e.g., size exclusion chromatography (SEC), field flow fractionation (FFF), and crossflow ultrafiltration (CFUF)) or chemical (e.g., XAD resin for fractionation of HSs) methods into simpler fractions that can be studied individually, and this has improved our understanding of natural colloids and their environmental functions. In addition, the development of sample preparation methods with minimum perturbation and new analytical tools (e.g., FFF, CFUF, TEM, and AFM) for colloid fractionation and size determination along with combining these methods has increased our knowledge of colloid size and functions dramatically (Baalousha et al., 2005 b; Baalousha and Lead, 2007; Gibson et al., 2007). In particular, a multimethod approach allows the determination of particle-size and nonsize parameters that are useful to describe colloidal structure such as shape, morphology, sphericity, and permeability (Baalousha et al., 2005a; Baalousha and Lead, 2007). Therefore, a multimethod approach is preferred (Buffle and Leppard, 1995b). Size distribution obtained from the different methods, even on the same sample and prepared with minimum sample perturbation methods, is often different. This can be explained by the differences in physical principles of the different techniques, measured parameters, detection limit, and analytical window of each method (summarized in Table 4). The measured size parameters include: physical size (microscopy techniques), hydrodynamic diameter (including a hydration layer, FFF), and radius of gyration (mass distribution within a particle, laser light scattering (LLS)). In addition, the size distribution may be related to particle number (microscopy techniques), mass (FFF), or surface area (Brunauer, Emmett, and Teller (BET)). Furthermore, the average diameter can be described by the number-, weight-, or z-average (Table 4).

3.05.5.2 Shape and Morphology Shape and morphology is one of the most important parameters to describe and classify natural colloids. Natural colloids frequently have anisotropic and irregular shape and are rarely, if not ever, isotropic (Balnois et al., 2003). This can be explained by the different chemical (e.g., pH, ionic strength, and acidity), mechanical (e.g., erosion, flow, and transport), and biological conditions to which natural colloids are exposed. Particle shape reflects material composition, release from the matrix, and transportation, and may be different from one site to another. Therefore, colloids have what may be termed a particle shape distribution. Microscopy observations show natural colloids from Rhine River (Figure 2(a)) with different shapes, including spherical, globular, irregular particles with defined angular edges, and fibrillar materials. Figure 2(b) shows natural colloids from Lake Bret, again with different shapes, mainly plate-like particles with some spheroids (Buffle et al., 1998). In addition, when colloids aggregate, many different shapes may form (see discussion in Section 3.05.7.2) that do not necessarily correspond to the primary particles. This illustrates the additional complexity in describing and defining colloids and makes quantification and incorporation of this parameter into usable models of behavior very difficult. For crystalline colloids such as clays, shape is partially determined, in addition to other parameters, by crystallographic structure (e.g., hexagonal shape of kaolinite vs. tubular shape of halloysite) and they are usually characterized by sharp edges. This might be the case in laboratory studies (and it is the case for synthesized NPs, described later) but is rarely the case in the natural environment due to erosion and degradation processes. Amorphous (noncrystalline) colloids such as organic macromolecules are characterized by globular or fibrillar structures with no sharp edges. Several methods have been used to quantify the shape of particles based on image analysis such as sphericity or circularity:

rffiffiffiffiffiffiffiffiffi 4pA C¼ P2

ð1Þ

where A and P are the area and perimeter of the particle, respectively. Others, such as aspect ratio (length/width), fractal dimension (see Section 3.05.7.2), etc. (Hentschel and Page, 2003), are frequently used. A major problem with these calculated parameters from image analysis is that they give a two-dimensional (2D) description, while shape and morphology are 3D. It is possible to overcome such problems by electron tomography measurements, that is, 3D reconstructions of images obtained at different electron microscopy (EM) stage tilt angles (Gontard et al., 2006), though this is very time consuming. Furthermore, particle morphology and aggregate structure can be determined by other methods such as static laser light scattering and small angle neutron scattering. As with other colloidal parameters, combination of analytical techniques has been used to produce other parameters that can describe particle shape and aggregate structure. For instance, combination of flow FFF (FlFFF) and static light scattering (SLS) gives a shape

0.1 nm height

Number

40.5 nm

Ambient air Liquid High resolution analysis can be performed under ambient pressure and in aqueous media

Time consuming, require large number of particles for representative PSD TEM require special sample preparation Require UHV

Spatial resolution

Size distribution

Analytical window

Sample environment

Difficulties

Advantages

Scanning a probe on a surface

High resolution, visual observation of the particles

UHV

41 nm

Number

0.1–0.2 nm

Interaction of electrons with matter

Direct observation of the particles, gives semi 3D information

UHV

430 nm

Number

5–20 nm

Interaction of electrons with matter

PSD, morphology, and topography

Principle

PSD, morphology crystallography structure, defects

PSD, shape, tip– specimen interaction forces

SEM

Measured parameter

HR-TEM

AFM

Particle size characterization techniques

Method

Table 4

Separation occurs in liquid media Possibility of hyphenation with other techniques such as light scattering and mass spectroscopy Time consuming, material losses, data interpretation, and representation

Representation of the calculated x potential

Easy, fast

Liquid

0.001–3 mm

o0.45 mm Liquid

Intensity based

Measuring hysteresis in scattered light intensity due to Brownian motion -

Diffusion coefficient, hydrodynamic diameter

DLS

Mass

o1 nm

Interaction of colloids with an applied field

Diffusion coefficient, hydrodynamic diameter

FlFFF

Possibility of sample damage Low spatial resolution UHV

Nondestructive, fast and averaging of properties

UHV

nm range

Volume

10 mm

Structural analysis of crystals, amounts of different crystalline phases, crystal size Elastic interaction of X-rays with matter

XRD

Representation of the measured surface area, UHV

Easy, fast, direct measurement of surface area regardless particle morphology

UHV

nm–mm range

Surface area

-

Adsorption of a gas into the surface of particles

Surface area

BET

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Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

factor, defined as the ratio of the radius of gyration to the hydrodynamic radius of the colloids (Baalousha et al., 2006b). Combination of FlFFF with TEM allows 3D characterization of colloidal particles (Baalousha et al., 2005a), while combination of FlFFF with AFM allows particle sphericity and permeability to be quantified (Baalousha and Lead, 2007).

3.05.5.3 Surface Coating There is a clear evidence from the 1970s onward that NOM adsorbs to macroscopic surfaces from electrophoretic measurements (Neihof and Loeb, 1975; Tipping and Higgins, 1982; Hunter and Liss, 1982), and more recently from surface force measurement by AFM (Mosley et al., 2003; Assemi et al., 2004; Sander et al., 2004). Other analytical tools such as AFM, FlFFF, and others have been used to determine the physical dimensions and structure of these films on macroscopic and nanoscopic surfaces, that is, NP–colloid interactions (Mayer and Xing, 2001; Lead et al., 2005; Baalousha et al., 2008; Gaebel et al., 2009), with thicknesses of HSs in the range 1–5 nm (Assemi et al., 2004; Gibson et al., 2007), while natural colloids are sometimes much larger. The formation of NOM surface coatings on colloids can dominate their environmental functions as well as their fate and behavior. For instance, surface coating may alter colloidal surface charge, hence the similarity in surface charges of colloidal particles in aquatic environment and their mutual repulsion, slowing down aggregation. Almost all environmental particles, regardless of chemical composition, are negatively charged due to the dissociation of surface functional groups on sorbed NOM (Hunter and Liss, 1982; Loder and Liss, 1985). Therefore, a useful approximation in terms of surface charge and aggregation may be to treat colloids as a single class of colloidal materials, irrespective of their nature (O’Melia, 1980; Filella and Buffle, 1993). However, these surface coatings may be patchy (Gibson et al., 2007), depending on the nature of the underlying substrate, the NOM type, and the solution conditions, meaning that this assumption does not always hold. Surface charge modification will have direct role on the stability, aggregation, and disaggregation of colloids (see Section 3.05.7.1). Surface coating is also likely to alter trace contaminant interaction with colloids. For instance, their formation will change the solid–solution partitioning of trace pollutants. NOM may also increase the sorption of organic pollutants such as carbazole, dibenzothiophene, and anthracene. In general, models describing reactions at inorganic surfaces assume that the surfaces are clean and free from organic matter, and models describing both inorganic and NOM binding to metals assume nonadditivity (Tipping, 2002).

of ionic solids (e.g., AgI); or (3) specific sorption of charged species (e.g., simple ions such as Ca2þ, surfactant ions, and polyelectrolyte chains such as HS or synthetic surfactants). The total colloidal surface charge is the sum of permanent and variable charges. The colloid surface charge must be balanced by equal and opposite charge in solution so that the colloidal system is electrically neutral. This balancing charge is created by an excess number of oppositely charged ions (counterions), and a deficit of similarly charged ions (co-ions) in the vicinity of the particle surface. This distribution of ions around a charged particle surfaces may be described by the electric double layer theory. Several models have been presented, though we only describe one model (i.e., Stern–Grahame–Gouy–Chapman model) as it is one of the most elaborate descriptions of the double layer theory (Figure 7). In this model, ions are distributed across two layers, a compact inner layer (Stern layer), where the counterions are immobile and a diffuse outer layer, which extends over a certain distance from the particle surface and decays exponentially with increasing distance into the bulk liquid phase. The distribution of ions in the diffuse layer depends on the concentration of the electrolyte, the charge of the ions, and the potential at the boundary between the compact inner layer and the diffuse outer layer. The potential at this interface is called the Stern potential. The potential at

Stern layer

Diffuse layer

– –

Colloid surface charge can be either permanent or variable. The permanent charge arises from the isomorphous substitution of cations within the colloid, for example, substitution of Si(IV) by Al(III) in kaolinite. The variable charge originates from chemical reactions at the colloidal surface: (1) ionization or dissociation of the surface functional groups (e.g., the dissociation of protons from carboxylic groups); (2) dissolution

+

+

+ –

+

– +

–

+

+

–

– +

–

– +  s ζ s/e xs

3.05.5.4 Surface Charge

Bulk solution

1/

x

Figure 7 Schematic diagram of the diffuse double layer (DDL) forming from the surface of a colloidal particle into the bulk solution. Abbreviations: zeta potential (z), electrostatic potential (C), electrostatic potential at the stern layer (CS), Euler’s number (e), Boltzmann constant (k). X is a distance from the surface, Xs is the shear plane, the distance from where ions and molecules are mobile and can be sheared off. Adapted from figure 2 in Handy RD, Von der Kammer F, Lead JR, Hassello¨v M, Owen R, and Crane M (2008) The ecotoxicology and chemistry of manufactured nanoparticles. Ecotoxicology 174: 287–314.

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

the shear plane, that is, the transition plane from fixed ions and water molecules to those which can be sheared off by fluid motion, is called the zeta potential (z), which can be calculated from the electrophoretic mobility measured by electrokinetic methods (e.g., electrophoresis). Under conditions of very low ionic strength, the decay of the potential between the Stern layer and the shear plane is negligible and the zeta potential can be seen as an approximation of the Stern potential. For more details about the different models describing the double layer, the reader is referred to the literature (Elimelech et al., 1995). Colloid surface charge can be determined indirectly by determining the zeta potential (z) of the particles from the measured electrophoretic mobility (m) which is the velocity of particles (v) per electric field unit (E), m ¼ n/E. Although measuring the electrophoretic mobility is straightforward, calculation of zeta potential is more complicated, for both colloids/NPs and when performed in environmental media. For instance, the conversion of the electrophoretic mobility to zeta potential is based on the assumption that the particles are approximately hard (nonpermeable) spheres (Delgado et al., 2007). Further models have been developed for soft (permeable) particles and applied to environmental colloids, where the concept of zeta potential is not physically meaningful (Duval et al., 2005). Permeable or semi-permeable colloidal models have been applied for environmental colloids such as HSs (Duval et al., 2005; Duval, 2007), and bacterial cells (Hayashi et al., 2001; Tsuneda et al., 2003; de Kerchove and Elimelech, 2005). In addition, for nonspherical particles, such as fibrils or fractal or porous aggregates, zeta potential values are likely to be inaccurate or misleading. Colloid surface charge plays an important role in determining their fate and behavior in the environment. Surface charge is one of the primary characteristics that determines colloidal stability, aggregation, and disaggregation. Altering surface charge through changes in solution chemistry (e.g., ionic strength, pH, and NOM) is the practical means of manipulating colloid stability, aggregation/disaggregation, and

sedimentation/deposition in engineered systems, while natural changes such as increased ionic strength in estuarine conditions will have the same effect.

3.05.5.5 Pollutant Binding and Behavior Surface reactions play an important role in environmental processes such as colloid stability (Johnson et al., 2005), dissolution rate (Johnson et al., 2004), trace contaminant speciation and transport (Joo et al., 2008) and can be important in catalyzing certain degradation reactions (e.g., chlorinated volatile organics). Colloids play an important role in regulating chemicals (e.g., contaminants and nutrients) in the environment due to their small size and consequently high surface area/volume (Figure 8). For instance, they often dominate the physicochemical speciation of trace elements and organic pollutants (Buffle, 1988; Doucet et al., 2006). A large proportion of these trace compounds (typically 40–90% or more) are adsorbed to colloids (Stumm, 1992; Stumm and Morgan, 1996). The binding of trace pollutants by colloids can be interpreted as a function of colloid size (Lead et al., 1999), chemistry of the colloidal phases (Lienemann et al., 1997), or both (Lyven et al., 2003; Baalousha et al., 2006a). It has been suggested that small colloids o50 nm (Lead et al., 1999) or o25 nm (Lyven et al., 2003) are capable of binding the largest fraction of total trace metals. In addition to size colloid chemical composition plays an important role in metal binding. Lyven et al. (2003) identified iron oxides and organic carbon as the main colloidal binding phases to which trace elements were associated with elements such as Cu and Zn associated to organic carbon and others such as Pb associated to colloidal iron oxides. In another study using the same coupled FlFFF–ICP–MS technique, Hassellov and co-workers found iron oxide as a major vector for metal transport, in particular Pb (Hassellov and van der Kammer, 2008). Other phases such as manganese oxides may also be important in trace element binding (Baalousha et al., 2006a). Dong and co-workers have investigated the

Nanoparticles

Macroscopic particles

Change in reactivity (a.u.)

Atomic clusters

107

10–10

10–9

10–8

10–7 Particle size (m)

10–6

10–5

Figure 8 General tendency for size-dependent reactivity change of a material as the particle transitions from macroscopic (bulk-like) to atomic. From Wigginton NS, Haus KL, and Hochella MF (2007) Aquatic environmental nanoparticles. Journal of Environmental Monitoring 9: 1306–1316.

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Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

sorption of Cd and Pb to surface coating (Fe and Mn oxides, and NOM) and found that they sorb significantly to metal oxide (Fe and Mn) colloids, but not to NOM (Dong et al., 2000, 2001). In particular, Fe and Mn oxides play an important role in the biogeochemical cycling of trace metals at the redox interface in lakes (Balistrieri et al., 1992a, 1992b), resulting in the removal of dissolved metals to the surface of Fe and Mn oxides as they form at the redox interface. In addition, the aggregation of colloids in natural waters results in the removal of metals from water column through a process known as colloidal pumping (Honeyman and Santschi, 1991; Stordal et al., 1996; Wen et al., 1997). As with inorganic contaminants, colloids may significantly influence the distribution, and fate and behavior of organic contaminants. For instance, majority of polycyclic hydrocarbons (PAHs) were found to be present in large (420 mm) flocs (Leppard et al., 1998), which were essentially aggregates of small colloids. Marvin et al. (2004) showed that PAHs were primarily associated with particles less than 2 mm in diameter. Majority of these particles were found to be fractal aggregates of HS. In marine systems, majority of polychlorinated biphenyls (PCBs) were found to be associated with particulate matter (41.2 mm), although in the fraction o1.2 mm, colloidal binding (40–80%) was dominant (Burgess et al., 1996). Up to 93% of PCBs were found to be associated with colloids in a coastal sea area (Totten et al., 2001). The interaction of selected pharmaceuticals (Maskaoui et al., 2007) and endocrine disrupting chemicals (EDCs) (Liu et al., 2005; Zhou et al., 2007) with natural colloids has also been more recently investigated. While the more hydrophobic pharmaceuticals showed a linear dependency of the Kcoc (colloidal organic carbon sorption coefficient) and the Kow (octanol–water partition coefficient), the Kcoc of the more hydrophilic EDCs was independent of the Kow, highlighting the importance of different binding mechanisms. Polychlorinated dibenzop-dioxins and dibenzofurans (PCDD/Fs) were found to be relocated from soil to groundwater associated with colloids (Hofmann and Wendelborn, 2007). Understanding the role of colloids in regulating chemicals in the environment has significantly improved our understanding of their bioavailability, toxicity, and transport. In the metal area, models based on equilibrium chemistry such as biotic ligand model (BLM) and free ion activity model (FIAM) have helped improving our understanding of metal toxicity (Campbell, 1995; Paquin et al., 2002; Slaveykova and Wilkinson, 2005). Models based on dynamic processes, although newer, are being used fairly extensively (van Leeuwen and Koster, 2004; van Leeuwen et al., 2005). The reader is referred to these references for a detailed discussion. In addition, it is now well known that contaminant and nutrient transport processes in marine and freshwater systems, and in surface and subsurface waters are dominated by the transport of colloids and particles (Dai et al., 1995; Santschi et al., 1997; Benedetti et al., 2002). For decades, processes of contaminant relocation in soil and groundwater were believed to occur predominantly in a two-phase system (the mobile liquid phase and the immobile solid phase) and a potentially mobile solid phase was neglected (McCarthy and Zachara, 1989). Colloid-facilitated transport is now a well-recognized process in porous media such as soils and aquifers. Small

colloids compete with the solid, immobile phase for trace contaminants sorption (e.g., metals; Chen et al., 2005), organic pollutants (White et al., 2005), and nutrients (Heathwaite et al., 2005) and increase the distances traveled by pollutants with respect to those predicted from noncolloidally bound components (Kaplan et al., 1995; McCarthy, 1998; Laegdsmand et al., 1999).

3.05.5.6 Interaction Forces As colloidal particles approach each other, different interaction forces take place, including electrical double layer, van der Waals, hydration, hydrophobic, and steric forces, which act over a relatively short distance and depend on surface properties and surface coating of particles. The electric double layer (long-range, repulsive) force results from the overlap of the double layer of two colloidal particles as they approach each other, resulting in a repulsive force, which opposes further approach:

V A ðh Þ ¼ 

" A 2R 2 2R 2 þ 2 6 h þ 4Rh ðh þ 2RÞ2 !# 4R 2 þln 1  ðh þ 2RÞ2

ð2Þ

This equation applies for two spherical particles of equal radius, R, at a surface to surface separation distance, h, apart along the center to center axis (Liang et al., 2007), where A is a constant, named ‘Hamaker constant’, which depends on the material properties such as density and polarizability. The effective Hamaker constant (Equation (3)) depends also on the dispersion medium, and is generally of order of magnitude 10–20–1021 J (Elimelech et al., 1995):

Aeff E

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2 Aparticle  Amedium

ð3Þ

The van der Waals forces, short-range attractive, arise from spontaneous electrical and magnetic polarizations as particles get close to each other, giving a fluctuating electromagnetic field within the media and in the gap between particles. For identical particles, sphere–sphere double layer interaction energy can be given by Equation (4). There are many expressions available based on various assumptions for sphere– sphere double layer interaction energy and readers are referred to the literature for more details (Bell et al., 1970; Carnie et al., 1994; Sader et al., 1995; McCormack et al., 1995; Stankovich and Carnie, 1996; Genxiang et al., 2001):

 2 kT VR ðhÞ ¼ 32peR g2 expðkhÞ ze

ð4Þ

For small values of surface or zeta potential (z), Equation (4) simplifies to

VR ðhÞ ¼ 2peRz2 expðkhÞ

ð5Þ

where e is the permittivity of the medium; R the particle radius; g the dimensionless functions of the surface potentials; k

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

the Boltzmann constant; T the absolute temperature (kelvin); h the surface–surface separation between particles (m); e the electron charge; and k the inverse of Debye–Huckel screening length (m–1). Equation (5) is applicable only if kR 4 5 and h oo R. For the general case of electrolyte solutions containing a number of dissolved salts, k is defined by

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 P 2 ni zi e k¼ ekT

ð6Þ

where n is the number concentration of ion i in the solution. Inserting numerical values appropriate to aqueous solutions at 25 1C and converting the ion concentration into molar terms gives

k ¼ 2:32  10 9

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X  ci z2i

ð7Þ

where c is the concentration of ions expressed in mol l1 and z the valency of the ions. The length 1/k is known as the thickness of the diffuse layer. Equation (7) shows that the increase in ionic strength results in a decrease in the thickness of the diffuse layer and a consequent decrease in the repulsive interactions among particles. Typical values of the diffuse layer thickness, 1/k, are in the range of 1–100 nm. The summation of the two forces gives the DLVO theory (named after Derjaguin and Landau, Verwey and Overbeek) (Equation (8)):

VT ¼ VA þ VR

ð8Þ

where VT is the total interaction energy; VA the attractive van der Waals energy; and VR the repulsive double layer energy. Although DLVO theory was found to be able to describe colloidal stability of simple colloidal systems, it has been found unable to fully describe colloidal behavior in aquatic and terrestrial environments (Grasso et al., 2002; Sander et al., 2004). This has been related to other, non-DLVO, forces such as hydration, hydrophobic, steric, and bridging forces. Hydration force results from the hydration layer that surrounds colloidal particles which might be different from the bulk water. For true contact to occur between particles, surfaces need to become dehydrated, which usually gives an extra repulsion to that induced by the double layer (Grasso et al., 2002). The hydrophobic forces result from the migration of the water from the distances between two hydrophobic colloidal particles, resulting in an attractive force (Elimelech et al., 1995). Steric force results from the interaction between particle surface coating (usually NOM in natural waters and stabilizing agents in case of NPs, see Section 3.05.5.3). As particles approach each other, the adsorbed layers come into contact, resulting in the interaction between these molecules. As these molecules are hydrated, any interaction will induce hydration repulsive forces as described in the previous section. The steric stabilization effect increases with increased surface coating load or the thickness of the adsorbed layer. Larger particles will need a thicker coating due to the increase of van der Waals attraction energy with particle size. Bridging results from the interaction of colloidal particles with long chain molecules such as polysaccharides in the natural environment, with long

109

chain molecules attaching to two or more particles. In such a case, particles can form aggregates even though they may be charged and repel each other, forming open flocs. Quantitative physico-chemical aggregation theory (DLVO theory) exists only for identical, compact, and spherical particles (homoaggregation). However, there is no such general theory for aggregation of a mixture of different particles (heterocoaggregation), in particular for aggregation involving polymers. Non-DLVO forces are complicated and difficult to describe, and no simple, comprehensive theory is yet developed. According to DLVO theory, parameters that affect colloidal stability are: ion type and concentration, particle size and particle surface charge, and z potential. Increased ionic strength results in a decreased double layer repulsive force (Equation (2)) because of a decreased diffuse layer thickness (Equation (7)). Polyvalent electrolytes induce larger decreases in the diffuse layer thickness than monovalent electrolytes and consequently induce greater aggregation. Both attractive and repulsive forces are proportional to particle size based on Equations (2) and (5), and generally electrostatic stability increases with increasing particle size. Increased zeta potential results in a higher colloidal stability as the electrostatic repulsive force is proportional to the square of z potential (Equation (2)), and so it is a key parameter in determining the stability of colloids. Nonetheless, the stability of colloidal particles in aquatic environment is often higher than expected on the basis of particle size, zeta potential, and ionic strength governing DLVO theory. This is likely to be related to the steric stabilization effect induced by NOM surface coating (Jekel, 1986), and possibly to the hydration effect.

3.05.6 Intrinsic Properties of Manufactured NPs NPs are not merely small crystals, and the nanoscale can be considered as an intermediate state of matter placed between bulk and molecular material. Alongside the particle size, morphology and surface charge play dominant roles in the chemical reactivity of the particle. These parameters will determine their ability, for instance, to enter the cell membranes and interact with different living organisms present in natural terrestrial and aquatic systems.

3.05.6.1 Size As particle sizes are decreased within the nanoscale range (and therefore take on different properties vs. the larger fine-sized particle types), alterations in their physical and chemical properties are found. It is not unreasonable to assume that the biological effects associated with exposures to NPs may also differ from their bulk counterparts (Warheit et al., 2008). Therefore, assessment of the potential health risks due to exposure of NPs is an emerging area in toxicology, exposure assessment, and health risk evaluations. Some effects that are only found in the NP size range are in the field of physics, for example, optical properties of NPs (gold NPs in different colors depending on their size, fluorescent QDs), superparamagnetism of small magnetic NPs (Aime et al., 2002), or supercooling of fluids in confined

110

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

geometries. Some of these effects are exploited in chemistry such as the high surface area for catalysis (Alvarez-Roman et al., 2004a) and adsorption (Alvarez-Roman et al., 2004a). The general interest in the monodisperse NPs has also arisen due to the possible applications in a wide range of domains (Poole and Owens, 2003). One of the important characteristics of all NPs is their unusually high specific surface area. This feature often has a considerable effect on the particle physical properties as well as being on its fate and behavior in natural environments (Christian et al., 2008). The physical and chemical properties of a material are also determined by the type of motion allowed for its electrons to execute, as in the case of QDs. The latter is determined by the space in which the electrons are confined (Braun et al., 2002). Unbound (unconfined) electrons have motion that is not quantized and can thus absorb any amount of energy given to them and use it simply to move more rapidly (Brus, 1986). Once bound in an atom, a molecule or a material, its motion becomes highly confined and quantization occurs. The allowed types of motion in atomic or molecular orbitals are found to have well-defined energies that are separated from one another (Braun et al., 2002). The smaller the space in which motion occurs (i.e., the stronger the confinement), the larger the energy separation between the allowed energies of the different types of motion becomes. Theoretically, if the physical size of the NP is reduced, it becomes comparable to or smaller than the Bohr radius. This would decrease the space in which the charge carriers (the excitons) move and thus confine their motion (Braun et al., 2002). For instance, in the case where the size of the semiconductor NPs becomes smaller than their Bohr radius, their band-gap energy increases (Alivisatos et al., 1988). Equally important, the energy of the band-gap absorption (and thus the NP color) and that of the emission increase, and become sensitive to the size of the particles (Heath, 1995; Wang and Herron, 1991). Thus, the optical and other physical and chemical (e.g., oxidation– reduction) properties of semiconductor NPs are size and shape dependent. As particle size becomes smaller, a greater fraction of atoms are at the surface and quantum effects tend to increase surface reactivity and energy, in general (Klaine, 2009; Wiesner et al., 2006, 2009). In some cases this does not occur, for example, due to nanoscale pit formation (Arugete et al., 2009). At the same time, NPs have a tendency to agglomerate and form larger structures. Thus, agglomeration can lead to a reduction in the number of atoms at the surface with a reduction in surface energy, although aggregation state may formally have no relation to SSA, dependent on size distribution and fractal dimension of aggregate (Buffle and Leppard, 1995a; Buffle and Leppard, 1995b; Zhang et al., 2007). Since coagulation half-lives of NPs are of the order of tens of microseconds to a few milliseconds (Preining, 1998), NP concentrations can decrease rapidly by agglomeration. Manufactured NPs, however, are specially coated to reduce agglomeration in order to exploit high surface reactivity for various useful ends.

3.05.6.2 Shape and Morphology Besides size and chemical composition, other NP properties such as shape and morphology may also affect NP transport,

bioavailability, and their toxic effects (Nel et al., 2006). The morphology of NPs is a key feature for exploiting their properties in several emerging technologies and diverse applications. For instance, selective optical filters (Ahmadi et al., 1996) and biosensors (Antognozzi et al., 1997) are among the many applications that use optical properties of metal NPs related to SPRs which depend strongly on the anisotropy of the particle shape; different shapes produce greater plasmon losses (Barth and Henry, 2004). Despite the great importance of the morphology of NPs, it is generally not well characterized and in practice almost never controlled. This situation is due to the intrinsic difficulty to accurately characterize the morphology of NPs and due to the limited number of ways known for controlling shape. The morphology of NPs depends on both kinetic (i.e., growth) and thermodynamic parameters (Adair and Suvaci, 2000). If the growth takes place far from equilibrium conditions (i.e., large supersaturation), the growth shape is not uniquely defined and depends on many parameters, such as the flux of growing material, structure of the support (if present), presence of defects and impurities, and confinement (i.e., template effect) (Adair and Suvaci, 2000). Generally, these parameters are not well controlled or not controlled at all. However, in the case of well-defined systems, it is possible to reduce the number of growth parameters and attempt to control the shape of growing particles. In the case of 2D growth, use of single crystal substrates with known surface diffusion anisotropy has enabled the preparation of 2D islands with shapes tuneable by the growth conditions (Roder et al., 1993). In the case of 3D growth, it is much more complicated to control all growth parameters. In terms of the toxicity related to the shape of the NPs, little is known, and majority of the studies reported in the literature are based on human inhalation exposures. Prior experience with asbestos and other fibrous aerosols indicates that the shape of the particles (i.e., their length and diameter) has a profound effect on toxicity. Smaller diameter fibers penetrate deeper into the respiratory tract, while longer fibers are cleared more slowly (Mossman et al., 1990; Oberdo¨rster et al., 2005). Engineered NPs come in various shapes such as spheres (e.g., dendrimers), tubes (e.g., SWCNT and MWCNT), plates (e.g., nanoclay flakes), fullerenes, and needles. While it seems likely that particle shape will affect the deposition, fate, and toxicity of the particles in the human body (Jia et al., 2005), few data about these effects are available. On the other hand, it has been suggested in the literature that there are two reasons that might account for the DNA damage caused by NPs. First, ROS generation and oxidative stress in the cell may cause oxidative damage to DNA through free radical attack. Previous work demonstrated that sunscreen TiO2 and ZnO can catalyze oxidative DNA damage in cultured human fibroblasts measured by comet assay (Dunford et al., 1997). In addition, this has been proved by the determination of 8-hydroxy-deoxyguanosine, a good marker of oxidative DNA lesion (Papageorgiou et al., 2007). However, it has been reported that CNTs exhibited greater genotoxicity than ZnO NPs which elicited more oxidative stress. Therefore, it is educible that the DNA damage caused by CNTs may come from mechanical injury and not oxidative effect. It is likely that CNTs might penetrate into cell nucleus through nucleopores,

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

and then destruct the DNA double helix (Pantarotto et al., 2004). Second, although several studies have shown that some spherical NPs such as titanium dioxide or silica NPs can also enter the nucleus (Geiser et al., 2005) and it has been demonstrated that C60 NPs can bind to and deform nucleotides (Zhao et al., 2005), CNTs induced significantly more DNA damage than other NPs with the sphere shape or crystal structure in our research. To combine the above two points, the genotoxicity of different NPs may primarily be due to particle shape rather than chemical composition.

3.05.6.3 Surface Properties At the nanoscale range, the properties of materials differ significantly from those corresponding to bulk materials of the same chemical composition due to the increased specific surface area and reactivity, which may lead to increased bioavailability and toxicity. The surface properties of NPs are one of the most important factors that govern their stability and mobility as colloidal suspensions or their aggregation into larger particles and deposition in aquatic systems (Navarro et al., 2008a). The aggregation of NPs released in the environment may be caused by several environmental parameters. For instance, in the case of TiO2, it has been reported that the NP aggregation behavior strongly depends on the pH and ionic strength of the liquid medium. Cationic and anionic species, and also the presence of HSs may affect the stability of TiO2 colloidal suspensions (Ottofuelling et al., 2007). It has also been reported in the literature that NP aggregation may have repercussions on their toxicity. However, the evaluation of the toxic effect of aggregates could be a difficult task if the specific surface area is not assessed, which is also involved in solubilization, adsorption, and catalytic properties (Kahru et al., 2008). In addition, in the case of metals, it is well covered in the literature that reactive chemical metal species depend not only on solubility, but also on all associated ions and on slight changes of pH. It is well known that surfaces and interfaces of particles are key components of nanoscale materials. As the particle size decreases, the amount of atoms found at the surface increases in comparison to the proportion of atoms found in the NP core. This implies that particles in the nanoscale are likely to be more reactive compared to their larger counterparts. However, when the potential health implications are considered, reactive groups on the surface of particle are also likely to influence the potentially toxicological effects when compared to nonreactive surfaces or coatings which tend to passivate (Warheit et al., 2008). Therefore, the shell of the NPs generated by the chemical modifications on their surfaces may also be important and relevant for their toxicity. In addition, surface coatings can be utilized to alter surface properties of NPs to prevent aggregation or agglomeration with different particle types facilitating particle dispersion and, at the same time, serve to passivate the NPs to moderate the effects of UV radiation-induced reactive oxidants (Warheit et al., 2008). Besides the importance of the NP core-shell dynamics for biological effects, different atomic planes on NP surfaces can lead to different results in toxicological studies. For instance, in the case of TiO2 NPs, two different crystal structures of titanium dioxide can be found, anastase and rutile, and

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despite their similar core, the hazard potential may be different (Hartmann et al., 2009). It has been reported in the literature that these differences in crystallinity may result in comparative differences in the potencies of pulmonary inflammatory and cytotoxic endpoints, ranging from benign to more moderate health impacts (Warheit et al., 2007). On the other hand, in the case of CNTs, it has been suggested that the small variations and defects in CNT surface, morphology, and physico-chemical features might modulate their toxicity. Mu¨ller and co-workers synthesized defect-free ground multiwall CNTs by heating at 2400 1C the material and structural defect-induced CNTs by grinding the material that had been heated at 2400 1C. They confirmed the presence of imperfections in the carbon framework by Raman spectroscopy. The role of the abundance of defects was also confirmed by microcalorimetry as the heat of adsorption of water vapor showed that heating CNTs at high temperature increased hydrophobicity and fully eliminated hydrophilic sites. Grinding of the material led to the creation of sites capable of interaction with water molecules, suggesting the formation of defects at the surface. These surface defects are subject to oxidation or the opening of the internal pores where water may condense. The CNTs were administered intratracheally (2 mg/rat) to Wistar rats to evaluate the long-term (60 days) lung response. The results show that the acute pulmonary toxicity and the genotoxicity of CNT were reduced in the case on defect-free CNTs, indicating that the intrinsic toxicity of CNT is mainly mediated by the presence of defective sites in their carbon framework (Mu¨ller et al., 2008). In general, the toxicological effects of different NPs may be attributed to their surface properties that originate from the specific nanosize but are also determined by chemical compositions. Shrinkage in particle size may create discontinuous crystal planes that increase the number of structural defects besides disrupting the well-structured electronic configuration of the material, so as to give rise to altered electronic properties on the particle surface (Oberdo¨rster et al., 2005; Donaldson and Tran, 2002). This could establish specific surface groups that could function as reactive sites. Surface groups can make NPs hydrophilic or hydrophobic, lipophilic or lipophobic, or catalytically active or passive. The extent of these changes and their importance strongly depends on the chemical composition of the material (Nel et al., 2006).

3.05.7 Environmental Fate and Behavior of Natural Colloids Although the fate and behavior of natural colloids and manufactured NPs are interlinked, this section reviews mainly the fate and behavior of natural colloids in the environment. Nonetheless, some of the references used are on manufactured NPs or model colloidal particles within the NP size range.

3.05.7.1 Aggregation/Disaggregation Colloidal aggregation and disaggregation is one of the most important processes occurring in the natural environment. It controls the fate and behavior and of natural colloids, including their transport and sedimentation. In addition, it

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controls the behavior of pollutants including their fate and behavior, transport, bioavailability, and toxicity. In the absence of surface coating (NOM or surfactants), aggregation/ disaggregation is mainly governed by particle size, x potential, and solution ionic strength as described by DLVO theory. However, this is only the case in well-controlled laboratory experiments and rarely occurs in natural environmental systems. In the natural environment, the situation is more complicated due to the presence of different types of cations and NOM. In these systems, aggregation of colloids is governed by several mechanisms, including electrostatic (i.e., charge) stabilization (see Section 3.05.5.6), steric (i.e., nanoscale surface film formation) stabilization (see Section 3.05.5.3), charge enhancement by NOM, charge neutralization by ionic strength or specifically by binding cations such as Ca, bridging (see Section 3.05.5.6) by fibrils and aggregated NOM. NOM molecules such as fulvic and humic acids (HAs) can enhance colloidal stability (Wilkinson et al., 1997a) by a mechanism known as steric stabilization in addition to enhancing colloid surface charge (Sander et al., 2004). However, in the presence of high concentration of divalent cations such as Ca2þ, HS can enhance aggregation via bridging mechanisms (Chen and Elimelech, 2007). The net effect will depend on surface coverage and the degree of charge alteration. For model compounds, it has been shown that the adsorption of negatively charged HSs to positively charged iron oxide will result in destabilization only for low surface coverage (Stumm, 1992; Ferretti et al., 1997; Baalousha et al., 2008). Other NOM molecules such as polysaccharides can induce aggregation by a bridging mechanism (Filella et al., 1993; Wilkinson et al., 1997a). The adsorption of small quantities of the polymer leads to colloidal aggregation by charge neutralization or colloid bridging, whereas the adsorption of larger quantities is thought to stabilize the colloidal suspension via steric stabilization mechanism. Several mechanisms may take place together resulting in enhanced aggregation. For instance, alginate-coated hematite NPs aggregate through electrostatic destabilization in the

500 nm

(a)

presence of monovalent cations (Naþ) according to DLVO theory. However, in the presence of CaCl2, aggregation increased more than that can be explained by the DLVO theory, and was explained by the formation of an alginate-coated hematite gel network and the cross-linking between unadsorbed alginate as shown in Figure 9, via Ca2þ bridging, that might form bridges between hematite–alginate gel structures (Chen et al., 2006). Microscopy analysis of freshwater colloids gives insight into these processes. Microscopy analysis often shows small inorganic colloids embedded in networks of fibrillar materials (see Figure 2(a)). Interaction of inorganic colloids with biopolymers is likely due to the minimal electrostatic repulsion because of low surface charge density of biopolymers (see Table 2). In such a situation, highly stable colloidal suspension might produce large aggregates in the presence of biopolymers. As biopolymers are very long in comparison with the colloid diameter, they can serve as long bridges between colloids. The attached colloid may interact with another polymer, leading to the formation of loose aggregate networks extending to large dimension. Further, HSs may aggregate as small spheroids along the fibril of biopolymers (Buffle and Leppard, 1995a), suggesting that HSs might interact with fibrils similarly to inorganic colloids. A full picture of the aggregation behavior of colloids in natural systems can be understood from microscopy analysis of natural colloids (Figures 2(a) and 2(b)), and is depicted in Figure 10. Disaggregation (i.e., breakage of colloidal aggregates) is as important as aggregation in determining colloidal size distribution, fate and behavior, and interaction with trace contaminants, but few studies are available on the disaggregation of model colloids or natural colloidal particles. Most disaggregation studies have concentrated on the effect of shear force (Newman and Stolzenbach, 1996; Bergendahl and Grasso, 1998). However, effect of solution conditions on disaggregation has rarely been considered. Altering solution conditions by dilution, changing pH, ionic strength, altering surface charge and chemical composition, may induce particle disaggregation or alter their aggregate structure (fractal

100 nm

(b)

Figure 9 Combined hematite-alginate gel aggregate in the presence of 6.1 mM CaCl2 at pH 5.2. (a) and (b) are TEM images of the same aggregate, but at different magnifications. From Chen KL, Mylon SE, and Elimelech M (2006) Aggregation kinetics of alginate-coated hematite nanoparticles in monovalent and divalent electrolytes. Environmental Science and Technology 40(5): 1516–1523.

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Small aggregates (stable suspension)

Gels

Large aggregate (unstable suspension)

Figure 10 Major types of aggregates formed in the three-colloidal component system: fulvic acid, small points; inorganic colloids, circles; rigid biopolymers, lines. Both fulvic acids and polysaccharides can also form gels, which are represented here as gray areas into which inorganic colloids can be embedded. From Buffle J, Wilkinson KJ, Stoll S, Filella M, and Zhang J (1998) A generalized description of aquatic colloidal interactions: The three-colloidal component approach. Environmental Science and Technology 32(19): 2887–2899.

dimension, see Section 3.05.7.2). For instance, synthetic polymers have been shown to be able to separate two aggregated colloids, even when the separation distance was on the order of few nanometers (primary minimum) (Ouali and Pefferkorn, 1994). Disaggregation of NOM (peat HA) was observed after dilution of a peat concentrate, and disaggregation rate increased with pH (Avena and Wilkinson, 2002). Recently, NOM (Suwannee River HA) has been shown to induce the disaggregation of iron oxide aggregates of NPs, likely due to formation of surface coating of NOM on the surface and pore surfaces of aggregates and thus the enhancement of surface charge as confirmed by electrophoretic mobility measurements (Baalousha, 2009). This induces an increase of the degree of repulsion within the aggregate matrix and results in aggregate rupture. There are two possible mechanisms of aggregate breakup based on aggregate structure: slow surface erosion and fast large-scale fragmentation. In surface erosion, small particles are separated from the surface of the aggregate, whereas in large-scale fragmentation, the aggregates split into pieces of comparable sizes (Jarvis et al., 2005). Disaggregation rate depends on aggregation mechanism, aggregate structure, presence of NOM, and solution composition. For instance, diffusion-limited aggregation mechanism results in the formation of highly branched aggregates with small fractal dimension that break up by fragmentation mechanism. Reaction-limited aggregation mechanism results in the formation of compact aggregates with large fractal dimension that favor breakup by surface erosion mechanism (Yeung and Pelton, 1996). Disaggregation depends also on the way aggregates interact with NOM, which may develop in two steps. The first fast step corresponds to coverage of the aggregate surface by NOM, and a second slower step corresponds to the diffusion of NOM through the already adsorbed layer and the reptation of HA into zones near to neighboring interfaces, that

is, it aggregates pores (Baalousha, 2009). It has been shown that the adsorption of polymer (polyvinylpyridine) to colloidal particle surfaces induced aggregate fragmentation after an initial lag time, which is the time required for polymer reptation within the porous matrix of aggregates and sorption to the surface of the particles (Ouali and Pefferkorn, 1994; Pefferkorn, 1995). Solution chemical composition including ionic strength and type of cations present in solution are also expected to influence disaggregation of colloidal aggregates, with faster disaggregation rate in the presence of monovalent cations and slower disaggregation rate in the presence of divalent cations. Clearly, more research is needed to investigate the possible disaggregation of natural environmental aggregates and the role played by water composition (ionic strength, cations, pH, and natural organic molecules).

3.05.7.2 Aggregate Structure and Fractal Dimension Aggregation may occur in two different modes: diffusion limited aggregation (DLA) and reaction limited aggregation (RLA). In DLA, particles are assumed to have no surface repulsion and aggregation occurs due to Brownian motion of particles. In RLA, repulsive forces due to electrostatic or other interactions may prevent the particles from aggregating and only a fraction of collisions are successful. These modes of aggregation result in formation of aggregates with different structures with open porous aggregates in the case of DLA and more compact aggregates in RLA mode (Figure 11). In low ionic strength such as freshwater systems, compact aggregates with RLA-type aggregate structures (Figure 11(b)) are likely to dominate due to the low collision efficiency. However, in high ionic strength such as marine systems, loose aggregates with a DLA-type structure (Figure 11(a)) are more likely to dominate due to the higher collision efficiency

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100 nm

100 nm (a)

(b) 1

Figure 11 TEM micrographs of iron oxide NPs (100 mg l Fe) at pH 6 (a) without HA and (b) with HA (5 mg l1) showing two different aggregation modes namely reaction limited aggregation in (a) and diffusion limited aggregation in (b). Adapted from figure 4 in Baalousha M, Manciulea A, Cumberland S, Kendall K, and Lead JR (2008) Aggregation and surface properties of iron oxide nanoparticles; influence of pH and natural organic matter. Environmental Toxicology and Chemistry 27: 1875–1882.

500 nm

500 nm

500 nm

(a)

(b)

(c)

500 nm

(d)

500 nm

(e)

Figure 12 Variation of the texture of DOM along the Adour estuary with salinity (a) 0, (b) 0.1, (c) 5.2, (d) 21.7, and (e) 23. From figure 4 in Baalousha M, Motelica-Heino M, and Coustumer P (2006) Conformation and size of humic substances: Effects of major cation concentration and type, pH, salinity and residence time. Colloids and Surfaces A: Physicochemical and Engineering Aspects 272: 48–55.

(Leppard et al., 1986, 1997; Wilkinson et al., 1999). In the presence of high ionic strength, HSs aggregate to small spheroids of about 10 nm or large, porous aggregates of several micrometers (Baalousha et al., 2005c, 2006b). However, the structure of the aggregates also depends on the interaction (residence time). The highly porous aggregates formed at high ionic strength in DLA mode (Figures 12(a)–12(c)) may become more compact (Figure 12(e)) over time due to neutralization of the remaining internal (within the aggregate structure) surface charge (Baalousha et al., 2006c). The structure of natural colloidal aggregates can be described by a parameter known as fractal dimension (Senesi and Wilkinson, 2008). A fractal object has a self-similar structure at all levels of magnification, that is, it can be subdivided into parts, each of which is a reduced-size copy to the whole structure. The fractal dimension can be described by a geometric power law scaling each dimensional geometry (volume (v) or mass (m) for three dimensions D3, projected area (A) for two dimensions D2, or perimeter (P) for one dimension (D1), and characteristic length scales (L) of the aggregate (Lee and Kramer, 2004)). D1 provides information about the morphology of the aggregate related to the irregularity of the aggregate boundary or perimeter, D2 provides information about the projected area of an aggregate, and D3

provides information about the mass distribution within the aggregate:

m or vp L D3

Ap L D2

P p L D1

ð9Þ

A summary of studies that applied the concept of fractal dimension to environmental colloidal particles and the technique used is given elsewhere (Filella, 2006). Although scattered, the fractal dimension values reflect the aggregation mechanisms; values of D3 of 1.6–1.9 indicate a DLA (Figure 11(a), D2 ¼1.7870.06, D3 ¼1.8770.06), while values B2.1–2.3 indicate an RLA (Figure 11(b), D2 ¼1.9570.01, D3 ¼ 2.0670.02). Majority of studies in the literature have applied the concept of fractal dimension to synthetic particles such as iron oxide, goethite (Hackley and Anderson, 1989), hematite (Amal et al., 1992; Zhang and Buffle, 1996), mon¨ sterberg tmorilonite, or fractionated organic compounds (O and Mortensen, 1992; Rice and Lin, 1993; Senesi et al., 1996; Senesi et al., 1997; Rice et al., 1999; Chakraborti et al., 2003). Few studies have applied the concept of fractal dimension to nonfractionated environmental samples such as fluvial particulate matter (Lartiges et al., 2001), colloids from natural aquatic colloid, for example, river and river bed sediment and agricultural field drainage (Jarvie and King, 2007), marine

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

snow, aggregates formed in mesocosm diatom bloom, estuarine and marine suspended particles, or biological aggregates in wastewater treatment plants. Besides experimental studies, computer simulation has been proved useful to understand colloidal aggregation, including the effect of physico-chemical properties, the effect of NOM, the aggregation mode, and the structural properties (fractal dimension) of the formed aggregates (Stoll and Pefferkorn, 1996; Stoll and Buffle, 1998). Fractal dimension is an important factor in understanding aggregate fate and behavior (see Section 3.05.7.3), and their interaction with other environmental components, for example, contaminants and nutrients. Adsorption/desorption hysteresis of contaminants to fractal aggregates can be explained by the blockage of the pores within the aggregates once sorption takes place, that is, variation in their fractal dimension (Cheng et al., 2004). Aggregate structure also influences disaggregation rate (see Section 3.05.7.1).

3.05.7.3 Transport and Sedimentation in Aquatic Media The settling behavior of a hard, nonpermeable sphere can be described in a relatively straightforward manner by Stokes’ law. However, aggregation of colloidal particles in natural waters results in the formation of large, fractal, and permeable aggregates (see Section 3.05.7.2) (Johnson et al., 1996; Lartiges et al., 2001). Thus, Stokes’ law is not suitable to describe the settling behavior of natural colloidal aggregate. The settling behavior of such aggregates depends on a drag force and permeability of solvent through the porous aggregates. Pores formed within the fractal aggregate will permit greater interior flow through the aggregate, resulting in a faster settling velocity. It has been demonstrated that fractal aggregates (with heterogeneous pore sizes) settle faster than predicted by Stokes’ law for impermeable spheres or permeable sphere models that specified aggregate permeability for a homogenous distribution of particles with aggregates (Logan and Hunt, 1987; Johnson et al., 1996), indicating that intra-aggregate flow reduces the drag for aggregates compared to that for the equivalent impermeable particles. As the fractal dimension increases, the permeability decreases and the fluid mechanics resembles more closely that of an impermeable sphere (Chellam and Wiesner, 1993). Nevertheless, the settling behavior of fractal aggregates is not well understood. The settling behavior of fractal aggregates depends on many properties, including porosity, size, permeability, and buoyant density, which need to be determined to predict fractal aggregate sedimentation. Several models have been developed to predict the sedimentation behavior of fractal aggregates (Tang et al., 2002; Tang and Raper, 2002). However, it is difficult to describe mathematically the nonhomogeneous distribution of pores within fractal aggregates, although this nonhomogeneous porosity has been expressed by assuming that the porosity of fractal aggregates varies radially from the center of gyration (Veerapaneni and Wiesner, 1996). Models for determining porosity, drag coefficient, and settling velocities of fractal aggregates are reviewed elsewhere (Tang and Raper, 2002), where it was concluded that, although several models have been presented to calculate the previous parameters, no single model can best describe the

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settling behavior of fractal aggregates. Thus, the use of any of these models generally for all types of fractal aggregates still needs to be confirmed.

3.05.7.4 Transport in Porous Media Colloid-facilitated transport is a very well-known process in porous media; mobile colloidal particles may act as carriers of strongly sorbing contaminants in subsurface materials (Kretzschmar et al., 1999; Grolimund and Borkovec, 2005). Understanding the processes (depicted in Figure 13) that control colloid transport in porous media, besides understanding their reactivity and contaminant binding (see Section 3.05.5.5), is essential to efficiently manage and remediate many environmental contaminants. It is also essential to understand the transport of microbial particles (e.g., bacteria and viruses) in the natural environment. Colloids are affected by many of the physical and chemical processes that influence solute transport such as advection, diffusion, dispersion, and sorption and desorption (known as attachment and detachment in colloid literature). Thus, colloid transport in porous media is, among other processes, governed by their physico-chemical properties (e.g., size, shape, and surface properties), the physico-chemical properties of the porous medium (e.g., grain size, surface properties), and the fluid properties (e.g., velocity, ionic composition, presence of NOM, density, and viscosity) (Kretzschmar et al., 1995; Bradford et al., 2002; Grolimund and Borkovec, 2005; Ahfir et al., 2007). Colloid transport/deposition in porous media can be thought of as occurring in two steps (McDowell-Boyer et al., 1986): 1. transport to the vicinity of the soil or sediment grains themselves (collector) by surface filtration, straining, diffusion, or physical–chemical mechanisms leading to collision and 2. attachment to the collector via electrostatic interactions between the colloid and the soil. There are several restrictions to movement of colloids through soil, including: (1) straining (some times called physical restriction or mechanical filtration) and (2) true filtration (McDowell-Boyer et al., 1986; McGechan, 2002). Straining is the trapping of colloid particles in the down-gradient pore throats that are too small to allow particle passage. Colloid retention by straining depends on both colloid and porous media properties. Complete straining (mechanical filtration) occurs when colloids are large enough to be physically excluded from entering all the soil pores, resulting in the formation of a filter cake or surface mat of colloids above the media. Incomplete straining occurs when size distribution of colloids is smaller than some of the medium pores; colloid transport may occur in the larger pore sizes, and colloid retention occurs in the smaller pore sizes. Straining may occur even for particles much smaller than the average grain size of narrowly distributed grains. Under strongly repulsive conditions with no physico-chemical filtration, straining filtration to become negligible, the ratio of grain size to colloid size must be larger than 125 (Xu et al., 2006).

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Translocation

Dissolution

leachates from vadoze zone seapage

Deposition

Mobilization ionic strength pH ionic composition hydraulic effects Is↓

pH?

2+

Ca

+

→ Na

Stabilization transport

Generation Secondary mineral formation Precipitation from over-saturation Dissolution of cements Filtration

Bacterial growth Eh↓ Eh ↓

pH?

pCO2 ↑2↑

Figure 13 Schematic plot of important processes influencing colloid behavior in the subsurface environment. Mobilization usually takes place when double layers expand or by changes of the surface charge (polarity þ /  to þ / þ or more often  /  ), hydrodynamic forces usually play a less important role for colloids. Generation occurs when new colloids are produced by precipitation from supersaturation or by dissolution of cements which contain colloidal particles (as carbonates or oxides) through changes in surrounding conditions as decrease in pH or redox potential or increase in the dissolved CO2. Removal of colloids is associated with dissolution of particles, their deposition onto the immobile matrix straining filtration in the pores. From v. d. Kammer F (2005) Characterization of Environmental Colloids applying Field-Flow Fractionation – Multi Detection Analysis with Emphasis on Light Scattering Techniques. Hamburg, Germany: Hamburg University of Technology.

True filtration covers a range of mechanisms with a common feature that particle dimensions are much smaller than the pores. Filtration mechanisms include diffusion, interception, and sedimentation. Diffusion is caused by the bombardment by water molecules undergoing Brownian motion; hence, it strongly depends on colloid size. Small colloids (o100 nm) in particular are deposited by collision to the porous media due to diffusion. Interception occurs when the particle passes closer than one particle radius from the collector surface and electrostatic forces come into play. This process is especially important for colloids/aggregates larger than 1 mm. Sedimentation of colloids is due to a difference in colloid to fluid density (mainly for larger (4200 nm) or dense colloids) (McDowell-Boyer et al., 1986). These three processes relate to the three main aggregation processes: perikinetic (diffusion), orthokinetic (shear), and differential settling. Small particles are often removed more efficiently by diffusive transport, whereas larger particles are often removed more efficiently by sedimentation and interception (Bradford et al., 2002).

Attachment is the removal of colloids from solution via collision and fixation to the porous media, and is typically assumed to be the primary process controlling colloids transport in porous media. Attachment depends on particle–particle, particle–solvent, and particle–porous media interactions including double layer, London–van der Waals, hydrodynamic, hydration, hydrophobic, and steric interactions (see discussion in Section 3.05.5.6). Colloid-attachment kinetics is controlled by the rate of transport to the solid surfaces and subsequent fixation to these surfaces. While the maximum size of mobile colloids will be limited by straining and pore velocity, concentration and size distribution of smaller colloids are controlled by physico-chemical filtration. Altogether, subsurface transport of small (1– 100 nm) colloids is limited mainly by the diffusion-driven collision rate, whereas the transport of larger colloids or aggregates (41 mm) is limited by straining (if colloid density equals fluid density) or sedimentation (for colloid density 4 fluid density).

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

Besides colloid and porous media properties, solution physico-chemistry (e.g., pH, ionic strength, NOM) also affects colloidal transport via aggregation/disaggregation and effective pore size distribution. For unsaturated porous media, colloid retention at the stationary, mobile, or transitional gas–water interface has to be taken into account when considering colloidal transport (Ouyang et al., 1996; Wan and Tokunaga, 1997; Lenhart and Saiers, 2002; Crist et al., 2004; Bridge et al., 2009).

3.05.8 Environmental Fate and Behavior of Nanomaterials The potential fate and behavior of nanomaterials in the environment is not yet well understood, and available studies are scarce. Determining the fate and behavior of nanomaterials in the environment requires understanding potential sources of nanomaterials, their fate in air, soil, and water, their transformation, degradation, and persistency. In addition, the fate of nanomaterials in the environment is likely to vary with the physical and chemical characteristics of the nanomaterials and the containing medium and with the interaction of nanomaterials and other environmental contaminants. Understanding the processes that control the fate and behavior of natural colloids (NPs) in water and soil (Section 3.05.7) and ultrafine particles in air (not presented here) will improve our understanding and ability to predict the fate and behavior of manufactured nanomaterials in these systems.

3.05.8.1 Exposure/Release of NPs According to The Nanotechnology Consumer Products Inventory, the most common material mentioned in the product descriptions was silver (259 products). Carbon was the second most referenced (82 products) which included fullerenes and nanotubes, followed by titanium dioxide (31), zinc oxide (24), silica (15), and cerium oxide (1). Among potential environmental applications of NPs, remediation of contaminated groundwater with nanoscale iron is one of the most wellknown examples (Tratnyek and Johnson, 2006). Regarding personal-care products, NPs of titanium dioxide and zinc oxide are included in toothpaste, beauty products, sunscreens (Serpone et al., 2007), and textiles (Yuranova et al., 2007). Metal oxide-based NPs are also increasingly used in fillers, opacifiers, ceramics, coatings, catalysts, semiconductors, microelectronics, prosthetic implants, and drug carriers (Reijnders, 2006). Photocatalytic properties of TiO2 may also be used for solar-driven self-cleaning coatings (Cai et al., 2006). Knowing the heavy manufacturing and use of NPs in commercially available products, it is therefore correct to assume that the household waste in the case of the metal oxide NPs is likely to end up in natural water bodies. For perspective on potential nanopollution, one may consider that 2 g of 100-nm-size NPs contain enough material to provide every human worldwide with 300 000 particles each (Hardman, 2006). As it was previously mentioned, decrease in particle size changes the physicochemical and structural properties of particles and in the case of NPs that is responsible for increased bioavailability and toxic effects (Nel et al., 2006). Due to the current commercial

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development of nanotechnology, the occupational and public exposure to NPs via inhalation, dermal absorption, and gastrointestinal tract absorption is supposed to increase dramatically in the coming years as well as their potential release in the environment. Because of their unique properties, including small size and corresponding large specific surface area, NPs are supposed to impose different degrees of biological effects from their corresponding analog micro-scaled materials (Oberdo¨rster et al., 2005; Nel et al., 2006). The extent of exposure and resulting adverse effects on human health and the environment remain mostly unknown (Jeng and Swanson, 2006; Lin et al., 2009). Thus, the studies on safety and (eco)toxicity of NPs are of extreme importance in order to support the sustainable development of nanotechnology. Gottschalk et al. (2009b) recently reported predictions of environmental concentrations of nano-TiO2, nano-ZnO, nano-Ag, CNT, and fullerenes for all environmental compartments (including sediments) of US, Europe, and Switzerland. The environmental concentrations were calculated by a probabilistic material flow modeling and compared to already published data from ecotoxicological studies (Gottschalk et al., 2009a). The most frequent values predicted by the model reported range from 0.003 (fullerenes) to 21 ng l1 (nano-TiO2) for surface waters and from 4 (fullerenes) to 4 mg l1 (nano-TiO2) for sewage treatment effluents (up to 16 mg l1 for TiO2 in surface waters, but may be larger in peak flows, where waters not always treated). For Europe and the US, the annual increase of manufactured NPs on sludge-treated soil ranges from 1 ng kg1 for fullerenes to 89 mg kg1 for nano-TiO2 (Gottschalk et al., 2009b). Blaser et al. (2008) calculated total Ag concentrations in surface waters which were by a factor 10–100 higher than the simulation results for nano-Ag reported by Gottschalk et al. and they also concluded that nano-Ag contributes only 1–15% to the total Ag into the environment. Kiser et al. (2009) have carried out measurements of Ti in sewage treatment plant sludge and reported concentrations ranging from 1 to 6 g Ti/ kg. These first measurements of manufactured NPs in the environment show concentrations in the same order of magnitude to Gottschalk et al. modeling estimates. The latter allows a first validation of the model to be reported in the literature. The results of Gottschalk et al. simulations indicated that risks to aquatic organisms may currently emanate from nano-Ag, nano-TiO2, and nano-ZnO in sewage treatment effluents for all considered regions and for nano-Ag in surface waters (Gottschalk et al., 2009b).

3.05.8.2 Fate in Water The potential fate and behavior of engineered nanomaterials, once they are released into the aquatic environment, can be understood in the light of the existing knowledge on the fate and behavior of natural colloidal particles (see Section 3.05.7). This knowledge suggests that the fate of nanomaterials in the aquatic environment can be influenced by variety of processes such as dispersion/diffusion, aggregation and disaggregation, interaction between NPs and natural water components, sedimentation, biotic and abiotic degradation, transformation and photoreaction/light. These reactions may alter the physical and chemical properties of nanomaterials and so alter their behavior in the aquatic environment.

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There have been few studies on the aqueous stability and aggregation of nanomaterials under environmentally relevant conditions. Brant et al. (2005) studied the aggregation and deposition of fullerene NPs in aqueous media at variable ionic strength. They found that while in the absence of electrolytes nC60 stayed stable over time, 0.001 M solution ionic strength (NaCl) was enough to destabilize the nC60 by screening their electrostatic charge and produce large aggregates that settle rapidly (Brant et al., 2005). The addition of HA has been shown to enhance the stability of fullerene suspension in the presence of NaCl and MgCl2 and low concentrations of CaCl2 (Chen and Elimelech, 2006). However, at high concentrations (above 10 mM) of CaCl2, enhanced aggregation of fullerene NPs was observed due to bridging mechanism by HA aggregates (Chen and Elimelech, 2007). Other research has found similar complex interactions between natural and manufactured nanomaterials (Baalousha et al., 2008; Giasuddin et al., 2007; Hyung et al., 2007). Extracted Suwannee River HA (SRHA) and natural surface water (actual Suwannee River water with unaltered NOM background) have been shown to stabilize MWCNT (Hyung et al., 2007). However, extensive flocculation of SWCNT (i.e., formation of floating aggregates and partial sedimentation of other aggregates) was observed when mixed with natural waters from a lake, presumably due to the high ionic strength and the presence of divalent cations such as Ca. Apparently, sorption of HSs enhances the stability and inhibits the aggregation of CNTs to a certain extent (Hyung et al., 2007). However, cations, particularly divalent cations such as Ca and Mg, reduce the stability of CNTs in the absence or presence of NOM surface coating. Disaggregation is as important as aggregation processes in determining the fate and behavior of nanomaterials, though few studies are available (Ouali and Pefferkorn, 1994; Baalousha, 2009). NOM has been shown to induce the disaggregation of iron oxide NP aggregates (5–10 mm) formed at pH 7, likely due to formation of surface coating of NOM on the surface and pore surface of the aggregates and thus the enhancement of surface charge (Baalousha, 2009). In addition, it has been shown that certain polymers are able to disaggregate latex particle (885 nm in diameter) aggregates (Ouali and Pefferkorn, 1994). However, polysaccharide or HA did not result in the disaggregation of polystyrene latex particle aggregates which was explained by the existence of strong interparticle forces within flocs which prohibited aggregate breakup upon adsorption of NOM (Walker and Bob, 2001). Fabrega et al. (2009b) found that SRHAs can cause partial disaggregation of AgNP aggregates by nanoscale film formation, although such disaggregation and film formation decreased short-term bacterial toxicity.

3.05.8.3 Fate in Wastewater NPs used in different applications can be released by material degradation or erosion and these NPs may find their way into wastewater treatment facilities and may end up either in the wastewater sludge or in the water effluent. Nonetheless, the fate and behavior of NPs, the impact they might have on wastewater treatment, and the impact that wastewater has on NPs are largely unknown and need further investigation. For

an overview of the potential behavior of NPs in different compartments of a wastewater treatment plant, the reader is referred to the review by Brar et al. (2010). Generally, NPs fate and behavior in wastewater treatment facilities are likely to be governed by processes such as aggregation/disaggregation, adsorption to colloidal or micron particles in the wastewater, and sedimentation. In a study on the behavior of model NP (cerium oxide) in a model wastewater treatment plant, Limach et al. (2008) found that the majority of the NPs could be captured through adhesion to cleaning sludge. Nonetheless, they found that a significant fraction of the NPs escaped the wastewater plant clearing system and up to 6 wt.% of cerium oxide was found in the effluent stream (Limbach et al., 2008). Another study on the removal of TiO2 in wastewater treatment plant suggests the removal of majority of the particles which was accumulated in the settled solids. Nonetheless, a small fraction in the effluent which was mainly in the size range o0.7 mm (Kiser et al., 2009). The sedimentation of NPs in sludge in wastewater treatment plant may result in the release of NPs into soil and subsequently into groundwater (Benn and Westerhoff, 2008). Surface coating and surface charge will potentially play an important role in their behavior in wastewater systems (Limbach et al., 2008). Uncoated NPs are likely to sediment and form part of the waste sludge as they are borne to aggregation. However, coated or functionalized NPs might be partitioned between the water effluent and waste sludge due to their inherent stability induced by the surface coating. In both cases, NPs need to be removed from both compartments to prevent further pollution. On the other hand, interaction of NPs with microorganisms might potentially inhibit activated sludge process, a major process in wastewater treatment, which may result in jeopardizing water treatment plant (Brar et al., 2010). Several studies have suggested different types of NPs, including silver, iron, ZnO, CuO, La2O3, SnO2, TiO2, CNTs, nC60, and other NPs are toxic to bacteria (Kang et al., 2007; Pal et al., 2007; Auffan et al., 2008; Hu et al., 2009; Fabrega et al., 2009a), and to biofilm (Fabrega et al., 2009b).

3.05.8.4 Fate in Soil The potential fate and behavior of engineered nanomaterials in soil can be understood in the light of the existent knowledge on the fate and behavior of natural colloidal particles (see Section 3.05.7.4). Nanomaterials are small enough to travel through soil pores. However, they can be sorbed to soil particles due to their high surface area, and therefore become immobilized. In addition, the formation of large aggregates of nanomaterials can immobilize them by filtration, sedimentation, or straining in smaller pores. At the moment, little information is available on the transport and fate of nanomaterials in the natural porous environment. However, some data are available from laboratory column studies using porous media (Lecoanet and Wiesner, 2004; Lecoanet et al., 2004; Schrick et al., 2004; Li et al., 2006; Yang et al., 2007), which suggests that transport is often relatively rapid and depends on the type of nanomaterials. Laboratory soil column experiment on iron oxide and zerovalent iron NPs shows that their mobility is more limited due

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

to the efficient filtration mechanisms of aquifer material. Field studies on iron oxide NP indicate that they may migrate only few centimeters to few meters from the point of injection and that their mobility depends on many factors such as particle size, solution pH, ionic strength, soil composition, and groundwater flow velocity (Schrick et al., 2004; Li et al., 2006). The zero-valent NPs are somewhat more mobile as they have been synthesized on supports acting as a delivery vehicle (Schrick et al., 2004; Yang et al., 2007). These delivery vehicles, including anionic hydrophilic carbon and poly(acrylic acid) (PAA), bind strongly to the iron, create highly negative surfaces, thus effectively reducing the aggregation among zerovalent iron particles and reducing the filtration removal by aquifer materials. Laboratory soil column experiments with Fe/hydrophilic carbon, Fe/PAA, and unsupported iron NPs suggest that the anionic surface charges can enhance the transport of iron NP through soil and sand packed columns in comparison with unsupported iron NPs (Schrick et al., 2004; Yang et al., 2007). In addition, the transport of iron NPs (2–10 nm) through porous media column (glass beads, unbaked and baked sand) can be highly enhanced by surface modification via surfactant sorption. Unmodified iron NPs were immobile and aggregated on porous media surfaces in the column inlet area (Kanel et al., 2007). Although surfactants and polymers enhance the transport of NPs, the role of NOM in NP-facilitated transport has not yet been investigated, but likely to be important. Further, the characteristics of the soil matrix may influence the diffusion and transport of NPs. PAA-modified nanoiron slurry has been found to travel easily through silica sand columns, but not loamy sand soil columns (Yang et al., 2007). The transport of water-stable nC60 aggregates underivatized C60 crystalline NPs, stable in water for months through a soil column, was investigated at different flow rates, while other column operating parameters remained fixed through all the experiments. The nC60 particles were observed to be more mobile at higher flow velocity due to less interaction time between the nC60 particles and the porous media (Cheng et al., 2005). Lecoanet and Wiesner (2004) studied the transport and removal of silica, anatase, and fullerene-based NPs in porous media. They found that the removal of anatase is less significant at higher flow rates. However, no dependence on velocity of particle passage through the porous medium was observed for silica particles and the fullerene-based NPs. This was explained by the very small value of the collision efficiency factor in the case of silica and the deposition of fullerene-based NPs on the porous media at higher flow rates after 1 void volume, which limits the interaction of these NPs with the porous media and reduces particles removal afterwards (Lecoanet and Wiesner, 2004). The discussion above, in addition to previous knowledge on colloidal transport in porous media, suggests that the mobility of NPs in soils depends on (1) NP physical–chemical characteristics, that is, size, shape, surface coatings, and stability; (2) the properties of the soil and environment, that is, clay, sand, colloids, NOM, water chemistry, and flow rates; and (3) the interaction of NPs with natural colloidal material, that is, surface coating, aggregation/disaggregation, and sorption to larger particles. Chapter Chapter 1.03 Managing Aquatic Ecosystems discusses the more theoretical aspects of particle movement in porous media.

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3.05.9 Conclusions and Recommendations This chapter has reviewed both colloids in natural aquatic and terrestrial systems and manufactured NPs in the same environmental systems. It is clear that similar physical and chemical properties dominate their behavior in the environment and that natural materials are a useful analog for understanding the highly topical area of NP environmental risk. However, some caution is required in making this link as NPs show many differences to environmental colloids such as tuneable properties, less complexity, and low but increasing concentrations. Clearly, there is also a benefit from two-way information flow and understanding of relatively well-defined manufactured NPs will inform our understanding of the natural colloidal and nanoparticulate chemistry and behavior. In particular, the following issues need to be addressed.

3.05.9.1 Environmental Fate and Behavior The existing literature on natural colloids suggests that their environmental fate and behavior are controlled by properties and processes such as surface properties, surface film formation, aggregation/disaggregation, and transport and sedimentation. This literature is essential in order to understand the likely fate and behavior of manufactured NPs, once released to the environment, as similar processes are expected to control their fate and behavior. Although advances have been made in understanding the fate and behavior of colloids and their role in environmental systems in the last few decades, much is still unknown due to the intrinsic complexity of natural colloids, the lack of appropriate experimental techniques, and the significant gap between theories and models, which were developed to describe well-controlled, simple laboratory systems and the reality of natural systems that contain heterogeneous mixture of particles. The development of nanoscience and the controllable synthesis of NP with different properties (e.g., size, shape, morphology, surface coating, etc.) will allow a better understanding of the fate and behavior of natural colloids through a better understanding and a systematic investigation of the properties, processes, and mechanisms controlling their fate and behavior. The synthesis of simple systems of NPs with tuneable properties will allow validating the existing theories on colloidal science and possibly developing new theories to minimize the gap between the simple systems of monodisperse spherical colloidal system and the real environmental situation.

3.05.9.2 Need for New Metrology and Analysis Tools Determining the concentration and physical and chemical properties of natural colloids and manufactured NPs in environmental and biological systems is essential to predict the environmental consequences of natural colloids and manufactured NPs. However, the diversity of nanomaterials and their properties make their identification and characterization a difficult task. In addition, the interaction of nanomaterials with the natural environmental or biological components provides an additional complexity to the system and so a significant metrological and analytical challenge. Therefore, it

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is important to adopt/develop new metrological and analysis tools, in particular measuring units, characterization methods, and measurement standards. This is still at an early stage and no published data are yet available. Properties that are important for the characterization of natural colloids and manufactured NPs include, but are not limited to, concentration, size and size distribution, molar mass, surface area, state of dispersion/agglomeration, composition, structure, surface charge, oxidation state, solubility, reactivity, and stability (Buffle and Leppard, 1995b; Powers et al., 2006, 2007). The characterization of natural colloids and manufactured NPs is an extensive laborious process, demanding the use of several techniques in parallel in order to achieve a high degree of accuracy and reliability. To date, few quantitative analytical tools for measuring natural colloids and NPs in natural systems are available (see Table 4 for some of these tools), which results in a serious lack of information about their occurrence and fate and behavior in the environment, and new tools are needed to investigate both natural and manufactured NPs. So far, there is an important debate on the best metrics (size, number, and surface area) to report concentration of NPs (Oberdo¨rster et al., 2005; Warheit et al., 2006; Wittmaack, 2007), though no clear conclusion is yet achieved and further research is needed. The development of structure–activity relationships (see Section 3.05.9.5) could be the best way to reach a conclusion. There is a need for certified reference materials and standards for characterization and the terminology to enable interlaboratory comparison and benchmarking in areas of natural colloids and manufactured NP characterization, behavior, toxicity, and others. A list of the available standards for manufactured NPs, which can also be applied to natural colloids, is given in Table 5. Recently, the National Institute of Standards and Technology (NIST) has issued a new reference

Table 5

standards for manufactured NPs (citrate stabilized gold particles nominally 10, 30, and 60 nm in diameter) targeted for laboratories studying the biological effects of NPs.

3.05.9.3 Understanding Complexity on the Nanoscale Although the nanoscale is a very small scale, the definitions of natural colloids (1–1000 nm) and manufactured NPs (1–100 nm) cover a relatively wide range, under which a large complexity may exist, including impurities, surface protruding, surface coating, and adsorbed materials. Such complexity inevitably alters the behavior of natural colloids and the toxicity of nanomaterials. Understanding the behavior and biological response of nanomaterials requires a good understanding of these materials at the nano, the atomic, or even, subatomic scale. This can only be achieved by combining the results of several powerful techniques (e.g., electron microscopy, AFM, and others) for studying material structure at these tiny scales. This will allow understanding how subtle nanoscale features of a material can give rise to changes in its physico-chemical and biological properties. In addition, the development of new analytical tools for nanotechnology will help understanding the variability at such a small scale, both for natural colloids and manufactured NPs.

3.05.9.4 Knowledge of Uptake and Toxicity of NPs Although the beneficial aspects of nanomaterials are well versioned and products containing nanomaterials are already in use, the potential impact of nanomaterials is still largely unclear. Several reports have suggested the negative impact of nanomaterials on the natural environment and living organisms, although more research is required to demonstrate such a negative impact and to determine the nano-properties responsible for it. So far, there is a consensus that surface

Available standards on nanotechnology and nanoparticles

Type

Standard

Description

Terminology

GB/T 19619-2004 IUPAC recommendations

Terminology for nanomaterials Nomenclature for the C60-Ih and C70-D5 h(6) fullerenes

Sizing

GB/T 13221-2004

GB/T 20307-2006 GB/T 20099-2006

Nanometer powder – determination of particle size distribution – small X-ray scattering method (ISO/TS13762) Determination of the specific surface area of solids by gas adsorption using BET methods (ISO 9277:1999) Particle sizing analysis – photon correlation spectroscopy (ISO 13321:1996) Representation of results of particle size analysis – part 2: calculation of average particle size/diameters and mements from particle size distribution (ISO 9276-2:2001, IDT) Representation of results of particle size analysis – part 2: characterization of a classification process (ISO 9276-4:2001, IDT) General rules of nanometer-scale length measurement by SEM Sample preparation dispersing procedures for powders in liquids

Nanomaterial specifications

GB/T GB/T GB/T GB/T

Nano-nickel powder Nano-zinc oxide Nano-calcium carbonate Nano-titanium dioxide

Handling/disposal

PD 6699-2:2007

GB/T 19587-2004 GB/T 19627-2005 GB/T 15445.2-2006

GB/T 15445.4-2006

19588-2004 19589-2004 19590-2004 19591-2004

Guide to safe handling and disposal of manufactured nanomaterials

Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems

properties (e.g., surface area, surface reactivity, atomic surfaces, redox state, surface charge, and surface coating) achieved by the reduction in particle size are responsible for their potential uptake and toxicity (Karakoti et al., 2006), though the exact nano-properties determining their toxicity are still unknown. This is largely related to the complexity of nanomaterials, the wide range of nano-properties that might be responsible for such a negative effect, the complexity of the toxicity test system, the lack of the required characterization, as well as, the lack of the analytical tools and protocols required for this purpose, and all of these aspects need further development.

3.05.9.5 Knowledge of Structure–Activity Relationships One of the key objectives of nanoscientisits is to develop benign nanomaterials, in order to allow a full benefit of the development of nanotechnology. This can only be achieved by determining the properties of nanomaterials responsible for the harmful effects and alter them when designing synthesis methods. A useful tool to achieve this goal is the structure– activity relationship. Structure–activity relationship is the process by which chemical structure is quantitatively correlated with a welldefined process such as biological (Sayes et al., 2006) or chemical (Liang et al., 2006) reactivity. For example, the biological reactivity (e.g., toxicity) can be expressed as a function of the concentration of the substance required to give a certain biological response. In addition, the physico-chemical properties or structure of the substance can be integrated into a mathematical relationship (Equation (10)) if they can be quantified by numbers. Such a relationship is called quantitative structure–activity relationship (QSAR) which can be used to predict the biological response of other materials based on their properties.

Activity ¼ f ðphysicochemical properties and= or structural propertiesÞ

ð10Þ

The development of such models is essential to understand the properties of nanomaterials controlling their behavior and toxicity and to reduce the amount of studies required to achieve these goals.

3.05.9.6 Next Generation NPs Currently nanotechnology uses primarily passive nanomaterials. However, future developments predict the increased use of the more active nanomaterials or nanostructures, for instance, in drug delivery. Four overlapping generations of nanotechnology products have been predicted to be developed in the period 2000–20: passive nanostructures, active nanostructures, systems of nanosystems, and molecular nanosystems (Roco, 2005). The first generation (after 2000) involved the basic discovery and production of passive nanostructures such as the simple components of NPs, nanotubes, nanolayers, and nanocoatings. They have steadystate structures and functions such as chemical reactivity or mechanical behavior during their usage (Renn and Roco, 2006). The second generation (B2005) involves active nanostructures that change their properties (morphology,

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shape, mechanical, electronic, magnetic, biological, etc.) during operation such as nanobiodevices, transistors, polymerbased targeted drugs (Yih and Al-Fandi, 2006; Park, 2007), etc. The third generation (B2010 onwards) includes systems of nanosystems which might self-assemble or self-organize, networking at the nanoscale to form larger architectures (Renn and Roco, 2006) such as artificial organs and electronic devices based on state variables (electron-spin, nuclear-spin or photonic state). The fourth generation (B2015/2020) includes molecular nanosystems, where each molecule in the nanosystem has a specific structure and plays a different role. Molecular machines might be designed by atomic manipulation and may be used as devices which will approach the way biological systems work. Whatever happens in the near future, it is certainly clear that massive and rapid changes are about to be brought about and it is incumbent upon us to be aware of these changes and as a society to use them in a beneficial manner, while minimizing any attendant risks. Current environmental studies and risk assessment programs are mainly concerned with the first generation of nanomaterials or passive nanomaterials, and even here there are fundamental uncertainty arising from our lack of knowledge. Further development of nanomaterials will involve larger and more complex phenomena and problems, and much work remains to be done.

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Relevant Websites http://www.nanotechproject.org The Project on Emerging Nanotechnologies.

3.06 Sampling and Conservation T Schulze, G Streck, and A Paschke, UFZ – Helmholtz-Centre for Environmental Research, Leipzig, Germany & 2011 Elsevier B.V. All rights reserved.

3.06.1 Introduction 3.06.2 General Aspects and Requirements of Sampling Environmental Waters 3.06.2.1 Objectives and Challenges of Sampling 3.06.2.2 Design of Sampling and Monitoring Programs 3.06.2.3 Types of Water Samples and Water-Sampling Methods 3.06.2.4 Sampling Site Selection and Sampling Frequencies 3.06.2.5 QC and QA 3.06.2.6 Safety Considerations 3.06.2.7 Standards, Guidelines, and Handbooks for Sampling of Water Samples 3.06.3 Handling and Conservation of Liquid Water Samples 3.06.3.1 Introduction 3.06.3.2 Alterations of Water Samples 3.06.3.3 Handling and Conservation of Water Samples 3.06.3.4 Selection of Sample Containers and Storage 3.06.4 Water Sampling Using Traditional Methods 3.06.4.1 Sampling of Surface Water 3.06.4.2 Sampling of Groundwater 3.06.5 Water Sampling Using Passive Sampling Technology 3.06.5.1 Introduction to Passive Samplers, Their Modeling, Calibration, and Quality Control 3.06.5.2 Passive Sampling of Nonpolar Organic Compounds 3.06.5.3 Passive Sampling of Polar Compounds Acknowledgments References

3.06.1 Introduction Sampling, in general, is an important prerequisite of a successful chemical, physical, or biological analysis. In terms of an integrated approach, sampling is a part of the analytical procedure and the beginning of the analytical chain. An analytical result cannot be better than the sample on which the analysis was performed due to the vulnerability of samples to artifacts, contamination, incorrect chemical treatment, and mislabeling during sampling and processing (Madrid and Zayas, 2007; Wilde, 2005). Thus, unskilled sampling and consecutive sample processing lead to imprecise, or in worst case, useless analytical results. Therefore, it is essential to implement and to carry out sampling programs and sampling with expertise. Keeping these considerations in mind, key issues and main challenges of sampling for scientific and regulatory purposes are (1) representativeness, (2) integrity of the samples collected, and (3) accuracy of the sampling procedures and design of the sampling or monitoring programs. The water samples require to be representative regarding the variability of each chemical, physical, or biological parameter observed in time and spatial distribution of the water body under investigation. It is important to ensure the integrity of the water samples, due to changes in the samples during collection, transport, and storage, prior to sample preparation and analysis. Design and implementation of sampling programs as well as selection of sampling methods done with

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accuracy are essential to achieve the goals of representativeness and integrity of samples and to gain valid results for correct conclusions and further decision making. Furthermore, professional training and a regular auditing of the field personnel who is performing sampling is mandatory because of their responsibility to ensure a sampling in compliance with quality assurance (QA) standards. In the year 2000, the European Community established the Water Framework Directive (WFD) (European Community, 2000) to improve and protect the quality of all water bodies (including surface, transitional, coastal, and groundwaters) at river-basin level in Europe. The WFD demands an integrative river-basin management and policy making regarding (European Community, 2000):

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protection and enhancement of the biological and chemical status of aquatic environments; a sustainable water use based on long-term protection of water bodies; a significant reduction of pollution in surface waters and groundwaters; and achievement of background levels of naturally occurring compounds

In 2006, the European Groundwater Directive (GD) was enacted to establish specific measures for the prevention and control of groundwater pollution in accordance with Article

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17 of the WFD (European Community, 2006a):

• • • •

criteria for the assessment of good groundwater chemical status; criteria for the identification and reversal of significant and sustained upward trends and for the definition of starting points for trend reversals; completion of the provisions for preventing or limiting inputs of pollutants into groundwater included in WFD; and prevention of the deterioration of the status of groundwater bodies.

Thus, the WFD and the GD enhance the requirements for the determination of target values and environmental quality standards (EQSs) regarding the sampling techniques and the objectives for the design of sampling programs. The aim of this chapter is to discuss the general scope, purposes, and requirements of the sampling process with a focus on water types as surface water (lakes, streams, and rivers), groundwater and wastewater. The sampling approaches reflect the requirements of environmental or chemical management programs such as the WFD, the GD, and the Registration, Evaluation, Authorization and Restriction of Chemicals (European Community, 2006b) including recent passive sampling techniques. In this chapter, surface water includes all standing or flowing waters on the surface of the inland (e.g., rivers, streams, and lakes), groundwater is the water present in the subsurface at the saturation zone, and a river basin is the land area where the surface- and groundwater run-off flows through a system of consecutive surface waters into the sea at a single river mouth, estuary, or delta (European Community, 2000).

3.06.2 General Aspects and Requirements of Sampling Environmental Waters 3.06.2.1 Objectives and Challenges of Sampling The objective of sampling is to collect a portion of water for chemical, physical, or biological analysis or other kind of testing in a specified procedure. A challenge is the volume of sample that should be small enough to be transported conveniently and handled in laboratory, while representing the part of environment sampled accordingly (American Health Association 2005; Kramer, 1994; Madrid and Zayas, 2007). Sampling includes the whole process of taking, storing, conserving, and transporting samples, since these are inherent parts of every sampling procedure. Furthermore, sampling is the starting point and integral part of the analytical process; hence, it can influence the accuracy of the analytical results significantly. The main challenges and key issues of sampling are (1) representativeness, (2) integrity of the samples collected, and (3) accuracy of the sampling procedures as well as the design of the sampling or monitoring programs.

3.06.2.2 Design of Sampling and Monitoring Programs The design of sampling and monitoring programs is a holistic approach that addresses the problem(s) of concern or reason(s) for sampling unambiguously and specifies the

objective(s) of the program and the indicators to be sampled clearly (Keith, 1990; Maher et al., 1994; Strobl and Robillard, 2008; Whitfield, 1988). However, many early water-monitoring programs have been designed without a consistent or logical design strategy resulting in failed studies and useless data collections (Maher et al., 1994; Strobl and Robillard, 2008), whereas today water-monitoring programs are practiced and are more focused to specific goals with selected variables or indicators (European Commission 2003a; Strobl and Robillard, 2008). In general, a minimum of required aspects should be considered during the design to establish an appropriate, effective, and representative monitoring program to achieve the monitoring objective(s) (Dixon and Chiswell, 1996; European Community, 2000; Maher et al., 1994; Strobl and Robillard, 2008; Whitfield, 1983):

• • • • • • • •

definition of monitoring goal(s) (scientific and regulatory); identification of suitable indicator(s) or parameter(s) (physical, chemical, and biological); selection of sampling sites (representativeness and reference conditions); choice of sampling strategy (number of samples, sampling frequency, and methods); verification of cost-effectiveness (comparison of alternative strategies, information gained versus cost of sampling); data analysis using computer-based tools (spatial and statistical analysis, models, and geographical information systems (GIS)); periodic review of sampling and quality control (QC) procedures as well as refinement and optimization in case of deficiencies; and periodic review of the adequacy of the monitoring goal(s).

Besides these general requirements, a monitoring program is river-basin specific and needs to be adapted, because of the diversity in catchment pressures (e.g., diffuse and point pollution sources), water body types, and hydromorphological and physicochemical characteristics of the river basin (European Commission 2003a; Strobl and Robillard, 2008). Regarding these considerations, the WFD (European Community, 2000) is exemplary for the establishment of monitoring programs and setup of monitoring guidelines at riverbasin level. The objectives for the establishment of sampling and monitoring programs for water bodies (i.e., groundwater and surface water) within the WFD were defined as follows (European Commission, 2003a):

• • • •

assessment of the environmental status of a water body and long-term changes in natural conditions, especially resulting from anthropogenic activity; estimation of pollution concentrations and loads, for example, to calculate trends or to monitor magnitude and impacts of accidental pollution; discovery of the reasons of water bodies’ failures to achieve EQSs where these are not identified; and ascertainment of reference conditions for water bodies.

Three types of monitoring strategies for the sampling and design of sampling programs were defined to achieve these

Sampling and Conservation

goals (Allan et al., 2006; European Community, 2000; Ru¨del et al., 2009):

•

• •

Surveillance monitoring. There is a provision of information and data to complete and validate impact-assessment procedures, to plan further monitoring activities, and to assess long-term changes of (aquatic) ecosystems. Operational monitoring. This is to classify the status of water bodies according to EQS and to estimate the changes of water bodies due to action plans. Investigative monitoring. It is undertaken at sites where surveillance monitoring shows that EQSs are not reached to identify the causes of the failure as well as to assess the magnitude and impact of accidental pollution events.

3.06.2.3 Types of Water Samples and Water-Sampling Methods The sampling strategy defined in the sampling or monitoring program and the aim of a scientific investigation itemize the types of samples to be taken and the method to achieve sampling. The types of samples as well as the water-sampling method should be considered and defined in the sampling program to ensure that the goals of the program can be achieved successfully. Basically, the characteristics of water and wastewater, the variability of water and wastewater flow, and the requirements of analysis are important factors for the decision on types of samples and sampling methods (Wardencki and Namies´nik, 2002). The collected types of water samples are (1) grab or spot samples and (2) composite samples (American Health Association, 2005; Kramer, 1994; Wardencki and Namies´nik, 2002):

•

•

Grab or spot samples. Single or discrete samples collected at a given location, time, and depth of water body that are representative only for the composition of the sampled medium during sampling time (usually seconds to minutes). Composite or integrative samples. Samples collected using pooled portions of grab samples or by using continuous collecting automated sampling devices and stored in one sample container that are representative for the average conditions during sampling period or samples obtained by means of passive samplers.

The different composite sampling types are defined as:

• •

•

Time-proportional composite sampling. Equal volumes of samples collected proportional to water flow at or during constant time intervals. Flow-proportional composite sampling. Samples that are taken proportional to the water flow either collecting an equal volume of sample in an interval depending on the volume of water passed the sampling point or by alteration of the volume of sample proportional to the flow at constant time intervals. Time-weighted sampling. Collecting samples continuously during an entire sampling time using the passive sampling technique.

The overall sampling methods can be divided into manual, automatic, and passive sampling techniques:

•

Manual sampling. Collection of discrete or composite samples using a hand device (e.g., bottle, scoop, bucket, or

•

•

133

hand pump); risk of errors are due to incorrect handling of the device. Automated sampling. Gathering of discrete or continuous samples using an automated device with a predefined program without human action; it eliminates human errors, but interferences with the equipment may occur (e.g., contamination by and sorption to the materials used in tubing, pumps, and collection vessels). Passive sampling. Time-weighted sampling technique for polar and nonpolar organic pollutants and metals based on the free diffusion (according to Fick’s first law of diffusion) of the analyte molecules from the sampled environment to a receiving phase with a very high affinity for the target analytes (Greenwood et al., 2009; Namies´nik et al., 2005).

The question of how to select between the types of samples and methods for the sampling depends on the goals of the sampling or monitoring program. During the implementation of WFD for the most water bodies in Europe, spot samples were identified to be suitable to achieve the goals of the monitoring programs, but in specific situations (e.g., flow conditions and temporal variations influence pollutant concentrations or calculations of pollution load are performed) more representative flow- or time-integrated sampling should be applied (European Commission, 2009). However, in recent reviews, the disadvantages and drawbacks of spot sampling regarding the monitoring strategies of integrative environmental protection programs such as the WFD were addressed (Greenwood et al., 2009; Madrid and Zayas, 2007):

•

•

•

spot water samples represent the water quality only at the moment of sampling and may not reflect the average water quality, particularly if the concentrations of pollutants fluctuate with space and time; significant errors can occur, especially when pollutants are present in only trace levels and large volumes must be collected for analysis, even with automated sampling methods; and measurement of truly or freely dissolved contaminants by most conventional approaches due to difficulties with the separation of this fraction (see Section 3.06.3.3).

Hence, spot water samples are representatives only if the source of contamination is essentially constant (Wardencki and Namies´nik, 2002). Nevertheless, in some cases spot sampling may be appropriate (e.g., sampling of protected groundwater wells and some well-mixed surface waters as well as for parameters needed to be analyzed in situ, e.g., pH, dissolved oxygen, and temperature) (American Health Association, 2005; Madrid and Zayas, 2007). For a more representative sampling, a higher frequency of sampling (i.e., automated sequential sampling) to provide composite samples and continuous online monitoring systems is recommended. Furthermore, the usage of passive sampling approaches discussed in Section 3.06.5 could be a good opportunity to tackle the indicated problems (Greenwood et al., 2009; Madrid and Zayas, 2007). Although they are not well recognized and established in official monitoring programs (Greenwood et al., 2009; Mills et al., 2009b), the international standardization regarding ISO 5667-23, ‘‘Determination of priority pollutants in surface water using passive sampling’’

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and recommendations in the guidance documents for the implementation of WFD (European Commission, 2009) as well as the activities in the Network of Reference Laboratories for Monitoring of Emerging Pollutants (Mills et al., 2009b) enhance this process. Further development of validation schemes for QC and quality assurance methods and standards for the use of passive sampling devices as well as successful demonstration of sampler performance alongside conventional sampling schemes can help to convince regulators of the fact that passive samplers are alternative and cost-efficient instruments for water monitoring. Thus, the standardization will lead to a broader acceptance of passive sampling methods for regulatory and routine monitoring purposes.

3.06.2.4 Sampling Site Selection and Sampling Frequencies The sampling site can be defined as the area, transect, or stretch of a water body where samples are collected and the sampling or monitoring point(s) as the exact location(s) where samples are taken within a sampling site. The sampling frequency is the interval in which a sample is collected. The choice of sampling sites and sampling frequencies depends on the objectives and goals of the respective sampling or monitoring program. Nevertheless, the selection of the sampling sites and points has been rather often more a decision by personal judgment based on pragmatic considerations such as accessibility and safety than on determination of the optimal sampling site for each indicator under investigation (Dixon and Chiswell, 1996; Maher et al., 1994). In consequence, the goals of monitoring programs in terms of an integrative risk assessment demanded, for example, by the WFD may fail because of nonexistent consistence of sampling areas or points for different parameters such as metals, macroinvertebrates, and organic compounds (von der Ohe et al., 2009). In the Annex V of the WFD, minimal criteria for the selection of sampling points are listed for the monitoring of surface waters (European Community, 2000, 2009):

•

•

•

Surveillance monitoring. The sampling points should be located at major river stretches and at the downstream end of relevant subcatchments, advantageously with fixed monitoring stations and automated sampling to obtain continuous and composite samples. Operational monitoring. The sampling points should be located in order to allow assessment of the pressures of point or diffusion of pollution sources such that the magnitude and impact thereof is recorded sufficiently and representatively for the entire water bodies. Investigative monitoring. The sampling points should be located such that the goals of the program are achievable (e.g., unknown exceedance of EQS or magnitude and impact of an accident).

In practice, the selection of the exact sampling points including water depths for water sampling depends on local conditions such as vertical and lateral mixing, and homogeneity of the water body (European Commission, 2009). For an integrated assessment of the chemical and biological status, the requirements of biological monitoring should be considered for

sampling site selection as well (European Community, 2000; von der Ohe et al., 2009). The distance from, for example, effluents of wastewater treatment plants and the confluence with tributaries should be considered to quarantine the vertical and horizontal mixing and homogeneity of the water body sampled (e.g., using tracer method as described in detail elsewhere) (ISO 5667-6, 2005; Kramer, 1994). The selection of groundwater sampling points depends on the setup of the groundwater monitoring network that should be representative for each groundwater body under observation to allow the assessment of possible chemical pressures and their impact on the groundwater (European Community, 2000). However, the implementation of a groundwater monitoring network is more difficult in comparison to surface water monitoring due to the three-dimensional nature of the groundwater system and the spatial and temporal variability of the groundwater body (see also Chapter 2.06 Mechanics of Groundwater Flow) (e.g., hydrogeology, hydrology, connected surface ecosystems, and pollution pressures) (European Commission, 2007a). Another critical factor of sampling is the sampling frequency, because the confidence intervals of measures are a function of the numbers of samples taken (Dixon and Chiswell, 1996; Strobl and Robillard, 2008). Frequency of monitoring predominantly depends on the characteristics of the water body and the monitoring site. The Annex V of the WFD (European Community, 2000) provides tabulated sampling frequencies for different indicators and environmental quality elements in terms of minimal required intervals to assess the quality of a water body under investigation which were refined for the practical implementation of WFD (European Commission, 2003a, 2003b, 2007, 2009). In general, computer tools and models can be applied for the selection of optimized sampling sites and sampling frequencies in order to avoid the common practice of designing monitoring networks on subjective factors using, for example, Kriging theory, analysis of variance, least-squares methods, Fuzzy logic, and GIS (European Community, 2003b; Strobl and Robillard, 2008). Further guidance for the selection of sampling site(s), sampling frequencies, and number of samples can be found for surface waters in ISO 5667-6 (2005) and Wilde (2005) and for groundwater in ISO 5667-22 (2009) and Nielsen and Nielsen (2007a) as well as in Annex V of the WFD (European Community, 2000).

3.06.2.5 QC and QA QC is essential for the appropriate application of sampling technologies in water monitoring. In general, QA measures should be implemented throughout all procedures including preparation, handling (transportation, deployment, and retrieval), and storage and processing. The level of QC applied to sampling varies depending on the project objectives and procedures involved. QC/QA procedures for sampling are outlined in a vast number of publications for traditional and passive sampling methods (Vrana et al., 2005a; Huckins et al., 2000) as well as in different ISO guidelines (ISO 5667-10, 1992; ISO 5667-14, 1998; ISO 5667-11, 2009; ISO 5667-22, 2009; ISO/DIS 5667-23, 2009; see also Chapter 3.07 Measurement Quality in Water Analysis).

Sampling and Conservation 3.06.2.6 Safety Considerations Safety considerations are issues of different national and international laws and regulations regarding personnel safety precautions. However, collection of water samples has some elements of danger due to dangerous sampling sites (e.g., river banks, boats, weirs, and sewage treatment plants) and intrinsic toxicity of the water samples collected as well as hazardous substances (e.g., acids) which are used for conservation of the samples. Personal protective clothing should be worn the whole time during sampling and handling samples (e.g., disposable gloves, eye protection, laboratory coats, respirators, and coveralls) to protect contamination and damage with toxic substances caused by skin or eye passage, respiration, or swallowing. Also, the sampling sites should be selected under safety aspects and dangerous sites should be avoided wherever possible.

3.06.2.7 Standards, Guidelines, and Handbooks for Sampling of Water Samples Table 1 lists the most important International Organization for Standardization (ISO) and European Commission guidelines and recommended handbooks related to water sampling. The standard guidelines of ISO and related national organizations are well established and validated, and hence, they are widely accepted for regulatory and legislative purposes (see also Chapter 3.11 Standardized Methods for WaterQuality Assessment). In general, these guidelines contain the principles for the design of sampling programs and sampling techniques, and QA for the sampling of surface water, groundwater, and wastewater. The National Field Manual for the Collection of Water-Quality Data of the US Geological Survey (USGS, 2008) and Standard Methods for the Examination of Water and Wastewater (American Health Association, 2005) are comprehensive manuals for the sampling and analysis of waters. The guidelines of the European Community reflect the recent developments and discussions regarding the common implementation strategy for the European WFD and are available free of charge from the website of European Communities. The Essential Handbook of Ground-Water Sampling (Nielsen and Nielsen, 2007a) and the handbook on passive sampling techniques in environmental monitoring (Greenwood et al., 2007a) contain techniques for groundwater and passive sampling in detail.

3.06.3 Handling and Conservation of Liquid Water Samples 3.06.3.1 Introduction The pretreatment of collected water samples is commonly necessary before chemical analysis due to loss of analytes, contamination, and other alterations, for example, change of pH, temperature, and dissolving of gases. This processing is the consecutive step after sampling and includes the composition, subsampling (splitting), preservation, and shipment of samples and depends on the target analytes and the intended purpose of the sampling campaign or program (Wilde et al., 2004). If the samples are not processed immediately during or after sampling (e.g., the measurement of pH and oxygen), a long-term stabilization or preservation is

135

Table 1 List of guidelines and recommended handbooks regarding sampling of water samples Guidance/handbook

Reference

Water quality – sampling – part 1: Guidance on the design of sampling programmes and sampling techniques Water quality – sampling – part 3: Guidance on the preservation and handling of water samples Water quality – sampling – part 4: Guidance on sampling from lakes, natural and manmade Water quality – sampling – part 6: Guidance on sampling of rivers and streams Water quality – sampling – part 10: Guidance on sampling of wastewaters Water quality – sampling – part 11: Guidance on sampling of groundwaters Water quality – sampling – part 14: Guidance on quality assurance of environmental water sampling and handling Water quality – sampling – part 20: Guidance on the use of sampling data for decision making – compliance with thresholds and classification systems Water quality – sampling – part 22: Guidance on the design and installation of groundwater monitoring points Water quality – sampling – part 23: Determination of priority pollutants in surface water using passive sampling National field manual for the collection of water-quality data (US Geological Survey) Common Implementation Strategy for the Water Framework Directive – Guidance document No. 7: Monitoring under the Water Framework Directive Common Implementation Strategy for the Water Framework Directive – Guidance document No. 11: Planning process Common Implementation Strategy for the Water Framework Directive – Guidance document No. 15: Guidance on groundwater monitoring Common Implementation Strategy for the Water Framework Directive – Guidance document No. 16: Guidance on groundwater monitoring in drinking water protection areas Common Implementation Strategy for the Water Framework Directive – Guidance document No. 19: Guidance on surface water chemical monitoring under the Water Framework Directive Passive Sampling Techniques in Environmental Monitoring Standard Methods for the Examination of Water and Wastewater The Essential Handbook of Ground-Water Sampling

ISO 5667-1, 2006

ISO 5667-3, 2003

ISO 5667-4, 1987

ISO 5667-6, 2005 ISO 5667-10, 1992 ISO 5667-11, 2009 ISO 5667-14, 1998

ISO 5667-20, 2008

ISO/DIS 5667-22, 2009 ISO/DIS 5667-23, 2009 USGS, 2008 European Commission, 2003a

European Commission, 2003b European Commission, 2007a

European Commission, 2007b

European Commission, 2009

Greenwood et al., 2007a American Health Association, 2005 Nielsen and Nielsen, 2007a

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Sampling and Conservation

recommended to protect the samples against changes of physical and chemical properties as well as the disposal of each component interfering with analytes and analytical procedures from the sample. This section is related to liquid water samples. Section 3.06.5 deals with the description of handling of passive samplers.

3.06.3.2 Alterations of Water Samples A main challenge of the sampling procedure is to prevent the alteration of the water samples with respect to their natural character. The major possible alterations of the natural character of water samples are (Parr et al., 1988; USGS, 2008; Wardencki and Namies´nik, 2002) as follows:

• •

Physical–chemical processes. Oxidation, reduction, hydrolysis, (de-)polymerization, volatilization, adsorption, absorption, diffusion, photo-degradation, precipitation, and degassing. Biochemical processes. Consumption of compounds by organisms (e.g., bacteriae or algae) due to their growth including formation of metabolic compounds.

Some basic chemical and physical properties are sensitive to quick changes after collection of sample, for example, temperature, pH, and dissolved gases (e.g., oxygen and carbon dioxide). Many organic compounds as well as metals and inorganic analytes such as nitrogen and phosphate species are sensitive to changes of pH; volatiles and dissolved gases may degas; and ions and hydrophilic and organic compounds may adsorb to surfaces of glass bottles. Changes in the reduction– oxidation potential can lead to precipitation or dissolution and other alterations of substances (e.g., iron and manganese). Biological activity in the samples is a reason for a wide range of changes. It influences, for example, the oxidation state of some compounds, and some substances (e.g., nitrogen, phosphorus, and organic compounds) are potential growing substrates for microbes and get lost. If possible, analyze the following parameters immediately in situ to avoid significant changes (see also Chapter 3.01 Sum Parameters: Potential and Limitations):

• • • • • • • • •

temperature; pH; specific conductance; turbidity; alkalinity; reduction–oxidation potential; dissolved gases; color; and odor.

and bottles, maximum elapsed time between sampling and analysis, and pretreatment (e.g., filtration, addition of a preservation agent). These requirements depend on the properties of the sample and analytes to be determined. The properties of samples may be different among the sample species (groundwater, surface water, and wastewater) and among the sample locations. These differences should be considered while planning the sampling and selection of methods for sampling, handling, preservation, and storage. Conservation methods such as cooling or adding of a preservation agent may be applied to avoid alterations of the natural character thereof. The pretreatment of the samples may include filtration of the samples to avoid, for example, sorption of metals and organic compounds to suspended particulate matter (SPM) or interactions between dissolved fraction and particulate-bound fraction due to addition of a preservative. Commonly used membrane filters, for example, for metals, have a pore size of 0.4–0.45 mm, and glass-fiber filters often used for organic compounds have a nominal particle size retention of 0.7– 1.2 mm with a similar SPM retention capacity comparing with membrane filters. However, the pore-size limits are an operational and somewhat arbitrary limit to separate SPM and the dissolved fraction. Because in this concept the role of colloids – which occur in a diameter ranging from 1 nm to 1 mm (see also Chapter 3.05 Natural Colloids and Manufactured Nanoparticles in Aquatic and Terrestrial Systems) – and other dissolved organic carbon (DOC) – which is present in natural surface and groundwaters and may not be retained by the filter – is completely ignored (Danielsson, 1982; Horowitz et al., 1996; Kramer, 1994; see also Chapter 3.01 Sum Parameters: Potential and Limitations). Nevertheless, if the filtration in situ is difficult or impossible, the addition of preservatives can cause an undesirable change of equilibrium between dissolved and particulate-bound fractions, for example, of metals, and hence, the filtration and possible addition of preservatives should be done as soon as possible when reaching the laboratory (Kramer, 1994). If the sampling container is used to collect the sample, for example, by immersing it in the water, the preservative must be added after sampling to avoid the loss. The chemicals used for preservation should have the bestavailable quality and should also be controlled for their blanks to avoid any contamination. Table 2 gives an overview of suitable conservation methods for different groups of analytes. The preservation agents should be added initially to the sample containers. If there is concern of interference of some determinants with used agents, then splitting of the sample is required to avoid interactions between the preservation agents.

3.06.3.3 Handling and Conservation of Water Samples

3.06.3.4 Selection of Sample Containers and Storage

The cooling of samples should start as soon as possible, for example, using ice or commercially available cooling packs or storing in refrigeration systems. Dry ice ought to be avoided due to freezing of the sample, breaking of glass beakers, and a possible change of pH in the sample (American Health Association, 2005). It is useful to consult the analyzing laboratory to determine the requirements for handling, preservation, and storage of the samples, such as the choice of containers

Besides the choice of preservation agent, the selection of the sample containers for transport and storage is of major importance for the integrity of the sample (American Health Association, 2005; Wilde et al., 2004). The containers and the cap-liners should be made of a material, which is appropriate to keep the sample in its natural condition and for the conservation of the sample as well as for the expected range of analytes. Fluorinated polymers (e.g., polytetrafluoroethylene

Sampling and Conservation

137

Table 2 Suitable conservation methods for water samples (American Health Association 2005; Jeannot, 1994; Wardencki and Namiesnik, 2002; Wilde et al., 2004) Conservation technique

Preservation agent

Determinant

Untreated Refrigeration (2–5 1C)

NA NA

Acidification to pHo2

HCl HNO3

Anions Acidity and alkalinity, BOD, bromides and bromine compounds, chlorophyll, chromium (VI), color, COD, conductivity, cyanide, DOC, iodide, iodine, nitrate, nitrite, odor, oil and grease, organic compounds (e.g. pesticides, PAH, PCB, VOC, organochlorine and organophosphorus pesticides, base/neutrals and acids), pH, phenols, phosphorus, silicates, suspended solids, sulfate, sulfide, surfactants, TOC, turbidity Hydrocarbons, oil and grease, mercury Cations, metalloids (antimony, arsenic, and selenium), oil and grease, phosphorus (total), total hardness, trace elements Ammonia, boron, COD, free and ionized ammonia, total hardness, Kjeldahl nitrogen, oil and grease, organic chlorine, permanganate index, phosphorus (total), surfactants (anionic), TOC Phenoxylalkanoic herbicides Phenols, phenol index Cyanide Nonionic surfactants Sulfide

H2SO4

Acidification to pHo4 Alkalinize to pH412 Addition of other agents

Formic acid H3PO4 or H2SO4 NaOH Formaldehyde Zinc acetate solution

(PTFE)) and quartz or borosilicate hard glass such as Pyrexs or Durans are the best materials for most inorganic determinants and organic compounds, respectively. However, high-density polyethylene bottles are convenient for inorganic compounds (Batley and Gardner, 1977) and often used due to the high costs of fluorinated polymers. Polyethylene (PE) is not suitable for organic compounds (Barcelona et al., 1985; House, 1994), because these may absorb to the PE or polymer compounds such as softeners and antioxidants can leach from the PE walls. Soft glass containers ought to be avoided due to leaching of constituents from the glass wall, for example, silica, sodium, and boron. Use only light-adsorbing hard glass containers with PTFE cap-liners for photosensitive organic compounds. Silanization of the glassware can be suitable for the determination of hydrophilic organic compounds such as bisphenol A, alkylphenols, and some pesticides, which may adsorb the glass surfaces. Containers should be pre-leached and cleaned in laboratory using suitable agents (e.g., phosphate-free detergents, acids, organic solvents, and ultra-cleanwater) to avoid any contamination. In situ pre-rinsing with the sample is generally not recommended to avoid loss of preadded preservative and through potential biasing of the results due to compounds that adhere to the walls of the sample container (e.g., SPM) (American Health Association, 2005; House, 1994). If there is intention to preserve and store samples by freezing, the containers should consist of HDPE or PTFE to avoid breakage. Table 3 summarizes the recommendations for containers for some common groups of analytes.

3.06.4 Water Sampling Using Traditional Methods 3.06.4.1 Sampling of Surface Water The selection of a representative sampling site or sampling point is one of the main challenges of the setup of a sampling or monitoring program. The principles of sampling site

selection were discussed above. A main problem of sampling point selection is probably the heterogeneity of the water body. The distribution and fate of chemicals in the water body are controlled by diffusion and advection (Schwarzenbach et al., 2003). Hence, the concentration of a pollutant may change throughout the water body resulting in a heterogenic vertical, horizontal, and lateral distribution of the pollutant (Artiola, 2004; Whitfield, 1983). A comprehensive guideline as to how to handle the problems of sampling site selection and how to obtain representative samples even from heterogenic sampling locations can be found elsewhere (Wilde et al., 2004). The sampling equipment is selected considering the type of water (e.g., lake and river), the sample requirements (e.g., size and analytes), and the objectives of the sampling program. The used sampler must be without risks of sample contamination by the construction materials of the samplers (sorption and/or release of compounds) and from previous use for sampling in other water bodies (memory effects) to avoid analytical artifacts (Keith, 1990). Different sampling devices were developed for (1) spot sampling, (2) sampling from specific depths, and (3) automatic sampling. Spot sampling is often done manually using bottles, scoops, or buckets immersed directly to the water body in a depth of 0.5–1 m as well as using depth-water samplers for stratified water bodies (Artiola, 2004; House, 1994). Bottles can be immersed to collect a surface sample using an extendable telescope rod with a holder for the bottle. There are commercially available different types of depth-water samplers (Table 4). The depth samplers are lowered down into the water using metered tag ropes and either opened or closed at the predefined water depth according to their sampling principle. Manual sampling devices are used, for example, from a boat, a bridge, or the river bank. Automated sampling devices are available in many different configurations for stationary and mobile operation. The sampling devices may be programmed and configured to sample discrete as well as flow- and time-proportional

138

Sampling and Conservation

Table 3

Recommended containers, maximum storage times, and typical sample volumes for water samples for exemplary water analytes

Determinant

Sample container

Alkalinity–acidity

P,GB P,GB Anions P,G P P P Biological oxygen demand (BOD) P,G P,G P,G Cations (except Cr6þ, Hg) P Chemical oxygen demand (COD) P,G P,G P(A),G(A) Cr6þ P Mercury (Hg) P(A),G(A) GB G Cyanide P,G GB P P Nitrogen and phosphorus (nutrients) P,G P

Storage time

Typical sample size (ml) Reference

24 h 24 h 48 h–28 days

100 4 100–200 125–500 100–200 250 1000 1000 1000 250 100 100 1000 500 1000 250

24 h–48 h 24 h 24 h 6 months 7 days 24 h 24 h 28 days 1 month 24 h 48 h

7–28 days 48 h

Chlorinated hydrocarbons Dioxins and furans

Ga (PTFE) Ga, PTFE-LC Ga, PTFE-LC 7 days

Dissolved organic carbon Hydrocarbons Nitroaromatics

G G Ga, PTFE-LC Ga Ga, PTFE-LC G, wide-mouth

Nitrosamines Oil and grease

Organochlorine pesticides Organophosphorus pesticides Organo-nitrogen herbicides Pesticides

Phenols

Polynuclear aromatic compounds Polychlorinated Biphenyls Total organic carbon (TOC)

Volatile organic compounds

G G Ga, G Ga, Ga, Ga Ga, G

28 days 15 days 7 days

PTFE-LC 1 day PTFE-LC PTFE-LC PTFE-LC

P,G, PTDE.LC G Ga, PTFE-LC Ga Ga Ga, PTFE-LC Ga Ga, PTFE-LC GB Ga, PTFE-LC G G Ga

1000 100 250 1000 500 100 125 1000 125 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000

7 days 1000 1000 2000 1000 500 o24 h–28 days 1000 500–1000 1000 7 days 1000 1000 7 days 1000 1000 7 days 100 40 125 14 days 40 7 days 7 days

American Health Association, 2005 Wardencki and Namiesnik, 2002 American Health Association, 2005 Nielsen and Nielsen, 2007b Wardencki and Namiesnik, 2002 Wilde et al., 2004 American Health Association, 2005 Wardencki and Namiesnik, 2002 American Health Association, 2005 Wilde et al., 2004 American Health Association, 2005 Wardencki and Namiesnik, 2002 American Health Association, 2005 Nielsen and Nielsen, 2007b American Health Association, 2005 Wardencki and Namiesnik, 2002 Wilde et al., 2004 American Health Association, 2005 Wardencki and Namiesnik, 2002 Wilde et al., 2004 Nielsen and Nielsen, 2007b American Health Association, 2005 Wardencki and Namiesnik, 2002 Wilde et al., 2004 Nielsen and Nielsen, 2007b Koester and Clement, 1993 Wardencki and Namiesnik, 2002 Wilde et al., 2004 Nielsen and Nielsen, 2007b Nielsen and Nielsen, 2007b Wilde et al., 2004 Nielsen and Nielsen, 2007b American Health Association, 2005 Wardencki and Namiesnik, 2002 Wilde et al., 2004 Jeannot, 1994 Nielsen and Nielsen, 2007b Jeannot, 1994 Nielsen and Nielsen, 2007b Koester and Clement, 1993 Wilde et al., 2004 American Health Association, 2005 Wardencki and Namiesnik, 2002 Wilde et al., 2004 American Health Association, 2005 Jeannot, 1994 Nielsen and Nielsen, 2007b Wilde et al., 2004 Jeannot, 1994 Nielsen and Nielsen, 2007b Jeannot, 1994 Nielsen and Nielsen, 2007b American Health Association, 2005 Nielsen and Nielsen, 2007b Wilde et al., 2004 Jeannot, 1994 Wilde et al., 2004

P, polyethylene; G, glass; GB, borosilicate glass; PTFE-C, Teflons-lined caps; Ga, amber borosilicate glass; P(A)/G(A), acid rinsed P or G.

Sampling and Conservation Table 4

139

Depth sampling devices for surface waters

Principle

Sampler name

Sampling volume (l)

Spot/depth sampler

Water-sediment bottle Ruttner sampler MERCOS sampler LIMNOS sampler modified Van Dorn sampler

1–2 1–2 2 1.4–7

samples. The water is delivered to the systems by peristaltic, syringe, membrane, cogged submersible, and eccentric submersible pumps (Wardencki and Namies´nik, 2002). Flowproportional sampling machines are connected to flow- or discharge-gauging systems and measure the flow in the water body to take a water sample after a predefined volume of water that has passed the sampling point or to take event-controlled samples, for example, during flood events. Time-proportional sampling devices take a certain volume of sample after a predefined time interval, for example, a volume of 20 ml taken every 15 min over 24 h to obtain a 24-h composite sample of approximately 2 l. The samples are delivered to sample bottles inside the machines using internal distribution systems and frequently cooled at 4 1C. The automatic devices are normally equipped with an automatic dosing system which always obtains a constant volume of water (Wardencki and Namies´nik, 2002). For more details regarding the principles of water-sampling techniques and sampling of surface waters, the reader is referred to Sturgeon et al. (1987), Wardencki and Namies´nik (2002), Wilde et al. (2004), and the respective ISO guidelines (ISO 5667-4, 1987; ISO 5667-14, 1998; ISO 5667-6, 2005; ISO 5667-1, 2006).

3.06.4.2 Sampling of Groundwater The methods for sampling of groundwater are mainly different from surface water sampling. The main challenges are the time- and cost-intensive accessing of groundwater, that is, drilling, construction, and maintenance of observation and monitoring wells, and the three-dimensional black-box character of the groundwater bodies (i.e., lack of visibility of the subground and its inherent hydrogeological, geochemical, and biological characteristics) (Nielsen, 2007b; Roy and Fouillac, 2004). Therefore, the planning and installation of a groundwater-monitoring network and groundwater sampling demands a high grade of experience and professionalism to achieve the goals of the monitoring or sampling program successfully. Thus, this section only gives a short overview regarding different types of observation and monitoring wells as well as samplers used for groundwater sampling. In hydrogeology, three types of wells are defined: (1) water supply wells, (2) observation wells, and (3) monitoring wells (Dalton et al., 2007; Wilde, 2005). The first well type is used for extraction of water for drinking, industrial process, and irradiation purposes, and usually equipped with a dedicated high-capacity pump; the second well type is used to collect hydrogeological data for observation of the aquifer characteristics and often equipped with piezometers; the third well

Water depths (m)

Reference

o30

Artiola, 2004 Majaneva et al., 2009 Freimann et al., 1983 Majaneva et al., 2009 Finucane and May, 1961

type is used for observation of physical, chemical, and biological characteristics of the aquifer. The wells for groundwater monitoring are installed using a variety of drilling techniques and well-construction methods, for example, driven well points and nested multilevel wells in separated or single boreholes to observe shallow, unconfined saturated zones, and multiple saturated zones (Barackman et al., 2004; Dalton et al., 2007; Lerner and Teutsch, 1995). For the sampling of the groundwater-monitoring wells, there are different devices commercially available (Barackman et al., 2004; Nielsen, 2007a):

•

•

•

•

Grab samplers. Open and point-source bailers, thief samplers, and syringe samplers; lowered down in the boreholes, particularly equipped with valves and mechanical, electrical, or pneumatic triggers to open/close valves or plugs to obtain samples in a certain depth. Suction-lift pumps. Peristaltic and surface centrifugal pumps; vacuum pumps operated at the ground surface connected to the sampler tubing; problematic due to alteration of the samples due to vacuum (e.g., degassing). Submersible positive displacement samplers. Gas-displacement pumps, bladder pumps, piston pumps, helical rotor pumps, gear-drive pumps, inertial-lift pumps; pumps submersed in groundwater table and using different mechanical concepts to lift the water to surface. Passive samplers. Passive diffusion samplers are used for groundwater sampler as well; the different types of passive samplers are discussed in Section 3.06.5.

The interested reader is directed to Barackman et al. (2004), Nielsen and Nielsen (2007a) and the respective ISO guidelines (ISO 5667-1, 2006; ISO/DIS 5667-22, 2009, ISO/DIS 566723, 2009) for further information regarding groundwater well construction, well development, and sampling of groundwater.

3.06.5 Water Sampling Using Passive Sampling Technology 3.06.5.1 Introduction to Passive Samplers, Their Modeling, Calibration, and Quality Control Passive samplers have been used in environmental monitoring since the beginning of the 1970s. The early designs were used to measure concentrations of gaseous pollutants in air, but since the late 1980s, passive samplers have been developed for monitoring concentrations of pollutants in water, soils, and sediments (Greenwood et al., 2007a). The same principles of operation apply to all passive sampler devices, for use in both

140

Sampling and Conservation

Curvilinear

Equilibrium

St

Linear

In the latter case, transport of samples from field to laboratory becomes very expensive. A range of passive samplers has been developed for monitoring environmental pollutants from a range of chemical classes, including metals, polar organics, nonpolar organics, organometallics, and volatile organics. Several reviews (Go´recki and Namies´ek, 2002; Vrana et al., 2005b; Stuer-Lauridsen, 2005; Seethapathy et al., 2008; Zabiegala et al., 2010) and the worldwide first anthology (Greenwood et al., 2007a) summarize these achievements. Sections 3.06.5.2 and 3.06.5.3 describe several of these devices and their applications more detailed for nonpolar and polar organic target compounds, respectively. Passive sampling of metal ions is not covered in this chapter; the readers are referred to the relevant literature (Greenwood et al., 2007b; Warnken et al., 2007; Mills et al., 2009a). While all the samplers have the same basic components, their structural configurations, handling properties, ease of use, and performance are widely different. They all have strengths and weaknesses, and it is important to select the sampler most appropriate for each particular problem to be investigated. For all passive samplers, uptake rates depend to a greater or lesser extent on temperature and turbulence of the water (Booij et al., 2007). The rate of uptake can also be affected by the growth of microorganisms (biofouling) on the surface of the diffusion limiting membrane. Further, all depend on the availability of reliable calibration data for the pollutants of interest. For samplers used for monitoring nonpolar pollutants, performance reference compounds (PRCs) have been developed. These are compounds (typically deuterated analogs of the compounds to be measured) that are loaded onto the receiving phase of the sampler prior to deployment, and that offload at a measurable rate. If the kinetics of uptake and offloading are isotropic, that is, the offloading rates of the PRCs are affected by temperature, turbulence, and biofouling in a manner similar to the uptake rates of pollutants, then the rates of loss of PRCs from the sampler can be used to correct the uptake rates of pollutants for the effects of those environmental variables. This approach can effectively provide in situ calibration of the samplers.

R

Amount sorbed / water concentration

air and water. Uptake of a chemical from the environment is by molecular diffusion. The samplers comprise a receiving phase that accumulates contaminants, and has a very high affinity for them so that the concentration at its surface is maintained close to zero, and a diffusion-limiting layer that separates the receiving phase from the bulk water environment. Hence, the mass of a contaminant accumulated is determined by its concentration in the water (more precisely, the gradient of its chemical potential between bulk water phase and receiving phase), the exposure time, and the sampling rate (Rs) of the device (i.e., an overall compound- and samplerspecific uptake rate constant). In case of diffusion-based samplers where the solutes diffuse through capillaries (pores) to the collecting phase, Rs is a product of the compound’s diffusion coefficient in water, the exchange surface area with the surrounding (i.e., the total area of capillary/pore openings), and the inverse of the diffusion path length. For permeation-based samplers, where a nonporous membrane encloses the receiving phase, the membrane/water partition coefficient is included as additional factor in Rs (Go´recki and Namies´ek, 2002). Most passive samplers measure only concentrations of freely dissolved analytes and not the total amount of analytes present in the water column. Fractions bound to SPM or to DOC are not measured or irrelevant due to either their exclusion by the diffusion-limiting layer, slow transport through it, or poor uptake by the receiving phase. In all passive samplers, the mass accumulated is used to determine the external concentration, but depending on sampler design and mode of operation, this can reflect either the equilibrium concentration or the time-weighted average concentration over the deployment period (days to months) (Figure 1). Where environmental concentrations fluctuate with time the kinetic samplers are used, but in more constant or slowly changing conditions, the equilibrium samplers are deployed. Since the samplers accumulate substances over a prolonged period the analytes are effectively pre-concentrated, and this can bring them above the level of quantification of the analytical method. It would be necessary to collect and extract large volumes of water in order to achieve a comparable sensitivity with spot sampling.

Time Figure 1 Passive samplers mostly operate in the linear uptake phase or in equilibrium with the surrounding water phase.

Sampling and Conservation

However, PRCs have not been developed for samplers for monitoring polar organics or inorganics (Mills et al., 2007). In the following, basic mathematical equations are given for the description of the uptake of compounds into a passive sampler. A first model of this process was derived by Huckins and co-workers at the US Geological Survey in the early 1990s for the semipermeable membrane device (SPMD) in analogy to those describing the dynamic exposure of aquatic organisms. The modeling approach can easily be applied to other passive samplers such as the membrane-enclosed sorptive coating (MESCO) device or the polar organic integrative sampler (POCIS) (see Sections 3.06.5.2 and 3.06.5.3). A more detailed discussion of the theory of passive sampling can be found in the literature (Booij et al., 2007; Huckins et al., 2006). The passive solute uptake in the receiving phase of a permeation-based sampler can be described by a first-order kinetics according to

   kov A a mSðtÞ ¼ m0 þ ðcW KSW VS  m0 Þ 1  exp  t KSW VS

ð1Þ

where mS(t) is the amount of compound accumulated in the sampler after the exposure time t, m0 the amount already in the sampling phase before exposure (blank, background level), cW the concentrations in water, KSW the partition coefficient between sampling phase and water, VS the volume of the sampling phase, kov the overall mass-transfer coefficient into the sampler, A the available exchange area (membrane surface), and a the membrane porosity (a ¼ 1 for nonporous membranes or if a membrane is not present). The coefficient in the exponential function in Equation (1) can be combined to an overall exchange constant kex, whereby the product in the numerator is usually named as sampling rate RS:

kex ¼

kov A a RS ¼ KSW VS KSW V S

ð2Þ

Substance enrichment follows approximately a linear trend until half-life of accumulation t1/2 is reached:

t1=2 ¼

ln 2 kex

ð3Þ

The passive sampler accumulates integrative up to this point in time; hence, it is possible to derive (from Equation (1)) an expression for the time-weighted average (TWA) concentration in the aqueous medium monitored:

CTWA ¼ W

mðtÞ  m0 RS  t

ð4Þ

This value is the primary goal of passive sampler application in water monitoring. For longer exposure times, when the sampler reaches distribution equilibrium with surrounding water phase, the term in square brackets in Equation (1) approximately equals 1 and the aqueous concentrations Ceq W are calculated according to

Ceq W ¼

mðtÞ  m0 KSW  VS

ð5Þ

141

The result of Equation (5) cannot be considered as a TWA concentration because it is not to deduce when equilibrium was reached and thus the analyte amount accumulated does not necessarily reflect all fluctuations in concentration during the complete sampling period. Instead, the value calculated from Equation (5) provides a snapshot of the concentration representative for the equilibrium period. For more details on the principles of equilibrium samplers, the reader is referred to Mayer et al. (2003). Other more sophisticated mathematical models are possible, depending on the degree of simplifying assumptions that are taken into account. However, this simple model based on the film theory for interfacial mass transfer and the inherent assumption of the additivity of mass-transfer resistances (Cussler, 1984) is able to describe the occurring phenomena (i.e., the data from field trials) reasonably well and serves in the design and evaluation of laboratory calibration experiments. Such calibration studies under controlled conditions are necessary because the sampling rate RS is a complex parameter depending not only on the aforementioned substance-specific coefficients and sampler properties/dimensions, but is also influenced by environmental factors such as water temperature, hydrodynamic conditions, and biofilm growth on the surface of the sampler. Partly, this also holds for KSW as the only specific factor in Equation (5). Different laboratory experiments are possible for passive sampler calibration and model equations to evaluate them mathematically given by Booij et al. (2007). The static-exposure design where the passive samplers are exposed to a single volume of contaminated water is used usually for determination of sampling rates under more quiescent conditions (e.g., relevant for sampler deployment in lakes or groundwater). Another batch-wise approach is to work with a periodic renewal of the exposure water. Here, several flow regimes can be realized by adjusting stirring/shaking. Paschke et al. (2006) demonstrated the feasibility of such serial batch tests for rapid MESCO calibration. A third option for RS determination in the laboratory is the continuous flow design where depletion of the water phase in the exposure chamber is prevented by a constant supply of freshly contaminated water. Also in such experiments, different conditions (flow regime, temperature, DOC, salt concentrations, etc.) can be arranged. Vrana et al. (2001a) used long glass columns with slowly upstream flowing contaminated water for SPMD and MESCO calibration. Furthermore, Vrana et al. (2006a) designed and performed continuous flow experiments in a tank with the samplers placed on a rotating carousel (allowing higher flow rates) for calibrating the Chemcatchers device. A similar design is applied successfully in short-term exposure testing of six different passive samplers for monitoring hydrophobic contaminants in spiked river water (Allan et al., 2010). However, it will not be possible to simulate natural exposure conditions for the passive samplers in their full diversity and complexity. As already mentioned above, a promising approach is the in situ calibration of passive samplers by using PRCs. The dissipation rate constants of these preloaded compounds from the receiving phase during field deployment of the sampler can be estimated and used for deriving sampling rates under site-specific field conditions.

142

Sampling and Conservation

The PRC approach is applicable at least for receiving phases that consist of a liquid or a nonpolar polymeric film where the dominant uptake process is absorption into the bulk phase, but not adsorption to the surface of the material. A recently reported study on the field performance of seven passive sampling devices for monitoring hydrophobic substances demonstrates this in situ calibration approach (Allan et al., 2009).

3.06.5.2 Passive Sampling of Nonpolar Organic Compounds In the last two decades, various passive sampling devices were designed for monitoring nonpolar pollutants in the aquatic systems. Especially, the determination of persistent organic pollutants is of relevance due to their tendency to bioaccumulate and their high toxic potential. These pollutants are present in the aquatic environment both dissolved and particle-bound (due to their hydrophobicity). Of primary interest for risk assessment is the bioavailable fraction which corresponds to the dissolved fraction (for most of exposure routes). With conventional sampling techniques (e.g., grab sampling), only the total content of the pollutants is obtained. Furthermore, analyses of grab samples provide information about very hydrophobic organic pollutant burden only if large-volume water samples (410 l) are processed. In this section, only those passive sampling devices are considered that are commercially available or at least under commercial development. Such a stage of technical maturation implies that (reliable) calibration data are generated for several classes of compounds and made available for the enduser and that the field applicability of the device is demonstrated. More detailed information about the variety of more recently reported passive sampler types for water monitoring, including field application of solid-phase microextraction fibers, can be found in periodically appearing review papers (e.g., Go´recki and Namies´nik, 2002; Vrana et al., 2005a; StuerLauridsen 2005; Ouyang and Pawliszyn 2007; Seethapathy et al., 2008; Zabiegala et al., 2010). SMPD. Among the permeation-based samplers, the socalled SPMDs, introduced by Huckins and co-worker at US Geological Survey in the early 1990 (Huckins et al., 1990, 1993, 2006), attained the greatest importance and widespread application. A SPMD consists of lay-flat low-density polyethylene (LDPE) tubing enclosing a thin film of triolein. SPMDs are proved to be most effective in their capacity to accumulate lipophilic contaminants. Commercially available standard SPMDs are used in many field campaigns for sampling of, for example, chlorinated hydrocarbons, polycyclic aromatics, and organochlorine pesticides in different aqueous environments (e.g., freshwater and marine systems, and groundwater wells). The main disadvantage of this sampler type is, besides the relatively high price, the complex sample preparation required to recover the accumulated pollutants from the collecting phase (triolein). This is usually achieved by dialysis using considerable amounts of organic solvents, followed by pre-concentration, solvent exchanges, and cleanup of the extracts before the chromatographic analysis (Petty et al., 2000; Huckins et al., 2000; Wenzel et al., 2004). A compilation of SPMD sampling rates is given in Huckins et al.

(2006). Aspects of field application of SPMDs are also discussed there as well as by Bergqvist and Zaliauskiene (2007). In the last decade several attempts have been made to develop devices, which avoid the drawbacks with SPMDs and make the passive sampling technology more attractive also for routine monitoring programs. Single-phase polymer films such as LDPE (Booij et al., 2003) or silicone strips (Smedes, 2007; Yates et al., 2007) and rods (Paschke et al., 2006) can be used directly to avoid the triolein traces in the extracts. However, for the processing of these polymer materials, considerable amounts of organic solvents are necessary in most cases. Data evaluation is not straightforward due to the stronger influence of hydrodynamics on the substance uptake over the uncovered surface of the sampling phase, especially if PRCs are not used. PBD. For monitoring volatile (nonpolar) organic compounds in groundwater, the most widely used device is the passive diffusion bag (PDB) developed by Vroblesky at the US Geological Survey (Vroblesky, 2007). The major advantages are its simplicity (a protected PE bag filled with water) and the possibility to use the conventional headspace gas chromatography for analysis. However, one has to bear in mind that this is an equilibrium sampling device which does not provide TWA concentrations. Information on PDB handling, processing, and data interpretation can be found in Vroblesky (2001a). Many applications are described and are also reported (Vroblesky, 2001b, 2007). Other samplers (for semi-/low-volatile hydrophobic organics) contain solid materials (granular adsorbents and compact polymeric sorbents) instead of a liquid organic receiving phase. This allows simpler extraction procedures or even thermo-desorption of the accumulated pollutants without previous sample preparation. Grathwohl and co-workers at the University of Tu¨bingen, Germany, for example, designed a dosimeter for the integrative sampling of organic compounds in groundwater (Martin et al., 2001; WeiX et al., 2007). This sampler consists of a porous ceramic tube which can be filled with different grained adsorbents, for example, with ion-exchange resin Amberlite IRA-743 or Tenax, and was tested for monitoring several polynuclear aromatic hydrocarbons (PAHs) in groundwater (Martin et al., 2003; Bopp et al., 2005). Concerning the subsequent thermo-desorption of the analytes from Tenax difficulties appear due to the (unexpected) water permeability of the ceramic tube. Also, the long lag phase and thus response time of the sampler can be a problem when exposed in aqueous systems with more fluctuating concentrations of target compounds. Chemcatcher(R). The so-called Chemcatchers developed by Greenwood and co-workers at the University of Portsmouth, UK, is another promising passive sampling device (Kingston et al., 2000; Vrana et al., 2006a, 2006b; Greenwood et al., 2007b). The nonpolar Chemcatchers version consists of a PTFE body containing a C18 EmporeTM disk as receiving phase. A 40-mm-thick LDPE disk (47 mm diameter) of diffusion-limiting membrane is placed on the top of the receiving phase. The PTFE body part supports both the C18 EmporeTM disk and the LDPE membrane and sealed them in place. Currently, an optimized design of the Chemcatchers body is under investigation at the University of Portsmouth

Sampling and Conservation

which is simpler to use and more cost efficient than the original PTFE body. Conditioning and extraction of receiving phase is carried out in most steps according to protocols for the conventional solid-phase extraction of water samples with C18 EmporeTM disks (see Vrana et al. (2006a, 2006b) for specific modifications). There are sampling rates with the Chemcatchers device available for many priority organic pollutants. Both temperature- and flow-dependent (Vrana et al., 2006a, 2006b, 2007) field applications are also performed successfully with this flexible sampler configuration (Vrana et al., 2007; Greenwood 2007b; Allan et al., 2009). MESCO. A decade ago, another promising sampler for nonpolar aquatic micropollutants was developed at the Helmholtz Centre for Environmental Research – UFZ Leipzig, Germany. Vrana et al. (2001b) described the use of coarse pieces of silicone-based sorbent material as collecting phase of a passive sampler, which is enclosed in a membrane bag during field exposure and can be retrieved lostless for the following processing. Different types of the membrane-enclosed sorptive coating (MESCO) device are tested meanwhile for time-weighted average (TWA) sampling of organic compounds in water (Paschke et al., 2007). MESCOs can consist of different types of silicone-collecting phases. Commercial TwisterTM bars (PDMS-coated stir bars from GERSTEL, Mu¨lheim a.d.R., Germany), silicone tubes, and silicone rods are tested so far. In some MESCO applications, cellulosemembrane bags were applied to envelope the receiving phase (Vrana, 2001b, 2006b), whereas in others cellulose was replaced by an LDPE because it has proved to be more stable to biodegradation in the field (Wennrich et al., 2003; Paschke et al., 2006). The advantages of using silicone tubes and rods are their nonfragility, low costs, and flexibility in processing with minimal solvent consumption (Van Pinxteren et al., 2010). Current investigations deal with optimization of the used membrane thickness and material (Paschke et al., 2007). MESCO sampling rates for PAHs, polychlorinated biphenyls (PCBs), and selected organo-chlorine pesticides can be found together with discussion of optional instrumental processing variants in the original papers mentioned above. Examples of MESCO field applications are also reported (Vrana et al., 2006b; Paschke et al., 2006, 2007; Allan et al., 2009). Finally, a recently published report on a field study in the River Meuse, The Netherlands, should be mentioned (Allan et al., 2009). There, the field performance of seven passive sampling devices for monitoring dissolved concentrations of PAHs, PCBs, hexachlorobenzene, and p,p0 -DDE was evaluated through simultaneous field exposure of 7–28 days. Data generated by the Chemcatchers, LDPE membranes, two versions of the MESCO sampler, silicone rods, silicone strips, and SPMDs were evaluated through PRC dissipation data, analyte masses absorbed by various samplers, and the comparison of TWA concentration data. Despite different modes of calculation, relatively consistent TWA concentrations are obtained from different samplers. The variability observed is likely due to the uncertainty of sampler–water partition coefficients and the extrapolation of analyte uptake rates at the high n-octanol–water partition coefficient (KOW) range from a narrow PRC data range. These issues are further investigated (see, Allan et al. (2010) for first attempt in this matter).

143

3.06.5.3 Passive Sampling of Polar Compounds The following section describes passive sampling of polar organic compounds (POCs). With the development of analytical chemistry, especially the progress in mass spectrometric techniques combined with liquid chromatography, POCs gained increasing attention in the last few years (Richardson, 2008, 2009). It was realized that POCs are frequent contaminants in aquatic ecosystems (Loos et al., 2009; Richardson, 2007) since POCs that are not readily degradable can enter rivers via wastewater treatment plants (Hewitt and Marvin, 2005; Reemtsma et al., in press; Reemtsma et al., 2006). Furthermore, POCs can contribute significantly to the whole toxicity of water or sediments (Bandow et al., 2009; Biselli et al., 2005; Pomati et al., 2006; Streck, 2009a). Different approaches exist to classify a compound as polar or nonpolar, either by considering their electric-dipole moment, whether functional groups are present that could undergo particular interactions with other molecules, or by means of their KOW as a simple but somewhat arbitrary discriminator (Schwarzenbach et al., 2003). Usually, many pesticides, especially herbicides, pharmaceuticals as well as organic wastewater contaminants are counted among POCs. Two special passive samplers have been developed to sample these compounds, that is, the polar organic chemical integrative sampler (POCIS) and the Chemcatchers. POCIS. POCISs consist of a sorbent enclosed by membrane disks made of polyethersulfone (poly(oxy-1,4-phenylsulfonyl1,4-phenyl, PES)). The microporous membrane, with a typical pore size of 0.1 mm and a thickness of 132 mm, allows water and dissolved substances to pass but excludes solid material such as SPM. Commercially available material used also for solid-phase extraction is employed as sorbents. Such material has usually particle sizes between 5 and 100 mm. In order to prevent the sorbents from being washed away, they are placed between two PES disks forming a sandwich (Figure 2). PES is not amenable to heat sealing, which is the standard sealing technique for other membranes used for passive samplers (e.g., PE for SPMDs). Therefore, another way had to be found to enclose the sorbents reliably. Devices that are commercially available consist of two stainless steel rings serving as compression holders. Other inert material (e.g., aluminum or PTFE) not interfering with the sampling process is suitable (Alvarez et al., 2004). The membrane sandwich with the sorbents is placed between two metal compression holders, which are then tightened by nuts and bolts. In principle, a wide variety of sorbents is suitable for sampling POCs. However, ready-to-use POCISs are available on the market in two different configurations, either with a triphasic admixture or with Oasiss HLB (EST Inc., St. Joseph, USA) (Alvarez et al., 2007). The triphasic admixture is composed of 80% of weight of the polystyrene divinylbenzene resin Isolute ENV þ and of 20% of AmbersorbTM 572 carbon dispersed on S-X3 BiodeadsTM. First POCISs were introduced with AmbersorbTM 1500 instead of AmbersorbTM 572; however, the first one is not produced any more. AmbersorbTM 572 is found to be an equivalent substitute. POCIS with the triphasic admixture, marketed under the name AQUASENSE-P with pesticide configuration, has been applied for sampling a variety of different compound classes. This includes not only

144

Sampling and Conservation Bolt hole

Compression ring Sorbent PES membrane

Figure 2 Setup of POCIS; the sorbent is enclosed by an upper and lower membrane hold by compression rings and fixed by bolts and nuts.

pesticides, but also steroidal hormones or compounds released from municipal wastewater treatment plants (Table 5). Oasiss HLB consists of a co-polymer (poly(divinylbenzolco-N-vinylpyrrolidon)) and has been found especially suitable for sequestering pharmaceuticals. POCISs with this type of sorbent are marketed as AQUASENSE-P with pharmaceutical configuration. Pharmaceuticals often have multiple functional groups and are therefore able to bind strongly to the carbon of the triphasic admixture. This can lead to relative low recoveries of pharmaceuticals from POCIS with the triphasic admixture (Alvarez et al., 2007). Further, a commonly used sorbent with POCIS is strataTM-X, which is a surface-modified styrene divinylbenzene polymeric sorbent suitable for a wide range of acidic, basic, and neutral compounds. POCISs with strataTM-X have, for example, been used to collect pesticides, wastewater-related compounds, or steroidal hormones (Cefas, 2007; Streck, 2009b). The membrane of a standard POCIS in contact with the water phase covers 41 cm2, which thus is the effective sampling surface area. POCIS with the triphasic admixture or with Oasiss HLB exhibits a surface area to sorbent mass ratio of 180 cm2 g1 while this reported ratio for POCIS with strataTMX is approximately 150 cm2 g1. Chemcatchers . The Chemcatchers-type passive sampler consists of a solid sorbent disk as receiving phase and membranes that cover the disk and govern the uptake of the compounds. Both are usually mounted on a sampler body made of PTFE (Kingston et al., 2000). Disks with different sorbents embedded in a polymeric matrix (PTFE) are commercially available under the trademark EmporeTM. Chemcatchers can target different classes of compounds depending on the sorbent disk and the choice of membranes. Sorbent–membrane combinations have been tested, for example, for hydrophobic compounds, for metals, as well as for POCs (Greenwood et al., 2007a). Table 6 contains examples of passive samplers with EmporeTM disks and different membranes employed for sampling of POCs. Sorbent disks without membranes were used by several authors for short-term sampling. Membranes slow down the uptake of compounds and influence the transfer velocity of compounds from the water phase to the sorbent (Figure 3). Thus, they extend the period in which the receiving phase has not yet reached its sorption capacity and continues linear uptake (kinetic regime). Time-weighted average

concentrations can be derived from the amount of chemical trapped on the receiving phase as long as the passive sampling device stays in the kinetic regime. Without membranes, the linear uptake occurs for a period of less than 10 days (Stephens et al., 2005, 2009), while membranes made of polysulfone or PES enhance this time to at least 21 or 30 days (Shaw et al., 2009; Tran et al., 2007). Besides affecting the period of the kinetic regime, membranes function as a protection of the sorbent disks from biofouling (Harman et al., 2009a; Macleod et al., 2007) and decrease the sensitivity of the system to changes in flow velocity (Booij et al., 2007) as well as to changes in concentrations of compounds. Field and lab procedures. POCIS and Chemcatchers are deployed in the field often within a protective canister which is made of perforated stainless steel plates (Figure 4). The protective canister deflects debris that otherwise may damage the membranes of the passive samplers. POCISs are deployed beneath the surface water typically for a period between a week and few months. Alvarez and co-workers reported that a reliable determination of water concentrations is still possible with sampling times of 56 days (Alvarez et al., 2004; JonesLepp et al., 2004). Deployment time of Chemcatchers depends on whether the EmporeTM disks are covered with membranes. Naked disks allow sampling time for a maximum period of 7–14 days (Table 6 and Figure 3). Transport from and to the lab into the field and sample storage should be done in cooled and protected, for example, airtight containers (Alvarez et al., 2007). For Chemcatchers, the transport in water-filled plastic zip-bags has been reported (Scha¨fer et al., 2008a) in order to keep the preconditioned EmporeTM disks wet. The next step after retrieval from the water phase is to dismount the passive sampler and separate the receiving phase from the membranes (Figure 5). For POCIS, it is recommended to clean the body of the sample holder and the exterior of the membranes prior to the disassembly using clean water and a soft brush in order to prevent debris, organisms, or particulate matter which could contaminate the sorbent material. This step is not necessary for the Chemcatchers, since the receiving phase is solidly embedded in the polymer matrix of the disk. Loose sorbent material retrieved from POCIS is flushed into a glass with suitable solvents, for example, methanol. Targeted compounds can then be eluted using an appropriate solvent. For the triphasic admixture a solvent mixture of methanol, toluene, and dichloromethane

Sampling and Conservation Table 5

Application of POCIS with different receiving phase

Receiving phase

Class of compounds

Reference

Triphasic admixture

Steroids (e.g., 17a-ethynylestradiol) Pesticides (e.g., triazines, organophosphates, and chloroacetanilides) Pesticides (e.g., triazines) Musk compounds (e.g., galaxolide, tonalide, and traseolide) Other wastewater related compounds (e.g., alkyl phosphates, phthalates, and N,N-diethyltoluamide) Alkylated phenols Estrogenic activity (YES-assay) Alkylphenols Alkylphenolethoxylate Bisphenol A Steroids UV-filters (e.g., benzophenone-4, benzophenone-3, 3-(4-methylbenzylidene)camphor, 2-ethyl-hexyl-4-trimethoxycinnamate) Steroids (e.g., 17a-ethinylestradiol, estriol, 17b-estradiol, and estrone) Wastewater related compounds (e.g., caffeine) Steroids (e.g., estrogenic and androgenic steroids) Pesticides (e.g., triazines, organophosphates, and thiocarbamates) Pharmaceuticals Pesticides Wastewater related compounds Bioanalysis (YES-assay) Pharmaceuticals (e.g., azithromycin ,fluoxetine, levothyroxine, and omeprazole) Alkylated phenols Azaarenes Polycyclic aromatic compounds Alkylated phenols Cresols Pharmaceuticals (e.g., amitriyptiline, doxepine, imipramine, and carbamazepine) Alkylphenols Alkylphenolethoxylate Bisphenol A Steroids Pharmaceuticals Antibiotics Wastewater-related compounds Pharmaceuticals Pesticides Antibiotics Wastewater-related compounds Pharmaceuticals Wastewater-related compounds Pesticides Pesticides (eg., terbutylazine, diethylterbuthylazine, isoproturon) Steroids (eg., 5a-dihydrotestosterone (DHT), estrone) Pharmaceuticals (eg., flutamide, tamoxifen) Pesticides (prometryn)

Alvarez et al., 2004, 2008

Oasiss HLB

StrataTM-X

145

Alvarez et al., 2009

Harman et al., 2009a Burki et al., 2006 Arditsoglou and Voutsa, 2008

Zenker et al., 2008

Sellin et al., 2009

Sellin et al., in press

Petty et al., 2004

Alvarez et al., 2004 Harman et al., 2008a

Harman et al., 2009b Togola and Budzinski, 2007 Arditsoglou and Voutsa, 2008

Bartelt-Hunt et al., 2009

Bueno et al., 2009

Macleod et al., 2007 Mazzella et al., 2007 Mazzella et al., 2008 Cefas, 2007

Rotter et al., unpublished data

146 Table 6

Sampling and Conservation Passive sampling devices for polar compounds containing an EmporeTM disk as receiving phase

Receiving phase (EmporeTM disk)

Membranes

Targeted class of compounds

Deployment timea,b

Reference

C18 SDB-XC C18

Polysulfone PES None

o9 days o21 days o7 day

Kingston et al., 2000 Tran et al., 2007 Stephens et al., 2005

SDB-RS

PES/none

3.2–10.3 daysc

Stephens et al., 2009

SDB-RS

PES/none

o30 days/o10 days

Shaw et al., 2009

SDB-XC SDB-XC SDB-XC

PES/none None PES/none

1 day and 9 days 10–13 days 6/30 days

Scha¨fer et al., 2008a Scha¨fer et al., 2008b Vermeirssen et al., 2009

SDB-RPS

None

6 days

Vermeirssen et al., 2009

SDB-XC

None

Nonionic herbicides Nonionic and ionic herbicides Herbicides (atrazine, diuron, hexazinone, flumetoron) Herbicides (diuron, atrazine, simazine) Herbicides (tebuthiuron, hexazinone, simazine, atrazine, diuron, ametryn, metolachlor) Pesticides (fipronil and chlorpyrifos) thiacloprid Pesticides and herbicides Pharmaceuticals, herbicides and pesticides Pharmaceuticals, herbicides and pesticides Herbicides and pesticides

14 days

Gunold et al., 2008

a

with/without membrane. time at which linear uptake was observed. c the capacity of samplers without PES-membrane was probably reached at 7.2 days. b

in the ratio of 1:1:8 has frequently been applied while for the processing of Oasiss HLB methanol has been used (Alvarez et al., 2005). Disks from Chemcatchers are processed for extraction either in an ultrasonic bath (Kingston et al., 2000; Shaw et al., 2009), by using a vacuum manifold (Tran et al., 2007) or by simply shaking the sorbent disks in a glass using solvents. Using an ultrasonic extraction method can detach particles of sorbent from the disk; therefore, a filtration step after extraction is recommended (Tran et al., 2007). The membranes are usually not extracted; however, it should be mentioned that membranes can contain a substantial fraction to the total amount of chemicals collected (Tran et al., 2007). Especially when the sampling time is short, an erroneous determination of water concentrations can occur. Extracts obtained from the passive samplers can then undergo the usual steps for chemical or biological analysis. Effect of environmental conditions on uptake. Conditions in environmental waters differ, for example, in temperature, pH, salinity, and flow velocity. These conditions can affect the uptake rates of compounds in passive samplers. Thus, the dependency of uptake rates from these conditions has to be determined usually in special laboratory or field experiments before passive samplers can be employed quantitatively. In the following, an overview is given how environmental conditions influence passive sampling. Temperature. Water temperature can have a large effect on the uptake of compounds into passive samplers. Membrane– compound partition coefficients are temperature dependent besides diffusion rates or sorption constants. This effect has been investigated by several research groups, since temperature changes occur regularly in the environment, for example, during the course of the year or when wastewater enters a river. In general, an enhanced water temperature leads to faster uptake; however, the effect is strongly compound dependent. Togola

and Budzinski (2007) reported that for ketoprofen the uptake rate into POCIS almost doubled, whereas for other pharmaceuticals such as carbamazepine uptake remained constant when the temperature was enhanced by 6 1C. For POCIS with strataTM-X as sorbent, an increase of up to a factor of 2.6 was found when the temperature was raised from 13 to 20 1C (Streck, 2009b). Mainly, musk compounds were considered in this study. However, bisphenols were unaffected by a temperature change. In a study using Chemcatchers, an Arrheniustype relationship was observed for a temperature range between 4 and 20 1C, and the uptake of atrazine and diuron increased with rising temperatures (Kingston et al., 2000). There is no model today to calculate compound-specific changes in uptake rates caused by variation of the water temperature. Therefore, calibration studies at different temperatures are highly important to adjust to varying conditions in the field (So¨derstro¨m et al., 2009). pH. POCs often possess many functional groups and can be present in the environment in ionized or neutral form, based on the pH. Physicochemical properties of the compounds are changing when a neutral compound becomes ionized or vice versa. Hence, influence of pH on uptake into passive samplers has to be considered. To date, only little information is available to what extent the pH value affects the uptake of compounds into POCIS or Chemcatchers. Only one study is known to the author in which the pH value was varied: Zhang et al. (2008) determined uptake rates for estrone, 17b-estradiol, 17a-ethynylestradiol, and bisphenol A, which have pKa values of 10.8, 10.5, 10.7, and approximately 10.0, respectively (Lewis and Archer, 1979; Staples et al., 1998). Uptake rates were assessed for pH values between 4 and 10, but no differences were found. Therefore, Zhang et al. (2008) concluded that both neutral and ionized forms were accumulated equally. However, since the pH value was lower

Sampling and Conservation

147

ng sampler −1

600

400

Diu

Ame

Sim

Teb

Atr

Met

Hex

Fip

200

0 0

5

10

(a)

15 Days

20

25

30 Figure 4 POCIS and protective canister ready for deployment.

ng sampler −1

1500

1000

Diu

Ame

Sim

Teb

Atr Hex

Met

between 0 and 35 g l1, salinity will probably play a minor part in most studies. Flow velocity. Uptake of compounds into a passive sampler is governed by several resistances to mass transfer which occurs through the water boundary layer, through the membrane, and within the sorbent itself. Work by Alvarez et al. (2004) noted that the aqueous boundary layer controls the uptake into POCIS, and the sampling rate RS can be written as

Fip

500

0 0 (b)

5

10

15 Days

20

25

30

Figure 3 Uptake of herbicides in Chemcatcher with (a) and without (b) a PES-membrane; with membranes the uptake remains linear for at least 30 days. Without membranes a quasi-linear uptake occurs for several days. Lines show (a) linear regression and (b) second order regression fit. Diu, diuron; Sim, simazine; Atr, atrazine; Hex, hexazinone; Ame, Ametryn; Teb, tebuthiuron; Met, metolachlor; Fip ¼ fipronil. Reproduced from Shaw M, Eaglesham G, and Mueller JF (2009) Uptake and release of polar compounds in SDB-RPS EmporeTM disks; implications for their use as passive samplers. Chemosphere 75: 1–7, with permission from Elsevier.

or just equal (for bisphenol A) to the pKa values, ionized forms of the investigated substances were only partly addressed with the study design. Salinity. Salinity can affect the sampling efficiency of passive samplers by enhancing the water ionic strength which decreases the hydrophilicity of compounds (Togola and Budzinski, 2007; Harman et al., 2008a). Another reason for reduced uptake rates with higher salinity can be due to the salting-out effect: With an increase of ionic strength, the solubility of compounds is exceeded and they are no longer available for passive sampling (Huckins et al., 1999). In an experiment varying salinity of water between 0 and 35 g l1 of salt, Togola and Budzinski (2007) found a decreased uptake into POCIS especially for basic pharmaceuticals. Neutral and acidic pharmaceuticals were not affected. Thus, variation in uptake rates due to salinity changes is compound dependent, and in fact not fully investigated yet. However, in the abovementioned study that lasted 7 days, the decrease in sampling rates amounted to maximal 64% for imipramine. Since only few applications of POCIS see differences in the salt content

RS ¼ A

  DW dW

ð6Þ

where A is the sampler surface, DW the diffusion coefficient of the compound in water, and dW the thickness of the aqueous stagnant film layer. With increasing flow velocity, the thickness of the aqueous layer diminishes which should lead to a faster uptake till the mass transfer is limited by the diffusion within the membrane or the sorbent. Such behavior has already been demonstrated for other passive samplers, for example, SPMDs (Huckins et al., 2006; Vrana and Schu¨u¨rmann, 2002). MacLeod et al. (2007) calibrated pharmaceutical type POCIS for three different flow velocities between 3 and 12 cm s1 and for quiescent conditions. Uptake rates showed no significant differences in the calibration experiments under flow conditions. This could indicate that already at 3 cm s1 aqueous boundary layer control no longer limits the mass transfer. For a part of the compounds, significant differences were found between flowing and quiescent conditions, which is in agreement with studies from other researchers (Alvarez et al., 2004). The increase in uptake rates for these compounds ranged from a factor of 3.1 for the pharmaceutical carbamazepine to 10.4 for biocide triclosan. However, it should be mentioned that the calibration experiment with quiescent water was operated at a temperature 6 1C lower, which partly could be responsible for lower uptake rates. The independence of uptake rates for nominal flow velocities between 13 to 51 cm s1 was also demonstrated for POCIS with strataTM-X (Streck, 2009b). A similar picture can be drawn for Chemcatchers. Both Gunold et al. (2008) and Vermeirssen et al. (2009) investigated the flow dependency for samplers with a naked EmporeTM SDB-XC disk. While Gunold observed no differences in uptake rates at two velocities (13.5 and 40 cm s1) in spiked tap water,

148

Sampling and Conservation

Exterior cleaning Disassembling and retrieval of sorbent

Transport to lab in airtight can/glass

Extraction of sorbent Retrieval of POCIS

Chemical analysis

Bioanalysis

Figure 5 Processing POCIS from retrieval in the field till analysis.

Vermeirssen reported increasing rates up to 37 cm s1 when calibrating with treated sewage effluent. However, also the last study indicated an asymptotic approach of uptake rates at higher flow velocities. Clear differences were observed for membrane covered and naked EmporeTM-type passive samplers. With membranes, the velocity at which uptake rates remained constant was lower (Vermeirssen et al., 2009). In summary, it appears that uptake rates of POCIS and Chemcatchers increase from quiescent to flowing conditions until at a certain flow velocity the aqueous stagnant film layer becomes so small that it does not control the mass transfer any more. However, more calibration data are needed to back this hypothesis. Biofouling. Another problem that can affect the uptake of compounds into POCIS and thus lead to larger errors in quantifying water concentrations is biofouling. Scha¨fer et al. (2008a) pre-fouled Chemcatchers with and without PES membrane for up to 9 days before exposing them to the pesticide thiacloprid within an artificial stream. Biofouling of the naked receiving phase led to a fourfold decrease of uptake. No biofouling was observed for samplers equipped with PES membrane demonstrating the protective function of PES for the period of deployment. With an experiment, comparing uptake kinetics in fouled and nonfouled POCIS, Harman et al. (2009a) found in general an increase of the uptake rates of alkylated phenols after biofouling. The reason for this behavior remained unexplained, and other groups of compounds may behave differently. More research is necessary to clarify the role of biofouling. Burst uptake and lag phase. Several researchers reported that in the beginning of a calibration experiment uptake rates for hydrophilic compounds are higher than expected leading to positive intercepts, whereas hydrophobic compounds show negative intercepts (Harman et al., 2008b). The transition from this burst uptake to the behavior of a lag phase can be approximately related to a log KOW of 3. The burst effect represents initial wetting of the membrane, which leads to a capillary effect. For more hydrophobic compounds, sorption to the membrane dominates probably in the early stages, suggesting a biphasic uptake. However, these effects are negligible for exposure times lasting several weeks (Alvarez et al., 2007).

In recent years, scientists and authorities realized that POCs could play an important role as contaminants in the aquatic environment. Passive samplers such as POCIS and Chemcatchers provide a powerful, easy-to-handle, and cheap tool to sample POCs from the water phase. They allow for an integrative sampling over days or weeks and are thus able to provide TWA concentrations. An integrative monitoring for such long sampling times is almost impossible and financially not feasible with traditional sampling methods. Due to their integrative character, POCIS and Chemcatchers allow further to sequester POCs at concentration levels that are difficult to reach with spot sampling. Furthermore, both types of passive samplers collect dissolved compounds only, which is the bioavailable fraction in the water phase. However, to date some limitations apply to these passive samplers. Especially, their ability to serve as a routine tool for quantitative analysis is limited. The knowledge about uptake rates for different compounds is still limited. In particular, progress in the prediction of dependency of uptake rates from environmental conditions like flow velocity, salinity, temperature, or biofouling is crucial. Several researchers are looking for suitable performance reference chemicals (PRCs) to adjust for uncertainties in uptake rates due to varying environmental conditions. However, to date the number of candidates for PRCs is very limited, and the applicability of the PRC approach with POCIS and Chemcatchers is still in debate. Nevertheless, several research groups are tackling the open questions, and tremendous progress has been made in the last few years. Therefore, polar passive samplers have good prospects to become a standard in water-quality monitoring in the coming future.

Acknowledgments This work was partly supported by the European Union through the Integrated Project MODELKEY (Contract-No. 511237-GOCE) and by the Academic Research Collaboration Programme of German Academic Exchange Service (DAAD) and British Council (Contract-No. D/07/09989).

Sampling and Conservation

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Relevant Website http://europa.eu.int Europa.

3.07 Measurement Quality in Water Analysis B Magnusson, SP Technical Research Institute of Sweden, Bora˚s, Sweden M Koch, Universita¨t Stuttgart, Stuttgart, Germany & 2011 Elsevier B.V. All rights reserved.

3.07.1 Introduction 3.07.2 Terminology 3.07.2.1 Measurand 3.07.2.2 Measurement Uncertainty 3.07.2.3 Metrological Traceability 3.07.2.4 Validation 3.07.2.5 Trueness 3.07.2.6 Precision 3.07.2.7 Limit of Detection 3.07.2.8 Limit of Quantification 3.07.3 How to Set the Analytical Requirement 3.07.4 Quality of Drinking Water Analysis 3.07.5 How to Assess the Quality in a Lab 3.07.5.1 Method Validation 3.07.5.1.1 Implementation of a standard method 3.07.5.1.2 Single laboratory (in-house) method validation 3.07.5.2 Metrological Traceability 3.07.5.3 Measurement Uncertainty 3.07.5.4 Quality Control 3.07.5.4.1 Internal QC 3.07.5.4.2 External QC – PT 3.07.6 Data Treatment 3.07.7 Conclusions Acknowledgments References

Glossary Measurand Quantity intended to be measured (VIM 2.3). Measurement uncertainty Non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used (VIM 2.26). Metrological traceability Property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations each contributing to the measurement uncertainty (VIM 2.41). Precision Closeness of agreement between indications or measured quantity values obtained by replicate

3.07.1 Introduction Many decisions in the water sector are based on the results of measurements. Therefore, it is essential that such results are reliable. The needed measurement quality can be achieved by validation that the test method is fit for the intended purpose and by establishing traceability of the results to stated references and an estimate of the uncertainty of measurement. The

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measurements on the same or similar objects under specified conditions (VIM 2.15). Trueness Closeness of agreement between the average of an infinite number of replicate measured quantity values (VIM 2.10) and a reference quantity value (VIM 2.14). Validation Parameter where the specified requirements are adequate for an intended use (VIM 2.45) and where verification is defined as the provision of objective evidence that a given item fulfills specified requirements (VIM 2.44).

ongoing quality control (QC; internal and external) assures that the measurement results (including uncertainty) are of the same quality as at the time of validation. Measurement quality should include both sampling and analysis. The analysis is treated in this chapter and sampling in Chapter 3.06 Sampling and Conservation. We had reliable measurement data for most constituents at the mg l1 level and nutrients at the mg l1 level many years

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back and we can, in many cases, compare the levels of these components in the environment over time and space. For background levels of trace elements and organic compounds at the ng l1 level, we can only in some cases compare measurement results that date back to some 20 years back, and still for many components the requirements for measurement quality are not met. Therefore, the analytical results cannot be compared. There is an ongoing development in both sampling and analysis and with increasing capability of detection of low levels of important components and improved sampling, the requirements will be fulfilled for more components. Important issues in improving quality in trace element analyses were the introduction of clean room laboratory facilities in the 1980s, intense development of samplers and hydrographical wires and trace analytical techniques such as graphite furnace atomic absorption spectrometry (AAS) and inductively coupled plasma mass spectrometry (ICP-MS). The issue of quality in analysis in the water sector is dealt with in several European Union (EU) projects. One of the most recent is EAQC-WISE (2010). The project European Analytical Quality Control in support of the Water Framework Directive (WFD) via the Water Information System for Europe (EAQC-WISE) aims to establish a general QC system and the ultimate objective is to develop a sustainable pan-EU quality assurance and QC (QA/QC) system for water, biota, sediment, and related soil monitoring data. In this chapter, measurement quality in chemical analysis in general is presented with examples from the water sector: (1) the needed terminology; (2) how to set the requirements; (3) the present status of measurement quality in water analysis for some selected parameters based on the requirements in EU directives; and (4) how each laboratory can demonstrate quality. The aim is not a basic introduction for beginners, but an overview which may stimulate further reading and study of the literature.

3.07.2 Terminology To describe and assess the measurement quality the following terms are essential: the general terms (measurand, measurement uncertainty, traceability, and validation) and the specific terms related to validation (trueness, precision, limit of detection (LOD), and limit of quantification (LOQ)). The definitions are mainly from the vocabulary in metrology (VIM), the internationally agreed vocabulary for measurements (BIPM, 2008b). VIM is a normative reference in ISO/IEC 17025:2005; therefore, this terminology applies to all accredited laboratories. Some additional terms used in validation are introduced in Section 3.07.5.1.

and sediments, the base for reporting is included in the measurand, for example, mass fraction (mg kg1) of Cd in a sediment sample delivered to the laboratory reported on dry basis (105 1C, 2 h).

3.07.2.2 Measurement Uncertainty Measurement uncertainty provides information on the level of confidence that can be placed on the measurement result. The estimate of measurement uncertainty is a requirement for accreditation and should be communicated, on request, to the customer to show the quality of the measurement. Measurement uncertainty is the quantitative expression of the doubt associated with the result. The result is often presented in the format value7expanded uncertainty. For example, 50 mg l17 10 mg l1 (expanded uncertainty k ¼ 2) corresponds to the interval 40–60 mg l1. We interpret this as: the measured value7expanded uncertainty is an estimate of the true value (VIM 2.11) and the true value is (with a stated probability of normally 95%) somewhere in this interval. The uncertainty interval and the relation between the reference (or estimate of true value) and the measured value are shown in Figure 1.

3.07.2.3 Metrological Traceability Traceability can refer to the documentation, that is, sampling procedure, laboratory, analyst, test method, etc., but when we are referring to traceability of measurement results as in ISO/ IEC 17025:2005 the results have to be traceable to the metrological references used. To be specific we here use the term ‘metrological traceability’. Ideally, the references should be values of national and international standards expressed in SI units. The metrological traceability can be achieved through chains of calibrations (VIM 2.39). For temperature and many other physical quantities (e.g., mass and time), the traceability is relatively easily established by the national metrology institutes. However, in chemistry the metrological traceability is realized by working standards for calibration which are normally prepared from reference materials: substances with defined purity, solutions of pure substances, or matrix reference materials.

Uncertainty interval y − U ... y + U

Difference (error)

y − U yR

y

y+U

3.07.2.1 Measurand The specification of the measurand normally includes the kind of quantity, the unit, the analyte to be analyzed, and the test item, for example, mass concentration (ng l1) of Cd in a seawater sample delivered to the laboratory or mass concentration (ng l1) of Cd in a seawater basin at the time of sampling. In the former case, the test item is laboratory sample, and in the latter the test item is the seawater basin. For biota

Reference value best estimate of the true value Measured value Figure 1 Relation between a measured value, y, with a given uncertainty interval and a reference value, yR. From Ivo Leito, University of Tartu, Estonia.

Measurement Quality in Water Analysis 3.07.2.4 Validation In VIM, validation implies a verification or check that the measurement procedure (VIM 2.6) or test method is fit for the intended purpose. ‘Fitness for purpose’ or ‘adequate for an intended use’ implies that the performance of the procedure (as specified by performance parameters, such as trueness, precision, LOD, and LOQ, besides the measurement uncertainty) meets the specified requirements.

3.07.2.5 Trueness Trueness is related to systematic measurement error (VIM 2.17) and is an expression of how close the mean of a set of results is to the reference value of a reference material. Trueness cannot be expressed numerically. It is normally expressed in terms of measurement bias (VIM 2.18). The bias is the difference between the mean value of several measurement results and a reference quantity value (VIM 5.18) as shown in Figure 2. Measurement bias is an estimate of the systematic error as shown in the figure. A measurement bias may be due to several causes such as an erroneous calibration, contamination, losses during sample

Mean

Bias

Reference quality value

Figure 2 Bias – the difference between the mean of several measurement results and a reference value. Reproduced with permission from LGC Limited.

treatment, or lack of selectivity. Selectivity is high, if the measurement result is independent of matrix components other than the measurand, that is, there are no significant interferences. An estimate of the bias for measurement results produced by a laboratory under intermediate precision (within-laboratory reproducibility) conditions can be obtained by applying the measuring procedure to one or several reference materials several times over a longer time period (e.g., 6 months) and calculating the mean value. The bias is then the difference between the mean value obtained with this procedure under intermediate precision conditions and the reference quantity value (VIM 5.18).

3.07.2.6 Precision Measurement precision is related to random measurement error (VIM 2.19) and is a measure of how close results are to one another. The term precision is used differently in measurement science and in common language. When we talk about measurement results within the analytical community precision expresses spread, but in common language it is synonymous with accuracy (closeness of agreement between a measured quantity value and a true quantity value of a measurand (VIM 2.13)). Measurement results cannot be corrected to remove the effect of random error but the size of the random error can be reduced by making replicate measurements and calculating the mean value. Measurement precision is expressed numerically using measures of imprecision such as the standard deviation calculated from results obtained by carrying out replicate measurements on a suitable material under specified conditions (Figure 3). Examples of specified conditions are: repeatability conditions, intermediate precision conditions (also called within-laboratory reproducibility conditions), or reproducibility conditions (for details on these terms, see Section 3.07.5.1).

Between batches

Between laboratories

Increasing s

Between injections

Within batch (replicates)

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Measurement repeatability

Intermediate measurement precision

Measurement reproducibility

Figure 3 The relationship between different estimates of precision illustrated in terms of the magnitude of the observed imprecision. As the conditions of measurement become more variable (e.g., moving from repeatability conditions to reproducibility conditions), the standard deviation of measurement results generally increases. Reproduced with permission from LGC Limited.

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3.07.2.7 Limit of Detection

Critical value



Measured value

Many analysts are familiar with calculating the LOD for a test method by multiplying a standard deviation, s (obtained from the results from the analysis of a blank sample or a sample containing a low content of the measurand) by an appropriate factor (typically between 3 and 5). The multiplying factor is based on statistical reasoning. The following text explains the background to the commonly used factor of 3.3. The aim, when determining the LOD, is to establish the lowest amount of the concentration of the analyte present in a sample that can be detected, using a given test method, with a specified level of confidence. Defining the LOD is a two-step process. First, a critical value is established. This value is set so that the probability of obtaining a measurement result that exceeds the critical value is no greater than a, if a sample actually contains none of the measurand. The critical value sets a criterion for declaring a sample to be positive. A false positive probability of a ¼ 0.05 is generally used; this leads to a critical value of approximately 1.65s (where s is the standard deviation of a large number of results for a blank sample or a sample containing a low content of the measurand, and 1.65 is the one-tailed Student t-value for infinite degrees of freedom at a significance level, a ¼ 0.05). The critical value is indicated on the vertical axis in Figure 4 to emphasize the fact that it is a measured value. The critical value is most conveniently expressed in terms of concentration, though in principle it may be any observation, such as peak area. Any result exceeding the critical value should be declared positive. However, if the true value for the concentration in a sample was exactly equal to the critical value (expressed in terms of concentration), approximately half of the measurement results would be expected to fall below the critical value, giving a false negative rate of 50%. This is illustrated by the distribution line in the middle of Figure 4. A false negative rate of 50% is obviously too high to be of practical use; the test method does not reliably give results above the critical value if the true value for concentration is equal to the critical value. The LOD is intended to represent the true amount of substance concentration for which the false negative rate is acceptable given the critical value. The acceptable false negative error, b, is usually set equal to the acceptable false positive error; this is largely for

Limit of detection



0 0 Distribution of results

True value 50% false negative rate if analyte concentration = critical value

Figure 4 Illustration of statistical basis of limit of detection calculations. Reproduced with permission from LGC Limited.

historical reasons. The International Union of Pure and Applied Chemistry (IUPAC) recommends default values of a ¼ b ¼ 0.05 (Currie, 1995). Using a ¼ b ¼ 0.05, the LOD needs to be 1.65s above the value specified for the critical value. This is illustrated by the shaded distribution on the horizontal axis in Figure 4. The factor for calculating the LOD with a ¼ b ¼ 0.05 is thus 1.65 þ1.65 ¼ 3.3.

3.07.2.8 Limit of Quantification The IUPAC recommendation is to set the LOQ to a factor times the measured standard deviation that is used to determine the detection limit. The factor is arbitrary but a factor of 10 is usually used. When the LOD is calculated as 3.3s, the ratio between LOQ and LOD is a factor of 3. With a factor of 10 for the LOQ, the repeatability expressed as CV% (coefficient of variation) is about 10% at the concentration level of LOQ.

3.07.3 How to Set the Analytical Requirement Analytical requirements shall be governed by the intended use of the results – what purpose do we have for the measurement (see Figure 5). In Chapter 3.06 Sampling and Conservation, ‘sampling and conservation’ uncertainty in general with reference to ISO 5667-20:2008 is discussed in detail. Here, we focus on the analytical requirement for the water sector. For Europe, the analytical requirements for the monitoring of ground- and surface water in the context of the WFD as well as for drinking water analysis are set in EU directives. The WFD 2000/60/EC (European Parliament and the Council of the European Union, 2000) established a framework for community action in the field of water policy, especially for groundwater, surface water, and coastal seawater. Environmental quality standards (EQSs) have been set in the daughter directive 2008/105/EC (QA/QC directive) (European Parliament and the Council of the European Union, 2008) for the priority substances mentioned in the WFD and for certain other pollutants in the form of limits for the annual average (AA-EQS) and for maximum allowable concentrations (MACEQS). Requirements on quality assurance and QC in the laboratories performing the monitoring are set in a separate directive 2009/90/EC published in 2009 (Commission of the European Communities, 2009). A maximum expanded uncertainty of measurement (k ¼ 2) of 50% of the EQS value is required for measurement results for water monitoring. The LOQ should be less than 30% of the EQS value. The first requirement of expanded uncertainty (k ¼ 2) of 50% of the EQS value for all parameters is very ambitious since the EQS values are very low for many substances (examples in Table 1) relative to the detection capability of analytical methods. Up to now there is only very limited information from interlaboratory tests to prove that these requirements can be met by the laboratories under routine conditions. The second requirement for the WFD directive is the LOQ. Compared with the requirements in the EU drinking water directive (see below) the lower limit is here set using LOQ

Measurement Quality in Water Analysis

157

Client Decision on result

Client issue

Data presentation

Define issue

Client Report on measurement

Decision on measurement

Evaluation

interface Measuring scientist

Sampling Analysis

Measurement

Measuring scientist Figure 5 The measurement cycle starting with the client issue and ending with a decision upon the result. Reproduced from SP Technical Research Institute of Sweden.

Table 1 Environmental quality standards (EQS) from 2008/105/EC for some selected parameters and extract from the minimum performance criteria for analyses according to WFD directive 2000/60/ EC laid down in the QA/QC requirement 2009/90/EC Parameter

Environmental quality standard (EQS) (mg l 1)

Maximum expanded uncertainty 50% of the EQS (mg l 1)

Limit of quantification (LOQ) 30% of the EQS (mg l 1)

Benzene Benzo(a)pyrene Brominated diphenylether Endosulfan Mercury

10 0.05 0.000 5

5 0.025 0.000 25

3 0.015 0.000 15

0.005 0.05

0.002 5 0.025

0.001 5 0.015

instead of LOD. The LOQ is commonly used as a reporting limit in analysis – a measurement result below the LOQ is generally reported as ‘less than’. The ratio of LOQ to LOD is often equal to 3 (e.g., if the LOD is defined as 3.3s and the LOQ is 10s). The reporting of data less than can cause problems when further calculations are performed (see Section 3.07.6). In the EU drinking water directive (Council of the European Union, 1998), there are three requirements: 1. trueness, 2. precision, and 3. LOD limit (see extract in Table 2). Specified requirements are given for each component related to a limit value – so-called parametric value. They are derived on the basis of relevant and extended toxicological data to ensure that water intended for human consumption can be

consumed safely on a lifetime basis, and thus represent a high level of health protection. The prescribed requirements are clear from the analytical point of view, and the additional, explanatory notes give good guidance on how to demonstrate that these requirements are met. Further guidance on this approach can be found in the literature (Thompson et al., 2002; EURACHEM, 1998) and in ISO 5725 (ISO 5725-1:1994; ISO 5725-2:1994; ISO 57253:1994; ISO 5725-4:1994; ISO 5725-5:1998; ISO 57256:1994). The precision requirements should be assessed under intermediate precision conditions (within-laboratory reproducibility conditions) and not under repeatability conditions. We also recommend assessing the bias under intermediate precision conditions. In the EU drinking water directive the requirements are based on trueness and precision, and in the WFD the requirements are based on measurement uncertainty which can be seen to encompass both trueness and precision. This reflects the change in terminology used to describe the measurement quality from accuracy (trueness and precision) to measurement uncertainty. The relation between the terms is demonstrated in Figure 6. A question that is still under discussion is how to combine trueness and precision data in order to obtain a required measurement uncertainty. We propose the following approach on the basis of the requirements in the EU drinking water directive: 1. We could use half the value of the precision requirement as a standard uncertainty component (standard deviation) since it is defined as two standard deviations (see note 2 in Table 2). 2. If we assume that the value for trueness is the maximum allowed bias, then the standard uncertainty component can be calculated from a rectangular distribution, which is the half-width of the interval divided by root of 3 (Eurachem, 2000).

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Measurement Quality in Water Analysis

Table 2 Extract from table ‘Parameters for which performance characteristics are specified’ from annex III of the EU drinking water directive (Council of the European Union, 1998) Parameter

Parametric value

Trueness % of parametric value (note 1)

Precision % of parametric value (note 2)

Limit of detection % of parametric value (note 3)

Ammonium Arsenic Benzene Benzo(a)pyrene Mercury Pesticides

0.5 mg l1 10 mg l1 1 mg l1 0.01 mg l1 1 mg l1 0.5 mg l1

10 10 25 25 20 25

10 10 25 25 10 25

10 10 25 25 20 25

Note 1. Trueness is the systematic error and is the difference between the mean value of the large number of repeated measurements and the true value. Note 2. Precision is the random error and is usually expressed as the standard deviation (within and between batch) of the spread of results about the mean. Acceptable precision is twice the relative standard deviation. Note 3. Limit of detection is either (1) 3 times the relative within batch standard deviation of a natural sample containing a low concentration of the parameter, or 5 times the relative within batch standard deviation of a blank sample. The terms ‘trueness’ and ‘precision’ discussed in notes 1 and 2 are further defined in ISO 5725.

Type of errors

Performance characteristics

Quantitative expression of performance characteristics

Systematic error

Trueness

Bias

(Total) error

Accuracy

Measurement uncertainty

Random error

Precision

Standard deviation repeatability / within-lab reproducibility/ reproducibility

Figure 6 Relationships between type of error, qualitative performance characteristics, and their quantitative expression (trueness and precision). Reproduced from Menditto A, Patriarca M, and Magnusson B (2007) Understanding the meaning of accuracy, trueness and precision. Accreditation and Quality Assurance 12: 45–47, with permission from Springer.

The standard uncertainties are then combined to an estimated maximum standard uncertainty (Equation (1)):

umax

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s   ffi limitprecision 2 limittrueness 2 pffiffiffi ¼ þ 2 3

ð1Þ

The EU drinking water directive specifies three different requirement levels. From these requirements an estimated maximum standard uncertainty is calculated according to Equation (1) and the maximum allowed expanded uncertainty is calculated at a confidences level of about 95% (coverage factor 2) – see Table 3.

Table 3 Requirements in the EU drinking water directive and the estimated maximum standard uncertainty and expanded measurement uncertainty Trueness (%)

Precision (%)

Maximum standard uncertainty (%)

Maximum expanded uncertainty (%)

10 20 25

10 10 25

7.6 13 20

15 25 38

The expanded uncertainty is calculated with a coverage factor of 2. All requirements are given in percent of the parametric value.

Measurement Quality in Water Analysis

These values are valid only at the parametric value stated in the drinking water directive. There is no other information on requirements at other concentration levels. The maximum expanded uncertainty requirement for WFD monitoring (QA/QC Directive) is slightly higher (50%) than for EU drinking water directive (Table 3). However, the parametric values (EQS) are much lower in the WFD and therefore the WFD-related performance criteria are highly challenging. It is still not clear whether these requirements can be met by routine water laboratories. In the next section, we look at the present status of drinking water analysis for analytical laboratories in Europe using Germany as an example.

3.07.4 Quality of Drinking Water Analysis There are a large number of interlaboratory comparisons in the field of water analysis, organized for the proficiency testing (PT – see Section 3.07.5.4.2) of water laboratories. From the results of these intercomparisons, information can be gathered on whether the analytical requirements are fulfilled. The sampling part is still to be evaluated. The following text deals with the analytical quality for drinking water analysis. The uncertainty of measurement for most components depends on the concentration, and the measurement uncertainty at the specific level (EQS or parametric value) has to be evaluated. Figures 7–11 show examples of CV% versus the mass concentration of the analyte for PTs or interlaboratory trials performed in Germany during the time period 2002–09. The CV% is calculated from all the laboratory results using the Q-method, a robust method described in ISO/TS 20612:2007. In addition, a function for CV% versus mass concentration according to ISO/TS 20612:2007 is fitted to the data (blue line). The CV% function indicates the average quality of the performance of the drinking water laboratories over some years. The parametric value from the European drinking water directive together with the required CV% is shown with a red line. The

159

CV% can be regarded as an estimate of the standard uncertainty (ISO/TS 21748:2004) if the laboratories use the same method. In order to assess the measurement quality, we have to compare these PT results with the requirements. In Table 4 the required maximum standard uncertainty estimated from the EU drinking water directive (interpreted in the way described above) is compared with the average CV% at the parametric value. This is compliant to the fact that in these PT rounds results in the range 72 standard deviations are assessed as successful. This exactly matches the requirements of the directive (expanded uncertainty with a factor of 2). Analytes where the requirements of the EU drinking water directive are fulfilled on average are indicated Yes. In cases, where the requirements on average are almost fulfilled have a No, and those where the requirements are clearly not fulfilled are indicated by a No. The results from these PT trials show that the performance requirements of the EU drinking water directive are far from the analytical reality for most laboratories. In some cases (e.g., most of the major components), the quality of the analyses is much better than the requirements, but in many cases, especially for many of the trace elements and most of the organic trace compounds it is very difficult to meet the requirements for most laboratories. However, single expert laboratories may have a higher measurement quality but maybe to an increased cost of analysis.

3.07.5 How to Assess the Quality in a Lab The main pillars for measurement quality in an analytical laboratory are: 1. 2. 3. 4.

validation, metrological traceability, measurement uncertainty, and QC.

45

Variation coefficient (%)

40 35 30 25 20 15 10 5 0 0

0.05

0.1

0.2

0.15

0.25

Mass concentration (µg l−1) BW 1/02 NW 1/03

BW 2/04 NW 2/05

BW 2/09 NW 4/07

BW 4/06

HH 10.LURV

Parametric value

Figure 7 CV% vs. mass concentration for benzo(a)pyrene. Results from PT exercises during 2002–09. The various PT rounds are indicated by different symbols. From www.aqsbw.de.

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Measurement Quality in Water Analysis

Variation coefficient (%)

25

20

15

10

5

0 0

BW 1/06

10

20

BW 2/03

30

40 50 60 Mass concentration (µg l−1)

HH 08

NW 1/07

NW 3/03

70

80

NW 3/04

90

100

Parametric value

Figure 8 CV% vs. mass concentration for lead. Results from PT exercises during 2002–09. The various PT rounds are indicated by different symbols. From www.aqsbw.de.

45 Variation coefficient (%)

40 35 30 25 20 15 10 5 0 0

2

4

6

8

10

12

14

16

18

20

Mass concentration (µg l−1) BW 2/06

BW 3/08

BW 4/03

NW 4/02

NW 4/03

NW 4/04

NW 2/07 Parametric value

Figure 9 CV% vs. mass concentration for tetrachloroethene. Results from PT exercises during 2002–09. The various PT rounds are indicated by different symbols. From www.aqsbw.de.

Validation demonstrates that the method used in this laboratory at a given time was fit for purpose and all significant effects on the measurement result were taken into account. Metrological traceability demonstrates that the measurement results are traceable to the metrological references used and measurement uncertainty provides information on the level of confidence that can be placed on the measurement result. QC assures that the measurement results (including uncertainty) are of the same quality as at the time of validation. The requirements on how to demonstrate measurement quality for

an analytical laboratory are laid down in chapter 5 in ISO/IEC 17025:2005.

3.07.5.1 Method Validation When a laboratory implements a test method for the first time, it is essential that the performance of the method is studied prior to the analysis of test samples to confirm that the method is suitable for the required application. Table 5 lists the aspects of method performance which may require study as part of a

Measurement Quality in Water Analysis

161

35

Variation coefficient (%)

25 20 15 10 5 0 0

0.1

0.2

0..3

0..4

0.6

0.7

NW 2/09

NW 4/06

0..5

0.8

0.9

Mass concentration (µg l−1) BW 1/02

BW 5.LÜRV

HH 2/05

HH 5.LÜRV

NW 2/04

Parametric value

Figure 10 CV% vs. mass concentration for atrazine. Results from PT exercises during 2002–09. The various PT rounds are indicated by different symbols. From www.aqsbw.de.

Variation coefficient (%)

12 10 8 −

6

× −+

4

−× -

−

+×

-

2

−

+

× -

−

−

-

0 0

BW 2/08 NI 1/06 − NW 1/04

20

40

BW 4.LÜRV NI 2/05 NW 3/06

60

80 100 120 Mass concentration (µg l−1)

BW 4/02 × NI 2/06 NW 1/09

140

160

BW 4/05 HH 4.LÜRV + NI 4/06 NI 4/05 Parametric value

180

200

NI 1/05 - NI 1/05

Figure 11 CV% vs. mass concentration for nitrate. Results from PT exercises during 2002–09. The various PT rounds are indicated by different symbols. From www.aqsbw.de.

validation exercise. The extent of the validation study carried out by a laboratory depends on the history of the method being implemented and the criticality of the application. Two scenarios commonly encountered in laboratories are:

• •

implementation of a standard method which has been previously validated through an interlaboratory study and development and validation of a method within a single laboratory for its own use (often referred to as ‘in-house validation’).

Guidance on validation is available in a number of texts including documents produced by Eurachem (1998) and IUPAC (Thompson et al., 2002).

3.07.5.1.1 Implementation of a standard method A standard method is a method that has been published by a national or international standards body (e.g., ISO), or by a sectoral organization. The validation of the method will have been carried out prior to publication (often by means of an

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Measurement Quality in Water Analysis

Table 4 Comparison of the maximum standard uncertainty with CV% obtained in proficiency tests (PT) Maximum standard uncertainty (%)

Average CV% in PT

Continued

Requirements fulfilled

Major components Ammonium Chloride Conductivity Fluoride Iron Manganese Nitrate Nitrite Oxidizability Sodium Sulfate

8 8 8 8 8 8 8 8 19 8 8

7 3 1 7 8 9 4 5 9 4 3

Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes

Trace elements and ions Aluminum Antimony Arsenic Boron Bromate Cadmium Chromium Copper Cyanide Lead Mercury Nickel Selenium

8 19 8 8 19 8 8 8 8 8 13 8 8

12 19 13 6 36 10 8 5 19 15 17 9 17

No Yes No Yes No No Yes Yes No No No No No

Polycyclic aromatic hydrocarbons Benzo(a)pyrene 19 Benzo(b)fluoranthene 19 Benzo(ghi)perylen 19 Benzo(k)fluoranthene 19 Indeno(1,2,3-cd)pyrene 19

30 21 28 22 27

No No No No No

Volatile organic trace compounds Benzene 19 1,2-Dichloroethane 19 Tetrachloroethene 19 Trichloroethene 19 Bromodichloromethane 19 Bromoform 19 Chloroform 19 Dibromochloromethane 19

26 23 20 20 15 16 15 16

No No No No Yes Yes Yes Yes

Pesticides and metabolites Aldrin Atrazine Bentazone Bromoxynil Chlortoluron 2,4-D 2,4-DB p,p0 -DDD p,p0 -DDE p,p0 -DDT Desethylatrazine Desisopropylatrazine Dichlorprop

26 17 27 29 18 29 34 25 25 30 24 30 23

No Yes No No Yes No No No No No No No No

19 19 19 19 19 19 19 19 19 19 19 19 19

Table 4

Dieldrin Dimethoate Diurno a-Endosulfan b-Endosulfan Endrin Fenoprop a-HCH d-HCH g-HCH Hexachlorobenzene Heptachlor Ioxynil Isoproturon Linuron MCPA MCPB Mecoprop Metazachlor Metolachlor Propazine Simazine 2,4,5-T Terbuthylazine

Maximum standard uncertainty (%)

Average CV% in PT

Requirements fulfilled

19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19

23 42 22 22 21 28 23 21 29 26 27 33 36 23 26 27 25 26 36 26 21 20 27 20

No No No No No No No No No No No No No No No No No No No No No No No No

The maximum standard uncertainty is estimated from EU drinking water directive according to Equation (1). In the column ‘Requirements fulfilled’ italics indicate requirement fulfilled and bolds indicate requirement not fulfilled. Bold italics indicate results not fulfilled but close to the requirement.

interlaboratory study). The organization of interlaboratory studies for method validation is described in ISO 5725. A laboratory wishing to use such a method, within its stated scope, will not generally need to carry out a full validation of the method prior to use. However, the laboratory must demonstrate that it can perform the method according to specifications and can achieve any performance criteria specified in the method such as targets for measurement repeatability or bias.

3.07.5.1.2 Single laboratory (in-house) method validation A more detailed validation study is required if a laboratory has modified a standard method or developed a method in-house. The parameters that require study depend on the scope of the method. Table 6 indicates the parameters which may require study during the validation of different types of methods. The validation study should aim to cover the scope of the method (i.e., representative analyte concentrations and sample types). Ideally, a precision study should address both repeatability and intermediate conditions (within-laboratory reproducibility) of measurement. The latter aims to provide an indication of the likely variability of results obtained from different batches of analyses (possibly produced by different analysts). Bias should be studied through the analysis of suitable certified reference materials (CRMs), if available. If there are no CRMs available, then alternative strategies are required – see Table 5.

Measurement Quality in Water Analysis Table 5

163

Method validation parameters

Parameter

Comments

Selectivity

The ability of the test method to determine the analyte(s) without interferences from other components (e.g., the sample matrix).

Precision

A measure of the dispersion of measurement results.

Repeatability, sr

Precision obtained under repeatability conditions of measurement: measurements made on the same material by a single analyst, using the same procedure, under the same operating conditions over a short time period. Often used to provide an estimate of within-batch variability of results.

Intermediate precision, sI or withinlaboratory reproducibility sRw

Precision obtained under intermediate conditions of measurement: measurements made on the same material using the same procedure, but over an extended time period and possibly by different analysts who may be using different pieces of equipment. Often used to provide an estimate of between-batch variability of results.

Reproducibility, sR

Precision obtained under reproducibility conditions of measurement: measurements being made on the same material using the same procedure but by different analysts working in different locations. Obtained from an interlaboratory study.

Trueness – bias

Estimated as the difference between the mean of several measurement results and a reference value. Experimental studies for estimating bias include:  the analysis of certified reference materials,  the analysis of spiked (fortified) samples, and  comparison with results obtained from a reference method and if none of these are available or suitable  comparison with results obtained from proficiency tests.

Measuring (working) range

The range of values over which the method has been demonstrated to produce results that are fit for purpose.

Limit of detection (LOD)

The minimum concentration of the analyte that can be detected with a specified level of confidence.

Limit of quantification (LOQ)

The lowest concentration of analyte that can be determined with an acceptable level of uncertainty.

Linear range

Part of the working range in which change in instrument/method response is directly proportional to the change in analyte concentration.

Ruggedness/robustness

The extent to which a test method is influenced by variation in operating conditions. Ruggedness testing evaluates how small changes in the method conditions affect the measurement result (e.g., changes in temperature, pH, reagent concentration).

Table 6 Method performance parameters required for the validation of in-house developed test methods for different types of analysis Parameter

Type of analysis Qualitative

Selectivity Precision Bias Limit of detection Limit of quantitation Linearity/ working range Ruggedness a

|

Quantitative Major componenta

Trace analysisb

Physical property

| | |

| | | |

| | |

|

3.07.5.2 Metrological Traceability As pointed out above, traceability can refer to the documentation, that is, sampling procedure, laboratory, analyst, method, etc., but as in ISO/IEC 17025:2005 we are dealing with traceability of measurement results. In other words, measurement results have to be traceable to the metrological references used. To be specific, we here use the wording metrological traceability (see Figure 12). An example of demonstration of traceability for a test method from a Eurachem leaflet (Eurachem, 2008) is given in Box 1.

| |

|

|

|

|

|

3.07.5.3 Measurement Uncertainty The basic requirements for uncertainty estimation are:

|

Major component: analyte concentration in the range 1–100% by mass or where detection of presence or absence is not an issue. b Trace analysis: analyte concentration less than 100 mg kg1 or where detection capability is important. Reproduced with permission from LGC Limited.

1. a clear definition of the measurand, that is, the quantity intended to be measured; 2. a comprehensive specification of the test method and the test items; and 3. a comprehensive analysis of the effects impacting the measurement results.

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Measurement Quality in Water Analysis

Figure 12 Example of traceability to the SI – temperature. The temperature of a sample can be traced back through a chain of calibrations to the reference, an SI value of temperature at 0 1C. Illustration by Douglas Hasbun, Sweden.

Box 1

Metrological traceability to the stated references – example from a Eurachem leaflet: mercury in tuna fish

A measurement result (mass fraction) of mercury in a tuna fish sample is 4.0370.11 mg kg1. The result is reported as total Hg on dry weight basis (105 1C, 2 h, determined on a separate sample portion) and the measurement uncertainty is reported with a 95% level of confidence (k ¼ 2). In this case, mercury was determined with a mercury analyzer (AAS cold vapor) after a microwave digestion. The samples are weighed on a balance with a calibration certificate relating the weight measured to the SI unit kg. The acid digest is diluted in a volumetric flask where the manufacturer supplies the traceability of the volume of the flask to a national standard. The calibration curve was made using a CRM, a mercury standard with a certificate stating a quantity value of 0.99870.005 mg l1 (k ¼ 2) and with traceability to pure mercury. The method is validated using an appropriate matrix CRM with a total mercury concentration of 1.9770.04 mg kg1 (k ¼ 2). This validation is a check on the method performance. The evidence required by the laboratory to demonstrate traceability for the mercury result is shown below: 1. 2. 3. 4. 5. 6.

mass concentration of the Hg solution – a certificate of the CRM solution, mass of sample – calibration certificate of the balance, volume of volumetric flask – calibration certificate of the manufacturer, drying temperature – calibration of oven, digestion conditions – check according to specifications, and drying time – ordinary clock or stopwatch.

An introduction to measurement uncertainty is presented in a Eurolab report (Eurolab, 2007) and for further reading we recommend the Eurachem guide (Eurachem, 2000) and the fundamental reference document Guide to the Expression of Uncertainty in Measurement (BIPM, 2008a). Examples of different approaches for uncertainty estimation that can be used are presented in Figure 13. It is important to note that for most instrumental methods, the uncertainty is proportional to concentration and therefore a relative uncertainty is appropriate at levels well above the LOQ. At the LOQ level, it is appropriate to report the uncertainty in absolute terms, that is, in concentration unit used. In new ISO standards, measurement uncertainty may be included in the standard. The work for a single laboratory will then only be to show that their uncertainty is similar or lower than the uncertainty given in the ISO standard. Today, the reproducibility standard deviation from an interlaboratory comparison according to ISO 5725 is given in many standards and this can often be used as an estimate of standard uncertainty (ISO/TS 21748:2004). However, each laboratory has to estimate its own measurement uncertainty and for laboratories that already have the method in routine use we can recommend the single laboratory validation approach using

data for within-laboratory reproducibility from internal QC and data for the uncertainty on bias from validation or external QC (PT). The basic principle including the alternative to enlarge the uncertainty due to an observed but unknown bias (Magnusson and Ellison, 2008) is presented in Box 2 extract from the Eurolab report (Eurolab, 2007). This single validation approach described above is presented in detail in a guideline from Nordtest or Nordic Innovation Centre (NICe) from 2003 – TR 537 (Magnusson et al., 2003). An example summary from this handbook is given below for ammonium in water with data from an expert laboratory. The estimated uncertainty for ammonium–nitrogen is at least a factor of 2 lower than for routine laboratories at this mass concentration level of around 200 mg l1. No reference materials are available and the bias estimate is therefore based on results from PT. Details of the calculations are given in the handbook TR 537. Example from Nordtest handbook TR 537 is given as follows. Ammonium in water by ISO 11732:2005. Background for the NHþ 4 2N example – automatic photometric method. The laboratory has participated in six PTs or interlaboratory comparisons. All results have been somewhat

Measurement Quality in Water Analysis

165

Definition of the measurand, list of uncertainty components

Interlaboratory approach

Intralaboratory approach

Yes

Mathematical model?

Evaluation of standard uncertainties

Law of uncertainty propagation GUM

Modeling approach

Method performance

No

Method accuracy ISO 5725

Proficiency testing ISO/IEC 17043 + ISO 13528

Use of values already published + Uncertainty on the bias and factors not taken into account during interlaboratory study ISO TS 21748

Variability + Uncertainty on the bias and factors not taken into account during interlaboratory study

Organization of replicate measurements, method validation

Adding other uncertainty contributions, e.g., uncertainty on the bias

Single-laboratory validation approach

PT or PT method performance study

Interlaboratory validation approach

PT approach

Empirical approaches Figure 13 The different approaches to uncertainty estimation presented in the Eurolab report Data from Eurolab (2007) Measurement uncertainty revisited: Alternative approaches to uncertainty evaluation. Technical Report 1/2007, Paris. http://www.eurolab.org (accessed April 2010).

Box 2 Single laboratory validation approach. From Eurolab (2007) Measurement uncertainty revisited: Alternative approaches to uncertainty evaluation. Technical Report 1/2007, Paris. http://www.eurolab.org (accessed April 2010). The basic principle behind this approach is the synthesis of uncertainty estimates from estimates of precision and estimates of bias: * * *

Measurement accuracy ¼ precision and trueness Measurement uncertainty ¼ within-laboratory reproducibility and uncertainty on bias Measurement uncertainty is estimated as a root sum of squares of a standard deviation s characterizing the (im)precision of the measurement and an estimate b accounting for measurement bias, which gives the standard uncertainty u according to the schematic equation:

u¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2 þ b2

Here, it is understood that measurement bias is investigated, and corrective actions are taken to remove/reduce such bias to the greatest possible extent. The bias-related uncertainty estimate accounts for the potential bias left after correction. In practice, however, it happens quite often that significant bias is found, but the data are not sufficient for deriving a sound correction. For example, it may be doubtful whether a single-level correction, based on measurements of a single standard, is applicable to the entire measuring range. Then additional measurements, for example, including another standard, should be made in order to characterize the bias to an appropriate degree. If this is not possible or not practical, a pragmatic alternative is to increase the uncertainty to account for the observed bias instead of attempting any correction.

higher than the nominal value. The laboratory therefore concludes that there may be a small positive bias. On average, the difference has been þ 2.2%. This difference can be considered as a possible bias but since we have no traceable

reference values no correction is applied. Since it is not corrected for in their analytical results, but exists, it is treated as another uncertainty component. The data from the PTs are shown in Table 7.

166

Measurement Quality in Water Analysis

In Table 8, combined uncertainty, uc is calculated from the control sample limits and bias estimation from results from PT that the laboratory has participated in. The sR from interlaboratory comparisons (ISO/TS 21748:2004) using the same method can also be used if a higher uncertainty is acceptable. Measurement uncertainty U (95% confidence interval) is estimated to 77% (Table 9). The customer demand is 710%. The calculations are based on internal QC (control chart limits) and results from PT. The use of the reproducibility standard deviation from the standard would lead to an expanded uncertainty estimate of 716%.

3.07.5.4 Quality Control 3.07.5.4.1 Internal QC For guidance in Internal QC the Nordtest handbook Trollbook (Hovind et al., 2007) gives an introduction with detailed examples. The following text is based on this Trollbook. Internal QC at the chemical analytical laboratory involves a continuous, critical evaluation of the laboratory’s own analytical methods and working routines. The control encompasses the analytical process starting with the sample entering the laboratory and ending with the analytical report. The most important tool in this QC is the use of control charts. The basis is that the laboratory runs control samples together with the test samples. The control values are plotted in a control Table 7 Evaluation of results for a laboratory from six proficiency tests of NHþ 4 –N in water Exercise

1999 1 2 2000 1 2 2001 1 2

Assigned value xref (mg l 1)

Laboratory result xi (mg l 1)

Difference (%)

sR (%)

Number of labs

81 73

83 75

2.4 2.7

10 7

31 36

264 210

269 213

1.9 1.4

8 10

32 35

110 140

112 144

1.8 2.9

7 11

36 34

þ 2.18

8.8

34

 X RMS (root mean square)

Table 8

chart. In this way it is possible to demonstrate that the test method performs within given limits. If the control value is outside the limits, no analytical results are reported and remedial actions have to be taken to identify the sources of error, and to remove such errors. Figure 14 illustrates the most common type of control chart, the X-chart. Several types of control charts are available but the main two are X-charts for control samples, blank samples, and recovery, and R-charts (R ¼ range) for test samples. X-charts are used to control both trueness and precision, and R-charts are used purely for controlling precision (repeatability). Normally, control charts are constructed using statistically calculated warning and action limits. In order to set robust control limits (warning and action limits), it is recommended (Hovind et al., 2007) that the standard deviation is calculated from a large number of results (preferably 460) during a longer time period (preferably 4 year). In order to be able to start the QC, preliminary control limits are set which are modified after, for example, 1 year. When a QC program is established, it is essential to have in mind the requirements on the analytical results and for what purposes the analytical results are produced – the concept of fit for purpose. From the requirement on the analytical results, the analyst sets up the control program: 1. 2. 3. 4.

type of QC sample, type of QC charts, control limits – warning and action limits, and control frequency.

The control limits are then set based on customer or legislative targets or on target measurement uncertainties (ISO/TS 13530:2009). Guidance on how to use target measurement uncertainty to set control limits can be found in examples 1 and 2 in the Trollbook. It is also important to have very basic and simple rules for deciding if a control value is in control – that is, if the analyst can report the results. The normal rules (ISO 8258:1991) for Table 9

Calculation of combined and expanded uncertainty

Measurand

2.25

Ammonium– nitrogen

Combined uncertainty uc

Expanded uncertainty U

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1:67 2 þ 2:71 2 ¼ 3:18%

3.18  2 ¼ 6.4E 7%

Calculation of standard uncertainties for a laboratory measuring NHþ 4 –N in water Value

Relative u(x)

Comments

Reproducibility within-laboratory, Rw  ¼ 200 mg l1 Rw Control sample X

Warning limits are set to 73.34%

1.67%

3.34% / 2 ¼ 1.67%

Method and laboratory bias Proficiency testing

RMSbias ¼ 2.25%

2.71%

Bias

u(xref) ¼ 1.5% Reproducibility between laboratories Standard method sR

8%

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RMS2difference þ uðx ref Þ2 sR uðx ref Þ ¼ pffiffiffiffiffiffiffiffiffi nLab

uðbiasÞ ¼

EN / ISO 11732 at level 200 mg l1

Measurement Quality in Water Analysis

167

70

µg l−1

65

60

55

50 1-Feb

22-Mar

10-Mar

28-Jun

16-Aug

4-Oct

22-Nov

10-Jan

28-Feb

Date of analysis Figure 14 Example of an X control chart for the direct determination of zinc in water. All control values in the green area (within the warning limits) show that the determination of zinc performs within given limits and the routine sample results is reported. Control values in the red area (outside the action limits) show clearly that there is something wrong and no routine sample results are reported. A control value in the yellow area is evaluated according to specific rules. From Hovind H, Magnusson B, Krysell M, Lund U, and Ma¨kinen I (2007) Internal quality control – handbook for chemical laboratories, 3rd edn. Nordtest Technical Report 569. http://www.nordicinnovation.net/nordtest.cfm (accessed April 2010).

deciding if a control value is in control are based on statistical process control and are, in the authors’ view, not directly applicable to chemical analysis. We recommend the following rules for in control proposed in the Trollbook. The three rules for QC from the Trollbook are as follows: 1. The method is in control if:

 the control value is within the warning limits and  the control value is between warning and action limit and the two previous control values were within warning limits

In this case the analyst can report the analytical results. 2. The method is in control but can be regarded as out of statistical control if all the control values are within the warning limits (maximum one out of the last three between warning and action limit) and if:

 seven control values in consecutive order gradually increasing or decreasing (10) and

 10 out of 11 consecutive control values are lying on the same side of the central line (10)

In this case, the analyst can report the analytical results but a problem may be developing. Important trends should be discovered as early as possible in order to avoid serious problems in the future. Any long-term trends are taken care of in the annual long-term evaluation of the control charts – see Trollbook. 3. The method is out of control if:

 the control value is outside the action limits and  the control value is between the warning and the action limit and at least one of the two previous control values is also between warning and action limit – the rule two out of three.

In this case, normally no analytical results can be reported. All results since last value in control was obtained must be reanalyzed.

3.07.5.4.2 External QC – PT PTs are useful to help the participating laboratories to assess their own performance and to identify gaps. Of course also customers and authorities may use such results to identify laboratories that fulfill their quality requirements. The usual procedure is that a PT provider prepares water samples and distributes them to the participating laboratories. After finishing the analyses the results of the measurements are sent back to the provider, where the results are compared with an assigned value determined either from external references or from the consensus of the participants. Statistical analysis is done and an assessment is made usually using a scoring procedure delivering a z-score. This z-score compares the difference of the participant’s result (x) from the assigned value (X) with a standard deviation for proficiency assessment ð^ sÞ (Equation (2)). The latter can be determined from the data set in the PT round or from external quality requirements, for example, for the water sector the standard deviation ð^ sÞ can be set equal to the standard uncertainty calculated from the WFD or the EU water directive (see above) (for more details, see ISO 13528:2005):

z¼

xX ^ s

ð2Þ

If we assume that the requirements are exactly met, we expect s, that is, |z| p 2.0. that 95% of the data will lie within X 7 2^ For an individual laboratory this means that values between 2 and 3 are questionable and |z| p 3 require corrective actions. In most cases, a consensus mean or median is used as assigned value and the comparison of the laboratory’s result with the assigned value, strictly speaking, only delivers the comparability of the result with the results of other laboratories. Only if we have a metrologically traceable reference value or we can assume that the assigned value is a good estimate for the true value we can calculate the overall bias for all the laboratories participating in the PT. Example of PT with traceable reference values is the International Measurement

168

Measurement Quality in Water Analysis

Evaluation Programme (IMEP). For the water sector there are recent developments to introduce traceable reference values into routine PTs for water analysis (Rienitz et al., 2007; Koch and Baumeister, 2008). If several different analytical techniques are used, it is recommended to calculate the bias for each technique. PT results can demonstrate the quality of measurements to customers, authorities, and accreditation bodies.

4. Beho¨rde fu¨r Soziales, Familie, Gesundheit und Verbraucherschutz, Hamburg; and 5. Niedersa¨chsisches Landesgesundheitsamt, Aurich. The authors acknowledge the following for their valuable contributions to this chapter: Vicki Barwick, Marina Patri¨ rnemark for contributing arca, Elizabeth Prichard and Ulf O to the discussion regarding terminology. Vicki Barwick for reviewing the chapter and contributing material on method validation.

3.07.6 Data Treatment The QA/QC directive for WFD directive also gives guidance on data treatment – how to calculate mean values if the data set contains values below LOQ. This is a critical issue and can often vary from laboratory to laboratory and rules are here needed so that mean values and sums are calculated in the same way. The following are proposed in the directive:

•

•

First is to calculate a mean value for one parameter over for, for example, a year: ‘‘Where the amounts of physicochemical or chemical measurands in a given sample are below the limit of quantification, the measurement results shall be set to half of the value of the limit of quantification concerned for the calculation of mean values.’’ Second is to calculate a sum of several parameters, for example, pesticides: ‘‘ y results below the limit of quantification of the individual substances shall be set to zero.’’

3.07.7 Conclusions There is a general need for improvement of analysis of trace components in the water sector. With the needs clearly defined, measurement quality can be assessed for laboratories in general using PT. The results (2002–09) show that the requirements set in the EU drinking water directive are met for main components including nutrients and conductivity. For trace elements/ions, the requirements are met for some parameters. For trace organic compounds, the requirements are met for the different trihalomethanes but not for most of the other organic compounds. For a single laboratory the quality can be assessed when a new test method is implemented using the main pillars (traceability, validation, and uncertainty). For the ongoing assessment on the measurement quality the internal and external QC are vital and this information should be open to customers, authorities, and accreditation bodies in order to assess the quality.

Acknowledgments The permission to use the data from the following PT providers for Figures 7–11 is gratefully acknowledged: 1. AQS Baden-Wu¨rttemberg, Universita¨t Stuttgart; 2. Landesinstitut fu¨r den o¨ffentlichen Gesundheitsdienst Nordrhein-Westfalen, Mu¨nster; 3. Landesamt fu¨r Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen, Recklinghausen;

References BIPM (2008a) Evaluation of measurement data – guide to the expression of uncertainty in measurement (GUM), JCGM 100:2008. http://www.bipm.org/en/publications/ guides/gum.html (accessed April 2010). BIPM (2008b) International vocabulary of metrology – basic and general concepts and associated terms (VIM), JCGM 200:2008. (Also published as ISO/IEC Guide 99:2007.) http://www.bipm.org/en/publications/guides/vim.html (accessed April 2010). Commission of the European Communities (2009) Commission Directive 2009/90/EC of 31 July 2009 laying down, pursuant to Directive 2000/60/EC of the European Parliament and of the Council, technical specifications for chemical analysis and monitoring of water status. Official Journal of the European Union L 201/36, 1.8.2009. Council of the European Union (1998) Council Directive 98/83/EC of 3 November 1998 on the quality of water intended for human consumption. Official Journal of the European Communities L 330/32, 5.12.98. Currie LA (1995) Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC recommendations). Pure and Applied Chemistry 67: 1699--1723. EAQC-WISE (2010) EAQC-WISE – European Analytical Quality Control in support of the WFD via the Water Information System for Europe. http://www.eaqc-wise.net (accessed April 2010). Eurachem (1998) The fitness for purpose of analytical methods: A laboratory guide to method validation and related topics. http://www.eurachem.org (accessed April 2010). Eurachem (2000) Quantifying Uncertainty in Analytical Measurement, 2nd edn. http:// www.eurachem.org (accessed April 2010). Eurachem (2008) Metrological traceability of analytical results – a Eurachem leaflet. http://www.eurachem.org (accessed April 2010). Eurolab (2007) Measurement uncertainty revisited: Alternative approaches to uncertainty evaluation. Technical Report 1/2007, Paris. http://www.eurolab.org (accessed April 2010). European Parliament and the Council of the European Union (2000) Directive 2000/60/ EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for community action in the field of water policy (Water Framework Directive, WFD). Official Journal of the European Communities L 327/1, 22.12.2000. European Parliament and the Council of the European Union (2008) Directive 2008/ 105/EC of the European Parliament and of the Council of 16 December 2008 on environmental quality standards in the field of water policy (QA/QC directive). Amending and subsequently repealing Council Directives 82/176/EEC, 83/513/ EEC, 84/156/EEC, 84/491/EEC, 86/280/EEC and amending Directive 2000/60/EC of the European Parliament and of the Council. Official Journal of the European Union L 348/84, 24.12.2008. Hovind H, Magnusson B, Krysell M, Lund U, and Ma¨kinen I (2007) Internal quality control – handbook for chemical laboratories, 3rd edn. Nordtest Technical Report 569. http://www.nordicinnovation.net/nordtest.cfm (accessed April 2010). ISO 5667-20:2008 Water quality – sampling – part 20: Guidance on the use of sampling data for decision making – compliance with thresholds and classification systems. ISO 5725-1:1994 Accuracy (trueness and precision) of measurement methods and results – part 1: General principles and definitions. ISO 5725-2:1994 Accuracy (trueness and precision) of measurement methods and results – part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. ISO 5725-3:1994 Accuracy (trueness and precision) of measurement methods and results – part 3: Intermediate measures of the precision of a standard measurement method.

Measurement Quality in Water Analysis

ISO 5725-4:1994 Accuracy (trueness and precision) of measurement methods and results – part 4: Basic methods for the determination of the trueness of a standard measurement method. ISO 5725-5:1998 Accuracy (trueness and precision) of measurement methods and results – part 5: Alternative methods for the determination of the precision of a standard measurement method. ISO 5725-6:1994 Accuracy (trueness and precision) of measurement methods and results – part 6: Use in practice of accuracy values. ISO 8258:1991 Shewhart control charts. ISO 11732:2005 Water quality – determination of ammonium nitrogen – method by flow analysis (CFA and FIA) and spectrometric detection. ISO 13528:2005 Statistical methods for use in proficiency testing by interlaboratory comparisons. ISO/IEC 17025:2005 General requirements for the competence of testing and calibration laboratories. ISO/TS 13530:2009 Water quality – guidance on analytical quality control for chemical and physicochemical water analysis. ISO/TS 20612:2007 Water quality – interlaboratory comparisons for proficiency testing of analytical chemistry laboratories. ISO/TS 21748:2004 Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty estimation. Koch M and Baumeister F (2008) Traceable reference values for routine drinking water proficiency testing: First experiences. Accreditation and Quality Assurance 13: 77--82. Magnusson B and Ellison SLR (2008) Treatment of uncorrected measurement bias in uncertainty estimation for chemical measurements. Analytical and Bioanalytical Chemistry 390: 201--213.

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Magnusson B, Na¨ykki T, Hovind H, and Krysell M (2003) Handbook for calculation of measurement uncertainty in environmental laboratories. Nordtest Technical Report 537. http://www.nordicinnovation.net/nordtest.cfm (accessed April 2010). Rienitz O, Schiel D, Gu¨ttler B, Koch M, and Borchers U (2007) A convenient and economic approach to achieve SI-traceable reference values to be used in drinkingwater interlaboratory comparisons. Accreditation and Quality Assurance 12: 615--622. Thompson M, Ellison SLR, and Wood R (2002) Harmonized guidelines for singlelaboratory. Validation of methods of analysis (IUPAC Technical Report) 74: 835--855.

Relevant Websites http://irmm.jrc.ec.europa.eu European Commission Joint Reasearch Centre, Institute of Reference Materials and Measurements; IMEP, Interlaboratory Comparisons. http://www.aqsbw.de Analytische Qualita¨tssicherung Baden-Wu¨rttemberg. http://www.eptis.bam.de EPTIS. http://www.iswa.uni-stuttgart.de Institut fu¨r Siedlungswasserbau,Wassergu¨te- und Abfallwirtschaft.

3.08 Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization S Thiele, BM Fuchs, and RI Amann, Max Planck Institute for Marine Microbiology, Bremen, Germany & 2011 Elsevier B.V. All rights reserved.

3.08.1 3.08.2 3.08.2.1 3.08.2.2 3.08.2.2.1 3.08.2.2.2 3.08.2.3 3.08.2.4 3.08.2.5 3.08.2.6 3.08.2.6.1 3.08.2.6.2 3.08.2.6.3 3.08.2.6.4 56.2.6.5 3.08.3 3.08.3.1 3.08.3.2 3.08.3.3 3.08.3.3.1 3.08.3.3.2 3.08.3.3.3 3.08.3.4 3.08.4 3.08.5 References

Introduction The Full-Cycle rRNA Approach Ribosomal RNA Sources of 16S rRNA Sequences Pure cultures Microbial communities Clone Libraries Sequencing Sequence Analysis Probe Design Accessibility of the probe Selection of the oligonucleotide probe label Quality check of probes Adjusting probe specificity Clone-FISH Fluorescence In Situ Hybridization Fixation and Permeabilization Hybridization with Monolabeled Oligonucleotide Probes Catalyzed Reporter Deposition Fluorescence In Situ Hybridization Embedding, permeabilization, and inactivation of endogenous peroxidases Hybridization Catalyzed reporter deposition Troubleshooting Cell Counting From Cell Detection to Ecological Function

3.08.1 Introduction The microscopic discovery of bacteria in dental plagues in 1683 by van Leeuwenhoek marks the beginning of the identification, localization, and quantification of microorganisms in their environment. In the late nineteenth century, Koch’s cultivation techniques with agar plates led to the discovery of most of the pathogens in short time. However, it took many more years to realize by staining techniques that readily culturable microorganisms represent only a minor fraction of the microbes present in the environment, for example, the marine pelagial (Jannasch and Jones, 1959). With the advent of molecular biology, uncultured organisms also became accessible to directed research in the late twentieth century. Since that time a wealth of new freshwater and marine microorganisms have been discovered, identified, and abundances in different environments have been measured. The focus has broadened from mainly free-living bacteria to complex microbial communities in various environments, such as sediments, biofilms, or tissues. Nevertheless, planktonic microorganisms in limnic and marine ecosystems remain of high interest. Bacterioplankton communities in freshwater systems were found to be mainly formed by Cyanobacteria, Betaproteobacteria, Bacteroidetes, and Gram-positive Actinobacteria

171 171 171 172 172 173 173 174 174 176 176 176 177 177 179 180 180 180 181 182 182 183 183 184 186 187

(Glo¨ckner et al., 1999; Simek et al., 2001; Zwart et al., 2002). In contrast, marine bacterioplankton communities consist of mainly Cyanobacteria (e.g., Prochlorococcus), Alphaproteobacteria (e.g., SAR11), Gammaproteobacteria (e.g., SAR86), Bacteroidetes (e.g., Polaribacter), and, interestingly, of Archaea from the marine group I (see Partensky et al. (1999) and Giovannoni and Stingl (2005) for reviews). Besides almost cosmopolitan bacteria such as SAR11, which can make up to 50% of the bacterioplankton cells in some marine environments (Morris et al., 2002), other microorganisms can be found only in specific environments. These environments may be characterized by different water temperatures or different nutrient availabilities, for example, members of the clade Roseobacter dwell in the Southern Ocean or in correlation with high concentrations of particulate organic matter (Selje et al., 2004; Buchan et al., 2005). Yet, other bacteria are restricted to extreme environments such as hot springs (Marsh and Larsen, 1953), saline lakes (Oren, 1999), hydrothermal vents (Jeanthon, 2000), and man-made systems such as activated sludge (Snaidr et al., 1997). These findings indicate that microbial life is of a broad variety and thus taxonomy and determination of microorganisms are not trivial. One of the first classification systems of microorganisms was invented by Ferdinand Cohn in 1872.

171

172

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

The system was based on morphological markers and classified six genera of bacteria as a clade of plants (Cohn, 1872). During the early twentieth century, more and more physiological and biochemical tests on pure cultures were added to the list of discriminative markers, resulting in a more detailed, but, not-yet-stable classification (Garrity et al., 2001). In the second half of the twentieth century, improved chemical analytics made it possible to include DNA base composition, the analysis of lipids, isoprenoid quinones, and cytochromes as well as cell-wall composition. In the late 1970s and early 1980s, new molecular biological techniques enabled another major step forward in the classification of microorganisms. The comparative sequence analysis of the RNA of the small subunit (SSU) of the ribosome finally stabilized our view of the tree of life and led to the recognition of the domains Bacteria, Archaea, and Eukarya (Woese et al., 1975; Woese and Fox 1977; Woese, 1987). In the following, we focus on a set of methods, called the ‘full-cycle ribosomal RNA (rRNA) approach’, and the determination and quantification of bacteria in the environment, using fluorescence in situ hybridization (FISH).

3.08.2 The Full-Cycle rRNA Approach The full-cycle rRNA approach was developed as a phylogenybased toolbox for cultivation-independent studies of microbial diversity and ecology. Figure 1 shows a flow diagram

of this approach, starting with different sources of sequence information. After DNA extraction, the 16S rRNA genes are amplified by polymerase chain reaction (PCR) using conserved primers. The amplified rRNA genes are singularized by cloning in a plasmid vector and transformation in competent Escherichia coli cells. They are then sequenced and submitted to sequence databases. Comparative sequence analysis is the basis for the design of oligonucleotide probes. Finally, these probes can be applied to environmental samples using FISH techniques (Amann et al., 1995).

3.08.2.1 Ribosomal RNA The rRNA provides some characteristics, which predestines this molecule as a phylogenetic marker. First of all, the rRNA is an integrated part of the ribosome, the protein factory of each cell, and has the same function in every organism. The rRNA molecules must have developed in early stages of life and thus are evolutionarily conserved even in its two- and three-dimensional structures. Although the primary structure, that is, the nucleotide sequences of rRNAs, is highly conserved throughout all organisms, some regions in the rRNA are more conserved than others. For example, while some regions are identical across all domains, other regions are more variable and specific for particular genera or even species. Today, the rRNA is widely accepted as a global marker for phylogenetic studies, which is essentially lacking artifacts from

Environmental/ Environmental/culture Culture Sample sample

Extracted Extracted nucleic Nucleicacid Acid DNA rRNA

Nucleic Nucleic acid acid probe probe

rDNA clones Clones

rDNA sequences Sequences Comparative analysis Analysis rDNA database Data Base Hybridization

Sequencing

Figure 1 Flow scheme of the full-cycle rRNA approach. Modified from Amann RI, Ludwig W, and Schleifer KH (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiological Reviews 59: 143–169.

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

lateral gene transfer (for reviews, see Olsen et al. (1986), Woese (1987), or Pace (2009)). The conserved sequence stretches on the rRNA provide a variety of technical advantages. Primers can be designed to amplify, for example, the 16S rRNA genes from almost all bacteria in environmental samples by PCR. rRNA is highly abundant in cells, making it suitable as a target for in situ hybridization studies. For example, a cell from a logarithmically growing E. coli culture contains up to 70 000 ribosomes. Ribosomes consist of two subunits. The large subunit (LSU) of the bacterial and archaeal ribosomes contains the 5S rRNA (B120 nucleotides) and the 23S rRNA (B3000 nucleotides), while the SSU contains the 16S rRNA (B1600 nucleotides). The unit [S] refers to Svedberg, a sedimentation coefficient in ultracentrifugation. In contrast to Bacteria and Archaea, the eukaryotic ribosome contains in the LSU 5S, 5.8S, and 28S rRNA, and in the SSU 18S rRNA. Due to the smaller size, the sequencing of the 16S/18S rRNA is more convenient, compared to the longer 23S/28S rRNA. Additionally, 16S/ 18S rRNA has a much higher information content compared to 5S or 5.8S rRNA. Therefore, the rRNA of the SSU, the 16S rRNA of Bacteria and Archaea, and the 18S rRNA of the Eukarya were chosen as the most suitable markers to build up a phylogenetic database. Since this chapter centers on bacterial and archaeal identification, we focus on 16S rRNA, although most techniques can be applied to 18S, 23S, or 28S rRNA as well.

3.08.2.2 Sources of 16S rRNA Sequences 3.08.2.2.1 Pure cultures The classification of pure cultures by the full rRNA cycle is mostly straightforward, while the identification of organisms from environmental samples needs a few more steps. In the case of cultured organisms, biomass can be either picked from an agar plate or retrieved from liquid media via centrifugation and washing. In order to extract nucleic acids, cells are broken up by enzymatic lysis or mechanical cell disruption. After removal of cell fragments and proteins by standard sodium

173

dodecyl sulfate (SDS)/chloroform extraction, DNA and RNA can be precipitated (Box 1; Zhou et al., 1996). The rRNA genes are amplified from the extracted DNA using general primers (Table 1). The amplicon is purified and directly subjected to sequencing. The achieved sequences have to be checked for sequencing errors before they can be assembled and imported into a database. By comparative sequence analysis, phylogenetic trees can be calculated. Finally, the sequences are deposited in a public database such as European Bioinformatics Institute (EBI) or GenBank. Based on the retrieved sequences, specific oligonucleotide probes can then be designed for the localization and quantification of the organisms in the environment.

3.08.2.2.2 Microbial communities The analysis of the diversity of complex microbial communities by comparative sequence analysis and the identification of microorganisms without cultivation involves some additional steps to singularize and characterize the different types of rRNA sequences. Figure 1 summarizes the steps involved in the full-cycle rRNA approach for microbial ecology. In brief, first the sample is taken directly from the environment followed by a DNA extraction, as described previously. This is a critical step since Bacteria and Archaea have very different cell-wall composition, making it often very demanding to retrieve nucleic acids from all cells present in a sample. In order to amplify the rRNA genes, PCR with general primer pairs targeting the 16S rRNA genes of, for example, almost all bacteria (Table 1) is conducted. This results in a large number of 16S gene amplicons from most of the organisms present in the sample. These DNA fragments can now be analyzed by several methods to estimate the diversity of the microorganisms present, for example, by amplified ribosomal DNA (rDNA) restriction analysis, a method using restriction patterns on agarose gels (e.g., Smit et al., 1997), or a combined method of PCR amplification and denaturing high-performance liquid chromatography used for the analysis of polymicrobial

Box 1 SDS-based DNA extraction. Modified from Zhou JZ, Bruns MA, and Tiedje JM (1996) DNA recovery from soils of diverse composition. Applied and Environmental Microbiology 62: 316–322 Remarks: Modified version for DNA extraction from polycarbonate filters. This protocol is suitable for extracting DNA of a molecular weight up to 200 kb. Careful handling of DNA to avoid shearing is mandatory. Never vortex the DNA and use tips with wide opening. 1. Add 13.5 ml extraction buffer (100 mM Tris–HCl (pH 8.0), 100 mM ethylene-diamine-tetra-acetic acid (EDTA) (pH 8.0), 100 mM Na-phosphate (pH 8.0), 1.5 M NaCl, 1% CTAB) and 100 ml proteinase K (10 mg ml1 ) to a polycarbonate filter of 25-mm diameter which has been cut into small pieces; incubate for 60 min at 37 1C on a shaker. 2. Add 1.5 ml SDS (20%) and incubate for 120 min at 65 1C. 3. Centrifuge at c.50 000 g (15 min at room temperature (RT)), transfer supernatant into a fresh tube and add 4.5 ml extraction buffer and 0.5 ml SDS (20%) and incubate for 10 min at 65 1C. 4. Centrifuge using the same conditions and add chloroform/isoamylalcohol (24:1) to the supernatant. 5. Mix carefully, centrifuge at 10 000 g for 10 min and decant upper aqueous phase into a fresh tube. 6. Repeat chloroform/isoamylalcohol extraction steps, precipitate DNA from the aqueous phase by adding 0.6 volumes of isopropanol, and incubate at RT overnight. 7. Centrifuge at c.50 000 g at RT for 20 min, decant supernatant, and wash pellet with 10 ml ethanol (80%). 8. Centrifuge for 10 min, carefully decant supernatant, and dry pellet at RT. 9. Resuspend pellet in 200 ml PCR water or TE buffer (1:10) and incubate at 4 1C overnight. 10. Transfer DNA into a 1.5 ml PP tube, reduce volume to about 50 ml in the speed vac and store at 4 1C.

174 Table 1

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization Standard primers for the amplification of 16S and 23S rRNA genes from environmental samples

Primer

Sequence (5 0 - 3 0 )

Target

Reference

GM3F (8F) GM4R (1492R) 27F Univ1390R Arch20F ARC21F Arch958 1492R L189R

AGA GTT TGA TCM TGG C TAC CTT GTT ACG ACT T GTT GAT CCT GGC TCA G GAC GGG CGG TGT GTA CAA TTC CGG TTG ATC CTG CCG GA TTC CGG TTG ATC CYG CCR G YCC GGC GTT GAM TCC AAT T GGT TAC CTT GTT ACG ACT T TAC TGA GAT GYT TMA RTT C

Bacteria Bacteria Bacteria Bacteria and Archaea Archaea Archaea Archaea Archaea Bacterial 23S

Muyzer et al. (1995) Muyzer et al. (1995) Lane (1991) Zheng et al. (1996) DeLong (1992) Massana et al. (1997) DeLong (1992) DeLong (1992) Yu and Mohn (2001)

Modifications: M ¼ adenine or cytosine, R ¼ adenine or guanine; S ¼ guanine or cytosine; V ¼ guanine, adenosine, or cytosine, Y ¼ thimidine or cytosine.

infections (Domann et al., 2003). Denaturing gradient gel electrophoresis (DGGE) can be applied to differentiate between different microorganisms based on the guanine–cytosine (GC) content of the DNA (Muyzer et al., 1993; Myers et al., 1987). In a gel electrophoresis with a polyacrylamide gel containing a linear gradient of denaturants, the amplified 16S rRNA genes are separated in the gel according to their different GC content. Denaturants in the gel, for example, urea and formamide, lead to a transition of double-stranded DNA amplicons to partially melted structures at a particular point and thus to a stop of the fragments along the gradient in the gel. Sequences with a higher GC content will stop later in the gel at stronger denaturing conditions. AT-rich amplicons will denature earlier, thereby leading to a banding pattern in the gel. DGGE is well suited to have first insights into the microbial diversity of an environment. Individual bands can be cut from the gel, the rRNA gene fragment can be re-amplified, and the partial 16S rRNA can be determined. Another method used for environmental samples is the terminal restriction length polymorphism method (T-RFLP), using a fluorescently labeled primer for the PCR amplification of the genes and a restriction digest combined with a fluorescence detection of the resulting fragments by capillary electrophoresis (Abdo et al., 2006; Liu et al., 1997). Different lengths of the restriction fragments lead to different mobility in the gel or the capillary. The detection of the fluorescent dyes and the migration time allows the calculation of the fragment length. Different fragment lengths of the 16S rDNA fragments are indicative for different taxa. Different environments will result in characteristic and discernable banding patterns. However, pattern techniques such as DGGE and T-RFLP, in general, have only a limited resolution to assess the overall microbial diversity.

3.08.2.3 Clone Libraries Compared to the other techniques described above, clone libraries provide often a more detailed picture of the microbial diversity present in a given habitat (Box 2). The PCR product is purified after the amplification by the use of a commercial kit, for example, from Qiagen (Qiagen, Hilden, Germany), and checked for purity, size, and DNA amount via an agarose gel electrophoresis. The purified amplicon gene is cloned into plasmid vectors, for example, pCR 4-TOPO (Invitrogen, Carlsbad, USA), transformed into competent E. coli cells, and

singularized by plating on media, giving rise to single clones. From isolated clones, the plasmid containing the 16S rRNA insert can be isolated using a kit for plasmid extraction, such as PureLink Quick Plasmid Miniprep Kit (Invitrogen, Carlsbad, USA). To check whether inserts of the correct size and type are present, the isolated plasmids can be screened by PCR or agarose gel electrophoresis. A screening step can also be done from the clones directly via PCR; however, for sequencing purposes, a plasmid extraction is highly recommended to avoid cell fragments, proteins, etc., that might have an influence on the sequencing process.

3.08.2.4 Sequencing Subsequently, Sanger sequencing is done with primers targeting the cloning vector, for example, M13F and M13R (Invitrogen, Carlsbad, USA) (Box 3). High-quality sequencing of the 16S rRNA insert from clones is commercially available from specialized companies, for example, GATC-Biotech AG (Konstanz, Germany). The 16S rRNA sequences have now been determined for virtually all B8400 validly described species, and additionally for more than 1000 000 PCR-retrieved sequences of yet-uncultured environmental microorganisms. High-quality 16S rRNA sequences of cultured organisms (B7700) of valid taxonomic rank are currently assembled and curated in the Living Tree Project (Yarza et al., 2008). In total 41000 000 sequences of prokaryotic 16S rRNA have been determined and collected in databases. The SILVA project (Pruesse et al., 2007) and the Ribosomal Database Project (RDP) (Cole et al., 2009) are two established databases. It should be noted that new technologies, such as pyrosequencing (Ronaghi et al., 1996; Sogin, 2009) of PCR-amplified 16S rRNA fragments, enable highly parallel sequencing of potentially millions of 16S rRNA fragments, but the sequences are still shorter and of lower quality than those determined by Sanger sequencing. However, the development of new technologies is fast. This will then allow for an even faster processing and a higher throughput of environmental samples.

3.08.2.5 Sequence Analysis For a first quick phylogenetic identification of the sequences, a BLAST search (BLAST, Basic Local Alignment Search Tool) can

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175

Box 2 Generation of a 16S rRNA gene clone library 1. Extracted DNA (see, e.g., Box 1) is checked for integrity and size by agarose gel electrophoresis. The DNA concentration is either determined spectophotometrically or estimated by using standards on the agarose gel. Do not use fragments smaller than 20 kb. 2. Amplify 16S rRNA genes via PCR reaction. 10  buffer 2 ml dNTPs (2.5 mM each dNTP) 2 ml 2 ml) (Optional: BSA 3 mg ml1 Primer (50 mM) each 0.2 ml Eppendorf Master Taq 0.04 ml ( ¼ 0.2 units) Template 10 ng Add water to 20 ml final Primer sequences specific for most Bacteria (Muyzer et al., 1995): GM3F (50 -AGAGTTTGATCMTGGC-30 ) GM4R (50 -TACCTTGTTACGACTT-30 ) Thermocycling for GM3F/GM4R primers: 4 min at 94 1C, 1 min at 94 1C, 1 min at 48 1C (needs to be optimized for the template), 3 min at 72 1C, go to step 2 and repeat for 18–35 cycles (less means less chimera), 60 min at 60 1C (in order to obtain 100% A-overhang), and hold at 15 1C. 3. Purify reactions (e.g., Qiagen kit or, if necessary, gel purification); pool reactions, and run agarose gel for quantification and purity control. 4. Use TOPO TA Cloning Kit, Invitrogen or pGEM-T-Easy Vector System, Promega. In order to clone your vector, follow the instructions in the supplied manual. 5. Pick the clones with toothpicks first onto an agar plate then into liquid medium in a microtiter plate (MTP; 100 ml LBAMP) or pick the clones directly into the liquid medium in a MTP (100 ml LBAMP). Incubate MTP overnight. 6. Check clones via a screening PCR with the vector primers M13F and M13R 10  buffer 2 ml dNTPs 2.5 mM 2 ml 2 ml BSA 3 mg ml1 5  enhancer 4 ml Primer (50 mM) each 0.2 ml 0.2 ml Eppendorf master Taq (1 U ml1) Template 0.5 ml (from overnight culture) Add water to 20 ml final 7. Run aliquot of reaction on agarose gel to select the positive clones and purify selected clones with Multiscreen HV/Sephadex G50 plates.

Box 3 Sequencing of the cloned 16S rRNA genes Remark: This reaction uses M13F and M13R primers. If you use different primers adjust annealing time and temperature, if necessary. 1. Prepare sequence reaction with M13F and M13R primer and run sequencing. 5 ml sequence reaction: 1 ml BIG DYE, 1 ml 2.5  reaction buffer, 1 ml primer (5 mM), 0.5–1 ml template ( ¼ purified product of the screening PCR), and add water to 5 ml final. Thermocycling: 20 s at 96 1C, 10 s at 96 1C, 5 s at 55 1C, 4 min at 60 1C, go to 2 and repeat 60 cycles, and hold at 20 1C. 2. Purify reactions with Multiscreen HV/Sephadex G50 plates.

be done (Altschul et al., 1990). The next step in the workflow is the alignment of the retrieved sequences for comparing them with the databases, for example, RDP or SILVA. Alignment programs, such as the one implemented in the ARB

program package (Ludwig et al., 2004) or available at the SILVA webpage (Pruesse et al., 2007), can be used to automatically align several hundreds of sequences in one batch. The aligned sequences are then subjected to phylogenetic

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analyses, resulting in the reconstruction of phylogenetic trees. The construction of phylogenetic trees should follow the standard operating procedure for phylogenetic inference (SOPPI; Peplies et al., 2008) using information of type strain sequences (Yarza et al., 2008). However, an in-depth description of the reconstruction of phylogenetic trees is beyond the scope of this chapter. The retrieved sequences can now be used to design probes in order to detect microorganisms in environmental samples. The reliability of such probes for the identification of cells in complex environments is strongly dependent on the quality of the 16S rRNA database. A comprehensive and well-maintained database is vital for rational probe design. Such a database can be found at the SILVA project site (Pruesse et al., 2007). Powerful software tools are required to manage such a database. We recommend the highly integrated software package ARB released by the Technical University of Munich (Ludwig et al., 2004). This program runs on different UNIX-based operating systems (including Linux); it is capable of maintaining 4500 000 aligned 16S rRNA sequences and allows the easy import of additional sequences from various sources. In an ideal world, probe design would be solely based on highquality, full-length sequences. In the real world, the public databases are filled with partial sequences and sequences that contain sequencing errors. The sequence gaps in partial sequences are severely hindering probe design by reducing the number of potential target regions. Databases with incomplete sequences also fail to provide reliable information about the current specificity of probes that have been designed in the past. However, the SILVA databases allow for the selection of high-quality sequences (e.g., Silva-Ref database) on the basis of different sequence qualifiers for optimal probe design. As of March 2010, the Silva-Ref database contained about 461000 high-quality sequences of 1200 base pair length, while the Silva-Parc database included all sequences with a minimal length of 300 base pairs and all qualities. By referring to such

Table 2

high-quality databases, oligonucleotide probes can be designed with specificities ranging from the species level up to levels such as phyla or even domain. Table 2 shows examples of probes which are frequently used in marine water samples. Additional probes for freshwater environments or man-made habitats such as wastewater treatment plants can be found at probeBase. This probe database was established by Loy et al. (2003) at the University of Vienna and it has currently more than 1500 entries.

3.08.2.6 Probe Design When none of the existing probes can be used, a new one has to be designed. For FISH, a probe length of 15–25 nucleotides – most often 18 nucleotides – is common. Sequence signatures serving as suitable target sites for nucleic acid probing can be conveniently searched with the PROBE_DESIGN tool within the ARB software package. First, a target group of organisms must be specified, for example, in a phylogenetic tree contained in ARB. The PROBE_DESIGN tool searches for possible signature sequences that are diagnostic for the selected species. The tool automatically excludes potential probe sequences which contain self-complementary regions with more than 3 nucleotides. The GC content of probe sequences influences their melting behavior. By default, this parameter is set in PROBE_DESIGN between 50% and 100% to ensure a tight binding. We recommend that the GC content of a newly designed probe should be between 50% and 70%, since a higher GC content could result in unspecific binding. Sometimes the PROBE_DESIGN tool cannot find a suitable probe target site. However, the program provides options for modifying the search parameters to look for signatures in subsets of the group originally selected, or by choosing to allow for the signature to be found in a defined number of species outside the target group (Ludwig et al., 2004). By the combinatorial use of probes with overlapping specificity, the

List of examples of frequently used probes for marine planktonic samples

Probe

Target organisms

Sequence (5 0 - 3 0 )

Arch915

Archaea

GTGCTCCCCCGCCAATTCCT

Eury806

CACAGCGTTTACACCTAG

0

GCTGCCTCCCGTAGGAGT GCAGCCACCCGTAGGTGT GCTGCCACCCGTAGGTGT ACTCCTACGGGAGGCAGC GGTAAGGTTCTGCGCGTT CAACGCTAACCCCCTCC

35 35 35 35 35 35

Amann et al. (1990a) Daims et al. (1999) Daims et al. (1999) Wallner et al. (1993) Neef (1997) Eilers et al., (2001)

Bet42a

Euryarchaeota marine group II Bacteria Supplement to EUB338 Supplement to EUB338 Control Alphaproteobacteria Clade Roseobacter and relatives Betaproteobacteria

GCCTTCCCACTTCGTTT

35

Manz et al. (1992)

Gam42a

Gammaproteobacteria

GCCTTCCCACATCGTTT

35

Manz et al. (1992)

CF319a Pla46

Bacteroidetes Planctomycetes

TGGTCCGTGTCTCAGTAC GACTTGCATGCCTAATCC

35 30

Manz et al. (1996) Neef et al. (1998)

Eub338 Eub338-II Eub338-III Non338 Alf968 Ros537

a

Formamide concentration in CARD-FISH hybridization buffer. More probes can be found at probeBase (http://www.microbial-ecology.de/probebase).

Competitor/helper sequence (5 0 - 3 0 )

cGam42a GCCTTCCCACATCGTTT cBet42a GCCTTCCCACTTCGTTT

FAa(%)

Reference

35

Stahl and Amann (1991) Teira et al. (2004)

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177

3.08.2.6.2 Selection of the oligonucleotide probe label

wavelength B525 nm (suitable for Alexa 488, Fluorescein, Atto 488, etc.). It is important to note that the CY5 and Alexa 647 derivatives emit light in the near infrared (see Table 3) and are only detectable with a charge-coupled device camera or on a confocal laser scanning microscope with red laser excitation. Concomitant to the selection of the fluorescent label, care should be taken such that the right optical filters are chosen for the detection of the respective dyes. Well-adapted optical filters with a high transmission in the emission spectrum of the dye and a strong and sharp blocking of the excitation light are essential for a confident detection of weak signals. In nutrient-depleted environmental samples, oligonucleotides carrying only one fluorochrome may not be sensitive enough for detection of cells with low ribosome content (Pernthaler et al., 2002). Polynucleotide probes with a length of 4100 nucleotides carrying several fluorochromes may solve this sensitivity problem (Trebesius et al., 1994; DeLong et al., 1999). However, these probes do not allow for single mismatch discrimination and they are, therefore, lacking specificity for narrow target groups on the level of species and genera. An alternative labeling technique that increases fluorescence signal intensity uses horseradish-peroxidase (HRP)labeled oligonucleotides and a subsequent detection of this enzymatic label by catalyzed reporter deposition FISH (CARD FISH). The fluorescent staining in this case is the result of a secondary incubation with fluorescently labeled tyramides. The covalent and therefore permanent deposition of these labeled reporter compounds occurs only within cells hybridizing with the HRP-labeled probe. CARD-FISH signals are significantly brighter than FISH signals obtained with the same probe (Scho¨nhuber et al., 1997). Hoshino et al. (2008) determined a signal amplification of 26- to 41-fold. However, cell permeabilization protocols need to be adjusted in order to enable the larger enzyme-labeled oligonucleotides to penetrate into cells (Pernthaler et al., 2002).

A range of fluorochromes is available for the labeling of nucleic acids. However, not all fluorochromes are equally suited as labels for oligonucleotides. Newly developed dyes, in particular, should be checked for nonspecific staining. Standard labels for in situ hybridization are the green fluorescein and the red tetramethylrhodamine derivatives (Table 3). These dyes are well suited for standard applications when the ribosome content of target cells is high. In addition, these dyes can be used in conjunction to label different probes in double staining experiments. CY3 and CY5 (cyanine dyes) are members of the indocarbocyanine family. The high signal intensity of these two dyes makes them the fluorochromes of choice for detection of small cells with lower ribosome content like bacterioplankton cells. CY3 and CY5 emit strong fluorescence due to their high quantum yields and high molar extinction coefficients (Table 3). Different excitation and emission wavelength need different filter sets. These sets are available in standard configurations, for example, 65 HE Alexa 488 shift free (Zeiss, Jena, Germany) for Alexa 488 fluorescent dye, or in more variable configurations, for example, BrightLine HC 475/35 (AHF Analysetechnik AG, Tu¨bingen, Germany) for dyes with excitation wavelength B475 nm and emission

Oligonucleotide probes are custom-made by solid-phase synthesis. In the last step of synthesis, a fluorochrome is added to the 50 end of the oligonucleotide. Purified probe stocks are frequently delivered lyophilized. Upon reconstitution with 100-ml sterile water, a probe synthesis at 0.02 mmol scale yields approximately a 1500 ng ml1 stock solution. To determine the exact probe concentration, the absorbance of the 1:100 diluted stock solutions at 260 nm should be measured, assuming that 1 OD260 nm ¼ 20 ng ml1 DNA. Furthermore, the labeling of the oligonucleotide should be checked. For a pure monolabeled oligonucleotide, the ratio of absorption of the dye (Adye) versus the absorption of the nucleic acids at 260 nm (A260) should match the ratio of the extinction coefficients (e) of the dye and oligonucleotide. The extinction coefficient e at 260 nm (e260) of an oligonucleotide can be estimated from its nucleotide composition as the sum of the extinction coefficients of the individual nucleotides (deoxyadenosine triphosphate (dATP) ¼ 15.4 cm3 mmol1, deoxycytidine triphosphate (dCTP) ¼ 7.3 cm3 mmol1, deoxyguanosine

selected group of organisms may be fully targeted. Whenever possible, the identification of microbial cells in complex environmental samples should not be based on a single oligonucleotide probe, but rather on probe sets which contain, for example, two probes targeting the population of interest.

3.08.2.6.1 Accessibility of the probe A problem that should be considered during the design of FISH probes is target site accessibility. The higher-order structure of the ribosome may hinder the binding of the probe to its target site. The 16S rRNA in situ accessibility for oligonucleotide probes has recently been studied for two members of the domain Bacteria (E. coli, Pirellula sp. strain 1), one eukaryote (Saccharomyces cerevisiae), and one archaeon (Metallosphaera sedula) (Figure 2; Fuchs et al., 1998; Behrens et al., 2003). Furthermore, a complete accessibility map for the 23S rRNA of E. coli has been published (Fuchs et al., 2001). These color-coded maps clearly demonstrate dramatic differences in the binding of different fully complementary 18-mer probes to one batch of fixed target cells, which seem to be most strongly influenced by the type of secondary structure that is targeted. Although the secondary structure of the ribosome is highly conserved and a consensus map could be developed from the 16S rRNA accessibility studies, each probe should be checked on their respective target group of organisms to ensure high probe signals (Behrens et al., 2003). Inaccessible target regions can be made accessible by the use of unlabeled oligonucleotides, called ‘helpers’ (Fuchs et al., 2000). These bind adjacent to the diagnostic probe, thereby opening the target region for the probe. Helpers should be a few nucleotides longer than the diagnostic probe, that is, if the probe is an 18-mer, the helper should be a 21-mer, to ensure a tight binding beyond the melting point of the diagnostic probe.

3.08.2.6.3 Quality check of probes

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1090

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Class V: 6−20%

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Class VI: 0−5%

190

Figure 2 Accessibility map of the 16S rRNA of Escherichia coli for fluorescently labeled oligonucleotide probes. From Behrens S, Ru¨hland C, Inacio J, et al. (2003) In situ accessibility of small-subunit rRNA of members of the domains Bacteria, Archaea and Eucarya to Cy3-labeled oligonucleotide probes. Applied and Environmental Microbiology 69: 1748–1758.

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization Table 3

179

Dye labels frequently used for oligonucleotide probes and their characteristics

Carboxy-fluorescein (FAM)b Fluoresceinb Alexa 488 Atto 488 CY3a Alexa 546 Carboxytetramethyl-rhodamine (TAMRA) Alexa 594 Atto 590 (rhodamine-derivative) CY5a Alexa 647

Excitation (7 10 nm)

Emission (7 10 nm)

492 490 494 501 512/552 554 540 590 594 625–650 651

518 520 517 523 565/615 570 565 617 624 670 672

Molecular weight (Da) 376 389 643 766 1079 466 820 792 1250

e (mol1 cm1) 79 000 77 000 71 000 90 000 150 000 112 000 91 000 92 000 12 000 250 000 270 000

Data compiled from a Amersham Biosciences. b Sambrook J, Fritsch EF, and Maniatis T (1989) Molecular Cloning: A Laboratory Manual, 2nd edn., vol. I. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, and Invitrogen. e molar extinction coefficient. Note: Fluorescein and derivatives are pH sensitive and exhibit maximum fluorescence at pHZ9.

triphosphate (dGTP) ¼ 11.7 cm3 mmol1, thymidine 50 triphosphate (dTTP) ¼ 8.8 cm3 mmol1, from Sambrook et al., 1989). Taking into account the extinction coefficient of the dye (edye; see also Table 3), the quality of labeled oligonucleotide can be estimated by calculating a ratio k according to the following:

k¼

e260 =edye A260 =Adye

Values of ko1 indicate an incomplete labeling of a probe, whereas values 41 point to the presence of additional, potentially unbound dye. Considering inaccuracies in the estimation of the extinction coefficients of oligonucleotides, k-values between 0.7 and 1.3 are acceptable. Working solutions are prepared at concentrations of 50 ng ml1 and stored in the dark at –20 1C. Only small portions of probe working solutions (50–100 ml) should be prepared, since repeated freeze–thawing may damage the probe and might result in a precipitation of the probe, which in turn leads to weak hybridization signals and high background. HRP-labeled probes can be ordered from Biomers (Ulm, Germany). They are shipped in a lyophilized state and subsequently suspended in sterile H2O. It is important not to freeze HRP-labeled probe solutions, repeatedly, but to prepare small aliquots which are stored in the refrigerator at 4 1C. For calculating the quality of the probe, it has to be taken into account that the enzyme has a broad absorption maximum at 404 nm, and therefore contributes to the measured absorbance at 260 nm. This has to be considered in the determination of the probe concentration by the following correction:

OD260 ðoligoÞ ¼ OD260  OD404  0:276 A peak ratio (A260/A404) of around 3 indicates a good labeling of probe with HRP (Pernthaler et al., 2004). For checking the enzymatic activity of the HRP, the Amplex Red Hydrogen

Peroxide/Peroxidase Assay Kit can be used (Invitrogen, Carlsbad, USA). Following the manual, first a calibration curve is done with a peroxidise standard to which the activity of the HRP-labeled probe is compared. This test is ultrasensitive, and therefore performed on a probe solution diluted 10 000 from the probe stock solution (Jo¨rg Wulf, MPI Bremen, personal communication).

3.08.2.6.4 Adjusting probe specificity It is important that previously reported probes are reevaluated regularly against an updated database, such as SILVA (Pruesse et al., 2007), for specificity and target group coverage (see also Amann and Fuchs, 2008). First, an in silico check using online tools such as probeCheck (Loy et al., 2008) should be performed. Ideally, the probe of interest shows at least a mismatch to all nontarget microorganisms. For the design of new probes, it is important to keep these discriminatory positions central because a mismatch between probe and nontarget rRNA at the 30 - or 50 - end of the oligonucleotide is only weakly destabilizing. The discriminatory effect of a single mismatch can be increased by competitor oligonucleotides (Manz et al., 1992). They help to increase probe specificity. These competitor oligonucleotides have been shown to strongly suppress unspecific probe binding to a particular one-mismatch sequence. Competitors are mostly applied as unlabeled oligonucleotides, which are fully complementary to the mismatch-containing nontarget sequence. The probes and competitors specific for the Gammaproteobacteria (GAM42a) and Betaproteobacteria (BET42a) are prominent examples for this concept (Manz et al., 1992). Subsequently, the optimal hybridization conditions need to be established to guarantee high specificity and good sensitivity of the probe. For this, a series of hybridizations is performed at increasing stringency either by increasing the temperature of hybridization or by increasing the concentrations of a denaturing agent such as formamide in the

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hybridization buffer (often referred to as melting curve; Figure 3). The changes in the fluorescence intensities of individual cells can be quantified by computer-assisted image analysis (Neef et al., 1996), by flow cytometry (Fuchs et al., 1998), or they can be semiquantitatively scored by a trained microscopist. The most desirable hybridization stringency is usually the one immediately before the target cell fluorescence begins to decrease. At this formamide concentration, the hybridization signal of the target cells is still maximal and that of nontarget organism should be low or absent. As a rule of thumb, an 18-mer oligonucleotide with a GC content between 50% and 60% will start to dissociate from its fully complementary rRNA target at a formamide concentration of approximately 30–40% in our standard buffer at 46 1C. Finally, new probes should be tested by FISH of isolates which have none, one, and more mismatches to the oligonucleotide at the optimized hybridization conditions (see Box 4).

This quite laborious method is based on the assumption that the temperature of dissociation from isolated rRNA is the same as from rRNA in fixed cells. On the other hand, 16S rRNA gene clones carrying the target sequence of a new probe can be used for adjusting the hybridization conditions (Clone-FISH). For this, the rRNA gene of interest must have been ligated in correct orientation into a vector with an inducible promoter upstream of the multiple cloning sites. Such clones are then grown with chloramphenicol and isopropylb-D-thiogalactopyranoside. This induction leads to an in vivo transcription of the cloned 16S rRNA gene and the accumulation of heterologous 16S rRNA of the uncultured organism inside the E. coli cell. After standard fixation, these induced E. coli cells, displaying the heterologous rRNA, can be used as analogs to cultured organisms for determining the melting point of probes (Schramm et al., 2002).

3.08.3 Fluorescence In Situ Hybridization 56.2.6.5 Clone-FISH

Probe conferred signal (sensitivity)

New probes are quite frequently designed to target yet uncultured microorganisms, which are only known from their rRNA gene sequences. In this case, it is not possible to test the probes using pure culture isolates. Two strategies are available to optimize the hybridization conditions in such a case. On the one hand, the cloned 16S rRNA gene of interest can be transcribed in vitro to RNA which is then blotted on a nylon membrane and hybridized with a labeled oligonucleotide at increasing levels of formamide (e.g., Pernthaler et al., 1998).

Target organism Nontarget organism

Temperature, formamide (stringency)

Specificity

Low

High

Low

Signal/noise = Max

Figure 3 Theoretical melting behavior of a probe to a target (straight line) and a mismatch containing organism (dotted line). The shaded area depicts the optimal working conditions for the probe.

Box 4

FISH with rRNA-targeted oligonucleotide probes can be used for identification, localization, and quantification of defined microbial populations in complex samples (Amann et al., 1995). The principle steps necessary for this phylogenetic staining (DeLong et al., 1989) are shown in Figure 4, and are described in more detail in the following sections (see Wagner et al. (2003) and Amann and Fuchs (2008) for reviews). Additionally, other FISH techniques provide powerful tools for environmental research and can be used to detect different target nucleic acids. Thus, transfer-messenger RNA (tmRNA) and messenger RNA (mRNA) can be used as targets for oligonucleotide probes. Within certain species, tmRNA can be used for discrimination of subspecies (Scho¨nhuber et al., 2001), while mRNA can be used to link expressed metabolic functions to cell identities (Pernthaler and Amann, 2004). Moreover, recognition of individual genes FISH using polynucleotide probes can be applied to detect certain genes within environmental samples and thus link the identity of a cell to its potential function (Zwirgelmaier et al., 2004). Another link of function, activity, and identity of microorganisms is provided by microautoradiography (MAR) FISH (e.g., Lee et al., 1999). However, in this chapter, we focus on FISH and the more sensitive CARD FISH.

3.08.3.1 Fixation and Permeabilization The first step in FISH analyses is the sampling and fixation of the cells. The most common fixatives are formaldehyde and ethanol. Fixation is critical for the whole FISH analysis. On the one hand, it prevents cell lysis, and thus preserves the cell

Melting curve analysis

1. Hybridize different microorganisms with none (full match) and one or more mismatches, according to the protocol (Boxes 6, 8, and 9) using different formamide concentrations (10–70% in steps of 5%) in the hybridization buffer. 2. Take pictures of the hybridized cells in an epifluorescence microscope using a fixed exposure time. 3. Analyze the brightness of the probe pictures via the program ImageJ and calculate mean brightness for the different formamide concentrations. The concentration with the highest differences of brightness values of the full match to mismatch organism should be used for hybridization (see also Figure 3). This is usually the concentration before the signal of the target cells is decreasing (see shaded area in Figure 3).

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

181

Sample (culture or environment) Fixation and permeabilization

Fixed and permeabilized cells

Flourescent dye Probe

Target (rRNA) Hybridization with fluorescent-labled oligonucleotide probes

Ribosomes

Hybridized cells

Quantification

Flow cytometry

Epifluorescence microscopy

Figure 4 Flow scheme of the analysis of an environmental sample by the FISH approach.

morphology. On the other hand, the permeabilization of the cell wall is necessary for the access of oligonucleotide probes to intracellular target sites. Bacteria, Archaea, and also fungi, algae, and protozoa have a broad variety of cell walls. Therefore, there is no single standard protocol available that fits all microorganisms (see Box 5 for basic protocols). Empirical optimizations should consider both, modification of individual steps of the standard protocols and additional treatments depending on the type of cell wall. Enzymatic digestions of the thick peptidoglycan layer of Gram-positive bacteria by lysozyme, the digestion of pertinacious cell walls using proteases, wax removal with solvents, the use of different detergents, or even short-time treatments in hydrochloric

acid are just some examples (Burggraf et al., 1994; Roller et al., 1994; Davenport et al., 2000; Pernthaler et al., 2004). All fixation protocols also disintegrate the cell membranes. Otherwise, the oligonucleotide probe could not diffuse to its ribosomal target sites. Consequently, cells fixed for FISH are always dead.

3.08.3.2 Hybridization with Monolabeled Oligonucleotide Probes Hybridization with fluorescently labeled oligonucleotide probes can be done in two formats. One format is the quick, but nonquantitative protocol for hybridization of cells on

182

Box 5

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

Fixation protocols for different sample types

1. Formaldehyde fixation of pure cultures with Gram-negative cell wall (Amann et al., 1990). A. Harvest cells during logarithmic growth from liquid medium by centrifugation of an aliquot of 2 ml by centrifugation for 10 min. at 10 000g, discharge supernatant and resuspend cells in 750 ml PBS buffer (145 mM NaCl, 1.4 mM NaH2PO4, 8 mM Na2HPO4, pH 7.4). B. Fix cells by adding 250 ml of a 4% PFA fixative (1% final concentration) and incubate for 1 h (for robust cells) to 24 h (for fragile cells) at 4 1C. C. Pellet cells by centrifugation (10 min at 10 000 g), discharge supernatant and thoroughly resuspend fixed cells in 500 ml PBS. D. Add 500 ml absolute ethanol and mix thoroughly, at this stage samples can be used for FISH or stored at  20 1C for several months. 2. Ethanol fixation of pure cultures with Gram-positive cell wall (Roller et al., 1994). A. Harvest cells during logarithmic growth by centrifugation of an aliquot (c.2 ml) for 10 min at 4000 g, discharge supernatant and wash cells in PBS buffer. B. Pellet cells by centrifugation (10 min at 10 000 g), discharge supernatant and add 500 ml PBS to resuspend cells thoroughly. C. Add 500 ml cold, absolute ethanol, at this stage samples can be stored at  20 1C for several months. 3. Fixation of planktonic samples (modified from Glo¨ckner et al. (1999)). A. Add formalin (37% formaldehyde) to a water sample to a final concentration of 1–3% and fix for 1–24 h at 4 1C; needs to be optimized for new sample types. B. Place a moistened support filter (0.45 mm pore size, cellulose nitrate, 47 mm diameter; Sartorius, Germany) and a membrane filter (0.2 mm pore size, white polycarbonate, 47 mm diameter; Millipore, Eschborn, Germany; shiny side up!) into a filtration tower; filter a known volume of the fixed sample by applying gentle vacuum; support filters may be utilized for several samples; for cell numbers of around 106 ml1, 10 ml of sample is generally sufficient. C. After complete sample filtration, wash with 10–20 ml of sterile H2O; remove H2O by filtration, put the membrane filter in a plastic petri dish, cover and allow air-drying. D. Store at  20 1C until processing; filters can be stored frozen for several months without apparent loss of hybridization signal. 4. Fixation of sediment/soil samples (Llobet-Brossa et al., 1998). A. Fix sediment samples with fresh formaldehyde solution (end concentration 1–4%) for 1–2 h at RT or max 24 h at 4 1C. B. Centrifuge at 16 000 g for 5 min, pour off supernatant and resuspend sample with 1  PBS pH 7.6, repeat washing step twice. C. Store sediment sample in a 1:1 mix of PBS/ethanol at  20 1C until further processing.

glass slides (Manz et al., 1992). For this, cells of pure cultures, enrichments or concentrated cell suspensions from environmental samples are spotted onto gelatin-coated slides, allowed to air-dry, and fixed by immersion in formalin or ethanol. These slides are then covered with hybridization buffer and probe, and incubated in a moisture chamber for several hours. After a short washing step, the cells can be embedded in antifading reagent for microscopic visualization. This hybridization technique is robust, yet not quantitative since cell loss from the slide during hybridization and washing cannot be completely ruled out. Nevertheless, this technique can be used in wastewater and food analytics because high numbers of bacteria are present and slides can be used as colonization devices. In water science, immobilization of cells on glass slides is frequently used for the analysis of wastewater treatment or activated sludge plants (Wagner et al., 1993; Daims et al., 2001). In limnic and marine systems, low cell numbers require cell concentration and the usage of polycarbonate membrane filters for analysis on microbial communities. Therefore, fixed cells are concentrated and immobilized on membrane filters of an adequate pore size (most often 0.2-mm pore-sized polycarbonate filters). This technique is more suitable for environmental samples with low cell concentrations, for example, marine or freshwater. Care must be taken such that the filtered volume is adjusted in a way that facilitates subsequent cell counting in the microscope. Filters are then cut into pieces (eight for a 25-mm-diameter filter, 16– 20 for a 47-mm-diameter filter), and these filter pieces are then used for hybridization with different probes (Table 3, Box 6). Again, a moisture chamber is used to avoid evaporation of the hybridization buffer and to ensure a high stringency of the hybridization. Hybridization is performed using a buffer/

probe mix (10:1) to cover the filter pieces. Incubation times should be in the range of 90 min to 3 h at a temperature of 46 1C. After several washing steps, filter pieces are stained with the DNA stain 40 ,6-diamidino-2-phenylindole (DAPI), and embedded in antifading reagent for microscopic analyses (Glo¨ckner et al., 1996).

3.08.3.3 Catalyzed Reporter Deposition Fluorescence In Situ Hybridization In environmental samples, single oligonucleotides carrying only one fluorochrome may not provide enough fluorescence signals to detect cells with low ribosome contents (Pernthaler et al., 2002). Polynucleotide probes with a length of more than 100 nucleotides labeled with several fluorochromes per molecule are an alternative (Trebesius et al., 1994; DeLong et al., 1999). However, these probes lack the specificity for narrow target groups such as species or genera. An alternative labeling technique that increases the fluorescence signal intensity uses HRP-labeled oligonucleotides. When using HRP-labeled probes, fluorescent staining results from a secondary incubation with fluorescently labeled tyramide. Each HRP-labeled probe catalyzes the covalent deposition of multiple labeled tyramides, resulting in a staining that is significantly brighter than those obtained with oligonucleotides labeled with a single fluorochrome (Scho¨nhuber et al., 1997; Hoshino et al., 2008). However, cell permeabilization protocols need to be adjusted in order to enable the larger enzyme-labeled oligonucleotides to diffuse into the cells (Pernthaler et al., 2002). Depending on the sample, the protocols for CARD FISH differ substantially, yet, there are generally two additional steps compared to FISH: (1) embedding in agarose and

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

(2) incubation with fluorochrome-labeled tyramide (Pernthaler et al., 2004).

3.08.3.3.1 Embedding, permeabilization, and inactivation of endogenous peroxidases Similar to FISH with monolabeled oligonucleotide probes, samples for CARD FISH are fixed with formaldehyde and filtered on polycarbonate filters. These filters are then embedded in low gelling point agarose (0.1%) and dried at a temperature between 20 and 50 1C. The embedded cells are stabilized by the agarose, and cell loss during permeabilization is prevented. In order to permeabilize the cells for the large HRP enzyme (B40 kDa), again no single standard protocol is available. However, permeabilization with lysozyme turned Table 4 NaCl concentration in the washing buffer according to % formamide of the hybridization buffer % formamide in hybridization buffer 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

[NaCl] in M final concentration 0.900 0.636 0.450 0.318 0.225 0.159 0.112 0.080 0.056 0.040 0.028 0.020 0.014

ml 5 M NaCl in 50 ml

9000 6300 4500 3180 2150 1490 1020 700 460 300 180 100 40

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out to be a very good method for most marine planktonic Bacteria (see Box 7 for a basic protocol). Archaea or cells with protective substances, such as exopolymers or waxes, could be permeabilized with substances described above (Section 3.08.4.1) or they require the development of new permeabilization protocols. Due to the use of HRP as the label for the oligonucleotide probes, naturally occurring peroxidases must be inactivated. Several bacteria produce, for example, peroxidases as a protection mechanism against peroxide which are formed from reactive oxygen species in aerobic environments (Farr and Kogoma, 1991). Such peroxidases could lead to false-positive signals in cell counts and thus must be inactivated before the CARD of tyramide, for example, by a short incubation in diluted hydrochloric acid. Furthermore, autofluorescence of certain cells could also lead to false-positive counts. An incubation of the cells with peroxide solution (3%) reduces autofluorescence signals to background levels.

3.08.3.3.2 Hybridization Several minor changes from the FISH protocol were made for the hybridization of cells with HRP-labeled probes (Box 8). Similar to monolabeled probes, moisture chambers are used to ensure stringency of the hybridization. However, the concentration of the hybridization buffer/probe mix can be chosen an order of magnitude lower (300:1 to 100:1, depending on the sample). Furthermore, a prolonged hybridization time of 2–8 h at 46 1C is recommended. Subsequent washing steps are crucial; filters must not run dry before the CARD procedure.

3.08.3.3.3 Catalyzed reporter deposition CARD of tyramide by HRP is known since two decades as a method of signal amplification (Bobrow et al., 1989). First used in immunoblotting and immunosorbent assays, the

Box 6 FISH using monolabeled oligonucleotide probes on membrane filters 1. Cut sections from membrane filters with a razor blade and label filter sections with a pencil, for example, by numbering them. 2. Put filter sections on glass slides (cells facing up!), several filter sections can be placed on one slide and for simultaneous hybridization with the same probe. 3. Prepare 2 ml of hybridization buffer in a microfuge tube (360 ml 5 M NaCl, 40 ml 1 M Tris/HCl, formamide % depending on probe, add water to 2 ml, add 2 ml SDS (10%)). 4. Remove an aliquot of 20 ml per filter piece into a separate cap and add 2 ml probe working solution (50 ng probe ml1) per filter piece. 5. Prepare moisture chamber by putting a piece of blotting paper into a 50-ml polyethylene tube and soaking it with the remaining hybridization buffer without probe (see above). 6. Carefully cover the filter section with the hybridization mix and place the slide with filter sections into the polyethylene tube (in a horizontal position). 7. Incubate at 46 1C for at least 90 min (maximum: 3 h). 8. Meanwhile prepare 50 ml of washing buffer in a polyethylene tube (X ml 5 M NaCl, depending on formamide concentration in the hybridization buffer (see Table 4)), 1 ml 1 M Tris/HCl, 500 ml 0.5 M EDTA (if formamide concentration of the hybridization buffer is higher than 20%), add to 50 ml with water and add 50 ml SDS (10%). 9. Quickly transfer filter sections into preheated washing buffer and incubate for 15 min at 48 1C (water bath). 10. Pour washing buffer with filter sections into a petri dish. Pick filter sections and rinse them by placing them into a petri dish with distilled H2O for several seconds, then let them air-dry on blotting paper. 11. For counterstaining, put filter sections on a glass plate, cover with c.50 ml of DAPI solution (1 mg ml1), and incubate for 3 min. Afterwards, wash filter sections subsequently for 1 min in distilled H2O and for 1 min in 80% ethanol to remove unspecific staining. Let air-dry. 12. Samples are mounted in a 4:1 mix of Citifluor and Vecta Shield. The filter sections have to be completely dry before embedding, otherwise part of the cells might detach during inspection. 13. Double-stained and air-dried preparations as well as filters mounted on slides can be stored in the dark at  20 1C for several days without substantial loss of probe fluorescence.

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Box 7

Embedding, permeabilization and inactivation of endogenous peroxidases for CARD FISH

Remark: Do not embed cells if flow cytometry should be used for analyses, and handle filters very carefully during the washing steps to prevent cell loss. Embedding 1. Boil low gelling point agarose (0.1%, gel strength should be approximately 1000 g cm2), fill the agarose in a pre-warmed petri dish and let it cool down to 35–40 1C. 2. Dip filter with both sides in the agarose and place it face down (shiny side with bacteria down!) onto a parafilm covered, even surface (e.g., glass plate), let dry; temperature for drying is not crucial, anything between 20 and 50 1C is fine. 3. Remove filters from surface by soaking in 80–96% ethanol. 4. Let the filter air-dry on a piece of tissue paper. Permeabilization Remark: Permeabilization with lysozyme proved to be the optimal method for most of the marine planktonic bacteria. 1. Incubate filter in 10–20 ml of fresh lysozyme solution in a small petri dish (10 mg ml1 in 0.05 M EDTA, pH 8.0; 0.1 M Tris–HCl, pH 8.0) for 60 min at 37 1C. 2. Wash in excess with MilliQ water. Inactivation of endogenous peroxidases 1. Incubate in 0.01 M HCl or waterous H2O2 solution (3%) for 10–20 min at RT. Wash filters well in excess MilliQ water and 96% ethanol and let the filter air-dry on a paper. 2. If you have problems with autofluorescence of your cells, an additional incubation in 3% H2O2 in water for 10 min at RT may help.

Box 8

Hybridization with HRP-labeled oligonucleotide probes

1. Prepare a humidity chamber by inserting a piece of tissue paper in a 50-ml tube and soak it with a formamide-water mix according to the formamide concentration of the hybridization buffer. Mix hybridization buffer (3.6 ml 5 M NaCl, 0.4 ml 1 M Tris–HCl pH 8.0, formamide (depending on probe, see Table 2),  ml sterile dH2O (depending on probe), 2.0 ml Blocking Reagent (10%, Roche, Basel; prepare according to manufacturer’s instructions), 2.0 g of dextran sulfate) and 20 ml SDS (20%)) with probe working solution (50 ng DNA ml1) in a ratio 300:1 (i.e., 1 ml) to 100:1 (i.e., 3 ml) depending on sample. For every filter piece 100 ml of hybridization mix should be calculated. 2. Dip each filter completely into the hybridization solution and place filters face up onto a parafilm covered glass slide; spread the rest of the solution evenly onto the filters. Close tube firmly and keep the tube in a horizontal position. 3. Incubate at 46 1C for 2–3 h (coastal water) or 6–8 h for oligotrophic/open ocean water samples. 4. Wash filters in prewarmed washing buffer (0.5 ml 0.5 M EDTA pH 8.0, 1.0 ml 1 M Tris–HCl pH 8.0,  ml NaCl (depending on probe, see Table 4 in standard FISH protocol) add dH2O to a final volume of 50 ml, then add 25 ml SDS (20%)) for 10 min at 48 1C. 5. Transfer filters to 1  PBS (do not let filter run dry!) and incubate for 15 min at RT. 6. To remove excess liquid, dab filter on blotting paper, but do not let filter run dry!

combination with oligonucleotide probes made this method suitable as an approach for microbial ecology (Box 9; Scho¨nhuber et al., 1997; Pernthaler et al., 2002). Thus, HRPlabeled probes can be used to enhance FISH signals by the use of peroxide as a catalyst for tyramide oxidation. H2O2 is the activating substrate of peroxidase. The peroxidase enters a radical form and transfers the radical to the fluorescently labeled tyramide. In this reaction, a proton is set free. The activated HRP radicalizes in a second step another fluorescently labeled tyramide molecule and is then reaching a resting stage, needing H2O2 for further activation (Veitch, 2004). The fluorescently labeled tyramide radicals bind covalently to electron-rich moieties within the cell, such as tyrosine sites in proteins close to the reaction site (Figure 5; Pernthaler, et al., 2002). These reactions are again carried out in a humidity chamber. Therefore, H2O2 (0.15% in phosphate-buffered saline (PBS) buffer) is mixed with the amplification buffer and fluorescently labeled tyramide (1 mg ml1) is added to the mix (see Box 9 for protocol). The filter pieces are then incubated at 46 1C for up to 45 min and washed thoroughly afterwards.

After DAPI counterstaining and embedding into antifading agent, the filter pieces can be analyzed via epifluorescence microscopy. For flow cytometric analyses, cells have to be removed from the filters again by mechanical and chemical treatments before counterstaining with DAPI (Box 10; Sekar et al., 2004). Subsequently, the resuspended cells can be counterstained and analyzed via flow cytometry (Sekar et al., 2004).

3.08.3.4 Troubleshooting Even though FISH and CARD FISH are rather robust methods, they are prone to errors during handling. Some of them are described in the following; however, in case of failure, we also recommend to consult the extensive check list provided by Wagner et al. (2003). First of all, some of the chemicals and buffers, such as formamide, dextran sulfide, and the hybridization buffer mix, are delicate and tend to decay over time. These reagents need to be refilled from the stock or prepared freshly in short time

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

185

Box 9 Catalyzed reporter deposition 1. Prepare a moisture chamber by inserting a piece of tissue paper in a 50-ml tube and soak it with 2 ml water. 2. Prepare a fresh solution of H2O2 (0.15% in PBS), keep it cool. 3. Mix amplification buffer with H2O2 solution in a ratio of 100:1 (as a guideline, the same volume as the hybridization mix is sufficient) and add fluorescently labeled tyramide (1 mg ml1) and mix well, keep in the dark. (The volume of labeled tyramide added strongly depends on the nature of the sample, start with 1000:1; if the signal is not sufficient: in-/decrease the ratio of added tyramide.) 4. Dip filter completely in the amplification mix, place filter sections face up on a parafilm covered glass slide and spread the rest of the amplification mix over the filters. 5. Put the slides into the humidity chamber and incubate at 46 1C for up to 45 min in the dark. 6. To remove excess liquid, dab filter on blotting paper and incubate in 1  PBS for 5–10 min at RT in the dark (or: 15 min at 46 1C on a shaker). 7. Wash filters thoroughly in excess with deionized water. Therefore, use a Bu¨chner funnel and MQ water to create a gyre in the funnel. Then wash filters thoroughly twice in excess in 96% ethanol (1–2 min), let them completely air-dry in the dark before counterstaining with DAPI. 8. For counterstaining, put filter sections on a glass plate, cover with 50 ml of DAPI solution, and incubate for 3 min; afterwards wash filter sections for several seconds in 80% ethanol to remove unspecific staining followed by rinsing in distilled H2O and air-drying. 9. Samples are mounted in a 4:1 mix of Citifluor and Vecta Shield; the filter sections have to be completely dry before embedding, otherwise part of the cells might detach during inspection. 10. Double-stained and air-dried preparations as well as filters mounted on slides can be stored in the dark at  20 1C for several days without substantial loss of probe fluorescence.

HRP HRP Hybridization with horseradish-peroxidase (HRP)-labeled oligonucleotide probes

HRP HRP

+ H2O2 Catalyzed reporter deposition (CARD) of fluorescently labeled tyramide

HRP

Protein + 2 H2O Figure 5 CARD FISH with HRP-labeled probes, using fluorescently labeled tyramines.

intervals. HRP-labeled probes should not be repeatedly frozen, because freezing might break the enzyme–oligonucleotide binding and decrease the activity of the enzyme, resulting in bad or no signals. Another frequent problem could be the lack of signals. This might have several reasons. The most trivial one is the complete loss of cells from the slide or filter during handling. It might also well be that during sampling too little cells have been transferred onto the filter or glass slide to be detected in the microscopic viewing field (no cells, no signals; Amann et al., 1995). Alternatively, the staining procedure by FISH resulted in low signal intensities due to low ribosome contents in the target cells, in which case, CARD FISH might enhance

the signals. If CARD FISH fails, most likely the cells are impermeable to the large HRP probes and need to be permeabilized more thoroughly by treatment with, for example, proteinases and lysozyme. Some newly developed probes could fail in FISH, although a positive control hybridization with an established probe such as EUB338 produces nice signals. In such a case, the target cells have been permeabilized and contain sufficient ribosomes for detection, and the most likely cause of failure is then inaccessibility of the probe target sites in the ribosome. The use of helper oligonucleotides here might increase the accessibility of the probe-binding site, resulting in a significant increase of the hybridization signals. Yilmaz and co-workers could also show that the low

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Box 10 Preparation of the CARD FISH stained cells for flow cytometry. From Sekar R, Fuchs B, Amann R, and Pernthaler J (2004) Flow sorting of marine bacteria after fluorescence in situ hybridization. Applied and Environmental Microbiology 70: 6210–6219 Remark: No standard protocol is available, different methods for cell removal should be combined for different samples. 1. Mechanical cell removal A. Vortexing for 15 min at 2500 rpm. B. Sonication at maximum power for 30 min in a water bath. 2. Chemical removal. Try the following chemicals for up to 2 h at 37 1C with subsequent 10 min vortexing, depending on samples. A. 1 M Tris–HCl B. PBS (1  ) with 0.05% Triton X-100 C. 150 mM NaCl with 0.05% Tween 80 D. 0.05 M pyrophosphate E. 1 M KCl F. 0.1% Pluronic F-80 G. 0.1% sodium cholate 3. Counterstain cells with DAPI (1 mg ml1). 4. Analyze cells in a flow cytometer, for example, MoFlo flow cytometer (BeckmanCoulter) or FACScalibur (BD biosciences).

A good method proved to be the incubation with NaCl–Tween 80 for 30 min at 37 1C.

efficiencies of FISH are often a kinetic problem, and may be overcome by the alteration of the probe sequence, for example, by designing a longer oligonucleotide probe (Yilmaz and Noguera, 2004; Yilmaz et al., 2006).

3.08.4 Cell Counting One of the aims of FISH-based analyses of bacterial communities is exact quantification. The common method to achieve quantitative cell counts is either flow cytometry or manual cell counting using epifluorescence microscopes. Flow cytometry has successfully been applied to sort environmental samples and to determine the composition of the microbial communities (Sekar et al., 2004). However, this approach requires a relatively large amount of sample, and quantitative measurements are biased toward large cells with high ribosome content due to a lack of sensitivity. Generally, cells with high ribosome contents are preferred for sorting. Therefore, manual counting using epifluorescence microscopes is recommended, even though this method is time consuming and can make up a major part of the experimental time. The quantification of taxa is most often based on determining the ratio of total DAPI-stained cells to probe-stained cells. To reduce counting errors, at least 1000 DAPI-stained cells should be inspected for probe signal. Due to the low sample throughput and subjectivity of manual counting, a semi-automated counting method (Cottrell et al., 2006) and an automatic counting system for epifluorescence microscopes were developed for FISH-based quantification of microbial communities (Pernthaler et al., 2003). Additionally, a semi-automated technique for structural investigations was invented by Daims et al. 2001. Based on image analysis, the technique of Pernthaler allows a high sample throughput and thus increases efficiency of the counting (see also Schattenhofer et al., 2009). Until now, these techniques can only be applied to planktonic samples, since

more complex samples, for example, sediment samples, exceed the differentiation capability of the machine. Nevertheless, several improvements in automatic counting have recently been made by Zeder and Pernthaler (2009). Nested focusing in bright field and fluorescence illumination, continuous life-image acquisition during focusing, multiple spot focus measurements to assess quality and topology of the filter section, and the usage of z-stacks to compensate for unevenness of the filter surface are used to perform a more reliable auto-focusing process (Zeder and Pernthaler, 2009). Furthermore, an artificial neural network was implemented into the focus routine as a quality check.

3.08.5 From Cell Detection to Ecological Function From the numerous applications since the introduction of FISH into the field of microbial ecology 20 years ago (DeLong et al., 1989; Amann et al., 1990b), only some examples are mentioned in the following. One recent application of fluorescence-labeled oligonucleotides was the quantification of the probably most abundant bacterial and archaeal taxa on Earth. The alphaproteobacterium SAR11 constitutes up to 50% of the bacterial community of the open-ocean surface waters, while marine group I crenarchaeota makes up to 40% of deep ocean waters (Karner et al., 2001; Morris et al., 2002; Schattenhofer et al., 2009). In addition, taxa with lower frequencies of about 1 in 1000, but of high ecological importance, were discovered using FISH, for example, bacteria catalyzing the anaerobic ammonium oxidization (anammox) in the Black Sea (Kuypers et al., 2003). Furthermore, not only natural environments were investigated via FISH, but also anthropogenic environments such as wastewater treatment plants, where biofilm cells of the genus Nitrospira could be subdivided in two sublineages (Maixner et al., 2006). Drinking water, probably one of the biggest future concerns of humankind, can be monitored with

Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization

FISH methods, for example, Enterobacteriaceae were detected with direct viable counts combined with FISH and laser scanning cytometry (Baudart et al., 2005), Bacteroides of human feces could be detected in drinking water experiments (Savichtcheva et al., 2005) and Helicobacter pylori was detected in drinking water biofilms (Braganca et al., 2007). Furthermore, FISH was applied to various monitoring approaches in wastewater treatment, for example, in combination with MAR (Hagman et al., 2008), as a control in the development of new wastewater reactors (Lalbahadur et al., 2005), following earlier application of FISH in wastewater treatment plants (Manz et al., 1994). These examples show that identification of individual cells by FISH can be applied not only for basic research on bacterial community, but also on water quality monitoring, wastewater treatment, and a variety of more water-related issues. By the combination of FISH with other methods, different aspects of microbial activity can be analyzed for single cells in a spatially and phylogenetically resolved manner. Active DNA synthesis could be assigned to particular bacterial cells by the combination of CARD FISH with the immunodetection of bromodeoxyuridine-labeled nucleotides (Pernthaler et al., 2002). The amount of rRNA synthesis can be estimated from FISH-based measurements of the ratio of precursor to mature rRNA (Oerther et al., 2000). The cellspecific uptake of stable isotope or radioactively labeled substrates can be determined by, for example, MAR FISH (Lee et al., 1999), Raman FISH (Huang et al., 2007), or a combination of nano-secondary ion mass spectrometry and in situ hybridization (Behrens et al., 2008; Li et al., 2008; Musat et al., 2008). Finally, FISH is now used for in situ identification of bacteria in dental plaque (Gmu¨r and Lu¨thi-Schaller, 2007) thereby coming back to the habitat in which bacteria were discovered by Leuwenhook. All these methods allow further insight into ecological processes and the important functions of bacterial or archaeal taxa in the environment. New FISH protocols could in future provide even better possibilities for researchers in different fields of microbiology to gain information about bacterial identity and activity. In a time of massive environmental metagenome sequencing and ever-increasing sequence databases, there is an urgent need for further probe development and the continuous optimization of microscopic techniques, including high-throughput automatic counting methods.

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labeled oligonucleotide probes. Applied and Environmental Microbiology 66: 3603--3607. Fuchs BM, Syutsubo K, Ludwig W, and Amann RI (2001) In situ accessability of Escherichia coli 23S rRNA to fluorescently labeled oligonucleotide probes. Applied and Environmental Microbiology 67: 961--968. Fuchs BM, Wallner G, Beisker W, Schwippl I, Ludwig W, and Amann RI (1998) Flow cytometric analysis of the in situ accessibility of Escherichia coli 16S rRNA for fluorescently labeled olinucleotide probes. Applied and Environmental Microbiology 64: 4973--4982. Garrity G, Boone DR, and Castenholz RW (eds.) (2001) Bergey’s Manual of Systematic Bacteriology, 2nd edn., vol. 1. New York: Springer. Giovannoni SJ and Stingl U (2005) Molecular diversity and ecology of microbial plankton. Nature 437: 343--348. Glo¨ckner FO, Amann R, Alfreider A, et al. (1996) An in situ hybridization protocol for detection and identification of planktonic bacteria. Systematic and Applied Microbiology 19: 403--406. Glo¨ckner FO, Fuchs BM, and Amann RI (1999) Bacterioplankton composition of lakes and oceans: A first comparison based on fluorescence in situ hybridization. Applied and Environmental Microbiology 65: 3721--3726. Gmu¨r R and Lu¨thi-Schaller H (2007) A combined immunofluorescence and fluorescent in situ hybridization assay for single cell analyses of dental plaque microorganisms. Journal of Microbiological Methods 69: 402–405. Hagman M, Nielsen JL, Nielsen PH, and Jansen JL (2008) Mixed carbon sources for nitrate reduction in activated sludge-identification of bacteria and process activity studies. Water Research 42: 1539--1546. Hoshino T, Yilmaz LS, Noguera DR, Daims H, and Wagner M (2008) Quantification of target molecules needed to by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition-FISH. Applied and Environmental Microbiology 74: 5068--5077. Huang WE, Stoecker K, Griffiths R, et al. (2007) Raman-FISH: Combining stable isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function. Environmental Microbiology 9: 1878--1889. Jannasch HW and Jones GE (1959) Bacterial populations in sea water as determined by different methods of enumeration. Limnology and Oceanography 4: 128--139. Jeanthon C (2000) Molecular ecology of hydrothermal vent microbial communities. Antonie van Leeuwenhoek International Journal of General and Molecular Microbiology 77: 117--133. Karner MB, DeLong EF, and Karl DM (2001) Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409: 507--510. Kuypers MMM, Sliekers AO, Lavik G, et al. (2003) Anaerobic ammonium oxidation by anammox bacteria in the Black Sea. Nature 422: 608--611. Lalbahadur T, Pillay S, Rodda N, et al. (2005) Microbiological studies of an anaerobic baffled reactor: Microbial community characterisation and deactivation of healthrelated indicator bacteria. Water Science and Technology 51: 155--162. Lane DJ (1991) 16S/23S rRNA sequencing. In: Stackebrandt E and Goodfellow M (eds.) Nucleic Acid Techniques in Bacterial Systematics, pp. 115--175. Chichester: Wiley. Lee N, Nielsen PH, Andreasen KH, et al. (1999) Combination of fluorescent in situ hybridization and microautoradiography – a new tool for structure–function analyses in microbial ecology. Applied and Environmental Microbiology 65: 1289--1297. Li T, Wu TD, Mazeas L, et al. (2008) Simultaneous analysis of microbial identity and function using NanoSIMS. Environmental Microbiology 10: 580--588. Liu WT, Marsh TL, Cheng H, and Forney LJ (1997) Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Applied and Environmental Microbiology 63: 4516--4552. Llobet-Brossa E, Rossello´-Mora R, and Amann R (1998) Microbial community composition of Wadden Sea sediments as revealed by fluorescence in situ hybridization. Applied and Environmental Microbiology 64: 2691--2696. Loy A, Arnold R, Tischler P, Rattei T, Wagner M, and Horn M (2008) ProbeCheck – a central resource for evaluating oligonucleotide probe coverage and specificity. Environmental Microbiology 10: 2894--2896. Loy A, Horn M, and Wagner M (2003) ProbeBase: An online resource for rRNAtargeted oligonucleotide probes. Nucleic Acids Research 31: 514--516. Ludwig W, Strunk O, Westram R, et al. (2004) ARB: A software environment for sequence data. Nucleic Acids Research 32: 1363--1371. Maixner F, Noguera DR, Anneser B, et al. (2006) Nitrite concentration influences the population structure of Nitrospira-like bacteria. Environmental Microbiology 8: 1487--1495. Manz W, Amann R, Ludwig W, Vancanneyt M, and Schleifer KH (1996) Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria

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Relevant Websites http://www.arb-home.de ARB Project. http://www.microbial-ecology.net Department of Microbial Ecology: probeCheck. http://greengenes.lbl.gov Greengenes Project. http://rsbweb.nih.gov/ij ImageJ. http://www.ncbi.nlm.nih.gov NCBI; BLAST. http://www.probebase.net probeBase: Department of Microbial Ecology. http://rdp.cme.msu.edu Ribosomal Database Project. http://www.arb-silva.de SILVA: FISH.

3.09 Bioassays for Estrogenic and Androgenic Effects of Water Constituents K Kramer, Technische Universita¨t Mu¨nchen, Freising, Germany & 2011 Elsevier B.V. All rights reserved.

3.09.1 Introduction 3.09.2 In Vivo Bioeffect Assays 3.09.2.1 Classic In Vivo Assays at the Organism Level 3.09.2.1.1 Uterine weight of rodents 3.09.2.1.2 Sexual development of female rodents 3.09.2.1.3 Oviduct weight assay 3.09.2.1.4 Sexual differentiation of birds 3.09.2.1.5 Sexual differentiation of reptiles 3.09.2.2 Molecular Biomarkers 3.09.2.2.1 Methods to determine vtg 3.09.2.2.2 Quantitative PCR 3.09.2.2.3 Alkali-labile phosphate method 3.09.2.3 Gene Expression Analysis 3.09.2.3.1 Genome-wide DNA microarrays 3.09.2.3.2 Subset microarrays 3.09.3 In Vitro Assays at the Cellular Level 3.09.3.1 Cell Proliferation Assays 3.09.3.2 Vitellogenin Assays 3.09.3.2.1 Culture systems and cell types for in vitro vtg assays 3.09.3.2.2 Analytical considerations for vtg determination in cell cultures 3.09.3.2.3 Determination of vtg protein 3.09.3.2.4 Determination of vtg mRNA 3.09.3.3 Reporter Assays 3.09.3.3.1 ED-reporter assays based on nonestrogen hormone receptors 3.09.3.3.2 ED-reporter assays beyond transactivation 3.09.3.4 Yeast-Based Assays 3.09.3.4.1 Initial yeast estrogen screens 3.09.3.4.2 Optimization of the initial YES 3.09.3.4.3 Subtype YES 3.09.3.4.4 ER mutants 3.09.3.4.5 Extension of the YES principle 3.09.4 Subcellular Assays 3.09.4.1 ER Preparation 3.09.4.2 Enzyme-Linked Receptor Assay 3.09.4.3 Fluorescence Polarization Assays 3.09.4.4 Biosensors 3.09.5 Conclusions Acknowledgment References

3.09.1 Introduction Exposure of wildlife species to contaminants can permanently modify the development of the reproductive and endocrine systems. For example, alligators at the Lake Apopka, Florida, were exposed to a number of contaminants derived from agriculture and municipals as well as a major pesticide (dicofol and dichlorodiphenyltrichloroethane (DDT)) spill (Woodward et al., 1993; Guillette et al., 1995). Alligator eggs from Lake Apopka showed elevated levels of p,p0 - dichlorodiphenyldichloroethene (DDE), p,p0 -DDD, dieldrin, and

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various polychlorinated biphenyls (PCBs; Heinz et al., 1991). Hatchlings from these eggs exhibited abnormal gonadal anatomy (Guillette et al., 1995). Another prominent example is 17a-ethynylestradiol (EE2), which is widely used as a human contraceptive. This substance is also administered for the treatment of prostate and/or breast cancer (Kuster et al., 2004). In addition to compounds in legal use, several strictly banned estrogens (Directive 96/22/EC), for example, dienestrol and diethylstilbestrol, which are applied as growth promoters during fattening of cattle, have been reported to occur in river sediments (Kuster et al., 2004).

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Although environmental contaminants have been suspected to play a role in reproductive dysfunctions, developmental abnormalities, and cancer hazards to wildlife and humans (e.g., Colborn and Clement, 1992; Guillette et al., 1999; Segner, 2005), the linkage among these processes is still controversially discussed (Safe et al., 2002). Particularly, aquatic wildlife appears to be at risk by bioeffective contaminants, because watercourses constitute a natural sink of industrial chemicals, pesticides, or hormones excreted by humans, for example, the natural steroid hormone, 17b-estradiol (E2) (Ternes et al., 2002; Rutishauser et al., 2004). E2 has various physiological functions. In addition to its role in the reproductive system of women, this estrogen also plays various roles in cardiovascular health, bone integrity, cognition, and behavior. Considering the functional diversity of estrogen, it is not surprising that estrogen is also implicated in the development or progression of numerous disorders, including cancer (breast, ovarian, and prostate), neurodegenerative diseases (Alzheimer’s disease and Parkinson’s disease), cardiovascular disease, endometriosis, insulin resistance, obesity, and lupus erythematosus. These diseases can be divided into two groups: those believed to be caused by excess of estrogen, such as breast cancer (Yager and Davidson, 2006) and endometrial carcinoma (Deroo and Korach, 2006), and those diseases such as Alzheimer’s disease (Pinkerton and Henderson, 2005) that may be relieved by estrogen treatment. Natural hormones such as E2 act through the endocrine system, which essentially consists of cells producing and releasing the hormone into the bloodstream, through which it is transported to the corresponding target cells. The physiological response generated in the target cell due to a hormone is a composite reaction, which is followed by degradation and excretion from the body. Thus, the effective concentration refers to that amount of hormone capable of inducing a specific response in the target cell of an organism and, in the case of E2, is modified by binding to serum proteins such as albumin and sex hormone-binding globulin (SHBG). Albumin is a nonspecific binding protein with a low affinity and specificity for E2, whereas SHBG has a high affinity and specificity for E2. In order to accurately measure the estrogenicity of a certain compound, several factors must therefore be taken into account: (1) affinity of the compound for the estrogen receptor (ER), (2) accumulation of the compound in the environment and the body, (3) degradation or metabolism of the compound in the environment and body, and (4) the availability of the compound to the target cell (Arnold et al., 1996a). The evaluation of estrogenic compound concentrations is further hampered by the occurrence of two different ERs (ERa and ERb; Walter et al., 1985; Mosselman et al., 1996). Different tissues are characterized by a variation in the expression levels of the two ER isoforms (Couse et al., 1997), as well as in the levels of specific coactivators and corepressors. The latter are critical to elicit an estrogenic response (Shang and Brown, 2002). Mice lacking one or both ERs were created to define the receptor functions. ERa knockout mice turned out to be infertile (male and female). They were characterized by diminished bone density and a disturbed breast development. ERb knockout mice developed normally but females showed very reduced fertility due to defects in both the ovary and the uterus (Couse and Korach, 1999; Krege et al., 1998).

The human ER (hER) isoforms contain a highly conserved DNA-binding domain and an assimilable distinctive conservation of the ligand-binding domain (LBD). In addition to its ligand-binding activity, the LBD has dimerization activity and a ligand-dependent activation function-2 (AF-2; Katzenellenbogen, 1996). In the absence of ligand, the ER is associated with heatshock proteins (Hsps) and is transcriptionally inactive (cf. Figure 1; Smith and Toft, 1993). In the presence of ligand, the ER dissociates from the Hsps. This facilitates the homodimerization and binding to a regulatory DNA sequence, the estrogen response element (ERE; Figure 1). This ERE-bound, ligand-occupied ER complex can either activate or suppress the transcription of downstream target genes (e.g., vitellogenin (vtg) or zona radiata in fish; see Figure 1) in a cell- and promoter-specific manner (Fujimoto and Katzenellenbogen, 1994; Tsai and O’Malley,

Follicle cell Oocyte GtH

N E2

SbG

vtg/Zr Secretion Hepatocyte vtg/Zr

E2 ER Hsp

Hsp Hsp

Gene expression

Hsp E2

ERE ER ER E2 ER

ER

vtg and Zr gene N

Figure 1 Estrogen synthesis in oocytes and subsequent primary hormone effect on gene expression in hepatocyte of rainbow trout (Oncorhynchus mykiss). Explanation in the text. GtH, Gonadotropin; E2, 17b-estradiol; SbG, steroid-binding globuline; ER, estrogen receptor; Hsp, Heat shock protein; ERE, estrogen-responsive element; vtg, vitellogenin; Zr, zona radiata; N, nucleus. Adapted from Alberti MC (2006) Erfassung und Bewertung von Genexpressionsmustern von Zebraba¨rblingen (Danio rerio) nach Belastung mito¨strogenen Substanzen. PhD Thesis, Technische Universita¨t Mu¨nchen.

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

1994). If the complex recruits coactivators and induces the transcription of a downstream target gene, this biological function is generalized as the transactivation or transcriptional activity of the ER. However, it should be noted that in the absence of ligand, the ER could also form a homodimer and bind to the ERE (Zhuang et al., 1995). The molecular interaction of estrogenic compounds with components of the endocrine system constitutes the background of bioeffect assays, which are subsequently described. Particularly, cell-based assays are highlighted thereafter, because they have the potential to deliver rather complex information at reasonable economic means. These bioassays can essentially be used to identify whether discharges to the environment cause biological effects, such as endocrine interference. If the tests are positive, chemical evaluation should be utilized to identify the cause in an analytical chemistry/toxicity-identification evaluation exercise. This strategy enables the determination of the causative agents, and the results obtained by applying bioassays in the first stage are used to direct attention to the detailed chemical analysis of fractions until reasonable correlations are attained. The entire process can be repeated applying an additional fractionation procedure until the chemical complexity of the fractions is reduced sufficiently. Thus, nontarget chemical analysis enables the detection or identification of unknown compounds responsible for the observed effect (Petrovic et al., 2004).

3.09.2 In Vivo Bioeffect Assays Disruption of estrogen signaling is a prominent effect described for xenobiotic compounds, and this chapter focuses on bioeffect assays for the detection and characterization of endocrine disruptors (EDs), which act through interference with E2-mediated signaling components. However, due to the conserved mechanism of nuclear-receptor (NR) action, analogous assays have been already or can be established in a similar manner for the detection of compounds interfering with comparable hormone receptors (Gray et al., 1997, 2002; Vinggaard et al., 2002; Hartig et al., 2002). Numerous in vitro and in vivo assays have been suggested as screens for estrogenicity and their performance as well as correlation have critically been evaluated (e.g., Shelby et al., 1996; Zacharewski, 1997; Ashby, 1998; Combes, 2000; O’Connor et al., 2002; Charles, 2004; Scrimshaw and Lester, 2003). There exist various concepts of grouping these assays for the effect-based analysis of endocrine-disrupting compounds, depending on the individual point of view. In biological terms, these test systems can be distinguished by the corresponding organization level of the involved biological components: organism, tissue, cell, or subcellular elements. Therefore, the assays described subsequently are assigned accordingly.

3.09.2.1 Classic In Vivo Assays at the Organism Level Bioeffect assays at the organism level are performed to evaluate the impact of EDs on the endocrine system. In this context, multigeneration reproduction studies are considered as the ultimate test system for identifying adverse effects (O’Connor et al., 1998). However, it is unlikely that in vivo tests will, in the

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long term, be utilized for monitoring the environment. Since they are expensive and time consuming, they are not regarded as ideal tools for an effective screening of EDs in economical terms (O’Connor et al., 1998). In addition, using animals for these investigations will raise critical ethical questions and encounter limited acceptance in the public (ECETOC, 1999). Despite this, in vivo studies are perfectly suited to validate in vitro techniques. Considering a reasonable range of species enables us to define different endpoints in order to obtain a comprehensive assessment of ED effects (Hoss et al., 2001). Subsequently, a few examples of well-established in vivo assays are briefly mentioned. This is followed by a more exhaustive description of in vivo assays using biomarkers and gene-expression profiling.

3.09.2.1.1 Uterine weight of rodents The rodent uterotrophic assay has, to date, been the most widely used in vivo assay. This test is considered to be the gold standard of estrogenicity (Korach and McLachlan, 1995; Gray, 1998). It is based on the ability of chemicals to stimulate uterine growth (Shelby et al., 1996; Beresford et al., 2000; Odum et al., 1997). In the classical uterotrophic assay, the test compound is administered with the diet to ovariectomized or immature female rats or mice, typically during several days (Buelbring and Burn, 1935; Kuch and Ballschmiter, 1999). Hormonally active compounds induce cell proliferation in the uterine mucous membrane. After explantation, the uterine weight is measured. The hormone effect of the test compound is determined by direct comparison to control animals treated with and without natural estrogens such as E2. The lowest observed effect dose (LOED) of this assay was determined at 104 mg E2 kg1 body weight. Significant uterine growth was observed on a daily dose of 0.4 mg kg1 body weight of E2, whereas phytoestrogen coumesterol was effective at 20 mg kg1 body weight per day (Baker et al., 1999). However, the results of the uterotrophic assay are dependent on various parameters such as animal species, procedure, and intervals of administration. Various estrogenic compounds such as o,p0 DDT, methoxychlor, chlordecone, and PCBs were identified using this in vivo assay (Guelden et al., 1998). Although the proliferative effect of natural estrogens on the female genital tract (e.g., the vaginal cornification) is considered as a reliable indicator of estrogenicity, this test is essentially not amenable for large-scale screening.

3.09.2.1.2 Sexual development of female rodents The administration of hormonally active compounds during the neonatal phase results in female rodents in premature sexual development, interference of the reproduction cycle (persistent vaginal estrus syndrome), and damage of the ovaries (Guelden et al., 1998). In addition, functional changes of the hypothalamus and the hypophyse were observed for rodents treated with o,p0 -DDT and chlordecone (Gellert et al., 1974; Gellert, 1978). These physiological alterations are considered as effect-related endpoints.

3.09.2.1.3 Oviduct weight assay This test is performed by feeding juvenile chicks up to several weeks, followed by the determination of the oviduct weight

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(Dorfman and Dorfman, 1953). Based on the administered dose and test duration, the weight can increase up to 100-fold as compared to untreated controls. Besides the enhanced cell proliferation, concentrations of glycogen, ovalbumin, and conalbumin are raised in the ovaries. The estrogenic effect of the insecticide DDT using this assay was investigated (Bitman et al. (1968)) as early as in the year 1968. A variant of this initial assay focuses on various anatomical effects using an in ovo exposure strategy (Biau et al., 2007). Morphological defects in the urogenital system of the developing chick embryo were observed for estrone and estriol, whereas ethynylestradiol revealed fewer effects. Estriol caused persistence of Mu¨llerian ducts in male embryos and hypertrophic oviducts of females. Estrone exerted comparable effects though at a lower rate. Kidney dysfunction was exclusively observed with estrone, in both males and females.

3.09.2.1.4 Sexual differentiation of birds The left ovary together with the accordant oviduct is usually completely developed in female birds, whereas the corresponding right organs degenerate during the embryonic phase. Male birds lack these organs since the Mu¨llerian ducts from which these are developed, become degenerate on both sides. However, on exposure to o,p0 -DDT, p,p0 -DDT, and methoxychlor, feminization of male embryos of the seagull can be observed (Fry and Toone, 1981). The feminization results in degeneration or absence of the right testicle and the development of oviducts, of which the one on the left side is well developed.

3.09.2.1.5 Sexual differentiation of reptiles The gender development of many reptiles such as tortoise, lizards, and alligators is not genetically determined, but depends on the incubation temperature of the eggs. For example, the tortoise Trachemys scripta develops at a temperature of 32 1C into female, and at 26 1C into male progeny. If estrogens are present in the egg shell during sex determination, ovaries are developed even at lower temperatures. This effect on eggs incubated at lower temperatures also applies for antiestrogens (tamoxifen), antiandrogens (triphenylethylene), polychlorinated biphenyls, and other xenobiotics (Wibbels and Crews, 1992; Bergeron et al. 1994).

3.09.2.2 Molecular Biomarkers Biomarkers indicating exposure to pollutants and their effects are increasingly applied to assess the quality of ecosystems. Biomarkers can be defined as measurements of body fluids, cells, or tissues that indicate in biochemical or cellular terms the presence of contaminants or the magnitude of the host response (Livingstone et al., 2000). A broad spectrum of potential biomarkers was applied to study endocrine disruption. In aquatic organisms, these included changes in hormone titers (steroid hormones and thyroid hormones), abnormal gonad development (cf. above), and alterations in distinct enzyme activities (i.e., aromatases) and protein levels such as vtg, zona radiata proteins, and spiggin (Matthiessen, 2003; Kleinkauf et al., 2004). In fish, one of the most

frequently used biomarkers for screening estrogenic activity of chemicals and environmental samples includes the induction of vtg synthesis (e.g., Kime et al., 1999; Tyler et al., 1999). In egg-laying vertebrates, estrogens activate the hepatic synthesis of vtg, a calcium-containing glycolipophosphoprotein (Wallace, 1985; Arukwe and Goksøyr, 2003). Synthesized in the liver, vtg is transported via the bloodstream to the ovary where it is incorporated and sequestered by the maturing oocyte (Tyler et al., 1988; Specker and Sullivan, 1994). The vtg synthesis is generally restricted to females, while males contain no measurable or very low vtg levels in their plasma (e.g., Silversand et al., 1993; Tyler et al., 1996, 1999). However, the hepatic production of vtg can be induced in male fish by exposure to exogenous estrogens (Mommsen and Walsh, 1988). Thus, the level of vtg in male fish is indicative of exposure to estrogenic compounds. For this reason, vtg induction in fish has become an accepted indicator of exposure to estrogenic substances. The induction of this biomarker in intact fish has been utilized for chemical screening and environmental monitoring (Thorpe et al., 2003; Burki et al., 2006). The induction of vtg can be monitored in shortterm in vivo tests with fish, or, alternatively, in isolated fish hepatocytes (Navas and Segner, 2006).

3.09.2.2.1 Methods to determine vtg In fish in vivo, vtg is usually measured as circulating protein in the plasma, or as vtg mRNA in the liver. For vtg protein quantification, biochemical methods such as determination of phosphoprotein phosphorus (Craik and Harvey, 1984) or alkali-labile phosphoprotein (cf. below; Pelissero et al., 1991) have been used. Further tests include immunochemical methods such as radioimmunoassay (RIA; Jobling et al., 1998), enzyme-linked immunosorbent assay (ELISA; Bon et al., 1997; Marx et al., 2001), and Western blotting (Sole´ et al., 2000). These immunochemical tests are based on the selective binding of antibodies to the biomarker. The vtg mRNA can be determined semiquantitatively by reverse transcriptase-polymerase chain reaction (RT-PCR, Ren et al., 1996), real-time PCR (cf. below; Celius et al., 2000; Alberti et al., 2005; Burki et al. 2006), microarray technology (cf. below; Hoyt et al., 2003), or RNAse protection/dot-blot assays (Islinger et al., 1999; Navas and Segner, 2006). In order to define the effect of EDs on biomarkers such as vtg synthesis, the test organism is typically exposed to the chemical, ideally at strictly controlled conditions. In our group, the zebra fish Danio rerio was used as model organism to evaluate the bioeffects of estrogenic substances. The exposition was performed in a flow-through system. The experimental setup was as shown in Figure 2. Up to 12 male zebra fish were exposed to estrogenic compounds for 11 days in each of the tanks. Each experiment included one fish tank containing 30 ng l1 EE2 as positive and four basins without any estrogenic compound as negative controls. Four female fish were kept in an additional tank lacking any estrogenic compounds for calibration purposes. Following an exposure period of 11 days, the zebra fish were analyzed according to the methods outlined next.

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1 6 4 2

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(b)

Figure 2 Experimental setup of the exposition system for zebra fish. (a) Schematic drawing 1: freshwater inlet with filter; 2: overfall; 3: supply basin (500 l); 4: peristaltic pump for water supply; 5: peristaltic pump for dosing the test compound; 6: prepared test solutions; 7: exposition basin (20 l), 8: outlet via peristaltic pump and filter. (b) Exposition lab for zebra fish experiments showing exposition basins and peristalting pumps according to (a). Adapted from Alberti MC (2006) Erfassung und Bewertung von Genexpressionsmustern von Zebraba¨rblingen (Danio rerio) nach Belastung mit o¨strogenen Substanzen. PhD Thesis, Technische Universita¨t Mu¨nchen.

3.09.2.2.2 Quantitative PCR Quantitative PCR (qPCR) can be used to determine the effective concentrations of estrogenic compounds. Following exposition in the flow-through tank system described above, liver tissues of the exposed animals were dissected. After RNA extraction and reverse transcription into complementary DNA (cDNA), qPCR was performed using specific primers, which enabled the selective amplification of target and control genes. Fluorescence curves of a typical qPCR are shown in Figure 3. The crossing point constitutes the crucial value of these fluorescence curves. It describes the cycle number, at which the fluorescence signal gains exponentially in strength. Increasing template cDNA in a sample leads to a decreasing crossing point and vice versa. Thus, the expression level of vtg can be deduced, if a calibrator (e.g., nonexposed female zebra fish) is included in the study. The relative expression level is determined by the ratio of the crossing points of the target gene vtg and a reference gene (e.g., b-actin). Positive controls (nos. 3 and 5), which were exposed to EE2, are characterized by lower crossing points as the corresponding curves at 5000 mg l1 genistein (nos. 4 and 6). Negative controls (nos. 7 and 8) did not evoke any fluorescence signal. The calibrator curves

(nos. 1 and 2) derived from exposed female fish were used for normalization of the different qPCR runs.

3.09.2.2.3 Alkali-labile phosphate method Most of the biomarker studies mentioned above have been used in vertebrates such as rodents or fish. However, there exists an increasing interest in studying ED effects on invertebrate species, such as bivalve mollusks (Porte et al., 2006). The latter are used worldwide in biomonitoring programs (Matozzo et al., 2008). There are few specific antibodies developed against bivalve vtg-like molecules for quantitative immunochemical methods (Li et al., 1998; Kang et al., 2003; Osada et al., 2003). Therefore, indirect methods are used to study ED-triggered alterations, such as increase in RNA contents, lipid deposition, glycogen depletion, increase in protein levels, calcium, magnesium, and phosphoproteins contents (Verslycke et al., 2002; Arukwe and Goksøyr, 2003; Marin and Matozzo, 2004). Among these methods, the measurement of phosphoproteins by the alkali-labile phosphate (ALP) method has been widely used in different aquatic organisms such as fish and mollusks (Kramer et al., 1998; Verslycke et al., 2002).

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Bioassays for Estrogenic and Androgenic Effects of Water Constituents 18 16

Relative fluorescence

14 1 2 3 4 5 6

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No. of cycles Figure 3 Fluorescence curves of a typical qPCR applying specific primers for vitellogenin 1 and b-actin1 (reference gene). Samples are derived from exposition of zebra fish with genistein. The experiment included 32 liver samples from exposition with genistein or EE2 (as positive control). Subsequently, experimental conditions eight curves are detailed. 1: vtg1-calibrator for comparison of different qPCR reactions; 2: X -actin1-calibrator for comparison of different qPCR reactions; 3: positive control, exposed with 30 ng l1 EE2, vtg1-primer; 4: sample, exposed with 5000 mg l1 genistein, vtg1-primer; 5: positive control, exposed with 30 ng l1 EE2, b-actin1-primer; 6: sample, exposed with 5000 mg l1 genistein, b-actin1-primer; 7: negative control, no sample added, vtg1-primer; 8: negative control, no sample needed, b-actin1-primer. Adapted from Alberti MC (2006) Erfassung und Bewertung von Genexpressionsmustern von Zebraba¨rblingen (Danio rerio) nach Belastung mit o¨strogenen Substanzen. PhD Thesis. Technische Universita¨t Mu¨nchen.

In fish, ALP levels have been shown to correlate with vtg levels determined by specific immunochemical and gene-expression techniques (Versonnen et al., 2004; Robinson et al., 2004). For example, a significant positive correlation was found between vtg levels measured by specific ELISA and ALP methods during an international intercalibration study using adult male zebra fish, which were exposed to E2 for 2–9 days (Porcher, 2003). However, the ELISA turned out to be more sensitive than the ALP technique (Ortiz-Zarragoitia and Cajaraville, 2005).

3.09.2.3 Gene Expression Analysis The modification of the gene-expression pattern in particular cell types is considered as one of the typical responses of an organism as either direct or indirect response to toxicant exposure (Nuwaysir et al., 1999; Steiner and Anderson, 2000). Some chemicals elicit toxic responses by initially damaging cellular components. Target cells typically respond by trying to repair the damage or to adapt to the injury, in part, through altering expression of appropriate repair genes. Other toxicants that modulate the endocrine system or cellular replication affect toxic responses directly by triggering signal transduction systems, leading to altered gene expression. It has been hypothesized that the spectrum of altered gene expression then determines the type and outcome of the toxic response. Thus, an approach to assess the toxicity of a given compound stems from the identification of gene-expression patterns elicited in a tissue or organ exposed to particular classes of chemicals. As gene and microarrays facilitate the quantitative analysis of thousands of gene-expression changes in a single experiment, transcript profiling can be used as a tool to predict toxic outcomes of exposure to particular chemicals (Naciff and Daston, 2004).

3.09.2.3.1 Genome-wide DNA microarrays A generic strategy was developed by Naciff and Daston (2004) to define a transcript signature, which is characteristic for chemicals with estrogenic activity. For this purpose, an oligonucleotide-based microarray was applied in standardized in vivo test systems, using the developing rat reproductive system (uterus and ovaries), at two life stages: fetal and prepubertal. For the evaluation of fetal and prepupertal response to EDs, the gene-expression profile induced by graded dosages of EE2, genistein, and bisphenol A (BPA) was evaluated, in order to establish dose–response relationships (McLachlan and Newbold, 1987; Diel et al., 2000). The gene-expression profiles were compared between treatment groups and controls (vehicle-treated animals), using a rat genome chip, which allows the evaluation of approximately 7000 rat-annotated genes and over 1740 expressed sequence tags. Scanned output files resulted in a signal value, corresponding to the level of expression for each transcript represented in the microarray, which was used to calculate the average fold change (Lockhart et al., 1996; Naciff et al., 2002). The exposure of prepubertal female rats to EE2 resulted in the expression modulation of more than 500 genes (out of 8740 evaluated genes) from the uterus and ovaries, compared with fetal exposure to the same chemical (Naciff et al., 2003). However, this response was only apparent at relatively high doses (1 and 10 mg EE2 kg1 d1). Over 45% of the genes regulated by estrogenic compounds (EE2, BPA, and genistein) in the fetal uterus were regulated, in the same direction by EE2 in the pubertal uterus/ovaries. This suggests that the exposure of female rats during fetal or prepubertal development to chemicals with estrogenic activity results in the alteration of gene expression in a developmental-stage-specific manner (Naciff and Daston, 2004; Naciff et al., 2002, 2003).

Bioassays for Estrogenic and Androgenic Effects of Water Constituents 3.09.2.3.2 Subset microarrays DNA microarray chips, which represent the entire genome of an organism, are perfectly suited to identify all ED-regulated genes (cf. above). However, once revealed, the array can be reduced to this subset of ED-responsive genes. The subset microarrays can be precisely tailored to meet individual experimental requirements in terms of number of replicates on a single chip, arrangement of individual genes, economic assay design, etc. An example for such arrays is presented in Figure 4. Yellow color indicates equal gene expression in both, exposed and control animals, whereas upregulated gene expression upon exposition results in red spots. Green color reveals downregulation. As majority of spots in the subset array on the left side of the depicted example in Figure 4 are yellow, the exposition with 1 mg l1 genistein did not evidently alter the expression levels compared to the control animal. However, following exposition with 5000 mg l1 genistein increases the number of red spots, which are indicative for upregulated genes in exposed animals (Figure 4, right). Bright red spots are located at position B7 (hemopexin, hpx), C2 (mesoderm posterior b, mespb), C10 (zona pellucida glycoprotein 2.4, zp2.4), D10 (zona pellucida glycoprotein 2.2, zp2.2), D12 (zona pellucida glycoprotein 3b, zp3b), and H12 (zgc:114012, vtg5) (Alberti, 2006).

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modulating factors. The interplay between ligand-bound ER and these factors determines the response depending on the target cell. In vitro assays are tests for the identification of potentially endocrine-active compounds, but cannot, therefore, be solely used to deduce the risk of endocrine-related adverse effects. Furthermore, as most synthetic EDs exert activity only at high doses, non-ED-related effects have to be considered carefully to assess the actual potential of EDs to affect human health. The assays subsequently described allow a ranking of a series of compounds that could be used to prioritize compounds for studies, which enable the detection of reproductive and developmental toxicities in vivo (Mueller, 2004). The most frequently used cellular assays to screen chemicals and environmental samples for (anti)estrogenic activity in ecotoxicology include cell proliferation assays, particularly the MCF-7-based E-screen (e.g., Soto and Sonnenschein, 1985; Blom et al., 1998) and transactivation or reporter gene assays with fish, mammalian, or yeast cells (e.g., Ackermann et al., 2002; Giesy et al., 2002; Hornung et al., 2003). In addition, integrative assessment approaches are based on ER-mediated vtg synthesis induced in isolated hepatocytes of rainbow trout and quantified in nonradioactive dot-blot/RNAse protection assay in parallel to comprehensive chemical analyses of estrogenic substances (Holllert et al., 2005).

3.09.3 In Vitro Assays at the Cellular Level Even stripped down to the cellular level, EDs interfere with the complex network of ER signal transduction and modulate coupled cell signaling pathways. The physiological effect is therefore not solely defined by the ligand structure, but rather by the interaction of the receptor–ligand complex with various

3.09.3.1 Cell Proliferation Assays Predominantly based on human cell lines, proliferation techniques utilize a number of endpoints to measure the cell proliferation induced by exposure to estrogenic compounds. In the presence of estrogen, particular cells are stimulated

Figure 4 Subset microarrays showing different samples of exposed male zebra fish. (Left) Hybridization with Cy3-labeled (green) sample of a male control and a Cy5-labeled (red) sample of a male zebra fish, which was exposed to 1 mg l1 genistein. (Right) Hybridization with Cy3-labeled sample of a male control and a Cy5-labeled sample of a male zebra fish, which was exposed to 5000 mg l1 genistein. Adapted from Alberti MC (2006) Erfassung und Bewertung von Genexpressionsmustern von Zebraba¨rblingen (Danio rerio) nach Belastung mito¨strogenen Substanzen. PhD Thesis, Technische Universita¨t Mu¨nchen.

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besides the enhanced proliferation for the synthesis of, for example, enzymes, growth factors, and progesterone receptors (PRs). Commonly used cells are estrogen-responsive MCF-7 or T47D human breast cancer cells and ZR-75 cells (White et al., 1994). The E-screen assay developed by Soto et al. (1992) is based on the increased growth of MCF-7 cells in the presence of estrogens (Soto et al., 1995). When a range of concentrations is tested, the method can differentiate between agonists, partial agonists, and inactive compounds (Korach and McLachlan, 1995). The E-screen assay compares cell yields in both, positive and negative controls with those from samples exposed to test compounds. Later on, the endpoint of the E-screen was modified to utilize a colorimetric endpoint, which was claimed to be faster and easier to perform than cell counting (Korner et al., 1998). A range of other means for quantifying cell growth has also been reported with the alamarblue and [3H]-thymidine-incorporation assays exhibiting enhanced sensitivity than cell counting as well as DNA and methylthiazoletetrazolium (MTT) assays (Desaulniers et al. 1998). Experimental protocols for proliferation of MCF-7 cells (and other estrogen-responsive lines) require that the media used for growth are stripped of steroids with dextran charcoal. The proliferative effect on estrogen-sensitive MCF-7 cell lines is inhibited, if they are treated in this manner. This inhibition can be compensated by addition of estrogenic compounds. Therefore, the E-screen does not determine the stimulation of cellular proliferation, rather than the reduction of the inhibitory effect on cell proliferation. The estrogenic response is calculated as relative proliferative effect (RPE) units, which corresponds to the maximum induced proliferation induced by the test compound compared to E2 (Koerner et al., 1999). This enables the discrimination between a potent agonist (RPE ¼ 100%) and a partial agonist (RPEo100%). In the past, several MCF-7 sublines were developed by cloning and by exposition to different selection conditions, for example, variation of culture media. As a consequence, these individual sublines display altered sensitivity toward estrogens. This has to be taken into account, if results obtained by E-screen techniques are compared, which are derived from different laboratories. Some of the developed cell lines even proliferate in estrogen-free media (Katzenellenbogen et al., 1987; Odum et al., 1998). This aspect essentially hampers standardization and validation of the method for the assessment of estrogenic compounds. Other disadvantages associated with cell-proliferation assays are due to the fact that mammalian cells exhibit tissue-specific differences in the expression of receptor subtypes (O’Connor et al., 1998; Scrimshaw and Lester, 2003).

3.09.3.2 Vitellogenin Assays The vtg response can be measured in isolated fish hepatocytes in a similar manner as described above for the analysis at the organism level. The application of this kind of bioassay has been suggested as an in vitro screen for identifying estrogenactive substances (Pelissero et al., 1993; Segner and Braunbeck, 2003). Compared to the corresponding in vivo assays, the benefit of the in vitro assay is that it is less laborious and more

cost efficient. Compared to other in vitro screening assays for estrogic compounds, for example, the recombinant yeast ER assay (cf. below), the hepatocyte vtg assay offers the potential to comprehensively detect effects of estrogenic metabolites, because the hepatocytes are metabolically competent and enable even the detection of antiestrogenic effects (Navas and Segner, 2006).

3.09.3.2.1 Culture systems and cell types for in vitro vtg assays One of the first reports using isolated liver cells of fish to study vtg synthesis was published by the group of Yves Valotaire and Farzad Pakdel (Maitre et al., 1986). While these first studies were focused on the physiological action of E2, it was in 1993 when Pelissero et al. (1993) as well as Jobling and Sumpter (1993) applied liver cell cultures of fish for assessing the estrogenic potency of phytoestrogens and xenoestrogens. Generally, the impact of various parameters, such as serum, medium constituents, or temperature, has to be generally considered for all kinds of cellular assays. These factors may significantly affect the endpoints of a cell assay, such as ED-induced alterations of vtg levels (Islinger et al., 1999; Pawlowski et al., 2000; Navas and Segner, 2006). The majority of published studies on vtg induction in isolated fish liver cells used monolayer culture systems (e.g., Kwon et al., 1993; Peyon et al., 1998; Navas and Segner, 2000). As an alternative to monolayer cultures, three-dimensional aggregate cultures of fish hepatocytes have been developed (Flouriot et al., 1993; Latonnelle et al., 2000). In these aggregates, the hepatocytes are arranged in a three-dimensional structure, similar to the arrangement characteristic for functional tissues in vivo. Flouriot et al. (1993) showed that for identical culture conditions including identical culture medium, the mRNA levels of vtg and ER in the aggregates were significantly higher than those observed in monolayer cultures. Moreover, the longevity and estrogen responsiveness of the liver cells were extended in the aggregates. Whereas in hepatocyte monolayers the level of ER mRNA rapidly decreased after 1 week of continuous culture, three-dimensional cultures maintained uncompromised functional vtg synthesis and secretion over a period of 30 days (Flouriot et al., 1993). Alternatively, an in vitro system can be based on liver slices (Schmieder et al., 2000; Shilling and Williams, 2000). Livertissue slices essentially benefit from the naturally preserved tissue organization. Tissue slices are not restricted to hepatocytes solely (as typically used in monolayer-based assays; Navas and Segner, 2006). They complement the basic function of hepatocytes with the entire spectrum of specialized cell types, which are necessary to sustain tissue homeostasis and therefore warrant a comparably uncompromised organ function and metabolism. Finally, one factor, which has a pronounced influence on basal and hormone-inducible vtg synthesis of isolated fish hepatocytes, is the physiological status and the endocrine history of the donor animal. The production of vtg varies between male and female hepatocytes (e.g., Smeets et al., 1999). Whereas liver cells isolated from male fish usually show no measurable vtg expression in hormone-free media, hepatocytes isolated from maturing females produce vtg

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

mRNA and protein without extracellular estrogenic stimulation. For instance, Pelissero et al. (1993) found no basal production of vtg protein in hepatocytes isolated from male rainbow trout or immature females. However, liver cells obtained from maturing females released vtg protein into the culture medium (Smeets et al., 1999; Navas and Segner, 2006). In this context, it is of crucial relevance whether the genderspecific difference of basal vtg production influences the sensitivity of the hepatocytes to estrogen treatments. As vtg synthesis essentially depends on the expression of the ER, hepatocytes of female origin, which have a higher constitutive expression of ER, may respond faster or be more sensitive to estrogen treatment than male hepatocytes. In line with this consideration, Riley et al. (2004) found that 107 M E2 was sufficient to significantly induce vtg production in hepatocytes isolated from female tilapia, while a significantly higher concentration of 104 M E2 was required in male hepatocytes. Consequently, the elevated basal secretion of vtg by female hepatocytes may obscure weak induction responses. For instance, Kordes et al. (2002) described that while 109 M EE2 were able to induce vtg secretion in liver cells isolated from male medaka (Oryzias latipes), this concentration did not elevate vtg secretion in hepatocytes isolated from mature females. Furthermore, Smeets et al. (1999) observed that the dose–response relationships for E2 were similar in hepatocytes from male and female carp. Despite this, female hepatocytes reached higher absolute levels of vtg than male hepatocytes. From these results, the authors concluded that the ER level in the hepatocytes influences only the absolute magnitude of vtg production but not the relative sensitivity for estrogens. Nevertheless, they suggested to use hepatocytes of male origin when assessing estrogenic potencies of chemicals, mainly because the induction of vtg can be detected more easily and with a higher induction factor in male hepatocytes, which show no or minor basal synthesis of vtg (Navas and Segner, 2006). The role of species/donor fish differences on the vtg response has been discussed by Latonnelle et al. (2000), who showed that rainbow trout hepatocytes cultured as aggregates and exposed to E2 after 4 days of culture (the minimal time necessary for the formation of aggregates) required 2 days to synthesize measurable levels of vtg. Sturgeon hepatocytes cultured under the same conditions required 6 days of E2 treatment until detectable levels of vtg were produced. It is tempting to ascribe this variation to a species difference. However, knowing that the rainbow trout hepatocytes originated from fish at the onset of the reproductive cycle, while the sturgeon hepatocytes were isolated from immature fish, it is more likely that a difference in the physiological status of the donor fish explains the observed variation in the lag time of vtg induction (Navas and Segner, 2006).

3.09.3.2.2 Analytical considerations for vtg determination in cell cultures The majority of studies investigating vtg induction in cultured fish hepatocytes used the natural estrogen E2 as a positive reference. In all publications, increasing vtg production has been reported with concomitantly raised concentrations of E2. Nevertheless, there seems to exist a pronounced variability

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concerning the minimal concentration of E2 eliciting a vtg production, which is significantly different from that detected in controls. Minimal effective E2 concentrations vary at a rather wide range from 1012 to 106 M. Reported EC50 values range from 1011 to 106 M E2, with a concentration centered around a concentration of approximately 109 M E2. The lag time between the time point when estrogen treatment starts and when vtg production becomes detectable is variable and may be influenced by estrogen concentration, culture conditions, analytical methods, physiological status of the donor fish, and species differences. Lower estrogen concentrations appear to require longer incubation periods when compared to higher estrogen concentrations (e.g., Peyon et al., 1993; Latonnelle et al., 2000; Okoumassoun et al., 2002). Several authors addressed the problem of establishing significant concentration–response curves of vtg induction with xenoestrogens in the hepatocyte assay (e.g., Islinger et al., 1999; Toomey et al., 1999). Xenoestrogens typically have low estrogenic potencies, and thus relatively high concentrations (107 to 104 M range, see above) have to be applied in order to induce a vtg response in cultured fish hepatocytes. Above a critical level, these high concentrations can exert cytotoxic reactions, which in turn result in decreased vtg synthesis (Navas and Segner, 2006). Furthermore, the cellular metabolism of the test compounds affects the time- and concentration-dependent response of hepatocytes to estrogens. Cultured fish hepatocytes maintain biotransformation capabilities (Segner and Cravedi, 2001) and metabolize E2 rather rapidly. It has been shown for several species (Peyon et al., 1998; Schmieder et al., 2000) that the half-life of E2 in in vitro liver preparations of fish is limited to approximately 0.5–2 h. The biotransformation of xenoestrogens by cultured fish hepatocytes has been demonstrated accordingly (Cravedi and Zalko, 2005). Thus, differences between test substances or between species in their rate of metabolic turnover can influence estrogenic potencies (e.g., Lindholst et al., 2003). Metabolism of test agents is of particular relevance for the detection of estrogen precursors, which become estrogenic by cellular metabolism. Contrary to other in vitro screens for estrogenic activity, such as ER-binding assays, cultured hepatocytes enable the detection of these estrogen precursors. A few studies have addressed the capability of cultured hepatocytes to detect estrogenicity resulting from xenobiotic metabolism. Petit et al. (1997) tested 32 substances in a recombinant yeast assay containing the rainbow trout ER as well as in cultured trout hepatocytes. They found that 30% of the substances were estrogenic exclusively in one of the two test systems, and hypothesized that this difference is due to metabolic differences between the two test systems. A case study was provided for nonylphenol (NP) derivatives. In the recombinant yeast system, 4-nonylphenol exhibited pronounced estrogenicity, while the estrogenic effect of nonylphenol ethoxylates decreased with the increasing length of the ethoxylate substituent. This decrease in estrogenic activity was associated with a strong decrease in binding affinity to the ER. As measured in a receptor-binding assay, the binding affinities of NP, NP-diethoxylate, and NP-heptaethoxylate were 270, 6700, and 47000 times, respectively, lower than that of E2 (Petit et al., 1997). In the hepatocyte assay, however, NPdiethoxylate showed an eightfold higher estrogenic potency

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than NP, suggesting that NP-diethoxylate with low ER affinity was metabolized into NP with higher ER affinity (Navas and Segner, 2006).

3.09.3.2.3 Determination of vtg protein In order to measure vtg in cultured cells, in principle, the same methods are applicable as employed for the analysis of vtg in vivo. ELISA was the analytical method of choice in most of the studies published to date. By this means, vtg protein can be measured either as secreted protein in the cell-culture medium or as intracellular protein in the cells (following homogenization). The ELISA procedure is an immunochemical technique based on detecting and quantifying antigens by selective antibodies. It can generate absolute (using a vtg standard curve) or relative values (without a standard curve, measuring only relative optical density readings). Comprehensive overviews are given, for example, by Nilsen et al. (2004).

3.09.3.2.4 Determination of vtg mRNA Alternatively to the immunochemical detection of vtg protein in hepatocyte culture supernatants, the mRNA level of transcribed vtg genes can be determined. The assay principle is similar to the methods applied for the corresponding analysis at the organism level (cf. above). Since the number of fish species for which vtg sequences are available is steadily expanding, mRNA-directed techniques can be applied for a broad range of different species. Islinger et al. (1999) introduced a dot-blot/RNAse protection assay employing digoxigenin-labeled RNA transcripts. Another assay is based on RTPCR (cf. above) as developed by Pawlowski et al. (2000) in isolated rainbow trout hepatocytes. In this method, the level of vtg mRNA is determined densitometrically. This enables at least a semiquantitative analysis of vtg mRNA levels. The amount of target mRNA is then typically calibrated by the mRNA of a housekeeping gene, for example, b-actin. The choice of a housekeeping gene can be crucial since its expression level may be not as constant as assumed and may

ER ER gene

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vary with culture conditions and treatments. The use of RTPCR typically improves the sensitivity of the assay, and thus enables the detection of vtg gene transcription at lower concentrations of xenoestrogens as compared to ELISA. In contrast to semiquantitative RT-PCR, properly designed real-time RT-PCR experiments (cf. above) allow a quantitative analysis of vtg mRNA levels, either by means of relative quantitation of vtg mRNA using the threshold approach (Funkenstein et al., 2004) or by means of absolute quantitation calibrated by a standard curve. The problem of calibrating the target mRNA against housekeeping genes, however, remains as in other mRNA techniques (Navas and Segner, 2006).

3.09.3.3 Reporter Assays Reporter assays as ED screening methods are based on the fact that steroid receptors are transcription factors, which induce expression of target genes after binding to specific DNA sequences in their promoter region. Transient transactivation assays, in which cells are transfected with the cDNA for ER and a reporter gene containing an ERE or an estrogen-responsive promoter, are widely used to measure ligand-induced ERmediated gene activation (Shelby et al., 1996). In this assay, yeast or mammalian cell lines lacking endogenous ER are transiently transfected with an expression plasmid carrying the gene encoding ERa, ERb, or any desired receptor variant along with an ER-responsive promoter or ERE linked to a chloramphenicol acetyltransferase (CAT) or luciferase reporter gene (cf. Figure 5). ER ligands induce a dose-dependent transcription of the reporter protein and can easily be monitored. Due to the high sensitivity of the available luciferase reporter vectors, very weak to highly potent estrogens can be analyzed. Furthermore, single compounds and chemical mixtures can be analyzed depending on ERE, ER-subtype, and cellular context for their estrogenicity and antiestrogenicity. This versatility is especially important if the tissue-specific estrogenic/antiestrogenic activity of the so-called selective ER modulators (SERMs), such as tamoxifen and raloxifene, is considered

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Figure 5 General principle of reporter-gene assays based on ERE-activation by the presence of E2 or ED in the sample. ER genes can be endogenously expressed or provided by transient or stable transfection. ERE reporter fusions are located on a separate plasmid. Popular reporter genes are, for example, b-galactosidase, luciferase, CAT, and gfp. See the text for detailed explanation.

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

(McDonnell, 1999). SERMs are synthetic estrogens applied for the treatment of hormone-dependent disorders. The SERMs differentially activate wild-type ER subtypes and variant forms expressing activation function 1 and 2 in human cells transfected with a pC3-luciferase construct, and these in vitro differences reflect their unique in vivo biologies (Safe et al., 2001). In addition to these transient ER-expressing systems, tissuespecific cell lines have been developed, which stably express the ERa or ERb gene (Jiang and Jordan, 1992; Mueller, 2004). However, as the production of ER is artificially enforced in cells, whose wild type is lacking this receptor, some of the cellular reactions may not necessarily reflect the physiological response of the analyzed cell type. Therefore, cell lines with endogenous ER expression were used for transactivation assays, in order to compensate these limitations. Examples are cell lines such as human breast tumor T47D or human ovarian BG-1 cells. Similar immortalized cell lines can be employed to measure transactivation on stably integrated or transiently transfected ERE reporter vectors (Legler et al., 1999; Rogers and Denison, 2000; Mueller and Korach, 2001; Wilson et al., 2004). As both ER subtypes have different primary sequences in their activation function 2-containing hormone-binding domains (Gustafsson, 1999; Ogawa et al., 1998), some ER subtype-selective ligands have been identified. These ligands differ in their binding affinities for the two receptors and exhibit variable agonistic or antagonistic attributes according to the ER subtype considered (Veeneman, 2005). Balaguer et al. (1999) established stably transfected ERa- or ERb-responsive reporter cell lines to reveal the ER subtype-selective activities of various compounds. These cell lines enable to determine the whole-cell affinity of ligands for hERa and ERb and to precisely compare their effects on transcriptional activation (Escande et al., 2006). The human cervix adenocarcinoma HeLa cell line was used by Escande et al. (2006) as a host to generate stable reporter cell lines, because it does not express endogenous ERs. The corresponding HELN–ERa and HELN–ERb reporter cell lines were generated by first transfecting HeLa cells with an estrogen-responsive reporter to obtain the HELN cell line. The reporter gene contains a luciferase gene driven by an ERE in front of the b-globin promoter and a neomycin phosphotransferase gene under the control of the SV40 promoter (Balaguer et al., 1999). In a second step, HELN cells were transfected with the corresponding ER subtype expressing plasmid to obtain the HELN–ERa and HELN–ERb cell lines, respectively. The Kd value, calculated from saturation curves, was approximately 0.1 nM for Erb, whereas an almost threefold higher affinity was determined for ERa. These Kd values were within the range generally reported for E2 binding to ERs in various systems (Kuiper et al., 1998). The reporter cells were used to compare the induction of transcriptional activity of ERa and ERb by various estrogen agonists and antagonists. For instance, raloxifene was identified as the most selective antagonist for ERa and RU486 as the most selective antagonist for ERb. Raloxifene exhibited an ERa-selective partial agonist/ antagonist function but a pure antagonistic effect through ERb (Barkhem et al., 1998). Another example for mammalian reporter assays was established employing the human breast cancer cell line T47D.

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This cell line was transfected with the Gal4 ER and luciferase gene constructs which resulted in sensitive and responsive cells (Legler et al., 1999). Chemical-activated luciferase gene expression (CALUX) cell line assays have been established in rat or mouse hepatoma cells, which were transfected with receptor-controlled luciferase reporter gene constructs.

3.09.3.3.1 ED-reporter assays based on nonestrogen hormone receptors Due to their anabolic effects, androgens are used to promote muscle strength in athletes and meat quantity in farm animals (Meyer, 2001; Evans, 2004). It has also been found that environmental chemicals can interfere with androgen action, thereby possibly contributing to the disruption of the endocrine system in wildlife and humans (Kelce and Wilson, 1997; Andersen et al., 2002). The androgen receptor (AR) is a liganddependent transcription factor that regulates specific gene expression by binding to specific hormone-response elements within the regulatory DNA sequences of androgen-responsive genes (Claessens et al., 2001). The basic mechanism is comparable to the corresponding estrogen system. The enhancer region of the mouse mammary tumor viral long terminal repeat (MMTV-LTR) promoter is the most widely used enhancer to study AR function. Four inverted repeats of the core sequence 50 -TGTTCT-30 within the MMTVLTR enhancer are recognized by: AR, glucocorticoid receptor (GR), PR, and mineralocorticoid receptor (MR; Glass, 1994), now classified as the members of the 3C group within the NR family. The MMTV promoter also contains several enhancer regions that can be addressed by transcription factors that may respond to other hormonal and cellular stimuli, thereby modulating steroid responses (Aurrekoetxea-Hernandez and Buetti, 2004; Uchiumi et al., 1998). Several stable reporter gene assays have been described for androgens. However, these systems still have several drawbacks, since they either have a low responsiveness, employ slowly growing prostatic cell lines, or are not selective in their response because of expression of other nuclear hormone receptors of the C3 class, activating the transfected reporter gene through non-AR-mediated mechanisms (Terouanne et al., 2000; Blankvoort et al., 2001; de Gooyer et al., 2003; Wilson et al., 2002). The full-length MMTV promoter has been used to generate a number of androgen-responsive reporter cell lines. Although this promoter is quite selective to AR, PR, and GR, it also contains a number of regulatory sites that can be targeted by different agents other than steroids (Uchiumi et al., 1998; Ouatas et al., 2002). MDA-kb2 is a derivative of a human breast-cancer cell line containing such a stably integrated MMTV-luciferase reporter (Wilson et al., 2002). In addition to responding to androgens, this cell line responds very strongly to glucocorticoids acting through the GR that is present endogenously. This makes the system unsuitable as a selective screening tool. However, the androgen specificity was improved by stable transfection of human prostatic PC-3 cells with human AR (hAR) and the MMTV-luciferase reporter, named PALM cells (Terouanne et al., 2000), CHOhARMMTVluc cells (de Gooyer et al., 2003), and COShARMMTVluc cells (Paris et al., 2002). So far, the only cell line

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that uses a simpler reporter construct, thereby avoiding influences by nonsteroidal regulatory pathways, is derived from the human breast-cancer cell line T47 D, stably transfected with a luciferase reporter under transcriptional control of the PB-ARE2 androgen response element (Blankvoort et al., 2001). This stable cell line shows additional hormone class specificity, as it mainly responds to progestins, due to the known overexpression of PR in T47D cells, and relatively low endogenous AR levels (Sutherland et al., 1988). Sonneveld et al. (2005) developed an androgen-reporter cell line that combines high specificity and sensitivity. They selected the human bone cell line U2-OS, in which the stably introduced hAR was highly active, whereas expression of other C3 class receptors was insignificant. Based on earlier observations (Quaedackers et al., 2001) and transient transfections using a panel of steroid receptors, steroid reporter plasmids, and different cell lines (HEK293, T47D, U2-OS, HeLa, and CHO), the osteoblastic osteosarcoma U2-OS cell line was chosen as the best candidate to serve as an appropriate platform for androgen-, estrogen-, glucocorticoid-, and progestinresponsive reporter cell lines. The selection was mainly based on the observation that the U2-OS cell line showed little or no endogenous receptor activity using reporter plasmids only, while it supported strong hormone-mediated responses when cognate receptors were transiently introduced (Quaedackers et al., 2001). Whereas no evidence for significant endogenous activity of AR, PR, or GR upon ligand stimulation was found, cotransfection of the appropriate receptors resulted in high reporter activity upon ligand treatment. Stable transfectants were selected from U2-OS cells transfected with the hAR, hPR, and hGR and the 3x HRE-TATA-Luc reporter construct, or with the hERa in combination with the 3x ERE-TATA-Luc reporter construct (Quadackers et al., 2001; Sonneveld et al., 2005). In this manner, distinct steroid reporter cell lines with the same cellular background (U2-OS) and comparable minimal promoter reporter constructs (multimerized response elements coupled to the TATA box and the luciferase reporter gene) were generated. These lines are characterized by high levels of induction (fold induction ranging between 30 and 80), high stability (usually more than 40 passages), high sensitivity (picomolar to nanomolar range), and high selectivity (Sonneveld et al., 2005). For example, the thereby derived AR CALUX cell line (Sonneveld et al., 2003) was remarkably selective for androgens, showing no substantial agonistic response to the (androgen) precursors DHEA, pregnenolone, and other natural steroid hormones. The most potent androgen was dihydrotestosterone (DHT), activating these cells with an EC50 of 0.13 nM. The AR CALUX cells showed high sensitivity toward all natural androgens tested, with the following range of potencies (EC50 values in nM): DHT (0.13), testosterone (0.66), and androstenedione (4.5). Remarkably, in contrast to the ER that is prevalently activated by environmental pollutants, the AR seems to be prone to antagonism rather than agonism (Sohoni and Sumpter, 1998; Willemsen et al., 2004). This correlation was revealed by testing pesticides with ERa agonistic activity, which were found to be also AR antagonists (e.g., o,p 0-DDT, p,p 0-DDT, methoxychlor). This phenomenon may contribute to the

feminization of male fish as described before. While only a small number of environmental compounds are currently known as AR agonists, some of them are quite potent antagonists. The AR CALUX bioassay readily classified chemicals according to their antiandrogenic properties when tested in the presence of EC50 concentrations of DHT. The well-known and widely used AR antagonists flutamide (IC50 5 1.3 mM), vinclozolin (IC50 5 1.0 mM), and cyproterone acetate (IC50 57.1 nM) clearly showed antagonistic properties (Sonneveld et al., 2005). Compounds, particularly at micromolar levels or higher, can occasionally nonspecifically repress responses in reportergene assays. This can be due to overall cytotoxicity, ultimately leading to cell death, but can also be due to more specific effects such as inhibition of protein synthesis or mRNA transcription. The latter effects precede the more general cytotoxic effects, with cell death as the least sensitive parameter. Therefore, controls should be assessing nonspecific repression of reporter gene activity rather than overall cytotoxicity and cell death. Constitutively expressed reporter genes, which are frequently used as controls, have the drawback that no bona fide constitutive promoters have been identified so far. Therefore, the use of these controls cannot be recommended. In the case of steroid receptor-mediated responses, an appropriate control for nonspecific inhibition is considered in the determination of the effect of the test compound on the reportergene activation by an excess of high-affinity agonist. For example, all inhibitory responses of the CALUX assay can be reversed by coincubation of the sample with excess DHT, demonstrating the specificity of the response (Sonneveld et al., 2005). Squelching of common cofactors by other nuclear (hormone) receptors is a well-known mechanism of interference and might therefore produce false-negative results. An example for this type of mechanism is the interference between PR and ER (Kraus et al., 1995). However, squelching does not seem to be prominent in U2-OS-derived CALUX bioassays, because they do not express high levels of steroid receptors other than the stably introduced receptor of interest. This was shown by the fact that progestins and glucocorticoids do not interfere with DHT- or E2-induced luciferase activity in the AR and ERa CALUX bioassays, respectively, while androgens do not show reduced E2-induced luciferase activity in the ERa CALUX bioassay. Another receptor shown to possess squelching effects with nuclear hormone receptors is the aryl hydrocarbon receptor (AhR). Interference of the AhR ligand dioxin on ER signaling was demonstrated in T47D cells (ER CALUX bioassay) expressing functional AhR (Legler et al., 1999), but not in U2-OS cells (ERa CALUX bioassay) not expressing AhR (Sonneveld et al., 2003).

3.09.3.3.2 ED-reporter assays beyond transactivation In addition to the analysis of ligand binding and ER transactivation, steroidogenesis of endogenous estrogens offers an endpoint to measure the physiological response to EDs. These assays include the measurement of the activity of steroidogenic enzymes such as aromatase (cf. below; Mak et al., 1999) and the quantification of the pattern of steroid biosynthesis (Gray, 1998). Other assays include the measurement of ER/coactivator association by glutathione-S-transferase

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

pull-down, fluorescence resonance energy transfer (FRET), or two-hybrid assays (below), and analysis of ER-mediated gene expression (Routledge et al., 2000; Jorgensen et al., 2000). These assays can be very helpful for the analysis of reaction mechanisms of selected xenoestrogens and elucidation of their putative role in tissue-specific estrogenic effects (An et al., 2001). A critical shortcoming of in vitro assays in general is the lack of metabolic competence of the used cellular systems. As many compounds have to be activated by metabolic biotransformation in order to obtain estrogenic potential, a reporter gene assay may not be able to identify compounds that are potent xenoestrogens in vivo due to their metabolism. In order to compensate for this frequently raised criticism, primary human and rat hepatocytes were employed that maintain metabolic competence in culture. Primary cell cultures can be used in transactivation assays and primary hepatocytes treated with the UV-filtering compound 4-methylbenzylidenecamphor were shown to be responsive in ER reporter-gene assays, because they are capable of producing estrogenic metabolites of the tested compound (Mueller, 2004; Mueller et al., 2003).

3.09.3.4 Yeast-Based Assays 3.09.3.4.1 Initial yeast estrogen screens The yeast Saccharomyces cerivisiae is one of the classic model organisms applied in biological research since decades. In contrast to multicellular organisms, it is the most basic eukaryotic model at a single-cell level, which provides functionality of many mammalian genes. Metzger et al. (1988) have shown that despite hERa being nonfunctional when expressed in bacteria, it retains its native ligand-activated transcriptional activity in yeast cells. This indicates that the underlying regulatory mechanisms have been highly conserved during evolution and constitute the fundamental finding on which all other yeast estrogen screens (YESs) are predicated. Some of the advantages of yeast-based assays compared to mammalian cell culture are the lower costs for nutrient media, shorter assay duration as a consequence of the higher growth rate, and the robustness toward toxic effects such as endotoxins or solvents (Breithofer et al., 1998). However, the latter aspect is discussed controversially in literature. For example, the nonpolar fractions of sediment extracts were reported to show poisonous effects on yeast cells, which hampered the measurement of estrogenic activity by YESs. In contrast, the nonpolar fraction did not significantly harm human endometrial adenocarcinoma cells (Hashimoto et al., 2005). Despite this controversy, the yeast system is the model of choice for testing environmentally relevant samples such as sewage effluents, because contaminants present in these usually nonsterile samples do not impair the cellular proliferation and ER activity measurement in yeast in contrast to mammalian cells (Ramamoorthy et al., 1997). Further benefits of this system are seen in the availability of a large variety of genetic tools, in order to install yeast variants, which provide a perfect match for a given application. As a fundamental limitation, screening techniques based on recombinant yeast have been criticized as such systems may not accurately

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mirror mammalian or human systems, for example, in terms of cell walls and molecular transport mechanisms as a prerequisite for exposure to xenoestrogens. Another potential disadvantage of YES is that it is unclear whether yeast cells metabolize proestrogens to estrogens. Therefore, results obtained by YES will require confirmation in mammalian cells. In the 1990s, a number of yeast transactivation assays were developed for the determination of estrogenic activity. One of the initial systems was developed by Lyttle et al. (1992). It consisted of an expression plasmid (YEpE12) encoding the hERa, the copper-inducible CUP promoter, and a tryptophan auxotrophy marker. The marker compensated for the lack of cell lines to grow on tryptophan-deficient media by recruiting the enzymes required for the synthesis of this amino acid. Yeasts were co-transfected with the reporter plasmid YRpE2. The reporter plasmid carried two copies of the vtg ERE, the iso1-cytochrome c (CYC1) promoter in fusion with the b-galactosidase-encoding lacZ gene and a uracil auxotrophy marker (Lyttle et al., 1992). A few years later, a recombinant yeast strain was developed by Ian Purvis in the Genetics Department at Glaxo for the identification of compounds that can interact with the hER. The DNA sequence of the hER was stably integrated into the yeast genome. In this system, the hER was functionally expressed retaining its binding capability to ERE upon hormone stimulation. These ERE sequences were located within a strong promoter sequence on the b-galactosidase-encoding reporter plasmid. In the same manner as in the assay described above, upon binding an active ligand, the estrogen-occupied receptor interacted with transcription factors and other transcriptional components (Katzenellenbogen et al., 1993) to modulate gene transcription. This caused expression of the reporter gene lac-Z and the produced b-galactosidase was secreted into the medium. Following secretion, the enzyme metabolized the chromogenic substrate, chlorophenolred-b-D-galactopyranoside (CPRG) into a red product that could be measured spectroscopically at 540 nm. This yeast assay offered a measuring range for E2 from 1.5 to 3072 ng l1 and was described to be highly reproducible (Routlegde and Sumpter, 1996). Therefore, the yeast assay could detect E2 at concentrations, which were 5 times lower than that of the MCF-7 cell system initially reported by Soto et al. (1992; cf. above). The entire procedure of these initial YES assays spanned over a duration of approximately 3–4 days including cell growth and exposition. A similar recombinant yeast screen described by Klein et al. (1994) also relied on the expression of b-galactosidase and was even applicable for screening blood plasma levels of estradiol. Arnold et al. (1996b) described an assay, analogous to the one described by Routledge and Sumpter, based on the hER-containing expression plasmid pSCW231-hER and the reporter plasmid YRPE2. This latter plasmid from McDonnell et al. (1991) contained two EREs linked to the lacZ gene. The EC50 of this assay was determined at approximately 0.2 nM for E2. Similar to the majority of YES assays, the test was performed in 96-well microtiter plates, allowing screening of multiple compounds over a wide range of concentrations. Another initial system was based on ubiquitin ER fusions, which turned out to be more stable as compared

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to the unfused hormone receptor (Graumann et al., 1996). Subsequent modifications at the N-terminus according to the N-end rule from Varshavsky (1992) showed effects on receptor expression level and activation function-1 (AF-1) domain on transactivation.

3.09.3.4.2 Optimization of the initial YES Initially developed YES systems revealed several shortcomings in the routine screening of EDs. For example, Vanderperren et al. (unpublished) found high blank values when applying the assay of Routledge and Sumpter (1996). A similar effect was observed when higher cell densities were used compared to the original protocol. These were the first hints that the chromogenic b-galactosidase substrate CPRG or chlorophenol red interfered with assay results. Vanderperren et al. (unpublished) found dose-dependent estrogenic effects caused by the substrates of the YES assay at a concentration of 165 mmol l1, which corresponded to the substrate quantity applied in the original protocol. At the time when the sample was added to the yeast cells, the medium already contained the CPRG substrate. Therefore, the estrogenic response in the assay seemed to be an additive effect of the test compound and CPRG/chlorophenol red. In an attempt to compensate this interference, De Boever et al. (2001) redesigned the assay. During the initial growth phase, the cells were brought into contact with the sample in the absence of CPRG. Subsequently, CPRG and cycloheximide were added simultaneously. The postponed addition of CPRG avoided synergistic interference with sample-induced ER activation. The cycloheximide inhibited ribosomal peptidyltransferase activity, which catalyzes the formation of peptide bonds during translation (Cooper and Bossinger, 1976). Thus, cycloheximide blocked any further b-galactosidase production. These modifications resulted in a significantly lower background b-galactosidase activity for the negative controls. As a consequence, the dose–response in the modified assay was increased when compared with the original assay (De Boever et al., 2001). In addition, the initial cell density was raised. Thus, the dose–response curve was obtained after an exposure phase with E2 for 24 h incubation and an additional 18 h incubation with the chromogenic substrate CPRG in the redesigned assay as compared to 3 days of incubation required for the original protocol. One of the most time-consuming steps of the original YES protocols was the release of the expressed reporter enzyme b-galactosidase from within the cell into the CPRG-containing substrate solution. A combination of an enzymatic and a chemical digestion step was introduced, in order to enable a faster conversion of CPRG to chlorophenol red (Schultis and Metzger, 2004). A higher permeability of yeast cells was achieved by lysing the cell membrane with the enzyme lyticase from Arthrobacter luteus (LYES assay). The LYES assay offered a substantially reduced incubation time of 7 h as compared to 3–5 days for the unmodified protocol. In addition, the EC50 value of the LYES assay was reduced by one order of magnitude as compared to the YES assay (Schultis and Metzger, 2004). The cell disruption in the LYES assay demanded some additional handling steps in the analysis procedure which

constituted a potential source of error. Although accelerated compared to initial YES protocols, the disruption was still considered as a time-consuming procedure. A solution to this inherent problem of classical enzyme-based reporter assays was suggested by implementing the green fluorescent protein (GFP) instead of b-galactosidase as a reporter gene (Bovee et al., 2004; Beck et al., 2005). GFP was derived from the jelly fish Aequorea victoria and emits green fluorescing light which can be measured directly without cell disintegration (Tsien, 1998). Furthermore, the chromophore of GFP is formed by intramolecular cyclization and subsequent dehydrogenation without adding any cofactors (Heim et al., 1994). Additional benefits of GFP are related ro the small molecular size, high solubility, and stability in a broad range of pH values (Zimmer, 2002). In order to create a GFP-reporter plasmid, Beck et al. (2005) inserted the coding sequence for red-shifted GFP (rsGFP) into the plasmid backbone of YRpE2 lacking the coding sequence of the lacZ gene. Fluorescence of the cell suspensions had already been measured after 4 h incubation of E2. A clone with a highly induced fluorescence level and a low basal GFP expression was selected from a panel of various yeast clones. The sensitivity for E2 of the GFP-based transactivation assay developed thereof was in the same range as for conventional YES. Furthermore, the potencies of various substances were reproducible in the yeast transactivation assay, independent of the reporter plasmid employed. However, the absolute range between baseline and plateau of the dose–response curve was higher in the yeast assay using b-galactosidase as a reporter than in the GFP assay.

3.09.3.4.3 Subtype YES Estrogens control transcriptional responses through binding to two different NRs, ERa and ERb, which share a similar architecture (Koehler et al., 2005; Harris, 2007). The carboxylterminal domain of the receptors is crucial for ligand binding, nuclear translocation, receptor dimerization, and modulation of target gene expression associated with coregulators (Tsai and O’Malley, 1994). This domain shows a mere sequence homology of 58% between ERa and ERb in contrast to the high similarity of the amino-terminal region (Mosselman et al., 1996). The sequence variability of ER subtypes is the basic cause for the respective binding affinities of estrogenic ligands and their variable agonistic transcriptional activities (Hermenegildo and Cano, 2000). In order to identify subtype-selective ER ligands, a bipartite recombinant yeast screen (BRYS) was developed by Liang et al. (2009). This subtype-selective assay involves the reporter-gene plasmid (YRp2ERE) and the ER expression plasmids (YEp– hERa or YEp–hERb). BRYa was constructed by YRp2ERE and Yep–hERa, whereas BRYb was constructed by YRp2ERE and YEp–hERb. As BRYa or BRYb each comprises exclusively one kind of ER, the ER subtype selectivity of the ligands can be elucidated. Propyl pyrazole triol (PPT) and diarylpropionitrile (DPN) are subtype-selective ER ligands. In the transcription assays, PPT and DPN displayed obvious ER subtype selectivity. PPT showed ERa preference. The b:a ratio of relative potency

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

(defined as 100  EC50(estrogen)/EC50(ligand)) was 0.67 and the b:a ratio of relative efficiency (defined as 100  (A2 – A1)(ligand)/(A2 – A1)(estrogen)) was 0.36. In contrast, DPN demonstrated ERb selectivity with a 21.57-fold ERb potency. The phytoestrogen genistein showed strong ERb potency selectivity with b:a ratio of 2710.54. PPT, on the other hand, preferentially activates ERa. The ratio of relative efficiencies and potencies between BRYa and BRYb was nearly threefold and 1.5-fold, respectively. DPN was shown to be an ERb selective agonist. The b:a ratios in relative potencies of DPN was 21.57. The ER subtype selectivity of both PPT and DPN was lower than those in 293 human embryonal kidney cells (Meyers et al., 2001) or HELN cell line stably expressing ERa or Erb (Escande et al., 2006). Engagement of coregulators present in mammalian cells may make the ER subtype selectivity more obvious. However, it is most likely that the compound and the ER subtype are the major factors to determine the agonistic or antagonistic effects. Thus, the BYRS could yield at least valuable hints on estrogenic ligand subtype selectivity (Liang et al., 2009). Coactivator YES. The ER subtype ERb differs from ERa with respect to tissue-specific expression, ligand selectivity and affinity, and selectivity in recruiting coactivators. For example, the affinity of ERa and ERb for NR boxes of pl60 coactivators is significantly influenced by the ligand that is bound to the ERs (Bramlet et al., 2001). Although the ligand-dependent activation level of reporter genes with ERE on their promoter is generally lower with ERb than ERa, the increase in their activation by ERb is larger than that by ERa (Routledge et al., 2000). By selectively recruiting coregulators to ERb, isoflavone phytoestrogens only activate ERb-mediated transcription pathways (An et al., 2001). Increasing understanding of the transcriptional regulation by estrogens suggests the necessity of detection systems for EDs providing enhanced accuracy, in which ERb and a suitable coactivator are employed. Otherwise, some chemicals that might affect estrogen systems could be missed. Lee et al. (2002) developed a yeast two-hybrid system in order to integrate the estrogen-dependent interaction between the hER ligand-binding domain (hER LBD) and various coactivator nuclear receptor-binding domains (NRBDs). For this purpose, the genes coding for GAL4–DNA binding domain (DBD) fused to hER LBD, and GAL4-transactivation domain (TAD) fused to NRBD were expressed from vector plasmids in S. cerevisiae strain YRG-2, which has a reporter construct on its chromosome. When the two fusion proteins associate with each other through the ligand-dependent interaction between hER LBD and NRBD, the GAL4TAD recruits the basal transcriptional machinery to the CYC1 promoter, resulting in the production of b-galactosidase. The b-galactosidase activity, therefore, reflects the strength of the interaction between hER LBD and NRBD, or the ability of estrogen-like chemicals that induce hER LBD association with NRBDs of the coactivators (Nishikawa et al., 1999; Peters and Khan, 1999; Sheeler et al., 2000). Six combinations of LBDs of the two hERa and b species and the three coactivators TIF2, SRC1, and ATB1 were tested by Lee et al. (2002) using the yeast two-hybrid system described above. The ligand specificity of the two most effective combinations of hER LBD and coactivator NRBD were

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analyzed by examining the effect of a variety of natural and synthetic steroids and phytoestrogens. For the combination of hERa LBD and TIF2 NRBD, E2 was most effective among the chemicals tested at 1010 M. The order of effectiveness was E24DES4estrone4coumestrol4genisteine4testosterone. In the case of hERb LBD and SRCl NRBD, the order of effectiveness of the above chemicals was similar, although DES produced higher b-galactosidase activity than E2 at 1010 M. This latter combination gave higher b-galactosidase activity for the chemicals tested and seemed more sensitive than the former combination. The lowest concentration that gave detectable b-galactosidase activity was 1010 M for DES, l08 M for estrone, 107 M for coumestrol, 106 M for genistein, and 106 M for testosterone. These concentration levels were one to two orders lower than those for the combination of hERa LBD and TIF2. The latter two-hybrid system that employed hERb LBD and SRCl NRBD was more sensitive to estrogenic chemicals than the former. The difference in ER subtypes seemed to be primarily influential, because the selectivity and affinity of their ligand-dependent association with coactivators are different (Mosselman et al., 1996; Ogawa et al., 1998). In the presence of xenoestrogens, ERb bound to coactivators SRC-la and TIF2 at much lower concentrations and potentiated reporter-gene activity more effectively than ERa in transiently transfected HeLa cells expressing SRC-le and TIF2 (Routledge et al., 2000). ERb showed a 30-fold enhanced binding affinity to genistein compared with ERa (An et al., 2001). Isoflavone phytoestrogens repressed the expression of a tumor necrosis factor-alpha (TNF-a) promoter region through the action of ERb but not ERa, although E2 represses this promoter more potently by binding to ERa (An et al., 2001). ERb has different binding affinities than ERa for the NR boxes of coactivators, which varied depending on the estrogens employed (Bramlet et al., 2001). Ligand-bound ERb but not ERa showed an especially high affinity for NR box IX of SRC-la, a longer splice variant of SRC-1. Thus, these observations suggest that the interactions of ERb with estrogenic chemicals were not the same as those of ERa. ERb tended to bind to coactivators SRC-la and TIF2 at much lower xenoestrogen concentrations (Routledge et al., 2000). The estrogenic activities of alkylphenols reportedly depended on their structural features in both, the position (from high to low, para4 meta4 ortho) and branching (tertiary4 secondary ¼ normal) of the alkyl groups joined to a phenyl ring (Edwin and John, 1997). In agreement with these results, Lee et al. (2002) found that 4-tert-OP has higher estrogenic activity than 4-n-OP, but that a number of 4-NP hydrocarbon isomers do not give higher activity than 4-NP, probably because of insolubility. In their two-hybrid system, a-naphthol and b-naphthol showed detectable activities. Indole, a natural metabolite of tryptophan that has not been classified as positive, was positive in the coactivator system. Polycyclic aromatic hydrocarbons such as phenanthrene and naphthalene were judged to be negative in this system, and showed only weak induction of b-galactosidase. The observed differences in the response to these aromatic chemicals probably reflect the structural differences between the two types of hER LBDs. Generally, these studies indicated that a carefully optimized combination of receptor subtype and coactivator could

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enable the detection of estrogenic activity in some chemicals that were not suspected of being positive (Lee et al., 2002).

3.09.3.4.4 ER mutants Subsequent to the initially established principle of the YES assay, several approaches have been pursued, in order to modulate this amazingly generic strategy for answering basic scientific questions or meeting practical requirements. Several researchers have focused on the molecular modification of the receptor itself aiming on altered functionality or ligandbinding capability. Most ligand–ERa structure–function experiments to date have applied directed mutagenesis to produce large mutant ERa libraries, which were then screened to identify ERa variants with altered ligand binding and activation profiles (Chen and Zhao, 2003; Jakacka et al., 2002; Montano et al., 1996). Similarly, directed evolution was applied to generate mutant libraries, which were screened for optimized variants using selection-based methods (Chen et al., 2004; Sitcheran et al, 2000). Affinity mutants. A panel of screening methods was established in order to isolate hERa mutants with altered transactivation potency. Initially described genetic screens of yeast have tended to isolate constitutively active C-terminally truncated proteins which lacked functional LBDs (Pierrat et al., 1994; Vegeto et al., 1992). This is due to the fact that the N-terminal AF-1 domain is the major transactivation domain in yeast. In order to eliminate the isolation of these constitutively active truncated mutant ERs, a rearranged reverse ER was developed, in which the mutagenized LBD was placed at the N-terminus of the receptor in order to restrict the possible genetic alteration of the LBD. Mutations within the LBD of the reverse ER, which generated stop codons, resulted in truncated proteins lacking a DBD. They were therefore transcriptionally inactive in the screening process. For this reason, yeast cells were cotransformed with a reporter plasmid containing two or three copies of ERE upstream of the reporter gene lacZ and an ER expression plasmid (Wrenn and Katzenellenbogen, 1993; Whelan and Miller, 1996; Eng et al., 1997). After generating a library of ER variants, the mutants were grown on selective agar plates containing the ligand of interest. The transactivation activity of ER was revealed by the b-galactosidase activity, which could be assayed using the chromogenic substrate X-gal (5-bromo-4-chloro-3-indolyl-b-D-galactopyranoside). A second screening method entailed the fusion of the ER to the GAL4 DBD, which interacts with the GAL4 upstream activating sequence (UAS) located upstream of an integrated GAL1-lacZ gene (Bush et al., 1996). The addition of a ligand induced the expression of the lacZ gene and could be again assayed using X-gal. However, both assays were described to suffer from moderate sensitivity and low throughput because they required multiple handling steps (Chen and Zhao, 2003). The third example for an effective screening strategy was based on the fusion of the LBD of hER.a to murine dihydrofolate reductase (DHFR) providing temperature-sensitive stability (Tucker and Fields, 2001). The association of an estrogenic compound with the LBD increased the stability or activity of the murine DHFR. This in turn resulted in increased cell growth. Although this method was sensitive and amenable to high-throughput screens, it seemed to mainly screen for

increased ligand affinity because, for example, the phytoestrogen genistein was not active in this system. The fourth approach providing a sensitive genetic screen amenable to high-throughput isolation of hERa mutants was based on the linkage of the transactivation activity of ER to the cellular growth rate (Chen and Zhao, 2003). In this system, a minimum promoter of the reporter plasmid produced a very low level of constitutive expression of the HIS3 gene: the HIS3 gene product (His3p) is an essential enzyme in the histidine biosynthesis pathway. In the presence of ligand, high-level expression of the inserted HIS3 gene was activated, enabling colony growth on histidine-deficient minimum medium. A library of variants produced by error-prone PCR in the region of LBD of hERa was used to isolate hERa mutants with altered transactivation activity. Three mutants showed increased response to E2 during agar plate screening. Based on its halfmaximal concentration, the best mutant was determined at 1.0 1011 M E2, which corresponds to 100-fold increased sensitivity to E2 as compared to the wild-type hERa. Selectivity mutants. A completely different approach of mutation experiments targeted on the alteration of the ligandbinding specificity of the ER. Thus, a new type of receptor could be generated, which enabled the analysis of target groups or combinations of different hormonal reactive compounds that are not bound by the wild-type protein. Besides this, the corresponding experiments shed light on molecular details to gain an understanding of receptor structure. For example, the ER and the AR selectively bind their physiological ligands with subnanomolar affinity. Although the chemical structures of E2 and testosterone differ only in the A-ring region, the affinity of hER. for testosterone is 10 000-fold weaker than that for E2 (Chen et al., 2004). However, the affinity of hAR for E2 is 44-fold weaker than that for testosterone (Toth et al., 1995). The reason for this strict discrimination can be deduced from crystallographic structures of the hERa LBD complexed with E2 and the human AR LBD (hAR LBD) complexed with testosterone. These studies revealed that the majority of the residues interacting with the ligand (14 out of 20) are different between the ER and the AR. Despite their low sequence homology (o20%), the ER LBD and the AR LBD share a similar structural motif consisting of 12 a-helices arranged in an antiparallel sandwich motif. Chen et al. (2004) altered the ligand-binding characteristics aiming on enhancement of the hER binding affinity toward testosterone. They applied error-prone PCR to introduce random point mutations into the LBD fragment and the yeast two-hybrid system to select ER variants with altered transactivation activity. In this system, almost the entire LBD domain together with the F domain was fused to the DNA sequence encoding the DBD of yeast transcription factor GAL4 in order to create the bait plasmid (pBD-GAL4 hERa). The corresponding prey plasmid encoded the human SRC-1 fused to the gene encoding the GAL4 activation domain (pGAD424 SRC1). In the presence of agonistic ligands, the LBD undergoes a conformational change and binds to SRC-1, which brings the GAL4 DBD and the GAL4 activation domain into close proximity. This in turn activates the transcription of the HIS3 reporter (cf. above). On minimal medium lacking histidine, the cell growth rate is proportional to the strength of the ligand–receptor interaction. Two rounds of directed evolution

Bioassays for Estrogenic and Androgenic Effects of Water Constituents were performed with a cutoff concentration of 108 M testosterone in the medium. The EC50 values of the final mutants for testosterone were thus increased 234–780-fold. The binding affinities of the variants were determined by direct and competitive hormone-binding assays to establish whether the improved transactivation potencies of these evolved hERa variants toward testosterone were the result of the corresponding changes in ligand-binding affinities. The variants exhibited affinities to testosterone in the nanomolar range (up to 38 nM corresponding to 7600-fold improvement over that of the wild-type hERa). Simultaneously, the variants had increased Kd values toward E2, ranging from 0.44 to 3.53 nM (Chen et al., 2004). Unlike the naturally occurring ARs or ERs, these in vitro evolved hERa variants represent promiscuous receptors for estrogens and androgens. Such dual-ligand class receptors could constitute the basis of new approaches in the screening of hormone-reactive compounds. By applying the same principle, new receptors could be tailored for the simultaneous detection of theoretically any combination of bioeffective compounds. Expanding this principle of protein modification, the ER was used as an evolvable template structure to design a functionally orthogonal gene switch to accommodate a variety of nonsteroidal compounds in its binding pocket, which do not activate the wild-type receptor (Chockalingam et al., 2005). Modification of the binding pocket was achieved by saturation mutagenesis. This method involves the replacement of each amino acid residue that is expected to contact the ligand with the 19 possible alternative amino acids. The generated receptor mutants were selected for binding to a synthetic ligand using the yeast two-hybrid screening system, similar to that described above, by monitoring cell growth on histidine-deficient minimal medium. Subsequently, the site-saturation mutagenesis was repeated with the resulting mutant. The ligand specificity could be further enhanced by random point mutagenesis of the LBD and phenotypic screening. Using the synthetic nonsteroidal compound 4,4’-dihydroxybenzil (DHB), a mutant hER.a that showed a 50-fold enhanced binding activity to DHB and a 140-fold reduced affinity to its natural ligand E2 was selected after two rounds of mutagenesis. After three further rounds of molecular evolution, a mutant receptor was identified that showed a fivefold enhanced binding activity to DHB and an over 106-fold reduced affinity to E2 when compared with the wild-type protein. Thus, the finally achieved affinity was enhanced by a factor of 107. Applying an analogous strategy Islam et al. (2009) created a novel receptor–ligand pair, which responded to concentrations of a synthetic ligand that did not activate the native receptor. The aromatic A-ring of the synthetic ligand carried the hydroxyl group in a position analogous to the hydroxyl group in E2. The D-ring being either a five-or a six-membered ring carried a keto-group similar to the E2 homolog estrone. Iterative rounds of site-specific saturation mutagenesis of a fixed set of ligand-contacting residues were performed, followed by random mutagenesis of the entire LBD. The affinity of the ligand to the wild-type receptor was 3.7 mM as determined by its effective concentration to promote half maximal growth (EC50) of the yeast reporter strain (Chockalingam et al., 2005). After three rounds of mutagenesis, the affinity of

207

the selected mutants was enhanced up to 0.055 mM. Thus, the resulting receptor mutant showed a 67-fold increased activity to the synthetic ligand. In contrast to directed evolution methods as described above, experimental evolution systems were expected to be even more efficient for generating useful receptor variants, because the latter involved the continuous generation of new ERa variants and benefited from the power of natural selection in large microbial populations. Experimental evolution assays require the organism’s fitness to be linked to the trait of interest being investigated. When this is accomplished, small variations in that trait, generated by spontaneous mutation in a large population, confer small differences in growth rate and fitness for individual yeast. With increasing number of generation cycles, yeast inheriting fitness-improving alleles rise in frequency and ultimately dominate the yeast population over time. The advantages of carrying out such experiments in yeast include their short generation time, well-characterized genome, and consequently, the feasibility to analyze the genetics responsible for new phenotypes during the course of experimental evolution. Corresponding investigations proved that significant differences in growth rate and fitness are induced by minute (o 10 pM) differences in ligand (E2) concentration added to the media. This indicated that variants with even subtle improvements in ERa signaling should be subjected to relatively strong selection. Experimental evolution studies in yeast have shown that small differences in fitness (o 2%) can be selected for and isolated from large populations of variants within a few hundred generations (Thatcher et al., 1998; Zeyl, 2005).

3.09.3.4.5 Extension of the YES principle The conversion of androgens to estrogens is catalyzed by the enzyme aromatase. Some ED may act in an indirect fashion, for example, by inhibiting aromatase activity. This would result in a decreased level of estrogens or an increased androgen concentration in the cell. Aromatase plays an important role in the expression of secondary sexual characteristics, maintenance of pregnancy, and brain sex differentiation (MacLusky et al., 1987). Furthermore, the enzyme has received considerable attention in breast-cancer development because high expression of the enzyme activity has been associated with a significant number of breast tumors (Miller and O’Neill, 1987; Silva et al., 1989; Reed et al., 1989). Therefore, aromatase inhibitors are administered as therapeutic agents for the treatment estrogen-dependent breast cancer by inhibiting estrogen production concomitantly to antiestrogen treatment. Phytoestrogens such as flavones are competitive inhibitors of aromatase (Kao et al., 1998; Wang et al., 1994). Some flavones can inhibit aromatase with Ki values similar to that of aminoglutethimide, an approved drug for treating breast cancer. The conventional method for aromatase inhibitor screening was based on an in vitro enzyme assay with human placental microsomes as source for the enzyme. However, the aromatase assay in such preparations depends on several components including short incubation time, the stability of NADPH–cytochrome P450 reductase, and aromatase content. Furthermore, the assay procedure is tedious and it would not be easy to adapt this assay for high-throughput screening.

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Although certain intact cells expressing aromatase, including human aromatase transfected mammalian cell lines (Zhou et al., 1990), provided an ideal in vivo model for aromataseinhibitor screening, the use of radioactive-labeled substrates was a disadvantageous feature of the assay. In contrast, the aromatase inhibitor screening method developed by Mak et al. (1999) relied on the coexpression of AR and aromatase in yeast cells carrying the androgen-responsive reporter plasmid (Figure 6). If the triple transformant yeast cells carrying the three plasmids were incubated with an aromatizable androgen (testosterone or androstenedione), the androgen diffused into the cell, where it either bound to the yeast-produced AR or was converted to estrogen by aromatase produced in yeast. If the conditions (concentrations and choice of substrates) favored the enzymatic reactions, most of the androgen was converted into estrogen but not bound to the AR. Therefore, it could not transactivate the yeast basal promoter linked to the androgen-responsive element (GRE/ PRE). The androgen metabolism within the yeast cells finally culminated in the inhibition or reduction of reporter-enzyme b-galactosidase induction. However, androgen-dependent transcriptional activation was apparent as reflected by the reporter-enzyme induction if an aromatase inhibitor was included in the yeast medium. Applying this system, aromatizable androgens such as androstenedione and testosterone were not able to induce the reporter enzyme in the absence of the aromatase inhibitor aminoglutethimide. However, ligand-dependent transcriptional activation was apparent in the presence of aminoglutethimide (1 mM). In contrast to aromatizable androgens, the nonaromatizable androgen, 5a-DHT, induced the reporter gene efficiently even in the presence or absence of aminoglutethimide. Thus, the system essentially enables to monitor environmental chemicals for their antiaromatase activity and for their interaction

with AR. Furthermore, it discriminates nonandrogenic aromatase inhibitors from inhibitors with androgenic activity. Single-gene studies provide important but limited information about biological systems, primarily, because most genes function as part of gene networks and molecular pathways. Fox et al. (2007) designed a generic and modular geneintegration strategy, which enabled to monitor the interaction of several components of the ERa signalling network. This evolvable ERa activity sensor (EERAS) yeast strain expressed hER.a and three ERE-driven genes that were installed as reporters of transcriptional activity induced by the network. The entire system was based on a recyclable vector with modular components to direct integration of multiple genes of interest to any target loci in the S. cerevisiae genome. The resulting EERAS strain contained a constitutively expressed ERa gene, an ERE-driven fluorescent reporter gene (ERE-yEGFP), and two ERE-driven metabolic genes (ERE-HIS3 and ERE-URA3), which were required for growth in medium deficient of histidine and uracil (Figure 7). The yEGFP activity in the EERAS strain emerged as an efficient and sensitive dose-responsive reporter of ERa signalling activity, reaching approximately sixfold activation above background at 4 h posttreatment, with an EC50 of 3.9 1010 M for E2 (Bovee et al., 2004). E2dependent growth was measured in synthetic drop-out (SD) media lacking histidine and uracil, respectively, to validate the functionality of the ERE-driven HIS3 and URA3 genes, and to determine whether growth of the EERAS strain depends on ERa signaling. The growth-related sensitivities were even higher than for the yEGFP reporter assay: EC50 values for growth in SD-His and SD-Ura were 4.7 1011 M and 7.8 1011 M, respectively. In SD-His-Ura medium, in which both ERE-driven genes are required for growth, the EC50 was 1.1 1010 M. The EERAS generally delivered sensitive doseresponsive GFP and growth curves over a broad range of

Aromatase gene Aromatase

ARE

AR AR gene

AR ARE

Androgen

Reporter gene

Reporter gene

Reporter protein

E2 Androgen receptor (AR) Gene expression

Androgen response element (ARE)

Figure 6 Principle of aromatase inhibitor screening by reporter assays based upon aromatase-catalyzed transformation of E2 to androgen. See the text for detailed explanation.

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

ER ERE

ER

Fluorescence GFP gene

ER

ER gene

ERE

URA3

Cell growth in uracil-deficient medium

HIS3

Cell growth in histidine-deficient medium

URA3 gene

ER ERE

209

HIS3 gene

Fluorescent protein

E2 Estrogen receptor (ER)

URA3 protein

URA3

HIS3 protein

HIS3

Estrogen response element (ERE) Gene expression

Figure 7 ERa gene interaction network controls EERAS yeast strain growth and yEGFP production. The EERAS yeast strain was engineered to express multiple reporter genes that control growth and yEGFP production in a ligand–ERa–ERE activity-dependent manner and to constitutively express the hERa gene. When no ligand is added, ERE-HIS3, ERE-URA3, and ERE-yEGFP reporter genes are not transactivated by ERa, yeast are unable to grow in synthetic dropout media lacking histidine or uracil, and yEGFP is not produced. When ligand is added to the media, ERa is activated, resulting in dose-responsive yeast growth and yEGFP production.

ligand concentrations, enabling the detection of, and differentiation between, ligands with strong, moderate, and weak activation of ERa in EERAS. EC50 values for ligands were comparable to, and in most cases more sensitive than, those reported by other yeast ERa reporter assays (Bovee et al., 2004; Fang et al., 2000; Soto et al., 1995).

3.09.4 Subcellular Assays In addition to tests based at the organism and cellular level, subcellular receptor-binding assays with ER preparations were used to screen chemicals and environmental samples for estrogenic activity. Measurement of ligand binding (e.g., xenoestrogen) to ER is performed by competitive assays with, for example, radioactive- or fluorescence-labeled E2 (e.g., Kuiper et al., 1997; Mueller et al., 2003). The implication is that binding triggers subsequent biological effects. The assays enabled to establish quantitative toxicity equivalents combined with a high level of reproducibility. Since these assays benefit from easy handling and comparably higher speed than cellular or organismic test systems, they are considered to match fundamental prerequisites for high-throughput screening of environmental samples.

green anole (Anolis carolinensis), and for the corresponding receptor rtER from rainbow trout (Onchorhynkiss mykiss). Alternative expression systems for ER preparations were based, for example, on baculovirus using Sf9 insect cells (Bolger et al., 1998). In our group, the yeast expression system of McDonnell et al. (1991) was used for the recombinant production of ER. The proteinase-deficient yeast strain BJ3505 was transformed with the expression vector pYEPE-10 containing the ER gene fused to an ubiquitin gene. Expression was controlled by the coppersensitive metallothionein promoter CYP. Addition of CuSO4 (1 mM) to the culture medium induced the production of the fusion protein, from which ubiquitin was enzymatically cleaved off after a short time. Figure 8 illustrates the timedependent expression of recombinant ER protein. The Western blot shows the appearance of a single band with a molecular weight of 66 kDa, the size of the hERa. A maximum yield of approximately 4–7 pmol ER mg1 total receptor protein was obtained after 16 h of expression. Longer expression periods resulted in a higher amount of total expression product but diminished percentage of functional ER. Subsequent purification of the ER was carried out by affinity chromatography on heparin-agarose, which is known to bind several DNA-binding proteins such as the ER (Hock et al., 2002).

3.09.4.1 ER Preparation Substantial amounts of the hER. are required for binding assays. A number of natural cell lines was used in order to obtain the receptor protein. However, bacterially expressed receptors for high-throughput testing have been developed for the hERa, for a reptilian receptor (aER) from the liver of the

3.09.4.2 Enzyme-Linked Receptor Assay The enzyme-linked receptor assay (ELRA) essentially employs the same principles as competitive immunoassays based on antibody–antigen interactions. The decisive reaction is the competitive binding of the estrogenic sample compound and

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66 kDa

1

4

8

16

Commercial ERα

Hours after CuSO4 induction Figure 8 Western blot of human estrogen receptor expressed by recombinant yeast. From Seifert M (2000) Bestimmung von O¨strogenen und Xenoo¨strogenen mit einem Rezeptorassay. PhD Thesis, Technische Universita¨t Mu¨nchen.

2

1

ER

E2−BSA conjugate Substrate 3

Product

4

Anti-ER antibody (biotinylated) Biotinylated horseradish peroxidase

Streptavidin

Figure 9 ELRA principle (1) E2-BSA is adsorbed to the individual wells of microtiter plates. (2) E2 standards and samples are incubated together with the ER. (3) Addition of biotinylated mouse antihuman estrogen receptor antibody to label bound ER. (4) Addition of a streptavidin–biotin enzyme system. Finally, the substrate solution is incubated and the resulting signal measured spectroscopically (not shown). Adapted from Alberti MC (2006) Erfassung und Bewertung von Genexpressionsmustern von Zebraba¨rblingen (Danio rerio) nach Belastung mito¨strogenen Substanzen. PhD Thesis, Technische Universita¨t Mu¨nchen.

the immobilized E2-derivative at a limited number of ER molecules. Essential steps of the procedure are shown in Figure 9. In order to improve the sensitivity of the ELRA, the chromogen substrate tetramethylbenzidine was replaced by the luminescent substrate luminol (Seifert, 2004). This modification significantly lowered the detection limit of the assay. Consequently, the higher sensitivity of the luminescent detection system allowed higher dilutions of several assay

components as well as the dilution of the samples. This in turn lowered the potential interference with the sample matrix. The chemiluminescent ELRA reached a detection limit below 20 ng l1 compared to 100 ng l1 for the chromogen ELRA. Figure 10 shows the calibration curve of the ELRA for E2. An inverse relation between enzymatic activity and effect concentration (E2) can be observed because of the competitive assay principle. The IC50 values derived from these calibration

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Therefore the polarization values are reduced (Schultis et al., 2002; Bolger et al., 1998).

1200 1000

3.09.4.4 Biosensors RLU

800 600 400 200 0

0.001 0.01

0.1

1

17β-Estradiol (μg

10

100

1000

l−1)

Figure 10 Calibration curve obtained with the luminescence ELRA for E2. RLU, Relative luminescence units. From Seifert M (2000) Bestimmung von O¨strogenen und Xenoo¨strogenen mit einem Rezeptorassay. PhD Thesis, Technische Universita¨t Mu¨nchen.

curves were used to determine the percentage of cross-reactivity, with E2 arbitrarily set at 100%. These cross-reactivities represent effect concentrations. In addition, the competitive binding assay provides relative binding affinities compared to positive controls (usually E2 or DES) and therefore enables the ranking and prioritization of several compounds for subsequent studies. Binding assays for other receptors, such as the ERb and AR, have also been developed (Kelce et al., 1994). Compared to the cell-based assays, the ELRA has one main disadvantage: although compounds may have bound to the receptor, the tests do not distinguish between subsequent agonistic or antagonistic effects (Holmes et al., 1998; Zacharewski, 1997). An agonist binds to a cellular receptor and triggers the response by mimicking the effects of the natural molecules in cells. In contrast, an antagonist inhibits receptor action by competition with the natural molecule or by interaction with other sites in the receptor. However, if the agonists and antagonists are present simultaneously in a sample, the ELRA delivers a correct sum parameter for receptor-binding substances (Seifert, 2004). As binding to the receptor does not necessarily reflect ER activation, a prioritization of endocrine-active compounds can not be solely based on binding assays. In contrast to bioassays, the interpretation of synergistic effects is limited. However, installed as a primary screening, ER-binding assays are considered as appropriate tools to decide whether or not further investigations are required employing more informative assay schemes as provided at the cellular or organism level.

3.09.4.3 Fluorescence Polarization Assays Receptor-binding assays with hER based on fluorescence polarization offer the benefit of fast and direct measurement even in the presence of unbound ligand (Checovich et al., 1995). A separation step for the removal of unbound ligand such as in the ELRA is not necessary. In these assays, estrogenic substances displace the fluorescent ligand from a slowly tumbling ER–ligand complex. With increasing concentration of a competing compound, more fluorescent ligands are displaced, which now unbound in solution tumble more rapidly.

In ER-based biosensors, the hER. was preferentially used as sensing element so far (Habauzit et al., 2007). The primary objective of sensor techniques was initially to evaluate the binding properties between the protein ER and different estrogenic compounds rather than sample measurement. In contrast to the broad range of different biosensor principles reported (optical, electrochemical, acoustic, etc.), the molecular formats applied were of intriguing similarity to the ERbased microplate assays (cf. above). The various formats can be assigned either to direct or to indirect interaction analysis between the ER and an estrogenic compound. Indirect binding assays are based on the competition between estrogenic test compounds and the E2 derivative covalently linked to the sensor surface by chemical (pentanediamine, obtained from an amide derivative of 17bestradiol-17-hemisuccinate and 1,5-pentanediamine; Usami et al., 2002) or biological spacers (BSA coupled with estradiol; Miyashita et al., 2005; Pearson et al. 2001). The covalent coupling to the sensor chip surface is mediated, for example, in the optical sensor BIAcore through carboxymethylated dextran. The ER functions a recognition element in the competition between the tested molecules and E2 bound at the sensor surface. Then a stream containing the ER is directed over the surface (Figure 11). In the absence of analyte, the ER is maximally bound to the immobilized E2. With increasing analyte concentrations, decreasing amounts of the ER are attached. The dissociation constant Kd of 2.3 1010 M deduced from those experiments (Seifert et al., 1999) corresponded very well with the values reported by other groups, where Kd values ranging from 4.2 1010 to 2.0 1010 M were determined with radiolabeled E2 (Olea et al., 1985). In direct binding assays, the ER-LBD fragment is bound on injection by an anti-ER antibody, which is covalently immobilized on the sensor chip surface. After addition of estrogenic compounds, the association to and dissociation from the ERLBD can be monitored online without any competition included (Rich et al., 2002). Another example based (as in the case of the BIAcore instrument) on surface plasmon resonance (SPR) sensors is the portable immunosensor described by Sesay and Cullen (2001) for the detection of hormone mimics. Murata et al. (2001) proposed a bioaffinity sensor based on the specific binding of estrogens to the receptor immobilized on a gold disk with cyclic voltammetry detection. This biosensor was applied for the detection of E2. There are, however, difficulties in terms of reproducibility of direct binding assays, because of general instrumental limitations of the most frequently applied SPR-based biosensors for the detection of small molecules. In addition, the affinity constant of the ER–LBD has been reported to be not identical as compared to the entire receptor protein (Haubitz et al., 2007). The complexation of E2 with spacer molecules for competition experiments alters the interaction properties of the molecules and their ER accessibility (Usami et al., 2002; Miyashita et al., 2005; Pearson et al., 2001). In addition, the mere Kd information is not sufficient to determine the

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2000 1500 Dissociation phase RU

1000 500 0 −500 −1000 −1500 11 500

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Time (s) Figure 11 Typical sensorgram obtained with ER and immobilized E2 derivative in a surface plasmon resonance (SPR) biosensor for the calculation of kinetic data. Symbols as explained in Figure 9. RU, response units. From Seifert M (2000) Bestimmung von O¨strogenen und Xenoa¨strogenen mit einem Rezeptorassay. PhD Thesis, Technische Universita¨t Mu¨nchen.

agonistic or antagonistic and the SERM effects (above) in organisms. Therefore, the Hill number can be used to determine agonist or antagonist activity. A Hill coefficient greater than 1 unity is indicative of cooperative action between the ligand and the receptor and a Hill coefficient less than 1 unity is indicative of negative cooperation. Despite this, properly designed SPR assays shed light on our understanding of these interactions by monitoring the speed of complex formation (association rate constant ka) and the complex stability (dissociation rate constant kd). For example, E2 rapidly (ka ¼ 1.3 106 s1) forms an unstable complex with ER (kd ¼ 1.2 103 mol1 l1 s1), whereas 4-hydroxytamoxifen slowly (ka ¼ 2.3 103 s1) forms a stable complex with ER (kd ¼ 4.1 105 mol1 l1 s1; Habauzit et al., 2007). DNA-binding assay. A few groups extended the simple biosensor-binding formats in order to functionally implement the transactivation activity of EDs. The principle of the detection is that the ER captures the estrogenic ligand, dimerizes, and then the complex binds to the ERE, which is immobilized at the transducer surface of a biosensor. The evaluation of the affinity between ER and the consensus DNA sequence ERE by SPR was introduced by Cheskis et al. (1997). Equilibrium constants and rate constant changes between ER and ERE were determined after a previous incubation with several estrogens. This method has also been applied by other groups for assessment of estrogenic action (e.g., Asano et al., 2004; Murata et al., 2004). A rapid formation of an unstable ER/ERE complex could be observed with E2. Conversely, slow association of a very stable ER/ ERE complex was characteristic for the ICI 182,780 antagonist. These results were validated by testing the binding efficiency of more than 30 endocrine compounds. Variation of both the binding properties and the stability of the ER/ERE complex was observed, depending on the ligand employed (Asano et al., 2004).

SPR-based technology was rarely used for differential characterization of the effect of estrogenic compounds, the activity of which is difficult to investigate by conventional methods. As this approach is complementary to other assays at the organism or cellular level, gene-reporter assays, and protein-based assays, SPR-based biosensors are considered to furnish additional information on the molecular activity of EDs (Habauzit et al., 2008).

3.09.5 Conclusions Endocrine bioeffect assays can be essentially assigned to in vitro and in vivo test systems. Both groups are characterized by individual and inherent pros and cons. Generally, there are a few established short-term in vivo assays that are applicable for the assessment of ED. The above-described uterotrophic and vaginal cell cornification assays represent the most frequently employed in vivo screening tools for assessing the estrogenic potential of substances such as water contaminants. A crucial difference between both the in vivo tests is the fact that vaginal epithelial cell cornification can be induced only by compounds considered to be estrogenic, whereas the uterotrophic assay responds to progesterone or testosterone. These short-term rodent assays were criticized because they may not possess sufficient sensitivity to identify xenobiotics with weak or specific endocrine-disrupting activities. It is conceivable that ED may elicit responses at the geneexpression level that may not be translated into immediate responses at the organ or tissue level but could subsequently predispose an individual or subpopulation to adverse effects at later stages of development. Assessment of ED is further complicated by the fact that many substances elicit species-, tissue-, cell-, and response-specific effects. For example,

Bioassays for Estrogenic and Androgenic Effects of Water Constituents

tamoxifen exhibits antiestrogenic activity in the breast and agonist activities in the uterus. This underpins the necessity for measuring a number of different endpoints in order to comprehensively evaluate ED potency. In order to assess the risk posed by ED to human and wildlife health, it has been further criticized that rodents do not express SHBG following parturition, which is a major determinant of the metabolic clearance and the bioavailability of sex steroids. Finally, in vivo assays are considered to be laborious, costly, and ethically questionable (Zacharewski, 1998). Particularly, the compensation of the latter issues is postulated as a crucial benefit of properly standardized in vitro assays, which are in addition potentially amenable to highersample throughput. Table 1 summarizes some of the pros and cons of the above-described in vitro test systems. These assays are recommended by the United States Environmental Protection Agency (US EPA), Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC), and the Organization for Economic Cooperation and Development (OECD, 2001). In vitro assays are not only suitable for the screening of ED, but are also acknowledged as powerful tools for mechanistic analysis of their mode of action. The versatility of the mechanistic assays may help to pin down a potential tissue-specific effect of suspected ED (Mueller, 2004). Competitive receptor binding assays provide relative binding affinities compared to positive controls and therefore

Table 1

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enable a ranking and prioritization of several compounds for subsequent analyses. However, binding to the receptor does not necessarily reflect activation of the hormone receptor. Therefore, a prioritization of endocrine-active compounds should not be based solely on binding assays. Reporter gene assays are suitable screening assays that provide agonistic and antagonistic potencies. Due to the high sensitivity of available luciferase reporter vectors, very weak to highly potent ED can be analyzed. Furthermore, single compounds and chemical mixtures can be analyzed depending on response element, receptor subtype, and cellular context for their agonistic and antagonistic impact. This versatility is especially important for instance in the context of tissue-specific (anti)estrogenic activity of selective ER modulators. In this context, the YES assay is the model of choice for testing environmental samples such as sewage effluents. In contrast to mammalian cell culture, these typically nonsterile samples do not impair the cellular proliferation and ER activity in yeast. Next to these transient ER-expressing systems, tissue-specific cell lines stably expressing the ER can be considered as screening tools. However, as the exogenous receptor is forced into a cell accustomed to the lack of ER, some effects measured may not reflect the physiological response of the analyzed cell type. To account for these limitations, cell lines with endogenous ER expression can be used for transactivation assays. For this purpose, cell lines with endogenous ER expression like human breast-tumor

In vitro assays for the measurement of estrogenic and antiestrogenic compounds according to Mueller (2004)

In vitro assay

Measured endpoint

Advantages

Limitations

E-Screen

Proliferation of ERa-positive cells

No defined ER expression, no mechanistic data

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Binding affinity to ERa or ERb

Measures physiological endpoint of estrogen action, measures estrogens and antiestrogens Simple, high-throughput method

ER binding to ERE

Binding affinity of Era or ERb to ERE

High-throughput method, various EREs can be used

(GST pull-down/FRET/) two-hybrid assay

Ligand-dependent association of ERa or ERb with coactivators

Transactivation assay in yeast or mammalian cellsa

ERa- or ERb- mediated activation of reporter

Analysis of gene expression

Expression of ER-regulated genes Activity of ER-regulated enzymes

Analysis of molecular interaction, defined ER subtype or ER domain, as well as coactivators can be used, measures estrogens and antiestrogens High-throughput method, measures estrogens and antiestrogens, can be done in metabolic competent cells to account for (anti)- estrogenic metabolites Analysis of physiological response, versatile, measures estrogens and antiestrogens Analysis of physiological response, measures estrogens and antiestrogens

Induction/inhibition of estrogen biosynthesis

Analysis of physiological response, measures ER-independent pathways

Analysis of enzyme activity

Analysis of steroidogenesisa

a

Does not measure ER activation, does not measure physiological response Does not measure ER activation, low sensitivity, does not measure physiological response Does not measure direct ER activation, low throughput, does not measure physiological response Does not measure physiological response

Low throughput Cell lines or primary cell cultures with active marker enzymes suitable only Cells with active steroidogenesis suitable only

Recommended for screening of xenoestrogens by the United States Environmental Protection Agency (US EPA) Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) and the Organization for Economic Cooperation and Development (OECD). Assay principles, advantages, and limitations apply in a similar manner to analoguos sytems for the analysis of other endocrine systems.

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T47 D cells (e.g., ER-CALUX, cf. above) can be employed to measure transactivation on stably integrated or transiently transfected ERE reporter vectors. Similar to these ER activity assays, test systems to measure effects on other endocrine systems, for example, (anti)androgenic activity are used for the analysis of endocrine-active compounds. However, all these in vitro assays allow a ranking of a series of compounds that should be used to prioritize compounds for studies that can detect reproductive and developmental toxicities in vivo. These in vivo studies then enable a valid risk assessment (Mueller, 2004). Therefore, in vitro and in vivo assays are typically combined for tailored monitoring strategies termed as test batteries. Due to time and cost limitations, it is technically not yet feasible to test the ED potential of all chemicals which can be found in ecosystems. However, in silico assays complementing in vitro and in vivo tests as a third component may have the potential to solve this fundamental problem of ED-assessment studies (Escher et al., 2006). There exist different modeling approaches, most prominently SAR and QSAR methods. The procedures are based on the idea that specific structural features of a compound are associated with ED activity. As chemicals including specific structural features are potential binders of a defined hormone receptor, these substructures can be selected as structural alerts for detecting chemicals as EDs in priority-setting programs in which large collections of chemicals are ranked (Shi et al., 2002). However, one of the bottlenecks of the majority of these models is that they deal with structure-binding relationships while EDs intervene on other targets and in several different ways. For example, EDs can speed up the metabolism of hormones or modulate the number of receptors. EDs can also affect natural hormone production by interfering with other signaling pathways. Therefore, advanced modeling approaches consider activity profiles instead of unique endpoints. Multiple endpoint analysis can be made from multivariate analysis (Devillers et al., 2006). Nevertheless, ED modeling is still its infancy and there is a need to develop models in various directions in order to enable the detection and correct assessment of this complex activity.

Acknowledgment The author thanks Martin Alberti and Martin Seifert for providing the figures as indicated.

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3.10 Online Monitoring Sensors G Orellana, C Cano-Raya, J Lo´pez-Gejo, and AR Santos, Complutense University of Madrid, Madrid, Spain & 2011 Elsevier B.V. All rights reserved.

3.10.1 Introduction 3.10.2 Sensors for pH Measurements 3.10.2.1 Electrochemical pH Sensors 3.10.2.1.1 pH electrodes based on redox reactions 3.10.2.1.2 Ion-selective electrodes for pH measurements 3.10.2.2 Optical pH Sensors 3.10.2.3 Optical versus Electrochemical pH Sensors 3.10.3 Sensors for Ionic Species 3.10.3.1 Ion-Selective Electrodes 3.10.3.2 Optical Ion Sensors 3.10.4 Sensors for Dissolved Carbon Dioxide 3.10.4.1 IR Spectrometry 3.10.4.2 The pCO2 Electrode 3.10.4.3 Optical pCO2 Sensors 3.10.4.4 Miscellaneous pCO2 Sensors 3.10.5 Dissolved Oxygen Sensors 3.10.5.1 Electrochemical Oxygen Sensors 3.10.5.2 Optical Oxygen Sensors 3.10.6 Sensors for Waterborne Ozone 3.10.7 Sensors for Waterborne Hydrocarbons 3.10.7.1 Oil-Spill Detection 3.10.7.2 Water-Quality Control 3.10.7.2.1 Sensors based on refractive-index changes 3.10.7.2.2 Sensors based on light scattering 3.10.7.2.3 Sensors based on absorption changes 3.10.7.2.4 Sensors based on emission changes 3.10.8 Sensors for Waterborne Organic Matter 3.10.8.1 Sensors for COD 3.10.8.2 Sensors for BOD 3.10.8.3 Sensors for TOC 3.10.9 Waterborne Chlorophyll Sensors 3.10.10 Sensors for Waterborne Pesticides 3.10.11 Sensors for Waterborne Toxins 3.10.12 Sensors for Waterborne Bacteria 3.10.13 Turbidity Sensors 3.10.14 Oxidation–Reduction Potential Sensors 3.10.14.1 Effect of pH on Oxidation–Reduction Potential Sensors 3.10.14.2 Effect of Temperature on ORP Sensors 3.10.14.3 Frequent Problems with ORP Sensors 3.10.15 Conductivity Sensors 3.10.15.1 Effect of Temperature 3.10.16 Conclusions Acknowledgments References

3.10.1 Introduction Nowadays, sensors are regarded as the electronic mimics of the human senses. Thanks to both physical and chemical sensors, we can constantly be aware of natural and human-made phenomena. In this way, it is possible to monitor the status of watercourses, oceans, underground aquifers, water-treatment

221 221 221 222 222 223 224 224 226 229 231 231 231 232 234 234 234 236 238 239 239 240 241 241 241 242 243 243 243 245 246 247 249 252 252 254 256 256 256 256 257 258 258 258

plants, distribution networks, etc., and take corrective actions as early as possible, before any damage is inflicted. This chapter provides an overview of the currently available sensors for water-quality monitoring and analysis. The definition of a sensor includes any device that is able to provide a measurement of a physical parameter (e.g., turbidity and conductivity), or the concentration of a chemical

221

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species (e.g., molecular oxygen, ions, toxins, and hydrocarbons) in situ, in real time and continuously. In situ implies that the monitoring device can be taken to the point of measurement and introduced directly into the target water body (e.g., a pH meter), or placed where sampling and analysis can be made without human intervention. Nevertheless, the sensor signal can be sent far away from the monitoring site to central sites where the actual control is located and decisions are made. Currently, in use are radio, global system for mobile connections (GSM), and satellite links. Real-time monitoring involves rapid response by the sensor to changes in the monitored parameter, so that immediate action can be taken if needed. However, while this is the ideal behavior of any sensing device, there are processes such as analysis of several chemical species (e.g., waterborne organic matter) that require sample pretreatment. The true sensor must perform this operation in situ and online at the expense of a longer response time. Despite this, the sensor response is much shorter than the time taken to perform manual sampling, which involves transport of samples to the laboratory and subsequent analysis. Continuous measurements imply that the sensor response is reversible (ideal situation) or, at least, it can be regenerated in situ without human intervention. The devices that do not fulfill this last feature are often called ‘dosimeters’, although many scientific and commercial publications also refer to them as ‘sensors’. This chapter is intentionally restricted to true sensors, that is, devices that meet the three requirements mentioned earlier. However, where commercially available online sensors are not accessible for an important water-quality parameter, or when manual tests are firmly established in the water-monitoring field, the corresponding dosimeters are briefly described. The dynamic range of an analyzer is normally regarded as the analyte-concentration interval that the instrument is able to measure in an effective manner, that is, from the detection limit to the maximum usable indication. The detection limit refers to the smallest amount of substance or element detectable by the sensing device (typically corresponding to an analytical signal equal to 3 times the standard deviation of the background noise). From the statistical point of view, the limit of determination is always higher than the limit of detection of the sensor. Therefore, even though many manufacturers state that a particular sensor for water monitoring is applicable within an analyte concentration range from 0 to x (units), it must always be understood that the lowest indication value is actually the limit of detection. Given the wide variety of chemical sensors developed for water monitoring and the different types of sampled waters (natural, potable, underground, industrial, recreational, recycled, wastewaters, etc.), it is impossible to provide a general critical view on the practical applicability of these devices. Nevertheless, a critical assessment of the advantages/disadvantages of the different sensors described in this chapter is included under each section.

fields. In this section, we describe pH sensors divided into two main groups, namely electrochemical and optical devices.

3.10.2.1 Electrochemical pH Sensors The potentiometric measurement of pH is based on the electrochemical cell:

Reference electrodejconcentrated KCljjtest solution jelectrode reversible to H þ ðaqÞ where, typically, the electrode reversible to hydrogen ions is a glass electrode that is assumed to exhibit Nernstian behavior. The electromotive force (emf) of this cell is given by the expression 0

E ¼ E 0 þ kN logfHþ g þ Ej

ð1Þ

where kN is the Nernst constant (RTln10/F), {Hþ} is the thermodynamic activity of the hydrogen ions, EJ is the liquid junction potential that arises from the ionic strength differences between the electrolyte solution of the reference electrode and the test solution, and E00 is the conditional potential of the cell which depends on the experimental conditions (such as the filling solution) in the pH electrode and the contribution of the reference electrode potential. During routine measurements, electrode readings in one or more reference solutions are compared to that of the test solution. Assuming that the EJ values in both the test and the reference solution are the same (DEJ ¼ 0), the pH of the test solution is, therefore, operationally defined as

pHðXÞ ¼ pHðSÞ þ ðES  EX Þ=kN

ð2Þ

where X and S indicate the test and reference solution, respectively.

3.10.2.1.1 pH electrodes based on redox reactions There are two types of indicator electrodes: metal- and carbonbased. Metal electrodes develop an electric potential in response to a redox reaction at the metal surface. The most common metal indicator electrode is made of platinum, which is relatively inert. An advantage of the metal oxide electrodes is that they have very low resistance, but may be subject to severe interference by redox reactions. Various types of carbon can be used as pH electrodes because the rates of many redox reactions on the carbon surface are fast enough. Although many such materials respond to pH without preliminary activation, the derivatization of carbon surfaces has allowed development of electrodes for pH measurements that offer advantages compared to other pH meters (Kahlert, 2008).

3.10.2.1.2 Ion-selective electrodes for pH measurements

3.10.2 Sensors for pH Measurements The acidity of water is probably the most widespread chemical parameter measured in both environmental and industrial

Ion-selective electrodes are different from metal electrodes in that the former do not depend on redox processes. The essential feature of an ideal ion-selective electrode is a thin membrane across which only the target ion can migrate. The most important ion-selective electrodes for pH determination

Online Monitoring Sensors

are glass electrodes, liquid membrane electrodes, and ionsensitive field-effect transistors (ISFETs). Glass pH electrode. The glass pH electrode used to measure water acidity is the most common example of an ion-selective electrode. The overall galvanic cell of a typical (combination) glass electrode incorporating both glass and reference electrodes can be represented by Reference electrode (internal) ( )

H+ (internal) ( )

Glass membrane

H+ (external) analyte

Reference electrode (external)

Glass electrode

The key to electrode selectivity lies in its glass membrane. The surface layers of the latter consist of fixed silicate groups associated with sodium ions ðOSiO2  Naþ Þ. When this electrode is dipped in water, the sodium ions exchange with the solvated protons in water and the surface is then described as ‘hydrated’. The glass membrane has an inner and outer hydrated layer. In these hydrated layers, the anion sites are covalently bound to the bulk of the glass and are fixed. However, the Hþ cations are mobile, being free to exchange with the external solution or with sodium ions in the body of the glass. When the electrode is placed in an aqueous solution of unknown pH, the activity of the Hþ ions in the test solution is likely to be different from the activity of the Hþ ions in the hydrated layer. This sets up a potential difference between the solution and the surface of the membrane. This boundary potential is determined by this difference in the activities. Beckman marketed the first pH glass electrode and meter in 1935. The glass pH electrode system used nowadays consists of a pH-sensitive measurement glass electrode and a separate reference electrode in a potassium chloride (KCl) gel-conducting solution (Figure 1). These electrodes are usually housed in the combination sensor, containing both electrodes, which is connected to an electronic meter with a signal amplifier and temperature compensation. The meter displays the pH reading, which may be uploaded to a computer or controller. A silver wire enclosed in the measurement electrode forwards a signal indicating the difference in acidity between the solutions inside and outside the glass membrane. The reference electrode has a stable potential, which is independent of the measuring solution and must be calibrated outside the system in a reference solution. The most commonly used reference is a silver/silver chloride electrode in a buffer. The measurement and reference electrodes complete a circuit through the water sample (via a permeable porous junction built in the glass wall, Figure 1) allowing measurements of the voltage generated by the glass electrode. Common glass pH electrodes are extraordinary sensors in that they operate within a typical temperature range of 0– 90 1C over the full pH range of 0–14 (14 orders of magnitude of the Hþ concentration!), although they require accurate temperature measurement and compensation. The pH signal generated by a glass electrode can drift, or lose accuracy, over time due to a number of factors including fouling, sensor instability, and interference from external equipment.

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Therefore, accurate pH measurements require an external recalibration procedure using standard solutions of known pH. Other potential pitfalls of the pH glass electrodes include fragility, difficult miniaturization, leakage of the reference electrode buffer into the sample solution, poor response in low ionic strength solutions, high background noise, and moderate signal-to-noise ratio. Liquid membrane pH electrodes. Liquid membrane pH electrodes (LMEs; including polymeric membrane electrodes) offer advantages over other types of pH sensors and their aquatic applications are promising. Their key component is a pH-sensitive membrane that contains a pH-selective material composed of a neutral proton carrier dissolved in a membrane solvent. The membrane solvent is not miscible with water forming an organic phase that separates the aqueous sample solution from the aqueous internal filling solution. The neutral carriers are capable of selectively extracting ions from aqueous solution into the membrane phase and transporting them across the organic phase by carrier translocation. Similarly to the glass electrode, a membrane potential is established during the process and can be measured against a reference electrode. Compared to glass pH electrodes, LMEs have shorter response times because of their lower inner resistance. Ion-sensitive field-effect transistors. The ISFET is an integrated device containing an ion-selective electrode and an insulatedgate field-effect transistor. In pH-sensitive FETs, the ion-selective layer consists of SiO2, Si3N4, Ta2O5, or Al2O3, currently Ta2O5 being the preferred pH-sensitive layer. Technical difficulties regarding the required encapsulation of the electronic components due to their sensitivity to moisture are the main problems of ISFET fabrication. These problems have been solved by advanced packaging technologies so that an

Wires to pH meter

Filling hole

AgCl covered silver wire Ag/AgCl reference electrode Reference electrode internal solution

Permeable porous junction Glass electrode internal solution Glass membrane Figure 1 Typical glass electrode for pH measurements.

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economical mass production of solid pH sensors is possible. The major problems that persist are in finding a compatible reference electrode and avoiding hysteresis effects. Nevertheless, ISFETs are sensitive over a wide pH range (0–14) and are able to measure faster and with less temperature dependence than glass electrodes. Unlike the latter, ISFET-based pH sensors possess a rugged structure, small size, and low impedance, being amenable to miniaturization and automation. Moreover, ISFETs are not expected to be sensitive to organic contaminants and redox species in natural environments. Representative electrodes for pH sensing are listed in Table 1.

3.10.2.2 Optical pH Sensors Optical pH sensors, also known as pH optodes, are based on pH-dependent variations in the optical properties of an indicator dye, which reacts reversibly with the protons or bases in the aqueous sample. The most popular designs use the pHdependent absorption or fluorescence of reagents that are weak electrolytes and exist in both acidic and basic forms over the pH range of interest (typically c. 4 units). The acid (hyaluronic acid (HA)) and the corresponding conjugated base (A) participating in the pH-dependent chemical equilibrium are selected to have different absorption or fluorescent properties:

HA ðcolor AÞ" H þ þ A  ðcolor BÞ

ð3Þ

Since the degree of dissociation depends on the solution pH, the acidity level can be determined by measuring the relative concentrations of both forms of the dye. It is important to point out that most reports on pH optodes ignore the effect of ionic strength on the dissociation equilibrium of the indicator. While this omission is acceptable in dilute solutions, it can lead to serious errors in some environments. Absorption- and reflectance-based pH sensors. Absorptionbased pH sensors operate under the Beer–Lambert law that relates the absorbance at the analytical wavelength (Al) of an aqueous solution of the indicator dye to the concentration (C) of its acidic and basic forms (Equation (4)):

Al ¼ log

P0;l ¼ el ðHAÞlCHA þ el ðA ÞlCA Pl

ð4Þ

where P0,l and Pl represent the incident and transmitted spectral radiant power at the analytical wavelength of the monochromatic radiation used to interrogate the system, respectively; l is the absorption path length and el is the molar absorption coefficient at the analytical wavelength of the acidic and basic species of the indicator dye. The measured absorbance is a function of the solution pH due to the effect of the latter on the acid/base equilibrium of the indicator dye. For practical fabrication of optical pH-meters, the indicator dye is usually immobilized onto a polymer support and interrogated using optical fibers to carry the light to and from the distal end where the sensing head is placed. If the supported dye is in particulate form rather than a film, it needs to be confined and separated from the sampled water by a Hþpermeable membrane. The Beer–Lambert law also holds in

transparent polymer supports but, if the indicator dye is adsorbed onto opaque materials, then diffuse reflectance rather than absorbance must be measured. In this case, the Kubelka– Munk function, that relates the absolute diffuse reflectance of the indicator material at the analytical wavelength (Rl) to the concentration of the immobilized absorbing species (C), is used:

f ðRÞ ¼

ð1  Rl Þ2 2:303el C ¼ 2R Sl

ð5Þ

where el has been defined above and Sl is the scattering coefficient of the indicator support material at the analytical wavelength. The latter is assumed to be independent of the immobilized dye concentration. As in the case of absorptionbased pH sensors, the overall dye concentration will be distributed among its acidic and basic forms (Equation (3)) depending on the pH. Therefore, the diffuse reflected color intensity at the chosen analytical wavelength will be a function of the sampled water pH. Fluorescence-based pH sensors. Fluorescence is particularly well suited for optical sensing due to the high sensitivity and selectivity of the emission phenomenon. For weakly absorbing solutions (Alo0.05), the fluorescence intensity at the analytical wavelength (IF,l) returning from the sensing head is directly proportional to the intensity of the exciting radiation (I0) and to the concentration of the fluorescent dye (C) in the sensor:

IF ¼ k0 I0 Fel lC

ð6Þ

where l is the absorption path length through the sensing layer, el is the molar absorption coefficient, F is the fluorescence quantum yield, and k0 is the fraction of the fluorophore emission that can be measured in each particular setup. Fluorescent indicator molecules are also immobilized onto a polymer support and the emission from the fluorescent material is a function of the sample pH due to the dependence of the indicator acidic and basic forms concentration on that parameter. Absorption versus fluorescence pH sensors. Absorption measurements are simple and easy to use but are not very sensitive, requiring the use of high concentrations of pH indicator dye and/or a thick sensing layer. Reflection configurations with bifurcated fiber bundles are often used to overcome this problem. In contrast, fluorescent measurements are much more sensitive and can be used for small-size sensors and/or low indicator concentration. Fluorescent sensors can be operated in the (absolute) emission intensity mode and/or the emission lifetime mode, depending on whether steady-state or pulsed excitation of the indicator dye is performed. Fluorescence lifetimes can also be determined by sinusoidally modulating the excitation light and measuring the (sinusoidally) modulated emission phase shift. The current light-emitting diodes (LEDs) have helped to make the latter method very popular. Sensors based on fluorescence lifetime measurements are preferable because of their decisive advantages over absolute intensity recording: it is intrinsically self-referenced, it has negligible signal drift due to independence of the decay time with luminophore

Online Monitoring Sensors Table 1

225

Some academic and commercial electrochemical sensors for pH determination

pH-sensitive electrode

pH range

Slope (mV/pH unit)

Response time

Interferences

Lifetime

pH electrode type

References

W/WO3 IrOx Functionalized graphite Functionalized carbon black Functionalized glassy carbon Functionalized glassy carbon Functionalized glassy carbon Functionalized carbon epoxy Functionalized carbon, organic binder (silicone, PTFE) Polypyrrole on PTFE Octyldibenzylamine

3–6 2–12 NA 2–7 4–12

 61.1  6477  59  59 59

40 s NA 2–3 s NA NA

NA Ni2þ NA NA NA

NA 2 months NA NA NA

Redox Redox Redox Redox Redox

a

1–11

55

NA

NA

NA

Redox

f

1–11

 54.7

10 min

NA

NA

Redox

g

1–12

 60

60 s

NA

NA

Redox

h

0–9

 58

o2 s

NA

NA

Redox

i

2–12 2–10

 37.8 56.5

50–100 s NA

NA Small effect of Naþ, Kþ, Ca2þ

NA NA

Redox LME

j

(2–55 1C) 2–11 2–12 4–9 0–14 (0–95 1C) 0–14 (0–100 1C) 1–12 (o80 1C) 0–14 (25 1C) 0–14

(20 1C) NA 55–58 39–42 NA

Few seconds NA NA NA

NA NA NA NA

530 days NA NA NA

ISE ISFET ISFET Redox

l

53–60

o30 s

Naþ

NA

ISE

p

NA

NA

NA

NA

ISE

q

59

NA

NA

NA

ISE

r

NA

1–5 s

NA

ISE

s

0–14 (0–60 1C) 0–14 (o135 1C)

53.23 (25 1C) NA

NA

Naþ error at pH 4 12.3 Hysteresis

6–12 months

ISFET

t

NA

NA

NA

ISFET

u

Glass Ta2O5 film Polyelectrolyte multilayers NA Glass Glass Glass Glass NA NA a

Fenster et al. (2008). El–Giar et al. (2007). c Szepesvary and Pungor (1971). d Jankowska et al. (1981). e Lawrence and Robinson (2007). f Holm et al. (2007). g Brown et al. (1976). h Li et al. (2002). i Scholz et al. (2005) and Kahlert et al. (2004). j Prissanaroon et al. (2005). k Cho et al. (1998). l Kaden et al. (2004). m Poghossian et al. (2003). n Scho¨ning et al. (2009). o Continuously self-calibrating sensor (http://www.sensorin.com). p Thermo Scientific (http://www.thermo.com). q Orbisint Memosens Glass Electrode (http://www.emc.co.nz). r pH/ORP monitor/controllers (http://www.myronl.com). s Sensorex (http://www.sensorex.com). t Argus ISFET (http://www.sentron.nl). u Tophit-H Glassless Memosens (http://www.emc.co.nz). NA, not available; PTFE, poly(tetrafluoroethylene). b

b c d e

k

m n o

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Online Monitoring Sensors

concentration, and fluctuations in light-source intensities and photodetector do not influence the measurements.

3.10.2.3 Optical versus Electrochemical pH Sensors The most significant advantage of spectrophotometric methods is their high precision. There are no liquid-junction or high-impedance problems with optical measurements, and calibration is inherent in prior knowledge of the thermodynamic properties of the indicator dye. However, interrogation of pH electrodes is simpler, and therefore electrochemical sensors are more economical than their optical counterparts. Moreover, the enormous dynamic range of the pH electrodes can never be beaten by the optical pHmeters. The latter can be a useful option when miniature sensors operating in a narrow pH range are needed, or when the optical sensing monitors developed successfully for other widespread water-quality parameters such as dissolved oxygen (see Section 3.10.5) can be directly used for pH measurements. A summary of optical pH sensors for water appears in Table 2.

molybdenum, sulfate, zinc, nickel, tin, etc.) can also be found in water discharged from industrial areas. Depending on the water to be monitored, the ion-concentration range may be very different. For example, according to the US Environmental Protection Agency (EPA), the highest concentration of arsenic to which an aquatic community can be exposed briefly without resulting in an unacceptable effect is 340 mg l1. However, this value is limited to only 0.018 mg l1 when considering waters for human consumption. It is obvious that a sensor system becomes more expensive with higher sensitivity, a factor that must be considered when evaluating the application needs. Some waterborne ionic species can easily be transformed into neutral ones in the gas phase by just changing the pH of the sample, facilitating their determination by indirect methods. This is the case of sulfur compounds, chlorides, and ammonium. For instance, upon acidification of the water sample to be monitored, sulfite, hydrogen sulfide, and sulfide ions yield sulfur dioxide and hydrogen sulfide gases according to the following reactions: þ SO2 3 þ 2H -SO2 ðgÞ þ H2 O

HS  þ H þ -H2 SðgÞ

3.10.3 Sensors for Ionic Species S 2 þ 2H þ -H2 SðgÞ The monitoring of ionic species in water is of particular interest when considering drinking-water applications or, in general, public health. However, it is also relevant in other aspects of environmental analysis, such as aquatic life or industrial processes. The relative importance of monitoring the different waterborne ions depends on the particular nature of the aqueous medium. Aluminum compounds, for instance, can be found in swimming pools due to their use as flocculating agents, or in wastewater discharges from aluminum smelting where it becomes harmful to aquatic life. The presence of ammonium in groundwaters is an indication of potential pollution. Cadmium and lead ions are associated with the industrial production of batteries, while calcium and magnesium ions are responsible for water hardness. Chloride and bromide are ubiquitous and, in forced irrigation systems, groundwater can be monitored for these ions to avoid excess salinity. The presence of phosphates is important for plant life, as they are nutrients; yet if a wastewater rich in phosphate detergents is found due to insufficient monitoring, it produces environmentally damaging algal blooms. Another element essential to plant growth is sulfur, which can also be harmful to humans if present as sulfite ion, a potent allergenic species. Sulfite and hydrogen sulfide are often used to eliminate residuals of chlorine in wastewaters, due to the strong reducing potential of these ions. However, dissolved free sulfides strongly promote corrosion of many metals. Fertilizers are rich in nitrogen compounds such as nitrates that are harmful when they are found at high levels in water. Nitrites and nitrates can also be pollutants in aquarium waters. Both potassium and sodium cations are present in drinking, process, and wastewaters, their monitoring being relevant in the food industry as well as in horticulture as components of fertilizers. Several other ionic species (chromate, cobalt, copper, iron, lead, manganese,

Obviously, the need for a pH change in the water sample requires more than a simple point-sensitive device. Moreover, many ion sensors require sample preconditioning. Therefore, manufacturers have developed total analytical systems containing fluidic tubing, peristaltic pumps, reagent reservoirs, sensing chambers, and cleaning devices that can be deployed in situ for continuous monitoring of waterborne ions. These systems require more frequent maintenance servicing than the simple sensors, and therefore are more expensive to operate. It has to be borne in mind that personnel costs are always much higher than the cost of any monitoring sensor network, a fact that must be carefully considered when evaluating the monitoring needs of a particular application. Most of the devices for waterborne ionic-species monitoring rely on electrochemical processes for detection of the analyte, but there have emerged in the market, an increasing number of instruments based on optical-detection schemes, due to the advantages they offer. However, many commercial systems or devices described in the literature for ion sensing cannot be defined as true sensors (see Section 3.10.1), but are rather dosimeters because of their irreversible response. However, due to their widespread use, they have been included in this subsection to let the reader judge whether they can be useful for his/her particular application.

3.10.3.1 Ion-Selective Electrodes Sensors based on electrical measurements (potentiometric, amperometric, and conductometric) are widely used for field applications due to their low cost, simplicity, and robustness. When available (see, for instance, Section 3.10.2), they allow

Table 2

Some optical pH sensors found in recent literature and commercial sources

Chemical reagent

l (nm)

pH range

Precision

Response time

Temperature range

Interferences

Lifetime

Transduction principle

References

Thymol blue p–Methyl red

435 540,

Seawater pH 1.0–3.5

0.001 pH unit NA

NA 15.8 s

NA 1–3.5 1C

NA NA

4 weeks NA

Absorption Absorption

a

4-CP-BPB Bromocresol green

445, 606 610

2.0–6.0 4.0–7.0

20% (RSD)

11.9 s 5 min

NA

NA

NA

Absorption

c

PoAnis/TSA

501

4.9–10.5

0.01 pH unit

NA

Ionic strength (Naþ, Liþ, Cl)

NA

Reflectance

d

NA

5 min (acid to basic) 5–22 min (basic to acid) r60 s

NA

NA

NA

Reflectance

e

2.0–10.0 Thymol blue

NA

Congo red Bromothymol blue

6.8–9.5

b

7.9–11.2 3.3–4.8

Organometalcarbonyl complexes

2100–1750 cm1

7–13

NA

NA

NA

NA

NA

Reflectance

f

Swellable hydrogel

NA

4–5

NA

r30 s

NA

NA

NA

g

Langmuir– Blodgett film

750, 780

11–13

0.001–0.1

E20 s

NA

NA

NA

Swellingdependent reflectance Evanescent wave absorption

[Ru(bpy)2(dhphen)] (ClO4)2

Exc. 415

1–8

r0.11 pH unit

2–5 min

NA

Oxygen

2 weeks

Fluorescence

i

Em. 612 Exc. 300

3.5–9.2

r0.5 pH unit

2 min

2073 1C

Cd2þ, Cu2þr5 mg l1

2m

Fluorescence (energy transfer)

j

Em. 535 Exc. 405/450

6.5–8.5

NA

r5 s

NA

Ionic strength

50 days

Fluorescence

k

1.5–5.0

NA

r60 s

NA

Ionic strength

1 month

Fluorescence

l

7.5–9.0

0.1 pH unit

r230 s

2172 1C

Temperature

NA

Fluorescence

m

1–9

0.01–0.06 pH unit NA

E1 min

NA

Not found

8 months

DLR Fluorescence

E90 s

NA

Not found

1 month

Fluorescence

BNS, BrN/PhR, BCP, BPB

HPTS TAPP/MBTD DHFA/DHFAE/ Ru(dpp)3 Mercurochrome APN /MBTD

Em. 520 Exc. 422/481 Em. 656/528 Exc. 516/468/ 530/505 Em. 540/554/600 Exc. 506 Em. 530 Exc. 393/479 Em. 524/530

5.80–8.80

h

n

o

(Continued )

Table 2

Continued

Chemical reagent

l (nm)

pH range

Precision

Response time

Temperature range

Interferences

Lifetime

Transduction principle

References

MAHPDS

6.5–9.0

0.05 pH unit

r216 s

NA

NA

NA

Fluorescence

p

5.5–8.6

NA

2 min

NA

NA

NA

Fluorescence

q

Phenol red

Exc. 404/457 Em. 510 Exc. 406/460/ 506 Em. 515/540 525

6.2–8.4

0.1

20 s

NA

NA

Absorption

r

NA

NA

3.5–8.5

0.005 pH unit

o40 s

2–50 1C

Total alkalinity: 40–140 ppm Ionic strength

2 years

Fluorescence

s

Phenol red

NA

6.5–8.5

0.1

NA

NA

fluorescent molecules NA

NA

Absorption

t

0.1

NA

NA

NA

NA

Reflectance

t

HPTS

Cresol red m-Cresol purple Thymol blue Brilliant yellow Phenol red Phenol red nylon Cresol red m-Cresol purple Thymol blue Brilliant yellow a

8.0–10.0 8.5–10.5 9.0–12.0 NA

7.0–9.0 6.5–8.5 8.0–10.0 8.5–10.5 9.0–12.0 9.0–12.0 7.0–9.0

Bellerby et al. (2002). Wong et al. (2005). c Lau et al. (2006). d Taboada Sotomayor et al. (1997). e Wro´blewski et al. (1998). f Creaser et al. (2002). g Michie et al. (1995). h Flannery et al. (1997). i Chan et al. (1998). j Jin et al. (2001). k Hulth et al. (2002). l Niu et al. (2005). m Schroeder et al. (2005). n Sanchez-Barragan et al. (2005). o Li et al. (2006). p Vuppu et al. (2009). q Aller and Zhu (2006). r eXacts Micro 7 þ pH (http://www.sensafe.com). s Optical pH sensors (http://www.polestartech.com). t Fiber optic pH sensor (http://www.oceanoptics.com). NA, not available; CI: confidence interval; RSD: relative standard deviation; 4-CP-BPB: (4-carboxyphenyl)-bromophenol blue; TBPSP: 3,4,5,6-tetrabromophenolsulfonephthalein; PoAnis/TSA: poly(omethoxyaniline) doped by p-toluene sulfonic acid; bpy: 2,20 -bipyridine; dhphen: 4,7-dihydroxy-1,10-phenanthroline; BNS: 6-bromo-2-naphthyl sulfate; BrN: a-bromonaphthalene; PhR: Phenol red; BCP: Bromocresol purple; BPB: Bromophenol blue; HPTS: 8-hydroxypyrene 1,3,6-trisulfonic acid trisodium salt; TAPP: meso5,10,15,20-tetra-(4-allyloxyphenyl)porphyrin; MBTD: N-(2-methacryloxyethyl)benzo[k,l]thioxanthene-3,4-dicarboximide; DHFA: 20 ,70 -dihexyl-5(6)-N-octadecylcarboxamidofluorescein; DHFAE: 20 ,70 -dihexyl-5(6)-N-octadecyl-carboxamidofluorescein ethyl ester; Ru(dpp)3: tris(4,7-diphenyl-1,10-phenanthroline)ruthenium(II); DLR: dual lifetime referencing; Mercurochrome: 20 ,70 -dibromo-50 -(hydroxymercuri)fluorescein; APN: N-allyl-4-piperazinyl-1,8-naphthalimide; MAHPDS: 6-methacryloyl-8hydroxy-1,3-pyrene disulfonic acid; FITC-dextran: fluorescein isothiocyanatedextran. b

Online Monitoring Sensors

in situ, continuous monitoring of the analyte over a wide concentration range and are unaffected by water parameters such as color and turbidity. Yet, electrochemical devices for ion sensing may lack the required precision and suffer from interfering species (false-positive readings, explained later). The so-called ion–selective electrodes (ISE; the pH electrode is actually an ion-selective electrode) are commercially available for most ionic species commonly present in water samples, namely ammonium, chloride, iodide, fluoride, nitrate, potassium, sodium, and many heavy metals. Amperometric measurements of a galvanic cell based on the electrical potential created across an ion-specific solid membrane due to the presence of the target ion, versus that of a reference electrode (Figure 2), allows determination of the analyte ion activity. The analyte-sensitive membrane is placed at the bottom of a small reservoir containing the (internal) reference electrode held at constant potential, and the sensor is introduced into the sampled water. For instance, the ISE for fluoride in water contains as an analyte-selective membrane, a single LaF3 crystal doped with EuF2 to create defects that improve conductivity of the F ions within the solid. However, the calcium ISE is based on a plasticized poly(vinyl chloride) (PVC) membrane containing an organic receptor that binds and transports Ca2þ across the film. This transportation is responsible for the buildup of an electrical potential between the two sides of the membrane, so that the measured current is proportional to the concentration of the target ion in the aqueous medium where the ISE is immersed. Table 3 summarizes some of the instruments, available in the market, that use ISEs for ion monitoring in water. This chapter does not aim to provide an extensive description of every commercial instrument, but to provide a general outline on the state of the art of this technology. In situ continuous sensing of waterborne bromide, calcium, chloride, and fluoride using solid-state transducers (i.e., the

mV Meter

ISE

RE

AgCl-coated wires

Internal electrolyte

Ion-selective membrane

AgCl-saturated KCl aq.solution

Liquid junction

Figure 2 Typical combination ion-selective electrode.

229

inner electrolyte is replaced by a semi-solid paste), and ammonium and nitrate using PVC-coated electrodes, is provided by Nexsens ISE WQSensors (Table 4). All of them show measurement reproducibility better than 4% and are based on screw-cap replaceable sensing modules, simplifying maintenance operations. They have a 1.8-m USB connector cable for direct data collection by a computer and for freeing them from individual power supplies. The Nico2000 ELIT ISE sensors (Table 5) are another example of commercial electrochemical sensors. The two-head configuration (reference þ ISE) allows the user to replace only the electrode without changing the head. A multicomponent analysis is offered with a configuration of one reference with up to six ion sensors. With a response time of just 10 s, this company offers an impressive list of sensors for waterborne ions. The Yellow Spring 6000 series is an example of combined multiparametric electrochemical/optical sensing modules (see below). Unlike the ISEs mentioned above, these sensor modules have their own power supply to improve the positional freedom for in situ monitoring. Capable of working from  5 to 50 1C, its chloride, nitrate, and ammonium sensors seem ideal for nutrient environmental monitoring. The latter two contain a PVC membrane impregnated with a specific reagent yielding a working range of 0–200 mg l1 (0.001–1 mg l1 resolution), while the chloride ISE uses a solid-state membrane that allows monitoring from 0 to 1000 mg l1 (with the same resolution as the other two). Horiba Process & Environmental Sensor Technology (NJ, USA) has developed a multiparameter sensing probe capable of simultaneously analyzing up to 13 different parameters including a variety of ions in the temperature range of 0–55 1C. The W-20 series is a probe with high-pressure tolerance allowing measurement of nitrate, chloride, and fluoride at depths up to 100 m in rivers, lakes, or even in the open sea, accompanied by its built-in memory capacity of 1 month data logging. One of its attractive features is its global positioning system (GPS) module that allows acquisition of the location and time of each measurement for detailed three-dimensional (3D) records. Both Analytical Technology Inc. (ATI) and Hach market sensors that rely on the change of the sample acidity to extract the analyte into the headspace, as described earlier. The ATI’s A15/79 total residual chlorine sensor actually measures the concentration of gaseous iodine in an indirect method, measuring 0.01–2000 mg l1 analyte in 3 min. It uses potassium iodide and a pH 4 buffer as reagents for the hypochlorite determination in water:

HClO þ 2KI þ HCl-I2 ðgÞ þ 2KCl þ H2 O

3.10.3.2 Optical Ion Sensors Optical sensors based on the absorption of light by the analyte ion and rugged miniature spectrometers are becoming very popular for in situ water monitoring due to their particular advantages compared to their electrochemical counterparts: no need of electrolyte, electrode, or membrane maintenance; sturdiness of the sensitive optical probes; absence of drift due

230

Online Monitoring Sensors

Table 3

Analytical data for some commercially available electrochemical sensors for waterborne ions

Ionic species

Sensor model

Dynamic range (mg l1)

Accuracy

Response Time (s)

Observations

Agþ, Br, NO3  , Liþ, ClO4  , Ca2þ, Naþ, Kþ, NH4 þ , S, Cl, CN, F, I, SCN Br, Cd2þ, Ca2þ, Cl, Cu2þ, CN, F, I, Pb2þ, SO4 2 , NO3  , Kþ, Agþ, S2 Br, Ca2þ, Cl, F, NH4 þ , NO3  ClO/Cl2 (as total chlorine)

AB 6000 seriesa

NA

NA

NA

Single-parameter monitoring

HI98184 and HI98185b

0.01–saturation (depending on the analyte) See Table 4

NA

NA

Multiparameter monitoring

NA

NA

Q45H/62-63d

0.02 mg l1

60

Cl2, ClO2, F

PCA 330 seriesb Chlori::lyserTMe Conexs DIAf

8% NA NA

180 120 NA

Multiparameter monitoring Single-parameter monitoring Multiparameter monitoring

Cl, NH4 þ and NO3 

YSI 6000 seriesg

0–0.2; 0–2; 0–20; 0–200 0–5 0.001–2; 0.01–10 0–50 (depending on the analyte) 0–200; 0–1000 (see text)

Replaceable membrane, single-parameter sensors Polarographic gas sensor

10%

NA

F

CA610h A15/82d IF–250i

10% 5% 30%

o260 90 NA

Replaceable membrane, single-parameter monitoring Single-parameter monitoring

Kþ Naþ

C–131i C–122i SODITRACEj 9245h AMTAXTM h

NA NA 10% 5% o5%

NA NA 120 180 o300

Gas-sensitive electrode

10% NA

NA 600

Single-parameter monitoring Multiparameter monitoring

10%

10

Replaceable membrane, multiparameter system

10% N and 15% Cl

60

Multiparameter monitoring

NA 10% 10% 10%

60 NA NA NA

Multiparameter monitoring Single-parameter sensors

0.03 mg l1 0.03 mg l1

180 180

Polarographic gas sensor Polarographic gas sensor

WQSensorsc

NH4 þ , Ba2þ, Br, Cd2þ, Ca2þ, Cl, Cu2þ, CN, F, I, Pb2þ, Hg2þ, NO3  , NO2  , ClO4  , Kþ, Agþ, Naþ, S2, SCN NH4 þ , NO3  , Cl

ELITl

0.1–10 0–1; 0–1000 0–20; 0–200; 0–2000; 0–10 000 339–3900 23–2300 1  106–10 1  105–10 0.02–5; 0.05–20; 1–100; 10–1000 0.1–1000 0.003–1000 (depending on the analyte) See Table 5

TROLL 9500m

0.14–14 000 N

NH4 þ , NO3  , Kþ NO3 

Ammo::lyserTM proe Monitor FAM Nitratej B-343i W-20 Seriesi

NH4 þ

þ









NH4 , Cl , CN , F , NO3 , NO2 

NO3  , Cl, Ca2þ, F, Kþ

SO3 2 H2S, S2 (as dissolved sulfide) a

Monitor FAM ammoniumj ES 9010k

A15/66d A15/81d

ASTI (http://www.astisensor.com). HANNA instruments (http://www.hannainst.com). c Nexsens (http://www.nexsens.com). d ATI (http://www.analyticaltechnology.com). e S::can (http://www.s-caNAt). f Grundfos Alldos (http://www.grundfosalldos.com). g YSI (http://www.ysi.com). h Hach (http://www.hach.com). i HORIBA (http://www.horiba.com). j SWAN (http://www.swan.ch). k Environnement S.A. (http://www.environnement-sa.com). l NICO 2000 (http://www.nico2000.net). m In-Situ Inc. (http://www.in-situ.com). NA, not available. b

0.35–35 500 Cl 0.1–1000 0.1–1000 14–1400 0.02–62 000 (depending on the analyte) 0–20; 0–2000 0–20; 0–2000

Multiparameter monitoring

Online Monitoring Sensors Table 4

Technical details on Nexsens ISE WQsensors

Table 5

231

Technical data for the ELIT ion sensors

Ion

Working range (mg l1)

Temperature range (1 C)

Known interferents

Ion

Dynamic range (mg l1)

Temperature range (1 C)

Known interferents

Br

0.4–79 900

0–80

Agþ Ba2þ

0.01–107 900 0.5–13 700

0–80 0–50

Ca2þ

0.02–40 000

0–40

I, Cl, S2, CN, NH3 Pb2þ, Hg2þ, Si2þ, Fe2þ, Cu2þ, Ni2þ, NH3, Naþ, Liþ, Trisþ, Kþ, Ba2þ, Zn2þ, Mg2þ CN, Br, I, OH, S2, NH3 OH Naþ, Kþ ClO4  , I, ClO3  , F

Br

0.4–80 000

0–80

Ca2þ

0.02–40 000

0–50

Cd2þ

0.1–11 000

0–80

Cl

1–35 000

0–80

ClO 4 CN 2þ Cu

0.2–99 600 0.03–260 0.006–64 000

0–50 0–80 0–80

F Hg2þ I

0.06–2000 0.2–201 000 0.06–127 000

0–80 0–80 0–80

Kþ Naþ NH4 þ NO3 

0.4–39 000 0.05–20 000 0.03–9000 0.3–62 000

0–50 0–50 0–50 0–50

NO2 

0.5–460

0–50

Pb2þ

0.2–20 800

0–80

S2 SCN

0.003–32 000 1–5800

0–80 0–80

Hg2þ, S2þ Ca2þ, Kþ, Naþ, Mg2þ, NH4 þ , Sr2þ Ag þ , CN, I, S2, Cl 3þ Al , Ba2þ, Fe2þ, Cu2þ, Sr2þ Agþ, S2, Cu2þ, Fe2þ, Fe3þ, Hg2þ, Pb2þ Br, CN, I, S2, Agþ Cl, I, NO3  , SCN I, S2, Agþ Agþ, Br, Cd2þ, Cl, Fe2þ, Hg2þ, S OH Agþ, S2 CN, S2, Agþ, S2 O3 2 Csþ, NH4 þ Most cations Kþ BF4  , Cl, ClO4  , CN,I, NO 2, HCO3 CN, CH3COO, F, Cl, NO3  , SO4 2 Agþ, S2,Cd2þ, Cu2þ, Fe2þ, Fe3þ, Hg2þ Ag þ , Hg2þ Br, Cl, I, Ag þ , S2, S2 O3 2

Cl

0.18–35 500

0–80

F NH4 þ NO3 

0.02 to saturation 0.014–1400 (N) 0.1–14 000 (N)

0–80 0–50 0–50

to a reference electrode; lack of electrical interferences; and ease of miniaturization. However, they may be subject to interference due to turbidity or the presence of species other than the analyte absorbing in the same region (e.g., dissolved organic matter or ions other than the monitored ones). For example, S::can (Austria) provides robust sensors for nitrate and nitrite monitoring by measuring the ultraviolet (UV)–visible (VIS) absorption spectrum of these ions in the water. Determinations are possible by using chemometrics, a term coined in the 1970s to design the use of statistical methods for the analysis of (instrumental) analytical chemistry data (Brereton, 2007). Registration of thousands of spectra from known samples allows training the optical sensors for recognizing the analyte pattern and quantifying it in the actual (complex) water matrices. The multiparameter Tethys UV400 instrument (Meylan, France) also relies on spectral absorption by the sample to provide quantitative information on ammonium, nitrate, phosphate, and hydrogen sulfide. By measuring the UV light absorption at 210–220 nm, the instrument provides a working range of 0–100 mg l1 for nitrate. The ammonium-detection method is based on increasing the pH of the water by the addition of sodium hydroxide to transform all dissolved NH4 þ into gaseous NH3, which has a distinct absorption around 200 nm. The sensor is able to measure 0–100 mg l1 of ammonia free of interferents since the detection occurs in the gaseous phase. By acidifying the sample upon addition of hydrochloric acid, this device enables the extraction of gaseous H2S from the dissolved HS. Phosphate determinations are made by colorimetric measurements, yielding a working range of 0–2 mg l1. Several other instruments also rely on absorption methods for multiparameter sensing purposes. The Swan AMI Phosphate monitor (Hinwill, Switzerland) for automatic and continuous measurements of 0.01–10 mg l1 phosphate in water uses the ammonium molybdate ISO 6878 colorimetric method to work for up to 6 months, with a response time of 10 min within a temperature range from  10 to 50 1C. Hach (Loveland, CO, USA) has developed several continuous monitoring single- and multiparameter optical chemical sensors. For instance, the MO42 Molybdate Analyzer

uses colorimetric catechol chemistry detection at 420 nm to monitor molybdenum oxoanions with a limit of detection of 0.03 mg l1 and a dynamic range of 0–5 mg l1. Being capable of working for up to 1 month of unattended operation, it shows readings every 2.5 min, ensuring proper monitorization. A cuprethol colorimetric method enables the APA 6000TM instrument to analyze copper (II) at 436 nm in two separate ranges, 0.05–2.0 mg l1 and 1–10 mg l1. This device is also capable of 1 month of unattended operation, and it measures readings every 4 min, yielding results with a resolution of 0.001 mg l1. The same company also provides several solutions to monitor water hardness (magnesium and calcium), using both the APA 6000TM low-range hardness analyzer and the SP510 instruments, monitoring in the working range of 0.05–10 mg l1 (as CaCO3). As in other instruments that use optical methods for the detection of nitrate and nitrite, the Hach NitrataxTM monitor takes advantage of the molecular N–O bond UV light absorption to obtain its concentration, using a second beam of light to eliminate interference from turbidity and dissolved organic matter. This reagent-free UV-absorption technique gives a dynamic range from 0.1 to 100 mg l1 (as N) with a resolution of 0.1 mg l1.

232

Online Monitoring Sensors

PhosphaxTM is the Hach instrument for phosphate optical detection. With a self-cleaning membrane, it is capable of detecting from 0.05 to 50 mg l1 (in phosphorous) with a 5-min response time and 3 months of unattended operation. Table 6 lists some of the instruments that use optical methods for continuous detection of ionic species in water. When considering optical monitoring of waterborne ions, there are very few instruments capable of in situ continuous sensing (basically nitrates and phosphates). However, there are several portable devices capable of detecting numerous

Table 6

ions in water samples by optical methods that require human intervention for sampling, conditioning, and testing operations. For instance, a portable photometer, such as the Hach DR 2700TM, provides fast results with its multiwavelength capability. It is able to detect an impressive number of ionic species with adequate limits of detection. Test strips are also a good example of widespread simple optical dosimeters as the changes in color of an immobilized specific reagent in the presence of the target analyte yield direct quantification. Even though this type of sensing

Some of the optical instruments commercially available for ionic species monitoring

Ionic species

Sensor model

Dynamic range (mg l1)

Accuracy

Response time (s)

Remarks

Cl2, ClO2 Cu2þ

AMI CODES-II CCa CL17b APA 6000TMb

0–1; 1–3; 3–5 0.035–5 0.05–2; 1–10

0.01; 0.06; 0.2 mg l1 5% 5%

120 o150 o240

Mo6þ

MO42b

0.03–5

5%

o150

NO3 2

ISUS V3c nitro::lyserTMd

0.007–28 0–70

0.028 mg l1 3%

NA NA

DPD method DPD method Colorimetric cuprethol chemistry Colorimetric catechol chemistry UV absorption UV–VIS spectrometry over the total range, multiparameter probe

multi::lyserTMd spectro::lyserTMd

3% 3%

NA NA

TPNA–300e

0–70 From 0 to 50 (N) (depending on the analyst) 0–2 (N)

NA

3600

YSI 9600f

0.025–10

5%

NA

NITRATAXTMb PHOSPHAXTMb

0.1–100 0.05–15; 1–50

5% 2%

60 o300

Series 5000b TPNA–300e

0.2–50 0–0.5 (P)

5% NA

660 3600

Monitor AMI Phosphatea UV400g

0.01–10

NA

600

From 0 to 79 (depending on the analyse)

NA

5



NO3 , NO2

PO4



3

NH4 þ , HS, S2, NO3  ,NO2  , PO4 3

Measures total nitrogen through alkaline potassium peroxodisulfate UV oxidation–UV absorption method Cadmium reduction– diazotization colorimetric method UV absorption Colorimetric molybdovanadate chemistry Measures total phosphorus by potassium peroxodisulfate UV oxidation–molybdenum blue absorption method Ammonium molybdate colorimetric method UV absorption and colorimetric Multiparameter instrument

a

SWAN (http://www.swan.ch). HACH (http://www.hach.com). c Satlantic (http://www.satlantic.com). d s::can (http://www.s-canat). e HORIBA (http://www.horiba.com). f YSI (http://www.ysi.com). g TETHYS Instruments (http://www.tethys-instruments.com). NA, not available. b

Online Monitoring Sensors Table 7

233

Examples of the test strips for ion analysis offered by three manufacturers

Ion

Dynamic range

Manufacturer

Ion

Dynamic range

Manufacturer

Al3þ

10–250 mg l1 0.01–0.25 mg l1 10–400 mg l1 N 1–50 mg l1 N 0.1–3 mg l1 0.005–0.5 mg l1 10–100 mg l1 0.5–20 mg l1 25–500 mg l1 50–500 mg l1 0.02–2 mg l1 0.1–50 mg l1 3–100 mg l1 10–1000 mg l1 10–300 mg l1 0.5–5 mg l1 0.05–2 mg l1 0.5–10 g l1 0.05–1 mg l1 5–100 mg l1 3–500 mg l1 0.1–3 mg l1 0.005–0.3 mg l1 0.02–5 mg l1 0.3–50 mg l1 20–500 mg l1 3–600 mg l1 (with photometer detection) 0.02–1.5 mg l1 2–100 mg l1 0.05–4 mg l1 0.02–1.6 mg l1

Merck Jenway Merck Jenway Merck

Mo6þ

5–250 mg l1 0.3–18 mg l1 50–1000 mg l1 2–80 mg l1 10–500 mg l1

Merck Jenway ITS

Merck Merck

NO 3

10–500 mg l1 1–30 mg l1 N

Merck Jenway

0.5–50 mg l1 100–3000 mg l1 2–80 mg l1 0.01–0.5 mg l1 N 0.15–10 mg l1 10–500 mg l1 0.05–4 mg l1 PO4 250–1500 mg l1 0.5–12 mg l1 200–1600 mg l1 2–100 mg l1 SO4 10–400 mg l1 0.05–4 mg l1 SO3 10–200 mg l1

ITS Merck

10–250 mg l1 0.02–1 mg l1 Zn 2–100 mg l1

Merck Jenway ITS

NH4 þ As3þ/5þ Ca2þ Cl

3þ/6þ

Cr

CrO4 2 Co2þ Cuþ/2þ

þ

Ag

Fe2þ/3þ

Pb2þ F Mn2þ

Hg2þ Ni2þ

ITS Jenway ITS Merck Merck Merck Jenway; ITS ITS Merck ITS Merck Jenway ITS

Merck ITS Jenway Merck Jenway ITS

mechanism does not constitute a true sensor (see Section 3.10.1), colorimetric test strips are very much in use for in situ semi-quantitative quick detection of ionic species in water samples. Several companies offer such convenient strips: for instance, the Merckoquants strips (Merck, Germany) allow visual detection and quantitation of numerous ions in aqueous media. Some test-strip methods require the use of a portable or handheld reflectophotometer to improve accuracy of analyte determination and reproducibility. Cases in point are the Jenway environmental test kits (Belgium) or Industrial Test Systems Inc. strips with their 3 mg l1 lead-detection test using the Hach LeadTrak Pocket Colorimeter II providing limits of detection lower than the US EPA requirement (15 mg l1). Table 7 summarizes the ions covered by the test strips offered by the above-mentioned manufacturers.

3.10.4 Sensors for Dissolved Carbon Dioxide Carbon dioxide is the major end product of organic carbon degradation in almost all marine environments. Fluctuations of the CO2 level are related to the net ecosystem metabolism. Four parameters define the marine CO2 system: pH, pCO2, dissolved inorganic carbon, and total alkalinity. The solubility of CO2 in the water is about 28 times that of other

NO 2

PO4 3 Kþ SO4 2 SO3 2 Sn2þ

Zn2þ

Merck

Jenway ITS Merck Jenway Merck Jenway Merck Jenway Merck Jenway Merck

hydrophobic gases such as O2 or N2. One of the most popular methods for carbon dioxide determination in the gas phase is infrared (IR) spectrometry. For measurements in aqueous solutions, the Severinghaus pCO2 electrode is the most common potentiometric sensor.

3.10.4.1 IR Spectrometry Carbon dioxide can be analyzed by IR spectrometry because of its strong stretching bands at 2350 and 650 cm1. By measuring the intensity of these bands, CO2 is quantified. This feature may be used to determine the carbon dioxide generated after acidification of aqueous samples and to calculate in this manner the total inorganic carbon (TIC) of the water. Total inorganic carbon (also called dissolved inorganic carbon (DIC)) includes all the carbon-containing inorganic species present in solution, namely, CO2, H2CO3, HCO3  , and CO3 2 . By acidification of the water sample, the acid–base equilibria of these species are driven to CO2 production. Nevertheless, the application of IR spectrometry to TIC determination is limited due to the strong IR absorption of water as well as the long optical path lengths required for analyses in the gas phase.

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3.10.4.2 The pCO2 Electrode The equilibrium between the gas phase and water obeys Henry’s law, which states that, for an ideally diluted solution, the gas vapor pressure of a volatile solute is proportional to its mole fraction in the aqueous solution. For surface waters (i.e., at atmospheric pressure), Henry’s law can be simplified to Equation (7):

½CO2  ¼ KH pCO2

ð7Þ

where KH represents the so-called Henry’s constant and pCO2 is the partial pressure of this gas. The pCO2 values can be determined by using an electrode such as the Severinghaus one, based on the detection of pH changes in an internal HCO3  aqueous solution caused by the incoming CO2. This solution is entrapped between a glass pH electrode and a hydrophobic gas-permeable membrane. The latter permits the flow of small, uncharged (gas) molecules such as CO2, but prevents the entrance of charged molecules (hydrogen ions, cations, anions, etc.). In natural aquatic environments, pCO2 varies widely between 0.1–104 (e.g., coastal ocean surface waters) and almost 200 atm (nearshore sediments). The Severinghaus electrode presents important drawbacks: interferences caused by basic or acidic gases, slow response time, and effects of osmotic pressure caused by variable salt conditions in the sample and in the inner electrolyte.

3.10.4.3 Optical pCO2 Sensors Like pH sensors, optical pCO2 sensors (optodes) for water are an alternative in specific applications. While electrochemical sensors employ a glass pH electrode to monitor pH changes, optical devices rely on immobilized pH-sensitive indicator dyes (see Section 3.10.2.2) to accomplish the pH transduction process within the internal electrolyte. As in electrochemical pCO2 sensors, the acidity of the internal solution at equilibrium depends on the concentration of carbonic acid produced upon hydration of the permeated CO2, which in turn is proportional to the partial pressure of the analyte in the sample. Based on the pH changes produced by CO2 diffusion through a gas-permeable membrane in an internal reservoir of hydrogen carbonate buffer, YSI Life Sciences (Yellow Springs, OH, USA) commercialized the first optical pCO2 sensor. The latter uses the pH-sensitive fluorescent dye hydroxypyrene trisulfonic acid (HPTS, pyranine) and ratiometric fluorescent measurements (measuring the green emission upon successive excitation at two wavelengths) to determine the dissolved CO2 concentration. Mills et al. introduced a new scheme to design optical pCO2 sensors. They incorporated a pH-sensitive dye into a hydrophobic polymer membrane (e.g., cellulose acetate butyrate) and replaced the hydrogen carbonate internal buffer by a lipophilic hydrated quaternary ammonium hydroxide. Depending on the pKa and concentration of the indicator dye used, such sensors allow quantification of trace levels of CO2 and show a fast response. However, the membranes tend to fog after prolonged immersion in water and, sometimes, are prone to dye leaching. A thorough description of the different

types of optical sensors for CO2 measurements can be found in the review by Mills and Eaton (2000). Orellana and co-workers (1992) patented a CO2 sensing mechanism based on luminescent Ru(II) polyazaheterocyclic complexes immobilized in hydrogels, that undergo irreversible proton transfer in their excited state from various Bro¨nsted acids. The polymer-supported indicator dye is separated from the sample by a thin silicone membrane. Permeation of the CO2 into the gel phase modifies the concentration of the internal buffer species (with different proton transfer ability, e.g., hydrogen phthalate and phthalic acid) changing the Hþtransfer quenching of the luminescent indicator dye (Figure 3). Both emission intensity and luminescence-lifetime-based interrogation can be used to fabricate the sensor using the same instrumentation than the one developed recently for dissolved oxygen sensing (see Section 3.10.5). This version is used by OptosenTM Interlab IE (Madrid, Spain) in the luminescent dissolved O2/CO2 monitors that they market. Ru(II) polypyridyl complexes can also be used in combination with colorimetric indicator dyes to manufacture luminescent sensors based on Fo¨rster resonance energy transfer (FRET) from the photoexcited metal dye (donor) to the coimmobilized colorimetric indicator (acceptor). Permeation of CO2 into the gel phase containing the two dyes lowers its pH leading to a color change. The spectral shift of the acceptor provokes a variation in the FRET efficiency with concomitant change in the emission lifetime of the donor. This principle forms the basis of the PreSens (Regensburg, Germany) dissolved CO2 monitoring system.

3.10.4.4 Miscellaneous pCO2 Sensors Martek Instruments (Raleigh, NC, USA) offers a dissolved carbon dioxide analyzer based on conductivity measurements carried out before and after degassing the water sample. The conductivity differences are due to the carbonate and bicarbonate species that carbon dioxide forms when it is in solution. Table 8 provides a summary of the prototype and commercial sensors for dissolved CO2 measurements.

* Ru

+ HB

* Ru –H+ +

B−

Ru

+ HB

Ru –H+ +

B−

Figure 3 Working principle of the luminescent CO2 sensor based on photoinduced proton transfer to excited Ru(II) polypyridyls (Orellana et al., 2000). The ground state complex is completely non-protonated (pKa ¼  1.9); however, its basicity increases more than 106-fold in its excited state due to the high-acceptor character (low-lying p* orbital) of the pyrazine ligands. Therefore, it undergoes efficient (irreversible) proton transfer from suitable Bro¨nsted acids present in the reservoir indicator phase (phosphate, hydrogen phthalate, acetic acid, H3Oþ, etc). The incoming CO2 hydrolyzes and reversibly increases the HB/B ratio leading to strong luminescence quenching of the indicator dye.

Table 8

Some prototype and commercial sensors for carbon dioxide determination in aqueous solution

Dynamic range (ppm)

Limit of detection (LOD) (ppm)

Precision

Response time

Recovery time

Temperature(s) tested (1 C)

Interferences

Lifetime

Transduction principle

References

30–180 200–1000 NA 0.044–880 0.17–880

NA NA NA 0.044 NA

5–120 s o130 min 42.6 s o126 s 16 s

NA NA 88.8 s 240 s 30 s

NA 5–23 10–30 25 25

NA Temperature HCl Not tested NA

NA 4 months NA NA NA

Electrochemical Fluorescence Fluorescence Absorption Fluorescence

a

0.18–440

NA

NA 71 ppm NA NA 5.8% (relative standard deviation, RSD) NA

o1 min

46 min

0–40

NA

Fluorescence

f

NA 0–900 Up to 50%

0.33 0.50 NA

NA o2% o2.0%

3 min 7 min 1 min

10 min 12 min NA

NA 5–35 20

H2 S, CH3COOH, temperature NA Temperature Oxygen (421%), temperature

4 weeks NA NA

Fluorescence Fluorescence Luminescence

g

(832 ppm) (at 201C and 1 bar) 0.3–4.4 0.05–7 hPa (0.07–10 ppm) 15–1500 4.4–400

0.3 0.04 hPa (0.06 ppm) 15 NA

NA NA

1–2 min o30 s

NA o40 s

NA 25–63

NA H2 S

12 months 2 months

Fluorescence Luminescence

j

710% 72%

o120 s NA

NA NA

0–60 0–50

NA NA

Electrochemical Electrochemical

l

NA

NA

o7 min

NA

20–40

NA  NO 2 , HSO3 , HOAc, HCOOH NA

NA

Fluorescence

n

NA

70.06%

o3 min

NA

15–45

Salinity, acids, SO2, HCl

6 months

Luminescence

o

NA

72.0 ppb

NA

NA

0–100

NA

Analyzer: 5 years

Conductivity

p

1–25% (16.65–416.22 ppm) (at 20 1C and 1 bar) 1–25% (16.65–416.22 ppm) (at 20 1C and 1 bar) 0–10 a

Wiegran et al. (1999). Tabacco et al. (1999) and Walt et al. (2000). c Amao and Nakamura (2005). d Oter et al. (2006). e Ertekin and Alp (2006). f Mu¨ller and Hauser (1996). g Burke et al. (2006). h Wolfbeis et al. (1998). i Orellana et al. (1992) and Interlab OptosenTM (http://www.interlab.es). j Nivens et al. (2002). k Neurauter et al. (2000). l InPros5000 Mettler Toledo (http://www.mtpro.com). m Orion carbon dioxide electrode (http://www.thermo.com). n YSI 8500 (http://www.ysilifesciences.com). o Carbon dioxide sensor (http://www.presens.de). p Martek Dissolved Carbon Dioxide Analyzer (http://www.martekinstruments.com). NA, not available. b

b c d e

h i

k

m

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3.10.5 Dissolved Oxygen Sensors Together with pH, dissolved molecular oxygen is one of the main analytes to be monitored in water. Oxygen levels in water are critical to determine the health of stream, river, and lake ecosystems. Efficient operation of wastewater-treatment plants requires continuous monitoring of the O2 concentration for aeration, activated sludge, and nutrient-removal control. Tight corrosion control (e.g., nuclear power plants) requires sensing of waterborne O2 at ppb levels. Moreover, O2 sensors are also used as transducers for monitoring other water-quality parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), etc. (see Sections 3.10.8 and 3.10.9). The first dissolved oxygen determination was performed by L. H. Winkler in 1888 using a colorimetric method based on a titration of oxygen with thiosulfate ðS2 O3 2 Þ and iodine (I2). The amount of the dissolved oxygen is proportional to the generated tetrathionate ðS4 O6 2 Þ, which is determined by reduction of I2 to iodide (I). In spite of being difficult to use for online sensing purposes, a 100 years later, this is still employed as a reference method for calibration of electrodes. Automatic measurement of dissolved oxygen based on potentiometric determination of the produced I has also been developed.

3.10.5.1 Electrochemical Oxygen Sensors In the mid-twentieth century, electrochemical methods gained importance due to their fast response, possibility of in situ operation, and analyte nondestructive character, either in an amperometric (voltage applied or intensity of current measured) or potentiometric (intensity applied and voltage measured) mode. The DOC is proportional to the intensity or voltage measured respectively. The cumbersome, dangerous, dropping mercury electrodes gave way to amperometric sensors with solid electrodes covered with gaspermeable membranes and which were capable of being miniaturized. The so-called amperometric ‘Clark electrode’ has probably been the most used O2 sensor so far, and has been the basis of the majority of commercial electrochemical sensors sold till date (see Table 9). In a Clark electrode, oxygen is reduced on a platinum cathode covered with an oxygen-permeable membrane:

O2 þ 4e þ 2H2 O-4OH Oxidation of silver metal occurs at the anode with formation of silver chloride from the chloride ions dissolved in the inner electrolyte solution:

Ag þ Cl  -AgCl þ e  The electrochemical cell has to be polarized at about 800 mV to ensure linearity between the oxygen consumed at the cathode and the measured current. The electrode destroys the oxygen molecules, thereby requiring a minimum water flow in order to maintain equilibrium at both sides of the

gas-permeable membrane. Three problems of the Clark electrode have been identified in continuous operation mode: (1) buildup of an impermeable layer of AgCl at the active anode surface that may lead to a drift in the sensor readings and eventually to failure; (2) production of OH ions at the cathode moves the potential of the inner electrolyte to negative values leading to a zero shift; and (3) Cl ions in the electrolyte eventually become depleted. These problems are normally corrected by the periodical maintenance operations of polishing the anode and replenishing the electrolyte. At the same time, the gas-permeable layer is also cleaned or changed in order to prevent algal biofouling. Moreover, there is a need to establish a polarization of the electrochemical cell, and therefore the Clark electrode needs about 10 min before the first measurement can be obtained. This warm-up time is dependent on the sensor geometry and size. The Clark electrode readings are indeed a function of the O2 partial pressure in the water. According to Henry’s law, the O2 depends on the water temperature and salinity, and on the atmospheric pressure (at 20 1C and ambient pressure of 1013 mbar, air-saturated water contains about 9 mg l1 of O2). Therefore, appropriate corrections for these parameters must always be applied in all sensors. Hydrogen sulfide, a by-product of the anaerobic metabolism of bacteria (e.g., on decaying organic matter), produces the most prominent interference on O2 measurements using the Clark electrode. Once it permeates the electrode membrane, H2S is converted into sulfide ion at the alkaline pH of the inner electrolyte. S2 reacts at the silver anode with formation of a stable precipitate of Ag2S, which passivates the electrode that eventually stops working. A particular amperometric system is the galvanic cell, where a spontaneous O2 reduction at the (platinum or other noble metal) cathode take places combined with simultaneous oxidation of a readily oxidizable sacrificial anode (e.g., lead or zinc)

O2 þ 4e þ 2H2 O-4OH Zn-Zn 2þ þ 2e  The formed Zn(OH)2 turns into zinc oxide flakes (or PbO in case of a lead anode) that, unlike the AgCl of the Clark electrode, detach from the anode surface and avoid the electrode drift in long-term continuous monitoring. The polarization needed to reduce oxygen (B800 mV) is provided by the dissimilar metals of the cathode and anode (i.e., no need of external voltage application). The galvanic O2 sensor may be regarded as a corrosion cell, where the corrosion rate is determined by the rate of oxygen consumed at the cathode. In spite of their intrinsically limited operational lifetime (typically 5 years before having to change the anode), it overcomes some of the drawbacks of the amperometric Clark electrode (neither warm-up waiting, nor electrolyte replenishment and anode servicing are required). While the galvanic cell is less sensitive to the presence of H2S, ammonia and high levels of dissolved CO2 produce stronger interference than in the case of the Clark electrode.

Online Monitoring Sensors Table 9

237

Some commercial sensors for waterborne molecular oxygen

Transduction principle

Model

Dynamic range (mg l1)

Precision

LOD

Response time (s)

Temperature range (1 C)

Electrochemical/ Clark cell Luminescence quenching Luminescence quenching

DO100a

0–20

0.2 ppm

0.1 ppm

900

0–55

Oxi::lyserTMb

0–25

1%

0.01 ppm

NA

0–50

ROXs Optical Dissolved Oxygen Sensorc Model Q45Dd

0–20

0.1 ppm

0.01 ppm

NA

NA

0–40

0.2%

0.05%

NA

 20–60

FDOs 700 IQe

0–20

0.01%

NA

o150

 5–50

TriOxmaticse

0–60

0.1%

NA

180

0–60

DC 300f

0–20

1.5%

0.01 ppm

NA

0–50

ORBISPHERE G1100g ORBISPHERE A1100g Model 9438h

0–20

72 ppb

0.6 ppb

o30

 5–50

0.05–2000

71%

0.1 ppb

30

 5–60

0–20

75%

NA

NA

 20–55

HI 9142i

0–19.9

71.5%

0.1 ppm

NA

0–50

ODOTj

0–20

71%

0.01 ppm

o60

 10–60

COS21D-Ak

0.001–20

71%

1 ppb

o60

 5–100

OPTISENS AAS 2000l

0–20

71%

NA

NA

0–50

RDOs PROm

0–20

70.2%

0.01 ppm

NA

0–50

OPTOSENTMn

0–40

70.2%

0.01 ppm

NA

0–60

LDOs Dissolved Oxygen Probeg PSt3o

0–20

70.1 ppm

0.01 ppm

60

0–50

0–45

70.4%

0.015 ppm

NA

0–50

PSt6o

0–1.8

73%

0.001 ppm

NA

0–50

Electrochemical/ galvanic cell Luminescence quenching Electrochemical/ galvanic cell Electrochemical/ galvanic cell Luminescence quenching Electrochemical/ galvanic cell Electrochemical/ galvanic cell Electrochemical/ Clark cell Luminescence quenching Electrochemical/ Clark cell Electrochemical/ Clark cell Luminescence quenching Luminescence quenching Luminescence quenching Luminescence quenching Luminescence quenching a

Stevens Water Monitoring Systems Inc. (http://www.stevenswater.com). S::can Messtechnik GmbH (http://www.s-caNAt). c YSI Environmental (http://www.ysi.com). d Analytical Technology Inc. (http://analyticaltechnology.com). e WTW GmbH (http://www.wtw.com). f OAKTON Instruments (http://www.4oakton.com). g HACH Company (http://www.hach.com) h ABB Inc. (http://www.abb.com). i HANNA Instruments Inc. (http://www.hannainst.com). j Neotek-Ponsel (http://www.neotek-ponsel.com). k Endress þ Hauser Inc. (http://www.endress.com). l KROHNE Messtechnik GmbH & Co. (http://www.krohne.com). m In-Situ Inc. (http://www.in-situ.com). n Interlab IE (http://www.interlab.es). o PreSens Precision Sensing GmbH (http://www.presens.de). NA, not available. b

238

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Although concentrations of oxygen are generally measured in situ amperometrically, potentiometry offers an alternative way of O2 sensing. Using the latter method, no analyte is consumed during the measurement and, at low oxygen concentrations, the logarithmic sensitivity of potentiometric sensors becomes an advantage over the linear sensitivity of amperometric sensors. Potentiometric oxygen sensors were initially developed to measure in the gas phase at high temperature; however, novel potentiometric sensors for determination of dissolved oxygen at ambient temperatures have been manufactured using transition metals (cobalt, zinc, and platinum) or metal oxides (ruthenium oxide, tungsten oxide, and iridium oxide) electrodes.

3.10.5.2 Optical Oxygen Sensors After the original Winkler method (mentioned earlier), optical methods for measuring O2 levels in water have also been developed. For instance, the absorption at 620 nm of indigo carmine has been employed to determine 1–8 mg l1 of dissolved oxygen. This method was an improvement on the 1925 method by Efimoff that employed an indigo carmine solution, glucose as reducing agent, and potassium carbonate as pH buffer. Further modification of this procedure also led to the detection of ppb levels of O2 in water. However, colorimetric methods require sample pretreatment, which makes their online use difficult. When H. Kaustsky discovered in 1939 the luminescence quenching of organic molecules by dissolved oxygen, he was not aware that this opened an extraordinary door to the sensor community. Bimolecular dynamic deactivation of the electronic excited state does not consume the analyte and depends on the quencher concentration (O2 in this particular case). It is observed as a reduction on both the luminescence intensity and lifetime of the indicator dye according to the

Stern–Volmer equation

I0 F0 t0 ¼ ¼ ¼ 1 þ KSV ½O2  ¼ 1 þ kq t0 ½O2  I F t

ð8Þ

where I, F, and t represent the luminescence intensity, quantum yield, and lifetime of the indicator dye, respectively (the subscript 0 means in the absence of O2), and KSV is the so-called Stern–Volmer constant. The latter is equivalent to the product of the bimolecular rate constant of the quenching reaction (kq) and the lifetime of the luminophore in the absence of O2 (t0). The linearity of the Stern–Volmer law is normally lost when the luminescent indicator dye is immobilized into a polymer support for manufacturing the actual oxygen sensor by usually attaching the luminescent film at the distal end of an optical fiber (Figure 4). Therefore, oxygen optical sensors can be based on luminescence intensity or lifetime changes as a function of the analyte level. Luminescence-based O2 sensors offer advantages over electrochemical devices including ease of miniaturization, lack of analyte consumption, faster response, robustness, and insensitivity to interfering agents (e.g., H2S, CO2, or NH3). The low maintenance, extended operational lifetime, and reliability of fiber-optic oxygen sensors based on transition metal (Ru, Pd, Pt, and Ir) luminescent complexes with polyazaheterocyclic chelating ligands (bipyridines and phenanthrolines, porphyrins, etc.) are so noticeable that every major manufacturer of environmental monitors is currently offering at least one model for in situ dissolved O2 measurements in water (see Table 9), rapidly phasing out the amperometric Clark electrode. The appropriate selection of a luminescent dye with a long excited-state lifetime and a supporting polymer material with a high O2 permeability are key issues in the design of the ideal oxygen optical sensor. In this way, materials such as fluorinated polymers, polystyrene, aerogels, organically modified silicates (ORMOSILs), or polydimethylsiloxane (silicone) have

10 000

Counts

1000

100

10

1 0

5

10

15

20

Time (µS) Figure 4 Luminescence decay profiles upon pulsed excitation of the O2 indicator dye tris(4,7-diphenyl-1,10-phenanthroline)ruthenium(II) dichloride embedded in a silicone film, for different O2 concentrations in water (from top to bottom: 0, 0.4, 0.8, 1.2, 1.6, 4.0, and 8.0 mg l1).

Online Monitoring Sensors

all been used with the aim of finding the ideal matrix for each particular application. Most of the sensing layers are microheterogeneous materials formed by the base polymer plus fillers, cross-linkers, plasticizers, etc. These components may play different roles such as reinforcement, providing dye compatibility, increasing flexibility or resistance, etc. One of the most prominent advantages of fiber-optic oxygen sensors is the ease of miniaturization at affordable cost. Therefore, micro-optical sensors have made possible in situ monitoring of aquatic environments in confined spaces such as marine sediments, microbial mats, lichens, biofilms, etc. Both electrochemical and optical sensors are subject to formation of biofilm over the luminescent sensing layer, or the electrode membrane is a critical component of continuous dissolved oxygen monitoring in high biofouling environment (wastewater-treatment plants, highly eutrophized rivers or lakes, tropical ocean bays, etc.). Vendors have developed different methods to avoid such deposits and prolong the operational lifetime of the sensors. Thus, antifouling materials (YSI Inc., Yellow Spring, OH, USA), pressurized air systems (Analytical Technology Inc., Collegeville, PA, USA), ultrasounds (WTW GmbH, Weilheim, Germany), integrated spray cleaning nozzles (KROHNE Messtechnik GmbH, Duisburg, Germany), and automatic mechanical wiping (HACH Co., Loveland, CO, USA), to name a few, are useful commercially available strategies to keep sensor biofouling at bay.

3.10.6 Sensors for Waterborne Ozone Ozone (O3) is a strong oxidizing gas with various industrial applications spanning from water treatment to microelectronics to pharmaceuticals. The needs of each industry in terms of concentration is very different and ranges from the high levels for water treatment, where ozone is used for purification and disinfection purposes on highly absorbing water media, to the residual ppb levels that can be found in cooling water reservoirs of the pharmaceutical or microelectronics factories. Therefore, the monitoring needs for process control and possible interferences are broadly different. For example, the O3 concentration in wastewater treatment should be monitored carefully to keep it at the appropriate concentration that leads to effective water disinfection but minimizes the dangerous bromate ion formation from bromide. Similar to O2 (see Section 3.10.5), continuous monitoring of ozone can be performed by two main methods: electrochemical and optical sensing. The former is based on the electrochemical reduction of ozone over the sensor electrode. The redox method has a few disadvantages in that it is a nonspecific reduction process wherein chlorine or other oxidizing gases present in the sample can interfere with the O3 measurements. To resolve this, several approaches have been proposed. New electrochemical amperometric ozone sensors have a membrane through which ozone diffuses and reaches the electrode. The presence of this selective membrane avoids major interference from other species but introduces a delay in the analysis and increases the equipment cost. Ozone monitoring in waters with high concentration of particulates or salts

239

pose additional maintenance challenges to avoid membrane obstruction, with consequent increase in operational costs. A more elaborate system is the DOM-1 monitor from Eco Sensors, Inc. (Santa Fe, NM, USA). It includes a stripping chamber to extract O3 from the liquid phase for analyzing it electrochemically in the gas phase. Apparently, the stripping separation method reduces the response time of the equipment considerably. As far as optical methods are concerned, O3 has been traditionally been measured by colorimetry after reaction with indigo derivatives:

−

O

O3S N H

H N

SO3− O3 SO3−

O

ε600 ≅ 20 000 M−1 cm−1

−

O O3S O N (SO − ) H 3

ε600 ≅ 0,0 M−1 cm−1

For instance, CHEMetrics ozone dosimeter (Calverton, VA, USA) employs indigo trisulfonate. This dye reacts instantly and quantitatively with ozone, bleaching its blue color in direct proportion to the amount of ozone present. Malonic acid is included in the formulation to prevent interference for up to 3 mg l1 of waterborne chlorine. Ozone displays a strong absorption band centered at 253.7 nm in the UV region, with an absorption cross section of 1.141 1017 cm2. In fact, such a strong absorption band has been in use for quite some time for O3 measurements in the gas phase. Recently, companies such as Horiba Advanced Techno (Northampton, UK), S::can Messtechnik GmbH (Wien, Austria), and IN USA Inc. (Norwood, MA, USA) have developed new optical O3 sensors for water analysis. In some cases (e.g., the Horiba OZ-96 sensor), the system designed for ozone monitoring in clean waters is similar to that used during the manufacturing processes of the semiconductor industry where no absorbing interferent species influence the measurements. This is not the case in the treatment of wastewater with large amounts of suspended solids and organic matter that obstructs the optical measurements. Nevertheless, some recently results from S::can Messtechnik with its Spectro::lyserTM monitor show excellent agreement between the electrochemical and optical determination of ozone concentration in wastewater (Figure 5). Such an accuracy in the O3 level monitoring with an optical sensor in a complex absorbing matrix can be obtained, thanks to an elaborate data treatment using advanced chemometrics tools. A summary of representative waterborne O3 sensors can be found in Table 10.

3.10.7 Sensors for Waterborne Hydrocarbons Hydrocarbon-in-water sensing can be divided into two applications with very different requirements and detection levels. Thus, the petrochemical industry has traditionally used sensors for detecting oil spills which have high levels of hydrocarbons in water and, very often, a very large monitoring area. To this

240

Online Monitoring Sensors 7.0

2 1.5

6.0

1

Ozone ( mg L−1)

5.0

0.5 0 50

4.0

70

90

3.0

2.0

1.0

0.0 0

50

150

100

200

250

Time (h) Figure 5 Overlaid of optical (red) and electrochemical (black) ozone sensor measurements in wastewater. Data courtesy of S::can Messtechnik GmbH.

Table 10

Some commercial sensors for waterborne ozone measurements

Transduction principle Model Amperometric Amperometric UV-VIS absorption UV-VIS absorption Amperometric Amperometric Amperometric UV-VIS absorption Amperometric

DULCOTESTsa Q45H/64b spectro::lyserTMc dFFOZ-Wd DOM-1e CRIUS 4800f 9185sc Ozone Sensorg OZ-96h OZ-50i

Dynamic range (mg l1) Precision

LOD (mg l1) Response time (s) Temp. range (1 C)

0.05–2 0–200 0–30 0–150 0–2 0–10 0–2 0–100 0.1–10

NA NA NA NA 30 1 5 NA NA

0.01 ppm 0.5% 0.015 ppm 1% 10% 75% 3% or 710 ppb 70.5 ppm 73%

5 NA NA NA 60 1800 90 NA o60

0–40  20–60 5–40 NA 20–30 0–40 0–45 5–30 5–40

a

ProMinent Dosiertechnik GmbH (http://www.prominent.de). Analytical Technology Inc. (http://www.analyticaltechnology.com). c S::can Messtechnik GmbH (http://www.s-canat). d IN USA Inc. (http://www.inusacorp.com). e Eco Sensors Inc. (http://www.ecosensors.com). f Process Instruments (UK) Ltd. (http://www.processinstruments.net). g Hach Co. (http://www.hach.com). h HORIBA Advanced Techno (http://www.horiba.com). i Bionics Instrument (http://www.bionics-instrument.com). NA, not available. b

end, sensors based on the characteristic immiscibility of hydrocarbons and water have been developed. On the other hand, environmental protection agencies concentrate their efforts in detecting low levels of hydrocarbons in drinking water or recreation areas. A similar target is aimed at by the naval industry through the International Maritime Organization (IMO), whose Marine Environment Protection Committee has established an upper limit of 15 mg l1 for the oil pollution by ships and mandates control of the emission to the oceans by installation of alarm systems (IMO resolution MEPC 107[49]). Therefore, careful consideration of the sought application for the hydrocarbon-sensing device is the first step to making the

right choice of the monitoring technology. Moreover, some of the marketed sensors (see ahead) do not discriminate among the different hydrocarbon types and volatile organic compounds (e.g., chlorinated hydrocarbons). These hydrocarbonsin-water sensors are reviewed here according to their technology rather than the exact targeted analyte.

3.10.7.1 Oil-Spill Detection Since most hydrocarbons have low solubility in water and their density is lesser than 1, the presence of relatively large amounts of hydrocarbon in water is manifested as an oily

Online Monitoring Sensors

thermal energy, primarily in the IR region (8000–14 000 nm or 1250–700 cm1). Therefore, during daylight, IR-based sensing devices do not need an excitation source to operate, an advantage in terms of cost and size against other optical sensors. However, IR sensors cannot detect oil-in-water emulsions under most circumstances and several factors can interfere with the measurements including seaweeds and the shoreline. Aromatic hydrocarbons, particularly those containing multiple condensed rings, are strongly fluorescent (Figure 6). Both, the characteristic spectral (excitation/emission) and decay features of the fluorescence can be monitored and correlated to the particular hydrocarbon for quantitative and forensic measurements, very often with the aid of chemometrics. Unfortunately, the fluorescence intensity of an oil spill in seawater, excited by sunlight, is c. 5 times lower than that required for detection. Therefore, unlike IR sensors, optical fluorosensors require the use of lasers operating in the ultraviolet (300–355 nm) or visible (488 nm, e.g., Ar ion) for excitation of the pollutant. For instance, the FLSs Fluorescent LIDAR System of Laser Diagnostic Instruments AS (Tallin, Estonia) can monitor vast territories and detect hydrocarbon pollution at ppm level in water. Airborne oil-spill detection systems working on the principle of scattering of low-frequency electromagnetic radiation are also commercially available. The interaction between microwave radiation and the waves generated by the wind on the ocean surface, results in a scattered radiation known as Bragg scattering. The presence of an oil layer on the ocean surface reduces sea-surface roughness and dampens wind waves. Therefore, the back-scattered radar power decreases, creating dark structures in the radar images that signal the polluted area. This is the principle of SeaDarQ BV (Hardinxveld-Giessendam, The Netherlands) and Miros AS (Asker, Norway) large oil-spill detection systems.

layer on the water surface. Such a situation has been used, for instance, by GE Analytical Instruments Inc. (Boulder, CO, USA) for developing the Leakwises oil-spill sensor. It is a floating device that continuously monitors the liquid surface using a high-frequency electromagnetic absorption technique. Since water absorbs more electromagnetic energy than hydrocarbons, changes in the absorption rate of water indicate the presence or buildup of a hydrocarbon layer. The sensor uses a frequency of 2.45 GHz where the effect of the difference between the water and hydrocarbon dielectric constants is maximum and the influence of salinity is greatly reduced. This sensor has a detection range of 0.3–25 mm oil layer but, being a floating device, it has operational limitations when the water-level variations or lateral currents are too high. Another floating hydrocarbon sensor is the 2114 HCF from Arjay Engineering Ltd. (Oakville, ON, Canada) that monitors the capacitance field between the probe and its concentric shield. As the volume of separated oil increases over the water surface, the probe capacitance changes. Conductivity sensors based on the large difference between dielectric constants of water and hydrocarbons have also been developed. The Expo Instruments Cobra monitor (Sunnyvale, CA, USA) has a conductive polymer that changes its resistance when hydrocarbons are adsorbed. Conductivity measurements are also the basis of the Waterra HS-1 sensor (Bellingham, WA, USA) to make inspections in narrow wells. An additional measurement with an ultrasonic sensor offers the possibility of determining the thickness of the hydrocarbon layer over the water. The sensing devices mentioned above are useful for detection of large amounts of hydrocarbon spills at a particular point. However, they may be insufficient for environmental protection agencies that have the mission of monitoring vast water areas. Two different strategies have been adopted to tackle this problem: (1) installation of several oil point detectors and (2) development of systems for very large surface surveillance. The latter has usually been realized by installing detectors in an aircraft that can scan a large area in a short period of time, searching for an oil spill. Hydrocarbon layers, which are optically thick fluids, absorb solar radiation and re-emit a portion of this radiation as

3.10.7.2 Water-Quality Control When the level of waterborne hydrocarbons lies in the mg l1 (ppm) or mg l1 (ppb) ranges, no oil layer builds up and the pollutants are either dissolved or suspended as micro-droplets. In these two situations, optical sensors may be the best choice.

0.12

25 × 106

0.10

15 0.06 10

0.04

5

0.02

0

0.00 350

400 Wavelength (nm)

Figure 6 Absorption and emission (lexc ¼ 340 nm) spectra of anthracene.

450

500

Intensity (a.u.)

Absorption

20 0.08

300

241

242

Online Monitoring Sensors

The most intuitive method to develop an optical hydrocarbon sensor is to use the intrinsic photophysical properties (absorption and/or emission of light) of the analyte itself. Aromatic hydrocarbons absorb strongly in the UV spectral region and both aromatic and aliphatic hydrocarbons display characteristic (narrow) absorption bands in the IR region. Aromatic hydrocarbons also display strong fluorescence that may be employed for more sensitive direct sensing as has been shown earlier. The difference of refractive index between water and hydrocarbons may also be the basis for alternative optosensing schemes. Moreover, some sensors capitalize on the light-scattering properties of the hydrocarbon micro-droplet suspensions in water to determine the pollutant concentration. In general, interferences from the matrix (e.g., strongly absorbing media or high levels of suspended particles) are bound to affect the performance of the above-mentioned optical sensors profoundly for waterborne hydrocarbon measurements (but not of indicator-mediated ones, see Section 3.10.7.2.4).

3.10.7.2.1 Sensors based on refractive-index changes The adsorption of hydrocarbons onto the cladding of a polymer-coated silica (PCS) optical fiber provokes a change in refractive index and therefore in the intensity of the light transmitted along the fiber by attenuated total reflection. This is the basis, for instance, of the fiber-optic chemical sensor developed by Petrosense Inc. (Las Vegas, NV, USA) CMS-4000. A popular class of fiber-optic sensors based on refractiveindex changes uses the so-called fiber Bragg gratings (FBGs) and long-period gratings (LPGs) as the analyte-sensitive device. In both cases, thanks to a periodic variation in the refractive index made to the fiber core, a selection of transmitted wavelengths is obtained. The latter shift upon a change in the grating refractive index, due to adsorption of the hydrocarbons. Nevertheless, depending on the adsorbent polymer and the monitored hydrocarbon type, reversibility of the refractive-index-based fiber-optic sensors may be an issue. Therefore, instrument manufacturers offer optional sensorcleaning systems.

3.10.7.2.2 Sensors based on light scattering The scarce solubility of hydrocarbons in water usually leads to micro-droplet formation even at ppm concentrations that produce scattering of the incident light (see Section 3.10.13). For instance, Dexil Corp. (Hamden, CT) PetroFLAGs is an offline hydrocarbon analyzer that includes extraction, filtration, and turbidity measurements to determine hydrocarbons in water samples. Multiwavelength light scattering (MWLS) with detection at different angles is used by Deckma GmbH (Hamburg, Germany) in their online OMD-7 MKII hydrocarbon detector, specially designed for installation on board ships for monitoring ocean pollution. Measurements at three different wavelengths and various angles of detection allow obtaining turbidity, and oil and solid concentrations with the same equipment. MWLS including the near-IR (NIR) region is used by Rivertrace Engineering Ltd. (Redhill, Surrey, England) in the OCD Xtra oil-in-water analytical system. Both the OMD-2005 monitor of DVZ Services GmbH (Syke, Germany) and the TF16-EX sensor of Optek-Danulat GmbH (Essen,

Germany) use combined light scattering and VIS–NIR absorption data for determination of oil concentrations in water. In general, sensors based on light-scattering measurements are strongly affected by suspended particles present in the sample. In particular, iron-oxide particles have been identified as the major source of interference in sensors installed on ships, and some manufacturers have developed special systems for their elimination or discrimination to avoid false-positive alarms of hydrocarbon presence in the water.

3.10.7.2.3 Sensors based on absorption changes The UV absorption of aromatic hydrocarbons has been used for the development of rugged optical sensors. For instance, S::can Messtechnik GmbH (Vienna, Austria) manufactures the Spectro::lyser system that measures the absorption between 220 and 390 nm to determine the concentration of benzene, toluene, ethylbenzene, and xylene (BTEX). It is a double-beam instrument that compensates light scattering from suspended solids, with a variable path length from 1 to 100 mm depending on the analyte and sensitivity required. OptekDanulat GmbH (Essen, Germany) offers its AF46-EX, a dualchannel UV absorption (254, 280, 290, 300, and 313 nm) sensor with optical paths from 1 to 500 mm. The Teledyne Analytical Instruments (City of Industry, CA) 6600 model includes an in situ ultrasound homogenizer to dissolve the oil in water before UV-absorption analysis and minimize interferences from scattering. Dissolved natural organic matter or UV-absorbing salts are other potential sources of interference. The use of NIR sensors for in situ water-quality control is limited by the strong absorption of water in these regions due to the combination and overtone O–H absorption bands. These bands are often stronger than the C–H absorptions of hydrocarbons, impairing direct determination of the latter in aqueous medium. Therefore, traditional (off-line) hydrocarbon-determination methods based on NIR spectroscopy must include a previous extraction step with freon (American Society for Testing and Materials (ASTM) Method D3921) or, nowadays, more environmentally acceptable solvents such as S-316 (ASTM Method D7066-04). An example is the portable InfraCals analyzer from Wilks Enterprise, Inc. (South Norwalk, CT, USA). Nevertheless, this procedure has particular interest for analyses where online monitoring presents special difficulties due to the presence of high levels of suspended solids or for the analysis of hydrocarbons in soil. Some online sensing devices do both extraction and analysis, with a 5– 30 min operational delay. For instance, Horiba’s OCMA-25 (Northampton, UK) includes extraction of waterborne hydrocarbons with S-316 and absorbance measurements in the IR region (3400 nm). The solvent-extraction step can be eliminated using spectroscopic techniques based on evanescent field absorption (EFA) measurements using polymer-coated optical fibers as the sensing elements and the NIR or mid-infrared (MIR) spectral ranges. The latter type of hydrocarbon sensors could only be developed after the appearance of silver halide-based optical fibers which display high transmission in the IR region. The EFASs fiber-optic sensor for in-situ monitoring of organic pollutants in water from Siegrist (Karlsruhe, Germany) capitalizes on the EFA principle. Novel approaches include planar

Online Monitoring Sensors

and liquid-core waveguide technology. On these waveguideevanescent optical sensors, the coating layer is the key part of the sensor, which determines the selectivity and sensitivity of the final device. Teflon, poly(dimethylsiloxane), polysiloxane with polysiloxane–xerogel, poly(vinyl chloride) (PVC), and low-density polyethylene (LDPE) have all been used to monitor aliphatic, aromatic, and chlorinated hydrocarbons in water. Inelastic Raman-scattering sensors based on Y-shaped fiberoptic reflection probes have also been used to monitor waterborne hydrocarbons. To improve the inherent low sensitivity associated with Raman measurements and to achieve detection limits in the low ppm range for chlorinated hydrocarbons (o1 ppm for trichloroethylene, 15 ppm for perchloroethylene, 15 ppm for chloroform, and 10 ppm for carbon tetrachloride), surface-enhanced Raman spectroscopy (SERS) has been proposed as an alternative. Nevertheless, to the best of our knowledge, no commercial Raman hydrocarbon sensor is currently available.

3.10.7.2.4 Sensors based on emission changes As mentioned above, aromatic hydrocarbons display a strong fluorescence in the UV-VIS that has been exploited for the development of sensitive monitors. Lieberman et al. (1991) described laser-induced fluorescence via optical fibers to measure the level of petroleum hydrocarbons in real time and in situ in seawater. An N2 laser (337 nm, 1.4 mJ, 800 s1 pulses) was coupled to a 10-m bifurcated silica optical fiber and the fluorescence collected by six other fibers concentrically distributed around fiber undergoing excitation. A photodiode array was used as the detector. With this equipment, emission decays and fluorescence spectra of the seawater could be collected and the levels of hydrocarbons continuously monitored. Commercial equipment based on the hydrocarbon intrinsic fluorescence has been available for long in the market. For instance, the TD-4100 system from Turner Designs Hydrocarbon Instruments Inc. (Fresno, CA, USA) detects hydrocarbons in a stream of water falling through an open chamber in which fluorescence is measured. No adsorption media or measuring flow cell is used and therefore problems of measuring-time delays, regeneration, and cell cleaning are eliminated. UV fluorescence measurements are also used by DMA Sorption ApS (Vedbæk, Denmark) in their Bilge monitor for hydrocarbons in water, reaching response times below 1 s with equipment installed on board ships. The FPM 605 from J.U.M. Engineering GmbH (Karlsfeld, Germany) and the CX6000 from Awa Instruments (Duluth, GA, USA) are further examples of hydrocarbon sensors for online water monitoring based on fluorescence measurements. In the Hydrosense 2410 from Arjay Engineering Ltd. (Oakville, ON, Canada), water flows down a special UV plate to maximize signal strength and stability of the fluorescence reading. In general, fluorescence determinations (particularly those based on time-resolved measurements) have the advantage of a lower interference from particles or bubbles that usually provoke false-positive alarms in light-scattering-based equipments. However, since most fluorescence sensors use the intrinsic fluorescence of the analyte itself, hydrocarbons

243

with low fluorescence-quantum yield or no emission at all (e.g., aliphatic or chlorinated) prevent the use of such devices for waterborne hydrocarbon monitoring. To overcome such problems and manufacture more general detectors, different strategies have been proposed. Development of indirect sensors where an immobilized luminescent indicator dye displays a solvatochromic effect on its emission is one of the most promising strategies. Thus, different luminescent dyes such as Nile Red (White et al., 1996) or a ruthenium complex (Castro et al., 2005) have been successfully used to that end. The latter is the base of the fiberoptic portable hydrocarbon-in-water sensor manufactured and recently commercialized by Interlab IEC (Madrid, Spain) within its line of OptosenTM monitoring systems. A compilation of representative sensors for waterborne hydrocarbons is listed in Table 11.

3.10.8 Sensors for Waterborne Organic Matter The level of organic pollutants in the river, reservoir, or wastewater is one of the most widely analyzed parameter because excess contamination of these substances in aquatic environments provokes serious damages to the ecosystem. However, while existing laboratory methods and analyzers are plentiful, there are very few sensors for online in situ continuous (or even near real-time) monitoring of the several indices related to the contents of waterborne organic matter (COD, BOD, and TOC, see ahead).

3.10.8.1 Sensors for COD The COD index is commonly used to indirectly measure the overall amount of organic compounds in water and is expressed in milligrams per liter (mg l1), which indicates the mass of molecular oxygen consumed per liter of solution in the complete oxidation process. COD measuring systems usually imply the total oxidation of the waterborne organic matter and concomitant measurement of the oxygen consumption for such oxidation. Traditionally, oxidation has been done using a strong oxidizing agent such as potassium permanganate (KMnO4). Currently, off-line test kits with potassium dichromate (K2Cr2O7) as oxidizing agent are the most popular and practical for routine applications. COD values are determined by means of a photometric method after digestion (Environmental Protection Agency method 410.4). COD online monitoring equipment based on traditional chemical oxidants, like TOC sensors, usually display disadvantages that mainly include the measuring delay time and the experimental error due to the partial (instead of full) oxidation of some organic and inorganic matter. To overcome these limitations, more efficient methods such as thermal (1200 1C) or ozone oxidation are included in new commercial equipments (see Table 12). A different system is the PeCODTM from Aqua Diagnostic (South Melbourne, Australia) that directly measures the photocurrent charge originating from the oxidation of organic species contained in a sample. The photocatalytic oxidation of organic matter takes places in a photoelectrochemical cell with a photoactive electrode (e.g., a layer of titanium dioxide nanoparticles coated on an inert

244 Table 11

Online Monitoring Sensors Some commercial sensors for waterborne hydrocarbons

Transduction principle

Analyzer model

Dynamic range (mg l1)

Precision

LOD (mg l1)

Response time (s)

Limitations/ interferences

Temp. range (1 C)

Electrical resistance High-frequency absorption UV fluorescence

Cobraa Leakwiseb

NA 0.3–25 mm

NA NA

NA NA

5 NA

NA NA

2–74 0–70

NA

710%

0.001–1000

o10

0–49

CMS-4000 OCMA-25e

0–20 000 0–100

715% 73%

o10 NA

o60 600

Fluorescent compounds NA None

0–50 0–40

OCD Xtraf

0–200

75 ppm

1

NA

NA

0–50

DMA ppm MonitorTMg OMD-7 MKIIh

0–40

710%

o15

1

NA

0–70

0–200

710%

5

10

NA

0–70

OMD-2005i PetroFLAGTMj

0–30 10–50 000

72% 710%

2 15

NA 900

NA Off-line system

1–65 2–35

InfraCalTMk

2–5000

70.1%

0.5%

600–900

Off-line system

4–45

0–100 0–1000 NA

70.3% 71.5% NA

0.1 ppm 0.1 1.5 mm

30 o12 NA

NA NA NA

5–40 10–39 0–50

70.1 710% 71 mm o71%

0.1 0.1 ppm NA o70.05%

NA o10 NA NA

NA NA NA NA

10–50 0–50 0–50 0–70

TF16-EXq

0–500 0–1000 0–600 mm Hydrocarbon dependent 0.5–500

o70.3%

o70.05%

NA

NA

0–40

HC 9010r

0.1–10

0.01 ppm

0.1 ppm

1800

NA

5–20

Teledyne 6600s Teledyne 4080s

0–200 0–1000

72% 72%

1% 0.1 ppm

o5 15

NA For C1 to C9 þ

0–50 4–43

EnviroFlu-HCt FLS-LIDARu SeaDarQv

0–200 NA NA

NA NA NA

0.1 ppb NA NA

NA NA NA

NA NA NA

0–40 NA NA

Miros ODSw

NA

NA

NA

NA

NA

NA

Refractive index change Solvent extraction þ IR absorption Multi-wavelength light scattering UV fluorescence Three wavelength, multiangle light scattering Absorption and scattering Solvent extraction, filtration, and turbidity Solvent extraction þ IR absorption UV absorption UV-fluorescence Conductivity and ultrasounds UV-fluorescence UV-fluorescence Capacitance UV absorption VIS–NIR absorption þ light scattering Solvent extraction þ IR absorption UV absorption Gas chromatography þ FID detection UV-fluorescence UV-fluorescence Long wavelength scattering Long wavelength scattering a

TD-4100c d

Spectro::lyserTM FPM 605 m HS-1n

l

Hydrosense 2410o CX6000p 2114-HCFo AF46-EXq

Expo Instruments Inc. (http://www.expoinstruments.com). GE Analytical Instruments Inc. (http://www.geinstruments.com). c Turner Designs Hydrocarbon Instruments Inc. (http://www.oilinwatermonitors.com). d Petrosense Inc. (http://www.petrosense.com). e Horiba (http://www.horiba.co.uk). f Rivertrace Engineering Ltd. (http://www.rivertrace.com). g DMA sorption (http://www.dma-sorption.dk). h Deckmahamburg GmbH (http://www.deckma.com). i DVZ group (http://www.dvz-services.de). j Dexil Corporation (http://www.dexsil.com). k Wilks Enterprise Inc. (http://www.wilksir.com). l S::can Messtechnik GmbH (http://www.s-caNAt). m J.U.M. Engineering GmbH (http://www.jum.com). n waterra USA Inc. (http://www.waterra.com). o Arjay Engineering Ltd. (http://www.arjayeng.com). p Awa Instruments (http://www.awa-instruments.com). q Optek-Danulat GmbH (http://www.optek.com). r Environnement S.A. (http://www.environnement-sa.com). s Teledyne Analytical Instruments Inc. (http://www.teledyne-ai.com). t TriOS Mess- und Datentechnik GmbH (http://www.trios.de). u Laser Diagnostic Instruments AS (http://www.ldi.ee). v SeaDarQ B.V. (http://www.seadarq.com). w Miros AS (http://www.miros.no). NA, not available. b

Online Monitoring Sensors Table 12

245

Some commercial sensors for the chemical oxygen demand (COD) of water

Oxidation method and transduction principle

Model

Heat þ O2 determination Oxidation þ photocurrent measurements UV-VIS absorption

QuickCODsa PeCODTM P100 analyzerb

O3 oxidation þ O3 differences UV254 absorption

CarbonVISs 700/1 IQc Phoenix 1010d UV 400e

Precision

LOD (mg l1)

Response time (s)

Temprature range (1 C)

0–100

NA

NA

60

NA

0–350

3%

0.2

30–300

NA

0–2500

3%

NA

NA

NA

0–100 000

5%

10

180–900

5–40

10 ppm

NA

10

0–50

Dynamic range (mg l1)

0–20 000

a

LAR Process Analyzers AG (http://www.lar.com). Aqua Diagnostic (http://www.aquadiagnostic.com). c WTW GmbH (http://www.wtw.com). d Endress þ Hauser Instruments AG (http://www.endress.com). e Tethys Instruments SAS (http://www.tethys-instruments.com). NA, not available. b

conductive substrate) and the changes in photocurrent are related to the COD value. No consumption of an oxidizing agent is involved but an electrolyte solution is needed. Since most organic compounds absorb in the UV region, the absorption spectra of water samples can yield COD data. No oxidation reagent is consumed as no sample treatment is performed, with consequent saving in time, device size, and system autonomy. Electrochemical methods have also been successfully applied to laboratory measurements. For instance, COD levels between 20 and 9000 mg l1 have been determined amperometrically with a boron-doped diamond electrode. Oxidation is carried out by the formed hydroxyl radical at the surfaces of the electrode upon water electrolysis. The current of the working electrode changes proportionally with the concentration of the organic matter as long as the physisorbed HO radicals are not depleted. Another amperometric sensor with a surface ground copper electrode has been used to measure 10–1000 mg l1 COD thanks to the catalytic action of copper. Table 12 summarizes representative COD sensors for water analysis.

3.10.8.2 Sensors for BOD BOD, also called biochemical oxygen demand, is another very common index of the quality of water based on quantification of the overall concentration of organic substances by their effect on the respiration of a microbial biomass. The conventional parameter of quality, dating back to 1908, is the socalled BOD-5 (or BOD5) method that measures the oxygen consumption of a sample at 20 1C over 5 days in the dark, by aerobic microorganisms deliberately introduced into the water sample in a closed container. The rate of oxygen uptake is nowadays measured by an oxygen sensor placed in the headspace. The values of the BOD-5 for the different waters can be accurately measured to comply with legislation but the index

is of no use for early warning of environmental damage (spills, runoffs, illegal discharges, etc.), industrial wastewater realtime monitoring, or for maximizing the efficiency of wastewater plant operation (optimization of the biological treatment by monitoring the instantaneous organic-matter level of the influent and the effluent). To overcome the pitfalls of the BOD-5 method, an electrochemical biosensor for BOD estimation was developed, as early as 1977, based on Karube’s work in Japan. The biosensor contains whole microorganism cells immobilized on an acetylcellulose membrane in contact with the water to be measured on the one side, and with a Clark-type oxygen electrode on the other (see Section 3.10.5). While the BOD-5 method uses a mixture of microorganism species, the Karube BOD sensor was based on the respiration of a population of Trichosporon cutaneum. The yeast degrades most organic compounds with concomitant decrease of the dissolved oxygen level producing a measurable response of the oxygen sensor. The microbial sensor BOD values linearly correlated with the BOD-5 values in the 0–60 mg l1 range of a glucose–glutamic acid (GGA) standard solution, with a 20-min response time. The sensor was marketed in 1983 (Nissin Electric Co.) and successfully used, for instance, to measure wastewaters from fermentation plants. Only phosphate buffer and GGA solutions were required for the daily measurements. Several improvements have been introduced in current BOD sensors to reduce their response time (down to 30 s), extend their operational lifetime before change of the sensitive terminal (more than a year), and to raise their sensitivity (limits of detection as low as 0.2 mg l1). These improvements have been possible thanks to the introduction of flow-injection analyzers (FIAs) to perform automatic water sampling, transport, dilutions and standardization, substitution of luminescent optical oxygen sensors (see Section 3.10.5) for the electrochemical devices, and the replacement of Pseudomonas putida, Pseudomonas fluorescens biovar, Bacillus subtilis, Stenotrophomonas maltophilia (among others), or even activated

246

Online Monitoring Sensors

sludge for the T. cutaneum, which is unable to degrade lessbiodegradable organic substances. There are, currently, only a few commercial online BOD analyzers. The Japanese ruggedized BOD 3300 and the benchtop a-1000 models of Central Kagaku Co. are based on the original Karube’s electrochemical O2 sensor respiration measurements to determine BOD levels between 0–500 and 2–50 mg l1, respectively, every 30–60 min. The Spanish Optosens-DBO in situ analyzer (Interlab Ingenierı´a Electro´nica) uses state-of-the-art luminescent measurements of dissolved O2 to interrogate respiration of the immobilized microbial biomass. The sensor allows BOD determinations in the 0–2000 mg l1 range every 20–60 min. However, the South Korean company Korbi has opted for a microbial fuel cell to degrade the sample in its online HABS-2000 analyzer, and correlate the generated electrical signal with the water BOD level (0.1–200 mg l1). In spite of the potential advantages of in situ online BOD sensing, these analyzers are not yet widespread due to (1) the lack of legislation enforcement to perform such measurements, (2) the difficulties often found to relate instant BOD readings with the traditional BOD-5 measurements for water samples with high levels of suspended organic matter, and (3) the recent availability of competing technologies such as TOC online analyzers (see Section 3.10.8.3).

3.10.8.3 Sensors for TOC TOC is the amount of bound carbon in waterborne organic compounds and is yet another nonspecific indicator of water

Table 13

quality often used as an alternative to COD or BOD measurements. In other to avoid interferences from waterborne inorganic carbon (IC), mainly from carbonate and hydrogen carbonate ions, a previous acidification and purging with inert gas of the water sample is included in some of the equipment. Traditionally, TOC analysis has included a first-digestion stage where both organic and inorganic matter is oxidized to CO2. Subsequently, the generated CO2 is quantified and the TOC calculated. A combination of persulfate acid addition, UV irradiation, ozone treatment ,and high temperature combustion (1200 1C) are the most common digesting methods depending mostly on the TOC concentration of the sample (see Table 13). The combination UV/persulfate is   based on the high oxidation potential of the SO 4 and OH radicals produced upon irradiation of S2 O8 2 and H2O, respectively. The mineralization step is the limiting process in terms of analysis times. Therefore, most commercial equipments show delay times of the order of several minutes. For very low TOC concentrations, Mettler-Toledo Thornton Inc. (Bedford, MA, USA) and GE Analytical Instruments (Boulder, CO, USA) offer the model 5000 and Check Point TOC sensors, respectively (see Table 13) that perform an online vacuum-ultraviolet (VUV) (185 nm) oxidation, reducing the total analysis time to less than a minute. Another current approach for low TOC samples is to digest the organic matter by generation of OH radicals by electrolysis or photochemical dissociation of water molecules. Such a reagent-free long operational lifetime system, coupled with a gas–liquid separator and a nondispersive infrared (NDIR) analyzer is used by National Aeronautics and

Some commercial sensors for waterborne total organic carbon (TOC)

Mineralization and transduction principle

Model

Dynamic range (mg l1)

Precision

LOD

Response time (s)

UV/heated persulfate þ CO2 IR detection O3/OH þ CO2 IR detection Combustion þ CO2 IR detection IR detection UV–VIS absorption UV/cold persulfate þ potentiometric CO2 detection UV–VIS absorption

Series 6700a

0–10 000

2%

NA

420

0–40

Series 6700a Series 6700a Series 6700a ProPS–Kitb COT 9010c

0–25 000 0–10 000 0–10 0–500 0–220

3% 3% 3% NA 0.01 ppm

NA NA NA NA 0.5 ppm

600 300 420 NA 1800

0–40 0–40 0–40 NA 5–30

0–150

NA

NA

NA

0–45

0–10 000

3%

NA

360

5–40

0.05 ppb (o5 ppb) 1% (45 ppb) 3%

0.025 ppb

o60 s

0–90

0.05 ppb

15 s

O3/persulfate þ CO2 IR detection UV oxidation þ differential conductivity

UV oxidation þ conductivity a

carbo::lyserTM II/IIId BioTectors Series 4e 5000TOCef

CheckPointg

Teledyne Technologies Company (http://www.teledyne.com). TriOS Optical Sensors (http://www.trios.de). c Environnement S.A. (http://www.environnement-sa.com). d S::can Messtechnik GmbH (http://www.s-canat). e Pollution Control Systems Ltd. (http://www.biotector.com). f Mettler-Toledo Thornton Inc. (http://us.mt.com). g GE Analytical Instruments (http://www.geinstruments.com). NA, not available. b

0–1

0–1

Temperature range (1 C)

10–40

Online Monitoring Sensors

Space Administration (NASA) for TOC analysis in the International Space Station. After the full oxidation step, detection of the generated CO2 gas takes places by means of IR absorption (at 2350 cm1 after purging the aqueous CO2 into the gas phase), potentiometric methods, or differences in conductivity of the water before and after mineralization. The continuous growth of optical sensors is slowly displacing traditional (mineralization–detection) systems in the TOC sensing field as well. Direct UV-VIS absorption of the dissolved organic and inorganic matter has been used by some companies such as S::can (Vienna, Austria) and TriOS (Oldenburg, Germany) for developing alternative in situ TOC sensors. Both analyzers use the entire UV spectral region (210–330 nm) while in other optical equipment, such as the Tethys UV 400 (Meylan, France), only absorption at a single wavelength (usually 254 nm) is monitored with the consequent loss of information. Suppression of the digestion stage dramatically reduces both the analysis time and instrument size, eliminates the consumption of oxidizing reagents, and increases the equipment power autonomy. Nevertheless, these analyzers are limited thus far to low TOC measurements. Compared to BOD measurements, COD and TOC online analyzers provide faster, more reproducible readings. However, the BOD index is more closely related to natural processes than either COD or TOC values because the former uses microorganisms to determine the level of waterborne organic matter (biodegradable organic matter). Additionally, COD readings are affected by the presence of both oxidizing and reducing inorganic matter and TOC has to be corrected by the IC values (mentioned earlier). A representative collection of TOC sensors is listed in Table 13.

Table 14

3.10.9 Waterborne Chlorophyll Sensors Phytoplankton photosynthetic efficiency is one of the biological signals that rapidly reacts to changes in nutrient availability as well as to naturally occurring or anthropogenic toxins (contaminants) and, therefore, is a useful indicator of the environmental water health. As early as in 1956, P. Latimer considered the yield of in vivo chlorophyll a fluorescence as an index of the photosynthetic efficiency. The fluorescence yield can be used as an approximation to chlorophyll concentrations and, in fact, some commercial equipments use this simple principle (see Table 14). Removal of interferences from other fluorescence substances and discrimination of the different algal groups (green Chlorophyta, blue–green Cyanobacteria, brown Heterokontophyta, Haptophyta, or Dinophyta) can be performed using several excitation wavelengths and recording an excitation spectrum characteristic of each group. However, fluorescence per unit chlorophyll is not constant but varies according to the photosynthesis rate and also in response to other factors such as prior exposure to excess irradiance. Correlations between the photosynthesis rate of algal cultures and the increase of the chlorophyll red fluorescence from the photosystem II in the presence of 3-(3,4-dichlorophenyl)1,1-dimethylurea (DCMU) were first observed by G. Samuelsson and co-workers in 1977. In the presence of the pesticide, green algae will strongly fluoresce due to the inhibition of the photosynthetic electron transport. A pronounced DCMU-induced emission increase is recorded when the photosynthetic activity is high (growing algal culture), while algae in the stationary phase of growth would be expected to show only a small DCMU-induced increase in fluorescence. Based on the comparison of fluorescence readings in the presence and absence of DCMU and using a

Some commercial sensors for waterborne chlorophyll

Transduction principle

Model

Dynamic range (mg l1)

Precision

LOD (mg l1)

Temperature range (1 C)

Multiple turnover fluorescence Fluorescence (exc. 470 nm) Fluorescence Single turnover fluorescence Fluorescence (exc. 470 nm) Fluorescence (exc. 470 nm) Fluorescence (three exc. wav.) Single and multiple turnover fluorescence Single turnover fluorescence

PhytoFlasha ECO FLNTUb YSI 6025 c FIRed ECO–FLb Manta2e Algae Torchf Fasttracka IIg

0–100 0.01–50 0–400 0.05–100 0–125 0–500 0–200 0–600

NA 1% 0.1 mg l1 NA NA 0.01 mg l1 0.2 mg l1 2%

0.15 0.01 0.1 NA 0.01 0.03 NA NA

 2–50 0–30  20–60 0–40 0–30 NA 0–30  10–40

Submersible FL3500/ SMh

NA

NA

NA

a

Turner Designs Inc. (http://www.turnerdesigns.com). WET Labs, Inc. (http://www.wetlabs.com). c YSI Environmental (http://www.ysi.com). d Satlantic Inc. (http://www.satlantic.com). e Eureka Environmental Instrumentation (http://www.eurekaenvironmental.com). f BBE Moldaenke GmbH (http://www.bbe-moldaenke.de). g Chelsea Technologies Group Ltd (http://www.chelsea.co.uk). h Photon Systems Instruments (http://www.psi.cz). NA, not available. b

247

0–55

248

Online Monitoring Sensors

parallel flow-through fluorometer, Cullen and Renger developed an online method and defined a fluorescence response index (FRI). Technical advances in electronics have lead to the development of new modulated techniques where the fluorescence yield of chlorophyll a can be determined without addition of any inhibitor agent. By repetitive application of short light pulses of saturating intensity, the fluorescence yield at complete suppression of photochemical quenching is repetitively recorded, allowing continuous plots of the photochemical and non-photochemical quenching. In the dark condition, due to emission quenching by the primary electron acceptor of the photosynthetic process (quinone), a low level of fluorescence emanating from the pigment bed is measured (F0) with a low intensity (not to drive photosynthesis, and in the absence of solar irradiance), exciting beam. When a darkadapted sample is exposed to a high-energy single turnover flash (10–100 ms), a single photoreduction of all the primary electron acceptor occurs, and the fluorescence rises from F0 to a maximum fluorescence level (Fm). Thus, the maximum quantum yield of photochemistry in PSII is given by (Fm – F0)/Fm. The use of multi-turnover systems that generate a longer saturating flash (200–10 000 ms), yields a higher increase in fluorescence due to the more effective photochemical quenching process. The combination of fluorescence techniques and satellite technology has been demonstrated to be a powerful tool for monitoring evolution of ecosystems (Figure 7). In this manner, the water-quality evolution in the Baltic sea, the California current, or the Atlantic ocean have all been studied by satellite fluorescence images of the waterborne cyanobacteria provided, among others, by NASA programs SeaWiFS and MODIS-Aqua.

3.10.10 Sensors for Waterborne Pesticides Pesticides are anthropogenic chemicals commonly used in agriculture. The increasing concern about groundwater pollution due to the use of these compounds requires a strong effort in order to detect such pollutants using reliable, economical, and rapid methods. Pesticides are toxic substances and some of them (e.g., the organophosphates) are powerful inhibitors of enzymes involved in the nerve functions. They normally display low environmental persistence but have acute toxicity, and therefore, there is a demand for fastscreening methods to detect low concentrations of these pollutants. Strict regulations are being enforced in Europe and other developed areas allowing a maximum concentration of 0.1 mg l1 of individual pesticide residues in drinking water. Unfortunately, many of the sensors that have been developed do not match such a detection limit but may still be used for other water-sensing applications (rivers, lakes, reservoirs, wastewater treatment plant inlets, consent discharges, rainfall runoff monitoring, etc.). Currently applied methods for the determination of organophosphates and other pesticides in water are mainly based on gas or liquid chromatographic analyses of water samples, which generally have the advantage of high sensitivity and selectivity. However, they are intrinsically off-line methods and involve several operations such as extraction, homogenization, clean-up of the sample, and concentration and analytical determination. Due to changes in effluent discharge rates as well as dynamic environmental conditions, the aquatic environment is subject to spatially and temporally changing concentrations of pollutants. Sampling-based techniques are usually incapable of tracking these changes and are therefore not suitable for field deployment. Consequently,

Figure 7 SeaWiFS image showing the average chlorophyll a concentration from October 1997 to April 2002. Image from the SeaWiFS Image Gallery courtesy of GeoEye.

Online Monitoring Sensors

there is still a need for in situ continuously operating sensing devices able to monitor pesticides in water at trace levels. Although different sensors have been proposed for their detection, most of them are only able to operate under the controlled laboratory environment or at best with very shortterm in situ measurements due to the fragility of the immobilized enzyme, the reagent/catalyst consumption, or insufficient sensitivity for field measurements. Consequently, only a few of them have reached the market so far. The majority of proposed pesticide sensors rely on either electrochemistry or optical measurements. The most popular electrochemical devices are biosensors based on enzyme-activity inhibition using potentiometry or amperometry as transduction principles. For instance, potentiometric methods for the assay of organophosphorous and carbamate pesticides are based on the inhibitory effect of such chemicals on the acetylcholinesterase (AChE) activity and detection using an electrode of subsequent pH changes caused by acetic acid release in the enzyme-catalyzed reaction:

Acetylcholine þ H2 OAChE -Acetic acid þ Choline Other enzymes such as organophosphate hydrolase (OPH) have also been used for sensor fabrication by monitoring the enzyme activity inhibition through amperometric detection of OPH-catalyzed electroactive hydrolysis products. Fiber-optic biosensors based on immobilized acetylcholinesterase are also known. In this case, acidity changes are monitored using pH-sensitive colorimetric or fluorometric dyes instead of a pH electrode (e.g., Abraxis LLC, Warminster, PA and Severn Trent Services, Ft. Washington, PA commercial kits for detection of aldicarb and dicrotophos pesticides). Alternatively, the enzyme-catalyzed hydrolysis of acetylated luminescent dyes can be followed using high sensitivity in automated optical dosimeters. Optical fiber sensors are of particular interest due to their robustness, remote-sensing capability, and absence of interferences by electromagnetic fields or surface potentials. Other important group of optical sensors for pesticide determination is that based on measurements of the intrinsic optical properties of the analyte (MIR absorption or native luminescence). The MIR technique is limited when dealing with aqueous solutions of an analyte due to strong background absorption of water in this region. However, fiberoptic evanescent wave spectroscopy (EWS) is a technique that allows in-situ MIR absorption spectroscopy in aqueous environments. The optical waveguides provide a rugged and versatile light-delivery system while EWS can provide suitably short path lengths through a highly absorbing medium such as water. Based on the principle of attenuated total reflection (ATR), a different approach for pesticide sensing using IR spectrometry has been developed by Janotta et al. (2003). A nonpolar organically modified sol–gel material is deposited on the optical fiber. If the thickness is 1.7 mm or more, it prevents interaction of the evanescent field with water and also extracts organophosphate pesticides from the solution. With this arrangement, detection limits below 500 ppb, for parathion, fenitrothion, and paraoxon, were attained and sensor

249

measurements could be performed directly in real-life samples such as river waters. Using the native luminescence of pesticides as an optical parameter, several sensors are found in literature for water analysis (Ca´pitan-Vallvey et al., 2001; Ruedas Rama et al., 2002; Salinas-Castillo et al., 2004; Dominguez-Vidal et al., 2007). In the course of the RIver ANAlyzer (RIANA) European project, Mallat et al. have developed a pre-commercial prototype based on a fluorescent immunoassay using labeled antibodies for pesticide determination in water (Klotz et al., 1998; Mallat et al., 1999). Nowadays, it is possible to find some commercial optical immunoassays based on absorption measurements for atrazine determination in water (e.g., Abraxis, Beacon Analytical Systems of Saco, ME and Strategic Diagnostics of Newark, DE, USA). In addition to atrazine, the latter two companies offer similar test kits for determination of other pesticides such as carbofuran, 2,4-dichlorophenoxyacetic acid, etc. For rapid qualitative analysis, Silver Lake Research Corporation (Monrovia, CA, USA) markets a colorimetric immunoassay for atrazine and simazine determination in a test-strip format. Recently, an immobilized microalgae-based fiber-optic biosensor for simazine determination based on chlorophyllfluorescence monitoring has been described (Pen˜a-Va´zquez et al., 2009). Chlorophyll fluorescence increases when toxicants such as simazine inhibit the algal photosystem II (see Section 3.10.9). Alternatively, microalgal biosensors may be based on the inhibition of the photosynthetic function (O2 production) in the presence of a pesticide or other toxicant. According to this scheme, a novel fiber-optic biosensor can selectively detect simazine at sub-microgram per liter level, using a dual head containing selected toxicant-sensitive and -resistant mutants of Dictiosphaerium chlorelloides immobilized on a porous silicone film and luminescent oxygen transduction (Orellana et al., 2009). In this manner, the lack of analyte specificity due to the nonspecific photosystem II response is overcome. One of the main problems in the development of microbial biosensors is the incorporation of the microorganisms into a suitable matrix that avoids leaching without affecting stability or rendering a significant loss of activity (Gupta and Chaudhury, 2007). Currently, sol–gel films are considered one of the best options to fabricate (reversible) robust optical chemical sensors and biosensors (Jero´nimo et al., 2007). It is also possible to find some commercially available whole-cell biosensors for pesticides and other toxic species in water. Such devices are based on the inhibition of the bioluminescence of Vibrio fischeri bacteria by the overall toxicants. These nonspecific sensors are commercialized by companies such as Strategic Diagnostics and Abraxis. A representative set of examples of pesticide sensors is listed in Table 15.

3.10.11 Sensors for Waterborne Toxins Toxins are poisonous substances produced by living cells or organisms that are active at very low concentrations (XiangHong and Shuo, 2008). Depending on the organism that produces them, toxins can be classified into bacterial toxins,

Table 15

Analytical figures-of-merit of some academic and commercial sensors for pesticide determination in water

Pesticide

Dynamic range

LOD

Precision (%)

Response time

Temperature tested (1 C)

Interferences

Lifetime

Transduction principle

References

Butoxycarboxime Trichlorfon Dimethoate Neostigmin Coumaphos

0.1–10 mmol l1 5–20 mmol l1 0.5–100 mmol l1 0.1–10 mmol l1 0.015–0.90 mg l1

NA

1% (RSD)

NA

NA

NA

NA

Electrochemical

a

0.002 mg l1

NA

NA

NA

NA

NA

Electrochemical

b

Trichlorfon Aldicarb Methiocarb

0.05–4.0 mg l1 0.045–5.0 mg l1 0.2–20 mg l1

0.04 mg l1 0.03 mg l1 0.08 mg l1

Chlorpyrifos-oxon Methyl parathion

2–8 mg l1 o5  106 M

0.5 mg l1 4  107 M

4.7 3.9

NA 60 s

NA RT

Phosphate buffer Ionic strength

8 days NA

Electrochemical Electrochemical

c

Paraoxon

4.6–46  106 M

9  107 M

5.8

5  10 M 5  10 5  107–5  106 M

1.5  10 M 1.1  107 M

5.8 3.7

NA

NA

NA

NA

Absorbance

e

Aldicarb

0.026–260 mg l1

NA

NA

30 min

NA

NA

Absorbance

f

Dicrotophos

0.14–1400 mg l1

Humic and fulvic acids, Ca2þ, Mg2þ

Aldicarb

(tested ranges) 0.026–260 mg l1

NA

NA

3 min

NA

NA

0.14–1400 mg l1

Colorimetric (qualitative)

g

Dicrotophos

Humic and fulvic acids, Ca2þ, Mg2þ

Alachlor

(tested ranges) 5–100 mg l1

5 mg l1

NA

15 min

NA

NA

NA

Mid-IR

h

Morestan

1.0–200.0 ng ml1

0.28 ng ml1

2.9

2h

RT

Not found

NA

i

Thiabendazole

10–800 ng ml1

2.35 ng ml1

0.93

NA

2070.5 1C

a-Naftol

NA

RT phosphorescence Fluorescence

Warfarin

2–40 mg ml1

0.54 mg ml1

1.26

Naptalam

8.1–300.0 ng ml1

8.1 ng ml1

2.7

NA

20 1C

NA

NA

k

Atrazine

0.001–100 mg l1

0.18 mg l1

1–9

15 min

NA

Desethylatrazine

NA

RT phosphorescence Fluorescence

(tested range) 1

7

8

pH

Carbofuran Paraoxon

Simazine

8

d

0.10 mg l

1 1

Atrazine

0.1–5 mg l

0.06 mg l

Atrazine

0.1–5 mg l1

NA

Atrazine

0.1–5 mg l1

0.1 mg l1

j

o-Phenylphenol

l

Deisopropylatrazine 3.5–15.2 (RSD) 3.9–22.8 (RSD) 2.6–16.7 (RSD)

15 min

NA

Desethylatrazine

NA

Absorbance

m

20 min

NA

Desethylatrazine

NA

Absorbance

n

50 min

RT

Desethylatrazine

NA

Absorbance

o

Atrazine

Z3 mg l1

Simazine

Z4 mg l1

Simazine

19–860 mg l1

a

Wollenberger et al. (1994). Ivanov et al. (2002). c Hildebrandt et al. (2008). d Wang et al. (1999). e Andres and Narayanaswamy (1997). f Organophosphate/Carbamate Screen Kit (http://www.abraxiskits.com). g Eclox Pesticide Strips (http://www.severntrentservices.com). h Walsh et al. (1996). i Capitan-Vallvey et al. (2001). j Ruedas Rama et al. (2002). k Salinas-Castillo et al. (2004). l Mallat et al. (1999). m Atrazine ELISA Kit (http://www.abraxiskits.com). n Atrazine Tube Kit (http://www.beaconkits.com). o Rapid Assay Kit (http://www.sdix.com). p Watersafe Pesticide Test Strip (http://www.silverlakeresearch.com). q Pen˜a-Va´zquez et al. (2009). NA, not available; RT, room temperature. b

NA

NA

10 min

RT

Not found

NA

Colorimetric (qualitative)

p

3.6 mg l1

5.6

30 min

NA

Atrazine, propazine, terbuthylazine, linuron

3 weeks

Fluorescence

q

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Online Monitoring Sensors

mycotoxins, and invertebrate and vertebrate toxins. Due to their high toxicity, effective analysis techniques are indispensable. The typical method widely used for the detection and quantification of biological toxins is high-performance liquid chromatography (HPLC) with UV, fluorescence, or mass spectrometric detection. These methods provide sensitive and specific analyses but have problems similar to that previously mentioned for pesticide determination (see Section 3.10.10): (1) they are labor intensive and not really suitable for screening large numbers of samples; (2) the extraction and cleanup processes involve numerous time-consuming steps; and (3) derivatization reagents have been used for converting the toxins into the correspondent fluorescent derivatives, which is a complex procedure and needs skilled personnel. For these reasons, rapid, sensitive and specific methods are needed for routine analysis and monitoring of water samples contaminated by these toxins. In this regard, several sensors and biosensors have emerged in the past decade for toxicity analysis. Several toxicity sensors for drinking-water protection have been evaluated by the US EPA Environmental Technology Verification Program for concentrations at and below the estimated human lethal concentration. Most of them are immunosensors based on the specific high affinity, antibody– antigen binding interactions and optical-detection techniques, such as those developed by Tetracore (Rockville, MD, USA), ADVNT Biotechnologies (Phoenix, AZ, USA), QTL Biosystems (New Kensington, PA, USA), and Response Biomedical Corp. (Vancouver, BC, Canada). Some of them are test strips that indicate the presence or absence of a certain toxin in a preestablished range (ADVNT, Tetracore). Immunosensors display some limitations such as (1) a strong dependence of the antibody-binding capacity under the assay conditions, for example, pH and temperature, and (2) the irreversible nature of the antibody–antigen interaction. A more complete description about fiber-optic immunosensors for waterbornetoxin detection, their different assay formats, and opticaldetection techniques can be found in the review by Marazuela and Moreno-Bondi (2002). Using a fluorescent-labeled amino-acid sequence, PharmaLeads (Paris, France) has developed a test kit for botulinum toxin determination. The company introduces both the fluorogenic label and a quenching substance in the aminoacid sequence and, when botulinum toxin reaches the quenching substance, segmentation occurs generating an intense fluorescence. Another biosensor has been described for aflatoxin B1 determination in river samples based on potentiometric measurements (Marrazza et al., 1999). In this case, the toxin affinity for polynucleotides is measured by its effect on the oxidation signal of the guanine peak of calf thymus DNA immobilized on the electrode surface. Other types of biosensors used for toxins determination are the cellular structure- and whole-cell-based devices. In this case, the living microorganism or a specific cellular component is used as the biorecognition element. For instance, Abraxis has developed a biosensor based on bioluminescence quenching of Vibrio fischeri bacteria, caused by the effect of toxins on their metabolism. The AbraTox Kit responds to global toxicity in water samples and can be used for pesticide determination (see Section 3.10.10) as well. These sensors

display advantages such as (1) whole cells or microorganisms are more tolerant to pH or temperature changes; (2) some microorganisms (i.e., bacteria, fungi, yeast, etc.) can be readily isolated from natural sources (river water, sediments, soil, activated sludge, etc.); (3) a single cell can contain all the enzymes and co-factors needed for detection of the analyte; (4) measurement is frequently possible without extensive preparation of the sample, and (5) biosensors can be easily regenerated by letting the cells re-grow. Limitations of this type of biosensors include longer response times and poorer selectivity compared with enzyme-based biosensors, although this feature can sometimes be turned into an advantage for certain applications (e.g., toxicity screening), as in the case of the Abraxis sensor. Representative biosensors for toxin-in-water detection can be found in Table 16. True sensor devices for waterborne toxins are still lacking but most applications (particularly bioterrorism early alert and detection) can be fulfilled with disposable dosimeters (see Section 3.10.1).

3.10.12 Sensors for Waterborne Bacteria Development of sensors for real-time detection of bacterial contamination in water supplies is a top but highly challenging priority. For this application, sensors should be sensitive enough, rapid, and robust with long operational lifetime (Ji et al., 2004). Until now, a significant number of detection methods have been developed using the optical, electrochemical, biochemical, and physical properties of the microorganisms (Hobson et al., 1996). Some of them have been commercialized, for example, those based on impedance measurements (Don Whitley Scientific, West Yorkshire, UK; Sy-Lab, Neupurkersdorf, Austria, and BioMerieux, Marcy l’Etoile, France). However, these methods are nonspecific, respond not only to bacteria but to all types of microorganisms present in the water sample, and are time consuming because they are based on microorganism growth. One rapid unspecific colorimetric method for total bacteria determination is that proposed by Palintest (Kingsway, Team Valley, England). Their test strips comprise nutrient agar for total aerobic count of bacteria and triphenyl tetrazolium chloride (TTC) dye which stains most colonies red for easy enumeration. The range of detection for bacteria is 103–107 colony-forming units (CFU) ml1 in water. Other types of biosensors have been developed recently for bacteria determination in water samples; these devices are sensitive, specific, and rapid in comparison to the previously cited methods. Some representative examples described in the literature are presented below. Optical bacteria sensors based on fluorescent nucleic acid stains, acting both as molecular-recognition elements and fluorescent reporters, have been described (Ji et al., 2004; Chuang et al., 2001; Chang et al., 2001). The working principle of these sensors is that the fluorescence quantum yield of some nucleic acid stains significantly increases upon binding to nucleic acids. This signal increase can be correlated to the amount of nucleic acid present in the sample. Since all organisms contain nucleic acids, these sensors are not specific and respond to all bacterial species.

Table 16

Some academic and commercial sensors for toxin determination

Toxin

Tested range 10

LOD 1

4

1

Precision

Response time

NA

E5 h

NA

Interferences

Transduction principle

References

Humic and fulvic acids, Ca , Mg

Absorption

a

NA

Ca2þ, Mg2þ

Luminescence

b

2þ

2þ

Anthrax Botulinum A and B

200–10 spores ml 0.004–0.3 mg l1

2  10 spores ml 0.004 mg l1

Ricin Botulinum A Ricin

0.0015–15 mg l1 5  105–0.3 mg l1 5  105–15 mg l1

0.0015 mg l1 5  105 mg l1 5  105 mg l1

Anthrax Ricin

200–5  106 spores ml1 0.05–15 mg l1

105 spores ml1 0.05 mg l1

NA

5 min

Ca2þ, Mg2þ

Fluorescence

c

Anthrax Botulinum A

200–1010 spores ml1 0.5–25 mg l1

4  105 spores ml1

NA

o15 min

Humic and fulvic acids, Ca2þ, Mg2þ

Fluorescence

d

Botulinum B Ricin Anthrax Botulinum A

0.3–1000 mg l1 1–50 mg l1 200–1010 spores ml1 0.5–25 mg l1

0.5 mg l1 1 mg l1 106 spores ml1 0.4 mg l1

NA

15 min

Humic and fulvic acids, Ca2þ, Mg2þ

Colorimetric (qualitative)

e

Botulinum B Ricin Anthrax Botulinum A and B

0.3–1000 mg l1 0.4–2000 mg l1 200–1010 spores ml1 0.01–0.5 mg l1

0.4 mg l1 0.4 mg l1 105 spores ml1 0.01 mg l1

NA

15 min

Humic and fulvic acids, Ca2þ, Mg2þ

Colorimetric (qualitative)

f

Ricin Botulinum A Botulinum B

0.035–15 mg l1 0.01–0.5 mg l1

0.035 mg l1 0.01 mg l1 0.01 mg l1

NA

30–60 min

Humic and fulvic acids, Ca2þ, Mg2þ

Fluorescence

g

Aflatoxin B1 Botulinum B

10–30 mg l1 0.0003–0.3 mg l1

10 mg l1 NA

NA r34%

2 min 60 min

NA Zn2þ, Fe2þ, Cu2þ

Electrochemical Bioluminescence

h

Ricin

0.015–15 mg l1

a

Enzyme-linked immunosorbent assay (ELISA) (http://www.tetracore.com). Bioveris (now within Roche) BioVerify Test Kits and M-Series M1 M Analyzer. c QTL Biosensor (http://www.qtlbio.com). d RAMP Immunoassay Test Cartridges (http://www.responsebio.com). e BADD Immunoassay Test Strips (http://www.advnt.org). f BioThreat Alert Immunoassay Test Strips (http://www.tetracore.com). g EzyBots A and EzyBots B Test Kits (http://www.pharmaleads.com). h Marazza et al. (1999). i AbraTox Kit (http://www.abraxiskits.com). NA, not available. b

r27%

i

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Online Monitoring Sensors

Specific biosensors for bacteria determination have been developed using RNA and DNA probes. Combined with the polymerase chain reaction (PCR, Belgrader et al., 1999), several devices with relatively short response times have been marketed (Applied Biosystems, Foster City, CA, USA; Idaho Technology, Salt Lake City, UT, USA; Invitrogen, Carlsbad, CA, USA). If the sequence of bases of a particular part of the DNA molecule is known, then the complementary sequence (probe) can be synthesized and labeled using a fluorescent reporter. The problem with these type of sensors is that they are limited when faced with unknown or genetically modified organisms. Strategic Diagnostics has commercialized an enzyme-based qualitative biosensor to detect the presence/absence of total coliforms and E. coli in water samples. The ColitagTM method capitalizes on the detection of two enzymes, namely b-glucuronidase and b-galactosidase, which are characteristic of E. coli and the coliform groups, respectively. Colitag detects total coliforms using the chromogenic substrate o-nitrophenyl-b-Dgalactopyranoside (ONPG). Upon hydrolysis by b-galactosidase, ONPG produces a distinct yellow color, confirming the presence of coliforms in the sample. For detecting E. coli, Colitag utilizes the fluorogenic enzyme substrate 4-methylumbelliferyl-b-D-glucuronide (MUG). Upon hydrolysis by bglucuronidase, MUG releases 4-methylumbelliferone. The latter reaction product fluoresces when exposed to UV light. The b-glucuronidase enzyme is specific to E. coli and observation of fluorescence differentiates this organism from other members of the coliform group. Colitag is able to detect just one CFU of E. coli and other coliform bacteria in 100 ml of water fulfilling the legal requirement in developed countries for potable water. Nevertheless, actual levels of bacteria present in drinking waters of less-developed countries or regions may be higher by 3–4 orders of magnitude. Bacteriophages or phage organisms can be employed as recognition elements to detect deadly bacteria such as E. Coli and Salmonella. The genetically engineered phages supplied by Biophage Pharma Inc. (Montreal, Canada), are viruses that recognize specific receptors on the surface of bacteria, to which they bind with extreme selectivity and sensitivity. The system works on the basis of monitoring the change in capacitance caused when the target bacteria attach to the sensing interface (Lei et al., 2008). The phages can also be tagged with fluorophores to render them optically responsive, and are immobilized on the surface of an addressable micro-LED array. The LEDs are used to excite the phage organisms and the fluorescence intensity is dependent on the concentration of specific bacteria attached to the phages (Lei et al., 2008b). The performance of representative sensing devices for waterborne bacteria is listed in Table 17.

3.10.13 Turbidity Sensors The main areas of application of the water-turbidity measurements lie on cell-density determinations, crystallization monitoring, filtration control, detection of suspended solids, quality testing, and flocculation monitoring. The units given by turbidity instruments are the so-called nephelometric turbidity units (NTUs) but in the sensing field it is also common

to use suspended sediment concentrations (SSCs) and particle size distribution (PSD). Among all methods, two are standardized and approved for turbidity determinations of freshwater and brackish water: Nephelometric Method 2130 B and ISO 7027. The attenuation of an IR beam (e.g., 850 nm) or visible radiation in the red region (e.g., 660 nm) is used to avoid interference due to absorption of organic matter dissolved in the water. Another option is to perform a multiwavelength analysis using a white light source and detect any effects due to absorption. To eliminate interference from extraneous light sources, the analytical beam can be pulsed at a rate of several kilohertz. Light-scattering-based instruments can operate on either of the two principles: transmission and nephelometry. In the transmission mode, the SSC is calculated using the loss of light through a determined optical path. This simple principle was used by P. A. Secchi to develop the first known turbidimeter, back in 1865. In Secchi’s method, a circular disk about 30 cm in diameter is lowered from above the water into the water column, and the point at which it disappears from sight is determined. In the nephelometric mode, SSC is calculated by the amount of light scattered by the suspended particles. This scattered light can be measured at 901 (901 nephelometry) or at 3017151 to the incident beam (backscattering). Depending on the application and the amount of suspended solid, transmission, 901 nephelometry or backscattering is chosen. Air bubbles are one of the major interferences in these types of systems but can be eliminated applying high pressure (Analytical Technology, Collegeville, PA, USA), introducing a bubble-trap chamber (Hach Co., Loveland, CO,USA), or compensating by statistical treatment of the measured values (Zu¨llig AG, Rheineck, Switzerland). In order to avoid the absorption of light and interference from bubble scattering, acoustic techniques can also be used. Acoustic measurements of suspended particles in the water are based on two approaches: the first method is to measure the attenuation of an acoustic pulse passing through the water column due to the suspended particles. Particle-size distribution and concentration within the water column can be derived but estimation of distribution, as a function of depth, cannot be inferred. Commercial equipments based on ultrasonic attenuation are currently in the market (e.g., Markland Specialty Engineering Ltd., Georgetown, ON, Canada). The second approach is by interpreting the backscattering, which is the scattering by the suspended particles back to the transducer known as Acoustic Doppler Current Profiler (ADCP). The latter is a state-of-the-art equipment in oceanography and hydrometry for current velocity. The ADCP works by transmitting pings of sound at a constant frequency into the water. As the sound waves travel, they ricochet off particles suspended in the moving water, and reflect back to the instrument. Due to Doppler effect, sound waves bouncing back from a particle moving away from the profiler have a slightly lowered frequency when they return. Particles moving toward the instrument send back higher frequency waves. This shift is used to calculate how fast the particle and the water around it are moving, while the intensity of the signal echoed by the suspended particles contains information on concentration. This technique has some intrinsic limitations. First, multifrequency ADCP instruments are needed in order to resolve

Table 17

Some academic and commercial sensors for bacteria quantitation in water

Bacteria

Tested range

LOD

Response time

Temperature range

Interferences

Lifetime

Transduction Principle

References

Escherichia coli Bacillus subtilis 23095

104–108 cells ml1

104 cells ml1

5 min

4–48 1C

Not found

7 months

Fluorescence

a

Pseudomonas aeruginosa Pseudomonas aeruginosa

2.4  105–2.4  107 cells ml1 0–5.4  107 cells ml1

2.4  105 cells ml1 NA

15 min 10 min

NA NA

NA NA

50 h (operational) 2 months

Fluorescence Fluorescence

b

Erwinia herbicola

5–500 cells

NA

7–15 min

NA

NA

NA

Fluorescence

d

Escherichia coli

0.2–106 cfu ml1

10 cfu ml1

NA

NA

Not found

NA

Fluorescence

e

Francisella

2  103–5  104 cfu ml1

103 cfu ml1

NA

NA

Humic and fulvic acids

NA

Fluorescence

f

tularensis, Yersinia

4

0.28–5  10 cfu ml

Not found

pestis, Bacillus anthracis, Brucella suis, Escherichia coli Francisella tularensis, Yersinia pestis Bacillus anthracis

200–5  104 cfu ml1 40–5  104 cfu ml1 0.2–5  104 cfu ml1 2  104–5  105 cfu ml1 0.28–5  103 cfu ml1 200–5  105 cfu ml1

Not Not Not Not

NA

Fluorescence

g

a

1

104 cfu ml1 102 cfu ml1 104 cfu ml1

o10 min

NA

found found found found

Ji et al. (2004). Chuang et al. (2001). c Chang et al. (2001). d Belgrader et al. (1999). e TaqMan E. coli 0157:H7 Detection System (http://www.appliedbiosystems.com). f R.A.P.I.D. System for the detection of Francisella tularensis, Yersinia pestis, Bacillus anthracis, Brucella suis, and Escherichia coli (http://www.idahotech.com). g PathAlert detection Kits for the detection of Francisella tularensis, Yersinis pestis, and Bacillus anthracis (http://www.invitrogen.com). NA, not available. b

c

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whether changes in echo intensity (EI) are associated with changes in sediment concentration, or changes in particle-size distribution. Luckily, most of the manufacturers already offer multi-frequency equipment (Teledyne RD Instruments, Poway, CA, USA; SonTek/YSI, San Diego, CA, USA). Second, an acoustic surrogate is the relation between particle circumference and ADCP frequency, and an error in SSC estimates has been found to increase as the ratio of particle circumference to acoustic wavelength approaches 1. A third limitation is that ADCPs are designed to detect acoustic frequency changes in current profiles and are less accurate in measuring the amplitude changes associated with EI measurements. Laser-diffraction instruments exploit the principles of small-angle forward-scattering angles. Thus, the method is mostly insensitive to change in particle color or composition. Sequoia Scientific, Inc. (Bellevue, WA, USA) and Shimadzu Corporation (Kyoto, Japan) provide this type of equipment. For instance, the LISST100X and LISST25X (LIST ¼ Laser InSitu Scattering and Transmissometry) are equipped with a 670 nm laser. Both record scattering at 32 angles, mathematically invert the readings to get the size distribution, and also scale it to obtain the volume-scattering function (VSF). Their working range is from 1 to 750 mg l1 and can be modified by increasing or reducing the optical path since it is a forward-scattering measuring technique. For very high SSC samples, the company has developed the LISST-INFINITIVE model that includes one single sample dilution step (50:1). A range from 1 to 500.000 mg l1 is achieved, but no data of a possible delay time on the reading is provided by the manufacturer. Estimation of SSC from fluid-density values computed from pressure measurements is a strong candidate for monitoring high sediment-laden flowstream. In fact, this inexpensive technology is designed to monitor SSC values over 10 g l1 since it is based on simultaneous pressure measurements with two exceptionally sensitive pressure-transducer sensors arrayed at different fixed elevations in a water column. The precision pressure sensors are available from several companies but only Waterlog (a division of design Analysis Associates, Inc., Logan, UT, USA) used them for turbidity measurements. Unfortunately, the system is no longer available. In fact, application of this technique in the field can be complicated by low signal-to-noise ratios associated with low SSC, turbulence, significantly large dissolved-solid concentrations, and watertemperature variations. Table 18 lists the most representative equipments for turbidimetry currently found in the environmental instrumentation market.

are affected by all oxidizing and reducing agents present in water so that they also are nonspecific measurements. The ORP is determined by measuring the difference of potential between an inert sensing half-cell (indicator electrode) and a reference half-cell (reference electrode) as in pH measurements. The sensing half-cell (typically platinum) acts as a platform for electron transfer to or from the sample. The standard hydrogen electrode (SHE) is the reference from which all standard redox potentials are determined, and has been assigned an arbitrary half-cell potential of 0.00 V. However, it is fragile and impractical for routine use, and therefore Ag/AgCl and saturated calomel (SCE) reference electrodes are used instead. The latter is nowadays phased out because it contains mercury. At equilibrium, the ORP is calculated as the emf of the overall galvanic cell:

EðORPÞ ¼ ½E0 þ ð2:3RT=nFÞ  ðlog½Ox=½RedÞ  Eref

ð9Þ

where E0 is the standard potential of the redox system versus SHE, R is the universal gas constant, T is the temperature in Kelvin, n is the number of electrons transferred, F is the Faraday’s constant, [Ox] and [Red] are the activities of oxidant and reductant species ,and Eref is the half-potential of the reference electrode at 25 1C. The readout of the ORP sensor is a voltage (relative to the reference electrode), with positive values indicating an oxidizing environment (ability to accept electrons) and negative values indicating a reducing environment (ability to donate electrons).

3.10.14.1 Effect of pH on Oxidation–Reduction Potential Sensors Some redox reactions are pH-dependent, for example, the reduction of the powerful disinfectant hypochlorous acid (HClO), widely used in water treatment as a by-product of chlorine solutions in water:

HClO þ H þ þ 2e  " Cl  þ H2 OðE0 ¼ 1:49 VÞ In this case, the ORP of water would be

EðORPÞ ¼ ½E0 þ ð2:3RT=nFÞ  logð½HClO½Hþ =½Cl Þ  Eref

ð10Þ

where the proton activity [Hþ] shows the ORP dependence on pH. If the redox reaction involved depends on the acidity, the solution pH must be controlled to achieve reliable ORP measurements.

3.10.14.2 Effect of Temperature on ORP Sensors

3.10.14 Oxidation–Reduction Potential Sensors The so-called oxidation–reduction potential (ORP, or simply redox) measures the capacity of an aqueous solution to either release or gain electrons by electrochemical reactions. Oxidation and reduction reactions control the behavior of many chemical species in drinking water, wastewater, and aquatic environments. Therefore, ORP values are used in a manner similar to pH values to determine water quality. ORP values

The ORP is directly dependent on the temperature of the sensing system according to Nernst equation above. The actual temperature effect depends on the ratio of activities of each redox couple present in solution. In most cases, electroactive species in solution are unknown and for this reason, temperature is not compensated in ORP sensors. Proper use of ORP sensors require that their calibration is done at the same temperature at which the measurement will be carried out. For this reason, some vendors provide tables containing the ORP

Online Monitoring Sensors Table 18

257

Some commercial sensors for water turbidimetry

Transduction principle

Model

Dynamic range

Precision

LOD

Response time (s)

Temperature range (1 C)

Infrared backscattering Infrared 901 nephelometry Infrared backscattering Red (660 nm) light 901 nephelometry White or Infrared light 901 nephelometry Infrared 901 nephelometry

AF46 CSa A15/76b OBS–3 þ c FilterTrak 660TMd MicroTOLe

0–200 g l1 0–4000 NTU 0–4000 NTU 0–5 NTU

NA NA NA 0.007 NTU

NA NA NA NA

0–55 0–50 NA 0–40

0–1000 NTU

NA 75% or 70.02 72% or 0.25 73% or70.005 75%

0.0001 NTU

NA

0–50

NTU Digital Sensorf NTU–S10f

0–4000 NTU

71%

0.01 NTU

o1

0–50

0–20 NTU

NA

NA

NA

 10–50

270WQg LISST100Xh VisoTurbs 700 IQi 6136 Turbidity Sensorj LATS–1k Model 502–ILl TU 810m Turbimax W CUS65-Cn TML–25o CT–CENSETMp ECO VSF3q

0–1000 NTU 1–750 mg l1 0–4000 NTU

71% NA 72%

NA o1 mg l1 0.05 NTU

5 NA NA

 10–50  10–45 0–60

0–1000 NTU

72% or 0.3

0.1 NTU

NA

NA

0–3.2 NTU 1–150 g1 0–4000 NTU 0–50 g1

NA 75% or 1 g/l 1 NTU o1%

0.001 NTU NA 0.05% NA

1 60 10 NA

NA 1–45 0–50 0–50

0–4000 NTU 1–200 NTU 0–25 NTU

o1% 72% NA

0.001 NTU o0.1 NTU 0.01 NTU

2 15 NA

0–50 0–50 0–30

EL400r

0–100 NTU

72%

0.01 NTU

NA

0–60

Infrared absorption and 901 nephelometry Infrared 901 nephelometry Laser diffraction Infrared 901 nephelometry Infrared 901 nephelometry Laser diffraction Ultrasonic attenuation Infrared 901 nephelometry Infrared absorption Infrared 901 nephelometry Infrared 901 nephelometry Three (red, blue, green) wavelengths, three angle (100, 125 and 1501) backscattering Infrared 901 nephelometry a

Aquasant AG (http://www.aquasant.com). Analytical Technology, Inc. (http://www.analyticaltechnology.com). c Campbell Scientific Ltd. (http://www.campbellsci.com). d HACH Company (http://www.hach.com). e HF scientific Inc. (http://www.hfscientific.com). f Neotek-Ponsel (http://www.neotek-ponsel.com). g NovaLynx Corporation (http://www.novalynx.com). h Sequoia Scientific Inc. (http://www.sequoiasci.com). i Wissenschaftlich-Technische Werksta¨tten GmbH (http://www.wtw.com). j YSI Environmental (http://www.ysi.com). k Shimadzu Corporation (http://www.shimadzu.com). l Markland Specialty Engineering Ltd. (http://www.sludgecontrols.com). m RODI Systems Corporation (http://www.rodisystems.com). n Endress þ Hauser Inc. (http://www.endress.com). o Zu¨llig AG (http://www.zuellig.ch). p CENSAR Technologies Inc. (http://www.censar.com). q Wetlab Inc. (http://www.wetlabs.com). r Tethys Instruments SAS (http://www.tethys-instruments.com). NA, not available. b

values for the (calibration) standard solution versus the reference electrode at different temperatures.

3.10.14.3 Frequent Problems with ORP Sensors ORP sensors can show a sluggish response in environmental waters if the platinum electrode of the probe has been contaminated. Common contaminants include hard-water deposits, oil/grease, or organic matter. It may take days to reach

the final ORP value in contaminated sensors and, therefore, the typical time frame of a sampling experiment (o1 h) is not sufficient to provide a correct reading. Naturally, if the electrode contaminant is redox-active, either in itself or because it contains redox-active impurities, the sensor reading will be erroneous until the contaminant is removed. In clean environmental water, there may be very few redoxactive species present and those that are present may be in very low concentration. In many cases, the concentration can be so

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low that the redox influence of the species is actually below the detection limit of the method. Finally, the surface composition of the electrode may not be ideal for the measurements in the selected medium. While platinum ORP electrodes are primarily composed of the metal itself (in a neutral state), it is well known that the surface of the electrode where the redox action takes place is coated with a molecular layer of platinum oxide (PtO). The Pt/PtO ratio can change over time, depending on the medium in which the probe is stored, and thus the electrode surface actually possesses its own potential that can be variable. If this surface potential is similar to the ORP potential of the medium, then the electrode response can be slow. Representative ORP sensors can be found in Table 19.

3.10.15 Conductivity Sensors Electrical conductivity or specific conductance (k) is defined as the ability of a liquid to conduct electricity. It is the reciprocal of resistivity and is measured in Siemens cm1. Conductivity is extensively used to characterize water supplies for municipal, commercial, hospital, and industrial uses. While individual ions cannot easily be differentiated, conductivity gives a measurement of the total ions present in the sample, reading being proportional to the combined effect of all the ions. Conductivity is sometimes expressed as parts per million of total dissolved solids (ppm TDS) and is used to monitor mineral concentration in some applications. Table 19

A large variety of conductivity equipment is now available to measure water quality ranging from ultrapure water (very low conductivity) to concentrated chemical water streams (high conductivity). Representative examples are listed in Table 20. Conductivity sensors for water analysis use two or four electrodes with a known surface area and are directly placed in the solution or built into a tube or vessel at a specified distance. The cell constant (kcell) refers to the distance between the electrodes divided by the electrode area. If conductivity is low (very dilute solutions) the electrodes are placed close together and the cell constants are between 0.1 and 0.01 cm1. If conductivity is high, they can be further apart and the cell constants can reach up to 10 cm1. The two-electrode conductivity sensors are based on amperometric measurements. In this case, a known potential difference (DV) is applied to the pair of electrodes and the resulting electric current (I) is measured. According to Ohm’s law (I ¼ DV/R) the resistance of the system (R) can be determined and related to conductivity by the following expression:

k ¼ kcell =R Unfortunately, the resistance in this method is not constant; salt deposition on the electrodes due to electrolysis can change it. For low to medium conductivity levels (o2 mS cm1) this method may be sufficient, but for greater accuracy and for higher conductivities, a potentiometric method is required.

Representative academic and commercial sensors for water oxidation–reduction potential (ORP) measurements

Dynamic range (mV)

Precision

Response time

Temperature range (1 C)

Pressure range

Lifetime

References

90–450 86–268

E1 s o30 s

25 20–25

NA NA

NA NA

a

NA

0–60

NA

NA

c

 2000–2000  500–500

NA 70.31,70.42, 70.49 mV (different ranges) 70.25, 70.5, 72 mV (different ranges) 72 mV 2% (RSD)

NA NA

0–50 0–55

NA NA

d

 999–999  2000–2000  2000–2000

71 mV 730 mV 71 mV

NA NA NA

0–50 0–60 0–60

NA NA NA

f

 700–1100

70.1% (RSD)

10 s

0–80

5 years

i

 1500–1500

NA

NA

0–100

NA o40 psi (276 kPa) NA NA o50 psi (345 kPa) 0–30 psi (0–207 KPa) 0–232 psi (0–1.6 MPa)

NA

j

 450–1100

a

Lee et al. (2007). Jang et al. (2005). c ORP Sensor (http://www.vernier.com). d HI 504 pH/ORP Controller with Sensor Check (http://www.hannainst.com). e WQ600 ORP Sensor (http://www.globalw.com). f ORP15 ORP/Temperature Instrument (http://www.ysiecosense.com). g P & S series ORP electrode (http://www.ionode.com.au). h 720II ORP Controller (http://www.myronl.com). i CSIM11–ORP (http://www.campbellsci.com). j Orbisint CPS12/CPS12D/CPS13 (http://www.endress.com). NA, not available. b

b

e

g h

Online Monitoring Sensors Table 20

259

Representative examples of academic and commercial sensors for water conductivity measurements

Measuring range

Precision

Cell constant (cm1)

Response time

Temperature range (1 C)

Pressure range

References

1–189 mS cm1 0.01 mS–1 mS (conductance) 0–1999 mS cm1 (different ranges) 0.02–500 mS cm1

o7% (RSD) 75.6 nS (SD) NA

2.02 NA 0.01–10

NA 160 ms NA

NA NA 0–100

NA NA 0–2 MPa

a

5%

NA

NA

0–100

d

0.005–7 mS cm1 NA

10% NA

NA 0.1–10.0

NA NA

0–50 0–70

0–40 000 mS (conductance)

1%

NA

NA

0–55

0.0001 mS cm1–2 S cm1 (different ranges) 0–4999 mS cm1 (different ranges)

1.5%

o0.933

NA

0–130

0.50%

1.0–10

NA

0–95

0–10 bar (0–1 MPa) NA o7.5 bar (750 KPa) 50 psi (345 kPa) o14 bar (1.4 MPa) NA

b c

e f

g

h

i

a

Lario-Garcı´a and Pallas-Areny (2006). Li and Meijer (2008). c Conductivity analyzers (http://www.yokogawa.com). d InPro 7100 (http://www.mt.com). e CS547A (http://www.campbellsci.com). f Conductivity sensors (http://www.sensorex.com). g WQ301 (http://www.globalw.com). h WTW (http://www.wtw.com). i YSI 3100/3200 (http://www.ysi.com). NA, not available. b

The potentiometric method for conductivity measurements uses four electrodes: the two outermost electrodes apply an alternating voltage. However, rather than directly measuring the current between these two electrodes, the four-electrode sensor measures the voltage drop across the two innermost electrodes. Operating with a known current condition, the resistance of the solution can be calculated using Ohm’s law. Unlike amperometric probes, potentiometric conductivity sensors are not limited by electrolysis and therefore can be used for a wider range of conductivities.

increasingly lower analyte levels truly online. Substance-specific monitoring is still a challenge particularly for organic water pollutants due to the vast diversity of chemical structures. Therefore, online monitoring sensors and off-line laboratory techniques, such as chromatography interfaced with mass spectrometry, will continue to coexist for many years to come. Each one fulfils an important mission and they are bound to complement each other. However, only reliable, affordable, robust sensors will provide the expected benefits to the humankind and the environment.

3.10.15.1 Effect of Temperature Conductivity in aqueous solutions increases with increasing temperature because of higher ion mobility. This dependence is usually expressed as a relative change per 1C, commonly as percent per 1C; this value known as the slope of the particular aqueous solution. For this reason, conductivity readings are normally referenced to 25 1C. Fortunately, temperature sensors are readily available and can be integrated into the electronic circuitry, it being possible to correct the conductivity value and to bring it to its equivalent value at 25 1C automatically.

3.10.16 Conclusions The text above proves to the reader that there is no shortage of sensors for water. However, legislation pressure and technology advancements are continuously driving the search for novel rugged monitoring devices capable of detecting

Acknowledgments We gratefully acknowledge support from the funding institutions that have made possible our research in this area: the Madrid Community Government (IV PRICYT ref. CM-S-505/ AMB/0374), the European Regional Development Fund, the European Social Fund, the Spanish Ministry of Science and Innovation (CTQ2006-15610-C02-01-BQU and CTQ200914565-C03-01), and the UCM-Banco Santander (GR58-08910072).

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Andres RT and Narayanaswamy R (1997) Fibre-optic pesticide biosensor based on covalently immobilized acetylcholinesterase and thymol blue. Talanta 44: 1335. Belgrader P, Benett W, Hadley D, et al. (1999) PCR detection of bacteria in seven minutes. Science 284: 449--450. Bellerby RGJ, Olsen A, Johannessen T, and Croot P (2002) A high precision spectrophotometric method for on-line shipboard seawater pH measurements: The automated marine pH sensor (AMpS). Talanta 56: 61--69. Brereton RG (2007) Applied Chemometrics for Scientists. Chichester: Wiley. Brown AP, Koval C, and Anson FC (1976) Illustrative electrochemical behavior of reactants irreversibly adsorbed on graphite electrode surfaces. Journal of Electroanalytical Chemistry 72: 379--387. Burke CS, Markey A, Nooney RI, Byrne P, and McDonagh C (2006) Development of an optical sensor probe for the detection of dissolved carbon dioxide. Sensors and Actuators B: Chemical 119: 288--294. Ca´pitan-Vallvey LF, Fernandez Ramos MD, Avidad R, and Deheidel MKA (2001) Determination of the pesticide morestan by means of a single-use phosphorimetric sensor. Analytica Chimica Acta 440: 131--141. Castro AM, Delgado J, and Orellana G (2005) Hydrocarbon in water sensing with PTFE membranes doped with a luminescent Ru(II) poly(pyridyl) complex. Journal of Material Chemistry 15: 2952--2958. Chan CM, Fung CS, Wong KY, and Lo W (1998) Evaluation of a luminescent ruthenium complex immobilized inside Nafion as optical pH sensor. Analyst 123: 1843--1847. Chang AC, Gillespie JB, and Tabacco MB (2001) Enhanced detection of live bacteria using a dendrimer thin film in an optical biosensor. Analytical Chemistry 73: 467--470. Cho DH, Chung KC, and Park MY (1998) Hydrogen ion-selective membrane electrodes based on alkyldibenzylamines as neutral carriers. Talanta 47: 815--821. Chuang H, Macuch P, and Tabacco MB (2001) Optical sensors for detection of bacteria. 1. General concepts and initial development. Analytical Chemistry 73: 462--466. Creaser CS, Hutchinson WE, and Stephenson GR (2002) An attenuated total reflectance optrode sensor incorporating infrared-active carbonylmetal probe groups. Sensors and Actuators B: Chemical 82: 150--157. Dominguez-Vidal A, Ortega-Barrales P, and Molina-Diaz A (2007) Environmental water samples analysis of pesticides by means of chemometrics combined with fluorimetric multioptosensing. Journal of Fluorescence 17: 271--277. El-Giar EE-DM and Wipf DO (2007) Microparticle-based iridium oxide ultramicroelectrodes for pH sensing and imaging. Journal of Electroanalytical Chemistry 609: 147--154. Ertekin K and Alp S (2006) Enhanced emission based optical carbon dioxide sensing in presence of perfluorochemicals (PFCs). Sensors and Actuators B: Chemical 115: 672--677. Fenster C, Smith AJ, Abts A, Milenkovic S, and Hassel AW (2008) Single tungsten nanowires as pH sensitive electrodes. Electrochemistry Communications 10: 1125--1128. Flannery D, James SW, Tatam RP, and Ashwell GJ (1997) pH sensor using Langmuir– Blodgett overlays on polished optical fibers. Optical Letters 22: 567--569. Gupta R and Chaudhury NK (2007) Entrapment of biomolecules in sol–gel matrix for applications in biosensors: Problems and future prospects. Biosensors and Bioelectronics 22: 2387--2399. Hobson NS, Tothill I, and Turner APF (1996) Microbial detection. Biosensors and Bioelectronics 11: 455--477. Holm AH, Vase KH, Winther-Jensen B, Pedersen SU, and Daasbjerg K (2007) Evaluation of various strategies to formation of pH responsive hydroquinoneterminated films on carbon electrodes. Electrochimica Acta 53: 1680--1688. Hulth S, Aller RC, Engstrom P, and Selander E (2002) A pH plate fluorosensor (optode) for early diagenetic studies of marine sediments. Limnology and Oceanography 47: 212--220. Ivanov AN, Lukachova LV, Evtugyn GA, et al. (2002) Polyaniline-modified cholinesterase sensor for pesticide determination. Bioelectrochemistry 55: 75--77. Jang A, Lee JH, Bhadri PR, et al. (2005) Miniaturized redox potential probe for in situ environmental monitoring. Environmental Science and Technology 39(16): 6191--6197. Jankowska H, Neffe S, and Swiatkowski A (1981) Investigations of the electrochemical properties of activated carbon and carbon black. Electrochimica Acta 26: 1861--1866. Janotta M, Karlowatz M, Vogt F, and Mizaikoff B (2003) Sol–gel based mid-infrared evanescent wave sensors for detection of organophosphate pesticides in aqueous solution. Analytica Chimica Acta 496: 339--348. Jero´nimo PCA, Arau´jo AN, Conceic¸a˜o BSM, and Montenegro M (2007) Optical sensors and biosensors based on sol–gel films. Talanta 72: 13--27.

Ji J, Schanzle JA, and Tabacco MB (2004) Real-time detection of bacterial contamination in dynamic aqueous environments using optical sensors. Analytical Chemistry 76: 1411--1418. Jin WJ, Costa-Fernandez JM, and Sanz-Medel A (2001) Room temperature phosphorescence pH optosensor based on energy transfer. Analytica Chimica Acta 431: 1--9. Kaden H, Jahn H, and Berthold M (2004) Study of the glass/polypyrrole interface in an all-solid-state pH sensor. Solid State Ionics 169: 129--133. Kahlert H (2008) Functionalized carbon electrodes for pH determination. Journal of Solid State Electrochemistry 12(10): 1255--1266. Klotz A, Brecht A, Barzen C, et al. (1998) Immunofluorescence sensor for water analysis. Sensors and Actuators B: Chemical B51: 181--187. Lario-Garcia J and Pallas-Areny R (2006) Constant-phase element identification in conductivity sensors using a single square wave. Sensors and Actuators A: Physical 132: 122--128. Lau KT, Shepherd R, and Diamond D (2006) Solid state pH sensor based on light emitting diodes (LED) as detector platform. Sensors 6: 848--859. Lawrence NS and Robinson KL (2007) Molecular anchoring of anthracene-based copolymers onto carbon nanotubes: Enhanced pH sensing. Talanta 74: 365--369. Lee JH, Seo Y, Lim TS, Bishop PL, and Papautsky I (2007) MEMS Needle-type sensor array for in situ measurements of dissolved oxygen and redox potential. Environmental Science and Technology 41: 7857--7863. Lei Yao H, Ghafar-Zadeh E, Shabani A, Chodavarapu V, and Zourob M (2008a) CMOS capacitive sensor system for bacteria detection using phage organisms. Proceedings of the 21st Canadian Conference on Electrical and Computer Engineering, pp. 877–880. Lei Yao H, Chodavarapu V, Shabani A, Allain B, Zourob M, and Mandeville R (2008b) CMOS imager microsystem for multi-bacteria detection. Joint 6th International Northeast Workshop on Circuits and Systems and TAISA Conference, pp. 137–140. Lieberman SH, Inman SM, and Theriault GA (1991) Laser-induced fluorescence over optical fibers for real-time in situ measurement of petroleum hydrocarbons in seawater. Oceans 1: 509--514. Li JP, Peng TZ, and Fang C (2002) Screen-printable sol-gel ceramic carbon composite pH sensor with a receptor zeolite. Analytica Chimica Acta 455: 53--60. Li X and Meijer GCM (2008) A high-performance interface for grounded conductivity sensors. Measurement Science and Technology 19: 1--7. Li ZZ, Niu CG, Zeng GM, et al. (2006) A novel fluorescence ratiometric pH sensor based on covalently immobilized piperazinyl-1,8-naphthalimide and benzothioxanthene. Sensors and Actuators B: Chemical 114: 308--315. Mallat E, Barzen C, Klotz A, Brecht A, Gauglitz G, and Barcelo D (1999) River analyzer for chlorotriazines with a direct optical immunosensor. Environmental Science and Technology 33: 965--971. Marazuela MD and Moreno-Bondi MC (2002) Fiber-optic biosensors – an overview. Analytical and Bioanalytical Chemistry 372: 664--682. Marrazza G, Chianella I, and Mascini M (1999) Disposable DNA electrochemical biosensors for environmental monitoring. Analytica Chimica Acta 387: 297--307. Michie WC, Culshaw B, McKenzie I, et al. (1995) Distributed sensor for water and pH measurements using fiber optics and swellable polymeric systems. Optical Letters 20: 103--105. Mills A and Eaton K (2000) Optical sensors for carbon dioxide: An overview of sensing strategies past and present. Quı´mica Analı´tica 19(supplement 1): 75--86. Mu¨ller B and Hauser PC (1996) Fluorescence optical sensor for low concentrations of dissolved carbon dioxide. Analyst 121: 339--343. Neurauter G, Klimant I, and Wolfbeis OS (2000) Fiber-optic microsensor for high resolution pCO2 sensing in marine environment. Fresenius Journal of Analytical Chemistry 366: 481--487. Niu CG, Gui XQ, Zeng GM, Guan AL, Gao PF, and Qin PZ (2005) Fluorescence ratiometric pH sensor prepared from covalently immobilized porphyrin and benzothioxanthene. Analytical and Bioanalytical Chemistry 383: 349--357. Nivens DA, Schiza MV, and Angel SM (2002) Multilayer sol–gel membranes for optical sensing applications: Single layer pH and dual layer CO2 and NH3 sensors. Talanta 58: 543--550. Orellana G, Lo´pez-Rodas MV, Costas E, Haigh D, and Maneiro E (2009) Biosensors Based on Microalgae for the Detection of Environmental Pollutants. PCT Pat. Appl. ES2008000465, 29 January 2009. Orellana G, Moreno-Bondi MC, Segovia E, and Marazuela MD (1992) Fiber-optic sensing of carbon dioxide based on excited-state proton transfer to a luminescent ruthenium(II) complex. Analytical Chemistry 64: 2210--2215. Oter O, Ertekin K, Topkaya D, and Alp S (2006) Room temperature ionic liquids as optical sensor matrix materials for gaseous and dissolved CO2. Sensors and Actuators B: Chemical 117: 295--301.

Online Monitoring Sensors Pen˜a-Va´zquez E, Maneiro E, Pe´rez-Conde C, Moreno-Bondi MC, and Costas E (2009) Microalgae fiber optic biosensors for herbicide monitoring using sol–gel technology. Biosensors and Bioelectronics 24: 3538--3543. Poghossian A, Berndsen L, and Schoning MJ (2003) Chemical sensor as physical sensor: ISFET-based flow-velocity, flow-direction and diffusion-coefficient sensor. Sensors and Actuators B: Chemical 95: 384--390. Prissanaroon W, Brack N, Pigram PJ, Hale P, Kappen P, and Liesegang J (2005) Fabrication of patterned polypyrrole on fluoropolymers for pH sensing applications. Synthetic Metals 154: 105--108. Ruedas Rama MJ, Ruiz Medina A, and Molina Diaz A (2002) Use of a solid sensing zone implemented with unsegmented flow analysis for simultaneous determination of thiabendazole and warfarin. Analytica Chimica Acta 459: 235--243. Salinas-Castillo A, Fernandez-Sanchez JF, Segura-Carretero A, and FernandezGutierrez A (2004) A facile flow-through phosphorimetric sensing device for simultaneous determination of naptalam and its metabolite 1-naphthylamine. Analytica Chimica Acta 522: 19--24. Sanchez-Barragan I, Costa-Fernandez JM, and Sanz-Medel A (2005) Tailoring the pH response range of fluorescent-based pH sensing phases by sol–gel surfactants coimmobilization. Sensors and Actuators B: Chemical 107: 69--76. Scholz F, Steinhardt T, Kahlert H, Poerksen JR, and Behnert J (2005) Teaching pH measurements with a student-assembled combination quinhydrone electrode. Journal of Chemical Education 82: 782--786. Scho¨ning MJ, Abouzar MH, and Poghossian A (2009) pH and ion sensitivity of a fieldeffect EIS (electrolyte–insulator–semiconductor) sensor covered with polyelectrolyte multilayers. Journal of Solid State Electrochemistry 13: 115--122. Schroeder CR, Weidgans BM, and Klimant I (2005) pH fluorosensors for use in marine systems. Analyst 130: 907--916. Szepesvary E and Pungor E (1971) Potentiometric determination of acids and bases with a silicone rubber-based graphite electrode as indicator electrode. Analytica Chimica Acta 54: 199--208. Tabacco MB, Uttamlal M, McAllister M, and Walt DR (1999) An autonomous sensor and telemetry system for low-level pCO2 measurements in seawater. Analytical Chemistry 71: 154--161. Taboada Sotomayor MP, De Paoli MA, and de Oliveira WA (1997) Fiber-optic pH sensor based on poly(o-methoxyaniline). Analytica Chimica Acta 353: 275--280. Vuppu S, Kostov Y, and Rao G (2009) Economical wireless optical ratiometric pH sensor. Measurement Science and Technology 20: 1--7. Walsh JE, MacCraith BD, Meaney M, et al. (1996) Sensing of chlorinated hydrocarbons and pesticides in water using polymer coated mid-infrared optical fibers. Analyst 121: 789--792. Walt DR, Tabacco MB, and Utamlal M (2000) Fiber Optic Sensor for Long-Term Analyte Measurements in Fluids.US Pat. 6285807, 4 September 2001. Wang J, Chen L, Mulchandani A, Mulchandani P, and Chen W (1999) Remote biosensor for in-situ monitoring of organophosphate nerve agents. Electroanalysis 11: 866--869. White J, Kauer JS, Dickinson TA, and Walt DR (1996) Rapid analyte recognition in a device based on optical sensors and the olfactory system. Analytical Chemistry 68: 2191--2202. Wiegran K, Trapp T, and Cammann K (1999) Development of a dissolved carbon dioxide sensor based on a coulometric titration. Sensors and Actuators B: Chemical B57(1–3): 120--124. Wolfbeis OS, Kovacs B, Goswami K, and Klainer SM (1998) Fiber-optic fluorescence carbon dioxide sensor for environmental monitoring. Mikrochimica Acta 129: 181--188. Wong LS, Brocklesby WS, and Bradley M (2005) Fibre optic pH sensors employing tethered non-fluorescent indicators on macroporous glass. Sensors and Actuators B: Chemical 107: 957--962. Wro´blewski W, Rozniecka E, Dybko A, and Brzo´zka Z (1998) Cellulose-based bulk pH optomembranes. Sensors and Actuators B: Chemical 48: 471--475. Xiang-Hong W and Shuo W (2008) Sensors and biosensors for the determination of small molecule biological toxins. Sensors 8: 6045--6054.

Relevant Websites http://www.abraxiskits.com Abraxiskits. http://www.advnt.org Advnt Biotechnologies. http://www.analyticaltechnology.com Analytical Technology.

http://www.appliedbiosystems.com Applied Biosystems. http://www.astisensor.com ASTi: Advanced Sensor Technologies, Inc. http://www.beaconkits.com Beacon Analytical Systems. http://www.biomerieux.es Biomerieux. http://www.biophagepharma.net BiophagePharma. http://www.campbellsci.com Campbell Scientific. http://www.dwscientific.co.uk Don Whitley Scientific. http://www.endress.com Endress þ Hauser. http://www.environnement-sa.com Environnement S.A. http://www.grundfosalldos.com Grundfos Alldos. http://www.interlab.es Grupo Interlab. http://www.hach.com Hach. http://www.hannainst.com HANNA Instruments. http://www.horiba.com HORIBA. http://www.idahotech.com Idaho Technology. http://www.sensafe.com Industrial Test Systems. http://www.in-situ.com In-Situ. http://www.invitrogen.com Invitrogen. http://www.ionode.com.au Ionode. http://www.jenway.com Jenway. http://www.martekinstruments.com Martek Instruments. http://www.merck-chemicals.com Merck. http://in.mt.com Metller Toledo. http://www.myronl.com Myron L Company. http://earthobservatory.nasa.gov NASA: Earth Observatory. http://lsda.jsc.nasa.gov NASA: Life Science Data Archive. http://modis.gsfc.nasa.gov NASA: MODIS. http://oceancolor.gsfc.nasa.gov NASA: Ocean Color Web. http://www.nexsens.com Nexsens Technology. http://www.nico2000.net NICO 2000. http://www.palintest.com Palintest. http://www.pharmaleads.com Pharmaleads. http://www.presens.de PreSens. http://www.qtlbio.com QTL Biodetection.

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3.11 Standardized Methods for Water-Quality Assessment$ BC Gordalla, Karlsruhe Institute of Technology, Karlsruhe, Germany & 2011 Elsevier B.V. All rights reserved.

3.11.1 3.11.2 3.11.3 3.11.3.1 3.11.3.1.1 3.11.3.1.2 3.11.3.1.3 3.11.3.2 3.11.3.2.1 3.11.3.2.2 3.11.3.3 3.11.3.4 3.11.3.5 3.11.4 3.11.4.1 3.11.4.2 3.11.4.3 3.11.4.4 3.11.4.4.1 3.11.4.4.2 3.11.4.5 3.11.4.6 3.11.4.7 3.11.4.7.1 3.11.4.7.2 3.11.4.8 3.11.5 3.11.6 References

Introduction Features of Standards and Standardization Standardization Organizations Delivering Water-Testing Standards and Their TCs International Organization for Standardization General ISO/TC 147 – water quality Further ISO committees relevant for water examination and quality aspects Comite´ Europe´en de Normalisation – European Committee for Standardization CEN/TC 230 – water analysis Further TCs in CEN relevant for water-testing issues Coordination of Activities in CEN and ISO and Mutual Adoption of Documents The Role of the NSBs ASTM Items Covered by Standardization in the Field of Water Examination Terminology, Analytical Strategies, Validation, and Quality Control Sampling, Sample Pretreatment, and Basic Operations Physical–Chemical and Other Basic Parameters for Water-Quality Assessment Methods for Determination of Individual Water Constituents and Defined Groups of Substances Inorganic water constituents Methods for determination of organic compounds or jointly determinable groups of compounds Radiological Methods Microbiological Methods Biotesting Biodegradability Ecotoxicity and bioeffect testing Methods for Assessment of Water Bodies Resume and Outlook List of Standards

3.11.1 Introduction Activities to design and collect standardized methods of water examination go back to the end of the nineteenth century. A very popular example is the often-cited Standard Methods for the Examination of Water and Wastewater (APHA et al., 2005), jointly edited by the American Public Health Organization (APHA), the American Water Works Association (AWWA), and the Water Environment Federation (WEF). This collection was first published in 1905 and has now grown to a 1200-page book that is revised every 5 years by an editorial board supported by a large number of experts. Water analysis is performed worldwide using standardized protocols that have been recommended, collected, or developed by different organizations on a national or super-national level, ranging from authorities, technical, scientific, or water management associations to organizations explicitly dedicated to standardization. Water-testing standards in the narrower sense are delivered by national, European, or international standards bodies. $

Dedicated to Dr. Sibylle Schmidt, who chaired the technical committees ISO/TC 147 – water quality and CEN/TC 230 – water analysis for many years.

263 264 265 265 265 265 266 267 267 268 268 268 269 269 270 270 273 275 275 278 280 280 283 283 285 286 286 286 301

This chapter mainly focuses on the standardization activities in the field of water examination of the Comite´ Europe´en de Normalisation (CEN) – the European Committee for Standardization – which coordinates European standardization, and of the International Organization for Standardization (ISO), which deals with standardization on an international level. In these organizations, the participating countries are represented by their national standards bodies (NSBs). In addition, the standards’ portfolio on water testing of American Society for Testing and Materials (ASTM) International will be considered, which is an international standardization organization that emerged from the former ASTM. It must be stressed that these standardization organizations develop standardized methods for water examination, but that it is not their task nor are they entitled to set legal norms for water quality. The major benefit of standardized methods is the comparability of results. Comparability is especially crucial for

1. long-term observations to evidence changes or trends; 2. large-scale monitoring programs, for example, of river catchment areas; and

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3. reasons of equal treatment, when measured analytical parameters serve to check compliance with legal norms or to calculate emission-based taxes.

the large group of effect-, toxicity-, and ecotoxicity-testing protocols are conventional parameters.

Justifiability is a further reason, which is why water-testing standards are useful for legal and supervision purposes. The most simple case is that distinct standardized methods are directly prescribed in laws or ordinances, as is practiced, for example, in German wastewater regulations, where the calculation of sewage taxes (AbwAG, 2005) as well as compliance with the wastewater ordinance (AbwV, 2002), which gives the minimum requirements for the quality of discharged wastewater, are based on parameters measured according to official standards. As an alternative to referring directly to standards, in some regulatory stipulations specifications are made for methods to be suitable to check compliance with legal norms. As water-testing standards are set up for a specified application range, and often statistical performance data are also given therein, use of a standard may ensure that the respective specifications are met. For instance, chemical monitoring of the so-called priority pollutants based on the European Water Framework Directive (WFD) (2000/60/EC, 2000; 2008/105/EC, 2008) has to be performed by validated and documented methods which meet specified performance criteria concerning measurement uncertainty and limit of quantification (2009/90/EC, 2009). The European Drinking Water Directive (98/83/EC, 1998) gives minimum requirements for trueness, precision, and limit of detection for most chemical ingredients to be analyzed (see Chapter 3.07 Measurement Quality in Water Analysis). For determination of microbiological parameters, this directive actually refers to distinct standards. In the US, the National Technology Transfer Act of 1995 (see the ‘Relevant websites’ section for NTTA) urges US authorities to make use of already-existing private sector standards wherever possible. Thus, the Environmental Protection Agency (EPA), which has developed a large methods portfolio for water examination of its own (see the ‘Relevant websites’ section for EPA), also refers to standardized methods published by standardization organizations, for example, in order to ensure compliance with the Safe Drinking Water Act or the Clean Water Act. A detailed inventory of standardized methods approved for national environmental analysis is available on the Internet (see the ‘Relevant websites’ section for NEMI). In many cases, standardized water-testing methods dedicated to defined analytes are based on analytical methods published in scientific literature, which have been transferred into a standardized version (1) being given a single text format and further specifications concerning scope and procedural details compared to a publication in a journal, (2) based on a consensus of experts going beyond peer review, and, often, (3) after a validation step exceeding in-house validation. Besides, there are several core parameters especially designed for water analysis to which a standardized protocol is inherent. Very prominent ones are the so-called conventional – operationally defined – parameters, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved organic carbon (DOC), or adsorbable organic halogens (AOX). Chapter 3.01 Sum Parameters: Potential and Limitations is dedicated to those parameters. In addition,

3.11.2 Features of Standards and Standardization Some features that are typical for water analytical standards delivered by standards bodies are outlined below, but many of them apply to standardized methods for water examination in general. Standards have a given format with pre-defined specifications to be made. Standardized methods and especially water-testing standards delivered by standard bodies are written in a given text format that may differ for the respective standardization organizations in wording, but for chemical parameters, generally specifications are made on the following items: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

14.

definition of the analytical parameter; definition of terms; normative references; principle of the method; scope (matrices, range of concentrations reliably determinable); interferences; sampling and conservation; reagents including specifications concerning purity, standards; apparatus, equipment; all steps of analytical procedure (calibration, determination); calculation/evaluation; expression of results; precision (and bias, where applies, e.g., results from an interlaboratory trial, repeatability, reproducibility, and recovery); and test report.

Of course, for microbiological examination or biological testing, the catalog of items has to be modified accordingly. An essential feature of water analytical standards is that they are set up for a defined scope with respect to concentration range and matrices. Specifications or requirements concerning the purity of chemicals and the performance of instruments are given in a general way, but not tailored to the commercial products of a special manufacturer, as an important objective of standards bodies is to facilitate the exchange of goods and services. Thus, materials, instruments, and methods which are unique or on which a monopoly is held are not suitable for standardization. Extended or additional information (e.g., detailed results of round-robin tests, example chromatograms, technical drawings, and additional references) is given in annexes to the standards. In CEN, ISO, and ASTM, a distinction is made between normative (mandatory) and informative (nonmandatory) annexes, the latter providing information that is illustrative or serves deeper understanding. In some cases, informative annexes specify modifications which allow an international standard to be used within respective national regulations. An example is the definition of the concept lowest ineffective dilution (LID) for wastewater testing in the informative annex of EN ISO 5667-16 and the specifications

Standardized Methods for Water-Quality Assessment

how to determine this quantity given in the informative annexes of several standards for ecotoxicity testing (e.g., EN ISO 20079, EN ISO 11348-1, -2, and -3). In standards of CEN, ISO, and ASTM, information on former versions is also given. ASTM standards can be obtained in a so-called redline version, where changes compared to the former version are marked. Extended rules for the format of a standard and the items to be covered therein are given in basic standards, for example, for ISO standards in ISO/IEC (2004). For ASTM, regulations concerning procedural details of standards development, or on format and style of the documents, are given in the Red Book (ASTM, 2007), the Green Book (ASTM, 2009a), and the Blue Book (ASTM, 2009b). Standards are the result of a formalized process of consensus building. Characteristics of the standardization process are openness to the public and participation of the so-called interested parties. In the case of standardized methods for water examination, the interested parties include 1. legislator and authorities, who set legal norms in the field of water quality and need methods to control compliance; 2. water suppliers, sewerage boards, and industries which release effluents, whose compliance is checked using the standardized methods or who might be charged with emission-based taxes based on the results; and 3. universities, manufacturers of analytical instruments, and analytical laboratories, who develop and apply the standardized methods.

The development of a standard is initiated by a formal new work item proposal to the standardization organization, more precisely, to the responsible technical committee (TC). The method protocols finally delivered as standards are developed in a regulated multistage process of elaborating, commenting, and voting. In CEN and ISO, a time schedule has to be met for this process, 2-, 3-, or 4-year development tracks are possible in ISO. In European standardization, the drafts of standards (prEN) are presented to the public in the member countries by the NSBs for comments. Further public involvement is established depending on the conditions in the respective countries, for example, by giving the opportunity to submit new work item proposals to the NSBs or by publicly announcing for participants in working groups (WGs) or interlaboratory trials. Standards are subject to quality control. In many cases, the standardization process includes a validation step to be prerequisite for a method to be considered as a standard for water examination. Validation might be based on certified reference materials or on a successful interlaboratory trial. The latter is mandatory in ISO/TC 147 and in CEN/TC 230 for standardized methods on continuously measurable variables. Interlaboratory trials are not restricted to chemical parameters, but are performed also for microbiological methods, biotests, and methods for ecological assessment. Validation is an important element of standardization, and sometimes experiences and results of interlaboratory trials are additionally published in journals (e.g., Reifferscheid et al., 2008; Stottmeister et al., 2009). In ASTM, a collaborative study to determine precision

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and bias of proposed new methods for water testing is obligatory (ASTM D2777). In ISO, CEN, and ASTM, standards once delivered are periodically checked if they are relevant and state of the art, and then either confirmed, revised, or withdrawn. In ISO or CEN, this decision is made after a routine query among the member countries every 3 or 5 years, respectively. ASTM performs a routine reballoting every 5 years.

3.11.3 Standardization Organizations Delivering Water-Testing Standards and Their TCs Within the standardization organizations, standardization work is organized in TCs dedicated to particular subject areas. The actual technical work on specific standards is done in WGs or task groups of project-related specialists. Their task is to develop the technical content of the standards (i.e., the method protocols), to elaborate the documents, and to organize validation measures (i.e., interlaboratory trials), if mandatory. WGs are usually disbanded after having finished their standardization project.

3.11.3.1 International Organization for Standardization 3.11.3.1.1 General ISO, founded in 1946, coordinates standardization activities on an international level. The member countries are represented in ISO by their NSBs. Membership is possible as a participating member (P-member) with the right and the obligation to vote on the documents, or with an observing status (O-member). In balloting processes, the vote of each P-member weighs equally (one country – one vote). The adoption of ISO standards into the respective national collections of official standards is optional; it is up to the NSBs to decide about this.

3.11.3.1.2 ISO/TC 147 – water quality This TC was created in 1971 in order to develop standardized methods for water quality control (Schmidt, 2001, 2003). According to its scope, ISO/TC 147 is in charge of ‘‘standardization in the field of water quality, including definition of terms, sampling of waters, measurement and reporting of water characteristics.’’ In addition, in situ sediments are dealt with in this committee. It was chaired by the US standards body American National Standards Institute (ANSI) until 1983, since then the secretary has been held by the German standards body Deutsches Institut fu¨r Normung (DIN) (Schmidt and Wunder, 1988; Schmidt, 1992). At present, 35 countries participate in ISO/TC 147 as P-members, and a further 52 countries are informed about the activities of the committee as O-members. About 30 active WGs are working on current standardization projects, organized in five subcommittees (SCs) according to the different subject areas to be covered for water examination (see Table 1). The WG on radiological methods is directly allocated to ISO/TC 147. Up to 1989, it was organized in a SC of its own. The majority of the documents authored by ISO/TC 147 is in the format of international standard (IS), which is the intended main deliverable of ISO. Some methods with a normative content are delivered as so-called technical

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Table 1

Subcommittees (SCs) and working groups (WGs) of the technical committee ISO/TC 147 – water quality current as of April 2010

Panel

Title

Chair

Members

TC 147 WG 4

Water quality Radiological methods

Germany (DIN) France (AFNOR)

35 P, 52 O

SC 1

Terminology

South Africa (SABS)

18 P, 26 O

SC 2 WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG

Physical, chemical, and biochemical methods Polycyclic aromatic hydrocarbons (PAH) Ion chromatography methods Flow analysis methods Precision and accuracy Antimony, arsenic, and selenium GC-MS for groups of nonpolar substances Glyphosate and AMPA PFOS and PFOA SPME Chloroalkanes Color determination Mercury determination Dissolved oxygen determination Discrete analysis Chlorinated naphthalenes Volatiles Chemical oxygen demand

Germany (DIN) The Netherlands (NEN) Germany (DIN) Germany (DIN) Germany (DIN) United Kingdom (BSI) The Netherlands (NEN) France (AFNOR) Japan (JISC) DIN Germany (DIN) DIN Germany (DIN) Norway (SN) Germany (DIN) Germany (DIN) United Kingdom (BSI) Canada (SCC) Germany (DIN) United Kingdom (BSI)

26 P, 25 O

19 33 38 48 52 53 55 56 57 59 60 61 62 63 NN NN NN

SC 4 WG WG WG WG WG WG WG WG WG

Microbiological methods E. coli and other coliforms Sulfite-reducing Clostridium Salmonella Legionella Analytical quality control of microbiological media Cryptosporidium/Giardia Uncertainty of measurement Legionella by PCR E. coli/coliforms with liquid enrichment

Germany (DIN) Germany (DIN) Austria (ASI) United Kingdom (BSI) The Netherlands (NEN) France (AFNOR) United Kingdom (BSI) Finland (SFS) France (AFNOR) USA (ANSI)

28 P, 17 O

2 5 7 10 12 13 15 17 19

SC 5 WG WG WG WG WG WG

Biological methods Toxicity – bacteria and biodegradability Toxicity – invertebrates Toxicity – fish Toxicity – algae and aquatic plants Biological classification Genotoxicity

Germany (DIN) Germany (DIN) Germany (DIN) Sweden (SIS) Norway (SN) United Kingdom (BSI) Germany (DIN)

24 P, 19 O

1 2 3 5 6 9

SC 6 WG WG WG WG WG

Sampling (general methods) Design of sampling programmes Conservation methods Rivers and streams including groundwater Drinking water and water used for food and beverage processing Sampling of sludges and sediments

United Kingdom (BSI) United Kingdom (BSI) The Netherlands (NEN) United Kingdom (BSI) United Kingdom (BSI) United Kingdom (BSI)

23 P, 20 O

1 3 4 6 11

NN, to come; P, participating members; O, orbserving members.

specification (TS) based on a lower level of consensus. Nonnormative documents with a more informative or guideline character are published in the format of a technical report (TR). The portfolio of ISO/TC 147 comprises almost 250 active standards, they are included in the list of standards at the end of this chapter. On the website of ISO TC/147, extended information about the scope and structure, current standardization projects, and the standards portfolio of this committee is available (see the ‘Relevant websites’ section for ISO/TC 147).

An overview of the different steps of the standardization process in ISO/TC 147, the subsequent document stages, and queries among the member countries is given in Figure 1. The development of a standard is initiated by a formal new work item proposal submitted by one of the participating NSBs, in most cases with a first working draft added. To have the standardization project included in the working program, not only a majority is prerequisite, but also at least five countries must be willing to actively participate in the concerned WG. In two document stages, committee draft (CD) and draft of

Standardized Methods for Water-Quality Assessment

a standard (DIS), the members are given the opportunity to submit technical comments on the method. The project leader deals with these comments. An important step for all methods on parameters that are continuously measurable is the validation by a round-robin test in order to estimate repeatability and reproducibility. The philosophy of those external interlaboratory trials in ISO/TC 147 is as follows: the mean value is the reference value. An identical sample has to be measured according to the overall procedure at least in duplicate, preferably in three or four replicates. The evaluation of the test is performed based on ISO 5725-2. The data are checked for type 1 (Grubbs test), type 2 (Grubbs test), and type 3 (Cochran test) outliers; the percentage of relative outliers should not exceed 25%. The minimum performance to be achieved is that at least eight valid data sets and 24 outlier-free single data should remain. The coefficient of variation of reproducibility (interlaboratory) CVR should not exceed 30%. If a true value does exist, there will be additional requirements concerning bias, depending on the respective method. In ISO/TC 147, consensus building, decisions, and votes are made in writing by means of queries among the participating countries. Nevertheless, ISO/TC 147, its SCs and WGs meet every 18 months for a 1-week meeting. At this meeting, TC 147 and the SCs discuss principle or strategic issues of future work, for example, possible new work item proposals as well as installation or disbanding of WGs. In parallel, the experts in the WGs deal with the technical details of the methods

to be standardized, the comments received from the member countries, and the design and organization of interlaboratory trials to be performed. At present, about 50 standardization projects are going on in ISO/TC 147, 40 dealing with development of new standards, the rest with revision of already-existing standards induced by the periodical 3-year-review inquiries.

3.11.3.1.3 Further ISO committees relevant for water examination and quality aspects Hydrometric and hydrogeological aspects of aquatic systems (e.g., flow, velocity and discharge measurements, sediment transport, and measurement of groundwater levels) are dealt with in a separate TC, the ISO/TC 113 – hydrometry. Furthermore, ISO TC/147 is in liaison with ISO/TC 190 – soil quality. In the documents delivered by ISO/TC 147, basic documents are cited that have been published by other TCs or institutions. Prominent examples are the standard ISO 3696 on water for analytical purposes authored by ISO/TC 47 – chemistry, or the standard ISO 5725 on accuracy, especially part 2 dealing with repeatability and reproducibility, which was created by ISO/TC 69 – applications of statistical methods. Concerning metrological terms, quantities, and units, ISO standards refer to the International Vocabulary of Metrology (VIM) (ISO/IEC Guide 99, 2007) and to the International Union of Pure and Applied Chemistry (IUPAC, 2007, cited in ISO 80000-9).

Document stage

A committee member (e.g., DIN) submits 1 NP (new work item proposal) P-members vote

Simple majority + 5 members nominate experts

SC members vote and comment

Approval by simple majority + <3 × disagreement

2

66% Approval + <25% disagreement

P-members vote

CD (committee draft)

Interlab. trial

DIS or ISO/TS (technical specification) ISO/TR (technical report) for non-normative contents

Enquiry on DIS (Draft International Standard)

Final text for processing as FDIS (Final Draft International Standard)

Formal vote on FDIS 5 (proof check by secretariat, resp., project leader)

Final text of international standard

Publication of International Standard

ISO International Standard

4 66% Approval +

Deliverable

WD (working draft)

Building expert consensus

Consensus building 3 within TC/SC P-members vote and comment

267

TS TR

<25% disagreement

6

ISO Standard

Figure 1 Standardization process – setting up an ISO standard. CD, DIS, and FDIS (final draft of an International Standard) are three official document stages that require balloting of the members’ standards bodies. In ISO/TC 147 – water quality, an interlaboratory trial for validation is mandatory.

268

Standardized Methods for Water-Quality Assessment

3.11.3.2 Comite´ Europe´en de Normalisation – European Committee for Standardization On a European level, standardization is organized in the CEN, founded in 1961 with the goal of European harmonization (see also the ‘Relevant websites’ section for CEN). The 31 member states are represented in CEN and in its TCs by their NSBs. Initiatives for standardization projects may come from the NSB, or directly from the European Commission (EC), in case standards are developed in order to support European legislation. In European standardization, one draft stage (prEN – draft of a European standard) is released. It is published in the three language versions English, French, and German and distributed in the member countries for public comments (CEN inquiry). A European Standard (EN) has to be adopted as a national standard in the 31 member countries and supersedes national standards on the same topic. Furthermore, once a standardization project has been adopted in CEN on a topic, on a national level, no new projects on that topic are initiated, nor are revisions of existing standards undertaken (standstill obligation).

3.11.3.2.1 CEN/TC 230 – water analysis The TC CEN/TC 230 – water analysis was created in 1990 in order to establish methods for existing and future EU Directives on water quality (Schmidt, 2003). An overview of the structure of this committee is given in Table 2. At present, this TC deals mainly with the development of methods suitable to implement the monitoring programs of the WFD (2000/60/ EC). In doing so, WG 2 focuses on biological and ecological assessment methods for aquatic systems and the characterization of water bodies, which has to be performed according to annex V of the WFD. The task of WG 1 is to develop methods that are suitable to monitor the environmental quality standards (EQS) for priority pollutants and certain other pollutants according to annexes VIII and X of the WFD, listed in the daughter Directive 2008/105/EC and that meet the performance characteristics specified in daughter Directive 2009/90/EC on technical specifications for chemical analysis, that is, LOQo30% of EQS, expanded measurement uncertainty (k ¼ 2)o50% of EQS (see Chapter 3.07 Measurement Quality in Water Analysis). A strong impulse for these activities arose from the official mandate M/424 of 2008 given

Table 2

by the EC to CEN/TC 230 in order to develop or improve standardized methods in support of the WFD. At present, the main efforts are directed at the parameters organochlorine pesticides, pentabromodiphenylethers, tributyltin compounds, chloroalkanes, polycyclic aromatic hydrocarbons (PAH), furthermore to phytoplankton sampling from inland waters, determination of algal biovolume, and fish sampling. An overview of the various efforts to coordinate research, standardization, and policy in order to implement the WFD and the role of standards bodies of the different European countries therein is given in Quevauvillier et al. (2007). A list of standards and standardization projects of CEN/TC 230 is available (see the ‘Relevant websites’ section for CEN/ TC 230). The TC, its WGs, and task groups meet annually to discuss strategic topics and technical aspects. Much of the detailed technical work is also performed in expert workshops. Official votings and comments on the documents are requested by queries among the member countries of the committee and given in writing. European standards are adopted by weighted votes, as is common on many European panels.

3.11.3.2.2 Further TCs in CEN relevant for water-testing issues TCs on water testing and TCs dealing with environmental solid matrices keep each other informed about their activities. This is because water quality is influenced by adjacent solid matrices, for example, by leaching or runoff processes. Another reason is that after a preceding digestion or extraction step, the primary step of chemical analysis for testing solid matrices often is the same as for aqueous samples. The CEN/ TC 400 – horizontal standards in the fields of sludge, biowaste, and soil, emerged from the eponymous task force CEN BT/TF 151, deals with the harmonization of analytical standards for those matrices to avoid duplicate work and standards portfolios. A particular matrix tested in water technology is the intermediate phase of sludge, being formed in wastewatertreatment plants or during flocculation in drinking water treatment. CEN/TC 308 – characterization of sludges is dedicated to this subject area. The standards portfolio and working program of CEN/TC 308 and the working program of CEN/TC 400 are available on the Internet (see the ‘Relevant websites’ section for CEN/TC 308 or CEN/TC 400, respectively.).

Working groups (WGs) and task groups (TGs) of the technical committee CEN/TC 230 – water analysis, current as of April 2010

Panel

Title

Chair

TC 230 WG 1 WG 2 TG 1 TG 3 TG 4 TG 5 TG 6 TG 7

Water analysis Physical and chemical methods Biological and ecological assessment methods Invertebrates Aquatic macrophytes and algae Fish monitoring Waterbody characteristics Quality assurance Marine ecological methods

Germany (DIN) NN United Kingdom (BSI) United Kingdom (BSI) The Netherlands (NEN) Sweden (SIS) United Kingdom (BSI) Austria (ASI) Norway (SN)

Standardized Methods for Water-Quality Assessment 3.11.3.3 Coordination of Activities in CEN and ISO and Mutual Adoption of Documents There are many cases, where standardization activities on the same subject area take place on an international level and a European level as well. Both ISO/TC 147 – water quality and CEN/TC 230 – water analysis deliver standards on waterquality examination. In principle, their working programs would overlap to a great extent, with the consequence that many of the experts would meet in WGs of CEN and additionally of ISO. As both TCs are chaired by the same country, a division of labor between ISO/TC 147 and CEN/TC 230 has been established in order to avoid double or contradictory work. This is possible, because the Vienna Agreement allows mutual adoption of standards developed in CEN and ISO. At present, technical work in CEN/TC 230 is done only on standards that are especially needed for the requirements of the WFD and that are not intended to be included into the ISO portfolio. The majority of physical, chemical, and microbiological methods, as well as testing methods for ecotoxicity or degradability, are developed in ISO/TC 147 and adopted into the European standards collection. This can be done by unique acceptance procedure (UAP). This means that the final ISO standard is presented for balloting in CEN and either accepted as an EN as is or not accepted. The alternative is the so-called parallel vote (PV), where a draft stage of the standard is circulated and commented among the members of both ISO/TC 147 and CEN/TC 230 (see the ‘Relevant websites’ section for Vienna Agreement). EN ISO standards make up 40% or 70% of the active standards of ISO/TC 147 or of CEN/ TC 230, respectively. For reasons of space, ISO standards adopted as European standards are only cited once in the list of standards at the end of this chapter, as EN ISO standards, which includes reference to ISO.

3.11.3.4 The Role of the NSBs The member countries participate in standardization work done on European and international levels by their NSBs. In detail, the NSBs 1. appoint experts to participate in the TCs, SCs, working or task groups of ISO and/or CEN; 2. may submit new work item proposals to ISO and/or CEN; 3. agree/disagree to new work item proposals of ISO and/or CEN; 4. give technical comments on draft stages (e.g., ISO/CD, ISO/DIS, and prEN) of standards in development; 5. cast national vote to final documents of ISO and/or CEN; 6. may adopt ISO standards as national standards; and 7. transpose European standards into national standards (CEN members only). In the field of water analysis, these tasks are fulfilled in Germany by the TC DIN NA 119-01-03 AA (Arbeitssausschuss – Wasseruntersuchung – TC – water analysis), which was created by the German Standards Body Deutsches Institut fu¨r Normung (DIN) – German Institute for Standardization in the early 1970s in order to deliver methods for water examination as German standards. This committee, consisting of experts representing the interested parties in Germany and supported

269

by about 30 WGs, mirrors the committees CEN/TC 230 and ISO/TC 147 and additionally develops water analytical standards on a national level, where these are required for special needs. A list of active standards and current standardization projects is available on the Internet (see the ‘Relevant websites’ section for DIN NA 119-01-03 AA). Standards passed by the TC DIN NA 119-01-03 AA Wasseruntersuchung (i.e., DIN, DIN EN, DIN ISO, or DIN EN ISO standards) are available as stand-alone standards, and besides, they are included into the German loose-leaf collection of standard methods for the examination of water, wastewater, and sludge (Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung – DEV) (Wasserchemische Gesellschaft and Normenausschuss Wasserwesen im DIN, 2010; see also the ‘Relevant websites’ section for DEV), edited and updated since 1935 by the German Water Chemical Society – a division of the German Chemical Society (GDCh). This collection of methods was taken as the basis for DIN standardization in the field of water analysis when it started in the 1970s, and up to now, the Water Chemical Society has been closely involved in the standardization activities. Thus, for national German standards authored by the AA Wasseruntersuchung (standards series 38 400), the process of method development and especially the interlaboratory trial has to be recorded in extended documentation, the so-called Validierungsdokument. These documents are hosted on the website of the GDCh (see the ‘Relevant websites’ section for GDCh).

3.11.3.5 ASTM ASTM is an industry-driven international standardization organization, which emerged from the former American Society for Testing and Materials, founded in 1898. It produces consensus-based standards for goods and services. The technical experts in the WGs represent producers, users, government, and academics. In ASTM, much impetus comes from the manufacturers of analytical instruments. New work items are often started at the initiative of the WGs. The WGs are responsible for validation of the methods. The prepared documents have to pass voting in the respective SC, in the main TC, and at ASTM society level. Besides membership of organizations, for example, companies or standards bodies, in ASTM, personal membership in the committees is possible as well. Personal members have the right to vote in ballots, with the restriction of one vote each per institution involved in the respective standardization project (one company, one vote). Methods for water examination are developed in the ASTM TC D19, created in 1932, with currently around 340 members (see the ‘Relevant websites’ section for ASTM D19). D19 meets twice a year for 4 days of technical meetings and a workshop on relevant topics, with approximately 70 participants. The technical work is organized in the SCs listed in Table 3. According to its scope (see the ‘Relevant websites’ section for ASTM D19 Scope), D19 deals with 1. sampling and analysis of water, waterborne materials, and wastes, water-formed deposits and fluvial sediments; 2. surface water hydraulics and hydrologic measurements; 3. the determination of the performance of materials or products used to modify water characteristics; and

270

Standardized Methods for Water-Quality Assessment

Table 3 water

Subcommittees of the ASTM technical committee D19 on

D19.02 D19.03

Quality systems, specification, and statistics Sampling water and water-formed deposits, analysis of water for power generation and process use, on-line water analysis, and surveillance of water Methods of radiochemical analysis Inorganic constituents in water Methods for analysis of organic substances in water Sediments, geomorphology, and open-channel flow Membranes and ion-exchange materials Water microbiology

D19.04 D19.05 D19.06 D19.07 D19.08 D19.24

4. the determination of the corrosivity or deposit-forming properties of water. ASTM D19 also covers hydrological, water technological, and corrosion aspects. A special SC is dedicated to testing methods in connection with membrane technology and ion exchange. Besides the matrices freshwater and wastewater, in D19 saline waters, brines, boiler-feed waters, and process waters are considered. Several methods deal with waters from contaminated sites and oil-contaminated waters. Furthermore, scalings and deposits constitute an item. The 290 standards are under the jurisdiction of the committee D19; they are published as stand-alone methods as well as in the Annual Book of ASTM Standards, volumes 11.01 and 11.02. In the list of standards at the end of this chapter, standards authored by the ASTM SCs D19.02, D19.04, D19.05, D19.06, and D19.24 are included. ASTM water-testing standards are delivered in the format of standard test method, dedicated to special analytes, standards practice, or standard guide, the latter dealing with general methodological aspects.

3.11.4 Items Covered by Standardization in the Field of Water Examination Water-testing standards are needed for suitable parameters to cover the following purposes: 1. to check the quality of water for human consumption and use or for technical applications; 2. to check the efficiency of treatment processes (e.g., drinking water treatment and wastewater treatment); 3. to monitor and assess the quality of natural waters; and 4. to estimate emissions and loads, especially of wastewaters and effluents. For illustration purposes, Table 4 gives an overview of the parameters which have to be analyzed according to the European Drinking Water Directive (98/83/EC, 1998) and for monitoring of natural waters as a consequence of the WFD. Many of them require very sensitive methods. This is particularly important for the EQS given in the directive 2008/ 105/EC (2008) for priority substances in the field of water quality (PS) and priority hazardous substances in the field of water quality (PHS), the latter being of concern because they are toxic, persistent, and bioaccumulative. In addition, in the

table given are the guideline values set by the World Health Organization (WHO) for some water ingredients, since many of the drinking water parameters have been included into the directive because of human-toxicological concerns (for human-toxicological aspects of water ingredients, see also Chapter 3.14 Drinking Water Toxicology in Its Regulatory Framework. Corrosiveness to the main, and taste and odor are further aspects covered. Traditional parameters for surveillance of domestic or municipal wastewaters are suspended matter, BOD, COD, phosphorus, and organically and inorganically bound nitrogen. Analytes to be checked in industrial wastewaters may be manifold and depend on the respective industry branches, for example, metals such as Hg, Cd, Cr, Ni, Pb, and Cu. Prominent examples for industrial wastewater parameters are AOX and cyanide.

3.11.4.1 Terminology, Analytical Strategies, Validation, and Quality Control Definition of terms is a prerequisite to unambiguously specify technical data and procedures. For this, most TCs in standardization deal with the definition of terms to be used in their respective standards portfolio. The SC 1 of ISO/TC 147 has defined about 900 terms given in the series ISO 6107 on vocabulary, parts 1 through 9. At present, these definitions are being transposed into a concept database, which is accessible free of charge in a read-only format (see the ‘Relevant websites’ section for ISO/CDB). In CEN, terms used in wastewater treatment are collated in EN 1085. For ASTM, the standard terminology relating to water is given in the standard ASTM D1129, for fluvial sediment in ASTM D4410. Analyte-dedicated specified methods make up the majority of the standards portfolio on water examination. Besides, there are some papers on general or on partial aspects of analytical procedures, for example, guideline papers dealing with strategies for the design of monitoring programs (ISO 5667-20, ASTM D5851, ASTM D5612, ASTM D6146, and ASTM D6145), or proficiency of laboratories (ISO/IEC 17025, ISO 13528, and ISO/TS 20612). Analytical quality control is dealt with in ISO/ TS 13530, and quality assurance of biological and ecological assessments in EN 14996. A current standardization project in ISO/TC 147/SC 2 is dedicated to the determination of measurement uncertainty (intended ISO 11352). An important issue is the validation of methods, for example, design and evaluation of interlaboratory comparisons, which is specified by ISO 5725-2 or ASTM D2777. ENV ISO 13843 gives guidance on the validation of microbiological methods. Sometimes the necessity may arise to prove that a given method is equivalent to an already-existing standard, for example, if in regulatory frameworks application of equivalent methods as alternatives to a recommended official standard is permitted. ISO/TS 16489 is a guideline for establishing the equivalency of results. Criteria for establishing equivalence among microbiological methods are given in EN ISO 17994.

3.11.4.2 Sampling, Sample Pretreatment, and Basic Operations Sampling is the first step of analysis. The manifold aspects of sampling are covered in the standard ISO 5667, which now

Standardized Methods for Water-Quality Assessment Table 4

271

Parameters relevant for the assessment of drinking water and of natural waters, and their parametric values, given in European Directives

Acrylamide Aluminum Ammonium Antimony Arsenic Alachlor Anthracene Atrazine Benzene Boron Bromate Brominated diphenylether Cadmium

Carbon tetrachloride C10–13 chloroalkanes Chloride Chlorfenvinphos Chlorpyrifos (chlorpyrifos-ethyl) Chromium Copper Cyanide Cyclodiene pesticides: aldrin, dieldrin, endrin, isodrin

Inland surface waters

Drinking water

2008/105/EC (2008)

98/83/EC (1998)

WHO (2006)

Environmental quality standard, annual average (AA-EQS)

Parametric value

Guideline value

0.10 mg l1 (CP) 200 mg l1 (IP, CM) 0.50 mg l1 (IP, CM) 5.0 mg l1 (CP) 10 mg l1 (CP) As a pesticide

0.5 mg l1a

20 mg l1 10 mg l1 (P) 20 mg l1a

As a pesticide 1.0 mg l1 (CP) 1.0 mg l1 (CP) 10 mg l1 (CP)

2 mg l1 10 mg l1a 0.5 mg l1 (T) 10 mg l1a (A,T)

5.0 mg l1 (CP)

3 mg l1

0.3 mg l1 (PS) 0.1 mg l1 (PHS) 0.6 mg l1 (PS) 10 mg l1 (PS)

0.0005 mg l1 (PHS) (PHS) r0.08 mg l1 (hardness class 1) Up to 0.25 mg l1 (hardness class 5) 12 mg l1 0.4 mg l1 (PHS) 1

0.1 mg l (PS) 0.03 mg l1 (PS)

S ¼ 0.01 mg l1

DDT total 1,2-Dichloroethane Dichloromethane Di(2-ethylhexyl)-phthalate (DEHP) Diuron Endosulfan Epichlorohydrin Fluoranthene Fluoride Hexachlorobenzene Hexachlorobutadiene Hexachlorocyclohexane Iron Isoproturon Lead Manganese Mercury Naphthalene Nickel Nitrate Nitrite

0.025 mg l1 10 mg l1 (PS) 20 mg l1 (PS) 1.3 mg l1 (PS) 0.2 mg l1 (PS) 0.005 mg l1 (PHS)

Nonylphenol (4-nonylphenol) Octylphenol (4-(1,10 ,3,30 -tetramethylbutyl)phenol) Pentachlorobenzene Pentachlorophenol

0.3 mg l1 (PHS) 0.1 mg l1 (PS)

4 mg l1 250 mg l1 (IP) As a pesticide As a pesticide 50 mg l1 (CP) 2.0 mg l1 (CP) 50 mg l1 (CP) Aldrin 0.03 mg l1 Dieldrin 0.03 mg l1

3.0 mg l1 (CP)

30 mg l1 50 mg l1 (P) 2 mg l1 70 mg l1 Aldrin þ dieldrin 0.03 mg l1

1 mg l1 30 mg l1a 20 mg l1 8 mg l1

As a pesticide As a pesticide 0.10 mg l1 (CP)

0.4 mg l1 (P)

1.5 mg l1 (CP)

1.5 mg l1

0.1 mg l1 (PS) 0.01 mg l1 (PHS) 0.1 mg l1 (9) (PHS) 0.02 mg l1 (PHS) 0.3 mg l1 (PS) 7.2 mg l1 (PS) 0.05 mg l1 (9) (PHS) 2.4 mg l1 (PS) 20 mg l1 (PS)

0.007 mg l1 (PHS) 0.4 mg l1 (PS)

0.6 mg l1 200 mg l1 (IP, CM) As a pesticide 10 mg l1 (CP) 50 mg l1 (IP) 1.0 mg l1 (CP) 20 mg l1 (CP) 50 mg l1 (CP) 0.50 mg l1 (CP, CM)

9 mg l1 10 mg l1 400 mg l1 (C) 6 mg l1 70 mg l1 50 mg l1 3 mg l1b 0.2 mg l1c

9 mg l1a (P) (Continued )

272

Standardized Methods for Water-Quality Assessment

Table 4

Continued

Pesticides – individual Pesticides – total Polycyclic aromatic hydrocarbons Benzo(a)pyrene Benzo(b)fluoranthene Benzo(k)fluoranthene Benzo(g,h,i)perylene Indeno(1,2,3-cd)pyrene Selenium Simazine Sodium Sulfate Tetrachloroethylene Trichloroethylene Tributyltin compounds (tributyltin-cation) Trichlorobenzenes Trichloromethane Trifluralin Trihalomethanes-total Vinyl chloride Tritium Total indicative dose Hydrogen ion concentration Oxidizability Total organic carbon (TOC) Conductivity Turbidity Color Odor Taste Escherichia coli Enterococci Clostridium perfringens (including spores) Coliform bacteria Pseudomonas aeruginosa Colony count 22 1C Colony count 37 1C a

Inland surface waters

Drinking water

2008/105/EC (2008)

98/83/EC (1998)

WHO (2006)

Environmental quality standard, annual average (AA-EQS)

Parametric value

Guideline value

0.10 mg l1 (CP) 0.50 mg l1 (CP) 0.10 mg l1 (CP) 0.010 mg l1 (CP)

0.7 mg l1a

(PHS) 0.05 mg l1 S ¼ 0.03 mg l1 S ¼ 0.002 mg l1

1 mg l1 (PS)

10 mg l1 10 mg l1 0.0002 mg l1 (PHS) 0.4 mg l1 (PS) 2.5 mg l1 (PS) 0.03 mg l1 (PS)

10 mg l1 (CP) As a pesticide 200 mg l1 (IP) 250 mg l1 (IP) 10 mg l1 (CP)f

As a pesticide 100 mg l1 (CP) 0.50 mg l1 (CP) 100 Bq l1 (IP) 0.10 mSv a1 (IP) Z6.5 and r9.5 pH units (IP, CM) 5.0 mg l1 O2 No abnormal change (IP) 2500 mS cm1 at 20 1C (IP, CM) Acceptable to consumers and no abnormal change (IP, CM)g 0/100 ml (MP, CM) 0/250 mle(MP) 0/100 ml (MP) 0/250 mle(MP) 0/100 ml (IP, CM) 0/100 ml (IP, CM) 0/250 mle (IP, CM) 0/250 mle(MP, CM) No abnormal change (IP) 100/mle (MP, CM) 20/mle (MP, CM)

10 mg l1 2 mg l1

40 mg l1 20 mg l1 (P)

300 mg l1 20 mg l1 d

0.3 mg l1a (C) 10 000 Bq l1 0.10 mSv a1

0/100 ml

0/100 ml

Considered to be carcinogenic, value calculated for an excess lifetime cancer risk of 105. Short-term exposure. c Long-term exposure. d The guideline values for dichloromethane (20 mg l1), bromodichloromethane (60 mg l1), tribromomethane (100 mg l1), trichloromethane (300 mg l1), dibromochloromethane (100 mg l1) should be complied with individually. e For water in bottles or containers. f Sum of tetrachloroethylene and trichloroethylene. g In case of surface water treatment, 1.0 NTU turbidity in the water extreatment works should be strived for. A, provisional guideline, because the calculated level is below the achievable quantification level. C, concentration at or below the health-based guideline may affect appearance, taste, or odor; CM, subject to check monitoring, performed more frequently than the audit monitoring which comprises all drinking water parameters; CP, chemical parameter; IP, indicator parameter; MP, microbiological parameter, P, provisional guideline, because available information on health effects is limited; PHS, priority hazardous substance in the field of water quality; PS, priority substance in the field of water quality. b

Standardized Methods for Water-Quality Assessment

comprises about 20 parts, some of them adopted as European standards. EN ISO 5667-1 gives a general guidance for the design of sampling programs and sampling techniques. In EN ISO 5667-3, conservation techniques for various analytes are outlined. These have to be applied if no specifications for conservation are given in the respective individual analytical standard. ISO 5667-14 gives a modus operandi of how to systematically take field blank samples, spiked samples, and replicate samples in order to control sampling errors, contamination, and variability of sampling at the successive steps from the sampling site to analysis in the laboratory. Sampling techniques are specified for sampling from various aquatic systems, that is, lakes (ISO 5667-4), rivers and streams (ISO 5667-6), groundwaters (ISO 5667-11 and ISO 5667-18), wet deposition (ISO 5667-8), and marine waters (ISO 5667-9). Specifications are also given for sampling of wastewaters (ISO 5667-10), drinking waters from waterworks and piped distribution systems (ISO 5667-5), and waters and steams in boiler plants (ISO 5667-7). In addition, sampling methods for suspended matter (ISO 5667-17), sediments (ISO 5667-12, EN ISO 5667-19), and sludges (ISO 5667-13 and ISO 5667-15) are available. Special requirements have to be met for sampling prior to microbiological analysis (EN ISO 19458) or biotesting (EN ISO 5667-16). The sampling of living beings of different trophic levels is a crucial issue in biological– ecological assessment of natural waters (see Section 3.11.4.8). At present, standardization projects are underway in ISO/ TC 147/SC 6 which deal with the sampling of drinking water distributed by tankers or means other than distribution pipes (intended ISO 5667-21), design and installation of groundwater sampling points (intended ISO 5667-22), and with the use of passive samplers in surface waters (intended ISO 566723). The issue of sampling and passive sampling in particular is treated in more detail in 00054. Further guidance documents describe standard practices such as digestion (ASTM D1971, ASTM D4309 – microwave heating, EN ISO 15587-1 – aqua regia digestion, EN ISO 15587-2 – nitric acid digestion) or spiking into aqueous samples (ASTM D5788; ASTM D5810). In ISO 3696 and ASTM D1193, specifications are given concerning the quality of reagent water for different purposes. In the ASTM portfolio, standard guides for ultra-pure water used in the electronic and semiconductor industries (ASTM D5127) and for bioapplications grade water (ASTM D5196) are available, furthermore standard practices for the preparation of substitute ocean water (ASTM D1141) or substitute wastewater (ASTM D5905). An essential step in the quantitative analysis is calibration. The statistical evaluation of the linear calibration function and a calibration strategy for nonlinear second-order calibration functions is dealt with in ISO 8466-1 and ISO 8466-2, respectively.

3.11.4.3 Physical–Chemical and Other Basic Parameters for Water-Quality Assessment The oldest parameters to assess primary acceptance of drinking water are the so-called sensoric parameters such as taste and odor. Organoleptic methods, which try to classify odor (ASTM D1292) and to roughly quantify it by threshold numbers (EN 1622), are available.

273

From very early on testing methods were developed for raw water and drinking water to ensure the efficiency of drinking water treatment. Turbidity (EN ISO 7027, ASTM D6698, ASTM D6855, and ASTM D7315) can be read as a surrogate parameter for possible presence of microorganisms. Measurement of temperature, pH value (ISO 10523, ASTM D1293, ASTM D5128, and ASTM D5464) and determination of alkalinity (EN ISO 9963-1, EN ISO 9963-2, and ASTM D1067) and CO2 (ASTM D513) serve to prove the stability of waters concerning carbonate balance. Chlorine concentration (EN ISO 7393-1, -2, -3, ASTM D1253) is checked to make sure sufficient disinfection of drinking water or pool water. Color can indicate natural organic matter, for example, humic substances, or metal compounds (Fe and Mn). The parameter is relevant for treatment aspects and in limnology as well. The standard EN ISO 7887 specifies several methods for measurement of color, comprising visual examination and comparison with color-matching solutions (hexachloroplatinate scale) and a photometric method, specified for l ¼ 436 nm, l ¼ 525 nm, and l ¼ 620 nm. The standard is being revised at present, in addition, to specify 410 nm as a suitable wavelength as well (see prEN ISO 7887:2009), since this wavelength is often used in limnological studies (Hongve and A˚kesson, 1996). Electrical conductivity (EN 27888) is a measure for electrolyte content. According to 98/83/EC (1998), it should not exceed 2500 mS cm1 (20 1C) in drinking waters for corrosiveness concerns. Oxygen is crucial wherever respiration has to be maintained ranging from aquatic wildlife to microorganisms. Therefore, oxygen concentration or saturation is a key parameter for the status of surface waters as well as an important variable in the biological treatment of wastewaters. For routine oxygen measurement, the application of electrochemical (EN 25814) and luminescent (ASTM D888) probes is specified. In Table 5, further fundamental parameters in water analysis are listed. The table reflects that saprobization and eutrification are major issues in water monitoring. Nitrogen species listed in the table are fecal indicators and document domestic wastewater influence. Furthermore, parameters to estimate some major pollutants from industrial effluents are included. Organic load is relevant in natural waters with respect to oxygen balance, and in drinking waters, because organic matter might support bacterial growth. Moreover, organic water constituents can react to form hazardous disinfection by-products during disinfection of drinking water or pool water. Therefore, there were efforts very early on to analytically capture the phenomenon of oxygen consumption as well as the quantity of overall content of organic matter in water. This has resulted in the sum parameters DOC and total organic carbon (TOC), which quantify and specify organic carbon (OC), and furthermore the COD and permanganate index, which are indicative of oxygen consumption or easily oxidizable substances (see also Chapter 3.01 Sum Parameters: Potential and Limitations). The parameter BODn (biochemical oxygen demand after n days) was designed to approach more natural conditions, as a protocol for quantifying oxygen consumption after inoculation with aerobic microorganisms after 5 or 7 days. As halogenated organic substances

274 Table 5

Standardized Methods for Water-Quality Assessment Standards that specify important conventional parameters for water examination and assessment

Total organic carbon (TOC), dissolved organic carbon (DOC) ISO 8245, EN 1484 (OC 0.3–1000 mg l1, oxidation by combustion, by addition of an oxidant, by UV radiation or other high-energy radiation; determination of CO2 formed directly or after reduction to methane by IR, titration, thermal conductivity, conductimetry, coulometry, CO2-sensitive sensors, or FID (methane)); ASTM D4129 (high-temperature oxidation and coulometric detection, 2–20 000 mg l1); ASTM D4839 (ultraviolet or persulfate oxidation and infrared detection, 0.1–4000 mg l1); ASTM D5904 (membrane conductivity detection, ultraviolet-persulfate oxidation, CO2 selective membrane, 0.5–30 mg l1); ASTM D7573 (high-temperature catalytic combustion and infrared detection) Chemical oxygen demand (COD) ISO 6060 (dichromate method, COD 30–700 mg l1) ISO 15705 (small-scale sealed tube method) ASTM D1252 (dichromate oxygen demand, macro-COD by reflux digestion and titration, micro-COD by sealed digestion and spectrometry, up to 800 mg l1) Biochemical oxygen demand after n days (BODn) ISO 5815-1 (dilution and inoculation procedure after addition of allylthiourea) ISO 5815-2 (undiluted sample) EN 1899-1 (allylthiourea addition) EN 1899-2 (undiluted samples) Permanganate index EN ISO 8467 (chloride concentration smaller than 300 mg l1) Adsorbable organic halogens (AOX) EN ISO 9562 Bismuth-active substances (cationic surfactants) ISO 7852-2 (surfactants using Dragendorff reagent) Methylene blue active substances (MBAS) index (anionic surfactants) ISO 7875-1, EN 903, ISO 16265 (CFA), ASTM D2330 (0.03–1.5 mg l1) Phenol index ISO 6439 (aminoantipyrine, spectrometric after distillation) EN ISO 14402 (FIA and CFA, 0.01–1 mg l1) ASTM D1783 (color reaction with 4-aminoantipyrine, chloroform extraction: 0–100 mg l1, direct photometric 40.1 mg l1) Oil and grease, hydrocarbon oil ISO 9377-2 (hydrocarbon oil index, solvent extraction and GC) ASTM D4281 (oil and grease, gravimetric determination, liquid–liquid extraction, soxhlet extraction) Nitrogen Nitrogen: EN ISO 11905-1 (oxidative digestion with peroxodisulfate, up to 5 mg l1) Kjeldahl-Nitrogen: EN 25663 (corresp. ISO 5663, after mineralization with selenium); ASTM D3590 (manual digestion/distillation, semiautomated colorimetric Bertholt) Chemically bound nitrogen (TNb): ISO/TR 11905-2 (chemiluminescence detection after combustion and oxidation); EN 12260; ASTM D5176 (pyrolysis and chemiluminescence detection, 0.5–1000 mg l1) Soluble silicates EN ISO 16264 (FIA and CFA) Cyanides Total cyanide: ISO 6703-1; EN ISO 14403 (CFA); ASTM D4374; ASTM D7284 (FIA); ASTM D7511(CFA, in-line UV digestion) Easily releasable cyanide: ISO 6703-2, ASTM D4374 Free cyanide: EN ISO 14403 (CFA), ASTM D4282 (microdiffusion) Cyanogen Chloride ISO 6703-3 (0.02–15 mg l1); ASTM D4165 Alkalinity Total and composite alkalinity: EN ISO 9963-1 Total alkalinity in seawater: ISO 22719 Carbonate alkalinity: EN ISO 9963-2 Sulfides Dissolved sulfide: ISO 10530 (photometry using methylene blue) Easily released sulfide: ISO 13358 (0.04–1.5 mg l1, stripping with nitrogen at pH 4, subsequent color reaction to methylene blue) Phosphorus Phosphorus (ammonium molybdate method): EN ISO 6878 Orthophosphate and total phosphorus: EN ISO 15681 Total phosphorus: EN ISO 11885 (ICP-OES, 40.1 mg l1); EN ISO 17294-2 (ICP-MS, 45 mg l1) Suspended solids EN 872; ISO 11923 (both separation with a glass-fiber filter)

Standardized Methods for Water-Quality Assessment

are of special concern, the sum parameter AOX is still as a key parameter for survey and taxation of effluents (Pluta and Rosenberg, 2005). Further sum parameters were created to estimate the content of surfactants and phenols, the latter being of concern because of odor problems. A class of substances determined in bulk in wastewaters to monitor the efficiency of grease separators includes lipophilic hydrocarbons (oil and grease), one of the very few parameters for which a gravimetric method is still an active standard (ASTM D4281). In ISO, a gravimetric method on low-volatility lipophilic substances, avoiding fluoro-chloro-hydrocarbons by using petroleum ether or n-hexane for extraction, is in preparation (intended ISO 11394). Besides sum parameters, Table 5 contains further analytical parameters that are operationally defined, as they imply that specified steps for separation, digestion, or release have to be observed. This holds for the parameters concerning cyanide, nitrogen, and sulfide. Specification of a species as dissolved is based on the convention of 0.45-mm membrane filtration, this operation accounting for the distinction between DOC and TOC or true and apparent color. The digestion procedure and its performance specify the total element content determined by ICP-OES (HNO3 in EN ISO 11885, or HNO3/HCl in ASTM D1976). Alkalinity and hardness are convention-based parameters as well. Some of the operationally defined parameters in Table 5 are based on laborious manual procedures. As they have to be analyzed in analytical laboratories for a large number of samples, miniaturization and automation were highly attractive. The BOD is the only example in the portfolio of ISO/ TC 147 that a small-scale sealed tube (ST) version has been standardized (ISO 15705). It is pointed out in this standard that the results might differ from those of the full-scale version (ISO 6060), and results of an interlaboratory trial comparing both methods for different matrices are given. Furthermore, flow analysis (flow injection analysis (FIA) or continuous flow analysis (CFA)) allows for a miniaturization and automation of these methods, since it has become technically feasible to integrate operations such as liquid–gas separation, digestion, or distillation into flow systems. Standardized flow analysis versions are available for several parameters given in Table 5 for example, for methylene blue active substances (ISO 16265), phenol index (EN ISO 14402), and total and free cyanide (EN ISO 14403). For flow-analysis determination of total nitrogen, in-line UV digestion is an element in the intended standard ISO 29441 (Kroon, 1993). If an operationally defined manual parameter is transposed into a miniaturized flow-analysis format, it is crucial that specified recovery checks have to be met. For determination of total or free cyanide, this is, for instance, the recovery of hexacyanoferrate(III) to be X90% or p5% , respectively, and the recovery of thiocyanate to be o1% in each case.

3.11.4.4 Methods for Determination of Individual Water Constituents and Defined Groups of Substances Concerning the determination of individual analytes in waters, a division can roughly be made between major components and micropollutants, and furthermore between inorganic and organic constituents.

275

3.11.4.4.1 Inorganic water constituents For a large number of individual inorganic water constituents, photometric or colorimetric manual single-parameter methods are available (see Tables 6 and 7). For chloride, calcium, and magnesium, titrimetric methods are still included in the standards collections. Manual single-parameter methods, though they are sensitive and reliable, require much expenditure of manpower, time, and chemicals. Standardized FIA and/or CFA versions are available now for determination of the prominent analytes ammonium (EN ISO 11732), nitrite and nitrate (EN ISO 13395), cyanide (EN ISO 14403), phosphate (EN ISO 15681), chloride (EN ISO 15682), silicate (EN ISO 16264), chromate (EN ISO 23913), and sulfate (ISO 22743). An alternative way to miniaturize and automatize manual single-parameter methods is the so-called discrete analyzers offered as analytical instruments. An initiative to deal with these instruments with respect to possible standardization has been started in ISO/TC 147/SC 2 (ISO new project 15923). The application of electrochemical sensors (ion-selective electrodes, see also Chapter 3.10 Online Monitoring Sensors) has been specified for selected analytes. The most prominent example is fluoride (ISO 10359-1, ASTM D1179). In the ASTM portfolio, ion-selective electrodes are considered for detection of bromide (ASTM D1246), chloride (ASTM D512), sulfide (ASTM D4658), and ammonia (ASTM D1426). As an automated multi-analyte method, ion chromatography (IC) has become widely used and is the method of choice for analysis of anions in many laboratories. Standards on IC are available for drinking waters and wastewaters (e.g., EN ISO 10304, see also Table 6). A current project in ISO deals with the ion-chromatographic determination of bromate after post-column reaction as a very sensitive method for an application range from 0.5 mg l1 upward (intended ISO 11206). Standard protocols are also available for ion-chromatographic determination of some cations (e.g., EN ISO 14911 and ASTM D6919). For a decade, capillary electrophoresis has been in discussion as a further multi-analyte flow method for routine applications (Kaniansky et al., 1999). The standard ASTM D6508 gives a specification for determination of several inorganic anions. Tailor-made classical single-analyte methods are available in the standards collections also for species of metal or metalloids, often based on reaction of metal cations with complexing agents in order to form a colored complex which could be photometrically determined (Table 7). Today, however, these substances are determined in most cases by methods of elemental analysis, unless speciation is required for special purposes (see Chapter 3.02 Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species). For instance, this applies for chromium, which is much more toxic in the oxidation state þ VI than in þ III; therefore, the species chromate has to be analyzed separately. In routine analysis, there is a trend toward elemental analytical methods based on plasma techniques (i.e., plasma optical emission spectroscopy or plasma mass spectrometry) as fast multi-element methods. In the standards EN ISO 11885 and ASTM D1976, specifications are given for multi-element determination by inductively coupled plasma atomic optical emission spectroscopy (ICP-OES),

276 Table 6

Standardized Methods for Water-Quality Assessment Standardized methods for determination of molecular ions of nonmetals

Ammonium ISO 5664 (distillation and titration,o10 mg per test portion); ISO 6778 (potentiometric,o50 mg l1); ISO 7150-1 (manual spectrometric method); EN ISO 14911 (IC, 0.1–10 mg l1) Ammonia nitrogen EN ISO 11732 (CFA and FIA, spectrometric detection, 0.1–10 mg l1); ASTM D1426 (ion-selective electrode, 0.5–1000 mg l1) Borate ISO 9390 (spectrometric, azomethine-H, 0.01–1 mg l1 B); ASTM D3082 (curcumin colorimetric-extraction method, 0.1–1.0 mg l1) Bromate EN ISO 15061 (IC, 0.5–1000 mg l1, after suitable sample pretreatment); ASTM D6581 (IC, 5–30 mg l1) Bromide EN ISO 10304-1 (IC, 0.05–20 mg l1); ASTM D1246 (ion-selective electrode, 0.5–1000 mg l1); ASTM D3869; ASTM D4327 (IC, 0.63–21.0 mg l1); ASTM D6581 (IC, 20–200 mg l1); ASTM D6508 (CIE, 1–50 mg l1) Chlorate EN ISO 10304-4 (IC, 0.03–10 mg l1); ASTM D6581 (IC, 20–500 mg l1) Chloride ISO 9297 (titrimetric with AgNO3, Mohr); EN ISO 10304-1 (IC, 0.1–50 mg l1); EN ISO 10304-4 (IC, 0.1–50 mg l1); EN ISO 15682 (flow analysis, photometric, and potentiometric detection, 1–1000 mg l1); ASTM D512 (mercurimetric titration, silver nitrate titration, and ion-selective electrode method); ASTM D4327 (IC, 0.78–26.0 mg l1); ASTM D4458; ASTM D6508 (CIE, 1–50 mg l1) Chlorite EN ISO 10304-4 (IC, 0.05–10 mg l1); ASTM D6581 (IC, 20–500 mg l1) Cyanide ISO 6703; EN ISO 14403 (CFA, 10–100 mg l1); ASTM D2036; ASTM D4282 Fluoride ISO 10359-1 (electro-chemical sensor, weakly contaminated water); ISO 10359-2 (digestion and distillation); EN ISO 10304-1 (IC, 0.01 –10 mg l1); ASTM D1179 (distillation: 0.1–2.6 mg l1, ion-selective electrode: 1–1000 mg l1); ASTM D3868; ASTM D4327 (IC, 0.26–8.49 mg l1); ASTM D6508 (CIE, 1–25 mg l1) Iodide EN ISO 10304-3 (IC, 0.1–50 mg l1); ASTM D3869 Nitrate ISO 7890-3 (photometric, sulfosalycilic acid); EN ISO 10304-1 (IC, 0.1–50 mg l1); EN ISO 13395 (flow analysis, 0.2–20 mg l1); ASTM D3867; ASTM D6508 (CIE, 1–50 mg l1) Nitrite ISO 6777 (spectrometric); EN ISO 10304-1 (IC, 0.05–20 mg l1); EN ISO 13395 (Flow analysis, 0.01–1.0 mg l1), ASTM D3867; ASTM D6508 (CIE, 1– 50 mg l1) Orthophosphate EN ISO 6878 (photometric, ammonium molybdate); EN ISO 10304-1 (IC, 0.1–20 mg l1); EN ISO 15681-1 (FIA, 0.01–1.0 mg l1); EN ISO 15681-2 (CFA, 0.01–1.00 mg l1); ASTM D4327 (IC, 0.69–23.1 mg l1); ASTM D6508 (CIE, 1–50 mg l1) Silicate EN ISO 16264 (FIA, CFA), ASTM D859 (colorimetric, 815 nm: 20–1000 mg l1, 640 nm: 0.1–5 mg l1) Sulfate ISO 22743 (CFA); EN ISO 10304-1 (IC, 0.1–100 mg l1); ASTM D516 (turbidimetric, 5–40 mg l1); ASTM D4130; ASTM D4327 (IC, 2.85–95.0 mg l1); ASTM D6508 (CIE, 1–50 mg l1) Sulfide ISO 10530 (in solution, photometrically with methylene blue); ASTM D4658 (ion-selective electrode, 0.04–4000 mg l1) Sulfite EN ISO 10304-3 (IC, 0.1–50 mg l1) Thiocyanate EN ISO 10304-3 (IC, 0.1–50 mg l1); ASTM D4193 (colorimetric, 0.1–2.0 mg l1) Thiosulfate EN ISO 10304-3 (IC, 0.1–50 mg l1)

Standardized Methods for Water-Quality Assessment

277

Table 7 Standardized methods for determination of metals/metalloids and their ions. Methods of elemental analysis based on plasma techniques are given in Table 8 Aluminum ISO 10566 (photometry, pyrocatechol, optical pathlength of 50 mm: up to 100 mg l1; optical pathlength of 10 mm: up to 500 mg l1); EN ISO 12020 (FAAS, 5–100 mg l1; GF-AAS, 10–100 mg l1); EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 6–60 mg l1); ASTM D857 (F-AAS, 0.5–5 mg l1) Antimony EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 10–100 mg l1); ASTM D3697 (HG-AAS, 1–15 mg l1) Arsenic EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 10–100 mg l1); EN 26595 (photometry, silver diethyldithiocarbamate, 0.001–0.1 mg l1); ASTM D2972 (photometry, silver diethyldithiocarbamate, 5–250 mg l1; GF-AAS, 5–100 mg l1; HG-AAS, 1–20 mg l1) Barium EN ISO 14911 (IC, 1–100 mg l1), ASTM D3651 (F-AAS, in saline waters, 1–5 mg l1); ASTM D4382 (GF-AAS) Beryllium ASTM D3645 (F-AAS, 10–500 mg l1; GF-AAS, 10–50 mg l1) Cadmium ISO 8288 (F-AAS, 1–50 mg l1); EN ISO 5961 (AAS, air-ethine flame: 0.05–1 mg l1; after electrothermic atomization: 0.3–3 mg l1); EN ISO 15586 (GFAAS, lowest determinable concentration 0.1 mg l1, optimal parameters 0.4–4 mg l1); ASTM D3557 (AAS, differential pulse anodic stripping voltammetry) Calcium ISO 6058 (complexometry, 2–100 mg l1); EN ISO 7980 (AAS, up to 50 mg l1 Ca and 5 mg l1 Mg); EN ISO 14911 (IC, 0.5–50 mg l1); ASTM D511 (complexometry, 1–1000 mg l1); ASTM D1126 (calcium hardness, EDTA complexometric (titrimetry), indicator hydroxynaphthol blue); ASTM D6919 (IC, 4.0–40.0 mg l1) Chromium ISO 9174 (AAS); EN 1223 (AAS); EN ISO 15586 (GF-AAS, lowest determinable concentration 0.5 mg l1, optimal parameters 2–20 mg l1) Chromium(VI) (chromate) ISO 11083 (photometry, 1,5-diphenylcarbazide); EN ISO 18412 (photometry, weakly contaminated water); EN ISO 23913 (FIA and CFA); EN ISO 10304-3 (IC); ASTM D1687; ASTM D5257 (IC, 1–1000 mg l1) Cobalt ISO 8288 (F-AAS, 3 methods); EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 6–60 mg l1); ASTM D3558 (F-AAS 0.1–10 mg l1; F-AAS, chelation–extraction 10–1000 mg l1; GF-AAS, 5–100 mg l1) Copper ISO 8288 (F-AAS, 3 methods); EN ISO 15586 (GF-AAS, lowest determinable concentration 0.5 mg l1, optimal parameters 3–30 mg l1); ASTM D1688 (AAS, chelation–extraction, 50–500 mg l1; F-AAS, direct 0.05–5 mg l1) Iron ISO 6332 (photometry, phenanthroline); EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 3–30 mg l1); ASTM D1068 (photometry bathophenanthroline, 40–1000 mg l1; F-AAS, 0.1–5.0 mg l1; GF-AAS, 5–100 mg l1) Lead ISO 8288 (F-AAS, 3 methods, 5–200 mg l1); EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 10–100 mg l1); ASTM D3559 (F-AAS, direct, 1–10 mg l1; F-AAS, chelation–extraction 100–1000 mg l1; differential pulse anodic stripping voltammetry, 1– 100 mg l1; GF-AAS 5–100 mg l1) Lithium EN ISO 14911 (IC, 0.01–1 mg l1); ASTM D3561 (F-AAS, in saltwater, 0.1–70 000 mg l1); ASTM D6919 (IC, 0.4–10.0 mg l1) Magnesium EN ISO 7980 (AAS, up to 50 mg l1 Ca and 5 mg l1 Mg); EN ISO 14911 (IC, 0.5–50 mg l1); ASTM D511 (AAS); ASTM D6919 (IC, 2.4–20.0 mg l1) Calcium þ magnesium ISO 6059 (EDTA, complexometry); ASTM D511 (complexometry, EDTA, Ca: 1.0–15 mg l1, Mg: 0.25–3.5 mg l1); ASTM D1126 (with hardness indicator chrome black T3) Manganese ISO 6333 (photometry, formaldoxime spectrometry, 0.01–5 mg l1); EN ISO 14911 (IC, 0.5–50 mg l1); EN ISO 15586 (GF-AAS, lowest determinable concentration 0.5 mg l1, optimal parameters 1.5–15 mg l1); ASTM D858 (F-AAS, direct 0.1–5 mg l1; F-AAS, chelation–extraction, 10–500 mg l1; GF-AAS, 5–500 mg l1) Mercury ISO 5666 (AAS cold vapor 0.1–10 mg l1; optional digestion by KMnO4/K2S2O8; reduction by tin(II) chloride or by NaBH4); ISO 16590 (AAS cold vapor 0.1–1 mg l1 with enrichment by amalgamation; reduction by tin(II) chloride or by NaBH4 after digestion by KMnO4/K2S2O8); EN ISO 17852 (atomic fluorescence, 10 ng l1 to 10 mg l1); EN 1483 (AAS cold vapor, 0.1–10 mg l1); EN 12338 (after enrichment by amalgamation 0.01–1 mg l1); ASTM D3223-02 (AAS cold vapor, 0.5–10 mg l1, stabilization with HNO3, oxidation with KMnO4/K2S2O8, reduction with SnSO4) (Continued )

278

Standardized Methods for Water-Quality Assessment

Molybdenum EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 6–60 mg l1); ASTM D3372 (AAS) Nickel ISO 8288 (F-AAS, 3 methods, 0.2–2 mg l1); EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 7–70 mg l1); ASTM D1886 (F-AAS direct 0.1–10 mg l1; F-AAS, chelation–extraction, 10–1000 mg l1, GF-AAS, 5–100 mg l1) Potassium ISO 9964-2 (F-AAS, air-ethine flame, 1–10 mg l1); ISO 9964-3 (F-OES, 0.1–10 mg l1); EN ISO 14911 (IC, 0.1–10 mg l1); ASTM D3561 (F-AAS, in saltwater); ASTM D4191 (F-AAS, 0.20–4.0 mg l1); ASTM D6919 (IC, 1.2–20.0 mg l1) Selenium ISO 9965 (HG-AAS); EN ISO 15586 (GF-AAS, lowest determinable concentration 2 mg l1, optimal parameters 15–150 mg l1); ASTM D3859 (GF-AAS, 2–100 mg l1; HG-AAS, 1–20 mg l1) Silver EN ISO 15586 (GF-AAS, lowest determinable concentration 0.2 mg l1, optimal parameters 1–10 mg l1); ASTM D3866 (AAS, chelation–extraction; GFAAS) Sodium ISO 9964-1 (F-AAS,); ISO 9964-3 (F-OES, 0.1–10 mg l1); EN ISO 14911 (IC, 0.1–10 mg l1); ASTM D3561 (F-AAS in saltwater); ASTM D4191 (F-AAS, 0.20–3.0 mg l1); ASTM D6919 (IC, 4.0–40.0 mg l1) Strontium EN ISO 14911 (IC, 0.5–50 mg l1); ASTM D3352 (AAS in saltwater, 5–2000 mg l1); ASTM D3920 (AAS, 0.1–1 mg l1) Thallium EN ISO 15586 (GF-AAS, lowest determinable concentration 2 mg l1, optimal parameters 20–200 mg l1) Vanadium EN ISO 15586 (GF-AAS, lowest determinable concentration 1 mg l1, optimal parameters 6–60 mg l1); ASTM D3373 (GF-AAS, 10–200 mg l1) Zinc ISO 8288 (F-AAS, 3 methods, 0.01–2 mg l1); EN ISO 15586 (GF-AAS, lowest determinable concentration 0.5 mg l1, optimal parameters 0.5–5 mg l1); ASTM D1691 (F-AAS direct, 0.05–2 mg l1; chelation–extraction 20–200 mg l1)

which comprise recommended emission wavelengths for observation, information on spectral interferences, examples for mixed calibration standards, and specification of the acid digestion procedure to precede determination of total element content. For the elements covered in the standards, estimated detection limits given therein for prominent wavelengths are listed in Table 8. ASTM has standardized an optical emission spectroscopy (OES) method using also direct-current plasma (DCP) (ASTM D4190). Standardized protocols for inductively coupled mass spectrometry (ICP-MS) are given for a large number of elements in EN ISO 17294 and in ASTM D5673 (see Table 8), which specify, for example, recommended analytical masses, and inform about isobaric and molecular ion interferences. An advantage of plasma OES is its large linear working range of about five decades. For many elements, ICP-MS is the most sensitive method, with a limit of quantification below 0.1 mg l1, but for refractory elements or elements with high affinity to oxygen, ICP-OES might work better (e.g., B, P, Si, and alkaline earth metals). As it is evident from Table 8, methods of plasma elemental analysis are also applicable to some nonmetals. Further standardized methods based on elemental analysis are included in Table 7. Flame OES (F-OES) is still relevant for the determination of sodium and potassium. For many elements, individual standards have been developed for determination by flame atomic absorption spectrometry (FAAS), in many cases with an optional chelation–extraction step for enrichment to increase sensitivity. In addition,

specifications for the more recent sensitive AAS version using graphite furnace atomization (GF-AAS) are given. In the standards EN ISO 15586 and ASTM D3919, procedural details concerning GF-AAS are collated for a larger group of trace metals. For the determination of mercury in low concentrations by AAS cold-vapor technique, several standardized methods are available, which differ in specifications concerning reagents for stabilization (HNO3 or K2Cr2O7), predigestion (KMnO4/ K2S2O8 or KBr/KBrO4), elimination of excessive oxidizing reagent, and reduction of mercury (SnCl2 or NaBH4) (see Table 7). In ISO 16590 and in EN 12338, an additional enrichment step by amalgamation is specified. In a current ISO standardization project of ISO/TC 147/SC 2, efforts are made to replace these standards by a single combined revised standard (intended ISO 12846) with harmonized procedures and after exclusion of some options. Besides AAS, atomic fluorescence spectroscopy (AFS) is applied for detection of mercury (EN ISO 17852). AFS is also intended for detection of arsenic, selenium, and antimony in current standardization projects as an option besides AAS (intended ISO 17378, ISO 17379, and ISO 23914). Voltammetry as a very sensitive method for determination of heavy metals is specified in ASTM standards for Cd (ASTM D3557) and Pb (ASTM D3559), furthermore in the German standard DIN 38406-16 for Zn, Cd, Pb, Cu, Tl, Ni, and Co, and in DIN 38 406-17 for uranium, which is of concern because of its toxicity to the kidneys and therefore an item in the analysis of drinking water and mineral water.

Standardized Methods for Water-Quality Assessment

279

Table 8 Standardized multielement methods based on plasma techniques, elements covered therein, and data on sensitivity or application range given in the standards Element

ICP-MS EN ISO 17294-2 a (mg l 1)

ICP-MS ASTM D5673 b (mg l 1)

ICP-OES EN ISO 11885 c (mg l 1)

ICP-OES ASTM D1976 d (mg l 1)

DCP-OES ASTM D4190 e (mg l 1)

Ag Al As Au B Ba Be Bi Ca Cd Ce Co Cr Cs Cu Dy Er Eu Fe Ga Gd Ge Hf Hg Ho In Ir K La Li Lu Mg Mn Mo Na Nd Ni P Pb Pd Pr Pt Rb Re Rh Ru S Sb Sc Se Si Sm Sn Sr Tb Te Th Ti

1 5 1 0.5 10 0.5 0.5 0.5 10 0.1 0.1 0.2 1 0.1 1 0.1 0.1 0.1

0.05 0.05 0.9

4 1 5

7 45 53

50–100

5

50–1000

0.3

50–1000

0.1

4 2 0.1 40 0.4 0.2

4

50–1000

0.03 0.07

1 1

7 7

50–1000 50–1000

0.03

2

6

50–1000

2

7

50–1000

0.5 0.1

0.3 0.1 0.3 0.1 200–1000 0.1 0.1 0.1 50 0.1 1 0.1 1 3 0.3 10 0.1 1 5.0 0.1 0.5 0.1 0.5 0.1 0.1 0.1 0.1 0.2 5 10 0.1 1 0.3 0.1 2 0.1

20 6 1 1 2 20

30 2 8

50–800

15

50–800

0.08

2 9 5

42

200–1000

0.08

13 4

0.1 0.1

0.2

5.0

7 3 60 0.1

32 75

50–600

0.03 1 (Continued )

280 Table 8

Standardized Methods for Water-Quality Assessment Continued

Element

ICP-MS EN ISO 17294-2 a (mg l 1)

ICP-MS ASTM D5673 b (mg l 1)

Tl Tm U V W Zn Zr

0.1 0.1 0.1 1 0.3

0.09 0.02 0.02 0.2

ICP-OES EN ISO 11885 c (mg l 1)

ICP-OES ASTM D1976 d (mg l 1)

DCP-OES ASTM D4190 e (mg l 1)

40

1 10 1 0.3

8

50–1000

2

50–1000

a

Lower limit of application range for the most sensitive isotope. Estimated instrument detection limit. c Limit of detection for the recommended or most sensitive wavelength given in the standard, conventional pneumatic nebulation. d Estimated detection limit. e Range covered in the study for the standard. b

3.11.4.4.2 Methods for determination of organic compounds or jointly determinable groups of compounds In the beginning of water analysis, organic water constituents were detected and quantified only as bulk or sum parameters. The progress in analytical chromatographic techniques, especially in gas chromatography (GC) and high-performance liquid chromatography (HPLC), made it possible to separate, identify, and determine organic substances that occur in water only in traces. This is performed by highly potent chromatographic separation methods combined with detectors based on various detection principles. Often, these analyses are preceded by an enrichment (concentration) step together with suitable cleanup. In parallel, in legal framework regulations have been made which call for advanced organic analytical methods. Organic compounds that have to be monitored according to the European Drinking Water Directive (98/83/EC, 1998) comprise acryl amide, benzene, 1,2-dichloroethane, epichlorohydrine, pesticides, PAH, tetrachloroethylene and trihalomethanes, and vinyl chloride (see Table 4). They are based on the guideline values for water ingredients with respect to human toxicological concerns given by the WHO (2006) or in some cases are even stricter. Organic micropollutants are also included in the list of priority substances in the field of water policy (2008/ 105/EC, 2008; see also Table 4). As organic substances considered for possible identification as priority substances, AMPA, bentazone, bisphenol-A, dicofol, EDTA, glyphosate, mecoprop, musk xylene, perfluorooctane sulfonic acid (PFOS), quinoxyphen, dioxins, and polychlorinated biphenols are listed in annex III of the Directive. Organic trace analysis makes up the main part of the biannual reviews by Richardson on current issues in water analysis, which also give an outlook on the contaminants considered for further regulations in the US, collated in the contaminant candidate list (CCL) (Richardson, 2003, 2005, 2007, 2009) (for extended information on emerging organic contaminants, see also Chapter 3.04 Emerging Contaminants). In Table 9, standardized methods for organic micropollutants or constituents are given. The majority of the

substances reflect the lifestyle of an industrialized society with civilized urban life and intensive agriculture. Some noxious substances to be determined are formed as unintended by-products of disinfection or oxidation during water treatment. The organic microconstituents to be dealt with are not exclusively xenobiotics, microcystins, for instance, are algal toxins of concern (Tillmanns et al., 2007; see also Chapter 3.14 Drinking Water Toxicology in Its Regulatory Framework). Several methods are dedicated to pesticides, which find their way into aquatic systems preferably by diffuse input and runoff. As pesticides and plant-treatment agents belong to various classes of substances based on their chemical nature, different chromatographic techniques or detection principles are applied for their determination. Besides chromatographic methods, a guideline for the determination of plant treatment and pesticide agents using selective immunoassays is given (ISO 15089). For determination of PAH, HPLC methods with fluorescence detection after liquid–liquid extraction (EN ISO 17993, ISO 7981-2) are available. A current standardization project in ISO/TC 147/SC 2 deals with the determination of PAH by GC-MS (intended ISO 28540). The majority of standardized chromatographic methods is based on column techniques. Thin-layer techniques have been specified for pesticides (ISO/TS 11370) and for PAH (ISO 7981-1). GC-MS (with negative ion chemical ionization – NCI) is also applied for the analysis of short-chain polychlorinated alkanes (SCCP) in a current standardization project (intended ISO 12010). Quantification is based on multiple linear regression calibration. Solid-phase micro-extraction (SPME) as an enrichment method has now become a subject of standardization. A document on the determination of plant-treatment agents and biocide products by GC-MS after SPME is already in the finaldraft stage in ISO (intended ISO 21708). ASTM published a standard practice on applicability of SPME for the analysis of volatile organic compounds (ASTM D6520) and specifies the use of this technique for the extraction of PAH from sediment pore waters prior to GC/MS analysis. Further recent developments considered for standardization as powerful methods are

Standardized Methods for Water-Quality Assessment Table 9

281

Standardized methods for determination of organic micropollutants. Most of them are groups of jointly determinable substances

Organic plant treatment ISO/TS 11370 EN ISO 11369 ISO 15089

and pesticide agents Automated multiple development (AMD) technique; Z50 ng l1 HPLC-UV, after solid–liquid extraction; for drinking water; LOQ 0.1 mg l1 Selective immunoassays

Organochlorine pesticides EN ISO 6468 GC, after liquid–liquid extraction; LOD 1–10 ng l1; validated for hexachlorobenzene, b-endosulfane, PCB 180, 1,2,4,5tetrachlorobenzene, a-HCH, dieldrine, p,p-DDE, p,p-DDT, PCB 28, PCB 52, PCB 101, PCB 138, PCB 153, PCB 194 ASTM D5175 GC, microextraction; LODo1 mg l1 ASTM D5812 Aldrine, chlordane, DCPA, 4,40 -DDD, 4,40 -DDE, 4,40 -DDT, dieldrin, endosulfane Phenoxyalkanoic herbicides, including bentazones and hydroxybenzonitriles EN ISO 15913 GC-MSD, after SPE and derivatization, for groundwater and drinking water, 450 ng l1 Chlorinated phenoxyacid herbicides ASTM D5317 GC-EC; bentazone, 2,4-D, 2,4-DB, 3,5-dichlorobenzoic acid, dichlorprop, pentachlorophenol (PCP), 2,4,4-T Chlorobenzenes EN ISO 6468

GC, after liquid–liquid extraction; LOD 1–10 ng l1

Biphenyls, polychlorinated (PCB) ISO 17858 HRGC/HRMS, after extraction EN ISO 6468 GC, after liquid–liquid extraction; LOD 1–10 ng l1 ASTM D5175 GC, microextraction Organotin compounds EN ISO 17353 GC, -AED, -FPD, or -MSD, after alkylation and extraction, working range 10–1000 ng l1 Chlorophenols EN 12673

GC, ECD or MSD, after acetylation, n-hexane extraction, 0.1 mg l1 to 1 mg l1

Polycyclic aromatic hydrocarbons (PAH) EN ISO 17993 HPLC, fluorescence detection, after after liquid–liquid extraction, 40.005 mg l1 in drinking water and groundwater, 40.01 mg l1 in surface water; 15 PAH ISO 7981-2 HPLC, fluorescence detection, liquid–liquid extraction; drinking and groundwater: 40.005 mg l1, surface water: 40.01 mg l1 ISO 7981-1 HPTLC (thin-layer chromatography), after liquid–liquid extraction; 40–240 ng l1 ASTM D7363 SPME, GC/MS, SIM; in sediment pore water Monocyclic aromatic hydrocarbons, naphthalene, and chlorinated compounds (volatile organic compounds) EN ISO 15680 GC, purge-and-trap, thermal desorption, 10 ng l1 to 100 mg l1 Explosives and related compounds EN ISO 22478 HPLC-UV-DAD, after SPE, 0.1–0.5 mg l1; in groundwater near ammunition waste sites or in drinking water; nitroluenes, amino nitro toluenes, nitrobenzenes, picric acid, oktogen, and hexogen Nitrophenols EN ISO 17495

GC-MSD, after solid-phase extraction, methylation, 40.5 mg l1

Glyphosate and aminomethyl phosphonic acid (AMPA) ISO 21458 HPLC, post-column derivatization and fluorescence detection; 40.05 mg l1 Parathion, parathion-methyl and organophosphorus compounds EN 12918 GC, after dichloromethane extraction, 0.01–1 mg l1 Organophosphate compounds ASTM D7597 LC/MS-MS, ESI (SRM); ethyl hydrogen dimethylamidophosphate, ethyl methylphosphonic acid, methylphosphonic acid, and pinacolyl methylphosphonic acid Organic nitrogen and phosphorus compounds EN ISO 10695 GC-NPD, after dichloromethane extraction, LOD 0.1–1.0 mg l1; after solid-phase extraction, LOD 0.012–0.060 mg l1 ASTM D5475

GC-NPD

Haloacetic acids, trichloroacetic acid, dalapon EN ISO 23631 GC-ECD or GC-MS, after liquid–liquid extraction using MTBE and derivatization using diazomethane, working range 0,5– 10 mg l1; bromochloroacetic acid, dibromoacetic acid, dichloroacetic acid, monobromoacetic acid, and monochloroacetic acid Highly volatile halogenated hydrocarbons DIN 38407-30 Headspace-GC; swimming pool waters; validated for the trihalomethanes CHCl3, CHBrCl2, CHBr2Cl, and CBr3Cl EN ISO 10301 GC, liquid–liquid extraction, LOQ 0.1–50 mg l1; static headspace, LOQ 0.1–200 mg l1 (Continued )

282

Standardized Methods for Water-Quality Assessment

ASTM D3973 GC, halogen specific detectors or MSD; 1–200 mg l1 ASTM D5316 Microextraction GC; 1,2-dibromoethane, 1,2-dibromo-3-chloropropane Purgable organic compounds (including organohalides) ASTM D3871 GC, dynamic headspace sampling, low mg l1 to low mg l1 range Phthalates EN ISO 18856 Microcystins ISO 20179

GC-MSD, after SPE, 0.02–0.150 mg l1 RP-HPLC, UV-DAD, suitable for control of WHO guideline value (1 mg l1), after SPE. For samples containing suspended algal biomass after a preceding liquid extraction step; validated for MCYST-RR, MCYST-YR, MCYST-LR

Alkylphenols EN ISO 18857–1

GC-MSD, liquid–liquid extraction (toluene), for non-filtered samples; validated for 4-(1,1,3,3-tetramethylbutyl)phenol and 4-nonylphenol (mixture of isomers); application range 0.005–0.2 mg l1 for 4-(1,1,3,3-tetramethylbutyl)phenol, 0.02– 0.2 mg l1 for 4-nonylphenol Nonylphenol, tert-octylphenol, nonylphenol monoethoxylate, nonylphenol diethoxylate ASTM D7485 LC/MS-MS, reporting range 100–2000 ng l1 ISO 24293 SPE, GC/MS; nonylphenol, individual isomers ASTM D7065 Nonylphenol monoethoxylate, nonylphenol diethoxylate Complexing agents EN ISO 16588

GC

Phenols, monovalent ISO 8165-1 ISO 8165-2 ASTM D2580

GC, extraction GC, derivatization GC, direct aqueous injection, 41 mg l1

Perfluorooctanesulfonate (PFOAS), perfluorooctanoate (PFOA) ISO 25101 LC/MS, SPE Polybrominated diphenylethers EN ISO 22032 GC/MS, EI or NCI, extraction, in sediment and sludge, LOQ (EI) 0.05–25 mg kg1 for tetra- to octabromo congeners, 0.3– 100 mg kg1 for decabromodiphenylether, lower for NCI Bisphenol A ASTM D7574

LC/MS-MS, MDL 20–600 ng l1

N-Methyl carbamates ASTM D7600 ASTM D5315

LC/MS-MS; aldicarb, carbofuran, oxamyl, and methomyl Direct aqueous injection HPLC post-column derivatization

Benzene ISO 11423-1 ISO 11423-2

Headspace-GC Extraction, GC

Tetra- to octa-chlorinated dioxins and furans ISO 18073 Isotope dilution HRGC/HRMS

liquid-chromatography tandem mass-spectrometry (LC-MS/ MS) and ultrahigh-performance liquid chromatography (UPLC).

3.11.4.5 Radiological Methods The estimation of radioactivity and the determination of radionuclides in water require special methods. In Table 10, available standards are listed. The European Drinking Water Directive (98/83/EC) considers radioactivity by giving parametric values for tritium (100 Bq l1) and a total indicative dose of 0.10 mSv a1 (excluding tritium, potassium-40, radon, and radon decay products). Gross alpha-activity and gross beta-activity measurements are suitable as a screening steps to estimate this quantity (Aurand and Ru¨hle, 2003). The standards ISO 9696 and ISO 9697, both recently revised, are wellestablished methods for this. A standard based on the liquid

scintillation counting (LSC) method, which requires reduced sample preparation and counting time (Forte et al., 2006), is in the final-draft stage (intended ISO 11704). This method is also applied in the active standard on measurement of tritium activity concentration (ISO 9698, under revision), and is intended for measurement of carbon-14 activity (ISO 13162, in preparation). Further standardization projects in ISO/TC 147 deal with the measurement of strontium (89Sr, 90Sr, intended ISO 13160) and polonium-210 activity concentration (intended ISO 13161) (for further information on radioactivity in waters, see also Chapter 3.03 Sources, Risks, and Mitigation of Radioactivity in Water).

3.11.4.6 Microbiological Methods Generally, the most common and most weighty hazards for human health that can arise from water are water-borne

Standardized Methods for Water-Quality Assessment Table 10 Parameter

283

Standardized methods for radiochemical examination and determination of radionuclides in water Standard

Alpha-activity ISO 9696 (thick source method), ASTM D1943 Beta-activity ISO 9697 (thick source method), ASTM D1890 Gross alpha- and beta- ISO 10704 (thin source deposit method), ASTM D7283 (liquid scintillation counting) activity Radionuclides ISO 10703 (activity concentration, by high-resolution gamma-ray spectrometry), ASTM 3649 Tritium ISO 9698 (liquid scintillation counting method), ASTM D4107 Radium ASTM D3454 (radium-226), ASTM D2460 (alpha-particle-emitting isotopes) Iron ASTM D4922 Strontium-90 ASTM D5811 Lead-210 ASTM D7535 Technetium-99 ASTM D7168 (solid phase extraction disk) Uranium ASTM D5174 (pulsed laser phosphorimetry), ASTM D6239 (high-resolution alpha-liquid-scintillation spectrometry), ASTM D3972 (alpha-particle spectrometry)

infections and outbreaks, induced by bacteria, viruses, or parasites. To grant the microbial safety of the distributed water is therefore the first-priority task of water suppliers. The philosophy of microbiological examination of drinking water is not only to prove the absence of definite pathogens, but also to prove that fecal influence on the water can be excluded. This is done by checking for suitable nonpathogenic indicator organisms that have to be absent, too. Microbial safety is an item for swimming pool waters and recreational waters as well. Furthermore, microbial fouling is of concern in technical systems. In Table 11, standardized methods available for the testing of water for microorganisms are listed. For some microbiological parameters to be checked routinely in drinking water, the European Drinking Water Directive (98/83/EC, 1998) directly refers to methods for examination specified in the following standards: EN ISO 9308-1 for Escherichia coli and for coliforms; EN ISO 7899-2 for enterococci; EN ISO 12780, now replaced by EN ISO 16266, for Pseudomonas aeruginosa; and EN ISO 6222 for culturable microorganisms as colony count. All these methods are based on membrane filtration. Alternative methods can be used, provided their equivalency has been shown according to EN ISO 17994. EN ISO 8199 gives a guideline for the calculation and reporting of test results. E. coli is the most important indicator organism for fecal pollution, but not all coliform bacteria are of fecal origin. In some countries, the whole group of thermotolerant coliforms is determined (ISO 9308-2) instead. The method EN ISO 7899-2 preferably captures enterococci species that originate from human or animal intestine. P. aeruginosa, which causes, for example, pneumonia, is especially of concern for immunodeficient persons. A further microbial parameter mentioned in the directive 98/83/EC (1998) is Clostridium perfringens and its spores, which are strict obligate anaerobes and conservative tracers for past and present pollution by parasites. This parameter is especially relevant for raw waters that are influenced by surface water. For its measurement, the directive gives an extended protocol based on membrane filtration. At present, a standard on the detection and enumeration of C. perfringens, suitable to match the specifications of the directive, is being developed in ISO/TC 147/SC4 (intended ISO 14189).

In warm water distribution systems with stagnant phases, Legionella bacteria can occur, which are a health hazard by respiratory uptake (e.g., in aerosols from showers). Therefore, warm water distribution systems are checked for Legionella in buildings, which are frequented by many persons. According to the German drinking water ordinance, monitoring for Legionella is mandatory in warm water systems of public buildings (TrinkwV, 2001). Up to now, active standards for microbiological analysis are all based on cultivation methods. Recently, a standardization project (intended ISO 12869) has been started in ISO/TC 147/SC 4 to consider the new technique of polymerase chain reaction (PCR) for the detection of Legionella. This method for the detection of bacteria is faster than the cultivation techniques, with the drawback that it does not discriminate between live and dead organisms (see also Chapter 3.08 Identification of Microorganisms Using the Ribosomal RNA Approach and Fluorescence In Situ Hybridization). As suspicions concerning microbial safety entail severe measures (e.g., restriction of water use), fast microbiological testing methods would be advantageous. The test repertoire given in Table 11 also comprises methods for detection of bacteriophages, viruses, and parasites. Coliphages are generally more resistant to chlorination than coliforms and may have some advantage over coliforms as an indicator of treatment efficiency in disinfected waters. The parasites Cryptosporidium and Giardia, which can get into raw waters for drinking water treatment by an unexpected shortcut to wastewaters, were the cause of serious outbreaks (see also Chapter 3.12 Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control). The yeast Candida albicans may serve as an indicator of recreational water quality. Sulfate-reducing bacteria produce hydrogen sulfide and might cause corrosion problems in pipes and pipelines. Iron bacteria utilize ferrous iron as a source of energy and cause deposits of ferric hydroxide, for example, in wells.

3.11.4.7 Biotesting Quantification of water ingredients, or proval of their absence is only one aspect of water-quality assessment. Further, an important approach is biotesting of waters or their

284

Standardized Methods for Water-Quality Assessment

Table 11

Standardized methods for microbiological examination

General Cleaning of equipment Bacterial retention of membrane filters Evaluation of membrane filters Evaluating and controlling microbiological colony count media Enumeration by culture Validation of microbiological methods Establishing equivalence of microbiological methods Sampling for microbiological analysis Special methods Adenosine triphosphate (ATP) content Aquatic bacteria Total and respiring bacteria Culturable microorganisms Microbial colony counts Total active biomass Coliform organisms, thermotolerant coliforms, presumptive Escherichia coli E. coli and coliforms

E. coli Intestinal enterococci

Enterococci Pseudomonas aeruginosa Clostridium perfringens Legionella

Campylobacter species Sulfite-reducing anaerobes (Clostridia)

Sulfate-reducing bacteria Iron bacteria Bacteriophages

ASTM D5245 ASTM F838 ISO 7704 ISO 9998 EN ISO 8199 ENV ISO 13843 EN ISO 17994 EN ISO 19458 ATP firefly (luciferin-luciferase)

ASTM D4012

Enumeration, acridine-orange epifluorescence directcounting Enumeration, microscopy, and acridine-orange INTformazan reduction Colony count, nutrient agar medium Plating methods In cooling tower waters, Kool Kount assay (KKA)

ASTM D4455

Detection and enumeration, multiple tube (most probable number) method Spores and vegetative cells, from water and extracted sediments, detection and enumeration, membrane filtration Detection and enumeration, miniaturized method, most probable number, inoculation in liquid medium Isolation and enumeration, two-step membrane filter procedure Detection and enumeration, miniaturized method most probable number, inoculation in liquid medium Detection and enumeration, membrane filtration Detection in water, using Enterolert Isolation and enumeration, membrane filtration

ISO 9308-2

Detection and enumeration, membrane filtration Detection and enumeration, by membrane filtration Detection and enumeration For waters with low bacterial counts, direct membrane filtration Thermotolerant, detection and enumeration Spores, detection and enumeration, enrichment in liquid medium Spores, detection and enumeration, membrane filtration

ASTM D4454 EN ISO 6222 ASTM D5465 ASTM D6530

EN ISO 9308-1

EN ISO 9308-3 ASTM D5392 EN ISO 7899-1 EN ISO 7899-2 ASTM D6503 ASTM D5259 EN ISO 16266 ASTM D5916 ISO 11731 EN ISO 11731-2 ISO 17995 EN 26461-1 (ISO 6461) EN 26461-2 (ISO 6461-2)

In water and water-formed deposits In water and water-formed deposits R-specific RNA bacteriophages, enumeration Somatic coliphages, enumeration Validation of methods for concentration Bacteriophages infecting Bacteroides fragilis, enumeration Coliphages, low level, in waters

ASTM D4412 ASTM D932 EN ISO 10705-1 EN ISO 10705-2 ISO 10705-3 ISO 10705-4

Viruses Enteroviruses Human enteroviruses

Recovery from wastewater sludges Recovery from waters Detection by monolayer plaque assay

ASTM D4994 ASTM D5244 EN 14486

Parasites Cryptosporidium oocysts and Giardia cysts

Isolation and identification

ISO 15553

Yeast Candida albicans

Enumeration

ASTM D4249

ASTM D6734

Standardized Methods for Water-Quality Assessment

constituents. For this, the standards portfolio of CEN and ISO contains testing methods on biodegradability and for ecotoxicity testing.

3.11.4.7.1 Biodegradability An important aspect of wastewater treatment is biodegradation; therefore, biodegradability is a key parameter for evaluation of wastewater constituents. It is an important parameter to assess the behavior of water ingredients in the aquatic environment as well. This has two aspects: (1) their elimination by biological processes or possible persistence and (2) their influences on the oxygen balance of Table 12

natural water, since oxygen is needed for aerobic degradation processes. In the 1960s, the issue of biodegradability called for attention due to scum formed on rivers from poorly degradable detergents. In Germany, an early assimilation/consumption test was elaborated in 1971 (DEV, 1971). An important impetus for testing of biodegradability was given by the initiative Registration, Evaluation, Authorization and Restriction of Chemicals (REACH, 2006). For this, the Organization for Economic Cooperation and Development (OECD) developed a series of degradability tests (OECD, 2008). In Table 12, methods in the portfolio of ISO/TC 147 and CEN/TC 230 for biodegradability testing are listed. Most of the

Standardized methods for testing biodegradability in aqueous media

Standard

Test

Static tests for ultimate aerobic biodegradability (duration 28 days) EN ISO 14593 CO2 headspace test Closed bottle with headspace, 2–40 mg l1 DOC Suitable for volatile test substances EN ISO 9408 Closed respirometer test At least 100 mg l1 ThOD Suitable for volatile test substances EN ISO 9439 Carbon dioxide evolution test 2–40 mg l1 DOC EN ISO 7827 Method by analysis of dissolved organic carbon (DOC) 2-40 mg l1 DOC ISO 10708

EN ISO 10707

EN ISO 9888

285

Two-phase closed bottle test (BODIS) Determination of biochemical oxygen demand, 100 mg l1 BOD Closed bottle test Analysis of biochemical oxygen demand Smaller than 10 mg l1 ThOD (about 2–5 mg l1 test substance), low degradation potential Suitable for volatile or inhibitory test substances Static test (Zahn–Wellens test) 50–400 mg l1, high degradation potential Specified for wastewaters

Semi-continuous test for ultimate aerobic biodegradability EN ISO 9887 Semi-continuous activated sludge method (SCAS) High degradation potential (bacteria concentration 1– 4 g l1 supended matter), duration: 12–26 weeks Effective residence time of wastewater: 36 h Tests for aerobic biodegradability at low concentrations ISO 14592-1 Shake-flask batch test with surface water or water/ sediment suspensions ISO 14592-2 Continuous flow river model with attached biomass EN ISO 11733 Activated sludge simulation test 10–20 mg l1 DOC; aim: prediction of concentration in the effluent; HRT: 6 h Test for ultimate anaerobic biodegradability in digested sludge EN ISO 11734 Measurement of biogas production Test substance: 20–100 mg l1 OC Static closed bottle test, duration up to 60 days

Analytical parameter

Increase of TIC; optional: decrease of DOC (for watersoluble substances) Oxygen demand optional: additionally decrease of DOC for water-soluble substances Release of CO2 Dissolved organic carbon, at least at 3 days within the test period Dissolved oxygen (ThOD or COD)

Dissolved oxygen

Dissolved organic carbon, after 3 h check for adsorption

Dissolved organic carbon

DOC or BOD

TIC

Methodological guideline papers and additional tests ISO TR 15462 Selection of tests for biodegradability ISO 16221 Determination of biodegradability in the marine environment ISO 18749 Adsorption of substances to activated sludge EN ISO 10634 Preparation of poorly water-soluble organic compounds for biodegradability testing

286

Standardized Methods for Water-Quality Assessment

current standards were developed in the early 1990s, many of them based on OECD protocols. The philosophy of the different approaches is discussed in Pagga (1997). The technical report ISO/TR 15462 on the selection of tests for biodegradability, revised in 2006, gives an extended overview of the different methods, and their principles and scopes. Degradation processes in the environment and in treatment plants are manifold, which are taken into account by different test designs. Biodegradation tests can roughly be divided into ones which are designed for aerobic or anaerobic conditions; they can be operated in static, semicontinuous, or continuous mode. The majority of the tests in Table 12 are static tests for ultimate aerobic biodegradability with a duration of 28 days. As inoculum, mixed populations are mandatory, in order to cover the variety of degradation processes in the environment. The inoculum and the state of adaptation of the microorganisms cannot be standardized (Pagga, 1997). In most cases, activated sludge from municipal wastewatertreatment plants is applied. The tests differ in the analytical parameters by which the degree of conversion is monitored. For ultimate aerobic biodegradation (total mineralization), this can, in principle, be performed by measurement of DOC, oxygen consumption, or CO2 formation. A comparison of CO2 production or oxygen uptake with DOC removal as a measure of ultimate biodegradation is given in Reuschenbach et al. (2003) and in Strotmann et al. (2004). In order to capture the elimination that is caused by biodegradation, biodegradability tests comprise a blank control, a test with a reference substance, an inhibition control, an abiotic elimination control, and an adsorption control. Inhibitory effects to activated sludge can be tested by standardized inhibition tests (e.g., EN ISO 8192 or ISO 15522). Suitable tests for inhibition or toxicity are also given in ISO/TR 15462. Most tests require water solubility for the tested substances. A guideline for preparation of poorly water-soluble substances for testing is given in EN ISO 10634. Headspace and closedrespirometer tests are suitable settings for volatile substances to be tested. Biodegradation tests can also be designed for marine systems, which require a different composition of the test media from that used for limnic systems (Pagga, 1997).

3.11.4.7.2 Ecotoxicity and bioeffect testing The goal of ecotoxicity testing in water analysis is to assess effects of environmental samples on survival, growth, or reproduction of aquatic wildlife. The tests are used for example to estimate the environmental impact of treated wastewaters released to natural waters and are therefore important elements in emission control (Thompson et al., 2005). Standards available for ecotoxicity testing are listed in Table 13. They are designed for different trophic levels, for example, bacteria, algae, higher plants, daphnia, rotifers, and fish. EN ISO 15088 specifies a fish egg test which was developed to replace the fish test for reasons of animal welfare (Pluta and Rosenberg, 2005). The repertoire of testing methods for aquatic toxicity covers different ecotoxic effects by estimating acute toxicity, chronic toxicity, and inhibition of growth or of other physiological functions. Bioeffects are also tested on a suborganismic level. This applies to the tests on genotoxicity given in the table.

Genotoxicity is one of the effects of main concern in the assessment of substances released to the environment. Current standardization projects in ISO/TC 147/SC 5 deal with methods suitable for the testing of sediments, for example, a contact test for the inhibition of dehydrogenase activity of Arthrobacter globiformis (intended ISO 10871) and a test for toxic effects of sediment and soil samples on growth, fertility, and reproduction of the nematode Caenorhabditis elegans (intended ISO 10872). Determination of vitellogin is being specified for physiological measurements on fish (intended ISO 23893-3). For the algal test species of EN ISO 8692, Desmosdesmus subspicatus and Pseudokirchneriella subcapitata, a growth inhibition test in a format using microtiter plates is in preparation. Concerning the expression of endpoints in algal or plant growth tests, where growth rate is used for the testing endpoint, ISO decided that in future standards the endpoint should be expressed as ErCx (concentration with x% effect), not as lowest observed effect concentration (LOEC) or no observed effect concentration (NOEC).

3.11.4.8 Methods for Assessment of Water Bodies The monitoring of water bodies is a traditional task of water analysis. The use of biological indicators for water quality, for example, for saprobic levels, there has been a long tradition as well (Vilela Junqueira et al., 2010; DIN 38410-1). Increased efforts in this field have been induced by the WFD (2000/60/ EC, 2000), which stipulates that the member states perform a classification and assessment of their water bodies. Near-naturalness is the key and target variable in this approach, which first has to be metrologically captured by appropriate descriptors in order to define a good status, and based on this, has to be maintained or restored by suitable measures. Concerning morphological features, a sophisticated classification within the main categories, rivers, lakes, transitional waters, and coastal waters, has to be made. The ecological status has to be assessed for surface waters through evaluation of the levels of phytoplankton (for lentic systems), aquatic flora, benthic invertebrate fauna, and fish fauna (for freshwaters and transitional waters) concerning composition and abundance. For phytoplankton, determination of biomass is also required. In freshwaters, fish populations have to be evaluated, in addition, with respect to their age structure. Table 14 gives an overview of already-existing standards for ecological or morphological classification of water bodies. The standards concerning the sampling of macro-invertebrates (EN 27828, EN 28265, EN ISO 9391, and EN ISO 8689-1, EN ISO 8689-2) are directly referred to in the WFD. The repertoire of methods is still in development. Current standardization projects deal with in vivo absorption techniques for the estimation of chlorophyll (CEN project 00230263), quantitative and qualitative investigation of marine phytoplankton (intended EN 15972), selection of sampling methods, and devices for benthic macro-invertebrates (intended EN ISO 10870), and with estimation of fish abundance with mobile hydroacoustic methods (intended EN 15910). Furthermore, guidance standards on selection and design of taxonomic keys (CEN project 00230275) and on design of multi-metric indices (CEN project 00230261) are in preparation. Biological–ecological assessment methods are

Standardized Methods for Water-Quality Assessment Table 13

287

Standardized methods for ecotoxicity and effects testing

General procedures, ISO/TR 11044 ISO 14442 ISO/TS 20281 EN ISO 5667-16

methodology Scientific and technical aspects of batch algae growth inhibition tests Algal growth inhibition tests with poorly soluble materials, volatile compounds, metals and wastewater Statistical interpretation of ecotoxicity data Guidance on biotesting of samples

Organismic tests EN ISO 7346-1 to -3 ISO 23893-1 ISO/TS 23893-2 ISO 10229 ISO 12890 EN ISO 15088 EN ISO 6341 ISO 10706 ISO 20665 EN ISO 16712 ISO 14669 ISO 20666 EN ISO 20079 EN ISO 10253 EN ISO 8692 EN ISO 10712 EN ISO 11348-1 to 3 EN ISO 8192

Test organism Freshwater fish (Brachydanio rerio Hamilton–Buchanan (Teleostei, Cyprinidae)) Freshwater fish Freshwater fish; rainbow trout (Oncorhynchus mykiss Walbaum (Teleostei, Salmonidae)) Embryos and larvae of freshwater fish Fish egg, Danio rerio Daphnia magna Straus (Cladocera, Crustacea) Daphnia magna Straus (Cladocera, Crustacea) Ceriodaphnia dubia (Cladocera, Crustacea) Amphipods Marine copepods Rotifers, Brachionus calyciflorus Higher water plants, Lemna minor Marine algae, Skeletonema costatum and Phaeodactylum tricornutum Freshwater algae, unicellular green algae Bacteria, Pseudomonas putida Bacteria, Vibrio fischeri

EN ISO 9509 ISO 15522 ISO 13641-1, -2

Bacteria, activated sludge for carbonaceous and ammonium oxidation Bacteria, activated sludge microorganisms Bacteria, activated sludge microorganisms Bacteria, anaerobic bacteria

Suborganismic tests EN ISO 21427-2 ISO 21427-1 ISO 13829 ISO 16240

for genotoxicity Induction of micronuclei, cell line V79 Induction of micronuclei, using amphibian larvae umu test Salmonella/microsome test (Ames test)

subject to validation measures as well, a document on design and analysis of interlaboratory comparison studies is being developed (intended EN 16101). In the field of morphological classification of water bodies, a standardization project is underway for assessment of the hydromorphological features of lakes (intended EN 16039).

3.11.5 Resume and Outlook Standards for water examination are available which are designed to assess the quality of waters concerning chemical, microbiological, biological, and ecological aspects, and to control the efficiency of water-treatment processes. They have been developed and are continuously updated by the interested parties in this field, comprising science, authorities, manufacturers of analytical instruments, industries, and users. The process of development and consensus building is moderated by standardization organizations, releasing the

Effect Acute lethal toxicity Biochemical and physiological measurement Prolonged toxicity, growth rate Toxicity Acute toxicity Inhibition of mobility, acute toxicity test Long-term toxicity Chronic toxicity Acute toxicity (marine estuarine sediment) Acute lethal toxicity Chronic toxicity, 48 h Growth inhibition (Duckweed growth inhibition test) Growth inhibition Growth inhibition Growth inhibition (Pseudomonas cell multiplication inhibition test) Inhibition of light emission (luminescent bacteria test) Inhibition of oxygen consumption Inhibition of nitrification Inhibition of growth Inhibition of gas production

standards as editors. On European and international levels, the NSBs of individual countries participate in the supernational standardization organizations CEN and ISO, whose combined and complementary efforts have created partially overlapping standards portfolios of about 300 standardized methods in total. A further collection of private sector standards on water testing has been elaborated by ASTM International, the former ASTM, who opened up for participation of international experts. Recent trends tend toward miniaturization and automation of methods. Standardization of methods consistently has to find its position between convenience of commercially available ready-to-use components and the requirements of traceability and disclosure. Furthermore, the variability of results is increasingly dealt with, not only for chemical analysis, but also for microbiological methods, ecological assessment, and sampling (Strub et al., 2009). On a European level, the requirements of the WFD and its daughter directives concerning the comparability necessary for meaningful ecological and

288

Standardized Methods for Water-Quality Assessment

Table 14

Standardized methods for assessment of water bodies

Hydromorphology EN 15843 EN 14614

Degree of modification Assessment of hydrological features

Biological and ecological assesment EN 14996

Quality assurance

Chlorophyll-a ISO 10260

Determination

Phytoplankton EN 15204

Enumeration, Utermo¨hl technique

Phytobenthos EN 15708

Surveying, sampling and laboratory analysis

Shallow running water

Benthic diatoms EN 13946 EN 14407

Routine sampling Identification, enumeration, interpretation

Rivers Running waters

Macrophytes EN 15460 EN 14184

Surveying Surveying

Lakes Running waters

Zooplankton EN 15110

Sampling

Standing waters

Macroinvertebrates EN ISO 9391 EN 27828 (ISO 7828) EN 28265 (ISO 8265) EN ISO 8689-1, -2

Sampling Handnet sampling (benthic) Quantitative samplers (benthic, on stony substrata) Interpretation and presentation (benthic)

Deep waters

Fish EN 14962 EN 14011 EN 14757

Sampling, selection of method Sampling, with electricity Sampling, with multi-mesh gillnets

Chironomidae, pupal exuviae EN 15196

Sampling and processing

Soft-bottom macrofauna EN ISO 16665

Sampling and sample processing

Marine systems

Hard substrate communities EN ISO 19493

Biological surveys

Marine systems

large-scale morphological assessment and concerning the sensitivity necessary to check for compliance with the lowlevel chemical EQS are a challenge for future work.

3.11.6 List of Standards ASTM D511-09 – Standard test methods for calcium and magnesium in water. ASTM D512-04 – Standard test methods for chloride ion in water. ASTM D513-06 – Standard test methods for total and dissolved carbon dioxide in water. ASTM D516-07 – Standard test method for sulfate ion in water. ASTM D596-01(2006) – Standard guide for reporting results of analysis of water. ASTM D857-07 – Standard test method for aluminum in water.

Rivers Rivers

Shallow freshwaters Rivers

ASTM D858-07 – Standard test methods for manganese in water. ASTM D859-05 – Standard test method for silica in water. ASTM D888-09 – Standard test methods for dissolved oxygen in water. ASTM D932-85(2009) – Standard test method for iron bacteria in water and water-formed deposits. ASTM D1066-06 – Standard practice for sampling steam. ASTM D1067-06 – Standard test methods for acidity or alkalinity of water. ASTM D1068-05e1 – Standard test methods for iron in water. ASTM D1125-95(2009) – Standard test methods for electrical conductivity and resistivity of water. ASTM D1126-02(2007)e1 – Standard test method for hardness in water. ASTM D1129-06ae1 – Standard terminology relating to water. ASTM D1141-98(2008) – Standard practice for the preparation of substitute ocean water. ASTM D1179-04 – Standard test methods for fluoride ion in water.

Standardized Methods for Water-Quality Assessment

ASTM D1193-06 – Standard specification for reagent water. ASTM D1246-05 – Standard test method for bromide ion in water. ASTM D1252-06 – Standard test methods for chemical oxygen demand (dichromate oxygen demand) of water. ASTM D1253-08 – Standard test method for residual chlorine in water. ASTM D1291-06 – Standard practice for estimation of chlorine demand of water. ASTM D1292-05 – Standard test method for odor in water. ASTM D1293-99(2005) – Standard test methods for pH of water. ASTM D1385-07 – Standard test method for hydrazine in water. ASTM D1426-08 – Standard test methods for ammonia nitrogen in water. ASTM D1429-08 – Standard test methods for specific gravity of water and brine. ASTM D1498-08 – Standard test method for oxidation-reduction potential of water. ASTM D1687-02(2007)e1 – Standard test methods for chromium in water. ASTM D1688-07 – Standard test methods for copper in water. ASTM D1691-02(2007)e1 – Standard test methods for zinc in water. ASTM D1783-01(2007) – Standard test methods for phenolic compounds in water. ASTM D1886-08 – Standard test methods for nickel in water. ASTM D1890-05 – Standard test method for beta particle radioactivity of water. ASTM D1943-05 – Standard test method for alpha particle radioactivity of water. ASTM D1971-02(2006) – Standard practices for digestion of water samples for determination of metals by flame atomic absorption, graphite furnace atomic absorption, plasma emission spectroscopy, or plasma mass spectrometry. ASTM D1976-07 – Standard test method for elements in water by inductively coupled argon plasma atomic emission spectroscopy. ASTM D2035-08 – Standard practice for coagulation–flocculation jar test of water. ASTM D2036-09 – Standard test methods for cyanides in water. ASTM D2330-02 – Standard test method for methylene blue active substances. ASTM D2460-07 – Standard test method for alpha-particleemitting isotopes of radium in water. ASTM D2580-06 – Standard test method for phenols in water by gas–liquid chromatography. ASTM D2777-08e1 – Standard practice for determination of precision and bias of applicable test methods of committee D19 on water. ASTM D2791-07 – Standard test method for on-line determination of sodium in water. ASTM D2908-91(2005) – Standard practice for measuring volatile organic matter in water by aqueous-injection gas chromatography. ASTM D2972-08 – Standard test methods for arsenic in water. ASTM D3082-09 – Standard test method for boron in water.

289

ASTM D3084-05 – Standard practice for alpha-particle spectrometry of water. ASTM D3223-02(2007)e1 – Standard test method for total mercury in water. ASTM D3325-90(2006) – Standard practice for preservation of waterborne oil samples. ASTM D3326-07 – Standard practice for preparation of samples for identification of waterborne oils. ASTM D3328-06 – Standard test methods for comparison of waterborne petroleum oils by gas chromatography. ASTM D3352-08a – Standard test method for strontium ion in brackish water, seawater, and brines. ASTM D3370-08 – Standard practices for sampling water from closed conduits. ASTM D3372-02(2007)e1 – Standard test method for molybdenum in water. ASTM D3373-03(2007)e1 – Standard test method for vanadium in water. ASTM D3414-98(2004) – Standard test method for comparison of waterborne petroleum oils by infrared spectroscopy. ASTM D3415-98(2004) – Standard practice for identification of waterborne oils. ASTM D3454-05 – Standard test method for radium-226 in water. ASTM D3557-02(2007)e1 – Standard test methods for cadmium in water. ASTM D3558-08 – Standard test methods for cobalt in water. ASTM D3559-08 – Standard test methods for lead in water. ASTM D3561-02(2007)e1 – Standard test method for lithium, potassium, and sodium ions in brackish water, seawater, and brines by atomic absorption spectrophotometry. ASTM D3590-02(2006) – Standard test methods for total Kjeldahl nitrogen in water. ASTM D3645-08 – Standard test methods for beryllium in water. ASTM D3648-04 – Standard practices for the measurement of radioactivity. ASTM D3649-06 – Standard practice for high-resolution gamma-ray spectrometry of water. ASTM D3650-93(2006) – Standard test method for comparison of waterborne petroleum oils by fluorescence analysis. ASTM D3651-07 – Standard test method for barium in brackish water, seawater, and brines. ASTM D3694-96(2004) – Standard practices for preparation of sample containers and for preservation of organic constituents. ASTM D3695-95(2007) – Standard test method for volatile alcohols in water by direct aqueous-injection gas chromatography. ASTM D3697-07 – Standard test method for antimony in water. ASTM D3856-95(2006) – Standard guide for good laboratory practices in laboratories engaged in sampling and analysis of water. ASTM D3859-08 – Standard test methods for selenium in water.

290

Standardized Methods for Water-Quality Assessment

ASTM D3864-06 – Standard guide for continual online monitoring systems for water analysis. ASTM D3865-09 – Standard test method for plutonium in water. ASTM D3866-07 – Standard test methods for silver in water. ASTM D3867-09 – Standard test methods for nitrite– nitrate in water. ASTM D3868-09 – Standard test method for fluoride ions in brackish water, seawater, and brines. ASTM D3869-09 – Standard test methods for iodide and bromide ions in brackish water, seawater, and brines. ASTM D3871-84(2003) – Standard test method for purgeable organic compounds in water using headspace sampling. ASTM D3875-08 – Standard test method for alkalinity in brackish water, seawater, and brines. ASTM D3919-08 – Standard practice for measuring trace elements in water by graphite furnace atomic absorption spectrophotometry. ASTM D3920-02(2007)e1 – Standard test method for strontium in water. ASTM D3921-96(2003)e1 – Standard test method for oil and grease and petroleum hydrocarbons in water. ASTM D3972-09 – Standard test method for isotopic uranium in water by radiochemistry. ASTM D3973-85(2003) – Standard test method for low-molecular weight halogenated hydrocarbons in water. ASTM D3974-09 – Standard practices for extraction of trace elements from sediments. ASTM D3975-93(2008) – Standard practice for development and use (preparation) of samples for collaborative testing of methods for analysis of sediments. ASTM D3976-92(2005) – Standard practice for preparation of sediment samples for chemical analysis. ASTM D3977-97(2007) – Standard test methods for determining sediment concentration in water samples. ASTM D3986-07 – Standard test method for barium in brines, seawater, and brackish water by direct-current argon plasma atomic emission spectroscopy. ASTM D4012-81(2009) – Standard test method for adenosine triphosphate (ATP) content of microorganisms in water. ASTM D4025-08a – Standard practice for reporting results of examination and analysis of deposits formed from water for subsurface injection. ASTM D4107-08 – Standard test method for tritium in drinking water. ASTM D4127-06 – Standard terminology used with ion-selective electrodes. ASTM D4128-06 – Standard guide for identification and quantitation of organic compounds in water by combined gas chromatography and electron impact mass spectrometry. ASTM D4129-05 – Standard test method for total and organic carbon in water by high-temperature oxidation and by coulometric detection. ASTM D4130-08 – Standard test method for sulfate ion in brackish water, seawater, and brines. ASTM D4165-06 – Standard test method for cyanogen chloride in water. ASTM D4188-08 – Standard practice for performing pressure in-line coagulation–flocculation–filtration test.

ASTM D4189-07 – Standard test method for silt density index (SDI) of water. ASTM D4190-08 – Standard test method for elements in water by direct-current argon plasma atomic emission spectroscopy. ASTM D4191-08 – Standard test method for sodium in water by atomic absorption spectrophotometry. ASTM D4192-08 – Standard test method for potassium in water by atomic absorption spectrophotometry. ASTM D4193-08 – Standard test method for thiocyanate in water. ASTM D4196-05 – Standard test method for confirming the sterility of membrane filters. ASTM D4199-82(2003) – Standard test methods for autoclavability of membrane filters. ASTM D4249-83(2005) – Standard test method for enumeration of Candida albicans in water. ASTM D4281-95(2005)e1 – Standard test method for oil and grease (fluorocarbon extractable substances) by gravimetric determination. ASTM D4282-02 – Standard test method for determination of free cyanide in water and wastewater by microdiffusion. ASTM D4309-02(2007) – Standard practice for sample digestion using closed vessel microwave heating technique for the determination of total metals in water. ASTM D4327-03 – Standard test method for anions in water by chemically suppressed ion chromatography. ASTM D4328-08 – Standard practice for calculation of supersaturation of barium sulfate, strontium sulfate, and calcium sulfate dihydrate (gypsum) in brackish water, seawater, and brines. ASTM D4374-06 – Standard test methods for cyanides in water-automated methods for total cyanide, weak acid dissociable cyanide, and thiocyanate. ASTM D4375-96(2006) – Standard practice for basic statistics in committee D-19 on water. ASTM D4382-02(2007)e1– Standard test method for barium in water, atomic absorption spectrophotometry, graphite furnace. ASTM D4410-03 – Terminology for fluvial sediment. ASTM D4411-03(2008) – Standard guide for sampling fluvial sediment in motion. ASTM D4412-84(2009) – Standard test methods for sulfate-reducing bacteria in water and water-formed deposits. ASTM D4453-02(2006) – Standard practice for handling of ultra-pure water samples. ASTM D4454-85(2009) – Standard test method for simultaneous enumeration of total and respiring bacteria in aquatic systems by microscopy. ASTM D4455-85(2009) – Standard test method for enumeration of aquatic bacteria by epifluorescence microscopy counting procedure. ASTM D4458-09 – Standard test method for chloride ions in brackish water, seawater, and brines. ASTM D4489-95(2006) – Standard practices for sampling of waterborne oils. ASTM D4517-04 – Standard test method for low-level total silica in high-purity water by flameless atomic absorption spectroscopy.

Standardized Methods for Water-Quality Assessment

ASTM D4519-94(2005) – Standard test method for on-line determination of anions and carbon dioxide in high-purity water by cation exchange and degassed cation conductivity. ASTM D4520-03(2008) – Standard practice for determining water injectivity through the use of on-site floods. ASTM D4581-86(2005) – Standard guide for measurement of morphologic characteristics of surface water bodies. ASTM D4638-03(2007) – Standard guide for preparation of biological samples for inorganic chemical analysis. ASTM D4658-09 – Standard test method for sulfide ion in water. ASTM D4691-02(2007) – Standard practice for measuring elements in water by flame atomic absorption spectrophotometry. ASTM D4698-92(2007) – Standard practice for total digestion of sediment samples for chemical analysis of various metals. ASTM D4763-06 – Standard practice for identification of chemicals in water by fluorescence spectroscopy. ASTM D4778-05 – Standard test method for determination of corrosion and fouling tendency of cooling water under heat transfer conditions. ASTM D4785-08 – Standard test method for low-level analysis of iodine radioisotopes in water. ASTM D4822-88(2008) – Standard guide for selection of methods of particle size analysis of fluvial sediments (manual methods). ASTM D4823-95(2008) – Standard guide for core sampling submerged, unconsolidated sediments. ASTM D4839-03 – Standard test method for total carbon and organic carbon in water by ultraviolet, or persulfate oxidation, or both, and infrared detection. ASTM D4840-99(2004) – Standard guide for sampling chainof-custody procedures. ASTM D4841-88(2008) – Standard practice for estimation of holding time for water. ASTM D4922-09 – Standard test method for determination of radioactive iron in water. ASTM D4962-02(2009) – Standard practice for NaI(Tl) gamma-ray spectrometry of water samples containing organic and inorganic constituents. ASTM D4994-89(2009) – Standard practice for recovery of viruses from wastewater sludges. ASTM D5074-90(2008) – Standard practice for preparation of natural-matrix sediment reference samples for major and trace inorganic constituents analysis by partial extraction procedures. ASTM D5127-07 – Standard guide for ultra-pure water used in the electronics and semiconductor industries. ASTM D5128-09 – Standard test method for on-line pH measurement of water of low conductivity. ASTM D5172-91(2004) – Standard guide for documenting the standard operating procedures used for the analysis of water. ASTM D5173-97(2007) – Standard test method for on-line monitoring of carbon compounds in water by chemical oxidation, by UV light oxidation, by both, or by hightemperature combustion followed by gas-phase NDIR or by electrolytic conductivity.

291

ASTM D5174-07 – Standard test method for trace uranium in water by pulsed-laser phosphorimetry. ASTM D5175-91(2003) – Standard test method for organohalide pesticides and polychlorinated biphenyls in water by microextraction and gas chromatography. ASTM D5176-08 – Standard test method for total chemically bound nitrogen in water by pyrolysis and chemiluminescence detection. ASTM D5196-06 – Standard guide for bio-applications grade water. ASTM D5241-92(2004) – Standard practice for microextraction of water for analysis of volatile and semi-volatile organic compounds in water. ASTM D5244-92(2004) – Standard practice for recovery of enteroviruses from waters. ASTM D5245-92(2005) – Standard practice for cleaning laboratory glassware, plasticware, and equipment used in microbiological analyses. ASTM D5246-92(2004) – Standard test method for isolation and enumeration of Pseudomonas aeruginosa from water. ASTM D5257-03 – Standard test method for dissolved hexavalent chromium in water by ion chromatography. ASTM D5258-02(2007) – Standard practice for acidextraction of elements from sediments using closed vessel microwave heating. ASTM D5259-92(2006) – Standard test method for isolation and enumeration of enterococci from water by the membrane filter procedure. ASTM D5315-04 – Standard test method for determination of N-methyl-carbamoyloximes and N-methylcarbamates in water by direct aqueous injection HPLC with post-column derivatization. ASTM D5316-98(2004) – Standard test method for 1,2dibromoethane and 1,2-dibromo-3-chloropropane in water by microextraction and gas chromatography. ASTM D5317-98(2003)e1 – Standard test method for determination of chlorinated organic acid compounds in water by gas chromatography with an electron capture detector. ASTM D5387-93(2007) – Standard guide for elements of a complete data set for noncohesive sediments. ASTM D5391-99(2009) – Standard test method for electrical conductivity and resistivity of a flowing high-purity water sample. ASTM D5392-93(2006) – Standard test method for isolation and enumeration of Escherichia coli in water by the two-step membrane filter procedure. ASTM D5411-05 – Standard practice for calculation of average energy per disintegration (e) for a mixture of radionuclides in reactor coolant. ASTM D5412-93(2005) – Standard test method for quantification of complex polycyclic aromatic hydrocarbon mixtures or petroleum oils in water. ASTM D5462-08 – Standard test method for on-line measurement of low-level dissolved oxygen in water. ASTM D5463-08 – Standard guide for use of test kits to measure inorganic constituents in water. ASTM D5464-07 – Standard test method for pH measurement of water of low conductivity.

292

Standardized Methods for Water-Quality Assessment

ASTM D5465-93(2004) – Standard practice for determining microbial colony counts from waters analyzed by plating methods. ASTM D5475-93(2002) – Standard test method for nitrogenand phosphorus-containing pesticides in water by gas chromatography with a nitrogen–phosphorus detector. ASTM D5540-08 – Standard practice for flow control and temperature control for on-line water sampling and analysis. ASTM D5542-04(2009) – Standard test methods for trace anions in high purity water by ion chromatography. ASTM D5543-09 – Standard test methods for low-level dissolved oxygen in water. ASTM D5612-94(2008) – Standard guide for quality planning and field implementation of a water quality measurement program. ASTM D5673-05 – Standard test method for elements in water by inductively coupled plasma-mass spectrometry. ASTM D5739-06 – Standard practice for oil spill source identification by gas chromatography and positive ion electron impact low resolution mass spectrometry. ASTM D5788-95(2005) – Standard guide for spiking organics into aqueous samples. ASTM D5790-95(2006) – Standard test method for measurement of purgeable organic compounds in water by capillary column gas chromatography/mass spectrometry. ASTM D5810-96(2006) – Standard guide for spiking into aqueous samples. ASTM D5811-08 – Standard test method for strontium-90 in water. ASTM D5812-96(2002)e1 – Standard test method for determination of organochlorine pesticides in water by capillary column gas chromatography. ASTM D5847-02(2007) – Standard practice for writing quality control specifications for standard test methods for water analysis. ASTM D5851-95(2006) – Standard guide for planning and implementing a water monitoring program. ASTM D5904-02 – Standard test method for total carbon, inorganic carbon, and organic carbon in water by ultraviolet, persulfate oxidation, and membrane conductivity detection. ASTM D5905-98(2008) – Standard practice for the preparation of substitute wastewater. ASTM D5907-09 – Standard test method for filterable and nonfilterable matter in water. ASTM D5916-96(2002) – Standard test method for detection and enumeration of Clostridium perfringens from water and extracted sediments by membrane filtration (MF). ASTM D5996-05(2009) – Standard test method for measuring anionic contaminants in high-purity water by on-line ion chromatography. ASTM D5997-96(2005) – Standard test method for on-line monitoring of total carbon, inorganic carbon in water by ultraviolet, persulfate oxidation, and membrane conductivity detection. ASTM D6071-06 – Standard test method for low-level sodium in high-purity water by graphite furnace atomic absorption spectroscopy.

ASTM D6091-07 – Standard practice for 99%/95% interlaboratory detection estimate (IDE) for analytical methods with negligible calibration error. ASTM D6104-97(2003) – Standard practice for determining the performance of oil/water separators subjected to surface runoff. ASTM D6145-97(2007) – Standard guide for monitoring sediment in watersheds. ASTM D6146-97(2007) – Standard guide for monitoring aqueous nutrients in watersheds. ASTM D6157-97(2003) – Standard practice for determining the performance of oil/water separators subjected to a sudden release. ASTM D6238-98(2003) – Standard test method for total oxygen demand in water. ASTM D6239-09 – Standard test method for uranium in drinking water by high-resolution alpha-liquid-scintillation spectrometry. ASTM D6301-08 – Standard practice for collection of on-line composite samples of suspended solids and ionic solids in process water. ASTM D6317–98(2004) – Standard test method for low level determination of total carbon, inorganic carbon and organic carbon in water by ultraviolet, persulfate oxidation, and membrane conductivity detection. ASTM D6362-98(2008) – Standard practice for certificates of reference materials for water analysis. ASTM D6501-09 – Standard test method for phosphonate in brines. ASTM D6502-08 – Standard test method for continuous measurement of on-line composite samples of low level filterable matter (suspended solids) and non-filterable matter (ionic solids) in process water by X-ray fluorescence (XRF). ASTM D6503-99(2009) – Standard test method for enterococci in water using Enterolert. ASTM D6504-07 – Standard practice for on-line determination of cation conductivity in high-purity water. ASTM D6508-00(2005)e2 – Standard test method for determination of dissolved inorganic anions in aqueous matrices using capillary ion electrophoresis and chromate electrolyte. ASTM D6512-07 – Standard practice for interlaboratory quantitation estimate. ASTM D6520-06 – Standard practice for the solid-phase micro-extraction (SPME) of water and its headspace for the analysis of volatile and semivolatile organic compounds. ASTM D6530-00(2006) – Standard test method for total active biomass in cooling tower waters (kool kount assay; KKA). ASTM D6568-00(2006) – Standard guide for planning, carrying out, and reporting traceable chemical analyses of water samples. ASTM D6569-05(2009) – Standard test method for on-line measurement of pH. ASTM D6581-08 – Standard test methods for bromate, bromide, chlorate, and chlorite in drinking water by suppressed ion chromatography. ASTM D6592-01 – Standard test method for portable chemiluminescent water quality determination.

Standardized Methods for Water-Quality Assessment

ASTM D6689-01(2006) – Standard guide for optimizing, controlling, and reporting test method uncertainties from multiple workstations in the same laboratory organization. ASTM D6696-05e1 – Standard guide for understanding cyanide species. ASTM D6697-01 – Standard test method for determination for chemical oxygen demand (manganese III oxygen demand) of water. ASTM D6698-07 – Standard test method for on-line measurement of turbidity below 5 NTU in water. ASTM D6734-01(2009) – Standard test method for low levels of coliphages in water. ASTM D6764-02(2007) – Standard guide for collection of water temperature, dissolved-oxygen concentrations, specific electrical conductance, and pH data from open channels. ASTM D6800-02(2007)e1 – Standard practice for preparation of water samples using reductive precipitation preconcentration technique for ICP-MS analysis of trace metals. ASTM D6808-02(2007) – Standard practice for competency requirements of reference material producers for water analysis. ASTM D6850-03(2008) – Standard guide for QC of screening methods in water. ASTM D6855-03 – Standard test method for determination of turbidity below 5 NTU in static mode. ASTM D6888-04 – Standard test method for available cyanide with ligand displacement and flow injection analysis (FIA) utilizing gas diffusion separation and amperometric detection. ASTM D6889-03 – Standard practice for fast screening for volatile organic compounds in water using solid phase microextraction (SPME). ASTM D6919-09 – Standard test method for determination of dissolved alkali and alkaline earth cations and ammonium in water and wastewater by ion chromatography. ASTM D6994-04 – Standard test method for determination of metal cyanide complexes in wastewater, surface water, groundwater, and drinking water using anion exchange chromatography with UV detection. ASTM D7065-06 – Standard test method for determination of nonylphenol, bisphenol A, p-tert-octylphenol, nonylphenol monoethoxylate and nonylphenol diethoxylate in environmental waters by gas chromatography mass spectrometry. ASTM D7066-04e1 – Standard test method for dimer/trimer of chlorotrifluoroethylene (S-316) recoverable oil and grease and nonpolar material by infrared determination. ASTM D7126-06 – Standard test method for on-line colorimetric measurement of silica. ASTM D7168-05e1 – Standard test method for 99Tc in water by solid phase extraction disk. ASTM D7237-06 – Standard test method for aquatic free cyanide with flow injection analysis (FIA) utilizing gas diffusion separation and amperometric detection. ASTM D7282-06 – Standard practice for set-up, calibration, and quality control of instruments used for radioactivity measurements.

293

ASTM D7283-06 – Standard test method for alpha- and betaactivity in water by liquid scintillation counting. ASTM D7284-08 – Standard test method for total cyanide in water by micro distillation followed by flow injection analysis with gas diffusion separation and amperometric detection. ASTM D7315-07a – Standard test method for determination of turbidity above 1 turbidity unit (TU) in static mode. ASTM D7316-06 – Standard guide for interpretation of existing field instrumentation to influence emergency response decisions. ASTM D7362-07 – Standard guide for rapid screening of vegetation for radioactive strontium aerial deposition. ASTM D7363-07 – Standard test method for determination of parent and alkyl polycyclic aromatics in sediment pore water using solid-phase microextraction and gas chromatography/mass spectrometry in selected ion monitoring mode. ASTM D7365-09 – Standard practice for sampling, preservation and mitigating interferences in water samples for analysis of cyanide. ASTM D7366-08 – Standard practice for estimation of measurement uncertainty for data from regression-based methods. ASTM D7485-09 – Standard test method for determination of nonylphenol, p-tert-octylphenol, nonylphenol monoethoxylate and nonylphenol diethoxylate in environmental waters by liquid chromatography/tandem mass spectrometry. ASTM D7511-09 – Standard test method for total cyanide by segmented flow injection analysis, in-line ultraviolet digestion and amperometric detection. ASTM D7535-09 – Standard test method for lead-210 in water. ASTM D7572-09 – Standard guide for recovery of aqueous cyanides by extraction from mine rock and soil after remediation of process releases. ASTM D7573-09 – Standard test method for total carbon and organic carbon in water by high-temperature catalytic combustion and infrared detection. ASTM D7574-09 – Standard test method for determination of bisphenol A in environmental waters by liquid chromatography/tandem mass spectrometry. ASTM D7575-10 – Standard test method for solvent-free membrane recoverable oil and grease by infrared determination. ASTM D7597-09 – Standard test method for determination of diisopropyl methylphosphonate, ethyl hydrogen dimethylamidophosphate, ethyl methylphosphonic acid, isopropyl methylphosphonic acid, methylphosphonic acid and pinacolyl methylphosphonic acid in water by liquid chromatography. ASTM D7598-09 – Standard test method for determination of thiodiglycol in water by single reaction monitoring liquid chromatography/tandem mass spectrometry. ASTM D7599-09 – Standard test method for determination of diethanolamine, triethanolamine, N-methyldiethanolamine and N-ethyldiethanolamine in water by single reaction monitoring liquid chromatography/tandem mass spectrometry.

294

Standardized Methods for Water-Quality Assessment

ASTM D7600-09 – Standard test method for determination of aldicarb, carbofuran, oxamyl and methomyl by liquid chromatography/tandem mass spectrometry. ASTM F660-83(2007) – Standard practice for comparing particle size in the use of alternative types of particle counters. ASTM F838-05 – Standard test method for determining bacterial retention of membrane filters utilized for liquid filtration. DIN 820-1:2009 – Normungsarbeit; Grundsa¨tze. DIN 8202:2009 – Normungsarbeit – Teil 2: Gestaltung von Dokumenten. DIN 820-34:2009 – Normungsarbeit – Teil 3 – Begriffe. DIN 820-4:2009 – Normungsarbeit – Teil 4: Gescha¨ftsgang. DIN 38406-16:1990 – Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung – Kationen (Gruppe E) – Teil 16: Bestimmung von 7 Metallen (Zink, Cadmium, Blei, Kupfer, Thallium, Nickel, Cobalt) mittels Voltammetrie (E 16) (German standard methods for the examination of water, wastewater, and sludge – cations (group E) – part 16: determination of 7 metals (zinc, cadmium, lead, copper, thallium, nickel, cobalt) by voltammetry (E 16)). DIN 38406-17:2009 – Deutsche Einheitsverfahren zur Wasser, Abwasser- und Schlammuntersuchung – Kationen (Gruppe E) – Teil 17: Bestimmung von Uran – Verfahren mittels adsorptiver Stripping-Voltammetrie in Grund-, Roh- und Trinkwa¨ssern (E 17) (German standard methods for the examination of water, wastewater, and sludge – cations (group E) – part 17: determination of uranium – method using adsorptive stripping voltammetry in surface water, raw water and drinking water (E 17)). DIN 38407-30:2007 – Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung – Gemeinsam erfassbare Stoffgruppen (Gruppe F) – Teil 30: Bestimmung von Trihalogenmethanen (THM) in Schwimm- und Badebeckenwasser mit Headspace-Gaschromatographie (F 30) (German standard methods for the examination of water, wastewater and sludge – jointly determinable substances (group F) – part 30: determination of trihalogenmethanes in bathing water and pool water with headspace-gas chromatography (F 30)). DIN 38410-1:2004 – Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung – Verfahren der biologisch-o¨kologischen Untersuchung (Gruppe M) – Teil 1: Bestimmung des Saprobienindex in FlieXgewa¨ssern (M 1) (German standard methods for the examination of water, wastewater, and sludge – biological–ecological analysis of water (group M) – part 1: determination of the saprobic index in running waters (M 1)). EN 872:2005 – Water quality – determination of suspended solids – method by filtration through glass fiber filters. EN 903:1993 – Water quality – determination of anionic surfactants by measurement of the methylene blue index MBAS (ISO 7875-1:1984 modified). EN 1085:2007 – Wastewater treatment – vocabulary; trilingual version EN 1085:2007. EN 1233:1996 – Water quality – determination of chromium – atomic absorption spectrometric methods. EN 1483:2007 – Water quality – determination of mercury – method using atomic absorption spectrometry.

EN 1484:1997 – Water quality – guidelines for the determination of total organic carbon (TOC) and dissolved organic carbon (DOC). EN 1622:2006 – Water quality – determination of the threshold odor number (TON) and threshold flavor number (TFN). EN 1899-1:1998 – Water quality – determination of biochemical oxygen demand after n days (BODn) – part 1: dilution and seeding method with allylthiourea addition (ISO 5815:1989, modified). EN 1899-2:1998 – Water quality – determination of biochemical oxygen demand after n days (BODn) – part 2: method for undiluted samples (ISO 5815:1989, modified). EN 12260:2003 – Water quality – determination of nitrogen – determination of bound nitrogen (TNb), following oxidation to nitrogen oxides. EN 12338:1998 – Water quality – determination of mercury – enrichment methods by amalgamation. EN 12673:1998 – Water quality – gas chromatographic determination of some selected chlorophenols in water. EN 12918:1999 – Water quality – determination of parathion, parathion-methyl, and some other organophosphorus compounds in water by dichloromethane extraction and gas chromatographic analysis. EN 13946:2003 – Water quality – guidance standard for the routine sampling and pretreatment of benthic diatoms from rivers. EN 14011:2003 – Water quality – sampling of fish with electricity. EN 14184:2003 – Water quality – guidance standard for the surveying of aquatic macrophytes in running waters. EN 14207:2003 – Water quality – determination of epichlorohydrin. EN 14407:2004 – Water quality – guidance standard for the identification, enumeration, and interpretation of benthic diatom samples from running waters. EN 14486:2005 – Water quality – detection of human enteroviruses by monolayer plaque assay. EN 14614:2004 – Water quality – guidance standard for assessing the hydromorphological features of rivers. EN 14757:2005 – Water quality – sampling of fish with multimesh gillnets. EN 14962:2006 – Water quality – guidance on the scope and selection of fish sampling methods. EN 14996:2006 – Water quality – guidance on assuring the quality of biological and ecological assessments in the aquatic environment. EN 15110:2006 – Water quality – guidance standard for the sampling of zooplankton from standing waters. EN 15196:2006 – Water quality – guidance on sampling and processing of the pupal exuviae of Chironomidae (order Diptera) for ecological assessment. EN 15204:2006 – Water quality – guidance standard on the enumeration of phytoplankton using inverted microscopy (Utermo¨hl technique). EN 15460:2007 – Water quality – guidance standard for the surveying of macrophytes in lakes. EN 15708:2009 – Water quality – guidance standard for the surveying, sampling, and laboratory analysis of phytobenthos in shallow running water.

Standardized Methods for Water-Quality Assessment

EN 15843:2010 – Water quality – guidance standard on determining the degree of modification of river hydromorphology. EN 25663:1993 – Water quality – determination of Kjeldahl nitrogen – method after mineralization with selenium (ISO 5663:1984). EN 25813:1992 – Water quality – determination of dissolved oxygen – iodometric method (ISO 5813:1983). EN 25814:1992 – Water quality – determination of dissolved oxygen – electrochemical probe method (ISO 5814:1990). EN 26461-1:1993 – Water quality – detection and enumeration of the spores of sulfite-reducing anaerobes (clostridia) – part 1: method by enrichment in a liquid medium (ISO 6461-1:1986). EN 26461-2:1993 – Water quality – detection and enumeration of the spores of sulfite-reducing anaerobes (clostridia) – part 2: method by membrane filtration (ISO 64612:1986). EN 26595:1992 – Water quality – determination of total arsenic – silver diethyldithiocarbamate spectrophotometric method (ISO 6595:1982). EN 26595:1992/AC:1992 – Water quality – determination of total arsenic – silver diethyldithiocarbamate spectrophotometric method (ISO 6595:1982). EN 26777:1993 – Water quality – determination of nitrite – molecular absorption spectrometric method (ISO 6777:1984). EN 27828:1994 – Water quality – methods of biological sampling – guidance on handnet sampling of aquatic benthic macro-invertebrates (ISO 7828:1985). EN 27888:1993 – Water quality – determination of electrical conductivity (ISO 7888:1985). EN 28265:1994 – Water quality – design and use of quantitative samplers for benthic macro-invertebrates on stony substrata in shallow freshwaters (ISO 8265:1988). EN ISO 5667-1:2006 – Water quality – sampling – part 1: guidance on the design of sampling programmes and sampling techniques (ISO 5667-1:2006). EN ISO 5667-1:2006/AC:2007 – Water quality – sampling – part 1: guidance on the design of sampling programmes and sampling techniques (ISO 5667-1:2006). EN ISO 5667-3:2003 – Water quality – sampling – part 3: guidance on the preservation and handling of water samples (ISO 5667-3:2003). EN ISO 5667-3:2003/AC:2007 – Water quality – sampling – part 3: guidance on the preservation and handling of water samples (ISO 5667-3:2003). EN ISO 5667-16:1998 – Water quality – sampling – part 16: guidance on biotesting of samples (ISO 566716:1998). EN ISO 5667-19:2004 – Water quality – sampling – part 19: guidance on sampling in marine sediments (ISO 566719:2004). EN ISO 5961:1995 – Water quality – determination of cadmium by atomic absorption spectrometry (ISO 5961:1994). EN ISO 6222:1999 – Water quality – enumeration of culturable microorganisms – colony count by inoculation in a nutrient agar culture medium (ISO 6222:1999).

295

EN ISO 6341:1996 – Water quality – determination of the inhibition of the mobility of Daphnia magna Straus (Cladocera, Crustacea) – acute toxicity test (ISO 6341:1996). EN ISO 6341:1996/AC:1998 – Water quality – determination of the inhibition of the mobility of Daphnia magna Straus (Cladocera, Crustacea) – acute toxicity test (ISO 6341:1996). EN ISO 6468:1996 – Water quality – determination of certain organochlorine insecticides, polychlorinated biphenyls and chlorobenzenes – gas chromatographic method after liquid–liquid extraction (ISO 6468:1996). EN ISO 6878:2004 – Water quality – determination of phosphorus – ammonium molybdate spectrometric method (ISO 6878:2004). EN ISO 7027:1999 – Water quality – determination of turbidity (ISO 7027:1999). EN ISO 7346-1:1997 – Water quality – determination of the acute lethal toxicity of substances to a freshwater fish (Brachydanio rerio Hamilton–Buchanan (Teleostei, Cyprinidae)) – part 1: static method (ISO 7346-1:1996). EN ISO 7346-2:1997 – Water quality – determination of the acute lethal toxicity of substances to a freshwater fish (Brachydanio rerio Hamilton–Buchanan (Teleostei, Cyprinidae)) – part 2: semi-static method (ISO 73462:1996). EN ISO 7346-3:1997 – Water quality – determination of the acute lethal toxicity of substances to a freshwater fish (Brachydanio rerio Hamilton–Buchanan (Teleostei, Cyprinidae)) – part 3: flow-through method (ISO 7346-3:1996). EN ISO 7393-1:2000 – Water quality – determination of free chlorine and total chlorine – part 1: titrimetric method using N,N-diethyl-1,4-phenylenediamine (ISO 73931:1985). EN ISO 7393-2:2000 – Water quality – determination of free chlorine and total chlorine – part 2: colorimetric method using N,N-diethyl-1,4-phenylenediamine, for routine control purposes (ISO 7393-2:1985). EN ISO 7393-3:2000 – Water quality – determination of free chlorine and total chlorine – part 3: iodometric titration method for the determination of total chlorine (ISO 73933:1990). EN ISO 7827:1995 – Water quality – evaluation in an aqueous medium of the ‘‘ultimate’’ aerobic biodegradability of organic compounds – method by analysis of dissolved organic carbon (DOC) (ISO 7827:1994). EN ISO 7887:1994 – Water quality – examination and determination of color (ISO 7887:1994). EN ISO 7899-1:1998 – Water quality – detection and enumeration of intestinal enterococci in surface and wastewater – part 1: miniaturized method (most probable number) by inoculation in liquid medium (ISO 78991:1998). EN ISO 7899-1:1998/AC:2000 – Water quality – detection and enumeration of intestinal enterococci in surface and wastewater – part 1: miniaturized method (most probable number) by inoculation in liquid medium (ISO 78991:1998). EN ISO 7899-2:2000 – Water quality – detection and enumeration of intestinal enterococci – part 2: membrane filtration method (ISO 7899-2:2000).

296

Standardized Methods for Water-Quality Assessment

EN ISO 7980:2000 – Water quality – determination of calcium and magnesium – atomic absorption spectrometric method (ISO 7980:1986). EN ISO 8192:2007 – Water quality – test for inhibition of oxygen consumption by activated sludge for carbonaceous and ammonium oxidation (ISO 8192:2007). EN ISO 8199:2007 – Water quality – general guidance on the enumeration of microorganisms by culture (ISO 8199:2005). EN ISO 8467:1995 – Water quality – determination of permanganate index (ISO 8467:1993). EN ISO 8689-1:2000 – Water quality – biological classification of rivers – part 1: guidance on the interpretation of biological quality data from surveys of benthic macroinvertebrates (ISO 8689-1:2000). EN ISO 8689-2:2000 – Water quality – biological classification of rivers – part 2: guidance on the presentation of biological quality data from surveys of benthic macroinvertebrates (ISO 8689-2:2000). EN ISO 8692:2004 – Water quality – freshwater algal growth inhibition test with unicellular green algae (ISO 8692:2004). EN ISO 9308-1:2000 – Water quality – detection and enumeration of Escherichia coli and coliform bacteria – part 1: membrane filtration method (ISO 9308-1:2000). EN ISO 9308-1:2000/AC:2008 – Water quality – detection and enumeration of Escherichia coli and coliform bacteria – part 1: membrane filtration method (ISO 9308-1:2000/Cor 1:2007). EN ISO 9308-3:1998 – Water quality – detection and enumeration of Escherichia coli and coliform bacteria in surface and wastewater – part 3: miniaturized method (most probable number) by inoculation in liquid medium (ISO 9308-3:1998). EN ISO 9308-3:1998/AC:2000 – Water quality – detection and enumeration of Escherichia coli and coliform bacteria in surface and wastewater – part 3: miniaturized method (most probable number) by inoculation in liquid medium (ISO 9308-3:1998). EN ISO 9377-2:2000 – Water quality – determination of hydrocarbon oil index – part 2: method using solvent extraction and gas chromatography (ISO 9377-2:2000). EN ISO 9391:1995 – Water quality – sampling in deep waters for macro-invertebrates – guidance on the use of colonization, qualitative and quantitative samplers (ISO 9391:1993). EN ISO 9408:1999 – Water quality – evaluation of ultimate aerobic biodegradability of organic compounds in aqueous medium by determination of oxygen demand in a closed respirometer (ISO 9408:1999). EN ISO 9439:2000 – Water quality – evaluation of ultimate aerobic biodegradability of organic compounds in aqueous medium – carbon dioxide evolution test (ISO 9439:1999). EN ISO 9509:2006 – Water quality – toxicity test for assessing the inhibition of nitrification of activated sludge microorganisms (ISO 9509:2006). EN ISO 9562:2004 – Water quality – determination of adsorbable organically bound halogens (AOX) (ISO 9562:2004).

EN ISO 9887:1994 – Water quality – evaluation of the aerobic biodegradability of organic compounds in an aqueous medium – semicontinuous activated sludge method (SCAS) (ISO 9887:1992). EN ISO 9888:1999 – Water quality – evaluation of ultimate aerobic biodegradability of organic compounds in aqueous medium – static test (Zahn–Wellens method) (ISO 9888:1999). EN ISO 9963-1:1995 – Water quality – determination of alkalinity – part 1: determination of total and composite alkalinity (ISO 9963-1:1994). EN ISO 9963-2:1995 – Water quality – determination of alkalinity – part 2: determination of carbonate alkalinity (ISO 9963-2:1994). EN ISO 10253:2006 – Water quality – marine algal growth inhibition test with Skeletonema costatum and Phaeodactylum tricornutum (ISO 10253:2006). EN ISO 10301:1997 – Water quality – determination of highly volatile halogenated hydrocarbons – gas-chromatographic methods (ISO 10301:1997). EN ISO 10304-1:2009 – Water quality – determination of dissolved anions by liquid chromatography of ions – part 1: determination of bromide, chloride, fluoride, nitrate, nitrite, phosphate, and sulfate (ISO 10304-1:2007). EN ISO 10304-3:1997 – Water quality – determination of dissolved anions by liquid chromatography of ions – part 3: determination of chromate, iodide, sulfite, thiocyanate, and thiosulfate (ISO 10304-3:1997). EN ISO 10304-4:1999 – Water quality – determination of dissolved anions by liquid chromatography of ions – part 4: determination of chlorate, chloride, and chlorite in water with low contamination (ISO 10304-4:1997). EN ISO 10634:1995 – Water quality – guidance for the preparation and treatment of poorly water-soluble organic compounds for the subsequent evaluation of their biodegradability in an aqueous medium (ISO 10634:1995). EN ISO 10695:2000 – Water quality – determination of selected organic nitrogen and phosphorus compounds – gas chromatographic methods (ISO 10695:2000). EN ISO 10705-1:2001 – Water quality – detection and enumeration of bacteriophages – part 1: enumeration of Fspecific RNA bacteriophages (ISO 10705-1:1995). EN ISO 10705-2:2001 – Water quality – detection and enumeration of bacteriophages – part 2: enumeration of somatic coliphages (ISO 10705-2:2000). EN ISO 10707:1997 – Water quality – evaluation in an aqueous medium of the ‘‘ultimate’’ aerobic biodegradability of organic compounds – method by analysis of biochemical oxygen demand (closed bottle test) (ISO 10707:1994). EN ISO 10712:1995 – Water quality – Pseudomonas putida growth inhibition test (pseudomonas cell multiplication inhibition test) (ISO 10712:1995). EN ISO 11348-1:2008 – Water quality – determination of the inhibitory effect of water samples on the light emission of Vibrio fischeri (Luminescent bacteria test) – part 1: method using freshly prepared bacteria (ISO 11348-1:2007). EN ISO 11348-2:2008 – Water quality – determination of the inhibitory effect of water samples on the light emission of Vibrio fischeri (luminescent bacteria test) – part 2: method using liquid-dried bacteria (ISO 11348-2:2007).

Standardized Methods for Water-Quality Assessment

EN ISO 11348-3:2008 – Water quality – determination of the inhibitory effect of water samples on the light emission of Vibrio fischeri (luminescent bacteria test) – part 3: method using freeze-dried bacteria (ISO 11348-3:2007). EN ISO 11369:1997 – Water quality – determination of selected plant treatment agents – method using high-performance liquid chromatography with UV detection after solid–liquid extraction (ISO 11369:1997). EN ISO 11731-2:2008 – Water quality – detection and enumeration of Legionella – part 2: direct membrane filtration method for waters with low bacterial counts (ISO 117312:2004). EN ISO 11732:2005 – Water quality – determination of ammonium nitrogen – method by flow analysis (CFA and FIA) and spectrometric detection (ISO 11732:2005). EN ISO 11733:2004 – Water quality – determination of the elimination and biodegradability of organic compounds in an aqueous medium – activated sludge simulation test (ISO 11733:2004). EN ISO 11734:1998 – Water quality – evaluation of the ‘‘ultimate’’ anaerobic biodegradability of organic compounds in digested sludge – method by measurement of the biogas production (ISO 11734:1995). EN ISO 11885:2009 – Water quality – determination of 33 elements by inductively coupled plasma atomic emission spectroscopy (ISO 11885:2007). EN ISO 11905-1:1998 – Water quality – determination of nitrogen – part 1: method using oxidative digestion with peroxodisulfate (ISO 11905-1:1997). EN ISO 11969:1996 – Water quality – determination of arsenic – atomic absorption spectrometric method (hydride technique) (ISO 11969:1996). EN ISO 12020:2000 – Water quality – determination of aluminum – atomic absorption spectrometric methods (ISO 12020:1997). EN ISO 13395:1996 – Water quality – determination of nitrite nitrogen and nitrate nitrogen and the sum of both by flow analysis (CFA and FIA) and spectrometric detection (ISO 13395:1996). EN ISO 14402:1999 – Water quality – determination of phenol index by flow analysis (FIA and CFA) (ISO 14402:1999). EN ISO 14403:2002 – Water quality – determination of total cyanide and free cyanide by continuous flow analysis (ISO 14403:2002). EN ISO 14593:2005 – Water quality – evaluation of ultimate aerobic biodegradability of organic compounds in aqueous medium – method by analysis of inorganic carbon in sealed vessels (CO2 headspace test) (ISO 14593:1999). EN ISO 14911:1999 – Water quality – determination of disþ 2þ 2þ 2þ 2þ solved Liþ, Naþ, NHþ 4 , K , Mn , Ca , Mg , Sr , and 2þ Ba using ion chromatography – method for water and wastewater (ISO 14911:1998). EN ISO 15061:2001 – Water quality – determination of dissolved bromate – method by liquid chromatography of ions (ISO 15061:2001). EN ISO 15088:2008 – Water quality – determination of the acute toxicity of wastewater to zebrafish eggs (Danio rerio) (ISO 15088:2007).

297

CEN ISO/TR 15462:2009 – Water quality – selection of tests for biodegradability (ISO/TR 15462:2006). EN ISO 15586:2003 – Water quality – determination of trace elements using atomic absorption spectrometry with graphite furnace (ISO 15586:2003). EN ISO 15587-1:2002 – Water quality – digestion for the determination of selected elements in water – part 1: Aqua regia digestion (ISO 15587-1:2002). EN ISO 15587-2:2002 – Water quality – digestion for the determination of selected elements in water – part 2: nitric acid digestion (ISO 15587-2:2002). EN ISO 15680:2003 – Water quality – gas-chromatographic determination of a number of monocyclic aromatic hydrocarbons, naphthalene, and several chlorinated compounds using purge-and-trap and thermal desorption (ISO 15680:2003). EN ISO 15681-1:2004 – Water quality – determination of orthophosphate and total phosphorus contents by flow analysis (FIA and CFA) – part 1: method by flow injection analysis (FIA) (ISO 15681-1:2003). EN ISO 15681-2:2004 – Water quality – determination of orthophosphate and total phosphorus contents by flow analysis (FIA and CFA) – part 2: method by continuous flow analysis (CFA) (ISO 15681-2:2003). EN ISO 15682:2001 – Water quality – determination of chloride by flow analysis (CFA and FIA) and photometric or potentiometric detection (ISO 15682:2000). EN ISO 15839:2006 – Water quality – on-line sensors/analyzing equipment for water – specifications and performance tests (ISO 15839:2003). EN ISO 15913:2003 – Water quality – determination of selected phenoxyalkanoic herbicides, including bentazones and hydroxybenzonitriles by gas chromatography and mass spectrometry after solid phase extraction and derivatization (ISO 15913:2000). EN ISO 16264:2004 – Water quality – determination of soluble silicates by flow analysis (FIA and CFA) and photometric detection (ISO 16264:2002). EN ISO 16266:2008 – Water quality – detection and enumeration of Pseudomonas aeruginosa – method by membrane filtration (ISO 16266:2006). EN ISO 16588:2003 – Water quality – determination of six complexing agents – gas-chromatographic method (ISO 16588:2002). EN ISO 16588:2003/A1:2005 – Water quality – determination of six complexing agents – gas-chromatographic method (ISO 16588:2002/Amd 1:2004). EN ISO 16665:2005 – Water quality – guidelines for quantitative sampling and sample processing of marine softbottom macrofauna (ISO 16665:2005). EN ISO 16712:2006 – Water quality – determination of acute toxicity of marine or estuarine sediment to amphipods (ISO 16712:2005). EN ISO 17294-1:2006 – Water quality – application of inductively coupled plasma mass spectrometry (ICP-MS) – part 1: general guidelines (ISO 17294-1:2004). EN ISO 17294-2:2004 – Water quality – application of inductively coupled plasma mass spectrometry (ICP-MS) – part 2: determination of 62 elements (ISO 17294-2:2003).

298

Standardized Methods for Water-Quality Assessment

EN ISO 17353:2005 – Water quality – determination of selected organotin compounds – gas chromatographic method (ISO 17353:2004). EN ISO 17495:2003 – Water quality – determination of selected nitrophenols – method by solid-phase extraction and gas chromatography with mass spectrometric detection (ISO 17495:2001). EN ISO 17852:2008 – Water quality – determination of mercury – method using atomic fluorescence spectrometry (ISO 17852:2006). EN ISO 17993:2003 – Water quality – determination of 15 polycyclic aromatic hydrocarbons (PAH) in water by HPLC with fluorescence detection after liquid–liquid extraction (ISO 17993:2002). EN ISO 17994:2004 – Water quality – criteria for establishing equivalence between microbiological methods (ISO 17994:2004). EN ISO 18412:2006 – Water quality – determination of chromium(VI) – photometric method for weakly contaminated water (ISO 18412:2005). EN ISO 18856:2005 – Water quality – determination of selected phthalates using gas chromatography/mass spectrometry (ISO 18856:2004). EN ISO 18857-1:2006 – Water quality – determination of selected alkylphenols – part 1: method for non-filtered samples using liquid–liquid extraction and gas chromatography with mass selective detection (ISO 188571:2005). EN ISO 19458:2006 – Water quality – sampling for microbiological analysis (ISO 19458:2006). EN ISO 19493:2007 – Water quality – guidance on marine biological surveys of hard-substrate communities (ISO 19493:2007). EN ISO 20079:2006 – Water quality – determination of the toxic effect of water constituents and wastewater on duckweed (Lemna minor) – duckweed growth inhibition test (ISO 20079:2005). EN ISO 21427-2:2009 – Water quality – evaluation of genotoxicity by measurement of the induction of micronuclei – part 2: mixed population method using the cell line V79 (ISO 21427-2:2006). EN ISO 22032:2009 – Water quality – determination of selected polybrominated diphenylethers in sediment and sewage sludge – method using extraction and gas chromatography/mass spectrometry (ISO 22032:2006). EN ISO 22478:2006 – Water quality – determination of certain explosives and related compounds – method using high-performance liquid chromatography (HPLC) with UV detection (ISO 22478:2006). EN ISO 23631:2006 – Water quality – determination of dalapon, trichloroacetic acid, and selected haloacetic acids – method using gas chromatography (GC-ECD and/or GCMS detection) after liquid–liquid extraction and derivatization (ISO 23631:2006). EN ISO 23631:2006/AC:2007 – Water quality – determination of dalapon, trichloroacetic acid, and selected haloacetic acids – method using gas chromatography (GC-ECD and/ or GC-MS detection) after liquid–liquid extraction and derivatization (ISO 23631:2006).

EN ISO 23913:2009 – Water quality – determination of chromium(VI) – method using flow analysis (FIA and CFA) and spectrometric detection (ISO 23913:2006). ENV ISO 13530:1998 – Water quality – guide to analytical quality control for water analysis (ISO/TR 13530:1997). ENV ISO 13843:2001 – Water quality – guidance on validation of microbiological methods (ISO/TR 13843:2000). ISO 78-2:1999 – Chemistry layouts for standards – part 2: methods of chemical analysis. ISO 3696:1987 – Water for analytical laboratory use; specification and test methods. ISO 5663:1984 – Water quality – determination of Kjeldahl nitrogen – method after mineralization with selenium. ISO 5664:1984 – Water quality – determination of ammonium – distillation and titration method. ISO 5666:1999 – Water quality – determination of mercury. ISO 5667-4:1987 – Water quality – sampling – part 4: guidance on sampling from lakes, natural and man-made. ISO 5667-5:2006 – Water quality – sampling – part 5: guidance on sampling of drinking water from treatment works and piped distribution systems. ISO 5667-6:2005 – Water quality – sampling – part 6: guidance on sampling of rivers and streams. ISO 5667-7:1993 – Water quality – sampling – part 7: guidance on sampling of water and steam in boiler plants. ISO 5667-8:1993 – Water quality – sampling – part 8: guidance on the sampling of wet deposition. ISO 5667-9:1992 – Water quality – sampling – part 9: guidance on sampling from marine waters. ISO 5667-10:1992 – Water quality – sampling – part 10: guidance on sampling of wastewaters. ISO 5667-11:2009 – Water quality – sampling – part 11: guidance on sampling of groundwaters. ISO 5667-12:1995 – Water quality – sampling – part 12: guidance on sampling of bottom sediments. ISO 5667-13:2009 – Water quality – sampling – part 13: guidance on sampling of sludges from sewage and water treatment works. ISO 5667-14:1998 – Water quality – sampling – part 14: guidance on quality assurance of environmental water sampling and handling. ISO 5667-15:2009 – Water quality – sampling – part 15: guidance on preservation and handling of sludge and sediment samples. ISO 5667-17:2008 – Water quality – sampling – part 17: guidance on sampling of bulk suspended solids. ISO 5667-18:2001 – Water quality – sampling – part 18: guidance on sampling of groundwater at contaminated sites. ISO 5667-18:2001/Cor 1:2008. ISO 5667-20:2008 – Water quality – sampling – part 20: guidance on the use of sampling data for decision making – compliance with thresholds and classification systems. ISO 5725-2:1994 including Technical Corrigendum 1:2002 – accuracy (trueness and precision) of measurement methods and results – part 2: basic method for the determination of repeatability and reproducibility of a standards measurement method. ISO 5813:1983 – Water quality – determination of dissolved oxygen – iodometric method.

Standardized Methods for Water-Quality Assessment

ISO 5814:1990 – Water quality – determination of dissolved oxygen – electrochemical probe method. ISO 5815-1:2003 – Water quality – determination of biochemical oxygen demand after n days (BODn) – part 1: dilution and seeding method with allylthiourea addition. ISO 5815-2:2003 – Water quality – determination of biochemical oxygen demand after n days (BODn) – part 2: method for undiluted samples. ISO 6058:1984 – Water quality – determination of calcium content – EDTA titrimetric method. ISO 6059:1984 – Water quality – determination of the sum of calcium and magnesium – EDTA titrimetric method. ISO 6060:1989 – Water quality – determination of the chemical oxygen demand. ISO 6107-1:2004, ISO 6107-2:2006, ISO 6107-3:1993, ISO 6107-4:1993, ISO 6107-5:2004, ISO 6107-6:2004, ISO 61077:2006, ISO 6107-8:1993 – Water quality – vocabulary. ISO 6107-2:2006/DAmd 1. ISO 6107-3:1993/Amd 1:2001. ISO 6107-8:1993/Amd 1:2001. ISO 6107-9:1997 – Water quality – vocabulary – part 9: alphabetical list and subject index. ISO 6332:1988 – Water quality – determination of iron – spectrometric method using 1,10-phenanthroline. ISO 6333:1986 – Water quality – determination of manganese – formaldoxime spectrometric method. ISO 6340:1995 – Water quality – detection and enumeration of Salmonella. ISO 6439:1990 – Water quality – determination of phenol index – 4-aminoantipyrine spectrometric methods after distillation. ISO 6461-1:1986 – Water quality – detection and enumeration of the spores of sulfite-reducing anaerobes (clostridia) – part 1: method by enrichment in a liquid medium. ISO 6461-2:1986 – Water quality – detection and enumeration of the spores of sulfite-reducing anaerobes (clostridia) – part 2: method by membrane filtration. ISO 6703-1:1984 – Water quality – determination of cyanide – part 1: determination of total cyanide. ISO 6703-2:1984 – Water quality – determination of cyanide – part 2: determination of easily liberatable cyanide. ISO 6703-3:1984 – Water quality – determination of cyanide – part 3: determination of cyanogen chloride. ISO 6777:1984 – Water quality – determination of nitrite – molecular absorption spectrometric method. ISO 6778:1984 – Water quality – determination of ammonium – potentiometric method. ISO 7150-1:1984 – Water quality – determination of ammonium – part 1: manual spectrometric method. ISO 7704:1985 – Water quality – evaluation of membrane filters used for microbiological analyses. ISO 7828:1985 – Water quality – methods of biological sampling – guidance on handnet sampling of aquatic benthic macro-invertebrates. ISO 7875-1:1996 – Water quality – determination of surfactants – part 1: determination of anionic surfactants by measurement of the methylene blue index (MBAS). ISO 7875-1:1996/Cor 1:2003

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ISO 7875-2:1984 – Water quality – determination of surfactants – part 2: determination of non-ionic surfactants using Dragendorff reagent. ISO 7888:1985 – Water quality – determination of electrical conductivity. ISO 7890-3:1988 – Water quality – determination of nitrate – part 3: spectrometric method using sulfosalicylic acid. ISO 7981-1:2005 – Water quality – determination of polycyclic aromatic hydrocarbons (PAH) – part 1: determination of six PAH by high-performance thin-layer chromatography with fluorescence detection after liquid– liquid extraction. ISO 7981-2:2005 – Water quality – determination of polycyclic aromatic hydrocarbons (PAH) – part 2: determination of six PAH by high-performance liquid chromatography with fluorescence detection after liquid–liquid extraction. ISO 8165-1:1992 – Water quality – determination of selected monovalent phenols – part 1: gas-chromatographic method after enrichment by extraction. ISO 8165-2:1999 – Water quality – determination of selected monovalent phenols – part 2: method by derivatization and gas chromatography. ISO 8245:1999 – Water quality – guidelines for the determination of total organic carbon (TOC) and dissolved organic carbon (DOC). ISO 8265:1988 – Water quality – design and use of quantitative samplers for benthic macro-invertebrates on stony substrata in shallow freshwaters. ISO 8288:1986 – Water quality – determination of cobalt, nickel, copper, zinc, cadmium, and lead – flame atomic absorption spectrometric methods. ISO 8466-1:1990 – Water quality – calibration and evaluation of analytical methods and estimation of performance characteristics – part 1: statistical evaluation of the linear calibration function. ISO 8466-2:2001 – Water quality – calibration and evaluation of analytical methods and estimation of performance characteristics – part 2: calibration strategy for non-linear second-order calibration functions. ISO 9174:1998 – Water quality – determination of chromium – atomic absorption spectrometric methods. ISO 9297:1989 – Water quality – determination of chloride – silver nitrate titration with chromate indicator (Mohr’s method). ISO 9308-2:1990 – Water quality – detection and enumeration of coliform organisms, thermotolerant coliform organisms and presumptive Escherichia coli – part 2: multiple tube (most probable number) method. ISO 9377-2:2000 – Water quality – determination of hydrocarbon oil index – part 2: method using solvent extraction and gas chromatography. ISO 9308-3:1998/Cor 1:2000 ISO 9390:1990 – Water quality – determination of borate – spectrometric method using azomethine-H. ISO 9696:2007 – Water quality – measurement of gross alpha activity in nonsaline water – thick source method. ISO 9697:2008 – Water quality – measurement of gross beta activity in nonsaline water – thick source method.

300

Standardized Methods for Water-Quality Assessment

ISO 9698:1989 – Water quality – determination of tritium activity concentration – liquid scintillation counting method. ISO 9964-1:1993 – Water quality – determination of sodium and potassium – part 1: determination of sodium by atomic absorption spectrometry. ISO 9964-2:1993 – Water quality – determination of sodium and potassium – part 2: determination of potassium by atomic absorption spectrometry. ISO 9964-3:1993 – Water quality – determination of sodium and potassium – part 3: determination of sodium and potassium by flame emission spectrometry. ISO 9965:1993 – Water quality – determination of selenium – atomic absorption spectrometric method (hydride technique). ISO 9998:1991 – Water quality – practices for evaluating and controlling microbiological colony count media used in water quality tests. ISO 10229:1994 – Water quality – determination of the prolonged toxicity of substances to freshwater fish – method for evaluating the effects of substances on the growth rate of rainbow trout (Oncorhynchus mykiss Walbaum (Teleostei, Salmonidae)). ISO 10260:1992 – Water quality – measurement of biochemical parameters – spectrometric determination of the chlorophyll-a concentration. ISO 10359-1:1992 – Water quality – determination of fluoride – part 1: electrochemical probe method for potable and lightly polluted water. ISO 10359-2:1994 – Water quality – determination of fluoride – part 2: determination of inorganically bound total fluoride after digestion and distillation. ISO 10523:2008 – Water quality – determination of pH. ISO 10530:1992 – Water quality – determination of dissolved sulfide – photometric method using methylene blue. ISO 10566:1994 – Water quality – determination of aluminum – spectrometric method using pyrocatechol violet. ISO 10703:2007 – Water quality – determination of the activity concentration of radionuclides – method by high-resolution gamma-ray spectrometry. ISO 10705-3:2003 – Water quality – detection and enumeration of bacteriophages – part 3: validation of methods for concentration of bacteriophages from water. ISO 10705-4:2001 – Water quality – detection and enumeration of bacteriophages – part 4: enumeration of bacteriophages infecting Bacteroides fragilis. ISO 10706:2000 – Water quality – determination of long-term toxicity of substances to Daphnia magna Straus (Cladocera, Crustacea). ISO 10708:1997 – Water quality – evaluation in an aqueous medium of the ultimate aerobic biodegradability of organic compounds – determination of biochemical oxygen demand in a two-phase closed bottle test. ISO 11083:1994 – Water quality – determination of chromium(VI) – spectrometric method using 1,5diphenylcarbazide. ISO/TR 11044:2008 – Water quality – scientific and technical aspects of batch algae growth inhibition tests.

ISO/TS 11370:2000 – Water quality – determination of selected organic plant-treatment agents – automated multiple development (AMD) technique. ISO 11423-1:1997 – Water quality – determination of benzene and some derivatives – part 1: head-space gas chromatographic method. ISO 11423-2:1997 – Water quality – determination of benzene and some derivatives – part 2: method using extraction and gas chromatography. ISO 11731:1998 – Water quality – detection and enumeration of Legionella. ISO 11734:1995 – Water quality – evaluation of the ‘‘ultimate’’ anaerobic biodegradability of organic compounds in digested sludge – method by measurement of the biogas production. ISO 11923:1997 – Water quality – determination of suspended solids by filtration through glass-fiber filters. ISO 12890:1999 – Water quality – determination of toxicity to embryos and larvae of freshwater fish – semi-static method. ISO 13358:1997 – Water quality – determination of easily released sulfide. ISO 13528:2005 Statistical methods for use in proficiency testing by interlaboratory comparisons. ISO/TR 11905-2:1997 – Water quality – determination of nitrogen – Part 2: determination of bound nitrogen, after combustion and oxidation to nitrogen dioxide, chemiluminescence detection. ISO/TS 13530:2009 – Water quality – guidance on analytical quality control for chemical and physicochemical water analysis. ISO 13641-1:2003 – Water quality – determination of inhibition of gas production of anaerobic bacteria – part 1: general test. ISO 13641-2:2003 – Water quality – determination of inhibition of gas production of anaerobic bacteria – part 2: test for low biomass concentrations. ISO 13829:2000 – Water quality – determination of the genotoxicity of water and wastewater using the umu-test. ISO 14442:2006 – Water quality – guidelines for algal growth inhibition tests with poorly soluble materials, volatile compounds, metals, and wastewater. ISO 14592-1:2002 – Water quality – evaluation of the aerobic biodegradability of organic compounds at low concentrations – part 1: shake-flask batch test with surface water or surface water/sediment suspensions. ISO 14592-2:2002 – Water quality – evaluation of the aerobic biodegradability of organic compounds at low concentrations – part 2: continuous flow river model with attached biomass. ISO 14669:1999 – Water quality – determination of acute lethal toxicity to marine copepods (Copepoda, Crustacea). ISO 15089:2000 – Water quality – guidelines for selective immunoassays for the determination of plant treatment and pesticide agents. ISO/TR 15462:2006 – Water quality – selection of tests for biodegradability. ISO 15522:1999 – Water quality – determination of the inhibitory effect of water constituents on the growth of activated sludge microorganisms.

Standardized Methods for Water-Quality Assessment

ISO 15553:2006 – Water quality – isolation and identification of Cryptosporidium oocysts and Giardia cysts from water. ISO 15705:2002 – Water quality – determination of the chemical oxygen demand index (ST-COD) – small-scale sealed-tube method. ISO 16221:2001 – Water quality – guidance for determination of biodegradability in the marine environment. ISO 16240:2005 – Water quality – determination of the genotoxicity of water and wastewater – Salmonella/microsome test (Ames test). ISO 16265:2009 – Water quality – determination of the methylene blue active substances (MBAS) index – method using continuous flow analysis (CFA). ISO/TS 16489:2006 – Water quality – guidance for establishing the equivalency of results. ISO/TS 16489:2006/Cor 1:2006. ISO 16590:2000 – Water quality – determination of mercury – methods involving enrichment by amalgamation. ISO 17381:2003 – Water quality – selection and application of ready-to-use test kit methods in water analysis. ISO 17858:2007 – Water quality – determination of dioxinlike polychlorinated biphenyls – method using gas chromatography/mass spectrometry. ISO 17995:2005 – Water quality – detection and enumeration of thermotolerant Campylobacter species. ISO 18073:2004 – Water quality – determination of tetra- to octa-chlorinated dioxins and furans – method using isotope dilution HRGC/HRMS. ISO 18749:2004 – Water quality – adsorption of substances on activated sludge – batch test using specific analytical methods. ISO 20179:2005 – Water quality – determination of microcystins – method using solid-phase extraction (SPE) and high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection. ISO/TS 20281:2006 – Water quality – guidance on statistical interpretation of ecotoxicity data. ISO/TS 20612:2007 – Water quality – interlaboratory comparisons for proficiency testing of analytical chemistry laboratories. ISO 20665:2008 – Water quality – determination of chronic toxicity to Ceriodaphnia dubia. ISO 20666:2008 – Water quality – determination of the chronic toxicity to Brachionus calyciflorus in 48 h. ISO 21427-1:2006 – Water quality – evaluation of genotoxicity by measurement of the induction of micronuclei – part 1: evaluation of genotoxicity using amphibian larvae. ISO 21458:2008 – Water quality – determination of glyphosate and AMPA – method using high-performance liquid chromatography (HPLC) and fluorometric detection. ISO 22719:2008 – Water quality – determination of total alkalinity in seawater using high-precision potentiometric titration. ISO 22743:2006 – Water quality – determination of sulfates – method by continuous flow analysis (CFA). ISO 22743:2006/Cor 1:2007. ISO 23893-1:2007 – Water quality – biochemical and physiological measurements on fish – part 1: sampling of fish, handling and preservation of samples.

301

ISO/TS 23893-2:2007 – Water quality – biochemical and physiological measurements on fish – part 2: determination of ethoxyresorufin-O-deethylase (EROD). ISO 24293:2009 – Water quality – determination of individual isomers of nonylphenol – method using solid-phase extraction (SPE) and gas chromatography/mass spectrometry (GC/MS). ISO 25101:2009 – Water quality – determination of perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA) – method for unfiltered samples using solid phase extraction and liquid chromatography/mass spectrometry. ISO 80000-9:2009 – Quantities and units – part 9: physical chemistry and molecular physics. ISO/IEC 17025:2005 – General requirements for the competence of testing and calibration laboratories. ISO/DIS 7887:2009 – prEN ISO 7887:2009 – Water quality – examination and determination of color.

References 2000/60/EC (2000) WFD – Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official Journal of the European Communities L 327: 1--73. 2008/105/EC (2008) Directive 2008/105/EC of European Parliament and Council on environmental quality standards in the field of water policy, amending and subsequently repealing Council Directives 82/176/EEC, 83/513/EEC, 84/156/EEC, 84/419/EEC, 86/280/EEC and amending Directive 2000/60/EC. Official Journal of the European Communities L 348: 84--97. 2009/90/EC (2009) Commission Directive 2009/90/EC of 31 July 2009 laying down, pursuant to Directive 2000/60/EC of the European Parliament and of the Council, technical specifications for chemical analysis and monitoring of water status. Official Journal of the European Communities L 201: 36--38. 98/83/EC (1998) Council Directive 98/83/EC of 3 November 1998 on the quality of water intended for human consumption. Official Journal of the European Communities L 330: 32--54. Abw AG (2005) Gesetz u¨ber Abgaben fu¨r das Einleiten von Abwasser in Gewa¨sser (Abwasserabgabengesetz – AbwAG) in der Fassung vom 18. Januar 2005 (Act on charges levied for discharging wastewaters into waters (wastewater charges act)). Bundesgesetzblatt Teil I Nr. 5: 114–119. Abw V (2002) Verordnung u¨ber Anforderungen an das Einleiten von Abwasser in Gewa¨sser (Abwasserverordnung – AbwV) in der Fassung vom 15. Oktober 2002 (Ordinance on requirements for the discharge of wastewaters into waters (wastewater ordinance)). Bundesgesetzblatt Teil I Nr. 74: 4047–4121. APHA (American Public Health Association), AWWA (American Water Works Association), and WEF (Water Environment Federation) (ed.) (2005) Standard Methods for the Examination of Water and Wastewater, 21st edn. Baltimore, MD: Port City Press. ASTM (2007) ASTM Technical Committee Officer Handbook (‘‘Red Book’’). http:// www.astm.org/COMMIT/RedBook5.pdf (accessed April 2010). ASTM (2009a) Regulations governing ASTM Technical Committees (‘‘Green Book’’). http://www.astm.org/COMMIT/Regs.pdf (accessed April 2010). ASTM (2009b) Form and Style for ASTM Standards (‘‘Blue Book’’). http:// www.astm.org/COMMIT/Blue_Book.pdf (accessed April 2010). Aurand K and Ru¨hle H (2003) Radioaktive Stoffe und die Trinkwasserverordnung. In: Grohmann A, Ha¨sselbarth U, and Schwerdtfeger W (eds.) Die Trinkwasserverordnung. Einfu¨hrung und Erla¨uterungen fu¨r Wasserversorgungsunternehmen und U¨berwachungsbeho¨rden, 4., neu bearbeitete Auflage, pp. 377–387. Berlin: Erich Schmidt Verlag. DEV (Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung) (1971) Verfahren L 12 – Assimilationszehrungstest. Weinheim: Wiley-VCH. Berlin: Beuth. Forte M, Bertolo A, D’Alberti F, et al. (2006) Standardized methods for measuring radionuclides in drinking water. Journal of Radioanalytical and Nuclear Chemistry 269: 397--401. Hongve D and A˚kesson G (1996) Spectrophotometric determination of water colour in Hazen units. Water Research 30: 2771--2775.

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IUPAC (International Union of Pure and Applied Chemistry) (2007) Quantities, Units and Symbols in Physical Chemistry, 3rd edn. Cambridge: Royal Society of Chemistry. ISO/IEC (2004) ISO/IEC Directives, Part 2 – Rules for the Structure and Drafting of International Standards, 5th edn. Geneva: ISO/IEC. ISO/IEC (2007) Guide 99 International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM). Geneva: ISO/IEC. Kaniansky D, Masa´r M, Mara´k J, and Bodor R (1999) Capillary electrophoresis of inorganic anions. Journal of Chromatography A 843: 133--178. Kroon H (1993) Determination of nitrogen in water: Comparison of a continuous flow method with on-line UV-digestion with the original Kjeldahl method. Analytica Chimica Acta 276: 287--293. OECD (Organisation for Economic Cooperation and Development) (2008) OECD guidelines for the testing of chemicals, pdf edn., ISSN 1607-310X. http:// oberon.sourceoecd.org (accessed April 2010). Pagga U (1997) Testing biodegradability with standardized methods. Chemosphere 35: 2953--2972. Pluta H-J and Rosenberg M (2005) German perspective. In: Thompson KC, Wahida K, and Loibner AP (eds.) Environmental Toxicity Testing, 1st edn., pp. 290--301. Boca Raton, FL: Blackwell and CRC Press. Quevauvillier P, Borchers U, and Gawlik BM (2007) Coordinating links among research, standardisation and policy in support of water framework directive chemical monitoring requirements. Journal of Environmental Monitoring 9: 915--923. REACH (2006) Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing council regulation (EEC) No 793/93 and commission regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/ EEC, 93/105/EC and 2000/21/EC. Official Journal of the European Communities 396: 1--851. Reifferscheid G, Ziemann C, Fieblinger D, et al. (2008) Measurement of genotoxicity in wastewater samples with the in vitro micronucleus test – results of a round robin study in the context of standardisation according to ISO. Mutation Research 649: 15--27. Reuschenbach P, Pagga U, and Strotmann U (2003) A critical comparison of respirometric biodegradation tests based on OECD 301 and related test methods. Water Research 37: 1571--1582. Richardson SD (2003) Water analysis: Emerging contaminants and current issues. Analytical Chemistry 75: 2831--2857. Richardson SD (2005) Water analysis: Emerging contaminants and current issues. Analytical Chemistry 77: 3807--3838. Richardson SD (2007) Water analysis: Emerging contaminants and current issues. Analytical Chemistry 79: 4295--4324. Richardson SD (2009) Water analysis: Emerging contaminants and current issues. Analytical Chemistry 81: 4645--4677. Schmidt S (1992) Wasseranalytik – Normung von Verfahren (Water analysis – standardization of methods). GIT Fachzeitschrift fu¨r das Laboratorium 1992: 621--630. Schmidt S (2001) No water, no life – water quality in ISO. ISO Bulletin January 2001: 10--14. Schmidt S (2003) International standardization of water analysis – basis for comparative assessment of water quality. Environmental Science and Pollution Research 10: 183--187. Schmidt S and Wunder H (1988) International anerkannte Verfahren in der Wasseranalytik. Zeitschrift fu¨r Wasser- und Abwasserforschung 21: 118--122. Stottmeister E, Heemken OP, Hendel P, et al. (2009) Interlaboratory trial on the analysis of alkyphenols, alkylphenol ethoxylates, and bisphenol A in water samples according to ISO/CD 18857-2. Analytical Chemistry 81: 6765--6773. Strotmann U, Reuschenbach P, Schwarz H, et al. (2004) Development and evaluation of an online CO2 evolution test and a multicomponent biodegradation test system. Applied Environmental Microbiology 70: 4621--4628. Strub MP, Lepot B, and Morin A (2009) Metrological aspects of collaborative field trials, including coping with unexpected events. Trends in Analytical Chemistry 28: 245--261.

Thompson KC, Wahida K, and Loibner AP (eds.) (2005) Environmental Toxicity Testing. Boca Raton, FL: Blackwell and CRC Press. Tillmanns AR, Pick FR, and Aranda-Rodriguez R (2007) Sampling and analysis of microcystins: Implications for the development of standardized methods. Environmental Toxicology 22: 132--143. TrinkwV (2001) Verordnung u¨ber die Qualita¨t von Wasser fu¨r den menschlichen Gebrauch vom 21. Mai 2001 (Trinkwasserverordnung – TrinkwV 2001). Bundesgesetzblatt 2001 I Nr. 24 S. 959 ff. http://bundesrecht.juris.de/ trinkwv_2001/index.html (accessed April 2010). Vilela Junqueira M, Friedrich G, and Pereira de Araujo PR (2010) A saprobic index for biological assessment of river water quality in Brazil (Minas Gerais and Rio de Janeiro states). Environmental Monitoring and Assessment 163: 545--554. Wasserchemische Gesellschaft and Normenausschuss Wasserwesen im DIN (eds.) (2010) Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlammuntersuchung. Herausgegeben von der Wasserchemischen Gesellschaft – Fachgruppe in der Gesellschaft Deutscher Chemiker in Gemeinschaft mit dem NormenausschuX Wasserwesen (NAW) im DIN Deutsches Institut fu¨r Normung e. V. 77. Lieferung 2010 (German standard methods for the examination of water, waste water and sludge. Edited by the Water Chemical Society – a Division of the German Chemical Society and the Standards Committee ‘‘Water Practice Standards’’ of the German Institute for Standardisation). Weinheim: Wiley-VCH; Berlin: Beuth. WHO (2006) Guidelines for Drinking-Water Quality. First Addendum to Third Edition. Volume 1: Recommendations. Geneva. http://www.who.int/ water_sanitation_health/dwq/gdwq0506.pdf (accessed April 2010).

Relevant Websites http://www.astm.org ASTM International; ASTM D19 and ASTM D19 Scope. ftp://ftp.cen.eu CEN; CEN Compass: The World of European Standards. http://www.naw.din.de DIN NA 119 Normenausschuss Wasserwesen (NAW) Startseite, DIN NA 119-01-03 AA. http://www.epa.gov Environmental Protection Agency (EPA): Test Method Collections. http://www.cen.eu European Committee for Standardization (CEN); Technical Committees, Workshops and other bodies; CEN/TC 230 Water analysis, CEN/TC 308 Characterization of sludges, and CEN/TC 400 Horizontal standards in the field of sludge, biowaste and soil. http://www.gdch.de Gesellschaft Deutscher Chemiker (GDCh), Liste vorhandener Validierungsdokumente. http://www.iso.org International Organization for Standardization; Standards Development, Technical Committees, List of ISO Technical Committees, TC 147, Work Programme; Vienna Agreement. http://cdb.iso.org ISO Concept Database (ISO/CDB). https://www.nemi.gov National Environmental Methods Index (NEMI), Chemical Methods. http://standards.gov Standards.gov; NTTA (National Technology Transfer Act). http://www.wiley-vch.de Wiley-VCH, DEV (Deutsche Einheitsverfahren zur Wasser-, Abwasser- und Schlamm-Untersuchung).

3.12 Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control TN Petney and H Taraschewski, Karlsruhe Institute of Technology, Karlsruhe, Germany & 2011 Elsevier B.V. All rights reserved.

3.12.1 3.12.2 3.12.2.1 3.12.2.1.1 3.12.2.1.2 3.12.2.1.3 3.12.2.2 3.12.2.2.1 3.12.2.2.2 3.12.2.2.3 3.12.2.2.4 3.12.2.2.5 3.12.2.2.6 3.12.2.2.7 3.12.2.3 3.12.2.4 3.12.2.5 3.12.2.5.1 3.12.2.5.2 3.12.2.5.3 3.12.2.5.4 3.12.2.5.5 3.12.2.5.6 3.12.2.5.7 3.12.3 3.12.3.1 3.12.3.1.1 3.12.3.1.2 3.12.3.1.3 3.12.3.1.4 3.12.3.1.5 3.12.3.1.6 3.12.3.1.7 3.12.3.2 3.12.3.2.1 3.12.3.2.2 3.12.3.2.3 3.12.3.2.4 3.12.3.2.5 3.12.3.2.6 3.12.3.2.7 3.12.3.3 3.12.3.3.1 3.12.3.3.2 3.12.3.3.3 3.12.3.3.4 3.12.3.3.5 3.12.3.3.6 3.12.3.3.7 3.12.3.4 3.12.3.4.1 3.12.3.4.2 3.12.3.4.3

Introduction Parasites Transmitted through Drinking Water Cryposporidiosis and Giardiasis Cryptosporidiosis Giardiasis Toxoplasmosis Amoebiasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Anthropogenic alterations to the environment Prevention and cure Recommendations Free-Living Amoeba Microsporidian Infections Dracunculiasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Food-Borne Parasites Transmitted through Freshwater and Marine Foods Opisthorchiasis and Clonorchiasis Parasite characterization Developmental cycles Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Intestinal Flukes Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Paragonimiasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Diphyllobothriosis Parasite characterization Developmental cycle Human involvement

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3.12.3.4.4 3.12.3.4.5 3.12.3.4.6 3.12.3.4.7 3.12.3.5 3.12.3.5.1 3.12.3.5.2 3.12.3.5.3 3.12.3.5.4 3.12.3.5.5 3.12.3.5.6 3.12.3.5.7 3.12.4 3.12.4.1 3.12.4.1.1 3.12.4.1.2 3.12.4.1.3 3.12.4.1.4 3.12.4.1.5 3.12.4.1.6 3.12.4.1.7 3.12.5 3.12.5.1 3.12.5.1.1 3.12.5.1.2 3.12.5.1.3 3.12.5.1.4 3.12.5.1.5 3.12.5.1.6 3.12.5.1.7 3.12.6 3.12.6.1 3.12.6.1.1 3.12.6.1.2 3.12.6.1.3 3.12.6.1.4 3.12.6.1.5 3.12.6.1.6 3.12.6.1.7 3.12.6.2 3.12.6.2.1 3.12.6.2.2 3.12.6.2.3 3.12.6.2.4 3.12.6.2.5 3.12.6.2.6 3.12.6.2.7 3.12.6.3 3.12.6.3.1 3.12.6.3.1 3.12.6.3.3 3.12.6.3.4 3.12.6.3.5 3.12.6.3.6 3.12.6.3.7 3.12.7 3.12.7.1 3.12.7.2 3.12.7.3 3.12.7.4

Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Anisakiasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Other Parasites with a Water-Dependent Life Cycle Fascioliasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Parasites Penetrating Human Skin on Contact with Freshwater Schistosomiasis (bilharziosis) Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Water-Dependent Vector-Borne Parasites Malaria Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Onchocerciasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Lymphatic Filariasis Parasite characterization Developmental cycle Human involvement Disease characteristics in humans Prevention and cure Anthropogenic alterations to the environment Recommendations Environmental Factors Influencing the Dynamics of Water-Associated Parasites Dam Construction and Irrigation Projects Land-Use Changes Mass Animal Husbandry: Cryptosporidia and Giardia Human Conflict, Political Considerations, and Healthcare Systems

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Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.7.5 El Nino 3.12.7.6 Climate Change 3.12.8 Synopsis 3.12.9 Conclusion Acknowledgments References

3.12.1 Introduction Humans are reliant on limited supplies of freshwater for drinking, hygiene, recreation, agriculture, and industry. The subsequent recycling of wastewater into usable surface water is thus necessary to maintain even reasonable water supplies (Postel et al., 1996; Pimentel et al., 1997). In addition, both freshwater and marine ecosystems provide the human population with a necessary but increasingly depleted source of highquality protein (Tidwell and Allan, 2001). Indeed, some human communities derive most of their protein from fish or waterdwelling invertebrates (Middendorp, 1992; Allan et al., 2005). This reliance makes the contact between humans and water an essential component in human life. However, water, as well as the animals harbored in it, also provides a continual source of contact with parasites and pathogens which can potentially cause major problems for humans, the subject of this chapter. Historically, there is evidence that for thousands of years, humans have either deliberately manipulated local hydrological patterns in order to ensure an adequate water supply (Shaw and Sutcliffe, 2003; Abdel Khaleq and Alhaj Ahmed, 2007), or unintentionally changed such patterns by modifying land use, such as deforestation, aimed at increasing agricultural land availability (O’Sullivan et al., 2008). Over the last 100 years, this manipulation has reached immense proportions (Vo¨ro¨smarty and Sahagian, 2000). It alters not only the hydrological patterns, but also the diversity of organisms populating the new habitat, among which are human parasites and pathogens. Thus, deforestation, with the aim of increasing agricultural land, often leads to a rise in the water table and the formation of wetlands suitable as breeding grounds for mosquitoes causing malaria (Yasuoka and Levins, 2007; O’Sullivan et al., 2008). For historical and technical reasons, diseases due to bacteria, fungi, and viruses fall under the responsibility of microbiologists and physicians. These pathogens usually do not have complex developmental cycles, and the likelihood of human infections can be substantially diminished by improving general sanitation and hygiene (Jacobsen and Koopman, 2004; Ashbolt, 2004), as well as vaccination for certain pathogens (e.g., hepatitis A virus) (Dagan et al., 2005). In contrast, disease agents belonging to the protozoa, parasitic worms, and arthropods, such as lice and ticks, fall under the scope of parasitologists and physicians. These species may be zoonotic, that is, they are predominantly animal parasites which can also infect humans, or parasites specific for humans (anthroponotic), such as Plasmodium falciparum, the cause of the most severe form of malaria, which does not have wild or domestic animal hosts. Parasites often show characteristic life cycles, for instance, being dependent on a specific snail serving as a first intermediate

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host and a variety of fish species as the second intermediate hosts (e.g., Andrews et al., 2008). Humans can act as the major final host in terms of supporting the persistence of the parasite in a certain area, or may be as an accidental host of a parasite which usually occurs in the birds or mammals, which function as reservoir hosts (Cox, 2002). In a final host, the parasite reproduces sexually, while in intermediate hosts it merely grows or reproduces asexually. Monoxenic life cycles, that is, those not requiring an intermediate host species, also exist. Studies on the epidemiology of disease agents often involve different aspects of ecology: the first intermediate host may benefit from eutrophication of its aquatic habitat or it may become displaced by a competing invasive species which is not able to transmit the parasite (Taraschewski, 2006). In the present times of global change, it is likely that the competitor is unintentionally introduced from a different continent or it may be naturalized as part of an eradication campaign directed against the parasite (Hudson and Greenman, 1998; Torchin and Mitchell, 2004; Taraschewski, 2006). As we explain in this chapter, parasite transmission is also affected by changes in land use, including irrigation, the building of dams, wetland restoration, etc. (Walsh et al., 1993; Patz et al., 2004). In industrialized countries, serious waterborne health hazards, such as malaria, either do not exist or are controlled; however, humans cannot be efficiently protected from parasites hosted in high abundance by stock animals or water birds or other reservoir hosts (Daszak et al., 2000). In contrast to infections by viruses or bacteria, thus far, no suitably effective vaccines against parasitic protozoa or worms are available. Thus, measures for water management are of great significance in view of the control or spread of waterborne parasitic diseases (Feenstra et al., 2000; Marino, 2007; Montgomery and Elimelech, 2007; Zhou et al., 2009). All wellplanned hydrological projects, such as the construction of dams or irrigation projects, should therefore always take into consideration the project’s potential impact on the human (and animal) populations caused by changes in the epidemiology of water-associated parasites. This is especially true for developing countries where the population is particularly susceptible to such hazardous pathogens (Oomen et al., 1994; Carr et al., 2004; Moe and Rheingans, 2006). Of the 14 major human diseases caused by parasites listed by Crompton (1999) as modified by Bush et al. (2001), seven of them have obligatory freshwater periods in their life cycles. All of these have a very substantial impact on human morbidity and mortality, particularly in developing countries. These include malaria, the ninth leading cause of human mortality worldwide, and several parasites which lead to diarrheal disease, the seventh leading cause of death (Mathers et al., 2007). Indeed, one of the key risk factors for human

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mortality in developing countries is ‘‘unsafe water, sanitation and hygiene’’ (Mathers et al., 2007). Here, we review human parasites associated with freshwater, brackish, or marine environments, emphasizing their transmission cycles in relation to their aquatic habitats and to hydrological engineering projects which may influence their distribution and rate of transmission and thus directly impact human health and well-being.

3.12.2 Parasites Transmitted through Drinking Water 3.12.2.1 Cryposporidiosis and Giardiasis These two diseases are commonly discussed together due to the similarities in their epidemiology (World Health Organization, 2002; Caccio` et al., 2005). The tough, long-lived cysts of both species (Figures 1–4) occur worldwide in aquatic environments with fecal contamination (World Health Organization, 2002). Their resistance to disinfectants used in water treatment, which is particularly true for Cryptosporidium spp., as well as the low infective dose make them a health danger even in developed countries such as the United States and countries of the European Union (MacKenzie et al., 1994; Ward et al., 2002; World Health Organization, 2002). Although not dealt with in detail, Cyclospora cayentanensis, which is related to Cryptosporidium, is a recently recognized species which causes a disease similar to cryptosporidiosis (Ortega et al., 1993; Shields and Olson, 2003).

3.12.2.1.1 Cryptosporidiosis Parasite characterization. Cryptosporidium spp., which are enteric coccidian protozoa lacking multicellular stages. They do not have an intermediate host (Figure 1). They belong to the phylum Apicomplexa, order Eucoccidiorida. There are at least 13 species of parasites, belonging to this genus (Xiao et al., 2004), which are capable of infecting a wide variety of vertebrate hosts, including fish, reptiles, birds, and mammals, but rarely amphibians (Fayer, 2004; Ramirez et al., 2004; Graczyk, 2007; Jirku et al., 2008). Of these, Cryptosporidium parvum is the most commonly reported species infecting mammals in general (Ramirez et al., 2004). There are a variety of vertebrate reservoir hosts, such as cattle (particularly calves), rodents, and potentially chickens (Chalmers et al., 1997; Sre´ter and Varga, 2000; Ramirez et al., 2004). In humans, two distinct types of Cryptosporidia usually occur, the oocysts of which are morphologically very similar but can be differentiated genetically (Clark, 1999). Until recently, divided into type 1 and type 2, C. parvum type 1 is now recognized as C. hominis while type 2 is C. parvum sensu stricto (Morgan-Ryan et al., 2002). Both species are pathogenic to humans (Chappell et al., 2006); however, only C. parvum appears to be frequently transferred from animals, usually cattle, to humans (Monis and Thompson, 2003). Developmental cycle. Cryptosporidium species occur in the environment as approximately 5 mm in diameter, round oocysts each of which contain four sporozoites (Figures 1 and 2). These oocysts are covered by a tough protective coat which is resistant to external factors, enabling them to survive in suitably moist, cool environments for 6 months or more

(Fayer et al., 1998; Fayer, 2004). Infection occurs through the ingestion of these oocysts (Clark, 1999). Once in their host, the oocysts move through the gut to the small intestine where they rupture and release the sporozoites. This tapering, slender stage adheres to the epithelial cells lining the gastrointestinal tract and then breaks the epithelial barrier leading to inflammation (Clark, 1999; Savioli et al., 2006). Within the cells, the parasite replicates by forming merozoites, which escape from ruptured cells to infect new cells and complete the asexual multiplication phase of the life cycle. Eventually, the merozoites differentiate into gamonts. These undergo sexual reproduction and generate oocysts containing sporozoites which are excreted in the feces and are capable of spreading the infection (Clark, 1999). In addition, as shown in Figure 1, oocysts may also rupture before being discharged with the feces leading to an autoinfection of the respective host individual. Human involvement. Cryptosporidiosis is one of the major causes of diarrheal disease in developing countries, while in industrialized countries it can also lead to serious outbreaks (Guerrant, 1997). Transmission to humans is usually via drinking water, which may or may not have been previously treated, including in developed countries such as Australia, Canada, the United Kingdom, and the United States (LeChevallier et al., 1991; Glaberman et al., 2002). The largest outbreak so far recorded was in Milwaukee, USA, with over 400 000 humans estimated to have been infected (MacKenzie et al., 1994). In the UK and the US, between 4% and 100% of surface-water samples were found to be contaminated with Cryptosporidium oocysts with contamination rates ranging from 0.1 to 10 000 oocysts per 100 l (Lisle and Rose, 1995). Infection can occur after ingestion of 10 or less oocysts (Okhuysen et al., 1999; Pereira et al., 2002). In addition, groundwater may also be contaminated (Hancock et al., 1998) and even if the water is treated before consumption, contamination may remain. In Canada, for example, 3.5% of treated water samples were contaminated compared to 6.1% of raw sewage samples and 4.5% of raw water samples (Wallis et al., 1996). Water used in recreation has also been implicated as a source of infection. In 2001, 358 cases of cryptosporidiosis were reported associated with a water park in Illinois in which contaminated water was present (Causer et al., 2006). Chlorine-resistant oocysts, frequent swimming, high density of bathers, and frequent use by very young children probably contributed to the outbreak. An unmonitored splash park, where guests have the opportunity to spray, splash, or pour water on one another, was the site of 154 cases of cryptosporidiosis in Idaho, USA (Jue et al., 2009). This outbreak was related to deficiencies in the technical control of backflow water into the system. In developing countries, children under the age of 5 years, and especially under 3 years, are most commonly affected, with an estimated 25% of all children being infected for the first time during this period (Tzipori and Ward, 2002). A number of Cryptosporidium species can be involved in these infections, and multiple species infections can occur in children as well as adults (Cama et al., 2007). In a study in Kampala, Uganda, children with persistent diarrhea (31%) were more likely to be infected by Cryptosporidium species than

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Thick-walled oocyst ingested by host

Recreational water 2

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j Thick-walled oocyst (sporulated) exits host

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Microgametes

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Figure 1 The life cycle of Cryptosporidium spp. Sporulated oocysts, containing four sporozoites, are excreted by the infected host through feces and possibly other routes such as respiratory secretions 1 . Transmission of Cryptosporidium parvum and C. hominis occurs mainly through contact with contaminated water (e.g., drinking or recreational water). Occasionally food sources, such as chicken salad, may serve as vehicles for transmission. Many outbreaks in the United States have occurred in waterparks, community swimming pools, and day-care centers. Zoonotic and anthroponotic transmission of C. parvum and anthroponotic transmission of C. hominis occur through exposure to infected animals or exposure to water contaminated by feces of infected animals 2 . Following ingestion (and possibly inhalation) by a suitable host 3 , excystation a occurs. The sporozoites are released and they parasitize the epithelial cells ( b , c ) of the gastrointestinal tract or other tissues such as the respiratory tract. In these cells, the parasites undergo asexual multiplication (schizogony or merogony) ( d , e , f ) and then sexual multiplication (gametogony) producing microgamonts (male) g and macrogamonts (female) h . Upon fertilization of the macrogamonts by the microgametes ( i ), oocysts ( j , k ) develop that sporulate in the infected host. Two different types of oocysts are produced, the thick-walled, which is commonly excreted from the host j , and the thin-walled oocyst k , which is primarily involved in autoinfection. Oocysts are infective upon excretion, thus permitting direct and immediate fecal–oral transmission. Reproduced with permission from CDC.

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Figure 2 Oocysts of Cryptosporidium sp. (c. 5 mm in diameter) released with the feces of a human host. The sporozoites inside the cysts are difficult to differentiate. Reproduced with permission from CDC.

those with acute disease (22%) and were also more likely to suffer from reduced growth and malnutrition than nutritionally healthy children (Tumwine et al., 2003). Mortality rate in cases of chronic cryptosporidiosis was high in this study, with 24/191 children dying (12.6%) compared to 34/545 (6.2%) for those without a C. parvum infection (Tumwine et al., 2003). A study in the Venda region of South Africa also showed a high prevalence of 50% in the older population group, 50–59 years (Samie et al., 2006). Transmission can also occur from animals to humans (particularly on farms); however, this would appear to be rare (Ramirez et al., 2004). There is little information available on the economic impact of cryptosporidiosis; however, a study of the 1993 outbreak in Milwaukee estimated total costs to be US$93 million divided into 31.7 million medical costs and 64.6 million in lost productivity (Corso et al., 2003). Disease characteristics in humans. In humans, cryptosporidia frequently cause diarrheal diseases. This is characterized by nausea, diarrhea, abdominal pain, low-grade fever, and weight loss. Symptoms usually last for 10–14 days and the disease is self-limiting (Clark, 1999), and in some areas high prevalences are not associated with significant disease possibly due to the development of an immune response (Estaban et al., 1998; Pantenburg et al., 2008). Cryptosporidia infection, including adventitious species other than C. parvum, in patients suffering from immunodeficiency diseases, such as human immunodeficiency virus– acquired immune deficiency syndrome (HIV-AIDS), as well as those with compromised immune systems such as transplant patients, can be life threatening (Kibbler et al., 1987; Matos et al., 2004). In these patients, cryptosporidiosis is characterized by chronic, profound diarrhea and, potentially, vomiting (Blanshard et al., 1992; Cama et al., 2007). Recent research has shown that immunosuppressed patients are susceptible to a variety of different Cryptosporidium species, and that infection with different species leads to different clinical pictures (Cama

et al., 2007). In addition, prolonged infections in immunocompromised patients can lead to spread of this intestinal pathogen to the hepatobiliary and pancreatic ducts causing pathology in these organs (Tzipori and Ward, 2002). Such chronic infections lead, for example, to disruption of the epithelial surface, fibrosis, and cellular infiltration. Lack of effective treatment exacerbates the problem (Tzipori and Ward, 2002). Diagnosis relies mainly on detecting Cryptosporidium oocysts in fecal samples, although this method is unreliable due to the similarity between Cryptospoidium species and certain other protozoan organisms occurring in the feces (Jex et al., 2008). Recently, molecular diagnostic methods using immunological assays and the detection and differentiation of nucleic acids have been developed (Jex et al., 2008). Prevention, cure. The inactivation or removal of cryptosporidia from drinking water is relatively difficult, with filtration and coagulation being most effective, while chlorination is relatively ineffective (Di Giovanni et al., 1999). In order to remove cryptosporidia, multiple processes should be employed including prevention of source contamination, and performing coagulation filtration and disinfection (Ro¨delsperger et al., 1999; Betancourt and Rose, 2004). Recently, The United States Environmental Protection Agency established new drinking-water standards (Long-Term 2 Enhanced Surface Water Treatment Rule) with the aim of reducing the risk of cryptosporidiosis. Chlorination is of little value and before disinfection, an effective particle separation is necessary. In addition, ultraviolet (UV) disinfection is effective (Clancy et al., 1998; World Health Organization, 2004). Fortunately, cryptosporidiosis is usually self-limiting in healthy, well-nourished, immunocompetent humans. Even though pharmaceutical research has been ongoing for many years, ‘‘there are no consistently effective, approved products for either animals or humans’’ (Fayer, 2004). This statement is supported by a meta-analysis carried out by Abubakar et al. (2007a, 2007b). Anthropogenic alterations to the environment. Untreated wastewater is a major source of potential infection with Cryptosporidium spp. (Ramirez et al., 2004). In an Israeli study, both surface and subsurface irrigation with effluent led to the accumulation of Cryptosporidium oocysts at depths ranging from the surface to 90 cm below the soil surface (Armon et al., 2002). In England, infected cattle, along with high rainfall which washed infective stages into a water supply, are suspected to have contributed to an outbreak of 55 000 cases (Rose, 1997). Ong et al. (1996) showed that significantly more Cryptosporidium oocysts were found downstream of cattle farms than upstream and that peak concentrations occurred during calving. Recommendations. Control of cryptosporidiosis can best be affected via adequate treatment of drinking water. The resistant nature of the oocysts, however, requires relatively sophisticated, multilevel treatment (Ro¨delsperger et al., 1999; Hambsch and Lipp, 2000) which is difficult to obtain in developing countries. Here, drinking water should be boiled before consumption. At a more basic level, the correct, hygienic disposal of human and animal feces and adequate wastewater treatment prior to recycling, for example, for irrigation, can considerably

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2 i

Contamination of water, food, or hands/fomites with infective cysts

Trophozoites are also passed in stool but they do not survive in the environment

1 i = infective stage d = diagnostic stage

Cyst

i d

Trophozoites

d Cyst

3

4

5

Figure 3 The life cycle of Giardia lamblia. Cysts are resistant forms and are responsible for transmission of giardiasis. Both cysts and trophozoites can be found in the feces (diagnostic stages) 1 . The cysts are hardy and can survive several months in cold water. Infection occurs by the ingestion of cysts in contaminated water, food, or by the fecal–oral route (hands or fomites) 2 . In the small intestine, excystation releases trophozoites (each cyst produces two trophozoites) 3 . Trophozoites multiply by longitudinal binary fission, remaining in the lumen of the proximal small bowel where they can be free or attached to the mucosa by a ventral sucking disk 4 . Encystation occurs as the parasites transit toward the colon. The cyst is the stage found most commonly in nondiarrheal feces 5 . As the cysts are infectious when passed in the stool or shortly afterward, person-to-person transmission is possible. While animals are infected with Giardia, their importance as a reservoir is unclear. Reproduced with permission from CDC.

reduce the likelihood of infection. Thus, the protection of surface-water reservoirs is recommended by restrictions to animal breeding and grazing in the surrounding area.

3.12.2.1.2 Giardiasis Parasite characterization. Giardia lamblia (synonyms G. duodenalis, G. intestinalis) is a flagellate protozoan or complex of morphologically similar protozoan species belonging to the phylum Sarcomastigophora, order Diplomonadida, which are characterized by duplication of their major organelles (Andrews et al., 1989; Thompson, 2000; Adam, 2001)

(Figures 3 and 4). It is estimated that there are 2.8 108 cases per year worldwide, making Giardia the major source of intestinal infections in developing countries, although it is also found in developed countries with increasing incidences of infection (Lane and Lloyd, 2002). The current taxonomic situation is complex with the genus now containing a number of new species. These appear to be relatively host specific (Monis et al., 2009). Developmental cycle. G. lamblia, like Cryptosporidium spp., has a monoxenic life cycle, that is, it does not depend on any intermediate or paratenic host (Figure 3). The 10–12-mm-long cyst (Figure 4) is the infectious stage. This form is resistant to

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Figure 4 Giardia lamblia cyst (c. 10–12 mm in length) excreted with human feces. Infection occurs by oral ingestion. From the slide collection of Werner Frankw, University of Stuttgart-Hohenheim, Germany.

various methods of water treatment, including chlorination and the use of ozone (Lane and Lloyd, 2002). Once in the host, excystation takes place to form an excyzoite before dividing into four trophozoites. These are 15 mm long and motile, possessing four pairs of flagella and a ventral disk for attachment to the intestinal wall, and nutrition is taken via phagocytosis from the intestinal contents. These remain in the intestine (Mu¨ller and Von Allmen, 2005) where they can either move in the intestinal lumen or attach to the microvillous brush-border of enterocytes by way of the adhesive ventral disk (Lane and Lloyd, 2002; Mu¨ller and Von Allmen, 2005) (Figure 3). G. lamblia is normally found on the surface of the lumen of the small intestine in a wide variety of mammals where the trophozoites reproduce asexually. The environmentally resistant cysts (Figure 4) are excreted with the feces and can be transmitted by the fecal–oral route either by direct contact or via contaminated food or, most commonly, through water (Thompson, 2000; Stuart et al., 2003). Contamination of freshwater occurs via fecal contamination from humans and possibly also from animal hosts such as livestock (Thompson, 2000). Human involvement. Humans are usually infected by ingesting contaminated water; this may be as drinking water or by swallowing water while swimming or during water-related recreational activity (Stuart et al., 2003). Direct transmission among children in day-care centers and between male homosexuals has also been reported (Meyers et al., 1977; Phillips et al., 1981; Polis et al., 1986; Rauch et al., 1990). Infection can also occur by eating raw vegetables, presumably irrigated with contaminated water (Osterholm et al., 1981; Stuart et al., 2003). G. lamblia is the most common cause of waterborne diarrhea in the USA in 46 states (Hlavsa et al., 2005) and outbreaks have also occurred in Australia, Canada, New Zealand, Sweden, Norway, and the United Kingdom (Thompson, 2000; Stuart et al., 2003; Nygard et al., 2006). Although zoonotic Giardia infection has been reported (Traub et al., 2004; Leonhard et al., 2007; Geurden et al., 2008), the extent to which this is the case currently requires more research based on recent molecular taxonomic work on the complex of Giardia species which has been divided into different

molecularly defined assemblages (Monis and Thompson, 2003; Thompson et al., 2008). As with Cryptosporidium, Giardia cysts cannot reliably be eliminated from drinking water by filtration, although the cysts are larger (LeChevallier et al., 1991). Indeed, in Canada, 18.2% of treated water samples were found contaminated with Giardia cysts compared to 73% of raw sewage and 21% of raw water samples (Wallis et al., 1996). In industrialized countries, Giardia transmission can also be related to inadequate hygiene at public recreational facilities such as swimming pools (Porter et al., 1988). In many developing countries, giardiasis occurs with high prevalences due to poor hygienic conditions (Sullivan et al., 1991; World Health Organization, 2002). Prevalences reach 19.5% in rural communities in Malaysia with those most at risk being less than 13 years old (Norhayati et al., 1998); a similar picture is found for the native Orang Asli children (24.9% prevalence; Al Mekhlafi et al., 2005). In Africa, prevalence rates of 9.8% (Sudan; Magambo et al., 1998), 45% (vegetable farmers in Eritrea; Srikanth and Naik, 2004), and 31% (Maasai children, Kenya; Joyce et al., 1996) have been reported. Disease characteristics in humans. The symptoms caused by G. lamblia in humans are very variable. In many individuals, the infection is asymptomatic (Adam, 1991). Should overt disease occur, G. lamblia may cause stomach and abdominal pain, and nausea followed by either severe acute or chronic diarrhea potentially leading to dehydration and weight loss due to malabsorption as the absorptive surface of the small intestine may become blocked by the high density of the flagellates (Adam, 1991; Thompson, 2004; Mu¨ller and Von Allmen, 2005). Infection may result in poor condition and growth in children due to reduced nutrient uptake (Sullivan et al., 1991; World Health Organization, 2002). The pathogenesis of giardiasis is still unclear but includes microvillus shortening, flattening, or atrophy. This subject has been reviewed in detail by Mu¨ller and Von Allmen (2005). The disease is often self-limiting with a spontaneous cure occurring after 2–6 weeks. If, however, it should become chronic, there are short or persistent periods of diarrhea (Mu¨ller and Von Allmen, 2005). Diagnosis was usually made by determining the presence of cysts in fecal samples using light microscopy. Recently, immunoassays and polymerase chain reaction (PCR)-based diagnostics have been developed (Regnath et al., 2006; Haque et al., 2007). Prevention, cure. As G. lamblia is larger than Cryptosporidium, its removal by filtration is easier and it is also substantially more susceptible to chlorination (Betancourt and Rose, 2004). Prevention or a substantial reduction in contamination can be achieved by adequate treatment of water, such as sedimentation and retention (Betancourt and Rose, 2004). Preventing contamination of water sources used for drinking and irrigation would cut the transmission route and prevent infection. In areas with insufficient hygienic standards, drinking water should be boiled. Unlike Cryptospridium spp., Giardia can be effectively treated in the human host. Tinidazole is the drug of preference, with a single dose being effective for most individuals (Petri, 2005). Metronidazole, a related drug, is also effective but

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

treatment 3 times a day for 5–7 days is recommended. Limited studies also indicate that nitrazoxanide may be as effective as trinidazole without the latter’s bitter taste. During pregnancy, paromomycin is recommended (Petri, 2005). Anthropogenic alterations to the environment. Substantial evidence is available showing that irrigation with wastewater leads to contamination of a wide variety of crop plants in various areas of the world (Thurston-Enriquez et al., 2002; Ensink et al., 2006). Experimental watering of mint, coriander, radish, and carrots with untreated wastewater, treated wastewater with sedimentation and 16 days retention, and freshwater, showed high levels of contamination for all crops when untreated wastewater was used (96, 254, 59, and 155 cysts kg1, respectively) (Amahmid et al., 1999). When these cultures were irrigated with treated or freshwater, no contamination could be detected. Cifuentes et al. (2000) found up to 300 Giardia cysts per liter of untreated wastewater in an agricultural area of Mexico. In this case, water retention in reservoirs led to a substantial reduction in the number of cysts to less than 6 l1. However, if sewage sludge was used for fertilizing fields, the likelihood of introducing Giardia (and Cryptosporidium) cysts to the environment was high (Straub et al., 1993; Gale, 2005). In the study by Ong et al. (1996) comparing Giardia and Cryptosporidium in two adjacent watersheds in which cattle production occurs, Giardia was not found in water samples collected from lakes and headwaters in either watershed, but was collected in almost 100% of samples of water which had passed through cattle pastureland, with a maximum of about 2000 cysts per 100 l at both sites. As for Cryptosporidium, the sites downstream of cattle ranches had significantly higher levels of Giardia cysts than those upstream, and the peak concentrations occurred with calving. The importance of global warming for Giardia are currently unknown, although increased temperature is likely to result in the colonization of areas previously too cold to support this species (Polley and Thompson, 2009). There is evidence, however, that the increased or extreme precipitation predicted for some areas is likely to increase the risk of contamination of otherwise safe water sources (Curriero et al., 2001). Recommendations. As for cryptosporidiosis, giardiasis control can be affected via adequate treatment of drinking water, including boiling. Again, as for cryptosporidia, the resistant nature of the cysts requires relatively sophisticated, multilevel treatment in water-purification plants, which is likely to be difficult to obtain in developing countries. In addition, the use of human and animal feces and inadequate wastewater treatment prior to recycling, for example, for irrigation, can considerably increase the likelihood of disease outbreaks. Plants deriving from such sources should be cooked before consumption and not used as salad.

3.12.2.1.3 Toxoplasmosis Toxoplasma gondii, the causative agent of toxoplasmosis, is a coccidian parasite (order Eimeriida) which has a very high prevalence in most human populations worldwide (Tenter et al., 2000). A wide variety of avian and mammal intermediate hosts, including pigs, sheep, and goats as well as poultry, game animals, and rabbits, have been recorded as intermediate

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hosts (Tenter et al., 2000). Infection usually occurs via eating meat-containing cysts or by ingestion of oocysts which occur in environments contaminated with the infected feces of the final hosts, cats. These are unsporulated and therefore not infectious when they are excreted. Sporogony is temperature dependent and the oocysts only become infectious 2–4 days after exposure to air (Lucius and Loos-Frank, 2008). Despite the high prevalence of infection with T. gondii in humans and cats (Tenter et al., 2000), the waterborne route of infection has usually been considered to play a minor role, with most infection considered to be caused by eating undercooked, contaminated meat (Tenter et al., 2000). Recently, however, more interest in possible infection through contact with water has been generated after a major outbreak of the disease involving an estimated 2894–7718 humans, including 100 acute cases, in Victoria, British Columbia, Canada. This outbreak was associated with an unfiltered but chlormainated municipal water supply (Bowie et al., 1997). In north Rio de Janeiro State, Brazil, there was an increased risk of seropositivity associated with drinking unfiltered water from sites accessible to contamination (Bahia-Oliveira et al., 2003). Toxoplasmosis is usually considered to be a benign infection in immunocompetent individuals except when the initial infection occurs in the months prior to or during pregnancy, in which case transplacental transmission to the fetus can occur. This can cause severe disease leading to mental retardation, microcephaly or hydrocephalus, or even prenatal death (Kravetz and Federman, 2005; Rorman et al., 2006). Clinical manifestation may also only become apparent after birth with the neonate being asymptomatic (Rorman et al., 2006). In the case of retinal toxoplasmosis, which may affect about 2% of the infected population of the United States, most individuals are thought to have been infected post-natally (Smith and Cunningham, 2002; Holland, 2003). T. gondii is one of the most common protozoan parasites causing opportunistic disease in immunocompromised individuals (Ferreira and Borges, 2002). Reduced immunity can lead to reactivation of a latent infection resulting in acute disease including severe meningoencephalitis and myocarditis (Ferreira and Borges, 2002). Dubey (2004) suggests that waterborne infection with toxoplasmosis can be avoided by not drinking unfiltered or, if this is not possible, uncooked water, and that access to water sources used for human consumption should be prevented for cats.

3.12.2.2 Amoebiasis 3.12.2.2.1 Parasite characterization Entamoeba histolytica is a protozoan parasite, belonging to the phylum Sarcomastigophora, order Entamoebida, which causes amoebic dysentery, predominantly in the tropics and subtropics (Figures 5 and 6). It has been estimated that this pathogen causes about 40 000–100 000 human deaths a year while 50 million individuals show clinical disease (Petri and Singh, 1999; Petri et al., 2000, 2002; Ackers and Mirelman, 2006). Information on E. histolytica published prior to the 1990s is of doubtful value as the single species recognized prior to this time was finally classified into two species, E. histolytica, which is potentially highly pathogenic to

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2 i Mature cysts ingested

d

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Cysts and tophozoites passed in feces A = noninvasive colonization B = intestinal disease C = extraintestinal disease 4 Trophozoites dd

Exits host

Multiplication Excystation 3

Trophozoites 4

d 5 Cysts

d

d

d

d

Figure 5 The life cycle of Entamoeba histolytica. Cysts and trophozoites are passed in feces 1 . Cysts are typically found in formed stool, whereas trophozoites are typically found in diarrheal stool. Infection by Entamoeba histolytica occurs by ingestion of mature cysts 2 in fecally contaminated food, water, or hands. Excystation 3 occurs in the small intestine and trophozoites 4 are released, which migrate to the large intestine. The trophozoites multiply by binary fission and produce cysts 5 , and both stages are passed in the feces 1 . Due to the protection conferred by their walls, the cysts can survive days to weeks in the external environment and are responsible for transmission. Trophozoites passed in the stool are rapidly destroyed once outside the body, and if ingested would not survive exposure to the gastric environment. In many cases, the trophozoites remain confined to the intestinal lumen ( A : noninvasive infection) of individuals who are asymptomatic carriers, passing cysts in their stool. In some patients the trophozoites invade the intestinal mucosa ( B : intestinal disease), or, through the bloodstream, extraintestinal sites such as the liver, brain, and lungs ( C : extraintestinal disease), with resultant pathologic manifestations. Reproduced with permission from CDC, modified by the authors of this chapter.

humans, and H. dispar, which is considered to be a commensal species in the human gut (Hamzah et al., 2006; Stauffer and Ravdin, 2003; Fotedar et al., 2007). However, these two species are morphologically indistinguishable making traditional microscopic diagnostic methods obsolete (Jackson, 1998;

Stauffer and Ravdin, 2003). Diagnosis today therefore relies on serological and molecular methods (Stauffer and Ravdin, 2003). Recently, a third morphologically identical species, E. moshkovskii, which can also be distinguished by molecular methods, has been detected in humans. Its cysts are also found

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Figure 6 Mature fecal cyst of Entamoeba histolytica (10–15 mm) typically showing four nuclei. From the slide collection of Werner Frankw.

in fecally contaminated water and, although it is considered to be primarily a free-living species, evidence is accumulating that it can cause diarrhea and other intestinal problems in humans (Tanyuksel et al., 2007; Fotedar et al., 2008). In addition to these species, Tanyuksel and Petri (2003) list Entamoeba coli, E. hartmanni, E. polecki, Iodamoeba bu¨tschlii, and Endolimax nana which are also found in the human gut but are considered to be harmless. Coinfections with different Entamoeba species are possible (Fotedar et al., 2007; Tanyuksel et al., 2007).

protein, must be considered a maladaptation of the parasite to its host (Stauffer and Ravdin, 2003). Infective cysts deriving from intra-intestinal trophozoites are shed in the feces to potentially be picked up by the next host (Figure 5; Lucius and Loos-Frank, 2008). The cysts can survive in moist, cool environmental conditions for 2–4 weeks with a maximum of 3 months if temperatures are above freezing – freezing and high temperatures are lethal (Schuster and Visvesvara, 2004a; Keene, 2006).

3.12.2.2.2 Developmental cycle

E. histolytica is the dominant, globally distributed, amebic pathogen of the human gut (Schuster and Visvesvara, 2004a). It is largely an anthropogenic pathogen but a variety of nonhuman primates, possibly pigs and occasionally dogs can be infected, although in the latter, infective cysts are rarely produced (Petri et al., 2002; Verweij et al., 2003; Schuster and Visvesvara, 2004a). Transmission occurs via the oral route, usually with consumption of water or food contaminated with human fecal material (Lucius and Loos-Frank, 2008). Most cases are confined to the intestine and are self-limiting, with a study in Vietnam showing infections in asymptomatic adult carriers having an average half life of 12.9 months; however, approximately 10% of carriers develop extra-intestinal disease (Blessmann et al., 2003; Stauffer and Ravdin, 2003). Tissue invasion, in particular if the brain is involved, can lead to death within a few days and immunologically naive individuals from industrial countries who spend some months in developing countries often become severely ill (Okhuysen, 2001; Lucius and Loos-Frank, 2008). Such invasive amoebiasis is 3 or more times more common in males than in females, although the sex ratio for asymptomatic individuals is the same (Acuna-Soto et al., 2000; Blessmann et al., 2002). Amebic dysentery is a relatively uncommon colitis with severe, often bloody, stools. In this form of the disease, the trophozoites lyse the epithelial cells of the large intestine and enter the mucosa and submucosa from where they can invade the

3.12.2.2.3 Human involvement As with cryptosporidia and G. lamblia, E. histolytica has a simple, direct life cycle (Figure 5). Infection occurs when metacysts (10–15 mm in diameter with four nuclei, Figure 6) are ingested orally via fecally contaminated water or food. Excystation takes place in the small intestine with the formation of eight metacystic trophozoites (Petri et al., 2002). Once mature, the trophozoites, which have a diameter of 20–60 mm, reproduce by binary fission in the large intestine where most remain in a commensal (apathogenic) relationship. However, some may change through some currently unknown mechanism, to larger, polyploid, hemophagous, metabolically more active forms which are pathogenic (Figure 6; Lucius and Loos-Frank, 2008). These penetrate into the colon wall causing flask-shaped ulcers. Less frequently, E. histolytica spreads through the portal vein to the liver causing amebic liver abscess, and rarely to the lungs or brain where it can cause potentially lethal abscesses (Ackers and Mirelman, 2006). If a lectin on the parasite’s surface attaches to the muco-glycoproteins that line the host’s intestinal lumen, a noninvasive gut infection ensues. In contrast, if the trophozoite penetrates the mucin layer and its lectin attaches directly to the surface of the host cell, a cascade of events occurs ultimately leading to invasive disease. Under these circumstances, replication and cyst formation do not occur. Thus, invasive disease, as determined by the lectin

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organs via the bloodstream (Lucius and Loos-Frank, 2008). The pathological process appears to be enhanced by the inflammatory response of the host (Ackers and Mirelman, 2006). Prevalence rates can be high, especially in children, with 28.4% of under-15-year-olds being infected in the southern Sudan (Magambo et al., 1998). A large-scale study of 289 children aged 2–5 years living in an urban slum in Bangladesh revealed that 80% were infected with E. histolytica over a 4-year period, 53% had a repeat infection, and that 4% developed clinically significant disease over a 2-year period (Haque et al., 2002, 2006). Both innate and acquired immune responses are capable of limiting the infection (Haque et al., 2002, 2006). Although developing countries in warm and temperate areas are most strongly affected, E. histolytica infections can also show high rates of infection, and potentially be a health hazard in industrialized nations, where it is often found predominantly in male homosexuals, with HIV infection possibly being an additional risk factor (Phillips et al., 1981; Ohnishi et al., 2004; Chen et al., 2007; Hung et al., 2008). The route of transmission in this case is likely to involve oral–anal sexual contact, thus differing from the usual water-based route (Allason-Jones et al., 1986). Although Campos-Rodrı´guez and Jarillo-Luna (2005) claim that such infections are usually asymptomatic and extra-intestinal amoebiasis is rare, invasive pathology has been reported from the USA, Japan, and Taiwan (Seeto and Rocky, 1999; Mitarai et al., 2001; Hung et al., 2008). In developed countries where fecal–oral transmission is rare among the general population, this disease is most commonly seen in immigrants coming from, and individuals who had visited, countries with prevalent disease (Haque et al., 2003).

3.12.2.2.4 Disease characteristics in humans Intestinal infections are often asymptomatic, although they may also cause dysentery. In the pathogenic, extra-intestinal form, symptoms of amebic colitis include abdominal cramps, watery or bloody diarrhea, and weight loss over a period of several weeks (Haque et al., 2003). These general symptoms make definitive diagnosis difficult as they are also commonly found in a variety of intestinal bacterial infections, although finding cysts with four nuclei in stool samples is diagnostic for the E. histolytica group (Haque et al., 2003; Tanyuksel and Petri, 2003). Severe but unusual disease progression includes acute necrotizing colitis, toxic megacolon, ameboma causing a bowel lesion, and peri-anal ulceration, as well as abscesses in the liver, lungs, or brain (Haque et al., 2002). These complications require early recognition and medical intervention, for example, acute necrotizing colitis, although rare, has an associated mortality of 40% (Ellyson et al., 1986). Interestingly, clinical characteristics vary geographically: in Egypt, invasive colitis is most common, while in South Africa and central Vietnam as well as in an outbreak in the Republic of Georgia in 1998 liver abscesses predominate (Barwick et al., 2002; Blessmann et al., 2002; Stauffer and Ravdin, 2003). In Vietnam, the use of river water was identified as a major risk factor (Blessmann et al., 2002). In addition to differences in food-preparation techniques between these countries, evidence of genetic variability in this species, even in samples from the same geographic location (Haque et al., 2002;

Haghighi et al., 2003; Simonishvili et al., 2005), suggests that more research should be invested in determining the population genetic structure of this parasite in relation to disease epidemiology and pathology (Bhattacharya et al., 2005). Antigen tests are used to confirm positive stool ova, and computer tomography can be used to visualize tissue abscesses for which molecular biological techniques have also been developed (Tanyuksel and Petri, 2003; Stauffer and Ravdin, 2003).

3.12.2.2.5 Anthropogenic alterations to the environment An outbreak of amebic disease involving liver abscesses in Tbilisi, Republic of Georgia, in 1998 was thought to be caused by either ‘‘inadequate municipal water treatment or contamination of municipal water in the distribution system’’ (Barwick et al., 2002). Data from El Azzouzia, Marrakesh (Morocco), where untreated, fecally contaminated wastewater is used in agriculture, indicate that amoebiasis affects 28% of the population compared to 6% in a control area without such irrigation (Melloul et al., 2002). Thus, it appears that parasite transmission is encouraged by expanding irrigation using wastewater.

3.12.2.2.6 Prevention and cure As with other diseases transmitted by the consumption of fecally contaminated water or uncooked food which had come into contact with such water, the adequate treatment of wastewater is necessary to control the disease (Smith and Perdek, 2004). Effective methods for eliminating Entamoeba spp. date back to the US army during World War II with precoat filtration (LeChevallier and Au, 2004). The cysts are resistant to chlorination and may require prolonged contact times before they are inactivated, although reducing pH and increasing temperature reduces the time needed for inactivation (LeChevallier and Au, 2004). In a Mexican study, advanced primary treatment with high sedimentation combined with sand filtration and chlorination reduced the number of protozoan cysts (Giardia and Entamoeba) by two orders of magnitude (Jiminez et al., 2001). Ozonation and UV radiation can also be effectively used (Schaefer et al., 2004). In areas with poor hygienic standards only safe, bottled, or boiled water should be consumed. Once the pathogen is present, the intra-intestinal stage can be treated with paramomycin and diloxanide furoate with the former showing a higher cure rate (85% vs. 51% in Hue, Vietnam; Blessmann et al., 2005). The visceral form responds to nitroimidazoles, especially metronidazole, to which about 90% of patients with mild-to-moderate symptoms respond (Haque et al., 2003; who provide a comprehensive treatment schedule). Recent studies also show that nitazoxanide is effective against both intestinal infection with E. histolytica as well as invasive amoebiasis (Rossignol et al., 2007).

3.12.2.2.7 Recommendations Adequate treatment of drinking water is capable of reducing this threat; however, the use of contaminated wastewater for irrigation can also lead to cysts occurring on vegetables grown for human consumption. Thus, the use of untreated wastewater for agriculture should be curtailed. Cooking vegetables

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

and drinking boiled water substantially reduce the chances of infection.

3.12.2.3 Free-Living Amoeba A number of species of free-living amoeba belonging to the genera Acanthamoeba, Naegleria, Balamuthia, and Sappinia are known to cause serious disease in humans and potentially in other animals including dogs, sheep, primates, horses, and cattle as well as birds, reptiles, and fish (Rideout et al., 1997; Schuster and Visvesvara, 2004b; Daft et al., 2005; Visvesvara, 2007b; Matin et al., 2008). Transmission is either via contact with contaminated soil or aquatic environments, potentially including seawater, where the free-living amoeba feed on bacteria (Schuster and Visvesvara, 2004b; Lorenzo-Morales et al., 2005a, 2005 b). Those capable of infecting humans must be thermotolerant to survive the normal human-body temperature of 37 1C (Schuster and Visvesvara, 2004b). Infection occurs via breaks in the skin, by cysts carried to the upper respiratory tract by air, or by amoeba carried by water (Schuster, 2002). Both immunocompetent and immunocompromised individuals may be affected (Schuster and Visvesvara, 2004b). Infection with Naegleria fowleri, which is the cause of the usually fatal primary amebic meningoencephalitis, is found in warm-water sources, such as swimming pools which lack sufficient chlorination, ponds, and flowing water (Cabanes et al., 2001; Tiewcharoen and Junnu, 2001; Schuster and Visvesvara, 2004b), and even wells (Shenoy et al., 2002; Blair et al., 2008). This species usually infects healthy children, adolescents, and young adults swimming or washing in such water, with infection occurring via the olfactory neuroepithelium (Schuster, 2002). A number of Acanthamoeba spp. as well as Balamuthia mandrillaris can also cause encephalitis and other disease syndromes, usually in immunocompromised hosts (Teknos et al., 2000; Torno et al., 2000; Marciano-Cabral and Cabral, 2003), and Acanthamoeba can also cause keratitis in healthy humans (Marciano-Cabral and Cabral, 2003; Kilvington et al., 2004; Jeong and Yu, 2005). A single case of amebic encephalitis has been reported in an immunocompetent patient caused by Sappinia pedata (originally identified as S. diploidea; Qvarnstrom et al., 2009), although the route of infection is unknown (Rocha-Azevedo et al., 2009).

3.12.2.4 Microsporidian Infections Microsporidia are single-celled, obligate intracellular, sporeforming, primitive fungal parasites which infect every major animal group from protozoans to humans (Bush et al., 2001; James et al., 2006). Although a wide variety of microsporidian species are known to infect humans, these will not be discussed in detail here as they are usually opportunistic parasites of immunocompromised patients. Occasionally, however, microsporidians have also been detected in healthy humans, often as self-limiting traveler’s diarrhea (Didier et al., 2004; Mathis et al., 2005). In Spain, Enterocytozoon bieneusi infection has been associated with diarrhea in geriatric patients (Mathis et al., 2005). In another study, infection prevalence reached 67.5% in immunocompetent individuals in Cameroon, suggesting that this situation may be much more common than

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previously considered. (Nkinin et al., 2007). Transmission can occur via several routes including the fecal–oral, oral–oral, ingestion of contaminated food, aerosol, and potentially direct person to person. In addition, contact with or drinking contaminated water has been associated with infections (Hutin et al., 1998; Mathis et al., 2005). Irrigation water, river water, as well as groundwater can be contaminated (ThurstonEnriquez et al., 2002; Didier, 2005). Sixteen of 25 samples taken from the River Seine in France over a period of 1 year were found to be contaminated with E. bieneusi (Fournier et al., 2000). More work is needed to determine the significance of this group of pathogens.

3.12.2.5 Dracunculiasis 3.12.2.5.1 Parasite characterization Dracunculus medinensis, the Guinea worm (Figures 7 and 8), is a nematode (round worm) belonging to the suborder Spirurina, family Dracunculidae. Dracunculiasis, which it causes, once occurred throughout much of the semiarid tropics of the Old World from Central and West Africa to Yemen and parts of India and Pakistan. An estimated 3.5 million people were infected in 1986 prior to the current eradication program which began in the mid-1980s (Hopkins et al., 2005). Eradication efforts have now restricted it predominantly to Sudan and Ghana, and to a lesser extent neighboring countries, with a mere 16 000 cases reported in 2004 (Steib, 1987; Hopkins et al., 2005). Recent data indicate that this trend is continuing at least for southern Sudan, with 5815 cases reported in 2007 but only 3618 in 2008 (Rumunu et al., 2009). Eradication has been possible as D. medinensis is one of the few anthroponoses dealt with here, that is, it is not zoonotic with the only final host being humans (Lucius and Loos-Frank, 2008).

3.12.2.5.2 Developmental cycle The life cycle of D. medinensis (Figure 7) is dependent on still freshwater bodies containing small, commonly occurring, crustaceans known as copepods which act as intermediate hosts (Figure 8; Hopkins, 1983; Steib, 1987). The more these pools are frequented by humans collecting drinking water, the better the chances are of transmission. Temperatures over 19 1C are also necessary for larval development of the most important intermediate host, in West Africa, Thermocyclops inopinus (Steib, 1987). Gravid female worms, which reach a length of 70–100 cm, take up a subcutaneous position usually on the lower leg or foot of their human host where they cause a prominent, painful blister (Hopkins, 1983). On contact with water, this blister ruptures, releasing about a half a million minute (0.3–0.6 mm long), motile L1 larvae into the water (Steib, 1987; Mehlhorn and Walldorf, 1988). Usually, the females die after the first mass discharge of the larvae. The males, which only reach lengths of 3–4 cm, live until they have fertilized a female. Some of the larvae are ingested by copepod crustaceans which act as intermediate hosts. The parasites develop in the hemocoel of this host to the infective thirdstage (L3) larvae (Figure 8), which infect humans who drink unboiled or unfiltered water containing the copepod intermediate hosts. Digestion of the copepods releases the larvae which move to the small intestine before penetrating the intestinal wall and migrating through the connective tissue to

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Human drinks unfiltered water containing copepods with L3 larvae Larvae undergoes two molts in the copepod and becomes an L3 larvae 6

1

i Larvae are released when copepods die. Larvae penetrate the host’s stomach and intestinal wall. They mature and reproduce 2

L1 larvae consumed by a copepod 5

Female worm begins to emerge from skin 1 year after infection 3

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Fertilized female worm migrates to surface of skin, causes a blister, and discharges larvae

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L1 larvae released into water from the emerging female worm

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Figure 7 The life cycle of Dracunculus medinensis. Humans become infected by drinking unfiltered water containing copepods (small crustaceans) which are infected with larvae of D. medinensis 1 . Following ingestion, the copepods die and release the larvae, which penetrate the host stomach and intestinal wall and enter the abdominal cavity and retroperitoneal space 2 . After maturation into adults and copulation, the male worms die and the females (length: 70–120 cm) migrate in the subcutaneous tissues toward the skin surface 3 . Approximately 1 year after infection, the female worm induces a blister on the skin, generally on the distal lower extremity, which ruptures. When this lesion comes into contact with water, a contact that the patient seeks to relieve the local discomfort, the female worm emerges and releases larvae 4 . The larvae are ingested by a copepod 5 and after two weeks (and two molts) have developed into infective larvae 6 . Ingestion of the copepods closes the cycle 1 . Reproduced with permission from CDC.

period of prepatency, the discharge of larvae into the transmission site takes about a year, ensuring that this happens under the favorable conditions of the dry season (Steib, 1987).

3.12.2.5.3 Human involvement

Figure 8 Dracunculus medinensis : infectious L3 larva (arrow) in a Thermocyclops copepod. Courtesy of Karl Steib.

the thorax (Lucius and Loos-Frank, 2008). Mating between male and female worms occurs after 60–90 days with 10–14 months being required before the eggs are mature and the female can migrate to be body surface. Due to this extended

Humans are the only final hosts for this species. The disease is distributed only in areas with a well-defined dry season with limited water availability and high densities of copepods (Steib, 1987). Transmission sites include ponds, cisterns, pools, and wells, which are frequented by many people and where humans can move into the water with their feet and legs. This is the case, for example, with the traditional step wells in which humans reach the water over a number of steps (Steib, 1987; Hopkins et al., 2005). Copepods can disperse from water body to water body in a variety of ways including transport by animals, such as waterbirds, although wind dispersal of eggs is also likely to be important (Ca´ceres and Soluk, 2002; Cohen and Shurin, 2003). In addition, it is also possible that copepod eggs lie dormant in sediment until the pond refills with rain (Bohonak and Jenkins, 2003). Thus, temporary water sources are likely to be infected (Steib, 1987).

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.2.5.4 Disease characteristics in humans Infection is usually asymptomatic until the female worm is ready to lay her eggs and moves to the skin surface (Ruiz-Tiben and Hopkins, 2007). During this movement within the body, symptoms such as slight fever, allergic symptoms, nausea, diarrhea, dizziness, rash with pruritis, and local erythema may occur (Ruiz-Tiben and Hopkins, 2007). The blister itself is painful once it develops, which often leads the patient to seek relief in water. Although the worm usually emerges from the legs or feet (85–90% of cases), other parts of the body, such as the arms, buttocks, and genitalia, may also be involved (RuizTiben and Hopkins, 2007). Migration of the worm through the tissues may also lead to the presence of the worm in other locations, potentially leading to a space-occupying lesion and abscess formation. Chronic disease may then occur, including joint inflammation and arthritis (Ruiz-Tiben and Hopkins, 2007). The open wound is also subject to secondary bacterial infections (Steib, 1987). No diagnostic methods, except the emergence of the female worm, are available, a fact which is negated by the success of control programs not requiring determination of the disease (Molyneux, 2009).

3.12.2.5.5 Prevention and cure There is neither a drug nor a vaccine capable of curing or preventing infection with D. medinensis (Ruiz-Tiben and Hopkins, 2007). It is therefore interesting that the near eradication of the disease, through the Dracunculiasis Eradication Program (Hopkins et al., 2008), is not based on medication or vaccination, but on human education and the distribution of water-filtration equipment (including simple cloth filters) disrupting the freshwater transmission cycle of the disease by removing the copepod intermediate host before drinking (Nwaorgu, 1991; Barry, 2007). Denying contact between the gravid female worm and water, for example, by using an occlusive bandage, will also interrupt the transmission cycle (Ruiz-Tiben and Hopkins, 2007). A current infection is traditionally eliminated by extracting the gravid female manually through the ruptured blister without allowing contact to a freshwater source, a method dating back to at least 1500 BC (Cox, 2002). Metronidazole is sometimes recommended as an anti-inflamatory agent (Bogitsh et al., 2005).

3.12.2.5.6 Anthropogenic alterations to the environment It has been shown that large dams with permanent water sources are associated with lower levels of dracunculiasis than when small, impermanent dams and ponds were used as water sources of people working in the fields, suggesting that small water sources, particularly those constructed by humans (Steib, 1987), are of more epidemiological significance than large ones (see also Scott, 1960; Belcher et al., 1975; Steib and Mayer, 1988; Tayeh and Cairncross, 1998). However, AdekoluJohn (1983) showed that construction of the Kainji Reservoir in northern Nigeria was responsible for the development of surrounding ponds by raising the water table, providing suitable habitat for the intermediate hosts, and thus promoting parasite transmission. Prevalences of dracunculiasis increased in the area from approximately 0% in 1960–64 to 25.6% in 1975–79, 10 years after the construction of the dam

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(Adekolu-John, 1983). Adekolu-John suggested that future studies take into account the role of the water level in Kainji Reservoir on the formation of ponds and surface water bodies along estuaries and streams. In times of civil unrest, large areas may not be accessible to Guinea-worm-control programs, as has occurred in southern Sudan. This kind of anthropogenic alteration could prevent the aim of complete eradication of D. medinensis in the near future (Hopkins et al., 2008; Rumunu et al., 2009).

3.12.2.5.7 Recommendations Hydrological projects in the affected areas of those countries in which D. medinensis is still found (Sudan, Ghana, Mali, Nigeria, and Niger; Hopkins et al., 2008) should aim at interrupting the transmission cycle of the disease either by providing the possibility of water filtration (even at a simple level) and/or by preventing gravid females from releasing their eggs into water bodies. The construction of sources allowing the collection of water without contact between the water and human extremities would also interrupt transmission. An accompanying program of educating the local population on the dangers of this disease should be carried through.

3.12.3 Food-Borne Parasites Transmitted through Freshwater and Marine Foods Aquaculture is rapidly overtaking natural harvesting as the main source of marine and freshwater food, with 47% of such food currently coming from this area (Food and Agriculture Organization, 2009; Subasinghe et al., 2009). This massive increase in aquaculture, particularly in Asia, has paved the way for the introduction to and propagation of a variety of parasites into the human food-production system in many parts of the world (Naylor et al., 2001). Surveys of aquaculture fish in different ponds in Vietnam showed that between 0.7% and 6.5% to as high as 44.6% contained zoonotic trematode species compared to 10.3% of wild fish (Chi et al., 2007; Hop et al., 2007; Thu et al., 2007). There are about 70 species of food-borne digenean trematodes known to be capable of infecting humans. All are hermaphroditic flatworms which have one or more aquatic snail species as intermediate hosts (World Health Organization, 2004). Most transmission takes place in freshwater but brackish and seawater are occasionally involved. These flatworms cause significant infections such as fascioliasis, clonorchiasis, opisthorchiasis, and paragonimiasis (Keiser and Utzinger, 2005). Somewhat dated estimates suggest that 18 million people are infected by fish-borne trematodes (World Health Organization, 1995a). We now have estimates of over 35 million being infected with opisthorchids trematodes alone (Lun et al., 2005; Andrews et al., 2008).

3.12.3.1 Opisthorchiasis and Clonorchiasis 3.12.3.1.1 Parasite characterization The digenetic trematode family Opisthorchiidae contains three species which are significant parasites of humans in East (Clonorchis sinensis) and Southeast Asia (C. sinensis and Opisthorchis viverrini), and in Europe, as well as all of the

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Russian federation except northern Siberia and the Far East (Opisthorchis felineus) (Kaewkes, 2003; Yossepowitch et al., 2004; Pozio, 2008) (Figure 9). Species of both of these genera, the adults of which inhabit the bile ducts of the human liver, are remarkably similar genetically (Park, 2006; Saijuntha et al., 2008), but differ in testicular morphology (with the testes occurring posteriorly) and the arrangement of the vitelline glands (Kaewkes, 2003). All three flatworms measure about 10 mm in length and have an anterior and a central ventral sucker for attachment to their host. Recent work suggests that O. viverrini is a species complex containing at least two species (Saijuntha et al., 2007; Andrews et al., 2008). As with most of the parasites discussed here, these helminths have long plagued human civilization. Archaeological records of C. sinensis come from Korea (several centimeters to 5 m below the level dated 668–935 AD; Han et al., 2003), China (Ming dynasty: 1368–1644 AD; Lun et al., 2005), and Japan (seventh century AD; Matsui et al., 2003). A corpse buried in 167 BC, the time of the Western Han dynasty in China (202 BC–24 AD), contained C. sinensis eggs in the gall

bladder which are morphologically identical to those found in recent times (Yang and Wei, 1984), but which differ genetically from current populations (Liu et al., 2007). The review by Sithithaworn et al. (2007) should be referred to for more detailed information on all species, while Lun et al. (2005) provide an excellent summary on C. sinensis.

3.12.3.1.2 Developmental cycles The life cycles of all three species of liver flukes are dependent on snail and cyprinid fish intermediate hosts inhabiting freshwater (Figure 9; Keiser and Utzinger, 2005; Sithithaworn et al., 2007). Adult C. sinensis worms are usually between 10–25 mm long and 3–5 mm wide (Lun et al., 2005). Final hosts are carnivorous mammals (such as dogs and cats) and humans eating raw or undercooked fish. Sexual reproduction occurs in these hosts with each mature worm producing between 1000 and 4000 eggs per day for at least 6 months (Lun et al., 2005). Once the operculated eggs (25–35 mm long and 15–17 mm wide; Figure 10) are released from the worms

Metacercariae in flesh or skin of fresh water fish are ingested by human host i

4 i = infective stage d = diagnostic stage

Free-swimming cercariae encyst in the skin or flesh of freshwater fish 3

5

Excyst in duodenum

Eggs are ingested by the snail 2

Miracidia

Sporocysts

Rediae

Cercariae

2a

2b

2c

2d

1

Embryonated eggs passed in feces d

6 Adults in biliary duct

Figure 9 The life cycle of a member of a liver fluke of the family Opisthorchiidae, modified by the authors of this chapter. The adult flukes deposit fully developed eggs that are passed in the feces 1 . After ingestion by a suitable snail (first intermediate host) 2 , the eggs release miracidia 2a, which undergo in the snail several developmental stages (sporocysts 2b, rediae 2c , cercariae 2d). The eyed and finned cercariae are released from the snail 3 and penetrate freshwater fish (second intermediate host), encysting as metacercariae in the muscles or under the scales 4 . The mammalian definitive host (cats, dogs, and various fish-eating mammals including humans) become infected by ingesting undercooked fish containing metacercariae. After ingestion, the metacercariae excyst in the duodenum 5 and ascend through the ampulla of Vater into the biliary ducts, where they attach and develop into adults, which lay eggs after 3–4 weeks 6 . The adult flukes (O. viverrini: 5–10 mm  1–2 mm; O. felineus: 7–12 mm  2– 3 mm) reside in the biliary and pancreatic ducts of the mammalian host, where they attach to the mucosa. Reproduced with permission from CDC, modified by the authors of this chapter.

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not appear to be a common phenomenon with the high rates of infection in humans not correlating with infection rates in other potential hosts in Thailand, Taiwan, or Korea (Sithithaworn et al., 2007).

3.12.3.1.3 Human involvement

Figure 10 Egg (25–35 mm  15–17 mm) of Clonorchis sinensis. Note the operculum (arrows) through which the miracidium-larva hatches. Reproduced with permission from CDC.

inhabiting the bile ducts, they reach the intestinal lumen and are excreted with the feces. If they are subsequently washed into a freshwater body which contains suitable snail first intermediate hosts (e.g., Bithynia spp., Parafossarulus spp.), then they may be ingested by these species. Embryonated eggs that are eaten by these intermediate hosts contain the miracidium stage which transforms to a sporocyst which then undergoes asexual reproduction giving rise to rediae (Lun et al., 2005). The rediae produce large numbers of long-tailed cercariae which are shed by the snails into the water. Prevalences in the snail hosts are low (0.05–0.07% for O. viverrini and 0.09–0.60% for C. sinensis; Sithithaworn et al., 2007). The cercariae actively search for the second intermediate, cyprinid fish host which they penetrate, forming encapsulated metacercariae which are infectious to the final host on ingestion (Lun et al., 2005). Prevalences in fish, in contrast to snails, are very high reaching 90–95% for both O. viverrini and C. sinensis (Lun et al., 2005; Sithithaworn et al., 2007). On ingestion, the metacercariae hatch in the small intestine and the juvenile worms migrate into the intra-hepatic bile ducts, attaching to the epithelium. As many as 1500 worms have been recorded in a severe human infestation (Lun et al., 2005). After about 4 weeks, egg production starts. Potential survival of individual worms may be over 25 years in untreated patients (Attwood and Chou, 1978) although Sithithaworn et al. (2007) suggest that O. viverrini survives in humans for about 10 years. The life cycles of O. felineus and O. viverrini are similar to that of C. sinensis, differing mainly in geographic distribution and the choice of snail and fish hosts (Kaewkes, 2003; Sithithaworn and Haswell-Elkins, 2003; Sithithaworn et al., 2007). In Europe, there is a low prevalence of O. felineus in humans suggesting that they are not a major host in the life cycle of this species. In areas in Italy where recent human outbreaks have been reported, the cats examined showed infection prevalences ranging from 23.5% to 40% (Pozio, 2008). This does

Worldwide there are an estimated 35 million people infected with these species, with 15 million in China alone, although this is likely to be an underestimate as very little data are available on O. viverrini from Laos, Cambodia, and southern Vietnam (Lun et al., 2005; Andrews et al., 2008). The frequent contamination of freshwater with egg-bearing human fecal matter is a significant component in the life cycles of C. sinensis and O. viverrini (Sithithaworn and Haswell-Elkins, 2003). Indeed, in some regions of China where clonorchiasis is common, toilets are deliberately built adjacent to fish ponds leading to high rates of contamination (Lun et al., 2005). Thus, inadequate, unhygienic disposal of human fecal matter plays a major role in effectively maintaining the life cycle of both C. sinensis and O. viverrini (Sithithaworn et al., 2007). Human infection occurs through eating raw, marinated, or inadequately cooked fish (Sithithaworn and Haswell-Elkins, 2003), which is a dietary component of the cultural system in many of the areas affected (Petney, 2001; Lun et al., 2005). Men are often more frequently and potentially more heavily infected than women for both C. sinensis and O. viverrini (Lun et al., 2005; Sithithaworn et al., 2007), which is also likely to be related to traditional eating patterns (Petney, 2001). Prevalence is lowest in preschool children, increasing to early adulthood after which no consistent variation can be found for either C. sinensis or O. viverrini; intensity of infection for both species tends to increase with age (Sithithaworn et al., 2007). Distribution and prevalence of the infection with these fish-borne liver flukes vary substantially depending on the suitability of the environmental condition for the intermediate hosts and the frequency of consumption of raw fish. C. sinensis is present in all Chinese provinces except those in the far west of the country (Lun et al., 2005). Mean provincial prevalences range from 0.04% to over 4.5% in Sichuan, but local prevalences may be over 75%, such as those recorded from Guanyuan, Dongyoung, Sanshui, and Shunde, where raw fish is a common component of the diet. In Guangdong, 18% of the 862 393 people examined were positive for C. sinensis (Lun et al., 2005). In the Republic of Korea, prevalence of infection is highly variable, ranging from 2.1% in Chuncheon to 31.3% in Haman (Lim et al., 2006). A similar pattern is present for O. viverrini in Thailand, where the south (0%) and central (3.8%) parts of the country are relatively free of infection compared with the north (19.3%) and the northeast (15.7%) where infection is highest (Sithithaworn et al., 2007). There is also a great deal of local variation in prevalence within provinces, ranging in Khon Kaen province from 2% to 71% (Jongsuksuntigul, 2002; Sriamporn et al., 2004). Nevertheless, education and control programs have reduced the overall prevalence in Thailand from 34% in 1992 to 10% in 2002 (Jongsuksuntigul and Imsomboon, 2003). A number of recent studies have shown high prevalences in Laos (Sithithaworn et al., 2006; Sayasone

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et al., 2007), but data for both Cambodia and southern Vietnam are largely lacking (Andrews et al., 2008). Epidemiological data for O. felineus are few and scattered, but rates of human infection usually seem to be low, probably due to the reduced likelihood of raw fish being consumed as a traditional part of the diet. In some areas of Byelorussia, the Ukraine, and western Siberia, however, prevalences range from 40% to 95% (see Yossepowitch et al., 2004). As it has become more fashionable to consume raw fish in Western countries, outbreaks on infection with O. felineus are becoming more common, with recent outbreaks in Italy being caused by the consumption of tench (Tinca tinca) (Armignacco et al., 2008; Pozio, 2008).

3.12.3.1.4 Disease characteristics in humans The symptoms and pathology associated with clonorchiasis depend on the intensity of the infection, its duration, the number of reinfections, and the susceptibility of the host individual (Min, 1984; Lun et al., 2005). Pathology is due to both local trauma and toxic irritation and can be divided into a number of phases eventually leading to hyperplasia, proliferation of connective tissue resulting in fibrosis of the bileduct wall within the liver (Min, 1984). These changes may eventually affect the liver generally. The onset of symptoms may be gradual or sudden. These include chills or fever up to 40 1C within a few weeks of infection followed by a potentially asymptomatic course. Should symptoms occur, these may be mild with gastrointestinal discomfort, diarrhea, anorexia, weight loss, fatigue, and hepatomegaly, or severe including portal cirrhosis and hypertension (Min, 1984). Complications can be severe, including pyogenic cholangitis, cholelithiasis, cholecystitis, and pancreatitiis (Min, 1984). Although infection with any of the three species leads directly to human morbidity (Sripa, 2003; Lun et al., 2005), to date O. viverrini is the only member to be officially recognized as a carcinogen, with long-term exposure potentially leading to cholangiocarcinoma (IARC, 1994; Meyer and Fried, 2007), although in Korea the prevalence of C. sinensis in different areas is positively correlated with incidence of this cancer (Choi et al., 2004; Lim et al., 2006). Worldwide cholangiocarcinoma is rare comprising 18.3% and 29.8% of all liver cancers in males and females, respectively, in the United States (Vatanasapt et al., 2002). The highest incidences of this cancer, however, occur in the distributional range of O. viverrini in Southeast Asia (Vatanasapt et al., 2002; Khuhaprema et al., 2007; Mathers et al., 2007). In the Khon Kaen area, liverfluke-related cancer comprises 89% of all liver cancers with an incidence of 97.4 per 100 000 for males and 39.0 per 100 000 for females, the highest in the world (Vatanasapt et al., 2002). Unfortunately, this cancer, which can eventually invade the liver, is usually not discovered in developing countries before it becomes inoperable and therefore fatal (Gores, 2003; Andrews et al., 2008). It has been responsible for millions of deaths in Southeast Asia (World Health Organisation, 2004). Although it is known that O. viverrini elicits a systemic immune response in its host, the mechanisms by which this occurs have been little investigated (Sithithaworn et al., 2007). In Korea, both the incidence and mortaliy rate due to cholangiocarcinoma are correlated with local prevalence of

C. sinensis (Lim et al., 2006). The most important risk factors for contracting cancer are being male, alcohol consumption, eating freshwater fish, and area of residence of which the last is the most significant (Lim et al., 2006). For O. felineus, the incubation period ranges from 2 to 4 weeks (Pozio, 2008). Infection with this species often begins with an acute infection characterized by high fever, nausea, abdominal pain, myalgia, and eosinophilia before becoming chronic (Sithithaworn et al., 2007; Armignacco et al., 2008). To date, the evidence implicating O. felineus with cholangiocarcinoma is insufficient to draw any definite conclusions (Watanapa and Watanapa, 2002); although some studies suggest that this is likely (Ruditzky, 1928; Bohl and Jakowlew, 1931; Ilyinskikh et al., 1998; Yossepowitch et al., 2004). Diagnosis of infection with opisthorchids is usually accomplished by detection of eggs in fecal samples (Khandelwal et al., 2008). This method, however, has low sensitivity and specificity, particularly in cases with light infections, and relies on the skill of the microscopist in indentifying the egg (Duenngai et al., 2008). Recently, PCR-based methods have been developed which substantially improve diagnosis (Duenngai et al., 2008). The presence of eggs in fecal samples is diagnostic for infection but care must be taken with identification due to the similarity of eggs to those of other food-borne trematodes (Sithithaworn et al., 2007).

3.12.3.1.5 Prevention and cure Control or prevention of food-borne trematode disease in human populations can be brought about by improved sanitation, health education, including dietary advice, and treatment (World Health Organization, 1995a; Jongsuksuntigul and Imsomboon, 2003). Health education, aimed at explaining the significance of the disease, how it is transmitted, the likelihood of reinfection, and how it can be controlled, particularly the role of eating raw fish, plays an important role in disease control and prevention (Jongsuksuntigul and Imsomboon, 2003). Effective prevention occurs when the consumption of raw or undercooked fish is stopped. In the past, this has proven to be difficult because of the traditional place of this food in the cultures of the people affected (Petney, 2001). Evidence from Thailand suggests that education programs were responsible for a decrease in the frequent consumption of fish from 14% to 7% between 1990 and 1994 (Jongsuksuntigul and Imsomboon, 1997). In Laos, an education program and treatment were associated with a reduction in disease prevalence from 62% to 34%. At the end of the education program, the village population understood the relationship between liver disease and the consumption of raw fish. In a control village without education, this was not the case (Strandgaard et al., 2008). Nevertheless, antihelminthic treatment needs to be provided regularly as infection rates can return to the original high levels within a year after initial treatment (Sornmani et al., 1984; Upatham et al., 1988). Treatment with a single or double dose of praziquantel is effective in elimination of adult worms in 98–100% of cases (Lun et al., 2005; Sithithaworn et al., 2007). In a recent outbreak in Italy, fish containing the metacercariae had been frozen at  10 1C for 3 days. Freezing at

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

this temperature should be prolonged and requires 5–7 days to be effective at killing the metacercariae but freezing at  28 1C is effective within 24 h (Armignacco et al., 2008).

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time, however, conditions may become suitable for the snails, and prevalences can again rise (Morley, 2007).

3.12.3.1.7 Recommendations 3.12.3.1.6 Anthropogenic alterations to the environment The complicated three-host life cycle of this group makes predications on changes in prevalence due to anthropogenic manipulation of the environment difficult. Range extensions and increases in abundance are dependent not only on suitable environmental conditions, but also on the presence of suitable snail and fish intermediate hosts. Sithithaworn et al. (2007) speculate that the increase in prevalence and transmission of C. sinensis in China may be due to the concurrent increase in aquaculture in the affected areas. Silver carp (Hypophthalmichthys molitrix) from aquaculture ponds in Vietnam had C. sinensis prevalences of 45% (Garrett et al., 1997). The freshwater gastropod Melanoides tuberculatus, which thrives in aquaculture ponds and has been introduced from its endemic range in Africa and Asia to much of the tropical and subtropical Americas, is a potential first intermediate host for C. sinensis (Lun et al., 2005; De Kock and Wolmarans, 2009), which is also found in aquaculture ponds (Garrett et al., 1997). This would have significant implications if C. sinensis was introduced, either in fish or in humans, into areas colonized by this species. Aquaculture products from O. viverrini endemic countries have shown an immense expansion in recent years (Keiser and Utzinger, 2005), including a variety of cyprinid species (Naylor et al., 2000). As O. viverrini metacercariae are resilient and can remain viable in fish muscle even if the fish is pickled or fermented (World Health Organization, 1995a), the export and import of fish and fish products worldwide provide a potential transfer route for these parasites. In addition, migrant workers or refugees coming from endemic areas are known to have carried infection with them (Schwartz, 1986; Peng et al., 1993). In Laos, the heightening of roads above flood levels has led to the creation of large numbers of ponds in paddy fields as well as in home gardens (Haylor et al., 1997; Bush, 2004). These ponds harbor both the snail and cyprinid fish intermediate hosts of O. viverrini (Sithithaworn et al., unpublished data). The fish are eaten raw or marinated leading to human infection, while lack of adequate, hygienic toilet facilities completes the epidemiological cycle. The predicted increase in temperatures for Southeast Asia (Christensen et al., 2007) is likely to reduce the developmental time of immature stages living in ectothermic hosts and potentially reduce the time available for cercariae to find a host (Poulin, 2005; Hudson et al., 2006), while the predicted increase in rainfall could lead to an increase in habitat suitable for the intermediate hosts. It is thus not clear how the climatic changes will affect the epidemiology of these parasites. Dam construction is known to have a substantial impact on the prevalence/presence of O. felineus. The construction of dams in the former Soviet Union initially led to a substantial reduction in the infection in second intermediate host fish due to the unsuitability of the new habitat for the first intermediate host snails (Potseluev, 1991; Morley, 2007). With

Recommendations follow those of Jongsuksuntigul and Imsomboon (2003) for O. viverrini and Lun et al. (2005) for C. sinensis both of which aim at breaking the cycle of transmission. From the point of view of the freshwater component of the life cycle, this involves the improvement of sanitary condition, and preventing the contamination of ponds and streams which contain snail and cyprinid intermediate hosts with infected fecal material. Toilets built alongside or even over fish ponds should be eliminated (Lun et al., 2005). In addition, all potential food sources of infection should be cooked before consumption. Pharmaceutical treatment of infection can reduce the number of eggs being introduced into freshwater bodies, as can effective education programs aimed at reducing the consumption of raw fish. However, due to the presence of reservoir hosts, the opportunities for completely interrupting the transmission cycle are limited.

3.12.3.2 Intestinal Flukes 3.12.3.2.1 Parasite characterization About 70 species of intestinal trematodes are known to infect humans worldwide (Yu and Mott, 1994); most of these belong to the families Heterophyidae and Echinostomatidae, although zoonotic representatives are found in at least 11 other families (Yu and Mott, 1994; Fried et al., 2004). Of these, 59 species occur in Southeast Asia (Chai et al., 2009). About 35 species contracted by eating raw or insufficiently prepared fish are significant for human health, potentially causing morbidity but rarely mortality, although many infections are probably asymptomatic (Yu and Mott, 1994; Chai et al., 2005). These are usually less well studied than opisthorchid and fasciolid liver flukes discussed elsewhere in this chapter (Yu and Mott, 1994; Graczyk and Fried, 1998). Nevertheless, most parts of the world have representatives of intestinal flukes infecting humans. Infection with metacercariae can occur via a variety of snail, crustacean, and other invertebrate hosts as well as fish, amphibian, and reptile vertebrate second intermediate hosts (Figure 11; Fried et al., 2004). Most species commonly parasitizing the human gut belong to the family Heterophylidae. They are related to the Opisthorchiidae, also possessing testes in the posterior part of their bodies, but unlike the latter, the hermaphrodite genital pore is surrounded by a specific structure called the gonotyl, often forming a genital sucker ornamented with tipped rodlets (Lucius and Loos-Frank, 2008).

3.12.3.2.2 Developmental cycle Minute intestinal flukes usually do not exceed 2–3 mm in length; accordingly, their eggs are usually a little less than 20 mm long (Figure 12). Like opisthorchids, all heterophyids use freshwater, prosobranch snails as first intermediate hosts inhabiting fresh, brackish, or seawater (Figure 11; Lucius and Loos-Frank, 2008). These consume the parasite’s eggs (Figure 12), excreted with the feces of the host, which contain a fully developed miracidium (Figure 12). The further development follows the same pattern as described for

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5 Cercariae penetrate the skin of fresh/brackish water fish and encyst as metacercariae in the tissue of the fish

4

Host becomes infected by ingesting undercooked fish containing metacercariae i

8 6

Metacercariae excyst in the small intestine

3 Cercariae released from snail Fish-eating mammals and birds can be infected as well 2 Snail host ingests eggs, miracidia emerge from eggs and penetrate the snail’s intestine

2a Sporocysts

2b Rediae in snail tissue

2c Cercariae

7 Adult in small intestine

d Embryonated eggs each with 1 a fully developed miracidium are passed in feces

i

= infective stage

d = diagnostic stage

Figure 11 Life cycle of the minute intestinal fluke Heterophyes spp. Adults release embryonated eggs each with a fully developed miracidium, and eggs are passed in the host’s feces 1 . After ingestion by a suitable snail (first intermediate host), the eggs hatch and release miracidia which penetrate the snail’s intestine 2 . Pirenella conica and Cerithidea cingulata are the snail hosts in the Middle East and Asia, respectively. The miracidia undergo several developmental stages in the snail, that is, sporocysts 2a , rediae 2b , and cercariae 2c . Many cercariae are produced from each redia. The cercariae are released from the snail. They have a finned tail and two eye spots resembling those of Clonorchis species 3 , and encyst as metacercariae in the tissues of a suitable euryhaline (brackish water) fish (second intermediate host) 4 . The definitive host becomes infected by ingesting undercooked or salted fish containing metacercariae 5 . After ingestion, the metacercariae excyst, attach to the mucosa of the small intestine 6 and mature into adults (measuring 1.0–1.7 mm  0.3–0.4 mm) 7 . In addition to humans, various fish-eating mammals (e.g., cats and dogs) and birds can be infected by Heterophyes species 8 . Reproduced with permission from CDC, modified by the authors of this chapter.

Figure 12 Eggs of Heterophyes heterophyes (c. 24 mm  14 mm) from the uterus of a gravid worm collected from the gut of a dog. From Taraschewski (unpublished).

opisthorchiids. The rediae shed a huge number of fin-tailed, eyed cercariae which actively swim in search of a suitable fish second intermediate host (Taraschewski, 1984). While penetrating the fish, the cercaria loses its finned tail, forming a metacercaria. On ingestion, this encapsulated, dormant larva is infectious to suitable final hosts, where it attaches to the intestinal mucosa with its ventral sucker, feeding on the outer layer of the intestinal wall using its oral sucker. The habitat where transmission occurs depends on the species involved. Heterophyids are found in water with a wide range of salinities. The miracidia and cercariae of Metagonimus yokogawai, are exposed to pure freshwater, while others, such as Stellanthchasmus falcatus, inhabit brackish water. The free-living stages of the genus Heterophyes also tolerate high salinities. H. heterophyes, which occur in mullet (a euryhaline fish of the family Mugilidae), in Egypt, tolerates salinities ranging from 20% to 60%, which corresponds with the euryhaline habitat

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

of the intermediate snail host, Pirenella conica. These mullet are traditionally eaten raw as salted fessikh (Taraschewski, 1984). For M. yokogawai and Haplorchis taichui, the consumption of uncooked, infected, cyprinid freshwater fish, in which the metacercariae are found, is the route of infection to humans (Yu and Mott, 1994). In a study in northern Thailand, Kumchoo et al. (2005) found all 15 species of cyprinid fish examined infected with Haplorchis taichui, with 540 of a total of 615 fish being infected. Heterophyid flukes, as well as members of the Echinostomatidae, parasitize birds and mammals, including dogs and cats, with species-specific host preferences. Heterophyid infections are usually zoonotic, and although in the Nile Delta, humans seem to be the major hosts, recent data are scarce (Taraschewski, 1984; Elsheikha and Elshazly, 2008). Not all intestinal flukes have three-host life cycles. Fasciolopsis buski cercariae encyst on the surface of aquatic plants (similar to Fasciola hepatica discussed later) (Fried et al., 2004). The resultant metacercariae are transmitted to humans by ingestion of raw or undercooked aquatic plants or by handling such plants resulting in the oral ingestion of metacercariae, or by drinking contaminated water. Fasciolopsis buski (family Fasciolidae) is the largest fluke parasitizing humans, reaching a length of approximately 8–10 cm and a width of 1–3 cm (Fried et al., 2004).

3.12.3.2.3 Human involvement Intestinal food-borne trematodes are acquired by eating raw or undercooked fresh, or brackish water or marine organisms, such as mollusks, crustaceans, insect larvae, squid, fish, and amphibians, which contain the infectious metacercariae (Fried et al., 2004). This includes the consumption of raw (koi pla) or fermented (pla som, pla ra) fish in the northeast of Thailand (Sithithaworn et al., 2007), and raw fish dipped in salt and vinegar (kinilaw) or snails (kuhol, kiambu-ay) in the Philippines (Belizario et al., 2007). The prevalence of intestinal fluke infection in human populations is variable (Hinz, 1996), but can be well above 50% in some areas (Bundy et al., 1991; Radomyos et al., 1998; Belizario et al., 2004), ranging to 100% in the Nghia Hung district of Vietnam where Haplorchis spp. made up 90.4% of all worms recovered (Dung et al., 2007). In such areas, the prevalence of heterophyid metacercariae in susceptible local fish species may be more than 80% with mean intensities between 100 and 250 metacercariae per fish (Kumchoo et al., 2005). Minute intestinal heterophyid flukes are widely distributed throughout the world, but human infections only occur commonly where fish is customarily consumed raw: in East Asia (M. yokogawai, Heterophyes nocens, and others), Southeast Asia (Haplorchis spp. and others), and Egypt (H. heterophyes). The highest heterophyid diversities have been recorded from Korea where 12 species of Heterophyidae and four species of Echinostoatidae and three species from other families have been found to infect humans (Chai and Lee, 2002), and from Thailand (23 indigenous species) (Nawa et al., 2005). In Tokyo, one of the most commonly found human parasite infections is due to Metagonimus yokogawai which is ingested with the local sushi and sashimi dishes (Nawa et al., 2005). Interestingly, along the Persian Gulf, the local population is

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not affected but expatriates from Korea were found to harbor H. heterophyes and H. dispar (Chai et al., 1986). It seems likely that the increased consumption of aquatic organisms in some countries and their export on a worldwide basis, the increasing popularity of eating raw and undercooked food, as well as growing levels of international travel have led to an increase in the incidence of infection with these helminths (Fried et al., 2004).

3.12.3.2.4 Disease characteristics in humans Disease characteristics vary depending on the species of trematode involved. Infection with members of the family Heterophyidae usually causes a mild inflammatory reaction; however, high worm burdens are associated with a more severe pathology including diarrhea, abdominal pain, anorexia, nausea, and vomiting (Taraschewski, 1984). Eggs may invade the mesenteric lymphatic system (Yu and Mott, 1994; Fried et al., 2004), although many questions on the method of invasion and pathology remain open (Elsheikha, 2007). H. taichui has been shown to imbed itself deeply in the human intestinal mucosa with the severity of the damage being proportional to the worm burden, as in other helminth species (Sukontason et al., 2005). For the Echinostomatidae, the symptoms depend on the intensity of infection and can lead to different degrees of focal necrosis and inflammation of the intestinal mucosa (Yu and Mott, 1994). Heavy infections may cause eosinophilia, abdominal pain, severe diarrhea, anemia, and anorexia (Fried et al., 2004). Diagnosis involves microscopic examination of stool samples for eggs, although the morphological similarity of the eggs makes an accurate diagnosis, to species level, difficult (Fried et al., 2004).

3.12.3.2.5 Prevention and cure Abdussalam et al. (1995) have detailed the measures required to control food-borne trematode infections. The two major phases include prevention of contamination of food with infective stages (metacercariae), and inactivation of the metacercariae that have entered the food. As with our discussion on the measures for the prevention of Clonorchis sinensis and Opisthorchis spp., both aim at blocking or at least reducing the rate of transmission. Prevention can be effected by eliminating intermediate hosts, such as freshwater snails, but such methods cannot be carried out on such a widespread basis to have more than local, temporary success, and, in addition, can cause potentially unwanted changes in the local environment (El-Sayed, 2001). Reducing infection of the snails by sewage treatment and better hygienic conditions will reduce the egg input from humans into the transmission cycle but not eggs from natural-reservoir hosts such as dogs and cats. In addition, education to avoid the consumption of infected food by deep freezing or heating to destroy the infective stages can prevent human infection (Fried et al., 2004). The usual method of treatment of adult intestinal fluke infection is with praziquantel (Fried et al., 2004).

3.12.3.2.6 Anthropogenic alterations to the environment The presence of intestinal trematodes from a variety of families has been confirmed in introduced aquaculture fish species in

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Southeast Asia where it presents a potential risk to humans both locally and as an export item (Hop et al., 2007; Thien et al., 2007, 2009). For example, the tilapia, Oreochromis niloticus, originating from Africa which is now cultivated worldwide in subtropical countries, is known to host metacercariae from various species of intestinal trematodes (Costa-Pierce, 2003; Hop et al., 2007; Thien et al., 2009). The use of human feces to fertilize fishponds provides a direct route for first intermediate host infection (Graczyk and Fried, 1998). The presence of domestic animals such as dogs, cats, and pigs as reservoir hosts for these parasites is likely to play a role in their transmission success. In a fish-farming community in Vietnam, fecal samples from 46.6% of cats, 35% of dogs, and 14.4% of pigs contained fishborne zoonotic intestinal trematode eggs including human pathogens such as H. taichui and H. yokogawai (Anh et al., 2009). Several heterophyids naturally occurring in the temperate Old World have been introduced to the Americas (Scholz et al., 2001). This happened following the dispersal of the freshwater/brackish water snail Melanoides tuberculata, which is possibly the most invasive aquatic gastropod known, and which benefits from the rapid growth of pond aquaculture. This species spreads along international trade routes with aquaculture organisms. Furthermore, M. tuberculata was intentionally introduced in South America and certain Caribbean Islands in order to outcompete and exclude the native Biomphalaria species which transmit schistosomiasis (see below, Taraschewski, 2006). The introduction of intermediate hosts of intestinal trematodes can lead to substantial problems in both cultured and native fish species (Mitchell et al., 2002). In Asia as well as in North America and Hawaii, the metacercariae of the gill trematode Centrocestus formosanus has led to losses among both cultured and wild fish species by causing severe gill damage due to high metacercarial burdens (Mitchell et al., 2000, 2005). In the United States, the introduction of a suitable snail intermediate host, Melanoides tuberculatus, was responsible for the spread of this species (Mitchell et al., 2000, 2005). This snail is also likely to be responsible for the introduction of C. formosanus and/or Haplorchis pumilio, which are potentially pathogenic to humans as well as fish, into the United States, Mexico, Brazil, and Venezuela (Radomyos et al., 1983; Giboda et al., 1991; Scholz et al., 2001; Boge´a et al., 2005; Sommerville, 2006; Diaz et al., 2008). In a study from Vietnam, heterophyid infections in fish raised in peri-urban wastewater-fed freshwater aquaculture systems were compared to specimens from units without wastewater influx. The latter showed heavier parasite infections (Hop et al., 2007). Data on the levels of oxygen and environmental toxins in the wastewater-fed ponds, however, were not provided. Nor was the density of snail intermediate hosts determined. Thus, whether the wastewater effected the snail population or the parasites themselves remains to be determined.

3.12.3.2.7 Recommendations The wide variety of intestinal flukes, together with their different intermediate and final hosts, makes recommendations difficult. In general, however, our recommendation for

C. sinensis should also be followed for this group. In endemic areas, control programs must take into account domestic animals as potential reservoir hosts (Anh et al., 2009). The introduction of M. tuberculata, and potentially of other intermediate hosts, to new areas will assist the spread of these parasites and should, if possible, be prevented.

3.12.3.3 Paragonimiasis 3.12.3.3.1 Parasite characterization The genera Paragonimus and Euparagonimus belong to the trematode family Paragonimidae. They contain over 50 species of flukes inhabiting the lungs, of which about 10 species are potentially harmful to humans (Doanh et al., 2007). These are distributed over much of East, South, and Southeast Asia as well as in parts of South and North America including the United States (DeFrain and Hooker, 2002) and Africa, with human infections being found in all of these areas (Blair et al., 1999; Liu et al., 2008). Final hosts include a variety of rodent species and also carnivores and primates. Domestic dogs and cats were found to be highly infected with prevalences reaching 84.6% and 66.5%, respectively, in certain areas of China (Liu et al., 2008). The prevalence of the most common human parasite Paragonimus westermani (the oriental lung fluke) and of congeners in samples of the crab intermediate hosts can reach high levels: Ecuador to 76.5% (Viera et al., 1992), Columbia 50% (Ve´lez et al., 2000), Laos 59% (Odermatt et al., 2007), and between 7.9% and 85.4% in various areas of China (mean 51.2%; Liu et al., 2008). Natural foci of infection are commonly found around small streams with waterside vegetation, which are inhabited by suitable intermediate hosts, and are frequented by mammals, for drinking purposes as well as for defecation (Ve´lez et al., 2000).

3.12.3.3.2 Developmental cycle Adult worms (15  8  5 mm), which are relatively short and compact with an oval shape and have cuticular spines, occur in the lungs of their mammalian hosts. Eggs are released into the environment either by sputum or, if swallowed, in the feces (Figure 13; Soh, 1962; Blair et al., 1999; Lucius and Loos-Frank, 2008). The dark brown eggs of P. westermani are approximately 90  55 mm in length (Figure 14). They hatch to miracidia in fresh or brackish water which actively swim to find a first intermediate snail host. A number of prosobranch genera, including Melanoides, Thiara, and Semisulcospira, have been incriminated as suitable intermediate host species (Lucius and Loos-Frank, 2008). After asexual reproduction involving a progression from sporocysts to rediae to cercariae, the second intermediate crab or freshwater crayfish host can be infected either by active-seeking cercariae or by ingestion of the first intermediate host (Liu et al., 2008). The cercariae encyst in this host to metacercariae. In China, 80 species of crab (Brachiura) belonging to five families are recognized as second intermediate hosts (Liu et al., 2008). If an inadequately cooked infected crab is eaten by the human final host, infection takes place when the metacercariae excyst in the duodenum. The immature worms then penetrate the intestinal wall 3–6 h after infection, migrate through the abdominal cavity, penetrate the diaphragm, and move into the pleural

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

5 4b

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Cercariae Humans ingest 6 inadequately cooked or pickled crustaceans containing metacercariae

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2 Embryonated eggs d 1 Unembryonated eggs

Adults in cystic cavities in lungs lay eggs which are excreted in sputum. Alternately, eggs are swallowed and passed with stool

Figure 13 The life cycle of Paragonimus spp. The eggs are excreted unembryonated in the sputum, or alternately, they are swallowed and passed with stool 1 . In the external environment, the eggs become embryonated 2 , and miracidia hatch and seek the first intermediate host, a suitable snail, and penetrate its soft tissues 3 . Miracidia go through several developmental stages inside the snail 4 : sporocysts 4a , rediae 4b, with the latter giving rise to many cercariae 4c , which emerge from the snail. The cercariae invade the second intermediate host, a crustacean, such as a crab or crayfish, where they encyst and become metacercariae. This is the infective stage for the mammalian host 5 . Human infection with Paragonimus species occurs by eating inadequately cooked or pickled crab or crayfish that harbor metacercariae of the parasite 6 . The metacercariae excyst in the duodenum 7 , penetrate through the intestinal wall into the peritoneal cavity, then through the abdominal wall and diaphragm into the lungs, where they become encapsulated and develop into adults 8 (7.5–12 mm  4–6 mm). The worms can also reach other organs and tissues, such as the brain and striated muscles, respectively. However, when this takes place, completion of the life cycles is not achieved because the eggs laid cannot exit these sites. Time from infection to oviposition is 65–90 days. Infections may persist for 20 years in humans. Animals such as pigs, dogs, and a variety of feline species can also harbor lung flukes. Reproduced with permission from CDC.

cavity. Immature worms can also be ingested by eating undercooked pork from wild boar which can act as paratenic hosts (Liu et al., 2008). Adult worms are found in cystic cavities in the lungs where they lay their eggs starting 65–90 days after infection (Liu et al., 2008). P. skrjabini, which is found predominantly in China, differs in behavior from P. westermani. Although it has a similar life cycle, only few individuals reach the lungs and develop into adults, with most juveniles migrating to other organs such as the muscles, liver, and brain where they cause ectopic lesions (Hu et al., 1982; Cui et al., 1998; Liu et al., 2008).

3.12.3.3.3 Human involvement A number of species can cause human infection with the most medically important species being P. westermani from Asia. Prevalences in humans vary but can be substantial, reaching up to 22.75% of the 17–22 age group of the population of the Cross River basin area of Nigeria (caused by Paragonimus uterobilateralis; Arene et al., 1998), and 20.9% in children under 15 years of age in a hyper-endemic area of Arunachal Pradesh in India, while antigen positivity against the excretory–secretory protein of adult worms was 51.7%, indicating contact with the parasite (P. hererotremus; Devi et al., 2007). Summarizing the data for China, Liu et al. (2008)

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Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.3.3.5 Prevention and cure As with all infectious agents that are ingested with undercooked fresh- and brackish water, or marine invertebrates or vertebrates, the transmission cycle can be broken by educating the populations of infected areas to heat their food sufficiently to denature protein and thus kill the metacercariae before consumption. This is also true for immature worms from paratenic hosts. The World Health Organization (WHO) recommends the use of either praziquantel, the drug of choice, or triclabendazole for the treatment of human paragonimiasis (World Health Organization, 2004).

3.12.3.3.6 Anthropogenic alterations to the environment

Figure 14 Eggs (c. 90 mm  55 mm) of a Paragonimus species. Reproduced with permission from CDC.

found reported prevalences between 1.5% and 33.7% in various Chinese provinces. There is currently a steady increase in the number of people infected with P. westermani in Japan which may be partly related to immigration from Southeast Asia and China (Obara et al., 2004). The tradition of these immigrants of eating raw freshwater food, including crustaceans, may lead to increased rates of autochthonous infections contracted within Japan (Takagi et al., 2009). Food-preparation methods other than heating, such as pickling, marinating, or salting, are not effective at killing the metacercariae (Yokogawa, 1965).

3.12.3.3.4 Disease characteristics in humans Following the ingestion of infected intermediate hosts, the progression of the disease is slow. The first symptoms for pulmonary infections include cough, chest pain, dyspnea, blood-tinged sputum, and, occasionally, fever (Obara et al., 2004; Liu et al., 2008). In Laos, where both P. westermani and P. heterotremus are known to infect humans, 12.7% of patients presenting with a chronic cough were infected by Paragonimus sp. (Odermatt et al., 2007). Extra-pulmonary paragonimiasis can also occur, although this is less common than the pulmonary form. It most commonly involves the central nervous system with invasion of the brain potentially leading to seizures, epilepsy, motor and sensory disturbances, and other neurological syndromes (Mac et al., 2007; Liu et al., 2008). Other sites potentially affected include the eyes (Wang et al., 1984), urinary bladder, skin, liver, and the pericardial area (Liu et al., 2008). The severity of disease is dependent on the length and intensity of infection (Liu et al., 2008). Paragonimiasis can be diagnosed by finding eggs in stool or sputum samples, although these are not present until 2–3 months after infection (Liu et al., 2008). Effective immunological tests are also available (Lee et al., 2007).

Economic globalization processes, such as aquaculture with the import of potentially parasitized crustaceans (Keiser and Utzinger, 2005), are likely to have been responsible for the recent finding of the first human infection in Brazil (Lemos et al., 2007). It has been suggested that the construction of the Three Gorges Dam in China will lead to an increase in human infection rates with P. skrjabini as the habitat provided by the dam will favor the population growth of suitable crab second intermediate hosts (Morley, 2007).

3.12.3.3.7 Recommendations The problem of paragonimiasis can be addressed at two levels: (1) by breaking or inhibiting the transmission cycle which can be effected by appropriate, hygienic sewage-disposal practices and by educating the population to reduce contamination of freshwater sources with saliva and to eat only sufficiently heated water-based food products and (2) by treating the affected population to reduce the number of eggs input into the environment. For paragonimiasis, unlike infection with Opisthorcis viverrini with humans as the dominant final hosts, the presence of a variety of zoonotic hosts, especially with companion animals living integrated in human communities, there is an alternative source of introducing large numbers of eggs into water bodies, which complicates control possibilities.

3.12.3.4 Diphyllobothriosis 3.12.3.4.1 Parasite characterization The zoonotic genus Diphyllobothrium (class Cestoda, order Pseudophyllidea) contains up to 14 species of tapeworm which occasionally infect humans, of which D. latum occurs most commonly (Scholz et al., 2009). These fish tapeworms occur throughout the Northern Hemisphere (Dick et al., 2001) as well as in some countries south of the equator such as Peru (Baer et al., 1967), Chile (Torres et al., 2004a, 2004b), Brazil (Tavares et al., 2005), and Argentina (Revenga, 1993). The distribution of the various pathogenic species differs; for example, D. dendriticum occurs with a circumpolar distribution at high latitudes above the distribution of D. latum (Curtis and Bylund, 1991). The two latter species, as well as D. nihonkaiense, have recently invaded and partly colonized new continents (Torres et al., 2004a, 2004b; Arizono et al., 2009).

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.3.4.2 Developmental cycle The natural life cycle includes fish-eating birds and mammals as definitive hosts, and freshwater or marine copepods and fish as first and second intermediate hosts, respectively (Figure 15; Scholz et al., 2009). The bodies of tapeworms are long and flat, consisting of a scolex (head) with two slit-like attachment grooves (bothria), a proliferation zone (neck), and a strobila with many proglottids (Figure 16). Diphyllobothrium spp. reach a length of up to 25 m with as many as 4000 hermaphrodite segments. The growth rate may be as high as 22 cm d1 and the adult worms can live 20 years or longer (Scholz et al., 2009). The eggs, which are operculate (35–80 mm long and 25–65 mm wide depending on species,

Infected crustacean ingested by small freshwater fish Procercoid larva released from crustacean, develops into plerocercoid larva 5

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Figure 17) are excreted with the feces after which they mature within 2–3 weeks. In pseudophyllid cestodes, the onchosphere larva hatching from the egg is surrounded by a ciliated epithelium which enables the larva to swim about in order to attract a copepod. On ingestion by such a first intermediate host, the coracidium develops into procercoid larvae. If the copepod is in turn eaten by a small freshwater fish, the procercoid is released and migrates into fish muscle where it develops into a plerocercoid larva, the stage which is infective to humans. Diphyllobothrium species differ in their preference for the second intermediate host. Larvae of D. latum are usually found in perch and pike, whereas D. dendriticum occurs more commonly in salmonoid fish (Curtis and Bylund, 1991).

Predator fish eats 6 infected small fish

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= infective stage

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Figure 15 The life cycle of Diphyllobothrium latum. Immature eggs are passed in feces 1 . Under appropriate conditions, the eggs mature (approximately 18–20 days) 2 and yield oncospheres which develop into ciliated coracidia 3 . After ingestion by a suitable freshwater crustacean (the copepod first intermediate host), the coracidia develop into procercoid larvae 4 . Following ingestion of the copepod by a suitable second intermediate host, typically minnows and other small freshwater fish, the procercoid larvae are released from the crustacean and migrate into the fish flesh where they develop into a plerocercoid larvae (sparganum) 5 . The plerocercoid larvae are the infective stage for humans. As humans do not generally eat undercooked minnows and similar small freshwater fish, these do not represent an important source of infection. Nevertheless, these small second intermediate hosts can be eaten by larger predator species, for example, trout, perch, and pike 6 . In this case, the sparganum can migrate to the musculature of the larger predator fish and humans can acquire the disease by eating these later intermediate infected host fish raw or undercooked 7 . After ingestion of the infected fish, the plerocercoid develop into immature adults and then into mature adult tapeworms which will reside in the small intestine. The adults of D. latum attach to the intestinal mucosa by means of the two bilateral groves (bothria) of their scolex 8 . The adults can reach more than 10 m in length, with more than 3000 proglottids. Immature eggs are discharged from the proglottids (up to 1 000 000 eggs per day per worm) 9 and are passed in the feces 1 . Eggs appear in the feces 5–6 weeks after infection. In addition to humans, many other mammals can also serve as definitive hosts for D. latum. Reproduced with permission from CDC.

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Figure 16 Diphyllobothrium egg; size c. 90 mm  55 mm. Note that the pseudophyllid tapeworm egg has an operculum (arrows), similar to that of trematode eggs, through which the coracidium larva escapes from the shell. Courtesy of Dr. Yves Jackson, Travel and Migration Medicine Unit, Geneva University Hospitals.

Humans are seldom infected by eating small uncooked fish; rather, infection occurs by eating larger predatory fish which have themselves consumed the smaller prey fish species in which the plerocercoid larvae have migrated into muscle tissue. If a human ingests such tissue, the plerocercoid larva develops into an immature, followed by a mature, tapeworm in the small intestine. Adult worms can produce more than a million eggs per day (Scholz et al., 2009), and D. latum proglottids containing eggs are visible in the stool around 15–45 days after fish consumption (Jackson et al., 2007; Scholz et al., 2009). D. latum has been reported to survive in humans for up to 25 years (Scholz et al., 2009). Prevalences in fish may be high; Oshima and Wakai (1983) report from Japan that the yearly infection rates for cherry salmon (Oncorhynchus masou) ranged from 15.9% to 48.8%. In Argentina, introduced rainbow (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis) were heavily infested with both D. latum (28% and 9%, respectively) and D. dendriticum (58% and 27%, respectively) (Revenga, 1993).

3.12.3.4.3 Human involvement A recent estimate suggests that up to 20 million people are infected worldwide (Chai et al., 2005). Human infections are generally associated with cold waters, with Finland and Alaska being the most affected areas (Scholz et al., 2009). Nevertheless, diphyllobothriasis, caused by a member of the family Diphyllobothriidae, has also been reported from tropical areas such as southern India (Pancharatnam et al., 1998) and Malaysia (Rohela et al., 2002). There is evidence, however, that human disease rates are declining in the United States, Asia, and most of Europe, although incidence in Russia, South Korea, Japan, and South America seem to be increasing (Scholz et al., 2009). In Europe, D. latum has reappeared in lakes around the Alps (Terramocci et al., 2001; Jackson et al., 2007). The association between Diphyllobothrium tapeworms and humans is ancient, with the eggs of Diphyllobothrium having

Figure 17 Diphyllobothrium latum: plerocercoid larva in the musculature of a pike from Como Lake, Italy. From Scholz T, Garcia HH, Kuchta R, and Wicht B (2009) Update on the human broad tapeworm (genus Diphyllobothrium) including clinical relevance. Clinical Microbiological Reviews 22: 146–160 and courtesy of the American Society of Microbiologists.

been found in coprolites or other human remains from Europe, North and South America, and the Middle East (Goncalves et al., 2003). Eggs from Peruvian mummies dated between 4000 and 5000 years old were recently identified as belonging to D. pacificum (Reinhard and Urban, 2003). The route of infection involves the consumption of uncooked, inadequately cooked, or smoked freshwater or marine fish (including sushi and sashimi), the intermediate hosts of Diphyllobothrium (Terramocci et al., 2001; Nawa et al., 2005). In Switzerland, marinated but uncooked perch (Perca fluviatilis) filets were consumed by 26 guests at a wedding, seven of whom had a confirmed D. latum infection with one additional case being probable (Jackson et al., 2007).

3.12.3.4.4 Disease characteristics in humans In many cases, infection is asymptomatic; however, diarrhea and abdominal pain occur in about 20% of cases and prolonged heavy infection may lead to intestinal obstruction, cholecystitis, or cholangitis (King, 2005; Scholz et al., 2009). Other symptoms reported include constipation, fatigue,

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

headache, and occasionally allergic reactions (Scholz et al., 2009). In addition, anemia due to vitamin B12 deficiency has been reported (Stabler and Allen, 2004). Diagnosis is usually based on the recovery of eggs from fecal samples but molecular techniques are also available (Scholz et al., 2009). In general, the parasite is only identified to the genus level using egg morphology (Scholz et al., 2009).

3.12.3.4.5 Prevention and cure As nonhuman final hosts such as dogs, cats, foxes, and wild pigs are also involved in the transmission cycle, sewage treatment alone cannot eliminate the disease (Curtis and Bylund, 1991). Avoiding eating raw fish means that transmission cannot take place. Cooking fish for at least 5 min at 55 1C kills the larvae, as does freezing at  20 1C for at least 24 h (Jackson et al., 2007). If an infection occurs, praziquantel is the drug of choice with niclosamide a possible alternative (Scholz et al., 2009).

3.12.3.4.6 Anthropogenic alterations to the environment The extensive increase in aquaculture with a subsequent transfer of fish for culture purposes could lead to transfer of infected individuals to new habitats. It has been suggested, for example, that cases of diphyllobothriasis in Brazil, where the disease was previously unknown, were caused by eating imported salmon from aquaculture production (Tavares et al., 2005; Cabello, 2007). In Chile, the establishment of salmon aquaculture coincided with the appearance and increase in abundance of diphyllobothriasis. Prevalences and mean intensities of D. latum and D. dendriticum are higher in introduced rainbow trout than in native fish species (Torres et al., 2004b; Cabello, 2007). Data from Russia, including Siberia, show that the construction of reservoirs is often followed by an increase in infected fish. This becomes apparent 3–4 years after impoundment and may result in the development of stable foci of diphyllobothriasis, especially if dam construction leads to an influx of human population into the area (Morley, 2007). Accordingly, the tapeworm changes its life cycle from primarily occurring in animals (zoophilic) to humans (anthropophilic). This is facilitated by the continuous discharge of untreated domestic sewage. However, the number of infected fish is lower where sewage mixes with industrial wastewater from chemical plants, reducing the copepod density (Morley, 2007).

3.12.3.4.7 Recommendations Adequate sewage treatment can reduce contamination of water sources with eggs excreted by infected human hosts, although alternative reservoir hosts may be present. Eating of marinated or cold smoked, raw fish should only occur after deep freezing.

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Pseudoterranova decipiens sensu lato, are parasitic nematodes, both of the family Anisakidae. Herring and cod worms usually spend their complete life cycle in the marine environment. The final hosts of these worms include marine mammals, while aquatic invertebrates, predominantly pelagic calanoid copepods and euphausiids (for Anisakis spp.) and different benthopelagic harpacticoid copepods, amphipods, and isopods (for Pseudoterranova spp.), act as intermediate, and a wide variety of cephalopod and fish species act as paratenic hosts (e.g., Abollo et al., 2001; Audicana et al., 2002; Klimpel et al., 2004). Recent molecular taxonomic examinations have shown that there are at least 9–14 species in the genus Anisakis and eight Pseudoterranova species, many of which had previously been lumped together either as A. simplex or as P. decipiens (Nadler et al., 2005; Mattiucci and Nascetti, 2006).

3.12.3.5.2 Developmental cycle Anisakis species are usually found in cetaceans (baleen and toothed whales), while Pseudoterranova species occur especially in pinnipeds such as seals (Chai et al., 2005). The heteroxenous life cycle of both nematodes principally follows the nematode life-cycle pattern, including four larval stages (L1–L4) and the adults in the final host. Unembryonated eggs of Anisakis spp. are produced by adult worms living in the intestinal tract of marine mammals (Figure 18). These are shed with the feces and become embryonated in water. There is some discrepancy in the literature on whether L2 or L3 larvae then hatch from the eggs (e.g., Smith, 1983; Koie, 2001; Klimpel et al., 2004). The third stage larvae of Anisakis spp., which are ingested by humans, are 2–3 cm long and 0.5– 1.0 mm in diameter (Bogitsh et al., 2005). The hatching larvae become free swimming and may be eaten by a pelagic copepod or euphausiid first intermediate host. If, in turn, this crustacean host is eaten by a cephalopod or fish, the L3 larvae migrate to the body cavity or viscera of this new transport host where they become encysted (Figure 19). Thus, the larvae can potentially be transferred from fish to fish. In this paratenic transfer, very large numbers of L3 larvae may accumulate in high trophic order carnivorous fish (Abollo et al., 2001). If the cephalopod or fish is eaten by a marine mammal, the larvae molt twice before reaching the adult stage (Audicana et al., 2002; Klimpel et al., 2004). In the life cycle of Pseudoterranova spp., partially embryonated eggs passed in seal feces settle onto the seabed where they complete development to the third stage larvae (L3) and hatch. Nematode larvae ingested by benthic and/or benthopelagic crustaceans hatch in their intestine and migrate to the peritoneal cavity. Various fish species serve as paratenic hosts, acquiring Pseudoterranova third-stage larvae through the food chain. Death of the fish host may lead to migration of both parasites from the visceral organs to muscle tissue, a process which may be enhanced by cold storage (Abollo et al., 2001). Humans are accidental hosts which are unsuitable for the parasites to continue their life cycle.

3.12.3.5 Anisakiasis 3.12.3.5.1 Parasite characterization

3.12.3.5.3 Human involvement

The long, thin and tapering herring worms, A. simplex sensu lato (i.e., a complex of closely related species), and a similar pathogenic species group, the cod- or seal worms

Humans are accidental hosts of both Anisakis and Pseudoterranova species both of which cause similar illnesses (Sohn and Seol, 1994; McClelland, 2002). Anisakiasis is caused by

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Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control Humans become incidental hosts through eating infected raw or undercooked seafood

7

i = infective stage d = diagnostic stage

Diagnosis of anisakiasis can be made by gastroscopic examination during which the 2-cm larvae can be removed

6

When fish or squid containing L3 larvae are ingested by marine mammals, the larvae molt twice and develop into adult worms. Adult worms produce eggs that are shed by marine mammals

d

1 Marine mammals excrete unembryonated eggs

Eggs become embryonated 2a in water and L2 larvae form in the eggs i 5 Fish and squid maintain L3 larvae that are infective to humans and marine mammals

2b After the L2 larvae hatch from eggs, they become free swimming

4 Infected crustaceans are eaten by fish and squid. Upon the host’s death, larvae migrate to the muscle tissues, and through predation, the larvae are transferred from fish to fish

3

Free-swimming larvae are ingested by crustaceans and they mature into L3 larvae

Figure 18 The life cycle of Anisakis spp. Adult stages of Anisakis simplex or Pseudoterranova decipiens reside in the stomach of marine mammals, where they are embedded in the mucosa, in clusters. Unembryonated eggs produced by adult females are passed in the feces of marine mammals 1 . The eggs become embryonated in water, and first-stage larvae are formed in the eggs. The larvae molt, become second-stage larvae 2a, and after the larvae hatch from the eggs, they become free swimming 2b. Larvae released from the eggs are ingested by crustaceans 3 . The ingested larvae develop into third-stage larvae that are infective to fish and squid 4 . The larvae migrate from the intestine to the tissues in the peritoneal cavity and grow up to 3 cm in length. Upon the host’s death, larvae migrate to the muscle tissues, and through predation, the larvae are transferred from fish to fish. Fish and squid maintain third-stage larvae that are infective to humans and marine mammals 5 . When fish or squid containing third-stage larvae are ingested by marine mammals, the larvae molt twice and develop into adult worms. The adult females produce eggs that are shed by marine mammals 6 . Humans become infected by eating raw or undercooked infected marine fish 7 . After ingestion, the anisakid larvae penetrate the gastric and intestinal mucosa, causing the symptoms of anisakiasis. Reproduced with permission from CDC.

the ingestion of nematodes of the genus Anisakis (Mattiucci et al., 1997) with raw or undercooked marine fish (Sakanari and McKerrow, 1989; Audicana et al., 2002; Nawa et al., 2005). Japan, where consumption of such food is common, accounts

for about 95% of the known anisakiasis cases worldwide, with about 2000 recorded every year (Audicana et al., 2002). In Europe, only about 500 cases had been reported up to 2002, of which almost all were from the Netherlands, Germany,

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

Figure 19 Larval Anisakis in a herring (arrow). Note the typical watchspring coil of the encysted L3 larvae. Courtesy of Dr. V. Etzel, Cruxhaven.

France, and Spain (Audicana et al., 2002). The number of cases appears to be increasing (Audicana et al., 2002), perhaps as a result of increased recognition of the disease and/or because of the more frequent consumption of raw or undercooked seafood. Another hypothesis suggests that increasing eutrofication of the sea leads to higher densities of mesozooplankton, comprising mostly herbivorous crustaceans (Micheli, 1999), although variation in infestation prevalence and intensity can also be related to changes in sea temperature and the population levels of the final host (McClelland et al., 2002; Midtgaard et al., 2003). Physicians do not usually have the capability of determining precisely which nematode species is involved.

3.12.3.5.4 Disease characteristics in humans Not only have pathological effects been associated with the ingestion of live worms but allergic effects have also been noted after ingesting either live or dead worms (Del Pozo et al., 1997; Audicana et al., 2002). The clinical course of infection by Anisakis or Pseudoterranova species is variable depending on the localization of the parasites (Casta´n et al., 2002). Two forms are recognized, gastric and intestinal, with the former predominating in some areas, such as Japan, while the latter is more common in Europe and is symptomatically more severe (Casta´n et al., 2002; Akbar and Ghosh, 2005). In noninvasive anisakiasis, the worms remain within the gastrointestinal tract and the infection is usually asymptomatic. Acute gastric anisakiasis usually involves abdominal pain, nausea, and vomiting, and occurs 2–5 h after eating infected seafood. The larvae can be detected by gastroscopy and removed (Akbar and Ghosh, 2005). In Japan, there is more widespread use of endoscopic techniques due to greater awareness of the disease (Audicana et al., 2002). Intestinal anisakiasis, which is difficult to diagnose, usually appears between 10 h and 2 days of ingesting the larvae, with the major symptom being abdominal pain (Matsui et al., 1985). This is usually a self-limiting condition which resolves within 3–12 days (Matsui et al., 1985). Migration of the larvae to other organs such as the pancreas, spleen, and lungs has been reported (Akbar and Ghosh, 2005). Allergic manifestations caused by the ingestion of anisakids, either in cooked or insufficiently cooked fish, can be

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serious, with more than 50% of patients requiring emergency treatment (Audicana et al., 2002). Symptoms include anaphylaxis, urticaria, angioedema, dermatitis, and airway hyperreactivity (Lopez-Serrano et al., 2000; Audicana et al., 2002; Nieuwenhuizen et al., 2006). Indeed, of the 625 submissions to the Sandiago Apo´stel Hospital in northern Spain for anaphylaxis between 1994 and 1999, 67 (10.7%) were caused by an allergy to A. simplex (Audicana and Kennedy, 2008). Allergens have been found in A. simplex which are resistant to both body heat and the digestive enzyme pepsin indicating that they can potentially lead to an allergic response in spite of digestive processes (Caballero and Moneo, 2004). The variability and generality of symptoms, particularly of chronic gastric anisakiasis, can lead to confusion in the diagnosis with other, potentially severe diseases such as peptic ulcer, appendicitis, Crohn’s disease, and cancer (Akbar and Ghosh, 2005). A definitive diagnosis can be made by finding worms in the stomach using gastroscopy. Abdominal ultrasonography can be used to diagnose intestinal infections but about half of the 15 cases reported by Casta´n et al. (2002) required histological examination of biopsy specimens. Immunoassays have also been developed both for diagnosing infections (Akbar and Ghosh, 2005) and for detecting allergic responses (Audicana et al., 2002).

3.12.3.5.5 Prevention and cure Humans are accidental hosts of these species and reproduction in humans does not occur. Thus, curing the disease will not lead to changes in the natural abundance of these parasites; in fact, humans act as an ecological sink. Abollo et al. (2001) suggest that better fisheries and aquaculturemanagement practices may help reduce the problem. Commercial fisheries often dispose of heavily infested viscera in the sea. Any fish feeding on these will be infected and act as paratenic hosts. In addition, the storing of whole fish (i.e., those still containing the viscera) on ice for several hours may enhance the number of L3 larvae migrating from the viscera to the muscle tissue, which is the source of infection for humans. This must be prevented. Heating fish to above 60 1C effectively kills the larvae and denatures the protein, a fact which should be propagated in education programs by the health authorities in affected regions (Audicana et al., 2002).

3.12.3.5.6 Anthropogenic alterations to the environment As the cycles of these parasites are predominantly marine, changes in land-based hydrological systems will have little influence on transmission dynamics. The effect of the substantial changes in such parameters as the temperature, salinity, and pH of seawater associated with global climate change are unclear, although eutrification can lead to an increase in intermediate host density (Micheli, 1999; McClelland et al., 2002; Midtgaard et al., 2003).

3.12.3.5.7 Recommendations As indicated by Abollo et al. (2001, discussed earlier) certain changes in fisheries practices may help reduce the likelihood of humans eating infected fish. Heating fish to temperatures

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causing protein denaturation before consumption will also prevent human infection.

3.12.4 Other Parasites with a Water-Dependent Life Cycle 3.12.4.1 Fascioliasis 3.12.4.1.1 Parasite characterization Fasciola hepatica, the common liver fluke, belongs to the trematodes as do the opisthorchids, the heterophyids, and Paragonimus spp. These hermaphroditic flatworms inhabit the biliary system of the liver (Figure 20; Lucius and Loos-Frank, 2008). Naturally occurring in Europe, it is now one of the world’s most widely distributed parasites, having been recorded from at least 51 countries ranging from Europe,

4a Sporocysts

4b Rediae

4c Cercariae

North and South America, Africa, Asia, Australia, and Oceania (Mas-Coma et al., 1999; Taraschewski, 2006; Laird and Boray, 2008). A second species, F. gigantica, the distribution of which overlaps that of F. hepatica in Africa and Asia, is also known to cause similar disease in humans and animals (Mas-Coma et al., 2005). Both are zoonoses infecting a variety of domestic and wild animals including cattle, sheep, buffaloes, equids, elk, red deer, hares, and occasionally kangaroos (Presidente and Beveridge, 1978; Mas-Coma et al., 2005). Prevalences in these hosts are variable but may be very high in some areas: a mean of 56.3% (range: 41.8–61.1 for nine sites) in sheep in the Upper Awash River basin in Ethiopia (Asrat et al., 2005), 44.2% (determined by enzyme-linked immunosorbent assay (ELISA), range 38.4–62.0 for four sites) for sheep in Spain (Ferre et al., 1995), and 0–56.8% in cattle in Cambodia (F. gigantica; Tum et al., 2004).

Metacercariae on water plant ingested by human, sheep, or cattle

5 Free-swimming cercariae encyst on water plants

6 i

In snail tissue

4 Snail

7 Excyst in duodenum 7

3 Miracidia hatch, penetrate snail

8

2 Embryonated eggs in water i = infective stage d = diagnostic stage

d 1 Unembroynated eggs passed in feces

8 Adults in hepatic biliary ducts

Figure 20 The life cycle of Fasciola hepatica. Immature eggs are discharged in the biliary ducts and in the stool 1 . Eggs become embryonated in water 2 , eggs release miracidia 3 , which invade a suitable snail intermediate host 4 , including the genera Lymnaea (Galba), Fossaria and Pseudosuccinea. In the snail, the parasites undergo several developmental stages (sporocysts 4a , rediae 4b, and cercariae 4c ). The cercariae are released from the snail 5 and encyst as metacercariae on aquatic vegetation or other surfaces. Mammals acquire the infection by eating vegetation containing metacercariae. Humans can become infected by ingesting metacercariae-containing freshwater plants, especially watercress and by metacercariae floating on water 6 . After ingestion, the metacercariae excyst in the duodenum 7 and migrate through the intestinal wall, the peritoneal cavity, and the liver parenchyma into the biliary ducts, where they develop into adults 8 . In humans, maturation from metacercariae into adult flukes takes approximately 3–4 months. The adult flukes (Fasciola hepatica: up to 30 mm  13 mm; F. gigantica: up to 75 mm) reside in the large biliary ducts of the mammalian host. Fasciola hepatica infect various animal species, mostly herbivores. Reproduced with permission from CDC, modified by the authors of this chapter.

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.4.1.2 Developmental cycle The eggs (Figure 21; c. 132  70 mm) are excreted in the feces which, when they have been washed or defecated into freshwater, embryonate over a period of 2–3 weeks, depending on the temperature, and release the miracidia larvae. These then invade the snail intermediate host. In Europe, the lesser pond snail, Lymnaea (Galba) truncatula (family Lymnaeidae) is the major first intermediate host. This is also true for the Andean Altiplano in South America and the higher altitudes in East and South Africa where this gastropod has been naturalized. Many lowland areas of the tropics and subtropics have been colonized by Lymnaea (Pseudosuccinea) columella deriving from the southern USA and the Caribbean. This species transmits the fluke in Argentina and Brazil. In other areas, such as South Africa, to which it has also been introduced, this snail shows no indication of field transmission. Currently, for Australia and New Zealand, the only known snail intermediate host is the indigenous Lymnaea tomentosa, although laboratory trials with introduced local L. columella showed that these could be infected successfully (Taraschewski, 2006). Development in the snail continues through sporocysts, redia, and cercariae, which are released into the water. These move preferentially onto the underside of partially submerged vegetation, and form a cyst which takes about 24 h to becoming infective (Figure 22). The cyst, which can also float on the surface of the water, must be ingested, with or without the vegetation, by the final hosts such as grazing ruminants, or by humans. Excystation takes place in the duodenum prior to penetration of the intestinal wall. The immature cysts enter the liver capsule through the peritoneum, and then migrate through the liver parenchyma where they feed on liver cells and blood before reaching the bile ducts. There, they develop into adults (5.0 1.3 cm length by width), attaining sexual maturity in about 12 weeks. The adults can live for up to 12–15 years (Bogitsh et al., 2005; Saba et al., 2004; Lucius and Loos-Frank, 2008).

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indicating that transmission can occur through water used for drinking and washing dishes, etc., which is collected from irrigation and drainage canals (Estaban et al., 2002; Mas-Coma et al., 2005). This hypothesis is supported by a significant positive association between F. hepatica and other waterborne protozoan diseases with direct life cycles, such as Cryptosporidium spp. and Giardia lamblia (Estaban et al., 2002). Although originating in Europe, the highest prevalences are now found in South America, in Peru, and particularly Bolivia where up to almost 100% infection rates in the rural human population have been recorded locally (Estaban et al., 1999; Mas-Coma et al., 1999, 2005). It is estimated that 180 million people are at risk worldwide and that between 2.5 and over 17 million are infected (Mas-Coma et al., 2005). In the Nile Delta in Egypt, where F. hepatica and F. gigantica co-occur, a mean of 12.8% of the human population was found to harbor liver flukes. Here, the number of cases has risen conspicuously during the last few decades (Estaban et al., 2003). In Egypt, as in the Andean countries of South America, women are more commonly infected than men, with school children, especially girls, having the highest prevalences and intensities of infection (Estaban et al., 1999, 2002, 2003; Marcos et al., 2006). In Vietnam and Cuba too, females are more likely to be infected than males, but adults have the highest infection rates (World Health Organization, 2007; Rojas et al., 2009). This species also has a long association with humans, having been found in mummified remains from over 5000 years ago in a continual progression through to the present day (Bouchet et al., 2003; Goncalves et al., 2003).

3.12.4.1.4 Disease characteristics in humans

Eating contaminated vegetable material is undoubtedly the most common mode of infection in humans (Mas-Coma et al., 2005); however, there is some suppositional evidence

The severity of symptoms depends on the intensity of infection, with low infection levels potentially being asymptomatic (Chen and Mott, 1990). There are several phases involved with infection by Fasciola spp. (World Health Organization, 2007). After ingesting infective metacercariae, there is an incubation phase lasting from a few days to several months. The acute hepatic stage begins with the migration of parasites from the body cavity into the liver capsule where they ingest hepatic tissue causing hemorrhage and inflammation. This stage is characterized by abdominal pain and

Figure 21 Unembryonated Fasciola hepatica egg (arrows: operculum) from fresh feces. Size c. 130 mm  70 mm. From the slide collection of Werner Frankw.

Figure 22 Squash prepared Fasciola hepatica metacercariae encysted on a plant. Diameter c. 250 mm. From the slide collection of Werner Frankw.

3.12.4.1.3 Human involvement

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Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

fever accompanied by a variety of other symptoms including fatigue, weight loss, nausea, vomiting, respiratory symptoms, and chest pain (Saba et al., 2004; World Health Organization, 2007). These symptoms usually clear once the flukes reach the bile ducts. On having reached their final habitat, the chronic latent phase begins during which the parasites mature and start laying eggs. This phase can last from months to years, with symptoms being nonspecific involving abdominal pain, nausea, fatigue, and weight loss (Saba et al., 2004). The latent phase can progress to a chronic obstructive phase with worms and debris periodically blocking the bile duct. This results in swelling, potentially with acute pancreatitis, jaundice, and cholestatic hepatitis. Bacterial superinfection can lead to acute cholangitis and cholecystitis (World Health Organization, 2007). In the post-infection stage, there are a variety of clinical sequelae and complications including biliary cirrhosis and gall stones (World Health Organization, 2007). Chronic fascioliasis can be a long-term disease lasting well over 10 years (Bogitsh et al., 2005; Saba et al., 2004). It is possible that the flukes may migrate to organs other than the bile ducts such as the pancreas, spleen, and kidney (Zali et al., 2004), brain (Ying et al., 2007), eye (Dalimi and Jabarvand, 2005), and spinal cord (Vatsal et al., 2006), where they cause pathological damage. In the Altiplano of Bolivia, the synergistic associations between fascioliasis and a variety of other pathogens are believed to cause the high morbidity rates and significant mortality in Aymara children (Mas-Coma et al., 2005). For a definitive diagnosis, it is necessary to confirm the presence of the parasite or its eggs. For the latter, repeated stool examinations are usually required. Immunological methods have also been developed. These are particularly important for early diagnosis when eggs have not yet been produced (Saba et al., 2004).

3.12.4.1.5 Prevention and cure Fascioliasis is predominantly a disease of domestic and herbivorous mammals in most parts of the world. In developed countries, where large herds of stock animals are managed, control programs involving treatment of infected stock are effective (Roberts and Suhardono, 1996; Kaplan, 2001). In developing countries, in which families are often poor and possess few cattle, particularly in tropical regions where water bodies are omnipresent, effective control at this level is much more difficult and stock are often left untreated (Roberts and Suhardono, 1996). This presents a substantial problem in terms of human infection as domestic stock act as reservoir hosts continually contaminating the environment. Various attempts to control the snail intermediate hosts have met with little success (Roberts and Suhardono, 1996). Furthermore, suitable mud snails, such as Lymnaea (Pseudosuccinea) columella, are highly invasive and their accidental introduction to areas that are unaffected by F. hepatica is difficult to prevent (Taraschewski, 2006). On the other hand, trials aimed at the introduction of a nonlocal gastropod capable of outcompeting and thus reducing population levels of the local Fasciola intermediate host have progressed substantially less than in the case of schistosome flukes (see later). In Cuba, biological control of the snail host Lymnaea (Fossaria)

cubensis with the planorbid Helsioma duryi and the thiarid Thiara granifera has been successful in certain habitats. However, Lymnaea (Pseudosuccinea) columella is able to coexist with the introduced competitors (Canete et al., 2004; Rojas et al., 2009). The globally invasive North American snail Physa acuta could be a candidate for such campaigns where nontropical temperatures occur (Dreyfuss et al., 2002). Recently, it was found to have invaded Lake Titicaca (Albrecht et al., 2008). Human infection can be prevented or strongly reduced by eliminating uncooked, potentially contaminated vegetable food from the diet, and by avoiding hand–water–mouth contacts, as well as by drinking water, coming from potentially contaminated sources, only after it has been boiled (Mas-Coma et al., 2005; Ashrafi et al., 2006). In addition, to reduce the chances of infection, cattle and sheep should be prevented from feeding near water sources such as ponds and streams. The currently recommended treatment for fascioliasis is triclabendazole 10 mg kg1 body weight as a single dose. Both immature and adult parasites are killed and cure rates are high (World Health Organization, 2007).

3.12.4.1.6 Anthropogenic alterations to the environment Endemic cycles of fascioliasis are dependent on the presence of water required by the lymnaeid snail intermediate hosts and of suitable final hosts such as cattle (or indeed humans if conditions are suitable). There is evidence from various areas of the world that irrigation has led to the introduction or increased the rate of infection of humans with Fasicola species, in part through the introduction of suitable intermediate snail hosts (Mas-Coma et al., 2005). In the Punto region of Peru, for example, school children had high prevalences of infection with F. hepatica after man-made irrigation systems were established (Estaban et al., 2002). Prevalences in humans ranged from 18.8% to 31.3%. After the construction of the irrigation system, both the intermediate lymnaeid snail host as well as the liver fluke quickly adapted to this environment (Estaban et al., 2002). In Egypt, the large Nile Delta has been transformed into an agricultural plain due to extensive irrigation resulting in high abundance of fascioliasis in animals and humans (Estaban et al., 2003). In the Upper Awash River Basin of Ethiopia, irrigation was associated with significantly increased prevalence of ovine fascioliasis in mid-altitude sites during the dry season and in lowland sites during both the wet and dry seasons (Asrat et al., 2005). In addition to developing countries, fascioliasis can also be a problem enhanced by irrigation in developed countries, such as Spain (Uriarte et al., 1985; Ferre et al., 1995), where irrigation canals increase habitat in which the survival of eggs over winter is possible (Luzon-Pena et al., 1992), the USA (Malczewski et al., 1975; Kaplan, 2001), and Australia, where irrigation was the variable providing the best explanation for the observed distribution of fascioliasis (Durr et al., 2005). In Cambodia, the irrigation of rice at the edges of flooded areas as the water level falls provides suitable aquatic habitat for the snail intermediate hosts of F. gigantica. This, together with the widespread contamination of dams and canals by cattle and water buffalo feces at certain times of the year when they are used as draught animals for preparing the fields, leads

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

to increased transmission rates for this species (Tum et al., 2004; Suon et al., 2006).

3.12.4.1.7 Recommendations In areas with a high abundance of Fasciola infections, the local populations should be educated not to consume raw plants or plant juices from water sources or their vicinity, or from irrigated fields. They should also not drink uncooked or unfiltered water from the environment and avoid washing kitchen utensils in potentially contaminated water. In many or most regions of developing countries, no potable water systems inside dwellings exist. Thus, inhabitants must obtain their water from irrigation or drainage canals (Estaban et al., 2002). Any initiative for improving agricultural or semi-agricultural landscapes should be accompanied by the establishment of a clean, tapwater-supply system. If this is financially not feasible, each village should at least be provided with the opportunity of obtaining drinking water and water for washing the dishes from a safe source. An Egyptian endemic area for human infection showed a markedly reduced prevalence of the disease after the construction and utilization of so-called ‘washing units’ in which water is appropriately filtered (Mas-Coma et al., 2005). In addition, irrigation canals and wet areas should be fenced off, at least in the vicinity of villages where people might be tempted to collect water. Municipalities in irrigated areas should provide veterinary and medical control of animal and human feces for Fasciola eggs. Should these be found, treatment with triclabendazole should be initiated. Irrigation schemes should always be planned including a parasitological assessment taking into consideration the likelihood of spreading fascioiasis (Estaban et al., 2002; Asrat et al., 2005).

3.12.5 Parasites Penetrating Human Skin on Contact with Freshwater 3.12.5.1 Schistosomiasis (bilharziosis) 3.12.5.1.1 Parasite characterization Schistosomes are digenean trematodes of the family Schistosomatidae (Loker and Brant, 2006). There are a large number of species belonging to the genus Schistosoma of which at least 12 are capable of infecting humans (Lucius and Loos-Frank, 2008). They inhabit the mesenteric veins, in most cases of the posterior intestine, feeding on blood (Figure 23). In addition to using the blood vessels as their microhabitat in their final hosts, schistosomes also differ from the usual flukes in other ways: they are dioecious, that is, are either male or female; and in both sexes, the body is not flat, but rounded. Along the ventral surface of the male, there is a slit, the canalis gynaecophorus, in which the long, thin female is kept, ensuring a permanently available sexual partner. Once mated, they can survive inside the human host for decades due to specific morphological and biochemical characteristics of their outer surface. Schistosome eggs do not have an operculum (Figure 24). Moreover, schistosomes do not have a second intermediate host; thus no metacercariae exist (Lucius and Loos-Frank, 2008).

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Molecular taxonomic characterization is likely to increase the number of known species infecting humans (Agatsuma et al., 2001, 2002; Webster et al., 2006; Zhao et al., 2009). In Schistosoma mansoni, the species with the largest distribution and the highest number of infected humans, 85 distinct haplotypes have been found belonging to five distinct lineages, with particularly high variation being present in East Africa (Morgan et al., 2005). Of the currently recognized species, the most important for humans include S. mansoni from Africa, parts of the Arabic Peninsula, Caribbean, and South America; S. intercalatum from Central African countries; S. haematobium causing urinary schistosomiasis in Africa, Madagascar, Iraq, and parts of the Arabian Peninsula; S. japonicum from East and Southeast Asia and the Western Pacific; and S. mekongi from the Mekong River Basin (Gryseels et al., 2006).

3.12.5.1.2 Developmental cycle The life cycles or the various Schistosoma species infecting humans are similar (Figure 23). Eggs released by mated females into blood capillaries passively penetrate the walls of the vessels via antigenic activity either to the intestine or urinary bladder to be excreted with the feces (e.g., S. mansoni) or urine (S. haematobium). On excretion, eggs contain a ciliated miracidium which hatches and seeks a suitable snail host when it contacts water – a process which can last for several hours before the miracidium dies if a host is not found. Intermediate hosts of the S. mansoni and S. haematobium groups are planorbid, air-breathing aquatic snails (Basommatophora) belonging to the genera Biomphalaria and Bulinus, respectively. These inhabit shallow water with a minimum temperature of 20 1C. In contrast, the S. japonicum group is transmitted by small aquatic or amphibic prosobranch snails of the genus Oncomelania. S. mekongi has the prosobranch snail Neotricula aperta as intermediate host. Once having penetrated the snail, the miracidia develop into sporocysts which finally shed cercariae (c. 500 mm long). These elongated, fork-tailed larvae (Figure 25) are released into the water, where they can survive for up to 5–10 h, during which time they actively seek a suitable final host (Lucius and Loos-Frank, 2008). On finding a host, the cercariae penetrate the skin, lose their tail, and develop into an immature worm which must enter a blood or lymph vessel to be transported to the lungs, and then the heart where they enter the arterial system and finally the portal artery. Here, the sexually mature, flat males (S. haematobium: 8–15 mm in length) find round female partners, which slip into the male’s gynaecophoral fold, mate, and then migrate to the organ of preference: S. mansoni, the mesentary of the veins usually of the large intestine or rectum; S. japonicum the mesentary of the veins of the large or small intestine; and S. haematobium the urinary bladder (Lucius and Loos-Frank, 2008). In addition to humans, all human pathogenic schistosomes have mammalian reservoir hosts. S. mansoni is predominantly a human parasite, although some primate species and rodents can harbor the parasite (Cameron, 1928; Duplantier and Se`ne, 2000). S. mekongi has been found in pigs and dogs (Urbani et al., 2002), while S. japonicum has a very wide host spectrum including at least 40 mammal species.

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5 Sporocysts in snail 4 (successive generations)

i = infective stage

Cercariae released by snail into water and free swimming

d = diagnostic stage

i Cercariae lose tails during 7 penetration and become schistosomulae

Penetrate skin 6

8 Circulation

3

Miracidia penetrate snail tissue

A Migrate to portal blood in liver and mature into adults 9

B

2

In feces

d

In urine

Eggs hatch releasing miracidia

C

C

S. japonicum A S. haematobium S. mansoni C B 1

10 Paired adult worms migrate to: A B mesenteric venules of bowel/rectum (laying eggs that circulate to the liver and shed in stools) C Venous plexus of bladder

Figure 23 The life cycle of Schistosoma spp. Eggs are eliminated with feces or urine 1 . Under optimal conditions, the eggs hatch and release miracidia 2 , which swim and penetrate specific snail intermediate hosts 3 . The stages in the snail include two generations of sporocysts 4 and the production of cercariae 5 . Upon release from the snail, the infective cercariae swim, penetrate the skin of the human host 6 , and shed their forked tail, becoming schistosomulae 7 . The schistosomulae migrate through several tissues and stages to their residence in the veins ( 8 , 9 ). Adult worms in humans reside in the mesenteric venules in various locations, which at times seem to be specific for each species 10 . For instance, S. japonicum is more frequently found in the superior mesenteric veins draining the small intestine A , and S. mansoni occurs more often in the superior mesenteric veins draining the large intestine B . However, both species can occupy either location, and they are capable of moving between sites; so, it is not possible to state unequivocally that only one species occurs in one location. S. haematobium most often occurs in the venous plexus of bladder C , but it can also be found in the rectal venules. The females (size 7–20 mm; males slightly smaller) deposit eggs in the small venules of the portal and perivesical systems. The eggs are moved progressively toward the lumen of the intestine (S. mansoni and S. japonicum) and of the bladder and ureters (S. haematobium), and are eliminated with feces or urine, respectively. Reproduced with permission from CDC.

Water buffalo, cattle, and pigs are known to be important hosts in China (Hotez et al., 1997; Ross et al., 1997; Wang et al., 2006). S. haematobium is found almost exclusively in humans but primates can also harbor this parasite (Taylor et al., 1972; Lucius and Loos-Frank, 2008).

3.12.5.1.3 Human involvement

Figure 24 Embryonated egg of Schistsoma mansoni from human feces. Note the lateral spine and the lack of an operculum (compare Figures 10, 12, and 14). Reproduced with, ermission from CDC.

Schistosomiasis is one of the major human diseases with an estimated 200 million people infected, of whom 120 million have symptomatic infections and 20 million have severe disease (Chitsulo et al., 2000). About 650 million people from 76 countries are at risk of contracting the disease. Most infections are in Africa (c. 97%), which contains 85% of the endangered global population. Children under and around the age of 14 are particularly at risk (Chitsulo et al., 2000; Engels et al., 2002; Steinmann et al., 2006).

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

Figure 25 Cercariae of Schistosoma mansoni showing the typical forked tail and lacking eyes, photographed with indirect fluorescent antibody stain to enable better visualization. Reproduced with permission from CDC.

Transmission of the disease to humans involves skin contact with water containing active cercariae. In certain parts of the world where schistosomiasis is endemic, most if not all water bodies are potentially infected and humans contacting water during their daily activities, such as irrigating fields, washing dishes or clothes, fishing, and collecting water for consumption, are at risk (Watts and El Katsha, 1997; Seto et al., 2007).

3.12.5.1.4 Disease characteristics in humans The disease characteristics in humans depend on which species of Schistosoma is present and the intensity of the infection and the individual immune responses (Gryseels et al., 2006). Double infections may be locally common. Initial penetration of the skin can cause a rash similar to swimmer’s itch, found in Europe and caused by the related species, Trichobilharzia ocellata, maturing in water birds, which is otherwise not pathogenic to humans (Hora´k and Kola´rˇova´, 2001). In endemic areas, chronic infections are common. Disease symptoms in these patients are usually associated with the deposition of eggs in various organs where they become the core of granulomas (Gryseels et al., 2006). Depending on the species and age of a female worm, between 20 and 3000 eggs (Figures 23 and 24) are discharged per day. Only about 50% of these succeed in passing through the wall of the intestine or urinary bladder, after which they are embryonated and excreted into the environment. The other half becomes stuck in the wall of organs, mainly the liver, or in other tissues to which they are dispersed in the blood stream. Urinary schistosomiasis is caused by S. haematobium. The most common symptom is blood in the urine due to inflammation caused by the ulceration induced by the eggs in the vesical and ureteral walls (Gryseels et al., 2006). Chronic infection can lead to fibrosis (the accumulation of collagen fibers around the eggs) and calcification of the urinary bladder and lower urinary tract. Chronic urinary schistosomiasis is associated with bladder cancer in Africa, including about 31% of all cancers in Egypt where the prevalence of S. haematobium is high (El-Rifai et al., 2000; El Mawla et al., 2001; Michaud, 2007).

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Hepatic schistosomiasis can develop on infection with S. japonicum, S. mansoni, and S. mekongi, with S. intercalatum causing limited symptoms and pathology. Two distinct syndromes are present. Inflammatory hepatic schistosomiasis is caused by the deposition of eggs in the presinusoidal periportal spaces of the liver (Gryseels et al., 2006). This is the main cause of hepatomegaly found in up to 80% of children and adolescents with schistosomiasis, with the symptoms being difficult to differentiate from those of malaria (Gryseels et al., 2006). Fibrotic schistosomiasis is a late consequence of infection occurring predominantly in young to middle-aged adults with a history of intense, long-term infections and showing high morbidity and potential mortality (Gryseels et al., 2006). Fibrosis in the periportal spaces leads to progressive occlusion of the portal veins, portal hypertension, and splenomegaly. This development can take 5–15 years in S. mansoni infections with more rapid development in S. japonicum (Gryseels et al., 2006). Hemorrhage from gastroesophageal varices is a serious, often fatal complication (Bandeira Ferraz et al., 2001; Lacerda et al., 1999). Acute schistosomiasis is a hypersensitivity response to the migration of the immature worms which occurs a few weeks to months after infection (Gryseels et al., 2006). It is most often described in individuals, such as tourists, who become infected while visiting endemic areas (Jelinek et al., 1996; Bottieau et al., 2006). Symptoms include the sudden onset of fever, myalgia, headache, fatigue, and cough. Later, abdominal symptoms can occur as the worms migrate to their final microhabitat (Bottieau et al., 2006; Gryseels et al., 2006). Symptoms usually disappear within 2–12 weeks, but some individuals become seriously ill with abdominal pain, weight loss, and diarrhea, shortness of breath, toxemia, and hepatosplenomegaly (Gryseels et al., 2006). Movement of eggs to organs other than the liver, such as the lungs (Waldman et al., 2001; Schwartz, 2002), kidneys (Barsoum, 2004), both male and female reproductive organs (Poggensee and Feldmaier, 2001; Leutscher et al., 2000), and the central nervous system (Abreu Ferrari de, 2004), have been reported, all of which can cause significant pathology. Determination of the presence of eggs in feces or urine remains the most important diagnostic technique (Gryseels et al., 2006). Immunoassays are also available but are unable to distinguish exposure from active infection and may crossreact with other helminth species (Gryseels et al., 2006).

3.12.5.1.5 Prevention and cure Although efforts to control schistosomiasis have been partially successful in some parts of the world, the number of people infected has probably not changed significantly and it remains a major health problem, particularly in Africa (Chitsulo et al., 2000; Engels et al., 2002). The main strategy aimed at controlling schistosomiasis is based on reducing transmission from humans to local water supplies by curing infections using the drug praziquantel (Engels et al., 2002; Gryseels et al., 2006). Treatment is also aimed at preventing morbidity, including bladder cancer, associated with long-term infections (El Mawla et al., 2001; Engels et al., 2002). The opportunities to interrupt the transmission cycle of these important waterborne parasites through

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chemotherapy, health education, and the prevention of sewage influx into surface waters and defecation on the banks of water bodies containing suitable intermediate hosts, each a difficult task, are limited due to the existence of reservoir hosts for most species. Some decades ago, various trials were carried out attempting to eliminate the intermediate snail hosts by releasing molluskicides into their habitats. However, no long-term, positive effects could be achieved despite conspicuous environmental damage (Perrett and Whitfield, 1996), although, recently, latex from various Euphorbia species has shown potential (Schall et al., 2001; Sermsart et al., 2005; Dos Santos et al., 2007). Biomanipulation, such as the introduction of a nonsusceptible competitor (Melanoides tuberculata, Thiara granifera, and other nonindigenous Old World snails; Pointier and Giboda, 1999), or a potentially predatory gastropod species (Lanistes carinatus, Marisa cornuarietis, Pila ovata; Pointier and McCullough, 1989; Hofkin et al., 1991; Pointier and David, 2004) into South America and the Caribbean habitats of Biomphalaria spp. transmitting S. mansoni, has been more successful. The introduction of M. tuberculata into these sites resulted in the interruption of transmission and the near-total disappearance of native planorbid (schistosome transmitting) snails, which were locally either partly or totally outcompeted and population levels sank dramatically (Taraschewski, 2006). Currently, the thiarid snails M. tuberculata and T. granifera have colonized the whole hydrographic system on the island of Martinique and maintain dense populations preventing an eventual recolonization by the planorbid intermediate hosts, thus allowing sustainable control (Pointier and Jourdane, 2000). M. tuberculata, however, can also act as an intermediate host for other helminth species of medical or veterinary importance (described earlier).

3.12.5.1.6 Anthropogenic alterations to the environment The construction of dams, lakes, and irrigation systems has substantially aided the spread of schistosomiasis (Steinmann et al., 2006). This can occur through increased habitat availability for the intermediate snail hosts, as well as a higher density of humans due to migration to agriculturally favorable irrigated sites, thus increasing fecal input into water sources. Such migration from infected to uninfected areas can lead to new disease foci (Malan et al., 1977; Idris et al., 2003). In their meta-analysis, Steinmann et al. (2006) show that, worldwide, 8.1% of the at-risk population live in proximity to irrigated areas and 5.4% in proximity to dams – a total of 106 million people. In general, studies comparing the prevalence of infection either in the presence or in the absence of a dam consistently show higher prevalences where dams are present. In Ghana, the construction of clay-core dams was associated with a change in prevalence of S. haematobium from 17% to 51% in 3 years (Hunter, 2003), while the extremely low levels of infection of about 1% prior to the formation of Lake Volta increased to levels of between 68% and 87% after the dam had filled (Paperna, 1969; Scott et al., 1982). For S. mansoni and S. haematobium, both irrigation and dam proximity can increase the risk of infection (Steinmann et al., 2006). In Burkina Faso, for example, non-irrigated areas had a prevalence of

S. haematobium in 14% of schoolchildren, but of 80% in areas where irrigation was established. For S. mansoni, which also occurs in Burkina Faso, the respective prevalences were 1.3% and 45% (Poda et al., 2003). Similar data are available from Liberia for the total population with S. haematobium relative prevalences being 11% and 42% and those for S. mansoni being 9% and 87% (Kazura et al., 1985). In Egypt, the building of the Aswan High Dam led to a shift of irrigation practices from natural, annual flooding events to perennial irrigation with permanent water in irrigation canals and drains (Lanoix, 1958; Malek, 1975, 1976). Prior to dam construction, the prevalence of infection ranged from 2% to 11%, but after the dam was built, it increased from 44% to 75% (Khalil, 1949; cited by Lanoix, 1958). In addition, a variety of studies show that the development and management of water resources can lead to the introduction of Schistosoma spp. in new areas where the human population had had no previous contact with the disease (Steinmann et al., 2006). In the Senegal River Basin, only S. haematobium was present before the construction of the Diama Dam. In less than 10 years after dam construction, not only had the prevalence of this species increased, but also S. mansoni occurred with prevalences ranging from 4% to 71% (Picquet et al., 1996). In former Zaire, the introduction of schistosomiasis was associated with mining activities which created new habitat for intermediate host snails and led to highly increased contact rates between humans (miners) and water (Polderman et al., 1985; Polderman, 1986).

3.12.5.1.7 Recommendations Oomen et al. (1994) provided effective, although somewhat dated, recommendations for regional disease-control measures in irrigation areas for schistosomiasis as well as filariasis, malaria, and onchocerciasis, diseases which are discussed next. Unfortunately, these recommendations have, in many cases, not been followed. Infected humans shed eggs in their urine (S. haematobium) or feces (other species), which begin the cycle of transmission when they reach a freshwater source with the appropriate snail host species. People working in fields who clean themselves in the water after defecation can pose a substantial problem. Supplying uncontaminated water for human consumption and sanitation to stop the eggs entering a suitable environment can break this cycle. This situation is complicated by the potential infection of freshwater sources via zoonotic hosts, particularly for S. japonicum for which water buffalo are effective reservoir hosts. In general, the development and management of water resources is an important risk factor for schistosomiasis, and hence strategies to mitigate negative effects should become integral parts in the planning, implementation, and operation of future water projects (Steinmann et al., 2006). There should, for example, be as little contact as possible between humans and infectious water sources. The Goral irrigation scheme (Oomen et al., 1994) shows that new housing for agricultural workers should be located outside of the irrigated area, canals, and river banks should be fenced off and foot bridges supplied to prevent wading through freshwater bodies, and newly erected villages should be provided with a safe

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

water supply. In addition, long, concrete watering troughs should be offered to herdsmen and the habitat made as unsuitable as possible for the snail intermediate hosts. During the construction of dams and canals, the workers should be made aware of the dangers of coming into contact with freshwater bodies and a base laboratory with medical personnel should be set up to control disease in the workers both before and during construction. If possible, dams, including microdams, should be constructed at altitudes above 2000 m where factors affecting the life cycle, such as temperature, are not as suitable as at lower altitudes (Alemayehu et al., 1998; Ghebreyesus et al., 2002).

3.12.6 Water-Dependent Vector-Borne Parasites 3.12.6.1 Malaria 3.12.6.1.1 Parasite characterization Plasmodium species are protozoa belonging to the Sporozoa (Apicomplexa, class Hematozoa) which have only single-cell stages in their development. All species in this taxon are obligatory endoparasites mostly living inside cells. Their developmental cycle includes a sporogonic, schizogonic, and a gamogonic phase of reproduction (Figures 26–28; Lucius and Loos-Frank, 2008). Within the hematozoa, the infection alternates between vertebrates, such as mammals, and bloodsucking arthropods. For the four Plasmodium species parasitizing red blood cells and causing human malaria, the vectors all belong to the mosquito genus Anopheles (Figure 29; Service, 2004). These are the final hosts of the disease, as sexual reproduction of the parasites occurs in them. Thus, humans act as intermediate hosts. The larvae of Anopheles mosquitoes live in nonpermanent aquatic environments, showing a parallel orientation to the water surface (Figure 30). As one of the major diseases infecting humans, malaria has a vast literature ranging back to over many years. The four major species of malaria infecting humans, Plasmodium falciparum, P. vivax, P. malariae, and P. ovale, are all anthroponoses, exclusively infecting humans in natural situations. Recently, however, substantial rates of infection with the zoonotic, simian malaria, P. knowelsi, have been found in humans in Malaysia (Singh et al., 2004; Cox-Singh et al., 2008). P. falciparum is pathogenically and numerically the most important malaria species worldwide, occurring predominantly in tropical and subtropical areas (Lucius and Loos-Frank, 2008). P. vivax is the most widely distributed species and accounts for 43% of human cases worldwide and more than 50% outside of Africa (Sattabongkot et al., 2004). It is present in warmer areas as well as in temperate zones with the 16 1C summer isotherm building the distributional boundary. P. malariae is substantially less common and occurs most commonly in West and East Africa, as well as the southwest Pacific, while P. ovale is uncommon and occurs predominantly in tropical Africa, New Guinea, the eastern parts of Indonesia, and the Philippines (Mueller et al., 2007; Lucius and Loos-Frank, 2008). There is a great deal of species overlap and mixed infections are not uncommon (Snounou and White, 2004; Mueller et al., 2007). Historically, malaria was much more widely distributed than it is today, occurring between latitudes 641 N and 321 S;

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reaching as far north as the United Kingdom and Scandinavia (Reiter, 2000; Hay et al., 2004; Sallares et al., 2004). In Finland, indigenous malaria caused by P. vivax died out within the last 200 years even though no or only limited countermeasures were carried out. The last autochthonous case occurred in 1954, with a gradual decrease in household size being considered responsible (Hulden and Hulden, 2009). Major control programs, directed at the vectors, including drainage of swampy areas which act as habitat for larval stages, and against the pathogens themselves by the use of antimalarial drugs, effectively eliminated the disease elsewhere in Europe by the late 1950s (Chwatt and De Zulueta, 1980). Malaria is also one of the parasitic diseases which is known to have influenced human history, at least at a local scale (Sallares et al., 2004). Economically, it is still a major burden at the individual, family and state levels (Sachs and Malaney, 2002; Russell, 2004). Sachs and Malaney (2002), for example, show that malarial countries have higher levels of poverty and lower rates of economic growth than their non-malarial counterparts.

3.12.6.1.2 Developmental cycle The life cycle of P. falciparum is illustrated in Figure 26. However, it is sufficiently similar to that of the other human malaria parasites for it to be used as a general life cycle. The female Anopheles mosquitoes from susceptible species, of which there are about 40 significant species worldwide (Service 2004) (Figure 29), ingest gametocytes (Figures 26 and 27) when they take a blood meal from an infected human. These undergo gamogony (the sexual cycle) in the mosquito. Fertilization of the female macrogamete with male microgametes leads to the formation, first, of a zygote followed by a motile ookinete which undergoes sporogony to form an oocyst. This then breaks releasing infective sporozoites. If the female mosquito then takes another blood meal, the slender, tipped sporozoites, which occur in the salivary gland, are injected into the blood of the human host where they follow an asexual reproductive cycle (hepatic schizogony) in the liver (extra-erythrocytic cycle). The ovoid to round schizonts (meronts) and the hepatic cells enclosing them rupture and merozoites are released which then invade the erythrocytes where many asexual reproductive cycles take place (Figure 28). Eventually, some of the merozoites inside the erythrocytes differentiate to gametocytes which are infective to female mosquitoes, completing the cycle (Figure 27). The female mosquitoes lay their eggs in a wide variety of different aquatic habitats ranging from clear, unpolluted water (e.g., A. culicifacies) to polluted waters in and near human settlements (e.g., A. stephensi), the choice of which is species dependent (Service, 2004). A. merus, for example, is a coastal species which shows peak densities at salinities 30–50% that of seawater (Mosha and Mutero, 1982; Tsy et al., 2003). Between 50 and 200 eggs are laid per oviposition event. These may be laid singly or as small, floating rafts and usually hatch into larvae (Figure 30) within a few days to weeks, depending on temperature (Service, 2004). Feeding on algae, bacteria, or other surface microorganisms, they develop through four stages before developing into motile pupae. After a few days,

Human liver stages Liver cell

Infected liver cell 2

Mosquito stages 12

11 Oocyst i

Ruptured oocyst

Release of sporozoites

1 i Mosquito takes a blood meal (injects sporozoites)

A Exo-erythrocytic cycle

4

Ruptured schizont

3 Schizont

C Sporogonic cycle

Human blood stages 5

10 Ookinete

Macrogametocyte

8 Mosquito takes a blood meal (ingests gametocytes)

Immature trophozoite (ring stage) d

B Erythrocytic cycle Microgamete entering macrogamete 9 P. falciparum Exflagellated microgametocyte

i

= infective stage

d = diagnostic stage

6 Ruptured schizont

7 Gametocytes d P. vivax P. ovens P. malariae

Mature d trophozoite

Schizont d 7 Gametocytes

Figure 26 The life cycle of Plasmodium spp. The species infesting humans differ for instance in the shape of the gametocytes which can be detected in stained blood smears (Figure 27) providing information on the specificity of the infection. In contrast, the schizonts (Figure 28), which in the erythryocytic cycle lead to the synchronized mass rupture of red blood cells and cause most of the pathogenicity, are more difficult to use for species-distinguishing diagnosis. The malaria parasite life cycle involves two hosts. During a blood meal, a malaria-infected female Anopheles mosquito inoculates sporozoites into the human host 1 . Sporozoites infect liver cells 2 and mature into schizonts 3 , which rupture and release merozoites 4 . (Of note, in P. vivax and P. ovale, a dormant stage (hypnozoites) can persist in the liver and cause relapses by invading the bloodstream weeks, or even years later.) After this initial replication in the liver (exo-erythrocytic schizogony A ), the parasites undergo asexual multiplication in the erythrocytes (erythrocytic schizogony B ). Merozoites infect red blood cells 5 . The ring-stage trophozoites mature into schizonts, which rupture releasing merozoites 6 . Some parasites differentiate into sexual erythrocytic stages (gametocytes) 7 . Blood-stage parasites are responsible for the clinical manifestations of the disease. The gametocytes, male (microgametocytes) and female (macrogametocytes), are ingested by an Anopheles mosquito during a blood meal 8 . The parasites’ multiplication in the mosquito is known as the sporogonic cycle C . While in the mosquito’s stomach, the microgametes penetrate the macrogametes generating zygotes 9 . The zygotes in turn become motile and elongated (ookinetes), 10 which invade the midgut wall of the mosquito where they develop into oocysts 11 . The oocysts grow, rupture, and release sporozoites 12 , which make their way to the mosquito’s salivary glands. Inoculation of the sporozoites into a new human host perpetuates the malaria life cycle 1 . Reproduced with permission from CDC.

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Figure 27 Typical banana-shaped gametocytes of Plasmodium falciparum (between erythrocytes not taking up the stain) in a Giemsa-stained smear of human blood. From the slide collection of Werner Frankw.

Anopheles

Culex

Figure 28 Giemsa-stained smear of human blood showing a late schizont of Plasmodium vivax. The infected, inflated erythrocyte has not yet ruptured. From the slide collection of Werner Frankw.

Figure 30 A larva of Anopheles sp. lying under the water surface and of a Culex sp., both in a typical position. Some species belonging to the family Culicidae are vectors of filariasis (see further). Reproduced with permission from CDC.

the pupal case splits and the adult mosquito emerges (Service, 2004). Males and females feed on sugary plant juices such as nectar; however, the females require a blood meal prior to egg development. The average longevity of females in tropical countries varies from 10 days to more than a month (Sattabongkot et al., 2004) which is particularly important epidemiologically, as a female mosquito must survive long enough to feed on an infected host, to allow time for the intraAnopheles sexual life-cycle stage, and to bite a new host. There are very substantial differences in the vectorial efficiency of the different Anopheles species which are able to transmit Plasmodium spp. to humans (Kiszewski et al., 2004). Figure 29 A feeding female Anopheles gambiae, one of the major vectors of malaria. Note the typical anopheline posture with the head and body in a straight line forming an angle to the surface compared to the typical culicine posture with the head bent at an angle to the body, which lies horizontal to the feeding surface (Figure 34). Reproduced with permission from CDC.

3.12.6.1.3 Human involvement The most recent major summary of human malaria, including information on prevalence and incidence, and treatment and control strategies is the 2008 malaria report published by the WHO. This report indicates that in 2006, there were an

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estimated 247 million cases of human malaria with some 3.3 billion people at risk. The yearly death toll is over a million people, mostly children (c. 91%) under the age of 5 years. In 2008, 109 countries were reported to have autochthonous malaria, while with growing tourism, an increasing number of travelers are bringing malaria back to non-malarial countries (Jelinek et al., 2002). The malaria situation in Africa is worse now than it was 20 years ago. Mortality has increased at least twofold and the efficiency of available low-cost antimalarial drugs is decreasing (Trape et al., 2002). Malaria caused by P. falciparum is the most pathogenic form and results in the highest mortality (Bogitsh et al., 2005). In highly endemic areas, regular reinfection after early childhood leads to partial immunity which reduces both morbidity and mortality in later infections (Kiszewski et al., 2004). In areas with low endemicity (e.g., mountainous regions or areas near the parasite’s distributional limits), however, such immunity cannot develop, and morbidity and mortality occur through all age groups. Malaria epidemics tend to occur where endemicity is relatively low (Kiszewski and Teklehaimanot, 2004). Forty to fifty years ago, there were great hopes that control programs would lead to the eradication of human malaria worldwide (Russell (1955), quoted by Hay et al., 2004). These hopes have not been fulfilled, predominantly because the mosquito vectors rapidly developed resistance to the various insecticides used for their control and because the Plasmodium pathogens developed resistance to the drugs developed for their destruction (Hemingway and Ranson, 2000; Wongsrichanalai et al., 2002). Today, malaria is present over large areas, particularly in tropical and subtropical countries, in many of which multiple drug resistance is present (Hay et al., 2004). However, the moderately pathogenic species, P. vivax, also occurs in higher latitudes (Chwatt and De Zulueta, 1980; Sattabongkot et al., 2004). Data from the USA document its potential reintroduction to states such as Virginia where suitable vectors are available (Pastor et al., 2002). Malaria is very much a disease associated with poverty (Sachs and Malaney, 2002). Even in historical times, high disease burdens of malaria occurred during times, for example, in Roman Italy, when social structures were breaking down (Carter and Mendis, 2002). In a general, worldwide analysis, McCarthy et al. (2000) calculated a significant negative correlation between malaria morbidity and the per growth rate of per-capita gross domestic product. The absolute negative growth impact of malaria exceeded 0.25% per year in a quarter of the 101 countries considered; in sub-Saharan Africa, the average annual growth reduction was 0.55%. A number of genetic mutations in humans have been favored by their ability to reduce human susceptibility to malarial parasites (Tishkoff and Verrelli, 2003). These include sickle-cell anemia, thalessemias, G6PD deficiency, and Duffy blood-antigen variations (Carter and Mendis, 2002).

3.12.6.1.4 Disease characteristics in humans There are variations between the timing and other characteristics of the human phase of the life cycles of the different malaria species (for a detailed tabular summary see Bogitsh

et al. (2005)). The duration of schizogony is critical for the development of sequential symptoms in humans. The initial symptoms of malaria are usually nonspecific and include nausea, fatigue, muscular pains, jaundice, fever, and/or diarrhea (Jelinek et al., 2002; Bogitsh et al., 2005). These can be mistaken for a less-severe disease such as influenza, hepatitis, or gastrointestinal infection (Lalloo et al., 2007). An additional complication in the diagnosis of malaria is the potential delay between contracting the disease and the outbreak of symptoms. The minimum incubation time is 6 days, but falciparum malaria may only occur one or more months after infection (Lalloo et al., 2007). Infections with P. vivax and P. ovale often only become apparent by 6 or more months, or even years, after infection (Jelinek et al., 2002). These species are known to have dormant stages in liver cells. Both the nonspecificity of the symptoms, as well as the potential delay between infection and the presentation of symptoms, mean that malaria should be suspected in any patient with a history of fever and travel to malarial countries (Lalloo et al., 2007). If the initial disease progresses to severe malaria, the clinical situation becomes more complex and potentially life threatening (World Health Organization, 2000; Trampuz et al., 2003; Greenwood et al., 2005). High rhythmic fever periods indicating the synchronized mass bursting of infected erythrocytes resulting from mature schizogonies is a typical feature of malaria infections. Infections with P. vivax and P. ovale are characterized by peaks of fever returning every second day, and for P. malariae, every third day. P. falciparum shows no highly specific pattern. Symptoms include cerebral involvement, pulmonary edema, acute renal failure, and severe anemia (World Health Organization, 2000; Trampuz et al., 2003). These may lead to the rapid deterioration in the clinical condition of the patient, including death, within hours or days (World Health Organization, 2000). Death is often caused, at least in part, by the formation of projections on the outer surface of P. falciparum-infected erythrocytes which lead to the entanglement of the red-blood cells and blocking of capillaries obstructing blood flow (Silamut et al., 1999; Mackintosh et al., 2004). Various forms of acute malaria occur depending in part on the immune status of the patient (Miller et al., 2002). In African children with a high exposure rate to falciparum-malaria hypoglycemia, severe anemia and cerebral malaria occur frequently, while acute respiratory distress is the most dangerous (World Health Organization, 2000; Schellenberg et al., 1999). Non-immune adults commonly suffer from jaundice, pulmonary edema, and renal failure (World Health Organization, 2000; Greenwood et al., 2005). Non-immune pregnant women represent a particularly susceptible group, with infection being associated with maternal anemia, intrauterine growth retardation, low birth weight, congenital infection, and neonatal mortality (Steketee et al., 2001). In the past, the diagnosis of malaria was based on finding infected blood cells in Giemsa-stained blood smears. This method is still used because it is cheap, can differentiate between the parasite species, and can quantify the parasitemia (Wongsrichanalai et al., 2007). Gametocytes of the most pathogenic species, P. falciparum, show a typical bananashaped appearance (Figure 27). Nevertheless, a variety of

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

other sensitive assays, including immunodiagnosis and detecting parasite DNA are available (Wongsrichanalai et al., 2007).

3.12.6.1.5 Prevention and cure Prevention can be effected by reducing the rate of transmission. Mosquito-control programs have been effective in the past, but the mass spraying of insecticides such as dichlorodiphenyltrichloroethane (DDT) has led to substantial ecological damage (Roberts et al., 1997; Keiser et al., 2005b) as well as the development of resistance in many species of mosquitoes (Hemingway and Ranson, 2000). Vector biocontrol by the mosquitofish, Gambusia affinis, today classified as one of the world’s 100 worst invasive alien species (Lowe et al., 2000), has caused extensive ecological damage and is no longer recommended (Bence, 1988; Howarth, 1991). Today, ecologically more acceptable methods, such as the use of Bacillus thuringiensis var. israelensis d-endotoxin which is specific against larvae of mosquitoes and blackflies and certain other freshwater insects, has been successfully used in North and South America, parts of Asia, as well as Africa and Europe (Lu¨thy and Studer, 1986; Walker and Lynch, 2007). Recent techniques to prevent biting or reduce biting rates, for example, by the use of insecticide-impregnated bed-nets have proved remarkably successful in some areas (Sexton, 1994; Shiff, 2002). Treatment regimes with various antimalarial drugs are provided in the following articles: Trampuz et al. (2003), Greenwood et al. (2005) and Lalloo et al. (2007). The drug regimen used is dependent on the potential resistance of the malarial parasites in the area in question. Multiple antimalarial drug resistance is unfortunately common and increasing in many parts of the world (Wongsrichanalai et al., 2002; White, 2004; Alfonso et al., 2006). Generally, the current treatment of preference involves artemisinin and its derivatives with the recommendation to combine this with other drugs in an attempt to reduce the development of resistance (World Health Organization, 2008). This recommendation, however, is currently not being adequately complied with (Butler, 2009). Although numerous attempts have been and are being made to develop a vaccine against the Plasmodium parasite, these have to date been unsuccessful (Girard et al., 2007; Todryk and Hill, 2007).

3.12.6.1.6 Anthropogenic alterations to the environment As pointed out by Patz et al. (2000), the various anthropogenic alterations constantly imposed on aquatic environments lead to habitat-dependent changes in mosquito communities. However, the wide variety of conditions under which at least one mosquito species which is able to transmit malaria can thrive, ensures that malaria remains endemic throughout Africa. In other regions, this rule may not apply. In areas of Thailand bordering Cambodia, the observed decrease in the abundance of Anopheles dirus, the main vector of P. falciparum in this region, was accompanied by a concurrent increase in the abundance of members of the Anopheles barbirostris/ campestris group. It has been suggested that these species might be important secondary vectors of P. vivax because of their

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high biting intensity, anthropophily, and susceptibility to only P. vixax (Sattabongkot et al., 2004). Thus, the effects of anthropogenic changes may be complex and not always predictable. Nevertheless, some patterns are apparent: 1. Dams and irrigation. In Africa, within areas of unstable malaria, increased densities of (new) vector species following the introduction of irrigation schemes usually leads to an increase in malaria incidence (Ijumba and Lindsay, 2001), for example, in Ethiopia (Ghebreyesus et al., 1999). In contrast, in most of sub-Saharan Africa with stable high endemicity levels, the establishment of irrigation has little impact or may even have a slightly positive effect if Anopheles funestus is replaced by A. arabiensis, which has a lower vectorial capacity (Ijumba and Lindsay, 2001). Indeed, irrigation may lead to greater wealth so that local people can afford more bed-nets and have better access to healthcare (Ijumba and Lindsay, 2001). However, based on literature dealing with both Fasciola spp. and Schistosoma spp., this hypothesis should be examined in more detail, as most of the humans populations affected by these parasites do not directly benefit from the introduction of irrigation schemes. In general, the higher the dams are built in mountainous areas, the lesser is the risk of malaria transmission. For example, the rate of development and indeed the abundance of the vector and parasite are determined by the temperature (Bayoh and Lindsay, 2004). With increasing altitude, the rate of development and potential for the successful completion of the life cycle decreases; as altitude decreases, prevalence of malaria increases (Drakeley et al., 2005; Kulkarni et al., 2006; Mboera et al., 2008). 2. Deforestation. This is a well-documented factor which often intensifies the malaria hazard. Microclimatic changes, that is, higher temperatures and sun-exposed habitats resulting from deforestation exert a strong effect on mosquito larval development. In large parts of Africa and South America, deforestation can lead to increases in malaria (Guerra et al., 2006). In Africa, members of the Anopheles gambiae complex, which has a high vectorial capacity, a long life span, and a preference for human blood, became more prevalent after deforestation (Minakawa et al., 2005). In Brazil, the local removal of rain forest, combined with mining and an increase in the human population, resulted in a 70% increase in malaria prevalence. Interestingly, the number of cases of P. falciparum increased disproportionately compared to P. vivax (Patz et al., 2000). In Trinidad, following deforestation, Erythrina spp. trees, which support large numbers of bromeliads, were planted to provide shade for cocoa growing underneath. A malaria epidemic followed as the bromeliads accumulated small pools of water which became the preferred breeding sites for the vector Anopheles bellator (Patz et al., 2000). Vittor et al. (2009) carried out a large-scale survey of the breeding sites of 17 Anopheles species in Peru. They showed that deforestation and associated ecological alterations were conductive to high larval and adult densities of Anopheles darlingi, one of the most important malaria vectors in South America. In the Bolivian part of the Amazon Basin, malaria increased fourfold between 1991 and 1998, largely owing to forest clearance

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(Harris et al., 2009). Deforestation does not always lead to an increase in malaria prevalence. In some areas, such as those parts of Southeast Asia where deep forest vectors, such as Anopheles dirus, have a high vectorial capacity, deforestation may also lead to a reduction in malaria incidence (Guerra et al., 2006; Obsomer et al., 2007; Petney et al., 2009). 3. Urbanization. For urban human populations, the malaria situation is similar to areas with low endemicity: low transmission, lack of immunity, high morbidity, and mortality in infected individuals in all age groups (Trape et al., 2002). Keiser et al. (2004) estimate an annual incidence of 24.8–103.2 million cases of urban malaria every year in Africa although prevalences vary greatly between urban areas. With a few exceptions, anopheline malaria vectors have not generally succeeded in adapting to urban life (Lines et al., 1994; Hay et al., 2005). This is markedly different to the vectors of the filarial worm Wuchereria bancrofti, some of which are well adapted to urban environments (see next). In the urban environment, anopheline breeding sites can usually be easily localized and the mosquitoes controlled by classical methods such as drainage and larvicide and indoor spraying (Trape et al., 2002). In rural towns in western Kenya, about 70% of all available mosquito habitats were found to be man made, half of them being cement-lined pits (Fillinger et al., 2004). Robert et al. (2003) carried out a meta-analysis of entomological inoculation rates from sub-Saharan African cities. These were 7.1 for city centers, 45.8 for peri-urban areas, and 167.7 for rural areas. The impact of urbanization in reducing transmission of malaria was most clear in areas with low seasonal rainfall. Thus, urbanization does not usually support high malaria prevalence (Hay et al., 2005).

3.12.6.1.7 Recommendations An understanding of the ecological requirements of potential malaria vectors should be obtained before carrying out any large-scale changes in land use or hydrological patterns in potential malarial areas. As indicted above, mosquitoes are sensitive to environmental change, and either increases or decreases in human malaria may result from such change. Changes in mosquito communities should be monitored and an assessment made of the vector capacity of the species currently present. In cases of deforestation/reforestation, the potential for migration of non-forest or deep forest, respectively, vectors of malaria into the area should be assessed and monitoring programs initiated. In urban environments, anopheline breeding sites can be easily localized and control using classical methods, including drainage, application of larvicide, and indoor spraying of surrounding buildings, carried out. In areas with low malaria endemicity, the introduction or intensification of irrigation should be accompanied by the establishment of healthcare stations for the local population.

3.12.6.2 Onchocerciasis 3.12.6.2.1 Parasite characterization Onchocerciasis is caused by infection with the nematode Onchocerca volvulus (family Onchocercidae) which is transmitted

via the bite of a female blackfly (Nematocera, family Simuliidae) which acts as an obligate intermediate host vector (Udall, 2007). For the species O. volvulus, only humans serve as final hosts. The round, pointed nematodes inhabit the subcuticular connective tissue and lymph system. One or more females are associated with nodules of connective tissue, seen as elevations of the skin. The microfilariae, which reach 100 million in a single host, can be found in various tissues. Long-term infections with these L2 larvae in the eye can lead to loss of sight due to which the disease is also called river blindness (Lucius and Loos-Frank, 2008). A number of other Onchocerca species parasitize stock and domestic animals as well as wildlife and can be of economic importance (Webster and Dukes, 1978; Uni et al., 2001; Marques and Scroferneker, 2004; Sre´ter and Sze´ll, 2008).

3.12.6.2.2 Developmental cycle Female O. vulvulus can live for up to 15 years, while males live for a shorter period. Once mated, females produce up to about 1000 active microfilariae (220–360 mm long) per day which then move around typically in the subcutaneous tissue, the eye, and the lymphatic system, and also in the peripheral blood, urine, and sputum, where they can live for 1–2 years (Figure 31). On biting a human, simulids ingest microfilariae with the blood meal. Some of these penetrate the blackfly’s midgut and migrate to the thoracic muscles where they develop over the next few weeks into infective L3 larvae. These in turn migrate to the head or proboscis of the blackfly and are transmitted to the human host during the fly’s next blood meal. Simulium species, which occur worldwide, are responsible for the transmission of the nematode throughout its range. As with mosquitoes, only the females require blood meals and bite the host. All Simulium species spend their larval and pupal life stages mainly as filter feeders in flowing freshwater habitats, although a few species are predatory (Service, 2004). The actual breeding habitat is species dependent, ranging from slow to rapidly flowing streams with high or low-flow volumes in mountainous areas or lowland (Service, 2004). The females of most species lay their strings of 150–500 eggs on partially immersed objects including vegetation and stones, although the Central American S. ochraceum lays individual eggs on the water surface while in flight (Service, 2004). The eggs are usually resistant to desiccation and the larvae sessile (Figure 32), although they can detach from the substrate, for example, when threatened. Pupae are protected by a silken cocoon which is attached to submerged stones or vegetation. S. neavei is unusual with immature stages being found on freshwater crabs (Lewis, 1960). Once the adults emerge, often simultaneously in very large numbers, they either float or crawl to the surface and take flight (Service, 2004). The taxonomy of the various species groups of the simulian vectors of O. volvulus is highly complex and has only become accessible with the development of molecular taxonomic techniques (Krueger and Hennings, 2006). There are currently over 50 recognized forms (species, cytoforms, and morphoforms) of the S. damnosum group ranging throughout sub-Saharan Africa to the Arabian Peninsula (Crosskey and Howard, 2004; Krueger and Hennings, 2006; Post et al., 2007).

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

Blackfly stages

1

Blackfly (genus Simulium) takes a blood meal (L3 larvae enter bite wound)

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Human stages 2

9

Subcutaneous tissues

Migrate to head and blackfly’s proboscis

i 8 L3 larvae

3 Adults in subcutaneous nodule

7

L1 larvae 5 Blackfly takes a blood meal (ingests microfilariae) 6 Microfilariae pentrate blackfly’s midgut and migrate to thoracic muscles

i

= infective stage

d

= diagnostic stage

4 Adults produce unsheathed microfilariae that typically are found in skin and in lymphatics of connective tissues, but also occasionally in peripheral blood, urine, and sputum.

d

Figure 31 The life cycle of Onchocerca volvulus. During a blood meal, an infected blackfly (genus Simulium) introduces third-stage filarial larvae onto the skin of the human host, where they penetrate into the bite wound 1 . In subcutaneous tissues, the larvae 2 develop into adult filariae, which commonly reside in nodules in subcutaneous connective tissues 3 . Adults can live in the nodules for approximately 15 years. Some nodules may contain numerous male and female worms. Females measure 33–50 cm in length and 270–400 mm in diameter, while males measure 19– 42 mm  130–210 mm. In the subcutaneous nodules, the female worms are capable of producing microfilariae for approximately 9 years. The microfilariae, measuring 220–360 mm  5–9 mm unsheathed, have a life span that may reach 2 years. They are occasionally found in peripheral blood, urine, and sputum but are typically found in the skin and in the lymphatics of connective tissues 4 . A blackfly ingests the microfilariae during a blood meal 5 . After ingestion, the microfilariae migrate from the blackfly’s midgut through the hemocoel to the thoracic muscles 6 . There, the microfilariae develop into first-stage larvae 7 and subsequently into third-stage infective larvae 8 . The third-stage infective larvae migrate to the blackfly’s proboscis 9 and can infect another human when the fly takes a blood meal 1 . Reproduced with permission from CDC.

3.12.6.2.3 Human involvement

Figure 32 Larvae of Simulium sp. on the underside of a stone in flowing water. Courtesy of Wikipedia.

Onchocerciasis is one of the major human parasitic diseases with an estimated 17.7 million people infected worldwide in 34 countries and a population at risk of 123 million (Udall, 2007). It is endemic over substantial parts of tropical Africa, the Arabian Peninsula, and parts of Central and South America (Udall, 2007). Movement of microfilariae to the eye can lead to significant pathological effects, with an estimated 500 000 people suffering from visual impairment and 270 000 from blindness (World Health Organization, 1995b). The microfilariae can survive for 1–2 years before dying. In the past, blindness caused by microfilarial infection of the eyes affected up to 50% of adults in some areas of the West African highlands while today, 99% of all humans suffering from river blindness live in Africa. Only the West African

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savannah form of O. volvulus leads to river blindness. This form is transmitted by different members of the S. damnosum species group (Basa´n˜ez et al., 2006). River blindness has led to significant socioeconomic effects, with the abandonment of fertile agricultural land near rivers and streams, as well as to psychological trauma due to disfigurement of the skin (Evans, 1989; Dadzie et al., 2002; Lazdins-Helds et al., 2003; May, 2007). Prior to the initiation of control programs in West Africa, microfilarial infection was higher in males than females and increased rapidly in both sexes until about the age of 25 years when it plateaued at 90% for males and 85% for females (Kirkwood et al., 1983). Average prevalence in the villages studied was 60% (range 5–93%) (Kirkwood et al., 1983).

3.12.6.2.4 Disease characteristics in humans Human disease is caused predominantly by the inflammatory response to dying microfilariae which can occur over a period of many years. The initial symptoms usually involve an intensely itchy, diffuse papular dermatitis. Once the disease becomes chronic, the cutaneous manifestation differentiates across a spectrum ranging from pruritic lichenification to asymptomatic depigmentation (Udall, 2007). These can be severe, with skin lesions, inflammation, and swelling accompanied by severe itching. Wasting and loss of elasticity are consequences. The formation of sub-dermal nodules (onchocercomata containing 2–50 female and 1–10 male worms) are also a common symptom (Udall, 2007). Several hundred such nodules may be present, ranging from 1 to 5 cm in diameter. In Africa, these occur frequently on the torso and hips while in South America they are more common on the head and shoulders (Udall, 2007). Microfilariae frequently migrate to the eyes where inflammation gradually leads to sclerosal opacification and blindness (Udall, 2007). Not only does onchocerciasis cause major morbidity in humans, a recent report also suggests that the life expectancy of those affected is reduced and that mortality is significantly correlated with microfilarial burden (Little et al., 2004). Savannah ocular involvement is severe while that found in forests is mild (Basa´n˜ez et al., 2006). Diagnosis of onchocerciasis in the past was based on finding evidence of microfilariae in skin biopsies (skin snips). Currently, biochemical and molecular biological methods, including antigen assays and PCR to detect parasite deoxyribose nucleic acid (DNA), are available (Udall, 2007).

3.12.6.2.5 Prevention and cure A number of onchocerciasis control programs have been largely successful in reducing the burden of this disease in both Africa and South America and reducing the morbidity and socioeconomic burden in these areas (Richards et al., 2001; Molyneux, 2005). Today, prevention involves two major strategies, vector control and treatment of humans, both aimed at breaking the transmission cycle (Basa´n˜ez et al., 2006). Vector control currently involves killing larval blackfly by applying insecticides to the rivers and streams in which they live (Davies, 1994; Le´veˆque et al., 2003). The insecticide of choice depends on the development of resistance of the blackflies, with different chemicals potentially being used on a

rotational basis (Hemingway and Ranson, 2000). The concentration of the larvae in these limited habitats makes the control effort much easier than that which would be required for the widely dispersed adult flies (Le´veˆque et al., 2003). The Onchocerciasis Control Program (OCP) in West Africa, which involved 11 countries and was financed by the WHO, the Food and Agriculture Organization (FAO), the United Nations Development Program (UNDP), the World Bank, and 22 additional donors from 1974 to 2002, was the most comprehensive and successful program to combat this disease (Hodgkin et al., 2007). More recently, the African Program for Onchocerciasis Control (APOC) was initiated aimed at functioning from 1995 to 2010, with additional promised support for national task forces until 2015 (Hodgkin et al., 2007). The concept of the OPC program was to combine chemotherapy for humans with ivermectin (Mectizan generously donated by Merck & Co. Inc.) with helicopter application of insecticides (the organophosphates temphos and chlorphoxin) as well as the biological larvicidal toxin produced by Bacillus thuringiensis var. israelensis (B.t.i.) into the riverine habitats of the simuliid larvae. The latter was mainly sprayed during the dry season in areas showing resistance to temphos. B.t.i. showed the lowest degree of environmental damage and impact on nontarget invertebrates including the most important natural predators of the vectors (Richards et al., 2001; Le´veˆque et al., 2003). However, it had only a limited effect in areas with algal blooms, invasive water hyacinths, or strong water flow. The insecticides need to be applied on a weekly basis as the development of larvae to pupae takes about this time. Together with the vast stretches of water courses needing treatment (40 000 km of river treated over 106 km2 by the OCP; Hougard et al., 1997) and the requirement for aerial spraying in some areas, control efforts become both expensive and time consuming (Le´veˆque et al., 2003). In addition, although claimed to be environmentally safe, Le´veˆque et al. (2003) have shown a limited effect on fish but changes in the community structure and taxa present to the level of family. Treatment of patients with ivermectin paralyses and kills microfilariae, preventing disease progress as well stopping the females from producing microfilariae for some months after treatment, thus interrupting the transmission cycle (Diawara et al., 2009). Ivermectin treatment should be repeated every 6–12 months. Although effective in controlling the disease, the elimination of transmission has proven difficult as, after an initial drastic reduction in the number of cutaneous microfilariae, these begin to reappear at 20% or more of the initial intensity within a year (Hoerauf et al., 2003; LazdinsHelds et al., 2003). However, recent evidence has shown the feasibility of eliminating onchocerciasis in certain endemic foci via ivermectin treatment alone (Diawara et al., 2009). The possibility of eliminating adult worms by killing their Wolbachia endosymionts using doxycycline is also being examined (Hoerauf et al., 2003; Udall, 2007). Other onchocerciasis control programs are more limited. The APOC only involves treatment of the infected human populations with ivermectin, as does the Onchocerciasis Elimination Program for the Americas (OEPA; 1991 to 2010) involving six Latin American countries (Richards et al., 2001; Le´veˆque et al., 2003).

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control 3.12.6.2.6 Anthropogenic alterations to the environment Some of the alterations to the environment brought about by humans are responsible for increasing the spread of disease: 1. Dams. The Simulium species which transmit O. volvulus inhabit a wide variety of flowing water environments with specific microhabitat preferences. Any changes to these habitats are likely to lead to changes in the species or community of Simulium species present. Thus, the damming of these watercourses can reduce or eliminate water flow reducing the amount of suitable habitat available for immature blackflies (Sutherst, 2004). However, the introduction of new habitats, such as spillways on dams, is likely to provide suitable habitat for certain species and may lead to the establishment of new foci (Patz et al., 2000; Sutherst, 2004). According to Taylor et al. (2009) the simuliid density along the the Sanaga River has increased due to the initiation hydroelectric schemes, perhaps due to the construction of spillways. Adewale et al. (1999) found abundant vectors around the Owena dam in Nigeria. This study showed that 0.4% of the blackflies were infected, while 0.3% had infective larvae. An annual transmission rate of 109 larvae per person per year was calculated which is slightly lower than in a rural area without direct association to a dam (131–189 larvae per person per year, Opara et al., 2008). This suggests that construction of the dam has not substantially reduced the abundance of the blackflies and the prevalence of infection with O. volvulus. 2. Deforestation. The effect of deforestation is variable depending on the local simuliid vector. For those species, such as S. neavei and S. woodi, which require shaded streams for breeding or are attracted to dense forest patches as adults, deforestation can lead to substantial reductions in abundance or to extinction (Muro and Raybould, 1990; Taye et al., 2000; Garms et al., 2009). However, an increase in open savannah areas can lead to a change in the vector species present, as we saw above for malaria, with an increased likelihood of transmission by savannah species. In the case of onchocerciasis, this is associated with the occurrence of the more severe form of the disease (Wilson et al., 2002; Adjami et al., 2004). Where forest vectors are present, movement of infected humans into forest areas, such as occurred in Central and South America, can lead to the introduction of O. volvulus to new areas (Sutherst, 2004). 3. Eradication programs. The OCP eradication project also emphasized a concern for local biodiversity and the implementation of long-term freshwater monitoring, a very progressive position among international development projects for this time. An independent ecological committee was in charge of risk assessment for all applied larvicides and for the documentation of aquatic biodiversity. This resulted in a substantial increase in our knowledge of the fauna and ecology of West African rivers. However, when the success of the project made available 25 million hectares (ha) of fertile riverside areas for resettlement and agriculture, ecological aspects were no longer taken into consideration. These newly available areas became subject to unplanned and unsuitable colonization. This led to

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land-use changes on an enormous scale with rampant deforestation, soil erosion, and loss of biodiversity. Pesticides far more hazardous than temephos and chlorphoxin, as well as fertilizers used in agriculture, leached into the rivers. Migrant fishermen, partly fishing with agricultural poisons, depleted local fish stocks and subsequently moved quickly to more pristine stretches of other rivers. Both fish and invertebrate species became endangered (Le´veˆque et al., 2003). Thus, although commencing with the praiseworthy aim of maintaining an intact, functioning ecosystem during the initial phase of the project, no forward ecological planning, based on the new availability of large natural areas, was made. Had this been done, the local population would have had access to a wide range of ecosystem services, such as sustainable resources and tourism, based on the conversion of part of the river and its surroundings to a national park containing a wide diversity of endangered plants and animals including attractions such as pygmy hippoppotami. West Africa, where onchocerciasis is most common, is undergoing extensive deforestation (Caspary, 1999; Schipper et al., 2008) and lags substantially behind East Africa in the area of ecotourism (Sournia, 1996). The intensive, uncontrolled use of riverine areas by humans implies that these options are either no longer possible or are severely reduced.

3.12.6.2.7 Recommendations Prior to the anthropogenic change of hydrological patterns, it is necessary to determine whether new, suitable habitats for Simulium vectors will be created allowing new foci of onchocerciasis to develop. Should such habitats be created, they should be monitored and if necessary, sprayed with an appropriate insecticide (dependent on the resistance status of the Simulium vectors) to prevent the establishment of the vector. Modern technology has provided a substantial impetus to the control of onchocerciasis. Satellite detection of water-flow rate has been used to calculate the amount of insecticide to be used in vector-control programs (Boyer et al., 1990). Geographic information system analysis can also be used to predict the areas where onchocerciasis prevalence is likely to be high and to apply appropriate control measures (Noma et al., 2002; Botto et al., 2005; Gebre-Michael et al., 2005). The d-endotoxin produced by B.t.i. is effective against blackfly larvae at very low concentrations (a few nanograms per milliliter). In West Africa, this has proved effective in large-scale programs aimed at blackfly control (Lu¨thy and Studer, 1986). As seen in the OCP project, it is not enough to control the vectors and the disease. Detailed planning on sustainable land use and the maintenance of functioning ecosystems and their services must also be made for the time after the project succeeds.

3.12.6.3 Lymphatic Filariasis 3.12.6.3.1 Parasite characterization Lymphatic filariasis is caused by any one of three species of nematode parasites (family Onchocercidae) transmitted by mosquitoes (Figure 33). Bancroftian filariasis, due to Wuchereria bancrofti, occurs throughout the tropics, while brugian filariasis, is found in the region from India across

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Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control Mosquito takes a blood meal (L3 larvae enter skin)

1 Mosquito stages

Human stages

8 Migrate to head and mosquito’s proboscis

7 L3 larvae

i 2 Adults in lymphatics

6

L1 larvae

3 4 Mosquito takes a blood meal (ingests microfilariae)

Adults produce sheathed microfilariae that migrate into lymph and blood channels

5 Microfilariae shed sheaths, penetrate mosquito’s midgut, and migrate to thoracic muscles d

i

= infective stage

d

= diagnostic stage

Figure 33 The life cycle of Wuchereria bancrofti. Different species of mosquitoes are vectors of W. bancrofti filariasis depending on geographical distribution. During a blood meal, an infected mosquito introduces third-stage filarial larvae onto the skin of the human host, where they penetrate into the bite wound 1 . They develop in adults that commonly reside in the lymphatics 2 . The female worms measure 80–100 mm in length and 0.24– 0.30 mm in diameter, while the males measure about 40  0.1 mm. Adults produce microfilariae measuring 244–296 mm  7.5–10 mm, which are sheathed and have nocturnal periodicity, except the South Pacific microfilariae which have the absence of marked periodicity. The microfilariae migrate into lymph and blood channels moving actively through lymph and blood 3 . A mosquito ingests the microfilariae during a blood meal 4 . After ingestion, the microfilariae lose their sheaths and some of them work their way through the wall of the proventriculus and cardiac portion of the mosquito’s midgut and reach the thoracic muscles 5 . There, the microfilariae develop into first-stage larvae 6 and subsequently into third-stage infective larvae 7 . The third-stage infective larvae migrate through the hemocoel to the mosquito’s proboscis 8 and can infect another human when the mosquito takes a blood meal 1 . Reproduced with permission from CDC.

Southeast Asia to China and the Philippines (due to Brugia malayi). On Timor, Flores, and other Indonesian islands, a third species, B. timori is involved (Michael and Bundy, 1997; Melrose, 2002; Service, 2004). The three species are similar in appearance with females reaching a length of 65–100 mm by 0.2–0.3 mm wide and males only about 40 mm by 0.1 mm. Microfilariae vary between 222 mm and 310 mm, depending on species, and are sheathed, that is, the L2 larva still carries the cuticle of the L1 larva as a loose sheath (Lucius and LoosFrank, 2008). There are more than a billion people at risk of contracting lymphatic filariasis (lymphedema and elephantiasis) worldwide, with an estimated 120 million affected in 80 countries and 40 million incapacitated or disfigured (Dreyer et al., 2000; Molyneux, 2003). The numbers infected are relatively equally divided between Africa, India, and Southeast Asia and the Pacific. Wuchereria bancrofti is responsible for about 90% of

cases of lymphatic filariasis. Bancroftian filariasis, caused by this species, is largely an urban disease (Michael and Bundy, 1997; Melrose, 2002). Only W. bancrofti is completely anthroponotic, lacking animal hosts (Service, 2004); the subperiodic form of B. malayi is a zoonosis known to infect primates as well as domestic cats and dogs (Chansiri et al., 2002; Melrose, 2004). All species are transmitted by anopheline (genus Anopheles) and by several genera of culicine mosquitoes (e.g., Aedes, Mansonia, and Ochlerotatus: Figure 33 and 34; Michael et al., 1994; Service, 2004), which in turn spend their egg, larval, and pupal stages in and/or on water (Service, 2004).

3.12.6.3.1 Developmental cycle Adult worms of all three species occur in the human lymphatic system (Figure 33) where they produce sheathed microfilariae

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

Figure 34 Armigeres subalbatus, a culicine mosquito which occurs from Pakistan in the west, all through Southeast Asia to Indonesia, and north to Japan and Korea. Note the typical culicine posture with the body horizontal to the surface and only the head bent downward. Larva, compare Figure 29. Reproduced with permission from CDC.

(L2 larvae) that migrate into the lymph and blood vessels. Mosquitoes feeding on the blood ingest the microfilariae which then shed their sheaths, penetrate the mosquito’s midgut, and migrate through the hemocoel to the thoracic muscles where they continue their development to L3 larvae (1.2–1.6 mm long). As for Onchocerca volvulus, there is no multiplication of the parasite in the vector. The larvae migrate through the head of the mosquitoes to the labium of the proboscis. During the next blood meal on a suitable host, the L3 larvae penetrate the labium and crawl on to the host’s skin. Then a lesion, usually the bite site of the mosquito, is required before they can enter the host and move to the lymphatic system. Larval development of W. bancrofti and B. malayi can take place in a number of mosquito species, but B. timori is probably only vectored by Anopheles barbirostris (Fischer et al., 2002; Service, 2004). In rural areas, in Africa, for example, W. bancrofti is transmitted by the Anopheles funestus group and members of the Anopheles gambiae complex: in freshwater A. gambiae sensu stricto and A. arabiensis; and in salty water A. melas and A. merus (Bockarie et al., 2009).

3.12.6.3.3 Human involvement Prevalence is initially age dependent, plateauing in early adulthood, but with infection often occurring early in childhood and potentially leading to disfigurement for much of the individual’s life (Weil et al., 1999; Witt and Ottesen, 2001; Melrose, 2004). In two studies from the 1990s, 25% of 5-yearolds on Haiti were already infected (Lammie et al., 1994, 1998), while in Tanzania, prevalence for the same age group reached 28% (Simonsen et al., 1996). In the Nile Delta of Egypt, antibody prevalences reach 30% in the 10–19-year age group, peak at over 40% for 20–29-year-olds then slowly decline to about 30% again between 60 and 80 years (Weil et al., 1999). There is some indication that females of reproductive age are less commonly infected than age-matched males (Melrose, 2004), and in India 79% of chronic cases occur in men (Ramaiah et al., 2000).

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Acute filarial disease is generally more common than the chronic disfiguring manifestations (Melrose, 2004). In some tropical and subtropical areas, the prevalence is continuing to increase, although elimination is easy and inexpensive, and various strategies have eliminated transmission in China, Japan, Korea, Thailand, and the Solomon Islands (Molyneux, 2003). It is mainly a disease of the poor, being associated with the expansion of slums in urban areas. There are two forms of both bancroftian and brugian filariasis: nocturnal periodic and nocturnal sub-periodic. In the periodic form, most microfilariae are found in the pulmonary blood vessels during the day while at night they migrate to the peripheral blood and lymph systems. These strains have adapted to mosquito vectors which feed during the night. Reduced periodicity is found in the sub-periodic form in which the microfilariae are present in the peripheral blood during the day as well as night (Service, 2004). The filarial worms live in the lymphatic system and have a fecund life span estimated at 5–15 years, producing millions of microfilariae in their lives (Melrose, 2004). Prevalences in endemic areas can be high. In their 1997 analysis, Michael and Bundy reported 17 of the 34 African countries where the disease is found to have countrywide prevalences of over 10%, with the four highest being in Guinea Bissau (37%), Comoros (27%), the Seychelles (24%), and Nigeria (22%). The highest prevalences, however, occur on Tonga (48%) and Papua New Guinea (39%) (Michael and Bundy, 1997). The socioeconomic and psychological burden of lymphatic filariasis is immense. India harbors 40% of all individuals with lymphatic filariasis. Ramaiah et al. (2000) estimated that this disease costs US$842 million per annum in treatment time and lost working time, equivalent to 0.62% of the gross national product at this time. A chronically infected male patient with acute episodes could lose up to US$50 per year, 15% of the total available income. In addition to the physical effects of the disease, the deformities which it causes lead to stigmatization of affected individuals (Melrose, 2004).

3.12.6.3.4 Disease characteristics in humans Filariasis shows various manifestations in humans. Many of those infected, including children, appear to be asymptomatic while in fact the adult worms and circulating microfilaria cause observable damage to the lymphatic system, tissue, and organs (Witt and Ottesen, 2001; Melrose, 2004). These infections may be a preliminary to chronic disease in later life (Witt and Ottesen, 2001). Acute disease can occur at any age with reports from babies as young as 3 months (Melrose, 2004). Fever (elephantoid fever) and chills accompany adeno-lymphadenitis characterized by severe inflammation of the lymph node and lymphatic vessels which lasts for about a week before resolving spontaneously (Melrose, 2004). Chronic disease leads to the most spectacular symptomatology, with elephantiasis being the final form. It usually starts with the accumulation of fluid in the extremities, scrotum, vulva, or breasts due to obstruction of the lymph vessels and can lead to severe hypertrophy of the skin and subcutaneous tissues (Dreyer et al., 2000; Melrose, 2004).

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Fortunately, elephantiasis only occurs in a small proportion of those suffering from the disease (Melrose, 2004). Infection has been reported to affect a wide variety of other organs including the kidneys, joints, eye, heart, and spleen. Details are provided in the comprehensive review of Melrose (2004). A variety of methods are available for diagnosing filariasis including the demonstration of microfilariae by either direct or concentration techniques, and the detection of filarial antigens, the detection of filarial antibodies, and the detection of parasite DNA (Melrose, 2004; Melrose et al., 2004).

3.12.6.3.5 Prevention and cure Reduction or elimination of the microfilariae can be achieved by administering a single dose of diethylcarbamazine (DEC) together with albendazole, which is effective over a year, in areas where onchocerciasis is not endemic (Hotez et al., 2007). The simultaneous administration of both albendazole and ivermectin removes 99% of filariae from the blood, also for a full year, and is recommended in areas where onchocerciasis is also endemic (Hotez et al., 2007). At the community level, these treatments effectively reduce transmission by preventing microfilarial uptake by mosquitoes. Both albendazole and DEC effectively kill the adult worms (Horton et al., 2000). Although this can lead to improvement in the disfiguring elephantiasis and hydrocele, particularly when the disease is in an early stage, it cannot cure them as progression of the disease is due to superinfection by fungi or bacteria. The latter can be treated by strict hygiene, minimizing the risk of infection, pharmaceutical treatment against the superinfecting agent, and measures to increase lymph flow (Dreyer et al., 2000). Lymphatic filariasis is also subject to a number of international control programs which have shown considerable success in recent years (Molyneux, 2003; Molyneux et al., 2003). In 2000, 2 million treatments were made available worldwide. This was increased to 60 million in 2002 through the generosity of GlaxoSmithKline which has donated albendazole, the drug of choice since 1998, and Merck, which now also supplies ivermectin for these programs (Molyneux, 2003). These treatment regimens have been successful; however, they must be applied continuously over many years and in many areas. Educating the local population is a necessary addition to the treatment. It is important to note, however, that individuals with densities of microfilariae as low as 3 ml1 blood can still infect mosquitoes and that, therefore, residual low-density infections after mass-treatment programs have the potential to cause a rapid re-emergence of the disease (Melrose, 2004). Vector control is also possible. Open breeding sites such as areas of flooded land, cess pits, and blocked drains can be treated with modern insecticides such as pyriproxyfen, an insect growth regulator, or the biological control agent, Bacillus sphaericus (Bockarie et al., 2009). This is related to B.t.i. but has a narrower range of potential target organisms of which the highly susceptible Culex quinquefasciatus, a major vector of W. bancrofti and a highly successful invading species, is one (Bockarie et al., 2009). In addition, residual house spraying and the use of permethrin-impregnated bed-nets have proven

to be highly effective by reducing the mosquito biting rate (Bockarie et al., 2009).

3.12.6.3.6 Anthropogenic alterations to the environment The nocturnal periodic form of W. bancrofti is transmitted over much of its range by C. quinquefasciatus which breeds preferentially in polluted water containing organic waste (including human and animal feces) such as cess pits, latrines, and drains (Subra, 1981; Raccurt et al., 1988). The unsanitary conditions in rapidly growing urban environments therefore provide ideal breeding grounds for this vector (Mian and Mulla, 1986; Calhoun et al., 2007; Chaves et al., 2009). C. quinquefasciatus is the cause for high prevalences of bancroftian filariasis in urban areas with low sanitary standards. As it is a night-biting mosquito, urban filariasis is associated with nocturnal periodicity of the microfilariae (Melrose, 2004). According to Bockarie et al. (2009), the distribution and abundance of C. quinquefasciatus is increasing in parallel to urbanization and human activity. Many rural pockets, which were relatively free of this mosquito, are becoming increasingly colonized. On the Ivory Coast, where bancroftian filariasis used to be an urban phenomenon, two villages were compared to each other: one with a traditional way of life, the other with modern conveniences (Dossou-Yovo et al., 1995). In the latter, where water pollution and household rubbish, cess pits, and septic tanks were abundant, the biting rate of the C. quinquefasciatus was significantly higher throughout the year compared to that of the traditional village. In Polynesia, a sub-periodic form of bancroftian filariasis occurs with a diurnal peak of microfilariae in the peripheral blood. An important vector of this form is Aedes polynesiensis which uses natural sites of water collection, such as leaf bracts and crab holes for breeding, as well as containers conveniently distributed by the human population such as tires, tins, jars, etc. (Service, 2004). In southern Ghana, the availability of irrigation canals late in the dry season led to a massive increase in the number of Anopheles gambiae mosquitoes. These had a filarial infection rate with W. bancrofti of 8.3%, comparable to those of the wet season (Dzodzomenyo et al., 1999). Indeed, Erlanger et al. (2005) indicate that ‘‘environmental changes due to water resource development and management consistently led to a shift in vector species composition and generally a strong proliferation of vector populations.’’ Irrigation projects in West Africa have been associated with an increase in W. bancrofti infections. Almost every house in villages bordering a man-made irrigation ditch contained a case of bancroftian filariasis. By contrast, only scattered cases were seen in villages two or more kilometers away from these ditches. Anopheles gambiae and A. funestus complex mosquito densities were up to 25 times higher in areas under water-resource development as compared to non-irrigated sites (Erlanger et al., 2005).. Little information is available on the role of deforestation in changing the prevalence of lymphatic filariasis. By analogy with malaria caused by Plasmodium falciparum, which in rural Africa is transmitted by the same vectors as W. bancrofti, we would predict that deforestation should also lead to an increased incidence of bancroftian filariasis. There is, however, a

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negative spatial association between the two parasite species (Kelly-Hope et al., 2006).

3.12.6.3.7 Recommendations As both bancroftian and brugian filariasis are transmitted by a wide variety of different mosquito species, each with its own breeding habitat requirements, complete control of vectors is unlikely. However, in the case of Culex quinquefasciatus discussed above, increased hygiene, water treatment, and the removal of water-filled tires and other containers containing organic material, such as rotting vegetation, could reduce the breeding success of this species in urban slums. Control using insecticides has been locally successful (Bockarie et al., 2009). Aedes polynesiensis and C. quinquefasciatus populations could potentially be reduced by removing litter which acts to collect water and thus can be used as breeding sites. In Africa, the non-urbanophilic Anopheles species can be controlled as indicated above for malaria, as these are often the same species transmitting the filarial parasites (Melrose, 2004).

3.12.7 Environmental Factors Influencing the Dynamics of Water-Associated Parasites The epidemiology of the parasites discussed above, although following set patterns, is not static but dynamic and potentially unstable. This dynamism is based on the way in which the parasite and its various intermediate and final hosts react to natural and man-made global and local changes in the environment. We provide general examples of the environmental factors influencing these epidemiological cycles and summarize the information presented for the various waterborne parasites treated earlier.

3.12.7.1 Dam Construction and Irrigation Projects With the continual increase in the human population, a reliable supply of high-quality freshwater, for human consumption, agriculture, and industry, has become of critical importance (Vo¨ro¨smarty and Sahagian, 2000; Oki and Kanae, 2006). This has led to a major increase in the construction of dams and the initiation of irrigation projects. Keiser et al. (2005a) cite the construction of 40 000 large dams, 800 000 small dams, and 272 million ha of irrigated land worldwide over the last 50 years. As discussed here, such projects can lead to major deterioration in the health status of the local population due to increases in parasite and pathogen-transmission rates. This is the result of increasing the amount of habitat required for the survival and/or reproduction of the vectors (Aedes, Anopheles, Culex, etc.), or intermediate hosts (e.g., snails) traditionally transmitting the disease in the respective area, or related indigenous or introduced species which also act as vectors colonizing the habitat after its alteration by a hydrological engineering project. This shift may take years before a new equilibrium is reached. Irrigation projects are aimed at increasing the availability of reliable water supplies for agriculture in areas with limited or only seasonal water supplies. Thus, such irrigation projects frequently supply the conditions required for the maintenance

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of parasitic diseases in areas where they would otherwise either not exist or would be of only limited significance. Schistosomiasis (Steinmann et al., 2006), malaria (Keiser et al., 2005a), and lymphatic filariasis (Erlanger et al., 2005) fit this pattern with known examples of increases in disease incidence with inadequately planned water-development projects. In all cases, consultation with specialists on the epidemiology of parasitic diseases including the ecology of their vectors or intermediate hosts could have predicted and reduced the health-risk consequences of the development projects. Zhang et al. (2002) and Li et al. (2007), for example, provided models showing a projected increase in schistosomiasis around the Three Gorge Dam and the Dongling Lake Region in China based on predicted hydrological and epidemiological data. Thus, parasitological predictions for hydrological projects are feasible. The Gu¨Neydogu Anadolu Projesi (GAP) project in southeastern Turkey includes the construction of 22 dams on the Euphrates and Tigris rivers with the aim of providing irrigation water to 1.7 million ha (10% of the surface area of the country) of arid land as well as to about 10% of the Gu¨neydogu Anadolu Projesi population (Aksoy et al., 1995). This ambitious project also appears to have more than doubled the incidence of malaria between 1990 and 1992 from 8680 cases to 18 676 cases (Aksoy et al., 1995). Other water-associated diseases from the area, including Entamoeba histolytica and Giardia lamblia, have also increased in incidence. Ak et al. (2006) have shown that dam construction and irrigation per se have not influenced the spread of these intestinal parasites, but that the elevated levels of parasitization are related to contamination of drinking water, raw green vegetables irrigated with feces-contaminated wastewater, the use of human fecal material as fertilizer, and the use of contaminated water to clean raw vegetables for consumption. Predicting the effects of hydrological change requires knowledge of all the parasite groups present or likely to move into the affected area. The creation of dams may have quite different effects on different parasite groups. For instance, it has been shown that the elimination of flowing-water habitats required by the simulian vectors reduced levels of onchocerciasis. The same hydrological changes, however, increase the area of standing-water habitat available for the breeding of mosquito species transmitting malaria and filariasis and for the intermediate snail hosts of schistosomiasis (Sutherst, 2004).

3.12.7.2 Land-Use Changes All free-living organisms have specific habitat requirements which will be impinged upon by changes in land use. These organisms include the vectors and intermediate hosts for the variety of parasite species which we have discussed above. Thus, as land use changes, the epidemiological cycles of the parasites actually or potentially present in a given area will also be modified; indeed, depending on the degree of impact, they may disappear or appear in areas where they were not previously present. Today, land-use changes are considered a major driving force in shifting epidemiological patterns (Patz et al., 2004). In terms of parasites with a waterborne transmission phase, land use is likely to have a major effect by

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changing both water-flow patterns as well as the quality of the water available (Scanlon et al., 2007). The massive increases in rainfed crop and pasture land over the last several hundred years at the expense of forest and grasslands has led to decreased rates of evapotranspiration and increased rates of recharge of groundwater and streamflow, together with changes in the chemical composition of the water with mobilization of salts, salinization, and leaching of fertilizers (Scanlon et al., 2007). All of these parameters can have a significant impact on species assemblages; for example, calcium is required for the growth of most snail species, including those involved as intermediate hosts for Schistosoma spp. (Williams, 1970; Nduku and Harrison, 1976; Madson, 1987). Seasonal changes in the abundance of Biomphalaria pfeifferi, an intermediate host of Schistosoma mansoni, are related to water temperature and flow rate (Woolhouse, 1992), while high magnesium levels have a negative impact on egg production (Harrison et al., 1966). Moreover, this species is rarely found in conditions with a sodium/calcium ratio of more than 2.4 (Schutte and Frank, 1964) and not at all with conductivities below 12 mS cm1 (Polderman et al., 1985). This indicates that multiple factors are involved in determining the distribution of B. pfeifferi (Utzinger et al., 1997). Deforestation provides many examples of an increased impact on the spread and development of Anopheles spp. mosquitoes leading to an increase in malaria incidence (Yasuoka and Levins, 2007). However, exception does occur. In Thailand, however, malaria can be caused by Plasmodium falciparum and P. vivax, the two most common species, and more rarely by P. malariae, P. ovale, and the zoonotic species P. knowlesi, the importance of which may have been underestimated (Chareonviriyaphap et al., 2000; Jongwutiwes et al., 2004; Cox-Singh et al., 2008). Malaria is most common in forest and scrub areas with the major vectors in the northeast of the country, mosquitoes of the Anopheles dirus complex, occurring preferentially in shady forest and forest-fringe habitats (Walsh et al., 1993; Yasuoka and Levins, 2007). During the first half of the last century, malaria was common in the northeast of Thailand (Petney et al., 2009), but the incidence of infection decreased rapidly as deforestation progressed (Petney et al., 2009). By 1992, only two of the then 17 northeastern provinces fell within the top 15 malarial provinces in Thailand (Thimasarn et al., 1995). Both these adjoining provinces were low on the list (13 and 15, respectively) and both are close to malarial areas in Laos and Cambodia. Between 25% and 31% of cases were considered to be imported (Thimasarn et al., 1995). Today, the northeast area is considered to lave a low risk of infection (Chareonviriyaphap et al., 2000). This reduction in risk is likely to be directly related to the reduction in forest area and thus the habitat required by the vector mosquito. Areas currently requiring control are predominantly hilly and forested on the borders with Burma and Cambodia (Chareonviriyaphap et al., 2000). Today, major increases in the area covered by rubber plantations, which provide the necessary shade for the freshwater habitats needed for breeding malaria mosquitoes, could herald the return of malaria to this area (Petney et al., 2009). Patz et al. (2004) discuss policy recommendations on landuse change in relation to infectious diseases. They stress the complexity of the cascades of factors influenced by even a

single change, which can affect disease emergence. Policy decisions involve specific health-risk factors, landscape and habitat change, and economic and education considerations. The recommendations involve (1) providing a conceptual model integrating land use into public health policy; (2) more research on the relationship between deforestation and infectious diseases; (3) the development of policies to reduce pathogen contamination; and (4) the creation of centers of excellence in ecology and health research with appropriate training courses.

3.12.7.3 Mass Animal Husbandry: Cryptosporidia and Giardia Wild and domestic animals are known hosts for a variety of zoonotic diseases affecting humans (Slifko et al., 2000; Simpson, 2002). The introduction of mass animal husbandry of cattle, pigs, and poultry, with the close confinement of many individuals in a small space, provided ideal conditions for the long-term survival and the transmission of a wide variety of parasites (Gajadhar et al., 2006). Cryptosporidium parvum and Giardia lamblia, which have been dealt with briefly earlier, are two species commonly contracted by humans through drinking water contaminated with fecal material. Outbreaks of C. parvum sensu stricto involving both cattle and humans have also been reported (Monis and Thompson, 2003; Peng et al., 2003; Soba and Logar, 2008), although the mass outbreak of cryptosporidiosis in Milwaukee, initially thought to be due to water contamination with cattle feces, was in fact due to human specific C. hominis (Gajadhar and Allen, 2004). Aquaculture is undergoing a massive growth phase. In 1970, only 3.9% of total aquatic animal food, including fish, crustaceans, mollusks, and other aquatic animals, came from this source. By 2006, this had increased almost 10-fold to 36%, while if fish alone were considered, 47% came from aquaculture (Food and Agriculture Organization, 2009). In China, 90% of fish food production derived from this source. The introduction of parasites to populations with such high densities is likely to lead to increased rates of transmission, depending on the presence of suitable intermediate hosts and contamination of the water with human waste (e.g., the eggs of fish-borne trematodes). The fact that a number of important intermediate hosts, such as the snail Melanoides tuberculata, are highly successful invading species, which are associated with aquaculture, facilitates the establishment of transmission cycles in new areas (Garrett et al., 1997).

3.12.7.4 Human Conflict, Political Considerations, and Healthcare Systems Human conflict, which in the future has been predicted to increasingly include the right to access freshwater (Toset et al., 2000; Hensel et al., 2006), is also capable of limiting the effectiveness of control campaigns against parasites with a freshwater transmission cycle. In 1980, the Centers for Disease Control in Atlanta, Georgia, proposed a global eradication campaign for dracunculiasis; this was later accepted by the United Nations. In 1988, the ministers of health from African countries agreed

Waterborne Parasitic Diseases: Hydrology, Regional Development, and Control

upon the eradication program aimed at being completed by 1995 (Hopkins et al., 1997, 2002). Today, 14 years after this deadline, a great deal of progress has been made toward the goal of eradication (Hopkins et al., 2002; Barry, 2007). Indeed, the number of cases has plummeted from almost 900 000 in 1989 to about 25 000, and of the 20 countries with endemic dracunculiasis at the beginning of the campaign, seven had interrupted transmission by 2002, including all countries outside of Africa (Hopkins et al., 2002). By 2007, dracunculiasis was only recorded from five countries, of which three had a total of a mere 170 cases, and only two, Sudan and Ghana, had more than a 1000 cases (Iriemenam et al., 2008). One stumbling block preventing complete eradication, however, remains – the civil war in Sudan and ethnic fighting in Ghana (Hopkins and Withers, 2002; Iriemenam et al., 2008). Data from 2007 indicate that 60.3% of all cases of dracunculiasis came from the Sudan and 37.7% from Ghana (Iriemenam et al., 2008); however, although there is commitment to and progress in fighting the disease in Ghana, and eradication has been successful in northern Sudan, there are still substantial difficulties in southern Sudan (Hopkins et al., 2005; Iriemenam et al., 2008) and it is highly unlikely that global eradication of this disease will be possible as long as these conflicts continue (Hopkins and Withers, 2002). The prevalence of malaria is also susceptible to the impact of conflict situations. Malaria transmission is often higher than usual in areas containing refugee populations due both to lack of control and medication, and to high population densities (Na´jera, 1996; Martens and Hall, 2000). In addition, the collapse of health services due to civil unrest after the break-up of the Soviet Union led to a substantial reemergence of malaria in Tajikistan (Pitt et al., 1998), while population displacement from Afghanistan to Pakistan after the Soviet invasion of the former in 1979 led to an increase in the local number of cases in refugees from 11 200 in1981 to 118 000 in 1991 (Rowland et al., 2002). Population movement within Afghanistan has led to the introduction of malaria to the Bamian Valley at an altitude of 2250–2400 m, well above its normal range (Rab et al., 2003).

3.12.7.5 El Nino The El Nino Southern Oscillation (ENSO) is a complex geophysical phenomena in the Pacific Ocean leading to major cyclic shifts in weather patterns, including changes in rainfall on a worldwide scale (Cane, 2005). These shifts in turn can have a significant influence on ecological processes in the affected areas (Jaksic, 2001; Stenseth et al., 2002). Historical evidence shows a correlation between ENSO events and malaria epidemics, which can be related to patterns of monsoon rainfall (Bouma and Van der Kaay, 1996; Zubair et al., 2008). Kovats et al. (2003), point out that malaria epidemics usually occur in areas where transmission rates are too low to lead to widespread immunity. In those areas where transmission is restricted by climate, even small climatic changes can trigger an epidemic. Increases in malaria cases have been associated with the ENSO in South America (Bouma and Dye, 1997; Poveda et al., 2001), Africa (Lindblade et al. (1999), but see Lindsay et al. (2000) who found a significant reduction in

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Malaria in highland Tanzania) and Southern Asia (Zubair et al., 2008). The relationship between malaria incidence and El Ninodriven climatic phenomena is sufficiently great to be incorporated into predictive models of malaria outbreaks (Thomson and Connor, 2001; Anyamba et al., 2006).

3.12.7.6 Climate Change Anthropogenically caused global climate change will lead to significantly elevated temperatures and changing rainfallpatterns worldwide (IPCC, 2007). The parasites dealt with here, all of which have temperature-dependent developmental phases and rely, at least during part of their lifecycle, on aquatic environments (i.e., rainfall), are climate dependent (Sutherst, 2004; Mas-Coma et al., 2008). Moreover, their dissemination within the environment is dependent on runoff patterns (Rose et al., 2001). Nevertheless, considering the importance of these parasites for human health, remarkably little concrete information is available on how climate change is likely to influence parasite transmission rates, and the information which we do have suggests that this influence may be highly complex and the resulting patterns not readily predictable (Marcogliese, 2001; Poulin, 2005). The relationship between the incidence of El Nino and malaria shows the potential close association between vectorborne pathogens and climate. This, together with the anthropogenically driven current global warming suggests that a new and unstable dynamic has entered such epidemiological cycles (Sutherst, 2004; Patz et al., 2005).

3.12.8 Synopsis Estimates indicate that about 1.1 billion people lack access to improved water supplies and that 2.6 billion lack adequate sanitation (United Nations Children’s Fund and World Health Organization, 2004). This coupled with the fact that between 4000 and 6000 children alone die in developing countries everyday due to diseases associated with contaminated water supply and poor hygenic conditions, presents a damning picture of water-resource policies in many parts of the world (Moe and Rheingans, 2006). As we have seen above, inadequately planned human manipulation of water resources aimed at improving access and agricultural availability can be directly involved in increasing the burden of disease (Montgomery and Elimelech, 2007). Waterborne human parasites represent a major component of this medical problem and its social and economic consequences. Of the 10 neglected tropical parasitic diseases listed by May (2007) as requiring urgent political action, five have been dealt with above indicating the importance of waterborne parasites generally. Some of the diseases caused by these parasites, such as malaria, have been strongly implicated in reducing the economic strength of entire countries and regions (McCarthy et al., 2000). Superimposed on the basic epidemiological characteristics of the parasites are the dynamic, human-based environmental changes taking place on a global basis. How are these changes likely to affect the epidemiology of the parasites discussed

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above? Can we predict future disease patterns? What precautions should be put in place to prevent significant local or regional increases in the burden of these parasites on the human population? How should water-development projects be planned to avoid the problem of potentially increasing the burden of human parasite infection? All of these questions are currently the subject of intense research and discussion. The freshwater ecosystems of the world are currently undergoing major modifications in their ecology brought about largely by anthropogenic means (Vo¨ro¨smarty and Sahagian, 2000; Jackson et al., 2001; Dudgeon et al., 2005). These changes have a number of major causes; directly by influencing flow patterns, and indirectly through land use, population, and climatic shifts (Bunn and Arthington, 2002; Dudgeon et al., 2005). Direct changes in flow pattern modify the physical characteristics of the stream and thus the habitat present for the aquatic plant and animal species which are present. As plants and animals often have well-defined ecological requirements and therefore preferences for certain habitats, modified flow patterns are likely to influence the entire biotic community, including the parasites and their hosts present (Bunn and Arthington, 2002). Nevertheless, it is not always easy to predict, either qualitatively or quantitatively, how such alterations in flow regimes will influence either the biotic community in general or the parasite species involved (Marcogliese, 2001; Bunn and Arthington, 2002). In many developed countries, progress in water management has usually been accompanied by adequate sewage treatment strongly reducing the contamination of surface and groundwater and ensuring the supply of safe drinking water to most segments of the population (Tulchinsky et al., 2000). In developing countries, there is still much to be done in this regard (Cave and Kolsky, 1999). Water works and sewagetreatment plants financed by foreign aid often prove to be problematic for the the developing countries involved, which do not have the funding, infrastructure, or technical knowledge to maintain them (e.g., Obi et al., 2007). Scanning through the recommendations for each species or group of parasite species, a pattern can be found which is dependent on the life cycle of the parasite. For the few species discussed, which use only humans as final hosts, such as the Guinea worm Dracunculus medinensis, breaking of the transmission cycle to humans is capable of eliminating the disease. This is much harder for both directly and indirectly transmitted diseases for which nonhuman reservoir hosts are present (zoonotic diseases), as in such cases breaking the transmission cycle to humans leaves a natural cycle in which the pathogen remains present. If control measures are then lessened, this cycle can again expand to include the human population. It is also clear from studies conducted within a parasite species or higher taxonomic groups, that each individual situation must be analyzed separately. Plasmodium falciparum malaria provides a good example: in Southeast Asia; deforestation has led to a very substantial reduction in the number of malaria cases, while in parts of South America and Africa, the opposite has been documented. In the case of diseases which are transferred directly via water, such as giaridiasis and cryptosporidiosis, control can be attained by appropriate water treatment. Even simple filtration can be effective for some parasites, such as the removal of the

intermediate copepod host of Dracunculus medinensis. This is, however, an unsatisfactory situation, particularly in developing countries, as a variety of organisms other than relatively large parasites, such as bacteria, which cannot be readily eliminated by filtration, can also act as major human pathogens (Lee and Schwab, 2005). Thus, for completely effective water treatment, additional procedures, such as chlorination, are recommended. In very poor areas where this is not feasible, boiling drinking water offers a safe alternative. The situation becomes even more difficult for those pathogens which are transmitted via contact between the human body and water, for example, Schistosoma spp., or which require a vector with an aquatic component in its life cycle, for example, the mosquitoes which transmit malaria, or the black flies which transmit Onchocerca volvulus. Filtration or other water-treatment options are unrealistic as these vectors occur throughout freshwater bodies covering large areas. Attempts at large-scale control of malaria using insecticides sprayed into such water bodies are often only partially effective, lead to insecticide resistance in the mosquitoes, and often cause very substantial environmental damage as the insecticides are nonspecific (here exceptions are the biocontrol agents B.t.i. and B. sphaericus). Pharmaceutical development and reducing contact rates between the vector and the human host, and potentially the development of vaccines are important goals for control. An often-neglected topic in discussing waterborne parasite control, which is of particular importance, is the status of state-run healthcare systems. The major eradication and control projects carried out through the WHO are run by internationally recognized experts on the parasites involved, include large teams of trained fieldworkers and have access to substantial funding. In many developing countries and some developed countries, such as Russia, however, basic medical care at the national level is inadequate, particularly for the poorer segments of society, and treatment outside of international schemes, is limited, poor, or nonexistent (Stilwell et al., 2004; Streefland, 2005; Gwatkin et al., 2007). Thus, in order to reduce the burden of waterborne parasitic diseases, not only is adequate planning of the use of national water resources required, but also the effective treatment of humans is necessary. Interestingly, a few waterborne parasites, such as Onchocerca volvulus, have stimulated the community of nations to develop ambitious eradication campaigns (discussed earlier), while others such as schistosomes, although being equally hazardous, have attracted less coordinated attention either by large international organizations or by the countries affected. Thus, there is also an irrational aspect to the way in which we evaluate the parasites and diseases discussed in this chapter.

3.12.9 Conclusion Parasitic diseases with a water-based transmission cycle present major medical problems in developing countries and potential problems in the industrialized world. Any changes in the hydrodynamic pattern of a given area are likely to affect these transmission cycles, most often to the detriment of the human population. Indeed, major increases in the incidence

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and prevalence of highly hazardous pathogens have been recorded on numerous occasions following anthropogenic alterations. It is therefore critical when designing hydrodynamic interventions, even on a small scale, anywhere in the world to assess the likelihood of increasing the medical and socioeconomic burden of parasites on the human population involved. Accordingly, all hydrological engineering projects should be accompanied by an evaluation of the likely parasitological consequences caused by changes in the characteristics of the water bodies involved.

Acknowledgments We would like to thank the Centers for Disease Control (CDC) in Atlanta, Georgia for permission to use the life cycle figures and many of the other images presented in this manuscript. Permission to use Figure 16 was obtained from the American Society for Microbiology. We also thank the estate of Prof. Werner Frank, Dr. Karl Steib, Dr. Yves Jackson, and Dr. V. Etzel for supplying images. Dr. Sven Kimpel (University of Du¨sseldorf) was kind enough to read and improve the section on anisakiasis. Alexandra Wenz and Jasmin Skuballa provided invaluable help with organizing and arranging the figures and formatting the manuscript. Pascal Baumgartner, Ba¨rbel Dausmann, Emanuel Heitlinger, Miriam Pfa¨ffle, Alexandra Plokarz, Ula Weclawski, and Claudia Zetlmeisl helped with the preparation of the manuscript.

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3.13 Bioremediation: Plasmid-Mediated Bioaugmentation of Microbial Communities – Experience from Laboratory-Scale Bioreactors M Hausner and M Starek, Ryerson University, Toronto, ON, Canada S Bathe, Studienstiftung des deutschen Volkes, Bonn, Germany & 2011 Elsevier B.V. All rights reserved.

3.13.1 3.13.1.1 3.13.1.2 3.13.1.3 3.13.1.4

Horizontal Gene Transfer-Mediated Bioaugmentation Microbial Aggregates Xenobiotics in Aquatic Environments Role of Catabolic Genes in Bioremediation Monitoring of Gene Transfer: Detection of Donor Strains and Emerging Transconjugants Using Culture-Independent Techniques 3.13.1.5 Objectives 3.13.2 Plasmid pWW0 3.13.2.1 Characteristics and Host Range 3.13.2.2 Plasmid pWW0 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge 3.13.2.3 Introduction of Plasmid pWW0 to Groundwater-Derived Microbial Communities 3.13.2.4 Utilization of Plasmid pWW0 for Bioaugmentation Purposes 3.13.3 Plasmid pJP4 3.13.3.1 Characteristics and Host Range 3.13.3.2 Plasmid pJP4 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge 3.13.3.3 Utilization of Plasmid pJP4 for Bioaugmentation Purposes 3.13.4 Plasmid pNB2 3.13.4.1 Characteristics and Host Range 3.13.4.2 Plasmid pNB2 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge 3.13.4.3 Utilization of Plasmid pNB2 for Bioaugmentation Purposes 3.13.5 Conclusions and Recommendations Acknowledgments References

3.13.1 Horizontal Gene Transfer-Mediated Bioaugmentation 3.13.1.1 Microbial Aggregates In natural environments, as much as 99% of microorganims are found in biofilms (Dalton and March, 1998) or other bioaggregates (Adav et al., 2010). Bioaggregates, such as activated sludge flocs, aerobic and anaerobic biofilms, microbial mats or marine snow, and aerobic or anaerobic granules, can be defined as accumulations of microbes embedded in extracellular polymeric substance (EPS) (Adav et al., 2010). EPSs are biopolymers of microbial origin consisting of polysaccharides, a wide variety of proteins, glycoproteins, glycolipids, and in some cases, extracellular DNA (e-DNA) (Flemming et al., 2007). From an ecological point of view, bioaggregates can be broadly subdivided based on their location relative to the surrounding environment. Bioaggregates can be associated with a surface (biofilms), can float at a liquid–air interfaces (microbial mats), or can be free-floating in a liquid in the form of microbial flocs. There exists a special type of bioaggregate referred to as granular sludge, with a minimum size of 200 mm (de Kreuk et al., 2007). These granular bioaggregates differ from microbial flocs in that they settle faster and do not coagulate with each other (de Kreuk et al., 2007).

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3.13.1.2 Xenobiotics in Aquatic Environments Bioremediation, microbially mediated degradation or detoxification of contaminants, shows potential for remediation of contaminated aquatic environments. Many xenobiotics become associated with bioaggregates (Bouwer, 1989; Wolfaardt et al., 1994) where they exert selective pressure on attached microbial communities. Given sufficient time, adaptation of microorganisms to the contaminant usually results. Microorganisms acquire the ability to survive in the presence of xenobiotics or to even utilize contaminants as carbon sources by one or a combination of the following mechanisms: (1) an increase in the numbers of specific degraders, (2) microbial community adaptation through mutations, or (3) acquisition of relevant genetic information by the community through horizontal gene transfer (HGT), leading to an eventual increase in the community’s biodegradation potential (Top et al., 2002; Top and Springael, 2003). By definition, bioaggregates are likely to possess microorganisms positioned in close proximity and are thus ideal environments for the occurrence of HGT (Wuertz et al., 2004). It is in this vein of thought that natural attenuation of environmental contaminants can be anthropologically altered. Practically speaking, it would be a difficult task to perform site-directed mutagenesis to a population of microorganisms in a natural environment with a high degree of precision, such that the

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population would be able to degrade the contaminant in question. The addition of nutrients to an environment with the intension of stimulating the growth of specific degraders is perhaps a more practical avenue of procedure, and is commonly referred to as biostimulation (Pandey et al., 2008). The approach of biostimulation, in essence, depends on the existence of a bacterial strain with specific metabolic capabilities in the vicinity of the contaminated area. The addition of new genetic capabilities, either chromosomally or plasmid encoded, can circumvent this caveat to biostimulation. This approach is referred to as bioaugmentation and it can initiate and accelerate bioremediation (Van Limbergen et al., 1998).

3.13.1.3 Role of Catabolic Genes in Bioremediation In general, genes encoding enzymes active in degradative pathways and genes encoding antibiotic or metal resistance are often located on plasmids or other mobile genetic elements such as transposons (Top et al., 2002). Plasmid transfer via conjugation (exchange of genetic information from one cell to another mediated by cell-to-cell contact) has been frequently detected in many environments, including soils, activated sludge, sediments, the rhizosphere and phyllosphere of plants, and model, engineered and natural biofilms. Bioaugmentation can be accomplished through the addition of a bacterial strain or a mixed culture with the required metabolic properties (i.e., the capability to degrade a relevant pollutant) to the indigenous microbial community (Kasai et al., 2007; Dabert et al., 2005). Nevertheless, previous work has shown that strains cultured in the lab under optimal conditions and then introduced to existing microbial communities may not survive under the new environmental

conditions and eventually are outcompeted to levels below detection limits (Bouchez et al., 2000a, 2000b; Tchelet et al., 1999). Interestingly, bioaugmentation aimed at improving nitrogen removal from wastewater has been associated with process inhibition (Bouchez et al., 2000a, 2000b), demonstrated by nitrification breakdown upon addition of a denitrifying microorganism. The authors in that particular study attributed the rapid decline in denitrifying microorganisms to grazing by higher-order protozoan ciliates. On the other hand, bioaugmentation via in situ genetic manipulation (the introduction of catabolic genes into an existing indigenous community by means of HGT via conjugation or transformation) may result in a lasting presence of the introduced degradative genes in an existing microbial community (Bathe, 2004; Bathe et al., 2004b, 2005, 2009). In this manner, catabolic enzymes encoded by genes on mobile genetic elements carried by an appropriate bacterial host are introduced to indigenous populations (Figure 1). Previous studies have demonstrated that conjugative catabolic plasmids of the incompatibility group P1 are frequently transferred to a broad range of proteobacterial recipients (Bathe et al., 2004a). In engineered systems such as wastewater treatment plants, cell densities and growth rates are usually high and the impact of HGT may be improved by a rapid division of transconjugants passing their newly acquired genetic information to the subsequent generations of bacteria (Wuertz et al., 2004). Similarly, transconjugants may serve as donor strains and enhance the spread of introduced genes throughout the indigenous microbial communities. Conjugation is likely to play a major role in spreading genetic information encoded on plasmids and can be exploited in bioaugmentation. Transfer of catabolic plasmids has been previously studied in model aerobic suspended growth microcosms (Bathe, 2004; Bathe et al.,

Reactor

Bacteria in wastewater Donor

(a)

Chromosomal DNA Contaminant molecule Degradative plasmid Single strand of plasmid DNA during conjugative plasmid transfer Transconjugant

(b)

Figure 1 Diagrammatic representation of plasmid-mediated bioaugmentation. (a) A normal (not bioaugmented) reactor where resident microorganisms degrade a portion of the contaminants present in the wastewater. (b) A bioaugmented reactor: a donor strain harboring a degradative plasmid is introduced into the reactor and subsequently transfers that plasmid via conjugation to resident bacteria, which become transconjugants and potential new plasmid donors. This results in the spread of the degradative plasmid throughout the existing microbial community, and therefore increased degradation of the contaminant.

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2004b, 2005; Boon et al., 2000; Molin and Tolker-Nielsen, 2003) as well as in soils (Dejonghe et al., 2000; Newby et al., 2000a, 2000b; Pepper et al., 2002).

3.13.1.4 Monitoring of Gene Transfer: Detection of Donor Strains and Emerging Transconjugants Using Culture-Independent Techniques In order to interpret the success or failure of gene-mediated bioaugmentation, it is useful to monitor the survival or potential disappearance of the introduced strain and/or the emergence of transconjugants (upon successful conjugation) coupled to the performance of the reactor. Since not all transconjugants can be cultivated (Bathe et al., 2004b), cultivation-independent in situ techniques which employ fluorescent marker genes together with microscopy (Sorensen et al., 2005) are useful. Fluorescent proteins such as the green fluorescent protein (GFP) from Aequorea victoria (Chalfie et al., 1994; Tsien, 1998) or the red fluorescent protein DsRed (drFP583, commercially available as DsRed) isolated from a Discosoma sp. coral (Yarbrough et al., 2001) have been used as markers for in situ monitoring of plasmid transfer (Bathe et al., 2004b, 2005; Boon et al., 2000; Nancharaiah et al., 2003) in different environments, including agar surfaces, the phylloplane, microbial wastewater communities, or biofilms (Bathe et al., 2004b; Christensen et al., 1998; Normander et al., 1998).

3.13.1.5 Objectives In this chapter, we summarize our experience with bioaugmentation of microbial communities using donor strains carrying one of the following catabolic plasmids: plasmid pNB2 (Bathe, 2004; Bathe et al., 2005, 2009) which encodes genes necessary for the 3-chloroaniline (3-CA) degradation pathway, plasmid pJP4 (Bathe et al., 2004a, 2004b), encoding genes for the degradation of 2,4-dichlorophenoxyacetic acid (2,4-D), and the TOL plasmid pWW0 (Nancharaiah et al., 2003, 2008; Venkata Mohan et al., 2009), which carries genes encoding enzymes for toluene degradation and other related compounds. We will emphasize wastewater environments and, where appropriate, include examples from other microbial habitats.

3.13.2 Plasmid pWW0 3.13.2.1 Characteristics and Host Range There are various incompatibility group 9 (IncP-9) TOL plasmids which have been isolated; the most used in laboratory settings is plasmid pWW0. The conjugative IncP-9 TOL plasmid pWW0, initially isolated from Pseudomonas putida-mt2 is 116 580 bp in size (Greated et al., 2002; Williams and Murray, 1974; Wong and Dunn, 1974; GenBank Accession No. AJ344068) and contains genes that encode enzymes for the degradation of toluene/xylene in addition to containing, and constitutively expressing, genes which are necessary for its transfer from host to recipient (Greated et al., 2002; Worsey and Williams, 1975). Plasmid pWW0 seems primarily to transfer among the Pseudomonas species. However, other species, including Escherichia coli, Erwinia chrysanthemi, Hydrogenophaga palleronii, Serratia, and Burkholderia species, have

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been shown to be recipients of pWW0 (Benson and Shapiro, 1978; Nakazawa et al., 1978; Nancharaiah et al., 2003; Ramos-Gonzalez et al., 1991; Sarand et al., 2000). However, the transfer of pWW0 depends not only on the donor strains and recipient pool but also on environmental factors such as nutrient concentration and temperature (Johnsen and Kroer, 2007; Fox et al., 2008). Conjugative transfer of plasmid pWW0 was accomplished efficiently on plates (Christensen et al., 1996), in biofilm or bioaggregates (Christensen et al., 1998; Nancharaiah et al., 2003), soils (Sarand et al., 2000), on bush bean leaves (Normander et al., 1998), and in activated sludge communities (Nancharaiah et al., 2003, 2008; Venkata Mohan et al., 2009). Previous studies demonstrated that transfer rates of pWW0 increase with donor growth rate (Smets et al., 1993) and substrate concentration (Smets et al., 1995). The transfer frequency for pWW0 can be as high as one transconjugant per donor if the conditions for the bacteria are optimal (Ramos-Gonzalez et al., 1991).

3.13.2.2 Plasmid pWW0 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge In our research, we use a green-fluorescent protein or redfluorescent protein labeled pWW0 for bioaugmentation studies. In this way, we can detect both donor cells and transconjugants microscopically. Previously, we investigated the transfer of pWW0 from its P. putida KT2442 donor to indigenous wastewater microbial communities (Nancharaiah et al., 2003; Venkata Mohan et al., 2009) in a laboratory scale (total volume of 1.6 l and a working volume of 1 l) and a pilot scale (total volume of 38 l and a working volume of 21 l) sequencing batch biofilm reactor (SBBR), using glass or clay beads, respectively, as carrier material for biofilm development. The laboratory-scale SBBR had a total volume of 1.6 l and a working volume of 1 l. The pilot scale SBBR had a total volume of 38 l and a working volume of 21 l. We showed that pWW0 was transferred to indigenous recipients in the laboratory-scale bioreactor with benzyl alcohol (BA) as the sole carbon source. We demonstrated that donor cells survived in the laboratory-scale bioreactor throughout the experimental period of 32 days. Concurrently, we were able to detect emerging transconjugants using confocal laser scanning microscopy (CLSM). The degradation rate of BA was enhanced from 0.98 mg BA min 1 prior to inoculation to 1.9 mg BA min 1 after donor inoculation. In contrast, in the pilot-scale bioreactor, donor cells disappeared 84 h after inoculation while transconjugants were not detected at all. The performance of the pilot-scale reactor with respect to BA removal was not affected by donor addition. Thus, the survival of a bioaugmented strain, conjugative plasmid transfer, and enhanced BA degradation was demonstrated in the laboratoryscale SBBR but not in the pilot-scale SBBR. We attributed the failure of bioaugmentation in the pilot-scale SBBR to insufficient selection pressure, since parallel experiments showed complete removal of BA even prior to donor addition. In another study (Nancharaiah et al., 2008), we showed successful bioaugmentation of aerobic granular sludge using P. putida KT2442 cells bearing plasmid pWW0. The study revealed both donor integration and transconjugant

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proliferation, indicating successful plasmid transfer to indigenous microbial communities in the granules, coupled to a significant increase in degradation of BA (used as sole carbon source). In contrast, control microcosms (with non-augmented granules) did not show any noticeable increase in BA degradation. This study showed that bioaugmentation of aerobic granular sludge via donor colonization and plasmid transfer is feasible for enhanced biodegradation. A recent study by Pei and Gunsch (2009) investigated the transfer of pWW0 from P. putida BBC443 to microbial communities in batch reactors inoculated with activated sludge from a municipal wastewater treatment plant. The results confirmed plasmid transfer to recipient cells. A higher initial concentration of activated sludge cells (potential recipients) was associated with the highest number of conjugation events. However, successful gene transfer was not coupled to an increase in toluene degradation rates, perhaps due to the presence of a high number of degraders originally present in the activated sludge inoculum.

or dependent on yet-undefined environmental factors. In the case of wastewater communities, donor cell persistence and/or plasmid transfer was linked to enhanced BA degradation. However, it could not be differentiated whether accelerated degradation of BA was linked to the persistence of donor cells, the emergence of transconjugants or both. On the other hand, no enhancement of toluene degradation was observed upon successful transfer of pWW0 to activated sludge communities in batch cultures (Pei and Gunsch, 2009). Similarly, the addition of a pWW0-bearing donor strain to simulated rockfracture aquifers did not result in enhanced toluene or BA degradation. Therefore, the factors governing the success of pWW0-based bioaugmentation of microbial communities for enhanced toluene degradation (e.g., donor cell:recipient cell ratio, size of aggregates harboring potential recipients, and effect of toluene concentration) need to be further investigated.

3.13.3 Plasmid pJP4 3.13.2.3 Introduction of Plasmid pWW0 to Groundwater-Derived Microbial Communities While bioaugmentation via HGT has been successful in wastewater remediation, as well as in some applications to subsurface microbial communities (Smets et al., 2003) or to endogenous endophytic microbial communities from poplar to improve groundwater contaminated with organic solvents (Taghavi et al., 2005), a current study in our lab on the applicability of plasmid pWW0-mediated enhanced degradation of toluene or BA has not yielded similar results in a model of groundwater flow in a rock-fracture aquifer. These flowcell microcosms represent one of the first attempts to determine if HGT could be a suitable means of increasing the bioremediation capacity of subsurface biofilms exposed to groundwater flow. The introduction of donor strain P. putida SM1443 that harbors pWW0 to this model system has not accelerated the removal of toluene or BA; furthermore, no indication of HGT has been detected. These results are in contrast to concurrent conjugation experiments which have been performed as agar plate mating experiments with the same donor strain and recipient microbial community (Starek, 2010). These matings display a low, yet detectable, level of HGT. There are obvious contrasts between our system and previous studies – such as variety and concentration of nutrients, fewer potential recipients with regard to both cell numbers and bacterial species, concentrations of dissolved oxygen and a directional flow of bulk fluid – which focus on wastewater. Early results seem to support the notion that HGT depends not only on donor and recipient compatibility, but also on environmental factors (Lambertsen et al., 2004).

3.13.2.4 Utilization of Plasmid pWW0 for Bioaugmentation Purposes In summary, recent studies have shown that plasmid pWW0 can be successfully transferred to microorganisms within wastewater/activated sludge communities, but our current studies with groundwater-derived mixed cultures indicate that pWW0 transfer in other types of environments may be limited

3.13.3.1 Characteristics and Host Range Plasmid pJP4 is an 80-kb, IncPl, broad-host-range conjugative plasmid of Alcaligenes eutrophus, encoding resistance to mercuric chloride and phenyl mercury acetate and degradation of 2,4-dichlorophenoxyacetic acid, 2-methyl-4-chlorophenoxyacetic acid, and 3-chlorobenzoate (Don and Pemberton, 1985). It has been shown previously that conjugative catabolic plasmids of the incompatibility group P1 are transferred between bacterial cells at a high rate and possess a broad host range within the proteobacteria (Bathe et al., 2004a; Pukall et al., 1996). After introduction of bacteria carrying plasmid pJP4 into soil microcosms, conjugative transfer to indigenous bacteria and subsequent enhancement of 2,4-D degradation was observed (Newby et al., 2000a, 2000b). Our own studies with plasmid pJP4 (Bathe et al., 2004a) showed that it can be transferred to a variety of microbial genera of the a, b, and g classes of the Proteobacteria, mostly to the families Rhizobiaceae and Comamonadaceae and the genus Stenotrophomonas. However, only transconjugants identified as P. putida and Delftia spp. strains were able to grow on 2,4-D as the sole carbon source.

3.13.3.2 Plasmid pJP4 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge We investigated the possibility of enhancing degradation of the xenobiotic model compound 2,4-D in a model, laboratoryscale SBBR, inoculated with activated sludge from a municipal wastewater treatment plant, using plasmid pJP4 (Bathe et al., 2004b). After introduction of a plasmid donor strain to a labscale SBBR operated without 2,4-D, the number of plasmidcarrying cells first dropped, and then increased after switching to 2,4-D as the sole carbon source. The donor cells were unable to grow in the synthetic wastewater which was used in our experiment with 2,4-D as the sole carbon source, so the emergence of plasmid-carrying cells was attributed to successful plasmid transfer to members of the indigenous wastewater communities. Transconjugants could be detected both

Bioremediation: Plasmid-Mediated Bioaugmentation of Microbial Communities

by culture-dependent and culture-independent methods in the 2,4-D degrading bioaggregates. In contrast to 90% 2,4-D degradation in the bioaugmented reactor within 40 h, a control reactor that did not receive the plasmid donor still contained 60% of the initial 2,4-D concentration even after 90 h. Reactors consisted of 1.0 l of synthetic wastewater with an initial 2,4-D concentration of 2 mM. After 460 min of aerated reaction time, the entire liquid content of the reactor was drawn out. This experiment clearly demonstrates the introduction of 2,4-D degradative genes into a microbial biofilm and indicates that HGT is a promising tool for bioaugmentation of reactors treating wastewater. Although transconjugants could be cultured from the bioaugmented reactor, they did not belong to strains corresponding to sequences from dominant denaturing gradient gel electrophoresis (DGGE) (Muyzer et al., 1993) bands in the 2,4-D degrading biofilm sample. DGGE is a culture-independent, DNA-based fingerprinting technique which allows for the estimation of microbial diversity in a sample. It also permits partial identification of dominant microbial community members. In our study, it was not clear if dominant DGGE bands corresponded to transconjugant species which could not be isolated on agar plates or if the isolated Ralstonia strain was the dominant transconjugant species. However, the lack of a corresponding DGGE sequence indicated that the abundance of the isolated transconjugant stain within the activated sludge community was low. A closer relationship between phylogenetic identity and metabolic functions of specific bacteria may be established by the application of fluorescence in situ hybridization (FISH) probes to identify cells expressing plasmid-associated fluorescent proteins, by the use of microautoradiography combined with FISH or stable isotope probing as reviewed previously (Gray and Head, 2001).

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Additionally, they may permit transfer of the plasmid to recipients which were not accessible by the initial donor strain (Bathe et al., 2004a).

3.13.4 Plasmid pNB2 3.13.4.1 Characteristics and Host Range Plasmid pNB2 is a broad host range IncP1 plasmid (88 kb) which carries genes for mercury resistance and for the degradation of aniline and 3-CA (Bathe, 2004) and was recently shown to be present in Comamonas testosteroni I2, a strain capable of aniline and 3-CA degradation (Boon et al., 2001). After conjugal transfer of this plasmid to Ralstonia eutropha JMP228, transconjugants gained the ability to degrade aniline, but not 3-CA. Therefore, pNB2 was described to be an anilinedegradative plasmid (Boon et al., 2001). Bathe (2004) investigated the involvement of the anilinedegradative plasmid pNB2 in degradation of 3-CA. Using plate matings of a P. putida strain containing pNB2 with a mixed bacterial culture derived from activated sludge and inoculation of the mating mixtures into batch cultures containing 3-CA, degradation of the compound was observed. A total of five different transconjugant strains were isolated from one of the batch cultures and two of them were able to degrade 3-CA. These two isolates were identified as C. testosteroni by partial 16S rDNA sequencing. Therefore, it was concluded that pNB2 carries a portion of the genes involved in the catabolism of 3-CA, but that the completion of the pathway must be provided by chromosomal genes in the host strain. The results suggest that pNB2 is a candidate plasmid which can be used in plasmid-mediated bioaugmentation of wastewater contaminated with chlorinated anilines.

3.13.3.3 Utilization of Plasmid pJP4 for Bioaugmentation Purposes

3.13.4.2 Plasmid pNB2 Transfer to Indigenous Microorganisms in Wastewater or Activated Sludge

In summary, our studies have shown that plasmid pJP4 can be successfully transferred to microorganisms within wastewater/ activated sludge communities and that plasmid transfer and the emergence of transconjugants is coupled to enhanced 2,4-D degradation. In the case of microbial wastewater communities, donor cell persistence and/or plasmid transfer was linked to enhanced BA degradation. However, it could not be differentiated whether accelerated degradation of BA was linked to the persistence of donor cells, the emergence of transconjugants or both. Even though transconjugants belonging to a number of different families were isolated, only a limited number of isolates, specifically those belonging to the Delftia and Pseudomonas genera were able to utilize 2,4-D as sole carbon source. The inability to utilize 2,4-D despite the presence of the plasmid may be caused by a lack of promoter recognition or translation of the produced mRNAs, failure to import the substrate, substrate toxicity and concentration, or incomplete assembly of the pathway if the host needs to provide additional catabolic genes. On the other hand, nondegrading transconjugants may serve as a plasmid reservoir, which might facilitate the adaptation of the community upon encountering the corresponding xenobiotic compound.

In a follow-up study, Bathe et al. (2005) tested the applicability of plasmid pNB2 for bioaugmentation of bacteria in model sequencing batch moving bed reactors (SBMBRs) containing wood chips as biofilm carrier material and receiving 3-CA. A setup of three biofilm reactors was studied, all initially inoculated with bacteria from activated sludge. One reactor (PB) received a P. putida pNB2 donor strain which, even though it carried the pNB2 plasmid, was not able to degrade 3-CA, due to a lack of chromosomally encoded genes required to complete the degradation pathway. A positive control reactor (P) received a 3-CA degrading C. testosteroni pNB2 transconjugant. A negative control reactor (N) received only the initial wastewater inocula but otherwise remained unchanged (i.e., no plasmid donors or 3-CA degrading strains were added). The results revealed that the positive control reactor (amended with a 3-CA degrading transconjugant) showed 3-CA degradation from the very beginning of the experiment. In contrast, degradation started after an initial lag period in reactor PB, amended with the plasmid donor (which was not able to degrade 3-CA, despite the presence of plasmid pNB2). No degradation was observed in the negative control reactor N, which received neither the

Table 1

Summary of plasmid-mediated bioaugmentation studies discussed in this contribution

Contaminant

Initial concentration (mg l 1)

Reactor type

Inoculum/ recipient community

Donor strain

Plasmid

Donor persistence

Transconjugant emergence

Enhanced degradation

Reference

BA

54

SBBR, pilot scale

Municipal WW

P. putida KT2442

pWW0

No

No

No

BA

62

Municipal WW

P. putida KT2442

pWW0

Yes

Yes

NDa

BA

108

Municipal WW

P. putida KT2442

pWW0

Yes

Yes

Yes

BA

541

Municipal WW

P. putida KT2442

pWW0

Yes

Yes

Yes

BA

2604

pWW0

No

No

No

173

Groundwaterderived mixed culture Municipal WW

P. putida SM1443

Toluene

SBBR, laboratory scale SBBR, laboratory scale SBR, aerobic granular sludge Continuous flowthrough chamber Flask

Venkata Mohan et al. (2009) Nancharaiah et al. (2003) Venkata Mohan et al. (2009) Nancharaiah et al. (2008) Starek (2010)

P. putida BBC443

pWW0

ND

Yes

No

Toluene

Continuous flowthrough chamber

Groundwaterderived mixed culture

P. putida SM1443

pWW0

No

No

No

2,4-D

303 415 (added as a slug of toluene to simulate a NAPLb spill) 442

Municipal WW

P. putida SM1443

pJP4

No

Yes

Yes

3-CA

80

Municipal WW

P. putida SM1443

pNB2

No

Yes

Yes

Bathe et al. (2004b) Bathe et al. (2005)

3-CA

100–400 (variable)

SBBR, laboratory scale SBMBR, laboratory scale Semicontinuous activated sludge, semicontinuous MBBR

Municipal WW

Comamonas testosteroni SB3

pNB2

ND

Yes

Yes, but variable

Bathe et al. (2009)

a

ND, not determined. NAPL, nonaqueous phase liquid.

b

Pei and Gunsch (2009) Starek (2010)

Bioremediation: Plasmid-Mediated Bioaugmentation of Microbial Communities

plasmid donor nor the 3-CA degrading transconjugant. Polymerase chain reaction (PCR) analysis showed that the P. putida donor abundance dropped in reactor PB (bioaugmented with the plasmid donor strain), but plasmid pNB2 abundance remained stable, indicating plasmid transfer to members of the indigenous wastewater communities. A number of different 3-CA degrading C. testosteroni strains carrying pNB2 were isolated from the plasmid donor bioaugmented reactor PB. In order to determine the most efficient bioaugmentation approach using plasmid pNB2, Bathe et al. (2009) investigated several strategies for achieving degradation of 3-CA in semicontinuous activated sludge reactors. The addition of a 3-CAdegrading C. testosteroni strain carrying the degradative plasmid pNB2 to a biofilm reactor resulted in complete 3-CA degradation together with spread of the plasmid within the indigenous biofilm population. A second set of reactors was bioaugmented with either a suspension of biofilm cells removed from the carrier material or with biofilm-containing carrier material (in this case, wood chips). 3-CA degradation was established rapidly in all bioaugmented reactors, followed by a slow adaptation of the nonbioaugmented control reactors. Variations in 3-CA concentration caused temporary performance breakdowns in all reactors. Duplicates of the control reactors deviated in their behavior but the bioaugmented reactors appeared more reproducible in their performance and population dynamics. The carrier-bioaugmented reactors showed an improved performance in the presence of high 3-CA influent concentrations in comparison the suspension-bioaugmented reactors. In contrast, degradation in one control reactor failed completely, but was rapidly established in the remaining control reactor.

3.13.4.3 Utilization of Plasmid pNB2 for Bioaugmentation Purposes Our studies using plasmid pNB2 clearly demonstrated that a successful plasmid-mediated bioaugmentation was achieved (Bathe et al., 2005, 2009). C. testosteroni was the dominant 3-CA degrading pNB2 transconjugant species isolated from the donor strain-augmented reactor PB. The study again demonstrated the potential of gene transfer to spread and establish xenobiotic degrading potential by dissemination of catabolic genes in particular.

3.13.5 Conclusions and Recommendations The studies discussed in this chapter are summarized in Table 1. Our studies have shown that plasmid transfer in bioreactors occurs with a subsequent formation and outgrowth of transconjugants, if a selective pressure in the form of the target compound is being applied. This was confirmed in our studies with three different degradative plasmids, namely the TOL plasmid pWW0, plasmid pJP4, and plasmid pNB2. On the other hand, we have also shown that successful bioaugmentation in one type of microbial community (i.e., wastewater) is not directly transferable to another type of microbial environments (i.e., groundwater-derived cultures). Further investigations are necessary to examine if plasmidmediated bioaugmentation can be applied to remove

375

xenobiotic compounds in industrial wastewater treatment systems and to reveal if a long-term bioaugmentation can be achieved using this approach. More research is required to analyze the role of fluctuations and threshold concentration of the target compound, the stability of the degradation potential, and thus the fate of plasmid-encoded catabolic genes in the absence of the target compound, the role and presence of other (possibly recalcitrant) carbon compounds, process conditions, and different ways of biomass retention specific or nonspecific for degrader populations. This should reveal if plasmid-mediated bioaugmentation is a generally applicable tool or if it is a technique that is best used to facilitate start-up and operation of specialized bioreactors receiving chemically defined and temporally stable wastewater.

Acknowledgments MH and MS acknowledge National Sciences and Engineering Council of Canada (NSERC) support (Discovery Grants Program– Individual; Grant No. 355606-2008 to MH).

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Pukall R, Tschape H, and Smalla K (1996) Monitoring the spread of broad host and narrow host range plasmids in soil microcosms. FEMS Microbiology Ecology 20: 53--66. Ramos-Gonzalez MI, Duque E, and Ramos JL (1991) Conjugational transfer of recombinant DNA in cultures and in soils: Host range of Pseudomonas putida TOL plasmids. Applied and Environmental Microbiology 57: 3020--3027. Sarand II, Haario H, Jorgensen KS, and Romantschuk M (2000) Effect of inoculation of a TOL plasmid containing mycorrhizosphere bacterium on development of Scots pine seedlings, their mycorrhizosphere and the microbial flora in m-toluateamended soil. FEMS Microbiology Ecology 31: 127--141. Smets BF, Morrow JB, and Pinedo CA (2003) Plasmid introduction in metal-stressed, subsurface-derived microcosms: Plasmid fate and community response. Applied and Environmental Microbiology 69: 4087--4097. Smets BF, Rittmann BE, and Stahl DA (1993) The specific growth rate of Pseudomonas putida PAW1 influences the conjugal transfer rate of the TOL plasmid. Applied and Environmental Microbiology 59: 3430--3437. Smets BF, Rittmann BE, and Stahl DA (1995) Quantification of the effect of substrate concentration on the conjugal transfer rate of the TOL plasmid in short-term batch mating experiments. Letters in Applied Microbiology 21: 167--172. Sorensen SJ, Bailey M, Hansen LH, Kroer N, and Wuertz S (2005) Studying plasmid horizontal transfer in situ: A critical review. Nature Reviews Microbiology 3: 700--710. Starek M (2010) Evaluation of the Transfer of the TOL Plasmid from Pseudomonas putida to Groundwater-Derived Biofilms in a Model Rock-Fracture Aquifer. MSc Thesis, Ryerson University, Toronto, ON, Canada. Taghavi S, Barac T, Greenberg B, Borremans B, Vangronsveld J, and van der Lelie D (2005) Horizontal gene transfer to endogenous endophytic bacteria from poplar improves phytoremediation of toluene. Applied and Environmental Microbiology 71: 8500--8505. Tchelet R, Meckenstock R, Steinle P, and van der Meer JR (1999) Population dynamics of an introduced bacterium degrading chlorinated benzenes in a soil column and in sewage sludge. Biodegradation 10: 113--125. Top EM and Springael D (2003) The role of mobile genetic elements in bacterial adaptation to xenobiotic organic compounds. Current Opinion in Biotechnology 14: 262--269. Top EM, Springael D, and Boon N (2002) Catabolic mobile genetic elements and their potential use in bioaugmentation of polluted soils and waters. FEMS Microbiology Ecology 42: 199--208. Tsien RY (1998) The green fluorescent protein. Annual Review of Biochemistry 67: 509--544. Van Limbergen H, Top EM, and Verstraete W (1998) Bioaugmentation in activated sludge: Current features and future perspectives. Applied Microbiology and Biotechnology 50: 16--23. Venkata Mohan S, Falkentoft C, Venkata Nancharaiah Y, et al. (2009) Bioaugmentation of microbial communities in laboratory and pilot scale sequencing batch biofilm reactors using the TOL plasmid. Bioresource Technology 100: 1746--1753. Wilderer PA and McSwain BS (2004) The SBR and its biofilm application potentials. Water Science and Technology 50: 1--10. Williams PA and Murray K (1974) Metabolism of benzoate and the methylbenzoates by Pseudomonas putida (arvilla) mt-2: Evidence for the existence of a TOL plasmid. Journal of Bacteriology 120: 416--423. Wolfaardt GM, Lawrence JR, Headley JV, Robarts RD, and Caldwell DE (1994) Microbial exopolymers provide a mechanism for bioaccumulation of contaminants. Microbial Ecology 27: 279--291. Wong CL and Dunn NW (1974) Transmissible plasmid coding for the degradation of benzoate and m-toluate in Pseudomonas arvilla mt-2. Genetical Research 23: 227--232. Worsey MJ and Williams PA (1975) Metabolism of toluene and xylenes by Pseudomonas putida (arvilla) mt-2: Evidence for a new function of the TOL plasmid. Journal of Bacteriology 124: 7--13. Wuertz S, Okabe S, and Hausner M (2004) Microbial communities and their interactions in biofilm systems: An overview. Water Science and Technology 49: 327--336. Yarbrough D, Wachter RM, Kallio K, Matz MV, and Remington SJ (2001) Refined crystal structure of DsRed, a red fluorescent protein from coral, at 2.0-A resolution. Proceedings of the National Academy of Sciences of the United States of America 98: 462--467.

3.14 Drinking Water Toxicology in Its Regulatory Framework H Dieter, Federal Environment Agency (UBA), Dessau-Roßlau, Germany & 2011 Elsevier B.V. All rights reserved.

3.14.1 3.14.1.1 3.14.1.2 3.14.2 3.14.2.1 3.14.2.2 3.14.2.3 3.14.2.3.1 3.14.2.3.2 3.14.2.4 3.14.2.4.1 3.14.2.4.2 3.14.2.4.3 3.14.2.5 3.14.2.5.1 3.14.2.5.2 3.14.3 3.14.3.1 3.14.3.2 3.14.4 3.14.4.1 3.14.4.2 3.14.4.3 3.14.4.3.1 3.14.4.3.2 3.14.4.3.3 3.14.4.3.4 3.14.4.3.5 3.14.5 3.14.5.1 3.14.5.2 3.14.5.3 3.14.6 3.14.6.1 3.14.6.2 3.14.6.2.1 3.14.6.2.2 3.14.6.2.3 3.14.6.2.4 3.14.6.2.5 3.14.6.2.6 3.14.6.2.7 3.14.6.2.8 3.14.6.2.9 3.14.6.3 3.14.6.3.1 3.14.6.3.2 3.14.7 3.14.7.1 3.14.7.2 3.14.7.3 3.14.7.4

Introduction Drinking Water: A Unique Medium to Support Life and Personal Hygiene Acceptability and Tolerability of Chemicals in Drinking Water According to Their Functionality and the Potential to Avoid Them From Chemical Hazards to Chemical Standards Basic Facts Historical Landmarks Objectives and Goals of Protection Objectives of protection Goals of protection Timescales to Protect Goals of Protection Precautionary standards (enduring protection of human beings and drinking-water resources) Scientific standards (guide values for lifelong protection of human beings) Remedial standards (action values to protect from shorter-than-lifetime exposure) Special Aspects to be Considered when Setting Standards for Drinking Water Standards for accepted chemicals in drinking water Standards to protect from adverse effects of chemicals in drinking water Panels and Institutions for Setting Drinking Water Standards National International Defining Standards to Prevent Human Health Risks from Drinking Water Qualification of Risks – Critical Toxic Endpoints Groups of Compounds of Specific Interest for Drinking Water Risk Quantification Chemicals exhibiting systemic effects with threshold Chemicals assumed to exhibit threshold-free systemic effects Local effects on humans of chemicals in drinking water Effect combinations Exposure A Holistic Approach for Defining Quality Goals or Standards for Drinking Water Chemicals Whose Regulation Will Primarily Concentrate on Avoidance of Adverse Effects Chemicals Whose Regulation Should Concentrate on an Optimal Ratio of Functional Exposure to Functional Intention Chemicals Whose Regulation Should Concentrate on Remote Emission Control Practical Regulation of Drinking-Water Quality Quality Assurance and Surveillance of Raw Water, Finished Water, Tap Water Paths and Significance of Exposure to Chemicals in Drinking Water Acidification of raw water Agrochemicals Emissions from abandoned waste sites Disinfection by-products Small supplies and health risks Organics leaching from drinking water reservoir coatings and armatures Hygienic aspects of corrosion products from domestic pipes and metallic materials Hygienic aspects of domestic posttreatment of drinking water Hygienic aspects of domestic water saving Significance and Hygienic Assessment of Exposure at the Tap Inorganics Organics The Author’s Short Conclusions Unintended Exposure By-Products of Disinfection and Oxidation Risk Assessment and Management Derogations from Limit Values

377 377 378 379 379 379 380 380 381 381 381 382 382 383 383 383 384 384 384 384 384 385 385 385 387 387 388 389 390 390 390 391 391 391 393 393 393 393 394 395 396 396 396 397 397 397 403 407 407 408 408 408

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3.14.7.5 3.14.7.6 3.14.8 3.14.8.1 3.14.8.2 3.14.8.3 3.14.8.4 3.14.8.5 References

Drinking-Water Installations Surveillance of Drinking Water Perspectives on Perception of Drinking Water How Pure Is Pure? Erroneous Reasons to Ask for Absolute Purity Some Good Reasons for Not Asking for ‘Absolute’ Purity Many More Good Reasons for Not Exhausting Strictly Health-Based Levels How to Best Realize the Social Concept of an Esthetically Acceptable Drinking Water?

3.14.1 Introduction 3.14.1.1 Drinking Water: A Unique Medium to Support Life and Personal Hygiene Water, besides oxygen, is the most important medium to support life and physiological or hygienic needs. Apart from its conceptual designation of ‘water for human consumption/ drinking water’ it refers additionally to specific uses of water in private and public facilities for purposes of personal hygiene. Several technical and principal aspects of drinking water hygiene and the safety of its distribution and accessibility endorse the view that water that meets physiological or personal hygiene demands optimally should exhibit an unobjectionable chemical and microbiological quality as well. A list ranking countries for shortage of freshwater index reveals per capita availabilities between 10 m3 (Kuwait) and almost 100 000 m3 (Canada). Sufficient water supply is considered possible from 1600 m3 upwards in hydrogeologically disparate countries as Kenya (985 m3), Ethiopia (1749 m3), Germany (1878 m3), Afghanistan (2986 m3), Mexico (4624 m3), and Portugal (6859 m3) UNEP (2003a). The minimum amount of drinking water to meet daily needs of personal hygiene and physiology seems to vary between 20 l per person, if available within 1 km of the user’s dwelling (WHO, 2008), and up to about 100 l in case of household connections (Cairncross and Valdmanis, 2010). This discussion or controversy on the technical feasibility and hygienic advantages of two parallel systems to distribute ‘service water’ in one and ‘drinking water’ in the other is as old as the idea of central supply. In Europe, it was decided at the end of the nineteenth century to have one single system of pipes to deliver water of unobjectionable quality to serve all purposes of personal hygiene and physiology (Kluge and Schramm, 1986). The forced domestic use of greywater in private houses would very probably not reduce the respective chemical load, neither of the environment, nor of the sewage treatment plants, or of the reused water (Donner et al., 2010). Unobjectionable drinking water is not necessarily sterile, but devoid of infectious concentrations of pathogens. It should never be allowed to exhibit any property or potential to impair human health. Furthermore, according to current technical (national and supranational) norms, it should be appetizing, cool, colorless and odorless, and low in germs. It should have an inoffensive taste and stimulate consumption. Its content of dissolved substances has to be kept, as far as possible, within technically feasible limits. It should not lead to corrosion of technical materials within

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the distribution system and households and be available at their hand-over points (‘taps’) in sufficient quantity and pressure. According to the general rule of hygiene, food production from raw materials should have, as far as possible, the same chemical and microbiological quality as the intended product, containing therefore, ideally, only technically inevitable contaminations or residues. The raw material of drinking water is the ‘raw water’ as taken or extracted from groundwater or (prepurified) surface water to transform it to drinking water by a more or less elaborate chemical or physical treatment. This leads to the present principle to rely, wherever possible, on raw water that requires only minimal or even no treatment – neither for health nor for technical or esthetic reasons. The same principle applies to any other life-supporting foods or principles we use or consume directly in their natural form, for example, sunlight, fruits, or air for breathing. A very important point when looking on our consumption of foods as biochemical energy carriers is the fact that, in contrast to foods, we do not consume or destroy (drinking) water to support our physiologic and hygienic needs. We only make use of it as a transporter of molecules, respectively, contaminants and their desired and undesired interactions. This is why in this context the central questions on the sustainability of human behavior toward ‘drinking water’ are not those dealing with technically available or absolute water quantities but with the tolerable degree and quality of technically or physiologically inevitable contamination of water after its intended use, its discharge as waste water, and its eventual reuse. The discussion on the question ‘which intensity of use and contamination of a regional water resource would correspond to optimal but neither maximal nor minimal quality claims’ has therefore to be negotiated on a societal level (Section 3.14.8). From the point of sustainability, ‘saving water for use’ means, therefore, preferentially ‘clean the used water’ as efficiently as reasonably achievable and reuse it after it has passed a sufficient number of (at least two to three) technical or natural safety barriers to retain infectious agents and chemical noxes. From several points of view (technical, hygienic, social, or psychological), the optimal fraction of water to be reused in moderately to highly populated areas is not qualified for being defined on the organizational level of small family homes or even single households. Instead, all qualitative and hydrogeological aspects of the natural regional water yield, its rate of renewal, and its technical availability must be considered to find the optimal technical structures and procedures to meet this important societal task to maintain the regional resources

Drinking Water Toxicology in Its Regulatory Framework

for drinking water as abundant, socially equitable, and as clean as possible. This purpose, ‘‘adequate sanitation of a standard which sufficiently protects human health and the environment,’’ shall particularly be ‘‘done through the establishment, improvement and maintenance of collective systems’’ (UN – Economic Commission for Europe, 1999). The only imperative and ‘absolute’ (scientific) quality requirement for any drinking water from any source consists in respecting health-based quality criteria as strictly as possible, whereas precautionary (not merely scientific) aspects of sustainability are best followed along the principle of keeping nonfunctional exposure ‘‘as low as reasonably (mostly technically) achievable’’ (ALARA). Within this discussion, the focus of what may be called achievable is not only on hard aspects such as human health but also on softer criteria such as esthetics or pureness of drinking water, taking into account, as WHO (2008) proposes, the socioeconomic, cultural, or ecologic contexts. It should include therefore criteria of technical functionality or efficiency of investments to minimize the inevitable preventable contamination right from the beginning to the end of the pipe. This holistic approach is also the best to minimize the risk that a consumer might refuse a subjectively unpleasant central supply and replace it by a seemingly more pleasant one being possibly present in his/her private backyard or in the hands of a street retailer. Taken together, limit values for substances in the drinking water system are not only a matter of science or human toxicology but also of sustainable drinking water hygiene (or pureness) and of safeguarding water resources as clean and as abundant as possible for future generations. The corresponding proactive limit values or standards are not to be defined purely by science. They need to be defined and fixed in a transparent societal decision process as well. Its stakeholders are bound to consider each reasonable option to define and quantify minimal maximal value(s) for the substance(s) under question, referring not only to toxicological but also to technical and hygienic advice, taking into account as many societal perspectives on drinking water as possible (see Section 3.14.8).

3.14.1.2 Acceptability and Tolerability of Chemicals in Drinking Water According to Their Functionality and the Potential to Avoid Them Proactive management of drinking water quality fits best with a three-dimensional rule of environmental hygiene, which is ‘‘Avoid useless, optimize functional and prevent harmful exposure’’ (Dieter, 1998). It leads directly to limit values that are functional from the management point of view. Only inevitable exposure, be it natural or technical/functional, needs to be limited or regulated at higher than zero (and sometimes even close to risky) levels. However, many substances functional on site find their way to distant environmental compartments off site. The aforementioned rule asks to regulate such useless and mostly anthropogenic exposure by means of the ALARA principle down to off site levels as close to a technical zero as possible, instead of allowing them to encroach on health-related maximal levels.

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Such a concept of avoiding drinking water contamination by prevention is certainly a safer plane of consensus between experts and stakeholders than the too ambitious concepts of defining never-ending numbers of adverse effect levels on the one side and analytical zero exposure on the other – especially for contaminants devoid of any benefit at their off-site point of exposure. Exposure of raw and drinking water to contaminants such as pesticides, industrial chemicals, or pharmaceuticals is useless as per definition. It is not acceptable but just tolerable, and preventing them from entering the drinking water system is an everlasting challenge in minimizing the on-site emission. In contrast, exposure of consumers via drinking water to corrosion products, corrosion inhibitors, disinfectants, and their disinfection by-products (DBPs) is never devoid of a functional aspect. Therefore, it seems acceptable on site at ‘residual’ minimal, albeit functionally inevitable levels (see Section 3.14.2.3.2). However, they should never touch healthrelated values and if they ever do, their functionality or corresponding risk/benefit ratio has to be critically reexamined. This concept of distinguishing between acceptable and tolerable anthropogenic exposure is reflected exactly in the conceptual distinction between tolerable and acceptable daily intake (TDI and ADI) of a chemical as it was promulgated in 1987 by WHO (1987). The toxicological meaning – maximum daily intake of a chemical without any appreciable health risk, in mg per kg body mass (kg bm1) per day – of both is identical; however, the database from which a numerical value is extracted very often is much less complete in the case of a TDI than of an ADI. The reason behind is the mere absence of benefit from exposure to unaccepted contaminants and similarly, as a consequence, also the absence of a systematic gathering of toxicological data to assess their presence in the environment. By contrast, geogenic constituents of raw water are reduced to a technical limit of avoidance only if their natural levels appear to be harmful for either human health or the technical distribution system. Such levels, if not reduced by reasonably feasible treatment, are also the upper limit for the usability of a resource or respectively for the tolerability of a natural but useless constituent under question in case of nature-oriented treatments. The same holds true for biogenic constituents such as cyanotoxins in reservoirs for drinking water, most of them associated with a high toxic potential for humans. The only way to manage their reduction below nontoxic levels is by stopping over-fertilization of affluent waters. Most prominent examples of constituents with high human toxicity are fluoride, arsenic (III þ V), uranium 2þ (UO2þ 2 ), and selenium (Se ). Technical hurdles include Mn (IV), Fe(III), and hardness (Mg2þ þ Ca2þ), the latter one also for the possible health and nutritional benefit (Cotruvo and Bartram, 2009). Drinking water containing high levels of HCO3 may support reabsorption of Ca2þ and Mg2þ in the renal tubuli, thereby intensifying their potential to prevent cardiovascular disease (Rylander, 2008). On the other hand, natural constituents may also be judged as acceptable if these are essential nutrients or trace elements at levels of total intake that would not exceed the acceptable range of oral intake (AROI) (WHO, 1998).

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3.14.2 From Chemical Hazards to Chemical Standards 3.14.2.1 Basic Facts The water molecule is a strong dipole with an excited or oxygen atom with a partial negative charge in its center. Its distorted tetrahedral structure and high polarity are the preconditions for liquid water organizing itself in the form of dynamic molecular clusters sticking loosely together by permanently opening and closing H2O-hydrogen bonds, containing 4–40 kJ mol1 each. Its high polarity is also why it separates ionic molecular structures (salts) into cations and anions and is able to gather and transport easily an almost innumerable variety of polar compounds from its adjacent environments. Therefore, there is simply no way to get absolutely pure water out of a natural drinking water resource, a water works facility or the end (tap or faucet) of any distribution system, including domestic drinking water installations (Hopp, 2004). Natural waters (and their main ingredients) that can serve as possible raw water for drinking water production can be differentiated into

• •

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rain water (dust, dissolved natural and anthropogenic gases); spring and ground or subsoil water (up to 0.2% solids, mainly Ca- and Mg-, but also Fe- and Mn-compounds, either particular or containing most commonly carbonate, sulfate, fluoride, nitrate, and chloride as counter ions); water from lakes and rivers (freshwater with less than 0.02% solids); and seawater (with 3.5% solids, 3% being table salt, the rest comprising about 50 different geogenic ions, each in a typical and stable oceanic concentration).

Most living beings contain in their nonfat compartments more than 50% (up to 99%, up to 73% in humans) of water as a solvent and transport medium, reaction component for anabolic and catabolic processes, and source of hydrogen for hydrating reactions and of hydrogen bonding to stabilize biologic macromolecules for function. The renal filtration rate per adult person is about 30% of the total blood volume of 1700 l d1, whereas daily net use of water for transport of metabolic waste comprises only 2.6 l per adult person. Fifty percent or more of this amount is normally replaced or ingested in the form of drinking water and 40% or less as constituent of food, whereas 10% is generated in mitochondria as endogenous oxidation water. Daily excretion is renal (1.6 l), dermal (0.7 l), pulmonal (0.2 l), and fecal (0.1 l). Daily loss of water lasting several days and more than 10–20% of the daily need can be dangerous for health. Drinking water might become a significant source of some minerals and essential metals only in individuals whose regular diets are low in these metals (Deveau, 2010). Seen from the other side, consuming drinking water in normal amounts with low-to-zero mineral content does not hold any danger for health if combined with a balanced diet.

3.14.2.2 Historical Landmarks The Corpus Hippocratum by Hippocratus contains some ancient but still valuable guidelines on drinking-water quality,

recommending to use rain water, to prefer running to stagnating water, and to judge its quality according to local circumstances (geology, vegetation, staining of metals, and general state of the inhabitant’s health) (Garbrecht, 1986). Hippocrates also provides early advice on individual possibilities to improve water quality for own human consumption. Improvement of water quality for human consumption by boiling, straining, storing in copper vessels, or filtration over charcoal has been known to date back to as early as several hundred years BC (State of Alaska, 2010). Conflicts between intentional and unintentional strains on natural water resources and their protection or vulnerability, respectively, can be traced back as far as 2000 years from now when close to the Roman aqueduct providing what is today’s French city Lyon with freshwater, a plaque was put up against unintentional contamination, bearing the inscription ‘‘ex autoritate imp(eratoris) Caes(aris) Trajani Hadriani Aug(usti) nemini arandi, serendi pangendive jus est intra id spatium agri, quod ductus destinatum est’’ (‘‘At the behest of Emperor Caesar Trajanus Hadrianus Augustus nobody shall be permitted to plough, seed, or plant within the space dedicated to protect this aqueduct’’ (translation by the author from the original Latin; German translation given by Kolkmann (1990))). At these early times, it was still sufficient to protect just a central pipe and its content as the water transported therein came from catchments yet untouched by human settlements and economic activity (Kolkmann, 1990). In European medieval times, protection of wells from being poisoned by individuals with bad intentions was regulated by strict laws to prosecute and punish such individuals strictly, but there was no such diktat to prevent unintentional degradation or poisoning of a water source. Only in the course of the second half of the nineteenth century did it become clear that penalizing was not sufficient to protect public water sources from more or less unintentional contamination. Instead, this novel task obviously was to be solved normatively on societal levels of central administrative law and public health police (and policy). One of the concurrent consequences was to close the many private and public urban wells in favor of providing everybody with a centrally controlled drinking water supply at a reasonable cost and to create on 1 April 1901, in Prussia, a worldwide first ‘private–public partnership’ to support interdisciplinary research, expert advice, and teaching in the field of water hygiene under the leadership of Prussia’s Ko¨nigliche Versuchs- und Pru¨fungsanstalt fu¨r Wasserversorgung und Abwasserbeseitigung (Royal Research and Testing Institution for Water Supply and Sewage Disposal). Due to the pioneering microbiological and epidemiological work done by John Snow, Louis Pasteur, and Robert Koch, up to the first quarter of the nineteenth century, publicly supplied water was thought to exhibit a potential for spreading or transmitting infectious diseases, but not those of chemical origin. There was even some speculation on industrial wastewater and its chemicals to purify the accepting rivers of dangerous fecal germs by their precipitation and disinfection (Kluge and Schramm, 1986). Therefore, healthbased maximal values for a number of highly toxic pesticides could not be implemented as was hoped by the US Public Health Service (PHS) as late as 1967, because the legal basis

Drinking Water Toxicology in Its Regulatory Framework

called upon (prohibition of infectious diseases) was not affirmed on behalf of a private suit. Despite this unclear early legal situation, already from 1914 on technically based maximal values for some chemical parameters occurring typically in connection with central water supply and potentially detrimental for them were decreed, such as technical guide values for copper and zinc by the PHS in the US, whereas in Europe, especially Germany, principal weight was put not so much on maximal values but rather, like in the 2nd German Empire, officially, on guiding rules to safeguard high-quality installation, operation, and surveillance of public water facilities to serve not only technical purposes. In 1915, the German water hygienist from Jena university, August Ga¨rtner, published his yet modern and comprehensive, epoch-making lifework (Ga¨rtner, 1915). Ga¨rtner emphasized the need to consider local circumstances and not just chemical or bacteriological numbers when evaluating a water supply. His forward-looking definition of ‘drinking water’ which should exhibit the same high quality also for any other domestic purpose is valuable up to the present day. Later on, in 1942, in the US, health-related permissible values for arsenic and lead and even a very low one (1 mg l1) for phenols were decreed by PHS (Larson, 1990), the latter group of chemicals being the first known group of large-scale drinking water contaminants from industrial sources. They had attracted attention since the 1920s in Europe, not for any high toxicity but as precursors for a closely related group of chlorinated DBPs, the chlorophenols, arising from phenols during drinking-water disinfection by chlorination and giving the finished water a strange pharmacy taste at the mg l1 range itself (Kluge and Schramm, 1986). After 30 more years, chlorination of drinking water became a main milestone in developing a system of health-related parametric values for systemic contaminants of drinking water, since Rook (1974) in Europe and Bellar et al. (1974) in the US had discovered independently the emergence of disinfection by-products during drinking-water chlorination (see review Hrudey (2009)). Also from 1974 on, a new legal basis in the USA (Safe Drinking Water Act, SDWA) opened the possibility of starting and implementing an extensive list of chemical (and microbiological) parameters, containing health-based maximum contaminant level goals (MCLGs) and technical maximum contaminant levels (MCLs), the latter deemed to be as close to the corresponding MCLG as feasible, the final value depending on the result of the risk/benefit-approach (Cotruvo and Regelski, 1990). A milestone away from the concept of a strictly scientific risk/benefit approach when assessing the presence of constituents, residuals, and contaminants in the drinking water system was set by the World Health Organization (WHO) in its 1984 guidelines for drinking-water quality WHO decided then to depart from its ‘‘previous practice of prescribing international standards for drinking-water’’ in favor of the ‘‘desirability of adopting a risk-benefit-approach (y) to national standards and regulations’’ (WHO, 1984a). This was not to state that any reduction of a chemical agent should be thought of being accomplished when reaching an up-to-then lower health-based level. Rather the contrary is meant, as

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WHO in 1984 stated this some pages later: ‘‘Although the guideline values describe a quality of water that is acceptable for lifelong consumption, the establishment of these guidelines should not be regarded as implying that the quality of drinking-water may be degraded to the recommended level. Indeed, a continuous effort should be made to maintain drinking-water quality at the highest possible level’’ (WHO, 1984b). This continued line of WHO to encourage resource protection and early surveillance instead of simply looking at compliance with strictly health-related values was further strengthened after that. This becomes evident when going over its 1993 published (especially part 4 of volume 1 of its most recent version) guidelines for drinking water (WHO, 2008). Its concept or plan of ‘water safety’ (WSP) takes compliance with any listed health-based guide values only as a minimum daily quality goal, thus bringing together the limit-value approach on the one hand and a preventive, sustainable risk-based water supply management approach with independent surveillance on the other hand. The concept followed by the author of this chapter to strictly differentiate between criteria of functionality, preventive avoidance, and adversity according to the origin of the agent when choosing one for chemical drinking-water standards fits perfectly with such precautionary approach.

3.14.2.3 Objectives and Goals of Protection 3.14.2.3.1 Objectives of protection In order to optimize legal compliance of drinking-water standards, it is good regulatory practice to allocate different parameters to different partial objectives of protection or points of surveillance in or at which the respective parameter(s) should not be exceeded: 1. The raw water ¼ drinking water before treatment. Parameters to control a. nontreated or untreatable geogenic constituents and b. environmental contaminants. 2. The finished water ¼ drinking water directly after treatment at the exit of the water-works facility. Parameters to control a. geogenic constituents possibly not properly eliminated by treatment, b. residuals from inevitable chemical treatment steps and their technical contaminants, and c. DBPs from drinking-water disinfection or oxidation in the water-works facility. 3. The delivered water ¼ drinking water at the handover point just before the water meter. Parameters to control a. delayed formation of DBPs in disinfected drinking water during its transport and distribution and b. inputs from transport and distribution (corrosion products, contaminants, etc.). 4. The tap water ¼ drinking water at the consumer’s faucet. Parameters to control a. corrosion products and contaminants from the domestic installation and its armatures.

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Summing up, limit values for chemical parameters in drinking water define and prescribe legally the minimum quality of an artificial or natural water serving as a source of raw water for drinking-water production, the maximally admissible change of quality during treatment and distribution of drinking water, or minimal improvements of quality by treating drinking water. Many countries apply or recommend competing quality goals on surface water in the form of scientifically based maximal contaminant or similarly defined regulatory levels. Very often they miss considering drinking-water as an objective of protection, even though in the precautionary context of drinking-water regulation, such standards could be set much lower than just based on ecotoxic potential. This is especially true for the ever-increasing number of relatively nontoxic, but highly polar and persistent environmental contaminants such as pharmaceuticals from human use, antibiotics, iodinated X-ray contrast media, veterinary pharmaceuticals, polar herbicides and metabolites, aminocarboxylate complexing agents, amines, and surfactants (Reemtsma and Jekel, 2002). Their special and environment-friendly affinity to the aquatic compartment goes in parallel with their potential to persist or accumulate continuously in reused water, although for economic, ecologic, and social ethics of water supply such reuse should gain continued significance and acceptance on a global scale (see Sections 3.14.1.1 and 3.14.8).

3.14.2.3.2 Goals of protection The legal concept limit value is characterized not only by its clear liability but also by the fact that different parametric values, depending on the chemical parameter under question, can be allocated to very different goals of protection. These correspond to different maximal values based on science, best technological treatment and avoidance of exposure, or just on acceptance of a contaminant in the object of protection, for example, drinking water. Human health is in fact an outstanding, but even within systems of drinking water supply, only one of several supposable goals of protection. Therefore, it seems reasonable to set limits on values or standards for chemicals in drinking water to support its original purity, given that original (or natural) purity of raw water for drinking-water production and use means at least its long-term compatibility with human health. Such standards should describe as exactly as possible the actual knowledge and technology to ‘‘maintain drinkingwater quality at the highest possible level’’ (WHO, 2008a). This means that standards for drinking water are optimally to be set as close as possible down to levels of best possible technical avoidance or natural background levels. Such levels normally are distinctly lower than those corresponding to human adverse effect thresholds or additional risks. Depending on parameters and protection goals is not only the option to a. protect human health, which is to be stressed for defining a numerical limit value, and also options for defining possibly lower parametrics than health-based values as there are b. indicators for optimal technical use and functioning of functional chemicals,

c. standards to ensure optimal technology for preventive avoidance of useless contaminants, d. indicators and goals of aesthetic quality (odor, taste, and purity), e. standards (indicators) to ensure resource protection and aquatic life, and f. standards (indicators) designed to facilitate or optimize quality surveillance. This chapter not only renders more precisely the regulatory framework for a toxicology of drinking water, but also intends to explain the scientific approach behind this expression to derive (a) health-based standards and to compare their criteria with those of stricter standards for (b) optimal technical function and (c) technical avoidance. A prominent example of a relatively high yet functionally minimal concentration is that of the disinfectant free chlorine (or of other less prominent disinfectants) in drinking-water. Chlorine-based disinfection of drinking water would not be sufficiently effective anymore below a minimum of 0.3 mg l1 free chlorine. However, if the raw water for drinking-water production and its distribution network are proven to exhibit steadily an unobjectionable microbiological and technical state, disinfection even for transport would not be required and the accepted minimal concentration of chlorine would accordingly be ‘zero’. Another example for accepted functional, and insofar inevitable, minimal concentration levels in the drinking-water system is DBPs, residuals from metal corrosion, or contaminants from materials in contact with drinking water, whose presence in drinking water is acceptable only if the processes, metals, and materials within the system appear technically indispensable or even irreplaceable. Levels of residuals and contaminants are deemed to encroach on health-based maximal levels only in technically inevitable or unfavorable situations. Otherwise, technical norms to ban their use, to constrict it on ALARA-compatible physicochemical or technical conditions to optimize a treatment process, should be implemented. Drinking water with a quality in accordance with standards as definable by options given earlier will always be of unobjectionable quality from the point of not only health but also (d) esthetics. However, they are only insofar hygienic (or precautionary) standards for drinking water as the criteria to derive them are a consequence of applying the earlier mentioned three-dimensional rule of environmental hygiene (Section 3.14.1.2). Standards for (e) resource protection and of aquatic (wild) life and (f) optimization of surveillance are not considered in this chapter.

3.14.2.4 Timescales to Protect Goals of Protection 3.14.2.4.1 Precautionary standards (enduring protection of human beings and drinking-water resources) Precautionary standards define drinking water as being ‘‘as pure as naturally possible or technically feasible.’’ They allow to compare desire and reality by reporting real concentrations on the more or less technical ALARA principle. The applicability of ALARA should be agreed upon by all stakeholders as

Drinking Water Toxicology in Its Regulatory Framework

the result of discussions on the basis of the environmental rule (Section 3.14.1.2). They stand for maintaining an optimal purity of drinking water far below health-based maximal values and for protecting the distribution system at a minimum of expenditure for continuous surveillance. In Germany’s drinking-water ordinance the ALARA principle is present in the form of a flexible rule, called ‘precept of minimization’. It requires keeping contamination of drinking water as low as possible and even below health-based maximal values, if this can be reasonably achieved. Precautionary standards for anthropogenic environmental contaminants may deviate upward from zero but be distinctly lower than health-based values if the transfer of the contaminants under question into aquatic environments was or seems to be technically inevitable. Examples for drinking water are the European Union (EU) standards for tetra-plus trichloroethylene (10 mg l1), four polycyclic aromatic hydrocarbons (0.2 mg l1) or 0.1 mg l1 per pesticide in drinking water.

3.14.2.4.2 Scientific standards (guide values for lifelong protection of human beings) Scientific standards are designed to protect

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human health (for details see Section 3.14.4), or distribution network/installations from technically adverse chemical agents, or drinking water from tasting or smelling unusual/disgusting.

They are conceived by stressing on 1. either the basic dogma 1 on the presence of an effect threshold for unacceptable adverse effects (see Section 3.14.4.3.1), or 2. basic dogma 2 on the absence of an effect threshold with an accordingly allocated and accepted additional risk or annoyance over background (see Section 3.14.4.3.2). If a parameter was decided to be regulated under dogma 1 and the heading scientific standard, its numerical value is chosen in a way that it is virtually (quantitatively) identical with the starting point of the most sensitive positive dose/adverse response curve in persons or installations/distribution systems of distinctly higher-than-average sensitivity during repeated, preferentially, lifelong exposure. Regulatory levels under dogma 2 exist only for humans, but neither to protect materials nor to protect humans from taste and odor. In drinking water, such levels are generally accepted to correspond with additional risks of 105 106 for contracting an irreversible adverse effect over background incidence in the course of a 70-year life-span exposure.

3.14.2.4.3 Remedial standards (action values to protect from shorter-than-lifetime exposure) Remedial standards for drinking water are designed for being applied on shorter-than-lifetime exposure. They protect the goal of protection from adverse effects or from a higher-thanaccepted risk only during that shorter time period. In principle, they can be derived not only to safeguard human health but also to address the functional safety of materials and systems.

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The rapid availability of remedial standards for drinking water is critical for informed decisions on whether the continuous access to a moderately or short-term contaminated drinking-water supply would create more or less risks for individual health and public hygiene than disconnecting or closing that supply. Even in those extremely rare situations where a drinking water might be contaminated up to an acutely toxic level, its use for most purposes of personal hygiene may not need to be excluded or delimited. Especially if weighing the risk from enhanced exposure to DBPs toward those from neglecting necessary disinfection of the same water, a correct decision will always vote in favor of continuing disinfection, hence supporting minimization of DBPs not by challenging disinfection but by its technical optimization. As far as human health goes, the concept of remedial standards means to allow exceeding standards for lifelong protection if sanitation, repair, or other remedy can definitely be expected to take place in less than 70 years. However, with actual levels of contamination being higher than regarded as safe for lifetime exposure, the risk for the health of the consumers must be reasonably excluded with equal dependability also in such situations. Compliance with any suggested protective measure in the form of formal water notices for the public during drinkingwater contamination incidents strongly depends on clear and pragmatic semantics of such notices. In a study with 107 undergraduate participants, warnings such as ‘do not drink’ or ‘boil and drink tap water’ revealed a number of partially risky behaviors and several strange correlations between independent behavioral patterns of one and the same person (Rundblad, 2008). Guidance to find remedial standards for acute to shortterm (r24 h up to a few weeks) exposure to spills from emergencies was recently published by WHO (2008). A remedial standard represents a defined aliquot in 2 l of drinking water of an acute reference dose (ARfD). An ARfD is the quantity on body mass basis of the amount of a spilled chemical (mixture) that can be ingested maximally within the exposure period without appreciable health risk. For pesticides, ARfDs have been published by the Joint FAO/WHO Expert Committee on Food Additives and short-term health advisories for chemical contaminants in drinking water were produced by US-EPA. A pragmatic approach to define longer-term allowable but distinctly less than lifelong exceedance may grant up to 100% of an ADI or TDI (lifelong tolerable or acceptable intake ¼ exposure per day (Section 3.14.1.2)) in the daily amount of drinking water while minimizing total exposure as soon as feasible below the ADI or TDI, respectively. Short-term exceedance of an ADI or TDI is considered not causing appreciable risk to health. This also applies to sensitive groups of the population as long as their exposure is not high enough to render toxic endpoints different from those considered for deriving an ADI or TDI more critical for the safety assessment than the latter ones. The allowance of an ADI or TDI for drinking water should be quantified by expert judgment on the basis of all modes and pathways of exposure to the same or similar compound(s). Such judgments would also have to consider the possibility of significant differences between intestinal

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resorption rates for chemicals, especially of dissolved metal ions, when comparing and combining their exposure from drinking water with that from food. A formalized and quantitative method or procedure to define allowable exceedance of lifetime tolerable exposure via drinking-water for up to several years, if ever necessary for not being strained to close a drinking-water supply, is practiced in Germany since 2003 (Federal Environmental Agency of Germany, 2010). The proposal for threshold compounds starts from quantifying a minimal lifelong unsafe exposure halfway between a lifelong (virtually safe) body dose (Bd) and the experimental or epidemiological no-adverse-effect level (NOAEL) which was chosen as the point of departure (PoD) to extrapolate on that Bd. The interpolation of a hazard-linked dose (HLD) between the Bd and the PoD harks back on widely accepted methods and conventions of toxicological risk quantification. The interpolation factor (IF) is quantitatively defined as the square root from exclusively those extrapolation factors (EFs) having been used as well to extrapolate experimental or epidemiologic data to the human target population for quantifying the Bd. The HLD equals IF  Bd. Both IF and EFs thus are equally conservative. They are always open for corrections in concert with improvements in quantifying the PoD. The larger the percentage by which the HLD encroaches on the safety margin between PoD and the Bd, the more completely the extrapolated data resemble human data. At the same time, IF decreases with data coming closer to data from humans (Dieter and Konietzka, 1995). An analogous procedure to define less than lifelong acceptable remedial standards was developed in Germany for nonthreshold carcinogens as well. It starts from proposing to accept over a lifetime up to 5-times higher than accepted additional cancer incidence IZ (see Section 3.14.4.3.2) per contaminant. The calculated IFs to allow for a 3- and a 10-year exceedance of a lifelong safe health-based maximal value (guide value, GV, see Section titled ‘Health-related chemical standards for drinking water’) are 6 and 17, respectively. The corresponding remedial less than lifelong tolerable exposure levels via drinking water are especially conservative insofar as they consider also the possibility of a higher sensitivity of small children (rapidly growing organisms) when compared to adults toward primarily genotoxic carcinogens (Schneider, 1999).

3.14.2.5 Special Aspects to be Considered when Setting Standards for Drinking Water 3.14.2.5.1 Standards for accepted chemicals in drinking water Legally defined and politically granted levels of exposure or contamination are not set arbitrarily but originate from accepted and realized proposals to benefit from the use of synthetic chemicals and natural resources with their constituents. There are many compounds whose presence in food or drinking water is linked with an intended functional benefit. They are accepted there in minimal values either

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because below such minimal level they would not be functional any more or

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because a raw water resource would fall out of favor for drinking-water production in case the contaminant is not accepted at a certain minimal level.

Examples are

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•

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Acceptance of geogenic constituents such as Pb, Cd, As, and F of raw water for drinking-water production up to healthbased maximal values but factually very often much lower natural or technically treated levels. Acceptance of anthropogenic residuals in drinking water up to a technical or health-based upper limit resulting from its transport through (or packaging in) pipes and armatures being more or less susceptible to corrosion. Acceptance of disinfectants in drinking water (and their DBPs) as far as their presence is linked indispensably to disinfection or other treatment to minimize microbial risks from drinking water and its corrosive properties.

3.14.2.5.2 Standards to protect from adverse effects of chemicals in drinking water If only functional but neither potentially dangerous nor technically avoidable contaminants of drinking water existed, the (technical) world of drinking water would not have the concept of risk society. However, any chemical load of drinking water provokes questions not only concerning its functionality, but also on its avoidance and drawbacks. Regarding the latter point, the following criteria serve to find the precise maximal numbers for tolerable contamination:

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potential drawback of a chemical for the drinking water to adversely affect consumer’s health; potential of a chemical to damage certain functions (e.g., pumps) or materials in the course of drinking-water extraction, treatment, and distribution; and potential of some chemicals to influence negatively the esthetic (taste and odor) properties, perceived by the consumer not as an annoyance but as a threat to health.

There are no other goals of protection from adverse effects to be covered by standards for drinking water, especially not for the protection of natural aquatic communities from pesticides, despite the fact that many consumers would be glad if their drinking water is safe not only for its original purpose but also for aquatic communities in their private domestic aquariums, ponds, or backyard pools. Drinking-water standards are also not a good measure for standards for protecting groundwater from contaminants in leachates from abandoned waste sites. Health-related standards for drinking water. A health-related standard or health-based GV for a chemical in drinking water is the maximal concentration of a chemical agent in mg l–1 drinking water that would not give any reason to be concerned about the consumer’s health if he or she regularly ingests such water (Section 3.14.4). This definition is valuable for any duration of exposure between very short term (a few days) to lifelong (Z70 years) and for which the standard under question was factually quantified (see Section 3.14.2.4.3). When going over the limits of a GV, there is an increasing health concern and finally danger when exceeding the HLD (see Section 3.14.2.4.3) with risk of an adverse health effect to occur such as if ingestion paths such as air or food contribute

Drinking Water Toxicology in Its Regulatory Framework

significantly (80% and more) to regular intake of the same or toxicologically similar compounds. On the other hand, regular exposure to a contamination below its GV is not likely to give any reason for health concerns via drinking water. Standards to protect materials and technical functionality of drinking water systems. These standards are designed to protect the whole system of drinking-water extraction, treatment, and distribution or parts of it from damage by water loss from corrosion, pump damage, or any technical failure. Most common causative agents are geogenic constituents of raw water such as ionic/particular Al, Fe, or Mn, and salts and protons as well. Manganese in drinking-water systems, such as iron at more than 0.2 mg l1, may lead to the accumulation of deposits and occasional turbidity if permanently present at more than 0.05–0.1 mg l1. Unspecific chemical and physical parameters such as conductivity, dissolved organic carbon, absorbable organic halogens, or even temperature protect the functionality of the system insofar as they make it easier to retrieve points of technical failures or indicate suspicious changes in raw water quality or composition. Such parametric values are, very often, to be set distinctly lower than would be possible on the basis of human health considerations. The technical risk assessment on which they are based upon does not use default (safety) factors but quantifies any yet acceptable technical risks in the form of technical parametric levels. These are defined by combining results from technical small-scale models and worst cases but yet realistic predictions for technically adverse or undesired chemical behavior. The aim is to safeguard the usability of a resource or system at minimal risk from either (failures of) technical treatment or technical damage. Geogenic and biogenic constituents, although nonfunctional and also harmless to (components of) the technical system, seem tolerable up to health-based maximal levels, especially if treatment is difficult and a resource with a lower natural background is not readily available. Esthetic-sensory standards. Some of these standards (taste, odor, color, and turbidity) are surrogates or indicators for less evident but more critical quality characteristics of the consumers’ tap water. Their noncompliance may correspond to still more unfavorable-to-adverse consequences for the system or the consumer than their own exceedance. Other esthetic-sensory thresholds or parameters defend the taste and odor neutrality of drinking water and hence its acceptance by the consumer, not as surrogate parameters but by their own nature. Sulfate in the presence of magnesium from 250 mg l1 on gives a bitter taste to the water, whereas aluminum above 0.2 mg l1 leads to an astringent taste and causes turbidity and color in the presence of iron. Manganese stains sanitaryware and laundry if exceeding 0.05–0.1 mg l1. Sodium chloride may taste salty at more than 400 mg l1. More parameters and their thresholds from which they could impair odor and taste of an otherwise unobjectionable drinking water have been described by WHO (2008). The sensual perception of esthetic-sensory parameters has highly subjective denotations (Doria et al., 2009). Accordingly, standards can be derived only from experience with humans, not from animal experiments. The slope and range of the dose/response curve from which a standard may be derived should also include distinctly higher-than-average sensitivities.

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The most common standards worldwide that impair the esthetic (odor and taste) quality of drinking water from 5 to 10 mg l1 on are alkylated aromatics (BTEX) and alkylated t-butylethers, such as methyl-t-butylether (MTBE) or its ethylanalog (ETBE). Simple expectations on (sensorially undetectable) purity of drinking water may be esthetically motivated as well. This pertains especially to contaminants possibly reaching the consumer with reused water and whose mere origin or known presence in his or her drinking water above a certain but by far not yet noxious level could merely be perceived as disgusting, for example, pharmaceuticals and their metabolites (see Section 3.14.8).

3.14.3 Panels and Institutions for Setting Drinking Water Standards 3.14.3.1 National National homepages or documents with extensive lists of parametric values for drinking water and criteria on which they are based are available from Australia (including the free quarterly newsletter Healthstream, responding to many actual questions relating to drinking water for an international expert readership), Health Canada, Japan, the New Zealand Ministry of Health, the European Union (EU-guideline 98/83/EC), and the US-EPA/Office of Water.

3.14.3.2 International The guidelines for drinking-water quality of the World Health Organization provide health-based guideline values for more than 100 chemicals (WHO, 2008). The guidelines are advisory in nature; for most countries, however, these guidelines provide the scientific point of departure in deriving national or supranational drinking-water standards. The guidelines, which are subject to rolling revision and ongoing update in response to new evidence, strongly encourage adapting standards to national priorities and to socioeconomic, cultural, and environmental contexts. For countries that do not have the resources to sustain their own regulatory-toxicologic capacities for deriving health-based drinking-water standards, the guidelines are the point of reference for standard derivation.

3.14.4 Defining Standards to Prevent Human Health Risks from Drinking Water 3.14.4.1 Qualification of Risks – Critical Toxic Endpoints The possibility of adverse effects of chemicals on human health is of highest regulatory interest if they are the result of permanent (chronic) exposure, for example, via drinking water as a medium for a steady lifelong consumption, often without the possibility or freedom of choice. Possible effects may occur either concomitantly with drinking water ingestion or only after a period of more or less irreversible substance or effect accumulation from earlier plus present exposure. Environmental contaminants, if using all paths of ingestion (oral with food or drinking water, dermal, and pulmonal), reach their target organ(s) by all these paths through the bloodstream, the so-called ‘central compartment’. They are

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then defined to act systemically, in contrast with those acting preferentially or exclusively along their port or route of entry into the body, then comprising the olfactorial pathway as well with its nerve endings being exposed directly to the environmental air. Critical toxic effects to be considered by proactive regulation of chemicals which may reach environmental compartments include

• • • • •

early irreversible damage (including cancer) to important organs, in their early functional and histopathologic manifestations; disturbance of normal growth and behavior; exposure-induced derailments of metabolism; teratogenic effects: impaired male and female fertility, sexual development and reproductive behavior with special consideration of acutely toxic exposure windows; and in general, any kind of early biochemical abnormalities (including endocrine disruption) as observed down to the lower end of a dose/response curve in an animal experiment or preferentially an epidemiologic setting.

Information on effect quality and potency is quantitatively exploitable only if exposure was performed experimentally (under reproducible conditions), hence preferably in animal experiments or in vitro tests which imitate the in vivo exposure as close as possible. Effect qualification and especially quantification by using human data is preferable if exposure is known to have caused the effect under question.

3.14.4.2 Groups of Compounds of Specific Interest for Drinking Water All these critical or toxicologically crucial endpoints of chemical toxicity are subject to different national and international procedures of proactive toxicological evaluation of chemicals during their examination for intended or functional but environmentally open use. Admissions are regulated or refused on the basis of their toxic potential or their potential to persist, accumulate, or dissipate in the environment. Hydrophilic properties are always weighted in favor of admission and there are, at first glance, good reasons to do so. By this way, however, drinking water not only remains the target compartment for more or less hydrophilic and toxic old chemicals but also turns more and more into the role as a sink for such environmental contaminants which slip through modern registration processes, because they arise only somewhere in the environment as metabolite(s) of an environmentally neutral and, accordingly, admitted parent compound (Schwarzenbach et al., 2006; Barnes et al., 2008). Such new analytes, albeit usually not very toxic, are often very hydrophilic and refractive to further environmental degradation. This supports their continuation in aquatic environments and drinking water and their circulation therein. Other new analytes, also called chemical transformation products, may even arise from different antecessor compounds only in the course of drinking-water chlorination or ozonation (Reemtsma and Jekel, 2002). A prominent but up-to-now unique proof of a very toxic transformation product is N-nitroso-dimethylamine (NDMA), a probable human carcinogen, which can be formed in raw water from dimethylsulfamide

(DMS), a so-called nonrelevant environmental metabolite (Dieter, 2010) of the agricultural fungicide tolylfluanide. NDMA was formed while treating DMS-containing groundwater with ozone for drinking-water production. The presence of catalytic traces of (geogenic) bromide is required for this surprising but highly undesired reaction (Arnold et al., 2010; Schmidt and Brauch, 2008). However, not only nonrelevant metabolites of pesticides on a long-term scale seem to be relevant for drinking water hygiene, but also pharmaceuticals, their metabolites, and transformation products. They occur worldwide at up to several mg l1 in surface water used for drinking-water production (Reemtsma and Jekel, 2002; Ternes and Joss, 2006; Bohannon, 2007; Cooper et al., 2008). Other groups of compounds with special relevance for drinking water are mainly naturally occurring organic constituents (as measured in the form of dissolved organic carbon (DOC)), environmental contaminants from industrial sources and human dwellings, biogenic toxins from cyanobacteria, residuals and DBPs of chemical water treatment, and corrosion products or contaminants from materials in contact with drinking water (WHO, 2008; Table 8.1). The experimental–toxicological material or database (see Section 3.14.4.3) to evaluate their toxic potential for humans such as by WHO is often missing or very limited. This very common regulatory toxicological situation asks for a remedy in the form of a toxicologically motivated surrogate approach to evaluate provisionally the presence of drinking-water-relevant chemicals on an incomplete-to-missing database under the aspect of preventive healthcare until the database improves (see Sections 3.14.5.3 and 3.14.6.3.2).

3.14.4.3 Risk Quantification The following is an extremely short basic introduction to what may be studied as regulatory toxicology in more detail and some variants either at WHO (2008) or with pertinent introductions into methods of human toxicological evaluation at Greim and Snyder (2008) and Leeuwen and Vermeire (2007) .

3.14.4.3.1 Chemicals exhibiting systemic effects with threshold In order to calculate a lifelong tolerable and health-related parametric concentration of a compound in drinking water, it is essential to know 1. the lifelong tolerable body dose Bd (discussed in this section), and 2. the percentage of Bd which would realistically be attributed to the daily intake of drinking water (see Section 3.14.4.3.5). In regulatory toxicology, the threshold concept is a rather pragmatic and not a scientific concept insofar as it refers not to the single cell as the smallest and most sensitive functional unit of life but to most sensitive subunits (cells) of population. In this sense, the Bd of the chemical under question is its population-based adverse effect threshold in the human target population as evaluated on the basis of scientific (human, animal, in vitro, and statistical) data by regulatory

Drinking Water Toxicology in Its Regulatory Framework

toxicologists. It is the regulatory-toxicological surrogate for the actual but scientifically mostly unknown adverse effect threshold in such a target group and is derived in a way to be never higher than the same threshold had it determined scientifically. In most cases, the Bd can even be assumed to be much lower, but in some unpredictable cases real threshold and Bd may be not only conceptually but also numerically identical. Lower doses than Bd by definition exert no observable adverse effect in any one of the target individuals (basic dogma 1). In contrast to a regulatory drinking-water level, given mostly in mg l1, the Bd is calculated or defined in mg of the chemical agent per kg body mass (mg kg1 bm). As such, and different from any environmental standard, the Bd is not directly accessible for regulatory interventions, except that each individual would be ready or forced to permanently equip himself or herself with a personal dosimeter. Toxicological information of sufficient quality to derive a Bd may be gained only in a few cases from an epidemiological (human) database, for example, from uptake of a chemical directly with drinking water. A main drawback of retrospective human studies is the difficulty to gain reliable and at the same time exact information on level and length of exposure due to its spatial and temporal variability (Legay et al., 2010). Therefore, the required information has mostly to be drawn from animal experiments controlled for exposure. Studies using water instead of food, air, or administration by gavage as exposure path are to be preferred. As far as the solubility, speciation, stability of the noxious agent, the frequency of daily exposure, and its resorption in the digestive tract are concerned, they reflect the real human exposure situation more closely. Even then, the uptake of drinking water and hence the noxious agent or chemical species must always be estimated as precisely as possible. Exposure paths different from water often exhibit distinctly lower rates of resorption than drinking water as has been shown for some heavy metals and/or their chemical speciation (Yokel et al., 2006). If there is no experiment available using water as a path of exposure, it may be allowed to change to systemic exposure via food or inhalation in exposure via water. It must be mentioned here that experiments, especially with exposure to lipophilic agents by gavage, are not suited as a basis to characterize and quantify their toxic potential in drinking water. Exposure via corn oil, the usual solvent in such study, is not continuous like from continuous drinking-water uptake, but fluctuates batch-wise between high and low. This may change, as observed with chloroform, the position and slope of the dose/response-curve for cancer due to precancerous or other cytoxicity so drastically, that the toxicokinetic and toxicodynamic behavior of the chemical agent should not be extrapolated from such studies on humans and their exposure by drinking water (see review Hrudey, 2009). The first section of the toxicological evaluation opens by defining an experimental NOAEL (in mg per kg of the experimental animal’s body mass). This NOAEL is the highest, yet ineffective, experimental dose of the chemical agent. The animal species or strain chosen for the experiment should not only exhibit a higher-than-average sensitivity but also be as similar as possible to conditions of exposure, physiology, and sensitivity of the human target population. It is also preferable

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to define a NOAEL by using the benchmark approach, a standard procedure to assess, if ever possible, data from several studies exhibiting sufficient experimental quality and consistent toxicological information (Murrell et al., 1998). A guidance document containing recommendations for the application of the benchmark approach was developed and several default assumptions are proposed (Kalberlah and Hassauer, 2003). For the applications considered in this comprehensive report, the comparison of both approaches when using the concept proposed by the report generally does not show great differences between the benchmark and the NOAEL approach in terms of the resulting POD. An adverse effect, called ‘delayed toxicity’ then, may not always show up concurrent with exposure, but weeks to years after its onset. This phenomenon has been known for a long time as a characteristic of some organophosphates, but also needs to be addressed as a possible mode of action of endocrine disruptors, for example, if they are ingested at critical exposure windows during pregnancy or puberty. A biomimetic mode of action may set a hormonal switch to exhibit adverse results only much later as was hypothesized, for example, by Hens (2007). The second section of the total evaluation procedure starts from the NOAEL agreed upon either by expert judgment or from a benchmark procedure. Sometimes, an NOAEL needs to be extrapolated by means of a specific extrapolation factor EFa between 3 and 10 from an experimentally observed lowest-observed-effect level (LOAEL). Another EF (EFb) of mostly 3 may save subchronic data for an assessment by extrapolating them on chronic exposure. The NOAEL eventually chosen is the procedural PoD from which a Bd is then defined. This is done in two more steps c and d within this second section. They extrapolate, starting from the PoD, the experimental data on humans by dividing the NOAEL by two single extrapolation factors EFc and EFd, one for interspecific (animal-human) variability of experimental–toxicological results, the other for the intraspecific variability between normal and sensitive humans. The numerical amount of both EFs varies between 1 and 10. The final amount of each depends on the quality of the database and its similarity with toxicologically critical parameters of human physiology and metabolism. Each factor may be subdivided in partial factors of 2.5–4 to cope separately with extrapolation of the agent’s experimental kinetic (uptake, distribution, metabolism, accumulation, and excretion) and dynamic (mechanism and mode of action) characteristics on humans. A compact outline on motivations and designation of safety factors (SFs) and extrapolation factors, respectively, was published by Ritter et al. (2007) and completed with a commentary by Konietzka et al. (2008). The actual (but scientifically unknown) effect threshold within the sensitive (target) population is assumed to be positioned somewhere within this safety margin EFc  EFd below the NOAEL, whereas the Bd equals by definition its lower limit and was calculated to resemble this intended result as close as is scientifically traceable. Only in some unpredictable (worst but still realistic) cases, Bd would be numerically identical with the (unknown) actual and then very low effect threshold. Some expert groups propose a fixed additional SF of 10 to cope with incomplete or questionable experimental data,

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missing of tests on very critical endpoints, or doubts about the correctness of a performed extrapolation. However, as a rule, higher than 3000 total factors (as a product of all applied EFs and the SF) will signal such a high incompleteness of the database, that provisional evaluation by a default method (as outlined in Section 3.14.6.3.2) will become imperative. The scientific acceptability of the four EFs as conventionally amounting to 3–10 has been positively underlined by the screening of a large toxicological–experimental database worldwide being used for deriving regulatory-toxicological recommendations (Kalberlah et al., 2003). The only convention missing is how to answer the question as to what percentage of the subgroup of the population identified as sensitive should still be protected by not exceeding a Bd. Such extremely sensitive individuals may, if at all, be found among that subgroup of the population carrying congenital anomalies of differing severity and representing about 2–4% of the general population (Nicolopoulou-Stamati et al., 2007). As seen by science, the regulatory-toxicological statement that Bd ¼ effect threshold in the most sensitive target population represents only a working hypothesis lacking real probative force. However, the mode of deriving a Bd guarantees its scientific acceptability and consistency as long as it does not slip into contradiction with either scientifically proven human data or animal data, which might be extrapolated without major scientific limitation on humans. In fact, a Bd would be identical with the actual or real effect threshold in a conceptual as well as a numerical sense only if derived on a scientifically unobjectionable epidemiologic (human) data basis.

3.14.4.3.2 Chemicals assumed to exhibit threshold-free systemic effects The most extensively described effect ascribed to this regulatory-toxicological category of chemical agents is cancer. The individual risk of contracting cancer by primarily DNAreactive agents is considered as being governed by chance (basic dogma 2), while other carcinogens may be regulated as threshold compounds if following US-EPA (2005) or the SCOEL of the European Union (Bolt, 2008). If according to toxic mechanism, the absence of a threshold is to be assumed, the carcinogenic potential at a given exposure level can be represented or given the form of a probability number for an additional population risk. Therefore, there is no virtually safe dose attribute of a chemical in the form of a Bd if it acts along a nonthreshold probability and not a thresholded discontinuity. Instead, a socially accepted and/or politically tolerated incidence I ¼ additional number X of cancers per chemical exposure unit and predefined size Z of population can be calculated. The regulatory equivalent for I is that concentration of the chemical in the compartment of regulatory interest (e.g., drinking water), which in the given exposure scenario corresponds with the accepted or tolerated value of I. In order to calculate a concentration acceptable for lifelong exposure, it is inevitable to anticipate how many cancers X per mg exposure per day and kg bm1 might be expected in the course of 70 exposure years. X is known or calculated preferably from epidemiological studies or, if those are missing, from high-dose long-term animal experiments and

extrapolating them on real world ¼ low to very low dose human exposure scenarios. The resulting number gets hypothetical in the same rate as knowledge on the underlying time course and biochemical mechanism of irreversible molecular interaction between the chemical and DNA turns out to be speculative. The only inevitable precondition for such a number is the principal possibility of adverse effects at very low, if not molecular, doses. WHO (2008), in its drinking-water guidelines, uses for calculating X the most conservative of all possible models, the linearized multistage model (LMS). It delivers higher numbers for X than all other models and describes therefore an absolute upper limit of risk per exposure unit. It is hence correct to assume that any risk calculated by using the LMS model is probably higher than the real-life risk from daily exposure. However, similar to the situation with Bd and its hypothetical identity with the factual threshold, X is supposed by the regulatory-toxicological community to reflect the factual risk in some existent but unpredictable cases. This is why X is a regulatory-toxicological surrogate for the unknown factual risk as long as scientific data do not support a lower, more conservative number. X, the additional risk, is given per unit of exposure in (mg d1 kg bm)1 and is called slope factor by WHO (2008). If real exposure in (mg d1  kg bm) is called Y, the individual additional risk R to contract cancer equals R ¼ X  Y (R has no dimension). If a total of Z persons are exposed to the same daily amount X of a carcinogenic and primarily DNA-reactive chemical during the course of 70 years, the total number of persons to contract the prognosticated cancer within this population would be IZ ¼ X  Y  Z. For means of comparing risks from different exposure situations, Z is often set as Z ¼ 105. The worldwide accepted and politically tolerated additional risk IZ by ingesting one chemical contaminant during 70 years with drinking water seems to amount from 105 to 106. In order to have Y available in its unit (mg d1  kg bm 1) and hence to enable calculation of IZ from the chemical’s concentration c ( ¼ a (mg l1)) in drinking water, it is at first necessary to multiply c by V ¼ 2(l d1  70  kg bm 1), the body mass-normalized daily drinking-water consumption, so that Y ¼ c  V ¼a  2/70 (mg d1  kg bm 1). Table 1 lists a number of primarily DNA-reactive and at the same time polar (water-soluble) chemical carcinogens with unit risk values as applied by WHO (2008).

3.14.4.3.3 Local effects on humans of chemicals in drinking water Some chemicals possibly present in drinking water exert their adverse effects not systemically but locally, close to the port of entry (e.g., on skin, mucous membranes, lung, stomach, and intestine). Their regulatory-toxicological evaluation is not possible along the models of tolerated body dose or respectively accepted additional incidence, although they might be separable according to basic dogmas 1 and 2 by science. Their adverse effect potential depends not on whether a tolerable and weight-normalized body exposure (TDI or ADI) might or might not be reached, but solely on the concentration in the exposure medium (e.g., drinking water) at the point of

Drinking Water Toxicology in Its Regulatory Framework Table 1 Presumably DNA-reactive and at the same time polar (water-soluble) chemical carcinogens with values of X as applied (a) by WHO (2008) and (b) by UBA (2005) Carcinogen

Increase of risk per (mg d1  kg bm1) (slope factor, SF a)

Increase of riska per (mg l1) (oral risk unit, RUob)

(a) N-Nitrosodimethylamine (a) Acrylamide (a) Benzo(a)pyrene (a) 1,2-Dibromo-3chloropropane (b) 2,4-Dintrotoluene (a) Vinyl chloride (a) Benzene (b) 2,4,6-Trinitrotoluene (a) 1,2-Dibromoethane

SF ¼ 3.3

r10  105

SF ¼ 0.7 SF ¼ 0.46 SF ¼ 0.33

r2  105 r1.4  105 r1.0  105

SF ¼ 0.17 SF ¼ 0.066 SF ¼ 0.033 SF ¼ 0.033 SF ¼ 0.023– 0.83 SF ¼ 0.016 SF ¼ 0.011 SF ¼ 0.0056

r0.5  105 r0.2  105 r0.1  105 r0.087  105 0.07  105  2.5  105

(a) 1,3-Dichloropropene (a) 1,2-Dichloroethane (a) Bromodichloromethane

r0.05  105 r0.033  105 r0.017  105

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water directive is its detection limit (0.1 mg l1). Its compliance presumes technical minimization of ECH leaching materials in contact with drinking water, according to ALARA . The most common parameters with GV for drinking water derived from or taking into account local effects on humans are copper and sulfate. The effect threshold of copper for triggering gastrointestinal complaints is positioned between 1 and 3 mg l1 (Araya et al., 2003), whereas the laxative effect of sulfate in unaccustomed persons seems to start at around 1000 mg l1 (for toxicity of copper as a systemic agent, see section titled ‘Copper’) The discussion on how to quantify a Bd to protect Nipresensitized persons from the systemic allergenic potential of systemic oral nickel exposure seems to have been concluded by WHO (2008) by deriving a systemic Bd based on the (assumed) reproductive toxicity of oral Ni2þ exposure. This Bd (5 mg kg1 bm) and its corresponding GVWHO (20 mg l1) are much lower than any earlier aspersed thresholds for the systemic allergenic potential of nickel in presensitized persons. Levels from 30 mg l1 on have been shown to be devoid of any health risk even for presensitized people (Alam et al., 2008).

a

SF ¼ Population-based probability to contract cancer during lifetime if he or she would permanently ingest 1 mg per day and kg body mass of the respective contaminant. The ratio between the highest and lowest SF is about 600. b Calculated from SF for a person with 70 kg bm1 and a lifelong consumption of 2 l drinking water per day

exposure. The main toxic endpoints to be discussed here are irritating and allergenic as well as local carcinogenic effects. Within the context of personal hygiene, including showering and bathing (target organs: skin and external mucous membranes), the concentration of a critical compound decides directly on the possibility of an adverse effect on up to 2.5 m2 skin surface. When using drinking water for preparing food (target organs: mucous membranes and epithelial cells in mouth and intestine) the initial concentration will be more or less diluted and/or the chemical be bound to or masked by body fluids and organic materials absent from drinking water. It is possible to examine in animal experiments whether local effects might be absent below a threshold concentration and what would be its numerical value. There are, however, huge differences in sensitivity between animals and humans when comparing different agents. It is therefore preferable to define the presence and numerical value of a possible threshold on the basis of retrospective epidemiologic studies, single-case observations, and prospective examination of voluntarily exposed persons. Epichlorohydrine (ECH), as an example, actually is a primarily genotoxic carcinogen, but due to its high reactivity it initiates tumors in mice only close to its port of entry, the forestomach, where it is also a strong irritant. As humans do not dispose on a forestomach, this information could not be used to derive a GV with the LMS model. Instead, WHO (2008) preferred to derive a Bd-based GV of 0.4 mg l1 by stressing basic dogma 1 and calculating a total safety margin of 10 000 between a human Bd for ECH and its NOAEL in mice for the precancerous endpoint forestomach hyperplasia. The parametric value for ECH of the present EU drinking

3.14.4.3.4 Effect combinations Drinking water is a potential sink for many environmental contaminants, especially the hydrophilic ones. It is not surprising to hear very often the fear their possible effects on humans via drinking water might combine in an additional, or even synergistic or (theoretically) antagonistic, manner. The possibility of effect combination, be it favorable or unfavorable for humans, is impossible to falsify or verify by experiment in each single case (Cassee et al., 1998). In cases where only retrospective evaluation is asked, the only way to proceed is to assess the exposure situation as it presents locally, considering and evaluating toxicological (kinetic and dynamic) similarity of all contaminants relevant for exposure and structure–activity relationships between them to help assess the mixture instead of its single components. Groups of contaminants being similar with respect to their adverse toxic mechanism and specific binding to (shared) targets can be assessed by means of the addition rule for ‘similar joint action’ or ’concentration additivity’ (Kortenkamp et al., 2009). It asks at first to compute the quotient between the measured and the tolerated or accepted concentration (the latter one being often a health-related indication value (HRIV) (see Section 3.14.6.3.2), but ideally a GV) for each component within similarly acting groups of the mixture. The groupspecific sums of these quotients are called risk index (RI). RI values lower than 1 indicate the absence of appreciable risk, but only regarding the group of compounds as evaluated by means of this RI under the precondition of similar joint action (WHO, 2008). The result of such group-specific assessments is distinctly more conservative than considering and evaluating just the presumably most toxic component of each group or mixture by neglecting all others (Kortenkamp et al., 2009). The parallel determination of several dissimilar groupspecific RI values should not indicate any risk if each RI remains lower than one since, under this condition, none of them would indicate trespassing of an effect threshold (Bd) or

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the possible exceedance of an accepted additional cancer incidence (IZ). An alternative to assess mixtures or combination of effects from a priori unknown chemical agents would be the integral experimental approach. It consists in identifying and comparing cytotoxic or genotoxic potentials from concentrated drinking water samples on the basis of results from standardized biological in vitro toxicity testing. An example of how to proceed was given for two treatment/distribution networks located near the source and at the mouth of River Po in Italy (Maffei et al., 2009). The short-term and long-time cell survival (endpoint cytotoxicity) and the degree of in vitro genotoxicity (as seen by the comet assay and the micronucleus test) of the water before and after disinfection by ClO2 varied considerably between both plants and depended on the drinking water’s residence time in each distribution network. However, the regulatory-toxicological evaluation of a contaminated drinking water with recourse to in vitro tests remains a matter of local or technical circumstances as long as conventions on the endpoints to be tested and how to evaluate the results are not standardized. There is undoubtedly an urgent need to define and standardize in vitro testing for more and new endpoints of crucial importance for the integrity of human health at different stages and conditions Anonymous (2010). Such tests would open the way to identify toxic potentials of hitherto unidentified chemicals and their mixtures on the basis of data from different human cells and tissues long before it is necessary to perform in vivo tests. The basic idea behind would be to define scientifically the presence of safety on a cellular level as long as scientific proof for the absence of toxicity for a population is not required or not yet possible (see Section 3.14.4.3.1, third paragraph). Support to assess mixtures by anticipating effect combinations on a molecular level could come in the near future from computational toxicology (Ekins, 2007).

3.14.4.3.5 Exposure Uptake of (contaminated) drinking water. The starting point for quantifying health-related tolerable exposure to a drinkingwater contaminant as specified by WHO (2008) is to assume lifelong consumption of 2 l of water per day and adult person, and possibly contaminated up to the contaminant’s GV. The amount of 2 l per day and person seems to have a reasonable epidemiological base as shown by data from Germany, where 50% of the 18- to 69-year-old population consumes up to nearly 700 ml drinking water per day and person and 98% up to nearly 2500 ml (Becker et al., 2001). Similar daily consumption was reported from other studies and countries. Variations may result mainly from climatic differences and gender-specific behavior underlying mean living times at home and work. An earlier approach proposes or calculates with only 1.4 l per day and person (WHO/IPCS, 1994). The difference between 1400 and 2000 ml per day per person is insignificant when looking on the considerable inexactness by which any toxicological evaluation is afflicted from which safe health-based standards may be derived. Allocation. There are, besides drinking water, some more important paths of exposure by which contaminants may

reach the human population, very often in much higher contingents of Bd than with drinking water. In order to regulate prospectively the possible appearance of environmental contaminants on virtually safe levels, it is not sufficient to agree upon the (maximal) daily uptake of water per person. It is also necessary to quantify the percentage of the contaminant’s Bd being allowed for in the daily amount of drinking water. Allocation is the term for attributing or allowing a certain percentage of the Bd (see Section 3.14.4.3.1) of a threshold contaminant on the daily drinking-water uptake. Allocation rate has to depend on guessed estimates on exposure fragmentation of the substance (group) under question among food, air, soil/dust, and drinking water; otherwise, there would be no way to control whether future exposure would stay within a predefined Bd. Allocation is not performed for carcinogenic contaminants characterized as primarily genotoxic and whose presence in the environment and drinking water is evaluated accordingly by comparing the additional exposure-controlled incidence I with an accepted IZ (see Section 3.14.4.3.2). The underlying reasoning is by far not that such allocation possibly could not be calculated. The reason is more fundamental in the sense that it is not advisable to assign any official allowances or allocations for primarily genotoxic chemicals because this would eventually legalize their appearance in environmental compartments. Instead the first, although not always successful, choice for managing these contaminants is to defeat their escape or development right where they are formed. Data on how exposure to different groups of environmental contaminants split over different exposure paths can be retrieved at best and with more or less preciseness from retrospective studies. They may in turn be used only conditionally for setting prospective standards. Depending on the exposure fragmentation to be expected factually, the acrossthe-board contribution of daily drinking-water is set usually between 10% and 50% of a Bd, dissolved in 2 l of water. As a rule, high default contributions are allowed for factually inevitable natural constituents of raw water (up to 50%), for DBPs exclusively from drinking-water disinfection and distribution, and for cyanotoxins occurring in drinking-water reservoirs (both up to 80%). Lower allowances (down to 10%) should apply on environmental contaminants such as pesticides or contaminants from industrial emissions. Alternatively, environmental contaminants may be regulated either by a health-related precautionary default, or by even a purely precautionary approach (see Section 3.14.6.3.2). On the other hand, if a Bd was derived from an experiment with intake by food instead of drinking water, the resorption of the chemical from drinking water might differ significantly from the experimental reference value. Especially for metals, resorption rates are often distinctly higher than from food. This may provisionally be accounted for by calculating the corresponding GV using an accordingly reduced allocation. Metals such as copper or manganese, if ingested by infants mainly with tea, juice, or formula prepared with drinking water from concentrates, may attain in the latter distinctly higher fractions than 10% of their Bd. It is advisable in these cases, in parallel with the higher allocation, to give special attention on toxic endpoints of possibly specific importance

Drinking Water Toxicology in Its Regulatory Framework

for weaned infants with regard to target organ(s), metabolism, and mode of action (World Health Organization/International Program on Chemical Safety, 1986) and to limit the content of the dry materials on these and other essential elements accordingly, if they are presented regularly and in significant amounts via drinking water. Concentrations representing more than 10% of TDI in 2 l of drinking water per day are often also observed with persons who, instead of drinking water from the source, consume regularly bottled water rich in minerals. Till date, there is no standard default assumption on the daily per capita consumption of bottled water and hence no international guideline as to how to derive health-based maximal values for bottled water. Such waters are regularly allowed to exhibit distinctly higher mineral contents than regulated drinking water, especially on fluoride, arsenic, and borate. For many minerals (e.g., sodium, chloride, sulfate, and manganese) there are no limitations at all. On the other hand, there are many mineral waters exhibiting much lower content of minerals than they often occur in drinking water.

3.14.5 A Holistic Approach for Defining Quality Goals or Standards for Drinking Water A holistic approach to define hygienically sustainable quality goals or standards for drinking water not only should include considerations dealing with adverse or annoying effects of chemicals on humans and their adverse effects on technical systems, but also should take into account optimizing functionality of intended exposure on site and technical or procedural avoidance of what can be called useless, albeit not necessarily risky, exposure off site. Such precautionary approach (Section 3.14.2.4.1) starts best with considering the origin of a chemical to be regulated. It turns out that the origin or (non)functional value of a chemical in a raw water/ drinking-water system is the factor that helps decide whether it should be regulated either by a health-based or a lower technical maximal or minimal concentration or rather by remote (on site) emission control long before health-based values can be exhausted off site in the drinking water. To make this clearer, Table 2 classifies a number of common (A) constituents of raw water, (B) residuals from functional use in the drinking-water system, or (C) contaminants of raw and possibly drinking water from remote emission, which are candidates for being regulated in drinking water according to their origin (A, B, or C) and (non-)functionality (B or C) in a raw water/drinking-water system. The criteria on why this was done in Table 2 are outlined in Sections 3.14.1.2 and 3.14.2.3.2.

3.14.5.1 Chemicals Whose Regulation Will Primarily Concentrate on Avoidance of Adverse Effects The natural constituents of group A are – almost by definition – difficult to avoid. Sometimes, if occurring at higher than not-adverse levels yet, it may be possible to treat raw water, otherwise it would be necessary to change the water source.

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The steps or elements to decide on an optimal limit value for group A chemical parameters are as follows: 1. define at first a tolerable or acceptable (yet) nonadverse effect concentration (GV) not to exceed as based on human toxicology or annoyance (see Section 3.14.4) or a more sensitive technical endpoint of adversity (see Section 3.14.2.3.2.); 2. define a natural reference concentration (natural value, NV), for example, in the form of an upper percentile of current geogenic background values of the same compound in the final raw water to be processed as it is; 3. compare GV with NV; if GV rNV; 4. improve the water by treating it according to ALARA to lower the adverse constituent’s level to a tolerable level TV rGV. TV is a precautionary maximal value, it should be fixed as limit value to control technical treatment for failure; and 5. if TV rGV is not reasonably achievable, change the raw water or (some) of its source(s) or wells; if this is not possible keep the TV as a provisional TVp and continue to improve treatment or accept any residual risk as counterbalanced by continued use of the source. Advice for finding GVs as based on adversity for humans is internationally available at WHO (2008), and also at national and supranational bodies as mentioned in Section 3.14.4. Some additional or new information is given in Section 3.14.6.3. Representative information on background levels to define NV is often difficult to find. Existing levels of TV as based on technical adversity or efficiency to eliminate potentially adverse constituents from drinking water are described in appropriate technical norms for construction materials in contact with drinking water or for treatment of drinking water.

3.14.5.2 Chemicals Whose Regulation Should Concentrate on an Optimal Ratio of Functional Exposure to Functional Intention Residuals and by-products on site from functional additives (group B) are evitable down to minimal yet functional levels in the measure as their use gains in efficiency by or despite minimizing on-site exposure. The steps or elements to decide on an optimal limit value for group B chemical parameters are as follows: 1. Define at first a tolerable or acceptable (yet) nonadverse effect concentration (GV) not to exceed on site as based on human toxicology (see Section 3.14.4) or annoyance or a more sensitive technical endpoint of adversity (see Section 3.14.2.3.2). 2. Define the minimal yet reliably functional or residual concentration (FV1) on site of the same compound for the desired process or function. 3. Compare GV with FV1; if FV1 Z GV. 4. Improve the process or functional efficiency according to ALARA down to a lower yet functional on site level of FV2oGV. FV2 should later on be described in a technical norm and would be a precautionary maximal value. Any

392 Table 2

Drinking Water Toxicology in Its Regulatory Framework Assessment and management of chemicals in drinking water by origin A–C of inputa (see text)

A: Natural constituents of raw water

 Presence unintentional  May be functional

B: Anthropogenic residues and side/corrosion products in drinking water  Presence intentional  From functional use (treatment þ distribution)

 Could be adverse

 Could be adverse

Optimal regulation either at beginning or end of pipe in the water-works utility – Aluminumb Ammonium Antimony Arsenicd – – – Borate – Cadmium Chloride

Optimal regulation somewhere in the pipe between source and consumer Acrylamidec Aluminumd – Antimonyf Arsenicf Alkylated benzenesg Benzo(a)pyreneg – – Bromated Cadmiumf –

Chromium Conductivity – Cyanide Cyanotoxinsi – Fluoride Irond Lead Manganesed Mercury Nickel

– – Copperf –

–

–

– –

Nitritek PAHg

– Protonsd Selenium Sodiumd Sulfate – – Uranium

– – – – – Disinfectants and DBPsd Vinylchloridec –

a

Epichlorohydrinec – Ironf Leadf – – Nickelf

Bold: routine surveillance; others: surveillance on verified suspicion (author’s suggestion). In raw water from acidified soils. c Control preferably by specifying materials in contact with drinking water. d After treatment. e Plant protection products from agriculture. f From armatures and/or pipes. g From coatings. h Origin industrial or from abandoned waste sites. i Control preferably cyanobacteria by limiting P load of resource. j Industrial chemicals, nonrelevant metabolites from PPPe, pharmaceuticals. k From nitrate in anoxic water stagnating in galvanized steel pipes. b

C: Anthropogenic contaminants of drinking water  Presence unintentional  Nonfunctional, coming from diffuse or point sources/spills  Could be adverse Optimal regulation at beginning of pipe (remote emission control) – – Ammoniume – – – Benzeneh Borateh – – – Chlorinated solventsh – – – – – – – – – – – New analytesj Nitratee Nitroaromatic compoundsj Nitritee – Perfluorinated compounds (PFCs) Pesticides þ relevant metabolitese – – – Sulfateh – Vinylchlorideh –

Drinking Water Toxicology in Its Regulatory Framework

FV needs to be fixed as a limit value as under improper technical conditions it could exceed the respective GV. 5. If an FV25FV1ZGV is not reasonably achievable, rebalance the functional or hygienic benefit of the process against giving it up. Advice for finding GVs as based on adversity for humans are internationally available at WHO (2008), and also at national and supranational bodies as mentioned in Section 3.14.4. Some additional or new information is given in Section 3.14.6.3. Existing levels of FV2 and FV1 as based on technical functionality of the respective compounds in the drinking water system are described in appropriate technical norms for construction materials in contact with drinking water or its treatment.

3.14.5.3 Chemicals Whose Regulation Should Concentrate on Remote Emission Control Anthropogenic environmental contaminants (group C) by definition are chemicals that occur at places off site from their functional use. They are evitable there down to technical or analytical zero levels only in the measure as their functional use on-site gains in environmental neutrality by minimizing chemical emission into off-site compartments. The steps or elements to inform on an optimal limit value for a group C chemical parameter are as follows: 1. Define at first a tolerable or acceptable (yet) nonadverse effect concentration not to exceed a certain value (GV or a precautionary default surrogate, HRIV, see Section 3.14.6.3.2) as based on human toxicology (see Section 3.14.4) or annoyance or a more sensitive technical endpoint of adversity (see Section 3.14.2.3.2). 2. Define from the point of drinking-water hygiene a tolerable level TV1 for the contaminant in drinking water as far as reasonably justifiable below its GV (or precautionary default surrogate, HRIV), albeit not higher than actually feasible or observed levels. 3. Compare TV1 by means of prognostic models with emission on site of the contaminant into the environment and with observations on its occurrence off site from or by its use on site. 4. Improve the environmental neutrality of the chemical’s onsite functional use, if TV1 is significantly lower than prognosticated or observed emission levels, by reducing its emission according to ALARA down to loads or levels to comply off site with TV1. Such minimized loads or levels should be fixed in technical norms and by legal admissions. TV1 would be a precautionary minimal value. It may be fixed as limit value only if there is a chance to establish clear-cut causal relations between loads or levels admitted on site and possible exceedance of TV1 off site. 5. If TF1 is not reasonably achievable by limiting emission on site, there is, as WHO (2008) proposes, a choice of options for informed decisions, depending on the cultural, technical, hydrogeological, or economic context: a. treat the water to comply with TV1 which should then be set as a limit value to control performance of treatment, or

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b. change to a less contaminated raw water source with a TVoTV1, or c. define a TV24TV1 as far as reasonably justifiable and achievable below the chemical’s GV (or HRIV) but not lower than prognosticated or observed levels; TV2 would only be a provisional precautionary value. Advice for finding GVs as based on adversity for humans is available at WHO (2008), and also at national and supranational bodies as mentioned in Section 3.14.4. Some additional or new information on a number of parameters is given in Section 3.14.6.3.

3.14.6 Practical Regulation of Drinking-Water Quality 3.14.6.1 Quality Assurance and Surveillance of Raw Water, Finished Water, Tap Water The quality of water bodies from which raw water is abstracted to produce drinking water should meet, as a minimum requirement, certain essential health and hygienic standards. Preferably, and wherever possible, its production should rely on raw water requiring only minimal or even no treatment – neither for health nor for technical or esthetic reasons (Section 3.14.1). If, traditionally, the conditions of abstraction and treatment processes are nature oriented, the raw water usually will meet these requirements and there will be no problem to ensure a drinking-water supply that is not only palatable and sound in health terms, but also acceptable in terms of environmental hygiene. In such drinking water, the actual concentrations of legal chemical parameters, apart from occasional geogenic constituents, are usually far below mandatory limit values. Regarding chemical parameters to which no formal limit values are ascribed, often an unspoken consensus between the precautionary healthcare sector, water supply companies, and the public is effective stating that unregulated environmental contaminants are to be kept off from drinking water, preferably by on-site emission control, and thus lend support at the end of (the drinking water) pipe to a highly defect-resistant error-friendly water abstraction and treatment technology. (An error-friendly system is designed to be highly resistant to technical failure and human error.) As an example, this basic consensus is reflected in the EU Drinking Water Directive 98/83/EC (1998) and their quantitative parametric values as being ‘‘minimum (quality) requirements,’’ and is also covered in essence by the wording of its Articles 4(1) and 5(3). For this purpose, it is the best to detect, quantify, and evaluate suspected contaminants already in the raw water. It would be capital to counteract the input of xenobiotics into the environment in a medium like drinking water just with human toxicological limit values even though such xenobiotics do not exhibit even a minimal benefit there. The highest priority is on closing the sources of xenobiotic input and on ‘‘effective protection of water resources used as sources of drinking water (y) from pollution from (y) agriculture, industry and other discharges and emissions of hazardous

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Drinking Water Toxicology in Its Regulatory Framework

substances’’ (UN – Economic Commission for Europe, 1999). A comprehensive introduction into managing the quality of groundwater as a drinking-water source under very different aspects is provided in Schmoll et al. (2006). According to these and other sources of knowledge or management advice, it would seem the wrong way to impose at the end of the pipe the responsibility for failures on toxicologists whose scientific expertise to assess the consequences may, at best, be prepared to retrospectively assess single cases where prospective assessments would have been required. It should be no surprise if some of them occasionally pipe up with proposals on precautionary values which then in consequence are intentionally misunderstood by interested stakeholders as being scientifically sound and indicating imminent danger if trespassed. No toxicologist or water supplier ever pretended of being able to protect water bodies by means of human toxicological maximal values for drinking water. This is so because the cooperation required for sustainable development in this area goes far beyond the formal responsibility of the water utility. Any toxicological limit value at the end of (drinking water) pipe would be of no help to clear the underlying relationship between cause and contamination or effect. To achieve practically sustainable results, the preferable way is to agree upon voluntary cooperation. In this context, there exist two kinds of control cycles, number I being closed and number II being open (Figure 1). Unfortunately, in the open control cycle II, legal limit values do not by far have yet the same regulatory potency as in control cycle I. Much too often, the question arises whether the limit values in cycle I are (yet) safe enough or how better maximal (or minimal) values could be made available, whereas preferably the mechanisms of control cycle II should be strained for triggering causal or functional sanitation (for groups of contaminants: see Section 3.14.4.2).

Regulatory circuit II open

Another, at first glance, better-defined source of pollution is migration of substances from materials in contact with water used for human consumption. The control cycle in which these materials and their contaminants are regulated is more or less open when regarding the seemingly neverending number of materials and products that penetrate this worldwide market. The fact that substances originating from this source and entering the drinking water are not contaminants but residues from formally permitted (?) use makes the situation hardly better. In fact, the most significant fraction of drinking-water contamination originates from badly conceived domestic drinking-water installations and materials whose emissions and improper maintenance very often adulterate drinking water delivered in unobjectionable quality from a central supplier to the handover point. For matters of practical surveillance, it is reasonable to take samples for measuring parameters open to change during transport or stagnation of drinking water in such a way that the analytical result represents the quality at the consumer’s tap and not somewhere on the way to the handover point (see also Section 3.14.2.3.1). Parametric values to be checked therefore at either the outlet of the water-works facility or only at the consumer’s tap are listed in Table 3. A sampling procedure to represent the average weekly ingestion by the consumer of corrosion products from Cu, Pb, and Ni with tap water was developed in Germany (UBA, 2004) and conforms to a demand of the EU drinking-water guideline 98/83/EC. A similar conclusion on developing even more sampling procedures to quantitatively describe mean domestic instead of point exposure could be drawn, for example, when regarding impact of (hot) water stagnation and temperature on DBPs in household water (Dion-Fortier et al., 2009).

Resources of drinking water

???

Compliance Comparison with limit values

Finding

Noncompliance Drinking water

Measurements

Regulatory circuit I closed

Distribution Treatment Figure 1 In-plant and external regulatory circuits when operating a drinking-water supply.

Drinking Water Toxicology in Its Regulatory Framework Table 3 Selection of chemical parameters to be checked ideally only at either the outlet of the waterworks facility, or the consumer’s (domestic) tap Place I or II of surveillance I. Outlet of waterworks Chemical parameters whose concentration usually is unsuspicious to increase in the distribution network

II. Consumer’s tap Chemical parameters whose concentration may increase in distribution network including domestic installation

Acrylamide (AA) Benzene Borate ðBO4 3 Þ Bromate Volatile chlorinated solvents (VCSs) Chromium (Cr) Cyanide (CN–) Fluoride (F–) Mercury (Hg) Nitrate ðNO3  Þ

Antimony (Sb) Arsenic (As) Benzo(a)pyrene (B(a)P) Cadmium (Cd) Copper (Cu)

Plant protection products and their metabolites (ppp) Selenium (Se)

Epichlorohydrin (ECH) Lead (Pb) Nickel (Ni) Nitrite ðNO2  Þ Polycyclic aromatic hydrocarbons (PAHs) Trihalogenmethanes (THMs) and other disinfection by-products (DBPs) Vinylchloride (VC)

3.14.6.2 Paths and Significance of Exposure to Chemicals in Drinking Water 3.14.6.2.1 Acidification of raw water Soils under long-term exposure to protons from air may glide into the aluminum- or iron–/aluminum range of buffering situated at pH 4.2–3.8 and 3.8–3.0, respectively. The buffering takes place by dissolution of corresponding metal compounds and the destruction of three-layered clay minerals, which are important filters against the input of organic xenobiotics. The place of HCO 3 is taken over by the anions of strong acids (sulfate and nitrate), whereas on the cationic side strongly acidic ions such as Al3þ, Fe2þ, and Mn2þ are increasing, possibly together with increased mobilization of toxicologically critical heavy metals ions (Svensson et al., 1987). Aluminum in concentrations above 2 mg l1 may heavily compromise the functionality of pumping systems for drinking water (Lu¨kewille and Heuwinkel, 1990). The present regulations to stop or confine soil acidification in many parts of the world are insufficient to stop this risky process of threefold concern for adverse technological, ecological, and human health endpoints. Increasing proton concentrations mobilize toxic or undesired metals not only in the subsoil, but also in metallic distribution systems, if the distributed water is not neutralized according to existing technical norms.

3.14.6.2.2 Agrochemicals Agricultural activity affects water bodies mainly by its input of nutrients such as nitrogen and phosphate salts and pesticides and their metabolites. Nitrate/nitrite and their potential to

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cause methemoglobinemia have attracted the interest of toxicologists for a long time (see Section 3.14.6.3.1). Overfertilization of lakes and reservoirs may also lead to massive growth of cyanobacteria and their corresponding contamination by highly hepato- and neurotoxic cyanotoxins up to hazardous levels in extracted drinking water, if not treated accordingly. Countries that do not regulate pesticides in drinking water by a precautionary limit value also deserve to be looked into by drinking-water toxicologists (see Section 3.14.6.3.2). Another toxicological observation is the microbial oxidation and mobilization of toxic heavy metals in the subsoil in connection with denitrification of nitrate from agriculture in reducing aquifers (Ko¨lle et al., 1983, 1987).

3.14.6.2.3 Emissions from abandoned waste sites Reams of chemicals are used in industrial processes or are present in products and wastes thereof, along with many more such degradation products. Their health hazard may vary considerably and it is necessary therefore to accordingly prioritize the hazard potential of those that are situated in water-catchment areas. Pilot surveys performed to detect highly mobile inorganic and organic parameters such as borate, sulfate, AOX, and GC-screening of abandoned waste sites or sources in drinkingwater catchment areas are able to discriminate potentially hazardous sources of underground emissions from the more harmless ones. Positive findings indicate the need for further explorations on the basis of an invariable list of contaminants with high toxicological priority (toxic potential) and, at the same time, unfavorable environmental behavior (mobility, persistence). Of the nine inorganic compounds from such a list, the first four were arsenic, nickel, chromium, and nitrite. The first four of its 15 organic compounds were benzene, vinyl chloride, trichloroethene, and tetrachloroethene. All eight compounds were also reliable indicators for the possibility that raw water for drinking-water production may be fed downstream by a contaminated aquifer (Schmoll et al., 2006)

3.14.6.2.4 Disinfection by-products Disinfection of drinking water renders the water not only in a desired microbiological state but, especially by chlorination, also creates chemical-toxicological risks. They result from the reaction of chlorine with chemical precursors naturally present in the water to be treated to hundreds of chlorinated compounds. Chloroform and, if the raw water contained bromide, (chloro-)bromoforms are the most prominent single compounds. The debate on whether chlorinated drinking water under practical conditions of exposure might or might not have a carcinogenic or other adverse health outcomes in humans has been ongoing for three decades. It discusses qualification and quantification of continued exposure mainly (see also Section 3.14.6.1, last paragraph). Neglect in considering spatial and temporal variations of DBP presence on their way to the consumer’s tap makes it very difficult to allocate adverse outcome and exposure and still more to analyze whether positive or negative results from different studies are consistent with

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Drinking Water Toxicology in Its Regulatory Framework

others (Legay, et al. 2010). The author of a recent comprehensive review (Hrudey, 2009) states ‘‘the epidemiologic evidence has generally been found to be insufficient to declare chlorination DBPs to be carcinogenic in humans.’’ The evidence in favor of DBPs as urinary bladder carcinogens has been the least inconsistent, but ‘‘there are no data to indicate that any of these compounds can contribute to bladder cancer by any mechanism’’ (Bull et al., 2001, as cited in Hrudey, 2009). Only studies with positively proven control for such complex confounding factors as lifestyle and exposure, allowing even for the hypothetical presence of an extremely potent carcinogen in significant concentrations among all DBPs, could bring this discussion to an end. As an example, such studies would definitively have to include the genetic disposition of exposed persons for expressing a toxicologically critical isoform of the detoxifying enzyme glutathion transferase and their exposure to DBPs from swimming pool water (Zwiener et al., 2007). The regulatory-toxicological situation here is somewhat similar to the one with nitrite/nitrate and their presumed potential to initiate bladder cancer as precursors of carcinogenic nitrosamines. As to the another suspected endpoint for human toxicity of DBPs, a number of reproductive outcomes suspected to be adverse under the influence of DBPs, the total of available epidemiologic data were exhaustively considered by Hrudey (2009). The present author’s own conclusion and that of Hrudey’s are that these data are still less supportive in favor of an increased incidence under exposure to DBPs than for bladder cancer. A technically feasible way to cope with this regulatorytoxicological challenge is to minimize the necessary concentration of disinfectants. This would not only minimize formation of DBPs from precursors but also improve microbiological quality and stability of drinking water. This is possible by applying routine raw water-directed procedures of drinking water treatment (Zwiener, 2002; Haberer, 1994). Starting from raw water which is not a protected groundwater and whose microbiological quality may therefore not be unobjectionable, there are several ways to achieve both goals, namely

• • • • •

prefer raw water with low DOC, minimize DOC by flocculation and oxidation before chemical disinfection, use disinfective treatment in the water-works facility by alternative agents with lower DBP-forming potential as there are chlorine dioxide, ozone, UV-irradiation, carefully maintain a microbiologically clean distribution network for being able to minimize dose of disinfectant for transport even up to its complete abandonment, and apply, if feasible (despite high energy expenditure), several modes of micro- or nanofiltration.

In case of natural or quasi-natural resources exhibiting, by definition, doubtful microbiological quality (rivers, reservoirs, and natural lakes), a priori effective treatment (before disinfection) is the best way to produce a microbiologically safe drinking water with minimal levels of DBPs.

An established procedure to produce microbiological safety, while minimizing the DBP formation potential of a raw water simultaneously, is its ozonation after different grades or steps of treatment (von Gunten, 2003; Bonacquisti, 2006). Pre-ozonation oxidizes organic carbon to epoxides, aldehydes, ketones, or acids which then may be eliminated by flocculation. However, if formed after intermediate ozonation, such compounds are excellent substrates for microbiological growth and concurrent water contamination. Such secondary ozonation must therefore be followed by percolation on activated carbon. This step is effective not only by adsorption, as the filter bed will soon support stabile bacterial mats which easily degrade these partially oxidized carbon compounds. After percolation follows the final ozonation to disinfect the water before distribution. Before this finished water is fed into its distribution net (see Section 3.14.2.3.2) it needs to be microbiologically stabilized for its way to the consumer’s tap by dosing minimal but demonstrably effective amounts of chlorine or chlorine dioxide not only directly after treatment but also at selected dosing points on the way to the consumer. With a distribution system in optimal technical state, even transport disinfection may routinely be abandoned as demonstrated in the city of Berlin for several decades (Jekel and Gruenheid, 2008) The desired consequences of the method outlined here to separate (process) oxidation from disinfection (for transport) are a minimal requirement of disinfectants and equally minimal DBP formation in the range of maximally 10–20 mg l1 to be detected at the consumer’s tap. Under such conditions and keeping the conclusions by Hrudey (2009) in mind, any discussion on increased incidence of cancerous or other toxic endpoints in populations that are long-term consumers of chlorinated drinking water would not only appear obsolete but also cast superfluous doubt on the health benefit of drinking-water disinfection by chlorination. The only DBP formed in significant amounts (from bromide) as a consequence of ozonation is bromate (see Section 3.14.6.3.1).

3.14.6.2.5 Small supplies and health risks Many countries or their supranational organizations prescribe, dispose of, or provide technical norms to support the legal quality of drinking water from small supplies and private wells if constructed in consistence with such norms. Water from wells surrounded by catchment areas lacking such consistence and hence adequate protection may exhibit high nitrate levels and microbial growth, giving cause for health concerns if ingested. The water of such supplies may also be acidified (see Section 3.14.6.2.1) and, accordingly, support corrosion of domestic installations. Risky levels of copper, lead, nickel, and other metals may be the consequence. Additionally, the presence of geogenic constituents with a high toxic potential (arsenic, fluoride, heavy metals, etc.) should be excluded before approving such water as safe for consumption. As an example, uranium in well water was the cause of nephrotoxicity in a family (Magdo et al., 2007). Similar cases are documented for intoxication by geogenic fluoride from small supplies (Hardisson et al., 2001) and in the form of a mass intoxication

Drinking Water Toxicology in Its Regulatory Framework

by geogenic arsenic ingested with drinking water from wells in Bangladesh and West Bengal in India (Meharg, 2005). Biofilms in containers used for storage of well water may be a reservoir of toxic microcystins from cyanobacteria if the origin of water and the conditions of storage support their growth (Kanke-Fosso et al., 2008). Common geogenic parameters such as iron or manganese may be tolerated at higher levels on the basis of human health considerations than for technical reasons in larger systems (see Section 3.14.6.3.1). Surveillance and control of small community supplies (applicable also to private wells) have been extensively treated by WHO (1997) and in a report ‘Protecting Public Health in Small Water Systems’ at an International Colloquium held at Montana Water Center in May 2004 ( Ford et al., 2005; Committee on Environmental Health, 2009).

3.14.6.2.6 Organics leaching from drinking water reservoir coatings and armatures Construction of facilities for drinking-water supply and choice of materials should be organized in such a way that additional surface coatings to prevent corrosion are not necessary, although such wishful thinking is not always practicable. Exposure to coatings and paints may be of significance as well in the course of pipe sanitation, especially of those with old tar-based coatings which are an important source of polycyclic aromatic hydrocarbons. Pipes being sanitized on site by coating with epoxide resin may turn into a source of migrating chemicals if after coating, necessary waiting times or maximal temperatures to operate the sanitized system are not respected. Bitumen-based coatings may contain alkylated benzenes that are of interest more for esthetics than for toxicity. Besides this, they may offer organic carbon for microbial growth and impair disinfection by chlorine by forming DBPs. They may be perceived by odor when they are already a few mg l1, whereas human toxicological guide values may reach levels of 100 mg l1 and more (WHO, 2008). Contaminations of this source are common and occur mostly in connection with unprofessional (incomplete) drying of freshly coated construction elements or because contaminated exhaust-air had contact with drinking water. Defective mixer taps may be a source of leaching control liquids such as toluene, alcohols, and waxes. For adequate surveillance see last paragraph of Section 3.14.6.1.

3.14.6.2.7 Hygienic aspects of corrosion products from domestic pipes and metallic materials The intensity of mutual interaction between drinking water and metallic materials/pipes depends on several factors:

• • •

chemical properties and metallurgical state of the material, inorganic constituents and electrochemical properties of the drinking water in contact with that material, and operating conditions (temperature, stagnation, domestic treatment, etc.) of drinking-water supply and domestic installation.

The most common metals to reach drinking–water by this way are copper, zinc, iron, cadmium, and lead. Nitrate in

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anaerobic drinking water may be chemically catalytically reduced to nitrite during stagnation in galvanized steel pipes. In order to prevent health and technical problems, it is necessary to avoid chemical corrosion of drinking-water pipes as far as possible. Technical norms to cope with this task are permanently created and updated in many countries. For reasons of practicability they cannot deal with the inorganic constituents and electrochemical properties of individual drinking waters, but only with commercial materials, products, and installations. The implementation of technical norms lies within the professional responsibility of the construction engineer. The alternative would be a normalized, chemically defined and all-round technically compatible hence uniform drinking water. Such a vision is neither desirable nor would it be feasible. The best predictions on the corrosive potential of drinking water within its technical environment can be given on the basis of its pH value. This is, at the same time, the most practical parameter to gain such information. For each metal there exists a specific, slightly alkaline pH interval in which its tendency to react with water crosses or finds a minimum. On the other hand, it is advisable not to exceed in real water its specific pHc of calcium carbonate saturation, since otherwise it would precipitate and gradually clog the pipes. Exceeding the pHc would therefore make it impossible to adjust the interval of minimal corrosion; the material under question would then be unemployable. Therefore, from the view of undesired chemical corrosion, a soft water with its own and relatively high pHc is always preferable to hard water and its lower pHc. Higher corrosive potentials are only to be found in unbuffered soft and acid waters with measured pH values of 6.5 and less. Depending on hardness and pHc of drinking water, the metallurgic properties of the used materials and the operating conditions, it is possible that some metals may exceed health-based maximal values if corrosion is not minimized by implementing the technical norms created for this purpose. A current example is copper. Its pH interval of technically and hygienically tolerable corrosion – depending on dissolved organic carbon and some other parameters – begins at the earliest at pH 7.4 (usually at pH 7.6). Since this pH is usually higher than the pHc of very hard water, the usability of uncovered (untinned) copper pipes to transport such water should be limited on pH values higher than 7.6. If the pH of the finished water exceeds a value of 7.8, control of copper content is required only in special situations (amendment of the German drinking-water ordinance as of 1 March 2010). The correct hygienic and exposure assessment of residues from metallic corrosion depends heavily on a correct representative sampling (see Section 3.14.6.1). As a rule, the mean concentration of a corrosion product at the consumer’s tap reaches its concentration maximally as measured in a 4-h stagnation sample.

3.14.6.2.8 Hygienic aspects of domestic posttreatment of drinking water Operating of small domestic devices for posttreating of drinking water (mainly by softening, reverse osmosis, or ultrafiltration) is, rarely, a reasonable alternative to (mis)trust a central supplier’s engagement to provide the consumer an

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unobjectionable product. Even for considerations unrelated to health, domestic devices, installations, and procedures to treat drinking water just before it reaches the tap are useful only in special situations. Unfortunately, the potential domestic operator or owner can only occasionally take the right functional and hygiene-related decision on his own but depends completely on the information provided to him/her by a plumber or the vendor/manufacturer of a treatment device. For different reasons, these parties are likely to overemphasize the advantages of their investment or devices. For the operator of a domestic installation, the only question of interest to be answered is whether the investment for a posttreatment device would save him from losses by later more expensive repairs that might occur from damage by increased corrosion. There is, however, no doubt that the professional design of a domestic installation and its correct completion later is the best warranty for its enduring functionality. Enduring functionality depends mainly on correct selection of construction materials as required by the characteristics of the water delivered by the local water supplier. Within the area of supply, the operator is the best addressee for questions regarding corrosiveness and other technical experience, whereas any question about its health compliance is answered best by the local health authority. An overall expert judgment of specific complaints, technical information, and possible desire for domestic comfort in an area of supply could reach the conclusion that a partial central softening of very hard water would be of advantage to all parties for economic, ecologic, and technical reasons; the only possible constraint for the supplier could be of keeping a minimal and nutritionally favorable concentration of Ca2þ instead of further increasing Naþ (Cotruvo and Bartram, 2009) (see also Section 3.14.1.2). Even when considering technical progress, there are always good hygienic reasons in favor of not interrupting the distribution pipe between the supplier and the consumer just for technical or comfort reasons. Each additional construction device, especially those that are the consumer’s private responsibility, creates additional risks because of human failure, faulty operation, and careless maintenance. Therefore, one single central (and partial) treatment for softening would always be the preferable alternative to many peripheral softening devices in single homes. Other technologies adapted for domestic use such as reverse osmosis, ultrafiltration, or ultraviolet (UV) irradiation are advisable for small supplies devoid of any reasonable possibility to benefit from central treatment and, in such cases, would be misclassified as posttreating methods.

3.14.6.2.9 Hygienic aspects of domestic water saving From the very inception of the existence of a central drinkingwater supply system, the question was raised whether it would be worthwhile to partially replace unobjectionable drinking water at home by water of minor quality (greywater) in the form of rain water collected from one’s private roof. The economic, ecologic, and technical benefit to expect from such replacement very often is heavily overestimated, and for hygienic risks the contrary is true. If not maintained properly and regularly or correctly stored and disinfected, the technical

equipment as well as the collected water itself is very likely to preserve a microbiologically objectionable condition (Schets et al., 2010). As far as chemical aspects go, such water is usually very soft and acidic since it is only occasionally adjusted to its pHc. As such, it will corrode the domestic installation (see Section 3.14.6.2.7) much more than if it is adjusted to its pHc. An important point from the perspective of personal hygiene and public health is that in moderately to densely populated areas none of the possible private investors would ever be able to renounce a central or piped supply completely, except they would have drinking water from a private well available to them to bridge periods of dryness. All others, in case of emergency, will always be tempted to reconnect themselves with the central supply, entailing all technical failures and adverse hygienic consequences which any hidden or unintentional technical default (e.g., back siphonage) might cause for the surrounding neighborhood or clients of the central supplier. Other hygienically questionable outcomes of decreased drinking-water turnover are increase of its stagnation in public pipes and that of wastewater and its concomitant fouling in sewerages. Any effort from the side of the supplier to clean the pipes and sewerages by flushing will undo such savings at the expense of those who anyhow were unable to afford it (see also Section 3.14.1.1). In fact, there are not many arguments in favor of an action to save, specifically, drinking water, besides some useful technical possibilities to limit superfluid domestic water use through regulation of flow or pressure by means of specific armatures. There should never be any argument at all against storage and use of rain water for agricultural or domestic irrigation. Matchless higher potentials to save (at inevitably much higher absolute needs of) water are hidden or slumbering in present modes and habits to produce certain foods and their consumption (UNEP, 2003b).

3.14.6.3 Significance and Hygienic Assessment of Exposure at the Tap In its monograph ‘Concern for Europe’s Tomorrow’ from 1995, WHO (1995) indicated the numbers of people and localities in Eastern Europe exposed to toxic chemicals via drinking water. The most important numbers relate to heavy metals (553 000 people from 123 localities), cyanides (10 000 people from 10 localities), phenolics (230 000 people from 57 localities), oil substances (1 million people from 1659 localities), and enhanced radioactivity (300 000 people from 100 localities). The following very short monographs come in alphabetical order. They concentrate on those parameters not dealt with in the rolling revision of the WHO (2008) drinkingwater guidelines or on those the author felt additional or more recent information than contained in the respective WHO background documents (the earliest dates from 2003) would be of interest for the reader.

3.14.6.3.1 Inorganics Arsenic (group A in Table 2). Inorganic arsenic (As) is a potent human carcinogen. WHO (2008) stated in its 2003 assessment

Drinking Water Toxicology in Its Regulatory Framework

that ‘‘although there is a substantial data base on the association between both internal and skin cancers and the consumption of arsenic in drinking-water, there remains considerable uncertainty over the actual risks at low concentrations’’ and therefore retained its earlier GVWHO of 10 mg l1 As for reasons of technical and analytical achievability. Applying the terminology of Section 3.14.5.1, this GV would be a TVp. It is frequently exceeded in sources for drinking water; in terms of exposed population (35–77 million people) the scale is greatest in Bangladesh (Smith et al., 2000; Meharg, 2005). On the other hand, a large ecologic study with 44 872 respondents revealed no association between As in 1 900 springs and groundwater wells containing 0.1–950 mg l1 As (of which15%410 mg l1 As) and incidence of As-related internal cancers, whereas other independent variables than As (race, smoking, sex, and overweight) showed some positive association. Most important, the study found no indication on an increased risk to contract cancer from exposure to inorganic As below 10 mg l1 As in drinking water (Han et al., 2009). A similar conclusion was also drawn earlier from large epidemiological cancer studies (Dieter, 1991) and is sustained by exhaustion of As detoxification only at regular daily ingestion of at least 200 mg As (Mazumder et al., 1988; Marcus et al., 1988; Rudel et al., 1996; Calderon et al., 1999). On the other hand, there is convincing evidence to assume monomethylarsonic acid (MMA) as a genotoxic intermediary product of As metabolism in mammals on the way to its excretion as dimethylarsonic (DMA) and trimethylarsonic acid (TMA) (Schuhmacher-Wolz et al., 2009). For the time being, it remains to be determined whether and, if ever, at which level the carcinogenicity of As might be mediated by one or more thresholded effects, the more as nutritional status, namely intake of methionine, protein, and cysteine may significantly affect the level of possible threshold(s) as recently shown by Heck et al. (2009) and earlier publications cited therein on other settings of As exposure. Nutritional, mechanistic, epidemiological, and other information was recently reviewed and used to evaluate the reliability of existing cancer risk assessments and to better quantify current assessments of noncancer health effects of As such as peripheral vascular disease, blackfoot disease, skin hyperpigmentation, and hyperkeratosis (Schuhmacher-Wolz et al., 2009). According to Schuhmacher-Volz et al. (2009), As at or below 100 mg l1 shows a broad spectrum of noncancer adverse effects on reproductivity, central nervous system (CNS; intellectual function), cardiovascular system, and skin. They provided consistent dose–response data from several new studies for skin hyperpigmentation and hyperkeratosis (thickened soles of feet). Based on the data for prevalence of such skin lesions from arsenic exposure in a population in Bangladesh and data on concurrent background arsenic exposure from food, the authors performed benchmark (BM) dose–response modeling and calculated a lower confidence limit of 109.2 mg d1 at a population effect level of 5% and set this LOAEL as PoD to derive a Bd. Their reference study (Ahsan et al., 2006) comprised a large rural population of more than 10 000 persons exhibiting a mean body mass of 50 kg for men as the most susceptible

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group for the selected, most critical toxic endpoint. They deemed an extrapolation factor to cope for interindividual differences in susceptibility not necessary, whereas the 5% response level at the PoD was assessed as unacceptable. Therefore, they used an extrapolation factor of 5 to derive a tolerable daily intake for As from all sources of TDI ¼ 0.45 mg kg1 bm, corresponding to a Bd of 31.5 mg per 70 kg bm and 22.5 mg per 50 kg bm, respectively. In Western countries, where daily intake of As is estimated to amount to 14 mg (Hughes, 2006) this TDI would be exceeded if 2 l of daily drinking water contain more than 9 mg l1. Consequently, for these countries the GV of WHO would be supported by this evaluation of noncarcinogenic health effects, whereas for countries with higher personal drinking water intakes, higher intake of As from food and lower mean body mass it could easily be too high. Bromate (group B in Table 2). Bromate was last assessed for drinking water in 2005 by WHO (2008) in the form of a GV for reasonably feasible minimization of exposure to bromate from bromide as oxidized during ozonation of drinking water. The assessment was based on risk calculation with IZ values (see Section 3.14.4.3.2) of 106, 105, and 104 mainly for kidney cancer at 0.2, 2, and 20 mg bromate per liter. This GVWHO was eventually set for reasons of analytical and technical (bromate levels after drinking water ozonation) feasibility at 10 mg l1. Delker et al. (2006) published extensive biochemical data and arguments in favor of a nonlinear ( ¼ thresholded) dose– response relationship for the carcinogenic potential of bromate in 2006. Bromate triggers this potential by oxidative stress, mainly 1O2, which was assessed by the authors as being about 3 times higher in the rat kidney than its thyroid (in which anyhow no clear relationship between dose and response was observed). The intracellular capacity of the enzymatic and other defenses to cope internally with oxidative stress from bromate exposure seems to be exhausted at doses of more than 5 mg kg1 bm and their capacity to protect DNA from oxidative damage from doses of at least 20 mg kg1 bm on or higher (Dieter, 2003). The failure to protect DNA from damage by oxidation manifests itself intracellularly above background levels of excised 8-hydroxy-desoxyguanosin, the main reaction product of DNA with oxidative stress from bromate. The transcription of responsible enzymatic excision repair enzyme (8-glykosylase) correlates closely with increased oxidative stress long before mutations or even cancer can be observed (Dieter, 2003). The reason is that ROS-mediated mitogen-activated protein kinase (MAPK) activation involved in the molecular mechanisms of BrO 3 -induced cell cycle arrest occurs independently of 8-OH-dG production (Zhanga et al., 2010). An assessment different from the one proposed by WHO (2008) and starting from a thresholded PoD (see Section 3.14.4.3.1) seems therefore supported by strong biochemical arguments and may lead to distinctly higher lifelong tolerable GV than could be concluded from WHO (2008). This view is further supported at least for relatively low exposure by data demonstrating effective detoxification of oxidative stress from bromate by reducing agents, especially glutathione, in the extracellular space (Chipman et al., 2006) and the effective chemical reduction of bromate by typical

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sulfide-containing gastric juices to Z99% within 20–30 min (Keith et al., 2006). Residual levels of bromate distinctly below 10 mg l1 after treating drinking water with ozone seem routinely feasible (Bonacquisti, 2006). There is therefore no argument against limiting bromate levels in finished water maximally at 10 mg l1. Within the context of definitions given in Section 3.14.5 and the earlier arguments in favor of an effect threshold of bromate for precancerous cytotoxicity, it would appear that FV1 o GV is an acceptable precautionary level to avoid nonfunctional exposure. The nonthreshold point of view would come to the contrary conclusion, namely FV1 4 GV, and hence would define this FV1 not as a precautionary but an ALARA-based level of minimization of exposure without impeding the underlying function. From this view, FV1 is acceptable on the basis of indispensable benefit of ozonation for drinking water hygiene and its outweighing of more significant chemical risks from other oxidation/disinfection processes, not on the basis of precautionary minimization. The bromate example is a convincing example on how it is possible to regulate a chemical in drinking water, despite strongly differing views on its toxic potential, in a consensual manner on the basis of reasonable achievability and the corresponding three-dimensional rule of environmental hygiene as outlined in Section 3.14.1.2. Copper (group B in Table 2). Copper (in the form of its corrosion products in drinking water from copper pipes) was last assessed in 2004 by WHO (2008) in the form of a GVWHO of 2 mg l1 total Cu as based on local acute adverse health effects of Cu compounds from corrosion of copper pipes on the digestive tract and on their astringent taste (see Section 3.14.4.3.3). The WHO background document reports also on possible risk groups for liver toxicity by copper. A high exposure risk group for this so-called ‘idiopathic copper toxicosis’ (ICT) seems to be infants growing up in households providing themselves with acid water (Section 3.14.6.2.1) from a private well (Section 3.14.6.2.5) and copper plumbing (Section 3.14.6.2.7) (Dieter et al., 1999). Infants growing up under regulated conditions of drinking-water supply do not seem to be at risk (Zietz et al., 2003). Data collected directly in the customers, homes by Zietz et al. (2003) may allow some kind of rough risk calculation for a large city on the basis of the following estimations:

• • • •

0.6 ¼ frequency of copper installations, 0.1 ¼ frequency of (strongly buffered) drinking water with a pH r7.4, 0.03 ¼ frequency of weaning infants right from birth, and 0.01 ¼ frequency of average exposure to 4 2 mg l1 Cu in drinking water, a hypothetical threshold for early signs of hepatic copper toxicity (Dieter et al., 1999).

The total population risk or possible incidence IICT for an infant in a family with regulated central supply to contract early signs of ICT should equal the total product of these numbers, hence IICT ¼ 0.6  0.1 0.03  0.01 ¼1.8 105. WHO (2008) reflects on the existence of a yet undiscovered congenital condition of copper hypersensitivity, taking shape as ICT in infants only under the influence of very early and

distinctly higher than average environmental copper ingestion. This condition was discovered and extensively characterized since a long time in parts of India under the name of congenitally determined Indian childhood cirrhosis (ICC) (Tanner, 1998) and discovered later on as being very similar or identical to the ecogenetic copper storage disease endemic Tyrolean infantile cirrhosis (ETIC) as discovered by Mu¨ller et al. (1996). All three diseases, ICT, ICC, and ETIC, are clinically identical but could not be characterized by a gene defect common with Wilson’s disease, the most prominent and longknown genetic disorder of copper metabolism (Wijmenga et al., 1998; Mu¨ller et al., 2003). Despite a successful effort to establish an animal model to study the genetic basis and etiologic outcome of ICT or even of all three (then identical?) diseases themselves (Haywood et al., 2004), it was not possible yet to identify their genetic substrate although there are good arguments to support the existence of such substrate in the form of mild mutations or polymorphisms common to ICT, ICC, and ETIC, respectively, in variable extents (de Bie, 2007). This view was also supported by WHO (2008) in its actual background document on copper from 2004. Assuming as a worst case, a fraction of 5 102 for such congenital disposition in a genetically heterogeneous population as being exposed to high environmental copper, the IICT calculated above would reduce to less than 106. This risk would be of the same order as the one accepted for risks from exposure to nonthreshold carcinogens (see Section 3.14.4.3.2). Its management, however, differs distinctly insofar as with copper, each single case of an unfavorable or risky exposure is easily possible to trace back down to the exposed individual, whereas such risk management hardly ever seems possible regarding environmental exposure to nonthreshold carcinogens. Unfortunately, due to the low incidence of ICT and the strong homeostatic regulation of peripheral copper even an enhanced liver load, the possibilities to develop an early and easily accessible biomarker for this unexplained copper storage disease are very limited (Uauy et al., 2008). There was some speculation on a causative association between toxic free serum copper, as ingested in inorganic forms from drinking water or other sources, and incidence of Alzheimer’s disease (AD). The main argument given was oxidative stress by copper and its binding to ‘‘all the molecules involved with AD brain pathology’’ (Brewer, 2009), although it was clearly demonstrated in a strongly controlled clinical trial with mild Alzheimer’s patients that long-term oral intake of 8 mg Cu can be excluded as a risk factor for AD as based on biomarker analysis in cerebrospinal fluid (Kessler et al., 2009) Fluoride (group A in Table 2). Fluoride, like possibly copper or manganese in infants or subgroups thereof, has a rather steep dose–response curve in humans. It increases the metabolic turnover of the bone by directly inhibiting osteoblastic acid phosphatase activity (Krishnamachari, 1986; Lau et al., 1989). Like with geogenic arsenic and manganese or anthropogenic lead, it is very important to examine a new source for its fluoride content, since, as with geogenic arsenic, selenium, and uranium, levels relevant for health can easily be exceeded (Fawell and Nieuwenhuijsen, 2003; see also Section 3.14.6.2.5).

Drinking Water Toxicology in Its Regulatory Framework

A recent example for osteosclerosis due to endemic fluorosis from drinking water has been described from an area in southern Turkey with levels of F in drinking water above 1.2 mg l1. Skeletal fluorosis was evident in patients with mean urine fluoride levels of 1.27 mg l1 (0.22–3.99 mg l1), whereas a mean urine value of 0.6 mg l1 F (0.18–1.35) was not associated with this disease (Tamer et al., 2007). WHO (2008) in its 2004 background document, like in 1993, affirmed again its fluoride GVWHO of 1.5 mg l1 from 1984. According to WHO, exposure to moderately higher levels would result in dental fluorosis and higher levels would eventually lead to skeletal fluorosis. National standards worldwide should, as recommended by WHO, vary according to regional regular drinking water uptake und fluoride ingestion from other sources which in total should not exceed the Bd of 6 mg F per 60 kg person or 0.1 mg kg1 bm F (for children up to 10 years). There is an ongoing discussion all over the world on whether drinking water would be an appropriate communal carrier of artificially added 0.5–1.0 mg l1 fluoride in order to prevent dental caries on a societal level. For several reasons, many countries or communities do not practice drinking-water fluorination, although its health benefit seems to be proven. A socioethical reason says drinking water should never be misused as a carrier of pharmaceuticals. Environmental arguments against drinking-water fluorination are the environmental persistence and the high ecotoxic potential of this element; more so as at least 95% of the added fluoride would miss its target while nevertheless accumulating in regional water cycles. Treatment of drinking water for fluoride removal would be rather costly, as common technologies are not effective for this purpose. This is why raw water containing more than 1.5 mg l1 F usually is not exploited for drinking-water production, except if the source is irreplaceable and may then be treated by reverse osmosis or filtration over hydroxylapatite. In countries allowing fluorination of table salt (usually in the range of distinctly less than 1 mg NaF per 1000 mg NaCl) it is recommended not to drink either bottled water or drinking water containing more than 0.7 mg l1 F regularly. A comprehensive review on benefit and risks of artificial fluoride exposure has been published by the Australian National Health and Medical Research Council, covering English-language publications from 1996 to 2006 (NHMRC, 2007). Incidents from technical failure of F dosage in the water works have been described (WQRA, 2009; Gessner et al., 1994) and form an important argument against drinkingwater fluorination, at least in supplies without appropriate preventive control measures or safety plans. Detection of overdosing may be too late to inform overexposed and acutely intoxicated people in due time. Lead (group B in Table 2). Due to the general decline in atmospheric lead pollution in the course of the last few decades, drinking water from lead pipes became the most important single source of lead exposure. An initial estimate for the EU is that potentially 120 million people, corresponding to 25% of all domestic dwellings, are at risk from enhanced lead exposure via drinking water (Hayes and Skubala, 2009).

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WHO evaluated lead last in 2003 (WHO, 2008) and set its GVWHO for drinking water at 10 mg l1 Pb. Its derivation related on Joint FAO/WHO Expert Committee on Food Additives (JECFA)’s provisional tolerable daily intake (PTDI) for lead which since 1986 is 3.5 mg kg1 bm for infants and small children. In 1993, JECFA extended it to all age groups. The allocation of PTDI corresponding with this GV is 50% in 0.75 l of drinking water as consumed daily by a formula-fed 5-kg infant. JECFA’S PTDI was considered by WHO (2008) as being sufficiently protective for the CNS, the most sensitive organ for lead toxicity. It impairs a number of CNS functions, namely intelligence, performance of attention and reaction, behavior, and level of hearing threshold. Protection from these effects should protect from systemic carcinogenicity of lead also; this was evaluated last in 2007 as exhibiting just limited evidence, even at much higher than its very low yet neurotoxic doses in humans (Deutsche Forschungsgemeinschaft, 2007). According to a recent review (Wilhelm et al., 2010a), the model of taking 100 mg l1 blood–lead (PbB) as a warning threshold below which adverse neurotoxic effects may not be expected was rejected recently ‘‘on the basis of linear and nonlinear effect extrapolation.’’ The authors state ‘‘it now appears to be certain that negative correlations between PbB and neuropsychological parameters also exist at PbB-levels below 100 mg/l.’’ Moreover, oral lead is suspected to affect onset of male and female puberty as indicated by several biological and anatomical parameters in three different populations already at PbB levels distinctly lower than 100 mg l1. Lead exposure through drinking water can easily be avoided. From the view of drinking-water hygiene, the conclusion is to prevent the risk groups – pregnant women, infants, and small children – from ingesting higher lead intakes than inevitable for the time being. They should consume food which had no contact with drinking water from lead pipes during its preparation. A reference PbB level to indicate, if exceeded, specific sources of lead exposure such as drinking water from lead pipes, has been set at 35 mg l1 Pb for infants and small children and 70 or 90 mg l1 for adult women and men, respectively (Umweltbundesamt, 2009b). The high toxicity of lead for humans was never in doubt in newer human history. Unfortunately, its toxic potential is too high despite exceptional technical performance for water plumbing. This was the reason to prevent its use for this purpose as early as 1790 (Duke of Wu¨rttemberg, Letter from 24 December 1790, on behalf of a new well order in which he insists on using iron or argil instead of lead because the latter could have adverse consequences for health) and to ban it formally in 1878 (Ko¨niglich-Wu¨rttembergisches Ministerium des Innern, 1878) both in the then South German Duke and later Kingdom of Wu¨rttemberg. Manganese (group A in Table 2). The health-based GV of WHO for manganese (Mn) is 0.4 mg l1. Its derivation dates from 2004 and is based on the upper limit of adult manganese intake of 11 mg d1, coming from dietary studies. This NOAEL was used by WHO (2008) as a PoD since ‘‘it is not believed that this amount of manganese in the diet represents an overexposure to the element.’’ Given this belief, 11 mg d1 would represent the upper limit of an AROI of Mn.

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The GV of WHO has the drawback to be based on a toxicologically unqualified AROI of mainly unsoluble Mn(IV) as it prevails in food, albeit qualified animal studies using drinking water to dose soluble Mn2þ are available. Moreover, an alternative approach to define a health-based GV for manganese should examine more closely the neurotoxic potential of Mn as known from workplace epidemiologic studies (Roels et al., 1999) and perhaps from a study with school children (Gongqiao et al., 1995). A 10-year-old boy, after having ingested a well water containing B1.1 mg l1 total Mn for at least 4 months and up to 5 years, exhibited strongly enhanced Mn levels in blood, urine, and hair. Neurological examinations revealed distinctly less than normal verbal and visual performance. The authors concluded on exposure via well water (for years?) to Mn as the main cause, but could not completely rule out exposure to other ‘toxic wastes’ (Woolf et al., 2002). Oral MnCl2, in a 240-day study with adult rats, changed their motor behavior at 10 mg kg1 bm Mn2þ and more (Bonilla, 1984). Young experimental animals are more sensitive to early intoxication by soluble Mn than adult ones. In a 2-year study with rats, early signs of Mn2þ intoxication were seen in young but not adult animals in the form of enhanced levels of the enzyme monoaminoxidase (Leung et al., 1982; Lai et al., 1982), a biochemical indicator fitting well with the systemic neurotoxicity of Mn in humans, called ‘manganism’ and exhibiting in late states symptoms similar to Parkinson’s disease. Higher sensitivity of young versus adult rats for the neurotoxic effects to orally ingested MnCl2 was confirmed in a short-time high-dose study (Dorman et al., 2000). Extensive information on the toxic and especially neurotoxic properties of Mn for primates (but without information on their dependence on chemical speciation) has been reviewed by Burton and Guilarte (2009). All neurological effects of Mn2þ as it is present in most (oxygen-poor) groundwaters were linked to minimal LOAELs of about 10 mg kg1 bm Mn2þ, corresponding to an estimated NOAEL of 1.4 mg kg1 bm Mn2þ seemingly suitable to be used as PoD (see Section 3.14.4.3.1). As Mn is an essential trace element with only a small margin between essentiality and toxicity, the intra-plus interspecific extrapolation should use a smaller than usual total factor (WHO, 2008). With a total factor of 10 to extrapolate from the PoD directly on a tolerable human Bd, its final number is Bd ¼ 10 mg 70 kg1 bm, a value identical with the earlier-mentioned upper limit of the AROI for soluble plus insoluble Mn but with the advantage of being based directly on toxicological

and not nutritional data. Moreover, this more toxicologically derived Bd opens the possibility of calculating a separate maximal value for Mn2þ in drinking water for preparing infants’ formula and for small children because the developing brain, similar to what is known from perinatal lead toxicity, is supposed to exhibit a special sensitivity for the neurotoxic potential of soluble Mn species like Mn2þ, the species to prevail in groundwater from single wells for drinking water, but not in de-manganized (oxygenated) water from public systems. When 20% of the present toxicological Bd are allocated on 0.75 l of drinking water for a child weighing 5 kg, a specially protective GV for infants of about 0.2 mg l1Mn2þ is obtained. The high potential for maternal and early life exposure to high levels of neurotoxic Mn2þ is underlined by findings in Bangladesh on a negative correlation between As and Mn with the consequence that waters seemingly safe with regard to As may actually be unsafe due to the presence of high soluble Mn which might even have provoked health effects erroneously ascribing them to arsenic (Ljung et al., 2009). This situation may by accentuated with weaned infants since they resorb Mn2þ from mother’s milk in distinctly smaller fractions than from liquefied dry milk. Natural radionuclides (group A in Table 2). In the third edition on radiological aspects of its guidelines for drinking-water quality, WHO (2008) proposes guidance for levels of radioactivity corresponding to a recommended reference dose level (RDL), that is, RDL ¼ 0.1 mSv a1 for drinking water as based on adult dose coefficients (DCs) for each radionuclide present in the specific sample. This RDL corresponds to an IZ (see Section 3.14.4.3.2) of about 104. If several nuclides are present in the same sample, the addition rule to cope with similar joint action would apply (see Section 3.14.4.3.4). WHO states that ‘‘the higher age-dependent dose-coefficients calculated for children (y) do not lead to significantly higher doses due to the lower mean volume of drinking water consumed by infants and children.’’ However, DCs for common natural nuclides in drinking water (Ra-228 and Ra226, and the decay products of Rn-222, namely Po-210 and Pb-210) exhibit much higher differences between adults and young children than their body mass-normalized intakes of drinking water (ICRP, 1996). The coefficients for two groups of children (0–1 and 1–2 years of age) and adults (417 years) are compiled in Table 4. The mean deviation of DCs between the one or the other children’s age group on the one side and adults on the other comprises factors of about 22 (0–1 a) and about 6 (1–2 a),

Table 4 Dose coefficients (DCs, (mSv kg1 bm)) as determined by ICRP (1996) for adults and two groups of children to calculate committed effective doses of some drinking-water-relevant natural radionuclides after oral uptake Age group

DC for Ra-228

DC for Ra-226

DC for Pb-210

DC for Po-210

0–1 a (infants) 1–2 a 417 a Ratio of highest to lowest DC

30 5.7 0.69 43.5

4.7 0.96 0.28 16.8

8.4 3.6 0.69 12.2

26 8.8 1.2 21.7

The mean deviation of DC between the one or the other children age group on the one side and adults on the other comprises factors of about 22 (0–1 a) and about 6 (1–2 a), whereas the respective drinking-water uptakes per person ( ¼ unit of exposure) vary only by maximal factors of 4 (0.5 l per 4-kg infant vs. 2 l per 70-kg adult) and 2 (1.0 l per 10-kg child vs. 2 l per 70-kg adult), respectively.

Drinking Water Toxicology in Its Regulatory Framework

whereas the respective drinking-water uptakes per person (¼ unit of exposure) vary only by maximal factors of 4 (0.5 l per 4-kg infant vs. 2 l per 70 kg adult) and 2 (1.0 l per 10-kg child vs. 2 l per 70-kg adult), respectively. It seems therefore justifiable to calculate effective doses from natural radioactivity in drinking water separately for children to see up to which levels of activity the RLD would yet be respected. The data not only from a comprehensive survey on natural radioactivity in drinking water from geologically suspicious areas but also from many unsuspicious ones in Germany support this view (Federal Office for Radiation Protection/Bundesamt fu¨r Strahlenschutz, 2009). If exclusively adult DCs are applied to transform measured activity values into effective doses, maximally 10% of the sampled public utility uses contain enough activity to ingest from Ra-228, Ra-226, Rn 222, Po-210, Pb-210, U-238, and U-234 to exceed the RDL of 0.1 mSv a1. When taking into account the higher sensitivity of infants for direct (genotoxic) DNA damage not only by chemicals (see Section 3.14.4.3.2) but also by irradiation, up to 22.5% of all sampled utilities would contain very high activity levels from these four radionuclides if the RDL of 0.1 mSv a1 should be used with respect to infants as well. The exposure scenarios assumed a yearly uptake of 170 l of drinking water by infants and of up to 730 l by adults, respectively. Nitrate (group C in Table 2). When evaluating nitrate in drinking water, one has to differentiate between its acute and chronic toxicity. Acute toxicity can be seen when a significant percentage (420%) of nitrate in the stomach is microbiologically reduced to nitrite and the latter then resorbed. Once in the blood, nitrite is able to oxidize Fe(II)hemoglobin (Hb) into Fe(III)Hb, the so-called methemoglobin (metHb). MetHb is unable bind oxygen and transport it from the lungs to peripheral tissues. If the metHb fraction becomes higher than 10%, the first signs of oxygen deficit may become apparent in peripheral tissues. The most probable target individuals for internal asphyxia from exposure to nitrite, called ‘methemoglobinemia’, are exposed infants since they exhibit, at the same time, low levels of physiological cytochrome b5-reductase in the blood and low gastric acidity, the latter allowing for survival of nitrate-reducing bacteria in the stomach. Although endogenous nitrate/nitrite may additionally arise in significant amounts also from internal oxidation of nitric oxide formed in macrophages and other cell types during infection (Speijers and van den Brandt, 2010), WHO (2008) in its 2007 background document on nitrate/nitrite assessed GVWHO ¼ 50 mg l1 nitrate as sufficiently protective for infants against methemoglobinemia. Continuous exposure to more than this GVWHO results in significantly higher than background (2% of Hb) metHb in the blood (Sadeq et al., 2008; Winton et al., 1971). If nitrate and nitrite are present simultaneously in drinking water, the addition rule for similar joint action applies (see Section 3.14.4.3), and it is recommended that nitrite should not exceed its GVWHO ¼ 3 mg l1 because its metHb-forming potential is estimated to exceed the one from nitrate by about 10-fold. Nitrate levels between 50 and 100 mg l1, according to WHO (2008), are safe only under close medical supervision and only if the nitrate-contaminated water is known as being microbiologically safe.

403

Chronic toxicity of nitrate has three aspects, its systemic toxicity, its goitrogenic properties, and its potential as indirect chemical precursor of carcinogenic N-nitroso-compounds as formed from amines and nitrite. Systemic chronic toxicity of orally ingested nitrate was detected in a long-term rat study in the form of growth inhibition at the earliest above a NOAEL of 370 mg kg1 bm nitrate. WHO (2008) confirmed this NOAEL as PoD to keep on its older ADI of 5 mg kg1 bm Na-nitrate or 3.7 mg kg1 bm nitrate ion, corresponding to a Bd of 259 mg d1 in a 70-kg person. With a relative source contribution of 10% in 2 l of daily drinking water, a health-based GV of 13 mg l1 for chronic toxicity of nitrate is obtained, hence just a quarter of the maximal value based on acute toxicity for babies. As nitrate 410 mg l1 is not normally a geogenic constituent of drinking water, its tolerability above this should be judged referring to its occurrence as an environmental contaminant (see Section 3.14.5). Its actual oral intake with drinking water due to agricultural over-fertilization in Europe amounts to 25% instead of 10% of the total daily intake (EFSA, 2008). Moreover, nitrate is the only environmental contaminant worldwide whose current GVWHO and even actual concentration in many ground- and drinking waters come very close to values not to be exceeded in order to protect a large group of the population (weaned babies) from acute health effects. The conclusion is that environmental contamination by this agricultural nutrient has exceeded since long time and in many parts of the world at any hygienically tolerable proportion or scale. The goitrogenic properties of nitrate were ascribed to its competition with iodine for transport into the thyroid. According to WHO (2008), this effect is weak or absent at or below its GV of 50 mg l1 nitrate if daily ingestion of iodine is sufficient (150–300 mg d1). However, already at 90 mg l1 nitrate exposure struma incidence was increased highly significantly in pregnant women and, possibly, in their children (Gatseva and Argirova, 2008). On the other hand, WHO in its Food Additives Report 50 states that nitrate, instead of inhibiting iodine transport, may stimulate it in humans even at threefold ADI exposure. A ‘‘further concern relating to the metabolism and toxic potential of dietary nitrate and nitrite is the potential formation in vivo of carcinogenic N-nitroso compounds from nitrite, or the derived nitrosating species, N2O3 and N2O4, and dietary amines’’ (Speijers and van den Brandt, 2010). Volunteers under a diet regimen rich in dietary amines excreted much more N-nitrosamines, especially N-nitroso-dimethylamine, if they ingested nitrate simultaneously at ADI levels (Vermeer et al. 1998). Due to complex interaction between exposure and cofactors such as meat intake (De Roos et al., 2003) or vitamin C and chewing gum (Mirvish et al., 1995), epidemiological studies on the relation between drinking-water nitrate and cancers provide, if any, only weak associations with either direction, positive or negative (Ward et al., 2005). A recent study by Chiu et al. (2007) on nitrate and bladder cancer is devoid of any individual information on the kind and source of nitrate exposure. Above all, it has none either for the preceding latency period for bladder cancer (20–40 years) or for the much more significant nitrate sources than drinking

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Drinking Water Toxicology in Its Regulatory Framework

water, which in this study exhibited a mean difference between maximal and minimal nitrate values of only 0.4 mg l1. Furthermore, the study did not consider smoking and forgot to ask how frequently the test persons went to the restroom or how much drinking water they had been ingesting, albeit all three parameters could influence bladder cancer incidence dramatically in one or the other direction (Jiang et al., 2008). Again, like with regulation of DBPs in drinking water, the only reasonable regulatory-toxicological conclusion from this complex situation – on the one side, rather conclusive biochemical results and on the other side very inconclusive epidemiologic data – is to minimize exposure wherever this seems reasonably feasible but not to wait until nonambiguous scientific conclusions may or may not be drawn from this huge and ever-increasing data pool of very heterogeneous quality. Uranium (group A in Table 2). Water-soluble uranium (UO2 2þ ) was last assessed by WHO in the year 2005 (WHO, 2008) in the form of a health-based GVWHO referring to toxicological work published until 2002, including information published up to 2005, on how to eliminate uranium from drinking water. The assessment used an LOAEL from a rat study as a threshold-PoD (see Section 3.14.4.3.1) for renal toxicity. The WHO set its GVWHO at 15 mg l1 U, allocating 80% of the corresponding Bd of 36 mg kg1 bm on 2 l of drinking water per day for a 60-kg person. UO2 2þ predominates at low concentration in the presence of Ca2þ in the form of Ca2UO2(CO3)3 (Bernhard and Geipel, 2007), being distinctly less toxic than UO2(CO3)4 as shown with rat renal epithelial cells (Gouget et al., 2005). A recent assessment (Konietzka et al., 2005) with additional consideration of human data by Kurttio (2005) and experimental data from rabbits (Gilman et al., 1998b) leads to the derivation of a somewhat lower TDI than that proposed by WHO, mounting to 0.3 mg kg1 bm and corresponding to a Bd of 20 mg d1  70 kg bm. Its derivation starts from the rabbitPoD of 50 mg kg1 bm (instead of 60 mg kg1 bm from rats) and further assumes, as supported by kinetic data from animals as cited in Konietzka et al. (2005) and also by human data from Zamora et al. (2003), a higher resorption of uranium by a factor of 5 in humans (1.5%) as compared with rabbits (0.3%). The human data from Kurttio (2002) indicated a range of 6–60 mg d1 for a possible Bd when regarding the relative change of the creatinine-normalized calcium and phosphate levels (fractional excretion’) in blood and urine and their dependence on U uptake with drinking water. By means of 10 indicators for kidney toxicity of uranium, Kurttio et al. (2006) demonstrated a lowest no-observed effect level linked to uptake of U with drinking water as being possibly located around 100 mg l1, corresponding with a calculated human NOAEL of 200 mg d1 in a study with 193 adults. Prat et al. (2010) explain the ‘‘lack of significant health effects’’ of U in the Kurttio et al. (2006) study by low resorption rates of U from drinking water since UO22þ should prevail there are two calcium-dependent species, Ca2UO2(CO3)32 (Prat et al., 2010). Similar findings on a low resorption of these complexes from the matrix ‘mineral water’ had been reported already earlier by Bernhard and Geipel (2007). Nevertheless,

when extrapolating an NOAEL of 200 mg d1 U with EFd ¼ 10 on sensitive humans, a Bd of 20 mg d1 is obtained which corresponds well with a Bd as based on data from Zamora et al. (1998) on a slight increase of lactate dehydrogenase (LDH) and glucose in the urine of their most sensitive study subjects. The maximal contribution of uranium exposure from paths other than drinking water in the ecologic studies by Kurttio et al. (2002, 2005, 2006) and the one by Zamora et al. (1998) should not have been higher and, rather, was less than 50%. Under this assumption, the above estimations of a Bd from human data and the one from the rabbit study agree within a factor of 2. Given that, the human data seem more reliable to derive, eventually, a health-based GV of 10 mg l1, allowing for concurrent ingestion of another 20 mg d1 portion of uranium with food. Taken together, the final result of this assessment is not different from that by WHO (2008) since it similarly corresponds to a Bd of 40 mg d1 or a TDI of 0.6 mg kg1 bm applicable on total ingestion of uranium from food together with drinking water. The actual GVUBA of 10 mg l1 seems, however, somewhat better founded than the somewhat higher GV of WHO since the former mainly refers to recent human data (including higher resorption) which prevail under reallife exposure conditions and relies especially on a more realistic 50:50 instead of 80:20 splitting of uranium exposure between food and drinking water. In order to detect higher than average uranium exposure, such as from a private well, it is recommended to have an internal indicator of enhanced exposure. The Federal Environment of Germany proposes for this purpose a reference value of 30–60 ng l1 U in the urine as based on an upper normal level of 30 ng l1 U (Federal Environment Agency/ Umweltbundesamt, 2005). The upper limit of this reference range might be exceeded at permanent ingestion of 10 mg l1 U with drinking water plus normal U uptake from food (Kurttio et al., 2002).

3.14.6.3.2 Organics Perfluorinated compounds (PFCs; group C in Table 2). Linear perfluorinated tensides (PFTs) have not been assessed yet for drinking water in the form of a health-based GV by WHO (2008). The two most important PFTs, perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS), are used as surfactants in a variety of industrial processes and consumer products. Due to their physical–chemical properties, they spread and persist in environmental media, wildlife, and humans, in the latter mainly as a consequence of dietary intake (Fromme et al., 2008). PFOA and PFOS have elimination half-lives of several years in humans. Biomonitoring studies proved a clear relation between plasma load and drinking water contamination. Hepatotoxicity, developmental toxicity, immunotoxicity, hormonal effects, and also a weak carcinogenic potency in animal studies have been described as main endpoints of health concern. Their potential to contaminate raw water resources and drinking water up to health-related values has recently been summarized by Wilhelm et al. (2010b).

Drinking Water Toxicology in Its Regulatory Framework

Exposure to PFOA via contaminated drinking water was observed at water districts in Little Hocking, Ohio, USA at up to 3.5 mg l1 (Emmett et al., 2006), in the German city of Arnsberg at up to 0.64 mg l1 (Skutlarek et al., 2006; Ho¨lzer et al., 2008), and in Minnesota, USA, at up to several mg l1 (MDH, May 2009). PFOA can be effectively removed from drinking water by percolation over activated carbon (Wilhelm et al., 2008). PFTs leach from contaminated soils and firefighting foams under areas contaminated by fire brigades either by training or by fighting against conflagrations. Wastewater, either from chemical industry or from communal wastewater disposal plants, causes diffuse aquatic contamination. Sums of various PFTs in drinking water from bank filtration along the rivers Rhine and Ruhr amount mostly to between 0.05 and 0.15 mg l1 (Wilhelm et al., 2010b). In June 2006, the German Federal Advisory Board on drinking water (TWK) proposed the first worldwide healthbased GV for safe lifelong exposure at 0.3 mg l1 to sums from PFOA and PFOS, assuming effect additivity for both compounds by the mode of similar joint action (TWK, 2006). In accordance with this addition rule, the sum of all quotients from a measured concentration and the respective healthbased reference value (HRIV or GV, see Section 3.14.6.3.2 or 3.14.2.5.2) is deemed not being 41. For PFOA, the GV of the German TWK represents about 10% in 2 l of drinking water d1 70 kg bm of the TWK’s provisional Bd for PFOA which equals 7 mg d1. This Bd was derived from an estimated NOAEL of PFOA for reproductive toxicity in rats by applying two extrapolation factors (EFs) of 10 each for inter- and intraspecies biologic variability and an additional SF of 10 to cope with uncertainties due to the much longer elimination half-time of PFOA in humans than in rats. Regarding PFOS, TWK reverted to an NOAEL of 0.025 mg kg1 bm for proliferation of peroxisomes in chronically exposed rats. This toxic endpoint was considered by TWK to be sensitive enough for being extrapolated immediately (with an intraspecific EF ¼ 1) on sensitive humans. On the other hand, due to the exceptional persistence of PFOS in humans as compared to rats, TWK applied an exceptionally high interspecific EF ¼ 30 as supported by toxicokinetic results from animals and humans. The Bd calculated this way for PFOS was 0.083 mg d1 and eventually turned out to be very similar to Table 5 Compound

PFBA PFPA PFHxA PFHpA PFOA PFBS PFPS PFHxS PFHpS PFOS

405

the one (0.1 mg d1) for PFOA. More details have been published on behalf of the German Federal Environment Agency (UBA) by Dieter (2007). It should be mentioned that published GVs for PFOA in drinking water differ considerably, the lowest (0.04 mg l1) coming from the New Jersey (NJ) Department of Environmental Protection (Post et al., 2009), while Tardiff et al. (2009) proposed a lifetime safe drinking-water equivalent level (DWEL), whose 10% allocation on 2 l d1  70 kg bm would correspond to a GV of about 1 mg l1 of PFOS or PFOA, resulting in a maximal value comparable to those from US-EPA (2009), the German UBA, or the Drinking Water Inspectorate of England and Wales (DWI, 2007). The most significant drawback of the very low NJ maximal value for PFOA seems to be inadequate extrapolation of rat serum PFOA levels on humans (Tardiff, 2009). Moreover, if ever the NJ maximal value would be toxicologically sound, then exposure from diet would primarily need to be reduced (Zhang et al., 2010). Shorter-chained PFTs (C4–C7) are less persistent than the C8 analogs and hence are introduced as replacements of outphased PFOA and PFOS. Some of these will, possibly, be the main future contributors to total PFT levels in raw and drinking water, since they dissolve better in water and hence are more difficult to remove by percolation over activated charcoal. An approach to assess shorter-chained PFCs and their mixtures is explained by Wilhelm et al. (2010b). It refers to a proposal launched by the Federal Environment Agency of Germany (Umweltbundesamt, 2009a) to provisionally assess the toxic potential of any single PFC mainly on the basis of its measured or anticipated elimination half-life from the human body and to decide accordingly on a possible maximum level value for the same compound in drinking water (called HRIV in the case of a provisional health-related indication value, or GV in the case of a scientifically based guide value, see Section 3.14.6.3.2 or 3.14.2.5.2). The range of possibly tolerable concentrations for any single PFT in drinking water extends between a minimal GV of 0.3 mg l1, as derived by TWK for the very persistent PFOA and PFOA or their sums, and a maximal GV of 7 mg l1 applicable only on the easily excreted PFBA as derived in February 2008 by the Minnesota Department of Health (MDH, 2009) as explained by Wilhelm et al. (2010b). Table 5 lists all HRIVs and GVs for PFTs as applied currently in Germany (Umweltbundesamt, 2009a). Additionally,

Health-related indication value, guide values, and long-term precautionary quality goal in drinking water for PFTs Long-term precautionary value (PV; precautionary quality goal) for each single PFC and their sums (Section 3.14.2.4.1)

0.1 mg l1

Health-related indication value (HRIV) (Section 3.14.6.3.2)

Guide value (GV) (Section 3.14.4.3.1)

F 3 mg l1 1 mg l1 0.3 mg l1 F 3 mg l1 1 mg l1 0.3 mg l1 0.3 mg l1 F

7 mg l1

0.3 mg l1

0.3 mg l1

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Drinking Water Toxicology in Its Regulatory Framework

the long-term precautionary quality goal for any PFT and their sums in drinking water is shown. All numbers are supported also by the German TWK. If shorter-chained PFTs than PFOA and PFOS are present concurrently in drinking water, the addition rule applies as well, assuming again the effect additivity by the mode of similar joint action (see earlier discussion). The health-based reference value on which the quotients to be summed up are based upon is either the GV (PFOS, PFOA, and PFBA) or a provisional HRIV (all others). Nitro- and nitroaminocompounds (NAC; group C in Table 2). Nitro- and nitroaminocompounds (NACs) are important industrial chemicals with a broad area of application. They serve mainly as readily available intermediates for dyes, pharmaceuticals, synthetic materials, and other applications. On their own, they are used as solvents, explosives, and pesticides. NACs are also emitted from many military areas or hot spots of abandoned explosives production sites. Due to their hydrophilic properties and relative stability, they may penetrate into aquatic systems and thus also into drinking water resources. The environmental fate of NACs is determined by biodegradation and photolytic degradation and results mainly in the formation of equally amino nitroaromatics and amino aromatic compounds. The WHO (2008) has not recommended drinking-water guideline values for any NAC except recently for nitrobenzene, for which a GV of GVWHO ¼ 8 mg l1 for cancerous and noncancerous endpoints was published in 2009. GVs for 19 NACs, including nitrobenzene, have been proposed by Wollin and Dieter (2005) and adopted as an official guide or advisory values in Germany (UBA, 2006). The threshold approach (Section 3.14.4.3.1) was used for 12 of them, resulting in corresponding GVs between 0.7 mg l1 (for nitrobenzene, similar to the 2009 GV from WHO) and 175 mg l1 (for octogen or HMX). Estimates of excess lifetime cancer risk (see Section 3.14.4.3.2) were used for evaluating five NCs, resulting in GVs between 0.05 mg l1 and 2 mg l1, depending on the individual NAC. GVs for the remaining 3- and 4-nitrotoluene were 10 mg l1 and 3 mg l1, respectively, referring to their organoleptic properties. Human exposure surveillance for those NACs which exhibit an aromatic moiety is routinely possible by monitoring hemoglobin adducts (HBAs) as proposed by Neumann et al. (1995), Sabbioni et al., (1996), and Thier et al. (2001). Although HBA levels do not immediately reflect the genotoxic/ carcinogenic potential or potency of a chemical under conditions of real exposure, they correlate usually much better with the internal biologically active dose than any assessment of external exposure. New analytes – and how to assess them? The evaluation in terms of environmental hygiene and human toxicology of substances which enter adjoining environmental compartments from (more or less) open applications or solid materials or formed there as metabolites is not legally regulated, except perhaps for certain metabolites from agricultural pesticides (Steinha¨user and Richter, 2002). Such new analytes very often have a high affinity to the aquatic environment, are very mobile there, and many of them are also persistent. The very polar ones, especially, may enter water sources by bank filtration and are difficult to retain

from drinking water, at least by nature-oriented treatment or by percolation over activated charcoal (Ternes and Joss, 2006). Their human toxicological potential can be prognosticated as being moderate to low, although the data to base such judgment when looking for single compounds are often sparse or missing completely (see Section 3.14.4.2). Sparseness of data for health-based evaluation of new analytes or emerging contaminants in drinking water may be concluded from a compilation by Schriks et al. (2010). Although Schriks et al. propose many GVs for drinking water, their rationales differ strongly by the quality of database. The underlying data originate very often from very old studies, short-time experiments, or exposure modes inapt to asses exposure from drinking water (gavage, inhalation, and injection). Extrapolation (rather: safety) factors to cope with such serious experimental and conceptual drawbacks are always more or less speculative and do not really help to describe sustainable drinking water for the future. On the other hand, missing or bad data do not automatically imply health risks, but they should, if possible, be completed in order to define or provide regulatorytoxicological certainty and safety. Up to this moment, it is necessary to have criteria available to evaluate the presence of substances in drinking water in terms of their toxic potential for humans even on a patchy database. These criteria must be allocated to concentrations that are at any rate equal to, but possibly lower than would otherwise be a sound health-based GV. To meet this goal, two systematic concepts exist to provisionally assess new analytes with patchy database: 1. an approach, called TTC concept, to provisionally define a threshold of toxicological concern in function of structural alerts or analogies as recently explained for application on nonrelevant metabolites from pesticides by MelchingKollmuss et al. (2010) and 2. a tiered experimental and exposure assessment approach (Dekant et al., 2010). The first way leads to a toxicologically safe, albeit provisional concentration (DWRTTC) for the analyte under question in drinking water; the second (and more laborious) one would open the possibility to assess, on a case-by-case basis, any new analyte above its DWRTTC and present in drinking water. Case-by-case risk assessments are the approach of choice to evaluate past anthropogenic contaminations of resources and environmental media during required sanitation (Section 3.14.2.4.3). In order not to risk such scenarios, quality of all waters for human use must strictly satisfy precautionary principles. A more effective (and less laborious) way to keep media and resources for the future in a sanitized (close to nature or unobjectionably safe) state is to avoid cause and need for case-specific assessments from the beginning. This situation in mind, a third approach may be recommended as a pragmatic and, at the same time, sustainable default approach (UBA, 2003).At its core are five provisional HRIVs (formerly called HPVs – health-related parametric values) to pragmatically assess the presence of new analytes on a patchy human toxicological database for purposes of healthrelated long-term chemical drinking water hygiene. These values eventually turned out to cover practically the same

Drinking Water Toxicology in Its Regulatory Framework concentration range 0.01–3 mg l1 as the four lower DWRTTC (ILSI, 2005) but the HRIV concept from UBA (2003) is simpler and therefore easier to handle. Depending on the completeness and informative value of the toxicological database for defined endpoints (Table 6), the HRIV of any new analyte with a patchy database is set for lifelong exposure at one of four concentration steps between 0.1 and 3.0 mg l1, each being a factor 3 away from the neighboring lower and higher one. The maximal HRIV is the same as the one derived by means of the TTC concept for Cramer’s toxicity class III, which according to Dekant et al. (2010) corresponds to 3 mg l1 instead of 4.5 mg l1 as was proposed yet in Barlow (2005). Table 6 Health-related indication values (HRIVs) as defined provisionally by the toxicological ‘default’ approach, called HRIV approach to assess presence of trace concentrations of new analytes in drinking water Designation

Numerical value (mg l1)

Explication

HRIV1

0.1

HRIV2

0.01 to o0.1

HRIV3

0.3

HRIV4

1.0

HRIV5

3.0

HRIVQSAR

0.1–3

HRIV6

43

For contaminants known to exhibit no genotoxic potential or if not tested as such in the absence of any other information For contaminants known to exhibit strong to weak genotoxic potential For contaminants known to be devoid of genotoxic potential, additional data on germ cell toxicity, immunotoxicity, and reproduction toxicity do not support any lower value Similar to HRIV3; additional data from at least one study on subchronic toxicity do not support any lower value Similar to HRIV4; additional data from the only available study on chronic toxicity do not support any lower value. An HRIV within this range may be set for a contaminant referring on known toxic potentials of similar structures (or elements thereof) as concluded by analogy or from QSAR considerations Similar to HRIV5; additional data on chronic toxicity do not support any value oHRIV5. Completeness of database may also allow to scientifically derive values4HRIV5. In most cases, an HRIV6 may be set as a scientific guide value (GV)

An HRIVx is lifelong tolerable and increases with completeness of database and decreasing severity of the toxic endpoint if not experimentally tested.

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Another very low HRIV is reserved for analytes to exhibit primarily a strong in vitro genotoxic (DNA-damaging) potential as detectable in routine Ames tests. Such analytes are recommended by UBA (2003) for being tolerated depending on their in vitro genotoxic potential at 10–100 ng l1 only. HRIVs have the regulatory-toxicological function of healthrelated, not absolute precaution. To define them in a first step does not normally ask for analyzing heterogeneous expert systems or for screening any QSAR data files, as is the case when using the TTC approach: HRIVs are easier to define for a specific analyte than an appropriate DWRTTC. However, if ever an HRIV definitely appears as too low for practical purposes and, if in a second step, the structure or elements to be assessed are not found in pertinent QSAR data files, the first-step HRIV should always be examined for plausibility or even tested in vitro before taking irreversible regulatory measures. In case of doubt, it is more reasonable to refer such measures on the ALARA-principle than on a toxicological hazard referring to no hazard but data gaps (see also Section 3.14.4.3.4). To summarize, the HRIV approach is nothing else but a kind of pragmatic TTC concept as designed specifically for drinking water. Its regulatory-toxicological reliability is comparable or even better. The rationale for all five HRIV levels and their gradation is UBA’s own and literature-based international experience from assessing many well-described hydrophilic (highly polar) drinking-water contaminants of very different grades of toxicity, both support the continued assumption that such assessments also in future never would result in lower (but mostly higher) GVs than the respective HRIV for the same or similar toxic endpoint. It is an approach to combine knowledge on toxicological relevance with knowledge on the practical ‘relevance’ of hydrophilic environmental contaminants for drinking water before or after treatment. Such a pragmatic approach to bridge the gap between problem identification and problem solving was asked for lastly by Fawell (2008). Application of the HRIV-approach on side products of oxidation and disinfection. Drinking water, containing new analytes below endpoint-specific HRIVs, according to UBA (2003) may also be considered as being sufficiently safe for toxicologically relevant oxidative transformation products, possibly being formed from new analytes during oxidative treatment steps. However, even drinking water, where one or several new analytes may be detected above an endpoint-specific HRIV, is suited for human consumption without imminent health risk. Only in special cases (relatively high concentration of a new analyte), examining the finished water for transformation products after oxidative treatment could be suggested if there are no further treatment steps to eliminate, absorb, or degrade a possibly hazardous polar compound. Application of the HRIV approach on pharmaceuticals in drinking water. The HRIV approach is specially suited for being applied for assessing from the point of regulatory toxicology, the presence of residues from pharmaceuticals and their metabolites in drinking water. Their major source of input into aquatic environments and from there via wastewater into reused drinking water is their excretion after intended use by humans (Ternes, 2007; Focazio et al., 2008). The human database for this group of new analytes at first glance seems quite complete, as their toxic potential normally is assumed of

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being linked to therapeutic dose (Webb et al., 2003). The underlying assumptions on the informative value of therapeutic dose on chronic toxicity may or may not be true. In any case, clinical tests as linked to therapeutic doses do not inform on effects possibly to observe at lower doses than normally used in preclinical studies with experimental animals. There are many examples (mostly old compounds) for the absence of systematic toxicological examination of typical systemic endpoints such as chronic toxicity, neurotoxicity, reproductive and developmental outcome, or even cancer. Moreover, serious side effects (expected and unexpected ones) in patients, usually occurring in frequencies around 1%, may seem acceptable when weighed against the drug’s benefit and the possibility to exclude any involuntary risk by accordingly informing the therapeutic target population. Most probably, such populations are incomparably much smaller than any population exposed to a large supply of drinking water. When taking the possibility into account, that unobserved effects in fact may be present but just have been overlooked due to inadequate testing, possible Bd and maximal values to assess pharmaceuticals and their metabolites in drinking water would have to be derived by using very large margins of safety between a Bd and any experimental outcome, amounting easily to 1000, 3000 or more (Bull et al., in press; personal communication by J. Cotruvo), signaling then such a high incompleteness of the database that assessment of pharmaceuticals in drinking water should preferably be performed using a precautionary default-approach. Only exceedance of an HRIV would then have to result in measures of management to either complete the respective compound’s database or reduce exposure via drinking water. The basic idea behind is to preferably define scientifically the presence of safety as long as scientific proof for absence of toxicity is not required or not yet possible. Application of the HRIV approach on nonrelevant metabolites of pesticides in drinking water. A recommendation similar to UBA (2003) but specified for a group of unregulated new analytes, being called nonrelevant metabolites of pesticides in the European Union, has been published (UBA, 2008) and its motivation and regulatory context explained (Dieter, 2010). The parent pesticides and their relevant metabolites are subject to an extensive admission process, whereas the database of the remaining, so-called nonrelevant metabolites is more patchy. Nevertheless, across the board, their toxic potential can be judged as being either lower or better documented than in the case of new analytes from other sources. This is why only the two upper HRIVs (1 mg l1 and 3 mg l1) are recommended to assess the presence of these new analytes for lifelong exposure from the point of drinking-water hygiene. Pesticides and their relevant metabolites. Many countries all over the world, except members of the EU, regulate agricultural pesticides and their metabolites in drinking water as if these were present there for functional reasons by assigning them strictly health-related maximum values as was explained by Hamilton et al. (2003). They model themselves mainly on WHO, although WHO, since 1984, never ceased to qualify its GVs as describing a minimal and not an optimal drinking water quality for lifelong consumption (see Section 3.14.2.2). This view of WHO goes well with the EU members’ view on

pesticides and their similarly regulated relevant metabolites in drinking water as nonfunctional contaminants. Consequently, in the EU, they are regulated in drinking water, as outlined in Section 3.14.1.2, by an (agro-)technical maximal value, factually a precautionary limit value of PV ¼ 0.1 mg l1 per compound, being more or less but always lower than a lowest possible health-based value (some old polychlorinated pesticides being the only exceptions). The same concentration of 0.1 mg l1 is recommended by UBA (2003, 2009) as a general precautionary value – PV – for any nongenotoxic drinking-water contaminant with patchy or missing database (see also Section 3.14.8). Cyanotoxins. Cyanotoxins are hepatotoxins and neurotoxins produced by cyanobacteria, also known as blue-green algae and commonly found in over-fertilized surface water. The hepatotoxins are mostly microcystins. Their chemical structure includes two variable amino acids and an unusual aromatic amino acid. They differ by lipophilicity and polarity, affecting toxicity as well. Microcystin-LR has been associated with most of the incidents of toxicity involving microcystins in most countries. It is a cyclic heptapeptide with a molecular weight of about 1000 Da. Neurotoxins are not considered as widespread in water supplies, and they do not appear to pose the same degree of risk from chronic exposure as microcystins. Some of them, such as anatoxin-a and -a(s), are highly toxic nerve poisons but have short biological half-lives. Toxic cyanobacteria also produce cytotoxic alkaloids; the most recently described is a tricyclic guanidine linked to a hydroxymethyl uracil. These alkaloids have been implicated in a variety of health effects, ranging from gastroenteritis to kidney disease. WHO (2008) proposes a provisional GV of 1 mg l1 for only one microcystin (microcystin-LR) and derived it from a 13-week rat study for its liver pathology. This GV represents 80% of the Bd of 2.4 mg per 60-kg person for only this compound. New data for the toxicity of cyanobacterial toxins are being generated (WHO, 2008a). Other organics. Many more organic compounds, besides the few groups described here, occur occasionally in drinking water. They have, if of some importance at the consumer’s tap, been partially addressed in Section 3.14.6.2. Extensive toxicological information on any of these compounds can be found at WHO (2008) and on those relevant in groundwater in compact form in Schmoll et al., 2006.

3.14.7 The Author’s Short Conclusions 3.14.7.1 Unintended Exposure Unintended exposure is synonymous with useless exposure. Useless exposure may be present in drinking water in the form of either geogenic or biogenic constituents or anthropogenic contaminants. Constituents must often be accepted and hence controlled accordingly at the end of pipe, which here usually is identical to its beginning. Treatment of drinking water to eliminate natural constituents, except for manganese and iron, is indicated only if health-based values are exceeded and a better

Drinking Water Toxicology in Its Regulatory Framework

source is not available, be this for direct use or for blending. Most relevant (hazardous) geogenic constituents are arsenic, fluoride, and soluble manganese. Lower than health-based natural levels of undesired geogenic constituents are not a sound reason for treating water. Minimal level goals for desired natural mineral constituents, for example, within the context of central softening of drinking water, are a reasonable option to provide a minimal proportion of the respective daily physiologic need. However, the consumers’ acceptance for fixing such regulatory minimal levels could turn into regulatory misuse of drinking water as carrier of involuntary medication. Contaminants must not be accepted but may be tolerated in drinking water as long as precautionary health-based levels are not exceeded and emission is controlled at the beginning (on site) instead the end (off site) of pipe. This should be done in all three dimensions of the basic rule of environmental hygiene and be implemented technically in terms of the ALARA principle. The only anthropogenic contaminant of drinking water to which WHO, since several decades, had to assign a maximal value based on acute human toxicity (for babies) is nitrate. This situation deserves any precautionary effort to reduce exposure to nitrate via drinking water also, more so as the discussion on the role of nitrate/nitrite as precursors of some forms of cancer in humans is far from being conclusive in terms of congruence between scientific certainty and healthcentered safety.

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the millions of people of very variable sensitivity and possibly exposed to hazardous chemicals, comprises a huge potential of irreversible and/or difficult to retrievable failure. This concerns both, technological equipment and health, if not protected as a whole and managed or surveyed accordingly. This is why drinking-water toxicology is not just a scientific method to allocate by the rule of three a certain proportion of a Bd on the daily amount of consumed drinking water and let the management go how and where it wants. Precautionary protection of drinking water and of its resources is best implemented if management and regulatory toxicology set their quality standards in close cooperation and knowledge of each other’s expertise in defining goals of protection and technical functionality. In order to achieve these goals, they convene on standards being at the same time not only scientifically stable and technically feasible, but also resistant to scientific error and technical failure. The most important part of regulatory toxicologists within this cooperation is their openness to intelligent guess and to formulate scientifically guided hypotheses on which events or changes in a given drinking-water system might give rise to toxicologically relevant change in future. By doing this, however, he or she should try to never glide into contradictions to basic toxicological science. They should also be always informed on most recent possibilities to theoretically identify and practically quantify and prognosticate adverse human health effects as early as possible, optimally on the cellular and molecular level.

3.14.7.2 By-Products of Disinfection and Oxidation By-products of DBP and oxidation (OBP) in many countries represent the highest proportion of xenobiotics present in drinking water. DBP formation is to be accepted if chemical disinfection of drinking water is indispensable in a given situation and need and choice of a disinfectant is optimized and minimized according to technical possibilities and expertise. The best way to minimize DBP without endangering disinfection is to minimize concentration of DBP precursors and to maintain the distribution net in a shape as unobjectionable as the drinking-water quality the consumers desire at their tap. Any discussions on health effects of DBP seem of relevance only when referring to trihalogenmethane (THM) values above about 50 mg l1 and even then only with exact (individual) knowledge of exposure right from the tap. Again, like with nitrate/nitrite, the best way to cope with this is precautionary reduction of exposure to DBP, while never impairing the efficiency of disinfection. OBP may be reasonably assumed to exhibit some toxicological relevance in finished water from waterworks where ozonation is not followed by percolation over activated charcoal. Some OBPs, however, might pass the filtration, and periodic surveillance of the filtrate or finished water for total mutagenic activity could help define safety instead of dealing with toxicological uncertainty.

3.14.7.3 Risk Assessment and Management The whole system of drinking water, from an ever-vulnerable resource to the technology of treatment and distribution up to

3.14.7.4 Derogations from Limit Values To decide on efficient and best-adapted measures to deal with exceedance of a maximal value, it is of crucial importance to know about the rationale behind such value in order to be able to adjust their management accordingly. Only in cases of acute incidents concurring with extreme exposure, it seems indicative to interrupt a contaminated supply for a short time (some hours to maximally 1 day or so) to retrieve exposure from the pipes. The overwhelming number of toxicologically relevant derogations relates on much lower maximal values designed for hygienic tolerance of lifelong exposure. Given this, a supply presumably never needs to be immediately interrupted for health reasons if such ‘low’ maximal value is exceeded. It could even be a substantial error to act against this rule, the more as such action would entrain significant microbiological health risks when impairing correct functioning of the waterborne sewage system.

3.14.7.5 Drinking-Water Installations Drinking-water installations, if not maintained and operated properly or if built with inappropriate construction materials, are a common reason for adulteration of the quality of finished water when it has passed the handover point. A general recommendation is to drink or prepare food only with a tap water of optimal quality, thus having no stagnation or (as a rule) at most 2–3 h of stagnation in domestic pipes behind it. Adulteration by stagnation routinely concerns only esthetic parameters or annoyance. However, specific toxic potentials for infants are always associated with lead from plumbing and

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sometimes possibly with copper in hard water coming from new copper plumbing.

3.14.7.6 Surveillance of Drinking Water Continued surveillance of chemical drinking-water quality should, if possible, concentrate on such parameters which in a given resource or technical context may be subject to change. Such concept of surveillance is the better justifiable, the better the characteristics of the resource are known (and accordingly protected) and the less technical equipment is involved in drinking water production. A resource where the main part of surveillance is done by nature itself is always preferable to a resource where such nature-oriented production of drinking water is not or only nearly achievable.

3.14.8 Perspectives on Perception of Drinking Water This subject needs to be treated in the context of perceptional aspects putting drinking water in relation to bottled water and wastewater.

3.14.8.1 How Pure Is Pure? Centrally supplied drinking water is deemed to be of a high quality but low-price medium to meet everybody’s essential daily fluid requirement, for preparing food, and to advance personal, domestic, and public hygiene. Its mode of central distribution and inherent difficulties to repair any mistake make it inevitable to keep resources and technical facilities in a microbiological and chemical state as clean and safe as possible on a short- and long-term scale. The mode of perception and hence acceptance of tap water by the consumer, however, follows not only such abstract health-related criteria but also esthetic ones such as color, smell, taste, turbidity, or – last but not least – purity. Such criteria are often much more ambitious than mandated by health considerations (Doria et al., 2009). On the other side, in highly populated regions, the main purpose of public surveillance and maintenance of drinking water cannot be an absolute purity unless the principle of Augias would be elated as authoritative to manage the eternal resource, in our domestic case called drinking water. In fact, water is an eternal source, at least where quantity and its potential to be cleaned, recycled, and reused forever is concerned. However, the answer to the question what degree of purity with regard to the contamination of a reused drinking water may seem socially acceptable below strictly health-based limits, should not be bargained from day to day. Instead, this question should rather be negotiated referring to criteria such as functionality and prevention of exposure, ecotoxicology, and overall rationality of water management, such as was proposed in Section 3.14.1.2, in the form of a three-dimensional rule of environmental hygiene to define inevitable and hence acceptable exposure between zero and adversity.

3.14.8.2 Erroneous Reasons to Ask for Absolute Purity Despite this basic intuition, erroneous demands in developed countries on an absolute quality of the daily water have

provoked a situation in which unique water resources all over the world are bottled and then are sold, although for horrendous prices, down the river (Arnold and Larsen, 2010). It is unlikely that the surge in bottled water consumption is about health benefits associated with such water. In most cases, bottled water is not of better quality than tap water (Parag and Timmons-Roberts, 2009). The majority of people from a recent study in Great Britain were satisfied with the quality of their tap water supply and believed it would not pose any adverse health risk, although they were consuming bottled water for some other reasons such as convenience, cost, or taste (Ward et al., 2009). One more reason may be that many people easily get insecure by news especially on pesticides or pharmaceuticals contaminating drinking water (IESK, 2008), without being able to rationalize their insecurity as is proposed in Section 3.14.6.3.2. Similarly, even an assumed higher mineral content of bottled versus tap water is perceived as an indicator of positive health benefit, although the majority of brands are quite poor in minerals. The more probable and irrational reason why so many people prefer bottled to tap water are the former’s increasing perception as an individual and ultrapure aquaceutical and the readiness of many consumers to pay (almost) any price to have their individual miracle water available everywhere (Petrie and Wessely, 2004). As a consequence, such globalization of many water treasures over usually huge distances asks for much energy, whereas one-way bottles increase the landfills. At the same time, publicly accessible rivers and lakes are deteriorating irretrievably in correlation with neglecting resource protection. There is no valid reason why people who do not drink bottled water should finance the handling and disposal of bottled water waste (Parag and Timmons-Roberts, 2009). While, on the one side, the production (and consumption) of 1 kg of meat asks for more than 10–15 m3 of water, there is, on the other side, only a minority of people who have the optimal daily amount of 100 l available; for others, even the daily minimum of 20 l is hardly affordable and often only in bad quality for horrendous prices from private dealers.

3.14.8.3 Some Good Reasons for Not Asking for ‘Absolute’ Purity In contrast to such discrepancy between private occupancy of and public need for high-quality drinking water, the principle of public drinking-water supply is an especially efficient variety of social ethics since it is sustainable and socially acceptable all around. Given the multibarrier source protection approach, it opens the way to use and reuse eternally the amount of publicly accessible freshwater while its available amount is limited by region. The sole condition is to measure and define the off-site acceptability of any residual contamination in all on-site dimensions of the environmental rule (Section 3.14.1.2) and the ALARA principle instead of exploiting more and more fresh (albeit geologically old) resources whose owners claim for or sell their ‘absolute’ purity. Many practical examples on how to recycle purified wastewater through multibarrier systems to finally produce an unobjectionable drinking-water quality meeting ALARA requirements have been collected in a valuable publication

Drinking Water Toxicology in Its Regulatory Framework

(Jime´nez and Asano, 2008). Such technical use of natural forces and facts unifies the economic, ecologic, and social ethic views on daily drinking water in a reasonable and practical manner. This holistic approach asks all stakeholders not to insist on their maximal (absolutely low, toxicologically exhaustive, economically maximally beneficial) quality criteria but to convene for defining an optimal drinking water, referring as exactly as possible on the (per capita) availability of regional freshwater yield. A proposal to put on a long-term scale such social contract into a number is to use 0.1 mg l1 for any anthropogenic contaminant of drinking water as a general precautionary and esthetic quality goal (see Section 3.14.6.3.2).

3.14.8.4 Many More Good Reasons for Not Exhausting Strictly Health-Based Levels Once the perception of drinking water by the consumer is confined to that of a solvent or even an abandoned sink for an unknown number of polar and persistent new analytes, it could quickly lose much of its present acceptance. Following Klo¨pffer and Wagner (2007), the adequate regulatory consequence is not releasing persistent chemicals into the environment – even if no negative effects are known. The longlasting efforts (tens of years since 1974) to avoid or to identify and quantify the risk potential of DBPs in drinking water may speak as an example in this context. Even these efforts are by far not finished yet. Once new analytes such as pharmaceuticals, pesticides, industrial chemicals, flagrants, or endocrine-disrupting compounds are present in the water cycle, they can hardly be retrieved technically. Therefore, the sociopolitical discussion on acceptable and tolerable contamination and risks caused by environmental contaminants in drinking water will go on. Public supply of drinking water is a task of social ethics. The innocence, called absolute purity, of a privately provided water or a transcendentally transfigured aquaceutical is an egoistic chimera. When talking about economic use of water and how to save it, it is indispensable to consider domestic drinking water strictly separated from water in general. Replacing drinking water by domestic graywater as separated from piped or bottled water for human consumption, in general, is a very inefficient, unsustainable concept to save water. The margins to reduce domestic flow rate are very small and below a certain per capita level very cost intensive (Sections 3.14.1.1 and 3.14.6.2.9), whereas huge margins to save water are to be observed worldwide in agriculture and industry.

3.14.8.5 How to Best Realize the Social Concept of an Esthetically Acceptable Drinking Water? Saving water in moderately to densely populated areas is best realized by perceiving wastewater not as a private but as a centrally collectable economic good. The esthetic concept called purity of the daily drinking water is best realized by strengthening esthetics of natural aquatic life together with the regional water cycle by artificial infiltration of groundwater and the use of purified, microbiologically safe wastewater for agricultural use. Stakeholders should convene to arrive at an

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agreement to answer the societal question ‘‘How much too much is just about little enough?’’ The result then may be considered a socially acceptable zero level. This level can neither be the up-to-date lowest analytical zero nor a merely health-based upper one. The first alternative would make drinking-water regulation a hostage of day-to-day analytic certainty, the second one of a never completely certain toxicology. A level of r0.1 mg l1 per any nongenotoxic analyte as recommended by UBA (2003) appears to be safe, socially acceptable, and technically feasible under any aspect not only of regulatory toxicology but also of drinkingwater hygiene and esthetics.

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Wilhelm M, Bergmann S, and Dieter HH (2010b) Occurrence of perfluorinated compounds (PFCs) in drinking water of North Rhine-Westphalia, Germany and new approach to assess drinking water contamination by shorter-chained C4–C7 PFCs. International Journal of Hygiene and Environmental Health 213: 224--232. Wilhelm M, Heinzow B, Angerer J, and Schulz C (2010a) Reassessment of critical lead effects by the German Human Biomonitoring Commission results in suspension of the human biomonitoring values (HBM I and HBM II) for lead in blood of children and adults. International Journal of Hygiene and Environmental Health 213: 265--269. Wilhelm M, Kraft M, Rauchfuss K, and Ho¨lzer J (2008) Assessment and management of the first German case of a contamination with perfluorinated compounds (PFC) in the Region Sauerland, North Rhine–Westphalia. Journal of Toxicology and Environmental Health – Part A 71: 725--733. Winton E, Tardiff RG, and McCabe LJ (1971) Nitrate in drinking water. Journal of the American Water Works Association 63: 95--98. Wollin KM and Dieter HH (2005) Toxicological guidelines for monocyclic nitro-, amino- and aminonitroaromatics, nitramines, and nitrate esters in drinking water. Archives of Environmental Contamination and Toxicology 49: 18--26. Woolf A, Wright R, Amarasiriwardena C, and Bellinger D (2002) A child with chronic manganese exposure from drinking water. Environmental Health Perspectives 110: 1--4. World Health Organization/International Program on Chemical Safety (WHO/IPCS) (1986) Principles for Evaluating Health Risks from Chemicals during Infancy and Early Childhood: The Need for a Special Approach, Environmental Health Criteria 59. Geneva: WHO/IPCS. WQRA (Water Quality Research Australia) (2009) Report on Brisbane Fluoride Incident. Health Stream 55 – September 2009. http://www.wqra.com.au/hsarch/HS55a.htm (accessed April 2010). Yokel RA, Lasley SM, and Dorman DC (2006) The speciation of metals in mammals influences their toxicokinetics and toxicodynamics and therefore human health risk assessment. Journal of Toxicology and Environmental Health, Part B 9: 63--85. Zamora ML, Tracy BL, Zielinski JM, Meyerhof DP, and Moss MA (1998) Chronic ingestion of uranium in drinking water: A study of kidney bioeffects in humans. Toxicological Sciences 43: 68--77. Zamora ML, Zielinski JM, Meyerhof D, Moodie G, Falconer R, and Tracy B (2003) Uranium gastrointestinal absorption: The f1 Faktor in humans. Radiation Protection Dosimetry 105: 55--60. Zhang T, Sun HW, Wu Q, Zhang XZ, Yun SH and Kannan K (2010) Perfluorochemicals in meat, eggs and indoor dust in China: Assessment of sources and pathways of human exposure to perfluorochemicals. Environmental Science and Technology (doi: 10.1021/es1000159). Zhanga X, De Silvaa D, Suna B, et al. (2010) Cellular and molecular mechanisms of bromate-induced cytotoxicity in human and rat kidney cells. Toxicology 269: 13--23. Zietz B, Dieter HH, Lakomek M, Schneider H, Kessler-Gaedtke B, and Dunkelberg H (2003) Epidemiological investigation on chronic copper toxicity to children exposed via the public drinking water supply. Science of the Total Environment 302: 127--144. Zwiener C (2002) Trihalomethanes (THMs), haloacetic acids (HAAs), and emerging disinfection by-products in drinking water. In: Reemtsma T and Jekel M (eds.) Organic Pollutants in the Water Cycle: Properties, Occurrence, Analysis and Environmental Relevance of Polar Compounds, pp. 251–286 (ISBN 978 3 527 31297 9). Weinhiem: Wiley-VCH. Zwiener C, Richardson SD, De Marini DM, Grummt T, Glauner Th, and Frimmel FH (2007) Drowning in disinfection byproducts? Assessing swimming pool water. Environmental Science and Technology 41(2): 363--372.

Relevant Websites http://scripts.oieau.fr Aqua-Lingua. http://www.umweltdaten.de Definition and derivation of health-related indication values (HRIV) for contaminants of drinking with patchy or nonexistent database, recommendation of Germany’s Federal Environment Agency (UBA) from March 2003, and the recommendation’s rationale in form of a commentary to it. http://www.epa.gov Drinking Water Health Advisories, Water Quality Criteria, EPA.

3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter SK Sharma, SK Maeng, and S-N Nam, UNESCO-IHE Institute for Water Education, Delft, The Netherlands G Amy, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia & 2011 Elsevier B.V. All rights reserved.

3.15.1 3.15.1.1 3.15.1.2 3.15.2 3.15.3 3.15.3.1 3.15.3.2 3.15.3.3 3.15.3.4 3.15.3.5 3.15.3.6 3.15.4 3.15.5 3.15.5.1 3.15.5.2 3.15.5.3 3.15.5.4 3.15.6 References

Introduction 417 Relevance of Organic Matter Characterization in Wastewater Treatment and Reuse 417 NOM and EfOM: Definition and Sources 417 Advantages of Bulk Water Characterization over NOM/EfOM Isolates 418 Bulk Water Analysis Protocols 418 Dissolved Organic Carbon 419 Dissolved Organic Nitrogen 419 Specific UV Absorbance 419 XAD-8/-4 Adsorption Chromatography 419 Fluorescence Excitation-Emission Matrix 419 Liquid Chromatography with Online Organic Carbon Detection 420 EfOM versus NOM Differences in Bulk Water Parameters 420 Application of Protocols to Case Studies 420 Soil Column Studies Simulating SAT Using Primary and Secondary Effluent 420 Water Reclamation Case Study (China) 421 Impact of Wastewater Treatment Plant Effluent on River Water Quality (USA) 423 Comparison of Removal of Bulk Organic Fractions from River Water and Wastewater Impacted River Water during Soil Passage 423 Summary 425 425

3.15.1 Introduction 3.15.1.1 Relevance of Organic Matter Characterization in Wastewater Treatment and Reuse Wastewater reclamation and reuse offers a great opportunity and promise as an alternative source of water to meet the everincreasing different water demands (municipal, industrial, agricultural, and environmental) of the growing world population. Furthermore, wastewater reclamation and reuse has now been accepted as a necessity and attractive option to reduce water scarcity in different parts of the world. This is evident from the rapid increase in water reclamation and reuse projects worldwide in the last decade. However, proper selection of wastewater treatment plant (WWTP) effluent treatment or polishing techniques to satisfy the requirements (for direct or indirect reuse and nonpotable or potable reuse) of the relevant water quality guidelines and regulations as well as that of the users is a challenge and prerequisite for sustainability of water reuse projects. In this context, a better understanding of the different constituents in the WWTP effluent and their fate during different subsequent treatment processes are important to ensure proper planning, design, and operation of wastewater reclamation and reuse systems. The organic matter present in WWTP effluents, commonly known as effluent organic matter (EfOM), is mainly a mixture of (1) background natural organic matter (NOM) from the drinking water, (2) soluble microbial products (SMPs) added during biological wastewater treatment, and (3) trace levels of

effluent-derived organic micropollutants (OMPs) and disinfection by-products (DBPs). Figure 1 presents a schematic of the urban water cycle illustrating the different components of EfOM. Therefore, in the water cycle, the characteristics of the EfOM (the concentrations and proportions of each of these components in the effluent) depend on the source of drinking water, type of drinking water treatment, water use in the service area, and type of wastewater treatment. Removal of EfOM is one of the main concerns in the treatment of WWTP effluents for water reuse applications as it impacts the treatability of the water for the intended application. The refractory organic compounds remaining after advanced water treatment are of special concern. EfOM is a DBP precursor, exerts high coagulant and oxidant demands, and influences nitrification and denitrification processes as well as the removal of OMPs by biodegradation. Some components of EfOM, for example, protein-like organic matter, are also responsible for the fouling of membranes and adsorbents. Furthermore, some esthetic (color, taste, and odor) and operational problems (corrosion and regrowth) associated with NOM in drinking water also affect wastewater reuse applications. In general, EfOM affects essentially all chemicals and biological processes in aquatic environments (Shon et al., 2006). By systematic characterization, the problematic organic matter fractions can be targeted for removal and transformation. Insight into the behavior of different fractions or constituents of organic matter present in water sources will

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Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter Natural organic matter (NOM)

Water treatment plant

Drinking water source

Biodegradable organic matter (BOM) Municipal (domestic) use

Soluble microbial products (SMPs) Wastewater effluent Wastewater treatment plant

Figure 1 Schematic of urban water cycle showing components of EfOM.

provide a better understanding of their fate, transport, and impact during water treatment and distribution. Therefore, proper characterization of the bulk organic matter present in WWTP effluents and after different treatment steps would be an important basis for the selection of the appropriate and cost-effective treatment processes and monitoring of the effectiveness of different treatment steps. A major constraint, however, is that much of our present knowledge on characterization protocols is based on drinking-water NOM, and there is a need to demonstrate their applicability to EfOM and to provide a basis for differentiating EfOM from NOM according to measured characterization protocols.

3.15.1.2 NOM and EfOM: Definition and Sources NOM is a complex heterogeneous matrix of organic compounds found in all natural waters. The type and amount of NOM in water depend on climatic conditions and hydrological regime as well as other environmental factors. NOM mainly consists of carbon, oxygen, and hydrogen. Depending on the source of NOM, nitrogen and sulfur can also be present. Furthermore, different cations and anions may be incorporated into NOM structure due to adsorption, complexation, and ion exchange. NOM consists of both humic (humic and fulvic acids) and nonhumic components. Humic/fulvic acids have molecular weight greater than 2000 Da, while that of fulvic acids ranges from 500 to 2000 Da. Fulvic acids represent the most watersoluble fraction of humic material. Humic molecules contain aromatic, carboxyl, carbonyl, methoxyl, and aliphatic units. In addition to humic substances, nonhumics such as hydrophilic acids, proteins, amino acids, polysaccharides, and other biopolymers also contribute to the NOM (Thurman, 1985; Owen et al., 1993; AWWARF, 2000; Drewes and Summers, 2002). Furthermore, organic matter in natural water sources may also contain many trace organic compounds contributed by human activities. Based on source or origin, NOM in water can generally be divided into three main types: 1. Allochthonous NOM. This type of NOM originates from the decay of terrestrial biomass or through soil leaching in the watershed, mainly from leaching of vegetative debris by runoff. It mainly consists of humic substances. The production and characteristics of this type of NOM are therefore related to vegetative patterns and to hydrologic and geological characteristics of the watershed.

2. Autochthonous NOM. This type of NOM originates from in situ sources, mainly algal organic matter (AOM), other phytoplankton, and macrophytes; components can be extracellular or intracellular organic matter consisting of macromolecules and cell fragments. The production of this type of NOM is therefore related to photosynthetic activity and decay products of algal matter. 3. EfOM. EfOM mainly consists of background drinking water NOM (dominated by humic substances) which is not removed during wastewater treatment plus SMPs formed during biological wastewater treatment. It also contains some OMPs introduced during domestic use and generated during water and wastewater treatment. The characteristics of EfOM therefore depend on the type of drinking water source and treatment as well as the type of wastewater treatment applied. NOM in lakes and reservoirs of moderate-to-high trophic status is often dominated by material generated in the water body (autochthonous material), whereas lower-order rivers and streams usually carry more NOM that is generated exterior to the water body (allochthonous NOM). Allochthonous NOM has a large C/N ratio (near 100:1), is highly colored, and has significant aromatic carbon content, whereas autochthonous NOM has relatively lower C/N ratios (near 10:1), is almost colorless, and has low aromatic carbon content (HDR Engineering Inc., 2001).

3.15.2 Advantages of Bulk Water Characterization over NOM/EfOM Isolates As organic matter present in water (NOM/EfOM) consists of thousands of distinct chemical species, its characterization based on analysis of individual compounds and their properties is not possible. The logical and well-accepted approach is to characterize NOM/EfOM by grouping constituents according to a set of fractions. Due to the diversity of the organic molecules in water and their relatively low concentrations compared to other solutes and ions in the inorganic matrix, methods are needed that can characterize NOM in dilute solutions containing a variety of other chemicals, or that can isolate NOM without altering their properties. Bulk water characterizations are more useful to water utilities and are relatively less analyst and time intensive. The results of bulk water characterization can be understood and

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

used by nonspecialists as well. Furthermore, they also provide opportunities for online monitoring and control of treatment processes. Isolation protocols (e.g., XAD-8 resin adsorption or reverse osmosis concentration), while producing purified and concentrated isolates, require processing of large volumes of water (e.g., hundreds of liters), do not provide 100% recovery of the organic matter, create possible artifacts, and are very analyst and time intensive (Gadmar et al., 2005). Some isolate protocols require pH adjustment which may also influence the NOM characteristics due to the possibility of degradation, decarboxylation, oxidation, and condensation reactions (Gaffney et al., 1996). Advanced instrumental techniques such as Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) spectroscopy have been used to characterize NOM isolates/fractions. Even with these methods, it is often difficult to obtain detailed information. Infrared spectra yield very broadband with significant overlaps that often cannot be resolved with consequent loss of information. Band broadening can also occur with NMR techniques due to the presence of free radicals in the humic structure. The focus of this chapter is on characterization of bulk water samples with minimal pretreatment (e.g., filtration).

3.15.3 Bulk Water Analysis Protocols Several analytical methods and procedures have been developed for characterization of NOM/EfOM in bulk water, due to the complexity and heterogeneity in properties and structures of the compounds present. Some of the most commonly used methods for distinguishing EfOM from NOM are in drinking water treatment and wastewater reuse practices, which are elaborated below.

3.15.3.1 Dissolved Organic Carbon The concentration of total amount of organic matter molecules present in water is generally quantified as total organic carbon (TOC), given that carbon (C) is the building element of organic compounds. TOC consists of two fractions: (1) dissolved organic carbon (DOC) and (2) nondissolved particulate organic carbon (POC). DOC is the organic carbon passing through a 0.45-mm filter and represents the majority and the chemically reactive fraction of the organic matter present. DOC in natural water varies with the type of water ranging from as low as 0.5 mg l1 for some groundwaters and seawater to over 30 mg l1 for colored waters from swamps (Thurman, 1985). The DOC concentrations of primary, secondary, and tertiary effluents from WWTPs used for soil aquifer treatment (SAT; laboratory and field studies) were in the range of 9–35, 2–24, and 5–20 mg l1, respectively (Sharma et al., 2008). DOC, measured with TOC analyzers, provides a bulk measure of the amount of organic matter present. DOC can be measured using two types of analytical methods: combustion and wet oxidation. Generally, combustion methods are more accurate in DOC measurement than wet oxidation methods, specifically for seawater with high chloride concentration

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(McKenna and Doering, 1995). The most commonly used DOC analysis methods include persulfate oxidation, ultraviolet (UV) irradiation, and a combination of the two (Sharp, 1993), specifically for the determination of low concentrations (Dafner and Wangersky, 2002).

3.15.3.2 Dissolved Organic Nitrogen Increasing use of nitrification–denitrification processes for municipal wastewater treatment leads to the presence of dissolved organic nitrogen (DON) as the main remaining form of nitrogen in the wastewater effluent. NOM also contains 1–5% of nitrogen by weight (Lee and Westerhoff, 2006). DON may act as nutrient and is a precursor of the carcinogenic DBP N-nitrosodimethylamine (NDMA), which is formed during disinfection using chlorine/chloramines. Measurement of DON is accomplished by a two-step process: first, involving elimination of dissolved inorganic nitrogen (DIN) by continuous dialysis through dialysis bags with a molecular weight cutoff (MWCO) of 100 Da, followed by direct measurement of DON as total nitrogen (TN) with a TN analyzer. Because the concentration of the DIN species is often considerably higher than that of the organic nitrogen species, DON measurements are potentially subject to substantial error. Pretreatment such as dialysis can improve the accuracy of DON measurement, separating DIN species (nitrate, nitrate, and ammonia) from DON (Lee, 2005); however, this method may be less useful for DON measurement in wastewater due to matrix effects.

3.15.3.3 Specific UV Absorbance UV absorbance (UVA) of a filtered (0.45 mm) sample at 254 nm (UVA254) is measured with a spectrophotometer to assess the humic contents or aromatic character (aromaticity) of the sample. The aromatic structure of NOM can absorb more UV light than an aliphatic structure. The UVA/DOC ratio, the specific UV absorbance (SUVA) expressed in l mg1 m1, is defined as the normalized UV absorbance of a water sample with respect to the DOC:

SUVA ¼

UV254 ðcm1 Þ  100 DOC ðmg l1 Þ

A SUVA value of 44 l mg1 m1 represents a water source dominated by humic substances and a SUVA o2 l mg1 m1 represents a source dominated by nonhumic material (Edzwald and Tobiason, 1999). Humic substances are more dominant in NOM, while nonhumic material is more dominant in EfOM. The SUVA of fulvic acid is higher than that of natural bulk waters (Westerhoff et al., 2001; Chow, 2006). In the case of comparing ozonated water and raw waters, ozonated water exhibits a lower SUVA value than the corresponding raw water because of the breakdown of aromatic structure by ozone. EfOM, compared to NOM, typically exhibits a lower SUVA.

3.15.3.4 XAD-8/-4 Adsorption Chromatography XAD-8 and XAD-4 resins (nonionic solid sorbents, Amberlite) have been used to isolate different organic matter from water.

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Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

The isolation procedure generally uses two types of XAD resin in series: XAD-8 resin first, followed by XAD-4 resin. This isolation technique, however, can also be used to determine polarity fractions of the DOC. An acidified water sample (pH 2.0) is first passed through a column of XAD-8 resin. The hydrophobic (HPO) fraction, mainly consisting of humic and fulvic acids, is quantitatively sorbed onto XAD-8 resin, with HPO-DOC calculated by the difference in initial DOC and the DOC of the XAD-8 effluent. The HPO fraction typically represents 50–65% of the DOC in water. The effluent from the XAD-8 column is then passed through a column of XAD-4 resin where the transphilic (TPI) fraction is adsorbed onto XAD-4. The hydrophilic (HPI) fraction is adsorbable neither on XAD-8 nor on XAD-4. These different fractions reflect differences in polarity, size, and charge density. HPO and TPI NOM components contain mainly acids and some neutrals, while hydrophilic NOM components contain mostly neutrals and bases (Thurman and Malcom, 1981; Malcolm and McCarthy, 1992; Labanowski and Feuillade, 2009). EfOM, compared to NOM, typically shows a greater proportion of polar organic matter.

3.15.3.5 Fluorescence Excitation-Emission Matrix Fluorescence spectroscopy is an extremely sensitive method that permits analysis of fluorescing organic matter fractions in water even at concentrations o1 mg l1. This method is useful to investigate the relative amounts of aromatic and nitrogen species, and to probe the origin of NOM (Chen et al., 2003). An excitation-emission matrix (EEM) is obtained by collecting the emission spectra over a series of excitation wavelengths. A fluorescence excitation-emission matrix (F-EEM) of a water sample is developed by scanning it over an excitation range of 240–450 nm by 10-nm increments and an emission range of 290–530 nm by 2-nm increments using spectrofluorometer. The result is a three-dimensional spectrum in which fluorescence intensity (arbitrary units) is represented as a function of excitation and emission wavelengths. There are three dominant peak areas observed in EEM: (1) humic/fulvic-like organic matter peak (at excitation ¼ 330–350 nm and emission ¼ 420–480 nm), (2) humic-like organic matter peak (at excitation ¼ 250–260 nm and emission ¼ 380–480 nm), and (3) protein-like organic matter peak (at excitation ¼ 250– 280 nm and emission ¼ 280–350 nm) (Baker and LamontBlack, 2001; Leenheer and Croue´, 2003). EfOM, compared to NOM, typically shows more protein-like and less humic-like organic matter. Based on an EEM, a fluorescence index (FI) can be calculated by the ratio of fluorescence intensities at emissions of 450 and 500 nm at an excitation 370 nm. A higher FI (B1.7 to B2.0) reflects organic matter of an autochthonous (microbial) origin, while a lower FI (B1.3 to B1.4) reflects organic matter of an allochthonous (terrestrial) origin. EfOM typically shows an autochthonous signature (higher FI), while NOM typically shows an allochthonous signature (lower FI) (Donahue et al., 1998; McKnight et al., 2001). Fluorescence is sensitive to factors such as pH, solvent polarity, temperature, redox potential of the medium, and interactions with metal ions and organic substances (Coble, 1996; Westerhoff et al., 2001; Leenheer and Croue´, 2003).

During F-EEM analysis of a sample, a uniform method of sample preparation is generally adopted. Filtered water samples are diluted to 1 mg l1 DOC with 0.01 N KCl, and adjusted to a pH of 3.0 before measurement. The EEMs of each sample are adjusted by subtracting an EEM of 0.01 N KCl (pH 3.0 adjusted with HCl) solution (set as a blank EEM) to remove Raman scatter peaks.

3.15.3.6 Liquid Chromatography with Online Organic Carbon Detection Liquid chromatography with online organic carbon detection (LC-OCD; also referred to as SEC-DOC, size-exclusion chromatography with DOC detection) is based on molecular size determination with gel permeation chromatography. The water sample is passed through a column of gel, and the extent to which fractions are retarded is a measure of their molecular size. Larger molecules that do not enter the gel pores pass through the columns, while smaller ones diffuse into the gel and take longer to pass through. This method thus provides an indication of the apparent molecular weight (MW) or molecular size (MS) of different classes of NOM fractions, including biopolymers (which include polysaccharides, organic colloids, and proteins), humic substances, building elements, low-molecular-weight acids (LMWAs), and low-molecularweight neutrals (LMWNs). An LC-OCD or SEC-DOC chromatogram can be represented either in terms of retention time or, if calibration chemicals are used, in terms of MW distribution – daltons (Amy and Her, 2004). A typical chromatogram of NOM present in surface water is shown in Figure 2 (Huber, 2007). The first fraction identified after approximately 25–45 min (first peak – largest molecular size) is the biopolymer peak with significant OCD only. The organic colloids and proteins present in this fraction provide responses in both OCD and UV detection (UVD). The second and third fraction responses in OCD and UVD are attributed to humic substances and building elements, respectively. The fourth response to OCD and UVD is attributed to LMWA. LMWNs comprise the last main fraction. LC-OCD can be used to effectively monitor polar NOM components with a lower SUVA, and has been successfully applied to monitoring changes in NOM associated with bank infiltration, SAT, ozone oxidation, coagulation, adsorption, bio-filtration, and membrane separation (Her et al., 2002). It has also been used to identify problematic NOM components in membrane fouling (Her et al., 2004a, 2004b).

3.15.4 EfOM versus NOM Differences in Bulk Water Parameters Previous studies have shown that there are some distinct differences between EfOM and NOM with respect to bulk water parameters, as listed below, which can be identified by using several bulk water analysis protocols (Frimmel and Abbt-Braun, 1999; Nam et al., 2008): 1. DOC and DON measurements show that EfOM has higher DOC, higher DON, and higher DON/DOC ratio compared to NOM. Organic matter from the wastewater-derived

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

421

12 Released from HSs after mid-oxidation Mw 350−500 g mol−1 Building blocks Mw 500−1200 g mol−1 Humics A) Comp. with IHSS B) Standards C) Ratio UV/DOC D) Retention time Acid hydrolysis and analysis of E) Peak form monosaccharides

10

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8

Acids and LMW humics Mw < 350 g mol−1 Due to salt peak (nonbuffered sample)

OCD: organic carbon detection UVD: UV detection at 254 nm OND: organic nitrogen detection

Biopolymers Mw > 20.000 g mol−1

Retention time and tests with IEX resins LMW neutrals

6

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4

No OC response (even before BP fraction) By Raleigh scattering, no quantification Inorganic colloids

2

UVD Nitrate OND

0 20

30

40

50 Retention time in minutes

60

70

80

Figure 2 Typical NOM chromatogram of a surface water sample. From Huber S (2007) LC-OCD applications. DOC-Labor Dr. Huber, Germany. http:// www.doc-labor.de/english_pages/What_is_LC-OCD_About/What_is_LC-OCD_about_2007-2.pdf.

2. 3.

4.

5.

samples exhibits lower C and O contents and a significantly higher amount of H, N, and S. EfOM has lower SUVA compared to drinking water NOM (as it is low in humic substances and high in DOC). Compared to NOM, EfOM has a relatively higher amount of hydrophilic (polar) components than HPO (nonpolar) components (i.e., HPI-DOC4HPO-DOC). EfOM exhibits more dominant protein-like peaks in F-EEM as it is rich in nitrogenous organic matter of wastewater origin. Drinking water NOM normally does not show pronounced protein-like peaks unless impacted by AOM. Furthermore, EfOM exhibits higher FI values, indicating that the EfOM source is from autochthonous and microbially derived sources. Due to its high DOC concentration and high concentration of nonhumics, EfOM also exhibits a more dominant biopolymer peak (higher polysaccharide or protein content) in LC-OCD or SEC-DOC chromatograms.

These results from different studies clearly show that bulk water characterization is an effective method for differentiating EfOM from NOM. The following discussion illustrates these differences through application of the protocols within the context of several case studies.

3.15.5 Application of Protocols to Case Studies 3.15.5.1 Soil Column Studies Simulating SAT Using Primary and Secondary Effluent DOC and SUVA. Soil column studies were conducted using 5-m-long columns filled with bio-acclimated silica sand (0.8– 1.25 mm) and operated at a hydraulic loading rate of 1.25 m d1 under different conditions to simulate SAT. Primary and secondary effluents from a full-scale WWTP were applied to the soil columns and the removals of bulk organic compounds were monitored (after acclimation of the soil columns for about 60 days) using different analytical protocols. Table 1 summarizes the average DOC and SUVA of primary and secondary effluents before and after soil columns. Table 1 shows that for both primary and secondary effluents, DOC levels decreased and SUVA values for both effluents are in the range 2–4, indicating that the effluent is a mixture of both humic and nonhumic organic matter fractions (and mixture of HPO and hydrophilic compounds). SUVA values increased with soil passage, showing the preferential removal of nonhumics over humics in the soil columns. The amount of DOC removed in the column is also an indicator of biodegradable dissolved organic carbon (BDOC), which was confirmed by a strong correlation between the DOC removal profile and biomass profile along the depth of the column.

422

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

F-EEM. Figure 3 shows typical F-EEM spectra of primary and secondary effluents before and after soil column passage. Both influent and effluent showed three characteristic peaks of humic/fulvic-like, humic-like, and protein-like organic matter fractions. It was observed that with soil passage, there was a reduction in the intensities of all the characteristic peaks for both primary and secondary effluents. Table 2 presents the average reduction in intensities of the characteristic peaks of different organic matter fractions during SAT. It shows that SAT preferentially removes the protein-like organic matter fraction over humic-like organic

Table 1

matter fractions. More than 54% and 90% of the protein-like organic matter peak intensities were removed from primary and secondary effluents over a 5-m length of soil column. However, the reductions of intensities of other organic matter fractions are limited. This clearly supports the SUVA results in Table 1 that SAT preferentially removes non-humiclike organic matter fractions. This also shows that effluents from SAT systems have reduced concentrations of nonhumic and relatively high concentrations of humic matter, and thus more resembled characteristics of the natural water NOM.

Average DOC and SUVA values of primary and secondary effluents before and after simulated SAT

Effluent applied

Before SAT

After SAT

1

Primary effluent Secondary effluent

1

DOC (mg l )

SUVA (l mg

36.071.1 11.670.4

2.3070.2 2.9070.2

1

m )

DOC (mg l1)

SUVA (l mg1 m1)

15.571.5 9.770.2

2.9570.3 3.4070.2

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Figure 3 F-EEM of primary and secondary effluents before and after SAT treatment (a) primary effluent; (b) primary effluent after SAT; (c) secondary effluent; and (d) secondary effluent after SAT.

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter 3.15.5.2 Water Reclamation Case Study (China) Changes in the characteristics of bulk organic matter were monitored during a wastewater reclamation case study in China under the EU Reclaim Water project (RECLAIM WATER, 2008a, 2008b). Secondary effluent was pretreated with coagulation (polyaluminum chloride ¼ 10–30 mg l1) and then ozonated (ozone dose ¼ 10–15 mg l1) before infiltration. Table 3 compares the DOC and TN concentrations after different treatment steps. The DOC removal after infiltration was about 60%. Analysis showed that background groundwater had much higher DOC than that of secondary effluent and groundwater abstracted after infiltration. Furthermore, the TN concentrations of secondary effluent and background groundwater at this site were comparable. F-EEM spectra for samples were also measured and the intensities of characteristic peaks in the spectra are presented in Table 4. Three characteristic organic matter fraction peaks were visible in the F-EEM spectra of all the samples. The humic/fulvic-like peak intensity decreased by about 30%, whereas the (second) humic-like peak intensity increased after SAT, indicating a breakdown of larger humic/fulvic-like

423

organic matter fractions or a peak shift. There was an increase in the protein-like peak after infiltration, indicating some groundwater pollution, as background groundwater also has a high protein-like peak and TN. The FI values of the samples were 41.5, indicating a mixture of both autochthonous and allochthonous organic matter.

3.15.5.3 Impact of Wastewater Treatment Plant Effluent on River Water Quality (USA) The effects of WWTP effluent on characteristics of NOM present in river water were analyzed by conducting XAD fractionations, F-EEM, and SEC-DOC measurements of samples (1) upstream of a WWTP, (2) WWTP effluent, and (3) downstream of WWTP. Table 5 summarizes trends showing the characteristics of NOM, EfOM, and EfOM-impacted waters in the Northeast USA watershed. Compared to the upstream NOM sample, the WWTP effluent EfOM sample exhibited relatively low SUVA values (1.63–2.13 l mg1 m1, average: 1.90 l mg1 m1), increased fraction of hydrophilic organic matter (average: 34.8%), and higher FIs (average: 1.418). The

Table 2 Average % reduction in intensities of the peaks of different organic matter fractions and corresponding DOC removal for primary and secondary effluents during SAT Effluent type

Protein-like

Humic/fulvic-like

Humic-like

DOC removal

Primary effluent Secondary effluent

54.5 90.3

18.3 5.9

16.6 3.9

56.9 16.4

Table 3

TOC and TN measurement of water samples from a case study site

Parameter

Secondary effluent

After coagulation

After ozonation

After infiltration (SAT)

Background groundwater (local)

DOC (mg l1) TN (mg l1)

5.4 32.2

4.4 30.2

4.7 31.1

2.1 28.2

7.3 29.6

Table 4

Intensities of characteristic peaks in F-EEM spectra of samples from case study site

Sample

Humic/fulvic-like

Humic-like

Protein-like

FI

Secondary effluent After coagulation After ozonation After infiltration Background groundwater

19.2 17.3 10.9 12.0 11.9

25.9 22.9 15.2 43.5 23.4

12.2 13.2 13.7 24.8 19.7

1.57 1.63 1.60 1.45 1.55

Table 5

Characterization results of upstream water, wastewater, and downstream water

Sample

UVA254 (cm1)

DOC (mg l1)

SUVA l (mg1 m1)

HPO-DOC (%)

TPI-DOC (%)

HPI-DOC (%)

FI

Upstream of WWTP WWTP effluent Downstream of WWTP

0.100 0.120 0.122

3.60 6.39 4.31

2.76 1.90 2.82

52.1 41.3 56.2

22.7 23.9 21.1

25.2 34.8 22.8

1.229 1.418 1.261

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

low SUVA of WWTP effluent implies that the DOC of EfOM is comprised of more nonaromatic (or less aromatic) organic carbon than NOM. The FIs of EfOM were higher than those of NOM, which means that the properties of EfOM that distinguishes it from NOM are mainly microbial in origin. EEM measurements for upstream water, wastewater, and downstream of WWTP (influent of drinking water treatment plant) clearly showed different peaks between NOM and EfOM (Figure 4). The EEMs of wastewater and wastewaterimpacted water exhibited the presence of protein-like substances at the range of excitation wavelength 260–290 nm and emission wavelength 320–370 nm, which were at a similar location with other studies, and this protein-like peak likely originated from SMPs present in biologically treated wastewater. SEC-DOC of surface waters and wastewaters is separated into three main peaks according to their molecular-weight distributions, as shown in Figure 5: the zone 1 peak represents large molecules, such as polysaccharides, proteins, and colloids; the zone 2 peak is attributed to humic substances and

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The effects of source water quality matrix on the removal of different organic matter fractions during soil passage were investigated by conducting soil columns studies using a river water and a mixture of river water and secondary effluent (1:1), simulating wastewater-impacted surface water sources. The soil column depth was 5 m and the hydraulic loading rate was 0.56 m d1. Table 6 presents the DOC, UVA254, and SUVA values of two different types of water tested before and after the soil passage. After the ripening (bio-acclimation) of the soil column for 60 days, the average steady-state DOC removals were 18.3% and 33.5% for river water and mixture of river water and secondary effluent, respectively. Higher DOC removal in the case

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3.15.5.4 Comparison of Removal of Bulk Organic Fractions from River Water and Wastewater Impacted River Water during Soil Passage

WW effluent (EFOM)

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building elements; and the zone 3 peak corresponds to lowmolecular-weight organic acids.

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−0.05 Retention time (s) Figure 5 SEC of DOC fractions for upstream water, wastewater, and downstream water from a Northeast USA watershed, and their molecular-weight (MW) distributions.

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter Table 6

Average DOC, UVA254, and SUVA values of river water and a mixture of river water and secondary effluent

Water type

Before soil passage

River water River water þ secondary effluent (1:1)

After soil passage

DOC (mg l1)

UVA254 (cm1)

SUVA (l mg1 m1)

DOC (mg l1)

UVA254 (cm1)

SUVA (l mg1 m1)

3.70 9.70

0.09 0.28

2.48 2.85

3.00 6.40

0.07 0.21

2.38 3.22

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of the mixture of river water and secondary effluent is likely due to higher concentration of biodegradable organic matter fractions present in secondary effluent. This shows that soil passage (bank filtration or artificial recharge) is effective in removal of bulk organic matter from wastewater-impacted water sources. For river water, SUVA decreased slightly during soil passage, while SUVA for the mixture of river water and secondary effluent increased with soil passage (Figures 6 and 7). Table 7 shows that reduction in F-EEM intensities for the mixture of river water and secondary effluent were 45.5% for

protein-like, 49.9% for humic/fulvic-like, and 51.4% for humic-like organic matter fractions. In comparison, for the river water, the reductions in peak intensities were 5.9% for protein-like, 2.8% for fulvic/humic-like, and 11.3% for humiclike organic matter fractions. Figure 8 shows the LC-OCD chromatograms for the river water and mixture of river water and secondary effluent before and after the soil passage. It was observed that there was preferential removal of biopolymer fractions while humics and other organic matter fractions were also removed to some

426

Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter

Table 7 Average % reduction in peaks of different organic matter fractions and corresponding DOC removal for river water and mixture of river water and secondary effluent during soil passage Water type

Protein-like

Humic/fulvic-like

Humic-like

DOC removal

River water River water þ secondary effluent

5.9 45.5

2.8 49.9

11.3 51.4

18.3 33.5

25 OCD UVD

Building blocks Humics (HS)

LMW acids and HS

20 Biopolymers Neutrals

Rel. signal response

RW+SE+OUT 15

RW+SE−IN 10

RW−OUT 5

RW−IN 0 0

20

40 60 Retention time (min)

80

100

Figure 8 LC-OCD chromatograms for the river water (RW) and mixture of river water and secondary effluent (RW þ SE) before and after the soil passage.

extent, which is in agreement with the results of F-EEM analysis. From LC-OCD chromatograms, it was estimated that biopolymer removals were 55% and 91% for river water and mixture of river water and secondary effluent, respectively. The corresponding removal efficiencies for humic fraction were 11% and 22%, respectively. For river water, the removal of building elements and neutrals were about 1% and 2%, respectively. Relatively higher removals of building elements (24%) and neutrals (36%) were observed for the mixture of river water and secondary effluent.

3.15.6 Summary Innovative NOM characterization tools, elucidating size, structure, and functionality are also applicable to samples containing EfOM and/or a mixture of NOM and EfOM. As

such, they provide a means of differentiating NOM from EfOM and elucidating wastewater impacts on drinking water sources through revelation of unique EfOM signatures.

References Amy G and Her N (2004) Size exclusion chromatography (SEC) with multiple detectors: A powerful tool in treatment process selection and performance monitoring. Water Science and Technology: Water Supply 4: 19--24. AWWARF (2000) Natural Organic Matter in Drinking Water: Recommendations to Water Utilities. Denver, CO: American Water Works Association Research Foundation. Baker A and Lamont-Black J (2001) Fluorescence of dissolved organic matter as natural tracer of ground water. Ground Water 39(5): 745--750. Chen J, LeBoeuf EJ, Dai S, and Gu B (2003) Fluorescence spectroscopic studies of natural organic matter fractions. Chemosphere 50: 639--647. Chow AT (2006) Disinfection byproduct reactivity of aquatic humic substances derived from soils. Water Research 40: 1426--1430.

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Lee W (2005). Occurrence, Molecular Weight and Treatability of Dissolved Organic Nitrogen. Doctoral Dissertation, Arizona State University, Tempe, AZ, USA. Lee W and Westerhoff P (2006) Dissolved organic nitrogen removal during water treatment by aluminum sulfate and cationic polymer coagulation. Water Research 40(20): 3767--3774. Leenheer JA and Croue´ J-P (2003) Characterizing dissolved aquatic organic matter. Environmental Science and Technology 37(1): 18A--26A. Malcolm RL and McCarthy P (1992) Quantitative evaluation of XAD 8 and XAD 4 resins used in tandem for removing organic solutes from water. Environment International 18: 597--607. McKenna JH and Doering PH (1995) Measurement of dissolved organic carbon by wet chemical oxidation with persulfate: Influence of chloride concentration and reagent volume. Marine Chemistry 48: 109--114. McKnight DM, Boyer EW, Doran PT, Westerhoff PK, Kulbe T, and Anderson DT (2001) Spectrofluorometric characterization of aquatic fulvic acid for determination of precursor organic material and general structural properties. Limnology and Oceanography 46(1): 38--48. Nam S-N, Krasner SW, and Amy GL (2008) Differentiating effluent organic matter (EfOM) from natural organic matter (NOM): Impact of EfOM on drinking water sources. In: Kim YJ and Platts U (eds.) Advanced Environmental Monitoring, ch. 20, pp. 259–270. Dordrecht: Springer. Owen DM, Amy GL, and Chowdhury ZK (1993) Characterization of Natural Organic Matter and Its Relationship to Treatability, 250p. Denver, CO: American Water Works Association Research Foundation. RECLAIM WATER (2008a) Monitoring of developed and adapted parameters. EU Reclaim Water Project. Milestone Report M 3.2. RECLAIM WATER (2008b) Water Reclamation Technologies. EU Reclaim Water Project. Deliverable No. D1.2/D5.2. Sharma SK, Harun CM, and Amy G (2008) Framework for assessment of performance of soil aquifer treatment systems. Water Science and Technology 57(6): 941--946. Sharp JH (1993) The dissolved organic carbon controversy: An update. Oceanography 6(2): 45--50. Shon HK, Vigneswaran S, and Snyder SA (2006) Effluent organic matter (EfOM) in wastewater: Constituents, effects, and treatment. Critical Reviews in Environmental Science and Technology 36(4): 327--374. Thurman EM (1985) Organic Geochemistry of Natural Waters. Dordrecht: Martinus Nijhoff/Dr Junk W Publishers. Thurman EM and Malcom R (1981) Preparative isolation of aquatic humic substances. Environmental Science and Technology 15: 463--466. Westerhoff P, Chen W, and Esparza M (2001) Organic compounds in the environment fluorescence analysis of a standard fulvic acid and tertiary treated wastewater. Journal of Environmental Quality 30: 2037--2046.

3.16 Chemical Basis for Water Technology P Huck, University of Waterloo, Waterloo, ON, Canada M Sozan´ski, Poznan´ University of Technology, Poznan´, Poland & 2011 Elsevier B.V. All rights reserved.

3.16.1 3.16.2 3.16.2.1 3.16.2.2 3.16.3 3.16.4 3.16.4.1 3.16.4.2 3.16.4.3 3.16.4.4 3.16.4.5 3.16.4.6 3.16.4.7 3.16.4.8 3.16.4.9 3.16.4.10 3.16.4.11 3.16.4.12 3.16.5 3.16.5.1 3.16.5.2 3.16.5.3 3.16.5.4 3.16.6 3.16.6.1 3.16.6.1.1 3.16.6.1.2 3.16.6.1.3 3.16.6.1.4 3.16.6.2 3.16.6.2.1 3.16.6.2.2 3.16.6.2.3 3.16.6.2.4 3.16.6.2.5 3.16.6.2.6 3.16.6.3 3.16.6.4 3.16.6.5 3.16.6.5.1 3.16.6.5.2 3.16.6.5.3 3.16.6.6 3.16.6.7 3.16.6.8 3.16.7 References

Introduction Goals and Processes for Water Treatment The Seven Goals Classification and Definition of Processes Used in Water Treatment Key Chemical and Physical Principles/Phenomena for Water Treatment Summary of Processes Used in Water Treatment Coagulation and Flocculation Sedimentation Flotation Filtration Membranes Disinfection Oxidation Gas–Liquid Transfer (Aeration and Air Stripping) Adsorption Biodegradation Ion Exchange (Including MIEXs) pH Correction The Evolving Nature of Water Treatment Increased Emphasis on Physical/Biological Processes The Evolving Role of Membranes Environmental Footprint Coping with Supply Constraints Addressing the Treatment Goals – From the Perspective of the Chemical, Physical, and Biological Processes Involved Particle Removal (Including Pathogens) Coagulation, flocculation, and sedimentation Flotation Filtration Membranes TOC Removal Enhanced coagulation MIEXs Biological treatment Adsorption Oxidation Nanofiltration Disinfection/Inactivation Maximizing Biological Stability Removal of Organic Chemical Contaminants Geosmin and MIB Pharmaceuticals and endocrine disrupting substances Volatile contaminants Inorganic Contaminants Maximizing Chemical Stability Maintaining Water Quality to the Consumer’s Tap Summary (Concluding Remarks)

429 429 429 430 431 432 432 434 435 435 436 437 438 438 439 440 442 442 442 443 443 443 443 444 444 444 444 444 445 445 445 445 446 447 447 447 447 447 448 448 458 460 460 463 463 464 466

429

430

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3.16.1 Introduction This chapter reviews drinking water treatment from the perspective of the chemical phenomena and processes on which it is based. The framework is provided by the definition of seven goals for water treatment: removal of particles (including pathogens), total organic carbon (TOC) removal, disinfection/inactivation, maximizing biological stability, removal of chemical contaminants, maximizing chemical stability of the finished water, and maintaining quality to the point of use or consumption. The treatment required in a given situation is defined by the gap between the incoming or raw water quality, and the required final or finished water quality. The latter is typically defined by regulation or guideline. Achievement of the goals involves meeting defined values for a number of specific water-quality parameters. Many of these parameters have been discussed in detail in other chapters of this volume. In some situations treatment may not be required to address all of the seven goals, because the raw water quality may already be acceptable in this regard. As is evident later in the chapter, there are often a number of alternative processes that can be used to meet a specific goal, and some processes are capable of simultaneously addressing more than one goal. ‘Master variables’, such as pH and TOC, can both influence and be influenced by a number of treatment processes, and therefore constitute important linking factors among processes. Water treatment is based on a number of important chemical and physical (and biological) principles or phenomena. These include equilibria, kinetics, surface phenomena, and mass transfer. These are each reviewed briefly with regard to their importance for water treatment. The next section summarizes each of the major processes used in water treatment, from the perspective of both the basic phenomena on which they are based and also important considerations for their application. This section is followed by a discussion of the evolving nature of water treatment. Although drinking water treatment has traditionally been a relatively conservative field, with many of the processes having been in use for decades or longer, the rate of change is currently increasing. This is being driven by factors such as the development of new technology (e.g., the recent substantial reduction in costs and improvement in performance of membranes) and other developments such as minimizing the environmental impact (including energy requirements) of treatment. Considerable emphasis is given in the chapter to reviewing applications of the processes that can be used to achieve each of the treatment goals. This includes coverage of newer processes or newer applications of processes that may be less well described in the literature.

3.16.2 Goals and Processes for Water Treatment 3.16.2.1 The Seven Goals Seven goals can be defined for the treatment of drinking water. These goals are independent but linked, and the need to which each of them needs to be addressed in a given treatment situation depends on the gap between the raw water quality and the regulatory or otherwise desired finished water quality.

Where more than one treatment goal must be met, a treatment plant will invariably consist of several processes in series. As discussed in the next section, some goals may be met by a combination of treatment processes, and some treatment processes are capable of simultaneously addressing more than one goal. Each of the goals is described briefly below, in the general order in which they would be addressed in a treatment facility. 1. Removal of particles. This goal also includes the removal of colloidal material and the physical removal of pathogenic microorganisms. The removal of particulate matter is necessary for operational, public health, and esthetic reasons. Obviously, water that is turbid is not pleasant to drink and would be rejected by consumers. Operationally, particulate matter would settle out in the distribution system, leading to problems. However, the public health reason is paramount. Particulate matter both interferes with disinfection/ inactivation processes and may have microorganisms adsorbed to it. Therefore, it is necessary that particle concentrations (traditionally measured by light scattering in the form of turbidity) be reduced to low levels early in a treatment process. 2. Reduction in the concentration of TOC. The natural organic matter (NOM) present in all waters to varying degrees (discussed in detail in Chapter 3.15 Characterization Tools for Differentiating Natural Organic Matter from Effluent Organic Matter) can interfere with treatment objectives, lead to the creation of undesirable treatment byproducts, and be esthetically undesirable, that is, it may produce a noticeable color in the water. Depending on the TOC level in the raw water, the level may need to be reduced during treatment, although TOC does not need to be completely eliminated. The objectives for TOC removal are to reduce disinfectant demand and by-product formation, to reduce membrane fouling where that is relevant, and to improve the stability of disinfectant residuals in the distribution system in jurisdictions where these are employed. 3. Disinfection and inactivation. In virtually all cases disinfection or inactivation of microorganisms must be provided. In terms of immediate and short-term risks to health, achievement of this goal is invariably more important than meeting of the other goals. Only true groundwater can be reliably considered to contain no pathogenic microorganisms; however, this should be demonstrated on a case-by-case basis. To provide an increased level of public health protection, some jurisdictions require disinfection of all groundwater supplies, even though testing of regulated microorganisms may demonstrate their absence. Although various disinfectants are possible, the most practical ones are normally chlorine, chlorine dioxide, ozone, and ultraviolet (UV). Although chlorine has historically played a significant role in events of public health, issues involving by-product formation are seeing chlorine-based disinfectants lose ground to other approaches. Chlorine dioxide is used in some cases but does not offer some of the other process benefits of ozone nor the inactivation capability against Cryptosporidium of UV. 4. Removal of chemical contaminants. Jurisdictions regulate the levels of organic and inorganic chemical substances that

Chemical Basis for Water Technology

are considered acceptable in water, based in general on an assessment of risk to public health. In most cases, these substances are considered to represent a long-term health risk, often based on a cancer outcome. A number of these substances are discussed in detail in Chapter 3.02 Trace Metal(loid)s (As, Cd, Cu, Hg, Pb, PGE, Sb, and Zn) and Their Species and Chapter 3.04 Emerging Contaminants. When such substances are present in raw water above acceptable levels, they must be removed during the treatment process. In general, the options for doing this involve transferring the substance from the water to another phase (e.g., by volatilization, precipitation, or adsorption), oxidizing the substance either chemically or biologically, or physically removing it by a membrane process. There may be a link between achieving this goal and providing disinfection/inactivation because chemical disinfectants also function as oxidants. 5. Ensuring biological stability. Meeting this goal involves minimizing the opportunity for bacterial regrowth in the distribution system. Essentially, this involves introducing biological processes into the treatment train to remove sources of nutrients and energy for microorganisms, principally bacteria. In most drinking-water systems, biodegradable carbon is considered the limiting nutrient; however, there are systems where phosphorus has been found to be limiting. Achievement of this goal is more important in jurisdictions where either no or a minimal disinfectant residual is maintained in the distribution system. Where such a residual is maintained, it can suppress biological growth. In treatment processes involving membranes, maximizing the biological stability of the water upstream of the membrane will reduce the extent of biofouling on the membrane, which is an important operational issue. 6. Maximizing chemical stability. Raw water is invariably in chemical equilibrium before it enters the treatment plant; however, a number of treatment processes act to disturb this equilibrium. A prime example of this is changes in pH, for example, due to the addition of coagulants. One of the most important effects of this is to disturb the calcium carbonate equilibrium, which can lead to problems in the distribution system, in particular related to corrosion. It is also possible that dissolved coagulant residuals can precipitate in the distribution system. Depending on the extent of pH change during treatment, it may be necessary to correct pH at the end of the treatment process. 7. Ensuring esthetic quality. For consumers, the esthetic quality of the water (i.e., taste and odor) is one of the most important factors determining confidence and acceptance. In general, odor is a more common problem than taste, and most odor problems are related to excessive growth of algae or cyanobacteria in the raw water. Concentrations of odorous substances can be reduced to acceptable levels during treatment, and to some extent these substances can be considered a particular class of chemical contaminants. However, odors can also arise or increase in the distribution system, most likely in relation to biological instability of the distributed water. Although threshold odor concentrations for various common odorous substances have been determined, odor perception varies among individuals, and some individuals may detect low levels not

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noticed by the majority of consumers. Although odorous substances themselves are not considered detrimental from a health perspective, meeting this goal is very important for drinking-water providers. It should however be noted that algae or cyanobacteria capable of producing odors may also be capable of producing toxins, which can have an adverse health effect.

3.16.2.2 Classification and Definition of Processes Used in Water Treatment Figure 1 shows the classification of both substances to be removed (contaminants) and processes in water treatment. At the top of the figure, the contaminants are classified by size: macroscopic, microscopic or colloidal, and truly dissolved. Simple treatment processes are classified into three categories according to their mechanism of action: physical, chemical, or biological. Complex processes, a category that applies to many of the processes actually used, involved two or all of these mechanisms. In the lower part of the figure, types of contaminants in each size range are linked to processes most commonly used to remove them, with the dominant mechanism(s) also identified. Thus, truly dissolved substances are removed by processes where chemical and/or biological mechanisms dominate. The most common treatment processes are described briefly in Section 3.16.4, and examples of their use are given in Section 3.16.6. Table 1 links processes to the achievement of each of the seven treatment goals identified previously. Although, for example, a number of processes can contribute to TOC removal, it is evident that really only a biological filtration process can improve the biological stability of the water. (Network or secondary disinfection is listed for completeness because it is used in a number of jurisdictions, although it is not really a treatment process. It does not of course remove substances causing biological instability, but rather masks or counteracts their effect (Huck and Gagnon 2004). Figure 2 shows the typical location in a treatment train where each of the goals would usually be met. (The figure is derived for surface waters requiring particle removal and for the more common case of a treatment train using granular media filtration rather than a membrane process.) The figure also gives a general indication of the number of processes that would typically contribute to the achievement of a given goal. Thus, removal of particles (and physical removal of pathogens) normally takes place early in the treatment train and a number of processes may contribute (e.g., coagulation, flocculation (sedimentation), and filtration). Disinfection or inactivation of pathogens would typically take place later in the treatment train and often is achieved in a single process (Figures 3 and 4).

3.16.3 Key Chemical and Physical Principles/ Phenomena for Water Treatment There are a number of well-known chemical principles or phenomena that are important for water technology. These are:

• • •

equilibrium, precipitation/dissolution, kinetics,

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Chemical Basis for Water Technology

1. Classification of contaminants based on size Macroscopic: Microscopic (colloidal): Dissolved:

greater than 10−5 m from 10−5 to 10−9 m less than 10−9 m

2. Classification of simple processes based on mechanism Physical processes (PF) Chemical processes (PCh) Biological processes (PB) 3. Classification of complex processes based on integration of mechanisms Integration of processes

Physicochemical processes (PF-Ch)

PB

Biophysical processes (PB-F)

h -C

PB -F

PB

Biochemical processes (PB-Ch)

PB-Ch-F

Biophysicochemical processes (PB-Ch-F)

PB-Ch

PF

PCh

4. Classification of contaminants and removal processes Contaminant Suspensions Emulsions Algae Parasites Bacteria

Colloids Macromolecules Bacteria Viruses

Molecules Ions

Size classification Macroscopic

Processes

Mechanism

Screening Sedimentation Flotation Rapid filtration Micro filtration UV radiation

Physical processes dominate

Microscopic (colloidal)

Coagulation Rapid filtration Chemical oxidation Biological filtration Ultrafiltration Chemical stabilization

Integration of physical, chemical and biological processes

Dissolved

Oxidation/reduction Chemical precipitation Ion exchange Biological filtration Chemical stabilization

Chemical and biological processes dominate

Figure 1 Classification of contaminants and processes in water treatment.

• • •

oxidation/reduction, complexation, and surface phenomena.

The relevance of each of these is described briefly below, and some examples in relation to the goals defined in Section 3.16.2 are given. These principles are referred to in Section 3.16.6, where the various treatment goals are addressed from the perspective of the chemical (and physical/biological) processes involved. As in any process, equilibrium is important in defining the end state that the process can reach, given enough time. It thus provides an upper or lower limit on what may be achieved. In addition to homogeneous (i.e., single-phase) equilibria, heterogeneous equilibria, both gas–liquid and liquid–solid, are important in water treatment. The adsorption of contaminants on activated carbon provides an important example of liquid– solid equilibria. Gas–liquid equilibria are important in air stripping processes to remove volatile contaminants from water.

Precipitation and dissolution reactions are a special class of equilibria that are important in water treatment. One of the ways of removing dissolved contaminants or target substances is to cause them to precipitate or co-precipitate, and then physically remove the precipitate from the system. The feasibility of this depends on the solubility product, and an example of a precipitation process is the removal of phosphorus from wastewater using either calcium or iron. Dissolution is important in terms of chemical additions and also in terms of applications such as the use of solid calcium carbonate to dissolve gradually and add alkalinity to water. Kinetic phenomena are arguably among the most important in water treatment, because of the generally limited residence time of water in engineered treatment systems. Naturally based treatment systems can take advantage of much slower reactions; however, the retention time of water in individual treatment process steps typically ranges from a few seconds to perhaps several hours. Continuing the example of the adsorption processes mentioned above, true equilibrium is

Chemical Basis for Water Technology Table 1

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Goals and Processes Goal

Process

Particle removal a

TOC removal

Coagulation/flocculation Sedimentation Flotation Rapid granular filtration Biological filtration Membranes (MF or UF) Membranes (NF) Disinfection/oxidationc Chlorine Ozone UV Ozone/UV or H2O2/UV Adsorption Air stripping Ion exchange (including MIEXs) Secondary disinfectiond pH correction

    ( ) 

()b () () ()

Disinfection/ inactivation

Biological stability

Chemical stability

() 

 () 

( ) ( )

Removal of chemical contaminants

 

() ()

()



   ()

()

Esthetic quality

()

()

 ()    



  

  

a

Including physical removal of pathogens. Not necessarily the principal goal of this process. c When used as oxidant, may also provide (some) disinfection. d Provision of residual for distribution system. b

Number of processes

N

Removal of particles and pathogens

Removal of TOC

Removal of chemical contaminants

Removal of taste and odor

Chemical stability

Biological stability Disinfection/ inactivation 1 Beginning

Typical location in treatment train

(Secondary disinfection) End

Figure 2 Treatment goals and processes.

not reached because of the limited time the water is in contact with the activated carbon. In the case of this process the kinetics or rate of mass transfer of the absorbing molecule to the adsorption site is invariably limiting, rather than the kinetics of the actual adsorption itself. Another process where the kinetics are very important and determine the physical design is chemical disinfection or inactivation of microorganisms. For example, regulations in the USA (and similar ones have been adopted in other countries or jurisdictions) specify the contact times that must be provided, based on essentially a first-order kinetic model. Oxidation/reduction processes (primarily the former) are important in water treatment. A common way of removing contaminants is to oxidize them, in some cases to a form that

is more easily removed. Whether oxidation or reduction will occur depends on the redox potential of the system. Common oxidizing agents added to water include chlorine and ozone. To an important extent, the action of those substances as chemical disinfectants is based on oxidation reactions. Complexation. Complex formation, involving either organic or inorganic ligands, is important in certain aspects of water treatment. For example, trivalent metal ions (aluminum or iron) added as coagulants form complexes that are important in terms of reducing the charge on colloidal particles. Humic substances, often the major fraction of NOM in water, can form complexes with metal ions, affecting their solubility and treatability. Trace organic contaminants can also complex with humic substances.

434

Chemical Basis for Water Technology Pressure-driven membrane processes

Macromolecules (humics), viruses

Particles, sediment, algae, protozoa, bacteria

Multivalent ions & DOM

Monovalent ions (Na+, Cl−)

Water molecules

Pore size 0.1−1 μm

MF

1−100 nm (>1000 Da)

UF

~ 1 nm (200−1000 Da)

NF

<1nm (<200 Da)

RO

Figure 3 Removal capabilities of membrane types. From MWH (2005) Water Treatment: Principles and Design, 2nd edn., figure 12-2, p. 957. Hoboken, NJ: Wiley.

Diffused aeration

Spray towers

Percent removal

90.0 Cross-flow tower

99.0

Packed tower 99.9 99.99 Not feasible

99.999 NH3

CHCl3 PCE

CH4

99.9999 10−1

100

101

102

103

Mass transfer. Mass transfer is important in heterogeneous systems, and also in some cases in homogeneous systems. For example, von Gunten (2003a) has indicated that oxidation reactions involving hydroxyl radicals are with a few exceptions essentially diffusion controlled. Mass transfer is described mathematically by Fick’s first and second laws and the driving force for mass transport is the difference between the actual and equilibrium concentration in a given phase. In the case of adsorption of organic contaminants on activated carbon, either film or surface/pore diffusion, rather than the actual adsorption itself, is the rate-limiting step. In gas–liquid processes, such as air stripping of volatile contaminants from water or the transfer of ozone into water, maximizing interfacial mass transfer is crucial for successful process design. This involves both minimizing the thickness of the stagnant layers and maximizing the renewal of the surface.

104

Henry’s constant (atm) Figure 4 Gas-liquid separation process selection diagram. From MWH (2005) Water Treatment: Principles and Design, 2nd edn., figure 14-1, p. 1166. Hoboken, NJ: Wiley.

Surface phenomena. Because water treatment can involve all three phases (liquid, solid, and gaseous), surface phenomena can sometimes be important. For example, the behavior of colloidal particles is largely determined by the surface charge that they carry. Surface phenomena play an important role in the removal of particles by granular media filtration and the removal of various contaminants by various types of membrane processes.

3.16.4 Summary of Processes Used in Water Treatment 3.16.4.1 Coagulation and Flocculation Coagulation is one of the most important processes in water treatment and one where the chemistry can be extremely complex. Basically, coagulation involves either neutralizing the charges on colloidal particles so they can agglomerate in a subsequent flocculation step, or adding substances that can either bridge between like-charged particles or enmesh them. The flocculated particles can then be removed by a particleseparation process (sedimentation, flotation, granular media filtration, or low-pressure membranes). An important early article discussing the chemical aspects of coagulation is that of Stumm and O’Melia (1968).

Chemical Basis for Water Technology

Colloids or particles in water carry a charge, which for some species is influenced by pH. This charge leads to the creation of the well-known electrical double layer around the particles. Particle stability due to electrical double layer interactions is described by the well-known Derjaguin–Landau– Verwey–Overbeek (DLVO) theory, and the electrical forces prevent the particles coming close enough so that the physical attractive forces can draw and keep them together. Coagulants act either by forming charged intermediates that adsorb at the particle surface, thus reducing the charge and the thickness of the electrical double layer (charge neutralization), or by forming an amorphous hydroxide precipitate that can enmesh the particles (sweep coagulation). The most common coagulants are salts of aluminum or trivalent iron, with aluminum being more commonly used. As Edzwald (1993) points out, particles (both mineral and organic) may also be stable in water because of hydrophilic effects due to bound water or steric interactions from adsorbed macromolecules. He also notes that NOM rather than the particles in water can control the dosage and selection of coagulants, arguing that at neutral or acidic pH values, humic and fulvic acid organic ligands complex aluminum, resulting in an aluminum demand that must be satisfied before precipitation of aluminum hydroxide can occur. The removal of NOM with aluminum coagulants can involve aluminum hydrolysis reactions, complexation with aluminum, and precipitation. It can also involve adsorption on aluminum hydroxide that can precipitate once the aluminum demand referred to above is satisfied. Although alum (aluminum sulfate) and ferric chloride are the most widely used coagulants, the most commonly used among others include sodium aluminate, polyaluminum chloride (PACl), and cationic organic polymers. PACl has been introduced in recent decades, and is often more effective in cold waters. This is because some of the intermediates formed by alum are in fact polymeric species and in cold waters the kinetics of their formation are slower. The hydrolysis reactions of both alum and PACl have been examined by Van Benschoten and Edzwald (1990). The term ‘enhanced coagulation’ refers to a regulatory requirement in the US having the objective to remove TOC by coagulation to reduce the formation of disinfection byproducts. Edzwald and Tobiason (1999) introduced the term ‘multiple objective coagulation’. On this basis, the optimum coagulation conditions are those that maximize removals of pathogens in downstream processes, result in low turbidity values and particle counts, and minimize residual dissolved aluminum concentrations in the water. Sludge production and operating costs of course also play a role in optimizing coagulation. The physical process of flocculation involves the aggregation of the destabilized colloidal particles into loose macroscopic structures referred to as flocs. The rate of floc formation is determined by the rate of contact of the destabilized particles. Although perikinetic flocculation can take place by Brownian motion, this is not sufficiently rapid to be of practical importance in water treatment, and therefore orthokinetic flocculation is used. This occurs by the input of energy into the fluid, most commonly by mechanical mixing, although in some cases due to the turbulence created in the

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water due to flow and the resulting loss of hydraulic head. The rate of flocculation, or rate of collision of primary particles per unit volume, is given by

Ji ¼ constant N2i d3i du=dz

ð1Þ

where Ji is the number of collisions of primary particles per unit time, Ni the number of primary particles per unit volume, di the diameter of the primary particles, and du/dz the local velocity gradient. Although Equation (1) indicates that increased time and energy input will reduce the number of primary particles, they also lead to breakup of flocs, which become more susceptible to shear forces as their size increases. Thus, there is an optimal Gt for flocculation, as well as optimal values for G and t, which are generally well established in practice. Often, flocculation will take place in several tanks in series, with progressively reducing Gt values to favor aggregation initially and minimize floc breakup in the later stages. Optimal G and t values will also be different depending on the downstream process, that is, whether sedimentation occurs or not and whether final particle separation is by classical granular media filtration or by a low-pressure membrane. In some cases, the so-called inline flocculation occurs without the use of a separate flocculation basin.

3.16.4.2 Sedimentation Sedimentation is one of the processes for which the development of theory has overtaken its application. In 1851, George G. Stokes analyzed the behavior of rigid, nonporous spherical particles in liquid under specific physical conditions. From a practical perspective, the important physical conditions he assumed were constant velocity under laminar flow conditions (Re r 0.4), and that the particles act individually. The force balance (the downward gravity force equals the sum of the upward buoyant and drag forces) leads to the wellknown Stokes’ law which allows calculation of the settling velocity for a given spherical particle under laminar flow conditions:

v ¼ gðrp  rÞd2p =18m

ð2Þ

where v is the settling velocity, g the acceleration due to gravity, rp the particle density, r the fluid (water) density, dp the particle diameter, and m the viscosity. The work of Stokes was subsequently verified experimentally, and supported the further development of the understanding of sedimentation through both theoretical and experimental work. The goal of the majority of this work was and remains attempting to develop mathematical relationships to describe the frictional resistance for a broad range of Reynolds numbers, and especially for nonspherical particles. In practice, Stokes’ law can only be used in a limited sense to describe sedimentation processes in water treatment. For example, the flocs developed as a result of coagulation do not follow Stokes’ law, because as they agglomerate their mass and size and therefore settling velocity increase. Also, actual suspensions are polydisperse, making their mathematical description more complicated. At higher concentrations of

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Chemical Basis for Water Technology

particles or flocs, they interfere with one another, leading to a behavior known as zone settling. The practical value of Stokes’ law is in illustrating the major influence of particle diameter and density on settling behavior. Sedimentation is a well-established process in water treatment, and in practice design and operation are based on established ranges of important factors. Key parameters in this regard include the surface loading rate of the sedimentation basin (e.g., cubic meters per square meter per unit time, which has the units of velocity), residence time in the basin (related to the depth of the basin), and design of the inlet and outlet structures to minimize turbulence within the basin. So-called shallow depth sedimentation devices are now commonly used in water treatment. In these units the sedimentation basin contains a number of either closely spaced parallel plates or tubes on an angle. The water flows upward between the plates or within the tubes, thus minimizing the distance a particle or floc has to settle to be removed, and substantially increasing the surface loading rate at which the basin can be operated.

3.16.4.3 Flotation Dissolved air flotation (DAF) is an alternative to sedimentation that has found increased application in drinking-water treatment over the last several decades. In terms of criteria such as turbidity and particles, DAF can produce an effluent that is essentially equivalent to that from sedimentation. DAF basically involves attaching air bubbles (typically 10–100 mm diameter) to particles, although the fundamental mechanisms of this attachment are not well understood. The attachment of the bubbles causes particles in the water to float to the top of the tank. DAF is therefore especially suited for light particles such as algae, colloidal, or particulate NOM (causing color), for low to moderate turbidity waters that produce a light floc upon coagulation, and for cold waters. In some waters that vary seasonally, it is possible that operation of DAF may only be required during part of the year. The flotation process is accomplished by introducing air to the water under pressure prior to the flotation tank. Usually, the water that is pressurized is a portion of the effluent stream that has been recycled, and this pressurized water is mixed with the incoming flow prior to the flotation tank. When the water enters the tank near the bottom, the pressure is released causing the formation of fine bubbles that attach to solid particles as they rise to the surface. The float containing the solids that accumulates on the surface of the tank is then skimmed off mechanically or allowed to overflow the tank. The treated water, referred to as the subnatant, is withdrawn from the tank lower down. Principles and applications of DAF are discussed in detail in several references (e.g., Edzwald, 1995, 2010; MWH, 2005). One of the most important factors affecting DAF performance is proper coagulation (MWH, 2005). Also important are floc characteristics, bubble size and rise velocity, air loading, and floc-bubble attachment. Proper coagulation is important for a good attachment of particles to air bubbles and the same fundamental principles of coagulation discussed earlier apply here. DAF must be followed by a final particle-separation step. Normally, this would be granular media filtration, but it may

also be a low-pressure membrane (microfiltration (MF) or ultrafiltration (UF)).

3.16.4.4 Filtration Granular media filtration has historically been one of the main processes used in water treatment. It initially started as what is now referred to as slow sand filtration, however, rapid filtration has now been in use for more than 100 years. Additional processes where a filtration mechanism occurs are bank filtration and underground passage. Increasingly, rapid granular media filtration is being replaced by low-pressure membranes (Section 3.16.4.5) for particle removal. The theoretical background and practice of granular media filtration are well described in various standard environmental engineering textbooks (e.g., MWH, 2005). Despite its apparent simplicity, rapid granular filtration is one of the most complicated processes in water treatment, due to the large number of factors and parameters that determine its effectiveness, the complexity of what actually occurs in the filter bed and the resulting difficulty in unambiguously and accurately determining these parameters as well as mathematically describing the process. The fundamental variables and parameters of filtration change as a function of time and filter bed depth. The generally accepted filtration theory of Iwasaki (1937) was developed for filtration of a uniform suspension at constant filtration velocity, under isotropic and laminar conditions by a uniform bed. Further work on the development of filtration theory conducted by, among others, Ives (1960), Mints (1966), Hereit (1973), and Adin and Rebhun (1977) has led to important theoretical advances; however, the conditions investigated are generally not readily translatable to full-scale filtration practice. In addition, in the mathematical relationships developed by the authors mentioned above, the characteristics of the suspensions, filter bed and other parameters of the filtration process are often defined using constants and the parameters that are difficult to determine experimentally. Therefore, the existing theories of filtration cannot be directly applied for the design and operation of filters in practice. They are however helpful in planning more fundamentally based filtration investigations and interpreting the results of such studies. The particles (including pathogenic microorganisms) removed by granular media filtration are generally smaller than the channels through which they pass among the media grains. The first step of the removal process involves the transport of the particle from the bulk of the water to the surface of a media grain. In the second step, attachment to and retention on the media grain, particle or floc characteristics as well as media characteristics become important. There is thus a strong connection between pretreatment (coagulation/flocculation and possibly sedimentation) and the success of a downstream filtration step. O’Melia and Stumm (1967) published a seminal paper on the importance of physical and chemical factors in filtration. A detailed discussion is also provided in MHW (2005: ch. 11). Granular media filtration involves a well-defined cycle, beginning when the filter is placed back into service following the removal of previously accumulated particles through backwashing. Following this initial period there is normally

Chemical Basis for Water Technology

an extended period when filter effluent quality is at its best and pressure drop (headloss) is rising although relatively slowly because of particles being accumulated in the bed. At some point, often on the order of 24 h, the filter is ready for backwashing. Backwashing is typically initiated based on either a headloss trigger, a filter effluent turbidity (or particle count) trigger, or following a specified time. The first trigger is preferable to the second, because it ensures that filter effluent quality remains acceptable throughout the run. For example, even relatively modest increases in turbidity at the end of the filter cycle can correspond to a significant deterioration in the removal capability for Cryptosporidium (Huck et al., 2002). There are a number of factors that influence filtration performance: media (type, diameter), bed depth, hydraulic loading, chemical pretreatment, backwashing, and temperature. There are generally accepted ranges for these parameters, although temperature can of course not be controlled. It is generally desirable to base full-scale design on pilot testing. As indicated earlier, the removal of particles by filtration is a complex process. However, a useful fairly simple approximate mathematical representation is given by

CL =Co ¼ eZL=d

ð3Þ

where CL is the particle concentration at depth L, Co the influent particle concentration, Z the constant for a given filter bed, and d the media diameter. Thus, deeper beds and smaller media diameters will improve removals, although in a less-than-proportional way. Because headloss increases as particle diameter decreases, there is an inherent conflict between headloss and particle removal. The L/d ratio is sometimes used for filter design; a value of 1000 or greater is generally considered reasonable and MWH (2005: ch. 11) notes a range of 1000–2000. Most large filters are open to the atmosphere and flow is by gravity, although some small filters may be enclosed and operated under pressure. The choice of media is one of the most important decisions to be made. For rapid filtration there are essentially two variations: a relatively standard dual media design, typically with anthracite on top of sand, or a deeper bed monomedia (usually anthracite and known as filter adsorbers or GAC filter caps) design, which typically uses coarser media and operates at higher flow rates. Alternative media may include granular activated carbon (GAC; sometimes to replace only the anthracite) for simultaneous removal of dissolved contaminants by adsorption. Catalytic media have also been used for iron and manganese removal. In addition, some newer media have been developed that may be advantageous in specific applications.

3.16.4.5 Membranes In recent years, membranes have increasingly been used in drinking-water treatment, largely because of their substantial decrease in cost. On a life-cycle cost basis, low-pressure membranes (MF and UF) are sufficiently competitive with conventional particle removal processes (i.e., chemically assisted granular media filtration) that they are now normally given at least initial consideration in any plant upgrade or new

437

design. Additional reasons for adopting membranes can include improved and more robust treatment performance. Although hypothetically a membrane could eliminate the need for disinfection, such a single-barrier process may not be approved by regulatory authorities. A detailed description of membrane processes may be found elsewhere (e.g., MWH, 2005). Although there is currently limited application of ceramic membranes in drinking-water treatment, the overwhelming majority of membranes are made of polymeric material. The usual types of membranes that would be applied in drinking-water treatment from freshwater sources are MF, UF, and nanofiltration (NF). Reverse osmosis (RO) is used in desalination of seawater and brackish waters, and in water reuse applications, although NF may also be used in the latter. MF and UF are referred to as low-pressure membranes, whereas NF and RO are high-pressure membranes. A general indication of the classes of substances that can be removed by the common membrane types is given in Figure 3. Thus, low-pressure membranes are used for particle/pathogen removal, NF for organics (TOC and some micro-contaminant) removal and softening, and RO for micro-contaminant and inorganics removal. Low-pressure polymeric membranes are typically manufactured in a hollow fiber format, in which individual fibers are grouped together into a bundle or cassette. NF and RO membranes are configured as a spiral-wound flat sheet. As is evident in Figure 3, each membrane type has a range of pore sizes, and a given membrane could be classified as being in either of two categories (e.g., MF or UF). Nominal pore size can be used as an initial criterion in selecting a membrane type. However, because of the normal variability in pore size and other factors affecting rejection by membranes (see Section 3.16.6), the removal of a specific entity or substance in a specific water by a given membrane normally needs to be quantified by direct testing. Water is pushed/pulled through a membrane by a difference in pressure. Recovery is the percentage of the feedwater that passes through the membrane as useful product or permeate, and feasible recovery decreases with decreasing membrane pore size. However, membranes having a larger pore size require less pressure differential and therefore are cheaper to operate. Flux refers to the flow of a substance (either water or a specific contaminant) through the membrane, per unit area of membrane surface, in a given time. Units commonly used to express water flux are liters per square meter per hour (Lmh). The amount of membrane surface area and therefore the capital cost is directly related to the operating flux that can be maintained on an ongoing basis. In addition to water temperature (i.e., viscosity), the design of the membrane itself, and specifically its pore size or pore-size distribution, is a very important factor in determining achievable flux. However, in practice all membranes become fouled and this normally substantially decreases the flux for a given pressure differential. Fouling is caused by accumulation, on the surface of the membrane or within the pores, of material rejected by the membrane. The types of fouling that can occur are:

•

colloidal and particulate, caused by nondissolved inorganic and organic matter in the water, including particulate microbial cell components;

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• • •

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organic, caused by components of the background TOC, including dissolved microbial cell components; biofouling, caused by the actual growth of microorganisms on the membrane surface; and inorganic, caused by the deposition or precipitation of inorganic salts on the membrane surface.

For low-pressure membranes that are backpulsed, a distinction can be made between hydraulically reversible and irreversible fouling. Chemical cleaning is normally used periodically with all membranes to remove accumulated foulants. The factors controlling the extent of fouling in a particular situation are the type of membrane and its pore-size distribution, the membrane material, the quantity and nature of fouling material present, the pretreatment applied, the cleaning regime used for the membrane, and the operating conditions, especially the flux. Fouling is a complex and important phenomenon, and further detailed discussion is outside the scope of this chapter. Understanding and mitigating fouling is an extensive and very active research domain at the time of writing. One recent important finding has been the demonstration of the importance of organic fouling for low-pressure membranes. Particularly important in this regard are biopolymers, consisting primarily of polysaccharides and protein-like material. Recent bench and pilot-scale investigations have shown rapid biological filtration to be effective in reducing bioorganic fouling of low-pressure membranes (e.g., Halle´ et al., 2009). Hijnen et al. (2009) have demonstrated the important role played by easily biodegradable carbon in the biofouling of high-pressure membranes. The importance of fouling in the present context is that pretreatment steps to reduce fouling have an important influence on the overall treatment train.

3.16.4.6 Disinfection Achieving proper disinfection is one of the most important goals in water treatment. In general, this goal is the only one which, if not achieved, can create an acute as opposed to a long-term health risk. For this reason many jurisdictions specify the level of disinfection that must be achieved. Although required disinfection levels are generally similar around the world, the specific values can be different in different jurisdictions. Normally, disinfection requires application of a process to reduce the concentrations of pathogenic microorganisms present in the raw water to levels representing a negligible health risk. Thus, raw waters that are more heavily contaminated will require greater reductions. Quantitative microbial risk assessment (QMRA) has been used recently in some cases to assist in arriving at appropriate disinfection requirements. Disinfection does not imply complete sterilization of the water. The term inactivation is often used in recent years with regard to protozoan pathogens and also applies to viruses, and therefore the term disinfection/inactivation is often utilized. Pathogens can also be physically removed, for example, by granular media filtration or by membranes, contributing to the overall reduction required. Inactivations or removals are normally expressed in logarithmic units. For example, three-

log removal means that initial microbial concentrations have been reduced by three orders of magnitude (99.9%). Because disinfection is widely described in various standard and specialized texts, the topic is treated only briefly in this section. After providing some general comments, the emphasis is on how disinfection processes may impact or be impacted by the rest of the treatment train. The three types of pathogenic microorganisms of greatest concern in drinking water are bacteria, viruses, and protozoans, specifically Giardia and Cryptosporidium. In general, the latter are most difficult to disinfect/inactivate, and will often drive process design and operation. (In the case of UV however, some viruses may be the most difficult to inactivate.) Historically, chlorine was the major disinfectant used worldwide, and its introduction approximately 100 years ago or more represents one of the great public-health advances of all time. However with the development in the 1970s of the ability to measure potentially hazardous chlorination byproducts such as trihalomethanes in water, alternatives were more actively sought. Ozone, which had been in greater use in Europe, came to be considered as the generally best alternative. However when Cryptosporidium became a concern in drinking water field in the 1990s, it became evident that the doses and contact times of ozone, especially at low water temperatures, could make it economically unattractive. At about this time, it was demonstrated that UV radiation could be effective against Cryptosporidium at reasonable dosages, and for approximately the past decade that water industry globally has seen a major increase in the use of UV for disinfection. Other disinfection agents may be used in special cases and small systems; however, the three just mentioned are the workhorses of municipal water treatment. Some jurisdictions require the maintenance of a disinfectant residual in the distribution system, and in cases where chlorine would lead to unacceptably high levels of chlorination by-products, the distribution system residual may be provided by chloramination, either by the addition of preformed chloramines at the end of the treatment process, or by the addition of ammonia to react with chlorine already present. In the case of the chemical disinfectants, chlorine and ozone, practice or regulations involve maintaining a desired concentration of the disinfectant in contact with the water for a specified period of time during treatment. In some jurisdictions a CT value is specified, which is the product of the disinfection concentration (C) and contact time (T), calculated in a prescribed way. In the case of UV disinfection, a fluence is specified, being the product of UV intensity and time. Contact times for chlorine are generally on the order of tens of minutes, those for ozone on the order of a few minutes, and those for UV on the order of a few seconds. The major interactions between disinfection and other treatment processes or requirements can be summarized as follows: 1. Is oxidation also required, for example, for the removal of odorous compounds? 2. What level of physical removal can the process train achieve? 3. What is the TOC concentration in the water at the point of disinfection? This will affect both the level of by-product

Chemical Basis for Water Technology

formation, as well as the initial disinfectant demand or UV transmittance. If the TOC level is too high, some TOC removal may be required prior to the disinfection step. 4. What is the pH of the water? Lower pHs are more advantageous for disinfection with chlorine because more of the hypochlorite acid formed by reaction of the chlorine with the water is present in the undissocciated form, which is more effective for disinfection. In a full-scale study with ozone, Urfer et al. (1999) demonstrated that both the lower pH and lower TOC provided by enhanced coagulation prior to ozonation were beneficial in maintaining a higher ozone concentration in the disinfection step. 5. What is the range of water temperatures? Lower temperatures lead to the need for greater disinfectant concentrations and/or contact times to maintain the same level of disinfection. In summary, the disinfection requirements for a particular water often have a major impact on treatment and need to be considered together with the other goals to be met, in order to arrive at an optimally configured treatment train.

3.16.4.7 Oxidation Oxidation is another important process in water treatment, and is capable of addressing several treatment goals (Table 1). The discussion herein includes the interplay between oxidation and other processes. As indicated in the previous section, the major chemical disinfectants (chlorine and ozone) serve as oxidants. In addition, UV used for disinfection can carry out oxidation by direct photolysis, and in combination with hydrogen peroxide can act as an advanced oxidation process (AOP), due to the generation of hydroxyl radicals. However, it should be noted that the UV doses necessary for effective oxidation of microcontaminants are generally much higher, perhaps by even an order of magnitude, then the doses generally required for disinfection/inactivation. Ozone on its own can act through either the molecular ozone or hydroxyl radical pathway and in combination with hydrogen peroxide can also serve as an AOP, because the production of hydroxyl radicals is greatly increased. Other oxidants such as potassium permanganate can be used in specific applications, such as some taste and odor control situations. Biological oxidation is also important and is discussed later in Section 3.16.4. Although in previous decades chlorine was used as an oxidant, this is becoming less common. This is related directly and indirectly to the formation of chlorination by-products, the indirect relationship being that other disinfectants (primarily ozone and UV) are replacing chlorine and so it is less commonly present in treatment processes. A detailed discussion of the use of oxidation to reduce concentrations of the odorous compounds geosmin and 2-methylisoborneol (MIB) is provided in Section 3.16.6. Comprehensive reviews of the use of ozone in drinking water for both disinfection and oxidation are provided by von Gunten (2003a, 2003b). In terms of the interrelationship between oxidation and other treatment processes, the following points can be noted:

• •

oxidation can assist in coagulation and filtration, ozonation of NOM increases its biodegradability,

• •

439

oxidation increases the polarity of NOM, potentially reducing its adsorb ability on activated carbon, and oxidation early in the treatment process can lyse algal and cyanobacterial cells before they can be physically removed.

Oxidation reactions, including those from UV, can produce by-products, because the oxidized substances are rarely mineralized completely. The chlorination by-products have been most studied, and while those from ozonation and UV are either considered to be less harmful or are present at much lower concentrations, ongoing research need is the identification of by-products, particularly from oxidation of trace contaminants such as pharmaceuticals. When hydrogen peroxide is used as part of an AOP, consideration must be given to removal of any hydrogen peroxide residual exiting the process. Urfer and Huck (1997) have demonstrated the removals achievable by biological filtration. Chemical reducing agents can also be used, and in previous practice have sometimes been used where high chlorination dosages were used (referred to as superchlorination).

3.16.4.8 Gas–Liquid Transfer (Aeration and Air Stripping) Gas–liquid transfer processes are important in water treatment, especially for the treatment of groundwater. They can be used as aeration to add oxygen to water, which increases the redox potential and may be important for biological processes. In some cases, they can be used to add carbon dioxide. They can also be used to strip volatile components from water, including excess carbon dioxide, hydrogen sulfide or methane, and volatile organic contaminants. The discussion here does not consider gas–liquid transfer with chemical reaction (e.g., ozone addition), which is more complicated. Although there are some general similarities between aeration and air stripping (because in both cases good contact between the water and air must be assured), the equipment used for the two types of processes is generally different. The following discussion focuses primarily on air stripping. Gas (air) stripping has been a mature process in the chemical processing industry for a long time and its principles are well understood. Its wider application to drinking-water treatment began several decades ago when volatile organic contaminants began to be of wider concern. Several excellent references are available (e.g., MWH, 2005) and the design process, including the use of commercially available software, is well established. The most important parameter with regard to the feasibility of air stripping for a particular contaminant is the Henry constant, discussed below. Because air stripping does not destroy contaminants but simply transfers them to another phase, treatment of the offgases is normally required. Both equilibrium and kinetics (rate) considerations are important for air stripping. In terms of equilibrium, the driving force for gas transfer between the water and the air is the difference between the existing and equilibrium concentrations in the two phases. At equilibrium, the concentration (partial pressure) of the contaminant in the gas phase (air) is proportional to its concentration in the water. This relationship is known as Henry’s law. Most gases and vapors follow Henry’s law in the range of interest in water treatment (Kavanaugh and Trussell, 1980). Henry’s constant, which is a

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Chemical Basis for Water Technology

measure of volatility for a particular substance, is given by the following equation:

H ¼ PT Y=X

ð4Þ

where H is Henry’s constant for a particular contaminant, PT the total pressure, Y the mole fraction (concentration) of the contaminant in the gas phase (air), and X the mole fraction (concentration) of the contaminant in the water. Because Henry’s constant is an intrinsic property of a particular gas, contaminants with a higher Henry’s constant will tend to partition more into the gas phase, that is, be more easily stripped from water. Conversely, a substance with a low Henry’s constant will be relatively soluble in water and therefore difficult to strip but easy to absorb into water. Henry’s constants for most contaminants of interest are tabulated in standard references. It should be noted that different units can be used to express Henry’s constant, which affects the numerical value. Therefore, when comparing Henry’s constants for different contaminants, one should check to be sure that they are reported in the same units. Henry’s constant is a strong function of temperature, increasing as the temperature increases. Although the temperature of groundwater is often relatively constant year-round, stripping towers are often located outside and therefore, depending on the local climatic conditions, the temperature of the process may vary considerably. Although the value of H decreases as the salt concentration of the water increases, for drinking water applications such changes can be ignored. Although pH does not have a direct effect on Henry’s constant, it can have an influence on the removal of some contaminants (such as hydrogen sulfide) that ionize, because only the unionized species is volatile. An example of the impact of pH on hydrogen sulfide removal is given by MWH (2005: 1175). The rate at which a volatile contaminant is transferred across the air–water interface depends on both the driving force (the difference between the actual and the equilibrium concentration) and the intensity of mixing at the interface. For the design of air stripping equipment, it is important to be able to determine the unit rate of gas transfer (i.e., contaminant removal per unit time and unit area of interface). There are three consecutive steps involved in gas transfer: transfer from the bulk water to the interface, transfer across the interface, and transfer away from the interface into the bulk of the gas (air). For most gas–liquid contacting systems of practical interest, flow of one or both phases is turbulent, and the rate of gas transfer must be estimated empirically. An overall mass-transfer coefficient can be calculated that incorporates the mass-transfer resistances in both phases. For most situations in water treatment, resistances in the water control the overall rate of mass transfer (James, 1985). The overall rate of mass transfer also depends on the interfacial area. Increasing this area (and ensuring that it is continuously renewed) requires energy, and therefore a balance must be struck in air-stripping design between increased mass transfer and increased energy costs. In turbulent flow, the specific interfacial area is difficult to determine. Therefore, the common practice is to measure the product of the masstransfer coefficient (KL or KG, the overall coefficient for the liquid or gas phase, respectively) and a (the total interfacial

area divided by the system volume, or specific interfacial area). This quantity KLa or KGa is known as the volumetric mass transfer coefficient and is the value typically provided by manufacturers for various air-stripping equipment. (It should be noted that the discussion above regarding the rate of gas transfer applies to situations in which the gas does not react in the water. Volatile contaminants to be removed by air stripping do not normally react in water.)

3.16.4.9 Adsorption Adsorption is a major process that can be used for the removal of chemical contaminants, and its use in water treatment is especially for the removal of organic contaminants. It is applicable to both groundwater and surface waters; however, it can be a relatively expensive process depending on the levels of contaminants to be removed and the presence of other substances in the water (such as background TOC) that also adsorb, reducing the capacity of the process for the contaminants of interest. By far, the most commonly used adsorbent in water treatment is activated carbon, which can be applied in two forms: GAC or powdered activated carbon (PAC). A detailed discussion of the theory of adsorption is outside the scope of this chapter. Basically, from the viewpoint of thermodynamics, the contaminant prefers to be on the surface of the carbon rather than in the water. The process of adsorption is not instantaneous however; therefore, the contact time between the activated carbon and the contaminant is important. Several excellent references (Sontheimer et al., 1988; MWH, 2005) provide more detail on the fundamentals of adsorption. Basically, activated carbon is produced by heating a carbon-based material such as coal or wood to high temperatures in the absence of oxygen. This modifies the surface properties of the carbon and creates a very high surface area (on the order of 1000 m2 g1). This large surface area is due to the extremely fine pores that are created in the carbon structure by the activation process. The smallest pores, referred to as micropores, are not much larger than the adsorbing contaminant molecules and are the ones responsible for the carbon’s capacity. The capacity for specific types of compounds is affected by the starting material and the manufacturing process, which in turn affect the chemistry of the carbon surface and the pore structure. Activated carbon is a heterogeneous material, meaning that there are different types of sites with different energies for adsorption. The properties of a given contaminant are also very important in determining the extent to which it will be adsorbed. For charged compounds, pH can play a major role. Important factors include molecular weight, chemical structure, and polarity. For practical purposes the effect of temperature on adsorption can be ignored in water treatment. Li et al. (2005) have developed a model to predict adsorption based on activated carbon and contaminant properties. The extent to which a given contaminant will adsorb at equilibrium is expressed by an isotherm. Although various equations can be used to describe isotherms, in water treatment practice the most commonly used one is the Freundlich equation:

Q ¼ KC 1=n

ð5Þ

Chemical Basis for Water Technology

where Q is the equilibrium adsorbed phase concentration of adsorbent, K the Freundlich adsorption capacity parameter, C the equilibrium liquid phase concentration of adsorbent, and 1/n the Freundlich adsorption intensity parameter. The literature contains a considerable amount of ‘purewater’ (e.g., distilled and/or deionized water) isotherm data for many common contaminants. Although an isotherm is specific to a given activated carbon, most of the available data have been obtained with carbons that are commonly available commercially. Because activated carbon is a very nonspecific adsorbent, it will also remove other substances in the water, primarily organics, which compete with the target compound(s) for adsorption sites. In practice, the most significant competition is from background TOC, which can reduce the capacity of the carbon substantially below that predicted from purewater isotherms (e.g., Graham et al., 2000a). For PAC, which is typically in contact with the water for at most on the order of an hour, direct competition is most important. For GAC, significant preloading of background TOC can also occur.The practical significance of competition and preloading is that pure-water isotherms can provide only an indication of relative adsorbability, and cannot be used for design. Because the contact times in water-treatment practice are rarely sufficient for even approximate equilibrium to be achieved, process kinetics and, in practice the available contact time, are important. The movement of a contaminant from the water to the surface of the carbon consists of three sequential steps: diffusion through the stagnant layer of water surrounding the carbon particle (film diffusion), diffusion within or along the surface of the carbon pore (pore or surface diffusion), and finally the actual adsorption at a site within the pore. Usually, one or other of the diffusion steps is rate limiting. This is true for both GAC and PAC. Although modeling and short-term laboratory testing of adsorption processes can be performed, they are especially complicated for GAC because of the preloading discussed above and because of potential biodegradation of some contaminants on the GAC. Yu et al. (2009a, 2009b) have addressed and modeled the significant impact of preloading on both kinetics and equilibrium for the adsorption of trace levels of selected pharmaceuticals and an endocrine disrupting compound. GAC is typically regenerated, whereas PAC is not. Regeneration involves heating the carbon under appropriate conditions and results in the restoration of most of its original adsorptive capacity. Detailed information regarding regeneration (reactivation) is provided by Sontheimer et al. (1988). Another important issue for GAC is the potential desorption of adsorbed contaminants. Because adsorption is an equilibrium process, a decrease in the influent concentration of a particular contaminant can cause some of it to be desorbed from the carbon and appear in the effluent. This can lead to the unusual situation in which the concentration of a contaminant is higher in the GAC effluent for a period of time than in the influent. In Section 3.16.6.5 the adsorption of odor-causing compounds is addressed in detail and the practical implications of issues considered in this section are discussed.

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3.16.4.10 Biodegradation Biological treatment has played an important role historically in drinking-water preparation in processes such as slow sand filtration, bank filtration, and underground passage. Within the last several decades, rapid biological treatment has been implemented globally in a number of treatment facilities, in the form of the biological activated carbon (BAC) process. This process combines ozonation, one of whose effects is to increase the biodegradability of NOM in water, with a biologically active carbon filter. Rapid biological treatment has also been implemented with nonadsorbing filter media instead of GAC, and without prior ozonation. By reducing the concentration of biodegradable organic matter (BOM) in the water, biological treatment reduces the opportunity for bacterial regrowth in the distribution system. This can also lessen problems such as taste and odor and corrosion. Within the last few years, biological treatment has attracted increasing interest in North America, in part because of stricter regulations on disinfection by-products. By removing at least part of the biodegradable portion of NOM, biological treatment reduces the concentration of chlorination by-product precursors. Since chlorine demand is also reduced, a lower chlorine dosage can be applied to maintain a given residual in the distribution system. Although biological treatment can also remove various substances from drinking water such as ammonia, iron, and trace organic contaminants (Rittmann and Huck, 1989; Halle´, 2010), its major application is for BOM removal. Comprehensive reviews on biofiltration are provided by Urfer et al. (1997) and Huck and Sozan´ski (2008). The benefit derived from biological processes in drinkingwater treatment is based on the ability of bacteria to oxidize substances present in water that can be referred to as biological instability. Although bacteria can also reduce substances, the focus of this discussion is on oxidation, which is more common. For oxidation to take place, there must be an electron donor, an electron acceptor, appropriate nutrients and suitable environmental conditions such as temperature, pH, and the absence of toxic substances. Common electron donors in water are organic matter and ammonia. Iron and hydrogen sulfide are among other substances that can serve as electron donors. Since drinking water must remain aerobic, the electron acceptor normally of interest in drinking-water processes is oxygen. The major nutrients aside from carbon are nitrogen and phosphorus. The various other micro-nutrients required by bacteria are normally present in appropriate amounts. For most drinking waters, the limiting nutrient is organic carbon, although phosphorus may also be limiting in some waters (Miettinen et al., 1997). Bacteria which can only use organic forms of carbon are referred to as heterotrophs, and are therefore the bacteria of interest for BOM removal. Part of the organic carbon is converted to cell material and may exit the process eventually. The remainder of the organic carbon is either transformed to other soluble organics with a lower energy level (i.e., more oxidized) or mineralized to carbon dioxide. Since waters used for drinking-water preparation generally contain low levels of nutrients compared to wastewaters,

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bacteria which dominate in this environment are referred to as oligotrophs. The bacteria of importance in biological drinking-water treatment exist as a biofilm, or agglomeration of bacterial cells and extracellular polymers on solid support surfaces such as filter media. Suspended or planktonic organisms are generally unimportant for removals because of their relatively low numbers and the relatively short process retention times. Biofilms in drinking-water treatment are generally much thinner than those in wastewater treatment, are normally not visible to the naked eye, and may be discontinuous or patchy. Any biological treatment process should be considered as an ecosystem, which may have additional levels of the food chain present, most commonly protozoa. The organic carbon oxidized by the bacteria is commonly referred to as the substrate. It is commonly assumed that bacterial growth is linearly related to substrate utilization through the yield coefficient. The most common models developed to describe drinkingwater biofiltration have been summarized by Urfer et al. (1997) and Huck and Sozan´ski (2008). For practical purposes, BOM removal can be approximated as a first-order process (Huck et al., 1994). The practical objective of any modeling is ultimately to provide a tool for process design and operation. A common design question is the required bed depth or contact time to achieve a given treatment objective. BOM is the most common electron donor and energy source of interest in drinking water. It is present in low concentrations, often less than 0.5 mg l1. It can be composed of many different organic substances and cannot readily be distinguished chemically from nonbiodegradable NOM. Therefore, some type of assay in which the BOM is biodegraded is required for its measurement. The biochemical oxygen demand (BOD) test used in wastewater treatment is far too insensitive to be used for drinking water. The initial BOM measurement method for drinking water was proposed by van der Kooij et al. (1982) and measures a parameter that he termed easily assimilable organic carbon (AOC). The AOC content of the sample is determined using a calibration curve and the measured bacterial growth. A subsequent modification (Haddix et al., 2004) uses strains that fluoresce. More recently, a different AOC method has been published by Hammes and Egli (2005). In critically reviewing the BOM methods available at that time, Huck (1990) divided the methods into those which measure bacterial growth and those which measure a change in dissolved organic carbon (DOC) concentration as a result of biodegradation. The parameter measured by these latter methods is termed biodegradable dissolved organic carbon (BDOC). Huck (1990) noted that the method chosen should relate to the purpose of the measurement: if the purpose is to control bacterial growth, then a method which measures bacterial growth should be used, whereas if the objective is to measure reduction in chlorine demand or chlorination byproduct precursors, then a method which measures DOC is more appropriate. A BDOC method which uses a column packed with a special type of glass beads was reported by Frias et al. (1992) and subsequently further refined (Kaplan et al., 1993). In the method, DOC is measured in the influent and effluent of the column. If a column is placed on-line at a given point in a

treatment plant or distribution system, the method can therefore give a BDOC value as quickly as DOC can be measured. van der Kooij et al. (1995) have developed the biofilm formation rate protocol and apparatus, which directly measures bacterial accumulation on rings (e.g., glass) simulating sections of pipe. Methods that provide only an overall measure of BOM may be insufficient for quantitative process design and optimization. Rather, knowledge and quantitation of the major BOM components may be required. In general, major components are carbohydrates, amino acids, biodegradable portions of humic substances, polysaccharides, protein-like material, aldehydes, oxoacids, and carboxylic acids. The latter three groups are expected to be important in ozonated waters. Some of the compounds in these groups are important in microbial metabolism and therefore may be expected to impact directly on bacterial growth. Polysaccharides and protein-like material have been shown to be important in fouling of low-pressure membranes (Haberkamp, 2008; Halle´ et al., 2009). There is ample evidence that ozone enhances the biodegradability of NOM. Therefore, increased levels of biological instability (e.g., AOC) will occur in the finished water if there is no biological treatment step following ozonation. However, to date, it has not been possible to develop generally applicable quantitative relationships between BOM levels and bacterial growth in distribution systems, because of the complexity of the processes involved.

3.16.4.11 Ion Exchange (Including MIEXs) Ion-exchange processes can be used for the removal of charged dissolved species from water. With the exception of the emerging use of magnetic ion-exchange (MIEXs) resins discussed below, they are less commonly used in municipal water treatment, and therefore are discussed in less detail. The principle of ion exchange involves exchanging target ions in the water for those on an ion-exchange resin. Resins are commonly in the form of beads, and the process functions as a packed bed reactor. Following exhaustion of the resin, it is regenerated with a concentrated solution containing the ion to be later exchanged into the water during the operating phase. Ion exchangers can be used for the removal of either cations or anions. In the case of the former, the process is most commonly used to remove hardness-causing calcium and magnesium, and is prevalent in home water softening units. Softening is also an important process in the treatment of boiler feedwater. Ion exchangers can also remove anions, for example, in water demineralization for advanced uses such as electronics or boiler feedwater, for the removal of nitrate in groundwater, and also for removal of organic ligands, as discussed below. A large number of commercial ion exchange resins have been developed. They are available with varying degrees of selectivity for specific items, since one of the limitations of the process is that ions other than the target one may also be removed in the process, sometimes preferentially, reducing its effectiveness. The impact of ion exchange on water quality also depends on the ion used to regenerate the resin. For example if an ion exchanger used for water softening is regenerated with sodium

Chemical Basis for Water Technology

chloride, as is commonly the case in home units, the sodium concentration of the water will increase. Regeneration of the resin with acid would increase the hydrogen ion concentration of the water being treated, lowering the pH. In terms of treatment process design, handling of the residues produced by ion exchange must be considered. For the removal of TOC (DOC) or some of its components, ion-exchange processes have also been investigated (e.g., Croue´ et al., 1999) and implemented (e.g., Hongve et al., 1999). The use of granulated iron hydroxide has also been investigated (Teermann and Jekel, 1999) and a review of the use of adsorptive/ion-exchange processes to remove humic substances has been written by Fettig (1999). However, in terms of wider application, the process of this type that is attracting increasing attention for TOC removal in recent years is the MIEXs process, originally developed in Australia. Pilotscale studies are being or have been conducted in a number of countries, and full-scale applications include a large plant in Perth, Australia, that has been operating for several years. In contrast to conventional ion-exchange processes, the MIEXs process is designed to be used in infrastructure that is very similar to that of conventional water-treatment plants. The theoretical basis for the ability of the MIEXs resin to remove DOC is that the resin has strong base functionality, and is therefore able to exchange (remove) weak organic acids (an important component of most DOC) at the neutral pH of most natural waters. The resin is highly selective and therefore can achieve effective removals. The resin is also resistant to physical attrition. Because the resin has a smaller particle size (mean diameter approximately 150 mm) but a comparable specific surface area (surface area per unit volume) compared to conventional ion-exchange resins, the MIEXs resin has considerably more external surface area. This leads to a higher rate of DOC removal. It also reduces fouling of the resin because less DOC is exchanged into the interior of the particles. A key feature of the MIEXs resin is that it contains a magnetic component. This allows the resin beads to agglomerate under the right hydraulic conditions and facilitates their separation from the water so the resin can be recycled.

3.16.4.12 pH Correction pH correction is done to ensure the chemical stability of the water. The chemical stability does not directly affect the quality of water for drinking or other uses; however, water that is not chemically stable leaving a treatment facility can undergo changes in the distribution system that would worsen its quality and cause problems (e.g., precipitation) for some uses. In principle, raw waters entering treatment are chemically stable, because of the generally long time that they have spent in the environment prior to entering a treatment plant. The addition of various chemicals during treatment may disturb this equilibrium, often by lowering the pH; therefore, in some cases it may be necessary to correct (raise) the pH at the end of the treatment process. Although the classical concern is to ensure equilibrium with respect to calcium carbonate, other issues may also be important, for example, the pH of the finished water can have a substantial impact on lead solubility in the distribution system (e.g., Lytle and Schock, 2005). An

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additional aspect of chemical stability is to ensure that dissolved coagulants (e.g., aluminum) do not precipitate in the distribution system.

3.16.5 The Evolving Nature of Water Treatment The approach and philosophy of water treatment continues to evolve, particularly over the last one or two decades. This is driven by technical changes within the field itself (e.g., the availability of new technologies), an increasing and more sophisticated range of contaminants to be addressed, increased regulatory requirements and in some cases the changing regulatory philosophy, as well as broader issues such as environmental footprint, energy requirements, and climate change. Robustness is being given increased importance in water treatment design. For example, Hrudey and Hrudey (2004) have highlighted the risk associated with changes in raw water quality, in terms of microbial pathogen outbreak events. A robust process can be defined as one that is able to produce excellent water quality under normal conditions, and to deviate minimally from that when challenged (Huck and Coffey, 2004). The potential for more variable weather patterns associated with climate change will place increased importance on treatment process robustness. Risk assist procedures (e.g., QMRA – Quantitative Microbial Risk Assessment; Haas et al., 1999) allow ‘worst case’ scenarios to be explored as part of plant design or upgrading.

3.16.5.1 Increased Emphasis on Physical/Biological Processes Early water-treatment processes such as slow sand and bank filtration relied essentially exclusively on natural physical and biological processes. The first major use of a chemical technology was the introduction of chlorine for disinfection. The addition of metal salts for coagulation and flocculation represented another use of chemical processes. As additional man-made contaminants had to be addressed, chemical processes such as oxidation came more to be used. However, in recent years there has been a desire to move away from a reliance on chemical processes. The origins of this can be traced to the 1970s when the discovery of chlorination by-products (Rook, 1974; Bellar et al., 1974) placed essentially the first upper limit (other than cost) on disinfectant addition. Since that time, there has been a desire to minimize chemical additions because of cost and also because of the production of by-products and/or residuals that need further handling and disposal. In addition, in some cases a philosophical resistance to the addition of chemicals to water has arisen. The consequence of this is that physical and biological processes are now favored. To some extent, this is facilitated by advances in technology such as improved membranes and improvements in apparatus for UV disinfection/inactivation. However, biological processes are also seeing a resurgence, for example, at the time of writing for the last several years in the USA.

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3.16.5.2 The Evolving Role of Membranes One of the most significant developments in water treatment in the last decade has been the much broader introduction of membrane processes. The initial application was of highpressure RO membranes for seawater desalination. However, membranes are now being routinely applied or at least evaluated for a much broader range of applications. Lowpressure membranes (MF and UF) are in many cases replacing conventional granular media filters for particle and pathogen removal. High-pressure (NF) membranes are being implemented for TOC and color removal, can be used for softening and can provide some removals of trace organic contaminants (e.g., Makdissy et al., 2007). This increasing use of membranes is consistent with the greater emphasis on physical processes noted in the previous section. In terms of particle removal, membranes are also more robust than granular media filtration, because they can provide an absolute barrier. However, membrane use is not entirely chemical free, because chemicals are required for cleaning. The use of biofiltration pre-treatment processes to reduce fouling in low-pressure (UF) membranes has however been successfully demonstrated (Halle´ et al., 2009; Huck et al., 2009). The robustness referred to above is being given increased importance in water-treatment design. For example, Hrudey and Hrudey (2004) have highlighted the risk associated with changes in raw water quality, in terms of microbial pathogen outbreak events. A robust process can be defined as one that is able to produce excellent water quality under normal conditions, and to deviate minimally from that when challenged (Huck and Coffey, 2004). The potential for more variable weather patterns associated with climate change will place increased importance on treatment process robustness.

3.16.5.3 Environmental Footprint As with other processes and activities in society, water treatment should seek to minimize its environmental footprint, consistent with providing good treatment. An important component of this is energy consumption, and the most obvious use of energy is in pumping, both the low-lift pumping to the treatment plant, possible pumping within the treatment process, and the high-lift pumping to the distribution system. While these are not directly water quality concerns, steps taken to minimize such energy use may have an impact on treatment (e.g., configuration of processes within the plant). One aspect of reducing energy use is to reduce water consumption, which may also delay required expansion of treatment facilities. Energy is also consumed during treatment to provide mixing, often associated with chemical additions. Therefore, minimization of chemical additions reduces energy requirements, including those associated with the manufacture and transport of the chemicals. Reduced addition of, for example, coagulant chemicals will also minimize solid residuals produced by the process.

3.16.5.4 Coping with Supply Constraints Increased population pressure and climate change will, in some cases, lead to the use of raw waters that may be more

difficult to treat, and to increase the need for water reuse. Seawater desalination will also increase. All of these will lead to more challenging treatment requirements, and will also favor the increased use of membrane processes. One option that may be pursued in some locations is the use of dual distribution systems, where only the water directly consumed is treated to meet all drinking-water requirements. While this approach can reduce treatment costs, it has of course its own associated health risks, which must be carefully evaluated and minimized. At a minimum, all water should be treated to be microbiologically safe. One option would be to partially treat all water centrally, and then provide a higher level of treatment for the water to be consumed, closer to the actual point of consumption, potentially at the neighborhood level. This approach, in addition to leading to a more robust system, helps to minimize problems of water quality deterioration in distribution systems. Although its costs would have to be evaluated carefully for a given situation, it is conceptually increasingly feasible because of the greater use of treatment processes such as membranes and UV disinfection that can be more amenable to automation and remote monitoring and control.

3.16.6 Addressing the Treatment Goals – From the Perspective of the Chemical, Physical, and Biological Processes Involved This section describes how the processes described in Section 3.16.4 can be used to address the various treatment goals identified in Section 3.16.2. The goal ensuring esthetic quality is addressed as part of the removal of chemical contaminants, because esthetic quality is often largely determined by odorous compounds that can be removed in treatment. A small section on maintaining water quality from the treatment facility to the consumer’s tap is also included. Because of the large area that has to be covered, this section can only provide general information on the processes that can be used to address specific goals. The focus is on the practical achievement of these goals, and where possible comprehensive review papers are cited that can provide much more specific detail. In this section, more detail is provided for some less common processes. The subsection on the removal of odorous compounds is quite detailed, both because there is less comprehensive review information in the literature, and also because the detailed description allows a number of important treatment factors and subtleties to be addressed, that are also relevant for the removal of other contaminants. It is said in water treatment that ‘‘every water is different’’ and thus resources such as the present chapter can fill an important role in suggesting processes that can be investigated on site, ideally at pilot scale, to define or improve the treatment process for given water.

3.16.6.1 Particle Removal (Including Pathogens) 3.16.6.1.1 Coagulation, flocculation, and sedimentation As indicated in Section 3.16.4, coagulation and flocculation are used as preparatory processes for solid separation. The final solids separation step will be either granular media

Chemical Basis for Water Technology

filtration or low-pressure membrane filtration (MF or UF). Except for raw waters having low suspended particle concentrations, sedimentation will normally be inserted prior to the final particle-separation step, to reduce the load on that step. Because of the preparatory nature of coagulation, flocculation, and sedimentation, specific applications are not described separately, but rather are discussed where appropriate in relation to other processes in the following sections. Flotation, as a newer and less widely practiced alternative to sedimentation, is discussed in the following section.

3.16.6.1.2 Flotation Although the same fundamental principles of coagulation discussed earlier apply, a small low-density floc is appropriate for DAF. Thus, optimal coagulation and flocculation conditions for DAF are likely to be different than those for sedimentation. Often flocculation time will be reduced, compared to those prior to sedimentation, and a higher flocculation energy may be advantageous. Polymers are rarely used (except possibly for the newer high-rate DAF processes) and the coagulant dose may be lower than for sedimentation. The coagulant dose may also need to be seasonally optimized, because of temperature and other factors. The rise velocity of a bubble depends upon its size and water temperature (i.e., viscosity). Because flow conditions should be laminar, it is possible to calculate the rise velocity of a bubble based on Stoke’s law. However, in practice, operating conditions such as hydraulic loading are determined based on optimization within known feasible ranges. The air loading (grams of air per cubic meter of influent water) is a function of the amount of air introduced into the recycle flow. The mass of air in the pressurized flow can be calculated using Henry’s law. A certain minimum air loading is required for successful operation. Above this minimum, an increase in air loading within a certain range will improve performance (i.e., reduce effluent turbidity). Beyond this point, further increases in air loading do not improve performance. The design basis of DAF is the hydraulic loading rate. Newer high-rate processes are capable of loadings in the range of 40 m h1 (MWH, 2005), compared to more traditional loadings of 10–20 m h1. Because of the number of factors that affect performance and because the fundamental mechanisms for floc-bubble attachment are not well understood, testing of DAF is required to establish its suitability in a given situation. Bench scale batch testing can be used to provide an initial indication of process feasibility and to aid in determining optimum coagulant and coagulant dosages. However, pilot-scale testing is required for the optimization of the various factors that affect process performance, and for reliable determination of process costs. DAF must be followed by a final particle-separation step. Normally, this would be granular media filtration, but it may also be a low-pressure membrane (MF or UF). Therefore, although the effluent turbidity and particle counts of the DAF effluent are very important, the optimum DAF conditions are those that will lead to optimum filtration or membrane performance. In addition to providing details regarding the design of DAF, MWH (2005) summarizes the advantages and disadvantages of

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DAF in comparison with other clarification technologies (e.g., sedimentation). As indicated earlier, DAF is generally not a good process where higher raw water turbidities are experienced. Valade et al. (2009) also provide guidance regarding the selection of DAF versus sedimentation and direct filtration. They recommend DAF for waters of relatively high quality: river sources (i.e., mineral turbidity) with average turbidity below 10 NTU (NTU, number of transfer unit) or reservoir sources (i.e., nonmineral turbidity) with average turbidity below 100 NTU. They place no upper limit on organic content and note that maximum turbidities in both types of waters can be higher. In a comprehensive review of DAF, Edzwald (2010) notes that DAF is especially effective treating water from reservoirs, raw waters containing algae, color or NOM, and waters with low mineral turbidity. He also notes that DAF is more efficient than sedimentation in removing Giardia cysts and Cryptosporidium oocysts.

3.16.6.1.3 Filtration As discussed in Section 3.16.4, rapid filtration, whose goal is particle removal, can provide some physical removal of pathogens, especially the cysts and oocysts of Giardia and Cryptosporidium, respectively. Filtration is a mature technology and the general range of operating conditions and expected performance are well established in the literature and in textbooks. In a number of jurisdictions, filtration effluent quality is specified in order to allow the filtration process to receive removal credit for organisms such as Giardia and Cryptosporidium. Filter effluent quality is typically specified in terms of turbidity, and more recently in some situations in terms of particle counts. Optimum design and operation of a filtration process typically requires site-specific investigations. Among the various investigations that have quantified removals of Giardia and Cryptosporidium is an investigation by Nieminski and Ongerth (1995) that examined both conventional and direct filtration, at both pilot and full scale. In a review of water-treatment processes for the removal of Giardia and Cryptosporidium, Betancourt and Rose (2004) summarize removals obtained by filtration in various studies. They note that these studies affirm the importance of proper coagulation for cyst and oocyst removal through all stages of conventional treatment. In a comprehensive pilot-scale investigation, Huck et al. (2002) observed a substantial decrease in removal at the end of the filter cycle, before filter effluent turbidity had substantially increased. Weiss et al. (2005) indicated that riverbank filtration had the potential to provide substantial reductions in concentrations of various microorganisms, relative to levels in raw water. Their investigations involved more than a year of monitoring at three full-scale riverbank filtration facilities in the US. They note that accurate removals for Giardia and Cryptosporidium could not be directly determined because of the low and variable levels of these pathogens in the raw waters.

3.16.6.1.4 Membranes With low-pressure membranes (MF and UF) replacing granular media filtration, one of their functions is of course to provide physical removal of microbial pathogens. A number of jurisdictions allow specified log removals to be claimed for

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these processes; however, monitoring and maintaining membrane integrity is critical for maintaining these removals. A well-cited study by Jacangelo et al. (1995a) evaluated the removal of protozoan cysts and oocysts and a model virus by six commercially available membranes (three MF and three UF). Three different source waters were used, and both benchand pilot-scale investigations were conducted. As long as the membranes remained intact, no cysts or oocysts were detected in the permeate, and physical straining appeared to be the primary removal mechanism. The extent of virus removal was membrane specific, ranging from o0.5 log to 46 logs. Phenomena contributing to removal appeared to be physical sieving or adsorption, cake layer formation, and the fouling state of the membrane.

3.16.6.2 TOC Removal The various processes available for TOC removal are discussed briefly in the following subsections. Their applicability in a particular water-treatment situation will depend on sitespecific circumstances, including the nature (i.e., composition) of the TOC. The optimum process for a particular water will also depend on the other treatment objectives being addressed, to which some or all of these processes may also contribute.

3.16.6.2.1 Enhanced coagulation In the US, enhanced coagulation requirements depend on raw water TOC concentration and alkalinity (e.g., Edzwald and Tobiason, 1999). Briefly, the required TOC percentage removals increase with increasing raw water TOC concentration and decrease with increasing alkalinity. The latter requirement is related to economic considerations. Raw waters having a TOC of 2 mg l1 or less are not required to practice enhanced coagulation. Edwards (1997) developed a model for predicting the DOC concentration remaining in the water after enhanced coagulation. Model inputs are coagulant dose, coagulation pH, and raw water UV254 and DOC. Vrijenhoek et al. (1998) investigated the removal of trihalomethane (THM) precursors and suspended particles from two surface waters by enhanced coagulation at pilot scale. They reported optimal removal of THM precursors at pH 5.5 and postulated that at this pH, humic substances were probably removed by formation of insoluble aluminum–humate complexes at a low alum dosage and adsorption to aluminum hydroxide precipitates at a high alum dosage. They noted that enhanced coagulation does not benefit raw waters with low specific UV absorbance (SUVA) values, but also noted that such waters may not form elevated concentrations of disinfection by-products (DBPs) because of the low humic fraction in the NOM. Yan et al. (2008) investigated enhanced coagulation of three well-characterized typical source waters in China with PACls. They found optimum NOM removal at pH 5.5–6.5 for all PACls, and recommended that basicity, speciation, and dosage of the coagulant should be optimized, based on the raw water alkalinity.

3.16.6.2.2 MIEXs The various process components required for an MIEXs installation are described by Slunjski et al. (2000). The first step involves contacting the water with the resin over a 10–30 min

detention time to allow the DOC to be exchanged on to the resin. Only a low-energy input is required to maintain the resin in suspension, because the magnetic attraction of the resin beads occurs over only a very short distance. Following this step, the resin–water suspension flows by gravity to the resin separation stage. The inlet of this vessel is designed to facilitate inter-particle collisions resulting in agglomeration of the magnetic resin beads. The agglomerates have a high settling velocity (Lange et al. (2001) report 25 m h1). This allows a high-upflow water velocity in the settling vessel, which prevents turbidity accumulation in the system. The settled resin is pumped back to the contractor for another DOC loading cycle while the treated water continues onward to the next process step. A small amount of used resin is continuously removed from the recycle line for regeneration and replaced with regenerated resin. The used resin is regenerated in a batch mode using a sodium chloride solution. The regenerant is reused a number of times although the chloride concentration of the regenerant must be restored to the original level prior to the next regeneration cycle (Lange et al., 2001). Slunjski et al. (2000) report that small additions of sodium hydroxide to the regenerant may also be beneficial for some waters and that periodic acid washes may be required when it is necessary to remove metal precipitates from the resin. The MIEXs process does not require pretreatment of the water (Slunjski et al., 2000). However in some cases, it might be advantageous to combine the process with coagulation for increased DOC removal. In testing for one treatment plant, the MIEXs process was shown to preferentially remove the lowermolecular-weight component of the DOC while alum coagulation removed the larger-molecular-weight DOC components (Lange et al., 2001). Those authors noted that MIEXs and coagulation would complement each other, regardless of the order of the two processes. In pilot testing for this plant, MIEXs (prior to coagulation) was shown to reduce the raw-water DOC (approximately 10 mg l1) by about 60%. Placing MIEXs ahead of coagulation would reduce the coagulant dose (and associated sludge production) needed for DOC removal and also reduce the need for pH correction following coagulation. Wert et al. (2005) evaluated MIEXs at pilot scale to remove DOC and bromide, and to assess the process as a pretreatment for ozonation. In this study treating Lake Mead water in the US, up to 30% DOC removal was obtained, leading to a measurable decrease in ozone decay rates. This led to a reduction in the transferred ozone dosages required for Cryptosporidium inactivation of 15–25%, and reduced bromate formation by 35%. In a bench-scale investigation, Boyer and Singer (2005) compared MIEXs with enhanced coagulation for the removal of DBP precursors and bromide. They studied raw waters from four drinking water facilities in California, having a range of raw water characteristics. They reported that MIEXs was more effective than coagulation in removing UV-absorbing substances and DOC. Using raw waters in the UK in a bench-scale investigation, Mergen et al. (2006) reported a substantial reduction in coagulant dose when coagulation was preceded by MIEXs. In pilot-scale investigations at four US locations, Singer et al. (2007) also examined the effectiveness of MIEXs for the

Chemical Basis for Water Technology

removal of DBP precursors, and found that it was generally more effective in this regard than enhanced coagulation. MIEXs removed substantial amounts of DOC and UVabsorbing substances, except in water that had a high concentration of total dissolved solids and a low SUVA value. Hydrophobic and transphilic DOC fractions were removed more effectively by MIEXs than was the hydrophilic fraction. The authors also noted that DBP speciation after MIEXs tended to shift from the fully chlorinated THM and haloacetic acid (HAA) components to their more brominated counterparts because bromide was less well removed than DOC. In further bench-scale investigations of three of the waters with quite different NOM character studied above (Mergen et al., 2006), Mergen et al. (2008) determined that the hydrophobicity of the DOC has an important effect on its removal using MIEXs. Although the water containing NOM with high hydrophobicity showed good DOC removal in the first cycle of contact with the resin, removal decreased substantially with subsequent use of the resin. This decrease was attributed to the blocking of resin sites by higher-molecularweight NOM present in the hydrophobic sample. For the water with more hydrophilic NOM, the DOC removal remained more consistent with subsequent use of the resin. In the water containing algogenic NOM, DOC was poorly removed and this was attributed to the greater presence of uncharged organics, considered likely to be carbohydrates and proteins. Son et al. (2005) conducted bench-scale investigations of MIEXs as a pretreatment for UF or MF membrane filtration. In considering MIEXs for a given application, total lifecycle costs should be determined and compared to other alternatives such as enhanced coagulation, BAC, or potentially NF. Costs would need to include residuals handling and disposal. Slunjski et al. (2000) presented cost estimates that showed that, for the Wanneroo plant in Perth, Australia, MIEXs was cheaper than either GAC adsorption or ozone combined with BAC. In summary, the MIEXs ion-exchange process represents a feasible technology for DOC removal. Although it does not have as long and extensive a full-scale operating history as alternative processes such as enhanced coagulation, it is beginning to be more widely applied at full scale. For a particular water where DOC removal is required, pilot testing is required for a reliable technical and cost comparison of MIEXs to other alternatives.

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In terms of BOM removal, water temperature was extremely important if it remained below 5 1C for a significant period of time. The removal of easily biodegradable substances such as ozonation by-products was significantly impaired at these lower temperatures. Above 5 1C, temperature had some influence, but not a major effect, on performance. The shortest contact times (on the order of a few minutes) were required for the removal of easily biodegradable ozonation by-products. Somewhat longer contact times were typically necessary for the removal of parameters measuring the biological stability of the water (e.g., AOC and BDOC). The longest contact times are generally required for the removal of chlorination by-product precursors and chlorine demand. Acceptable removals of the easily biodegradable compounds, and perhaps AOC or BDOC, may be achievable within the common range of contact times used for conventional particle-removal filtration. At temperatures above about 10 1C, Huck et al. (2000) found that the choice of filtration media (anthracite vs. (exhausted) GAC) had no measurable impact on BOM removal. At lower temperatures, GAC performed better than anthracite. GAC filters also recovered more quickly from process perturbations. The difference between GAC and anthracite was also greater for individual BOM components such as oxalate than for overall parameters such as BDOC. A previously developed non-steady-state biofiltration model was refined in the project and could successfully predict trends seen in the experimental work. One of the most important practical predictions from the model was that less readily biodegradable substances were better removed in the presence of easily biodegradable compounds. This supports the common observation (mentioned above) that ozonation prior to a biofilter improves biological filtration performance by creating more easily biodegradable substances. In more recent work treating a challenging river water, Halle´ et al. (2009) demonstrated the ability of rapid biofiltration (without prior coagulation or ozonation) to measurably reduce both hydraulically reversible and irreversible fouling of UF membranes. This was due to the ability of the biofilters to reduce the concentrations of biopolymers (polysaccharides and protein-like substances) in the raw water. It is thus evident that biological filtration can be used to provide at least partial removal of TOC in water treatment. Its applicability in a given situation will depend on the specific treatment objectives.

3.16.6.2.3 Biological treatment

3.16.6.2.4 Adsorption

In examining the removal of humic substances by biological filtration, Huck (1999) noted that investigations reported in the literature have shown only very limited biodegradation of unozonated humic substances under water-treatment conditions. However, several investigations had demonstrated substantial removals of humic substances by biofiltration following ozonation. By cleaving and modifying humic molecules, ozonation creates product molecules with increased biodegradability and diffusivity. A later experimental investigation of single-stage biological filtration (i.e., particle and BOM removal in the same filtration step) was conducted by Huck et al. (2000), largely at full scale.

In principle, adsorption on GAC could be used for TOC removal. For example, Jacangelo et al. (1995b) compared GAC to membrane processes (NF) and enhanced coagulation for the removal of NOM. In a qualitative summary they rated GAC adsorption (with regeneration) as providing very good NOM removal and as being generally intermediate in cost between enhanced coagulation and NF. In general, because of the relatively short time to exhaustion of GAC for TOC removal (on the order of months), adsorption on GAC would not be a preferred option for TOC removal, unless other processes could not be used, or unless GAC was also addressing other treatment objectives.

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Chemical Basis for Water Technology

3.16.6.2.5 Oxidation In addition to removing specific organic contaminants, oxidation can, in principle, also be used to reduce the concentration of TOC in water, and several example investigations are therefore cited. Carr and Baird (2000) examined mineralization of TOC at bench scale using ozone and ozone peroxide. They tested prepared standards, well waters from a groundwater recharge basin, and secondary and tertiary effluent from a wastewater reclamation plant. They found that the use of ozone alone or in combination with hydrogen peroxide may have practical limits for reducing concentrations of TOC. Thomson et al. (2004) used UV (low-pressure mercury vapor lamps) alone and in combination with hydrogen peroxide to investigate NOM removal from the highly colored surface water, high in TOC. They combined this process with biological treatment. They also found that although their preliminary study indicated that NOM could be removed, the UV doses used were thousands of times greater than those used for disinfection. It would therefore seem that, from a practical perspective, the use of oxidation processes for TOC removal may be restricted to specific cases where other processes cannot be used.

3.16.6.2.6 Nanofiltration Because of their pore size, NF membranes can remove at least a portion of the TOC. In a given situation, an important factor governing the extent of removal will be the pore-size distribution of the particular membrane being used. In terms of specific applications, Eriksson (1988) has cited the use of NF for the removal of color and TOC from surface water in Florida. In an article mentioned previously, Jacangelo et al. (1995b) compared NF to GAC adsorption and enhanced coagulation. In their qualitative comparison they rated NF as providing the best NOM removals, but also likely being the most expensive of the three processes. (They do note that, at the time of their article, costs for NF had been decreasing more rapidly than those for other processes and of course costs for NF have further decreased subsequently.) They note that because NF is not highly complex with regard to operation and maintenance, it is particularly attractive for small systems. Processes involving membranes (essentially NF pore size 1.5–5 nm) have been in use for TOC removal in small systems for several decades in Norway (Ødegaard et al., 1999).

3.16.6.3 Disinfection/Inactivation As indicated in Section 3.16.4, addressing the disinfection/ inactivation goal is arguably the most important in water treatment, in terms of minimizing acute health risk. Specific applications of processes to achieve this goal are not addressed herein, because they are well described in various standard reference works. In some jurisdictions, disinfection conditions, such as dose and contact time, are prescribed by regulation. As noted earlier, the major chemical disinfectants (chlorine, ozone, and potentially chlorine dioxide) also function as oxidants, as can UV. Together with the addition of substances such as hydrogen peroxide to promote the generation of hydroxyl radicals, ozone and UV can also function as AOPs. For this reason, as indicated in Section 3.16.4, process decisions with regard to disinfection should take into

consideration the need for oxidation in the process train to achieve other treatment goals. In addition to Section 3.16.6.2.5 on the use of oxidation to reduce concentrations of TOC, a detailed discussion is provided in Section 3.16.6.5 on the use of oxidation to reduce concentrations of odorous compounds.

3.16.6.4 Maximizing Biological Stability van der Kooij (2000) has provided a comprehensive discussion of biological stability. As indicated in Section 3.16.4.10, organic carbon is normally the limiting nutrient in drinking water, and maximizing biological stability therefore normally involves minimizing the concentration of BOM in the treated water. The necessary target BOM level may be sitespecific, and will be lower if no disinfectant residual is maintained in the distribution system. Because of their high surface area, filters make the best bioreactors to reduce BOM levels during treatment. Effective biofiltration can be obtained in first-stage filters that also provide particle removal (Huck et al., 2000) and in secondstage GAC filters (e.g., Pre´vost et al., 2005). Urfer et al. (1997) have critically reviewed the various factors affecting biofiltration. These include contact time, surface area, media, temperature, nature and concentration of the influent BOM, and biofilm disruption (due to backwashing). Another important concept (Huck et al., 2000) is whether biofiltration is managed or operated in the background. The former implies maximizing the performance of biofiltration without compromising other treatment objectives, whereas the latter implies focusing on other treatment objectives and accepting the degree of biological treatment that the filters thus operated are able to deliver. In general, biological filtration has classically been considered for four treatment objectives: the removal of easily biodegradable substances created during treatment (normally ozonation by-products) or present in the raw water, the removal of the biodegradable fraction of chlorination by-product precursors, the enhancement of biostability, and the removal of certain types of odorous compounds. More recently, biofiltration has been shown effective for the removal of specific trace contaminants (Halle´, 2010) and for the reduction of organic fouling in low-pressure membranes (Halle´ et al., 2009). Although contact time is a crucial variable, Zhang and Huck (1996) and Huck (1999) have shown the importance of considering a new parameter referred to as dimensionless contact time, which also includes reactor surface area and substrate parameters. It is, of course, important that disinfectant residuals not be present in biological filters.

3.16.6.5 Removal of Organic Chemical Contaminants As indicated in Figure 2, a number of different processes can be used to remove (trace) organic contaminants. The key to successful process selection and design is to take advantage of the most appropriate property of the substance to be removed. For example, the volatile contaminants in groundwater lend themselves to removal by some type of air stripping process, where they are transferred to the gas phase.

Chemical Basis for Water Technology

The processes for the removal of trace organic contaminants are basically oxidation, volatilization, adsorption, and membranes. Oxidation may, of course, be either chemical or biological. In a particular situation, more than one process may contribute to the removal of a specific contaminant. As will be evident, background TOC plays an important role in processes used for trace contaminant removal. The following section deals with the removal of the odorous compounds, geosmin and MIB. It is written in considerable detail, allowing us to address many important issues regarding the removal of these contaminants. As mentioned previously, it also illustrates the complexities and subtleties involved, many of which are directly relevant for the removal of other types of organic contaminants.

3.16.6.5.1 Geosmin and MIB Introduction. Problems of taste and odor continue to be perhaps the most difficult faced by drinking-water providers, and successful resolution of these problems is extremely important for consumer acceptance of the finished water. The complexity of taste and odor problems is due to the many possible sources of taste and odor in drinking water. Biological sources, such as cyanobacteria and other microorganisms, can produce a number of known and as-yet unidentified odor– causing compounds. Some of these have odor thresholds in the low nanogram per liter level. Anthropogenic or man– made chemicals can also cause taste and odor when they are discharged into drinking-water sources either in municipal/ industrial effluents or during spill events. Tastes and odors can also be created during treatment itself, in particular by the action of chlorine on some substances in the raw water. Tastes and odors can also arise in the distribution system as a result of microbiological activity, the reaction of disinfectants with organic matter, emissions from pipes and reservoir coatings, and possibly the diffusion of pollutants through plastic pipes. Home plumbing can also be a source of taste and odor. Temperature is an important factor in taste and odor episodes. Not only do higher temperatures lead to greater biological growth in distribution systems, but they generally lead to greater consumer perception of tastes and odors. Because of the complex nature of taste and odor, the successful resolution of a problem requires that the odor be properly characterized. Classifications proposed for tastes and odors are useful in this regard, as they give insights into the possible types of taste- or odor-causing compounds. Taste and odor can be analyzed both by sensory methods (i.e., using either the human nose or mouth as the detector) and by chemical techniques. In recent decades, the water industry has adapted the flavor profile analysis (FPA) method from the food and beverage industry. This is superior to the previously used threshold odor number (TON) technique, in that it provides both a characterization and an approximate quantitation of the taste or odor. Chemical analysis is however necessary for the identification of specific odorous compounds. The chromatographic sniffing technique provides a means of combining sensory and instrumental methods to identify individual odor components. Although the first line of defense against taste and odor problems is a high-quality source water, this represents an

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unattainable ideal for many water utilities. Mitigation for taste and odor therefore consists of attempting to prevent formation of the offending substances in the raw water, typically by cyanobacteria and potentially algae control, removing these substances in the treatment plant, usually by adsorption, or transforming them during treatment by oxidation or by microbial activity (biodegradation) into less offensive substances. The complexity of odor treatment is due to the fact that the overall odor of a water may be composed of a number of odorous compounds of various odor types and intensities. The most information available in the literature regarding treatment is for naturally occurring taste and odor compounds, especially the most commonly occurring ones, geosmin and MIB. Although this section therefore focuses on these compounds, many of the considerations presented are also valid for other odorous compounds, and indeed for nonodorous trace contaminants. Aeration can be used for the removal of volatile compounds, especially the rotten egg odor caused by hydrogen sulfide. AWWARF-LE (1987) reported that air stripping is effective for compounds with a Henry’s law constant greater than 103 m3 atm mol–1. It is not effective for geosmin and MIB, which have Henry’s law constants approximately two orders of magnitude lower. Conventional processes (e.g., coagulation, flocculation, sedimentation, and granular media filtration) have very limited usefulness against taste and odor. At the doses typically used for disinfection, chemical disinfectants such as chlorine, chlorine dioxide, and ozone may achieve at least partial success against some types of odors. Membrane processes on their own, especially the low-pressure membrane processes MF and UF, are not effective, because the odorous molecules are small enough to pass through the membrane. Some removals may be achieved with NF membranes. If there is no predisinfection, coagulation/flocculation/ sedimentation can remove organisms such as cyanobacteria and actinomycetes before they can be lysed by disinfection and release odorous substances. Ashitani et al. (1988) reported that both geosmin and MIB were present in raw water in dissolved form and in suspended form associated with the cyanobacteria (blue–green algae) from which they originated. The geosmin and MIB in suspended form were well removed by coagulation and sedimentation alone. However, breakpoint prechlorination caused leakage of geosmin and MIB from the host cells into the water, as also reported for geosmin by Gammie (1987). Both geosmin and MIB were decomposed by sunlight in the presence of free residual chlorine. Decomposition for the conditions tested was on the order of 50%. The three processes most effective against taste and odor are oxidation, adsorption on activated carbon, and biodegradation – these are discussed in detail in the following. Except possibly for the addition of PAC, the installation of a process for taste and odor control can be relatively expensive. However, oxidation, adsorption, and biofiltration are also able to achieve other treatment objectives, and this may be important in assessing overall cost-effectiveness. It should be noted that if geosmin and MIB are present, toxic cyanobacterial metabolites may also be present. Consideration of

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these is outside the scope of this section; however, treatment may be needed to deal with them as well, and some of the same processes discussed herein would apply. Oxidation. Oxidation is one of the key technologies that can be used for odor removal. As described earlier (Section 3.16.4) basically all of the common oxidizing substances and methods (i.e., UV) that can be used for disinfection can also be used for oxidation. Thus when taste and odor must be addressed as a treatment objective, this may influence the choice of disinfection method employed. Our understanding with respect to the use of oxidation processes for taste and odor control over the last 15–20 years has developed from the suspicion that the hydroxyl radical was important, to certainty that it is, and to the determination of kinetic parameters for hydroxyl radical oxidation, the socalled AOPs. Comparison of various oxidants. In laboratory studies, Glaze et al. (1990) evaluated a number of oxidants for the removal of six model taste and odor compounds (including geosmin and MIB) spiked into Colorado River water. In addition to conventional oxidants, the authors evaluated three AOPs: ozone with hydrogen peroxide, ozone with UV light, and hydrogen peroxide with UV light. (This was some of the first reported work for these AOPs.) The authors concluded that conventional oxidants (chlorine, chloramines, chlorine dioxide, potassium permanganate, and hydrogen peroxide) are unable to control taste and odor problems due to geosmin and MIB. AOPs were more successful, but the authors noted that they must be evaluated for factors such as cost and effectiveness. They found that ozone was able to oxidize geosmin and MIB without the addition of either hydrogen peroxide or UV radiation and attributed this effect to the fact that some constituents in natural waters were able to react with ozone to form highly reactive radicals, presumably the hydroxyl radical. Following on the work of Glaze et al. (1990), Ferguson et al. (1990) showed at pilot scale that PEROXONE (ozone in combination with hydrogen peroxide) required a significantly lower applied ozone dosage than ozone alone, to oxidize geosmin and MIB. Dietrich et al. (1995) examined the oxidation of six odorous and nonodorous algal metabolites by chlorine, chlorine dioxide, and permanganate. In some cases, the oxidants had little effect on the odor characteristic of the compounds (e.g., b-cyclocitral and phenethyl alcohol). In other cases, the oxidants created new odors from previously odor-free compounds (e.g., palmitic and linoleic acids), and in yet other cases the oxidants decreased, eliminated, or changed odors from odorous metabolites (e.g., linolenic acid (which produces a watermelon odor) and 2 t,6 c-nonadienal). There were many similarities in results obtained with the three oxidants. This research confirmed that elimination of one odor by oxidation can result in the formation of others. AWWARF-LE (1995: ch. 3) reported that, in comparison with chlorine, chloramination generally produces lower concentrations of odorous by-products such as iodoforms, chlorophenols, and aldehydes. However, chlorination is more efficient at removing a number of odors associated with anaerobic conditions, that may be described as septic, decaying

vegetation, swampy, and fishy. Chlorination is also more effective than chloramination at stopping the formation of medicinal iodoform. The use of potassium permanganate for taste and odor control appears to be specific to particular substances (AWWARF-LE, 1995). The authors note that in documented case studies involving KMnO4 and other treatments simultaneously, there has not been a careful evaluation to determine the controlling mechanism or mechanisms for odor removal. The authors also note that the effect of the background organic carbon matrix on taste and odor control by KMnO4 is also not clear. In evaluating the removal of odorous compounds by various oxidants, Jung et al. (2004) concluded that higher doses of ozone might be sufficient to control geosmin and MIB to below the odor threshold, which they identified as being 30 ng l1 for both compounds (i.e., higher than usual). The effect of several oxidants on MIB concentrations in the presence of cyanobacteria was investigated by Tung et al. (2004). Raw water samples were incubated to allow for the development of high concentrations of MIB (1000– 2000 ng l1), with as much as 70% of the MIB being contained within the algae cells. (Most of the algae were in fact cyanobacteria.) Under the conditions tested, ozone was much more effective than chlorine or potassium permanganate; however, all oxidants caused cell damage and release of intracellular MIB. Oestman et al. (2004) examined the ability of chlorine and chloramines to mask geosmin and MIB using two sensory analysis approaches – a statistical pairwise comparison test and FPA. The overall conclusion from this work was that neither chlorine nor chloramines are likely to be effective for reducing or masking other odors (such as geosmin and MIB) in drinking water. Bruchet and Duguet (2004) have provided a good summary on the role of various oxidants in removing, masking, and generating tastes and odors. They note that the main application of chlorine or chloramines as oxidants for removing odors that are either present in the raw water or that may develop in the distribution system is for the elimination of sulfide odors or related odors caused by anaerobic conditions. Although one or both oxidants can also remove several other odors, both are completely ineffective in removing most of the other odorous substances, including geosmin and MIB. The authors also note that chlorinous oxidants themselves impart a chlorinous odor at rather low concentrations. In contrast to the work by Oestman et al. (2004) reported previously, Bruchet and Duguet (2004) state that chlorine can mask other odors. They specifically cite the reduction in the musty taste caused by MIB as the concentration of free chlorine increased. They note that an important implication of this is that when the chlorine concentration decreases in the distribution system, other odors that were previously masked may reappear or increase in intensity. Bruchet and Duguet (2004) state that, at the time of writing of their article, the most efficient process for dealing with taste and odor problems was oxidation (ozone alone or in combination with hydrogen peroxide) in combination with adsorption on activated carbon. (It should be noted that recent work involving the combination of UV and hydrogen peroxide indicates that this process can be a viable alternative

Chemical Basis for Water Technology

to AOPs involving ozone.) In terms of the final disinfectant, Bruchet and Duguet (2004) note that it sometimes may be necessary to switch from chlorine to chlorine dioxide to minimize chlorinous or medicinal odors when phenols or iodides are present in the raw water. They also indicate that an AOP is highly desirable for dealing with taste and odor problems that may be caused by chemical spills in the source water. Finally, they also note that the best treatment processes for taste and odor removal are also the most efficient in dealing with cyanobacterial toxins, should those be present. Oxidation involving ozone. As noted previously (Section 3.16.4.7), ozone oxidation can occur either via molecular ozone or via hydroxyl radicals. This section includes studies where part of the oxidation may be due to the presence of hydroxyl radicals, even though their generation was not necessarily a deliberate objective. AWWARF-LE (1995) noted that additional work is needed to assess the health impacts of ozonation by-products, identify other by-products that create tastes and orders in treated water, and identify intermediate by-products that may be formed during the oxidation of taste and odor compounds. It is also noted that additional research is needed to identify intermediate by-products formed by the use of an AOP, as well as their potential health effects. Morioka et al. (1993) approximated the removal of geosmin or MIB by ozonation as a first-order reaction. Decomposition rates in natural waters were primarily influenced by the concentrations of carbonate ion and humic substances, attributable to scavenging effects on hydroxyl radicals. In synthetic waters, low concentrations of humic substances appeared to act as promoters. Ho et al. (2004) examined the effect of water quality and also the character of NOM on the ozonation of geosmin and MIB, using synthetic waters containing two hydrophobic NOM fractions isolated from two French surface waters and sodium bicarbonate. The NOM fraction having higher SUVA values (i.e., greater aromatic content) and higher average molecular weight showed higher ozone decomposition rates and higher apparent rate constants for the degradation of geosmin and MIB. This was attributed to the greater production of hydroxyl radicals. The authors also reported that the addition of bicarbonate stabilized the concentration of the concentration of ozone, thus reducing the removal of geosmin and MIB. The authors state that these results support the hypothesis that the destruction of geosmin and MIB is primarily through the OH radical since a lower ozone decomposition leads to less production of hydroxyl radicals. They noted that MIB was more resistant to ozonation than geosmin. An important implication of the work by Ho et al. (2004) is that for waters low in alkalinity, the advantage provided by an AOP compared to ozonation alone would be expected to be less than for waters higher in alkalinity. Because ozone decay is more rapid when radical scavenging occurs, in evaluating a given water, it may be possible to use the measured ozone decay rate as an indicator of the need for an AOP, compared to ozonation alone. Nerenberg et al. (2000) present preliminary full-scale ozonation/biofiltration results from a treatment plant on Lake Michigan, USA, whose source water contained musty/earthy odors. For MIB concentrations in the raw water ranging up to

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about 40 ng l1, removals due to ozonation were between approximately 35% and 65%. As has been mentioned by others (e.g., Hrudey et al., 1995), AWWARF-LE (1995) note that ozone has the possibility to produce odors, sometimes characterized as fruity, attributable to aldehydes. It would be reasonable to expect that odors would be produced where the original odor had a complex character. Oxidation involving UV. The increasing use of UV for disinfection in drinking-water treatment in recent years has given an additional impetus to the evaluation of UV (alone or in combination with H2O2) for odor removal. Most of the research has focused on the traditional taste and odor compounds, geosmin and MIB. Rosenfeldt et al. (2005) reported on bench-scale investigations using UV and UV/H2O2 to remove geosmin and MIB from a surface water (Lake Michigan in USA). At a fluence of 1000 mJ cm2, low-pressure and medium-pressure direct UV photolysis (i.e., without hydrogen peroxide addition) removed 10% and 25–50% of the compounds, respectively. At the same fluence, the addition of hydrogen peroxide resulted in more than 70% removal. For MIB oxidation in the presence of H2O2, medium-pressure UV provided consistently faster oxidation than low-pressure UV. The authors also calculated the electrical energy per order (EEO), which is defined as the electrical energy (in kilowatt hours) required to decrease the concentration of a target compound by one order of magnitude in 1000 US gallons (3785 l) of water. Although this is a very useful value for process comparison, the authors note that it is very specific with respect to reactor geometry, UV lamp, the specific contaminant being investigated, and background water quality. They note that their calculated EEO values of less than 5 kWh for 90% oxidation of geosmin or MIB (with the addition of hydrogen peroxide) are lower than previously reported by others, and they attribute this difference to the important role played by background water quality in determining EEO. Although costs based on EEO values reflect only the cost of the electricity needed to run the UV lamps, this does give a general indication of process feasibility. It should be noted that the fluences for which the authors cited various percentage removals (1000 mJ cm2) are a factor of 25 larger than a value of 40 mJ cm2 frequently used for disinfection. At least one manufacturer of full-scale UV disinfection equipment offers a reactor system where additional UV lamps can be turned on and hydrogen peroxide can be fed when an odor event occurs in the raw water (see Trojan Technologies website). Gray (2006) also examined the removal of various odor compounds by UV photolysis, and by UV in combination with H2O2, in bench-scale experiments. In addition to geosmin and MIB, he also investigated 2-isopropyl-3-methoxypirazine (IPMP), two-isobutyl-3-methoxypirazine (IBMP), as well as four unsaturated aldehydes (E2-heptenal, E2,E4-nonadienal, E2,E4-decadienal, and E2,E4-heptadienal). Significant removal of all compounds was obtained with hydrogen peroxide doses as low as 1.5 mg l1 and a UV fluence greater than 500 mJ cm2 for a given water quality, and UV/H2O2 was able to provide up to twice the removal of geosmin, MIB, IPMP, and IBMP than UV alone. For the aldehydic compounds,

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moderate removal was obtained with direct UV photolysis (i.e., UV alone), and geosmin, MIB, and the pyrazines were most resistant to treatment, regardless of whether or not hydrogen peroxide was present. The results indicate that, if odors in a given water were due only to the aldehydic compounds, an AOP might not be necessary. Experiments with different hydrogen peroxide doses and UV fluences that are typically used for disinfection (40– 200 mJ cm2) demonstrated that high concentrations of hydrogen peroxide would be necessary to treat any of the compounds investigated. The experiments with hydrogen peroxide verified that hydroxyl radical-driven reactions contribute only partially to the removal of the aldehydic compounds, whereas they play a very important role in the removal of geosmin, MIB, IBMP, and IPMP. In related pilotscale work, Gray (2006) reported that, as has been found by others, geosmin was consistently better removed than MIB with the combination UV/H2O2. The bench-scale investigations conducted by Gray (2006) provide quantitative information for the removal of these compounds, that could be useful in treatment process design. However, as has been stated elsewhere, the influence of background water quality on the performance of UV and UV/ H2O2 processes means that investigations must be conducted for a particular water. As has been suggested by others, Gray (2006) notes that his results point to the possibility of using a UV reactor designed for disinfection to also deal with seasonal taste and odor problems, by increasing the UV fluence and adding hydrogen peroxide. Oxidation summary. As with other treatment processes, many studies have focused on an individual water or location and the vast majority of the available literature is for the removal of geosmin and/or MIB. The major findings for oxidation processes can be summarized as follows:

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Conventional oxidants (i.e., chlorine-based) have more limited success than ozone or AOPs for odor removal. The use of potassium permanganate for taste and odor control appears to be specific to certain substances. Ozone can be very effective for odor control, and provides oxidation by means of both molecular ozone and the hydroxyl radical. The relative concentrations of these two species are strongly influenced by the background characteristics of a particular water, especially the concentration of the carbonate ion and the level of humic substances. Although the carbonate ion acts as a scavenger of hydroxyl radicals, the impact of humic substances is more complex as they can be both a consumer of hydroxyl radicals and a promoter of their formation. An implication of the scavenging effect of carbonate ions is that, for low-alkalinity waters, the advantages of an AOP involving ozone (i.e., PEROXONE) versus ozone alone may be less than in higher alkalinity waters. In recent years, the usefulness of AOPs has increasingly been demonstrated for taste and odor control. The most common configurations are the combination of ozone and hydrogen peroxide (referred to as the PEROXONE process) and the combination of hydrogen peroxide and UV. The latter process has gained increasing popularity because of

the increasing use of UV for disinfection. However, it should be noted that the UV fluences required for effective odor removal are much higher than those conventionally used for disinfection. At least one manufacturer has developed a process where additional UV lamps can be turned on and hydrogen peroxide fed during an odor event. The following additional points are of note:

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Oxidants can create odors (or change their character) as well as remove them. This is true for both chlorine-based oxidants and for ozone. The odors produced by ozone, sometimes characterized as fruity, are more likely to occur where the original odor is of complex character. The main application of chlorine or chloramines as oxidants to deal with odors is for the removal of sulfide odors and related odors caused by anaerobic conditions. Conflicting information appears in the literature regarding the ability of chlorine to mask odors. A switch from chlorine to chlorine dioxide may help minimize chlorinous or medicinal odors. Various workers have reported that geosmin is consistently better removed than MIB with UV/hydrogen peroxide. The parameter electrical energy per order (EEO) used for UV processes is useful but is very specific with respect to reactor geometry, UV lamp, the specific contaminant being investigated and background water quality. One study investigating the removal of aldehydic odorous compounds found moderate removals with direct UV photolysis (i.e., UV alone, without hydrogen peroxide edition). Not a great deal of work has been done on AOP by-products, to determine whether there may be compounds formed that are of health concern.

Adsorption. The overview on absorption and absorption of various compounds are given as follows. Introduction. As noted in Section 3.16.4.9, the adsorbent almost universally used in water treatment is activated carbon, applied as either PAC or GAC. PAC is particularly useful for periodic taste and odor episodes. GAC contactors as a separate treatment stage for taste and odor are relatively expensive. Filter adsorbers, combining filtration and adsorption in one step, are less expensive; however, the contact time for adsorption may be insufficient for effective removals, because of the shallower GAC layer. For GAC, biodegradation will inevitably also occur unless there is a disinfectant residual throughout the contactor or the duration of a pilot study is only several weeks, that is, too short for a biofilm to become properly established. Some authors investigating adsorption of odorous compounds on GAC mention biodegradation, while others do not. Biodegradation can essentially be neglected as a removal mechanism for PAC, because the contact time of the carbon with the water is typically at most a few hours, and therefore too short for biofilm development. AWWARF-LE (1995) provide good coverage of adsorption in chapter 4 of their book on taste and odor control, including a number of case studies, and discuss the effects of oxidants. They note that in all cases the most important aspect of activated carbon use is the competition with background organic matter or with other contaminants. As will be discussed

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extensively later in this section, this competition reduces, often substantially, the capacity of activated carbon for specific odorcausing compounds. Although AWWARF-LE (1995) present extensive isotherm data, the quantitative use of this information for design purposes, as is the case with any isotherm data, must be done with extreme caution because of the previously mentioned competition effects. Isotherms can however provide a good indication of the relative adsorbabilities of different odorous compounds. The results of specific investigations on both PAC and GAC are reported below. In general, less literature is available for the removal of odor compounds using GAC than with PAC. Powdered activated carbon. Crozes et al. (1999) reported on a bench-scale program for rapid testing of various odor treatments (oxidation and adsorption). During the testing, the predominant raw water odor characteristic was earthy/musty. Using standard jar tests, the investigators evaluating PAC found that a wooded-based carbon was better than coal-based carbons for odor removal but indicated that economic analyses should also be performed. Gillogly et al. (1999) described a simplified method for determining PAC dose required to remove MIB. Using natural water with different initial concentrations of MIB, they showed that the percentage removal was independent of the initial concentration for a given PAC dose. They note that this relationship is specific for each type of PAC and natural water. They state that it is not valid at very high MIB concentrations, but they tested it at MIB concentrations up to 178 mg l1, which is far above raw water concentrations that normally would be encountered. They demonstrated the robustness of their approach by applying it to four different source waters and 14 different activated carbons. Graham et al. (2000b) also determined that the percentage removal of geosmin and MIB in natural waters by a given carbon dose was independent of initial concentration in the range of 40–180 ng l1. Cook et al. (2001) also confirmed that percentage removal for a given carbon dose was independent of starting concentration. Gillogly et al. (1999) note that their procedure gives a minimum dose and that the actual dose will be higher in a given water-treatment plant if equilibrium is not achieved. As the authors note, the value of their work is that it demonstrates that only a single isotherm test with a given carbon is necessary for a particular water. The authors also note that other investigators (Newcombe et al., 1994) have established that characteristics of the carbon such as surface area and micropore volume cannot be consistently used to predict a priori how well MIB would be removed. In another study, Gillogly et al. (1998) found that the ability of PAC to adsorb MIB was significantly reduced by free chlorine. They also found that, because chlorine can oxidize adsorption sites containing MIB, chlorine can cause the immediate release of adsorbed MIB back into the water. Graham et al. (2000b) found that equilibrium adsorption capacity for MIB and geosmin on PAC in natural waters was at least a factor of 10 lower than in laboratory water (i.e., without the presence of NOM). They also showed that modeling could be successful when a single equivalent background compound (EBC) was employed to simulate the competition from the background NOM. In natural waters, somewhat lower

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adsorption of the odorous compounds was observed at pH 5.5 versus 7.5. It was hypothesized that this was probably because of the enhanced adsorption of NOM at the lower pH. Cook et al. (2001) note that the PAC dosage required is influenced strongly by the type of activated carbon, the presence of NOM, and the contact time. They also note that the effects of NOM concentration and character are not straightforward and could not be interpreted in terms of the parameters they measured. The addition of PAC with coagulant chemicals had no effect on geosmin or MIB removal; however, higher turbidity did reduce removal efficiency. The authors described an approach for predicting the required PAC dose, taking into account the kinetics of adsorption and were able to construct a PAC dose table. The important aspect of this work is that it assists in making realistic predictions for a specific treatment plant, where the actual contact time of the PAC is not long enough for equilibrium to be reached. However, they noted that full-scale confirmatory testing was required. Cook et al. (2001) also investigated adsorption isotherms for geosmin and MIB. They found that geosmin was more strongly adsorbed than MIB and that the adsorption was less sensitive to background water quality. They proposed that, for a given carbon, only one isotherm would be required to predict geosmin removal in a wide range of waters, whereas for MIB an isotherm would be required for each water. Jung et al. (2004) also found geosmin to be better adsorbed than MIB on PAC. Newcombe and Cook (2002) describe the various factors influencing the removal of tastes and odors by PAC, focusing on geosmin and MIB. Significant findings are as follows:

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In general, good-quality microporous carbons (e.g., coconut or coal-based) will be better than wood-based carbons, although at shorter contact times the superior adsorption kinetics of the chemically activated wood-based carbons may be advantageous. The removal at contact times of 2 h or less is more important than the equilibrium removal capacity of the carbon. The adsorption kinetics for geosmin and MIB depend on both the equilibrium capacity of the carbon and the volume of larger transport pores that allow rapid access to the adsorption sites. The fact that geosmin has a slightly lower solubility than MIB and is flatter in structure helps to explain its observed greater adsorbability. Only some of the background NOM present in natural waters will compete for adsorption sites on carbon. The majority of the competitive effect is stated to be due to direct competition for adsorption sites by small, uncharged NOM compounds with low UV absorbance. In one study, the competing NOM was found to be less than 10% of the total NOM. Therefore, bulk characterization and concentration parameters for NOM would not be expected to be specific enough to directly predict the extent of competition for a particular water. A user-friendly computer program has been developed for application of the model at full scale. Prechlorination is not recommended when PAC is used for taste and odor control. This is consistent with findings reported by Gillogly et al. (1998).

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In some cases, the simultaneous application of PAC and alum or other coagulants can increase the PAC dose required. In general, a higher alum dose because of a higher turbidity will likely lead to a higher required PAC dose than that predicted by the homogeneous surface diffusion model (HSDM).

Graham et al. (2000a) conducted a survey of full-scale watertreatment plants in the US and Canada using PAC for control of earthy–musty odors. The survey showed that most plants (60%) applied PAC at the rapid mix step. Bench-scale investigations conducted as part of the study showed that, under the operational constraints of a conventional treatment plant, the greatest removal of odorous compounds occurred when the PAC was added prior to coagulation. The adsorptive capacity of PAC for the odorants was reduced in the presence of oxidants. No consistent significant relationships were found in attempting to correlate performance with 11 different adsorption and physical properties for five PACs tested. None of the parameters typically used to characterize carbon adsorption performance (such as molasses number, iodine number, etc.) consistently predicted the effectiveness of a PAC for removing geosmin or MIB. GAC. Chen et al. (1997) examined GAC for removal of MIB to below the odor threshold concentration. Pure-water isotherms at realistic equilibrium concentrations (o10– 100 ng l1) showed that the bituminous carbon had the highest capacity, followed by the peat, lignite, and wood carbons. The authors also noted the reduction in capacity in natural water due to competition. In agreement with results reported earlier in this section, they found that the ranking of carbons according to traditional parameters such as the iodine or tannin number was inconsistent with the rankings obtained from the pure-water isotherms. Again, consistent with results reported above, the authors found that, under the conditions tested, the capacities were reduced by about an order of magnitude (factor of 10) in the natural water. It should be noted that isotherm tests with natural waters essentially only measure direct competition from NOM, and not the preloading effect discussed in Section 3.16.4.9. Kim et al. (1997) reported on pilot-scale investigations including GAC for the removal of taste and odor causing substances in a water-treatment plant in Korea. The compounds investigated were geosmin, MIB, IPMP (2-methoxy3-isopropyl-pyrazine), IBMP (2-methoxy-3-isobutyl-pyrazine), and TCA (2,3,6-trichloroanisole). The pilot plant consisted of coagulation/flocculation/sedimentation and rapid sand filtration followed by two parallel trains. Train 1 had ozonation followed by GAC, whereas train 2 had GAC only. The EBCTs in the GAC columns in both trains ranged from 10 to 15 min. The entire investigation lasted for 2 years. The raw water concentrations of the odorous compounds were between approximately 10 and 30 ng l1, except for IBMP and MIB, which were present at approximately 80 ng l1. The authors reported that the GAC filters following ozonation showed an increased BAC effect. The treatment process including ozonation was capable of reducing the five substances to less than the TON with an EBCT of more than 15 min, regardless of the iodine number of the GAC. This investigation, along with others, shows that while a multistep

process can be successful in treating odors, GAC alone will not necessarily provide sufficient removals. Ho (2004) studied the removal of cyanobacterial metabolites (MIB, geosmin, and the toxin microcystin) from drinking water using ozone and GAC. An important finding was that laboratory-scale minicolumn experiments, combined with the HSDM, were not effective in predicting GAC breakthrough at two different pilot plants. This was attributed primarily to the biological degradation taking place at the pilot plants that could not be modeled by the HSDM. Ndiongue et al. (2006) assessed the remaining odor removal capacity of GAC filter caps using four pilot-scale columns with the same bed depths as full-scale filters and fed with the same water received by the full-scale filters. Geosmin and MIB were spiked into the influent of the pilot-scale filters, at concentrations ranging up to several hundred nanograms per liter. The first experiments conducted by Ndiongue et al. (2006) were designed to assess the possible losses of the spiked compounds to the apparatus and therefore were conducted before the media was added to the columns. Such investigations are important because of the very low concentrations of the compounds being investigated, and the very high surface-to-volume ratio of pilot-scale equipment, in comparison with full-scale facilities. Similar losses were observed for both geosmin and MIB, ranging between 23% and 40%. Results obtained later in the study indicated that system losses may have decreased over time. Following the system loss experiments, media was added. Three columns contained different depths (25–95 cm) and types of used carbon while a fourth contained fresh carbon. The columns were operated continuously for approximately 2.5 months. Three experiments of several days’ duration each were conducted, in which the odor compounds were spiked. Under the conditions tested by Ndiongue et al. (2006), none of the pilot filters were able to reduce geosmin and MIB concentrations to below the commonly cited odor thresholds of 4 and 9 ng l1, respectively. The low adsorption capacity was attributed to fouling of the carbon by background TOC; although biodegradation may have played some role in this investigation, its contribution was considered minimal. The significance of this investigation is in demonstrating how quickly the capacity of GAC for target compounds is reduced by the adsorption of background TOC. Since GAC will never be completely fresh when an odor episode strikes, GAC alone may have limited success as a treatment strategy in dealing with such episodes. Newcombe et al. (1997) provide a good discussion of the impact of NOM on adsorption on GAC. They note the direct competition effect, as discussed earlier for PAC, and also the fouling or preloading effect, essentially only relevant for GAC. The authors report that the competitive effect between NOM fractions and MIB was largest for the smallest NOM fraction (having an UF molecular weight o500). Their explanation is that this fraction was the most similar in size to MIB and therefore could directly compete for the same adsorption sites. Hepplewhite et al. (2004) confirmed the main findings reported just above, including the fact that the most competition was provided by the low-molecular-weight NOM compounds. They also discuss the complexity of doing kinetic analysis. They

Chemical Basis for Water Technology

state that the rate of adsorption in the initial stages is strongly affected by the equilibrium capacity of the carbon, as well as by the carbon’s pore structure and competition from NOM. They also note that large NOM compounds can restrict, although not block, access to the adsorption sites in a carbon with substantial mesopore volume. For a carbon with primarily micropores, low-molecular-weight NOM components can restrict access to these pores (as well as compete for adsorption sites) and thus affect adsorption kinetics. MacKenzie et al. (2005) assessed the impact of different GAC reactivation procedures on both the ability of the carbon to remove MIB, and its ability to retain MIB once an odor episode had ended. The authors suggest that, for virgin carbons, the Brunauer–Emmet–Teller (BET) surface area is an important factor for MIB removal, although other GAC properties such as pore-size distribution and surface chemistry may also play a role. However, for the carbon reactivated using various procedures, there was no relationship between BET surface area and number of bed volumes to breakthrough. The authors indicate that this could suggest that surface chemistry is more important for reactivated carbons, although they also note that reactivation will have an impact on pore size. This information is significant for a water-treatment plant that may be planning to reactivate its carbon. Several investigations on the modeling of GAC performance, while not conducted using odorous compounds, show that background organic matter affects not only the capacity of GAC for target contaminants, but also the kinetics of adsorption. Carter and Weber (1994) conducted investigations with both a natural and a synthetic water to investigate the impact of preloading by background organic matter on the adsorption of TCE. They used an initial TCE concentration of approximately 50 mg l1. Although this is of course substantially higher than typical concentrations of odorous compounds, it likely does not preclude application of their findings to odor removal. They found that, for both waters, both equilibrium capacities and rates of adsorption decreased with increased periods of preloading. In contrast to the findings of several other investigators, they noted that in the natural water (from the Huron River) the effect of preloading reached a plateau after several weeks. Jarvie et al. (2005) used pilot- and field-scale data from 11 studies to attempt to improve the prediction of GAC adsorber performance. The data represented both surface and groundwater sources, and were for the removal of various synthetic organic compounds in the microgram per liter range. They found that the background organic matter in both ground and surface waters could significantly reduce both adsorption capacity and kinetics, and were able to obtain some trends regarding the way in which the diffusivity within the GAC particle changed as a function of time and depth of the bed. They considered that their modeling approach was sufficiently developed to make crude design calculations. They noted that their approach provided exceptionally better estimates of GAC use rates (compared to actual data) than the standard massbalance approach based on isotherm data that is generally used to obtain a quick determination of GAC requirements. More recently, Yu et al. (2007) reported on the reduction of both adsorption rate and capacity on GAC preloaded with background organic matter. In laboratory tests, they

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investigated the impact on adsorption for the pharmaceutical naproxen. The initial naproxen concentration was 500 ng l1, which is at the upper end of the range at which odorous compounds such as geosmin and MIB may be observed. Pirbazari et al. (1993) published a GAC adsorber design protocol based on computer modeling for the removal of offflavors (taste and odor compounds). The model was used to simulate full-scale adsorbers, although the authors note that biodegradation was not considered and therefore their cost estimates would be conservative. The modeling results confirmed experimental data obtained in various investigations showing the major influence of background organic matter on GAC capacity. For the conditions chosen by Pirbazari et al. (1993), the breakthrough times for MIB were at most on the order of a few weeks. This calls into question the practical feasibility of using GAC in an adsorptive mode only (i.e., not as a support medium for a biofiltration process) for odor control. The value of the authors’ work lies both in the development of a protocol for predicting full-scale adsorber capacity (with recognized limitations) from small scale tests, and also in showing the complexity of such a task. Sagehashi et al. (2005) reported on a bench-scale investigation of a process involving both a high silica zeolite adsorbent (USY) and ozonation. They obtained good results for the removal of MIB. It will be interesting to watch for possible further development and larger-scale testing of this process. If such testing yields positive results, the process may represent a possible future alternative for taste and odor control. Adsorption summary. The major findings are as follows.

• •

•

•

In general, geosmin is better removed through adsorption than MIB. Both PAC and GAC can be effective for odor removal. PAC is generally well suited to episodic odor events and, in general, seems to be more frequently used than GAC. GAC may be more suitable if odor conditions last for much or all of the year; however, if GAC is installed, it is more likely to also have other treatment objectives. In this case, the optimal operating conditions, and even the choice of carbon type, may not be the same for all contaminants. A clear indication from the literature is the tremendous impact of background water quality, specifically NOM (TOC) on adsorption. TOC is present at the milligram per liter level, whereas odorous substances occur at the nanogram per liter level. Low-molecular-weight NOM components compete very effectively with the odorous compounds for adsorption sites and larger NOM molecules can block or hinder access to these sites, also reducing the kinetics of adsorption. NOM affects both PAC and GAC; however, in the latter case the effect will be more severe because of the extended exposure of GAC to NOM in the water, whether or not the odorous compounds are present. The equilibrium capacity of activated carbon for odorous compounds has been shown to be at least a factor of 10 lower in actual water than in laboratory water, due to the effect of NOM. It has also been demonstrated that measuring standard parameters such a water’s TOC is not helpful in predicting the impact on adsorption. For GAC, the original adsorptive capacity can be substantially reduced

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relatively quickly (i.e., within a few weeks) by the background TOC. As a consequence of the effect of NOM, GAC alone may not be sufficient in eliminating odor problems; however, it can be a very effective component of an overall treatment strategy. In odor-removal investigations on GAC, biodegradation of the odorous compounds is not always considered. Biodegradation will lead to increased removals, and is discussed in detail in the next section. Biodegradation essentially does not contribute to removals when PAC is used, because the contact times are typically at most only a few hours. Several investigations have shown that for a given water and PAC dose, the percentage removal of an odorous compound is independent of its initial concentration, in the concentration range of the compounds normally expected in natural waters. This is very helpful for predicting performance in a given water where the influent concentration may be different than that tested. In the application of PAC for geosmin and MIB removal, the removal at contact times of 2 h or less (i.e., the time typically available in practice) is more important than the equilibrium removal capacity of the carbon. The adsorption kinetics for these compounds depend on both the equilibrium capacity of the carbon and the volume of larger transport pores in the carbon that allow rapid access to the adsorption sites. Adsorption isotherms (typically carried out much longer times) even with the actual water will typically overestimate the removals by PAC obtainable in practice. The ability of PAC to absorb MIB has been found to be significantly reduced by the presence of free chlorine. Therefore, chlorination upstream of PAC application is not recommended.

Additional findings are as follows:

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In terms of predicting a carbon’s capacity for odor removal, traditional parameters such as iodine and molasses numbers are not useful. Characteristics of the carbon such as surface area and micropore volume also cannot be consistently used to predict removals. In general, good-quality microporous carbons (e.g., coconut- or coal-based) will be better than wood-based carbons for the removal of geosmin and MIB. However, at shorter contact times (in PAC applications), the better adsorption kinetics of the chemically activated wood-based carbons may be advantageous. Differing results have been reported for the impact of coagulants or adsorption by PAC, with some investigations showing no impact. In conducting experiments, it is important to take into account the possible adsorption of odorous compounds onto the walls and surfaces of laboratory-scale apparatus, particularly when it is new.

Biological treatment. Introduction. The use of biological processes (i.e., biological filtration) has been increasingly investigated in recent years for odor removal. Most of these investigations have been conducted at laboratory or pilot scale and, as with other treatment

technologies, have focused largely on the removal of geosmin and MIB. As discussed in Section 3.16.4.10, important factors affecting the performance of biofiltration include contact time, temperature, the absence of a disinfectant or oxidant residual in the filter, and, to some extent, the choice of media. The degradation of substances such as odor compounds present at very low concentrations (typically at nanogram per liter levels) occurs by secondary utilization. This means that the amount of biomass in the filter is determined by the overall amount of biodegradable carbon present in the water, as measured by a parameter such as AOC or BDOC, which serves as the main carbon and energy source for the biofilm. In filters where the media is GAC, except for the initial operating period, the adsorptive capacity is essentially exhausted and therefore the removals taking place are due to biodegradation, and the media is essentially functioning as a biomass support. In some cases, GAC has been shown to be a better support medium than sand or anthracite; however, this is not always the case. Most investigations have been conducted for what is referred to as rapid biological filtration. Several investigations that have been conducted for bank filtration are also included at the end of the section. Identification of specific microorganisms responsible for biodegradation of geosmin and MIB. Tanaka et al. (1996) isolated two bacteria capable of degrading MIB from the backwash water of a pilot-scale biological filter at Lake Biwa in Japan. The identified bacteria belonged to the genera Pseudomonas and Enterobacter. Using lake water, Lauderdale et al. (2004) isolated and characterized a bacterium capable of aerobically degrading MIB. The bacterium was determined to be most closely related to Bacillus fusiformis and Bacillus sphaericus. Lauderdale et al. (2004) note that other authors, including Tanaka et al. (1996), have reported that oxidation is a significant microbial pathway for MIB transformation. In investigations involving bench-scale biological sand filters, Ho et al. (2006) identified a Sphingomonas species as a potential geosmin degrader. During the onset of geosmin degradation in a bench-scale biofiltration study, Hoefel et al. (2006) detected the predominance of three bacteria, most similar to previously cultured species of Sphingopyxis alaskensis, Novosphingobium stygiae, and Pseudomonas veronii. Subsequent investigation showed that degradation of geosmin, whether present as either the sole carbon source or when spiked into sterile water from a reservoir, occurred only when all three isolates were present. None of the isolates were demonstrated to be able to degrade geosmin either individually, or in any combination of two. In a full-scale water-treatment plant, the biofilm community (including nondegraders of odorous compounds) that will be sustainable over the longer term will be composed of those organisms best able to compete under the conditions present. Bench- and pilot-scale investigations. Saito et al. (1999) reported on the microbial degradation of geosmin obtained with the backwash water from a pilot-scale biological filter (and also with activated sludge from a wastewater-treatment plant), using batch-suspended growth experiments. They found geosmin to be more difficult to degrade microbiologically than was the case

Chemical Basis for Water Technology

with MIB, which the group had reported on previously (Tanaka et al., 1996). A reason for this is not given, nor is it immediately evident from a review of the two papers. However, it is possible that this difference could be attributed to differences, even subtle ones, in experimental conditions. The degradation potential of MIB and its producer via biofilm process was examined by Sugiura et al. (2003). The authors took biofilm from a full-scale biological treatment facility and performed suspended growth experiments. They found that degradation decreased as pH increased, in the range from 7.4 to 8.6. The rise in pH also corresponded to a decrease in ciliated species in the biofilm. They attributed the predation and degradation of the filamentous cyanobacterium Phormidium tenue (which was considered to be the exclusive producer of MIB), and its intracellular MIB, primarily to protozoa such as ciliates. In the full-scale facility, removal of the MIB released from P. tenue as a result of their degradation was attributed to bacteria in the biofilm. They concluded that abundant ciliates species in the biofilm and pH near neutral were important factors for the efficient removal of MIB and P. tenue. Huck et al. (1995) used a bench-scale biofilm reactor to attempt to determine kinetic parameters for the biodegradation of geosmin and 2,4,6-trichloroanisole. In experiments conducted over a relatively short period of time (the exact duration is not specified), the bioreactors did not effectively remove the compounds. It is important to note that the biofilm was established using amended river water without the odor compounds, and for the actual experiments the odor compounds were then fed in a synthetic solution. Based on their results, the authors do not recommend a biofilter as the primary treatment process for the removal of these compounds when it is not acclimated and when it receives an organic and hydraulic load in the normal range for rapid filters and GAC contactors. The acclimation aspect is important for intermittent odor events. However, the authors note that the poor removals they obtained may be due to nonacclimation of the system to either the carbon sources in the synthetic water used or the odor compounds themselves. An important aspect of the study by Huck et al. (1995) is that it documents the preliminary investigations undertaken to determine and minimize losses of the odorous compounds (by adsorption, degradation in the feed container, and possibly volatilization) in the experimental system. These investigations showed that such losses can be considerable, and must be taken into account for studies at realistic (i.e., low) concentrations in bench-scale systems, with their high surfaceto-volume ratios. (An evaluation of losses to the surfaces of experimental apparatus during adsorption experiments was reported in the previous section (Ndiongue et al., 2006).) Hrudey et al. (1995) described odor removal by biological treatment processes in a pilot plant containing conventional treatment and ozonation followed by three parallel two-stage biologically active filtration trains. The first-stage filters contained either anthracite/sand or GAC/sand and the second stage in all three trains contained a GAC contactor. The overall conclusion from this study was that a pilot-scale treatment process including ozonation and biologically active GAC was effective in removing a complex raw water odor to levels judged to be essentially odorless by FPA. It is however

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considered likely that adsorption played some role in the overall removals. The authors stress the importance of the finding that ozonation alone, under the conditions used, was not effective for dealing with this odor event. Terauchi et al. (1995) reported on biological filtration for the removal of a musty odor, determined to be largely due to MIB. They conducted pilot-scale investigations over 2 years using a porous ceramic media with a diameter of about 5 mm. The depth of the media was 1.5 m, the filtration rate was 170 m d1 (approximately 7 m h1) and the contact time was about 13 min. During the season of natural odor, the total MIB concentration and the ratio of insoluble to total MIB varied substantially. Removal efficiencies of the filter were in the range of 60–80% for influent concentrations up to several hundred nanograms per liter of soluble MIB. Outside of the natural odor season, the investigators added MIB to the filter influent and the percentage removals were about the same. The authors report that as a result of their study, biofiltration was installed at full scale. Elhadi et al. (2004) reported on the startup phase of investigations on the removal of geosmin and MIB using small pilot-scale biofilters. Two columns were operated in parallel – each contained 50 cm of GAC (PICA P-830) over 25 cm of sand. One of the columns contained fresh GAC, while the other contained exhausted GAC from a water-treatment plant. The hydraulic loading rate was 7.5 m h1, corresponding to an empty bed contact time of 5.6 min. The columns were fed with dechlorinated tap water that was amended with the two odor compounds (each at target concentrations of 100 ng l1), several typical ozonation by-products, as well as nitrogen and phosphorus. The low-molecular-weight ozonation by-products, fed at a total concentration of approximately 800 mg l1, were added to simulate the effect of ozonation upstream of a biological filter. The investigations were conducted at room temperature (approximately 20 1C). In the study by Elhadi et al. (2004) an acclimation period close to 2 months was required for biological removals to be fully developed (this period did include a 3-week period during which the odor compounds were not fed, to simulate intermittent odor events). Once acclimation had occurred, biological filtration achieved 80–90% removal of geosmin and 50–60% removal of MIB. After several months (i.e., when the removals were essentially biological without a substantial contribution from adsorption), the removals of both odor compounds on exhausted GAC were only a few percentage points lower than on fresh GAC. The importance of an acclimation period suggests that biofiltration alone could not necessarily successfully handle a rapidly developing geosmin and MIB odor event, where the biomass did not have recent prior exposure to these compounds. Results of investigations carried out over several years using the same experimental setup were reported by Elhadi et al. (2006). Factorial experiments were designed to quantify the effects of the major factors expected to influence removals of geosmin and MIB during biological filtration: temperature, geosmin/MIB concentration, media type, and the concentration of easily BOM (to simulate the presence or absence of upstream ozonation). The experimental conditions were as described above (Elhadi et al., 2004) and were chosen to represent reasonable rapid filtration practice.

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Although the geosmin and MIB were fed either at increasing concentrations (ramped) or at a constant maximum concentration of 100 ng l1 (not ramped), Elhadi et al. (2006) discuss in detail only results at 100 ng l1. The investigations showed that temperature, geosmin and MIB concentration (i.e., whether the concentration had been ramped or not), media type, and BOM level were all important factors affecting removals. The best removals (60% for geosmin and 40% for MIB) were obtained in the exhausted GAC filters at the higher temperature (20 1C) and in fact under these conditions, the BOM level was relatively unimportant. At influent concentrations of 25 ng l1 at both BOM levels, sufficient geosmin and MIB removal occurred in the GAC filters at 20 1C to produce an effluent that was within the low odor-threshold range. The better removals obtained by Elhadi et al. (2006) on exhausted GAC versus anthracite were observed at both BOM levels, but were more significant at the lower BOM level. For both geosmin and MIB, somewhat lower removals were obtained when the concentrations were ramped up to 100 ng l1 than when they were fed at that level from the beginning. Importantly, a quantitative relationship between biomass levels (as measured by the phospholipid method) and geosmin and MIB removals was not found, although there was a qualitative relationship. The results of the investigation by Elhadi et al. (2006) show that, once acclimated, biofiltration is capable of providing appreciable removals of geosmin and MIB under typical rapid filtration conditions. However, depending on the influent concentration, biofiltration alone will not necessarily reduce odor concentrations to below the threshold level. If biofiltration is coupled with upstream oxidation using ozone, additional easily biodegradable carbon compounds will be produced that will likely enhance the efficiency of the downstream biofiltration process for odor removal. In terms of media, GAC, even if exhausted for adsorption, would appear to be a better biomass support medium than anthracite. Uhl et al. (2006) investigated the removal of geosmin and MIB in pilot-scale biological filters receiving an untreated surface water in Sweden. At an empty bed contact time of 30 min and a temperature of 15 1C, biofiltration through either GAC or crushed expanded clay reduced an initial concentration of 20 ng l1 of geosmin and MIB by at least 97%. At a lower temperature (6–12 1C) where biomass concentrations were also lower, the GAC showed similar removal efficiency, but the efficiency was considerably lower in the expanded clay biofilter. Adsorption was shown to play an important role in removals in the GAC filter, even though the GAC had been in operation for nearly 4 years treating surface water. Summers et al. (2006) also reported investigations on the biodegradation of MIB in laboratory-scale filters having an EBCT of 7 min. Acclimated biologically active sand and activated carbon were obtained from the Greater Cincinnati Water Works in Cincinnati, USA. The GAC had been in use there for approximately 10 months and the sand had been in use for at least 2 years. After 4 months of operation, approximately 50% MIB removal was obtained by the acclimated biologically active sand filters at room temperature, and no removal was observed at 4 1C. (Nonacclimated biologically active sand filters at room temperature averaged only 7% removal after

4 months.) The biologically active GAC filters showed MIB removals of about 65% (whether this removal was also seen at the lower temperature is not specified). Ho et al. (2007) examined biodegradation of geosmin and MIB in biologically active laboratory sand filters and determined degradation rates in batch bioreactors inoculated with biofilm from one of the sand filters (no solid media was present in the bioreactor experiments). The batch experiments showed that the biodegradation of geosmin and MIB was pseudo-first-order, with rate constants ranging between 0.10 and 0.58 d1. The rate constants were dependent on the initial concentration of the microbial inoculum, but not on the initial concentration of geosmin or MIB when target concentrations of 50 and 200 ng l1 were used. Rate constants were demonstrated to increase when the biofilm was re-exposed to both taste and odor compounds. While these rate constants provide useful information, it should be noted that they cannot be directly applied to biofilters, where the concentration of biomass per unit reactor volume will be different. In another investigation with a laboratory sand biofilter, Ho et al. (2006) reported a lag period in excess of 75 days before greater than 95% removal was obtained for geosmin and MIB. Although the filter sand had been obtained from an operating water treatment plant, during the laboratory experiments it was fed with water from another location, which may help to explain the delay time. Initially, the EBCT was 15 min, and after significant initial breakthrough of both compounds, their removals steadily increased after the first 9 days. On day 79, the EBCT of the column was increased to 30 min, which corresponded to a further increase in the removal of geosmin and MIB to greater than 95%. For comparison, the authors note that geosmin and MIB appeared to be more difficult to degrade biologically than the nonodorous but toxic cyanobacterial metabolite, Microcystin-LR. As part of her investigations, Elhadi (2004) also determined rate constants for geosmin and MIB removal in pilot-scale biofilters. She found that zero-, first-, or second-order kinetics could describe her data equally well, and adopted a zero-order model for simplicity. Observed rate constants for geosmin and MIB ranged from approximately 2 to 8 ng l1 min1 at 20 1C. She also conducted experiments at 8 1C and was able to determine temperature coefficients for the rate constants. Full-scale investigations. AWWARF-LE (1995) includes a chapter on biological removals that provides information on full-scale practical experience in the Paris, France area. Nerenberg et al. (2000) reported preliminary investigations of ozone/biofiltration for MIB and geosmin removal at a full-scale plant. (The results relating to ozonation have been discussed in a previous section.) They reported that the ozonation/biofiltration combination could effectively remove MIB to below threshold concentrations, and that biofiltration played an important role in this reduction. During the period of investigation, the typical empty bed contact time was 17 min based on total bed height, or 11 min based on the height of the GAC only. The GAC had been in service (without regeneration) for 6 years, and therefore the role of adsorption in MIB removal was considered unlikely to be significant. The authors’ removals ranged from 26% to 64%. In full-scale biological sand filters in Cincinnati, USA, Metz et al. (2006) observed sustained biological removal of MIB

Chemical Basis for Water Technology

(80%) and geosmin (50%) over a 6-year period. During this time, the filter influent concentrations of TOC, geosmin, and MIB exhibited considerable variability. They noted that no disinfectant was added ahead of the filters and that the filter influent was not ozonated. They also noted that, during the investigation, the filter media was replaced in all filters over a period of several years and the hydraulic loading increased by 20%. Neither of these changes presented a problem with regard to reaching the utility’s taste and odor goals. Although there is little information available regarding odor removal for other (at least partly) biological processes such as slow sand or bank filtration, Ju¨ttner (1995, 1999) has reported on removals of various compounds, including monoterpenes and geosmin. In an investigation on the Ruhr River in Germany (Ju¨ttner, 1995), the aquifer where riverbank filtration occurred was anoxic, while the following slow sand filters were aerobic. Removals differed among compounds, and the schmutzdecke at the top of the slow sand filters played an important role. Another study on the Neckar River in Germany (Ju¨ttner, 1999) investigated removals of fragrance compounds through anoxic bank filtration. Efficient removals, attributed to microbial degradation, were observed after a relatively short distance from the river. Biofiltration summary. The major findings regarding the use of biological processes for taste and odor removal can be summarized as follows:

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Biofiltration can be a useful process for the removal of the taste and odor compounds, geosmin and MIB. For relatively low levels of these compounds, biofiltration under typical rapid filtration operating conditions may be able to provide filter effluent values below the odor threshold. For higher influent levels, an effective process combination may be oxidation followed by biofiltration. A number of investigations have shown that biofiltration requires an acclimation period before reaching its full potential for removals. Thus, biofiltration alone may not be a suitable process when odor events occur intermittently. GAC, essentially exhausted with respect to its adsorption capacity, has been shown to be a somewhat better medium than anthracite for biofiltration for taste and odor removal. Conflicting results are reported in the literature with regard to the relative ease of biodegradation of geosmin and MIB. There is no immediate explanation for this difference. Several investigations have determined kinetic parameters for the biodegradation of geosmin and MIB. One investigation using suspended growth batch bioreactors determined the reaction to be pseudo-first-order. Although rate constants were determined, these are not directly applicable to biofilters. Another investigation using pilot-scale filter columns concluded that the removals could be equally well represented by zero-, first-, or second-order kinetics. The zero-order model was chosen for convenience and rate constants were determined. Although most studies have been conducted at bench or pilot scale, several full-scale investigations have demonstrated good levels of removal under rapid filtration conditions. Several investigations have identified specific bacteria capable of degrading geosmin or MIB. In one investigation,

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degradation of geosmin occurred only when three specific bacterial species were present simultaneously. There is limited information available on the removal of taste and odor compounds by other processes with an important biological component, such as riverbank and slow sand filtration. However, several studies have shown effective removals by these processes.

3.16.6.5.2 Pharmaceuticals and endocrine disrupting substances Research in this area is relatively recent; however, there is sufficient information to provide reliable indications. One of the most important points is that this group of substances encompasses a large number of compounds having various chemical properties. Thus, blanket statements cannot be made, and process selection must be based on the types of compounds to be removed. Alternatively, several processes might be used to remove a range of compounds. Of course, these compounds are present at extremely low concentrations, and this has both process and analytical implications. As with other types of organic compounds, important considerations are whether the compound is destroyed or simply transferred to another phase as is (i.e., in the case of adsorption), and whether the treatment process (e.g., oxidation) transforms the original compound into something that may be of equal or greater concern. A number of researchers (e.g., Huber et al., 2003; Crosina et al., 2006; Zwiener and Frimmel, 2000) have examined the removal of PhACs and EDS by oxidation. Biodegradation has also been reported. For example, Halle´ (2010) found that drinking water biofilters removed some compounds effectively, whereas others were refractory to biodegradation. Yu et al. (2008, 2009a, 2009b) extensively investigated the adsorption of a small group of compounds. The background TOC substantially reduced the effectiveness of both PAC and GAC. Removals by membrane processes have been investigated by various researchers in recent years. Bellona et al. (2004) reviewed factors affecting the rejection of organic compounds by NF and RO membranes. In terms of solute properties the following were found to be most important: molecular weight, molecular size (length and width), acid dissociation constant (pKa), hydrophobicity/hydrophilicity (log Kow), and diffusion coefficient. Important membrane properties affecting rejection include molecular weight cutoff, pore size, surface charge (measured as zeta potential), hydrophobicity/hydrophilicity (measured as contact angle), and surface morphology (measured as roughness). Feed water characteristics such as pH, ionic strength, hardness, and the presence of organic matter were also found to have an influence. An immediate conclusion that can be drawn from this review is that obviously removals will be very compound and site specific, and no easy generalizations are possible. As part of their review, Bellona et al. (2004) developed rejection diagram, which is essentially a decision tree giving a general indication of the range of rejection to be expected for a particular compound as a function of certain solute and membrane properties. Kim et al. (2007) developed a transport model for trace organic compounds through NF and RO membranes. Both

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neutral and charged compounds were considered. They found that convection was the dominant transport mechanism for most compounds; however, diffusion was important for more hydrophobic nonpolar compounds. Convection was more important for NF membranes as well. They included a useful graphical summary of their modeling results, showing combinations of membrane and compound properties where factors such as pore size or membrane zeta potential would contribute to improved rejection. Comerton et al. (2009) studied the influence of NOM and cations on NF rejection of pharmaceutically active compounds and endocrine disruptors. They used synthetic and natural waters, including a membrane bioreactor (MBR) effluent, and conducted experiments over a period of 48 h. They concluded that, in general, fouling and the presence of NOM increased rejection, whereas it was reduced by an increase in cation concentration. Verliefde et al. (2009a) developed a method for predicting the transport of uncharged organic compounds through NF and RO membranes. The method requires the following solute and membrane parameters: solute size, membrane pore size, and solute-membrane affinity (intermolecular free energy). They note that these parameters can be relatively simply determined experimentally. Solute-membrane affinity (measured by contact angle) reflects phenomena such as hydrophobic attraction, hydrogen bonding, and dielectric effects. The authors found good agreement between model predictions and experimental data and note that the influence of solutemembrane affinity on rejection is very high. In a second study Verliefde et al. (2009b) examined the influence of fouling by pretreated surface water on NF rejection of PhACs. Fouling-induced changes in membrane surface hydrophobicity altered the extent of partitioning and therefore rejection for both hydrophobic and hydrophilic pharmaceuticals. The authors note that the impact of fouling on rejection determined in their laboratory experiments cannot be quantitatively extrapolated to full-scale units, because of differences in flow and other conditions. Thus although they found some rejections to decrease by more than 40% and others to increase by more than 15% due to fouling, they state that in full-scale units fouling would probably not cause changes in rejection (before and after fouling) of more than 5–10%. Yangali-Quintanilla et al. (2009) also examined the impact of fouling on rejection of PhACs and endocrine disrupting substances by NF membranes. In these laboratory experiments, sodium alginate was added as a foulant. For the NF200 membrane, fouling either decreased or increased rejection, depending on the class of compound. For the NF-90 membrane, the rejections of hydrophobic compounds were not affected by fouling, although increases in rejections of hydrophilic neutral compounds were observed. Makdissy et al. (2007) examined the removal of endocrine disrupting and pharmaceutically active compounds by four commercially available polyamide NF membranes. Two waters were investigated – ultrapure water and a surface water with a DOC of approximately 6 mg l1 and a SUVA value of 2.8 l mgC1 m1. Both size exclusion and electrostatic repulsion played a role in rejection, depending on the compound and membrane. Lower removals were observed in the natural water compared to ultrapure water.

Another investigation by Yangali-Quintanilla et al. (2010) developed a quantitative structure activity relationship (QSAR) model to predict rejection of pharmaceuticals and endocrine disruptors by NF membranes. The developed model, using interactions between membrane characteristics, operating conditions, and compound properties, was able to satisfactorily predict rejection. It is evident from the preceding paragraphs that NF can be successfully used to achieve at least partial, and sometimes good, removal of pharmaceutically active and endocrine disrupting compounds. By extension, NF would be expected to achieve at least some removal of other trace organics that may have been less extensively studied. It is however also evident that removals are compound and membrane specific, and are influenced by fouling. Therefore, quantitative a priori predictions for a given situation cannot reliably be made at present. This is not surprising given the complexity of the removal mechanisms involved. Thus, approaches such as QSAR offer promise for developing tools that could provide at least semiquantitative predictions. It is also evident that, depending on the compound and degree of removal that might be required, NF might need to be used in combination with another technology such as oxidation, absorption, or potentially biodegradation.

3.16.6.5.3 Volatile contaminants Although volatile contaminants can potentially be removed by oxidation or adsorption on activated carbon, for substances with sufficiently high Henry’s constants, air stripping can be an effective technology for removal. This section therefore addresses air stripping, because the other technologies have been discussed previously in relation to other types of organic contaminants. For air stripping, the principal factors controlling process selection are Henry constant (Section 3.16.4.8) and the required degree of removal. Figure 4 shows the type of system that can be used under various combinations of Henry’s constant and required percent removal. Stripping often requires what is referred to as a water-in-air system in which the air is in contact with either droplets of water or a thin film of water. Packed towers or columns are one of the most efficient types of water-in-air systems and are commonly used for air stripping. The packing material creates turbulence, thus maximizing and renewing the contact between the air and water surfaces. Thus, the process is also known as a packed bed process. Various types of standard packing materials are available. The diameter of the column is a function of the air and water flowrates. The height of the column packing required to achieve the desired removal of a given contaminant is the product of two quantities: the height of a transfer unit (HTU) and the NTU. HTU is a function of the efficiency of mass transfer and therefore will change with seasonal temperature changes. NTU is a function of the difficulty of removing a contaminant from the water and is related to the difference between the actual and equilibrium concentrations. In cases in which a very high percent removal of a volatile contaminant is required, it is therefore often most efficient to provide most of

Chemical Basis for Water Technology

the removal using air stripping and final polishing using adsorption on activated carbon. MWH (2005: 1177) reported that packed towers commonly provide one to four NTUs. Both the fundamentals of packed tower air stripping and detailed design calculations, including those for determining HTU and NTU, are provided by MWH (2005). They noted that commercially available software is commonly used for design. In summary, air stripping can be an effective process for the removal of volatile contaminants. Initial design for a given situation can usually be done based on information in the literature and knowledge of the contaminant concentrations present. Therefore, only brief confirmatory pilot testing is normally required, unless the process is part of a multistep treatment train for which overall optimum conditions must be determined.

3.16.6.6 Inorganic Contaminants For reasons of space, this section focuses on the removal of several metals. Iron and manganese, which are discussed first, are common problems in groundwater. Although they are generally not considered to represent health issues, elevated levels of iron and/or manganese are important from an operational and esthetic point of view. One of the most common treatments applied to groundwater is the removal of iron and/ or manganese where their levels exceed prescribed values. Arsenic, which is also addressed, is a serious contaminant from a health perspective, and for this reason allowable limits for this metal are low and, in some jurisdictions, have been set even lower in the last few years. Iron and manganese. Iron and manganese are metals appearing in raw waters in many regions of the world. These waters (generally groundwaters) have a low redox potential, and therefore the metals are generally present as divalent ions – FE(II) and Mn(II). They can be present either as mineral compounds, aquo complexes, or complexed with organic matter. The mechanism for iron and manganese removal takes advantage of the low solubility of hydrated iron and manganese oxides: Fe2O3  nH2O and MnO2  nH2O, allowing these substances to be removed from water using simple technologies.

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Therefore, an important component of treatment technologies is to oxidize iron and manganese to the Fe(III) and Mn(IV) states, respectively, so that these relatively insoluble oxides can form. Two major ways to achieve this transformation are:

• •

increasing the oxidation potential of the water by adding oxygen from the atmosphere and increasing the pH of the water by the addition of bases such as Ca(OH)2.

The choice of the optimal treatment in a given situation can be determined experimentally. Technology based on increasing the oxidation potential, Eh, using oxygen from the air is generally used when the iron and/or manganese are present as mineral compounds and the concentration of dissolved organic matter, especially humic substances, is minimal. The technology shown schematically in Figure 5 is based on aeration and rapid filtration through an active bed. For large concentrations of iron (above 10 mgFe l1), sedimentation is added to the process. In enclosed (pressure) systems the water entering the filters has divalent manganese (Mn(II)) and iron either in the form of Fe(OH)3 or unoxidized Fe(II). The effects of iron and manganese removal are governed by catalytic oxidation reactions on the active external layer of the media of the filter beds. In open systems, where more time is available for aeration, and CO2 is more easily removed, the water flow into the filters has iron mostly in the oxidized form (Fe(III)4Fe(II)), and divalent manganese. Iron(III) in water is present as either micro- or macroparticles and therefore the process of flocculation occurring in the filter beds is also important for removal. The oxidation of iron and especially manganese in filter beds has an autocatalytic character. This fact increases the opportunity to use technology for iron and manganese removal that does not require the addition of reagents. This is especially important for manganese removal, because at pH values typical for groundwater following aeration, manganese can be only oxidized catalytically by an active oxidizing filter bed after iron removal has taken place. The reactions for the catalytic oxidation of manganese are shown in Figure 6.

Processes

Oxidation of water by aeration

Hydrolysis of Fe2+, Mn2+ Oxidation Fe2+ → Fe3+

Hydrocomplexes and microflocs of Fe(OH)3

Flocculation Flocs of Fe(OH)3

Separation of Fe(OH)3 flocs in filter bed

Water with iron removed, manganese present as Mn(OH)2

Catalytic oxidation Mn2+ → Mn4+ and removal in active layer of filter bed

Water with Fe and Mn removed

System components Aerators and pipes transporting water to filter

Upper layer of filter bed, where iron removal takes place

Lower active layer of filter bed, where manganese removal takes place

Figure 5 Chemical reactions and processes for iron and manganese removal using aeration and rapid filtration.

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Chemical Basis for Water Technology Adsorption + Oxidation Reduction

Mn(IV)

Mn(II) Stage I:

Mn(OH)2

MnO(OH)2

+

(Water)

Mn(III) =

Active coating in filter bed

Mn2O3 + 2H2O Growth and reduction of active coating

Oxidation Mn(III) Mn2O3

Stage II:

Mn(IV) +

1 O 2 2

Growth and reduction of active coating

+

2H2O

=

(Water)

2MnO(OH)2 Active coating in filter bed

Figure 6 Schema for catalytic oxidation of Mn(II) in an active filter bed.

Table 2

Reactions and selected process parameters for the oxidation of Fe(II) and Mn(II)

Oxidant

Reaction

Oxidant demand

Precipitate

Iron removal O2 O3 Cl2 ClO2 KMnO4

4Fe2þ þ O2 þ 10H2O-4Fe(OH)3k þ 8Hþ 2Fe2þ þ O3 þ 5H2O-2Fe(OH)3k þ O2 þ 4Hþ 2Fe2þ þ HOCl þ 5H2O-2Fe(OH)3k þ Cl þ 5Hþ Fe 2þ þ ClO2 þ 3H2 O-FeðOHÞ3 k þ ClO2  þ 3Hþ 3Fe 2þ þ MnO4  þ 2H2 O-3FeðOHÞ3 k þ MnO2 þ 5Hþ

mg/mgFe(II) 0.14 0.43 0.64 1.21 0.94

mg/mgFe(II) 1.9 1.9 1.9 1.9 2.4

Manganese removal O2 O3 Cl2 ClO2 KMnO4

2Mn2þ þ O2 þ 2H2O-2MnO2k þ 4Hþ Mn2þ þ O3 þ H2O-MnO2k þ O2 þ Hþ Mn2þ þ HOCl þ H2O-MnO2k þ Cl þ 3Hþ Mn 2þ þ ClO2 þ 2H2 O-MnO2 k þ 2ClO2  þ 4Hþ 3Mn 2þ þ 2MnO4  þ 2H2 O-5MnO2 k þ 4Hþ

mg/mgMn(II) 0.29 0.88 1.29 2.46 1.92

mg/mgMn(II) 1.58 1.58 1.58 1.58 2.64

In most practical situations, active oxidizing beds are filtration media (most frequently quartz) whose grains are coated with a permanent layer of MnO2. The compounds have amphoteric characteristics, and therefore at pH values lower than their iso-electric points (in the range from approximately 7.5 to 8.5) they take on a proton to become positively charged, and above that point they are negatively charged. In the usual pH range of aerated groundwaters, hydrated iron oxides are generally positively charged. Because the surface of quartz filter media in this pH range has a negative charge, iron oxides tend to adsorb and flocculate on these beds. In the filter beds used for iron and manganese removal, two characteristic zones are formed (Figure 5):

•

•

the upper iron removal zone, in which iron removal occurs through catalytic oxidation of Fe(II) and retention of the formed oxides through flocculation of the transported microparticles and particles of iron oxides, and the lower manganese removal zone where catalytic oxidation and retention of manganese through the development of a layer of MnO2 that forms on the media grams occur.

If the pH rises above 9.5 or Eh is increased by the addition of a chemical oxidant, the distinction between the zones disappears. A characteristic feature of the structure of Fe2O3 and MnO2

formed as a result of heterogeneic catalytic oxidation in oxidizing filter beds is their great hydrodynamic stability in comparison with oxides formed under homogeneous conditions (high pH and high Eh). This means that high filtration velocities can be used in these beds. Oxidation using oxygen from the air is determined and limited by its oxidation potential, whose value under atmospheric conditions is defined by

EhðO2 Þ ¼ 1:228  0:0591 pH

ð6Þ

This value is sufficient for the oxidation of divalent iron, whose oxidation under homogeneous conditions requires in general a potential not lower than that given by

EhðFeðIIÞÞ ¼ 1:057  0:1773 pH  0:0591 log½FeðIIÞ

ð7Þ

The value of Eh(O2) is a bit too low for the oxidation of divalent manganese, which under homogeneous conditions requires the addition of a stronger chemical oxidant, such as ozone, chlorine, chlorine dioxide, or potassium permanganate. The reactions for the oxidation of divalent iron and manganese with various oxidants are shown in Table 2 and include the oxidant demand and solids (precipitate) production. It is important to emphasize that the reactions shown

Chemical Basis for Water Technology

in Table 2 are simplified and do not represent the complex mechanism of these processes. The proposed use of chemical oxidation for either new or modernized water-treatment facilities should be evaluated experimentally, in investigations examining the choice of oxidant as well as the effect of process and water-quality parameters (oxidant dose, contact time, water flow rate, TOC, etc.). It is important to know in advance the effect on the process of additional reactions, the need for oxidant dosages above the stoichiometric requirement, the influence of pH and TOC on the rate of oxidation, and the possibility of formation of undesirable odors, especially if chlorine is used. Based on full-scale experience, ozone is the preferred oxidant. The successful use of ozone at the lowest dose depends on changes in the pH of the water, however, requires that the dosage be carefully controlled, because too high a dosage can lead to the oxidation of divalent manganese to permanganate. The presence of TOC, and especially humic substances, limits the use of potassium permanganate as an oxidizing agent. Experimental investigations have shown that the rate of oxidation of divalent iron and manganese is determined primarily by Eh, pH, TOC, concentrations of iron and manganese, temperature, and in waters having low alkalinity, also on the buffering capacity of the water. Technologies based on increasing the pH are often based on the premise that, in addition to iron and manganese removal, softening of the water is required using chemical precipitation. Iron and manganese are removed together with calcium and magnesium because of the high pH of the softening process. In waters where the iron and manganese are originally present in the form of Fe(HCO3)2 or Mn(HCO3)2, the iron and manganese can be removed as carbonate precipitates in the pH range of 8–8.5 by the addition of either lime or soda ash. Technologies based on increasing the pH are more costly than those based on increasing the redox potential, especially if the latter is achieved by aeration. In conclusion, with regard to technologies for iron and manganese removal, it is important to emphasize the great complexity and not completely understood mechanisms of many reactions and processes used to achieve this goal. The complexity is also due to the participation of microorganisms in the process, for example, Gallionella ferruginea, that can use divalent iron and manganese in their metabolism. The biological contribution to iron and manganese removal requires an acclimation time. Arsenic. Arsenic compounds are significant harmful substances that are not detectable by taste in water. In various parts of the world, for example, in eastern India and in Bangladesh, arsenic contamination of groundwater is a major problem. This compound is a semi-metal, appearing primarily in compounds in the oxidation state of As(III) and/or As(V). The natural sources of arsenic are primarily arsenopyrites. Processes for arsenic removal from water include:

• • • •

chemical and biological oxidation of As(III) to As(V), which is more easily removed from water; coagulation and chemical precipitation (co-precipitation); adsorption; and membranes.

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In practice, chemical oxidation can be achieved using ozone, potassium permanganate, manganese dioxide, and Fenton’s reagent (H2O2/Fe(II)). Coagulation and chemical precipitation can be used successfully for the removal of As(V) by the addition of hydrolyzing iron coagulants (Fe(III)) at low pH values. Effective arsenic removal can also be achieved in groundwater by iron and manganese removal technologies based on aeration and rapid filtration. Increased effectiveness of arsenic removal at increased raw water concentrations of Fe(II) and Mn(II) has been confirmed. For adsorption, the following absorbents have been used: activated carbon, aluminum or iron oxides, and fly ash. pH has been demonstrated to be very important for arsenic removal, and removal is generally greater when pH is less than 7. The best adsorption effects on iron and aluminum oxides have been obtained. Activated carbon has been shown to successfully remove arsenic after being impregnated with copper or Fe(II). Promising results have also been obtained for the adsorption of arsenic on fly ash. Jekel (1994) has discussed arsenic removal and Jekel and co-workers have reported investigations with granular ferric hydroxide (Driehaus et al., 1998; Sperlich et al., 2008). Success has also been reported with NF (Moore et al., 2008), provided the arsenic is in the As(V) form.

3.16.6.7 Maximizing Chemical Stability The chemical stability of water leaving the treatment plant is important so that undesirable reactions do not take place in the distribution system. A major consideration with respect to chemical stability is the calcium carbonate equilibrium. The carbonate or bicarbonate concentrations of the water are typically expressed as the alkalinity. Raw waters, either groundwater or surface water, are normally at equilibrium with respect to calcium carbonate when they enter the treatment facility. This equilibrium can be disrupted by the addition of treatment chemicals such as alum that lower the pH and consume alkalinity. A pH that is too low will result in a water that is referred to as being aggressive. Such a water will dissolve metals and lead to corrosion in distribution system pipes. Lack of chemical stability can have other undesirable effects such as the precipitation of alum flocs (aluminum hydroxide) in the distribution system. Normally, regulations specify the pH range in which the water leaving the treatment plant must lie. (Therefore in some cases if the pH of the raw water is too low, it may need to be raised even if it has not been decreased during treatment.) Sometimes, the tendency of the water to either dissolve or precipitate calcium carbonate is also specified. Such tendency can be measured by a parameter such as the Langelier index, which relates the actual pH to the pH for equilibrium, although other approaches have also been investigated (MWH, 2005: ch. 11). Depending on the extent to which the pH has been reduced during treatment, it may be necessary to raise it again at the end of the process. (Although some treatment processes such as lime softening may increase the pH, such processes are much less commonly used and the usual situation is that the pH will be reduced rather than increased during treatment.) Another important aspect with respect to pH in the distribution system is its impact on the solubility of lead species

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Chemical Basis for Water Technology

(e.g., Lytle and Schock, 2005). This may be another reason for raising pH during treatment. In general, the addition of chemicals to increase the pH prior to distribution of the water will be done at the end of the treatment process. Thus, this step will have no impact on other treatment steps with the possible exception of disinfectant addition to maintain a residual in the distribution system. In practice, there are only several standard chemicals that are used for pH correction (most commonly sodium hydroxide or lime). Typically, the choice of chemical will be made on the basis of cost and operational considerations. Treatment plant designers are normally able to make these choices on the basis of past experience. Therefore, it is unlikely that there would be a need to experimentally investigate these alternatives as part of treatment process investigations.

3.16.6.8 Maintaining Water Quality to the Consumer’s Tap Hydraulically, the role of the distribution system is to convey water from the treatment plant to the end-user. From the viewpoint of water quality it has been said, somewhat tonguein-cheek, that the distribution system is a large and complex reactor, whose sole purpose is to degrade water quality. While this statement is obviously an exaggeration, it does contain several important messages. The first of these is that it is important to view the distribution system as a reactor in which physical, chemical, and biological processes occur. Unfortunately, in almost all cases these processes have the effect of degrading, rather than the improving, water quality. Minimization of these effects therefore means making the distribution system as inefficient a reactor as possible. An important aspect of this is to make the water leaving the treatment plant as physically, chemically, and biologically stable as possible, to minimize the opportunity for further reaction in the distribution system. This is not defined as a separate goal in addition to the seven; however, it is the reason for the fifth and sixth goals addressed previously (maximizing biological and chemical stability), and is an important contributing reason for the first and second goals (particle removal and TOC removal, respectively). This section addresses the relationship of achieving these goals to maintain distribution system water quality. Several important physical aspects of the distribution system that do have a direct bearing on water quality are pipe materials and pipe diameter and the hydraulic residence time in various parts of the system. The hydraulic residence time or water age is affected by demand in relation to pipe size and by the extent to which dead ends are avoided by looping of pipes. (It should be recalled that a distribution system is probably never at steady state and that water flows at a given point may change direction several times a day. Therefore, it makes sense to speak only of average hydraulic residence times.) In terms of pipe materials, the presence of metallic pipes can mean that corrosion plays a substantial role in water quality. Of course, the presence of older lead pipes or lead connections means that lead levels in water can be unacceptably high. Pipe diameter is important because many reactions affecting water quality take place at the pipe surface. Smaller diameter pipes have a higher surface-to-volume ratio and therefore provide a greater opportunity for such reactions to take place. The fact

that smaller diameter pipes are often located at the ends of the system where water age may be high (and disinfectant residual low) means that growth of biofilms through bacterial utilization of BOM is likely to be highest in these areas. Physical stability of the water leaving the treatment plant is probably the easiest to obtain, compared to biological or chemical stability. If treated water is low in turbidity, this will minimize the deposition of particles in the distribution system. Similarly, minimizing the soluble concentrations of coagulant (e.g., aluminum) will minimize the opportunity for precipitation of alum floc in the system. Chemical stability with respect to the calcium carbonate equilibrium will minimize either precipitation, or dissolution of existing protective scale. As discussed in Section 3.16.6.4, biological stability means avoiding the regrowth of bacteria and the consequent formation of biofilm in the distribution system. As noted previously, various factors can influence the extent to which regrowth may occur. This is discussed in more detail by Huck and Gagnon (2004). In many parts of the world, a disinfectant residual (invariably involving chlorine in some form) is maintained in the distribution system. One of the effects of this is to control regrowth. If a residual is not maintained, or in parts of the system where the residual has declined to a low value, other factors such as the amount of BOM in the water become important with regard to regrowth (Huck and Gagnon, 2004). The degree of biological stability required in the finished water at a given treatment plant depends on the extent to which regrowth may occur in the plant’s distribution system and the method chosen to manage it (i.e., maintaining a residual or not). Maintaining a microbiologically safe water throughout the distribution system requires both adequate disinfection at the treatment plant and the absence of direct contamination of the distribution system. The maintenance of a disinfectant residual is often seen as a way of guarding against such potential contamination. The low residual typically maintained in a distribution system might provide limited protection against a massive contamination incident; however, a loss of residual would indicate that a problem had occurred. Rapid detection of such a problem requires of course that residuals be monitored at various points in the distribution system with sufficient frequency. In systems where a disinfectant residual is maintained, the issue becomes maintaining an appropriate residual to the end of the system without having an excessively high residual entering the system. A high initial residual could lead to unacceptable levels of disinfection by-products, may be esthetically unacceptable, and also carries additional costs. Therefore, the disinfectant (often chlorine) demand of the treated water and of the distribution system itself is important. The chlorine demand of the treated water will be reduced by measures taken to reduce TOC levels (goal 2). The chlorine demand of the system itself is a function of pipe material, pipe surface area, and the presence of deposits and/or biofilm on the pipes. Replacement of metallic with nonmetallic pipes and minimization of biofilm growth will reduce this demand. Reducing water age by appropriate hydraulic measures will facilitate maintaining a residual to the end of the system. In some jurisdictions, chloramines rather than free chlorine are used to provide a more stable distribution system residual.

Chemical Basis for Water Technology

For systems where a disinfectant residual is not maintained, microbial safety of the distributed water requires a system of high physical integrity, as well as vigilant monitoring and response to detect and address any intrusion of contamination. Corrosion of metallic pipes is a function of water quality. As stated previously, lead levels can be a problem where lead materials are present in the distribution system pipes, connections, or appurtenances. Both iron-based and copper pipes are susceptible to corrosion. For many waters, the issue of corrosion can be addressed by maintaining a slight precipitating tendency with respect to the calcium carbonate equilibrium. This is often expressed through an appropriate positive value of the Langelier index. For other waters, other indices may be required. pH is a master variable with respect to the various equilibria involved and therefore corrosion control involves maintaining a sufficiently high pH. These matters relate to the chemical stability of the water, which has been discussed in Section 3.16.6.7. The corrosion of metallic pipe material can also create esthetic problems because of the presence of iron precipitates. Other esthetic problems may arise because of the formation of taste and odor in the distribution system. In certain cases, pipe materials may impart a metallic taste. Odors may arise because of the growth of certain types of microorganisms. In general, this issue can be addressed by maintaining biological stability. Although the discussion above has been in the context of distribution system pipes, it is important to recognize that treated water-storage reservoirs are important distribution system components. It is necessary to ensure that there is no possibility for contamination of the water in these reservoirs, and also that they are operated in such a manner as to avoid an excessively high water age. Although distribution system maintenance issues are outside the scope of this chapter, it should be noted that regular distribution system flushing is important for maintaining water quality. In addition, an appropriate pipe replacement program is crucial for maintaining this important element of municipal infrastructure. In summary, in the absence of accidental or deliberate contamination incidents, maintaining water quality to the consumer’s tap primarily involves maximizing the physical, chemical, and biological stability of the water leaving the treatment plant.

3.16.7 Summary (Concluding Remarks) The development of water treatment as a self-standing discipline has taken place over approximately the past 100 years. This development has not taken place evenly, but rather has occurred as a series of waves. An example of a very recent period of rapid change is the emergence of the use of UV radiation for disinfection. The speed with which this technology has come into use in water treatment is almost unparalleled in what is by nature a conservative discipline. Advances in the discipline of water treatment depend on a sound knowledge of important fundamental principles and an ability to apply them to produce very practical outcomes.

465

Treatment is one element of the overall system required for the provision of safe drinking water. Within the context of an appropriate regulatory framework, this system can be considered to consist of five elements: a good source, effective treatment, secure distribution, appropriate monitoring, and an appropriate and timely response to an adverse monitoring result. To ensure public health protection, the system for supplying drinking water must be robust. The concept of robustness extends the so-called multibarrier principle to include the human and institutional elements of the system as well as the physical ones. It is useful to approach water treatment from the point of view of the goals that treatment must meet. It is possible to identify seven such goals: particle (and associated pathogen) removal, removal of TOC, disinfection/inactivation, removal of chemical contaminants, achievement of biological stability, achievement of chemical stability, and achievement of an appropriate aesthetic quality. While all of these goals are important, disinfection/inactivation is paramount for protecting public health from acute risk. The complexity of treatment required in a given situation depends on the raw water quality, that is, on the number of goals that treatment must address for a given water. Some goals (e.g., disinfection/inactivation) are normally achieved through a single treatment process, whereas others may be addressed by several processes in the treatment system or train. There is often a choice of processes to address a particular goal. Maintaining water quality in the distribution system to the consumer’s tap is important but is not an explicit treatment goal because it is as much dependent on the physical layout and quality of the distribution system as it is on treatment itself. Effective treatment should minimize the risk to the consumer, be optimized and be sustainable. The latter point implies a preference for physical and biological processes and the minimization of chemical additions and treatment residuals such as sludges. Investigations of water-treatment processes can range from very fundamental to very applied. As an example, in an applied investigation, the hypothesis being tested may be: will a membrane work for the treatment of this particular water? Investigations may be conducted at various scales: laboratory, pilot, and demonstration (effectively full) scale. For investigations, proper application of the general principles of experimental design is important, with some special considerations for water treatment, such as testing several processes in parallel to ensure that the impact of any changes in raw water quality will be experienced by all processes. In practical investigations, analysis of the results must include an estimate of the capital and operating costs of the processes or systems capable of meeting the treatment objectives. This chapter briefly discusses the seven previously identified goals for water treatment and important chemical and physical principles involved, and summarizes the major processes used, with a focus on municipal (public) water supplies. For a detailed discussion of the processes, the reader less familiar with the processes may wish to consult standard environmental engineering works. The second half of the chapter directly links goals and processes, providing a critical review of the processes that can be used to meet a particular goal. In particular situations, achieving the goal may require contributions from several processes in the treatment train, and of

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course some processes (e.g., oxidation) can contribute to more than one goal. Achievement of the first goal, the removal of particles (including the physical removal of pathogenic microorganisms), is normally required for surface waters and for groundwater under the direct influence of surface water. Classically, particle removal has been achieved by a series of processes (coagulation, flocculation, and normally either sedimentation or flotation) culminating in granular media filtration. More recently, low-pressure membranes have come to replace granular media filtration even in large treatment plants. The optimal pretreatment required for membranes will probably be different than that for granular media filtration. In some cases, a low-pressure membrane used for particle removal may be able to operate without pretreatment. TOC removal may be required to reduce disinfection byproduct formation, to increase the stability of disinfectant residuals, and possibly to reduce membrane fouling. Although various approaches can be used to remove TOC, coagulation at low pH (approximately six or less) can be very useful, especially for waters of high humic content. Biological processes can reduce concentrations of biodegradable TOC. The biological stability of a water leaving the treatment plant is important to maintain water quality in the distribution system and minimize bacterial regrowth. Carbon is usually considered to be the limiting nutrient in drinking water and therefore biological stability is enhanced by the removal of BOM during treatment. Biological filtration, involving the growth of bacteria as a biofilm on the filter media, is the process normally used to reduce levels of BOM. Biological processes also play an important role in slow sand filtration, riverbank filtration, and underground passage of water following treatment. Biofiltration is not required for most groundwaters, which already contain lower levels of BOM. Biological processes must occur upstream of chlorination. Ozonation will increase the level of easily biodegradable material. Therefore although ozonation prior to biofiltration is helpful, it also means that if ozonation is used, it should be followed by biofiltration. Treatment should seek to minimize BOM levels (i.e., maximize biostability) of the finished water while meeting the other treatment objectives. The goal of reducing chemical contamination is water specific, in that the need to address this goal depends on the extent to which chemical contamination of the raw water exists. As examples of organic chemical contaminants that are important for the goal of addressing esthetic quality, the removal of the common odorous compounds, geosmin and MIB, is addressed in considerable detail to illustrate the three major processes that can be used (oxidation, adsorption on activated carbon, oxidation, and biodegradation) and to permit discussion of important water quality and other factors affecting the performance of these processes in specific situations. Many of these factors are relevant for the removal of other chemical contaminants by these processes. Removal technologies for the important inorganic substances, iron, manganese, and arsenic are also reviewed. The goal of ensuring chemical stability is important to minimize either precipitation in the distribution system or corrosion of metal distribution system piping and household

piping and fixtures. pH plays an important role in chemical stability and is also important from the perspective of controlling the concentration of substances such as lead at the consumer’s tap. The goal of esthetic quality is important because consumers want water that tastes and smells well. Odor is often more of a problem than taste. Although many odorous substances present in raw water are of natural origin and are not generally considered to have adverse health effects, it is difficult to convince people that the water is safe to drink if it tastes or smells bad. While not a specific treatment goal, the maintenance of water quality to the consumer’s tap is a very important issue. Water quality rarely, if ever, improves in the distribution system; rather, it normally degrades. Quality degradation is normally most evident at long residence times and is associated with the decay of the disinfectant residual. (The maintenance of a disinfectant residual is the approach used in much of the world to counter biological instability and maintain microbiological quality in the distribution system.) The extent of quality degradation is a function of the chemical, biological, and physical instability of the water leaving the treatment plant. However, the age, physical condition, and layout of the distribution system are also significant factors. From a treatment perspective, minimizing quality degradation in the distribution system involves making it as inefficient a reactor as possible, by maximizing the biological, chemical, and physical stability of the water leaving the treatment plant. Although some goals (e.g., disinfection) can be achieved by a single treatment process, others, such as TOC removal, may be accomplished by more than one process. For high-quality groundwater, the only treatment goal required may be disinfection. However for some groundwaters and for virtually all surface waters, more than one treatment goal must be addressed. Therefore, treatment processes must be combined into systems. In general, there is a logical sequence in which the processes would be combined, as discussed in the text. For example, particle removal of normally occurs early in the treatment train. In conclusion, the vital importance of high-quality and reliably available drinking water for the development and maintenance of society cannot be overemphasized. The design of robust and sustainable systems for the provision of such water must be based on the sound application of the relevant fundamental principles. Treatment is a vital component of such systems. The treatment goals that must be addressed for a particular water must be defined, and the processes required to meet those goals determined and optimally implemented.

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Relevant Websites http://www.trojanuv.com Trojan Technologies.

Preface – Water-Quality Engineering K Hanaki, University of Tokyo, Tokyo, Japan & 2011 Elsevier B.V. All rights reserved.

Water technology has been ever growing. It is an essential set of technologies for sustainable human society. Traditional technology, or better called just skill, to obtain, purify, and supply water was developed in the ancient era in various regions of the world. Great efforts have been made to obtain safe and adequate water as an essential resource to human life. However, still, billions of people in the world have no access to safe water. Moreover, large numbers of people have no chance to use a proper sanitation system, and this eventually deteriorates water quality and decreases the available safe water resources. Water resources are renewable theoretically. Used water does not disappear but is renewed to freshwater through evaporation by the power of solar energy. Solar energy is a natural distillation system to remove impurities present in water. However, the help of water technology is needed to maintain this renewing function in the modern world in which human activity overwhelms the natural purifying function. Conventional water technology was used as a black box through which water was purified without knowing the mechanisms, which control the physical, chemical, and biological reactions used in purification. However, such empirical use of technology cannot further improve or develop the technology. Many researchers and practitioners have developed theory-based technology, rather than mere empirical skill, for purifying water. The function of each unit process was studied and the mechanisms of separation, role of microorganisms, and process characteristics were clarified. A significant amount of knowledge has been accumulated. This knowledge improves process performance and reliability. Human beings also developed tools to examine the micro- or nanoscale reaction. Modern technology needs to be based on a deep and broad understanding of theory. Water technology is not isolated from other technologies. Many innovations to upgrade water-technology performance have been tried by applying new technologies from other fields. Membrane technology that originated in a field such as medical science or chemical engineering is an example. Nowadays, water treatment is one of the largest application areas of membrane technology. The purpose of water technology has been expanded from purification of water to water generation, energy and resource recovery. This is a practical and important area to which new

technology can be applied. Water availability is limiting human settlements. The supply of water produced from seawater or even moisture can break through this limitation. The requirements for water technology differ very much from one place to the other. The key factors are target compounds to be removed, resource and energy consideration, capacity of operating human resources, as well as economic resources. For example, a safe water-supply system in leastdeveloped areas needs technology, which can be used without frequent and sophisticated maintenance. However, such technology does not mean cheap and old technology. Newly developed innovative technology has a higher chance of implementation than old technology. Water management needs policy and system technology rather than simple connection of unit technologies. A distributed wastewater treatment system needs reliable and economically and technologically reasonable treatment technologies. A nutrient removal policy for eutrophication can be realized by introducing a technologically reasonable combination of secondary and advanced treatments. The water technology is a system technology. Resource and energy limitation has become a key factor for sustainability. Substantial amount of material use threatens the world’s resources, and energy use provokes the climate change problem. Saving resource and energy is now an indispensable aspect of water technology. The necessity of energy and resource saving further changes water technology. The current global situation regarding climate change and resource limitation enhances the recovery of resource and energy. Wastewater contains organic matter, which is biomass; therefore, obtaining carbon-neutral energy is possible. Water technology is now forming an important part of business worldwide. Every country needs safe water and environmental protection from wastewater. Technology development, implementation, and maintenance provide substantial opportunities for business. This volume includes theory, practice, and recent development of these wide range of water technologies, although all such innovative technologies cannot be included. There is no single answer to any of the particular cases. Among many options, one should choose a technology system considering the local social, economic, and engineering aspects. This volume would help such a technology choice.

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4.01 Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations G De Feo, University of Salerno, Fisciano (SA), Italy LW Mays, Arizona State University, Tempe, AZ, USA AN Angelakis, Institute of Iraklion, Iraklion, Crete, Greece & 2011 Elsevier B.V. All rights reserved.

4.01.1 4.01.2 4.01.3 4.01.4 4.01.5 4.01.6 4.01.7 4.01.8 References

Aqueducts Minoan and Greek Aqueducts Roman Aqueducts Cisterns and Reservoirs Water Distribution Systems Fountains Drainage and Sewerage Systems and Toilets Discussion and Conclusions

Prolegomena The past is the key for the future ‘Hydor (Water) is the beginning of everything’ Thales from Miletus (c. 636–546 BC).

Humans have spent most of their existence as hunting and food-gathering beings. Only in the last c. 9000–10 000 years, they discovered how to grow agricultural crops and tame animals. Such revolution probably first took place in the hills to the north of Mesopotamia. From there the agricultural revolution spread to the Nile and Indus Valleys. During this agricultural revolution, permanent villages replaced a wandering existence. About 6000–7000 years ago, farming villages of the Near East and Middle East became cities. Hydraulic technology began during antiquity long before the great works of such investigators such as Leonardo da Vinci (1452–1519) and Isaac Newton (1642–1727), and even long before Archimedes (287–212 BC) (Mays, 2008). During the Neolithic age (c. 5700–3200 BC), the first successful efforts to control the water flow were driven (such as dams and irrigation systems) due to the food needs and were implemented in Mesopotamia and Egypt (Mays et al., 2007). Urban water-supply and sanitation systems are dated at a later stage, in the Bronze Age (c. 3200–1100 BC). Regarding the technological principles related to water and wastewater, today it is well documented that many are not achievements of present day, but date back to 3000–4000 years ago. These achievements include both water and wastewater constructions (such as dams, wells, cisterns, aqueducts, sewerage and drainage systems, toilets, and even recreational structures). These hydraulic works also reflect advanced scientific knowledge, which allowed the construction of tunnels from two openings and the transportation of water both by gravity flow in open channels and by pressurized flow in closed conduits. Certainly, technological developments were driven by the necessities to make efficient use of natural resources, to make civilizations more resistant to destructive natural elements, and to improve the standards of life. With respect to the latter, the Greek (including Minoan) and

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Roman civilizations developed an advanced, comfortable, and hygienic lifestyle, as manifested from public and private bathrooms and flushing toilets, which can only be compared to the modern one, re-established in Europe and North America in the beginning of the last century. Minoan technological developments in water and wastewater management principles and practices are not as well known as other achievements of the Minoan civilization, such as poetry, philosophy, sciences, politics, and visual arts. However, archaeological and other evidence indicate that, during the Bronze Age in Crete, advanced water management and sanitary techniques were practiced in several palaces and settlements. This period was called by the excavator of the palace at Knossos, Sir Arthur Evans, as Minoan after the legendary King Minos. Thus, Crete became the cradle of one of the most important civilizations of mankind and the first major civilization in Europe. One of the major achievements of the Minoans was the advanced water and wastewater management techniques practiced in Crete during that time. The advanced water distribution and sewerage systems in various Minoan palaces and settlements are remarkable. These techniques include the construction and use of aqueducts, cisterns, wells, and fountains, the water-supply systems, the construction and use of bathrooms and other sanitary and purgatory facilities, as well as wastewater and stormwater sewerage systems. The hydraulic and architectural function of the water-supply and sewer systems in palaces and cities are regarded as one of the salient characteristics of the Minoan civilization. These systems were so advanced that they can be compared with the modern systems, which were established only in the second half of the nineteenth century in European and American cities (Angelakis et al., 2010). Water and wastewater technologies developed during the Minoan, Greek, and Roman civilizations are considered in this chapter. Emphasis is given to the water resources development such as aqueducts, cisterns, wells, distribution systems, wastewater and stormwater sewerage systems construction, operation, and management beginning since Minoan times (second millennium BC). The achievements to support the

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hygienic and the functional requirements of palaces and cities during this time were so advanced that could be paralleled only to modern urban water systems that were developed in Europe and North America only in the second half of the nineteenth century (Angelakis and Spyridakis, 1996). It should be noted that hydraulic technologies developed during the Greek and Roman periods are not limited to urban water and wastewater systems. The progress in urban water supply was even more admirable, as witnessed by several aqueducts, cisterns, wells, and other water facilities discovered (Koutsoyiannis et al., 2008). These advanced Minoan technologies were expanded to the Greek mainland in later periods of the Greek civilization, that is, in Mycenaean, Archaic, Classical, Hellenistic, and Roman periods. In this chapter, a rather synoptic description of the main concepts of water and wastewater management during the Minoan, Greek, and Roman civilization is attempted. The main principles and challenges are also discussed.

4.01.1 Aqueducts Aqueducts were used to transport water from a source to the locations where the water was needed, either for irrigation or for urban water supplies, and have been used since the Bronze Age. Aqueduct bridges are man-made conduits for transporting water across rivers, streams, and valleys. As a matter of fact, a systematic evolution of water management in ancient Greece began in Crete during the early Bronze Age, that is, the Early Minoan period (c. 3500–2150 BC) (Myers et al., 1992; Mays, 2007). Starting the Early Minoan period II (c. 2990–2300 BC), a variety of technologies such as wells, cisterns, and aqueducts were used (Mays, 2007).

4.01.2 Minoan and Greek Aqueducts The water distribution system at Knossos, as well as the mountainous terrain and available springs made possible

the existence of an aqueduct (Mays, 2007; Mays et al., 2007). The Minoan inhabitants of Knossos depended partially on wells, and mostly on water provided by the Kairatos River to the east of the low hill of the palace, and on springs. Indications suggest that the water-supply system of the Knossos palace initially relied on the spring of Mavrokolybos (called so by Evans), a limestone spring located 450 m southwest of the palace (Angelakis et al., 2007; Evans, 1921–1935; Mays et al., 2007). In later periods with the increase of population, other springs at further longer distances were utilized. Thus, an aqueduct made of terracotta pipe could have crossed a bridge on a small stream south of the palace which carried water from a perennial spring on the Gypsadhes hill (Graham, 1987; Mays, 2007). A second example of an aqueduct was found in Tylissos (see Figure 1(a)). Parts of the stone aqueduct, with the main conduit at the entrance of the complex of houses, and other secondary systems led the water to a cistern dated at c. 1425–1390 BC (Mays et al., 2007). Other aqueducts were in Gournia, Malia, and Mochlos. These technologies were further developed during the Hellenistic and Roman periods in Crete, and were transferred to continental Greece as well as other Mediterranean locations (Angelakis et al., 2007; Angelakis and Spyridakis, 2010). In the Archaic and the Classical periods of the Greek civilization, aqueducts were built similar to the ones built by the Minoans and Mycenaeans. One of the most renowned watersupply systems is the tunnel of Eupalinos on Samos Island. In fact, it is the first deep tunnel in history that was dug from two openings with the two lines of construction meeting at about the central point of the distance. The construction of this tunnel was made possible by the progress in geometry and geodesy that was necessary to implement two independent lines of construction that would meet (Koutsoyiannis et al., 2008; Mays et al., 2007). The Samos aqueduct system includes the 1036-mlong tunnel and two additional parts for a total length greater than 2800 m. Its construction started in 530 BC, during the tyranny of Polycrates and lasted 10 years. It was in operation until the fifth century AD (Koutsoyiannis et al., 2008).

Figure 1 Ancient Minoan and Greek aqueducts: (a) aqueduct entering Tylissos showing the stone cover and (b) Peisistratean aqueduct consisting of terracotta pipe segments and elliptical pipe openings in each pipe. Copyright permission with LW Mays.

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Obviously, there are several other acknowledged aqueducts in Greek cities since water supply was regarded a crucial and indispensable infrastructure of every city (Tassios, 2007). Aqueducts (either tunnels or trenches) were always subterranean due to safety and security reasons. Usually, at the entrance of the city, aqueducts would branch out in the city to feed cisterns and public fountains in central locations. The aqueducts were pipes (usually terracotta) laying in the bottom of trenches or tunnels allowing for protection. One or more pipes in parallel were used depending upon the flow to be conveyed. The terracotta pipes (20–25 cm in diameter) fit into each other and allow access for cleaning and maintenance by elliptic openings that were covered by terracotta covers (Mays, 2007; Mays et al., 2007). Water conveyed by aqueducts typically originated from karstic springs. As the history teaches us, the presence of natural springs was a prerequisite for the selection of an area to settle. As a matter of fact, the Acropolis at Athens had an aquifer and a spring named Clepsydra. With the intensified urban development as well as the increase of population, the natural springs were not able to cover the water demand. Thus, the increasing water scarcity was remedied by transferring water from distant springs by aqueducts, digging wells, and constructing cisterns for rainwater storage. In Athens all these alternatives coexisted: the Peisistratean aqueduct (see Figure 1(b)) constructed by the end of the sixth century BC was accompanied with numerous wells and cisterns. Legislative and institutional tools were developed in Athens in order to wisely and effectively manage a water-supply system with public and private elements (Mays et al., 2007; Koutsoyiannis et al., 2008). Subsequently, the technologies developed in ancient Greece were transferred to the Greek colonies both to the east in Ionia (Asia Minor, nowadays Turkey) and to the west in the Italian peninsula, Sicily, and other Mediterranean sites, most of which were founded during the archaic period. A brilliant example of this was the founding of Syracuse (on Sicily) as a colony of Corinth in 734 BC (Mays et al., 2007). Later, during the Hellenistic period, further developments were accomplished by the Greeks in the construction and operation of aqueducts due to the progress in science which led a new technical expertise. Hellenistic aqueducts usually used pipes as well as they continued to be subterranean for safety reasons (war, earthquakes, etc.). The scientific progress

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in hydraulic (especially due to Archimedes, Hero of Alexandria) allowed the construction of inverted siphons at large scales to convey water across valleys (lengths of kilometers, hydraulic heads of hundreds of meters) (Koutsoyiannis et al., 2007, 2008; Mays, 2007; Mays et al., 2007).

4.01.3 Roman Aqueducts Springs, by far, were the most common sources of water for aqueducts even with the Romans. Water sources for the Greeks and Roman systems included not only springs, percolation wells, and weirs on streams, but also lakes that were developed by building dams. At ancient Augusta Emerita, at present-day Merida, Spain, the Roman water system included two reservoirs created by the construction of the Cornalvo and the Proserpina dams. The Proserpina dam is an earthen dam, approximately 427 m long and 12 m high. The Cornalvo dam is an earthen dam, approximately 194 m long and 20 m high with an 8 m dam crest width. Both of these dams are still used in the present day, obviously with modifications over the years. Dams were built in many regions of the Roman Empire. Aqueducts consisted of many components, including open channels and pipes. The main types of conduits used by the Romans are: (1) open channels (rivi per canales structiles), (2) lead pipes (fistuli plumbei), (3) earthenware (terracotta) pipes (tubili fictiles), and (4) wood pipes. Open channels were built using masonry or were cut in the rock and flows were driven by gravity, while the lead pipes were used for pressurized conduits including inverted siphons. A scheme representing the general path of a whole aqueduct with the basic elements is presented in Figure 2. Obviously, there are many system configurations that were built by the Romans and Greeks; however, the drawing presents the major components, including the siphon (inverted siphon) which was used in some systems. Various types of pipes constructed by the Romans included terracotta, lead, wood, and stone. One of the most impressive Roman aqueducts in Roman Greece is that in the Aegean island Lesvos (Figure 3). It is probably a work of late second or early third century AD. It was mainly used for water supply of Mytilene town, the capital of the island, and for water supply and irrigation of the southeastern area of the island, by transporting water from the lake of Megali Limni (big lake), at the Olympus mountain,

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Figure 2 Flow sheet and components of a Roman aqueduct: (1) source – caput aquae; (2) steep chutes (dropshafts); (3) settling tank; (4) tunnel and shafts; (5) covered trench; (6) aqueduct bridge; (7) inverted siphon; (8) substruction; (9) arcade; (10) distribution basin/castellum aquae divisorium; (11) water distribution system. From De Feo G and Napoli RMA (2007) Historical development of the Augustan aqueduct in Southern Italy: Twenty centuries of works from Serino to Naples. Water Science and Technology: Water Supply 7(1): 131–138.

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Figure 3 Part of the impressive Roman aqueduct rises 600 m west Moria, a Lesvian village at 6 km from Mytilene town: (a) general view of the remains and (b) the base of columns. Copyright permission with AN Angelakis.

where the construction begins. The aqueduct was also fed by other secondary springs, such as the springs at the Agiassou area (i.e., Karini). It was passed through a very anomalous landscape relief; thus, it includes parts on the soil surface, tunnels, and bridges. The total length of the Lesvos aqueduct is 26 km, with a uniform slope of 0.0096 m m1. Its depth ranges from 0.65 to 1.10 m and its width from 0.35 to 0.64 m (Karakostantinou, 2006). Its maximum capacity is estimated to be of 25 000 m3 d1 a along the distance of 26 km, a route that was entirely supported by gravity. Today, the maximum water supply of the town (15 000 m3 d1) is pumping from springs of Ydata located in a lower level of that of Karini (Mytilene Municipal Enterprise for Water Supply and Sewerage, 2009, personal communication. Mytileni, Greece). Its remains at the village of Moria are 170 m long and 27 m in height and consist of 17 arches, also called Kamares laying on their column (Figure 3(a)). Each opening is divided in three successive arches based on columns. The masonry is constructed with the use of emplekton system (Karakostantinou, 2006). The columns and arches were constructed from large blocks of gray marble taken from the island; these materials were very strong and resistant to decay (Figure 3(b)). The distribution of the arches along the openings consists of three at a time – up and down – for every opening. The openings are delimited by columns, and each column has an abacus. Siphons (Figure 2(g)) were built by the Romans also, in fact many of the siphons may very well have been started by the Greeks and completed by the Romans. The siphons included a header tank for transitioning the open channel flow of the aqueduct into one or more pipes, the bends called geniculus, the venter bridge to support the pipes in the valley, and the transition of pipe flow to open channel flow using a receiving tank. Locations of siphons included Ephesus, Methymna, Magnesia, Philadelphia, both Antiochias, Blaundros, Patara, Smyrna, Prymnessos, Tralleis, Trapezopolis, Apameia, Akmonia, Laodikeia, and Pergamon (Mays et al., 2007; Tassios, 2007). These siphons were initially built with terracotta pipes or stone pipes (square stone blocks to which a hole was

carved) such as the inverted siphon at Patara (Turkey), shown in Figure 4 (Haberey, 1972). As shown in the figure this siphon was constructed from carved stone segments. Nevertheless, the need for higher pressures naturally led to the use of metal pipes, specifically from lead. One of the largest siphons was the Beaunant siphon of the aqueduct of the Gier River which supplied the Roman city of Lugdunum (Lyon, France). This siphon had nine lead pipes with a total length of 2.6 km. This siphon was 2600 m long and 123 m deep with an estimated (Hodge, 2002) discharge of 25 000 m3 d1. Pergamon was a city in western Turkey at the present-day city of Bergama. The Helenistic aqueducts constructed were the Attalos, the Demophon, the Madradag, the Nikephorium, and the Asklepieion. The Roman aqueducts constructed were the Madradag channel, the Kaikos, and the Aksu. The Madradag aqueduct which had a triple pipeline (terracotta pipe) of more than 50 km long included an inverted siphon (made of lead) longer than 3.5 km with a maximum pressure head of about 190 m (Mays et al., 2007; Tassios, 2007). It took another 2000 years later before another pipeline was constructed that could bear a higher pressure (Fahlbusch, 2006). In particular, the Attalos aqueduct was the first pipeline (buried of fired clay, and 13 cm inner diameter) in Pergamon, and it was probably constructed in the middle or second half of the third century BC, bringing water from a spring in the mountains north of Pergamon (Fahlbusch, 2006; Mays, 2007; Oziz, 1987, 1996). The Romans built mega water-supply systems including many magnificent structures. As a matter of fact, Roman aqueducts became very famous all over the world, with Rome’s water-supply system being considered one of the marvels of the ancient world (Hodge, 2002; De Feo and Napoli, 2007; De Feo et al., 2009b; Mays, 2007; Mays et al., 2007). In fact, the Romans were urban people and consumed enormous amount of drinking water in order to supply baths, public and decorative fountains, residences, garden irrigation, flour mills, aquatic shows, and swimming pools (Hodge, 2002; Tolle-Kastenbein, 2005; De Feo and Napoli, 2007; De Feo et al., 2009b; Mavromati and Chryssaidis, 2007). However, the

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Figure 4 Inverted siphons. (a) Inverted siphon at Patara (Turkey) made of stone pipes. (b) Reconstruction of siphon of the aqueduct of Gier, near Beaunant, France that supplied water to Ancient Lugdunum, showing ramp of siphon with header tank on the top and the nine lead pipes of the siphon. (a) From Mays LW (ed.) (2010) Ancient Water Technologies. Dordrecht: Springer and (b) From Haberey W (1972) Die ro¨mischen Wasserleitungen nach Ko¨ln. Bonn: Rheinland-Verlag.

Roman aqueducts were not built with the primary purpose of providing drinking water, nor to promote hygiene, but rather to supply the thermae and baths or for military purposes (Hodge, 2002; De Feo and Napoli, 2007; De Feo et al., 2009b). The description of the ancient Roman water-supply system is contained in some recommendations of the Latin writers: Vitruvius Pollio (De Architectura, book VIII), Plinio the Elder (Naturalis Historia, book XXXVI), and Frontinus (De Aquaeductu Urbis Romae). Roman hydraulic engineering borrowed from the experiences and techniques of the Greeks and Etruscans. However, the size of the works as well as the technical-organizational features of distribution started with them. The common Greek practice was based on underground conduits, following courses determined by terrain features (Martini and Drusiani, 2009). The Etruscan civilization flourished in central Italy from the VIII century BC onward. The Etruscan talent for water and land management is highlighted by the existence of an imposing number of works (tunnels and channels) spread over their territories of Latium and, to a lesser amount, of the other Etruscan areas (Bersani et al., 2010). The construction of an ancient Roman aqueduct was not different from the modern practice, with several modern technologies coming from Roman engineering. The building of an aqueduct started with the search for a spring. Water was collected after permeating through vaults and walls of

draining channels and settled. From the spring, water flowed into an open channel flow and air was present over the water surface (Monteleone et al., 2007). The water in the aqueducts descended gently through concrete channels. During the route, there were multitiered viaducts, inverted siphons, and tunnels to exceed valleys or steep points. At the end of its course, the channel entered into a so-called piscina limaria, a sedimentation tank to settle particulate impurities. Then, the channel flowed into a partitioning tank called castellum divisorium where there were some walls and weirs to regulate the water flowing into the urban pressure pipes (De Feo and Napoli, 2007; Monteleone et al., 2007). Rome originally used water directly from the river Tiber as well as wells and many small springs existed inside its town area, such as Acque Lautole, Acque Tulliane, Fonte Giuturna, and Fonte Lupercale. However, since the fourth century BC, Rome gradually built aqueducts (Bono and Boni, 1996). Aqua Appia was the first aqueduct built in Rome in 312 BC. It was entirely underground for a total length of around 16.561 km, equivalent to 11 190 passus (1 passus ¼ 1.48 m) and an average flow rate of 73 000 m3 d1, corresponding to 1825 quinariae (1 quinaria B40 m3 d1) (Table 1; Panimolle, 1984). It is important to specify that a quinaria has not been scientifically defined. As a matter of fact, a quinaria was a pipe of 2.3125 cm diameter and there is no unanimity on how much water is a quinaria (Rodgers, 2004). During the subsequent

8

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Table 1

Characteristics of the 11 Imperial Age Roman aqueducts

Location

Dating

Length (km)

Aqua Appia Anio Vetus Aqua Marcia Aqua Tepula Aqua Julia Aqua Virgo Aqua Alsietina Aqua Claudia Anio Novus Aqua Traiana Aqua Alexandrina Average Total

312 BC 273 BC 144 BC 127 BC 33 BC 19 BC 2 BC 52 AD 52 AD 109 AD 226 AD

16.561 63.640 91.331 17.800 22.830 20.875 32.882 68.977 86.876 58.000 22.000 45.616 501.772

Underground length (km (%)) 16.472 (99.5%) 63.312 (99.5%) 80.286 (87.9%) 12.470 19.040 32.814 53.620 72.964

(54.6%) (91.2%) (99.8%) (77.7%) (84.0%)

43.872 (86.8%) 350.978

Average slope (m km1)

Flowrate (m3 d1)

0.6 3.6 2.7 5 12.4 0.2 6 3.8 3.8 3.8 1 3.9

73 000 175 920 187 600 17 800 48 240 100 160 15 680 184 280 189 520 113 100 21 025 102 393 1 126 325

From Panimolle G (1984) Gli Acquedotti di Roma Antica (The Aqueducts of Ancient Rome). Rome: Edizioni Abete; Adam JP (1988) L’Arte di Costruire presso i Romani. Materiali e Tecniche (Roman Building: Materials and Techniques). Milan: Longanesi; Bono P and Boni C (1996) Water supply of Rome in antiquity and today. Environmental Geology 27: 126–134; Hodge AT (2002) Roman Aqueducts & Water Supply, 2nd edn. London: Gerald Duckworth; Rodgers RH (2004) Sextus Iulius Frontinus. On the Water-Management of the City of Rome. De Aquaeductu Urbis Romae. Cambridge: Cambridge University Press.

500 years, 10 more aqueducts were constructed. The last great aqueduct built in Rome in ancient times was the 22-km-long Aqua Alexandrina. On the whole, the 11 Imperial Age Roman aqueducts had a total flow rate of 1.13 106 m3 d1 and a total length of more than 500 km. Since the population of Rome at the end of the first century AD was about 500 000 inhabitants (Bono and Boni, 1996), a mean specific discharge of B2000 l inhabitant1 d1 was produced. This value is extraordinary if compared with the current specific water use of B200–300 l inhabitant1 d1. Nowadays, the popular but inaccurate image is that Roman aqueducts were elevated throughout their entire length on lines of arches, called arcades. Roman engineers, as their Greek predecessors, were very practical and therefore whenever possible the aqueduct followed a steady downhill course at or below ground level (Hansen, 2006). As a matter of fact, Table 1 shows that on average 87% of the length of the Rome’s aqueduct system was underground. The longest aqueduct in the Roman world was constructed in the Campania Region, in Southern Italy. It is the Augustan Aqueduct Serino-Naples-Miseno, which is not well known due to there being no remains of spectacular bridges, but it was a masterpiece of engineering. The Serino aqueduct was constructed during the Augustus period of the Roman Empire, probably between 33 and 12 BC when Marcus Vipsanius Agrippa was curator aquarum in Rome, principally in order to refurnish the Roman fleet of Misenum and secondarily to supply water for the increasing demand of the important commercial harbor of Puteoli as well as drinking water for big cities such as Cumae and Neapolis. The main channel of the Serino aqueduct was approximately 96 km long, and had seven main branches to towns such as Nola, Pompeii, Acerra, Herculaneum, Atella, Pausillipon, Nisida, Puteoli, Cumae, and Baiae (De Feo and Napoli, 2007; De Feo et al., 2010). In summary the Romans made great contributions to the advancement of the engineering of aqueducts. Fahlbusch

(2006) points out the following from examination of many aqueducts: 1. size of the aqueduct channel was chosen according to the estimated discharge and the size varied along the course of the aqueduct; 2. the cross section was large enough for people to walk through the channel for repair and maintenance, particularly to remove calcareous deposits; and 3. the cross section was kept constant allowing manifold uses for encasings, especially the soffit scaffoldings for the vaults in a kind of industrialized construction.

4.01.4 Cisterns and Reservoirs In general, cisterns were usually constructed in order to store rainwater for domestic use (private houses), with a volume in the order of dozens of cubic meters, while reservoirs were realized in order to store flowing water with a volume in the order of thousands of cubic meters (Tolle-Kastenbein, 2005; De Feo et al., 2010). The Minoan and Mycenaean settlements used cisterns a 1000 years before the classical and Hellenistic-Greek cities. Cisterns were used to supply (store runoff from roof tops and court yards) water for the households through the dry summers of the Mediterranean. In ancient Crete, in particular, the technology of surface and rainwater storage in cisterns for water supply was highly developed and has continued to be used in modern times. One of the earliest Minoan cisterns was found in the center of a pre-palatial house complex at Chamaizi dating back to the turn of the second millennium BC. It is located on the summit of a hill and its rooms were situated around a small open court with a deep circular rock-cut cistern, 3.5 m in deep and with a diameter of 1.5 m, lined with brickwork in its upper part (Davaras, 1976; Mays et al., 2007; Angelakis and

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Spyridakis, 2010). Four of the earliest Minoan structures which may be considered to be large cisterns were built in the first half of the second millennium BC at Pyrgos-Myrtos (Ierapetra), Archanes, Tylissos, and Zakros (Cadogan, 2007; Mays et al., 2007; Angelakis and Spyridakis, 2010). While, at Phaistos, water supplied to cisterns depended on precipitation collected from rooftops and courts, a supplementary system was needed to satisfy the needs of water supply, especially in this particular area where agriculture was widely practiced. Thus, water was probably taken from wells in a location southwest of the palace which was rich in groundwater and surface water, and from the river Ieropotamos located to the north, at the foot of the Phaistos hill (Gorokhovich, 2005; Mays et al., 2007; Angelakis and Spyridakis, 2010). There were also cisterns on the high grounds above the Minoan palace in Malia, in a site lying in a narrow plain between the mountains and the sea. At the famous Phaistos palace, cisterns depended on precipitation collected from rooftops and yards. A supplementary system of water supply was needed to satisfy the needs of water supply, especially in those areas where agriculture was intensive. The cisterns were connected to small channels collecting spring water and/or rainfall runoff from catchment areas. The use of cisterns preceded channels or aqueducts in supplying the palace and the surrounding community with water (Mays et al., 2007; Angelakis and Spyridakis, 2010). Most Greek houses had a cistern supplied by rainwater for purposes of bathing, cleaning, houseplants, domestic animals, and even for drinking during shortages of water. Most likely, the water was of a quality that would be subpotable using today’s standards. Aristotle in his Politics (vii, 1330 b) written around 320 BC asserted that ‘‘cities need cisterns for safety in war.’’ During this time a severe 25-year drought required the collection and saving of rainwater. Also about this time cisterns were built in the Athenian Agora for the first time in centuries (Crouch, 1993; Mays, 2007). In particular, in the ancient Greek city of Dreros on Crete, there is a rectangularshaped cistern with dimensions of approximately 13.0  5.5  6.0 m3 (Antoniou et al., 2006; Mays, 2007). In ancient Crete, the technology of surface and rainwater storage in cisterns is continued to be used even today. Four of the earliest Minoan structures which may be considered to be large cisterns were built in the first half of the second millennium BC (the time of the first Minoan palaces) at PyrgosMyrtos (Ierapetra), Archanes, Tylissos, and Zakros (Angelakis et al., 2010). The Tylissos cistern is shown in Figure 5(a). This technology has been further improved during the Hellenistic and Roman periods. An impressive pillar of two interconnected cisterns, 40 m deep cut in the rock, has been discovered in ancient city Eleutherna (Figure 5(b)). The dimensions of the two cisterns are 40  25 m2 and the depth 4.5 m. The city flourished in the early Christian times and the water was transported from a spring through an aqueduct of about 3 km long to the cisterns. The water supply of the city including the thermes was transported through a 150-m-long channel with dimensions of 1.5  2.0 m2. The advanced water-supply technologies developed in Minoan Crete were expanded and improved during the Roman domination of the Greek world. Two such examples with a relatively small but impressive cistern in Minoan city and one of the two huge cisterns

9

(of about 3000 m3 each) in Aptera city in the western Crete are shown in Figures 5(c) and 5(d), respectively. During the classical age (the period between the Archaic and Roman epoch), the political situation was characterized in the Greek world (mainly Greece and Asia Minor) by wars among the various cities. In this period, no springs or deep wells existed, so cisterns were constructed to collect rainfall during the winter season. These cisterns were dug into the rock and were mostly pear-shaped with at least one layer of hydraulic plaster that prevented water loss. The cisterns varied in size from 10 m3 to thousands of cubic meters and possibly supplied more than 10 000-people baths and thermes. To prevent contamination of water the mouth of the cistern was covered to keep out dust and debris, and to prevent light from entering, avoiding the growth of bacteria and algae. Reservoirs constructed by the ancient Romans were set low in the ground, or actually underground, and roofed over, by means of concrete vaulting. The roofing vaults were supported by rows of columns, piers, or wall pierced with doors to allow the water to circulate. In some cases, the floor was slightly concave with a drain in the middle, to permit cleaning (Hodge, 2002; De Feo et al., 2010). In general, in the Roman world the reservoirs had two functions: a reservoir could be a reserve for use when the aqueduct ran low or by adding in a little from the tank everyday to supplement supplies until the aqueduct discharge picked up again. When the daily consumption exceeded what the aqueduct could bring in, at least in the hours of daylight, the reservoir was topped up every night to meet the next day’s demands (Hodge, 2002; De Feo et al., 2010). An example of a Roman reservoir is the Bordj Djedid at Carthage in Tunisia, into which the Carthage aqueduct emptied after a run of no less than 90.43 km from its source. This great reservoir was oblong, 39.0 154.6 m2, the size of an entire city block, and subdivided into 18 transverse compartments. Its capacity was 25 000–30 000 m3, representing about a day and a half’s discharge for the aqueduct (Hodge, 2002; De Feo et al., 2010). Remaining in Tunisia, in the center of the city of Dougga/Thugga, there are two very large reservoirs. The first one is the Ain El Hamman reservoir with five aisles, while the second one is the Ain Mizeb reservoir with seven aisles. The two reservoirs have a combined storage volume of 15 000 m3 (Tolle-Kastenbein, 2005; De Feo et al., 2010). Large reservoirs were constructed not only in Northern Africa but also in Europe, especially in Italy and in Turkey. Since a Roman thermae required an enormous quantity of water for its functioning, a huge reservoir had to be constructed. As a matter of fact, the reservoir of the Baths of Caracalla (located in an area of over 100 000 m2) could contain over 80 000 m3 in the numerous cells, situated into two parallel aisles and onto two floors. The oldest baths of Traiano received water supply from a reservoir of around 10 000 m3 (Tolle-Kastenbein, 2005; De Feo et al., 2010). The greatest baths of Diocletian occupied about the same area as those of Caracalla (a rectangle of about 356  316 m2) and closely resembled them in the plans. The reservoir by which the baths were supplied was fed by the aqua Marcia, the volume of which was increased by Diocletian. It was trapezoidal in shape, 91 m in length, with an average width of 16 m. This reservoir, called Botte di Termini (Barrel of Termini), was

10

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Figure 5 Minoan, Hellenistic, and Roman water collection and storage cisterns: (a) Minoan at the ancient town of Tylissos; (b) Hellenistic at the city of Eleutherna; (c) Roman at the Minoa town; and (d) Roman at town of Aptera. Copyright permission with AN Angelakis.

destroyed during 1876 in order to build the Termini railway station, whose name derives from that of the baths (De Feo et al., 2010). In the three centuries of the Roman imperial age, the reservoirs were designed in almost all the architectural forms and in almost all the techniques of masonry known: arcs (especially transversal arcs), turned (especially barrel vault), carrying pillars or groups of pillars, walls of stones and bricks, opus caementicium; while columns were still not used. In fact, the columns were introduced by architects famous for their works of hydraulic engineering in the present-day Istanbul. They created a host of columns hidden in the heart of the capital of the Roman Empire (Tolle-Kastenbein, 2005; De Feo et al., 2010). As a matter of fact, the name of the first reservoir means ‘with a 1001 pillars’. It is the Binbirdirek reservoir which was built under the order of Philoksenos, a Senate member in the Constantinus I period of the fourth century. During the Roman period, Istanbul’s water requirements were met by water brought from distant parts of Thrace. For this reason, the Byzantines built large reservoirs in order to be able to withstand long sieges (De Feo et al., 2010). The Binbirdirek reservoir covered an area of 3640 m2 and had a capacity of around 32 500 m3 of water. It measured 66  56 m2 and was carried by 224 columns consisting of

16 rows, each one having 14 columns, all of which are equal in length, and every column carries the signature of its master (‘1001’ was used to emphasize the great number of columns). There is a thick overlapping astragal running round the columns carrying the vaults and arches and they are in the form of a truncated pyramid and are without decoration. The relief cross on one of the columns is good proof that the reservoir was built in the fourth century, after the Byzantines accepted Christianity. In order to construct ceilings 14–15 m2 high, a second layer of columns was fixed over the marble rings on the first layer of columns. When the palace was destroyed in the sixth century, the cistern was restored. After the Ottoman conquest of Istanbul in 1453, new reservoirs were built and the Binbirdirek was no longer used (De Feo et al., 2010). One of the magnificent historical constructions of Istanbul is the Yerebatan Saray (or Basilica Cistern), located near the southwest of Ayasofya (Hagia Sophia). This huge reservoir was rebuilt by the emperor Justinian (527–565) after the Nika revolt (532). It is a large, vaulted space; the roof rests on 12 rows of 28 marble columns, which are about 9 m high. As the total surface is 65 138 m2, the maximum capacity is almost 85 000 m3, which was brought to this cistern from a well B20 km away with a new aqueduct, also built by

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Justinian. It was used to provide water to the imperial palace (hence the name, imperial cistern). The 336 columns (246 are still visible) were brought to the Basilica Cistern from older buildings. Again, it is narrated that 7000 slaves worked in the construction of the cistern. In fact, the cistern borrowed its name from Ilius Basilica in the vicinity (Lendering, 2008; Ku¨ltu¨r, 2008; De Feo et al., 2010). Another huge Roman reservoir in ancient Constantinopolis (today’s Istanbul) is the Sultan’s Cistern. We do not have any verifiable scientific evidence for its construction date; at the earliest, it could be late fourth century AD, judging by the presence of crosses carved into the upper parts of the column heads. It has a rectangular plan and the whole is divided into five equal rectangular parts by the use of 28 columns, with 7 in granite and 21 in marble, placed equidistant from each other, also supporting the roof with vaulted arches (De Feo et al., 2010). The last Roman underground hydraulic marvel is the spectacular Piscina Mirabilis in Misenum, in the Southern Italy. The Piscina Mirabilis is located in the present-day Municipality of Bacoli, in Miseno (the ancient Misenum), up the hill facing the sea in the bay of Naples. It was constructed during the Augustan Age in order to supply water to the Classis Praetoria Misenensis (Adam, 1988; Hodge, 2002; De Feo and Napoli, 2007; De Feo et al., 2010). The Piscina Mirabilis is a gigantic reservoir 72 m long and 27 m large, with a volumetric capacity of 12 600 m3 of water (Figure 6). It is dug in a tufa hill and has two step entrances in the northwest, the Ancient Roman entrance and southeast corners, the latter closed. Forty-eight pillars, arranged on four rows serving as a support to the barrel vault, divide it into five principal aisles on the long sides (Figure 7(a)) and 13 secondary aisles on the short sides (Figure 7(b)), giving it the majestic look of a cathedral. The long walls were built in opus reticolatum (reticular work) with brick bonding courses and by the technique of the tufa stone pillars, both covered with a thick waterproof layer of opus signinum (pounded terracotta). There is a basin of 1.10 m, probably a polishing pool, which is a waste bath for the maintenance of the reservoir, in the floor of the nave. It was used as a Piscina limaria for the periodical cleaning of the reservoir (Figure 7(c)). The water was lifted through a series of openings (doors) in the vault along the central nave, hydraulically to the covering terrace of the reservoir, and from there, flowed in channels to the urban area. These doors appear casually opened in the roof (Figure 7(d)), with an irregular realization being noted (Adam, 1988; Hodge, 2002; De Feo and Napoli, 2007; De Feo et al., 2010). Russo and Russo (2007) estimated a total daily demand of 12 000 m3 of water for Misenum, including 4000 m3 for the fleet and 8000 m3 for daily demands and for the thermal baths and gardens (based upon daily individual requirements of 100 liters per capita and equal requirements for thermal baths and gardens). The estimated total daily demand is similar to the capacity of the Piscina Mirabilis. Close to the Piscina Mirabilis are two other large cisterns, probably belonging to large villas, the Grotta Dragonaria and Cento Camerelle (Nerone’s jail). In Pozzuoli, the aqueduct served several cisterns, notably the Piscina Cardito (55 16 m2) from the second century, and the Piscina Lusciano (35  20 m2) from the first century AD (De Feo and Napoli, 2007; De Feo et al., 2010).

11

4.01.5 Water Distribution Systems Water distribution systems are aimed at distributing water from reservoirs or aqueducts to the end users. The modern systems are based on the use of pipes. Regarding this aspect, the Minoan society was surprisingly modern. As a matter of fact, in the Knossos palace, the water supply was furnished by means of a network of terracotta pipe conduits (60–75 cm flanged to fit into one another and cemented at the joints) beneath the floors at depths that vary from a few cm up to 3 m (Koutsoyiannis et al., 2008; Angelakis and Spyridakis, 2010). Possibly, the piping system was pressurized (Mays, 2007). Similar terracotta pipes were discovered in some other Minoan sites. In particular, Tylissos was one of the important cities in Ancient Crete during the Minoan era, flourishing (2000–1100 BC) as a peripheral center dependent on Knossos. From the aqueduct, secondary conduits were used to convey water to a sedimentation tank (Figure 8; Mays, 2010) constructed of stone before its storage to the cistern shown in Figure 5(a). Terracotta pipes have also been found at Vathypetro, as well as in the Caravanserai (Guest House), south of the Knossos palace with some also having been found scattered in the countryside (Angelakis and Spyridakis, 2010). The study of the ruins of Pompeii gives a clearer understanding of a Roman urban water distribution system. But this statement does not mean that all Roman cities are identical to Pompeii. The ending point of a Roman aqueduct was the castellum divisorium which had the double function of serving as a disconnection between the aqueduct and the urban distribution network as well as dividing the water flow to various uses and/or geographical areas of the city (Figure 9). In the beginning, Pompeii was not supplied by the Serino aqueduct. As there were no springs in Pompeii, wells were dug to supply water. It is also very likely that Pompeii received water via an aqueduct from the mountains due northeast of Avella. The town must have had a long-distance water supply, quite some time before the Augustan Age, probably around 80 BC. When the Serino aqueduct was built under Augustus, it crossed the course of the older Avella aqueduct between the Apennines and Mount Vesuvius, and both aqueducts were united into a single system (De Feo and Napoli, 2007). The castellum divisorium of Pompeii was housed inside a large brick building near the Vesuvian gate (Figure 10(a)). The supply channel entering the building is 30  25 cm (Figure 10(b)). The flow in this distribution structure was allowed to expand into a wide, shallow tank, separated into three equal compartments (masonry structures) (Figure 10(c)). Flow from each compartment entered a lead pipe. Some feel that the three pipes were connected separately to public fountains, the second to the thermal baths and the third to private users (Hodge, 2002; Russo and Russo, 2007). From the exits the water flowed into lead pipes. There is also the distinct possibility that the three pipes were directed to different geographical areas of Pompeii. Assuming that the pipes did convey water separately to the three major uses as presented by Hodge (2002), the central pipe was directed to the public fountains and had a 30 cm external diameter, whereas the two side ones were 25 cm in diameter. The three gates were of different heights. Thus, the highest gate, which was that serving private houses, cut off their supplies until and unless the water level in

12

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations 1.2

4.9

1.2

4.0

1.2

4.0

1.2

4.0

1.2

4.9

1.2

11.4

27.0

1.2

4.3 4.3 1.2 4.3 4.3

( Measures in meters )

1 2 3 4 5

2.0

1.2

4

Legend

N

11.4

9.4

A

Inlet water Ancient Roman entrance - 1 Piscina Limaria Outlet washing water Ancient Roman entrance - 2

1

B

1.2

4.9

1.2

4.3

2

1.2

4.3

Longitudinal section A-A

1.2

4.3

1.2

4.3

1.2

72.0

1.2

A

4.3 1.2

3

A

Plan of the Roman Piscina Mirabiliis

1.2

3.0

5

1.2

4.3

1.2

4.3

1.2

4.9

1.2

B

10.4

Trasversal section B-B Figure 6 Plan and sections of the Piscina Mirabilis. Modified from De Feo G, De Gisi S, Malvano C, and De Biase O (2010) The greatest water reservoirs in the ancient Roman world and the ‘‘Piscina Mirabilis’’ in Misenum. Water, Science and Technology: Water Supply 10(4) (in press).

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

13

Figure 7 Piscina Mirabilis: (a) a cross aisle; (b) a longitudinal aisle; (c) internal piscina limaria; and (d) a hole in the barrel vaulted roof.

the main body of the castellum rose high enough to spill over it and start flowing down the channel; on the contrary, the lowest gate (that in the center) governed access to the public fountains, which, if the water level sank, were thus the least to dry up. The private users had no minimum water entitlement until the needs of the public fountains and thermal baths had been satisfied (Hodge, 2002). From the castellum divisorium, the three pipes lead the water to different parts of the city filling water towers: the castellum secondarium or castellum privatum (Figure 10(d)). The water

towers were lead tanks positioned on top of brick masonry pillars, 6 m tall, located at crossroads and connecting small numbers of customers. They also supplied public fountains. The single user had to pay to obtain water for his premises. The water was metered by means of bronze orifices, the calices connecting the customers’ pipes (usually quinariae pipes) to the castellum privatum lead tank. In Pompeii, case calices were placed at the bottom of the lead tanks, and pipes fit into cavities left in the brick pillars (Hodge, 2002; Monteleone et al., 2007).

14

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Figure 8 Water system at Tylissos, Crete, Greece with sedimentation tank in foreground with stone channel connecting to cistern in background. (Mays, 2010, Copyright permission with LW Mays).

Aqueduct

Castellum divisorium

Head 18 m

Castellum secondarium Head 6m

Figure 9 Flow sheet of a Roman urban water distribution systems based on Pompeii. Modified from Hodge AT (2002) Roman Aqueducts & Water Supply, 2nd edn. London: Gerald Duckworth.

The lead tank on the water tower acted as a disconnection between the system at high pressure upstream and the customers’ pipes downstream. Connecting water derivation pipes elsewhere in the castellum privatum was against the regulations. The only connection available had to be arranged with the water office discussing the quantities for consumption. This water-supply system clearly shows that water towers could break from the pressure built up in the mains descending from the initial castellum divisorium at the top point of the city, with excess water overflowing into streets drains. As shown in Figure 9, the maximum height of water over the tap was about 6 m, without accounting for the pressure losses in the delivering pipes (Hodge, 2002; Monteleone et al., 2007). Lead pipes (Figure 11) in Pompeii are of the same construction and appearance as found in other Roman cities. The water taps found in Pompeii were also similar to those found in other Roman cities. Only a small number of houses had

a water pipe that supplied a private bath or basins in the kitchen, in the toilet, or in the garden.

4.01.6 Fountains The Minoan civilization gave an extraordinary contribution to the development of water management practices also in terms of fountains. The main examples of Minoan fountains are subterranean structures supplied with water directly or from springs via ducts. The construction of steps or alternatively the shallow basins indicates that water was taken out with the use of a container. This recalls the type of fountain of the later Classical and Hellenistic period called arykrene. The most typical of them is that of the Zakro palace. Another fountain similar to the Tykte was found at the Guest House (Caravanserai) of Knossos in the Spring Chamber. A ritual function of

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

15

Figure 10 Pompeii: (a) brick building near the Vesuvian gate housing the castellum divisorium; (b) inside castellum divisorium; (c) supply channel; and (d) a castellum secondarium.

the particular fountains is also argued, as artifacts of ritual content have also been unearthed. Another type known in later periods as rookrene, which constantly provided freshwater, was also found in Zakro with two zoomorphic waterspouts. Finally, a remarkable fragment from a fresco composition depicting a fountain of a supposedly Minoan garden was found in the House of Frescoes in Knossos (Angelakis and Spyridakis, 2010). During the Roman period, public fountains were usually located in the street. For example, in Pompeii the fountains were located at fairly evenly spaced intervals of about 100 m, and it was rare for anyone to carry their water for more than 50 m (Hodge, 2002). The simplest form of street fountain was normally equipped with an oblong stone basin, typically about 1.5 1.8 m2 and 0.8 m high, into which the spout discharged, and which presumably was normally full. The fountains were deliberately designed to overflow in order to clean the street (Hodge, 2002; De Feo et al., 2010). Not far from the city of Pompeii, in the District of Salerno, there is a Roman gallery in rock in the village of Sant’Egidio del Monte Albino in the Sarno River basin. The gallery was constructed in order to supply a public fountain which stands on the structure of an ancient Roman villae (the Helvius

villae). The Helvius fountain was a public fountain, but it was quite different from the public fountains in nearby Pompeii (Figure 12(a)). As a matter of fact, the Helvius fountain was constructed neither by means of matched slabs nor in limestone nor in Vesuvian stone. It was built as a single block of white marble. Moreover, there is another particular aspect which differentiates the Helvius fountain from the Pompeian fountains (Figure 12(b)). The Helvius fountain has a sculptural decoration on the three available sides representing the river Sarno along its path from the spring toward the sea (De Feo et al., 2010). Figure 13 shows two additional Roman fountains that are quite different from those previously mentioned. Figure 13(a) shows a fountain in Chersonesos (Crete) and Figure 13(b) the Fountain of Trajan in Ephesus (Turkey), dedicated by Aristion, AD 102/114.

4.01.7 Drainage and Sewerage Systems and Toilets Drainage systems were used for the disposal of surplus water, and were found both in cities (to carry rainfall, overflow from fountains and bathrooms) and in the country (to prevent

16

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Figure 11 Components of lead pipe system found in Pompeii: (a) lead pipe and joint found along the street; (b) junction box; and (c) manifold. Copyright permission with LW Mays.

flooding in the fields). Sewerage systems were used for the conveyance of domestic wastewater, and were only found in cities, where they were necessary due to a high population density (Hodge, 2002). However, in most cases, combined systems of flow rates composed mainly of rainfall runoff and wastewater were applied. The Minoan civilization also gave an extraordinary contribution to the development of water management practices in terms of drainage and sewerage systems. As a matter of fact, Minoan palaces were equipped with elaborate storm drainage and sewer systems (MacDonald and Driessen, 1988). Open terracotta and stone conduits were used to convey and remove stormwater and limited quantities of wastewater.

Pipes, however, were scarcely used for this purpose. Larger sewers, sometimes large enough for a man to enter and clean, were used in Minoan palaces at Knossos, Phaistos, and Zakro. These large sewers may have led to the conception of the idea of the labyrinth, the subterranean structure in the form of a maze that hosted the Minotaur, a hybrid monster. The end section of the main part of the sewerage system of the Knossos palace is shown in Figure 14(a). The outlet of the Phaistos palace system appears to be similar (Figure 9(b)). Note that Evans (1921–35) and Darcque and Treuil (1990) considered that the main part of the system had been planned and constructed originally in Middle Minoan time. The main disposal sites at the Knossos and Zakros palaces were directed

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

17

Figure 12 Public fountains: (a) in Pompeii (matched slabs) and (b) in the basin of the Sarno river (single block of white marble).

Figure 13 Roman fountains: (a) fountain in Hersonissos (Crete) and (b) remains of the fountain of Trajan in Ephesus (Turkey), dedicated by Aristion, AD, 102/114. Copyright permission with LW Mays.

to the Kairatos River and to the sea, respectively. However, there are indications that in the palace of Phaistos and in the villa of Agia Triadha, cisterns were also used as disposal sites of surface water, along with appropriate landforms. Particularly in the palace of Phaistos, agricultural land located in the south site of the palace was used as disposal site of the both the wastewater and stormwater instead of the river Ieropotamos crossing the northern site of the Phaistos hill. In all cases of palaces and cities, there is an increased slope of the central sewers toward of their outlets; thus, anaerobic conditions have been maintained and the odors have been avoided. In addition to the very effective drainage and sewerage systems, some palaces had toilets with flushing systems operated by pouring water in a conduit. However, the best example of such an installation was found on the island of Thera (Santorini) in the Cyclades, Greece. This is the most eloquent and best-preserved example belonging to the early late-Minoan period (c. 1550 BC) in the Bronze Age settlement of Akrotiri, which shares the same cultural context of Crete (Angelakis and Spyridakis, 2010). At the beginning, for some centuries, the collection and discharge of rainwater runoff was managed by separate sewers.

As a matter of fact, rainwater was carried in simple channels carved into the rock in cities with bedrock (i.e., the Acropolis of Athens). Otherwise, the channels were covered with rocks. A system for the simultaneous discharge of both rainwater and domestic sewage was invented during the Greek period (Tolle-Kastenbein, 2005). Ancient drainage and sewerage systems were usually developed on four levels. The initial channels coming from buildings (first order) ended in street channels of second order, which prosecuted in principal channels with an increasing size (third order) and ended in a final huge collection channel (fourth order), usually present only in big cities. The great drain of Athens was first designed as a rainwater drainage system. However, in the first quarter of the fifth century BC, it received domestic sewage and ended in a huge collection channel (fourth order) similar to the Roman Cloaca Maxima (Tolle-Kastenbein, 2005). The Cloaca Maxima is the best-known ancient urban drain. Tradition ascribes its construction to Tarquinius Priscus, king of Rome 616–578 BC. The Cloaca Maxima (4.2 m high, 3.2 m wide) was covered by stone vaulting, while its bottom was paved with basalt pavers. It combined the three functions of

18

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Figure 14 Outlet of the central Minoan sewerage and drainage systems: (a) palace of Knossos and (b) palace of Phaistos. Copyright permission with AN Angelakis.

wastewater and rainwater removal and swamp drainage. As it is well known, the exit from the Cloaca Maxima drain into the river Tiber still exists in Rome, but now partly hidden by the modern Lungotevere Embankment (Hodge, 2002). The street drains of Pompeii are very famous. At the time of the famous Vesuvius eruption, the drains existed only in the area around the forum. The streets were a sort of open channel conveying water coming from public fountains, rainwater, and segregate sewage. Therefore, as shown in Figure 15, streets had raised sidewalks (50–60 cm high) with stepping stones (pondera) at the street corners to enable pedestrians to cross from one side to the other without stepping down (Hodge, 2002). Toilets have a long history. The first evidence of the purposeful construction of bathrooms and toilets in Europe comes from Bronze Age Minoan (and Mycenaean) Crete in the second millennium BC (Vuorinen et al., 2007). In the palace of Knossos, rainwater was probably used to flush the toilet near the Queen’s Hall (Figure 16; Angelakis et al., 2005). The Hellenistic period is considered more progressive for the sanitary and purgatory engineering during the antiquity, although the considerable spreading of these systems occurred during the Roman era. The Romans applied the earlier techniques in larger constructions, using the advantages of their

building methods with concrete walls and vaulted roofing. Moreover, due to their improved aqueduct technologies, they could provide natural water flow in most public latrines. It is also evident that such structures and installations have survived until the end of the ancient world and have been implemented during the beginning of the Byzantine period. The customs of the new religion, Christianity, modified some of the structures in terms of privacy in bathing facilities (Antoniou and Angelakis, 2009). During the Hellenistic era lavatories improved significantly, followed by their spread throughout the Roman Empire. The features of the typical ancient lavatory are the bench-type seats with keyhole-shaped defecation openings and an underneath ditch. The ditch was both a water-supply conduit for flushing and a sewer. Figure 17 shows remains of a public toilet in Ephesus (Turkey) illustrating the bench seats, the defection openings, and the small channel on the floor for cleaning the sponghia. The lavatory was usually situated in the area of the building most convenient for water supply and/or sewerage. In many cases, the water for the flushing was reused either after other domestic or communal activities. Despite privacy, lavatories were used in antiquity by many people simultaneously, from two to three people in the small domestic latrines and up to 60 people in the larger public latrines

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

19

Figure 15 Stepping stones (pondera) in Pompeii.

Wooden seat

Door jamb

Gypsum floor

likely lacked running water and they were commonly located near the kitchens. All this created an excellent opportunity for the spreading of intestinal pathogens (Vuorinen et al., 2007). Hygienic conditions in both types of toilets must have been very poor, and consequently intestinal diseases were diffused. Dysentery, typhoid fever, and different kinds of diarrheas are likely candidates for diagnoses. Unfortunately, descriptions of the intestinal diseases in the ancient texts are so unspecific that the identification of the causative agent is a very problematic venture. Studies of ancient microbial DNA might offer some new evidence for the identification of microbes spread by contaminated water (Vuorinen, 2010).

Sewer

Seat

Hood

Sewer

Flushing conduit 1m Doors

Figure 16 Section and plan of ground-floor toilet in the residential quarter of palace of Minos. From Angelakis AN, Koutsoyiannis D, and Tchobanoglous G (2005) Urban wastewater and stormwater technologies in ancient Greece. Water Research 39: 210–220.

(Antoniou, 2010). Lavatories were used throughout the Roman Empire, with a more or less monumental appearance. The reader is referred to Antoniou (2010) for a detailed discussion of ancient Greek lavatories. Toilets during the Roman era can be divided into two groups: public and private. A public toilet was frequently built near to or inside a bath so that it was easily entered from both inside and outside of the bath. The abundance of water that was conducted to the bath could also be used to flush the toilet. Piped water for flushing private toilets seems to have been a rarity. The Romans, however, lacked something similar to our toilet paper. They probably used sponges or moss or something similar. In public toilets, the facilities were common to all. They were cramped, without any privacy, and had no decent way to wash one’s hands. The private toilets most

4.01.8 Discussion and Conclusions In the Minoan, Greek, and Roman cities, and other settlements, water supply varied according to local conditions, determined by climate (mainly rainfall), surface and ground water, and terrain. In these periods, various water-supply and wastewater systems and techniques were developed and applied, such as collection and storage facilities, wells and groundwater abstraction aqueducts, water distribution and use, construction and use of fountains, sewers, bathrooms, and other sanitary facilities and even recreational uses of water. These advanced technologies, which have been used in prehistoric Crete since about 4500 years ago, were subsequently expanded during the Mycenaean and then the Archaic, Classical, and Roman periods. In light of these historical and archaeological evidences, it turns out that the progress of present-day urban water and wastewater technologies as well as comfortable and hygienic living is not as significant as we tend to believe (Angelakis and Koutsoyiannis, 2003). However, a burst of achievements in water and

20

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

Figure 17 Public toilet in Ephesus (Turkey): (a) the bench-shaped seats were constructed of stone slabs with another vertical stone slab that covered the opening from the void between the floor and the seat and (b) the small channel (half-pipe-shaped cross-section) on the floor in front of the seat had a continuous flow of water for cleaning the sponghia (the toilet paper of the time). Copyright permission with LW Mays.

wastewater technology was accomplished throughout the centuries of the ancient Greek and Roman civilization. With a few exceptions, the basis for present-day progress in water transfer is clearly not a recent development, but an extension and refinement of the past. In fact, the surprising features are the similarity of ancient water methodologies with those of the present and the advanced level of water and wastewater management used by the ancients. Greek and Roman technological developments in water and wastewater management principles and practices as well as other achievements of those civilizations, such as poetry, philosophy, sciences, politics, and visual arts, are not known. To put in perspective the ancient water and wastewater achievements discussed in this chapter, it is important to examine their relevance to modern times and to harvest some lessons. The relevance of ancient hydraulic works should be examined in terms of the evolution of technology, the technological advances, homeland security, and management principles. The Romans, whose empire replaced the Greek rule in most part of this area, inherited the technologies and developed them further by changing their application scale from small to large and implementing them to almost every large city. The Greek and Roman water technologies are not only a cultural heritage but also the underpinning of modern achievements in water and wastewater engineering and management practices. Apparent characteristics of technologies and management practices in many ancient civilizations are durability and sustainability. Also, there have been integrated management practices, combining both large-scale and small-scale constructions and measures that have allowed cities to sustain for millennia. Currently, engineers use return period for the design of hydraulic structures as dictated by design standards and economic considerations. Sustainability, as a design principle, has

entered the engineering lexicon within the last decade. Naturally, it is difficult to estimate the design principles of ancient engineers but it is notable that several ancient works have operated for very long periods, some until recent times. Thus, wastewater and stormwater drainage systems were functioning in Bronze Age settlements and continued during the Greek and Roman periods. These include the construction and use of bathrooms and other sanitary and purgatory facilities, as well as wastewater and storm sewer systems. In fact, the hydraulic and architectural function of sewer systems in palaces and cities are regarded as one of the salient characteristics of Minoan civilization. They were so advanced that they can be justly compared with their modern counterparts. The durability of some of the constructions that operated up to present times, as well as the support of the technologies and their scientific background by written documents, enabled these technologies to pass to present societies despite regressions that have occurred through the centuries (i.e., in the Dark Ages). The development of science and engineering is not linear but often characterized by discontinuities and regressions. Bridges from the past to the future are always present, albeit oftentimes they are invisible to those who cross them! Thus, in addition to many ancient constructions that have been continuously or intermittently in operation to date, substantial information from ancient Greek and Roman written sources has also been preserved (Angelakis and Koutsoyiannis, 2003). Thus, the major achievements were accomplished during the Greek and Roman civilizations. As a result, they represent the state-of-the-art structures that were technically feasible at that time. For example, the aqueduct of ancient Samos, called ‘&mj´istomon’ or ‘bi-mouthed’ (thus pointing out that it was constructed from two openings), is an important hydraulic monument, indicating that it was possible in the ancient world to design and construct technologically advanced water transportation projects on a large scale.

Water and Wastewater Management Technologies in the Ancient Greek and Roman Civilizations

From the preceding synoptic discussion, certain conclusions might be suggested for further reflection and systematic investigation: 1. The water and wastewater hydraulics works in Minoan, Greek, and Roman civilizations are sometimes not too different from the modern practice, since present technologies descend directly from that time’s engineering. 2. Minoan, Greek, and Roman water and wastewater public works are characterized by simplicity, robustness of operation, and the absence of complex controls. 3. The meaning of sustainability in modern times should be reevaluated in light of Minoan, Greek, and Roman hydraulic works and water and wastewater management practices. 4. Technological developments based on sound engineering principles can have extended useful lives. 5. In areas of water shortage, development of a cost-effective and environmental friendly water resources management practice, based on Minoan, Greek, and Roman civilizations principles, is essential.

References Adam JP (1988) L’Arte di Costruire presso i Romani. Materiali e Tecniche (Roman Building: Materials and Techniques). Milan: Longanesi. Angelakis AN and Koutsoyiannis D (2003) Urban water resources management in ancient Greek times. In: Stewart BA and Howell T (eds.) Encyclopedia of Water Science, pp. 999--1007. New York: Dekker. Angelakis AN, Koutsoyiannis D, and Tchobanoglous G (2005) Urban wastewater and stormwater technologies in ancient Greece. Water Research 39: 210--220. Angelakis AN, Lyrintzis AG, and Spyridakis SV (2010) Urban water management in Minoan Crete, Greece. E-Water (in press). Angelakis AN, Savvakis YM, and Charalampakis G (2007) Aqueducts during the Minoan era. Water Science and Technology: Water Supply 7(1): 95--101. Angelakis AN and Spyridakis SV (1996) The status of water resources in Minoan times – a preliminary study. In: Angelakis A and Issar A (eds.) Diachronic Climatic Impacts on Water Resources with Emphasis on Mediterranean Region, pp. 161–191. Heidelberg: Springer. Angelakis AN and Spyridakis DS (2010). Water supply and wastewater management aspects in ancient Greece. Water Science and Technology: Water Supply 10(4) (in press). Antoniou G, Xarchakou R, and Angelakis AN (2006) Water cistern systems in Greece from Minoan to Hellenistic period. In: Angelakis AD and Koutsoyiannis D (eds.) Proceedings of 1st IWA International Symposium Water and Wastewater Technologies in Ancient Civilizations, pp. 457–462. National Agricultural Research Foundation, Iraklio, Greece, 28–30 October 2006. Antoniou GP (2010) Ancient Greek lavatories: Operation with reused water. In: Mays LW (ed.) Ancient Water Technology. Dordrecht: Springer. Antoniou GP and Angelakis AN (2009) Historical development bathrooms (toilets) and other sanitary and purgatory structures in Greece. In: Proceedings of 2nd IWA International Symposium on Water and Wastewater Technologies in Ancient Technologies. Bari, Italy, 28–29 May 2009. Bersani P, Canalini A, and Dragoni W (2010) First results of a study of the Etruscan tunnel and other hydraulic works on the ‘‘Ponte Coperto’’ stream (Cerveteri, Rome, Italy). Water Science and Technology: Water Supply 10(4) (in press). Bono P and Boni C (1996) Water supply of Rome in antiquity and today. Environmental Geology 27: 126--134. Cadogan G (2007) Water management in Minoan Crete, Greece: The two cisterns of one Middle Bronze Age settlement. Water, Science and Technology: Water Supply 7(1): 103--112. Crouch DP (1993) Water Management in Ancient Greek Cities. New York: Oxford University Press. Darcque P and Treuil R (eds.) (1990) The storm drains of the east wing at Knossos. Special Issue: L’habitat e´ge´en pre´historique. Bulletin de Correspondance Helle´nique, Supple´ment 19: 141–146. Davaras K (1976) Guide to Cretan Antiquities. Park Ridge, NJ: Noyes Press.

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De Feo G, De Gisi S, Malvano C, and De Biase O (2010) The greatest water reservoirs in the ancient Roman world and the ‘‘Piscina Mirabilis’’ in Misenum. Water, Science and Technology: Water Supply 10(4) (in press). De Feo G, De Gisi S, Malvano C, et al. (2010) The Roman aqueduct and the Helvius’ Fountain in Sant’Egidio del Monte Albino, in Southern Italy: A historical and morphological approach. In: Proceedings of 2nd IWA International Symposium on Water and Wastewater Technologies in Ancient Technologies. Bari, Italy, 28–29 May 2009. De Feo G, Malvano C, De Gisi S, and De Biase O (2009b) The ancient aqueduct from Serino to Beneventum in Southern Italy: A technical and historical approach. In: Proceedings of 2nd IWA International Symposium on Water and Wastewater Technologies in Ancient Technologies. Bari, Italy, 28–29 May 2009. De Feo G and Napoli RMA (2007) Historical development of the Augustan aqueduct in Southern Italy: Twenty centuries of works from Serino to Naples. Water Science and Technology: Water Supply 7(1): 131--138. Evans SA (1921–1935) The Palace of Minos at Knossos: A Comparative Account of the Successive Stages of the Early Cretan Civilization as Illustrated by the Discoveries, vols. I–IV, London: Macmillan (reprinted by Biblo and Tannen, New York, USA, 1964). Fahlbusch H (2006) Water management in the classic civilization. In: Proceedings of La Ingenieria Y La Gestion Del Agua a Traves de Los Tiempos. Universidad de Alicante, Spain, with the Universidad Politechnica de Valencia, Alicante, Spain, 30 May–01 June 2006. Gorokhovich Y (2005) Abandonment of Minoan palaces on Crete in relation to the earthquake induced changes in groundwater supply. Journal of Archaeological Science 32: 217--222. Graham JW (1987) The Palaces of Crete. Princeton, NJ: Princeton University Press. Haberey W (1972) Die ro¨mischen Wasserleitungen nach Ko¨ln. Bonn: RheinlandVerlag. Hansen RD (2006) Water and wastewater systems in imperial Rome. http:// www.waterhistory.org (accessed February 2010). Hodge AT (2002) Roman Aqueducts & Water Supply, 2nd edn. London: Gerald Duckworth. Karakostantinou A (2006) The Roman Aqueduct of Moria, Lesvos. Volos, Greece: Department of Elementary Education, University of Thessaly (in Greek). Koutsoyiannis D, Mamassi N, and Tegos A (2007) Logical and illogical exegeses of hydrometeorological phenomena in ancient Greece. Water Science and Technology: Water Supply 7(1): 13--22. Koutsoyiannis D, Zarkadoulas N, Angelakis AN, and Tchobanoglous G (2008) Urban water management in ancient Greece: Legacies and lessons. ASCE, Journal of Water Resources Planning and Management 134(1): 45--54. Ku¨ltu¨r AS¸ (2008) The History of the Basilica Cistern. Istanbul, Turkey. http:// www.yerebatan.com/english/itarihce.html (accessed July 2010). Lendering J (2008) Constantinople (Istanbul): Basilica Cistern. Istanbul, Turkey. http://www.livius.org (accessed July 2010). MacDonald CF and Driessen JM (1988) The drainage system of the domestic quarter in the Palace at Knossos. British School of Athens 83: 235--358. Martini P and Drusiani R (2009) History of the water supply of Rome as a paradigm of water services development in Italic peninsula. In: Proceedings of 2nd IWA International Symposium on Water and Wastewater Technologies in Ancient Technologies. Bari, Italy, 28–29 May 2009. Mavromati E and Chryssaidis L (2007) Aqueducts in the Hellenic area during the Roman Period. Water Science and Technology: Water Supply 7(1): 139--145. Mays LW (2007) Ancient urban water supply systems in arid and semi-arid regions. In: Proceedings of International Symposium on New Directions in Urban Water Management. UNESCO, Paris, France, 12–14 September 2007. Korea Water Resources Association, http://www.kwra.or.kr (accessed February 2010). Mays LW (2008) A very brief history of hydraulic technology during antiquity. Environmental Fluid Mechanics 8(5): 471--484. Mays LW (ed.) (2010) Ancient Water Technologies. Dordrecht: Springer. Mays LW, Koutsoyiannis D, and Angelakis AN (2007) A brief history of urban water supply in antiquity. Water, Science and Technology: Water Supply 7(1): 1--12. Monteleone MC, Yeung H, and Smith R (2007) A review of ancient Roman water supply exploring techniques of pressure reduction. Water Science and Technology: Water Supply 7(1): 113--120. Myers JW, Myers EE, and Cadogan G (1992) The Aerial Atlas of Ancient Crete. Berkeley, CA: University of California Press. Oziz U (1987) Ancient water works in Anatolia. Water Resources Development 3(1): 55--62. Oziz U (1996) Historical water schemes in Turkey. Water Resources Development 12(3): 347--383. Panimolle G (1984) Gli Acquedotti di Roma Antica (The Aqueducts of Ancient Rome). Rome: Edizioni Abete.

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Rodgers RH (2004) Sextus Iulius Frontinus. On the Water-Management of the City of Rome. De Aquaeductu Urbis Romae. Cambridge: Cambridge University Press. Russo F and Russo F (2007) Pompei. La Tecnologia Dimenticata (Pompeii. The Forgotten Technology). Naples: ESA – Edizioni Scientifiche e Artistiche. Tassios TP (2007) Water supply of ancient Greek cities. Water Science and Technology: Water Supply 7(1): 165--191.

Tolle-Kastenbein R (2005) Archeologia dell’Acqua (Water Archaeology). Milan: Longanesi. Vuorinen HS (2010) Water, toilets and public health in the Roman era. Water Science and Technology: Water Supply 10(4) (in press). Vuorinen HS, Juuti PS, and Katko TS (2007) History of water and health from ancient civilizations to modern times. Water Science and Technology: Water Supply 7(1): 49--57.

4.02 Membrane Filtration in Water and Wastewater Treatment Y Watanabe and K Kimura, Hokkaido University, Sapporo, Japan & 2011 Elsevier B.V. All rights reserved.

Membrane Application to Water Purification Current Status Membrane Fouling Main foulant Affinity of main foulant for membranes Membrane Filtration Systems for Controlling Fouling Channel flocculation in monolith ceramic membrane Pre-coagulation/sedimentation in hollow-fiber UF/MF membrane Hybrid submerged MF membrane system PVDF Membrane filtration with pre-ozonation Membrane Application to Wastewater Treatment Current Status of MBRs Mechanism of Membrane Fouling Effect of membrane permeate flux on fouling Effect of membrane material on fouling Fouling potential of carbohydrate assessed by lectin affinity chromatography

4.02.1 Membrane Application to Water Purification 4.02.1.1 Current Status The mainstay of water purification technology in the twentieth century was sand filtration, but since the late 1980s, membrane filtration technology using RO/NF/UF/MF membranes has been applied to the water and wastewater treatment, desalination, and water reuse (RO, reverse osmosis; NF, nanofiltration; UF, ultrafiltration; MF, microfiltration).

3500 Start of RO research in USA (1953)

3000

Water / wastewater treatment (UF/MF)

President J.F.Kenedy approved RO desalination as a national project (1961)

2500 Cryptosporidium infection in Milwaukee (1993)

2000 Enhanced regulations of surface water in USA (1998)

1500 Enhanced water works law in Japan (2001)

1000

Brackish water desalination / wastewater reuse (NF / RO)

2005

2000

1995

1990

1980

1975

1970

1965

1960

0

1955

500

1950

Global accumulative amount of permeate (×104 m3 d−1)

23 23 23 24 30 36 36 40 43 45 47 47 48 49 54 57 60

Figure 1 shows the historical development of membrane technology in the water and wastewater treatment. Membrane filtration has small foot print, extremely high solid–liquid separation ability, and its maintenance is easy. Water purification plants in the United States, the Netherlands, France, Australia, and Japan have introduced the membrane filtration process. Figure 2 shows the recent increase in the amount of water produced by the membrane filtration, which includes water purification, desalination, and wastewater treatment.

1985

4.02.1 4.02.1.1 4.02.1.2 4.02.1.2.1 4.02.1.2.2 4.02.1.3 4.02.1.3.1 4.02.1.3.2 4.02.1.3.3 4.02.1.3.4 4.02.2 4.02.2.1 4.02.2.2 4.02.2.2.1 4.02.2.2.2 4.02.2.2.3 References

Sea water desalination (RO)

“If we could produce fresh water from salt water at a low cost that would indeed be a great service to humanity, and would dwarf any other scientific accomplishment” John F. Kennedy Figure 1 Development of membrane filtration. MF, microfiltration; NF, nanofiltration; RO, reverese osmosis; UF, ultrafiltration.

23

24

Membrane Filtration in Water and Wastewater Treatment Global amount of water produced by membrane processes 35 000 000 32 000 000 m3 d–1, 2006 SWRO

Amount of water (m3 d–1)

30 000 000

Increase by 25% each year

NF+BWRO 25 000 000

LP+MF+UF

20 000 000 15 000 000 10 000 000

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

1990

5 000 000

Figure 2 Increase in purified water by membrane filtration. BWRO, brackish water reverese osmosis; LP, low pressure; MF, microfiltration; NF, nanofiltration; SWRO, seawater reverese osmosis; UF, ultrafiltration.

Table 1

Large-scale water purification plants in world wide

Country

Place (plant name)

Capacity (103m3d1)

Construction year

Membrane

Water source

USA Canada Singapore USA USA USA Canada UK Germany USA

Minneapolis (Fridley Plant) Mississanga, Ontario Chestnut Minneapolis (Columbia Heights) Racine, Wisconsin Thornton, Colorado Kamloops, British Columbia Clay Lane Roetgen/Aachen San Joaquin, California

360 302 273 265 189 187.5 160 160 144 136

2011 (to be built) 2006 2003 2005 2005 2005 2005 2001 2005 2005

UF UF UF UF UF UF UF UF UF UF

Surface Lake Surface Surface Surface Surface Surface Ground Reservior Surface

Source: Japan Water Research Center, Hot News in water works, No. 56.

Table 1 shows the large-scale water purification plants using membrane filtration. All plants in the table use the UF membrane but a plant using monolith ceramic MF membrane with the capacity of 173 000 m3 d1 is under construction in Japan. There has been a significant progress in the development of new robust MF membranes with new polymers such as PVDE and FTFE for water and wastewater treatment. Combining robust MF membranes and the other processes such as coagulation, ozonation, biological/chemical oxidation, and powdered activated carbon adsorption and chemically enhanced physical cleaning makes very efficient water purification system. They are very effective in the application to the large-scale water purification plant. The trend toward membrane filtration is expected to spread worldwide during this century. However, there are several limiting factors applying the UF membrane and MF membrane to the water purification. Among them, fouling in membrane is a major obstacle to widespread use of this technology. The authors have been studying the mechanism and control of membrane fouling in water treatment. This chapter

summarizes the authors’ research on membrane application to the water purification.

4.02.1.2 Membrane Fouling Several physical membrane cleaning methods such as hydraulic backwashing and air scrubbing have been developed and used routinely in many existing membrane plants to minimize membrane fouling. Despite routine physical membrane cleaning, membrane filtration resistance gradually increases over a long period of operation, indicating that membrane fouling cannot be completely controlled by physical cleaning. Fouling that cannot be controlled by physical cleaning is defined here as physically irreversible fouling. Control of physically irreversible fouling is important for the reduction of operation cost in a membrane process because this type of fouling develops even when a very efficient physical cleaning is carried out. Physically irreversible membrane fouling can only be canceled by chemical cleaning. However, chemical cleaning of the membrane should be limited to a minimum frequency because repeated chemical

Membrane Filtration in Water and Wastewater Treatment

cleaning may shorten the membrane lifetime and disposal of spent chemical reagents poses another problem. Membrane fouling strongly depends upon the structure of membrane (average size, size distribution, and density of pores). Surface morphology and roughness are surely involved in it. However, this chapter describes the effect of only nominal pore size and materials of membrane on the membrane fouling.

4.02.1.2.1 Main foulant In a number of previous studies on fouling of membranes used for water treatment, natural organic matter (NOM), composed of a variety of nonbiodegradable organic compounds including humic substances, has been shown to be the major constituent causing membrane fouling. However, it is still not clear which fraction of NOM causes membrane fouling. In early works, hydrophobic fractions of NOM, such as humic substances, were considered to be the major foulants. Hydrophobic interaction and electrostatic interaction were the explanations for the binding between hydrophobic NOM and membranes. More recently, hydrophilic NOM with features of carbohydrate or protein has been reported by several researchers to be the major foulant. As explanations for the binding between hydrophilic NOM and membranes, van der Waals attraction and hydrophobic interaction between membranes and hydrophobic domains in hydrophilic NOM have been suggested. In addition to NOM, metals and metal– NOM complexes have been reported as the constituents affecting membrane fouling (Yamamura et al., 2007a, 2007b). Physically reversible fouling and physically irreversible fouling have not been distinguished in many previous studies. In addition, many previous studies were based on short-term experiments, which are not sufficient for observing physically irreversible fouling. As a result, knowledge of physically irreversible fouling occurring in membrane filtration in drinking water treatment is very limited; therefore, further studies need to be carried out with special emphasis on physically irreversible fouling for more efficient use of membranes. In particular, investigation of the characteristics of components that cause physically irreversible fouling would be useful for the establishment of a new protocol of fouling control. In this study, three MF/UF membranes that had been fouled in long-term filtration of surface water used as a drinking water source were investigated in terms of the recovery of water permeability by chemical cleaning and the characteristics of the foulant causing physically irreversible fouling. Based on the results obtained from various analyses, a hypothesis regarding the evolution of physically irreversible fouling is proposed. Three different hollow-fiber membranes were used in this study. Two of them were MF membranes and the other was a UF membrane. The two MF membranes had the same nominal pore size of 0.1 mm but were made from different polymers such as polyethylene (PE; Mitsubishi Rayon, Tokyo, Japan) and polyvinylidene fluoride (PVDF; Asahikasei Chemicals, Tokyo, Japan). The UF membrane had a molecular weight cut-off of 100 000 Da and was made from polyacrylonitrile (PAN; Toray Industries, Tokyo, Japan). Using these three different membranes, pilot-scale membrane

25

filtration tests were carried out in parallel using the Chitose River surface water. This river flows through peat area and its surface water contains many humic substances. The concentration range of total iron and aluminum was 0.7–1.7 and 0.05 and 0.7 mg l1. About 75% of them were larger than 0.45 mm. The PVDF and the PE membranes were submerged in separate tanks and were operated under vacuum. The PAN membrane was housed in a vessel and was operated under pressure. All membranes were operated in the outside-in flow mode. The three membranes were operated with identical run cycles (filtration: 30 min; air scrubbing: 30 s; hydraulic backwashing: 60 s) at the same constant flux of 0.65 m3 m2 d1. Hydraulic backwashing was not accompanied by the addition of chlorine. When membrane fouling became significant in the submerged MF membranes despite the implementation of periodical backwashing, membrane modules were taken out from the tanks and were cleaned by spraying pressurized water on the membrane surface. The average quality of the feed water and that of membrane permeates are shown in Table 2. In the feed water, large portions of aluminum (78%) and iron (75%) were present as suspended solids (40.45 mm), while manganese, calcium, and organic matter were mainly present in dissolved forms. Aluminum and iron were effectively removed by the tested membranes due to the strict solid–liquid separation. On the other hand, removal of manganese, calcium, and organic matter was not significant in any of the membranes. This implies that the sizes of manganese, calcium, and dissolved organic carbon (DOC) were smaller than the pore sizes of the tested membranes. The UF membrane showed slightly higher rates of removal of DOC and UV absorbance than those of the two MF membranes, reflecting the difference between membrane pore sizes of the MF and UF membranes. However, the concentration of aluminum in the PAN membrane was slightly higher than the concentrations in the MF membranes. No reasonable explanation for this is available at present. Figure 3 shows the changes in transmembrane pressure (TMP) in the three membranes. The rates of increase in TMP in the three membranes were considerably different. As expected, the tightest membrane (PAN) showed the highest rate of increase in TMP. The rates of increase in the two MF membranes were different despite the fact that they had the same nominal pore size. This clearly indicates that the materials of the membrane have a substantial influence on the

Table 2

Average raw water quality during experiment

Temperature (1C) pH Turbidity (NTU) UV absorbance at 220 nm (cm1) UV absorbance at 260 nm (cm1) TOC (mg 11) DOC (mg 11) THMFP (mg 11) Manganese (mg 11) Soluble manganese (mg 11) Ammonia Nitrogen (mg 11)

11.5 7.11 16.54 0.411 0.099 2.43 2.29 0.086 0.100 0.074 0.22

DOC, dissolved organic matter; THMFP, trihalomethane formation potential; TOC, total organic carbon.

26

Membrane Filtration in Water and Wastewater Treatment 200

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Time of additional physical cleaning

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120

80

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Operation time (days) Figure 3 Time course changes in transmembrane pressure (TMP) difference adjusted to 20 1C equivalent value considering the change in water viscosity. PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride; TMP, transmembrane pressure.

evolution of membrane fouling. Interestingly, the results obtained in this study showing that the PE membrane was less fouled than the PVDF membrane are opposite to the results of a previous study focusing on membrane fouling in membrane bioreactors (MBRs) used for municipal wastewater treatment. This implies that characteristics of foulants in the case of drinking water treatment were different from those in the case of wastewater treatment. Further investigation is needed to understand the influence of membrane material on the rate of fouling. In all of the tested membranes, increase in TMP was not constant and rapid increases in TMP were seen several times. After the rapid increases in TMP, however, the value of TMP gradually declined due to the periodical backwashing except for the case of the PVDF membrane. On days 31 and 41, an additional physical cleaning (spraying pressurized water on the membrane surface) was needed to maintain the permeability of the PVDF membrane. This additional physical cleaning worked well and substantial reduction in TMP in the PVDF membrane was seen after cleaning. Chemical cleaning was not carried out at that time. Based on the observations mentioned above, it is assumed that the rapid increases in TMP shown in Figure 3 were caused by the accumulation of cake on the surfaces of the membranes. The three dashed lines shown in the figure are assumed to represent the evolution of physically irreversible fouling in the three membranes, which accumulated and remained despite of the implementation of periodical backwashing and additional physical cleaning. As seen in Figure 3, the rates of occurrence of physically irreversible fouling in the three membranes were different. To investigate the features of constituents that were responsible for physically irreversible fouling, the foulants were desorbed from the fouled membranes at the termination of the operation and then their chemical characteristics were analyzed. When the pilot operations were terminated, fouled membranes were taken out from the filtration units. The

membrane fibers were immediately brought to the laboratory in a container filled with distilled water. First, each membrane fiber was manually wiped with a sponge and thoroughly rinsed with distilled water, which was carried out to minimize the influence of the accumulated cake causing physically reversible fouling in subsequent tests. By visual inspection, no accumulated cake was found on the membrane after wiping with a sponge. Using the wiped membranes, tiny membrane modules of 40 cm2 in membrane area were assembled and pure water permeability of the fouled membrane was measured by applying 30 kPa of pressure difference. Filtration was continued until a constant permeate flow rate was achieved (typically in 15 min). After measuring the pure water permeability, tiny membrane modules were soaked in various chemical solutions at 20 1C for 24 h. The chemical solutions used for cleaning were Milli-Q water, NaClO (700 ppm as free available chlorine), NaCl (0.1 M), NaOH (pH 12), HCl (pH 2), ethylenediaminetetraacetic acid(EDTA) (20 mM), and oxalic acid (0.5%). Recoveries in pure water permeability by the chemical cleaning were evaluated and the chemical solutions containing the foulants desorbed from the membranes were analyzed. Membrane specimens that were not used for assembling the tiny membrane modules were divided into two portions and were soaked in a solution of sodium hydroxide at pH 12 or hydrochloric acid at pH 2. Because a large amount of membrane specimens was available in this study, this process enabled extraction of a sufficient amount of organic matter for advanced analysis (e.g., Fourier transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectra). Figure 4 shows the degree of restoration of the fouled membranes in terms of pure water flux by chemical cleaning with various reagents. In this figure, the ratio of pure water flux after chemical cleaning (J1) to the flux before chemical cleaning (J0) is used to express the degree of flux restoration. As described earlier, chemical cleaning was carried out after

Membrane Filtration in Water and Wastewater Treatment PE

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Figure 4 Effect of chemical membrane cleaning (J0: pure water flux before chemical cleaning, J1: pure water flux after chemical cleaning). EDTA, ethylenediaminetetraacetic acid; MQ, milli-Q water; PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride.

manually removing reversible cake that had accumulated on the membrane. Therefore, it can be considered that the restoration shown in Figure 4 represents removal of the foulants causing physically irreversible membrane fouling. Actually, manual sponge cleaning carried out prior to chemical cleaning had little effect on the permeability of the fouled membranes, indicating that fouling seen at the termination of the longterm operation could be attributed mainly to physically irreversible fouling. As seen in the figure, in the case of the PVDF and PAN membranes, NaCl (0.1 M) and EDTA (20 mM) were not effective in mitigation of physically irreversible fouling in this study. Figure 4 also shows that alkaline solution (NaOH) was more efficient than acid solutions (oxalic acid and HCl) for recovery of permeability of the PVDF and PAN membranes. The oxidizing agent (NaClO) exhibited the best cleaning performance in recovery of permeability of the PVDF and PAN membranes. This implies that organic matter was mainly responsible for the evolution of physically irreversible membrane fouling in the PVDF and PAN membranes. In contrast, in the case of the PE membrane, which exhibited the least membrane fouling in the continuous run (Figure 3), the degree of recovery of water permeability following cleaning with acid, alkaline, and oxidizing reagents were comparable. This suggests that the contribution of metals to the physically irreversible fouling in the PE membrane was significant. Desorption of membrane foulants was carried out at the termination of the pilot operation. As stated above, to ensure that physically reversible cake was removed from the membrane surface, each membrane fiber was carefully wiped with a sponge prior to desorption tests. Although both aluminum and iron in the raw water were effectively removed by the membranes tested, only iron was desorbed from the fouled membranes at a significant amount. This suggests that aluminum in the feed water was rejected or deposited on the membrane surface and subsequently removed by the periodical backwashing. In contrast, iron was likely to cause the physically irreversible fouling to some extent. In the cleaning with HCl solution, not only metals but also organic matter were desorbed from the fouled membranes, particularly from the PVDF membrane. Figure 5 shows the FTIR spectra of the foulants desorbed from the fouled membranes by HCl solution. Interestingly, there were significant similarities among the three spectra. All of

1080

HCI-PVDF HCI-PE HCI-PAN 2000

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Wave number (cm–1) Figure 5 FTIR spectra of membrane foulant desorbed with HCl (pH 2) solution. PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride.

the spectra had a dominant peak near 1080 cm1, which is an indication of their carbohydrate character. Therefore, the carbohydrate-like organic matter was thought to be the main constituent in the foulants desorbed with HCl solution regardless of membrane type. In a study by Kabsch-Korbutowicz et al., it was shown that a large portion of organic matter desorbed from the fouled membrane by acid or chelating agents formed complexes with metals. Similarly, in the present study, the carbohydrate-like organic matter and metals (mainly iron) desorbed with HCl solution were assumed to form complexes and cause physically irreversible fouling. It has been reported that carbohydrate can form a complex with iron. As previously mentioned, NaOH solution restored the membrane permeability to a larger extent and desorbed a larger amount of organic matter from the fouled membranes than did HCl solution. Therefore, analysis of the foulants desorbed from the membrane with NaOH solution would be more useful in understanding the fouling, compared to the case of HCl solution. The value of specific ultraviolet absorbance (SUVA) is considered to be a surrogate measurement of aromacity of organic matter, and a high SUVA value corresponds to organic matter consisting of a large amount of double-bond or aromatic structures. The values of SUVA determined for the foulants desorbed by NaOH solution were much lower than those for the feed water on average. This

28

Membrane Filtration in Water and Wastewater Treatment 1080 1660

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Figure 6 Infrared spectra of membrane foulant desorbed with NaOH (pH 12) solution. PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride.

200

implies that a relatively hydrophilic fraction of the organic matter in the feed water was responsible for the physically irreversible fouling. Interestingly, the value of SUVA determined for the foulants was similar among the foulants desorbed from the three membranes. This indicates that the characteristics of the foulants desorbed from the three membranes might be similar, but this turned out to be false as discussed later. FTIR spectra of the foulants desorbed with NaOH solution from the three membranes are presented in Figure 6. There were significant similarities in the spectra obtained for the three membranes. In these spectra, peaks near 1660 and 1540 cm1 were significant. They are assigned to amido-I and II bands, respectively. In all spectra, a broad peak near 1080 cm1 was seen. This peak is an indicator of carbohydrate character. FTIR spectra shown in Figure 6 are not similar to those of humic substances. This suggests that humic substances were relatively minor components in the foulant responsible for the physically irreversible fouling. CPMAS 13C NMR spectra of the foulants desorbed with NaOH solution from the membranes are presented in Figure 7. A general similarity among the foulants desorbed from the three membranes was found in NMR analysis as well. Although a proteinaceous nature of the foulants in the membranes can be seen by peaks near 175 and 55 ppm, carbohydrate (peak at 75 ppm) was dominant in the foulant regardless of the membrane type. The aromatic carbon signal (110–165 ppm) was minor in the spectra for the two MF membranes (PVDF and PE) but was pronounced in the spectrum for the PAN membrane. This indicates that the contribution of the humic fraction of NOM to the evolution of physically irreversible fouling was more significant in the PAN membrane than in the two MF membranes. The humic fraction would be smaller than carbohydrate, as shown later. Thus, it is reasonable to assume that the contribution of the small humic fraction would become more significant in a UF membrane (PAN in this case) than in MF membrane (PVDF and PE in this case). The amount of calcium desorbed with NaOH solution was significant in the case of the PAN membrane. This calcium might have formed a complex with humic substance as suggested by several researchers. Nevertheless,

160

120 80 Chemical shift (ppm)

40

0

Figure 7 CPMAS 13C NMR spectra of membrane foulants desorbed with NaOH (pH 12) solution. PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride.

carbohydrate was dominant in the foulant desorbed form the PAN membrane as well, as shown in Figure 5. As shown above, both FTIR and NMR analyses demonstrated that carbohydrate was a dominant component causing physically irreversible fouling regardless of the type of membrane. Carbohydrate has, however, a hydrophilic nature, and hydrophobic interaction between the membranes and carbohydrate is therefore not a reasonable explanation for the participation of carbohydrate in physically irreversible fouling. To elucidate the fouling mechanisms involved in the continuous operation, changes in rejection rate of both humic acid and carbohydrate in the operation were investigated using HPLC-SEC with UV/DOC detectors. Figure 8 shows the representative molecular weight distribution of organic matter contained in the feed water used in this study. As seen in the figure, organic matter contained in the feed water could be roughly divided into two fractions: large molecules with a hydrophilic nature (little UV absorbance) and small molecules with a hydrophobic nature (high UV absorbance). A similar molecular weight distribution of organic matter was found in previous studies. It is thought that large molecules mainly consisted of carbohydrate, while small molecules mainly consisted of humic acid. Figure 9 shows changes in the removal of the large and small molecules by the three membranes determined by HPLC-SEC with UV/DOC detectors. In the case of the PVDF membrane, about 15% of the fraction of smaller organic molecules mainly composed of humic substances was initially removed. As the operation period became longer, however, the rate of removal of the small organic molecules declined and eventually no removal of small molecules was achieved by the PVDF membrane. The size of the small molecules should be considerably smaller than the nominal pore size of the PVDF membrane (0.1 mm); therefore, the sieving effect was discounted as an explanation for the initial removal of small organic molecules by the PVDF membrane. Rather, the initial

Membrane Filtration in Water and Wastewater Treatment

29

Humic acid DOC

900 600

Colloidal carbohydrate or protein

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0 UV

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0.02 107 106 105 104 103 102 Molecular weight (Da)

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Figure 8 Molecular size distribution of dissolved organic matter in the Chitose river surface water. DOC, dissolved organic carbon.

removal of the small molecules can be attributed to adsorption on/in the PVDF membrane. In contrast to the small molecules, the rate of removal of the large organic molecules by the PVDF membrane gradually increased during the operation. When the removal of the small molecules declined to a negligible level, the removal of large organic molecules increased by almost 100%. A similar trend was also seen for the other two membranes. Based on these observations, the following hypothesis regarding the evolution of physically irreversible fouling is presented. First, small molecules mainly composed of humic substances are adsorbed on/in membranes by hydrophobic interaction. As a result of adsorption of the small molecules, the sizes of membrane pores decrease and it becomes possible for large molecules mainly composed of carbohydrates to plug the pores and cause physically irreversible fouling. Also, adsorbed humic substances could work as glue for carbohydrates and facilitate the capture of carbohydrates on/in membranes. The examined PVDF was assumed to be more hydrophobic than the PE membrane because hydrophilic modification was provided for the PE membrane by the manufacture. It is likely that the hydrophobic PVDF membrane adsorbed humic substances more rapidly than did the hydrophilic PE membrane. As a result, the PVDF membrane should achieve complete rejection of carbohydrates earlier than the PE membrane (Figure 9). In discussion made above, it is assumed that foulant causing physically irreversible fouling originated from the feed water. Another possible origin of the foulant might be biofilms that cannot be removed by backwashing. It was reported that both carbohydrate and humics were excreted by microorganisms. Although the possibility that excretion from biofilms was the main source of the foulant which cannot be completely eliminated, it would be discounted by the following reasons: (1) evolution of reversible fouling (indication of biofilm formation) did not always dominate in the operation of the membranes as shown in Figure 3; (2) occasional increases in physically reversible fouling shown in Figure 3

could be explained by increases in turbidity in the feed (data not shown); and (3) water temperature was low (i.e., 5–10 1C) in the operation. To deal with the issues discussed above more precisely, establishment of the methods that can distinguish the origin of organic matter is indispensable. The following points were derived from the measurement of the zeta potentials of membranes before and after the longterm operation. The decrease in rejection of small molecules during the operation might be attributable to a decrease in favorable electrostatic interaction (repulsion) since the zeta potential of the tested membranes became slightly less negative after operation as a consequence of carbohydrate deposition. In this study, it was assumed that the decrease in favorable electrostatic interaction was not the main reason for the decrease in rejection of small molecules both because of the initial zeta potential that was close to neutral and because of the small changes in the zeta potentials after use. However, further investigation is needed to determine the influence of surface conditions of membranes on binding of NOM to membranes. To confirm the experimental results showing that carbohydrate-like substances are main substances causing the physically irreversible fouling, the authors carried out the bench-scale study where the surface water samples taken from four different sources such as Toyohira River (central Hokkaido), Kusiro River (eastern Hokkaido), Inba Lake (Chiba prefecture), and Yodo River (Osaka prefecture). Toyohira River water (total organic carbon, TOC ¼ 0.8 mg l1) is relatively clean. Kushiro river water (TOC ¼ 0.9 mg l1) is rich in humic substances. Inba Lake water (TOC ¼ 5.7 mg l1) is polluted and eutrophicated by the domestic wastewater. Yodo River water (TOC ¼ 1.8 mg l1) contained a lot of treated wastewater. Tiny membrane module with the surface area of 1.44 103 m2 was prepared with hollow-fiber membranes made of PVDF. The pore size of membranes was 0.1 mm. Membrane filtration was carried out by a peristaltic pump, and the constant-flow-rate mode of operation was applied. Permeate flux was fixed at 1.5 m d1 for all filtration experiments.

30

Membrane Filtration in Water and Wastewater Treatment PVDF 30

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Figure 9 Changes in removal rate of large molecules (carbohydrate) and small molecules (humic acid). PAN, polyacrylonitrile; PE, polyethylene; PVDF, polyvinylidene fluoride.

Hydraulic backwashing was performed every 15 min. The duration and pressure of backwashing were 30 sec and 50 kPa, respectively. The organic matter in the four water sources was concentrated using RO (Nanomax 95, Millipore), and its recovery, defined as (DOC mass after concentration by RO)/(DOC mass before concentration), was 0.95, 0.81, 0.80, and 0.91 for Toyohira River, Inba Lake, Kushiro River, and Yodo River, respectively. Fractionation of organic matter contained in the isolates was carried out using the procedure described by Croue et al. They used the DAX-8 and XAD-4 resins. The portion that passed through both the DAX-8 and XAD-4 column was denoted the hydrophilic (HPI) fraction. The portion that retained on DAX-8 resin was denoted the hydrophobic (HPO) fraction. The portion that retained on XAD-84 was denoted the transphilic (TPI) fractions. The HPO and TPI fractions were eluted by backwashing with 2 l of 0.1 N NaOH at 100 ml min1. Each of the three fractions was desalinated by the electric dialysis until its electric conductivity became less

than 0.5 mS O1. The HPI and HPO fractions were diluted to a concentration of 2.0 mg TOC l1 with Milli-Q water and used as the feed water for the bench scale experiment. Figure 10 shows the FTIR spectra of the organic matter in the hydrophobic and hydrophilic fractions of the water from each of the four sources. FTIR analysis is a powerful tool for identifying the functional groups in organic matter and, together with the SUVA, provides useful information about the characteristics of organic matter in the feed waters. As seen in the spectra of the HPO fractions, the organic matter in the hydrophobic fraction was highly aromatic. For all the spectra of HPO fraction, a general similarly was seen in two broad peaks around 1400 and 1620–1660 cm1. These peaks are an indication of their aromatic character. The HPO fractions also seemed to contain alkyl aromatic sulfonates, as evidenced by the peaks of an aromatic sulfonic acid group (1035 and 1009 cm1) and the alkyl group (2930 cm1). In the spectra of the HPI fractions, on the other hand, a high peak at 1080 cm1 is seen for all the sources. This peak is assigned to C–O stretching of polysaccharide or aliphatic alcohol, which represent the carbohydrate-rich nature of HPI organic matters. The spectra of the HPI fractions of Inba Lake water and Yodo River water not only show the signature of carbohydrate-like substances but also have sharp peaks at 1620 and 1660 cm1 corresponding to carboxylic acid. These peaks, in combination with the peak at 1080 cm1, might indicate the presence of alginate-like substances in the feed water. The changes in TMP during filtration through the MF membrane made of PVDF differed between the HPO fraction and the HPI fraction are shown in Figure 11. Regardless of the NOM source, the TMP for the HPO fraction increased by less than 7 kPa and the TMP for the HPI fraction increased by more than 30 kPa. This clearly indicates that the HPI fraction of NOM is a major component affecting the development of physically irreversible fouling. The major differences in the characteristics of organic matter between the HPO fraction and the HPI fraction are in aromaticity and size. The organic matter in the HPO fraction consisted mainly of aromatic humic substances less than 6000 Da in size, while the HPI fraction was rich in carbohydrate-like substances having sizes between 100 000 and 1000 000 Da. These findings indicate that the development of physically irreversible fouling was caused not by aromatic humic substances but by carbohydrate-like substances. In authors’ study investigating the affinity between NOM and membrane surfaces, it was concluded that the physico-chemical interaction with the surface of membrane was more significant for carbohydrate-like substances (with hydroxyl groups) than for humic-like substances (with carboxyl groups). As a consequence, it can be hypothesized that large carbohydrate-like substances can accumulate on the membrane surface, interact with it strongly, and thereby cause physically irreversible fouling. Although some researchers suggested that physically reversible fouling is largely due to the HPO fraction of NOM, the development of physically reversible fouling was not obvious for the HPO feed waters, probably because the organic particles in the HPO fraction are smaller than the membrane pores in this study. Rather, some of the HPI fractions were found to contribute to the physically reversible fouling as well.

Membrane Filtration in Water and Wastewater Treatment Hydrophobic

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4000 3500 3000 2500 2000 1500 1000

4000 3500 3000 2500 2000 1500 1000

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Figure 10 Fourier transform infrared (FTIR) spectra of the natural organic matter (NOM) in hydrophobic (HPO) and hydrophilic (HPI) fractions of raw water from different sources: HPO fraction of water from (a) Toyohira river, (b) Lake Inbanuma, (c) Kushiro river, and (d) Yodo river; HPI fraction of water from (e) Toyohira river, (f) Lake Inbanuma, (g) Kushiro river, and (h) Yodo river.

Fraction contributing to memberane fouling (TOC = 2 mg l–1)

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32

Membrane Filtration in Water and Wastewater Treatment

In particular, the filtration of the HPI fractions of Lake Inbanuma water and Yodo River water induced the evolution of the physically reversible fouling to a large extent. These HPI fractions were found to contain a large amount of macromolecular polysaccharides with a negative charge at neutral pH, in which the electrostatic repulsion between the negatively charged polysaccharide and accumulated polysaccharides or membrane surface would occur. Such an electrostatic repulsion would help to weaken the binding of organic molecules to each other and thereby enable the accumulated organic matter to be easily removed by physical cleaning.

4.02.1.2.2 Affinity of main foulant for membranes In our previous study on pilot-scale filtration using hydrophilic and hydrophobic membranes, NMR analysis of the foulant demonstrated significant contribution of carbohydrate-like substances to the evolution of fouling. It was also shown that the nature of membrane materials affected the rate of accumulation of carbohydrate-like substances. However, the reason for the preferential binding of carbohydrate-like substances to membranes remains unclear. Elucidation of the physicochemical interactions between membranes and carbohydratelike substances is needed for understanding the mechanism of fouling involving carbohydrate. Several research groups have already demonstrated the usefulness of atomic force microscopy (AFM) force measurement for the quantification of the affinity between a carboxylmodified microsphere and the surfaces of NF/RO membranes. Carboxyl-modified microspheres were used as a surrogate of humic substances in their studies. Taking into account the hydroxyl-rich characteristics of carbohydrate, AFM force measurement using hydroxyl-modified microspheres and membranes would provide useful information about the affinity of carbohydrate-like substances to membranes, which has been reported in recent studies on fouling as reviewed above. Two MF membranes with the same nominal pore size of 0.1 mm were used in this study. One membrane was made of PE (Mitsubishi Rayon Engineering, Tokyo, Japan) and the other was made of PVDF (Asahi Kasei Chemicals, Tokyo, Japan). These two membranes were chosen because they are now used in many full-scale plants. Prior to the AFM force measurement, new membranes were filtered with Milli-Q water for 6 h so as to wash out impurities remaining on the membrane surface. Because of hydroxyl-rich nature of carbohydrate, Polybeads-hydroxylate microspheres (Cosmo Bio, Tokyo, Japan) were used as surrogates for carbohydrate-like substances. For comparison, Polybeads-carboxylate microspheres (Cosmo Bio, Tokyo, Japan) were also used in the AFM force measurement. In previous studies, carboxyl-modified microspheres were used as surrogates of humic substances. Both microspheres used in this study were made of polystyrene (3 mm). The characteristics of these microspheres are shown in Yamamura et al. (2008). The colloidal probes used in the AFM force measurement were prepared by attaching the microspheres to the top of a silicon nitride tip (NP-S: Veeco Instruments Inc., New York, USA) as previously described (Figure 12). Attachment of the microspheres to the cantilever tip was carried out with a micromanipulator with the aid of a

Figure 12 Scanning electron microscope image of a polystyrene bead (3 mm) glued to the top of a cantilever tip.

scanning electron microscope (TINY SEM, Technex Lab, Tokyo, Japan). After preparation, the colloidal probes were stored in a refrigerator (4 1C) prior to use. The spring constants of carboxyl- and hydroxyl-colloidal probes determined by thermal fluctuation method were 84 and 92 pN nm1, respectively. These values were used for converting cantilever deflections to loading forces. An atomic force microscope (MFP-3D, Asylum Research, Santa Barbara, CA) was used for the force measurements. Measurements were carried out in buffered water (1.0 mM NaHCO3, pH 6.8) with a trigger point of 50 nm. Divalent cations such as calcium or magnesium were not added to the buffered solution so as to prevent the formation of a bridging between polystyrene of microspheres and the membrane surface. Taking the heterogeneities of local membrane surfaces into account, measurements of force curves were made at three different locations. At each location, more than five force curves were obtained. All force curves obtained by the AFM force measurement were originally expressed as a function of force determined on the basis of the scanner position in the AFM instrument. The scanner position was converted to the separation distance by determining the onset of constant compliance between the scanner position and cantilever deflection (i.e., where cantilever deflection becomes a linear function of piezo-scanner position) and subtracting this value from all other scanner position values. In AFM force curves, the separation distance at which the interaction became either repulsive or attractive was identified as the point where the measured force is either positive or negative, respectively. At separation distances greater than this value, no force was considered to be acting on the colloidal probe and the zero force region of the plot was determined. An AFM force measurement gave two force curves: an approaching force curve and a retraction force curve. The affinity of the colloidal probe to the surface of the membrane was expressed by the adhesion force, Fad, which is defined as the force needed to separate the two from contact. Fad is determined on the basis of the maximum value of cantilever deflection in a retraction force curve (dmax) as shown in

Membrane Filtration in Water and Wastewater Treatment

33

0

Adhesion force (nN)

Deflection (nm)

12 –100 Maximium deflection –200 dmax

10 8 6 4 2

–300

0 PE (a)

0.0

0.2 0.4 0.6 0.8 Separation distance (μm)

1.0

PE

PVDF

PVDF

(b)

Figure 13 A representative retraction force curve.

Figure 14 Adhesion forces of (a) carboxyl-modified and (b) hydroxylmodified microspheres to polyethylene (PE) and polyvinylidene fluoride (PVDF) membrane surfaces in buffered solution (pH 6.8).

Figure 13. In contrast, interaction between the colloidal probe and the membrane surface when the probe was approaching the membrane surface (similar to the situation in which carbohydrate-like substances approach membranes by convection flow) was also assessed by the effective distance of the forces shown in an approaching force curve. The affinity between a carbohydrate-like substance and membrane surface would change as a result of fouling. Therefore, AFM force measurement was also carried out with membranes previously fouled in a pilot operation to investigate the change in affinity. Because of the difficulty in regular sampling of membrane specimens from the PVDF membrane module used in the pilot study, the investigation of change in affinity of the carbohydrate-like substance was carried out only with the PE membrane. Pilot-scale membrane filtration was carried out at the Kamiebetsu water purification plant (Ebetsu, Japan) using Chitose River surface water as raw water. Characteristics of the raw water used for the pilot operation are described elsewhere. In authors’ previous study using the same water, it was found that carbohydrate-like substance was dominant in the foulant causing physically irreversible fouling. After passage of the grit chamber, the raw water was delivered to the membrane units without any pretreatment. The PE membrane, which had the same properties as those described before, was assembled (3 m2) and horizontally immersed in a 300 l submersion tank. The operation was conducted using a vacuum. The filtration flux was set at a constant value of 1.0 m3 m2 d1. During the operation that continued for 49 days, periodic physical cleaning was carried out by filtration for 30 min, air scrubbing for 30 s, and hydraulic backwashing for 60 s, as recommended by the manufacturer. When the membrane was rapidly fouled or the value of TMP became excessive, the submerged membrane module was taken out from the submersion tank and was cleaned by spraying pressurized water on the membrane surface. During the pilot-scale operation, membrane fibers were sampled from the center of the membrane module six times: on days 1, 3, 5, 16, 23, and 39. After cutting the fibers, corresponding channels were closed with epoxy glue to prevent leakage, and the permeate flow rate was adjusted to maintain a constant flux of 1.0 m3 m2 d1. To check for membrane breakage, turbidity of the permeate was monitored. After

membrane fibers had been cut, they were immediately brought to the laboratory in a container filled with distilled water (resulting pH of 6.570.5), and the surface of the membrane specimen was manually wiped with a sponge and rinsed with distilled water thoroughly. This step was carried out to ensure the removal of the accumulated cake (i.e., effect of physically reversible fouling) and to specifically focus on physically irreversible fouling in this study. It was found that manual sponge cleaning had little effect on permeability of the fouled membranes at the termination of the operation, indicating that physically irreversible fouling was dominant in the pilot operation. A portion of membrane fibers was examined in a zeta potential meter (ELS-8000, Otsuka Electronics, Osaka, Japan) at pH 7.0 and 5 mM KCl. The other membrane fibers were stored in Milli-Q water until use for AFM force curve measurements. Figure 14 shows the adhesion forces (Fad) of (a) carboxylmodified and (b) hydroxyl-modified microspheres to clean PVDF or PE membranes, which were determined from the maximum values in the retraction force curves (Figure 13). From Figure 14, it is obvious that the adhesion force of the hydroxyl group was much greater than that of the carboxyl group regardless of membrane. The difference in values shown in Figure 14 is explained by differences in a balance of three relevant forces: (1) electrostatic interaction, (2) hydrogen bond (or electron transfer interaction), and (3) van der Waals interaction as seen in Figure 15. The hydrogen bond and the van der Waals interaction work as attraction forces, while the electrostatic interaction is considered to be repelling force because of the negatively charged nature of both microspheres and membrane surfaces. The electrostatic repulsion is governed by Coulomb’s force, which is proportional to the product of the two different charges to be considered. The charges of the two functionally modified microspheres were comparable. Thus, similar levels of Coulomb’s force would be exerted on carboxyl- and hydroxyl-modified microspheres with the membranes. On the other hand, the van der Waals interaction between a microsphere and a flat surface is known to be proportional to the radius of the microsphere The two types of microspheres used in the present study had the same radius of 3 mm, and therefore the levels of van der Waals attraction were also considered

34

Membrane Filtration in Water and Wastewater Treatment

Carboxyl group (COOH)

–130 mV

Hydroxyl group (OH)

Electrostatic

–126 mV

Van der Waals interaction

–4 mV

–4 mV Hydrogen bond

Figure 15 Three relevant forces: (1) electrostatic interaction, (2) van der Waals interaction, and (3) hydrogen bond.

to be similar. Considering the balance of the three relevant forces, it is reasonable to conclude that the difference in the adhesion force shown in Figure 14 may be attributed to the hydrogen bond. A hydrogen bond is generated by electron transfer reaction between electronegative atoms (e.g., O, N, F, and Cl) and H atoms that are covalently bound to similar electronegative atoms. The two functional groups examined in the present study (i.e., carboxyl and hydroxyl groups) have the possibility of forming hydrogen bonds due to their high polar nature, but the bounding power largely depends on their pKa values. If pKa value is larger than pH of the solution, the functional group is protonated, contributing to the formation of a strong hydrogen bond. In contrast, in the condition of pKa being less than pH of the solution, the functional group dissociates, resulting in an insignificant hydrogen bond. To make sure the dissociation condition of two functional groups, an investigation of the adhesion force as a function of pH is considered to be appropriate. However, because of low resistance of available AFM cells to extreme pH condition, the authors could not figure out the dissociation condition of two functional groups. In previous studies in which the pKa values of hydroxyl- or carboxyl-modified microspheres were investigated, it was estimated that carboxyl groups have pKa values between 3 and 6 and hydroxyl groups have pKa values between 9 and 13. Assuming that the pKa values obtained in those previous studies could be applied to the present study, the carboxyl groups were dissociated whereas the hydroxyl groups were not dissociated in the adhesion force measurements carried out at pH 6.8 (Figure 14). In the present study, the difference stated above presumably caused the remarkable difference in adhesion force of the two types of microspheres. An additional remark that should be made for Figure 14 is that the adhesion forces of hydroxyl-modified microspheres to the PVDF membrane and the PE membrane were quite different. As shown in Figure 14, the binding power of the hydroxyl group was much greater for the PVDF membrane than for the PE membrane. According to Ducker et al., the adhesion value possibly varies depending on surface roughness. The difference between the roughness of the PVDF membrane and that of the PE membrane was insignificant, suggesting a limited effect of roughness on the difference in adhesion force. Rather, difference in polymer materials seemed to affect the binding force of hydroxyl-modified microspheres: binding power of the hydrogen bond largely depends on hydrogen

bonded pairs. It is known that PVDF has two fluoride atoms that are arranged symmetrically with a center carbon atom, while PE has only hydrogen atoms along with carbon chain. Generally, the higher the electronegativity of the bounded atom, the greater the binding energy of the hydrogen bond becomes. Because of the high electronegative nature of fluoride atoms, a strong hydrogen bond would be formed between the surface of the PVDF membrane and hydroxyl-modified microspheres. Based on the fact that carbohydrate has many hydroxyl groups in its structure, the hydrogen bond seems to play an important role in the accumulation of carbohydrate-like substances on membranes, as indicated by previous studies on fouling. The hydrogen bond is considered as a semi-irreversible reaction, and the value of binding energy is between 10 and 40 kJ mol1, which is stronger than that of typical van der Waals attraction (B1 kJ mol1). Because of such a strong and semi-irreversible binding ability of the hydrogen bond, it is probably very difficult to remove carbohydrate-like substances from membranes by physical cleaning (e.g., backwashing) once they have adhered to the membranes by hydrogen bonds. The data shown in Figure 14 suggest that more carbohydrate would accumulate on a membrane made from polymers containing atoms with high electronegativity. For the prevention of accumulation of carbohydrate on membranes used for water treatment, it would be desirable to choose membranes that are fabricated with polymers that do not contain atoms with high electronegativity in their structure. Figure 16 shows the approaching force curves repeatedly measured with (a) carboxyl- and (b) hydroxyl-modified microspheres for new PE and PVDF membranes. As shown in the figure, features of approaching force curves were completely different depending on the type of microspheres. As the carboxyl- modified microspheres approached the membrane surface (Figure 16(a)), they encountered repulsive interaction due to repulsive electrostatic interactions between the negatively charged microspheres and the negatively charged membrane surface. It is shown in Figure 16(a) that the interaction became apparent within a distance of about 20 nm for both membranes, demonstrating that the two membranes exerted similar electrostatic repulsion against the carboxyl-modified microspheres. This is consistent with the results of measurement of zeta potentials of the membranes: the two membranes exhibited similar negative charges.

PE 0.5 0.25

0.5 0.25

0.0

0.0 0

25

50

75

0

Distance (nm)

(a)

25

50

75

Distance (nm) 0.5

0.5 PE

PVDF

0.25

0.25

0.0

0.0

–0.25

–0.25

Force (nN)

Force (nN)

35

PVDF

Force (nN)

Force (nN)

Membrane Filtration in Water and Wastewater Treatment

–0.5

–0.5

–1.0

–1.0

–1.5

–1.5

–2.0

–2.0 0

25

50

75

0

Distance (nm)

(b)

25

50

75

Distance (nm)

Figure 16 Approaching force curves of (a) carboxyl-modified microspheres and (b) hydroxyl-modified microspheres to the PE membrane (left panels) and the PVDF membranes (right panels) in buffered solution (pH 6.8).

6 Adhesion force (nN)

Adhesion force (nN)

6 5 4 3 2 1

4 3 2 1 0

0 0 (a)

5

5

10 15 20 25 30 35 40 Operation time (days)

0 (b)

5

10 15 20 25 30 35 40 Operation time (days)

Figure 17 Changes in adhesion force of carboxyl-modified microspheres (a) and hydroxyl-modified microspheres (b) to PE membranes that were sampled during the pilot-filtration test.

In contrast, as the hydroxyl-modified microspheres approached the membrane surface (Figure 16(b)), rapid decrease in the bending stresses of the cantilever or jump-in attraction forces appeared after gradual increase in repulsion force. The increase in attractive force was probably due to hydrogen bonds between the hydroxyl groups of microspheres and the membrane surface. The effective distances of hydrogen bonds were around 15 and 5 nm in the case of the PVDF and the PE membranes, respectively. This was in accordance with the strong adhesion force of the hydroxyl-modified microspheres to the PVDF membrane discussed above. The results shown in Figure 16 suggest that hydrogen bonds between foulants and membranes can be significant only when they are transported to the region where the

membrane surface is very close. Before entering the region where hydrogen bonds can be significant, foulants need to overcome repulsive forces if they bear negative charges. Otherwise, they do not adhere to the membrane surface and subsequently cause membrane fouling. Strongly negativecharged particles/molecules (e.g., humic acid) are less likely to reach the membrane surface: in contrast, it is expected that carbohydrate-like substances relatively easily access to the membrane surface because of their electrostatically neutral nature. This is an additional explanation why carbohydratelike substances have recently been reported to be major foulants. Figure 17 shows the changes in adhesion forces (Fad) of (a) carboxyl- or (b) hydroxyl-modified microspheres to the PE

36

Membrane Filtration in Water and Wastewater Treatment

membranes, which were sampled during the pilot-scale filtration on days 0, 1, 3, 5, 16, 23, and 39. Adhesion force shown in the figure was determined by the same procedure as that used for obtaining the data shown in Figure 14. As clearly shown in Figure 14, adhesion forces of both hydroxyl-modified and carboxyl-modified microspheres changed to a large extent as a result of fouling. In the case of carboxyl-modified microspheres, the adhesion force decreased rapidly to a value of 0.06 nN within 1 day and remained at a low level until the end of operation. One possible reason for the reduction in binding force of carboxyl-modified microspheres was the increase in electrostatic repulsion. The charge of the membrane surface changed from  11 to  28 mV during the pilot operation (Yamamura et al., 2008), which resulted in greater electrostatic repulsion between negatively charged microspheres and the membrane surface. In authors’ previous fouling study using the PE membrane carried out at the same plant, it was shown that negatively charged substances (e.g., humic substances) also accumulated on/in the membrane during the long-term filtration. Accumulation of such negatively charged substances presumably decreased the charge of the membrane surface. As shown in Figure 17, adhesion force of the hydroxylmodified microspheres also declined rapidly, but the values of Fad for the hydroxyl-modified microspheres were much larger than those for the carboxyl-modified microspheres except for on day 39. This result partially explains why hydrophilic NOM dominated over humic substances and was shown to be a major foulant in previous studies on fouling: hydrophilic NOM actually has a great binding power to the membrane due to hydrogen bonding.

The exponential reduction of adhesion force seen with hydroxyl-modified microspheres could presumably be explained by the decrease in binding sites available on the membrane surface due to membrane fouling and/or by the increase in repulsive forces between negatively charged microspheres and the negatively charged membrane surface.

4.02.1.3 Membrane Filtration Systems for Controlling Fouling In order to reduce the membrane fouling, we need to produce the membrane resistant to fouling and to construct hybrid membrane systems which include the existing treatment processes such as coagulation, activated carbon adsorption, and biological/chemical oxidation. Figure 18 describes such a concept, considering the size, concentration, and chemical properties of the substances to be removed.

4.02.1.3.1 Channel flocculation in monolith ceramic membrane Coagulation–flocculation process has been widely used to form aggregates (flocs), which include many fine particles contained in the raw water, for the efficient solid–liquid separation in the sedimentation basin and sand filter. Tambo and Watanabe published several papers describing the floc density and flocculation kinetics for the better understanding of flocculation process. They presented the floc density function and GC0T value. The floc density function describes the quantitative relationship between the size and effective (buoyant) density of flocs. The exponent Kr in the function is related to the fractal dimension (D) for the aggregates formed

Impurities mm Suspended matters

Organic–inorganic soil (clay, microorganisms, highmolecular-weight humics, etc.) Silts MF–UF Algae filtration Protozoa (Cryptosporidium, Giardia, etc.) Bacteria

µm Impurity size

Protein Colloidal matters

Coagulation + MF–UF

Coagulation / sedimentation + MF–UF filtration

Oxidized substances (SiO2, Fe2O3, Al2O3, MnO2, etc.) Humic acids Virus

nm

Dissolved matters

Å

Adsorption, ion exchange + MF–UF

Saccharoid

Ozonation, activated carbon adsorption, biological oxidation + MF–UF filtration

Taste and odor producing inorganic ions (Fe2+, Mn2+, etc) Fulvic acids NF filtration Synthetic organic compounds (DDT, BHC, PCB,) Inorganic compounds (arsenic, antimony, seleninum, etc.)

Concentration Figure 18 Design matrix of hybrid membrane filtration systems. DDT, dichlorodiphenyltrichloro ethane; BHC, benzene hexachloride; MF, microfiltration; NF, nanofiltration; PCB, polychlorinated biphenyl; UF, ultrafiltration.

Membrane Filtration in Water and Wastewater Treatment

in cluster–cluster aggregation (CCA) as D ¼  3  Kr. Kr is a function of the aluminum to turbidity (ALT) ratio, which is defined as Al dosage(mg/l)/suspended solid concentration (mg l1) in raw water, and has the value of 1.00 and 1.25 for the ALT ratio of around 1/100 and 1/20, respectively. These values coincide with the fractal dimension D determined for the reaction and diffusion limited case (2.05 and 1.75), respectively. Tambo and Watanabe have proposed that the GC0T value is more useful than GT value proposed by Camp as the criterion of flocculation. These research results have been included in the membrane filtration process to improve the filterability of the membrane (Yonekawa et al., 2004). In Japan, membrane filtration plant has increased its treatment capacity since the mid-1990s. Tokyo Metropolitan Water Works Authority constructed a plant with the total capacity of 80 000 m3 d1 in April 2007 using hollow-fiber MF membranes made of PVDF. It is currently the largest plant in Japan. There has also been innovation in the membrane material and membrane module. The monolith ceramic membrane was developed in 1988 and its advances have been remarkable as seen in Figure 19. Figure 20 describes the detail of the monolith ceramic membrane. By the end of 2008, 81 plants with monolith ceramic membrane have been under operation in Japan and the maximum capacity of the plants is about 40 000 m3 d1. The pre-coagulation has been provided to all of these plants to strengthen filterability for stable filtration performance for a

Configuration

Unit

Stage Length

mm

Diameter

mm

Channel number

wide range of raw water turbidity and enhancement of the removal of viruses and dissolved organic substances. The authors have clarified the characteristics unique to monolith ceramic membrane with pre-coagulation by referring to the behavior of microparticles. The region exists in the monolith channel with the optimum G and GC0T value for good flocculation. The flocculation of microparticles offers the reduction in the membrane fouling. The laminar flow model within dead-end hollow-fiber membranes has been presented in many studies. For example, Fujita and Takizawa developed Equation (1) from the energy equation and the material balance in the course of filtration:

  dp v 8m ¼ 1 dv g rdkðp  p0 Þ

ð1Þ

where p is the static pressure (m), v the axial velocity within hollow fiber (m s1), g the gravitational acceleration (m s2), m the viscosity (kg m1 s1), r the water density (kg m3), d the internal diameter of hollow fiber (m), k the membrane filterability (s1) and p0 the external pressure of membrane (m). Considering the characteristic values (d ¼ 4 104 m, k ¼ 6 106 s1) of the typical hollow fiber, the first term in Equation (1) is much smaller than the second term. Neglecting the first term, an appropriate equation to calculate an expanded approximate axial velocity in a fibre can be derived. In the case of monolith ceramic membrane (d ¼ 2.5 103 m,

Tube 1985

Monolith 1988

1990

1994

2001

2006

1000

1500

30

10 1

19

37

180 61

2000

Channel diameter

mm

7

4

3

Membrane area

m2

0.02

0.24

0.35

0.48

15

24

Packing density

m2 l–1

0.25

0.34

0.50

0.63

0.6

0.63

1.5m3 8.9m2 1000 Module capacity (m3 d–1 module)

2.5

Industrial use

Application

100

10

Figure 19 Advance in monolith ceramic membrane.

37

13m2

Water purification m–2

d–1

1.8m3 73m2

m–2

d–1

2.5m3 m–2 d–1

5m3 m–2 d–1

150m2

240m2 module–1

38

Membrane Filtration in Water and Wastewater Treatment

Figure 20 Detail of monolith ceramic membrane (META water product).

k ¼ 5 105 sec1), however, the first term in Equation (1) cannot be neglected to derive an appropriate equation for calculating axial velocity in a monolith channel. Without neglecting the first term in Equation (1), the authors have developed Equation (2) to calculate an expanded approximate axial velocity in a monolith channel:

  v2 v ¼ vf coshðaxÞ  b pf  pe þ f sinhðaxÞ 2g rgdk 4dk 2 ; a¼ 2 b ¼ 8m d b

ð2Þ

where pf and vf are the pressure (m) and velocity at inlet of monolith channel (m s1), respectively. On the other hand, the membrane filterability k in the monolith membrane has a certain distribution. To facilitate analysis of the flow pattern on the basis of this distribution, a five-channel model with three levels of filterability was created, as described in Figure 21. Solving Equation (3) under the material balance and appropriate boundary conditions, the equation for axial velocity in the five channel model has been derived as

vi ¼ vfi coshðai xÞ  bi ðpfi  pe Þsinhðai xÞ ði ¼ 0; 1; 2Þ

ð3Þ

where pfi is the total pressure at channel inlet (m) and pe the external static pressure of membrane (m). The calculated flow pattern in the monolith ceramic membrane module is shown in Figure 22. A concentrate flowing out through outlets of channels 1 and 2 with lower filterability is drawn into channel 0 with higher filterability. It was also confirmed that the dead-end point is located at the position with an axial velocity vi ¼ 0 in channel 0.

In the channel of 1 m long, axial velocities calculated by Equation (9) are shown in Figure 21 for the membrane flux of 2 m3 m2 d1. The G value in the channels 0–2 was calculated at about 40 s1, which is in the range of optimum values proposed by Camp. On the other hand, the mean hydraulic residence time in the channels 0–2 was about 50 s. Therefore, the GT value in the channel is only about 2000, which is too small compared with the Camp’s proposed values. However, good flocculation was observed in the channel, because the GC0T value in the part of channel is high enough for good flocculation, explained as below. Using the data shown in Figure 23, the distribution of the local G values within the channel 2 under the membrane flux of 2 m3 m2 d1 is described as seen in Figure 23. Considering the velocity distribution in the channel and high concentration of coagulated microparticles reflected by membrane filtration, the GC0T value may be high enough for a good flocculation in the region with the local G value of 40–100 s1. In this context, C0 is defined as the coagulated microparticle concentration near the entrance of such a region. Figure 24 shows the experimental setup (large and small monolith membrane module) and sampling points. The top and bottom portion of the both modules were made of transparent material to enable a visual observation of flocs using video camera. Raw water was taken from the Kiso River near Nagoya city. The dosage of coagulant (polyaluminum chloride, PACl) was fixed at 1 mg Al l1. For rapid mixing condition, G value was fixed at 150 s1 and hydraulic detention time at 300 s. The filtration mode was dead end and membrane flux was fixed at 2 m3 m2 d1. The specifications and operation conditions of the two membrane modules are described in this chapter.

Membrane Filtration in Water and Wastewater Treatment

39

Eq. (3) Velocity equation for a 5-channels model

5 channels

Average velocity in channels (m s−1)

Channel no.0 Permeability k = 5.80 × 10–5[s–1]

Channel no.1 Permeability k = 4.65 × 10–5[s–1]

Channel no.2 Permeability k = 4.07 × 10–5[s–1]

Channel no.1 Permeability k = 4.65 × 10–5[s–1]

Channel no.0 Permeability k = 5.80 × 10–5[s–1]

i = fi cos(i x ) – i (p f,i – pe) sinh(i x ) (i = 0, 1, 2)

0.04

Channel no. 0 Channel no. 1 Channel no. 2

0.03 0.02

0.01

2 m3 m–2 d–1

0 0

0.2

0.4

0.6

0.8

1.0

Channel axial coordinate (m) Figure 21 Five-channel model and filterability k.

Module casing

Dead-end point

90%

Membrane

96.5%

Feed

Figure 22 Flow pattern in monolith module.

With the laser diffraction scattering-type particle-size distribution cell holder (Horiba LY-073), the particle-size distribution was measured to verify the predicted flocculation phenomena and its effect on the filterability of the monolith ceramic membrane. The behavior of microparticles with the size of 0.5–15 mm in the channel with lower filterability was also measured to identify the critical particle size. Polystyrenetype latex particles (JSR Stadex/Dynospheres: 0.5, 3, 5, 10, 15 mm, specific density of 1.05) were used as model particles.

The authors also investigated the correlation between microparticle concentration and TMP using the effluent from a conventional rapid sand filtration process, as shown in Figure 25. There exists a clear relationship between them. It would suggest a significant effect of the flocculation on the filterability in the monolith channel, because the microparticles, larger than 1 mm in the shear field, are subject to a lift force such as the lateral migration and shear-induced diffusion which are proportional to square and cubic power of the equivalent particle diameter, respectively, as described in Figure 26. There were no visual flocs in the bottom portion of the module where coagulated microparticles entered. Visual flocs, however, blew out at the maximum velocity of 3–8 mm s1 from the lower filterability channels in the upper portion of the module. From the analytical result with five channel model, the average outflow velocity at the membrane top was estimated to be 2–4 mm s1. The maximum flow velocity in laminar flow is twice the average velocity. Therefore, the analytical result has been confirmed by the visual experiment. The authors measured the concentration of polystyrenetype latex particles with the size range of 0.5–15 mm in the influent and effluent of the membrane. There were almost no particles in the effluent. It demonstrated that the latex particles of smaller than 15 mm are deposited onto the membrane surface in the course of membrane filtration. This result can explain the correlation of the variation of microparticle number in raw water and TMP as seen in Figure 25. The experiment on the flocculation in the monolith channel was carried out to prove that good flocculation occurs in the channel and will improve the filterability of the membrane. From the theoretical analysis, the average flow velocity in the channel with lower filterability is about 0.5 mm s1 in

40

Membrane Filtration in Water and Wastewater Treatment Recovery 90%

Channel diameter 2.5 mm

Let’s consider “flocculation condition in channel” Channel length 1000 mm

G value 20 sec–1

Especially, near the membrane surface G value 20−100 s –1 : desirable value for flocculation Contact Time

40

60

80

Enough : laminar velocity is very low 100 Concentration Highly concentrated : accompanied by filtration Flux 2 m3 m–2 d–1 Figure 23 Profile of G values in monolith channel with lowest k.

Frequency (volume based ) (%)

20

SP3

16

SP4

12 SP2 SP1

8

Sp4

4 Filtrate

0 1

10

100 Particle size (µm) Small membrane

Coagulant (PACI) M

M

P SP1

SP2

SP3

Figure 24 Experimental setup and sampling points (SPs). PACl, polyaluminum chloride.

the region of 1–200 mm from the surface, so the detention time is between a few tens of minutes and few hours. The G value in the zone is between 20 and 100 s1. The floc size distribution in each sampling point is seen in Figure 24. Flocs are lifted up by laminar flow and carried away from the outside of the channel. Therefore, the space near the membrane surface might be considered to be a high efficient field for coagulating the charge neutralized microparticles.

Figure 27 shows a schematic image of phenomenon occurred in the channel when the pre-coagulation is prepared. In order to confirm the flocculation effect on the improvement of ceramic membrane filterability, the authors carried out an additional experiment using the small module with the Nishitappu River water. It is a very clean water with annual average turbidity and DOC of 1.3 TU and 0.6 mg l1, respectively.

Membrane Filtration in Water and Wastewater Treatment

41

CSF treated water

Run 6

Pore size

Flux

Interval

Pressure

Recovery

1.0 mm

20 m3 m–2 d–1

15 min

300 kPa

93.3%

TMP (kPa)

50 10

40 5 TMP Microparticle 30 29 Oct.

30 Oct.

Microparticle count (103 ml–1) (0.5–1.0 µm)

15

0

31 Oct. Date

Figure 25 Correlation between transmembrane pressure (TMP) and microparticle concentration. CSF, coagulation/sand filtration.

0 Monolith ceramic membrane

Back transport –2 log cm s–1

F1 ux Membrane

Log transport velocity (cm s–1)

2 Minimum size of particle that will not deposit on membrane

0

DpL = 56 µm

d–1

–4 0.8 µm

–6

DpL = 87 µm

–8 –10

Ultrafiltration flux

–4

–8 –4

m–2

Microfiltration flux

–2

–6

2

m3

Brownian R = 0.03 cm u = 133 cm s–1 T = 20 °C –3

–3

Shear Calculation conditions

–2 –1 0 1 Particle diameter: log Dp (µm)

2

3

Channel diameter = 2.5 mm Water temperature = 20 °C Channel entrance

Lateral migration

–2 –1 0 1 Log particle diameter (µm)

Middle point of channel 2

Back-transport velocity and critical flux Figure 26 Particle size and lift force.

Figure 28 shows the experimental result and confirms the effect of flocculation on the fouling reduction. Further improvement is possible using the chemically enhanced backwashing (CEB) with acidic solution. Coagulant addition of 1 mg Al l1 to the monolith ceramic MF membrane system also improved the virus log removal efficiency up to 7. Figure 29 shows the experimental verification of the effect of the CEB on the membrane filterability. The reason behind the

improvement may be the removal of microflocs attached to the membrane surface by the ECB.

4.02.1.3.2 Pre-coagulation/sedimentation in hollow-fiber UF/MF membrane The surface water from Chitose River and Nisitappu River was used as the raw water in the experiment. Table 2 summarizes

42

Membrane Filtration in Water and Wastewater Treatment

 L = uo2 dp3/(32  ro2)

Lateral migration Shear-induced diffusion

 S = 0.05 uo dp2/(4 ro2)

Ceramic membrane surface

Disaggregated floc particles

Lift force

Ceramic membrane surface u (membrane flux) (b) Aggregation

(a) Carrying near the membrane accompanied with filtration

(c) Lifting from membrane

Figure 27 Schematic image of effect of channel flocculation.

80

4 TMP Membrane flux

TMP (kPa at 25 °C)

3 Back washing interval: 4h

2h

40

2

20

1

0 1/4

Membrane flux (m d–1)

Precoagulation

60

0 1/19

2/3

2/18

3/5

3/20

4/4

Figure 28 Effect of channel flocculation on transmembrane pressure (TMP) change.

CEB (acid)

Experimental flow Coagulation

TMP (kPa)

Mn oxidization

Ceramic membrane

40

10 m3 m–2 d–1

30

8 m3 m–2 d–1

20

6 m3 m–2 d–1

10 0 04 Jan.

4 14 Jan.

m3

m–2

d–1,

24 Jan.

with CEB

without CEB 03 Feb. Date

13 Feb.

Figure 29 Effect of CEB, chemically enhanced backwashing on TMP change under high flux operation.

23 Feb.

Membrane Filtration in Water and Wastewater Treatment

Considering the data shown in Figure 33 and Table 3, it may be concluded that the higher DOC removal in the precoagulation/sedimentation gives better performance of UF membrane filtration. Even though the TMP used by PACl and PSI was almost the same at about 3300 h of filtration time (the actual TMP reached about 100 kPa, which is the recommended TMF for chemical cleaning), TMF used PSI has always been lower than that by AS and PACl. Figure 33 shows the comparison of removal efficiency of DOC among the three coagulants. PSI gave the best removal efficiency resulting in the best filtration performance. With Nishitapu River water, the TMP increased in each operating condition as seen in Figure 34. When the Nishitapu River water was filtered at constant flow rate of 1.1 m d1 directly by using UF membrane, the filtration time to reach 100 kPa of TMP was only 300 h in spite of low organic content and low inorganic content. However, the filtration time for coagulated water was 4 times longer than that. In addition, hypochlorite solution was added

Transmembrane pressure at 25 °C (kPa)

the average raw water quality of Chitose river during the experiment (Jang et al., 2004). With Chitose River water, the pilot plant consists of a rapid mixing tank, a jet mixed separator (JMS) with inclined tube settlers, and three hollow-fiber UF or MF membrane filters as described in Figure 30. The JMS is a simple but effective solid/liquid separator with several vertical porous plates in a channel; microflocs are flocculated under the turbulent flow produced by the water jets and larger parts of grown flocs settle between the plates; subsequently, residual small flocs are removed in the inclined tube settlers. The effective volume of JMS with inclined tube settlers is 7.0 m3 and flow rate to the JMS was 120 m3 d1, corresponding to the hydraulic detention time of 84 min. The operating conditions of this pilot plant are summarized (refer to Jang et al., 2004). Four processes of the pilot experiment were carried out. In processs 1 and 2, the aluminum sulfate (AS) with activated silicate and PACl was used as coagulant. The water was fed from outside to inside of hollow-fiber UF membrane, which is made of specially polymerized PAN with nominal average pore size of 0.01 mm, at a constant permeate flow rate of 0.9 m d1. The physical cleaning with back washing and air scrubbing was carried out to prevent fouling in a time interval of 30–60 min. In processes 3 and 4, polysilicato-iron (PSI) which is inorganic polymeric iron coagulant was used as coagulant PSI has a molecular ratio of Fe to Si of 1:1–1:5, but we used the molecular ratio with 1:1 in this pilot plant experiment. Coagulant dosage and coagulation pH were 0.21 mmol Fe l1 and 6.2, respectively. Results of TMP trends, for Chitose River water, with increasing UF filtration time in process 1 are shown in Figure 31. Figure 32 shows the comparison of the TMP among processes 1, 2, and 3 using the same UF membrane and AS, PACl, and PSI as coagulant, respectively.

150

Raw-UF Coa.-UF Sed.-UF

120 90 60 30

Flux: 0.9 m d–1

0 0

500 1000 1500 2000 Membrane filtration time (h)

Jet mixed separator (JMS)

P

Rapid mixing tank Permeate

Permeate

Permeate

Drain P

Compressor

Figure 30 Schematic description of pilot plant.

2500

Figure 31 Effect of pre-coagulation/sedimentation on performance of ultrafiltration (UF) membrane system.

Coagulant

Chitose river water

43

P

P

44

Membrane Filtration in Water and Wastewater Treatment

during backwashing term; the UF membrane filterability was significantly improved. These results were also obtained in the case of using MF membrane as seen in Figure 35.

4.02.1.3.3 Hybrid submerged MF membrane system

Transmembrane pressure at 25 °C (kPa)

The hybrid MF membrane system is a combination of submerged membrane and the other processes such as the powdered activated carbon adsorption and chemical/biological oxidation. The membrane system has been developed to purify raw waters with low quality containing a lot of soluble matter such as biodegradable organics, humic substances, manganese, and ammonia nitrogen. In the hybrid system, soluble less-biodegradable organics are adsorbed to the

120

Sed.-UF (PSI: 0.21 mmol-Fe l–1)

100

Sed.-UF (PACI: 0.19 mmol-Al l–1) Sed.-UF (AS: 0.37 mmol-Al l–1)

80

powdered activated carbon, and suspended particles including powdered activated carbon are separated by the membrane filtration. The soluble biodegradable organics, manganese and ammonia nitrogen, are biologically or chemically oxidized. In the case of chemical oxidation (with prechlorination), soluble manganese is oxidized with chlorine and the catalytic reaction of powdered activated carbon, and the oxidized manganese is removed by membrane separation. Ammonia nitrogen is also oxidized by chlorine in a pre-chlorination tank. In the case of biological oxidation (without prechlorination), the iron oxidizing bacteria and ammonia oxidizing bacteria, which are concentrated in submerged membrane tank, oxidize the soluble manganese and ammonia, respectively. A schematic diagram of the pilot plant is shown in Figure 36 (Suzuki et al.,1998). The volume of membrane submerged tank and the surface area of submerged membrane were 4 m3 and 86–120 m2, respectively. Detention time in the mixing tank was 10–15 min. The raw water was fed into the mixing tanks. Four types of polytetrafluoroethylene (PTFE) membranes were used. When the first, second, and third type of membranes were used, the

60 Table 3

Physically irreversible resistance

40 Run 1(125 days)

20

Run 2(73 days)

Module Module Module Module A B A B

0 0

500

1000 1500 2000 2500 3000 3500 Membrane filtration time (h)

Figure 32 Effect of various coagulants on performance of ultrafiltration (UF) membrane system.

Membrane flux (m3m2d1) Physically irreversible filtration resistance (1011 m1)

0.2 0.29

0.6 1.52

0.4 0.69

0.8 2.48

100 Removal efficieny of DOC (%)

Removal of UF

Removal of coagulants

Removal of sedimentation

80 PSI

60 PACI

40

AS

20

Run 1

Run 2

RW -U F C oa -U JM F SU F

RW -U F C oa -U JM F SU F

RW -U F C oa -U JM F SU F

RW -U F C oa -U JM F SU F

0

Run 3

Run 4

• Direct UF; around 15%, precoagulation / sedimentation; 35–60% • The highest DOC removal efficiency was obtained in run 4 Figure 33 Removal efficiency of dissolved organic carbon (DOC) with various coagulants. AS, aluminium sulfate; PACl, polyaluminum chloride; PSI, polysilicato-iron.

Transmembrane pressure at 25 °C (kPa)

Membrane Filtration in Water and Wastewater Treatment

45

Raw-UF (hypochlorite solution was not added, flux: 1.1 m d–1) Coa.-UF (hypochlorite solution was not added, flux: 1.1 m d–1) Coa.-UF (hypochlorite solution was added, flux: 1.1 m d–1) Coa.-UF (hypochlorite solution was added, flux: 1.7 m d–1)

140 120 100 80 60 40 20 0 0

500

1000

1500

2000

Membrane filtration time (h)

Transmembrane pressure at 25 °C (kPa)

Figure 34 Effect of operation condition on performance of ultrafiltration (UF) membrane system.

100

Raw-MF (hypochlorite solution was added) Coa.-MF (hypochlorite solution was added)

80 60 Flux: 1.4 m d –1

40 20 0 0

100 200 300 400 Membrane filtration time (h)

500

Figure 35 Effect of precoagulation on microfiltration (MF) membrane system.

raw water to the pilot plant was taken from the existing water purification plant, which had already contained the powdered activated carbon in the concentration of 5–30 mg l1. In the first tank, hypochlorite was added when the chemical oxidation was applied, and the sludge containing biomass and activated carbon were returned from the membrane submerged tank and mixed with the raw water in the second tank. The same powdered activated carbon (average diameter of 10 mm) was dosed into the second tank at the constant concentration of 13 mg l1 when the fourth PTFE membrane was used. PACl was added to coagulate the small suspended particles in the third tank. The pretreated water was fed into the submerged tank where the hollow-fiber PTFE membranes were submerged and intermittent aeration was performed to supply the oxygen to the microorganisms. Air was supplied for 1 min. with the intensity of 0.64 N m3 min1. every 4 min. The raw water in the pilot plant study was surface water from Chitose River. It contained many humic substances, soluble manganese, and ammonia nitrogen. The average raw water quality is given in Table 2. The dosage of hypochlorite in the mixing tank was 4–6 mg Cl2 l1. Coagulant dosage was 2–3 mg Al l1.

In the pilot plant experiments, symmetric or composite PTFE membrane with nominal pore size of 0.1 mm was used. The thickness of skin layer in the composite membrane was changed at 60, 30, and 15 mm. The pore density was about 80%. The skin layer thickness and pore density of the newest composite PTFE membrane are about 15 mm and 80%, respectively. The hybrid membrane system is able to efficiently remove the soluble matter such as organics, manganese, and ammonia nitrogen. The soluble manganese and ammonia nitrogen were oxidized biologically or chemically and small humic substances were adsorbed to the powdered activated carbon. The removal efficiency of TOC, E260, and trihalomethane formation potential (THMFP) is shown in Figure 37, and the comparison in the removal efficiency of the soluble manganese and ammonia nitrogen between chemical oxidation with prechlorination and biological oxidation without prechlorination was made in Figure 38 when the composite PTFE membrane with the skin layer thickness of 30 mm was used. Dosage of powdered activated carbon was fixed at 13 mg l1. As previously reported by the authors, chemical oxidation is necessary to oxidize soluble manganese when the raw water temperature become less than 10 1C. The authors also reported that improved filtrate quality can contribute to keep a higher flux. Figure 39 shows the change of the permeate flux, TMP, and raw water temperature during the experiment with the symmetrical membrane. In this experiment, the hybrid MF membrane system was operated without prechlorination. The average flux was relatively low at less than 0.3 m d1 and the TMP increased to 70 kPa after 5 months of operation. To improve the permeability of the PTFE membrane, the structure of membrane was changed from symmetrical to composite. Permeability of the composite membranes with different skin layer thickness was compared in the pilot plant experiment. The thinner the skin layer thickness, the better the permeability. Figure 40 shows the change of the membrane flux, TMP, and raw water temperature with increasing operation time when the newest composite PTFE membrane was used. It demonstrates that TMP was very stable under a high flux of 1.2 m d1. It is about 4 times higher than that in the symmetrical membrane.

46

Membrane Filtration in Water and Wastewater Treatment Hybrid submerged PTFE MF membrane system including coagulation, carbon adsorption and biological oxidation Submerged MF membranes PAC Cl2 PACl Raw water

C

P Suction pump

Compressor

P

Storage tank of permeate

P Circulation pump Hydraulic retention time = 1.5 h Figure 36 Schematic description of pilot plant. PAC, powdered activated carbon; PACl, polyaluminum chloride.

4 3.5

Raw water Raw water (soluble) Membrane filtrate

0.12 0.1

2003/9/1~2004/8/31 0.099 0.086

mg l–1

2.5

2.43

1/cm mg / l

3 2.29

2 1.5

1.17

0.08 0.06 0.04

0.031

1 0.02

0.5 0

0.017

0 TOC

E260

THMFP

Figure 37 Removal efficiency of total organic carbon (TOC), E260, and THMFP in hybrid system.

4.02.1.3.4 PVDF Membrane filtration with pre-ozonation Combination of ozonation with membrane filtration is effective for the prevention of membrane fouling. Japanese membrane manufacturing companies have developed the ozone-resisting membrane module made of PVDF with potting material having a high resistance to ozone. In the developed membrane module, water containing residual ozone can be directly filtered. It is reported that this system can provide consistently high permeate flux for various raw waters, especially high turbidity water and secondary treated municipal wastewater. We studied the effect of residual ozone on fouling reduction using the ozone resisting PVDF membrane.

Figure 41 shows the schematic diagram of the experimental system. The same raw water was used as the hybrid membrane system. The average water quality is shown in Table 2. In experimental runs 1-1 and 1-2, ozone dosage was 2.0 and 4.2 mg O3 l1, in which the residual ozone concentration was 0.73 and 1.13 mg O3 l1, respectively. The ozone contact time was of 7.8 min in all experimental runs. In runs 2-1 and 2-2, ozone dosage was 1.4 and 1.9 mg O3 l1, in which the residual ozone concentration was 0.41 and 0.61 mg O3 l1, respectively. Figure 42 shows the TMP change with increasing operation time in run 1 where the constant permeate flux mode operation under 3.5 m d1 with physical cleaning of backwashing

Membrane Filtration in Water and Wastewater Treatment 0.3

mg l–1

0.15

0.2

0.090

0

0.1

0.05

Manganese

0.01

0.016

0.05

0.15

0.002

0.1

2003/11/13–2004/8/31 with prechlorination (chemical oxidation)

0.100

0.15 0.105

mg l–1

0.2

0.25

2003/6/25–11/13 without prechlorination (biological oxidation)

0

Ammonia nitrogen

Manganese

0.01

0.25

Raw water Raw water (soluble) Membrane filtrate 0.23

Raw water Raw water (solouble) Membrane filtrate

0.079

0.3

47

Ammonia nitrogen

30

Flux Water temperature

0.6

25 20

0.4

15 10

0.2

5

30 Nov

31 Oct

1 Oct

1 Sep

3 Jul

100 90 80 70 60 50 40 30 20 10 0

0

2 Aug

0

Water temperature (°C)

0.8

3 Jun

TMP (kPa)

Membrane flux (m d–1)

Figure 38 Removal efficiency of Mn and NH4–N in hybrid system.

2002

0

30

60

90 Operating days

120

150

Figure 39 Transmembrane pressure (TMP) changes and permeates flux (symmetrical PTFE membrane with pore size of 0.1 mm).

and air scrubbing was carried out. It clearly demonstrates that the residual ozone reduced the membrane fouling (Lee et al., 2004). After the continuous operation for about 1800 h, chemical cleaning of fouled membrane was conducted. The following three chemical solutions were used: NaOH solution of 1%, NaClO solution of 5 mg l1, and oxalic and nitric acid of 2%

and 5%. Figure 43 shows the extracted TOC in each chemical solution. As seen in Figure 41, preozonation with residual ozone significantly decreased the attached organic substances to the membrane causing the physically irreversible fouling. It may come from the following two ozone-induced reactions: degradation of organic substances and destabilization of particles on the membrane surface. The ozone-induced particle

Membrane Filtration in Water and Wastewater Treatment 50

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Average membrane Flux: 1.2 m d –1 40

:

30 Temperature

Flux

Flux

Water temperature

20 10

Water temperature (°C)

Membrane flux (m d–1)

48

0

100 90

TMP (kPa)

80 70 60

Chemical washing

50 40 30 20

0

30

60

90

120 150 Operating days

180

30 Aug

31 Jul

1 Jul

1 Jun

2 May

2 Apr

3 Mar

2 Feb

3 Jan

0

4 Dec

10

210

240

270

Figure 40 Transmembrane pressure (TMP) changes and permeates flux (new composite PTFE membrane with a skin layer of 15 mm and porosity of about 80%).

destabilization reaction has been reported by many researchers. In the other experiment, we measured the size distribution of fouling particles in the backwash water and found that the average size of the particles was about 20 and 50 mm without and with ozonation, respectively. Increasing particle size increased the rate of back transport of organic particles, leading to the decrease in the accumulation of organic particles on the membrane surface. In this experiment, permeate TOC (i.e., DOC) concentration in the membrane filtration system without and with preozonation was the same as 2.4 mg l1 but E260 and E260/DOC were 0.062 and 0.034 cm1, and 0.026 and 0.013 cm1 mg1 l1, respectively. These results demonstrate that the biodegradability of organic particles increased due to the oxidation by O3.

4.02.2 Membrane Application to Wastewater Treatment 4.02.2.1 Current Status of MBRs Necessity of recycling use of water has been recognized to resolve the shortage of water resources. Municipal wastewaters seem to be an important water resource for recycling use. MBR is a key technology for creating the reclaimed water resource. MBR has been applied to the municipal wastewater treatment since the 1980s. The first-generation MBR combines a crossflow-type membrane with outside bioreactor and mixed liquor

is recirculated into membrane module. The operation pressure is high and recirculation pump is needed. In addition, it is reported that microorganism activity decreases due to the recirculation of the mixed liquor. The second-generation MBR submerges membrane module directly in the bioreactor. As a result, circulation pump is not needed and the operating pressure is low. Submerged MBRs have been preferred due to their lower energy consumption and smaller footprint compared with recirculated MBRs. However, it is reported that accumulated dissolved organic matter in the bioreactor decreases the membrane permeability in the submerged MBR more seriously compared with the first-generation MBR. In 2005 the European Commission decided to boost the development and application of MBR processes for municipal wastewater treatment through financing a 3-year research project within the scope of the 6th framework program: AMEDEUS (accelerate membrane development for urban sewage purification). Within AMEDEUS an analysis of the potential for MBR standardization was carried out. Based on an extensive survey of the MBR industry, the White Paper was published to provide a comprehensive overview of the market interest/expectation and technical potential of going through a standardization process of MBR technology in Europe. Due to the predominance of submerged MBR system in municipal applications, representing 99% of the installed membrane surface in Europe in the period 2002–05, the study focuses only on this configuration.

Membrane Filtration in Water and Wastewater Treatment

49

Preozonation-PVDF MF membrane system O3

Permeate

Chitose River water Ozonationmembrane Residual O3

PVDF membrane Back wash P

Run-4.1, 4.2 Run-5.1, 5.2

Air

Ozonation tank Retention tank O3 O2 Ozonationmembrane No residual O3

Back wash P

Run-4.3 Run-5.3

Air

O3 removal tank Membrane Run-4.4 Run-5.4

Pressurized membrane ⇒ Pore size: 0.1 μm (MF) ⇒ PVDF (polyvinylidenefluoride)

Back wash P

Air Figure 41 Experimental system of preozonation and membrane filtration.

300 Run 1 Run 2 Run 3 Run 4

TMP at 25 °C (kPa)

250 200 150 100 50 0

0

300

600

900

1200

1500

1800

Operation time (h) Figure 42 Effect of preozonation and residual ozone on membrane filtration. TMP, transmembrane pressure.

Figure 44 shows the number of municipal and industrial MBRs in Europe. In Japan MBR technology has been applied to the water recycling for some large business, commercial and residential complex buildings such as Roppongi Hills and Tokyo Mid Town. Membrane fouling deteriorates the membrane permeability and consequently increases energy consumption in MBR. A seriously fouled membrane must be cleaned with chemical reagents, which are costly. In addition, disposal of chemical reagents after membrane cleaning is an issue of concern, and the frequency of chemical membrane cleaning

should therefore be minimized. Thus, there is a need for efficient control of membrane fouling in MBR. In order to develop methods for efficient MBR operation, a better understanding of the mechanism of membrane fouling in MBR is needed.

4.02.2.2 Mechanism of Membrane Fouling Membrane fouling is a major obstacle for wider application of MBRs. Membrane fouling results in reduced performance, severe flux decline, high-energy consumption, and frequent

50

Membrane Filtration in Water and Wastewater Treatment 14 NaOH NaClO (Oxalic + nitric) acid

Detached organic substances per unit permeate volume (mg-TOC m–3)

12 10

Without O3

8 6 4 Residual O3 2 0 Run 1-1

Run 1-2

Run 1-4

Figure 43 Effect of residual ozone on amount of detached organic carbon.

600

Number of installations

With standardization?

Industrial (> 20 m3 d–1) Municipal (> 500 p.e.)

500 400

> 50 per year 300 200 > 20 per year 100 0 7

99

<1

98

19

99

19

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

Year Figure 44 Number of MBRs in Europe. From AMEDEUS (2006).

membrane cleaning or replacement. In order to establish strategies for controlling membrane fouling, an understanding of the mechanisms of membrane fouling is indispensable. Many factors that might influence membrane fouling in MBRs have been reported. Attention has been given to various design and operating parameters such as airflow rate in the reactor, membrane configuration, membrane flux, concentrations of mixed liquor-suspended solids (MLSSs), and solids retention time. Figure 45 describes several factors affecting the membrane fouling in MBRs (Yamato et al., 2006). In this part, the authors focus on two important operating parameters, membrane flux and membrane material.

4.02.2.2.1 Effect of membrane permeate flux on fouling Experiments were carried out at the Soseigawa Municipal Wastewater Treatment Center, Sapporo, Japan. Characteristics of the raw wastewater of this plant can be found in Kimura et al. (2005). The examined wastewater is classified as weak. Feed wastewater for the MBR examined in this study was

delivered from the grit chamber of the facility. Hollow-fiber MF membrane modules made of PVDF that had a total surface area of 1.3 m2 each and nominal pore size of 0.4 mm (Mitsubishi Rayon, Tokyo, Japan) were used in this study. Two identical membrane modules were submerged in the same MBR tank (350 l) and filtered the same mixed liquor suspension side by side as described in Figure 46 (Kimura et al., 2005, 2008a, 2008b). To investigate the influence of difference in membrane flux on fouling, the two modules were operated at different fluxes. Any differences between the two modules could be attributed solely to the influence of membrane flux. In this study, two long-term operations of the MBR (runs 1 and 2) were carried out. In run 1, one module (module A) was operated at 0.2 m3 m2 d1, while the other (module B) was operated at 0.6 m3 m2 d1. In run 2, modules A and B were operated at 0.4 and 0.8 m3 m2 d1, respectively. In each run, new membranes were used. Continuous monitoring was initiated in September 2005 for run 1 and in September 2006 for run 2. In the MBR, aeration was continuously carried out at the flow

Membrane Filtration in Water and Wastewater Treatment * Water purification

51

Module configuration, material (PE, PVDF, PFTE) hydrophilicity/hydrophobicity, porosity, pore size

Membrane

Turbidity, humic substances Characteristics of raw water Temperature, TOC, pH, alkalinity

Membrane fouling

Operating conditions

Molecular-level analysis

Permeate flux, aeration intensity, TMP, HRT, SRT, F/M ratio

Characteristics of mixed liquor

Hybrid system

MBR Multifunctional system

MLSS, viscosity, EPS/SMP, dissolved matter, DO, particle-size distribution, activity of microorganisms

Figure 45 Factors relating to the membrane fouling in MBR. DO, dissolved oxygen; EPS, extracelluar polymeric substance; HRT, hydraulic retention time; MLSS, mixed liquor-suspended solid; PE, polyethylene; PVDF, polyvinylidene fluoride; SMP, soluble microbial product; SRT, solid retention time; TMP, transmembrane pressure; TOC, total organic carbon.

Real wastewater

No. 1

Flux Membrane material

No.1

No.2

0.2

m3

No. 2

m2 d–1

0.6 m3 m2 d–1

PVDF

Nominal pore size

0.4 µm

Surface area

1.3 m2

MLSS

12000 mg l–1

HRT

10.2 h

SRT

111 days Organic analysis

Characterization of foulant NaOH (pH 11) Figure 46 Experimental setup – effect of flux. HRT, hydraulic retention time; MLSS, mixed liquor-suspended solid; SRT, solid retention time.

rate of 3.5 m3 h1. Intermittent filtration (12-min filtration and 3-min pause) was also carried out. MLSS concentration in the MBR was maintained at 11 g l1 and resulting SRT was 110 days in run 1, while MLSS and SRT in run 2 were 12 g l1 and 65 days, respectively. The degree of membrane fouling was estimated by membrane filtration resistance calculated by the following equation:

J ¼ DP=m=Rt

where J is the membrane permeate flux (m3 m2 s1), DP the TMP difference (Pa), m the water viscosity (Pa s), and Rt the total membrane filtration resistance (m1). In run 1, operation of the MBR equipped with the two membrane modules was continued for 125 days. As mentioned before, the membrane fluxes were different for the two modules: 0.2 m3 m2 d1 for module A and 0.6 m3 m2 d1 for module B. As expected, increase in the filtration resistance in module B was much faster than that in module A. However, increase in the filtration resistance in module B was still slow,

52

Membrane Filtration in Water and Wastewater Treatment

and filtration using both modules could be continued for 125 days without any chemical cleaning in run 1. A similar result was obtained in run 2 where operation of the MBR was continued for 73 days: increase of filtration resistance in module A with the low flux (0.4 m3 m2 d1) was much slower than that in the other. Chemical cleaning of the membranes was not carried out in run 2, either. Membrane fouling can be categorized into two types: physically reversible fouling and physically irreversible fouling. Physically reversible fouling can be cancelled by physical cleaning, whereas physically irreversible fouling needs chemical cleaning to be cancelled. Control of physically irreversible fouling is essential for reduction of operating costs of MBRs because physically reversible fouling can be mitigated as long as an efficient physical cleaning is carried out. Most of the existing MBRs are operated with routine practices of physical cleaning (e.g., backwashing). To specifically focus on physically irreversible fouling in this study, each membrane module was intensively cleaned by spraying pressurized water and wiped with lab paper at the end of the operations, and then water permeability was measured. Table 3 summarizes the magnitude of physically irreversible resistance measured at the end of the operations. As mentioned above, in run 1, membrane flux of module B was set 3 times higher than that of module A. However, the difference in the degree of physically irreversible fouling between the two modules was larger than threefold. The degree of physically irreversible fouling in module B was about 5 times larger than that in module A. In the operation of module B, increase in physically irreversible fouling was 1.7 times more rapid than that of module A on the basis of volume of the suspension filtered. A very similar result was obtained in run 2. Although the difference in membrane flux between the two

modules was twofold in run 2, the degree of physically irreversible fouling in module B operated at the higher flux was about 4 times larger than that in module A operated at the lower flux. These indicate that the degree of physically irreversible fouling in an MBR is not directly linked to the volume of the suspension filtered but differs depending on membrane flux. To investigate the features of constituents that were responsible for membrane fouling in the pilot runs, organic matter was desorbed from the fouled membranes at the terminations of the operations and was then analyzed. When the pilot operations were terminated, membrane modules were taken out from the reactors and were disassembled. Each membrane fiber was manually wiped with lab paper in order to remove accumulated cake that could be physically removed. Desorption of organic matter from the fouled membranes was carried out by soaking the membranes in alkaline solution (sodium hydroxide) for 24 h. Solution pH was set at 11. After desorption, measurements of TOC were carried out. The remaining solutions were subsequently processed with electric dialysis for desalination and lyophilized for advanced analyses. Amounts and characteristics of the foulants that caused physically irreversible fouling in the two experiments were investigated by analysis of foulants extracted from the fouled membranes with sodium hydroxide. Figure 47 shows the amounts of foulants desorbed from the fouled membranes. Data in Figure 47 are shown on the basis of a unit membrane surface area. As seen from Figure 47, the amounts of organic matter, which were expressed in extracted concentrations of TOC, carbohydrate, and protein, were not significantly different between the two modules in both runs. Although the amounts of organic matter desorbed from module B were slightly larger than those desorbed from

90

Amount of desorbed organic matter (mg m−2)

80 70

Module A (run 1) Module B (run 1) Module A (run 2)

60 Module B (run 2) 50 40 30 20 10 90 Total organic carbon

Figure 47 Amount of foulants desorbed from fouled membrane.

Carbohydrate

Protein

Membrane Filtration in Water and Wastewater Treatment

module A, the differences were much smaller than the difference seen for the filtration resistances shown in Table 3. On the contrary, the amount of protein desorbed from module A was larger than that from module B in run 2. These data imply that quality of organic matter rather than quantity of organic matter should be focused for an explanation of the differences. Figure 48 shows the composition of monosaccharides in the foulants desorbed from the fouled membranes. Very interestingly, the presence of rhamnose was large in the foulants desorbed from module B that was operated with the higher fluxes in both runs. Figure 49 shows amino acid compositions in the foulants desorbed from the two membranes. Compared with the monosaccharide analysis shown in Figure 48, in the case of amino acid composition, differences between the two modules were not significant in both runs. However, in both runs, the presence of glutamic acid (GLU) and aspartic acid (ASP) was more pronounced in the foulants desorbed from module A operated with lower fluxes. As

Module B (run 2)

Fucose Rhamnose

Module A (run 2)

Arabinose Galactose

Module B (run 1) Glucose Mannose

Module A (run 1) 0

20

40 60 Percent

80 100

Figure 48 Composition of monosaccharides in foulants.

shown in Figures 48 and 49, it was found that the composition of foulants that accumulated in the two modules differed despite the fact that the two modules filtered the same suspension at the same time. These differences can be discovered by comprehensive measurements of carbohydrates and protein. FTIR spectra of the foulants obtained from the fouled membranes are presented in Figure 50. Difference in characteristics of the foulant caused by the difference in membrane flux was clearly shown in this analysis and a good reproducibility of the analysis is shown in Figure 50. In both runs, the foulants desorbed from the membranes operated at higher fluxes showed more pronounced peaks around 1550 cm1 than those operated at lower fluxes. Peaks around 1550 cm1 in an FTIR spectrum are assigned to amide groups. Figure 51 shows 13C NMR spectra of the desorbed foulants. Similar to the FTIR spectra, reproducibility of the analysis was confirmed. In the case of NMR analysis, a significant difference between the two modules can be found in the peaks at 105 ppm, which is attributed to anomeric carbon, and minor differences were repeatedly found in the region between 130 and 160 ppm, which corresponds to aromatic carbon. As described above, characteristics of foulants in MBRs differed significantly when membrane flux was different. This difference in characteristics of the foulants associated with difference in membrane flux can probably be explained by the size distribution of organic matter in the mixed liquor suspension of the MBR. Organic matter in the suspension exists in a variety of size distributions. It is likely that some types of organic matter tend to exist as large particles/molecules, while others exist as small ones. A particle/molecule never causes membrane fouling unless it is transported to the membrane surface. In the case of submerged MBRs, whether a particle/ molecule can reach the membrane surface or not, is totally

PHE LEU ILE MET VAL PRO ALA GLY LYS ARG HIS TYR CYS

Module B (run 2)

THR SER GLU ASP

Module A (run 1)

Module A (run 2) Module B (run 1)

0

2

4

6

8 Percent

Figure 49 Amino acid composition in foulants.

53

10

12

14

54

Membrane Filtration in Water and Wastewater Treatment

Module A (run 1)

2000

1800

1600

1400

1200

1000

800

600

Module A (run 2)

2000

1800

Wave number (cm–1)

1600

1400

1200

1000

Module B (run 1)

2000

1800

1600

1400

1200

800

600

Wave number (cm–1)

1000

800

600

Module B (run 2)

2000 1800

Wave number (cm–1)

1600

1400

1200

1000

800

600

Wave number (cm–1)

Figure 50 Fourier transform infrared (FTIR) spectra of foulants in fouled membranes.

Module A (run 1)

250

200 150 100 50 Chemical shift (ppm)

Module A (run 2)

0

250

Module B (run 1)

250

200 150 100 50 Chemical shift (ppm)

200 150 100 50 Chemical shift (ppm)

0

Module B (run 2)

0

250

200 150 100 50 Chemical shift (ppm)

0

Figure 51 Nuclear magnetic resonance (NMR) spectra of desorbed foulants.

dependent on a balance between the rate of convection flow toward the membrane associated with suction pressure (i.e., membrane filtration) and the rate of back-transport from the membrane mainly provided by turbulence caused by aeration. The size of a particle/molecule plays a key role in determining back-transportation rate. In this study, rates of convection flow toward the membranes were different in the two modules, and consequently different types of constituents could be transported to each module despite the fact that the two modules filtered the same suspension. As a result, characteristics of the membrane foulants in the two modules could become different. A high membrane flux can attract an increasing fraction of particles/ molecules to the membrane by overwhelming back-transportation rate. Larger particles/molecules with larger back-transportation rates are then pulled to the membranes and might

cause fouling when membrane flux is high. Among large particles/molecules in the mixed liquor suspension, there seem to be fractions that would cause severe membrane fouling. This would be a good explanation for the results showing that the degree of physically irreversible fouling caused by filtration of a specific volume of suspension differed considerably depending on membrane flux.

4.02.2.2.2 Effect of membrane material on fouling Two different bunches of hollow-fiber membranes made from different polymers (i.e., PE and PVDF) were separately bundled and submerged in a single reactor side by side. Both tested membranes had the same nominal pore size (0.4 mm). Total surface areas of the PE and PVDF membranes were 3 and 1.3 m2, respectively. These membranes were kindly supplied

Membrane Filtration in Water and Wastewater Treatment

pilot run. In the early stage of the operation, Rt of the PE membrane increased much faster than that of the PVDF membrane. The rate of increase in Rt of the PE membrane was fairly constant. Chemical cleaning of the PE membrane was carried out on day 76. The efficiency of the cleaning was so high that almost complete recovery of membrane permeability was observed. After the chemical cleaning, filtration using the PE membrane was restarted and Rt of the PE membrane increased at a rate that was comparable to that observed before the cleaning. Physical cleaning was carried out for the PE membrane at the end of operation (day 140) and was found to be ineffective. Almost no recovery of membrane permeability was achieved by the physical cleaning. This means that membrane fouling in the PE membrane that occurred after the chemical cleaning was irreversible fouling. Increase of Rt of the PVDF membrane was minimal in the early stage of the operation. Around day 60, Rt of the PVDF membrane suddenly started to increase. Physical cleaning of the PVDF membrane was carried out on day 89. Before the physical cleaning, accumulation of a gel-like substance that was considerably different from ordinary sludge cake was observed on the membrane surface by visual inspection. Rt of the PVDF membrane was substantially reduced by the physical cleaning and the operation was restarted. A rapid increase in Rt of the PVDF membrane was just after the physical cleaning. This rapid increase in Rt might have been due to changes in properties of the mixed liquor during that time (details given later). At the end of the operation (day 140), physical cleaning of the PVDF membrane was carried out and resulted in substantial reduction in filtration resistance. This was in contrast to the results obtained for the PE membrane. The filtration resistance that accumulated in the PVDF membrane was thought to be mainly reversible resistance. In Figure 53, evolution of irreversible fouling in the PVDF membrane can be estimated by connecting the points recorded just after the implementation of physical cleaning. As mentioned above, the increase in Rt of the PE membrane observed after day 76 directly reflected the evolution of

Concentration (mg l–1)

by Mitsubishi Rayon Co., Ltd. (Tokyo, Japan). Filtration was carried out with the constant flow rate mode of operation using suction pumps. Membrane permeate flux was fixed at 0.4 m3 m2 d1 for both membranes. Intermittent filtration (12-min filtration and 3-min pause) was also carried out. MLSS concentration in the reactor was maintained at 11 g l1 by a daily extraction of excess sludge. Aeration was continuously carried out in the reactor at the flow rate of 3.5 m3 h1. Hydraulic retention time and solid retention time were 6.1 h and 34 days, respectively. When membrane fouling became significant, membrane modules were taken out from the reactor and were cleaned physically or chemically. Physical membrane cleaning was carried out by spraying pressurized water on the membrane surface. Chemical membrane cleaning was carried out by submerging the membrane modules in a solution of sodium hypochlorite (500 ppm) and hydrochloric acid (pH 2). The degree of membrane fouling was evaluated by membrane filtration resistance (Rt). Figure 52 shows time course changes in TOC concentrations measured in permeates from both membranes. There was no obvious difference in TOC concentrations in the permeates throughout the operation. It was confirmed that the difference in pore sizes of the two membranes was negligible. Figure 53 shows time course changes in Rt observed in the

10 PE

PVD

5

0

0

20

40

60 80 100 Elapsed time (days)

120

140

Figure 52 Time course changes in total organic carbon (TOC) concentration in permeates.

PE

Flux = 0.4 m d–1 MLSS = 10 g l–1 F/M = 20.4 SRT = 34 day

Total filtration resistance (1011 m–1)

10

PVDF

Physical cleaning Chemical cleaning

9

55

PE PVDF

PE PVDF

8 7 6 5 PE

4 3 2

PVDF

1 0

0

20

40

60 80 100 Elasped time (days)

120

140

Figure 53 Time course changes in Rt (effect of membrane materials). The type of fouling was differed depending on membrane materials. PE: physically irreversible fouling was dominant; PVDF: most of the fouling observed for the PVDF membrane was physically reversible. MLSS, mixed liquor-suspended solid; SRT, solid retention time.

56

Membrane Filtration in Water and Wastewater Treatment

irreversible fouling. A comparison of the rates of increase in irreversible resistance observed in the two membranes showed that the rate of increase in the PE membrane was much faster. It should be noted that two membranes, having the same pore sizes, simultaneously filtered the same mixed liquor at the same permeate flux in the pilot run. Thus, the observed difference can be attributed to the properties of polymer materials. It can be concluded that PVDF is superior to PE in terms of prevention of irreversible fouling in MBRs used for treatment of municipal wastewater. In this study, the nominal pore size of both membranes was 0.4 mm. Particles with sizes close to the nominal pore size were assumed to affect membrane fouling. Thus, changes in the concentration of organic particles that were smaller than 1 mm (denoted hereafter as sub-micron-sized organic matter) were monitored. Whole sub-micron-sized organic matter was further divided into four fractions: between 0.65 mm and 1 mm, between 0.45 mm and 0.65 mm, between 0.1 mm and 0.45 mm, and less than 0.1 mm. This fractionation was carried out by successive filtrations using membrane filters with different pore sizes. Figure 54 shows the time course changes in the concentrations of each fraction in the mixed liquor of the pilot-scale MBR. As can be seen from Figure 54, between day 60 and day 100, the concentration of organic matter with particle size between 0.1 and 0.45 mm increased remarkably, although the concentration of TOC in both permeates was fairly constant. Possible reasons for this increase in sub-micron-sized organic matter are low temperature and/or increase in feed concentration. During the period between day 60 and day 100, a gradual decline in temperature and an increase in TOC concentration in the feed were observed at the same time. These changes might be responsible for the increase in concentration of organic matter with particle size between 0.1 and 0.45 mm at that time. It has been reported that production of soluble microbial product (SMP) is promoted as the organic loading rate increases. More accumulation of data is needed to identify the factors causing changes in characteristics of mixed

liquor in MBRs, which are thought to be related to membrane fouling in MBRs. In the case of the PVDF membrane in pilot run, as stated before, reversible fouling became significant during this period. On the other hand, in the case of the PE membrane, change in the rate of increase in filtration resistance was not significant during this period. These observations imply that organic matter that accumulated in the reactor during the period between day 60 and day 100 had a feature to promote formation of a cake layer (i.e., reversible fouling) on the PVDF membrane but that it did not affect fouling in the PE membrane. It is possible that features of the foulant differ depending on the membrane polymer material. Figures 55 and 56 show the changes in carbohydrate and protein, respectively. In these figures, data measured for permeates from both membranes and for mixed liquor in the MBR are shown. With respect to mixed liquor, measurements were carried out after filtering samples with a 0.5-mm filter. Concentrations of carbohydrate and protein in the permeates were relatively constant throughout the operation. The concentration of dissolved carbohydrate in the reactor significantly increased during the period between day 60 and day 100, when the concentration of sub-micron-sized organic matter increased (see Figure 54). At that time, increase in dissolved protein concentration in the mixed liquor was insignificant. Therefore, the increase in sub-micron-sized organic matter in the mixed liquor was likely to be due to the increase in carbohydrate. It is possible that carbohydrate accumulating between 60 and 100 days had different features from those observed in other periods. Unfortunately, however, the phenol–sulfuric acid method used for measurement of carbohydrate does not provide any information about features/composition of carbohydrate. To investigate the changes in composition of carbohydrate that accumulated in the reactor during the operation, monosaccharide composition analysis was conducted. Figure 57 shows the change in monosaccharide composition of dissolved organic matter in the MBR.

Flux = 0.4 m d–1 MLSS = 10 g l–1 F/M = 20.4 SRT = 34 day

Total filteration resistance (1011 m–1)

20 0.65 –1.0 µm 0.45–1.0 µm 0.1–1.45 µm < 0.1 µm

15

10

5

0 0

20

40

60 80 100 Elasped time (days)

120

140

Figure 54 Time course changes in concentration of each fraction in mixed liquor (submicron-sized organic matter – effect of membrane materials). When the organic matter with particle size of 0.1–0.65 mm increased: PVDF – rapid increase in physically reversible fouling was observed; and PE – there is no obvious change in the rate of membrane fouling. MLSS, mixed liquor-suspended solid; PE, polyethylene; PVDF, polyvinylidene fluoride; SRT, solids retention time.

Membrane Filtration in Water and Wastewater Treatment 2.5 Dissolved PE permeate PVDF permeate

30

Concentration (mg l–1)

Concentration (mg l–1)

40

20

10

0

20

40

60 80 100 Elapsed time (days)

120

140

2 1.5 1 0.5

35

Dissolved PE permeate

30

PVDF permeate

25 20 15 10 5 0

20

40

60 80 100 Elapsed (days)

120

0

20

40

80 100 60 Elapsed time (days)

120

140

Figure 57 Changes in monosaccharide composition in dissolved organic matter.

40 Concentration (mg l–1)

Fucose Rhamnose Arabinose Galactose Glucose Mannose

0 0

Figure 55 Changes in carbohydrate. PE, polyethylene; PVDF, polyvinylidene fluoride.

0

57

140

Figure 56 Changes in protein. PE, polyethylene; PVDF, polyvinylidene fluoride.

From Figure 57, it can be seen that all types of monosaccharide exhibited an upward trend after day 60. Fucose, arabinose, and glucose increased significantly. Carbohydrates that were mainly composed of those monosaccharide increased during the period between day 60 and day 100 and might have caused reversible fouling in the PVDF membrane. On the other hand, it seemed that those carbohydrates did not affect membrane fouling in PE membrane as the rate of fouling in the PE membrane was fairly constant regardless of increase of those constituents at that time. To investigate features of constituents responsible for irreversible fouling that had occurred in the pilot run, organic matters were desorbed from the fouled membranes at the end of the continuous operation and were then analyzed. When the pilot operation was terminated, both the PE and the PVDF membrane modules were taken out from the reactor and were disassembled. Membrane fibers were rinsed with tap water and were manually wiped with a lab paper. This was done to remove accumulated cake that could be physically removed, allowing us to eliminate the bias caused by reversible fouling and to focus on the irreversible membrane fouling. Desorption of organic matter from the fouled membranes was carried out by soaking the membranes in an alkaline solution (sodium hydroxide) at 25 1C for 24 h. The solution pH was set at 11.

Analysis of organic matter desorbed from the fouled membranes was conducted at the termination of the continuous operation, and the difference between the PE and the PVDF membranes was investigated. As described in the experimental section, intensive physical cleaning was carried out prior to desorption of foulants from the membranes. Thus, the following discussion will be made for filtration resistance that cannot be cancelled by physical cleaning (i.e., irreversible fouling). Table 4 shows the results of analysis in the desorption test. The data shown in Table 4 are expressed on the basis of a unit surface membrane area (mg m2). As stated before, at the end of continuous operation, irreversible fouling resistance in the PE membrane was greater than that in the PVDF membrane. Nevertheless, a greater amount of organic matter was desorbed from the PVDF membrane than from the PE membrane on the basis of a unit membrane surface area. One possible explanation for this is that features/compositions of the organic matters desorbed from the two membranes were different and consequently the magnitude of fouling differed on the basis of a unit mass of organic matter. If this is the case, the data shown in Table 4 suggest that organic matter desorbed from the PE membrane had a higher fouling potential than that desorbed from the PVDF membrane. SUVA and carbohydrate/protein ratio (C/P) determined for the organic matters desorbed from the two membranes are also shown in Table 4. These two indexes are lumped ones and therefore provided limited information on features of organic matter. Nevertheless, the difference between the organic matters desorbed from the two membranes in terms of SUVA and C/P was obvious, and it can therefore be assumed that features of the foulant causing irreversible resistance differ depending on the membrane polymer material.

4.02.2.2.3 Fouling potential of carbohydrate assessed by lectin affinity chromatography In a number of previous studies, carbohydrates have been pointed out to be major foulants in MBRs. The authors’ previous study indicated that some fractions of organic matter contained in the mixed liquor of MBRs would cause severe membrane fouling than do other fractions. They also reported

58

Membrane Filtration in Water and Wastewater Treatment

Table 4

Characteristics of organic matter desorbed from fouled membranes

Membrane

TOC(mg m2)

Carbohydrate (mg m2)

Protein (mg m2)

Carbohydrate/Protein

UVA260 (cm1)

SUVA (m1 mg1 l)

PE PVDF

3.3 9.8

3.3 8.3

8.5 11.6

0.38 0.72

0.092 0.142

3.17 2.45

PE, polyethylene; PVDF, polyvinylidene flouride; SUVA, specific ultraviolet absorbance; TOC, total organic carbon; UVA260, ultraviolet absorbance at 260 nm.

Mixed liqour of MBR Filtration (0.45 µm) Dissolved organic matter

Control

Removal of polysaccharides by lectin affinity column

Effluent of each column Dead-end filtration test Figure 58 Experimental procedure. MBR, membrane bioreactor.

that composition of foulants was different depending on the membrane material implying that characteristics of organic matter that caused severe membrane fouling are closely related to characteristics of membrane. At present, however, little is known about the details of carbohydrates that cause membrane fouling in MBRs. Information on characteristics of the organic matters causing severe membrane fouling in relation to the types of membrane should be very useful for establishing a strategy to mitigate membrane fouling in MBRs (Miyoshi et al., 2010; Yamamura et al., 2008). In this study, affinity chromatography was applied with a variety of lectins to assess the fouling potential of some specific carbohydrates contained in mixed liquor suspension in an MBR. Lectins are proteins with the ability to bind specific carbohydrates with high selectivity. By changing the types of lectin in affinity chromatography, different types of carbohydrates can be removed from the liquid phase. After that, reductions in the degree of fouling potential associated with the removal of specific carbohydrates by lectins were evaluated. This investigation was carried out for different membrane and the results were compared with each other in order to assess the effects of membrane materials on fouling potential of carbohydrates. In addition, organic matters retained in lectin columns were eluted and were then characterized. Based on the results obtained in this study, factors associated with difference in fouling potential of specific carbohydrates caused by difference in membrane materials will be discussed. Continuous operation of a pilot-scale MBR was conducted at Soseigawa Municipal Wastewater Treatment Center in Sapporo, Japan. A pilot-scale MBR was operated with baffled MBR (BMBR) configuration (Kimura et al., 2008b). Hydraulic retention time (HRT) and solid retention time (SRT) were set

Table 5

Lectins used in assessment of fouling potential

Lectin

Binding specificity

Aleuria aurantia lectin (AAL) Concanavalin A (Con A)

al–6, al–2, al–3 Fuc

Datura stramonium agglutinin (DSA) Lens culinaris agglutinin (LCA) Maackia amurensis lectin (MAM) Ricinus communis agglutinin (RCA) Sambucus sieboldiana agglutinin (SSA) Wheat germ agglutin (WGA)

a-D-Man in type N-glycans hybrid type high Man bianntenary and hexaantennary, All O-types, a-Glu Galbl–4GlcNAc, GlcNAc a-D-Man in di- and tri-complex-type Nglycans with core a-Fuc, a-Ghi NeuAca2–3Gal b-Gal, GalNAc NeuAca2-6Gal/GaINAc b-GlcNAc, sialic acid, GlcNAcbl, 4GlcNAcbl, 4GlcNAc, chitoriose

From Kaku H et al. (2007), Journal of Biochemistry 142: 393–401; Opitz L, et al. (2008) Vaccine 25: 939–947; Greenwel P, et al. (2008) International Journal of Pharasitology 38: 749–756.

around 2.9 h and 35 days, respectively. As a result, MLSS concentration in the reactor was 16.471.4 g l1. Solution containing dissolved organic matter was obtained by centrifugation (4800 rpm; 5 min) followed by filtration using a membrane filter paper with a pore size of 0.45 mm. Figure 58 shows the experimental procedure employed in this study. Carbohydrates with high affinity to the lectin in the column were retained. As a result, filtration resistance should be lowered in the subsequent filtration test if the retained carbohydrates had high fouling potentials. Evaluation of fouling potential was also conducted for the solution without passage through a lectin column as a control test. Commercially available pre-packed lectin–agarose columns (Seikagaku Corporation, Tokyo, Japan) were used in this study. Table 5 lists the lectins used in this study and their binding specificities (Kaku et al., 2007; Opitz et al., 2008; Greenwel et al., 2008). All columns were washed with 60 times of gel volume of phosphate buffer saline pH 7.2 (PBS) and then 50 ml of sample solution containing dissolved organic matter in the mixed liquor of MBR was loaded. The liquid which passed through a lectin column was collected and subsequently applied to dead-end filtration test to evaluate the fouling potential. After passage of sample solution, the column was washed with 10 times of gel volume of PBS to remove nonbinding constituents. Bound constituents were eluted with PBS containing elution reagent and were then analyzed. The elution reagents used for each lectin are

Membrane Filtration in Water and Wastewater Treatment

summarized in Table 6. In this study, characterization of organic matter eluted from lectins in terms of characteristics of sugar (e.g., monosaccharide composition) was not performed. This is because some lectins have monosaccharide or disaccharide as elution reagent (Table 6) which can interfere with the results of sugar analysis. In this study, organic matters eluted from lectins were characterized by means of excitation–emission matrices (EEMs) because this method does not suffer from interference by elution reagents. Dead-end filtration tests were conducted for the assessment of fouling potential of polysaccharides in mixed liquor suspension in an MBR. Flat-sheet membranes were used for the dead-end filtration test. In this study, two different membranes were used in dead-end filtration test. One was made of polypropylene (PP; Kubota, Osaka, Japan) and the other was made of PVDF (Toray, Tokyo, Japan). Nominal pore size values of PP and PVDF membrane were 0.4 and 0.1 mm, respectively. Operating pressures of PP and PVDF membrane were 20 and 30 kPa, respectively. These values were selected to equalize Table 6

Elution reagent for each lectin

Elution reagent

Lectin

L-Fucose Methyl-a-D-glucoside Chitooligosaccharide Ethylenediamine Lactose N-Acetyl-D-glucosamine

AAL Con A, LCA DSA MAM RCA, SSA WGA

From Kaku H et al. (2007), Journal of Biochemistry 142: 393–401.

60

59

pure water permeation flux of new membrane for both membranes. Effective surface areas of the membranes were 13.9 cm2. Pure water permeability was measured for the membranes before and after filtration of 5 ml of sample solution (i.e., effluents from the lectin columns), and the difference between them was used to evaluate the fouling potential of carbohydrates. Pure water permeability was evaluated by membrane filtration. Figure 59 shows filtration resistance developed in the filtration of sample solutions. Regardless of the types of membrane, some lectins were effective for reduction in filtration resistance while the others were not. This indicates that the fouling potential differed depending on the types of carbohydrates. Among the tested lectins, wheat germ agglutin (WGA) and concanavalin A (Con A) which were used for visualization of carbohydrate involved in membrane fouling, were not effective for reduction in fouling potential regardless of the types of membrane. The results obtained in this study clearly indicate that carbohydrates which can be visualized by those lectins do not represent all of the membrane fouling. WGA recognizes N-acetyl-glucosamine (Table 5), which is the major component of peptide glycan in the cell wall of bacteria. It was likely that the contribution of the carbohydrates derived from cell debris to membrane fouling was relatively low compared to those with other origins. Aleuria aurantia lectin (AAL) was effective for the reduction of fouling potential for both membranes. Carbohydrates which have higher affinity to this lectin would cause severe membrane fouling at least in the two membranes tested in this study. In contrast, some lectins reduced fouling potential for only one type of membrane. For example, Sambucus sieboldiana agglutinin (SSA) and Maackia amurensis lectin (MAM) were

PVDF membrane

50 40

Filtration resistance (1011 m–1)

30 20 10 0 Control 60

WGA

RCA

LCA

Con A

SSA

AAL

MAM

DSA

RCA

LCA

Con A

SSA

AAL

MAM

DSA

PP membrane

50 40 30 20 10 0 Control

WGA

Figure 59 Filtration resistance development. AAL, Alueria aurantia agglutinin; Con A, concanavalin A; DSA, Datura stramonium agglutinin; LCA, Lens culinaris agglutinin; MAM, Maackia amurensis agglutinin; PP, polypropylene; PVDF, polyvinylidene fluoride; SSA, Sambucus sieboldiana agglutinin; RCA, Ricinus communis agglutinin; WGA, wheat germ agglutinin.

Membrane Filtration in Water and Wastewater Treatment

300

400 350 300

450

Excitation (nm)

350

Excitation (nm)

Excitation (nm)

400 350 300 250

250

400 350 300 250

250 300 350 400 450 500

250 300 350 400 450 500

250 300 350 400 450 500

250 300 350 400 450 500

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Emission (nm)

Emission (nm)

Emission (nm)

SSA

AAL

MAM

450

Excitation (nm)

450 400 350 300

400 350 300 250

250

DSA

450

450

Excitation (nm)

Excitation (nm)

400

Con A

450

450

250

Excitation (nm)

LCA

RCA

WGA 450

Excitation (nm)

60

400 350 300 250

400 350 300 250

250 300 350 400 450 500

250 300 350 400 450 500

250 300 350 400 450 500

250 300 350 400 450 500

Emission (nm)

Emission (nm)

Emission (nm)

Emission (nm)

Figure 60 Excitation–emission matrix (EEM) spectra of organic matter eluted from lectin columns.

effective for the reduction of fouling potential in PP membrane while reductions in fouling potential by those lectins were negligible in the case of PVDF membrane. On the other hand, effectiveness of Ricinus communis agglutinin (RCA), which was slightly effective for the reduction of fouling potential in PVDF membrane, was not recognized in PP membrane. Those results clearly indicate that types of carbohydrates that cause severe membrane fouling were different for two membranes examined in this study. A fouling potential of specific carbohydrate would depend heavily on characteristics of a membrane. Since many carbohydrates found in activated sludge system coexist with other organics, characteristics of the organic matter associated with the carbohydrates are thought to be important for fouling potential of retained carbohydrates. To investigate the characteristics of the organic matter associated with the carbohydrates retained by lectins, EEM fluorescence spectra analysis was applied to the organic matters eluted from lectins. Figure 60 shows EEM fluorescence spectra of the organic matter eluted from lectin columns. Basically, shapes of EEMs obtained for different samples were different indicating that types of organic matter retained by lectin were different depending on the types of lectins. However, the shapes of EEMs obtained for the organic matters eluted from Con A and LCA were similar. Both EEMs have two major peaks located on Ex/ Em ¼ 325 nm/425 nm and 275 nm/425 nm. These two peaks can be attributed to humic acid-like substances. Similarity in the shapes of EEMs can also be seen between the organic matter eluted from RCA and SSA. Those EEMs have one major peak around Ex/Em ¼ 325 nm/400 nm that can be attributed to humic acid-like substances that have features similar to those found in ocean and one minor peak around Ex/ Em ¼ 275 nm/425 nm. As can be seen in Table 6, elution reagent of organic matter for Con A and LCA is the same (methyl-a-D-glucoside). RCA and SSA also have the same elution

reagent (lactose). It can be assumed that lectins which have the same elution reagent have high affinity to similar structure of polysaccharides. As a result, carbohydrates adsorbed onto Con A and LCA or RCA and SSA were likely to overlap to some extent. The similarities in the shapes of EEMs obtained for the organic matter eluted from Con A and LCA or RCA and SSA could partly be explained by those overlapping. However, the similarities in the shapes of EEMs did not coincide with the degree of reductions in fouling potentials by each lectin. For example, LCA was slightly effective for reduction in fouling potential in PVDF membrane but Con A was not. SSA was effective for PP membrane but was not for PVDF membrane. In contrast, effectiveness of RCA on reduction in fouling potential can only be recognized in PVDF membrane. Those results clearly indicate that fouling potentials are not related to characteristics of organic matters associated with carbohydrates retained by lectins. Difference in structures or properties of sugar chain which could not be assessed by EEM analysis would play an important role in determining fouling potentials. Taking into consideration the results of dead-end filtration test that indicated an influence of membrane materials on fouling potential of specific carbohydrate, investigation on the relationship between characteristics of sugar chain in the polysaccharides which have high fouling potentials and characteristics of membrane (e.g., surface morphology, roughness) will be important to elucidate the interactions between carbohydrates and membranes. Further studies regarding this point are needed.

References Greenwel P et al. (2008) International Journal of Pharasitology 38: 749–756. Jang N-Y, Watanabe Y, and Minegishi S (2004) Performance of ultrafiltration membrane process combined with coagulation/sedimentation. Water Science and Technology 51(6–7): 209--219.

Membrane Filtration in Water and Wastewater Treatment

Kaku H et al. (2007) Journal of Biochemistry 142: 393–401. Kimura K, Nishisako R, Miyoshi T, Shimada R, and Watanabe Y (2008a) Baffled membrane bioreactor (BMBR) for efficient nutrient removal from municipal wastewater. Water Research 42: 625–632. Kimura K, Miyoshi T, Naruse T, and Watanabe Y (2008b) The difference in characteristics of foulants in submerged MBRs caused by the difference in membrane flux. Desalination 231(1–3): 268--275. Kimura K, Yamato N, Yamamura H, and Watanabe Y (2005) Membrane fouling in pilotscale membrane bioreactors (MBRs) treating municipal wastewater. Environmental Science and Technology 39: 6293--6299. Lee S, Jang N, and Watanabe Y (2004) Effect of residual ozone on membrane fouling reduction in ozone resisting microfiltration (MF) membrane system. Water Science and Technology 50(12): 287--292. Miyoshi T, Tsuyuhara T, Aizawa E, Kimura K, and Watanabe Y (2010) Fouling potentials of polysaccharides in MBRs assessed by lectin affinity chromatography. Water Science and Technology 62. Opitz L et al. (2008) Vaccine 25: 939–947. Suzuki T, Watanabe Y, Ozawa G, and Ikeda S (1998) Removal of soluble organics and manganese by a hybrid MF hollow fibre membrane system. Desalination 117(1–3): 119--129.

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Yamamura H, Chae S-R, Kimura K, and Watanabe Y (2007a) Transition in fouling mechanism in microfiltration of a surface water. Water Research 41: 3812--3822. Yamamura H, Kimura K, Okajima T, Tokumoto H, and Watanabe Y (2008) Affinity of functional groups for membrane surfaces: Implications for physically irreversible fouling. Environmental Science and Technology 42(14): 5310--5315. Yamamura H, Kimura K, and Watanabe Y (2007b) Mechanism involved in the evolution of physically irreversible fouling in microfiltration membranes used for drinking water treatment. Environmental Science and Technology 41(19): 6789--6794. Yamato N, Kimura K, Miyoshi T, and Watanabe Y (2006) Difference in membrane fouling in membrane bioreactors (MBRs) caused by membrane polymer materials. Journal of Membrane Science 280(1–2): 911--919. Yonekawa H, Tomita Y, and Watanabe Y (2004) Behavior of micro-particles in monolith ceramic membrane with pre-coagulation. Water Science and Technology 50(12): 317--325. Watanabe Y and Yonekawa H (2008) Flocculation and its inclusion into membrane filtration. Lecture note of Academic Summer School on particle separation in water and wastewater treatment, organized by IWA Specialist Group on Particle Separation (in press as a book published by IWA publishing).

4.03 Wastewater Reclamation and Reuse System HL Leverenz and T Asano, University of California at Davis, Davis, CA, USA & 2011 Elsevier B.V. All rights reserved.

4.03.1 4.03.2 4.03.3 4.03.4 4.03.5 4.03.6 4.03.6.1 4.03.6.2 4.03.6.3 4.03.6.4 4.03.6.5 4.03.6.6 4.03.7 4.03.7.1 4.03.7.2 4.03.7.3 4.03.8 References

Foundation of Water Reuse Water Reuse Terminology and Definitions Reclaimed Water Applications Water-Quality Considerations Treatment Technology Infrastructure for Water Reuse Storage Facilities Distribution Systems Centralized Systems Decentralized Systems Satellite Systems Point-of-Use Treatment Source Control Salinity and Toxic Constituents Source Separation Graywater Future Directions for Water Reuse

63 63 63 65 65 67 67 67 68 69 69 69 70 70 70 70 70 71

4.03.1 Foundation of Water Reuse

4.03.2 Water Reuse Terminology and Definitions

Inadequate water supplies and water-quality deterioration represent serious contemporary concerns for many municipalities, industries, agriculture, and the environment in various parts of the world. Several factors have contributed to these problems such as continued population growth in urban areas, contamination of surface water and groundwater, uneven distribution of water resources, and frequent droughts caused by extreme global weather patterns. Water reclamation and reuse accomplishes two fundamental functions: (1) the treated effluent is used as a water resource for beneficial purposes, thereby reducing potable water demands and, (2) where effluent is returned to the environment, improves overall water quality in the receiving water, which is often used subsequently as potable water supply and habitat. The foundation of water reuse is built upon three principles: (1) providing reliable treatment of wastewater to meet strict water-quality requirements for the intended reuse applications, (2) protecting public health, and (3) gaining public acceptance. Whether water reuse is appropriate for a specific locale depends upon careful economic considerations, potential uses for the reclaimed water, and the relative stringency of waste discharge requirements. Public policies can be implemented that promote water conservation and reuse rather than the costly development of additional water resources with considerable environmental expenditures. Through integrated water resources planning, the use of reclaimed water may provide sufficient flexibility to allow a water agency to respond to short-term needs as well as increase the reliability of long-term water supplies (Asano and Levine, 1995).

Early developments in the field of water reuse are synonymous with the historical practice of land application for the disposal of wastewater. With the advent of sewerage systems in the nineteenth century, domestic wastewater was used at sewage farms and by 1900 there were numerous sewage farms in Europe and in the United States. While these sewage farms were used primarily for waste disposal, incidental use was made of the water for crop production or other beneficial uses. During the past century, the growing need for reliable water has resulted in the development of a number of water reclamation and reuse projects. Wastewater reclamation is the treatment or processing of wastewater to make it reusable, and water reuse is the use of treated wastewater for beneficial purposes such as agricultural irrigation and industrial cooling. Reclaimed water is a treated effluent suitable for an intended water reuse application. In addition, direct water reuse requires the existence of pipes or other conveyance facilities for delivering reclaimed water. Indirect reuse, through discharge of an effluent to receiving water for assimilation and withdrawals downstream, is recognized to be important but does not constitute planned direct water reuse. In contrast to direct water reuse, water recycling normally involves only one use or user and the effluent from the user is captured and redirected back into that use scheme. In this context, water recycling is predominantly practiced in industry. To facilitate communication among different groups associated with water reuse, it is important to understand the terminology used in the arena of water reclamation and reuse. Water reclamation and reuse definitions commonly used are summarized in Table 1.

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64

Wastewater Reclamation and Reuse System

Table 1

Water reuse terminology and definitions

Term

Definition

Agricultural water use Aquifer Beneficial uses

Water used for soil cultivation, crop production, and livestock uses. Geological formations that contain and transmit groundwater. Many ways water can be used, either directly by people, or for their overall benefit. Examples include municipal water supply, agricultural and industrial applications, navigation, fish and wildlife, and water contact recreation. The part of water withdrawn that is evaporated, transpired, incorporated into products or crops, consumed by humans or livestock, or otherwise removed from the immediate water environment; also referred to as water consumed. Incorporation of reclaimed water directly into a potable water supply system. Domestic water use includes water for normal household purposes, such as drinking, food preparation, bathing, washing clothes and dishes, flushing toilets, and watering lawns and gardens. A collective term that includes loss of water from the soil by evaporation and by transpiration from plants. The infiltration or injection of natural waters or reclaimed waters into an aquifer, providing replenishment of the groundwater resource or preventing seawater intrusion. Planned potable reuse indirectly allowing mixing and assimilation by discharge into an impoundment or natural body of water, such as in domestic water supply reservoir or groundwater. Water in industry is used for cooling, transportation, as a solvent, and as an ingredient of the finished products. The principal water users in industry are thermal and atomic power generation. Artificial application of water on lands to assist in the growing of crops and pastures or to maintain vegetative growth in recreational lands such as parks and golf courses. Turf and landscape irrigation systems in ways that enable the efficient and safe application of reclaimed water in such places as golf courses, public parks, playgrounds, school yards, and athletic fields. The water withdrawals made by the populations of cities, towns, and housing estates, and domestic and public services and enterprises. Also includes water used to directly provide for the needs of urban populations, which consume high-quality water from city water supply systems. All water reuse applications that do not involve either indirect or direct potable reuse. Water suitable for human consumption without deleterious health risks. The term, drinking water is a preferable term better understood by the community at large. An augmentation of drinking (potable) water directly or indirectly by highly treated reclaimed water. Municipal wastewater that has gone through various treatment processes to meet specific water-quality criteria with the intent of being used in a beneficial manner (e.g., irrigation). The term recycled water is used synonymously with reclaimed water, particularly in California. The process of tapping into a sewer main and extracting wastewater locally, which can then be treated in a satellite or decentralized treatment plant and reused for beneficial purposes. State of California regulations for how treated and recycled water is used and discharged that is listed in Title 22 of the California Administrative Code. The state-wide Water Recycling Criteria are developed by the Department of Health Services and enforced by the nine State Regional Water Quality Control Boards. Used water discharged from homes, business, cities, industries, and agriculture. Various synonymous uses such as municipal wastewater (sewage), industrial wastewater, and storm water. Treatment or processing of wastewater to make it reusable with definable treatment reliability and meeting appropriate water-quality criteria. The use of wastewater which is captured and redirected back into the same water use scheme such as in industry. However, the term ‘water recycling’ is often used synonymously with water reclamation. The use of treated wastewater for a beneficial use, such as agricultural irrigation and industrial cooling.

Consumptive use Direct potable reuse Domestic water use Evapo-transpiration Groundwater recharge Indirect potable reuse Industrial water use Irrigation water use Landscape irrigation Municipal water use

Nonpotable reuse Potable water Potable reuse Reclaimed water (also, recycled water) Sewer mining Title 22 regulations

Wastewater Water reclamation Water recycling Water reuse

4.03.3 Reclaimed Water Applications In the planning and implementation of water reclamation and reuse, the reclaimed water application generally governs the type of wastewater treatment needed to protect public health and the environment, and the degree of reliability required for each sequence of treatment processes and operations. In principle, wastewater or any marginal quality waters can be used for any purpose as long as adequate treatment is provided to meet the water-quality requirements for the intended use. The dominant applications for the use of reclaimed water include: agricultural irrigation, landscape irrigation, industrial recycling and reuse, and groundwater recharge. Among them, agricultural and landscape irrigation are widely practiced throughout the world with well-established health protection guidelines and agronomic practices.

From a global perspective, water reuse applications have been developed to replace or augment water resources for specific applications, depending on local water use patterns. In general, water reuse applications fall under one of seven categories: (1) agricultural irrigation, (2) landscape irrigation, (3) industrial reuse, (4) groundwater recharge, (5) environmental and recreational uses, (6) nonpotable urban uses, or (7) indirect or direct potable reuse. The relative amount of water used in each category varies locally and regionally due to differences in specific water use requirements and geopolitical constraints. Notable aspects of each of these water reuse applications are given in the following:

•

Agricultural irrigation represents the largest current use of reclaimed water throughout the world. This reuse

Wastewater Reclamation and Reuse System

•

•

•

•

•

•

category offers significant future opportunities for water reuse in both industrialized countries and developing countries. Landscape irrigation is the second largest user of reclaimed water in industrialized countries and it includes the irrigation of parks; playgrounds; golf courses; freeway medians; landscaped areas around commercial, office, and industrial developments; and landscaped areas around residences. Many landscape irrigation projects involve dual distribution systems, which consist of one distribution network for potable water and a separate pipeline to transport reclaimed water. The reclaimed water pipelines are normally color-coded with purple color in the United States. Industrial activities represent the third major use of reclaimed water, primarily for cooling and process needs. Cooling water creates the single largest industrial demand for water and as such is the predominant industrial water reuse either for cooling towers or for cooling ponds. Industrial uses vary greatly and water-quality requirements tend to be industry specific. To provide adequate water quality, supplemental treatment may be required beyond conventional secondary wastewater treatment. Groundwater recharge is the fourth largest application for water reuse, either via spreading basins or via direct injection to groundwater aquifers. Groundwater recharge includes groundwater replenishment by assimilation and storage of reclaimed water in groundwater aquifers, or establishing hydraulic barriers against salt-water intrusion in coastal areas. Recreational and environmental uses constitute the fifth largest use of reclaimed water in industrialized countries and involve nonpotable uses related to land-based water features such as the development of recreational lakes, marsh enhancement, and stream flow augmentation. Reclaimed water impoundments can be incorporated into urban landscape developments. Man-made lakes, golf course storage ponds, and water traps can be supplied with reclaimed water. Reclaimed water has been applied to wetlands for a variety of reasons, including habitat creation, restoration and/or enhancement, provision for additional treatment prior to discharge to receiving water, and provision for a wet weather disposal alternative for reclaimed water. Nonpotable urban uses include fire protection, air conditioning, toilet flushing, construction water, and flushing of sanitary sewers. Typically, for economic reasons, these uses are incidental and depend on the proximity of the wastewater reclamation plant to the point of use. In addition, the economic advantages of urban uses can be enhanced by coupling with other ongoing reuse applications such as landscape irrigation. Potable reuse is another water reuse opportunity, which could occur either by blending in water-supply storage reservoirs or, in the extreme, by direct input of highly treated wastewater into the water distribution system. Although the likelihood of implementing this option in the most locations is remote, a successful example includes the City of Windhoek, Namibia (Asano et al., 2007).

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4.03.4 Water-Quality Considerations The acceptability of reclaimed water for a given water reuse application is dependent on the physical, chemical, and microbiological quality of the water. The effects of physical parameters (such as pH, color, temperature, and particulate matter) and chemical constituents (such as chlorides, sodium, heavy metals, and trace organics) on vegetation, soil, and groundwater are well known, and recommended limits have been established for these constituents. In contrast to the agronomic considerations associated with chemical constituents that may be present in wastewater, pathogenic constituents may present health considerations for the distribution and use of reclaimed water. The recommended guidelines and regulations related to reclaimed water quality are found in State of California (2000), World Health Organization (2006), and Asano et al. (2007). Source control programs can limit the input of chemical and microbiological constituents that may present health, environmental, or irrigation concerns or that may adversely affect treatment processes and subsequent acceptability of the reclaimed water for specific uses. In some arid and semi-arid regions, the level of total dissolved solids is a major quality concern, and source control measures are considered for domestic water users such as restrictions on water softener use. Assurance of treatment reliability is an obvious, yet sometimes overlooked, quality control measure. Water-quality considerations in water reuse applications are extremely important especially where health and environmental issues are of concern. Unless the product water is of sufficient quality to meet the required criteria and regulations for the intended reuse application, acceptance by the potential users or beneficiaries will not occur. By the same token, over-treatment that is excessive for its intended use is a waste of resources in terms of energy, labor, equipment, and money.

4.03.5 Treatment Technology Treatment technologies used for the production of reclaimed water typically follow conventional secondary treatment. These technologies include depth and surface filters, membranes, carbon adsorption, disinfection, and advanced oxidation. The type of treatment processes that are selected to produce reclaimed water will depend on several factors, including the quantity and quality of reclaimed water required and the life cycle costs of the reclaimed water system. However, in terms of water quality, virtually any quality of reclaimed water that is desired can be produced using currently available technology. A summary of these technologies and the specific constituents that are removed is presented in Table 2. Additional details on the technologies described in Table 2 can be found in Asano et al. (2007) and Tchobanoglous et al. (2003). With further refinement and development, the cost and robustness of these technologies is improving. Membranes represent the most significant development as several new products are now available for a number of water and wastewater treatment and water reuse applications.

Table 2

Unit operations and processes used for the removal of classes of constituents found in wastewater for reuse applications

Unit operation or process

Constituent class Suspended solids

Secondary treatment Secondary with nutrient removal Depth filtration Surface filtration Microfiltration Ultrafiltration Dissolved air flotation Nanofiltration Reverse osmosis Electrodialysis Carbon adsorption Ion exchange Advanced oxidation Disinfection Natural processes Source control

Colloidal solids

Organic matter (particulate)

x x x x x x x

Dissolved organic matter x x

x x x

x x x x x x

Nitrogen

Phosphorus

x

x

Trace constituents

x x x x x x

x

x

x

x x x x x

x

x

x

x

Bacteria

x x x x

x

Protozoan cysts and oocysts

Viruses

Energy needs

þ þþ

x

x

Total dissolved solids

x x

x x x

x x

x x x x x x x

x x x x

x x

x x

x x

x

x

Modified from Asano T, Burton FL, Leverenz H, Tsuchihashi R, and Tchobanoglous G (2007) Water Reuse: Issues, Technologies, and Applications. New York: McGraw-Hill and Tchobanoglous et al. (2003).

þ þ þ þ þ þ þ þ þ þ þ þ  

þ þ þ þþ þþ

þþ

Wastewater Reclamation and Reuse System

Membranes had been limited previously to water softening and desalination, but are now being used increasingly for wastewater applications to produce high-quality reclaimed water suitable for reuse. Treatment trains that incorporate membrane filtration are capable of producing several grades of product water that can serve a range of water reuse applications. Desalination of reclaimed water is also being done by means of reverse osmosis and electrodialysis. Increased levels of contaminant removal not only enhance the product water for reuse, but also lessen health risks. Further, the cost of producing high-quality reclaimed water has decreased considerably, largely due to the development of low-pressure membranes and the entrance of a number of suppliers in the competitive marketplace. Chlorination remains as the most widely used disinfection technology and its effectiveness is enhanced by improved reclaimed water quality. Increased removal of particulate matter and the development of ultraviolet disinfection technology also improve the applicability of reclaimed water for many more applications. Advanced oxidation is also an important technology for reducing or removing trace constituents and emerging contaminants to safe levels, especially for indirect potable water reuse applications. While not specifically identified in Table 2, natural treatment systems, including oxidation ponds, constructed wetlands, sand filtration, bank filtration, soil/vadose zone filtration, and anaerobic processes, are also used commonly in water reuse. In addition, practices such as urine segregation, graywater systems, and source control of specific constituents can have a significant impact on overall water quality and the type of treatment system that is required. The specific application of these technologies is site specific and, therefore, subject to local constraints. Natural treatment processes often experience a higher level of variability due to the reduced level of control that can be applied. However, in terms of energy usage, these processes can be used to accomplish water reuse with significantly lower power output. Thus, natural processes should be considered and implemented where feasible to reduce the overall carbon footprint of the water reuse system.

4.03.6 Infrastructure for Water Reuse In areas where reclaimed water is distributed widely, for example, in urban lawn irrigation projects, a substantial redistribution system is usually required. An alternative reclaimed water project that minimizes the installation of pipelines is where reclaimed water is added directly to the potable water reservoir. However, this type of reuse, indirect potable reuse, requires a higher level of treatment compared to water used for lawn irrigation but takes advantage of the preexisting infrastructure used to distribute potable water. Thus, the type of reclaimed water system must be evaluated carefully, depending on the level of treatment that is attainable reliably and the expense to install pipelines for the reclaimed water. The primary factors governing distribution and storage facilities include the location of the reclaimed water treatment plant and the location and demand requirements of the reclaimed water users. The principal facilities needed for the

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delivery of reclaimed water are storage tanks, pumping stations, and transmission and distribution pipelines and depend on the overall type of reclaimed water. Important issues and factors, typical to most reclaimed water distribution and storage projects, are planning and implementation issues, planning and conceptual design of distribution and storage facilities, design of pipelines, design of pumping facilities, operation and maintenance of pipelines and pumping stations, design of storage facilities, and operational issues in reclaimed water storage. Planning and implementation issues that must be addressed when considering storage and distribution facilities for a reclaimed water project include: 1. the type, size, and location of physical facilities; 2. the interrelationship between the potable and reclaimed water systems, that is, is the reclaimed water system being installed in an area where an existing potable water system exists or is a dual distribution system (for potable and reclaimed water) needed?; and 3. the involvement of the public during the planning and implementation process; the public may be affected directly by facilities siting and construction. A brief description of key infrastructure used for water reuse systems is presented in the following sections.

4.03.6.1 Storage Facilities Storage of reclaimed water is required in situations where there is a difference in the production and utilization of reclaimed water, such as in cases where water is stored for nighttime irrigation and where there is a seasonal use of reclaimed water such as agricultural irrigation. Elevated storage tanks are used to regulate the system pressure. The size of storage required should be determined from a flow analysis. Storage reservoirs can be above- or below-ground tanks, open reservoirs, or an aquifer. The type of storage to be used is determined by the site constraints and requirements of the application. Open reservoirs are subject to contamination from recreational and wildlife activities, thus requiring treatment prior to distribution for reuse. However, the open reservoir can also be viewed as an environmental buffer, which is used in cases of indirect potable reuse. The facilities to store and distribute the reclaimed water to potential users can be planned and designed once the source of reclaimed water and the location and nature of the water reuse areas and demands are known. In most respects, facilities for the storage and distribution of reclaimed water are similar to those for potable water. Because of the characteristics of reclaimed water and the potential changes in water quality that may occur over time, care must be taken during the planning, design, and operation of distribution and storage facilities to prevent or mitigate any effects.

4.03.6.2 Distribution Systems A reclaimed water system may be planned, designed, and installed as a system totally separate from the potable water system or planned as part of a dual distribution system that provides both reclaimed and potable water to the service area (see Figure 1). The integrated planning, design, and

68

Wastewater Reclamation and Reuse System Remote community with independent collection and treatment system Remote community or development serviced by independent satellite reclamation plant connected to centralized collection system To local reuse Waste solids

Satellite reclamation plant

Trunk sewer

High-rise building (typical)

(c)

Satellite reclamation plant

(d)

Decentralized reclamation system

(e)

Centralized treatment facility

To local reuse Receiving water body Dual distribution system

Central collection system

Nonpotable in-building reuse

Screenings In-building reclaimed water distribution system

Waste solids Satellite reclamation plant

Effluent recycle (at low flow, if needed) To local reuse

To outdoor reclaimed water system Flow equalization tank

Central collection system

(b)

Waste to centralized system

Reclaimed water storage tank Onsite treatment process for water reclamation and reuse

(a)

Figure 1 Definition sketch for various types of water reuse systems: (a) interception-type satellite, (b) extraction-type satellite, (c) upstream-type satellite, (d) decentralized, and (e) centralized. Modified from Asano T, Burton FL, Leverenz H, Tsuchihashi R, and Tchobanoglous G (2007) Water Reuse: Issues, Technologies, and Applications. New York: McGraw-Hill.

construction of a dual system offers advantages in both water resource management and cost savings, as discussed in AWWA (1994) and Okun (2005). Substituting reclaimed water for potable water is one of the primary purposes of dual distribution systems. The use of reclaimed water for nonpotable purposes serves to conserve the potable water supply for use where drinking water quality is needed. In the planning of a dual distribution system, if the reclaimed water is used for fire fighting in lieu of potable water, the potable water pipelines and storage can be sized for delivery of domestic flows and not fire flows. Potable

water-quality benefits accrue because pipeline and storage sizes are reduced which in turn reduces the residence time in the potable water system. Long residence times can result in the loss of disinfectant residual and may promote the regrowth of microorganisms, which can affect bacterial quality, and tastes and odors. The distribution system for reclaimed water can be designed to provide unrestricted, on-demand service, or the reclaimed water can be provided in restricted hours. Because the principal use of reclaimed water in urban areas is for landscape irrigation, which is applied generally during the

Wastewater Reclamation and Reuse System

nighttime hours to minimize human contact and evaporation loss, unrestricted service may result in a high peak flow demand. The peak demand may be several times higher than the daily average flow rate available for producing reclaimed water. Storage reservoirs are, therefore, needed to meet maximum hourly demands. Where reclaimed water is used for fire fighting, emergency storage can serve as a backup for the distribution system when pumping stations or pipelines are out of service for maintenance or repair.

4.03.6.3 Centralized Systems The use of centralized or regional wastewater collection and treatment facilities for the production of reclaimed water is practiced extensively in developed urban regions and other densely populated areas (see Figure 1(e)). For some reuse applications, such as indirect potable reuse through reservoir or groundwater augmentation, centralized facilities are well justified. However, when a centralized collection system is not available, or it is desirable to have independent treatment facilities, decentralized and satellite wastewater systems may be an option. Decentralized wastewater reclamation systems have been used widely for landscape irrigation in suburban areas, thereby reducing demand on potable supplies in addition to other benefits. In areas located adjacent to a centralized collection system, satellite facilities may also be used to meet some of the reclaimed water demand. While satellite facilities share some common characteristics with the decentralized systems, satellite systems are differentiated because they have a direct connection to a centralized wastewater collection system and therefore do not have to manage or store solids on site.

4.03.6.4 Decentralized Systems Decentralized wastewater management (DWM) systems are used most commonly in semi-urban, rural, and remote areas, where installation of a centralized sewer system is not feasible (see Figure 1(d)). However, in some areas decentralized systems are used instead of centralized sewers to limit and control the type of development in a given area. Decentralized treatment systems present a significant challenge for the design engineer due to the need for high-quality reliable performance in light of a number of constraints, including long periods of time between maintenance activities, lack of redundant systems, high variability in flow rate and constituent concentrations, and site-specific factors. Decentralized systems are an integral component of smartgrowth community design initiatives in unsewered areas and an element of sustainable development because of the potential for low-impact wastewater management and other advantages presented below. Further, due to practical and economic limitations, it is recognized that it is not possible or desirable to install centralized sewers to service all areas in the United States. Therefore, DWM systems are necessary for the protection of public health and environment and for the development of long-term strategies for the management of water resources. DWM is defined as the collection, treatment, and reuse of wastewater at or near the point of waste generation (Crites and Tchobanoglous, 1998). Decentralized facilities may be used

69

for wastewater management from individual homes, clusters of homes, subdivisions, and isolated commercial, industrial, and agricultural facilities. The wastewater flow rate, quality, and flow distribution will depend on the types of activities taking place as well as the scale of the application.

4.03.6.5 Satellite Systems In most collection and treatment systems, wastewater is transported through the collection system to a centralized treatment plant located at the downstream end of the collection system near the point of disposal. Oftentimes, opportunities for instituting water reuse applications, especially for agricultural and landscape irrigation or groundwater recharge, are limited as the points of use are located remotely from the wastewater-treatment facilities. The infrastructure costs for storing and transporting reclaimed water to the points of use are often prohibitive, thus making reuse uneconomic. An alternative to the conventional approach of transporting reclaimed water from a central treatment plant is the concept of satellite treatment at upstream locations with localized reuse. Residuals generated by satellite treatment process are discharged to the collection system for processing downstream at the central treatment plant. Satellite treatment systems generally fall into three categories: (1) interception type, (2) extraction type, and (3) upstream type. Each of these types of satellite systems is described further below. The distinction between satellite types is made because the characteristics of the wastewater to be treated, the treatment technologies that will be used, and the infrastructure needed to implement them are somewhat different, and, in some cases, quite different. Interception type. In the interception type, as illustrated in Figure 1(a), the wastewater to be reclaimed is intercepted before it reaches the collection system. Typical applications for this type of satellite system are for reuse in high-rise commercial and residential buildings. The quantity of flow to be intercepted and reclaimed will depend on the local and seasonal water reuse requirements. Typically, all of the flow from an individual building will be intercepted for reuse. In some cases, it may be necessary to supplement the intercepted flow with potable water. Should excess flow occur, it would be discharged to the collection system. Extraction type. In the extraction type, as illustrated in Figure 1(b), the wastewater to be reclaimed is extracted (mined) from a collection system main, trunk, or interceptor sewer. Typical applications for this type of satellite system are for reuse in landscape, park, and greenbelt irrigation; for reuse in nearby high-rise commercial and residential buildings; and for commercial and industrial cooling tower applications. The quantity of flow to be extracted and reclaimed will depend on the local and seasonal water reuse requirements, especially so for landscape irrigation applications. Upstream type. In upstream type, as illustrated in Figure 1(c), the wastewater reclamation facilities are used to reclaim water from developments located at the extremities of a centralized collection system and where opportunities for water reuse (e.g., golf course and median strip irrigation) are available and the capacity of the collection system is limited. Typical applications for this type of satellite system are for new

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housing developments and remote commercial centers and research parks. The quantity of flow to be intercepted and reclaimed in upstream satellite systems will depend on the local and seasonal water reuse requirements. In general, all of the flow from a housing development will be intercepted for reuse. In some cases, however, it may be necessary to divert some of the flow directly to the centralized collection system, before or after treatment.

wastewater-treatment processes include strong disinfectants, fabric softeners, chemical sanitizers for holding tanks, chemotherapy medications, high amounts of oils or grease, and brine from water softeners. In larger systems a degree of anonymity exists that makes it difficult to identify the particular source of an offending discharge and there is less individual responsibility for performance and operational matters, as these activities become the responsibility of the municipality.

4.03.6.6 Point-of-Use Treatment For multiple water reuse applications, the economic question that must be addressed is whether it is more cost effective to (1) produce multiple grades of reclaimed water to meet the quality criteria for all users, (2) produce reclaimed water of a single quality that meets all criteria, or (3) produce a single grade of quality that meet most criteria and provide treatment at or near the point of use in special circumstances. Typically in water reuse system that involves multiple uses and a single quality of product water, reclaimed water-quality requirements are determined by a major user that requires the highest quality. For example, if a reclaimed water distribution system is to provide water for landscape irrigation, high-rise building toilet and urinal flushing, and industrial cooling towers, the microbial requirements for toilet and urinal flushing will be critical, whereas industrial cooling tower usage may require nitrogen and phosphorus removal to control biological growth, scaling, and corrosion. Thus, polishing treatment as required for a given application can be applied at the point of use may prove more economical depending on site-specific circumstances.

4.03.7 Source Control The spectrum of household products used on a daily basis will increase the overall salinity of the resulting wastewater. Other chemicals or compounds discharged with wastewater may be toxic to treatment organisms or plants irrigated with the treated effluent. Because the removal of salts and toxic constituents is beyond the scope of most small wastewater-treatment applications, source control or dilution may be required for some irrigation-type reuse applications.

4.03.7.1 Salinity and Toxic Constituents Ions commonly added to wastewater from domestic water use that contribute to salinity include the cationic species (such as sodium, calcium, magnesium, and potassium), and anionic species (such as bicarbonate, carbonate, chloride, fluoride, and sulfate). A potential advantage of decentralized treatment systems is that individuals who use the system have direct control of the problematic constituents entering the wastewater stream. While the concentration of salts in the water is typically low enough not to be of concern for most applications, if the discharge of brine from regenerating water softeners or the use of toxic chemicals is not compatible with a particular process or reuse application, these issues can be discussed with the system users, who also have an interest in proper operation of the system. Examples of substances which have been implicated in negative impacts to

4.03.7.2 Source Separation Source separating systems include facilities that are used to separate solid and liquid wastes without commingling with the bulk wastewater stream. Human waste can be segregated, with or without the use of water, with composting systems and waste incinerating systems. Collection and processing of human waste (and food waste in the case of in-sink food waste grinders) with a composting toilet or separate wet-composting system can reduce the size of downstream wastewater management systems and produce a compost material that can be used for landscaping purposes (Del Porto and Steinfeld, 1999). Source separation can also be used for liquid wastes, including urine diversion and graywater separation. Because of the high nutrient value of urine, toilets have been developed that divert urine to a separate holding tank for reuse in agriculture (Ecosan, 2003). Similarly, graywater is often considered for reuse due to the reduced presence of pathogens and organic matter. The level of maintenance and user participation required for source separating systems should be considered carefully when selecting these systems, as many of these processes have failed to work adequately in the field. However, in some areas, where limiting conditions exist, source separating systems may be a preferred alternative.

4.03.7.3 Graywater The water from bathing, hand washing, and clothes washing (not including soiled diapers), collectively known as graywater, is sometimes managed separately from human waste because it is relatively free of pathogens, organic matter, and trace constituents. When graywater is separated, wastewater from kitchen sinks, automatic dishwashers, and food waste grinders is discharged typically with toilet flushing water, collectively known as blackwater (note that drainage from kitchen sinks is included in household graywater in Australia). Separated graywater may be treated and reused more easily than combined graywater and blackwater. Some system designs incorporate direct drainage of graywater to mulch basins for tree irrigation, therefore not requiring treatment or storage and greatly reducing the system cost and maintenance needs (Ludwig, 2000). Separated blackwater may be treated separately or discharged to a collection system. Graywater systems are usually expensive to retrofit into a building, and therefore should be included, if possible, during building planning and construction. In some areas, the use of graywater for irrigation and toilet flushing is recommended during periods of water shortages. Management of graywater systems may present challenges if there is insufficient planning.

Wastewater Reclamation and Reuse System

4.03.8 Future Directions for Water Reuse In many parts of the world, agricultural irrigation using reclaimed water has been practiced for many centuries. Landscape irrigation such as irrigation of golf courses, parks, and playgrounds has been successfully implemented in many urban areas for over 30 years. However, salt management in irrigated croplands and landscapes may require special attention in many arid and semi-arid regions. Beyond irrigation and nonpotable urban reuse, indirect or direct potable reuse needs careful evaluation and closes public scrutiny. It is obvious from public health and acceptance standpoints that nonpotable water reuse options must be exhaustively explored prior to any notion of indirect or direct potable reuse, although modern technology is beginning to obviate these criteria. Reservoir augmentation as well as groundwater recharge with reclaimed water and direct potable water reuse shares many of the public health concerns encountered in drinking water withdrawn from polluted rivers and reservoirs. Three classes of constituents are of special concern where reclaimed water is used in such applications: (1) enteric viruses and other emerging pathogens; (2) organic constituents including industrial and pharmaceutical chemicals, residual home cleaning and personal care products and other persistent pollutants; and (3) salts and heavy metals. The ramifications of many of these constituents in trace quantities are not well understood with respect to long-term health effects. For example, there are concerns about exposure to chemicals that may function as endocrine disruptors; also, the potential for development of antibiotic resistance is of concern. As a result, regulatory agencies are proceeding with extreme caution in permitting water reuse applications that affect potable water supplies. In each case in the United States where potable water reuse has been contemplated, alternative sources of water have been developed in the ensuing years and the need to adopt direct potable water reuse has been avoided. As the proportional quantities of treated wastewater discharged into the receiving water increases, much of the research which addresses indirect potable reuse via reservoir augmentation and groundwater recharge and direct potable water reuse is becoming of equal relevance to unplanned indirect potable reuse such as municipal water intakes located downstream from

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wastewater discharges or from increasingly polluted rivers and reservoirs. Examples include New Orleans, Louisiana on the Mississippi River, and the Rhine Valley communities along the Rhine River in Germany and The Netherlands. Reclaimed water is a locally controllable water resource that exists right at the doorstep of the urban environment, where water is needed the most and priced the highest. Closing the loop of the water cycle not only is technically feasible in industries and municipalities but also makes economic sense. While direct potable reuse of reclaimed water is more or less a possibility, reservoir augmentation and groundwater recharge with advanced wastewater-treatment technologies are a viable option backed by the decades of experience in Arizona, California, Florida, New York, and Texas as well as in Australia, Israel, Germany, The Netherlands, and the United Kingdom. Water reuse has become an essential element of future water resources development in integrated water resources management in many parts of the world.

References Asano T (ed.) (1998) Wastewater Reclamation and Reuse, Water Quality Management Library, vol. 10. Boca Raton, FL: CRC. Asano T, Burton FL, Leverenz H, Tsuchihashi R, and Tchobanoglous G (2007) Water Reuse: Issues, Technologies, and Applications. New York: McGraw-Hill. Asano T and Levine AD (1995) Wastewater reuse: A valuable link in water resources management. Water Quality International 4: 20--24. AWWA (1994) Dual Distribution Systems, AWWA Manual M24, 2nd edn. Denver, CO: American Water Works Association. Del Porto D and Steinfeld C (1999) The Composting Toilet System Book: A Practical Guide To Choosing, Planning, and Maintaining Composting Toilet Systems, an Alternative to Sewer and Septic Systems. Concord, MA: The Center for Ecological Pollution Prevention. Ecosan (2003) Ecosan – closing the loop. In: Proceedings of the 2nd International Symposium on Ecological Sanitation. Lubeck, Germany: GTZ. Ludwig A (2000) Create an Oasis with Greywater, 4th edn. Santa Barbara, CA: Oasis Design. Okun DA (2005) Dual systems to conserve water while improving drinking water quality. In: 20th Annual WateReuse Symposium. Denver, CO. State of California (2000) Water recycling criteria. In: Title 22 Code of Regulations, Division 4, Sections 60301 et Seq., 2 December 2000. World Health Organization (2006) WHO Guidelines for the Safe Use of Wastewater, Excreta and Greywater. Volume II: Wastewater Use in Agriculture. Geneva, Switzerland: WHO.

4.04 Seawater Use and Desalination Technology S Gray, Victoria University, Melbourne, VIC, Australia R Semiat, Grand Water Research Institute, Technion, Israel M Duke, Victoria University, Melbourne, VIC, Australia A Rahardianto and Y Cohen, University of California, Los Angeles, CA, USA & 2011 Elsevier B.V. All rights reserved.

4.04.1 4.04.2 4.04.2.1 4.04.2.2 4.04.2.2.1 4.04.2.2.2 4.04.2.2.3 4.04.2.2.4 4.04.2.3 4.04.2.3.1 4.04.2.3.2 4.04.2.4 4.04.2.5 4.04.2.5.1 4.04.2.6 4.04.2.7 4.04.3 4.04.3.1 4.04.3.2 4.04.3.3 4.04.3.3.1 4.04.3.3.2 4.04.3.4 4.04.3.4.1 4.04.3.4.2 4.04.3.4.3 4.04.3.4.4 4.04.3.4.5 4.04.3.4.6 4.04.3.4.7 4.04.3.5 4.04.4 4.04.4.1 4.04.4.2 4.04.4.3 4.04.4.4 4.04.4.5 4.04.5 4.04.5.1 4.04.5.1.1 4.04.5.1.2 4.04.5.1.3 4.04.5.2 4.04.5.2.1 4.04.5.2.2 4.04.5.3 References

Introduction Seawater Water Quality Evaporative Techniques Pretreatment Multi-stage flash Multi-effect distillation Vapor compression Membrane Processes Pretreatment Reverse osmosis Desalination Process Costs Quality of Water Produced Increase in water hardness/water stabilization Environmental Aspects Energy Issues Brackish Water Brackish Water Desalination Applications Brackish Water Desalination Technologies Common Process Configuration RO/NF process configuration ED/EDR process configuration Major Challenges Concentration polarization and membrane mineral scaling Mitigation of membrane mineral scaling Managing the impact of feedwater-quality variation Enhancing water recovery Concentrate disposal Specific contaminant removal Cost of brackish-water desalination Future Developments Desalination of Wastewater for Reuse Water Quality Pretreatment RO Processes Final Water Quality Concentrate Disposal Alternative Technologies Membrane Distillation Brief history Membrane distillation configuration The Memstill project Forward Osmosis Background Recent developments Capacitive Deionization

73 74 74 76 76 76 78 79 80 80 82 84 86 86 86 87 88 88 89 90 90 90 91 92 93 94 94 96 97 97 98 99 99 99 101 102 102 102 102 103 103 103 103 103 104 105 105

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4.04.1 Introduction Water, water, every where,

And all the boards did shrink;

Water, water, every where,

Nor any drop to drink

The above lines from Samuel Taylor Coleridge’s The Rime of the Ancient Mariner are often quoted to highlight the abundance of seawater but our inability to use it because of its high salt content. People require water of low salinity or freshwater for consumption, and typical values of less than 500 mg l1 total dissolved solids (TDS) are often used by health regulators to specify salinity requirements for human consumption (National Water Quality Management Strategy, 2004). It is, therefore, not surprising that many believe that distillation processes have been used to produce freshwater since the fourth-century BC, although the first documented case appears to be from the early seventeenth century when Japanese sailors boiled seawater in pots and collected the condensate in bamboo tubes. The world’s first industrial desalination plant is considered to have been commissioned in 1881 on the island of Malta, while Saudi Arabia had its first desalination plant installed by the Ottoman Turks in Jeddah, 1907. The application of desalination technology grew throughout the twentieth century, with many applications in the Middle East and on water-scarce islands. However, at the end of the twentieth and start of the twenty-first century, there has been a rapid increase in the desalination capacity, and Figure 1 shows the rapid growth in installed global desalination capacity between 1980 and 2009 (Global Water Intelligence, 2009). This rapid growth has been driven by population growth and

changing climatic conditions that have lead to lower rainfall or altered rainfall patterns, and has resulted in many communities becoming water stressed. Figure 2 is an estimate of regions that will be experiencing various degrees of water scarcity by 2025, and the World Health Organization (WHO; World Health Organisation, 2010) estimates that one in three people in the world are affected by water scarcity (Seckler, 1998). In an effort to combat water-scarcity issues, communities are treating poorer-quality water sources, and desalination of seawater, brackish groundwater, and salty wastewater is increasingly practiced. Coupled with the need for increased use of desalination technology there has been a dramatic decrease in the cost of desalination, with approximate costs of 20–35 cm3 for brackish water, 30–40 cm3 for wastewater, and 50–100 cm3 for seawater. Figure 3 shows the dramatic decrease in unit costs for seawater desalination over the period 1990–2003. The increased affordability and need for desalination have resulted in many communities becoming increasingly dependent upon desalination technologies. Initially, largescale desalination was predominantly confined to areas with severe water limitations such as the Middle East or island communities; however, desalination is increasingly practiced by communities that have traditionally relied upon surface water sources, such as London, Singapore, Chennai, and Sydney. The removal of salt is thermodynamically more difficult than the removal of solid particles or large-molecular-weight molecules, as the osmotic pressure of the salt solution must be overcome (see Chapter 4.11 Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis). Therefore, the energy required for desalination is generally greater than that for other treatment processes, although the energy required is a strong function of salt concentration. The thermodynamic minimum amount of energy required for desalination of seawater is 0.79 kW h1 m3 if water is taken from an infinite salt solution (Semiat, 2008).

New desalination capacity 1980−2009 8 Commissioned 7 Capacity (million m3 d−1)

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Figure 1 Global desalination capacity. From Global Water Intelligence (2009) New desalination capacity 1980–2009-chart. http:// www.globalwaterintel.com/archive/10/10/analysis/new-desalination-capacity-1980-2009-chart.html (accessed April 2010).

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Projected water scarcity in 2025

Physcial water scarcity

Little or no water scarcity

Economic water scarcity

Not estimated

Figure 2 Global Watering. From Seckler D, Amarasinghe U, Molden D, de Silva R, and Barker R (1998) World water demand and supply, 1990 to 2025: Scenarios and Issues, International Water Management Institute, Research Report 19, http://iwmi.cgiar.org/Publications/ IWMI_Research_Reports/PDF/PUB019/REPORT19.PDF

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If the water recovery is increased to 50%, then the minimum energy requirement is 1.09 kW h1 m3, because water is extracted from incrementally higher salt concentrations as the water recovery increases. These thermodynamic values are the absolute minimum amount of energy required, and actual desalination must use more than this. Typically, commercial seawater-desalination plants use between 4 and 10 kW h1 m3 depending upon the type of plant installed. These energy requirements compare to values of o1 kW h1 m3 for alternative water supplies such as dams, storm

water, and recycled water (Leslie and Myraed, 2009). The greater use of desalinated water has coincided with climatechange concerns arising from greenhouse-gas emissions, and subsequently communities have sought to limit the greenhouse-gas emissions from seawater desalination by the use of energy from renewable sources (Crisp, 2009; Voutchkov, 2009). Although the energy used for operating an entire water and wastewater-treatment supply might only be 15% of the energy used for heating water in some Western communities (Kenway et al., 2008), the focus on energy efficiency that

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pervades modern communities dealing with climate change implies that energy use will remain an issue for seawater desalination and be a key focus for new desalination technologies that are being developed. This chapter outlines the common desalination processes used for treatment of seawater, brackish water, and wastewater, and comments on operating issues and performance of these processes. It focuses on reverse osmosis (RO) membrane systems for treatment of the three types of waters considered and thermal desalination systems for seawater desalination, as these two processes are the dominant commercial processes. Discussion of the desalination of wastewater focuses on fouling chemistry, as another chapter (see Chapter 4.03 Wastewater Reclamation and Reuse System) explains wastewater reclamation and reuse systems. Additionally, a brief outline of alternative desalination processes is provided as many such processes are being developed.

4.04.2 Seawater 4.04.2.1 Water Quality About 97% of the water on Earth is found in the seas and oceans that cover approximately 70% of the Earth’s surface. Salt content and concentration vary slightly from place to place. Open oceans contain approximately 3.5% weight salt, while smaller closed seas may contain higher concentrations. For instance, the Mediterranean Sea contains close to 4% salt, while the Red Sea and the Persian Gulf contain 4.2% dissolved salts. Lower concentrations can be found in other closed seas, such as the Baltic Sea where salt concentration changes during the year from 0.5% to 1.5%, while the salinity of the Black Sea salinity is below 2%. The typical salt composition of seawater is shown in Table 1 (Water Chemistry, 2010), while Figure 4 shows a global distribution of ocean salt concentrations. Sodium and chloride represent the most abundant cations and anions in the sea, and the concentration of magnesium is significantly greater than that of calcium. Besides the spatial variation in salt concentrations across the globe, the amount of CaCO3 also varies with depth. CaCO3 is saturated in the surface layer of seawater and below saturation concentration at lower levels (Le Gouellec et al., 2006). This is of significance for RO membrane systems, since Table 1

Seawater salt composition

Component

Concentration (%)

Calcium Magnesium Sodium Potassium Bicarbonate Sulfate Chloride Bromide Total dissolved solids

0.042 0.13 1.07 0.04 0.015 0.27 1.94 0.007 3.5

From Water Chemistry (2010) Nitto-Denko – Hydranautics. http://www.membranes. com/docs/papers/04_ro_water_chemistry.pdf (accessed April 2010).

the concentration of salts increases along the RO membrane during the process and precipitation of salts fouls the membrane. The bromide and boron concentrations in seawater are low, but their concentrations are significant as they can adversely affect the treated water quality, particularly for RO treatment systems. While bromide rejection is high through RO membranes (90–95%), the permeate still contains sufficient bromide to cause bromate issues should ozonation be used as a means of disinfection. Where bromide contributes to taste and odor issues in the system, such as in Perth, Australia, low bromide concentration in the final treated water has been specified (0.1 mg l1; Crisp, 2009). Boron is present as boric acid, in a concentration of approximately 5 ppm. Boric acid is a small molecule that can penetrate through RO membranes so the product may contain around 1 ppm of boron. This is important due to the sensitivity of many crops to boron content in irrigation. New RO membranes can reject up to 90% of the boron compared to 30–70% rejection for standard RO membranes. High boronrejecting membranes have the potential to minimize problems due to boron, although currently there is little long-term operational experience with these membranes. Seawater also contains organic contaminants that come from living marine creatures. These contaminants include all types of small to large molecules, colloids and viruses, bacteria, algae, and larger living or nonliving suspended matter. These contaminants also need to be removed along with salt to attain drinking-water standards (Morse et al., 1979) and to prevent fouling of membrane and thermal desalination systems. It is important that particulate foulants are removed prior to treating the water in RO systems as particles around 1 mm in size can block spacers in RO modules, while small particles o1 mm in size can lead to particle fouling of RO membranes. Particle fouling is less of an issue for thermal desalination processes. Algal blooms or red tides can cause temporary but significant increases in turbidity and total organic carbon (TOC), leading to rapid fouling of membranes. The high TOC concentrations not only lead to greater organic fouling of membranes, but also provide food for microorganisms to grow on the membranes. Pseudomonas, Bacillus, Arthrobacter, and Corynebacterium are the bacteria usually associated with biofouling in seawater RO systems (Voutchkov, 2008). In areas prone to red tides or oil spills, sensors are often used to detect their presence and the feed stopped if algae or oil is present. The concentration of organic molecules typically varies from o0.2 mg l1 in ocean waters not affected by freshwater sources (rivers, stormwater, and wastewater) or algal blooms to as high as 8 mg l1 or more for waters impacted by freshwater sources (Voutchkov, 2008). Additionally, the composition of the organic compounds can vary, with the lowmolecular-weight compounds being typically between 20% and 50% of the TOC, low-molecular-weight acids and neutrals between 18% and 25%, humic substance between 26% and 52%, and polysaccharides between 1% and 14%. Such variations in composition can have a significant effect on biofouling, as the presence of easily biodegradable compounds, such as polysaccharides, can increase the level of biological activity in the desalination process, thereby increasing the

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Figure 4 Global surface seawater salinity levels. From Antonov JI, Locarnini RA, Boyer TP, Mishonov AV, and Garcia HE (2006) In: Levitus S (ed.) World Ocean Atlas 2005, Volume 2: Salinity. NOAA Atlas NESDIS 62, 182pp. (CD-ROM). Washington, DC: US Government Printing Office. Permission from NOAA’s National Oceanographic Data Center. http://serc.carleton.edu/eslabs/corals/4c.html (accessed May 2010).

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degree of biofouling. It has been observed in a number of plants that temporal increases in TOC over a 1–2- week period is linked to increases in biofouling (Voutchkov, 2008).

4.04.2.2 Evaporative Techniques 4.04.2.2.1 Pretreatment Thermal processes are not very sensitive to the initial concentration of seawater and are also less sensitive to suspended particles than membrane-based systems. A simple straining filtration technique to remove coarse particles is usually suitable. De-aeration is needed to remove oxygen and to reduce the possibility of noncondensing gases accumulating, as these can cause corrosion within the thermal desalination process. Simple ejector-condensers are often used for this purpose. Thermal processes are more sensitive to possible precipitation of calcium salts than membrane processes, mainly gypsum on heat-transfer surfaces.

4.04.2.2.2 Multi-stage flash Multi-stage flash (MSF) distillation is still considered as the most common and simple technique in use. It has been operated commercially for more than 40 years (Awerbuch, 1997b). The technique is based on condensing low-pressure steam to produce heat for evaporation of seawater. A schematic presentation of an MSF desalination plant is shown in Figure 5. The process is based on slightly pressurizing the seawater feed and passing it through long closed pipes and a series of flash chambers. Condensing vapor, generated in the flash chambers is used to heat the feedwater in the pipes. Energy is added to the system to heat the feedwater to the initial high temperature of approximately 120 1C, and lowpressure steam is commonly used as the heating source. The low-pressure steam is usually extracted from a power station. The heated seawater feed is introduced into a series of flash chambers where it is allowed to flash along the bottom of the chambers. The pressure is reduced along the chambers so that water continues to flash in each chamber. This generates vapor

Steam

that passes through mist eliminators before condensing on the seawater feed pipes. The condensate is collected from the pipes and pumped out as the plant product. Part of the concentrated brine is recycled and mixed with the feed to increase the recovery ratio, and the rest is pumped out and rejected into the sea. Energy is transferred to heat the seawater feed as the vapor condenses on the condenser pipes containing the seawater feed. The sensible heat of condensation is recovered to produce vapor. Based on an enthalpy balance, the water-recovery ratio is low and recirculation of the brine is required in order to increase water recovery. The energy consumption in this technique is high, and is associated with heating of the feedwater, low sensible-heat recovery, and pumping of feed and brine recirculation. The energy efficiency, the size of the plant, and the cost involved are affected by design parameters such as (1) the number of stages from the high-temperature feedentrance point to the brine exit, (2) the recirculation ratio, (3) the temperature of the preheated feed seawater, (4) heattransfer quality of the condensing vapor, (5) improved utilization of the heat rejected with the product and the rejected brine, and (6) controlling and preventing scale formation and prevention of accumulated noncondensable gases. Working on extracted steam at the end of a power station, at a temperature of about 120 1C, the typical energy consumption is estimated to be 7–9 kW h1 m3, depending on the gain output ratio (GOR) (Semiat, 2008). Corrosion is of concern in MSF systems, as water of very high purity is corrosive by nature. Corrosion is related to the operational temperature, the existence of dissolved oxygen in the water, and the choice of materials for the heat-transfer surfaces used in the heated seawater environment. Stainless steels are used for the pipe work and epoxy-coated wrought iron for the vessels. De-gassing is achieved in the pretreatment by the use of strippers or vacuum systems and the addition of hydrazine is also practiced. Typical temperatures vary between 110 and 120 1C at the hot end, down to seawater temperature at the cold end.

Vent ejector

Brine heater

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Brine

Product water

Figure 5 Schematic presentation of a multi-stage flash (MSF) desalination plant, MSF Sidem Design. Adapted from http://www.acwasasakura.com/ images/msf.gif (accessed 24 April 2010).

Seawater Use and Desalination Technology

An important advantage of this process, when compared with other distillation processes, is that scale does not precipitate on heat-transfer surfaces, but in the flash chambers. This enables the heat-transfer surfaces to remain clean, and cleaning of the system is rarely required. The quality of the product from this technique, as in other evaporation techniques, is extremely good. Product water usually contains less than 50 ppm of TDS. TDS is carried into the product via small drops that pass through the mist eliminators, rather than via the vapor phase. Better quality of water, down to 10 ppm TDS can be produced for industrial purposes if greater efforts are made to reduce carryover of mist. Due to lack of salts, the product water is aggressive and can cause corrosion. It is usually passed through a bed of lime to increase the calcium-carbonate concentration, or it is mixed with another source of water to stabilize the water and prevent corrosion. MSF processes are usually associated with large-scale cogeneration plants, where waste heat from power plants is available. The simple design and ability to be unaffected by scale make MSF a robust process that is easy to operate. However, the trade-off is higher energy consumption compared to alternative thermal and membrane-based systems and relatively high production costs. Information regarding the cost of this process may be found in Jassim and Ismail (2004).

4.04.2.2.3 Multi-effect distillation Multi-effect distillation (MED) is considered a more sophisticated and more energy-efficient evaporation technique than MFS systems (Awerbuch, 1997b). Multi-stage evaporation has been used for many years for the purpose of solution concentration, crystallization, solution purification, etc., and has also been used for seawater desalination, for the last 45 years. The method is based on a low-temperature source of energy. The main source of energy is spent steam emerging at the exit of a steam-operated power station, but alternatives also include low-level steam or hot fluid from other sources. Figure 6 describes the schematics of a horizontal tube MED unit. The steam enters the plant and is used to evaporate

79

heated seawater. The secondary vapor produced is used to generate tertiary steam at a lower pressure. This operation continues along the plant from stage to stage. The primary steam condensate is returned to the boiler of the power station. The technique is based on double-film heat transfer, where latent heat is transferred in each stage from condensing of steam through the heat-transfer piping to the evaporated falling film of seawater. The process is repeated up to 16 times in existing plants, between the upper possible temperature and the lower possible cooling water, which depends on seawater temperature. Condensate accumulates from stage to stage as product water. A vacuum pump removes the accumulated noncondensable gases, together with the remaining water vapor, after the last condensation stage in order to maintain the gradual pressure gradient inside the vessel. The pressure gradient is dictated by the saturation pressure of the feedstream and the saturation pressure of the condensing steam leaving the last stage and condensed by cooling with seawater. Typical pressure gradients of 5–50 kPa across the system (o5 kPa per stage) are typical. Steam condenses in common MED installations inside horizontal pipes where seawater evaporates on the other side as it falls down the tube bundle. Heat transfer in double-film condenser–evaporators is a very efficient mechanism that controls the process and can operate with a low-temperature driving force across the tube walls. The heat transfer is bounded by the increasing boiling-point elevation along the plant as the salt concentration increases with removal of water on one hand, and prevention of surface boiling by keeping the on-temperature difference so that boiling does not start, on the other. The performance ratio, or the GOR, which refers to the number of tons of water produced per ton of initial steam, is considered high. The ratio in MED can be up to 15 compared to a maximum of 10 for MSF. Therefore, the energy or thermal efficiency is essentially higher for MED than it is for MSF (Ophir and Weinberg, 1997). Energy utilization is increased with the number of stages. If low-cost heat at lower temperature is available, optimization of operation conditions may lead to a lower number of stages. Additionally, lower-temperature operation allows the use of low-cost heat-transfer surfaces without the problems of severe

MED and TVC process schematic Heat-recovery evaporator

Heat-rejection condenser Heat-pressure steam

Recycle vapor

Heat-pressure steam

NCG Seawater

High-pressure steam (for an TVC plant) Low pressure steam (for an MED plant)

Freshwater Brine

Condensate tank Condensate return pump

Intermediate feed pump

Coolant

Polyphosophate Feed pump

Figure 6 Schematics of a horizontal tubes multi-effect distillation (MED) plant, IDE Design. From http://www.ide-tech.com/ (accessed May 2010).

80

Seawater Use and Desalination Technology

corrosion and reduces the likelihood of CaSO4 precipitation on the tubes, and hence improves plant reliability. Low temperature differences and good wetting of the surfaces prevent scaling. Therefore, if a plant is operated below 70 1C, it is possible to use aluminum pipes, reduce corrosion, and operate at sub-saturation conditions for gypsum up to 60% recovery. Additionally, there is no need to remove oxygen below 70 1C, as corrosion rates are very low and cleaning is less frequent. The capacity of an MED plant is usually less than 30 000 m3 d1, but the modular design enables several trains to be built adjacent to each other to enable larger overall plant capacity. MED system designs can vary, with vertical or horizontal tubes, or flat-sheet heat exchangers, arrangement of the stages horizontally or vertically, and co-current or countercurrent flow of seawater against the direction of produced steam. Such variations in design affect the ease of cleaning heat exchangers, the pumping of water flows, and energy losses in the system, and sometimes, specific process designs are developed based on the site conditions. Designs also differ with regard to scaling potential, as the path of the circulating brine in connection to calcium sulfate hydrate saturation may vary. Co-current operation is advantageous in this respect, since the calcium sulfate hydrate saturation level increases when the water temperature reduces. In co-current operation, the highest calcium sulfate hydrate concentrations occur at the lowest temperatures, where higher saturation levels must be reached before precipitation occurs. In countercurrent operation, however, the opposite is true and the highest calcium sulfate hydrate concentrations occur at the highest temperature where the saturation levels before precipitation occur are lower. This is an important design consideration for scale control and for water circulation and pumping expenses. Good water distribution, evenly distributed on the heat-transfer tubes is essential to reduce scaling. Co-current operation takes place in the MED-Metropolitan Water District (MWD) tower design, where the highest temperature is obtained at the lowest calcium sulfate concentration. In this design, the brine temperature–concentration curve along the tower is closed, almost parallel to the CaSO4 saturation–temperature curve. More information on the cost of MED may be found in Ophir and Lokiec (2004).

4.04.2.2.4 Vapor compression The vapor compression (VC) technique is similar in operation to the MED process, as condensation of vapor from each stage is used to generate vapor from brine in the next stage. Heat transfer usually takes place, as in MED, in the form of a double-falling film, which is an effective heat-transfer mechanism. Seawater is preheated against the brine discharge and the product water leaving the system. This is a heat-pump process, where the latent heat of the condensing vapor is used to make more vapor on the other side of the heat-transfer surface. However, vapor generated during the last stage of the production process is compressed to higher pressure, followed by an increase in temperature, after which it is recycled to the first stage of the process where it condenses and the heat of condensation is used to evaporate feedwater. In contrast,

vapor from the last stage of an MED or MSF process is condensed and mixed with the product water. Therefore, VC allows the sensible heat associated with the last stage to be used in the evaporation process and hence is more energy efficient than MED and MSF. The main need for energy is, therefore, for elevating the pressure of vapor from the last stage to provide the driving force for heat transfer in the first stage. The process usually comprises one to six stages. The operating temperature may be chosen for the best optimization of the process, as the upper temperature is determined by the number of stages and the lower temperature by the flow rates and the properties of the vapor. Compressing of lowtemperature gases is expensive due to their density and/or specific volume. A part of the brine recirculates to increase water recovery. A common approach to recycling of the vapor is the use of a mechanical compressor that operates at relatively low pressure and high specific vapor volume, but thermal compression is also practiced by mixing with higherpressure steam. Figure 7 presents a schematic view of a mechanical VC unit. Mechanical VC benefits from the fact that it requires only an electrical source of energy from the grid or from a diesel generator, and energy consumption is between 7 and 8 kW h1 m3. A source of steam and a source of electricity for water evaporation and pumping are needed for the thermal compression process. The process can operate close to a power station where steam and electricity are readily available, or it may use hot gases from different sources to generate lowpressure steam, for example, from a gas turbine or a diesel generator. VC operates mainly at small scale, in small installations such as hotels or refineries. The maximum reported capacity is of the order of 5 000 m3 d1 using two mechanical compressors. Higher capacities, up to 10 000 m3 d1, may be achieved with thermal compression. The ability of VC to operate at low temperatures makes it possible to use simple metals such as aluminum, with almost no corrosion attack and prevention of scale formation. The use of electricity makes the technique compatible for use in parallel with other desalination techniques, as in hybrid operation for optimization of energy consumption. A modern compressor presents efficiency of up to 80%. The quality of the product is similar to that of other evaporation techniques. The technique may also be used for part removal of salts that are at saturation level, in cases of low-boiling-point elevation. More information on VC can be found in El Dessouky (2004).

4.04.2.3 Membrane Processes Desalination with RO membranes is based on applied dynamic pressure to overcome the osmotic pressure of the salt solution in feedwater. The term osmotic pressure represents a property of a solution, containing dissolved matter, salts in water, starch, or sugar, such as that found in the roots of most plants. The relatively high concentration of this solution allows transfer of water from the land surrounding the root through the membrane skin of the root. Applying a pressure to the concentrated solution on one side of the membrane will stop the flow of water. This pressure is defined as the osmotic pressure of the solution. Higher applied pressures on the

Seawater Use and Desalination Technology

81

MVC process schematic Evaporative condenser

Heat transfer tubes

Vapor compression system

Decoder seperator

NCC removal auxiliary condenser

Seawater supply pump Feed dosage pump Product storage

Feed

Vacuum pump

Brine

Brine pump

Feed heat exchanger

Water vapor

Product pump

Product

Recircular pump

NCG

Sea

Scale inhibitor

Figure 7 Schematic presentation of a horizontal tube, single-stage vapor compression (VC) desalination unit, IDE Design. From http://www.idetech.com/ (accessed May 2010).

Post treatment

Low-pressure pump

Membranes High-pressure pump

Feed water

Product tank

Concentrate

Pre-treatment Energy-recovery unit To concentrate disposal

Product water

Figure 8 Schematic presentation of reverse osmosis (RO) desalination plant. From Semiat R and Hasson D (2009) Sea-water and brackish water desalination with membrane operations. In: Drioli E and Giorno L (eds.) Membrane Operations. Innovative Separations and Transformations. Weinheim: Wiley-VCH Verlag GmbH & CO. KGaA. ISBN: 978-3-527-32038-7.

concentrated solution side, well above the osmotic pressure, will overcome the solution properties and transfer water from the concentrated solution through a membrane to produce freshwater. This is the basis of the RO process, which allows water-selective permeation through the membrane from the saline side to the freshwater side (Faller, 1999). Salts rejected by the membrane stay in the concentrate stream and are removed from the membrane by the flow of fresh salt solution along the membrane. Removal of permeate, the freshwater product, occurs via a permeate tube on the lower-pressure side of the membrane (see Chapter 4.11 Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis for more details).

Figure 8 depicts a schematic flow sheet of a typical RO desalination plant. Feed pretreatment for the removal of suspended material, bacteria, and organics is performed by either media filtration or now increasingly by UF or MF modules in modern plants (Semiat and Hasson, 2009). If residual chlorine is present, it is removed by active carbon filters or sodium metabisulfite. The feedwater is pumped into the RO module where water selectively passes through the membrane to produce freshwater. The high-pressure pump used to feed the RO-membrane module may be connected on a single shaft with the motor and a turbine, as is the case in the Eilat seawater plant, in order to recover the energy content of the pressurized concentrate. Energy-recovery devices such as

82

Seawater Use and Desalination Technology

independent turbines for secondary stages, pressure exchangers, and other techniques may also be used. The concentrate is disposed back into the ocean. Each of these unit processes are discussed in turn, along with the water-quality issues that affect process design.

4.04.2.3.1 Pretreatment Membrane-based desalination systems are reliant on thin, semipermeable membranes to separate water from brine, and these membranes are sensitive to contaminants in water. Therefore, extensive pretreatment is required to provide highquality water to RO membranes. This is a general requirement for high-pressure membranes and applies equally to seawater, brackish water, and wastewater feeds. Indeed, many performance issues can be traced back to poor pretreatment of the feedwater and subsequent fouling or scaling of the membrane surface. The standard industry test for the suitability of feedwater quality with respect to particle load is the silt density index (SDI) (ASTM, 2002). This test is based on measuring the time taken to filter the initial 500 ml of water through a 0.45 mm membrane at a constant pressure of 207 kPa, and the time taken to filter a second 500 ml of water after 15 min of filtration. The time taken for these amounts of water to pass reflects characteristics of the filter cake that has developed, and an equation is used to provide a single SDI number. Low values of SDI represent high-quality feedwater and high numbers poor-quality feed. Generally, SDI values o3 are considered suitable for RO feedwater, values between 4 and 5 represent adequate feedwater quality for slow flux decline, and values above 5 reflect poor quality. While SDI is used as the industry standard test, it is commonly known that it is not suitable for all waters, that it is sensitive to the test conditions and results may vary with slight changes in technique (Mosset et al., 2008). There have been several attempts to develop an improved testing method to determine the suitability of feedwater quality, such as the modified fouling index (MFI) (Boerlage et al., 2003), but currently these have not been adopted by the industry. The common approach to removal of particles is screening, to remove coarse particles and debris, followed by coagulation, sand filtration, membrane filtration, or dissolved air filtration (DAF). A screen with bar racks (75–100 mm) is used to remove large debris from seawater. This is followed by fine bar screens (3–10 mm) to remove finer material and protect downstream processing units. Further screen or grit removal chambers may also be used prior to filtration, particularly if microfiltration (MF) or ultrafiltration (UF) systems are installed. Coagulation of seawater prior to filtration uses ferric salts such as ferric sulfate or ferric chloride. Aluminum salts are not preferred, as it is difficult to maintain low dissolved aluminum levels and this can cause subsequent membrane-fouling problems. Similarly, overdosing of ferric salts can increase the dissolved iron concentration, which leads to membrane fouling downstream and regular jar tests are required to maintain the correct coagulant dose. Coagulation tanks (approximately 30 min coagulation time) or in-line static mixers may be used; however, static mixers are not recommended

when there are large flow variations due to their inability to provide adequate mixing under these circumstances. Dual-media pressure filtration with anthracite and sand is commonly used, with typical filtration rates between 15 and 20 m3 m2 h1. Gravity filters are also used when algae are likely to be present, but lower filtration rates of 10 m3 m2 h1 are typical. If the turbidity of the feedwater is regularly high (430 NTU), then sedimentation prior to sand filtration may also be used. The addition of coagulant allows sand filters to remove particles as small as 0.5 mm as well as some natural organic matter. Backwash of the sand bed is performed in order to remove the accumulated particles that are either sent to the sea or, where environmental regulations are stricter, to landfill. Small desalination plants sometimes use beach wells during the pretreatment stage. Seawater passage through the soil layers around the wells serves as a sand-filtration unit. This operation is limited due to the low possible capacity of a single well. DAF uses small air bubbles to capture contaminants and float them to the surface of the tank where they are removed by skimming. Oils, algae, and suspended matter attach to air bubbles and rise to the surface where they accumulate before being skimmed out for disposal. The water is often passed through a sand filter following DAF treatment to ensure that water with low suspended solids is fed to the desalination stage, and at the Tuas seawater desalination plant, the filtration units are incorporated into the DAF. DAF has the advantage of removing dissolved and suspended organic matter and oils that cannot be removed using sand filtration, and can treat waters with turbidity up to 50 NTU. This makes DAF suitable for handling waters prone to algal outbreak. MF and UF systems are now available for filtration of seawater and are increasingly being used, with seawater desalination plants in Yu-Huan (China), Fukuoka (Japan), Saudi Arabia, and Turkey using UF pretreatment. The advantage of using MF or UF pretreatment is that these systems can remove particles and colloids as small as 0.2 mm for MF and 0.02 mm for UF, and have more than 4 log removal of bacteria. This leads to consistently high treated water quality, with turbidity consistently less than 0.1 NTU and SDI less than 3. Iron coagulation is generally used to remove organic compounds and to enable fluxes between 50 and 100 l m2 h1 to be achieved. Both pressure and vacuum systems are used, with vacuum systems requiring less coagulant prior to the membrane and pressure systems being less sensitive to source-water temperatures. For cold waters less than 15 1C, pressure systems are more economically attractive. Finer screening is required before MF/UF systems than for dual-media filters, and screening of particles down to 120 mm or less in size is required. This is because the hollow-fiber MF/ UF systems may be cut or punctured by shells or sand particles. Furthermore, embryonic barnacles need to be removed to prevent colonization within the MF/UF systems and a 120-mm screen is able to achieve this. MF and UF systems can also be prone to fouling by organic compounds of biological origin and do not treat algae-laden waters efficiently. Chemically enhanced backwashing with chlorine solutions of 25– 100 mg l1 chlorine is used to control this fouling, and it may be used 1 or 2 times a day.

Seawater Use and Desalination Technology

Waste streams from media filtration systems are generally only half the volume of waste streams generated by MF/UF systems. Typically between 2% and 4% of feedwater is rejected in the waste stream in media-filtration systems, while between 5% and 8% of the feed might be rejected in MF/UF filtration. The higher waste associated with MF/UF systems is associated with their more frequent backwashing and the additional waste streams are associated with chemically enhanced backwashing and chemical cleaning of the membranes. Ion exchange is also used in some cases, such as in Palmachim, Israel. The ion-exchange resins are regenerated by the brine concentrate, and the calcium and magnesium released from the ion-exchange resin stabilize the product water. Nanofiltration (NF) membranes were suggested as a means to remove Ca/Mg and SO4 ions, but the cost for such an operation is high (Wang et al., 2009; Hassan et al., 1998; Hilal et al., 2007). Disinfection of the feedwater by chlorine compounds is done in some instances, in an effort to reduce biofouling of the RO membranes. The sensitivity of the RO membranes to chlorine implies that feedwater needs to be dechlorinated prior to treatment by RO membranes, and activated carbon is needed for this purpose. Unfortunately, in many cases, activated carbon allows bacteria to grow after the dechlorination stage, and, in some cases, it increases the impact of biofouling at the RO stage. Therefore, disinfection of the feedwater is no longer done in modern plants. The final stage of pretreatment is often cartridge filtration, particularly for systems that use media filters rather than MF/ UF systems. The cartridge filters are included to protect the RO membranes by removing contaminants that have made it through the pretreatment stage and to filter out particles associated with the breakthrough of media filters. The filters have nominal pore sizes between 1 and 25 mm and may need to be changed every 6 months in systems with good pretreatment or every 6 weeks if the source water is more challenging. Calcium carbonate is saturated on the surface layer of seawater and is below saturation concentration at lower levels. This is of significance for RO-membrane systems, since the concentration of salts increases along the RO membrane during the process, along with the level of supersaturation. This salt may, therefore, precipitate on the membrane and cause clogging and reduce the flux of freshwater. Acidification and addition of anti-scalants are often practiced, but it is now considered unnecessary because it is believed that the high concentration of brine assists in preventing precipitation. Ba and Sr salts are also in supersaturation in seawater; yet, their concentration is low and causes minimal damage to the membranes. CaSO4 has three different forms of salts, yet they are all below supersaturation levels in the brine leaving the desalination plant. More detailed information on pretreatment before membrane-seawater-desalination systems can be found in Voutchkov (2008) and Voutchkov and Semiat (2008).

4.04.2.3.2 Reverse osmosis The RO process relies on semipermeable membranes to selectively pass water from salt solutions. The building

83

materials for RO membranes are usually polymers such as cellulose acetates, polyamides, or polyimides. The membranes are semipermeable and made of thin layers about 200 nm thick that are adhered on to a thicker support layer. A few types exist, such as symmetric, asymmetric, and thin-film composite membranes. The membranes are usually built as long sheets, separated by spacers, and spirally wound around the product tube. In some cases, tubular or capillary membranes are used or even hollow fibers. Sidney Loeb developed the first modern RO membrane based on cellulose acetate (Loeb and Sourirajan, 1963; Loeb, 1981). Modern membranes are made of polyamides and polyimides that have better rejection properties, longer life, and require less energy. RO membranes are usually sensitive to changes in pH and the recommended pH range for polyamide membranes is between pH 2 and 11, but they can work for a short time (30 min) at extreme conditions of pH, such as 1 or 13, for cleaning purposes. Small concentrations of oxidizing substances such as chlorine, chlorine oxides, and ozone can severely damage the membrane skin. So also can a wide range of organic materials and biological organisms such as algae and bacteria, and this is one reason why high-quality pretreatment is required. The process takes place at ambient temperature, and RO membranes are usually stable only up to 35–40 1C. However, variations in water temperature within this range still influence membrane performance. The flux through a membrane increases with rising water temperature as the viscosity of water decreases. Higher temperatures increase both the water flux and salt flux, and lower rejections are obtained. Using hot seawater flowing from the cooling system of a large power plant, as is sometimes practiced, increases the efficiency as the membranes are able to operate at higher flux. A schematic presentation of an RO desalination process is shown in Figure 8. Seawater is pumped through the pretreatment stage before high-pressure pumps feed the water to the RO modules. Water then penetrates the membrane and freshwater is recovered in the permeate stream. The highpressure purged concentrate contains energy that may be recovered using turbines or pressure-exchange devices. The osmotic pressure of seawater, as an example, varies between 24 bars to twice as much for the concentrate at 50% recovery. Operating pressures therefore, vary between 60 and 80 bars for seawater desalination in order to allow sufficient permeation of water through the membrane at the relatively high concentrations of the brine along the pressure vessel. Water recoveries of 35–50% are usual, with lower water recovery obtained in closed seas, such as the Red Sea or the Persian Gulf, due to higher salt concentration. The requirement of a two-pass process during RO and the occurrence of membrane fouling are discussed in the following. The need for a two-pass process. RO membranes are able to selectively pass water, but the separation is not 100% and small amounts of salt and organic compounds are also able to pass through the membrane into the permeate. The level of salt passage increases as the salt concentration of the water being desalinated increases, and therefore, more salt passes through at the end of the RO process where the salt concentration is higher than at the beginning.

84

Seawater Use and Desalination Technology

The quality of water produced, therefore, depends on membrane rejection properties together with the degree of water recovery and the system design. Relatively small, uncharged molecules such as carbon dioxide, silica, and boric acid may penetrate the membrane and reduce water quality. Silica and CO2 concentrations in the permeate usually present no issues for downstream use where the presence of CO2 assists in dissolving CaCO3 in the stabilization process. The presence of silica can be a problem for specialized industrial uses such as the fabrication of microelectronic components, where ultrapure water is required. However, salt and boron permeation through RO membranes remains problematic. Seawater contains approximately 5 ppm boron, which reduces to a value slightly greater than 1 ppm of boron after treatment through a RO membrane. Boron is an important component for plant growth; however, high concentrations are harmful and cause significant reduction in yield for many crops. Boron may be removed from water by ion exchange (Nadav, 1999), by using secondary or higher RO stages (Redondo et al., 2003; Glueckstern and Priel, 2003; see Figure 9; Faigon and Liberman (2003)), by increasing the pH of the water on the feed side of the membrane (RodriguezPastor et al., 2001; Prats et al., 2000), and by using electrodialysis reversal (EDR) applied on the product. A combination of these techniques is also suitable (Sagiv and Semiat, 2004). The current demand in Israel is to produce water containing less than 0.4 ppm (of boron) in Ashkelon and less than 0.3 ppm in Hadera – a plant that has been operating since early January 2010. The reason for this is related to the recovery of wastewater that remains after the use of desalinated water. Boron reaches the wastewater from different sources, and this may damage crops irrigated by treated wastewater. This requirement for low boron concentrations in the treated water has resulted in Ashkelon using up to three permeate stages of RO membranes to remove boron to concentrations below 0.4 ppm. This also results in a significant reduction of the dissolved salt concentration. Reduction of salt content in the product obtained after seawater reverse osmosis (SWRO) is a by-product of boron removal by a second RO stage applied on the product. If boron removal is performed using another technique, a secondary RO stage is still needed to improve the TDS quality, as concentrations of above 600 ppm are still found after the first pass. The types of membranes used in each pass differ, with SWRO membranes used in the first pass where the concentration of salt is very high and brackish water reverse osmosis (BWRO) membranes used in the second pass where the salt concentration is lower. SWRO membranes have higher rejection of salt, while BWRO membranes operate at higher flux enabling cost reductions to be achieved. Typical operating fluxes are 13–17 l m2 h1 in the first SWRO stage and 30–40 l m2 h1 in the second BWRO stage. The improvement in membrane rejection of boron and salts has reduced this problem; however, there is still a need for a BWRO second stage to partially treat the product water in order to produce water of a satisfactory quality. The high-pressure driving force for SWRO is reduced along the module in a single-pass system. The mode of operation using six to eight membranes in a module, at a constant pressure (minus the friction along the membranes) reduces

the driving force applied to desalinate the water. The difference between the operating pressure and the osmotic pressure at the first membrane may be 40–45 bars, while at the exit, on the last membrane it is of the order of 15–20 bars. It is obvious that the flux declines along the module to less than 30% of the starting flux of the first membrane. The flux depends on the operating pressure, the operating temperature, as well as on the salt concentration. Different membranes are available in the market; yet the order of magnitude of flux is up to 40 l m2 h1. The difference in driving force causes uneven load on the different membranes, that is, higher pressure drop at the entrance causes significant concentration polarization (CP) close to the membrane surfaces at the entrance to the membranes modules, and hence increased fouling. Operating an increased number of passes allows changes in the operating pressure to get a better distribution of the driving force. Theoretically, using a large number of brine stages while maintaining low operating pressure, just above the osmotic pressure, may save some energy in the process, down to the theoretical minimal thermodynamic energy for separation, but at the expense of the equipment, which would have to bear a high number of membranes and pressure vessels. Between the two extremes, it is possible to use two or three passes to gain some energy reduction and improved operational conditions. A two-stage operation can be used with two pumps that feed the two stages in a series; the first pump elevates the feed to 35–45 bars, while the second pump takes the concentrate of the first stage and increases the pressure for the second stage. The second stage may be operated by a turbine that is based on the brine of the first stage to increase the pressure to the second stage (e.g., Tuas SWRO Plant, Singapore). Membrane fouling and cleaning. Despite all precautions taken to improve the quality of water entering the membranes, membrane fouling occurs and frequent cleaning is required. While salt precipitation mainly occurs in brackish-water membranes, corrosion products may also accumulate, usually close to the spacer between the membranes. Suspended matter that was not removed during the pretreatment process may also accumulate on membrane surfaces, along with dissolved organic matter that concentrates in the thin layer close to the membrane skin. The latter may enhance biofilm growth on membranes if bacteria also reach the membrane surface. Accumulation of a fouling layer on the membrane is a process that enhances itself, with the rate of fouling increasing as fouling progresses. Clogging of a membrane reduces the flux through the membrane and the overall performance of the plant. All this calls for frequent membrane cleaning. The exact timing is dictated by the need to control fouling to a low level, while reducing the frequency of cleaning to maintain production capacity and minimize the consumption of chemicals. Initial acidification of the seawater feed may release CO2 from the water and may reduce the possibility of CaCO3 precipitation. The accumulation of organic matter may be removed partially by using sodium hydroxide (saponification). Membranes are also frequently cleaned with acid that releases some contaminants from membrane surfaces, particularly inorganic foulants. The cleaning process needs to be performed while the solutions are flowing along the membrane in order to remove fouling species that are released

Recovery 45% SWRO

Recovery 45% SWRO

Recovery 45% SWRO

2nd RO

Front product

2nd RO

Front product

2nd RO

Front product

3rd RO

4th RO

Boron select. IX

Weak acid IX

3rd RO

4 Stage boron removal system

25%

50%

25%

34%

36%

30%

10%

Boron <0.4 ppm Chloride ~ 120 ppm

2nd RO recovery – 80–85% Overall recovery – 41.4%

2 Stage boron removal system + Selective IX

Boron <0.4 ppm Chloride ~ 40 ppm

2nd RO recovery – 50% 3rd RO recovery – 90–95% Overall recovery – 44.1%

3 Stage boron removal system + WAIX

Boron <0.4 ppm Chloride ~ 35 ppm

2nd RO recovery – 80–85% 3rd RO recovery – 90–95% 65% 4th RO recovery – 90–95% Overall recovery – 44.5%

25%

Figure 9 Boron-removal processes. From Redondo J, Busch M, and De-Witte JP (2003) Boron removal from seawater using FILMTECTM high rejection SWRO membranes. Desalination 156: 229–238. Adapted from Faigon M and Liberman B (2003) Pressure center and boron removal in Ashkelon desalination plant. IDA World Congress on Desalination and Water Reuse (BAH03-181), International Desalination Association, Paradise Island, Bahamas.

Scheme C

Scheme B

Scheme A

86

Seawater Use and Desalination Technology

during the cleaning process. Excessive suspended solids in this stage may accumulate in the spacer between the membranes and clog the flow path. Unlike other filters and even MF/UF membranes, NF and RO membranes are not subjected to a backwashing process. The main reason is the fear that the delicate layer that determines the separation properties of the membrane may be removed during the backwash process, causing severe damage to the membrane. However, it was found recently, that controlled osmotic backwashing, applied without damaging pressure, may be useful in removing contaminants from the membrane. The technique may be applied in different ways:

•

•

•

•

Shut down the feedwater occasionally for a short time. This will allow immediate osmotic backwash of the membrane. Water will penetrate the membrane at fluxes that are a function of the local salt concentration along the membrane. More water will penetrate the high-concentration locations that are more prone to scale deposition and might dissolve small, precipitated scale on the membrane (Sagiv and Semiat, 2005; Sagiv et al., 2008). Reduce the operating pressure of the concentrate side of the membrane to a pressure below the osmotic pressure in the system. Water will penetrate the membrane but the backwash flow rate will be reduced. Allow a wave of highly concentrated solution to pass through the feed channel without changing the operating condition. Backwash flow will increase but it is necessary to maintain a stock of a highly concentrated solution for this task (Liberman, 2004a, 2004b). Increase the permeate pressure to a level that allows back flow. This pressure should be below the concentrate side pressure on the feed side. Masaaki and Toshiyuki (2001)issued patents on a similar backwash using air pressure from the permeate side. However, this requires high-pressure piping on the permeate side of the membrane as well, and this will increase equipment cost significantly.

4.04.2.4 Desalination Process Costs Most of the evaporation processes were built along the coast of the Persian Gulf and some were also built on remote islands. Usually, they are connected to power stations. The real cost of these systems is not clear due to different calculations based on the real cost of energy. Estimations for SWRO are about US$0.55–1 m3 of freshwater produced. There are promising signs for reducing desalination costs by analyzing the cost components. Table 2 presents an estimated cost breakdown of desalinated water produced in a modern plant, taking into account partially, the changing trend of energy costs. The main constituent is, of course, the capital and financial cost. This is composed of the cost of the main items of the equipment: feed tanks, pretreatment filtration units, pumps, turbines and piping, controls, membranes, and membranes housing post-treatment and product tanks. One way to reduce desalination costs is to seek possibilities for cost reduction in each of the above items of equipment. Some items of equipment are restricted to the desalination industry and their cost may simply go down due to market forces. Investment in sophisticated automation and

Table 2

Cost estimation – modern project

Item

Cost (%)

Capital Energy Chemicals Manpower Replacement parts Membranes replacement Insurance Overhead

34 38 5 3 9 5 1 5

Table 3

Power usage in RO sweater plant with partial second stage

Item

Cost (%)

High-pressure pump High-pressure pump, 2nd unit Product transfer pumps Seawater supply Pretreatment system Miscellaneous

80.6 3.8 6.7 4.5 2.6 1.8

From Wilf M (2004) Fundamentals and cost of RO–NF technology. In: Proceedings of the International Conference on Desalination Costing, pp. 18–31. Limassol, Cyprus.

control equipment can result in lower water costs by maintaining stable high throughputs and savings in manpower costs. As can be seen from Table 2, manpower costs are no longer significant, since modern desalination plants may operate largely unattended. Energy is the main cost component to consider. The energy cost can be reduced by the use of a dedicated gas-turbine power station. A dedicated power station is more efficient than the grid because it is insensitive to the familiar sine-curve power consumption, due to fluctuations between day versus night and summer versus winter electricity demand. Modern devices for energy conservation also act to reduce energy cost though at the expense of increased capital cost. Wilf (2004) presents in Table 3 all the energy-demand components in a two- pass RO desalination plant. Others may claim that the relative low-pressure pumping costs are higher due to the distances to and from the plant. More information on RO costing history can be found in Glueckstern (2004). Costing information on the Ashkelon plant, which has been the largest RO desalination plant in the world for the last 4 years may be found in Kronenberg (2004) and Velter (2004). Figure 10 shows a picture of the Ashkelon plant, which has an operating capacity of 108 million m3 yr1. Compliance with proper operational procedures and implementation of a careful maintenance program can also reduce desalination costs by minimizing replacements of damaged membranes, lessening the use of cleaning chemicals, and reducing the inventory of membranes and spare parts. Membranes are often replaced after 5 years, but good pretreatment and cleaning practices can extend membrane life to 10 or more years (Montgomery et al., 2006). The design of a desalination plant in which it is envisaged that the operators are insufficiently trained, will invariably be based on exaggerated safety factors. Well-trained and

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Figure 10 The Ashkelon plant – 100 million m3 yr1 during 2005. From IDE Technologies Ltd. http://www.ide-tech.com/projects/ashkelon-israel-botswro-plant (accessed April 2010).

experienced operators can extract a higher production capacity from a desalination plant by identifying and debugging bottlenecks. Electrodialysis (ED), or EDR, is operated by applying a direct current (DC) electrical field across membrane stacks. Ions are transferred through semipermeable membranes into concentrated streams, leaving behind dilute salt solution. This was considered to be a promising technique, mainly attributed to the relative insensitivity of the membranes for fouling, and due to the thermodynamic transfer properties of this technique. Unfortunately, the technique did not succeed in taking the naturally expected position among other processes. Currently, the technique is used mainly for brackish-water desalination, and water purification (Thampy et al., 1999). EDR membranes are also used to remove special salts such as nitrates from slightly polluted waters. Strathmann (2004) reports the costing of the ED process.

includes an increase in the pH level, addition of Ca (up to the level of about 100 ppm as CaCO3), and alkalinity, namely HCO 3 (also to a level up to 100 ppm as CaCO3), according to local water regulations. This is often achieved by carbon dioxide addition or sulfuric acid addition to limestone. The need for addition of magnesium salt is currently under review by the WHO, as magnesium helps prevent heart disease. Additionally, trace amounts of sulfur are required to ensure good plant growth. Desalinated seawater contains bromide, and when disinfected with chlorine, brominated by-products such as bromochloroacetonitrile may form. In most applications, the presence of these compounds is sufficiently low so as to not compromise the final water quality (Agus and Sedlak, 2009), but it should be considered when using seawater desalination plants.

4.04.2.6 Environmental Aspects 4.04.2.5 Quality of Water Produced Thermal processes typically produce water containing between 5 and 50 ppm of TDS, with the relative concentration ratios of mineral ions similar to that in the feed seawater. Therefore, the problem of high boron concentrations in the product water does not exist. Feedwater containing dissolved volatile organic compounds, however will generate, unless special care is taken, water contaminated with the same components. This is true for both RO and evaporation techniques.

4.04.2.5.1 Increase in water hardness/water stabilization The product water of both thermal and membrane-desalination process is aggressive, tends to corrode iron pipes, and dissolves protective layers containing calcium and other salts on the inner sides of the mains. This may result in the phenomenon called ‘red water’, which is a release of corrosion products by water that dissolves the pipes’ protective layer of CaCO3. Water needs, therefore, post treatment that usually

Desalination processes may be characterized by their effluent discharges to the environment, the air, the nearby land, and to the seas. Desalination is dependent on energy and usually uses energy derived from fossil-fuel sources. Air pollution is associated with energy production, that is, emission of NOx, SO2, volatile compounds, particulate, CO2, and water, etc., either by using electricity produced by a conventional power station or by using a dedicated power station. Operation of dedicated gas turbines at high-efficiency levels will reduce the amount of contaminants to the atmosphere. Several desalination plants in Australia have offset their energy use with renewable energy, such as the Perth desalination plant (Crisp, 2009). The Perth plant included the construction of an 82-MW wind farm. While this produces sufficient power to drive the desalination plant, the intermittent nature of the power source means that base-load power sources are still required. This increases the cost of water as electricity is a significant cost in desalination operations, but it is

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used to offset the carbon dioxide emissions associated with desalination. Effluents of desalination plants contain relatively highly concentrated water, which depend on the water recovery from the feed brine. In case of seawater desalination, rejected brine is concentrated almost to twice the concentration of the original seawater solution. The concentrate also contains chemicals used in the pretreatment of the feedwater. The latter may contain low concentrations of anti-scalants, surfactants, and acid added to the feedwater that reduces the pH. To this may be added occasional washing solutions or rejected backwash slurries from feedwater pretreatment. At small-scale operation, the problem is mild and no serious damage to marine life is likely. At large scales of water production, the problem is more serious; however, dilution and spreading of effluents usually solves the problem. Natural chemicals that do not harm the environment will probably replace the added chemicals, in the future. The current trends in concentrate disposal is to transfer it into the deep sea, sending jets upward in a few directions, at an angle to the horizon, enabling the concentrate to be diluted very close to the concentration of the sweater. Another approach is to mix the concentrate with the cooling water leaving a large power station before discharge. These techniques minimize the influence of the concentrate on the sea environment.

4.04.2.7 Energy Issues Desalination, as a separation process, needs energy. The current specific energy for RO desalination was reduced significantly during the last decade and it is now not far from the limiting theoretical thermodynamic minimum. This has been achieved by the development of large pumps with efficiencies as high as 92%, and modern efficient turbines and energyrecovery devices. The newer devices are the turbocharger, pressure exchanger, or work exchangers – the names adopted by different producers represent efficient ways to recover the energy content of the high-pressure concentrate. More details on energy-recovery devices may be found in Voutchkov and Semiat (2008). Turbines are used to turn the concentrate pressure into the velocity of jets that spin a wheel. This is used either to reduce the power consumption of the motor that drives the pump, or in conjunction with the turbine pump, to boost the pressure of the feed to a second stage. There were other methods that were used to exchange the pressure of the brine concentrate by simple devices that transfer the pressure to the seawater feed. Using these new techniques, significant reductions in power consumption have been achieved. For example, processing of Mediterranean seawater at a recovery of 50% needs only 2.7 kW h1 m3 produced by recovering the concentrate energy with turbines. Pressure exchangers can go even lower to 2.2 kW h1 m3 water produced. Since more energy is consumed for the feed and concentrate pumping and for the pretreatment stages, the overall energy needs are below 3.7 kW h1 m3 produced from seawater. The energy cost of an optimized desalination plant is around 30–40% of the total cost of water. The cost is based on optimization of the operation and the exchange between energy and equipment. This optimization is made during the

design of the plant; yet, the cost of energy may vary significantly during the lifetime of the project. During the course of writing this chapter, the cost of natural oil increased significantly in comparison with the cost at the design stage of, say, the Ashkelon plant. It is difficult to change the optimal design of the plant after it is built. However, using RO membranes while considering possible changes of energy cost, it is possible to minimize the losses by designing for lower possible energy consumption at the expense of equipment costs. A 100-million m3 RO based seawater desalination plant demands an electrical energy supply of less than 40 MW. A dedicated power station can work at much higher efficiency than a regular power station for this purpose, since it is operated constantly without the known sine wave that represents day–night and summer–winter changes in consumption. Higher efficiency is expected for gas turbines, since the high temperature of the gases may also be used. Therefore, the real energy needed is lower than that for other common uses. Environmentalists are often heard criticizing the levels of energy consumption for water desalination. Water is needed for the many people on Earth and for meeting their basic needs. This is of higher priority for the use of energy compared to its use in air-conditioning and/or large, high-energy consuming cars. More on energy consumption and comparison with other forms of energy usage may be found in Semiat (2008). Environmental concern arising from the CO2 green-house effect associated with the use of hydrocarbon fuel, has led to the goal of supplying desalination energy from renewable energy sources. While writing this chapter, the cost of a barrel of oil is higher than US$70. With this trend, renewable energy sources may be soon compatible and economic for general electricity production. At this stage, they will also be suitable for desalination purposes. No doubt, more effort should be directed toward the use of renewable sources of energy. The real test, however, for any new source of energy is its acceptance as a common source of energy. The savings on CO2 emissions need to replace other forms of energy use and not be used for the very delicate issue of desalination for freshwater production. Using nuclear energy, which is currently more expensive than fossil-fuel-derived energy, is dangerous in areas where political instabilities exist. It is also problematic where the technology is not available locally and the technology, expertise, and skilled manpower need to be imported. A possible way for efficient use of energy in a sufficiently large desalination plant is to design a hybrid plant consisting of a membrane unit and/or a VC unit (Awerbuch, 1997b), using electrical energy, and a multi-effect evaporation plant, using heat. Such an operation is common in the chemical industry. The energy costs are minimized by coupling the desalination plant with a dedicated power plant generating electricity and waste heat at optimal economic conditions. One of the benefits that can be claimed from the day– night, summer–winter electricity-production cycle is that it can produce desalinated water during the night when lower power is consumed. The main disadvantage is that the desalination equipment will not be in use for a high percentage of the time. This is unlikely to be economical, since, as in any modern plant, the production cost is more expensive if the equipment is not in full use. In other words, an efficient

Seawater Use and Desalination Technology

desalination plant needs to be operated round the clock, 24 h a day, 365 days a year, with exceptions for maintenance only. During this time, it needs the full supply of energy, at the lowest cost. Since energy is so important for desalination, a few comments on possible energy use that may significantly reduce desalination costs are made. With respect to the use of spent energy from large steampower plants, it is well known that steam cycle power stations purge large amounts of energy at the steam-condensing stage at the turbine exit. This source of heat may be combined with a thermal desalination technique in order to supply the primary steam, as in MSF and MED (Awerbuch, 1997a, 2004). A modern, efficient power station releases the exhausted steam at around 35–40 1C, which is too low for the proper operation of the desalination plant. It is required, therefore, to release steam at elevated pressure and temperature using backpressure turbines that fit in with the desalination-plant needs. This, of course, will cause some loss of production at the power-generation plant; therefore, there is a need to integrate and optimize the two processes together. Such designs are extremely difficult to perform if two different authorities are involved – power and water production (El-Nashar, 1997). This type of hybridization was successfully employed in the Persian Gulf countries when the same authority controlled the two industries. However, there is always a difference between the demand for electricity and the demand for water, and this is the main reason it is not implemented in other places.

4.04.3 Brackish Water 4.04.3.1 Brackish Water Desalination Applications Brackish water, generally defined as water with TDS content between that of freshwater (r500 mg l1 TDS) and seawater (33 000–48 000 mg l1 TDS), can occur naturally as brackish groundwater in subsurface saline aquifers, as surface water due to natural erosion, or as a result of seawater mixing with river water (in estuaries) or groundwater (in coastal aquifers). Natural brackish water, particularly brackish groundwater, exists in most continents in quantities almost equal to or more than fresh groundwater and surface waters combined (Shiklomanov, 1993). Human activities can also cause fresh surface water and groundwater resources to become brackish through consumptive use and increase in their salt loading. For example, excessive groundwater pumping from coastal aquifers can cause salt-water intrusion that extends the brackish water zone of mixing inland, while saline return flows from irrigated agricultural lands can increase the salt loading of surface waterways. Some agricultural irrigation practices such as subsurface tile drainage often generate agricultural drainage waters that are highly brackish. Similarly, mining activities can also generate mine-drainage waters that are brackish and contaminated with heavy metals. In the past, brackish-water desalination applications have been limited to small-scale municipal and industrial applications. It is especially popular in the USA as it accounts for the majority (B77%) of the nation’s total online-desalination capacity (Committee on Advancing Desalination Technology, 2008). One of the early large-scale inland brackish-water

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desalting plant, the Yuma Desalting Plant in Arizona (USA) (Lohman, 2003), was completed in 1993 for the purpose of supplementing Colorado River water deliveries to Mexico (via desalting brackish agricultural water return flows; B2500 mg l1 TDS). Although its production capacity remains the largest in the world for a brackish-water desalting plant (272 500 m3 d1 or 72 million gallons a day (MGD)), the plant was operational for only two occasions in 1994 and has remained offline (although well maintained). With dwindling freshwater supplies and maturing RO/NF and ED/EDR process technologies, desalting of under-utilized brackish groundwater and surface-water resources has attracted significant interest. Some recent (2000–10) large-scale brackish-water desalting installations include the Aigu¨es Ter-Llobregat’s (ATLL) Plant (Spain; 220 000 m3 d1), the Al Wasia Plant (Saudi Arabia; 200 000 m3 d1), the El Atabal Plant (Spain; 165 000 m3 d1), the Wadi Ma’in Plant (Jordan; 135 000 m3 d1), and the K. B. Hutchison Plant (USA; 104 000 m3 d1). All of these are RO plants, except for the ATLL plant, which is an EDR plant. Feedwater quality plays a major role in both the design and operation of brackish-water desalination processes. Brackish waters vary greatly in ionic composition and content, both temporally and geographically, depending on hydrogeologic conditions and related human activities (i.e., industrial or agricultural). For example, in California’s San Joaquin Valley, one of the most productive agricultural regions in the US, tile drainage of irrigated agricultural lands generates brackish waters with a wide salinity range (3000–30 000 mg l1 TDS). In Texas (USA), subsurface aquifers with salinity ranging from 1000 to 10 000 mg l1 TDS have been estimated to hold as much as 3 trillion m3 of brackish groundwater. Major solutes in brackish waters, such as sodium, chloride, calcium, sulfate, and bicarbonate ions, typically originate from water reactions with minerals such as halite, gypsum, anhydrite, calcite, and dolomite. Other common minor solutes include silicates, iron, strontium, barium, fluoride, selenium, and boron. Some examples of brackish-water composition are listed in Table 4.

4.04.3.2 Brackish Water Desalination Technologies The choice of brackish-water desalination technologies and process configuration is a site-specific combination of many factors, including source-water quality, target productivity, product-water quality requirements, brine-disposal options, and other local conditions and regulations (e.g., permits). Pressure-driven membrane processes of RO and NF are predominant brackish-water desalting technologies, while electrochemically driven membrane processes of ED and EDR still maintain important niche applications. In applications where high-purity product is of importance, electrodeionization (EDI) processes have been integrated for product water polishing. Cross-flow RO/NF operation relies on applying sufficient pressure at the feed side of ion-rejecting RO/NF membranes to overcome the hydraulic resistance of water permeation, as well as the osmotic pressure difference between the feed and permeate side of the membranes. The world’s first municipal RO plant was commissioned in 1965 in Coalinga (CA, USA) for desalting brackish water, just shortly after the first cellulose acetate RO membrane (with practical hydraulic resistance and

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Table 4

Composition of brackish water from a variety of sources (USA)

Analyte (mg l1)

El Paso Water Utilities, TX Airport Wellsa

Panoche Water District (San Joaquin Valley, CA), DP-25 Wellb

Indian Wells Valley Water District, CAa

Colorado River Water, Yuma, AZc

TDS Arsenic Barium Bicarbonate Boron Bromide Calcium Chloride Fluoride Magnesium Nitrate Potassium Selenium Silica Sodium Strontium Sulfate

3170 75 0.05 176 1370 0.61 38.4 0.11 15.9 29.4 745 301

8500 274 23.5 492 1190 255 337 4.3 0.47 31.4 1810 78 4080

1630 0.0052 370 1.74 164 236 1 49 72 6.1 0.059 45 333 1.55 570

941 0.1 212 95 164 34.5 11.6 165.5 1.24 322

a

Data on ElPaso Water Utilities, TX and Indian Valley Water District, CA, form Committee on Advancing Desalination Technology (2008) Desalination a National Perspective, Science and Technology Board, National Research Council. Washington, DC: National Academies Press. b Data on Panoche Water District, CA, from Cohen Y and Christofides P (2010) Reverse Osmosis Field Study, Final Report, DWR-WRCD Agreement 46000534-03, Task Order No. 22, California Department of Water Resources, 16 June 2010. c Data on Colorado River Water, Yuma, AZ, from Rahardianto A, Gao J, Gabliech CJ, Williams MD, and Cohen Y (2007) High recovery membrane desalting of low-salinity brackish water: Integration of accelerated precipitation softening with membrane RO. Journal of Membrane Science 289: 123–137.

salt rejection) was invented at the University of California, Los Angeles (UCLA) (Loeb, 1984). Over the past two to three decades, significant advances in polyamide thin-film-composite (TFC) membranes have resulted in a new class of RO and NF membranes with high water permeability (i.e., low hydraulic resistance) and salt rejection, enabling cross-flow RO/NF to operate at applied pressure levels approaching the limit imposed by thermodynamics (i.e., the osmotic pressure difference at the concentrate end). With larger pores than the ones in RO membranes, NF membranes can selectively reject divalent ions and molecular solutes over monovalent ions. Although fouling-resistant RO/NF membranes remain elusive, improvements in TFC membrane designs (i.e., lower surface roughness and near-neutral surface charge) have made present membranes less prone to fouling by suspended particulates/ colloids. Present commercial RO/NF membranes are packaged as modular spiral-wound membrane elements of standardized dimensions, with fluid channels formed by feed and permeate spacers. An RO element can typically operate up to 15% water recovery with a nominal salt rejection of about 98–99.7%. Operating pressures are normally in the range of 10–41 bars (150–600 pound force per square inch (psi)) for brackish water RO membrane elements, depending on feedwater salinity (i.e., osmotic pressure). Due to its relative simplicity and ease of operation and maintenance, RO has become the primary workhorse in brackish-water desalting. NF elements usually operate at much lower pressures (B7 bar/100 psi), with divalent ion rejection of 50–98% and monovalent ion rejection of 20–75%. As a stand-alone or a feedwaterpretreatment system, NF is increasingly being applied for the removal of hardness ions (e.g., calcium and magnesium),

organics, and specific contaminants (iron, nitrates, pesticides, herbicides, etc.). As alternatives to RO and NF, electrodialysis (ED, EDR, or EDI) relies on electric field to transport ions from diluate (feed) compartments to concentrate compartments across flat-sheet ion-exchange membranes. In an array of alternating cation and anion membranes, separated by alternating diluate and concentrate compartments, anions/cations migrate from diluate compartments toward anode/cathode plates, passing through anion-exchange/cation-exchange membranes, and become trapped in concentrate compartments due to rejection by adjacent cation-exchange/anion-exchange membranes. With the development of the first ion-exchange membranes in 1948, the first commercial ED unit was built by Ionics and deployed at an oil-field campsite in Saudi Arabia in 1953 (Reahl, 2006). The first ED plant in the USA was erected in the city of Coalinga (1958) (Reahl, 2006) less than a decade before the operation of the world’s first RO plant at the same city (1965) (Loeb, 1984). In the mid-1970s, Ionics introduced the EDR process as an improvement on its ED process, whereby DC flow through the ED membrane stack is periodically reversed, along with simultaneous interchange of the product and brine stream flows (Reahl, 2006). The periodic reversal is touted to minimize the formation of mineral scale on ion-exchange membranes. Furthermore, uncharged materials (e.g., some forms of silica) do not accumulate near membrane surfaces. As ion removal is readily controlled by the applied voltage, ED/EDR processes are also relatively more flexible for producing a wider range of product-water purity than RO. ED/EDR processes, however, become less efficient with increasing product-water purity (i.e., due to reduced

Seawater Use and Desalination Technology

solution conductivity); ion removal is therefore typically in the range of 50–95%. ED and EDR processes remain important in niche applications, especially for treating low-salinity brackish-water sources (o3500–4000 mg l1 TDS) where a cost advantage over RO/NF may be present. EDR is sometimes chosen over RO in desalting challenging feedwaters with high membrane fouling/scaling tendency (e.g., waters with high silica content) at high water-recovery levels. Another important development in electrodialysis was in the late 1980s, when EDI was first commercialized (Grebenyuk and Grebenyuk, 2002). EDI incorporates ion-exchange resins within ED compartments to enhance ion transport and provide a substrate for electrochemical reactions. Specifically, target ions displace Hþ and OH ion sites at ion-exchange resins within the feedwater (diluate) compartments before migrating through the anion-exchange/cation-exchange membranes into the concentrate compartment. In addition to driving the migration of ions, the applied electric field also drives water-splitting reactions that continuously regenerate ion-exchange resins in situ (with Hþ and OH ions), eliminating the need to regenerate ion-exchange resins using additional chemicals. While EDI cannot be reliably used to directly desalt brackish water, it has important applications in RO permeate water polishing with respect to specific contaminants – a less-chemical intensive alternative to conventional mixed-bed ion-exchange process.

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number of pressure vessels decreases from one stage to another in order to compensate for the decline in the retentate stream cross-flow velocity in the axial downstream direction (i.e., due to water recovery). In order to cope with increasing osmotic pressure in the downstream direction, inter-stage booster pumps in multistage processes can be employed to operate RO/NF stages at increasingly higher pressure ranges. Such use of inter-stage booster pumps, from a thermodynamic viewpoint, allows RO/NF to operate closer to the reversible process and thus consume less energy for desalting (Zhu et al., 2008). A two-stage system with a 2:1 array (i.e., the first stage has twice as many pressure vessels than the second stage) is common for brackish water desalting at water-recovery levels in the range of 60–80%, while three stages (3:2:1 array) are needed for higher water-recovery levels (Figure 11). If an inter-stage booster pump in a two-stage systems is used, permeate production at equal water recoveries in the first and second stage would correspond to the energy-optimal operation (Zhu et al., 2008). It is noted that, a two-stage configuration with a booster pump may require a larger totalmembrane area than without a booster pump in order to achieve the same product water recovery. However, when high water recovery is targeted in brackish water desalting, the additional membrane cost is usually offset by the reduction in energy cost associated with using an inter-stage booster pump (Zhu et al., 2008).

4.04.3.3 Common Process Configuration The present RO/NF and ED/EDR systems consist of modular building blocks that can be configured to meet productivity and product-quality requirements. These configurations are briefly discussed next.

4.04.3.3.1 RO/NF process configuration A typical RO/NF system for brackish water desalting consists of two or more stages, with each stage consisting of pressure vessels arranged in parallel (Figure 11). Each pressure vessel can usually accommodate up to six to seven spiral-wound RO/ NF elements. The system is typically designed so that the

4.04.3.3.2 ED/EDR process configuration A typical ED/EDR process employs plate-and-frame stacks of membrane cell pairs, with each cell pair consisting of an anion-exchange and cation-exchange membrane that are separated by concentrate and diluate stream spacers. These plateand-frame membrane stacks can be arranged as a series of one or more hydraulic stages (Figure 12). Electrical staging can also be done to improve system performance and flexibility (Figure 12). The number of stacks in series (and thus membrane surface area), in addition to current density, determines the product (the final diluate stream) quality and thus the

Single stage Two-stage (2:1 array) C

F Pump

C

F Pump

P P Three stage with booster pump (3:2:1 array) F

Booster pump

C P

Figure 11 Typical arrangements of pressure vessels for RO/NF membrane elements. F, Feed; C, concentrate; P, Permeate.

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+ HS-1

+ +

ES-1

HS-1 HS-2

HS-1 HS-2

−− − −

HS-3 ES-2

+

Figure 12 Examples of hydraulic staging (HS) and electrical staging (ES) of ED/EDR membrane stacks. Adapted from US Bureau of Reclamation (USBR) (2003) Desalting Handbook for Planners, 3rd edn. Washington, DC: USBR.

water recovery. Each membrane stack can typically reduce salinity by up to about 60% (Reahl, 2006). Single- and twostage systems are most common, but three- and four-stage systems can also be competitive (compared to RO/NF; Reahl, 2006).

4.04.3.4 Major Challenges Achieving and maintaining optimal product-water-recovery levels (with respect to overall desalination operating costs) are perhaps the most important challenges in the application of membrane technology (RO, NF, ED, and EDR) for brackishwater desalting. Product-water-recovery level dictates the volumetric rate of desalted water production relative to that of residual concentrate waste generation. With increasing product water recovery, the volume of residual concentrate waste is reduced, increasing the available options for residual concentrate management (i.e., treatment and disposal). Optimal product-water-recovery levels in brackish-water desalting are highly dependent on feedwater quality, target production capacity, and locally available methods of concentrate disposal. As the costs associated with managing residual desalination concentrate is typically high, especially at inland locations, high levels of product water recovery (85–95%) are often required for optimal desalting operations. Effective feedwater pretreatment for preventing membrane fouling (i.e., colloidal/particulate deposition on membranes and feed-spacer blockage) is an operational prerequisite in membrane-based desalting (i.e., RO/NF/ED/EDR) of brackish water. Typical foulants in brackish water include suspended and colloidal particulates/organic matter, as well as dissolved organics and biological entities (that may contribute to organic fouling and biofilm formation). Particulates/organic

matter removal via conventional coagulation, flocculation, and sedimentation processes followed by media filtration are common in brackish-water desalting applications. MF and UF processes, however, are increasingly being applied due to their superiority in providing stable influent quality to membranedesalting operations with respect to particulates, colloids, as well as bacteria. In-line coagulation and media filtration are sometimes used in conjunction with MF/UF in order to enhance contaminant removal, minimize MF/UF pore plugging, and reduce MF/UF particulate loading (Huang et al., 2009). In some cases, media filtration may be sufficient to remove particulates due to very low concentrations of suspended solids and organic matter (e.g., as in the case of some brackish groundwater). In other cases, pretreatment to remove specific constituents such as dissolved iron, manganese, and sulfides may be necessary as their oxidation may lead to in situ precipitation in membrane systems (US Bureau of Reclamation, 2003). Feedwater disinfection by chlorination/ chloroamination is also used sometimes when biofouling is of a concern (e.g., in brackish wastewater). However, dechlorination may be necessary prior to membrane-desalting operations because, unlike present EDR membranes which have high chlorine resistance, present polyamide TFC RO membranes can only tolerate very low levels of chlorine/ chloroamine residuals (i.e., carryover from pretreatment) due to oxidation of the polyamide active layer. Careful selection of plant equipment and piping materials are also necessary to avoid material leaching that can contaminate feedwater with foulants or membrane-damaging substances. For example, contamination of RO feedwater with phthalate ester from reinforced polyester pipe have been shown to cause fouling and damage to RO membranes (Hasson et al., 1996).

Seawater Use and Desalination Technology

Effective feedwater pretreatment methods for mitigating membrane fouling (particulates/colloidal fouling, as well as biofouling) are available and routinely employed in RO desalination. However, the main bottleneck that remains to achieving high product water recovery in brackish water desalting is membrane mineral scaling – the deposition and crystallization of sparingly soluble mineral salts on membrane surfaces (e.g., gypsum (CaSO4  2H2O), BaSO4, SrSO4, CaCO3, SiO2, etc.). Mineral scaling can occur in pressure-driven (RO/NF) and electrochemically driven (ED/EDR) membrane processes when dissolved mineral-salt concentrations near membrane surfaces are brought above solubility limits with increasing product water recovery. Mineral scale blocks membrane surfaces and thus degrades membrane performance (e.g., permeate flux decline in RO/NF and increase in electrical resistance in ED/EDR). The primary strategy of membrane-scaling mitigation is feedwater conditioning, which involves dosing of chemical additives to alter feedwater chemistry. Common feedwater conditioning methods include feedwater pH adjustment and anti-scalant treatment. Feedwater pretreatment to remove inorganic and organic particulates/colloids is also critical not only to minimize blockage of feed channels and particulate/colloidal fouling, but also to minimize the presence of solid surfaces that can promote the nucleation of mineral-salt crystals. Common feedwater pretreatment/conditioning methods do not remove mineral-scale ionic precursors of mineral scalants. These methods typically allow membrane-desalting operations to concentrate feedwater to limited solution supersaturation levels with respect to mineral scalants (Hydranautics, 2008). The upper limit of solution supersaturation levels, as constrained by the effectiveness of feedwater pretreatment and conditioning methods, impose a limit on product water recovery (i.e., the membrane mineral scaling threshold) at a level that is often suboptimal with respect to overall desalination operating costs. Furthermore, the difficulty in identifying membrane-scaling threshold in real time, coupled by temporal variations in feedwater quality, often leads to operation of RO/NF or ED/EDR processes at waterrecovery levels well below the membrane mineral scaling threshold. In order to achieve and maintain desalting operation at optimal water-recovery levels, several key challenges must be addressed, including managing the impact of feedwater-quality variability, early detection and mitigation of membrane mineral scaling, methods for enhancing water recovery, and management of residual-desalination concentrate (i.e., treatment and disposal).

Feed Q f,C f Pp

4.04.3.4.1 Concentration polarization and membrane mineral scaling One of the primary factors affecting membrane fouling and mineral-scale formation in RO/NF and ED/EDR processes is CP. As separation process takes place at the membrane– solution interface, the concentrations of solutes near the membrane surface are higher relative to the bulk solution. In cross-flow RO/NF processes, pressure-driven convective flux of solution toward the membrane, coupled by ion rejection and water permeation at the membrane–solution interface, leads to the accumulation of solute near the membrane surface, generating a concentration-boundary layer along the axial flow direction (Figure 13). CP enhances the osmotic pressure difference across the membrane, reducing the net pressure driving force for water permeation. In the case of ED/EDR processes, ions are transported from one solution compartment to another under the influence of an applied electric field, passing through or rejected by ion-exchange membranes. The rate of ion transport to ion-exchange membranes, and thus the efficiency of the separation process, is limited by mass transfer in the concentration-boundary layer that develops near the membrane–solution interface. In both RO/NF and ED/EDR processes, local hydrodynamics strongly affect CP, leading to spatial variation of solute concentrations near membrane surfaces. Mitigation of membrane mineral-scale formation must therefore consider not only average CP levels in membrane systems, but most importantly the local CP extremes that may occur, particularly at flow-stagnation points (Lyster et al., 2009; Rahardianto et al., 2006). Advanced numerical methods (e.g., two-dimensional (2D) and 3D computational fluid dynamics) can be used to elucidate the impact of CP on scale formation (Lyster et al., 2009; Lyster and Cohen, 2007). A simple experimental procedure has also been developed for predicting average CP levels in RO membrane elements, based on measurements of permeate-flux decline induced by the osmotic pressure of saline solutions (relative to a salt-free solution) (Sutzkover et al., 2000). With the establishment of solution supersaturation due to CP, the process of membrane mineral-scale formation can take place. Specifically, crystal nucleation and growth may occur in the concentration-boundary layer and directly on the membrane surface (Gilron and Hasson, 1987; Lee et al., 1999). Growth of deposited or surface-nucleated precipitates leads to the formation of impermeable mineral scale that progressively blocks membrane-active area. Mineral scalants commonly encountered in brackish-water desalting include gypsum, calcium carbonate, strontium sulfate, barium sulfate, silicates, U (x)

Cb H/2 Cm(x ) Jw(x)

93

Concentrationboundary layer

Concentrate Qc,Cc RO membrane Permeate Qp,Cp Pp

Figure 13 Cross-flow RO in a membrane channel. Qf, Qc, and Qp refer to volumetric flow rates of feed, concentrate, and permeate streams, respectively; Cf, Cc, and Cp represent solute concentration in feed, concentrate, and permeate streams, respectively; Cm(x) is the solute concentration at the membrane surface; Cb is the solute concentration in the bulk solution; and U(x) is the cross-flow velocity.

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and calcium phosphate. The kinetics and thermodynamics of mineral-scale formation and the resulting scale structure and morphology are influenced by water chemistry, solution composition, solution saturation levels, temperature, pressure, surface properties, and near-surface hydrodynamic conditions (So¨hnel and Garside, 1992), as well as the presence of crystallization retarders (e.g., antiscalants). Coagulant carryover (e.g., alum) from pretreatment coagulation can cause membrane scaling by associating with other components such as silica (e.g., aluminum silicates).

4.04.3.4.2 Mitigation of membrane mineral scaling The most common feedwater conditioning methods for mitigating mineral-scale formation are feedwater pH adjustment and antiscalant treatment (Taylor and Jacobs, 1996). Owing to the acid–base chemistry of carbonic acid, pH adjustment via acid dosing (HCl or H2SO4) can lower the supersaturation level of carbonate minerals by keeping carbonate ions protonated. Antiscalant treatment involves dosing of antiscalant chemicals that kinetically retard scale formation. Antiscalants do not prevent crystallization but delay nucleation and retard growth of mineral salt crystals to an extent that depends primarily on solution supersaturation level and antiscalant type and dose. Most antiscalants are formulations of polyelectrolytes (e.g., polyacrylic acids, carboxylic acids, polymaleic acids, organo-phosphates, polyphosphates, phosphonates, etc.) with molecular weight ranging from 2000 to 10 000 Dalton (Hydranautics, 2008). Dispersants are also commonly included in antiscalants formulations; their function is to keep bulk-formed crystals and colloids in suspension, minimizing their deposition and contribution to scale formation. The upper limits of antiscalant treatment effectiveness are usually specified by manufacturers in terms of a maximumsolution supersaturation level with respect to the specific mineral scalant of concern, quantified as SIx ¼ IAP/Ksp (where IAP and Ksp are the activity and solubility products for ions that form mineral salt x, respectively). Antiscalant manufacturers, for example, typically recommend that SIg (gypsum) and SIb (barium sulfate) be kept below 2.3–4 and 60–80, respectively, in order to ensure effective antiscalant treatment (Hydranautics, 2008). As membrane scaling is often a slow kinetic process that may involve multiple types of mineral scalants, solubility considerations alone may be insufficient for determining the appropriate type and optimal dosing of antiscalants. It has been reported that overdosing can cause certain antiscalants to precipitate out of solution (Hydranautics, 2008), as well as to increase biofouling potential (van der Hoek et al., 2000). Therefore, it is often necessary to resort to experimental methods to determine the specific antiscalant effectiveness for the water source under consideration. Antiscalants, for example, can be ranked based on dosage-induction time relationships for the expected maximum levels of supersaturation in the membrane systems of interest, using solution conditions of interest (e.g., composition, pH, and temperature) (Shih et al., 2004). The effectiveness of various types of antiscalants in preventing membrane mineral scaling has been assessed in membrane systems for the case of calcium carbonate (Hasson et al., 1998; Drak et al., 2000; Lisitsin

et al., 2005; Lisitsin et al., 2009), gypsum (Hasson et al., 2001, 2003), silica (Semiat et al., 2001, 2003a, 2003b), and calcium phosphate (Greenberg et al., 2005). Novel methods have also been developed to assess the effectiveness of antiscalants via direct optical imaging of membrane surfaces in real time during the membrane-separation process (Kim et al., 2009), as well as for rapid off-line membrane analysis (Rahardianto et al., 2006). In Figure 14, for example, the impact of aluminum chlorohydrate (ACH), ferric chloride, and poly-DADMAC (poly-diallyldimethylammonium chloride) flocculants/ coagulants on antiscalant effectiveness (Flocon 260) in retarding gypsum scale formation is compared based on optical images from membrane-scaling runs conducted at the same initial CP level (Kim et al., 2009). In addition to feedwater conditioning (i.e., antiscalant treatment and feedwater pH adjustment), adjustment of operating conditions to alter CP levels can also facilitate the mitigation of membrane mineral scaling. In EDR processes, for example, polarity reversal with simultaneous interchange of feed and concentrate flows periodically renews the CP layer and can therefore reset the crystallization induction time. In RO/NF processes, CP level is typically minimized by maintaining a reasonable level of permeate flux near the membrane-element-concentrate fluid exit and providing a sufficiently high value of cross-flow velocity. Recently, feedflow reversal in RO/NF operation has been demonstrated as an effective approach for mitigating membrane scaling in some brackish-water desalting applications (Uchymiak et al., 2009). Using this approach, periodic changes of the feed-flow direction reverses the axial direction of the concentration– boundary-layer development. In the forward-flow direction, membrane areas near the concentrate fluid exit, which are prone to scale formation (near fluid exit), are exposed to higher solute concentration. By reversing the flow, just before scaling occurs, the same membrane area becomes exposed to lower solute concentration, thereby resetting the crystallization induction times. Feed-flow reversal, however, is most effective when the feedwater is undersaturated with respect to the mineral scalants of concern. In addition to feed-flow reversal, the feasibility and effectiveness of osmotic backwashing of spiral-wound RO membrane elements have also been demonstrated (Sagiv and Semiat, 2005; Sagiv et al., 2008). Unlike pressure-based backwashing in MF and UF systems, osmotic backwashing involves changes in pressureconcentration conditions across the membrane to induce osmotic driving force for periodic reversal in permeate-flow direction. The method has been shown to be effective in minimizing membrane fouling without causing delamination of the polyamide active layer of RO membranes.

4.04.3.4.3 Managing the impact of feedwater-quality variation Feedwater-quality variability has important implications for brackish-water desalting since optimal RO/NF and ED/EDR plant designs are source-water dependent; plants operate best when the feedwater quality is consistent. Feedwater quality governs the required feed pretreatment to prevent membrane fouling and scaling, and the required applied pressure (for RO/NF) or electrochemical driving forces (for ED/EDR) for

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Figure 14 Optical images of membrane surfaces at three different times during membrane scaling tests: (a) without additives, and with the addition of (b) with antiscalant and aluminum chlorohydrate (ACH) H (c) antiscalant and ferric chloride, and (d) antiscalant and poly-DADMAC. Adapted from Kim M-M, Au J, Rahardianto A, et al. (2009) Impact of conventional water treatment coagulants on mineral scaling in RO desalting of brackish water. Industrial and Engineering Chemical Research 48: 3126–3135.

achieving a given water-recovery level. In developing a brackish-water desalting plant, for example, the feedwaterintake location and hydrogeologic conditions (e.g., salinity variation with depth and time for groundwater, etc.) must be investigated carefully given the potential for spatial and temporal variability in source-water quality. The desalting process must then be selected, designed, and configured to cope with site-specific challenges with respect to energy requirements, membrane-scaling mitigation, and residual concentrate waste management. Plant-operating conditions must be selected and adjusted to cope with temporal fluctuations in water-production level that may be imposed due to feedwater-quality variations, which in turn can significantly affect CP, membrane-scaling tendency, and energy consumption. Present RO/NF and ED/EDR plants commonly manage temporal variability in source-water quality by applying process control (automated or manual) for the sole purpose of maintaining process productivity. Process-control systems, however, can be designed to enable adaptive operation of these processes. For RO/NF processes, for example, optimal time-varying operating policy with respect to energy consumption has been

proposed (Zhu et al., 2009). Specifically, recent work has demonstrated that, in order to maintain a constant permeate flow in the presence of feed-salinity fluctuation, feedwaterflow rate and operating pressure can be selected to minimize energy consumption. The work suggests that, by applying a real-time optimization routine in a control system, adaptive operation of RO/NF processes with respect to temporal feedwater-quality variation can keep energy consumption at minimum. Present RO/NF and ED/EDR plants commonly operate at reduced water-recovery levels in order to enable safe operation when feedwater quality can drive the process toward the fouling and/or mineral-scaling thresholds. This conservative process operation is a precaution that must be taken since present traditional measures of plant-performance trends (e.g., primarily permeate-flux decline and salt passage in RO/NF desalting) do not guarantee sufficient early detection of membrane fouling and mineral scaling. Although various methods of scale and fouling detection have been proposed (Chen et al., 2004), it is only recently that real-time early detection of the onset of scale formation has become possible (Uchymiak et al., 2007). For RO/NF processes, for example,

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the use of a high-pressure flat-sheet membrane cell, either with a transparent window (Uchymiak et al., 2007) or a completely transparent RO/NF cell (Rahardianto et al., 2008), allows real-time digital imaging of the membrane surface. For scale detection, the membrane monitor would typically receive a sidestream from a tail element of the RO plant where the retentate concentration is highest. The system can be adjusted such that the level of solution supersaturation at the membrane monitor’s membrane surface is at or higher than that for the last RO membrane module, thereby ensuring that mineral scale would be detected first on the monitor’s membrane surface. An illustration is shown in Figure 15 of early detection of gypsum crystals on the membrane surface prior to the detection of measurable flux decline. This type of scale detection in RO/NF processes can potentially be adapted to ED/EDR processes and can provide an important monitoring capability to enable safe membrane-desalting operations that adaptively vary waterrecovery levels close to temporally varying membrane-scaling threshold levels (resulting from water-quality variation). Extending membrane monitoring for online-biofouling detection is also a challenge that merits pursuit in order to enable the design of effective RO/NF and ED/EDR operational strategies.

4.04.3.4.4 Enhancing water recovery At inland locations, it is desirable to operate brackish-water desalting at high water recovery in order to reduce the volume of generated residual concentrate for locally feasible options of concentrate disposal to become cost effective. To achieve high levels of water recovery, coupling of RO and EDR processes in a series configuration have proven to be effective in some industrial applications (Reahl, 1990, 2006; e.g., Figure 16). In such cases, the use of EDR to desalt RO concentrate is particularly effective when silica is the primary mineral scalant of concern since EDR is not constrained by uncharged species. RO/NF, ED/EDR, and their integrated processes, however, still require scale-mitigation methods; the traditional scale-mitigation methods only retard the onset of mineral scaling (antiscalants, polarity reversal, etc.), but do not remove scale precursors, and thus are constrained to a threshold recovery limit. To overcome this limitation, the integration of intermediate concentrate demineralization (ICD) in a two-step membrane-desalting operation is a promising strategy (e.g., Figure 17). The function of ICD is to remove mineral-scale precursors and thus reduce the membranescaling potential of the concentrate from a primary-membrane desalting step, allowing a secondary RO desalting step to enhance product-water recovery.

(a)

Relative permeate flux (F/F0 )

1 mm

(b)

1 A 0.8

B C D

0.6

0.4 0

5

10

15

20

25

30

35

Time (h) (c)

(d)

Figure 15 Optical images of gypsum scale (a–d) and flux decline (bottom) for the corresponding RO scaling test for which initial gypsum saturation index (GSI) at the membrane (LFC-1) surface was 2.09. The images, a–d were taken at 0, 5, 20, and 30 hs. Adapted from Uchymiak M, Bartman AR, Daltrophe N, et al. (2009) Brackish water reverse osmosis (BWRO) operation in feed flow reversal mode using an ex situ scale observation detector (EXSOD). Journal of Membrane Science 341: 60–66.

MgO

Clarifier (for SiO2reduction)

EDR diluate

RO

−

UF

+

EDR Concentrate waste

Feed Product water

Figure 16 Coupling of RO, EDR, and chemical precipitation for high-recovery desalting. Adapted from Reahl E (2006) Half A Century of Desalination with Electrodialysis, Technical Paper TP1038EN. GE Water and Process Technologies.

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Brine Intermediate concentrate demineralization (ICD):

Secondary RO desalting (SRO) Antiscalant

Soda ash MF

Primary RO desalting (PRO) Solids (CaCO3)

Antiscalant

Feed

PRO concentrate Product water

MF

Figure 17 Schematic of a brine-treatment process for RO concentrate volume reduction from a primary RO desalting process. Brine treatment process consists of intermediate concentrate demineralization (ICD) via chemical precipitation and microfiltration (MF), followed by secondary RO desalting. Adapted from Zhu A, Rahardianto A, Christofides PD, and Cohen Y (2010) Reverse osmosis desalination with high permeability membranes – cost optimization and research needs. Desalination and Water Treatment 15: 256–266.

The two methods of ICD via chemical precipitation and seeded precipitation are discussed in the following. ICD via chemical precipitation. A two-step RO system with ICD (Figure 17) via chemical precipitation of calcium carbonate as a scale-precursor-removal step has been evaluated (Rahardianto et al., 2007) and pilot-tested (Gabelich et al., 2007) for the desalination of Colorado River water (B700– 1000 mg l1 TDS). The precipitation process is analogous to the classical lime-soda or caustic softening processes (i.e., precipitation softening) (AWWA, 1999). Alkaline dosing (e.g., pH adjustment with NaOH, lime, or soda ash) of the primary RO concentrate in a precipitation reactor (e.g., solid contact reactor) induces precipitation of primarily calcium carbonate, depleting the concentration of calcium in the aqueous phase, thus reducing the RO concentrate saturation index with respect to calcium-bearing mineral scalants (e.g., calcium carbonate and gypsum) to well below saturation. As an alternative to alkaline dosing, CO2 stripping has also been shown to be effective for inducing CaCO3 precipitation in brackish waters (Lisitsin et al., 2008). An added benefit of ICD via precipitation softening is the potential co-precipitation of other mineral-scale/fouling precursors, such as of Ba2þ, Sr2þ, silica, and also adsorptive removal of natural organic matter. The permeate production with the above approach demonstrated overall water recovery of up to 95%, provided that good pH control was maintained in the precipitation reactor along with antiscalant makeup to control silica scaling in the secondary RO step. In cases in which silica is the mineral scalant that limits water recovery, the use of EDR instead of RO for the secondary RO desalting step may be beneficial. To improve the efficiency of solid–liquid separation in precipitation softening, fluidized bed reactors and integrated precipitation–filtration systems have been proposed (Graveland et al., 1983; Oren et al., 2001; Sluys et al., 1996). ICD via seeded precipitation. Primary RO (PRO) desalting can potentially generate concentrate that is in meta-stable supersaturation with respect to various mineral salts. Such

behavior is attributed to slow crystal-nucleation kinetics and/ or the application of antiscalant treatment (which retards precipitation). The generated supersaturation level in the PRO concentrate can be utilized to drive precipitation by crystal growth in the intermediate concentrate-demineralization stage, initiated by crystal seeding. In this case, seed-crystal surfaces provide crystal growth sites for precipitation reactions to occur, which would lead to concentrate de-supersaturation (Rautenbach and Linn, 1996; Bremere et al., 1999; Yang et al., 2008). The de-supersaturated PRO concentrate can then be further desalted in a secondary RO step. Thus, unlike demineralization by chemical precipitation, de-supersaturation by seeded precipitation avoids the use of dissolved chemical reagents to generate the precipitation driving force. Antiscalant carryover from the PRO, however, may retard seeded precipitation and significantly reduce the rate of de-supersaturation. Methods have been proposed for deactivating antiscalants prior to crystal seeding, such as the use of coagulants (Yang et al., 2007, 2008) and low-dose lime pretreatment (Rahardianto et al., 2010). The rate of de-supersaturation can also be enhanced by integrating a membrane-concentrator unit to increase solution supersaturation above preexisting levels in the PRO concentrate. In this case, a specially designed membrane-concentrator unit is required in order to avoid membrane fouling and/or scaling. For example, Rautenbach and Linn (1996) described the use of disk-tube NF modules on concentrate primary RO concentrate generated from desalting of dumpsite leachate (Figure 18). High overall RO recovery (495%) was achieved, but periodic NF feed-channel flushing (every 30 s) and alkali cleaning (every 250–300 h) were required as preventive actions against fouling and/or scaling.

4.04.3.4.5 Concentrate disposal An extensive survey in the USA revealed that brackish-water desalting plants employ the following methods for concentrate disposal, in the order of decreasing frequency of use: surface

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Seawater Use and Desalination Technology Crystallizer RO NF Feed water

RO

RO

Purified water

RO

Concentrate reject

Figure 18 Process schematic for high-recovery desalting of dumpsite leachate. Adapted from Rautenbach R and Linn T (1996) High-pressure reverse osmosis and nanofiltration, a ‘‘zero discharge’’ process combination for the treatment of waste water with severe fouling/scaling potential. Desalination 105: 63–70.

water discharge, discharge to sewers, deep-well injection, evaporation ponds, spray irrigation, and zero liquid discharge (ZLD) (Mickley, 2006). Concentrate discharge to surface water and sewers accounts for about B70% of plants surveyed, while deep-well injection accounts for B20% (Mickley, 2006). Most of the plants (B80%) do not treat the concentrate waste before disposal, while the rest apply minimal treatment such as aeration, pH adjustment, degassification, air stripping, or defoaming. Concentrate discharge into surface water/sewers are typically available only in the case of desalting plants that are small or located near coastal areas (e.g., ocean outfall), while deep-well injection systems are typically used in larger inland desalting plants and in remote locations. Nevertheless, concentrate conveyance over long distances for ocean outfall, while costly, has also been practiced in inland desalting. To avoid precipitation of mineral salts (due to supersaturated concentrate) in long-distance concentrate-disposal pipelines, antiscalant treatment, concentrate-stream isolation from the atmosphere (to prevent CO2 release to air), and avoidance of particulate contamination are important (Semiat et al., 2004). Evaporation ponds and spray irrigation have also been used for concentrate disposal, but are land intensive and thus are used less frequently. A system for enhancement of evaporation pond performance known as the wind-aided intensified evaporation (WAIV) process, involves periodic circulation of pond brine over wettable surfaces, designed to increase the effective evaporative surface area (Gilron et al., 2003). This results in enhanced evaporation, which also depends on wind speed and direction in addition to relative humidity. It has been reported that with the above approach, evaporative capacity per area footprint can be increased 450% in a typical Middle East dry climate. Concentrate disposal via ZLD processes typically employ industrial evaporation methods (e.g., single-/multiple-effect evaporators, VC evaporators, evaporative crystallizers, and spray dryers) that can be readily deployed but at high energy and capital costs.

4.04.3.4.6 Specific contaminant removal Brackish-water sources, especially those impacted by human activities, often contain specific contaminants that must be

removed in brackish-water desalting. Brackish water impacted by agricultural activities, for example, may contain elevated concentrations of boron, nitrates, pesticides, and selenium, while those impacted by mining operations may contain elevated concentrations of arsenic and other heavy metals. Most of these contaminants are readily removed in desalting operations by RO/NF or ED/EDR. In some cases, the generated concentrate may require treatment before disposal, but it depends on the concentrate-disposal methods and related permitting/environmental issues. In addition, polishing the permeate (or diluate) water may be required in order to comply with strict water-quality regulations. For example, ion exchange or EDI are common for removal of specific contaminants. In the case of boron, RO/NF and ED/EDR processes are typically ineffective when operated at near-neutral pH as boron primarily exists as uncharged boric acid. The deprotonated form of boron (borate) is highly rejected in RO/NF processes (490–95%), but this requires operation at high pH (pH 410) that may increase the tendency for membrane scaling by carbonate minerals. To allow operation at high pH for enhanced boron removal, a multi-pass RO/NF configuration (e.g., Figure 9) can be utilized in which the permeate from a primary RO/NF is desalted at an elevated pH level in a secondary RO process. It is noted, however, that EDI can also be effective for product-water polishing for boron removal (Wen et al., 2005).

4.04.3.4.7 Cost of brackish-water desalination Major cost items in brackish-water desalination typically include costs of electrical energy, chemical additives (antiscalant and acid for scale mitigation), membrane replacement, concentrate treatment for recovery enhancement, brine disposal, capital depreciation, and financial interests. It is not feasible to arrive at generalization of the cost structure of brackish-water desalination because capital, operating, and financial costs are highly site specific. Nevertheless, one should expect that the energy cost of brackish-water desalting to be much lower than seawater desalting due to lower salinity of brackish water. Therefore, the cost of chemical additives (e.g., antiscalants) can become a significant portion of the total operating cost. Concentrate management (treatment and disposal), however,

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99

(b) Multi-step RO desalting w/ ICD 92% Water recovery operating cost: US$0.45 m−3

(a) Single-step RO desalting 58% Water recovery operating cost: US$0.71 m−3 Electrical energy

RO membrane Electrical energy

Antiscalant

MF operation Brine disposal

RO membrane

ICD chemicals Antiscalant Brine disposal

MF operation

Figure 19 Operating cost estimates for (a) single-step RO desalting at 58% water recovery and (b) multi-step RO desalting with ICD at 92% water recovery (see Figure 17). Cost estimates are for desalination of San Joaquin Valley agricultural drainage water (8500 mg l1 TDS), assuming deep-well injection for concentrate disposal. Data from Zhu A, Rahardianto A, Christofides PD, and Cohen Y (2010) Reverse osmosis desalination with high permeability membranes – cost optimization and research needs. Desalination and Water Treatment 15: 256–266.

tends to be the primary cost component in brackish-water desalting, especially at inland locations where disposal options are limited. An illustration of the significant impact of brine management (treatment and disposal) on operating costs is illustrated in Figure 19 for the case of agricultural-drainage-water desalting (8500 mg l1 TDS; Zhu et al., 2010). In this example, the effectiveness of antiscalants against gypsum scaling is expected to limit RO water recovery to 58%, leading to a large volume generation of RO concentrate. The operating cost is estimated to be high at US$0.71 m3 product; a large portion of this cost is for concentrate management (81%), given an estimated brine-disposal cost of US$0.8 m3 brine (e.g., via deep-well injection, western San Joaquin Valley; Johnston et al., 1997). Recovery enhancement to 92% recovery via ICD and secondary RO can reduce the total operating cost to BUS$0.45 m3 product. Although the contribution of concentrate-management costs (i.e., ICD chemicals for concentrate treatment and brine disposal via deep-well injection) to the total operating cost is lowered to 64%, this concentraterelated cost remains significant. Total costs of present brackish-water desalting plants vary significantly, representative of the high variability of local resources (e.g., feedwater quality and availability of concentrate-disposal sites). For example, a survey of six brackish groundwater RO desalting plants built during the past decade in Texas (4500–104 000 m3 d1 or 1.2–27.5 MGD production capacity) revealed total production costs between US$0.33 and 0.69 m3 product, with approximately 60–70% attributed to operation and maintenance (O&M) costs and the remainder being financial-interest costs (Arroyo and Shirazi, 2009). Of particular interest in the plants surveyed is the K. B. Hutchison plant (capacity of 104 000 m3 d1; total permeate production cost of US$0.65 m3), in which almost 30% of the capital expenditure was associated with the deep-well injection system for brine disposal (Committee on Advancing Desalination Technology, 2008). Finally, it is noted that

comparative analysis has shown that ED/EDR plants may be more cost effective than RO/NF plants when feedwater salinity is less than about 3700 mg l1 TDS (US Bureau of Reclamation, 2003).

4.04.3.5 Future Developments Significant improvements in the performance and cost effectiveness of pressure- and electrochemical-driven desalting technologies over the past two decades have enabled brackishwater desalting applications to meet anticipated water demands, while moving forward toward water sustainability. To sustain this growth, further technology developments are needed for optimal use of brackish-water resources, considering the spatial distribution and temporal variability of source-water availability and characteristics, energy sources, and concentrate-management options. Concentrate management, in particular, continues to be the primary cost impediment in many brackish-water desalting applications, especially at inland locations. Improvements are needed in the treatment and utilization of desalination concentrate (i.e., brine). Lesschemical intensive methods for scale-precursor removal, for example, would reduce costs associated with water-recovery enhancement in brackish-water desalting. There is also potential for chemical recovery from brine streams, such as acid/ base chemicals and mineral commodities. Significant opportunities exist for the development of smart membrane systems that can operate autonomously and remotely for optimal desalting of brackish-water resources. Such systems could be suitably located at sites that would match local water demands with availability of local brackishwater resources, as well as minimize costs associated with concentrate disposal and chemical and energy use. For example, smart membrane systems that can operate adaptively near fluctuating membrane fouling and scaling threshold levels, due to feedwater-quality variations, could enable one to maximize water recovery in real time and thus reduce

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concentrate-disposal costs. This would require further development of software and devices that can provide high-fidelity sensing of membrane fouling and scaling and timely automatic actuation of preventive actions. Integration of fouling and scaling mitigation methods that are less chemical intensive would also be highly beneficial for smart water systems. Development of high-permeability membranes for RO/NF desalting is not expected to lead to significant reduction in the cost of brackish-water RO desalination, especially since recent RO/NF membranes already allow for cross-flow RO/NF operation near the thermodynamic restriction (Zhu et al., 2008). Further developments of membranes and membrane processes that are fouling/scaling tolerant are needed, as well as membranes and processes that are highly selective for targeted contaminants.

4.04.4 Desalination of Wastewater for Reuse Wastewater reclamation is increasingly practiced around the world to meet industry, domestic, and agricultural needs. When the salinity of the reclaimed wastewater is too high for its intended use, a desalination stage may be required to produce water of appropriate quality. The cost of desalination processes means that desalination of reclaimed wastewater for agricultural purposes is rarely practiced, and the main uses of desalinated reclaimed water are for industrial and domestic uses. RO desalination is the dominant desalination technique used, as it requires far less energy (0.7 kW h1 m3; Leslie and Myraed, 2009) than thermal processes for this application. Therefore, the discussion in this section focuses on membrane-based desalting processes.

4.04.4.1 Water Quality Desalination of reclaimed wastewater and brackish groundwater are similar in some respects, as both have similar TDS concentrations, particularly when compared to seawater. However, the higher concentration of organic compounds and the large variability in their nature means that the operational issues are significantly different, with a high tendency for biofouling existing in reclaimed wastewater systems. Additionally, the higher concentration of phosphates in reclaimed wastewaters may also lead to scaling by calcium phosphates rather than calcium carbonate and gypsum, as is the case for brackish water systems. The quality of water feed to a wastewater reclamation plant depends upon the level of treatment in the preceding wastewater treatment plant, and only secondary or tertiary wastewater effluents would be considered suitable for processing in a reclamation plant. Secondary treated effluent refers to wastewater that has been treated by sedimentation followed by a biological process such as treatment in an activated sludge plant. Tertiary treatment refers to secondary treatment followed by a filtration step, such as media filtration, so that the turbidity and TOC concentrations are generally lower, and if coagulation with metal salts is used, then the phosphate concentration will also be reduced (Henriksen, 1963). Table 5 shows typical water qualities for secondary and tertiary wastewater treatment plant effluents.

Table 5

Typical wastewater treatment effluentsa Secondary treatment

pH Turbidity (NTU) Total suspended solids (mg l1) TOC (mg l1) TDSa Nitrate (mg l1) NH3 – N (mg l1) Phosphate (mg l1)

Tertiary treatment

6.5–8.2 5–25 25–35

6.5–8.2 o3 5–10

10–20 500–1500 20–30 3–10 3–8

4–10 500–1500 a 5–20 a 0.4–5 a 0.5–5

a

Lower limits for biological nutrient removal plants: Note: TDS concentrations lower than 500 mg l would not usually be considered for desalination. From Montgomery Watson Harza (Adham S, Burbano A, Chiu K, and Kumar M) (2006) Development of a reverse osmosis/nanofiltration (RO/NF) knowledge base. Report of the California Energy Commission. Pasadena, CA: California Energy Commission; and Water Corporation (1999) Personal communication, Water quality data from wastewater treatment plants.

4.04.4.2 Pretreatment Water Factory 21 in Orange County, California, USA, was an early successful example of wastewater reclamation. It began operation in 1976 and operated for approximately 30 years. Its pretreatment system initially consisted of chemical clarification and settling, ammonia stripping, re-carbonation, multimedia filtration, and activated carbon adsorption prior to being fed into RO membranes. However, extended operation of this plant revealed that carbon fines blocked the entrance to the spiral wound RO elements, while the ammonia stripper cooled the water leading to lower productivity through the RO membranes. Additionally, residual ammonia is converted to chloramines and assists with biofouling control. The pretreatment process before RO was then altered to lime clarification, re-carbonation, chlorination, and media filtration (Asarno, 1998). Water Factory 21 has now been replaced by the Groundwater Replenishment Scheme, in which the pretreatment process is now MF (Montgomery et al., 2006). The change in pretreatment trends to the use of MF and UF systems is a result of the very high quality of water produced, improvements in the operability of MF and UF systems, and relative reductions in their price. A 2006 survey of desalination systems by Montgomery Watson Harza (2006) showed that of the 14 wastewater-reclamation plants included in the survey, only one had a conventional pretreatment stage (coagulant, sedimentation, and media filtration), whereas nine had only MF or UF, two had both conventional and membrane pretreatment; and the remaining two had only cartridge filtration. The high number of plants with only membrane pretreatment shows that this is the recent trend, and it is increasing in popularity. Water quality from MF or UF systems is usually o0.2 NTU and often consistently lower than 0.1 NTU, and TOC concentrations are reduced from 5–15 mg l1 to 3–14 mg l1. Greater TOC reductions can be achieved if coagulant is used, with ferric chloride or ferric sulfate more commonly used than aluminum-based salts. It is noted that high TOC content may suggest high concentrations of biopolymers, such as

Seawater Use and Desalination Technology

101

Cl2 Lime

To RO Wastewater treatment

Lime sludge Lime clarification

Air re-carbonation

Dual-media filtration

Backwash To RO Cl2 Coagulant, pH Cl2, backwash MF/UF

To RO Air

MBR

Figure 20 Various options for pretreatment stages before wastewater-reclamation desalination.

polysaccharides and proteins (Jarusutthirak et al., 2002), which are strong foulants of MF and UF systems and are easily biodegradable, thereby promoting biofilm growth. Flux through the MF/UF membranes is typically 30–40 l1 m2 h1. Either pressure driven or submerged MF or UF membranes may be used in the pretreatment stage (see Figure 20). The decision to choose either pressurized or submerged MF or UF is based on cost and operability. Submerged systems can be easily fitted into a tank, and so are cheaper for large units or when retro-fitting to an existing tank. However, pressurized systems can be packaged in the manufacturer’s plant and have greater capacity to increase throughput during peak-flow periods. Hypochlorite is often added before MF or UF, as it reacts with ammonia in the wastewater to form chloramines that limit biofilm growth on the membranes. If the concentration of ammonia in the feedwater is low, ammonia may be added before the addition of hypochlorite although this is seldom a necessity. Chloramines have been the preferred disinfectant for controlling biofilm growths on MF, UF, and RO membranes because of their lower oxidizing potential relative to chlorine and hypochlorite. Reasonably low but effective chloramine doses (1–4 mg l1) may be applied safely to suppress biofilm growth while minimizing oxidative degradation of membranes. As chloramine tolerance of membranes may vary due to the catalytic effects at high temperature, low pH, or presence of transition metals, optimal chloramine dose should be determined for the specific source water of interest, membrane type, and operating conditions. New membrane materials such as polyvinylidene fluoride (PVDF) and polyethersulfone (PES) systems are chlorine tolerant, and chemically enhanced backwashing (CEB) strategies are in use currently. Chlorine in concentrations between 25

and 100 mg l1 is added to the backwash water and the membranes are soaked in the chlorine solution for 5–10 min. This would typically occur 1–2 times a day, and decreases the requirement for chemical cleaning of the membranes. Chemical cleaning is often required as contaminants can irreversibly foul the membranes with extended filtration times. Chemical cleaning is usually performed with caustic surfactant solutions to remove organic contaminants, while ethylenediaminetetraacetic acid (EDTA) and/or citric acid may be added to remove inorganic foulants. Chemical cleaning would typically take place every 6–8 weeks without chemically enhanced backflush (CEB) and after more than 6 months with CEB. Membrane bioreactors (MBRs) are also being considered for use in pretreatment before RO membranes for wastewaterreclamation plants. Qin et al., (2006) have demonstrated the use of an MBR RO system in place of a conventional wastewater-treatment plant followed by MF or UF pretreatment. They were able to demonstrate that the MBR pretreatment produced water with lower TOC concentrations than the conventional wastewater-treatment plant–MF system, producing TOC in the range of 4.9–5.1 mg l1 compared with 6.8–6.9 mg l1 for the conventional-MF pretreatment. This improved water quality, although only slightly, enabled the RO system to operate at higher fluxes of 22 l m2 h1 compared to 17 l m2 h1 when fed with conventional-MF treated water. The final RO permeate also improved, with TOC values of 24–33 ppb rather than 33–53 ppb. Again, while this was only a slight improvement in water quality, it was significant for the semiconductor industry using the reclaimed water as their requirement was for ultrapure water. Additionally, if being used for potable purposes, the lower TOC value indicates reduced organic components present in the wastewater,

102

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lessening the potential health risks associated with trace organic compounds, such as endocrine disruptors, in the final product water. Acid and antiscalant addition occurs after the MF/UF or MBR pretreatment stage, to reduce the likelihood of scaling in the RO stage. Addition of acid to lower the pH to around 6.5 is common (Zach-Maor et al., 2008), and the antiscalant type depends upon the likely mineral scalant.

4.04.4.3 RO Processes RO desalting of wastewater can usually achieve 70–90% water recovery using a multi-stage process configuration (e.g., Figure 11). Recovery levels of 70–75% are typically achieved using a two-stage RO desalting process, while recovery levels between 75% and 88% typically require a three-stage RO desalting process. Brackish-water membranes are used for wastewater treatment, as wastewater has salinity levels similar to those of brackish water. The operating pressure varies between 7 and 25 bar, with greater pressures required to achieve high recoveries because of the increase in the final salt concentration. Flux through the membranes varies considerably, with reported values between 14 and 27 l m2 h1 (Montgomery et al., 2006). The recommended design flux is usually quoted as between 15 and 20 l m2 h1. Fouling in wastewater-reclamation plants can vary from that in brackish water, as the higher organic carbon content of the feed increases the potential for biofilm growth, specific organic compounds in the feedwater may foul the membrane, and higher phosphate concentrations may lead to calcium phosphates precipitating rather than calcium carbonate or gypsum. The addition of chloroamine before the MF or UF process enables the disinfectant to control biofilm growth in both the pretreatment and desalination stages. This strategy is generally successful, with cleaning of membranes required between 3 and 6 months. Other approaches to biofilm control, such as chlorine dioxide dosing (Wise et al., 2004) and UV disinfection prior to the RO unit (Lo´pez-Ramı´rez et al., 2003) have been suggested but further studies are required before they can be implemented. Treatment of wastewater from domestic sources does not usually pose any significant problems, but industrial waters contain a wide range of compounds not generally present in domestic sewage and perhaps only present in the particular wastewater catchment. This has caused problems for a number of commercial systems, such as the Wollongong Recycled Water Plant, NSW, Australia (Borse et al., 2009). The Wollongong wastewater contained specific organic compounds, tert butyl phenol, 2-methylthio-benzothiazole, trichlorphenol, and trichlorocresol, that have the potential to foul membranes even when present in small doses and which may not be easily detected by membrane autopsies. Generally, these fouling issues can only be managed by operating at higher pressures or lower fluxes and by developing specific cleaning regimes. Additionally, these specific fouling issues are difficult to detect because of the low concentrations of the specific foulants, the difficulty in detecting them via membrane autopsies, and on occasions due to the intermittent presence of the contaminants. Bench- and pilot-scale field studies are therefore

recommended when treating wastewaters from industrial sources because this is the most reliable approach for detecting such issues. Mineral scaling of membranes is an issue, particularly when operating at high water recoveries. The control of mineral scaling is similar to that for brackish water systems, with acid addition prior to RO treatment to increase the solubility of inorganic mineral scalant, and the use of antiscalants. Calcium phosphate scaling arising from the high concentrations of phosphate in the feedwater, however, is more difficult to control with antiscalants (Greenberg et al., 2005); presently, feedwater pH adjustment appears to be the best available option for controlling calcium phosphate scaling. Alternatively, control of phosphate via coagulant addition in the pretreatment stream is also an option, but higher doses of coagulants are required than that for turbidity removal alone (Henriksen, 1963).

4.04.4.4 Final Water Quality Permeate TOC concentrations are low, around 50 ppb, and TDS concentrations are approximately 20–30 mg l1. Water of this quality is corrosive, and stabilization of permeate by mixing with nondesalination effluent is frequently practiced along with the addition of a residual disinfectant such as chlorine. If the water is to be used for industrial purposes, however, it is sometimes delivered in the high-purity form to be used in cooling towers and boilers. In Singapore, the semiconductor industry requires ultra-high-purity water; therefore, stabilization of recycled water is not in practice. The Luggage Point recycled water facility in Brisbane, Queensland, similarly does not stabilize the water delivered to a petroleum refinery where it is used in cooling towers and a demineralized water plant.

4.04.4.5 Concentrate Disposal The presence of nutrients and synthetic and natural organic compounds in wastewater brine complicates its disposal in comparison to brackish water brines (CH2M HILL, 2009). The treatment of wastewater brines in evaporation ponds is usually unaffected by these contaminants, but discharge to waterways or coastal marine environments may require additional treatment. For instance, the Bundamaba advanced watertreatment plant in Brisbane, Australia, reclaims wastewater for industrial use and indirect potable-water reuse and releases the brine concentrate into the Brisbane River that feeds into Morton Bay. The discharge of nutrients in this region is regulated because of the sensitive ecology, and treatment of the brine concentrate for nutrient removal is required. The pretreatment sludge is thickened with the aid of polymers, centrifuged, and the centrate mixed with the brine concentrate. The combined concentrate is treated via a fixed-film nitrification process following which it enters a denitrification stage that requires methanol addition to assist the process (Davies, 2009). The Bundamba brine-concentrate treatment process uses a conventional nitrogen-removal strategy, but there have also been studies investigating the use of wetlands to remove nutrients and metals from brine concentrate (Kepke et al., 2009).

Seawater Use and Desalination Technology

Sites at Luggage Point, Queensland, Australia and Oxnard, California, USA have pilot-tested the treatment of wastewaterreclamation brine by surface flow and vertical-upflow wetland cells. Initial results demonstrated 70–80% reductions in nitrate, up to 50% reduction in selected metals (B, Cu, Cr, and Mo) under some conditions, and increases in concentrations of other metals (As, Al, Cd, and Mg) and TDS. The increase in load of some metals was due to leaching from the soil, while the TDS increase was the result of plant transpiration reducing the water volume. Additional trials are required before the effectiveness of this approach is fully understood.

4.04.5 Alternative Technologies The large increase in demand for desalination technologies and the relatively higher energy requirements compared to other water-treatment processes have led to the intense search for alternate desalination technologies. The most notable of these processes are discussed in this section.

4.04.5.1 Membrane Distillation Membrane distillation (MD) is a desalination process which brings membranes into thermally based processes such as MED. Therefore, the theoretical approaches to assess MD performance, just as in distillation, stem from the enthalpy of evaporation, and the potential advantages lie in the functions of the membrane, which include

• • •

•

Containment of the evaporating surface (vapor–liquid interface) thus allowing more control of the system in certain applications. Efficient packing of controlled, regular membrane geometry for smaller process footprint. Novel aspects of fluid handling to allow for more functional setups, such as better heat efficiencies, and further footprint reduction. For example, in direct-contact MD (DCMD) mode, higher fluxes are observed due to reduced resistance allowed by the intimate contact of the cooling fluid on the permeate side of the membrane. Moreover, in the air-gap MD (AGMD) mode, a single compact module performs heat recovery simultaneously during desalination, providing improved heat recovery when compared with direct-contact MD. Cheaper materials for constructing the membrane modules (i.e., polymer-based materials) when compared to systems consisting of corrosion-resistant metals.

However, there are also some disadvantages of MD when compared to traditional thermal desalination processes such as MSF, MED, and VC, such as

• • •

Lower heat-transfer coefficients and mass-transfer coefficients compared to traditional thermal desalination processes such as MED, MSF, or VC. The heat efficiency as measured by the GOR is still lower than that of traditional thermal processes. For low temperature VC or MED (70 1C), however, aluminum transfer surfaces are used and corrosion is not an issue, thereby reducing any advantage in the use of construction materials made of polymer in MD.

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So, while there are some potential advantages in the application of MD, it has struggled to find application in the water industry because of the efficiency of traditional thermal processes and RO processes. The recent renewed interest in MD research will need to find specific applications where the advantages of MD can be realized. Applications such as brine treatment, the utilization of available low-grade heat (o70 1C), which traditional thermal processes find difficult to use, or applications requiring a small footprint process appear to be best suited to MD. Such processes have not been economic to consider previously, but with greater pressure on water resources and increasing costs of brine disposal in some locations (lined evaporation ponds), this economic equation may change.

4.04.5.1.1 Brief history The first patents for MD were filed in the 1960s (Bodell, 1963; Weyl, 1967), but the process never progressed into commercial utilization due to the high cost of the membranes and the process at that time. Moreover, RO was being developed, and when weighed up at a time of low-cost electricity, RO was by far superior in producing water at lower-cost per unit volume. Recently, with rising energy costs and awareness of greenhouse-gas emissions associated with electricity production needed for RO, MD has seen a recent resurgence (Curcio and Drioli, 2005). This is coupled with an increase of interest in membrane systems, as there are now numerous industries manufacturing membranes in commercial quantities at competitive prices. It is important to note here that these membrane advancements have been motivated for MF and application in advanced clothing applications (i.e., Gortex-type membranes), and not MD specifically. The range of hydrophobic membranes including polypropylene, PVDF, and polytetrafluoroethylene that have recently emerged make excellent candidates for MD. These improved membranes have led to increased flux and lower fouling, but limitations associated with energy efficiency have not been assisted by these improvements.

4.04.5.1.2 Membrane distillation configuration Generally, an MD system is made up of a hydrophobic porous membrane over which the saline water is passed. As the membrane is hydrophobic, the liquid feed cannot penetrate when the pressure is lower than the liquid entry pressure (LEP) of the membrane. When estimating LEP in MD, it is important to take the largest pore because any breakthrough of liquid has significant consequence to salt rejection. For example, on a 0.5-mm pore-size rated membrane, LEP calculated would be 270 kPa, but in practice, is at least 130 kPa. Such pressures are suitable for most MD setups, but higher LEPs can be achieved with UF membranes in the MPa region as their pore sizes are around 20 nm. These pressures are essential for the design of the system whereby the pressure of the feed must not exceed the LEP. There must also be an allowance factored into the design to allow increasing backpressure as a result of module fouling since feed flow is typically maintained constant for effective MD performance. As a process, there are four MD configurations: DCMD, AGMD, sweep-gas (SGMD), and vacuum (VMD), each of which is shown in Figure 21.

Seawater Use and Desalination Technology

Permeate (a)

(b)

Pore Permeate-vapor

Permeate-vapor (c)

Sweeping gas

Feed

Feed Pore

Pore

Pore

Membrane

Membrane

Vacuum

Cooling plate

Cool feed

Membrane

Permeate

Feed

Membrane

Hot feed

104

(d)

Figure 21 Four membrane distillation (MD) setups commonly applied, direct-contact MD (DCMD) (a), air-gap MD (AGMD) (b), vacuum MD (VMD) (c), and sweep-gas MD (SGMD) (d). From Zhang J, Duke M, Ostarcevic E, Dow N, Gray S, and Li J-D (2009) Performance of new generation membrane distillation membranes. Water Science and Technology: Water Supply 9(5): 501–508.

For water treatment, DCMD and AGMD are the most widely researched MD processes for desalination, while SGMD and VMD are generally applied for volatile organic compound (VOC) removal (Curcio and Drioli, 2005). With regard to desalination, AGMD shows a lot of promise due to its potential for high energy recovery resulting from its inherent ability to allow for simultaneous evaporation and condensation (heat recovery) within the module, as shown in Figure 22, which is similar to the MED concept.

4.04.5.1.3 The Memstill project The Memstill project is a major MD development project using the AGMD process for its potential as a lower-energy desalination alternative to RO (Dotremont et al., 2009). It has been claimed that because of its thermal driving force and large surface areas, low-grade heat from waste sources makes it more cost efficient than conventional pressure-driven (i.e., electric) RO. However, operating at MD’s optimal energy efficiency comes at the expense of low flux and high surface areas, and use of waste heat requires a heat source of compatible energy value. Table 6 shows the latest developments of the Memstill process. The first Memstill pilot plant with M28 module began operation at the Senoko Incineration Plant, Singapore, with raw seawater fed from the Straits of Johor. The second and third Memstill pilot studies with M32 and M33 modules were conducted in the Netherlands at the E.ON Benelux power plant (Dotremont et al., 2009). Both were fed with seawater from the Port of Rotterdam. Progress in demonstrating improved efficiency for Memstill has seen heat efficiencies come down to about one-third of the M28 module design. While the processes in MD are similar to the thermal processes presented earlier, the use of membranes instead of metallic heat-transfer surfaces increases the resistance to heat and mass transfer, and low flux across the membranes results. This leads to the need for large surface areas, and high packing densities, as has been achieved for other membrane-based

Figure 22 Concept schematic of the Memstill process. From Dotremont C, Kregersman B, Sih R, Lai KC, Koh K, and Seah H (2009) Seawater desalination with Memstill Technology – a sustainable solution for the industry. Paper presented to IWA Membrane Technology Conference, Paper 190. Beijing, China, 1–3 September.

processes, implying that it could result in a compact device with lower footprint. However, the energy needs are basically similar to those of the thermal processes. Special designs may allow process operation similar to MSF or MED. The thermal processes are fed by low-value heat sources. However, the main question is whether it is possible to obtain high GOR as obtained with the existing thermal processes, particularly

Seawater Use and Desalination Technology Table 6

105

Summary of previous Memstill pilot installations

Module used

M28

M32

M33

Location of testing Duration of testing Absolute flux (l m2 h1) Internal heat recovery (%) Heat consumption (kW h1 m3)

Singapore, Straits of Johor March 2006 to June 2007 0.25 30 278–556

The Netherlands, Port of Rotterdam October 2006 to January 2007 2.5 50 111

The Netherlands, Port of Rotterdam April 2008 to October 2008 3 90 97–111

From Dotremont C, Kregersman B, Sih R, Lai KC, Koh K, and Seah H (2009) Seawater desalination with Memstill Technology – a sustainable solution for the industry. Paper presented to IWA Membrane Technology Conference, Paper 190. Beijing, China, 1–3 September.

given the lower temperatures used, which limit the temperature difference and hence the heat recovery. If cooling water is used to condense the vapor via cooling devices, it requires much higher energy than in the regular thermal desalination. This has led to much higher energy consumption (low GOR and high pumping energy) and high cost of equipment (large membranes). Capturing the heat of condensation from the vapor in these processes, however, has the potential to produce high GORs. A certain amount of heat recovery from the permeate is also possible, but the extent of heat recovery is currently much lower than that from the heat of condensation (Table 6).

4.04.5.2 Forward Osmosis 4.04.5.2.1 Background The process of osmosis involves the migration of water from a less-concentrated saline solution across a water-permeable (and salt impermeable) membrane to a more concentrated saline solution. This process is energetically favorable as the system seeks to increase entropy (i.e., mixing). As shown in Figure 23, forward osmosis (FO) works on this principle to harness water, typically from naturally saline sources, such as seawater, by drawing it through the semipermeable membrane into a very different synthetic saline solution at a higher concentration than the original feedwater. The special aspect of FO is that the high-concentration synthetic saline draw solution contains different salts that are more practically separated than the original saline source. Clearly, the energy input to drive FO is in the regeneration and recirculation of the draw solution. According to Bolto et al. (2007), FO has been applied to contaminated waters, drawing water into fluids containing electrolytes and sugars for military applications. In such applications, the draw solution is not regenerated, as the sugars that constitute the draw solution are consumed along with the clean water in the final product. Many uses of FO such as this are practical, and a good comprehensive review on FO was made in 2006 (Cath et al., 2006). However, the use of FO as a continuous desalination process in which the draw solution is recycled has several obstacles even now, with the major process issues being the need to develop compact, stable membrane systems specifically for FO, and to improve the ease of regeneration of the draw solution which must be nontoxic.

4.04.5.2.2 Recent developments RO has also been used to remove the water from a NaCl draw solution as shown in Figure 24. RO brine concentrates from a

Concentrated draw solution recycle Saline feed water

FO membrane unit

Draw-solute separation

Potable water

Brine Diluted draw solution Figure 23 Forward osmosis (FO) concept processes. From McCutcheon JR, McGinnis RL, and Elimelech M (2005) A novel ammonia–carbon dioxide forward (direct) osmosis desalination process. Desalination 174(1): 1–11.

groundwater source up to 17 500 mg l1 were further desalinated by the FO/RO process. The system’s recovery reached 90% but performance was limited by scaling salts. FO may not emerge as an energy-saving process, but due to its operating conditions, may be more suitable in environments which normally foul membranes in conventional RO. This is mostly due to its ability to operate in the feed solution at a much lower pressure and so, for example, in highly turbid water, particles are not forced into the membrane pores leading to blockage (Bolto et al., 2007). The assumption that low-flux FO is economical is yet to be demonstrated. Opportunities may present themselves in harnessing renewable or waste energy for desalination, much as in thermal distillation processes. The draw solution can be regenerated by heat and thus can be adapted to harness solar thermal energy or low-grade waste heat. This is the case for the ammonia and carbon dioxide mixing with the water to form ammonium bicarbonate, which can be removed by heating to around 60 1C (McCutcheon et al., 2005). However, energy is needed not only to distil the ammonium carbonate, but also to pump water from the sea, as in RO processes, and for cooling to remove the heat of adsorption of the ammonium carbonate to regenerate the draw solution. Evaporation of water vapor occurs during the distillation of the ammonium carbonate from the solution, and distillation down to very clean, ammonia-free product is

106

Seawater Use and Desalination Technology

Concentrated DS

FO

RO

Feed Draw solution

T C Diluted DS

High pressure pump

Product water

Figure 24 Flow diagram of an FO and RO integrated process to purify water From Martinetti CR, Childress AE, and Cath TY (2009) High recovery of concentrated RO brines using forward osmosis and membrane distillation. Journal of Membrane Science 331(1–2): 31–39.

+ +

+ + +

+

+ +

+

Carbon aerogel

+

+ electrode __ _ _Negative _ _ _ _

_

Treated water Brackish water

+

+

+

+

+

Positive electrode Figure 25 Graphical representation of capacitive deionization (CDI) process. From Gabelich CJ, Xu P, and Cohen Y (2010) Concentrate treatment for inland desalting. In: Escobar IC and Schafer AI (eds.) Sustainable Water For Future Use – Water Recycling Versus Desalination, vol. 2, 1st edn., pp. 295–326. Amsterdam: Elsevier.

necessary. This requires considerable energy and needs to be performed under vacuum conditions, similar to evaporation desalination techniques. The capital cost of VC is high, as is the energy demand. An estimation of the energy consumption for this process has been made by Semiat et al. (2010), who calculated that it would require 13 7 3 kW h1 m3 and that the final permeate would contain 9 mg l1 ammonium. The permeate would require further processing, via a process such as ion exchange, to reduce the ammonium concentration to an acceptable level (o1 mg l1).

4.04.5.3 Capacitive Deionization Capacitive deionization (CDI) is a conceptually simple technique to remove salts from water. It works by passing the saline water over a charged electrode surface which literally causes the ions (e.g., NaCl, CaCO3, and CaSO4) to stick to the electrode. This concept is shown in Figure 25.

Once saturated, the charge must be reversed and the released ions redirected to the discharge brine stream. Clearly, more surface area available to the ions per unit volume of material is desired for economical performance. Hoang et al. (2009) carried out a recent review of the field, identifying key carbon-based materials being investigated to improve efficiency: carbon aerogels, activated carbon cloth with metal oxide nanoparticles, and carbon nanotubes. When treating an artificial brackish water of 2000 mg l1 TDS (Welgemoed and Schutte, 2005), CDI required only 0.59 kW h1 m3 to recover 70% of the water at a permeate concentration of 500 mg l1. An example of such development is reported by Zou et al. (2008), who used high-surface-area activated carbon to effectively reduce the salinity of water in laboratory trials, and also found that modification of titania nanoparticles increased the electrosorption efficiency. There are, however, a number of issues that CDI technology must address before it can be an economic alternative to

Seawater Use and Desalination Technology

RO and ED for the treatment of brackish waters. While sorption capacities of up to 80 mg TDS g1 of aerogel have been claimed, the capacities of carbons in actual application trials has only managed B8 mg TDS g1 of aerogel (Gabelich et al., 2001). This arises from ion adsorption being based on ionic hydrated radius, so that only pores greater than 20 nm in diameter are suitable sorption sites (Gabelich et al., 2002). Additionally, fouling of carbon aerogels by organic compounds readily occurs, which limits the adsorption capacity of the carbon electrodes (Gabelich et al., 2001, 2002; Lee et al., 2008). Commercial systems are now available and are finding application in polishing ultrapure water or treating lowsalinity brackish or wastewaters (Farmer et al., 1996). Limitations of CDI as a concentrate-minimization technology include (1) preference for removal of monovalent ions over divalent, (2) limited sorption capacity of carbon-based electrode materials, and (3) organic fouling to which the electrodes are prone when used on natural waters (Gabelich et al., 2010).

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Relevant Websites http://serc.carleton.edu EarthLabs. http://www.ide-tech.com IDE Technologies.

4.05 Abstraction of Atmospheric Humidity PA Wilderer, Technische Universitaet Muenchen, Institute for Advanced Study, Munich, Germany E Davydova and Y Saveliev, Meteo-Systems, Zug, Switzerland & 2011 Elsevier B.V. All rights reserved.

4.05.1 4.05.2 4.05.3 4.05.3.1 4.05.3.2 4.05.3.3 4.05.3.3.1 4.05.3.3.2 4.05.3.3.3 4.05.3.3.4 4.05.4 4.05.4.1 4.05.4.1.1 4.05.4.1.2 4.05.4.1.3 4.05.4.2 4.05.4.3 4.05.4.4 4.05.4.4.1 4.05.4.4.2 4.05.4.5 4.05.4.5.1 4.05.4.5.2 4.05.4.6 4.05.5 4.05.5.1 4.05.5.2 4.05.5.3 4.05.5.4 4.05.5.5 4.05.6 References

Introduction Volume of Water in the Atmosphere Fundamentals of Rainfall Generation Preliminary Remarks Water in the Atmosphere Atmospheric Processes of Precipitation Formation Thermodynamic processes Unit processes Thermodynamic approach to explaining precipitation events Electrical processes Innovative Abstraction Methods Condensation Technology General remarks Proposed technologies Research needs Fog Collection Generating Clouds with the Aid of Heat Islands Cloud Seeding Development of the technology Evaluations and recommendations Rainfall Enhancement by Cloud-Particle Charging Scientific background Development of the technology Evaluation and Recommendations Rainwater Collection, Purification, and Storage Incentives for Action Rainwater Collection Pollution and Purification of Stormwater Runoff Purification of Stormwater Runoff in Decentralized Treatment Units Large-Scale Storage of the Collected Rainwater Overarching Aspects

4.05.1 Introduction Since the middle of the nineteenth century, the number of people on our planet has been increasing at an unprecedentedly high rate. In 1959, 2.5 billion people lived on the Earth. Within 50 years, the world’s population had risen to about 6.7 billion, with an annual growth rate of about 82 million (Anonymous, 2008). Even more dramatic is the rate at which the population of urban areas has increased. In 2008, about 50% of the world’s population lived in urban areas, half of them in cities of 500 000 inhabitants or less, and the other half in cities of up to even 30 million people. By the year 2035, more than 70% of the global population is expected to live in cities. Moreover, a large proportion of the world’s population today lives in coastal areas, in strips about 100 km wide along various shorelines (Anonymous, 2006). As a consequence of such growth in the population, the demand for water and food, land and infrastructure, and

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commodities and energy has also risen, both globally and locally. Simultaneously, the emission of solid, liquid, and gaseous wastes has proliferated, polluting not only land and air, but also aquifers (groundwater), rivers, lakes, coastal areas, and oceans. These water resources, which are crucial for satisfying the water needs of humans and animals, agriculture, industry, and the planet’s entire ecosystem, are adversely exploited by this pollution. It is universally known that water is the essence of life. In contrast to all other living beings on the Earth, humans require water not only for life-enabling functions, but also for a multitude of other purposes arising from the human desire to create a lifestyle superior to that offered by nature. We use water for showering, recreational bathing, running washing machines and dishwashers, flushing toilets, washing cars, and many other activities. Farmers use water for growing crops and quenching the thirst of domesticated animals. Water is needed to produce paper, steel, and textiles, just to name a few.

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Additionally, water is used for various cooling purposes. All of this equates to a global demand for freshwater that grows overproportionally with respect to the growth of the human population on the Earth. Thus, it is fair to assume that water shortages in many parts of the world are caused not only by changes in climatic conditions and by the enormous growth of the human population, but even more by the insatiability of the human race’s demand for and consumption of water, food, and commodities. The traditional way of satisfying the human water demand is to abstract water from natural resources such as aquifers and surface water bodies (rivers and lakes). Where the water demand has exceeded the capacity of these natural resources, people have invented and implemented a variety of methods to capture rainwater or to melt snow and ice. During rainy seasons, rainwater from roofs and other sealed surfaces is collected and stored in tanks (cisterns). This method is referred to as rainwater harvesting. On a larger scale, holding ponds and dams (reservoirs) are built to ensure water supply to people, small enterprises, industry, power plants, and, importantly, agriculture during prolonged droughts. There are four main reasons why these traditional methods are no longer sufficient to meet the water demands of people, industry, agriculture, and the biota (plants, animals, and bacteria): 1. As mentioned above, the growth of the human population in general and the growth of cities in particular, as well as people and industry’s unquenchable demand for freshwater due to intensification of water usage by all economic sectors have increased the need for abstraction of water from natural and man-made water resources. 2. Extensive use of land for human settlements and industrial complexes has diminished recharge of natural water

resources, groundwater in particular. In many areas, the groundwater table has dropped to a critical point (Mervis, 2009). 3. Excessive use of fertilizer and pesticides, unintended infiltration of leachates from municipal and industrial landfills, intrusion of seawater into aquifers, and discharge of poorly treated wastewater into rivers, lakes, and dams have caused major deterioration of surface water and groundwater quality. 4. In many regions of the world, global warming and the subsequent change of climatic conditions have led to severe irregularities of precipitation (Bates et al., 2008). Regions such as California, the Mediterranean countries, and Australia are reporting unprecedented drought situations. Dams are empty (Figure 1); rivers, fields, and gardens are drying up (Pearce, 2006). To maintain their water supply, many municipalities are now being forced to consider alternative sources of water. The city of Brisbane in Australia, for instance, decided to implement advanced treatment of wastewater, pump it back to and blend it with the water in the Wivenhoe dam, and use it as a source of municipal water (Figure 2). This project has been completed but has not been fully commissioned after rainfall intensity increased in the year 2009, and due to a public backlash over discharging treated wastewater to the water supply of Brisbane City. As early as 1998, Singapore embarked on a program of desalinating treated wastewater for further use in industry (Anonymous, 2002; Tortajada, 2006). The city of Perth in Australia, along with many other large cities in the world, has constructed plants for desalination of seawater. Desalination of brackish water is being considered in some other areas.

Figure 1 Low water of Lake Wivenhoe, Australia, after 6 years of drought. Photo taken by the author.

Abstraction of Atmospheric Humidity

Figure 2 Western corridor recycled water project designed to overcome water shortage-situation in the Brisbane metropolitan area, Australia; wastewater is considered as an alternative source of water. PP, power plant; WWTP, wastewater treatment plant. Adapted from McCann B (2008) Australia’s largest recycled water project. Water 21, Journal of the International Water Association (London) 21: 42–44.

Although advances in membrane technology have made these solutions feasible and affordable, the high costs and high energy consumption limit the broader applicability of such high-tech solutions. There is one other source, which needs to be taken into consideration when looking for solutions to the problem of water shortages – atmospheric humidity. This chapter summarizes potential methods of harvesting atmospheric humidity, and outlines the technology used to abstract this humidity for human consumption.

4.05.2 Volume of Water in the Atmosphere The generic source of freshwater on the Earth is the atmosphere (Figure 3; Rekacewicz, 2002). Water reaches the surface of the Earth as a result of precipitation in the form of rain, snow, graupel, or hail. Some of the rainwater evaporates on its way to the surface and thereafter. Thus, it is transferred back to the atmosphere before it can be used. Some of this water is taken up by plants and animals, and stored for some period of time in plant and animal tissues. Another portion of the rainwater infiltrates geological formations (aquifers) consisting of porous material (gravel and sand), or rock crevices and caverns. The rest flows above ground toward lakes, wetlands, and eventually to the open sea. The quantity of water in the atmosphere is subject to constant and highly dynamic changes. As part of the overall water cycle, atmospheric water is continuously replenished by evaporation from surface waters (sea, lakes, rivers, swamps, etc.), and by evapotranspiration performed by global biota in general, and by plants in particular.

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According to estimates published by Gleick (1996), the total amount of freshwater on the Earth is 34 650 000 km3, of which 13 200 km3 is present in the atmosphere, either in the form of gaseous water (water vapor) or as liquid water droplets forming fog or clouds (Figure 4). In comparison to the overall volume of water on the Earth (1 385 984 000 km3), the water content of the atmosphere appears to be rather low. However, if we were to distribute the water contained in the atmosphere among the 6.8 billion people on the Earth, at any given time each person would receive about 2000 m3. Considering that agriculture requires about 75% of the freshwater being consumed, and only 6% of the overall water consumption relates to domestic usage (drinking, cooking, body care, washing clothes, as well as water consumption by small enterprises), there is still plenty of water available in the atmosphere (about 115 m3 per person at any given time) to satisfy the basic needs of people on the Earth. In conclusion, it is well worth considering the water vapor contained in the atmosphere as a supplementary source of water – not only for human consumption but for agricultural irrigation and industrial purposes as well.

4.05.3 Fundamentals of Rainfall Generation 4.05.3.1 Preliminary Remarks The following basic information relates to the various forms of atmospheric water, the processes leading to fog and cloud formation, and about methods of accessing atmospheric water to mitigate water shortages at local and regional scales. It is not intended to provide an in-depth scientific review. Readers who are interested in digging deeper into the knowledge base of atmospheric physics and meteorology are advised to consult the relevant textbooks and scientific journals (e.g., Rogers and Yan, 1996; Steinfeld and Pandis, 2006).

4.05.3.2 Water in the Atmosphere Water exists in the atmosphere in all three thermodynamic aggregate states, as gas, as a liquid, and in the solid state of ice. The gaseous state of atmospheric water is termed water vapor or simply vapor. Changes of water-aggregate states drive a rich variety of atmospheric processes affecting weather and climate. The change of aggregate state from vapor to liquid is termed ‘condensation’, whereas the reverse process of change from liquid to vapor is termed ‘evaporation’. The change of aggregate state from liquid to solid is termed ‘freezing’, whereas the reverse process of change from solid to liquid is termed ‘melting’. Vapor can also be directly converted into ice, bypassing the liquid-state stage. This change of aggregate state from vapor to solid is termed ‘vapor deposition’ or simply ‘deposition’. Evaporation of ice, the direct change of aggregate state from solid to vapor bypassing the liquid-state stage, is the reverse process. Water vapor is invisible to the naked eye. The amount of vapor in a unit volume of air can be described in terms of water vapor partial pressure, that is, the pressure of vapor contributing to the total pressure of all gases comprising atmospheric air. At a given temperature, partial vapor pressure

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Vapor transport

Precipitation 9000 km3

Precipitation 458 000 km3

Precipitation 110 000 km3

Evapotranspiration 65 200 km3 Evaporation 502 800 km3

Evaporation 9000 km3 Lakes

River runoff 42 600 km3

Infiltration

Ocean Area of internal runoff 119 million km2

Groundwater flow 2200 km3

Area of external runoff 119 million km2

Oceans and seas 361 million km2

Note: The width of the blue and gray arrows are proportional to the volumes of transported water

Figure 3 Graphical representation of the water cycle on the Earth. Reproduced from Rekacewicz P (2002) Vital water graphics. United Nations Environmental Programme/GRID-Arendal. http://maps.grida.no/go/graphic/world_s_water_cycle_schematic_and_residence_time (accessed March 2010), Philippe Rekacewicz, UNEP/GRID-Arendal, with permission.

cannot be increased indefinitely, that is, the amount of vapor which a given volume of air can hold is limited. This limit is reached when the number of molecules evaporating from water or ice surfaces equals the number of molecules condensing into liquid water or depositing onto ice. Such an equilibrium state is termed ‘saturation’. At saturation, vapor is termed saturated, and the partial pressure of vapor is termed ‘saturation vapor pressure’. Saturation vapor pressure increases with air temperature, which means that a volume of cold air can hold a smaller amount of vapor than the same volume of warm air. A common measure of vapor content in the air is relative humidity (RH), defined as the ratio of partial vapor pressure to saturation vapor pressure at a given temperature, usually expressed as a percentage. At saturation, RH is 100%.

The temperature at which the vapor at a given partial pressure is saturated is termed ‘dew point’. If air is cooled, RH may exceed 100% (in this case, the vapor is termed supersaturated), unless surfaces which can be wetted or, at subzero temperatures, surfaces with a structure similar to that of ice, are available allowing the vapor to condense or deposit, respectively. Small airborne particles called aerosols, for example, fine sand, dust, ash, soot, bacteria, and pollen, are almost always present in the atmosphere. Some of those particles with surfaces on which vapor may condense or deposit are called ‘condensation nuclei’ (CNs) and ‘ice nuclei’ (INs), respectively. Typically, the supersaturation of atmospheric vapor does not exceed 1–2% as the latter condenses or deposits on INs

Abstraction of Atmospheric Humidity

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Water in 13 167 km3

− Atmosphere − Surface

125 431 km3

− Permafrost soil

277 197 km3

− Aquifers

10 429 530 km3

− Glaciers

23 804 275 km3 0

10 000 000

20 000 000

km3

Water in 13 167 km3

− Atmosphere − Plants and animals

1 109 km3

− Rivers

2 218 km3

− Soil

16 909 km3

− Wetlands

11 780 km3

− Lakes

93 415 km3 0

40 000

80 000

km3

Figure 4 Graphical presentation of the various categories of freshwater on the Earth. Data from Gleick PH (1996) Water resources. In: Schneider SH (ed.) Encyclopedia of Climate and Weather, vol. 2, pp. 817–882. New York: Oxford University Press.

and/or CNs, which grow into ice crystals and liquid droplets, respectively. Areas laden with airborne cloud particles such as ice crystals and/or liquid droplets, formed as a result of air cooling below the dew point, appear visually as either clouds or fog. The term cloud is used when a clear interface exists between the particle-laden space and the atmosphere below. In contrast, the term fog is used when no such bottom interface exists, and the particle-laden space meets the surface of the Earth (land or a body of water). The efficiency of aerosols acting as CNs and INs varies depending on their chemical composition and surface properties. In particular, cloud formation and development are sensitive to atmospheric pollution (Steinfeld and Pandis, 2006). Due to their hygroscopic properties, sea-salt particles are considered to be excellent natural CNs, causing liquid droplets to grow well beyond their normal size (Biswas and Dennis, 1971). Elementary sulfur, originating from dimethyl sulfide (DMS), a volatile compound generated by marine algae, is another example. In general, the ability to act as CNs varies for different types of aerosols, which makes cloud formation and development sensitive to atmospheric pollution (Steinfeld and Pandis, 2006). Droplets are microscopic in size (typical diameter ranges between 10 and 20 mm). They are light and tend to remain airborne. In contrast, drops are comparably large, and are heavy enough to fall by virtue of gravity.

4.05.3.3 Atmospheric Processes of Precipitation Formation 4.05.3.3.1 Thermodynamic processes Condensation, deposition, and freezing are thermodynamic processes of the aggregate-state change accompanied by the release of latent heat. As a result, the surrounding air becomes warmer. The reverse processes of evaporation and melting result in the cooling of the surrounding air. Evapotranspiration refers to the process of evaporation facilitated by plants and animals.

4.05.3.3.2 Unit processes Clouds are a potential source of precipitating water, but not every cloud delivers precipitation. Rainfall and/or snowfall can only materialize after the cloud particles have reached a threshold weight beyond which gravity takes effect. Although intensive research has been conducted over the past years, knowledge about the governing processes in clouds is still incomplete. In the following, processes which play a major role in the generation of rain and snowfall are briefly described. Figure 5 illustrates the network of interconnected processes in clouds. The capture of supercooled cloud droplets by snow crystals is termed as ‘riming’. When two droplets collide, we term this process as ‘collision’. Coalescence occurs when two droplets fuse.

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-The source -

Evaporation

Evaporation Water vapor

Condensation

Melting

Cloud droplets

Ice crystals Coalition coalescence

Riming

Coalition coalescence

Condensation

Evaporation

Melting

Condensation

Condensation

Melting Snow flakes

Rain drops Riming Pr

ec

Lakes

ipi

ip rec

ion

n

tio

ita

tat

P

River runoff 42 600 km3

Infiltration

Ocean -The demand -

Figure 5 Network of processes involved in the development of precipitation. Adapted from Houze RA (1993) Cloud Dynamics, pp. 573–578. San Diego, CA; London: Academic Press; and background picture from Rekacewicz P (2002) Vital water graphics. United Nations Environmental Programme/GRID-Arendal, http://maps.grida.no/go/graphic/world_s_water_cycle_schematic_and_residence_time (accessed March 2010).

The schematic presented in Figure 5 may be somewhat misleading as it provides the impression of a rather universally applicable and static interaction of processes. In reality, however, the concert of processes is heavily affected by site-specific boundary conditions, and by natural and man-made impacts. Moreover, it is subjected to time variations high in frequency and amplitude. As shown in Figure 6, there exists a highly complex system of causes and effects which needs to be understood when attempting to influence weather conditions, and to trigger or enhance precipitation with the aim of mitigating local or regional drought situations.

4.05.3.3.3 Thermodynamic approach to explaining precipitation events If the temperature increases, the air can hold a larger amount of vapor and the corresponding vapor-saturation pressure increases. With rising temperature, the actual partial pressure of vapor falls below the saturation pressure, and RH falls below 100%. The molecule balance at the surface no longer exists and the evaporation of liquid water or ice into the air becomes the dominant process, which, if continued for a limited volume of air, will eventually bring the system back to the equilibrium state of saturation.

Abstraction of Atmospheric Humidity

e.g., - Cosmic rays - Solar radiation - Electrical charge distribution

Altitude

Orography

Geographic latitude

Albedo

e.g.,

e.g., - Air currents - Thermal uplift

- Air pollution - Contrails Vegetation lakes and wetlands ocean

Lakes

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Infiltration

Land use urbanization

e.g., - Volcano outbreak - Forest and bush fire - Man-made emissions

River runoff 42 600 km3

Lakes

Infiltration

River runoff 3 42 600 km

Ocean

Ocean

(b)

(a)

Distribution stratification Processes • Adhesion • Advective transport • Attraction • Coalescence • Coalition • Condensation • Deposition Temperature • Evaporation • Air • Freezing • Droplets • Melting Distribution • Ice • Repulsion scattering • Riming Electric charges • Turbulent mixing • Macroscopic • Microscopic Forces • Air currents • Buoyancy Riv R iiv ver runof ve runoff off o fff • Gravity 3 Density • Aerosols • Gases • Ice • Nuclei • Water droplets • Water vapor

Lakes

In Infi IInf nfiltra nfifiltration n ltratition ltra on on

• Factors • Conditions • Processes

Are subjected to time-dependent changes

42 600 42 00 km

Lakes Ocean

(c)

Infiltration

River runoff 42 600 km3 Ocean

(d)

Figure 6 Attempt to visualize the complexity of weather related processes in the atmosphere: (a) geographic boundary conditions; (b) physical and chemical boundary conditions; (c) internal system parameters; and (d) all external and internal factors, conditions, and processes change, often rapidly, with time. Background pictures adapted from Rekacewicz P (2002) Vital water graphics. United Nations Environmental Programme/GRIDArendal. http://maps.grida.no/go/graphic/world_s_water_cycle_schematic_and_residence_time (accessed March 2010).

If the temperature decreases below the dew point, the amount of vapor which can be held by air also decreases, as does the corresponding vapor-saturation pressure. As a result, RH exceeds 100%. In this case, the air is considered to be supersaturated with vapor. This state of supersaturation is unstable as the excess vapor will condense on any available surface (liquid or solid) until the partial vapor pressure is reduced to the saturation pressure, and the system is brought back to equilibrium.

The vapor-saturation pressure over ice is lower than that over liquid water (Bergeron, 1935, 1949). Therefore, ice particles may grow faster than liquid droplets and eventually absorb more vapor by deposition than droplets do by condensation. As the water vapor is consumed by the growing cloud particles, its partial pressure decreases. When the partial pressure of vapor falls below the vapor-saturation pressure, the air becomes undersaturated with respect to liquid water while still being supersaturated with respect to ice. At this point, the

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Abstraction of Atmospheric Humidity

surrounding droplets will evaporate while ice particles continue to grow at the expense of droplets. This mechanism is known as the Bergeron process. Atmospheric aerosols, both of natural and anthropogenic origin, can dramatically affect microphysical processes in clouds. Water droplets are microscopic in size. Most of them grow by condensation to a size of about B105 m in diameter. Due to the surface tension of liquid water, the vapor-saturation pressure over small droplets is higher compared to that of a flat water surface. Therefore, smaller droplets require a higher supersaturation to break the size barrier to condensational growth. This size barrier can be surpassed if a solute such as salt reduces the saturation pressure enough to mitigate the effect of surface tension. Soluble aerosols (e.g., particles of sea salt which are abundant over oceans), when collected by and dissolved in liquid droplets, may alter the effective vaporsaturation pressure over droplets. Aerosols of surface-active substances emitted from industrial areas may also affect the saturation pressure over water droplets. As a result, the droplet spectrum (the distribution of number concentration over droplet size) may be significantly affected. As the collision efficiency of droplet coalescence increases, rainfall may be initiated and enhanced in intensity (Facchini et al., 1999). Pure water droplets can remain in liquid form even down to temperatures near  42 1C (Smith, 1999). Supercooled droplets are typically abundant in cold clouds (i.e., clouds at temperatures below freezing point). When brought into contact with a body of ice or any other substance with similar surface characteristics (e.g., silver iodide particles), the supercooled water may freeze almost instantly. This process is known as ‘contact freezing’ (Sastry, 2005). As a result of contact freezing, ice particles can be instantly formed from a liquid droplet bypassing the relatively slow Bergeron growth of ice-forming nucleus, which involves liquid droplet reprocessing via evaporation. In turn, the produced ice particle may continue to grow either by vapor deposition or by further merging with the next supercooled droplet. The produced particles, when grown large enough, may reach the surface as ice conglomerates, such as snow and graupel, or, if melted as they descend, as rain drops. Rain is also produced by warm clouds (i.e., clouds at a temperature above freezing point) during summer and in tropical areas. Therefore, it has to be assumed that processes other than those related to ice production cause the generation of rainfall. At a certain stage of droplet growth in a warm cloud, droplet merging by collision becomes a growth process which is considered to be even more efficient than condensation. As the force of gravity exerted on a droplet increases more with the size of the latter than the competing force of air viscosity, larger droplets fall faster than smaller droplets. As they descend, the former (in this context termed ‘collectors’) may collide and merge with the latter, thus growing in mass and size and falling faster. In this classic description, such a droplet-merging process is known as ‘collision–coalescence’, or simply ‘coalescence’ (Battan, 2003). The coalescent growth of a descending collector accelerates during its travel through the swarm of small droplets in cloudy air until reaching the cloud base, which may result in the formation of a drop, a liquid water particle, a millimeter in size. On exiting the cloud,

the descending drops start to evaporate as the surrounding air is no longer saturated. Depending on the vertical air-humidity profile and size spectrum of the produced drops, some of the latter may reach the surface of the Earth as rainfall. In turbulent air, droplets can move in different directions with different velocities, generally determined by a combination of the forces of gravity and viscosity in highly variable air motions, causing them to collide as in the case of classic collision–coalescence. Such a turbulent coalescence is sometimes called turbulent coagulation. There is no guarantee, however, that all geometrically possible collisions will result in merging small droplets with the collector, that is, coalescence. Some droplets may be deflected by airflow around the collector surface. Droplets may also coalesce temporarily and then separate, or coalesce temporarily and then separate breaking into a number of smaller droplets. The collision efficiency defines the ratio of actual number of collisions to the number of collisions which are geometrically possible. Moreover, the coalescence efficiency has to be taken into account, defined as the ratio of the number of successful coalescence events to the number of collisions. In addition, it has to be considered that, as in the case of classic collision–coalescence, the presence of large droplets, even in a small number concentration, may significantly enhance turbulent coalescence (Riemer and Wexier, 2005). It was Aitken who discovered the importance of CNs in 1880. Due to their force of attraction to water, sea-salt particles may be considered as prominent CNs frequently available in the atmosphere above the ocean. Elementary sulfur is another candidate for nucleation. It originates, for instance, from DMS generated by marine algae. Dust and fine sand are other media. Dust particles may comprise inorganic or organic matter. The latter may originate from blooming plants (pollen), forest fires, road traffic, or industrial operations. Finally, but importantly, ice crystals are rated as very important CNs and play a major role in the process of cloud formation. Understanding the properties of clouds is still limited by difficulties surrounding the problem of adequately describing the processes of cloud-droplet nucleation and growth. Small changes in droplet population may significantly influence the formation and size of cloud droplets and precipitation (Facchini et al., 1999). Solutes affect the equilibrium and nonequilibrium properties of water. The depression of the ice equilibrium melting point is one such example. Koop et al. (2000) found that homogeneous nucleation of ice from supercooled aqueous solutions is independent of the nature of the solute, but depends only on the ratio between the watervapor pressures of the solution and of pure water under the same conditions. In addition, the authors found that the presence of solutes and the application of pressure have a very similar effect on ice nucleation. A thermodynamic theory for homogeneous ice nucleation was suggested which expresses the nucleation rate coefficient as a function of water activity and pressure. Cloud droplets are so small in size and so light that gravity has little effect on them. The rather uniform size of these particles suggests that the rate of condensation matches fairly well with the rate of evaporation processes. For the particles to grow, gain weight, and eventually descend to the surface of the Earth as rain or snow, additional forces need to take effect.

Abstraction of Atmospheric Humidity

Condensation of water vapor onto the surface of droplets is one of the growth mechanisms to be considered. Ice particles can grow in a similar fashion by the condensation and subsequent freezing of water vapor. For water vapor to condense onto a liquid droplet, the air surrounding the droplet must be supersaturated with respect to water. Likewise, for condensation onto an ice particle, the air must be supersaturated with respect to ice. At sub-freezing temperatures, supersaturation with respect to ice occurs at a lower RH compared to supersaturation with respect to water (Bergeron, 1935, 1949). Therefore, in the same environment, ice particles will grow faster than water droplets, particularly when the ice particles are supercool with respect to the temperature of the air (ambient temperature). As the water vapor is depleted, the environment will become subsaturated with respect to water but will still be supersaturated with respect to ice. At this point, the water droplets will evaporate while the ice particles continue to grow. Thus, ice particles will grow at the expense of water droplets. For the Bergeron process to proceed, the cloud must be positioned at an altitude where the air temperature is well below freezing point (cold cloud). With respect to rainfall generation from warm clouds, the collision–coalescence process described by Battan (2003) may be considered as an alternative to the Bergeron process. As mentioned above, salt particles, because of their hygroscopic properties, attract water when present in the air, causing water droplets to grow well beyond the normal size of cloud droplets (Biswas and Dennis, 1971). As these droplets become heavy, they likely start descending through the swarm of cloud droplets toward the Earth’s surface. On their way, they will inevitably collide with smaller cloud droplets, making some, but not all, merge with the larger droplets unless there are surface-active chemical substances present, which lower the surface tension of water (Facchini et al., 1999). Subsequently, the larger droplets grow in size, and the speed of descent increases. Once the drops exceed 100 mm in diameter, rain drops of 1 mm and larger develop within minutes.

4.05.3.3.4 Electrical processes Cloud processes are also affected by electric charges on cloud particles. Similar to aerosols, electric charges and the resulting electric forces affect cloud properties and the process of precipitation formation via a variety of microphysical mechanisms. The first basic research in this field was conducted by Charles T. R. Wilson, who received the Nobel prize in 1927 for his method of making the paths of electrically charged particles visible by the condensation of vapor. Based on Wilson’s findings, Bernard Vonnegut was the first to develop this concept further (Phelps and Vonnegut, 1970). He and his colleagues discovered that the presence of electric forces enhanced coalescence and the formation of larger drops during collisions. This led the authors to speculate that electrical charges in clouds could aid in the coalescence of droplets and thus initiate rainfall. They argued that negative ions that flow to the positively charged tops of thunderstorm clouds and the point-discharge positive ions that are carried from the Earth toward an electrified cloud base were not necessarily dissipative of cloud electrification. Some of these ions could be

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moved in the convective overturn associated with a growing cloud, resulting in point-discharge ions being carried by updrafts high in the cloud where they attract more negative ions to the cloud. These, in turn, become attached to cloud particles near the cloud boundary and are transported downward by the unfolding of air in and around a growing cloud. Noting that intense rainfall often did not develop in clouds prior to the electric discharge, they proposed an electrostatic precipitation explanation based on the rearrangement of charges around the lightning channels (Moore and Vonnegut, 1973, 1997; Vonnegut 1984, 1995). Harrison and Ambaum (2008, 2009) have added more information to the work of Vonnegut and his colleagues, and endorsed a hypothesis on the role of ions in nucleation of cloud droplets, their growth into raindrops, and the resulting precipitation events. Today, it is known that all clouds – even non-thunderstorm clouds – are electrified to a certain degree. Although complex charge configurations may take place under highly variable atmospheric conditions, cloud particles are predominantly charged positively at the top and negatively at the bottom, thus forming a so-called space charge in those areas. In a cloud, this space charge maintains the associated electric field. Processes of cloud charging and precipitation formation are intrinsically linked. One effect of electric charges on cloud particles is called the electro-hygroscopic effect, that is, the reduction of the size barrier for the growth of water droplets. As with salt and other solutes, condensation is facilitated when droplets are electrically charged. Although the droplet charge required to enhance activation is substantial, Harrison and Ambaum (2008) demonstrated that sufficient charging occurs at the edges of even weakly electrified clouds. Mean droplet charges in the order of 100 elementary charges have been observed near cloud boundaries of non-thunderstorm clouds (Beard et al., 2004). In this context, the term ‘elementary charge’ is to be understood as the positive electric charge carried by a single proton or the negative electric charge carried by a single electron. Cloud droplets with a radius of 3 mm carrying an average 1500 elementary charges have been observed at the base of mountain-top stratocumulus clouds (Twomey, 1956). Charging a haze droplet to 1000 elementary charges can reduce the critical supersaturation to 0.5% (Harrison and Ambaum, 2008). Another effect of electric charges is the enhancement of droplet coalescence by electric forces between charged droplets (Khain et al., 2004). The concept of electrically enhanced coalescence was introduced by Phelps and Vonnegut (1970). Regardless of the relative electrical polarities of the colliding particles, the net electric force between them is always attractive due to electrostatic image forces (Tinsley et al., 2000). For droplets of similar charges, there is a long-range repulsive force, but by virtue of turbulence, droplets may be brought into a range within which image forces take effect. To effectively increase the collision efficiency, a droplet should possess at least a few hundred elementary charges. Not only collision efficiency but also coalescence efficiency is increased in the process of electrically enhanced coalescence. Beard et al. (2002) demonstrated that the coalescence efficiency for collisions between weakly charged cloud droplets with a radius of 55–105 mm is greater than 91%. The efficiency is likely to be greater than 95% even when the droplet charges are

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insufficient for a significant enhancement of collision efficiency. The coalescence of droplets may also be enhanced by their polarization in an external electric field (Schlamp et al., 1976). As in the case of electrically enhanced liquid-droplet coalescence, electric forces may also augment the merging of other cloud particles such as nonwater aerosols and ice crystals. For example, liquid droplets may collect (scavenge) charged salt particles and therefore grow. Electrically scavenged nonsolute aerosols may trigger supercooled droplets to freeze at elevated temperatures (immersion freezing). Contact freezing of supercooled droplets by electroscavenged ice nuclei is a particularly efficient freezing mechanism of ice-particle generation (Tinsley et al., 2000). Another effect is that the electrostatic field of electric charges directly facilitates the freezing of supercooled cloud droplets and thus the production of ice nuclei. Water molecules possess their own electric dipole moment (Figure 7). The oxygen atoms are more negatively charged than the hydrogen atoms, and the molecule has a bent shape. This means that water molecules attract each other electrostatically. Clustering is likely to occur, causing enhancement of the electrical dipole moment. Further enhancement may occur when these clusters are exposed to corona ion emission. If random motion of the molecules is low, the molecules tend to line up in an orderly fashion with the positively charged part of one molecule next to the negatively charged part of another molecule. In an electric field, water molecules will rotate to line up with the field. As Wei et al. (2008) were able to experimentally demonstrate, this is favorable for the freezing of supercooled water at elevated temperatures. The electric field polarizes cloud droplets. Each of the dipoles exerts an attractive force on others above or below, causing them to collide and become larger. This, in turn, triggers the collision–coalescence process. As a result, raindrops develop, which are larger in size compared to those that develop in an electrically neutral environment (Jermacans and Laws, 1999). Cloud and fog formation under an electric field was studied by Teramoto and Ikeya (2000) using a Wilson cloud chamber containing a supercooled atmosphere of ethanol. The electric field necessary to generate dense clouds was about

Slightly positive

H

O

H

Slightly negative Figure 7 Electric dipole characteristic of the water molecule.

4 kV m1. Positive ions produced by ionization condensed the nuclei for the generation of fog and clouds. Plume clouds were generated from a needle electrode. Clouds get electrically charged by external and internal mechanisms. The latter are related to processes which lead to precipitation. The generation of ice is believed to play a major role in internal cloud charging, that is, separation of opposite sign charges into different cloud areas. In thunderclouds, where the internal charging is intense and the positive feedback between charging and precipitation formation is strong, a liquid droplet may grow up to the size of raindrops within seconds. The sudden appearance of heavy rain, often called ‘cloudburst’, during thunderstorms is in agreement with this theory (Moore et al., 1964). Detailed discussions about a number of internal charging mechanisms are outside the scope of this chapter, but can be found in many publications, for example, in a book by McGorman and Rust (1988). External cloud charging relies on the electrical conductivity of the air within the global electric circuit (GEC). The latter is a model for the atmospheric electric system (Wilson, 1929). This system may be described as an electric circuit where electric charges are assumed to be separated by the polar-cap electric potentials generated by the solar wind and global thunderstorm activity mainly in the convective tropic regions. Figure 8 provides an overview of the respective mechanisms. In a simplified form, the charge separation in the GEC by thunderstorms can be described as follows. The negative thundercloud charge is transported to the surface of the Earth by ground flashes. The same process applies to a smaller amount of the positive charge. The rest of the positive charge leaks out of the cloud as the surrounding air becomes slightly conductive due to the presence of atmospheric ions. As the electrical conductivity of air sharply (quasi-exponentially) increases with altitude, most of this leak current is guided to the upper atmosphere, where it is distributed over the globe and maintains the ionosphere at a potential of about 250–300 kV with respect to the ground. The overall system resembles a spherical capacitor. In fairweather regions, the atmospheric ions are driven by the electric field (i.e., gradient of ionosphere-to-Earth potential), thus forming a leakage current with a density of about 1–4 pA m2. Atmospheric ions, which are constantly produced by cosmic rays, and at a lesser rate by surface radioactivity, are the carriers of the leakage current. The current which flows across the vertical column resistance is known as the ambient or fairweather current (Harrison, 2005). A schematic diagram of the GEC is given in Figure 9. Put simply, the essence of charge separation in the above process of external cloud charging is as follows. The initial charge separation on a microscopic scale occurs when pairs of ions of opposite polarities are created by energetic particles, mainly of cosmic origin. Then, the ions of opposite polarities are dragged apart in opposite directions by electric forces in the electric field of the GEC. Positive ions are moved downward, while negative ions are moved upward. Eventually, some of these ions get stuck on cloud particles, thus charging them positive for particles close to the top and negative for particles close to the bottom cloud boundaries. It is important to note that any charge separation requires energy input. The initial energy input to separate ion charges

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Solar wind modulation + + + + + + + ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Ion-neutral Cluster ions chemistry Recombination Attachment +− to aerosols Air ions Reduced − ion mobility Ionization + Enhanced + Corona + − + − space charge Convection Turbulent ions Aerosol − − D current transport − rift − − − −− − − cur nucleation − − − − Radon gas ren Enhanced t + + + + + E-field + − + + + + + − − − − − − − − − + − − − + − − − −− − − − −− − − Cosmic rays

Figure 8 Atmospheric processes relevant to ion–aerosol–cloud interactions. Reproduced from Harrison RG and Carslaw KS (2003) Ion–aerosol– cloud processes in the lower atmosphere. Reviews of Geophysics 41–3: 1012. Copyright 2003 American Geophysical Union. Reproduced by permission of American Geophysical Union.

Cosmic rays

Ionosphere

−

−

+ ++

− −

+

+ +−++ +

Charge separation in thunderclouds

− −− +

+

−

Fair-weather current −

− −− −−−−− −− − − + − − ++ − +−−

−

−

+

− + − + − − − − + − − −− +

− +

+ −

−

−+ − + − −− + − + − + + − − −+ − −

Planetary surface Figure 9 Schematic diagram of the global electrical circuit. Adapted from Harrison RG (2005) The global atmospheric electrical circuit and climate. Survey Geophysics 25: 441–484; inserted photo: Carina Hansen – Fotolia.com

to microscopic distances (i.e., to create an ion pair) is provided by energetic particles. The energy input required to separate ion charges to macroscopic distances is provided by the GEC, which acts as a generator of electric power.

All clouds, layered clouds in particular, get externally charged by the fair-weather current because the electrical conductivity of cloudy air is typically many times less than that of clear air at the same altitude (Zhou and Tinsley, 2007).

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This is mainly due to the attachment of ions to droplets and ice crystals that the cloudy air is laden with. The links between cloud electrification and precipitation have an important aspect. External charging may facilitate precipitation formation by electrical mechanisms in all clouds, including those where the internal charging processes are not yet developed. The latter applies to non-thunderstorm clouds which are sensitive to external charging, especially those of layered structure. Thunderstorm clouds provide an electric remote assistance for precipitation formation in non-thunderstorm clouds by virtue of the initial cloud charging over long distances on a global scale.

cooling agent is passed along, while on the other side there is air containing some humidity. This humidity turns into liquid water which subsequently can be collected and used (Figure 13). The idea has been picked up by a number of companies. In the following part, three examples are presented: 1. The Dutch company, Dutch Rainmaker BV, proposed to use electricity generated by a wind turbine to drive a heat pump, cooling the inner wall of a shaft holding the blades, and the

4.05.4 Innovative Abstraction Methods 4.05.4.1 Condensation Technology 4.05.4.1.1 General remarks As explained in Section 4.05.3.3, the transfer of water from the gaseous to the liquid state, that is, condensation, requires saturation, and even supersaturation of the air and the availability of a surface at which condensation can take place. Saturation is temperature dependent. Condensation begins as soon as the temperature falls below the dew point. When this happens, for instance, on a clear night on a spider web, dew drops form and accumulate on the meshes of the web (Figure 10). Similarly, condensed water accumulates on leaves early in the morning after a clear night (Figure 11). The condensation process continues when the surface upon which condensation occurs is artificially cooled. This process is termed as ‘forced condensation’, and can be visualized by a simple experiment: Figure 12 shows a pitcher filled with iced water. Almost instantaneously, a puddle forms at the bottom of the glass as condensation occurs. Eventually, the liquid water, which accumulates on the surface, starts to flow downward.

Figure 11 Condensation of liquid water at a leaf serving as condensation surface. Photo: makuba – Fotalia.com

4.05.4.1.2 Proposed technologies The process of forced condensation can technically be applied to convert atmospheric humidity into liquid water to be used for various purposes. All that is needed is a surface upon which condensation may proceed. On one side of the surface a

Figure 10 Condensation of liquid water at spider net strings serving as condensation surface. Photo: Dalia Ruckiene – Fotalia.com

Figure 12 Condensation of liquid water at the outside of a pitcher filled with iced water.

Abstraction of Atmospheric Humidity

electricity generator or a heat pump, respectively. Ambient air is blown through the shaft. Water vapor contained in the air condenses on the wall, drips down, and is collected in a storage tank at the bottom of the installation (Figure 14).

At the company’s test facility in Harlingen, a prototype was producing around 0.5 m3 of water per day based on a relative humidity of 45% and a wind speed of 2 m s1. Under the same conditions, the full-scale version is expected to produce between 7000 and 8000 l d1. 2. Another technology has been developed by the UK-based company, Grimshaw. Proposed is a unique large-scale condensation structure for the city of Las Palmas in the Canary Islands (Figure 15). The structure was designed in a bold sculptural form as a backdrop to an outdoor amphitheater. Technically, this apparatus may best be described as a tube-and-fin-type condenser. Cold seawater is pumped through the condenser elements to cool the outer condensation surface. If the atmospheric conditions are right, wind-driven humid air from the ocean passes through the structure. Water vapor condenses on the tube surfaces; the condensed water drops down, is collected at the bottom of the structure, and then transported by gravity to an underground storage tank. From there, the water may be treated by appropriate physical and chemical methods, and used for domestic purposes. In this example, the proposed pipe length was 400 m, and the condenser area was 1000 m3. The temperature of the seawater at the inlet point is typically about 9 1C. The air temperature at Las Palmas varies between 15 and 23 1C in summer, and between 12 and 18 1C in winter. Thus, the water production of the unit was calculated to be 1120 m3 per day in summer and 530 m3 per day in winter (Pawlyn, 2007).

Interfacial Boundary wall layer Temperature

Liquid water film Warm

Cold

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Flux of water vapor < Dew point temperature

Distance

Flow of liquid water Figure 13 Schematic representation of forced condensation of water vapor at a cooled surface.

Wind turbine

Cooling of the innerpart of the shaft

Heat pump

Air filter Air flow Blower

Collection basin

Effluent

Figure 14 Simplified schematic representation of a water abstraction device driven by a wind turbine. Illustration based on a sketch provided by Dutch Rainmaker bv.

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Abstraction of Atmospheric Humidity Condensation structure

Humid air from sea

Amusement theater Return flow

Collection of condensed water

Water for further use Underground storage

Cold seawater intake Figure 15 Condensation of humid air blown from sea through an artistically designed structure – simplified overview based on a drawing of Pawlyn M (2007) An architecture of water purification. In: Huber H, Wilderer PA, and Paris S (eds.) Water Supply and Sanitation for All, pp. 131–136. Berching, Germany: Hans Huber AG. Photo by permission of Grimshaw, London, UK.

3. The third example of condensation technology has been developed by a UK-based consortium comprising Seawater Greenhouse Ltd., Exploration Architecture Ltd., and Max Fordham & Partners LLP. It is called the Sahara Forest Project, and it aims to create a growing environment in hot and dry parts of the world (such as the Sahara), and produce deionized water comparable in quality to rainwater, from seawater using solar energy. The proposed method is based on the so-called seawater greenhouse concept developed by Paton (2001). Its purpose is to provide desalination, cooling, and humidification in an integrated way, using solar energy as the main source of power. Davies et al. (2004) described this concept in some detail. In the Sahara Forest Project, the seawater greenhouse concept is combined with the concentrated solar power (CSP) technology, which is already applied at various locations around the world. Solar radiation is concentrated and focused on a heat-exchanger system to create steam that drives conventional turbines to produce electricity. CSP plants produce large amounts of surplus heat which can be used to evaporate seawater. The vapor may be distributed in the greenhouse structures where it is converted by natural condensation into deionized water, and used to water trees, shrubs, and food crops inside and outside of the greenhouses (Figure 16). Eventually, a forest ecosystem develops outside of the greenhouses which has the potential to change the microclimate of a region. It may even work as a biotic pump, which means that humidity from the ocean is attracted, clouds are formed, and precipitation

occurs in the former desert (Makarieva and Gorshkov, 2007).

4.05.4.1.3 Research needs As sufficient basic knowledge on condensation is already available, exploitation of this knowledge in order to overcome water shortages in arid countries deserves attention. The three examples described above clearly demonstrate the potential of condensation technologies. The field is wide open for more innovative ideas and concepts followed by applied research, technology development, and field trials. Certainly, condensation-based technology will not solve the water crisis at large. Nevertheless, the deployment of many small-scale solutions will assist in the mitigation of local water-scarcity problems.

4.05.4.2 Fog Collection Fog is differentiated from clouds by the fact that it occurs close to the Earth’s surface (land, lakes, or sea), whereas clouds are located high in the lower atmosphere in the form of either horizontally oriented layers (stratus, cirrus) or vertically oriented accumulations (cumulus). Fog, as do clouds, consists of very small droplets of liquid water (see Section 4.05.3). Fog droplets have diameters of about 1–10 mm. Under the influence of gravity alone, these droplets fall very slowly (o1 to about 5 cm s1) and are thus readily subjected to wind force. Horizontal winds of a few meters per second cause even the largest fog droplets to travel horizontally (Schemenauer and Cereceda, 1994a).

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Ocean Forest plantation Greenhouse installation

Concentrated solar power plant Figure 16 Schematic representation of the Sahara Forest Project proposed by Seawater Greenhouse Ltd, Exploration Architecture Ltd, and Max Fordham & Partners LLP. Reproduced from Pawlyn M (2007) An architecture of water purification. In: Huber H, Wilderer PA, and Paris S (eds.) Water Supply and Sanitation for All, pp. 131–136. Berching, Germany: Hans Huber AG.

Fog droplets are readily intercepted when they come into contact with surfaces, for instance, with leaves or plant stems. They accumulate on such surfaces, forming large drops and even films of liquid water. This is similar to (but should not be confused with) the surface accumulation of liquid water caused by condensation. Some of the water accumulated on leaves and stems may be taken up by plants or consumed by small animals. Excess water eventually drips or flows to the ground and contributes to soil moisture. Thus, in many arid areas, fog is the only source of liquid water to support vegetation and animal life. Persistent fog not only provides water, but also controls the natural water management of ecosystems. In the humid tropics, these regions are referred to as cloud forests (Kerfoot, 1968; Stadtmiiller, 1987; Goodman, 1982). Trees are potentially good collectors of fog. Indeed, trees have been used by man as fog collectors for centuries. In the seventeenth and eighteenth centuries, stories about three fogcollecting trees which grew on Hierro Island (Canary Islands) received widespread public attention (Glas, 1764). The trees were discovered in 1565 by Antonio Hernandez who coined them as ‘fountain trees’ (Figure 17) because the water captured on the leaves accumulated in large quantities before dripping down like a shower of rain. The water was collected in cisterns, which were divided in two parts, one for people and the second for cattle and other animals. Today, this concept is depicted on the coat of arms of Hierro Island. The trees belonged to the species of endemic laurel trees (Ocotea foetens). They stood atop cliffs where fog arrived on an almost regular basis from the ocean. Until a huge hurricane uprooted them in 1610, they served as major water sources for the pre-Hispanic population living on the island. In 1945, Don Zo´simo Herna´ndez Martin planted a laurel tree at one of the previous sites. The project was scientifically supervised by Alain Gioda, who in 1993 received the Rolex prize for his groundbreaking work (Gioda et al., 1993). Since 1993, this tree has been providing water obtained from fog in a manner very much like that described by Glas (1764).

Another example of a natural fog-collection system is that found by Schemenauer and Cereceda (1992a) in the Dhofar region of southern Oman. Two small intertwined olive trees stand in a windy environment where fog is almost constantly present. The vertical cross section of a tree can collect water at a rate of about 10 l m2 d1. Over an 83-day observation period in 1990, 580 l d1 of water was harvested (Schemenauer and Cereceda, 1994a). Realizing that fog is a rich source of water, and also due to the rapidly increasing need for water, particularly in rural areas of developing countries, in the early 1980s, Schemenauer and his colleagues commenced extensive research, development, and implementation of projects aimed at mitigating waterscarcity threats around the world (Schemenauer and Joe, 1982; Schemenauer and Cereceda, 1993). The results of these projects have been published in major scientific journals and presented at various international conferences and workshops. Key findings can be summarized as follows:

• •

•

•

•

Capturing and collecting fog is to be understood as a physical–chemical separation process. Fog droplets are separated from the air in which they are distributed. Interception by plant material (leaves, stems, etc.) followed by accumulation and storage can be mimicked by placing in a fog-laden environment, a material which is able to attract water droplets. For the collection of fog droplets, deep, three-dimensional net-like structures made of flat sheets of hydrophobic plastic material proved to be superior to two-dimensional sieve-like structures. Good collection efficiencies were achieved with a doublelayer net made of black polypropylene ribbon (flat ribbon of about 1 mm width and 0.1 mm thickness). The ribbons are woven into a net with a pore size of about 1 cm. Greater amounts of water can be collected when the droplet-laden air (fog) is driven toward and through the net by wind. To achieve optimal droplet-detention time within the net, airflow velocity within the net should not exceed

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Figure 17 Historic drawing and Hierro0 s coat of arms, both depicting the Fountain Tree used to harvest fog on Hierro Island, and the cistern to collect and store the captured water.

•

•

•

•

•

and should not drop below an optimal velocity range. Optimal collection efficiencies were achieved at air-flow velocities between about 3 and 8 m s1 (Schemenauer and Cereceda, 1994b). Due to their small size and mass, fog droplets travel mostly parallel to the surface of the land. Therefore, the fog collectors (racks holding the net) should be oriented in a vertical direction. Fog droplets attached to the surface of the ribbon merge with other droplets by virtue of coalescence. Eventually, the accumulated water loses hold and drips down into a collection gutter. Field measurements of the collection efficiency of the net at the centerline of a large collector gave values of about 66% (wind speed: 3.5–6.5 m s1). This is in good agreement with the theoretical collection efficiency for a single ribbon once the areal coverage of the net is taken into account. At El Tofo, Chile, 50 fog collectors, each consisting of 48 m2 net area, were constructed in 1987. The average water production from this collector system was approximately 3 l m2 d1. This equates to an average production of 11 000 l d1. Production rates varied with the prevailing meteorological conditions from zero on clear days to a maximum of about

•

100 000 l d1, which provided 330 people living in a nearby village with 33 l per capita per day. Due to the remote location of the El Tofo plant, the effluent of the fog collectors was of good quality. It contained some marine salts and soil dust, but little contamination from anthropogenic sources (Schemenauer and Cereceda, 1992b). The measured water quality satisfied the drinking water standards of the Chilean government and the World Health Organization (WHO).

Many fog-collector systems have been installed in South America, Africa, Oman, and Canada. Figure 18 illustrates the typical installation of a fog-collecting system. A simple structure holds nets through which fog passes. The captured water is collected in a half pipe below the net and directed into a storage tank. From the results obtained, it can be concluded that fog collection is a viable and effective low-cost method to abstract water from the atmosphere and use it for domestic purposes, agricultural irrigation, and as a protective measure against forest fires (Walmsley et al., 1999). Foggy days are frequent in many regions of the world, even in some desert areas. In such locations, the collection of fog

Abstraction of Atmospheric Humidity

Figure 18 Typical setup of a fog collection system. Background photo: Fotalia.

for subsequent domestic use appears to be a most attractive method of overcoming water-scarcity situations. Of course, fog-collection technology on its own will not solve the water crisis. However, the deployment of many small-scale solutions will, at least, help mitigate water-scarcity problems. It is therefore worthwhile to further develop fog-collection technologies. Of particular interest here are innovative materials, with special focus on the surface properties of the materials to be used to capture fog droplets. Additionally, more research is required to improve the fog-capturing capacity of the threedimensional structure of collection nets. Finally, the quality of the collected water in relation to the atmospheric conditions at various sites needs to be carefully monitored.

4.05.4.3 Generating Clouds with the Aid of Heat Islands When there are no clouds in the sky, rain can hardly be expected to fall. With this in mind, scientists and engineers have been searching over the last few decades for ways to trigger cloud development. In order to plan and execute cloud-generation technology, it is essential to start with a sound knowledge of how this phenomenon occurs in nature (Battan, 2003). The fundamentals of cloud formation and of processes leading to precipitation have been described earlier in Section 4.05.3. As shown in Figure 3, cloud formation is very often stimulated by thermal uplift. Air containing humidity is lifted up into regions of the atmosphere where the temperature is low enough for conditions of saturation, even supersaturation, and the formation of cloud droplets. Taking this sequence of processes into account, it appears that any method which enhances thermal uplift could favor cloud formation. To trigger a thermal uplift, a relatively large area of land needs to be covered with a material of low albedo that readily absorbs short-wave solar radiation and converts it into longwave radiation (i.e., heat). Subsequently, the temperature of air above such an area would rise, causing the air to ascend. A piece of land which exhibits high-level radiation-absorbing properties is called a heat island. Bare, rocky land of volcanic origin may possess such properties, but so would man-made settings, such as a city where the roads are paved

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with asphalt, and roofs made of red tiles (Black and Tarmy, 1963). Such environments may be classified as ‘unintended’, in contrast to purpose-built heat islands. To purposely create a heat island, it is necessary to pave a large area of land with a material such as asphalt, preferably close to the ocean. On sunny days, small deep-pressure systems may develop above the artificial heat island. Air would be sucked from the ocean or from wetlands where humidity is typically high. Vapor would thus be transported in large quantities toward the heat island and up into the sky leading to the accumulation of cloud droplets and, subsequently, to the development of convective clouds. These clouds may then be transported by the dominant wind some distance away from the heat island depending on wind direction and speed, and also on a variety of other prevailing meteorological factors in the site above the target area (Figure 19). Brenig et al. (2001) developed such a system based on the layout of an artificial heat island. He coined this method Geshem Rain System (Geshem means rain in Hebrew). The sequence of processes characterizing the heat-island concept was first described in the 1950s and 1960s by Malkus and Stern (1953), Malkus (1963), and Black and Tarmy (1963). The theoretical arguments of Malkus and Stern were based on very rough solutions of the hydrodynamic equations governing atmospheric flows. The observational data were derived from a small, flat island in the West Indies that had been thoroughly studied in order to understand the mechanisms which lead to frequent development of cloud rows in an area where clouds were typically not present. The study by Malkus and Stern stimulated some further studies on the potential of urban areas to serve as heat islands. As mentioned above, radiation-absorbing materials are typically used to build roofs and roads. These materials absorb solar radiation better than the vegetation in the surrounding countryside. The larger the area that is covered with such materials, the greater the expected heat flux toward the lower atmosphere. At the same time, small particles emitted by cars (e.g., abrasion of brakes and tires), by industrial activities, and heating devices will be uplifted as well. Rainfall is likely to occur in the downwind region. This phenomenon was studied by Shepherd et al. (2002) for unintended heat islands in areas such as Houston, Texas, by examining data measured by a radar system on board the Tropical Rainfall Measuring Mission (TRMM) satellite of National Aeronautics and Space Administration (NASA). This system measures rainfall rates, droplet size, and latent heat. Two main effects were observed in and near the southern US cities studied: first, an average temperature difference of 3 and 5 1C between the cities and the surrounding area was measured; and second, significantly higher rainfalls and thunderstorms occurred in an area away from the cities in the prevailing wind direction (Burian and Shepherd, 2005; Shepherd et al. 2002). Studies performed by Brenig et al. (1995, 2001, 2005) revealed that there is a lower limit for the size of the artificial heat island and a higher limit for its reflectivity beyond which the buoyancy produced would not be enough to lift the air to condensation altitude. The value of the minimum area for the low-reflectivity surface is a function of its albedo. For lower albedo values, a smaller heat-island surface produces about

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The Geshem rain system Cumulus Rain Wind Rain

d

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Aquifer

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Figure 19 Schematic representation of the Geshem Rain System, the engineered version of the general heat island concept. Reproduced from Brenig L (2007) Making rain on arid regions: The Geshem Rain System. Water and Environmental Exchange, Sevilla (Spain). http://physfsa.ulb.ac.be/IMG/pdf/ brenig07.pdf (accessed August 2010).

the same thermal rising motion for the air as a larger but lessabsorbing black surface. Moreover, these limiting values are also dependent on factors such as wind speed, atmospheric stability, and the thermal parameters of the radiation-absorbing material used. For higher wind speeds over a ground surface of a given albedo and a given size, the buoyancy effect gets weaker since the air flows faster over the hot surface and, consequently, has less time to absorb the heat ascending from the ground. For higher stability of the lower atmospheric layers, the ascending motion of the air will be counteracted by a stability effect. These are some examples of the rather complex set of physical relationships that govern the influence of a solar-absorbing surface at ground level on the local atmospheric circulation. According to Brenig (2001, 2005), for a heat-island system (Geshem Rain System) to function efficiently, the following conditions need to be met:

•

• •

The material constituting the heat island should reach a minimum temperature excess of 40–50 1C with respect to the surrounding area. In this case, the size of the heat island can be kept as small as 60–90 km2. The general orientation of the heat island should be with its longest axis parallel to the mean wind direction. Ideally, the center of the artificial heat island should be located at a distance between 10 and 30 km from the coast (Yoshikado, 1992, 1994). Closer to the ocean, the clouds generated by heat-island forces may be subject to diurnal

land–sea wind variations, and transported to the ocean rather than to inland areas. One of the major drawbacks of the Geshem Rain System is the enormous amount of space required to build an artificial heat island. In order to reduce this area, research is needed to identify materials exposing a maximum radiative absorption capacity and a low albedo at minimum cost. In collaboration with experts in land management and regional planning, possibilities should be investigated and assessed to deploy artificially built heat islands for multiple purposes, for example, for heating up the air and for electricity generation. In no case, however, should these heat islands compromise the functioning of local ecosystems or the supply of agricultural products. Triggering the development of cumuliform clouds does not necessarily lead to rainfall, as discussed in Section 4.05.3. In order to make optimal use of the water contained in the artificially generated clouds, research should focus on advances in technology which enable the generation and enhancement of rainfall.

4.05.4.4 Cloud Seeding 4.05.4.4.1 Development of the technology It is commonly known that clouds do not always bring precipitation to the surface of the Earth. From the literature review in Section 4.05.3, we have learned that precipitation can only occur when water particles, liquid or ice, gain sufficient

Abstraction of Atmospheric Humidity

weight to overcome the prevailing buoyancy in clouds and travel downward to the surface of the Earth. However, on their way, particles are subject to the process of evaporation. Only those drops or snowflakes make it to the grounds which, during their travel time, are not transformed back into water vapor. Taking into account the complexity of processes and boundary conditions shown in Figures 5 and 6, engineered harvesting of clouds appears possible only when, under actual meteorological, time-dependent conditions, the cloud particles (water droplets or ice crystals) are allowed to form, are triggered to merge, grow large, and gain weight. In principle, enhanced growths of cloud droplets could be achieved if the air temperature within clouds could be artificially lowered. In the case of clouds containing supercooled droplets, rapid cooling – for instance, by injecting dry ice (frozen CO2) – is a potential trigger for turning droplets into ice particles. It can be expected that the ice particles will grow since condensation with respect to ice is particularly high. Vincent Schaefer was the first to suggest this idea and investigate its applicability. In the laboratory, he was able to achieve positive results, and on 13 November 1946, he conducted a field test in the vicinity of Mt. Greylock (MA, USA). In this test, an airplane flew across a supercooled stratus cloud and dropped dry ice particles along its flight track. Within minutes, the texture of the clouds significantly changed, and below the cloud, snowflakes were detected. Under the leadership of Irving Langmuir, Nobel prize laureate for chemistry (1932), and in cooperation with Vincent Schaefer and Bernard Vonnegut, experiments continued. The group tried to find a substance which would be as effective as dry ice, but which would work at temperatures closer to the freezing point of water. It was Vonnegut (Battan, 2003) who identified silver iodide (AgI) as a potential seeding material. Silver iodide is highly soluble in water (3 107 g per 100 ml at 20 1C). It is used in photography and as an antiseptic in medicine. With respect to cloud physics, it is worth mentioning that the crystalline structure of AgI is similar to that of ice crystals. Thus, by injecting AgI crystals into a supercooled cloud, the availability of CNs is likely to be improved. Subsequently, supercooled cloud droplets are likely to develop, providing the opportunity for these droplets to instantaneously change into ice particles. Subsequently, the ice particles may grow in size and weight due to condensation processes. For silver iodide to convert into its crystalline structure, it has to be exposed to high temperatures at which the substance vaporizes. Cooling the AgI vapor results in very small crystals, 0.01–0.1 mm in size. They are similar in structure to ice crystals, and thus behave in a manner similar to that of CNs. The problem is, however, that these crystals deteriorate very quickly due to solar radiation. Thus, it is crucial to deliver the crystals to clouds as quickly as possible, by rockets or airplanes. Not only inorganic but also living bacteria have been considered as potential media for ice nucleation. Levin et al. (1987) studied the ice-nucleating properties of a number of Gram-positive and Gram-negative bacteria. They concluded that there is no reason to disqualify active bacteria as cloudseeding agents.

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Of particular interest in this context is the bacterial strain Pseudomonas syringae, which is known to cause ice nucleation at temperatures as low as  8 1C. Drainas et al. (1995) performed studies on the genetic properties of this strain, and explored the reasons why it causes water to freeze at temperatures below 0 1C. On ground, however, these bacteria are known to be responsible for severe surface-frost damages in plants. Tegos et al. (2001) conducted experiments to produce cellfree active INs for biotechnological applications in efforts to avoid detrimental effects once rain comes into contact with plants. A freezing temperature threshold of about  7 1C was observed. It is foreseeable that sooner or later, this type of seeding agent will be used in atmospheric studies as well. The method described above is often referred to as one based on the static-phase hypothesis. In other words, it is based on cloud microphysics. It is assumed that precipitation efficiency can be increased by altering the dynamics or air motion in clouds due to the latent heat release of growing ice particles, redistribution of condensed water, and counteraction against evaporation of cloud droplets, ice crystals, snowflakes, and raindrops. The second basic hypothesis of cloud seeding is the dynamic-phase hypothesis, which is based on the dynamics of clouds. Here, cloud seeding is focused on enhancement of the vertical air currents in clouds. Klatt’s (2000) description of the dynamic phase hypothesis is explained below. Cumuliform clouds are dependent on the presence of a persistent updraft. Air within the updraft experiences adiabatic cooling as it rises, and at some point it will become supersaturated with respect to water. The updraft speed is proportional to cloud buoyancy, the latter being a function of the temperature difference between the cloud and its environment. As water vapor is converted into liquid droplets or ice particles, it releases latent heat to the cloud, thus increasing temperature. This will enhance the updraft and increase watervapor influx. Positive feedback will occur as the increasing quantity of water vapor condenses, deposits, and releases even more latent heat. Particles suspended in the updraft may eventually grow large enough to overcome the upward velocity of the updraft and fall to the ground as precipitation. Precipitation has a very detrimental effect on the cloud. Clouds, which develop in areas where the shear is weak, will have a vertically oriented updraft. In this situation, precipitation which forms will fall straight down through the updraft. The weight of the drops and the drag created as they fall will dissipate the updraft. In addition, the precipitation removes large amounts of water from the cloud, which the updraft can no longer replenish. Once the updraft has ceased, the cloud will quickly evaporate. Observations have shown that seeding does enhance the transformation of cloud particles from liquid to ice (Sax and Keller, 1980; Hallett, 1981). According to the static-phase hypothesis, clouds should be seeded to achieve about 1–10 ice crystals per liter at temperatures warmer than  15 1C. In contrast, the proponents of the dynamic-phase hypothesis suggest seeding clouds such that more than 100–1000 ice crystals per liter may develop. This corresponds to seeding as much as 200–1000 g of silver iodide (Cotton, 1997).

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Dry ice and silver iodide enhance the ice crystal concentrations in clouds by either nucleating new crystals or freezing cloud droplets. They both belong to the category of glaciogenic materials. It should be realized that both substances can only be effective when clouds are exposed to temperatures well below the freezing point. In tropical areas, however, the air temperature remains above the freezing point even at high altitudes. To propagate enhancement of rainfall in such areas, the injection of water drops, dilute saline solutions, or grinded salt has been suggested. The primary objective of introducing these types of materials is to jump-start the coalescence process (Biswas and Dennis, 1971; Murty, 1989; Czys and Bruintjes, 1994). For seeding with salt, the term hygroscopic seeding is often used.

4.05.4.4.2 Evaluations and recommendations Over the past few decades, many attempts have been made to gain a deeper understanding of the underlying physics, to document the responses of cloud seeding, and to validate the causality, that is, the cause-to-effect relationship. The review in Sections 79.3.3.2 and 79.4.4.1 suggests that knowledge about cloud physics and the processes leading to precipitation has reached an advanced level, although more research is needed to consolidate the knowledge base. It is commonly agreed that the success of cloud seeding should be evaluated in terms of the amount of rain or snow that reaches the ground. The development of cloud particles heavy enough to fall is certainly a prerequisite for precipitation, but it is an insufficient criterion from an economic viewpoint. Only rain and snow which reaches the ground can be considered beneficial for humankind, industry, and nature – or detrimental if it exceeds a certain level of volume. Proving that a particular seeding exercise has caused rain or snow has been and still is a matter of controversial discussions. The problem here is that weather conditions are not only complex, but also highly variable in space and time (see Figure 6). Even if the cause-to-effect relationship could be scientifically verified, there is no guarantee that the public will accept this as an unarguable fact. Many people assume that a rain event can only be attributed to natural processes or to a divine power. A farmer whose land receives rain during a drought would most likely be reluctant to pay for this blessing, but would be more inclined to offer up a prayer to the heavens. Beginning with the early trials carried out by Schaefer and Vonnegut, efforts have been made to demonstrate that cloud seeding enhances rainfall or snowfall on the ground. Over time, the level of sophistication has increased – in terms of both test design and the evaluation of results using statistical methods. However, when Battan (2003) first published his book in 1962, he came to the conclusion that despite the evidence that cloud seeding leads to the enhancement of precipitation on the ground, more scientific knowledge was needed to better understand the physical mechanisms involved. He cited a report published in 1955 by the World Meteorological Organization, in which the authors state that a net increase in precipitation had not yet been demonstrated beyond reasonable doubt in any seeding operations. Years later, and after a multitude of field trials, Bruintjes (1999)

provided a critical review of cloud-seeding experiments. His critique is backed up by Garstang et al. (2004), who refer to a report of the US National Research Council (NRC) issued in 2004, which states that the field of atmospheric science is now in a position to answer many of the crucial questions that have impeded or blocked progress in weather modification in the past. However, the authors of the NRC report could still see no convincing scientific proof that cloud seeding works. Boe et al. (2004) countered that there is ample evidence that winter-fog modification, snowpack augmentation, and glaciogenic and hygroscopic seeding enhance rainfall, even though the magnitude of the effects may be difficult to quantify with precision. Qiu and Cressey (2008), referring to the failure of seeding operation during the Olympic games in Beijing, again expressed doubts about the effectiveness of cloud seeding. The question here is how to measure reliably the success of cloud seeding. Statistical analysis has been and still is the method of choice. To execute statistical analysis, it is commonly agreed that cloud-seeding experiments must be randomized. Although this method is well established in science, for fundamental reasons, the results of statistical analysis cannot prove that cloud seeding produces an effect such as rainfall on the ground. Statistical analysis can only provide information about levels of confidence and probability (List, 2005). Apparently, the statistical probability of cloud-seeding operations being successful is rather low. However, this conclusion might be misleading. As some experiments were randomized, the timeline of the three-dimensional meteorological conditions during and after the seeding event was not properly taken into account (see Figure 6). The actual meteorological conditions must be understood, however, as being decisive for success or failure. It is widely accepted that actual meteorological conditions are only occasionally appropriate for cloud seeding. If cloud-seeding operations are executed irrespective of the prevailing meteorological conditions, then it is little wonder that the success rate is rather limited. Nevertheless, according to the current state of scientific knowledge, it is fair to hypothesize that a seeding exercise under the right meteorological conditions will lead to precipitation on the ground (List, 2005). This hypothesis is yet to be proven incorrect. Future research and development efforts should concentrate on the development of a holistic physical hypothesis that incorporates all the major processes governing the generation of cloud droplets, ice particles, and eventually precipitation. Mathematical models need to be further developed to better understand the dynamics of meteorological conditions in time and space, and in response to the peculiarities of the region under consideration. Cloud seeding could eventually evolve into a technology which is predictable in effect, and could be pinpointed to a certain region. It would then become an attractive technology for investors. The proponents of cloud seeding must understand, however, that rainfall generation and enhancement are measures which can help solve regional or global water-supply problems only when treated as a part of an integrated waterresources management (IWRM) approach. Application of the technology has to be governed by national and international laws to ensure that conflicts of interest are avoided and

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sustainable development of regions is secured (for more information see Section 4.05.6). There is another concern not properly shared by the majority of cloud seeders and their economic and political proponents. Chemical substances and bacteria injected into clouds will inevitably end up on the ground and affect the biosphere. Silver, for example, is known to be potentially toxic. Similarly, bacteria such as Pseudomonas syringae are toxic (Drainas et al., 1995). Seeding clouds with substances which are or may be harmful for plants, animals, or humans is not only a shortsighted concept, but ethically and ecologically unacceptable.

4.05.4.5 Rainfall Enhancement by Cloud-Particle Charging 4.05.4.5.1 Scientific background In Section 4.05.3.3.3, we discussed the importance of atmospheric electricity on the evolution of cloud particles, and on subsequent occurrence and intensity of precipitation. It was mentioned that Phelps and Vonnegut were probably the first to consider the role of electricity in the processes leading to precipitation (Vonnegut and Moore, 1958; Phelps and Vonnegut, 1970) and to realize that the intended introduction of electrical charge into existing clouds could trigger or augment precipitation. Since then, numerous attempts have been made to develop weather-modification methods and devices for applications such as fog dissipation and precipitation enhancement by means of cloud-particle charging. As previously mentioned, there are two main microphysical mechanisms of cloud modification by particle charging: (1) electrically enhanced coalescence and (2) electro-freezing of supercooled droplets by contact nucleation. To be effective, each mechanism requires a certain minimum charge per electrically active cloud particle. For example, enhancing coalescence–collision efficiency requires hundreds of elementary (electric) charges on droplets with a radius of 10–20 mm (Khain et al., 2004). Charges per particle of the same order of magnitude are required for effective electro-freezing (Tinsley et al., 2000). Cloud particles charged sufficiently to significantly modify cloud-development processes are referred to hereinafter as supercharged particles. A common method to supercharge cloud or artificial aerosol particles (e.g., water droplets produced with a sprayer) is to deploy a direct current (DC) corona discharge device producing unipolar, that is, predominantly of the same sign, air ions. The particles are then directly charged by ion attachment. Negative ions are preferred as, compared to positive ions, they have a higher mobility and, therefore, a slightly higher particle charge is achievable in the same configuration. In its general form, a DC corona discharge device, suitable for charging airborne particles with the negative sign in the socalled aerosol chargers, comprises two electrodes connected to a high-voltage direct current (HVDC) source: the cathode having one or more surface parts with a high curvature, called an emitter electrode of corona discharge, or simply emitter, and the anode with a smooth surface, called a collector electrode of corona discharge, or simply collector. For example, one or more needles with sharp pins or thin wires can be used as the emitter. In this configuration, negative air ions produced in a strong electric field around a needle, pin, or wire

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surface, drift toward the positively charged collector, thus forming an electric current passing through the air. This current is often referred to as an ionic current. Aerosol particles, carried by an air stream through the ion-drift zone between the emitter and collector, become negatively charged in this zone by direct ion attachment. These particles are then introduced (seeded) into a cloud or fog. A number of designs of aerosol particle chargers have been proposed for cloud and fog modification, for example, those described in the patent applications of Marks (1980) and Khain et al. (2003). Seeding techniques using airborne carriers or ground-based chimney-like conduits have been described by Khain et al. (2003). In practice, however, direct supercharging of cloud particles with aerosol chargers and seeding those particles into a large volume of cloudy air would need to overcome severe engineering difficulties. The average charge on a particle which can be achieved by ion attachment is approximately proportional, among other factors, to the particle size and logarithm of the so-called unipolarity factor, which is the ratio of the number concentration of the dominant-sign ions to the concentration of the opposite-sign ions. In order to supercharge cloud particles, especially small ones, the corresponding unipolarity factor should also be sufficiently high. As ions of the sign opposite to that of corona ions are always present in the air, the required unipolarity can be maintained only within a limited area of the ion-drift zone around the emitter. Therefore, only a fraction of charged aerosols would be supercharged, with the rest of them charged below the supercharging threshold. Another problem is that a strong electric field of the nearby highly charged particles may reduce the ion-production rate of corona discharge (Smith, 1972; Loveland et al., 1972), which requires the prompt removal of the charged aerosols away from the emitter. On the other hand, removal of those particles and the overall charger performance will be limited by the time required for particle supercharging. Due to the sheer size of clouds, a large number of aerosol chargers would probably be required for precipitation enhancement by seeding with charged water particles. Once removed from an ion-drift (charging) zone, particles remain supercharged for only a limited time due to their (nonequilibrium) charge decay, posing the challenging problem of distributing such an unstable seeding medium over a large volume of cloudy air within that time. This would probably require the costly deployment of a number of airborne carriers such as aircrafts or drones and/or chimney-like conduits. In practice, direct supercharging of cloud particles in sufficient amounts with aerosol chargers is difficult to achieve and costly to implement in an application on a reasonable scale, thus making it uncompetitive with conventional (chemical) cloud seeding. Therefore, a practical approach to the problem should be focused on other means of increasing the electric charge on cloud particles. One solution to the problem based on the enhancement of the natural charging processes in non-thunderstorm clouds has been found after rigorous scientific scrutiny of Russian-engineered devices for cloud and fog modification, undertaken by the research team of Meteo Systems AG, Switzerland, and supported by recent research in the field of electrical processes in cloud

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microphysics, for example, links between solar activity and climate. Since non-thunderstorm layered clouds produce a large share of the total precipitation on the Earth, they appear to be attractive targets for modification. As discussed in Section 4.05.3.3.3, the upper and lower boundaries of such clouds are sensitive to electrification by the fair-weather atmospheric electric current. The density of space charge produced by this external cloud charging is proportional to the fair-weather current density (Harrison and Ambaum, 2008). Therefore, electrical processes of precipitation formation in non-thunderstorm clouds are modulated by fair-weather atmospheric electric current and, in principle, can be influenced by engineers, provided the technical means for controlling that current are in place. The density of the (ohmic) fair-weather current is determined by local values of air conductivity and the gradient of the ionosphere-to-Earth potential of the GEC, that is, the intensity of the fair-weather electric field. The conductivity of air depends on local factors such as atmospheric ion-production rate, number concentration, and type of atmospheric aerosols. The intensity of the fair-weather electric field, although strongly correlated with global thunderstorm activity, is subject to variations due to a number of factors. At a given location, the vertical profiles of air conductivity and fair-weather electric field exhibit significant variations, while the density of the fair-weather electric current usually changes very little with altitude. Many observations have provided evidence that weather variables are strongly correlated with the fair-weather electric current. Cyclical and irregular variations in solar activity modulate ionization rates and hence the fair-weather electric current in the lower atmosphere. Recent studies, based on the observed sensitivity of weather variables to variations of solar activity, strongly indicate that the input of cosmic rays is not negligible. Cosmic rays comprise particles with a high-energy potential originating from both solar and nonsolar sources (Tinsley, 2000; Carslaw et al., 2002; Palle et al., 2004; Harrison and Ambaum, 2008). The evidence of a statistical relationship of precipitation with cosmic-ray flux was first presented by Kniveton and Todd (2001). This relationship was examined for the Beijing area by Zhao et al. (2004). Comparative analysis of heavy rainfall correlations with cosmic rays, varying with different locations around the Mediterranean basin, was provided by Mavrakis and Lykoudis (2006). In contrast to the case of a DC corona discharge where the produced ions are unipolar, natural energetic particles produce pairs of air ions of the opposite sign, a process called ‘bipolar ionization’. Due to the motion of ions driven by the fair-weather electric field through clear-to-cloudy air interfaces with high conductivity gradients, the initial microscopic charge separation by bipolar ionization results in a macroscopic charge separation by the accumulation of multiple charges of the same sign on cloud particles, negative at the bottom and positive at the top of clouds, that is, external cloud charging. As this process is scaled with the density of the ohmic fair-weather electric current, increasing charges on cloud particles could be achieved by increasing the number concentration of atmospheric ions, determined by the air ionization rate, or increasing the fair-weather electric field

strength. In theory, augmenting natural bipolar ionization by providing an artificial source of additional bipolar ionization, for example, by means of a laser beam, might be an option. Depending on altitude, about 2–15 ions s1 are naturally produced in 1 cm3 of the troposphere, the layer of the atmosphere where most precipitating clouds form. Taking into account the sheer size of clouds, simple calculations would not engender much hope for achieving an artificial ionization rate comparable to that of natural ionization. Another option is to increase the fair-weather electric field strength at cloud altitudes. This can be achieved locally by the accumulation of negative electric charges below clouds. In this configuration, the electric field of those charges points in the same direction (downward) as the fair-weather electric field, that is, the latter is augmented. The elevation height of such an electric field source above the surface of the Earth should be sufficiently high with respect to the induced image charge at the surface with a finite conductivity, which further reduces the electric field with distance from the source. In terms of electrostatics, the electric field produced by an electric field source and its image charge fades with distance as quickly as the field of electric dipole. In practice, charging atmospheric aerosol particles which will then act as charge carriers appears to be feasible. In contrast to the direct charging of cloud particles, aerosol particles should not necessarily be charged to a supercharging threshold, and this can be achieved by means of ground-based facilities. The produced plume of space charge formed by charged aerosol particles is then elevated by convective updrafts. The lifetime of space charge accumulated by aerosols is typically in the range of 15–40 min. This allows the spacecharge plumes to be elevated to altitudes of up to several hundred meters and even more, depending on the atmospheric conditions. Summarizing the above, the principle of weather-modification methods by means of unipolar air ionization at lowelevation heights above the surface of the Earth is based on a local electrical disturbance of the GEC caused by the longlasting space charge of aerosol particles acquired by the attachment of produced ions. Under certain conditions, this may lead to the enhancement of the electric field strength at altitudes of non-thunderstorm clouds and thus external charging of the latter in the GEC due to a high conductivity gradient on the clear-to-cloudy air interface on cloud boundaries. This additional artificial charging, which appears as an increase in cloud particle electric charge, negative near the bottom and positive near the top of clouds, changes electrically sensitive cloud microphysical processes, such as droplet condensation, coalescence, and freezing of cloud droplets by contact with charged aerosol particles acting as contact-freezing nuclei. In this way, a local artificial modulation is added to the GEC. This artificial modulation can be used as a base for weather-modification applications at local or regional scales.

4.05.4.5.2 Development of the technology The first experiments using corona discharge devices for fog dissipation were reportedly carried out in the Soviet Union before World War II, although a detailed description of the trialed devices cannot be found in the literature available now.

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Using charged particles of fine sand for cloud and fog modification was also considered at that time. However, as was later stated by Smirnov (1992), delivering artificial charges to clouds was a major challenge. Vonnegut et al. (1957) were the first who hypothesized the existence of a link between aerosol charging near ground level and electrification of non-thunderstorm clouds. A hypothesis describing convective cloud charging based on the physical delivery of aerosol particles, charged near ground level, into clouds was proposed. To test this hypothesis, a large open-air DC corona discharge installation was proposed to charge natural aerosol particles in large volumes of atmospheric air. In that configuration, the emitter electrode in the form of a straight wire, several kilometers long with a diameter of 0.2 mm, was elevated on masts at 10 m above the ground and powered with an HVDC source at a voltage of 10 kV relative to the ground, the latter acting as the collector electrode of corona discharge. The external cloud-charging mechanism was not understood at that time. Although the results of experiments conducted by Vonnegut’s team to prove convective cloud charging were inconclusive, the new concept of charging atmospheric aerosols with open-air DC corona-discharge installations, aiming to remotely modify the electrical state of clouds, was introduced. Vonnegut et al. (1962) performed aircraft measurements of the electric potential gradient at different altitudes above the plume of aerosols charged with a 14-km long wire. They observed that the produced space charge mixed rapidly in the lower atmosphere and caused large perturbations in the fairweather electric potential gradient, which were extended downwind 10 km or more. When there was convection, the charge was rapidly carried aloft by thermal updrafts. Bradley and Semonin (1969) carried out similar measurements and attempted to detect precipitation–modification signals with the assessment techniques available at that time. In the early 1970s, experiments on warm-fog dissipation with aerosol chargers were carried out in the USA (Loveland et al., 1972). Electrical fog-dissipation experiments resumed in Russia from the 1990s onward. Large open-air corona-discharge systems were used comprising an emitter electrode in the form of a long thin wire supported and elevated to a few meters height above the ground with one or more poles. The wire was connected to the negative electrode of an HVDC source, the positive electrode of which was earthed. In a number of proposed embodiments, the wire was arranged in different ways and different support structures used in attempts to improve the basic design of Vonnegut et al. (1958). In other embodiments, such as that shown in Figure 20, metallic collector electrodes electrically coupled with the earthed positive electrode of HVDC source were provided. Fog-dissipation experiments were conducted using various embodiments on a trial-and-error basis. According to members of one of the Russian fog-dissipation research teams, best results were achieved with embodiments where the wire was arranged in parallel segments when wound in one strand around the sides of a wooden pyramidal frame (Rostopchin et al., 2001). A number of such emitter-electrode assemblies supported by individual poles, or grouped on a rectangular

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7m

100 m

(+)

(−)

100 m Figure 20 Corona discharge installations used for fog dissipation in Russian airports.

rack and supported by multiple poles, were operated at a voltage of 50–70 kV. In peer-reviewed literature, the studies of electrical fog dissipation in Russia were presented, for example, by Afanasiev et al. (1996) and later by Chernikov and Khaikine (1999). Although the primary concern of research, technical development, and field tests on weather modification, by means of ground-base DC corona-discharge installations, was the dissipation of fog, later such installations were also used in cloud-modification experiments conducted with varying degrees of success in Russia and other countries such as the USA, Mexico, the UAE, and Australia. In Russia, the press reported a number of trials, some of them commercial, performed with ground-based corona systems to reduce rainfall during harvesting and public events, or increase it during dry seasons, in particular, for extinguishing peat and forest fires. By the end of the last century, characterized by a sharp economic downturn in the former Soviet Union, a number of Russian experts in the field of electrical weather modification, previously employed by Soviet state meteorological institutions, had reportedly joined companies based in Mexico and the USA, such as ELAT SA (Mexico City), Earthwise Technology Inc. (Dallas, TX, USA), and Ionogenics (Marblehead, MA, USA). Marketed by the competing companies under various names, the technology was referred to either as ionization of the local atmosphere (IOLA) or as electrification of the atmosphere (ELAT). Support and funding were sought from both US and Mexican governments. According to media reports, limited funding was granted to ELAT by the Mexican government for field trials in a drought-affected area in Mexico. According to a report by Moore (2004), Earthwise Technology proposed corona-discharge systems that were 7 m high, shaped like short open-topped air-traffic control towers, housing proprietary ion generators and blowers to lift the produced space charge. The ELAT installations were rather simple in appearance, consisting of a 37-m-high central tower surrounded by 8-m posts arranged hexagonally at a distance of 150 m. The tower and posts were interconnected by the

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emitter wires, electrically coupled to an HVDC source powered by a 2-kW generator. Field trials were carried out by Meteo Systems AG, Switzerland, in the United Arab Emirates (in the year 2006), and later in Australia (in 2007 and 2008) under the scientific-assessment program of the University of Queensland, Australia. During 4 months of operations, a number of major rain-enhancement events were observed and quantitatively evaluated. Performance analyses based on measurements of ionic current and the assessment of aerosol-charging efficiency were carried out for different configurations of the corona installation deployed in field tests, which was based on the pyramidal design of Rostopchin et al. (2001). The major achievement of Meteo System’s research team was discovering the basic principles of weather modification by unipolar aerosol charging with ground-based systems. This opened the way for Meteo Systems to engineer a number of high-performance installations of the next generation, which are optimized for particular weathermodification applications. This would have been impossible without understanding the physical processes upon which the technology is based. Until recently, the mechanisms of how the space charge introduced into the lower atmosphere affected cloud development were not well understood. Field experiments relied on a trial-and-error approach. The prevailing belief, inherited from the former hypothesis of convective cloud charging, was that space charge should be delivered into a cloud in order to cause electrical modification of the latter. This and other hypotheses, such as those based on the vertical ion transport to clouds, were strongly criticized by the scientific community, and any observed evidence was not accepted but questioned, with reference to natural weather variability which is still not reliably predictable. However, as List (2005) stated, this reservation applies to any weather-modification technology regardless of the methods and techniques used.

4.05.4.6 Evaluation and Recommendations Although a large number of experimental trials have been carried out over the past years using ground-based coronadischarge installations, there is not a single report available in the scientific literature containing detailed results of the trials, and practical experiences. To the knowledge of the authors, only one paper has been published which provides, in the appendix section, some general information about trials that were planned for Mexico and Webb County, TX, USA, from 1996 to 2002 (Kauffman and Ruiz-Columbien, 2005). In contrast to strategies adopted by companies involved in marketing cloud-seeding technologies, the proponents of ionization-based technologies were very protective in their activities. As a result, some elements within the scientific community see the electrical weather-modification technology as voodoo science (Park, 2000). Subsequently, potential clients (e.g., representatives of water authorities) approach the technology with suspicion even when water scarcity is severely threatening local people and the agricultural community. The review of the scientific fundamentals of the ionizationbased technology presented in Section 4.05.4.5.1 reveals that this technology is far from being scientifically untenable. When applied in a scientifically sound manner, the technology

exhibits a significant potential, even higher than that of cloud seeding. During cloud-seeding operations, only a fraction of the available cloud cover can be influenced. In contrast, clouds covering a significantly larger area can be modified by remote cloud charging at low cost, especially if a grid of multiple ground-based installations is deployed. Moreover, the technology is highly scalable and suitable for applications which cannot be implemented or are difficult to implement by cloud seeding. In summary, the ionization-based technology, if accepted by the scientific community and supported in more detail with scientifically sound knowledge, has enormous potential to become a viable and widely used weather-modification technology. As in the case of cloud seeding, acceptance of the ionization technology will come with sound evaluations of achieved success. The chosen method of evaluation should not necessarily be based on randomized trials. As discussed in Section 4.05.4.4.2, application of the method is likely to be successful when done under the right meteorological conditions. In particular, the presence of updrafts is essential for vertical plume transport and thus for the success of operations. The art of applying the technology successfully depends on knowledge and correct interpretation of actual meteorological conditions in the three-dimensional space around the ion emitter, taking into account the variation of these conditions over time. Therefore, monitoring meteorological conditions, data collection and evaluation, with the aid of mathematical models, and operation of the technology according to the results of data evaluation are the three pillars on which any success rests. Applying the technology in such a manner is highly recommended. Unipolar ion emission should not be confused with the emission of electromagnetic waves, which are suspected of posing some health hazards. Nevertheless, the question whether or not the ionization-based technology is environmentally friendly needs to be scrutinized. It is highly unlikely that the emitted ions impose any threat to plants, animals, or commercial applications such as air traffic. Indirectly, health risks cannot be excluded, however. Corona discharge may cause generation of hazardous gases such as nitrogen oxides and ozone. The latter is known to be a strong oxidant, which may react with atmospheric pollutants. In a worst-case scenario, oxidation reactions may be incomplete, leaving molecules behind which may be toxic or even carcinogenic. It is highly recommended to invest in research to minimize the release of hazardous gases, for example, by optimizing the operating regime of corona discharge, and to clarify the extent of incomplete oxidation and the resulting toxic residues prior to commercial applications of the technology. As mentioned in Section 4.05.4.4.2, weather-modification technologies alone cannot solve the water-supply problems on the Earth unless the technology is treated a part of IWRM. In the case of the ionization-based technology, integration into the general policy of regional water management is particularly important since the methods affect rather large areas. At the ground, conflicts between different interest groups are very likely to develop, for instance, conflicts between agriculture which needs rainfall, and the tourist industry which wants clear skies. Political conflicts may arise between states when the technology is applied in areas close to borders.

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The question about which authority owns the atmospheric humidity requires international regulations, presumably at the United Nations (UN) level. Last but not the least, regulations need to be set with respect to liability. In the case of heavy rainfall caused by the application of any weather-modification technology, damages and even accidents may occur. In this case, is it the authority which ordered application of the technology, or the company which operated the technology that is legally responsible? Agreements at state level and with the major insurance and re-insurance companies need to be settled prior to commercial applications.

4.05.5 Rainwater Collection, Purification, and Storage 4.05.5.1 Incentives for Action As already mentioned in Section 4.05.2, there is enough water available in the atmosphere to support life on the Earth, and to satisfy the demands of people, agriculture, and industry, today and, most probably, in the future. The water contained in the atmosphere is transferred to the surface of the Earth by means of natural or deliberately forced precipitation processes. Once on the ground, the water may evaporate, be intercepted and used by plants, penetrate into the groundwater table, or form surface water bodies such as wetlands, lakes, creeks, or rivers (Figure 3). To minimize evaporation losses, the water can be collected on the spot, infiltrated, and stored in underground aquifers for subsequent use, or collected and stored in cisterns, ponds, or dams. One serious problem is that, on the local scale, precipitation in the form of rain or snow does not necessarily correspond to the actual demands of nature and humans – neither in time nor in intensity. There are areas which regularly receive high volumes of precipitation, far more than is actually needed. The west coast of the south island of New Zealand is a good example of the too-much-rain dilemma. In the area of Hokitika, for instance, annual rainfall exceeds 2800 mm. On the other hand, there are areas where rainfall is rare but water demand is extremely high. Southern California, where the annual rainfall is less than 40 mm, is an example of this extreme condition. Worldwide, people have tended to settle in areas where sunny days are predominant. Unfortunately, such areas are frequently affected by water shortages (e.g., Dubai), necessitating huge technical and financial efforts to satisfy urban and peri-urban water demand. Likewise, agricultural production is concentrated in sunny areas where solar radiation allows highquality grains, vegetables, and fruit to grow, provided enough water is available from local or distant sources. Water shortages may even develop in wet countries when the populations of cities and the resulting water demand exceed the capacity of rainfed water resources. The problems of the London metropolitan area are an example of this.

4.05.5.2 Rainwater Collection As people need water in sufficient quantities year round, collecting rainfall and temporary storage has been common practice in arid and semiarid countries ever since humankind

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shifted from migrating, gathering, and hunting to settlements and farming. In areas notoriously exposed to long-lasting periods of dry weather, people have learned to survive by collecting rainwater from roofs, and by storing the water in cisterns placed above or below ground. This traditional method of collecting and storing rainwater is called rainwater harvesting. With the advent of modern water technologies people have lost interest in rainwater harvesting, even in areas not served with tap water. This was certainly an important factor in the improvement of water supply in poorly served areas when organizations such as the Centre of Science and Environment (CES), based in New Delhi, India, started to revitalize the concept of rainwater harvesting. In 2004, the achievements of CES were rewarded with the Stockholm Water Prize. Since then, this old-fashioned but life-securing method has begun to receive worldwide attention, again. To help people understand that the perils resulting from lack of water can be mitigated by taking individual initiatives, CES produced a video which depicts a man in distress as rain starts pouring and a gust of wind blows away his umbrella. Eventually, the umbrella lands upside down on the road and fills quickly with rainwater. This is observed by passersby who excitedly start collecting rainwater in whatever container is available, including a police officer’s helmet. This video was an eye-opener for many people in the world who suddenly realized that, besides high-tech solutions, there are simple ways of coping during droughts. In Australia, for instance, under the pressure of a 6-year drought, a modern version of rainwater harvesting has become common practice (Lancaster, 2005). Large-scale rainwater-collection plants could be built, based on the upside-down umbrella metaphor, using devices which may only be opened in the case of a rain event. The advantage would be that pollution of the collected water by deposits, and loss of water through evaporation, could be minimized. The collected water could then be diverted to a storage tank to protect it against quality deterioration caused by algae growth, for example, as depicted in Figure 21.

4.05.5.3 Pollution and Purification of Stormwater Runoff Although the collection and storage of rainwater is certainly a clever method to deal with water shortages, it is not without drawbacks. Rainwater is by nature low in mineral-salt content. When consumed in large quantities, it may affect the osmotic pressure at a cellular level, resulting in health risks. More severe health risks result from pollution picked up by the raindrops when passing through the atmosphere, and by the rainwater once it comes into contact with the collection surfaces (roofs, terraces, courtyards, roads, etc). Analyses performed by Wallinder et al. (1998), Athanasiadis (2005), Schriever (2007), and Helmreich (2009), and by many others have revealed that rainwater collected from roofs and roads is severely polluted in rural areas, and to a much larger extent in cities (Fuchs et al., 2002). The collected water may contain soluble and particulate as well as dissolved materials. Airborne pollutants such as SO2, NOx, NH4, and volatile organic substances (VOSs) are of particular concern, as is

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Figure 21 Upside-down-umbrella farm to collect rainwater in arid areas: a visionary approach. Collecting devices open when it rains (right), and fold up during dry weather conditions (left) to allow free access to the native land.

small particulate matter (respirable dust). A multitude of organic and inorganic substances may be picked up from roofs and roads; some of these are dissolved in others in particulate form, for example, leaves and droppings of birds, cats, and other animals. Some pollutants may have deposited on the surface during dry periods. During the rain event, they get washed away and transferred into the collected rainwater. The resulting concentration of pollutants is especially high at the beginning of the rain event (first flush). Of particular concern are abraded materials from tires, brakes, catalytic converters in cars, and road covers because these materials contain heavy metals such as zinc, copper, cadmium, chromium, and platinum. Mangani et al. (2005) measured, during first-flush situations, concentration values of 346 mg l1 for copper, 412 mg l1 for zinc, and 37 mg l1 for lead. Organic pollutants of concern are benzene, poly-aromatic carbon (PAC), methyl- and ethyl-tert-butyl-ether (MTBE and ETBE, respectively). In Munich, Germany, the mean concentration of dissolved organic carbon (DOC), as a sum parameter for all those substances, was monitored over a period of 2 years. The concentration of DOC was in the range of 20 mg l1 in the runoff from a heavily frequented highway (Helmreich, 2009). The total organic carbon (TOC) was 70 mg l1 on average, and the total suspended solids was more than 350 mg l1. With respect to runoff from metal roofs, particularly high concentrations of copper and zinc, as a result of corrosion and subsequent wash-off effects, were detected by Athanasiadis (2005) and Schriever (2007). The wash-off rate for copper varied between 0.7 and almost 2 g m2 a1. For zinc, a mean value of 3.7 g m2 a1 was measured (Helmreich, 2009). In some cases, the concentration of zinc exceeded the 30 mg l1 margin. Copper concentrations in the runoff from a copper roof varied between 0.4 and 11 mg l1.

4.05.5.4 Purification of Stormwater Runoff in Decentralized Treatment Units Rainwater collected from roofs and roads can be considered as a supplementary source of water for households, industry, and agriculture, provided the collected rainwater is properly treated. Particulate material as well as dissolved pollutants need to

Figure 22 Buried road runoff treatment plant next to a gully of a highway in Ishijama, close to Lake Biwa, Japan.

be removed or, at the very least, lowered in concentration. If the treated water is designated for human consumption, disinfection is necessary to achieve hygienic safety. Since the extent of water demand and the availability of runoff hardly match actual water needs, temporary storage in containers (cisterns), ponds, or dams has to be provided. When the stormwater runoff is collected from a large area, the water is sent to a central treatment plant for purification. Such plants are large in size, and the treatment units often are placed in a standby phase as these can only be operated when rainfall actually occurs. Matsui et al. (2001) developed a decentralized concept for road runoff treatment and infiltration of the treated water into an aquifer. The pilot unit was positioned next to an individual street gully (Figure 22). Such treatment units are small since the volumetric loading remains comparably low even under peak-flow conditions. The treatment could be limited to

Abstraction of Atmospheric Humidity

filtration, absorption, and ion-exchange processes as barriers against the transfer of pollutants into the subsoil and the aquifer (Matsui and Lee, 2003). Filtration into the porous subsoil would take care of the removal of pathogenic organisms. The above idea was adopted by Koenig (1999) who proposed to pass the collected roof runoff through a layer of biologically activated top soil for filtration and biodegradation, and store the water in a buried cistern made out of prefabricated concrete for further use or infiltration into the aquifer (Figure 23). The system was commercialized and is used at various locations in Germany. The individual units are designed to receive runoff from roofs, 150 or 300 m2 in size. The prefabricated units are available in different volumetric sizes (3–10 m3). The total depth of the units varies between 2.4 and 3.1 m.

Soil layer consisting of special substrate

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Boller and his team developed a special filtration method which combines solid–liquid separation and the binding of heavy metals to granular iron-hydroxide (Boller and Steiner, 2002; Steiner and Boller, 2006). Advanced scientific investigation followed by field trials was conducted in Munich, Germany, by Athanasiadis (2005), Athanasiadis and Helmreich (2005), Athanasiadis et al. (2006), Hilliges (2007), and Helmreich (2009). In Munich, chemically conditioned clinoptilolite was selected as an ion exchanger to remove dissolved heavy metals from roof runoff (Athanasiadis and Helmreich, 2005). The treated clinoptilolite is packed in a filter column with the intention to remove, in addition, heavy metals in particulate form by filtration processes. Figure 24 shows the experimental setup which, after extensive testing, was applied at full scale at the bottom of a large copper-roofed building in Munich

Option: inlet

Inlet or overflow Manhole with cover Drainage pipe with coconut-fiber sleeve Empty conduit to domestic water station Monolithic container

Outlet, e.g., Mall percolation box

Figure 23 Rainwater storage reservoir with a soil filter top for purification of the collected roof runoff.

Emergency overflow

Effluent

Packing

Influent

Hydro-cyclone

Sedimentremoval pipe Figure 24 Schematic of the treatment system used to purify stormwater runoff from a copper-roofed building in Munich, Germany, according to Helmreich B (2009) Stoffliche Betrachtung der dezentralen Niederschlagswasserbehandlung (Pollution of stormwater runoff to be treated in decentralized system). Berichte aus Siedlungswasserwirtschaft. Munich, Germany: TU.

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Copper roof copper already patinated

Clinoptilolite filter cage

Manhole cover Figure 25 Copper-roofed building in Munich, and a view into the treatment chamber with the casket holding the granular clinoptilolite filter material.

(Figure 25). The total roof surface area is 4800 m2. Ten treatment units were installed but only four of them, each serving a roof area of 500 m2, were monitored. The influent was first introduced into a hydro-cyclone chamber to remove particulate material. The filtration chamber was packed with 750 kg of conditioned clinoptilolite, grain size 1–2 mm. For conditioning, the sieved clinoptilolite particles were submerged in a 1 M NaCl solution at room temperature for 24 h. Afterward, the treated material was washed 3 times with deionized water. During exposure to NaCl, the ion-binding sites were expected to be occupied by sodium ions, and it was assumed that sodium ions were readily exchanged by heavy metal ions during the purification process. During a 1-year observation period, 20 rainfall events were monitored. Samples of the rainwater before and after coming into contact with the copper roof, and of the effluent of the filter, were collected. The total volumetric loading of the four units was 744 m3. The copper concentration in the influent of the filter units varied between 38 and 980 mg l1. The effluent concentration varied between 19 and 84 mg l1. Considering mass loading, copper could be retained by 97%. Comparable removal values were observed for zinc. Similar experiments were performed with the aim of purifying runoff of a busy main road in downtown Munich, Germany. On average, 57 000 motor vehicles use this road every day. The catchment area of the filter system was about 300 m3. The composition of the road runoff differs significantly from the runoff of the copper roof discussed above. It contains a much higher load of particulate material, both inorganic and

organic. To remove large and heavy particles at the curb site, a simple and easily cleanable sedimentation/filtration trench was installed, followed by a hydro-cyclone placed in the lower part of the treatment unit (Figure 26). Particles of smaller size and lower density were removed by means of a fine sieve with a mesh size of 0.7 mm. Finally, the water was forced to pass through a medium consisting of a low-cost granular activated carbon material (1–2.5 mm in size) based on brown coal, before it was sent to a seepage trench. The experimental unit was monitored for 6 months. During this period, 63 major rainfall events were observed. The concentration of the various organic and inorganic pollutants varied across a wide range (DOC between 3.6 and 80 mg l1, PAC up to 1.3 mg l1). During winter, a significant increase in salt concentration was measured. The sodium concentration varied between 17 and 10 400 mg l1. The median concentration of zinc was 1 mg l1. The copper concentration varied between less than 0.1 and 0.6 mg l1. During the observation period, the removal rate of all organic pollutants and heavy metals was in the range of 90– 95%. The filter system installed along the curb eliminated firstflush effects almost entirely. Enhanced salt concentration during winter did not affect treatment efficiency in any significant way.

4.05.5.5 Large-Scale Storage of the Collected Rainwater Rainwater collected from large areas is traditionally stored in reservoirs and dams open to the atmosphere and to sunlight. Here, the problem is that some of the collected water is lost to

Abstraction of Atmospheric Humidity

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Manhole Handle to remove the fine sieve for cleaning Effluent

Packing Fine sieve Influent

Hydro-cyclone

Sediment trap Figure 26 Schematic representation of the treatment system used to purify road runoff, according to Helmreich B (2009) Stoffliche Betrachtung der dezentralen Niederschlagswasserbehandlung (Pollution of stormwater runoff to be treated in decentralized system). Berichte aus Siedlungswasserwirtschaft. Munich, Germany: TU.

evaporation. A wide variety of pollutants, pathogenic organisms among them, are transmitted from the atmosphere and from the surrounding land to the water stored in reservoirs. Moreover, algae growth leads to deterioration of the water quality as it transmits metabolites into the water, and also via biological degradation processes which follow the decay of algae. To keep the collected water from deteriorating in quality and quantity, infiltration into underground storage compartments is advisable. This process is commonly termed ‘groundwater recharge’. Strobl and his colleagues (Strobl and Zunic, 2006) developed a so-called infiltration dam concept (Figure 27). Infiltration dams serve two basic functions: as barriers preventing flooding of downstream areas and as reservoirs for the recharge of downstream aquifers. Stormwater runoff from upstream areas is collected and temporarily stored in the reservoir. To enable controlled groundwater recharge, the effluent of the reservoir is introduced to the downstream wadis (a dry riverbed that contains water only during times of heavy rain) for percolation (Figures 28 and 29). The flow is regulated so that evaporation losses are kept to a minimum. The water introduced in the wadis infiltrates toward the aquifer under the force of gravity. By raising the groundwater table, saltwater intrusion from the ocean can be counteracted. Thus, groundwater recharge can be considered as a measure to control salinization of aquifers. During the time the water resides in the aquifer, chemical interactions may take place with rock, gravel, and sand materials whereby the water picks up minerals. Filtration processes take place as water passes through sand and gravel layers. Both of these processes in combination are known to contribute to a significant improvement of water quality. Most likely, the water can be considered hygienically safe, and can be used as drinking water, and also for irrigation of gardens and agricultural fields. Full-scale trails carried out in Wadi Ahin, northern part of the Sultanate of Oman, combined with numerical modeling

(Haimerl et al., 2002; Haimerl, 2004; Strobl and Zunic, 2006) demonstrated the feasibility of this innovative approach. During a case study conducted in 1996, almost 4 million m3 of water was collected and temporarily stored in the reservoir. The stored water was discharged into the wadi system at a rate of 3 m3 s1. The efficiency of groundwater recharge increased with the moisture content of soil, with the water level of the surface flow, and the time of infiltration. These are obviously the aspects to be considered in efforts to further improve the efficacy of the technology. During the observation period, 85% of the water accumulated in the reservoir was able to be transferred to the aquifer. Evaporation losses were only around 0.1%, and 2.5% was captured in the topsoil.

4.05.6 Overarching Aspects Traditionally, water for human, industrial, and agricultural consumption is abstracted from rainfed sources such as rivers, lakes, and aquifers. After purification and transportation to customers, the water is used for specific purposes such as drinking, cleaning, and irrigation. Some of the water evaporates and thus gets incorporated in the original source, that is, atmospheric vapor. Most of the used water, however, is discharged into natural water bodies such as rivers, is transported downstream, and, in many cases, is abstracted again for subsequent use. In such cases, we are talking about unintended water reuse. As discharge of polluted water in rivers and lakes poses threats to both aquatic organisms and downstream users, and because such threats disturb the functioning of the aquatic environment as well as downstream economies, efforts have been made to regulate the abstraction of water from the Earthbased resources, and discharge of the used water back into the aquatic environment. Over recent decades, it has been realized that water management on the level of entire river basins is necessary to secure the economic, ecological, and social development of regions – in short: sustainable development.

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Rain in the mountains

Wells

Reacharge dam Flood detention basin

Surface runoff in wadi channels

Sea

Aquifer

Seawater intrusion into aquifier

Figure 27 Schematic representation of the concept of infiltration dam concept, provided by Zunic, Institute of Water and Environment, TUM, Germany.

Wadi inflow Reservoir Dam Outlets to wadi channels

Rock

Groundwater

Figure 28 Discharge of the water from the reservoir into wadi channels for subsequent percolation toward the aquifer. Schematic provided by Zunic, Institute of Water and Environment, TUM, Germany.

The United Nations Declaration on Environment and Development (Anonymous, 1972) may be considered as the starting point of what is now called the IWRM concept. IWRM is an alternative to the dominant sector-by-sector, top-down water management of the past. Water resources are

now managed at the basin or watershed level. The watersupply side is taken into account simultaneously with the water-demand side. IWRM integrates the use of land, groundwater, surface water, and coastal water. It integrates the interests of upstream and downstream regions. An

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Figure 29 Wadi channels in the Wadi Ahin area, Oman. The wedded riparian zone indicated progress of infiltration. Photo provided by Zunic, Institute of Water and Environment, TUM, Germany.

intersectoral approach is taken to decision making. IWRM calls for the integration of policy, regulatory, and institutional frameworks, such as the implementation of the polluter-pays principle, water-quality norms and standards, and marketbased regulatory mechanisms. According to the Global Water Partnership’s definition, IWRM is a process which promotes the coordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems (Anonymous, 2000; Anderson et al., 2008). The European Water Framework Directive (WFP) implemented in the European Union in October 2000 (Kaika, 2003) is an example of IWRM application. It calls for legal responsibility for the quantitative and qualitative status of all water bodies in Europe, including marine waters. It is a framework in the sense that all member states are committed to taking appropriate action to reach the common goal of achieving and maintaining high ecological quality of all surface and groundwater bodies. So far, however, atmospheric humidity and its quality are not included in the WFP, although they should be. Similar legislation has been implemented in many other parts of the world, including in developing countries. In Kenya, for instance, a Water Resource Management Authority (WRMA) has been established. It is a state corporation under the Ministry of Water and Irrigation, established under the Water Act 2002, and charged with being the lead agency in water-resources management (Anonymous, 2004). With the advent of methods designed to make active use of atmospheric water as an alternative water resource, a new dimension is added to the IWRM concept. Some of these methods are described in Section 4.05.4, and are comprehensively categorized in Figure 30. Other methods may be

developed in the very near future in response to the steady increase of water shortages around the world, caused by factors such as climate change, population growth, growth of urban areas, and lifestyle changes. A variety of options are available when it comes to the question of how to deal with the water abstracted from the atmosphere. Some of these options are shown in Figure 30. Precipitation generated by cloud seeding or ionizationbased technologies may be directed toward agricultural fields for sustained crop growth. In this case, farmers would be expected to pay for the service. Alternatively, it could be directed toward forest areas to prevent the outbreak of fires and the subsequent loss of property value and biodiversity. In this case, governmental organizations or insurance companies may be obliged to cover costs. To meet diurnal, weekly, and seasonal variations in water demand, temporary storage is certainly an option to be considered. The size of the storage facilities may vary from a small tank up to a large dam. Atmospheric water is low in salt content. When human consumption is concerned, mineralization of the water may need to be considered. When coming into contact with various surfaces, the water may pick up pollutants, dirt, droppings of animals, and even heavy metals (see Section 4.05.5.3). While being stored in a reservoir, the water may deteriorate in quality. In summary, the water needs to be treated prior to delivery to consumers in order to secure the health and welfare of the customers. The costs of abstracting, holding, treating, and delivering the water should be covered by the consumers (domestic and industry) based on volumetric consumption. This option is still a controversial subject in many regions of the world, however. Surprisingly, methods of abstraction of atmospheric humidity have not been taken into account by regulating

Abstraction of Atmospheric Humidity

Rainwater harvesting

Fog collection

Heat island

Clud seeding

Ionization

No regulations, yet

Condensation

Source: water vapor

Cloud formation and precipitation

Collection and storage

Piping or bottling

Purifying

Metering and prizing

Consumption Options:

S

S

S

S

S

Regulations exist and are mostly enforced

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People − Industry − Agriculture/forestry − Groundwater recharge Natural precipitation

Engineered capturing

Delivery

Figure 30 Overview of technologies to abstract atmospheric humidity, and the various options of treating the captured water on its way to the customers (people, industry, and agriculture).

authorities around the world, even though methods such as cloud seeding have been in use since 1946 (see Section 4.05.4.4). It is also surprising that atmospheric water has no obvious ownership – at least to the best knowledge of the authors (Wilderer, 2009). Influencing the atmosphere with the aim of changing weather conditions is not regulated anywhere in the world, with the exception of weather modification for hostile, military purposes (Anonymous, 1976). Over the centuries weather conditions have very often been decisive in military confrontations (Durschmied, 2000); therefore, it is only to be expected that engineered weather modification with the aim of abstracting atmospheric humidity to the advantage of one party or another may engender conflicts among stakeholders, regions, and even state authorities. Thus, it is high time to enter into national and supranational agreements concerning the exploitation of atmospheric humidity. Moreover, it is necessary to clarify liability issues, particularly regarding insurance. It has to be made clear as to which authority has the power to decide when and where certain weather-modification actions are allowed to be conducted. The authority in charge would then be responsible in case the activity has unintended extreme consequences, for instance, flooding, release of avalanches or mudslides, or car accidents.

Consequently, insurance companies need to be prepared to cover damages of any kind in case things do not go according to plan. A discussion of this type may appear superficial when considering technologies which obviously have only a very local effect, such as condensation or fog-collection technologies. In principle, however, the international community and governments everywhere would be well advised to take the negative effects of humidity abstraction seriously before a major accident occurs. Other than under natural conditions, it will not be force majeure which causes misery, but deliberate man-made actions.

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4.06 Safe Sanitation in Low Economic Development Areas BJ Cisneros, Universidad Nacional Auto´noma de Me´xico, Coyoaca´n, Mexico & 2011 Elsevier B.V. All rights reserved.

4.06.1 4.06.2 4.06.3 4.06.3.1 4.06.3.1.1 4.06.3.1.2 4.06.3.1.3 4.06.4 4.06.4.1 4.06.4.1.1 4.06.4.1.2 4.06.4.1.3 4.06.4.1.4 4.06.4.1.5 4.06.4.1.6 4.06.4.1.7 4.06.4.2 4.06.4.3 4.06.4.4 4.06.5 4.06.5.1 4.06.5.2 4.06.5.3 4.06.5.3.1 4.06.5.3.2 4.06.5.3.3 4.06.5.3.4 4.06.5.3.5 4.06.6 4.06.6.1 4.06.6.1.1 4.06.6.1.2 4.06.6.1.3 4.06.6.1.4 4.06.6.1.5 4.06.6.1.6 4.06.6.2 4.06.6.2.1 4.06.6.2.2 4.06.6.3 4.06.6.4 4.06.6.4.1 4.06.6.4.2 4.06.6.4.3 4.06.6.5 4.06.6.5.1 4.06.6.5.2 4.06.6.5.3 4.06.6.5.4 4.06.6.5.5 4.06.6.5.6 4.06.6.5.7 4.06.6.5.8 4.06.6.5.9

Introduction Historical Background Sanitation as Part of The Hydrological Cycle or Properly Closing the Water Loop Sources of Pollution Municipal discharges Industrial discharges Nonpoint and nonconventional pollutant sources to water Pollutants Biological Pollutants Viruses Bacteria Protozoa Helminth eggs Biological indicators Emerging pathogens Biological analytical techniques Conventional Parameters Emerging Pollutants Risks Sanitation in Low-Income Countries: A Complex Current Situation Sanitation Needs a Definition Millennium Development Goals Present Situation General overview Regional situation Situation at the national level Low-income countries sanitation specificities Sanitation Costs Wastewater Management Systems Basic Sanitation Facilities Traditional latrines Ventilated improved pit latrine Septic tank Composting toilets Pour-flush toilets Additional recommendations to set up basic sanitation facilities Toilets Water-saving toilets Toilets not using water Sludge Extraction from On-Site Sanitation System Sewerage Systems Small sewers Conventional sewers Pluvial sewers Wastewater Treatment Conventional pollutants treatment Pathogens treatment Emerging chemical pollutants Slow filtration Waste stabilization ponds Wetlands Land treatment Reservoirs and water storage tanks Upflow anaerobic sludge blanket

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4.06.6.5.10 4.06.6.5.11 4.06.6.5.12 4.06.6.5.13 4.06.6.6 4.06.6.7 4.06.7 4.06.7.1 4.06.7.1.1 4.06.7.1.2 4.06.7.1.3 4.06.7.2 4.06.7.3 4.06.7.3.1 4.06.7.3.2 4.06.7.3.3 4.06.7.3.4 4.06.8 4.06.9 4.06.9.1 4.06.9.2 4.06.9.2.1 4.06.9.2.2 4.06.9.2.3 4.06.10 4.06.10.1 4.06.10.2 4.06.10.3 4.06.10.4 4.06.11 4.06.12 References

Activated sludge Coagulation–flocculation Rapid filtration Disinfection Sanitation and Wastewater Treatment Costs Criteria for Selecting Wastewater Treatment Processes Wastewater Disposal versus Reintegration Soil Disposal or Reintegration of Used Water to Soil and to Groundwater Leach drains Evapotranspiration beds Soil aquifer treatment and aquifer storage recovery system Disposal into Surface Water Bodies or Reintegration of Used Water to Surface Water Bodies Reuse Types of water reuse Unintentional reuse Intentional or planned reuse Graywater reuse Sludge and Excreta Management Policy Integrated Water Resources Management Need for an Own Policy for Developing Countries Issues to address Challenges to face Strategies that can be used Funding Funding Options Why Sanitation Needs to be a Public Process Why Private Participation can be Involved Differences between Low- and Middle-Income Countries Science and Innovation: Need to Develop Individual Knowledge Conclusions

4.06.1 Introduction Before reading this chapter, it should be considered whether it is justifiable to have a specific section dealing with sanitation for low economic development areas (developing countries). Evidently, the editors of this book think so. The reasons include

• •

an increasing evidence that wastewater quality in high and low economic areas is different regarding some parameters that determine treatment options and differences in economic conditions necessitate alternative solutions not only at the technical level but also in terms of the ways to implement them.

To protect health, raise the quality of life, and increase the economic level, a good sanitation service is required in developing countries. While in developed countries, sanitation coverage is almost 99% as a result of a clear commitment of governments to provide it as part of the public services, in developing ones it is only around 50% (WHO–UNICEF, 2006). In addition, in the developed countries, the term sanitation applies not only to the installation of sewers but also to the full implementation of systems for the safe disposal and reuse of treated wastewater, sludge, and septage. In contrast, in developing countries, the term sanitation mostly

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applies to the use of sewers not always ending in treatment plants. In fact, reported sanitation figures frequently do not reveal the disposal of wastewater or excreta uncontrolled into the environment, the existence of malfunctioning wastewater treatment plants, or the use of rudimentary and inefficient basic sanitation facilities sometimes contributing to increased environmental pollution rather than to control it. As a result, waterborne diseases affect millions of people in the developing world, and the water quality of surface and groundwater bodies is increasingly deteriorating. The aim of this chapter is to assist the process of increasing sanitation in low-income regions by contrasting the differences in needs and solutions’ options with high-income regions. Most technical publications have traditionally grouped developing countries together as low-income societies without considering that in them there are high- and low-income areas and that among the latter ones there are several factors that create differences that need to be taken into consideration to provide suitable solutions, that rarely fall under the logic used in developed countries to provide sanitation. Most people lacking sanitation include the millions of poor people (Figure 1) living under precarious institutional conditions and under an economical and social situation that avoids the use of conventional solutions. This renders the provision of sanitation in low-income areas a major challenge.

Safe Sanitation in Low Economic Development Areas Living below the poverty line

Living above the poverty line

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Total

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Million people

1500

1000

500

0 Low-income countries

Middle-income countries

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Figure 1 Poverty distribution of the global population without access to basic sanitation in low- and middle-income countries (with information from Lenghton et al. (2005).

4.06.2 Historical Background The history of sanitation is mainly about three aspects: toilets, sewers, and final disposal. As sanitation is a broken subject in developing countries, the story of these three is also the same. When mankind was nomadic and lived in very small communities, sanitation was not an issue. Nature could absorb human wastes. Later, when villages grew, there was the need to set up special practices and facilities. In ancient Egypt (B3000 BC), each household had the responsibility to dispose of their garbage and excreta at the communal dump, in irrigation canals, or in open fields. Irrigation canals were the first drainage and waste disposal systems. At that time, toilets were a luxury that only the wealthier people could afford in cities. Toilets were carved of limestone, and the used water was disposed of into pits in the streets (MSU, 2009). Flushing toilets – some of them communal – existed in India since the twenty-sixth century BC. Reports on the use of toilets and other safe sanitation practices in ancient civilizations from Asia, Latin America, and Africa were common in places where nowadays lack of sanitation is a problem. The earliest covered sewers reported are from the Indus Civilization (2600–1900 BC) where Pakistan is located today. Cities used sewers to control inundations caused by pluvial water. The Cloaca Maxima or Roman sewer dates from around 600 BC. Initially, it was an open drain that was covered and left below the urban level, as the city building space became costly (Wikipedia, 2009). Later, when water began to be supplied in large quantities to households, getting rid of the used water became a problem and water was considered as a waste. It was then when sewers were found to be a useful infrastructure to convey wastewater out of the city in addition to stormwater. Concerning disposal, land application of wastewater and excreta has a long tradition in many countries. For centuries, farmers in China used human and animal excreta as fertilizers. The oldest references to the use of excreta in aquaculture come from some Asian countries, where it was employed to increase fish production (WHO, 2006). Further, even now in China, Mexico, Peru, Egypt, Lebanon, Morocco, India, and Vietnam

wastewater is used as a source of crop nutrients (Jime´nez and Asano, 2008). According to Rusong (2001), in contrast to the ‘mechanical’ ideas predominant in industrial societies, human ecological thoughts in ancient China emphasized the use of systems advocating ‘man and nature as one’. This principle is considered as equivalent to the sustainability principle and is based on terms describing concepts that are dissociated in modern civilizations, such as

• • • • •

Tian – heaven or nature; Di – Earth or resources; Ren – people or society; Wuxing – the five fundamental elements and movements within any ecosystem, that need to be in equilibrium by promoting and restraining each other; and Zhong Yong – describing that things should never go to their extremes but should be kept at equilibrium.

For several centuries, based on these ecological principles, China has developed and supported 21% of the world’s population with only 7% of the world’s arable land and less than 7% of the world freshwater resources (Rusong, 2001). Once again, similar conceptions can be found in ancient civilizations from Asia, Africa, and Latin America, in the same places where there are environmental crises now.

4.06.3 Sanitation as Part of The Hydrological Cycle or Properly Closing the Water Loop The urban water cycle is a relatively new concept used to analyze water quality problems in cities (Jime´nez, 2009b), which is depicted in Figure 2. It is useful in identifying conventional and nonconventional sources of pollution, in particular those that are specific to developing countries. It is important to understand the difference in order to be able to apply proper solutions to sanitation that go beyond the simplistic approach of merely installing wastewater treatment plants. A similar analysis could be made for rural areas.

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Water from: Rivers Lakes Reservoirs Water distribution through the network.

Water treatment plant Possibly

Wastewater treatment plant Disposal Occassionally

Rain Air pollution

Water tanks, water vendors, water bottlers

Urban agriculture with wastewater

Infiltration from Water network Ground water

Polluted rivers or open wastewater drains

Rivers lakes reservoirs

To the next city

Agriculture

Considerably higher

sewerage septic tanks industries storage tanks

Irrigation

Infiltration

Aquifer

Figure 2 Hydrological urban cycle. Differences compared to developed countries are shown in red. Adapted from Jime´nez B (2003) Health risks in aquifer recharge with recycle water. In: Aertgeerts R and Angelakis A (eds.) State of the Art Report Health Risk in Aquifer Recharge Using Reclaimed Water, pp. 54–172. Rome: WHO Regional Office for Europe.

The urban water cycle is important because of the large increase in urban population that is being experienced worldwide. By 2030, the urban proportion of the global population is expected to be around 60%. Over the next 50 years, in developing countries, most of the population growth will occur in urban and periurban areas. Furthermore, most of the 19 cities with the most rapid growth are located in chronically water-short regions in the developing world (UNHabitat, 2006). Providing water sources to urban areas from the developing world is a challenge because nearly one-third of the population (31.2% compared to a 6% in developed countries in 2001) are poor people living in slum areas. The slum growth rate is of 2.37%, a value significantly higher than the average world urban growth rate of 1.78%.

4.06.3.1.1 Municipal discharges

pollutants, such as biological, biodegradable, and nonbiodegradable organic matter, and heavy metals, in that order of importance. The content of almost all these of pollutants is similar around the world, tending to be more concentrated in arid and semiarid areas because of lack of water. In some cases, higher concentrations of pollutants result from increased industrialization of cities. Unfortunately, even when treated, municipal discharges introduce used water containing used compounds, some of which are pollutants, to water bodies. Municipal wastewater is never treated to recover its original quality (the one it had at the water source) as the selfcleansing and dilution capability of nature is used to complete the task. This is confirmed by the increasing amount of trace pollutants, such as endocrine disrupters, found in water sources. The presence of these compounds might be considered as an indicator that we have surpassed the natural depollution capability of the environment. Despite this, the idea of using water bodies or soil to depollute wastewater is still very common, and it could be reduced in water bodies as the depollution capability is lost as result of the water temperature increase due to climate change. In developing countries, the environment is frequently used to depollute wastewater, included when not treated at all, explaining the low quality of water bodies and the widespread presence of diarrheic diseases.

Municipal discharges are those produced by cities and small towns. They are considered to be point sources of pollution where they are produced and collected in sewers and thus disposed of as a well-identified source. When not treated, the main environmental concerns relate to conventional

Industrial wastewater has very variable quality and volume depending on the type of industry producing it. It may be highly biodegradable or not at all, and may or may not

4.06.3.1 Sources of Pollution Traditionally, pollution sources are classified as point and nonpoint sources. Municipal and industrial wastewater discharges are considered to be point sources, while agriculture (considered as the surface return flow from irrigation), storm runoff, and a wide variety of others are considered as nonpoint sources (Jime´nez, 2009a).

4.06.3.1.2 Industrial discharges

Safe Sanitation in Low Economic Development Areas

contain compounds recalcitrant to treatment. These include organic synthetic substances or heavy metals whose content in developing countries’ wastewater may be considerably different (in quantity and quality) from that of developed ones. The main concern with industrial wastewater is the increasing amount (in quantity and variety) of synthetic compounds contained in and discharged to the environment. A list of the most common pollutants in industrial discharges can be found in Jime´nez (2009a). Due to the difficultly in tracking toxic compounds and their fate, combined with the need to use complex and costly treatment methods to remove them from wastewater, it is advisable and cost effective to consider the implementation of cleaner production methods in industries (such as the replacement of toxic recalcitrant compounds with others that are less harmful or not harmful at all) and, also to raise awareness of society to reduce the use of such types of compounds (Jime´nez, 2009b).

4.06.3.1.3 Nonpoint and nonconventional pollutant sources to water Water pollutants come not only from urban and municipal wastewater discharges, but also from nonpoint sources, some of which are not perceived as such. Most of the nonpoint sources have been initially recognized as such by groundwater experts (Foster et al., 2003) who realized that soil (urban or rural) was an important means of transporting pollution to ground and surface water through complex interactions. A list of such pollutants is presented in Table 1 and a detailed description of some of the pollution sources can be found in Jime´nez (2009a).

4.06.4 Pollutants In this section, the types of different pollutants are reviewed, emphasizing those of special interest in developing countries.

4.06.4.1 Biological Pollutants Biological pollutants are the major threat to low-income countries as diseases caused by them are rapidly manifested and have important effects on children and the elderly, sometimes even resulting in fatalities. According to WHO (2004), diarrheal diseases accounts for an estimated 4.1% of the total daily global disease burden and is responsible for 1.8 million deaths every year. It is estimated that 88% of that burden is attributable to unsafe water supply, sanitation, and hygiene. Biological pollutants cause hydraulic diseases that are frequently divided into three categories: 1. Waterborne diseases that are caused by pathogenic organisms ingested when consuming water polluted with fecal contamination or food irrigated with polluted water. Examples of these types of diseases are giardiasis and amebiasis. 2. Water-washed diseases that are caused by the lack of safe water or simply any water for hygiene purposes. Disease transmission is linked to skin or eye contact. An example is trachoma, a disease that causes blindness. Some 6 million people have been blinded by trachoma. Another 150 million need treatment, and an estimated 500 million are at

151

risk. The disease is endemic in 55 countries, with only China and India accounting for 2 million cases. Productivity losses caused by trachoma are estimated to be US$2.9 billion (WHO, 2004). 3. Water-based diseases that are caused when water accumulates and stagnates, promoting the breeding of vectors such as mosquitoes that cause dengue or malaria. There are four groups of organisms that can be found in waste and polluted water: viruses, bacteria, protozoa, and helminths (in the form of eggs, Jime´nez (2003)). The general characteristics of these organisms can be found in specialized literature. In the following sections, properties relevant to developing countries will be highlighted for each type of group. A list of pathogens that have been detected in wastewater is presented in Annex 1. The main aspect to highlight is the notable difference in the quantity and variety of pathogens found in wastewater between developed and developing countries (Table 2).

4.06.4.1.1 Viruses Viruses are the smallest (0.01–0.3 mm) infectious agents. There are more than 150 types of enteric viruses capable of producing infections or illnesses that multiply in the intestine and are expelled in feces. Unlike bacteria, pathogenic viruses are found in wastewater and feces when people are infected, independently of whether they display symptoms. In regions where viral diseases are endemic, they are constantly isolated from wastewater. The presence of viruses and their concentration in wastewater is linked to the season of the year and the age distribution of the population. Concentrations are usually higher during summer and lower in the autumn months. The composition, type, and especially the content of viruses contained in wastewater are poorly known, particularly in developing countries, as a result of the complex and costly analytical techniques required to identify them (Jime´nez, 2003). The enteric viruses most relevant to man are enteroviruses (polio, echo, and coxsackie viruses), Norwalk, rotaviruses, reoviruses, caliciviruses, adenoviruses, and hepatitis A viruses. Rotaviruses are responsible for between 0.5 and 1 billion cases of diarrhea per year in children under 5 years of age in Africa, Asia, and Latin America and up to 3.5 million deaths. Usually, between 50% and 60% of the cases of children with gastroenteritis that are hospitalized are caused by rotaviruses. Reoviruses and adenoviruses are the main causes of respiratory illness, gastroenteritis, and eye infections and have been isolated from wastewater. To date, there is no evidence that the human immunodeficiency virus (HIV) causing the acquired immunodeficiency syndrome (AIDS) can be transmitted via a waterborne route. It is recognized that low virus levels may cause infection or illness; wastewater contains thousands of them, some of which are much more resistant to chlorine disinfection than bacteria (Jime´nez, 2003). Viruses discharged in polluted water can migrate long distances in soil and groundwater. The reported horizontal migration varies between 3 and 400 m, while vertical migration ranges from 0.5 to 70 m depending on soil conditions.

152 Table 1

Safe Sanitation in Low Economic Development Areas Sources of pollution for surface and groundwater

Origin

Urban infrastructure Water network Sewerage system Septic tanks and latrines Storage or treatment ponds Storage tanks Municipal landfills Hazardous wastes confinement sites Highways drainage soakways Pipelines Injection wells Cemeteries Urban activities Industries Factories and small commerce Irrigation of amenity areas Application of ice melting substances Transport and transference of material Storage of substances in tanks and reservoirs

Main polluting agents

Relative importance

Concern Developing countries

Developed countries

Cl, NMA ED, F, N, OM, T, PCP, sediments ED, N, OM, PCP Variable DBP, HC, OM, T ED, H, OM, PCP, S, T A, ED, EP, H, HC,NMA OM, PCP, S, T EP, S, T HC, OM, T ED, H, OM, PCP, S, T F, M, N, NMA, OM

þ þ þ þ þ þ

þ þ þ þ þ þ

þ þþ þ þ þ þ

þþþ þþ

þþþ þþþ

þ þ

Variable, more relevant synthetic compounds Variable, more relevant synthetic compounds N, P, T NMA, T HC, T Depending on the type of substance stored

þþþ

þþþ

þþ

þþþ

þþþ

þþ

þ þ þ þþ

þ þ þ þþþ

þþ þþ þ þ

þ þ þ þ þ

þ þ þ þ

þ þ þ þ þ þ

þ þ þ þ þ

þ

þ

Urban disposal options Unsewered sanitation Transportation of polluted water in channels or rivers Nontreated sewage disposal in soil with impact on water bodies Nontreated sewage discharge in rivers and lakes Treated wastewater disposal Sludge disposal Uncontrolled dumping sites

EP, F, N, OM, T EP, F, H, HC, N, OM, T ED, EP, F, OM, N, PCP, S, T

þþþ þþ þþþ

þþþ þþþ þþþ

  

ED, EP, F, N, OM, PCP, S, T DBP, ED, EP, N, NMA, PCP ED, EP, F, N, OM, PCP S, T ED, EP, H, OM, PCP, S, T

þ þ þ þ

þþþ þ þ þþþ

 þ þþ 

Other urban sources Atmospheric pollutants deposition Urban run-off Saline intrusion Industrial accidental spillage

A, EP, H, HC, N, M A, B, EP, HC, M NMA EP, T, HC

þþ þþþ þþ þ

þ þ þ þ

þ þþ þþ þ

þþ þþ þ þ

Industrial sources Industries located in urban or rural areas, in general

Variable, mostly synthetic compounds

þþþ

þþþ

þþ

Agricultural sources First use water Treated wastewater Nontreated wastewater

N, P EP, N, P, S EP, F, N, OM, P, S,

þþþ þ þþþ

þþþ þ þþþ

þþþ þþ þ

þþ

þþþ

þ

þþþ þþ

þþþ þþþ

þ 

Rural areas On-site sanitation systems and unsewered areas Storage of substances in tanks and reservoirs Disposal of solid wastes Transportation of polluted water in channels or rivers

EP, F, N, OM, Depending on the type of substance stored EP, ED, F, H, NMA, OM, PCP, S, T EP, F, H, HC, N, OM, T

þþ þ þ þþ

Adapted from Jime´nez B (2009a) Coming to terms with nature: Water reuse new paradigm towards integrated water resources management Encyclopedia of Biological, Physiological and Health Sciences, Water and Health, Vol. II: Life Support System, pp. 398–428. Oxford: EOLSS Publishers/UNESCO; Jime´nez (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324 Paris: UNESCO Leiden: Taylor and Francis Group. (a): May include industrial compounds. (b): Only present in industrial areas. A: Acids; Cl: Residual chlorine; DBP: Disinfection by-products; ED: Endocrine disrupters; EP: Emerging pollutants; F: Fecal pathogens; H: heavy metals; HC. Hydrocarbons; N: Nutrients; NMA: Nonmetal and anions; OM: Organic matter; P: Pesticides; PCP: Personal care products; S: Salinity; T: Toxics; þ : Magnitude increase.

Safe Sanitation in Low Economic Development Areas Annex 1

153

Biological disease-causing agents that have been reported in wastewater

Agent

Classification

Illness

Adenoviruses (31 to 51 types) Arbovirus Astroviruses (five types) Calcivirus or Norwalk agent Coronavirus Coxsackie A (enterovirus) Coxsackie B (enterovirus)

Viruses Viruses Viruses Viruses Viruses Viruses Viruses

Echovirus (enterovirus) Enterovirus 68–71

Viruses Viruses

Flavirus Hepatitis A virus Hepatitis E virus Norwalk virus Parvoviruses (three types) Poliovirus (enterovirus) Reoviruses (three types) Rotaviruses (four types) Snow Mountain Agent Small and round viruses Yellow fever viruses Brucella tularensis Campylobacter jejuni Escherichia coli enteropathogenic Legionella pneumophila Leptospira spp., 150 types

Viruses Viruses Viruses Viruses Viruses Viruses Viruses Viruses Viruses Viruses Viruses Bacteria Bacteria Bacteria Bacteria Bacteria

Clostridium perfringens Mycobacterium leprae Mycobacterium tuberculosis Salmonella spp., 1700 a 2400 strains (parathyphi, schottmuelleri, etc.) Salmonella thyphimurium Shigella spp., 4 types Treponema pallidum-pertenue Yersinia enterocolitica Vibrio cholerae Aspergillus fumigatus Candida albicans Balantidium coli Cyclospora cayetanensis Cryptosporidium parvum Entamoeba histolytica Giardia lamblia Naegleria fowleri Plasmodium malariae Trypanosoma spp. Toxoplasma gondii Ancylostoma duodenale Ascaris lumbricoides Echinococcus granulosis Enterobius vermicularis Necator americanus Schistosoma spp. Strongyloides stercoralis Taenia solium Trichuris trichiura Toxocara spp.

Bacteria Bacteria Bacteria Bacteria

Respiratory illness, conjunctivitis, vomiting, diarrhea Arboviral disease Vomiting, diarrhea Vomiting, diarrhea Gastroenteritis, vomiting, diarrhea Meningitis, fever, herpangina, respiratory illness Myocarditis, congenital heart anomalies, rash, fever, meningitis, respiratory illness, pleurodynia Meningitis, encephalitis, respiratory illness, rash, diarrhea, fever Meningitis, encephalitis, respiratory illness, acute hemorrhagic conjunctivitis, fever Dengue fever Infectious hepatitis Hepatitis Epidemic vomiting and diarrhea, gastroenteritis Gastroenteritis Poliomyelitis, paralysis, meningitis, fever Not clearly established Diarrhea, vomiting, gastroenteritis Gastroenteritis Diarrhea, vomiting Yellow fever Tularemia Gastroenteritis, diarrhea Gastroenteritis Acute respiratory illness, Legionnaire’s disease Leptospirosis (septic meningitis, jaundice, neck stiffness, haemorrhages in the eyes and skin) Gaseous gangrene, food poisoning Leprosy Pulmonary and disseminated tuberculosis Salmonellosis

Bacteria Bacteria Bacteria Bacteria Bacteria Fungi Fungi Protozoa Protozoa Protozoa Protozoa Protozoa Protozoa Protozoa Protozoa Protozoa Helminths Helminths Helminths Helminths Helminths Helminths Helminths Helminths Helminths Helminths

Typhoid fever, paratyphoid or salmonellosis Bacillary dysentery, Shigellosis Yaws (frambuesia) Gastroenteritis, Yersiniosis Cholera Aspergillosis Candidiasis Mild diarrhea colonic ulceration, dysentery, balantidiasis Severe infectious, dehydration: diarrhea, nausea, vomiting Diarrhea and cryptosporidiosis Amoebic dysentery Giardiasis Amoebic meningo-encephalitis Malaria Trypanosomiasis Congenital or postnatal, toxoplasmosis Anaemia, ancylostomiasis Ascariasis Hyadatidosis Enterobiasis Anaemia Schistosomiasis Diarrhea, abdominal pain, nausea, Strongylodiasis Taenisis, cysticercosis Diarrhea Fever, abdominal pain, nausea

The presence of biological disease-causing agents is not necessarily an indication of a confirmed risk. From Jime´nez B (2003) Health risks in aquifer recharge with recycle water. In: Aertgeerts R and Angelakis A (eds.) State of the Art Report Health Risk in Aquifer Recharge Using Reclaimed Water, pp. 54–172. Rome: WHO Regional Office for Europe.

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4.06.4.1.2 Bacteria Bacteria are single-celled microorganisms ranging from 0.2 to 10 m in size with different shapes. They reproduce and grow in an appropriate environment at defined ranges of temperature, Table 2 Comparison of the biological pollutant content in wastewater from developing and developed countries Organism Enteric viruses, PFU 100 ml1 (U, I) Salmonella, MPN 100 ml1 (M, U, F, SA,

IN,

Developed world

Developing world

102–104 100–104

104–106 106–109

104–106 101 1–103 1–103

106–4107 103 102–103 ND

H)

Fecal streptococci, No. 100 ml 1 (U, B, K) Protozoan cysts, organisms l1 (U, M) Giardia lamblia, cysts l1 (U, E, K) Cryptosporidium parvum, oocysts l1 (U, E)

Helminth ova, egg l1

1–9

6–800

Data from: E, England; H, Holland; In, India; I, Israel; K, Kenya; M, Mexico; SA, South Africa; U, USA; ND, No data. Adapted from Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO Leiden: Taylor and Francis Group.

Table 3

salinity, pH, etc. They may or may not be encapsulated. The environmental distribution of bacteria is ubiquitous and has different nutritional requirements. Many species of bacteria are not harmful to man. In fact, some even live inside humans forming intestinal colonies. Bacteria are expelled in feces at high concentrations (Jime´nez, 2003). Table 3 shows some characteristics of pathogenic bacteria that can be found in the feces of infected people. In wastewater, pathogenic bacteria are always present but at a variable concentration, depending on the local health conditions. As shown in Table 3, due to the high rate of diseases caused in developing countries, Salmonella, Shigella, and Helicobacter pylori are bacteria of importance as agents causing endemic diseases. In contrast, Vibrio cholerae is present only when an epidemic exists.

4.06.4.1.3 Protozoa Protozoa are the group of parasites most closely associated with diarrheas. They are single-celled organisms (2–60 mm in size) that develop in two ways: as trophozoites and as cysts. Infections are produced when mature cysts are consumed. Cysts are resistant to gastric juices and transform themselves into trophozoites in the small intestine, lodging in the wall where they feed on bacteria and dead cells. In time,

Characteristics of some bacteria frequently found in wastewater (with information from Jime´nez (2003) and Lenghton et al. (2005))

Characteristics and effects in humans Escherichia coli is commonly found in wastewater at high concentrations. Different E. coli strains can cause gastroenteritis in both animals and humans and pose a high risk to newborns and children under 5 years of age. E. coli strains implicated with human diseases are: (1) enteropathogenic E. coli ; (2) E. coli that is the common cause of traveler’s diarrhea, which provokes a liquid and profuse diarrhea with some mucosity, nausea, and dehydration; (3) enteroinvasive E. coli that invades the intestinal mucus lining like Shigella spp., and (4) E. coli (EHEC) that produces a similar toxin to Shigella causing hemorrhagic colitis. Infective doses are relatively low (102 organisms). Salmonella spp. is frequently present in wastewater at content always lower than that of fecal coliforms by 1–2 log. There is a wide variety of strains capable of infecting humans and animals. The incidence in humans is lower than in animals and has a seasonal variation. The most severe form of salmonellosis is typhoid fever caused by Salmonella typhi. Typical symptoms are chronic gastroenteritis with diarrhea, stomach cramps, fever, nausea, vomiting, and headache. In severe cases, collapse and death might occur. Transmission is through ingestion of polluted water or food, and is very common in developing countries. Infective dose is of the order 105–108 microorganisms, but for Salmonella typhi doses as low as 102–103 have been reported. Shigella is similar to Salmonella spp. but less frequent in wastewater. There are more than 40 strains, but S. sonnei and S. flexeneri represent almost 90% of total wastewater isolations. It rarely infects animals and lives for a shorter period in the environment. One route of transmission is through swimming in polluted water. Shigella spp. produces bacillary dysentery or shigellosis. This is light watery diarrhea that can develop into full-blown dysentery. The symptoms are fever, nausea, vomiting, abdominal pain, migraine, and myalgia. The classic form of dysentery is characterized by the expulsion of feces containing blood with or without mucus. The infective dose is less than 103 microorganisms. Helicobacter pylori is found in wastewater. Its major habitat is the human gastric mucosa. Three species are human pathogens: H. pylori, H. fennelliae, and H. cinaedi. The pathway of transmission is not entirely clear but water could be involved. In developing countries, H. pylori is acquired early in childhood, and up to 90% of children are infected by the age of 5. This contrasts with the low infection rate during childhood observed in developed countries (0.5–1%). Campylobacter jejuni usually is a pathogen to animals but it can cause severe gastroenteritis in humans. The main source of infection is nonchlorinated water supplies. Mycobacterium tuberculosis along with M. balnei (marinum) and M. boris causes pulmonary diseases and tuberculosis. For M. tuberculosis, contaminated water is the main source of infection. Vibrio cholerae is the cause not only of epidemic but also eight pandemics, the last one between 1990 and 1995. Cholera epidemics are caused by V. cholerae group O1 and some non-O1. Symptoms are abundant liquid diarrhea with significant loss of hydro-electrolytes and severe dehydration associated with vomiting. V. cholerae is rare in developed countries but frequent in poor ones. Humans are the only known hosts. The most frequent pathway of transmission is water, either through direct consumption or when used to irrigate produce that is consumed uncooked. Fish grown in polluted water are another source of transmission. Since 2007, there have been outbreaks of cholera in India, Iraq, Congo, Vietnam, and Zimbabwe. In 2005, West Africa suffered more than 63 000 cases of cholera, leading to 1000 deaths.

Safe Sanitation in Low Economic Development Areas Table 4

155

Protozoa related to sanitation problems and that are of interest for developing countries (with information from Jime´nez (2003))

Characteristics and effects in humans Entamoeba histolytica is one of the most important parasites detected in municipal wastewater and is commonly known as Amoeba. Trophozoites measure 20–40 mm and t cysts 10–16 mm. Amoebae usually lodge in the large intestine; occasionally they penetrate the intestinal wall, traveling and lodging in other organs. They are the cause of amoebic and hepatic dysentery. Entamoeba histolytica infects 10% of the world’s population – mostly in the developing world – resulting in approximately 500 million infected persons; there are between 40 and 50 million cases of invasive amebiasis per year resulting in up to 100 000 annual deaths (placing it second after malaria in mortality caused by protozoan parasites). Ninety-six percent of these cases occur in poor countries, especially on the Indian subcontinent, West Africa, the Far East, and Central America. Giardia spp. are common in wastewater as it frequently causes endemic diseases. It especially affects children under 5 suffering from malnutrition. The total number of sick people is of the order 1.1 billion, 87% of whom live in poor countries. Giardia spp. is the most common parasite of humans but water is not necessarily the main pathway of transmission. Cysts (that are 8–14 mm long and 7–10 mm wide) can survive in water bodies for long periods, especially in winter. Giardia lives in the intestines of a large number of animals as trophozoites. The disease is characterized by very liquid and smelly explosive diarrhea, stomach and intestinal gases, nausea, and loss of appetite. Cryptosporidium spp. is a parasite widespread in nature. Oocysts are resistant to chlorine and due to their small size (4–7 mm) are difficult to remove from water, as many other protozoan. Cryptosporidium spp. infects a large spectrum of farm animals and pets and was recently recognized as a human pathogen that is why it is considered as an emerging pathogen. Cryptosporidium spp. is capable of completing a life cycle within the same host and causing reinfection. Once an individual has been infected, the person carries the parasite for life and can be reinfected. The disease rate in developing countries has been poorly studied, in particular due to the higher occurrence of other types of diseases. Cryptosporidiasis in developing countries has shown a greater incidence among immune depressed people and in rural areas (Snelling et al., 2007). The main symptoms of cryptosporidiasis are stomach cramps, nausea, dehydration, and headaches. Although it is known that the infectious dose varies between 1 and 10, outbreaks have always been associated with large concentrations in water.

trophozoites become once again cysts that are expelled in feces. Infected persons may or not display symptoms. Protozoa do not reproduce in the environment, only in their host. However, they are able to survive in the environment and remain active for periods ranging from some months to up to several years, depending on the environmental conditions. Most intestinal protozoa are transmitted through polluted water and food contaminated with polluted water or unsanitary handled (Jime´nez, 2003). Table 4 shows the characteristics of some protozoa. In the developing world, the more relevant protozoa because of their effects on humans are Giardia and Amoeba. Cryptosporidium is a threat to developed countries, as was unfortunately demonstrated in Milwaukee, US, when 403 000 people became ill and more than 50 died after an infection was transmitted through the drinking water supply (Hrudey and Hrudey, 2004).

4.06.4.1.4 Helminth eggs Helminths are worms some of which are parasites in humans. Where helminths are the origin of waterborne diseases, they are mainly transmitted through the consumption of contaminated food (crops, meat, or fish). Helminths can also be transmitted through the oral–fecal route and, therefore, hygiene is important as a factor in their control. As helminths are associated with turbid water, they normally are not a concern in drinking water. Helminths are pluri-cellular worms and because of this they are poorly addressed in environmental microbiology books. The eggs – their infective form – are microscopic and travel along with wastewater. Helminths occur in different types and sizes (from 1 mm to several m in length), and have diverse and complex life cycles compared to most of the microorganisms known in the sanitary field (Jime´nez, 2008a).

Before infecting humans, in some cases, they may have an intermediary host as is the case for Schistosoma spp. that temporarily lives in snails. There are three different types of helminths: (1) plathelminths or flat worms, (2) nemathelminths, nematodes or round worms, and (3) annelids. If plathelminths have their body formed by segments, they are called cestodes; if not, they are then called trematodes. Only the first two types are of sanitary importance. Although common in sanitary engineering literature, it is improper to use the terms nematodes, Ascaris, and helminths as synonyms. This misunderstanding comes from the fact that Ascaris (a nematode) is the most common helminth egg in wastewater and sludge. A list of helminth eggs found in wastewater and sludge and its classification can be found in Jime´nez (2008a). Helminthiases are diseases of high incidence in developing countries compared with developed ones. Globally, there are around 1–2 thousand million people suffering of helminthiases but most of them are from developing countries where it affects up to 10% of the population. The incidence rate may reach 90% in regions where poverty and poor sanitary conditions prevail. In contrast, in developed countries, helminthiases’ incidence is at the most 1.5% and affects mainly poor immigrants (Jime´nez, 2008a). Helminthiases have different manifestations but, in general, they cause intestinal wall damage, hemorrhages, deficient blood coagulation, and undernourishment. They can degenerate into cancer tumors. Helminthiases affect mainly children, the elderly, and poor people (Jime´nez, 2008). Around 94% of the more than 4 billion cases of diarrhea in the world are caused by helminths (Murray and Lo´pez, 1996). There are several kinds of helminths with different local names (Annex 2). This along with the fact that it is hard to properly identify them clinically unless a costly laboratory analysis is performed, makes it difficult to track the actual incidence of all the

156

Safe Sanitation in Low Economic Development Areas

Annex 2

Examples of local names given to helminth and helminthiases diseases

Common name

Technical name

Examples of local names given to diseases

Number of infected people (million)

Region affected

Foodborne trematodes and schistosomiasis

Trematode

4240

Found in 74 countries.

Blood fluke

Schistosoma

Trematodiases, clonorchiasis, schistosomiasis, fasciolasis Schistosomiasis, bilharziosis or snail fever

200 half of which live in Africa (20 with severe consequences)

Liver fluke

Clonorchis sinensis

Clonorchiasis

40 (10% of the world’s population thought to be at risk)

Liver fluke

Fasciola hepatica and F. gigantica

Fascioliasis

Intestinal Fluke

Fasciolopsis buski

Fasciololopsis

Hookworms

Ancylostoma duodenale

Ancylostomiasis, anchylostomiasis, helminthiasis, miners’ anemia, tunnel disease, brickmaker’s anemia and Egyptian chlorosis Necatoriasis

Asia, Africa, and South America. (80% of whom live in sub-Saharan Africa China, Russian Federation, Republic of Korea, Vietnam Temperate areas of Africa, Europe and Central/ South America Kazakhstan, Lao Peoples0 s Democratic Republic, Poland, Russian federation, Thailand, Turkey, Ukraine, Viet Nam Middle East, North Africa, India and (formerly) in southern Europe

Necator americanus

Tapeworm

All cestode

Tape worm Tapeworm

Taeniasis, Cysticercosis

Roundworm nematode

Taenia Hymenolepis nana and diminuta All nematode (Ascaris, Toxocara, Trichuris Enterobius) Ascariasis lumbriocides

Pinworm Whipworm

Enterobius vermicularis Trichuris trichiura

Roundworm

1300

The Americas, Subsaharan Africa, Southeast Asia, China, and Indonesia Asia, Africa, South America, parts of Southern Europe and pockets of North America

Nematode infection

4000

Latin America, Asia, Africa, far East

Ascariasis

1500

Africa, Asia and Latin America, Far East

Oxiuriasis Enterobiasis Trichuriasis

600 1050

helminthiases. That is why frequently figures are underestimated. Technically, helminthiases take their name from their causative agent. For instance, trichuriasis is named after Thrichuris. Ascariasis, affecting nearly 1500 million people, is the most common of the helminthiases and is endemic in Africa, Latin America, and the Far East. Even though the mortality rate is low, most of the people infected are children under 15 years of age with problems of faltering growth and/ or decreased physical fitness. Around 1.5 million of these children will probably never bridge the growth deficit, even if treated (Silva et al., 1997; Jime´nez, 2008a).

The helminthiases’ infective agents are the eggs, not the worms. Actually, worms cannot live either in wastewater or in sludge because they need a host. Helminth eggs are transmitted through (1) the ingestion of crops polluted with wastewater or sludge, (2) direct contact with polluted sludge or fecal material, and (3) the ingestion of polluted meat or fish (Jime´nez, 2008a). Each type of helminth has its own pathways of infection. Eggs of different helminths generally occur in different shapes, sizes, and resistances (Figure 3). As a result of the higher incidence of ascariasis, in wastewater and sludge, these

Safe Sanitation in Low Economic Development Areas

Egg fertile roundworm Ascaris 40−80 μm × 25−50 μm

Ascaris egg, four-cell stage

Ascaris egg. With eight or more cells

Ascaris egg with a young worm (200−300 × 14 μm)

Ascaris egg, the shell loses resistance to allow hatching

Ascaris egg hatching

Nonfertile Ascaris egg 80−90 μm × 30−40 μm

Egg of the tapeworm Hymenolepis nana 30−47 μm

Egg of the tapeworm Taenia 30−40 μm

Egg of the tapeworm Hymenolepis diminuta egg 80 μm Hymenolepis diminuta 70−80 μm

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Hymenolepis diminuta hatching

Figure 3 Examples of helminth eggs most frequently observed in wastewater and sludge, Photographs courtesy of Catalina Maya, Treatment and Reuse GROUP, UNAM.

are the eggs found in the highest concentrations (Figure 4). The percentage of types of helminths might vary from one region to another following the disease’s pattern. Due to differences in health conditions in developed and developing countries, their helminth eggs content is very different in wastewater and sludge (Table 5). Eggs contained in sludge are not always viable and infectious. To be infectious, the larvae need to develop, and, for that, a certain temperature and moisture are needed. The necessary conditions are frequently met in soil or crops, where eggs are deposited when polluted wastewater, sludge, or excreta is used as fertilizer. Under such conditions, the larvae develop in 10 days. According to previous information (that has not been updated using better analytical techniques), Ascaris eggs remain viable 1–2 months in crops and many

months in soil, freshwater, sewage, feces, night soil, and sludge – periods which are much longer than those for microorganisms (Jime´nez, 2008a, Figure 5). This high resistance is due to a cover composed of 3–4 layers that gives mechanical resistance to eggs and protects them from desiccation, strong acids and bases, oxidants, reducing agents, detergents, and proteolytic compounds (Jime´nez, 2008a). The resistance of different helminth eggs genera under environmental conditions has not been reported in literature. To inactivate helminth eggs, it is recommended to raise the temperature above 40 1C for 10–20 days for Ascaris or to reduce moisture levels below 5%. These conditions are not ease of use during wastewater treatment; thus, helminths are usually removed from wastewater to be subsequently inactivated in sludge. Helminth ova of interest in the sanitary field

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Egg of the roundworm fertile Toxocara 85−95 μm

Toxocara larva inside the egg, infective stage (300−400 × 40 μm)

Toxocara egg, two-cell stage

Toxocara egg, four-cell stage

Toxocara hatching

Toxocara larva

Egg of the whipworm fertile Trichuris egg, infectious stage Trichuris 50−54 mm × 22−23 μm)

Egg 50−60 μm × 20−30 μm of pinworm Enterobius vermicularis with larva

Figure 3 Continued.

Trichosomoides 80 μm × 50 μm egg of a nematode with larva

Trichuris egg hatching

Trichosomoides sp. with damaged larva

Safe Sanitation in Low Economic Development Areas

Mexican wastewater

Mexican sludge

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South African ecosan sludge

90 80 70 60 50 40 30 20 10

Uncinaria

p. Taenia sp

Enterobiu

s spp.

. oides spp Trichosom

spp. Toxocara

pis spp. Hymenole

spp. Trichuris

Ascaris s

pp.

0

Figure 4 Content of different helminth egg genera in Mexican wastewater and sludge and from an on-site sanitation system in South Africa. Data from Maya C, Jime´nez B, and Schwartzbrod J (2006) Comparison of techniques for the detection of helminth ova in drinking water and wastewater. Water Environment Research 78(2): 118–124 and Jime´nez B and Wang L (2006) Sludge treatment and management. In: Ujang Z and Henze M (eds.) Municipal Wastewater Management in Developing Countries: Principles and Engineering, pp. 237–292. London: IWA Publishing.

Table 5 Helminth ova content in wastewater and sludge from different countries Country/region

Municipal wastewater (HO l1)

Sludge (HO g1 TS)

Developing countries Brazil Egypt

70–3000 166–202 6–42

Ghana Jordan Mexico

No data 300 6–98 in cities Up to 330 in rural and peri-urban areas 840 800 60 9 No data No data 1–8

70–735 75 Mean: 67; maximum: 735 76 No data 73–177

Morocco Syria Ukraine France Germany Great Britain United States

No data No data No data 5–7 o1 o6 2–13

From Jime´nez B (2008a) Helminth ova control in wastewater and sludge for agricultural reuse. Water reuse new paradigm towards integrated water resources management. In: Grabow WOK (ed.) Encyclopedia of Biological, Physiological and Health Sciences, Water and Health, Vol. II. Life Support System, pp. 429–449. Oxford: EOLSS Publishers/UNESCO.

measure 20–80 mm, have a specific density of 1.06–1.2, and are very sticky. These properties are used to remove eggs from wastewater (Jime´nez, 2008a). Helminth ova criteria. As shown in Table 5, not all wastewater and sludge contain significant amounts of helminth ova. For this reason, they are not included in all countries’ wastewater, sludge, or fecal sludge norms, as is the case with biochemical oxygen demand (BOD) or fecal coliforms, which are universal parameters used to design wastewater treatment (Jime´nez, 2008a). Based on toxicological and epidemiological studies, the World Health Organization WHO (2006) suggested a value of r1 egg l1 in wastewater intended for the irrigation of crops that are eaten uncooked. Wastewater used for the culture of fish should contain 0 egg l1, since trematode eggs (Schistosoma spp., basically) may multiply in an intermediary host (a snail) before infecting fish and humans. For excreta, the recommended criterion is of 1 egg g1total solids (TS).

4.06.4.1.5 Biological indicators Thermotolerant coliform bacteria (commonly referred as fecal coliforms) are the group most frequently used as indicators of fecal pollution because they behave in a similar way to most pathogenic bacteria in the environment, and, during treatment, they are abundant and easy to determine.

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Fresh and wastewater Ascaris lumbricoides ova E. histolytica cysts Shigella spp / Vibrio cholerae Fecal colifrom / Salmonella

Enteroviruses

0

20

40

80

60

100

120

140

100

120

140

Days

Crops Ascaris lumbricoides ova E. histolytica cysts Vibrio cholerae Shigella spp Fecal coliform / Salmonella Enteroviruses

0

20

40

80

60

Days

Soil Ascaris lumbricoides ova → Shigella spp / Vibrio cholerae / E. histolytica cysts Fecal coliform / Salmonella Enteroviruses

0

20

40

60

80

100

120

140

Days Figure 5 Survival time of different pathogens in fresh and wastewater, soil and crops at 20–30 1C. Data from Feachem R, Bradley D, Garelick H, and Mara D (1983) Sanitation and Disease: Health. pp. 349–356. New York, NY: Wiley.

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Thermotolerant coliforms are less specific indicators of fecal contamination than Escherichia coli, since they may sometimes arise from nonfecal sources, especially in tropical climates (WHO, 2004). However, it is becoming increasingly evident that they are not useful to simulate the behavior of all enteric viruses, protozoa – in particular with regard to Giardia and Amoeba – and helminth eggs that are of concern in low-income regions. Despite this, it is frequently, but wrongly, assumed that fecal coliforms are indicators of all kinds of biological pollution. Even though they can be useful indicators of fecal pollution in developed countries’ drinking water, this is not always the case for water and wastewater from developing ones, owing to the presence of a wider variety and larger quantities of microorganisms (Jime´nez, 2009). This does not mean that fecal coliforms are not useful for developing countries; it simply means that care must be taken to select additional indicators for specific purposes, such as for wastewater and sludge reuse in agriculture and aquaculture. In these cases, the helminth egg content (WHO, 2006) needs also to be specified. It is worth mentioning that the treatment procedures to inactivate helminth eggs are frequently developed using Ascaris eggs as models as they have been informally considered as indicators for all helminth eggs, although this has not been fully proven experimentally. In other cases, Taenia saginata or Ascaris galli, types of eggs that are rarely present in wastewater, are used to test treatment procedures.

4.06.4.1.6 Emerging pathogens Some pathogens that are not usually followed during conventional monitoring have been linked to outbreaks in developed countries. These pathogens have been called ‘emerging’ pathogens. They have led to new regulations as well as to improvements in water and wastewater treatment procedures. Some of the microorganisms considered as emerging pathogens are Giardia lamblia, Cryptosporidium parvum, Cyclospora cayetanensis, Blastocystis hominis, Legionella pnuemophila, E. coli 0157H7, Campylobacter, Mycobacterium, and Norovirus (Jime´nez, 2009b). In developing countries, some of these pathogens are endemic, while others have either not been studied or not reported as disease-causing agents.

4.06.4.1.7 Biological analytical techniques Assessing the biological quality of water is always a challenge due to the diversity of organisms and the need for different and proper methods to identify and enumerate them, some of which are complex, time consuming, and costly. In the following sections, a short description on the techniques used for different type of organisms is described. Viruses. Identification and quantification of viruses in wastewater, sludge, or excreta is complicated due to the low level of recovery from wastewater and the need to use complex and costly techniques to analyze them. A laboratory requires 14 days, on average, to determine the presence or absence of a virus in water and another 14 days to identify them, using conventional procedures. Polymerase chain reaction (PCR) techniques have considerably speeded up the process, as they can be used to determine viruses online. These techniques are based on the amplification of a single or few copies of a piece

161

of DNA allowing the identification of different types of viruses. However, quantification with the precision required in the sanitary field remains a challenge. In addition, the method is sophisticated, and requires highly specialized equipment and highly trained personnel. Due to these difficulties, it is sometimes preferred to detect bacteriophages, that is, bacteria infected by viruses. Bacteriophages are used as informal indicators of viruses and not been linked to human diseases; therefore, their presence has no health significance (Jime´nez, 2003). Bacteria. As mentioned previously, thermotolerant bacteria are the common accepted indicator of bacterial fecal pollution. They are detected by using a selective medium and incubating it after inoculation at 35 or 3770.5 1C and/or 44 or 44.570.25 1C, depending on the medium used. The materials and equipment used for this analysis are very common in most wastewater laboratories. PCR techniques to detect E. coli are useful as well. Protozoa. There are enough accessible techniques to determine the presence of the main protozoan pathogens in wastewater and sludge; however, fewer techniques are available to quantify them with the required precision for the sanitation field. The presence of protozoa on samples does not necessarily always imply a risk, since this requires them to be also viable. To determine the viability, several days are required. PCR techniques for protozoa are not as well developed as they are for bacteria and viruses. Helminth eggs. Helminths eggs require laborious techniques to detect them and even more so to enumerate them. Fortunately, the technique is readily available and does not use complex equipment, although it does require well-trained laboratory personnel. Currently, there is no standardized method and most of the few laboratories trained to detect them are using either different analytical procedures or similar ones with modifications. Moreover, most of the laboratories, instead of reporting the total content of helminth eggs, only report the Ascaris content, as is done in developed countries where it is frequently the single type of helminth eggs present (Jime´nez, 2008a). Analytical techniques for quantifying helminth eggs can be divided into two: direct and indirect techniques (Jime´nez, 2008a). The first consists of separating helminth ova from the other particles contained in wastewater or sludge (where there are many) and then identifying and counting different genera using a microscope. Some examples of these techniques used the US-EPA (United States-Environment Protection Agency), the membrane filter, the Leeds I and Leeds II, and the Faust techniques. The most widely used technique seems to be the US-EPA (1992). A comparison of the performances of the above-mentioned methods has been made by Maya et al. (2006). The recovery rate among them varies from 20% to 80%. Sensitivity for each notably varies as well and not all are capable of measuring the criteria values set by WHO (2006) of 1 egg l1 for wastewater and 1 egg g1 TS for sludge. The second types of techniques are indirect ones, and these have been applied only for wastewater. They are based on measuring either the total suspended solids (TSS) content or the particle size distribution (PSD), and then correlating the concentration to the helminth egg content. Calibration curves need to be established for each type of wastewater and

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Box 1 Endocrine compounds. From Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO Leiden: Taylor and Francis Group. Endocrine disruptors are chemicals that mimic hormones or have antihormone activity interfering with the functioning of endocrine systems in various living species. They derive from many sources including pesticides, persistent organic pollutants, nonionic detergents, and human pharmaceutical residues. Some of them have been identified in municipal wastewater and many of them may persist in the environment for some time. Endocrine disruptors have been also found in drinking water. Their presence in recycled waters also raises broader questions about the risks and benefits of water recycling and our approaches to anticipating the emergence of new contaminants. Human health effects potentially linked to exposure to these chemicals include breast, prostate, and testicular cancer; diminished semen quantity and quality, and impaired behavioral, immune or thyroid functions in children. Although direct evidence of adverse health effects in humans is lacking, reproductive abnormalities, altered immune function, and population disruption potentially linked to exposure to these substances has been observed in amphibians, birds, fish, invertebrates, mammals, and reptiles. Notably, feminization or masculinization on male or female animals, respectively, has been reported.

treatment process. Nevertheless, it is a worthwhile method because the helminth egg determination costs US$7–12 if TSS are used, and US$3 with the PSD, instead of US$70, which is the cost of direct methods. It is important to distinguish between fertile viable and nonfertile eggs as only the viable eggs are infectious. This can be done visually using stains or by incubation at 26 1C for 3–4 weeks (Jime´nez, 2008a).

4.06.4.2 Conventional Parameters Conventional parameters as understood in this text are those commonly used to design or select wastewater and sludge treatment processes worldwide, and they refer mainly to the organic matter content (measured as BOD or COD – biological or chemical oxygen demand), or suspended solids. In general, they are similar worldwide except for the heavy metals content that in general –and especially for sludge – is notably lower in developing countries than in developed ones (LeBlanc et al., 2008) as result of the difference at the industrialization level. However, at a local level, metal content in some industrialized areas of developing countries, notably where metal or tanning industries are placed, may be high. A detailed description of conventional parameters and their significance can be found in Jime´nez (2009a).

4.06.4.3 Emerging Pollutants The term (chemical) ‘emerging pollutant’ is used to describe a wide variety of complex organic chemical compounds that are candidates for future regulation and that have not usually been monitored. To detect them, complex and costly analytical equipment is needed, such as GC-MS or GC-MS-MS (gas chromatography coupled with one or two mass spectrometers) as these are the only ones capable to measure the very low concentrations at which the pollutants are present (in the order of micro- or ng l1) and to identify them. Emerging pollutants have been detected in untreated wastewater, treated wastewater, surface water, groundwater, and even in drinking water of both developed and developing countries (some). Among the countries that have measured and detect emerging pollutants, the following can be cited: Austria, Brazil, Canada, Finland, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Switzerland, UK, and USA (Jime´nez, 2009b). The sources of emerging pollutants are diverse. They come from nonpoint sources, municipal wastewater (treated or nontreated), and industrial discharges. They are also the result

of the improper disposal of solid wastes. Two groups of compounds that are considered as emerging pollutants are: endocrine disrupter compounds (Box 1) and personal care and pharmaceutical products (PCPPs). Wastewater treatment processes have not been designed to remove them; thus, they are randomly removed during conventional treatment. From the limited literature currently available, emerging pollutants – as other organic compounds – are concentrated in sludge during wastewater treatment. Initial risk studies suggest minimal ecological and health effects through biosolids recycling to soils (LeBlanc et al., 2008). As most of these pollutants have only been recently studied, the knowledge of their fate, transport, behavior during treatment, and risks is still poor in the sanitary engineering field. Chemical emerging pollutants, in general, are not considered at the moment as a priority for the developing world as there are more pressing health and environmental pollutants of concern.

4.06.4.4 Risks It is important to bear in mind that the simple presence of a pathogen or a toxic chemical in wastewater, sludge, or excreta does not necessarily mean that a negative effect will occur. For that, several other things need to happen. These include (1) the need for a compound/pathogen to reach a certain concentration; (2) the existence of a pathway for transmission to human or the environment; (3) the ingestion or presence of a certain dose to cause long- or short-term effects; (4) sufficient exposure times to the pollutant; and (5) sufficient sensitivity of a person or of the environment to pollutants. In addition, it should be remembered that, for humans, water is not the only source of risk, as food and air are also sources of pollutant ingestion and, in some cases, they may be the main ones. In terms of the differences of biological risks to humans in developing and developed countries, there are additional aspects to consider as humans develop immunity to pathogens depending on the type of environment they are exposed to, and thus infectious doses may be higher. Genetic history, nutrition, and the combination of social patterns also intervene. For these reasons, data developed for developed countries are not always applicable to developing ones to perform risk analysis. In order to quantitatively assess risks, it is necessary (1) to establish the type and quantity of given microorganisms in a region, (2) to know the actual infectious dose, and (3) to define and evaluate the possible infection route. To

Safe Sanitation in Low Economic Development Areas

quantitatively evaluate the risk from a chemical or microbial pollutant, several methodologies are available in literature, but the data needed to apply them may be lacking for special cases in developing countries.

In 2002, the World Summit on Sustainable Development (WSSD) provided a definition for basic sanitation that, besides considering the service itself, considered its impact on human health. This definition comprises the following:

• 4.06.5 Sanitation in Low-Income Countries: A Complex Current Situation 4.06.5.1 Sanitation Needs a Definition Sanitation is a term that has a clear meaning in the developed world. However, for the developing one, there is need to have a better definition. Traditionally, sanitation has been reported as the percentage of the population having access to the service. In practice, this service in low-income regions ranges from simple access to sewers that are discharging the wastewater just behind households or into the streets to sewers connected to sophisticated wastewater treatment plants coupled with water reuse projects and comprising safe sludge management practices. For basic sanitation – sanitation provided in rural or poor periurban areas, the term sanitation includes a wide variety of on-site sanitation options going from simple pit to highly comfortable package treatment plants, which may or may not be functioning. To overcome this, the Joint Monitoring Programme (JMP) from WHOUNICEF proposed in 2000 to introduce the term ‘improved sanitation’. Improved sanitation is a system in which excreta are disposed of in such a way that the risk of fecal–oral transmission to users and to the environment is reduced (WHO–UNICEF, 2008). Table 6 shows which options qualify as improved sanitation and which do not. Table 6 Improved and unimproved sanitation facilities according to WHO–UNICEF (2008) Improved

Unimproved

Connection to public sewer or septic tank Pour-flush latrine Pit latrine with slab VIP latrine Ecological sanitation

Service or bucket latrine Traditional latrine Public latrine or shared toilet Open pit or pit latrine without a slab Open defecation in bush or field

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• • • • • •

the development and implementation of efficient household sanitation systems; the improvement of sanitation in public institutions, especially in schools; the promotion of safe hygiene practices; the promotion of education and outreach focusing on children, as agents of behavioral change; the promotion of affordable and socially and culturally acceptable technologies and practices; the development of innovative financing and partnership mechanisms; and the integration of sanitation into water resources management strategies in a manner that does not negatively affect the environment (it includes protection of water resources from biological or fecal contamination).

As a result, the WSSD’s focus is not only on the construction of a particular number of toilets but also on the effective improvement of health and hygiene through basic sanitation. However, still new elements are needed to be added as problems caused by lack of sanitation are combined with those arising from the lack of economic resources and frequently also with lack of water in societies lacking even from social, economical, and political rights (Box 2).

4.06.5.2 Millennium Development Goals The Millennium Development Goals (MDGs) are drawn from the actions and targets contained in the Millennium Declaration that was adopted by 189 nations and signed by 147 heads of state and governments during the UN Millennium Summit held in New York City on September 2000 (WHO– UNICEF, 2009). They comprise eight goals and 21 quantifiable targets. Water is part of the 7th Goal under Target 7c: ‘‘Reduce by half the proportion of people without sustainable access to safe drinking water and basic sanitation.’’ Fulfilling this target represents the challenge of providing safe water supply to 1.1 million people and safe sanitation to 2.6 million people within 15 years.

Box 2 What sanitation should include, with some information from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan. * * * * * * * * * * *

Safe collection, storage, treatment and disposal, reuse, or recycling of human excreta (feces and urine). Drainage and safe disposal, reuse, or recycling of household wastewater (often referred to as sullage or grey water). Management, minimization, reuse, and recycling of solid wastes (trash or rubbish). Use of goods producing less solid wastes. Drainage, safe management, and even reuse or recovery of storm water. Treatment and disposal, reuse, or recycling of sewage effluents and wastewater by products. Collection and management of industrial waste products, and, the promotion of cleaner industries, vis-a`-vis water. Management of hazardous wastes (including hospital wastes and chemical, radio-active, mining, petrochemical, and other dangerous substances). The use of sanitation as a way to properly reintegrating water, organic matter, and nutrients into the environment in order for them to be safely used again. Provision of water in a sufficient amount to maintain clean households and to allow proper hygienic habits. The recognition of a right for sanitation at the same level of the right to water. The sanitation as an instrument to differentiate social classes, gender, children, and ethnic groups.

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Box 3 Some figures for global sanitation * * * *

For each four persons that do not have access even to a simple pit latrine, six have it. For each one person that does not have access to sanitation, another one has it. In rural areas, for each two persons, only one person has access to a sanitation service. For each 7 l of wastewater that is nontreated, 1 l is treated.

4.06.5.3 Present Situation Reporting figures concerning the state of sanitation in the developing world is a difficult task. First, there is a lack of information; second, the information available is generally presented in a heterogenic way; and third, different sources tend to contradict each other despite national and international efforts to produce consensus.

4.06.5.3.1 General overview The worsening situation with regard to sanitation in developing countries can be described using different indicators (Box 3). Contaminated water and poor sanitation account for the vast majority of the 1.8 million child deaths each year from diarrhea – almost 5000 every day – making it the second largest cause of child mortality (UNDP, 2006). The expansion of water services is essential to reduce the burden of waterrelated diseases and to improve the well-being of a large part of the world’s population. It is also vital for economic development and poverty alleviation (WHO, 2004). According to the figures presented by WHO–UNICEF (2006), despite the efforts made and due to population growth, between 1990 and 2004, the population with access to sanitation services has increased from 2569 million to 3777 million (47%), while the net number of people without improved sanitation decreased by only 98 million.

4.06.5.3.2 Regional situation The difference between the level of sanitation in developed and developing countries is high: 99% versus 50% (Table 7). However, between 1990 and 2004, the percentage of people with access to improved sanitation increased from 35% to 50% with countries’ variations ranging from 37% to 88% (WHO–UNICEF, 2006). The difference observed between rich and poor countries is also observed between urban (77%) and rural (33%) areas from developing countries and as well between rich and poor people living there following the inequities of wealthy distribution.

4.06.5.3.3 Situation at the national level The sanitation coverage as percent of the population with service per country is presented in the map of Figure 6 for the year 2004. Annex 3 contains a table with countries with less of 60% of the total, urban, or rural population.

4.06.5.3.4 Low-income countries sanitation specificities Sanitation in developing countries is quite a complex issue, because the lack of it is combined with other several problems, some of which are geographically described on the Maps 1–8

Table 7

Sanitation coverage per region for 2004

Region World Developed regions Commonwealth of independent states Developing regions Northern Africa Sub-Saharan Africa Latin America and the Caribbean Eastern Asia Southern Asia South-eastern Asia Western Asia Oceania

Coverage as % of the population Total Urban Rural 59 80 39 99 100 98 83 92 67 50 73 33 77 91 62 37 53 28 77 86 49 45 69 28 38 63 27 67 81 56 84 96 59 53 81 43

Coverages below 60% are highlighted in red and those above 80% in blue. Data from WHO–UNICEF (2006) Meeting the MDG Drinking Water and Sanitation Target: The Urban and Rural Challenge of the Decade. Geneva: WHO and UNICEF.

from Annex 4. By analyzing these maps, the following conclusions may be drawn: 1. Several low-income countries are located in arid or semiarid regions; thus, besides sanitation problems, they face the problem of water scarcity. 2. Many of the areas under greatest stress (where people are already overexploiting rivers by tapping water that should be reserved for environmental flows) coincide with areas that are heavily developed for irrigation to provide water for food, that is, mostly in developing countries. 3. Water withdrawal for agriculture is mainly performed in developing countries as a result of low water availability and the high dependence of agriculture. 4. Areas where poverty and hunger are prevalent coincide with areas lacking sanitation. 5. In the future, it seems that the situation may worsen as water availability will decrease in the countries already experiencing water-related problems, including lack of sanitation. As result of the past and present situations, sanitation has different aspects on developing countries that cannot be described simply using the percent of population-covered index. In the following, some of these aspects will be described. Basic sanitation versus sanitation. Providing services for excreta management in poor rural or urban areas is frequently known as basic sanitation. Thus, it has to do with excreta management rather than with sewerage and wastewater treatment plants (Box 4 and Figure 7). The quality of the service is frequently associated with peoples’ economic level, and thus, is

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165

Pacific Ocean

Equator Pacific Ocean Population using improved sanitation, in percentage Less than 50% 50 to 75% 76 to 90% 91 to 100% No data

Equator Atlantic Ocean

Indian Ocean

Source: World Health Organization

Figure 6 Sanitation coverage per country in 2004 (with information from WHO–UNICEF, 2006).

also a sign of status. Another aspect to consider is that the lack of basic sanitation frequently is associated with lack of water. LeBlanc et al. (2008) highlights that research and experience suggest the following hierarchy of risk to human health:

•

‘‘living in a dense community without basic sanitation4(is more risky thany) irrigation of crops with untreated, pathogencontaminated wastewater4use of untreated, pathogen-contaminated excreta or wastewater sludge on soils4use of untreated, pathogencontaminated animal manures on soils4use of treated manures, wastewater, or biosolids on crops4use of these treated materials in accordance with strict modern regulations that address heavy metal and chemical contaminants.’’

•

•

• • •

Differences on sanitation services. Possibly, one of the aspects that contributes the most to render sanitation in developing countries a challenge is the variety of needs and circumstances arising from social differences. As shown in Figure 8, for instance, poor people not only are less served but also the quality of the services is lower. One of the deepest disparities is between urban and rural areas as for the former the coverage is twice as much than for the latter in developing countries. Traceable differences in sanitation services have been reported as well among indigenous and nonindigenous people and minorities such as castes and women (Box 5). Among these differences, the following common challenges can be identified:

•

The need to provide the service in poor areas with large population increases.

•

For urban areas, a very fast service demand growth in slums that are spread out in cities, have high population density, and there is no land to place the infrastructure. For rural areas, the need to assist a population frequently dispersed and hence at higher cost. The need to fund projects combining liquid and solid waste collection and treatment infrastructure. The need to develop new or different management structures to provide services in social and political complex areas. The need to include health education and awareness programs on sanitation projects. The need to use public funding to provide services that are to be subsided. The existence of regions having high income where services can be provided in a similar way to developed countries.

Sanitation versus wastewater treatment. As described previously, sanitation coverage does not necessarily result in wastewater being treated or safely disposed of. To illustrate this, figures for the situation in some developing countries are provided. Two comments on this figure are that (1) it is really difficult to find data on wastewater treatment, notably for the Asian and African regions and (2) although there should not be a full correspondence between the sanitation coverage and the wastewater treatment – as some people are served using basic sanitation facilities – the figures should not be as different as they are for some countries. In Latin-America, for instance, although the sanitation coverage was 78% in 2006, only 18%

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Annex 3 Classification of countries per range of sanitation coverage, with information Total

Urban

Sanitation Coverage o 20% Ghana Guinea Cambodia Burkina Faso

Comoros

Sao Tome and Principe

Congo Congo, Democratic Republic of Coˆte d’Ivoire Gabon Guinea-Bissau Haiti India Lao People’s Democratic Republic Lesotho Liberia Madagascar Mauritania Micronesia, Federated States of Mozambique Namibia Nepal Sao Tome and Principe Solomon Islands Somalia Sierra Leone Sudan Timor-Leste Togo Sanitation coverage 4 40 but o 60% Azerbaijan Afghanistan Belize Angola Bolivia Bangladesh Botswana Benin Cameroon Bolivia Cape Verde Botswana China Burkina Faso Equatorial Guinea Burundi Gambia Cambodia

Continued

Total

Urban

Rural

Indonesia Kenya Kiribati

Cameroon Comoros Central African Republic Coˆte d’Ivoire

Iraq Kazakhstan Kyrgyzstan

Congo, Democratic Republic of the Equatorial Guinea Ethiopia Guinea-Bissau Haiti India Kenya Kiribati Korea, Democratic People’s R. Liberia Madagascar Mali Mauritania

Marshall Islands

Rural

Cape Verde Solomon Islands Togo Micronesia, Federated States of

Ethiopia Niger Chad Eritrea Sanitation coverage 4 20% but o 40% Afghanistan Chad Angola Congo Bangladesh Eritrea Benin Gabon Burundi Ghana Central African Guinea Republic

Annex 3

Azerbaijan Belize Brazil China El Salvador Lao People’s Democratic Republic Lesotho Mongolia Nepal Peru Senegal Timor-Leste Yemen

Korea, Democratic People’s R. Kyrgyzstan Maldives Mali Mongolia Nicaragua Nigeria Pakistan Papua New Guinea Rwanda Senegal Swaziland Tajikistan Tanzania, United Republic Uganda Vanuatu Yemen Zambia Zimbabwe

Mozambique Namibia Nicaragua Niger Nigeria Somalia Rwanda Sudan Sierra Leone Swaziland Tanzania, United Republic of Uganda Zambia

Maldives

Mexico Moldova, Republic of Morocco Palau Panama Pakistan Papua New Guinea Philippines South Africa Tajikistan Turkmenistan Vanuatu

Viet Nam Venezuela Zimbabwe

From WHO–UNICEF (2006) Meeting the MDG Drinking Water and Sanitation Target: The Urban and Rural Challenge of the Decade. Geneva: WHO and UNICEF.

of the wastewater was treated (CONAGUA and WWF, 2006). To give an idea of the situation in other regions, for the year 2004, when the Latin America and the Caribbean region reported a treatment capacity of 14%, this was of the order of 35% for Asia and nearly 0% for sub-Saharan Africa (WHO/ UNICEF, 2000; Figure 9).

4.06.5.3.5 Sanitation Costs Colombia Djibouti Egypt Fiji French Guiana Gambia Guyana Honduras Indonesia

According to Lenghton et al. (2005), the amount of money needed to fulfill the sanitation MDGs ranges from US$24 billion to US$42 billion representing, in mean conditions, an annual average investment of US$2.2 billion. To put these figures in perspective, the above-mentioned authors mention that each year Europe and the United States spend US$17 billion on pet food and Europe spends US$11 billion on ice cream. The overall cost estimation of the current water and sanitation deficit is of the order of US$170 billion, equivalent to 2.6% of developing countries’ gross domestic product (GDP). For each US$1 invested for sanitation, the economic

Safe Sanitation in Low Economic Development Areas

167

Annex 4

No data

Upper middle income countries ($3,056 - 9,386)

High - income countries ($9,386 or more)

Low middle - income countries ($766 - 3,056)

Low- income countries ($766 or less)

Map 1 Economic income per country, with information from World Bank 2009.

No data

<10

10−25

25−50

50−75

>75 %

Map 2 People living at under 2 USD/day, UNDP, 2006 with data from http://earthtrends.wri.org/povlinks/index.php (Continued )

168

Safe Sanitation in Low Economic Development Areas

No data

<500

500−1000

1000−1700

1700−5000

>5000

Map 3 Renewable water resources (surface and ground water) per inhabitant for 2005, with data from: FAO-Aquatat, 2009 http://www.fao.org/nr/ water/aquastat/globalmaps/

No data

<10

10−25

25−50

50−75

>75 %

Map 4 Water stress or water use intensity index (surface and groundwater withdrawal as percentage of the total renewable water resources) for 2001, with information from http://www.fao.org/nr/water/aquastat

Safe Sanitation in Low Economic Development Areas

No data

<5

5−10

10−20

20−45

169

>40 %

Whit data from 2001 Map 5 Surface water and groundwater withdrawal for agricultural purposes as percentage of the total actual renewable water resources for 2001, with information from http://www.fao.org/nr/water/aquastat/globalmaps

No data

<5

5−15

15−25

25−35

>35−50 %

>50 %

Map 6 Prevalence of undernourished people as percentage of total population for 2002–2004, with information from http://www.fao.org/nr/water/ aquastat/globalmaps (Continued )

170

Safe Sanitation in Low Economic Development Areas

Improved sanitation prevalence 75−100

0−50

50−75

20−40

Note: Data on prevalence of improved sanitation are for 2000. Diarrhea Data on prevalence of diarrhea are for various years, prevalence 1991−2000, and indicate prevalence in two weeks (%) before may vary by season. Because country surveys were administered at different times, data are not comparable across countries.

10−20 0−10

with data from FAO-AQUASTAT, 2007 Map 7 Prevalence of diarrhea and improved sanitation 2000 With information from: United Nation Children’s Fund Programme and The Joint Monitoring Programme Lenghton et al. (2005) UNPD Earthscan.

Vistuta Oder Elba Rhine Meuser

Dnleper Don

Kura

Darya

Tigris & Euphrates

Colorado Rio Grande Grande de Santiago Balsas

Syr

Panuco

Amu Darya

Indus

Yangtze Ganges

Narmada Tapti Krisna Volta

Jubba

Mahanadi

Huang He Hong Chao Phrya

Godavari

Limpopo Orange

<500

500−1000

1000−1700

1700−4000

4000−10000

<10000

No data

Map 8 Projected annual renewable water supply per person by river basin for the year 2025. With information from From: Water Resources eAtlas, 2007 http://earthtrends.wri.org/pdf_library/maps/2-4_m_WaterSupply2025.pdf

Safe Sanitation in Low Economic Development Areas

171

Box 4 Some challenges to provide basic sanitation in low-income countries * *

* *

Open defecation is practiced by 48% of the population in Southern Asia and 28% in sub-Saharan Africa. In Ouagadougou, the capital of Burkina Faso, the access to sanitation facilities is 53% while the figure for the country is 15.6%, a figure that reduces to only 10% for rural areas (Paskalev, 2008). In Yaounde´, Cameroon’s capital with 2 000 000 inhabitants, the available facilities for most people (88%) are external and in shared proprieties (Figure 7). Basic sanitation and sanitation figures reported are not the same. For instance, for Cote d’Ivoire, a coverage of 45% is reported for rural areas, but, in fact, 36% refers to basic facilities and only 9% to adequate systems (Angoua, 2008).

Percentage of people covered (%)

50 40 30 20 10 0 Flush toilets indoor, 75 l

External latrine, 50 l

Common latrine, 25 l

Private latrine, 20 l

Common latrine, 20 l

Other

Figure 7 Type of toilets used in Yaounde indicating the amount of used water per day and inhabitant: estimation for 2007. Data from Mfoulu N (2008) Cameroon. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 169–179.

Flush toilet

Pit latrine

No facility

100 80

%

60 40 20

Colombia, 2005

Kyrgyzstan, 1997

Namibia, 2000

Peru, 2000

Poorest 20%

Richest 20%

Poorest 20%

Richest 20%

Poorest 20%

Richest 20%

Poorest 20%

Richest 20%

Poorest 20%

Richest 20%

0

Zambia, 2001−02

Figure 8 Type of facilities provided for the richest and poorest quintiles in some countries. Data from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan.

return would be between 3 and US$34, depending on the region and the type of technologies used (WHO–UNICEF, 2004). Studies performed in Egypt and Peru showed that just providing access to flush toilets reduced the risk of infant death by 57–59% (Lenghton et al., 2005).

4.06.6 Wastewater Management Systems Even if sanitation represents an economic benefit, its cost is still important to societies in which this is not the only

requirement. Therefore, it is useful to combine options that involve building infrastructure with others that do not (such as washing or cooking produce that has been irrigated with polluted water) in order to improve health conditions while the sanitation services can be gradually provided. Such an approach is described in WHO (2006). In the next sections, options to build up wastewater management systems are reviewed. A wastewater management system (WWMS) is understood in this chapter as the combination of one or several of the following components: (1) basic sanitation facilities or toilets; (2) wastewater collection systems (sewers) or

172

Safe Sanitation in Low Economic Development Areas

Box 5 Women and sanitation (with information from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan.) One explanation for the low effective demand for sanitation is gender inequality. Women tend to place a higher value on household toilets than do men for a number of reasons, among them privacy, cultural norms, care-giving responsibilities, and the risk of sexual harassment and assault. In addition, the unique sanitation needs of women and girls (e.g., during menstruation and during and after pregnancy) receive little recognition when discussions about sanitation and hygiene occur. Yet, the limited political and personal power of women in many developing countries means that some of sanitation’s strongest advocates are virtually absent from decision making and priority-setting processes.

% Sanitation

% Wastewater treatment

100 80 60 40 20

Yemen

Tunisia

Syria

Panama

Pakistan

Morocco

Mexico

Libya

Lebanon

Jordan

Iran

India

Guatemala

Ghana

China

Brazil

Algeria

0

Figure 9 Percentage of sanitation coverage and treated wastewater. Data from CONAGUA and WWC (2006) Regional Document for the Americas Prepared for the 4th World Water Forum. Ciudad de Me´xico, Mexico, 16–22 March. Vienna: UN, Hashimoto (2009, personal communication), Wikipedia (2009), and Bahri A (2008) Water reuse situation on the Middle Eastern and North African countries. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs, pp. 27–48. London: IWA Publishing.

excreta extraction mechanisms; (3) wastewater treatment plants; (4) sludge management and disposal units; and (5) wastewater disposal or reuse facilities. Before presenting these components in detail, the two options in which they can be managed (centralized or decentralized) are discussed. Conventionally, to handle wastewater, sewers connected to wastewater treatment plants have been used. This is known as a centralized system and is a well-mastered and well-managed technology approach applicable to cities, provided funds for its construction and operation are available. In terms of operation, centralized systems are often cheaper and easier to handle than decentralized ones. For isolated slums and dispersed rural areas and even for cities where new sewerage systems is too costly, it is advisable to use decentralized wastewater management systems. In these, sewers of reduced size result in a lower capital cost (around 30%) due to the smaller diameter and length of the used pipelines. In addition, they offer the following benefits (Lenghton et al., 2005; Correlje and Schuetze, 2008): (1) they allow investments to be made stepwise, in line with available funds, local development, and population growth; (2) they are used in smaller areas of service that are easier to manage; (3) they allow the use of different technologies to provide services to different socioeconomic groups; and (4) they facilitate the reuse of water on-site. Nevertheless, all these advantages need to be assessed in practice, as they cannot be taken for granted

universally. As for many water utilities, decentralized systems represent a higher number of systems to manage, which is difficult and complex; to overcome this limitation, centralized management of decentralized systems is recommended. This way it is possible to ensure high performance and reliable operation, reduce costs, and also ensure the need for specialized operators (Hughes et al., 2006).

4.06.6.1 Basic Sanitation Facilities From a technical point of view, there are four important components to consider when providing a basic sanitation service: (1) the type of toilet, (2) the storage facility for feces which frequently are associated to the toilet, (3) the way in which feces are extracted from the pit, and (4) their further management. This section deals with the first two components. Their main characteristics are discussed here; for design, it is recommended to consult specialized books. A good option to begin with is the United Nations Environment Programme (UNEP) website (see section titled ‘Relevant websites’).

4.06.6.1.1 Traditional latrines Latrines are the most widespread type of on-site sanitation facility. They are used in rural settings and deprived areas in cities. They consist of a makeshift pit dug in the ground and

Safe Sanitation in Low Economic Development Areas

generally covered with any material (a wooden, plant, or metallic cover, whichever is available). When latrines are full they can be emptied (this is unpleasant and has an associated cost) or closed to build another one (this requires the availability of land).

4.06.6.1.2 Ventilated improved pit latrine These latrines, instead of having a single vault, are made up of a shallow pit divided into two 1–2 m3 vaults. Their major advantage is that they are a permanent facility due to the alternate use of each pit. The name comes from the inclusion of a properly designed pipe allowing ventilation, which also requires a screen to avoid the accumulation of flies. The pit cover is made of precast concrete, wood, palm leaves, or metallic material, and is removable. Emptying is performed manually in low-income areas, but can be done mechanically every 3–4 years. The ventilated improved pit (VIP) latrine with multiple pits can be built for collective use, such as in schools, markets, fueling stations, and administrative buildings (Mamadou, 2008).

4.06.6.1.3 Septic tank The septic tank is commonly used as primary treatment in rural areas, low-income urban settings, isolated households, or on sites where soil is not suitable for the installation of sewers (Jime´nez and Wang, 2006). They are built where a constant water supply is available and are used to partially treat domestic wastewater and to digest the settled sludge. They remove around 50% of the organic matter and suspended solid content in 2–4 days. For sludge digestion, 0.5–1 year is required; during this time, sludge is mineralized and its volume is reduced. Septic tanks are made up of a series of communicating chambers. They must be water sealed to avoid underground infiltration and are built using bricks, mortar, or concrete. A variation of the septic tank is the Imhoff tank, having the advantage of a shape that allows the removal of suspended solids and the control of foul odors in a better manner. Septic tanks need to be periodically cleaned (1–2 times per year, leaving 20% of the mature sludge as inoculum for digestion). This represents an additional cost that cannot always be afforded by poor people. Septage (the slurry taken out of septic tanks) is sent to wastewater treatment plants or treated separately. To treat septage, lime is frequently added until a pH of 12 is reached, over a period of 30 min (Jime´nez and Wang, 2006). Effluents from septic tanks are discharged into trenches for subsoil infiltration or diverted to the sewerage system (when available). Septic tanks are widespread sanitation systems but are often responsible for environmental pollution due to poor purification effects and leakages notably affecting groundwater.

4.06.6.1.4 Composting toilets Composting toilets are characterized by the separation of urine and feces. For this reason, they are also referred to as urine diversion (UD) toilets. They are constructed with two vaults or chambers. When the first vault is full, the pedestal is moved over to the second vault, and the first hole is closed. When the second vault is full, the first vault is emptied and so on. The urine is diverted to a soakaway. In comparison to VIP

173

latrines, they have a lower cost associated with emptying the pits (Snyman, 2008). Urine is collected in small cans (10–20 l) and can be used to enrich the soil after a stabilization period of 30 days. Feces are treated using an aerobic composting process. To control odors and to assist in the mineralization of feces, materials, such as ashes or pieces of wood, are used daily to raise the pH. The pathogens in fecal matter are inactivated over time through the drying process so they can be safely removed by the owner at no cost to the municipality. Once the sludge is digested, disinfected, and removed, it is used as fertilizer. UD toilets are seen as a viable option for rural applications. The main reasons are that they are cost-effective and, since the rural community is accustomed to the use of manure, the UD toilet is socially acceptable. However, its use in periurban areas is more problematic. The emptying of the vaults requires large-scale programs for which small businesses can contribute to the emptying of tanks (from UD or VIPs) either manually, using appropriate safety equipment, or by the use of a tanker. The disposal of the fecal matter in periurban areas is challenging due to the lack of land. If space allows, fecal sludge is buried on-site. Where this is not feasible, the sludge is blended into the waterborne system. This frequently leads to the complete overloading of the wastewater treatment plant (Snyman, 2008). There are several options of composting toilets (see section titled ‘Relevant websites’).

4.06.6.1.5 Pour-flush toilets Pour-flush toilets have been developed based on traditional flush toilets, which rely upon a water seal to perform cleansing and to control odors and insect infestations. The system works via a manual flush, where 2–3 l of water are poured into the toilet. The water, urine, and excreta are collected in an anaerobic chamber, which works similarly to a septic tank. The chamber needs to be periodically emptied and the partially treated wastewater needs to be disposed of, normally to land (Hughes et al., 2006). In the context of water-scarce areas, a very interesting option is combining graywater reuse with basic sanitation using pour-flush toilets. This concept was developed by United Nations International Children’s Emergency fund (UNICEF) on a system called the Wise Water Management scheme (Godfrey et al., 2007). This system was conceived to provide both water supply and sanitation services for water-scarce areas and can be used for both rural and lowincome urban areas. It was conceived in Madhya Pradesh, India, a densely populated and poor area. The WWMS uses groundwater as the primary source of water and also includes rainwater harvesting, used to dilute groundwater when polluted with fluoride to reduce its content for human consumption (Figure 10). First-use water is employed for cooking, handwashing, and bathing. Water from these two activities is recovered and properly treated in a sand filter to be used for toilet flushing and kitchen garden irrigation. The graywater reuse system can be installed independently of the rainwater harvesting system. By matching water demands, in quantity and quality, to different conventional and nonconventional water sources, the WWMS increases water availability by nearly 60%. Sanitation using low-consumption reused water flush

174

Safe Sanitation in Low Economic Development Areas

Cooking and human consumption

Fresh water source Ground or surface water

House cleaning activities

Pluvial water

Bathing

Gray water treatment

Hand washing

Reclaimed water Kitchen garden

Soil disposal

Toilet flushing

Leach pit

Soil disposal

Soil disposal

Figure 10 Flow diagram of the Wise Water Management Scheme.

Box 6 Poor people have a globalized attitude towards excreta management As described for Senegal by Ba (2008), in most poor areas of the developing world, water from baths and in some cases from showers are routed to septic tanks from which the effluent is sent to infiltration wells or trenches. Kitchen and laundry water is generally poured directly into the street, discharge areas in the wild, a well, a nearby river, or riverbed. Wastewater and noncollected solids are also frequently mixed creating breeding sites, odor problems, and development of flies.

toilets has proven sustainable under the prevailing local conditions and has eradicated open defecation.

4.06.6.1.6 Additional recommendations to set up basic sanitation facilities One important aspect to keep in mind when selecting the technology is that facilities need to be operational and, to achieve this, there is a need to sustain them under operation from the economical, technical, and cultural perspectives. Investment costs are linked to the type of sanitation system selected, the construction materials, and labor. Frequently, to reduce costs, cheap materials and the users are employed to build the facilities. However, this may result in failures, as cheap material frequently means low quality and the users are not people experienced enough, even if trained. It is thus preferable to invest in good and durable material and to use experienced workers. In India, for instance, sanitation programs using professional well-trained masons are being implemented in which the same masons for whom sanitation is a source of income become at the same time sanitation promoters. Norms and institutional capacity to provide basic sanitation constitute another weak link in the complex chain needed to implement and provide services. How to build institutions, policies, and human resources to provide successful sanitation services is better known in high-income countries

than in developing ones. Each country/region needs to look for the proper way to solve their problems. Finally, concerning basic sanitation, it needs to be considered that in several places, providing basic sanitation means to change open defecation habits and to handle domestic solid wastes (Box 6). It means as well to properly dispose of the toilet paper.

4.06.6.2 Toilets Under this section, only the toilets using less water or none at all are described as compared to the others (pour flushing toilets using 415 l of water is a well-known technology widely spread commercially). Concerning these toilets, one aspect to highlight is that even if convenient from the point of view of the used water, care must be taken when designing treatment plants as wastewater will be not only lower in volume but also highly concentrated, notably in terms of its organic matter content.

4.06.6.2.1 Water-saving toilets These toilets are based on the same working principles as common flush toilets but they are specially designed to fully operate with less water (6–8 l). In such toilets, it is possible to select either a full flush (with 4, 6, or 9 l depending on the model) for solids or a half flush (2–4.5 l) for liquids.

Safe Sanitation in Low Economic Development Areas

These toilets are also available with separate drainage for urine to reduce the impact of nutrients and pharmaceuticals on the sewage and to facilitate the reuse of urine as a fertilizer. However, most water-saving toilets available on the market are designed to be connected to typical drainage systems. There are several technological options on the market, some of which use a vacuum to transport feces at a much higher cost. The investment cost for low-volume toilets is comparable to high-volume toilets. However, dual flush toilets may cost more than common ones (nearly double). The installation of water-saving toilets must be stimulated by education (e.g., in the form of campaigns to raise awareness concerning watersaving issues), water metering, and pricing. Water-saving urinals, using 1–3 l, are also available (Correlje and Schuetze, 2008).

4.06.6.2.2 Toilets not using water The idea of dry toilets is not new. They have been used for thousands of years in East Asia (China, Japan, and Korea). Dry toilets are available as industrial prefabricated products and can also be constructed in local workshops; however, knowhow for its good operation and to avoid foul odors is required. Investment, construction, or installation costs vary significantly and depend on the specific system and design. The cost ranges from low investment for simple dry toilets to comparatively high cost for industrialized composting toilets. Due to the large size of the storage and composting chambers, these toilets require a large space underneath; if this is not possible, then they need to be regularly emptied and feces need to be transported to treatment facilities. User acceptance depends on cultural background and awareness. Generally, people who are already using flush toilets do not readily switch to dry toilets because the image of dry toilets is less attractive than that of flush toilets.

4.06.6.3 Sludge Extraction from On-Site Sanitation System Equally important as the type of on-site sanitation system selected is the provision of all the services associated. Past experiences (Water Decade, 1980–90) have shown that massive sanitation infrastructure provision without a proper planning of the whole scheme can be a complete failure (Kone´, 2010). Besides the technical aspects that are discussed later, the most worrying aspect is the lack of financial, institutional, and regulatory framework in most of the developing countries to establish the network required. Management of on-site sanitation infrastructure comprises on-site sanitation systems emptying, fecal sludge haulage, treatment, and safe reuse or disposal (Kone´, 2010). Fecal sludges refer to sludge collected from on-site sanitation systems such as latrines, nonsewered public toilets, or septic tanks. The criteria to select an extraction method – a task that is never pleasant – depend on (1) the TS content and (2) the funds available. Sludges with less than B2% TS, such as those produced in septic tanks, can be pumped; but, for the rest of facilities producing all sludge with 10% TS, pits need to be emptied using cesspit trucks or manually by laborers (Kone´, 2010). Even though when mechanically emptied and water is used for toilet cleansing, 20–50% of the contents in the lower pit part need to be manually emptied to extract the thicker

175

sludge. The use of mechanical equipment allows carrying away the sludge several kilometers for disposal on controlled sites or on treatment facilities, but this is often expensive and needs proper equipment and skilled laborers. In contrast, when sludge is manually emptied, this is deposited in nearby lanes or on open spaces representing a source of risk. According to Kone´ (2010) 30–50% of the on-site sanitation facilities from West African countries are emptied manually. In addition, in almost every developing country, fecal sludge collection and haulage are conducted by private entrepreneurs. However, their important role and responsibilities as key stakeholders are not yet fully recognized and legalized (Kone´, 2010).

4.06.6.4 Sewerage Systems 4.06.6.4.1 Small sewers In many low-income areas, the sanitation problem begins with the lack of sewerage. One option is to build sewers of small extent coupled with on-site sanitation systems. Sewers carry the treated effluent to disposal (usually to soil for infiltration, to irrigation canals, or into water receptors), to wastewater treatment plants, and/or to reuse sites located within a short distance. As these sewers frequently convey partially treated wastewater (such as septic tank effluents), they are designed for self-cleaning using a high wastewater velocity and/or a steep slope. This option is applicable for rural areas or urban ones where adequate land is available. Another option is to use simplified sewers. These are recommended where an uncertain population increase is occurring, as normally happens in periurban areas or slums. Small sewers are built to reduce the infrastructure and maintenance costs, as well to allow high operational flexibility. Inspection chambers such as manholes are replaced by inspection cleanout. The life expectancy of such sewers is in the order of 20 years rather than the 30 years quoted for conventional sewers. Such sewers are short and shallow (Hughes et al., 2006). One example of simplified sewers are condominial ones in which pipelines are laid through housing lots instead of on the side street, in a way that allows isolated and stepwise construction (UNEP, 2002). Condominial sewers were developed in the 1980s in Brazil with the aim of extending sanitation services to low-income communities. This technology has now become a standard sanitation solution for some urban areas in Brazil, irrespective of income levels. Condominial sewers reduce the per capita costs of service by replacing the traditional model of individual household connections to a public sewer with a model in which household waste is discharged into branch sewers, and eventually into a public sewer through a group (or block) connection (Watson, 1999 cited in Lenghton et al., 2005).

4.06.6.4.2 Conventional sewers These are structures that are bigger and deeper than those previously discussed. Details for design can be found in conventional literature on sewers.

4.06.6.4.3 Pluvial sewers Many developing countries are located within regions subject to tropical storms, or in areas where there are only two seasons per year: wet and dry. Therefore, urban hydraulic infrastructure

176

Safe Sanitation in Low Economic Development Areas

needs to be designed accordingly to have sewers that can handle large peaks of stormwater and the normal wastewater flows (wastewater treatment plants should also be capable of dealing with the varying wastewater characteristics in quantity and quality, at least in large cities). Sewers in tropical areas produce a high amount of sediments to be disposed off, which turns out to be a peculiar and difficult-to-solve problem not frequently commented upon in specialized literature but that needs proper methods to extract sludge and handle it. In addition, when conveyed in sewerage systems, stormwater must be treated in treatment plants at the same time as wastewater; but, if transported separately, it can be discharged to surface water or into wells for groundwater infiltration receiving treatment in soil. In this case, it must be kept in mind that stormwater quantity and quality are determined by rainfall, catchment processes, and human activities, which cause its flow and composition to vary in space and time. Normally, for the first rains of the year, stormwater has higher suspended solids, heavy metal content, and bacterial numbers than nontreated wastewater, and lower dissolved solids, nutrients, and oxygen demand than secondary-treated sewage effluent.

4.06.6.5 Wastewater Treatment Wastewater treatment is the typical method applied for sanitation, and is the predominant option used in developed countries for that purpose. Although it cannot be considered a caveat for all the negative impacts produced by wastewater, it is still a very important option, and, in many cases, the only one. There are several steps to treat wastewater. The primary step basically serves to remove easily decantable and floating solids. The secondary one, generally a biological process, is used to remove biodegradable (mostly) dissolved suspended material. The tertiary step is used to refine the quality of the effluent produced by a secondary treatment. It may have different purposes, most commonly being the removal of nutrients (N and P). As the treatment steps were conceived following treatment needs, in practice, they are usually implemented in separate tanks or in well-defined sections of wastewater treatment facilities; however, it is possible to use compact processes eliminating physical separation among steps and thus reducing costs (Jime´nez, 2003). Wastewater treatment plants are not common facilities in low-income countries. In contrast to developed countries, in developing ones, the sanitation figure (50% according to WHO–UNICEF (2006)) does not include the treatment of wastewater, which barely reaches 15% (US-EPA, 1992). Moreover, when available, the treatment merely consists of a primary step or including eventually a secondary step that is not always properly functioning. In many developing countries, the main issue concerning treatment is still the proper disposal of feces, particularly in low-income urban or rural areas. This, combined with a high content of pathogens in wastewater, sludge, or fecal sludge, implies the need to properly select the treatment process in order to effectively control disease dissemination. In general, coupling any kind of secondary wastewater treatment process (biological or physico-chemical) with a filtration step before disinfection will considerably reduce the pathogen content. However, this is rarely feasible for economic reasons and therefore it is sensible to consider the use

of other technologies alone or combined with other type of intervention methods to build up a multiple barrier system to control wastewater risks (Jime´nez, 2009b). In the following sections, guidance will be provided to support the selection for treatment options, based on the type of pollutants.

4.06.6.5.1 Conventional pollutants treatment To address problems caused by suspended solids, organic matter, nutrients, and fecal coliforms, there is a wide variety of available technologies supported by literature and practical results. Their affordability in economic terms and the suitability of the processes for local conditions are among the important aspects to consider for developing countries. It is beyond the scope of this chapter to provide a full description of treatment technologies for conventional pollutants, which can be found elsewhere in the literature. Table 8 shows the removal of pollutants by different processes so that it is possible to identify those acting upon the same type of pollutants.

4.06.6.5.2 Pathogens treatment Table 9 presents organisms’ removal or inactivation achieved by different wastewater treatment processes. This table is a guide for selecting a process. However, to design complete treatment schemes, the operating conditions need to be properly selected as well as the pre-and post-treatment. Table 9 differs from the one presented by WHO (2006) in showing the removal efficiency data for helminth eggs in terms of a percentage instead of log removal. This is because helminths eggs’ content is by far much lower and log units are meaningless. For developing countries, the removal of protozoa and helminths eggs is the main concern, considering their content and the occurrence of diseases caused by these types of agents. To remove protozoa, filtration is a good treatment option. Conditions used to remove Cryptosporidium oocysts – the targeted protozoan for developed countries – can be used as well to remove protozoa relevant to developing countries. Helminth eggs are not affected by conventional disinfection methods (chlorination, ultraviolet (UV) light, or ozonation); thus, they are first removed from wastewater using sedimentation, coagulation–flocculation, or filtration processes to be subsequently inactivated in sludge (Jime´nez, 2008a). Removal occurs because eggs are particles 20–80 mm in size. It is estimated that for contents of 20–40 mg l1 of TSS in treated wastewater, the concentration of eggs is around 3– 10 eggs l1, while for values below 20 mg l1 it is around 1 egg l1or less (Jime´nez, 2008a). However, for a process to be reliable, besides the removal efficiency attained, it is important for it to produce an effluent with constant concentration.

4.06.6.5.3 Emerging chemical pollutants The removal efficiency of emerging chemical compounds during conventional treatment can be found in Jime´nez (2009b). It is recommended that experimental tests be performed under laboratory conditions, prior to treatment selection. In the following, a description of main wastewater treatment processes is made, highlighting aspects that are relevant to developing countries, notably concerning their efficacy to control pathogens.

Table 8 Removal of pollutants by different wastewater treatment process that can be used to buildup a multiple barriers treatment scheme (with information from Jime´nez (2003); Jime´nez (2009), and Correlje and Schuetze (2008) Cost 1 Low Medium High Sophistication/complexity Low Medium High

Pollutant

Process

ONSS

PS

BT

BT + NR

CF

FI

NO

NO

NO

NO

NO

NO

Suspended solids Dissolved solids

Cl-D

UASB

LmP

NO

3

NO 3

UV-D

O-D

NPh

NO

NO

NO

NO

NO

NO

BOD

NO

TOC

NO

Volatile organics

NO

Heavy metals

NO

Nutrients

NO

Viruses∗

NO

Bacteria∗

NO

2

NO 2

NO

Protozoan∗

NO

21

NO

7

NO

11

7

NO

11

NO

NO

NO

NO

NO

NO

NO

9

NO

NO

Disinfection by products Chemical emerging pollutants

3,4,5

5

6

NO

UV-O

NO

NO

NO

NO

NO

NO

NO

NO

NO

Pp

Ads

3

NO

NO

NO

UF

NO

NO

NF

RO

NO

9

NO

11

11

MF

15

?

No on AC

9

2

9

2

9

2

11

12

No on AC

12

8

8, 12

Oz-O

NO NO

NO NO

Cl-O

NO

NO

8 NO

3

9

2, 10

Helminth eggs Pesticides

10

WT

NO

NO

21

SAT

8 20

19

?

12,13

12

14

5

16

NO

17

17

18

Processes: AC, activated carbon; Ads, adsorption; BT, biological treatment (any technology); BT+ NR, biological treatment with nutrient removal; CF, coagulation−flocculation (any technology) Cl-O, chlorine oxidation Cl-D, chlorine disinfection; FI, filtration; Clo, chlorine oxidation; Lmp, lime precipitation; MF, microfiltration; UF, ultrafiltration; NF, nanofiltration; NPh, natural photolysis; O-D, disinfection with ozone; ONSS, on-site sanitation systems; Oz-O, ozone oxidation; PS, primary sedimentation; Pp, precipitation; RO, reverse osmosis; UV-O, UV light oxidation; UASB, upflow anaerobic sludge blanket; SAT, soil aquifer treatment and river bank filtration; UV-D, UV-light disinfection; WT, wetlands. Low removal 1 2 3 4

Medium removal

High removal

Depending on the treatment level (primary, secondary, or tertiary). Depending on the type of technology used. Might increase the content. Mostly in biological secondary treatment plants; widely depending on the chemical composition of the pollutant; removal might represent only the transformation of the compound or its adsorption into. 5 Depending on the specific compound. 6 If coupled with chemicals. 7 Produce the pollutant as by-product or increase its value. 8 With low reliability. 9 For phosphorus. 10 Depending on the operating conditions.

11 12 13 14 15 16 17 18 19 20 ?

Noxious by-products can be formed. If there is no competition with organic matter (BOD or COD). Doses are several orders of magnitude higher than those used for disinfection. If granular carbon is used. High for nonpolar organic compounds with log KOW > 2 and when there is no competition with organic matter. Medium to high depending on the presence of cations and organic matter. High but not for low molecular weight uncharged compounds. Effective for several EC but not for carbamazepine, primidone, and iodinated X-ray contrast media. High for some EC, as it depends on the strength of solar irradiation removal will be different for different latitudes, or conditions. Can be enhanced with photosensitizers. Unknown or insufficient information ∗, Can be removed or inactivated. NO, not applicable for the pollutant.

178 Table 9

Safe Sanitation in Low Economic Development Areas Reduction or inactivation of different biological pollutants in wastewater

Treatment process

Log unit microorganisms removal

Removal (%)

Viruses

Bacteria

Protozoan (oo)cysts

Helminth eggs

Natural systems Waste stabilization ponds, WSP Wastewater storage and treatment reservoirs Constructed wetlands

1–4 1 to 2/4 1–2

1–6 1 to 3/6 0.5–3

1–4 1–2 0.5–2

90–100a, e, HR 70–95a, d, LR, g 90?a, e, L, R

Primary treatment Primary sedimentation Chemically enhanced primary treatment or advanced primary treatment Anaerobic upflow sludge blanket reactors, UASB Filtration

0–1 1–2 0–1 0–1

0–1 1–2 1–2 0–0.5

0–1 0.5–2 0–1 0–1

90a, LR 90–99a, 60–96a, 90–95

Secondary treatment Activated sludge þ secondary sedimentation

0–2

1–2

0–1

90–95a,

0–1 1–2

1–1 1–2 1–2

0–0.5 0–1

85–90c 95–100c 90c

Tertiary treatment Coagulation/flocculation High-rate granular sand filtration Dual-media filtration Membrane bioreactors

1–3 1–3 1–3 2.5 to 46

0–1 0–3 0–1 3.5 to 46

1–3 0–3 1–3 46

95–99a, 90–99a, 100c 100c

Disinfection Chlorination (free chlorine) Ozonation UV irradiation

1–3 3–6 1 to 43

2–7 2–6 2 to 44

0–1.5 1–2 43

0a, f, b 30–70b 0c

Trickling filters þ secondary sedimentation Aerated lagoon or oxidation ditch þ settling pond Slow filtration

e, HR e, LR

L, R

e, HR f, HR

a

Have been tested at full scale. From laboratory data. c Theoretical efficiency based on removal mechanisms. d Total helminth egg removal is only achieved when wetlands are coupled with a filtration step. e Tested with high helminth egg content. f Tested only with low helminth egg content. g Efficiency highly depends on size and operating conditions, notably the hydraulic retention time. LR, low reliability; HR, high reliability. Based on Shuval HI, Adin A, Fattal B, Rawitz E, and Yekutiel P (1986) Wastewater irrigation in developing countries: Health effects and technical solutions. World Bank Technical Paper No. 51. The World Bank, Washington; WHO (1989) Guidelines of the Safe Use of Wastewater and Excreta in Agriculture and Aquaculture. Prepared by D. Mara and S. Cairncross: Geneva: WHO. Von Sperling (2003, 2004); Rose (1999); Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO Leiden: Taylor and Francis Group; WHO (2006) Guidelines for the Safe Use of Wastewater, Excreta and Greywater, Vol. 2: Wastewater Use in Agriculture. Geneva: WHO. b

4.06.6.5.4 Slow filtration Slow filtration is recognized in water potabilization as an efficient method to control microbial pollution in rural and low-income communities. The few studies carried out on slow filtration of wastewater have demonstrated a removal range of 60–80% of suspended solids and 1–2 E. coli log, with coarse sand (Jime´nez, 2003). In rural areas, it may be coupled with absorption wells, irrigation reuse, or a soil aquifer treatment (SAT) system.

4.06.6.5.5 Waste stabilization ponds Waste stabilization ponds (WSPs) are shallow basins that use natural factors such as biodegradation, sunlight, temperature, sedimentation, predation, and adsorption to treat wastewater (Mara, 2004). WSPs are capable of removing organic matter with efficiencies similar to the activated sludge process and all kind of pathogens. They are easy to design and operate but require long retention times (several weeks). WSP systems

comprised several ponds connected in series. Lagoons are made through the shallow excavation of around 1–2 m, and they are frequently unlined to reduce investment costs. After a period of time, soil percolation and sedimentation form an impermeable barrier. If the water table is very high at the site, ponds need to be impermeable from the beginning. WSPs remove up to 6 bacteria log, up to 5 viruses log, and almost all the protozoa and helminth ova. To control Cryptosporidium spp., almost 38 days’ retention time is needed (Jime´nez, 2008). In developing countries with wet warm climates, the use of stabilization ponds is recommended if land is available at a reasonable price. For arid and semiarid regions, high evaporation rates limit their application as there is a net loss of water of 20–25% due to evaporation. This, in addition, increases the salinity of the effluent limiting its use for agricultural irrigation (Jime´nez, 2008). Sludge production in ponds is low but if extracted it needs disinfection as helminth ova remain viable in ponds for more than 9 years (Nelson et al., 2004).

Safe Sanitation in Low Economic Development Areas

WSPs can be coupled with aquaculture systems that are shallow ponds or wetlands where fish, duckweed, or aquatic vegetables are produced as is frequently done in Indonesia, China, and Thailand. Ponds can be used to produce only one crop such as duckweed that is used as food for the next pond where grass carp are grown. Different species can also be cultured in the same pond, as happens in nature. To operate the system, wastewater is applied to ponds at the required rate (estimated in terms of the organic load applied per hectare of ponds per unit time), and the organic matter and the nutrients contained serve as food for plant and animal production (Hughes et al., 2006). In order to avoid health problems, wastewater needs to be previously disinfected according to WHO guidelines (2006).

4.06.6.5.6 Wetlands Constructed wetlands are used to naturally remove organic matter, pathogens, and nutrients from wastewater through biodegradation, adsorption, or filtration in a similar way to WSPs. Nutrients are also removed by plant uptake and pathogens by competition and sun UV-light inactivation (Jime´nez, 2003). Wetlands are shallow ponds where aquatic macrophytes are planted in soil, sand, or gravel. There are three main types: surface-flow, horizontal-flow subsurface, and vertical-flow systems. Juncus spp. or Phragmites are commonly used plants but any local plant can be employed. Construction requires expertise and skilled labor. Once installed, operation is relatively easy. Wetlands remove nitrogen, phosphorus, and heavy metals. Up to 90–98% of thermo-tolerant coliforms, 67–84% of MS2 coliphages, and 60–100% of protozoa are inactivated or removed using hydraulic retention times of 4–5 days. In practice, pathogen removal is highly variable and depends on climate, type of wetland, and the kind of plant used. To completely remove helminth ova, it is necessary to couple wetlands with filtration, otherwise effluent with variable content may be produced. Breeding of mosquitoes and unpleasant odors can be a problem if wetlands are not operated correctly. Subsurface wetlands are used to avoid mosquito breeding (Correlje and Schuetze, 2008). Wetlands are a good solution for wastewater treatment in urban or rural areas where space is available; as a rule of thumb, 0.5–2.5 m2 per person is required for the treatment of graywater and 1–3 m2 per person for domestic wastewater. They are considered environmentally sound technology by UNEP for the treatment of graywater and stormwater urban runoff. They are used as secondary or tertiary treatment units, in which case, they treat effluents from septic tanks, anaerobic ponds, upflow anaerobic sludge blanket (UASB) reactors, or conventional wastewater treatment plants. Treated wastewater can be reused for agricultural irrigation, although its nutrient content is low. Wetlands have been used in Bangladesh and China to treat wastewater and to cultivate fish and ducks. In addition, they have the advantage of producing a low quantity of sludge.

4.06.6.5.7 Land treatment Soil can be used to treat wastewater by infiltration. It has a greater depollution capacity than water receptors, as there is no limit for the oxygen transfer needed for biodegradation.

179

Land-based treatment is recognized as an environmentally sound technology by UNEP (2002) that has a low cost when used for primary effluents. Among its disadvantages is the high demand for land (Jime´nez, 2003). In the case of land treatment, depollution takes place in the unsaturated zone through biodegradation, adsorption, ion-exchange filtration, and precipitation. For the removal of organisms, in addition to predation and humidity, the temperature also plays a role. Heavy metals and trace organic compounds (such as emerging pollutants) are removed mainly by adsorption. To operate, wastewater is to be applied at specific rates; if pretreatment is needed primary sedimentation or sand filtration might be used (Brissaud and Salgot, 1994; Jime´nez, 2003; Bouwer, 2002). In developed countries, pre-treatment usually consists of a secondary treatment. Wastewater application occurs in cycles at a rate that depends on the soil infiltration characteristics. In a typical situation, the cycle involves 1 week of wastewater flooding where infiltration is reduced by organic buildup, and 1 week of drying where bacteria consume the organic matter and soil drying takes place. There are several types of land treatment options in specialized literature that can be consulted. For efficient functioning, hydraulic loads (29–111 m3 m2 yr1) and mass loads should be limited. To avoid aquifer pollution, application of wastewater (preferably partially treated) is restricted to sites where groundwater is a minimum of 3 m in depth. Applied as primary or secondary treatment, land treatment produces a consistently high-quality effluent (TSS o1 mg l1, organic carbon 3 mg l1, and total nitrogen 6 mg l1, with a phosphorus removal of almost 50% with minimal pre-treatment). As tertiary treatment, it removes 492% of BOD, 85% of COD, 100% of TS, 455% of detergents, 499% of ammoniacal nitrogen, 55% of total nitrogen, and 98% of phosphorus. Land treatment is effective for the removal and/or inactivation of helminth eggs, protozoa, bacteria, and even viruses (Jime´nez, 2003).Treated wastewater can be used for irrigation or any other use and can be collected on the surface or underground.

4.06.6.5.8 Reservoirs and water storage tanks Reservoirs or wastewater storage tanks can be used as well to treat wastewater. While wastewater is stored during the wet season to provide water for irrigation during the dry season, pathogens are removed or inactivated via sedimentation, UVsunlight inactivation, predation, and other similar processes, which also occur in WSPs. Nevertheless, the efficiency is lower. Procedures for designing wastewater storage and treatment reservoirs are detailed in Juanico´ and Milstein (2004) and Mara (2004). Reservoirs and storage tanks are easy to operate and maintain, and if considered as part of the irrigation system, they result in a low investment cost. However, they facilitate vector breeding if they are not well maintained and operated, and algal development in effluents may interfere with irrigation applications. Effluent storage reservoirs remove 2  4-log of viruses, 3  6-log of bacterial pathogens, and 1  2-log units of protozoan (oo)cysts. If treatment reservoirs are operated as batch systems with retention times over 20 days, the complete removal of helminth eggs can be achieved (Juanico´ and

180

Safe Sanitation in Low Economic Development Areas

Milstein, 2004). In addition to large storage reservoirs, small storage ponds can be utilized for pathogen removal when used for urban agriculture irrigation as intermediate water storage reservoirs. Such reservoirs reduce the helminth ova content by around 70% (Keraita et al., 2008).

initial helminths egg content of 20–120 eggs l1, effluents with 3–10 eggs l1 are produced (Jime´nez, 2008). Other biological secondary treatment options include aerated ponds, oxidation ditches, and trickling filters. Much specialized literature exists describing the processes that are used to treat effluents before discharge into water bodies.

4.06.6.5.9 Upflow anaerobic sludge blanket The UASB is used to remove organic biodegradable matter. A UASB is a kind of attached system where microorganisms adhere to themselves, forming flocs. UASBs are considered as the most successful anaerobic process applied to treat wastewater due to low hydraulic retention time compared to other anaerobic processes thanks to the high density of biomass attained in the blanket (Campos, 1999). The reactor is designed to not only produce the biological reaction but also to sediment and filter suspended solids from wastewater. In addition, sludge retained in the bottom part of the reactor is anaerobically digested (Campos, 1999). The UASB produces better results when the wastewater has a high organic matter content. As by-products, it produces methane and partially treated sludge. The gas can be used as a source of energy, while the sludge remaining, after proper treatment to control the pathogen content, can be used to fertilize soil. UASBs remove 65–75% of BOD and COD and helminth eggs through filtration in the sludge blanket and through sedimentation. However, their efficiency with regard to the removal of helminth eggs is very variable. From wastewater containing 64–320 eggs l1, they produce effluents with 1–45 eggs l1 (60–96% removal). Therefore, UASBs are frequently coupled with other treatment process such as stabilization ponds or filtration to completely and reliable remove helminth ova and to inactivate other pathogens. Several stand-alone UASB plants or those coupled with WSP are currently under operation in Curitiba, Brazil. UASB reactors require careful design and operation to avoid bypasses (Campos, 1999). The construction, operation, and maintenance of improved anaerobic technology such as biogas installations require considerable expertise and skilled labor as well as space (Correlje and Schuetze, 2008). UASB reactors have a low capacity for tolerating toxic loads, need several weeks to start up the process, and require a post-treatment step.

4.06.6.5.10 Activated sludge It is the most common way to treat wastewater in developed countries. Compared to other secondary biological processes, activated sludge is effective for pathogen control as it removes 10% more than trickling filters. Both sedimentation and aeration play an important role in this. Sedimentation eliminates heavy and large pathogens, while aeration promotes antagonistic reactions between different microorganisms, causing their elimination. As a result of becoming entrapped within the flocs (which are subsequently sedimented), there is fairly good removal of small nonsedimentable microorganisms, such as Giardia spp. and Cryptosporidium spp., which remain concentrated within the sludge (Jime´nez, 2003). Helminths eggs are also removed, but due to continuous difficulties in achieving efficient and reliable sedimentation of suspended solids in secondary decanters, protozoan and helminths eggs may be found in effluents along with flocs. For an

4.06.6.5.11 Coagulation–flocculation This is a process that was almost abandoned for the treatment of municipal wastewater in the 1960–70s due to the high sludge production, which considerably increased the overall wastewater treatment cost. The introduction of new chemical products, in particular flocculants, combined with the possible reuse of treated effluent for agricultural irrigation and ocean disposal, has been instrumental in its reintroduction. Coagulation–flocculation removes helminths eggs while preserving nutrients and organic matter in contents suitable to grow plants. When this process is applied using low coagulant doses combined with a high molecular weight and high charge density flocculants, it is called chemical enhanced primary treatment (CEPT). If, a high-rate settler is used instead of a conventional settler, it is referred to as advanced primary treatment (APT). As a result, CEPT has a total hydraulic retention time of 4–6 h while, for APT, this is only 0.5–1 h. Among the coagulants that have been used, iron and alum compounds are the most common. APT removes 50–80% of protozoan cysts (Giardia, Entamoeba coli, and E. histolytica) and 90–99% of helminths eggs. From a content of up to 120 eggs l1, an APT can consistently produce an effluent containing 0.5–2 eggs l1. This process produces an effluent with a low content of suspended solids or turbidity, which leads to greater disinfection efficiency, either with chlorine or with UV light. Likewise, the process allows the use of sprinkler irrigation in high-tech countries or countries where water is scarce. The effluent quality is improved by the soil effect, and aquifers can be used as water supply storage (Jime´nez, 2003, 2008). APT and CEPT are useful in middle- and high-low-income countries on large urban areas as an economical alternative to an activated sludge process as the treatment cost for APT is one-third of this process when considering sludge treatment and disposal within 20 km. Coagulation–flocculation can also be applied as a tertiary treatment after a biological process. This is a very good method to remove enteric viruses (Jime´nez, 2003).

4.06.6.5.12 Rapid filtration Rapid filtration (at rates over 2 m3 m2 h1) is very efficient in removing protozoa and helminth eggs from wastewater, primary effluents, and biological or physicochemical effluents. It removes 90% of fecal coliforms, Salmonella, Pseudomonas aeruginosa and enteroviruses, 50–80% of protozoan cysts (Giardia, Entamoeba coli, and E. histolytica), and 90–99% of helminths eggs. Efficiency can be increased to easily reach 499% if coagulants are added (Jime´nez, 2008). For helminth ova removal, rapid filtration is performed in silica sand filters with 0.8–1.2 mm media size, a bed depth of at least 1 m and filtration rates of 7–10 m3 m2 h1. The helminth ova content

Safe Sanitation in Low Economic Development Areas in the effluent is constantlyo0.1 HO l1 in filtration cycles of 20–35 h for primary effluent (Jime´nez, 2003, 2008).

4.06.6.5.13 Disinfection The challenge for any disinfection method is that microorganisms respond differently. Efficiency depends on the disinfecting agent, the type and content of microorganism, the dosage, and the exposure time. The water matrix has as well a relevant influence, which becomes more important as its concentration and complexity increase. The most common disinfection processes for wastewater are chlorination, ozonation, and UV-light disinfection. 1. Chlorination. It is the most widely used process to control microorganisms. It is effective for the inactivation of bacteria, less so for viruses and protozoa, and not at all for helminth eggs. With regard to virus and bacteria, chlorine has inactivation efficiencies of up to 5–7 log. However, chlorine is a very reactive agent and, therefore, before attacking microorganisms, it reacts with many substances contained in wastewater, in particular with organic matter, hydrogen sulfide, manganese, iron, nitrites, and ammonia. As a result, chlorination is a process that, in order to be efficient, needs to be applied at the end of treatment schemes to avoid interferences. If, in treated wastewater, ammoniacal nitrogen and organic matter are still presented, chloramines and organo-chlorinated compounds are formed. These are compounds that increase cancer risks. Notwithstanding such risks, it is always preferable to chlorinate wastewater as microbial diseases have faster and often more dramatic health effects (Jime´nez, 2003). 2. Ozonation. Ozone is very effective at inactivating viruses and bacteria. It inactivates 3–4 log concentration units in a very short time, provided there is a low demand for oxidizing agents by wastewater. There is abundant information in the literature concerning the design and operation of the processes. Required ozone doses for several microorganisms are also available in the literature but, frequently, they are not affordable. As happens with chlorine, by-products generated during ozonation are a source of concern as many of them have been reported in the literature as toxic (Jime´nez, 2003). 3. UV light. Nowadays, UV-light disinfection closely competes with chlorination because it does not generate by-products that are too costly to remove from wastewater. Besides, compared to chlorination, UV light does not need storage facilities, does not imply the handling of hazardous chemicals, and uses very small-size treatment tanks as disinfection contact times are very small (in the range of seconds or minutes). Furthermore, due its simplicity of operation and high adaptive potential, it is suitable for rural and isolated communities.

4.06.6.6 Sanitation and Wastewater Treatment Costs Figure 11 presents estimated cost for different sanitation options, including from basic sanitation system to wastewater treatment plants. Simple services certainly are much cheaper to provide, but they do not necessarily represent what the society wishes to have due to the comfort level. As cost is an

181

important barrier to spread sanitation services, one would expect that these data is a well-known parameter. Despite this, in many developing countries there are no reference costs, as exist in developed ones. As result of this situation, in many bids, costs are established using international data that do not necessarily reflect the local conditions (Table 10). Differences are due not only to build the sanitation facilities but also for the use of fuel and electricity, two important inputs to operate wastewater treatment plants. Sludge management and disposal (Figure 12) is another source of different affecting costs (Figure 12). Table 10 also shows that the cost of emptying onsite sanitation systems is not negligible.

4.06.6.7 Criteria for Selecting Wastewater Treatment Processes The selection criteria for wastewater treatment processes are presented in Table 11, emphasizing the needs of developing countries.

4.06.7 Wastewater Disposal versus Reintegration After treating wastewater, the next step is its disposal. Recently, some researchers have suggested (Asano, 2009) to use the term ‘dispersion’ instead of ‘disposal’ in order to change the perception of getting rid of used water, but this term has to an extent the connotation of wanting to dilute a problem. In this chapter, the term ‘reintegration’ is introduced in order to emphasize that water needs to be returned to the environment or used once again (reuse). By reintegrating the water to the environment, the responsibility of using it and then restoring it back to the environment in a proper way may be realized. As, well water can be reintegrated into the hydraulic cycles in which is been used by the society, thus reducing the negative impact of extracting water from the environment beyond the amount needed for ecological use (environmental flow). Water can be reintegrated to the environment by discharging it to the soil or into water bodies. In the following, different ways to reintegrate used water are discussed. This is followed by discussing the reintegration of water through reuse.

4.06.7.1 Soil Disposal or Reintegration of Used Water to Soil and to Groundwater Soil reintegration (disposal) consists of discharging treated or nontreated water into land. As discussed in the Section 4.06.6.5 the soil may act as a treatment step if a proper management is provided. The options to reintegrate treated wastewater into the environment are presented below. After discharging used water to soil, it will be evaporated, infiltrated, or will percolate to reach surface or groundwater bodies. The extent of each of these will depend on the soil and local conditions.

4.06.7.1.1 Leach drains They are used mostly for on-site sanitation effluents. They consist of a trench in which partially treated wastewater is discharged to allow its infiltration to the subsoil. The seepage in the trench allows uniform disposal of the wastewater over a given area. The leach drain is often filled with gravel or highly

182

Safe Sanitation in Low Economic Development Areas Estimated cost per person in USD

10

Improved traditional practice

45

Simple pit latrine Ventilated improved latrine

65

Pour flush latrine

70

Septic tank latrine

160

Sewer connection with local labor

175

Connection to conventional sewer

300

Sewer connection and secondary wastewater treatment

450

Tertiary wastewater treatment

0

800

200

400

600

USD

800

Figure 11 Estimated cost for different options (with information from van de Guchte, and Vandeweerd, 2004).

Table 10 Comparisons of costs for wastewater treatment, diesel, and electricity in selected countries for the year 2008 (with information from LeBlanc et al. (2008)) Country

USD per m3 of wastewater

USD per 1000 l diesel fuel

Countries with high sanitation coverage England 2.98 2152 Norway 2.92 2292 Austria 1.24 1897 Australia 1.14 1234 USA 0.92 753 New Zealand 0.73 990 Russian 0.42 800 Federation Canada 0.39 1073 Italy 0.39 1899 Countries with low sanitation coverage Czech Republic 2.93 1752 Jordan 2.30 700 Slovakia 1.47 1764 Hungary 1.39 1697 Turkey 0.59 3588 Senegal 0.35 1044 Bulgaria 0.31 1298 China 0.08 834 Iran 0.05

Cameroon Nigeria Mali Ethiopia

Per truckload to empty latrines

USD per 1000 l diesel fuel

120 45 38.2 16.50

1120 935 1061 742

USD per kW h1 of electricity

0.29 0.07 0.18 0.11 0.04 0.12 0.08 0.26 0.26 0.06 0.14 0.14 0.17 0.17 0.59 0.09 0.03 USD per 1 kW h  1of electricity 0.12 0.21 0.06

permeable material and a perforated pipe – from which used water is distributed – is placed in the centre at about 0.2 m beneath the soil surface. The perforated pipe is typically around 0.1 m in diameter (Hughes et al., 2006). The size of the trench depends on the wastewater load and the soil type, groundwater depth, and precipitation. Leach drains are not recommended disposal options if the groundwater table is close to the surface (e.g.,o 0.5 m depth) or the soil has low permeability (e.g.,o3 mm d1).

4.06.7.1.2 Evapotranspiration beds They are convenient where soil is highly impermeable (e.g., clay) but can also be used in permeable soil from where water is both evaporated and infiltrated. In each case, plants are positioned to increase evapotranspiration and to remove nutrients from wastewater. If a limited area is available, evapotranspiration beds can be used in conjunction with a seepage trench. To increase dispersal of the wastewater throughout the whole bed, perforated pipes surrounded by gravel are used. The design of the bed should ensure it is large enough to hold wastewater loading and pluvial precipitation while, at the same time, providing sufficient water and nutrients to plants (Hughes et al., 2006).

4.06.7.1.3 Soil aquifer treatment and aquifer storage recovery system Soil disposal can be coupled with soil treatment in the soil aquifer treatment–aquifer storage recovery system (SAT-ASR). An aquifer storage recovery system (ASR) consists of holding water in an appropriate underground formation, where it remains available in such a way that it can be recycled by extraction when needed. An ASR can have several objectives, some of which are (Dillon and Jime´nez, 2008; Jime´nez, 2003) temporary or long-term storage; decrease of disinfection by-products; reestablishment of underground water levels;

Safe Sanitation in Low Economic Development Areas

183

60

%

40

20

China

Slovakia

Bulgaria

Turkey

Czech Republic

England

Russian Federation

Canada

Japan

Austria

Norway

USA

0

Figure 12 Estimated percentage of total wastewater treatment costs required for wastewater sludge treatment and management (with information from LeBlanc et al., 2008).

maintenance or improvement of underground water quality; prevention of saline intrusion; deferment of expansion of water supply systems; aggressive water stabilization; hydraulic control of contaminant plumes; and compensation of soil salinity lixiviation. The major advantages of underground storage is that evaporation losses are considerably lower than dams (B1%) and do not have the eco-environmental problems associated with them (Dillon and Jime´nez, 2008). Aquifers can be an economical option to reintegrate water to the environment in arid and semi-arid countries where it remains available for future use. They are also convenient in densely populated urban areas where, besides storing treated water, aquifiers can store stormwater runoff.

4.06.7.2 Disposal into Surface Water Bodies or Reintegration of Used Water to Surface Water Bodies Effluents from treatment plants can be used for the augmentation of surface water bodies, in which the effluent is diluted with freshwater and reused as a source for water. The water quality of receiving water should be preserved to facilitate a safe water supply. For this, it is important to control pollutant content in the effluent, notably pathogens, organic matter, and nutrients (especially for surface water bodies with slow flow). Two aspects need to be monitored: oxygen depletion in rivers and eutrophication in dams and lakes. To avoid oxygen depletion, biodegradable organic matter needs to be removed before introducing the wastewater. There is considerable literature available concerning this aspect as it has been the main target for most wastewater treatment processes. Control of eutrophication is achieved by removing N and/or P from effluents; this is an operation costly to perform in wastewater treatment plants for most developing countries. As an

alternative, land treatment can be used or treated wastewater used first for agricultural irrigation recovering it from the agricultural drainage before sending it to on lakes. Eutrophication of dams and lakes is a frequent problem in developing countries; alternatives for its control are discussed in Box 7.

4.06.7.3 Reuse Reuse is another option to reintegrate water to the environment but through its use. Due to the increase in the human population and the increased use of water for almost all human activities, water is becoming scarce and new tools are needed to use it better. Such tools are (1) the efficient use of water (using less water for the same activity – this is beyond the scope of this chapter) and (2) water reuse. Water reuse is a key component to alleviate the mismatch between water supply and water demand. At the global level, water availability is of around 8500 m3 inhab1 yr1 but with important variations at a regional, national, and local level. For instance, it is estimated that around 700 million people (11% of the total population) in 43 countries live in areas with less than 1000 m3 inhab1 yr1. By the year 2025, 38% of the total world population will live under such water stress, increasing to 50% (in 149 countries) by the year 2050 (UNDP, 2006). As shown in Maps 3, 4, and 9 (Annex 4), most of the affected people live in developing countries. For these countries, three aspects can be highlighted concerning water stress and water demand. First, water is needed for economic development and a better quality of life (even if industrialized countries are not completely making an efficient use of water; they use 30–50 times more water than developing ones (UN/WWAP, 2003)). Second, agriculture is the dominant user of water worldwide, but, in addition, for developing countries, agriculture is usually the

184

Safe Sanitation in Low Economic Development Areas

Table 11

Criteria for selecting wastewater treatment operation and processes

Process applicability  Must be evaluated based on past experience, data from full-scale plants, published data, and from pilot and full-scale plant studies.  If few data or unusual conditions are encountered (atypical wastewater characteristics) pilot plant studies are essential. For developing countries: – Since much less experience is available, a good wastewater characterization is needed as well as a request during bids that the applicability of the processes should be demonstrated before construction. – Bids should encourage operating at lower costs at the same pace the process is optimized. – Technology complexity need to be in agreement with the type of community being served: rural areas, rural isolated areas, small urban towns, large towns, and megacities (low-, middle-, and high-income urban and periurban areas densely or dispersed populated). – Possibility to combine treatment technologies with soft intervention methods (management). Performance

 Performance needs to be expressed not only in terms of the effluent quality but also on its allowed variability, and both must be consistent with the effluent discharge requirements and the possible use of treated wastewater.

 Performance needs also to be considered in terms of its reliability, as it may vary according to the process type. Reliability is very important when the effluent is to be reused or treated water is to be discharged into sensitive aquatic environments. For developing countries:  Performance should be verified in terms of the disinfection needs locally required. Influent wastewater variability

 Consider wastewater characteristic variations in probabilistic terms.  Consider wastewater variability in terms of climate change impacts and climate variability. For developing countries: – It is important to have a statistically representative wastewater characterization considering parameters not only defined in norms but also those that might interfere with the treatment processes or the future use of treated water. – Design data should not be based on bibliography data, especially that coming from other countries. – Since segregation and pretreatment of industrial discharge is not common, there are high chances that the wastewater to be treated will contain inhibiting constituents. An evaluation of these is important but not as intensive as the one required for the characterization of the targeted treatment parameters. – Consider wastewater quantity and quality possible variation if programmes to reduce water consumption (such as the use of water less toilets) are to be implemented. Reliability

 Achievable performance needs to be expressed in statistical terms and in short and long terms, taking into account water flow and wastewater quality variations. For developing countries: – Unusual situations and emergencies are common. Selecting robust albeit more expensive processes might be cheaper long term, both economically as well as in terms of the negative effects that malfunctioning can produce. Process sizing

 Reactor sizing is based on the governing reaction and kinetic coefficients. If kinetic data are not available, process loading criteria are used, but not always with good results, even in developed countries. For developing countries: – Most of the available information used in the design of biological process comes from the developed world, where wastewater and climatic conditions, among others, are different, and so bibliographic kinetic data and load criteria use should be avoided as much as possible. – For coagulation–flocculation process doses and mixing conditions determine at laboratory conditions are essential to minimize cost and sludge production. – For disinfection processes conditions need to be determined or checked up using laboratory data – If experimental data are not available, the adjustment of published data to local conditions, such as pressure and temperature, should always be checked in bids. Applicable flow range and flow variations

 The process should be matched to the expected ranges of flow rates. Moreover, whenever possible, considering the presence of stormwater, notably considering impacts of climate change. For developing countries: – For those located in regions with high pluvial precipitation concentrated in short periods of time, treatment processes must be able to deal with flow and major variations in quality. – Alternatively, the use of flow equalization tanks and their cost should be considered. – Processes that can be operated as modules than can be easy to start should be preferred to match variable influents in terms of quantity and quality. Residual treatment and disposal  The types and amounts of solid, liquid, and gaseous residuals produced must be estimated. Use pilot plant studies to identify and quantify residuals.

Safe Sanitation in Low Economic Development Areas Table 11

185

Continued

For developing countries: – By-products and wastewater treatment residues are often disregarded in proposals in order to offer a lower operating cost. To avoid this, it is important to clearly state in bids that any residues must be quantified and the management options considered within costs. Sludge processing  Design, operation, and maintenance must have the same degree of investment and complexity of its management as that of the wastewater treatment. For developing countries:

 Revalorization of sludge as biosolids (treated sludge) for soil fertilization, erosion control, or land remediation are to be considered as a priority.  For urban areas, use of biosolids to cover landfill cells can be an interesting disposal option. Climatic constraints

 Temperature affects the reaction rate of most chemicals and biological processes; therefore, local water temperature should be taken into account when selecting a processes. For developing countries: – In most developing countries temperature is relatively high, so problems arise due to high temperatures not low ones. High temperature may accelerate odor generation and also limit solubilization of gases such as oxygen. In densely populated urban areas, temperatures may rise even more than expected due to the ‘heat islands’ phenomena. Environmental constraints  Environmental factors, such as prevailing winds, may restrict or affect the use of certain processes, especially where odors are produced near residential areas.  A wastewater treatment plant may have negative impact on the environment if not properly designed.  The disposal site restrictions of the treated wastewater need to be considered regardless of the norms to be met. Water and sludge reuse

 Water reuse can be a way of making wastewater treatment more attractive in economic terms.  For countries located in water-stressed areas, besides being ecologically sound to reintegrate water to the environment as disposal option, reuse serves to alleviate water scarcity. For developing countries – Land degradation is costing 5–10% of their agricultural production (Young, 1998) and fertilizers have often a prohibitive cost for farmers; in both cases, biosolids can be used to remedy these problems. Ancillary processes

 Wastewater treatment plants are often accompanied by ancillary (complementary) processes that do not necessarily directly relate to the wastewater treatment process, such as power plants, special storing facilities for reagents, etc. It is important therefore to know, before selecting a process, what are those needs, their cost and viability to obtain them from the local market. Chemical requirements  The type and amount of chemicals to be used need to be considered as well as their cost and market availability, both now and in the future.  If chemicals are added during the treatment of wastewater or sludge and these are to be reused, their selection needs to be compatible. For developing countries: – Although the use of chemicals is often prohibited, an economic comparison is worth making, especially if chemicals are locally available. Energy requirements

 The present and future cost of the energy used is something to consider.  In selecting and designing wastewater treatment plants, the location, efficient use of energy, and the possibility of recovering/producing energy for in-plant use must form part of the selection criteria that in the long term will contribute to properly closing the urban water cycle.

 The energy foot print of the wastewater and sludge treatment plant should be minimized to contribute to the reduction of GHG (greenhouse gases). Personnel requirements

 The amount of people as well as their skill levels need to be well defined. For developing countries – The most common situation is a high availability of low-skilled personnel working for low salaries. Thus, selected processes may have a high labor demand but cannot be very sophisticated. Alternatively, intense training programs should be considered; nevertheless, high indexes of personal rotation are frequently experienced in developing countries when personnel are trained. Complexity and compatibility

 Define operational needs under routine and emergency conditions.  Define the type and need for repairs.  It is important that the items selected be compatible for efficient operation. For developing countries: – It should be considered that cheap or obsolete equipment may become costly if frequent repair is needed. – Equipment and spare parts must be available within an appropriate period of time. Obsolete equipment is very difficult to repair. (Continued )

186

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Table 11

Continued

– Normally, few items are produced or available locally, therefore overall equipment selection needs to consider compatibility between different equipment traders. Adaptability  Many treatment plants will need to adapt to future conditions and not all systems have the same capability to be adapted. Economic life-cycle analysis

 Cost evaluation must consider initial capital cost and long-term operating and maintenance costs. The plant with lowest initial capital investment may not be the most effective with respect to operating and maintenance costs.

 The nature of the available funding will affect the choice of the process. Land availability

 It is important to consider the size of the selected treatment process with respect to available land, including buffering zones for future expansions. For developing countries: – There is not always land or cheap land available, as frequently believed. – Considering the fast growth of cities in the developing world and the possibility of building plants in modules, it is very useful to consider buffering zones to increase treatment capacity, complete the treatment process or even to avoid building human settlements near to the facilities. Public acceptance

 Communities reject systems producing foul odors or vector breeding. Communities also tend to more readily accept natural process that are integrated with the landscape.

 Low-income communities accept better technologies that are a source of jobs for local people than rich ones. Adapted from Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO Leiden: Taylor and Francis Group.

Box 7 Eutrophication Control (with information from Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO; Leiden: Taylor and Francis Group.) Eutrophication is a process in which plants (such as water lilies or hyacinths (Eichornia crassipes), hydrilla (Hydrilla verticillata), cattail –(Thypa sp.), and duckweed (Lemna sp.)) proliferate in surface water bodies due to the presence of high concentrations of phosphorus and/or nitrogen that may come from wastewater, treated effluents, or agricultural runoff. It is commonly observed in polluted lakes or dams, but problems in low flow rivers and agricultural canals have also been observed. Aquatic plants cover the water surface preventing sunlight and oxygen from entering the water. Other negative effects that are provoked are (1) oxygen depletion in the hypolimnion; (2) release of Fe, Mn, NH4, and heavy metals from the sediments; (3) vector breeding, such as Schistosomas and mosquitoes; (4) loss of biodiversity, especially in higher trophic levels; (5) displacement of native species, (6) obstruction of hydroelectric plants and irrigation canals and drains; and (7) restrictions on tourist, recreational, and fishing activities. To reduce aquatic weed density (plants m2), five methods are available: *

*

*

*

*

Biological control. It consists of using living organisms to control weeds. In theory, it is a cheap option as no equipment or chemicals are required but it has an associated labor cost in order to perform maintenance. To be completely effective, the rate of grazing needs to be higher than the plant growth rate, which is very difficult to match in practice. A wide variety of fish, arthropod, fungi, and bacteria have been used for this purpose. Mechanical Control. These methods remove or cut weeds into pieces using mechanically or manually operated equipment. It is an expensive option that can play a role in quickly reducing the extent of infested areas prior to the application of another control method. Chemical control. Pesticides are also used to control weeds. Some substances that have been used are terbutryn, diquat, 2,4-D, glyphosphate, paraquat, and simazine. However, due to their toxicity, they can only be applied under controlled conditions and for a limited period of time. Water level control. In this method, the water level is decreased so the weeds located close to the edges of the water body dry out. The applicability of this method is limited to dams where water levels can be controlled, and to the dry season in which rain would not convey plants once again to the water. Nutrient control. Weed growth is caused by high N or P content in water, and so, lowering their concentration through wastewater treatment is another alternative. Unfortunately, the cost remains high.

Due to their low efficiency or cost implications, in practice, two or more methods are often used to control weeds.

main source of income and the main mean to feed a growing population. Third, the increasing demand for water by municipalities and industries is increasing the competition for its use with farmers. It is estimated that, in developing countries, water withdrawals will increase more (27%) than in developed ones (UNDP, 2006). Among the uses demanding water, sanitation needs to be considered and, in that respect, water reuse may be a component in some areas to promote it through the alleviation of water demand, saving water for

sanitation facilities or through coupling projects to treat wastewater with reclamation ones.

4.06.7.3.1 Types of water reuse Two types of water reuse can be distinguished: nonintentional and intentional or planned. As, in several developing countries, lack of sanitation is generating nonintentional reuse, national policy will need to encourage controlled options

Safe Sanitation in Low Economic Development Areas

instead of promoting practices to start up water reuse. This is the biggest difference with developed countries, where reuse is being promoted once wastewater is treated.

4.06.7.3.2 Unintentional reuse In literature, water reuse is considered merely as an activity where wastewater is intentionally treated to be used once again. Therefore, water reuse is understood as an artificial man-made practice. However, unintentional reuse also exists as part of the natural hydrological cycle, but this is frequently not acknowledged. (Jime´nez, 2009a). ‘Nonintentional’, ‘nonplanned’ or ‘incidental’ water reuse describe situations where used water is mixed with (or becomes part of) the water supply. In most cases, this unplanned reuse is difficult to identify, although it would be important to acknowledge it in order to properly control it. The nonplanned use of water is at the origin of the presence of emerging chemical pollutants in water sources and the reason why drinking water standards are becoming increasingly comprehensive and stringent and more sophisticated technologies to treat water are needed (Jime´nez, 2009b). Nonplanned reuse of wastewater is happening for agricultural irrigation, aquifer recharge, and human consumption. 1. Nonplanned reuse for agriculture. Three-quarters of the total irrigated area worldwide is located in developing countries, and, as a consequence, there is a high dependence on water for food production. Frequently, due to lack of sanitation in these countries, wastewater is used to irrigate land. This is a practice that happens almost naturally because of the combination of the high demand for water for irrigation (81% of total use compared to only 45% in developed countries, Figure 13), the availability of wastewater, the productivity boost that the added nutrients and organic matter provide, and the possibility to sow crops all year round (Jime´nez, 2006). It is estimated that at least 20 million hectares in 50 countries (around 10% of irrigated land) are irrigated with raw or partially treated wastewater (WHO, 2006).

Agriculture

Approximately one-tenth of the world’s population consumes crops irrigated with wastewater, diluted or not. As an example, in Hanoi, Vietnam, wastewater is used in the production of 80% of the vegetables consumed locally (Ensink et al., 2004). The use of nontreated wastewater is also common for urban agriculture, which is practiced in urban and periurban areas of arid or wet countries where there is local demand for fresh food products, and people live on the verge of poverty with no job opportunities (Jime´nez, 2009b). For urban agriculture, wastewater flowing in open channels is used to irrigate very small urban plots of land where trees, fodder, or any other product that can be introduced to the market in small quantities (flowers and vegetables) or be used as part of the family diet are grown (Ensink et al., 2004). In terms of volume, reuse of nontreated wastewater is at least 6 times higher than of treated wastewater (Jime´nez, 2006; Jime´nez and Asano, 2008). As a consequence, any sanitation project in localities using wastewater should consider its actual use. 2. Unintentional reuse for water recharge. Since groundwater is not water that can be observed as in lakes or dams, very often its pollution and nonintentional recharge is not perceived. Infiltration may result from agricultural irrigation, leakages from wastewater and water urban networks, unlined dams, tanks or reservoirs, and on-site sanitation systems. Little information on the extent of this problem is reported in literature, but some cases (a summary is presented in Table 12) have been described highlighting the importance of this phenomenon as a source of water supply. For the one referring to the Tula Valley, it has been the best documented (Jime´nez, 2008b) that recharge with wastewater amounts to at least 25 m3 s1, and the aquifer is used to supply 500 000 people. Infiltration and pollution of groundwater supplies varies from negligible to severe, and the recognition of unplanned reuse is needed in order to advance understanding of how to manage the risks. This may involve continuing groundwater recharge with water of improved quality and/or separating the

Municipal

Industrial

Low income countries Middle income countries High income countries

Developing countries Developed countries

World 0%

20%

187

40%

60%

80%

Figure 13 Water use in developing and developed countries (with information from Earth Trends, 2009).

100%

188 Table 12 aquifers

Safe Sanitation in Low Economic Development Areas Examples of unintentional indirect potable reuse via

City

Recharged water

Groundwater uses

Hanoi, Vietnam

Sewer, storm water

Hat Yai, Thailand

Drainage canals, on-site sanitation facilities Primary effluent Mix industrial effluent

Irrigation and drinking Drinking

Ica Valley, Peru Leon, Mexico Merida, Mexico Mexico City (southern part), Mexico Santa Cruz, Bolivia Sana’a, Yemen Tula Valley, Mexico

Sewer, storm water On-site sanitation facilities On-site sanitation facilities Cess pits Untreated effluent

Drinking Irrigation and drinking Drinking Drinking

Drinking Irrigation and drinking

Adapted from Dillon P and Jime´nez B (2008) Water reuse via aquifer recharge: Intentional and unintentional practices. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and needs. London: IWA Publishing.

recharge areas further from points of water abstraction. Appropriate monitoring information will allow the most cost-effective investments to be identified (Dillon and Jime´nez, 2008). 3. Nonintentional reuse for human consumption. Nonintentional reuse for human consumption occurs as described previously, not only through aquifer recharge but also through surface water sources when effluents, treated or nontreated, are discharged into them. This has been documented in developed countries. For instance, in the River Thames in England, during dry periods, 70% of the water used as supply downstream comes from treated effluent. In California’s Santa Ana River, a large part of the supply consists of treated wastewater (Gray and Sedlak, 2003) and in Berlin, 17–35% of the city’s water supply comes from an advanced treated effluent that is discharged to a nearby water supply (Jekel and Gruenheid, 2008). The increasing evidence of the presence of emerging contaminants in water sources is an indication of the nonintentional reuse of water. Information on this subject for developing countries is very poor, and possibly only reported as pollution cases. Recognizing the nonintentional reuse of water for human consumption will help society to acknowledge that water reuse is unavoidable in the future and also to understand that, to properly reintegrate used water to the environment is needed. For this, tools other than wastewater treatment plants will be needed.

4.06.7.3.3 Intentional or planned reuse According to Asano (1998), wastewater reclamation involves the treatment or processing of wastewater to make it reusable; and wastewater reuse or water reuse is the beneficial use of treated water. Planned reuse may be performed for agricultural

irrigation, industrial purposes, environment restoration, and municipal uses. 1. Reintegrating water for irrigation. Most of the world’s poorest people, 800 million to 1 billion rural people, live in arid areas and depend directly on natural resources, including water, for their livelihoods (Dobie, 2001). In such a context, safe wastewater reuse can be a sanitation option that could also be coupled with food security and economic development goals. Under prevailing land and water management practices, a balanced diet represents a depleting water use of 1300 m3 inhab1 yr1, which is 70 times more than the 50 l inhab1 d1 required for basic household water needs (SIWI-IMWI, 2006). For several middle- and low-income countries, agriculture is currently, and will continue to be, a key sector representing 80% of export earnings. Limited and unreliable access to water is a determining factor in agricultural productivity in many regions, a problem rooted in rainfall variability that is likely to increase with climate change (Lenghton et al., 2005). To feed this sector, water reuse can be one option. Planned reuse of water for agricultural irrigation in developing countries is a convenient strategy for many reasons (Jime´nez and Gardun˜o, 2001; Jime´nez, 2006, 2009a; WHO, 2006; Keraita et al., 2008), such as • It is an easy option to increase controlled reuse when nontreated wastewater is already in use as it allows more profitable and safe products. • It can be a low-cost option to manage wastewater and to reintegrate water into the environment. • It allows the reclamation of nutrients (N and P, to increase soil fertility) and organic matter (to improve soil characteristics) at no cost. • Particularly in (but not limited to) arid and semi-arid areas, it permits higher crop yields, as it allows crops to be sown year-round due to higher water availability. • Due to the availability and reliability of water, crops with better profitability can be selected. • It avoids discharging pollutants to surface water bodies (which have a considerably lower treatment capability than soils). • It is possible to recharge certain type of aquifers through infiltration. • It can be part of a strategy to secure food and increase poor people’s income in water-scarce areas. To obtain all the advantages from reusing wastewater for agriculture in planned projects, it is important (1) to control possible negative effects (Jime´nez, 2006; WHO, 2006) such as those related to health; (2) to keep in mind that in many cases nontreated wastewater is being reused at low or even no cost by poor farmers and, hence, they will be unable to afford reuse costs; and (3) from the legal aspect, the historical use of nontreated wastewater by farmers confers riparian rights. 2. Reintegrating water for industrial reuse. Industrial reuse (reclamation of wastewater from a different use, i.e., reuse of a municipal effluent for industrial cooling) differs from municipal and agriculture reuse as it involves the private sector that has its own rules and well-defined needs driven

Safe Sanitation in Low Economic Development Areas

by economic factors (Jime´nez and Asano, 2008). Before reusing water, industries always prefer to implement watersaving projects as these immediately reflect on their budgets; for reusing water, investments to provide proper treatment and monitoring programs are needed. To promote industrial reuse, the best government strategy is to provide incentives rather than setting compulsory regulations (Jime´nez and Asano, 2008). Among the different industrial reuse options, cooling is the most popular due to its high water demand, and the possibility of using secondary-treated municipal effluents, sometimes coupled with filtration or softening processes. As a consequence, power plants located near urban areas are potential sites of industrial water reuse. 3. Reintegrating water to the environment. More than 1.4 billion people live in river basins where the intense use of water threatens freshwater ecosystems (Smakhtin et al., 2004). Reintegrating water to the environment is a practice that is gaining momentum, as it is being recognized that (1) the environment needs water and (2) the environment has the same entitlement to water as other uses. Unfortunately, these two aspects are better recognized by developed countries than developing ones. Overuse of water tends to occur in regions heavily dependent on irrigated agriculture or where there is rapid growth of densely populated areas (UNDP, 2006), two characteristics common in developing countries. Among the more prominent examples (UNDP, 2006) of water overuse, the exploitation of the Yellow River basin, in northern China, can be cited: Human withdrawal currently leaves less than 10% of the flow remaining in the river. The river ran dry 600 km inland for a record 226 days in 1997. The drying up of the river caused a drop in agricultural production averaging 2.7–8.5 million tons a year, with losses estimated at US$1.7 billion for 1997. The purified effluent from sewage treatment plants can be used for the augmentation of river flows, to raise the level of wetlands or lakes, to recover dried lakes, or even to create new lakes or wetlands. In doing so, biodiversity may recover. Care must be taken when restoring water into water bodies to preserve or improve the actual quality of water. Used water reclamation can be combined with rainwater reclamation. Water reuse with environmental restoration can be coupled with projects of urban image improvement or programs to provide better facilities at recreational areas. 4. Restoring water to aquifers. Aquifer recharge is not, itself, a use of reclaimed water but is often part of the pathway to reuse. It is a convenient way to reintegrate water into the environment but can be used only under certain circumstances related, in particular, to the type of soil and groundwater. Aquifer recharge can be performed to recover groundwater levels, to control saline intrusion, to augment drinking water sources, to protect and, in some cases, to improve underground water quality, to protect surface water bodies from contamination by effluents, to increase water availability for any use, and simply to store water for the future (Dillon and Jime´nez, 2008; Corrleje et al., 2008). Intentional recharge with reclaimed water can play a role in providing balanced storage and supplemental treatment for water (Bouwer, 2002; Dillon and Toze, 2005). It also provides low-cost storage that occupies a minimum of

189

valuable urban land, while stored water is protected from pollution and evaporation. There are two methods to recharge aquifers. The first is known as land-spread infiltration where treated wastewater infiltrates through soil by gravity. This option has relatively low operating and maintenance costs. The second method for recharge is direct well injection. In this option, wells are used to convey a highly treated effluent directly to aquifers. Regulation to recharge aquifers are very different from one country to another; some are set at a national level while others are defined using a case-by-case approach (Jime´nez, 2003). Most of the projects to recharge aquifers are found in developed countries. In developing ones, some examples are found in Atlantis, South Africa (for drinking and agricultural purposes, using pond infiltration), in Windhoek, Namibia (for drinking purposes and using injection wells), in New Delhi, India (for irrigation using infiltration ponds for treated urban wastewater and stormwater), in Beijing, China (for drinking purposes using wells and recharge basins), and in Mexico City, Mexico (for drinking purposes on a limited scale and using infiltration ponds; Dillon and Jime´nez, 2008). In all these cases, wastewater is treated to at least at a secondary level (see section titled ‘Relevant websites’). 5. Reintegrating water for municipal use. In 20 years, 60% of the world’s population will be living in cities (UN, 2006). This being the case, more water will be needed for municipal use and, at the same time, more municipal wastewater will be produced. This situation, therefore, represents an opportunity to increase municipal wastewater reuse. Water reuse in cities represents an opportunity to conveniently treat wastewater, with environmental and even economic advantages. Opportunities to reuse wastewater in cities are classified into two groups: (1) those demanding relatively low-quality water and involving low health risks, and (2) those demanding high-quality water where health risks are high. In the first group, there are several types of uses, such as: (a) the filling of recreational lakes or the operation of fountains; (b) car, truck, or street washing; and (c) green area irrigation. Options demanding high water quality include reuse for drinking supply. Around the world, there are successful examples of both types of reuse, low risk options being the most common. Water reuse for human consumption, although less common, is no less important. Moreover, the only two examples of the reuse of water for human consumption in the world are notably from two countries from the developing world: Namibia and Singapore (Box 8).

4.06.7.3.4 Graywater reuse Graywater (i.e., domestic wastewater not containing toilet wastewater) is more accessible for reuse as it is less contaminated than wastewater, notably in terms of (but not limited to) pathogens. Typical sources of graywater are bathing, laundry, dishwashing, and food preparation. Due to its comparably low and easily degradable contamination, it can be relatively easily treated for reuse. Graywater reclamation entails the production of less wastewater to be treated in

190

Safe Sanitation in Low Economic Development Areas

Box 8 Reuse of wastewater for human consumption in Namibia and Singapore Windhoek, Namibia, has been reusing wastewater for human consumption for more than 40 years (Van der Merwe et al., 2008) as result of an original idea in 1956. Since its operation, no measurable health risk has been observed and neither have people drinking reused water displayed associated health problems. The reclamation plant has undergone several modifications to improve the technology used. The quality of the water supplied can be consulted every day in the local newspaper. The amount of water reused is around 250 ls1, which is distributed after dilution by a factor of 1–3 with first-use water. The monitoring program for the facility represents 20% of the operating costs, and is performed by the wastewater treatment plant and also by three independent laboratories. The system is operated using a multiple barrier concept that goes beyond the wastewater treatment plant. The astute words ‘‘Water should be judged by its quality; not its history’’ are attributed to Dr. Lucas van Vuuren (van der Merwe et al., 2008), one of the pioneers of the Windhoek reclamation system. This refers to the fact that fear of reused water should be based on rational aspects. The other example of direct reuse of wastewater for human consumption comes from Singapore (Funamizu et al., 2008) and is known as the NEWater project. It started in 2003 and uses a secondary effluent that is further treated with a membrane system (microfiltration (MF) and reverse osmosis (RO)) and UV-light disinfection. The water produced is cleaner than tap water as it fulfills all the requirements set by US-EPA and WHO for drinking purposes. Treated water is channeled to a reservoir, from which it is taken as supply after dilution with first-use water. Water is distributed through the network for use for domestic and industrial purposes. When the NEWater project was launched, it operated at a rate of 870 l s1. This will be progressively increased to reach 2400 l s1 by 2011 (B0.5% and 2.5% of total water consumption, respectively). In both cases, Namibia and Singapore, before the implementation of the reuse programs, stringent industrial pre-treatment programs and segregation of industrial effluent from the sewer were put in place.

centralized plants. Graywater reuse is performed at the same facilities where it is produced and, as a result, a short storage time is needed (1 day retention time). Graywater reuse can be performed individually (for a single home) or collectively (several groups of houses or larger buildings). Treated graywater may be used for watering plants, kitchen gardens, and for the safe augmentation of ground- or surface water. Treatment can be very simple or highly sophisticated, ranging from simple manually operated sand filters to biomembrane reactors, hence, covering the needs for rural areas or buildings located in upmarket areas in megacities. Further details on design and operation can be found in Correlje and Schuetze (2008). Graywater reuse can be as well an important component for basic sanitation, as described in Section 4.06.6.1.

4.06.8 Sludge and Excreta Management As the quantum of wastewater treatment is still low in developing countries, little information is available concerning the actual situation. LeBlanc et al. (2008) performed a survey in some countries showing that the tendencies are the following: 1. For middle-income countries. From information coming from 10 middle-income countries, including Africa (Namibia and South Africa), the Middle East (Iran, Jordan and Turkey), Asia (China and Russian Federation), and Latin America (Brazil, Colombia and Mexico), it is shown that wastewater treatment facilities serve mostly urban areas using preliminary, primary, and, in some cases, secondary processes. For rural or poor periurban areas, basic sanitation facilities are provided. Although sludge is produced in these facilities, this is not always managed as part of the sanitation service. The disposal options for the sludge from wastewater treatment plants produced are landfill dumping, dumping into sewers, storage at wastewater treatment plants, land application, and agricultural reclamation. Land application and agricultural reclamation are options

limited by space problems, while the use of landfills is restricted in densely populated urban areas, where solid wastes compete for space with sludge. As sludge production is still low in the few wastewater treatment plants available, sludge management policies are novel, and are still in a maturation phase. Some of these policies offer new approaches different to those used in developed countries (LeBlanc et al., 2008). With regard to fecal sludge, the main constraint for their management is the cost to empty on-site sanitation systems as these are often located in inaccessible areas, are large in number, and are frequently highly dispersed. It is noted that the high cost of latrine emptying is not sustainable, even for large municipalities. Extracted fecal sludge is often buried on-site, dumped into landfills or sewers or sent to uncontrolled discharge sites. Discharge of sludge and fecal sludge in sewers often lead to surpass the wastewater treatment plants’ capacity when available. 2. For low-income countries. Data from different African countries (Burkina Faso, Cameroon, Coˆte D’Ivoire, Ethiopia, Mali, Mozambique, Namibia, Nigeria, Senegal, and South Africa) demonstrated a similar situation focused on the need to provide basic sanitation services either in rural or urban areas. Few cities have complete sewerage systems and, when available, sewers frequently feed into partially functioning wastewater treatment plants. In these countries, the use of on-site sanitation systems, such as septic tanks, bucket latrines, pit latrines, and dry latrines, produces fecal sludge, which is often ‘contaminated’ with domestic waste. In dense informal settlements, the challenges to properly handle fecal sludge are significant as besides the technical constraints other factors related to the social, political, and cultural aspects come into play. Fecal sludge handling includes the need to provide reliable and low-cost options to emptying the facilities, to provide proper and affordable treatment and transportation, and to have suitable sites for safe disposal. Literature exists concerning the alleviation of sludge and fecal sludge disposal and revalorization problems, not all of which is relevant for developing countries. Common issues in

Safe Sanitation in Low Economic Development Areas

properly managing sludge and excreta in developing countries are as follows (LeBlanc et al., 2008; Jime´nez, 2006, 2008):

• •

• •

Conventional sludge and excreta treatment options used in industrialized countries do not necessarily achieve the levels of pathogen inactivation required for its safe reuse. Nutrients, organic matter, and energy are resources available in fecal and wastewater sludge that should be utilized as best as possible. There are examples around the world showing the feasibility and convenience of reclaiming them. Applying properly treated excreta and biosolids to soils in a safe way can contribute to soil fertility and with it to food security; it can also raise income for poor farmers. Proper management of excreta and wastewater sludge can significantly reduce releases to the atmosphere of potent greenhouse gases such as methane and contribute to carbon sequestration in soils.

191

4.06.9.1 Integrated Water Resources Management In order to consistently provide sustainable water services, it is recommended that an integrated water resources management (IWRM) approach is used. This approach is useful to analyze situations such as when

• • • •

4.06.9 Policy

•

The MDG Target 10 stating ‘‘Reduce by half the proportion of people without sustainable access to safe drinking water and basic sanitation is considered under Goal 7: Ensuring environmental sustainability’’ (Box 9). Therefore, sanitation is to be provided in a sustainable framework which, in practice, means to provide a service comprising much more than was expected in the past. To implement it, a proper policy is needed.

• • •

multiple barrier system comprising solutions that go beyond the construction of wastewater treatment plants need to be implemented to protect health and the environment; sanitation needs to be provided as a tool (sometimes indispensable) to have clean water supplies and to provide a safe water supply (Box 10); sanitation is coupled with projects contributing to food security, job opportunities, increases in exportation, soil erosion control, efficient use of water, etc.; sanitation needs to be provided over a wide area rather than to a single section of it to effectively control negative environmental impacts; sanitation needs to be part of a three R concept system (reduce, reuse, and recycle); sanitation is considered as part of a cycle in which wastewater is properly reintegrated to the environment; sanitation needs to consider the impacts caused by climate change; projects need to be designed, operated, and/or managed by different institutions, sectors, basin agencies, or even countries;

Box 9 What does sustainability mean? ‘‘A process that promotes the coordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems’’, UN-Water, 2008 According to LeBlanc et al., 2008, elements defining sustainability are * * * * * * * * * * *

dealing transparently and systemically with risk, uncertainty, and irreversibility; ensuring appropriate valuation, appreciation, and restoration of nature; integrating environmental, social, human, and economic goals in policies and activities; providing equal opportunities and community participation; conservation of biodiversity and ecological integrity; ensuring inter-generational equity; recognizing the global integration of localities; a commitment to best practice; avoiding net losses of human or natural capital; implementing principles for continuous improvement; and providing good governance.

Box 10 The Bissau case, with information from Correlje AF and Schuetze T (2008). Every Drop Counts: Environmentally Sound Technologies for Urban and Domestic Water Use Efficiency. Division of Technology, Industry and Economics, TU Delft. India: United Nations Environment Programme. Bissau, Guinea, in West Africa is a city attracting huge numbers of people from the surrounding countryside. Most of them have settled in squatter new areas around the old colonial center. During a study performed in the 1990s, it was found that the newly piped water taps ran dry several times per day. As a result, many people returned to the old wells. These were often more contaminated than before because the new pit latrines installed close to the wells polluted the groundwater. Groundwater quality was also impacted by solid waste thrown into the pits dug for the production of adobe blocks to build new houses. Moreover, the new network of gutters was now efficiently removing most of the clean rainwater that used to recharge the groundwater. The gutters caused an extra problem. On the edge of the settlements, where the gutters ended, storm water peaks caused serious soil erosion. This created problems for a newly developed scheme of vegetable gardens on the urban fringe, and even threatened houses.The original problem – the lack of water in piped water taps – was related to electrical power failures causing water pumps to stop. Similar situations can be encountered in many developing countries and they cannot be easily solved as long as their roots are not properly and integrally tackled.

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good technical solutions needing proper social, economic, and political policies are to be put in place; and wastewater, treated or not, is being nonintentionally reused.

• • •

4.06.9.2 Need for an Own Policy for Developing Countries Developed countries, through experience, research, and technological innovations have progressively improved their sanitation services and have developed systems that are what they need. However, as described in this chapter, the problems they have faced and the problems they are now facing, although similar, are not the same as those confronted by developing countries. Thus, there is a need for low-income nations to develop their own processes using part of the developed countries’ experience. To contribute to this process, a definition of the issues to address and the challenges to face is provided in the following.

4.06.9.2.1 Issues to address The issues that need to be addressed are as follows:

• •

•

Low sanitation coverage lagging behind population growth, needing an intense effort in order to be tackled. Need/importance to couple sanitation programs with others addressing problems such as food security, low income, and soil erosion control. In practice, this requires increased efforts of coordination. Lack of sanitation as a component of poverty, and therefore, as a problem that cannot be completely solved if its roots are not properly addressed (Box 11).

•

•

Lack of sanitation, particularly in vulnerable groups that, due to their own characteristics, are often more difficult to provide services for. A growing population, notably in urban areas and, within them, in slums. Higher vulnerability to the negative impacts of economic and climatic change on sanitation needs. For low-income countries, lack of economic capacity to deal with the cost of covering the sanitation MDG targets and, for middle-income countries, the need to mobilize funding required to put sanitation above other needs. The proper management of sludge and excreta, two byproducts often not considered as part of sanitation targets of funding programs.

4.06.9.2.2 Challenges to face The challenges to be encountered are listed below: 1. The lack of political will and commitment at the highest level (WHO/UNICEF, 2000) is a barrier that is greater than, for instance, the lack of economic resources, the capacity for building, or the acquisition of appropriate technology, since all these may be overcome by a strong political support. In order to develop political will, politicians and society need to appreciate the value of sanitation. An understanding that it is through the provision of water supply and sanitation that industrialized countries build up strong societies with good health and good economic conditions is needed (Box 12). 2. The second challenge is to put in place accountability mechanisms to ensure that resources provided to fulfill

Box 11 The sanitation problem in Cameroon (with information from Mfoulu N (2008) Cameroon. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 169–179. Vienna: UN). In Cameroon, some houses are equipped with a 2 m-deep hole for a latrine, surrounded by pieces of timber. When the hole is full, it is covered with earth and medicinal or aromatic plants, and another facility is built. If the family has no land to dig another hole (as frequently happens), they call the tanker to empty it at a cost of US$120. Sometimes, while the family saves up the money, excreta overflows and pollutes the nearby area where wells and boreholes are located, threatening drinking water quality. When feces are removed by tanker trucks, they are often dumped into rivers or the forest, because there are no treatment facilities. Houses in modern residential areas have septic tanks, and their effluents are directed into wells for filtration. Often, this does not happen in the correct way because builders have not mastered the technology. Some collective residential areas, universities, and hospitals are connected to sewers that convey wastewater to a treatment plant, from where treated water is directed to a river. But still, there are people without access to any of the facilities described above who go into the bush to relieve themselves on the spot. Villagers continue to use this practice because they have no choice.

Box 12 Clean meansy yy healthy? Mexico City produces 21% of Mexico’s gross domestic product (GPD) (US$12 500 per capita). After the swine flu (H1N1 flu) outbreak in May 2009, a loss of US$144 million was experienced solely due to the shutting down of restaurants, and US$35.2 million were lost due to the closure of public transport for just 10 days. To allow the city to return to normal conditions, health experts advised constant handwashing and the disinfection of school toilets. At this point, politicians realized that 200 public schools had no water at all, 195 had malfunctioning toilets and 90 more had no facilities at all. Before the swine flu epidemic, politicians had not understood the link between water, sanitation, and health and had not addressed this problem, although on many occasions parents’ associations had requested the services. The president of one parents’ association commented on the news that, in contrast to most Mexicans, he believed that the swine flu had been a blessing as it was the only way to ensure proper sanitation facilities at schools. The Mexico City government invested US$56 million on the school program ‘Clean means healthy’.

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Box 13 Need for new type of institutions, with information from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan. Water and sanitation service agencies are typically modeled after utilities in industrial countries, and as such are organized around the goals of maximizing operational efficiency for public sanitation components (trunk sewers and treatment plants) rather than providing services to poor people, slums, disadvantaged groups, etc. As result, in, in developing countries, experience and institutional structures to provide the type of services needed is deficient. As a result, services are being provided by other means. Data from India indicate that as much as 8% of rural households across the country invest their own money and use small private providers to construct latrines. Self-provision accounts for about 1 million privately installed septic tanks in Manila and in Jakarta. Research in Africa confirms that the role of the small-scale private sector in sanitation provision is significant. These findings are further supported by data from the JMP (WHO-UNICEF Joint Monitoring Programme): between 1990 and 2000, the increase in the number of people served by sanitation reported by the JMP was much larger than the expected impacts of the public investment that occurred during this period. The reorientation of public programs to either modify their structures or to promote and assist the provision of sanitation services by small private and even familiar companies is needed. This does not currently occur in developed countries.

the MDGs (public and private from donors) will be used wisely and for what they were originally intended for. 3. The third challenge involves a broader aspect. Even if sanitation programs are put in place, if poverty is not properly addressed, most of the solutions provided will be unsustainable. This will possibly lead in the future to adding addressing poverty to the already lengthy list of reasons why sanitation has failed in developing countries (this list already comprises financing, institutions, education, the need for decentralization, and the need for private participation).

• • • • •

4.06.9.2.3 Strategies that can be used Although there is no recipe for success, strategies that can be considered when developing plans for sanitation include the following (Jime´nez and Gardun˜o, 2001; Jime´nez, 2003, 2006; Lenghton et al., 2005; UNDP, 2006; WHO, 2006; LeBlanc et al., 2008; Correlje and Schuetze, 2008): To develop policies:

•

•

•

•

Take time to perform proper planning in order to identify the resources (human and economic) needed to design, build, operate, and maintain facilities, and to develop policies and institutions. Do not initiate projects for which this has not been previously defined, otherwise there is a risk of losing any investments made (a case in point is the existence of many facilities installed around the world, which have been subsequently abandoned). Take time to define how much money is needed, supported by experts with no commercial interest, specifically not those from companies that are potential participants in bids. Define needs and priorities using the best available information even if it does not come from the water sector. Priorities can be set by using the methodology proposed by Lenghton et al. (2005), which considers actual water service coverage, and mortality due to gastrointestinal diseases and density of settlements, considering urban and rural areas. Evaluate risks using quantitative methodologies to properly identify and prioritize problems, and select solutions accordingly (in terms of size, and economic and human resource investments). As much as possible during the planning stage, involve sectors related to the solutions other than the water sector

• • •

(e.g., the federal, regional, and local governments, ministers of the environment, urbanism, agriculture, land use, transport, economic development, social development, finance, etc.). Couple sanitation programs with programs related to food security, soil remediation, and economic development. Produce efficient, affordable, and enforceable norms and set goals for them that are easy to understand. Promote innovation at all levels (institutional –Box 13–, financial, regulatory, and technological). Combine different intervention methods to control problems; consider not only of sewers, latrines, and wastewater treatment plants. Consider water reuse and the safe reintegration of sludge and fecal excreta as an important part of the overall sanitation program. Promote the management of the environment in an integrated way, even considering climate change effects. Design monitoring programs that wisely use resources by including information that WILL be used. Use the new information obtained to evaluate and improve the program. Review the program to ensure it covers the specific targeted population sectors (women, the poor, rural areas, etc.) and meet the defined goals.

For funding:

• • •

•

be creative in finding solutions to funding needs; extend financial support to the poorest households to ensure that sanitation is an affordable option; discern whether there is an absolute lack of resources for expanding water supply and sanitation coverage, or if there is a need to redistribute potentially sufficient existing resources; and develop and put into practice transparent mechanisms to easily and rapidly transfer monetary resources from central to local institutions.

For institutional design:

• • •

Develop national and local political institutions that reflect the importance of sanitation in terms of social and economic progress. Promote institutions throughout government that use or at least understand concepts of integrated management, not only for water. Develop institutions where innovation and solidarity are considered as a virtue.

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•

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Consider the need to have as part of the institutions welltrained and highly professional personnel.

progressively controlling drawbacks; this can be done by promoting controlled reuse rather than adopting vanishing current practices. Incorporate reuse as part of the sanitation standards.

For norms and regulations:

•

•

To set up programs:

•

•

• • • • •

Identify which problems should be addressed by using norms (compulsory), criteria (recommendations), or other type of tools (such as incentives and education). Set appropriate and affordable sanitation risk-based standards, designed to contribute to solving local problems that can be reviewed over time to integrate experience. These should be able to be adapted to new and better conditions in order to move progressively to an ideal situation. Allow the development of norms that are adapted to local needs and capabilities (Table 13). Sanitation systems are often adopted from other developed countries without sufficient adaptation and users tend to put in place an idealized solution in which a uniformly high level of service is provided and the technology to be used is already set. Set up regulations that combine different intervention methods to control risks that are not based only on wastewater treatment plants. Keep in mind that parameters selected are to be enforced and they will demand economic and human resources for. Review the whole legal framework related to the standard so they can fit in and be implementable. Set up standards using a participatory approach, which includes stakeholders and expert participation, notably coming from local universities. Where noncontrolled reuse is already in place, regulations need to maintain the benefits already obtained while

Table 13

•

•

• • • • • •

Perform a national inventory of the actual needs and solutions to be implemented to manage wastewater, excreta, and sludge, include a survey on water reuse possibilities to couple them with sanitation solutions when feasible. Implement policies by promoting incentives rather than imposing rules and fines; but when rules are to be observed, be firm on decisions, and inform society in order for it to be perceived that jeopardizing the health of others is important. As there is no universal solution, support a wide range of sanitation technologies and service levels that are technically, socially, environmentally, and financially appropriate. Promote innovation to have both technically and economically feasible technologies to deal with local pollutants, notably for the high and varied pathogen content. Implement pilot plant programs to test policies and use the information obtained to retrofit your program before scaling it up (Box 14; Spaliviero and Carimo, 2008). Empower local authorities and communities with the authority, resources, and professional capacity required. In order to fund the maintenance and expansion of services, local governments and utilities should ensure that users who can pay, do so. Carry out training programs addressing all stakeholders needs, from plumbers to politicians.

Some aspects to consider when setting regulations

Aspect

Advantages

Disadvantages

Definition of fixed treatment option(s) to use and inclusion of predefined treatment design and operating criteria.

– Reduces the need for monitoring and surveillance. – Renders project implementation easier.

Selection and use of the best indicators as parameters.

Reduces monitoring and surveillance cost.

Selection of normal monitoring parameters and establishment of limits for each one. Use of epidemiological local data.

– Facilitates surveillance.

– Limits innovation – Encourages bias in regulators who will be responsible for both selecting the method of control and meeting objectives. – May lead to nonviable schemes from an economic point of view. – Introduces the idea that indicators are the best and ideal parameters to define pollution. – Most of the current best indicators have been proven effective for developed countries but have not been tested for all conditions in developing countries. – May give a false impression of safety. – Cannot be universal or static over time. – Increases supervision costs.

– Introduce protection for local problems.

Use of toxicological tests.

– Data available internationally. – Helps to establish cause–effect relationship.

Use of risk evaluation models.

– Help governments to make rational decisions.

– Information not always available for all of the diseases currently present. – Often render norms too stringent. – For diseases originating from microbial pollution do not correspond to local conditions when diseases are endemic. – Difficult to explain their meaning to the population.

Adapted from Jime´nez B (2003) Health risks in aquifer recharge with recycle water. In: Aertgeerts R and Angelakis A (eds.). State of the Art Report Health Risk in Aquifer Recharge Using Reclaimed Water, pp. 54–172. Rome: WHO Regional Office for Europe.

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Box 14 Development of a stepwise program in Mozambique (with information from Spaliviero M and Carimo D (2008) Mozambique. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 431–437. Vienna: UN.) Following Mozambique’s independence in 1975, the government identified sanitation as one of the key components to improve health conditions. As such, in 1976, the Ministry of Health launched an intensive national campaign for the self-help construction of latrines. Many thousands of latrines were constructed during a relatively short period. However, there were numerous problems, including insufficient awareness about environmental conditions, a lack of technical guidance in latrine design and construction, and shortages of critical building materials. Consequently, many of the latrines became structurally unsafe and unusable. In response, a research project was initiated in 1979 to ‘‘identify and develop a suitable technology and method for large-scale implementation of improved sanitation in periurban areas.’’ The result was the development and successful pilot testing of an appropriate and cost-effective technology. From 1979 to 1994, around 135 000 improved latrines were produced. In addition, an awareness campaign was carried out on the use of the latrine, hygiene promotion, and capacity building. In 1996, the program was extended to the rural areas. Prior to 1998, more than 230 000 latrines were constructed and installed. In December 1998, the program was formally transferred to the National Directorate of Water Affairs. Overall, it has been a long and steady scaling-up process over more than 10 years that ended by ensuring a progressive withdrawal of the government from latrine production. The emphasis now is given to decentralization and privatization for the services, although the responsibility for the program remains with the government. From this experience, some lessons learned, are *

*

*

•

•

•

• •

Although technology must be simple, it is important for massive use to ensure its local production and commercialization. There must be several types of sanitation facilities with different prices in order to commercialize. A good network needs to be established between users (periurban communities, the government, nongovernmental organizations (NGOs), small private companies, and donors) to ensure that the program progressively developed its own dynamism. Latrines need to be emptied and the service needs to be provided.

Implement programs to segregate and/or pre-treat industrial discharges to sewers to render municipal wastewater treatment more affordable and to avoid the presence of noxious compounds in treated wastewater and sludge that will limit their revalorization options. As wastewater, sludge, and excreta management regulation compliance often depend on the work of different ministries, coordinate the work of such institutions taking care that the objectives of each are compatible. Develop public indicators to follow up progress globally and also consider the implementation of indicators to follow specific targets such as wastewater treatment coverage, safe reintegration of treated water to the environment, and sludge and fecal excreta management. Attention should also be provided to deprived sectors (women, poor people, slums, dispersed rural areas, etc.) Seek to validate your indicators by a third independent party such as a university or a non-governmental organization (NGO). Verify that the same information is provided international, nationally, and locally.

To raise support for the program:

•

•

Make it understandable to all that lack of sanitation means a barrier for economic development is an unsustainable way to manage the environment, is at the origin of local pollution problems, contributes to water scarcity as it reduces water availability, and increases vulnerability and reduces the capacity to adapt to climate change. All of these issues have broad support among society and different groups, not all of which are concerned by sanitation for the poor. Build community-level initiatives through government interventions aimed at scaling up best practice.

•

Create awareness of the nonplanned reuse of wastewater and the importance of investing in it as an option to make clean water accessible for any use.

4.06.10 Funding Figure 14 shows the investments made for water supply and sanitation from 1990 to 2000; it can be observed that, in the past, most efforts were orientated to water supply and cities, leaving sanitation (only about one-fourth of investments made for water supply) and rural areas far behind. Figure 15 shows the origin of investments. In the case of Asia and LatinAmerica, almost all the finances have come from governments, while, for Africa, it represented nearly a half. From the previous analysis, it is evident that there is need to invest money to catch up with the level of services needed. Before calling for funding, it is convenient to analyze (preferably only within each country, without the input of donors or enterprises) what the money should be used for. To sustainably increase sanitation coverage, economic resources are needed not only to build sanitation infrastructure, but also for planning according to local needs and possibilities, developing research and technology, and developing institutional capacity in a local context. Unfortunately, most of the time, funding is provided only for some of these activities (mostly for infrastructure); one major reason being that, often, this is the only type of funding that is sought.

4.06.10.1 Funding Options There are two funding options: public or private, each of which has different modalities. For public funding, the money comes from federal or local governments either directly from tax revenues or user charges, or, indirectly through crosssubsidies from users who can afford to pay, private-sector investment, or international and national loans. Private sector

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Latin America and The Caribbean

Asia

Africa

8

Billions USD

7 6 5 4 3 2 1 0 Urban water supply

Rural water supply

Urban sanitation

Rural sanitation

Figure 14 Investments made in billions of USD between 1990 and 2000 per region for rural and urban water supply and sanitation. Data from WHO/ UNICEF (2000) Global Water Supply and Sanitation Assessment Report, Joint Monitoring Programme for Water Supply and Sanitation. Geneva: WHO.

7 National investment

External support

6

Billion USD

5 4 3 2 1 0 Africa

Asia

Latin American and The Caribbean

Figure 15 Origin of the investments made in billion USD between 1990 and 2000 for sanitation per region. Data from WHO/UNICEF (2000) Global Water Supply and Sanitation Assessment Report, Joint Monitoring Programme for Water Supply and Sanitation. Geneva: WHO.

investments and national and international loans are to be paid from taxes, the difference is only that payments differ in time and are used simply because it is very difficult to finance sanitation projects directly from users. As a result, people who pay for the services are not always the same who will be using them. Private aid is made available by private enterprises or NGOs. Private funding is used simply because developing countries have greater needs than economic resources. The participation of private enterprise cannot be taken for granted as there are several factors that actually inhibit their participation. These include low accessibility to loans from towns and municipalities, the need to organize projects that have payback periods of 20 years, and the need to recover costs through water tariffs (Lenghton et al., 2005). Private funding includes not only international or national firms, but also self-

provision schemes provided by nonconventional private enterprise. These nonconventional private enterprises have been called by some ‘informal’ although for several developing countries, they have in many cases proven to be more formal, useful, and to provide more reliable services than formal ones. For example, in India, an NGO named Sulabh has installed 5500 pour-flush toilets that are operated on a fee-paying basis and are maintained by attendants who live at the facilities. Through providing good reliable service, Sulabh’s facilities have become a model for sustainable public sanitation services. This shows that there is growing knowledge and capacity provided by small and even family-run companies that are capable of producing significant and innovative improvements in access to sanitation. Financing strategies are specific for each country and situation and depend on the political will, the compatibility with

Safe Sanitation in Low Economic Development Areas

existing institutional arrangements, the degree of community involvement in decision making, the available economic and financial resources, and the prevailing social and cultural preferences, among other aspects. When either private or public funding is used, some key elements to make a good use of it according to Lenghton et al. (2005) are

•

• • •

Maximum scalability. The selected financing strategy needs to be one that can be scaled up quickly and in a straightforward manner to allow for rapid increases in the population served. Minimal transaction costs. Full financial accountability. Closed revenue cycle, that is, financially viable in the sense that all capital and operating costs are fully covered – either through user fees, government subsidies, or external finance.

4.06.10.2 Why Sanitation Needs to be a Public Process Sanitation is of public interest (Box 15) and hence is a public process. In order to implement what needs to be provided is, for the governments, to identify the main requirements, the areas of responsibility, the risks associated, who is responsible for what, the different options to address needs, and the associated costs. Once this is performed, it is required to review, set up or adapt the legal and institutional framework, and to educate all the persons involved (from society to politicians, experts, regulators, private companies and functionaries,

197

besides children and women). Sanitation management (basic sanitation facilities management, wastewater collection, treatment and reintegration, by-product management, and risk control) requires the coordination of different public institutions, society, academia, private enterprises, and in some cases, even different countries. Therefore, the government is needed to set up the programs.

4.06.10.3 Why Private Participation can be Involved Today, around the world, it is still mostly government agencies that construct and operate wastewater collection and treatment systems. However, private companies are contracted to conduct operations in many places, and all countries have significant commercial enterprises built around collecting excreta and septage and managing wastewater sludge and biosolids, mostly in cities. Theoretically, private companies, if well used by the government, could be useful to increase sanitation coverage if the level of society is raised and private companies are not used to increase the already-considerable differences existing between economic social classes. Nevertheless, private participation is not increasing in sanitation. After steadily increasing at a global level between 1990 and 1997, it began to decrease (Lenghton et al., 2005). There are many reasons for this, one of which is that it is not easy to build up successful schemes combining private and public interests.

Box 15 How industrialized governments approached funding for sanitation (with information from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan.) In general, in developed countries, public water infrastructure components have been highly subsidized by governments, reflecting an understanding that the public health benefits of sanitation generate substantial positive external gains that merit public investment. In Britain, for example, urban authorities borrowed more than d7.7 million for sewerage work during the period 1880–91. Eventually, the public provision of sanitation became an uncontroversial and indeed, an expected part of life. Similarly, for many municipalities in the United States, public financing of sanitation infrastructure was seen as the only option for ensuring investment adequate to protect public health. In the nineteenth century, Boston, for example, had lower-than-expected connection rates among households to the city’s new water and sewer network; this prompted the city to cover the cost of service pipes for all unconnected households. In 1850, an influential state sanitary survey concluded that governments must accept responsibility for financing public sanitation infrastructure because, left to their own devices, a large proportion of Massachusetts residents would be unable or unwilling to take on personal responsibility to conduct their lives in accord with recommended sanitary principles. Until recently, grants of up to 70% or more were provided for innovative sanitation technologies in the United States.

Table 14

Type of service and technology more suitable for private and public participation

Type

Modality

Type of service

Technology needed

Private

Public or private sector provision

Sewerage plus wastewater treatment plants

Self-provision

Septic tank systems

Low or normal volume flush water closets; house connections; sewers, biological or physicochemical treatment centralized or decentralized operated. Septic tanks; soakaway pits or absorption trenches; water closets or pour-flush toilets Squat slabs over pits or connected to offset pits

Public

Provision by public, private businesses or NGOs

Pour-flush toilets VIP latrines Nonventilated pit latrines Public latrines

Public water closets; public VIP latrines; public pour-flush toilets; public nonventilated latrines

Adapted from Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan.

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One aspect to keep in mind concerning public and private participation is that for the sanitation field, these funding options combine better with certain type of sanitation systems, characterized in terms of their size and used technology (Table 14).

4.06.10.4 Differences between Low- and Middle-Income Countries Low-income countries need to invest 10–30% of their GDP to fulfill their MDGs (Lenghton et al., 2005). For some, these are figures difficult to reach even if the use of loans is considered. For them, external donors can play an important role. Middleincome countries have fewer needs and more economical capacity to meet their MDGs. For some, it is estimated that they could use up to 15% of their GDP, and hence it is considered that no external finance is needed (Lenghton et al., 2005). Moreover, this situation, from the point of view of some authors, offers to inform the private sector of great opportunities to conduct a business and, as a result, in several middle-income countries private funding is being promoted. One possible risk, which needs to be considered by local government and known by society in general, is that through private participation and international loans, technology and sanitation schemes from other countries are promoted, which

Receipts of royalties and licence fees (USD/person) 2004 120

109.3

100 80

do not always effectively solve local problems in the cheapest and most efficient way. Another risk is the use of the money for additional purposes. To deal with this, it is important, on the one hand, for the government to be accountable and, on the other hand, for society to demand transparency. In any case, it is certain that developing countries need to be creative to raise funds for sanitation. One option is to raise them as part of other projects in which sanitation can be a component; these include those considering goals for food security, health, land remediation, environmental problems control, and adaptation to climate change, for which several donors may be available. As an example, carbon credits could be used to fund projects to manage sludge and fecal sludge.

4.06.11 Science and Innovation: Need to Develop Individual Knowledge In developed countries, a complex and complete system of public agencies, private companies, equipment vendors, consultants, scientists, engineers, operators, and supporting professional and educational organizations makes sanitation possible. Promoting this organizational and human capacity in developing countries is one of the challenges on the path to increasing adequate sanitation, wastewater reuse, and proper fecal sludge and wastewater sludge management. Science and innovation are needed in developing countries to reduce their intense dependence on developed countries. Unfortunately, in many situations, technology originating in high-income countries is still preferred and implemented. However, this may not match the actual needs or promote local Table 15 Information concerning the first three Prosab research phases (with information from Andreoli et al. (2008))

60 40

0.8

20 0

High inco

me

Middle income

Area

Number of projects

Public resources (million USD)

Water Wastewater Sludge Solid waste

12 30 16 13

2509.00 3931.00 1845.00 1548.00

Total

71

9833.00

17.3

0

Low inco

me

World

Figure 16 Receipts of royalties and license fees in countries with different income (with information from UNDP, 2006).

Box 16 Research program for sanitation in Brazil (with information from Andreoli et al., 2008, Garbossa LHP, Lupatini G, and Pegorini ES (2008) Brazil. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 131–146. Vienna: UN.). The Brazilian Sanitation Research Programme (Prosab) is a public program that has received financial support for different projects since 1996. Its goal is to develop and optimize existing technologies for water supply, wastewater treatment, and solid residues management. For that, its objectives are * *

* *

to establish the state of the art of technology; to adapt or develop technology to provide sanitation services in local and regional conditions, and to meet the different needs of all population sectors, preserving and restoring the environment; to make technology and knowledge part of the public domain; and to support participatory processes, creating cooperative research networks to discuss subjects.

The total investment for the three phases listed is around US$9 million distributed as shown in Table 15, in which investments made for salaries and scholarships are not considered. Both, research papers and technological innovation, were produced from this program.

Safe Sanitation in Low Economic Development Areas

economic development. In some other cases, developing countries are even used as laboratory testing grounds for new magic solutions. In low- and middle-income countries, examples can be found where a significant part of the investment made for wastewater treatment plants is used to pay for the intellectual property rights of the processes, as happens with many other activities. In Figure 16, it is shown that royalties received because of patents in developing countries are nonexistent or low while those for developed countries are high; sanitation could be in the future another source of this dependency and inequity. On the top of this, some of these processes do not solve actual problems and, as a result, around the world, several places can be found where new solutions for providing sanitation to poor people have been installed in series unsuccessfully. This situation has two negative effects: first, it discourages donors from making further investments and, second, it makes local people wary of possible solutions. The only way to prudently overcome this is to promote the development of technology by people immersed in local problems. For this purpose, investment in education and local research is important (Box 16 and Table 15). As presented here, the solution to sanitation problems can be combined with the solutions to other problems. The possibility therefore exists to develop new and individual technologies, to adapt the existing ones, and even to rediscover ancient local solutions. In parallel, the same can be done with policies to manage water.

4.06.12 Conclusions At an international level, there is current mobilization to support and improve sanitation conditions in developing countries. This mobilization is being expressed in terms of donors, private participation, and international aid agencies support. From this chapter, it is concluded that there are many reasons explaining why providing sanitation in developing countries is different to the solutions implemented in developed ones; therefore, care must be taken to not to use the aid to implement projects, which may prove not successful. For this reason, it is important to promote that each country defines first its needs and works defining programs. As the challenges to provide sanitation are many and very complex (policy definition, technologies to be used, education and awareness programs implementation, development of adequate institutional capacity, finding new financing options, etc.) it is important for developing countries to share among them their knowledge and experiences in the framework of the so-called South–South cooperation. Sanitation is an important pillar to develop wealthy societies (in terms of health and economic capacity) and, for this reason, governments should promote investments in this field that are to be properly and responsible managed. The only way to assure this is to promote, allow, or to demand a participatory approach. Finally, the water situation in developing countries has some bright sides. The first consists in the fact that the wide divisions observed in developed countries within the water sector (water supply and wastewater experts) does not exist or is not so pronounced. This allows easier understanding and

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promotes the integrated management of the problem. The second has to do with the high degree of solidarity existing among the population, which may play an important role in speeding up a sanitation program proven successful and contributing to raising the quality of life.

References Andreoli CV, Garbossa LHP, Lupatini G, and Pegorini ES (2008) Brazil. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 131--146. Vienna: UN. Angoua KM (2008) Cote d’Ivoire. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 269--277. Vienna: UN. Asano T (1998) Wastewater Reclamation and Reuse, Vol. 10: Water Quality Management Library. Lancaster, PA: Technomic Publishing. Asano T (2009) The role of wastewater reuse in water resources management. In: Primer Simposio Internacional del Caalca. Monterrey, Mexico, 13–14 April (in Spanish). Ba S (2008) Senegal. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 487--494. Vienna: UN. Bahri A (2008) Water reuse situation on the Middle Eastern and North African countries. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs, pp. 27--48. London: IWA Publishing. Bouwer H (2002) Artificial recharge of groundwater: Hydrogeology and engineering. Hydrogeology Journal 10: 121--142. Brissaud F and Salgot M (1994) Infiltration percolation as a tertiary treatment. In: Colloque Scientifique Et Technique International, ‘‘Mieux Gerer L0 Eau’’, pp. 391–398. Marseilles, France. Campos JR (1999) Tratamento De Esgostos Sanitarios Por Processo Anaero´bio E Disposicao Controlad No Solo, 1st edn. Sa˜o Carlos, Brazil: Prosab. (in Portuguese). CONAGUA and WWC (2006) Regional Document for the Americas Prepared for the 4th World Water Forum. Ciudad de Me´xico, Mexico, 16–22 March. Correlje AF and Schuetze T (2008) Every Drop Counts: Environmentally Sound Technologies for Urban and Domestic Water Use Efficiency. Division of Technology, Industry and Economics, TU Delft. India: United Nations Environment Programme. Dillon P and Jime´nez B (2008) Water reuse via aquifer recharge: Intentional and unintentional practices. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA Publishing. Dillon P and Toze S (eds.) (2005) Water quality improvements during aquifer storage and recovery, Project #2618, AWWARF. Dobie P (2001) Poverty and the Drylands. Nairobi: United Nations Development Programme, Drylands Development Centre. Ensink J, Mahmood T, Van der Hoek W, Raschid-Sally L, and Amerasinghe F (2004) A nationwide assessment of wastewater use in Pakistan: An obscure activity or a vitally important one? Water Policy 6: 197--206. Feachem R, Bradley D, Garelick H, and Mara D (1983) Sanitation and Disease: Health. pp. 349–356. New York, NY: Wiley. Foster S, Gardun˜o H, Tuinhof A, Kemper K, and Nanni M (2003) Urban Wastewater as Groundwater Recharge Evaluating and Managing the Risks and Benefits, GWMATE Briefing Note Series No. 12. Oxford: World Bank. Funamizu N, Huang X, Chen GH, Jiangyong H, and Visvanathan C (2008) Water reuse in Asia. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues, and Needs. London: IWA Publishing. Godfrey S, Labhasetwar P, Swami A, Parihar G, and Dwivedi H (2007) Water safety plans for grey water in tribal schools. Waterlines 25(3): 8--10. Gray JL and Sedlak DL (2003) Removal of 17-b-estradiol and 17-a-ethinyl estradiol in engineered treatment wetlands. In: International Conference on Pharmaceuticals and Endocrine Disrupters. National Ground Water Association, Minneapolis, MN, 19–21 March. Hrudey SE and Hrudey EJ (eds.) (2004) Safe Drinking Water: Lessons from Recent Outbreaks in Affluent Nations. London: IWA Publishing.

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Hughes R, Ho G, and Kuruvilla M (2006) Conventional small and decentralized wastewater systems in developing countries. In: Ujang Z and Henze M (eds.) Principles and Engineering. Lyngby, Denmark: IWA Publishing. Jekel M and Gruenheid S (2008) Indirect water reuse for human consumption in Germany – the case of Berlin. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA Publishing. Jime´nez B (2003) Health risks in aquifer recharge with recycle water. In: Aertgeerts R and Angelakis A (eds.) State of the Art Report Health Risk in Aquifer Recharge Using Reclaimed Water, pp. 54--172. Rome: WHO Regional Office for Europe. Jime´nez B (2006) Irrigation in developing countries using wastewater. International Review for Environmental Strategies 6(2): 229--250. Jime´nez B (2008a) Helminth ova control in wastewater and sludge for agricultural reuse. Water reuse new paradigm towards integrated water resources management. In: Grabow WOK (ed.) Encyclopedia of Biological, Physiological and Health Sciences, Water and Health, Vol. II. Life Support System, pp. 429--449. Oxford: EOLSS Publishers/UNESCO. Jime´nez B (2008b) Unplanned reuse of wastewater for human consumption: The Tula valley, Mexico. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA Publishing. Jime´nez B (2009a) Coming to terms with nature: Water reuse new paradigm towards integrated water resources management Encyclopedia of Biological, Physiological and Health Sciences, Water and Health, Vol. II: Life Support System, pp. 398--428. Oxford: EOLSS Publishers/UNESCO. Jime´nez B (2009b) Wastewater risks in the urban water cycle. In: Jime´nez B and Rose J (eds.) Urban Water Security: Managing Risks, p. 324. Paris: UNESCO Leiden: Taylor and Francis Group. Jime´nez B and Asano T (eds.) (2008) Water reclamation and reuse around the world. In: Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA Publishing. Jime´nez B, Austin A, Cloete E, and Phasha C (2006) Using Ecosan sludge for crop production. Water Science and Technology 5(54): 169--176. Jime´nez B and Gardun˜o H (2001) Social, political and scientific dilemmas for massive wastewater reuse in the world. In: Davis C and McGinn RE (eds.) Navigating Rough Waters: Ethical Issues in the Water Industry. American Water Works Association Jime´nez B and Wang L (2006) Sludge treatment and management. In: Ujang Z and Henze M (eds.) Municipal Wastewater Management in Developing Countries: Principles and Engineering, pp. 237--292. London: IWA Publishing. Juanico´ M and Milstein A (2004) Semi-intensive treatment plants for wastewater reuse in irrigation. Water Science and Technology 50(2): 55--60. Keraita B, Jime´nez B, and Drechsel P (2008) Extent and implications of agricultural reuse of untreated, partly treated and diluted wastewater in developing countries. Cab Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 3(58): 15. Kone´ D (2010) Making urban excreta and wastewater management contribute to cities’ economic development: A paradigm shift Water Policy, Vol. 12, No. 4, pp. 602–610. LeBlanc R, Matthews P, and Roland P (2008) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource UN-HABITAT, Vienna, 632pp. Lenghton L, Wright A, and Davis K (eds.) (2005) Health, Dignity and Development: What Will It Take? Millennium Development Goals. London: Earthscan. Mamadou SD (2008) Mali. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 413--418. Vienna: UN. Mara D (2004) Domestic Wastewater Treatment in Developing Countries. London: Earthscan. Maya C, Jime´nez B, and Schwartzbrod J (2006) Comparison of techniques for the detection of helminth ova in drinking water and wastewater. Water Environment Research 78(2): 118--124. Mfoulu N (2008) Cameroon. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 169--179. Vienna: UN. Murray C and Lo´pez A (1996) Global Health Statistics. Cambridge: Harvard University Press. Nelson K, Jime´nez B, Tchobanoglous G, and Darby J (2004) Sludge accumulation, characteristics, and pathogen inactivation in four primary waste stabilization ponds in central Mexico. Water Research 38(1): 111--127. Paskalev A (2008) Bulgaria. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 149--153. Vienna: UN.

Rusong W (2001) System consideration of eco-sanitation. In: China Proceedings of the First International Conference on Ecological Sanitation. Nanning, China, 5–8 November. Shuval HI, Adin A, Fattal B, Rawitz E, and Yekutiel P (1986) Wastewater irrigation in developing countries: Health effects and technical solutions. World Bank Technical Paper No. 51. The World Bank, Washington. Silva N, Chan M, and Bundy A (1997) Morbidity and mortality due to Ascariasis: Reestimation and sensitivity analysis of global numbers at risk. Tropical Medicine International Health 2(6): 19--28. SIWI-IMWI (2006) Water – more nutrition per drop. Towards sustainable food production and consumption patterns in a rapidly changing. In: World Stockholm International Water Institute (SIWI) and the International Water Management Institute, p. 36. Stockholm, Sweden. Smakhtin V, Carmen R, and Do¨ll P (2004) Taking into account environmental water requirements in global-scale water resources assessments. Comprehensive Assessment of Water Management in Agriculture, Research Report 2. Colombo: International Water Management Institute. Snelling W, Xiao L, Ortega-Pierres G, et al. (2007) Cryptosporidiosis in developing countries. Journal of Infection in Developing Countries 1(3): 242--256. Snyman F (2008) South Africa. Faecal sludge management. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 514--516. Vienna: UN. Spaliviero M and Carimo D (2008) Mozambique. In: LeBlanc RJ, Matthews P, and Richard RP (eds.) Global Atlas of Excreta, Wastewater Sludge, and Biosolids Management: Moving Forward the Sustainable and Welcome Uses of a Global Resource: UNHSP, pp. 431--437. Vienna: UN. UN (2003) Water for people, water for life. The United Nations World Water Development Report. Barcelona, Spain: UNESCO. UNDP (2006) United Nations Development Programme Human Development Report 2006 Beyond Scarcity: Power, Poverty and the Global Water Crisis. New York, NY: Palgrave Macmillan. UNEP (2002) International Source Book on Environmental Sound Technologies for Wastewater and Stormwater Management, United Nations Environment Programme, International Environmental Technology Centre, Osaka. pp. 319–398. London: IWA Publishing. UN-Habitat (2006) The State of the World’s Cities Report 2006/7; The Millennium Development Goals and Urban Sustainability: 30 Years of Shaping the Habitat Agenda. London: Earthscan. UN/WWAP (/WWAP, 2003) United Nations/World Water Assessment Programme. USA: UN. US-EPA (1992) Guidelines for Water Reuse. Washington, DC: Office of Wastewater Enforcement and Compliance. Van de Guchte C and Vandeweerd V (2004) Targeting sanitation. Our Planet 14(4): 19--21. Van der Merwe N, du Pisani P, Menge J, and Ko¨nig E (2008) Water reuse in Windhoek, Namibia: 40 years and still the only case of direct water reuse for human consumption. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA Publishing. WHO (1989) Guidelines of the Safe Use of Wastewater and Excreta in Agriculture and Aquaculture. Prepared by D. Mara and S. Cairncross: Geneva: WHO. WHO (2004) Guidelines For Drinking-Water Quality: Recommendations, 3rd edn., vol. 1. Hong Kong, China: WHO. WHO (2006) Guidelines for the Safe Use of Wastewater, Excreta and Greywater, Vol. 2: Wastewater Use in Agriculture. Geneva: WHO. WHO/UNICEF (2000) Global Water Supply and Sanitation Assessment Report, Joint Monitoring Programme for Water Supply and Sanitation. Geneva: WHO. WHO/UNICEF (2004) Meeting the MDG Drinking Water and Sanitation Target: A MidTerm Assessment of Progress. Geneva: WHO and UNICEF. WHO–UNICEF (2006) Meeting the MDG Drinking Water and Sanitation Target: The Urban and Rural Challenge of the Decade. Geneva: WHO and UNICEF. WHO–UNICEF (2008) Progress on Drinking Water and Sanitation: Special Focus on Sanitation. Geneva: WHO and UNICEF.

Relevant Websites http://www.windhoekcc.org.na City of Windhoek. http://earthtrends.wri.org Earth Trends: Environmental Information; Earthtrends 2009.

4.07 Source Separation and Decentralization TA Larsen and M Maurer, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Du¨bendorf, Switzerland & 2011 Elsevier B.V. All rights reserved.

4.07.1 4.07.2 4.07.2.1 4.07.2.2 4.07.2.3 4.07.2.3.1 4.07.2.3.2 4.07.2.4 4.07.2.5 4.07.2.5.1 4.07.2.5.2 4.07.2.5.3 4.07.2.5.4 4.07.2.5.5 4.07.2.6 4.07.3 4.07.3.1 4.07.3.2 4.07.3.3 4.07.3.3.1 4.07.3.3.2 4.07.3.4 4.07.3.5 4.07.3.5.1 4.07.3.5.2 4.07.3.5.3 4.07.3.5.4 4.07.3.5.5 4.07.3.6 4.07.4 4.07.4.1 4.07.4.2 4.07.4.3 4.07.4.3.1 4.07.4.3.2 4.07.4.4 4.07.4.5 4.07.4.5.1 4.07.4.5.2 4.07.4.5.3 4.07.4.5.4 4.07.4.5.5 4.07.4.6 4.07.5 4.07.5.1 4.07.5.2 4.07.5.3 4.07.5.3.1 4.07.5.3.2 4.07.5.3.3 4.07.5.4 4.07.6 References

Introduction Gray Water Production Rate and Composition of Gray Water Reuse Purposes and Regulation The Risks of Gray Water Reuse Hygienic risks The risks of organic contaminants and salts Decision Making and Public Perception of Gray Water Recycling Treatment Technologies for Gray Water No treatment Storage Physical–chemical treatment Biological treatment Disinfection and removal of micropollutants Summary Urine Production Rate and Composition of Urine Reuse Purpose and Regulation Risks Associated with Source Separation of Urine Hygienic risks The risks of organic contaminants and salts Public Perception of Urine Source Separation Treatment Technologies for Urine No treatment Storage Physical–chemical treatment Biological treatment Hygienization and removal of micropollutants Summary Feces Production Rate and Composition of Feces Reuse Purposes and Regulation The Risks of Source Separation of Feces Hygienic risks The risks of organic contaminants Decision Making and Public Perception of Source Separation of Feces Treatment Technology for Feces No treatment Storage Physical–chemical treatment Biological treatment Hygienization and removal of micropollutants Summary Combined Domestic Wastewater Production Rate and Composition of Combined Domestic Wastewater The Risks of On-Site Treatment of Combined Wastewater Two Examples of On-Site Treatment Technologies for Combined Domestic Wastewater Septic tanks Johkasous On-site uncontrolled anaerobic digestion Summary Outlook

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4.07.1 Introduction Toward the end of the 1990s, a special issue on sustainable sanitation was published by Water Science and Technology, a journal of the International Water Association (IWA). In this issue, several authors discussed the issue of source-separation technologies in the area of wastewater management (Henze, 1997). All major sources of combined wastewater were covered: industrial wastewater (Visvanathan and Hufemia, 1997), storm water (Boller, 1997), gray water (domestic wastewater without toilet waste; Jeffrey et al., 1997), urine (Hana¨us et al., 1997; Larsen and Gujer, 1997), and black water (combined toilet waste; Otterpohl et al., 1997). Whereas separate discharge of storm water and separate treatment of industrial wastewater were already well-known concepts, source separation for domestic wastewater was at that time primarily recognized as an inexpensive sanitation technology for poor, rural areas. For urban areas, only sewerbased wastewater management was considered suitable. The above-mentioned special issue challenged this conventional wisdom with the idea that source separation could potentially compete with end-of-pipe technologies for all wastewaters, even in urban areas. The main concepts behind this idea were that, on the one hand, it would be more resource efficient to treat very concentrated solutions such as urine (Larsen and Gujer, 1997) and that, on the other hand, local recycling of (gray) water would increase flexibility of the urban water management system (Jeffrey et al., 1997). Since 1997, the acceptance of resource efficiency as a leading principle for sustainable urban water management has gained ground, recently resulting in the publication of a new paradigm for sustainable wastewater management based on resource recovery (Guest et al., 2009). Additionally, a number of studies have been published showing that source separation has the potential of being more resource efficient than end-ofpipe technology, depending on the specific choice of technology (e.g., Lundin et al., 2000; Hellstro¨m et al., 2008; Remy and Jekel, 2008). Moreover, it has also been shown that the evaluation of source-separating technologies is sensitive toward stakeholder preference (Borsuk et al., 2008), and also Guest et al. (2009) emphasize the importance of stakeholders for decision making. Additionally, emerging issues such as the problem of micropollutants may call for new approaches to urban water management and, again, we and other authors have argued that this problem may be best tackled close to the source (Larsen et al., 2004; Kujawa-Roeleveld and Zeeman, 2006; Joss et al., 2008). For source-separated waste streams, two different management approaches are possible: either on-site treatment or transport to a plant for (semi)centralized treatment. Most approaches to source separation are more decentralized than typical wastewater treatment plants; however, some authors explicitly foresee transport of the different source-separated waste streams to a (semi)centralized plant (see, e.g., Oldenburg et al., 2007). Others, for example, Larsen et al. (2009), argue that at least for urine, an on-site approach may be the more productive road to take. In this chapter, we explicitly look at decentralized treatment of source-separated waste streams, but we do not limit the scope to any specific size of the system. Besides, in most cases, the technologies in

question are still so new that issues that are more fundamental are of interest than the exact scale of application. A typical argument for centralized solutions is the possibility to achieve economies of scale in treatment plants (see compilation in Maurer (2009)), ignoring the tendency of conveyance systems to show a diseconomy of scale or to be scale neutral at best (Adams et al., 1972; Maurer et al., 2009). Only where sewers are deemed too expensive are decentralized technologies considered. With technical development, decentralized technologies may become more competitive, as exemplified by the progress in membrane technology (DiGiano et al., 2004). Membranes are becoming better and cheaper, due to technological development and mass production and, at the same time, the economic costs for sewers may increase due to growing pressures, such as the effects of climate change and increasing planning uncertainty, thereby reducing the useful functional life span of the sewer infrastructure (Maurer, 2009). As discussed by Larsen and Gujer (2001), decentralized treatment options could become more attractive if treatment technology for source-separated waste streams becomes integrated into household technology instead of the prototype wastewater treatment plants that we know today. For on-site treatment of urine, for example, we have calculated that about 260–440 USD/person would be available for investment in household technology for nutrient elimination directly from urine (Maurer et al., 2005), a benchmark which seems reachable with mass-produced devices. In the following sections, we give an overview of the fields of source separation for gray water, urine, and feces. Gray water refers to combined domestic wastewater without toilet waste. Source-separated urine may or may not include flush water. Feces can be collected with or without urine, and with or without flush water. Water-diluted feces including urine are conventionally known as black water, whereas water-diluted feces without urine have been termed brown water. Additionally, we added a short section on the on-site treatment of combined domestic wastewater (gray water þ black water). We base this overview on an extensive literature review, and it is no coincidence that the literature list contains very few items prior to 1996. The field of source separation for urban wastewater management is still very immature and this is why we deliberately refrain from drawing any conclusions with respect to the suitability of the different approaches. We intend to encourage the development of the entire field and the readers must draw their own conclusions. All the sections on source-separated waste streams have the same organization: after a short introduction of the field, we look at production rate and composition, reuse purposes and regulation, risk aspects, and public perception. At the end of each section, we review the literature on the different treatment options for the waste source in question. The literature list is long and we hope that this chapter will encourage interested readers to delve into the fascinating new field of source separation for sustainable urban water management.

4.07.2 Gray Water The most obvious target for source separation is gray water (combined wastewater without toilet waste) because of its

Source Separation and Decentralization

value as an alternative to drinking water, especially for nonpotable purposes. With the combined effect of increasing water demand from a growing world population and higher incidences of drought in many areas due to climate change, alternative water sources such as gray water will obviously gain importance in the decades to come. For logistic reasons, a decentralized approach to gray water recycling for nonpotable purposes is often taken. It is attractive to avoid a second distribution net, and for water, which is not of drinking water quality, a shorter residence time in the system is favorable. This approach will however also have to be considered from a risk perspective as discussed in Section 4.07.2.3. Gray water transport to a (semi)centralized treatment plant and back again to the households as nonpotable water as suggested by Bingley (1996) is not discussed here. An alternative to the decentralized recovery of gray water would be a decentralized treatment of toilet waste only, leaving the possibilities for a centralized treatment of gray water to drinking-water quality. This would even be possible via conventional wastewater treatment, infiltration, and drinking water treatment – an approach that is today often adopted more or less deliberately with combined wastewater (Wintgens et al., 2005). Without toilet waste, this approach could appear more appealing to the public, avoiding the toilet-to-tap notion. Moreover, the problems of accumulated particulate matter in sewers caused by steadily progressing water-saving measures (see Section 4.07.2.2 for a discussion) will be much less severe if the sewers are used only for gray water transport. However, such an approach would require centralized planning and would not allow the people directly concerned by water scarcity to solve their immediate problem. In this section, we confine our discussion to the decentralized approach to gray water treatment and reuse. In most cases, water reuse is the dominating reason for decentralized gray water treatment; however, where no sewers are available, urban hygiene or environmental protection may be in the foreground (see, e.g., Carden et al., 2007). Gray water, which refers to domestic wastewater without toilet waste, is generally considered more attractive for reuse than combined wastewater, from the point of view of both esthetics and pathogenic organisms. However, there are different types of gray water. Often, gray water from bath, shower, and washbasin is termed light gray water, whereas gray water from kitchen and laundry is termed dark gray water. It is disputed which type of gray water is best suited for reuse. Whereas Christova-Boal et al. (1996) do not recommend the reuse of kitchen gray water because they consider it as highly polluted and a source of many undesirable compounds, for example cooking oil, Li et al. (2009) suggested that for better

Table 1

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treatability, kitchen gray water should always be included where biological treatment is foreseen.

4.07.2.1 Production Rate and Composition of Gray Water The amount of gray water produced greatly varies from 15 l/ person/day in rural areas of water-scarce countries such as Jordan (Halalsheh et al., 2008) to more than 100 l/person/day in many parts of Europe. Consequently, the strength of gray water also varies, even without accounting for the large differences between light and dark gray water. In different literature reviews, very different ranges of gray water concentrations are reported, often due to the fact that the type of gray water is not indicated. For instance, a thorough literature review by Eriksson et al. (2002) revealed concentrations of organic matter in gray water in the range from 13 to 8000 mgCOD l1, which is not really helpful for a characterization. A number of authors have estimated the typical daily load of organics to different types of gray water (Table 1). Since these estimates are already corrected for different water-consumption patterns, they should be more robust than the concentrations measured in different settings. However, these estimates stem from a limited number of European countries and care should be taken to extrapolate to other cultures. It is generally understood that the volume of kitchen gray water is small (e.g., 5% of total gray water (Christova-Boal et al., 1996); 20% (Almeida et al., 1999); or 30% (Friedler, 2004)), but containing a substantial part of the organic matter (expressed as chemical oxygen demand (COD); e.g., 40% (Almeida et al., 1999) or 42% (Friedler, 2004)). There are, however, authors, who see this differently. For instance, Henze and Ledin (2001) allocated 50% of gray water volume and 85% of gray water COD to kitchen gray water. According to Bester et al. (2008), household wastewater is today one of the most important sources of xenobiotics to the urban water cycle, whereas the relevance of industrial point sources is decreasing. The occurrence of xenobiotics in gray water has been documented by very few authors. Based on the information available on the composition of common Danish household products, Eriksson et al. (2002) identified at least 900 different organic chemical substances and compound groups in gray water. Based on an environmental hazard identification of 211 of these compounds, the authors categorized 66 compounds as priority pollutants; 34 of these were different types of surfactants. The remaining 700 compounds could not be evaluated. In an analytical gas chromatography–mass spectrometry (GC-MS) screening study, Eriksson et al. (2003) identified 191 different organic compounds in gray water from an apartment building in

Suggested values for typical gray water production (water and total organic matter)

Volume (l/cap/day) COD (gCOD/cap/day) N (gN/cap/day) P (gP/cap/day)

Henze and Ledin (2001)

Almeida et al. (1999)

DWA (2008)

Vinnera˚s et al. (2006a)

Average

99 54 1.9 0.47

71 63

108 47 1.0 0.50

100 52 1.4 0.52

94 54 1.4 0.5

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Copenhagen. The concentration of the compounds was semiquantitatively assessed and found to be within the range of 101–102 mg l1. Based on a questionnaire, it was possible to predict the presence of many, but far from all of these, compounds. The reasons for this were partially lack of detailed product information, and partially incomplete information from the tenants. In a search for hazardous substances, Palmquist and Hana¨us (2005) looked specifically for 81 organic substances, and found 46 of them in gray water from a Swedish housing area with separate collection of gray water. In addition, the content of microbial contamination in gray water greatly varies. Typical literature values are summarized in Table 2, and compared to typical values for combined wastewater. A more detailed discussion of microbial indicators of gray water quality can be found, for example, in Albrechtsen (2002), Winward et al. (2008a), and Gilboa and Friedler (2008).

4.07.2.2 Reuse Purposes and Regulation Reclaimed gray water is typically intended for toilet flushing, cleaning purposes, car washing, and irrigation. Toilet flushing is the typical example of indoor use, and irrigation the typical example of outdoor use, and most advantages and risks associated with decentralized reuse of gray water can be illustrated by these two applications. There are many different quality standards for recycled wastewater, with an emphasis on hygienic quality, biochemical oxygen demand (BOD), and turbidity. In Table 3, we list typical ranges of mandatory quality parameters that should not be surpassed, but for any practical purpose, one will of course have to adhere to local Table 2

regulations. Most noticeable is the large deviation between the requirements, even for the same reuse purpose. Li et al. (2009) made a suggestion for new guidelines, but without presenting any risk analysis. Examples of such risk analyses can be found in Ottoson and Stenstro¨m (2003) and Huertas et al. (2008). If gray water is only used for toilet flushing, there is a clear limit to the water savings that can be achieved. Today, it is often assumed that use of gray water for toilet flushing will save around 30% of the total drinking-water consumption. Modern toilets, however, use 6 l for a large flush and 3 l for a small flush, and even smaller flush volumes have been attempted (e.g., a 4/2-l toilet). A typical person gives rise to one large and four to five small flushes a day, which with a 6/3-l flush toilet amount to 18–21 l/person/day (see also Table 7 for an outlook on future water-saving toilets). From Table 1 it is thus obvious that in the long term only a smaller part of the total gray water production will be required for this special purpose. The requirement of water for irrigation can of course only be quantified for a specific setting. Water saving is generally considered environmentally friendly, but as shown by Parkinson et al. (2005), a reduction of water consumption in a conventional setting based on combined sewers is not without problems. Especially the high water-saving capacity of gray water reuse for flushing of oldfashioned toilets will typically lead to a higher rate of sedimentation in sewers, with higher emissions of pollutants when these sediments are mobilized during combined sewer overflow events. Furthermore, anaerobic degradation of the organic sediments may lead to methane emission and sewer corrosion. If gray water reuse (or alternatively water saving) is

Typical content of microorganisms in gray water compared to typical values in combined wastewater

Parameter

Unit

Gray water

Bathroom

Laundry

Combined wastewater

Total coliform Fecal coliform

# ml1 # ml1

105–107 101–106

100–105 ND*–103

100–104 101–102

109–1011

ND*, nondetectable in 100 ml. Based on data compiled by Henze M and Ledin A (2001) Waste and wastewater characteristics and its collection. In: Lens P, Zeeman G, and Lettinga G (eds.) Decentralised Sanitation and Reuse, Integrated Environmental Technology Series, pp. 56–72. London: IWA; Eriksson E, Auffarth K, Henze M, Ledin A (2002) Characteristics of grey wastewater. Urban Water 4(1):85–104, Ottoson J, Stenstro¨m T.A (3003) Faecal contamination of greywater and associated microbial risks. Water Research 37(3): 645–655; Friedler E, Kovalio R, Ben-Zvi A (2006) Comparative study of the microbial quality of greywater treated by three on-site treatment systems. Environmental Technology 27(6): 653–663; and Birks R and Hills S (2007) Characterisation of indicator organisms and pathogens in domestic greywater for recycling. Environmental Monitoring and Assessment 129(1–3): 61–69.

Table 3

Examples of regulation of gray water use (maximum values, unless otherwise stated)

Parameter

Unit

Domestic reuse

Toilet flushing

Irrigation

Li et al. (2009)a

Turbidity BOD Total coliform Fecal coliform Residual chlorine

NTU mg l1 # ml1 # ml1 mg l1

1–90 10–45 NDb–100 4–20 40.2 to 41

5 5–20 100–1000 0.03–10 40.2 to 41

20

2 10 100 10 Z1

a

50 0.03–10 40.2 to 40.4

Suggested guideline for unrestricted urban reuse. ND in 100 ml. Based on values compiled by Jefferson B, Laine A, Parsons S, Stephenson T, Judd S (2002) Technologies for domestic wastewater recycling. Urban Water 1(4): 285–292, Eriksson E, Auffarth K, Henze M, Ledin A (2002) Characteristics of grey wastewater. Urban Water 4(1): 85–104, Winward G.P, Avery L.M, Frazer-Williams R, et al. (2008a) A study of the microbial quality of grey water and an evaluation of treatment technologies for reuse. Ecological Engineering 32(2): 187–197, and Li F, Wichmann K, Otterpohl R (2009) Evaluation of appropriate technologies for grey water treatments and reuses. Water Science and Technology 59(2): 249–260. b

Source Separation and Decentralization

the only decentralized measure taken in a catchment, this potential effect should be analyzed.

4.07.2.3 The Risks of Gray Water Reuse Gray water is contaminated by pathogens and carries a large amount of potentially problematic micropollutants (Eriksson et al., 2002, 2003; Palmquist and Hana¨us, 2005). In order to control the risks of gray water reuse, Salgot et al. (2006) recommended an elaborate scheme of surveillance based on a consideration of analytical costs. Obviously, with decreasing size of the recycling scheme, the possibilities of monitoring decrease. Even in a centralized setting, a dual pipe system with nonpotable water can be problematic. A recent large pilot study in the Netherlands (Oesterholt et al., 2007) with production of nonpotable water from surface, rain, and groundwater led to health problems. The main problems were linked to cross-connections in the dual pipe system, drinking of tap water intended for outdoor use, and the production of aerosols during toilet flushing. It was also observed that biological instability of the water leads to growth of Legionella. As a consequence, all pilot studies were canceled and larger settings with dual pipe systems forbidden in the Netherlands. Smaller settings are still allowed, but only with rainwater or groundwater as source. Although the Dutch experience was gained in a centralized and not a decentralized setting, some conclusions may still be drawn:

• • • •

the more complex the dual system is, the larger the risk of cross-connections; outdoor taps lead to a larger risk of drinking nonpotable water than toilet flushing; biological stability to prevent growth of Legionella must be provided; and the justification of any wastewater reuse for nonpotable purposes must be carefully examined.

4.07.2.3.1 Hygienic risks Generally, fecal contamination is considered the major hygienic risk from recycling of gray water. This is reflected in the use of fecal coliforms as an indicator organism in gray water (see Table 3). Although virus may be more critical than bacteria, many authors still favor fecal coliforms as an indicator (see, e.g., Dixon et al., 1999b). For a discussion of more advanced risk models, see, for example, Ottoson and Stenstro¨m (2003) or Huertas et al. (2008). The main discussion of hygienic risks connected to decentralized gray water reuse is the question of scale. Dixon et al. (1999b) stated the importance of population size for the range of risk, and distinguished between the multiuser and single family water reuse, where for the latter the hygienic risks are obviously much lower. This is also reflected in public perception as reported by Jeffrey and Jefferson (2001): people are generally more favorable with respect to recycling their own gray water for toilet flushing than using gray water from somewhere else. The authors contrast this with a possible lower health risk from more centralized schemes, where

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authorities can control the quality of the recycled gray water. The most critical situation, however, may occur in the middle situation, as illustrated by Albrechtsen (2002): a gray water recycling scheme in a multistory building is large enough that one family with a contagious illness can put all the other families at risk, but still too small for efficient monitoring of treatment efficiency. For pathogens such as Legionella, which may proliferate in a technical system for nonpotable water, especially if the water has not been biologically treated (Oesterholt et al., 2007), the risk would however also be present at a very small scale. Although we have found no examples in the literature of problems occurring due to Legionella in an existing gray water system, gray water recycling systems offer favorable conditions for their growth (biofilms and high temperatures), which must therefore be prevented (Dixon et al., 1999b). Consequently, for single family recycling of gray water, these authors suggest focusing more on system design (e.g., treatment of gray water and prevention of biofilm) than on measuring the microbial quality.

4.07.2.3.2 The risks of organic contaminants and salts Nonpathogenic pollutants from gray water are primarily considered a potential problem for the environment, and not for public health (Oesterholt et al., 2007). The major contributions to xenobiotics in the urban water cycle stem from household and service applications, including personal care products, detergents, etc., which will be present in gray water (Bester et al., 2008). Although the anthropogenic origin of organic compounds in gray water may lead to problems, the same anthropogenic origin also holds a potential for improvements at the source. Since most substances in gray water are not related to the improvement of human health, there may be more scope for their re-engineering than in the case of pharmaceuticals (see Sections 4.07.3.3.2 and 4.07.4.3.2). Besides salt in arid climate, organic compounds leading to a major change in soil structure may be the most serious threat to the sustainability of using gray water for irrigation. Two types of organics have been shown to increase water repellency of soils: surfactants, with a hydrophobic and a hydrophilic end (Wiel-Shafran et al., 2006), and oil and grease, which are strongly hydrophobic compounds (Travis et al., 2008). The former compounds originate mainly from bathroom and laundry gray water, whereas oil and grease are mainly found in kitchen gray water. In general, salts could be a major problem, as well as specific toxic inorganic compounds such as boron (Gross et al., 2005). Whereas the general problem of salt can only be solved by reducing the amount of salt-containing gray water used for irrigation or by separating salt and water by reverse osmosis (RO) or evaporation, a specific problem such as boron plant toxicity may be solved at the source. In Israel, for instance, the concentration of boron in detergents has been limited by regulation (Gross et al., 2005). For decentralized use of gray water for irrigation in the own garden, the household itself has a certain amount of control over the contaminations in the gray water. As discussed in Section 4.07.2.5.1, this may however not be of much use, if the gray water is not treated. Moreover, the risks are rather long term, which may be less important to a household that

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has to optimize its living on a short-term basis. Furthermore, through leakage of micropollutants to groundwater, the risks may spread to the larger community.

4.07.2.4 Decision Making and Public Perception of Gray Water Recycling Decision making in the area of decentralized gray water recycling is not easy. Scha¨fer and Beder (2006) analyzed the complexity of the issue within the context of the precautionary principle and discussed a number of uncertainties concerning human health, environmental quality, technology, politics, and socioeconomic issues. As a consequence of these uncertainties, the authors conclude that a precautionary approach requires transparency and public participation. However, only little work on public participation in the planning of gray water reuse has been internationally published. Based on a survey in the Melbourne area, ChristovaBoal et al. (1996) reported that people are generally interested in reusing gray water from the bathroom and from laundry, with a preference for using the recuperated water in the garden. However, people are interested to invest in gray water recycling only if the payback period is very short (2–4 years). Similarly, Neal (1996) found a very high approval rate of using gray water for irrigation (79% total support, 17% support a little, and nobody against). As discussed above, Jeffrey and Jefferson (2001) found that people prefer recycling their own gray water for toilet flushing rather than using gray water from somewhere else. In a hotel in Spain, however, guests were generally satisfied with using recycled gray water for toilet flushing, possibly due to the acute water scarcity experienced in the area (March et al., 2004). In a survey in Oman involving 1365 people, Jamrah et al. (2008) reported that 76% of the respondents accept recuperated gray water for irrigation, 66% for toilet flushing, and 53% for car washing. From a larger study in Australia, where the different perception of desalinated and recuperated gray water was compared, Dolnicar and Scha¨fer (2009) concluded that people generally accept recycled gray water as the more environmentally friendly option; however, from the point of view of public health, they consider desalinated water the safer alternative. People are also generally more disgusted about the idea of recycled gray water, despite an assumption of identical water quality. However, desalinated water is not generally preferred over recycled gray water, only for close-to-the-body applications. For irrigation, for instance, recycled gray water is preferred. This is in accordance with the older study by Christova-Boal et al. (1996) cited above.

4.07.2.5 Treatment Technologies for Gray Water From the above discussion, it is clear that gray water recycling will generally demand some sort of treatment, and, in this chapter, we briefly review the possible technologies. Most of them are well known from mainstream wastewater treatment and, for a more general introduction to the technologies, we refer to the standard wastewater treatment literature. As compared to treatment of urine and feces, the technology applications are very close to conventional treatment of combined wastewater.

The question preceding any discussion on treatment options is: What are the objectives of the treatment? For reuse of gray water, the treatment objectives are basically different from the aims of wastewater treatment. Whereas the question of receiving water quality dominates the discussion of conventional wastewater treatment, the risk in connection with reuse is the dominating issue of gray water treatment. The purpose of reuse determines the quality criteria; however, in nearly all cases, hygienic quality is of high importance. Since some storage and transport of the treated gray water before reuse will nearly always be necessary, the stability of the gray water is thus essential. Furthermore, for irrigation purposes, the risk of not only pathogens but also organic compounds is important (Section 4.07.2.3.2). The quality parameters are highly disputed, but we refer to the original literature for a discussion. The most important uncertainties are connected to the choice of indicator organisms for pathogens (see Section 4.07.2.3.1) and the potential risks of xenobiotics and surfactants (see Section 4.07.2.3.2). Furthermore, the possibilities of quality monitoring in decentralized settings are very limited, and robustness of treatment will therefore be one of the most important criteria for technology choice.

4.07.2.5.1 No treatment In many cases, gray water (or even combined wastewater) is reused without any treatment at all. Although this is not to be recommended, we have found one apparently successful example of intended gray water reuse without any treatment at all. This is the so-called hand basin toilet, where a small washbasin is placed above the flush water reservoir of the toilet (commercially available). The water used for hand washing thus flows directly into the reservoir. Apparently, the short transport and residence time prevent the buildup of odor, and the very local recycling without any risk of crossconnections limits the risk of distributing pathogens. We have found no information on the risk of Legionella, which could, in principle, grow on surfaces in the reservoir. The hand basin, however, is used in Australia, where drinking water is typically chlorinated; in countries without chlorination, problems even with this simple system could be larger. In all other cases, reuse of untreated gray water must be assumed to hold very large risks. Pathogens are a risk for all applications and, for irrigation, surfactants, oil, and grease represent a major risk to soil structure (see Section 4.07.2.3.2). It is interesting to note that some authors (e.g., Krishnan et al., 2008) assume that gray water from kitchen and laundry (dark gray water) is unlikely to cause severe pollution, because it is easily degraded in nature. For irrigation purposes, however, even degradability may not avoid problems. As shown by Gross et al. (2005) and Wiel-Shafran et al. (2006), surfactants may adsorb to soil particles and be resistant to biological degradation.

4.07.2.5.2 Storage If gray water is to be reused within the general water cycle of the household, some sort of storage will be necessary in order to balance production and demand. Based on experimental evidence and modeling, Dixon et al. (1999a) suggested that storage before treatment would be beneficial because

Source Separation and Decentralization

sedimentation in the storage tank would lead to a reduction of the organic load to the treatment stage. Depletion of oxygen and development of unpleasant odors due to anaerobic degradation of organic matter in the sediments would have to be counteracted through some sort of aeration, and, obviously, sludge should not be allowed to accumulate in the storage tank. Nolde (2000) presented an example of such a storage tank, but for practical reasons also recommended storage after treatment. One main purpose of the treatment technologies discussed subsequently is to stabilize the treated water for storage and prevent regrowth of microorganisms in the storage tank.

4.07.2.5.3 Physical–chemical treatment Although several authors state that for on-site treatment, only biological treatment will be able to stabilize gray water in order to prevent regrowth (e.g., Jefferson et al., 2000) and produce a stable sludge that will not give rise to odors (e.g., Abu Ghunmi et al., 2008), there are many attempts to treat gray water with physical–chemical methods, also in decentralized settings. The resulting gray water is stabilized by disinfection. From a process engineering point of view, there are good reasons for selecting a physical–chemical treatment method in a decentralized setting. The obvious advantage of nonbiological technologies as compared to biological technologies for on-site application is the higher resistance toward toxic chemicals and long absences. Moreover, the highly variable organic loads experienced in gray water, the large amount of nondegradable COD, and the shorter hydraulic residence times (HRTs) that can be obtained in a chemical– physical system would favor physical–chemical treatment (Rivero et al., 2006). Physical treatment. In a recent review, Li et al. (2009) compared the effectiveness of different physical treatments (different types of filtration, mainly sand and membrane filtration) and concluded that physical treatment alone is not sufficient to reach good gray water quality, except perhaps for very low loaded gray waters. It should, however, be noted that, for example, for toilet flushing, the amount of light gray water produced in a household would be sufficient (see Section 4.07.2.2). Nghiem et al. (2006) considered the use of on-site ultrafiltration (UF) for gray water recycling promising and also Friedler et al. (2008a, 2008b) suggested UF either alone or followed by RO as an attractive technology for decentralized gray water treatment. Direct membrane filtration takes up little space and delivers water of excellent quality, despite large fluctuations of gray water production rate and quality. The negative aspects of direct membrane filtration are connected to organic and biological fouling and inorganic scaling of the membranes, leading to rapid decline of flux. There are different ways of responding to this challenge. Friedler et al. (2008a, 2008b) tested two types of pretreatment: chlorination or coagulation with ferric chloride. Coagulation was found to be the better treatment method for reducing fouling in the UF membrane, and, in contrast to chlorination, it did not increase inorganic scaling of the RO membrane. Oschmann et al. (2005) and Nghiem et al. (2006), in contrast, investigated in detail the mechanisms leading to fouling in membrane

209

reactors treating gray water. Especially calcium in combination with humic acids and particulate matter seems to play a major role. Unfortunately, typical drinking-water calcium concentrations around 0.5 mM present the worst case for fouling, whereas at concentrations above 3 mM no fouling was observed. As a consequence, Nghiem and Scha¨fer (2006b) suggested pretreatment to reduce the amount of particulate matter (similar to the approach taken by Friedler et al. (2008a, 2008b)) and/or chemical cleaning and backwashing to control fouling. Nghiem et al. (2006) showed that ordinary household bleach would probably be suitable for chemical control, which would be favorable for decentralized applications. We have found no recommendations of the simpler filter systems such as sand filtration as the sole treatment method for gray water. On the contrary, a number of authors discourage such applications due to their low efficiency with respect to removal of organic matter and microorganisms (e.g., Li et al., 2009). Chemical treatment. With respect to the chemical treatment systems, Li et al. (2009) reviewed work on coagulation, ion exchange, granular activated carbon, and photocatalytic oxidation. Not much work has been performed in this area, and it is therefore difficult to judge the potential of these technologies. It seems that coagulation and ion exchange are best suited for low-strength gray water (Lin et al., 2005; Pidou et al., 2008), whereas photocatalytic oxidation combined with microfiltration also delivers good results for higher-strength gray water (Rivero et al., 2006; Li et al., 2009). Like in the case of UF, however, solving the problems of membrane fouling is essential for achieving economic operation. Physical–chemical pre- and posttreatment. Sedimentation is a common pretreatment step for any gray water treatment. It occurs naturally in storage tanks if gray water is stored before treatment (see Section 4.07.2.5.2). Other common pretreatments are coagulation or filtration through sand or soil (see, e.g., Gross et al., 2007a, 2007b). The main object of pretreatment is a rapid removal of organic matter in order to reduce the load to a biological reactor or prevent fouling of membranes. Posttreatment is a polishing step intended to remove organics and microorganisms, which were not removed in the main treatment step – examples include sand filters (Friedler et al., 2006), solar photocatalytic oxidation (Gulyas et al., 2005), and RO (Friedler et al., 2008a, 2008b).

4.07.2.5.4 Biological treatment Biological treatment is highly favored for gray water treatment because it leads to stabilization of the organic material and thereby lowers the risk of microbial regrowth during storage. In a more centralized, nonbiological production plant of nonpotable water from mainly surface water, Oesterholt et al. (2007) reported on regrowth of Legionalla, which would of course be a serious health risk also in decentralized settings. For the effectiveness of biological treatment, the source of gray water is highly important. Degradability and nutrient availability depend primarily on the sources of gray water with kitchen gray water as the primary source of BOD (Friedler, 2004) and nutrients (Li et al., 2009). One of the main questions

210

Source Separation and Decentralization

when evaluating biological degradability of gray water is the question whether the organic compounds are not a priori biodegradable, or whether nutrients limit degradability. Obviously, the nutrient requirements for degradation of organic matter will depend on sludge production and, consequently, on the solids retention time (SRT) in the biological reactor. The longer the SRT, the lower the sludge production and, as a result, the lower the nutrient requirements will be (see any standard textbook on biological treatment of wastewater). Equally, anaerobic treatment will require fewer nutrients than aerobic treatment, due to the lower yield coefficient of anaerobic organisms. Jefferson et al. (2001) cited literature values of required COD:N:P ratios of 100:20:1, 250:7:1, and 100:10:1 for aerobic treatment. For nutrient-deficient light gray water (from the bathroom), Jefferson et al. (2001) found that balancing the COD:N:P ratio certainly increased biodegradability, whereas the effect of adding micronutrients was more complex to evaluate. Krishnan et al. (2008) dealt with nutrient-deficient dark gray water (from kitchen and laundry; in a Malaysian setting) with a surprisingly high COD:N:P ratio of 100:1.82:0.76, and found a clear improvement of biodegradability after balancing the nutrients. The optimal COD:N:P ratio was determined to be 100:5:1 for an SRT of 13 days. Obviously, one has to be careful when comparing gray water from different cultural settings. Whereas European authors consider kitchen gray water to be rich in nutrients (see, e.g., Li et al., 2009), this may not be true in other cultures. Aerobic treatment. Aerobic treatment is generally considered the most promising technology for treatment of gray water for reuse, because it stabilizes the gray water with respect to organic matter. Typical reactors for decentralized treatment of gray water include biofilm reactors, sequencing batch reactors, and membrane bioreactors (MBRs). A special case is the recycled vertical flow bioreactor, a combination of a small wetland with a trickling filter, presented by Gross et al. (2007a, 2007b). In principle, all biological reactors can be used, provided it is possible to run them in a decentralized setting. It is no coincidence that we have found no examples in the literature of conventional activated sludge reactors, which are less suitable for decentralized settings. In older (Jefferson et al., 2000) as well as more recent reviews (Li et al., 2009), the MBR is consistently highlighted as the most successful technology for gray water treatment, not only as compared to other aerobic reactors, but also as compared to nonbiological treatments. The reasons are obvious: MBRs combine the advantages of biological treatment (stabilization of organic matter) with the advantages of membrane filtration (removal of suspended matter and microorganisms). The disadvantages are costs, energy consumption, and fouling. At a total water price of 1.46 USD m3, Friedler and Hadari (2006) showed that MBRs were only economically feasible in very large buildings (larger than 160 flats). In comparison to the more conventional technologies, however, there is still room for technical improvement of MBRs. It is, of course, also possible to add a filtration unit after a biological treatment, for example, a sand filter or a membrane. For all biological technologies, with the possible exception of the MBR, a subsequent disinfection step is necessary to render the water safe from a hygienic point of view (Jefferson et al., 2000).

Constructed wetlands are more natural systems, and will normally not be termed reactors. For water reuse, they offer some advantages. First of all, they are effective, relatively cheap, and have low environmental impact (Memon et al., 2007). Furthermore, for outdoor applications, there is virtually no risk of cross-connections with drinking-water pipes. However, they are space intensive, and thus in competition with other space-consuming activities. For areas with little available space, the green roof water recycling system has been developed (Frazer-Williams et al., 2008). Constructed wetlands are more advantageous in warmer climates, where biological activity can be maintained over the entire year. In very cold climates, they may be possible, but at the expense of very high HRTs. Gu¨nther (2000), for instance, described a constructed wetland in Sweden with an HRT of 1 year in order to bridge the winter, whereas Dallas et al. (2004) presented a case study of a reed bed in Costa Rica with an HRT of 7.9 days that achieved sufficient reduction of pathogens. Without getting into a detailed discussion of wetland technology for gray water treatment (that can be found elsewhere, see, e.g., Frazer-Williams, 2007), the latter example from Costa Rica presents an interesting concept for out-contracting of the maintenance of the reed bed and a very impressive cost reduction by replacing the expensive crushed rock carrier material with a waste product (used polyethylene terephthalate bottles). Anaerobic treatment. Anaerobic treatment of municipal wastewater is a relatively new area. Typically, one would consider this type of treatment only for very concentrated wastewater flows. However, as discussed in Section 4.07.2.1, in some countries with a very low per capita gray water production, gray water is a high-strength wastewater with concentrations of several thousand gCOD m3. Equally problematic, however, is the low degradability of many gray water organic compounds under anaerobic conditions, including some surfactants. In a recent review on gray water treatment, Li et al. (2009), therefore, concluded that anaerobic treatment is not suitable for gray water treatment. Leal et al. (2007) hypothesized that either inhibitory substances or a lack of trace elements may be responsible for poor COD removal from gray water under anaerobic conditions and suggested to look into the potential of a combined anaerobic and aerobic treatment. The problem of methane emissions from anaerobic treatment is discussed in Section 4.07.4.

4.07.2.5.5 Disinfection and removal of micropollutants Depending on the strength of the gray water, disinfection is normally only considered as a last step of treatment, because of the interference of the disinfection method with organic compounds. As described in Section 4.07.2.3.1, the risk from pathogens in gray water is high, and in many cases, disinfection is a necessary last step of a treatment sequence (see, e.g., Nolde, 2000). Disinfection. Chlorination with hypochlorite is a common method for disinfection of wastewater; besides, for reuse of gray water, chlorine is the most prevalent disinfectant (Winward et al., 2008b). Chlorination is generally considered effective and residual products prevent regrowth of microorganisms (March et al., 2005). In many cases, a certain

Source Separation and Decentralization

residual concentration of chlorine is required by regulation (Table 3). A main problem of the decentralized use of chlorine is the variable production and quality of gray water, leading to a variation in the chlorine demand (March et al., 2002). If too much hypochlorite is dosed, this is wasteful and leads to the typical odor of chlorinated water, whereas if too little is used, the disinfection purpose is not reached. Furthermore, many viruses and protozoans are resistant to chlorination, and in a decentralized setting it may be inconvenient for households to add the necessary chemicals (Fenner and Komvuschara, 2005). Chlorination also gives rise to toxic by-products, which are unwanted (March et al., 2004). The most critical issue of chlorination is the lack of effect on particle-associated microorganisms. Winward et al. (2008b) showed that such microorganisms were resistant to chlorination and thus concluded that removal of larger aggregates of microorganisms is essential when chlorination is used for disinfection of gray water. Ultraviolet (UV) disinfection would be a more elegant and potentially cheaper method of providing hygienic safety in decentralized settings, without any addition of chemicals. However, also for UV disinfection, shielding of microorganisms by particles occurs. Lack of disinfecting residuals may be considered a drawback of this technology, although Nolde (2005) found evidence that for properly treated gray water, this is not problematic. Fenner and Komvuschara (2005) developed and verified a theoretical model of UV disinfection of gray water and concluded that the practical limits of gray water disinfection (defined as a log 4 reduction of fecal coliforms) are found at a concentration of suspended solids above 60 mg l1 and a turbidity of 125 NTU. For biologically treated gray water with a turbidity of 1.5 NTU, Gilboa and Friedler (2008) found total removal of a number of pathogens at a UV dose of 69 mJ cm2, but they also found heterotrophic bacteria, which apparently were resistant even to high UV doses of 439 mJ cm2. These bacteria showed regrowth within 6 h of UV treatment. Winward et al. (2008d) showed that larger particles shield the microorganisms more from UV light than smaller particles and suggested the combination of biological treatment with filtration in order to improve the efficiency of UV disinfection. A well-accepted method of disinfection in households would be the use of plant essential oils, and an obvious application would be the use as a regrowth inhibitor after UV treatment. Although origanum oil has proved effective for this purpose in well-treated gray water (turbidity 2 NTU, total suspended solids o1 mg l1), the amount of oil required seems to make this approach too expensive (Winward et al., 2008c). Removal of micropollutants. Removal of micropollutants from gray water in decentralized settings is not a main focus of research. Obviously, the same methods as used for centralized treatment (e.g., chemical oxidation, activated carbon, and membrane processes; see Bolong et al. (2009) for a recent review) could also be used for gray water recycling. Micropollutants can be oxidized with chlorine, chlorine dioxide, ozone, and other chemical oxidants (Lee et al., 2008). Andersen et al. (2007) investigated the oxidation of parabens by chlorine dioxide in biologically treated gray water. Since already the biological treatment removed more than 97% of

211

the parabens in the gray water, the effluent was spiked with 5 or 10 mg l1 of parabens. The result of the chemical oxidation was good (498% removal of parabens), but there is no discussion of interference with organic matter, should this technology be used in gray water that has not been biologically treated. A general discussion of the removal of micropollutants from wastewater by membrane processes is found, for example, in Kim et al. (2007). Scha¨fer et al. (2006) have looked specifically at retention of biphenyl A in the process of direct UF of gray water in a decentralized setting. It appears, however, that the major effect of biphenyl A retention in these experiments was adsorption onto the membrane and organic matter. The problem of resuspension of micropollutants during backwash of the membrane was looked into by Nghiem and Scha¨fer (2006a).

4.07.2.6 Summary The main purpose of decentralized gray water treatment is the reuse of water within the household or for irrigation. The most severe problem of gray water reuse in the household is linked to hygiene, whereas the problem of irrigation is mainly surfactants, which may render soils water repellent. Biological, especially MBR, technology is the gold standard of gray water treatment, because of the stabilizing effect on the treated water. In order to prevent bacterial regrowth and Legionella, chlorine is often used for disinfection. However, the right choice of treatment technology and disinfection method is largely disputed as discussed in this section. A main nontechnical issue is the lack of acceptance for gray water reuse, especially for close-to-body applications. The most-accepted indoor use of recycled gray water for toilet flushing may be out-competed by water-saving toilets, and more general reuse of gray water may be necessary in areas with severe water scarcity.

4.07.3 Urine In this chapter, we define urine very broadly, as fresh or stored urine, and with or without flush water. In domestic wastewater, around 80% of the nitrogen and 50% of the phosphorus stem from urine (DWA, 2008). Separating these nutrients efficiently at the source would result in a fairly balanced C:N:P ratio at the treatment plant and thus eliminate the need of advanced nutrient elimination (Larsen and Gujer, 1996; Wilsenach and van Loosdrecht, 2006). Furthermore, where treatment plants do not exist and nutrient removal is required for water pollution control (e.g., due to eutrophication of coastal areas), urine source separation would be the technology of choice (Larsen et al., 2007). Compared to a typical denitrifying treatment plant with a nitrogen-removing capacity of 50–60%, urine separation is attractive. At the same time, the nutrients in urine could be used beneficially in agriculture as a fertilizer. This is favorable especially in view of the limited phosphorus resources (see, e.g., Cordell et al., 2009), and where farmers consider commercial fertilizers too expensive (Medilanski et al., 2006).

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Urine also contains a good part of the micropollutants in wastewater stemming from the human metabolism (Lienert et al., 2007a), and separating and treating it at the source could be an efficient way of reducing the load of micropollutants to the aquatic environment (Larsen et al., 2004). Some urineseparating toilets also give rise to water saving and can therefore be seen as an alternative to gray water recycling for toilet flushing. Source separation of urine comes in two fundamentally different technical variations. The more important variation from a quantitative point of view is the traditional urineseparating dry toilet. A newer version of this type of toilet has been available for at least 30 years (Winblad, 1994), and drastically improves the management of dry feces. It is installed in large numbers in many areas without access to flush toilets, for instance, in China, where nearly 700 000 such toilets were installed by 2003 (Kva¨rnstro¨m et al., 2006; Figure 1). The European type of urine-separating toilet, often termed the NoMix toilet, was invented in Sweden in the early 1990s (Hellstro¨m and Johansson, 1999). It has been installed in very large numbers in pilot projects in Sweden, mainly motivated by the depletion of the phosphorus resources. The NoMix flush toilet (Figure 2) has also been used in most of the other European pilot projects and in recent years also in some Swedish municipalities, where the technology is subsidized for environmental reasons (Kva¨rnstro¨m et al., 2006). Obviously, urine is collected undiluted in the dry toilets, whereas it may be more or less diluted when collected in a flush toilet. Different NoMix flush toilets are available in the market. The main problems of these toilets are linked to clogging of pipes, as reported by Hellstro¨m and Johansson (1999) and Udert et al. (2003c). For a comprehensive discussion of the rationale for introducing urine source separation, the reader may refer to Larsen et al. (2001), Lienert and Larsen (2007), and Larsen et al. (2009).

Figure 1 A dry urine-separating toilet of the type that is often installed in China. & Edi Medilanski 2008, Eawag.

4.07.3.1 Production Rate and Composition of Urine Based on several years of international experience, new data for the production rate and composition of urine have recently become available (DWA, 2008; Vinnera˚s et al., 2006a). These are reported in Table 4 and compared to literature data for fresh urine compiled by Udert et al. (2006). As compared to the data for gray water, it is striking how close these values are. However, it should be noted that the data stem from European countries and, that in an international context, they are much more variable. For example, van Drecht et al. (2003) predicted a variation in human nitrogen excretion of a factor of 4, depending on diet.

4.07.3.2 Reuse Purpose and Regulation In contrast to gray water recycling, we will look at urine source separation not only from the point of view of (nutrient) reuse, but also from the point of view of treatment for water pollution control. Whereas treatment of gray water and conventional combined wastewater only differs very slightly, treatment of urine is closer to the treatment of concentrated streams such as supernatant from sludge treatment or industrial wastewater. To our knowledge, however, currently no regulations for this purpose exist. From a resource point of view, it would be of advantage to reuse the nutrients in urine. This is especially true for phosphorus, normally produced from phosphate rock, which is a limited resource. Many authors discuss the availability of phosphate (e.g., Driver et al., 1999; Zapata and Roy, 2004), and depending on the assumptions, known reserves will meet the requirements for the next 50–300 years. However, of more immediate concern may be the quality of the available phosphate rock, especially their content of cadmium (Driver et al., 1999; Smil, 2000; Isherwood, 2000). For nitrogen, the main

Figure 2 A typical flush urine-separating (NoMix) toilet. & Ruedi Keller 2008, Eawag.

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Table 4 Suggested values for typical urine production (volume, nitrogen and phosphorus) compared to literature data on fresh urine compiled by Udert et al. (2006)

Volume (l/cap/day) Organic matter (gCOD/cap/day) Nitrogen (gN/cap/day) Phosphorus (gP/cap/day) Potassium (gK/cap/day)

Udert et al. (2006)

DWA (2008)

Vinnera˚s et al. (2006a)

Values used in this work

1.3 13 12 0.93 2.8

1.4 10 10.4 1.0 2.5

1.5

1.4 10 11 1 2.7

concern is energy and price, since nitrogen is abundantly available in the atmosphere. Maurer et al. (2003) discussed the energy issues in detail, and showed that it is possible, but challenging, to compete with industrial ammonia production. The reuse of urine in agriculture is in many European countries basically forbidden, because it is not specifically allowed, for example in Austria (Starkl et al., 2007) and in Switzerland (Pronk et al., 2007). One exception is Sweden, where urine from many pilot projects or even from full-scale urine source separation in rural areas is applied to agricultural fields without previous treatment, but after storage of several months (Kva¨rnstro¨m et al., 2006). In Switzerland, a temporal permission to use treated urine for research purposes could be obtained, under the condition that the product would be hygienically safe and free from micropollutants (Pronk et al., 2007).

4.07.3.3 Risks Associated with Source Separation of Urine Risks of urine source separation are normally considered only when urine is used as a fertilizer in agriculture. As for gray water reuse, the risks are associated with hygiene and other organic and inorganic contaminants in urine. However, for the handling of source-separated urine in the household, other issues may be of importance. Due to the risk of explosion, for example, the production of dry ammonium nitrate calls for a proper risk analysis before being introduced at the household level, and since ammonia is toxic, proper ventilation is essential (Udert et al., 2006).

4.07.3.3.1 Hygienic risks Normally, urine is considered sterile, when it leaves the human body, but certain diseases do lead to excretion of pathogenic organisms (Santos et al., 2004; Vanchiere et al., 2005) and prions (Reichl et al., 2002) via urine. Of more practical importance, however, is the fecal contamination occurring at the toilet. In one system of urine source separation, Scho¨nning et al. (2002) found that urine was contaminated with approximately 9 mg feces per liter. A detailed risk assessment is found in Ho¨glund et al. (2002b), concluding that virus must be considered the most critical parameter. It is very difficult to estimate typical concentrations of microorganisms in source-separated urine, since, for instance, the typical indicator organism Escherichia coli rapidly dies of in a urine storage tank (Ho¨glund et al., 2000). From the limited information that is available in literature, Enterococci seem to be present at the highest levels, reaching in one case a level of 105 ml1 at the bottom of the urine storage tank (where particles and microorganisms accumulate; Ho¨glund et al., 2000).

11 1.0 2.7

4.07.3.3.2 The risks of organic contaminants and salts As compared to gray water, urine is much less problematic in agriculture. Due to the proximity to animal urine, it must be taken for granted that urine will normally not cause any damage to soil structure. However, it is unclear whether salt could be a problem under very dry conditions (Lienert and Larsen, 2007). It is generally recognized that urine contains a significant amount of excreted micropollutants. Based on an extensive literature study, Lienert et al. (2007a) found that on average two-thirds of the micropollutants from the human metabolism are excreted via urine, and one-third via feces. However, due to the more lipophilic character of the compounds excreted in feces, Lienert et al. (2007b) estimated that the ecotoxic potential from these two excretion pathways is about equal. In addition, Winker et al. (2008) concluded on the basis of a literature review that the excretion of micropollutants via urine is relevant. Since pharmaceuticals are produced with the explicit purpose of biological action, source control is generally difficult.

4.07.3.4 Public Perception of Urine Source Separation It is frequently argued that people will never accept urine source separation, because they have no personal interest in changing a well-functioning system. However, many research results show the opposite: most people actually find the idea interesting and support it, provided there would be environmental advantages of changing the system. Currently available NoMix toilets are acceptable to most users if they are not responsible for maintenance themselves (e.g., in public buildings), and many people can even envisage living in a home equipped with NoMix toilets (Lienert and Larsen, 2006). In a comparison of pilot projects on urine separation in different European countries, urine source separation appeared to be generally well accepted, although blockages, smell, or more time-consuming cleaning were unpleasant for the users (Lienert and Larsen, 2010). Based on interviews in a housing estate in Austria equipped with NoMix toilets, Starkl et al. (2007) reported that about half of the tenants would prefer to change back to a normal toilet again, due to these problems. However, in view of the present state of technology, it is more surprising that half of the tenants would actually prefer to keep these toilets. To be widely accepted, it is obvious that NoMix toilets must evolve to approximately the same comfort standard as experienced with other modern toilets. In addition, the use of a urine-based fertilizer in agriculture is generally seen positively by the public (Pahl-Wostl et al., 2003; Lienert and Larsen, 2010), as well as by farmers (Lienert et al., 2003). However, for the recycling to agriculture, the risk

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aspect dominates and, for the farmers, obviously convenience and costs are equally important.

4.07.3.5 Treatment Technologies for Urine As compared to gray water, the goals of urine treatment are more diverse, and, for reuse, nutrients and not water are of interest. Like in the case of gray water, stabilization, hygienic quality, and removal of any undesirable organic matter are important, and also the concentration of one or more of the important nutrients in a smaller volume of water or as dry matter could be an important goal (Maurer et al., 2006). Moreover, nutrient removal for water pollution control can be a goal if no reuse is possible or desired.

adding a urease inhibitor. To our knowledge, there has been no attempt to keep urine sterile, but urease has been successfully inhibited. Specific urease inhibitors are available, but they have low efficiencies and negative side effects (Benini et al., 1999). Immediate addition of a strong acid, however, can keep pH below 4 for more than 250 days by inhibiting urea hydrolysis and at the same time improving the inactivation of pathogens (Hellstro¨m et al., 1999). Reducing pH after hydrolysis of urea can also be achieved by acid, but about 4 times more acid would be required. An alternative would be partial biological nitrification, discussed in Section 4.07.3.5.4.

4.07.3.5.3 Physical–chemical treatment 4.07.3.5.1 No treatment The traditional use of source-separated urine is the direct use of fresh urine in agriculture, and, even today, this is common practice in some areas (Kva¨rnstro¨m et al., 2006). From the point of view of hygiene, this is only advisable if special care is taken to avoid contamination of eatable products. Much more common, however, is the infiltration of source-separated urine from dry toilets (Anonymous, 2005), which obviously is not a good idea from the point of view of environmental protection. The motivation for this practice is the improved stabilization of feces that is obtained in the urine-separating dry toilets.

4.07.3.5.2 Storage The effect of storage on the chemical composition of urine has been discussed in detail by Udert et al. (2006). Storage is the only process that has been tested in depth for its ability to reduce potential health risks from microbial contamination of urine. A large number of experiments on the inactivation rates of selected bacteria and virus in source-separated urine were conducted by Ho¨glund et al. (1998, 1999, 2000, 2002a, 2002b). It was shown that three parameters determine the success of the process: temperature, storage time, and pH. Temperature was the most important parameter: whereas storage for 35 days at 20 1C and a pH larger than 9 removed 90% of the activity of rhesus rotavirus, no significant decrease in activity was observed at 4 1C. Based on this broad experience, Ho¨glund et al. (2002b) concluded that a storage time of at least 6 months at 20 1C without pH control would be sufficient to produce a safe fertilizer. Storage of urine, however, may also have negative effects. Precipitation of phosphorus will result in the accumulation of a nutrient-rich sludge at the bottom of the storage tank (Udert et al., 2003c), which is at the same time the most critical part of urine from a hygienic point of view (Ho¨glund et al., 2000). Perhaps more seriously, due to the high pH value in the tank, ammonia may evaporate from tanks through ventilation (Udert et al., 2006; Rossi et al., 2009). Stabilizing urine for storage would therefore be of advantage. Since precipitation of phosphorus and evaporation of ammonia are both due to a high pH, lowering the pH in the urine solution is the most important measure suggested. There are basically two possibilities: either preventing a pH increase through inhibition of urease activity or subsequently reducing pH. Preventing a pH increase would be possible by keeping the solution sterile (e.g., by applying a membrane process) or by

Apart from just spreading urine as a liquid on the fields, the simplest imaginable recovery method for the nutrients in urine would be concentration by evaporation. However, evaporation of urine is limited by two main factors: ammonia volatilization and energy consumption. Loss of ammonia only occurs at a high pH and the same pH-reducing strategies as for storage could be adopted (see Section 4.07.3.5.2). Energy recovery or energy cascading, for example, recovering the energy in the vapor for heating warm water in the household, can reduce energy consumption. Energy recovery in decentralized settings is of course a challenge. However, a small-scale vapor compression distillation system developed for space applications operates with 85% energy recovery (Wieland, 1994). An alternative would be the evaporation from nonhydrolyzed urine. Mayer (2002), cited in Maurer et al., (2006), evaporated nonhydrolyzed urine at 78 1C and 200 mbar, producing a viscous solution containing about 10% nitrogen. Moreover, freezing could be used for producing a concentrated solution. Repeated freezing of urine at –14 1C resulted in the concentration of 80% of the nutrients in 25% of the original volume (Lind et al., 2001), a result that was generally confirmed by Gulyas et al. (2004). However, the energy consumption of the chosen technology was relatively high. Volume reduction can, in principle, also be obtained by RO. The efficiency of RO for ammonia retention depends heavily on pH because the retention of the charged molecules (NHþ 4 ) is much better than the retention of the uncharged NH3. Dalhammar (1997, Behandling och koncentrering av humanurin, Royal Institute of Technology, Stockholm, Department of Biochemistry and Biochemical Technology, report, personal communication, cited in Maurer et al. (2006)) performed experiments with hydrolyzed, but acidified, urine at a pH of 7.1. At a concentration factor of 5 (at a pressure of 50 bar), around 70% of all three main nutrients (N, P, and K) were recovered in the concentrate. Thorneby et al. (1999) obtained similar results at a pressure of 30 bar with liquid manure, but with a much higher nutrient recovery (490%). In decentralized settings, problems of energy recovery and fouling and/or scaling must be expected (Maurer et al., 2006). The most widely applied physical–chemical treatment of urine is the precipitation of phosphorus. This is an attractive technology for decentralized recovery of phosphorus because the residuals produced in the reaction are small. Consequently, the precipitation product can be easily collected, for instance, with the normal solid waste from households.

Source Separation and Decentralization

Furthermore, technologies of phosphorus precipitation are known from conventional wastewater treatment, especially from the treatment of concentrated liquids, for example digester supernatant. For an overview of existing technologies, see, for example, Wilsenach and Van Loosdrecht (2002) and De-Bashan and Bashan (2004). Udert et al. (2003c) investigated the naturally occurring precipitation in urine conducting pipes and found mainly struvite (magnesium ammonium phosphate, MAP), hydroxyapatite (a calcium phosphate), and calcite (a carbonate mineral). For technical recovery of phosphorus from urine, we have only found examples of struvite precipitation. Struvite is an attractive precipitate because it is rapidly formed on the addition of magnesium (Ronteltap et al., 2007a), for example as magnesium oxide, magnesium hydroxide, or magnesium chloride. As an additional advantage, struvite is a good slow-release fertilizer (Johnston and Richards, 2003). Models for the thermodynamic equilibrium and the kinetics of phosphorus precipitation in urine have been set up by Udert et al. (2003b, 2003d). An overview of solubility products for struvite is presented by Ronteltap et al. (2003) and a simplified solubility product determined by Ronteltap et al. (2007a). It would be equally attractive if nitrogen could also be precipitated to form a solid product. Unfortunately, there are only two possible nitrogen precipitates, and there are problems with both. One is the commercially available slowrelease fertilizer isobutylaldehyde-diurea (IBDU), a complex of urea, and isobutyraldehyde (IBU), which is however not very suitable for the recovery of nitrogen from urine. Behrendt et al. (2002) have shown experimentally that high urea concentrations and excess IBU are necessary. Applying 5 times more IBU than should be necessary from the stoichiometry of the process only resulted in the precipitation of 75% of the urea as IBDU. The other possibility is the precipitation of nitrogen as struvite, at the cost of adding about 25 times more phosphorus than available from urine. The process is perfectly possible (Tu¨rker and C¸elen, 2007), but only sustainable on a large scale if phosphorus is recovered from the precipitate. Note that the degree of recovery must be very high if the process should not be wasteful in phosphorus. In niche applications, where there is a market for the struvite produced, this technology may be applied. For the recovery of nitrogen, a number of other physical– chemical processes are available, a promising one being ammonia stripping. This process is well known from the treatment of digester supernatant, but energy consumption is relatively high (7 kWh m3 treated liquid; Siegrist, 1996). Ammonia can be adsorbed either in water under pressure (resulting in a 10% ammonia solution at a pressure of 5 bar; Behrendt et al., 2002) or in sulfuric acid (also resulting in a 10% ammonium sulfate solution; Siegrist, 1996). As for struvite precipitation, the advantage of urine over digester supernatant for this process is the naturally high pH of urine, avoiding the large dosage of chemicals for pH regulation. Ion exchange is often suggested as a possible method of producing an attractive fertilizer product from urine. Naturally occurring zeolites have high affinities for ammonium and have been used for recovery of ammonia from urine (Lind et al., 2000; Ba´n and Dave, 2004). Combined with struvite precipitation, Ba´n and Dave (2004) obtained a relatively high

215

recovery rate dosing 15 g l1 of zeolite, however, at a very high effluent concentration of 1000 gN m3. Furthermore, it has not been possible to reproduce these results with such low concentrations of zeolite and, with more typical concentrations, the process becomes unattractive due to the large amount of residuals produced.

4.07.3.5.4 Biological treatment For removal of nitrogen from wastewater, the efficiency of biological treatment is well proven. In concentrated solutions, however, it is still a matter of dispute whether biological removal or physical–chemical recovery would be the best solution (see Maurer et al. (2003) for a discussion of the energy issues of nitrogen recovery). Biological recovery is possible in the form of stabilization of ammonia, with or without physical–chemical volume reduction. Biological recovery of phosphate with the Bio-P process known from conventional wastewater treatment plants has, to our knowledge, never been attempted from concentrated solutions. Nitrification is very suitable for decreasing pH of urine, thereby achieving a stabilization of ammonia. Stabilization is relevant for storage (Section 4.07.3.5.2), for transport and spreading, and as a pretreatment for evaporation (Section 4.07.3.5.3). Nitrification, either to nitrite or to nitrate, produces acid that will eventually stop the biological process when a pH around 6 is achieved. Based on the chemistry of urine, one would expect a partial nitrification of urine to lead to a ratio of ammonia to nitrite or nitrate of approximately 1:1. Experimentally, this was confirmed by Johansson and Hellstro¨m (1999) and Udert et al. (2003a). The latter authors ran different reactor systems, leading to either ammonium nitrate or ammonium nitrite. Nitrite is toxic, but at low pH, chemical oxidation with oxygen will easily convert it to nitrate (Udert et al., 2005). A welcome side effect of biological treatment is the removal of the typical urine odor and more than 80% of COD (Udert et al., 2008). For removal of nitrogen, either heterotrophic or autotrophic biological denitrification can be applied. For heterotrophic denitrification, the process used for the treatment of combined wastewater, an organic substrate is needed to reduce nitrate or nitrite to nitrogen gas. Autotrophic denitrification, or the anammox process (Strous et al., 1998), is applied, for instance, for the treatment of supernatant from sludge dewatering. In this process, ammonia is oxidized under anaerobic conditions with nitrite by autotrophic microorganisms. Udert et al. (2003a) showed that the ammonium nitrite solution produced from urine is suitable for autotrophic denitrification, and Udert et al. (2008) found that ammonia removal from urine by partial nitrification and a combination of heterotrophic and autotrophic denitrification is possible in a single reactor with a removal efficiency of approximately 80%. However, urine is a complex solution (see Udert et al., 2006) with high concentrations of ammonia and salt, and research is still necessary before stable biological nitrogenconverting processes can be run in a decentralized setting. Inhibition of nitrifiers is still not completely understood and the process is sensitive especially to auto-inhibition. Larsen et al. (2009) suggested that the use of genetic methods for

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identification of microorganisms may be a promising route for developing stable biological treatment of source-separated waste streams such as urine in decentralized settings.

4.07.3.5.5 Hygienization and removal of micropollutants Hygienic parameters in urine are primarily of importance for reuse purposes, whereas micropollutants may be of importance in the receiving waters and for reuse. Technically, there are many ways of disinfection or sterilization of any solution (heat, pressure, UV, etc.), but none of these methods have been tested for urine. Only storage (see Section 4.07.3.5.2) has been explicitly investigated for its ability to reduce the amount of pathogens in source-separated urine, although many of the treatment steps discussed in this chapter are expected to have an influence on its hygienic properties (see Maurer et al. (2006) for an overview). When a fertilizer based on urine is produced, the separation of nutrients and micropollutants is important, whereas elimination of micropollutants is relevant for water-pollution control. Separation processes are normally based on membranes or precipitation, whereas removal processes rely on oxidation or adsorption (Larsen et al., 2004). Fortunately, struvite precipitation is an efficient way of separating phosphorus and micropollutants (Ronteltap et al., 2007b). For an overview of the efficiency of different urine treatment methods with respect to the removal of micropollutants, the reader is referred to Escher et al. (2006). The same separation methods have a certain influence on hygienic parameters; however, as stated above, we have found no systematic research on this topic. In contrast to gray water, where RO can be expected to deliver water of the highest quality, also with respect to microorganisms and micropollutants, this is obviously not true when the goal is to produce a safe fertilizer product from urine. For this purpose, micropollutants and microorganisms must take a different route than the nutrients. Other membrane processes have been tested for their ability to separate nutrients and micropollutants. Electrodialysis is based on ionexchange membranes, with an apparent pore size of approximately 200 Da (Kim et al., 2003), which could be expected to retain micropollutants. Pronk et al. (2006a) showed that a separation of nutrients and micropollutants is in fact possible. The efficiency of nutrient recovery was 90%, with a 90% separation of micropollutants, and the concentration factor was around 3. In order to prevent a pH increase in the concentrate containing the nutrients, Pronk et al. (2006b) tested the use of bipolar membranes, which were effective in batch experiments. However, for real-life applications, improvements of the system are necessary (discussed in detail by Pronk et al. (2006b)). Nanofiltration is a well-known possibility of retaining different micropollutants (see, e.g., Kimura et al., 2004; Nghiem et al., 2004). Pronk et al. (2006c) have shown that it is possible to separate nutrients and micropollutants in source-separated urine with microfiltration, provided urea is not hydrolyzed. At optimal conditions, more than 90% removal of micropollutants was observed. Obviously, micropollutants can also be chemically oxidized in urine. However, organic matter and ammonia will compete with the oxidation of micropollutants. Pronk et al.

(2007) tested the use of ozone for the oxidation of micropollutants in untreated urine and found between 80% and complete removal of different compounds at an ozone dose of around 1 g l1. Although this seems high as compared to conventional wastewater treatment, urine is of course produced at a much lower rate and contains a substantial amount of micropollutants (see Section 4.07.3.1). In addition to the processes presented here, it is possible, in principle, to remove micropollutants by adsorption on active carbon or other adsorbents. It can be expected that the presence of high amounts of COD in urine strongly interferes with the adsorption process. However, a biological process can remove more than 80% of the organic matter (Udert et al., 2008), which will drastically improve the efficiency not only of oxidation, but also of adsorption processes.

4.07.3.6 Summary Urine source separation is publicly well accepted, given that toilet comfort is not compromised. Due to the dominating role of urine with respect to nutrients, the technology has a high potential for efficient nutrient recovery and removal. However, there is still much scope for further technical development before urine source separation can compete with existing wastewater treatment technologies. Socioeconomic issues have not been discussed in this chapter, but play a major role, for the successful introduction in industrialized and fast-industrializing countries. In dry sanitation systems in developing countries, urine source separation is already standard technology, but there is a lack of suitable technologies for further processing of urine.

4.07.4 Feces In this chapter, we consider feces with or without urine, and with or without flush water. Diluted combined toilet waste (feces þ urine) is known as black water, whereas diluted feces alone (without urine) are termed brown water. Dry as well as diluted feces may contain toilet paper. From the point of view of urban water management, feces compose the single most important source of pathogens, and it is difficult to find a satisfying solution to this problem. Sewer-based wastewater management effectively removes feces from the immediate urban environment, but the actual removal of microorganisms depends on how advanced the treatment plants and how leak-proof the sewers are. Really effective removal is only achieved with membrane reactors, with disinfection of the effluent, and with perfect sanitary sewers. At the same time, and for obvious reasons, this fraction seems to be the most critical one to handle in a decentralized setting. However, doing so in a safe way can result in a dramatic improvement of urban hygiene and is thus an urgent matter where sewers are not appropriate. Feces, however, also contain important nutrients and where agricultural production is limited by the availability of fertilizer, it seems worthwhile to reuse feces – or alternatively the nutrients contained in feces – as an important resource. It should be noted that the main contribution of feces to agriculture is phosphorus, whereas nitrogen and potassium are

Source Separation and Decentralization

mainly contained in urine. The importance of the organic content of feces for soil improvement is disputed, but according to Jo¨nsson et al. (2004), the improvement is hard to distinguish if feces are added based on the phosphorus requirements of the soil.

4.07.4.1 Production Rate and Composition of Feces Until recently, the production rate and composition of feces were more relevant to medical doctors than to wastewater professionals. However, this has changed during the last decade and some of the data reported in Table 5 are based on extensive experimental research in Sweden (Vinnera˚s et al., 2006a) and Germany (DWA, 2008).

4.07.4.2 Reuse Purposes and Regulation Reuse of feces is relevant for agriculture and aquaculture. Like for sewage sludge, reuse is relevant not only from a resource point of view, but also as a disposal option. In densely populated areas, the only sustainable options for feces are reuse or total oxidation (e.g., incineration), either directly or via sludge production in a treatment plant. Nutrient recycling and total oxidation may be combined, for example, by using ashes from incinerated feces as a fertilizer – an option that would allow recycling of phosphorus without hygienic risks (Scho¨nning and Stenstro¨m, 2004). In areas where reuse of nutrients is essential, recycling of feces is desirable, under the condition that hygienic risks are minimized. WHO (2006) has set up new guidelines for this approach, replacing the older guidelines from 1973 and 1989. Although these guidelines are only recommendations and regulations only available nationally, we here present the recommendation of the World Health Organization (WHO) guidelines as one example of how regulation could look (Table 6). Since monitoring is difficult, regulation based on best technical practice may be the more successful approach.

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organic and inorganic pollutants, these aspects are normally overshadowed by the higher risks of pathogens.

4.07.4.3.1 Hygienic risks Feces contain around 1011–1013 cells g1 and are the main source of microorganisms in wastewater. These microorganisms are however only critical when the individual excreting them is infected. In a comprehensive review, Scho¨nning and Stenstro¨m (2004) discussed the prevalence of different critical bacteria, virus, protozoan, and helmints in different environments. They generally define a conservative approach to risk analysis, where the most resistant organism is chosen as indicator organism. It is not possible to define one specific indicator organisms that will always be appropriate, but some guidance with respect to the choice in specific situations will be found in the review cited above. The main conclusion to bear in mind is that there is a major difference between detecting fecal contamination (which may be done by looking for fecal coliforms) and controlling whether fecal matter has been treated to a hygienically safe level.

4.07.4.3.2 The risks of organic contaminants There is not much work available on organic contaminants in feces. In a literature study, Lienert et al. (2007a) found that about one-third of the pharmaceuticals and hormones excreted by humans end up in feces, whereas the rest is excreted via urine. However, since the compounds excreted via feces tend to be more problematic than those excreted via urine, there is an estimated 50–50 distribution of the ecotoxicological risk potential between urine and feces (Lienert et al., 2007b). Since pharmaceuticals are produced with the explicit Table 6

WHO guidelines for reuse of feces in agriculture

Verification monitoring Recommendation for storage

o1 g1 total solids of Helminth eggs o1000 g1 total solids of E. coli 2–20 1C: 1.5–2 years 420–35 1C: 41 year pH49: 6 months

4.07.4.3 The Risks of Source Separation of Feces Feces are the main source of hygienic risks from wastewater and any discussion on risk connected to feces is thus dominated by the hygienic aspect. Although feces also contain

Table 5

Suggested values for typical production of feces Ciba-Geigy (1977)

Wet mass (kg/cap/day) COD (gCOD/cap/day) Dry mass (g/cap/day) Nitrogen (gN/cap/day) Phosphorus (gP/cap/day) Potassium (gK/cap/day) Toilet paper (g/cap/day) a

Data compiled from WHO (2006) Excreta and greywater use in agriculture. In: WHO Guidelines for the Safe Use of Wastewater, Excreta and Greywater, vol. 4, ISBN 92 4 154685 9. http://www.who.int/water_sanitation_health/wastewater/gsuww/en/ (accessed March 2010).

Henze and Ledin (2001)

a

Vinnera˚s et al. (2006a) 0.14

0.1–0.23

60 21; 34b 1.1 0.55 1.1

30 1.5 0.50 1.0 8.5

DWA (2008)

Values used in this work

0.14 60

0.14 60 30 1.5 0.5 0.9 8.5

1.5 0.5 0.7

Mean values for adults for different diets; the lower value corresponds with a ‘European’ diet, while the higher value corresponds with a diet ‘rich in fibers’. Mean values from two different samples of adults (7 and 24 people). Since the relative density of feces (compared to water) is close to 1, data on volume are reported as wet mass. b

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Source Separation and Decentralization

purpose of biological action, source control is generally difficult.

4.07.4.4 Decision Making and Public Perception of Source Separation of Feces Since many diseases are transmitted via feces-to-mouth contact, there are good reasons why feces are considered repulsive. However, as described by Avvannavar and Mani (2008), the societal context will determine the ‘‘ystrict and unwritten rules and taboo of how to behave when excreting.’’ These taboos also lead to the fact that hardly any scientific literature on the acceptance of different types of feces handling is available, despite the high importance of this issue for urban hygiene and public health. For a detailed overview of societal attitudes to the handling of feces and urine in different contexts, the reader is referred to Avvannavar and Mani (2008). In the literature, we have found only very few practical acceptance studies connected to feces, and all these focus on the acceptance of dry sanitation. In a study on large-scale dry sanitation in an urban area in Mexico, Cordova and Knuth (2005) found that when the toilets were well functioning (which was the case in four out of five sites), users were very satisfied. Similarly, our own experience with pilot projects in China indicates that the proper technical implementation is essential for the success of a pilot project and the acceptance of the technology (Medilanski et al., 2007). It is generally known that it is difficult to assess questions of technology acceptance if the respondents have no access to the technology in question. However, in a study on rural wastewater management in Austria (Starkl et al., 2007), such hypothetical options were discussed in focus groups. In this study, focus-group participants strongly opposed compost toilets because they anticipated a higher demand for space and maintenance, and feared that the toilets would smell. Experience from a rural area in Sweden (Kva¨rnstro¨m et al., 2006) indicates that the Swedish rural population is much more open toward dry sanitation systems. For environmental reasons, about 200 urine sourceseparating flush and dry systems were installed in a rural municipality and user satisfaction was high. Interestingly, in a follow-up investigation, 87% of the users with a dry system said that they would have installed this system even without the 50% subsidy that they received from the municipality, whereas only 23% of the people with a urine-separating flush toilet said the same. Experience from pilot projects indicate that the high acceptance of dry urine-separating toilets is connected with the fact that these toilets are completely odorless, because they are much better ventilated than conventional and urine-separating flush toilets (Kva¨rnstro¨m et al., 2006). Besides, note that conventional bathrooms are heavily ventilated (Tung et al., 2010), and that the ventilation of dry toilets will not automatically have negative effects on energy consumption. All studies emphasize that successful technology implementation and comfort for the user are the most important issues for acceptance. This corresponds with the results found for urine source-separating toilets (see Section 4.07.3.4): if source separation makes sense from an economic and/or ecological point of view, people are generally ready to accept such technologies, but they do require a level of bathroom

comfort, which is at least comparable to the level that they are used to. It is interesting to realize that people often associate dry toilets with increased problems of smell, whereas practical experience shows that in fact the opposite may be the case.

4.07.4.5 Treatment Technology for Feces The main goal of decentralized feces treatment is to improve the hygienic and esthetic quality (including also the removal of odor), and, in some cases, also to gain energy and reduce the amount of material to be transported. Hygiene is characterized by pathogen die-off, which is considered a first-order reaction (at least during shorter periods), where the organismspecific rate constant is considered a function of temperature, pH, and water activity (Scho¨nning and Stenstro¨m, 2004). Esthetic qualities are related to a change in physical appearance, where color and odor are significantly altered (Avvannavar and Mani, 2008). The main decisions with respect to the options of practical treatment technology for feces are taken very early in the system, namely at the point of the toilet. The most important distinctions are between dry and flush toilets and between toilets with and without urine source separation. However, Table 7 toilet type

Concentration of organic matter in feces as a function of

Flush volume (l/flush)

Concentration (gCOD l1)

Comments

Typical older American toilet Typical older European toilet Typical modern European toilet Possible development of modern toilet NoMix toilet, type Dubletten/ Gustavsberg Possible development of Dubletten/Gustavsberg Existing vacuum toilets Possible development of vacuum toilet NoMix vacuum toilet Possible development of NoMix vacuum toilet

Feces related

Urine only

13 6 6

13 6 3

0.92 2.0 3.3

4

2

5.0

6

0.2

8.8

4

0.2

13

1 0.5

1 0.5

12 24

1 0.5

0.2 0.1

33 67

Dry toilets With urine Without urine (100% urine separation)

% dry matter 6 23

Assumptions (Ciba-Geigy, 1977): 140 g feces/cap/ day with a dry matter content of 23% and 1.5 l urine/cap/day (dry matter B65 g/cap/day). Additives and toilet paper are not considered

Calculations based on 5 flushes/cap/day, one of those being feces related (Friedler et al., 1996), and a feces production of 60 gCOD/cap/day (Table 4). 100% compliance of the toilet user with respect to flushing behavior is assumed. Toilet paper is not accounted for.

Source Separation and Decentralization

with respect to the concentration of organic matter from flush toilets, there is a gradient from high-flush toilets without urine separation, resulting in highly diluted feces, to very waterefficient devices, where organic matter ends up in a very concentrated form. In Table 7, we give some examples of the resulting concentration of organic matter in separately collected feces depending on the type of toilet. Particle separation after flushing, for example with a whirlpool surface tension separator (Vinnera˚s, 2004), can result in very high feces concentrations similar to the ones found in 1-l vacuum toilets. For toilets with particle separation as well as for watersaving NoMix toilets, the mode of dealing with toilet paper is essential for the resulting concentration of feces dry matter. In both cases, flushing away toilet paper used in the urine-only mode (terminology from Friedler et al. (1996)) will lead to considerably lower concentrations of dry matter. For all other toilets, the addition of toilet paper and/or additives will increase the dry matter concentration. As seen from Table 7, the concentration of COD in separately collected feces from flush toilets varies from typical wastewater (1000 mgCOD l1) to very concentrated activated sludge (up to nearly 7% COD). The dry matter content of feces from dry toilets varies from 6% (without urine separation; more than half of the dry matter is made up by salt from urine) to 23% (with 100% urine separation), without accounting for toilet paper and/or additives.

4.07.4.5.1 No treatment The no-treatment option for source-separated feces is frequent in many developing countries, either in the form of open defecation (Avvannavar and Mani, 2008) or in the form of disposal of toilet waste from on-site sanitation in agriculture, aquaculture, receiving waters, or just simply in the urban or peri-urban environment (Ingallinella et al., 2002). It is needless to discuss that in more densely populated areas, this option is unacceptable for the purpose of urban hygiene, but it should be kept in mind as a base scenario when evaluating the alternatives presented in the following.

4.07.4.5.2 Storage In Section 4.07.3, we introduced storage as a treatment method, because this is a relevant option for urine. However, for feces, there are two distinct types of storage: those which are only intended to keep feces contained, until they can be transported to a central treatment (most often in the form of fecal sludge (FS), see Section 4.07.4.5.1), and those which are intended to stabilize feces. Here, we only discuss the latter type of storage. For feces, it is not very clear where storage ends and physical–chemical treatment begins. When feces are stored, most often at least some drying will take place, and with the addition of conditioners, for example ash for increasing the pH value of the fecal matter, also hygienic issues are pursued. However, pure storage would have a certain effect on the microbial quality, as seen for urine (Section 4.07.3.5.2); but with the higher content of pathogens and the lower pH values, the required storage time is considerably higher (Scho¨nning and Stenstro¨m, 2004). Obviously, there are many problems of pure storage of feces, mainly caused by smell and flies, and,

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where possible, some type of conditioning is preferred (see Section 4.07.4.5.3).

4.07.4.5.3 Physical–chemical treatment Solid–liquid separation and drying. For FS, the feces–liquid mix arising from decentralized storing of toilet waste, solid–liquid separation is obtained in settling–thickening tanks or on sludge drying beds (planted or unplanted; Strauss and Montangero, 2002). Whereas the settling–thickening tank is a pretreatment, which should be followed by further drying or biological treatment, the drying bed combines percolation of water with drying to produce a product that can be used in agriculture. Ingallinella et al. (2002) reported good results of a planted drying bed, with a count of live helminth eggs of 2 g1 TS, which is in accordance with the older WHO recommendations, and only a factor of 2 higher than the 2006 recommendations referred to in Section 4.07.4.2. Although the percolate from drying beds is of better quality than from the settling tanks, it still requires treatment (Strauss and Montangero, 2002). In dry toilets, feces are in many cases further dried by the addition of structural material such as saw dust, not only decreasing the relative humidity by adding more dry mass, but also allowing for better evaporation of water. Ventilation can be passive or forced. Decreasing humidity helps kill pathogens (Scho¨nning and Stenstro¨m, 2004) and reduces nuisance such as smell and flies. A special type of drying takes place in a precomposting tank, often described by the German word Rottebehaelter (Gajurel et al., 2007, 2004, 2003), which consists of a simple filter bag, where feces from a water-flushed toilet are collected and drained. This is a further development of the septic tank, which is widespread also in rural areas of industrialized countries (Section 4.07.5.3.1). According to the articles cited above, this filter bag provides an (outdoor) odor-free storage and drying of fecal matter obtained from flush toilets. After 3 months of storage, a product is obtained with a suitable water content for the sludge to be treated by vermicomposting (Gajurel et al., 2007; see Section 4.07.4.5.4, for a discussion of vermicomposting). Additives. A classical way of stabilizing feces is the addition of ash, which will increase pH and decrease humidity, and thereby increase the rate of pathogen die-off (Scho¨nning and Stenstro¨m, 2004). At the same time, the covering of feces with ash improves the esthetics in a dry toilet. A newer technology is the addition of either urea or ammonia, which has proved to be effective for all types of fecal pathogens. For details on dosage, temperature, and storage requirement, the reader is referred to Nordin et al. (2009), Ottoson et al. (2008), and Vinnera˚s et al. (2003). Physical–chemical oxidation. We have found no recent technical papers on physical–chemical oxidation of feces, but Scho¨nning and Stenstro¨m (2004) suggest that incineration would be the optimal treatment method for this type of bio waste, eliminating all hygienic risks and allowing for the recycling of phosphorus from the ashes. An on-site approach is suggested (i.e., incineration directly in the toilet) in order to avoid hygienic risks associated with transport. These same authors also note that heating feces sufficiently would have the

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Source Separation and Decentralization

same result on pathogen elimination – an option, which may be easier to realize in practice and which is already under development in on-site settings for thermophilic composting (see Section 4.07.4.5.4). For closed life-support systems, as they may be considered for long-term space missions, physical–chemical oxidation of human waste, including feces, has been considered (Upadhye et al., 1993), but we have found no literature on actual experiments. Upadhye et al. (1993) calculated a heat of combustion of about 22 kJ g1 dry mass of feces (lower heating value at 25 1C), which in principle would be available to evaporate the water content of feces. Based on an energy requirement for evaporating water of approximately 2.6 kJ g1, feces with up to 88% water content (see also Table 7) can be burned energy neutral. It is obviously attractive to imagine a toilet with internal combustion of feces, where the resulting ash contains many of the nutrients contained in feces (including phosphorus), presenting the minimum amount of residues possible (B7 g/cap/day; Ciba-Geigy, 1977), and eliminating effectively all fecal pathogens and organic micropollutants. However, there are equally obvious reasons, why nobody (to our knowledge) has tried out this process in practice: drying and burning feces (and toilet paper) in a bathroom is a complex and potentially risky undertaking. Some experience exists with respect to the combustion of manure, on the household scale in a number of developing countries and, on a larger, more technical scale, in industrialized countries with an overproduction of animal manure. From the latter experience, it is known that potential emissions of air-polluting compounds such as CO, NOx, and SOx must be taken into account (Florin et al., 2009; Lundgren and Pettersson, 2009) and that the amount and type of inorganic elements in different types of bio waste increase the risk of slagging and ash deposition in the combustion system (Miller and Miller, 2007). From this very limited experience, it seems that it would be rewarding, but far from trivial, to develop incineration technology for feces, especially in an on-site setting.

4.07.4.5.4 Biological treatment Aerobic treatment. Composting is the most widespread method of aerobic treatment of feces, with co-composting with organic solids being one of the standard treatment methods for FS in developing countries (Strauss and Montangero, 2002). These and other authors (e.g., Scho¨nning and Stenstro¨m, 2004), however, are of the opinion that most of the composting takes place at ambient temperatures with insufficient pathogen elimination. This is supported by a study of the survival of fecal coliforms in dry solar composting toilets in Mexico, where most of these toilets did not comply with the existing regulation (Redlinger et al., 2001). However, of those that did comply, nearly all were composting toilets with optimal solar exposure, confirming the importance of correct technical application of these single compost toilets. Thermophilic composting or co-composting at temperatures higher than 50 1C is given much attention in the scientific literature because a hygienic end product can be obtained with this technology (Ingallinella et al., 2002). From a technical point of view, there are several ways of obtaining the

necessary temperatures. Due to the heat-producing aerobic processes, sufficiently high temperatures can be obtained through co-composting with organic solid waste either in a compost heap (Kone´ et al., 2007) or in a single well-insulated toilet (Niwagaba et al., 2009). Solar heating of the toilets, as discussed above, may also be effective. In the specific compost heap referred to earlier, feces and organic solid waste were mixed in the ratio of 1:2, leading to a reduction of helminth eggs below the WHO guidelines of o1 viable egg/g TS (WHO, 2006). In the single toilet, a feces to solid waste ratio of 3:1 was apparently sufficient to obtain a reduction of E. coli 43 log10 and a reduction of Enterococcus spp. of 44 log10 units. However, based on experience, the authors conclude that mixing of the material is crucial in order to kill pathogens in the entire sample. Without addition of solid waste, no bacterial inactivation was observed. In the compost heap as well as in the single toilet, regular turning of the compost (approximately once a week) was required. In a single toilet, high temperatures can also be obtained through external heating. Since the temperature in such a setting can be chosen more freely, it makes sense to determine the optimal operational temperature. Lopez Zavala et al. (2004) showed that the rate of aerobic degradation increased with temperature between 20 and 60 1C, whereas no reaction took place at 70 1C. Using sawdust as a matrix and assuring that oxygen was not limiting for the process, stabilization of feces was obtained within 24 h at a temperature of 60 1C, whereas 72 h were required at 50 1C. According to the safety zone diagram set up by Feachem et al. (1983), the 24 h at 60 1C would be sufficient to ensure a safe end product, whereas at least 96 h would be necessary at 50 1C. However, for practical applications, the WHO guidelines from 1989 (cited in Scho¨nning and Stenstro¨m (2004)) recommend at least 1 month aerobic composting in piles at 55–60 1C; presumably because, in practice, the fecal matter will never be homogeneously mixed. The principle of a newer Japanese composting toilet (called the Bio-toilet) may help overcome some of the problems of the more conventional compost toilets. This toilet is based on automatic dosing of sawdust, an external heating system, and mechanical mixing (Nakagawa et al., 2006), and the authors model the die-off of virus as a function of temperature and water content. It should be noted that although from a hygienic point of view, heating alone would be sufficient, the esthetic quality of the fecal matter will profit from a biological process (Avvannavar and Mani, 2008). Vermicomposting. If FS is collected as dry matter, earthworms may be used for aerobic degradation, in a process called vermicomposting. For treatment of feces, this process has been studied far less than the more conventional composting discussed previously. However, some experience exists for treatment of sewage sludge as well as for on-site treatment of feces in Australia (Bajsa et al., 2003), and for on-site treatment of feces in Indonesia (Malisie et al., 2007) and Germany (Gajurel et al., 2007). Since vermicomposting is only possible within the relatively narrow limits of 70–90% water content (Edwards, 1995), feces from flush toilets can only be vermicomposted after dewatering, whereas feces from dry urine-separating toilets would fall within this limit (see Table 7). Vermicomposting will decrease the number of

Source Separation and Decentralization

pathogens (Bajsa et al., 2003), but there is still no conclusive evidence how effective this process is for feces (Gajurel et al., 2007). Treatment of black water in an MBR is not a typical technology choice, but it has been tested in laboratory scale (Atasoy et al., 2007) with very good results for COD and nitrogen removal (96% and 89%, respectively) and 100% removal of total coliforms. The specific energy demand was 2.30 kWh m3 at a concentration of 1218 gCOD m3 and 188 gTKN m3, which would correspond to approximately 5.75 W/person in this special case. However, based on Tables 4 and 5, we had to make these calculations based on the assumption that the black water production in this special investigation was very high (around 60 l/cap/day), which in the case of MBRs is relevant for energy consumption. Biological removal of odor has not been discussed in connection with source separation of feces, but in densely populated areas, good ventilation of the bathroom (see Section 4.07.4.4) must be followed by effective odor removal from the off-gas. Sato et al. (2001) suggested that the (very small amount of) malodorous compounds stemming from feces and urine could be efficiently degraded in a biological process. The authors identified that 90% of the substances responsible for the smell of stored wastewater are fatty acids (acetic, propionic, and butyric acid), which are well degradable in biological systems. Anaerobic treatment. Anaerobic digestion is a standard treatment method for sewage sludge, and also for the treatment of black water, anaerobic treatment is very well documented. The main arguments for anaerobic treatment of black or brown water as opposed to aerobic treatment are the possible recovery of energy (most often as methane) and the preservation of nutrients under anaerobic conditions (KujawaRoeleveld and Zeeman, 2006). The lower heating value of organic matter in the form of methane is 12.5 kJ g1 COD (Rittmann, 2006), resulting in a maximal energy content of feces of 750 kJ/cap/day or 8.7 W/cap (based on specific feces production as reported in Table 4). Up to 65% of the organic matter in feces can be transformed into methane (Feng et al., 2006), resulting in a potential maximal energy production of 5.7 W/cap/day. Anaerobic digestion of feces can be enhanced by the addition of kitchen refuse, a typical way of increasing COD concentration (Otterpohl et al., 1999). Kujawa-Roeleveld and Zeeman (2006) reviewed the more general knowledge about anaerobic treatment of black water, and the following summary is based on this article. A number of technical applications have been developed, mainly the completely stirred reactor (CSTR), the accumulation (AC) system, and the up-flow anaerobic sludge blanket (UASB) reactor. For nutrient recovery from the effluent, principally, the same technologies are available as for urine (see also Section 4.07.3.5). The main factors of importance for reactor performance are SRT and temperature. For energy balances, also the COD concentration of the influent is important: the higher the concentration, the less energy will be necessary for any heating anticipated and the lower the amount of methane lost to the atmosphere. Posttreatment of the anaerobic effluent will often be necessary in order to comply with effluent quality either to the receiving water (COD and nutrients) or to agriculture (pathogens and micropollutants).

221

Like for other treatment methods, higher temperature and higher residence time will lead to higher pathogen removal. For a CSTR treating source-separated feces, a mathematical model for the influence of HRT and temperature was developed and calibrated at 55 1C, using fecal coliforms and intestinal enterococci as indicator organisms (Lu¨bken et al., 2007). In accordance with the general understanding of pathogen die-off, this model predicts that pathogen inactivation in such a reactor is only possible at temperatures beyond 50 1C. It is well known that ammonia is inhibitory to methanogenesis, which is one of the reasons that anaerobic digestion of brown water (liquid feces without urine) is sometimes preferred to anaerobic digestion of black water (Otterpohl et al., 1999). In an experimental investigation of the influence of free ammonia (NH3) and total ammonia concentration (NH3 þ NHþ 4 ; Lay et al., 1998), it was found that first signs of inhibition of methanogenesis occur at a total ammonia concentration of 1.7 gN l1, which would occur at a toilet flush volume of around 6–7 l/cap/day. Existing vacuum toilets would result in a toilet flush volume of around 5 l/cap/day (Table 7), and thus be just below this critical volume. The same authors also found that the lag phase in a batch experiment depends on the free ammonia level, identifying a shock level of 0.5 gN l1 of free ammonia (NH3). In dry systems without urine separation, one must expect ammonia inhibition of methanogenesis, which is also found experimentally (Chaggu et al., 2007). It should be noted that with a urine-separating toilet of the type Dubbletten/Gustavsberg, the same COD concentration can be obtained as with a conventional vacuum toilet (Table 7), with much simpler technology. As a further advantage, separate treatment of urine will avoid any risk of ammonia inhibition in an anaerobic treatment process. Anaerobic treatment is typically not foreseen for a single household and many projects operate on a scale from about 100 persons and upwards (see, e.g., Zeeman et al., 2008; Otterpohl et al., 1997). It has been shown that no microbial risk should arise from gas usage (Vinnera˚s et al., 2006b), but we have found no other risk analyses of decentralized gas production, neither with respect to possible greenhouse gas emissions nor with respect to any possible danger of explosions. However, in order to reduce the loss of methane, it is important to maximize COD concentration, for example, by using a urine-separating vacuum toilet, an approach that would also increase process stability (Elmitwalli et al., 2006).

4.07.4.5.5 Hygienization and removal of micropollutants Since one of the main objectives of feces treatment is hygienization, we have discussed this topic in nearly all the paragraphs of Section 4.07.4.5. Shortly summarized, pH, time, and temperature are the main parameters determining whether hygienic requirements can be reached. In addition, ammonia has proved to be effective for the removal of microorganisms. Since ammonia is an important plant nutrient, where some surplus is required for efficient plant uptake, this technology has a great potential for recycling of feces to agriculture as illustrated by the Peepoo bag, the self-sanitizing single-use biodegradable toilet (Vinnera˚s et al., 2009).

222

Source Separation and Decentralization

Due to the organic nature of feces, there is no explicit way of removing only micropollutants from feces. From conventional wastewater treatment, it is known that a number of micropollutants are degraded in aerobic treatment, and there is no reason to believe that this would not also be the case for aerobic treatment of feces. We refer to the standard wastewater treatment literature on this topic. However, the typical treatment method for black and brown water is anaerobic treatment, and the few studies on anaerobic degradation of pharmaceuticals, which we have found, do not show much degradation (Mes et al., 2008). With aerobic treatment of the effluent from anaerobic treatment of black water, however, at least the soluble micropollutants may be degraded (de Mes et al., 2007). It is obvious that processes such as total oxidation and incineration will also eliminate the problem of micropollutants.

4.07.4.6 Summary With respect to urban hygiene, feces make up the most important part of wastewater. Separate feces handling is not only challenging, but also rewarding. The most important decisions concerning technology choice are taken with the choice of toilet. Two main factors are decisive: water consumption and separation of urine. A dry toilet allows for fundamentally different treatment technologies as compared to a flush toilet, but most conveyance systems require a certain amount of flush water. Urine source separation is essential for dry toilets and advantageous for water-saving toilets with anaerobic treatment. For reasons of energy conservation, anaerobic treatment of feces from flush toilets is often preferred, whereas drying or biological aerobic processes are common for feces from dry toilets. It is no surprise that for the products, esthetics and hygiene are the most important parameters.

4.07.5 Combined Domestic Wastewater There is a long tradition for decentralized treatment of combined wastewater in sparsely populated areas where conveyance systems are either too expensive or technically not feasible. There is a large wealth of technologies and options available to treat combined wastewater and it would exceed the scope of this chapter to review the entire literature on these small and well-known treatment plants. However, we briefly discuss the main trends of newer research into onsite treatment of domestic wastewater and thereby illustrate the problems of this approach. The advantages, however, are evident: treating combined wastewater on-site avoids the problems of reinventing household devices, and concentrates the efforts on established and tested technologies that wastewater treatment professionals feel comfortable with. Additionally, on-site treatment enables the simplification or even the abolishment of large and investment-intensive conveyance networks. We exemplify our main points on on-site treatment of combined wastewater with two types of common treatment technology that represent the two sides of the spectrum: (1) the septic tank, which is the simplest of all possible systems and abundant in many rural areas all over the world, and (2) the Japanese johkasou as a standardized mass-produced enhanced treatment unit.

Table 8 Suggested values for typical production of combined purely domestic wastewater (water, total organic matter, nitrogen and phosphorus) based on Tables 1, 4, and 5 and the assumption of an average 6-l toilet standards resulting in 30 l/cap/day of water for toilet flushing Volume (l/cap/day) COD (gCOD/cap/day) Nitrogen (gN/cap/day) Phosphorus (gP/cap/day)

130 120 14 2.0

4.07.5.1 Production Rate and Composition of Combined Domestic Wastewater Numbers for the production rate and composition of combined wastewater often include other types of wastewater than domestic. The numbers indicated in Table 8, in contrast are ‘estimated’ instead of ‘intended’ (the program does not allow me to erase the word ‘intended’) intended for purely domestic wastewater. Like for gray water, large variations of the water production rate must be expected.

4.07.5.2 The Risks of On-Site Treatment of Combined Wastewater Traditionally, the hygienic risks of on-site treatment have been avoided by subsurface evacuation through a septic tank. Since the household does not get into contact with the on-site treatment plant, the same direct safety level as for centralized treatment is obtained. For aboveground on-site treatment, there is a potential hygienic risk, especially if the household itself is involved in maintenance and sludge removal. In Japan, where such systems are frequent, this risk is minimized by an elaborate scheme for control, maintenance, and sludge handling by professional companies (Yang et al., 2001). More difficult to avoid is the risk of contamination of receiving waters, that is, groundwater (e.g., in the case of septic tanks) or surface water. The main difference to central treatment is the generally uncontrolled discharge of wastewater, due to low treatment efficiencies, malfunctioning, or leaks. The risk of groundwater contamination from septic tanks is well known and widespread. Wistrom et al. (2003) referred to extensive literature on this problem in the United States, explicitly mentioning contamination of drinking water wells with fecal coliforms, nitrate, and phosphorus from septic tank treatment of combined wastewater. The same problems are observed with the johkasous in Japan (Yang et al., 2001; see also Sections 4.07.5.3.1 and 4.07.5.3.2). The risks from reusing treated wastewater are well known and the discussion is not much different from the discussion on reuse of gray water (see Sections 4.07.2.2, 4.07.2.3, and 4.07.2.4). An obvious difference of treated combined wastewater is the potential to return nutrients to agriculture.

4.07.5.3 Two Examples of On-Site Treatment Technologies for Combined Domestic Wastewater 4.07.5.3.1 Septic tanks The traditional Western on-site wastewater treatment process for areas with low population density is an underground

Source Separation and Decentralization

223

septic tank. Not only in the US, but also in other countries such as Germany and France, this is the most widespread onsite treatment technology for domestic wastewater (EPA, 2002). The technology is based on primary sedimentation and HRT to reduce pathogens. The removal of particulate matter reduces clogging in the subsurface outflow. Sludge reduction through anaerobic digestion is an important issue in order to reduce the frequency of septic tank emptying (e.g., Fleckseder and Krejci, 1982). However, due to low subsurface temperature, only acidification may take place, resulting in the transformation of sludge into soluble COD instead of methane production (EPA, 2002). As discussed in Section 4.07.5.3.3, uncontrolled methane production may not be the process of choice from a global-warming point of view, but it is obvious that mobilized organic COD can also cause problems in some settings. There are several other reasons that traditional septic tanks are not an adequate solution for today’s problems, even in rural areas: poor nutrient removal (e.g., Gill et al., 2008), risk of microbial contamination of aquifers (e.g., Scandura and Sobsey, 1997), and contamination of aquifers with micropollutants (e.g., Godfrey et al., 2007). Since septic tanks are widespread, and the problems are recognized, there are many attempts to improve the technology. In some cases, septic tank effluent is further treated to avoid contamination of receiving water (e.g., Hu et al., 2007); in other cases, the septic tank itself is upgraded for enhanced biological treatment (e.g., in the form of an UASB septic tank (Al-Shayah and Mahmoud, 2008) or simply with filter material (Zhang et al., 2009)). There is no doubt that both approaches are possible, and with enough effort, they will both lead to success. However, if the simplicity of the septic tank approach shall be retained and treatment requirements are high, such solutions tend to be very space demanding, moving toward systems based on natural self-purification. For more compact solutions, simplicity tends to get lost. It is consequently a question, whether it will not be cheaper to go for an on-site reactor in the first place, which can be mass-produced. The principles of such on-site reactors can be well discussed based on the Japanese example of the johkasou (Section 4.07.5.3.2).

by Yang et al. (2001). There are two types of johkasous: the tandoku-shori johkasou for treatment of toilet waste (not discussed in Section 4.07.4.5 because there is no information in English on this technology) and the gappei-shori johkasou for combined wastewater. With the latter type, the wastewater of about 10 million people in rural Japan is treated. The johkasous are mass-produced and typically consist of a sedimentation chamber, where primary sludge is anaerobically degraded like in a septic tank, followed by biological aerobic treatment. The biological compartment is often based on filter and contact media, but activated sludge systems also exist (Nakajima et al., 1999). Similar to the situation of septic tanks, there is a trend toward improvement of the johkasous, because they do not sufficiently address today’s problems of environmental pollution, even in rural areas. In contrast to the septic tanks, however, the improvements of johkasou technology more closely mirror the developments in central treatment: improved nutrient removal (nitrification/denitrification), reduction of tank volume with membrane technology, improved disinfection of the effluent, and reuse of sludge for agriculture. Similar systems are also in use in Europe, with or without membranes. An interesting feature is the thermal destruction of sludge, reducing the need for emptying and maintenance (Wistrom et al., 2003). In some cases, the sedimentation step is omitted in order to avoid odor problems from primary sludge, thus allowing for in-house installations and the use of filter bags for sludge dewatering (Abegglen et al., 2008). In Table 9, we report some results on volume, BOD and nitrogen removal, and energy consumption of different types of gappei-shori johkasous, all from Yang et al. (2001). It is obvious that the use of membrane johkasou drastically improve the quality of wastewater treatment (at a similar or lower specific tank volume), but at the expense of higher energy consumption. Denitrification in a membrane johkasou is obtained either in a separate denitrification tank (Ohmori et al., 2000) or by using the anaerobic first sedimentation reactor for denitrification (Yang et al., 2001). In both cases, denitrification relies on recirculation of nitrified wastewater.

4.07.5.3.2 Johkasous

Considering the high global warming potential (GWP) of methane, it is surprising that widespread on-site technologies for disposal of domestic wastewater still rely on uncontrolled

4.07.5.3.3 On-site uncontrolled anaerobic digestion Due to a pronounced lack of information on johkasous in English, the following summary is mainly based on an article

Table 9

Volume, BOD, and nitrogen removal and energy consumption of different types of gappei-shori johkasous

Specific volume Energy consumption Percentage of reactors keeping the following targets: BODo5 mg l1 and T-No10 mg l1 BOD and T-No20 mg l1 BODo20 mg l1 and T-N420 mg l1 BOD 4 20 mg l1 and T-No20 mg l1 BOD 4 20 mg l1 and T-N420 mg l1

Type A

Type B

Type C

m3/cap W/cap

0.6–0.7 16–18

0.5 18

0.6 14–21

% % % % %

64 18 11 7

81 13 3 3

83 15 2

Membrane 0.5 32 100

Type A: with contact aeration tank; type B: with biofilm filtration tank; type C: with moving bed biofilm tank; membrane: membrane johkasou, only lab results. Based on Yang XM, Yahashi T, Kuniyasu K, and Ohmori H (2001) On-site systems for domestic wastewater treatment (johkasous) in Japan. In: Lens P, Zeeman G, and Lettinga G (eds.) Decentralised Sanitation and Reuse, Integrated Environmental Technology Series, pp. 256–280. London: IWA.

224 Table 10

Source Separation and Decentralization The global warming potential (GWP) of uncontrolled anaerobic degradation of COD from combined domestic wastewater

GWP(20) of CH4 (timescale 20 years) GWP(100) of CH4 (timescale 100 years) Specific CH4 production from COD Specific GWP(20) of methane production from COD Specific GWP(100) of methane production from COD Specific production of COD Assumed primary sludge production (25% of COD) Assumed primary sludge degradation (50% of sludge) GWP(20) based on assumptions above GWP(100) based on assumptions above Specific CO2 production from electricity production CO2 production from typical 10 W/cap WWTP

gCO2 =gCH4 gCO2 =gCH4 gCH4 =gCOD gCO2 =gCOD gCO2 =gCOD gCOD/cap/day gCOD/cap/day gCOD/cap/day gCO2 =cap=day gCO2 =cap=day gCO2 =Wh gCO2 =cap=day

72 25 0.25 18 6 120 30 15 270 90 0.8 192

a a b

a a d

e f

a

IPPC (2007: ch. 2, p. 212). Based on a mass conservation of theoretical COD (Gujer and Larsen, 1995). c From Table 8. d Typical values from centralized treatment of municipal wastewater (Gujer, 2007). Note that the anaerobic degradability is for primary and secondary sludge; for primary sludge it will be higher. e Based on EU 15 electricity mix from 1997 (European Environmental Agency, 2002). f A typical advanced wastewater treatment plant (WWTP) consumes around 10 W/person of electrical power (1 W/cap ¼ 24 Wh/cap/day). b

anaerobic digestion. For septic tanks, the methane production depends on the subsurface temperature (see Section 4.07.5.3.1), whereas the better-controlled johkasous are more likely to give rise to methane gas production. We therefore briefly illustrate the implications of uncontrolled methane production from on-site reactors. Since it is difficult to obtain any exact data on sedimentation in septic tanks and johkasous, let alone data on anaerobic degradability, we only present specific data and one example based on primary sedimentation in treatment plants (Table 10). Methane production from COD elimination is based on the concept of mass conservation of theoretical COD (Gujer and Larsen, 1995). The GWP is presented for a time period of 20 and 100 years (the latter timescale normally being considered of relevance). For comparison, we also present the GWP of electricity production, because electricity is the dominant energy form used for wastewater treatment technologies. Obviously, the net greenhouse gas emission from electricity production greatly varies (from close to zero for renewable energy sources to about 1 g CO2/Wh for small coal-fired power plants; Bettle et al., 2006); however, for simplicity, we use a value corresponding approximately to the European electricity mix (0.8 g CO2/Wh; European Environmental Agency, 2002). From Table 10, we can get an idea whether methane production from on-site anaerobic degradation of primary sludge is relevant or not. A typical modern wastewater treatment plant has an electricity consumption of about 10 W/person (corresponding to 240 Wh/cap/day), whereas the membrane johkasous are about 3 times as energy intensive (Table 9). We thus see that in the short term (20-year timescale), the GWP of methane production from primary sludge is similar to the GWP arising from electricity use in a modern centralized treatment plant. Due to the much shorter lifetime of methane in the atmosphere, it is obvious that the longer the time horizon, the smaller the contribution will appear. In conclusion, from a global warming perspective, the energy use for enhanced treatment is justified, especially if the electricity used has a small carbon dioxide footprint.

One way of reducing anaerobic digestion of primary sludge in johkasous, for example, is denitrification in the sludge compartment as described in Section 4.07.5.3.2. In septic tanks, at least, where nitrification and recycling of nitrified wastewater are difficult, separate nitrification of urine could be a good alternative. From Table 9 it can be concluded that about 10 g of NO 3 N/cap/day can be removed by denitrification, corresponding to around 29 g/cap/day of COD removal (based on mass conservation of theoretical COD; Gujer and Larsen, 1995). In principle, this would be more than enough to suppress anaerobic degradation of primary sludge, even if the assumptions made in Table 10 are conservative. More detailed investigations would however be necessary to test these assumptions, and obviously denitrification processes may also give rise to global warming, if not properly controlled.

4.07.5.4 Summary Decentralized treatment of combined wastewater is a typical application in rural areas of industrialized countries. In this section, we have discussed two exemplary decentralized technologies: (1) the septic tank, which is the simplest of all possible systems and abundant in many rural areas all over the world, and (2) the Japanese johkasous as a standardized massproduced enhanced treatment unit. Where the septic tank is an underground technology, with only periodical emptying, the johkousa requires an institutional setup for emptying, maintenance, and control. Today, it is recognized that both types of reactors do not fulfill modern requirements for waterpollution control, even in rural areas and they are both further developed. Whereas the septic tanks are developed more in direction of systems relying on natural self-purification, the johkasous are developed along the lines of conventional central wastewater treatment. From a global warming perspective, the energy use for enhanced treatment is justified, especially if the electricity used has a small carbon dioxide footprint.

Source Separation and Decentralization

4.07.6 Outlook We do not draw special conclusions from this chapter. As illustrated in Section 4.07.1, there are good reasons to work on the development of decentralized technologies based on source separation, but it is way too early to conclude which technology or technologies will be the choice of the future. However, also decentralization without source separation holds promise for a more resource-efficient future: without the large expenditures for long-lived sewer systems, flexibility is retained for introducing source separation later on, when such technologies have matured. We hope that the overview in this chapter will inspire more scientists to leave the well-thread path of centralized wastewater treatment and get involved in decentralization and source separation.

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Source Separation and Decentralization Udert KM, Larsen TA, and Gujer W (2003d) Estimating the precipitation potential in urine-collecting systems. Water Research 37(11): 2667--2677. Udert KM, Larsen TA, and Gujer W (2005) Chemical nitrite oxidation in acid solutions as a consequence of microbial ammonium oxidation. Environmental Science and Technology 39(11): 4066--4075. Udert KM, Larsen TA, and Gujer W (2006) Fate of major compounds in sourceseparated urine. Water Science and Technology 54(11–12): 413--420. Upadhye RS, Wignarajah K, and Wydeven T (1993) Incineration for resource recovery in a closed ecological life support system. Environment International 19(4): 381--392. Van Drecht G, Bouwman AF, Knoop JM, Beusen AHW, and Meinardi CR (2003) Global modeling of the fate of nitrogen from point and nonpoint sources in soils, groundwater, and surface water. Global Biogeochemical Cycles 17(4): ARTN1115. Vanchiere JA, White ZS, and Butel JS (2005) Detection of BK virus and simian virus 40 in the urine of healthy children. Journal of Medical Virology 75(3): 447--454. Vinnera˚s B (2004) Separation efficiency in a whirlpool surface tension separator, separating faeces and toilet paper for nutrient recovery – pilot-scale study. Water Science and Technology 50(6): 115--121. Vinnera˚s B, Hedenkvist M, Nordin, A, and Wilhelmson A (2009) Peepoo bag: Selfsanitising single use biodegradable toilet. Water Science and Technology 59(9): 1743–1749. Vinnera˚s B, Holmqvist A, Bagge E, Albihn A, and Jonsson H (2003) The potential for disinfection of separated faecal matter by urea and by peracetic acid for hygienic nutrient recycling. Bioresource Technology 89(2): 155--161. Vinnera˚s B, Palmquist H, Balme´r P, and Jo¨nsson H (2006a) The characteristics of household wastewater and biodegradable solid waste – a proposal for new Swedish design values. Urban Water Journal 3(1): 3--11. Vinnera˚s B, Scho¨nning C, and Nordin A (2006b) Identification of the microbiological community in biogas systems and evaluation of microbial risks from gas usage. Science of the Total Environment 367(2–3): 606--615. Visvanathan C and Hufemia AMM (1997) Exploring zero discharge potentials for the sustainability of a bottle washing plant. Water Science and Technology 35(9): 181--190. WHO (2006) Excreta and greywater use in agriculture. In: WHO Guidelines for the Safe Use of Wastewater, Excreta and Greywater, vol. 4, ISBN 92 4 154685 9. http:// www.who.int/water_sanitation_health/wastewater/gsuww/en/ (accessed March 2010). Wieland PO (1994) Designing for Human Presence in Space – an Introduction to Environmental Control and Life Support Systems, NASA RP-1324, appendix E/F, pp. 227–251. http://trs.nis.nasa.gov/archive/00000204/01/rp1324.pdf (accessed March 2010). Wiel-Shafran A, Ronen Z, Weisbrod N, Adar E, and Gross A (2006) Potential changes in soil properties following irrigation with surfactant-rich greywater. Ecological Engineering 26(4): 348--354. Wilsenach J and van Loosdrecht M (2002) Separate urine collection and treatment – Options for sustainable wastewater systems and mineral recovery. STOWA Report No. 2001–39, ISBN 90-5773-197-5. Utrecht: STOWA.

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Relevant Websites http://www.novaquatis.eawag.ch Novaquatis: ein Querprojekt der Eawag. www.roevac.com Roediger Vacuum: World Leading Systems for the Collection of Wastewater Using Vacuum.

4.08 Modeling of Biological Systems M Wichern, T Gehring, and M Lu¨bken, Institute of Environmental Engineering, Bochum, Germany & 2011 Elsevier B.V. All rights reserved.

4.08.1 4.08.2 4.08.2.1 4.08.2.1.1 4.08.2.1.2 4.08.2.1.3 4.08.2.1.4 4.08.2.2 4.08.2.3 4.08.3 4.08.3.1 4.08.3.1.1 4.08.3.1.2 4.08.3.2 4.08.3.3 4.08.3.4 4.08.3.5 4.08.4 4.08.4.1 4.08.4.2 4.08.4.2.1 4.08.4.2.2 4.08.4.2.3 4.08.4.2.4 4.08.4.2.5 4.08.4.3 4.08.4.3.1 4.08.4.3.2 4.08.5 4.08.5.1 4.08.5.2 4.08.5.2.1 4.08.5.2.2 4.08.5.2.3 4.08.5.2.4 4.08.5.2.5 4.08.5.3 4.08.5.3.1 4.08.6 4.08.6.1 4.08.6.2 4.08.6.2.1 4.08.6.2.2 4.08.6.2.3 4.08.6.3 4.08.6.3.1 4.08.6.3.2 4.08.6.3.3 4.08.6.3.4 References

Introduction Mathematical Modeling of Biochemical Processes Biological Processes Carbon removal Nitrification Denitrification Biological P-elimination Modeling of Hydraulic Conditions in Activated Sludge Plants Modeling of Biochemical Processes Modeling of Biological Processes in Activated Sludge Systems Introduction Activated Sludge Model No. 3 EAWAG BIOP Module WWTP at Koblenz WWTP at Hildesheim WWTP at Duderstadt Calibrated Biochemical Parameters and COD Influent Fractionation Soil Filters Introduction Material and Methods Pilot-scale sand filter Analytical methods Mathematical model Biological processes Biofilm modeling Results and Discussion Model calibration and simulation results Sensitivity analysis Waste Stabilization Ponds Introduction Material and Methods Description of the pilot pond Mathematical model Hydraulic concept Algal processes Physico-chemical processes Results and Discussion Model calibration and simulation results Anaerobic Treatment Introduction Material and Methods Analytical methods Reactor operation Mathematical model and sensitivity functions Results and Discussion Calibration of the ADM 1 Modeling reactor performance Sensitivity analysis for the biochemical parameters and the inflow fractioning Simulation of the energy balance

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4.08.1 Introduction

4.08.2 Mathematical Modeling of Biochemical Processes

Sanitary engineering is an extremely complex field of work. Practical experience and understanding of basic principles of engineering sciences, biology, hydrology, and computer science, as well as of social science and economics are necessary to deal with water issues in an efficient and sustainable manner. Sanitary engineering covers the specific fields of drinking water, water supply, sewage disposal, wastewater treatment, and river water quality. In many cases, there are overlaps and intersections between individual areas of expertise. In this chapter, the focus is on the application and development of mathematical models for wastewater treatment. Mathematical models can supply important information for the basic understanding of biochemical microbial conversion in technical systems, solve questions of optimal plant operation to save energy or investment costs, and contribute to the management of highly complex systems. Especially in developing countries with their demands for new and sustainable water solutions, integral approaches have to be developed to bring together technical, economic, and social objectives. To achieve these goals, mathematical modeling is an essential tool. Section 4.08.2 provides a short introduction to the mathematical modeling of biochemical processes. In Section 4.08.3, results from the calibration and validation of the Activated Sludge Model No. 3 (Gujer et al., 1999) for German wastewater are presented, including detailed information on how to deal with nitrogen and enhanced biological phosphorus removal in the mathematical model. Section 4.08.4 discusses the dynamic modeling of biochemical processes in soil filters. Apart from issues like how to calculate substrate conversion and how to anticipate soil clogging, this section informs about the modeling of biofilms, discusses how the growth of the microbial community which is formed on the single grains is limited by diffusion, and points out how the detachment of cells is one of the most sensible factors influencing the results of biofilm modeling. Section 4.08.5 describes the simulation of facultative lagoons and maturation ponds. The newly developed model expands the ASM 3 by adding algae growth and decay, sun radiation, ammonia stripping, wind impact, and ionic equilibrium as variables. Section 4.08.6 then presents the mathematical modeling of anaerobic processes. The Anaerobic Digestion Model No. 1 (Batstone et al., 2002) is calibrated for agricultural substrates and extended by equations to calculate energy balances.

Biological processes in wastewater-treatment plants (WWTPs) are based on natural processes in waterways. In waterways, microorganisms oxidize carbon into CO2, as well as water and ammonia nitrogen into nitrate nitrogen and elementary gaseous nitrogen. These conversion processes take place with the help of both suspended and attached biomass, with the latter finding its growth areas on stones and plants. Although conversion processes in attached and suspended biomass are similar, the mathematical modeling of attached biomass is considerably more difficult. There, substance conversion can be restricted by diffusion processes of the substrate to the cells. For suspended biomass, it is assumed that the diffusion of substrate into the interior of the floc is no rate-limiting step. Although substance conversion processes in waterways and WWTPs are based on the same principles, in WWTPs considerably higher conversion rates are achieved. This is due to the amount of provided substrate and the quantity of active biomass which is retained in the system through sedimentation tanks. The increased demands on environmental protection and further development of the process technology are met through the dimensioning of WWTPs with complex biochemical models. Apart from the dimensioning, mathematical simulation is used for the optimization of existing treatment plants, to achieve better effluent concentrations, lower sludge production, and oxygen input and to minimize operational costs. The following sections serve to explain the basic principles of the mathematical modeling of biochemical processes. Thus, this chapter deals with the hydraulic presentation of the flow stream, biological conversion processes, and sedimentation of biomass in the secondary clarifier tank. The aim of biological wastewater treatment is to remove those substances contained in the wastewater which are hazardous for humans and/or nature, or to change them in such a way as to render them harmless. Biological wastewater treatment realizes and technically utilizes the natural processes of transforming pollutants into inorganic and organic final products by microorganisms. In WWTPs, these conversion processes take place in the activated sludge tank, where wastewater and activated sludge are mixed and aerated. The oxygen necessary for the biological degradation is provided by aeration implements. From the activated sludge tank, the wastewater–sludge mixture flows into the secondary clarifier tank, where the activated sludge separates from the treated wastewater (see Figure 1). The sludge settling in the secondary

Clarifier Inflow

Effluent Aerated tank Surplus sludge

Return sludge Figure 1 Activated sludge plant consisting of aerated tank and secondary clarifier tank.

Modeling of Biological Systems

clarifier tank is conveyed back into the activated sludge tank as recirculation sludge, while the treated wastewater flows off. During the biological processes, the amount of activated sludge increases and is then removed as surplus sludge. Activated sludge processes are characterized by aerobic environment (dissolved oxygen available), anoxic zones (no dissolved oxygen available, but bound oxygen in the form of nitrate), and anaerobic zones (neither dissolved nor bound oxygen available). While aerobic and anoxic zones are essential to remove chemical oxygen demand (COD), nitrogen, and phosphorus, anaerobic zones in activated sludge processes are mainly established in order to facilitate enhanced biological phosphorus elimination.

4.08.2.1 Biological Processes 4.08.2.1.1 Carbon removal One crucial aspect for the dimensioning of the aeration and for the amount of produced surplus sludge in municipal WWTPs is the elimination of carbon. The oxidizable carbon can approximately be described as biochemical oxygen demand (BOD) or COD. Nowadays, COD is increasingly used to describe the organic load of a WWTP. COD represents the oxygen amount necessary to oxidize organic carbon into CO2 and water. The major advantage of COD is that it is easy to create the COD balance. The principle of this balancing for an activated sludge plant is presented in Figure 2 as an example. It becomes obvious that the influent COD must be equal to the sum of effluent COD, oxygen demand, and surplus sludge.

4.08.2.1.2 Nitrification During nitrification, ammonium nitrogen (NH4–N) is biochemically converted into nitrate nitrogen. This process is affected by bacteria which oxidize inorganic nitrogen into nitrate nitrogen (NO3–N), and which commonly are referred to as nitrifiers. These are obligatorily autotrophic microorganisms, which require CO2 and thus do not need any further organic carbon for the growth of their biomass. Nitrification takes place in two stages: first nitritation, then nitratation, both with different microorganisms.

4.08.2.1.3 Denitrification Denitrification is the conversion (reduction) of nitrate into gaseous nitrogen (N2). Many heterotrophic bacteria (denitrifiers) are able to consume the nitrate oxygen instead of

bound oxygen. If oxygen is available (aerobic milieu), the microorganisms will, as a rule, always prefer the O2 respiration; only in times of oxygen shortage and presence of nitrate and/or nitrite (anoxic milieu) will they switch to denitrification. Thus, nitrate is removed from wastewater by denitrification only if there is a shortage of dissolved O2.

4.08.2.1.4 Biological P-elimination Next to the elimination of nitrogen, the removal of phosphorus from the wastewater is of major importance. Phosphorus removal is achieved by bacteria which, under certain conditions, take up an increased amount of phosphate. By extracting these bacteria with the surplus sludge, phosphorus can be removed. The increased phosphate incorporation of the bacteria occurs if the organisms are first subjected to oxygen-free (anaerobic) zones, where they deliver phosphate into the wastewater (release), and then are moved to aerated (aerobic) zones, where they increasingly incorporate phosphate. Generally, enhanced biological P-elimination is thus coupled to a switching between anaerobic and aerobic or anoxic conditions.

4.08.2.2 Modeling of Hydraulic Conditions in Activated Sludge Plants Today, one-dimensional (1D) or multi-dimensional hydrodynamic models are used for complex simulations of river water quality. Hydrodynamic models are particularly recommended if highly unsteady effluent conditions must be expected. These models are based on the St.-Venant equations, which fulfill continuity conditions, and on equations of motion. 1D models with rectangular profiles are used for waterways if measuring data are not available in sufficient quantity and quality, or if an integrated approach is needed, where sewer system, WWTP, and river have to be considered together. Plug-flow reactors, which imitate the situations in actual waterways, are a sensible solution if strongly unsteady waterway conditions appear more rarely, if the target is to make statements about the capacity of the respective river in regard to the substance degradation of stationary loads, or if annual balances of substance loads should be determined. This reactor type should also be used for the simulation of activated sludge plants. Strong plug-flow is observed, for instance, in recirculation ditches. Figure 3 depicts the substrate degradation in a plug-flow reactor.

2500 kgO2 d−1 demand Clarifier Inflow Aerated tank

Effluent 500 kgCODd−1 Surplus sludge

5000 kgCODd−1

2000 kgCODd−1

Return sludge Figure 2 Exemplary COD balance for an activated sludge plant.

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c(t)

c (t)

Reactor length Time

Figure 3 Simplified substrate degradation in a plug-flow reactor.

Figure 5 Substrate degradation in a batch reactor over time.

substrate concentration C (if the reaction rate is assumed as k*C):

c (t)

C¼

C0 1 þ ðk  V=QÞ

ðg m 3 Þ

ð3Þ

where k is the reaction rate constant (d1). This also corresponds to the equation for the first CSTR reactor:

C1 ¼

Time Figure 4 Substrate degradation in the CSTR reactor.

ðg m 3 d 1 Þ

C2 ¼

C1 1 þ ðk  V=QÞ

ðg m3 Þ

ð5Þ

ðg m3 Þ

ð6Þ

Thus, the following is a general rule:

Cn ¼ where C is the substrate concentration (g m ), t the time (d1), Q the flow rate (m3 d1), A the reactor cross-section area (m2), z the space (m), and r the substrate conversion rate (g m3 d1). Often recirculation ditches are modeled not as a plug-flow reactor, but as a series of fully mixed reactors. Based on the hydraulic regime, it should be determined with how many fully mixed reactors (completely stirred tank reactors, CSTRs) the plug-flow reactor can be approximated. Mathematically, a cascade of an infinite number of CSTRs in line corresponds to a plug-flow reactor. Substrate degradation in a CSTR can be approximated as shown in Figure 4. One can assume that the substrate concentration in the reactor is equal to that of the reactor effluent. The corresponding differential equation is as follows (a constant reactor volume provided):

ðg m 3 d 1 Þ

ð4Þ

ð1Þ

3

dC Q ¼ ðC0  CÞ  r dt V

ðg m3 Þ

with C1 being the effluent concentration of the reactor (g m3). The following applies to the second reactor, if its influent concentration is the effluent concentration of reactor 1:

The corresponding differential equation expresses the dependence of concentration on both flowing distance and time:

dC Q dC ¼ r dt A dz

C0 1 þ ðk  V=QÞ

ð2Þ

with V being the volume (m3) and C0 the inflow substrate concentration (g m3). For steady-state conditions (substrate concentrations do not change with time), it can be easily explained how a series of fully mixed reactors will provide approximately same results as a CSTR cascade. In steady states, the following equation results, broken down for

Cn1 1 þ ðk  V=QÞ

If all previous equations are considered, for a reactor n the following can be concluded:

 Cn ¼

1 1 þ ðk  V=QÞ

n C0

ðg m3 Þ

ð7Þ

where n is the number of reactors in serie. In a batch reactor with a fixed volume, biochemical reactions are investigated without reactor input or output (Figure 5). With wastewater treatment, for instance, respiration experiments are run to determine the respiration activity of the sludge with different substrates. The mathematical description is quite simple:

dc ¼r dt

ðg m 3 d 1 Þ

ð8Þ

with r being the conversion rate in (g m3 d1).

4.08.2.3 Modeling of Biochemical Processes Based on the previous considerations, the following section serves to explain how biological degradation processes are described mathematically. The following considerations will be based on the CSTR reactor. Conversion rates (kinetics) of biological processes are often described using Monod kinetics, with

Modeling of Biological Systems

differentiation into growth kinetics and inhibition kinetics. With growth kinetics, the conversion rate rises with an increased substrate supply; with the inhibition kinetics, the conversion rate decreases due to inhibition of biochemical processes. A typical example of growth kinetics can be found in nitrification. The conversion increases with the presence of ammonium. At WWTPs, inhibition kinetics gain importance, for instance, with denitrification, which may eventually come to a standstill by oxygen. Figure 6 shows a typical example to describe the dependence of the conversion rate on a substrate – growth kinetics at lower substrate concentrations and inhibition kinetics at higher ones. The kinetics of the process illustrated in the diagram then can be described:

m ¼ mmax

S KI ðd1 Þ S þ KS KI þ S

ð9Þ

where m is the growth rate (d1), mmax the maximal growth rate (d1), S the dissolved substrate (g m3), and KS and KI the substrate half-saturation and inhibition concentrations (g m3), respectively. Monod kinetics can be further simplified depending on substrate concentrations (Figure 7). If it can be assumed that substrate is always available in more than sufficient amounts (SbKS), the growth term becomes 1. Then, if inhibition is not considered, the following applies: m ¼ mmax, resulting in an equation of zero order. In cases when substrate is available only at very low concentrations, for instance below the half-saturation constant KS, the reaction rate can be reduced to r ¼ m*S (equation of first order). Apart from the degradation rate of a process, the substance conversion as such must be described. This happens on the basis of chemical equations. A typical example is the degradation of carbohydrates into biomass, CO2, and water (Gujer, et al. 1999):

C6 H12 O6 þ 2:45O2 þ 0:71NH3 -0:71C5 H7 NO2 þ 2:45CO2 þ 4:58H2 O 0.6

ð10Þ

This equation is often used to determine the yield rate (Y). The yield states how many grams of biomass develop from 1 g of influent substrate. The yield rate is commonly rendered in COD units. For the equation mentioned above, the following applies:

CODðC5 H7 NO2 Þ 160 Y ¼ 0:71 ¼ 0:71 CODðC6 H12 O6 Þ 192 ¼ 0:59 ðgcod;biomass g1 cod;

  dXB V ¼ Q XB;o  XB  rV dt

rV ¼ 1mmax

/max

ðg d1 Þ

S So XB V  1bXB V S þ KS So þ Ko

ð12Þ

ðg d1 Þ

ð13Þ

where Ko is the oxygen haf-saturation concentration (g m3) and b the biomass decay rate (d1). For each process, the stoichiometric factor and kinetics are multiplied. Then, the single processes relevant for one parameter (here: S, So, and XB) are summarized. Stoichiometric factors are determined on the basis

0.4

0.3

0.2

0.1

0 10 S/Ks Figure 6 Growth and substrate inhibition with one substrate.

ð11Þ

with XB,0 being the inflow biomass concentration (g m3). Substrate conversion is described by the term r*V, which results from the matrix. It is comprised of the stoichiometric factor and kinetics, with both factors being multiplied by each other. For the biomass XB, the following applies:

0.5

5

inflow Þ

In a simplified manner, both the chemical equation and degradation kinetics are summarized in a matrix (see Table 1). The matrix contains two processes (biomass growth and decay) and three substrates (COD: S, XB: biomass, oxygen: SO). The matrix serves to present processes in a clearly structured manner and can be translated into the differential equations for any substrate. This is explained below for a CSTR reactor, using the biomass as example. Generally, the differential equation for each substrate comprises influent load per time, effluent load per time, and substrate conversion per time. Thus, for the biomass XB in a CSTR reactor, the following applies:

Growth inhibition

0

235

15

20

236

Modeling of Biological Systems

1

0th Order

1st Order

0.9 0.8

/max

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

5

10

15

20

S/Ks Figure 7 Reduction of the Monod kinetics according to the frame conditions in equations of 0th and 1st order.

Table 1

Matrix notation

Description

Stoichiometry S

Biomass growth

1  Y

Biomass decay

Kinetic: process rate p S0

XB 1 1

of chemical equations. Each process is comprised of a chemical equation and kinetics. If all substrates are regarded in the balance, the COD of the products must be equal to the COD of the reactants, which means that the COD balance is closed. In the present case, the following applies for the biomass growth:

SðsubstrateÞ þ So ðoxygenÞ-XB ðbiomassÞ þ H2 O þ CO2 ð14Þ As the COD of H2O and CO2 is zero, the equation can be reduced, and the following results:

SðsubstrateÞ þ So ðoxygenÞ-XB ðbiomassÞ

ð15Þ

If this equation is expressed using the yield coefficient Y, the following applies:

1  ð1  YÞ ¼ Yð2Þ

ð16Þ

This means that from one unit of substrate, there results Y units of biomass under reduction of 1  Y units of oxygen. It must be considered here that oxygen has a negative COD, so that a negative sign before (1  Y) needs to be included into the equation. Two transformations then yield the stoichiometric factors, which can also be found in the matrix:

1 1Y  ¼ 1 ð2Þ Y Y   1 1Y  þ1  ¼0 ðÞ Y Y

ð17Þ

ð18Þ

ð1  Y Þ  Y

mmax

1

bX B

S So XB S þ K S So þ K o

4.08.3 Modeling of Biological Processes in Activated Sludge Systems 4.08.3.1 Introduction In sanitary engineering, the most extensive experience has been made with the modeling of activated sludge systems. The first models were developed as early as at the beginning of the 1980s and led to the development of the Activated Sludge Model No. 1 (Henze et al., 1987). Nowadays, processes such as carbon removal, nitrification, and denitrification as well as the biological and chemical phosphorus removal are considered in the models. The focus of later models is on the description of enhanced biological phosphorus removal (EBPR). In an anaerobic environment, phosphorus is released when substrate is stored into cell biomass. In the following aerobic and anoxic conditions, polyphosphate accumulating organisms (PAOs) grow on stored substrate and are simultaneously take up phosphate to larger extent than they released before. To describe EBPR mathematically, models such as the EAWAG-BioP-Module (Rieger et al., 2001) in connection with the Activated Sludge Model No. 3 (ASM3; Gujer et al., 1999), the TUD model from the University of Delft (Murnleitner et al., 1997), the model from Barker and Dold (1997), and the Activated the Sludge Model No. 2d (Henze et al., 1999) are widely used. It is common to all models that Petersen matrix (Petersen et al., 2000) is used for the description of the biochemical processes. In this chapter, the focus is on investigations that were accomplished by ASM 3 to

Modeling of Biological Systems

model COD and nitrogen removal, and on the EAWAG BioP module to describe EBPR. The computations were accomplished by using software Simba 4.2 (2005) and Matlab/ Simulink (2005).

4.08.3.1.1 Activated Sludge Model No. 3 The Activated Sludge Model No. 3 was published in 1999 by the IWA task Group on Mathematical Modelling for Design and Operation of Biological Wastewater Treatment (Gujer et al., 1999). Better possibilities to identify biological processes today have resulted in the development of ASM 3 to simulate nitrification, denitrification, and degradation of COD. ASM 3 describes the storage of organic substrates, and the decay of heterotrophic organisms is modeled by the endogenous respiration. Heterotrophic yields are considered separately for aerobic and anoxic environments. The effect of redox conditions on the hydrolysis was assumed to be negligible. The hydrolysis of nitrogen was combined with the process of COD hydrolysis. Decay rates for endogenous respiration of heterotrophic and autotrophic organisms are reduced under anoxic conditions (Nowak, 1996; Siegrist et al., 1999). The following simulations were based on findings of Koch et al. (2000).

4.08.3.1.2 EAWAG BIOP Module The EAWAG Bio-P Module (Rieger et al., 2001) extends ASM 3 by processes which describe biological phosphorus removal. General principles of ASM 3 can be found again in EAWAG BioP Module. Endogenous respiration replaces lysis, different yields for aerobic and anoxic conditions are used, and the anaerobic decay was neglected for the PAOs. Eleven processes

237

were implemented additionally into ASM 3 to describe EBPR. Fermentation of COD is not modeled as a single process, nor was glycogen considered as an additional substrate pool for growth. Processes of chemical precipitation were integrated additionally in the same way as they were used in ASM 2d (Henze et al., 1999).

4.08.3.2 WWTP at Koblenz The wastewater treatment plant Koblenz was extended until 1992 to a population equivalent of PE ¼ 320000. The plant was designed for pre-denitrification and consists of a primary treatment unit, a first biological stage using trickling filter (only operated in the case of storm water flow), and a second biological stage built as activated sludge system. Rectangular tanks with horizontal flow are used as sludge sedimentation tanks. The WWTP is characterized by two different lines with different tanks in series and different operational conditions. In both lines, strong substrate gradients can be found, such that the plug-flow characteristic needs to be considered in the models. Figure 8 shows the treatment system. Both lines, consisting of activated sludge tanks and secondary settling tank were modeled in detail. Measured data was available in the inflow for total COD, dissolved COD, and easily degradable COD. The influent load of the new line was different from the old one, as wastewater from paper production was dumped into the new line. The influent COD was fractioned as follows: 61.5% XS, 10% XI, 16% XH, 3% SI. Measured easily degradable COD was 10% of total COD after pretreatment in the trickling filters. Data were achieved by respiration measurements in batch reactors. In the activated

Old line

TF Denitrification reactor

Nitrification reactor

SST Outflow

Bypass RS

Inflow

SS

TF New line

Denitrification reactor

Nitrification reactor

SST Outflow

TF

RS

SS

Bypass Figure 8 Wastewater treatment plant at Koblenz. TF, trickling filter; SST, secondary settling tank; RS, return sludge; SS, surplus sludge.

238

Modeling of Biological Systems

sludge tank total suspended solids (TSSs) and the effluent concentrations of ammonia and nitrate were measured. To consider the plug-flow character of the systems, measured data of oxygen at different points were modeled. Sludge retention times were 11.6 and 10.4 days, respectively. Phosphorus removal was not modeled for the WWTP at Koblenz. Chemical precipitation was only integrated in the simulation as precipitation sludge to consider its effect on sludge age. Precipitated sludge was considered as inert fraction of influent TSS. Because of the plug-flow character of the system (e.g., 1:8 width:length at the new line), the plant was modeled with several tanks in line. Measured oxygen data were available at six points of the old line and four points of the new line. The average oxygen concentrations were quite high (3.2 gO2 m3), especially in the old line. Measured oxygen concentrations are used in the simulation by reading the data from files and comparing and adapting the oxygen values in the simulation with the measured data by proportional-integral-derivative (PID) controllers. The results of ammonia nitrogen effluent simulation are depicted in Figure 9. During the calibration, the nitrification capacity was raised, as with the standard parameters of ASM 3 no sufficient nitrification could be realized. Therefore, the maximum autotrophic growth rate mN was increased to 1.5 d1. Average ammonia nitrogen concentrations in the effluent of the aerated tanks were simulated well. The dynamic behavior shows sufficient results. Modeling of denitrification was easier at the WWTP Koblenz. Despite a low BOD/TKN inflow ratio of 3, low average nitrate nitrogen effluent concentrations of 6 g m3 could be modelled well (TKN, total Kjeldahl nitrogen). The ratio of anoxic heterotrophic decay to aerobic decay was 50%

4.0

4.08.3.3 WWTP at Hildesheim The WWTP at Hildesheim was equipped with a biological stage that allows for nitrification, denitrification, and EBPR. The plant is operated according to the ISAH process. The main asset is that the return sludge is denitrified in a separate anoxic tank in order to prevent any possible impairing of phosphate release in the anaerobic tank through recirculated nitrate. Of four lines planned for the activated sludge plant, two were built and started in July 1987 (Figure 11). The other two lines have both been running since 1997. Operational experiences from the old lines were taken into consideration for the upgrade of the plant. The WWTP at Hildesheim is operated with simultaneous denitrification. The circulation ditch is modeled with two to three layers representing different substrate concentrations over the depth (Figure 12). The circulation ditch is 2 m deep while the upper layer is 71 cm. The lower layer was devided in two layers (65 cm each). One cannot assume that the installed surface aerators (rotors) accomplish complete mixing of the wastewater over the complete depth. In contrast, strong oxygen gradients can be measured. Data showed that the ratio of velocities of the upper and lower part of the tank was 1.8. Modeling of three layers also allows for simulation of anaerobic zones near the bottom of the tank. Oxygen calibration was done based on oxygen sensors in a depth of 30 cm below surface. For simulation of the plant at Hildesheim, measured online-data of total COD, filtrated COD, TKN, and ammonia nitrogen were used.

NH4−Neff, AST, sim

25

NH4−Neff, AST, meas. old line

NO3−Neff, AST, sim

20

NO3−Neff, AST, meas. old line

NO3−N (mg I−1)

NH4−N (mg I−1)

5.0

(ZNO3,end,H ¼ 0.5). This value was recommended by Koch et al. (2000). The results are shown in Figure 10.

3.0 2.0 1.0 0.0 1.0

2.0

3.0 4.0 Time (days)

5.0

6.0

10 5 0

7.0

0

5.0

NH4−Neff, AST, sim

25

4.0

NH4−Neff, AST, meas. new line

20

NO3−N (mg I−1)

NH4−N (mg I−1)

0.0

15

3.0 2.0 1.0 0.0

1

2

3 4 Time (days)

5

6

7

NO3−Neff, AST, sim NO3−Neff, AST, meas. new line

15 10 5 0

0.0

1.0

2.0

3.0 4.0 Time (days)

5.0

6.0

7.0

Figure 9 Simulation results compared with measured data of ammonia nitrogen (g m3), old line (left), new line (right). Gray line: simulated values, black line: measured data.

0

1

2

3 4 Time (days)

5

6

7

Figure 10 Simulation results compared with measured data of nitrate nitrogen (g m3), old line (left), new line (right). Gray line: simulated values, black line: measured data.

Modeling of Biological Systems

Inflow

SST

Denitrification reactor Nitrification reactor

Anaerobic reactor

239

Outflow

Surplus sludge

DNRS reactor

Figure 11 Flow sheet of the WWTP at Hildesheim. SST, secondary settling tank; DNRS, denitrification–recirculation sludge.

D

A

D

A

A

A/D

A/D

D/P

D/P

Inflow

Outflow A/P D

D

Internal recirculation Figure 12 Simplified scheme of model for the recirculation ditch with different layers at the WWTP at Hildesheim (D: mainly anoxic, A: mainly aerobic, P: mainly anaerobic).

Respirations tests revealed a readily degradable COD to be 16% of total COD. Additional online data were available in the effluent for NH4–N, NO3–N, and PO4–P. For the calibration of the TSS in the aerated tank, we used a measured factor of 1:1 gCOD gTS 1 . Simulation results (ASM 3 with EAWAG BioP module) of nitrogen species showed excellent correspondence with measured data, as shown in Figure 13. The influent fractioning in % of total COD was as follows: 49% XS, 15% XI, 9% XH, 11% SI and 16% SS. The results are presented in Figures 13 and 14. Nitrogen effluent values at Hildesheim were simulated without further calibration of biological parameters. Nitrification was increased only slightly by setting the maximum autotrophic growth rate to 1.1 d1. The half-saturation coefficient of heterotrophic biomass for oxygen was calibrated to 0.5 gO2 m3, which resulted in better denitrification. Figure 14 shows the simulated and measured PO4–P data. As can be seen, the PO4–P peak was modeled very well. The polyphosphate storage rate qPP was increased from 1.5 to 2.3 d1 and the maximum storage of polyphosphate Kmax was changed from 0.2 to 0.25 g m3.

4.08.3.4 WWTP at Duderstadt The WWTP at Duderstadt was designed for intermittent denitrification with an integrated pre-anaerobic volume in a

round aerated tank (Figure 15). Enhanced biological phosphorus removal could be established. The plant is operated with a high sludge retention time of about 25 days and without a primary settling tank. Thus, higher concentrations of particulate components in the influent of the AST can be expected. The influent fractioning in % of total COD was as follows: 63% XS, 10% XI, 14% XH, 3% SI and 20% SS. The intermittently aerated tanks are operated through oxygen and ammonia control. Influent concentrations of TSS calculated by ASM 3 were increased by an additional inert fraction to model measured TSS data. For Duderstadt, nitrification and phosphorus removal was increased again. The highly dynamic situation in the effluent of the activated sludge tank is quite difficult to model. The saturation coefficient of nitrogen for autotrophic biomass KNH,N was set to the value of 0.5 gN m3 to model the effluent ammonia nitrogen concentrations. As can be seen in Figure 16 (left), the dynamic simulated curve of ammonia nitrogen still did not always reach minimum effluent ammonia nitrogen concentrations of 0 mg l1. However, the simulation was improved considerably compared with results achieved when modeling biomass decay is independent of the redox milieu. Furthermore, the maximum autotrophic growth was changed to a typical value of 1.4 d1. The NO3–N and PO4–P effluent concentrations showed good results, as can be seen in Figure 16 (right). The

240

Modeling of Biological Systems

NH4−N (g m−3)

20.0

0.0

1.0

2.0

3.0 4.0 Time (days)

5.0

6.0

denitrification capacity was increased with the saturation coefficient of oxygen for the heterotrophic organisms KO,H ¼ 0.5 g m3. At Duderstadt, the EBPR was modeled with a polyphosphate storage rate qPP increased from 1.5 to 1.7 d1. Remarkable, however, is the PO4–P peak on three days of simulation time. Differently than measured, the model showed increased concentrations in the effluent of the secondary settling tank. PO4–P effluent concentrations are found in situations with high phosphorus inflow or in cases with suddenly high COD input. This can result in a high PO4–P release in the anaerobic tank without phosphorus being taken up that fast in the aerobic and anoxic zones. Yet, data analysis showed that here discrepancies can be found because of a rainwater event with approx. 80% increased inflow in between day 2.5 and 3. Further simulations showed that measuring data in the case of rainy weather could be modeled better with a PHA uptake rate of qPHA ¼ 12 d1. Calibrated in this way, greater discrepancies in dry weather situation occurred. Thus, in the end the PHA uptake rate was not changed.

0.0

1.0

2.0

3.0 4.0 Time (days)

5.0

6.0

4.08.3.5 Calibrated Biochemical Parameters and COD Influent Fractionation

15.0 10.0 5.0 0.0

NH3−N (g m−3)

25.0 20.0 15.0 10.0 5.0 0.0

Figure 13 Simulation results compared with measured data of NH4–N (left) and NO3–N (right) (g m3) for a representative load at the WWTP at Hildesheim. Gray line: simulated values; black line: measured data.

PO4-P [g m−3]

10.0 8.0 6.0 4.0 2.0 0.0 0.0

1.0

2.0

3.0 4.0 Time [d]

5.0

6.0

Figure 14 Simulation results compared with measured data of PO4–P (g m3) for a representative load at the WWTP at Hildesheim. Gray line: simulated values; black line: measured data.

In Table 2, the values of the kinetic and stoichiometric parameters that were adapted to simulate the measured data of the plants at Koblenz, Hildesheim, and Duderstadt are presented. The basis for these parameters are publications by Koch et al. (2000) for ASM 3 and Rieger et al. (2001) for the EAWAG BioP Module. It is noteworthy that for German municipal wastewater, the nitrification, denitrification, and phosphorus removal values in the model had to be increased slightly. The maximum autotrophic growth rate was mN ¼ 1.0–1.7 d1, that is, within typical values recommended by Koch et al. (2000) and Rieger et al. (2001). These authors assume that these values result from biofilm growth in activated sludge tanks. Koch et al. (2000) believe that higher CO2 stripping with increased pH is the reason for the calibrated maximum autotrophic growth rates. In few cases in literature, reduced half-saturation coefficients of oxygen KO,N are described to model better nitrification. Wentzel and Ekama (1995) recommend KO,N ¼ 0.02 g m3 for the simulation of activated sludge with

5.0

NH4−N [mg I−1]

4.0 3.0 2.0 1.0 0.0 0.0

1.0

2.0

3.0

4.0 5.0 Time (days)

6.0

7.0

8.0

9.0

Figure 15 Simulation results compared with measured data in the effluent of the AST for NH4–N (g m3) at the WWTP at Duderstadt. Gray line: simulated values; black line: measured data.

Modeling of Biological Systems

ASM 2 (Henze et al., 1995). In the STOWA report from the Netherlands, Hulsbeek et al. (2002) give KO,N ¼ 0.4 g m3, as does Seggelke (2002) for the simulation of the pilot plant at Gu¨mmerwald with ASM 2d (Henze et al., 1999). However, in

NH3−N (mg I−1)

5.0 4.0 3.0 2.0 1.0 0.0 0.0

1.0

2.0

3.0

4.0 5.0 6.0 Time (days)

7.0

8.0

9.0

PO4−P (mg I−1)

3.0 2.5 2.0

241

batch experiments the half-saturation coefficient for oxygen could be measured quite well (KO,N ¼ 0.5 g m3). Thus, following the publication of Koch et al. (2000), no change of this value can be recommended here. To model a better flux for autotrophic biomass in the activated sludge plant at Duderstadt (intermittent denitrification), the half-saturation coefficient for ammonia nitrogen was reduced to KNH,N ¼ 0.5 gN m3. Only by lower ammonia nitrogen concentrations could be described in simulation. Discrepancies in literature for KNH,N are significant (Horn and Hempel (1997): KNH,N ¼ 0.5 g m3; Seggelke (2002): KNH,N ¼ 0.1 g m3 with ASM 2d; Makinia et al., (2005): KNH,N ¼ 0.2 g m3 with ASM 3). The value calibrated here seems to be realistic. Furthermore, enhanced biological phosphorus removal was examined. The calibration was done via the storage rate of polyphosphate qPP and the maximum polyphosphate content of the biomass Kmax. The polyphosphate content of PAO biomass is between 0.1 and 0.4 gP g1 COD (e.g., Rieger et al. according to Wentzel and Ekama (1997), (2001), 0.38 gP g1 COD 0.4 gP g1 COD according to Johansson et al. (1996)). Here, calibration of Kmax showed Kmax ¼ 0.20–0.25 gP g1 COD.

1.5 1.0

4.08.4 Soil Filters

0.5

4.08.4.1 Introduction

0.0 0.0

1.0

2.0

3.0

4.0 5.0 6.0 Time (days)

7.0

8.0

9.0

Figure 16 Simulation results compared with measured data in the effluent of the SST for NO3–N (left) and PO4–P (right) (g m3) at the WWTP at Duderstadt. Gray line: simulated values; black line: measured data.

Planted soil filters and wetlands are used to treat municipal and industrial wastewater with low concentration of particulate material (Brix and Arias, 2005; Molle et al., 2005). Soil filters and wetlands are built as different kinds of systems with horizontal and vertical flow, with horizontal-flow filters being used rather for COD and nitrate nitrogen elimination, vertical-flow filters for nitrification. Through mathematical

Table 2 Calibrated biochemical parameters for simulation of the wastewater-treatment plants (Wichern, 2010) at Hildesheim, Duderstadt, Koblenz, and Gu¨mmerwald compared with values published by Gujer et al. (1999), Koch et al. (2000), and Rieger et al. (2001) Parameter

Unit

Gujer

Koch/Rieger

Hildesheim

Duderstadt

Koblenz

Gu¨mmerwald

Nitrification mN KO.N KNH.N

d1 gO2 m3 gN m3

1.0 0.5 1.0

0.9–1.8 0.5 1.0

1.1 0.5 1.0

1.4 0.5 0.5

1.5 0.5 1.0

1.7 0.5 1.0

Denitrification KO.H

gO2 m3

0.2 0.5

0.2 0.33

0.5 0.5

0.5 0.33

0.2 0.5

0.2 0.33

0.2 1.5 0.2

0.25 2.3 0.2

0.2 1.7 0.2

0.03 0.03 0.035 0.010 0.005 0.014

0.03 0.04 0.04 0.010 0.007 0.014

ZNO3,end,H P removal Kmax qPP KPO4.PP

gP gCOD 1 d1 gP m3

N and P content of chemical oxygen demand fractions iNSS 0.03 0.03 gN gCOD 1 0.03 0.03 iNXS gN gCOD 1 iNXI 0.035 0.035 gN gCOD 1 iPXI 0.010 gP gCOD 1 iPXS 0.005 gP gCOD 1 iPBM 0.014 gP gCOD 1

0.2 1.2 0.2 0.04 0.04 0.04

0.03 0.03 0.035 0.010 0.005 0.014

242

Modeling of Biological Systems

modeling of many different processes involving wastewater treatment on planted soil filters, the achievement of a more indepth understanding is expected, which might help further improvement of the utilization of these systems. It is possible to reproduce a simplified reality in a sensible way and extend the often insufficient 1D consideration of soil or sand filters. A calibrated model is an efficient tool to optimize the filter operation – taking into account the impact of the sand/gravel mixture, the wastewater feeding, the temperature, and the oxygen input. Another important phenomenon is the filter clogging, which frequently restricts the purification capacity of the filter and also may impair the utilization of the model. A crucial task for sand filter modeling is to describe substrate conversion that occurs through the biofilm which settles predominantly in the upper areas of the soil filters and on the roots of the plants (Langergraber, 2005). An important step to compare biofilm models with different levels of accuracy and level was achieved in Wanner et al. (2006). A wide range of models from 1D to 3D has been tested to describe the highly complex phenomena that occur in biofilms. Yet, researchers have still not agreed on a standard biofilm model. While the identification of biological and biofilm-specific processes such as diffusion, or the attachment or detachment of particles have been done to a large extent during recent years (Wanner and Gujer, 1986; Gujer and Wanner, 1990; Horn and Hempel, 1997; Wanner and Reichert, 1996), the quantification of single processes in natural systems such as soil or sand filters is still very difficult due to the inhomogeneity of the substratum and substrate. Both identification and quantification can be aided by mathematical modeling. Moreover, even though there has been important progress in the modeling of soil filters, for instance, through the analyses of McBride and Tanner (2000), Langergraber (2003), Dittmer et al. (2005), or Henrichs et al. (2007), complex multi-dimensional models are only rarely being used in practice. Despite the complexity of substrate conversion and hydrology, 1D models are still in use for dimensioning and process optimization (Rousseau et al., 2004; Kadlec, 2000). One reason for this is still the poor availability of measuring data, especially for processes occurring in the biofilm.

4.08.4.2 Material and Methods 4.08.4.2.1 Pilot-scale sand filter Experimental data from a pilot-scale plant consisting of a preliminary SBR, sand filter, and a storage tank (Figure 17) were used to verify the model. The first layer of the filter consisted of sand and gravel (U ¼ 2.93, d60 ¼ 0.63 mm, d10 ¼ 0.21 mm, diameter 0.06–3 mm) with a porosity of 35%. The bottom with a height of 10 cm was composed of gravel. The sand filter had a volume of 0.55 m3 and a depth of 70 cm. In the SBR, nitrate was reduced with the influent COD load. If necessary, methanol was dosed additionally to improve denitrification. Nitrification of ammonium occurred in the vertical-flow sand filter that was fed discontinuously with different kinds of wastewaters (Lindenblatt et al., 2007). During the feeding with landfill leachate, the SBR was operated in four cycles, and in five cycles during the feeding with municipal wastewater (see Table 3). Landfill leachate was taken from a dumping ground in Bavaria. Domestic waste was collected in a covered storage ground together with industrial and commercial waste, rubble, polluted soil, and residues from WWTPs. For landfill leachate, a COD reduction of 30% and an ammonium removal of 80% is required. The planted sand filter was capable of treating ammonium peaks of 10 gNH4N m2 d1 and hydraulic loads of up to 200 l m2 d1. To avoid filter clogging, particular material was mainly removed by sedimentation and substrate conversion in the SBR. Furthermore, analyses of the treatment of municipal wastewater stemming from a municipal WWTP were run at the pilot-scale plant. Table 3 summarizes the load cases used for the model calibration.

4.08.4.2.2 Analytical methods The analytical methods employed for TSSs, volatile suspended solids (VSSs), COD, AOX BOD were based on German Standard Methods (DEV, 1981) for the examination of water, wastewater, and sludge. Ammonium, nitrate, nitrite, and COD were measured spectro-photometrically (Dr. Lange ISIS 6000).

4.08.4.2.3 Mathematical model Here, an integrated model simulating biological processes and geometric distribution of the biofilm on the sand grains was used (Wichern et al., 2008a). The model was capable of

Recirculation

Inflow

Sand filter Storage tank Sequence Batch reactor

Figure 17 Schematic view of the pilot plant.

Outflow

Modeling of Biological Systems Table 3

243

Simulated load cases for the sand filter

Load case

qa (mm d1)

NH4–N ðgm 2Filter d 1 Þ (g d1)

COD ðgm 2Filter d 1 Þ (g d1)

NO3–N ðgm 2Filter d 1 Þ (g d1)

1

161

2

127

3

127

4

161

5

161

6

184

7

294

7.4 5.9 5.1 4.0 6.1 4.8 6.4 5.0 5.6 4.4 8.2 6.46 7.9 6.24

11.4 9.0 7.9 6.3 9.4 7.4 10.6 8.3 12.6 10.0 41.1 32.5 73.2 57.8

3.2 2.5 3.2 2.5 5.1 4.0 1.1 0.9 1.6 1.2 0.1 0.07 0.2 0.1

a

Hydraulic surface loading rate. Influent data for cases 1–5 with landfill leachate and cases 6 and 7 with municipal wastewater.

describing substrate degradation, clogging phenomena in the filter and nitrification capacity of the autotrophic metabolism. Biological processes include two different biomass groups, namely heterotrophic and autotrophic nitrogen consumers. A (quasi/discrete) 2D approach was adopted considering horizontal layers of filter in line, each composed of one completely mixed bulk water volume and biofilm on the substratum surface. Thus, it was possible to calculate substrate gradients through both the depth of the biofilm and the depth of the sand filter, an idea that was also used by Horn and Telgmann (2000) when simulating an upflow biofilter. The model was implemented on the AQUASIM software (Reichert, 1998), utilizing one biofilm reactor compartment for each filter layer. For all the simulations, a total of four layers were utilized. A schematic view of the filter configuration in the model is displayed in Figure 18.

used, who calculated biofilters for secondary denitrification. The remaining growth area on the sand grains and the maximum biofilm thickness is mathematically connected to the biological biofilm model. The sand grains in filter are represented by spheres of uniform size. The grains can touch each other at up to eight points. Where the spheres touch, biomass growth on the surface of the spheres is not possible. Furthermore, the available surface for new biomass growth depends on the biofilm thickness of existing biomass. Figure 19 illustrates this relation. Figure 19 describes the background of the developed equations. It is possible to determine the loss of biofilm surface area, ALoss, between two grains of sand (considered as spheres) in relation to the radius r of the single spheres and the thickness of the biofilm (LF):

ALoss ¼ BpLFð2r þ 2LFÞ ðm 2 Þ

4.08.4.2.4 Biological processes In the model, the growth and decay of heterotrophic and autotrophic biomass under aerobic and anoxic conditions were considered. The decay processes are described with endogenous respiration for both organism groups. From the measured data of the sand filter for municipal wastewater, it became apparent that COD effluent values were very low (B20 g m3 CODhom). Hardly any substances were discharged in particulate form. To describe the complete degradation of particular matter, two degradable fractions were defined. XXS means COD which is degraded very slowly resulting from biomass inactivation, SS means the mixed fraction for COD that is degraded via heterotrophic growth (Gujer et al., 1999). The implementation of XXS allows for modeling very slowly degradable compounds at high sludge ages in the filter. Tables 4 and 5 describe the entire biochemical model.

4.08.4.2.5 Biofilm modeling The biofilm properties are a determinant step in the sand filter model development. Here, the basic idea of Horn (1999) was

ð19Þ

with B representing the number of contact points per sphere. The equation is based on the calculation of the surface of spherical segments. In order to be able to estimate the number of grains of sand in the sand filter, the porosity of the sand filter must be known. In the case of hexagonal packing with the tightest possible structure (HCP grid) and a cubic areacentered structure (FCC grid), the porosity is 0.26. In nonidealized pouring cases, however, the porosity is considerably higher. For sand/gravel mixtures, values up to 0.50 have been documented. Thus, the number of grains of sand in a test reactor depending on the porosity e amounts to

N¼

VReactor  VPores ð1  eÞ  VReactor ¼ ðÞ p 3 VBall d 6 Ball

ð20Þ

with N being the number of sand grains (–), e the porosity (–), and VReactor, VPores and VBall the reactor, free pores, and sand grain volumes (m3), respectively. Considering the number of

244

Modeling of Biological Systems Inflow

17 cm

Inflow Biofilm reactor compartment1 Bulk liquid

Biofilm Biofilm reactor Compartment 2 70 cm

Diffusion

Biofilm growth Biofilm reactor compartment 3

Detachment

Biofilm reactor compartment 4 Outflow Outflow Figure 18 Flow sheet of the model setup for the sand filter.

Table 4

Stoichiometric matrix of the sand filter biofilm model (Wichern et al., 2008a) Process

XH

XN

1

Aerobic heterotrophic growth

1

2

Anoxic heterotrophic growth

1

3 4

Aerobic heterotrophic decay Anoxic heterotrophic decay

1 1

5 6 7 8

Aerobic heterotrophic inactivation Anoxic heterotrophic inactivation Aerobic maintenance Anoxic maintenance

1 1

9

Aerobic autotrophic growth

10 11

Aerobic autotrophic decay Anoxic autotrophic decay

12

Hydrolysis

XXS

1 YH 1  YH 

1 1 1 1 1

SNH

1 1

SO

1 YH 1 IB þ IBS YH IB IB IB þ IBS

IB  IBS IB–IBS IBS IBS 

1  IB YN

IB IB 1

contact points and the diameter of the spheres allows for calculating the remaining biofilm surface ARemaining:

ARemaining ¼ Npd2Ball  NALoss ðm 2 Þ

SS

ð21Þ

Figure 20 makes clear that for the given values and depending on the number of contact points, a complete reduction of the remaining biofilm surface appears already at an

IB þ IBS

SNO

SI

1 YH 

1 1  YH 2:9 Y H

1 1  2:9

1 1  2:9 4:6  Y N  YN 1

1 YN 1  2:9

1

existing biofilm thickness between 60 and 110 mm. Thus, the possible biofilm thickness available for the growth of the biofilm in the sand filter stays within a relatively narrow range. Hence, it can be concluded that the biological processes which lead to the production of particulate components must be contained in the case of surface limitation. For this restriction, the following Monod-type term was introduced:

MLim ¼

ðLFmax  LFÞ ðÞ ðLFmax  LFÞ þ KLF

ð22Þ

Modeling of Biological Systems Table 5

Kinetic matrix of the sand filter biofilm model Process

Process rate (T0 ¼ 20 1C)

1

Aerobic heterotrophic growth

mH

2

Anoxic heterotrophic growth

mH ZH

3

Aerobic heterotrophic decay

bH

4

Anoxic heterotrophic decay

bH ZD

5

Aerobic heterotrophic inactivation

bH;Inakt

6

Anoxic heterotrophic inactivation

bH;Inakt ZD

7

Aerobic maintenance

mH

8

245

Anoxic maintenance

SS SO LFmax  LF XH K S þ SS K O þ SO ðLFmax  LFÞ þ KLF SS K OH SNO LFmax  LF XH K S þ SS K OH þ SO SNO þ K NO ðLFmax  LFÞ þ KLF

SO XH SO þ K O K OH SNO XH SO þ K OH SNO þ K NO SO LFmax  LF XH SO þ K O ðLFmax  LFÞ þ KLF K OH SNO LFmax  LF XH SO þ K OH SNO þ K NO ðLFmax  LFÞ þ KLF SS Main

mH

Main

KS ZH

Main

þ SS K O SS

KS

Main

SO XH þ SO

Main

K OH SNO þ SS K OH þ SO SNO þ K NO

XH Main

SNH SO2 LFmax  LF XN K N þ SNH K OA þ SO ðLFmax  LFÞ þ KLF

9

Aerobic autotrophic growth

mN

10

Aerobic autotrophic decay

bN

11

Anoxic autotrophic decay

bN ZD

12

Hydrolysis

SO XN SO þ K O

K OH SNO XN SO þ K OH SNO þ K NO X XS kH K X þ X XS =X H

Sand grain Biofilm Missing area percontact point

LF

r

Bulk liquid Figure 19 Idealized presentation of the sand body using spheres with the same diameter (left), missing growth area per contact points between two spheres (right).

where MLim is the surface limitation term to the growth rate (–), LFmax the maximal biofilm thickness (m), and KLF the half-saturation coefficient limiting the thickness of the biofilm growth (m). To avoid filter clogging, substrate conversion and particle detachment need to be included into the model. Biofilm obstruction – mainly in the two upper layers of the

model – is expected particularly at higher substrate concentrations. At the bottom of the filter, no detachment takes place, as the biofilm thickness is below the base thickness of 50 mm. Biofilm detachment has been a research focus for several years now. Different models – among others from Trulear and Characklis (1982), Wanner and Gujer (1986), and

246

Modeling of Biological Systems

Remaining area (m2)

4000

B=8 B=7 B=6 B=5 B=4

3000 2000 1000 0 0

25

50

75

100

125

150

Biofilm thickness, LF (µm) Figure 20 Dependence of the remaining surface for biofilm growth in relation to the existing biofilm thickness for e ¼ 0.35, dsphere ¼ 0.6 mm, and vreactor ¼ 0.55 m3.

Kreikenbohm and Stephan (1985) – have translated microbial findings into mathematical equations. Here, a detachment rate based on the findings of Wanner and Gujer (1986), Morgenroth and Wilderer (2000), and Horn et al. (2003) was developed further:

dLF ¼ kD rF ðLF  LFBase Þ3 dt

ðm d1 Þ

ð23Þ

where kD is the detachment rate of the biofilm (m g1 d1), rF the biofilm density (g m3), and LFBase the minimal biofilm thickness (m). This equation allowed for a decent reproduction of the detachment processes in the sand filter. As a result from model calibration, a basic biofilm thickness of LFBase ¼ 50 mm was assumed. Thinner biofilms are not subject of biomass detachment. Oxygen input into the sand filter with vertical flow was calculated with the equations provided by Platzer (1999), who assumed a value of 1.0 gO2 h1 m2 for the oxygen input through diffusion. In the present simulations, this value was reduced slightly to 0.9 gO2 h1 m2. For air saturation, an oxygen flux of 0.4–0.5 gO2 h1 m2 was reported by Casey et al. (1999) for membrane-aerated biofilms, which indeed are less porous.

4.08.4.3 Results and Discussion

oxygen inhibition constant (KO,H) was increased from 0:5to2:0 gO2 m3 , which results in a better denitrification capacity, especially for low COD inflow concentrations. The diffusion coefficients used for nitrogen components are typical of biofilms (see Table 6) and have been used, among others, by Horn (2003) and Rauch et al. (1999). Wanner and Reichert (1996) assumed a value of DSs ¼ 1.0 104 m2 d1. Polprasert et al. (1998) documented a diffusion coefficient for the COD in planted soil filters of DSs ¼ 2.2 104 m2 d1. For oxygen, Horn (2003) documented a value of DO2 ¼ 2:2  10 4 m 2 d 1 ; Wanner and Reichert (1996) gave a value of DO2 ¼ 1:0  104 m2 d1 . Table 7 compiles the influent data and measurement data for the investigated system. Deriving from scenario calculations, further results are depicted to predict the best sand filter operation. The model was well capable of reproducing the different load cases. Conversion of ammonium nitrogen and COD is simulated for landfill leachate, landfill leachate with additional dosing of methanol into the SBR (5–20 ml d1 pure methanol) and municipal wastewater. Ammonia nitrogen effluent concentrations are consistent with results published by Cooper (2005) for wetlands with vertical flow. The simulated degradation of substrates over the depth of the planted sand filter is presented in Figure 21. The concentration of nitrate nitrogen rises necessarily with the degradation of ammonium nitrogen. Moreover, model calculations show that in municipal wastewater the nitrification takes place more slowly and in deeper layers of the filter. Furthermore, nitrification started after COD degradation had already progressed to a high degree. For the treatment of landfill leachate, the ratio between degradable COD and hardly degradable COD is much lower and the latter must be hydrolyzed first; thus, nitrification takes place much earlier there.

4.08.4.3.2 Sensitivity analysis A sensitivity analysis (SA) was conducted to verify the importance and influence of biochemical parameters. Furthermore, the influence of the number of contact points of the single sand grains was investigated. For the biochemical parameters, the SA procedure is based on the publication of Kim et al. (2006), who used the single parameter method (SVM) slope technique to investigate activated sludge models. The effluent quality index (EQ) is defined as

4.08.4.3.1 Model calibration and simulation results Table 6 summarizes the biochemical parameters used in the model. The calibrated biochemical parameters are based on the standard parameter set developed for municipal wastewater for the ASM 3 (Koch et al., 2000; Wichern et al., 2002). The hydrolysis rate kH describes the supply of very slowly degradable COD XXS for biological conversion at high sludge ages that usually do not occur in flocculent activated sludge systems. Thus, the hydrolysis rate kH was calibrated significantly lower (kH ¼ 2.0 d1). To describe the flux and degradation of ammonium, the autotrophic growth rate was calibrated to 1.8 d1 – which is at the upper end of typical ASM 3 values. As the soil filter in reality is not as homogeneous as the geometric model assumes, there are still nitrate-reducing zones in the sand filter that are not penetrated by oxygen at all. To consider this on the model scale, the

EQ ¼ bCOD CODe þ bNH4  NH4 Ne þ bNO3  NO3 Ne ðÞ ð24Þ The values 1, 10, and 1 for the weighting factors b of COD, NH4–N and NO3–N were chosen to reflect the importance of ammonium nitrogen removal in the planted sand filter. The sensitivity index DEQ is calculated as follows (Ref: referring to the calibrated reference simulation, Var: referring to the varied parameter).

  DEQ ¼ bCOD CODe;Ref  CODe;Var    þ bNH4 NH4 Ne;Ref  NH4 Ne;Var    þ bNO3 NO3 Ne;Ref  NO3 Ne;Var  ðÞ

ð25Þ

For the SA, 10 model parameters were changed from 50% to 200% of their original values in 10% steps, which resulted in

Modeling of Biological Systems

247

Table 6 Stoichiometric and kinetic parameters after calibration (T ¼ 20 1C) compared to the values of the ASM 3: Koch et al. (2000) and Wichern et al. (2002) Parameter

Unit

Heterotrophic biomass d1 mH

Sand filter

3 0.07 35 0.07 0.50a 0.07 0.75 0.07 1b 1c 0.5 0.5c 2.0

Koch/Wichern

3 0.07

mH_Main

d1

bH

d1

bH,Inact

d1

KS KS_Main KO KO_Main KOH

g g g g g

KNO KNO_Main

g m3 g m3

0.5 0.5

0.5

ZD ZH YH kH kX

g g1 d1 g m3

0.5 0.5 0.67 2 1

0.5/0.33 0.5 0.64d

Autotrophic biomass mN

d1

bN

d1

YN KN KOA

g g1 g m3 g m3

1.8 0.105 0.15 0.105 0.24 0.5e 0.4f

0.9–1.7 0.105 0.15 0.105 0.24 1.0 0.5/0.13

Other parameters IBS IB kD KLF DNH DNO DO2 DSs d

g g1 g g1 m2 d1 m m2 d1 m2 d1 m2 d1 m2 d1 g m3

m3 m3 m3 m3 m3

0.30 0.07

10 0.5 0.5

0.03 0.03 0.07 0.07 466 1.0  106 1.8  104 1.8  104 1.7  104 2.2  104 60 000

Description

Maximum heterotrophic growth rate Temperature factor Maximum heterotrophic rate of the sustenance metabolism Temperature factor Maximum heterotrophic endogenous decay rate Temperature factor Maximum heterotrophic inactivation rate Temperature factor Half-saturation concentration for COD Half-saturation concentration for COD (maintenance metabolism) Half-saturation concentration for oxygen Half-saturation concentration for oxygen (maintenance metabolism) Half-saturation concentration for oxygen (inhibition of the denitrification) Half-saturation concentration for nitrate nitrogen Half-saturation concentration for nitrate nitrogen (maintenance metabolism) Reduction factor for the anoxic heterotrophic decay process Reduction factor for the anoxic heterotrophic growth process Heterotrophic yield Maximum hydrolysis rate of the very slowly degradable COD Half-saturation concentration during the hydrolysis process Maximum autotrophic growth rate Temperature factor Maximum endogenous autotrophic decay rate Temperature factor Autotrophic yield Half-saturation concentration for ammonium nitrogen Half-saturation concentration for oxygen Nitrogen incorporated into all COD fractions except for the biomass Nitrogen incorporated into the biomass Detachment rate of the biofilm (detachment) Half-saturation coefficient limiting the thickness of the biofilm growth Diffusion coefficient for ammonium nitrogen Diffusion coefficient for nitrate nitrogen Diffusion coefficient for oxygen Diffusion coefficient for dissolved COD Biofilm density

a

Hulsbeek et al. (2002): bH ¼ 0.05–1.6 d1 for the ASM 1. Wanner and Reichert (1996) as well as Morgenroth and Wilderer (2000): KS ¼ 5 g m3, KS ¼ 2.5 g m3. c Horn et al. (2003): KS,Main ¼ 1 g m3, KO2,Main ¼ 0.2 g m3. d Results from the multiplication of the yield rates for substrate storage and heterotrophic growth. e Horn and Hempel (1997), KN ¼ 0.5 g m3; Makinia et al. (2005), KN ¼ 0.2 g m3, ASM 3. f Morgenroth and Wilderer (2000): KOA ¼ 0.1 g m3. COD, chemical oxygen demand. b

150 simulation runs. The sensitivity of the maximum heterotrophic growth rate can be seen in Figure 22. The nitrification capacity is influenced by oxygen concentrations that were affected by heterotrophic COD conversion. An increased diffusion coefficient for oxygen resulted in better COD removal, especially in the first 10 cm of the sand filter. Nitrification capacity was partly lost because of elevated COD removal.

Furthermore, especially with a lower autotrophic growth rate there is also a decrease in nitrification. A higher autotrophic yield leads to a significantly increased autotrophic biomass, and, due to the competition for oxygen, to a decreased COD removal. In general, it can be observed that the diagram does not change significantly when the COD weighting factor bCOD is increased to 10. The maintenance

248

Modeling of Biological Systems

Table 7

Selected results and corresponding measuring data for seven load cases with and without methanol dosage into the SBR

Case/scenario

Case 1 Case 2 Case 3 Case 4 (methanol) Case 5 (methanol) Case 6 Case 7 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

Influent sand filter

Effluent sand filter

Simulation

COD/NH4–N measured (g m3)

NH4–N- surface load 1 (g m2 reactor d )

CODhom Meas/ Sim (g m3)

NH4–N Meas/ Sim (g m3)

NO3–N Meas/ Sim (g m3)

NH4–N degradation 1 (g m2 reactor d )

71.0/46.4 62.6/40.2 74.2/48.2 66.0/39.8 79.0/35.0 225.1/44.7 250.2/27.0 164.3/32.7 193.6/38.5 247.6/49.2 274.6/54.6 330.9/65.8

7.4 5.1 6.1 6.4 5.6 8.2 7.9 6.0 7.0 9.0 10.0 12.0

60.6/57.8 50.6/50.4 60.0/60.1 53.9/52.8 61.7/61.3 17.3/18.6 19.7/19.1 –/12.5 –/15.1 –/19.6 –/19.8 –/20.3

0.1/0.1 0.0/0.1 0.3/0.1 2.4/0.1 1.4/0.1 o1.0/0.25 0.8/0.31 –/0 –/0.1 –/6.7 –/17.3 –/41.0

62.0/66.5 59.5/65.9 81.6/88.3 44.0/46.7 38.3/45.1 27.1/31.2 21.9/21.1 –/36.5 –/39.6 –/24.6 –/18.1 –/5.3

7.4 5.1 6.1 6.4 5.6 8.1 7.8 6.0 7.0 7.8 6.8 4.5

Results for COD, NH4–N, and NO3–N (left: measuring; right: simulation); load cases 1–5: landfill leachate; load cases 6 and 7: municipal wastewater. Moreover, influent loads and degradation performance of the filter in different simulation scenarios with municipal wastewater.

COD (mg l−1)

300 250 Case 6 (municipal wastewater)

200 150

Case 1-5 (landfill leakage)

100

Case 7 (muncipal wastewater)

50 0 0

10

20

30 40 50 Depth (cm)

60

70

80

NH4−N (mg l−1)

60 50

Case 6 (municipal wastewater)

40 30

Case 1-5 (landfill leakage)

20 Case 7 (muncipal wastewater)

10 0 0

10

20

30 40 50 Depth (cm)

60

70

80

Figure 21 COD (left) and NH4–N (right) concentrations (mg l1) for all seven load cases related to the depth of the sand filter (load cases 1–5: landfill leachate; load cases 6–7: municipal wastewater).

process of heterotrophic biomass and the autotrophic yield are getting more sensitive with higher bCOD. Afterward, the effect of the number of contact points of the single grains was analyzed. The contact points were changed from a minimum of 4 to a maximum of 7. This variation directly affected the remaining area for the biofilm growth. Figure 23 shows the effects of the number of contact points on the substrate removal for ammonium and COD.

As expected, the COD and ammonium concentrations in the bulk liquid decrease over the filter depth. With the number of contact points higher than 6, substrate conversion and removal efficiency decreased extensively. The available area for biofilm growth limits the biomass growth. If the pore volume is filled by existing biomass, new bacteria can only grow when biomass detachment has occurred or existing biomass has been inactivated and hydrolyzed. Substrate conversion is considerably higher if new biomass grows. In Figure 24 one can see that according to model results due to limited growth area, an increased number of contact points leads to thicker biofilms in the examined depths (8.75, 26.25, 43.75, and 61.25 cm). Biofilm thickness increases for 4 to 6 contact points because for nearly constant substrate degradation the same quantity of biomass is necessary. In the case of 7 contact points, the pore volume is not sufficient to maintain substrate degradation. Furthermore, Figure 25 reveals that biofilm thickness in the upper layers of the sand filter is higher than in the lower ones. Apart from the impact on biofilm thickness, the biomass composition also changes when contact points are varied. Figure 25 shows the effect of different numbers of contact points on the biomass composition of the sand filter in depths of 8.75 and 26.25 cm. Depicted are the ratios of autotrophic biomass XA per total biomass and autotrophic XA plus heterotrophic biomass XH per total biomass. When contact points are varied between B ¼ 4–6, there has hardly any effect on biomass composition, not until there are 7 contact points. Mathematical modeling and microbial analyses showed higher quantities of autotrophic biomass than one would expect for municipal wastewater treatment. This happens because COD was mainly removed in the upstream SBR that worked as sedimentation reactor and denitrification tank. In the investigated system, the sand filter serves mainly for nitrification. Compared to municipal wastewater, the COD/NH4– N ratio (15:1) in the filter influent is considerably lower (5:1).

Modeling of Biological Systems

H H_main

125

Sensitivity index dEQ (−)

249

kOH kH N

100

75

50

25

0 60

80

100

120

140

160

180

200

Parameter variation (%) Figure 22 Results from sensitivity analysis for exemplary model parameters based on 150 simulation runs.

COD [g m−3] for B = 4 COD [g m−3] for cal. B = 5 COD [g m−3] for B = 6 COD [g m−3] for B = 7 COD [g m−3] for B = 6,5

COD (mg l−1)

250 200 150 100 50 0 0

10

20

60

70

80

NH4-N [g m−3] for B = 4 NH4-N [g m−3] for cal. B = 5 NH4-N [g m−3] for B = 6 NH4-N [g m−3] for B = 7 NH4-N [g m−3] for B = 6,5

250 NH4-N (mg l−1)

30 40 50 Depth (cm)

200 150 100 50 0 0

10

20

30 40 50 Depth (cm)

60

70

80

Figure 23 Effect of the number of contact points of the single grains of sand on the effluent concentrations of COD (left) and NH4–N (right). COD, chemical oxygen demand.

4.08.5 Waste Stabilization Ponds 4.08.5.1 Introduction Waste stabilization ponds (WSPs) are a very appropriate method of wastewater treatment in developing countries, where the climate is most favorable for this application. Their lower implementation costs and operational simplicity are commonly regarded as their main advantages. However, the processes that occur in wastewater-treatment ponds still are not completely understood. Environmental factors such as sun

radiation, wind, biological processes, and hydrodynamics have as yet not been fully analyzed or are difficult to validate with experimental data. The design of WSPs is mostly based on empirical equations (Pano and Middlebrooks, 1982; von Sperling and Chernicharo, 2005), and there are only a few published mathematical models to simulate the dynamics of such complex systems (e.g., Buhr and Miller, 1983). Juspin et al. (2003) developed an adaptation from the River Water Quality Model No.1 (Reichert et al., 2001) to simulate highrate algae ponds, including the influence of daily light variations. Here, ASM 3 (Gujer et al., 1999) is presented for the modeling of facultative and maturation ponds, with extensions to consider important processes relevant for WSPs: algae photosynthesis, growth and endogenous respiration, gas exchange for oxygen, carbon dioxide, and ammonia, ionic equilibrium processes, and pH calculation processes (Gehring et al., 2010). Solar radiation and wind velocity were implemented as model parameters. Solar radiation is the principal factor influencing algae growth and pathogen removal (which is not evaluated in the model). Although algae photosynthesis is identified as one of the main sources of oxygen input in facultative WSPs, superficial oxygen transfer has been identified as a relevant factor under conditions with high wind velocities (Ro et al., 2006; Pelletier and Chapra, 2008). Up to now, however, wind impact is rarely quantified and included into mathematical models for ponds. Wind can also play an important role in nitrogen removal. Smith and Arab (1988) identified turbulence as a main factor in the liquid–atmosphere interface for free ammonia desorption. Ni (1999) also verified the high importance of air velocity to determine the mass transfer coefficient. The authors analyzed 30 mechanistic models regarding ammonia release in cases of manure treatment. Significant ammonia desorption rates from WSPs were also related in Shilton (1996) and Rumburg et al. (2008). Biological ammonia nitrogen removal through nitrification followed by denitrification is another possible path, as published by Hurse and Connor (1999) and Zimmo et al. (2003).

250

Modeling of Biological Systems 80

B = 4−6 depth 8.75 cm B=7

Biofilm thickness (µm)

60

B = 4−6 depth 26.25 cm B=6 B = 4−6 depth 43.75 cm

40

B=5 B=4

20 B=6

B = 4−6 depth 61.25 cm

B=5 B=4

0 0

5000

10 000

15 000

20 000

Time (min) Figure 24 Effect of the number of contact points of the single grains on biofilm thickness.

1.0 0.8

Ratio of XA+XH, depth 8.75 cm

0.6 0.4

Ratio of XA, depth 8.75 cm

0.2 0.0 0.00 0.01

1.2 Biomass fractions (−)

0.03

0.04

0.05

1.0 Ratio of XA+XH depth 8.75 cm

0.8

Ratio of XA, depth 8.75 cm

0.6 0.4 0.2 0.0 0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.6 Ratio of XA, depth 26.25 cm

0.4 0.2 0.0 0.00 0.01

0.02

0.03

0.04

0.05

0.06 0.07

Depth of the biofilm (mm) 1.2

Ratio of XA+XH + XXS, depth 26.25 cm

1.0 Ratio of XA+XH, depth 26.25 cm

0.8

Ratio of XA, depth 26.25 cm

0.6 0.4 0.2 0.0 0.00 0.01

0.07

Depth of the biofilm (mm)

Ratio of XA+XH, depth 26.25 cm

0.8

(b)

Ratio of XA+XH + XH, depth 8.75 cm

Ratio of XA+XH + XXS, depth 26.25 cm

1.0

0.06 0.07

Depth of the biofilm (mm)

(a)

(c)

0.02

1.2 Biomass fractions (−)

Ratio of XA+XH+XXS, depth 8.75 cm

Biomass fractions (−)

Biomass fractions (−)

1.2

(d)

0.02

0.03

0.04

0.05

0.06 0.07

Depth of the biofilm (mm)

Figure 25 Effect of the number of contact points b of the single grains of sand for: (a) B ¼ 4, depth 8.75 cm; (b) B ¼ 4, depth 26.25 cm; (c) B ¼ 7, depth 8.75 cm; and (d) B ¼ 7, depth 26.25 cm. From Wichern M, Lindenblatt C, Lu¨bken M, and Horn H (2008a) Experimental results and mathematical modelling of an autotrophic and heterotrophic biofilm in a sand filter treating landfill leachate and municipal wastewater. Water Research 42: 3899– 3909.

4.08.5.2 Material and Methods 4.08.5.2.1 Description of the pilot pond For the model calculation, experimental data from a pilot pond system gathered in the city of Floriano´polis in southern Brazil (Santa Catarina) were used. The system consisted of

three ponds in line (anaerobic–facultative–maturation), fed with leachate from an approximately 15-year-old municipal waste landfill (Silva, 2007). The results of the dynamic simulation presented here refer to the facultative (Fac) and maturation (Mat) ponds. Each pond had a volume of 1.1 m3, a

Modeling of Biological Systems surface area of 1.2 m2, and a depth of 1.0 m. The main characteristics of the influent and effluent of both ponds are summarized in Table 8. Wind was measured at an automatic hydrological station. Sun radiation measurements were collected in a solar station of the BSRN/ (271380 S, 481300 O; BSRN – Baseline Surface Radiation Network/WMO – World Meteorological Organization project).

Table 8

Mean effluent parameters of the three ponds

Parameter

Unit

Anaerobic Facultative Maturation

COD COD filtered COD/BOD5 Total suspended solids Total ammonia nitrogen Nitrate nitrogen Chlorophyll a Dissolved oxygen pH Temperature

g m3 1456 g m3 n.m. 6.8 g m3 301 g m3 505 g m3 8 mg l1 n.m. g m3 n.m. 8.7 1C 25.9

1233 1065 7.3 239 208 6 65.3 3.7 8.8 25.6

743 504 7.3 146 53 4 93.4 3.9 8.9 25.2

n.m., parameter not measured; BOD, biochemical oxygen demand; COD, chemical oxygen demand. Adapted from Gehring T, Silva J, Kehl O, et al. (2010) Modelling waste stabilization ponds with an extended version of ASM 3. Water Science and Technology 61(3): 713–720.

Table 9

251

For better characterization of the ponds, some parameters such as chlorophyll a, oxygen, pH, and temperature were measured at three different depths: 0.2, 0.5, and 0.8 m. They are referred here to as top, middle, and bottom layers, respectively. Average values in the different layers are depicted in Table 9.

4.08.5.2.2 Mathematical model Additional processes extending ASM 3 for simulations of facultative and maturations ponds were taken as proposed in Gehring et al. (2010), in order to describe the influences of sun and wind, algae biomass, gas exchange, and ionic equilibrium. The model implementation was done in Simba 4.2 (2005).

4.08.5.2.3 Hydraulic concept In order to understand the hydraulic behavior in a pond, it is crucial to describe the substrate flux in detail. Each pond was assumed as a combination of three completely stirred tank reactors (CSTRs). These CSTRs represented the depth of the system, with stratified layers: bottom, middle, and top (schematic view in Figure 26). The flow was constant with 60 l d1, resulting in a hydraulic retention time of 18 days. Sedimentation was neglected in the model, as it was considered irrelevant in the experimental observations.

4.08.5.2.4 Algal processes According to experiments, each gCOD m3 of algae had a concentration of 12 mg l1 chlorophyll a. Two different growth processes, based on ammonia and nitrate, and an endogenous

Mean measurements in three depths of the ponds

Parameters

Unit

Facultative pond

1

Chlorophyll a Dissolved oxygen pH Temperature

mg l g m3 1C

Maturation pond

Top

Middle

Bottom

Top

Middle

Bottom

79 3.9 8.8 26.9

61 3.7 8.8 25.1

57 3.4 8.8 24.8

163 4.5 9.1 25.7

110 4.2 8.9 23.4

101 3.9 8.7 23.0

Adapted from Gehring T, Silva J, Kehl O, et al. (2010) Modelling waste stabilization ponds with an extended version of ASM 3. Water Science and Technology 61(3): 713–720.

Gas exchange

Inflow

Gas exchange

Top layer 0.4 m3

Middle layer 0.35m3

Middle layer 0.35 m3

Bottom layer 0.35 m3

Bottom layer 0.35 m3

Facultative pond

Maturation pond

1m

Top layer 0.4m3

Figure 26 Pond configuration in the model.

Outflow

252

Modeling of Biological Systems

respiration process were included in ASM3 in analogy to RWQM No. 1 (Reichert et al., 2001). Process rates are displayed in Table 10. Light attenuation through depth is usually described by Beer’s law, where the attenuation coefficient is considered according to the absorption properties of water. In WSPs, light absorption is mainly determined by the concentrations of gilvin (dissolved yellow matter), algae, and tripton (inanimate particulate matter) (Curtis et al., 1994). Heaven et al. (2005) observed that only a few light attenuation coefficients for WSPs have been published so far and highlighted their importance to mathematical description of algae

Table 10

1

processes. According to Pelletier and Chapra (2008), the photosynthetic available radiation (PAR) was assumed to be 47% of total light radiation. Attenuation across the water depth was calculated from the Beer–Lambert equation to determine the available light radiation IAV. The light attenuation parameter Kd was defined as function of the mixed liquor suspended solids in the tank:

Iav ¼ 0:47 I eKd H Kd ¼ a1 þ a2 XTSS

ðW m2 Þ

ð26Þ

ðm1 Þ

ð27Þ

Kinetic matrix of the waste stabilization ponds model Process

Process rate

Hydrolysis

kH

X XS K X þ X XS =X H

Heterotrophic processes 2

Aerobic storage

3

Anoxic storage

4

Aerobic growth

5

Anoxic growth

6

Aerobic endogenous respiration

7

Anoxic endogenous respiration

8

Aerobic respiration of XSTO

9

Anaerobic respiration of XSTO

SO SS K O þ SO K SS þ SS KO SS SNO k STO ZNO X H K O þ SO K SS þ SS K NO þ SNO SCO2 SO SNH X STO =X H mH X H K O þ So K NH þ SNH K CO2 þ SCO2 K STO þ X STO =X H SCO2 KO SNH X STO =X H SNO mH ZNO X H K O þ SO K NH þ SNH K CO2 þ SCO2 K STO þ X STO =X H K NO þ SNO SO XH bH SO þ K O KO SNO bN ZD XH SO þ K O SNO þ K NO SO bH X SO þ K O STO KO SNO bN ZD X SO þ K O SNO þ K NO STO

k STO X H

Autotrophic processes 10

Aerobic growth

11

Aerobic endogenous respiration

12

Anoxic endogenous respiration

SNH SO2 SCO2  XN K N þ SNH K OA þ SO K SCO2 A  SCO2 SO bN XN SO þ K O KO SNO bN ZD XN SO þ K O SNO þ K NO

mN

Algae processes 13

Growth with SNH

14

Growth with SNO

15

Aerobic endogenous respiration Physicochemical processes

16

Equilibrium1: HCO3  =CO2

17

Equilibrium 2: NH3 =NH4 þ

18

Gas exchange 1: O2

19

Gas exchange 2: CO2

20

Gas exchange 3: NH3

SNO þ SNH SNH K N;ALG þ SNO þ SNH K NH;ALG þ SNH K NH;ALG SNO þ SNH mALG K N;ALG þ SNO þ SNH K NH;ALG þ SNH SO bALG  X ALG SO þ K O;ALG mALG

  SH þ SHCO3 kACO2 SCO2  K a CO2   SH þ  SNH3 kAIN SNH4  K a IN A klaO2 ðSsat O2  SO2 Þ V A klaCO2 ðSsat CO2  SCO2 Þ V A klaNH3 ðSsat NH3  SNH3 Þ V

IAV exp ð1  ð1=K I ÞÞX ALG KI IAV expð1  ð1=K I ÞÞ X ALG KI

Modeling of Biological Systems with I being the light radiation (W m2), H the depth (m), a1 the light attenuation constant from water color and turbidity (m1), a2 the light attenuation constant factor from suspended solids (m3 g1 m1), and XTSS the total mixed liquor solid concentration (g m3).

4.08.5.2.5 Physico-chemical processes Wett and Rauch (2003) described the importance of the inorganic carbon balance for nitrification in activated sludge systems treating highly concentrated ammonia wastewaters. They found it necessary to include the ionic equilibrium and also the stripping of carbon dioxide in the ASM models to better represent these processes. The carbon dioxide release as a function of pH was found to be of major importance for a correct evaluation of free ammonia concentrations (Ni, 1999). Here, the free ammonia concentration SNH3 ðgm3 Þ and carbon dioxide SCO2 (molC m3) were added into the model as new state variables. Thus, the ionized ammonium concentration SNH4 ðgm3 Þ could be dynamically determined as the difference of SNH (total ammonia) and SNH3 . The concentrations of free ammonia and carbon dioxide in the liquid phase were defined through two equilibrium processes, considering the acid/base pairs: SCO2 =SHCO3 and SNH4 =SNH3 . Both equilibrium equations and the equilibrium constants were set according to Reichert et al. (2001) and are depicted in Tables 10 and 11. Determination of pH was also necessary. It was realized through a charge balance, considering the influence of ionized ammonia, nitrate, and alkalinity. All equivalent charges together with the dissociation products of water, Hþ and OH, amounted to zero. Three different gas transfer processes between top layer and atmosphere were considered to determine the mass transfer of oxygen, free ammonia, and carbon dioxide, as shown in Table 10. The gas transfer rate was determined according to a convective mass transfer, which depends on the concentration gradient between atmosphere and liquid. If

Table 11

253

dissolved concentrations exceed atmospheric concentrations, the rates are negative and gas is released from the liquid. If rates are positive, there occurs gas absorption from the atmosphere. The oxygen transfer coefficient, klaO2, was calculated by the following equation according to Ro and Hunt (2006), who developed an empirical equation based on 297 data points from transfer coefficients published in the last 50 years. This equation was recommended by the authors to be applied to WSP:

klaO2 ¼ 0:24  170:6S1=2  U1:81 c 10



ra rw

1=2

ðm d1 Þ

ð28Þ

where Sc is the dimensionless Schmidt number, U10 is the wind velocity 10 m over the ground (m s1), and patm and pw are the atmosphere and water densities, respectively (kg m3). Saturation of oxygen was defined through an empirical equation, and carbon dioxide and free ammonia saturations as functions of Henry’s constant and the atmosphere pressure of the gas:

Ssat

O2

¼ 13:89  0:3825T þ 0:007311T 2 ðg m3 Þ

ð29Þ

ðmol m3 Þ

ð30Þ

 0:00006588T 3 Ssat

gas i

¼

patm gas KH

i

with T being the temperature (1C), KH the Henry constant (atm m3 mol1), and patm_gas_i the partial pressure in the atmosphere (atm). Wind measurements were corrected to a height of 10 m with the seventh-root profile, and the mass transfer coefficients from carbon dioxide and ammonia were normalized to the oxygen transfer coefficient considering the surface renewal theory (Ro and Hunt, 2006). Figure 27 displays the schematic view of the processes in the model, with the three biomass groups, algae, heterotrophic

Kinetic parameters after calibration (T ¼ 20 1C) compared to the values of the River Water Quality Model No. 1 (Reichert et al., 2001)

Parameter

Unit

WSP model

Reichert

Description

Algae biomass mALG bALG KN,ALG KO,ALG KNH4,ALG KI

d1 d1 g m3 g m3 g m3 W m2

2 0.1 0.1 0.2 0.1 1200

2 0.1 0.1 0.2 0.1 500

Maximum algae growth rate Algae decay coefficient Half-saturation constant for nitrogen Half-saturation concentration for oxygen Ammonia inhibition constant for growth with nitrate Light limitation and saturation coefficient

4.15  104 7.75  108 1  1014 1  105 1  105 0.03 44 4  106 2  109

4.15  104 3.87  107 -

CO2/HCO 3 equilibrium coefficient Inorganic nitrogen equilibrium coefficient Water dissociation equilibrium coefficient Equilibrium rate for carbon dioxide/bicarbonate Equilibrium rate for inorganic nitrogen Henry’s constant to carbon dioxide Henry’s constant to free ammonia Atmospheric pressure from carbon dioxide Atmospheric pressure from free ammonia

Physicochemical parameters gH m3 KaCO2 KaIN gH m3 kw gH m3 d1 kACO2 d1 KANH3 atm m3 mol1 KHCO2 atm m3 mol1 KHNH3 atm patmCO2 atm patmNH3

254

Modeling of Biological Systems

Sun radiation

Wind

Heterotrophs Algae

HCO3−

CO2

O2

Inflow

NH3

Autotrophs

Outflow

NH4+

pH calculation

Light attenuation Figure 27 Extended version of the ASM 3 for the simulation of WSP processes.

and autotrophic biomass, the ionic equilibriums, the gaseous transfers, and the presence of sun and wind.

Meas. COD (g m−3)

2500

−3

Meas. CODs (g m )

Sim. COD (g m−3) Sim. CODs (g m−3)

4.08.5.3.1 Model calibration and simulation results Table 11 shows the adopted parameter values for the new processes for the simulation of both ponds. As observed from experimental data, mean COD degradations in the facultative and maturation ponds were approximately 50%. Determined COD fractions in the inflow to the facultative pond are as follows: inert fraction SI ¼ 45% of total COD, and readily and slowly biodegradable substrate fractions SS ¼ 35% and XS ¼ 20%, respectively. Measured environmental data, sun radiation and wind velocity, were applied in the simulations with their mean values per hour and temperature (25 1C). The results of COD removal are depicted in Figure 28. The heterotrophic growth rate determined by model calibration (0.52 d1) is almost 4 times smaller than found for activated sludge (Gujer et al., 1999). The reduced rate could be explained by the high concentration of ammonia in the leachate (Li and Zhao, 2001). This was also reported by other authors (Yang et al., 2004; Lee et al., 2000), who have reported the effect of free ammonia in the liquid phase on heterotrophic growth and oxygen consumption rates. The mean concentrations of free ammonia obtained from model calculations in the facultative and maturation ponds were 11.4 and 1.5 g m3, respectively; both concentrations could explain the inhibiton of the heterotrophic biomass growth. In the model, oxygen input was considered from two different sources: algae growth and wind aeration. The low values of chlorophyll a suggest that wind is most important here. Model calculations showed a mean oxygen input from wind in the facultative pond of 14.4 and in the maturation pond of 11.1 gO2 m2 d1. The TSS effluent concentrations for both ponds are presented in Figure 29. These results are directly correlated to algae growth, due to their influence on light inhibition. However, more experimental data are necessary to establish the relationship of chlorophyll a, TSS, and available light to photosynthesis. Here, light attenuation coefficients were used

1500 1000 500 0 0

50

100 150 Time (days)

Meas. COD (g m−3)

2500

−3

Meas. CODs (g m )

200

250

Sim. COD (g m−3) Sim. CODs (g m−3)

2000 COD (g m−3)

4.08.5.3 Results and Discussion

COD (g m−3)

2000

1500 1000 500 0 0

50

100 150 Time (days)

200

250

Figure 28 COD effluent measured and simulated results in the facultative pond (left) and maturation pond (right). COD, chemical oxygen demand. From Gehring T, Silva J, Kehl O, et al. (2010) Modelling waste stabilization ponds with an extended version of ASM 3. Water Science and Technology 61(3): 713–720.

in accordance with Jupsin et al. (2003): a1 ¼0.3 and a2 ¼ 0.032. Algae concentrations in both ponds treating leachate wastewater were very low, with 93 and 63 mg l1 chlorophyll a concentrations in the maturation and facultative pond, respectively. Commonly reported data for facultative ponds are in the range of 500–2000 mg l1 (Mara et al., 1992). One

Modeling of Biological Systems

Simulated algae concentrations and pH for the facultative pond are shown in Figure 30. Algae biomass achieved in simulations was in the range of experimental data and also followed the observed seasonal variations. Despite the strong influence of depth on light availability to photosynthesis, good mixing of the three pond layers equalized the algae concentrations. The model was not able to explain peaks of chlorophyll a at the end of the experiments. The same goes for pH values. In contrast, the pH calculations met the experimental data for the first 150 days (cf. Figure 30). The ammonia dissociation constant showed a strong influence on the pH results, and thus on many other parameters. As no data for the ammonia dissociation constant in leachate were available, this value was adjusted to fit experimental data. The value found corresponds to one-fifth of pure water (Reichert et al., 2001). For animal manure, Ni (1999) found that the dissociation constant ranged from one-fifth to one-sixth of that of ammonia in pure water. Gas release that occurs exclusively in the top layer explains why pH variations are bigger in the first layer than in the other two. In both ponds, the nitrate concentrations were low. Variations were small, with average effluent values of 6 gNO3 2N m3 in the outflow of the facultative pond and 4 gNO3 2N m3 after the maturation pond. According to measurement data, the oxygen concentrations never fell below 1 mg l1 in either pond, which

reason for low chlorophyll a could also be free ammonia concentrations. Concentrations in the order of 0.02 g m3 may inhibit the growth of various algae species (Azov and Goldman, 1982). In our simulations, no inhibition function was considered and only the light inhibition/saturation constant KI was calibrated to fit the algae concentration. The final calibrated value was 1200 W m2. Other algae parameters from processes 13, 14, and 15 were maintained, as suggested by Reichert et al. (2001).

500

Meas. COD (g m−3)

Sim. COD (g m−3)

Meas. CODs (g m−3)

Sim. CODs (g m−3)

TSS (g m−3)

400 300 200 100 0 125

175 200 Time (days)

150

225

250

Figure 29 TSS effluent measured and simulated results. TSS, Total suspended solids.

600

255

12

Top

500 10

400 300

8

200 100

Chrolophyll a (mg m−3)

0 500

6 12

Middle

400 10

300 200

8

100 6 12

0 500 Bottom 400

10

300 200

8

100 0 60

80

100

120

140

160

180

Time (days) Figure 30 Chlorophyll a and pH measured and simulated results in the facultative pond in three layers.

200

220

6 240

Modeling of Biological Systems

Meas. Fac. ammonia (g m−3) Meas. Mat. ammonia (g m−3)

1000

Sim. Fac. ammonia (g m−3) Sim. Mat. ammonia (g m−3)

800 600 400 200 0 50

0

100 150 Time (days)

200

250

60 55 50 45

1200

800

400

6

0

8.8 800

4

700 2

600

40 35

9.0

900

65 Chrolophyl a (mg m−3)

Total ammonina nitrogen (g m−3)

70

Sun radiation (W m−2)

Figure 31 Total ammonia nitrogen in effluent, measured, and simulated results.

investigated (575–705 gN m3) here, pH was adjusted above 11. This makes the comparison of these investigations with present results more complicated. The same applies to other published stripping rates from different sources with different pH and nitrogen concentrations. Rumburg et al. (2008) reported fluxes from a plant-scale anaerobic dairy waste lagoon of 2.6–13.0 gN m2 d1 determined through tracer experiments. Experimental stripping rates measured for domestic WSPs (Zimmo et al., 2003; Camargo Valero and Mara, 2007) are very much below these values. Figure 32 illustrates the interaction of some model variables and their variation throughout the day. Presently apparent is direct correlation of sun radiation and chlorophyll a concentrations. Algae growth directly follows sun radiation. Daily peaks of both parameters show a gap of 4–5 h. On day 204, with sun radiation below 300 W m2, algae biomass decreased very quickly. It is still possible to visualize the influence of algae nitrogen uptake that was suggested as the main path for nitrogen removal in WSPs treating domestic wastewater (Camargo Valero and Mara, 2007). Daily peaks of chlorophyll a concentrations corresponded to daily minima of ammonia concentrations. Apart from the oxygen production due to photosynthesis, it was not possible to establish a direct relation between chlorophyll a and the dissolved oxygen concentrations, which are also influenced by variations of the organic load rates and wind velocities. The developed model may be one step toward more detailed WSP modeling which considers complex interactions between microbial and physical/chemical processes. More

8.4 8.2 8.0 7.8

500

30 200

8.6

pH

Tital amount nitrogen (g m−3)

indicates that no denitrification occurred there. More information about the nitrification calibration and nitrate concentration simulation results can be found in Gehring et al. (2010). The simulated concentrations of the total ammonia nitrogen effluent values showed good results for both ponds (Figure 31). The average ammonia stripping rates were 18.2 and 4.5 gN m2 d1 in the facultative and maturation pond, respectively. Unfortunately, there are not many published data available for comparison. Smith and Arab (1988) and Cheung et al. (1997) measured high ammonia stripping rates in free tanks (without aeration) treating landfill leachate. Calculated values ranged between 45 and 142 gN m2 d1. Although initial ammonia concentrations in both studies were very similar to the ponds

Dissolved oxygen (g m−3)

256

202

204

206

208

0 210

7.6

Time (days) Figure 32 Example of the dynamic behavior of the WSP model (top layer of the maturation pond between days 200 and 210). The presented variables include: total ammonia nitrogen (black line), chlorophyll a (gray line), dissolved oxygen (dashed gray line), pH (dashed black line), and the measurements from sun radiation (at the top in dark gray line).

Modeling of Biological Systems

detailed experimental data would help to better understand the findings and to include additional processes such as phosphorus removal, anaerobic digestion in bottom layers, and more sophisticated hydraulic concepts.

4.08.6 Anaerobic Treatment 4.08.6.1 Introduction In the following, results from the anaerobic treatment of manure are presented. Anaerobic processes are widely used, especially for industrial and agricultural wastewater that have much higher COD concentrations than typically found in municipal wastewater. For a few years, energy crops have been appreciated for their potential of producing energy. To be more independent from traditional sources such as oil and gas, agriculturists are being subsidized in order to produce not only food, but to cultivate energy-rich substrates such as maize, rye, and grass as well. To stabilize and increase the reactor operation, these substrates are ensilaged, and cattle, pig, and chicken manure is used as co-substrate. With manure, sufficient trace elements are added to avoid process inhibitions which have been reported to take place sometimes in anaerobic processes run with pure energy crops. To assure hygienic quality of the treated co-substrates and manure, detailed microbial analyses have been executed (e.g., Lebuhn et al., 2005). Manure as one major source of environmental pollution from livestock farming should be treated efficiently in order to avoid its hazardous impact on soil and groundwater. As yet, only little research has been done on the modeling of agricultural biogas plants. Angelidaki et al. (1993), who described the inhibition of the anaerobic processes by ammonia, and Angelidaki et al. (1999), who investigated the cofermentation of agricultural substrate and fats, dealt with the fermentation of manure. Both papers highlight the importance of a detailed fractioning of the input substrate, due to the fact that proteins, carbohydrates, and lipids have different degradation paths in the anaerobic process. Myint et al. (2007) focused on the hydrolysis and acidogenesis in the dry digestion of cattle manure with process TS concentrations higher than 20%. Complementing modeling results are summarized by Batstone et al. (2006), who described the adaptation of ADM 1 (Batstone et al., 2002) to sludge treatment and various industrial wastewaters; recent publications deal with the calibration of ADM1 to pure energy crops (e.g., Amon et al., 2007; Gerin et al., 2008; Wichern et al., 2009; Koch et al., 2009). Apart from the ADM1 calibration for the digestion of cattle manure and co-substrates (Wichern et al., 2008b), this chapter presents further information on parameter sensitivity and energy balances for the optimization of reactor operations. The latter findings were based on research by Lu¨bken et al. (2007). The presented results showed the importance of mathematical modeling to improve reactor operation, increase methane yield, and decrease impact on the environment.

4.08.6.2 Material and Methods

257

State Research Centre for Agriculture. The 3500-l fermenter was discontinuously fed with liquid manure from cattle farming and total mixed ratio (TMR – fodder for cows). The reactor was operated at 38 1C under mesophilic conditions. The contact time of the substrate in the fully mixed reactor amounted to 21 days. Measurements were based on German standard methods. In order to characterize the substrate in terms of carbohydrates, proteins, and fats, a method according to Van Soest and Wine (1967) and Weender (described in Naumann and Bassler (1993)) was applied. This application resulted in a fractionation of the organic matter between crude protein, crude fat, crude fiber, and nitrogen-free extract (Weender analysis). The so-called van Soest analysis allows for the determination of neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL). The carbohydrates were further divided into hemi-cellulose (NDF– ADF), cellulose (ADF–ADL), and lignin (ADL). The total biogas production was measured by the drum chamber gas meter TG5/5 (Ritter, Germany). Values for biogas production were normalized. Methane and carbon dioxide were quantified by means of the infrared two-beam compensation method with pressure compensation (measuring error as specified:72%). Oxygen and hydrogen were measured by electro-chemical sensors (measuring error as specified:73%).

4.08.6.2.2 Reactor operation The investigated anaerobic reactor was operated with a substrate mixture of liquid manure of cattle and TMR. The co-substrate TMR was composed of 43% corn silage, 18% gramineous silage, 12% crop groats, 9% water, 7% soy pellets, 7% cow grain, and 4% hay. The mean influent volume flow amounted to 175 l d1. This resulted in a volumetric load rate of 3.6 kgVS m3 d1 and a COD load of 15.3 kgCOD d1. COD in the effluent was reduced by 30–35% compared to the influent COD. The COD/TS ratio of the inflow substrate was iCOD/TS ¼ 1.2 kgCOD kg1 TS . The dry gas production was measured to be 3.65 m3Gas d1 (287 lBiogas kg1 VS ) at operation temperature. The pH value in the reactor was relatively constant at 7.6. Table 12 shows the characteristics of the inflow substrate in detail.

4.08.6.2.3 Mathematical model and sensitivity functions In 2002, the IWA Task Group on Mathematical Modelling of Anaerobic Digestion Processes presented the Anaerobic Digestion Model No. 1 (Batstone et al., 2002). ADM 1 is a highly complex model, characterized by 19 biochemical conversion processes and 24 substances. To investigate the application of ADM 1 to agricultural substrates, a sensitivity analysis of both inflow COD fractioning and biochemical parameters was run. The applied technique is called SVM slope technique, published by Kim et al. (2006), and was adapted here for use with ADM 1. The calibrated ADM 1 model (see Table 13) was used as reference parameter set. An effluent base quality EQ was defined:

EQ ¼ bCOD CODe þ b CH4 CH4 % þ b CO2 CO2 % þ bgasflow qgasflow ðÞ

ð31Þ

4.08.6.2.1 Analytical methods Analyses of the agricultural substrates were run by the Institute of Agricultural Engineering and Animal Husbandry, Bavarian

with CODe being the COD in the effluent (g m3), CH4% the percentage of methane in the gas, CO2% the percentage of

258

Modeling of Biological Systems

carbon dioxide in the gas, qgasflow the dry gas flow in m3 d1, and b the weighting factors (10, 20, 20, 20). The sensitivity index DEQ results from a procedure where each model parameter was changed incrementally by 10% (Ref: referring to reference simulation, Var: varied parameter):

been conducted to analyze the substrate (see Table 12). The following equations are implemented to define particular model fractions:

  XPr ¼ FM  TS  iCOD=TS  RP

  DEQ ¼ bCOD CODRef ;e  CODVar;e  þ bCH4 jCH4 %Ref  CH4 %Var j þ bCO2 jCO2 %Ref  CO2 %Var j   þ bgasflow qgasflow;Ref  qgasflow;Var  ðÞ

ð32Þ

4.08.6.3 Results and Discussion 4.08.6.3.1 Calibration of the ADM 1 The inflow fractioning of the total COD is of highest importance for the calibration of the ADM 1 and strongly affects the gas composition. For this, detailed measurements have Table 12

Characteristics of the influent substrate

Parameter

Unit

Manure

TMR

TS VS COD iCOD/VS VFAtotal Alkalinity pH NH4–N Raw protein Raw fiber Raw lipid NfE NDF ADF ADL

(%) (%TS) (kg m3) (kgO2 kg1 VS ) (g m3) (mmol l1) (–) (g m3) (% TS) (% TS) (% TS) (% TS) (% TS) (% TS) (% TS)

6.1 81.4 76 1.53 6657 241.9 7.4 2289 12.2 17.8 4.3 47.1 47.1 33.9 20.1

50.3 93.7 609 1.29 3765 48.2 4.9 1345 19.2 17.2 2.6 54.7 50.0 23.4 19.6

ADF, acid detergent fiber; ADL, acid detergent lignin; COD, chemical oxygen demand; NDF, neutral detergent fiber; NfE, nitrogen-free extract; TMR, total mixed ratio; TS, total solids, VS, volatile solids; and VFAtotal, total volatile fatty acids. From Wichern M, Lu¨bken M, Schlattmann M, Gronauer A, and Horn H (2008b) Investigations and mathematical simulation on decentralized anaerobic treatment of agricultural substrate from livestock farming. Water Science and Technology 58(1): 67–72.

Table 13

ðkgCOD d1 Þ

ð33Þ

  XLi ¼ FM  TS  iCOD=TS  RL ðkgCOD d1 Þ

ð34Þ

The two parameters proteins XPr and lipids XLi are defined by the fresh mass FM (kgFM d1), the TS (%), the COD content of manure iCOD/TS, as well as raw protein RP (%TS) and raw lipids RL (%TS), respectively. The calculation of carbohydrates XCH and inert material is more complicated and is based on additional information from the Van Soest analysis (Van Soest and Wine, 1967).

   XI ¼ FM  iTS=FM  iCOD=TS  ADL þ ðADF  ADLÞnon



deg

ðkgCOD d1 Þ

ð35Þ

To quantify the inert material, the load of lignin ADL (%TS) is needed. ADF (%TS) comprises lignin and cellulose. Measurements showed that cellulose is degraded by 28%, which is reasonable according to Fuchigami et al. (1989). The low degradation of COD implies that manure had been degraded in the animal intestines before:

  XCH ¼ FM  iTS=FM  iCOD=TS h   ðRF þ NfeÞ  ADL þ ðADF  ADLÞnon

i deg

ðkgcod d1 Þ

ð36Þ

Raw fiber RF (%TS) and nitrogen-free extract Nfe (%TS) represent the total content of carbohydrates, whereas the latter part of the equation describes the inert part consisting of lignin and nondegradable cellulose. If Equations (33)–(36) are applied, the particulate COD, which is 80% of the total COD, can be divided as follows: raw protein Xpr ¼ 13.6%, raw fat Xli ¼ 4.0%, carbohydrates Xch ¼ 54%, and inert material XI ¼ 28.4%. All particular material was split during the disintegration process into the aforementioned fractions. To fulfill the nitrogen mass balance after the disintegration step (Blumensaat and Keller, 2004), the nitrogen content of the composite and inert material was fitted to NXC,I ¼ 0.0014 moln g1 COD. Acetate, propionate, butyrate, and valerate were measured as 5.2%, 3.5%, 2.0%, and

Calibrated biochemical ADM 1 parameters for the treatment of cattle manure (see also Wichern et al., 2008b)

Parameter

Description

Unit

ADM 1 value

Calibrated

Notes

kDis km,ac pHUL,acid pHLL,acid km,pro KS,pro KS,H2 NXC,I

Disintegration constant Acetate uptake rate Upper pH limit for acidogens Lower pH limit for acidogens Propionate uptake rate Half-saturation coefficient for propionate uptake Half-saturation coefficient for hydrogen uptake Nitrogen content of composite and inert material

d1 g g1 d1

0.5 8 5.5 4 13 0.1 7  106 0.002

0.05 4.2 8 6 4.5 0.34 1.65  105 0.001 4

1 1 1 1 2 2 1 1

g g1 d1 kg m3 kg m3 molN m3

(1) Values determined by best fit between dynamic simulation and measurement. (2) Values in accordance with Angelidaki et al. (1999).

Modeling of Biological Systems

0.6% of CODtot, respectively. Calibration was mainly done with the disintegration constant changed to kDis ¼ 0.05 d1 and the acetate uptake rate to km,ac ¼ 4.2 g g1 d1. The complete list of calibrated biochemical parameters can be found below in Table 13.

4.08.6.3.2 Modeling reactor performance Exemplary modeling results are depicted in Figure 33. Results for gas flow (Figure 33 (left)) and gas composition (Figure 33 (right)) showed only smaller deviations between measuring and simulation results. Besides gas flow and gas composition, propionate and acetate concentrations were also calibrated (not shown).

4.08.6.3.3 Sensitivity analysis for the biochemical parameters and the inflow fractioning Identifying sensitive biochemical parameters is not only necessary to better understand the applied model, it is also most useful to identify sensitive inflow parameters that should be measured in great detail to avoid false results or misleading conclusions. In the following, results from sensitivity analyses are presented. It becomes apparent that for cattle manure and co-substrates, the hydrolysis rate kHyd, the uptake rates for amino acids km,aa and sugars km,su, the hydrogen inhibition constant KI,H2,pro, and the half-saturation coefficients for propionate KS,pro and hydrogen KS,H2 are less sensitive. In contrast, the disintegration rate kDis, the acetate uptake rate

400 300 200 100 0 0

100

10

20

30 40 Time (days)

Meas. CH4 (%) Sim. CH4 (%)

50

60

Sensitivity index dEQ (−)

250

kdis khyd km,ac

200

km,ac High sensitivity

300 100 50

Low sensitivity 0

Meas. CO2 (%) Sim. CO2 (%)

50

75

80 60

100 125 150 Parameter variation (%)

175

250

40 20 0 0

10

20

30 40 Time (days)

50

60

Figure 33 Gas flow in m3 d1 (left) and dry gas composition in % (right). From Wichern M, Lu¨bken M, Schlattmann M, Gronauer A, and Horn H (2008b) Investigations and mathematical simulation on decentralized anaerobic treatment of agricultural substrate from livestock farming. Water Science and Technology 58(1): 67–72.

Sensitivity index dEQ (−)

Gas flow (m3 d−1)

km,ac, the ammonia inhibition constant KI,NH3, and the biomass decay rates kdec are more sensitive. When applying the sensitivity analysis of Kim et al. (2006), the results very much depend on the weighting factors b. Here, gas volume and gas composition (CH4 and CO2) were considered to have higher importance than COD effluent values. If the acetate uptake rate km,ac and the inhibition constant for ammonia KI,NH3 were further reduced compared with the calibrated parameter set, this would result in complete process inhibition. If, for instance, measured acetate concentrations are available, in ADM 1 the parameters km,ac, KI,NH3, and KS,ac notably affect the acetate simulation results. In many cases, these three parameters cannot be identified exactly. From the mathematical point of view, various typical parameter sets can be identified, which leads to an even simulation quality. However, the modeling engineer has to decide which parameter set is reasonable from the microbial and engineering points of view. Figure 34 presents the results of the sensitivity analysis for the inflow fractioning of COD after the disintegration step. The total sum of composite material stayed the same; only the fractions of proteins, carbohydrates, lipids, and inert material were changed. The diagram outlines the importance of inert material and carbohydrates fraction for the gas flow and gas composition. For the investigated reactor and substrates, proteins are less sensitive. The quantity of ammonia nitrogen in cattle manure is much higher than the incorporated nitrogen in the COD fractions (Figure 35).

Meas. gas ammonia (g3 d−1) Sim. gas flow (m3 d−1)

500

Gas composition (%)

259

200

kdis khyd km,ac km,su km,aa

200 300 100 50 0 50

75

100 125 150 Parameter variation (%)

175

200

Figure 34 Sensitivity index DEQ for exemplary parameters of ADM 1.

260

Modeling of Biological Systems

fpr,xc fli,xc fch,xc fxi,xc

Sensitivity index dEQ (−)

200 150 High sensitivity 100 50

Low sensitivity

0 50

75

100 125 150 Parameter variation (%)

175

200

with Qin being the inflow rate (kg s1), H the conveyor height (m), r the density of the pumped media (kg m3), g the acceleration of gravity (m s2), tp the time for pumping (h d1), and Zecc_worm the efficiency degree (–). The efficiency degree strongly depends on the type of the pump used and can vary considerably. For the eccentric worm pump used for the pilotscale digester, Z values range between 0.3 and 0.7. Here, we considered that reactor feeding was 15 min once a day. The required energy for the stirrer ensuring good mixing and sufficient contact between substrate and microorganisms depends on reactor volume, reactor geometry, and viscosity of the medium:

Ploss stir ¼ V liq S ts

Figure 35 Sensitivity index DEQ for the COD inflow fractioning of cattle manure.

4.08.6.3.4 Simulation of the energy balance As a next step, a dynamic energy balance model was derived, which considers energy production and consumption. The model was derived by Lu¨bken et al. (2007). Energy consumption in a reactor results from pumping, stirring, substrate heating, and the compensation of radiation loss, whereas energy is mainly produced by utilizing the energy contained in biogas. The basic equation for the dynamic energy balance model is

 dPnet  prod loss ¼ Pelect  Ploss  P pump stir dt 

prod loss loss þ Pprod therm  Prad  Psub heat þ Pmic heat



ðkW h d1 Þ

ð37Þ

with Pnet being the net energy production of the digester (kW h prod d1), Pelect the electrical energy production (kW h d1), Ploss pump the mechanical power of the pump (kW h d1), Ploss stir the prod mechanical power of the stirrer (kW h d1), Ptherm the thermal energy production (kW h d1), Ploss rad the radiation loss (kW h the heat requirement for substrate heating (kW h d1), Ploss sub heat 1 d1), and Pprod mic heat the microbial heat production (kW h d ). The single terms were calculated as follows:

Pprod elect

¼ QG PCH4 HC Z elect

1

ðkW h d Þ

Pprod therm ¼ QG PCH4 HC Ztherm

ðkW h d1 Þ

ð38Þ ð39Þ

with QG being the biogas production (m3 d1), PCH4 the methane content (%), HC the calorific value of methane (kW h N m3), Zelect the electrical degree of efficiency () and Ztherm the thermal degree of efficiency (). Using Equations (38) and (39) assumes a co-generation plant for heat and power. When biogas is used in a combined heat and power plant (CHP), it is of utmost importance for cost-effective plant operation to use both mechanical/electrical energy and thermal energy. Electrical energy is produced in a CHP unit with a mechanical/electrical efficiency degree of approximately 35%, whereas the thermal efficiency degree is approximately 50%:

Ploss pump ¼ Qin H r g tp

1 Zecc

worm

ðkW h d1 Þ

ð40Þ

ðkW h d1 Þ

ð41Þ

with Vliq being the liquid volume (m3), S the specific power of the stirrer (kW m3), and ts the time for stirring (h d1). Here it was supposed that the stirrer is used every half hour for 10 min with a consumption of 0.005 kW m3:

  Ploss rad ¼ Kheat trans Tliq  Tambient Vliq    þ Tgas  Tambient Vtot  V liq  2 24  ðkW h d1 Þ r 1000

ð42Þ

with Kheat_trans being the heat transfer coefficient (Wh m2 h1 K1), Tliq the temperature of the substrate within the digester (K), Tambient the ambient temperature, Vliq the liquid volume (m3), Tgas the gas temperature (K), Vtot the total digester volume (m3), and r the radius of the digester (m). Ambient temperature varied between 25 and  10 1C:

Ploss sub

heat

  1 ¼ Qin c Tdigester  Tsubstrate 3:6

ðkW h d1 Þ

ð43Þ

with Qin being the reactor inflow (m3 d1), c the heat capacity of the substrate (kJ kg1 K1), Tdigester the temperature of the digester (K), and Tsubstrate the temperature of the stored substrate (K:

Pprod mic heat ¼

X j¼512

DEj f j rj Vliq

1 3:6

ðkW h d1 Þ

ð44Þ

with DEj being the energy released to the environment due to microbial activity of process j (kJ mol1), fj the molar mass per gCOD of the educt of process jðmolgCOD 1 Þ, rj the kinetic rate equation of process j of ADM 1 (kgCOD m3 d1), and Vliq the liquid volume (m3). The thermodynamic parameters of selected anaerobic biochemical reactions are presented in Table 14. Long-chain fatty acids (LCFAs) are represented by palmitate, and glucose stands for monosaccharide. The thermodynamics of amino acids were calculated for glycine and alanine degradation in the Stickland reaction. Figure 36 suggests that energy production was nearly constant for the investigated period. As energy consumption strongly depends on the respective season, the lowest energy consumption was calculated in summer time (July) and the maximum in winter time (December). For the investigated pilot-scale digester, heating of substrate needed the most

Modeling of Biological Systems Table 14

261

Thermodynamics of biochemical reactions implemented in ADM 1 DH0f (kJ DG0f (kJ DG0’ (kJ DG0T’ (kJ ATP (M1) M1) M1) M1) M1)

Reaction

DE (kJ M1)

Glucose: 1. C6H12O6 þ 2H2O-2CH3COO þ 2Hþ þ 2CO2 þ 4H2 89.5  136.0  215.7 2. 3C6H12O6-4CH3CH2COO þ 2CH3COO þ 6Hþ þ 2CO2 þ 2H2O  183.4  231.5 -311.3  47.5  224.1  264.0 3. C6H12O6-CH3CH2CH2COO þ Hþ þ 2CO2 þ 2H2    þ þ CH3CH(NHþ  4.0  51.1  51.1 3 )COO þ 2CH2(NH3 )COO þ 2H2O-3CH3COO þ CO2 þ 3NH4

Amino acids: Palmitate: CH3(CH2)14COO þ 14H2O-8CH3COO þ 7Hþ þ 14H2 Valerate: CH3CH2CH2CH2COO þ 2H2O-CH3CH2COO þ CH3COO þ Hþ þ 2H2 Butyrate: CH3CH2CH2COO þ 2H2O-2CH3COO þ Hþ þ 2H2 Propionate:CH3CH2COO þ 2H2O-CH3COO þ CO2 þ 3H2 Acetate: CH3COO þ Hþ-CH4 þ CO2 Hydrogen: 4H2 þ CO2-CH4 þ 2H2O

966.2 136.2 137.0 204.7 16.2  63.2

670.2 88.2 88.2 71.7  75.7 –32.7

391.1 48.3 48.3 71.7  35.8  32.7

 225.53  313.35  271.70  53.13

4 4/3 3 1/3

 25.53  246.69  121.70  36.46

378.22 14/6 494.88 46.24 0.875 89.99 46.17 0.75 83.67 65.87 0.50 90.87  39.84 0.25  27.34 –31.36 1/4 –18.86

Values for the thermodynamic parameters refer to the molar mass of the selected educts. The fraction of glucose which degrades via the first, second, and third reactions is: (1) ¼ 50%, (2) ¼ 35%, and (3) ¼ 15% (Lu¨bken et al., 2007).

20.0 (e) 18.0

(e)

(e)

(e)

(e)

(e) 16.0

P (kWh d−1)

14.0 12.0 10.0 8.0 6.0 (c) 4.0 2.0 0.0

(c) (b) (d) (a) Jul. 04

(c) (b) (a)

(d)

Aug. 04

(c) (b)

(c) (b) (a)

(d)

Sep. 04

(a)

(d)

Oct. 04 Months

(b)

(a)

(d)

Nov. 04

(c) (b)

(a)

(d)

Dec. 04

Figure 36 Comparison of the simulated energy production and the simulated energy consumption: (a) energy consumption of stirrer, (b) energy consumption due to radiation loss, (c) energy consumption due to substrate heating, (d) energy production due to microbial activity, and (e) sum of thermal and electric energy production of CHP.

energy, followed by radiation loss. Other factors were negligible. Heat produced by microbial degradation of organics made about 10% of the energy necessary for substrate heating.

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4.09 Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens H Furumai, F Nakajima, and H Katayama, The University of Tokyo, Tokyo, Japan & 2011 Elsevier B.V. All rights reserved.

4.09.1 4.09.2 4.09.2.1 4.09.2.2 4.09.2.3 4.09.2.4 4.09.2.5 4.09.2.6 4.09.3 4.09.4 References

Introduction Physicochemical Characterization of Road Dust and Soakaway Sediment Contamination by Polycyclic Aromatic Hydrocarbons and Their Source Size and Density Distributions of PAHs in Road Dust Evaluation on Heavy Metal Retention in Road Dust and Soakaway Sediments Identification of Elements in Individual Particles by Electron Probe Microanalysis Leaching Potential of Heavy Metal in Road Dust Runoff Behavior of Particle-Associated PAH Pathogenic Pollution in a Seaside Park after CSO Summary

4.09.1 Introduction Urbanization increases the variety and amount of pollutants carried into receiving waters. Therefore, urban environmental water is very susceptible to pollutants from urban activities, which have an adverse effect on water quality and aquatic ecosystems. The sources of the discharge of such pollutants are categorized into two types: point and nonpoint sources. Point source refers to pollutant sources which are easily identified facilities discharging pollutants to the environment. Factories and sewage treatment plants are included as representatives in this category. In many countries, the quality of the discharged water from these point sources is regulated by water pollution control laws and can be managed by a wide range of treatment technologies. Nonpoint source pollution is generally considered to be a diffuse source of pollution not associated with a specific point of entry into water bodies. The urban sources of pollutants, such as vehicles and urban surface materials, are called nonpoint sources. Nonpoint source pollution occurs with rainfall or snowmelt. The water from rain or snow dissolves the

265 266 266 267 267 270 270 271 272 274 275

atmospheric pollutants, washes off the pollutants on the impervious surfaces, and finally flows into rivers, lakes, and coastal waters. Impervious surfaces, such as building roofs, traffic roads, and parking lots, are constructed during urban development. During rainfall and other precipitation events, these surfaces carry polluted runoff to drainage system, instead of allowing the water to percolate through soil. The urban runoff behavior shows the difference between combined and separate sewer systems as shown in Figure 1. In Japan, separate sewer systems have been installed in many cities since 1970 to improve water pollution control in public water bodies. However, combined sewer systems are in place for historical reasons in old and large cities, including Tokyo or Osaka. The combined sewer systems are sewers that are designed to collect rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. Most of the time, combined sewer systems transport all of their wastewater to sewage treatment plants, where it is treated and then discharged to water bodies. During periods of heavy rainfall or snowmelt, however, the wastewater volume in a combined sewer system can exceed the

Combined sewer overflow

River

Combined sewer

Storm sewer

River

Primary treatment

Sanitary sewer Treatment Secondary treatment plant

Combined sewer system

Treatment plant

Secondary treatment

Separate sewer system

Figure 1 Two types of sewer systems and combined sewer overflow.

265

266

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

capacity of the sewer system or treatment plant. For this reason, combined sewer systems are designed to overflow occasionally and discharge excess wastewater directly to nearby streams, rivers, or other water bodies. This phenomenon is called combined sewer overflow (CSO) and has been recognized as a serious source of environmental water pollution. Naturally, the CSO and the nonpoint source pollution are difficult to control, since the water is irregularly discharged. In order to control water pollution and manage water quality in urban environmental waters, we have to investigate the irregularly occurring wet weather pollution phenomena and their pollutant sources. The monitoring or sampling of such irregular water discharge requires special devices and/or incurs high labor costs. The source responsible for the pollution is often unclear, not least because the water runoff itself is a natural phenomenon and the pollutant sources are diverse; responsibility is thus difficult to assign. As such, the significance of nonpoint source pollution in the water environment tends only to be recognized after the controlling system of the point sources has been spread well in the society. This chapter includes the following: 1. characterization of urban nonpoint pollutants and urban runoff behavior and 2. pathogenic pollution in coastal area after CSO. The first part introduces research on physicochemical characterization of road dust and sediments in infiltration facilities as pollutant source in urban area. Infiltration facilities have been constructed mainly aiming at inundation control in rapidly urbanized areas by reduced storm-water peak flows. These infiltration facilities are likely to contribute to reduction of nonpoint pollutant loads from urban surfaces. Therefore, accumulated sediment in infiltration facilities should be regarded as secondary pollutants in urban runoff pollution and a possible source to groundwater contamination. The second part explains microbial contamination in Tokyo Bay after CSO events, in which enteric virus behavior as well as bacterial indicators, such as total coliforms and Escherichia coli, were extensively investigated.

environments and aquatic ecosystems. Hence, control strategies for PAHs in urban runoff are required to ensure human and ecosystem safety. The effective control can be achieved by investigation of contamination levels of PAHs and understanding their sources. However, there have been limited attempts carried out for quantitative assessment of comparative contribution of various PAH sources to road dust. Road dust has been recognized as bringing a large volume of PAHs into the water environment via road runoff (Brown et al., 1985; Maltby et al., 1995a, 1995b; Boxall and Maltby, 1995). Possible PAH sources in road dust include diesel vehicle exhaust, gasoline vehicle exhaust, tire, pavement (asphalt or bitumen), and oil spill. Based on the enrichment factor (EF), Takada et al. (1990) identified vehicle exhaust as the primary PAH contributor to road dust collected from roads with heavy traffic, while atmospheric fallout was more significant in residential areas in Tokyo. Pengchai et al. (2004, 2005) estimated the comparative contribution from potential PAH sources in road dust samples, using a statistical approach based on a large number of reported PAH profiles. Seven types of PAH sources were defined: diesel vehicle exhaust, gasoline vehicle exhaust, tires, asphalt–pavement, asphalt or bitumen, petroleum products excluding tires and asphalt, and combustion products except those in vehicle engines. As many as 189 PAH data of possible sources were obtained from literature and by additional sampling and measurement. The obtained source data were categorized into seven possible groups as shown in Table 1: diesel vehicle exhaust [D], gasoline vehicle exhaust [G], tire [T], asphalt– pavement [P], asphalt or bitumen [A], petroleum products, excluding tire and asphalt [O], and combustion products, except for those in vehicle engines [E]. Using cluster analysis combined with principal component analysis, the 189 source data were classified into 11 source groups based on the content percentage of 12 individual PAHs (12-PAH profiles), as shown in Table 2. It could be interpreted that the 12-PAH profiles of samples in S1, which have pyrene, benzo(ghi)perylene, and fluoranthene as the predominant PAH species (43%, 19%, and 13%), indicated [T] because all the [T] data were included in S1 and, in reverse, most of the

4.09.2 Physicochemical Characterization of Road Dust and Soakaway Sediment 4.09.2.1 Contamination by Polycyclic Aromatic Hydrocarbons and Their Source Micropollutants, such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals, are widely distributed in dust, soils, and sediments, and are found in roof and road runoff (Hoffman et al., 1984; Takada et al., 1990; Sansalone and Buchberger, 1997; Roger et al., 1998; Heaney et al., 1999; Fo¨rster, 1999; Krein and Schorer, 2000; Chebbo et al., 2001; Furumai et al., 2002; Brenner et al., 2002; Murakami et al., 2003). PAHs are known to be acutely toxic, genotoxic, and carcinogenic compounds (Phillips, 1983; Hagris et al., 1984; Baumann, 1998). Hoffman et al. (1984) estimated that 36% of environmental PAH input was due to urban runoff; for the highermolecular-weight PAHs, the figure was 71%. Urban runoff has been recognized as an important PAH pathway to water

Table 1

Number of collected data as possible PAH sources

Source category

Number of PAH data Data reported in Pengchai et al. (2004)

Diesel vehicle exhaust [D] Gasoline vehicle exhaust [G] Tire [T] Asphalt-pavement [P] Asphalt or bitumen [A] Petroleum products excluding tire and asphalt [O] Combustion products except for those in vehicle engines [E] Total

2 4 8 8 8

Literature cited in Pengchai et al. (2004) 77 49

3 10 20

30

159

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens Table 2

267

Classification of PAH profiles in 189 source data S1

Average PAH profiles Phenanthrene (Ph) Anthracene (An) Fluoranthene (Fr) Pyrene (Py) Benzo(a)anthracene (Ba) Chrysene (Ch) Benzo(k)fluoranthene þ benzo(b)fluoranthene (Bf) Benzo(a)pyrene (Bpy) Indeno(1,2,3-cd)pyrene (In) Dibenz(a,h)anthracene (Db) Benzo(ghi)perylene (Bpe) Total % Sample number belonging to each group Diesel vehicle exhaust [D] Gasoline vehicle exhaust [G] Tire [T] Asphalt-pavement [P] Asphalt or bitumen [A] Petroleum products, excluding tire and asphalt [O] Combustion products, except for those in vehicle engines [E] Total number of samples

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

6 1 13 43 0 3 3 4 6 1 19

26 4 22 31 4 4 2 2 2 1 2

7 43 9 8 3 3 8 4 7 4 4

3 27 15 13 26 1 4 3 2 4 2

4 7 3 14 11 10 28 0 1 21 1

31 8 1 2 7 10 27 0 2 7 5

16 3 6 11 5 12 15 8 6 3 15

17 2 6 19 2 2 10 31 1 8 1

11 3 51 6 4 4 7 5 3 0 5

1 2 0 0 1 1 6 11 23 46 9

48 15 8 10 5 2 3 3 1 2 2

100

100

100

100

100

100

100

100

100

100

100

1

49 8

5 2

10

8

2 1

2 2

1

20 21

8 2

9

6 9

1 1

1

61

8

10

8

3

19

7

5

7

5

2

2 7 6

3

56

From Pengchai P, Nakajima F, and Furumai H (2005) Estimation of origins of polycyclic aromatic hydrocarbons in size-fractionated road dust in Tokyo with multivariate analysis. Water Science and Technology 51(3–4): 169–175.

data in S1 were from [T]. Likewise, S2 and S3 implied [D] and S7 represented [P] and [A]. S6 and S10 were minor groups having only three data each. S11 included a large number of source data in various categories and was difficult to be translated. Thirty-seven dust samples on nine streets in Tokyo were collected and subjected to PAH analysis both with and without particle size fractionation. Multiple regression analysis was applied to estimate the sources of the PAHs in the dust samples. The result demonstrated that the abrasion of tires and asphalt–pavement contributed a certain amount of PAHs to road dust, in addition to diesel vehicle exhaust, which has been recognized as the main source of PAHs in road dust.

4.09.2.2 Size and Density Distributions of PAHs in Road Dust Particle size and density are important parameters in wash-off processes for urban runoff. PAH distribution in harbor sediment fractions has been reported in both size and density (Ghosh et al., 2000; Rockne et al., 2002; Ghosh et al., 2003). Rockne et al. (2002) revealed that 85% of the total PAHs in Piles Creek sediment was found in the light fractions (o1.7 g cm3), despite the fact that light density components comprised only 4% of the total sediment mass. In addition, they suggested that the preferential sequestration in the Piles Creek sediment was likely due to the presence of detrital plant debris. Ghosh et al. (2000) showed that the coal-/wood-derived particles (specific gravity o1.8) constituted only 5% of Milwaukee Harbor sediment by weight, but contained 62% of the total PAHs.

Murakami et al. (2005) reported PAH concentrations in size- and density-fractionated road dust collected in Japan, as shown in Figure 2. The percentage contribution by weight of light density particles to the total deposition mass was less, but the light fractions accounted for a significantly higher ratio of PAH mass in road dust. It is suggested that light fractions in road dust contribute significantly to storm-water contamination, despite their minor contribution to the total deposition mass, due to their high PAH contents as well as their physical property of high mobility. The cluster analysis revealed that there was a significant difference in the PAH profiles between locations rather than between size fractions, density fractions, and period of sampling. Apart from the locations, the PAH sources might differ due to sampling time or size fractions. Multiple regression analysis indicated that asphalt/pavement was the major source of road dust in residential areas, and that tires and diesel vehicle exhaust were the major source of road dust in heavily trafficked area.

4.09.2.3 Evaluation on Heavy Metal Retention in Road Dust and Soakaway Sediments Infiltration facilities have the potential to serve as both sinks and sources of urban nonpoint pollutants during the process of groundwater recharge by storm water. Surface sediments found in infiltration facilities are known to have high heavy metal content (Mikkelsen et al., 1996; Datry et al., 2003; Dechesne et al., 2004). This implies that promoting urban storm-water infiltration may result in a pollutant transport from urban surface to groundwater. To minimize the

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

12-PAHs (µg g−1)

40 20 106−250 µm (H)

63−106 µm (H)

0.6−63 µm (H)

106−1250 µm (L)

63−106 µm (L)

0.6−63 µm (L) 80 60 40

250−2000 µm (H)

106−250 µm (H)

0.6−63 µm (H)

63−106 µm (H)

250−2000 µm (L)

106−250 µm (L)

20 0

(c)

Hongo Street

63−106 µm (L)

12-PAHs (µg g−1) 250−2000 µm (H)

(b)

106−250 µm (H)

10 0

60

106−250 µm (H)

63−106 µm (H)

0.6−63 µm (H)

20

63−106 µm (H)

250−2000 µm (H)

63−106 µm (H)

106−250 µm (H)

0.6−63 µm (H)

250−2000 µm (L)

0

106−250 µm (L)

10

30

Light particles: 44 ± 8%

0.6−63 µm (H)

20

Hongo Street

250−2000 µm (L)

Light particles: 3.4 ± 1.0%

12-PAHs (%)

30

40

17-Nov-03 10-Feb-04

100

50

Hongo Street

Shakujii

80

0.6−63 µm (L)

40

100

0 0.6−63 µm (L)

106−250 µm (H)

63−106 µm (H)

0 0.6−63 µm (H)

0 106−1250 µm (L)

10 63−106 µm (L)

10

0.6−63 µm (L)

Deposition mass (%) (a)

20

106−250 µm (L)

20

30

106−1250 µm (L)

17-Nov-03 10-Feb-04

17-Nov-03 10-Feb-04

Light particles: Nov: 28 ± 10% Feb: 33 ± 3%

0.6−63 µm (L)

30

40

63−106 µm (L)

Light particles: Nov: 4.0 ± 1.4% Feb: 0.69 ± 0.03%

Shakujii

63−106 µm (L)

40

50

Shakujii

12-PAHs (%)

50

0.6−63 µm (L)

Deposition mass (%)

60

63−106 µm (L)

268

Figure 2 Mass distribution (a), total 12-PAH distribution (b), and total 12-PAH content (c) in road dust by size and density fractions (mean7SE). L, light particles; H, heavy particles. From Murakami M, Nakajima F, and Furumai H (2005) Size- and density-distributions and sources of polycyclic aromatic hydrocarbons in urban road dust. Chemosphere 61: 783–791.

groundwater pollution, we have to understand the physicochemical characteristics of urban road dust and sediments in infiltration facilities. Heavy metal has higher concern of contamination because of higher exchangeability and leaching potential than PAHs. Boller (1997, 2004) described that heavy metals accumulate in various environmental components such as the sediments in the receiving via sewer system waters, whereas infiltration facilities functioned to control the accumulation of heavy metals as a short-term measure. Accumulation and potential release of heavy metals were investigated in infiltration facilities installed in Tokyo (Aryal et al., 2006, 2007). It is necessary to evaluate the multifunctions of infiltration facilities such as inundation control, groundwater recharge, and pollutant retention. Sixteen soakaway sediments, whose depths ranged widely, were then collected from bottom to top with a plastic pipe from the four sublocations in April 2004. In soakaway, with a 410 cm depth, 10 cm of surface of sediment was collected. Five road dust and two soils in pervious areas were collected in the same area in September 2004. Five road dust from heavily used roads (hereinafter referred to as heavy traffic road dust), where traffic volume ranged from 17 030 to 36 666 vehicles per day, was collected in Bunkyo Ward, Tokyo, Japan, in November 2004. Road dust was collected using a Hitachi CV-100S6 vacuum cleaner from a road gutter. Dry weather periods were more than 1.5 days for preliminary investigation and

sampling of soakaway sediments and more than 4 days for sampling of road dust and soils. Table 3 shows heavy metal contents in soakaway sediments, road dust from heavy traffic road dust and the residential area, and soils in pervious area. Heavy metal contents in thick and thin soakaway sediments are separately shown in the table. Aluminum, Mn, Fe, and As contents in heavy traffic road dust were lower than those in soils in pervious area. On the contrary, Cr, Ni, Cu, Zn, Cd, and Pb contents in heavy traffic road dust were approximately 2–8 times as high as those in soils in pervious areas. These heavy metals, except Cu in road dust from the residential area, were higher than soils in pervious area and lower than heavy traffic road dust. It was revealed that these heavy metals were derived from urban traffic activities. In particular, Pb has remained the key pollutant in heavy traffic road dust even since the early 1980s, when leaded gasoline was not a major source of Pb in aerosol in Japan (Mukai et al., 1993). Nevertheless, the sources of Pb in aerosol and road dust are not still clearly known (Mukai et al., 1993; Shinya et al., 2006). Further investigations are needed to apportion the sources of Pb. Chromium, Ni, Cu, Zn, Cd, and Pb contents in sediments, dust, and soils are plotted in Figure 3. It is interesting to note that thick soakaway sediments contained Cr, Cd, and Pb at significantly higher levels than thin soakaway sediments. In particular, thick soakaway sediments contained Cd and Pb at

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens Table 3

269

Heavy metal contents in soakaway sediments, road dust and soils in pervious area (mean7SE)

Al (g kg1) Cr (mg kg1) Mn (mg kg1) Fe (g kg1) Ni (mg kg1) Cu (mg kg1) Zn (mg kg1) As (mg kg1) Cd (mg kg1) Pb (mg kg1)

Thick soakaway sedimenta n¼5

Thin soakaway sedimentb n ¼ 11

Heavy traffic road dust n¼5

Road dust from the residential area n¼5

Soil in pervious area

5174 9776c 780760 4372 5474 4007180 17007100 1171 2.370.2c 230720c

4874 6675c 910780 4673 4973 210740 12007200 1071 1.570.1c 140720c

2674 180710 780780 5375 9073 730780 16007100 7.971.1 1.570.2 180720

3272 7077 700780 4075 5275 120720 10007300 7.470.6 0.9470.21 63718

8071 5270 120070 6873 4472 16070 240730 9.870.7 0.4570.10 2473

n¼2

a

Soakaway sediment whose depth was Z8 cm. Soakaway sediment whose depth waso8 cm. c Significant difference between thick and thin soakaway sediments (p o 0.05). n, number of samples. b

0

0

4000

0

4

3000 2000 1000 0

400 Cd Content (mg kg−1)

Zn

Content (mg kg−1)

3 2 1

Pb 300 200 100

Heavy traffic road dust Road dust from the residential area Soil in pervious area

0 Thin soakaway sediment

Heavy traffic road dust Road dust from the residential area Soil in pervious area

Thin soakaway sediment

Thick soakaway sediment

0 Thick soakaway sediment

Content (mg kg−1)

20

500

Heavy traffic road dust Road dust from the residential area Soil in pervious area

50

40

1000

Thin soakaway sediment

100

60

Cu

Thick soakaway sediment

150

80

Ni

Content (mg kg−1)

Cr

Content (mg kg−1)

Content (mg kg−1)

200

1500

100

250

Figure 3 Chromium, Ni, Cu, Zn, Cd, and Pb contents in soakaway sediments, road dust, and soils in pervious area. (Thick soakaway sediment: sediment depth was Z8 cm; thin soakaway sediment: sediment depth was o8 cm.)

higher levels than road dust from the residential area and soils in pervious areas. Cadmium and Pb contents in thick soakaway sediments were higher than or equal to those in heavy traffic road dust. This indicated that soakaway sediments work

as adsorbents to retain heavy metals. Dissolved metals in runoff water possibly have been adsorbed on the soakaway sediments for the long-term operation. The clogging with deposited sediments in infiltration facilities causes the low

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

1

10

10 000

Pb (count)

1000

Heavy traffic road dust

Both Cr and Pb

(n = 40) (n = 9) (n = 4)

Only Pb

Cr (count) 100 1000 10 000 10 000 1000 1

100

1

10

( ( (

1 Only Cr

1

10

n = 14) n = 2) n = 2)

100 10

Pb (count)

270

Road dust from the residential area Soakaway sediment Yellow road line marking (raw material) Yellow road line marking (curb) Yellow road line marking (road surface)

1

(n = 9) (n = 7) (n = 6)

100 1000 10 000 Cr (count)

Figure 4 The X-ray intensities of Cr and Pb during WDS measurement of the identified particles in heavy traffic road dust, road dust from the residential area, soakaway sediment, and yellow road line markings. Only Cr: particles containing Cr but not Pb; only Pb: particles containing Pb but not Cr; both Cr and Pb: particle containing both Cr and Pb. From Murakami M, Nakajima F, Furumai H, Tomiyasu B, and Owari M (2007) Identification of particles containing chromium and lead in road dust and soakaway sediment by electron probe microanalyser. Chemosphere 67(10): 2000–2010.

infiltration rate. The longer contact time in runoff infiltration through thick soakaway might cause the accumulation of heavy metals at higher levels than those through thin soakaway sediments. At the same time, it is of concern that limited adsorption of heavy metals on soakaway sediment finally causes groundwater contamination. It will be necessary to find out the breakthrough characteristics of heavy metals accumulated on sediments to prevent groundwater contamination through infiltration facilities. It was found that thick soakaway sediments (Z8 cm) contained traffic-related heavy metals, such as Cr, Cd, and Pb, at significantly higher levels than in road dust and thin soakaway sediments (o8 cm), possibly due to adsorption phenomena. Adsorption and desorption of heavy metals are related to their speciation, which differs among source materials as well as storm-water characteristics such as pH. It is important to gain an understanding of both the source and the fate of heavy metals in infiltration facilities.

4.09.2.4 Identification of Elements in Individual Particles by Electron Probe Microanalysis Electron probe microanalysis (EPMA) has been used to identify individual particles and to find the sources and their carrier particles. EPMA measurement has advantages of being able to target an individual specific particle as well as to determine multiple elements. The use of EPMA is practicable for source apportionment and investigation of carrier particles for Cr and Pb in road dust and soakaway sediment, considering that these metals were widely distributed in urban areas and were adsorbed by soakaway sediment. In most previous studies, EPMA has been applied to identify one-by-one particles comprising aerosol or road dust. To apportion the sources of a specific heavy metal, such as Cr and Pb, it is necessary to distinguish very minor individual particles containing the heavy metal at significantly high levels from other

particles in aerosol or dust by wavelength dispersive spectrometry (WDS) map analysis. We collected road dust and soakaway sediment in Tokyo and applied them to EPMA analysis to distinguish individual particles containing Cr and Pb at significantly high levels. Figure 4 shows the X-ray intensities during WDS measurement of road dust and soakaway sediment as well as yellow road line marking, which has been known to contain lead and chromium at high levels. Analysis of variance (ANOVA) testing showed that the X-ray intensities of Pb in the identified particles were significantly higher in heavy traffic road dust than in road dust from the residential area and soakaway sediment, whereas no significant difference was observed for the X-ray intensities of Cr in the identified particles among heavy traffic road dust, road dust from the residential area, and soakaway sediment. This indicates that particles containing Pb at high levels were more common in heavy traffic road dust than in soakaway sediment at the individual particle level, whereas the Pb content in soakaway sediment (340710 mgPb kg1) was approximately twice as high as that in heavy traffic road dust (17070 mgPb kg1) at the conglomerate level. In addition, Welch’s test in heavy traffic road dust showed that the X-ray intensities of Pb in the identified particles containing both Cr and Pb were significantly higher than those in the identified particles containing high levels of Pb only, whereas the X-ray intensities of Cr in the identified particles containing both Cr and Pb were lower than those in the identified particles containing only Cr. These results reveal that heavy traffic road dust contains Pb source materials that also contain Cr, and that different source materials contain only Cr at high levels. Figure 4 also shows the results of three yellow road line marking samples. The plots of X-ray intensities of Cr versus Pb were almost linear (1:1) for the identified particles containing both Cr and Pb in heavy traffic road dust. The line was a close fit with the plots of three yellow road line markings. One identified particle

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

containing both Cr and Pb, which deviated from other points, may have originated in a different source material.

pollution level. It is reported that the EF more than 10 implies the remarkable anthropogenic pollution (Han et al., 2005):

4.09.2.5 Leaching Potential of Heavy Metal in Road Dust

Leached concentration (µg l−1)

1000

100

10

EF ¼

ðX=AlÞsample ðX=AlÞref

where X denotes metal content, Al is aluminum content, and ref is reference natural soil. Figure 6 shows the relationship between EF and leached concentration of these metals. It is clear that anthropogenic pollution is significant for Pb and Cr. Regarding Cr, all the leached concentration from heavy traffic road dust was higher than the 10% value of cleanup criteria, while that of road dust from residential area was lower than the 10% value. Although there was high anthropogenic pollution in Pb, their leached concentration was not so high compared to the cleanup criteria, except for two samples. It is also shown that leaching of Cd is not so significant. On the contrary, attention is needed for leaching of As, while their anthropogenic pollution level was low.

4.09.2.6 Runoff Behavior of Particle-Associated PAH The monitoring of urban runoff often requires highly sophisticated equipment and human organizations to catch rainfall events whenever they happen. Runoff water quality varies with rainfall patterns and antecedent pollutant deposition conditions. Due to the difficulties inherent in regular monitoring of urban runoff, mathematical models are utilized to simulate rainwater runoff and pollutant transportation. Such models are useful in evaluating the effectiveness of pollution-control measures in protecting the water environment from nonpoint source pollution. Runoff models for suspended solids (SSs) have been developed by many researchers (Sartor and Boyd, 1972; Tomonvic and Makishimovic, 1996; Furumai et al., 2001; Hijioka et al., 2001; Uchimura et al., 1997). These models can 1000 Leached concentration (µg l−1)

Surface sediments found in infiltration facilities are known to have high heavy metal content. While sediments in infiltration facilities function to accumulate heavy metals as ‘sinks’, sediments that have high heavy metal contents are possible ‘sources’ to aquatic environments along with desorption processes. Infiltration facilities have the potential to serve as both ‘sinks’ and ‘sources’ of urban nonpoint pollutants during the process of groundwater replenishment by storm water. Leaching tests on road dust are necessary to clarify the transport of heavy metals in water cycle in urban areas. Figure 5 shows the result of leaching tests (liquid/solid ratio was 10 l/ kg-dry; pH ¼ 5.8–6.3; 6 h under room temperature) conducted to evaluate the leaching characteristics of heavy metals from road dust in a heavy traffic area and in a residential area in Tokyo. The results show that the leached concentrations of Cr, Fe, Ni, and Cu from road dust in the heavy traffic area were significantly higher than those from road dust in the residential area. Additionally, the leached fractions of Cr from road dust in the heavy traffic area were also significantly larger than those from road dust in the residential area. The leached Cr from road dust seems to be derived from traffic sign markers such as yellow paint. There were different tendencies in leaching characteristics from fine to coarse fractions among the heavy metals. Leaching tests on size-fractionated road dust revealed that the leached concentrations of Al, Cr, Cu, As, and Cd were higher in their fine fractions (o106 mm), whereas the leached concentrations of Mn, Zn, and Pb were higher in their coarse fractions (106–2000 mm). Among the tested heavy metals, Cr, As, Cd, and Pb are listed as toxic parameters of drinking water quality standard and Soil Contamination Countermeasures Act in Japan. Since heavy metals are contained in natural soil, it is not enough to discuss the anthropogenic pollution by their contents. Therefore, the following equation of EF was proposed by Zoller et al. (1974), which can be used to evaluate the anthropogenic

271

Cr(VI ) 100 As, Cd, Pb 10 Cr(VI)

1

As, Cd, Pb N.D. 10 Enrichment factor

1 1 Road dust in residential area Heavy traffic road dust

Cr

Cr* Mn Fe* Ni* Cu* Zn

Cd

Pb

Road dust in residential area

N.D. Al

As

As

Cd

Pb

Figure 5 Leached concentration of heavy metals from road dust (Murakami et al., 2006). Asterisks refer to significantly higher from heavy traffic road dust.

Cr

As

100

Cd

Pb

Heavy traffic road dust

Figure 6 Relationship between enrichment factor (EF) and leached concentration of Cr, As, Cd, and Pb (Murakami et al., 2006). Solid and dashed lines represent the cleanup criteria in the Soil Contamination Countermeasures Act and those 10% values, respectively.

272

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

be utilized in the simulation of particle-associated pollutants such as PAHs. The runoff of particle-associated micropollutants is thought to depend on particle size distribution. Several field surveys have shown that micropollutants are attached to fine sediments and particles (Sansalone and Buchberger, 1997; Roger et al., 1998; Murakami et al., 2005; Sartor and Boyd, 1972). It has been reported that the runoff and sedimentation characteristics of fine particles are different from those in the coarse fraction (Furumai et al., 2002; Brenner et al., 2002; Roger et al., 1998; Tomonvic and Makishimovic, 1996; Andral et al., 1999). Therefore, the SS runoff models need a modification of particle categorization to extend their application to PAH runoff models. Urban surface category is also an important factor in runoff modeling. Hijioka et al. (2001) proposed a SS runoff model with two particle size categories and two urban impervious surface types: fine (smaller than 45 mm) and coarse (larger than 45 mm) particles on roads and roofs. In the model, roof runoff was characterized as a faster process than road runoff because roofs have steeper slopes and smoother surfaces. As shown in the previous section, the PAH composition varies according to emission source. Murakami et al. (2003) revealed differences in PAH compositions between roof dust and road dust. The result of cluster analysis on PAH profiles in

the size-fractionated dust showed that the roof dust formed a separate cluster to the road dust, irrespective of either particle size or roof structure. Factor analysis revealed that phenanthrene, indeno(1,2,3-cd) pyrene, and benzo(ghi)perylene were important PAHs for distinguishing the road dust and the roof dust. The result of the factor analysis also suggested that the contribution of tires, pavements, or asphalts to PAHs was greater in road dust than in roof dust, and that the contribution of vehicle exhaust emission to PAHs was greater in roof dust than in road dust. A nonparametric test indicated that the PAH content was higher in the fine dust (smaller than 106 mm) than in the coarse dust (larger than 106 mm). A model was developed by Murakami et al. (2004), explaining the dynamic runoff behavior of particle-associated PAHs. In the model, roads and roofs were considered separately as impervious surfaces, and particle sizes were classified into fine and coarse fractions (Figure 7). A field survey for model development was conducted in a densely populated area in Japan. Consideration of two types of road dust with different mobility is conceptually useful to explain the PAH profiles in runoff particles. Such a model scheme achieves good agreement with observations of SS and PAH runoff behavior for fine particles, except during heavy rainfall. To improve the disagreement, it may be necessary to take account of

400

0 ‘Road’ (PAHs) ‘Roof’ (PAHs) Observed value (PAHs)

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30

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40 5:50

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‘Road’ (PAHs) ‘Roof’ (PAHs) Observed value (PAHs)

120

Time Figure 7 Simulated and observed runoff of particle-associated PAH in residential area (Murakami et al., 2004).

Rainfall intensity (mm h−1)

Rainfall intensity

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

additional sources of SSs and PAHs washed off by heavy rainfall.

4.09.3 Pathogenic Pollution in a Seaside Park after CSO CSOs have been recognized as a serious source of environmental water pollution. However, very limited field surveys have been conducted to evaluate the magnitude of the problem and the duration of its impact on receiving waters in Japan, where rather conventional and less informative water quality parameters have been monitored to assess risk of infection. Therefore, long-term monitoring data and high-quality information are required to estimate the impact of CSO events on human health. Studies in the United States investigated the relationship between waterborne infectious diseases and rain events using public records from 1971 to 1994 (Rose et al., 2000, 2001). They concluded that 20–40% of disease instances were due to contamination during heavy rain events, suggesting that public health could be improved by upgrading rainwater management systems in urban areas. The conventional bacterial parameters, such as total and fecal coliforms, are good indicators of fecal contamination; however, they are less reliable as an index of viral or protozoan contamination because these pathogens behave differently in the water environment. To assess the risk of infection caused by CSO events, both traditional bacterial indicators and viruses should be monitored in the receiving water bodies. Katayama et al. (2004) conducted spatial and temporal monitoring to investigate the fate of pathogens and indicator bacteria in the coastal area in Tokyo. They evaluated the magnitude and duration of the impact of CSO events spatially and temporally after rain events. In particular, the fate of enteric viruses that cause gastroenteritis (noroviruses G1 and G2 and enteroviruses) was investigated. The fates of the viruses as well as conventional indicators were evaluated by serially

monitoring the receiving water body after CSO events. However, their monitoring data are not sufficient to discuss difference behaviors of bacteria and virus after CSO event as well as under normally fine weather conditions. Therefore, we conducted a 2-month survey to evaluate the effects of rainfall on the fate of human adenoviruses, total coliforms and E. coli in coastal water in the Odaiba area in Tokyo Bay (Haramoto et al., 2006). The Odaiba area is suspected to be contaminated with the effluents from several domestic wastewater treatment plants. The Odaiba Seaside Park is located near the sampling site, and more than 1 million people visit the park for recreational purposes annually. Although playing on the beaches is allowed, swimming in the sea is prohibited. To compare the behavior of viruses and indicators, the duration and frequency of sampling were prioritized using only one sampling site and determining limited parameters. The sampling point at Odaiba Seaside Park is shown in Figure 8. Samples were collected from 4 August to 15 October 2004. During the survey period, a total of 774 mm of rainfall was observed, including some heavy rainfall events caused by typhoons. Samples were usually collected in the morning, delivered to the laboratory within a few hours on ice, and analyzed for human adenoviruses, total coliforms, and E. coli. Total coliforms and E. coli in 10 ml of coastal water were determined by an m-Coliblue broth membrane filtration procedure (Millipore, Tokyo). The acid rinse method (Katayama et al., 2002) was used for concentrating the virus from 1000 ml of coastal water samples as in the previously described study. Viral DNA was extracted using commercially available methods, followed by polymerase chain reaction to determine the concentration of viral genomes. At the same time, a decimal dilution series of DNA from human adenovirus serotype 40 were used to create a calibration curve. Total coliforms and E. coli were detected in all 47 tested samples with geometric mean concentrations of 68 CFU ml1 (range: 1.8–3700 CFU ml1) and 4.4 CFU ml1 (range: 0.15– 280 CFU ml1), respectively. On the other hand, human

Sampling point

Odaiba Seaside Park

Tokyo Bay

Figure 8 Sampling points in Tokyo Bay.

250 m

273

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens Rainfall

Human adenoviruses

E. coli

Total coliforms

250

Human adenoviruses, total coliforms, or E. coli (PDU ml−1 or CFU ml−1)

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200 100 10

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adenoviruses were detected in 38 (81%) of 47 samples at a maximum concentration of 5.5 PDU ml1. The daily change in the concentrations of human adenoviruses, total coliforms, and E. coli in coastal water is shown in Figure 9 (from 23 August to 10 September). The concentrations of these microorganisms increased after rainfall events. For instance, following a heavy rainfall from 4 to 5 September (84.5 mm), the concentration of human adenoviruses, total coliforms, and E. coli increased from 0.14 to 5.5 PDU ml1, from 13 to 240 CFU ml1, and from 2.0 to 55 CFU ml1, respectively. These increased concentrations decreased gradually to the level before the rainfall event within a few days to 0.24 PDU ml1, 21 CFU ml1, and 1.9 CFU ml1, respectively, on 8 September. The relationship between the concentration of human adenoviruses and that of total coliforms or E. coli was determined. As shown in Figure 10, a moderate positive correlation (r ¼ 0.536) was observed between the logarithms of the concentration of human adenoviruses and that of E. coli among the adenovirus-positive samples. Human adenoviruses were chosen as a target virus, and total coliforms and E. coli were used as indicator bacteria to compare the fates of viruses and bacteria in CSO-contaminated coastal water. After a rainfall event, the concentration of the tested microorganisms in coastal water usually increased by 10–100 times, followed by a gradual decrease to the level before the rainfall event within a few days. It suggests that E. coli could be used as an indicator of human adenovirus contamination in coastal water susceptible to CSO. Total coliforms and E. coli are present in the feces of both humans and animals, while human adenoviruses are present only in human feces. The results of this study indicate that untreated sewage, or CSO, could be a major source of these microorganisms in Tokyo Bay. Interestingly, this monitoring study indicated that the high contamination of human adenoviruses following heavy rainfall persisted for at least a few days after the event. Accordingly, recreational activities in the

Human adenoviruses (PDU ml−1)

Figure 9 Occurrence of human adenoviruses, total coliforms and E. coli in coastal water (from 23 August to 10 September 2004) (Haramoto et al., 2006).

1 r = 0.536 0.1 0.01 0.001 0.0001 0.1

10

10

100

1000

10 000

E. coli (CFU ml−1) Figure 10 Relationships between concentrations of human adenoviruses and E. coli in coastal water samples from the same location on several days (Haramoto et al., 2006).

contaminated area after rain will pose a higher risk of infection by human adenoviruses.

4.09.4 Summary Urban road dust, as a representative pollutant on urban surfaces, was well characterized using several analytical and statistical methods. Source and distribution of PAHs and heavy metals in road dust were discussed referring to research papers on characterization of urban road dust. The desorption process of heavy metals was also reported, looking at their accumulation process in infiltration facilities for urban inundation control. The heavy metal desorption/adsorption phenomena in the soil collected near the infiltration facilities were further assessed. EPMA is a powerful method to distinguish individual particles containing heavy metals at significantly high levels in

Urban Nonpoint Source Pollution Focusing on Micropollutants and Pathogens

road dust and soakaway sediment. This type of element composition analysis could be applied to more environmental samples in order to enable source apportionment and investigation of the carrier particles. Those research results greatly enhance our understandings of urban nonpoint pollutant behavior in infiltration process, although the interaction between pollutants and soils seems dependent on soil characteristics. In addition to urban nonpoint source pollution, CSOs have been recognized as one of the serious sources of pollution to the water environment during rain events. This chapter focuses on pathogenic pollution after CSO events, which is closely related to human health risk at bathing and water amenity activities. The intensive monitoring work indicated that concentration of tested microorganisms increased after a rainfall event and then gradually decreased to the level before the rainfall event within a few days. There was no significant difference in the concentration of the tested microorganisms between the increasing and decreasing tide groups, suggesting that the impact of rainfall on the fate of microorganisms is stronger than that of tidal movement. The behaviors of a pathogenic virus as well as bacterial indicators were gradually elucidated by the long-term sampling. In addition, techniques of molecular biology and virus concentration enabled us to study the fate of human pathogenic viruses in a CSO-impacted water body. This chapter summarizes the research results in the research work related to wet weather pollution phenomena in urban area, consisting of urban nonpoint source pollution and CSO. We should pay more attention to irregularly occurring events as well as to stable discharge of pollutant load so that we could protect water environment from urban pollution sources. It means that sustainable urban water environment can be developed by sufficient understanding and scientific diagnosis of current situation. Then it is necessary to propose and prepare effective countermeasures and their implementation toward the sustainability of urban water environment, coexisting with sound urban activities.

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Brown RC, Pierce RH, and Rice SA (1985) Hydrocarbon contamination in sediments from urban stormwater runoff. Marine Pollution Bulletin 16: 236--240. Chebbo G, Gromaire MC, Ahyerre M, and Garnaud S (2001) Production and transport of urban wet weather pollution in combined sewer system: The ‘‘Marais’’ experimental urban catchment in Paris. Urban Water 3: 3--15. Datry T, Malard F, Vitry L, Hervant F, and Gibert J (2003) Solute dynamics in the bed sediments of a stormwater infiltration basin. Journal of Hydrology 273: 217--233. Dechesne M, Barraud S, and Bardin JP (2004) Spatial distribution of pollution in an urban stormwater infiltration basin. Journal of Contaminant Hydrology 72: 189--205. Fo¨rster J (1999) Variability of roof runoff quality. Water Science and Technology 39(5): 137--144. Furumai H, Balmer H, and Boller M (2002) Dynamic behavior of suspended pollutants and particle size distribution in highway runoff. Water Science and Technology 46(11–12): 413--418. Furumai H, Hijioka Y, and Nakajima F (2001) Modelling and field survey on washoff behaviour of suspended particles from roofs and roads. In: Brashear RW and Maksimovic C (eds.) Urban Drainage Modelling – Proceedings of the Specialty Symposium of the World Water and Environmental Resources Congress, pp. 225--237. Reston, VA: American Society of Civil Engineers. Ghosh U, Gillette JS, Luthy RG, and Zare RN (2000) Microscale location, characterization, and association of polycyclic aromatic hydrocarbons on harbor sediment particles. Environmental Science and Technology 34: 1729--1736. Ghosh U, Zimmerman JR, and Luthy RG (2003) PCB and PAH speciation among particle types in contaminated harbor sediments and effects on PAH bioavailability. Environmental Science and Technology 37: 2209--2217. Hagris WJ, Jr., Roberts MH, Jr., and Zwerner DE (1984) Effects of contaminated sediments and sediment-exposed effluent water on an estuarine fish: Acute toxicity. Marine Environmental Research 14: 337--354. Han JS, Moon KJ, Ryu SY, Kim YJ, and Perry KD (2005) Source estimation of anthropogenic aerosols collected by a DRUM sampler during spring of 2002 at Gosan, Korea. Atmospheric Environment 39: 3113--3125. Haramoto E, Katayama H, Oguma K, Koibuchi Y, Furumai H, and Ohgaki S (2006) Effects of rainfall on the occurrence of human adenoviruses, total coliforms, and Escherichia coli in seawater. Water Science and Technology 54(3): 225--230. Heaney JP, Pitt R, and Field R (1999) Innovative urban wet weather flow management systems. EPA/600/R-99/029. Cincinnati, OH: US Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory. Hijioka Y, Nakajima F, and Furumai H (2001) Modified models of wash-off from roofs and roads for non-point pollution analysis during first flush phenomena. In: Brashear RW and Maksimovic C (eds.) Urban Drainage Modelling – Proceedings of the Specialty Symposium of the World Water and Environmental Resources Congress, pp. 275--286. Reston, VA: American Society of Civil Engineers. Hoffman EJ, Mills GL, Latimer JS, and Quinn JG (1984) Urban runoff as a source of polycyclic aromatic hydrocarbons to coastal waters. Environmental Science and Technology 18: 580--587. Katayama H, Okuma K, Furumai H, and Ohgaki S (2004) Series of surveys for enteric viruses and indicator organisms in Tokyo Bay after an event of combined sewer overflow. Water Science and Technology 50(1): 259--262. Katayama H, Shimasaki A, and Ohgaki S (2002) Development of a virus concentration method and its application to detection of enterovirus and Norwalk virus from coastal seawater. Applied and Environmental Microbiology 68: 1033--1039. Krein A and Schorer M (2000) Road runoff pollution by polycyclic aromatic hydrocarbons and its contribution to river sediments. Water Research 34: 4110--4115. Maltby L, Boxall ABA, Forrow DM, Calow P, and Betton CI (1995b) The effects of motorway runoff on freshwater ecosystems: 2. Identifying major toxicants. Environmental Toxicology and Chemistry 14(6): 1093--1101. Maltby L, Forrow DM, Boxall ABA, Calow P, and Betton CI (1995a) The effects of motorway runoff on freshwater ecosystems: 1. Field study. Environmental Toxicology and Chemistry 14(6): 1079--1092. Mikkelsen PS, Ha¨fliger M, Ochs M, Tjell JC, Jacobsen P, and Boller M (1996) Experimental assessment of soil and groundwater contamination from two old infiltration systems for road run-off in Switzerland. Science of the Total Environment 190: 341--347. Mukai H, Furuta N, Fujii T, Ambe Y, Sakamoto K, and Hashimoto Y (1993) Characterization of sources of lead in the urban air of using ratios of stable lead isotopes. Environmental Science and Technology 27(7): 1347--1356. Murakami M, Nakajima F, and Furumai H (2003) Distinction of size-fractionated road and roof dust based on PAHs contents and profiles. Journal of Japan Society on Water Environment 26(12): 837--842.

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Murakami M, Nakajima F, and Furumai H (2004) Modelling of runoff behaviour of particle-bound polycyclic aromatic hydrocarbons (PAHs) from roads and roofs. Water Research 38(20): 4475--4483. Murakami M, Nakajima F, and Furumai H (2005) Size- and density-distributions and sources of polycyclic aromatic hydrocarbons in urban road dust. Chemosphere 61: 783--791. Murakami M, Nakajima F, Furumai H, and Kato Y (2006) Leaching characteristics of heavy metals in road dust in Tokyo. Journal of Japan Society on Water Environment 29: 731--735. Murakami M, Nakajima F, Furumai H, Tomiyasu B, and Owari M (2007) Identification of particles containing chromium and lead in road dust and soakaway sediment by electron probe microanalyser. Chemosphere 67(10): 2000--2010. Pengchai P, Furumai H, and Nakajima F (2004) Source apportionment of polycyclic aromatic hydrocarbons in road dust in Tokyo. Polycyclic Aromatic Compounds 24(4–5): 713--789. Pengchai P, Nakajima F, and Furumai H (2005) Estimation of origins of polycyclic aromatic hydrocarbons in size-fractionated road dust in Tokyo with multivariate analysis. Water Science and Technology 51(3–4): 169--175. Phillips DH (1983) Fifty years of benzo(a)pyrene. Nature 303: 468--472. Rockne KJ, Shor LM, Young LY, Taghon GL, and Kosson DS (2002) Distributed sequestration and release of PAHs in weathered sediment: The role of sediment structure and organic carbon properties. Environmental Science and Technology 36: 2636--2644. Roger S, Montrejaud-Vignoles M, Andral MC, Herremans L, and Fortune JP (1998) Mineral, physical and chemical analysis of the solid matter carried by motorway runoff water. Water Research 32: 1119--1125.

Rose JB, Daeschner S, Easterling DR, Curriero FC, Lele S, and Patz JA (2000) Climate and waterborne disease outbreaks. Journal of the American Water Works Association 92(9): 77--87. Rose JB, Epstein PR, Lipp EK, Sherman BH, Bernard SM, and Patz JA (2001) Climate variability and change in the United States: Potential impacts on water- and foodborne diseases caused by microbiologic agents. Environmental Health Perspectives 109(5): 211--221. Sansalone JJ and Buchberger SG (1997) Characterization of solid metal element distributions in urban highway stormwater. Water Science and Technology 36(8– 9): 155--160. Sartor JD and Boyd GB (1972) Water pollution aspects of street surface contaminants. EPA/R2/72/081. Washington, DC: US Environmental Protection Agency, Office of Research and Monitoring. Shinya M, Funasaka K, Katahira K, Ishikawa M, and Matsui S (2006) Lead isotope ratios in urban road runoff. Water Science and Technology 53(2): 185--191. Takada H, Onda T, and Ogura N (1990) Determination of polycyclic aromatic hydrocarbons in urban street dusts and their source materials by capillary gas chromatography. Environmental Science and Technology 24: 1179--1186. Tomonvic A and Makishimovic C (1996) Improved modeling of suspended solids discharge from asphalt surface during storm event. Water Science and Technology 33(4–5): 363--369. Uchimura K, Nakamura E, and Fujita S (1997) Characteristics of stormwater runoff and its control in Japan. Water Science and Technology 36(8–9): 141--147. Zoller WH, Gladney ES, and Duce RA (1974) Atmospheric concentrations and sources of trace metals at the South Pole. Science 183: 198--200.

4.10 Constructed Wetlands and Waste Stabilization Ponds C Polprasert, Thammasat University, Pathumthani, Thailand S Kittipongvises, Asian Institute of Technology, Pathumthani, Thailand & 2011 Elsevier B.V. All rights reserved.

4.10.1 4.10.1.1 4.10.1.2 4.10.1.3 4.10.2 4.10.2.1 4.10.2.2 4.10.2.2.1 4.10.2.2.2 4.10.2.3 4.10.2.3.1 4.10.2.3.2 4.10.2.3.3 4.10.2.3.4 4.10.2.3.5 4.10.3 4.10.3.1 4.10.3.1.1 4.10.3.1.2 4.10.3.1.3 4.10.3.2 4.10.3.2.1 4.10.3.2.2 4.10.3.2.3 4.10.4 4.10.4.1 4.10.4.1.1 4.10.4.1.2 4.10.4.1.3 4.10.4.2 4.10.4.2.1 4.10.4.2.2 4.10.5 4.10.5.1 4.10.5.1.1 4.10.5.1.2 4.10.5.2 4.10.5.3 4.10.6 References

Introduction Millenium Development Goals, Water Supply and Sanitation Problems Potentials of Natural Systems Objectives and Scope Principles of CW and WSP Systems for Wastewater Treatment and Reuse Symbiotic Reactions among Aquatic Plants, Algae, and Bacteria Types and Functions of CW and WSP Systems Constructed wetlands types Wastewater stabilization pond types Major Mechanisms for Wastewater Treatment and Recycling in CW and WSP Systems BOD5 removal SS removal N and P removal Heavy metals Pathogen removal Design Criteria and Operation of CW and WSP Systems CW Design Criteria and Operation Empirical approach Rationale approach Operation, maintenance, and troubleshooting of CW WSP Design Criteria and Operation Empirical approach Rationale approach Operation, maintenance, and troubleshooting for WSP Case Studies of CW and WSP CW Case Studies CW treatment of an industrial wastewater CW treatment of a municipal wastewater CW treatment of fecal sludge or septage WSP Case Studies WSP treatment of industrial wastewater and fish production WSP treatment of wastewater containing endocrine disrupting chemicals Emerging Environmental Issues versus Potentials of CW and WSP Food Production Biomass production in CW Biomass production in WSP Energy and Water Conversation and Climate Change Mitigation CW and HRAP as Future Life-Support Technology Summary

Nomenclature Ac d Io Is ks kT

Cross-sectional area Dispersion number Amount of visible solar energy penetrating a smooth water surface Saturation light intensity Hydraulic conductivity Reaction rate at temperature T

k20 Pa Qa Qe Qi td Vv /

277 277 277 278 278 279 280 280 280 282 282 282 283 283 283 284 284 284 285 286 287 287 287 288 289 289 289 290 290 291 291 292 292 292 292 293 295 296 296 298

Reaction rate at temperature of 20 1C Algal productivity Average flow rate Effluent flow rate Influent flow rate Area doubling time Void volume Media porosity

277

278

Constructed Wetlands and Waste Stabilization Ponds

4.10.1 Introduction

The United Nations (UN, 2005, 2008) member states and international organizations have committed their actions to achieve the millenium development goals (MDGs), which include the issues of poverty alleviation, sustainable development, and environmental protection. One of the MDGs is to provide water supply and sanitation for all by the year 2025, with an interim target of halving the proportion of people living in extreme poverty or those without adequate water supply and sanitation by the year 2015. To meet the 2025 target, with continued population growth, around 2.9 billion people will need to be provided with improved water supply and about 4.2 billion people will need to be serviced with improved sanitation.

4.10.1.1 Millenium Development Goals, Water Supply and Sanitation Problems Presently, the world population is projected to increase from the current number of 6 billions to 9 billions in 2050 (Figure 1). Urbanization and rapid population growth are the main driving forces of adverse environmental impacts worldwide. Increase in the number of the global population will result in increased energy and food demands and environmental problems, especially with regard to the aspects of water supply and sanitation. Supporting evidence for the above statement is the result of a World Health Organization report (WHO, 2000) which showed that in the year 2000 around 1.1 billion people (about 18% of the world population) did not have access to improved water supply (Figure 2), and 2.4 billions (about 40% of the world population) were without access to any sort of improved sanitation facility (Figure 3). The vast majority of those without access to water supply and sanitation services are in Asia.

4.10.1.2 Potentials of Natural Systems The current situations of water supply and sanitation, as stated in Section 4.10.1.1, suggest that employing conventional technologies alone will not be adequate to manage the increasing quantity of human wastes. This is mainly due to the

World population: 1950−2050 10 9 9 billion

Population (billions)

8 8 billion

7 7 billion

6 6 billion

5 5 billion

4 4 billion

3 3 billion

2 1

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2040

2030

2020

2010

2000

1990

1980

1970

1960

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0

Year Figure 1 World population increases with human advances in science and technology (US Census Bureau, 2007).

Total unserved: 2.4 billion Total unserved: 1.1 billion

5% 2%

7% 2% Asia

13% Asia

Africa

Africa

Latin America and the Caribbean Europe

28%

Latin America and the Caribbean Europe

63% 80% Figure 2 Distribution of the global population not served with improved water supply, by region (WHO, 2000).

Figure 3 Distribution of the global population not served with improved sanitation, by region (WHO, 2000).

Constructed Wetlands and Waste Stabilization Ponds

high investment and operation costs of conventional technologies such as activated sludge, tricking filter, and sewerage systems, which require highly skilled manpower for maintaining. Natural systems such as constructed wetlands (CWs) and waste stabilization ponds (WSPs) are being considered as a potential alternative technology for wastewater treatment. Because they utilize the photosynthetic reactions of aquatic plants and algae to produce gaseous O2 for bacteria to biodegrade organic matter, the CW and WSP systems are usually less expensive to construct and operate than the conventional treatment systems (Table 1; Stowell et al., 1980). The CW and WSP systems offer several advantages as a wastewater treatment process as they could significantly reduce 5-day biochemical oxygen demand (BOD5), suspended solids (SSs), nitrogen (N), phosphorus (P), as well as metals, trace organics, and pathogens present in wastewaters or sludge (Polprasert, 2007). There is considerable yield of biomass through the production of plants, algae, and fish, which could be utilized as animal feeds or human foods (Figure 4). Some of these biomass materials could be converted to become compost fertilizer and alternative energy sources such as biogas, ethanol, and biodiesel oils (Haag, 2007). By integrating biotechnology and nanotechnology with these natural systems, their potentials in treating wastewaters containing toxic compounds and in resource recovery are expected to be improved considerably (Figure 5; Stern, 2006).

279

Figure 4 Food production from natural systems.

4.10.1.3 Objectives and Scope This chapter describes the potentials of some natural systems such as CW and WSP for wastewater treatment and resource recovery. The principal reactions occurring in the CW and WSP systems and the treatment mechanisms occurring in these natural systems such as photosynthesis, microbial reactions, symbiotic reactions of algae and bacteria and aquatic plants will be explained, including their potentials for resources’ recovery through the production of human and animal foods and alternative energy sources. Performance of full-scale CW

Table 1 Costs and energy requirements of conventional and aquatic treatment systems Item

Treatment plant size 378.5 m3d1

3785 m3d1

Conva Aquaticb Conv 0.71 0.37 Capital cost (US$  106) 21 O & M cost (US$/year  103) 35 Energy, (kJ/year  109) 0.93 0.53

Aquatic

1.60 0.90 117 74 5.06 2.19

a

Activated sludge þ chlorination. Primary clarification þ artificial wetlands þ chlorination. O & M ¼ operation and maintenance. kJ ¼ kilojoules. Modified from Stowell R, Ludwig R, Colt J, and Tchobanoglous G (1980) Towards the Rational Design of Aquatic Treatment Systems. Paper presented at the ASCE Convention, Portland, OR, USA, 14–18 April. Department of Civil Engineering, University of California, Davis, CA, USA.

b

Figure 5 Integrated natural systems for wastewater treatment: (a) attached-growth WSP and (b) integrated nano-phytotechnology CW.

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Constructed Wetlands and Waste Stabilization Ponds

and WSP systems in the treatment of municipal and industrial wastewaters under different climatological conditions and additional benefits derived from the production of algae, fish, agricultural products, and energy will be presented. Emerging issues and research topics of CW and WSP are discussed.

matter decomposition by bacteria, respectively:

NH 3 + 7.62O 2 + 2.53H 2 O

CW is a wastewater treatment system consisting of shallow ponds or channels cultured with emerging aquatic plants. The processes by which wastewater is treated include a wide range of interacting biological, physical, and chemical mechanisms. WSPs are shallow man-made basins into which wastewater continuously flows and from which, after a retention time of several days (rather than several hours in conventional treatment processes), a well-treated effluent is discharged. WSP systems comprise a series of anaerobic, facultative, and maturation ponds, or two or more such series in parallel. Anaerobic and facultative ponds are designed primarily for BOD5 removal, while maturation ponds are for SS and pathogen removal; although some BOD5 removal occurs in maturation ponds and some SS and pathogen removal occurs in anaerobic and facultative ponds.

C7.62 H8.06 O 2.53 N

Facultative bacteria

5CO 2 + NH 3 + 2H 2 O

Wastewater Algae CO2

O2

Bacteria

Aerobic zone Fish

Effluent

Facultative zone Anaerobic zone

Figure 6 Schematic diagram of treatment mechanisms in WSP.

Effluent outlet for FWS CW

Slope 1% CW media (sand and gravel) Clay liner or membrane Figure 7 Schematic diagram of horizontal-flow CW.

ð2Þ

Sunlight

Emergent plants Influent wastewater

ð1Þ

Note: In Equation (1) C7.62 H8.06 O2.53 N represents algal cells, and in Equation (2) C5H7O2N represents organic matter. Equations (1) and (2) explain the symbiotic relationships between algae and bacteria in a facultative WSP in which these natural reactions could be utilized in wastewater treatment without the addition of mechanical energy. The algal cells and zooplankton, which graze on them, can be used as food for herbivorous fish (Figure 6). A schematic diagram of the CW process illustrating the symbiotic reactions between aquatic plants and bacteria is shown in Figure 7. Similar to the algal photosynthesis, the aquatic plants perform photosynthesis under sunlight and the produced O2 is transferred from leaves to the root zones where bacteria will utilize it to decompose the organic matter. Other reactions occurring in a CW system such as adsorption and plant uptake will, respectively, result in better removal of SS and nutrients (such as N and P). Depending on climatic conditions, the harvested plant biomass can be used as animal

4.10.2.1 Symbiotic Reactions among Aquatic Plants, Algae, and Bacteria The relationship between the phototrophic micro-algae and bacteria is often classically illustrated as mutual reactions between aerobic bacteria and algae cells. In essence, with sunlight the algae produce oxygen that is used by aerobic and facultative bacteria in organic matter decomposition. The algae benefit by utilizing the CO2 produced by bacterial respiration along with the released nutrients to derive energy and fix carbon for growth via photosynthesis. If C5H7O2N represents organic matter, Equations (1) and (2) represent the photosynthetic oxygen production by algae and organic

Algae

+ 7.62O 2 C5 H7 O 2 N + 5O 2

4.10.2 Principles of CW and WSP Systems for Wastewater Treatment and Reuse

Sunlight

Effluent outlet for SF CW

Constructed Wetlands and Waste Stabilization Ponds

foods or converted to become compost fertilizer. Since there is no need for energy inputs, the operation and maintenance of CW units are cheaper than those of conventional treatment systems (see Table 1).

4.10.2.2 Types and Functions of CW and WSP Systems The CW and WSP systems have been designed and operated in various ways to treat municipal and industrial wastewaters. The variations include physical design, hydraulic flow patterns, and organic loading rates. The major types of these variations are given in the following.

4.10.2.2.1 Constructed wetlands types CWs are often classified into two basic types: free water surface (FWS) in which the water surface is maintained 10–50 cm above the CW bed and subsurface flow (SF) in which the water level is maintained below the CW bed. Examples of emergent aquatic plants growing in CW beds are shown in Table 2. Details of these two CW types and their functions are presented as follows. Free surface flow CW. According to US EPA (2000), FWS CW typically includes one or more shallow basins or channels with a barrier to prevent seepage to sensitive ground waters and a submerged soil layer to support the roots of selected emergent vegetation, inlet and outlet structures to distribute and collect wastewater, control water levels, and maintain hydraulic retention times (HRTs). In FWS CW treatment, wastewater at a relatively shallow depth of 10–50 cm flows over the vegetated soil surface (Figure 8) and the intended flow path through the system is horizontal. BOD5 matter is degraded by the bacteria attached to the surface of the CW media and plant roots using O2, photosynthetically produced by the plant leaves, that is transferred to the soil–water matrix. N and P are removed by plant uptake and sedimentation while, due to long HRT, most SS matters would settle down in the CW beds and some could be Table 2

removed by filtration and adsorption. Due to less exposure of the wastewater to sunlight, pathogen removal by CW systems is usually not adequate. Table 3 shows the functions of aquatic plants in CW treatment systems. Subsurface flow CW. SF CW consists of a set of basins or channels with barrier to prevent seepage, and a suitable depth of porous media, gravel, rock, and different soil, that supports the growth of emerging vegetation (Figure 7). Through proper design of outlet structure, the wastewater level is maintained to be below the CW bed. Wastewater flows horizontally (Figure 7) or vertically (Figure 9) through the medium and is purified during the contact with the surfaces of the medium and the root zone of the vegetation by the physical, chemical, and biological reactions, similar to those occurring in FWS CW. Some of the major differences between FWS and SF CWs are presented in Table 4.

4.10.2.2.2 Wastewater stabilization pond types WSP technology is one of the most important natural methods for wastewater treatment. There are four types of WSPs, namely, anaerobic ponds, facultative ponds, maturation ponds, and high-rate algal ponds. The major features and functions of these ponds are given in Table 5 and are described as follows. Anaerobic ponds (APs). APs are normally employed to treat wastewaters with high organic concentrations. With long HRTs of 10–50 days and depths of 3–5 m, anaerobic bacteria growing in AP could biodegrade the incoming BOD5 and accumulated SS resulting in the production of biogas such as methane (CH4), CO2, and hydrogen sulfide. The important

Emergent plants Wastewater distribution

Emergent aquatic plants for wastewater treatment

Common name, scientific name

Cattail, Typha spp. Common reed, Phragmites communis Rush, Juncus spp.

Bulrush, Scirpus spp.

a

281

Distribution

Common in Southeast Asia Common in America and Europe Common in America and Europe Common in America and Europe

ppt ¼ part per thousand.

Desirable temperature (1 C)

Maximum Optimum salinity pH tolerance (ppt a)

Effluent outlet Slope 1% Rhizome network

Soil, sand or gravel

10–30

30

4–10

10–30

45

2–8

15–25

20

5–7.5

Table 3 systems

4–9

Roots and stems in water column  Surfaces for microbial growth  Media for filtration and adsorption

Liner

Figure 8 Schematic diagram of horizontal-flow FWS CW.

15–25

20

Functions of emergent aquatic plants in CW treatment

Stems and leaves above water surface  Light attenuation and preventing algae growth  Minimizing wind effects  Transfer of oxygen to root zone

282

Constructed Wetlands and Waste Stabilization Ponds Influent wastewater Emergent plants

Effluent outlet

Slope 1%

CW media (sand and gravel)

Clay liner or membrane

Figure 9 Schematic diagram of vertical-flow SF CW. Table 4 Differences between free water surface and subsurface flow constructed wetland systems Free water surface

Subsurface flow

Typically long, narrow channels with an impermeable liner to prevent seepage With emergent vegetation Wastewater flows horizontally at a shallow water depth and in CW media and is purified by microorganisms attached to plant stalks, litter, and on medium surface

Trench or bed with impermeable liner to prevent seepage

Media: usually soil, sand, and gravel Odor and mosquito problems likely

With emergent vegetation Wastewater flows laterally or vertically through the medium and is purified by microorganisms attached on the surfaces of the root zone of the vegetation and the medium surface (biofilm) Media: sand and gravel Odor and mosquito problems less likely

Modified from US EPA (2000) Free Water Surface Wetlands for Wastewater Treatment: A Technology Assessment Washington, DC: Office for Water Management, US Environmental Protection Agency.

stages of anaerobic degradation are hydrolysis, acid formation, and methane formation, all of which occur in appropriate steps and rates for effective BOD5 reduction. As 50–60% of the biogas is CH4, which has calorific value of 9000 kcal m3, equivalent to 13 kcal g1or 211 kcal g1 mol1 (Polprasert, 2007), the CH4 gas could be captured and utilized as an alternative fuel in heat or electricity generation. Facultative ponds (FPs). Among the different types of WSPs, FP is most commonly used for wastewater treatment. With a HRT of 5–20 days and depths of 1.5–3 m, FP utilizes the algal– bacterial symbiotic reactions in which gaseous O2 photosynthetically produced by the algae is used by the facultative bacteria in biodegrading the influent organic matter. Byproducts of bacterial decomposition, such as CO2 and NH3– N, serve as nutrients for the algae (Figure 6).

As shown in Table 5 the medium organic loading rates of 100–300 kg BOD5 ha1 d1 applied to FP usually result in 80– 90% BOD5 removal. The nutrients such as N and P are also removed by algal uptake and sedimentation to the pond bottom. The FP effluent normally contains algal cells, which could make its SS concentrations and color not suitable for discharge into receiving waters. Therefore, maturation ponds should be used in series to polish the FP effluent and further reduce pathogenic microorganisms. On the other hand, since algal cells are rich in protein and nutrients, FP effluent could be used as feed to fish ponds or irrigation water to grow crops. More details of these applications are provided in a later section. Maturation ponds (MPs). MP receives the effluent from an FP and its size and number depend on the required microbiological and SS quality of the final effluent. MP units are shallow (1–2 m in depth) with less vertical stratification and, due to the low organic loading rates (Table 5), their entire volume is well oxygenated throughout the day. Algal population in MP is much more diverse and less in concentrations because most of them settle down to the pond bottom. The main removal mechanisms, especially of pathogens and fecal coliforms, are ultraviolet (UV) light inactivation, high pH during day time because CO2 is uptaken by algal cells (Equation (1)), grazing by protozoa, and sedimentation with SS to the pond bottom. High-rate algal ponds (HRAPs). HRAP conventionally takes the form of a continuous channel equipped with an aeratormixer to recirculate the contents of the ponds and is used for both production of algal biomass and wastewater treatment. It is characterized by large area/volume ratios, and shallow depths in the range of 0.2–0.6 m to allow sunlight to penetrate the whole pond depth (Table 5). To minimize short-circuiting, baffles are normally installed in the pond to make length/ width ratio of the channel greater than 2/1. A diagram and photograph of HRAP are shown in Figures 10 and 11, respectively. Effluent overflow from HRAP, containing high algal suspension, normally goes into an algal separation unit. The

Constructed Wetlands and Waste Stabilization Ponds Table 5

283

Comparative features of waste stabilization pond

Feature

Anaerobic pond

Facultative pond

Maturation pond

High-rate algal pond

Depth (m) HRT (days) OLR, (kg BOD5 ha1 d1) Microorganisms responsible for BOD5 reduction Major functions

3–5 10–50 High, 4300 Anaerobic bacteria

1.5–3 5–20 Medium 100–300 Facultative bacteria and algae

1–2 5–10 Low, o100 Aerobic bacteria

0.2–0.6 1.5–8 Medium 100–200 Aerobic bacteria and algae

Pretreatment, BOD5 reduction Biogas, CH4, CO2

BOD5 reduction

Polishing, SS and pathogen reduction -

Algal biomass production and BOD5 removal Algal biomass

By-products

SS in forms of algal and bacterial cells

Influent Baffles Effluent to algal separation unit or fish pond

Aerator and mixer

Wastewater flow Figure 10 Schematic plan of high-rate algal pond.

temperature, light intensity, mixing or agitation, pond depth, and HRT. It is generally known that light intensity is the important factor for photosynthesis and therefore, algal production, while temperature influences the biodegradation rate of the organic matter. Under appropriate HRT and organic loading rate (Table 5), the average production of algal biomass in HRAP is 70 or 35 tonnes ha1 yr1 of algal protein (algal cells contain about 50% protein). These algal cells could be used as fish or animal foods and as biomass to produce biofuels (Polprasert, 2007).

4.10.2.3 Major Mechanisms for Wastewater Treatment and Recycling in CW and WSP Systems As stated in the previous section, there are several mechanisms occurring in CW and WSP systems responsible for wastewater treatment. These mechanisms are physical, chemical, and/or biological, which may take place individually or in combination in removing certain pollutants from the influent wastewater. Although there are emergent plants growing in CW and algae cells growing in WSP, the treatment mechanisms of these two natural systems are similar.

4.10.2.3.1 BOD5 removal

Figure 11 High-rate algal pond at Royal Chitrlada Palace, Bangkok, Thailand.

effluent obtained after the algae have been separated is expected to have BOD5 of 20 mg l1 and DO of 0.5 mg l1. Because of these advantages, HRAP has, in recent years, received increasing attention as a means of both treating wastewater and producing algal biomass for food or energy production. Factors affecting the performance of HRAP and algal production include available carbon and nutrient sources,

As stated in the previous section, BOD5, chemical oxygen demand (COD), or organic matter removal in CW and WSP units is due mainly to the biodegradation reactions of bacteria as shown in Equation (2) using O2 photosynthetically produced by the emergent plant and algal cells, respectively. Polprasert et al. (1998) found biofilm bacteria growing on surfaces of the media and root zones in the CW beds to be the major organisms responsible for BOD5 removal. For the WSP systems, both suspended bacteria growing in the pond water and biofilm bacteria growing on the pond’s side walls were found to be responsible for BOD5 removal (Polprasert and Agarwalla, 1994). By-products of the bacterial reactions such as CO2 and NH3 are used by the emerging plants and algae cells for photosynthesis under sunlight. Since photosynthetic and bacterial reactions are dependent on climatological conditions, tropical areas having both plenty of sunlight and high temperature would be ideal for the CW and WSP systems to function effectively.

4.10.2.3.2 SS removal SS removal is very effective in both types of CWs and is accomplished mainly through adsorption on the media’s surface

284

Constructed Wetlands and Waste Stabilization Ponds

and sedimentation. Controlled dispersion of the influent flow to CW units with proper diffuser pipe design can help to ensure low velocities for SS removal. Since WSP systems are operated at long HRT, most SSs would settle down to the pond bottom, while some may be adsorbed on the algal cells, forming larger aggregates and be removed by sedimentation. In general, high algal growth in FP results in high SS concentrations in the FP effluent, which can be polished further in MP where, under low organic loading and long HRT, there is less algal growth, algal grazing by protozoa, and sedimentation, all leading to low SS concentrations in the MP effluent.

Table 6 Treatment mechanisms of constructed wetland and waste stabilization pond systems Treatment mechanisms

Constructed wetland

Waste stabilization pond

BOD5 removal

Emerging plantsbacterial symbiotic reactions (effective)a Filtration, adsorption, some sedimentation (effective)a Plant uptake, adsorption, nitrification/ denitrification (effective)a

Algal-bacterial symbiotic reactions (effective)a

SS removal

N and P removal

4.10.2.3.3 N and P removal Being nutrients, N and P are uptaken by emergent plants and algal cells for their growth and, consequently, removed from the influent wastewater. The range of N and P removal in CW by plant uptake was reported to be 10–50% (Sawaittayothin and Polprasert, 2007; US EPA, 1988), whereas the percent N and P removal in WSP by algal biomass uptake was 80–90% and less than 50%, respectively (Pano and Middlebrooks, 1982; Silva et al., 1995; Mara et al., 1992). Nitrification and denitrification can be significant reactions responsible for N removal in CW and WSP systems if suitable environmental conditions conducive to the growth of nitrifying bacteria (oxic conditon) and denitrifying bacteria (anoxic condition) are maintained. N removal via nitrification and denitrification reactions in CW and WSP systems was reported to be 20–40% and 10–30%, respectively (Sawaittayothin and Polprasert, 2007; WEF, 2001). P removal in CW systems is due mainly to adsorption on the CW media and sedimentation and was found to be about 50%. Improved P removal efficiencies of more than 90% could be achieved when oyster shells were used as CW media to adsorb P (Park and Polprasert, 2008). P removal in WSPs is not effective and is in the low removal range of 20–30% (WEF, 2001).

4.10.2.3.4 Heavy metals The predominant removal mechanisms of heavy metals in CW are precipitation and adsorption. Precipitation is enhanced by increased pH in the CW system (Equation (1)), which occurs during photosynthesis. The large surface areas of CW media and root zones are also effective for heavy metal adsorption. Removals of Cu, Zn, and Cd at 99%, 97%, and 99%, respectively, for CW units operating at HRT of 5.5 days in Santee, California, have been reported (US EPA, 1988). Visesmanee et al. (2008) found that CW units operating at HRT of 5.8 days could remove more than 99% of Cd through adsorption. Heavy metal removal in WSP is caused mainly by precipitation and was reported to be about 90% (WEF, 2001).

4.10.2.3.5 Pathogen removal Pathogen removal in CW beds is due mainly to adsorption on CW media and natural decay caused by unfavorable conditions. The growth of emergent plants usually prevents UV light from reaching the CW beds and, consequently, from inactivating the pathogens present in the influent wastewater. FP and MP are exposed to sunlight and UV light, which is effective in inactivating pathogens present in the influent

Heavy metals

Plant uptake, adsorption (effective)

Pathogen removal

Adsorption, natural decay (not effective)a

Sedimentation (not effective)a Algal uptake, nitrification/ denitrification, sedimentation (effective)a Algal uptake, sedimentation ( not effective)a UV light, predator grazing, sedimentation, high pH, natural decay (effective)a

a

Denote relative efficiency and may vary from places to places due to climates and operating conditions.

wastewater. Other mechanisms responsible for pathogen dieoffs in WSP are the high pH during algal photosynthesis (Equation (1)), grazing by zooplanktons, sedimentation, and natural decay. In general, pathogen removal in WSP could be 99.99% which is more effective than those of 50–90% obtained in CW systems (WEF, 2001). A comparison of major treatment mechanisms occurring in CW and WSP systems is given in Table 6.

4.10.3 Design Criteria and Operation of CW and WSP Systems This section describes design criteria and environmental requirements for CW and WSP systems. Methods for the design and operation of these systems based on empirical and rationale approaches are given.

4.10.3.1 CW Design Criteria and Operation The presence of emergent plants and media in CW beds, which have effects on the flow characteristics, need to be taken into considerations in the design and operation of CW systems. Depending on climatic conditions, evapotranspiration caused by emergent plants, especially during day time, could result in water loss in the CW effluent more than 50% of the influent. The media size and shape and its porosity have effects on the specific surface area and HRT of the CW systems. In designing a CW unit, average flow rate (Qa) and actual HRT (HRTa) should be used in calculation. The values of Qa can be determined from

Qa ¼

Qi þ Qe 2

ð3Þ

Constructed Wetlands and Waste Stabilization Ponds

where Qi and Qe are the influent and effluent flow rates, respectively, of a CW unit, m3 d1. The values of Qi can be obtained from municipal or industrial sources, which generate the wastewaters. The value of Qe depends on evapotranspiration rates, which can be determined from the data of similar CWs or from agricultural stations; otherwise, actual measurements at some pilot-scale CW units should be done during a summer period. The value of HRTa can be determined from

HRTa ¼

Vf Qa

ð4Þ

where V is the volume of CW bed, m3, that is, length  width  depth of liquid (L  W  D) in the CW bed, Vv the void volume in the CW bed, m3, and f the media porosity or Vv/V, decimal. Table 7 shows the approximate porosity values of sand and gravel commonly used as media in CW beds. It should be noted that, due to accumulation of solids and biofilm growth, the media porosity or void fraction values will change with the operation time of CW. For practical purposes, the f value of SF CW could be taken as 0.30 (Table 7). For FWS CW whose liquid depth is maintained 10–60 cm above CW bed, the f values could be taken as 0.70 (WEF, 2001). Due to the complex interactions among the physical, chemical, and biological processes occurring in CW, the available design criteria do not include all of these aspects, but are based on either empirical approach or rationale approach (first-order reaction rate and plug-flow conditions), which are described in the following.

285

North America and Australia. Based on these and other data, a summary of CW design guidelines is shown in Table 9, which should be applicable for CW located in temperate and tropical areas. Depending on wastewater characteristics, the BOD5 and TN loading rates of CW could be higher than those given in Table 9 if they are operated under tropical conditions. Due to better contact between wastewater and media in SF CW units (Figure 9), the maximum hydraulic and BOD5 loading rates can be higher than those of the FWS CW units. As emergent plants can grow and decay in CW beds, vegetation harvesting should be done regularly and more frequently under tropical conditions to maintain satisfactory performance. To avoid media clogging, pretreatment of the influent wastewaters by at least sedimentation would reduce accumulation of settleable solids in the CW beds. SF CW units can be operated in horizontal-flow or verticalflow modes (Figure 12). The cross-sectional area (Ac) of an SF CW unit is calculated from (US EPA, 1988)

Ac ¼ Qa ðks SÞ1

ð5Þ

where Ac is the cross-sectional area of SW CW bed perpendicular to the flow direction, m2, ks the hydraulic conductivity, m3 m2 d1 (Table 7), and S the bed slope, as fraction or decimal, Table 9. US EPA (1988) recommended that the values of ksS or Qa Ac1should be less than 8.6 m d1 to ensure sufficient contact time between the wastewater and media, which is essential for effective wastewater treatment.

4.10.3.1.1 Empirical approach

4.10.3.1.2 Rationale approach

Both FWS and SF CW systems have been applied to treat wastewaters at several locations worldwide with satisfactory results. Table 8 shows performance data of CW located in

As previously mentioned, the complex reactions occurring in CW beds responsible for wastewater treatment have made it difficult to develop a comprehensive model to predict

Table 7

Media characteristics for CW systems

Media type

D10a effective size (mm)

Porosity (f)

Hydraulic conductivityb (ks) (m3 m2 d1)

Coarse sand Gravelly sand Medium gravel

2 8 32

0.28–0.32 0.30–0.35 0.36–0.40

100–1000 500–5000 10 000–50 000

a

D10 ¼ effective size of the media at 10% (cumulative weight of total, or the media size such as 10% by weight are smaller). Hydraulic conductivity ¼ flow rate divided by area perpendicular to flow direction. Modified from WEF 2001. Natural Systems for Wastewater Treatment, Manual of Practice FD–16, 2nd edn. Alexandria, VA: Water Environment Federation. b

Table 8

Summary of BOD5 and SS removal from CW

Project

Listowel, Ontario Santee, CA Sydney, Australia Arcata, CA Emmitsburge, MD Gustine, CA

Flow (m3 d1)

17 240 11 350 132 3785

Type

BOD5 (mg l1)

SS (mg l1)

Reduction (%)

FWS SF SF FWS SF FWS

60 120 30 40 60 150

110 60 60 40 30 140

82 75 86 64 71 84

10 30 5 13 20 25

8 5 4 30 8 20

Modified from Polprasert (2007) Organic Waste Recycling, Technology and Management, 3rd edn. London: IWA Publishing.

93 90 92 28 73 86

Hydraulic surface loading rate (m3 m2 d1)

900 1540 410

286 Table 9

Constructed Wetlands and Waste Stabilization Ponds Summary of wetland design considerations

Design consideration

Maximum water depth (cm) Bed deptha (cm) Minimum aspect ratiob Bed slopec (%) Minimum hydraulic retention time (day) Maximum hydraulic loading rate (cm d1) Minimum pretreatment Configuration Maximum loading (kg ha1 d1) BOD5 TN Additional considerations

Constructed wetland Free water surface

Subsurface flow

10–60 Not applicable 2:1

Water level below ground surface 30–90 2:1

1–5 5–10

1–5 5–10

2.5–5

6–8

Screening and sedimentation Multiple units in parallel and series

Screening and sedimentation Multiple units in parallel and series

100–110 60 Mosquito control with mosquito fish; vegetation harvesting regularly

80–120 60 Vegetation harvesting regularly

Figure 12 Horizontal and vertical- flow modes of SF CW: (a) horizontalflow wetlands and (b) vertical-flow wetlands.

a

Bed depth is to support plant growth. Aspect ratio is length/width (L/W ), in this case for horizontal-flow CW. c Bed slope is for horizontal-flow mode. Modified from Polprasert (2007) Organic Waste Recycling, Technology and Management, 3rd edn. London: IWA Publishing and WEF (2001). Natural Systems for Wastewater Treatment, Manual of Practice FD–16, 2nd edn. Alexandria, VA: Water Environment Federation.

as shown in the following information:

b

treatment efficiency. Reed et al. (1988) suggested a first-order reaction and plug-flow model in a CW bed as follows:

Ce ¼ e kT t C0

ð6aÞ

where Ce, C0 are the effluent and influent concentrations (g m3), kT the reaction rate (¼ k20 (1.06)T20, T the liquid temperature (1C), and T (¼ HRTa) the actual hydraulic retention time, day. For BOD5 removal, k20 ¼1.104 d1 for SF CW and k20 ¼ 0.687 d1 for FWS CW. The higher k20 value of SF CW was due mainly to better contact between the wastewater and the media or bacteria responsible for BOD5 degradation. However, due to a lower f value of SF CW, the volume or area of SF CW required to achieve the same treatment efficiency is usually higher than that of FWS CW, as shown in Example 1. A study by Polprasert et al. (1998) found the activity of biofilm bacteria growing in CW beds to be much more significant than suspended bacteria in organic or COD removal.

Example 1. Compare the surface areas of an FWS CW unit and an SF CW unit required to achieve the treatment efficiency

Flow rate (Qa) ¼ 760 m3 d1 Influent BOD5, Co ¼ 130 mg l1 Effluent BOD5, Co ¼ 10 mg l1 Liquid temperature ¼ 25 1C Media porosity, f ¼ 0.75, for FWS CW f ¼ 0.3, for SF CW Depth, d ¼ 0.5 m, for SF CW (to support cattail roots) D ¼ 0.7 m, for FWS CW (cattails root depth of 0.5 m þ water depth of 0.2 m). For FWS CW,

k25 ¼ k20 ð1:06Þ2520 ¼ 0:678ð1:06Þ5 ¼ 0:907 For SF CW,

k25 ¼ k20 ð1:06Þ2520 ¼ 1:104ð1:06Þ5 ¼ 1:477 BOD5 removal

C0 ¼ e kT t Ce For FWS CW,

10 ¼ e 0:907t ; tFWS ¼ 2:8 days 130 For SF CW,

10 ¼ e 1:477t ; tSF ¼ 1:7 days 130 t¼

LWDf Vf tQ ¼ ; AFWS or ASF ¼ Qa Qa Df

ð6bÞ

Constructed Wetlands and Waste Stabilization Ponds

where AFWS or ASF are surface areas of FWS CW or SF CW, respectively:

AFWS ¼

2:828  760 ¼ 4093:87 m2 ¼ 0:410 ha 0:7  0:75

•

1:737  760 ASF ¼ ¼ 8800:8 m2 ¼ 0:88 ha 0:5  0:3 AFWS ; L:W ¼ 10:1-205:20 ASF ; L:W ¼ 10:1-300:30

4.10.3.1.3 Operation, maintenance, and troubleshooting of CW Two operational considerations associated with CW for wastewater treatment are mosquito control and plant harvesting, in addition to system perturbations, which can occur from time to time. Mosquito control. Mosquito problems may occur in FWS CW units located in the tropics especially when they are overloaded and under anaerobic conditions. Strategies used to control the mosquito population include effective pretreatment to reduce total organic loading; step feeding of the influent wastewater stream with effective influent distribution and effluent recycle; vegetation management; natural controls, principally by mosquitofish (Gambusia affinis), in conjunction with the above techniques; and application of man-made control agents (or pesticides). In general, natural controls are preferred because of a concern that man-made control agents might develop resistant strains of mosquito (Wieder et al., 1989). Plant harvesting. The frequency of plant harvesting in CW treatment systems depends on several factors, such as climate, plant species, and their growth/decay rates. Regular plant harvesting will minimize the accumulation of decayed biomass, maintaining system performance and reducing congestion at the water surface. Where a segmented wetland system is used, drying each segment separately allows harvesting with conventional equipment (Wieder et al., 1989). The frequency of plant harvesting should be in accordance with the plant’s growth rate. In the tropics and under favorable conditions, the area doubling time of most emergent aquatic plants is 2–3 months (Polprasert, 2007). System perturbations and operation modifications. Perturbations generally are of two types (Girts and Knight, 1989): (1) predictable perturbations, which can be predicted and occur periodically and (2) unpredictable perturbations, which are unforeseen in the design phase or which occur so infrequently that incorporation into the design would entail unnecessary expense. Some of the suggestions to minimize perturbations on CW operation given by the US EPA (2000) are listed below. FWS CW. Routine operation and maintenance (O&M) requirements for FWS CW include hydraulic and water depth control, inlet/outlet structure cleaning, wetland vegetation management, mosquito and vector control, and routine monitoring. The following points for specific treatment performance are suggested.

•

If N or P removal is required, the land area required for FWS CW could be large. The removal of N in biological

•

•

287

processes such as nitrification/denitrification and plant uptake requires longer HRT. The P, metals, and some persistent organics removed by the system are bound in the wetland sediments and accumulate over time. In cold climate areas, low temperatures reduce the rate of BOD5 removal and the biological reactions. An increased HRT can compensate for this, but the increased wetland size required in extremely cold climates may not be cost effective or technically feasible. The bulk water in most FWS CW systems is essentially anoxic, limiting the potential for rapid biological nitrification of ammonia. Increasing the wetland size and, therefore, the HRT, may compensate for this. Mosquitoes and other insect vectors can be a problem. FWS CW systems can remove fecal coliforms by at least one log from typical municipal wastewaters, but may not be sufficient to meet discharge limits in all locations and supplemental disinfection or polishing units may be required. The situation is further complicated because birds and other wildlife in the wetland produce fecal coliforms.

SF CW. Routine O&M requirements for SF CW are similar to those of FWS CW and include hydraulic, water depth control, inlet/outlet structure cleaning, wetland vegetation management, and routine monitoring. The water depth in the SF CW may need periodic adjustment on a seasonal basis or in response to increased resistance over long term from the accumulating detritus in the media void spaces. Mosquito control should not be required for SF CW systems as long as the water level is maintained below the media surface. Vegetation management and other specific treatment performance in SF CW should follow the same procedures indicated for the FWS CW. Due to its low porosity, an SF CW will require larger land area than an FWS CW (see Example 1) and a conventional treatment process.

4.10.3.2 WSP Design Criteria and Operation The mechanisms responsible for wastewater treatment in WSP are complex and similar to those of CW, but in this case it is the algal cells, not emergent plants, growing in the pond water that perform photosynthesis and produce O2 for the bacteria to biodegrade the influent wastewater (Figure 6). The available design criteria of WSP based on empirical and rationale approaches are presented.

4.10.3.2.1 Empirical approach Empirical approach for WSP design is based mainly on relationships between organic loading rates and HRT and the treatment performance of WSPs. In general, the design criteria of AP, FP, MP, and HRAP could follow those stated in Table 5 from which satisfactory results of the treatment performance could be expected.

4.10.3.2.2 Rationale approach Since FP is the major type of pond effective for organic (BOD5 or COD) removal, the formula for completely mixed and assuming first-order kinetics is commonly used in FP design for

288

Constructed Wetlands and Waste Stabilization Ponds

both organic and fecal coliform reductions (Marais, 1974):

Ce 1 ¼ C0 1 þ kT t

and Bhattarai (1985) proposed a dispersion model for WSP:

d¼

ð7Þ

ð8Þ

The value of kT for fecal coliform reduction is given by

kT ¼ 3:6ð1:19ÞT20

ð9Þ

The terms Ce, Co, T, and t are as defined previously. In general, the value of t depends on the magnitude of treatment required (Ce/Co) and the calculated t value is used to determine the pond volume or the pond surface area if a pond depth is chosen (see Table 5). To achieve maximum fecal coliform die-off, Marais (1974) recommended that ponds (FP or MP) should be of the same size and laid out in series. In this case, Equation (10) is applicable for designing FP and MP for fecal coliform die-off:

Ce 1 ¼ C0 ð1 þ kT tÞn

Example 2. Design a facultative pond (FP) to treat an industrial wastewater flow of 1000 m3 d1 with a soluble BOD5 of 500 mg l1. Assume the following conditions apply: 1. 2. 3. 4. 5. 6.

ð10Þ

where n is the number of ponds of identical size in series. Other terms are as defined previously. Since the hydraulic conditions in WSP are neither completely mixed nor plug flow, but partially mixed, Wehner and Wilhelm (1956) derived an equation for which a dispersion number (d) included:

  1 4a exp Ce 2d     ¼ C0 ð1 þ aÞ2 exp a  ð1  aÞ2 exp a 2d 2d

Influent SS ¼ negligible Effluent BOD5 ¼ 30 mg l1 First-order BOD5 removal-rate constant ¼ 0.30 d1 at 20 1C Temperature coefficient ¼ 1.05 at 20 1C Average pond temperature ¼ 25 1C Pond depth ¼ 2 m

Solution 1. Determine the BOD5 removal-rate constant.

k25 ¼ k20 ð1:05ÞT20 ¼ 0:38 d1

2. Determine the detention time. From Equation (7),

ð11Þ

30 1 ¼ 500 1 þ 0:38t

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi where a ¼ 1 þ 4ktd, with k being the first-order reaction rate, 1 d , and d the dispersion number, dimensionless. The value of d of a reactor can be determined by tracer study (Levenspiel, 1972; Metcalf and Eddy, 2003). Polprasert

t¼

16:67  1 0:38

BOD5 or fecal coliform remaining

60 50 40 d=1 30 d = 0.25 d=0

20 10 0 0

0.5

1

1.5

2

2.5 k.t

Figure 13 Design formula chart for partially mixed reactors.

ð12Þ

where u is the kinetic viscosity, W the width of the pond, L the length of fluid travel path from influent to effluent, and D the liquid depth of pond. Equation (12) was validated with WSP in USA and Thailand with satisfactory results. A design flowchart of Equation (11) is given in Figure 13 to simplify the calculation for BOD5 or fecal coliform reduction. In general, depending on HRT, d, and inlet/outlet arrangements, the values of d for most FP and MP are in the range of 0.1–0.5. The k values for BOD5 and fecal coliform removal of Equation (11) are different from those given in Equations (8) and (9) and need to be determined separately.

The values of kT for BOD5 reduction is given by

kT ¼ 0:3ð1:05ÞT20

0:184½tuðW þ 2DÞ 0:489 ðWÞ 1:511  ðLDÞ1:489

3

3.5

4

4.5

Constructed Wetlands and Waste Stabilization Ponds

t¼

15:67 0:38

289

Table 10 Summary of light saturation intensities (Is) for different freshwater algae Is

T ¼ 41:24 days Species

Temperature (1 C)

Chlorella pyrenoidosa

25 25 26 25 25

3. Determine the required pond area.

Pond volume ¼ 42  1000 ¼ 42 000 m 3

Chlorella vulgaris Scenedesmus obliquus Chlamydomonas reinhardti

Choose a pond depth of 2 m.

42 000 m 3 2m ¼ 21 000 m 2

Surface area ¼

Illuminance (ft-candle)

Irradiance (gcal cm2 d1)

250 500

51.8 36 82.1 18 36

500

36

500

25

Modified from Goldman (1979) Outdoor algal mass cultures. II. Photosynthetic yield limitation. Water Research 13: 119–136.

Therefore, the required pond is about 2.1 ha. Use three ponds in parallel; each pond’s surface area ¼ 7000 m2 (length  width ¼ 140  50 m). Depending on the effluent standards or reuse, MP could be built in series to the FP to further polish the FP effluent.

50 Algal yield (P )

HRAP. There are several rationale methods available for HRAP design which can give different results depending on wastewater characteristics, climates, and operational procedures. Since the main objective of HRAP is to produce algal biomass, Goldman (1979) derived the following model in which the effect of algae decay was excluded:

60

40 30 20 10

Pa ¼ 0:28 Is ½In 0:45 I0 =Is þ 1

ð13Þ 2

1

where Pa is the algal productivity (dry weight), g m d , Is the saturation light intensity, gcal cm2 d1, and I0 the amount of visible solar energy penetrating a smooth water surface, varying from 0 to 800 gcal cm2 d1. The values of Is depend on temperature and algal species as shown in Table 10 and for most HRAP are in the range of 30– 80 gcal cm2 d1. The I0 value depends on latitude and weather conditions, which can range from 0 to 800 gcal cm2 d1. Figure 14 shows effects of I0 and Is on algal productivity. The average production of algae is reported as 70 or 35 ton ha1 yr1 algal protein; comparing with the productivity of conventional crops, wheat 3.0 (360 kg protein), rice 5.0 (600 kg protein) and potato 40 (800 kg protein) ton ha1 yr1 (Becker, 1981). With the design criteria as given in Table 5, the efficiency of BOD5, N and P removal could be expected to be more than 80%. As shown in Figures 10 and 11, the algal concentration in the HRAP liquid could be maintained up to 500 mg l1, which need to be harvested for further reuse and making the treated effluent suitable for discharging into a receiving water. Details of algal harvesting and reuses can be found in Polprasert (2007).

4.10.3.2.3 Operation, maintenance, and troubleshooting for WSP As with any wastewater treatment systems, good performance of WSP depends on regular maintenance of the system, which could be categorized into the following aspects: Control of short circuiting. Short circuiting of flow occurs to varying degrees in most WSP and could cause the actual HRT

0 0

100

200

300

400

500

600

700

Total sunlight irradiance (I0) Figure 14 Algal yield (Pa) as a function of total solar irradiance (Io) according to Equation (13).

to vary from 25% to 90% of the theoretical design HRT (Middlebrooks et al., 1982). The use of multiple ponds operated in series and multiple port inlet structures is effective in reducing short circuiting. In-basin baffles similar to those used in HRAP can also be effective. Control of seepage. To avoid water seepage and inflow of groundwater, lining of the pond bottom and inner dike surfaces is strongly recommended for permeable soils. Control of sludge accumulation. Sludge will accumulate to varying degrees on the pond bottom, but most of the accumulation will occur at or near the inlet structures. Occasional removal of the accumulated sludge should be done for maintaining the desired HRT and minimizing short circuiting. Control of scum and aquatic plants. Scum accumulation on the pond surface may occur in AP and FP, which could reduce the effective pond volume and HRT. Although scum accumulation is useful for maintenance of anaerobic conditions in AP, too much of them will interfere with hydraulic flow and should be regularly removed. There may be growth of aquatic plants such as duck weeds, for example, Lemna sp. and water hyacinth (Eichhornia crassipes) on the surface of FP and MP, which will prohibit the availability of sunlight for algal photosynthesis and reduce the pathogen die-offs due to less

290

Constructed Wetlands and Waste Stabilization Ponds

UV light penetration. Excessive growth of these aquatic plants will result in accumulation of decayed plant biomass, leading to the impairment of effluent quality. The growth of these aquatic plants should be prevented in these ponds or regular harvesting has to be done.

4.10.4 Case Studies of CW and WSP Natural systems such as CW and WSP have been applied for the treatment of wastewaters originating from municipal, industrial, and agricultural resources in several regions of the world. Some of the successful case studies and their performance results are presented in this section.

4.10.4.1 CW Case Studies

During the period of September 2001–May 2002, these two SF CW units were operated at the following conditions: hydraulic loading rates of 60–160 l m2 d1; organic loading rates of 57–140 kg BOD5 ha1 d1; and HRT of 1.4–4.0 days. The treatment performance of these CW units was found satisfactory (Table 11) with the effluent BOD5 and SS concentrations being 4 and 10 mg l1, respectively. Because of the nitrification reactions occurring at the CW beds, there was an increase in NO3–N concentrations from 0.06 mg l1 in the influent to 4.75 mg l1 in the effluent. This treated water is being sold to some factories located in the ESIE for uses in factory air-cooling and other processes. Due to prolific growth of the emerging vegetation under tropical conditions, plant harvesting was done once in 4 months, with the annual yields of 130–150 ton ha1 yr1 (wet weight). These harvested plants are being used to make furniture and other decorations, becoming another income-generation avenue for the ESIE.

4.10.4.1.1 CW treatment of an industrial wastewater As several industrial wastewaters contain both heavy metals and organic compounds, the required treatment processes should be those that can effectively remove these pollutants. A case study of CW application for the treatment of an industrial wastewater is given as follows: Eastern Seaboard Industrial Estate (ESIE), Rayong province, Thailand. The ESIE is located in Rayong province, 200 km east of Bangkok, Thailand (Polprasert, 2006). The industries at ESIE are required to pre-treat their wastewaters to remove heavy metals and other toxic compounds in accordance with the Thailand effluent standards, prior to discharging into combined sewers and mixing with other domestic wastewaters. The combined wastewater flow rate was about 7000 m3 d1. Part of this combined wastewater is being treated by two pilot-scale SF CW in series, each with a dimension of 35 18  0.8 m (length  width  depth). The CW beds are lined with high-density polyethylene sheet and filled with 1-cm gravel (Figure 15). The wastewater is applied intermittently over the CW beds in a downward vertical flow, and the percolates are collected through underdrainage pipes. Cattails, bulrushes, and canna are the primary vegetation grown in these CW beds (Figure 16).

4.10.4.1.2 CW treatment of a municipal wastewater Rehabilitation of wastewater treatment system on Phi Phi Island. After the catastrophic Tsunami disaster of December 2004,

Figure 16 CW at Eastern Seaboard Industrial Estate (ESIE), Rayong province, Thailand.

Unit dimensions for each unit Area = 630 m2 Width = 18 m Length = 35 m Figure 15 Schematic diagram of pilot-scale CW units (Koottatep et al., 2001).

Media arrangements Top soil = 10 cm Sand layer = 15 cm Gravel layer = 55 cm

Constructed Wetlands and Waste Stabilization Ponds

several infrastructures and dwellings on Phi Phi Island, a worldfamous tourist attraction of Krabi province in Southern Thailand, were drastically damaged (Figure 17) besides hundreds of deaths (Koottatep et al., 2007). The existing wastewater treatment systems were heavily destroyed, resulting in the discharge of untreated wastewater into the sea. Even before the year 2004, the 4-m depth WSP units at a design capacity of 400 m3 d1 Table 11

Treatment performance of the CW units

Parameter Average concentration (mg l1) a

BOD5 COD SS TKN NH3–N NO3–N TP a

Overall removal b

Influent Effluent unit 1

Effluent unit 2

(%)

90 230 98 24.1 14.2 0.06 7.0

4 19 10 4.6 3.3 4.75 1.5

95.5 91.7 89.5 81.1 76.7 -c 78.5

20 50 16 14.5 10.8 0.53 4.7

Percolate of CW unit 1. Percolate of CW unit 2 in series. c Increased, likely due to nitrification reaction. From Polprasert C (2006) Design and operation of constructed wetlands for wastewater treatment and reuses. In: Ujang Z and Henze M (eds.) Municipal Wastewater Management in Developing Countries: Principles and Engineering. London: IWA Publishing. b

291

were not properly functioning because of poor maintenance. Local people and tourists perceived these ponds as stinky and smelly units locating in the center of the island. With the generous support of the Danish government, the wastewater treatment systems were rehabilitated using the integrated CW system. The challenges in the rehabilitation of the wastewater treatment systems on Phi Phi Island were due to the public resistance to the previous wastewater treatment systems, which were not well maintained, whereas conventional systems such as activated sludge processes were not preferable because of the high costs as well as the sophistication in operation and maintenance. Based on several public consultative meetings, the CW system was considered to be a promising treatment alternative for this island. Furthermore, because of the freshwater scarcity, it was suggested that the CW system should be able to produce the effluent suitable for plant irrigation. The conceptual design of the CW systems included the integration of different flow patterns in series: vertical flow; horizontal SF and FWS in order to ensure the effluent quality. This integrated CW system was designed at the capacity of 400 m3 d1 for treating effluent of septic tanks and gray water from households, restaurants, hotels, and other dwellings. To prevent odor problem at the manholes and to avoid rainwater infiltration, the wastewater was collected by a separated sewer system and pumped to the CW units. Figure 18 shows ‘the integrated flower and the butterfly’ CW design. The wastewater is fed to the flower vertical flow

Figure 17 Damages of WSP systems on Phi Phi Island (Koottatep et al., 2005).

292

Constructed Wetlands and Waste Stabilization Ponds

and horizontal SF CW units before feeding to the butterfly FWS CW units (Koottatep et al., 2007). The total surface area of the CW units is 8200 m2, each having 60 cm of gravel media. The treated effluent is collected at a 100-m3 underground tank where the local people can use it for irrigating their gardens; otherwise it is discharged to the sea. The CW units are planted with Canna, Heliconia, and Scirpus, most of which are colorful flowers, in vertical flow; horizontal SF and FWS CW units, respectively. In addition, some other plants and flowers are planted with the landscaping design as a community park (Figure 18). According to the design criteria of CW units, the integrated CW system can treat the wastewater with BOD5 and TKN concentrations of 100 and 20 mg l1, respectively. The performance of the integrated CW system has been found to be satisfactory and the effluent quality could meet the effluent standards for discharge into receiving waters (Table 12) or for agricultural reuse (Table 17).

4.10.4.1.3 CW treatment of fecal sludge or septage In tropical regions where most of the developing countries are located, septic tanks and other onsite sanitation systems are the predominant form of the storage and pre-treatment of excreta and wastewater, thus generating fecal sludge or septage (Koottatep et al., 2005). Uncontrolled septage management could endanger public health, hence the environmental protection for effective management strategies. Four demonstration CW units have been constructed at the Asian Institute of Technology, Pathumthani province, Thailand (Figure 19), to treat septage collected from Bangkok city. Each CW unit has a surface of 5  5 m2 with a substrata depth of 65 cm and a freeboard of 1 m. Cattail plants are grown in these CWs units and the solid loading rate of 250 kg TS m2 yr1 has been applied at the 6-day percolate impoundment. The COD, TS, and TKN removal efficiencies of these CW units were in the range of 80–96%. The biosolid accumulations in each of the CW units during the 7 years of operation were about 80 cm. There was no removal of these biosolids and the CW performance was not impaired probably due to extensive

growth of the cattail roots, which enhance the CW bed permeability and transfer O2 from the leaves to the root zone for bacterial degradation. The biosolids were found to contain viable helminth eggs below the standard for agricultural use (Table 17).

4.10.4.2 WSP Case Studies 4.10.4.2.1 WSP treatment of industrial wastewater and fish production A WSP unit, located in Yen So commune, Hanoi city, Vietnam, is being fed with water from the Kim Nguu River, which is heavily polluted with domestic and industrial wastewaters (Figure 20). The area of this WSP is 17 ha with depth of 1.2–1.5 m and is operated at an HRT of 7 days. Herbivorous fish are being cultured in this WSP. The characteristics of the Kim Nguu River water are: BOD5 ¼ 70–80 mg l1, TSS ¼ 80–120 mg l1, TN ¼ 50– COD ¼ 120–150 mg l1, 1 60 mg l , As ¼ 0.03–0.04 mg l1, Hg ¼ 0.0005–0.0016 mg l1, and fecal coliforms ¼ (2 –9)  108 MPN (100 ml)1. According to Toan (2008), the removal efficiencies of BOD5, TSS, and TN by this WSP were found to be 60%, 40%, and 75%, respectively. However, there were only about 98% removal of fecal coliform and the WSP water still contained fecal coliform concentrations above the safe limit for fish production (Table 17). The heavy metal (including As) concentrations were found to be within the EU threshold values or the Codex

Table 12

Effluent Standards from municipal sources

Parameters

Concentrations

pH BOD5 SS TKN Fat and oil

5–9 20–50 mg l1 30–50 mg l1 35–40 mg l1 20 mg l1

From MOI (2005). Wastewater Treatment Standards. Bangkok, Thailand: Ministry of Industry.

Figure 18 Bird-eye view of The Flower and The Butterfly CW system (Koottatep et al., 2007).

Constructed Wetlands and Waste Stabilization Ponds

293

4.10.4.2.2 WSP treatment of wastewater containing endocrine disrupting chemicals Endocrine disrupting chemicals (EDCs) are synthetic organic chemicals introduced to the environment by anthropogenic inputs. Although they may be present in the water environment at low concentrations, their ability to bioaccumulate and biomagnify in the food chains can pose health risks to humans and animals. This study reported the performance of three wastewater treatment plants in the removal of estrogenicity from wastewaters. The WSP units with HRT of 7–10 days demonstrated 90–95% removal of estrogenicity, higher than those of the tricking filters, which could remove about 40% estrogenicity. The mechanisms responsible for EDC removal in these WSP units were hypothesized to be biodegradation, adsorption on the settleable solids, and sedimentation. The MP with HRT of 26–47 days could enhance further estrogenicity removal, hence demonstrating the effectiveness of the WSP system in EDC removal (Gomez et al., 2007).

4.10.5 Emerging Environmental Issues versus Potentials of CW and WSP

Figure 19 Demonstration CW units have been installed at the Asian Institute of Technology, Pathumthani province, Thailand.

The world’s population has grown significantly during the past 100 years and is expected to increase further from the present number of about 6 billions in 2008 to about 9 billions (a 50% increase) in 2050 (Figure 1). The high population growth, together with industrialization, will obviously result in more demands for energy as shown in Figure 21. In addition, there will be increases in quantity of municipal and industrial wastewaters that need to be managed in a satisfactory manner. As indicated in the previous sections, some natural systems such as CW and WSP, if properly designed and operated, can treat these wastewaters to the degree comparable to secondary or tertiary treatment. Other advantages of CW and WSP include their potentials for waste recycling through the production of food for humans or animals, energy and water conversation, and climate change mitigation. Examples of these potentials are given in the following.

4.10.5.1 Food Production The operation of CW and WSP systems could lead to the production of biomass, which could be converted to or utilized as foods for humans or animals or other useful products. The principles of waste recycling and biomass production in these systems are described below.

4.10.5.1.1 Biomass production in CW Figure 20 Kim Nguu river (Toan, 2008).

threshold value. Fish production in this pond of 4–5 ton ha1 yr1 has been reported and the harvested fish are being sold for public consumption. With the above information, these fish should be well cooked before eating and preventive measures such as depuration should be implemented to minimize health risks from pathogen infection.

The growth of emerging aquatic plants in a CW unit can be described by daily increment factor as proposed by Mitchelle (1974):

Nt ¼ N0 xti

ð14Þ

where Nt, N0 are the final and initial numbers of plants, ti is the time interval for plant growth, day and x is the daily incremental factor, dimensionless.

294

Constructed Wetlands and Waste Stabilization Ponds 800 History

Projections 608 563

600 Quadrillion Btu

695 652

512 462 398

400 284

309

347

365

200

20 30

20 25

20 20

20 15

20 10

20 05

20 00

19 95

19 90

19 85

19 80

0

Figure 21 World marketed energy consumption, 1980–2030 (EIA, 2008). Note: 1 quadrillion Btu ¼ 2.928  1011 kilowatt-hour (kWh).

The area doubling time (td) is the time when Nt/N0 ¼ 2 and, from Equation (14), can be determined from

td ¼

ln 2 ðln Nt  ln N0 Þ=ti

Table 13 Proximate compositions (% dry weight) of some aquatic plants and alfalfa hey Species

Ash

Crude protein

Fat

Cellulose

Typha latifolia Justicia americana Sagittaria latifolia Alternanthera philoxeroides Orontium aquaticum Alfaalfa hay

6.9 17.4 10.3 13.9 14.1 8.6

10.3 22.9 17.1 15.6 19.8 18.6

3.9 3.4 6.7 2.7 7.8 2.6

33.2 25.9 27.6 21.3 23.9 23.7

ð15Þ

The value of td varies with plant species, climates, water quality, and other environmental conditions. Under tropical conditions, the td of several emerging plants can range from 4 to 8 weeks; the td values are longer for CW units located in temperature climates. The knowledge of td is useful to determine the frequency of plant harvesting without interfering with treatment performance or the biomass productivity. If necessary, the td value can be determined from experiments with pilot-scale CW units in order to obtain accurate biomass productivity. Westlake (1963) reported the productivity ranges of emerging aquatic plants as 27–77 ton of dry organic matter ha1 yr 1. The proximate composition of some emerging aquatic plants, shown in Table 13, indicates their crude protein contents to be 10–20%, similar to alfalfa hay. A promising technique to use the harvested aquatic plants from CW as animal food is to convert them to silage (NAS, 1976). This is accomplished by chopping the aquatic plants into small pieces (1–2 cm) and packing them in a silo under anaerobic conditions. The organic acids such as acetic acids and lactic acids produced in the silo will keep the pH at about 4 and avoiding putrefaction of the biomass. After about 20 days of silaging, the silaged products could be used as supplemental feeds to animals such as cattle. The harvested aquatic plants can be used as a raw material to make compost fertilizer. However, since the cell walls contain high cellulose content (Table 13), which is not easily biodegradable, the aquatic plants should be chopped into small pieces first (1–2 cm preferable) before composting. Because aquatic plants have a high C content, they should be mixed with other wastes that have high N contents (such as sludge or manure) so that the mixture C/N ratio becomes 30/1, optimum for composting process. Further details

From Boyd CE 1974. Utilization of aquatic plants. In: Mitchell DS (ed.) Aquatic Vegetation and Its Use and Control, pp. 107–115. Paris: UNESCO.

of aquatic plants composting can be found in Polprasert (2007).

4.10.5.1.2 Biomass production in WSP Due to the long HRT and without media, there appears to be more potential for biomass production in WSP in the form of algal and fish protein biomass, as described in the following: Algal protein production. Algal cells have high protein content (about 50%) and subsequent harvesting of algae growing in HRAP for human or animal consumption will be a financial incentive for wastewater treatment. The data of average algal productivity presented in Table 14 suggested that protein production through algal cultivation is far more effective than cultivating other conventional crops. The chemical compositions of different algae are presented in Table 15, which show them to have higher protein contents than soya bean. Due to the rapid generation of algae, HRAP treating wastewaters and operating outdoors under ambient conditions will have growth of mixed algal species such as those shown in Table 15. The harvested algal cells from HRAP can be fed to animals (such as cattle, pigs, poultry, and fish) and these animals are used as food for humans. This strategy would lengthen the food chains, minimizing health risks and

Constructed Wetlands and Waste Stabilization Ponds Table 14

Comparative protein productivity

Crop

Biomass productivity (tonnes ha1 yr1)

Protein content (%)

Protein productivity (tonnes ha1 yr1)

Algae Wheat Rice Potato

70 3 5 40

50 12 12 20

35 0.36 0.60 0.80

From Becker EW (1981). Algae mass cultivation – production and utilization. Process Biochemistry 16: 10–14.

Table 15 Chemical composition of different algae compared with soya (% dry matter) Component

Scenedesmus

Spirulina

Chlorella

Soya

Crude protein Lipids Carbohydrates Crude fiber

50–55 8–12 10–15 5–12

55–65 2–6 10–15 1–4

40–55 10–15 10–15 5–10

35–40 15–20 20–35 3–5

From Becker EW (1981). Algae mass cultivation – production and utilization. Process Biochemistry 16: 10–14.

promoting better social acceptance. Except for Spirulina sp. which has soft cell walls, most of the waste-grown algae have thick cell walls which are not easily digestible by nonruminant animals (such as poultry). Therefore, these cell walls need to be ruptured by heat or acid treatment before feeding to the nonruminants. Hintz et al. (1966) reported that the waste-grown algae (Chlorella and Scenedesmus) were 73% digestible when fed to ruminant animals such as cattle and sheep, and were only 54% digestible when fed to pigs. The digestible energy content for the ruminants was 2.6 kcal g1 algae. These algae were found to supply adequate protein to supplement barley for pigs. Alfalfa–algae pellets, when fed to lambs, resulted in higher weight gains than alfalfa pellets alone. Algae are basically not palatable to most livestock, but this may be overcome by pelletizing the processed algae with usual feed of the particular animal, such as steam barley in the case of cattle. In general, the waste-grown algae appear to have potential as a livestock feed because of the high contents of protein and other valuable substances (Table 15). Algal cells may also serve as a source of steroids. The concentration of steroids in algae is variable but significant amount may be found in some algal species. Algae may also contain up to 0.2% dry weight as carotenoids (Paoletti et al., 1976). Some medicinal products have been isolated from algae (Volesky et al., 1970). Despite the high potentials of applying HRAP for wastewater treatment and protein biomass production, the key challenge to this technology is the selection of an algal harvesting technique that is effective and economical. The algal biomass growing in HRAP is usually in the microscopic unicellular forms with sizes ranging from 10 to 100 mm. These algal cells need to be removed or harvested from the HRAP water so that the treated effluent will have low SS that meets the discharge standards; the harvested algal cells could be

295

processed for further reuse. Polprasert (2007) listed several algal harvesting technologies such as micro-straining, paperprecoated belt filtration, flocculation, flotation and centrifugation, and so on. Due to the wide range of conditions and objectives encountered, the selection of an algal-harvesting technology and its application should be approached on a case-by-case basis, considering the advantages and disadvantages of each technology and the intended uses of the algal biomass. Fish protein production. The use of waste-grown algae as food for herbivorous fish (feeding on algae and plants) or omnivorous fish (feeding on plants or animals) will minimize the problems of algal harvesting and algal digestibility mentioned above. Common herbivores and omnivores that could be fed with waste-grown algae are: Tilapia (Oreochromis niloticus), Chinese common carp (Cyprinus carpio), grass carp (Ctenopharyngoden idelia), and so on. These fish could be raised in ponds fed with harvested algal cells from HRAP or they could be raised directly in FP or MP to graze on the algae present in the pond water. The reuse of municipal and agricultural wastes through the production of algal and fish protein has been practiced in several countries (Table 16). Fish yields from this practice vary widely ranging from 1 to 10 ton ha1 yr1 depending on wastewater characteristics, climates, water quality, and fish pond management. To minimize health risks, the World Health Organization proposed that wastewaters to be fed to fish ponds should be pre-treated to have the microbiological quality as shown in Table 17 (Mara and Cairncross, 1988). Another measure believed to reduce the transfer of pathogens is to raise carnivorous fish for human consumption using the herbivorous fish from waste-fed ponds as feed. The waste-grown fish can be used as feed for carnivorous fish or shrimps, which have high market value when sold for human consumption. The biological value of protein in fish meal is 75–90%, which is quite high (Williamson and Payne, 1978) and is suitable for feeding to pigs and chicken. For this purpose, fish in waste-fed ponds can be reared at a higher stocking density and shorter growing period to obtain high fish yield. Fish can be sundried, grounded, and mixed with other food stuffs to increase the value of fish meal. Experiments were conducted at Asian Institute of Technology (AIT) to investigate the feasibility of using Tilapia grown in septage-fed ponds as fish feed ingredients for carnivorous fish of high market value such as snakehead (Channa striata) and walking catfish (Clarias batrachus and C. macrocephalus) (Edwards et al., 1988). Tilapia harvested from the septage-fed ponds was used either fresh, or processed as silage and mixed with other feed ingredients prior to feeding to the carnivores. A silage was produced by adding 20% carbohydrate (cassava) to minced Tilapia in the presence of Lactobacillus casei. It was found that growth of these carnivorous fish in ponds fed with Tilapia fish meal, fresh Tilapia, or silaged Tilapia was comparable with that fed with marine trash fish (conventional fish feed). In many cases, depuration, which is a natural process to remove some microorganisms or toxic and malodorous compounds from the fish body, can be applied to improve quality of the waste-grown fish. By putting contaminated fish

296

Constructed Wetlands and Waste Stabilization Ponds

Table 16

Fish yields in waste-fed ponds

Country

Type of waste

Yield (kg ha1 yr1)

Remark

Israel Philippines Indonesia Poland India China Munich, Germany

Cattle manure Biogas slurry Livestock waste Sugar beet wastes Wastewater Wastewater Wastewater

10 950 8000 7500 400–500 kg ha1 – growing season 958–1373 6000–10 000 500 kg ha1

Extrapolated from experiment

Hungary

Wastewater

1700 kg ha1

USA

Wastewater effluent, 37% wastewater Septage, loading ¼ 150 COD ha1 d1

126–218 5000 5000–6000

AIT, Thailand

Indian carp Common carp, per growing season Polyculture, Chinese carp and common carp, per growing season Channel catfish Silver carp, bighead carp Tilapia (extrapolation)

Modified from Polprasert C (2007). Organic Waste Recycling, Technology and Management, 3rd edn. London: IWA Publishing.

Table 17 Tentative microbiological quality criteria for the aquacultural use of wastewater and excreta Reuse process

Fish culture Aquatic macrophyte culture

Viable trematode eggsa (arithemetic mean number per liter or kg)

Fecal coliforms (geometric mean number per 100 ml or per 100 g)b

0 0

o104 o104

a

Clonorchis, Fasciolopsis, and Schistosoma. Consideration should be given to this guideline only in endemic area. b This guideline assumes that there is a one log 10 unit reduction in fecal coliforms occurring in the pond, so that in-pond concentrations are o1000 per 100 ml. If consideration of pond temperature and retention time indicates that a higher reduction can be achieved, the guideline may be relaxed accordingly. From Mara DD and Cairncross AM (1988). Guidelines for the Safe Use of Wastewater and Excreta in Agriculture and Aquaculture: Measures for Public Health Protection. Geneva: World Health Organization.

in clean water for 1–2 weeks, most of the contaminants retained on or in the fish body could be cleansed out. The nutritional value of waste-grown fish has shown to be better than those grown using other sources of feed. Most of the nutrition applied in the form of waste is converted to protein rather than fat as reported by Moav et al. (1977) and Wohlfarth and Schroeder (1979) . The use of waste-grown algae as feed for herbivorous fish (Tilapia) was reported by Edwards et al. (1987). Extrapolated fish yields approaching 20 ton ha1 yr1 were obtained in the 4 m3 concrete pond system based on 3-month growing periods under ambient, tropical conditions. A linear relationship was established between fish yields and means algal concentration in the fish ponds, in which an algal concentration of 70 mg l1 in the pond water was considered to be high enough to produce good fish growth. Higher algal concentrations were not recommended since it might lead to zero DO concentrations in the early morning hours. Another study by Edwards et al. (1988) applied septic tank sludge to 200 m2 earthen ponds to grow algae, which were subsequently grazed by

Tilapia. At the organic loading rate of 250 kg COD ha1 d1 and stocking density of 20 fish m2, the fish productivity of about 11 ton ha1 yr1 was obtained.

4.10.5.2 Energy and Water Conversation and Climate Change Mitigation The increasing uses of fossil fuels have resulted in global warming and climate change such as rising seawater levels, severe drought, and flooding. Natural systems such as CW and WSP are cheaper to build and operate and consume less energy in operation than conventional treatment systems (Table 1). Accordingly, these natural systems use less fossil fuels, generate less green house gases (GHGs), and contribute to climate change mitigation. A case study on GHG reduction and water conversation was conducted by Kittipongvises (2008) at the Sanguan Wongse Industries (SWI) factory in Nakorn Ratchasima, northeastern Thailand, which employed a covered AP to treat 7000 m3 d1 of its starch-processing wastewater. There was more than 90% reduction of COD and the produced CH4 gas of 50 000–80 000 m3 d1 was being used as heat in tapioca processing and in electricity generation (Figure 22). This CH4 utilization resulted in mitigation of GHG emission of 300 000 ton CO2eq-yr1 which, based on the Clean Development Mechanisms (CDM) and Certified Emission Reductions (CER) rate of Euro 10 per ton CO2eq, would yield an additional income of Euro 3 000 000 (or 120 million baht). Furthermore, financial gains from replacement of heavy fuel oil and grid-fed electricity were about US$1000 000 and US$250 000 per annum, respectively. The treated wastewater from the covered lagoon was used to irrigate nearly tapioca crops, hence contributing to water conservation. A recent report by Haag (2007) indicated that the potential of biodiesel production of about 90 000 l yr1 from algae cells grows in a 1-ha HRAP; soya could produce biodiesel to only 450 l ha1 yr1, canola about 1200 l ha1 yr1and oil palm about 6000 l ha1 yr1. Algae cells could yield about 50% of their weight in oil, higher than oil palms, which typically yield about 20%. Oils squeezed from algal cells are biocrude, the

Constructed Wetlands and Waste Stabilization Ponds

297

Figure 22 Anaerobic ponds with captured biogas for electricity generation (Kittipongvises, 2008).

Figure 24 A biosphere on Mars utilizing CW and HRAP. Figure 23 International Space Station (www.science.national geographic.com/science, cited on 19 January 2009).

precursor of biodiesel, which needs to be processed further to convert them to become biodiesel. The rest of the algal biomass can be converted into ethanol and animal feed. Besides the economic advantages, converting waste-grown algae to

biodiesel would contribute to global warming mitigation (algae need CO2 for photosynthesis, Equation (1)) and the produced biodiesel should not pose health and/or social problems.

298

Constructed Wetlands and Waste Stabilization Ponds

4.10.5.3 CW and HRAP as Future Life-Support Technology The current progress in space exploration should eventually lead to a possibility that human beings can stay in space, for example, at the International Space Station (ISS, Figure 23) for long duration. To minimize launch costs and re-supply requirements, the treatment and recycling of water by growing algal cells and aquatic plants for use as human foods at the ISS may be necessary, and the CW and HRAP technologies as described in this chapter appear relevant. Since there is more than 95% content of CO2 in the air of the Mars’ atmosphere, the potential of applying CW and HRAP to produce algal cells and food production in a biosphere on Mar could be considered, as shown in Figure 24.

4.10.6 Summary Increased global population and industrialization have become a major challenge for government and private sectors to adequately manage the generated wastewaters. The current problems of energy and food crisis and global warming have made the effort to treat the wastewaters more difficult. Natural systems such as CW and WSP are alternative technologies which are low cost, but can provide high treatment efficiencies comparable to or even better than those of conventional wastewater treatment systems. The principal mechanisms responsible for wastewater degradation are the interactions between emergent aquatic plants (in case of CW) or algae (in case of WSP) and bacteria growing in these systems. Through photosynthetic reactions, O2 produced by the emerging aquatic plants or algal cells are used by the bacteria in biodegrading the incoming organic matters and the biodegradation by-products such as CO2 and NH3 are used by the plants and algae for their photosynthesis and growth. The relatively long HRT occurring in these natural systems results in effective sedimentation, adsorption, and plant uptake of SS and nutrients. Pathogen die-offs in WSP are due to UV light exposure and high pH conditions prevailing during photosynthesis, resulting in acceptable concentration of fecal microorganisms in MP effluent for discharge into a receiving water body or reuses in irrigation and aquaculture. Pathogen die-offs in CW are less effective because the emerging aquatic plants prevent sunlight from reaching the wastewaters. Since there are physical, chemical, and biological reactions occurring in these natural systems and their performance is dependent to a large extent on climates, the available design criteria are based on empirical or rationale approaches. The empirical method for CW and WSP design is based mainly on relationships between organic loading rates, HRT, and their treatment performance. The rationale approach for design of a CW unit is the plug-flow model and assuming first-order kinetics in which the reaction rate is temperature dependent. Since there are emerging aquatic plants and media in the CW bed, the effect of bed porosity on the actual HRT and, consequently, the CW volume and surface area has to be taken into consideration. In WSP design, the completely mixed model assuming first-order kinetic is commonly used for FP and MP design for both organic matter and fecal coliform reductions. The

first-order reaction rates are temperature dependent, similar to that of CW. The operation of CW and WSP results in the production of biomass, which could be converted to or utilized as food for human and animals or other useful products. The harvested aquatic plant can be used as a raw material in making compost fertilizer or silage for use as animal feeds. The nutritional contents of these aquatic plants are similar to those of alfalfa hay. The algal productivity from HRAP is estimated to be 70 ton (dry weight) ha1 yr1, much higher than other terrestrial crops. Since algal biomass contains about 50% protein, they can be used as food for animal and herbivorous fish, which could consequently serve as human foods. Some medicinal and chemical products have been isolated from algal cells for pharmaceutical and industrial purposes. Due to the presence of algae in FP and MP water, herbivorous fish can be raised in these ponds and the harvested fish, if properly processed, could be used as animal or human foods. The AP system offers high opportunity for energy recovery through the production of CH4 gas. A case study on AP treatment of a tapioca-processing wastewater showed that the captured CH4 gas could be converted to heat for tapioca processing and electricity generation, hence, a financial return to the industry and contributing to the reduction of global warming problems. With the current progress in the space exploration, human beings could stay in space or eventually on the Moon or Mars for a long duration. The natural systems such as CW and WSP as discussed in this chapter could be considered as possible technologies for life-support systems in the production of O2 and biomass.

References Becker EW (1981) Algae mass cultivation – production and utilization. Process Biochemistry 16: 10--14. Boyd CE (1974) Utilization of aquatic plants. In: Mitchell DS (ed.) Aquatic Vegetation and Its Use and Control, pp. 107--115. Paris: UNESCO. Edwards P, Polprasert C, Rajput VS, and Pracharaprakiti C (1988) Integrated biogas technology in the Tropics. 2. Use of slurry for fish culture. Waste Management and Research 6: 51--61. EIA (2008) International Energy Outlook. Washington, DC: Energy Information Administration, US Department of Energy. http://www.eia.doe.gov/oiaf/ieo/ index.html Girts MA and Knight RL (1989) Operations optimization. In: Hammer DA (ed.) Constructed Wetlands for Wastewater Treatment: Municipal, Industrial, and Agricultural. Chelsea, MI: Lewis. Goldman JC (1979) Outdoor algal mass cultures. II. Photosynthetic yield limitation. Water Research 13: 119--136. Gomez E, Wang X, Dagnino S, et al. (2007) Fate of endrocrine disrupters in waste stabilization pond systems. Water Science Technology 55: 157--163. Haag AL (2007) Algae bloom again. Nature 447: 520--521. Hintz HF, Heitman H Jr., Weir WC, Torell DT, and Meyer JH (1966) Nutritive value of algae grown on sewage. Journal of Animal Science 25: 675--681. IPCC (2007) An Assessment of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: Intergovernmental Panel on Climate Change. Kittipongvises S (2008) Potential of Clean Development Mechanism (CDM) Activities for Greenhouse Gases Reduction at a Starch-Processing Factory in Thailand. Master’s Thesis, Asian Institute of Technology, Thailand. Koottatep T, Polprasert C, and Laugesen C (2007) Integrated eco-engineering design for sustainable management of fecal sludge and domestic wastewater. Journal of Korean Wetland Society 9: 69--78. Koottatep T, Polprasert C, Oanh NTK, et al. (2001) Septage dewatering in vertical-flow constructed wetlands located in the tropics. Water Science and Technology 44: 181--188.

Constructed Wetlands and Waste Stabilization Ponds Koottatep T, Surinkul N, Polprasert C, Kamel AS, and Strauss M (2005) Treatment of septage in constructed wetlands in tropical climate-lessons learnt after seven years of operation. Water Science and Technology 51: 119--126. Levenspiel O (1972) Chemical Reaction Engineering, 2nd edn. New York, NY: Wiley. Mara DD, Alabaster GP, Pearson HW, and Mills S (1992) Waste Stabilisation Ponds: A Design Manual for Eastern Africa. Leeds, UK: Lagoon Technology International. Mara DD and Cairncross AM (1988) Guidelines for the Safe Use of Wastewater and Excreta in Agriculture and Aquaculture: Measures for Public Health Protection. Geneva: World Health Organization. Marais G (1974) Fecal bacteria kinetics in waste stabilization ponds. Journal of the Environmental Engineering Division, ASCE 100: 120--139. Metcalf and Eddy Inc. (2003) Wastewater Engineering: Treatment and Reuse, 4th edn. New York, NY: McGraw-Hill. Middlebrooks EJ, Middlebrooks CH, Reynolds JH, et al. (1982) Water Stabilization Lagoon Design, Performance, and Upgrading. New York, NY: Macmillan. Mitchelle DS (1974) Aquatic Vegetation and Its Use and Control. Paris: UNESCO. Moav R, Wohlfarth G, and Schroeder GL (1977) Intensie polyculture of fish in freshwater ponds. I. Substitution of expensive feeds by liquid cow manure. Aquaculture 10: 25--43. MOI (2005) Wastewater Treatment Standards. Bangkok, Thailand: Ministry of Industry. NAS (1976) Making Aquatic Weeds Useful: Some Perspectives for Developing Countries. Washington, DC: US National Academy of Sciences. Pano AE and Middlebrooks J (1982) Ammonia nitrogen removal in facultative wastewater stabilization ponds. Journal of the Water Pollution Control Federation 54: 344--351. Park WH and Polprasert C (2008) Roles of oyster shells in an integrated constructed wetland system design for P removal. Ecological Engineering 34: 50--56. Paoletti C, Phushparaj B, Florenzano G, Capella P, Lercker G (1976) Unsaponifiable matter of green and blue-green algal lipids as a factor of biochemical differentiation of their biomass: I. Total unsaponifiable and hydrocarbon fraction: Lipids 11: 258-–265. Polprasert C (2006) Design and operation of constructed wetlands for wastewater treatment and reuses. In: Ujang Z and Henze M (eds.) Municipal Wastewater Management in Developing Countries: Principles and Engineering. London: IWA Publishing. Polprasert C (2007) Organic Waste Recycling, Technology and Management, 3rd edn. London: IWA Publishing. Polprasert C and Agarwalla BK (1994) A facultative pond model incorporating biofilm activity. Water Environment Research 66: 725--732. Polprasert C and Bhattarai KK (1985) Dispersion model for waste stabilization ponds. Journal of Environmental Engineering, ASCE 111: 45--49. Polprasert C, Khatiwada NR, and Bhurtel J (1998) Design model for COD removal in constructed wetlands based on biofilm activity. Journal of Environmental Engineering, ASCE 124: 838--843. Reed SC, Middlebrooks EJ, and Crites RW (1988) Natural Systems for Waste Management and Treatment. New York, NY: McGraw-Hill.

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Sawaittayothin V and Polprasert C (2007) Nitrogen mass balance and microbial analysis of constructed wetlands treating municipal landfill leachate. Bioresource Technology 98: 565--570. Silva SA, De Oliveira R, Soares J, Mara DD, and Pearson HW (1995) Nitrogen removal in pond systems with different configurations and geometries. Water Science and Technology 31: 321--330. Stern N (2006) The Economics of Climate Change. London: HM Treasury. Stowell R, Ludwig R, Colt J, and Tchobanoglous G (1980) Towards the Rational Design of Aquatic Treatment Systems. Paper presented at the ASCE Convention, Portland, OR, USA, 14–18 April. Department of Civil Engineering, University of California, Davis, CA, USA. Toan TQ (2008) Potential of Domestic Wastewater Reuse in Vietnam: A Case Study in Wastewater Fed Fish Ponds in Yenso Commune, Hanoi City. Master’s Thesis, Asian Institute of Technology, Thailand. UN (2005) Monitoring Progress Towards the Achievements of Millennium Development Goals. http://unstats.un.org/unsd (accessed February 2010). UN (2008) The Millennium Development Goals 2008 Report. New York, NY: United Nations. US Census Bureau (2007) International Data Base. July 2007 version. Washington, DC: US Census Bureau. US EPA (1988) Design Manual – Constructed Wetlands and Aquatic Plant Systems for Municipal Wastewater Treatment. EPA/625/1-88/022. Cincinnati, OH: United States Environmental Protection Agency. US EPA (2000) Free Water Surface Wetlands for Wastewater Treatment: A Technology Assessment. Washington, DC: Office for Water Management, US Environmental Protection Agency. Visesmanee V, Polprasert C, and Parkpian P (2008) Long-term performance of subsurface-flow constructed wetlands treating Cd wastewater. Journal of Environmental Science and Health, Part A – Toxic/Hazardous Substances and Environmental Engineering A43: 765--771. Volesky B, Zajic JE, and Knettig E (1970) Algal products. In: Zajic JE (ed.) Properties and Products of Algae, pp. 49--82. New York, NY: Plenum. W.E.F (2001) Natural Systems for Wastewater Treatment, Manual of Practice FD–16, 2nd edn. Alexandria, VA: Water Environment Federation. Wehner JF and Wilhelm RH (1956) Boundary conditions of flow reactor. Chemical Engineering Science 6: 89--93. Westlake DF (1963) Comparison of plant productivity. Biological Review 38: 385--425. WHO (2000) Global Water Supply and Sanitation Assessment 2000 Report. Geneva: World Health Organization. Wieder RK, Tchobanoglous G, and Tuttle RW (1989) Preliminary considerations regarding constructed wetlands for wastewater treatment. In: Hammer DA (ed.) Constructed Wetlands for Wastewater Treatment: Municipal, Industrial, and Agricultural. Chelsea, MI: Lewis. Williamson G and Payne WJA (1978) Animal Husbandry in the Tropics, 3rd edn. London: Longman. Wohlfarth GW and Schroeder GL (1979) Use of manure in fish farming – a review. Agricultural Wastes 1: 279--299.

4.11 Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis AG Fane, CY Tang, and R Wang, Nanyang Technological University, Singapore & 2011 Elsevier B.V. All rights reserved.

4.11.1 4.11.1.1 4.11.1.2 4.11.1.2.1 4.11.1.2.2 4.11.1.2.3 4.11.1.2.4 4.11.1.3 4.11.1.3.1 4.11.1.3.2 4.11.1.3.3 4.11.1.3.4 4.11.2 4.11.2.1 4.11.2.2 4.11.2.3 4.11.3 4.11.3.1 4.11.3.2 4.11.3.2.1 4.11.3.2.2 4.11.3.3 4.11.3.4 4.11.4 4.11.5 4.11.5.1 4.11.5.2 4.11.5.2.1 4.11.5.2.2 4.11.5.2.3 4.11.5.2.4 4.11.6 4.11.6.1 4.11.6.2 4.11.6.2.1 4.11.6.2.2 4.11.6.3 4.11.6.4 4.11.6.5 4.11.7 4.11.7.1 4.11.7.2 4.11.7.3 4.11.8 References

Introduction The Range of Membrane Processes Role in Water Supply, Sanitation, and Reclamation Water treatment Desalination Water reclamation Wastewater MBR Status of Development Seawater desalination Water reclamation Water treatment Membrane bioreactors Membrane Types and Properties Membrane Types and Important Membrane Properties Membrane Properties for RO and NF Membranes Membrane Properties for MF and UF Membranes Membrane Materials and Preparation Polymeric Membrane Materials Hollow Fiber Preparation Mechanism of membrane formation Fabrication of hollow fiber membranes TFC Membrane Preparation Ceramic Membrane Preparation Membrane Characterization Membrane Modules The Role of the Module Module Types Spiral-wound module Tubular module Hollow fiber module (contained) Submerged module Basic Relationships and Performance Membrane Flux and Rejection Transport Inside a Membrane – Basic Relationships Transport models for MF and UF membranes Transport models for RO membranes Transport toward a Membrane – Concentration Polarization Factors Affecting Membrane Performance Membrane Fouling Membrane Process Operation Crossflow versus Dead-End Operation System Components Energy and Economic Issues Conclusions

Nomenclature a A Am

B molecular radius water permeability coefficient (solutiondiffusion model) membrane area

C  C

301 302 302 302 302 304 304 305 305 305 305 306 306 306 308 309 312 312 312 312 317 319 319 319 321 321 321 322 322 322 323 324 324 326 327 327 328 329 330 332 332 332 333 333 333

solute permeability coefficient (solutiondiffusion model) solute concentration average solute concentration inside the membrane

301

302

Cb Cf Ci Cm Cp Cwm dh D Dsm Dwm Jcrit Js Jw K KKC Ksm lm Lp Ls m˙ s Mw P Pm Pp

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

bulk concentration feedwater concentration molar concentration of dissolved species i solute concentration near the membrane surface solute concentration in the permeate water concentration of water inside the RO rejection layer hydraulic diameter diffusion coefficient of solute diffusion coefficient of solute inside the RO rejection layer diffusion coefficient of water inside the RO rejection layer critical flux solute flux water flux mass transfer coefficient Kozeny–Carman coefficient solute partitioning coefficient into the RO rejection layer thickness of the rejection layer water permeability solute permeability coefficient solute mass flow rate molecular weight hydraulic pressure hydraulic pressure near the membrane surface hydraulic pressure of the permeate water

4.11.1 Introduction Membrane technology is used in the water industry to improve the quality of water for use, reuse, or discharge to the environment. Membranes range from finely porous structures to nonporous and can remove contaminants such as bacteria and protozoa down to ions. The advantages of membrane technology include its modular nature, allowing application at very large or small scale, the quality of the product water, the relatively small footprint, and, in some cases, the lower energy usage. Increased water scarcity, coupled with steady improvements in membrane performance, costs, and energy demand, will see a steady growth in membranes in the water industry into the foreseeable future.

4.11.1.1 The Range of Membrane Processes Membranes used for purification and separation can be defined as semipermeable thin films. The semipermeable property means that membranes may be able to transport water but not bacteria (microfilters) or salts (reverse osmosis, RO). Other membranes are able to transport salts but not water (electrodialysis). The family of membrane processes is depicted in Figure 1 which shows the driving force for transport

Qp rp R Rapp Re Rf Rg Rint Rm Rsys S Sc Sh T u v Vw Y e k g d p pm pp r s

volumetric flow rate that permeates through the membrane pore radius rejection apparent rejection of a membrane Reynolds number foulant hydraulic resistance universal gas constant (R ¼ 8.31 J mol1 K1) intrinsic rejection of a membrane membrane hydraulic resistance overall rejection – or rejection at a system level specific surface area Schmidt number Sherwood number absolute temperature (K) flow velocity kinetic viscosity molar volume of water recovery membrane porosity ratio of solute (or particle) diameter to the pore diameter viscosity of water boundary layer thickness osmotic pressure osmotic pressure at the membrane surface osmotic pressure of the permeate water reflection coefficient tortuosity of the membrane

and the size range of the species involved. For driving forces DC and DE, the species transported are solutes and water transport is low. For the liquid-phase pressure-driven processes (DP), water is transported and other species are partially or wholly retained. Gas-phase separations are also possible, such as N2/O2 or CO2/CH4, and details can be found elsewhere (Hagg, 2008). Membrane distillation (MD) is a process driven by temperature difference (DT) using hydrophobic membranes with vapor-filled pores. The DT provides a vapor pressure driving force for water vapor transport; a detailed review can be found elsewhere (Khayet, 2008). The membrane processes of interest in the water industry are the pressure-driven liquid-phase processes summarized in Table 1. The microfiltration/ultrafiltration (MF/UF) range can be regarded as a continuum. These membranes are typically produced by phase inversion, and small changes in preparation technique adjust the nominal pore size. The nanofiltration/reverse osmosis (NF/RO) membranes also represent a continuum and are usually produced as thin-film composite (TFC) structures. Section 4.11.2 gives details of the various types of membrane and their properties. Section 4.11.3 describes membrane preparation and Section 4.11.4 shows how membrane properties are characterized. Section 4.11.5 explains the role of the membrane module (housing) and

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

303

Applications Organics Driving force

Ionic Macromolecular Colloidal Fine Dialysis

ΔC

Liquid membranes Pervaporation

ΔE

Electrodialysis

Reverse osmosis Nanofiltration Ultrafiltration ΔP Microfiltration Gas sep.

ΔT

Filtration

Membrane distillation

0.01 nm

1 nm

10 nm

100 nm

1000 nm 104 nm 1 μm 10 μm

105 nm 100 μm

Figure 1 The family of membrane processes (driving forces and applications size range).

Table 1

Typical properties of pressure-driven membranes Microfiltration

Ultrafiltration

Nanofiltration

Reverse osmosis

Pore size (nm)

50–10 000

1–100

B2

o2

Water permeability (l m2 h1 bar1) Operating pressure (bar) MWCO (Da)

4500

20–500

5–50

0.5–10

0.1–2.0

1.0–5.0

2.0–10

10–100

Not applicable

1000–300 000

4100

410

Bacteria, algae, suspended solids, turbidity Polymeric, inorganic

Bacteria, virus, colloids, macromolecules

Di- and multivalent ions, natural organic matter, small organic molecules Thin-film composite polyamide, cellulose acetate, other materials (Schafer et al., 2005)

Dissolved ions, small molecules

Targeted contaminants in water Membrane materials

Polymeric, some inorganic

Adapted from Winston and Sirkar (1992) and Mulder M (1996) Basic Principles of Membrane Technology, 2nd edn. Dordrecht: Kluwer.

Thin-film composite polyamide, cellulose acetate

304

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

introduces the various module types. Section 4.11.6 provides the basic relationships describing membrane performance.

4.11.1.2 Role in Water Supply, Sanitation, and Reclamation The needs of the water industry (water supply, sanitation, and reclamation) are to remove various contaminants to make the water fit for purpose. Potable water requires removal of pathogens, organic species, and salts to specify limits. Some industrial waters have even tighter limits, for example, boiler feedwater, or ultrapure water for microelectronics. Wastewater treatment, such as sanitation, involves biological treatment and subsequent polishing. Water reclamation takes treated wastewater and upgrades it to high quality for industry or indirect potable reuse (IPR). Industrial water applications are very broad but could encompass water treatment to industry standards, water recovery and recycle, and treatment for discharge. It will be evident that the pressure-driven liquid-phase membrane processes, with the properties given in Table 1, provide the means to achieve the above separations. In many cases, the membrane process is used in combination with another unit operation (such as a bioreactor) in a hybrid membrane process. In other cases, low-pressure (MF/UF) membranes are used as pretreatment to high-pressure (NF/ RO) membranes in dual membrane processes. Figure 2 is a simplified generic flow sheet of a membrane process as applied in the water industry. In addition to the

Feedwater

Pretreatment

membrane separation, it is not uncommon to have both pretreatment and posttreatment steps. It should also be noted that contaminant removal inevitably results in reject (waste) streams that have to be dealt with. Input streams may also be involved in pre- and posttreatment. The following are brief descriptions of water industry applications of membranes. Table 2 summarizes the membrane process configurations for the range of applications and water sources.

4.11.1.2.1 Water treatment Table 2 identifies five water treatment applications (WT.1– WT.5), based either on low-pressure MF/UF membranes in hybrid processes or on tighter NF membranes. The water sources involved are low salinity. WT.1 is the most typical configuration for membrane-based water treatment plant (Kennedy et al., 2008); the posttreatment may be simpler depending on the natural organic matter (NOM) removal achieved upstream. Disinfection by chlorine is usually applied to maintain a residual in the distribution system. The lowpressure membranes are predominantly hollow fibers, either contained or submerged (see Section 4.11.5). In most applications the suspended solid content of the source is relatively low and this allows operation in dead-end mode (see Section 4.11.7), with cycles of filtration and backwash, typically over 30–60 min. The WT.2 option, combining membranes and powdered activated carbon (PAC), is used in cases where the

Membrane separation

Posttreatment

Product water

Reject Figure 2 Generic flow sheet of membrane process.

Table 2 Application

Membrane process configurations in the water industry Source water

Membrane process

Pretreatment or hybrid

Posttreatment

Target removals

Water treatment WT.1 Surface WT.2 Surface WT.3 Surface WT.4 Surface WT.5 Ground

MF/UF MF/UF NF NF NF

Coagulation PAC Filtration Coagulation þ filtration Filtration

AOT, BAC, Dis Dis Dis AOT Dis

NOM, turbidity, pathogens Taste/odor, trace organics NOM Trace organics, taste/odor Hardness

Desalination D.1 D.2

Brackish ground Seawater

RO RO

Filtration Media filtration or MF/UF

Dis Ca addition

Salinity Salinity

Reclamation R.1 R.2

Treated wastewater Wastewater

RO RO

MF/UF MBR

AOT AOT

Pathogens, trace organics Pathogens, trace organics

MF/UF

Screening

Dis

BOD, turbidity, pathogens

Membrane bioreactor MBR.1 Wastewater

AOT, advanced oxidation treatment (UV, etc.); BAC, biologically active carbon; BOD, biochemical oxygen demand; Dis, disinfection (chlorination, etc.); MBR, membrane bioreactor; MF, microfiltration; NOM, natural organic matter; PAC, powdered activated carbon; UF, ultrafiltration.

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

water may encounter trace organics or taste and odor (Lebeau et al., 1998). Applications WT.3–5 use NF membranes on low-salinity surface- or groundwaters. NF operates at higher pressures than MF/UF and also requires crossflow to control fouling, rather than dead-end operation, and consequently has a higher energy demand. WT.3 is popular in Norway (Wittmann and Thorsen, 2005), where raw waters are high in NOM and energy is relatively plentiful. WT.4 makes use of the ability of NF to remove organics of 100–200 Da size, and is exemplified by the treatment of river water in France to remove trace herbicides (Wittmann and Thorsen, 2005). In some locations, NF is also used on groundwater to remove calcium hardness.

4.11.1.2.2 Desalination Saline water sources range from brackish groundwater to seawater. In these applications, the key membrane process is RO. The role of pretreatment is to protect the RO membranes from various foulants (see Section 4.11.6.5), and posttreatment prepares the product water for discharge. Application D.1 (Table 2) is brackish water desalination. These are usually modest-size plants for local water supplies. A major challenge for this application is how to dispose of the plant reject and which is a high-salinity stream. Various options are available (Voutchkov and Semiat, 2008), including evaporation ponds and subsurface disposal. Application D.2 is desalination of seawater, which is effectively a limitless source. In this case, the high salinity and high osmotic pressure require an operating pressure of 60–70 bar, making seawater reverse osmosis (SWRO) more energy intensive than the other membrane/water options. Pretreatment has to be able to minimize various forms of fouling (inorganic, organic, and biofouling). Most SWRO plants with feedwater from ocean intakes opt for either media filtration, or increasingly use low-pressure membranes for pretreatment. In the case of SWRO, the reject (brine) stream is typically 50% of the intake flow, and is discharged back into the ocean, subject to adequate arrangements for rapid dispersion of salinity. Prior to discharge, the pressurized brine passes through energy recovery devices which can recover 495% of the pressure energy in the brine for pressurizing a portion of the feed. The product water is usually conditioned by addition of calcium ions to satisfy World Health Organization (WHO) requirements (Cote et al., 2008). A detailed description of desalination by RO is given in Voutchkov and Semiat (2008).

4.11.1.2.3 Water reclamation Municipal wastewater provides the second limitless source of water. Reclamation processes convert secondary effluent (R.1 in Table 2) or raw wastewater (R.2) into water of exceptionally high quality. Process R.1 is more common as it builds on the existing municipal wastewater infrastructure. Secondary effluent has relatively low suspended solids, total organic carbon (TOC), and salinity, which makes it very attractive as a source water. It also tends to be located close to where it could be reused. All major reclamation plants use dual membrane arrangements with the low-pressure (MF/UF) pretreatment membranes operating in dead-end cycles, similar

305

to MF/UF water treatment plant, and providing very low solids feed to the RO. Due to the low-salinity feed, the RO operates at much lower pressures and with higher recoveries (75% vs. 50%) than SWRO. This means that the reclamation plant can produce high-quality water at approximately half the energy and costs of SWRO (see Section 4.11.7.3). A comprehensive review of membrane reclamation plant and comparison with SWRO can be found elsewhere (Cote et al., 2008). Posttreatment in R.1 is typically ultraviolet (UV) which provides an added barrier to virus and also oxidizes trace organic compounds, possibly present at ppb levels in the RO permeate. Flow sheet option R.2 uses a membrane bioreactor (MBR, see Section 4.11.1.2.4) in place of the conventional activated sludge processes (CASPs) combined with MF/UF. This option would probably be favored in a green field site due to the smaller foot print and the reported better-quality feed (lower TOC) to the RO (Cote et al., 2008). The high-quality water produced by the dual membrane reclamation process is suitable for demanding industrial applications and for IPR.

4.11.1.2.4 Wastewater MBR The CASP combines a wastewater bioreactor and a settling tank. The membrane bioreactor (MBR) replaces the settling tank with low-pressure membranes. The advantages of the MBR include an improved effluent quality (solids-free, potentially lower TOC) and significantly smaller foot print. The membrane separation allows the biomass mixed liquor to be increased to 10–20 g l1, compared with the o5 g l1 in the CASP. This improves organics removal and can reduce the excess waste sludge for disposal. Typically, MBRs operate with mixed liquors of 10–12 g l1, which is a compromise to avoid raised viscosity that would impair oxygen transfer. MBRs have either submerged (immersed) membranes in the bioreactor, or external side-stream membranes connected so that mixed liquor can be cycled through the modules. Submerged membranes use either hollow fibers in bundles (or curtains) or flat sheets, vertically aligned, operated under suction. Side-stream modules use large bore hollow fibers. Pretreatment for MBRs is usually fine screening to eliminate sharp objects that could damage the membranes. Posttreatment depends on the fate of the permeate, but at a minimum it would involve disinfection. Membrane fouling (see Section 4.11.6.5) is a major challenge in MBRs, due to the complex nature of the mixed liquor with its biomass floc, colloids, and macrosolutes. Fouling is mitigated by careful selection of the operating flux and by maintaining a vigorous crossflow induced by air sparging below the membranes or in the membrane loop. A comprehensive review of MBR fouling is available (Le-Clech et al., 2006). More detailed information on MBRs can be found in Judd (2006) and Lieknes (2009).

4.11.1.3 Status of Development Membrane technology in the water industry has grown from a research curiosity to mainstream applications over a brief period of 50 years. This section outlines the major developments and milestones and summarizes the status of the various applications.

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4.11.1.3.1 Seawater desalination The application of membranes in the water industry started in the 1960s, following the invention of the cellulose acetate (CA) RO membrane by Loeb and Sourirajan (1964). The CARO membrane was an alternative to thermal desalination of seawater and brackish water to provide drinking water. Over the next two decades, seawater RO steadily developed, but was considered relatively costly and energy intensive, suitable for niche applications. A major advance occurred in the late 1970s with the invention of the TFC membrane (see Section 4.11.3.3) by Cadotte (1977). The TFC significantly improved both water flux and salt retention and has become the basis for modern SWRO membranes. The ability to tune the chemistry of the TFC separating layer has allowed steady improvements in performance. Both CA-RO and TFC-RO membranes are produced in continuous flat sheets. The housing, or module (see Section 4.11.5) developed to package these membranes, is the spiralwound module (SWM). This is produced in standard sizes, typically 8 in (203 mm) diameter and 40 in (1016 mm) long, which allows interchange between products. Over the 30-year period from 1978 to 2008, the SWM has steadily improved, with a drop in real cost (1/12), an increased life (2.3 ), an improved water production rate (2.5 ), and a reduced salt transmission (1/7) (Birkett and Truby, 2007). The SWM is now the module of choice for RO, NF (and some large-scale UF in other industries). However from the mid-1960 s to the late 1990s, there were two SWR options, one the SWM, the other the hollow fiber RO membrane produced by Dupont and others. The HFRO was initially predominant in larger-scale SWRO plant. However, the SWM became more competitive because of the better performance of the TFC membrane, its slightly lower pretreatment requirements, and its evolution into an interchangeable standard module. The SWM is now the predominant option for large SWRO and reclamation plant. Current (2010) trends are to larger 16-in-diameter SWMs with 4  the water output per element. Membrane developments include the thin-film nanocomposite (TFNC), which incorporates highly permeable zeolite nanoparticles into its separation layer (Jeong et al., 2007). Energy demand and costs for SWRO have steadily declined (see Section 4.11.7), largely due to the introduction of high-efficiency energy recovery devices that recover the pressure energy in the RO concentrate stream. Desalination capacity by SWRO now exceeds that of thermal processes. Plant capacities of several hundreds of ML d1 are not uncommon and applications continue to grow due to increasing water scarcity and population growth.

4.11.1.3.2 Water reclamation Water reclamation with membranes commenced at a significant scale in the 1970s with Water Factory 21 in Orange County California. This plant took secondary-treated municipal effluent and used physical/chemical pretreatment prior to RO. Today, this plant uses low-pressure membrane (UF) pretreatment as do all the major reclamation plants. Water reclamation processes provide significant augmentation of water supplies in some locations. For example, the Singapore NeWater plants (Seah et al., 2008) provide about 20% of

Singapore’s water supply. The high-quality water produced by water reclamation plant tends to go to industrial usage, with a portion going to IPR. The likely trend will be for more membrane-based IPR schemes, due to lower cost and energy demand than SWRO and increased confidence in its water quality.

4.11.1.3.3 Water treatment Water treatment with low-pressure membranes is now well established, but the growth only started in the early 1990s. Prior to this, membranes were considered too expensive for the production of a low-cost product, such as water – the exception being SWRO in niche areas. Several factors have contributed to the rapid growth in low-pressure membrane water treatment, including tighter treatment standards in the US prompted by a serious cryptosporidium outbreak in 1993, a concerted effort by manufacturers to reduce membrane plant costs and the adoption of dead-end with backwash operation (see Section 4.11.7.1) with lower energy demand. The current low-pressure membrane options are hollow fibers either in submerged or in pressurized modules (see Section 4.11.5), and both concepts appear to be equally popular. There is a small, but growing, interest in the use of ceramic membranes for water treatment, particularly in Japan. Claimed benefits are higher fluxes and longer membrane lifetimes. Higher-pressure NF membranes are also well established for effective removal of hardness and organic contaminants, including trace pollutants such as herbicides and endocrine disruptors (EDCs). NF is a more expensive option than low-pressure UF or MF, but has application in special cases (Wittmann and Thorsen, 2005).

4.11.1.3.4 Membrane bioreactors MBRs for wastewater treatment have been around since the late 1960s. In the early period, a major incentive was to increase the biomass mixed liquor (MLSS) to high levels (420 g l1) to reduce excess sludge production. However, this exacerbated fouling and reduced oxygen transfer. In addition, the high cost of membranes favored relatively high fluxes that required vigorous crossflow and high-energy input to control fouling. As a result, the MBR was another niche membrane application until the early 1990s. The significant growth in MBRs has been due to innovations in design and operation. First, the lower cost of membranes allowed a lower design flux with less intrinsic fouling. Second, the use of a two-phase flow, such as bubbling, has been found to control fouling with substantially reduced energy costs. Third, the use of submerged (immersed) membranes provided an alternative MBR configuration, besides allowing retrofitting to existing plants. The current status is that MBR applications are becoming widespread in industry and increasingly in municipal use. There tend to be more, but smaller, plant in industry and some large (4100 ML d1) municipal plant (Lieknes, 2009). MBR designs are not standardized and popular options include submerged membranes, with either hollow fibers or flat sheets, or pressurized side-stream vessels with hollow fibers. All applications use the two-phase flow to control fouling. While current MBRs are all aerobic processes, there is a significant R&D effort in anaerobic MBRs (AnMBRs), with the

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

incentive of energy recovery in biogas. Future applications of AnMBRs can be anticipated.

4.11.2 Membrane Types and Properties A pressure-driven membrane is a selective barrier for separation. Its selectivity and permeability depend strongly on its pore characteristics (pore size, pore-size distribution, and porosity). Thus, membrane pore structure is one of the most important properties affecting membrane performance. Depending on the pore structure, pressure-driven membranes can be classified into RO, NF, UF, or MF membranes. Other important membrane properties include membrane hydrophilicity, surface charge, roughness, etc. These properties are discussed in this section.

•

4.11.2.1 Membrane Types and Important Membrane Properties Membrane pore structure is one of the most important properties of a membrane, as this largely determines the selectivity as well as the permeability of a membrane. Pressuredriven membranes can be classified into porous and nonporous membranes on the basis of membrane pore size (Table 1). MF and UF membranes are porous membranes that can be operated at low pressures. For this reason, MF and UF membranes are also frequently referred to as low-pressure membranes. The pore structure of MF and UF membranes can be observed using an electron microscope. In contrast, the rejection layer of a typical RO membrane does not appear to have any visible pores under an electron microscope. RO membranes are believed to be nonporous. In some literature, subnanometer pore size is reported for RO membranes based on their rejection properties of dissolved ions and small organic molecules. Finally, an NF membrane is an intermediate between a tight UF membrane and a loose RO membrane. Both RO and NF membranes require relatively high operating pressure, and they are also referred to as high-pressure membranes. The performance parameters and pore size range for MF, UF, NF, and RO membranes are summarized in Table 1:

•

MF membranes typically have pore sizes ranging from 0.05 to 10 mm. Corresponding to their relatively large pore sizes, MF membranes have high permeability (4500 l1 m2 h1 bar1) and can be operated in a low-pressure range (typically from 0.1 to 2.0 bar). MF membranes are used to retain particulates whose size is greater than membrane pore size. They can be fabricated from both polymeric and

Symmetric MF membrane

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inorganic materials with either symmetric or asymmetric structures (Figure 3). For symmetric MF membranes, the pore diameters do not vary over the entire cross section of the membrane, and the thickness of the membrane determines its flux. Depending on the manufacturing method used, MF can also be asymmetrically structured, but the pores of the active layer are not much smaller than those of the supporting substructure. UF membranes have pore sizes ranging from 1 to 100 nm. This pore size range allows them to be used for removing bacteria, viruses, colloids, and macromolecules from a feedwater. The selectivity of a UF membrane is commonly represented by its molecular weight cutoff (MWCO), which is defined as the molecular weight of the solute that achieves a 90% rejection by the membrane. The MWCO of typical UF membranes is in the range of 1–300 kDa. A larger MWCO indicates that the membrane has a lower rejection ability and that it has a larger pore size. The MWCO of a membrane can be used to determine the pore size of the membrane by relating the molecular radius (a) of the solute molecules to its molecular weight (Mw):

a ¼ 0:33M0:46 w

ð1Þ

(valid for dextran, a in A˚ and Mw in Da, from Aimar et al. (1990)). UF membranes typically have an asymmetric structure (Figure 3) to maximize its membrane permeability. A very thin (0.1–1 mm) active or selective skin layer with fine pores is supported by a highly porous 100–200-mm-thick substructure. The pore diameters may increase from one side of the membrane (the skin layer) to the other (supporting sublayer) by a factor of 10–1000 (Strathmann, 1990). Its separation characteristics and mass flux are determined mainly by the feature of the skin layer (pore size, pore-size distribution and thickness, etc.), while the porous sublayer serves only as a mechanical support. Because of such an asymmetric structure, UF membranes gain excellent separation performance and considerable strength from the fine pore size of the thin active layer, and encounter little mass transfer resistance from the open supporting substructure. Typical permeability ranges from 20 to 500 l m2 h1 bar1. The normal operating pressure ranges from 1.0 to 5.0 bar.

•

RO membranes are able to remove small organic molecules and dissolved ions, including monovalent ions such as Naþ and Cl. These membranes have subnanometer pores and their separation properties are generally reported in terms of water permeability and sodium chloride rejection. They

Integral asymmetric UF membrane

Figure 3 Structures of symmetric and asymmetric membrane. MF, microfiltration; UF, ultrafiltration.

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•

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

can be further subdivided into SWRO membranes and brackish water RO (BWRO) membranes. SWRO membranes are used for seawater desalination, and they generally have high sodium chloride rejection (499%). Such high rejection is typically achieved using a highly crosslinked dense rejection layer which tends to have a low water permeability (o1 l m2 h1 bar1). High pressure (460 bar) is required for SWRO operation to overcome the osmotic pressure of seawater as well as the large hydraulic resistance of the membrane. Compared to SWRO membranes, BWRO membranes have lower sodium chloride rejection (495%) but higher water permeability (1–10 l m2 h1 bar1). Relatively lower operating pressure (10– 20 bar) is required. Some BWRO membranes are marketed as low-energy or high-flux RO membranes. NF membranes are similar to RO membranes in that they can retain dissolved ions as well as some small organic molecules. The difference between NF and RO is that NF membranes typically have low rejection to monovalent ions such as Naþ (10–90%). The rejection of dissolved ions depends strongly on their valence, and divalent and multivalent ions tend to be better rejected. Due to their ability to effectively remove calcium and magnesium ions, NF membranes can be used for water softening. Many NF membrane manufacturers also provide rejection data on solutes other than sodium chloride, such as magnesium chloride, magnesium sulfate, or glucose. NF membranes can be operated at significantly lower pressure levels (o10 bar) compared to those for RO membranes due to their higher water permeabilities (5–50 l m2 h1 bar1).

There are two types of structure for RO and NF membranes. One is an integral asymmetric structure formed by the phaseinversion method, and the other is a TFC structure formed by interfacial polymerization method (see Section 4.11.3.3). An integral asymmetric RO/NF membrane is made of one polymer material and has a thin, permselective skin layer with a thickness of 0.1–1 mm supported by a more porous sublayer. The membrane’s flux and selectivity are determined by the dense skin layer, while the porous sublayer has little impact on the membrane separation properties. In contrast, a TFC RO/ NF membrane is made of two or more polymer materials. The most important TFC composite membranes are made from crosslinked aromatic polyamide by the interfacial polymerization method, on a microporous polymer such as polysulfone (PS) support layer, followed by a reinforcing fabric (see Section 4.11.2.2). In addition to the membrane separation properties, other important membrane properties include the following. Chemical, mechanical, and thermal stability. A good membrane shall be mechanically stable. For many applications, pH, temperature, and chlorine tolerance are important considerations. For example, RO membranes synthesized from CA are susceptible to hydrolysis and biodegradation. These membranes can only be used within a narrow pH and temperature range, which is one of the major causes for phasing out of this type of RO membrane. Modern RO membranes are typically based on polyamide chemistry with a TFC structure. Unfortunately, TFC RO membranes have low chlorine resistance (Kwon et al., 2006, 2008). Where chlorine disinfection is

needed for biofouling control, the free chlorine level needs to be carefully controlled to prevent unacceptable membrane damage. Hydrophilicity. A hydrophilic membrane is water like, that is, water has a strong affinity to its surface and it has a tendency to wet the surface. In contrast, a hydrophobic membrane does not interact favorably with water. The relative hydrophilicity/hydrophobicity can be determined by contactangle measurements (Section 4.11.4). In general, a hydrophilic membrane surface is preferred as it tends to enhance water permeability and reduces membrane fouling propensity. A hydrophobic membrane is preferred for some special applications (such as MD and some membrane contactors) where transmission of liquid water through the membrane needs to be prevented. Surface charge. A membrane surface can gain surface charge due to either its charged functional groups or preferential adsorption of some specific ionic species. For example, most TFC polyamide RO membranes are negatively charged at neutral pH due to the presence of carboxylic groups (–COO) (Tang et al., 2007a). Membrane surface charge plays an important role in fouling. A positively charged particle may have a strong tendency to deposit on a negatively charged membrane surface due to electrostatic attraction (Jones and O’Melia, 2000). In addition, rejection of ionic species as well as membrane permeability can be affected by surface charge for RO, NF, and tight UF membranes as a result of electrostatic interaction (Donnan exclusion effect; see Schafer et al. (2005)). For example, Childress and Elimelech (2000) reported that both salt passage and water flux were maximum at the pore isoelectric point (BpH 5) for an NF membrane. Membrane surface charge has also been used to correlate the transport of some trace organic solutes (Kimura et al., 2003). Surface roughness. Surface roughness has been reported as an important parameter for RO and NF membrane fouling. A rougher membrane surface tends to promote fouling likely due to reduced shear force over the membrane surface and increased membrane nonhomogeneity (e.g., nonuniform flux distribution over a membrane surface) (Vrijenhoek et al., 2001; Tang and Leckie, 2007).

4.11.2.2 Membrane Properties for RO and NF Membranes The physiochemical properties, such as surface roughness, hydrophobicity, and rejection properties, of RO and NF membranes strongly depend on the chemistries forming these membranes (Petersen 1993; Tang et al., 2007a, 2009a, 2009b). There are mainly two types of RO membranes in the market: (1) asymmetrical CA-RO membranes formed by phase inversion and (2) TFC polyamide RO membranes formed by an interfacial polymerization process (Petersen, 1993). CA membranes, though they have hydrophilic and smooth membrane surfaces, have low resistance to hydrolysis and biodegradation. In addition, their separation properties (permeability and rejection) are inferior to modern TFC-PA membranes. A comparison between typical CA and polyamide RO membranes is presented in Table 3. Both CA and polyamide can be used to form NF membranes. In addition, other polymers (e.g., polyvinyl alcohol (PVA) and sulfonated PS) and inorganic materials (e.g., some metal oxides) can also be

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Table 3

309

Comparison between cellulose acetate (CA) and thin-film composite (TFC) reverse osmosis (RO) membranes

Parameter

CA RO membrane

TFC polyamide RO membrane

Permeability

Low

High (B5 l m2 h1 bar1 for brackish water RO)

NaCl rejection (%)

85–98

95–99.9

Surface hydrophilicity

Very hydrophilic

Less hydrophilic

Surface roughness

Smooth

Rough surface with valley-and-ridge structures

Maximum temperature (1C)

30

45

Stable pH range

4.5–6.5

3–10 (2–11 for some membranes, e.g., with a polyvinyl alcohol surface coating)

Resistance to hydrolysis

Good

Chlorine resistance

Low. Unstable at pHo4.5 or pH46.5, and accelerated hydrolysis at high temperature Stable at low levels (o1 ppm)

Resistance to biodegradation

Low

Relatively good

Polyamide ~ 0.05 – 0.3 μm

Polysulfone ~ 20 – 50 μm

Backing layer ~ 200 μm Figure 4 Schematics of a thin-film composite polyamide membrane. The composite PA membrane typically comprises three distinct layers – a thin, dense selective layer (the polyamide layer) of 50–300 nm in thickness, a polysulfone support layer of 20–50 mm in thickness, and a nonwoven fabric backing layer.

used for NF synthesis. A thorough review of RO and NF chemistry is available elsewhere (see Petersen (1993)) for RO and (Schafer et al., 2005) for NF membranes. The current section focuses on the structure and properties of TFC polyamide RO and NF membranes (Petersen, 1993). A typical TFC polyamide membrane comprises a dense polyamide rejection layer of B100 nm in thickness on top of a microporous PS or polyethersulfone (PES) support (Figure 4). The PS/PES layer, typically casted on a nonwoven fabric layer of 100–200 mm in thickness, provides a mechanical support to the rejection layer. Among the three layers, the polyamide rejection layer is the most critical one, as most of the membrane properties (e.g., permeability, rejection, surface charge, roughness, and surface hydrophilicity) are determined by this ultrathin dense rejection layer (Tang et al., 2007a; Petersen, 1993). In addition, the mechanical properties of the different layers are important, as RO and NF membranes need to withstand high pressures. The PS or the polyamide layer may deform mechanically and become more compact under high pressure. This reduces the membrane permeability with time, a phenomenon known as membrane compaction. Most commercial TFC-RO membranes for water treatment are formed by interfacial polymerization of aromatic amine

Low tolerance to free chlorine (o0.1 ppm)

monomers (such as m-phenylenediamine (MPD) in an aqueous solution) and aromatic acid chloride monomers (such as trimesoyl chloride (TMC) in an organic solvent). Since the discovery of this reaction scheme by Cadotte and the development of the first commercial fully aromatic TFC RO membrane FT30 by FilmTec&, the reaction scheme and its variations have been widely used to prepare most commercial TFC-RO membranes (Petersen, 1993). In general, fully aromatic TFC RO membranes formed in this way have a high degree of crosslinking (i.e., the fraction of crosslinked repeating units; see Figure 5) (Tang et al., 2007a). Increased crosslinking tends to increase salt rejection but decrease water permeability. Commercial BWRO membranes (e.g., XLE from Dow FilmTec and ESPA3 from Hydranautics) have a water permeability in the range of 4–8 l m2 h1 bar1 and sodium chloride rejection 495% (Table 4). Seawater RO membranes have lower water permeability but much higher salt rejection (499%). It is worth noting that rejection of a solute (R) is not an inherent property of an RO membrane, as it also depends on the operating conditions such as applied pressure. A more fundamental property of a nonporous RO membrane is the solute permeability coefficient (B). The NaCl permeability coefficient is also tabulated in Table 4 for some commercial RO membranes. The solute permeability coefficient can be determined from rejection test results via

B ¼ AðDP  DpÞðR 1  1Þ

ð2Þ

Typical fully aromatic polyamide RO membranes formed by TMC and MPD have rough membrane surfaces, with a rootmean-square (RMS) roughness (ridge-and-valley) on the order of 100 nm (Table 4). The surface is negatively charged at neutral pH due to the deprotonation of carboxylic (–COOH) functional group (Tang et al., 2007a; Childress and Elimelech, 1996). The typical contact angle for an unmodified membrane is 40–501. Some posttreatment steps, such as the application of a coating layer or additives, might be involved to protect the

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O

O

C

C

C

H N

H N

O

O

C

C

O

H N

NH

COOH

NH

n

1–n

(a)

O

O

C

C

C

N

N

O

O

C

C

O

N

N

COOH n

(b)

1–n

Figure 5 Typical chemistry for interfacially formed TFC-RO and NF membranes. (a) Fully aromatic polyamide based on trimesoyl chloride and m-phenylenediamine. (b) Semi-aromatic polyamide based on trimesoyl chloride and piperazine. Modified from Tang CY, Kwon YN, and Leckie (2009b) Effect of membrane chemistry and coating layer on physiochemical properties of thin film composite polyamide RO and NF membranes. I. FTIR and XPS characterization of polyamide and coating layer chemistry. Desalination 242: 149–167; and Petersen RJ (1993) Composite reverse-osmosis and nanofiltration membranes. Journal of Membrane Science 83: 81–150.

Table 4

Physiochemical properties of thin-film composite (TFC) polyamide reverse osmosis (RO) and nanofiltration (NF) membranes

Chemistry

Type

Membrane

A (l m2 h1 bar1)a

MPD þ TMC, no coating

SWRO BWRO BWRO BWRO

SWC4 XLE LE ESPA3

0.80 6.04 4.29 7.52

MPD þ TMC, PVA coating

SWRO BWRO BWRO BWRO

SW30HR LFC1 LFC3 BW30

0.85b 3.96 2.81 3.96

MPD þ TMC, no coating

NF NF

NE90 NF90

9.04 11.2

PIP þ TMC, no coating

NF NF

HL NF270

12.8 14.5

B for NaCl (l m2 h1)

R for NaCl (%)a

RMS roughness (nm)

Contact angle (1 )

Zeta potential (mV) at pH 9

0.11 3.02 2.60 5.58

99.0 96.5 95.8 94.9

135.6 142.8 95.7 181.9

48.8 46.4 47.2 43.1

–20.9 –27.8 –26.1 –24.8

0.11b 1.52 0.59 1.17

99.6b 97.3 98.5 97.9

54.4 135.8 108.4 68.3

30.9 20.1 22.8 25.9

–1.7 –13.2 –6.5 –10.1

91.5 94.4

72.4 129.5

46.3 44.7

–21.0 –37.0

21.3 56.9

7.2 9.0

27.5 32.6

–26.0 –41.3

11.6 9.17 653 152

a

The permeability of all membranes except BW30 was evaluated using ultrapure water at an applied pressure of 1380 kPa (200 psi). Rejection of all membranes except BW30 was determined using a 10 mM NaCl solution at pH 7. b The permeability and rejection for BW30 was calculated from the membrane manufacturer’s specification. The testing pressure for BW30 was 55 bar for a feedwater containing 32 000 mg l1 NaCl. MPD, m-phenylenediamine; PIP, piperazine; RMS, root-mean-square; TMC, trimesoyl chloride. Adapted from Tang CY, Kwon YN, and Leckie JO (2009a) Effect of membrane chemistry and coating layer on physiochemical properties of thin film composite polyamide RO and NF membranes II. Membrane physiochemical properties and their dependence on polyamide and coating layers. Desalination 242: 168–182.

membrane surface and/or improve membrane properties. Membrane surface properties, such as hydrophilicity and surface charge density, are largely determined by the top-most layer. Consequently, the application of a surface coating can greatly affect these properties. For example, PVA, a neutral and hydrophilic polymer, has been widely used for coating RO membranes to achieve improved membrane properties. The

PVA-coated RO membranes are less rough and less charged (Table 4). They are significantly more hydrophilic, with a contact angle of only 20–301 (Table 4 and Figure 6), and thus they are expected to be less prone to membrane fouling. Many commercial membranes marketed as low-fouling composite RO membranes are PVA coated. Some examples are the low fouling composite (LFC) series from Hydronautics and

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

BW30 from Dow FilmTec (Tang et al., 2009a). Commerical PVA-coated membranes tend to have better rejection than uncoated ones, but their permeability is lower probably due to the additional hydraulic resistance from the coating layer (Tang et al., 2009a). Other commonly used coating materials and additives reported in the literature include poly(ethylene oxide-b-amide) (Pebaxs), chitosan, and sodium alginate (Schafer et al., 2005; Louie et al., 2006). Similar to TFC RO membranes, TFC NF membranes can be formed by the fully aromatic polyamide chemistry (such as MPD reacting with TMC). Some commercial examples include NF90 from Dow FilmTec and NE90 from Saehan Industries. With a less crosslinked polyamide compared to typical RO membranes, these TFC-NF membranes have lower sodium chloride rejection (B90–95%) and higher water permeability of about 10 l m2 h1 bar1. Similar to fully aromatic RO membranes, MPD þ TMC-based NF membranes have negatively charged membrane surfaces with ridge-and-valley type of roughness. Their contact angle is also similar to MPD þ TMC-based RO membranes. Some other TFC-NF membranes are semi-aromatic where an aliphatic amine monomer is used with the aromatic TMC (Petersen, 1993). The most widely used amine monomer for semi-aromatic TFC-NF membranes is piperazine (PIP) (Petersen, 1993; Schafer et al., 2005). Some commercial examples of PIP þ TMC-based NF membranes include NF270 from Dow FilmTec and HL from GE Osmonics. Poly (piperazinamide) NF membranes have higher fluxes, lower

311

rejections, and smoother and more hydrophilic membrane surfaces than fully aromatic NF membranes. In Table 4, it is apparent that there is a strong trade-off between water permeability and salt rejection – a more water permeable membrane tends to have lower rejection and higher solute permeability.

4.11.2.3 Membrane Properties for MF and UF Membranes Porous MF and UF membranes can be formed by a wide range of structures, materials, and formation methods (refer to Section 4.11.3). Therefore, the properties of MF and UF membranes can vary significantly. By far, the most important consideration for porous membrane selection is the pore structure (e.g., pore size, pore-size distribution, porosity, tortuosity, and thickness of the active separation layer), as their flux and retention properties are mainly determined by these properties. In addition, membrane flux and rejection can be marginally affected by some material properties, including hydrophilicity. In general, hydrophilic membranes are preferred due to their lower fouling tendency and higher water permeability. There are many different pore structures for porous membranes (Figure 7). A track-etched MF membrane typically has straight cylindrical pores. These pores are perpendicular or slightly oblique to the membrane surface. The membrane permeability for cylindrical-pore membranes can be

BW30 membrane

EPSA3 membrane

Figure 6 Contact-angle measurement for membranes BW30 and ESPA3. The PVA-coated membrane BW30 is much more hydrophilic than the uncoated membrane ESPA3. Unpublished photos.

Cylindrical pores

Stacked spheres

Spongy structure

Cylindrical pores perpendicular or oblique to membrane surface (e.g., track-etched MF) Pores typically not interconnected

Interconnected pores form by the space between nearly spherical particles (inorganic membranes, foulant layer)

Interconnected pores. Most phaseinversion polymeric membranes have this type of pore structure

Transport equation:

Transport equation:

Hagen–Poiseuille Equation

Kozeny–Carman equation

Transport equation: Hagen–Poiseuille equation, or Kozeny–Carman equation

Figure 7 Types of membrane pore structures. Modified from Mulder M (1996) Basic Principles of Membrane Technology, 2nd edn. Dordrecht: Kluwer.

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determined by the Hagen–Poiseuille equation:

Lp ¼

er2p 8Ztlm

ð3Þ

where Lp is the water permeability of porous membranes, e the membrane porosity, rp the pore radius, Z the viscosity of water, t the tortuosity of the membrane, and lm the thickness of the rejection layer. Another type of well-defined pore structure closely resembles stacked spheres (Figure 7), a structure typical for inorganic porous membranes prepared from spherical particles. Interconnected pores are formed by the space between the particles. The same pore structure can also be used to describe foulant cake layers formed by the deposition of colloids or suspended particles. Stacked-spheres pore structure can be modeled by the Kozeny–Carman equation:

Lp ¼

e3 Kð1  eÞ 2 S 2 Zlm

ð4Þ

where K is the Kozeny–Carman coefficient and S the specific surface area of the particles. For perfect spherical particles, S ¼ 3/rp, K ¼ 5, and e ¼ 0.4. Many other porous membranes have much more complicated pore structures. For example, polymeric membranes prepared via phase inversion may have a spongy structure. The pores tend to be highly interconnected with a high tortuosity and a wide size distribution. A lognormal pore-size distribution is sometimes assumed for modeling purpose (Aimar et al., 1990). The Hagen–Poiseuille and/or Kozeny–Carman equation are widely used to approximate the transport properties of this type of membrane. The pore structural parameters of porous membranes prepared by different methods are summarized in Table 5.

4.11.3 Membrane Materials and Preparation Polymers are the most popular materials used for membrane fabrication. Being membrane materials, the polymers should demonstrate thermal and chemical stabilities, good mechanical strength, and ability to form flat sheet or hollow fiber membranes easily. The two major techniques for membrane preparation include the phase-inversion process and interfacial polymerization, which are widely used for commercial membrane productions.

Table 5

Most of materials such as polymer, ceramic, metal, carbon, and glass can be used to make membranes. Among these, polymeric materials are the most popular ones used for membrane fabrication. Being membrane materials, the polymers should demonstrate thermal stability over a wide range of temperatures and chemical stability over a range of pH, and possess good mechanical strength. In addition, they can also be processed into flat sheet or hollow fiber membranes easily. Some commercial and representative polymeric membrane materials are introduced in Table 6 (Ren and Wang, 2010). Commercial UF/MF membranes can be made by various materials ranging from fully hydrophilic polymers, such as CA, to fully hydrophobic polymers, such as polypropylene (PP). PS, PES, polyacrylonitrile (PAN), and polyvinylidene fluoride (PVDF) are between the two extremes. A hydrophilic surface tends to resist attachment due to absorption by organics, but it has the disadvantage of being less robust compared with hydrophobic membranes. In order to reduce membrane fouling tendency, the hydrophobic polymers are modified through various approaches such as the use of additives as pore formers, or blending with a hydrophilic polymer, or posttreatment. Figure 8 provides the relative hydrophilicity of commonly used polymeric materials. The pros and cons of different membranes made by different polymers are summarized in Table 7 (Pearce, 2007).

4.11.3.2 Hollow Fiber Preparation 4.11.3.2.1 Mechanism of membrane formation Hollow fiber membranes can be made either by a phase-inversion process or by a melt spinning plus stretching process (Mulder, 1996; Strathmann, 1990). Normally, UF membranes are produced by the phase inversion, which makes the membrane a precisely controlled asymmetric structure by varying the pore size over a wide range, while MF membranes can be fabricated by either process. Phase inversion refers to the process by which a polymer solution (in which the solvent system is the continuous phase) inverts into a swollen three-dimensional macromolecular network or gel (where the polymer is the continuous phase) (Kesting, 1985). The essence of phase inversion is the appearance in a polymer solution of two interdispersed liquid phases (a polymer-rich phase and a solvent-rich phase) due to the change of the state of the polymer solution caused by the

Pore structure of different types of porous membranes

Pore structure

Pore size (mm)

Pore-size distribution

Porosity

Tortuosity

Symmetry

Fabrication method

Microfiltration Stacked spheres Stretch pores Cylindrical pores Spongy structure

0.1–20 0.1–3 0.05–5 0.1–10

Narrow Wide Nearly uniform Wide, log-normal

0.1–0.2 High, up to 0.9 Low, up to B0.1 0.3–0.7

Low Straight pores Straight pores Tortuous pores

Symmetrical Symmetrical Symmetrical Symmetrical/asymmetrical

Sintering Stretching Etching Phase inversion

Ultrafiltration Spongy structure

0.001–0.1

Wide, log-normal

0.01–0.2

Tortuous pores

Asymmetrical

Phase inversion

Adapted from Mulder M (1996) Basic Principles of Membrane Technology, 2nd edn. Dordrecht: Kluwer.

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313

Commercial and representative polymeric membrane materials

Polymer Cellulose and cellulose acetate (CA)

Structure

Properties

 Cellulose is highly hydrophilic and OH

OH

O

HO HO

OH

HO O

O HO

O

OH

HO O

OH O

OH

OH

OH

O

CH

CH

O

CH

CH

CH

O n

CH CH

OCCH3

CH2

OCCH3

CH2

O

CH3CO O

O

membrane preparation.

 Cellulose acetate, diacetate, triacetate and

O

CH

n

CH CH

OH OCCH3

 Cellulose is mainly used for dialysis their blends are widely used to make MF, UF, and RO membranes.  Cellulose and cellulose acetate membranes are susceptible to hydrolysis and microbial attack.  They are only stable over limited pH range between 4 and 6.5.

n

Cellulose O

crystalline.

OH

O

OCCH3 O

Cellulose triacetate

Cellulose acetate

 PS is an amorphous polymer.  It belongs to the group of high-

Polysulfone (PS) O

O

SO2

performance polymers with excellent chemical and thermal stability.  PS is mainly used to form UF, MF, and gas separation membranes.  PS is also used to form the porous support layer of many RO, NF, and some gas separation membranes.

Udel polysulfone

 PES membranes have very high chemical

Polyethersulfone (PES)

and thermal stability.

 Like PS material, PES membranes are affected by aromatic hydrocarbons or ketones.  PES membranes are slightly less  hydrophobic than PS membranes.  PES membranes are mainly used in UF, MF, and dialysis.

Radel A polyethersulfone

Radel H polyethersulfone Polyacrylonitrile (PAN)

CH2

CH

 PAN possesses superior resistance to

n

C

hydrolysis and oxidation.

 It is mainly used to prepare UF membranes

N

and porous supports of composite membranes. Polyetherimide (PEI)

 PEI is an amorphous thermoplastic with

O O N

H3C

O

CH3

N

O

O O

n

characteristics similar to the related plastic polyether ether ketones (PEEKs).  PEI cannot be used in contact with chloroform and dichloromethane.  It is a good material for the fabrication of the integrally asymmetric membranes for gas separation and pervaporation.  PEI is also used to fabricate the support of composite flat sheet membranes. (Continued )

314 Table 6

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Continued

Polymer Polyamide (PA)

Structure

Properties

H N

CH2

O

H

C

N

x-1

CH2

O

O

N

C

CH2

x

n

O

 A PA is a polymer containing monomers of

C

 The basic aliphatic polyamides are referred

y-2

amides. n

as nylons which possess good thermal stability and mechanical strength, and are resistant to many organic solvents.  PA is used as the thin dense layer for RO and NF, but it has lower chlorine tolerance  The porous polyamide membranes have been commercialized for many years.

Aliphatic polyamides (nylon x or nylone x,y)

Polyimide (PI)

O

O

O

C

C

C

N

 Polyimides exhibit excellent thermal and

CH3

C

C

O

O

chemical stability because of their high glass transition temperature.  P84 is an amorphous commercial copolyimide with excellent resistance to many organic solvents.  Matrimide 5218 is another commercial polyimide.  Both polymers can be used as a material for making gas separation and NF membranes.

H N

C H 80%

n

20%

Copolymide P 84 O

O

O

C

C

C

CH3 N

N C

C

O

O

CH3

CH3

n

Matrimid 5218

Polyether ether ketones (PEEKs)

PEEK: O

O

C

O n

Polycarbonate (PC)

CH3 C CH3

Polyvinylidene fluoride (PVDF)

 PEEK has exceptional heat and chemical

SPEEK: O

stability.

C n

SO3H

 The high insolubility in common solvents makes PEEK membranes successfully being used in chemical processes as a solvent-resistant membrane.  The sulfonated PEEK (SPEEK) is soluble in common solvents, which can be used for the preparation of hydrophilic membranes or ion-exchange membranes.

 PC is a transparent thermoplastic with

O

high-performance properties.

O C O n

 It is mainly used for track-etched membranes with well-defined pore structures and very good mechanical strength.  PC can also be used to make UF and MF membranes by the phase-inversion process.

H

F

 PVDF is semi-crystalline with a very low

C

C

 It is the most popular and available

H

F n

hydrophobic membrane material to be used for making MF by phase-inversion process.  It has excellent chemical resistance and thermal stability.  It is resistant to most inorganic and organic acids and tolerant to a wide pH range.

glass transition temperature.

(Continued)

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315

Continued

Polymer

Structure

Polypropylene (PP)

Properties

CH2

CH

 PP membrane is normally hydrophobic

n

with high chemical stability.

CH3

 Hydrophobic PP membrane is ideal for the filtration of aggressive solvents.

 PP membranes are much cheaper than PTFE membranes. Polytetrafluoroethylene (PTFE)

F

F

C

C

F

F n

 PTFE is highly crystalline and demonstrates a very high resistance to chemical attack.  It cannot be formed by phase-inversion techniques.  PTFE membranes are formed by melt extrusion followed by stretch cracking.  The membranes are hydrophobic and need pre-wetting with a nonpolar solvent before use.

Adapted from Mulder M (1996) Basic Principles of Membrane Technology, 2nd edn. Dordrecht: Kluwer, and from Ren JZ and Wang R (2010) Preparation of polymeric membranes. In: Wang LK, Chen JP, Hung YT, and Shammas NK (eds.) Handbook of Environmental Engineering, vol. 13, ch. 2. Totowa: Humana Press.

Hydrophilic

CA

Hydrophobic

PES

PAN

PS/PVDF

PP

PTFE

Table 7

Pros and cons of different membranes

Polymer

Properties

CA

Good permeability and rejection characteristics Susceptible to hydrolysis Limited pH resistance Chlorine tolerant and fouling resistant

PES, PVDF, PS, PAN

Ability to modify properties through polymer blend Good strength and permeability PVDF best for flexibility and use with air scour PES best for polymer blending and UF rating

PP

Susceptible to oxidation Limited blend capability

CA is naturally hydrophilic PS, PES, PAN, and PVDF are naturally quite hydrophobic, but can be blended with additives and pore formers to make a moderately hydrophilic membrane PP and PTFE are hydrophobic, and are difficult to modify

Figure 8 Relative hydrophilicity of commonly used polymeric materials. CA, cellulose acetate; PAN, polyacrylonitrile; PES, polyethersulfone; PS, polysulfone; PVDF, polyvinylidene fluoride; PP, polypropylene; PTFE, polytetrafluoroethylene. Modified from Pearce G (2007) Introduction to membranes: Membrane selection. Filtration and Separation 44: 35–37.

alteration of its surrounding environment or operating conditions, followed by crystallization, gelation, or vitrification. In other words, a liquid polymer solution is precipitated into two phases: (1) a polymer-rich phase that will form the matrix of the membrane; (2) a polymer-poor phase that will form the membrane pores in an unstable nascent membrane structure. The porous asymmetric membrane morphology is then fixed according to the subsequent solidification process. There are different approaches to make the polymer solution precipitate, such as cooling, immersion in a nonsolvent coagulant bath, evaporation, and vapor adsorption. Depending on the change of the operating parameters that induce the phase inversion, two different separation mechanisms are involved:

•

Thermally induced phase separation (TIPS). The precipitation is achieved by decreasing the temperature of the polymer

CA, cellulose acetate; PAN, polyacrylontrile; PES, polyethersulfone; PP, polypropylene; PS, polysulfone; PVDF, polyvinylidene fluoride. Adapted from Pearce G (2007) Introduction to membranes: Membrane selection. Filtration and Separation 44: 35–37.

•

solution. This process can be used for PVDF membrane preparation. Diffusion-induced phase separation (DIPS). Diffusional mass exchange, because of the contact of the polymer solution with a nonsolvent, leads to a change in the local composition of the polymer film and then precipitation is induced. CA, PS, PES, PVDF, and PAN membranes are made by this method.

TIPS. It is one of the main approaches for the preparation of microporous membranes. In the TIPS process, a polymer is dissolved into a solvent or a mixture of solvent and nonsolvent, and a homogeneous solution is formed only at elevated temperature. By cooling down the homogeneous

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liquid phase. At this moment, the polymer-rich phase starts to solidify and forms the solid membrane structure, and the polymer-lean phase forms the pores. An interpenetrating three-dimensional network can be obtained. DIPS. The invention of the first integral asymmetric membranes by DIPS was a major breakthrough in the history of RO and UF membrane development (Loeb and Sourirajan, 1964; Kesting, 1985). DIPS can be realized through immersing the casting solution in a nonsolvent coagulant bath, evaporating the solution and using vapor adsorption. Figure 10 illustrates the concepts of three DIPS processes. For an immersion precipitation process, at least three components of polymer, solvent, and nonsolvent are involved. The membrane formation can also be illustrated with a ternary phase diagram as shown in Figure 11. In the diagram, four different regions are shown: one solution phase (region I), liquid–liquid two phases (region II), liquid–solid two phases (region III), and one solid phase (region IV).

solution, the phase separation is induced. Once the polymerrich phase is solidified, the porous membrane structure can be created by removing the solvent via extraction. Membrane formation by TIPS can be illustrated by the phase diagram of a polymer solution as a function of temperature from the basic point of thermodynamics, as shown in Figure 9. From the phase diagram, it can be seen that there exists three different phase areas of homogeneous solution phase I, the liquid–liquid demixing (metastable areas II, III, and unstable area IV) and one crystalline phase V, which are separated by a binodal curve, a spinodal curve, and a crystallization curve. If a homogeneous polymer–solvent mixture at a temperature TA, as indicated by point A in Figure 9, is cooled to the point M, the solution separates spontaneously into two phases after it crosses the spinodal curve. Upon further cooling to the temperature Tgel, as indicated by point B, the composition of the polymer-rich phase reaches point B00 , and the point B’ represents the solvent-rich, the polymer-lean

Unstable Liquid phase I

TA

Metastable

A

Critical point

Temperature

Binodal curve Spinodal curve IV

III

II

M

ve

V

B″

B

B′

Tgel

cur

iz

tall

s Cry

n atio

Liquid–solid demixing

0 A solvent-rich phase

Polymer composition A polymer-rich phase

1

An interpenetrating three-dimensional network Figure 9 Schematic phase diagram of thermally induced phase separation. Modified from van de Witte P, Dijkstra PJ, van de Berg JWA, and Feijen J (1996) Phase separation process in polymer solutions in relation to membrane formation. Journal of Membrane Science 117: 1–31.

Immersion precipitation (coagulation bath) NS

Vapor adsorprtion

Solvent evaporation

NS

S S Casting solution (polymer + solvent + nonsolvent + additives) Support Figure 10 Representation of three DIPS processes (S: solvent; NS: nonsolvent). Modified from Ren JZ and Wang R (2010) Preparation of polymeric membranes. In: Wang LK, Chen JP, Hung YT, and Shammas NK (eds.) Handbook of Environmental Engineering, vol. 13, ch. 2. Totowa, NJ: Humana Press.

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P (polymer)

IV

1

Vitrification S0

2-1: Path 2

S2 1 S1 K >1 2-1

2-3

III 2-3:

K <1 2-2

A

2-2:

S1 K <1

II

I

S1

S3 S (solvent)

N (nonsolvent) Critical point Binodal

Spinodal

Gelation boundary

Figure 11 Schematic diagram of membrane formation process for DIPS. Modified from Ren JZ and Wang R (2010) Preparation of polymeric membranes. In: Wang LK, Chen JP, Hung YT, and Shammas NK (eds.) Handbook of Environmental Engineering, vol. 13, ch. 2. Totowa, NJ: Humana Press.

Bore fluid Dope inlet

Nitrogen

Spinneret

Dope fluid

Water spray

Bore fluid

Syringe pump

Coagulation bath

Flushing bath

Figure 12 Schematic diagram of a hollow fiber spinning line.

Point A represents the initial composition of a casting solution. If the polymer solution is immersed into a nonsolvent bath, there are two possible composition paths, 1 and 2 (2-1, 22, 2-3). For path 1, the polymer solution undergoes a glass transition and goes into the solid phase IV directly. Consequently, the solution becomes a homogeneous glassy film. For the path 2, gelation dominates the formation of the porous membrane morphologies. When the composition path crosses the binodal curve and reaches point S1, liquid–liquid phase separation (S1 - S2 þ S3) occurs. The polymer-rich phase is represented by point S2 and polymer lean phase is represented by point S3. Depending on the location of point S1, three different nascent membrane morphologies are formed by (a) the nucleation and growth of the polymer-poor phase (path 2-

1), which leads to a morphology with dispersed pores; (b) the spinodal decomposition (path 2-2), which leads to a bi-continuous network of the polymer-poor and polymer-rich phases without any nucleation and growth due to instantaneous demixing; and (c) the nucleation and growth of the polymerrich phase (path 2-3), which leads to low-integrity powdery agglomerates. Such membrane morphology is not practical and thus it rarely happens in membrane formation.

4.11.3.2.2 Fabrication of hollow fiber membranes Polymeric hollow membranes were first introduced in 1966 (Mahon, 1966). A schematic diagram of a hollow fiber spinning line is shown in Figure 12. Hollow fiber membranes can

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residual solvent for further characterization (Strathmann, 1990; Shi et al., 2008). Figure 13 shows the morphology of poly(vinylidene fluoride-co-hexafluropropylene) (PVDF-HFP) asymmetric microporous hollow fiber membranes made by the DIPS process (Shi et al., 2008). Being an extremely complex process, the fabrication of hollow fiber membranes requires highly sophisticated mechanical, thermodynamic, and kinetic considerations. The spinning parameters involved are summarized in Figure 14. Mckelvey et al. (1997) have provided detailed guidance on how to control macroscopic properties of hollow fiber

be fabricated by either wet or dry-jet wet spinning process, where an air gap between the spinneret and the coagulation bath can be zero or a certain value. Basically, the polymer dope container is connected to a N2 gas cylinder or a pump. The dope is dispensed under pressure or pumped through a spinneret at a controlled rate, and goes through an air gap before immersing into a coagulation bath. Water is (typically) used as external coagulant, while a mixture of milli-Q Water and a solvent with varying ratios is used as the bore fluid. The nascent hollow fiber is taken up by a roller at a free falling or controlled velocity and stored in a water bath to remove

Figure 13 Cross-section morphology of the hollow fibers spun from the PVDF-HFP/NMP dopes without an additive. Reproduced from Shi L, Wang R, Cao YM, Liang DT, and Tay JH (2008) Effect of additives on the fabrication of poly(vinylidene fluoride-co-hexafluropropylene) (PVDF-HFP) asymmetric microporous hollow fiber membranes. Journal of Membrane Science 315: 195–204, with permission from Elsevier.

Formula Bore fluid

Dope

Approaching ratio Thermodynamic

Temperature Dope solution

Shear

Bore fluid (approaching coagulation ratio) Shear rate Spinneret

Shear stress

Shear flow

Velocity distribution

Die swell

Temperature, humidity Die swell

Elongation

Relaxation Air gap

Evaporation Elongation rate

Dynamic Elongational

Elongation stress flow Temperature Approaching coagulation ratio Coagulation

Stretching Solidification Posttreatment

Figure 14 Parameters involved in a dry-jet wet spinning process for hollow fiber membranes. Modified from Ren JZ and Wang R (2010) Preparation of polymeric membranes. In: Wang LK, Chen JP, Hung YT, and Shammas NK (eds.) Handbook of Environmental Engineering, vol. 13, ch. 2. Totowa, NJ: Humana Press.

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

membranes using dominant process parameters, including spinneret design, dope extrusion rate, air gap distance, bore fluid extrusion rate, solvent concentration in the bore fluid, vitrification kinetics, etc., and how to determine the optimal macroscopic properties. The effect of shear rate on the performance and morphology of hollow fiber membranes can also be found in the literature (Qin et al., 2001).

4.11.3.3 TFC Membrane Preparation In addition to the integral asymmetric membranes, which are produced by the phase-inversion process, composite membranes are also widely used in industry. For instance, many NF and RO membranes are formed with a composite structure (see also Section 4.11.2). A typical composite membrane is shown schematically in Figure 4. Normally, composite membranes are made in a two-step process: (1) fabricating a microporous support and (2) depositing/casting a barrier layer on the surface of the microporous support layer. This approach provides great flexibility for selecting different materials to tailor the membrane structure and properties. Today, the most important technique for manufacturing composite membranes is the interfacial polymerization of reactive monomers on the surface of a microporous support membrane. This technique was developed in the mid-1970s (Cadotte, 1977). A PS microporous membrane is soaked in an aqueous solution containing 0.5–1% polyethyleneimine, and then brought in to contact with a 0.2–1% solution of toluene diisocyanate in hexane. These two reagents react rapidly on the membrane surface, forming the selective layer of the composite membrane. A heat curing step leads to further crosslinking of the polyethyleneimine, which extends into the pores of the support membrane (Strathmann, 1990). More information on TFC membrane properties is given in Section 4.11.2.2.

4.11.3.4 Ceramic Membrane Preparation Similarly to polymeric membranes, ceramic membranes have been developed for many process applications, including MF and UF in the water industry. They are more thermally and chemically stable than polymer membranes with much greater mechanical strength and higher structural stability, which allows ceramic membranes to be used in harsh environments. Generally, the structure of ceramic membranes is asymmetric, consisting of a macroporous support, one or two mesoporous intermediate layers, and a microporous (or a dense) top layer. The support layer provides mechanical strength, the middle layers bridge the pore-size differences between the support and the top layers, and the thin top layer determines the separation. The preparation of a ceramic membrane support normally involves the following steps: (1) forming particle suspensions; (2) packing the particles in the suspensions into a membrane precursor with a certain shape using various shaping techniques such as slip casting, tape casting, extrusion, and pressing; and (3) sintering the membrane precursor at elevated temperature (Li, 2007). The separation layers of composite ceramic membranes are made of SiO2, Al2O3, ZrO2, and TiO2 materials, which can be formed on a membrane support via dip-coating, sol–gel, chemical

319

vapor deposition (CVD), or electrochemical vapor deposition (EVD), followed by repeated firing steps. A detailed description of ceramic membrane preparation methods can be found in Li (2007). The phase-inversion method which is commonly employed to spin polymeric hollow fiber membranes can also be used to prepare inorganic hollow fibers in combination with sintering step. The protocol of preparation is depicted in Figure 15. Basically, a desired spinning dope (a mixture of ceramic powder and polymer solution) is formed and passed through a spinneret to enter a water bath for precipitation. After posttreatment, the hollow fiber precursors are heated in a furnace to remove the organic polymer binder, and then calcined at a high temperature to allow the fusion and bonding to occur. Figure 16 shows the morphology of Zirconia hollow fibers membranes made by this method (Liu et al., 2006). Various factors such as the sintering temperature and the ratio of the selected ceramic powders to the polymer binders will affect the structure and performance of the resultant membranes.

4.11.4 Membrane Characterization Membrane characterization is critical to the understanding of the chemistry and structure of membranes and to the identification of causes of membrane failures. This section also briefly reviews a wide range of commonly used characterization techniques, including pore-structure characterization, microscopic methods, as well as spectroscopic methods for information on membrane chemistry. Membrane characterization is the basis for establishing the chemistry–structure–properties relationship that is a critical aspect in any new membrane development process. This can guide subsequent membrane optimization to achieve excellent separation efficiency and fouling resistance. Characterizing and understanding membrane chemistry and properties is also the key for selecting suitable membranes for a given application. A wide range of characterization methods have been applied to membrane characterization. This section briefly summarizes some of the most commonly used techniques (Table 8): Performance tests. This involves measurements of membrane permeability and rejection of various solutes using a filtration setup. The performance data (e.g., rejection of probe molecules such as dextran or polyethylene glycol) can be used to determine pore-size distribution of a membrane (Aimar et al., 1990; Schafer et al., 2005). Membrane porometry. Various porometry methods have been used to determine the pore-size distribution of MF and UF membranes. The bubble point method, which measures the pressure at which air bubbles start to pass through a wetted MF membrane, can be used to characterize pores 40.15 mm. Another method commonly used for MF membrane pore characterization is the mercury intrusion method, which is suitable for pores ranging from 3.5 to 1000 nm. Other methods include gas adsorption, permporometry, and thermoporometry, which are suitable for characterizing smaller pores. A comprehensive review of these methods can be found in Nakao (1994).

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Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

Mixing of inorganic powder, polymer binder, and solvent

Suspension

Spinneret Dope fluid

Wet spinning

Sintering Figure 15 Protocol of preparing inorganic hollow fibers using modified phase-inversion method.

Figure 16 Morphology of zirconia hollow fiber membranes: (a) cross section and (b) outer surface Reproduced from Liu LH, Gao SJ, Yu YH, Wang R, Liang DT, and Liu M (2006) Bio-ceramic hollow fiber membranes for immunoisolation and gene delivery. I: Membrane development. Journal of Membrane Science 280: 762–770, with permission from Elsevier.

Microscopic methods. Microscopic methods are useful for the visualization of membrane morphology and structure. They can be used to study the surface features of a membrane as well as its cross section. Microscopic methods are also widely used for membrane pore-size characterization (Kim et al., 1990). Where a foulant cake layer is present, microscopic investigation can provide great details about the morphology, structure, and properties of the layer. Conventional visible light microscopy is routinely used for membrane visualization due to its low cost and easy operation. However, its resolution is usually limited due to the relatively large wavelength of visible light. In contrast, electron microscopic methods, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), offer much better resolution. SEM has been widely used to characterize the surface and cross section of both clean and fouled membranes at a resolution as good as 5 nm for polymeric samples. Where ultrathin (o100 nm in thickness) sections can be prepared, TEM can

provide a large amount of detail on the structural information of membranes and foulants (Tang et al., 2007a; Freger, et al., 2005). Other commonly used microscopic methods include atomic force microscopy (AFM) and confocal laser scanning microscopy (CLSM). AFM can provide valuable information on the surface features of a membrane, and it has become the standard method for characterizing membrane roughness. On the other hand, CLSM is a powerful method for biofilm characterization. Spectroscopic methods. Spectroscopic methods provide essential information on the structure and chemistry of a membrane. For example, Fourier transform infrared spectroscopy (FTIR) is able to identify various chemical bonds in a membrane based on its adsorption of infrared irradiation (Tang et al., 2007a). Both X-ray photoelectron spectroscopy (XPS) and energy dispersive spectroscopy (EDX) are widely used for identifying elements present in membranes. XPS is a highly surface-sensitive technique, with the ability to measure

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Table 8

321

Commonly used membrane characterization methods

Type

Instrument

Information

Performance test

Membrane filtration setup

Permeability, rejection, and pore-size distribution inferred from transport models

Membrane porometry

Bubble point, mercury intrusion, gas adsorption, permporometry, thermoporometry

Information on membrane pore structure

Microscopic methods

SEM TEM AFM CLSM

Surface/cross-section features Cross section of membrane/foulant Roughness, surface morphology Foulant structure/composition

Spectroscopic methods

FTIR XPS EDX EIS

Membrane/foulant functional groups Elements/chemical binding Elemental mapping of foulants Structural information of sublayers

Other methods

Goniometer Streaming potential AFM force measurement

Hydrophobicity Surface charge Interaction force

AFM, atomic force microscopy; CLSM, confocal laser scanning microscopy; EDX, energy dispersive spectroscopy; EIS, electrical impedance spectroscopy; FTIR, Fourier transform infrared spectroscopy; SEM, scanning electron microscopy; TEM, transmission electron microscopy; XPS, X-ray photoelectron spectroscopy.

elemental composition and chemical binding information for the top 1–5 nm depth of the surface region (Tang et al., 2007a). The technique is able to detect all elements except hydrogen with detection limits around 0.01 monolayer or 0.1% of the total elemental concentration. Compared to XPS, EDX is less sensitive. Nevertheless, EDX offers a unique advantage as it can be coupled to an SEM or TEM, which allows it to analyze microscale features at specific locations and to construct elemental mapping. EDX has been widely used in membrane autopsy to analyze chemical compositions of membrane foulants (Khedr, 2003). Recently, electrical impedance spectroscopy (EIS) has been used to characterize the layered structures of TFC-RO and NF membranes. This method is able to provide structural information (e.g., thickness and electrical properties) of each sublayer of a composite membrane (Coster et al., 1996). Other surface characterization techniques. Goniometer and streaming potential analyzer are widely used to characterize membrane surface hydrophobicity (via contact-angle measurements) and surface charge (via zeta potential measurements), respectively (Tang et al., 2009a). AFM interaction force measurement is an emergent technique for membrane surface and foulant layer characterization (Bowen et al., 1999; Lee and Elimelech, 2006; Tang et al., 2009c). Using a colloidal cantilever probe with well-defined surface chemistry and calibrated spring constant, the AFM force measurement technique can be used to measure tiny interaction forces (B1 nN) between a membrane surface (either clean or fouled) and the probe. Such interaction forces correlate well with membrane fouling behavior (Tang et al., 2009 c; Lee and Elimelech, 2006).

4.11.5 Membrane Modules The membranes described in Sections 4.11.2 and 4.11.3 are produced as hollow fibers, tubes, and flat sheets. To use these membranes in large-scale processes, it is necessary to

incorporate them into a membrane module. Important features of modules include packing density, ease of cleaning, and flow distribution. Several module geometries have been developed suited to the range of membranes and their applications in the water industry.

4.11.5.1 The Role of the Module The membrane module, or element, has two major roles: (1) supporting the membrane and (2) providing efficient fluid management. Membranes are typically produced as flat sheets, tubes, or hollow fibers. The flat sheet and tubular forms are not self-supporting and the membranes must be placed on a porous support able to withstand the applied pressure and also facilitate permeate removal. Hollow fibers can be selfsupporting, and operate outside-to-in or inside-to-out. The latter is also called lumen feed, where the lumen is the bore of the hollow fiber. Good fluid management is vital for efficient membrane processing. The hydrodynamic conditions in the boundary layer at the membrane surface control the concentration polarization (CP) (see Section 4.11.6.3), which directly influences membrane performance. The various module designs deal with feed-side flow in different ways, attempting to balance boundary-layer mass transfer and the feed channel pressure losses. Fluid management also pertains to the downstream, permeate side of the membrane, because resistance to flow determines the downstream pressure losses and the net transmembrane pressures (TMPs). Several characteristics are potentially important in module design and are summarized in Table 9. In what follows, the various modules are described (Section 4.11.5.2), and their characteristics are summarized in Table 10.

4.11.5.2 Module Types This section describes the most common modules used in the water industry. One early approach, not described, was the

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plate-and-frame module which used stacks of flat sheet membranes on porous supports separated by flow channel spacers. Limitations due to packing density, pressure containment, and labor-intensive membrane replacement lead to the development of the SWM (see below).

4.11.5.2.1 Spiral-wound module The SWM is the predominant design for RO and NF applied to the water industry (see Section 4.11.1.3 for some of the historical developments); as such, it is the workhorse for SWRO and water reclamation plant. The SWM uses flat sheet membranes sealed by gluing on three sides to form leaves attached to a permeate channel (tube) along the unsealed edge of the leaf. Inside each leaf is a permeate spacer which is a porous matrix designed to support the membrane without compression, and to have a high hydraulic conductivity for permeate flow to the permeate tube (see Figure 17(a)). A net-like feed channel spacer fits between the leaves and defines the channel height (typically B1 mm). Several leaves are fixed to and then wound around the permeate tube and given an outer rigid casing (Figures 17(b) and 17(c)). The module has an anti-telescoping end cap which provides support to counter axial pressure drops. It should be emphasized that the feed channel spacer plays an important role as it enhances the effect of crossflow and promotes boundary layer mass transfer that controls CP (see Section 4.11.6.3). Table 12 (Section 4.11.6) provides information about the mass transfer correlations for different spacer geometries.

Table 9

The SWM comes in a standard diameter of 8 in (203 mm), but 2.5 and 4 in are also used for pilot or small scale, and 16 in SWMs are being introduced. The SWM is fitted into standard pressure vessels which can take several elements connected in series with O-ring seals to prevent bypassing and feed-to-permeate flow. Up to eight modules could be present in a pressure vessel, and many vessels are connected in an array (examples given in Section 4.11.7.2). A large desalination plant could have 20 000–50 000 modules.

4.11.5.2.2 Tubular module Tubular modules have the membrane surface on the inside of the tubes. They have several niche applications at the medium scale. Diameters are in the range 5–25 mm. The modules are similar to the shell and tube heat exchanger (Figure 18) with tubes connected in parallel and series. Some designs have the membrane tubes inserted into porous metal support tubes, and are able to withstand pressure for RO and NF. In other cases, the tubes are self-supporting and the burst pressure of the tubes limits it to UF/MF applications. Tubular modules are also produced in ceramic materials as multichannel monoliths with UF or MF capability; there are reported applications in water treatment. For RO and NF tubular modules are operated with crossflow in the turbulent flow regime which provides good control of CP, but at a relatively high energy cost. This type of module is suitable for feeds with high turbidity. An interesting example is remote-area water treatment which uses tubular NF with automatic foam ball cleaning for chemicalfree water treatment of colored waters (see reference 12 in Fane (2005)).

Module characteristics of importance

Characteristic Packing density Energy use

Fluid management

Standardization Replacement Cleaning

Table 10

Significant influence on

4.11.5.2.3 Hollow fiber module (contained)

System size, footprint, and (probably) cost Costs ¼ f {operating pressure, flow rate, flow resistance, flow regime} Concentration polarization, flux/ pressure relationship, fouling, and cleaning Flexibility in terms of choice of membrane supplier Maintenance and labor costs System availability, downtime, and time-averaged production

Hollow fiber membranes are self-supporting, that is, the walls can be strong enough to avoid collapse or bursting. Outer diameters are in the range of 0.5–1.0 mm with inner lumen diameters of o0.3 to 0.8 mm. Hollow fiber modules (HFMs) are either contained (filtration under pressure) or submerged (filtration under suction); Section 4.11.5.2.4 deals with submerged modules. Contained HFMs involve thousands of fibers arranged in a bundle and potted by epoxy in an outer shell (Figure 19). The design is similar to the shell and tube design for tubular membranes, but can be operated with feed in the shell side (out-to-in) or feed in the lumen (in-to-out), depending on the membranes and the application. HFMs with shell-side feed are externally pressurized and some RO hollow

Characteristics of different module concepts

Characteristic

Spiral wound

Tubular

Hollow fiber

Submerged

Packing density (m2 m3) Energy use Fluid/fouling management

High (500–1000) Moderate (spacer losses) Good (no solids) Poor (solids) Yes Element Can be difficult (solids)

Low–moderate (70–400) High (turbulent) Good

High (500–5000) Low (Laminar) Moderate (in-to-out) Poor (out-to-in) No Element Backflush (MF/UF)

Moderate Low Moderate

Standardization Replacement Cleaning

No Tubes (or element) Good – physical cleaning possible

No Element (or bundle) Backflush (HF) (MF/UF)

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323

Permeate spacer

Membrane leaf

Feed spacer

Permeate tube

Flux (a)

(b)

Feed flows axially Feed Feed-side spacer

Permeate

Membranes

(c)

Permeate flows inward to collector tube

Membrane support and permeate-side spacer

Figure 17 (a) Spiral-wound module showing membrane leaves and spacers. (b) Spiral-wound module with leaves wrapped around permeate tube. (c) Spiral-wound module showing flow paths. (a–c) Reproduced from Fane AG (2005) Module design and operation. In: Schaefer AI, Fane AG, and Waite TD (eds.) Nanofiltration – Principles and Applications, pp. 67–88. Oxford: Elsevier, with permission from Elsevier.

‘Shell’

Membranes (‘tubes’)

Retentate

Feed

Permeate Figure 18 Tubular module (shell-and-tube arrangement). Reproduced from Fane AG (2005) Module design and operation. In: Schaefer AI, Fane AG, and Waite TD (eds.) Nanofiltration – Principles and Applications, pp. 67–88. Oxford: Elsevier, with permission from Elsevier.

fibers can withstand high pressures up to the level of SWRO. In some special cases, seawater applications with hollow fibers of cellulose triacetate are used (Kumano and Fujiwara, 2008); their major advantage is the ability to withstand chlorine to control biofouling. However, in the vast majority of seawater RO desalination plants the HFM has been superseded by the SWM.

HFMs with shell-side feed are commonly used in lowpressure membrane applications (UF and MF), such as water treatment and pretreatment (see Table 2). These applications often use dead-end operation (see Section 4.11.7.1) with intermittent backwash from the lumen to the shell. Compared with submerged HFMs, the contained modules have a wider range of TMPs available. HFMs with lumen-side feed are also used for water treatment and pretreatment. Operation is either with crossflow or with dead-end flow, depending on the solids content (low solid favors use of dead-end with backwash). Intermittent two-phase (air–liquid) flow is often applied to HFMs during the backwash cycle. Continuous two-phase flow may be implemented in cases where the HFM (lumen-side feed) is used with high solids, such as MBRs (see Section 4.11.1.2.4).

4.11.5.2.4 Submerged module Submerged (or immersed) modules involve membranes positioned in a flooded tank at atmospheric pressure typically open at the top. The liquid to be filtered is fed to the tank and permeate is removed from the module under suction, either

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Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Hollow fibers

Feed

Retentate

Permeate

Shell

Lumen

Figure 19 Contained hollow fiber module. Reproduced from Fane AG (2005) Module design and operation. In: Schaefer AI, Fane AG, and Waite TD (eds.) Nanofiltration – Principles and Applications, pp. 67–88. Oxford: Elsevier, with permission from Elsevier.

Feed

Permeate

Hollow fibers or flat sheets

intermittent air scour. Flat sheets are not used in these applications because they cannot be backwashed. In these low solid operations both submerged and contained HFMs appear to be equally popular. In some cases, submerged HFMs may offer marginal cost advantages, but they have less turn-up/turndown capability and are heavier than contained HFMs. For high solid content feeds, such as MBRs, submerged modules are either hollow fibers or flat sheets, with continuous or rapidly intermittent air scour, and occasional backwash (hollow fibers). Submerged modules are more popular than contained modules for MBRs. There is no standardization in submerged membranes and there are many commercial suppliers; for example, Judd (2006) describes 12 different MBRs using submerged membranes. A more detailed account of submerged membranes can be found elsewhere (Fane, 2008).

4.11.6 Basic Relationships and Performance

Waste

Air

Figure 20 Submerged membrane module.

by a pump or by gravity. The concentrate is removed continuously or intermittently from the tank. Figure 20 depicts the general features of a submerged membrane system, which include: 1. an open tank (no pressure vessel), 2. modules in bundles of fibers or vertically aligned flat plates, 3. permeate removed by suction, and 4. TMPs o1 atm. Submerged modules use low-pressure MF and UF membranes; they are unsuitable for NF or RO due to the limited TMP. For low solid feeds, such as water treatment or pretreatment to RO, submerged hollow fibers are commonly used. Operation is usually dead-end cycles with regular backwash and

Membrane performance (such as flux and rejection) is determined by the mass transport inside a membrane as well as the transport toward the membrane surface. The mass transport inside a membrane defines the basic relationship between flux and the driving force. In addition, it determines the intrinsic retention properties of the membrane. Due to its retentive nature, solutes transported toward a membrane will tend to accumulate near the membrane surface, leading to a higher solute concentration near the surface compared to the bulk concentration. This phenomenon is known as CP. Another important phenomenon in pressure-driven membrane processes is membrane fouling, that is, the deposition of contaminants on a membrane surface and/or inside membrane pores. Both CP and fouling can adversely affect membrane flux and rejection. Thus, they need to be carefully controlled in membrane operation.

4.11.6.1 Membrane Flux and Rejection Flux and rejection are among the most important performance parameters for any membrane process. Membrane flux of a given species can be defined as the mass (or volume) of that species passing through a unit membrane area within a given duration. For applications in water and wastewater treatment,

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

the water flux is of particular interest, as this directly relates to the membrane productivity and thus process economics (refer to Section 4.11.7 for more details). Water flux Jw is typically defined as

ð5Þ

where Qp is the volumetric flow rate that permeates through the membrane and Am is the membrane area. Typical units for water flux in the literature include m3 m2 s1, m d1, mm s1, l m2 h1 and (US) gallons per ft2 per day (gfd). Similar to the definition of water flux, the mass flux of a solute Js can be defined as the mass flow rate m˙ s passing through the membrane normalized by membrane area:

Js ¼

m˙ s Am

ð6Þ

The solute flux is commonly given in kg m2 s1, kg m2 h1, mol m2 s1, or mol m2 h1. The retention ability of a membrane in water applications is expressed by the membrane rejection. The intrinsic rejection of a membrane Rint can be defined as (Ho and Sirkar, 1992)

Rint ¼ 1 

Cp Cm

ð7Þ

where Cm is the solute concentration near the membrane surface and Cp is the solute concentration in the permeate water. Here, Cp can be determined from the ratio of the solute mass flux to the volumetric water flux by

Cp ¼

Js Jw

ð8Þ

The intrinsic rejection Rint defined in Equation (7) relates the solute concentration in the permeate water Cp to that near the membrane surface Cm. Usually, Cm is not known as a priori.

Table 11

Thus, a more commonly used rejection parameter in practice is the apparent rejection Rapp, which relates Cp to the bulk feed concentration Cb (Ho and Sirkar, 1992):

Rapp ¼ 1 

Qp Jw ¼ Am

325

Cp Cb

ð9Þ

In a similar fashion, an overall observed rejection Rsys (rejection at the module or system level) can be defined based on the feedwater concentration Cf :

Rsys ¼ 1 

Cp Cf

ð10Þ

The overall rejection is usually lower than the intrinsic rejection. This can arise due to two main reasons: (1) concentration polarization which leads to a higher concentration near the membrane surface compared to the average bulk concentration (Cm Z Cb), and/or (2) high membrane recovery so that the average bulk concentration experienced by a membrane is greater than the feedwater concentration (Cb ZCf). Thus, Rint Z Rapp Z Rsys. The concentration polarization phenomenon will be discussed in greater detail in Section 4.11.6.3, and the effect of membrane recovery is discussed in Section 4.11.6.4. It is also worth noting that the retention mechanism for porous membranes (MF and UF) is different from that for nonporous membranes (RO) (Table 11). Particles and solutes are retained by porous MF or UF membranes by size discrimination, that is, a sieving mechanism (Mulder, 1996). Particles larger than membrane pore size are completely retained, while smaller particles are less retained. In contrast, selectivity of an RO membrane is based the solution-diffusion mechanism (Mulder, 1996). Solute or solvent absorbs into the nonporous membrane on the feedwater side, diffuses through the rejection layer under a chemical potential gradient, and desorbs on the permeate water side. Separation of different species is achieved based on their different ability to partition into the rejection layer as well as their different ability to

Rejection mechanisms for porous and nonporous membranes

Membrane type

Rejection layer

Rejection mechanism(s)

Water flux and solute rejection model(s)

MF

Porous

Sieving

Water flux: Hagen–Poiseuille equation; Kozeny–Carman equation Rejection: Ferry equation; Zeman and Wales equation; other pore models (Nakao, 1994)

UF

Porous

Sieving

Water flux: Hagen–Poiseuille equation; Kozeny–Carman equation Rejection: Ferry equation; Zeman and Wales equation; surface forcepore flow model; hindered transport models

NF

In between tight UF and loose RO

Solution-diffusion, sieving, Donnan exclusion

Solution-diffusion model; solution-diffusion-imperfection; preferential sorption-capillary flow model; surface force-pore flow model; Donnan equilibrium model; extended Nernst–Planck model

RO

Nonporous

Solution-diffusion

Solution-diffusion model; solution-diffusion-imperfection; preferential sorption-capillary flow model; surface force-pore flow model

MF, microfiltration; NF, nanofiltration; RO, reverse osmosis; UF, ultrafiltration. Adapted from Schafer AI, Fane AG, and Waite TD (2005) Nanofiltration – Principles and Applications. Oxford: Elsevier; Ho WS and Sirkar KK (1992) Membrane Handbook. New York: Chapman and Hall; and Nakao S (1994) Determination of pore size and pore size distribution. 3. Filtration membranes. Journal of Membrane Science 96: 131–165.

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diffuse through the rejection layer. RO membranes generally allow relatively high absorption of water molecules and faster diffusion of water molecules through the rejection layers (Mulder, 1996). In comparison, a typical solute (such as sodium chloride) has lower sorption onto RO membranes and slower diffusion through them, which results in a lower solute concentration in the permeate compared to that of the feedwater. As NF membranes are in between tight UF membranes and loose RO membranes, transport inside NF membranes may be governed by both solution-diffusion and sieving mechanisms (Schafer et al., 2005). In addition, charge repulsion (Donnan exclusion) can be important for the rejection of charged species (Schafer et al., 2005). Both porous membrane models and the solution-diffusion model are discussed in more detail in Section 4.11.6.2.

4.11.6.2 Transport Inside a Membrane – Basic Relationships

ð11Þ

where Lp is the water permeability coefficient of the pressuredriven membrane. In Equation (11), Dp represents the osmotic pressure difference between the membrane surface pm and permeate water pp. The osmotic pressure of dilute solutions can be determined by the van’t Hoff equation:

p ¼ Rg T S Ci

•

applying a positive Pm while maintaining Pp around atmospheric pressure (Pp B 0); this is typically done for most RO and NF applications, as well as for many MF and UF applications and applying a negative Pp (suction or partial vacuum) while maintaining an atmospheric Pm, which is widely used for submerged membranes (see Section 4.11.5.2.4).

The Darcy’s law for pressure-driven membranes is also commonly presented in terms of membrane hydraulic resistance Rm and dynamic viscosity of the permeating water Z by

Jw ¼

DP  Dp ZRm

1 ZLp

ð14Þ

The Darcy’s law (Equation (11) or Equation (13)) is applicable for both porous and nonporous membranes. A more sophisticated model available in the membrane literature is the irreversible thermodynamics model (Mulder, 1996; Bitter, 1991), which recognizes that membrane processes are not under thermodynamic equilibrium due to the continuous free energy dissipation and entropy production. According to the irreversible thermodynamics model, the volumetric flux Jv and the solute flux Js are given by the following equations, respectively (Mulder, 1996; Bitter, 1991):

Jv ¼ Lp ðDP  sDpÞ

ð15Þ

  sÞJv þ Ls DC Js ¼ Cð1

ð16Þ

where Lp is the water permeability, s the reflection coefficient,  the average solute concentration inside the membrane, Ls C the solute permeability coefficient, and DC the solute concentration across the membrane (DC ¼ Cm – Cp). Equation (15) takes a similar form to that of Equation (11), except a reflection coefficient s is introduced in the former. For s ¼ 1, two equations become identical. In effect, s is an indicator of a membrane’s ability to separate a solute from the solvent, and its value is usually between 0 and 1:

•

ð12Þ

where Rg is the universal gas constant (R ¼ 8.31 J mol1 K1), T the absolute temperature in kelvin, and Ci the molar concentration of dissolved species i. Thus, for a 0.01 M NaCl, SCi ¼ 0.02 M as there are 0.01 M sodium ions (Naþ) and 0.01 M chloride ions (Cl). The osmotic pressure term appears in Equation (11) only if the solute under concern is retained by the membrane. Similar to the osmotic pressure difference, DP in Equation (11) is the TMP, that is, the difference between the pressure near membrane surface Pm (which is identical to the applied pressure on the feedwater side) and that in the permeate water Pp. A positive TMP can be achieved by:

•

Rm ¼

and

The permeate water flux of a membrane can be related to its driving force (i.e., the net pressure difference across the membrane) following the phenomenological Darcy’s law:

Jw ¼ Lp ðDP  DpÞ

where the membrane hydraulic resistance Rm is related to its water permeability by

ð13Þ

•

•

For s ¼ 0, the membrane has no selectivity with respect to the solute. One example is the rejection of dissolved salts by porous membranes. As MF and (most) UF membranes do not retain dissolved salts, no osmotic pressure difference will be developed across these membranes (i.e., Dp ¼ 0 and  ¼ Cp ¼ Cm). This leads to Jv ¼ LpDP and Js ¼ Cm Jv. As a C result of the complete leakage of solute, the volumetric flux only depends on the hydraulic pressure difference and the solute flux arises solely from convective transport. For s ¼ 1, the membrane has ideal separation properties so that the solute and the solvent transport through the membrane are independent and uncoupled to each other. As a result, the convective transport term (the first term in Equation (16)) is zero, and solute transport through the membrane is purely by diffusion. This leads to Jv ¼ Lp(DP  Dp) and Js ¼ LsDC. A special example of this is rejection by high-retention RO membranes (Section 4.11.6.2.2). A real membrane typically has a s between 0 and 1, which indicates that the solute transport is partially coupled to the solvent transport (Bitter, 1991).

The Darcy’s law and the irreversible thermodynamic model treat a membrane as a black box. The effect of membrane structure and properties on the transport parameters (e.g., Lp and Ls) is not reflected in these models. For this reason, mechanistic models are preferred. Section 4.11.6.2.1 discusses transport models for porous MF and UF membranes, whereas Section 4.11.6.2.2 briefly reviews the solution-diffusion model commonly applied to RO membranes.

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis 4.11.6.2.1 Transport models for MF and UF membranes The water flux of MF and UF membranes can be described by the Hagen–Poiseuille equation for cylindrical-pore membranes or the Kozeny–Carman equation for stacked-sphere pore structure (refer to Section 4.11.2.3):

Jw

er2p DP 8Ztlm

ðHagen2Poiseuille equationÞ

327

potential gradient. According to the solution-diffusion model, the water flux through an RO membrane is proportional to the net applied pressure (DP  Dp), whereas the solute flux is proportional to the concentration difference across the membrane (DC):

ð17Þ

Jw ¼ AðDP  DpÞ

ð21Þ

Js ¼ BDC

ð22Þ

and

or

e 3 DP Jw ¼ Kð1  eÞ 2 S 2 Zlm ðKozeny2Carman equationÞ

ð18Þ

The Hagen–Poiseuille equation and the Kozeny–Carman equation state that the water flux of a porous membrane is proportional to the applied pressure difference. The proportionality constant (i.e., the water permeability Lp) is a function of membrane pore structure (porosity, pore size, type of pores, etc.), the thickness of the rejection layer, and the viscosity of the permeating solution. The osmotic pressure difference Dp does not appear in these models because MF and UF membranes do not retain dissolved salts so that their reflection coefficient s is zero (refer to the irreversible thermodynamics model and Equation (15)). For some special cases where s is not zero (e.g., osmotic pressure due to macromolecules that can be retained by UF membranes), the osmotic pressure difference term may need to be considered as well. Rejection of solutes (or particles) by porous membranes is based on the sieving mechanism. A simple rejection equation based on Poiseuille flow for cylindrical-pore membranes was derived by Ferry (1936):

Rint ¼ ½lð2  lÞ2

ðfor lo 1Þ

ð19Þ

and

Rint ¼ 1

ðfor l  1Þ

ð20Þ

where l is the ratio of solute (or particle) diameter to the pore diameter. Ferry’s equation clearly suggests that rejection increases as the size of particle increases relative to the pore size. Strictly speaking, Ferry’s equation is applicable only for solid spherical particles in cylindrical pores. In addition, the interaction between particles in the pores and that between a particle and the pore wall are not considered. More sophisticated models are available in the literature (Ho and Sirkar, 1992; Nakao, 1994). Solute–solute and solute–pore interactions as well as membrane pore-size distribution are considered in some models (e.g., the surface force-pore flow model (Ho and Sirkar, 1992)).

4.11.6.2.2 Transport models for RO membranes One of the most widely used transport models for RO membranes is the solution-diffusion model. This model assumes that (1) both the solvent and the solute absorb into the rejection layer and (2) they diffuse through the nonporous layer independent of each other under their respective chemical

where A and B are the respective water and solute permeability coefficients in the solution-diffusion model. Comparing the solution-diffusion model and the irreversible thermodynamics model shows that the two models take the same form if the reflection coefficient s is set to unity in Equations (15) and (16). The advantage of the solution-diffusion model is that the transport coefficients (A and B) in this model can be linked to membrane properties:

A¼

Dwm Cwm Vw Rg Tlm

ð23Þ

Dsm Ksm lm

ð24Þ

and

B¼

where Dwm and Dsm are the diffusion coefficient of water and that of solute inside the rejection layer, respectively; Cwm the concentration of water inside the rejection layer; Vw the molar volume of water; and Ksm the solute partitioning coefficient into the rejection layer. The solution-diffusion model suggests that a high-flux RO membrane shall have higher water absorption and also allow fast diffusion of water molecules (Equation (23)), which requires a lower degree of crosslinking of the rejection layer. However, reduced crosslinking will lead to a significantly enhanced diffusion of solutes and thus a much greater B value. This explains the strong trade-off relationship between water permeability and salt permeability for RO membranes, as discussed in Section 4.11.2.2. The intrinsic rejection of an RO membrane can be determined by

Rint ¼

 1þ

B AðDP  DpÞ

1 ð25Þ

As both A and B are inversely proportional to the rejection layer thickness lm, the solution-diffusion model suggests that increasing rejection layer thickness alone does not improve membrane rejection. The intrinsic rejection of an RO membrane can be improved by (1) preferential sorption of water molecules compared to solute molecules, (2) enhanced diffusion of water molecules through the rejection layer relative to solute molecules, and (3) increased applied pressure. The solution-diffusion model can be extended to include pore flows due to membrane imperfections (the solution-diffusion-imperfection model). Other models, such as the

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Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

preferential sorption-capillary flow model and the surface force-pore flow model, assume that the rejection layers of RO membranes are microporous. For NF membranes where electrostatic interaction is an important consideration, the Donnan equilibrium model and the extended Nernst–Planck model have also been applied. A review of these models is available (Ho and Sirkar, 1992; Schafer et al., 2005).

4.11.6.3 Transport toward a Membrane – Concentration Polarization The solute concentration near a pressure-driven membrane surface is typically higher than the bulk concentration as a result of rejection by the membrane. The concentration gradient adjacent to the membrane surface leads to a diffusion of solute molecules back to the bulk solution. When the back diffusion balances with convective transport of solutes toward the membrane, a steady concentration polarization profile is established (Figure 21). Based on the mass balance of the solute in the control volume shown in Figure 21, the following equation can be established for describing the solute concentration C as a function of distance x for a one-dimensional problem:

Jw C  Jw Cp  D

dC ¼0 dx

ð26Þ

with the boundary conditions given by

C ¼ Cb

at

x¼0

ð27Þ

C ¼ Cm

at

x¼d

ð28Þ

and

Solving Equations (26)–(28) leads to

Cm  Cp ¼ expðJw =KÞ Cb  Cp

ð29Þ

where K is the mass transfer coefficient (K ¼ D/d), and exp (Jw/K) is the concentration polarization modulus. Equation (29) is the boundary layer film model. By substituting Equation (7) into Equation (29), we have

Cm expðJw =KÞ ¼ Cb Rint þ ð1  Rint ÞexpðJw =KÞ

ð30Þ

For the special case where Cp is negligible (i.e., Rint B1), Equation (30) becomes

Cm ¼ expðJw =KÞ Cb

ð31Þ

Equation (29) clearly shows that CP increases at higher water flux and reduced mass transfer coefficient. Thus, the membrane surface concentration Cm can be significantly higher than the bulk concentration at high flux and/or low mass transfer coefficient. The mass transfer coefficient can be determined from the Sherwood number Sh by relating Sh to Reynolds number Re and Schmidt number Sc (Table 12):

Sh ¼ a  Re b Sc c ðdh =LÞd

ð32Þ

In Equation (32),

Sh ¼

Kdh D

ð33Þ

Re ¼

dh u v

ð34Þ

v D

ð35Þ

Jw

Sc ¼ Cm

where dh is the hydraulic diameter, u the flow velocity, and v the kinetic viscosity. Equation (32) can be rearranged to give the following form (assuming c ¼ 1/3):

JwC

D

dC dx

JwCp

Cb

Boundary layer thickness 

Membrane

X

Cp

Figure 21 Concentration polarization over a membrane surface.

K ¼ a  D 2=3 u b v 1=3b d bþd1 Ld h

ð36Þ

According to Equation (36), the mass transfer coefficient is proportional to D2/3. This suggests that bigger molecules are more likely to suffer from severe concentration polarization as a result of their lower diffusion coefficient. Another important point is that the mass transfer coefficient can be enhanced at larger flow velocity in a crossflow module. However, this is usually at the expense of increased pressure drop across a membrane module (pressure difference between module inlet and outlet) (Ho and Sirkar, 1992; Schafer et al., 2005; Schock and Miquel, 1987). In addition, large crossflow may damage the membrane surface (such as formation of wrinkle structures). Typical crossflow velocities for SWMs are 10–90 cm s1,

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Table 12

329

Mass transfer correlations

Geometry

Flow region

Correlation

Notes

Channel or tube

Laminar

Sh ¼ 1:62Re 0:33 Sc 0:33 ðd h =LÞ0:33

Turbulent

Sh ¼ 0:644Re 0:5 Sc 0:33 ðd h =LÞ0:5 Sh ¼ 0:023Re 0:8 Sc 0:33 Sh ¼ 0:023Re 0:875 Sc 0:25

Fully developed flow (L40.029dh), 100oRe Sc dh/Lo5000 Developing flow (Lr0.029dh) Scr1 1rScr1000

Stirred cell

Laminar Turbulent

Sh ¼ 0:285Re 0:55 Sc 0:33 Sh ¼ 0:044Re 0:75 Sc 0:33

8000rRe r 32 000 32 000rRe r 82 000

Spacers filled channels

Laminar

Sh ¼ 0:644Re 0:5 Sc 0:33 ðd h =LÞ0:5 Sh ¼ 0:644k dc Re 0:5 Sc 0:33 ð2d h =LÞ0:5

Turbulent

Sh ¼ 0:065Re 0:875 Sc 0:25

Ladder type spacer (Da Costa et al., 1994) Diamond-type spacer, correction factor kdc is a function of spacer geometry (Da Costa et al., 1994) Schock and Miquel (1987)

Adapted from Ho WS and Sirkar KK (1992) Membrane Handbook. New York: Chapman and Hall; and Fane AG (2005) Module design and operation. In: Schaefer AI, Fane AG, and Waite TD (eds.) Nanofiltration – Principles and Applications, pp. 67–88. Oxford: Elsevier.

while much higher crossflow velocities may be used for tubular modules due to their large tube diameter.

4.11.6.4 Factors Affecting Membrane Performance The transport toward a membrane surface is discussed in Section 4.11.6.3 and that inside a membrane has been discussed in Section 4.11.6.2. By combining these two aspects together, the water flux and the apparent rejection of a membrane can be determined by

Jw ¼

Rapp ¼

DP  expðJw =KÞðpb  pp Þ ZRm

ð37Þ

Rint Rint þ ð1  Rint Þ expðJw =KÞ

ð38Þ

Clearly, concentration polarization has a negative effect on both water flux and apparent rejection of a membrane; thus, Jw and Rapp can be significantly reduced at higher CP modulus, exp(Jw/K). This effect is more severe for larger molecules at lower crossflow and higher flux (or higher applied pressure). For systems or modules operated at low recovery (say recovery Yo0.1), the average bulk concentration Cb in the membrane system can be approximated by the feedwater concentration Cf . However, Cb can be significantly larger than Cf for systems with high recovery. The reject water (e.g., brine in RO) concentration Cc can be determined by

Cc ¼ Cf ð1  YÞRapp

ð39Þ

while the average bulk concentration in the system is approximated by

Z Cb D

Cf

y

ð1  YÞRapp dY

0

Y

¼ Cf

1  ð1  YÞ 1Rapp Yð1  Rapp Þ

ðfor 0r Rapp o 1 and 0o Y r 1Þ

ð40Þ

Rsys D 1 

1  ð1  YÞ 1Rapp Y

ð41Þ

For typical applications, the feedwater concentration is given. Both the reject stream concentration and the average bulk concentration increase at higher recovery. Figure 22 shows the increase in rejection (brine) concentration and the correspondence osmotic pressure as a function of recovery for different feed concentration. For a feedwater containing 35 000 ppm NaCl (typical seawater conditions), the osmotic pressure of the brine is about 5.6 MPa at 50% recovery. Even for a feedwater with only moderate salt concentration (1000 ppm NaCl, typical wastewater reclamation conditions), this osmotic pressure can be substantial (B0.8 MPa at a recovery of 90%). As higher recovery increases the average bulk concentration and the corresponding osmotic pressure, this leads to reduced average flux as well as reduced system rejection (Equation (41)). For this reason, recovery for typical seawater RO desalination plants is limited to 50%, and that for wastewater reclamation plants is below 80%. The effect of operating conditions (applied pressure, crossflow velocity, recovery, and temperature) on membrane performance is summarized below (Figure 23):

•

Applied pressure. At low applied pressure (thus low flux level), concentration polarization is not significant. As applied pressure increases, water flux increases linearly initially according to the Darcy’s law (Equation (37)). For a porous MF membrane, the solute rejection remains constant based on the Ferry model (Equation (19)). In contrast, the solute rejection also increases for RO based on the solution-diffusion model (Equation (25)). However, at high applied pressure and water flux, concentration polarization becomes important. Further increase in applied pressure will lead to a significant concentration polarization and thus significant increase in osmotic pressure. Such an increase in osmotic pressure can offset the increase in applied pressure, resulting in a significant deviation from the linear flux–pressure relationship. The increased membrane surface concentration will also result in lower apparent membrane rejection.

330

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis 10 000 Osmotic pressure

1000 ppm feed

Brine concentration (g l–1)

5000 ppm feed 1000

35 000 ppm feed

85 MPa

100

8.5 MPa 5.6 MPa

10

1.4 MPa 0.85 MPa

1 0

50

100

Recovery (%) Figure 22 Brine concentration and osmotic pressure as a function of recovery for a high-retention RO membrane.

Water flux

Solute rejection (for MF) Δ

Water flux Performance

Performance

Solute rejection (for RO)

Applied pressure

Solute rejection

Cross-flow

Water flux Solute rejection

Recovery

Performance

Performance

Water flux

Solute rejection

Temperature

Figure 23 Effect of operating conditions on RO membrane performance. Adapted from Ho WS and Sirkar KK (1992) Membrane Handbook. New York: Chapman and Hall.

•

• •

Crossflow velocity. Increasing crossflow tends to improve both water flux and apparent rejection of a membrane as a result of reduced CP. A plateau is usually observed at high crossflow where further increase in crossflow velocity is less effective (mass transfer is no longer a limiting factor). Recovery. Increasing recovery leads to an increase in average bulk concentration. This reduces both water flux (due to increased osmotic pressure) and system rejection. Temperature. Higher operating temperature tends to increase both water flux and solute flux due to improved diffusion through the membrane rejection layer. However, the increase in solute flux is usually more drastic compared to the enhancement in water flux. Consequently, membrane rejection tends to decrease.

Besides the operating conditions mentioned above, membrane fouling can also have profound effect of the performance of a membrane. Fouling is discussed in more detail in Section 4.11.6.5.

4.11.6.5 Membrane Fouling Membrane fouling is the deposition of contaminants on a membrane surface or inside membrane pores (Figure 24). According to the nature of the foulants, fouling can be classified into scaling (precipitation of insoluble salts), colloidal fouling, organic fouling, and biofouling (formation of a biofilm). Fouling leads to an additional hydraulic resistance (foulant resistance Rf) and therefore a lower water

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

331

Pressurized vessel Crossflow velocity (sweeping, limiting boundary-layer thickness and CP control cake-layer formation)

Feedwater

Insoluble salts

Concentrate

Microorganisms

Organic macromolecules

Inorganic colloids

Membrane Permeate flux Figure 24 Illustration of membrane fouling in a pressurized crossflow module.

permeability of the fouled membrane:

DP  Dp ZðRm þ Rf Þ

ð42Þ

The net effect of fouling is either reduced water flux at constant applied pressure or increased TMP to maintain a constant water flux. In either way, the energy demand to treat a unit volume of water can be increased significantly. Both CP and fouling can reduce water flux during constant pressure operation. CP happens within the boundary layer near the membrane surface, and it is fully reversible. Once water flux is reduced to a low level, CP disappears. The timescale for CP to reach a stable condition or to disappear is usually very short (in seconds to a fraction of a minute (Chong et al., 2007)). In contrast, foulants attach onto a membrane during membrane fouling. Membrane fouling typically occurs over longer timescales (hours to days or months), although rapid fouling can happen under some unfavorable conditions. Although CP and fouling are two different phenomena, they are closely related to each other. Severe CP can accelerate membrane fouling as a higher foulant concentration is experienced by the membrane surface. On the other hand, the formation of a cake layer on membrane surface can potentially reduce the mass transfer coefficient which results in a severe cake enhanced concentration polarization (Chong et al., 2007). Membrane fouling can be affected by many different factors, such as feedwater characteristics, membrane properties and module/system design, and hydrodynamic conditions over a membrane surface. In general, hydrophilic membranes with smooth surfaces have lower tendencies for fouling. A good module and system design improve mass transfer over the membrane surface (such as the use of spacer in SWMs and aeration in submerged membrane bioreactors (see Section 4.11.5)). Membrane fouling can be strongly affected by feedwater solution chemistry such as pH and ionic composition (Tang et al., 2007b). Unfavorable solution conditions (such as high ionic strength and hardness) can lead to severe colloidal and organic fouling by making membrane–foulant and foulant–foulant interactions less repulsive. Feedwater

Strong form Jcrit Flux

Jw ¼

Pure water flux

Weaker form Jcrit

Transmembrane pressure Figure 25 Critical flux in membrane operation. Modified from Bacchin P, Aimar P, and Field RW (2006) Critical and sustainable fluxes: Theory, experiments and applications. Journal of Membrane Science 281: 42–69.

contains high levels of sparingly soluble salts which are more susceptible to scaling formation, while the presence of microorganism and nutrients may promote biofouling. Pretreatment (such as removal of certain contaminants and pH adjustment) can be used to condition the feedwater for minimizing its fouling potential. Finally, hydrodynamic conditions are important for membrane fouling. Increased crossflow, thus enhanced mass transfer, helps minimize membrane fouling as well as CP. On the other hand, high membrane permeate flux tends to promote both CP and fouling problems. The concept of critical flux has been widely used in the membrane fouling literature. The critical flux concept states that membrane fouling is minimal below a threshold flux value (the critical flux Jcrit). Above the critical flux, significant fouling occurs. The theoretical basis has been extensively discussed in a review paper by Bacchin et al. (2006). In essence, the critical flux is the minimum flux needed to overcome the surface force and back diffusion of foulants such that fouling occurs. The critical flux can be classified into the strong form and weak form (Figure 25). In the strong form, the experimental flux versus TMP curve for a feedwater is compared to

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Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis

its pure water flux curve, and the critical flux is the flux at which the experimental flux starts to deviate from the pure water flux. In its weak form, it is recognized that rapid surface or pore adsorption of macromolecules may occur which reduces the membrane permeability. Thus, the experimental flux in these cases is always below the pure water line. Nevertheless, the experimental flux is still linear with respect to the TMP over a wide range. The weaker form of critical flux is the flux at which the experimental flux starts to deviate from its linear trend (Figure 25). Subcritical flux operation is usually preferred to avoid successive membrane fouling. Membrane critical flux can be increased by increasing mass transfer rate (such as increasing crossflow velocity, bubbling, and vibration) and avoiding unfavorable solution conditions.

(42), neglecting osmotic pressure):

dDP dRf ¼ ZJi dt dt

ð43Þ

After a specified period, tc, or at a predetermined maximum pressure drop, the flux is stopped and the deposit is removed by backwashing and (usually) vigorous aeration. The cycle times would typically be about 30 min and the backwash o5 min. This mode of operation is batch-continuous, and net flux would be slightly less than the imposed flux, that is, for tc of 30 min and for tBW of 5 min, the net flux is about 80% of imposed flux (allowing for loss of product in backwash water). Over time, due to fouling, a residual resistance may build up. This can usually be controlled by a cleaning cycle, such as chemically enhanced backwash.

4.11.7 Membrane Process Operation 4.11.7.2 System Components Membrane process operation requires consideration of whether the feed is delivered in crossflow or dead-end mode, and this depends on the nature and concentration of the contaminants to be removed. While the membranes (Sections 4.11.2 and 4.11.3) and the modules (Section 4.11.5) are the key components, the overall system includes pre- and posttreatment processes and various options for the arrangement of modules. The energy demand in most cases is directly related to the required input pressure and the fractional recovery (product/feed). The potential for energy recovery is significant in the high-pressure SWRO process. In the MBR, energy for air scour is important. The cost of water production using membranes has steadily fallen, and, in some cases, is equivalent to conventional processes.

Membrane process systems comprise membranes and modules as key components. Important additional components are the intake systems, the pretreatment steps, the feed pumps, the posttreatment steps, the energy recovery devices, and concentrate disposal method. Figure 2 is a simplified flow sheet of membrane process configurations in the water industry and Table 2 summarizes the pre- and posttreatment steps involved. Other ancillary components could be chemical addition to control fouling, membrane cleaning systems, and

4.11.7.1 Crossflow versus Dead-End Operation In many membrane applications in the water industry, such as SWRO, RO reclamation, NF water treatment, and MBRs, the aim is to operate at a steady-state production rate with continuous crossflow for controlling concentration polarization (Section 4.11.6.3) and fouling (Section 4.11.6.5). In these applications, an important consideration is how the boundary layer is influenced by crossflow velocity which depends on the flow rates and the design of the module. Any membrane application where the feed fluid is caused to move tangentially to the membrane surface is in crossflow mode. For example, MBRs with submerged membranes are operated in crossflow mode, due to the effect of continuous air scouring that creates a two-phase flow across the membrane surface. However, in the water industry, some applications are not operated in the crossflow mode. These include water treatment and pretreatment prior to RO. These processes use low-pressure membranes (MF and UF) and the feed streams have relatively low levels of suspended solids or turbidity. For these feeds, it is feasible to operate without continuous crossflow or surface shear, and this can reduce energy costs. This mode of operation is called dead-end filtration (or frontal filtration), and the key feature is that the deposition of retained species is allowed to grow. A typical cycle commences with a clean membrane (after backwash) and at constant imposed flux (Ji) the TMP rises according to Equation (43) (from Equation

(a)

(b)

(c)

Stage 1

1st pass

Stage 2

Stage 3

2nd pass

Figure 26 (a) Parallel connection. (b) Tapered cascade 3:2:1 array. (c) Two pass connection.

Membrane Technology for Water: Microfiltration, Ultrafiltration, Nanofiltration, and Reverse Osmosis Table 13

333

Approximate energy demand and costs for membrane applications to water industry

Membrane application

Energy demand (kWh m3)

Production cost (USD m3)

Reference

Seawater RO RO reclamation MBR Water treatment

3.2–3.8 1.0–1.5 o0.8 o0.3

0.5–0.75 B0.3 Similar to conventional Similar to conventional

Voutchkov and Semiat (2008) Cote et al. (2008) Cornel and Krause (2008)

MBR, membrane bioreactor; RO, reverse osmosis.

integrity testing facilities. Integrity tests are important in water treatment to check if damaged fibers are present. One popular test is the air pressure decay test (a damaged fiber shows rapid pressure loss) (Kennedy et al., 2008). Membrane modules can be connected in various ways, in series and parallel, as depicted in Figure 26. The low-pressure applications tend to have modules connected in parallel with permeate taken to a common header (Figure 26(a)). The feed is similar to all modules, although in a large system the feed may be staged. In a typical SWRO plant, the modules are arranged in both series and parallel (Figure 26(b)). The firststage pressure vessels are connected in parallel, the number of paths depending on the maximum allowable flow per module. Within the pressure vessel there would be 6–8 SWMs connected in series, such that toward the vessel outlet, the concentration builds up and the flow drops due to permeate removal. The process is continued in the second and possibly third-stage pressure vessels as shown in Figure 26(b). In this example, the second and third stages have fewer vessels in parallel as the net volumetric flow has dropped; this is known as a tapered cascade and the example is a 3:2:1 cascade. The permeate from the stages is often blended. Feed pumps may be augmented by interstage pumps to maintain pressuredriving force along the cascade. Another option is depicted in Figure 26(c), which shows a two-pass arrangement with permeate from the first set of membranes having further treatment in a second set of membranes. This approach is used if there is a need for greater removals of specific contaminants, such as boron.

1.56 kWh m3 reported (Truby, 2008). It should be noted that these values are for the RO stage only and it is usual to report plant data including seawater intake pumps, pretreatment, and other miscellaneous plant energy use. These add 0.6– 1.0 kWh m3 to energy demand (Voutchkov and Semiat, 2008). Table 13 summarizes typical energy data. The energy demand for RO reclamation is significantly less than SWRO, due to the much lower pressures required (about one-fourth of SWRO). Based on differences in O and M costs (Cote et al., 2008), the energy demand would be less than 50% of SWRO. For the MBR, a major energy demand is air scour to control fouling. Typical energy demand for MBR processing municipal wastewater is 0.75–1.0 kWh m3 (Cornel and Krause, 2008) and developments promise lower energy usage. Finally, treatment of surface water by membranes, using dead-end with backwash, has a modest energy demand of typically o0.3 kWh m3. Production costs follow similar trends to the energy demands. Table 13 gives indicative costs. It should be noted that the range could be considerable and depends on scale of operation (small plant typically have more costly product). SWRO is most costly, but the cost of production is only o0.1 cents US per liter. RO reclamation delivers water at about 50% of the cost of SWRO. For the low-pressure processes, it is now evident that both the MBR and membrane water treatment have similar costs to conventional processes for green field sites. The marginally higher energy costs are offset by the smaller foot print and infrastructure costs.

4.11.7.3 Energy and Economic Issues

4.11.8 Conclusions

Energy demand and production costs are important parameters for the water industry. As can be anticipated the greater the required pressure or the more fouling the feed, the greater the energy demand and cost. This means that energy and cost ranking is in the order, SWRO 4 RO reclamation 4 MBR 4 water treatment. A guide to the intrinsic energy demand can be obtained by noting for a flow of Q (m3 s1), with feed pressure P (Pa or N m) and a recovery Y (volume product/volume feed) the energy demand is QP/QY ¼ (P/Y)  (1/3.6 106) (correcting W s m3 to kWh m3). For SWRO operating at a feed pressure of 70 bar and recovery of 0.5, the intrinsic energy usage can be estimated as 3.9 kWh m3. However, modern RO plants use pressure energy recovery on the brine stream which could return about 1.7 kWh m3, giving a net energy of about 2.2 kWh m3. Even lower values have been achieved for SWRO under carefully optimized conditions, with a value of

Membrane technology is playing an increasingly significant role in the water industry. Membranes are applied across the spectrum from seawater desalination, through wastewater treatment and reclamation, to surface water treatment. The technology continues to advance with improved membranes and processes. The energy demands and production costs have steadily declined and, in some cases, are similar to conventional processes, but with better-quality water products.

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4.12 Wastewater as a Source of Energy, Nutrients, and Service Water P Cornel, A Meda, and S Bieker, Technische Universita¨t Darmstadt, Darmstadt, Germany & 2011 Elsevier B.V. All rights reserved.

4.12.1 4.12.2 4.12.2.1 4.12.2.2 4.12.2.2.1 4.12.2.2.2 4.12.2.3 4.12.3 4.12.3.1 4.12.3.2 4.12.3.3 4.12.4 4.12.4.1 4.12.4.2 4.12.5 4.12.5.1 4.12.5.2 4.12.5.2.1 4.12.5.2.2 4.12.5.2.3 4.12.5.2.4 4.12.5.2.5 4.12.6 4.12.6.1 4.12.6.1.1 4.12.6.1.2 4.12.6.1.3 4.12.6.1.4 4.12.6.2 4.12.6.3 4.12.6.3.1 4.12.6.3.2 4.12.6.3.3 4.12.6.3.4 4.12.6.3.5 4.12.7 4.12.7.1 4.12.7.2 4.12.7.3 4.12.7.4 4.12.8 References

Introduction Resources of Interest Energy Nutrients Nitrogen Phosphorus Water Origin and Amounts of Resources Energy Nutrients Water Energy Caloric Heat Degradable Organic Constituents Nutrients Nitrogen Recovery Phosphorus Phosphorus recovery during wastewater treatment Phosphorus recovery from sewage sludge – wet chemical technology Phosphorus recovery from sewage sludge – thermochemical technologies Products from phosphorus recovery processes Exemplary applications of phosphorus recovery Water Reuse Reuse Options Agricultural reuse Intra-urban reuse as service water Industrial reuse Groundwater recharge Fit for Purpose, Quality Requirements Treatment Options and Energy Requirements Physical and chemical methods Biological treatment Disinfection Other methods Energy requirements Recovery Fosters Decentralization Water Energy System Scale Case Study: Qingdao Summary and Outlook

4.12.1 Introduction With continuously improving analytical techniques, it becomes more and more obvious that municipal wastewater represents a multisubstance mixture containing probably several hundreds of different substances. Normally, human excrements play a major role. Urine and feces contain nonexploited residuals of ingested food and

337 338 339 340 340 341 342 344 344 350 350 352 352 353 356 357 358 358 358 358 358 359 360 361 361 362 363 364 365 366 366 367 367 368 368 368 369 369 369 370 371 372

their degradation products. Fats, proteins, carbohydrates, and other carbon compounds; ammonia, urea, and other organic nitrogen compounds; organically bound phosphorus and dissolved ortho-phosphate; organic sulfur compounds; and potassium are the main components. Their amounts can be estimated and balanced per person. In addition, metals/heavy metals such as iron, copper, and zinc are essential trace elements in human nutrition. They are taken up with food,

337

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Wastewater as a Source of Energy, Nutrients, and Service Water

excreted via feces/urine, and thereby end up in the wastewater in the same way as salts, sodium chloride in particular, and natural hormones such as estrogens. Human excrements are also the main source for pathogens, coliforms, viruses, protozoans, and helminth eggs. Adding chopped organic (kitchen) waste to wastewater, as is practiced in some cases, increases the load of organic carbon, nitrogen, and phosphorus compounds. Calcium and magnesium salts are introduced via drinking water; and nitrate, sulfate, iron, and manganese are from geogenic sources, for example, via the use of groundwater as drinking water resource. Salt concentrations are increased by washing, dishwashing, and other cleaning agents, disinfectants and salts for water softening, and the regeneration of ion exchangers in domestic appliances and installations (potassium, sodium, calcium, and magnesium). Depending on the composition of the washing/cleaning agents, polyphosphates and zeolith A (source of aluminum) are also introduced into the wastewater. Due to the use of perborate as a bleaching agent, boron can be found in urban wastewater at an average concentration of around 1 mg l1. Boron can become toxic at levels only slightly greater than those required by plants for good growth (Lazarova and Bahri, 2005). Copper and zinc in wastewater mainly originate from domestic installations and roof runoff. In some parts of the world, they are also the source of lead in wastewater. The main component of halogens in wastewater is chlorine. Sources of organic halogens are mainly from commercial and industrial activities. Concentrations can vary significantly, and the typical amount for municipal wastewater in Germany is approximately 100 mg l1 measured as adsorbable organic halogens (AOXs) (Imhoff, 2007). During the last few years, one of the main focuses of research has been on the so-called ‘micro-pollutants’, substances of ecotoxicological relevance which differ considerably in their chemical character and their physical behavior. There are acidic, neutral, as well as alkaline compounds with hydrophilic as well as hydrophobic character. In common, they have a low biological degradability. Here, the term micro-pollutants includes drugs, diagnostics, cleaning and personal care products, industrial chemicals, as well as endocrinically active substances which are found in wastewater in low concentrations, that is, generally in ng l1 to mg l1 levels. Hospitals and nursing homes, industrial and commercial activities are some of the sources; however, the main source is domestic wastewater. In the European Union, approximately 3000 different pharmaceuticals are in use, including analgetics, antirheumatics, antibiotics, antiepileptics, lipid-lowering drugs, beta blockers, cytostatic drugs, radiopaque materials, contraceptives, tranquilizers, and virility drugs (Ternes, 2004). In addition, there are cleaning and personal care products, such as shampoos and other hair care products, bath essences, skin, oral and dental care products, soaps, sunscreen agents, as well as perfumes and aftershaves. These care products normally contain persistent fragrances such as musk, ultraviolet (UV) blockers, and preservatives, which reach the wastewater by human excretion or during/after external use. While the output of pharmaceuticals in Germany varies – depending on

the kind of drug – between a few kilograms per substance and several tens of tons for lipid-lowering drugs, antirheumatics (up to 500 Mg a1), and antibiotics (Forth et al., 1996, cited in Wilken and Ternes, 2001), without considering veterinary medicines, in 1993, more than 550 000 t personal care products were produced (Ternes, 2004). Some pharmaceutical products, for example, contraceptives or antibiotics, belong to the group of so-called endocrine disruptors. This term includes those substances affecting the hormonal balance. They may be not only natural hormones (estrogens), but also substances, which function like hormones without being hormones in the actual sense (xeno-estrogens). The hormones with the highest known activity are the natural estrogen 17b-estradiol and the synthetic estrogens 17a-ethinylestradiol and mestranol (contraceptives). Their concentration in wastewater is in the ng l1 level (Kunst et al., 2002). Besides hormones, a large variety of other substances with undesirable/unintentional endocrine activity gets into the wastewater, from industrial as well as municipal sources. Among many others, relevant substances are nonylphenols, bisphenol A (BPA), organotin compounds, benzylbutylphthalate, dibutylphthalate, as well as some pesticides, pharmaceuticals, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs). A broad range of very different substances reaches the wastewater via commercial and industrial dischargers. These include the noble metals such as gold and silver, detergents, and disinfectants, partly based on phosphorus, acids, and bases; there is hardly any outline, and as Kroiss states: ‘‘ The only compounds which can reliably be prevented from the wastewater y are those which are not produced’’ (Kroiss, 2004).

However, wastewater consists of H2O to more than 99.5%, a purity which many purchasable products will never reach. This means that, on the other hand, potentially valuable substances are present in extreme dilution, in concentrations of mg l1 down to ng l1, and mixed with all sorts of other substances. This is a result of the conventional flushing sewer concept and makes an economic reuse of individual valuable substances at least questionable. When talking of wastewater as a resource, one has to face the ambivalent questions:

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What prerequisite the wastewater has to fulfill to become a resource? Under which conditions wastewater constituents may be called a valuable substance and when should it be named a pollutant? J Does the classification depend on the purpose of use? Nutrients can be valuable in irrigation water; however, they can have harmful impacts on water bodies. J Does the classification depend on the concentration of the substance in the wastewater? J Are bonding form, admixtures, and/or impurities responsible to turn a resource into a pollutant? J Are effort and complexity of separating the substance from the wastewater and its recycling the reason? The energy demand? The emission of greenhouse gases?

Wastewater as a Source of Energy, Nutrients, and Service Water

J

Is the shortage of the substance an issue? That is, the real shortage in the sense of the finite nature of resources, as it seems to be the case for phosphorus and/or the economic shortage.

These questions seem trivial at first sight. However, they can hardly be answered with a universally valid explanation, the more so as the answers seem to be subject to changes during time, as can be demonstrated with the example of phosphorus/phosphate. Until 30 years ago, phosphorus in wastewater did hardly attract any interest. Only later, phosphorus moved into the focus of wastewater treatment, when its eutrophicating impact on freshwaters and saline waters with low water exchange rates became obvious. Methods for phosphorus elimination were developed and were introduced into wastewater treatment plants almost all over Central Europe. The task was to separate phosphorus. Nowadays, hardly 15 years later, efforts have started to reclaim phosphorus from wastewater to be used as resource. What has changed? Neither the quality and the bonding form of phosphorus in wastewater has changed, nor its quantity/concentration. Obviously, our point of view has changed, initiated by new insights and the interdependency with improved technical prospects for recovery. These new methods enable the economic recovery and recycling of phosphorus and thereby transform phosphorus from being a pollutant to a resource. Whether we consider a water constituent as valuable substance or as potentially hazardous material depends on the regional conditions and is subject to temporary changes. In order to justify the efforts for an improved materials flow management and technical measures for concentration/enrichment, purification, and recovery of substances, it is therefore important to critically analyze the relevance of these measures and to evaluate them in comparison with alternative options of energy conversion and fertilizer production.

4.12.2 Resources of Interest The 2000 UN General Assembly Millennium Meeting established eight Millennium Development Goals (MDGs) with targets to be achieved by 2015 starting from the baseline in 1990. One of the water-related targets is ‘‘Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation.’’ The MDG further states that ‘‘In 2002, nearly half of the developing world (2.5 billion people) had no access to proper sanitation,’’ most of those (1.98 billion) in Asia (UNESCO, 2006). Will we meet the targets? The WHO and UNICEF Joint Monitoring Program 2008 summarizes (WHO and UNICEF, 2008):

•

•

‘‘ The world is on track to meet the MDG target on drinking water. Current trends suggest that more than 90% of the global population will use improved drinking water sources by 2015. The world is not on track to meet the MDG sanitation target. Between 1990 and 2006 the proportion of people without improved sanitation decreased by only 8 percentage points.’’ At least two-thirds of the population in 34 countries are not using improved sanitation facilities.

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‘‘Without an immediate acceleration in progress, the world will not achieve even half the sanitation target by 2015. Based on current trends, the total population without improved sanitation in 2015 will have decreased only slightly, from 2.5 billion to 2.4 billion. At the current rate, the world will miss the MDG sanitation target by over 700 million people. To meet the target, at least 173 million people on average per year will need to begin using improved sanitation facilities.’’ Wilderer pointed out that in order to meet the MDGs, every day wastewater facilities encompassing collection, transportation in sewers and treatment units must be built serving 900 000 people within the years 2005–15, considering 300 working days per year, (Wilderer, 2005a). This does not seem realistic at all. Against this background, the question is: What would municipal water and wastewater management look like, if we designed it on the drawing board, with our present knowledge and experience, but without including already existing supply and disposal infrastructure? The current concept of urban sanitation is based on the perception to avoid direct contact of humans with their own wastewater and flush away the pathogenic organisms, germs, and viruses potentially contained in the feces together with the urine and wastewater from shower, kitchen, and laundry. The implementation of flushing toilets and the flushing sewer concept is of course a success story in reducing diseases and offers high convenience to the users. To avoid negative ecological and economic impacts to the receiving water bodies, wastewater treatment technology was developed and systematically further developed and implemented. The main objective of wastewater treatment is to convert pollutants into less problematic substances prior to discharge to any surface water body (Wilderer, 2005b). The current system hardly makes use of valuable substances such as nutrients, organic matter as energy source, fatty acids as raw material in industrial chemistry, and of the purified water itself (Wilderer, 2005b). Against this background, the economic, social, and ecological necessity for resource-conserving handling of water and energy as well as the identification and development of potentials for resource recovery from wastewater should be looked at first. From the multitude of compounds and resources existent in water, the following topics – according to the chapter’s title – are focused on: 1. energy, 2. nutrients, phosphorus in particular, and 3. water.

4.12.2.1 Energy Finite fossil fuels as well as the increase in atmospheric concentrations of greenhouse gases due to anthropogenic activities have given priority to the worldwide discussion and efforts to increase energy efficiency in all processes. There are multifold interactions between water and energy:

• •

Electric energy can be generated by using hydro power. Water is necessary for energy production.

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•

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Wastewater as a Source of Energy, Nutrients, and Service Water

Water is used for mining fuels. Taking into account the local conditions of Queensland/Australia, Keller estimates an amount of 2–3 l of water per electric kW h generated from coal (Keller, 2008). Cornel and Meda (2008a) estimate the specific water demand for agricultural energy production via biogas generation in Germany at approximately 300 l kW h.

•

Power plant cooling uses water.

Moreover, vice versa, there is manifold use of energy in water supply and disposal:

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•

•

Supply and conveyance. If necessary, water is conveyed from large depths or across large distances, for example in southern California which imports approximately 50% of its water supply from the Colorado River and the State Water Project (California Energy Commission, 2005: 144). For California, a range of 0–1.06 kW h m3 for water supply and conveyance is mentioned in the Integrated Energy Policy Report. For southern California, with its particularly unfavorable situation, it is even 2.35 kW h m3. In Germany, the city of Stuttgart is supplied with water from Lake Constance, 150 km to the south, thereby creating an energy demand for pumping of approximately 1.1 kWh m3 (Bodensee Wasserversorgung, 2010). This figure makes clear that, even in a country with overall sufficient water resources such as Germany, water may be scarce on a local scale, requiring energy-intensive transports of water over long distances. Treatment. The energy demand for generating drinking water from existing water resources and eliminating impurities varies from almost zero to several kilowatt-hours per cubic meter. The Integrated Energy Policy Report states it to be up to 4.2 kW h m3. For desalination of brackish water and saltwater, currently one has to calculate approximately 4 kW h m3 (Keller, 2008). In Germany, as a relatively water-rich country, the energy consumption for water catchment and treatment ranges from 0.21 to 0.40 kW h m3 (Ro¨dl and Partner, 2006, 2007). Distribution. The energy demand for distributing water to consumers is mainly caused by costs for pumping and depends on topography, distances, pipe cross sections, water pressure as well as size and age of the distribution system. Between 0.18 and 0.32 kW h m3 are quoted for California (California Energy Commission, 2005: 144). Ha¨hnlein mentions 0.54 kW h m3 for treatment and distribution for a large German city, whereby more than half is caused by energy for pumping (Ha¨hnlein, 2008). Ro¨dl and Partner (2006, 2007) quote 0.06–0.17 kW h m3 as energy consumption for water distribution in Germany. In addition, water losses caused by leakages have to be considered. Water loss rates of up to 50% are not uncommon in urban distribution systems (UNESCO, 2009: 58). High losses require the extraction, treatment, and transport of greater volumes of water than the customer demand requires. As some leakage finds its way into community waste or storm water collection, it will be treated by the local wastewater plant causing additional energy demand with no beneficial use.

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•

End consumers. At the consumer’s end, the main energy users are posttreatment of water such as softening, filtration, disinfection, as practiced in some places, pressure increase especially in multistory buildings, and – in the first place – hot water production for personal hygiene, washing, and dishwashing. For urban uses (residential, commercial, and industrial) in California in 2001, the Integrated Energy Policy Report states an electricity consumption of 27 887 GW h and a natural gas consumption of 4220 US therms, corresponding to 123 646 GW h (1 US therm ¼ 29.3 kW h). Considering a water use in the urban sector of 11 128 million m3, one can calculate an average end-use energy intensity of 2.5 kW h m3 considering only electricity and 13.6 kW h m3 considering both electricity and natural gas. Neglecting any losses, the latter value is equivalent to an average water temperature increase of almost 12 1C within the household. Wastewater collection and treatment. The energy demand for collecting and treating wastewater depends – on the one hand – on the length of the sewer system and the topography, and – on the other hand – on the treatment requirements, the selected treatment technique, and the size of the wastewater treatment plant. Energy demand values as quoted for California, 0.29–1.2 kW h m3, may increase due to stringent quality requirements for nitrogen and phosphorus discharge values, for helminth eggs, and other hygienic parameters or by the commitment to specific treatment techniques, for example, aerobic membrane-activated sludge systems or desalination membranes. For comparison, in Germany, the average energy demand for wastewater treatment (without collection) amounts to 0.44 kW h m3 including infiltration water and partially storm water (Haberkern et al., 2008). Water discharge. Last but not least, energy is needed for operating the electric pumps required to discharge the effluent of the wastewater treatment plants to the receiving water body (o0.11 kW h m3) or for further water treatment and use.

Table 1 points out the ranges of energy intensities in the water cycle using the example of California and Germany. The data are expressed as energy consumption per water volume (kW h m3) and energy consumption per person and year (kW h(C  a)1), assuming a specific water consumption of 100 m3(C  a)1 for California according to Asano (2007), and 45 m3(C  a)1 for Germany according to the Federal Statistical Office of Germany (FSO) (FSO, 2009). Although the energy intensities are nonadditive, they reflect the ranges of the energy demand for the water use cycle from approximately 0.5 to almost 7 kW h m3 without the waterrelated energy use of the end users. The latter is in average approximately 2.5 kW h m3 in electrical power and 13.6 kW h m3 considering both electricity and natural gas. Altogether, approximately 15% of the total energy consumption of California are classified as water-related, increasing to 19% when including agricultural irrigation (Reiter, 2008). Considering these numbers, the California Energy Commission arrives at the conclusion: ‘‘The link between energy and water use in the state is an important facet of California’s

Wastewater as a Source of Energy, Nutrients, and Service Water Table 1

341

Energy intensities in the water cycle (on the basis of California Energy Commission, 2005, p. 144 and Ro¨dl and Partner, 2006, 2007)

Water cycle segment

Range of energy intensity California 1

kW h

Supply and conveyance Treatment Distribution Wastewater collection and treatment Wastewater discharge

Germany 3

1

m

kW h

(C  a)

1a

kW h1 m3

kW h1 (C  a)1b

Low

High

Low

High

Low

High

Low

High

0 0.03 0.18 0.29 0

1.06 4.23 0.32 1.22 0.11

0 3 18 29 0

106 423 32 122 11

0.21

0.40

9

18

0.06 0.39

0.17 0.83

3 25c

8 80c

a

Assuming a specific water consumption of 100 m3 (C  a)1 according to Asano (2007). Assuming a specific water consumption of 45 m3 (C  a)1 according to FSO (2009). c From Keicher K, Krampe J, and Steinmetz H (2008). Eigenenergieversorgung von Kla¨ranlagen. Korrespondenz Abwasser 55(6): 644–650. b

energy system. While the most immediately recognizable aspect of this link is large-scale hydroelectric generation, the amount of energy used by the state’s water infrastructure and water end-users is at least equally significant – and growing fast. The Energy Commission evaluated the relationship between water and energy systems to better understand this link and determine what, if any, mutually beneficial strategies can be developed to improve both the water and energy sectors. As a result of this initial work, the Energy Commission determined that much can be done to improve both systems’’ (California Energy Commission, 2005: 138). It should be mentioned that in other countries water consumption and specific energy intensities might be much lower than in California. In Germany, for example, private households consume only 45 m3(C  a)1 compared to 100 m3(C  a)1 in California. Combined with the lower specific energy demand due to shorter transport distances, less energy-intensive water-processing techniques (e.g., no seawater desalination plants), and the use of more energy-efficient wastewater treatment processes, the energy demand of the entire water cycle amounts to only 30–120 kWh (C  a)1, compared to 50–700 kW h (C  a)1 in California. In addition, just how much energy can be produced from the wastewater’s organic matter? With 110–120 g COD (C  d)1 (chemical oxygen demand (COD)), the total energy content per person and day is approximately 0.4 kW h. Currently, approximately 0.05–0.1 kW h thereof can be generated as electric energy. That is, depending on the specific water consumption per capita, around 0.15–0.7 kW h m3 and as such, far less than required for water supply, distribution, collection, and treatment (see Section 4.12.4.2 for a detailed description). Thus, wastewater as a source of energy means that besides the potential recovery of energy from wastewater, the entire technology chain starting from water supply, treatment, and distribution to water use at the consumer’s end as far as wastewater collection, treatment, and discharge has to be considered and energetically optimized. The biggest saving potential lies with the reduction of the energy consumption. In Section 4.12.4, the recovery potentials are looked at more specifically and the greenhouse gas emissions will be taken into account.

4.12.2.2 Nutrients The main nutrients in wastewater are nitrogen and phosphorus.

4.12.2.2.1 Nitrogen Nitrogen is available worldwide in sufficient amounts. It is one of the most common elements. By far the largest quantity is found in the atmosphere which consists of approximately 78 vol.% N2. Thus, raw material for nitrogen-based fertilizers is available in sufficient quantities; however, their production from elementary nitrogen or air, respectively, using, for example, the Haber–Bosch process is energy-intensive. In the Haber–Bosch process nitrogen and hydrogen react – at high pressure and increased temperature and in the presence of catalysts – according to the equation

N2 þ 3H2 ¼ 2NH3 þ 92:1 kJ Depending on the process, approximately 9–13 kW h kg1 NH3–N are needed (Mundo, 1970). According to Larsen et al. (2007), a value of 12.5 kW h kg1 NH3–N can be assumed respectively 9–11 kW h kg1 NH3–N according to EFMA (2000). The incentive to recover nitrogen from wastewater, mostly present as the ammonium ion, therefore results from the energy-saving potential rather than a finiteness of nitrogen itself. Considering that the nitrogen excretion amounts to 11 g per person and day, that is, approximately 4 kg per person and year, the energy-saving potential is a maximum of 40 kW h per person and year, in case all the ammonium ions contained in wastewater could replace ammonia produced by the Haber–Bosch process. In addition, around 3.9–6.9 kWh (C  a)1 could be saved at the wastewater treatment plant (WWTP) in case nitrification/denitrification could be omitted. (The energy consumption for nitrogen removal on WWTP is discussed in Section 4.12.3.2) Therefore, recovery of nitrogen from wastewater seems substantial and worth to be considered. Besides the general assessment of mass and energy balances, the positive response to a series of pragmatic questions is a precondition for the realization of the direct use of

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Wastewater as a Source of Energy, Nutrients, and Service Water 4.12.2.2.2 Phosphorus

nitrogen in wastewater as fertilizer or the use of recovery techniques:

•

Phosphorus is an essential element for all organisms. Besides carbon, hydrogen, oxygen, and nitrogen, phosphorus is one of the vital components of the DNA and the key element of the energy supplier adenosine triphosphate (ATP). Phosphorus is an essential nutrient. Already Justus von Liebig (1803–73) identified phosphorus as the limiting factor for plant growth. As a vital cell component, phosphorus cannot be replaced by any other element. This is why phosphorus is different from other resources, such as fossil fuels, where there are potential alternatives, or from nitrogen fertilizers, which can be technically produced from air nitrogen via the Haber–Bosch process. In nature, phosphorus passes through several interconnected cycles (Figure 2). The inorganic cycle describes the cycle from erosion, transport to the oceans, sedimentation, tectonic uplift, and alteration of phosphate-containing rocks into plant-available phosphates in soil (Emsley, 1980, 2001; Filippelli, 2002). The cycle time of this cycle is several million years, that is, in human spaces of times, phosphate transported into the oceans can be considered as lost for agricultural use. Besides the inorganic phosphorus cycle, there are two organic cycles attached describing phosphorus as part of the food chain. One of the cycles takes place on land (soil–plants– humans/animals–organic waste–soil) and the other in water. The cycle time of these cycles is between a few weeks and up to 1 year (Emsley, 1980, 2001; Bennett and Carpenter, 2002). These originally natural closed cycles are interrupted when phosphorus compounds in animal and human excrements are not used in fertilization. Then, phosphate contained in wastewater is partly transported to the oceans via the discharge systems, partly fixed in sewage sludge, which is deposited in landfill sites or incinerated; in the latter case, phosphorus contained in the ash is deposited in landfill sites or in subterranean storage. The procedure can be similar with organic fertilizers (solid and liquid manure) from intensive stock rearing. The deficit is balanced by chemical fertilizers, that is, the mining of phosphate-rich deposits in the earth’s crust. In Figure 2, the geological and biological cycles are illustrated, including changes due to human impact. The quality of rock phosphate not only depends on its phosphate concentration, but also on its concentration of harmful substances, cadmium and uranium in particular (UBA, 2001; Kratz, 2004). In order to restrict cadmium concentrations in processed ores and, where required, in mineral fertilizers, one has to expect increasing costs for the processing of rock phosphate (ATV-DVWK, 2003) in the future.

What is the required energy input for the recovery process? Is this energy demand less than the realized savings by taking into account storage, transport, and the fertilizing techniques themselves?

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What about the storage suitability of nitrogen? As fertilizers can only be used during vegetation periods, storage ability, space requirement, product stability, and its manageability are decisive cost factors.

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How is the quality of the fertilizer, that is, concentration, contamination, and plant availability? Is this quality constant and guaranteed? What about manageability? Is it feasible to use common equipment for spreading? Is the handling hygienically acceptable?

Last but not least, when discussing this matter one has to add certain constraints, as only a small percentage of the nitrogen employed in fertilizer production can be brought back to agricultural use, even with 100% recovery of the nitrogen existent in the wastewater. Only a fraction of the produced nitrogen reaches the consumer, is taken up with food, excreted, and reaches the wastewater. In vegetarian food, approximately 14% of the nitrogen applied as fertilizer is contained in the food, with meat, only 4% reaches the consumer, as shown in Figure 1 (Galloway et al., 2003 cited in Kroiss, 2006). Correspondingly low is the potential rate of nitrogen fertilizer recovered from wastewater in the fertilizer cycle. According to other authors, the percentage of nitrogen applied as fertilizer that finally reaches the consumer via the food chain and may then potentially be recovered from wastewater, ranges from 14% to 20% (Maurer et al., 2003; Zessner et al., 2010). Even considering these higher numbers, it is clear that nitrogen recovery cannot close the loop of the anthropogenic nitrogen cycle; however, it can make a small but important contribution in reducing the chemical fertilizer consumption. Similar circumstances apply to phosphorus recovery from wastewater, with the important difference that phosphorus is a limited, irreplaceable element. Therefore, its reclamation should be realized with higher priority.

N fertilizer produced 100

−6

N fertilizer applied 94

−47

N in crop

N in feed

N in store

N consumed

47

31

7

4

−16

−24

−3

Figure 1 Fate of nitrogen produced by the Haber–Bosch process in the course of meat production (Galloway et al., 2003, cited in Kroiss, 2006).

Wastewater as a Source of Energy, Nutrients, and Service Water

Human, animal Detergent Industry

Sewer system Organic waste

Plants

343

WWTP

Fertilization Agriculture Mining Erosion

Weathering

Rivers, oceans

Phosphate rock

Cycle times Sedimentation

Tectonic uplift

1−5 years 106−109 years

Sediments

??? years

Figure 2 Geological (inorganic) and organic (land) phosphorus cycles (Bennett and Carpenter, 2002 (cited in Pinnekamp, 2002) modified including human impacts, phosphorus cycle in water not included).

The price increase for phosphorus which has been observed in recent years, however, is attributed to speculations rather than being a consequence of increasing treatment costs. Rising energy costs also play a role, either directly using thermal processes or indirectly using the acid process with phosphate rock digestion, as the price for the required sulfuric acid also strongly depends on energy prices. However, in contrast to nitrogen, the availability of phosphorus is limited. Based on the assessment of the future consumption of phosphorus fertilizers, the availability of presently payable natural phosphate deposits is forecasted to be approximately 60–240 years (Steen, 1998, cf. IFA, 1998). Possible impacts on these estimations are the population development and thus the consumption of fertilizers as well as the activation of those natural phosphate deposits, which are not payable under present technical and economic views. Regional phosphate deposits vary distinctively. Currently, approximately two-thirds of the phosphate rocks are mined in USA, Morocco, and China. In addition, considerable amounts are mined in Russia, Brazil, Israel, Jordan, South Africa, and Tunisia. With regard to phosphorus recovery, there are also questions as to where recovery takes place (within the wastewater treatment process), what are the efforts, and in which form phosphorus is reclaimed (see Section 4.12.5).

4.12.2.3 Water Water resources are limited worldwide and with the still growing demand present a globally increasing problem. Thereby, the population of threshold and developing countries in arid and semi-arid regions as well as densely populated regions and megacities are affected in particular. Besides climatic conditions and a generally uneven distribution of water resources, population growth, increasing per capita water

consumption, conflicts of use, and increasing urbanization are the main causes for the growing (regional) shortage of freshwater. Often, a rather unsustainable handling of existing water resources and the contamination of surface and groundwater aggravate the situation. According to the forecast of the World Water Development Report (UNESCO, 2006) and based on the worst-case scenario, approximately 7 billion people in 60 countries will be confronted with water shortage until the middle of the current century if the present consumption habits do not change. The best-case scenario predicts that still at least 2 billion people in 48 countries will suffer from water shortage. In addition, experts from the Intergovernmental Panel on Climate Change (IPCC, 2007) forecast a further increase of global water shortage due to the effects of global climate change. Water is subject to regionally uneven distribution. In order to describe the (regional) water availability, different indices are used which on the one hand reveal tendencies, on the other hand should be carefully interpreted as to their absolute quantity and should be critically challenged (Box 1). One such parameter is the specific per capita availability of renewable freshwater, a number which depicts the calculatory potential and not the used water volume. The water intensity use index (also called water stress index) expresses the ratio of the mean total annual water withdrawal to the total renewable freshwater resources (Jime´nez and Asano, 2008). Table 2 gives threshold values for the two parameters used to characterize water stress situations. At a global level, water availability for 2006 was 8462 m3 (C  a)1, but at a regional level it varies from as little as 1380 m3 (C  a)1 in the Middle East and North Africa to almost 53 300 m3 (C  a)1 in Oceania. These figures do not reflect the situation of individual countries or within a country. A list of the countries that are water-scarce according to the

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Box 1

Wastewater as a Source of Energy, Nutrients, and Service Water

Can statistics lie?

Does Germany have a water stress problem similar to, for example, Spain, as the water stress indexes in Table 4 indicate? Everybody who knows these countries is surprised by this result. On the one hand, there is Germany’s green landscape and forests throughout the whole summer, with enough rain to almost abandon irrigation, and there is the dry landscape in Spain, on the other hand, where millions of Germans spend their holidays each year because of the nice, rain-free weather and where intensive agriculture is not thinkable without irrigation. Why does this subjective difference not match the statistical data? One key might be that the withdrawal data do not distinguish between consumptive and nonconsumptive uses. Water extracted for consumptive uses – especially for irrigation, where it is evaporated by plants – is no longer available for other uses, and as a consequence puts higher pressure on water resources than nonconsumptive uses such as cooling in power plants, where most of the water is returned to the water body with almost no quality deterioration and can be used again. The same is true for most of the municipal and industrial waters, which do not disappear by use but are returned to the rivers as (treated) wastewater and might be used again downstream. In addition to the fact that the water, after nonconsumptive use, is still available and, as a result, puts less pressure on the water resources, it has to be considered that in the published statistical data this amount of water is counted as leaving the countries by rivers and, as a consequence, lowers the calculated water resources and increases the water stress index. As this example shows, the method for calculating water stress indexes is questionable and needs at least careful interpretation. The numbers for Germany clearly illustrate this fact. Using the data of the FAO – the data of other sources, for example, German Federeal Statstical Office (FSO, 2009) are up to 15% lower – the water stress index for Germany can be calculated as 31% by dividing the withdrawal of 47 050 million m3 a1 by the natural renewable water resources of 154 000 million m3 a1. The statistical yearbook of Germany states that about 26 000 million m3 a1 of the withdrawn water is cooling water (FSO, 2009) which is mainly returned immediately to the same water body where it has been taken from. Subtracting the amount of cooling water, the water stress index would drop from 31% to (47 – 26)/ 154 ¼ 14%. In addition, 98% of the roughly 18 000 million m3 a1 of all industrial and municipal wastewaters are treated adequately and returned to the surface water. Together with the cooling water, these used waters amount to 44 000 out of 47 000 million m3 a1 which are still available after they have been used. Statistically they might be counted twice, once as withdrawal and a second time as leaving the country by rivers (Cornel and Meda, 2008a).

Table 2

Threshold values of two parameters used to characterize water stress situations Situation

Influence on water reuse

Based on per capita availability of renewable freshwater in m3(C  a)  1 Water stress o1700 The region begins to experience water stress and the economy or human health may be harmed. Chronic water o1000 The region experiences frequent water scarcity and scarcity water supply problems, both short- and long-term. Absolute water o500 The region completes its water supply by desalting stress seawater, overexploiting aquifersa or performing unplanned water reuse. Minimum survival o100 Water supply for domestic and commercial uses is level compromised, since the total availability is not enough to fulfill demand for all uses (municipal, agricultural, and industrial). Based on water intensity use index (WIUI) or water stress index (WSI) Water stress 420% The region is experiencing severe water supply problems that are addressed by reusing water (planned or not), overexploiting aquifers (by 2–30 times), or desalting seawater.

Under these circumstances, developing a water reuse program is recommended. Reuse activities have to be put in place. Urgent planned water reuse measures need to be implemented. Under such circumstances the current economic development model is unsustainable.

Integral water management programs including planned water reuse and recycling are vital to the economy.

a Although groundwater overexploitation has been observed even in countries with water availability over 4000 m3 (C  a)1. From Jime´nez and Asano T (eds.) (2008) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA.

water availability per capita index and the WIUI index are presented in Tables 3 and 4, respectively. Note that the list of countries change according to the index used. No country from Oceania, North America, or South America is listed, although it is well known that for some of them, at a regional level, water problems do exist, for example, in Australia, United States (Florida, California, etc.), Great Britain (London), and Mexico City. Almost independent from a country’s water availability, water supply of megacities poses a special challenge as its

water demand by far exceeds the local supply. Long transport distances and/or overexploitation of groundwater reserves with, in many cases dramatic, ecological and economical consequences are common, as examples from all continents would verify. Intra-urban water reuse of adequately treated wastewater or wastewater side streams is a sustainable measure of integrated water resource management by which the demand for potable water can be reduced by up to 50%. In many countries, water reuse is already an indispensable necessity and common practice in water management. In

Wastewater as a Source of Energy, Nutrients, and Service Water Table 3

345

List of water-stressed countries according to the water availability per capita index

Minimum survival level

Absolute water stress

Chronic water scarcity

Water stress

o 100 m3 (C  a)1

100–500 m3 (C  a)1

500–1000 m3 (C  a)1

1000–1700 m3 (C  a)1

Libya Jordan Bahrain Yemen Israel Algeria Oman Tunisia

Egypt Morocco Cyprus

Lebanon Syria

Asia (excluding Middle East) Maldives

Singapore

-

Pakistan Korea, Republic India

Central America and Caribbean Bahamas

Barbados

S.Kitts and Nevis Antigua and Barbuda

Haiti

Europe -

Malta

-

Denmark Czech Republic Poland

Sub-Saharan Africa -

Djibouti

Cape Verde Kenya Burkina Faso

Rwanda South Africa Malawi Eritrea Comoros Zimbabwe Ethiopia Burundi Lesotho

Middle East and North Africa Kuwait Gaza Strip United Arab Emirates Qatar Saudi Arabia

From Jime´nez and Asano (eds.) (2008). Water Reuse: An International Survey of Current Practice, Issues and Needs London: IWA

future, it will play an essential part in sustainable water resource management and will be one of the greatest challenges of the twenty-first century. In terms of reuse, treated wastewater meeting the respective demands according to its designated use has to be considered as valuable, usable, and locally available water resource. Thereby, water reuse has a share in reducing the discrepancy between continuously increasing water consumption and limited water resources (DWA, 2008). Depending on the respective boundary conditions, there are various reasons for water reuse (EPA, 2004): 1. Local or regional water shortage often occurs in arid and semi-arid regions, and also in metropolitan areas and megacities, where – nearly independent of the annual rainfall – the local demand is by far higher than the local or regional availability. Against this background, intraurban multiple use, such as greywater treatment and reuse as service water or use of treated wastewater for irrigation of parks, sports fields, and cemeteries is one possibility to reduce the specific freshwater consumption to such a level as needed for cooking, drinking, and personal hygiene. Another possibility is to use service water in those cases

2.

3.

4.

5.

where freshwater quality is not essential, thus reducing the wastewater load on the receiving waters at the same time. Water scarcity and droughts occur in arid and semi-arid regions in particular. Here, economic use of water is a must. This means, multiple water use with subsequent reduced quality standards and with – according to the reuse purpose – adjusted interim treatments. Protection of water resources, that is, within the frame of Integrated Water Resource Management Concepts freshwater extraction which does not exceed the water renewing rate. In many cases, water reuse and use of service water can represent an alternative to freshwater extraction. Economic factors can be a driving force for intensified water recycling, in particular in industrial water management, and also in hotel resorts and irrigation. In many countries, where fees have to be paid for drinking water supply as well as wastewater disposal, water reuse pays off twice. The possibility to use the water ingredients, for example, as fertilizer or the option of heat recovery from water can be of additional interest. Energy saving and minimization of the emission of greenhouse gases are new drivers, as local water treatment

346 Table 4

Wastewater as a Source of Energy, Nutrients, and Service Water List of water-stressed countries according to the water intensity use index

1000–3000%

500–1000%

100–500%

50–100%

20–50%

Saudi Arabia Libya Qatar

Yemen Oman Israel Jordan Iraq Syria

Tunisia Algeria Iran

Morocco Afghanistan Lebanon Cyprus

Asia (excluding Middle East) Turkmenistan

-

Uzbekistan Azerbaijan Pakistan

Bangladesh India Japan

Kazahkstan Armenia Korea, Republic Sri Lanka China Thailand Singapore

Central America and Caribbean -

-

Barbados

-

Cuba

Europe -

-

Malta Hungary Moldova, Rep

Belgium Netherlands Romania Ukraine

Bulgaria Spain Germany Poland Italy Denmark France Portugal

Sub-Saharan Africa -

-

Mauritania Sudan

Niger Somalia

Mauritius South Africa Swaziland Zimbabwe Eritrea

Middle East and North Africa United Arab Emirates

From Jime´nez B and Asano T (eds.) (2008). Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA.

and reuse can be by far more efficient in terms of energy savings than long-distance transport of freshwater, its potential intensive treatment, and subsequent wastewater treatment. 6. Political reasons, such as the independency of water supply from neighboring countries, which can be a strong motivation for multiple water use. Besides the above mentioned reasons, water shortage can result from extensive, often subsidized water use for agricultural irrigation. In combination with the export of agricultural products from water-scarce countries (e.g., Israel and Spain), large amounts of water are indirectly exported as so-called virtual water aggravating water shortage. All these reasons lead to the increasing importance of wastewater as resource whose use is in ideal accordance with the idea of sustainability. Water reuse should therefore be an indispensable part of the respective Integrated Water Resource Management system. Figure 3 shows the per capita reuse rate in m3 (C  a)1 and the percentage of reuse water in relation to the total water extraction (data from Earthtrends (2009)). The illustration

clearly shows that already today water reuse is given high priority in most of the world’s arid and semi-arid countries.

4.12.3 Origin and Amounts of Resources 4.12.3.1 Energy In order to minimize the consumption of energy and the emission of greenhouse gases, one has to consider a large number of individual segments of water supply and disposal as far as the detailed discussion of individual techniques. It starts with water supply and disposal, as pointed out in Section 4.12.2. In Figure 4, the values as listed in Table 1 (cf. Section 4.12.2) are presented graphically, supplemented by data for the energy intensity of recycled water treatment and distribution from the same report. It has to be mentioned again that the shown data might be higher than average since they are specific for the situation in California, USA, a region with high overall energy consumption and locally severe water scarcity. Nevertheless, they give a good impression of the relative impact of the different segments of the water supply and disposal chain on the total energy intensity.

Wastewater as a Source of Energy, Nutrients, and Service Water

347

70 Reuse per capita

Reuse/extraction

m3 (c.a)−1, (%)

60 50 40 30 20 10 Singapore

Malta

Jordan

Tunisia

Chile

Syria

Bahrain

Saudi Arabia

Cyprus

UAE

Mexico

Kuwait

Israel

Qatar

0

Figure 3 Reuse rate per capita in m3 (C  a)1 and percentage of reuse water in relation to the total water extraction. Data from Earthtrends (2009) Water Resources and Freshwater Ecosystems. http://earthtrends.wri.org/searchable_db/index.php?theme ¼ 2.

Source

Water supply and conveyance 0−1.06 kWh m3

Water treatment

Water distribution

0.026−4.23 kWh m3

0.18−0.32 kWh m3

End use

Recycled water treatment

Recycled water distribution

Agricultural Residential Commercial Industrial

0.11–0.32 kWh m3 Wastewater discharge 0−0.11 kWh m3

Wastewater treatment

Wastewater collection

0.29−1.22 kWh m3

Source

Total water use cycle energy intensity (without end use energy demand) 0.53−5.3 kWh m3

Figure 4 Water use cycle and energy intensities for California. Numbers according to California Energy Commission (2005), p. 144. Adapted from Reiter P (2008) Reducing the Water Utility’s Footprint Through Utility Sponsored End-Use Efficiency. Speech at the IWA World Water Congress Vienna 2008, 8–12 September 2008.

For the further treatment of treated wastewater to be used as service water and its distribution, the energy report quotes 0.1–0.3 kW h m3. This is a fraction of the energy intensities for water supply, treatment, and distribution, and points to the fact that water reuse not only preserves water resources, but may also be reasonable under the aspects of energy consumption and the emission of greenhouse gases. Although the range of the values is rather large and will vary according to the boundary conditions, particularly in the segments supply and conveyance and treatment, the following facts can be deduced:

•

Transport of water and wastewater as well as water distribution and wastewater collection present a significant

• •

factor within the energy balance. This favors small-scale treatment and reuse of recycled water. The use of recycled water from treated wastewater is much more energy-efficient than desalination of brackish water or saltwater (0.11–0.32 kW h m3 compared to 2–4 kW h m3). The partial replacement of potable water by reclaimed water for nonpotable use does not only conserve water resources but can also be a significant contribution to the reduction of the overall energy consumption.

This demonstrates the close link between water consumption, water reuse, and energy. However, as boundary conditions and specific energy consumption can vary significantly, there cannot be general recommendations for water and energy

348

Wastewater as a Source of Energy, Nutrients, and Service Water

resource management, and thus for water reuse; all the more, as the energy demand for the generation of reuse water strongly depends on the raw water quality and the treated wastewater side stream, respectively, as well as the required standards for the reuse water. Bieker et al. (2009) show that for the complete treatment of greywater 0.6–1.2 kW h m3 can be estimated for reaching nonpotable reuse water quality for intra-urban use (disinfection not included). The lower value of 0.6 kW h m3 when using the conventional activated sludge process or biological aerated filters, the upper value of 1.2 kW h m3 when using membrane bioreactors for biological treatment. For disinfection, for example, with ozonation, UV irradiation or membrane filtration, additional 0.035–0.4 kW h m3 must be calculated (Haberkern et al., 2008). In case an additional desalination step is used downstream, for example, in order to use the recycled water for groundwater replenishment or for direct or indirect potable reuse, the energy demand has to be assessed at the top end. Keller (2008) states an energy demand of 0.9–1.2 kW h m3 alone for the further treatment of already biologically treated wastewater to become so-called purified recycled water, via microfiltration, reverse osmosis, UV/H2O2 disinfection, and chlorination. The reuse water was treated to meet the highest standard of drinking quality through a seven-barrier treatment system (Keller, 2008) (see also Western Corridor, 2009). The operator of the new Goreangab water reclamation plant in Windhoek, Namibia, which, since 2002, daily produces approximately 21000 m3 drinking water from a mixture of secondary effluent and reservoir water, quotes an energy

demand of 1.34 kW h m3. This amount includes the whole multibarrier process chain, consisting of powdered activated carbon, preozonation, coagulation, flocculation, dissolved air flotation, dual media filtration, ozonation, biological activated carbon, two-stage granular activated carbon, ultrafiltration, chlorination, and stabilization including the raw water and high-lift pumps. Of course, these figures only reflect the energy for operation and cannot replace thorough life cycle assessments. The examples point out the quality-dependent range of the energy demand and place special emphasis on the question of required quality. Thus, consequent energy management also means to supply the quality required for the respective use, that is, fit for purpose, water for garden irrigation, toilet flushing, or street cleaning does not necessarily be of potable water quality. The above-mentioned data for the energy consumption per m3 water are important parameters which can be compared transnationally only in combination with the per capita water use. This mainly applies to concentration-dependent energy demand values, such as wastewater treatment. Figure 5 illustrates the large range of the municipal water withdrawal per capita, based on data published by the Food and Agriculture Organization (FAO, 2009). (The municipal water withdrawal considers the quantity of water withdrawn primarily for the direct use by the population and is usually computed as the total water withdrawn by the public distribution network. It might include public services, private gardening, small enterprises, and that part of the industries, which is connected to the municipal network and includes

700 FAO 2009

600

Eurostat 2009

l (C·d)−1

500

400

300

200

100

Bahrain Qatar USA Australia Kuwait United Arab Emirates Italy Japan Sweden Spain Greece Belgium-Luxembourg Norway France Israel Portugal Czech Republic Malta Austria Saudi Arabia Switzerland Denmark Finland Poland Jordan India Germany Namibia Singapore United Kingdom China Netherlands Eritrea Central African Republic Cambodia Chad

0

Figure 5 Municipal water withdrawal per capita and day according to FAO (2009) and Eurostat (2009).

Wastewater as a Source of Energy, Nutrients, and Service Water

water losses (FAO, 2009).) One can clearly see that water withdrawal does not depend on water availability alone. All countries with municipal water withdrawal above 400 l (C  d)1 can be called rich, even though some of them belong to the arid or semi-arid countries. The data depict a comparison of the countries; however, it should be pointed out that for some countries clearly divergent data are reported by different sources. As to these variations, data from Eurostat, the database of the European Commission, for the ‘‘total water abstraction by public water supply’’, which in some cases deviate significantly, have been added to the figure. Since the water withdrawal for the municipal network may contain transport losses, supply for industry and commerce, public buildings, etc., data for ‘‘water consumption for private households’’ for some European countries according to Eurostat (2009) are additionally shown in Table 5. In comparison to the municipal water withdrawal, the per capita water use in

Table 5

According to FSO (2009). According to Asano (2007). n.a.: not available. b

households is considerably lower. For example, in Germany, the municipal water withdrawal is 174 l (C  d)1, whereof 122 l (C  d)1 are used in private households (FSO, 2009), and Asano (2007) quotes a specific water consumption for the USA of 274 l (C  d)1 against a water withdrawal of 573 l (C  d)1. Specific withdrawal factors range from 12 to 14 l (C  d)1 in Chad, Cambodia, and the Central African Republic, to 100– 300 l (C  d)1 in many European countries. Up to 600 l (C  d)1 are used in Qatar and Bahrain, two of the driest countries in the world, where a large percentage of water is generated by energy-intensive seawater desalination. Thus, the energy demand for the processing of drinking water for Qatar and Bahrain is estimated as 600 l (C  d)1  4 kW h1 m3  365 d a1/1000 l m3 ¼ 876 kW h (C  a)1. This value compares to 27.5 kW h (C  a)1 in Germany (193 l (C  d)1 0.39 kW h m3 365 d a1/1000 l m3 for drinking water supply, conveyance, and treatment).

Municipal water withdrawal per capita in some countries according to different sources

Chad Cambodia Central African Republic Eritrea Netherlands China United Kingdom Singapore Namibia India Jordan Poland Finland Germany Denmark Switzerland Saudi Arabia Austria Malta Czech Republic Portugal France Israel Belgium-Luxembourg Norway Greece Spain Sweden Japan Italy United Arab Emirates Kuwait Australia USA Qatar Bahrain a

349

Municipal water withdrawal FAO (2009) (l (C  d)1)

Municipal water withdrawal Eurostat (2009) (l (C  d)1)

Water consumption for private households Eurostat (2009) (l (C  d)1)

12 12.4 13.7 18.1 83.3 87.7 95.6 101 103 132 139 150 179 193 209 232 241 248 277 282 285 288 288 301 301 315 318 334 373 381 397 449 493 573 581 660

n.a. n.a. n.a. n.a. 215 n.a. 339 n.a. n.a. n.a. n.a. 150 211 174a 214 360 n.a. n.a. 94 187 253 255 n.a. 188 487 207 357 268 n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

n.a. n.a. n.a. n.a. 122 n.a. n.a. n.a. n.a. n.a. n.a. 86 n.a. 122a 123 229 n.a. 121 75 91 151 n.a. n.a. 104 213 97 164 144 n.a. 203 n.a. n.a. n.a. 274b n.a. n.a.

350

Wastewater as a Source of Energy, Nutrients, and Service Water

Considerable differences in the per capita energy consumption arise from wastewater treatment as well. For example, when taking the per capita consumption of 274 l (C  d)1 for USA and 122 l (C  d)1 for Germany as a basis, annual wastewater volumes of 100 and 44.5 m3 (C  a)1 result, respectively. Combined with the specific energy consumption of 0.3–1.22 kW h m3 (see Table 1), the energy needed for wastewater treatment for USA is estimated to be 30–122 kW h (C  a)1. Thereby, wastewater treatment plants with nutrient elimination will rather be in the upper level. In Germany, with an average water consumption of 122 l (C  d)1, the average annual energy demand for wastewater treatment (without sewer system) is 34.9 kW h (C  a)1 (0.44 kW h m3) with a range of approximately 25 kW h (C  a)1 to more than 80 kW h (C  a)1 (Keicher et al., 2008). Such numbers should only be compared with great caution because of the specific boundary conditions, such as separate or combined sewer system, vacuum, pressurized or gravity flow sewer, topography, effluent quality standards, treatment level of the wastewater treatment plant, sludge treatment, off gas treatment, etc. All the same, it is obvious that the specific daily water and wastewater amount is decisive for the energy consumption and only the comparison of per capita values is appropriate and assessable. Energy and water are linked inseparably. Reducing the water consumption generally leads to a reduction of the energy consumption. However, here as well, there are exceptions, and one should examine and evaluate every single case. The second aspect when considering energy and water is: How much energy does wastewater contain? Here, different forms of energy have to be taken into account. It has to be distinguished between 1. the potential energy in dependence of the geodetic height which can be of importance at least in high-rise buildings or in topographies with large differences in altitude; 2. the thermal energy which in particular was input for hot water generation at the consumer’s end; and 3. the chemically bound energy which is mainly stored in organic water ingredients.

•

15 K1.16 W h (l  K)1 ¼ 254 kW h (C  a)1. This is by far higher than the potential energy estimated above. In other words, the amount of energy which is released to the environment when 1 l of water cools by 1 K corresponds to the calculated potential energy of 1 l of water retained at an altitude of 426 m. The chemically bound energy can be defined by the COD. The maximum potential amount of methane per kilogram COD can be calculated stoichiometrically. CH4 þ 2O2 ¼ CO2 þ 2H2O, that is, the COD per mol methane is 64 g O2. With a molar volume under standard conditions (0 1C, 1 atm) of 22.41 l, the CH4 equivalent of COD converted under anaerobic conditions is 22.41/64 ¼ 0.35 l methane g1 COD ¼ 350 l methane kg1 COD. Considering the energy potential of methane, 802 kJ mol1, the result is 802 kJ mol1 15.625 mol methane/kg COD ¼ 12.53 MJ kg1 COD, that is, 3.48 kW h kg1 COD. Based on a daily COD load per capita of 110–120 g, the maximum theoretical energy content is 139–152 kW h (C  a)1, in case the entire COD could be transferred to methane and could be utilized.

Table 6 summarizes the estimates, independent from the fact that the total energy content does not allow any conclusion on the reclaimable and technically usable percentage. The comparison shows that the major part of the energy contained in wastewater is stored as thermal energy and is reclaimable as close to the source as possible, whereas the percentage of chemically bound energy is smaller; however, it can be transported in the wastewater via the sewer system almost without losses. The recovery of potential energy from wastewater seems promising only in high-rise buildings or respective topographies. Under normal conditions, the potential energy is much smaller than thermal or chemically bound energy by several orders of magnitudes. Annotation: It might be promising, with high-rise buildings or hillside locations, to treat wastewater from upper floors or higher altitudes, respectively, to be used as service water in the lower floors/altitudes and thereby minimize costs for pumping freshwater. Whereby one can assume that the energy needed for pumping, in consideration of losses in the pipes

The following estimation will show the order of magnitudes:

•

•

When neglecting frictional losses etc., the potential energy is directly proportional to the height. Considering a medium water consumption of approximately 122 l (C  d)1 as in Germany, the energy content of wastewater (Epot ¼ m  g  h) in a height of 50 m is 122 kg (C  d)1 9.81 ms2  50 m ¼ 59 841 kg  m2 s2 ¼ 59 841 Ws ¼ 6.1 kW h (C  a)1. The thermal energy stored in wastewater mainly results from hot water generation and therefore basically affects greywater, that is, wastewater from showers, baths, washing machines, and possibly the kitchen. The maximum recoverable thermal energy is calculated via the specific heat capacity of water, the temperature gradient, and the water quantity according to the equation Etherm ¼ cp  DT  m. The specific heat capacity of water is 4.18 kJ (kg  K)1. This means, the amount of heat stored in water per liter and kelvin is 4.18 kJ ¼ 1.16 W h. Considering, for example, a greywater volume of 40 l (C  d)1 and an available DT of 15 1C, the thermal energy amounts to 40 l (C  d)1

Table 6 Estimated energy content of wastewater as potential, thermal, and chemically bound energy Calculated energy kW h (C  a)1

Potential energy (122 l (C  d)1, 50 m height) Thermal energy 40 l (C  d)1, DT ¼ 15 1C (or 120 l (C  d)1 with DT ¼ 5 1C) Chemically bound energy 110–120 g COD (C  d)1 a

6.1

Energy equivalent for driving a car for how many meters/day?a 9

254

1000

139–152

523–571

7 l (100 km)1 (34 mpg); 10 kW h l1 fuel.

Wastewater as a Source of Energy, Nutrients, and Service Water

and the efficiency of pumps and motors, is more than twice as much as the potential energy contained in the pumped water.

4.12.3.2 Nutrients In Table 7, an overview is given on the yearly loads of nutrients and the wastewater streams they mainly occur in, using the example of Germany (Otterpohl, 2002). As can be seen from the data in the table, nitrogen mainly occurs in urine as urea, in a daily quantity of 11–13 g per capita. With conventional wastewater disposal and treatment using the activated sludge process, nitrogen is physiologically bound in the activated sludge to approximately 20–30%. Depending on the process, the remaining part stays in the wastewater as ammonium ion, is nitrified to nitrate with an energy input of 2.15 kW h kg1 N ( ¼ B6 kW h (C  a)1), or Table 7 Annual loads of N, P, K, and COD and their distribution among the wastewater side streams Greywater

Urine

Feces

Volume Compound

m3 (C  a)1 kg (C  a)1

25–100

B 0.5

B 0.05

N P K COD

B4–5 0.75 1.8 30

B3% B10% B34% B41%

B87% B50% B54% B12%

B10% B40% B12% B47%

From Otterpohl R (2002) Options for alternative types of sewerage and treatment systems directed to improvement of the overall performance. Water Science and Technology 45(3): 149–158.

351

nitrified/denitrified which requires approximately 1.5– 2.9 kW h kg1 N ( ¼ B3.5–6.9 kW h1 (C  a)1, see Box 2). A utilization of nitrogen as fertilizer might be realized by the use of nitrogen-containing reclaimed water for agricultural irrigation and/or by using sewage sludge as biosolids on cropland. With the latter, only that share of nitrogen, which is bound in biomass, can be utilized. In Germany, with typical wastewater treatment processes including nitrification/denitrification with nutrient elimination, only a small part of the nitrogen is returned with the socalled biosolids to the nutrient cycle. Dockhorn calculated 48 000 Mg-N a1 out of 507 000 Mg-N a1 in the influent of the wastewater treatment plant, that is, a percentage of just below 10% is made use of (Dockhorn, 2007: 14). Phosphorus. Based on an average phosphorus load of 1.8 g P (C  d)1 (ATV-DVWK A 131, 2000) in the raw wastewater, with German boundary conditions and a per capita wastewater flow of 200 l (C  d)1 the influent concentration is around 9 mg l1. An average of approximately 11% of the incoming phosphorus load is removed with the primary sludge during primary settlement (ATV-DVWK A 131, 2000). In biological wastewater treatment, approximately 28% of the incoming phosphorus load is incorporated into the biomass and removed with the surplus sludge, even without specific phosphorus removal processes. Based on the permitted discharge concentrations of 1 or 2 mg l1, respectively, approximately another 50% of the incoming phosphorus load has to be removed specifically, either by biological or by chemical–physical P removal processes or their combination. In summary, this means approximately 90% of the incoming phosphorus load is incorporated into the sewage sludge. In Figure 6, the phosphorus balance for a

Box 2 Energy consumption for nitrogen removal The energy demand for nitrification/nitrogen elimination is estimated as follows. For the oxidation of 1 g NH4–N, stoichiometrically 4.57 g oxygen is necessary. Since the nitrifying bacteria consume some nitrogen for biomass production, the net oxygen consumption for the elimination of 1 g NH4–N via nitrification is 4.3 g oxygen. Assuming an oxygen transfer efficiency of 2 kg O2/kW h, the resulting energy input for aeration is 2.15 kW h kg1 N solely for nitrification (6.0 kW h (C  a)1, assuming an annual nitrified quantity of approximately 2.77 kg N (C  a)1). In case nitrate is successively denitrified, a fraction of the oxygen input (stoichiometrically 2.9 g O2/g N) can be used (recovered) for the oxidation of a part of the wastewater’s organics. Assuming that 85% of the nitrate is denitrified – the rest of the nitrate is lost via the effluent – the specific oxygen demand amounts to 1.83 g O2 g1 N and the specific energy demand for aeration to 0.92 kW h kg1 N. For total nitrogen elimination (additional denitrification), the energy demand for recirculation and agitation of the anoxic reactor volume has to be added. For recirculation 0.49 kW h (C  a)1 is estimated with the following assumptions: specific wastewater quantity: 200 l(C  d)1; recirculation factor: 300%; delivery head of recirculation pump: 0.50 m; efficiency of recirculation pump: 60%. For agitation 0.84 kW h (C  a)1 is estimated with the following assumptions. Specific volume of anoxic tank: 50 l/C; power density for agitation: 2 W m3. The total additional energy demand of 1.33 kW h (C  a)1 referred to as the annual denitrified quantity of approximately 2.36 kg N (C  a)1 results in 0.57 kW h kg1 N. Finally, the theoretical energy demand for nitrogen elimination results in 1.48 kW h kg1 N. Another estimation of the energy consumption for nitrogen elimination can be derived from real energy consumption data of wastewater treatment plants. Roth (1998) reports an energy consumption for aeration on wastewater treatment plants with nitrogen elimination of 14.2 kW h (C  a)1. The fraction of aeration energy due to nitrogen elimination is estimated at 3.12 kW h (C  a)1 (22%) considering the above-mentioned consumption for nitrification, the recovery from denitrification and the consumption for oxidation of carbon compounds (specific organic load: 16.4 kg BOD5 (C  a)1, specific oxygen demand: 1.1 kg O2kg1 BOD5). For the energy consumption for recirculation and agitation of the anoxic volume Roth (1998) quotes to be 2.01 respectively 1.75 kW h (C  a)1. The total energy consumption results in 6.88 kW h (C  a)1. Referred to the annual quantity of nitrified/denitrified nitrogen of approximately 2.36 kg N (C  a)1, the specific energy consumption results in 2.91 kW h kg1 N. It has to be remarked that the calculated energy requirements for nitrogen removal only apply to the denitrified nitrogen and not to the total quantity of nitrogen removed. Nitrogen removal by sludge production can be classified as a side effect of carbon removal. In fact, the energy demand to remove nitrogen contained in sludge needs to be calculated separately, depending on the specific sludge stabilization process, for example, sludge digestion with return of the supernatant to the biological treatment or separate treatment with for example, nitration/denitration, annamox, or deammonification.

352

Wastewater as a Source of Energy, Nutrients, and Service Water

Influent

Effluent

EBPR and precipitation PS 1.8 g (C.d)−1 100%

0.2 g (C.d)−1 11%

SS 0.5 g (C.d)−1 28%

0.9 g (C.d)−1 0.2 g (C.d)−1 50 % 11%

Approximately 90% in sludge Figure 6 Phosphorus balance for a typical municipal wastewater treatment plant in Germany with biological phosphorus removal and/or precipitation. PS, primary sludge; SS, surplus sludge; EBPR, enhanced biological phosphorus removal.

typical German municipal wastewater treatment plant with phosphorus removal is illustrated schematically.

4.12.3.3 Water Water demand. How much water do we need/consume?

• • •

•

3

1

1–3 m (C  a) is needed for drinking and cooking alone. Approximately 50 m3 (C  a)1 is consumed in European private households (equates to approximately 140 l (C  d)1). Approximately 230 m3 (C  a)1 is the specific consumption of European private households, public services, and industrial activities (not including energy generation; for comparison: USA: 266 m3 (C  a)1; Africa: 25 m3 (C  a)1 (Zehnder, 2003)). 41700 m3 (C  a)1 is the total consumption, including food production.

As shown in Figure 7, by far the largest part is used for food production. One kilogram of bread needs approximately 2 kg of wheat (dry weight of total plant) (Allan, 1997; Zehnder, 1997). In order to produce this quantity of plant material, the plants take up at least 1 m3 of water which they mainly release to the atmosphere as transpiration losses. The rule of thumb 1 m3 of water for 1 kg of bread holds true only in case of optimum conditions. In reality, the consumed water quantity is much higher. American farmers, for example, consume approximately 4 m3 of water per kilogram bread equivalent, and in the tropics approximately 5 m3 of water are used per 1 kg of rice, instead of the required 2 m3 (Zehnder, 2001). In order to generate foodstuff which is essential for providing a sufficient nutrition with 2500 kcal d1, presently 500– 1000 m3 (C  a)1 with vegetarian diet and 1200–1500 m3 (C  a)1 with a diet where 500 kcal out of the 2500 kcal are supplied by meat are needed (Zehnder, 2001, 2003). The higher demand for the nutrition of nonvegetarians derives from an approximately 10 times higher water demand per produced energy unit (kcal) for meat compared with vegetarian food. Even with optimal irrigation, that is, minimizing water losses, one has to calculate 250 m3 (C  a)1 for vegetarians and 680 m3 (C  a)1 for nonvegetarians (Zehnder, 2001, 2003).

1−3 m3 (C.a)−1 drinking and cooking

ca 50 m3 (C.a)−1 for private households

ca 180 m3 (C.a)−1 for industry and public services 800−1400 m3 (C.a)−1 for agriculture for foodstuffs production

Figure 7 Annual water demand (Europe) (Cornel and Meda, 2008a).

Against this background, it seems that the recovery of water from private households can only make a small contribution toward the reduction of water shortage. However, looking at the drinking water management of large cities, one gets a completely different impression. Beijing, London, Los Angeles, Mexico City, Singapore, Tehran, and Tokyo, cities of different development status and different locality, all have in common that the local drinking water demand by far exceeds the locally available resources. Drinking water supply can only be assured with large efforts and occasionally with severe impacts on the environment. Excessive exploitation of existing water resources, lowering of the groundwater level, energyintensive and costly transport of water over many hundreds of miles, and energy-intensive desalination of seawater are only few of the consequences of the current water supply of the megacities’ population. Moreover, most of the water is only used for the transport of pollutants. The demand of high-quality, potable water could be significantly reduced by reusing reclaimed water. One possibility would be intra-urban use of treated greywater for toilet flushing and other uses where potable water quality is not required as will be shown in Section 4.12.6.1.2. Another possibility is the direct or indirect reuse of reclaimed water as potable water, as practiced, for example, in Singapore, Windhoek, Australia, and California. Highly treated reclaimed water is introduced either directly into the potable water supply up- or downstream of the water treatment plant or into a raw water supply such as potable water storage reservoirs or groundwater aquifers (Asano, 2007: 6). Accordingly, the quality requirements are higher than with nonpotable use, and the required energy for treatment is higher as well. On the other hand, mass restrictions,

Wastewater as a Source of Energy, Nutrients, and Service Water

unavoidable with greywater use, fall away, and a dual supply system, coupled with the potential risk of cross-connection (as exists in buildings with parallel use of potable and service water) is avoided. ‘‘Water reuse is particularly attractive in the situation where available water supply is already overcommitted and cannot meet expanding water demands in a growing community. Increasingly, society no longer has the luxury of using water only once’’ (Asano, 2007). One might add that water reuse saves not only valuable water resources, but might also reduce the energy demand of the water cycle and contributes to reduce the emission of greenhouse gases. However, in places where water is sufficiently available and can be supplied with low energy input, water recycling might be relevant only in case a significant cost reduction can be achieved. In such cases, water supply does not reduce water availability.

Heating circuit

353

User

35 °C

50 °C Condenser Expansion valve

Compressor

Heat pump

Evaporator 12 °C

6 °C Sewer c. 15 °C

4.12.4 Energy 4.12.4.1 Caloric Heat One possibility for energy recovery from wastewater is heat recovery. Domestic wastewater presents a permanently available, year-round heat source with comparably high temperatures, in particular with separate sewer systems and low contents of sewer infiltration water. The heat contained in wastewater can be utilized via heat exchangers and heat pumps. Heat exchangers installed in the sewer transfer the heat from the wastewater to a heat exchanger fluid. Subsequently, in a second heat exchanger, the so-called heat pump evaporator, the heat is transferred to a working fluid with a lower boiling point. As a result of the energy input, the refrigerant evaporates and is then compressed by using electric energy. In doing so, the temperature is increased to a usable level. In a third heat exchanger, the so-called condenser, the vapor releases its heat to the heating circuit. Thereby, the pressurized refrigerant liquefies again. After the expansion and cooling of the refrigerant within the expansion valve of the heat pump, the refrigerant cycle starts anew (Figure 8). With such installations in the sewer system, the cooling normally amounts to 2–3 1C. For example, with 45 m3 (C  a)1 and a heat capacity of 1.163 W h (l  K)1 approximately 105–157 kW h (C  a)1 is produced. To assess the reasonability of this method of heat recovery, it is important to consider the ratio between the useable heat capacity and the added electric power. This characteristic is called coefficient of performance (COP) and is calculated via the heat pump efficiency Zhp and – derived from the second law of thermodynamics – the maximum COP (reciprocal value of the Carnot efficiency):

COP ¼ Zhp  ðThot =ðThot  Tcold ÞÞ When taking into account friction, pressure losses, temperature gradients during heat transfers, and losses during compression, one can assume a heat pump efficiency factor of approximately 0.45–0.5. In Table 8, COP values for cold water temperatures of 12 and 25 1C, respectively, and two hot water temperatures, that is, 40 1C for a low-temperature heating

Heat exchanger Figure 8 Schematic diagram of a heat pump.

Table 8 Coefficient of performance for heat pumps under different operating conditions Tcold / Thot

12 1 C

25 1 C

40 1C 65 1C

5.6 3.2

10.4 4.2

circuit and 65 1C for a hot water boiler (nominal temperature 460 1C, because of Legionella) are compared to each other. Assuming an efficiency factor of 50%, COP values of 5.6 and 3.2, respectively, for cold water temperatures of 12 1C, and 10.4 and 4.2, respectively, for cold water temperatures of 25 1C are the result. A COP of 3.2, for instance, indicates that 3.2 times more heat is made available than electric energy has to be provided for the heat pump. This is a significant advantage compared to electric warm water heating. However, taking into account the power plant efficiency in power generation and transmission losses in the power grid, assuming a total efficiency factor of approximately 30–35% for power generation and transport/distribution, just the same amount of heat energy is released as in case of direct local use of primary energy for heating (3.2  0.3 ¼ 98%). When also considering that due to fluctuations in temperature, biofilm formation on heat exchangers, etc., the actual efficiency (annual average value) is lower than the COP value indicates, the potentials and limits of heat recovery from cold wastewater become apparent. However, the table also shows that the COP is significantly higher with wastewater (greywater) of 25 1C. This means, in order to achieve optimum energy recovery, the process has to take place as close as possible to the origin of the hot water, for

354

Wastewater as a Source of Energy, Nutrients, and Service Water

example, via heat recovery from greywater close to its source. Therefore, efficient heat recovery also asks for preferably decentralized use of the generated heat, thus minimizing energy losses from the generated hot water on its way to the consumer. In Section 4.12.7, the potential of heat recovery is illustrated with the example of a semicentralized supply and disposal center. Via heat exchanger and heat pump, the caloric heat of the (treated) greywater, which is available as service water for toilet flushing, is utilized. This method pays off twice. Heat recovery is efficient as the temperature of greywater is comparably high, and an additional positive effect is that with cooling the service water, the potential of microbial recontamination is reduced significantly. If required, heat pumps can also be used for supplying cooling energy instead of heat. In this case, heat is added to the wastewater instead of taken from it. However, with equal temperature gradients, heat pumps are more efficient for heating than for cooling. The reason is that the waste heat from the compressor can be used in the heat mode but not in the cooling mode.

4.12.4.2 Degradable Organic Constituents As described in Section 4.12.3.1, the chemically bound energy is directly proportional to the COD. Stoichiometrically, 1 kg COD equals 350 l methane (under standard conditions), which corresponds to a net calorific value of 3.48 kW h. Accordingly, 0.39–0.42 kW h are chemically bound in the daily wastewater load of approximately 110–120 g COD/C, amounting to 139–152 kW h annually. In Germany, the specific energy consumption of wastewater treatment plants using the activated sludge process with nutrient removal and sludge dewatering is – depending on the plant size – between 25 and 35 kW h (C  a)1 for plants 4100 000 PE (population equivalent) and 55–80 kW h (C  a)1 for plants o5000 PE (Keicher et al., 2008). The manual Energie in Kla¨ranlagen (energy in wastewater treatment plants) quotes a guideline value of 23–30 kW h (C  a)1 for the energy consumption of wastewater treatment plants with C and N removal and a plant size 430 000 PE and an optimum value of 20–26 kW h (C  a)1 (MURL NRW, 1999). Comparing the estimated energy content of wastewater, that is, 139–152 kW h (C  a)1, with the specific energy

Table 9

consumption, that is, 20–30 kW h (C  a)1, the assumption seems likely that wastewater treatment plants do not need to be energy consumers, but rather net producers of renewable energy, even with aerobic activated sludge plants; as, for instance, Shizas and Bagley (2004) conclude. Looking closer, one will note that though the total energy content is an interesting upper limit value, it does not reveal the percentage that can be transferred into electricity or usable heat. These values depend on the requirements for the discharge quality as well as on the treatment technology itself. The wide range of standards and the large number of different processes make comparative evaluations difficult. In the following assessment, only wastewater treatment plants with C and N and P removal – as it is the standard treatment in the European Community for sensitive areas – are compared. In Western Europe, USA, and Japan, the activated sludge process is the most commonly used technique for municipal wastewater treatment. Thereby, the organic carbon is oxidized to CO2 and bound in the produced biomass by approximately 50% each. Nitrogen, bound as ammonium, is first oxidized to nitrate and then reduced to N2 (nitrification/denitrification). Parts of the nitrogen as well as phosphorus are bound in biomass. The remaining phosphate is removed either biologically or via precipitation/flocculation. In the review below, it is presumed that primary sludge as well as excess sludge is stabilized anaerobically and that the produced biogas is used to produce energy in a block-type thermal power station or micro-turbine. What is the energy balance for such a standard configuration? The hydraulic retention time (HRT) in the primary sedimentation as well as the sludge retention time (SRT) as a function of temperature in the activated sludge process are the fundamental parameters for oxygen demand and thus energy consumption in the activated sludge process on the one hand and the amount of biogas and thus the producible electricity on the other hand. In Table 9, the energy demand for oxygen supply in the biological treatment and the electric energy generation from biogas for a model WWTP with 100 000 PE and for a design temperature of 12 1C are estimated, with varying SRT and HRT for primary sedimentation. In Figure 9, the values are depicted graphically. As expected, from an energy point of view, it is favorable to remove as much biomass as possible with the

Energy demand for oxygen supply and electric energy generation from biogas for different SRT and HRT for primary sedimentation

HRT primary sedimentation in h

SRT in d

Biogas l (C  d)1

O2 demand g (C  d)1

Energy demand for O2-supplya kW h (C  a)1

Net calorific valueb (total in biogas) kW h (C  a)1

Electric energy from biogasc kW h (C  a)1

0 1 2 1 1

13 13 13 4 25

10.2 18.2 21.0 20.4 17.2

86 69 63 40 74

15.6 12.5 11.6 7.3 13.5

24.4 43.6 50.3 48.8 41.1

8.1 14.4 16.6 16.1 13.6

a

Oxygen transfer efficiency under process conditions: 2 kg O2 kWh1. Methane content in biogas: 66%. c Z ¼ 0.32 (32% electricity, 68% losses and heat). b

Oxygen demand g (C·d)−1

Oxygen demand g (C·d)−1

Biogas production l (C·d)−1

Biogas production l (C·d)−1

100

100

80

80

g (C·d)−1, l (C·d)−1

g (C·d)−1, l (C·d)−1

Wastewater as a Source of Energy, Nutrients, and Service Water

60 40 20

355

60 40 20 0

0 Without Primary Primary primary sedimentation sedimentation sedimentation 1h 2h

SRT=4 d*

SRT=13 d**

SRT=25 d**

Primary sedimentation with HRT = 1h, *: Onlycarbonaceous elimination **: Nitrificationand denitrification

Nitrification and denitrification, SRT = 13 d

Figure 9 Influence of SRT and HRT in primary sedimentation on oxygen demand and biogas production for a model WWTP with 100 000 PE and for a design temperature of 12 1C. Modified from MURL NRW (1999) Handbuch Energie in Kla¨ranlagen. Du¨sseldorf (Germany): Ministerium fu¨r Umwelt, Raumordnung und Landwirtschaft des Landes Nord-Rhein Westfalen.

primary sludge and treat it anaerobically rather than oxidize the biomass with the need of oxygen input. Comparing the values of the last column of Table 9 with the energy demand for aeration of the aerobic tank in the activated sludge process which is approximately 28–30 kW h (C  a)1 (Bo¨cker and Dichtl, 2001), it becomes clear that it is not possible to run an energy-autarkic operation in these plants with existent technologies and efficiency rates. However, a series of measures for minimizing the energy demand can be derived from these data, which have to be proved case by case:

•

• • •

•

Maximizing of the solids removal prior to the activated sludge process - increase of the biogas production - minimizing/reduction of the oxygen demand (annotation: when using machines, for example, micro-sieves, their energy demand has to be considered). Shortening of the SRT, for example, by two-step process management - maximizing the sludge production, minimizing the oxygen demand. Implementation of energetically more efficient processes, for example, trickling filters, rotating biological contactor, biodisk, etc., where possible. Improvement of the energy efficiency rate for converting biogas into electricity. The electrical efficiency is currently 26% for block-type thermal power station (Schro¨der and Schrenk, 2008), and could be increased by using modern equipment with efficiency rates up to 39% at the optimum operating point, or, in the future, using fuel cells with expected efficiency rates up to 50% (Schro¨der, 2007). Increase of the biogas volume via enzymatic additives or via disintegration; it has to be proved on an individual basis that the potential increase of the energy output is not lower than the invested energy.

•

•

•

Increase of the biogas volume via cofermentation of organic waste, for example, the addition of kitchen, market, or restaurant wastes, contents of grease separators, etc. Strictly speaking, this is not the way to energy autarky of wastewater treatment plants, as additional external energy carriers are cotreated which are not part of the wastewater cycle (cf. Section 4.12.7). Reduction of the nitrogen amount. The energy demand for nitrification/denitrification can be assumed to be 1.5–2.9 kW h kg1 Nremoved, as shown in Box 2. With 11 g N (C  d)1 and considering that the nitrogen bound in biomass is not nitrified/denitrified, for N removal alone approximately 3.5 to 6.9 kW h (C  a)1 are needed. Local N elimination, for example, by not introducing urine to the wastewater, would result in significant energy saving. However, these energy savings have to be compared to the additional energy demand resulting from storage, disposal, treatment, transport, and use. This also applies to the direct use of urine as fertilizer, as costs for spreading and transport might be relevant due to low N concentrations in the fertilizer. Implementation of alternative N removal processes with lower energy demand, such as nitration/denitration, annamox, or deammonification. Using these methods to treat process water can reduce the internal return load and thereby contribute to energy savings.

Keeping in mind that the main task of a wastewater treatment plant is to treat wastewater, one has to make sure in each single case that measures toward increasing the biogas volume or saving energy do not deteriorate the discharge quality. For example, increased removal of organic substrate during primary sedimentation can lead to substrate shortage during denitrification; cofermentation can lead to an increase in the return load of the wastewater treatment plant and disintegration to an increase in hardly degradable COD, etc.

Wastewater as a Source of Energy, Nutrients, and Service Water

Furthermore, with all measures one should consider the efforts needed and prove whether there are other possibilities which can be realized with equal efforts and yet higher energy savings. The energy consumption of German wastewater treatment plants is estimated to be 4.2–4.4 109 kW h a1 (Schro¨der and Schrenk, 2008), while the total energy demand (2005) is 3.955 1012 kW h a1 (BMWi/BMU, 2006: 8). This means that the energy demand of wastewater treatment plants constitutes only 0.12% of the total annual energy demand in Germany (electricity, oil, gas, etc.), around 1% of the used electric power of 611 109 kW h a1 (BMWi/BMU, 2006: 51) and equals approximately 1/5 of the annual consumption of electricity which is caused by the standby mode of electrical equipment (22 109 kW h a1 in 2004 (UBA, 2008)). On the other hand, wastewater treatment plants are often the largest energy consumers of the municipalities. Energy savings in wastewater treatment plants should not be questioned by any means, as only via the sum of many small individual measures the overall goal of a general reduction of the energy consumption can be achieved. However, one should always be aware of dimensions and significance. Besides the aerobic process, there are anaerobic processes with biogas utilization. Anaerobic ponds and other anaerobic treatment methods, converting organic load to methane gas energy efficiently but emitting methane directly to the atmosphere, are not dealt with in this context, as the effect of methane as greenhouse gas is 25 times higher compared to CO2 and, therefore, in no way, can be called sustainable (IPCC, 2007: 141). To reach the same discharge quality with anaerobic treatment as in the discussed activated sludge process, the design has to consist of an anaerobic plant which can convert approximately 35–45% of the COD to methane (at the common wastewater temperature of maximal 15–25 1C, depending also on residence time and plant design (Urban, 2009)), and subsequent aerobic treatment to remove the remaining carbon compounds and for nitrification/denitrification. Due to the lower sludge production when using the anaerobic process compared to the aerobic biological treatment, the fixation of nitrogen and phosphorus is lower as well. When considering an anaerobic conversion of 40% of the daily COD load of 120 g COD (C  d)1, which can be achieved only at a water temperature 420–25 1C as might be typical for example, in Brazil, South Africa, Thailand, and parts of China and USA (Ruhr-Universita¨t Bochum, 2005), a rough estimation of the potential methane production results in 48 g COD (C  d)1  350 l CH4 kg1 COD ¼ 16.8 l (C  d)1 methane, that is, approximately 25.5 l (C  d)1 biogas. This is only slightly more than that produced in anaerobic sludge stabilization (see Table 9). However, this theoretical quantity cannot be achieved in practice, as on the one hand, sulfates in wastewater impair the production of methane and, on the other hand, part of the produced methane remains dissolved in the water phase and is stripped to the air until aerobic conditions are reached. This is doubly unfavorable, as this gas cannot be used for the conversion to electricity and a large part of the achieved CO2 savings of the anaerobic process is compensated again due to the higher greenhouse gas equivalent of methane.

COD of dissolved methane at 0.66 bar partial pressure Dissolved methane Dissolved methane at 0.66 bar partial pressure 100 80 mg l−1

356

60 40 20 0 0

5

10

15

20

25

30

35

40

Temperature (°C) Figure 10 Dissolved methane vs. temperature and COD of dissolved methane.

How much methane is dissolved? The solubility of methane in water depends on the partial pressure of methane in the gas phase and on the water temperature. Figure 10 shows the progression for the temperature range 10–35 1C and a partial pressure of 0.66 bar as can be assumed for biogas. The methane concentrations are 13–21 mg l1, that is, 52– 85 mg l1 COD is dissolved as methane. In other words, considering a wastewater with, for example, 400 mg l1 COD of which approximately 40% are reduced to methane anaerobically (Urban, 2009) at 20–25 1C, approximately 40% (64 mg l1 out of 160 mg l1) of the produced methane remains dissolved in water, cannot be used as biogas but is stripped in the subsequent aerobic plant and emitted to the atmosphere. Against this background, the usable methane volume of 16.8 l (C  d)1 in the selected example is reduced to 10.1 l (C  d)1 respectively 3679 l (C  a)1, corresponding to a calorific value of 36.6 kW h (C  a)1 and – under the assumption of a conversion efficiency rate of 32% – an electricity yield of 11.8 kW h (C  a)1. This amount is by far not sufficient to aerobically decompose the residual carbon content (which still amounts to 60% of the raw wastewater load) and provide enough energy for nutrient removal. Here, one has to consider that the amount of nitrogen to be nitrified/denitrified and the phosphate load to be precipitated are higher in relation to the organic load than in raw wastewater, as – due to the lower amount of sludge in the anaerobic pre treatment – only 10% of the nutrients are bound in the biomass. Table 10 gives an example of COD removal, methane production, and electricity yield for anaerobic wastewater treatment for different temperatures, based on data from Urban (2009). However, the increased greenhouse gas emission caused by methane emitted to the atmosphere remains the actual obstacle and needs new technical solutions. Cakir and Stenstrom (2005) compare aerobic wastewater treatment, including sludge digestion, with anaerobic wastewater treatment at 20 1C. By including the energy balance and greenhouse gas emissions of CO2 and CH4, only with concentrations above 300–700 mg l1 BODu (corresponding to approximately 400–950 mg l1 COD, with BODu: ultimate

Wastewater as a Source of Energy, Nutrients, and Service Water

357

Table 10

COD removal, methane production, and electricity yield for anaerobic wastewater treatment for different temperatures

T 1C

Dissolved methane mg l1

COD of dissolved methane mg l1

COD conversion a %

COD removed b mg l1

Produced methane c (total) l (C  d)1

Produced methane (dissolved) l (C  d)1

Produced methane (usable) l (C  d)1

Electricity yield kW h (C  a)1

15 20 25 30

19 17 15 14

76 68 61 56

27 35 44 56

109 139 177 226

11.4 14.6 18.6 23.7

7.9 7.1 6.4 5.9

3.5 7.4 12.1 17.8

4.0 8.7 14.1 20.7

a

Temperature dependency according to Urban (2009). COD of organics converted in methane (both dissolved and in biogas), based on COD concentration of 400 mg l1. c Based on 120 g COD (C  d)1. b

biochemical oxygen demand ¼ 1.46 BOD5 (von Sperling and Chernicharo, 2005) and BOD5/COD ¼ 0.5, depending on the sludge residence time), anaerobic wastewater treatment emits less greenhouse gases than aerobic treatment. From their investigations, the authors conclude ‘‘a technology to recover dissolved methane would make anaerobic treatment favorable at nearly all influent strengths.’’ The topic of nutrient removal was not further discussed in the publication. Consequentially, only with concentrated wastewater with temperatures above 20 1C and efficient methane recovery and utilization, anaerobic pretreatment can be a satisfactory alternative to greenhouse gas emissions and energy consumption. Again, in the individual case and particularly considering the requirements for nutrient elimination, anaerobic/aerobic process combinations and energy-efficient aerobic technologies in combination with sludge digesters have to be compared. Only with favorable conditions, that is, high concentrations and small water volumes, an energy-autarkic operation combined with high discharge quality can be realized. Via codigestion of organic (kitchen) waste, this goal can be achieved even in plants with nutrient elimination.

4.12.5 Nutrients In wastewater, the nutrients nitrogen and phosphorus exist in the dissolved form, nitrogen mostly as ammonium, and to a small percentage in organic nitrogen compounds (e.g., proteins and urea), while phosphorus mainly exists as inorganic phosphate and to a small percentage as organically bound phosphorus. The main source of phosphorus and nitrogen are human excrements. The per capita loads are approximately 11–13 g N (C  d)1 and 1.8–2 g P (C  d)1, respectively. This equals a ratio of 6:1 per weight, and a molar ratio of B13:1 N:P. Nitrogen and phosphorus compounds in domestic wastewater are in excess of what is required for the growth of microorganisms in wastewater treatment plants. With common aerobic wastewater treatment and depending on the plant’s configuration and the sludge retention time, approximately 20–30% of the nitrogen and 30–40% of the phosphorus are bound in the excess sludge. In plants with enhanced biological phosphorus removal and/or phosphorus elimination by chemical precipitation, up to 95% of the phosphorus are bound in the sludge.

In the simplest case, the utilization of nitrogen and phosphorus as fertilizer is carried by using (treated) wastewater for agricultural irrigation. Thereby, nutrient concentration in irrigation water might be too high and need to be controlled in order to avoid over-fertilization of the soils, especially when reuse water is the sole water resource for irrigation. Cornel and Meda (2008a) show that with usual European nutrient concentrations of approximately 55 mg N l1 and approximately 7 mg P l1 in the discharge of wastewater treatment plants without nutrient elimination, the amount of wastewater used for the irrigation should be limited. Taking for example the cultivation of wheat, the total amount of water required by the plants is between 6000 and 10 000 m3 ha1. In the case of irrigation with treated wastewater, the amount of irrigation water has to be limited to 1000–3800 m3 ha1, in order to prevent the nutrient input exceeding the required amount of 60–210 kg N ha1 (Cornel and Meda, 2008a). Depending on the nutrient concentration in the wastewater and the evaporation rate, such estimations undergo considerable variations. If no dilution water such as rainwater, surface water, or others is available, a partial nutrient elimination might be necessary, which can be effectively realized, for example, by complete nitrification or nitrification/denitrification of a partial wastewater flow and subsequent blending with the remaining flow. Basically, the utilization of nutrients with irrigation water is limited to irrigation periods. The storage of nutrientrich water is problematic, because of the risk of heavy algae growth in open storage or due to clogging and quality problems in the case of storage in aquifers. Disinfection of ammonium-rich wastewater causes problems as well. This may lead to different treatment objectives during the year. While nutrients may remain in the treated water during irrigation periods (treatment limited to carbon removal), the water must be treated for storage or discharge during nonirrigation periods; thus, nutrient removal may be required in order to avoid harmful effects on the water reservoir, the underground storage or on the receiving surface water body (Cornel and Weber, 2004). WWTPs applying different operation modes during different seasons can fulfill these varying requirements. A process concept for wastewater treatment with variable operation modes for the seasonal production of nutrientrich irrigation water and nutrient-poor discharge water is proposed, for example, by BMBF (2009) and Meda and Cornel (2010).

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Wastewater as a Source of Energy, Nutrients, and Service Water

Additionally, in many cases wastewater availability and agricultural lands – especially in fast-growing urban areas – are wide apart from each other. This means that investment and operation costs as well as the energy input for transport will increase. In many cases, agricultural use of sewage sludge is practiced by which those nutrients bound in the sludge can be recycled. However, due to the potential contamination with heavy metals, trace organic constituents, etc., agricultural sewage sludge application is discussed controversially and is on the decline, at least in Western and Central Europe. In Germany, for example, the application of sewage sludge in agriculture decreased from 42% in 1995 to 31% in 2003 (Meda et al., 2007). Besides, controversial views exist about the plant availability of chemically precipitated phosphorus, particularly when iron is used for chemical P removal in the treatment plant. In this case, the phosphorus is of low plant availability and may even reduce the plant availability of already-existing phosphorus (Ro¨mer and Samie, 2002a, 2002b).

4.12.5.1 Nitrogen Recovery In his keynote speech at the Nutrient Recovery Conference in May 2009 in Vancouver, Dr. James Barnard stated ‘‘Nitrogen can be recovered from wastewater, but the cost of recovery far exceeds that of fixing nitrogen from the atmosphere’’ (Barnard, 2009). >Attempts were made to strip and recover ammonia at elevated pH values from the effluent and recover as ammonium sulfate, but the method was not economically viable (Barnard, 2009). There are also research reports about stripping ammonia from the so-called process water, that is, wastewater side streams with high concentrations of ammonium (41000 mg l1), as they occur during the dewatering of digested sludge. By stripping at high pH values, ammonia is transferred to the gaseous phase, and subsequently transformed to ammonium salts or ammonia water, that is, aqueous solution with an ammonia content of approximately 25% per weight (Kollbach and Gro¨mping, 1996). However, these processes rather serve the reduction of the ammonium return load than nitrogen recovery. Moreover, they are energy-intensive and need large amounts of chemicals. In practice, only single cases have been realized. Combined precipitation, together with phosphate as struvite (magnesium ammonium phosphate (MAP)) from such concentrated process waters seems more promising. Under appropriate conditions, phosphorus and nitrogen can be removed by adding magnesium salts and reclaimed as valuable products (see Section 4.12.5.2). However, here as well only a small percentage of nitrogen is removed as struvite since the molar ratio of N:P is 13:1 in wastewater (as mentioned above) compared to 1:1 in struvite (unless additional phosphorus is added in the required molar ratio.) Another method for control and recovery of nutrients is urine separation. Urine contains 70–90% of the nitrogen contained in wastewater and approximately 50% of the phosphorus besides some 50% of potassium (see Table 7, Section 4.12.3.2), whereas its volume is less than 1%. Urinediverting toilets can be either water flushed or dry, depending on economic and cultural boundary conditions. They separate

urine from feces, the latter being then collected separately. Urine is almost pathogen-free. During storage, the pH value increases due to hydrolysis, which again contributes to the disinfection of the product. By separating urine, wastewater treatment plants are relieved, and with completely separating urine, nitrification/denitrification steps can be omitted. However, practical experience shows that in reality only a fraction of the expected urine is collected separately, thus reducing the recovery rate considerably. Larsen and Lienert (2007) report a collection rate of 60–75% (demonstration project in Switzerland), and Genath (2009) only 30–40% (demonstration project in Berlin, Germany). Tilley et al. (2009) report a mean collection rate of 30% with a range of 10–75% for a community-based project on urine separation and nutrient recovery in Nepal. The use of urine as fertilizer is particularly attractive in those countries where distances between urine source and its place of application are short. This mainly applies to rural areas and those places where the cost for fertilizers is unaffordable for many people. In case of increased transport distances, upgrading the nutrient concentration should be considered, as the nitrogen concentration in urine is only approximately 1% per weight compared to 40% per weight in commercial fertilizers. Maurer et al. (2003) have calculated that the 10-fold upgrading of the urine concentration via vaporization is more energy-efficient (approximately 5 kW h (kg N)1) than producing ammonium fertilizers via the Haber–Bosch process. Generating MAP (struvite) from urine, however, seems to be more energy-intensive with regard to ammonium alone, but by including phosphorus, the energy balance becomes favorable again (Maurer et al., 2003). Yet, one has to consider that the stoichiometric imbalance between N and P in urine is even more distinctive than in wastewater. Last but not least, one of the main unknowns in conjunction with the use of urine as fertilizer is the fate of pharmaceuticals and endocrine disruptors. Ultimately, by assessing energy and cost balances in the individual case, while taking into account urine storage, transport, distance to the user, and techniques of fertilizer spreading, decision supports toward the usefulness of separate urine collection can be developed. Besides ecological and economical questions, the acceptance of the toilet users and farmers as well as hygienic manageability is decisive.

4.12.5.2 Phosphorus Phosphorus recovery from wastewater has enjoyed great interest over the last 20 years. Thereby, the focus of research and development of new technologies is, on the one hand, on precipitation/crystallization from the aqueous phase, whereby concentrated process water streams are favorable, and, on the other hand, on the recovery from sewage sludge and sludge ashes. As described in Section 4.12.3.2, with the latter, recovery potentials are particularly high.

4.12.5.2.1 Phosphorus recovery during wastewater treatment The implementation of phosphorus recovery during wastewater treatment allows for the separation of already dissolved

Wastewater as a Source of Energy, Nutrients, and Service Water

phosphorus, applying relatively basic technologies. Thereby, phosphorus-rich side streams or process water with phosphorus concentrations 450 mg l1 are economically feasible. One big advantage of phosphorus recovery during wastewater treatment is the possibility of combining it with phosphorus removal. Investigations of recent years showed that phosphorus recovery is particularly successful in combination with biological phosphorus removal in side streams (supernatant liquor of the anaerobic stabilization) or from process water during sludge treatment. The phosphorus-rich water is fed into a precipitation/crystallization tank, where phosphorus is removed as calcium phosphate or MAP (struvite) by adding calcium or magnesium salts and, where need be, seed crystals (cf. Figure 11).

4.12.5.2.2 Phosphorus recovery from sewage sludge – wet chemical technology The wet chemical treatment of sewage sludge involves that in a first step the phosphorus bound in the sewage sludge is dissolved by adding acid or base, in combination with temperature if necessary. Thereby, in most cases (heavy) metals are redissolved as well. After removal of the insoluble compounds, phosphorus can be separated from the phosphorusrich water, for example, via precipitation, ion exchange,

nanofiltration, or reactive liquid–liquid extraction (cf. Figure 12). The same technologies can be applied to recover phosphorus from sewage sludge ash. The advantage here is that by disintegrating the organic matter – including all organic pollutants – there is an enrichment of phosphorus and consequently higher phosphorus concentrations in the liquid phase occur. In contrast to sewage sludge, solid–liquid separation after alkaline or acidic treatment is significantly easier to realize due to the exclusively inorganic formation of the sewage sludge ash (Schaum, 2007).

4.12.5.2.3 Phosphorus recovery from sewage sludge – thermochemical technologies Through specific thermochemical treatment of sewage sludge ash, it is possible to remove heavy metals and, at the same time, improve the plant availability of phosphorus (cf. Rhenania process). Based on the thermochemical approach, ashes are exposed – under suitable conditions – to chlorine-containing substances, potassium chloride or magnesium chloride, and treated thermally. With temperatures 41000 1C, a large percentage of the heavy metals is turned into heavy metal chlorides which vaporize, thus removing them from the ashes (Kley et al., 2005). The heavy metals are captured at flue gas treatment.

2+ 2+ Ca , Mg , seed crystals Process water rich in phosohorus Treated water depleted in phosphorous

Calcium phosphate MAP − struvite Figure 11 Phosphorus recovery from the liquid phase during wastewater treatment (Cornel and Schaum, 2009).

Release of phosphorous/metals

Acid base Sludge ash Energy Residues

(a)

Release of phosphorous/metals

359

Precipitation/ crystallization liquid−liquid extraction ion-exchange nanofiltration

(b)

Residues

Calcium phosphate MAP − struvite

Separation of phosphorous

Figure 12 Phosphorus recovery from sewage sludge and sewage sludge ash – wet chemical technologies (Cornel and Schaum, 2009).

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Wastewater as a Source of Energy, Nutrients, and Service Water

4.12.5.2.4 Products from phosphorus recovery processes With few exceptions, most of the processes include phosphorus separation by precipitation/crystallization of calcium phosphate or MAP (struvite). Calcium phosphate, for example, hydroxyl apatite, is a product directly comparable to rock phosphate. Thereby, one has to keep in mind that in practice the kinetics of calcium phosphate precipitation plays a major role than thermodynamic equilibrium considerations. Thus, in most cases, spontaneous precipitation of calcium phosphate from the solution does not occur at all or only at very high oversaturation. However, the separation of calcium phosphate can be achieved by adding seed crystals, such as sand (Giesen et al., 2005) or calcium silicate hydrate (Berg, 2005), which are able to initiate the precipitation/crystallization process of calcium phosphate. To produce MAP, it is necessary to provide a stoichiometric ratio of magnesium, ammonium, and phosphate of 1:1:1. Filtrates from sludge dewatering are particularly suitable for MAP precipitation as only magnesium has to be added, cf. ATV-DVWK (2000, 2005). Due to thermodynamics, the separation of calcium phosphate and MAP only takes place in alkaline pH medium (pH value approximately 8–10; cf. Stumm and Morgan, 1996 and Wu and Bishop, 2004). Due to the ammonium contents, the utilization of MAP in the phosphate industry is limited. However, direct use as fertilizer seems to be possible. Laboratory-scale tests showed that the uptake of phosphorus from MAP on acidic and neutral soils is comparable to the uptake of triple superphosphate (Richards and Johnston, 2001; Ro¨mer, 2006).

4.12.5.2.5 Exemplary applications of phosphorus recovery Wastewater: crystallization of calcium phosphate – Crystalactors (The Netherlands). DHV Water (The Netherlands) developed a crystallization process for the recovery of phosphorus. Thereby, a so-called Crystalactors is used, a cylindrical fluidized-bed reactor with, for example, sand as seed material. By adding calcium, phosphorus crystallizes on the seed material (quartz sand) in a fluidized bed at pH values of approximately 9, thus forming calcium phosphate. Due to the crystallization, the pellets grow, and separation is possible (Giesen et al., 2005). In 1993, the process was realized in side streams of the wastewater treatment plants Geestmerambacht (230 000 PE) and Heemstede (35 000 PE), Germany, in combination with biological phosphorus removal. With a concentration of 60– 80 mg l1 the phosphorus-rich supernatant liquor from the stripping tank of the biological phosphorus removal unit is fed into the Crystalactors. Here, 70–80% of phosphorus are eliminated and separated. In order to minimize precipitation of calcium carbonate, the pH value is decreased in the inflow area, thus inducing the stripping of carbon dioxide. The separated phosphorus-rich pellets are utilized as a substitute for rock phosphate in the phosphate industry (Giesen et al., 2005). Wastewater: crystallization of calcium phosphate – P-RoC (Germany). Via the application of suitable seed crystals, such as calcium silicate hydrate, a by-product from the building materials industry, a process was developed in the Forschungszentrum Karlsruhe (Germany) which allows

the separation of phosphorus without the dosage of further chemicals. Phosphorus-rich water is fed into a crystallization reactor. By adding seed crystals, calcium phosphate is formed which can then be separated. The phosphorus removal rate is approximately 80%, almost independent of the organic constituents of the water. The phosphorus-rich product can be used in agriculture as well as in the phosphate industry. The process was investigated in pilot-plant scale. Recently, it was shown that the calcium silicate hydrate can also be applied directly to sewage sludge for phosphorus removal and recovery (Petzet and Cornel, 2009). In this process, the calcium silicate hydrate is added to the digester where it removes and recovers phosphorus released during anaerobic stabilization, thus providing a solution for operational problems related to the enhanced biological process removal (EBPR) process such as struvite scaling and high P return loads to the head of the treatment plant. Process water from sewage sludge treatment: crystallization of MAP (Japan, Canada, Germany). Repeatedly, there have been reports about incrustations of pipes following the digestion step, in particular in those wastewater treatment plants with biological phosphorus removal. Due to the formation of ammonium during digestion in combination with dissolved phosphorus and magnesium, slight changes in the pH value can induce spontaneous precipitation of MAP, which can lead to the incrustation of pipes (cf. Heinzmann and Engel, 2005). In this context, processes have been developed, for example, in Japan, Canada, and Germany, which focus on the formation and separation of MAP. In the case of the PHOSNIX process developed by Unitika (Japan), phosphorus- and ammonium-rich process water is fed into a fluidized-bed reactor. The pH value is adjusted to approximately 8.5–9 by adding sodium hydroxide solution, and by adding magnesium MAP crystals are formed which can then be separated. By applying this process, phosphorus removal rates of approximately 90% can be achieved. The generated product can be used in agriculture. Since 1987, an industrial-scale plant is operating in Japan (Ueno, 2004). Similar technologies have been investigated in Canada, OSTARA process (Prasad et al., 2007) operated in industrial scale since 2007, and in Germany, PRISA process – which is so far not realized in industrial scale (Pinnekamp and Montag, 2005). Digested sludge: wet chemical re-dissolution and crystallization of MAP – Seaborne process (Germany). In winter 2006, the Seaborne process was put into operation at the wastewater treatment plant Gifhorn (Germany), a municipal treatment plant with approximately 50 000 PE. Following anaerobic stabilization, sulfuric acid is added to acidify the digested sludge achieving a pH value of approximately 3. In order to improve dewaterability, hydrogen peroxide is added and the sludge is dewatered. The dewatered sludge is thermally recycled via a mono-sewage sludge incineration plant. Heavy metals are precipitated by adding sodium sulfide and separated with a belt filter press. Subsequently, magnesium hydroxide is added and the pH value is increased by adding sodium hydroxide solution. This procedure results in the precipitation of MAP, which can be separated by centrifuges and used in nutrient recycle. The residual water passes an ammonium/ammonia stripping with subsequent acidic wash,

Wastewater as a Source of Energy, Nutrients, and Service Water

thus producing diammonium sulfate. The water from which the nutrients have been removed is fed into the inflow of the wastewater treatment plant (Mu¨ller et al., 2005; Wittig, 2007). Sewage sludge ash: wet chemical redissolution and sequential precipitation – SEPHOS process (Germany). In the case of the sequential precipitation of phosphorous (SEPHOS) process, the first step is the elution of the sewage sludge ash with sulfuric acid. After removing undissolved residuals, the pH value in the filtrate is increased stepwise, whereas at pHo3.5 aluminum phosphates precipitate. The heavy metals such as copper and zinc remain dissolved and precipitate at pH values 43.5. Aluminum phosphate, poor of heavy metals, can be used in the electrothermal phosphate industry. By an alkaline treatment of aluminum phosphate (advanced SEPHOS process) phosphorus as well as aluminum is dissolved. By adding calcium, precipitation of calcium phosphate can be achieved. Aluminum stays in solution and can be recycled as coagulant. Respective investigations have been carried out at the Institute IWAR of Technische Universita¨t Darmstadt (Schaum, 2007). Sewage sludge ash: thermochemical treatment (Germany). Based on the thermochemical approach, ashes are exposed – under suitable conditions – to chlorine-containing substances, potassium chloride or magnesium chloride, and treated thermally. With temperatures 41000 1C, a large percentage of the heavy metals are turned into heavy metal chlorides which vaporize, thus removing them from the ashes (Kley et al., 2005; Prinzhorn 2005). The thermochemical treatment of the sewage sludge ash/chloride mixtures is performed in quasiclosed systems, for example, rotary furnaces. The chlorides are discharged via the gas phase with subsequent precipitation during flue gas cleaning. By applying the mentioned chlorides, potassium and/or magnesium phosphates are formed which can then be used in agriculture. By subsequent specific dosage of nitrogen and/or potassium – following the removal of heavy metals – various multinutrient fertilizers can be produced. After pellets are formed, they can be used as granulates (Prinzhorn, 2005). Respective investigations are carried out within the frame of the EU research project – SUSAN – Sustainable and Safe Re-use of Municipal Sewage Sludge for Nutrient Recovery (SUSAN, 2008). The startup of a pilot plant in Leoben (Austria) with a load of 4000 Mg a1 ash was in 2008. Probably the most cost-efficient method is the direct use of sewage sludge ashes as substitute for phosphate rock. One precondition for electrochemical phosphorus recovery is that iron concentrations in the ash are low, as this leads to the formation of low-grade iron phosphate within the process reducing the phosphate yield. Thus, only ashes from sewage sludge treatment plants with biological phosphorus elimination or precipitation with aluminum salts can be used. Furthermore, concentrations of copper, zinc, and other heavy metals should be as low as possible. Currently, the use of sewage sludge ashes as phosphate rock substitute in the fertilizer industry is investigated in various institutions. With 7–8%, the phosphorus concentrations are somewhat lower when compared to 12–15% in phosphate rock. Concentrations of several heavy metals, especially copper and zinc, are higher, while cadmium and uranium concentrations are considerably lower in the ashes.

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Considering German boundary conditions, annual costs of phosphorus recovery are estimated at 2–5 h per capita. These costs equal approximately 2–4% of the specific annual costs of approximately 124 h (C  a)1 for wastewater treatment and disposal (BGW/ATV-DVWK, 2003).

4.12.6 Water Reuse Wastewater can be a valuable resource that contains the resource water in concentrations of more than 99.5%. After adequate treatment, that is, adapted to its subsequent application, water can be a valuable product to be reused. Thus, water reuse is an essential component of integrated water resource management, not only in arid and in water-deficient areas, but increasingly also in most of the densely populated urban areas, where water demand and supply diverge widely, at least regionally. Water reuse opens up new water resources and reduces the demand for potable water. Potential regional or local lacks of water can be closed. In addition, water reuse reduces the discharge of (treated) wastewater into water bodies. In regions where water supply may be energy-intensive and costly due to extensive transportation and/or pumping, water reuse can be an alternative with lower energy consumption and lower costs than using freshwater. Moreover, finally yet importantly, valuable freshwater resources, such as high-quality groundwater, can be preserved via the alternative use of reclaimed water. Today, the reuse of wastewater for agricultural irrigation is practiced in almost all arid regions. Particularly in threshold and developing countries of Latin America, Asia, and Africa, raw wastewater or insufficiently treated wastewater is used directly in crop irrigation. Often, this is done deliberately, in order to use the nutrients N and P as well as the organic load for forming humus, but often also without being aware of the health risks for farmers, farm laborers, and consumers. This is also true for the indirect and unplanned use of wastewater, where untreated wastewater is discharged into rivers and downstream river water is used for irrigation. ‘‘This major health concern makes it imperative to governments and the global community to implement proper reuse planning and practices, emphasizing public health and environmental protection, during this era of rapid development of wastewater collection and treatment’’ (EPA, 2004). An additional challenge is water supply and disposal in large cities. According to UN-HABITAT (2006) until 2050 approximately 75% of the world’s population will live in cities, 20% in urban areas and cities of 1–5 million inhabitants. The majority of approximately 80% will be living in threshold and developing countries, over half the world’s urban population in Asia. ‘‘According to the conclusions of various water reuse surveys (Lazarova et al., 2001; Mantovani et al., 2001), the best water reuse projects, in terms of economic viability and public acceptance, are those that substitute reclaimed water in lieu of potable water for use in irrigation, environmental restoration, cleaning, toilet flushing, and industrial uses’’ (EPA, 2004). Intra-urban reuse of water for utilizations, which do not require drinking water quality, offers a high potential to save valuable water resources and reduce (waste-)water discharge.

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Wastewater as a Source of Energy, Nutrients, and Service Water

The freshwater consumption can be reduced by more than 30– 40% when reclaimed water is used for toilet flushing, gardening, irrigation, etc. (Bieker et al., 2009). However, intra-urban water reuse fosters the transition from conventional centralized to nodal, semicentralized supply and treatment systems, with short distances from the firsthand user to the treatment units and back to the secondhand reusers. This will minimize the energy for transport and treatment and offers the chance to recover heat from wastewater, especially from greywater. Again, to avoid any health risks, proper planning and professional operation of water reclamation plants have to be guaranteed. Intra-urban reuse and agricultural reuse are not mutually exclusive; on the contrary, they can be combined, as intraurban water reuse is a so-called nonconsumptive use. Nonconsumptive uses are all kinds of uses by which water is not physically lost but undergoes just a change in its quality, for example, in power plant cooling, where most of the water is returned to the water body with almost no quality deterioration and can be used again. The same is true for most of the municipal and industrial waters that do not disappear by use but are returned to the rivers as (treated) wastewater and might be used again downstream. Agricultural irrigation, on the other hand, is a consumptive use because water is evaporated by the plants and is no longer available for other uses. Therefore, consumptive uses put higher pressure on water resources than nonconsumptive uses and should be considered as the last step of a reuse chain.

4.12.6.1 Reuse Options The fields of application for water reuse are manifold. According to Asano (2007, p. 24), the following categories of water reuse applications for reclaimed water originating from treated municipal wastewater can be established:

• •

• • • •

Agricultural irrigation: Crop irrigation and commercial nurseries. Nonpotable intra-urban uses: Toilet flushing, landscape irrigation like in parks, golf courses, greenbelts, residentials, cemeteries, freeway medians, school yards, fire protection, and air conditioning. Industrial recycling and reuse: Cooling water, boiler feed, and process water. Recreational/environmental uses: Lakes and ponds, streamflow augmentation, fisheries, and snowmaking. Groundwater recharge: Groundwater replenishment, saltwater intrusion control, and subsidence control. Potable reuse: Blending in water supply reservoirs, blending in groundwater, and direct pipe-to-pipe water supply.

Corresponding to the manifold applications, the requirements on the water quality as well as on the treatment technology strongly vary. In the following, reuse for agricultural irrigation, nonpotable intra-urban reuse, industrial recycling, and groundwater recharge are briefly discussed. For detailed reviews, the publications of Asano (2007) and Jime´nez and Asano (2008) should be referred to.

4.12.6.1.1 Agricultural reuse The reuse of treated wastewater for agricultural irrigation presents by far the largest potential. Worldwide, more than 70% of the used freshwater is for agricultural irrigation (United Nations, 2003). While in Central Europe, the water demand for agricultural production is currently provided by sufficient rainfall during vegetation periods, in many countries of Latin and South America, Africa, and Asia between 70% and more than 90% of the annual water demand is needed for agricultural irrigation. Figure 13 shows the percentage of water withdrawals for agricultural use in different regions. Due to its high water demand, agriculture is also by far the largest reuser of water. In most arid regions, wastewater utilization is common practice and a necessity. In Figure 14, those countries with the largest surface areas under irrigation – according to their own statements – with untreated and/or treated wastewater are listed. Although the data are afflicted with great uncertainties and the degree of wastewater treatment will vary enormously, nonetheless, the diagram shows the importance of water reuse for agricultural irrigation. ‘‘It is estimated that at least 20 000 000 ha in 50 countries are irrigated with polluted water (United Nations, 2003), either directly or indirectly, and that 10% of the world’s population consume crops produced with wastewater (Smit and Nasr, 1992). The relative importance of this practice varies by country; in Hanoi, Vietnam, for instance, up to 80% of the vegetables consumed are produced with wastewater (Ensink et al., 2004)’’ (Jime´nez and Asano, 2008: 21). Farmers use wastewater for irrigation, as it is constantly available and, in addition, contains nutrients and humus formers (IMWI, 2003; Jime´nez and Gardun˜o, 2001). Only few are aware of the health risks arising from handling nondisinfected reuse water or are able to foresee the consequences of irrigation, such as salinization of soils or the risk of groundwater pollution. One phenomenon, which is often left unnoticed, is the socalled urban agriculture. What is meant is the cultivation of small parcels of land (0.5–2 ha) in urban and peri-urban areas for producing fruit trees, fodder, flowers, and vegetables. Here, often, untreated wastewater is used for irrigation. It is estimated that several million farmers are practicing urban agriculture and up to 50% of the vegetables offered for sale are being produced this way (Cornish and Lawrence, 2001; IMWI, 2003; Jime´nez and Asano, 2008). Despite the mentioned large numbers, the percentage of reuse water is only approximately 1% of the total water demand for agricultural uses. This leads to the question: What contribution can the reuse of treated wastewater make toward the reduction of water shortage? The proportions make clear that the reuse of municipal wastewater can only contribute modestly within the total water balance and is far from sufficient as a sole source for agricultural production. Even if one does not take into account that availability and demand do not necessarily coincide and under the assumption that the total amount of household wastewater can be used for agricultural irrigation, with 50 m3 a1 from private households and a typical irrigation efficiency rate for sprinkler irrigation of 65%, it is possible to irrigate an area of approximately 20–80 m2 sufficiently, depending on the type of plant (Table 11). This is a

Wastewater as a Source of Energy, Nutrients, and Service Water

Agriculture

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100% 80% 60% 40% 20%

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Figure 13 Water withdrawals by sector for 2006 (Jime´nez and Asano, 2008).

rather small contribution compared to the area of several thousand square meters that is needed for food production per person. When specific yields are taken as a basis, the product quantities that can be generated by using only municipal water for irrigation can be estimated (Table 12). This illustration, as well, shows that these amounts are comparably small and are by far not sufficient to cope with the annual demand on food and energy per person. Although agricultural reuse of wastewater cannot solve the problem of global water shortage and thereby cannot guarantee safe food supply on its own, it presents a certain contribution to sustainability. As discussed earlier, agricultural reuse is not the only reuse option: it can be seen as the last link of consumptive use within a chain of nonconsumptive uses with decreasing quality demands (multiple reuses).

4.12.6.1.2 Intra-urban reuse as service water The objective of intra-urban reuse of water (after respective adequate treatment) is to preserve local/regional drinking water resources. Interim storage is hardly necessary, as water availability and demand occur almost synchronically, delayed to each other only by a few hours. However, due to dual piping for drinking and service water, etc., technical efforts are higher. Intra-urban water reuse facilitates the reduction of the specific drinking water consumption (directly and year-round) to the quantity needed for cooking, drinking, and personal hygiene. Figure 15 shows typical water uses, exemplarily for the city of Qingdao, China, with an average water consumption of 109 l (C  d)1 (BMBF, 2006) and for USA with a specific water consumption of 274 l (C  d)1 (Asano, 2007: 11).

Although the specific water consumption differs, in both cases the quantity of slightly polluted greywater from showers and washing machines is sufficient to cover the demand of water needed for toilet flushing. It is comparably easy to treat greywater, as it mainly contains only organic impurities, washing agents, and personal care products. Due to mostly low concentrations of N and P in greywater (cf. Table 7 in Section 4.12.3.2), nutrient removal is expected to be dispensable; however, disinfection is usually required in order to reach quality standards like 0.2 mg l1 residual chlorine content at the extraction point (GB/T 18920-2002). Just by using adequately treated greywater, the demand of potable water could already be reduced by 30%. At the same time, the amount of wastewater to be treated would decrease by the same amount (cf. Section 4.12.7). Since treated greywater is used for toilet flushing and subsequently treated as blackwater, blackwater treatment represents the last barrier prior to discharge into the environment. Persistent compounds, such as fragrances and other compounds in personal care products that may only be partially removed during greywater treatment, are of no concern in toilet flushing and can be further eliminated during blackwater treatment. Direct reuse of reclaimed water in households fosters nearby treatment on various accounts. On the one hand, in case treatment is carried close to the greywater’s origin, its high temperature can be used for heat recovery. On the other hand, collection and distribution pipes are short and costs for pumping and losses during transport are low. One of the most important questions is the consumers’ acceptance of reclaimed water. Social-empirical studies have revealed that the more is known about the origin (and whether it is from the own surroundings), the higher is the acceptance (Jeffrey and Jefferson, 2004). Particularly with decentralized, quarter-, or district-based,

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Wastewater as a Source of Energy, Nutrients, and Service Water Ha irrigated with untreated wastewater (China out of scale) 0

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Chinaa Mexico India Chilec,a Syna Pakistan Colombia Argentin SAa Ghana Vietnam Peru Turkey Morocco Egyptc Kuwaitb Sudan Tunesia Nepalc Bolivia Ha irrigated with treated wastewater Chile Mexico Israelc,a Egypta Cyprus Italya Argentin Australia UAEc USA Jordan Turkeya Syriab Tunesia Kuwaitc Omanc France Libyac S. Arabia Germany

Note : Information may vary from source. Some countries report agricultural wastewater use without mentioning the amount of hectares involved. aData are confusing. bNo data available, although the practice is reported. cSurface might be greater. Figure 14 The 20 countries reporting the largest surface areas under irrigation with treated and untreated wastewater. From Jime´nez (2006) and (Jime´nez and Asano, 2008).

(grey-)water treatment, heavily polluted wastewater from industry and commerce can be excluded from reuse. Certainly, in case the complete wastewater stream is treated adequately and used as reclaimed water, considerably more water can be reused. This is mostly done in central treatment plants. Then, intra-urban reuse is reasonable in rather central units, such as for fire protection through reclaimed water fire hydrants, in industry, in commercial uses such as vehicle washing facilities, laundry facilities, street cleaning, or for irrigating public parks and recreation centers, athletic fields, highway medians, and shoulders, landscaped areas, and golf courses. As illustrated above, intra-urban water reuse not only preserves valuable resources, but often it is also more energy-efficient and more cost-effective. In particular, this is true in case:

• •

freshwater has to be transported over long distances, inferior raw water quality requires high efforts in drinking water treatment,

• •

seawater has to be processed for drinking water use, and stringent surface water discharge requirements are given.

Water reclamation facilities must provide the required treatment to meet appropriate water quality standards for the intended use. Usually, the discharge of wastewater treatment plants with nutrient elimination is filtrated in order to remove residual solids and is disinfected. Most standards require a defined residual chlorine concentration at the extraction point. Because urban reuse usually involves irrigation of properties with unrestricted public access or other types of reuse where human exposure to the reclaimed water is likely, reclaimed water must be of a higher quality than may be necessary for other reuse applications (see also Section 4.12.6.2).

4.12.6.1.3 Industrial reuse Industrial reuse and recycling is mainly driven by economic forces. Thereby, the main focus is on internal water recycling.

Wastewater as a Source of Energy, Nutrients, and Service Water Table 11 Irrigable area for different agricultural crops, with 50 m3 a1 of water and an irrigation efficiency rate of 65% Agricultural crop

Beans Cabbage Rice Sorghum Wheat Tomatoes Peanuts Corn/maize Cotton Sunflowers Lemons Bananas

Demand on irrigation water per vegetation period (l m2)

Irrigable area (m2)

Min

Max

Medium

300 380 350 450 450 500 500 500 700 800 900 1200

500 500 700 650 650 700 800 800 1300 1200 1200 2200

89 77 72 63 63 57 55 55 37 35 33 21

Data for demand on irrigation water from Lazarova V and Bahri A (2005) Water Reuse for Irrigation – Agriculture, Landscapes and Turf Grass. Boca Raton, FL: CRC Press.

Table 12 Theoretically producible quantity of foods in case 50 m3 a1 of water are used with an assumed irrigation efficiency rate of 65% (specific yield according to Katalyse, (2008), specific irrigation demand according to Zehnder (2003)) Product

Producible quantity (kg)

Sorghum Corn/maize Wheat Clover (Trifolium) Tomatoes Cucumbers Oranges Sunflowers Cotton Bread

133 45–95 45–65 72 52 47 17 4.9 0.01 22–33

Electric energya

166 kW h electric energy (Benergy equivalent of 17 l petrol/diesel)

a

Estimation based on Rosenwinkel (2006).

Qingdao, PR China

Cooking/dish washing 25

Cleaning Drinking 3 7

Toilets flushing 33

365

USA Showers + baths 33

Faucets 42 Dish washers 4

Washing machines 8

Other domestic 5

Toilets flushing 76

Showers + baths 52

Washing machines 57

Figure 15 Typical water uses, left for the city of Qingdao, PR China, right for the USA (values in l (C  d)1) (BMBF, 2006; Asano, 2007).

The reuse of water from municipalities and other providers is less attractive, as it produces dependencies, requires quality controls and negotiations with external contractors (Jime´nez and Asano, 2008). Utilizing reuse water for cooling is probably the most common reuse, as, among others, large quantities of water are needed, and quality requirements are comparably low. Usually, filtration and softening to avoid clogging and scaling are sufficient. Power plants are large consumers of reclaimed water (Jime´nez and Asano, 2008). Water recycling and reuse in industry in developed countries is an established and well-developed practice. This can be pointed out with the example of Germany. The volume of water utilized in the German manufacturing industry amounts to roughly 30 200 million m3 a1 (thereof 22 400 million m3 a1 for cooling), the freshwater supply to 6200 million m3 a1. The difference represents the volume of recycled water and amounts to 24 000 million m3 a1, a number which exceeds the total amount of municipal wastewater (9695 million m3 a1) by a factor of 2.4 (quantities for the year 1998, according to the Federal Statistical Office of Germany (FSO) (FSO, 2001; Cornel and Meda, 2008b). The use factor for water including cooling water – which is defined

as the quotient of utilized to provided water – varies between 1.3 in the textile industry up to 21.5 in the vehicle industry. Use factors indicate the extent of water reuse among different industry branches. Table 13 gives use factors among different industry branches. The values are increasing, for example, in the food industry from 3.5 in 1980 to 4.2 in 1998. They might vary in different countries and even in different regions as the driving force for water reuse in industry is, quite often, economics. Thus, reuse depends on water prices and wastewater fees on the one hand and on water treatment costs for adequate standards for internal reuse purposes on the other hand.

4.12.6.1.4 Groundwater recharge One has to differentiate between the so-called unintended groundwater recharge through intensive irrigation and the purposeful recharge with reclaimed water for reaching one of the followings goals:

• •

to prevent saltwater intrusion into coastal aquifers, to control or prevent ground subsidence,

366 Table 13

Wastewater as a Source of Energy, Nutrients, and Service Water Industrial water quantities and use factors for Germany

Industry

Manufacturing industry Food Metal Vehicle Textile Paper Chemical Power plants (public) Municipal wastewater

Wastewater

Water supply

Total million m3 a1

Thereof cooling water million m3 a1

Provided million m3 a1

Utilized million m3 a1

Thereof for cooling million m3 a1

Use factor

6008 363 822 86 175 547 3455 25 984 9695

4243 162 667 45 131 264 2639 25 842 -

6207 416 873 93 183 610 3422 26 559 -

30 226 1728 6018 1989 242 3485 11 836 67 734 -

22 486 834 4925 1092 172 816 10 594 57 457 -

4.9 4.2 6.9 21.5 1.3 5.7 3.6 2.6 -

From Cornel P and Meda A (2008b) Water reuse in Central Europe: The current situation. In: Jime´nez B and Asano T (eds.) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA.

• •

to ensure seasonal storage of potable or nonpotable water, and posttreatment for future applications.

Infiltration and percolation of reclaimed water use the natural purification processes within the soil, that is, filtration, adsorption, ion exchange, precipitation, biological degradation, etc., thus leading to additional cleanup and equalizing the water quality. In addition, the soil functions as a safety barrier. At the same time, by passing through the soil and being mixed with real groundwater, the water loses its identity as reclaimed water. Hereby, the acceptance is increased. Typical retention times in the so-called Soil Aquifer Treatment (SAT) are 20–50 days. The share of infiltrated water in the extracted and used water generally is between 40% and more than 90%. Detailed information on hydrology and the degradation of pollutants during SAT, riverbank filtration, and dune filtration can be taken from the literature (Fox et al., 2001; Drewes et al., 2001; Brauch and Schmidt, 2009; Oaksford, 1985). In case seasonal storage, for example, of irrigation water, is the main objective of infiltrating reclaimed water into the aquifer, prevention of evaporation losses, salinization, and algae growth, as they occur in surface reservoirs, are predominant. Storage processes require profound knowledge of the hydrological and geological conditions. The retention time can be much higher than with SAT. Normally, adequate pretreatment including nutrient elimination is required in order to protect aquifer and groundwater.

•

•

4.12.6.2 Fit for Purpose, Quality Requirements One precondition for unobjectionable and successful water reuse in the long term is the consistent compliance with defined quality standards. Here, various aspects have to be taken into account:

•

Hygiene/protection of human health. In order to protect human health, limit values for pathogens (bacteria, viruses, and helminth eggs) and toxic substances have to be defined and met. When defining these limit values, the type of

•

•

contact between humans and wastewater plays a decisive role. References can be taken from the guidelines (WHO, 2006). Soil protection/plant protection. In order to protect soils, first of all it is important to restrict the concentration of salts and heavy metals. A well-known example is the potential of soil salinization due to irrigation with sodium-containing wastewater. This reduces the water conductivity of the soil and therefore leads to harvest depression. Decisive factors for defining the required water quality are type of soil (texture, grain size, permeability, chemism, and specific salt content), climate (aridity, precipitation quantity and distribution, humidity, and wind), type of plant (nutrient balance in the soil), and irrigation technique (sprinkler irrigation or subsurface irrigation, irrigation quantity, and frequency). Besides the total salt content, the concentration of several ions toxic to plants, such as boron, chloride, and sodium, matters. These ions are taken up via the root system or via the leaves in case of sprinkler irrigation. Boron has a narrow tolerance range and can be toxic already in concentrations slightly above the essential concentration for plant growth. Boron reaches municipal wastewater in the form of perborate, that is, as bleach in washing agents and disinfectants, whereas NaCl exists as a regeneration salt for water-softening ion exchangers in dishwashers. Groundwater protection. Although professional application of irrigation tries to avoid infiltration into groundwater, in practice it is hardly possible to prevent irrigation water from reaching the aquifer at least to a certain degree. However, in case the aquifer is required as seasonal storage for irrigation water, its protection should be of special interest. Application of efficient irrigation techniques. Irrigation techniques also put demands on wastewater quality. Substances with corrosive impacts and insoluble substances should be eliminated during wastewater treatment, in order to prevent clogging of pipes and damage of equipment. Acceptance. In order to get the acceptance of the public, esthetic aspects also have to be considered. Wastewater for irrigation should therefore be as odorless as possible and colorless.

Wastewater as a Source of Energy, Nutrients, and Service Water

•

Storage during nongrowing seasons. In case wastewater for irrigation has to be stored during nongrowing seasons, specific quality standards for storage have to be met, for example, standards for nutrient contents in order to prevent excessive algae growth in case of aboveground storage or to minimize nutrient input into the groundwater in case of subsurface storage.

The US Environmental Protection Agency (EPA, 2004) has listed standards for wastewater quality for defined wastewater ingredients and their impact on wastewater reuse systems, see Table 14.

4.12.6.3 Treatment Options and Energy Requirements Treatment options conditions:

• • • • • •

depend

on

numerous

• •

Optional treatment steps for generating reclaimed water also include

• • • • • •

filtration to remove residual suspended solids; microfiltration and ultrafiltration to remove colloidal solids; nanofiltration, reverse osmosis, and electrodialysis to remove dissolved solids; if applicable or necessary, advanced oxidation, carbon adsorption, and ion exchange to remove trace constituents; disinfection with chlorine, UV, ozone, membranes, or others; and safety chlorination (Table 15).

4.12.6.3.1 Physical and chemical methods

purpose of reuse; quality requirements for use, storage, and transport; legal requirements and quality standards; irrigation technique in case water is used for irrigation; temperature when storing and tendency towards microbial recontamination in intra-urban reuse; and economic options.

preliminary treatment, such as screens and grit chambers; primary treatment, such as clarification and fine screens, sometimes enhanced by chemical treatment; secondary treatment, that is, biological treatment, with or without nitrogen and phosphorus removal and tertiary treatment, also referred to as advanced treatment, is generally defined as anything beyond secondary treatment.

Table 14

It might involve coagulation, flocculation, clarification, filtration, and disinfection.

boundary

Levels of wastewater treatment are generally classified as

• •

367

Starting with municipal raw wastewater, the first treatment objectives are the reduction of the solids content in order to protect the irrigation system and the irrigated soils. Furthermore, high solid contents can reduce the effect of the subsequent disinfection step. Mechanical treatment should therefore be considered as first treatment step, supported by chemical precipitation, if needed. Besides reducing the solid contents up to 90% and the organic load up to 70%, in combination with micro-sieving and/or sedimentation, this so-called enhanced primary treatment can also reduce healthrelevant parameters, such as the concentration of helminth eggs (up to three orders of magnitude), bacteria, and protozoa (up to two orders of magnitude). However, quality standards as listed in Table 14 cannot be met and residual COD concentrations are not low enough for subsequent disinfection. Conclusion. Compared with the use of raw wastewater for agricultural irrigation, the application of physical–chemical

The 20 countries reporting the largest surface areas under irrigation with treated and untreated wastewater

Parameter

Significance for water reuse

Range in secondary effluents

Treatment goal in reclaimed water

Suspended solids

Measure of particles. Can be related to microbial contamination. Can interfere with disinfection. Clogging of irrigation systems. Deposition

5–50 mg l1

o5–30 mg l1

1–30 NTU 10–30 mg l1 50–150 mg l1 5–20 mg l1 o10–107 cfu 100 ml1 o1–106 cfu 100 ml1 o1–10 l1 o1 –100 l1 -

-

o0.1–30 NTU o10–45 mg l1 o20–90 mg l1 o1–10 mg l1 o1–200 cfu 100 ml1 o1–103 cfu 100 ml1 o0.1–5 l1 o1/50 l o0.001 mg-Hg l1 o0.01 mg-Cd l1 o0.1–0.02 mg-Ni l1 4450 mg-TDS l1 0.5–41 mg-Cl l1

10–30 mg N l1

o1–30 mg l1

0.1–30 mg P l1

o1–20 mg l1

Turbidity BOD5 COD TOC Total coliforms Fecal coliforms Helminth eggs Viruses Heavy metals

Inorganics Chlorine residual Nitrogen Phosphorus

Organic substrate for microbial growth. Can favor bacterial regrowth in distribution systems and microbial fouling Measure of risk of infection due to potential presence of pathogens. Can favor biofouling in cooling systems

Specific elements (Cd, Ni, Hg, Zn, etc.) are toxic to plants and maximum concentration limits exist for irrigation High salinity and boron (41 mg l1) are harmful for irrigation To prevent bacterial regrowth. Excessive amount of free chlorine (40.05 mg l1) can damage some sensitive crops Fertilizer for irrigation. Can contribute to algal growth, corrosion (NH4–N), and scale formation (P)

From Jime´nez B and Asano T (eds.) (2008) Water Reuse: An International Survey of Current Practice, Issues and Needs. London: IWA.

368

Wastewater as a Source of Energy, Nutrients, and Service Water

Table 15

Treatment levels achievable with some typical treatment trains

Secondary treatment Tertiary treatment

Suspended solids mg l1 Turbidity NTU BOD5 mg l1 COD mg l1 Ntot mg l1 PO4-P mg l1

Activated sludge

None (secondary effluent) 10–30 Granular media filtration o5–10 o5 Filtration þ GACa Coagulation/flocculation o5–7 Coagulation þ filtration o1 Aerated biofilter (BAF) 2–10 Maturation ponds 20–120

5–15 o0.5–5 o0.5–3 o10 o0.5–2 0.5–5 -

15–25 o5–10 o5 o5–10 o5 o5–15 o5–35

40–90 30–70 5–20 30–70 20–40 20–50 40–150

10–50 10–35 10–30 10–30 o5–25 10–30 5–25

6–15 4–12 4–12 o1–5 o1–2 4–12 2–6

Trickling filter

None (secondary effluent) 20–40 Granular media filtration 10–20 Coagulation/flocculation o5–10

5–15 10 0.5–5

15–35 15–35 o5–10

40–100 30–70 30–60

15–60 15–35 10–30

6–15 6–15 4–12

MBR

None (secondary effluent)

o5

5–50

o5–20

o1

o0.1–0.5

o0.1–10

a

Granulated activated carbon. From Lazarova V and Bahri A (2005) Water Reuse for Irrigation Agriculture, Landscapes and Turf Grass. Boca Raton, FL: CRC Press, p. 170.

treatment techniques presents a large improvement, as these methods allow the significant reduction of solids and partial elimination of the organic load and pathogens. In addition, investment costs for these techniques are much lower compared to biological wastewater treatment plants, thus facilitating the implementation in economically weak countries as well. Hence, physical–chemical techniques for wastewater treatment represent a transitional solution for improving the water quality and can therefore be the first step to be realized rapidly in those countries where raw wastewater is currently used for irrigation. However, the disinfection ability of only physically– chemically treated wastewater has to be viewed critically. High residual concentrations of organic substances and turbiditycausing substances put an efficient and ecologically sound disinfection into question. Depending on the disinfection method, there is potential for the production of undesirable by-products.

4.12.6.3.2 Biological treatment The second important treatment objective is the reduction of organic ingredients. Even though this treatment might not be obligatory for agricultural irrigation (organic compounds could even have a positive, improving effect on light sandy soils), it is very important for subsequent treatment, storage, and distribution of the irrigation water. This treatment step minimizes the potential of microbial recontamination and the risk of clogging of the pipes via the production of biofilms and allows for the efficient application of disinfection methods. Other important parameters are the nutrients, as mentioned earlier. For agricultural use there might be an advantage in not eliminating the nutrients but using their fertilizer effect; however, overfertilization and groundwater contamination should be prevented. Nutrient control is essential, in particular with seasonal storage of treated irrigation water in aboveground reservoirs, natural lakes, or aquifers. Quality standards as listed by EPA (see Table 14) show that the elimination of phosphorus is not inevitable; however, limit values of nitrogen can only be met with N elimination or with low-strength wastewater.

Conclusion. The discharge quality of biological wastewater treatment plants normally fulfills the quality standards regarding the solids content, organic ingredients, and nutrients. Deficits might be microbiological requirements, inorganic ingredients such as salts (sodium in particular) and boron as well as micro-pollutants.

4.12.6.3.3 Disinfection Water reuse requires a sufficient water quality adjusted to the intended purpose. In wastewater treatment plants, pathogens in terms of bacteria, viruses, parasites, and helminth eggs occur in concentrations far in excess of WHO guideline values and those quality parameters as listed in Table 14, respectively. In order to protect the health of people who come in direct contact with irrigation water, such as farmers and irrigation technicians, and also uninvolved people in the vicinity of sprinkler irrigation units and consumers who might be exposed to pathogens indirectly via the consumption of field crops, disinfection of irrigation water is good practice nowadays. One may do without, provided that health risks can be excluded by applying other measures, for example, irrigation techniques, off-times for irrigation before the harvest, cleaning measures before consumption, etc. The DWA topics ‘Assessment of process steps for the treatment of wastewater for reuse’ gives an overview on how different process steps affect the reduction of pathogens (DWA, 2008). The data are presented in log scale and they are additive, that is, by combining different process steps the total reduction degree can be estimated. In addition, the WHO guidelines (WHO, 2006) give information on nontechnical measures that reduce the risk of infection via irrigation water even further.

4.12.6.3.4 Other methods In case the salt concentration of the treated water is too high for direct use, desalination steps with reverse osmosis or ion exchanger can be installed downstream. This is necessary in particular in those cases when almost only municipal wastewater is used for irrigation, as practiced for example in Israel. Salts partly derive from drinking water. In households, the increase in salinity results from adding softening agents in

Wastewater as a Source of Energy, Nutrients, and Service Water Table 16 Range of energy demand for some selected biological treatment options according to Lfu (1998) Plant size Small a kW h m3 Aerated wastewater ponds 0.34 Biological contactor 0.23 Trickling filter 0.31 Activated sludge with aerobic sludge 0.61 stabilization Activated sludge with nutrient elimination 0.48

Large b kW h m3 0.29 0.25 0.17 0.312) 0.302)

to be broadly applicable, any alternative management method must be as transparent as practical to users, allowing them to pay a service fee and then flush and forget it, as they do in conventional, centralized systems.’’ Moreover, adds: ‘‘All system components need to be managed to the needs of the technologies employed. Operations and maintenance can not be left to the sole discretion of individual users’’ (Venhuizen, 1997). The following principles define the minimum requirements for a sustainable urban water resource management, at least in densely populated urban areas in which local/ regional water shortage is a considerable challenge for the coming decades:

•

a

Small stands foro1000 PE; large foro5000 PE. b Large for 10 000–100 000 PE.

• washing machines and dishwashers, while there might be an additional increase due to evaporation in open reservoirs. With desalination, one has to take into account that on the one hand approximately 20–30% of the water volume is lost as concentrate, and that on the other hand, the concentrate or regenerate needs further treatment or disposal. In addition, those salts are eliminated which have to be added again later as plant nutrients or trace elements.

4.12.6.3.5 Energy requirements

369

•

•

The level of comfort has to be assured. As for any alternative system, the handling of water reuse systems must be at least as easy and as reliable as in conventional systems and processes. The cost of new systems must not exceed that of existing structures, yet, in the ideal case, should be below the cost of conventional systems. This includes all parts of the system, from the processing of drinking water to the distribution and collection of wastewater and its treatment as well as the treatment and disposal of sewage sludge. Distribution pipes and sewers are important cost factors, regarding investment and operation and maintenance as well as tying up capital for a long time. Accordingly, pipe and sewer lengths should be kept as low as possible in order to minimize capital and operational expenditures. Professional operation is inevitable. Operation as well as maintenance has to be carried out by qualified personnel. Resource conservation is an essential aspect of sustainable water management. Water withdrawal has to be balanced with the natural regeneration rates. To succeed in this point, the percentage of potable water for transporting organic waste, feces, and pollutants has to be minimized respectively, at least in most densely populated areas around the world or just as much in water-scarce regions. Valuable constituents in the wastewater offer an additional approach toward resource conservation. They should be reclaimed, including water for further use. Thereby, the respective hygiene standards for all material streams to be reused or reclaimed have to be provided for.

The energy demand varies widely for the different treatment options. For mechanical treatment DWA (2009) indicates 1– 2 W h m3 for sedimentation (with or without flocculation) and 1–20 W h m3 for sieving (with or without flocculation). Aerobic biological treatment exhibits the highest energy demand. For German conditions, Lfu (1998) indicates 0.17– 0.61 kW h m3, depending on the adopted treatment and the plant size (see Table 16). For disinfection, for example, with ozonation, UV irradiation or membrane filtration, additional 0.035–0.4 kW h m3 must be calculated (Haberkern et al., 2008).

•

4.12.7 Recovery Fosters Decentralization

Against this background, the question arises as to the recommendable size of alternative sanitation systems with emphasis on water reuse and nutrient recovery. The required professional operation, ensuring control and quality standards as well as safeguarding the hygienic safety of drinking water and service water, involves a minimum size for operation units. The minimization of transport costs, the potential of heat recovery from warm wastewater, and the social acceptance of water reuse favor compact-scale decentralized systems. In order to determine the recommendable size of alternative sanitation systems, the fundamental results of the present article are summarized in the following.

As illustrated in the previous chapters, water and nutrients are valuable, yet scarce resources. During the last few years, various approaches toward alternative sanitation concepts have been developed with the aim to reduce water consumption and enable the recovery of nutrients (Cornel, 2007; Larsen and Gujer, 1997; Otterpohl et al., 1997; Venhuizen, 1997; Wilderer and Schreff, 2000; Zeeman et al., 2000). The spectrum ranges from low-tech processes to high-tech solutions with high demands on plant operation and maintenance. Here as well, there is no general solution. While compost toilets with subsequent agricultural application have proved to be practicable in rural areas, there are logistic and hygienic objections against their use in densely populated regions. As Venhuizen said ‘‘Many people have claimed ‘sewerless society’ would minimize problems’’ (with installing sewers). ‘‘Most of them propose composting toilets and other nonstandard plumbing, which would require significant lifestyle changes. But

•

4.12.7.1 Water As described above, water is a scarce resource in most urban areas worldwide (UN Water, 2007; BMZ, 2006). A general reduction of the daily demand can be achieved by applying use-dependent water qualities and domestic water reuse. Intra-

370

Wastewater as a Source of Energy, Nutrients, and Service Water

urban water reuse requires the water to be treated for use as service water, whereby the treatment efforts increase according to the pollution load as well as the needed quality. This means that the lower the water pollution and required water quality, the more cost-efficient the treatment. Greywater, a slightly polluted partial flow deriving from showers, hand wash basins, washing machines, etc. can be treated with relatively small efforts as its temperature is elevated and the nutrient content is low enough not to require nutrient elimination to reach service water quality. Water reuse requires dual piping for potable and nonpotable water as well as dual sewers in case grey- and blackwater (wastewater excluding greywater) are to be separated. In order to minimize capital and operational expenditures, distances have to be kept short. Due to the separate collection of greywater, blackwater accumulates as a separate material flow. High concentrations facilitate the reclamation of nutrients from blackwater and the sewage sludge, generated during the treatment process, respectively. In case service water can be reused in close surroundings of its production, there are also possibilities to reuse treated blackwater locally. In any case, local treatment and discharge into the receiving water bodies are reasonable, in order to keep local water amounts within the natural environment, minimize sewer lengths and related investment and operation costs.

4.12.7.2 Energy Water reuse of partial flows proves efficient also for the energy balance of water systems wherever water has to be transported over long distances or has to be treated energy-intensively because of low qualities. The processing of potable water from seawater via reverse osmosis requires a multiple of the energy needed to process service water from greywater. In figures, around 3–4 kW h m3 are needed compared to approximately 0.5–1 kW h m3 for producing reclaimed water from treated wastewater (Keller, 2008) or for treating greywater. (see Section 4.12.2, respectively Section 4.12.3.1). As described earlier, separating different water flows proves to be reasonable: greywater from showers and washing machines with considerably higher temperatures allows a significantly more efficient utilization of the temperature gradient in generating caloric heat, compared to energy recovery from combined sewer systems on lower temperature levels. However, applications for using the recovered energy are needed, and in order to minimize heat losses, those should be located as close to the place of origin as possible. One positive side effect of energy recovery is the reduced risk of microbial recontamination in the cooled down, reclaimed water that usually has to be stored for a short time to bridge the gap between supply and demand. The lower the storage temperature, the lower is the risk of microbial recontamination in the storage and distribution systems and the lower is the demand of chemicals for disinfection.

4.12.7.3 System Scale The size of a system plays a decisive role regarding costs of sanitation systems. Thereby, sewer and pressurized distribution

grids cause a significant share in the cost of the overall system (Gu¨nthert and Reicherter, 2001). Unaccounted losses of treated high-quality water during distribution in pressure pipes, which can amount to 40–50%, also present a significant cost factor and should therefore be taken into account when assessing the system’s scale. Against the background of the reuse of material flows (after prior treatment) and therefore the need of dual piping and sewer systems, investment and operation costs of the grid may be one of the limiting factors for the system size. Existing concepts such as DeSaR or ecosan follow this approach and focus on decentralized systems. However, in (fast-growing) urban areas with high population densities, the existing small-scale, on-site solutions do not seem to be feasible. Professional operation, stringent and reliable hygiene standards and its professional monitoring, as well as small footprints are indispensable, because of the expected comfort and because of epidemics prevention. When referring to treatment costs, economies of scale for treatment and operation have to be considered. Specific costs of large treatment plants can be reduced considerably by introducing larger-scale systems (Gu¨nthert and Reicherter, 2001; Reicherter, 2003). Regarding intra-urban areas with high population density, from the economic point of view one has to balance largescale plants generating economies of scale (in the plant sector) and small-sized, compact systems with short piping and sewer lengths. This is in accordance with the ecological point of view: optimum resource conservation requires a minimum size of technical plants, yet, at the same time, a compact piping and sewer system in order to minimize the energy input. From the sociocultural point of view, the focus is on hygienic harmlessness and comfort, the latter being, feasible with larger structures at lower costs. Thus, the optimum scale for reclaimed water application infrastructure is beyond the conventional centralized systems with supply and disposal for entire megacities, but rather lies in an effective materials flow management that is able to incorporate regional/local boundary conditions. Regarding economies of scale on the one hand and soft skills of infrastructure systems, such as flexibility, planning safety, and degree of capacity utilization, which all favor rather smaller systems on the other hand, latest research shows that the recommendable size of integrated semicentralized systems for new development areas ranges between 50 000 and 100 000 inhabitants (Bieker et al. (2010); BMBF, 2006). Anyhow, one has to bear in mind that size optima depend on local boundary conditions, treatment techniques, and on the advances in control technology. Taking the above into consideration, it becomes clear that a holistic approach must be chosen to fulfill the requirements of resource savings (ecological aspects), financial interests (economical aspects), and hygiene and safety needs (sociocultural aspects) in terms of sustainable water management. At the same time, the requirements convey that there cannot be a universal solution for everywhere, but the individual regional and locals circumstances and interests (including the financial bearing capacity of the region, the educational status of the people, climatic conditions, traditions, even religious concerns, etc.) need to be considered in order to find an adapted and locally-fitted solution (Wilderer, 2005b).

Wastewater as a Source of Energy, Nutrients, and Service Water

In the next section, a case study for a development area of 20 000 inhabitants in the city of Qingdao, P.R. China, is exemplified.

4.12.7.4 Case Study: Qingdao In 2004, 109 l of potable water per capita and day were used in Qingdao (BMBF, 2006) and as water becomes scarce, in future it will need to be generated from seawater. As depicted in Figure 16, 41 l (C  d)1 greywater from showers, baths, and laundry are produced, whereas 33 l (C  d)1 are needed for toilet flushing. Thus, the first consideration was to reuse treated greywater for toilet flushing in this completely new planned housing area. This reuse fosters the semicentralized approach and thus a supply and treatment center (STC) for the 20 000 inhabitants in this development area. As solid waste is disposed far outside of the city and not used for energy generation, in a second step the co-processing of the organic solid waste fraction was incorporated in the case study. Figure 16 provides an insight into the material and energy flows of such an integrated (solid waste and water) semicentralized – larger than on-site but smaller than centralized – supply and treatment system. In comparison to the sectored centralized approach, the integrated semicentralized approach can achieve large reduction rates in material and energy flows. Toilet flushing is operated with service water gained from greywater, thus saving 30% of potable water. Higher water reduction rates can be achieved by treating the whole amount of the arising greywater (greywater light plus hand washbasins and kitchens) and locally using it for irrigation of public

371

greens. The flexibility of the semicentralized approach allows an application-optimized operation, also in terms of service water. The treated greywater for nonpotable use in private households has to meet high quality standards. The example of China exposes the needed quality level: according to the Chinese water quality standard for urban miscellaneous water consumption (GB/T 18920-2002), water for toilet flushing has to fulfill the following requirements:

• • • • • •

TDSr1500 mg l1, BOD5r10 mg l1, NH4-Nr10 mg l1, anionic surfactantsr1 mg l1, coliformsr3 l1, and residual chlorineZ0.2 mg l1.

The integration of sewage sludge and waste treatment leads to an increase of the overall system efficiency and a decrease of the amount of residues to be disposed. At the same time, the sludge is stabilized and a solution for the currently tense and severely deficient treatment situation of wastewater sludge (openPR, 2008; Bfai, 2008) is given. The gained biogas from the integrated anaerobic treatment of sludge and waste is energetically sufficient to provide the electric energy demand of the treatment of all considered material flows (greywater, blackwater, and integrated sludge and waste treatment) within a semicentralized STC and even to produce a surplus of electric energy. An energy self-sufficient operation of the integrated STC is in that case possible. In terms of figures, the system demand is 25–50 W h (C  d)1 respectively 9–18 kW h (C  a)1 for greywater

Water treatment

Service water

76 l (C • d)−1 41 l (C • d)−1 Greywater

Greywater treatment Sludge

250 g (C • d)−1

9a−18b kWhelectr. (C • a)−1 Heat recovery 117−131 kWhcalor. (C • a)−1

Recyclables RDF Residual and biowaste 750 g (C • d)−1

Blackwater

68 l (C • d)−1

Waste and sludge treatment Process water

73 kWhelectr. (C • a)−1 610 g (C • d)−1 residuals

Sludge

Blackwater treatment

20* kWhelectr. (C • a)−1 68 l (C • d)−1(for discharge)

aActivated bMBR:

sludge treatment membrane biological reactor

Figure 16 Material and energy flows in an integrated semicentralized supply and treatment system (scenario greywater light reuse) – the case of Qingdao, P.R. China (calculated with 160 l biogas(C  d)1100 l CH4(C  d)11 kW htotal energy(C  d)1300 W hel.(C  d)1).

372

Wastewater as a Source of Energy, Nutrients, and Service Water

treatment, according to the chosen treatment method. Additionally, 55 W h (C  d)1 respectively 20 kW h (C  a)1 are needed for blackwater treatment. The conversion of biogas into electricity generates approximately 300 W h (C  d)1 respectively 110 kWh (C  a)1. Approximately 100 Wh (C  d)1 respectively 36 kWh (C  a)1 are needed for solid waste treatment, so there is a surplus of 200 W h (C  d)1 respectively 73 kW h (C  a)1. Deducing the system needs for greywater and blackwater treatment, an energy surplus of 95– 120 W h (C  d)1 respectively 35–44 kW h (C  a)1 is to be reflected in the electric energy budget. Additionally, the caloric heat of the separated greywater can be recovered. Assuming a level of efficiency of heat pumps between 0.45 and 0.5 (see Section 4.12.4.1), between 320 and 360 W h (C  d)1 respectively 117 and 131 kW h (C  a)1 of caloric heat can be gained from greywater for heating purposes, while assuring a reduction of bacterial regrowth in the service water for intraurban reuse. In addition, the conversion of biogas into electricity generates approximately 700 W h (C  d)1 respectively 256 kW h (C  a)1 caloric heat, most of which is used for the thermophilic digestion process. It has to be stated that from the energy point of view the reuse of wastewater of any quality is only recommendable, if the energy needed for treatment is lower than the energy demand for the transport of other water sources – as long as those other sources are locally available in sufficient quantities. Concurrently, the energy production from waste and sludge improves the carbon footprint of the STC. The energy is (nearly exclusively) gained from organic material, the wastewater treatment sludge as well as bio-waste and residuals. Using the biogas out of this sludge and waste, not only the energy bill is reduced to a minimum, but also the CO2 balance of the whole system is significantly improved. Ongoing research is going to clarify the carbon dioxide balance in a more detailed manner. Of course, the described scenario is only one out of several. Different boundary conditions cause different solutions, with different techniques at different scales. Nevertheless, water reuse as well as integrated energy recovery and nutrient recovery widen the future prospects of modern, reliable resource-conserving, economical, and sustainable infrastructure solutions.

4.12.8 Summary and Outlook Wastewater is a multisubstance mixture containing urine and feces, a multitude of hygienically questionable germs, potential pathogens and helminth eggs, personal care products, bleaching agents, pharmaceuticals and endocrine-disrupting compounds, and inorganic pollutants such as heavy metals and salts. Yet, wastewater also contains potential nutrients such as nitrogen and phosphorus as well as organic constituents, which are potential sources of energy. The most important resource, however, can be water itself which, in domestic wastewater, accounts for more than 99.5%. Quantitatively, agricultural reuse of adequately treated water represents by far the largest potential for water reuse. In some countries, for example, Israel, more than 75% of municipal wastewater is reused for agricultural purposes.

Depending on local conditions, nutrients contained in the wastewater can also be utilized. Hereby, care is to be recommended to prevent overfertilization. However, one of the real challenges lies in dealing with water supply and demand for irrigation not coinciding in terms of time and/or location, requiring long-distance transport and storage of reclaimed water. Further challenges are the salt content of the wastewater, as salt can accumulate in soil, as well as the hygienic quality of the reclaimed water with respect to farmers, farm laborers, and consumers. With climate changes on the one hand and the increasing cultivation of energy crops on the other hand, one has to expect the demand for irrigation water to increase as well. With the latter, the acceptance of reclaimed water will be high, as these plants are not used for food production. Another large field of application is intra-urban reuse as nonpotable water. Increasing urbanization and ever-growing megacities aggravate the gap between water demand and availability (regional/local). Via intra-urban water reuse, implemented in new housing or development areas, the specific freshwater demand can be reduced by 30% up to 50%. Intra-urban reuse fosters nodal, semicentralized supply and treatment in order to minimize cost- and energy-intensive multiple transport. As intra-urban reuse is mostly nonconsumptive, multiple water use is possible, for example, treated greywater for toilet flushing and, subsequently, treated blackwater for irrigation and nutrient utilization. Reuse of water can contribute to the preservation of valuable freshwater resources. At the same time, water reuse might contribute in saving electric power. In particular, this is the case when freshwater has to be transported over long distances or elaborate treatment is required, for example, processing of potable water from brackish water or seawater. The generation of reclaimed water only requires a fraction of the energy needed for the desalination of seawater. Regarding the generation of energy from wastewater, the largest potential lies in heat recovery, for example, for heating of shower or laundry water, which is the more efficient, the warmer the wastewater is. Caloric heat recovery fosters nearby, decentralized units and is most effective by using relatively warm greywater. Generating electric power from organic water constituents is subject to tight limits. Only in exceptional cases, with low requirements on discharge quality or in combination with cofermentation, it will be possible to run a self-sufficient operation of aerobic wastewater treatment plants with sludge digestion. (Strictly speaking, co-fermentation is an antagonism to self-sufficient wastewater operation, as the organics do not stem from the wastewater itself.) With anaerobic wastewater treatment plants, one has to consider that with temperatures below 23 1C, 40% of the COD are degradable at best. Furthermore, with a temperature range of 15–23 1C and a methane partial pressure of approximately 0.66 bar in the digester, approximately 16–19 mg l1 methane, that is, 64–76 mg l1 COD, remain dissolved in the treated wastewater. With current techniques, this methane cannot be used for energy production; even worse, in the subsequent treatment steps, the methane is stripped and thus increases the greenhouse gas emissions. As the effect of methane as greenhouse gas is 25 times higher when compared to CO2, methane emissions are not to be neglected.

Wastewater as a Source of Energy, Nutrients, and Service Water

Besides potassium and others, wastewater contains the nutrients phosphorus and nitrogen. While nitrogen is available worldwide in sufficient amounts, exploitable phosphorus resources seem to be finite. Regarding nitrogen recovery, the main question is: What is the energy input for the reclamation from wastewater in comparison with the production of nitrogen fertilizers via the Haber–Bosch process? While separate urine collection and its use as fertilizer substitute will be an alternative in small-scale rural areas, questions of logistics, energy-efficiency, hygiene, and acceptance will likely oppose its application in megacities. Moreover, the direct use might be restricted by the contained pharmaceuticals and priority pollutants. On the other hand, in the last few years, numerous processes have been developed by which phosphorus can be recovered from wastewater, sewage sludge, or sewage sludge ash, cost-efficiently and with unobjectionable hygiene quality (Petzet and Cornel, 2010). The phosphorus-containing products can be used either directly as fertilizer substitute, for example, as MAP (struvite), or as substitute for phosphate ore. Considering German boundary conditions, the per capita annual costs are 2–5 h, that is, 2–4% of the annual wastewater fees. Concluding remarks. We need new visions in order to cope with the arising challenges in infrastructure components within the urban century. Urbanization rates, never seen before, lead to new stages of resource scarcity within the cities of today and tomorrow. Conventional solutions offer reliable techniques and systems, but we need to see the bigger picture: Water reclamation will become an integral part of integrated water resource management in most places of the world, especially where no infrastructure has to be planned and built. Water recycling fosters decentralized, nodal structures. Moreover, water supply and treatment will need to be linked increasingly to energy demand and energy recovery. Thus, new technical developments and the enhancement of existing processes are necessary as well as changes in administrative structures, in order to enable people and institutions to take on responsibility for implementation, reliable, and long-lasting functioning, control, and maintenance of (new) infrastructure systems. The abolition of artificial barriers isolating water supply, wastewater treatment, solid waste treatment and energy management, and the involvement of the consumers are further challenges to be met. As we look into the future, we can expect more differentiation and the coexistence of large-scale centralized systems and small- and medium-scale de- and semicentralized solutions, adapted to the respective climate zone, settlement structure, population density, as well as the respective stage of development.

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4.13 Advanced Oxidation Processes M Sievers, CUTEC-Institut GmbH, Clausthal-Zellerfeld, Germany & 2011 Elsevier B.V. All rights reserved.

4.13.1 4.13.2 4.13.2.1 4.13.2.1.1 4.13.2.1.2 4.13.2.2 4.13.2.3 4.13.2.3.1 4.13.2.3.2 4.13.3 4.13.3.1 4.13.3.2 4.13.3.3 4.13.4 4.13.4.1 4.13.4.2 4.13.4.3 4.13.4.4 4.13.5 4.13.5.1 4.13.5.2 4.13.5.3 4.13.5.4 References

Introduction Fundamentals Generation of Free Radicals Homogeneous processes Heterogeneous processes Reaction Mechanisms Reaction Systems Competition kinetics Reaction modeling Guidance for Selecting an AOP Criteria to be Considered Cost-Related Factors of Ozone-Based Processes Cost-Related Factors of UV-Based Processes Description of Processes Ozonation Photo-Chemical Oxidation Fenton and Photo-Fenton Processes Process Combinations Full-Scale Applications Ozone-Based AOPs UV-Oxidation Processes Fenton Process Wet Air Oxidation

4.13.1 Introduction Advanced oxidation is used for various applications in wastewater treatment, water reclamation, indirect potable water reuse, drinking water production, and recently in micro-pollutant control of sewage treatment effluents. Compared to other technologies (e.g., membrane filtration, adsorption, ion exchange, evaporation, and stripping), the organic compounds in water are degraded rather than concentrated or transferred into different phases. Advanced oxidation processes (AOPs) have the ability to generate elevated concentrations of hydroxyl radical dOH, a strong oxidant capable of complete oxidation of most organic compounds into carbon dioxide, water, and mineral acids or salts. The attribution of advanced oxidation to the hydroxyl radical was explicitly mentioned by Glaze et al. (1987) at first. Besides the generation of hydroxyl radicals, many other free radicals are produced, but the hydroxyl radical is the species dominating the pollutant degradation efficiency. The free radical chemistry makes AOPs interesting to the destruction of recalcitrant, anthropogenic and toxic organic water pollutants, bacteria, viruses, and, last but not least, the emerging micropollutants also called as trace pollutants/organics. The advantage of AOPs is the relative high reaction power of hydroxyl radical. In literature, this reaction power is often expressed in terms of electrode potential versus hydrogen

377 377 377 377 385 386 387 387 389 394 394 395 395 396 396 396 398 399 399 400 402 404 404 404

electrode of redox reaction, but this is chemically not correct since the standard redox potential is only related to electron transfer, whereas the OH radical reacts by three different pathways and mostly on hydrogen abstraction pathway in the field of water treatment. As a result of the high reaction power, reactions with dOH radicals are very fast, often close to diffusion-controlled rates, and nonselective (Buxton et al., 1988). Due to the high oxidative and nonselective character of hydroxyl radicals relative to other oxidants, AOPs enable the conversion of nonbiodegradable into biodegradable compounds as well as the generation of undesirable by-products. Therefore, AOPs often need careful control of oxidant dose and/or strategies to avoid or minimize by-product formation. As a consequence, each application needs feasibility studies in laboratory and pilot scale before applying. Due to the large number of process options for AOPs and the limited space for this chapter, only a brief overview about fundamentals, reactions, applications, etc., can be given and some of the processes must have been omitted, unfortunately.

4.13.2 Fundamentals The fundamentals of AOPs include the generation and type of radicals, the fate and type of reactions, the fate of compounds

377

378

Advanced Oxidation Processes

during reactions, etc. Generally, radical reactions include three sections: (1) the starting section by generating radicals, (2) the radical reaction section, and (3) termination section by radical recombination. For generating radicals, there are several different options. Not only dOH radicals are generated, but also other radicals may have sufficient contributions as part of chain reactions depending on conditions of applications and processes. AOPs can be classified as indicated in Table 1 by the way of generation of radicals. Three main classes are given: (1) direct generation of radicals by physically based processes, (2) generation of radicals by the addition of oxidants, and (3) generation of radicals by the use of solid catalysts. Many AOPs include combinations of (1)–(3). Further classification may include the type of energy supply to activate radicalstarting reactions: (1) ultraviolet (UV) light irradiation at different wavelengths, (2) electrochemical power, and (3) temperature. The chemistry of AOPs is very complex due to the many reactions involved. Many details on improving the process efficiency are expected to be still unknown, and an example for this opinion may be the recently quantified contribution of advanced oxidation in ozonation processes during initial phase ozone decomposition, or the progress in development of UV light lamps as well as of new catalysts, for example, for solar radiation approaches.

Table 1

4.13.2.1 Generation of Free Radicals 4.13.2.1.1 Homogeneous processes The direct generation (without addition of oxidants) is as follows: Ionizing irradiation. For ionizing irradiation treatment, which is often called as electron beam (e-beam) process, high-energy electrons are produced and passed through the water. The generation of an electron is similar to a television by emission from a cathode and subsequent electrostatic acceleration. In e-beam processes for environmental applications, the acceleration potential of electron accelerators may range between 1 and 10 MeV (Mincher and Cooper, 2003). By ionizing irradiation, reactive species of both reducing and oxidizing character are generated. The high-energy electron dissipates its energy and generates the following species with indicated G values in terms of mmol J1 in neutral water

Overview and classification of advanced oxidation processes

Processes of free radical generationa

Type of external energy supplyb Without

Solar irradiation

Homogeneous oxidant type addition No addition

Ozone

Combined oxidants Heterogeneous oxidant type addition No addition

Ozone Hydrogen peroxide

UV lamp irradiation

Cavitation

V–UV irradiation

Acoustic cavitation Hydrodynamic cavitation

UV/cavitation O3/UV

Ozonation

Hydrogen peroxide

a

It is not possible to give a comprehensive description of all AOP processes, and therefore, the more commonly investigated and/or applied AOP processes are described in this article while for the others the reader is referred to the literature.

Fenton H2O2/O3

Photo-Fenton

Zero valent Fe Fe–gAl2O3

TiO2/solar

O3/activated carbon het.c Fenton H2O2/activated carbon H2O2/transient metal

O3/TiO2/solar Het. photo-Fenton

H2O2/UV Photo-Fenton H2O2/O3/UV

TiO2/UV

O3/cavitation

Heat/pressure

Electron beam

Pressurized ozonation

H2O2/cavitation H2O2/O3/ cavitation

UV/cavitation

Cu (Loprox) Cu (wet air oxidation)

UV/cavitation/ ozone Het. photo-Fenton

OMP process

Classification only by radical generation, for pollutant degradation at least one oxidant, e.g., oxygen must be present at least. Electrochemical power is an additional type of external energy supply, but has been omitted due to dependency on electrode material. c Het., heterogeneous. b

Ionizing irradiation

Advanced Oxidation Processes at 107 s after electron injection (Buxton et al., 1988):

H2 O

Irradiation

0.28 • OH + 0.27 e aq + 0.06 • H

ð1Þ

+ 0.07 H 2 O 2 + 0.27 H 3 O + + 0.05 H 2 Mainly, three types of radicals are formed in parallel: (1) OH radical, (2) solvated aqueous electron eaq  , and (3) hydrogen atom dH. The dOH radical can be generated directly by decomposing of electronically excited water (reaction (2)) or by producing and subsequent decomposing of a water radical cation (reactions (3) and (4)) (von Sonntag, 2006). By decomposing water radical cation an electron is generated, which becomes a solvated electron eaq  , the most powerful reducing agent with reduction potential of  2.77 V (Mincher and Cooper, 2003). It should be noted that Hd is the conjugate acid of hydrated electron eaq  with pKa (Hd) ¼ 9.1 (Buxton et al., 1988): d

H2 Oþ -d OH þ Hþ

ð2Þ

e  þ nH2 O-eaq 

ð3Þ

H2 O -d OH þd H

ð4Þ

The products of reactions (2)–(4) subsequently act as reactants leading to a summarized reaction (1) at a given time shortly after electron injection. A series of different reactions including their second-order rate constants describing pure water radiolysis is given by Buxton et al. (1988). They also give a direct comparison of second-order rate constants of selected organic chemicals for dOH radical, solvated aqueous electron eaq  , and hydrogen atom dH. Some additional important aspects of radiation chemistry, physics of energy absorption, as well as properties of dH and solvated electron can be found briefly in von Sonntag (2006). Some mechanistic aspects of solvated electron reductions (based on eaq  generation by Birch reduction) as an alternative for waste remediation are described in Getman and Pittman (2003). It should be noted that radiation technique has also been widely used to clarify basics of free-radical chemistry, for example, of DNA damage (von Sonntag, 2006). Vacuum UV (VUV) irradiation. VUV irradiation is a photochemical process characterized by the low wavelength of emitted UV irradiance below 200 nm, for example, at 185 nm by low-pressure Hg lamps and at 172 nm by Xenon excimer lamps. At these wavelengths, direct photolysis of water takes place:

H2 O þ hnð1727 12 nmÞ-d OH þd H

ð5Þ

The penetration depth of VUV irradiation in water is reduced to a thin layer of maximum 70 mm depth at 172 nm. In the layer, the oxygen is rapidly depleted by peroxyl radical reactions (described later hereinafter) and reactions of Hd with molecular oxygen forming HO2 d radical. Although the quantum yield of water homolysis (reaction (5)) is relatively high at a value of 0.42 for 172 nm (Heit et al., 1998), the process is not effective for high total organic carbon (TOC) concentrated

379

wastewater (Legrini et al., 1993). Increase of process performance is only possible by improved mass transfer of oxygen into the layer. Otherwise this process is limited to the treatment of water and wastewater containing relatively low concentrations of pollutants (Legrini et al., 1993). Cavitation. Hydrodynamic and acoustic cavitations are the principal cavitation types of concern. Hydrodynamic or hydraulic cavitation occurs at lower frequencies below 20 kHz and is produced by pressure variation in a flowing liquid caused by velocity variation. Acoustic cavitation ranges from 20 kHz to 1 MHz, and is a result of pressure variation in a liquid caused by ultrasound. Ultrasound irradiation provides acoustical waves in the irradiated fluid by means of electromechanical transducer. Cavitation is produced in the rarefaction cycle of acoustical wave and the cavitation bubbles disappeared upon the next compression. The phenomenon of interest in sonochemistry is the transient cavitation: once a slow growing bubble is produced, the bubble will become unstable after a number of cycles and its size increases dramatically followed by a fast collapse. During the quasi-adiabatic collapsing phase, the temperature and pressure in the bubbles increase up to several thousand degrees (Flint and Suslick, 1991; Tauber et al., 1999) and several hundreds of bar, respectively. Under such extreme conditions, the water molecules undergo thermal dissociation to yield hydroxyl radicals and hydrogen atom radicals:

H2 OðgÞ-d HðgÞ þd OHðgÞ

ð6Þ

During acoustic cavitation the bubble collapses with higher intensity compared to hydrodynamic cavitation and, therefore, the temperatures and pressures during acoustic cavitation are higher. Once produced radicals, radical recombination reactions occur in both gas and liquid phase and by far the main products of water sonolysis are hydrogen (H2) and hydrogen peroxide (H2O2) (Destaillats et al., 2003). Hydrogen peroxide then can cause many secondary reactions. The reaction of free radicals released by collapsing bubbles with both volatile and nonvolatile water pollutants is therefore always a secondary reaction from the energy balance point of view (Destaillats et al., 2003). This may change for disinfection purposes using the more energy efficient, but also less radicalproducing hydrodynamic cavitation since those systems are widely established. The generation with the addition of oxidants is as follows: The main oxidants available for hydroxyl and other free radical generation without the use of catalysts are hydrogen peroxide and ozone. Also, chlorine and permanganate can be used for generation of free radicals, but these are not treated in this chapter. Some properties of ozone and hydrogen peroxide are summarized in Table 2. Ozone. Ozone cannot be stored because it decomposes to oxygen after generation. As a consequence of this, ozone must be produced onsite by ozone generators, which commonly generate gas streams containing ozone. For application of ozone to water and wastewater treatment gas–liquid contact reactors are necessary; thus, ozonebased AOPs always involve the efficiency of the gas–liquid transfer of ozone, especially due to the relatively low ozone

380

Advanced Oxidation Processes

solubility in water and the deriving mass transfer limitations. The influence of gas–liquid transfer on ozone reaction rates is described later in the text. The ozonation process is a well-known water-treatment process and widely used on a large-scale basis in drinking water treatment due to the combined activity as a disinfectant, strong oxidant, and discoloration agent. However, the process remained as black box for a long time with empirical optimization of process parameters, for example, the ozone dose transferred to water. Recently, ozonation is much better understood and therefore defined as an intrinsic AOP, because free radical reactions are always involved in ozonation of natural water or wastewater. This has been proved by different studies (e.g., Elovitz and von Gunten, 1999; Buffle et al., 2006a, 2006b). During ozonation of natural water or wastewater, pollutants are oxidized via two reaction pathways (Hoigne and

Table 2 Some properties of established oxidants – hydrogen peroxide and ozone Property

Unit

Molar mass Boiling point at 1013 mbar Melting point at 1013 mbar Density at 1013 mbar, 0 1C Max. limit for ambient level Odour threshold UV absorption at 253.7 nm Solubility in water at 0 1C Acidity (pKa)

Oxidants

g mol1 K K kg m3 ppm ppm 1/(M cm) gl1

Ozone value

H2O2 value

48 161.5 80.6 2.14 0.1 0.02 3300 1.05

34 423 273 1.46 Not relevant Not relevant 18.6a Miscible 11.6

a

UV absorption for conjugate base HO2  : 240 1/(M cm).

Bader, 1979; Elovitz and von Gunten, 1999): 1. Direct oxidation of water compounds with ozone. Ozone is a selective oxidant reacting directly with inorganic (e.g., Fe(II), Mn(II), NO2  , HS, As(III)) and organic pollutants (e.g., double bonds, amines, sulfur containing compounds, and activated aromatic rings) often at lower reaction rates compared to dOH radical reaction. 2. Oxidation of water compounds via dOH radical generation. In contrast to direct ozone action, this highly reactive free radical reaction is reacting with almost organic pollutants.

The value of contribution of each pathway depends on several factors such as ozone dose, scavenging capacity of wastewater matrix, pH, etc., and may change during oxidation process. Therefore, the ozonation process has been subdivided into two phases, the (1) initial phase with rapid decomposition of ozone, for example, first 20 s in natural water and (2) the second phase of ozone decrease as shown by Buffle et al. (2006b) (see Figure 1). Recently, three phases have been identified No¨the et al. (2009a) by subdividing the first initial phase into a very fast ozone decomposition phase for first 1 mg of total organic carbon (TOC) corresponding to B0.3-s reaction time. The transformation of O3 into dOH radical may yield 50% (Buffle and von Gunten, 2006) and depends on present reactive moieties of natural organic matter (NOM), for example, carbonates, amines, and phenols. The mechanism of radical generation in natural water as proposed by Buffle and von Gunten (2006) is based on direct superoxide formation by amines and additionally on a direct e-transfer and the oftenreported subsequent ozonide anion radical reaction by phenols (see reactions (7) and (8)). However, it is most important to note that reaction (8) is not correct. The recent knowledge about subsequent reactions is described by reactions (26)–(29), indicating that the yield of dOH radical generation

60

60

Ozone (μm)

60 50 40 30 20 10 0

50 40 0

30

3

6

9

Ozone (μm)

CQFS

40

Ozone (μm)

50

12

Time (s)

20

20

10

10

0

0 0

(a)

30

300

600 Time (s)

900

1200

0 (b)

5

10 15 Time (s)

20

25

Figure 1 Ozone stability in (a) natural water and (b) wastewater. CQFS, continuous quench-flow system. Reprinted from Buffle et al. (2006b) Measurement of the initial phase of ozone decomposition in water and waste water by means of a continuous quench-flow system: Application to disinfection and pharmaceutical oxidation. Water Research 40: 1884–1894, Copyright (2006), with permission from Elsevier.

Advanced Oxidation Processes

381

0.6

kO3 = 1 M–1 s–1 10 M–1 s–1

0.4

River Sihl, 15 °C, pH 8

Fraction P reacting with •OH

0.8

Lake Zürich, 15 °C, pH 8

Porrentruy, 10°C, pH 7.2

1

100 M–1 s–1 1000 M–1 s–1

0.2

0 10–10

10–9

10–8 Rct value

10–7

10–6

Figure 2 Fraction of micro-pollutant P reacting with dOH as a function of Rct. From Elovitz and von Gunten (2006) Hydroxyl radical/ozone ratios duringozonation process. I. The Rct concept. Ozone: Science and Engineering 21(3): 239–260, reprinted by permission of Taylor & Francis Group, http://www.informaworld.com.

is lower: 

O3 þ PhO -O3

d

þ PhO

d

ð7Þ

O3 d þ Hþ -d OH þ O2 ðincomplete reaction summary of different reactionsÞ ð8Þ The transient dOH concentrations during initial phase calculated from wastewater ozonation have been found 100 times higher than those occurring in natural water AOP process H2O2/O3 (Buffle et al., 2006c) and therefore, the addition of H2O2 does not seem useful in this initial phase, but maybe later, depending on the water contaminants. The contribution of each pathway to the oxidation of pollutants can be determined by the Rct concept introduced by Elovitz and von Gunten (1999). The idea behind the Rct concept is the use of an dOH-probe. A suitable dOH-probe consists of very low reactivity with O3 and high reactivity with dOH (e.g., p-chlorobenzoic acid (pCBA)); its disappearance during ozonation is an indirect measure of dOH concentration. The Rct value represents the ratio between concentrations [dOH] and [O3] at any time and can be determined in laboratory batch system (Elovitz and von Gunten, 2006) or more accurately over subsecond timescales by continuous quench-flow system (CQFS; Buffle et al., 2006b). The calculated dOH concentration further allows the calculation of O3 and dOH radical exposure to a wastewater by considering the measured ozone decomposition rate during ozonation. With the calculated exposures and the second-order rate constants kO3 and kOH for direct ozone and dOH radical oxidation, respectively, the extent of oxidation of a compound can be predicted for batch reactions as well as for plug-flow reactors by

 Z t   ½P ¼ exp  ½O3  dtðkO3 þ kOH Rct Þ ½P0 0

ð9Þ

with [O3] as time-dependent function (Elovitz and von Gunten, 2006). It is worth noting that Rct changes with reaction time and is only constant within reaction phases, which depend on wastewater characteristics. Therefore, the reaction phases of the related Rct values should be identified before. While second-order rate constants k have been determined for many compounds (see, e.g., database in NIST (2009)), the time-dependent functions of [O3] and [dOH] exposures need to be determined for each wastewater. Assuming the kOH rate constant of 5 109 M1 s1 for dOH radical reactions, the fraction of a compound reacting with d OH radical can be calculated as a function of Rct value for different ozone reaction rate constants kO3. Summarizing these calculations in a figure of Elovitz and von Gunten (1999) (see Figure 2), the importance of dOH radical oxidation during ozonation has been elucidated as significant for all compounds with kO3o104 M1 s1. Hence, wastewater ozonation can generally be categorized as an ozone-based AOP. Influence of gas–liquid transfer on ozone reaction rates. Bubble columns or similar reactors are commonly used for ozone treatment. In these reactors, the overall reaction rate depends on both the transfer rate of ozone from the gas bubbles to the liquid and the rate of reaction of ozone with water pollutants and other species. This process can be described theoretically based on the mathematical model of film theory (Lewis and Whitman, 1924). A model of a gas–liquid reactor considering fast and slow reactions is described, for example, in Benbelkacem et al. (2004), involving ozone as gas component A and nonvolatile pollutant as liquid component B. Concentration profiles at the interface of gas and liquid can be predicted by this model by solving the mass balances within the film using finite difference Runge–Kutta method. A typical gas–liquid concentration profile of liquid film model is illustrated in Figure 3 for a common type of a bubble column.

382

Advanced Oxidation Processes

Cg C

Cg,out

∗

E

1

C ∗1

C 2,bulk

C 2,bulk

D Gas

Liquid film

C1,bulk

C 1,bulk Bulk liquid Cg,in

Figure 3 Sketch of semibatch gas–liquid reactor (left hand) and typical concentration profile predicted by liquid film model (right hand).

Different reaction regimes of ozone reactions have been identified and the Hatta number Ha was defined to describe the ratio between kinetic and diffusion regime (Equation (10)). In addition, the enhancement factor E (Equation (11)) and the depletion factor (Equation (12)) have been introduced to describe the flow rates of gas component at gas– liquid interface:

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi k CB;bulk DA Ha ¼ ; kL  dCA dx x¼0 E¼ kL ðCA  CA;bulk Þ

ð10Þ



DA

  dCA dx x¼L D¼  kL ðCA  CA;bulk Þ

ð11Þ

DA

ð12Þ

with kL being the mass transfer coefficient (m s1), k the reaction rate constant (m3 mol1 s1), DA the diffusivity of component A (m2 s1), CA the dissolved gas concentration within the film (mol m3), and CA the equilibrium concentration at the gas–liquid interface (mol m3). By calculating E and D the gas flow rate reacting within the film can be calculated as the difference between flow rate entering and leaving the liquid film:

NA;film ¼ ðE  DÞ kL S ðCA  CA;bulk Þ

ð13Þ

where NA,film is the reaction rate within the liquid film (mol s1) and S the interfacial surface (m2). Results by Benbelkacem et al. (2004) on model calculations for a semi-batch ozonation process taking into account the overall mass balances and second-order reaction type show that four typical kinetic regimes can be observed: (1) the slow kinetic regime (Hao0.02), where the reaction occurs in the bulk liquid and E and D are equal to 1; (2) the specific slow kinetic regime (0.02oHao0.3), where the reaction is negligible within the liquid film and E and D are still B1, this condition is usual to ones for determining mass transfer coefficients; (3) the intermediate kinetic regime (0.3oHao3), where the reaction occurs in both the bulk and film liquid and the mass transfer is accelerated by the chemical reaction (E

and D are not equal to 1); and (4) the fast kinetic regime (Ha 44 3), where the reaction occurs in the liquid film, D is close to zero, while E obtained a high value. Details of concentration profiles and the mass transfer limitations are discussed in Benbelkacem and Debellefontaine (2003) and Benbelkacem et al. (2004). From these discussions, the kinetic regime of batch ozonation processes can be identified by the progress of ozone concentration in off-gas. Ozone/UV process. Free radical generation during ozonation will be accelerated by UV light irradiation. Generally, different ranges of UV light as shown in Figure 4 can be applied: (1) long-wave UV-A, also known as near-UV light radiation, (2) middle-wave UV-B, and (3) short-wave UV-C, also known as far-UV light radiation. For ozone/UV processes, the UV-C range is of main interest due to the high absorbance of ozone in this range. Figure 4 also indicates different typical UV radiation spectra of commonly used low-pressure, low-intensity and mediumpressure, high-intensity mercury lamps together with the relative UV adsorption for DNA. The most effective UV light wavelengths for microbial inactivation are considered to between 255 and 265 nm. The UV light is commonly generated by striking an electric arc between two electrodes in a specially designed lamp, which contains a vapor or gas mixture (e.g., mercury vapor). The emission of UV is based on the energy generated by the excitation of mercury vapor or gas mixture. The initiating reactions of free radical generation are proposed to be a two-step process of light-induced homolysis of ozone:

O3 ðhno 310 nmÞ-O2 þ Oð1 DÞ

ð14Þ

Oð 1 DÞ þ H2 O-d OH þd OH

ð15Þ

Although the UV-C absorbance of ozone is much stronger than that of hydrogen peroxide, the dOH quantum yield is very low in the range of 0.1 (Reisz et al., 2003). A recombination of OH radicals (Equation (16)) in the solvent cage as well as a photolysis of ozone dissolved in water (Equation (17)) leads to production of hydrogen peroxide which has been verified by Peyton and Glaze (1988) as the main primary product of ozone photolysis: d

OH þ d OH-H2 O2

O3 þ H2 O-H2 O2 þ O2

ð16Þ ð17Þ

Advanced Oxidation Processes

Cosmic rays

Gamma rays

X-rays

Ultraviolet

Visible light

383

Radio waves

Infrared

10–3 m

Relative lamp output

100 nm

Short-wave UV (UV-C)

200

Middle-wave Long-wave UV UV (UV-A) (UV-B) 280

315

400 nm

15

0.6

10

0.4

5

0.2

0 220

240

260

280 300 Wavelength (nm)

320

Relative DNA absorbance

Vacuum UV

0 340

Typical low-pressure low-intensity UV lamp Typical medium-pressure high-intensity UV lamp Figure 4 Range of UV radiation and radiation spectra of different UV lamps.

Further OH-radical generation is proposed by photolysis of hydrogen peroxide and by reaction of ozone with HO and HO2  . Hydrogen peroxide (H2O2). Hydrogen peroxide is a commercially available, thermally stable chemical of acceptable on-site storage capability. It is not only an oxidizing species as aforementioned in Table 1, but also acting as a reductant with E1 ¼  0.7 V. Compared to the use of the other common oxidant, ozone, limitation of solubility in water as well as mass transfer can be neglected and hydrogen peroxide forms a weak acid in water by dissociating to yield the peroxide anion, sometimes called as hydroperoxide:

H2 O2 $ HO2  þ Hþ ðpKa ¼ 11:7Þ

ð18Þ

H2O2/UV process. The photolysis of hydrogen peroxide decomposes the molecule into two dOH radicals by Equation (19), as commonly agreed. UV-C irradiation must be used for photolysis due to the UV absorption of H2O2 in this range:

H2 O2 þ hn-½2d OHcage

ð19Þ

The OH radicals undergo two pathways: (1) recombination in the cage and (2) diffusion out of the cage; therefore, the yield for free OH radicals is 50%. The reaction rate of photolysis depends on pH (see, e.g., Nicole et al., 1990) with increasing rates at higher pH values. A possible explanation of this effect is the higher molar absorption coefficient of peroxide anion at 253.7 nm (Glaze et al., 1987). H2O2/O3 process. This process has been discovered by Staehelin and Hoigne´ (1982), and is sometimes called perozone or peroxone process. The original reaction mechanism concept of Staehelin and Hoigne´ (1982), has been slightly modified by Sein et al. (2007) and recently revised by Mere´nyi et al. (2009). According to Staehelin and Hoigne´ (1982), the reaction mechanism is described by following six reactions ((20)–(25)) expressing that 2 mol OH-radicals may be theoretically generated by 1 mol H2O2 and 2 mol O3. Due to recombination reactions of radicals (e.g., O3 d þ HO2 d ), the yield should always be lower in practice.

H2 O2 $ HO2  þ Hþ HO2  þ O3 -HO2 d þ O3 d ðelectron transferÞ þ HO2 d $ Od 2 þ H pKa ¼ 4:8

ð20Þ ð21Þ ð22Þ

384

Advanced Oxidation Processes O3 d þ Hþ $ HO3 d

ð23Þ

HO3d -dOH þ O2

ð24Þ

HO2 d þ H2 O2 -d OH þ H2 O þ O2

O2d þ O3 -O2 þ O3d

ð25Þ

Iron undergoes a catalytic redox cycle reaction changing its oxidation state between þ II and þ III (reactions (30), (31), (34), and (35)), while dOH scavenging by reaction (33) is expected to be minimal because generation of dOH is catalytic in iron (Pignatello et al., 2006). Although the hydroxyl radical is generated by reaction (30), both reactions (30) and (31) may initiate the sequence of reactions depending on oxidation state of iron, initially added to the process. The reaction with ferrous iron by reaction (30) is several orders of magnitude faster than the reaction with ferric iron by reaction (31). This results in a slow reaction rate limited by reaction (31) after quick and complete oxidation of initially added Fe(II). The reaction rate is thus independent of type of initially added iron. Different speciation of both ferrous and ferric iron as a function of pH must be considered to understand the reaction mechanisms in detail. The ferrous and ferric irons predominantly exist in acidic solutions as hexaquo ion Fe II ðH2 OÞ6 2þ and Fe III ðH2 OÞ6 3þ , respectively, and undergo hydrolysis depending on pH (for ferrous iron: Wells and Salam, 1965, 1968; for Ferric iron: Gallard et al., 1999; summarized in Pignatello et al. (2006)). Due to the complexity of Fe(III) hydrolysis and its high impact on reaction rates, great care is required to obtain well-defined iron salt solutions. To ensure this, some advice for investigators from Pignatello et al. (2006) is given hereinafter: (1) dissolution of ferric salts in neutral water immediately starts hydrolysis and, therefore, concentrated stock solution should be prepared to below 0.1 M and diluted in acidified water Z0.1 M Hþ; (2) the total iron should be kept below 1 104 M in acidic solution less than 102 M Hþ; (3) locally high pH should be prevented for adjusting pH in the acidic range and use of bicarbonate is recommended rather than hydroxide solution; and (4) interferences with colloidal oxides should be avoided and solutions should be used within few hours after preparation. Hydrolyzed species can be detected by turbidity and slight yellow–orange color. The optimum pH values for pollutant oxidation rates have been found at approximately pH 3. This is due to the fact that the rate-limiting step of reaction (33) is due to precipitation of catalyst Fe(III) above pH 3, while the optimum pH of the reaction (32) is around pH 4 with a reaction rate 7 times higher than at pH 3 (Pignatello et al., 2006). Due to the catalytic character of iron reactions, the concentration of iron can be reduced to minimum amounts of ppm of the wastewater volume depending on pollutant type and amount. The peroxide-to-iron molar ratio is usually in the range of 100–1000. It should not be forgotten that not only iron is able to generate radicals. Other transition metals such as copper, nickel, cobalt, and chromium may also undergo the so-called Fenton-type reactions (Goldstein et al., 1993; Koppenol, 1994; von Sonntag, 2006). Photo-assisted Fenton reaction.The Fenton reaction may be improved by additional application of UV, UV/visible light, and near-infrared irradiation. The improvement relates to

Reactions (25) and (26) have been extended by reactions (26)–(28) (Mere´nyi et al., 2009) due to the reinvestigation of theoretical OH radical yield, which is only half of formerly assumed value for ozone (von Sonntag, 2008). Thermo-kinetic and quantum-chemical calculations give rise to the suggestion of short-lived nonradical adduct formation of HO4  by reaction (28) and subsequent dismutation by reaction (29) and HO2 d =O2 d radical reactions (Mere´nyi et al., 2009) taking into account the aspect that the kinetics of free radical chemistry often over-runs thermodynamics:

O3 d $ Od þ O2

ð26Þ

O d þ H2 O$ d OH þ OH

ð27Þ

OH  þ O3 -HO4  ðadduct formationÞ

ð28Þ

HO4  $ HO2 d þ Od 2

ð29Þ

Homogeneous Fenton reaction. The Fenton reaction is one of the first intensively studied AOP processes (Fenton, 1894; Haber and Weiss, 1932, 1934; historical review in Koppenol (1993)), which has been already used four decades before to enhance the reaction of hydrogen peroxide with I (see Scho¨nbein, 1857; mentioned in von Sonntag (2006)). The reaction is iron based using different oxidation states of iron. A detailed review and description of fundamental chemistry of Fenton and photo-Fenton was given by Pignatello et al. (2006) and an overview related to solar-based photoFenton can be found in Malato et al. (2009). The classical mechanism for Fenton reaction has been introduced by Haber and Weiss (1932, 1934) and revised by Barb et al. (1949, 1951a, 1951b) and contains a sequence of seven reactions:

FeðIIÞ þ H2 O2 -FeðIIIÞ þ OH þd OH

ð30Þ

FeðIIIÞ þ H2 O2 -FeðIIÞ þ HO2 d þ Hþ

ð31Þ

OH þ H2 O2 -HO2 d þ H2 O

ð32Þ

OH þ FeðIIÞ-FeðIIIÞ þ OH 

ð33Þ

FeðIIIÞ þ HO2 d -FeðIIÞ þ O2 Hþ

ð34Þ

FeðIIÞ þ HO2 d þ Hþ -FeðIIIÞ þ H2 O2

ð35Þ

HO2 d þ HO2 d -H2 O2 þ O2

ð36Þ

d

d

In addition, the conjugate base of HOd2 , the superoxide anion O2 d undergoes reactions analogous to reactions (34)– (36). Another reaction for hydroxyl radical generation mentioned in different papers (reaction (37)) can be neglected due to extremely low reaction rate constant (k37 ¼ 3 M1 s1;

Koppenol et al., 1978):

ð37Þ

Advanced Oxidation Processes

faster reactions as well as to higher yield of inorganic products (Pignatello, 1992; Kiwi et al.,1994; Lei et al., 1998; De Laat et al., 1999). Due to the photochemistry of Fe(III) an enhancement of the rate-limiting step of Fenton process (reaction (31)) is achieved. Fe(III) complexes are usually present in water and wastewater and involve ligands of any Lewis base (e.g., OH, H2O, HO2  , Cl, R–COO, RNH2, and R–OH). Due to absorption of photons the complexes undergo a ligand-to-metal charge transfer (LMCT) excitation and a subsequent dissociation reaction to Fe(II) and an oxidized ligand:

Fe III ðLÞn þ hn-ðFe III ðLÞnÞ  -Fe II ðLÞn1 þ Lox

ð38Þ

The advantages of photo-assisted Fenton to classical (thermal) Fenton are manifold (Pignatello et al., 2006): (1) the reduced iron can undergo reaction with hydrogen peroxide to yield dOH radical (reaction (30)); (2) the oxidation of ligand may lead to further degradation of pollutants; and (3) Fe(III)-hydroxy complexes present in mildly acidic solutions of pH 3–4 may undergo photo-reduction to form dOH radicals directly (reaction (39)):

FeðOHÞ 2þ -Fe 2þ þ dOH

ð39Þ

The yield of reactions (38) and (39) depends on wavelength and light absorption properties, which are also affected by water contaminants , the so-called inner filter effects as well as of ligand types (Faust and Hoigne, 1978; Benkelberg and Warneck, 1995). It is worth noting that visible light/sunlight has been identified as an appropriate polychromatic radiation source, since it is able to enhance photolysis of ferric iron complexes by overcoming inner filter effects (Gernjak et al., 2003; Oliveros et al., 1997). Also, the increase of temperature up to 50 1C reduces the demand of hydrogen peroxide (Pignatello and Day, 1996) and/or enhances the reaction rate (Sagawe et al., 2001; Go¨b et al., 2001) and may increase up to 5 times (Gernjak et al., 2006), while the optimum has been found at 55 1C due to improved hydrogen peroxide consumption at higher temperatures (Torrades et al., 2003). As a consequence, insulated and hybrid reactors have been developed (Sagawe et al., 2001; Farias et al., 2010) allowing both photochemical and thermal solar irradiation.

4.13.2.1.2 Heterogeneous processes The use of solid catalysts in water and wastewater treatment is based on the science of heterogeneous catalysis involving five reaction steps: (1) diffusion of reactants to the surface of catalyst, (2) adsorption of reactants onto the surface, (3) reaction on the surface, (4) desorption of products off the surface, and (5) diffusion of desorbed products. Noble metals (e.g., Ir, Pd, Pt, Rh, and Ru) and metal oxides of different metals such as Cu, Mn, Co, Cr, V, Ti, Bi, and Zn have been commonly used as heterogeneous catalysts. The catalysts are immobilized on supports, which may be classified by their nature to inorganic and organic supports. The tasks of supports are to: (1) increase the surface area of catalytic material, (2) decrease sintering, and (3) control useful lifetime of catalysts (e.g., Matatov-Meytal and Sheintuch, 1998).

385

Materials such as carbon black, metal oxides (e.g., TiO2 and Al2O3), silica, zeolite, glass and carbon fibers, ceramic materials, pillared clays (Al–Cu and Al–Pt), and many others have been used as supports for catalysts. Heterogeneous catalysis is currently a dynamic field driven by new developments, for example, in catalyst preparation, immobilization techniques, surface modification techniques, and, last but not least, the use and characterization of nanosized particles. However, heterogeneous catalysis in water phase is, to date, of limited application due to unsolved problems in the deactivation of catalysts by poisoning, sintering, or leaching. These problems have still to be overcome to ensure suitable lifetime of catalysts and economic applications. Some processes currently receiving more interest are the heterogeneous photo-Fenton process and the semiconductor TiO2 photo-catalysis due to the possible use of solar radiation, and the electrochemical oxidation due to recent advances in electrode material development, for example, doped diamond electrodes. Semiconductor photo-catalysis. Photo-catalysis is a process that generally may involve photons (by UV or solar radiation) and a catalyst either in liquid (homogeneous photo-catalysis) or in solid phase (heterogeneous photo-catalysis). The most investigated semiconductor photo-catalyst is TiO2 in anatase form. It is an inexpensive mass product, chemically and biologically inert and resistant to chemical and photo-corrosion. Other semiconductors of lower importance are, for example, a-Fe2O3, SrTiO3, WO3, ZnO, and ZnS. The initial step of photo-catalysis is the absorption of photons by the catalyst. UV-irradiation of TiO2 with wavelength o380 nm leads to an excited state, which is commonly explained by the band-gap model illustrated in Figure 5: through absorption of photons the electrons in the valence band of the semiconductor TiO2 are transferred to the valence conduction band, thus creating electron vacancies, also called as electron deficiencies or holes. Electron–hole pairs develop (Equation (40)) and the electron-depleted valence band hole (hþ) has a high reduction potential of 2.9 V versus normal hydrogen electrode (NHE) for oxidizing most of the pollutants present in wastewater:

TiO2 þ hn-TiO2 ðe þ hþ Þ

ð40Þ

An electron transfer to the valence band hole (hþ) either from the adsorbed substrate (Fox et al., 1991) or from the adsorbed solvent molecules (H2O and OH; see Pichat, 1991) has been identified for generating radicals. In water-treatment processes, the adsorbed H2O and OH have probably higher impact on radical generation by Equations (41) and (42), respectively, due to their higher concentration:

TiO2 ðhþ Þ þ H2 Oad -TiO2 þdOHad þ Hþ

ð41Þ

TiO2 ðhþ Þ þ OHad  -TiO2 þdOHad

ð42Þ

Molecular oxygen can act as an acceptor of the valence band electron (VBe) released by the excited TiO2 valence band electron hole (hþ) and enables further radical generation by forming the protonated form of superoxide anion

386

Advanced Oxidation Processes Acceptor Reduction

hν

O2

Adsorption

Acceptor

Energy

e–

Conduction band

Conduction band

Eg Eg

e–

•–

O2

Change carriers formation

Recombination

Donor+ Valence band

h+

Valence band

Oxidation

Further degradation P•• H+ +

h+

Donor

Reduction

P

OH•

H2O

Oxidation Adsorption

(Equation (43)) and subsequent dismutation to hydrogen peroxide or peroxide anion. Then, further dOH radicals are generated by dismutation of hydrogen peroxide or by reaction of Equation (44) with hydrogen peroxide as further electron accepting species. Enhancing the trapping of electrons and holes before recombination is one of the keys for optimizing TiO2 photo-catalysis. Electrons can be trapped within 30 ps after excitation and holes within 250 ns (Rothenberger et al., 1985), while interfacial charge transfer takes place in a period of nanoseconds to milliseconds (Martin et al., 1994):

VBe  þ O2 -O2 d

ð43Þ

VBe  þ H2 O2 -OH þdOH

ð44Þ

The reaction rate of photo-catalysis may be enhanced by the addition of hydrogen peroxide (Ollis et al., 1991; Matthews, 1991), but it is still pH dependent due to changes in adsorption/desorption rates and electron–hole separation efficiencies. The advantage of TiO2 photocatalysis is the potential use of solar radiation due to the spectral absorption characteristic of TiO2 in UV-A regions (see Figure 6). This may receive more interest in future due to more sustainable energy source and possible reduction of carbon dioxide emission. However, photo-catalysis by TiO2/UV is well known as a very slow AOP compared to other AOPs, which is mainly directed to the very low quantum yield (e.g., Ishibashi et al., 2000), even more in the case of solar radiation, where not only the spectral absorption of TiO2 is relatively low but also the energy content of solar radiation is less compared to UV-C radiation. This problem may be rather negligible for the removal of trace pollutants due to very low concentrations resulting in reduced reactor dimensions, which may be economic. On the other hand, the use of other semiconductor materials (e.g., SnO2, ZnO, WO3, GaAs, and GaP) is under investigation due to their higher band-gap wavelength enabling higher absorption of solar radiation and the possible increase of yielding photons. Another option under investigation is the

Arbitrary units

Figure 5 Scheme of band-gap model of semiconductor TiO2 with electron–hole pair. Redrawn from Malato S, Ferna´ndez-Iba´nez P, Maldonado MI, Blanco J, and Gernjak W (2009). Decontamination and disinfection of water by solar photocatalysis: Recent overview and trends. Catalysis Today 147: 1–59, Copyright (2009), with permission from Elsevier.

250

TiO2 Solar spectrum

300

350 400 Wavelength (nm)

450

500

Figure 6 TiO2 absorption spectrum compared with solar radiation spectrum. Redrawn from Malato S, Ferna´ndez-Iba´nez P, Maldonado MI, Blanco J, and Gernjak W (2009) Decontamination and disinfection of water by solar photocatalysis: Recent overview and trends. Catalysis Today 147: 1–59, Copyright (2009), with permission from Elsevier.

doping of semiconductor materials, for example, by Cr, to shift the absorbance to higher wavelengths of visible light, the so-called red-shift. Also, the doping with nonmetallic elements such as sulfur, nitrogen, and carbon could be adopted in order to extend the absorbance wavelength range of TiO2 and enhance photo-catalytic activity (Thompson and Yates, 2008). However, problems of deactivation, leaching, or chemical stability have still to be overcome.

4.13.2.2 Reaction Mechanisms Once generated an dOH radical, it reacts very fast, that is, often close to diffusion-controlled rates (Buxton et al., 1988). The radical reactions can be classified to (1) addition reaction, (2) hydrogen abstraction, and (3) electron transfer. The addition reaction is the preferred pathway, if possible. The yield of different reactions in relation to dOH yield depends on the moieties present in water, for example, (1) hydrogen abstraction may be negligible when C ¼ C and C ¼ N double bonds are present (So¨ylemez and von Sonntag, 1980) and

Advanced Oxidation Processes

(2) electron transfer yield increases, when the potential sites of d OH radical addition are substituted by halogens and may dominate for Br (see hereinafter). Despite the fact that radical reactions described in this chapter are mainly addressed to dOH, not only dOH radicals may be involved in free-radical reactions for water and wastewater treatment. Also, inorganic, carbon-centered, heteroatom-centered, or peroxyl radicals can be involved depending on specific compounds in polluted water. Some additional details about radical formation and compiled reaction rates of these radicals can be found in Buxton et al. (1988) and von Sonntag (2006). However, overall reaction rates of AOPs derived from wastewater and water-treatment investigations are commonly traced back to dOH reaction rates. This should be kept in mind, for example, for possible interpretation of results in wastewater treatment by AOPs. Another fact not treated in this chapter is the possible change of redox property of the radical from reducing species to oxidizing ones (readers are referred to, e.g., von Sonntag, 2006). One of the main difficulties in application of AOP is based on the influence of radical scavengers in the (waste)water matrix. Scavengers could inhibit the pollutant degradation reactions, and the predominant effect of scavengers in a given wastewater depends on all involved moieties of compounds in the water. One important scavenger is the carbonate in its different dissociation forms, mostly existing in natural water and wastewater. Other scavengers many times present in these water types are NOMs often called humic substances, whereas phenol and amine are present as repeating and promoting moieties of NOM. Addition reactions. The hydroxyl radical reacts readily with C ¼ C (reaction (45)), C ¼ N, and S ¼ O (except SO4 2 , reaction (46)) double bonds, and with transition metal ions (reaction (47)) by addition reaction leading to an adduct formation and subsequent adduct decomposition. Due to its electrophilic character, electron-rich positions at carbon atom are preferably attacked, for example, the C5 ¼ C6 double bond is rather attacked at C5 than at C6, due to the electron-richer region at C5 (von Sonntag, 2006): H + OH

H OH

ð45Þ

the H-abstraction chain reaction scheme is often used to explain oxidation of organic compounds, it is important to know that peroxyl radicals are known as weak oxidants due to its low reduction potential and may act as one-electron oxidants toward strong electron donor compounds. They, therefore, do not preferably undergo an auto-oxidation chain reaction at common water-treatment AOP conditions, but the elimination of superoxide radical (reaction (51); see von Sonntag and Schuchmann, 1991; von Sonntag, 2006). The peroxyl radical chemistry is much more complex as peroxyl radicals undergo a number of unimolecular reactions, not only HO2 d =O2 d elimination reaction through the formation of a double bond but also addition to C ¼ C double bond, or O-transfer reactions (for compiled details, see von Sonntag (2006)):

HRH þ dOH-HR d þ H2 O

ð48Þ

HRd þ O2 -HROOd

ð49Þ

HROO d þ HRH-ROOH þ HR d ðnot at common wastewater conditionsÞ

ð50Þ

HROO d -HO2 d þ R

ð51Þ

From the above reactions, it is obvious that the presence of oxygen significantly influences the radical reaction system. If oxygen is absent or not sufficiently present, reaction (49) is of minor importance compared to other reactions and the radical recombination by reaction (54) prevails. On the other hand, a very high amount of molecular oxygen improves the relative proportion of bimolecular recombination (von Sonntag and Schuchmann, 1991). Most of the carbon-centered radicals react fast and irreversible with O2 following reaction (49), while other organic compounds do not so, for example, hexandienyl and thiyl radicals react only reversible with O2 and phenoxyl and other hetero-centered radicals do not react with O2 (von Sonntag, 2006). The rate of reaction cycles depends on the presence of inhibitors/scavengers. One of the well-known scavengers is carbonate in its different dissociated forms. The reactions of carbonate and bicarbonate lead to carbonate radicals, which are mostly known as poor reactants: d

R2 S ¼ O þd OH-R2 SðOd ÞOH-RSðOÞOH þ R d Tl 2þ þ d OH-HOTl 2þ -Tl 2þ þ H2 O

ð46Þ

ð47Þ

Hydrogen abstraction reactions. Based on the bond dissociation energy of HO–H higher than C–H bond, a hydrogen atom can be removed from organic compound HRH (Equation (48)), thus forming a carbon-centered radical dR. Many authors propose a chain reaction to be initiated by reaction of d R with molecular oxygen (Equation (49)) producing a peroxyl radical, which may subsequently react with other organic compounds by reaction (50), and ideally lead in its final step to carbon dioxide, water, and inorganic salts. However, since

387

OH þ HCO3  -H2 O þ CO3 d

ð52Þ

OH þ CO3 2 -OH þ CO3 d

ð53Þ

d

Electron transfer reactions. The addition reaction and the electron transfer are in competition and although the electron transfer is thermodynamically favored, the addition reaction is often preferred, while the direct electron transfer has been rarely observed (von Sonntag, 2006). Direct electron transfer is favored, for example, in reactions with halogenated phenolate ions, because the ortho- and para-position of preferred HOd addition, are blocked by substituents and the electron transfer might become dominant (von Sonntag, 2006). The yield of electron transfer contributing to the total reaction increases with the size of halogen and dominates for Br and I by 73% and 97%, respectively. The other reactions with

388

Advanced Oxidation Processes

preferred electron transfers are reactions with thiol and thiolate (Akhlaq and von Sonntag, 1987). Radical recombination reactions. The radical recombination reactions terminate the chain reactions by yielding combined molecule whereas larger molecules decompose, for example,

HROO d þ HROO d -ROH þ ROH þ O2

ð54Þ

4.13.2.3 Reaction Systems Reaction systems may be described by different reaction pathways and sequences. The complexity of these systems is exemplary, illustrated by H2O2/UV and ozone/UV–ozone/ H2O2 processes. The ozonation process is treated separately to the ozone/UV/H2O2 reaction system due to different phases of ozone decomposition by direct and indirect ozone action and the direct formation of the ozonide radical anion O3 d .

4.13.2.3.1 Competition kinetics The sequence of radical reactions may be most affected by promoting and inhibiting/scavenging compounds depending on the water/wastewater characteristics. Not only the reactions but also the dissociation equilibriums depending on pH and the progress of pH during oxidation must be taken into account. The same goes for heterogeneous catalysis where, for example, the charge of surface and subsequently the adsorption/desorption kinetics are affected by pH value. Besides (1) pH, some common parameters, which may scavenge the removal of target pollutants, are (2) carbonate/ bicarbonate, (3) dissolved oxygen, (4) nitrate ion, and (5) NOM, (6) OH, (7) Fe2þ, (8) primary and secondary alcohols, (9) phosphate ion PO3 4 , and (10) specific chemicals (pBA, tert-butyl alcohol, etc.). 1. The pH value has direct and indirect effects. A direct effect is that the hydrated electron eaq  is scavenged by hydrogen ion at pH values below neutral pH to form the hydrogen atom. At pH higher than neutral, the dOH radical may dissociate to produce the hydrogen ion and oxygen anion ( d OH$ O d þ H þ ; pKa ðd OHÞ ¼ 10:8). Indirect effects are, for example, through the carbonate/bicarbonate concentration (see (2)) or the hydroxide anion concentration (see (6)). 2. The total carbonate concentration in water is part of the carbon dioxide buffer system in natural water and most types of wastewater due to dissolution of biologically generated or atmospheric CO2 to produce carbonic acid and the subsequent dissociation of carbonic acid. The scavenging effect of carbonate ion CO3 2 and bicarbonate ion HCO3  is described by reactions (54) and (55), where the carbonate ion is the more important hydroxyl radical scavenger due to 50 times higher reaction rate constant. Considering the carbonate–bicarbonate dissociation equilibrium of pKa value of 11.5, the dOH scavenging prevails at high pH, where most commonly a reduced reaction rate has been found compared to neutral and acidic pH, thus indicating an inhibition of radical reaction in pollutant degradation at higher pH. 3. The role of dissolved molecular oxygen is different for various AOPs. Mostly, it accelerates the reaction rates of

4.

5.

6.

7.

8.

photochemical and Fenton reactions (e.g., Sun and Pignatello, 1993; Kim and Vogelpohl, 1998; Bossmann et al., 2001; Legrini et al., 1993), and , in addition, serves as an oxidant. The oxygen incorporated into organic pollutants may originate partly from dissolved oxygen as shown by isotope-labeled 18O2 (Kunai et al., 1986); subsequently, dissolved oxygen is desirable to promote reduced consumption of hydrogen peroxide. In contrast to this, especially in radiolysis the oxygen will be rapidly reduced by solvated electron eaq  and Hd to produce superoxide radical anion O2 d . The second-order reaction rate constants are very high at 1.9 1010 and 2.1 1010 M1 s1. The superoxide radical anion is a reducing species and relatively inert compared to hydrated eaq  , and thus the ebeam process efficiency will be reduced by the presence of oxygen (Mincher and Cooper, 2003). In other AOPs the superoxide radical anion plays an important role, especially in ozone-based and ozone-involved AOPs because the relatively low reactive superoxide radical reacts fast with ozone resulting after sequence of fast reaction in generation of further dOH radicals. At high concentration levels, the nitrate ion NO3  can act as an electron eaq  scavenger and, for example, for the ebeam process the product after a sequence of reactions is nitrite NO2 . This eaq  scavenging may affect the recombination of eaq  and dOH radical in radiolysis, thus enhancing the oxidative reactions with dOH (Mincher and Cooper, 2003). However, the nitrite ion generation can be compensated by ozone addition (Mincher and Cooper, 2003). The NOM could initiate or inhibit the target radical reactions depending on the type and quantity of different moieties of NOM. Humic acids as a part of NOM also act as initiator or inhibitor depending on its concentration (Xiong and Graham, 1992). Especially during ozonation, new electron-rich moieties may be produced by direct ozone reaction. These moieties are then involved in the generation of new free radicals No¨the et al. (2009b). Some important moieties of NOM are amines and phenols (Buffle and von Gunten, 2006). The hydroxide anion is often mentioned as a possible initiator for ozone-based systems, but it should be noted that the reaction rate is low (70 M1 s1) and therefore mostly negligible. OH can react directly with ozone forming the anion of hydrogen peroxide, which reacts also with ozone generating an ozonide anion radical O3 d and a superoxide radical HO2 d , both known as chain carriers for hydroxyl radical generation. Dissolved Fe2þ may play a role as initiating species for dOH radical formation following the Fenton reaction. Higher concentrations of Fe2þ could be expected if Fe(II) salts are used as precipitators in pretreatment steps of water and wastewater treatment. Primary and secondary alcohols are often called promoters for ozone-based systems due to the radical chain decomposition cycle by production of the chain carrier superoxide (HO2 d =O2 d ). Methanol (MeOH) is a typical model compound for investigating ozone-based AOPs. Phosphate can act as an efficient inhibitor in concentrations at 50 mM (Staehelin and Hoigne´, 1985). In Fenton

Advanced Oxidation Processes

389

electron transfer reactions with strong oxidants. This is one of the reasons for the differences in UV/H2O2 systems compared to UV/ozone systems. The path (h) in Figure 7 indicates that the organic radicals may initiate polymerization of unsaturated organic compounds in cases of lack of oxygen. It is worth noting that multi-chromatic or so-called broadband UV lamps are commonly used in H2O2/UV systems; therefore, direct photolysis by UV may also be a significant part of H2O2/UV reaction systems, especially in cases where the transmission of wastewater is relatively high. Ozone/UV and ozone/H2O2 reaction system. The reaction system of ozone-based systems is summarized by Figure 8 considering the H2O2/UV reaction system from Peyton (1990) and Legrini et al. (1993). The hydroxyl radicals can be generated by (a) photolysis of O3, (b) reaction of HO 2 with O3, or (c) photolysis of hydrogen peroxide, which is either a product of photolysis of ozone as indicated by path (d) as well as a product of ozone reaction with unsaturated organic compounds. The sequence of dOH radical reactions is comparable to the peroxyl radical formation of UV/H2O2 process. In these ozone-based processes, the peroxyl radicals may be considered as true propagators of the radical reactions due to the fast reaction of superoxide radical anion with ozone as indicated by path (e). Ozone reaction system. The ozone reaction mechanism in natural water and wastewater systems may be classified in two phases, the (1) initial phase, and the (2) second phase of ozone decrease (Buffle and von Gunten, 2006). During the initial phase, the ozone decomposition is very fast due to involvement of higher amount of free radical generation. The first initial phase can be subdivided further into a very fast and a fast phase No¨the et al. (2009b). The species amine and phenol (Ph), both present as moieties of NOM, accelerate the ozonide radical anion O3 d formation either by direct formation or by direct electron transfer, respectively. The ozonide

reactions phosphate and iron(III), in addition, may precipitate forming Fe(III)–phosphate complexes and subsequently inhibiting the Fe(II)–Fe(III) cycle and reducing the dOH radical generation. 9. Some chemicals well known as inhibitors are acetate, tertbutanol (t-BuOH), and p-chlorobenzoic acid (pCBA). The latter one is also known as species with very low direct reaction with ozone and thus useful as an dOH probe for ozone-based reactions. Chloride (Jayson et al., 1973; Pignatello, 1992) and bromide (Zehavi and Rabani, 1972) may also serve as inhibitors at high concentrations due to the generation of weakly reactive radical products. Sulfate ions inhibit the Fenton reaction due to the formation of a mixture of FeSOþ 4 and Fe(SO4)2 complexes (Pignatello et al., 2006; Pignatello, 1992), which cannot be coordinated by hydrogen peroxide (De Laat and Lee, 2005). H2O2/UV reaction system. The oxidation of organic pollutants by H2O2/UV process may be summarized by reaction system of Peyton (1990) and Legrini et al. (1993) as shown in Figure 5. One started with reaction (17), indicated as reaction path (a) in Figure 5, the hydrogen abstraction by the dOH radical yielding the peroxyl radical is proposed and indicated as path (b) and (c). The peroxide radical may then undergo four possible pathways, (d) heterolysis and forming of organic cation and superoxide radical anion, or (e) 1,3 hydrogen shift and homolysis into hydroxyl radical and carbonyl compounds, or (f) back-reaction to oxygen and HRd, or (g) hydrogen abstraction starting a chain of oxidation reactions. The superoxide radical anion O2 d dominates in neutral solutions by pKa (HO2 d ) ¼ 4.8 (see, e.g., Czapski and Bielski, 1993; Behar et al., 1970) and a solvolysis reaction yielding hydrogen peroxide has been proposed by path (i). It should be noted that the superoxide radical anion is not very reactive (E1 ¼  0.33 V; compilation of rate constants in Bielski et al. (1985)), except for

H2O O 2– • (i)

RH+

H2O2

RO (e)

(d) (a) RHO2•

O2

H2O HO•

(f)

HRH

h

(c)

•

RH

(b) (g)

O2 (h) HRH

RHO2H

Polymer products

Figure 7 The reaction system of H2O2/UV process (Peyton, 1990; Legrini, et al., 1993). Redrawn with permission from Legrini O, Oliveros E, and Braun AM (1993) Photochemical processes for water treatment. Chemical Reviews 93: 671–698, Copyright 1993, American Chemical Society.

390

Advanced Oxidation Processes HO2• HO2– H2O

O2– • (i)

RH+

h

H2O2

RO (e)

H+

(d) (a) RHO2•

O2

(f)

HRH

(c)

RH

h

HO3•

HO•

•

H2O

(b)

O2

O2

(g)

O– •

O3–•

O3

(h) HRH RHO2H

Polymer products

OH –

Figure 8 The reaction system of ozone/UV and ozone/H2O2 process.

RH+

O2– •

HRH

RHO2•

RHO2H

HO2• O3 O2

OH –

RH •

A H2O A

•+

O– •

O2 O3– • O2

HO •

H2O

H+

HO3•

HRH

O2

Figure 9 The reaction system of ozone in natural water and wastewater.

radical anion O3 d then undergoes reactions to generate dOH radicals (Buffle and von Gunten, 2006). Recent studies No¨the et al. (2009a) indicated that the radical formation does not stop after degradation of involved moieties. It is interesting to note that there is a continuous generation of dOH radicals during ozonation of wastewater effluents due to the formation of new electron-rich moieties by direct reaction with ozone No¨the et al. (2009a). The reaction system is summarized in Figure 9. It indicates the linkage with other AOPs by reaction of ozone with the superoxide radical anion O2 d =HO2 d formed by ozone/H2O2, ozone/UV, and ozone-activated carbon processes. The dOH

radical subsequently generated (reaction (21)) undergoes the same peroxyl radical reaction sequence as described before depending on scavenging capacity of water matrix and pollutants such as NOM and carbonate.

4.13.2.3.2 Reaction modeling Reaction modeling mainly comprises models of chemical reaction, mass transfer, fluid dynamic, and, in addition, fluence rate if photons are involved. Chemical models are restricted to the sequence of reactions considering reaction rate constants, reaction equilibria, etc., with the main objective to allow the determination and evaluation of reaction mechanisms and

Advanced Oxidation Processes

reaction competitions. Mass transfer and fluid dynamic models may be combined with chemical models, but the main objectives of these combined models are (1) the determination and optimization of design of reactor and process and/ or (2) the reduction of the experimental effort for upscaling of reaction system from laboratory to technical scale. Many different models of each of the above-mentioned topics can be found in literature, for example, free-radical reaction models, gas–liquid contact reactors, ozone reaction systems, heterogeneous catalytic systems, and fluence rate models. However, the examples briefly described hereinafter have been restricted to (1) chemical model examples illustrating both the complexity as well as the usefulness, for example, for oxidation of micro-/trace pollutants, and (2) photochemical reaction system to exemplify the relation of different factors on process efficiency which are also valid analogously for photo-catalytic processes. Chemical reaction model. The chemical models for AOPs are based on reaction rates with hydroxyl and other radicals, which are described by second-order rate law corresponding to Equation (55):

Rr ¼ kox;p  Cox  Cr

ð55Þ

with kox being the reaction rate constant, Cr the concentration of reactant r, and Cox the concentration of oxidant. The oxidant could be ozone or hydroxyl radical. Reported steady-state hydroxyl radical concentrations of H2O2/O3/UV systems are in the range of 1011–109 mol l1 (Glaze et al., 1987). Due to the fast reactions the concentration is expected to be low, but it depends on different factors, for example, concentration of reactants, photon flux, etc. Some reaction rate constants and dissociation equilibria are listed in Tables 3 and 4 for hydroxyl radical reactions as well as for ozone reactions. A large compilation of several hundreds reaction rate constants is listed in Buxton et al. (1988) or can be found most completely at Notre Dame Radiation Laboratory/National Institute of Standards and Technology (NDRL/NIST) Solution Kinetics Database (NIST, 2009). Rates for ozone-based systems for some emerging micro-pollutants can be found in Dodd (2008). Reactions with NOM expressed as dissolved organic carbon (DOC) (Reisz et al., 2003; No¨the et al. (2009a)) have been considered; therefore, the modeling of degradation of several trace organics in natural water as well as in urine was possible. Several approaches for kinetic modeling have been proposed in the literature. In ozonation processes, the ozone decomposition rate is one of the key parameters in plant operating. For natural waters, the ozone decomposition rate in the first initiating decomposition phase substantially depends on hydroxyl radical reactions and needs to be modeled adequately. For example, for better understanding of the reaction system, Buffle et al. (2006b) investigated conceptually the effect of reactive moieties distributions ci on ozone decomposition applying a model of coupled differential equations, which describe the temporal behavior of homogeneous, isothermal chemical reactions. This example illustrates that a more detailed simulation is very useful to understand the operationally observed instantaneous ozone demand based on parallel reactions of different moieties of NOM with

391

different oxidants. More than 1000 rate constants of reactions with ozone and hydroxyl radicals have been extracted from National Standard Database (NIST, 2002) and deployed by a program code called ACUCHEM (Braun et al., 1988), which can handle up to 80 simultaneous reactions and 40 species. Another possibility for kinetic modeling is the use of the chemical kinetics simulator free of charge by downloading from IBM Almaden Research Center website. This simulation tool has been used, for example, to evaluate the contribution of H2O2 formation during wastewater ozonation to yield dOH radical (Noethe et al., 2009). The aforementioned type of modeling refers mainly to mechanistic aspects of reactions, whereas the modeling of elimination rates is more useful from the operators and engineers point of view. Established parameters for possible use in ozonation reaction models are the chemical oxygen demand (COD) or the DOC (Beltra´n et al., 1995, 2001; Steensen, 1998; Wenzel, 1998). Since both the COD and DOC are summarized parameters comprising various compounds of different reaction rates, they therefore do not give suitable simulation results with one reaction rate constant. To overcome this problem, the DOC can be classified into several fictive groups of pollutants with different apparent reaction rate constants for each group of DOC. The advantage of this approach has been confirmed by No¨the et al. (2009a) for simulation of micro-pollutant elimination. The DOC of the wastewater was subdivided into three group components of DOC containing their corresponding concentration ci:

cðMatrixÞ ¼

n X

ci

ð56Þ

i¼1

The ozone decomposition rate then depends on these group components by Equation (57) and the group component elimination rates can be modeled by reaction (58) with the prerequisite that the ozone decomposition rate for each group component is known. Then the concentrations (ci) can be adapted iterative numerically by minimizing the error between simulation and experiment No¨the et al. (2009a):



n d½O3  X ki ½O3 ci ¼ dt i¼1

ð57Þ

dci ¼ ki ½O3 ci dt

ð58Þ



An analogous scheme in grouping the summary parameter TOC can be found, for example, in Zazo et al. (2009) for modeling the elimination rate of one pollutant by Fenton process. They simulated the pollutant degradation by a consecutive reaction scheme. The measured TOC in solution is always a sum of the different TOC fractions consisting of pollutants with concentration TOCA that reacts easily with hydroxyl radical forming a by-product fraction TOCB, that also react itself with dOH but at slower reaction rates forming final fraction TOCC. Fluence rate distribution models. In H2O2/UV and other photochemical reaction systems, the fluence, often called UVdose, and the UV absorbance of wastewater must be considered besides the chemical reactions. Not only AOP

392 Table 3

Advanced Oxidation Processes Reaction rate constants for ozone reactions and OH radical reactions

Reactions

Rate or equilibrium constants: M2 s1, M1 s1, s1, or dimensionless

Refs. and notes

Ozone reactions O3 þ DOC-O3 d þ DOCdðþÞ O3 þ DOC-O2 d þ DOCðoxidizedÞ O3 þ DOC-O2(1Dg) þ DOC(oxidized) O3 þ DOC-DOC(peroxide) O3 þ O2 d -O3 d þ O2 d OH þ O3 -HO2 d þ O2 O3 þ HO2  -d OH þ O2 þ O2 d ONOO  þ O3 -ðd NO2 þ O2 d þ O2 Þ? NO2  þ O3 -NO3  þ O2

B105 in hydr. urine B103 in ED diluate

Dodd (2008) Dodd (2008)

1.5  109 1.1  108 2.8  106 B5  106 6.0  105

Sehested et al. (1983) Sehested et al. (1984) Staehelin et al. (1982) Dodd (2008) Liu et al. (2001)

Hydroxyl radical reactions OH þ DOC-DOCd ( þ H2O, for Habstr.) d OH þ HCO3  -CO3 d þ H2 O d OH þ CO3 2 -CO3d þ OH d OH þ NH3-dNH2 þ H2O d OH þ O3 -HO2 d þ O2 d OH þ H2 O2 -O2 d þ H2 O þ Hþ d OH þ H2 O2 -H2 O þ HO2 d d OH þ HO2  -O2 d þ OH þ Hþ d OH þ OH-Od þ H2O d NO2 þ dOH-ONOOH ONOO þ dOH-dNO þ O2 þ OH MTBE þ dOH TAME þ dOH DIPE þ dOH TBF þ dOH TBA þ dOH Arsenic trioxide þ dOH Bromide ion þ dOH Carbon tetrachloride þ dOH Chlorate ion þ dOH Chloride ion þ dOH Chloroform þ dOH CN þ dOH Dibromochloropropane þ dOH 1,1-Dichloroethane þ dOH 1,2-Dichloroethane þ dOH HCN þ dOH Hydrogen sulfide þ dOH Hypobromous acid þ dOH Hypoiodous acid þ dOH Iodide ion þ dOH Iodine þ dOH Iron þ dOH p-Dioxane þ dOH Tetrachloroethylene þ dOH Tetrachloroethylene þ dOH Tribromomethane þ dOH Tribromoethylene þ dOH Tribromomethane þ dOH Vinyl chloride þ dOH PNDA þ dOH-OH-PNDA Domoic acid þ dOH Kainic acid þ dOH

B3  108 8.5  106 3.9  108 9  107 1.1  108 2.7  107 2.7  107 7.5  109 1.2  1010 4.5  109 4.8  109 1.6  109 2.8  109 3.01  109 5.6  108 7.3  108 1.0  109 1.1  1010 2.0  106 1.0  106 4.3  109 5.0  105 7.6  109 1.5  108 1.8  108 2.0  108 6.0  107 1.5  1010 2.0  109 5.6  104 1.1  1010 1.1  1010 3.2  108 2.8  109 2.6  109 1.0  107 1.8  108 4.2  109 5.0  106 1.2  1010 1.25  1010 9.22  109 2.46  109

Westerhoff et al. (2007e) Buxton et al. (1986) Buxton et al. (1986) Neta et al. (1978) Sehested et al. (1984) Christensen et al. (1982) Kwon et al. (2009) Christensen et al. (1982) Buxton et al. (1988) Merenyi et al. (1999) Goldstein et al. (1998) Crittenden et al. (2005) Sutherland et al. (2007) Sutherland et al. (2007) Sutherland et al. (2007) Sutherland et al. (2007) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Crittenden et al. (2005) Kwon et al. (2009) Jones et al. (2009) Jones et al. (2009)

d

DOC, dissolved organic carbon; MTBE, methyl tertiary butyl ether; TAME, tertiary amyl methyl ether; DIPE, di-isopropyl ether; TBF, tertiary butyl formate; TBA, tertiary butyl alcohol; HCN, hydrogen cyanide; PNDA, p-nitrosodimethylaniline.

Advanced Oxidation Processes Table 4

393

Reaction rate constants for carbonate radical reactions, superoxide radical reactions, and other radical reactions Rate or equilibrium constants: (M2 s1), (M1 s1), (s1), or dimensionless

Refs. and notes

B3  106

Canonica et al. (2005e)

B3  104 8  105 5.6  107 3.7  106

Dodd (2008) Behar et al. (1970) Behar et al. (1970) Goldstein et al. (1998)

1.5  109 B105

Sehested et al. (1983) Dodd (2008)

9.6  107 6.7  109

Christensen et al. (1988) Huie et al. (1993)

2.8  103 3.6  109 5.2  1010 3.7  104 1.1  105 B2.5  109 B100

Elliot et al. (1989) Buxton et al. (1986) Bu¨hler et al. (1984) Bu¨hler et al. (1984) Bu¨hler et al. (1984) Dodd (2008) Dodd (2008)

NO2 þ DOC-DOCdðþÞ þ NO2  NO2 þ DOCd-DOCNO2 d NO þ DOCd-DOCNO d NO2 þ O2 d -O2 NOO d NO2 þ HO2 d -O2 NOOH

1.7  106 3.5  109 3.2  109 B7  105 B3  105

Buxton et al. (1970) Merenyi et al. (1999) Goldstein et al. (2005) Lazlo et al. (1998h) Dodd (2008)

B3  109 B3  109 4.5  109 1.8  109

Dodd (2008) Dodd (2008) Løgager et al. (1993) Løgager et al. (1993)

Other reactions (nitrogen-based) ONOO  - d NO þ O2 d ONOO  þ CO2 -0:33d NO2 þ 0:33CO3 d

2.0  102 2.9  104

ONOO  -NO3  ONOO-dNO2 þ Od ONOOH-dNO2 þ dOH ONOOH-NO3  þ Hþ ONOOH þ H þ -NO3  þ 2Hþ ONOOH þ H2O þ Hþ-HNO2 þ H2O2 þ Hþ HNO2 þ H2O2 þ Hþ-ONOOH þ H2O þ Hþ O2 NOO -d NO2 þ O2 d O2 NOO -NO2  þ O2 O2 NOOH-d NO2 þ HO2 d

B8  106 B106 3.5  101 9.0  101 4.3 1.1  101 9.6  103 1.0 1.4 2.6  102

Goldstein et al. (2005) Goldstein et al. (2005), Lymar et al. (1995) Goldstein et al. (2005) Goldstein et al. (2005) Goldstein et al. (2005) Goldstein et al. (2005) Merenyi et al. (2003) Merenyi et al. (2003) Merenyi et al. (2003) Goldstein et al. (1998) Goldstein et al. (1998) Goldstein et al. (2005)

Reactions

Carbonate radical reactions CO3 d þ DOC-DOCdðþÞ þ CO3 2 CO3 d þ NH3 -d NH2 þ HCO3  CO3 d þ H2 O2 -HO2 d þ HCO3  CO3 d þ HO2  -O2 d þ HCO3  ONOO  þ CO3 d -d NO þ O2 þ CO3 2 Superoxide radical reactions O3 þ O2 d -O3 d þ O2 O2 þ DOCðoxidizedÞ -O2 þ DOC O2 d þ HO2 d -HO2  þ O2 d NO þ O2 d -ONOO d

dðÞ

Other radical reactions O3 d -Od þ O2 O2 þ Od -O3 d O3 d þ Hþ -HO3 d HO3 d -O3 d þ Hþ HO3 d -d OH þ O2 DOCd þ O2-DOCOOd DOCOO d -DOCðoxidizedÞ þ HO2 d DOCOO d þ OH  -DOCðoxidizedÞ þ O2 d Od þ H2O-dOH þ OH d NO2 þ Od-ONOO d NO þ HO2 d -ONOOH NH2 O2 d -d NO þ H2 O d

d

reactions but also direct photolysis of organic compounds are often involved in theses processes. The fluence or UV-dose is the area-specific total radiant energy of all wavelengths passing from all directions through a cross sectional area in terms of, for example, J m2. UV light lamps have different range of UV emission as indicated by Figure 4. Modeling of fluence rate distribution in UV reactor allows simulation of photolysis-induced AOP kinetics and thus the degradation of photo-reactant component. The fluence rate models must be combined with computational fluid dynamics (CFD) as well as with the aforementioned chemical models and/or models of direct pollutant photolysis to calculate the

overall oxidation rate and photoreactor performance. An example can be found in Sozzi and Taghipour (2007). For fluence rate distribution modeling, the so-called multiple point source summation (MPSS) model (Blatchley, 1997) may be used. In this model the UV lamp is approximated as a series of point source emitting diffuse radiation to calculate the UV irradiance distribution. The fluence rate E is estimated at each point by

Eðr; zÞ ¼

n X i¼1

  p li exp s ð r  r Þ w l r n4pl2i

ð59Þ

394

Advanced Oxidation Processes

with z and r being the axial and radial distances, P the total lamp output, sw the absorption coefficient of the medium, rl the lamp sleeve radius, and li the distance from a specific point (r, z) to ni point source out of the total of n sources. The rate E(r, z) as a term of the local radiant power E is then useful to calculate the local degradation rate as a function of local energy absorption, quantum yields, oxidant and pollutant concentration, as well as reaction rate constants. The fluence rate model can be improved by considering refraction and reflection at the UV-lamp–water–sleeve interface (air–quartz–water) and the geometry of the lamp by a series of cylindrical elements instead of points (Bolton, 2000). Experimental validation is absolutely essential to consider the effect of interference of UV-absorbance between target pollutant and water matrix as well as its dependence on depletion or degradation. More accurate simulations include the validation of fluence of UV lamp by the use of an actinometer. An actinometer is defined as a chemical/physical system that determines the number of photons in a beam integrally or per unit time. A possibly useful tool for experimental validation may be the procedure of defining shape factors and classifying the wastewater absorbance into several groups of different UV absorbance and TOC degradability (Muret et al., 2000). More difficult than aforementioned modeling of artificial UV reaction systems is the simulation of solar-radiation-based processes since the solar radiation may change every day and even every minute depending on weather and the angle between sunlight source and reactor position. The reader is referred to Farias et al. (2009) and Imoberdorf et al. (2007), in which the authors describe the modeling and absorbance of polychromatic sunlight and the elimination rates of pollutants during different sunlight fluent rates as well as the quantum efficiencies in multi-annular photo-catalytic reactor. It is important to note that they developed a model of reduced parameter to enable less complex simulation supporting the applicability of solar-based reaction systems. It seems to be necessary to operate an actinometer in parallel for evaluation of actual fluence rates enabling suitable plant operation to reach target treatment effluents.

4.13.3 Guidance for Selecting an AOP As previously mentioned, each AOP for a particular application needs feasibility studies in laboratory and pilot scale before application due to the necessity of strategies to minimize by-product formation and/or careful control of oxidant dose. In principle, a preselection of one or more specific AOPs based on exclusion criteria is most useful. For example, costs of alternative treatment processes such as (1) membrane processes including concentrate treatment, (2) activated carbon adsorption including regeneration of adsorbent, (3) evaporation, or (4) combustion, etc., can be used for the estimation of maximum limits for operating costs which can be used for calculating the maximum oxidant dosage, energy demand, chemicals for pH adjustment, and disposal of waste sludge (Fenton reactions) before starting laboratory investigations.

4.13.3.1 Criteria to be Considered Before selecting a specific AOP or a combination of AOPs for a particular application, several factors should be considered such as (a) water quality, (b) the yield of hydroxyl radicals with a given AOP, (c) the amount of radical scavengers, (d) the required energy input of the AOP system, (e) the oxidant residual (if used) and its impact on downstream processes, (f) plant design, and (g) investment and operational costs. The criteria (a) to (e) are preferably evaluated by lab-scale studies whereas criteria (f) and (g) are generally referred to pilot-scale investigations for validation. Suitable consideration of these factors is only possible by adequate knowledge about analytical and process equipment as well as some key data on costs for equipment. Water quality. The tasks associated with AOPs are (1) reduction of toxicity or endocrine disrupting effects by transformation or mineralization of target compounds, (2) partial oxidation of biologically refractory dissolved organic compounds to improve the subsequent biodegradation, (3) color removal, (4) odor removal, and (5) disinfection. Many water-quality parameters such as summarized parameters (COD, DOC, suspended solids, heavy metals, alkalinity, color, colony forming units (CFUs), etc.) are regulated and thus the analytical methods for these parameters are well known and not subject of this chapter. The yield of hydroxyl radical of a given AOP. The yield of dOH radicals depends on wastewater characteristics, process configuration, and type of AOP. The yield is mostly determined indirectly by the evaluation of pollutant degradation rates for different process configurations, because the direct detection of hydroxyl radical is very difficult due to its fast reactive character and the resulting extremely low dOH concentration. A more detailed evaluation of dOH radical contributions to an oxidation process is possible with the use of a suitable dOH probe, which allows the identification of a characteristic product by taking into account the competition kinetics. The use of dOH probes allows the calculation of dOH concentration. For example, pCBA has been identified as a suitable dOH probe for ozonation processes (Elovitz and von Gunten, 1999; No¨the et al. (20009a), elucidating that d OH yield of ozonation is at least 13% in the third phase No¨the et al. (2009a) and much higher in the first initial phase. Using such dOH probes, the comparison of dOH radical yield of different AOPs is possible for each particular application. The presence, type, and quantity of radical scavengers. The competition reactions are strongly affected by scavengers as described earlier. AOPs rely on a suitable ratio of number of d OH radicals generated to the number of radicals consumed in reactions to the number of radicals decomposed by termination reactions. This ratio may lead to several suitable AOP applications even in the presence of inhibiting scavengers due to increased efficiency of target reaction cycles depending on water and wastewater characteristics and the relation of inhibiting to promoting effects of compounds and moieties. The required energy input of the AOP system. The most important energy-consuming factor of different AOPs is the power required, for example, for ozone generation and ozone gas-to-liquid mass transfer at ozone applications or artificial

Advanced Oxidation Processes

UV irradiation at UV-based applications. Other examples are the power needed for cavitation systems, e-beam processes, or electro-chemical reactions. The energy for pumping, mixing, and solid/liquid separation is approximately at the same level compared to that for non-AOP systems and therefore out of the focus hereinafter. One of the keys for the improved applicability of ozone in the last few decades was the enhanced energy efficiency in ozone generation. Several factors such as temperature, materials, purity of liquid oxygen supplied, as well as construction details of ozone generator influence the energy demand for ozone generation which has been reduced to date to approximately 6–10 kWh kg1 ozone generated (Ried et al., 2009). Also, UV systems have been optimized in the last few decades relating hydrodynamics, fluence rate, oxidant dosage, etc. The systems are now much more compact, a result mainly based on improved fluid dynamics and absorbance efficiency supported by CFD simulation combined with fluence rate models. In addition, the lifetime and fluence rate of UV lamps have been increased significantly. Moreover, the fouling/scaling problem has been reduced by the application of cleaning devices. Since the fouling/scaling problem is mainly addressed to the temperature increase at the quartz sleeve surface to water, new lamp developments, for example, in plasma-induced UV lamps, may overcome these problems. Plant design. The design of AOP system must address the minimization of by-product, for example, by careful control of oxidant dose and residual oxidant in AOP effluents considering its impact on downstream processes (e.g., biological systems). The bromate issue is one of the well-known byproduct examples, which needs a careful control of ozone dosage as a prerequisite besides others. The plant design concerns not only the AOP itself but also the process integration and combination with non-AOP systems. As discussed later hereinafter, AOPs mostly produce biodegradable compounds from biologically refractory organics. If AOPs are selected as final treatment step, complete mineralization and negligence of environmental impact through by-product generation should be ensured. However, this may cause increased treatment costs and failure of costefficient application. An often better strategy, and mostly not to be missed for drinking water production, is to integrate AOPs as an essential part of multi-stage water and wastewater-treatment concept

considering the benefits of AOPs compared to other processes. This may cause a reduction of the required oxidant to a level as low as possible for supporting cost-efficient applications. Investment and operational costs. In general, investment and operational costs are very specific for each application depending on various site-specific factors. All the points must be considered for the optimization of the investment and operational costs. Additional very specific cost factors of interest are, for example, (1) the costs for chemicals such as ferric/ ferrous solutions or hydrogen peroxide, (2) the lifetime of UV irradiation lamps, ozone generator, reaction system considering, for example, scaling, fouling, corrosion problems, and catalysts, if used, concerning inactivation, leaching, etc., (3) the available space for treatment system, (4) local energy costs, (5) costs and local presence of system service, (6) operational flexibility, etc. Despite the fact that costs will change every time depending on market structure, some costs should be mentioned hereinafter, because it may be useful for possible evaluation of new applications either by indicating the necessity of cost reductions and process optimization for new applications or by giving a benchmark enabling the development of new AOPs (e.g., solar-based photo-catalytic processes).

4.13.3.2 Cost-Related Factors of Ozone-Based Processes One of the main specific cost factors is the concentration of pollutant in the water to be oxidized and the ratio of mass of oxidant to mass of pollutant both contributing to the specific ozone dosage per volume of wastewater. Figure 10 summarizes different regions of ozone dosages for different applications. The ozone costs are mainly influenced by the energy demand of ozone generator and the costs for oxygen supply for ozone generation costs, which also depend on the size of ozone generator. Taking the previously mentioned 10 kWh kg1 and assuming 0.2 Euro/kWh as a basis for cost calculation, the specific energy costs per cubic meter wastewater allocated to ozone generation will range from 1 to 40 Eurocent m3 wastewater. Additional costs to be considered are the oxygen supply and the energy costs for gas–liquid mass transfer of ozone into water, which depend mainly on the size and configuration of the ystem. A typical oxidant-to-pollutant ratio for COD elimination is 2–4 kgO3 kg1COD. Optimization of the specific ozone dosage, or generally speaking the oxidant dosage, is crucial for the application of

AOX-/COD-elimination Odor control Decoloration Micropollutant control Disinfection 0 1

5

10

25

395

50

100

200

Ozone dosage (gO3 m–3wastewater) Figure 10 Range of ozone dosage and related treatment effects. Courtesy of ITT Wastewater GmbH Wedeco redrawn with minor changes.

396

Advanced Oxidation Processes

cost-effective AOPs. Several strategies have been established to reduce the ozone dosage: (1) partial oxidation to produce biodegradable compounds from recalcitrant organics, (2) pretreatment by removal of radical scavenging compounds, for example, precipitation and particle removal, (3) pretreatment by more cost-effective concentration of recalcitrant pollutants and subsequent treatment of concentrates, for example, the retentate of membranes processes, or the regeneration of adsorbents by AOPs, (4) improving the yield of OH radical generation by optimizing the process combination of oxidants and UV irradiation as well as their sequence of use, and (5) optimization of number and location of oxidant dosing points considering reaction kinetics and by-product control.

4.13.3.3 Cost-Related Factors of UV-Based Processes The cost efficiency of UV-based processes depends mainly on the energy demand of UV system and the hydrogen peroxide dosage. While the hydrogen peroxide dosage still has the analogous cost-related dependencies as mentioned in previous ozone paragraph and also needs to be minimized by optimization tasks, the energy efficiency of UV irradiation mainly depends on the UV light absorption characteristics of the wastewater. If the water matrix absorbs the main fraction of UV irradiance, the efficiency is low and the process may become uneconomic. Other AOPs are then expected to be more cost efficient. To allow comparison of different AOPs, the figure-of-merit concept introduced by Bolton and Bircher (1996) should be useful. A figure-of-merit considers the electrical energy demand per order of magnitude of oxidative degradation EE/O in relation to the volume of wastewater or to the mass of pollutant and can be calculated either from continuously operated or from batch-operated systems by Equations (60) and (61), respectively:

EE=O ¼

V0l

EE=O ¼

Pel ðcontinuous systemsÞ  logðc0 =cÞ

ð60Þ

Pel  t  1000 ðbatch systemsÞ VR  logðc0 =cÞ

ð61Þ

This approach is useful since the depletion of summarized pollutant parameters, for example, characterized by TOC, does not follow the second-order rate law due to the change of nature of TOC during oxidation. It may also be useful for scale-up tasks. While the electrical energy demand for ozone generation and subsequent mass transfer as well as for UV systems could be directly inserted in Equations (60) and (61), the oxidant hydrogen peroxide needs to be considered indirectly by converting the costs of hydrogen peroxide to the energy equivalents at site-specific conditions. It should be noted that the EE/O values may also be calculated for both electrochemical and solar-irradiated systems (Bolton et al., 2001). Typical EE/O values range from 0.1 to 1–5 kWh m3 whereas the lower range is assigned to disinfection issues and the higher values to COD-degradation with AOP H2O2/UV. Table 5 exemplifies some typical EE/O values at different H2O2 dosages, indicating that (1) EE/O is reduced at higher oxidant dosages and (2) efficiency may decrease slightly for

Table 5 Some typical EE/O values for atrazine degradation in lake water by UV/H2O2 process (Kruithoff et al., 2007) H2O2 dosage (g m3)

EE/O pilot scale (bench scale) (kWh m3)

4 8 15 25

1.50 1.30 1.00 0.70

(1.30) (1.15) (0.90) (0.65)

scale-up from bench to pilot scale. Due to the relative low dosage of oxidant in this case study, the total EE/O decreases but at higher dosage values typical for high-loaded industrial wastewater, the EE/O may not decrease due to possible stronger energy-equivalent contribution of H2O2, which is based on the costs of H2O2 and the energy equivalent of the costs. It is also worth noting that the EE/O may increase due to decrease of degradation efficiency after several months of UV lamp operation caused by fouling and/or scaling. An example of this decreasing efficiency was taken from Kruithof et al. (2007) for pesticide degradation in IJssel lake raw water located in The Netherlands, which was implemented as pretreatment step to potable water production (Figure 11). This decrease should be avoided or compensated by automated cleaning and adaptation of power input to UV lamp, respectively. A comparison of three different AOPs (1) H2O2/UV, (2) ozone/UV, and (3) H2O2/O3 by EE/O approach has been provided by Mu¨ller and Jekel (2001) for atrazine degradation. To enable such a comparison, the production costs of oxidant hydrogen peroxide have been converted to energy equivalents based on local energy costs. The calculated EE/O values range from 0.90 to 2.92 kWh m3 for the ozone/UV process, from 1.67 to 2.27 kWh m3 for the UV/H2O2 process, and from 0.102 to 0.135 kWh m3 for the O3/H2O2 process. This indicates that the O3/H2O2 process has been identified as the best option in this case by far. However, it is important to note that photochemical-based AOP often includes an optimization potential with respect to, for example, the strong impact of hydrodynamics or the type and fluence rate of UV lamp. This optimization procedure has not been documented and therefore the comparison just relies only on the used system layout. A strong indication of the existing optimization potential is the EE/O values provided by Mu¨ller and Jekel (2001) for the UV/ozone process, which have been found lower for a two-step process compared to the higher values of the onestep process.

4.13.4 Description of Processes 4.13.4.1 Ozonation A typical ozonation scheme is given in Figure 12. The process consists of (1) an oxygen gas supply, (2) an ozone generator, (3) a gas–liquid contact tank, which, in principle, is the ozone reactor, (4) a degassing tank, with decomposing residual ozone in gas phase, (5) a cooling unit for ozone generator, (6)

Advanced Oxidation Processes

397

100 After start-up After 2000 h Degraddation rate (%)

80

60

40

20

0 Atrazine

Pyrazone

Diuron

Bentazone

Bromacil

Figure 11 Pesticide degradation in pretreated Ijssel Lake Water at the beginning and after 2000 h of plant operation.

5–32 °C

Electrodes

Cooling unit

Fan

Ozone

Ozone destructor

Liquid oxygen tank

400 V FI

FI

Demister (option) Ozone generator Controls + power supply Treated water

Wastewater

Diffusors

Diffusors

Ozone reaction tank

Figure 12 Ozone system layout. Courtesy of ITT Wastewater GmbH Herford (formerly Wedeco) with minor modification.

a power supply, and (7) a control unit. Different online-sensor devices could monitor the ozone feed gas flow and its ozone concentration, the ozone concentration in off-gas and the dissolved concentration O3 after reaction chamber. These allow control of variable ozone dosages depending on water flow rate. The water or wastewater flows through the reaction chamber, where the ozone feed gas can be supplied by different gas–liquid contact systems. Various contact systems can be used, depending on water flow, reaction kinetics, and required ozone dosage: (1) gas diffuser (e.g., a ceramic disk), (2) two-phase side stream, or (3) main stream injector. The reaction chamber may be constructed as a pressurized reaction

tank and/or as a cascaded reaction system comprising baffles for avoiding short-cut flow. Important parameters to be measured or calculated for optimizing purposes are (1) ozone dose as difference of gaseous input and output mass flow, (2) the aqueous ozone concentration if not zero, (3) the ozone exposure, (4) the hydroxyl radical exposure, (5) the pollutant-specific ozone dose, and (6) for batch process, time-dependent oxidation rate of pollutant; for continuous process, pollutant concentration at input and output at different retention times considering hydrodynamic flow regime. If interference effects are expected, for example, by DOC, carbonate, etc., additional analytical measurements are necessary.

398

Advanced Oxidation Processes

The water to be treated enters the UV photo-reactor after injection of suitable hydrogen peroxide dose, which is often well distributed by static mixers. Depending on the reaction kinetics and possible side reactions, additional H2O2 injection points at different reactors within a series of reactors have been considered. In few applications, the pH value has been lowered by an acid to shift the carbonate–bicarbonate equilibrium, and thus reducing the dOH radical scavenging capacity. After oxidative treatment neutralization may be necessary to meet discharge requirements. Photochemical processes rely significantly on (1) emission spectrum and intensity of UV lamp, (2) the absorbance of pollutants, (3) possible UV interferences, and (4) fouling and scaling characteristics of wastewater. Several types of established UV lamps are listed in Table 6 with respect to their typical operating parameters. The low pressure–low intensity (LP–LI) and low pressure–high intensity (LP–HI) lamps are usually applied for disinfection and combined UV/ozone processes, respectively, while medium pressure–high intensity (MP–HI) lamps are most useful for H2O2/UV applications. However, this

In combined ozone/UV processes, the ozone is transferred into the wastewater either before entering UV-system or simultaneously to oxidation at different points of reaction chamber.

4.13.4.2 Photo-Chemical Oxidation A typical photo-oxidation treatment system’s layout based on H2O2/UV process is illustrated in Figure 13. Commonly, a UV-oxidation system comprises (1) a photo-reactor containing one or more UV lamps, (2) a hydrogen peroxide storage tank, (3) a UV lamp control panel, (4) a cleaning unit for the UV-transmissive quartz tubes, (5) an inert gas cooling system for UV lamps (only medium pressure lamps), (6) a power supply, and (7) a control unit with UV-intensity sensor as most important sensor device to control the fluence rate by automated cleaning and/or adapting the UV lamp power. The hydrodynamic characteristics inside the photoreactor is often controlled, for example, by baffles or special guiding plates.

Treated wastewater

Acid

Base

UV lamp

Hydrogen peroxide

Baffle Reactor

Wastewater Static mixer

Static mixer Oxidation unit

Figure 13 UV-oxidation system layout.

Table 6

Characteristics of different UV lamp types

Characteristic

Emission type Peak output wavelength Germicidal output to input Energy output at 254 nm Power consumption Lamp current Lamp voltage Operating temperature Mercury vapor pressure Lamp length Lamp diameter Sleeve life Ballast life Estimated lamp life

Unit

– nm % W W mA V 1C mmHg m mm Year Year h

Type of lamp MP–HI

LP–HI

LP–HI

Polychromatic 200–400 10–15 – 1 000–10 000 Variable Variable 500–900 102–104 0.05–1.95 Variable 1–3 1–3 3000–8000

Monochromatic

Monochromatic 253,7 30–40 25–27 40–100 350–550 220 35–50 0.007 0.75–1.5 15–20 4–6 410 8000–12 000

LP–LI, low pressure–low intensity; LP–HI, low pressure–high intensity; MP–HI, medium pressure–high intensity.

25–35 60–400 200–1200 Variable Variable 60–100 0.01–0.8 Variable Variable 4–6 410 7000–10 000

Advanced Oxidation Processes

position is part of a controversial discussion due to different operational costs for cleaning, cooling, lamp life, etc. Known UV interferences affecting the efficiency of H2O2/ UV process are nitrate410 ppm, nitrite410 ppm, phosphate41%, chloride41%, COD41000 ppm, and ferrous ion (Fe3þ)450 ppm. Scaling may occur at concentrations of Caþ 450 ppm, Fe3þ450 ppm, and Mg2þ41000 ppm.

A final filtration unit to remove fine aggregates may be necessary to meet discharge requirements. To enhance the Fenton reaction, the reaction system may be heated and temperature controlled at approximately 50 1C to take advantage of faster reactions and reduced oxidant dosage (see Pignatello et al., 2006). The Fenton process can easily be upgraded to a photo-Fenton process by the implementation of a second cycle loop with UV irradiation (see photo-Fenton box in Figure 14). This allows reduction of all, the catalyst dose, the amount of precipitates, as well as the sludge to be disposed. On the other hand, optimization due to fouling/scaling risks as well as energy minimization due to additional power that is required are necessary. This has to be proved before application.

4.13.4.3 Fenton and Photo-Fenton Processes The general Fenton process layout consists of (1) storage tanks for acid, base, hydrogen peroxide, and ferrous or ferric salt solutions, (2) first pH control unit to dose acid and control acidic conditions, (3) injection system for ferric or ferrous salt solutions, (4) injection system for hydrogen peroxide, (5) stirred tank reaction system comprising up to three reaction tanks in cascade, (6) second pH control unit to dose a base and control neutral conditions, (7) flocculation system with flocculant dosage, (8) sedimentation tank, (9) water filtration unit, (10) dewatering device for sediment which has been separated from the sedimentation tank, (11) a power supply, and (12) a control unit. Figure 14 illustrates a typical flow scheme of Fenton process. The acid, catalyst(s), and the oxidant hydrogen peroxide are mostly injected into a cycle loop of reaction system. The pH control is easy to operate, whereas the optimal catalyst and hydrogen peroxide dosage as well as the reaction time should be previously determined and validated. Optimal pH is known at approximately pH 3; however, higher values (e.g., up to pH 4) are sometimes applied. After processing Fenton reactions the wastewater is neutralized with caustic, lime slurry or another base, and thus ferric ion complexes precipitate. For improving the settling efficiency of precipitates and subsequent dewatering efficiency, flocculants are often added.

4.13.4.4 Process Combinations AOPs will take their advantage mostly in combination with other processes. One of the best-known examples is the combination of AOPs and biological (BIO) processes. Two approaches have been established. (1) Minimization of costs and optimization of BODproduced-to-CODremoved ratio by consciously reducing the oxidant dosage to enable partly oxidation. This approach leads to a transfer of COD removal from AOP to biological process. Different system layouts have been realized for ozone-based AOPs from sequential process schemes AOP–BIO, BIO–AOP, and BIO–AOP–BIO to continuous cycle loop process between BIO and AOP depending on the concentration level of pollutant. The latter process is interesting from engineering point of view due to higher potential for process optimization and by-product control by varying the cycle flow rate in addition to oxidant dosage. (2) The second approach is a combination of AOPs and

Optional installation for photo-Fenton Catalytic oxidation UV reactors

Post filtration, e.g., fixed bed Effluent

Fe 3+ (catalyst) acid H2O2 (oxidant)

Raw wastewater M

Additives

Chemicals

• Coagulants • Neutralization agents

• Flocculation agents

M

M

Mixing and buffer tank

M

OI

Precipitation and neutralization

Figure 14 General system layout for Fenton reaction treatment.

399

Sludge treatment

Filter press

400

Advanced Oxidation Processes

artificial groundwater recharge for removal of organic micropollutants; the improved removal of both transformed micropollutants as well as NOM offers an advantage for by-product control in drinking water production processes involving chlorine as disinfectant. Another option for a combined process is to concentrate the toxic or recalcitrant organics before applying AOPs. Different concentrating processes exist, for example, membrane processes and adsorption processes. The combined processes can be useful when the alternative processes are easily and cost effectively operated and the concentrates are classified as hazardous waste; or in case of absorption, if a new process of heterogeneous catalytic oxidation may operate well.

4.13.5 Full-Scale Applications Applications described hereinafter cover AOPs for drinking water supply as well as wastewater and waste treatment due to the reason that AOPs are involved in all fields, and the boundaries between drinking water and wastewater treatment are fading. The latter one has gained significant attention by new findings in early 1990s about linkage between the occurrence of endocrine disrupting chemicals (EDCs) in wastewater receiving water bodies and the reproductive impacts on aquatic life. Ever since, many studies have been started with focus on occurrence of trace contaminants in effluents of wastewater treatment plants (WWTPs), surface water (rivers, lakes, etc.), groundwater, and drinking water. New analytical methods for detecting trace contaminants in sub-mg l1 concentration range have led to an increased number of so-called emerging contaminants, micro-pollutants, and EDCs and currently to the status of a very dynamic field in wastewater

Diffusive sources (agriculture atmospheric deposition, transport systems, sewer leakage, etc.)

and water treatment. Nowadays, it has become clear that WWTP effluents form a significant emission source for pharmaceuticals, EDCs, personal care and health products, and other nonbiodegradable hazardous contaminants for regional surface water and raw water. Figure 15 illustrates schematically the link between emission sources and drinking water supply. Emission sources can be classified as diffusive- and point-source emissions. The diffusive-source emissions cannot be controlled by wastewatertreatment options and often cause direct contamination of groundwater. However, controlling the diffusive contamination has been realized in the European Union (EU) for some toxic compounds, for example, by removing the chemicals from the market (some priority substances of EU, see hereinafter), restriction of imports, etc. The point-source emissions are connected to wastewater streams and therefore regularly received by surface water bodies. This emission situation generally allows several treatment options at different locations 1–5 in Figure 15. AOPs may play an important role especially as an essential part of a multi-barrier water-treatment concept for the degradation of toxic, mutagenic, carcinogenic as well as bio- and ozone-refractory organic pollutants. Some of these pollutants are classified as drinking water-relevant compounds indicating that these compounds are passing through natural or closely natural drinking water plants. Tables 7 and 8 summarize the degradability by AOPs of hazardous as well as trace organics in relation to the source character and the possibility for oxidation and detoxification.

4.13.5.1 Ozone-Based AOPs Full-scale ozone-based AOPs for treating process water, wastewater, cooling water, and landfill leachate are implemented

Point sources Domestic WWTP 1

Industrial WWTP 2

Hospitals 3

Surface water body

Water work

Groundwater

4

5

Point of use

5

Point of use

5

Point of use

Drinking water Figure 15 Treatment options for AOPs in context to different pollution sources and linkage between wastewater and drinking water. WWTPs, wastewater treatment plants.

Advanced Oxidation Processes

401

Table 7 List of priority substances of EU framework directive, the main pollution sources, and the degradability of the organic compounds by advanced oxidation processes (AOPs) including ozonation Compound

Class

Main pollution source

Degradability by AOP (only organics)

Alachlor Atrazine Benzene

Herbicide Herbicide Industrial chemical

Agriculture Agriculture Industrial WW, atmospheric

99% 80–99% Almost completely

Lead

Heavy metal

(Inorganic)

C10-13-Chlorinated paraffins Cadmium

Flame retardant Heavy metal

Chlorfenvinphos Trichloromethane Chlorpyrifos Diethylhexyl phthalate (DEHP) 1,2-Dichloroethane Dichloromethane

Insecticide Industrial chemical Insecticide Plastics softener Industrial chemical Halogenated hydrocarbon

Rainwater, sewage, industrial WW Not known Rainwater, agriculture, atmospheric Agiculture Industrial WW Agriculture, atmospheric Atmospheric, rainwater, sewage Industrial WW Industrial WW, atmospheric

Diuron Endosulfan Hexachlorobenzene Hexachlorobutadiene

Herbicide Insecticide Industrial chemical Industrial chemical

Agriculture Agriculture Industrial WW No production and use in EU

Almost completely Up to 97% 20%

Isoproturon Lindane (g-hexachlorocyclo-hexane) Nickel

Herbicide Insecticide Heavy metal

Almost completely Almost completely (Inorganic)

Nonylphenol

Industrial chemical

Agriculture Agriculture (sewage, rainwater) Rainwater, erosion, mining, atmospheric Sewage

Octylphenol Polyaromatic carbons Polybromated diphenylethers

Industrial chemical Industrial chemical Industrial chemical

Automobile traffic, rainwater Atmospheric Diffuse (sewage)

Pentachlorobenzene Pentachlorophenol Mercury Simazine

Industrial chemical Industrial chemical Heavy metal Herbicide

Sediments (old contaminations) Industry Erosion, drainage, sewage Agriculture

495% (Inorganic) Yes

Hexabutyl distannoxane (organic tin) Trichlorobenzene Trifluralin

Industrial chemical Industrial chemical Herbicide

Water traffic Atmospheric Agriculture

Yes 70–97%

worldwide in chemical, pharmaceutical, textile, pulp and paper, semiconductor, and other industries. In this field, more than 1000 ozonation plants have been installed from 1954 to 1999 according to Bo¨hme (1999), most of them for process water conditioning (660). Within this context, the combined ozone-biology leachate treatment is a typical German exception relying on early strong requirements since 1989 to meet effluent limits below 200 mgCOD l1. However, this is a good example for being a more cost-efficient combined AOPprocess in the 1990s compared to other technologies such as stand-alone AOPs, absorption, or membrane processes. Recently, this approach receives an increasing interest for industrial wastewater treatment as well as for sewage posttreatment. Table 9 summarizes different wastewatertreatment applications based on ozone-biology process combinations as far as known to the author. Sewage may contain high fractions of refractory compounds, for example, originating from industrial sites. In the case of a Danish sewage treatment plant, the main fraction originates from pharmaceutical industry and due to planned

Almost completely (Inorganic) Almost completely Almost completely Almost completely Almost completely Almost completely

Almost completely Almost completely Up to almost completely

extension of its production capacity an adequate upgrading of sewage treatment plant was decided (Ried et al., 2009). The industrial wastewater is characterized as almost biorefractory containing, for example, the pharmaceuticals furosemid, sulfamethizol, and ibuprofen in much higher concentrations compared to the commonly known sub-mg regions of micro-pollutants. Due to the high fraction of biorefractory organics, the COD of the treatment plant effluent needs to be reduced from 120 to below 75 mg l1. The system was designed for an ozone dosage of 180 kg h1 ozone per 1200 m3 h1 wastewater and 15 min of reaction time, and enables the COD reduction of B40%. This was confirmed by effluent concentrations of approximately 65–75 mgCOD l1. It is worth noting that a complete removal of pharmaceuticals was detected at a much lower ozone dosage of 10–30% compared to COD removal design dosage. However, for safe operation issues, the ozonation system has been extended with a hydrogen peroxide unit, thus enabling perozone process. From the operating conditions, the costs were estimated to approximately 0.2 Euro m3

402

Advanced Oxidation Processes

Table 8

List of selected pharmaceuticals, the removal efficiency of sewage plants, and the degradability by AOPs including ozonation

Chemical group

Pharmaceutical compound

Removal by sewage treatment plant (%)

Removal by AOPs (posttreatment) (%)

Antibiotic

Ciprofloxacin Clarithromycin Erythromycin Sulfamethoxazol Trimethoprim

83 66 45 51 20

91 84 84 90 82

Lipid regulator

Benzafibrate Clofibrin acid Fenofibrin acid

68 (n ¼ 14) 17 (n ¼ 7) 46 (n ¼ 4)

83 (n ¼ 2) 71 (n ¼ 3) 63 (n ¼ 1)

Beta-blocker

Atenolol Metoprolol Sotalol

9 (n ¼ 1) 57 (n ¼ 4) No data

65 (n ¼ 2) 87 (n ¼ 2) 97 (n ¼ 2)

Analgesic anti-inflammatory

Acetylsalicylic acid Diclofenac Ibuprofen Paracetamol Phenazone

88 42 83 92 33

Not relevant 92 (n ¼ 9) 92 (n ¼ 2) 91 (n ¼ 1) no ref.

Antiphlogistic

Indometacin Propyphenazone

66 (n ¼ 3) No data

71 (n ¼ 2)

Anti-epileptic

Carbamazepine

12 (n ¼ 14)

96 (n ¼ 3)

Broncholytic

Theophylline

Biodegradable (n ¼ 1)

Not relevant

Hormones

17b-estradiol 17a-ethinylestradiol

84 (n ¼ 11) 74 (n ¼ 10)

85 (n ¼ 3) 85 (n ¼ 6)

Contrast media

Diatrizoate Iomeprol Iopamidol Iopromide

9 (n ¼ 2) 9 (n ¼ 2) 9 (n ¼ 2) 33 (n ¼ 5)

30 66 44 64

Cytostatic

Cyclophosphamide Ifosfamide

17 (n ¼ 1) 3 (n ¼ 3)

No ref. No ref.

(n ¼ 5) (n ¼ 3) (n ¼ 2) (n ¼ 7) (n ¼ 2)

(n ¼ 2) (n ¼ 15) (n ¼ 16) (n ¼ 2) (n ¼ 1)

(n ¼ 1) (n ¼ 2) (n ¼ 2) (n ¼ 8) (n ¼ 3)

(n ¼ 1) (n ¼ 1) (n ¼ 2) (n ¼ 5)

AOPs, advanced oxidation processes; n ¼ number of considered references.

wastewater (Ried A, 2009; ITT Wastewater, personal communication). Another example for an ozone-based AOP concerns the improvement of safety and reliability of drinking water-supply system, which has been recently implemented at first time for contaminated groundwater pretreatment. A process flow scheme is given by Figure 16. A part of the raw water is ozonized while hydrogen peroxide is injected to the other part. Both streams are then mixed together passing a static mixer and subsequently entering the oxidation reactor. Optimum process parameters validated by pilot trials are: 2.5 g m3 ozone dosage, H2O2/O3 ratio equal to 0.7 (g g1), and mean hydraulic retention time of 10 min. The plant was designed to treat 64 000 m3 d1 of groundwater contaminated by approximately 20 mg l1 of hydro-chlorinated carbons in two parallel lines. Due to the increased chloride dioxide consumption in the waterworks after startup of AOP system, the dosage of hydrogen peroxide has been reduced to the ratio of 0.5 (g g1) with only slightly decrease to 75% of pollutants removal rate. The operating costs have been estimated to be below 0.01 Euro m3. Another field of application of ozone-based AOPs is the minimization of excess sludge production of aerobic

treatment systems. While several ozone systems have been implemented in Japan, only few case-study systems are under operation in Europe. Currently, this relies mainly on higher sludge disposal costs in Japan compared to Europe. The established systems ozonize the sludge in a cycle loop to aerobic basin, which is an effective tool to minimize sludge production below the yield of 0.11 kg VSS per kgCOD of raw wastewater input. The specific ozone dosage for B100% sludge reduction ranges between 0.12 (Paul and Debellefontaine, 2007) and 0.18 kgO3/kgTSreduced (Sakai et al., 1997). It is worth noting that the 100% reduction has been reached only by release of some suspended solids via final clarifier as well as by increase on soluble COD in the effluent. Detailed information about process description, efficiency, different applications, costs, etc., can be found in Yasui et al. (1996), Sakai et al. (1997), Paul and Debellefontaine (2007), and Sievers et al. (2004). Although the reduction potential is very high and effective, the main competition processes are not processes combined to aerobic system but rather to anaerobic systems with the aim to improve energy yield from waste sludge. The latter one seems to be more sustainable in a period of increasing discussion about importance of renewable energy. Although there are some investigations about combining

Table 9

List of combined ozone-biology applications

Location

Wastewater origin

Target pollutant(s)

Process integration

Flowrate (m3 h1)

Generator size (kgO3 h1)

Ozone dose (kg m3)

Year

Reference

Lang Papier WWTP Ochtrup

Paper industry Textile industry

COD Color þ AOX þ PVAL

Bio-O3-Bio Bio-O3

580 160

2  50 26

0.172 0.075

1999 1992

Ried et al. (2000) Ried et al. (2007)

WWTP Prato, Italy WWTP Ranica, Italya

Textile þ sewage Textile þ sewage

Color þ surfactants Color þ surfactants

Bio-O3 Bio-O3

5000 2500

4  40 2  28

0.032 0.112

1992 2006

Kaulbach (1993) Ried et al. (2009)

Catania, Italy Tubli TSE, Bahrain

Industry þ sewage Sewage

COD Colony forming units

Bio-O3 Bio-O3

4800 8333

4  15 3  48

0.012 0.017

2002 2002

Ried et al. (2007) Ried et al (2007)

SCA-Laakirchen LLTP*** Hellsiek, Germany LLTP Braunschweig, Germany LLTP Mu¨nster, Germany LLTP Fernthal, Germany LLTP Bornum, Germany WWTP Kalundborg, Denmark Wacker Chemie, Germany DOW Bo¨hlen, Germany BASF Schwarzheide– Germany WWTP Regensdorf, Switzerlanda WWTP Ilkeston, Great Britaina

Paper industry Leachate

COD COD

Bio-O3-Bio BioQuint

1100 15

75 24

0.068 0.533

2005 1997

Liechti (2005) Ried et al (1999)

Leachate

COD

Bio-O3-Bio

25

3  12

1.440

1992

Ried et al. (2007)

Leachate

COD

Bio-O3-Bio

10.5

25

0.952

1994

Ried et al. (2007)

Leachate þ extern

COD

Bio-O3-Bio

6.25

34

1.920

1993

Leachate

COD

BioQuint

6.25

8

1.280

1995

Steegmans et al. (1995) Ried et al. (1999)

Industry þ sewage

COD

Bio-O3-Bio

1000

2  90

0.180

2003

Ried et al. (2003)

Chemical industry

COD

Bio-O3

100

43

0.430

2006

Ried et al. (2007)

Chemical industry Chemical industry

COD Nitroaromates

Bio-O3 Bio-O3-Bio

30 20

20 25

0.666

2004 2000

Ried et al. (2007) Ried et al. (2007)

Sewage

Micropollutants

Bio-O3-sand filter

B400

5

0.006

2007

Ried et al. (2009)

Sewage

Micropollutants

Bio-O3

B40

0.8

0.01

2008

Ried et al. (2009)

a

Case studies LLTP***, landfill leachate treatment plant. COD, chemical oxygen demand; AOX, adsorbable organic halogen compounds; PVAL, polyvinylalcohol.

404

Advanced Oxidation Processes H2O2

CIO2 Static mixer

Raw water

Reactor

Water reservoir

Injector

Pump O3 /O2 Figure 16 Ozone/hydrogen peroxide (perozone) process for raw water pretreatment.

AOPs and anaerobic systems onsite at sewage treatment plants, no onsite application exists to date.

separation. The separated condensate is of high quality appropriate for rinse water input, and the up-concentrated electrolyte is also of high quality suitable for input to Watt’s basic electrolyte tank.

4.13.5.2 UV-Oxidation Processes The H2O2/UV process is a well-established AOP. In 1996, more than 200 UV/oxidation treatment installations were put up for process water, groundwater, and drinking water (see, e.g., AOT Handbook Calgon Corp., 1996). Some of the contaminants besides others to be oxidized were: 1,1-dichloroethane (DCA), 1,1,1-trichloroethane (TCA), hydrazine, Nnitrosodimethylamine (NDMA), trichloroethylene (TCE), perchloroethylene (PCE), methyl ethyl ketone, TNT, phenol, vinyl chloride, benzene, chlorobenzene, toluene, xylene, pentachlorophenol (PCP), nitro-glycerin, total petroleum hydrocarbons, PAHs, etc. During the last decade, this process has received increasing interest in industrial wastewater treatment (e.g., electroplating, circuit printing, chemical, pharmaceutical industry, etc.) for degradation of complexing agents, regeneration of electrolytes of surface plating baths, treatment of rinsing water, and process water recovery. An interesting example and representative for a sustainable and economic process-integrated solution is the regeneration of plating baths by UVoxidation of aged electrolyte solutions. This enables not only (1) the degradation of accumulating residuals for less waste emission, but also (2) an increased product surface quality, (3) reduced metal electrolyte consumption, (4) improved water recovery of rinse water leading to zero water discharge, and (5) the use of reaction heat released by UV-oxidation in an integrated concept of evaporation/condensation. This combined process solution is an excellent example for integrating AOPs in industrial production processes reaching less emission up to zero wastewater discharge as well as additional resource-sparing objectives (Dams et al., 2008). The entire process is illustrated in Figure 17 pointing out (1) a nickel electrolyte bath for surface plating, (2) a rinsing cascade, (3) a waste reception tank for aged nickel electrolyte as well as discharged rinsing water, (4) a UV-reaction system, and (5) a Watt’s basic electrolyte tank. The UV-reaction system is treating the mixture of rinsed wastewater and aged nickel electrolyte in a loop. The TOC concentration decreases by UV-oxidation, and parallel to this the electrolyte is up-concentrated by water evaporation and condensate

4.13.5.3 Fenton Process The classical Fenton process is probably one of the oldest AOPs applied to remove hazardous compounds from wastewater. This process has been widely applied in different fields such as paper, chemical, pharmaceutical, textile, de-inking, TNT-production, and metal industry. It has been usually applied for wastewaters with COD range from 1 to 100 g l1 as a cost-efficient alternative to wet air oxidation (WAO) or incineration. Some authors identified the classical Fenton process as sometimes more cost efficient to the H2O2/UV or other AOPs (see Pignatello et al., 2006); however, this cannot be generalized, because the cost efficiency depends on so many different factors that each application requires a careful engineering and subsequent cost calculation. Although the optimal temperature for Fenton process has been found at approximately 50–60 1C, many applications operate at higher temperatures up to 100–130 1C. In these applications a second catalyst is often mentioned besides Fe, probably Cu.A compilation of fields of Fenton process application can be found in Suty et al. (2004). Recently, different modified Fenton processes received increasing interest in research and development up to pilot scale, namely (1) photo-Fenton, (2) solar-Fenton, (3) electroFenton, and (4) heterogeneous fixed-bed Fenton.

4.13.5.4 Wet Air Oxidation There exist some well-established applications in WAO, for example, (1) Bayer LOPROXs process, (2) WAO ATHOSs, (3) Zimmermann process (ZIMPRO), and (4) VerTech deep shaft. All these processes have similar operating conditions, high pressures and high temperatures – up to subcritical conditions. While the LOPROX process operates at 3–20 bar and 120–200 1C at hydraulic retention times (HRT) below 3 h, the ATHOS process reaches approximately 54 bar and 250 1C. Both processes use homogeneous Cu ion (if needed) as catalyst and oxygen as oxidant. The ZIMPRO process has been the first WAO introduced by Zimmermann (1958) with several installations in USA while the VerTech process is unique using

Advanced Oxidation Processes

405

Plating process

Rinsing process

Product

Product

Nickel electrolyte 1

2 Bath /rinse discharge Renewed rinse water

Heat pump

Cooling tower

UV reactor

4

3

5

Waste reception tank

Watt’s basic electrolyte

Oxidant Figure 17 Process scheme for water, heat, and metal recovery by integrated application of UV-oxidation type Enviolets (Dams et al., 2008).

a 500–1200 m deep-shaft reaction system. The WAOs are often characterized as relative expensive processes; however, the economic application seems to be possible for larger installations between 3000 and 15 000 tons of total solids per year, as the recently increased number of ATHOSs applications indicates. This process enables mineralization of 80–90% of COD and 95% sludge volume reduction.

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406

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Relevant Websites http://www.almaden.ibm.com IBM Almaden Research Center.

4.14 Biological Nutrient Removal GA Ekama, University of Cape Town, Cape Town, South Africa & 2011 Elsevier B.V. All rights reserved.

4.14.1 4.14.2 4.14.3 4.14.4 4.14.4.1 4.14.4.2 4.14.4.2.1 4.14.4.2.2 4.14.4.2.3 4.14.4.3 4.14.4.3.1 4.14.4.3.2 4.14.4.3.3 4.14.4.3.4 4.14.4.3.5 4.14.4.4 4.14.4.4.1 4.14.4.4.2 4.14.4.4.3 4.14.4.4.4 4.14.4.5 4.14.4.6 4.14.4.7 4.14.5 4.14.5.1 4.14.5.1.1 4.14.5.1.2 4.14.5.1.3 4.14.5.2 4.14.5.2.1 4.14.5.2.2 4.14.6 4.14.6.1 4.14.6.2 4.14.6.3 4.14.6.4 4.14.7 4.14.7.1 4.14.7.1.1 4.14.7.1.2 4.14.7.1.3 4.14.7.1.4 4.14.7.1.5 4.14.7.1.6 4.14.7.1.7 4.14.8 4.14.9 4.14.9.1 4.14.9.2 4.14.9.3 4.14.9.3.1 4.14.9.3.2 4.14.9.4 4.14.9.5

Introduction System Configuration and Organism Groups Transformations in the Biological Reactor Wastewater Characterization Introduction Carbonaceous Organic (C) Materials Carbonaceous material (COD) fractions Quantification of COD fractions Analytical formulation for COD Nitrogenous Materials Nitrogenous material fractions Quantification of N fractions (Nti) Analytical formulation Maximum specific growth rate of nitrifiers at 20 1C Typical wastewater TKN characteristics Phosphorous materials Phosphorus fractions Quantification of P fractions Analytical formulation Typical wastewater phosphorus characteristics Inorganic Dissolved, Settleable, and Nonsettleable Solids Other Materials Wastewater Characterization for Plant Wide Modeling Modeling Biological Behavior Biological Growth Behavior Stoichiometry and kinetics Monod growth kinetics for utilization of RBSO Active site surface kinetics for hydrolysis/utilization of BPO Organism Decline Endogenous respiration Death regeneration AS System Constraints Mixing Regimes Solids Retention Time or Sludge Age Nominal Hydraulic Retention Time Connection between Sludge Age and Hydraulic Retention Time Model Development – Completely Mixed Aerobic System Building Up the Model in Stages Unbiodegradable particulate organics (Supi) Unbiodegradable soluble organics (Susi) Biodegradable organics Complete utilization of soluble biodegradable organics The mass balance on oxygen Complete utilization of BPOs Integration of biodegradable and unbiodegradable organics models The COD (or e  ) Mass Balance The AS System Steady-State Equations for Real Wastewater Effluent COD Concentration ISS Concentration Process Design Equations For the influent For the system Active Fraction of the Sludge Steady-State Design Chart

409 409 410 410 410 411 411 414 415 417 417 418 419 420 420 420 420 421 422 423 423 424 425 426 426 426 427 428 429 429 430 431 431 431 432 432 432 432 433 435 435 437 439 440 440 441 442 442 443 443 443 443 444 445

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4.14.9.6 4.14.10 4.14.11 4.14.11.1 4.14.11.2 4.14.11.3 4.14.12 4.14.12.1 4.14.12.2 4.14.13 4.14.14 4.14.14.1 4.14.14.2 4.14.14.3 4.14.15 4.14.15.1 4.14.15.1.1 4.14.15.1.2 4.14.15.2 4.14.15.3 4.14.15.3.1 4.14.15.3.2 4.14.15.3.3 4.14.16 4.14.17 4.14.18 4.14.18.1 4.14.18.2 4.14.18.3 4.14.19 4.14.19.1 4.14.20 4.14.20.1 4.14.20.2 4.14.20.3 4.14.20.3.1 4.14.20.4 4.14.20.5 4.14.20.6 4.14.21 4.14.21.1 4.14.21.2 4.14.22 4.14.22.1 4.14.22.2 4.14.22.3 4.14.23 4.14.23.1 4.14.23.2 4.14.24 4.14.24.1 4.14.24.2 4.14.24.3 4.14.24.4 4.14.24.5 4.14.24.5.1 4.14.24.5.2 4.14.25 4.14.25.1 4.14.25.2

The Calculation Procedure Reactor Volume Requirements Determination of Reactor TSS Concentration Reactor Cost SST Cost Total Cost Carbonaceous Oxygen Demand Steady-State (Daily Average) Conditions Daily Cyclic (Dynamic) Conditions Daily Sludge Production System Design and Control System Sludge Mass Control Hydraulic Control of Sludge Age Flow and Load Equalization Tanks Selection of Sludge Age Short Sludge Ages (1–5 days) Conventional plants Aerated lagoons Intermediate Sludge Ages (10–15 Days) Long Sludge Ages (20 Days or More) Aerobic plants Anoxic–aerobic plants Anaerobic–anoxic–aerobic plants Sludge Age – The Dominant Driver for Size Nitrification – Introduction Nitrification Biological Kinetics Growth Growth Behavior Endogenous Respiration Nitrification Process Kinetics Effluent Ammonia Concentration Factors Influencing Nitrification Influent Source Temperature Unaerated Zones Maximum allowable unaerated mass fraction DO Concentration Cyclic Flow and Load pH and Alkalinity Nutrient Requirements for Sludge Production Nitrogen Requirements N (and P) Removal by Sludge Production Nitrification Design Considerations Effluent TKN Nitrification Capacity Mass of Nitrifiers (MXA) and Nitrification Oxygen Demand (FOn) Nitrification Design Example Wastewater Characteristics Nitrification Process Behavior Biological Denitrification Interaction between Nitrification and N Removal Benefits of Denitrification N Removal by Denitrification Denitrification Kinetics Denitrification Systems The Ludzack–Ettinger system The four-stage Bardenpho system Denitrification Kinetics Denitrification Rates Denitrification Potential

446 446 447 447 447 447 448 448 448 448 450 450 452 453 453 453 453 454 454 456 456 457 457 458 459 459 459 460 460 461 461 463 463 463 463 465 465 466 466 468 468 469 470 470 471 472 472 472 472 474 474 474 474 475 476 476 476 476 476 479

Biological Nutrient Removal 4.14.25.3 4.14.25.4 4.14.26 4.14.26.1 4.14.26.2 4.14.26.3 4.14.26.3.1 4.14.26.3.2 4.14.26.3.3 4.14.26.3.4 4.14.27 4.14.27.1 4.14.27.2 4.14.28 4.14.28.1 4.14.28.2 4.14.28.3 4.14.28.3.1 4.14.28.3.2 4.14.28.3.3 4.14.28.3.4 4.14.28.3.5 4.14.28.3.6 4.14.28.3.7 4.14.28.3.8 4.14.28.3.9 4.14.29 4.14.30 4.14.30.1 4.14.30.2 4.14.30.2.1 4.14.30.3 4.14.30.3.1 4.14.30.3.2 4.14.30.3.3 4.14.30.3.4 4.14.31 4.14.31.1 4.14.31.1.1 4.14.31.1.2 4.14.31.1.3 4.14.31.1.4 4.14.31.1.5 4.14.31.1.6 4.14.31.1.7 4.14.31.2 4.14.31.3 4.14.31.3.1 4.14.31.3.2 4.14.31.3.3 4.14.32 4.14.32.1 4.14.32.2 4.14.32.3 4.14.33 4.14.33.1 4.14.33.2 4.14.33.3 4.14.33.4 4.14.33.5

Minimum Primary Anoxic Sludge Mass Fraction Denitrification – Influence on Reactor Volume and Oxygen Demand Development and Demonstration of Design Procedure Review of Calculations Allocation of Unaerated Sludge Mass Fraction Denitrification Performance of the MLE System Optimum recycle ratio a The balanced MLE system Effect of influent TKN/COD ratio MLE sensivity diagram System Volume and Oxygen Demand System Volume Daily Average Total Oxygen Demand Biological Excess Phosphorus Removal Introduction Principles of BEPR Mechanism of BEPR Background Biological P removal microorganisms Prerequisites Observations Biological P removal mechanism Fermentable COD and slowly biodegradable COD Functions of the anaerobic zone Influence of recycling oxygen and nitrate to the anaerobic reactor Denitrification by PAOs Principles of Maximizing BEPR Model Development for BEPR Early Developments RBO and Anaerobic Mass Fraction NDBEPR system kinetics Enhanced PAO Cultures Enhanced culture development Enhanced culture kinetic model Simplified enhanced culture steady-state model Steady-state mixed culture NDBEPR systems Mixed Culture Steady-State Model Division of Biodegradable Organics between PAOs and OHOs Subdivision of influent RBO Conversion of FBSO Effect of recycling nitrate or oxygen Steady-state FBSO conversion equation Mass of VSS in the NDBEPR system PAO P release P removal VSS and TSS Sludge Masses in the Reactor (System) BEPR System Design Considerations Process volume requirements Nitrogen requirements for sludge production Total oxygen demand Influence of BEPR on the System Influence on VSS, TSS, and Carbonaceous Oxygen Demand VSS Composition P/VSS ratio Factors Influencing Magnitude of BEPR Sludge Age and Anaerobic Mass Fraction Raw and Settled Influent Influence of Influent RBO Fraction Influence of Recycling Nitrate and Oxygen to the Anaerobic Reactor Subdivision of the Anaerobic Reactor into Compartments

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4.14.34 4.14.34.1 4.14.34.2 4.14.34.3 4.14.34.4 4.14.34.5 4.14.35 4.14.36 References

Denitrification in NDBEPR Systems Introduction Experimental Basis for Denitrification Kinetics in NDBEPR Systems Denitrification Potential in NDBEPR Systems Principles of Denitrification Design Procedures for NDBEPR Systems Analysis of Denitrification in NDBEPR Systems Denitrification in the UCT System Conclusion

4.14.1 Introduction To comply with more stringent effluent legislation, the functions of the activated sludge (AS) system have expanded to progressively include the biological removal of carbon (C), nitrogen (N), and phosphorus (P). Not only have these expansions increased the complexity of the system configuration and its operation, but, concomitantly, the number of biological processes influencing the effluent quality and the number of compounds involved in these processes have increased. With such complexities, designs based on experience or semi-empirical methods no longer will give optimal performance; design procedures based on more fundamental behavioral patterns are required. To meet this requirement, over the past two decades various research groups have contributed to developing conceptual and mathematical steadystate design and dynamic kinetic simulation models for the biological nutrient removal (BNR) AS system. These models have progressively included aerobic chemical oxygen demand (COD) (carbon) removal and nitrification (Marais and Ekama, 1976; Dold et al., 1980), anoxic denitrification (van Haandel et al., 1981; WRC, 1984; Dold et al., 1991 [UCTOLD]; Henze et al., 1987 [ASM1]), and anaerobic/anoxic/aerobic biological excess phosphorus removal (BEPR; Wentzel et al., 1990, 1992 [UCTPHO]; Henze et al., 1995 [ASM2]). The models enable system design and operational parameters to be readily identified, provide guidance in selecting values for these parameters, and quantify the expected behavior of the system. For mathematical modeling of wastewater-treatment systems, generally two levels of mathematical models have been developed, steady state and dynamic kinetic simulation. The steady-state models have constant flows and loads and are simple to use. This simplicity makes these models very useful for design. In these models, complete descriptions of system parameters are not required, but rather the models are oriented to determine the important system design parameters from performance criteria using algebraic equations with explicit solutions. The dynamic models are much more complex than the steady-state ones and have varying flows and loads with the result that time is included as a parameter. Although dynamic simulation models are useful for predicting the timedependent system response of an existing or proposed system, they comprise time-based differential equations which require numerical integration with computer software to generate solutions. Also, their complexity demands that many more kinetic and stoichiometric constants need to be supplied and

513 513 514 518 519 520 520 521 521

all the system design parameters have to be specified. In fact, the steady-state models are very useful for calculating the initial conditions required to build dynamic simulation models (such as reactor volumes, recycle and waste flows, and values for the various concentrations in the reactor(s)) and for cross-checking simulation model outputs. Hence, steady-state models based on the same bioprocess principles but with simplified equations that yield closely similar results are not only a very useful complement to the complex dynamic simulation models but also give insight into the dynamic models. In this chapter, attention is focused on the steadystate models for biological organics (COD, C), N, and P removal, and where relevant their links to the dynamic models are discussed. First, an overview of the fundamental principles and functional organisms on which both the steady-state and dynamic kinetic models are based is presented.

4.14.2 System Configuration and Organism Groups The expansion in function of the AS system to include biological N and P removal has been accomplished by manipulating: (1) the system configuration, through the incorporation of multiple in-series reactors with various interreactor recycles or timed aeration cycles, some aerobic and others not and (2) the wastewater characteristics, through primary sedimentation and acid fermentation of primary sludge. The objective of these manipulations is to create environmental conditions in the AS system that are conducive to the optimal growth and action of organisms that naturally perform the biological reactions necessary to treat the wastewater – aerobic zones/periods for nitrification and organics removal, anoxic zones/periods for denitrification and organics removal, and anaerobic/aerobic sequence of zones/periods with the influent fed to the anaerobic zone/period for biological excess P removal (BEPR). In the highly diverse mixed cultures that develop in these AS systems, for the purpose of design, only the behavior of whole populations or groups of organisms with the same function is considered. The principal organism groups, their functions, and the zones in which these functions are performed are summarized in Table 1. From Table 1, for the design of nitrification–denitrification (ND) BEPR-AS systems, three organism groups and their interactions need to be taken into account (Wentzel et al., 1992): (1) heterotrophic organisms unable to accumulate polyphosphate (polyP), termed ordinary heterotrophic organisms (OHOs); (2) heterotrophic organisms able to

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Table 1 Principal organism groups included in models for activated sludge systems, their functions and the zones in which these functions are performed Organism group

Biological process

Ordinary heterotrophic organisms (OHOs, unable to accumulate polyphosphate)

 COD removal and ammonification

Phosphorus accumulating (heterotrophic) organisms (PAOs, accumulate polyphosphate).

Autotrophic nitrifier organisms (ANOs)

Zone Aerobica þ

(organic degradation; release of organic N as ammonia, NH4 )  Denitrification (organic degradation; ammonification; reduction of nitrate nitrite – NO3  -NO2  -N2 )  Fermentation (conversion of FBSO to VFA)

Anoxica

Anaerobica

 P release

Anaerobic

(VFA uptake; PHA storage)  P release (VFA uptake; PHA storage)  P uptake (PHA degradation; denitrification?)  P uptake; P removal (PHA degradation; DO uptake)

Anoxic Anoxic Aerobic

 Nitrification

Aerobic

(NH4 þ -NO2  -NO3  ; DO uptake) a

In this chapter, the following working definitions apply: aerobic – presence and/or influx of dissolved oxygen (DO) and nitrate/nitrite; anoxic – absence and zero influx of DO but presence and/or influx of nitrate/nitrite; Anaerobic – absence and/or no influx of dissolved oxygen (DO) and nitrate/nitrite.

Sludge constituents

Enmeshed with sludge mass

Biodegradable

Transforms to active organisms

Settleable Suspended Precipitable

Enmeshed with sludge mass Transforms to set. solids

Biologically Transferred utilizable to Nonprecip and bio util

Biomass in reactor all settleable none supended

Unbiodegradable

Unbiodegradable

Inorganic mass all settleable none suspended

Biodegradable

Transforms to active organisms Enmeshed with sludge mass Transforms to active organisms

Biodegradable

Organic volatile settleable solids (VSS)

Escapes with effluent

Inorganic set. solids (ISS)

Unbiodegradable

Reaction

Total settleable solids (TSS)

Soluble Dissolved Organic

Particulate Suspended Settleable Partic

For the AS system, it is necessary to characterize the wastewater physically (soluble, nonsettleable (colloidal and/or suspended), settleable, organic, and inorganic) and biologically (biodegradable and unbiodegradable). The physical, chemical, and biological transformations of the organic and inorganic wastewater constituents that take place in the biological reactor are outlined in Figure 1. Some of these transformations are important for achieving the required effluent quality, while others are not important for the effluent quality but are important for the system design and operation. In the Figure each of the wastewater organic and inorganic fractions have soluble and particulate fractions, the latter of which subdivides further into suspended (nonsettleable) and settleable ones. Each of the three organic subfractions, in turn, has biodegradable and unbiodegradable constituents. The inorganic particulate subfraction comprises both settleable and suspended (nonsettleable) constituents, while the soluble inorganic subfraction comprises both precipitable and nonprecipitable and biologically utilizable and nonbiologically utilizable constituents. In the biological reactor the biodegradable organics, whether soluble, nonsettleable, or settleable, are transformed to OHOs (XBH), which become part of the organic (volatile) suspended solids (VSSs) in the reactor. When these organisms die, they leave behind unbiodegradable particulate (but not soluble) organics, called endogenous residue, comprising

Inorganic

4.14.3 Transformations in the Biological Reactor

Wastewater constituents

Soluble

accumulate polyP, generically called phosphorus accumulating organisms (PAOs); and (3) autotrophic nitrifier organisms (ANOs) mediating nitrification. This chapter focuses on all three groups which together accomplish carbon (COD), N, and P removal via their normal bioprocesses.

Solids Gas

Escapes as gas

Escapes with effluent

Figure 1 Global transformation reactions of organic and inorganic wastewater constituents from the particulate and soluble forms in the solid and liquid phases to the solid phase as sludge constituents, and gas and liquid phases escaping to the atmosphere and with the effluent, respectively.

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mainly unbiodegradable cell wall material (XEH). This endogenous residue becomes part of the VSS mass in the reactor. If the reactor includes anaerobic zones/period, the readily biodegradable organics are converted to short-chain fatty acids and transformed to PAOs (XBG), which also become part of the VSSs in the reactor. When the PAO organisms die, they also leave behind unbiodegradable particulate (but not soluble) organics, called endogenous residue, comprising mainly unbiodegradable cell-wall material (XEG). The unbiodegradable suspended and settleable organics (XI) from the influent become enmeshed with the OHO and endogenous residue masses. Together, these five constituents (XBH þ XEH þ XBG þ XEG þ XI) form the organic component of the settleable solids that accumulates in the biological reactor (VSS, Xv) of the BNR system. If the system comprises only aerobic or anoxic and aerobic zones/periods, the PAOs will be absent, and the organic VSS (Xv) will comprise only the OHOs (XBH) and their endogenous residue (XEH) and the unbiodegradable particulate organics (UPO, XI) from the influent. The inorganic settleable and suspended constituents, together with the precipitable soluble inorganics from the influent, form the fixed inorganic component of the settleable solids mass (ISS) in the reactor. The sum of the organic (VSS) and inorganic settleable solids is the total suspended solids (TSS, Xt). The biologically utilizable soluble inorganics are absorbed by the biomass and become part of it or are transformed to the gas phase, in which case they escape to the atmosphere. The soluble inorganics (anions and cations) absorbed by the OHO and PAO biomass become part of the biomass intracellular dissolved constituents. When the solids from the reactor (TSS) are dried in the VSS and TSS tests procedure, these intracellular dissolved inorganics precipitate and add to the (fixed) ISS from the influent. This is particularly relevant for BEPR systems which stimulate the growth of PAOs – their polyP content adds up to 8 times more inorganics content than the OHOs. The nonprecipitable and nonbiologically utilizable soluble inorganics escape with the effluent. Because of the efficient bioflocculation capability of the organic AS mass, all the solids material, whether biodegradable or unbiodegradable, organic or inorganic, get enmeshed with and become part of not or the settleable solids. Very little suspended or colliodal (nonsettleable) solids mass is formed in the reactor, but when it does it cannot be retained in the system anyway and escapes with the effluent, unless membranes are used for solid–liquid separation instead of secondary settling tanks.

4.14.4 Wastewater Characterization 4.14.4.1 Introduction The AS system comprises a biological reactor and a secondary settling tank (Figure 2). Irrespective of whether or not biological N and/or P removal are included, many different biological and physical processes take place in the biological reactor, and the physical process sedimentation takes place in the secondary settling tank. These processes form the basis for subdividing the influent wastewater C, N, and P materials into subfractions. On entry of the influent into the biological reactor, the particulate (suspended) materials, which include

Aeration

Influent

Aerobic reactor

Waste flow Secondary settling tank Effluent

Sludge recycle Figure 2 Activated sludge system with biological reactor and secondary settling tank with excess sludge withdrawal direct from the reactor.

both settleable and nonsettleable, organic and inorganic material, are enmeshed (a biologically mediated flocculation) and become part of the AS mixed liquor, which is virtually all settleable (Figure 1). The soluble (dissolved) materials, both organic and inorganic, remain in solution. In the biological reactor, the bacteria present will act on the biologically utilizable material, termed ‘biodegradable’, whether organic or inorganic, soluble or particulate, and transform these to other compounds or products, either gaseous, soluble, or particulate: The gaseous products escape to the atmosphere, the particulate products become (or remain) part of the mixed liquor solids, and the soluble products become (or remain) dissolved in solution. The nonbiologically utilizable organics, termed ‘unbiodegradable’, will not be transformed and will remain in either the soluble or particulate form. Therefore, recognizing that biological processes take place in the reactor, the first major division of the influent is based on whether the material is ‘biodegradable’ or ‘unbiodegradable’. After biological treatment the flow passes from the biological reactor to the solid–liquid separation system, sometimes membrane reactor but usually a secondary settling tank. In the secondary settling tank, the bioflocculated particulate materials making up the mixed liquor (whether organic or inorganic, biodegradable, or unbiodegradable) settle out and are returned to the biological reactor. The particulate components of the mixed liquor entering the settling tank are thus retained in the system. All the soluble components of the mixed liquor (whether organic or inorganic, biodegradable, or unbiodegradable) cannot settle out and escape with the effluent. The settling behavior in the secondary settling tank therefore forms the basis for subdividing the influent unbiodegradable material into subfractions: the influent unbiodegradable material passes unmodified through the biological reactor to the secondary settling tank; ideally, all the particulate (including the enmeshed influent nonsettleable particulates) material settles out in the secondary settling tank and these constituents are therefore termed unbiodegradable particulate; the soluble constituents cannot settle out so that these constituents are termed unbiodegradable soluble. With regard to the influent biodegradable material, because practically all of this material gets biologically transformed to biomass in the biological reactor preceding the secondary settling tank, it cannot be subdivided into subfractions based on its behavior in the secondary settling tank; subdivision of the biodegradable material is based on the rates of transformation/ utilization by the bacteria in the biological reactor. As it

Biological Nutrient Removal

happens, the soluble organic constituents are more easily utilizable than the particulate ones, so that the physical size of the organics also plays an important role in their rate of utilization. For this reason, physical separation tests can be used to assist in the identification of the readily and slowly biodegradable organic material (COD) fractions. From the above, to assess the performance of the AS system, the wastewater constituents need to be characterized: (1) biologically, that is, as biodegradable (biologically utilizable) or unbiodegradable (biologically nonutilizable) material and (2) physically, that is, as soluble or particulate material. Therefore, for steady-sate and dynamic kinetic models based on fundamentals of biological behavior for AS systems, with or without biological N and P removal treating raw or settled wastewater, it is necessary to divide the influent constituents into at least three fractions: (1) biodegradable, (2) unbiodegradable soluble, and (3) unbiodegradable particulate. This general, but not complete, wastewater characterization structure conforms to the biological degradation and physical solid/liquid separation processes that take place in the AS system. When only organic (C) material removal is considered, this structure is applied to the organic (COD or carbonaceous) constituents of the wastewater; with additional nitrification or nitrification and biological N removal, it is also applied to the N constituents; with C, N, and P material removal, it is applied to all three of these groups. The quantity or concentration of each constituent fraction is assessed chemically. In AS models, the COD test forms the basis for specifying the various fractions of organic or carbonaceous (C) material, the total Kjeldahl nitrogen (TKN) and the free and saline ammonia (FSA) tests form the basis for specifying the various nitrogen (N) constituents, and the total phosphorus (TP) and orthophosphate (OP) tests form the basis for specifying the phosphorous (P) constituents of the wastewater. This section provides some detail on the characterization of the COD, N, and P constituents of the wastewater using these tests.

4.14.4.2 Carbonaceous Organic (C) Materials The COD test measures the electron-donating capacity of the organics in the wastewater, which closely approximates the free energy available in the organics (WRC, 1984). For AS system design, it is necessary to quantify, to various degrees, the constituents making up the organic (C) material (measured as COD), as these significantly affect the system response, for example, carbonaceous oxygen demand, sludge production, denitrification, and phosphorus removal. The extent of characterization required for the organic materials depends on the design objectives for the AS system – if N and/or P removal are incorporated, information additional to the three groups of organics is required, which are presented below.

4.14.4.2.1 Carbonaceous material (COD) fractions The first division of the influent COD (Sti) is based on whether the COD fraction undergoes biological degradation or not, that is, into biodegradable COD (Sbi) and unbiodegradable COD (Sui) respectively.

415

Unbiodegradable subfractions. The influent unbiodegradable organics (COD) are subdivided into two fractions, unbiodegradable soluble organic COD (USO, Susi) and UPO COD (Supi). Both fractions are accepted to be unaffected by biological action in the system so that at steady state, the fluxes (mass/d) of these materials that enter the system are equal to the fluxes that exit the system. As both fractions are unbiodegradable, their differentiation is based on their behavior in the secondary settling tank. The USO (Sus) passes out in the secondary settling tank overflow and appears as COD in the effluent. Since the USO (Sus) flows out with the effluent, it has a direct influence on the effluent COD concentration. It can be accepted that for AS systems with sludge ages greater than about 3 days, the effluent soluble COD (say o0.45 mm filtered) (Suse) is closely equal to the influent unbiodegradable soluble COD (Susi) (Ekama et al., 1986). This assumes that (1) the influent (and any generated) soluble biodegradable COD has been completely utilized and (2) negligible unbiodegradable soluble organics are generated during biological treatment in the reactor. Over the many years of research into AS systems, both assumptions have come to be accepted as reasonable and are implicitly incorporated in most models, for example, the steady-state one of Marais and Ekama (1976) and WRC (1984). The latter assumption also has been incorporated in the more complex mixed culture AS simulation models, for example, the IWA-AS model No 1 (ASM1) (Henze et al., 1987), UCTOLD (Dold et al., 1991), UCTPHO (Wentzel et al., 1992), and IWA ASM2 (Henze et al., 1995). A literature review by Dold et al. (1986) demonstrates the validity of this latter assumption. Experimental work by Torrijos et al. (1994), in which the respirometry and utilization of the more exactly defined colloidal (0.2–50 mm) and soluble (o0.1 mm) organic fractions from real wastewater were examined, further validates this assumption, and in addition two other important assumptions included in the kinetic models, that is, (1) that the soluble and colloidal organic fractions are utilized simultaneously but at markedly different rates and (2) that variability in utilization rates of different sized organics within the soluble (o0.1 mm) group is very small and markedly faster than the organics in the colloidal group, so that insofar as kinetics of utilization and hence wastewater characterization are concerned, it is sufficiently accurate to recognize only two biodegradable fractions: a soluble rapidly biodegradable one comprising dissolved organics and a lumped particulate (suspended) slowly biodegradable one comprising both nonsettleable and settleable organics. The UPOs (Sup), such as paper, hair, and other fibrous material, are enmeshed in the biological reactor mixed liquor which settles out in the secondary settling tank. Thus, UPOs (Sup) are retained in the system to accumulate as UPOs (VSS), which essentially are all settleable. At steady state, the flux of Sup entering the system with the influent is equal to the flux of this material, enmeshed with the mixed liquor VSS (and denoted XI), exiting via the sludge waste stream (Figure 2). From a mass balance, the mass of unbiodegradable organic solids that accumulate in the reactor from the influent is equal to the daily influent mass load of this material multiplied by the sludge age. Thus, the influent UPO (Supi) has a direct effect on the mixed liquor solids (VSS) mass in the reactor, and

416

Biological Nutrient Removal

therefore on the system volume requirements for a selected mixed liquor solids concentration. Unlike the USO (Sus), of which negligibly little is generated by the biological reactions, UPO material is generated in significant quantities by the biological reactions and also enmeshed (and hence all settleable) in the biological reactor mixed liqour. Owing to the different origin of this unbiodegradable particulate material, called endogenous residue (and denoted XEH), it is accounted for separately from the influent UPOs that accumulate in the reactor (XI), so that in the AS model, the contribution of each of these two UPO materials to the mixed liquor organic concentration in the reactor can be assessed. Biodegradable subfractions. Subdivision of the biodegradable organics (Sbi) into biodegradable soluble (BSO, Sbsi) and particulate (BPO, Sbpi) subfractions depends on the requirements for the system to be designed. For a completely aerobic system, irrespective of whether nitrification is included or not either intentionally or unintentionally, subdivision of the biodegradable organics into its subfractions is not required for design. Knowing the influent biodegradable COD concentration and the average flow per day gives the biodegradable COD load (flux, kgCOD/d) on the plant; knowing the biodegradable COD load (FSbi, where the prefix F denotes flux) and selecting a sludge age, the daily carbonaceous oxygen requirements (FOc, kgO d1) and the active OHO mass (MXBH, where the prefix M denotes mass, kgVSS) and UPO masses (MXEH þ MXI) that make up the mixed liquor organics in the

reactor (MXv ¼ MXBH þ MXEH þ MXI), which conventionally are measured with the VSSs but can be measured with the COD also (as in ASM1 and 2), can be estimated from the steady-state model (see Section 4.14.9.3). However, if denitrification and/or biological P removal are included, then subdivision of influent biodegradable organics (Sbi) into subfractions is essential. The first subdivision of Sbi is into readily biodegradable (soluble) COD (Sbsi) and slowly biodegradable (particulate) COD (Sbpi) (Figure 3). This division has been based on observed biological responses of AS mixed liquor to domestic wastewater (Dold et al., 1980; AS (short sludge age cyclic loading, plugflow reactors, batch tests)); two distinct rates of utilization of domestic wastewater biodegradable COD were apparent with either oxygen or nitrate as electron acceptor (aerobic or anoxic conditions, respectively) (Dold et al., 1980; van Haandel et al., 1981; Ekama et al., 1986; Still et al., 1996; Gabb et al., 1991; Ekama et al., 1996a). A fraction (called readily biodegradable soluble organics, RBSO) was taken up rapidly by the sludge and metabolized, giving rise to a high oxygen or nitrate utilization rate, respectively. The other fraction (called slowly biodegradable particulate organics (BPOs), COD) was taken up much more slowly and metabolized, giving rise to oxygen or nitrate utilization rates about 1/10 of the rate with RBSO. To explain these observations, the RBSO was hypothesized to consist of simple soluble molecules that can be readily absorbed by the organism and metabolized for energy and cell synthesis, whereas the BPO was assumed to be

Total COD (S ti)

Biodegradable (S bi)

Heterotroph active biomass (Z BHi)

Slowly biodegradable (particulate) (Sbpi)

Readily biodegradable (soluble) (Sbsi)

Short-chain fatty acids (SCFA) (Sbsai)

Unbiodegradable (S ui)

Unbiodegradable particulate (Supi)

Unbiodegradable soluble (Susi)

Fermentable RBCOD (F-RBCOD) (Sbsfi)

Figure 3 Complete subdivision of the influent organic material (measured as COD) showing the five fractions required for steady-state design of biological N and P removal systems and dynamic modeling of the fully aerobic and anoxic–aerobic (for N removal) activated sludge systems; although usually zero, an active heterotrophic organism (OHO) concentration is also shown for wastewaters that may contain significant concentrations of this.

Biological Nutrient Removal

made up of particulate/complex organic molecules that require extracellular adsorption and enzymatic breakdown (hydrolysis) prior to absorption and utilization (Dold et al., 1980; Torrijos et al., 1994; van Haandel et al., 1981), that is, the division is a biokinetic one. Under dynamic loading of activated sludge (short sludge age cyclic loading, plugflow reactors, batch tests) two distinct rates of utilization of domestic wastewater biodegradable COD were apparent with either oxygen or nitrate as electron acceptor (aerobic or anoxic conditions respectively) (Dold et al., 1980; van Haandel et al., 1981; Ekama et al., 1986; Still et al., 1985, 1996; Gabb et al., 1991; Ekama et al., 1996a). A fraction (called readily biodegradable COD, RBCOD) was taken up rapidly by the sludge and metabolized, giving rise to a high oxygen or nitrate utilization rate respectively. The other fraction (called slowly biodegradable COD, SBCOD) was taken up much more slowly and metabolized, giving rise to oxygen or nitrate utilization rates about 1/10 of the rate with RBCOD. To explain these observations, the RBCOD was hypothesized to consist of simple soluble molecules that can be readily absorbed by the organism and metabolized for energy and cell synthesis, whereas the SBCOD was assumed to be made up of particulate/complex organic molecules that require extracellular adsorption and enzymatic breakdown (hydrolysis) prior to absorption and utilization (Dold et al., 1980; Torrijos et al., 1994). The difference in molecule size between RBSO and BPO has been used to classify the RBSO as a biodegradable soluble COD and the BPO as a biodegradable particulate COD. This difference in molecule size of the RBSO and BPO has also led to the development of physical separation methods to assist in the quantification of these organic fractions (e.g., Dold et al., 1986; Ekama et al., 1986; Mamais et al., 1993; Wentzel et al., 1995, 1999, 2000; Mbewe et al., 1994, 1998). However, it must be remembered that the physical distinction between soluble and BPOs (COD) is not directly related to the behavior in the secondary settling tank, as is the case for the unbiodegradable COD subfractions, but is an approximation of the difference in response of the organisms to the two biodegradable COD fractions. The RBSO is soluble and therefore is exposed to biological treatment only as long as the liquid remains in the reactor, that is, for the hydraulic retention time, which is relatively short (B6–24 h). However, the rate of RBSO utilization is high and for sludge ages greater than about 3 days, the concentration of RBSO in the effluent is negligible even though the retention time is relatively short. Accordingly, for design of fully aerobic systems, knowledge of the influent RBSO concentration is not required – it can be safely assumed that all the RBSO will be utilized in the system. For the BPO, the extracellular breakdown (hydrolysis) is slow and is the limiting rate in the utilization of BPO. Although the rate of BPO utilization is relatively slow, the BPO does not appear in the effluent. This is because on entry of the influent into the bioreactor, the BPO becomes enmeshed in the mixed liquor, settles out in the secondary settling tank, and is retained in the system. Therefore, unlike the soluble biodegradable organics (RBSO, BSO) which are exposed to biological treatment for only as long as the liquid remains in the system, that is, hydraulic retention time, the biodegradable

417

particulate organics (BPO) are exposed to biological treatment for as long as the particulate (now settleable) material is retained in the system (i.e., for the sludge age). Therefore, even though the utilization of the BPO is around (1/10)th that of the RBSO, because the sludge age of most AS systems is usually more than 10 times longer than the hydraulic retention time, the BPO usually is completely utilized as well. From simulation studies using dynamic kinetic models (such as the one of Dold et al. (1980, 1991)) all the BPO is completely utilized for sludge ages greater than about 2 or 3 days and temperatures greater than about 20 1C (5–6 days at 14 1C). Accordingly, for design of fully aerobic systems using the steady-state model, knowledge of the RBSO (BSO) and BPO (UPO) subdivision is not required – it is sufficient to assume that all the biodegradable COD will be utilized in the system. However, when denitrification and/or BEPR are included, knowledge of the RBSO is essential and a more refined characterization as defined in Figure 3 is required. For denitrification, the rate of denitrification depends on, inter alia, whether RBSO or BPO serves as electron donor (substrate), and the relative proportion of these two organic types will thus influence the amount of N removal. For BEPR the magnitude of the phosphorus removal is strongly linked to the influent RBSO concentration. Furthermore, with BEPR, the RBSO needs to be subdivided into two subfractions (Figure 3) (Wentzel et al., 1990). With BEPR, the organisms mediating BEPR, called PAOs, take up and store intracellularly volatile fatty acids (VFAs) in the anaerobic reactor with associated P release (called sequestration). The amount of VFA that the PAOs take up in the anaerobic reactor determines the proportion of the biodegradable COD that these organisms obtain and therefore their active mass in the system, which in turn determines to a large extent the amount of P removal that is achieved (see Section 4.14.31.1). The VFA is derived from that present in the influent (part of the RBSO) and is also generated in the anaerobic reactor by acid fermentation (Table 1). The rate of VFA uptake is so rapid that it can be assumed that all VFA in the influent will be taken up in the anaerobic reactor by the PAOs (Wentzel et al., 1985). The RBSO that is not in an VFA form is called fermentable RBSO (FBSO) and will be acid-fermented by the OHOs in the anaerobic reactor to VFA which then can be sequestered by the PAOs. The rate of this fermentation reaction is slower than the VFA uptake rate (Wentzel et al., 1985), and the amount of FSBO fermented to VFA depends on the influent FBSO concentration and system design. Thus, for accurate design of BEPR, the RBSO needs to be subdivided into two subfractions, VFA (Sbsai) and FBSO (Sbsfi). Heterotrophic (OHO) active biomass. In some wastewatercharacterization schemes, an active OHO concentration is included as part of the total organic material in the influent (shown dotted in Figure 3). This may be necessary for wastewaters collected in well-aerated sewers (unintentionally in steep sloping ones or intentionally to reduce biocorrosion) so that biological activity that would normally take place in the biological reactor can already take place to a significant extent in the sewer. In some wastewaters, as much at 20% of the total COD has been measured to be active OHO mass (Kappeler and Gujer, 1992). In these aerobic sewer systems, nitrification can also take place to a considerable extent (detectable by

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Biological Nutrient Removal

nitrate concentrations in the influent) so that the AS system is seeded not only with a significant OHO mass, but also with nitrifiers (ANOs). This seeding effect does not influence the design of the AS system much, particularly when primary sedimentation is included, because most of the biomass settles out as primary sludge. However, should the AS system receive a considerable nitrifier (ANO) organism seed, this would manifest in the system as a higher than usual maximum specific growth rate of nitrifiers at 20 1C (mAm20). Usually, the seeding of OHOs and ANOs is negligible, unless some recycle stream at the plant returns waste activated sludge (WAS) into the influent flow or some other wastewatertreatment plant (WWTP) discharges WAS into the sewer. This has been observed by Mbewe et al. (1994), who developed a batch test procedure for measuring the influent OHO active mass (and the RBSO fraction); they measured that in normal domestic wastewater with a relative short trunk sewer (B5 km), the influent OHO concentration (as COD) was less than 6% of the total COD concentration, whereas at a plant where WAS was recycled to the influent, it was 15% (Wentzel et al., 1995, 1998, Lee et al., 2006). In the steady-state model, the seeding effect of OHOs is considered negligible and ignored because by far the greater proportion of these organisms develop in the biological reactor. In the wastewater-characterization schemes of the UCT (Dold et al., 1991) and IWA (Henze et al., 1987) kinetic simulation models, influent heterotrophic and nitrifier organism fractions are recognized, but may or may not be included in model implementation.

4.14.4.2.2 Quantification of COD fractions Quantification of the COD fractions is based principally on monitoring the response of AS to the different COD fractions (Ekama et al., 1986; Dold et al., 1991; Henze et al., 1994). Measurement of influent unbiodegradable soluble COD (Susi) is relatively simple: for sludge ages greater than 3 days, all the RBSO is utilized in the biological reactor and the BPO and unbiodegradable particulate COD (Supi) are enmeshed in the mixed liquor and will settle out in the secondary settling tank. Also, negligible soluble COD is generated during the biological transformation of the biodegradable COD. Thus, the only soluble COD in the effluent is the USO (Sus) of the influent. By running a steady-state laboratory-scale unit at a long sludge age (410) days and measuring the filtered (0.45 mm) effluent COD, Suse can be determined. At steady state, Susi ¼ Suse. Estimates of Susi can be obtained also by flocculating and filtering samples taken at the end of 24 h aerobic batch tests and measuring filtrate (o0.45 mm) COD (Mbewe et al., 1994; Wentzel et al., 1998). Measurement of UPO (Supi) and influent biodegradable organics COD (BO, Sbi) presents more difficulties because both contribute to the mixed liquor VSS in the biological reactor, UPO (Supi) directly by generating inert VSS mass (XI) and BO (Sbi) indirectly by generating OHO active biomass (XBH) and endogenous mass (XEH). UPO (Supi) and BO (Sbi) can be estimated simultaneously by running a laboratory-scale unit at a long sludge age (415 days), and comparing the measured mixed liquor concentration (conventionally as VSSs, but can also be measured with the COD test) and carbonaceous oxygen utilization with those calculated from the steady-state model with different estimated Supi values (see

Section 4.14.9.3). Provided a 100% COD balance over the experimental system is obtained, the value for Supi that gives a theoretical mixed liquor concentration (VSS or COD) and carbonaceous oxygen utilization equal to measured values will be the Supi concentration for the specific wastewater (Ekama et al., 1986). If the COD mass balance is not 100%, the mixed liquor concentration comparison will provide the more accurate estimate for Supi. A long sludge age is selected because the mixed liquor VSS concentration becomes more sensitive to Supi as the sludge age increases. Having found Supi and Susi, Sbi then can be found by difference. Overall, the Supi, Susi and Sbi must provide consistency between theoretical and measured responses at different sludge ages. Various methods have been developed to quantify RBSO, based on either (1) the different rates of utilization of RBSO and BPO – a bioassay test (short sludge age cyclic loaded systems, batch tests) or (2) the hypothesized difference in molecule size between RBSO and BPO – a physical separation (filtration, flocculation/filtration) (see Ekama et al., 1986; Dold et al., 1986; Henze, 1992; Mamais et al., 1993; Mbewe et al., 1994; Torrijos et al., 1994; Henze et al., 1994, Wentzel et al., 1995; Still et al., 1996; Ekama et al., 1996a; Wentzel et al., 2000). It must be remembered that the physical separation techniques are an approximation only of the biokinetic division of the biodegradable COD. The degree of success with the physical separation techniques depends on the method used: these include filtration through various pore-size paper, glass fiber, or membranes, with or without preflocculation. With preflocculation, all the particulate (settleable and nonsettleable) material is flocculated to settleable or filterable material (mimicking the bioflocculation process in the bioreactor), leaving the filtrate with only the soluble constituents. Without preflocculation, the various filter media, from molecular mass cutoff membranes through to filter paper with relatively large pore sizes (e.g., 1 mm), retain decreasing proportions of the particulate (suspended) material on the filter medium, and produce filtrates with increasing proportions of particulate (suspended) materials. Without preflocculation, even the filtrate that passes through a 0.45-mm filter membrane should not be regarded as entirely soluble material (Dold et al., 1986); it approximates the soluble constituents, but some particulate material does pass through the 0.45 mm membrane and is included with the soluble constituents in measurements of filtrate COD. For this reason, it is imperative that when wastewater is physically characterized by means of various separation tests, the type of separation method used is specified; without this no estimate can be made of the proportion of particulate material included with the soluble material so that no reliance can be attached to the results. Also, irrespective of the physical separation technique used, the filtrate will include both biodegradable and unbiodegradable material; an independent estimation of the unbiodegradable material is required to determine the biodegradable fraction of the soluble COD, as discussed above. In the conventional tests to quantify RBSO outlined above, both VFA and FBSO will be measured as RBSO (Figure 3). Accordingly, where VFAs in the wastewater are appreciable (e.g., in systems with acid fermentation of the primary sludge) and BEPR is to be included in the AS system, an additional test will be required to differentiate the two RBSO fractions –

Biological Nutrient Removal

direct measurement of the VFA (by gas chromatography or acid titration (Moosbrugger et al., 1992)) is the most practical.

419

VSS are related via the COD to VSS ratio (fcv):

XIi ¼ Supi =f cv ¼ f S0 up Sti =f cv

ðmgVSS l1 Þ

ð6Þ

4.14.4.2.3 Analytical formulation for COD For analysis and use in the steady-state model, accepting the influent OHO concentration is zero, the scheme indicated in Figure 3 can be expressed mathematically as follows: Biodegradable and unbiodegradable COD fractions:

Sti ¼ Sui þ Sbi

ð1Þ

where Sti is the total influent COD concentration (mgCOD l1), Sui the unbiodegradable influent COD concentration (mgCOD l1), and Sbi the biodegradable influent COD concentration (mgCOD l1). Each of the two fractions on the right-hand side of Equation (1) is again subdivided. Unbiodegradable COD fractions. The unbiodegradable COD concentration consists of two components, soluble and particulate, that is,

where, XIi is the UPO concentration in the influent expressed as VSS (mgVSS l1) and, fcv the COD to VSS ratio of the UPOs, (1.48 mgCOD/mgVSS). It should be noted that this particulate organic material cannot be directly measured as VSS in the influent. The VSS in the influent consists of both biodegradable and UPOs. This combined particulate organic VSS material can only be separated into its unbiodegradable and biodegradable constituent components by means of biodegradability tests, such as those described by Ekama et al. (1986). The justification for using a uniform COD/VSS ratio for all three components of the reactor VSS is given in Section 4.14.4.3.2. Biodegradable COD fractions. The biodegradable COD concentration is found from Equation (1) as follows:

Sbi ¼ Sti  Sui Sui ¼ Susi þ Supi

ðmgCOD l1 Þ

ð7aÞ

ð2Þ and from Equation (5)

where, Susi is the unbiodegradable soluble influent COD concentration (mgCOD l1) and, Supi the unbiodegradable particulate influent COD concentration (mgCOD l1). It is convenient to express Susi and Supi in terms of the total COD concentration Sti, that is, 1

Susi ¼ f S0 us Sti

ðmgCOD l Þ

ð3Þ

Supi ¼ f S0 up Sti

ðmgCOD l1 Þ

ð4Þ

Sbi ¼ Sti  Sti ðf S0 up þ f S0 us Þ ðmgCOD l1 Þ ¼ Sti ð1  f S0 up  f S0 us Þ ðmgCOD l1 Þ

From Figure 3 the biodegradable COD (Sbi) is divided into readily biodegradable soluble COD (Sbsi) and slowly biodegradable particulate COD (Sbpi). Each can be expressed in terms of Sbi as follows:

Sbsi ¼ f Sb0 s Sbi where fS’us is the fraction of total COD which is unbiodegradable soluble, (mgCOD/mgCOD) and fS’up the fraction of total COD which is unbiodegradable particulate (mgCOD/ mgCOD). Hence, from Equation (2)

Sui ¼ ðf S0 us þ f S0 up ÞSti

ðmgCOD l1 Þ

ð5Þ

The terminology of the symbols defining the different wastewater fractions is as follows: fS’up ¼ fraction (f) of the substrate COD (S) which is (denoted by the prime) unbiodegradable (subscript u) and particulate (subscript p). Because the prime comes immediately after the subscript capital S for COD, total COD is implied. Hence, fSb’s is not the same as fS’bs. The former is the fraction (f) of the biodegradable COD (subscript Sb) which is (prime) soluble (s), whereas the latter is the fraction (f) of the total COD (subscript S) which is (prime) biodegradable and soluble (subscript bs). This difference can be noted in Equation (9). Similarly fN’a is the fraction (f) of the total TKN (subscript N) which is (prime) ammonia (sub a) and fSbp’N is the fraction (f) of the biodegradable particulate COD (subscript Sbp) which is (prime) nitrogen (subscript N). Since by convention the mixed liquor solids concentration in the biological reactor is expressed in terms of VSS units rather than COD units, it is convenient to express the UPO material in terms of its equivalent influent volatile solids concentration (XIi). This is readily accomplished by noting that the COD and

ð7bÞ

ðmgCOD l1 Þ

ð8aÞ

and

Sbpi ¼ ð1  f Sb0 s ÞSbi

ðmgCOD l1 Þ

ð8bÞ

where fSb’s is the fraction of influent biodegradable COD which is readily biodegradable (mgCOD/mgCOD). The readily biodegradable COD can also be expressed in terms of the total COD (Sti); thus, substituting for Sbi from Equation (7) in Equation (8) yields

Sbsi ¼ f Sb0 s ð1  f S0 up  f S0 us ÞSti ¼ f S0 bs Sti

mgCOD l1

ð9aÞ ð9bÞ

where fS’bs is the fraction of total COD that is readily biodegradable (mgCOD/mgCOD). If a preflocculated o0.45 mm filtrate is accepted as soluble (which is not unrealistic, Mamais et al., 1993 and Mbewe et al., 1994), then 1. the filtered influent COD concentration gives the sum of the two soluble COD fractions, that is,

Influent : Filt COD ¼ Susi þ Sbsi

ðmgCOD l1 Þ

ð10Þ

2. the difference between the influent unfiltered and filtered COD concentrations gives the two particulate COD

420

Biological Nutrient Removal Table 2 Summary of measurement and calculation procedure for influent organic COD concentrations from experimental results

fractions, that is,

Influent: Total COD  filt COD ¼ Sbpi þ Supi

ðmgCOD l1 Þ

COD

3. the filtered effluent COD concentration gives the unbiodegradable soluble COD concentration, that is,

Effluent: Filt COD ¼ Susi

ðmgCOD l1 Þ

ð12Þ

The RBSO concentration (Sbsi) therefore is closely given by the difference between the filtered influent and effluent COD concentration (Equation (10) minus Equation (12), Wentzel et al., 2000) and the BPO (Sbpi) is given by the difference between the total particulate COD concentration (Equation (11)) and the unbiodegradable particulate COD concentration (Supi) known from the experimentally measured (or assumed) fS’up value. For BEPR systems, the readily biodegradable COD (Sbsi) is subdivided in fermentable readily biodegradable COD (Sbsfi) and VFA (Sbsai), that is,

Sbsi ¼ Sbsfi þ Sbsai

ð13Þ

Each of these can be expressed in terms of Sbsi:

Sbsai ¼ f Sbs0 a Sbsi

ð14Þ

Sbsfi ¼ f Sbs0 f Sbsi

ð15Þ

where fSbs’a is the fraction of readily biodegradable COD which is VFA (mgCOD/mgCOD) and fSbs’f the fraction of readily biodegradable COD which is fermentable (mgCOD/ mgCOD). The VFA can be expressed also in terms of the total COD, Sti, that is,

Sbsai ¼ f S0 bsa Sti

Sym

Source/method Unfiltered influent Filtered effluent (i) Flocculation filtrationa Filt infl–Filt effl (2) (ii) Directly by bioassay From steady-state systems (1)  (2)  (3)  (4) Directly by GC or by acid titration (3)  (6) Batch testsb

ð11Þ

ð16Þ

where fS’bsa is the fraction of total COD which is readily biodegradable VFA (mgCOD/mgCOD). Calculation of the different COD concentrations from experimental results is summarized in Table 2. The effect of these COD fractions on the AS system is discussed in Section 4.14.7.1.

4.14.4.3 Nitrogenous Materials As for the COD (or electron-donating capacity) of the organic material, the nitrogen content of the organic also is subdivided into different fractions. As noted earlier, fractionation of the N material is necessary only if nitrification, or ND, are included in the plant. The subdivision of the reduced N is shown in Figure 4 and is based on the same principles as those applied to COD. Assessment of these fractions is with the TKN and FSA tests. The TKN test measures both the FSA and the nitrogen bound in organic compounds (i.e., the organic N). In certain wastewaters, nitrate and nitrite (oxidized N) may be present but the TKN test does not include these (only the reduced N). Most municipal wastewaters will not contain nitrate or nitrite because in most sewerage systems the wastewater will be in a

1 2 3

Total USO RBSO

Sti Susi Sbsi

4 5 6 7 8

UPO BPO VFA FBSO ActiveOHO

Supi Sbpi Sbsai Sbsfi ZBHi

a

Flocculation prior to filtration provides more accurate results (Mamais et al., 1993; Mbewe et al., 1994). b ZBHi is the COD of the active OHO concentration in VSS terms (XBHi). The batch test method is described by Wentzel et al. (1995) and Ubisi et al. (1997a, b).

deoxygenated state and any nitrate entering the system is likely to be denitrified before it reaches the WWTP.

4.14.4.3.1 Nitrogenous material fractions The first subdivision of the influent TKN (Nti) is into FSA (Nai) and organically bound N (OrgN, Noi), see Figure 4. The FSA is immediately available for incorporation into the bacterial mass or for nitrification to nitrite or nitrate, if the environmental conditions in the system are appropriate for this. However, the organic N has to be converted to ammonia (FSA) by the action of organisms in the bioreactor (a process called ammonification), before it becomes available for incorporation into the bacterial mass, or for nitrification to nitrite or nitrate. In describing and modeling the ammonification of organic N to ammonia, it is accepted in the steadystate model and ASM2 (not ASM1) that this is linked to the organic material (COD) biological degradation (Henze et al., 1995, 2008). Therefore, each of the organic fractions has associated an organic N content with it. When the biodegradable COD is hydrolyzed/utilized for cell synthesis, the associated organic N is released to the bulk liquid as ammonia, which together with its influent ammonia counterpart participates in further biologically mediated reactions. The unbiodegradable COD fractions are not biologically broken down in the bioreactor and so the associated organic N also will not be affected and remains part of the organic material. Accordingly, the organic N is subdivided into subfractions in exactly the same fashion as the organic material COD is subdivided. Unbiodegradable subfractions. As with the COD, the influent organic unbiodegradable N (Noui) is subdivided into organic unbiodegradable soluble (Nousi) and organic unbiodegradable particulate (Noupi) subfractions (see Figure 4). By implication, these fractions are associated with the unbiodegradable COD fractions Susi and Supi, respectively (Figure 3). Thus, both these organic N fractions are unaffected by biological activity. The Nousi, which is associated with Susi, will pass through the system and be discharged with the effluent. The Noupi, which is associated with Supi and hence also with the unbiodegradable particulate VSS originating from the influent (XIi), is

Biological Nutrient Removal

421

TKN (Nti)

Organic N (Noi)

Ammonia N (Nai)

Heterotroph active biomass N

Biodegradable (Nobi)

Biodegradable particulate (Nobpi)

Unbiodegradable (Noui)

Biodegradable soluble (Nobsi)

Unbiodegradable particulate (Noupi)

Unbiodegradable soluble (Nousi)

Figure 4 Subdivision of the influent organic and inorganic material of N as measured by the TKN test. The organic N component is subdivided in the same way as the organic material as measured by the COD test (see Figure 3).

enmeshed in the sludge, settles out in the secondary settling tank, and is retained in the system. This fraction (denoted fSup’N) basically represents the N content of the UPO material (e.g., paper, hair, and other fibrous material) in the wastewater and exits the system via the waste sludge stream (Figure 2). Just as the COD/VSS ratio of the AS mixed liquor (fcv) is accepted to be the same for the active OHO (XBH), the endogenous residue (XEH) and the inert UPOs that accumulate in the reactor from the influent (XI), so also the TKN/VSS ratio (fN mgN/mgVSS) of these three mixed liquor constituents. The justification for this is the same as for the COD/VSS ratio (fcv) (see Section 4.14.4.3.2). Biodegradable subfractions. The biodegradable organic N (Nobi) is associated with the biodegradable COD (Sbi). Accordingly, Nobi can be subdivided into two subfractions (Figure 4), organic biodegradable soluble (Nobsi) and organic biodegradable particulate (Nobpi), associated with Sbsi and Sbpi, respectively (Figure 3). When the biodegradable COD fractions are utilized for organism metabolism and synthesis, the associated organic N fractions are broken down (ammonified) to FSA which, with its influent counterpart, participates in further biologically mediated reactions. As the Sbi is virtually completely utilized for all sludge ages greater than 3 days for fully aerobic systems and 6–8 days for unaerated–aerated N and P removal systems, it can be assumed that the associated (biodegradable) organic N (Nobi) is virtually completely ammonified to FSA. Consequently, in the steady-state model, subdivision of Nobi into soluble and particulate subfractions is not required for fully aerobic systems or for unaerated–aerated systems for biological N and P removal. Provided the sludge age is long enough, all the Sbi will be utilized in these systems

and so all the associated Nobi will become available as FSA. For the latter systems the subdivision of the biodegradable COD into RBSO (soluble) and BPO (particulate) is not required for quantifying the removal of the biodegradable COD (and organic N) itself, but rather for quantifying the magnitudes of denitrification of nitrate and biological P removal. At short sludge ages and very large unaerated sludge mass fractions, utilization of the biodegradable COD may not be complete. In this case, in terms of the steady-state design models and their use in wastewater characterization presented here, higher unbiodegradable particulate and soluble COD fractions (fS’up and fS’us) and concentrations (Supi and Susi) will be noted, and hence also proportionally higher unbiodegradable particulate and soluble organic N concentrations (Noupi and Nousi) compared with systems in which all the biodegradable COD is utilized.

4.14.4.3.2 Quantification of N fractions (Nti) With regard to measurement of the nitrogen fractions, the TKN (Nti) and FSA (Nai) are measured directly by the tests bearing these names; the organic nitrogen (Noi) is found from the difference between the TKN and FSA test concentrations. Difficulties in quantification arise when subdividing the Noi into unbiodegradable (Noui) and biodegradable (Nobi) subfractions. Numerous comparisons of the observed responses of laboratory-scale systems with those predicted by the steadystate model indicate that subdivision of the organic N into subfractions and quantification of these subfractions are important because they determine the effluent organic N concentration, the N content of the WAS, and the amount of

422

Biological Nutrient Removal

organic N ammonified to ammonia which is then available for incorporation into cell mass or for nitrification to nitrite and nitrate. However, the magnitudes of the two unbiodegradable organic N fractions (Nousi and Noupi) are relatively small compared to the influent TKN (Nti). Also, as mentioned above, the subdivision of the biodegradable organic N (Nobi) into soluble and particulate subfractions (Nobsi and Nobpi, respectively) is of little consequence in the steady-state model because it is accepted that all the biodegradable COD (Sbi) is utilized, with all the associated Nobi becoming available as ammonia. The organic unbiodegradable soluble N (Nousi) can be estimated by following the same procedures used to measure Susi: As all the biodegradable COD (Sbi) is utilized for systems with long sludge ages, all the organic biodegradable N (Nobi) associated with Sbi must have been broken down to FSA (Na). Accordingly, all the organic N present in the filtered (o0.45 mm) effluent from such a system must be due to Nous. Thus, by running a steady-state unit at a sludge age greater than 3 days and measuring the filtered (0.45 mm; effluent samples do not need to be preflocculated because of bioflocculation by the mixed liquor) effluent organic N (effluent TKN-FSA), Nouse is determined; at steady state, Nousi ¼ Nouse because, as for the Sus, it is accepted that no significant unbiodegradable soluble organic N (Nous) is generated by the biological processes taking place in the reactor. From experimental results, Nousi is very low and usually is of little consequence, except where very low effluent N standards based on total N (reduced þ oxidized, TN ¼ FSA þ OrgN þ NOx) are set (Pagilla et al., 2009). The UPO N concentration in the influent (Noupi) is determined from its accepted association with UPO (Supi). The UPO (Supi) accumulates in the reactor as organic VSS solids (XI). Therefore, the N associated with Supi (i.e., Noupi), will also accumulate in the reactor with its associated XI. Because the biodegradable and unbiodegradable particulate COD fractions cannot be individually tested for in the influent wastewater, it follows that this also applies to the biodegradable and UPO N fractions. This can only be done by means of biodegradability tests, the same tests by means of which estimates of the biodegradable and unbiodegradable particulate COD fractions are obtained, by monitoring the nitrogen content of the mixed liquor VSS (or COD) that accumulates in the reactor. From experimental data on the TKN concentration of the mixed liquor in the biological reactor, it was found that the TKN/VSS concentration ratio (fN) remained approximately constant irrespective of sludge age from 3 to 30 days, despite the fact that the proportions of the three constituent fractions of the mixed liquor (i.e., active OHOs (XBH), endogenous residue (XEH) and unbiodegradable particulate (XI)) change appreciably with sludge age. The fraction of XI (i.e., N) is therefore constant and equal to that of the other VSS constituent fractions, at about 0.1 mgN/mgVSS. With the TKN content of the UPOs in terms of VSS (fN) at say 0.10 mgN/ mgVSS, the TKN content of this same material in terms of COD (fSup’N) would be 0.10/1.48 ¼ 0.068 mgN/mgCOD (i.e., fSup’N ¼ fN/fcv or Noupi ¼ fN XIi ¼ fN Supi/fcv ¼ fSup’N Supi mgN l1). Having determined Nousi and Noupi, Nobi can be found by difference (Nobi ¼ Noi  Nousi  Noupi). For the steady-state

design model, as noted above, it is not required to subdivide Nobi into soluble and particulate subfractions (Nobsi and Nobpi, respectively). However, for the dynamic simulation models, this subdivision is required and can be done with the aid of TKN and FSA test results of filtered and unfiltered influent samples once the two unbiodegradable concentrations are known from experimental systems, analogous to quantifying the COD subdivisions in Section 4.14.4.2.3.

4.14.4.3.3 Analytical formulation For use in the steady-state model, the relationships indicated in Figure 4 can be expressed as follows: The influent total TKN (Nti) is divided into FSA (Nai) and organic N (Noi):

Nti ¼ Nai þ Noi

ðmgN l1 Þ

ð17aÞ

The Nai can be expressed in terms of Nti:

Nai ¼ f N0 a Nti

ðmgN l1 Þ

ð17bÞ

where fN’a is the fraction of influent TKN which is ammonia (mgN/mgN). The Noi can be subdivided in exactly the same way as the COD, that is, soluble, particulate, biodegradable, and unbiodegradable. The first subdivision is into biodegradable (Nobi) and unbiodegradable (Noui) subfractions:

Noi ¼ Nobi þ Noui

ðmgN l1 Þ

ð18Þ

The Noui can be further subdivided into soluble (Nousi) and particulate (Noupi) subfractions:

Noui ¼ Nousi þ Noupi

ðmgN l1 Þ

ð19Þ

It is convenient to express Nousi in terms of Nti:

Nousi ¼ f N0 ous Nti

ðmgN l1 Þ

ð20aÞ

where fN’ous is the fraction of influent TKN which is organic unbiodegradable soluble N (mgN/mgN). Alternatively, Nousi can be expressed in terms of its associated COD fraction, Susi:

Nousi ¼ f Sus0 N Susi

ðmgN l1 Þ

ð20bÞ

where fSus’N is the fraction of unbiodegradable soluble COD which is nitrogen (mgN/mgCOD). The Noupi can be expressed in terms of the influent unbiodegradable particulate COD (Supi), or in terms of its volatile solids counterpart (XIi):

Noupi ¼ f Sup0 N Supi ¼ f Sup0 N XIi f cv

ðmgN l1 Þ ð21Þ

where fSup’N is the fraction of the influent unbiodegradable particulate COD which is nitrogen (E0.068 mgN/mgCOD). From Equations (17) to (21) the organic biodegradable N (Nobi) can be found by subtracting Nai, Nousi, and Noupi

Biological Nutrient Removal

423

Table 3 Summary of measurement and calculation procedure for influent TKN concentrations from experimental results

from Nti:

Nobi ¼ Nti ð1  f N0 a  f N0 ous Þ  f Sup0 N XIi f cv

ð22aÞ

¼ Nti ð1  f N0 a Þ  f Sus0 N Susi  f Sup0 N Supi

ð22bÞ

For the purposes of steady-state design, it is not necessary to subdivide Nobi into soluble (Nobsi) and particulate (Nobpi) subfractions (Section 4.14.21.1), but if required (as included in the dynamic models of Dold et al. (1991)) this can be done as follows:

Nobi ¼ Nobsi þ Nobpi

ðmgN l1 Þ

As done above for the unbiodegradable N subfractions, each of the biodegradable N subfractions can be expressed in terms of either the influent TKN or its associated influent COD subfraction:

ðmgN l1 Þ

Nobsi ¼ f N0 obs Nti ¼ f Sbs0 N Sbsi

ðmgN l1 Þ

ð23aÞ ð23bÞ

where fN’obs is the fraction of the influent TKN which is organic biodegradable soluble (mgN/mgN), fSbs’N the fraction of the influent readily biodegradable COD which is nitrogen, (mgN/mgCOD) and

Nobpi ¼ f N0 obp Nti ¼ f Sbp0 N Sbpi

ðmgN l1 Þ

ðmgN l1 Þ

ð26Þ

3. the difference between the effluent filtered TKN and FSA concentrations gives the unbiodegradable soluble organic N (Nousi):

ðmgN l1 Þ

Unfiltered influent Filtered influent (1)  (2) Filtered effluent Filtered effluent (5)  (4) From steady-state systems and Supi Floc filt inf TKNa  (2)  (4) (1)  filt inf TKN  (5) Batch testsb

Preflocculated. From the N content (fN ¼ 0.068 mgN/mgCOD) of the respective COD concentrations.

b

From the above, the soluble biodegradable organic N (Nobsi) is found by difference from Equations (26) and (27). The UPO N (Noupi) can be calculated if the unbiodegradable particulate COD concentration Supi (or fraction, fS’up) is known from Equation (21). The BPO N (Nobpi) can therefore be calculated by difference from Equation (25). Calculation of the different TKN concentrations from experimental results is summarized in Table 3.

Experimental observations on a number of different municipal wastewater flows have indicated that the maximum specific growth rate of the nitrifiers at 20 1C (mA20) can vary greatly in value and appears to be specific to each waste flow, that is, it appears to be a wastewater characteristic. In particular, the value for mA20 appears to be very sensitive to the industrial component of the wastewater presumably because various industries discharge metals, elements, and organics which act in an inhibitory fashion on the ANOs. The magnitude of mA20 can have a significant influence of the design of nutrient removal systems, particularly on the selection of sludge age and unaerated mass fraction. Therefore, for optimal design it is most desirable to have an estimate of mA20. This estimate can be obtained by means of batch tests (Still et al., 1996; Dold et al., 1991) or by running a completely mixed single reactor at a sludge age of about 8 days, imposing a sequence of aerobic and anoxic periods and measuring the rate of nitrate increase during the aerobic period (van Haandel et al., 1981; WRC, 1984). The data thus obtained can be used to calculate mA. By applying the well-established relationship between mA and temperature (see Section 4.14.20.2), the value at a particular temperature within the normal wastewater range (8–281C) can be determined.

2. the difference between the filtered influent TKN and FSA concentrations gives the two soluble organic N fractions, that is,

Nousi ¼ Nte  Nae

Nti Nai Noi Nae Nte Nousi Noupi Nobsi Nobpi NXBHi

ð24bÞ

ðmgN l1 Þ ð25Þ

ðmgN l1 Þ

a

Total FSA OrgN Effluent FSA Effluent TKN USOrgN UPOrgN BSOrgN BPOrgN ActiveOHO

Source/method

4.14.4.3.4 Maximum specific growth rate of nitrifiers at 20 1C

1. the difference between the unfiltered and filtered influent TKN concentrations gives the two particulate organic N fractions, that is,

Filt TKN  FSA ¼ Nousi þ Nobsi

1 2 3 4 5 6 7 8 9 10

Sym

ð24aÞ

where fN’obp is the fraction of the influent TKN which is organic biodegradable particulate (mgN/mgN) and, fSbp’N the fraction of the influent slowly biodegradable COD which is nitrogen (mgN/mgCOD). In terms of the TKN characterization block diagram (Figure 4) and accepting that the influent active OHO concentration is zero and that the preflocculated o0.45 mm filtrate is soluble material, then:

Unfilt TKN  filt TKN ¼ Noupi þ Nobpi

TKN

ð27Þ

where Nte is the Filtered effluent TKN concentration and, Nae the effluent FSA concentration.

4.14.4.3.5 Typical wastewater TKN characteristics Full waterborne sanitation wastewater N characteristics vary widely due to the varying degree of hydrolysis of the urea in urine (organic N) to ammonia but about 75% of the TKN is FSA, and 25% organic N, which as percentages of the TKN comprise 1–3% unbiodegradable soluble (Nousi), 8–10%

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Biological Nutrient Removal

unbiodegradable particulate (Noupi) and 10–14% biodegradable (Nobi).

4.14.4.4 Phosphorous materials As for the carbonaceous and nitrogenous materials, the phosphorus (P) material is also subdivided into fractions (Figure 5). This fractionation is only necessary if P removal (biological or chemical) is required in the system. The fractionation of the influent phosphorus follows the same scheme used for characterization of the influent nitrogen. Assessment of the influent phosphorus fractions is with the TP and OP tests. The TP test measures soluble orthophosphate, condensed orthophosphates (pyro, meta, and other polyPs), and the phosphorus bound in organic compounds and is denoted Pti. The OP (also orthoP) test measures principally the orthophosphates but a small fraction of some condensed phosphates also may be included. In this chapter, all P concentrations measured by the OP test are termed ‘soluble orthophosphate’ (Psi) and the difference in P concentration between the TP and OP tests is called ‘organic P’. As for the nitrogenous material, the Pti is divided into the same five fractions.

4.14.4.4.1 Phosphorus fractions The first subdivision of total influent phosphorus (Pti) is into soluble orthophosphate (Psi) and organically bound P (Poi) (Figure 5). In both raw and settled municipal wastewaters, the soluble orthophosphate fraction predominates, ranging between 70% and 90% of the TP. The main source of orthophosphates are detergents which can contribute up to

50% of the total phosphate load (Wiechers and Heynike, 1986). Thus, in countries where phosphate-free detergents are used, the relative contribution of the orthophosphates will be lower, so also the influent TP concentration in relation to the COD and TKN (Gleisberg, 1993). The soluble orthoP (Psi) is immediately available for incorporation into bacterial mass and, if the system is appropriately designed, for BEPR. The organic phosphorus (Poi) needs to be converted to orthophosphate (Psi) by the action of organisms in the bioreactor (if possible) before it becomes available. Because of this source of orthophosphate in the biological reactor, it is important in design of BEPR systems to measure the influent phosphorus with the TP test to take account of the organic phosphorus in the influent; if only soluble orthoP (Psi) is measured, the BEPR required by the system to achieve a specified effluent orthoP concentration will be underestimated. For description and modeling the conversion of organic P to orthoP, it is accepted that the conversion is linked to the COD biodegradadation, as was done for the organic N. Therefore, each of the COD fractions has associated with it an organic P content. When the biodegradable COD is utilized for cell synthesis, the associated organic P is released as orthoP. When the unbiodegradable particulate COD is enmeshed in the sludge mass, the associated organic P is similarly enmeshed; when the unbiodegradable soluble COD flows through the system and appears in the effluent, the associated organic P will do likewise. Accordingly, subdivision of the organic P follows the subdivision of the COD and the organic N.

Total P (Pti)

Ortho P (Psi)

Heterotroph active biomass P

Organic P (Poi)

Biodegradable (Pobi)

Biodegradable particulate (Pobpi)

Biodegradable soluble (Pobsi)

Unbiodegradable (Poui)

Unbiodegradable particulate (Poupi)

Unbiodegradable soluble (Pousi)

Figure 5 Subdivision of the influent organic and inorganic material in terms of P as measured by the total P test. The organic P component is subdivided in the same way as the organic material as measured by the COD test (see Figure 3).

Biological Nutrient Removal

Unbiodegradable subfractions. As with the COD, the organic unbiodegradable P (Poui) is subdivided into organic unbiodegradable soluble (Pousi) and organic unbiodegradable particulate (Poupi) subfractions (Figure 5). By implication, these subfractions are associated with the unbiodegradable organics, USO (Susi) and UPO (Supi), respectively. Thus, both these organic P fractions are unaffected by biological activity. The Pousi, associated with Susi, will pass through the system to be discharged in the effluent. The Poupi, associated with Supi, will be enmeshed in the sludge, settle out in the secondary settling tank, and be retained in the system with the associated inert particulate organics (XI); hence, this P fraction leaves the system via the waste sludge stream (Figure 2). Biodegradable subfractions. The organic biodegradable P (Pobi) is associated with the biodegradable COD (Sbi). Accordingly, Pobi can be subdivided into two subfractions (see Figure 5), organic biodegradable soluble (Pobsi) and organic biodegradable particulate (Pobpi) associated with Sbsi and Sbpi, respectively. When the biodegradable COD fractions are utilized for metabolism and synthesis, the associated organic P fractions are broken down to soluble orthoP which, with its influent counterpart, is available for use in biologically mediated reactions. As Sbi is virtually completely utilized for all sludge ages greater than 3 days (see Section 4.14.4.2.1), it can be assumed that the associated biodegradable organic P (Pobi) is virtually completely broken down to soluble orthoP. Consequently, for design, subdivision of the Pobi into subfractions is not required; it can be assumed that all the influent organic biodegradable P (Pobi) will become available as soluble orthoP (Ps) in the bioreactor.

4.14.4.4.2 Quantification of P fractions To measure the phosphorus (P) fractions, the influent soluble orthoP (Psi) is measured directly by a colorimetric test of that name; the influent total P (Pti) can be measured by first subjecting the sample to an acid digestion step in which the organic P is oxidized to orthoP followed by the colorimetric test for orthoP. The organic P (Poi) is found by the difference between Pti and Psi. Subdivision of Poi into subfractions is important because one of them, the unbiodegradable soluble organic P concentration (Pousi), determines the minimum effluent TP concentration that can be achieved. Even if in the BEPR system, all the biodegradable organic P (Pobi) is converted to OP and all the OP is removed, the effluent TP concentration contains Pousi. In this respect, the setting of the effluent P standard can have a profound influence on the design of the plant and whether or not to include effluent filtration. If the standard is specified in terms of TP, then Pousi and the P content of any suspended solids (which can be high in BEPR plants) escaping with the effluent are included in the effluent TP. With 10% P/VSS content, losing an average of about 10 mgSS l1 with the effluent and with a Pousi of 0.3 mgP l1, the effluent P concentration already exceeds 1 mgP l1 TP even if all the OP has been successfully removed. In South Africa, the effluent P standard (where it needs to be met) is set at 1 mgP l1 dissolved OP which excludes Pousi and the P in the effluent suspended solids. For the effluent, the unbiodegradable particulate P concentration (Poupi) is not important because this fraction accumulates with the UPOs in the reactor

425

Table 4 Summary of measurement and calculation procedure for influent TP concentrations from experimental results TP 1 2 3 4 5 6 7 8 9 10 a

Total P OrthoP OrgP Effluent OP Effluent TP USOrgP UPOrgP BSOrgP BPOrgP ActiveOHO

Symbol

Source/method

Pti Psi Poi Pse Pte Pousi Poupi Pobsi Pobpi PXBHi

Unfiltered influent Filtered influent (1)  (2) Filtered effluent Filtered effluent (5)  (4) From steady-state systems and Supia Filt inf TPa  (2)  (4) (1)  filt inf TP  (5) Batch testsb

In contrast to the COD and N, sample NOT preflocculated. From the P content (fP ¼ 0.02 mgP/mgCOD) of the respective COD concentrations.

b

biomass. The magnitudes of the two unbiodegradable organic P concentrations (Pousi and Poupi) are very small compared with the total influent P (Pti), usually less than 0.5 and 3.0 mgP l1, respectively. The subdivision of the organic biodegradable P (Pobi) into soluble and particulate concentrations (Pobsi and Pobi respectively) is of no consequence in the steady-state model – because it is accepted that all the biodegradable COD (Sbi) is utilized, all the associated Pobi becomes available as soluble orthoP (Ps), which either is removed (taken up by biomass) or exits with the effluent. The four organic P fractions (Figure 5) are, like the four organic N fractions (Figure 4), difficult and tedious to measure. If, on the same fully aerobic system in which the low N fractions are measured, unfiltered TP and filtered (o0.45 mm) TP and orthoP concentrations are also measured on the influent and effluent samples, then the four organic P fractions can be calculated (see Table 4). Note that the filtered sample cannot be preflocculated because this will cause flocculation of the soluble P fractions also; however, because the organic P is small relative to the TP, excluding the preflocculation step prior to 0.45 mm filtration is reasonable. As with Noupi, Poupi is determined from its accepted association with Supi. Expressed in terms of COD units Poupi ¼ 0.03 (mgP/mgVSS)XIi (mgVSS l1 influent) ¼ 0.03 (mgP/mgVSS)Supi (mgCOD l1 influent)/1.48 (mgCOD/mgVSS) ¼ 0.02 (mgP/mg unbiodegradable particulate COD) Supi (mgCOD ll influent) Equation (33). Having determined Pousi and Poupi, Pobi can be found by difference: Pobi ¼ Poi  Pousi  Poupi. In the steady-state model, as noted above, it is not required to subdivide Pobi into soluble and particulate subfractions (Pobsi and Pobpi, respectively), but this can be done if required in the same way as the equivalent COD or N fractions defined above, that is, with the aid of Total P and orthoP analysis of filtered and unfiltered influent and effluent samples from long sludge age fully aerobic AS systems.

4.14.4.4.3 Analytical formulation For use in the steady-state design procedures, the relationships indicated in Figure 5 can be expressed as follows: The influent total P (Pti) is divided into soluble orthoP (Psi) and organic P (Poi):

Pti ¼ Psi þ Poi

ðmgP l1 Þ

ð28Þ

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Biological Nutrient Removal

The Psi can be expressed in terms of Pti:

Psi ¼ f P0 s Pti

1

ðmgP l Þ

ð29Þ

where fP’s is the fraction of influent total P which is soluble orthoP (mgP/mgP). The Poi can be subdivided into biodegradable (Pobi) and unbiodegradable (Poui) subfractions:

Poi ¼ Pobi þ Poui

1

ðmgP l Þ

ð30Þ

The Poui can be further subdivided into soluble (Pousi) and particulate (Poupi) subfractions:

Poui ¼ Pousi þ Poupi

ðmgP l1 Þ

ð31Þ

ðmgP l1 Þ

ð32aÞ

where fP’ous is the fraction of influent total P which is organic unbiodegradable soluble (mgP/mgP). Even though theoretically it would be more logical to define Pousi as a fraction of the organic P (Poi) rather than the total P (Pti) because Pousi is essentially part of Poi, defining Pousi in terms of Pti obviates having to know the organic P concentration (Poi), which requires both the total P and orthoP concentration to be measured. Defining Pousi in terms of Pti requires only Pti to be measured, which is required in any event due to the conversion of organic P to orthoP in the reactor. The Pousi can also be related back to the organic material COD of which it forms part, being a fraction fSus’P of the unbiodegradable soluble organics:

Pousi ¼ f Sus0 P Susi

Pobsi ¼ f P0 obs Pti ¼ f Sbs0 P Sbsi

ðmgP l1 Þ

ðmgP l1 Þ ðmgP l1 Þ

Pobpi ¼ f P0 obp Pti

ðmgP l1 Þ ðmgP l1 Þ

Poupi ¼ f Sup0 P Supi ¼ f Sup0 P XIi f cv

ðmgP l Þ

ð36bÞ

Unfilt TP  filt TP ¼ Poupi þ Pobpi

ðmgP l1 Þ

ð37Þ

2. the difference between the influent filtered TP and orthoP concentrations gives the two soluble P concentrations, that is,

ðmgP l1 Þ

ð38Þ

3. the difference between the filtered effluent TP and OP concentrations gives the unbiodegradable soluble organic P (Pousi), that is,

Filt Effl TP  Effl OP ¼ Pousi

ðmgP l1 Þ

ð39Þ

ð33Þ

where fSup’P is the fraction of the influent unbiodegradable particulate COD which is phosphorus ( ¼ 0.020 mgP/ mgCOD). From Equations (28) to (33), the organic biodegradable P (Pobi) can be found by subtraction:

Pobi ¼ Pti ð1  f P0 s  f P0 ous Þ  f Sup0 P XIi f cv

ð34aÞ

¼ Pti ð1  f P0 s Þ  f Sus0 P Susi  f Sup0 P Supi

ð34bÞ

For the purposes of steady-state design (Section 4.14.21.1), it is not necessary to subdivide Pobi into soluble (Pobsi) and particulate (Pobpi) subfractions because all of it is converted to orthoP, but if required (as included in the dynamic simulation models for BEPR) this can be done as follows:

Pobi ¼ Pobsi þ Pobpi

ð36aÞ

1. the difference between the influent unfiltered and filtered TP concentrations gives the two particulate organic P concentrations, that is,

Filt TP  OP ¼ Pousi þ Pobsi

1

ð35bÞ

where fP’obp is the fraction of the total P which is organic biodegradable particulate (mgP/mgP) and fSbp’P the fraction of slowly biodegradable (particulate) COD which is P (mgP/mgCOD). If o0.45 mm (or o0.10 mm, but not preflocculated) filtered samples are considered soluble, then, like for the nitrogenous materials,

ð32bÞ

where fSus’P is the fraction of unbiodegradable soluble COD which is P (mgP/mgCOD). Similarly, the Poupi can be expressed in terms of the influent unbiodegradable particulate COD (Supi), or in terms of its VSS counterpart (XIi):

ð35aÞ

where fP’obs is the fraction of the total P which is organic biodegradable soluble (mgP/mgP), fSbs’P the fraction of readily biodegradable (soluble) COD which is P (mgP/mgCOD), and

¼ f Sbp0 P Sbpi

It is convenient to express Pousi in terms of Pti:

Pousi ¼ f P0 ous Pti

As done above for the organic unbiodegradable N subfractions, each of the biodegradable P subfractions can be expressed in terms of either the influent TP or its associated influent COD subfraction:

From the above, the soluble biodegradable organic P (Pobsi) is found by difference from Equations (38) and (39). The UPO P (Poupi) can be calculated (from Equation (33)) if the unbiodegradable particulate COD concentration, Supi (or fraction fS’up) is known. The BPO P (Pobpi) can therefore be found by difference from Equation (37). Calculation of the different TP concentrations from experimental results is summarized in Table 4.

4.14.4.4.4 Typical wastewater phosphorus characteristics Full waterborne sanitation wastewater phosphorus characteristics also vary widely depending on the P content of detergents. Generally, for raw (unsettled) wastewater about 75% of TP is dissolved orthoP (Psi) and 25% organically bound P (Poi). Of the TP, this latter fraction comprises 0–2% unbiodegradable soluble organic P (Pousi), 10–15% UPO P (Poupi), and 8–12% biodegradable (hydrolyzable to orthoP) organic P (Pobi).

Biological Nutrient Removal 4.14.4.5 Inorganic Dissolved, Settleable, and Nonsettleable Solids The wastewater characterization described above focuses on the organic material. Inorganic material also influences the WWTP. Like the organic material, the inorganic material can be divided into particulate and dissolved constituents (Figure 1). The particulate constituents comprise settleable and nonsettleable material. The concentration of inorganic dissolved solids (IDSs, but conventionally called TDSs, due to wide application to waters with low dissolved organics) does not affect the WWTP except where some of the dissolved constituents are inhibitory, like heavy metals, or excessively high in the case of seawater waste collection systems. For most terrestrial water waste collection systems, the IDSs that enter the WWTP exit it via the effluent. A very low concentration is taken up biologically (5–20 mg l1) for growth, such as Ca, Mg, and K, in particular in BEPR systems. Also under certain circumstances, some dissolved inorganic solids may precipitate in the biological reactor, but this is generally very low as well, unless intentionally increased by chemical precipitation by, for example, simultaneous Fe or Al dosing for chemical P removal (Figure 1). Strictly speaking, FSA is inorganic. This is removed from the wastewater by incorporation into the sludge mass or transfer to gas phase, either as free ammonia via gas stripping or as dinitrogen gas via the biologically mediated processes of nitrification and denitrification. Generally, in activated sludge systems very little ammonia is removed by gas stripping due to the low pH (7–8) relative to that required for ammonia stripping (49.5). The particulate inorganic solids (more usually called suspended solids, Standard Methods (1985) and hence ISS) do affect the AS biological reactor solids concentration. These solids comprise grit, sand, silt, clay, and similar materials. Generally, these solids have a biofilm growth on their surfaces which significantly reduces their settling velocity, especially for the smaller particles. The large grit and sand particles, which enter the collection system via leaking joints and holes in the sewer pipes and groundwater ingress or even as cleaning material from some low-income communities, are usually removed in grit collection units as one of the first unit operations in primary treatment at WWTPs. These solids are highly abrasive and significantly reduce the life of mechanical equipment such as pumps and also can accumulate in settling tanks and biological reactors. The medium-sized influent inorganic particulate solids can be removed by primary sedimentation (and hence is the settleable fraction) and become part of the primary sludge. The concentration of organic and inorganic solids removed in primary sedimentation defines the volatile to total solids ratio (VSS/TSS) of the primary sludge. The inorganic solids not removed in primary sedimentation (nonsettleable particulates), which are the very small particles, enter the biological reactor and become enmeshed with the biological reactor mixed liquor (all settleable) and hence are retained in the system forming the inorganic component of the mixed liquor, usually measured as the ISS concentration in the reactor. If primary sedimentation is not included, then the influent ISS entering the biological reactor is much higher, with the result that the ISS concentration in the reactor is also much higher.

427

The ISSs in the biological reactor (which are all settleable) do not arise only from the influent ISS in the raw or settled wastewater. Also contributing to the measured reactor ISS is intracellular dissolved inorganic solids, which, when dried in the test procedure, precipitate as inorganic solids and hence are measured as ISS. This adds to the ISS from the influent. From an evaluation of experimental data collected over 10 years in the UCT Wastewater Laboratory with real and artificial wastewaters fed to N and N and P removal systems over a range of sludge ages from 8 to 20 days, Ekama and Wentzel (2004) calculated that OHOs contain about 0.15 mg ISS/mg OHO organic (volatile) suspended solids (VSSs). This 0.15 mg ISS/mg OHOVSS value was validated with data from the literature (van Haandel et al., 1998) by Ekama et al. (2006a). For the PAOs, the ISS residue was additionally 3.29 times their polyP content, with a maximum ISS of 1.3 mgISS/mgPAOVSS when their P content was 0.38 mgP/mgPAOVSS under aerobic P uptake BEPR conditions. The consequence of this is that the ISS concentration in the reactor depends on many factors such as (1) influent ISS concentration, (2) sludge age, and (3) hydraulic retention time of the biological reactor, (4) wastewater influent COD concentration and (5) characteristics (fS’up and fS’us), and (6) the magnitude of BEPR. Roughly for an N removal system treating raw wastewater at a long sludge age (15–20 days), the influent ISS contributes about two-thirds of the reactor ISS, the OHOs contributing the remaining onethird. For a N and P removal system treating settled wastewater at a short sludge age, the influent ISS contributes about onesixth of the reactor ISS, the OHOs one-sixth and the PAOs the remaining two-thirds. Because the influent ISS concentration in settled wastewater is much lower (Bone-third) than in raw wastewater, the VSS/TSS ratio of the mixed liquor in the reactor treating raw wastewater is lower (0.75–0.80) than that treating settled wastewater (0.83–0.87). Including BEPR reduces the mixed liquor VSS/TSS ratio significantly below these approximate values. Details on how to determine the influent ISS concentration are given by Ekama and Wentzel (2004) and to calculate the reactor ISS concentration and VSS/TSS ratio are given in Sections 4.14.9.2 and 4.14.31.6.

4.14.4.6 Other Materials Other physical and chemical parameters also influence the AS system and therefore also need to be measured. The main ones are temperature, H2 CO3  alkalinity, and pH. The former has a strong influence on the rates of biological activity: the lower the wastewater temperature, the slower the biological rates; in particular, ND is affected. The H2 CO3  alkalinity and pH also play an important role. Most of the biological reactions in AS proceed optimally around a neutral pH (7–8). Some of the biological reactions (e.g., ND) influence the pH by releasing or taking up hydrogen ions (Hþ), with the result that the ability of the wastewater to resist pH changes (buffer capacity) is important. The H2 CO3  alkalinity plays a central role in establishing the pH buffer capacity of the wastewater. These aspects are discussed in Section 4.14.20.6 which deals with ND, the two biological reactions that most markedly influence H2 CO3  alkalinity and pH. Other inorganic chemical constituents such as magnesium, calcium, potassium, sodium, chlorides, and sulfates are

428

Biological Nutrient Removal

generally of minor significance and need not be routinely measured for wastewater characterization. These inorganic dissolved constituents are needed as trace elements only for biological growth and, in wastewaters, are usually present well in excess of the bacterial requirements, with the result that the greater part of these constituents generally remain dissolved and exit the AS system in the liquid (effluent) stream. However, with BEPR, the cations magnesium and potassium play an important role and quantities greater (5–8 times) than those for normal AS growth are required. Therefore, there may be wastewaters where these cation concentrations in the influent are too low with respect to that required for BEPR, in which event the BEPR would be adversely affected (Lindrea et al., 1994). Many municipal wastewaters also contain potentially toxic metals and elements (PTMEs) such as cadmium (Cd), lead (Pb), nickel (Ni), mercury (Hg), zinc (Zn), copper (Cu), chrome (Cr), cobalt (Co), arsenic (As), fluorine (F), selenium (Se), molybdenum (Mo), and boron (B). The greater part of these PTMEs are in a particulate (nonsettleable or settleable) form and generally accumulate in the sludge mass (primary or secondary) formed at the treatment plant. If the final sludge produced at the treatment plant contains PTMEs exceeding specified limits, then restrictions are placed on the final disposal of the waste sludge.

4.14.4.7 Wastewater Characterization for Plant Wide Modeling Modeling the AS system does not require the carbon composition (total organic carbon, TOC) of the five wastewater organic groups (VFA, FBSO, USO, BPO, and UPO) to be known. Nearly all of the CO2 gas generated by the bioprocesses is stripped from the water via mixing and aeration devices (So¨temann et al., 2005a). It is sufficient to know the COD, TKN, and TP concentrations fractionated into the five organic groups as described in Sections 4.14.4.2–4.4. However, to model the anaerobic digester (AD), the TOC of at least the biodegradable organic groups (VFA, FBSO, and BPO) is also required because the CO2 gas that escapes together with the methane gas establishes the CO2 partial pressure in the AD head space, which with H2CO3 alkalinity (dissolved CO2) establishes the digester pH (So¨temann et al., 2005b, 2005c). So, for the plant-wide models which link ASM1 (Henze et al., 1987) for AS systems and ADM1 (Batstone et al., 2002) for the AD, a compound transformer is interposed between the ASM1 and ADM1 models, which converts the compounds exiting the AS system to the form required for the input to the AD system, while maintaining COD, N and P mass balances. This conversion also adds a carbon composition to the AD input compounds (Volcke et al., 2006). In a different approach, Ekama (2009) established a stoichiometric composition including carbon for the five organic groups in the influent wastewater. The generic composition stoichiometry is in the CxHyOzNaPb form where different x, y, z, a, and b values apply to the five organic groups VFA, FBSO, USO, BPO, and UPO, the first three in their dissolved form and the last two in their settleable and nonsettleable forms. Five unknowns (x, y, z, a, and b) require five facts to determine them. The five facts are the four mass ratios (fcv – (gCOD/g), fC

–(gC/g), fN –(gN/g), and fP – (gP/g)) and the mass balance (fC þ fH þ fO þ fN þ fP ¼ 1) in which the COD replaces fO (gO/ g) and the mass balance replaces fH (gH/g) (Volcke et al., 2006; Ekama, 2009). Therefore, to determine the x, y, z, a, and b values, four ratios need to be determined, viz., COD, TOC, OrgN, and OrgP mass ratios (fcv, fC, fN, and fP) where mass is the VSS for the particulate organics (settleable and nonsettleable) and mass for the dissolved organics. Three of these ratios (fcv, fN, and fP) for the USO and UPO have been in use in AS models already for a long time and were presented in Sections 4.14.4.2–4.4. Also, it was accepted that for the AS system fcv, fN, and fP ratios for the UPO are 1.48 mgCOD/mgVSS, 0.10 mgN/mgVSS, and 0.025 (or 0.03) mgP/mgVSS, respectively, and the same ratios have been accepted for the AS OHO and PAO biomass (without polyP), XBH, XBG, and their endogenous residue, XEH and XEG. Similarly, the N/COD and P/COD ratios of the USO were defined in Sections 4.14.4.2– 4.4. Adding TOC fractionation in exactly the same way as the COD fractionation (Figure 3) and applying the fractionation principles set out above, the fcv, fC, fN, and fP ratios can be determined for all the wastewater organic groups, remembering that the composition of the raw wastewater BPO (settleable þ nonsettleable) is different to that of the settled wastewater BPO (nonsettleable only), due to the removal of the settleable BPO and UPO in the primary sludge (PS). Here, it is interesting to note that the primary settling tanks (PSTs) remove a far greater proportion of UPO (more than twothirds) than BPO (o1/3rd) (Wentzel et al., 2006). This is fortuitous for the AS system because it significantly reduces the UPO, which requires reactor volume (Section 4.14.9.3) and retains a high proportion of BPO, which is required for denitrification. Not all ratios of all the organic groups can be measured, for example, while it is possible to measure the TOC/COD (fC/fcv), OrgN/COD (fN/fcv), and OrgP/COD (fP/fcv) ratios of the USO on filtered WWTP effluents, it is not possible to measure a representative mass of these organics, so one of the four required ratios needs to be guessed – Ekama (2009) used fcv ¼ 1.42 mgCOD mg1). The ratios of the settleable BPO and UPO can be measured in long retention time ADs fed PS, for which the effluent particulate organics comprise mostly UPO (and a very small proportion of AD biomass) and the influent particulate organics comprise the settleable BPO and UPO (Ekama et al., 2006a). An important aspect of this work was determining whether or not wastewater UPO (Supi) and OHO and PAO endogenous residue (XEH and XEG) remained unbiodegradable in the AD. From measured UPO fractions (fS’up) in raw and settled wastewater, the unbiodegradable particulate COD fraction of PS (fSPS’up) can be calculated by mass balance around the PST. In fact, the COD, TKN, and TP concentrations of the PS can be fractionated into the five organic groups represented in the same block diagrams as for raw and settled wastewater (Figures 3–5) by applying the COD, N, P, and VSS (and ISS) mass balances around PST (Wentzel et al., 2006;, So¨temann et al., 2006; Ekama et al., 2006b). The unbiodegradable COD fraction of primary sludge (fSPS’up) so determined matched closely the unbiodegradable COD fraction measured in ADs fed PS (Wentzel et al., 2006, Harding et al., 2009). The same was found for WAS – the unbiodegradable

Biological Nutrient Removal

COD fraction of the WAS calculated from the steady-state AS model (Section 4.14.9.3) matched closely the unbiodegradable fraction measured in long retention time ADs fed the WAS (Ekama et al., 2006b, Harding et al., 2009). So it could be accepted that the unbiodegradable organics, as measured in the AS system (influent UPO and endogenous residue of each of the OHOs and PAOs), remain unbiodegradable in the AD, which results in these organics passing through the WWTP unchanged. Poinapen and Ekama (2010) applied the stoichiometric organics composition approach for characterizing the BPO in PS to biological sulfate reduction (BSR). From stoichiometric modeling of BSR, they found that most organics, including the BPO in PS, are carbon deficient for BSR in that they can donate more electrons for sulfate reduction than supply carbon for the alkalinity increase in this requires. So all of the COD and C of the utilized BPO (and the very low soluble organics) go to sulfide and H2CO3* alkalinity (dissolved CO2), respectively (and very little to AD biomass). Therefore, PS BPO carbon composition could be determined from the H2CO3* alkalinity generated in the BSR AD. With the stoichiometric composition of the five organic groups known (most measured and some estimated), the influent wastewater characteristics (dissolved, nonsettleable, and settleable) COD, TOC, TKN, TP, and VSS concentrations before (raw) and after (settled) primary settling and of the PS are defined in their block diagram structure by the CxHyOzNaPb composition of the five organics groups (VFA, FBSO, USO, BPO, and UPO) and the FSA and OP concentrations. This allows plant wide stoichiometric modeling with both steady-state (Ekama, 2009) and dynamic kinetic models (Brouckaert et al., 2010), which includes compound products that do not have COD (e.g., CO2) and the proton (Hþ) balance, both of which affect the mixed weak acid/base systems (inorganic carbon, ammonia, VFA, phosphate, and sulfide) that establish the pH in which the bioprocesses occur. Wastewater characterization including stoichiometric CHONP composition therefore opens the way to include, in these models, the three-phase (aqueous, gas, and solid) mixed weak acid/base physical chemistry processes to predict aqueousphase concentrations and pH, gas-phase partial pressure of gases, and mineral precipitation, all very important in anaerobic digestion.

4.14.5.1 Biological Growth Behavior 4.14.5.1.1 Stoichiometry and kinetics To transform a conceptual model into a quantitative kinetic model requires both stoichiometry and kinetics. Stoichiometry gives the quantitative relationships between the various compounds of the conceptual model. For example, for aerobic OHO growth on a simple organic compound such as glucose, the reactants (inputs) are glucose (substrate or electron donor) and oxygen (electron acceptor) and the products are more biomass, water, and CO2. From the bioenergetics of such an aerobic growth process, it can be shown that the mass of organisms formed (anabolism) and oxygen utilized (catabolism) were in a fixed proportion of the mass of organics (substrate) utilized. This fixed proportion is governed by the yield coefficient (YH) and the COD/VSS ratio (fcv) of the organisms. Hence, YH and fcv are stoichiometric constants because they define quantitatively the relationship between the compounds involved in the biological processes (reactions), namely

ðmgVSS l1 Þ

DXBH ¼ YH DSb

DO ¼ ð1  f cv YH Þ DSb

ðmgO l1 Þ

ð40Þ ð41Þ

Although stoichiometry gives the quantitative relationships between the different compounds involved in the biological processes, kinetics considers the rate at which these biological processes take place. Stoichiometric relationships can be changed to kinetic relationships by including the time interval over which changes in the compounds take place, for example, from Equations (40) and (41),

DXBH DSb ¼ YH Dt Dt

ðmgVSS l1 h1 Þ

DO DSb ¼ ð1  f cv YH Þ Dt Dt

ðmgO l1 h1 Þ

ð42Þ ð43Þ

Equations (42) and (43) are kinetic relationships linking the rates of active OHO (XBH) and associated oxygen utilization (O) to the rate of substrate (Sb) utilization via the stoichiometric constants YH and fcv. Mathematically, as the time interval Dt gets infinitesimally small, the finite time interval Dt becomes the derivative dt, viz.,

dXBH dSb ¼ YH dt dt

ðmgVSS l1 h1 Þ

dO dSb ¼ OUR ¼ ð1  f cv YH Þ dt dt

4.14.5 Modeling Biological Behavior To model biological behavior of organisms in WWTPs requires a conceptual model of their behavior in the presence and absence of an external substrate. The former addresses the utilization of biodegradable substrate via anabolism (synthesis of cell mass) and catabolism (generation of energy) by the relevant organism group, such as to the utilization of readily biodegradable organics (RBOs) by the OHOs, or the utilization of FSA by the ANOs. The behavior of organisms in the absence of an external substrate addresses the decline in organism numbers or mass under these conditions. The conceptual models of organism growth and decline on which the AS kinetic models are built are presented below.

429

ðmgO l1 h1 Þ

ð44Þ ð45Þ

Note from Equations (40)–(45) that the COD (or e) balance is conserved because



dSb dXBH dO ¼0 þ f cv ðÞ  dt dt dt

ðmgCOD l1 h1 Þ

ð46Þ

In Equation (46), both the substrate and oxygen decrease and the active organism concentration increases as would happen in an aerobic batch test in which soluble organics and OHOs are mixed in aerated water. In the biological growth process, in which the reactants (inputs) are organics and oxygen and the products (outputs) are organisms, carbon dioxide and water,

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the electrons (COD) in the organics are conserved in organisms formed and water produced. However, because it is not possible to measure the concentration of the water produced, the oxygen utilized is measured instead. Oxygen utilization (OU) measurement: (1) is permissible due to the proportionality between oxygen utilized and water produced (4e þ 4Hþ þ O2-2H2O), (2) is relatively simple in AS reactors (e.g., Randall et al. 1991), and (3) is in conformity with the COD test. Because oxygen is a reactant in the growth process (i.e., a decreasing input) and replaces the water product (i.e., an increasing output), the sign of the oxygen utilization term in the COD mass-balance Equation (46) is changed to positive by multiplying it by a negative sign. This is done in all mathematical models which are formulated on the basis of COD balance (such as ASM1, UCTOLD, ASM2, and UCTPHO) to ensure that the COD in fact balances, that is, over a welldefined time interval the COD of the organisms formed (fcv DXBH) plus the oxygen utilized (DO) must be equal to the COD utilized (DSb). When an OHO population under aerobic conditions is brought into contact with biodegradable organics of soluble readily biodegradable (RBSO) and particulate slowly biodegradable (BPO) forms (see Figure 3), their response may be described qualitatively (conceptually) as follows: 1. The soluble (RBOs) passes directly through the cell wall and is metabolized (utilized) at a high rate. 2. The particulate slowly biodegradable organics (BPO) is enmeshed or entrapped in the sludge mass and some of it is adsorbed onto the active organisms. These enmeshment and adsorption reactions are rapid and effectively remove most of the particulate and colloidal organics from the wastewater. The adsorbed organics are broken down via a biologically assisted hydrolysis process to smaller and simpler organics by extracellular enzymes and the simpler organics are transferred through the cell wall and metabolized in the same manner as the RBOs in (1) above. The rate of the hydrolytic enzymatic breakdown is relatively slow and is the limiting (slowest) rate in the overall growth process on slowly biodegradable organics, only about onetenth of the rate for the RBOs.

4.14.5.1.2 Monod growth kinetics for utilization of RBSO The utilization rate of RBSO is described qualitatively via Monod kinetics. Monod kinetics is described in some detail below because it is a very useful mathematical expression in biological process modeling. Not only is it applied directly to model the utilization of all soluble substrates, such as utilization of RBSO by OHOs and FSA by ANOs (nitrification), its form is used to model the hydrolysis/utilization of BPO and also as swithing functions for the progressive phasing-in and -out of biological kinetic processes as environmental conditions in reactor(s) change from aerobic (dissolved oxygen, DO present) to anoxic (DO absent) conditions and vice versa. Monod (1950) defined the term specific growth rate (m) as the increase in organism concentration per unit time (dXBH/ dt) per average concentration of organisms present. Mathematically, this is equivalent to logarithmic growth and is

Monod specific growth rate curve 3.0 Specific growth rate (d−1)

430

Maximum specific growth rate = UHm = 2.5 d−1

2.5 2.0 1.5

Half-saturation coefficient Ks = Substrate concentration at which UH = UHm/2 = 10 mg COD l−1

1.0 0.5 0.0 0

10 20 30 40 50 Substrate concentration (mgCOD l−1)

60

Figure 6 Monod specific growth rate (mH, d1) vs. soluble substrate concentration (Sbs, mgCOD l1) curve.

expressed as

dXBH 1 ¼ mH dt XBH

ðmgOHOVSS=ðmgOHOVSS dÞÞ

ð47Þ

Further, Monod conducted batch experiments in which the soluble biodegradable substrate concentration of defined substrates such as glucose remained essentially constant and, in different chemostat (flow through) reactor tests, observed how the specific growth rate (mH) of certain organism species in pure culture changed at different defined soluble organic substrate concentrations (Sbs). The form of the relationship observed by him between mH and Sbs is shown in Figure 6 and can be expressed mathematically as

dXBH 1 mHm Sbs ¼ mH ¼ dt XBH KS þ Sbs

ð48Þ

where mHm is the maximum specific growth rate (d1) and KS the half-saturation coefficient, which is the concentration of the substrate at which the specific growth rate is half the maximum (mg l1) (Figure 6). Monod determined the mHm and KS constants for different organism species in pure culture growing on various soluble organic substrates and found that the mHm and KS values were different for different organism species–substrate type combinations. Despite this, the Monod equation (Equation (48)) was adopted into biological WWTP modeling in which not only the active OHO population is highly diverse but also the soluble biodegradable organics are innumerable. Because clearly not all the OHO species and soluble organic compounds could be modeled individually in biological WWTP models (level of organization too low), measures are adopted that lump together all the OHO species in a single surrogate organism group, that is, the active part of the volatile settleable solids (OHOVSSs) and the innumerable biodegradable organics into a single substrate, that is, the COD. Therefore, the units for the mHm and KS constants in the Monod equation as applied to modeling biological WWTPs are mgActiveOHOVSS/(mgActiveOHOVSS d) and mgCOD l1 respectively, as shown in Equation (48).

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From Figure 6 and Equation (48), there are two extremes of specific growth behavior described by Monod’s equation: (1) When the substrate concentration is very high in relation to KS so that KS contributes negligibly to the denominator (SbsdKS), changes in Sbs do not change mH which remains constant at mHm. Hence, the specific growth rate is zero order with respect to the substrate concentration, that is, (mH ¼ mHmS0bs ). (2) When the substrate concentration is very low in relation to KS so that Sbs contributes negligibly to the denominator (KSdSbs), changes in Sbs change the rate in proportion to Sbs. Hence, the specific growth rate is first order with respect to the substrate concentration (mH ¼ mHm/KSS1bs ). Monod’s equation (Equation (48)) therefore changes from first order to zero order with respect to the substrate concentration as the substrate concentration increases from zero to several times higher than the KS value. This characteristic is very useful in biological process modeling. First, it is a selfcontrolling kinetic rate; when the substrate concentration is high, the rate is a maximum and when the substrate concentration is zero the rate is zero. Second, the Monod term is a useful switching function if a biological process is required to operate only under aerobic conditions. To achieve this, the biological process rate equation is multiplied by the Monod term O/(KO þ O) where O is the dissolved oxygen (DO) concentration and KO is the sensitivity of the biological process rate to DO. If the DO concentration is high, then the process rate is zero order with respect to DO and process proceeds at its defined rate independent of the DO concentration. If the DO concentration is zero, the biological process rate also is zero as required for an exclusively aerobic process. If the DO concentration is less than KO, then the biological process will be less than half its maximum rate and dependent on the DO concentration and if the DO concentration is greater than KO, the biological process rate will be more than half its maximum rate. The KO value therefore controls the sensitivity of the biological process to DO; if KO is high, say 3 mg l1, then the biological process rate is lower than 75% of its maximum value up to a DO concentration of 9 mgO l1, which is the approximate saturation value for DO in freshwater aerated with air at 1 atm pressure (760 mmHg) and 20 1C; if the KO value is low (0.1 mgO l1), then the biological process rate reaches 95% of its maximum value at a DO concentration of 2 mgO l1. The above switching function can also be applied if an anoxic process needs to be switched on simultaneously as an associated aerobic process is switched off. To achieve this, the anoxic biological process rate is multiplied by 1  the above Monod term, that is, 1  O/ (KO þ O) ¼ KO/(KO þ O). Clearly, the Monod term is a relatively realistic and very convenient mathematical means whereby biological processes can be switched on or off in conformity with the conditions in the biological reactor and hence is extensively used in AS simulation models (e.g., ASM1 and ASM2). Substituting the Monod (equation Equation (48)) for the specific growth rate (mH, Equation (47)) yields the rate of growth of active OHOs as a function of substrate RBSO concentration, viz.,

dXBH mHm Sbs XBH ¼ dt KS þ Sbs

ðmgOHOVSS l1 h1 Þ

ð49Þ

dSbs mHm Sbs XBH ¼ dt YH KS þ Sbs

ðmgCOD l1 h1 Þ

dOs mHm Sbs XBH ¼ OURs ¼ ðÞ ð1  f cv YH Þ dt YH KS þ Sbs ðmgO l1 h1 Þ

431

ð50Þ

ð51Þ

where dOs/dt (or OURs) is the oxygen utilization rate (OUR) for OHO synthesis.

4.14.5.1.3 Active site surface kinetics for hydrolysis/ utilization of BPO The particulate slowly biodegradable organics (BPO, Sbp), comprising all the nondissolved organics, when mixed AS including active OHOs, are enmeshed into the sludge mass by a biologically assisted flocculation process. Because AS has such a strongly flocculent nature, this enmeshment process is efficient and rapid and effectively removes most of the particulate organics, both nonsettleable (colloidal) and settleable, from the wastewater liquid phase onto the AS solids phase (Senm). Therefore, if after a contact time as short as 1 h between the municipal wastewater particulate organics and AS, the sludge mass is allowed to settle, a COD removal of over 80–85% is achieved, the RBSO (Sbs) through rapid utilization and the BPO (Sbp) through rapid enmeshment and adsorption. This is in fact the principle on which the contact stabilization AS is based (Gujer et al., 1975a, 1975b; Alexander et al., 1980). Once enmeshed, some of the BPOs (Sbp) are adsorbed onto the active OHOs (XBH) in the AS. This adsorption process, which is also a physical one like the enmeshment, brings the active organisms into close contact with the particulate organics. The organisms have a finite capacity to absorb particulate organics. Conceptually, the adsorption process is modeled as an active site surface kinetic reaction approach in which particulate organics are adsorbed onto the organism mass until their active sites are all occupied, at which point the organisms are saturated with particulate organics (XS, mgVSS l1) (Dold et al., 1980). Once adsorbed, the particulate organics (XS) are broken down into small RBOs via a biologically assisted enzymatic hydrolysis process. The RBSO product of this hydrolysis process then enters the organism through the cell wall and is utilized by the same anabolic and catabolic growth processes as the RBSO of the wastewater. The adsorption rate is modeled as a first-order rate with respect to the enmeshed biodegradable COD concentration (Senm) and the active OHO concentration (XBH) and also includes a term that describes the degree of saturation of the adsorption sites on the organisms, viz., (fma  XS/XBH), where XS /XBH is the concentration ratio of adsorbed biodegradable organics (as VSS) and the active OHOs (also as VSS). When the adsorption sites are all full, the XS /XBH ratio equals fma and the adsorption rate is zero. In the UCTOLD dynamic simulation model (Dold et al., 1991), fma is assigned a value of 1 indicating that the active OHOs can adsorb their own mass of particulate organics (XS), viz.,

  dSenm XS ¼ Ka Senm XBH f ma  dt XBH

432

Biological Nutrient Removal

where Senm is the enmeshed particulate slowly biodegradable organics concentration (BPO, mgCOD l1), Ka the specific BPO adsorption rate [l/(mgVSS d)], XS the adsorbed particulate slowly biodegradable organics concentration (mgVSS l1), and fma the maximum adsorbed organics to active OHO concentration ratio (mgVSS/mgVSS). In the adsorption, hydrolysis, and utilization processes of BPO, the hydrolysis process is the limiting one because it has the slowest rate. This process therefore governs the overall specific growth rate of the active OHOs on BPOs. Unlike the soluble organics, the utilization rate of which is governed by its concentration in the liquid phase (see Equation (50)), the rate of hydrolysis of adsorbed particulate organics (XS) is governed by the degree of saturation of the active adsorption sites XS/XBH. From a comparison of predicted and experimental results, Dold et al. (1980) found that (1) the Monodtype equation best described the relationship between the specific hydrolysis rate and the XS/XBH ratio; (2) the power on the XS/XBH ratio, which defines the surface to volume ratio of the material on which the reaction takes place, was 1, that is, the active OHOs could be viewed simply as a planer surface rather than a spherical one (for which n ¼ 2/3); and (3) the soluble RBSO and particulate BPO are hydrolyzed/utilized simultaneously by the active OHOs. Because the specific OHO growth rate on the BPO hydrolysis product is as fast as the specific hydrolysis rate, the former is equal to the latter and is expressed by

Kmp ðXS =XBH Þ dXBH 1 ¼ þYH ½Ksp þ ðXS =XBH Þ dt XBH

ð52Þ

where Kmp is the maximum specific hydrolysis rate mgCOD/ (mgOHOVSS d) and Ksp the half-saturation coefficient for hydrolysis of BPO (mg COD/mg COD). The associated OUR is directly proportional to the specific hydrolysis/utilization rate of BPO via the catabolic stoichiometric factor (1  fcvYH) (see Equations (43) and (44)). It is mainly in the hydrolysis/utilization of the BPO that the UCTOLD and IWA ASM No1 models differ (Dold and Marais, 1986, Hu et al., 2003). The former includes instantaneous enmeshment (Senm), a defined kinetic adsorption process (Senm-XS), and the BPO (XS) specific hydrolysis/ utilization rate (XS-XBH) is governed by the XS/XBH ratio. ASM1 (and ASM2) includes only instantaneous enmeshment, which directly becomes adsorbed BPO (Senm ¼ XS), and the specific hydrolysis rate is governed by the ratio Senm/XBH. Furthermore, in ASM1, the BPO hydrolysis product (RBSO, Sbs) is not directly utilized in a combined hydrolysis/utilization process as in the UCTOLD model. Instead, the BPO hydrolysis/products are released to the bulk liquid and utilized as RBSO taken up from the liquid phase like the wastewater RBSO via the Monod kinetics Equation (50). Therefore, in ASM1, only RBSO is utilized for OHO growth and associated oxygen utilization. Further details on the biological growth kinetics of the UCTOLD and ASM No1 simulation models can be found in Dold et al. (1980, 1991) and Henze et al. (1987), respectively, and are compared by Dold and Marais (1986) and Hu et al. (2003).

4.14.5.2 Organism Decline 4.14.5.2.1 Endogenous respiration Experimentally, it has been observed that after all the biodegradable COD, whether of a soluble or particulate form, added to an aerated batch test on real AS has been utilized, oxygen continues to be utilized and the VSS concentration decreases for 20 days or longer. There are various conceptual models which seek to describe this simultaneous oxygen utilization and VSSs decrease in the absence of an externally added substrate. The most widely accepted is called the endogenous respiration and mass loss model. This model dates back to the earliest empirical AS models and is used even today in the dynamic kinetic models for the ANOs and PAOs. The problem with this model was not so much its concept and mathematics but the determination of the kinetic and stoichiometric constants associated with it. Once the determination of endogenous respiration rate independently of the yield coefficient (YH) was established via batch tests on AS harvested from continuous steady-state systems (Marais and Ekama, 1976; van Haandel et al., 1998), this model is acceptable and so is retained for the steady-state OHO AS model. In the endogenous respiration conceptual model, in the absence of an externally available substrate, the organism utilizes the organics (COD) of its own cell mass to generate energy via catabolism for essential cell functions. The utilization of the organism organics in which the e of these organics are passed to oxygen accounts for the continued simultaneous VSS decrease and utilization of oxygen. However, in order to quantitatively match the observed changes in VSS reduction and oxygen utilization, it appears that associated with this endogenous respiration process is the generation of unbiodegradable endogenous VSS residue. So mathematically, organism endogenous mass loss/respiration is modeled as follows: Over a defined period of time, dt, the decrease in active organism concentration (XBH) is proportional to the concentration of organisms present, viz.,

dXBH ¼ bH XBH dt

ðmgOHOVSS l1 h1 Þ

ð53Þ

where bH is the endogenous respiration rate (d1). However, the decrease in active organism VSS concentration is not all biodegradable organics. A fraction, fEH, is unbiodegradable and accumulates as endogenous residue denoted XEH. Hence, the endogenous residue accumulation rate is given by

dXEH ¼ þf EH bH XBH dt

ðmgEVSS l1 h1 Þ

ð54Þ

where fEH is the unbiodegradable fraction of the OHOs. The biodegradable part of the active organism concentration decrease is therefore (1  fEH)bHXBH mgVSS l1 h1. These biodegradable organics the organisms use catabolically to generate energy for maintenance of essential cell functions. All the electrons (e) of these biodegradable organics are therefore passed to oxygen to generate the energy. As (1  fEH)bHXBH is the rate of utilization of biodegradable

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organism organics in VSS units, to express this in oxygen or COD units, it needs to be multiplied by the COD/VSS ratio fcv of the OHOs. Because these organics are used catabolically and none is conserved in new organism mass, all the electrons of the biodegradable organism organics are passed to oxygen. Hence, the utilization rate of organism organics is the OUR for endogenous respiration, viz.,

dOe ¼ OURe ¼ ðÞf cv ð1  f EH ÞbH XBH dt

ðmgO l1 h1 Þ ð55Þ

where dOe/dt (or OURe) is the OUR for endogenous respiration (mgO l1 h1) and fcv the COD/VSS ratio of the organism organics (mgCOD/mgVSS). Again because oxygen is decreasing (utilization to form water), it is negative but as it is a reactant and not a product in the biological process, it is given an additional negative sign to make the overall OUR for endogenous respiration þ ve to maintain the COD balance. The equations (53)–(55) satisfactorily model the changes in VSS concentration and OUR with time in the absence of an externally added substrate. From many aerobic batch tests on AS harvested from continuous flow laboratory scale systems at 8, 14, and 20 1C, and accepting an endogenous residue fraction fEH ¼ 0.20 from McKinney and Symons (1964), Marais and Ekama (1976) found that the bH rate (1) did not change the sludge age, (2) was 0.24/d at 20 1C and (3) was mildly temperature sensitive between 8 and 20 1C with an Ahrrenius constant ybH ¼ 1.029, viz.,

bHT ¼ bH20 ð1:029ÞðT20Þ

ðd1 Þ

ð56Þ

This endogenous respiration rate was subsequently confirmed by van Haandel et al. (1998) and found to be slightly more temperature sensitive between 20 and 30 1C with a ybH ¼ 1.04.

4.14.5.2.2 Death regeneration The second conceptual model for describing the continuous oxygen utilization and VSS reduction with time in the absence of an externally added substrate is called death regeneration. In this model the active organisms die and lyse (release) all their organic material to the bulk liquid. The unbiodegradable part of this released organic material, which is all of a particulate nature, remains as endogenous residue or organism ‘‘skin and bones’’. The biodegradable part is utilized by the remaining active organisms via the identical biological growth processes of anabolism and catabolism as influent wastewater organics to form new active organism mass. The oxygen utilized is now that required for the synthesis of new organism mass from the biodegradable organics of the organisms that have died. The death regeneration model concept was incorporated into the UCTOLD AS model by Dold et al. (1980). From an evaluation of this model it was found that if it was assumed that all the biodegradable organics are utilized, acceptable for the steady-state model, then there was no difference between this model and the endogenous respiration model. However, to reproduce the observed experimental results, the death rate 0 ) (b’H) and unbiodegradable fraction of the organisms (fEH needed to be 0.62/d and 0.08, respectively. The similarity of

433

the two conceptual models for aerobic steady-state conditions is demonstrated in Table 5. Incorporation of the death regeneration model into the dynamic kinetic model, in which the utilization of slowly BPO is modeled with different kinetic rates than the RBSO and not all of these fractions are necessarily completely utilized, it was found that for aerobic conditions the two models yielded virtually identical results provided most of the COD was utilized. To achieve similarity it was necessary to accept that the biodegradable COD released by the dead organisms was the same as influent BPO and that no soluble organics of the biodegradable or unbiodegradable form were released by the dead organisms. Although for aerobic systems there was little to choose between the endogenous respiration and death regeneration models because both yielded equally close predictions, the latter is conceptually more consistent because it does away with the young fat organism and old thin organism outcome of the endogenous respiration model. With death regeneration, 99% of the active organisms are replaced in 7.4 days so there is a continuous generation of new organisms growing on the biodegradable organics of the dead ones. Also, an unbiodegradable fraction of 0.08 is a more reasonable value for microorganisms than the high 0.2 value for endogenous respiration model. Although for aerobic conditions and sludge ages longer than about 3 days (i.e., 495% biodegradable COD utilization) the two conceptual models gave virtually identical results (Dold et al., 1980), the advantage of the death regeneration model became evident when modeling anoxic–aerobic conditions. First, no change in organism behavior, and therefore kinetic equations, was needed in the model when oxygen and/ or nitrate became limiting. Under these conditions, the growth processes (i.e., RBSO and BPO utilization) cease but the death processes continue, leading to an accumulation of enmeshed and adsorbed BPO from the dead organisms until oxygen and/or nitrate again become available. Because this led to improved simulations for anoxic–aerobic systems (Van Haandel et al., 1981), the death regeneration model was incorporated into the dynamic kinetic model (e.g., UCTOLD and ASM1). However, because it is assumed in the steady-state model that all the biodegradable organics are utilized, the endogenous respiration model is retained for the steady-state model as it leads to much simpler process equations (Marais and Ekama, 1976; WRC, 1984; Henze et al., 2008) which are presented in this chapter.

4.14.6 AS System Constraints Basically, all aerobic biological treatment systems operate on the same principles, that is, trickling filters, aerated lagoons, contact stabilization, extended aeration, etc., differ only in the conditions under which the biological reactions are constrained to operate, called system constraints. The AS system comprises the flow regime in the reactor, the size and shape, number and configuration of the reactors, recycle flow, influent flow, and other features either incorporated deliberately or present inadvertently or unavoidably. Whereas the response of the organisms is in accordance with their nature, that is, biological process behavior, that of the system is governed by a

434

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Table 5 Comparison of the endogenous respiration (End-Res) and death regeneration (Dth-Reg) models for describing organism behavior in the absence of an externally supplied substrate Parameter

End-Res

Dth-Reg

Units

Active organism mass loss rate (bH) Unbiodegradable fraction of organisms (fEH) COD/VSS ratio of active organisms (fcv) At the start of 1 day Hence COD at start ¼ fcv100 Active VSS lost in 1 day ¼ bH100 Active organisms remaining ¼ (1  bH)100 COD of active organisms lost ¼ fcvbH100 Endogenous residue formed ¼ fEHbH100 COD of endogenous residue ¼ fcvfEHbH100 Biodeg. organics released ¼ (1  fEH)bH100 COD of biodeg. org released ¼ fcv(1  fEH)bH100 New active organisms formed ¼ YH 84.4 Oxygen utilized ¼ (1  fcvYH)fcv(1  fEH)bH100 Active mass at the end of 1 day

0.24 0.20 1.48 100 148 24 76 35.5 4.8 7.1 19.2 28.4 0 28.4a 76

0.62 0.08 1.48 100 148 62 38 91.8 4.8 7.1 57.0 84.4 38.0 28.4 76

mgActiveVSS/mgActiveVSS/d mgUnbioVSS/mgActiveVSS mgCOD/mgVSS mgVSS/l active organisms mgCOD/l active organisms mgVSS/l active organisms mgVSS/l active organisms mgCOD/l active organisms mgVSS/l endogenous residue mgCOD/l endogenous residue mgVSS/l biodeg. organics mgCOD/l biodeg. organics mgVSS/l active organisms MgO/l mgVSS/l active organisms

Endogenous respiration model

Death regeneration model

Endogenous residue formed 4.8%

Endogenous residue formed 4.8%

Oxygen utilized 19.2%

New active VSS formed 38%

Active VSS remaining 76%

Active VSS remaining 38%

Active VSS after 1 day 76%

Active VSS after 1 day 76%

Unbiopart of XBH = End residue Active VSS loss d−1 bHXBH

Biodeg part (1−fEH)b HXBH Active VSS remaining (1−bH)XBH

Oxygen utilized 19.2%

Oxygen demand for synthesis ′ )b′HXBH (1−fcvYH)fcv(1−fEH

fEHbHXBH

Oxygen demand for end resp = COD biodeg XBH fcv(1−fEH)b HXBH

New active VSS synthesized ′ )b′HXBH YHfcv(1−fEH

Biodeg COD of dead active VSS ′ )b′HXBH fcv(1−fEH

Net active VSS loss Active VSS gain

Active VSS loss d−1 b′HXBH

Active VSS regrown Active VSS remaining (1−b′H)XBH

Unbio VSS of XBH loss = end residue ′ b′HXBH fEH

a

Oxygen utilized ¼ COD of biodegradable organics released.

combination of the organism behavior and the physical features that define the system, that is, the environmental conditions or system constraints under which the biological processes are constrained to operate.

4.14.6.1 Mixing Regimes In the AS system, the mixing regime in the reactor and the sludge return are part of the system constraints and therefore

Biological Nutrient Removal

influence the response of the system – hence, consideration must be given to reactor mixing regimes. There are two extremes of mixing: completely mixed Figure 7(a) and plugflow Figure 7(b). In the completely mixed regime, the influent is instantaneously and thoroughly mixed (theoretically) with the reactor contents. Hence, the effluent flow from the reactor has the same compound concentrations as the reactor contents. The reactor effluent flow passes to a settling tank; the overflow from the tank is the treated waste stream; the underflow is concentrated sludge and is recycled back to the reactor. In the completely mixed system, the rate of return of the underflow has no effect on the biological reactor except if an undue sludge buildup occurs in the settling tank. The shape of the reactor is approximately square or circular in plan, and mixing is usually by mechanical aerators or diffused air bubble aeration. Examples are extended aeration plants, aerated lagoons, Pasveer ditches, and singlereactor completely mixed AS plants. In a plugflow regime, the reactor usually is a long-channeltype basin. The influent is introduced at one end of the channel, flows along the channel axis, and is mixed by air spargers set along one side of the channel or horizontal shaft surface aerators. Theoretically, each volume element of liquid along the axis is assumed to remain unmixed with the elements leading and following. Discharge to the settling tank takes place at the end of the channel. To inoculate the influent waste flow with organisms, the underflow from the settling tank is returned to the influent end of the channel. This creates an intermediate flow regime deviating from true plugflow conditions depending on the magnitude of the recycled underflow. Conventional AS plants are of the intermediate flow regime type with sludge return recycle ratios varying from 0.25 to 3 times the average influent flow rate. If the recycle ratio is very high, the mixing regime approaches that of completely mixed. Intermediate flow regimes are also achieved by having two or more completely mixed reactors in series, or by step

Aeration

Influent

Aerobic reactor

Waste flow Secondary settling tank Effluent

Sludge recycle

(a)

Aeration Influent

Waste flow Secondary settling tank

Aerobic reactor

435

aeration. In the latter, the influent is fed at a series of points along the axis of the plugflow-type reactor. Both configurations require, for inoculation purposes, recycling of the settled sludge from the settling tank(s) to the start of the channel reactor. The mean kinetic response of an AS system (i.e., sludge mass, daily sludge production, daily oxygen demand, and effluent organics concentration) is adequately, indeed accurately, given by assuming that the system is completely mixed and the influent flow and load are constant. This allows the reactor volume, the mass of sludge wasted daily, and average daily oxygen utilization rate to be determined by relatively simple formulations. Peak OURs which arise under cyclic flow and load conditions can be estimated subsequently quite accurately by applying a factor to the average OUR. These factors have been developed from simulation studies with the simulation models on aerobic and anoxic–aerobic systems operated under cyclic and under constant flow and load conditions (see Section 4.14.27.2).

4.14.6.2 Solids Retention Time or Sludge Age In the schematic diagrams for the AS system (Figure 7), the waste (or surplus) sludge is abstracted directly from the biological reactor. The common practice is that the waste sludge is abstracted from the secondary settling tank underflow. Sludge abstraction directly from the reactor leads to a method of control of the sludge age, called the hydraulic control of sludge age, which has significant advantages for system control compared to abstracting wastage via the underflow. The sludge age, Rs in days, is defined by

Rs ¼

Mass of sludge in reactor Mass of sludge waste per day

ðdaysÞ

ð57Þ

By abstracting the sludge directly from the reactor, the sludge concentrations in the waste flow and biological reactor are the same. If a sludge age of, say, 10 days is required, one-tenth of the volume of the reactor is wasted every day. This can be achieved by a constant waste flow rate, Qw (l d1), where Qw is the volume of sludge to be wasted daily. Hence,

Rs ¼

X Vp Vp ¼ X Qw Qw

ðdaysÞ

ð58Þ

where Vp is the volume of the biological reactor (l) and Qw the waste flow rate from reactor (l d1). Equation (58) assumes that the mass of sludge in the secondary settling tanks is negligible relative to that in the biological reactor. This assumption is reasonable when the system is operated at relatively high recycle ratios (B1:1) and the sludge age is longer than about 3 days (see Section 4.14.14).

4.14.6.3 Nominal Hydraulic Retention Time Effluent

Sludge recycle (b)

Figure 7 (a) Activated sludge system with a completely mixed reactor mixing regime and (b) plugflow/intermediate reactor mixing regime.

In AS theory, the volume of the process per unit of volume of influent flow is known as the nominal hydraulic retention time (HRT), that is,

Rhn ¼

Vp Qi

ðdaysÞ

ð59aÞ

436

Biological Nutrient Removal

where Rhn is the average nominal hydraulic retention time (days) and Qi the daily average influent flow rate (l d1). When the sludge return flow from the secondary settling tank (Qs) and any other mixed liquor recycle flow entering the reactor (Qa) are included, the retention time is called the actual hydraulic retention time (Rha), viz.,

Rha ¼

Vp Rhn ¼ Qi þ Qs þ Qa 1 þ s þ a

ðdaysÞ

Mass-balance boundary Influent Q i, S bsi

4.14.6.4 Connection between Sludge Age and Hydraulic Retention Time From the above definitions, it can be seen that there are two parameters that relate to time in the system: (1) the sludge age (Rs), which gives the length of time the particulate material remains in the reactor and (2) the nominal hydraulic retention time (Rhn), which gives the length of time the liquid and dissolved material remains in the reactor. In AS systems which do not have solid–liquid separation with membranes or secondary settling tanks (SSTs), such as aerated lagoons, the sludge age and nominal hydraulic retention time are equal, that is, the liquid/dissolved material and the solids/particulate material remain in the reactor for the same length of time. When solid–liquid separation is included, then the liquid and solid retention times are separated and Rs4Rhn. However, long sludge ages (Rs) lead to large sludge masses in the reactor, which, in turn, lead to large reactor volumes (Vp). Therefore, even with solid–liquid separation, as Rs gets longer, so also does Rhn. This link between Rs and Rhn is neither proportional nor linear and depends on (1) the wastewater organic (COD or BOD5) concentration and (2) the reactor suspended solids concentration (TSS). For biological nutrient removal AS systems, the sludge age is around 10–25 days and the nominal hydraulic retention time around 10–24 h.

4.14.7 Model Development – Completely Mixed Aerobic System The system equations are derived by doing mass balances over the AS system on the compounds identified as important for the model. The mass balance considers the rates of increase and decrease in the mass of a particular compound due to this compound’s inflow to and outflow from the system and its production or degradation via the biological processes in the reactor. To define the system, the inflows to it and outflows from it for the mass balance, a boundary needs to be set. The elements within the boundary constitutes the system. The system boundary can be drawn around the reactor only because the biological processes take place in the reactor. However, when this is done, the recycle flow constitutes an inflow to the system and complicates the final steady-state model equations considerably. Therefore, in the interests of the simplicity, the boundary for the mass balances includes both the biological reactor and the settling tank, and only one

Waste flow Qw, XBH, XEH, Sbs

Vp, XBH XEH, S bs OURc QR

ð59bÞ

where Rha is the actual hydraulic retention time (days), s the sludge underflow recycle ratio (Qs/Qi), and a the mixed liquor recycle ratio (Qa/Qi).

Oxygen in Oi

Effluent Qe = Q i − Q w Sbse

Figure 8 Completely mixed activated sludge system showing system boundary (dotted lines) for mass-balance equations.

inflow (influent, Qi) and two outflows (effluent, Qe, and waste, Qw) need to be considered (Figure 8). In the mass balance, it is assumed that (1) no biological activity takes place in the settling tank, thereby confining biological activity to the reactor only and (2) the water mass flow into the system (entering over the boundary) is equal to the water mass flow out of the system (exiting over the boundary). Water losses due to, for example, evaporation or gains due to, for example, biological activity (from oxygen being utilized as terminal electron acceptor) are considered negligible (o1%) in terms of the estimated accuracy of the model (B5%). Hence, the generic compound mass balance over a short time interval, Dt, is given by

3 3 2 3 2 Mass flow Mass flow Mass 7 7 6 7 6 6 4 change 5 ¼ 4 into 5  4 out of 5 system system in system 3 3 2 2 Mass loss Mass gain 7 7 6 6 by þ4 by 5 54 bioprocess bioprocess 2

ð60Þ

This mass-balance equation is applied to each compound (or system variable) of interest in the model. For the steady-state model for organic material removal only, these are 1. 2. 3. 4. 5. 6.

UPOs (XI) (from Supi in the influent), unbiodegradable soluble organics COD (Sus), active OHO organisms (XBH), endogenous residue (XEH), biodegradable COD (Sb), and oxygen (O).

4.14.7.1 Building Up the Model in Stages To gain greater insight into the behavior of the various components in the AS system, the model is built up in stages by considering first the behavior of the influent particulate (Supi) and soluble (Susi) unbiodegradable organics in the system and then that of the biodegradable organics. The influent UPOs (Supi in COD units or Xli in VSS units) cannot be measured directly in the wastewater because they are mixed with the BPOs; likewise, the influent unbiodegradable soluble organics are mixed with the biodegradable soluble organics. These unbiodegradable constituents can only be determined by treating the wastewater in a long sludge age mostly aerobic AS system to ensure that virtually all the biodegradable organics are

Biological Nutrient Removal

utilized. The proportion of the influent particulate and soluble organics that are not degraded are then accepted to be the influent unbiodegradable particulate and soluble COD concentrations Supi and Susi, respectively. To see how these two unbiodegradable constituents behave in the AS system, the stoichiometric and steady-state equations for these two constituents are derived and discussed first. Thereafter, the biodegradable organics of the wastewater are considered. The complete steady-state model for the AS system for all four wastewater organic fractions is then developed by adding the component equations of the four different wastewater organic constituents. This approach also provides insight into the only reliable method developed to date for determining the UPO concentration (Supi) or fraction (fS’up) (Ekama et al., 1986; Wentzel et al., 1999).

437

flows, (flux) viz.,

MXI ¼ Vp XI ¼ Mass XI in reactor FXIi ¼ Qi XIi ¼ Flux XI into reactor

ðmgVSSÞ

ð64Þ

ðmgVSS d1 Þ

ð65Þ

where the prefix M denotes mass and F denotes flux of the compound that follows it. Note that with this nomenclature all compound concentrations in the reactor are multiplied by the reactor volume to transform these to mass in the reactor (mg) and compound concentrations in the influent (Qi), waste (Qw), and effluent (Qe) flows are multiplied by their respective flows to transform these to fluxes (mg d1). Substituting Vp/Qi for Rhn in Equation (62) and multiplying through by Vp yields

4.14.7.1.1 Unbiodegradable particulate organics (Supi) The material, which comprises hair, cellulose, fibrous material, cloth remnants, and other UPOs, accumulates in the biological reactor. It settles out with the AS in the secondary settling tank and therefore is retained in the system. The only stream by which this material exits the system is via the waste flow Qw. This material is denoted XIi in VSS units in the influent and is denoted XI in VSS units in the reactor. Applying the general mass-balance equation (60) to this material yields

Vp dXI ¼ Qi XIi dt  0  Qw XI dt þ 0  0

ð61Þ

where dXI is the change in XI concentration in the reactor in terms of the VSS (mgVSS l1) and dt the time interval of the mass balance. In the mass balance it is assumed that (1) the settling tank is 100% efficient in that all the XI material is captured and returned to the biological reactor and (2) by definition of unbiodegradable, no gains or losses of this material take place by bioreaction. Dividing both sides of Equation (61) by Vpdt yields

dXI Qi Qw XI ¼ XIi  dt Vp Vp and noting that Qi/Vp ¼ 1/Rhn and Qw/Vp ¼ 1/Rs yields

dXI XIi XI  ¼ dt Rhn Rs

ð62Þ

Now at steady state, the transient dXI/dt is zero because the XI concentration in the reactor has reached equilibrium between the inflow and outflow masses of this compound with the influent and waste flows, respectively. So, setting dXI/dt ¼ 0 and solving for the reactor UPO concentration XI which accumulates from the influent UPOs (XIi) yields

XI ¼ XIi Rs =Rhn

ðmgVSS=l reactorÞ

ð63Þ

For design Equation (63) is not very useful because it includes the nominal hydraulic retention time Rhn which is not known because the reactor volume is not yet known. This difficulty can be eliminated by considering mass and mass

Vp XI ¼ Qi XIi Rs and applying Equations (64) and (65) yields

MXI ¼ FXIi Rs

ð66Þ

Equation (66) shows that at steady state the mass of UPOs (as VSS) in the reactor is equal to the flux of this material into the reactor times the sludge age Rs. Because XIi is found from the influent unbiodegradable particulate COD concentration, that is, XIi ¼ Supi/fcv, the mass of this material in VSS units that accumulates in the reactor at steady state is given by

MXI ¼ FSupi =f cv Rs ¼ Qi ðSupi =f cv ÞRs

ð67Þ

Dividing Equation (67) through by FSupi gives the mass of UPOs as VSS in the reactor per flux of this material in the influent, viz.,

MXI Rs ¼ FSupi f cv

ðmgVSS=ðmgCOD=dÞÞ

ð68Þ

A plot of MXI/FSupi versus Rs from Equation (68) is shown in Figure 9. It can be seen that the mass of UPOs as VSS increases linearly with sludge age and that the production rate or yield coefficient of this material, given by the slope of the line, is 1/fcv ¼ 1/1.48 ¼ 0.676 mg VSS/mgCOD. In COD units, the production rate or yield of this unbiodegradable organic material fcv1/fcv ¼ 1.0 mgCOD/mgCOD. This means that all the unbiodegradable organics are conserved as VSS (or its COD equivalent) in the reactor and the only reason why the yield is 0.676 mgVSS/mgCOD is because in the influent this material is expressed in COD units, whereas in the reactor it is expressed in VSS units. If the UPOs are expressed in the same units, then the yield is 1 mgVSS/mgVSS or 1 mgCOD/mg COD. This is the result that would be obtained if a suspension of UPO is made and treated in an AS system. The influent would have a COD/VSS ratio of around 1.48 mgCOD/mgVSS (which can be measured by doing the COD and VSS tests on the influent). The mass of UPO which accumulates in the reactor as VSS (provided it all settles out in the settling tank) at steady state would be proportional to the sludge age. The effluent

438

Biological Nutrient Removal Reactor VSS mass For unbiodeg. particluate organics

Hence,

mgVSS/(mgCOD/d)

20

15

10

5

0 0

5

10 15 20 Sludge age (R s)

25

30

Figure 9 Mass of VSS in reactor per mg COD load per day applied to the reactor for unbiodegradable particulate organics and biodegradable organics.

COD concentration would be zero (no dissolved organics) and the OUR in the reactor also zero (no biodegradable organics). For example, if the suspension of UPO in water has COD and VSS concentrations of 112.5 mgCOD l1 and 76.0 mgVSS l1, respectively, that is, a COD/VSS ratio fcv ¼ 1.48 mg COD/mgVSS, and this is fed to a 15.2 l AS reactor at 15 l d1, then from Equations (66) or (67), the mass of UPOs in the reactor at steady state MXI at 20 days sludge age would be MXI ¼ 15(112.5/1.48)20 ¼ 22804 mgVSS. This mass is diluted into the 15.2 l reactor volume giving, from Equation (64), a reactor VSS concentration of XI ¼ MXI/Vp ¼ 22804/ 15.2 ¼1500 mgVSS l1. Because the steady-state mass of XI in the reactor is linear with sludge age, at 10 days sludge age, the VSS concentration would be 750 mgVSS l1.

4.14.7.1.2 Unbiodegradable soluble organics (Susi) Applying the mass-balance equation (60) to the unbiodegradable soluble organics yields

V p dSus ¼ Qi Susi dt  Qe Sus dt  Qw Sus dt þ 0  0

ð69Þ

where Sus is the unbiodegradable soluble organics concentration in the reactor. Additional subscripts ‘i’ or ‘e’ denote concentration in the influent or effluent, respectively. Noting that

Qe þ Qw ¼ Qi

ð70Þ

from conservation of water mass in the system, Suse ¼ Sus from complete mixing regime, Vp/Qi ¼ Rhn from Equation (59a), dSus/dt ¼ 0 at steady state, and dividing Equation (69) by Vpdt, yields

dSus =dt ¼ ðSusi  Sus Þ=Rhn ¼ 0

Sus ¼ Suse ¼ Susi

ðmgCOD l 1 Þ

ðQe þ Qw ÞSuse ¼ Qi Susi FSuse ¼ FSusi

ðmgCOD d 1 Þ ðmgCOD d 1 Þ

ð71Þ

From Equation (71), at steady state the unbiodegradable soluble organics pass through the system unchanged from the influent to the effluent and waste flows, and the reactor (Sus) and effluent (Suse) concentrations are equal to that in the influent. Also, because it is assumed that the water mass is conserved, the flux of unbiodegradable soluble organics exiting the system via effluent and waste flows is equal to the flux of unbiodegradable organics entering the system via the influent flow. For example, if a 15 l d1 wastewater flow with a 52.5 mgCOD l1 unbiodegradable soluble COD concentration (Susi) were treated in a 15.2 l AS reactor at a long sludge age, then the effluent and waste flow unbiodegradable soluble COD concentrations would also be 52.5 mgCOD l1.

4.14.7.1.3 Biodegradable organics The behavior of the system with biodegradable organics is now demonstrated with a hypothetical wastewater comprising only soluble biodegradable organics. This approach is also consistent with the Monod kinetics for modeling OHO growth behavior: these kinetics are valid only for soluble RBOs (see Section 4.14.5.1.2). Once the system equations for soluble biodegradable organics have been developed, they will be modified for real wastewater comprising soluble and BPOs. When a wastewater comprising only soluble biodegradable organics (Sbsi) is treated in an AS system (1) active OHOs increase due to growth on Sbs, (2) Sbs decreases, (3) oxygen is utilized for synthesis of OHOs (Os), (4) OHOs decrease due to endogenous respiration resulting in (5) generation of endogenous residue and (6) oxygen utilization (Oe). So, the variables for which the system equations need to be developed are XBH, XEH, Sbs, and oxygen utilization (Oc ¼ Os þ Oe). These variables, together with the system design parameters (Qi, Vp, Qw, and Qe) are shown diagrammatically in the AS system in Figure 8. Applying the mass-balance to the OHO concentration XBH (mgVSS l1) assuming that: (1) the influent XBH concentration is 0; this is of course not true because there will always be some OHOs in the influent; however, relative to the mass of OHOs that grow in the reactor, the mass of OHOs that are seeded into the system is negligible; (2) the settling tank is 100% efficient resulting in no loss of OHOs with the effluent; this also is not realistic but again the mass of OHOs lost with the effluent under normal circumstances is negligible relative to the mass of OHOs intentionally harvested from the system via the waste flow; (3) the growth rate of OHOs is given by Equation (49); and (4) the loss rate of OHOs by endogenous respiration in mgVSS l1 d1 is given by Equation (53), then

Vp dXBH ¼ 0  0  Qw XBH dt mHm Sbs XBH Vp dt  bH XBH V p dt þ KS þ Sbs

Biological Nutrient Removal

Dividing through by Vpdt and noting that Qw/Vp ¼ 1/Rs yields

dXBH XBH mHm Sbs þ XBH  bH XBH ¼ dt Rs KS þ Sbs

ð72Þ

Because the biodegradable COD concentration in the reactor (Sbs) will be low in the completely mixed reactor, the full Monod term in Equation (72) can be replaced by the simplified first-order growth rate. Then expressing the OHO specific growth rate in terms of the maximum specific substrate utilization rate Km instead of the maximum specific growth rate mHm yields

dXBH XBH þ YH Kv Sbs XBH  bH XBH ¼ dt Rs

ð73Þ

where

Kv ¼ Km =Ks ¼ mHm =ðYH Ks Þ ðl=ðmgVSS dÞ1 Þ Applying the mass-balance equation to the endogenous residue concentration (XEH) and assuming, as for the OHOs, no endogenous residue in the influent, a 100% efficient settling tank, and the rate of XEH generation in mgVSS l d1 given by Equation (54), we obtain

Vp dXEH ¼ 0  0  Qw XEH dt þ f EH bH XBH V p dt Dividing through by Vpdt and noting that Qw/Vp ¼ 1/Rs yields

dXEH XEH þ f EH bH XBH ¼ dt Rs

ðmgVSS l1 d1 Þ

ð74Þ

Applying the mass balance to the soluble biodegradable COD concentration and noting that because of complete mixing conditions in the reactor, the soluble Sbs concentration in the effluent and waste flow is the same as that in the reactor, that is, Sbse ¼ Sbsw ¼ Sbs , yields,

Vp dSbs ¼ Qi Sbsi dt  Qw Sbs dt  Qw Sbs dt  Qe Sbs dt mHm Sbs þ0 XBH Vp dt mgCODl1 d1 YH KS þ Sbs Dividing through by Vpdt and noting that Qi/Vp ¼ 1/Rhn yields

dSbs ðSbsi  Sbs Þ mHm Sbs ¼  XBH dt YH KS þ Sbs Rhn

ð75Þ

Again, as was done in Equation (73) for XBH above, substituting the simplified first-order equation for the full Monod equation yields

dSbs ðSbsi  Sbs Þ  Kv Sbs XBH ¼ Rhn dt

ð76Þ

Equations (72), (74), and (75) are valid differential equations for the active OHO (XBH), endogenous residue (XEH), and soluble biodegradable organic (Sbs) concentrations in the completely mixed AS system. Because these equations include the full Monod equation, simple solutions for XBH, XEH, and Sbs cannot be found. Therefore, to generate solutions for XBH, XEH, and Sbs, these three equations need to be simultaneously integrated numerically for which a computer program or simulation package is required. The solutions so generated will be

439

realistic provided realistic values for the OHO kinetic constants for the specific soluble biodegradable organics (YH, bH, fEH, mHm, and KS) are given as input. When this is done, it will be noticed that after an initial start-up period, which can be several sludge ages long, the solutions for XBH, XEH, and Sbs will reach a constant concentration, if the influent flow and Sbs load on the reactor are constant with time. During the initial start-up period, the transients dXBH/dt, dXEH/dt, and dSbs/dt become smaller and smaller until they reach zero at which time steady state is reached for the constant flow and organic load conditions. If the flow and organic load are not constant but vary cyclically over the day in an identical cyclic pattern for each successive day, then after an initial start-up period, a dynamic steady state is reached. Under dynamic steady-state conditions, XBH, XEH, and Sbs also will vary cyclically over the day in response to the cyclic flow and organic load over the day, and this variation will be identical for each successive day. This variation in XBH, XEH, and Sbs will be around an average XBH, XEH, and Sbs concentrations and these average concentrations will be virtually the same as those achieved under steady-state conditions if the organic load on the system over the day under constant flow and load conditions is the same as for the cyclic flow and load conditions. Mathematically, the cyclic loading average XBH, XEH, and Sbs concentrations are called the particular integral and the daily variation around these averages is called the complementary function (Figure 10). If the flow and organic load over the day are constant, then at steady state there is only a particular integral; the complementary function is zero because there is no variation in XBH, XEH, and Sbs concentrations in the reactor over the day. For the AS model, the particular integral (i.e., the steadystate or cyclic loading average concentrations) is very important because they give a close approximation of the concentrations in the reactor in response to the daily average organic load on the reactor. While Equations (72) and (75) are only valid for soluble biodegradable organics fed to the completely mixed AS system, and therefore will not generate valid numerical solutions for real wastewater comprising soluble and particulate organics (see Sections 4.14.5.1.2 and 4.14.5.1.3), the conclusion regarding the importance of the particular integral or steady-state solution is valid and also applies to the system treating real wastewater. The problem is finding simple equations for these steady-state concentrations for the real wastewater system. This is achieved first by substituting the simplified first-order Monod equation into Equation (72) for XBH and Equation (75) for Sbs to give Equations (73) and (76), respectively, and second by making the reasonable assumption (discussed below) that all the biodegradable organics are broken down in the system. Setting the transients in Equations (73), (74), and (76) to 0 yields

0 ¼ YH Kv Sbs XBH  bH XBH  XBH =Rs from which

Sbs ¼

ð1 þ bH Rs Þ Kv YH Rs

ðmgCOD l1 Þ

ð77Þ

which is the steady-state solution for the soluble biodegradable COD concentration in the reactor (Sbs, mgCOD l1). Note that the Sbs concentration is independent of the influent

440

Biological Nutrient Removal

Start-up period

Steady state

Concentration (mg I−1)

Cyclic flow and load response Steady state Particular integral Transient zero Steady-state solution

Constant flow and load response

Dynamic steady state Complementary function Transient not zero

Initial condition

Time (days) Figure 10 Calculated concentration vs. time response profile from a selected initial condition for constant and cyclic flow and load conditions showing changing concentration during start-up and constant or cyclically varying concentration when steady state is reached.

organic concentration (Sbsi). This implies that the steady-state residual Sbs concentration is the same irrespective of the magnitude of the influent Sbsi concentration. Note also that Sbs is dependent on sludge age (Rs) and not on the nominal hydraulic retention time (Rhn). From Equation (74)

0 ¼ f EH bH XBH  XEH =Rs from which in turn

XEH ¼ f EH bH Rs XBH

ðmgVSS l1 Þ

ð78Þ

which is the steady-state solution for the reactor endogenous residue concentration (XEH, mgVSS l1). From Equation (76)

0 ¼ ðSbsi  Sbs Þ=Rhn  Kv Sbs XBH from which in turn

Kv Sbs ¼ ðSbsi  Sbs Þ=ðRhn XBH Þ

ð79Þ

A simple equation for the steady-state OHO concentration cannot be found from Equation (79) alone. Multiplying Equation (77) through by Kv makes the right-hand side equal to KvSbs, which is the same as the right-hand side of Equation (79). Setting the right-hand side of these two equations equal and solving for XBH yield

XBH ¼

YH Rs ðSbsi  Sbs Þ ð1 þ bH Rs Þ Rhn

ðmgVSS l1 Þ

ð80Þ

which is the steady-state solution for the active OHO concentration in the reactor in mgVSS l1. If Sbs is solved for from Equation (79), it will be found that

Sbs ¼

Sbsi ð1 þ Kv XBH Rhn Þ

ðmgCOD l1 Þ

ð81Þ

This equation is similar to that used for fecal coliform die-off in oxidation ponds (Marais, 1974) except that the die-off rate is KvXBH. It appears to indicate that the residual soluble biodegradable organic concentration Sbs is a function of the influent Sbsi concentration and dependent on the nominal hydraulic retention time (Rhn) and not on the sludge age (Rs) in apparent contradiction to Equation (77). However, Sbs does not increase with increase in Sbsi because the effective utilization rate KvXBH is not constant; the higher the Sbsi, the higher the XBH and the higher the utilization rate KvXBH. This increased KvXBH rate compensates for the increased Sbsi so that the same Sbs concentration is obtained. This equation for Sbs is not useful because it requires the (1) active OHO concentration XBH and (2) nominal hydraulic retention time Rhn to be known, which at the design stage are not. Because the influent wastewater comprises only soluble biodegradable organics (Sbsi), the VSSs concentration in the reactor (Xv) is the sum of the OHO and endogenous residue concentrations, viz.,

Xv ¼ XBH þ XEH ðmgVSS l1 reactor volumeÞ ¼ XBH þ f EH bH Rs XBH ¼ XBH ð1 þ f EH bH Rs Þ YH Rs ðSbsi  Sbs Þ ¼ ð1 þ f EH bH Rs Þ ð1 þ bH Rs Þ Rhn

ð82Þ

As in Equation (63) for the UPOs accumulating in the reactor from the influent (XI), the appearance of Rhn in Equation (82) is not helpful in design because it requires the reactor volume (Vp) to be known. Therfore, Equation (82) is converted to mass of VSS in the reactor (MXv) by substituting Vp/Qi for Rhn and multiplying through by Vp yielding

Vp Xv ¼ Qi ðSbsi  Sbs Þ

YH Rs ð1 þ f EH bH Rs Þ ð1 þ bH Rs Þ

Now VpXv in the equation is the mass of VSSs in the reactor which from the earlier mass nomenclature is MXv mgVSS. The (Sbsi  Sbs) is the biodegradable COD concentration change between the influent and effluent denoted DSbs mgCOD l1.

Biological Nutrient Removal

Multiplying this concentration change by the influent flow gives the flux of soluble biodegradable organics degraded per day in the system and is denoted FDSbs mgCOD d1. Hence, at steady state, the mass of VSS in the reactor is given by

MXv ¼ FDSbs

YH Rs ð1 þ f EH bH Rs Þ ð1 þ bH Rs Þ

MXBH

YH Rs ¼ FDSbs ð1 þ bH Rs Þ

MXEH ¼ f EH bH Rs MXBH

soluble biodegradable organics across the system (FDSbs) is equal to the flux (load) of these organics on the system (FSbsi), viz.,

FDSbs ¼ Qi Sbsi  ðQw þ Qe ÞSbs ¼ Qi Sbsi  Qi Sbse ¼ Qi ðSbsi  Sbse Þ

ð83Þ

from which it can be seen that the mass of VSS in the reactor MXv when treating a wastewater with only soluble biodegradable organics is proportional to the flux of biodegradable COD degraded FDSbs. Converting the reactor concentrations XBH and XEH to masses in the reactor by noting that Sbsi  Sbs ¼ DSbs, Rhn ¼ Vp/ Qi, VpXBH ¼ MXBH, QiDSbs ¼ FDSbs, and VpXEH ¼ MXEH yields

¼ Qi Sbsi ¼ FSbsi Substituting FSbsi for FDSbs into Equations (84), (85), and (83) yields

MXBH ¼ FSbsi ðmgVSSÞ ðmgVSSÞ

ð84Þ

To determine the masses of OHO VSS (MXBH), endogenous residue VSS (MXEH), and total VSS in the reactor (MXv) from Equations (83)–(85) for the soluble biodegradable organics in wastewater, the flux change (mgCOD d1) in soluble biodegradable organics (FDSbs) needs to be known, which in turn requires the residual soluble biodegradable organics concentration not degraded in the reactor (Sbs ¼ Sbse) to be known. The residual Sbs concentration can be calculated from Equation (77) noting that Kv ¼ mHm/(KsYH) from Equation (73). Assigning appropriate values to mHm, Ks, and YH allows one to determine Kv. For soluble RBOs such as glucose and acetate, typically mHm is between 1 and 5 d1 and Ks between 5 and 20 mgCOD l1 for OHOs isolated from AS (Richard et al., 1982). Accepting mHm ¼ 2.25 d1, Ks ¼ 10 mgCOD l1, and YH ¼ 0.45 mgVSS/mgCOD yields Kv ¼ 0.5 l/(mgOHOVSS d). Accepting bH ¼ 0.24 d1 at 20 1C allows the residual unbiodegraded Sbs concentration to be calculated for a selected sludge age from Equation (77). When this is done, it will be noticed that even at very short sludge ages (B3 d), Sbs is very low at around 2 mgCOD l1. If the influent soluble RBO concentration (Sbsi) is higher than 200 mgCOD l1, which are common soluble biodegradable organic concentrations in real wastewater, then in excess of 99% biodegradable organic material breakdown takes place in the system. Therefore, for soluble biodegradable organics, it is reasonable to assume that all the organics are degraded and that the change in flux of

YH Rs ð1 þ bH Rs Þ

ðmgVSSÞ

MXEH ¼ f EH bH Rs MXBH YH Rs ¼ FSbsi f EH bH Rs ð1 þ bH Rs Þ

ð85Þ

4.14.7.1.4 Complete utilization of soluble biodegradable organics

441

MXv ¼ FSbsi

ð86Þ

ðmgVSSÞ

ð87Þ

YH Rs ð1 þ f EH bH Rs Þ ðmgVSSÞ ð1 þ bH Rs Þ

ð88Þ

Knowing the OHO kinetic and stoichiometric constants (see Table 6) allows the mass of VSS in the reactor per mass of biodegradable COD load per day, that is, MXv/FSbsi to be calculated and plotted versus sludge age (Figure 11). From Figure 11, it can be seen that the OHO VSS mass in the reactor increases from about 1.0 to 1.5 kgVSS/(kgCOD/d) with increasing sludge age from 5 to 15 days. Increases in sludge age above 15 days lead to only a marginal increase in OHO VSS mass, that is, 1.6 kgVSS/(kgCOD/d) at 30 days sludge age. In contrast, the endogenous residue VSS mass is very low at short sludge ages and increases almost linearly with sludge age from 0.2 at 5 days to 2.0 kgVSS/(kgCOD/d) at 30 days sludge age. At 25 days sludge age the OHO and endogenous residue VSS masses are equal at 1.6 kgVSS/(kgCOD/d) giving a total VSS mass in the reactor of 3.2 kgVSS/(kgCOD/d). Figure 11 also shows that the proportion of the OHOs in the VSS mass in the reactor decreases as sludge age increases; this proportion is 80% and 50% at 5 and 25 days sludge age, respectively (see Section 4.14.9.5). A comparison of the mass of VSS in the reactor per flux organic load on the reactor (MXv/FSbsi) from biodegradable soluble organics (which are accepted to be all utilized) with that from UPOs is given in Figure 12. It can be seen that much less VSS mass accumulates in the reactor with biodegradable organics (YH ¼ 0.45 mgVSS/mgCOD, bH ¼ 0.24 d1) than

Table 6 Stoichiometric and kinetic constants and their temperature dependency for the OHOs in the steady-state carbonaceous degradation activated sludge model Constant

Symbol

Temperature dependence

Theta (y)

Standard value at 20 1 C

Yield coefficient (mgVSS/mgCOD) Endogenous respiration rate (d1) Endogenous residue fraction (–) COD/VSS ratio (mgCOD/mgVSS)

YH bH fEH fcv

Remains constant bHT ¼ bH20 y (T20) Remains constant Remains constant

1 1.029 1 1

0.45 0.24 0.2 1.48

From Marais GvR and Ekama GA (1976) The activated sludge process part 1 – steady state behaviour. Water SA 2(4) 163–200.

Eq. no.

56

442

Biological Nutrient Removal Reactor VSS mass for biodegradable organics only

4

mgVSS/(mgCOD/d)

Volatile mass, MXV 3

Active OHO mass, MXBH

2

1 Endogenous residue mass, MXEH 0 0

5

10 15 20 Sludge age (R s)

25

30

Figure 11 Active OHO (MXBH), endogenous residue (MXEH), and volatile suspended solids (MXv ¼ MXBH þ MXEH) per mass biodegradable COD per day (MXv/FSbsi) vs. sludge age for wastewater with biodegradable organics only.

Reactor VSS mass for biodeg. and unbiodeg. organics

20

mgVSS/(mgCOD/d)

From unbiodegradable 15

reaction and the carbon (energy) of these organics catabolized exit the system as CO2 (is lost as heat). Only twothirds of the soluble biodegradable organic electrons accumulate in the reactor as particulate OHO organic (VSS) material. 2. If the endogenous respiration rate were zero (bH ¼ 0.0 d1), then according to Equation (86) the mass of OHO per organic load per day (MXBH/FSbsi) would increase linearly with sludge age at a slope of YH ¼ 0.45 mgVSS/mgCOD (see Figure 12, the YH ¼ 0.45 mgVSS/mgCOD, bH ¼ 0.0 line) and VSS mass would be two-thirds of that for UPOs (see (1) above). However, bH ¼ 0.24 d 1 which means that for every day of sludge age, 24% of OHO VSS organics is lost, of which 20% (fEH ¼ 0.20) accumulates as unbiodegradable particulate endogenous residue and increases the VSS mass in the reactor. The electrons of the remaining 80% biodegradable OHO organics VSS lost every day (1  fEH ¼ 0.8) are passed to the oxygen (endogenous respiration oxygen utilization, Oe), the carbon of which exits the system as CO2 and the energy of which is lost as heat. From the above it is clear that the reduced VSS mass in the reactor per organics COD load per day with biodegradable organics is due to oxygen utilization for synthesis and endogenous respiration which are the result of the biological redox reactions in which the electrons, carbon, and energy of biodegradable organics from the influent and OHOs are passed to oxygen, released as CO2 and lost as heat, respectively. The difference in terms of the VSS mass in the reactor per organic load per day between unbiodegradable particulate and soluble biodegradable organics is therefore due to oxygen utilization which conforms to the COD (e) mass balance.

4.14.7.1.5 The mass balance on oxygen Applying the general mass balance (Equation (60)) to the oxygen in the system yields

COD lost via oxygen utilization = energy lost as heat

10

5

3 3 2 3 2 3 2 Mass Mass Mass O2 Mass 6 change 7 6 flow O 7 6 flow O 7 6 utilized 7 27 27 7 7 6 6 6 6 7 7¼6 76 76 6 4 in O2 in 5 4 into 5 4 out of 5 4 by OHOs in 5 system system system system 2

From biodegradable 0 0

5

10

15

20

25

30

Vp dOr ¼ FOin dt  dOr ðQw þ Qe Þdt  OURc Vp dt

Sludge age (R s) Figure 12 Mass VSS (MXv) in reactor per mass COD load /d on the reactor vs. sludge age for unbiodegradable particulate organics and biodegradable organics. The difference is the COD (or e) passed to oxygen which is proportional to the energy in the biodegradable organics lost as heat.

with UPos (YH ¼ 0.676 mgVSS/mgCOD, bH ¼ 0.0 d1) – from about 65% less at 5 days sludge age to 81% less at 25 days sludge age. There are two reasons for this: 1. The yield of OHO VSS electrons is 0.45 mgVSS/mgCOD (or 0.45 1.48 ¼ 0.66 mgCOD/mgCOD) so that one-third of the electrons in the organics (1  fcvYH ¼ 1 0.66 ¼ 0.34) are passed to oxygen (synthesis oxygen utilization, Os) to form water in the OHO catabolic part of the synthesis

where dOr is the dissolved oxygen concentration in the reactor (mgO l1) and, OURc the OHO oxygen utilization rate (mgO l1d1). Normally, the mass of oxygen exiting the reactor via the effluent and waste flows is negligible in comparison with that entering the reactor by the aeration device (FOin, mgO d1). Also even when the reactor DO concentration is changing so that the transient dOr/dt is not zero, this too has a negligible effect in the oxygen mass balance compared with the mass of oxygen transferred to the water by the aeration device and that utilized by the OHOs (OURcVp dt). Hence, the rate of oxygen mass input by the aeration device is equal to the mass rate of oxygen utilized by the OHOs, viz.,

FOin ¼ OURc Vp ¼ FOc

ðmgO d1 Þ

ð89Þ

Biological Nutrient Removal

Now OURc is the OHO OUR, which is the sum of the OURs for synthesis (OURs) and endogenous respiration (OURe):

OURc ¼ OURs þ OURe

ðmgO l

1

substituting Equation (84) for MXBH into Equation (91) yields

3

2 6 FOc ¼ FDSbs 4ð1  f cv YH Þ þ f cv ð1  f EH ÞbH

1

d Þ

Synthesis

ðmgO d Þ

Now from the biological kinetic behavior of growth (Equation (51))

OURs ¼ ð1  f cv YH Þ

dSbs Kms Sbs ¼ ð1  f cv YH Þ XBH dt Ks þ Sbs

ðmgO l1 h1 in reactorÞ

OURs ¼ ð1  f cv YH ÞKv Sbs XBH

and accepting that all the soluble biodegradable organics are degraded, that is, FDSbs ¼ FSbsi yields

 FOc ¼ FSbsi ð1  f cv YH Þ þ f cv ð1  f EH ÞbH

 YH Rs ð1 þ bH Rs Þ

ðmgO d1 Þ

which for the simplified first-order conditions reduces to

From Equation (79),

Kv Sbs XBH ¼ ðSbsi  Sbs Þ=Rhn ¼ Qi ðSbsi  Sbs Þ=Vp Therefore,

OURs ¼ ð1  f cv YH ÞQi ðSbsi  Sbs Þ=Vp This makes sense from our kinetic understanding, that is, oxygen for synthesis is the catabolic part (1  fcvYH) of the organics degraded. The oxygen utilized for endogenous respiration comes from Equation (55):

OURe ¼ f cv ð1  f EH ÞbH XBH

YH Rs 7 5 ð1 þ bH Rs Þ

Endog: resp:

1

FOc ¼ FOs þ FOe

443

ðmgO l1 d1 in reactorÞ

that is, COD equivalent of the biodegradable OHO VSS concentration that disappears. Hence,

ð92Þ

From Equation (92), it can be seen that the mass of oxygen utilized by the OHOs (FOc) is a function of the OHO stoichiometric and kinetic constants and the sludge age. Knowing the values of the stoichiometric and kinetic constants from Table 6, the mass of oxygen utilized by the OHOs per mass organic load per day [FOc/FSbsi, (mgO/d)/(mgCOD/d)] is plotted versus sludge age (Rs) as shown in Figure 13. From Figure 13, it can be seen that as the sludge age increases so the flux of oxygen utilized (total demand, FOc) per flux organic load increases, but the increase becomes smaller as the sludge age increases. The synthesis oxygen demand (FOs) is constant because all the biodegradable organics are utilized and transformed to OHO VSS mass. The increase in total oxygen demand is due the increasing oxygen demand from endogenous respiration (FOe), which increases because the longer the OHO VSS mass remains in the reactor, the more of this mass is degraded via endogenous respiration, the electrons, carbon, and energy of which are passed to oxygen, changed to CO2, and lost as heat, respectively. Clearly, synthesis is the biological process whereby influent biodegradable organics are transformed to OHO VSS mass (anabolism) with OHO oxygen demand

OURc ¼ ð1  f cv YH ÞQi ðSbsi  Sbs Þ=Vp þ f cv ð1  f EH ÞbH XBH

kgO/d per kgCOD/d load on reactor

1.0

ðmgO l1 h1 in reactorÞ ð90Þ

Total FOc

To obtain the flux of oxygen utilized FOc, the rate per liter reactor is multiplied by Vp, that is,

FOc ¼ OURc Vp ¼ OURs Vp þ OURe Vp ¼ ð1  f cv YH ÞQi ðSbsi  Sbs Þ þ f cv ð1  f EH ÞbH XBH VP which in terms of the mass and flux nomenclature yields

FOc ¼ ð1  f cv YH ÞFDSbs þ f cv ð1  f EH ÞbH MXBH

kgO/d per kgCOD/d

0.8

Endogenous respiration FOe

0.6

0.4

0.2 1

ðmgO d Þ

Synthesis FOs

ð91Þ

From Equation (91) it can be seen that the synthesis OUR is proportional to the mass of biodegradable organics degraded per day and the endogenous respiration OUR proportional to the mass of active OHOs in the reactor. From Equation (84), the mass of active OHOs in the reactor is related to the flux of biodegradable organics degraded, so

0.0 0

5

10 15 20 Sludge age (R s)

25

30

Figure 13 The synthesis, endogenous respiration, and total OHO oxygen utilization rates (demand) versus sludge age in kgO/d per kg COD/d biodegradable organic load on the reactor.

444

Biological Nutrient Removal

an associated electron transfer to oxygen and an energy loss as heat (catabolism), and endogenous respiration is a process whereby now the organism biodegradable organics are degraded via catabolism to CO2 with a further oxygen demand and energy loss as heat. The electron transfer to oxygen results in the much lower accumulation of VSS mass in the reactor compared with UPOs (Figure 12). In Figure 12, the difference between the YH ¼ 0.676 mgVSS/mgCOD and the YH ¼ 0.45 mgVSS/mgCOD  bH ¼ 0.0 d1 line values at a selected sludge age (say 10 days) is the accumulated oxygen consumed for synthesis over the 10 days sludge age period. Similarly, the difference between the YH ¼ 0.45 mgVSS/mgCOD  bH ¼ 0.0 d1 and the YH ¼ 0.45 mgVSS/mgCOD and bH ¼ 0.24 d1 line values in Figure 12 at a selected sludge age (say 20 days) is the accumulated oxygen consumed for endogenous respiration over 20 days sludge age period. From this it can be seen that (1) the YH value governs the proportion of the influent biodegradable organic COD (e) that is conserved as an OHO organics (balance passed the oxygen for synthesis) and (2) the bH rate governs the rate of breakdown of the organism biodegradable organics, in which the COD (e) of these organism organics is passed to the oxygen in the endogenous respiration process. At sludge ages of 5, 10, and 25 days, of the influent biodegradable organic COD, 38%, 29%, and 21% remains as sludge solids COD and 62%, 71%, and 79% is oxygen utilized, which represents energy lost as heat. It can be seen that even at short sludge ages, the greater proportion of the influent biodegradable organic COD (e), carbon, and energy is passed oxygen, transformed to CO2 and lost as heat, respectively, in the system.

4.14.7.1.6 Complete utilization of BPOs The influent BPOs, both settleable and suspended (Sbpi), are mostly slowly biodegradable. These slowly BPO become enmeshed and absorbed within the AS flocs and become part of suspended VSS sludge mass in the reactor (see Section 4.14.5.1.3). As part of the VSS sludge mass, these organics settle out with the sludge mass in the setting tank and are returned to the biological reactor. Undegraded particulate organics therefore do not escape with the effluent but remain part of the sludge VSS mass in the system; the only exit route for the undegraded BPOs is via the waste flow (Qw) with the waste sludge. The time available for the breakdown of the particulate slowly biodegradable organics by the OHOs is therefore related to the solids retention time or sludge age of the system. It has been found from experimental and modeling work with real wastewater that if the sludge age is long (43 days for fully aerobic systems at 20 1C), then virtually complete breakdown of the BPOs takes place in the system. Hence, at long sludge ages the residual biodegradable organic concentration, both soluble and particulate, not broken down can be accepted to be very small. From this an important assumption and simplification can be made for the steady-state model, that is, it is not necessary to make a distinction between soluble and BPOs; all are transformed to OHO VSS mass. However, it must be remembered that, although reasonable, this assumption that all the biodegradable organics are

degraded cannot be proved because any residual biodegradable soluble and particulate organics not degraded in the system are implicitly included with the unbiodegradable soluble and particulate organic fractions, respectively.

4.14.7.1.7 Integration of biodegradable and unbiodegradable organics models In Sections 4.14.7.1.1–4.14.7.1.3, the behavior of the four organic constituents of real wastewater in the completely mixed system was examined individually with hypothetical wastewaters containing only the single constituent: 1. 2. 3. 4.

UPOs (Supi, mgCOD l1), unbiodegradable soluble organics (Susi, mgCOD l1), biodegradable particulate organics (Sbpi, mgCOD l1), and biodegradable soluble organics (Sbsi, mgCOD l1).

However, because the biodegradable organics are accepted to be completely utilized at sludge ages longer than about 3 days, these organics can be considered a single constituent as all are transformed to OHO mass through the biological growth process. To obtain the steady-state AS model for the completely mixed system treating the real wastewater, the equations derived for each individual constituents are simply combined. Combining the three constituents into the 15 l d1 wastewater flow (Qi) and treating this in the 15.2 l (Vp) 20 day sludge age (Rs) in a system result in a reactor VSS concentration (Xv) of 3256 mgVSS l1, an oxygen utilization rate (FOc) of 6798 mgO d1 or (Oc) 18.63 mgO l1 h1 and an effluent COD concentration (Suse) of 52.5 mgCOD l1. In the same way as here the answers from the steady-state equations are added; below (Section 4.14.9.3) the equations themselves are added to give the complete steady-state model for real wastewater COD removal. The major conclusion from the above is that if a completely mixed aerobic AS system were operated by hydraulic control at a long sludge age (say 20 days) and fed unknown real wastewater at a constant flow and load for a sufficiently long time to reach steady-state conditions, then by measuring the effluent COD concentration (Suse), the reactor VSS concentration (Xv), and the oxygen utilization rate (OURc or FOc), the wastewater unbiodegradable soluble and particulate organic concentrations (Susi and Supi) or fractions (fS’us and fS’up) can be calculated. In such an experiment, it would be sufficient to measure the effluent COD and the reactor VSS concentrations only to determine Supi. However, it is also important to measure the oxygen utilization rate to allow the COD balance of the experimental results to be checked. The closer the COD balance is to 100%, the more reliable the Supi estimate. Despite considerable research efforts to find simpler and less laborious methods of wastewater Supi or fS’up measurement (Wentzel et al., 1999; Torrijos et al., 1994), the only reliable method to date is that outlined above (Ekama et al., 1986).

4.14.8 The COD (or e) Mass Balance In the AS system, COD theoretically must be conserved so that at steady state the COD flux out of the system must be equal to the COD flux into the system over a defined time interval. The

Biological Nutrient Removal COD (e) of the influent organics is (1) retained in the unbiodegradable particulate and soluble organics, (2) transformed to OHO mass and therefore conserved in a different type of organic material, or (3) passed onto oxygen to form water. Therefore, in general, the COD (or e) balance over the AS system at steady state is given by

"

445

can be seen that at all sludge ages the influent COD flux is the same and constant (equal to 11 250 and 6750 kgCOD for raw and settled wastewater respectively). Also, the effluent COD flux is the same for the raw and settled wastewater (788 kgCOD d1) because the effluent COD concentration is the same at the USO concentration of 53 mgCOD l1. The flux

# " # Mass of COD ðe  Þ Mass of COD ðe  Þ ¼ output per day input per day 3 3 2 3 2 2 Mass of Mass of Mass of 7 2 Mass of oxygen utilized 3 2 Mass of 3 7 6 7 6 6 6 soluble 7 6 soluble 7 6 particulate 7 7 6 7 6 7 6 6 7 7 6 7 7 6 7 6 6 6 COD in 7 þ 6 COD in 7 þ 6 COD in 7 þ4 by OHOs for COD 5 ¼ 4 COD input 5 7 7 6 7 6 6 per day breakdown per day 4 effluent 5 4 waste flow 5 4 waste flow 5 per day per day per day

ð93Þ

 Qe Ste þ Qw Ste þ Qw Xv fcv þ Vp OURc ¼ Qi Sti

where Ste is the effluent total soluble COD concentration (mgCOD l1), Xv the VSS concentration in biological reactor (mgVSS l1), and OURc the carbonaceous (for organic material degradation) oxygen utilization rate in reactor (mgO l1 h1). In Equation (93), the first two terms represent the soluble organics that exit the system via effluent and waste flows, the third term the particulate organics that exits the system via the waste flow, and the fourth term represents the mass of oxygen utilized for biodegradable organic material breakdown by the OHOs. Noting that from Equations (70), (58), (89), and (65)

of COD exiting the system via the waste VSS (FXv) decreases as sludge age increases and concomitantly the flux of oxygen utilized (FOc) increases as sludge age increases so that their sum is constant all sludge ages (equal to 11 250  788 ¼10 462 and 6750  788 ¼ 5962 kgCOD d1 for raw and settled wastewater respectively). This transfer from VSS (COD) to oxygen takes place via the endogenous respiration process and because the endogenous respiration process continues for longer, the longer the sludge age, more COD (electrons) is transferred from the VSS sludge mass to oxygen the longer the sludge age.

ðQe þ Qw ÞSte ¼ Qi Ste ¼ FSte

Example wastewaters 12 000

Qw Xv ¼ Vp Xv =Rs ¼ MXv =Rs ¼ FXv 10 000

Qi Sti ¼ FSti the general COD mass balance is given by

ð94Þ

where FSte is the COD flux of soluble organics exiting system via effluent and waste flows (mgCOD d1), fcvMXv/Rs ¼ FXv the COD flux of particulate organics (VSS) exiting system via waste flow (mgCOD d1), and FOc flux of oxygen utilized by OHOs for biodegradable organic material breakdown (carbonaceous) (mgO d1). The carbonaceous oxygen utilization flux FOc is the sum of the oxygen utilized for the synthesis of new organism mass from the biodegradable organics (FOs) and the oxygen utilized for internal generation of energy for essential cell functions from organism biodegradable organics (endogenous respiration (FOe), Equation (91)). The COD balance (Equation 94) is shown for the example raw and settled wastewater in Figure 14 versus sludge age. It

COD flux out (kgCOD d−1)

Vp OURc ¼ FOc

FSte þ f cv MXv =Rs þ FOc ¼ FSti

Raw FSti 22 C 14 C

8000

Settled FSti

FOc Raw

6000

4000

Settled FOc

2000

FXv Raw

Settled FXv Settled FSte

0 0

5

10

Raw 15

20

25

30

Sludge age (days) Figure 14 COD fluxes (kgCOD/d) exiting the fully aerobic activated sludge reactor via effluent (FSte), waste sludge (fcv FXv) and oxygen utilization (FOc) versus sludge age for the example raw and settled wastewaters.

446

Biological Nutrient Removal

The COD mass balance is a very powerful tool for checking not only the data measured on experimental systems (Ekama et al., 1986) but also the results calculated for design from the steady-state model.

In most cases, the effluent VSS and TSS concentrations are too low to measure reliably with the VSS and TSS tests. Alternative methods for measuring low solids concentrations in the effluent have been developed; for the VSS, via the filtered and unfiltered COD concentrations, that is, from Equation (95b).

4.14.9 The AS System Steady-State Equations for Real Wastewater Once it is recognized that all the organics in the influent, except the unbiodegradable soluble COD, either is utilized by the microorganisms to form OHO mass, or remains in the process and accumulates as inert sludge mass, it follows that the mass of sludge produced and the carbonaceous oxygen demand in the system are stoichiometric functions of the flux of COD to be treated daily; the greater the daily COD flux (FSti), the greater the sludge production (FXv) and carbonaceous oxygen demand (FOc). The steady state model equations below give the effluent COD concentration comprising the unbiodegradable soluble organics, the masses of sludge generated in the reactor, and the average daily oxygen demand for organic material removal as a function of the total organic (COD) load per day, the wastewater characteristics, that is, the unbiodegradable soluble and particulate COD fractions (fS’us and fS’up) and the sludge age. The kinetic and stoichiometric constants in the equations, that is, the specific yield coefficient (YH), the specific endogenous mass loss rate (bH), the unbiodegradable fraction of the OHOs (fEH), and the COD/VSS ratio of the sludge (fcv), as well as their temperature dependencies are given in Table 6.

4.14.9.1 Effluent COD Concentration Under normal AS process operating conditions, where the sludge ages are in excess of 8 days (to ensure nitrification and biological nutrient removal), the nature of the influent organics in municipal wastewaters is such that the COD concentration in the effluent is inconsequential in the process design – the soluble readily biodegradable COD fraction is completely utilized in a very short period of time (o1 h) and the particulate COD, whether biodegradable or unbiodegradable, is adsorbed or enmeshed in the sludge flocs, and settles out with the sludge in the secondary settling tanks. Consequently, the effluent COD concentration is comprised virtually wholly of the unbiodegradable soluble COD (from the influent) plus the COD of the sludge particles which escape with the effluent due to imperfect operation of the secondary settling tank. Hence, the effluent COD concentration, Ste, is approximately given by

Ste ¼ Suse

ðmgCOD l1 Þ

ð95aÞ

for filtered samples, or,

Ste ¼ Suse þ f cv Xve

ðmgCOD l1 Þ

ð95bÞ

for unfiltered samples, where Suse ¼ unbiodegradable COD in the effluent ¼ Susi ¼ fS’us Sti (mgCOD l1) (see Equation (3)); Xve ¼ volatile solids concentration in the effluent (mgVSS l1); and fcv ¼ COD/VSS ratio of the volatile solids ¼ 1.48 mgCOD/ mgVSS.

Xve ¼ ðunfiltered Ste  filtered Ste Þ=f cv

ð96Þ

and, for TSS, via the turbidity, once a turbidity versus TSS concentration calibration curve for the AS has been prepared (Wahlberg et al., 1994).

4.14.9.2 ISS Concentration The ISS concentration from the influent accumulates in the reactor in the identical way as the UPOs Equation (66), that is, the mass of influent ISS in the reactor is equal to the daily flux of ISS into the reactor FXIOi times the sludge age (Rs), viz.,

MXIO ¼ FXIOi Rs

ðmgISSÞ

ð97aÞ

ðmgISS d1 Þ

ð97bÞ

where

FXIOi ¼ XIOi Qi

and XIOi ¼ influent ISS concentration (mgISS l1). The influent ISS is only part of the ISS measured in the reactor. The OHOs (and PAOs if present) also contribute to this concentration. For fully aerobic and ND systems, where only OHOs comprise the active biomass, the OHOs contribute about 15% of their OHOVSS mass to the ISS (Ekama and Wentzel, 2004). It appears that this ISS mass consists of intracellular dissolved solids, which, when a sludge sample is dried in the TSS procedure, precipitate as ISS. If this is so, then theoretically, this TSS contribution of the OHOs (and PAOs if present) should strictly be ignored even though it manifests in the TSS test, because being intracellular dissolved solids, it does not add to the actual ISS load on the secondary settling tank. However, because this ISS mass has always been implicitly included in the TSS test result in the past, it will be retained because SST design procedures have been based on the measured TSS result. Including the OHO ISS mass yields for non-BEPR systems,

MXIO ¼ MXIOi Rs þ f iOHO MXBH

ðmgISSÞ

ð98aÞ

¼ MXIOi Rs þ f iOHO f avOHO MXv

ðmgISSÞ

ð98bÞ

where favOHO is the fraction of the VSS mass as active OHOs (see Section 4.14.9.4) and fiOHO the inoganic content of the OHOs ( ¼ 0.15 mgISS/mgOHOVSS). For BEPR systems, the ISS in the PAOs needs to be included also. For aerobic P uptake BEPR, fiPAO ¼ 1.30 mgISS/ mgPAOVSS, that is, 9 times higher than for OHOs. For anoxic P uptake BEPR, the P content (fXBGP), and hence the ISS content (fiPAO), of the PAOs is lower and variable. Calculating the reactor ISS concentration for NDBEPR systems with PAOs is presented in Section 4.14.31.4 later.

Biological Nutrient Removal 4.14.9.3 Process Design Equations 4.14.9.3.1 For the influent The flux of total organics (FSti, mgCOD d1), biodegradable organics (FSbi, mgCOD d1), and unbiodegradable particulate organics (FXIi, mgVSS d1) are

FSti ¼ Qi Sti

ð99Þ

FSbi ¼ Qi Sbi

ð100aÞ

¼ Qi Sti ð1  f S0us  f S0up Þ ð100bÞ ¼ FSti ð1  f S0us  f S0up Þ FXli ¼ Qi Xli

ð100cÞ ð101aÞ

¼ Qi f S0up Sti =f cv

ð101bÞ

¼ FSti f S0up =f cv

ð101cÞ

MXv ¼ MXBH þ MXEH þ MXI YH Rs ð1 þ f EH bH Rs Þ þ FXLi Rs ¼ FSbi ð1 þ bH Rs Þ   ð1  f S0us  f S0up ÞYH Rs f S0up ¼ FSti Rs ð1 þ f EH bH Rs Þ þ ð1 þ bH Rs Þ f cv ðmgVSSÞ

4.14.9.3.2 For the system

MXBH ¼ XBH Vp

ð102aÞ

MXEH ¼ XEH V p

ð102bÞ

MXi ¼ Xi Vp

ð102cÞ

MXv ¼ Xv Vp

ð102dÞ

ð106Þ

MXIO ¼ FXIOi Rs þ f iOHO MXBH ðmgISSÞ

ð107Þ

and hence

MXt ¼ MXv þ MXIO  ð1  f S0us  f S0up ÞYH Rs ¼ FSti ð1 þ f EH bH Rs þ f iOHO Þ ð1 þ bH Rs Þ  f S0 up XIOi ðmgTSSÞ ð108Þ þ þ Rs f cv Sti The VSS/TSS ratio of the sludge (fi) is

fi ¼

The masses of OHO VSS (MXBH, mgVSS), endogenous residue VSS (MXEH, mgVSS), unbiodegradable organics VSS (MXI, mgVSS), volatile settleable solids VSS (MXv, mgVSS), and the TSS (MXt, mgTSS) in the system are

447

MXv MXt

ðmgVSS=mgTSSÞ

ð109Þ

If the influent ISS concentration (XIOi) is not known, then the reactor TSS mass (MXt) can be calculated from an estimated VSS/TSS ratio (fi) of the sludge, that is,

MXt ¼ MXv =f i

ðmgTSSÞ

ð110Þ

The flux of oxygen utilized (FOc, mgO d1) is

 FOc ¼ FSbi ð1  f cv YH Þ þ f cv ð1  f EH ÞbH ¼ FSti ð1  fS0us  fS0up Þ   ð1  f cv YH Þ þ f cv ð1  f EH ÞbH



YH Rs ð1 þ bH Rs Þ

 YH Rs ðmgO d1 Þ ð1 þ bH Rs Þ

ð111Þ FOc ¼ OURc Vp MXBH ¼ FSbi

ð102eÞ

YH Rs ð1 þ bH Rs Þ

YH Rs ¼ FSti ð1  f S0us  f S0up Þ ð1 þ bH Rs Þ ðmgVSSÞ

Vp ¼ MXt =Xt ð103Þ

MXEH ¼ f EH bH Rs MXBH YH Rs ¼ FSbi f EH bH Rs ð1 þ bH Rs Þ ¼ FSti ð1  f S0us  f S0up Þ

YH Rs f EH bH Rs ð1 þ bH Rs Þ

ðmgVSSÞ

MXI ¼ FXli Rs ¼ ðmgVSSÞ

Knowing the mass of total settleable solids (MXt) in the reactor, the volume of the reactor is determined from the value specified for the MLSS concentration, Xt (see Section 4.14.10):

ð104Þ f S0up FSupi Rs ¼ FSti Rs f cv f cv ð105Þ

ðl; m3 ; or MlÞ

ð112Þ

Knowing the volume Vp, the nominal hydraulic retention time, Rhn, is found from the design average dry weather flow rate Qi from Equation (59a). The above design equations lead to the following important conclusions for the steady-state model: the mass of VSSs in the reactor is a function principally of the daily flux of COD on it and the sludge age. Similarly, the mass of TSS in the reactor is a function principally of the daily flux of COD and ISS on the reactor and the sludge age. Consequently, insofar as the mass of sludge in the reactor is concerned, it is immaterial whether the flux of COD (and ISS) arises from a low daily flow with a high COD (and ISS) concentration, or a high daily flow with a low COD (and ISS) concentration. Provided FSti (and FXIOi) is the same in both cases, the masses of sludge (TSS) in

448

Biological Nutrient Removal

the reactor will be virtually identical. However, the hydraulic retention times will differ, being long in the first and short in the second case, respectively. The hydraulic retention time, therefore, is incidental to the COD (and ISS) fluxes, the reactor VSS (and TSS) masses, and the daily flow – it serves no basic design function in the steady-state model. Design criteria for the AS reactor volume based on hydraulic retention time should therefore be used with extreme caution because they implicitly incorporate specific wastewater strength and characteristics values typical for the regions for which they were developed.

4.14.9.4 Active Fraction of the Sludge The active VSS mass MXBH in the reactor is the live OHO mass which performs the biodegradation processes of the organic material. The other two volatile solids masses, MXEH and MXI, are inactive and unbiodegradable and do not serve any function insofar as the biodegradation processes in the system are concerned. They are given different symbols because of their different origin, the MXI is UPOs from the influent wastewater and the MXEH is UPOs produced in the reactor via the endogenous respiration process. The active OHO fraction of the volatile solids in the reactor favOHO is given by

f avOHO ¼

MXBH MXv

ðmgOHOVSS=mgVSSÞ

ð113Þ

Substituting Equations (103) and (106) for MXBH and MXv and rearranging yields

f S0up ð1 þ bH Rs Þ 1 ¼ 1 þ f EH bH Rs þ f avOHO f cv YH ð1  f S0up  f S0us Þ

ð114Þ

where fatOHO is the active OHO fraction of the VSS mass. If the TSS mass is used as the basis for determining the active fraction, then the active fraction of the sludge mass with respect to the total settleable solids, fatOHO, is given by

f atOHO ¼ f i f avOHO

ð115Þ

where fi is the VSS/TSS ratio of the AS. The active fractions favOHO or fatOHO give an indication of the stability of the waste sludge, which is related to the remaining biodegradable organics in the sludge mass. The only biodegradable organics in the VSS mass are those of the OHOs which in terms of the steady-state model is 80% (1  fEH) of the OHO mass. Hence, the higher the active fraction, the greater the proportion of biodegradable organics in the sludge mass and the greater the utilizable energy content of the sludge mass. For an AS (or digested primary sludge) to be stable, the remaining utilizable organics in it should be low so that it will not generate odors through further significant biological activity. Ekama et al. (2006b) and Harding et al. (2009) have shown experimentally that the UPOs of the influent (UPO, Supi) and the endogenous residue organics (XEH) generated in the AS system remain unbiodegradable in aerobic and anaerobic digestion. Therefore, the biodegradable COD fraction of primary sludge and WAS can be calculated from its wastewater characteristics (fS’up and fS’us) and AS active

fraction, respectively. Sludge used as a soil conditioner needs to be stable because its primary purpose is to provide nutrients and unbiodegradable organic content to the soil (Korentajer, 1991); unstable sludges applied to agricultural land lead to an undesirable high oxygen demand in the soil through significant residual biological activity.

4.14.9.5 Steady-State Design Chart The design equations set out above form the starting point for aerobic and anoxic–aerobic N removal AS system design, from the relatively simple single reactor completely mixed aerobic system to the more complex multireactor anoxic–aerobic systems for biological nitrogen removal. When BEPR is included in the AS system the above equations do not give an accurate estimate for the VSS and TSS masses in the system. With BEPR, a second group of heterotrophic organisms, the PAOs need to be considered, which have different stoichiometric and kinetics constants producing more VSS and TSS mass per mass organics (COD) utilized. Incorporation of the PAOs in the steady-state model is presented in Section 4.14.31. For the more complex anoxic–aerobic systems, the above basic equations apply if the assumptions made in their derivation apply. Provided this is the case, the effects of nitrification (see Section 4.14.17) and ND (see Section 4.14.24) and the associated oxygen demands can be formulated as additional equations to the basic equations above. That the above simplified approach is adequate for design rests principally on the assumption that the biodegradable organics are completely utilized. This has been established by the close correlation achieved between the mean response of the more complex anoxic aerobic systems predicted by the more complex general kinetic model (and validated experimentally), with that calculated by the above basic equations and the additional equations for ND. The close correspondence between this simplified steady-state model and the more complex general kinetic simulation model is demonstrated for anoxic–aerobic systems including anoxic–aerobic digestion by So¨temann et al. (2006). Indeed, the simplified models developed in this chapter form the basis for hand calculations to (1) develop design input information for and (2) check output results from the dynamic simulation models. Other assumptions on which the steady-state model is based are that (1) the mass of active OHOs seeded into the system with the influent is negligible in comparison with that which grows in the reactor and (2) there is no loss of solids in the effluent from the secondary settling tanks, (3) water mass is conserved, (4) a 100% COD balance is achieved, and (5) active OHO loss is modeled as endogenous respiration. It is important to take cognizance of these assumptions in the model. With regard to the assumption of complete utilization of biodegradable organics, if this is not the case, then the mass of sludge produced per day increases and the carbonaceous oxygen demand decreases below those predicted by the basic equations. The reason for these deviations lies in the kinetics of degradation of the slowly biodegradable particulate material – if, for example, the aerobic fraction of the sludge mass is too small, the BPOs are only partially utilized and residual BPOs accumulate in the system as additional VSS like UPOs. Concomitantly, the carbonaceous oxygen demand is

Biological Nutrient Removal

reduced because less biodegradable organics are utilized. Clearly, for such situations the simple steady-state equations are inappropriate – approximate solutions are sometimes obtainable by simulation using the general kinetic models

such as UCTOLD (Dold et al., 1991) or ASM1 (Henze et al., 1987). Graphs of Equations (103)–(111) and (113)–(115) are shown in Figures 15(a)–15(d) per COD flux (FSti). The values

Settled wastewater

Raw wastewater

10

10 T = 20 °C YH = 0.45 bH = 0.24 fcv = 1.48 fEH = 0.20

T = 20 °C YH = 0.45 bH = 0.24 fcv = 1.48 fEH = 0.20

f S′up = 0.15 f S′us = 0.07 f i = 0.75 8 Sludge mass (kg/kgCOD on reactor)

MXt 6 MXv

4 MXi 2

f S′up = 0.04 f S′us = 0.12 f i = 0.83

6 MXt 4 MXv MXi

2

MXEH

MXBH MXEH

MXBH 0

0 20 15 10 Sludge age (days)

5

0 (a)

25

0

30

5

FOc 0.6

0.4

0.4 favOHO 0.2 fatOHO

0.0 10 15 20 Sludge age (days)

25

0.8

FOc 0.6

0.6 favOHO

0.4

0.4 T = 20 °C YH = 0.45 bH = 0.24 fcv = 1.48 fEH = 0.20

0.2

fatOHO 0.2

0.0

0.0 5

30

1.0 f S′up = 0.04 f S′us = 0.12 f i = 0.83

0.8

Active fraction

0.8

Oxygen demand (kg/kgCOD on reactor)

Active fraction

f S′up = 0.15 f S′us = 0.07 f i = 0.75

0.6

0

25

Settled wastewater

0.2

(b)

20

1.0

1.0

0.8

15

Sludge age (days)

Raw wastewater 1.0 T = 20 °C YH = 0.45 bH = 0.24 fcv = 1.48 fEH = 0.20

10

(c)

0.0 0

30 (d)

Oxygen demand (kg/kgCOD on reactor)

Sludge mass (kg/kgCOD on reactor)

8

449

5

10 15 20 Sludge age (days)

25

30

Figure 15 Active (MXBH), endogenous (MXEH), insert (MXI), volatile (MXv), and total (MXt) masses (kg) of settleable solids in the reactor per kgCOD d1 organic flux on the reactor for (a) the example raw wastewaters and flux carbonaceous oxygen demand (FOc, kgO d1) per kgCOD d1 organic flux on the reactor and active fraction with respect to volatile solids (favOHO) and total solids (fatOHO) vs. sludge age for (b) the example raw wastewaters vs. sludge age from 3 to 30 days at 20 1C, (c) the settled wastewaters and flux carbonaceous oxygen demand (FOc, kgO d1) per kgCOD d1 organic flux on the reactor and active fraction with respect to volatile solids (favOHO) and total solids (fatOHO) vs. sludge age for (d) settled wastewaters vs. sludge age from 3 to 30 days at 20 1C.

450

Biological Nutrient Removal

Table 7 Influent wastewater COD characteristics for the example raw and settled wastewaters Wastewater characteristic

Unbiodegradable soluble COD fraction (fS’us) Unbiodegradable particulate COD fraction (fS’up) Influent ISS (XIOi)a mgISS l1

Wastewater type Raw

Settled

0.07 0.15 47.8

0.12 0.04 9.9

a

These influent ISS concentrations give VSS/TSS (fi) values for the raw and settled wastewater systems at 20 days sludge of 0.75 and 0.83 mgVSS/mgTSS, respectively.

of the kinetic constants, YH, bH, fcv, and fEH are listed in Table 6 for 20 1C and have been validated in extensive laboratory and pilot-scale investigations over the years. The values of the unbiodegradable particulate and soluble COD fractions (fS’up and fS’us, respectively) and the AS VSS/TSS ratio (fi) are listed in Table 7 and are values for the example raw and settled municipal wastewaters that will used throughout this chapter. Figures 15(a) and 15(c) give the masses MXBH, MXEH MXI, MXv and MXt in the reactor for a unit flux of COD applied per day to the reactor for sludge ages from 3 to 30 days for the example raw and settled wastewaters, respectively. Figures 15(b) and 15(d) show the associated flux of oxygen to be supplied for organic material degradation (FOc) and the active fraction of the sludge with respect to VSS (favOHO) and TSS (fatOHO) for sludge ages from 3 to 30 days for the example raw and settled wastewaters, respectively. For a particular wastewater, the volume of the reactor at two different sludge ages will be in direct proportion to the TSS mass (MXt) in the reactor if the same reactor concentration Xt (mgTSS l1) is specified. The graphs show that the active mass increases fairly rapidly with an increase in sludge age up to about 10 days, after which it increases only marginally. The carbonaceous oxygen demand shows a similar behavior, increasing significantly from 3 to 10 days sludge age and thereafter more gradually. In contrast, the fractions of endogenous and inert solids increase rapidly relative to the active mass at sludge ages greater than 10 days and consequently in systems treating raw wastewater at sludge ages longer than 10 days; only a relatively small fraction of the sludge mass are active OHOs; most of the VSS sludge mass is unbiodegradable organic material from the influent and the endogenous process.

4.14.9.6 The Calculation Procedure The calculation procedure to generate the design results required for a certain sludge age is as follows: Select the wastewater characteristics fS’up, fS’us, and XIOi which are believed to best represent the unbiodegradable particulate and soluble COD fractions and ISS content of the wastewater. Then calculate 1. Supi (Equation (4)), Susi (Equation (3)), and XIi (Equation (6)) 2. FSti (Equation (99)) and/or FSbi (Equation (100)), FXIi (Equation (101)) and FXIOi ( ¼ Qi XIOi); 3. select the sludge age Rs;

4. MXBH (Equation (103)), MXEH (Equation (104)), MXI (Equation (105)), MXv (Equation (106)), MXIO (Equation (107)), or select fi, MXt (Equation (108) or (110)); 5. Select Xt, Vp (Equation (112)); 6. FOc (Equation (111)) and OURc (Equation (102e)); 7. Rhn, (Equation (59a)); and 8. Ste (Equation (95)). In this design procedure the input COD and its characteristics will be governed by the specific waste flow. The parameter that requires selection is the sludge age; this will depend on the specific requirements from the WWTP such as effluent quality, that is, organic COD removal only, nitrification, N removal, biological P removal, and the envisioned sludge treatment facilities, that is, whether or not primary settling is included, the stability of the WAS, etc. Specification of the sludge age, therefore, is an important design decision and requires special consideration (see Section 4.14.15). For illustrative purposes, however, the effect of sludge age on the various design results such as reactor volume, oxygen demand, and sludge production and nutrient (N and P) requirements for sludge production are demonstrated in the following sections.

4.14.10 Reactor Volume Requirements Once the mass of sludge in the reactor is known from a specified sludge age and influent organic COD flux, the reactor volume is determined by diluting this mass of sludge to a specified TSS concentration (Xt). From the volume, the nominal hydraulic retention time, or aeration time for fully aerobic systems, is fixed (by Equation (59)). Hydraulic retention time therefore is immaterial in the design procedure – it is a consequence of the mass of sludge in the reactor and a selected TSS concentration. This point was mentioned earlier but bears repeating because some design procedures lay stress on retention time or aeration time as a basic design parameter, an approach that can result in serious miscalculation of the reactor volume requirements. Compare, for example, two plants operating at the same sludge age, both receiving the same organic load (kgCOD d1) but the first at high influent COD concentration and low flow and the second at a low concentration and high flow. If designed on a specified hydraulic retention time, the volume of the first will be much smaller than that of the second but the sludge mass in the reactors will be the same. Consequently, the first plant may have an inordinately high TSS concentration which may cause problems in the secondary settling tank. Therefore, retention time is a completely inappropriate basis for design and other purposes such as a criterion for comparing the reactor volume requirements of different plants. Figure 16 shows the reactor volume requirements versus sludge for the example raw and settled wastewaters obtained from Equations (99)–(115). The reactor volume requirements may also be determined from the equivalent COD load per person equivalent (PE), also shown in Figure 16 for a raw WW COD load of 0.10 kg COD/person/day. Hence, treating the example raw WW at a sludge age of 20 days and a TSS concentration of 4 kgTSS m3, a reactor volume of 145 l

Biological Nutrient Removal Reactor volume 350

3.5 Raw wastewater load = 0.10 kgCOD/(PE d) 40% COD removal in PST

2.5

300

250

Reactor TSS Kg m−3

3 200

2.0 4 Raw 1.5

150

5

1.0

Liters per PE

m3/kgCOD per day on treatment plant

3.0

100

3 4

0.5

451

WW) systems – in a survey of 45 full-scale AS plants in the Netherlands, Stofkoper and Trentelman (1982) found significantly higher DSVIs in settled WW systems than in raw WW systems (see Ekama and Marais, 1986). The effect of WW strength and sludge settleability, as well as other factors such as the peak wet weather flow (PWWF) to average dry weather flow (ADWF) ratio (or peak flow factor fq ¼ PWWF/ADWF), WW and AS characteristics (fS’up, fS’us, fi) and construction costs, can all be taken into account by determining the reactor concentration from a construction cost minimization analysis (Ho¨rler, 1969; Dick, 1976; Riddell et al., 1983; Pincince et al., 1995). In such an analysis, the construction cost of the reactor(s) and the SSTs is determined as a function of the reactor TSS concentration. The reactor concentration at which the combined construction cost of the reactor(s) and the SST(s) is a minimum, is the design reactor concentration.

50

5

4.14.11.1 Reactor Cost Settled 0

0.0 0

5

10 15 20 Sludge age (days)

25

30

Figure 16 Reactor volume requirements in m3 kg1 COD raw WW load per day vs. sludge age at different average reactor TSS concentrations for raw and settled WW (assuming 40% COD removal by primary sedimentation). Reactor volume requirements in liter or person equivalent (PE) is also given on the right-hand vertical axis based on a raw WW COD contribution of 0.10 kg COD/person equivalent.

per PE is required or 1.45 m3 kg1 COD applied per day to the WWTP. The comparative reactor volume requirements for settled WW per kgCOD load per day on the WWTP also is shown in Figure 16 taking due consideration of the COD fraction removed by primary sedimentation (40% for the example settled wastewater) and the reduction in settled wastewater UPO fraction (fS’up) this causes (Table 6). From Figure 16, treating settled WW at a sludge age of 20 days and reactor TSS concentration of 4 kgTSS m3 requires a reactor volume of 0.55 m3 kg1 raw WW COD load per day on the WWTP or 55 l per PE. Therefore, a significant reduction in reactor volume can be obtained by means of primary sedimentation – 62% for the example raw and settled WWs at 20 days sludge age.

4.14.11 Determination of Reactor TSS Concentration The choice of the reactor concentration can be done empirically from past experience with similar WWs or selected from design guidelines such as those from Metcalf and Eddy (1991), for example, for conventional systems (with primary sedimentation) 1500–3000 mgTSS l1 or extended aeration (without primary sedimentation) 3000–6000 mgTSS l1. Differences in the reactor TSS concentration for raw and settled WWs arise because (1) the WW flow per kgCOD load on the reactor for raw WW is significantly greater than that for settled WWs and (2) sludge settleability in conventional (settled WW) systems can be poorer than with extended aeration (raw

For selected WW and AS characteristics (fS’up, fS’us, XIOi, or fi), sludge age (Rs), and organic COD load on the reactor (FSti Reactor), the mass of TSS in the reactor (MXt) can be determined from the steady-state model (Section 4.14.9) and remains constant at a fixed sludge age. The reactor volume as a function of the reactor TSS concentration Xt is found from Equation (112), viz., Vp ¼ MXt/Xt m3, where Xt is the reactor concentration in kgTSS m3. To estimate the cost of the reactor from the volume, empirical functions relating the construction cost of the reactor to the volume are required. Such functions show (1) that as the reactor becomes smaller so its construction cost gets lower (Figure 17) and (2) a benefit of scale effect in that it is cheaper (per m3) to build a large reactor than a small one.

4.14.11.2 SST Cost On the basis of the flux theory, Ekama et al. (1997) show that provided the underflow recycle ratio R is above the critical minimum value, the maximum overflow rate at PWWF (qPWWF, m h1) of the SST is a function of only the reactor (or feed) solids concentration (Xt) and the sludge settleability. Therefore, the maximum overflow rate in the SST must not exceed the settling velocity of the AS at the feed concentration (Xt). For a selected sludge settleability, if the reactor (Xt) concentration increases, the settling velocity of the sludge decreases, with the result that the maximum overflow rate in the SST must be lower for higher Xt. Hence, the required surface area for the SSTs (AST) gets larger as the reactor concentration increases. Therefore, as the biological reactor becomes smaller with increasing concentration, the SST area and its construction increase.

4.14.11.3 Total Cost The total cost of the reactor–SST system is the sum of the reactor and SST costs. Qualitative results for the example raw and settled WWs are given in Figure 17, ignoring that the reactor volume and SST diameter may have upper and lower size restrictions. For real WWTPs, the reactor and/or SST may

452

Biological Nutrient Removal Cost minimization 20

Cost minimization

20 RAW WW Rs = 20 days DSVI = 120 ml g−1 Reactor conc. for minimum cost

10

Reactor conc. for minimum

15

Cost

Cost

15

Settled

Total

10

Reactor

Total

5

5 Reactor SST

SST

0

0 0

2

4

6

8

10

Reactor conc. (kg TSS m−3)

0

2

4

6

8

10

Reactor conc. (kg TSS m−3)

Figure 17 Reactor, secondary settling tank, and total construction costs to estimate the reactor TSS concentration for minimum total cost in single reactor and SST units for the example raw and settled wastewaters.

need to be split into two or more equal sized modules to bring the volume and diameter within the limit ranges. Basically, the reactor volume depends on organic load (FSti) and the SST surface area on hydraulic load (PWWF). This is the reason why in Figure 17 the cost of the SST for the raw and settled wastewaters is the same but the cost of the reactor for the raw wastewater is higher than for the settled wastewater. From cost minimization analyses such as that above, generally it will be found that the range of reactor concentration for minimum construction cost (1) is higher for higher influent WW strengths (BOD5, COD), (2) is higher for longer sludge ages, and (3) is higher for raw WW than settled WW at the same strength, because these three changes all increase the size of the biological reactor relative to that of the settling tank, (4) is lower for higher peak flow factors (fq), and (5) is lower for poorer settling sludges because these two changes all increase the size of the settling tank relative to that of the biological reactor. A universal optimum therefore cannot be specified. In countries with low WW strengths and short sludge age plants (e.g., North America), the reactor concentration tends to be low (2000–3000 mgTSS l1) and in countries with high WW strengths and long sludge age plants (e.g., South Africa), the reactor concentration tends to be high (4000–6000 mgTSS l1) as the example WWs demonstrate.

4.14.12 Carbonaceous Oxygen Demand 4.14.12.1 Steady-State (Daily Average) Conditions The mean daily carbonaceous oxygen demand per kgCOD load on the reactor (FOc/FSti Reactor) is calculated from Equation (111). For sludge ages longer than 15 days the increase in FOc/FSti Reactor is small with further increase in sludge age for both raw and settled wastewater (Figures 15(b) and 15(d)). The FOc/FSti Reactor for raw and settled wastewater is usually within 10% of each other, with the demand for settled wastewater being the higher value. This is because compared to raw wastewater, a higher percentage of the total organics

(COD) load in settled wastewater is biodegradable. For example, the wastewaters at 20 days sludge age, the FOc/FSti Reactor is 0.604 kgO/kgCOD for raw wastewater and 0.653 kgO/ kgCOD for settled wastewater. Although there is only a small difference in FOc/FSti Reactor between raw and settled wastewaters, there is a large difference in the oxygen demand (FOc) because primary settling removes a significant proportion of the WWTP organic load as primary sludge (PS). For settled wastewater, this is given by 0.653  (1– 0.40) for 40% COD removal in PSTs, which gives 0.38 kgO/ kgCOD load on the WWTP. For the raw wastewater, it would remain 0.604 kgO/kgCOD load on the treatment plant, making the settled wastewater oxygen demand 37% lower than that for the raw wastewater. Clearly, primary sedimentation leads to significant aeration energy savings – because primary settling tanks remove about 30–50% of the raw influent COD, the carbonaceous oxygen demand for settled wastewater generally will be about 30–50% lower than that for raw wastewater. The carbonaceous oxygen demand is the oxygen demand for the oxidation of the influent organics (COD) and the associated OHO endogenous process only. In N removal systems, oxygen is also required for nitrification, which is the biological oxidation of ammonia to nitrate by autotrophic nitrifiers. However, with denitrification, which is the biological reduction of nitrate to nitrogen gas by facultative heterotrophic organisms, some of the biodegradable organics are utilized with nitrate as electron acceptor, for which oxygen is then not required. Thus, denitrification leads to a reduction in the oxygen demand. The total oxygen demand for a N removal system therefore is the sum of the carbonaceous and nitrification oxygen demands less that saved by denitrification. The procedures for calculating the oxygen demand for nitrification and the oxygen saved by denitrification are discussed in Sections 4.14.22.3 and 4.14.27.2. The equations given here for calculating the carbonaceous oxygen demand are based on the assumption that all the biodegradable organics are utilized with oxygen as electron acceptor, that is, for fully aerobic systems.

Biological Nutrient Removal

453

Sludge production

4.14.12.2 Daily Cyclic (Dynamic) Conditions Owing to the daily cyclic nature of the organic (COD) load on the reactor, the carbonaceous oxygen demand will vary concomitantly over the day. The TKN load on the reactor also varies over the day in an approximately similar fashion as the organic load. Generally, the COD and TKN loads on the reactor increase in the morning due to increases in both flow and COD and TKN concentration reaching a peak at around noon. Thereafter, the COD and TKN loads decrease reaching a minimum during the nighttime hours of 2–4 am due to decreases in both flow and COD and TKN concentration. The peak-to-average and minimum-to-average load ratios and the time of day these occur depend on the catchment that the particular treatment plant serves, such as size of population, layout of the catchment and industrial activity. Generally, the smaller the catchment, the lower the flow and COD and TKN loads but the greater the peak to average flow and load ratios and the lower the minimum to average flow and load ratios. As the TKN load and its variation over the day and the nitrification process have a profound influence on the daily average and peak total oxygen demands, empirical methods to estimate the peak oxygen demand from the average for fully aerobic nitrifying systems are discussed in Section 4.14.27.2. Fully aerobic AS systems with sludge ages longer than 3 days are likely to nitrify at temperatures 414 1C. Moreover, a sludge age of 3 days is around the limit of validity for the steady-state AS model because at sludge ages lower than this the assumption that all the biodegradable organics are utilized is not valid. Therefore, there is little merit in developing empirical methods for estimating the peak oxygen demand for fully aerobic systems without nitrification.

4.14.13 Daily Sludge Production The mass of sludge produced per day by the AS system is equal to the mass of sludge wasted per day from it via the waste flow and is called WAS or secondary sludge. From the definition of sludge age (see Equation (58)), the mass of sludge TSS produced per day (flux) FXt is given by the mass of sludge in the system MXt divided by the sludge age (Rs), that is,

FXt ¼ MXt =Rs

ðmgTSS d1 Þ

ð116Þ

Substituting Equation (108) for MXt and simplifying yield the sludge produced per day per mgCOD load on the biological reactor, that is,   ð1  f S0 us  f S0 up YH Þ f S0 up XIOi FXt ¼ þ ð1 þ f EH bH Rs þ f iOHO Þ þ ð1 þ bH Rs Þ FSti f cv Sti

ðmgTSS=dÞ=ðmgCOD=dÞ

ð117Þ

A plot of the daily TSS produced per unit COD load on the biological reactor (Equation (117)) versus sludge age (Rs) is shown in Figure 18 for the example raw and settled wastewaters. It can be seen that the mass of sludge produced in the AS system (per unit COD load on the biological reactor) decreases as the sludge age increases for both raw and settled wastewater but the rate of decrease is negligible at sludge ages longer than about 20 days. Treating settled wastewater results

kgTSS/d per kgCOD/d on reactor

0.5

0.4

Raw 0.3

Settled

0.2

T = 20 °C YH = 0.45 bH = 0.24 f cv = 1.24 f EH = 0.20

0.1

0.0 0

5

10

15

20

25

30

Sludge age (days) Figure 18 Daily sludge production in kgVSS d1 and kgTSS d1 per kgCOD load per day on the biological reactor for the example raw and settled wastewaters at 14 1C.

in lower secondary sludge production per unit COD load on the biological reactor than treating raw wastewater. This is because the unbiodegradable particulate COD fraction (fS’up) and inorganic content (XIOi/Sti) in settled wastewater are significantly lower than that in raw wastewater. Temperature effects on secondary sludge production are small – sludge production at 14 1C is about 5% greater than at 22 1C, a difference which is completely masked by the uncertainty in the estimates of the wastewater characteristic fS’up and the VSS/TSS ratio (fi) of the sludge if the influent ISS concentration (XIOi) is not measured. Although the secondary sludge production treating settled wastewater is lower than that treating raw wastewater, the total sludge mass treating settled wastewater is higher because the total sludge production includes both the primary and secondary sludges; at plants treating raw wastewater, only secondary sludge is produced. In systems treating raw wastewater, the primary sludge is in effect treated in the AS reactor itself. From the COD balance, the more the oxygen utilized in the system, the lower the sludge production and the lower the active fraction of the sludge (Figures 14 and 15). Therefore, because the carbonaceous oxygen demand is much higher when treating raw wastewater, the overall sludge production is much lower compared with settled wastewater. Generalizing the above observations and taking into account the active fraction of the WAS as an indication of the remaining biodegradable organics, there are two extremes in approach to designing WWTPs with AS for biological treatment, viz., 1. Treating settled wastewater at a short sludge age (say 5–8 days). This results in a very small AS system with low oxygen

454

Biological Nutrient Removal

demand and a high sludge production with high energy content, that is, high remaining biodegradable organics in both the primary and secondary (waste activated) sludges requiring further stabilization treatment before disposal, or 2. Treating raw wastewater at a long sludge age (say 30 days). This results in a very large AS system with high oxygen demand and a low sludge production with a low energy content, that is, no primary sludge and low remaining biodegradable organics (low active fraction) in the secondary sludge, not requiring further stabilization treatment before disposal. The daily production of secondary and primary sludges is the mass of sludge that needs to be treated and disposed of by downstream sludge handling methods. Sludge treatment and disposal for BNR systems, in particular, should not be seen as separate from the design of the AS system. In fact, all unit operations of the WWTP from raw wastewater pumping to ultimate disposal of the sludge should be viewed as an integrated system where the design of one unit operation depends on the unit operations before it, and decisions on its design may affect the design of unit operations following it.

4.14.14 System Design and Control The parameter of fundamental importance in the design and control of the AS system is the sludge age, which governs the mass of sludge to be wasted daily from the system. The sludge age can and should replace completely the food to microorganism ratio (F/M, kgBOD or COD load per day per kgMLSS or MLVSS in reactor) or equivalently the load factor (LF) as a reference and control parameter, in particular if nitrification is required. The sludge age can be fixed by a simple control procedure if the system is appropriately designed. This control procedure is simpler and operationally more practical and reliable than procedures based on the F/M or LF, which basically seek to control the mass of sludge in the system by controlling the reactor MLSS concentration at some specified value.

4.14.14.1 System Sludge Mass Control By far the most common AS system control procedure involves keeping the sludge MLSS concentration in the reactor at some specified value. At best this sludge concentration is specified by design or at worst, established from operational experience on the plant behavior, which is usually the concentration that can be contained in the system by the SSTs. This approach does not control sludge age, only the mass of sludge in the system. In fact, in some instances, it may not even be the sludge mass that is controlled via the reactor concentration, but the settled volume at 30 min (SV30) in the 1l measuring cylinder. If the SV30 is greater than say 450 ml l1, then sludge is wasted until it reaches this value again. This approach was developed to obviate the need for measuring the reactor sludge concentration and with it, the sludge concentration in the reactor varied with the sludge settleability (SVI). This approach was acceptable before nitrification became obligatory and at least ensured that the sludge could be contained in the system while maintaining a low effluent suspended solids (ESS)

concentration. However, with this method there is no control of the F/M, the LF, the sludge mass, the reactor concentration, or the sludge age, a situation which is completely untenable when nitrification is required. While nitrification is a simple process to cater for in design – just make the sludge age long enough and provide sufficient oxygen – it imposes a completely different control regime on the operation of the system. It requires the sludge age to be controlled at a fixed value. If the F/M or LF are controlled, then to keep these parameters within the desired limits, not only does the reactor concentration need to be measured regularly, but also the daily BOD5 (or COD) load. This requires extensive sampling and testing of the influent BOD5 (or COD) concentration and flow pattern over the day to determine the daily COD (or BOD) mass load. Controlling the sludge age requires measuring the reactor MLSS concentration and the mass of sludge wasted per day. Usually, the waste sludge is abstracted from the SST underflow to benefit from its thickening function. However, the sludge concentration of the underflow varies considerably over the day with the daily cyclic flow through the plant (Figures 19(a) and 19(b)). Therefore, to know the sludge mass wasted via the underflow, it is necessary to measure the underflow concentration, waste flow rate, and duration each time sludge is wasted. Therefore, to know the LF or sludge age, intensive testing of the influent and/or reactor and underflow concentrations are required. This may be manageable at large plants where the technical capacity is adequate, but on small plants, both the LF and sludge age usually are not known. As a consequence, nitrification is sporadic, partial, or stops altogether during periods of poor sludge settleability, which results in high sludge wastage and hence short sludge age. Even if the reactor concentration were accurately controlled with modern control equipment such as automated wasting and on-line reactor concentration measurement, this still does not control the sludge age. With reactor concentration control at the same value throughout the year and a stable organic load on the plant (zero urban development), the sludge age decreases during winter because sludge production per kgCOD load increases with decrease in temperature due to the lower endogenous respiration rate. Although the decrease is relatively small, decreasing the sludge age is nevertheless the opposite of what should be done to the system in winter to keep the ammonia concentration low. This is particularly relevant to plants operated at sludge ages close to the minimum for nitrification, (Section 4.14.20.3), which is common practice in developed countries to squeeze as much capacity as possible out of the plant because space for extensions is limited. If the reactor concentration is controlled and the organic load on the plant progressively increases, which is usually the case in developing countries where often urban growth is constrained by WWTP capacity, the sludge age decreases progressively with time (Figure 20a). Inevitably, on one cold winter day, nitrification will have stopped. The operators think the cause is a toxic batch of wastewater (which it could be but is not, in this instance) and hope that nitrification returns soon – it does not, because the sludge age is too short, certainly not until summer when the wastewater temperature increases. Thereafter, if sludge concentration is controlled, loss of nitrification will become a seasonal occurrence.

Biological Nutrient Removal

455

Reactor TSS conc.

16 000

5

4 8000 Reactor Xt 4000 Low flow Low XR

High flow High XR

0 24h00

06h00

18h00

24h00

High flow period low recycle ratio (for constant QR)

Low flow period high recycle ratio (for constant QR)

Xt

1.0

Constant MLSS conc

3

Increasing organic load

2

Decreasing sludge age

0 0

QI + QR QI

1.3 1.2 1.1

1

Time of day

(a)

QI + QR

12h00

Reactor MLSS (gTSS l−1)

Concentration (mg I−1)

SST underflow, XR 12 000

5

Xt

10

20

15

25

30

Sludge age (Rs)

(a)

QI

Reactor TSS conc. 5

Settled sludge XR

QR

Q I = influent flow rate (m3 h−1) Q R = recycle flow rate (m3 h−1) X t = reactor concentration (mgTSS I−1) X R = recycle concentration (mgTSS I−1) (b)

4

XR

= (Q I + Q R)X t/Q R = (1 + R)Xt /R R = recycle ratio = Q R/Q I

Figure 19 (a) Experimental data from a full-scale activated sludge plant illustrating the virtually constant reactor concentration compared with the widely varying SST underflow concentration over the day. From Nicholls HA (1975a). (b) Increased sludge accumulation and higher underflow sludge concentration at high influent flow periods (right) than at low influent flow periods (left) at constant recycle flow rate.

Reactor MLSS (gTSS l−1)

QR

1.3 1.2 1.1 1.0

3

Increasing organic load

2

1 Constant sludge age 0 0 (b)

When nitrification is required, not only should sludge age be controlled, but also the SST can no longer serve the dual purpose of clarifier and WAS thickener. To obtain high WAS concentrations, the underflow recycle ratio must be low (o0.25:1), which results in long sludge residence times in the SST (Figure 19(b)). The long sludge residence time stimulates denitrification in the SSTs causing floating (or rising) sludge on the SST surface, in particular in summer when wastewater temperature is high (420 1C). In fact, in the tropics, the climatic region of most developing countries, it may not be possible to operate an AS system that does not nitrify even at very short sludge ages. So the problem of rising sludge due to denitrification can take place in plants even where nitrification is not a requirement. This happened at Brazilia WWTP, which had a low return sludge ratio (0.25:1) – it nitrified even at 3 days sludge age and suffered from floating sludge all the time. If the sludge age was reduced to stop nitrification, the COD removal deteriorated below an acceptable level. So once

Increasing reactor MLSS

5

10

15

20

25

30

Sludge age (Rs)

Figure 20 (a) With reactor TSS controlled at a constant concentration, sludge age decreases as sludge production increases due to increasing organic load or wastewater temperature decrease. (b) With sludge age control, the reactor TSS concentration increases with increase in sludge production due to increase in organic load or decrease in wastewater temperature.

nitrification takes place, whether intentionally by design or unavoidably, one must cater to denitrification in appropriate reactor (anoxic) zones and increase the underflow recycle ratio (B1:1) to minimize rising sludge in the SSTs due to denitrification. Clearly, once nitrification takes place, whether as a requirement for N removal or unavoidably due to system conditions, one is forced to abandon using the SSTs as WAS thickeners. If one has to thicken WAS in a separate unit, whether from the underflow or reactor, one may as well waste sludge directly from the reactor and derive the significant

456

Biological Nutrient Removal

operational benefit of hydraulic control of sludge age. It is simple, requires very little testing, and establishes the sludge age almost exactly. It results in stable year-round nitrification and is strongly recommended for AS systems where nitrification is required, even where sophisticated reactor concentration control measures can be applied.

4.14.14.2 Hydraulic Control of Sludge Age Hydraulic control of sludge age was first proposed and implemented in a generalized form by Garrett in 1958, and is based on a method of modified wastewater aeration implemented by Setter et al. (1945). If a sludge age of 10 days is specified, (1/10)th of the reactor volume is wasted daily, if 20 days, 1/20th is wasted daily, that is, Qw ¼ Vp/Rs (Equation (58)). For plants with low levels of technical support, a satellite settling tank or a dewatering drying bed completely independent of the SSTs can be provided to which the daily WAS flow from the reactor is discharged – for plants with a higher level of technical support, a dissolved air flotation unit would be best (Bratby, 1978, Bratby et al., 2008), which also minimizes P release from BEPR sludges (Pitman, 1999). The supernatant is returned to the reactor and the thickened sludge is pumped to the sludge treatment/disposal part of the plant. This procedure establishes very closely the desired sludge age because the mixed liquor concentration does not change significantly over the day (Figure 19(a)). An important point about hydraulic control of the sludge age is that irrespective of the flow through the plant, if a fixed fraction of the volume of the reactor is wasted every day, the sludge age is fixed. If the COD mass load per day on the plant remains constant, the sludge concentration will remain constant automatically. If the COD mass load increases, the sludge concentration will increase automatically, to maintain the same sludge age (Figure 20b). Thus, by monitoring the reactor concentration and its changes at a fixed sludge age, an indirect measure is obtained of the long-term changes in COD load on the plant. With time, the reactor concentration may increase indicating that the organic load on the plant is increasing. Hydraulic control of sludge age is very easy for the operator – (s)he just needs to check that the flume/pipe is not blocked and is running at the correct flow rate; the reactor MLSS concentration does not even have to be measured very often. By means of the hydraulic control procedure, the sludge age may be changed by simply changing the volume wasted per day. If say, the sludge age is reduced from 25 to 20 days by hydraulic control, the full effect of the change will become apparent only after about half a sludge age. Thus, the biomass has an opportunity to adapt gradually to the change in F/M and LF. Hydraulic control of sludge age is particularly relevant to plants with sludge ages longer than about 5 days because for these plants the mass of sludge contained in the SSTs is a relatively small fraction of the total mass of sludge in the system. At sludge ages shorter than 5 days the mass of sludge in the SSTs can become appreciable with respect to the total mass of sludge in the system, particularly when the sludge settleability gets poor (DSVI4150 ml g1). When the mass of sludge in the SSTs is significant, hydraulic control will have to

take cognizance of this and accuracy of the control will require additional testing. Hydraulic control of sludge age devolves a greater responsibility on the designer and removes responsibility from the plant operator – oftentimes operator ingenuity had to work around design inadequacies by force fitting the biological processes into the designed constraints to achieve the best effluent quality. It becomes essential that the designer calculates the sludge mass more exactly, to provide sufficient reactor volume under the design organic load to allow for the required reactor concentration at the specified sludge age. Also, the settling tank surface area, underflow recycle ratio, and aeration capacity must be accurately sized for the particular wastewater and sludge age of the system. If these aspects are catered for adequately, then with hydraulic control of the sludge age, plant control is simplified and, on small-scale plants, may even do away with the requirements for solids and SVI tests except at long intervals. Hydraulic control of sludge age makes parameters such as LF and F/M redundant and introduces an entirely different attitude to system control. It is eminently practical and establishes the desired sludge age to ensure year-round nitrification. When nitrification is a requirement, sludge age control also becomes a requirement, and then the hydraulic control of sludge age is the easiest and most practical way. Moreover, with hydraulic control of sludge age the mode of failure of the plant is completely different than with solids mass control. With the solids mass control the plant fails by nitrification stopping and a high effluent ammonia concentration, a nonvisible dissolved constituent which also is difficult to remove by other means. With sludge age control, the plant fails more obviously – sludge solids over the secondary settling tank effluent weirs and high effluent suspended solids concentration. At plants managed with low levels of technical capacity, this is more like to prompt remedial action.

4.14.14.3 Flow and Load Equalization Tanks In BNR systems of any sludge age, aeration control is a particularly vexing problem under cyclic flow and load conditions, because the system is affected by too high or too low DO concentrations in the aerobic zone. Too high DO concentrations are unnecessarily expensive and result in oxygen recycle to the anoxic (and the anaerobic zone if BEPR is included), thereby reducing the potential for N and P removal; too low DO concentrations cause nitrification efficiency to decline and possibly poor settling sludges to develop. Although some good DO control systems have been developed over the years, the cost of providing aeration capacity and SST surface area for the peak flow has prompted research into alternative control solutions such as flow and load equalization. Furthermore, most of the diurnal variation in system variables such as ammonia, nitrate, and phosphate concentration is not induced by the biological processes but by the hydraulic flow variation. To minimize hydraulic flow variation, an equalization tank is provided upstream of the AS system and outflow from this tank is controlled in such a manner that the cyclic fluctuations in flow and load are damped to very small values. The tank is controlled by

Biological Nutrient Removal

microcomputer which calculates the tank outflow rate that best damps the projected inflow of the next 24 h. This flow equalization approach has been tested at the Goudkoppies BNR plant (Johannesburg, RSA) and showed great potential for reducing aeration and other control problems in nutrient removal plants (Dold et al., 1982, 1984).

4.14.15 Selection of Sludge Age Selection of the sludge age is the most fundamental and important decision in the design of an AS system, particularly when biological nutrient removal is included. The sludge age selected for a plant depends on many factors, some of which are listed in Table 8 such as stability of the system, sludge settleability, whether or not the waste sludge should be suitable for direct discharge to drying beds, and most important of all, the quality of effluent required, that is, is COD removal only acceptable, must the effluent be nitrified, are N and P removal required. Several of the factors have already been discussed earlier and will not be repeated here. Only a few clarifying and additional comments on Table 8 are made in the following sections.

Table 8

457

4.14.15.1 Short Sludge Ages (1–5 days) 4.14.15.1.1 Conventional plants These plants are operated in the conventional configuration, that is, a semi-plugflow configuration, but modified systems such as contact stabilization, step aeration, step feed, and others are also implemented. Short sludge age plants have been extensively used in Europe and North America before N (and P) removal became requirements. Their main objective is COD removal only, for which sludge ages of 1–3 days are sufficient. BOD5 or COD reductions range from 75% to 90%. The removal achieved depends on the wastewater characteristics, the operation of the plant (in particular, the management of the transfer of the sludge between the reactor and SSTs), and the efficiency of the SSTs. Because predatory activity of protozoan organisms on the free swimming bacteria is limited at short sludge ages, the nonsettling component (or dispersion) of the AS flocs is high which causes turbidity and high effluent COD (Chao and Keinath, 1979; Parker et al., 1971). It is accepted in Table 8 that short sludge age plants would not normally nitrify. For temperate and high-latitude regions, where wastewater temperatures are generally below 20 1C, this would be the case. However, in tropical and low-latitude

Some important considerations in the selection of sludge age for the activated sludge system

Sludge age

Short (2–5 days)

Intermediate (8–15 days)

Long (4 25 days)

Types

High rate, step feed, aerated lagoons, contact stabilization, pure oxygen

Extended aeration, orbal, carousel, BNR systems

Objectives

COD removal only

Similar to high rate but with nitrification and sometimes denitrification. BNR systems COD removal, nitrification, biological N removal, and/or biological P removal

Effluent quality

Low COD, high ammonia, high phosphate, variable

Low COD, low ammonia, low nitrate high/low phosphate, relatively stable

Low COD, low ammonia, low nitrate, low phosphate, usually stable

Primary settling

Generally included

Usually included

Usually excluded

Activated sludge quality

High sludge production, very active, stabilization required

Medium sludge production, quite active, stabilization required

Low sludge production, inactive, no stabilization required

Oxygen demand

Very low

High due to nitrification

Very high due to nitrification and long sludge age

Reactor volume

Very small

Medium to large

Very large

Sludge settleability

Generally good, but bulking by nonlow F/M filaments like S. natans, 1701, Thiothrix possible

Good at low sludge age and high aerobic mass fractions; but generally poor due to low F/M filament growth like M. parvicella

Can be good with high aerobic mass fractions, but generally poor due to low F/M filament growth, particularly M. parvicella

Operation

Very complex due to AS system variability and 11 and 21 sludge treatment

Very complex with BNR and 11 and 21 sludge treatment

Simple if without 11 and 21 sludge treatment, but BNR system is complex

Advantages

Low capital costs, energy self-sufficient with anaerobic digestion

Good biological N (and P?) removal at relatively low capital cost

Good biological N (and P?) removal No 11 and stable 21 sludge Low sludge handling costs

Disadvantages

High operation costs, effluent quality variation

Complex and expensive sludge handling costs

Large reactor, high oxygen demand, high capital cost

COD removal, biological N removal, biological P removal

458

Biological Nutrient Removal

regions, where wastewater temperatures can exceed 25–30 1C, short sludge systems would normally nitrify; in fact, it would be difficult to stop them doing so. For these situations, it is best to accept nitrification as inevitable and design the system accordingly. Furthermore, it would be advantageous to include a small primary anoxic zone (B15–20% anoxic mass fraction, see Section 4.14.24) in the system to denitrify a considerable proportion of the nitrate generated even if N removal is not required – this increases the minimum sludge age for nitrification, reduces oxygen demand, recovers alkalinity, and reduces the risk sludge flotation and high effluent COD due to denitrification on the SST bottom. Biological P removal is possible at short sludge ages of 3–5 days – the PAOs are relatively fast-growing heterotrophs (compared with the ANOs). In the absence of nitrification, an unaerated zone would be anaerobic (i.e., no nitrate or oxygen present or entering it) and provided the readily biodegradable (RB) COD and short-chain fatty acids (VFAs) are available from the influent, BEPR will take place. The original Phoredox system developed by Barnard (1976) is based on a two-reactor anaerobic–aerobic system. The minimum sludge age for BEPR is temperature dependent, increasing as temperature decreases and is around 3–5 days at 14–20 1C (Mamais and Jenkins, 1992). At these temperatures, the minimum sludge age for nitrification is significantly longer than that for BEPR, so that nitrification generally would not take place with the result that the adverse effect of nitrate on the BEPR would be absent. However, in warmer climates the minimum sludge age for nitrification and BEPR is similar, and ensuring a low nitrate recycle to the anaerobic reactor by including also anoxic zones is essential if BEPR is required (Burke et al., 1989). If BEPR is not required, the nitrification changes the two-reactor unaerated – aerated system from a P removal one to an N removal one.

4.14.15.1.2 Aerated lagoons Aerated lagoons, as distinct from aerated oxidation ponds in which oxygenation is supplemented by algae, are essentially high-rate AS systems because the oxygen demand is supplied wholly by aerators. There are essentially two types of aerated lagoons, suspension mixed and facultative. Suspension mixed aerated lagoons have sufficient energy input per unit volume by the aeration equipment to keep the sludge in suspension. In facultative lagoons this energy input is insufficient and settlement of solids onto the lagoon floor takes place. The biodegradable solids in the sludge layer so formed degrade anaerobically, as in an oxidation pond. Kinetically, suspension mixed lagoons are flow-through AS systems, and can be modeled as such. Their nominal hydraulic retention time equals their sludge age, and the waste (Qw) and effluent (Qe) flows are one and the same and equal to the influent flow (Qi). Hence, the volume of the aerated lagoon per unit COD load is very large compared with the conventional short sludge age systems, which have hydraulic retention times about 1/20th of the sludge age. The effluent from a suspension mixed aerated lagoon has the same constituents as the mixed liquor in the lagoon. The COD removed from the system via the oxygen demand is relatively small so that the COD in the effluent is generally

unacceptable for discharge to receiving waters. In fact, the principal objective of all short-age plants is to act as biologically assisted flocculators, which (biologically) transforms the influent soluble biodegradable organics to settleable organism mass and enmesh with this the influent BPO and UPOs to a form a settleable sludge that allows effective liquid– solid separation. In conventional short sludge age plants, the waste sludge is transferred to the sludge treatment facility; in the aerated lagoon systems, the effluent (with the waste sludge) usually flows to a second pond, that is, an oxidation pond or a facultative aerated lagoon, to allow the now readily settleable particulate material to settle to the lagoon floor to produce a relatively solids free and low COD effluent. The sludge that accumulates on the tank floor undergoes anaerobic stabilization. Aerated lagoons find application principally as low-technology industrial waste-treatment systems where organic strengths are high, the load varies seasonally, and nitrification is not required.

4.14.15.2 Intermediate Sludge Ages (10–15 Days) Where nitrification is obligatory because of a low effluent FSA concentration standard, this will govern the minimum sludge age of the AS system. For nitrification, the sludge ages required are 5–8 times longer than those for COD removal only, depending on the temperature. In the temperate regions where water temperatures may fall below 14 1C, the sludge age is not likely to be less than 10–15 days, taking due consideration of some unaerated zones in the reactor for denitrification (and biological P removal). In this range of sludge age, the effluent COD concentration no longer plays a role in the design. For sludge ages longer than about 4 days, protozoan organism predation of free swimming bacteria is high and flocculation good so particle dispersion is low. Also, virtually all soluble biodegradable organics are broken down, with the result that the effluent COD (or BOD) concentration remains approximately constant at its lowest achievable value, that is, the unbiodegradable soluble COD concentration. The effluent ammonia concentration also plays a minor role in design because the nitrification kinetics are such that once nitrification is achieved, it is virtually complete provided sufficient oxygen is supplied. Even though the effluent standards may require an effluent ammonia concentration, say o10 mgFSAN l1, once nitrification takes place the concentration is not likely to be greater than 2–4 mgN l1. Consequently for nitrification, the sludge age of the system is fixed principally by the requirement for nitrification. The method for calculating the minimum sludge age for nitrification is given in Section 4.14.20.3. Once a sludge age of say 25% longer than the minimum is selected, the effluent FSA concentration is mostly affected by the system operating conditions than by the nitrification process itself, that is, oxygen supply limitations, variation in ammonia load, uncontrolled loss of sludge, and pH of the mixed liquor. With low alkalinity wastewaters, nitrification can cause a significant reduction in effluent pH, often as low as 5. This not only causes problems with the nitrification process itself, that is, noncompliance with the effluent ammonia standard, but

Biological Nutrient Removal

also produces aggressive effluents that can do considerable damage to concrete surfaces. To reduce these problems and derive the other advantages of oxygen and alkalinity recovery (see Section 4.14.24.2), the policy of deliberate biological denitrification is advocated whenever nitrification is likely, even if N removal is not required. However, once nitrification is required and biological denitrification is incorporated in the system, sludge ages longer than 10–15 days may be required and the system falls into the long sludge age category. In nitrifying aerobic AS plants, there is always the possibility of denitrification in the SST. This problem is exacerbated by the system control procedure of abstracting the waste sludge from the settling tank underflow (Section 4.14.14.2). At low underflow recycle ratios, sludge retention in the SST is long leading to denitrification (Figure 19(b)). Henze et al. (1993) estimated that between 6–8 and 8–10 mgN l1 nitrate needs to be denitrified to cause sludge flotation at 10 and 20 1C, respectively. The concentration of nitrate denitrified increases as (1) sludge retention time in the SST increases, which is dependent on the recycle ratio and peak flow conditions; (2) active fraction of the sludge increases, that is, greater at shorter sludge ages (Figure 15); (3) temperature increases; and (4) mass of unutilized enmeshed biodegradable organics increases which is higher at shorter sludge ages and greatest at the peak load condition (Ekama et al., 1997). The above demonstrates that, for plants where nitrification takes place, the SST should not serve the dual purpose of solid–liquid separation and waste sludge thickening; the hydraulic control of sludge age should be employed; and deliberate denitrification should be included in the system (Section 4.14.14.2). These modifications will ameliorate the problem of sludge flotation by denitrification in the SST, but may not completely eliminate the root cause, that is, high nitrate concentrations in the mixed liquor. In order to reduce the construction cost of the AS system, reductions sludge age need to be made. Moreover, a reduction in sludge age also increases both biological N and P removal per mass organic load (WRC, 1984; Wentzel et al., 1990) and this would be particularly beneficial for low temperature wastewaters (10–15 1C) where nitrification is required. To try to reduce the sludge age required for nitrification, and hence the biological reactor volume per Ml WW treated, internal fixed media have been placed in the aerobic reactor (Wanner et al., 1988; Sen et al., 1994; Ekama and Wentzel, 1999b). The nitrifiers that grow on the fixed media are not subject to the mixed liquor sludge age and aerobic mass fraction with the result that both can be reduced. However, the effectiveness of the internal fixed media has not been as good as expected, and they yield a rather low cost/benefit ratio. Successful reduction of sludge age down to 8–10 days has been achieved with external nitrification (Bortone et al., 1996; Sorm et al., 1996; Hu et al., 2000, 2001) and this system starts finding application at full scale (Vestner and Gu¨nthert, 2001; Muller et al., 2006). With external nitrification, the nitrification process is removed completely from the suspended AS and transferred to an external fixed medium system like a trickling filter. With nitrification independent of the BNRAS mixed liquor, the sludge age can be reduced to around 8–10 days. Such a reduction reduces the biological reactor volume requirement per M l WW treated by about a one-third

459

without negatively impacting either biological N or P removal. Moreover, the sludge settleability improves significantly (DSVIB60–80 ml g1) compared with conventional BNR systems, which further increases the capacity of the system (Hu et al., 2000). Source separation of urine may also produce wastewaters, comprising feces flush water (brown), bathroom and kitchen water (gray), with sufficiently low influent TKN concentrations to obviate nitrification in the WWTP (Wilsenach, 2006; Mbaya et al., 2010). Comparing intermediate sludge age plants with high rate plants, the oxygen demand per kgCOD (including nitrification) is doubled (except with external nitrification, for which it is halved), the system volume is 3–4 times larger, the daily sludge mass wasted is reduced by 40%, and active fraction is much lower. Intermediate sludge age plants are much more stable than high-rate plants, requiring less sophisticated control techniques or operator intervention (excepting external nitrification), thereby making these plants more suitable for general application. At intermediate sludge ages, the active fraction of the waste sludge is still too high for direct discharge to drying beds. Consequently, some form of waste sludge stabilization would need to be incorporated in the WWTP, for example, aerobic or anaerobic digestion. The former has the advantage of ease of operation but the disadvantage of energy costs for oxygen supply; the latter has the advantage of energy generation from the biogas but the disadvantage of complexity of operation. Even with energy recovery by anaerobic digestion of waste sludge, because of the low mass of sludge wasted from the AS plant and high oxygen demand per kgCOD load, energy self-sufficiency at intermediate sludge ages is not possible. However, on large plants (B500 000 PE) where technical supervision and operator expertise are of a high level, energy costs can be reduced by gas production from AD, particularly if energy costs continue to increase as they have over the past decade. Ekama (2009) found that the green house gas emission (CO2) from two widely differing WWTPs treating the same wastewater is virtually the same if the residual biodegradable organics (COD) in the final disposed sludge is the same, viz., (1) a long sludge age (30 days) extended aeration AS system treating raw wastewater and (2) a short sludge age (8 days) AS system treating settled wastewater with anaerobic digestion of primary sludge and aerobic or anaerobic digestion of WAS with beneficial combustion/flaring of methane gas. In any event, the CO2 emitted by WWTPs (B20 g CO2-C/PE/d), together with that generated by their energy consumption at a fossil-fuel power station (B40 g CO2-C/PE/d at 800 kWh Ml1) is very low in comparison with that generated by (1) domestic energy consumption (B4000 gCO2-C/PE/d at 10 kWh/PE/d), which is only one-third of the total PE energy consumption, (2) motor car driving (B2000 gCO2-C/PE/d and 30 km/PE/d) or (3) even just breathing (B180 gCO2-C/ PE/d for a 6000 kJ d1 diet). So, from a sustainability point of view, the treated water produced at the WWTP has a far greater value than trying to maximize energy recovery from WWTP at the expense of effluent quality. ‘‘Minimization of energy requirement for wastewater treatment is an important goal but has a lower priority than the human and environmental health which is closely related to efficient water quality management’’ (Svardal and Kroiss, 2009).

460

Biological Nutrient Removal % Biodegradability of sludges residual biodegradable organics (COD)

% Biodegradability

80

4.14.15.3.2 Anoxic–aerobic plants

70

Particulate

60

Soluble

50 40 30 20 10 0

1

2

3

4

5 6 7 Sludge type

8

9

10

Figure 21 % residual biodegradable organics remaining in stabilized wastewater sludges treated with different stabilization system types: (1) Raw unsettled wastewater; (2) Zimpro humus þ 11  high soluble COD; (3) anaerobically digested 11 þ WAS  high VFA; (4) anaerobically digested 11 only  high VFA; (5) anaerobically digested 11, 1st stage  low VFA; (6) Zimpro humus þ 11  low soluble COD; (7) anaerobically digested 11, 2nd stage  low VFA; (8) DAF thickened WAS; (9) an. digested 11 þ WAS, single stage  low VFA; and (10) aerobically digested WAS. 11, primary sludge; VFA, volatile fatty acids; WAS, waste activated sludge.

4.14.15.3 Long Sludge Ages (20 Days or More) 4.14.15.3.1 Aerobic plants Long sludge age aerobic plants are usually called extended aeration plants. The principal objective of long sludge systems is to obviate primary (11) and secondary (21) sludge treatment. These plants therefore treat raw wastewater and the sludge age is chosen so that the active fraction (or residual biodegradable organics) of the waste sludge is sufficiently low to allow its direct discharge to sludge drying beds. The sludge age required to produce a sludge sufficiently stable so as not to generate odor problems is uncertain and will depend on the temperature and climatic conditions, that is, whether or not the sludge can be dried sufficiently quickly before it starts stinking, but probably exceeds 30 days. Interestingly, from a survey of the residual biodegradable organics in wastewater sludges treated by different sludge stabilization systems, Samson and Ekama (2000) found that aerobically digested WAS contained the lowest residual biodegradable organics (10%) compared with wet air oxidized (Zimpro) and anaerobically digested primary sludges (25–60%, Figure 21). Extended aeration plants are very stable in operation and require less supervision than their short sludge age counterparts. Although the volume requirements and oxygen demand per unit COD are very high, the relative ease of operation makes the system the preferred one for small communities. The reactor configurations that can be operated in the extended aeration mode are many and the particular one that suits the specific application is chosen by the designer. These include single completely mixed reactor systems, Orbal, oxidation ditch and Carousel multichannel systems, and multireactor anaerobic anoxic and aerobic systems for BNR.

Once the sludge age exceeds 20–25 days, nitrification is inevitable and is advisable for reasons cited above to incorporate denitrification in the system, which at these long sludge ages would not affect the stability of nitrification. Furthermore, if required, BEPR can also be included for little extra cost. In fact, biological N and P removal are significantly greater with raw wastewater than the settled wastewater due to the higher organic load. To include N (and P) removal, the reactor is subdivided into unaerated (anoxic and anaerobic) and aerated zones in a variety of configurations. Denitrification takes place in the unaerated but mixed zones receiving nitrified mixed liquor via recycles from the aerated zones to give the socalled ND systems. The ND systems include: the four stage Bardenpho, which incorporates primary and secondary anoxic reactors; the modified Ludzack Ettinger (MLE), which incorporates only a primary anoxic reactor; the Orbal, Carousel, and oxidation ditch systems in which the anoxic zones are created along different lengths of the same long channel reactor; and the intermittently decanted extended aeration (IDEA) systems in which aerators are swithched off for different time periods over the day. Although incorporation of denitrification imposes some additional constraints on the design, at long sludge age, these are minor provided the aeration capacity of the plant is sufficient to ensure efficient nitrification under all expected conditions (Section 4.14.20).

4.14.15.3.3 Anaerobic–anoxic–aerobic plants When the BEPR is required, an initial anaerobic reactor is included in the configuration that receives the influent wastewater but minimal oxygen and nitrate via the sludge recycles. For BEPR, assurance of a zero nitrate discharge to the anaerobic zone is critical for achieving good P removal and is an additional constraint on the design when including BEPR in extended aeration systems. The extent of BEPR achieved will depend on a number of factors, mainly the influent RB COD concentration, the TP/COD ratio, and the degree to which nitrate can be excluded from anaerobic reactor, which depends on the influent TKN/COD ratio. The waste sludge from extended aeration systems, including BEPR, has the potential to release high P concentrations. This can be dealt with in specially designed dewatering/drying beds with sand filter under drains and weir overflows, which allow the drying bed also to operate as a dewatering system. While discharging waste sludge directly to the drying bed, the under drain and overflow are monitored for P concentration (with kit dipsticks) and when this gets to say 5 mgP l1, sludge wastage to the drying bed and the return of supernatant to the head of the works is stopped. The relatively small volume of high P liquor that drains from the drying bed thereafter is either chemically treated or irrigated at the WWTP site. The dewatering capability of the drying bed allows significantly more sludge to be discharged to it than drying beds without these dewatering features.

4.14.16 Sludge Age – The Dominant Driver for Size For organic material (C) removal only, the sludge age of the system is short and hence the reactor volume small

Biological Nutrient Removal Dominant drivers for size of activated sludge system

(8) Sludge settleability

Apply uncertainty/ sensitivity analysis to 1−8 O r g a n i c

N I T

N D

E B P R

Kinetic parameters (change only rarely)

(1−7) Wastewater characteristics

(6) Influent RBCOD fraction

(7) Influent P load FP ti − kgP/d

Clarifier models

FX t

Oxygen demand FO2 − kgO2 d−1

(1) organic load FS ti − kgCOD/d (2) Unbio part COD fraction − f S’up (3) Nitrogen load FN ti − kgTKN-N/d (4) Nitrifier max (5) WW temperature

Reactor conc. (Xt)

461

Reactor volume (Vp)

FO2

SST area (A sst)

Mass TSS in reactor (MX t) Sludge production (FXt − kg TSS d−1) Reactor volume (V p) S L U D G E

A G E

f xa

SST area (Asst)

f xd Recycle: high (1:1)

Anoxic mass fraction (f xd)

Effluent NO3

Anaerobic and anoxic mass fractions (f xm = f xa + f xd) Effluent NO3 and PO4

Figure 22 Important wastewater characteristics required to be known for different activated sludge systems – fully aerobic, nitrification, nitrification– denitrification, and biological excess P removal – and the interrelationships that affect sludge age and effluent quality.

(Figure 22). Essentially, only the organic (COD) load and unbiodegradable particulate (fS’up) and soluble (fS’up) COD fractions need to be known (Table 9). The organic load and unbiodegradable particulate COD concentration (Supi) strongly affect sludge mass in the reactor and daily sludge production. The unbiodegradable soluble COD concentration fixes the filtered effluent COD concentration from the system. Also, the organic load and sludge age fix the daily oxygen demand (kgO d1) and the peak hydraulic load fixes the secondary settling tank surface area and peak oxygen demand. If nitrification is required from the system, more wastewater characteristics are required to be known (Table 9). The most important of these are the maximum specific growth rate of the nitrifiers at the standard wastewater temperature of 20 1C (mAm20) and the minimum wastewater temperature (Tmin), both of which fix the minimum sludge age for nitrification (Rsm). The system sludge age (Rs) must be selected longer than the minimum for nitrification and the higher the system to minimum sludge age ratio (Rs/Rsm), the lower the effluent ammonia concentration and the more damped its variation in response to diurnal nitrogen load variation. Also required for nitrifying systems is the daily nitrogen load (both TKN and FSA) so that the components making up the N material in the influent can be determined. For nitrification, the maximum specific growth rate of the nitrifiers is regarded a wastewater characteristic and not a model kinetic constant because it is different in different wastewaters.

With biological nitrogen removal (ND), a part of the biological reactor volume is intentionally not aerated. The sludge mass in the unaerated (anoxic) reactor as a fraction of the sludge mass in the whole reactor is the anoxic mass fraction (fxd, Figure 22). The larger the anoxic mass fraction, the more nitrate can be denitrified but the longer the minimum sludge age for nitrification becomes over that for fully aerobic conditions. So for ND systems, the biological reactor gets larger because the required sludge ages get longer. Also an additional wastewater characteristic needs to be known, that is, the influent RB COD concentration (or fraction, fS’bs) because a high proportion (up to half) of the nitrate denitrified in the primary anoxic reactor is due to this wastewater constituent – if the influent RBSO (COD) concentration is not known, the effluent nitrate concentration cannot be calculated accurately with either steady-state or dynamic simulation models. With BEPR, the daily wastewater phosphorus load (both total P and orthoP) needs to be known so that the components making up the P material in the influent can be determined. With BEPR the influent RBSO concentration (which includes the short-chain fatty acids, VFA) is very important and establishes the extent of biological P removal that can be achieved. If the influent RBSO concentration is not known, the biological P removal that can be achieved cannot be calculated accurately. The influent RBSO is indirectly the food source for the PAOs that mediate the BEPR process. The

462

Biological Nutrient Removal

Table 9 Wastewater characteristics requiring specification for different single sludge activated sludge systems. Characteristics marked NB (nota bene) are very important and required to be accurately known for accurate design. Wastewater characteristics

Units

Symbol

System type Fully aerobic

1. 2. 3.

4.

5. 6.

7. 8. 9.

Mean influent COD concentration Average flow Average temperature  Maximum  Minimum Influent COD fractions  Unbiodegradable soluble (USO)  Unbiodegradable particulate (UPO)  Readily biodegradable (RBSO)  Fermentable readily biodegradable  Volatile fatty acids (VFAs) Mean influent TKN concentration Influent TKN fractions  Ammonia  Soluble unbiodegradable organic N Influent total P concentration ANO max. specific growth rate at 20 1C Influent inorganic suspended solids

Non Nit.

Nit.

ND

NDBEPR

mgCOD l1 l d1

Sti Qi

|NB |NB

|NB |NB

|NB |NB

|NB |NB

1C 1C

Tmax Tmin

| |

| |NB

| |NB

| |NB

mgN l1

fS0 us fS0 up fS0 bs fSbs0 f fSbs0 a Nti

| | -

| | |NB

| | |NB |NB

| | |NB | | |NB

mgP l1 d1 mgISS l1

fN0 a fN0 ous Pti mAm20 XIOi

|

| | |NB |

| | |NB |

| | |NB |NB |

purpose of the anaerobic zone, which receives the influent wastewater, is to allow the PAOs to take up the VFA fermentation products generated from the influent RBSO. Nitrate (or DO) which enters the anaerobic zone results in utilization of some of the influent RBSO by OHOs, which reduces the VFA products available to the PAOs and hence the biological P removal. The difference between the influent P concentration and the BEPR that can be achieved establishes the effluent P concentration. Very low nitrate (and DO) concentrations in the recycles entering the anaerobic zone are essential for maximum BEPR. This imposes important requirements on the denitrification required in the anoxic zones. If the influent TKN/COD concentration ratio is too high, then low nitrate concentrations cannot be achieved in the anoxic zone(s) and methanol dosing may be required. High N removals in the anoxic zones requires large anoxic reactor(s), which together with the anaerobic zone results in large unaerated mass fractions, which in turn requires long sludge ages to ensure nitrification. Unless specific strategies are applied to keep the sludge age low, such as external nitrification (Sorm et al., 1996; Hu et al., 2000; Muller et al., 2006) or adding fixed media into the aerobic zone (Wanner et al., 1988; Sen et al., 1994) to reduce the system sensitivity to the minimum sludge age for nitrification, NDBEPR systems will have long sludge ages, especially where wastewater minimum temperatures are low. The above demonstrates that wastewater characteristic determination is the most important aspect of modeling WWTPs, whether using steady-state or dynamic simulation models. Uncertainty in wastewater characteristics (and sludge settleability) results in a commensurate uncertainty in calculated oxygen demand, sludge production, reactor volume, and effluent quality. So, uncertainty/sensitivity analyses should be applied to the wastewater characteristics rather than to the

kinetic and stoichiometric parameters of the model(s). In fact, only rarely should the kinetic and stoichiometric parameters of the model be changed (except the maximum specific growth rate of nitrifiers which is regarded a wastewater characteristic). Fitting all the effluent quality concentrations, sludge production, and oxygen demand to laboratory, pilotand full-scale plant data can be achieved by changing the wastewater characteristics only, provided the data conform to mass balances (water, COD, N, and P). More often than not, model predictions cannot be made to conform to measured data because the measured data do not conform to mass balance and continuity principles. Only when the data conform to mass-balance and continuity principles and changing the wastewater characteristics cannot yield a good correlation between model predictions and measured data, should kinetic and stoichiometric parameters of the model be changed, but such change(s) should be based on bioprocess basics and not simply because ‘it makes the model fit’.

4.14.17 Nitrification – Introduction The term nitrification describes the biological process whereby FSA is oxidized to nitrite and nitrate. Nitrification is mediated by specific chemical autotrophic organisms with behavioral characteristics that differ significantly from the heterotrophic (OHO) ones. Whereas the OHOs obtain their carbon (anabolism) and energy (catabolism) requirements for biomass synthesis from the same organic compound(s), the autotrophic nitrifying organisms obtain their carbon requirement (anabolism) from dissolved CO2 and their energy requirement (catabolism) for biomass synthesis from oxidizing ammonia to nitrite and nitrite to nitrate. This difference results in the autotrophic nitrifiers having much lower biomass growth

Biological Nutrient Removal

coefficients (one-fifth) than the OHOs. The objectives in this chapter are to review briefly the kinetics of nitrification, to highlight the factors that influence this biological process, and set out the procedure for designing a nitrifying aerobic or anoxic-aerobic AS system. It has been well established that nitrification is due to two specific genera of autotrophic bacteria, the ammonia oxidizing organisms (ANOs) and the nitrite oxidizing organisms (NNOs). Originally, it was thought that only nitrosomonas and nitrobacter mediated nitrification but recent molecular techniques have shown that there are several genera of nitrifying organisms. Nitrification takes place in two sequential oxidation steps: (1) ANOs convert FSA to nitrite and (2) NNOs convert nitrite to nitrate. The nitrifiers utilize ammonia and nitrite principally for synthesis energy requirements (catabolism) but some ammonia is also used anabolically for synthesis of cell mass nitrogen requirements. The ammonia requirement for synthesis, however, is a negligible fraction of the total ammonia nitrified to nitrate by the nitrifiers, at the most 1%. Consequently, in steady-state models it is usual to neglect the synthesis nitrogen requirements of the nitrifiers and to consider the nitrifiers simply to act as biological catalysts in the nitrification process. This stoichiometric approach greatly simplifies the description of the kinetics of the process. The two basic stoichiometric redox reactions in nitrification are:

NH4 þ þ ð3=2ÞO2 ðANOsÞ-NO2  þ H2 O þ 2Hþ 

NO2 þ ð1=2ÞO2 ðNNOsÞ-NO3



ð118aÞ

463

nitrification sequence is therefore the ammonia conversion to nitrite by the ANOs. So from a steady-state modeling point of view, one needs to consider the kinetics of this organism group only. Because the nitrite produced is virtually immediately further nitrified to nitrate, it is assumed that the ANOs nitrify ammonia to nitrate directly and the kinetics of nitrification reduce to the kinetic behavior of the ANOs. Experimental investigations by Downing et al. (1964) showed that the nitrification rate can be formulated in terms of the Monod equation. In fact, Monod kinetics was applied to nitrification before it was applied to model the kinetics of organic material breakdown by heterotrophic organisms. The successful application to nitrification prompted Lawrence and McCarty (1972) to apply it to AS. Monod established that (1) the mass of organisms generated is a fixed fraction of the mass of substrate (in this case ammonia) utilized and (2) the specific rate of growth, (i.e., the rate of growth per unit mass of organisms per unit time) is related to the concentration of substrate surrounding the organisms. From (1),

MDXBA ¼ YA MDNa

ð119Þ

where MDXBA is the mass of nitrifiers generated (mgVSS), MDNa the mass of ammonia as N utilized (mgFSA-N), and YA the nitrifier yield coefficient mgVSS/mgN. Taking the changes over a time interval Dt and assuming the changes are very small, one can write

  dXBA dNa ¼ YA  ðmgANOVSS l1 d1 Þ dt dt

ð120Þ

ð118bÞ

Stoichiometrically, the oxygen requirements for the first and second reactions are 3/2  32/14 ¼ 3.43 and 1/2  32/ 14 ¼1.14 mgO/mgN (also written as mgO/mgFSA-N). Hence, the stoichiometric conversion of ammonia to nitrate, both expressed as N, requires 2  32/14 ¼ 4.57 mgO/mgN utilized. Taking into account the ammonia utilized for synthesis of nitrifier cell mass, the oxygen requirement per mgFSA-N nitrified is slightly less, with reported values down to 4.42 mgO/ mgFSA (Ekama, 2009). This approach is adopted in the simulation models such as ASM1 (Henze et al., 1987) and is one reason for the small difference in the predicted results between steady-state stoichiometric models and the more complex simulation models.

4.14.18 Nitrification Biological Kinetics 4.14.18.1 Growth In order to formulate the nitrification behavior, it is necessary to understand the basic biological growth kinetics of ANOs. The rate of conversion of ammonia to nitrite, by the ANOs is generally much slower than that of nitrite to nitrate by the NNOs. Therefore, under most circumstances in municipal WWTPs, any nitrite that is formed is converted virtually immediately to nitrate. As a consequence generally very little nitrite (o1 mgN l1) is observed in the effluent from a plant operating on an influent that does not contain substances that inhibit the NNOs. The limiting rate in the two-step

From (2) Downing et al. (1964) developed the following relationship, known as the Monod equation

mA ¼

mAm Na Kn þ Na

ðmgVSS=mgVSS dÞ

ð121Þ

where mA is the specific growth rate at ammonia concentration Na (mgANOVSS/mgANOVSS/d), mAm the maximum specific growth rate (mgANOVSS/mgANOVSS/d), Kn the halfsaturation constant, that is, the concentration at which mA ¼ 1/2 mAm (mgN l1), and Na the bulk liquid ammonia concentration (mgN l1). The Monod constants maximum specific growth rate mAm and half-saturation coefficient (also known as the affinity coefficient) Kn for the ANOs are sensitive to temperature, generally decreasing as temperature decreases. An additional subscript T on the symbols refers to temperature (1C). The growth rate is given by the product of the specific growth rate and the ANO concentration (XBA):

  dXBA mAmT Na ¼ mAT XBA ¼ XBA dt KnT þ Na ðmgANOVSS l1 d1 Þ

ð122Þ

The rate of ammonia conversion is found by combining Equations (120) and (122), viz.,

dNa 1 mAmT Na ¼ XBA dt YA KnT þ Na

ðmgFSA-N l1 d1 Þ

ð123Þ

Biological Nutrient Removal

dNn dNa 1 mAmT Na ¼ XBA dt dt YA KnT þ Na

ðmgNO3 -N l

1

1

d Þ ð124Þ

where Nn is nitrate concentration (mgNO3  N l1). The oxygen utilization rate associated with nitrification is based on the stoichiometric oxygen requirement of 4.57 mgO/ mgFSA-N nitrified to nitrate calculated above, viz.,

dOn dNa dNn ¼ OURn ¼ 4:57 ¼ 4:57 dt dt dt

0.5

5

UAm or KAm

0.4

4 UA = UAm Na /(Kn + Na) UAm = Maximum specific growth rate Kn = Half-saturation coefficient

0.3

3

0.2

2

0.1 Kn

UAm20 = 0.45/d; KAn = UA20 /YA YA = 0.10 mgVSS/mgN Kn = 1.0 mgN l−1

0.0

ðmgO l1 d1 Þ

0 0

ð125Þ Assuming stoichiometric conversion of FSA to nitrate as in Equations (124) and (125) slightly overestimate the nitrate generation and oxygen consumption because a small proportion (1%) of the FSA taken up by the nitrifiers is used for cell synthesis. Based on the empirical organism cell mass formula C5H7O2N, Ekama (2009) shows that for 1 mgFSA-N taken up, 0.99 mgN nitrate and 0.076 mgANOVSS is generated and 4.42 mgO is utilized. Application of the Monod growth kinetics to nitrification by Downing et al. (1964) is probably one of most successful applications of microbiological kinetic research to wastewater treatment, so much so that the Monod kinetics is commonly used today to express the rates of many biological processes in terms of the growth limiting nutrient concentrations. Monod growth kinetics requires three constants to be known: the yield coefficient (YA), the maximum specific growth rate (mAm), and the half-saturation coefficient (Kn). The yield coefficient for nitrifying organisms represents the net organism mass produced per unit mass of substrate nitrogen utilized. Evidence that this coefficient is not constant but can vary with the conditions of growth was presented in the 1960s when the nitrification model was developed. However, Downing et al. (1964) stated that the different VSS concentrations obtained from different YA values are inconsequential to the experimentally determined maximum specific growth rate, mAm, provided a consistent pair of mAm and YA are used. This is because the mAm is obtained from an observed maximum specific nitrification rate, KAm mgFSA-N nitrified/(mgANOVSS d), which is equal to mAm/YA. If YA is selected low, the mAm will be high and vice versa. To avoid confusion about the experimentally determined mAm rates, a standard YA ¼ 0.10 mgVSS/mgFSA or 0.15 mgCOD/mgFSA has been adopted in steady-state and dynamic simulation AS models for municipal WWTPs.

4.14.18.2 Growth Behavior In Figure 23 the relationship between the specific growth rate, mA, the specific substrate (FSA) utilization or nitrification rate, KA, and the bulk liquid FSA concentration, Na, is shown, as described by the Monod equation (Equation (122)). The rate constants selected are mAm20 ¼ 0.45 d1, YA ¼ 0.10 mgANOVSS formed/mgFSA-N nitrified, making KAm ¼ 4.5 mgFSA-N/

1

Specific nitrification rate

Because in the steady-state model the nitrification process is accepted to be stoichiometric, that is, the nitrifying organisms act only as a catalyst to the process, the rate of nitrate formation is equal to the rate of FSA conversion, that is,

Specific growth rate (d−1)

464

10 20 30 40 Ammonia concentration (mgN I−1)

50

Figure 23 The Monod specific growth rate equation for nitrification at 20 1C.

(mgANOVSS d) and Kn20 ¼1.0 mgN l1. The interesting feature of this nitrifier growth behavior is that, because Kn is so low at B1 mg(FSA-N) l1, the nitrification rate is virtually at maximum for concentrations 42 mgFSA-N l1. However, at concentrations o 2mgN l1, the rate rapidly declines to zero. The implication of this is that when nitrification takes place, it will be nearly complete (provided all other requirements are met – see below) but the ammonia concentration is not readily reduced to zero.

4.14.18.3 Endogenous Respiration It is generally accepted that all organisms undergo some form of biomass loss due to maintenance or endogenous energy requirements. This behavior manifests when a biomass has completely utilized its external substrate – its VSS decreases and it continues to utilize oxygen with time. This process is called endogenous respiration. Different organisms have different endogenous respiration rates. For the OHOs, it is quite high (bH20 ¼ 0.24 d1), whereas for the nitrifiers (ANOs), it is low (bA20 ¼ 0.04 d1). The endogenous respiration process for the ANOs is modeled in exactly the same way as that for the OHOs, that is,

dXBA ¼ bAT XBA dt

ðmgANOVSS l1 d1 Þ

ð126Þ

where bAT is the specific endogenous mass loss rate for nitrifiers at T (1C), mgANOVSS/(mgANOVSS d).

4.14.19 Nitrification Process Kinetics The basic AS system modeled for nitrification is the single completely mixed reactor system with hydraulic control of sludge age (see Figure 2). This system under steady-state conditions provides the information necessary for design of nitrification. The principal steady-state solution required for this is the effluent ammonia concentration (Nae). This solution forms the basis for the analysis of the nitrification process behavior and provides the information for the design of an AS

Biological Nutrient Removal

465

system including this process. This information is also sufficient to understand the modeling of the nitrification process in AS simulation models such as ASM1.

Table 10 Kinetic constants and their temperature sensitivity for ANOs accepted in most activated sludge models Kinetic constant

at 20 1 C

Temp. coeff.

4.14.19.1 Effluent Ammonia Concentration

Yield coefficient, YA (mgVSS/mgFSA) Endogenous respiration rate, bA (d1) Half-saturation coefficient, Kn (mgFSA l1) Maximum specific growth rate mAm (d1)

0.1 0.04 1 Varies

1 1.029 1.123 1.123

A mass balance on the change in nitrifier mass MDXBA over the completely mixed system at steady state is given by

MDXBA ¼ Vp DXBA ¼

mAmT Na XBA Vp Dt  bAT XBA Vp Dt KnT þ Na  XBA Qw Dt ðmgANOVSSÞ

60 Rs = Rsm

where Vp is the reactor volume (l) and Qw the waste sludge flow rate from the reactor (l d1). Dividing by VpDt yields

ð127Þ

Under steady-state (constant flow and load) conditions, DXBA/ Dt is zero and from Equation (58), Qw/Vp ¼ Rs. Substituting these and solving for the reactor ammonia concentration (Na), and therefore also from the definition of completely mixed conditions, the effluent ammonia concentration (Nae) yields

50 Ammonia (mgN I−1)

DXBA mAmT Na Qw XBA  bAT XBA  XBA ¼ Dt KnT þ Na Vp

Rs < Rsm

Rs > Rsm Am20 = 0.33 d−1 Kn20 = 1.0 mgN I−1

40

Influent ammonia concs

30

Rsm = For different influent ammonia concentrations

20

10

KnT ðbAT þ 1=Rs Þ Na ¼ Nae ¼ mAmT  ðbAT þ 1=Rs Þ

1

ðmgN l Þ

ð128Þ 0

From Equation (128), the ammonia concentration (Na) in the reactor and effluent (Nae) are independent of the specific yield coefficient (YA) and the influent ammonia concentration (Nai). Using mAm20 ¼ 0.33 d1 and Kn20 ¼1.0 mgN l1 at 20 1C, and taking bAT ¼ 0.04 d1 (Table 10), a plot of Equation (128) with Nae versus sludge age Rs is given in Figure 24. At long sludge ages Nae is very low and remains so until the sludge age is lowered to about 4 days. Below 4 days, Nae increases rapidly and in terms of Equation (128) can exceed the influent FSA concentration, Nai. This clearly is not possible so the limit of validity of Equation (128) is Na ¼ Nai. Substituting Nai for Na in Equation (128) and solving for Rs give the minimum sludge age for nitrification, Rsm below which theoretically, nitrification cannot be achieved, that is,

Rsm ¼

1 ½mAmT =ð1 þ ðKnT =Nai ÞÞ  bAT

ð129Þ

This minimum sludge age varies slightly with the magnitude of Nai (Figure 24) – higher Nai gives a slightly lower Rsm. The effect of Nai on RSm is very small because the magnitude of KnT is very small relative to Nai (o5%). So for Nai 420 mgN l1 (rarely will it be lower than this), and noting that Kn20B1 mgN l1, then KnT/Nai is negligibly small with respect to 1 (o5%). So substituting zero for KnT/Nai in Equation (129) yields

Rsm ¼

1 mAmT  bAT

ðdaysÞ

ð130Þ

For all practical purposes, taking into account the uncertainty in mAm, Equation (130) adequately defines the minimum sludge age for nitrification. Conceptually, Equation (130)

0

2

4 6 Sludge age (days)

8

10

Figure 24 Effluent ammonia concentration vs. sludge age for the steady-state nitrification model.

states that if the net nitrifier multiplication rate (inverse of the net maximum specific growth rate, mAm  bA) is slower than the harvesting rate of the nitrifiers via the sludge waste flow rate, then the nitrifiers cannot be sustained in the system and nitrification cannot take place. At sludge ages lower than the minimum for nitrification, nitrifiers are washed out of the system and so are called washout sludge ages. This concept of washout can be applied to any group of organisms in a bioreactor, and defines the sludge age below which the bioprocess will not take place because the organisms mediating this process are not sustained in the system. The virtually constant value for Rsm insofar as the influent FSA concentration is concerned (for the fixed values of mAmT and bAT) and the rapid decrease in effluent FSA concentration at sludge ages slightly longer than RSm is due to the very low Monod half saturation concentration for the nitrifiers (Kn20). This feature causes that in a particular plant, as the sludge age is increased, once Rs4Rsm, a high efficiency of nitrification will be observed, provided the FSA is the growth limiting nutrient for the ANOs, that is, all other requirements such as oxygen are met. Consequently, under steady-state conditions with increasing sludge age, kinetically, one would expect an AS system either not to nitrify at all, or, if it nitrifies, to nitrify virtually completely depending on whether the sludge age is

466

Biological Nutrient Removal

shorter or longer than the minimum (Rsm), respectively. Conversely, as sludge age decreases, one would expect an AS system to nitrify virtually completely and then quite suddenly cease to nitrify depending on whether the sludge age is shorter or longer than the minimum (Rsm), respectively. This behavior sometimes occurs in full-scale AS systems, where for many years the system has nitrified virtually completely, and suddenly one winter it stops nitrifying and produces very high effluent FSA concentrations. Provided the oxygen supply is not limiting, what happens in these situations is that over the years, the organic (COD) load on the system has increased and in order to maintain the reactor VSS concentration at the required level, the sludge wastage rate (Qw) has been increased, which reduced the sludge age. Then, coupled with a low winter temperature, the system sludge age falls below the minimum and nitrification ceases. This cannot happen with hydraulic control of sludge age, where a fixed proportion of the reactor volume is wasted daily to establish a constant sludge age. However, the secondary settling tank may become overloaded as the reactor TSS concentration increases with time, depending on the settleability with the AS (see Section 4.14.14). An operator therefore can choose the way an AS system fails with increasing organic loading – it does not have to be with nitrification, and so also with N removal.

4.14.20 Factors Influencing Nitrification From the discussion above, it can be seen that there are a number of factors that affect the nitrification process, the minimum sludge age required to achieve it, and the effluent FSA concentration from the AS system: 1. the magnitude of the kinetic constant mAm20 because this rate can vary considerably in different wastewaters; 2. temperature because it decreases the mAm20 rate and Kn20 coefficient; 3. unaerated zones in the reactor because ANOs are obligate aerobes and can grow only under aerobic conditions; 4. DO concentration because Monod kinetics presumes that FSA is the growth-limiting nutrient implying that the oxygen supply must be adequate; 5. cyclic flow and load conditions because FSA is dissolved and therefore the reactor (and effluent) FSA concentration is affected by the instantaneous actual hydraulic retention time; most FSA not nitrified during the actual hydraulic retention time escapes with the effluent; and 6. pH in the reactor because the mAm20 is strongly suppressed by pH outside the 7–8 range. These six factors are discussed further below.

4.14.20.1 Influent Source The maximum specific growth rate constant mAmT has been observed to be quite specific for the wastewater and also to vary between different batches of the same wastewater source. This specificity is so marked that mnmT should not be classified as a kinetic constant but rather as a wastewater characteristic. The effect appears to be of an inhibitory nature due to some substance(s) in the influent wastewater. It does not appear to

be a toxicity problem because a high efficiency of nitrification can be achieved even with a low mAmT value if the sludge age is increased sufficiently. These inhibitory substances are more likely to be present in municipal wastewater flows having some industrial contribution. In general, the higher the industrial contribution, the lower mAmT tends to be, but the specific chemical compounds that cause the reduction of mAmT have not been clearly defined. A standard temperature of 20 1C has been adopted for reporting mAm rates to take into account the effect of temperature. A range mAm20 values have been reported in the range of 0.30–0.75 d1 for municipal wastewaters. These two limits will have a significant effect on the magnitude of the minimum sludge age for nitrification. Two systems, having these respective mAm20 values, will have Rsm values differing by 250%. Clearly due to the link between the sludge age and mAmT, the latter’s value should always be estimated experimentally for optimal design. In the absence of such a measurement, a low value for mAmT necessarily will need to be selected to ensure that nitrification takes place. If the actual mAm is higher, the sludge age of the system will be longer and the reactor volume larger than necessary. However, the investment in the large reactor is not lost because in the future the plant will be able to treat a higher organic load at a shorter sludge age. Experimental procedures to determine mAm20 are given in the literature (e.g., WRC, 1984). The bn20 rate is taken as constant for all municipal wastewater flows at bn20 ¼ 0.04 d1. Its effect is small so that there is no need to enquire closely into all the factors affecting it. Little information on effects of inhibitory agents on KnT is available; very likely KnT will increase with inhibition.

4.14.20.2 Temperature The mAmT, KnT, and bAT constants are sensitive to temperature with a high-temperature sensitivity for the first two, while the endogenous rate is accepted to have the same low-temperature sensitivity as that for OHOs, viz.,

mAmT ¼ mAm20 ðyn ÞðT20Þ ðd1 Þ

ð131aÞ

KnT ¼ Kn20 ðyn ÞðT20Þ ðmgN l1 Þ

ð131bÞ

bAT ¼ bA20 ðyb ÞðT20Þ ðd1 Þ

ð131cÞ

where yn is the temperature sensitivity for nitrification ( ¼ 1.123) and yb the temperature sensitivity for endogenous respiration for ANOs ¼ 1.029. The effect of temperature on mAmT is particularly strong. For every 6 1C drop in temperature, the mAmT value halves which means that the minimum sludge age for nitrification doubles. Design of systems for nitrification, therefore, should be based on the minimum expected system temperature. The temperature sensitivity of KnT is also strong, doubling for every 6 1C increase in temperature. This does not affect the minimum sludge age for nitrification, but it does affect the effluent FSA concentration – the higher the Kn value, the higher the effluent FSA at Rs b Rsm. However, the faster mAmT rate at the higher temperature compensates for the higher KnT value so that the effluent FSA decreases with increase in temperature.

Biological Nutrient Removal

The effect of unaerated zones on nitrification can be formulated based on the following assumptions: 1. Nitrifiers, being obligate aerobes, grow only in the aerobic zones of a system. 2. Endogenous mass loss of the nitrifiers occurs under both aerobic and unaerated conditions. 3. The proportion of the ANOs in the VSS in the unaerated and aerated zones is the same so that the sludge mass fractions of the different zones of the system reflect the distribution of the nitrifier mass as well. From 1–3 above, it can be shown that if a fraction fxt of the total sludge mass is unaerated (i.e., (1  fxt) is aerated), the effluent ammonia is given by

KnT ðbAT þ 1=Rs Þ Nae ¼ mAmT ð1  f xt Þ  ðbAT þ 1=Rs Þ

ð132Þ

Equation (132) is identical in structure to Equation (128), if one views the effect of the unaerated mass (fxt) as reducing the value of mAmT to mAmT(1  fxt), which conforms with (1) to (3) above. This sludge mass fraction approach is compatible with the nitrification kinetics in the AS kinetic models such as ASM1 and ASM2 (Henze et al., 1987, 1995) and UCTOLD and UCTPHO (Dold et al., 1991; Wentzel et al., 1992). In these models, nitrifier growth takes place only in the aerobic zone and endogenous respiration in all the zones. This sludge mass fraction approach is not compatible with the aerobic sludge age approach, which is used in some ND AS system design procedures (WEF, 1998; Metcalf and Eddy, 1991). In the aerobic sludge age approach, it is assumed that the growth and endogenous processes of the nitrifiers are active only in the aerobic zone, with neither processes active in the unaerated zone(s). This aerobic sludge age approach is not compatible with kinetic models and so significantly different predictions can be expected for the nitrification behavior from the aerobic sludge age-based design procedures and kinetic models. Following the same reasoning as that preceding Equation (132), it can be shown that the minimum sludge age for nitrification Rsm in an ND system having an unaerated mass fraction, fxt, is

Rsm ¼

1 mAmT ð1  f xt Þ  bAT

ð133Þ

Alternatively, if Rs is specified, then the minimum aerobic sludge mass fraction (1  fxm) that must be present for nitrification to take place is found by substituting Rs for Rsm and fxm for fxt in Equation (133) and solving for (1  fxm), that is,

ð1  f xm Þ ¼ ðbAT þ 1=Rs Þ=mAmT

ð134Þ

or equivalently, from Equation (134), the maximum allowable unaerated sludge mass fraction at a sludge age of Rs is

f xm ¼ 1  ðbAT þ 1=Rs Þ=mAmT

ð135Þ

For a fixed sludge age, Rs, the design value for the minimum aerobic sludge mass fraction (1  fxm) should always be significantly higher than that given by Equation (134), because

nitrification becomes unstable and the effluent ammonia concentration increases when the aerated sludge mass fraction decreases to near the minimum value as given by Equation (134) in the same way as when the sludge age (Rs) approaches the minimum for nitrification (Rsm). This situation is exacerbated by cyclic flow and ammonia load conditions (see below). Consequently to ensure low effluent ammonia concentrations, the maximum specific growth rate of nitrifiers must be decreased by a factor of safety, Sf, to give the minimum design aerobic sludge mass fraction; from Equation (134),

ð1  f xm Þ ¼ ðbAT þ 1=Rs Þ=ðmAmT =Sf Þ

ð136aÞ

The corresponding maximum design unaerated sludge mass fraction, from Equation (136a), is

f xm ¼ 1  Sf ðbAT þ 1=Rs Þ=mAmT

ð136bÞ

With the aid of the temperature dependency equations for nitrification (Equation (131)), the maximum unaerated sludge mass fraction (fxm) from Equation (136b) is shown in Figure 25 for Sf ¼ 1.25 and mAm20 rates from 0.25 to 0.50 at 14 1C. This shows that fxm is very sensitive to mAmT. Unless a sufficiently large aerobic sludge mass fraction (1  fxm) is provided, nitrification will not take place and consequently nitrogen removal by denitrification is not possible. In fact, the selection of the maximum unaerated sludge mass fraction to achieve near complete nitrification and a required degree of N removal is the single most important decision that is made in the design of the BNR AS system because it defines the system sludge age and, for a selected reactor MLSS concentration, also the reactor volume. From Equations (132) and (136), it can be shown that at fxm for constant flow and ammonia load (i.e., steady-state conditions)

Nae ¼ KnT =ðSf  1Þ ðmgN l1 Þ

ð137Þ

From Equation (137), if Sf is selected at say 1.25 or greater at the minimum wastewater temperature, the effluent ammonia

0.80 Maximum unaerated sludge mass fraction

4.14.20.3 Unaerated Zones

467

Temperature = 14 °C

Factor of safety = 1.25

Recommended maximum 0.60 50

0.

40 36 0.

0.

0.40

30

0.

25

0.

0.20

Am20

0.00 0

10

20 30 Sludge age (days)

40

Figure 25 Maximum unaerated sludge mass fraction required to ensure nitrification vs. sludge age for maximum specific growth rates of nitrifiers mAm20 of 0.25–0.50 d1 at 14 1C for Sf ¼ 1.25.

468

Biological Nutrient Removal

concentration (Nae) will be lower than 2 mgFSA-N l1 at 14 1C for Kn20 ¼1.0 mgN l1. Although Kn is higher at higher temperature, Nae will decrease with increase in temperature because at constant sludge age, Sf increases with increase in mAmT. Consequently, for design the lower expected temperature should be selected to determine the sludge age and the aerobic mass fraction. If this is done, using say Sf ¼ 1.25, then it can be accepted from Equation (137) that the effluent ammonia concentration is below 2 mgN l1 at the lowest temperature and around 1 mgN l1 at 20 1C. In this way, explicitly calculating Nae with Equation (132) is not necessary because provision for near complete nitrification has been made by the selection of Sf. Clearly, selection of the mAm20 and Sf values has major consequences on the effluent FSA concentration and economics (size) of the ND AS system.

4.14.20.3.1 Maximum allowable unaerated mass fraction The above equations allow the two most important decisions in the design of an NDAS system to be made, the maximum unaerated sludge mass fraction and sludge age to ensure near complete nitrification. Evidently from Figure 25, for mAm20 4 0.50 the unaerated mass fraction at 14 1C can be as high as 0.7 at a sludge age of 40 days. Such a high unaerated mass fraction is apparently also acceptable at RsZ10 days at 20 1C. However, there are additional considerations that constrain the unaerated mass fraction – sludge age selection. 1. Experience with laboratory-scale ND (and NDBEPR) systems has shown that at unaerated mass fractions greater than 0.40, the filamentous bulking can become a problem, in particular at low temperatures (o16 1C). Systems with low unaerated mass fractions of o0.30 show greater tendency for good settling sludges (Musvoto et al., 1994; Ekama and Wentzel, 1999a; Tsai et al., 2003). 2. For design of BNR plants for high N and P removal, the unaerated sludge mass fraction fxm usually needs to be high (440%). If the mAm20 value is low (o 0.40 d1, which will be the usual case in designs where insufficient information on the mAm20 is available), the necessary high fxm magnitudes will be obtained only at long sludge ages (Figure 25). For example, if mAm20 ¼ 0.35 d1, then with Sf ¼ 1.3 at Tmin ¼ 14 1C, an fxm ¼ 0.45 (Equation (136b)) gives a sludge age of 25 days and for fxm ¼ 0.55 a sludge age of 37 days. Long sludge ages require large reactor volumes – increasing Rs from 25 to 37 days increases the reactor volume by 40%, whereas fxm increased only 22%. Also, for the same P content in the sludge mass, the P removal is reduced as the sludge age increases because the mass of sludge wasted daily decreases as the sludge age increases. Consequently, for low mAm20 values, the increase in N and P removal that can be obtained by increasing the unaerated sludge mass fraction above 0.50–0.60 might not be economical due to the large reactor volumes this will require, and might even be counterproductive insofar as it affects P removal. A sludge age of 30 days probably is near the limit of economic practicality which, for low mnm14 ¼ 0.16 values, will limit the unaerated mass fraction to about 0.5. At higher mnm14 values, the sludge ages allowing 50% unaerated mass fractions decrease significantly again indicating the advantages of determining

experimentally the value of mAm20 to check whether a higher value is acceptable. 3. An upper limit to the unaerated mass fraction is evident also from experimental and theoretical modeling of the BNR system. Experimentally at 20 1C with Rs ¼ 20 days, if fxm 40.70, the mass of sludge generated is found to increase sharply. Theoretically, this happens for fxm 4 0.60 at T ¼ 14 1C and Rs ¼ 20 days. The reason is that for such a high fxm, the exposure of the sludge to aerobic conditions becomes insufficient to utilize the adsorbed and enmeshed BPOs. This leads to a decrease in active mass and oxygen demand and a buildup of enmeshed nondegraded organics. When this happens, the system still functions in that the COD is removed from the wastewater, but the degradation of the COD is reduced; the system begins to behave as a contact reactor of a contact-stabilization system, that is, a bio-flocculation with minimal degradation. This critical state occurs at lower fxm as the temperature is decreased and the sludge age is reduced. From the above discussion, it would appear that the unaerated mass fraction should not be increased above an upper limit of about 60%, as indicated in Figure 25, unless there is a specific reason for this (Tsai et al., 2003).

4.14.20.4 DO Concentration High DO concentrations, up to 33 mg l1, do not appear to affect nitrification rates significantly. However, low oxygen concentrations reduce the nitrification rate. Stenstrom and Poduska (1980) have suggested formulating this effect as follows:

mAmO ¼ mAm

O ðd1 Þ KO þ O

ð138Þ

where O is oxygen concentration in liquid (mgO l1), KO the half-saturation constant (mgO l1), mAmo the maximum specific growth rate (d1), and mAO the specific growth rate at DO of O mg l1. The value of KO ranges from 0.3 to 2 mgO l1, that is, at DO values below KO the growth rate will decline to less than half the rate where oxygen is present in adequate concentrations. The wide range of KO probably has arisen because the concentration of DO in the bulk liquid is not necessarily the same as inside the biological floc where the oxygen consumption takes place. Consequently, the value will depend on the floc size, mixing intensity, and oxygen diffusion rate into the floc. Furthermore, in a full-scale reactor the DO will vary over the reactor volume due to the discrete points of oxygen input (with mechanical aeration) and the impossibility of achieving instantaneous and complete mixing. For these reasons, it is not really possible to establish a generally applicable minimum oxygen value – each reactor will have a value specific to the conditions prevailing in it. In nitrifying reactors with bubble aeration a popular DO lower limit, to ensure unimpeded nitrification, is 2 mgO l1 at the surface of the mixed liquor. Under cyclic flow and load conditions the difficulties of ensuring an oxygen supply matching the oxygen demand and a lower limit for the DO concentration are difficult.

Biological Nutrient Removal

4

2

3

2

1

0.0 0

0 0 (a)

Max. effl. FSA/steady-state FSA ratio

Amplitude of influent flow and FSA conc. 1.00 0.75 0.50 0.25

6

Steady-state FSA conc. (mgN I−1)

Max. effl. FSA/steady-state FSA ratio

4

8

1

2

3

4

5

T = 22 °C: Raw sewage

10

5

8

0.8

0.0 Amplitude of influent flow and FSA

6

0.6

1.00 4

0.4

0.75 0.50

2

0.2

0.25

0.0

0 2

6

R s /R sm ratio

1.0

Steady-state effluent FSA

(b)

Steady-state FSA conc. (mgN I−1)

T = 14 °C: Raw sewage Steady-state effluent FSA

10

469

4

6

8

10

12

14

16

R s /R sm ratio

Figure 26 (a) Maximum to steady-state effluent FSA concentration ratio vs. sludge age to minimum sludge age for nitrification ratio for influent flow and ammonia concentration amplitude (in phase) of 0.0 (steady state) 0.25, 0.50, 0.75, and 1.0 at 14 1C. (b) Maximum to steady-state effluent FSA concentration ratio vs. sludge age to minimum sludge age for nitrification ratio for influent flow and ammonia concentration amplitude (in phase) of 0.0 (steady-state) 0.25, 0.50, 0.75, and 1.0 at 22 1C.

Where storm flows are not of long duration, flow equalization is a practical way to facilitate control of the DO concentration in the reactor. In fact, most of the diurnal variations in reactor dissolved concentrations are a direct consequence of diurnal flow variation – negligibly little is due to the kinetic rates of the biological processes, especially at long sludge ages. In the absence of flow equalization, amelioration of the adverse effects of low DO concentration during peak oxygen demand periods occurs by increasing the sludge age to significantly longer than the minimum necessary for nitrification, that is, by effectively increasing Sf.

4.14.20.5 Cyclic Flow and Load It is well known both experimentally and theoretically with simulation models that under cyclic flow and load conditions the nitrification efficiency of the AS system decreases compared with that under steady-state conditions. From simulation studies, during the high flow and/or load period, even though the nitrifiers are operating at their maximum rate, it is not possible to oxidize all the ammonia available, and an increased ammonia concentration is discharged in the effluent. This in turn reduces the mass of nitrifiers formed in the system. Equivalently, the effect of diurnal variation in flow and load is to reduce the system sludge age. The average effluent ammonia concentration from a system under cyclic flow and load conditions is therefore higher than that from the same system under constant flow and load (steady-state conditions). The adverse effect of the diurnal flow variation becomes more marked as the fractional amplitude of the flow and load variation increase and is ameliorated as the safety factor Sf increases. Simulation studies of the diurnal flow effect show a relatively consistent trend between the maximum or average effluent FSA concentrations under diurnal conditions and the steady-state effluent FSA concentration versus the ratio of system sludge age and the minimum sludge age for nitrification (Rs/Rsm). For mAm20 ¼ 0.45 d1 (other constants in

Table 10), Figures 26(a) (for 14 1C) and 26(b) (for 22 1C) show the maximum (average not shown) effluent FSA concentration as a ratio of the steady-state effluent FSA concentration versus the system sludge age as a ratio of the minimum sludge age for nitrification (Rs/Rsm) for a single reactor fully aerobic system receiving cyclic influent flow and FSA load as in-phase sinusoidally varying flow and ammonia concentration, both with amplitudes of 0.25, 0.50, 0.75, 1.00, and 0.0 (steady state). For example, at 14 1C (Figure 26(a)) if the system sludge age is 2 times the minimum for nitrification, the maximum effluent FSA concentration is 8 times the steadystate value. From Figure 26(a), the latter is 0.8 mgN l1 so the maximum is 8  0.8 ¼ 6.4 mgN l1. From Figures 26(a) and 26(b), clearly the greater the diurnal flow variation and the lower the temperature, the higher the maximum (and average) effluent ammonia concentrations. This can be compensated for by increasing Sf, which has the effect of increasing the sludge age or decreasing the unaerated mass fraction of the system. This obviously has an impact on the effluent quality and/or economics of the system. The importance of the selection of mAm cannot be overemphasized. If the value of mAm is selected higher than the actual value, even with a safety factor Sf of 1.25–1.35, the plant is likely to produce a fluctuating effluent ammonia concentration, with reduced mean efficiency in nitrification. Hence, conservative estimates of mAm (low) and Sf (high) are essential for ensuring nitrification and low effluent ammonia concentration.

4.14.20.6 pH and Alkalinity The mAm rate is very sensitive to the pH of the mixed liquor outside the 7–8 range. It seems that the free ammonia (NH3) and nitrous acid (HNO2) act inhibitorily when their respective concentrations increase too high. This happens when the pH increases above 8.5 (increasing (NH3)) or decreases below 7

470

Biological Nutrient Removal

(increasing (HNO2)); optimal nitrification rates are expected for 7opHo8.5 with sharp declines outside this range. From the overall stoichiometric equations for nitrification (Equation (118a)), nitrification releases hydrogen ions which in turn decreases H2CO3* Alkalinity of the mixed liquor. For every 1 mgFSA that is nitrified 2  50/14 ¼ 7.14 mg Alkalinity (as CaCO3) is consumed. Based on equilibrium chemistry of the carbonate system (Loewenthal and Marais, 1977), equations linking the pH with H2CO3* Alkalinity for any dissolved carbon dioxide concentration can be developed. These relationships are plotted in Figure 27. When the H2CO3* Alkalinity falls below about 50 mg l1 as CaCO3 then, irrespective of the carbon dioxide concentration, the pH becomes unstable and decreases to low values. Generally, if nitrification causes the H2CO3*Alkalinity to drop below about 50 mg l1 (as CaCO3), problems associated with low pH will arise at a plant, such as poor nitrification efficiency, effluents aggressive to concrete, and the possibility of development of bulking (poor settling) sludges (Jenkins et al., 1993). For any particular wastewater, the effect of nitrification on pH can be readily assessed, as follows: for example if a wastewater has a H2CO3*Alkalinity of 200 mg l1 as CaCO3 and the expected production of nitrate is 24 mgN l1, then the expected H2CO3*Alkalinity in the effluent will be (200  7.14  24) ¼ 29 mg l1 as CaCO3. From Figure 27, such an effluent will have a pH o7.0. Wastewaters having low Alkalinity (capital A denotes H2CO3* Alkalinity) are often encountered where the municipal supply is drawn from areas underlain with sandstone. A practical approach to treating such wastewaters is to (1) dose lime or better (2) create an anoxic zone(s) to denitrify some or all of the nitrate generated. In contrast to nitrification, denitrification takes up hydrogen ions which is equivalent to generating Alkalinity (see Section 4.14.24.2). By considering nitrate as electron acceptor, it can be shown that for every milligram of nitrate denitrified, there is an increase of 1  50/ 14 ¼ 3.57 mg Alkalinity as CaCO3. Hence, incorporating denitrification in a nitrification system causes the net loss of

10 0.5 1.0 2.0 5.0 10.0

Mixed liquor pH value

8 Carbon dioxide concentration (mg I−1 as CaCO3)

6

Saturation ~ 0.5 mg I−1 as CaCO3

4

Alkalinity to be reduced usually sufficiently to maintain the Alkalinity above 50 mg l1 as CaCO3 and consequently the pH above 7. In the example above, where the Alkalinity in the system is expected to decline to 29 mg l1 as CaCO3, if 50% of the nitrate were denitrified, the gain in Alkalinity would be (0.5  24  3.57) ¼ 43 mg l1 as CaCO3 and will result in an Alkalinity of (29 þ 43) ¼ 72 mg l1 as CaCO3 in the system. In this event the pH will remain above 7. For low Alkalinity wastewaters, it is imperative, therefore, that denitrification be built into nitrifying plants, even if N removal is not required. Incorporation of unaerated zones in the system influences the sludge age of the system at which nitrification takes place so that cognizance must be taken of the effect of an anoxic or unaerated zone in establishing the sludge age of a nitrifying– denitrifying plant (see Section 4.14.20.3). In the AS systems treating reasonably well buffered wastewaters, quantifying the effect of pH on nitrification is not critical because pH reduction can be limited or completely obviated by including anoxic zones, thereby ensuring Alkalinity recovery via denitrification. However, in poorly buffered wastewaters, or wastewaters with high influent N (such as AD liquors), the interaction between the biological processes, pH, and nitrification is the single most important one for the N removal AS system. Hence, it is essential to include the effect of pH on the nitrification rate for such wastewaters to quantify this important interaction. From Equation (121), the specific growth rate of the ANOs (mA) is a function of both mAm and Kn. It was shown above that the minimum sludge age is dominated by the magnitude of mAmT; it is only very weakly influenced by KnT. At RscRsm, the effluent ammonia concentration (Nae), although low, is, relatively speaking, significantly higher for larger KnT values: for example, if KnT increases by a factor of 2, the effluent ammonia concentration will increase correspondingly by the same factor (Equation (132)). Consequently, the value of KnT is significant insofar as it governs the effluent ammonia concentration once nitrification takes place at RscRsm. Several investigations have been made to understand the effect of pH on mAmT. These investigations generally have not separated out the effect of pH on mAmT and KnT so that most data are in effect lumped parameter estimates of mAmT. Almost no information is available on the effect of pH on KnT by itself. Quantitative modeling of the effect of pH on mAm has been hampered by the difficulty of accurately measuring the effects of pH on nitrification. Studies have shown that mAm can be expressed as a percentage of the highest value at optimum pH. Accepting this approach and that mAm is highest and remains approximately constant in the pH range for 7.2opHo8.0 but decreases as the pH decreases below 7.2 (Downing et al., 1964; Loveless and Painter, 1968), So¨temann et al. (2005a) modeled the mA  pH dependency as For 5opHo7.2,

2

mAmpH ¼ mAm7:2 yns ðpH7:2Þ 0 −100

0

100 Alkalinity (mg

200 I−1

300

as CaCO3)

Figure 27 Mixed liquor pH vs. H2CO3* alkalinity for different concentrations of carbon dioxide.

400

ð139aÞ

where yns is the pH sensitivity coefficient (E2.35). Declining mAm values at pH48.0 have been observed and it has been noted that nitrification effectively ceases at a pH of about 9.5 (Malan and Gouws, 1966; Wild et al., 1971; Antoniou et al., 1990). Accordingly, for pH47.2, So¨temann et al.

Biological Nutrient Removal

(2005a) proposed Equation (139b) to model the decline in the mAm from pH 47.2 to 9.5 as a function of mAm7.2 using inhibition kinetics as follows:

mAmpH ¼ mAm7:2 KI

Kmax  pH Kmax þ KII  pH

ð139bÞ

where KI ¼ 1.13, Kmax ¼ 9.5, KIIE0.3. The overall effect of pH on mAm is modeled by combining Equations (139a) and (139b), which is given by Equation (139c) and shown in Figure 28. It can be seen that in the range pH ¼ 7.2–8.3, the change in mAmpH is small, with mAmpH/mAm7.2 40.9:

mAmpH ¼ mAm7:2 2:35ðpH7:2Þ KI

Kmax  pH Kmax þ KII  pH

ð139cÞ

where 2.35(pH7.2) is set ¼ 1 for pH47.2,

KI

Kmax  pH ¼1 Kmax þ KII  pH

for pH o7.2 and mAmpH ¼ 0 for pH49.5. Experimental data from the literature are also shown in Figure 28 to provide some quantitative support for Equation (139c). At low pH (o7.2), data from Wild et al. (1971) and Antoniou et al. (1990) fit the equation reasonably well. Very few data are available for pH48.5, but the few points from Antoniou et al. (1990) show reasonable agreement with Equation (139c). Accordingly, Equation (139c) was accepted to calculate mAmpH in the pH range 5.5–9.5. From Equation (139c), the minimum sludge age for nitrification (Rsm) at different pH and temperature (T) and unaerated mass fraction (fxm) is given by

Rsm ¼ 1=½mApHT ð1  f xm Þ  bnT 

ðdaysÞ

ð140Þ

The problem with nitrification in low alkalinity wastewater is that the pH obtained is not known, because it is interactively 1.2

Fraction Unm/Umm7.2

1 Eq (139b) 0.8 0.6 0.4 Eq (139a) Eq (139b) 0.2 0 4

5

6

7

8

9

10

471

established between the degree of nitrification, loss of alkalinity, pH, and mApHT. To investigate this interaction, the biological kinetic ASM1 model for carbon (C) and nitrogen (N) removal was integrated by So¨temann et al. (2005a) with a two-phase (aqueous-gas) mixed weak acid/base chemistry kinetic model to extend application of ASM1 to situations where an estimate for pH in the biological reactor is important. This integration, which included CO2 (and N2) gas generation by the biological processes and their stripping by aeration, made a number of additions to ASM1, inter alia the above effect of pH on the autotrophic nitrifiers (ANOs). From simulation of a long sludge age ND AS system with incrementally decreasing influent H2CO3* Alkalinity, when the effluent H2CO3* alkalinity fell below about 50 mg l1 as CaCO3, the aerobic reactor pH dropped below 6.3, which severely retarded nitrification and caused the minimum sludge age for nitrification (Rsm) to increase up to the operating sludge age of the system. The simulation confirmed the earlier conclusion that when treating low H2CO3* alkalinity wastewater (1) the minimum sludge age for nitrification (Rsm) varies with temperature and reactor pH and (2) for low effluent H2CO3* alkalinity (o50 mg l1 as CaCO3), nitrification becomes unstable and sensitive to dynamic loading conditions resulting in increases in effluent ammonia concentration, reduced nitrification efficiency, and as a result lower N removal. For effluent H2CO3* alkalinity o50 mg l1, lime should be dosed to the influent to raise the aerobic reactor pH and stabilize nitrification and N removal.

4.14.21 Nutrient Requirements for Sludge Production All live biological material and some unbiodegradable organic compounds contain nitrogen (N) and phosphorus (P). The organic sludge mass (VSS) that accumulates in the biological reactor comprises active organisms (XBH), endogenous residue (XEH), and UPOs (XI), each of which contains N and P. From TKN and VSS tests conducted on AS, it has been found that the N content (as N with respect to VSS, fn, mgN/mgVSS) ranges between 0.09 and 0.12 with an average of about 0.10 mgN/ mgVSS. Similarly, from total P and VSS tests, the P content (as P with respect to VSS, fp, mgP/mgVSS) of AS in purely aerobic and anoxic aerobic systems ranges between 0.01 and 0.03 with an average of about 0.025 mgP/mgVSS. From the steady-state model, the relative proportions of the active organisms (XBH), endogenous residue (XEH), and UPOs (XI) change with sludge age. Yet, it has been found that the fn value of the VSS is relatively constant at 0.10 mgN/ mgVSS. This indicates that the N content of the active organisms (XBH), endogenous residue (XEH), and UPOs (XI) is closely the same; if they were significantly different, it would be observed that fn changes in a consistent manner with sludge age. Likewise, for fully aerobic systems, the P content of the three constituents of AS is approximately similar at 0.025 mgP/mgVSS.

pH Figure 28 Maximum specific growth rate of nitrifiers, as a fraction of the rate at pH 7.2, vs. pH of the mixed liquor. (F), Model; (), Malan and Gouws (1966); ( ), Downing et al. (1964); (  ), Wild et al (1971); and (m), Antoniou et al. (1990).

4.14.21.1 Nitrogen Requirements The mass of N (or P) incorporated into the sludge mass is calculated from a N balance over the completely mixed AS

472

Biological Nutrient Removal

system (Figure 2) under steady-state daily conditions, viz., TKN flux out ¼ TKN flux in TKN flux in ¼ Qi Nti (mgN d1) TKN flux out ¼ TKN flux in Qe and Qw

Noting that Qw þ Qe ¼ Qi and Qw ¼ Vp/Rs yields

Qi Nte ¼ Qi Nti  f n Xv Vp =Rs from which

Nte ¼ Nti  f n MXv =ðRs Qi Þ ðmgN l1 Þ

ð141Þ

where Nte is the effluent TKN concentration (mgN l1). The term fnMXv/(RsQi) is denoted Ns and is the concentration of influent TKN in mgN l1 that is incorporated into sludge mass and removed from the system bound in the particulate sludge mass in the waste flow (Qw): 1

Ns ¼ f n MXv =ðRs Qi Þ ðmgN l

influentÞ

ðmgN l1 Þ

ð143Þ

From Equation (141), under daily average conditions, the concentration of N per liter influent required for incorporation into sludge mass is equal to the N content of the mass of sludge (VSS) wasted per day divided by the influent flow. Substituting Equation (106) relating the mass of sludge (VSS) in the reactor (MXv) to the daily average organic load on the reactor (FSti), cancelling Qi and dividing by Sti yields the concentration of N required per liter influent for sludge production per mgCOD/l organic load on the reactor, viz.,

  ð1  f S0 us  f S0 up ÞYH f S0 up Ns ¼ fn ð1 þ f EH bH Rs Þ þ Sti ð1 þ bH Rs Þ f cv ðmgN=mgCODÞ

Nae ¼ Nai þ Nobsi þ Nobpi  ðNs  Noupi Þ ðmgN l1 Þ

ð142Þ

From the N mass balance, this Ns concentration does not include the N in dissolved form in the waste flow. The soluble TKN concentration in the waste flow is the same as the effluent TKN concentration, Nte, which is soluble N in the form of ammonia (Nae) and unbiodegradable soluble organic N (Nouse). Therefore, from Equation (141), provided nitrifiers are not supported in the AS reactor so that nitrification of ammonia to nitrate does not take place, the effluent TKN concentration Nte is given by

Nte ¼ Nti  Ns

organics (Nobsi and Nobpi) is released as FSA when these organics are broken down. This FSA adds to the FSA in the reactor from the influent. Some of the FSA in the reactor is taken up by the OHOs to form new OHO biomass. Some of the OHO biomass in the reactor is lost via the endogenous respiration process. The N associated with the biodegradable part of the OHO biomass is released back to the FSA pool in the reactor but the N in the unbiodegradable endogenous residue part remains as organic N bound in the endogenous residue VSS. Due to these interactions, it is possible that the effluent FSA concentration from a non-nitrifying AS system is higher than the influent FSA concentration – this occurs when the influent TKN comprises a high biodegradable organic N fraction. If the conditions are favorable for nitrification, the net FSA concentration in the reactor is available for the ANOs for growth with the associated generation of nitrate. Unless taken up for OHO growth or nitrified, the FSA remains as such and exits the system with the effluent. So in the absence of nitrification, the effluent ammonia concentration Nae is given by

ð144Þ

The influent TKN comprises ammonia and N bound in organic compounds of a soluble and particulate and biodegradable and unbiodegradable nature. The unbiodegradable organics, some of which contain organic N, are not degraded in the AS system. The influent unbiodegradable soluble organic N (Nousi) exits the system with the effluent (and waste flow) streams. The UPOs are enmeshed with the sludge mass in the reactor and so the organic N associated with these organics exits the system via the daily waste sludge (VSS) harvested from the system. The N bound in the biodegradable

ð145Þ and the effluent TKN (Nte) concentration by

Nte ¼ Nouse þ Nae

ðmgN l1 Þ

ð146Þ

The same approach is applied for the phosphorus (P) requirement for sludge production. Accepting that the P content of the AS in the fully aerobic system without BEPR is 0.025 mgP/mgVSS, the effluent total P (TP) concentration Pte is given by

Pte ¼ Pti  Ps

ðmgP l1 Þ

ð147Þ

where

Ps MXv f p Ns ¼ fp ¼ Sti Rs Qi f n Sti

ðmgP l1 influentÞ

ð148Þ

4.14.21.2 N (and P) Removal by Sludge Production A plot of Equations (144) and (148) versus sludge age is given in Figure 29 for fn ¼ 0.10 mgN/mgVSS, fp ¼ 0.025 mgP/mgVSS for the example raw and settled wastewaters. It is evident that higher concentrations of TKN and TP are required for sludge production for raw than for settled wastewaters. This is because greater quantities of sludge are produced per mgCOD organic load on the reactor at the same sludge age when treating raw wastewaters (see Section 4.14.13). Also, the N and P requirements decrease as the sludge age increases because net sludge production decreases as sludge age increases. Generally, for sludge ages greater than about 10 days, the N removal from the wastewater attributable to net sludge production is less than 0.025 mgN/mgCOD load on the reactor. As influent TKN/COD ratios for domestic wastewater are in the approximate range 0.07–0.13 (Figure 29), it is clear that only a minor fraction of the influent TKN (A in Figure 29) is removed by incorporation into sludge mass. Additional N removal (B in Figure 29) is obtained by transferring the N from the dissolved form in the liquid phase to the gas phase

Biological Nutrient Removal Nutrient requirements 0.035 Approximate range of influent TKN/COD and P/COD ratios of municipal wastewaters

0.12 0.10

0.030 0.025

0.08

0.020

0.06

0.015 B

0.04

0.010 Raw

0.02

0.005 A

P requirement (mgP/mgCOD)

N requirement (mgN/mgCOD)

0.14

Settled

0.00

0.000 0

5

10 15 20 Sludge age (days)

25

30

Figure 29 Approximate minimum nutrient N and P requirements as mgN l1 influent TKN and mgP l1 influent total P per mgCOD l1 organic load on the activated sludge reactor vs. sludge age for the example raw and settled wastewaters at 20 1C together with influent TKN and TP to COD concentration ratio ranges for municipal wastewater.

by autotrophic nitrification and heterotrophic denitrification, which transforms the nitrate to nitrogen gas in anoxic (nonaerated) reactor(s). The details of heterotrophic denitrification are presented below. From Figure 29, normal P removal by incorporation into biological sludge mass is limited at about 0.006 and 0.004 mgP/mgCOD for raw and settled wastewaters respectively, effecting a TP removal of about 20–25% from average municipal wastewaters. As transformation of dissolved orthoP to a gaseous form is not possible, to increase the P removal from the liquid phase, additional ortho-P needs to be incorporated into the sludge mass. This can be achieved in two ways: (1) chemically and/or (2) biologically. With chemical P removal, iron or aluminum chlorides or sulfates are dosed to the influent (pre-precipitation), to the AS reactor (simultaneous precipitation) or to the final effluent (post-precipitation). The disadvantage of chemical P removal is that it significantly increases (1) the salinity of treated wastewater, (2) the sludge production due to the inorganic solids formed, and (3) the complexity and cost of the WWTP. With biological P removal, the environmental conditions in the biological reactor are designed in such a way that a specific group of heterotrophic organisms (called PAOs) grow in the AS reactor. With the accumulated polyPs, these organisms have a much higher P content than the OHOs, as high as 0.38 mgP/ mgPAOVSS (Wentzel et al., 1990). The more PAOs that grow in the reactor, the higher will be the mean P content of the VSS sludge mass in the reactor and therefore the higher the P removal via the wasted sludge. With a significant mass of PAOs present, the mean P content of the VSS sludge mass can increase from 0.025 mgP/mgVSS in aerobic systems to 0.10– 0.15 mgP/mgVSS in biological N and P removal systems. The advantage of biological P removal over chemical P removal is that (1) the salinity of the treated wastewater is not increased, (2) sludge production is increased only between 10% and 15%, and (3) the system is less complex and costly to operate.

473

A disadvantage of biological P removal is that, being biological, it is less dependable and more variable than chemical P removal. The biological processes which mediate biological N and P removal in AS systems and the different reactor configurations in which these take place are described in Section 4.14.28.

4.14.22 Nitrification Design Considerations The kinetic equations describing the interactions between the FSA and the organic N are complex and have been developed in terms of the growth–death–regeneration approach in AS simulation models such as ASM1 and ASM2. However, for steady-state conditions assuming (1) all the biodegradable organics are utilized in the reactor and (2) a TKN mass balance over the AS system, a simple steady-state nitrification model can be developed from the nitrification kinetics and the N requirements for sludge production considered above. This model is adequate for steady-state design and from it some general response graphs are developed below for the example raw and settled wastewaters. Dynamic system responses can be determined with the simulation models once (1) the AS system has been designed and sludge age, zone and reactor volumes and recycle flows are known and (2) the steady-state concentrations have been calculated to serve as initial conditions for the simulation. In the nitrifying AS system design, the (1) effluent FSA, TKN, and nitrate concentrations and (2) the nitrification oxygen demand need to be calculated.

4.14.22.1 Effluent TKN The filtered effluent TKN (Nte) comprises the FSA (Nae) and the unbiodegradable soluble organic N (Nouse). Once mAm20, fxt, Rs, and Sf have been selected, the equations for these concentrations are: 1. Effluent FSA (Nae). Nae is given by Equation (132), which applies only if Rs4Rsm, which will be the case for Sf41.0. 2. Effluent soluble biodegradable organic nitrogen concentration (Nobse). The biodegradable organics (both soluble and particulate) are broken down by the OHOs releasing the organically bond N as FSA. In the steady-state model, it is assumed that all the biodegradable organics are utilized. Hence, the effluent soluble biodegradable organic N concentration (Nobse) is zero. 3. Effluent soluble unbiodegradable organic nitrogen concentration (Nouse). Being unbiodegradable, this concentration of organic N flows though the AS system with the result that the effluent concentration (Nouse) is equal to the influent concentration (Nousi), that is,

Nouse ¼ Nousi

ð149Þ

where Nousi is the influent soluble unbiodegradable organic nitrogen, mgOrgN-N l1 ¼ fN0 ous Nti, where fN0 ous is the soluble unbiodegradable organic N fraction of the influent TKN (Nti). The two nonzero effluent TKN concentrations (FSA, Nae and OrgN, Nouse) are soluble and so exit with the effluent (and

474

Biological Nutrient Removal

waste flow). The soluble (filtered) TKN in the effluent (Nte) is given by their sum, that is,

Nte ¼ Nae þ Nousi

ðfiltered TKNÞ

ð150Þ

If the effluent sample is not filtered, the effluent TKN will be higher by the concentration of TKN in the effluent VSS, that is,

Nte ¼ Nae þ Nouse þ f n Xve

ðunfiltered TKNÞ

nitrogen required for sludge production per mgCOD applied (from Equation (144)). The nitrification capacity to influent COD concentration ratio (Nc/Sti) of a system can be estimated approximately by evaluating each of the terms in Equation (153) as follows:

•

ð151Þ

where Xve is the effluent VSS concentration (mgVSS l1) and fn the N content of VSS (B0.1 mgOrgN-N/mgVSS).

•

4.14.22.2 Nitrification Capacity From a TKN mass balance over the AS system and Rs 4 Rsm, the concentration of nitrate generated in the system (Nne) with respect to the influent flow is given by the influent TKN (Nti) minus the soluble effluent TKN (Nte) and the concentration of influent TKN incorporated in the sludge wasted daily from the AS system (Ns), that is,

Nne ¼ Nc ¼ Nti  Nte  Ns

ð152Þ

The Ns concentration is determined from the mass of N incorporated in the VSS mass harvested from the reactor per day (Equation (142)). The mass of VSS in the reactor (MXv) does not have to include the VSS mass of nitrifiers because this mass, as mentioned earlier, is negligible (o2–4%). In Equation (152), Nc defines the ‘nitrification capacity’ of the AS system. The nitrification capacity (Nc) is the mass of nitrate produced by nitrification per unit average influent flow, that is, mgNO3-N l1. In Equation (150), the effluent TKN concentration (Nte) depends on the efficiency of nitrification. In the calculation for the maximum unaerated sludge mass fraction (fxm) at a selected sludge age, if the factor of safety (Sf) was selected 41.25 to 1.35 at the lowest expected temperature (Tmin), the efficiency of nitrification be high (495%) and Nae generally will be less than 1–2 mgN l1. Also, with Sf 41.25 at Tmin, Nae will be virtually independent of both the system configuration and the subdivision of the sludge mass into aerated and unaerated mass fractions. Consequently, for design, with Sf41.25, Nte will be around 3–4 mgN l1 provided that there is reasonable assurance that the actual mAm20 value will not be less than the value accepted for design and that there is sufficient aeration capacity so that nitrification is not inhibited by an insufficient oxygen supply. Accepting the calculated fxm and selected sludge age (Rs) at the lower temperature, then at higher temperatures the nitrification efficiency and the factor of safety (Sf) both will increase so that at summer temperatures (Tmax), Nte will be lower, approximately 2–3 mgN l1. Dividing Equation (152) by the total influent COD concentration (Sti) yields the nitrification capacity per mgCOD applied to the biological reactor, Nc/Sti, viz.,

Nc =Sti ¼ Nti =Sti  Nte =Sti  Ns =Sti

ð153Þ

where Nc/Sti is the nitrification capacity per mgCOD applied to the AS system (mgN/mgCOD), Nti/Sti the influent TKN/ COD concentration ratio of the wastewater, and Ns/Sti the

•

Nti/Sti: This ratio is a wastewater characteristic and obtained from the measured influent TKN and COD concentrations – it can range from 0.07 to 0.10 for raw municipal wastewater and 0.10 to 0.14 for settled wastewater. Nte/Sti: Provided the constraint for efficient nitrification is satisfied at the lowest temperature (Tmin), the effluent TKN at Tmin (Nte) will be low at B2–3 mgN l1, that is, for influent COD concentrations (Sti) ranging from 1000 to 500, Nte/Sti will range from 0.005 to 0.010. At Tmax, NteE1– 2 mgN l1 making the Nte/Sti ratio lower. Ns/Sti: Given by Equation (144).

A graphical representation of the relative importance of these three ratios to the nitrification capacity, Nc/Sti, is shown in Figure 30(a) (for 14 1C) and 30(b) (for 22 1C) and were generated by plotting Nc/Sti versus sludge age for selected influent TKN/COD (Nti/Sti) ratios of 0.07, 0.08, and 0.09 for the example raw wastewater and settled wastewater for 40% COD and 15% TKN removal in primary settling, viz., 0.113, 0.127, and 0.141. Also shown are the minimum sludge ages for nitrification at unaerated sludge mass fractions of 0.0, 0.2, 0.4, and 0.6 for the example mAm20 value of 0.45 d1. For a particular unaerated sludge mass fraction, the plotted values of Nc/Sti are valid only at sludge ages longer than the corresponding minimum sludge age. These figures show the relative magnitudes of the three terms that affect the nitrification capacity versus sludge age and temperature. 1. Temperature. To obtain complete nitrification at 14 1C (for a selected fxm), the sludge age required is more than double that at 22 1C. The corresponding nitrification capacities per influent COD at 14 1C show a marginal reduction to those at 22 1C, because sludge production at 14 1C is slightly higher than at 22 1C due to the reduction in endogenous respiration rate of the OHOs. 2. Sludge age. For a selected influent TKN/COD ratio (Nti/Sti), the nitrification capacity (Nc/Sti) increases as the sludge age increases because the N required for sludge production decreases with sludge age, making more FSA available for nitrification. However, the increase is marginal for Rs410 days. 3. Influent TKN/COD ratio (Nti/Sti). Clearly, for both raw and settled wastewater, at any selected sludge age, the nitrification capacity (Nc/Sti) is very sensitive to the influent TKN/COD ratio (Nti/Sti). An increase of 0.01 in Nti/Sti causes equal increase of 0.01 in Nc/Sti. For the same Nti/Sti ratio for raw or settled wastewater, the nitrification capacity (Nc/Sti) for raw wastewater is lower than for settled wastewater because more sludge (VSS) is produced per unit COD load from raw wastewater than from settled wastewater because the unbiodegradable particulate COD fraction (fS’up) in raw water is higher than in settled wastewater. Apart from this difference, an increase in influent TKN/COD ratio will result in an equal increase in nitrate concentration (nitrification capacity) per influent

Biological Nutrient Removal

WW Char fS’us Raw 0.07 Settled 0.12

0.10

0.05 Raw wastewater

0.15

0.0 0.2 0.4 0.6 −1 Unaerated mass fraction 14 °C; UA20 = 45 d bA20 = 0.04 d−1; Sf = 1.25

0.00 0 (a)

fS’up Settled wastewater 0.15 0.141 0.04 0.127 0.113 TKN/COD ratio 0.10 0.09 0.08

Nitrification capacity

Nitrification capacity

0.15

5

10 15 20 Sludge age (days)

25

fs’us 0.07 0.12

fs’up 0.15 0.04

Settled wastewater 0.141 0.127

0.10

0.113 TKN/COD ratio 0.10 0.09 0.08

0.05 Raw wastewater

0.00

30

WW Char Raw Settled

475

0.2 0.6 22 °C; UA20 = 0.45 d−1 0.0 0.4 −1 b Unaerated mass fraction A20 = 0.04 d ; Sf = 1.25 0

5

(b)

10 15 20 Sludge age (days)

25

30

Figure 30 Nitrification capacity per mgCOD applied to the biological reactor vs. sludge age for different influent TKN/COD concentration ratios in the example raw and settled wastewaters at 14 1C (a) and 22 1C (b). Also shown as vertical lines are the minimum sludge ages required to achieve nitrification for Sf ¼ 1.25 for unaerated sludge mass fractions of 0.0, 0.2, 0.4, and 0.6.

COD. This decreases the likelihood, or makes it impossible, to obtain complete denitrification using the wastewater organics as electron donor. This will become clear when denitrification is considered below. Because primary settling increases the influent TKN/COD ratio, N removal via nitrification denitrification is always lower with settled wastewater than with raw wastewater. However, this lower N removal comes with the advantage of a smaller biological reactor and lower oxygen demand resulting significant savings in reactor and oxygenation costs.

4.14.22.3 Mass of Nitrifiers (MXA) and Nitrification Oxygen Demand (FOn) Once nitrification takes place because the sludge age of the system is longer than the minimum required for nitrification, the mass of nitrifiers (MXA, mgVSS) in the reactor is calculated from the flux of nitrate generated (FNne) in the same way as the mass of OHOs (MXBH) was calculated from the flux of biodegradable organics (Equation (86)), viz.,

MXBA ¼ FNne YA Rs =ð1 þ bAT Rs Þ ðmgVSSÞ

ð154Þ

where FNne is the flux of nitrate generated ¼ (Qe þ Qw)Nne ¼ Qi Nne (mgN d1) and Nne is given by Equation (152). The oxygen demand for nitrification is simply 4.57 mgO/ mgN times the flux of nitrate produced, that is,

FOn ¼ 4:57 FNne ¼ OURn Vp

ðmgO d1 Þ

ð155Þ

Table 11 Raw and settled wastewater characteristics required for calculating effluent N concentrations from nitrification AS systems Influent WW characteristic

Sym

Raw

Influent TKN (mgN l1) Influent TKN/COD ratio Influent FSA fraction Unbio sol orgN fraction Unbio partic VSS N content

Nti fns fN0 a fN0 ous fn

60 0.08 0.75 0.03 0.1

Influent pH Influent Alk mg l1 as CaCO3 ANO max spec growth rate Influent flow rate (M l d1)

Alk mAm20 Qi

7.5 200 0.45 15

Seta 51 0.113 0.88 0.034 0.1 7.5 200 0.45 15

a

Settled wastewater (WW) characteristics must be selected/calculated to be consistent with the raw wastewater ones and mass balances over the primary settling tanks, e.g., soluble concentrations must be the same in settled wastewater as in raw wastewater.

organics (COD) removal (see Section 4.14.9.5). The wastewater characteristics for the raw and settled wastewaters for COD removal are listed in Table 7 and the additional characteristics required for nitrification are listed in Table 11. The nitrifier kinetic constants in Table 10, adjusted for wastewater temperatures 14 and 22 1C, were applied. No adjustment to mAm20 for pH was made, that is, an effluent Alkalinity 450 mg l1 as CaCO3 was assumed. Also, it is accepted that all the biodegradable organics are degraded and their N content released as ammonia so the effluent soluble biodegradable organic N concentration (Nobse) is zero.

4.14.23.2 Nitrification Process Behavior

4.14.23 Nitrification Design Example 4.14.23.1 Wastewater Characteristics Design of a nitrification AS system without denitrification is considered below. For the purpose of comparison, the nitrifying AS system is designed for the same wastewater flow and characteristics accepted for the design of the AS system for

From Equation (20a), the unbiodegradable soluble organic nitrogen in the effluent is Nouse ¼ Nousi ¼ 1.8 mgN l1 for raw and settled wastewater The ammonia concentration available for nitrification (Nan) is the influent TKN concentration (Nti) minus the N concentration required for sludge production (Ns) (Equation (142)) and the USO N concentration in the effluent

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Biological Nutrient Removal

(Nouse), viz.,

Nan ¼ Nti  Ns  Nouse

ðmgN l1 Þ

ð156Þ

If the sludge age of the system is shorter than the minimum required for nitrification (RsoRsm), no nitrification takes place and the effluent nitrate concentration (Nne) is zero. The effluent ammonia concentration (Nae) is equal to the nitrogen available for nitrification (Nan, Equation (156)). If Rs4Rsm for Sf ¼ 1.0, most of the FSA available for nitrification is nitrified to nitrate and the effluent nitrate concentration (Nne) is the difference between Nan (Equation (156)) and the effluent FSA concentration given by Equation (132). For both RsoRsm and Rs4Rsm, the effluent TKN concentration (Nte) is the sum of effluent ammonia and unbiodegradable soluble organic nitrogen concentrations (Nte ¼ Nae þ Nouse). For RsoRsm, no nitrification takes place so the effluent nitrate concentration (Nne) is zero and the effluent ammonia concentration (Nae) is given by Nan (Equation (156)). The nitrifier sludge mass (MXA) and the nitrification oxygen demand (FOn) are both zero because Nne is zero. With increasing sludge age starting from Rs ¼ 0, Nae from Equation (132) is first negative (which is of course impossible) and then 4Nan (which is also not possible). For a sludge age slightly longer than Rsm, the Nae falls below Nan. From this sludge age, nitrification takes place and for further (even small) increases in sludge age, the Nae rapidly decreases to low values (o4 mgN l1). Hence for Rs4Rsm, the effluent ammonia concentration (Nae) is given by Equation (132), the effluent TKN concentration by Nte ¼ Nae þ Nouse, and the effluent nitrate concentration (Nne) by

Nne ¼ Nan  Nae ¼ Nti  Ns  Nte

ðmgN l1 Þ

ð157Þ

With nitrification, the nitrifier biomass and nitrification oxygen demand are given by Equations (154) and (155). Substituting the influent N concentrations for raw and settled wastewaters and the values of the kinetic constants at 14 1C into the above equations, the results at different sludge ages were calculated. In Figure 31(a), the different effluent N concentrations from the system versus sludge age for raw and settled wastewater at 14 1C are shown. In Figure 31(c) are shown the nitrifier sludge mass (as a % of the reactor VSS mass) and nitrification oxygen demand for raw and settled wastewater at 14 1C. Also shown in Figure 31(c) are the carbonaceous and total oxygen demands for raw and settled wastewater at 14 1C. The calculations were repeated for 22 1C and shown in Figures 31(b) and 31(d). Figures 31(a) and 31(b) show that once the sludge age is approximately 25% longer than the minimum required for nitrification, nitrification is virtually complete (for steady-state conditions) and comparing the results for raw and settled wastewater, there is little difference between the nitrification oxygen demand and the concentrations of ammonia, nitrate, and TKN in the effluent. The reasons for this similar behavior are: (1) the primary settling tank removes only a small fraction of the influent TKN and (2) settled wastewater results in lower sludge production, so that the FSA available for nitrification in raw and settled wastewater is nearly the same. Once

nitrification takes place, temperature has relatively little effect on the different effluent N concentrations. However, a change in temperature causes a significant change in the minimum sludge age for nitrification. Considering Figures 31(a) and 31(b), for RsoRsm, the effluent ammonia concentration (Nae) and hence the effluent TKN concentration (Nte) increase with increasing sludge age up to Rsm because Ns decreases for increases in Rs. For Rs4Rsm, Nae decreases rapidly to o2 mgN l1 so that for Rs41.3Rsm, the effluent TKN concentration is o4 mgN l1. The increase in nitrate concentration (Nne) with an increase in sludge age for Rs41.3Rsm is mainly due to the reduction in N required for sludge production (Ns). This is important for BNR systems – increasing the sludge age increases the nitrification capacity (see Figure 30) so more nitrate has to be denitrified to achieve the same N removal. Figures 31(c) and 31(d) show that the nitrification oxygen demand increases rapidly once Rs4Rsm but for Rs41.3Rsm, further increases are marginal irrespective of the temperature or wastewater type. This nitrification oxygen demand represents an increase of 42% and 65% above the COD for the raw and settled wastewater. However, the total oxygen demand for treating settled wastewater is only 75% of that for treating raw wastewater. In order that nitrification can proceed without inhibition by oxygen limitation, it is important that the aeration equipment is adequately designed to supply the total oxygen demand; generally, heterotrophic organism growth takes precedence over nitrifier growth when oxygen supply (or ammonia) becomes insufficient. This is because heterotrophic organisms can grow adequately with DO concentrations of 0.5–1.0 mgO l1, whereas nitrifiers tend to require higher DO concentrations. Just as the effluent FSA concentration rapidly decreases for Rs4Rsm, the nitrifier sludge mass rapidly increases once Rs4Rsm, is slightly higher at 14 1C than at 22 1C due to the lower endogenous respiration rate. Also, because the concentrations of nitrate produced with raw and settled wastewater are closely similar (B40 mgN l1), the nitrifier sludge mass is approximately the same at the same sludge age (B430 kgVSS at 10d sludge age and B900 kgVSS at 30 day sludge age). Because with raw wastewater so much more sludge mass is produced than with settled wastewater, the nitrifier sludge mass is a much smaller proportion of the VSS mass with raw waste water (B1.4% at 10 day sludge age) than with settled wastewater (B3.3% at 10 day sludge age). Comparing the nitrifier sludge mass to the heterotrophic sludge mass, as in Figures 31(c) and 31(d), the nitrifier sludge mass comprises o4% of VSS mass even at high TKN/COD ratios for settled wastewater and so is ignored in the determination of the VSS concentration in the AS reactor treating domestic wastewater. It is worth repeating that primary sedimentation removes only a minor fraction of the TKN but a significant fraction of COD (15% and 40% in this example). Even though the settled wastewater has a lower TKN concentration than the raw wastewater, the effluent nitrate concentration does not reflect this difference. This is because the N removal for sludge production is lower for settled than for raw wastewater. Consequently, the nitrate concentration for settled wastewater is nearly the same as for raw wastewater – for different

(a)

Nitrogen-FSA, TKN, NO3 0.0

0.2

0.4

0.6

0.8

1.0

1.2

0

14 °C

5

0

FOc

5

Rsm

Ns

%Nit

25

25

30

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

30

Raw WW Settled WW

Raw WW Settled WW

FOn FOn

FOc

FOc

FOt

FOt

10 15 20 Sludge age (days)

%Nit

10 15 20 Sludge age (days)

Nte

10

Nouse = 1.8 mg N l−1 Nte = Nae + Nouse

Nne

Influent TKN

Ns

0

Nte

Rsm

20

30

40

50

60

14 °C

% Nitrifier VSS

(b)

(d)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 0

0

5

22 °C Rsm

5

FOn FOn

%Nit

FOt

FOt

25

25

30

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

30

Raw WW Settled WW

22 °C

Raw WW Settled WW

FOc

FOc

10 15 20 Sludge age (days)

%Nit

10 15 20 Sludge age (days)

Ns

10

Nouse = 1.8 mg N l−1 Nte = Nae + Nouse

Nne

Influent TKN

Ns Nte

Rsm

20

30

40

50

60

70

Figure 31 Effluent ammonia (Nae), TKN (Nte), and nitrate (Nne) concentrations and N required for sludge production (Ns) vs. sludge age at 14 1C (a) and 22 1C (b) and nitrification (FOn), carbonaceous (FOc) and total (FOt) oxygen demand in kgO/kgCOD load and % nitrifier VSS mass vs. sludge age at 14 1C (c) and 22 1C (d) for the example raw and settled wastewater.

(c)

Oxygen demand (kgO/kgCOD)

Nitrogen-FSA, TKN, NO3 Oxygen demand (kgO/kgCOD)

70

% Nitrifier VSS

478

Biological Nutrient Removal

wastewater characteristics, it can be higher than raw wastewater. In contrast, the maximum N removal by denitrification using the wastewater organics as electron donor, called the denitrification potential, mainly depends on the influent COD concentration and this concentration is significantly reduced by primary sedimentation. This may result in a situation where it may be possible to obtain near-complete nitrate removal when treating raw wastewater but not when treating settled wastewater. The difference in COD and TKN removal in PSTs therefore has a significant effect on the design of BNR systems.

4.14.24 Biological Denitrification

ages can be in the usual fully aerobic short sludge age range of 3–6 days. Unaerated zones should still be incorporated to derive the benefits of denitrification in the event nitrification does take place. When it does not, the unaerated zone will be anaerobic (no input of DO or nitrate) instead of anoxic, and some BEPR may take place. Because BEPR is not required and therefore not exploited to the full, whether or not it takes place is not important because it does not affect the system behavior very much. With some BEPR, the sludge production will be slightly higher (o5%) per COD load, the VSS/TSS ratio and oxygen demand both somewhat lower (by about 5%). However, BEPR may result in mineral precipitation problems in the sludge treatment facilities if the WAS is anaerobically digested.

4.14.24.1 Interaction between Nitrification and N Removal Nitrification is a prerequisite for denitrification – without it biological N removal is not possible. Once nitrification takes place, N removal by denitrification becomes possible and should be included even when N removal is not required (see Section 4.14.14) by incorporating zones in the reactor that are intentionally unaerated. Because the nitrifiers are obligate aerobes, nitrification does not take place in the unaerated zone(s), so to compensate for this, the system sludge age needs to be increased for situations where nitrification is required. For fully aerobic systems and a wastewater temperature 14 1C, a sludge age of 5–7 days may be sufficient for complete nitrification, taking due consideration of the requirement that the effluent FSA concentration should be low even under cyclic flow and load conditions (Sf41.3). For anoxic – aerobic systems, a sludge age of 15–20 days may be required when a 50% unaerated mass fraction is added (Figure 25). Therefore, for plants where N removal is required, invariably the sludge ages are long due to (1) the uncertainty in the mAm20 value, (2) the need for unaerated zones, and (3) the guarantee of nitrification at the minimum average winter temperature (Tmin). For plants where nitrification is a possibility and not obligatory, uncertainty in the mAm20 value is not important and unaerated zones can be smaller, with the result that sludge Table 12

4.14.24.2 Benefits of Denitrification In the design of fully aerobic systems discussed above, it was suggested that when nitrification is not obligatory but a possibility, unaerated zones should still be incorporated in the system to derive the benefits of denitrification. These benefits include (1) reduction in nitrate concentration which ameliorates the problem of rising sludge from denitrification in the secondary settling tank (Section 4.14.14), (2) recovery of alkalinity (Section 4.14.20.6), and (3) reduction in oxygen demand. With regard to (3), under anoxic conditions, nitrate serves as electron acceptor instead of DO in the degradation of organics (COD) by facultative heterotrophic organisms. The oxygen equivalent of nitrate is 2.86 mgO/mgNO3-N which means that 1 mg NO3-N denitrified to N2 gas has the same electron-accepting capacity as 2.86 mg of oxygen. In nitrification to nitrate, the FSA donates eight electrons (e)/mol, the N changing from an e state of  3 to þ 5. In denitrification to N2, the nitrate accepts 5 e/mol, the N changing from an e state of þ 5 to 0. Because 4.57 mgO/mgFSA-N are required for nitrification, the oxygen equivalent of nitrate in denitrification to N2 is 5/8  4.57 ¼ 2.86 mgO/mgNO3-N (Table 12). Therefore, for every 1 mg NO3-N denitrified to N2 gas in the anoxic zone, during which about 2.86/(1  fcvYH) ¼ 8.6 mgCOD

Comparison of nitrification and denitrification processes in single sludge activated sludge systems Nitrification

Denitrification

Form Function Half-reaction Organisms Environment

Ammonia (NHþ 4) Electron donor Oxidation Autotrophs Aerobic

Nitrate (NO 3) Electron acceptor Reduction Heterotrophs Anoxic

Compound Oxid. no.

NH4 þ 3 Nitrification (oxidation)

N2 0

NO2  þ3

8 e/atom N ¼ 4.57 mgO/mgN Net loss Denitrification (reduction) 5e/atom N ¼ 2.86 mgO/mgN Nitrification: 4.57 mgO/mgNH4-N nitrified to NO3-N Denitrification: 2.86 mgO recovered/mg NO3-N denitrified to N2 gas Therefore, denitrification allows at best 62.5% (5/8 or 2.86/4.57) recovery of the nitrification oxygen demand.

NO3  þ5

Biological Nutrient Removal Effluent TKN liquid phase

Raw wastewater TKN/COD = 0.08 1.0

Oxygen demand (kgO/kgCOD on reactor)

14 °C 22 °C

479

Total incl. nitrification

~5%

N in gas phase ~75%

0.8

N in sludge solid phase ~20%

0.6 Total incl. nitrif. and denit. 0.4

N nitrified (transformed in liquid phase) and possibly denitrified (transferred to gas phase)

Carbonaceous

Possibility of nitrification 0.2

Possibility of denitrification

Figure 33 Exit routes for nitrogen in single sludge nitrification denitrification activated sludge systems.

0.0 0

5

10

15 20 Sludge age (days)

25

30

Figure 32 Carbonaceous, total including nitrification and total including nitrification and denitrification oxygen demand per unit COD load on the biological reactor vs. sludge age for the example* raw wastewater. *Note: All the figures in this part which show the behavior of the various activated sludge system configurations were generated from the example raw and settled wastewater characteristics.

is utilized, 2.86 mg less oxygen needs to be supplied to the aerobic zone. Because the oxygen requirement to form the nitrate from ammonia is 4.57 mgO/mgNO3-N, and 2.86 mgO/mgNO3-N is recovered in denitrification to N2 gas, a maximum of 2.86/4.57 (or 5/8ths) ¼ 0.63 of the nitrification oxygen demand can be recovered. A comparison of the nitrification and denitrification reactions is given in Table 12. Under operating conditions, it is not always possible to denitrify all the nitrate formed with the result that the nitrification oxygen recovery by denitrification is about 50% (see Figure 32). Therefore, once the possibility of nitrification exists, it is always worthwhile to consider including intentional denitrification because of the recovery of alkalinity and oxygen. With regard to oxygen, if the oxygen supply is insufficient to meet the combined carbonaceous and nitrification requirement, areas in the aerobic reactor will become anoxic. Under oxygen limited conditions, the aerobic mass fraction in the aerobic reactor will vary depending on the COD and TKN load on the plant over the day. At minimum load, oxygen supply may be adequate so that nitrification may be complete whereas, at peak load, oxygen supply may be insufficient so that nitrification may cease (partially or completely) and denitrification will take place on the accumulated nitrate. This behavior is exploited in the single-reactor ND configurations such as the ditch or Carousel-type systems.

4.14.24.3 N Removal by Denitrification In biological N removal systems, the N is removed by transfer from the liquid phase to the solid and gas phases. About 20% of the influent N is incorporated in the sludge mass (Figure 33) but the bulk of the N (i.e., about 75% when complete denitrification is possible) is removed by transfer to the gas phase via nitrification and denitrification (Figure 33). In the nitrification step, the N remains in the liquid phase because it is transformed from ammonia to nitrate. In the denitrification step, it is transferred from the liquid to the gas phase and escapes to the atmosphere. When complete denitrification is achieved, a relatively small fraction of the influent TKN (B5%) remains in the liquid phase and escapes as total N (TKN þ nitrate) with the effluent. For aerobic conditions, the problem of the designers is to calculate the mass of oxygen electron acceptor required by the OHOs (and ANOs) for the utilization of the known mass of organic electron donors (organics and ammonia) available. For anoxic conditions, the problem is the opposite. Here, the problem is to calculate the mass of electron donors (COD) that are required to denitrify a known mass of electron acceptors nitrate. If sufficient electron donors (COD) are not available then complete denitrification cannot be achieved. The calculation of the nitrogen removal is essentially a reconciliation of electron acceptors (nitrate) and donors (COD) taking due account of (1) the biological kinetics of denitrification and (2) the system operating parameters (such as recycle ratios and anoxic reactor sizes) under which the denitrification is constrained to take place. The electron donors (or COD or energy) for denitrification can come from two sources: (1) internal or (2) external to the AS system. The former are those present in the system itself, that is, those in the incoming wastewater or generated within the biological reactor by the AS itself; the latter are organics imported to the AS system and specifically dosed into the anoxic zone(s) to promote denitrification, (e.g., methanol,

480

Biological Nutrient Removal

acetate, and molasses; Monteith et al., 1980). Here, the focus is on internal COD sources for denitrification, but the principles and procedures are sufficiently general to be adaptable to include external COD (energy) sources also.

4.14.24.4 Denitrification Kinetics There are three internal organics sources, two from the wastewater and one from the AS sludge mass itself. The two in the wastewater are the two main forms of organics (i.e., RBSO) and slowly biodegradable organics (BPO)). The third is slowly biodegradable organics generated by the biomass itself through death and lysis of organism mass (also known as endogenous mass loss/ respiration). This self-generated BPO is utilized in the same way as the wastewater BPO, but is recognized separately because of its different source and rate of supply to that of the influent. The RBSO and BPO (influent or self-generated) are degraded via different mechanisms by the OHOs. The different RBSO and BPO degradation mechanisms lead to different COD utilization rates. The RBSO comprises small simple dissolved organic compounds that can pass directly through the cell wall into the organism, for example, sugars and short-chain fatty acids. Accordingly, the RBSO can be used at a high rate which does not change significantly whether nitrate or oxygen serves as terminal electron acceptor (Ekama et al., 1996a). Simulation models use the Monod equation to model the utilization of RBSO by OHOs under both aerobic and anoxic conditions. The BPO comprises large particulate or colloidal organic compounds, too large to pass into the organism directly. These organics must be broken down (hydrolyzed) in the slime layer surrounding the organism to smaller components, which then can be transferred into the organism and utilized. The extracellular BPO hydrolysis rate is slow and forms the limiting rate in the utilization of BPO (Section 4.14.5.1.3). This hydrolysis rate is much slower under anoxic conditions than under aerobic conditions – only about one-third (Stern and Marais, 1974, van Haandel et al., 1981). This introduces a reduction factor Z in the BPO hydrolysis rate equation for anoxic conditions (Equation (159) below). Research has indicated that the utilization of RBSO is simultaneous with the hydrolysis of BPO. Also the rate of RBSO utilization is considerably faster (7–10 times) than the rate of BPO hydrolysis so the denitrification rate with influent RBSO is much faster than with BPO. Therefore, the influent RBSO is the preferred organic for denitrification and the higher this concentration in the influent with respect to the total COD, the greater the N removal.

4.14.24.5 Denitrification Systems As a result of the different degradation mechanisms and rates of RBSO and BPO utilization, the position of the anoxic zone in the biological reactor significantly affects the denitrification that can be achieved. There are many different configurations of single sludge ND systems but from the point of view of the source of the organics (electron donors), these can be simplified to two basic types of denitrification or combinations of these. The two basic types utilizing internal organics are: (1) post-denitrification, which utilizes self-generated

endogenous organics and (2) pre-denitrification, which utilizes influent wastewater organics. With post-denitrification (Figure 34(a)), the first reactor is aerobic and the second is unaerated. The influent is discharged to the aerobic reactor where aerobic growth of both the heterotrophic and nitrifying organisms takes place. Provided the sludge age is sufficiently long and the aerobic fraction of the system is adequately large, nitrification will be complete in the first reactor. The mixed liquor from the aerobic reactor passes to the anoxic reactor, also called the secondary anoxic reactor, where it is mixed with stirring. The outflow from the anoxic reactor passes through an SST and the underflow is recycled back to the aerobic reactor. The BPO released by the sludge mass via the death of organisms provides the energy source for denitrification in the anoxic reactor. However, the rate of release of energy is low, so that the rate of denitrification is also low. To obtain a meaningful reduction of nitrate, the anoxic mass fraction of the

Anoxic reactor

Aerobic reactor

Waste flow Settler

Influent

Effluent

Sludge recycle

(a)

s

Anoxic Aerobic reactor reactor Mixed liquor Recycle

Waste flow

a

Settler

Influent

Effluent

I

Sludge recycle

(b)

Primary Aerobic anoxic reactor reactor Mixed liquor Recycle a

s

Secondary anoxic reactor Reaeration reactor Waste flow Settler Effluent

Influent

(c)

Sludge recycle

s

Figure 34 (a) The post-denitrification single sludge biological nitrogen removal system. (b) The modified Ludzack–Ettinger single sludge biological nitrogen removal system proposed by Barnard (1973), including the primary anoxic reactor only. (c) The four-stage Bardenpho single sludge biological nitrogen removal system, including primary and secondary anoxic reactors.

Biological Nutrient Removal

system (i.e., the fraction of the mass of sludge in the system that is in the anoxic reactor) must be large and this may cause, depending on the sludge age, cessation of nitrification. Thus, although theoretically the system has the potential to remove all the nitrate, from a practical point this is not possible because the anoxic mass fraction will need to be so large that the conditions for nitrification cannot be satisfied particularly if the temperatures are low (o15 1C). Furthermore, in the anoxic reactor, ammonia is released through organism death and lysis, some of which passes out with the effluent thereby reducing the total nitrogen removal of the system. To minimize the ammonia content of the effluent, a flash or re-aeration reactor sometimes is placed between the anoxic reactor and the SST. In this reactor, N2 gas is stripped from the mixed liquor to avoid possible sludge buoyancy problems in the SST and the ammonia is nitrified to nitrate to assist with compliance of ammonia standards but it reduces the overall efficiency of the nitrate reduction of the system. For these reasons, post-denitrification has not been widely applied in practice.

4.14.24.5.1 The Ludzack–Ettinger system Ludzack and Ettinger (1962) were the first to propose a single sludge ND system utilizing the biodegradable organics in the influent as organics for denitrification. It consisted of two reactors in series, partially separated from each other. The influent was discharged to the first, or primary anoxic reactor which was maintained in an anoxic state by mixing without aeration. The second reactor was aerated and nitrification took place in it. The outflow from the aerobic reactor passed to the SST and the SST underflow was returned to the aerobic (second) reactor. Due to the mixing action in both reactors, an interchange of the nitrified and anoxic liquors was induced. The nitrate which entered the primary anoxic reactor was denitrified to nitrogen gas. Ludzack and Ettinger reported that their system gave variable denitrification results, probably due to the lack of control of the interchange of the contents between the two reactors. In 1973, Barnard proposed an improvement to the Ludzack– Ettinger system by completely separating the anoxic and aerobic reactors, recycling the underflow from the SST to the primary (first) anoxic reactor and providing a mixed liquor recycle from the aerobic to the primary anoxic reactor (Figure 34(b)). These modifications allowed a significant improvement in control over the system N removal performance of the system with the mixed liquor recycle flow. The RBSO and BPO from the influent stimulated high rates of denitrification in the primary anoxic reactor and much higher reductions of nitrate could be achieved than with post-denitrification, even when the pre-denitrification reactor of this system was substantially smaller than the post-denitrification reactor. In this system, called the Modified Ludzack–Ettinger (MLE) system, complete nitrate removal cannot be achieved because a part of the total flow from the aerobic reactor is not recycled to the anoxic reactor but exits the system with the effluent. To reduce the possibility of flotation of sludge in the SST due to denitrification of residual nitrate, the sludge accumulation in the SST needed to be kept to a minimum. This was achieved by having a high underflow recycle ratio from the SST, equal to the mean influent flow (1:1).

481

4.14.24.5.2 The four-stage Bardenpho system In order to overcome the deficiency of incomplete nitrate removal in the MLE system, Barnard (1973) proposed including a secondary anoxic reactor in the system and called it the fourstage Bardenpho system (Figure 34(c)). Barnard considered that the low concentration of nitrate discharged from the aerobic reactor to the secondary anoxic reactor will be denitrified to produce a relatively nitrate-free effluent. He included a flash or re-aeration reactor to strip the nitrogen gas and to nitrify the ammonia released during the denitrification. Although in concept the Bardenpho system has the potential for complete removal of nitrate, in practice this is not possible except when the influent TKN/COD concentration ratio is quite low, o0.09 mgN/mgCOD for normal municipal wastewater at 14 1C. The low denitrification rate and ammonia release (about 20% of the nitrate denitrified) results is an inefficient use of the secondary anoxic sludge mass fraction. As a result of the competition between the aerated and unaerated sludge mass fractions from the requirement to nitrify, (Section 4.14.20.3) usually it is better to exclude the secondary anoxic (and re-aeration) reactor and enlarge the primary anoxic reactor and increase the mixed liquor recycle ratio.

4.14.25 Denitrification Kinetics 4.14.25.1 Denitrification Rates The denitrification behavior in the primary and secondary anoxic zones is best explained by considering these reactors as plug-flow reactors. However, the explanation is equally valid for completely mixed reactors because the denitrification kinetics are essentially zero order with respect to nitrate concentration (van Haandel et al., 1981; Ekama and Wentzel, 1999b). Owing to the two different kinds of biodegradable organics (RBSO and BPO) in the influent wastewater, the denitrification in the primary anoxic reactor follows two phases (Figure 35(a)) – an initial rapid phase where the rate is defined by the simultaneous utilization of RBSO and BPO (K1 þ K2) and a second slower phase where the specific denitrification rate (K2) is defined by the utilization of only BPO originating from the influent and self-generated by the sludge through organism death and lysis. In the secondary anoxic reactor, only a single slow phase of denitrification takes place (Figure 35(b)), the specific rate (K3) being about two-thirds of the slow rate (K2) in the primary anoxic reactor (Stern and Marais, 1974; van Haandel et al., 1981). In the preceding aerobic reactor all the RBSO and most of the BPO of the influent has been utilized with the result that in the secondary anoxic reactor the only biodegradable COD available is BPO from organism death and lysis; the slow rate of supply of this BPO governs the K3 rate and causes this rate to be slower than the K2 rate. The values of the K rates are given in Table 13. A further specific K rate (K4) has been defined for denitrification in intermittently aerated anoxic aerobic digestion of WAS (Warner et al., 1986). This rate is only two-thirds of the K3 rate in the secondary anoxic reactor (Table 13), but sufficiently high to denitrify all the nitrate generated in aerobic digestion of WAS if the 6 h aeration cycle is 50% anoxic and 50% aerobic. Denitrification in anoxic–aerobic digestion adds

482

Biological Nutrient Removal

NO3−N concentration

NO3−N concentration

K1XBH

K2XBH

1st

(a)

K3XBH

Single phase

Second phase

(b)

Time

Time

Figure 35 Nitrate concentration of vs. time profiles in primary anoxic (a) and secondary anoxic (b) plugflow reactors, showing the three phases of denitrification associated with the K1, K2, and K3 rates. In the primary anoxic the initial rapid rate K1 is attributable to the utilization of the influent RBSO and the second slower rate K2 to the utilization of BPO from the influent wastewater and self-generated by organism death and lysis. In the secondary anoxic reactor, the rate K3 is attributable to the utilization of the self-generated BPO only. Table 13

K denitrification rates and their temperature sensitivity

Symbol

20 1 C

y

14 1 C

22 1 C

Equation

K120a K220a K320a K420a

0.72 0.1 0.1 0

1.2 1.08 1.029 1.029

0.241 0.06 0.06 0.04

1.036 0.118 0.08 0.05

158 159 160 161

a

Units – mgNO3-N/(mgOHOVSS d).

the benefits of denitrification to this system, that is, zero alkalinity consumption, oxygen recovery, improved pH control, reduced chemical dosing (Dold et al., 1985), and additionally a nitrogen free dewatering liquor. This last advantage is significant considering the high N content of WAS compared with primary sludge. The constancy of K1, K2, K3 (and K4) specific denitrification rates under constant flow and load conditions can be explained in terms of the kinetics of RBSO and BPO organics utilization included in the AS simulation models such as ASM1 developed later. The utilization of RBSO organics is modeled with the Monod equation and expressing the K1 rate in terms of this yields

K1 ¼

ð1  f cv YH Þf cv mHm Ss 2:86YH Ks þ Ss

where

Ss E 1 ðmgNO3 -N=ðmgOHOVSS dÞÞ Ks þ Ss

ð158Þ

In the plugflow and completely mixed primary anoxic reactor, the Monod term SS/(KS þ SS) remains close to 1 down to low RBSO concentrations because the half-saturation concentration (KS) is low. Accepting YH ¼ 0.45 mgVSS and

fcv ¼ 1.48 mgCOD/mgVSS yields K1 ¼0.26 mH mgNO3-N/ (mgOHOVSS d). So for the measured K1 ¼0.72 mgNO3-N/ (mgOHOVSS d) (Table 13), the mHm must have been about 2.8 d1. This mHm rate is in the range of mHm rates measured in AS systems. In investigating the kinetics of RBSO utilization in aerobic and anoxic selectors, Still et al. (1996) and Ekama et al. (1996a, b) found mHm values ranged between 1.0 d1 in completely mixed reactor systems and 4.5 d1 selector reactor systems, which yields K1 denitrification rates around 0.26 mgNO3-N/(mgOHOVSS d) for completely mixed type systems and 1.17 mgNO3-N/(mgOHOVSS d) for systems in which a selector effect (high mH) has been stimulated in the OHO biomass. The utilization of BPO is expressed in terms of the activesite surface hydrolysis kinetic formulation, which has the form of a Monod equation, except the variable is the adsorbed BPO to active OHO ratio (Xs/XBH), not the bulk liquid BPO concentration. Hence, the K2, K3 (and K4) rates are given by

K2 ¼ K3 ¼ K4 ¼

ð1  f cv YH Þ ZKh ðXs =XBH Þ 2:86f cv YH ½Kx þ ðXs =XBH Þ mgNO3 -N=ðmgOHOVSS dÞ

ð159Þ

where XS/XBH is progressively lower in primary (K2) secondary (K3) and anoxic–aerobic digestion (K4).

Biological Nutrient Removal

In the constant flow and load primary and secondary anoxic plugflow reactors, the (Xs/XBH) ratio changes very little due to the reduced anoxic hydrolysis rate including the Z. The reason for the K2 being higher than K3 arises from different concentrations of adsorbed BPO relative to the active OHO concentration (Xs/XBH) (Figure 36). In the primary anoxic reactor, the ratio is high because adsorbed BPO originates from the influent and OHO death. In the secondary anoxic, the ratio is lower because BPO originates only from OHO death. For the K2 and K3 denitrification rates, there is no simple relationship between the K rates and the ZKh because the adsorbed BPO to OHO ratio (Xs/XBH) is different in the primary and secondary anoxic reactors (and aerobic digester) and varies somewhat with sludge age and unaerated sludge mass fraction. It was concluded that the K1, K2, K3, and K4 denitrification constants have no direct kinetic significance; their constancy is the result of a combination of kinetic reactions which show little variation with sludge age in the range 10–30 days. Temperature does affect the K rates but once these have been adjusted for temperature, again the K rates show little variation at different sludge ages (van Haandel et al., 1981). It can be concluded both from experimental observation and theoretical kinetic points of view that acceptance of constant K2 and K3 rates is acceptable for steady-state design. This is in fact done to estimate the denitrification potential (Dp) of an anoxic reactor under constant flow and load conditions. With regard to K1, this rate can change significantly because the RBSO utilization rate can change appreciably depending on the mixing regime in the anoxic (or aerobic) reactor (Ekama et al., 1986, 1996a, b and Still et al., 1996). However, its variation does not affect ND design significantly because normally primary anoxic reactors are sufficiently large to allow complete utilization of RBSO even when the utilization rate (mHm) is low. In fact, the denitrification design procedure requires that all the RBSO is utilized in the primary anoxic reactor which introduces a minimum primary anoxic sludge mass fraction (fx1 min) and a minimum a-recycle ratio (amin) to

0.12

Specific denit rates (K )

K2 0.10 0.08 0.06

K3 K4

0.04 0.02 0.00 0.0

0.1

0.2

0.3

0.4

XS /XBH ratio (mgCOD/mgCOD) Figure 36 Specific denitrification rate (K) vs. adsorbed SB organics to OHO biomass concentration ratio (XS/XBH), showing the primary anoxic (K2), secondary anoxic (K3), and anoxic–aerobic digestion (K4) specific denitrification rates.

483

ensure this. These concepts can also be used for anoxic selector reactor design (Ekama et al.,1996a). The simulation model was applied also to anoxic–aerobic digestion of WAS. It was found that the model predicted accurately both aerobic and anoxic–aerobic digester behavior under constant and cyclic flow and load conditions and validated the K4 specific denitrification rate (Warner et al., 1986); no significant adjustment to values of the kinetic constants was necessary.

4.14.25.2 Denitrification Potential The concentration of nitrate (per liter influent flow Qi) that an anoxic reactor can denitrify biologically is called that reactor’s denitrification potential. It is called a potential because whether or not it is achieved depends on the nitrate load on the anoxic reactor(s). If too little nitrate is recycled to the anoxic reactor, all the recycled nitrate will be denitrified and the actual removal of nitrate, that is, denitrification performance, will be lower than the potential. In this case the denitrification is system (or recycle) limited. An increase in the system recycle ratios will increase nitrate load on the anoxic reactor and hence also the denitrification. Once the recycle rates are such that the nitrate load on the anoxic reactor(s) equals the denitrification potential of the reactor, then the system denitrification performance is optimal and the recycle ratios are at their optimum values. At this point the anoxic and aerobic reactor nitrate concentrations are just zero and the lowest possible, respectively. Increasing the recycle rates above the optimum increases the nitrate concentration in the anoxic reactor outflow above zero but this does not improve the denitrification performance because the system has now become biological or kinetics limited. The denitrification potential of the anoxic reactor(s) has been achieved and more nitrate cannot be denitrified by the particular anoxic reactors and wastewater. Indeed, increases in the recycle ratios above the optimum values are uneconomical due to unnecessary pumping costs and introduce unnecessary additional DO into the anoxic reactors which causes an undesirable reduction in denitrification performance and increase in effluent nitrate concentration. The principle of denitrification design therefore hinges around (1) calculating the denitrification potential of the anoxic reactor(s); (2) setting the nitrate load imposed on the anoxic reactor(s) equal to the denitrification potential; and (3) calculating the recycle ratios associated with this condition. The recycle ratios so calculated are the optimum values. From the above discussion, it is clear that critical in the design for denitrification is calculation of the nitrate load and denitrification potential. The nitrate load is calculated from the nitrification capacity, which is the concentration of nitrate per liter influent flow (Qi) generated by nitrification (Section 4.14.22.2, Equation (152)). The nitrification capacity (Nc, mg N l1 influent) was shown above to be approximately proportional to the influent TKN concentration (Nti). The denitrification potential is calculated separately for the utilization of the RBSO and BPO. The RBSO gives rise to a rapid denitrification rate so that it can be assumed that it is all utilized in the primary anoxic reactor. This is in fact an objective in the design. Accordingly, the contribution of the RBSO to the denitrification potential is simply the catabolic component of its

484

Biological Nutrient Removal

electron-donating capacity in terms of nitrate as N. Therefore, in the complete utilization of the influent RBSO, a fixed proportion (1  fcvYH) of the RBSO electrons (catabolic component) will be donated to NO3  reducing it to N2. Thus, knowing the influent RBSO concentration and assuming it is all utilized, the denitrification potential of this RBSO is given by

Dp1RBSO ¼ f Sb0 s Sbi ð1  f cv YH Þ=2:86 ðmgNO3 -N l

1

influentÞ

components of the RBSO and BPO yields the total denitrification potential of primary and secondary anoxic reactors, that is,

Dp1 ¼ Dp1RBSO þ Dp1BPO ¼ f Sb0 s Sbi ð1  f cv YH Þ=2:86 þ Sbi K2 f x1 YH Rs =ð1 þ bH Rs Þ ¼ Sbi ff Sb0 s ð1  f cv YH Þ=2:86 þ K2 f x1 YH Rs =ð1 þ bH Rs Þg ðmgN l1 influentÞ

ð163Þ

ð160Þ Dp3 ¼ Dp3RBSO þ Dp3BPO

where Dp1 RBSO is the denitrification potential of the influent RBSO in primary anoxic reactor, Sbi the influent biodeg. COD (mgCOD l1), fSb0 s the RBSO fraction of Sbi, YH the OHO yield coefficient (0.45 mgVSS/mgCOD), and 2.86 the oxygen equivalent of nitrate. For the BPO, this substrate contributes to denitrification in the primary anoxic reactor and the secondary anoxic reactor. The denitrification potentials for the BPO are formulated on the basis of the K2 and K3 specific denitrification rates, respectively. These K rates are a simplification of the kinetic equations describing the utilization of BPO from the influent and/or from organism death and lysis and have a basis in the fundamental biological kinetics incorporated in the AS simulation models such as ASM1 (Henze et al., 1987). The K rates define the denitrification rate as mgNO3  -N denitrified per day per mgOHOVSS mass in the anoxic reactor. To determine the denitrification potential contributed by the BPO, the mass of OHOVSS produced per liter influent flow and the proportion of this mass in the primary and/or secondary anoxic reactors needs to be calculated and multiplied by the K2 or K3 rates. From the steady-state AS model for organics removal (Section 4.14.9.3), the OHO mass in the system (MXBH) is calculated from the biodegradable COD load (Equation (103)). Of this MXBH mass, a fraction fx1 and/or fx3 is continuously present in the primary and/or secondary anoxic reactors, respectively, that is, fx1 and fx3 are the primary and secondary anoxic sludge mass fractions, respectively. The OHOVSS mass in the primary and/or secondary anoxic reactors per liter influent flow is therefore given by

f x1 MXBH =Qi ¼ f x1 Sbi ðYH ÞRs =ð1 þ bH Rs Þ ðmgOHOVSS l1 influent in primary anoxicÞ

f x3 MXBH =Qi ¼ f x3 Sbi ðYH ÞRs =ð1 þ bH Rs Þ ðmgOHOVSS l1 influent in secondary anoxicÞ

Multiplying these masses by the respective K rates gives the primary and secondary anoxic reactor denitrification potentials attributable to BPO (Dp1BPO, Dp3BPO), viz.,

Dp1BPO ¼ K2 f x1 MXBH =Qi ¼ K2 f x1 Sbi YH Rs =ð1 þ bH Rs Þ ð161Þ Dp3BPO ¼ K3 f x3 Sbi YH Rs =ð1 þ bH Rs Þ

ð162Þ

This approach is valid because the K2 and K3 rates are continuous for the entire sludge residence time in the anoxic reactor(s), provided the nitrate concentration does not decrease to zero (Figure 35). Combining the denitrification potential

¼ 0 þ Sbi K3 f x3 YH Rs =ð1 þ bH Rs Þ ðmgN l1 influentÞ ð164Þ In Equations (163) and (164), the K2, K3, and bH rates are temperature sensitive, decreasing as the temperature decreases. The temperature sensitivity of these rates has been measured and is defined in Tables 13 and 6. From Equations (163) and (164), it can be seen that the denitrification potentials are directly proportional to the biodegradable COD concentration of the wastewater (Sbi). This is expected because in the same way that the oxygen demand is directly related to the COD load, so also is the nitrate demand (which is called the denitrification potential) because both oxygen and nitrate act as electron acceptor for the same organic degradation reactions. For the same size anoxic reactor, the primary anoxic has a much larger denitrification potential (by about 2–3 times) than the secondary anoxic because (1) K2 is larger than K3 and (2) more importantly, the RBSO makes a significant contribution to the denitrification potential in the primary anoxic reactor. For this reason the RBSO needs to be accurately specified to ensure reliable estimates of the N removal that can be achieved. For a normal municipal wastewater with an RBSO fraction (fSb0 s) of about 25% (with respect to biodegradable COD), the RBSO contributes about one-third to half of Dp1 depending on the size of the primary anoxic reactor and temperature. In a system where a high degree of N removal is required, between one-fourth and one-third of the carbonaceous oxygen demand is met by denitrification, which reduces the carbonaceous oxygen demand in the aerobic reactor by the same amount. As mentioned earlier, this reduction represents about half of the oxygen that was required to produce the nitrate by nitrification (see Figure 32). From Equation (164), the influent RBSO contribution to the denitrification potential of the secondary anoxic reactor is zero. This is because all the RBSO is utilized either in the preceding primary anoxic and/or in aerobic reactors. However, the Dp3 RBSO term has been included in Equation (164) in the event an external carbon source such as methanol, acetic acid, or high-strength organic wastewater is dosed into the secondary anoxic reactor to improve the denitrification. The sludge mass fraction approach above is valid because the fraction of the VSS (MXv) or TSS (MXt) masses that is OHO mass (MXBH) is constant for specified wastewater characteristics and sludge age and equal to the active fraction (fatOHO or favOHO – Equations (114) and (115)) and very closely the same in the anoxic and aerobic reactors of the system. Therefore, the anoxic and aerobic sludge mass fractions are the same whether calculated from the VSS, TSS, or OHO masses; for example, in an MLE system with anoxic and aerobic reactor volumes

Biological Nutrient Removal of 3000 and 6000 m3, respectively, one notes that nearly one-third of the OHO, VSS, and TSS masses in the system are in the anoxic reactor, and hence the anoxic sludge mass fraction is 0.33.

4.14.25.3 Minimum Primary Anoxic Sludge Mass Fraction In Equation (163), it is assumed that the initial rapid rate of denitrification is always complete, that is, the actual retention time in the primary anoxic reactor is always longer than the time required to utilize all the influent RBSO. This is because in Equation (163), the denitrification attributable to the influent RBSO is stoichiometrically expressed, not kinetically – it gives the concentration of nitrate the K1 rate removes when allowed sufficient time to reach completion. By considering the actual retention time (say t1) required to complete the first phase of denitrification (Figure 35(a)), and noting that t1(a þ s þ 1) is the minimum nominal hydraulic retention time to achieve this, it can be shown that the minimum primary anoxic sludge mass fraction fx1min to remove all the influent RBSO at a rate of K1 mgNO3-N/(mgOHOVSS d) is

f x1min ¼

f Sb0 s ð1  f cv YH Þð1 þ bHT Rs Þ 2:86K1T YH Rs

ð165Þ

Substituting the values of the kinetic constants into Equation (165), yields fx1mino0.08 for Rs 410 days at 14 1C. This value is much lower than most practical primary anoxic reactors so that Equation (163) will be valid in most cases. Equation (165) also applies to sizing anoxic selectors provided K1 (or mH) is appropriately selected (see Section 4.14.25.1, Equation (158); Ekama et al., 1996a).

4.14.25.4 Denitrification – Influence on Reactor Volume and Oxygen Demand From the design approach to nitrification (Equation (136)) and denitrification (Equations (163) and (164)), it can be seen that the design for N removal is done entirely using sludge mass fractions and does not require the volume of the reactor to be known. The volume of the reactor is obtained in the identical fashion as for the fully aerobic system and follows from the choice of the TSS concentration (Xt) for the reactor (Section 4.14.11). The volume of the reactor so obtained is then subdivided in proportion to the calculated primary and/or secondary anoxic and aerobic sludge mass fractions. Consequently, N removal design is grafted directly into the aerobic system design and for the same design reactor TSS concentration and sludge age, a fully aerobic system and an anoxic–aerobic system for N removal will have the same reactor volume. Research has indicated that there are many factors that influence the mass of sludge generated for a given sludge age and daily average COD load, and alternating anoxic–aerobic conditions is one of them. However, relative to the uncertainty in organic (COD) load and unbiodegradable particulate COD fraction and their daily and seasonal variation, these influences are not large enough from a design point of view to be given specific consideration in the design procedure. From a design point of view, the only significant difference between aerobic and anoxic–aerobic conditions is the oxygen demand

485

and this difference needs to be taken into account for economical design (Figure 32).

4.14.26 Development and Demonstration of Design Procedure It was concluded above that the influent wastewater characteristics that need to be accurately known are the influent TKN/COD ratio and RBSO fraction. These have a major influence on the nitrification capacity and denitrification potential, respectively, and hence on the N removal performance and minimum effluent nitrate concentration that can be achieved by biological denitrification. The effect of these two wastewater characteristics on design will be demonstrated below with numerical examples generated from the example raw and settled wastewaters with different influent TKN concentrations and RBSO fractions. The design of biological N removal is developed and demonstrated below by continuing the calculations with the example raw and settled wastewater characteristics listed in Tables 7 and 11. The only additional characteristic required for the denitrification design is the influent RBSO fraction (fSb0 s), which is 0.25 and 0.385 of the biodegradable COD for the raw and settled wastewaters, respectively. The results obtained so far for the COD removal and nitrification calculations for sludge ages 3–30 days are shown in Figures 14, 15, and 31.

4.14.26.1 Review of Calculations For the raw wastewater characteristics (i.e., fS0 up ¼ 0.15 mgCOD/mgCOD, fS0 us ¼ 0.07 mgCOD/mgCOD, Tmin ¼ 14 1C, Sti ¼ 750 mgCOD l1 – see Table 7) and 20 days sludge age, and accepting the nitrogen content of the volatile solids (fn) to be 0.10 mgN/mgVSS, the nitrogen required for sludge production Ns ¼ 17.0 mgN l1 (Equation (144)). From Section 4.14.23.2, the effluent biodegradable and unbiodegradable soluble organic nitrogen concentrations (Nobse and Nouse) are 0.0 and 1.80 mgN l1, respectively. From Equation (132) the effluent ammonia concentration Nae is 2.0 mgN l1. The effluent TKN concentration (Nte) is the sum of Nouse and Nae (Equation (150)) and hence Nte ¼ 3.8 mgN l1. The nitrification capacity (Nc) is found from Equation (152) and for the example raw wastewater (Nti ¼ 60.0 mgN l1; TKN/COD ¼ 0.08 mgN/mgCOD) at 14 1C is

Nc ¼ 60:0  17:0  3:8 ¼ 39:2 mgN l1 The nitrification oxygen demand, FOn is found from Equation (155), that is,

FOn ¼ 4:57Nc Qi ¼ 4:57  39:2  15  106 mgOd1 ¼ 2687 kgOd1 and the mass of nitrifier VSS in the reactor is given by Equation (154), that is,

MXBA ¼ 0:1  20=ð1 þ 0:034  20Þ  39:2  15 ¼ 702 kgVSS

486

Biological Nutrient Removal

In the design, because it is intended to reduce the nitrate concentration as much as possible, the alkalinity change in the wastewater will be minimized; assuming that 80% of the nitrate formed is denitrified, the H2CO3* alk change ¼ 7.14Nc  3.57 (nitrate denitrified) ¼  7.14  39.2 þ 3.57  0.80  39.2 ¼ 168 mg l1 as CaCO3. With an influent H2CO3* alk of 250 mg l1 as CaCO3 the effluent H2CO3* alk ¼ 250–168 ¼ 82 mg l1 as CaCO3, which, from Figure 27, will maintain a pH above 7 (see Section 4.14.20.6).

4.14.26.2 Allocation of Unaerated Sludge Mass Fraction In nitrogen removal systems, the maximum anoxic sludge mass fraction available for denitrification, fxdm, can be set equal to the maximum unaerated sludge mass fraction fxm at the minimum temperature, that is,

f xdm ¼ f xm

ð166Þ

where fxm is given by Equation (136) for selected Rs, mnmT, and Tmin. This is because for N removal systems, unaerated sludge mass need not be set aside for the anaerobic reactor. In N and P removal systems, some of the unaerated sludge mass (0.12–0.25) needs to be set aside for the anaerobic reactor to stimulate BEPR. This sludge mass fraction, called the anaerobic sludge mass fraction and denoted fxa, cannot be used for denitrification. For BEPR to be as high as possible, no nitrate should be recycled to the anaerobic reactor so that zero denitrification takes place in this reactor. So, for the purposes of this development and demonstration of denitrification behavior, it will be accepted that the maximum unaerated sludge mass fraction available at 20 days sludge age (fxm) is all allocated to anoxic conditions, that is, fxdm ¼ fxm ¼ 0.534.

4.14.26.3 Denitrification Performance of the MLE System 4.14.26.3.1 Optimum recycle ratio a In the MLE system, the anoxic sludge mass fraction is all in the form of a primary anoxic reactor, that is, fx1 ¼ fxdm ¼ fxm. The denitrification potential of the primary anoxic reactor Dp1 is found from Equation (163), that is, for the example raw wastewater at 14 1C and fxm ¼ fxdm ¼ fx1 ¼0.534, Dp1 ¼ 52.5 mgN l1. The only additional wastewater characteristic required to calculate Dp1 is the influent RBSO (Sbsi) concentration or fraction (fSb’s), which for the example raw and settled wastewaters are given in Table 14, that is, 0.25 and 0.385 with respect to the biodegradable COD (Sbi), respectively. In the MLE system, if the nitrate concentration in the outflow of the anoxic reactor is zero, then the nitrate concentration in the aerobic reactor (Nnar) is equal to Nc/ (a þ s þ 1), that is, the nitrification capacity in mgN l1 influent flow diluted by the total (no nitrate containing) flow entering the aerobic reactor which is (a þ s þ 1) times the influent flow, where a and s are the mixed liquor and underflow recycle ratios (with respect to the influent average dry weather flow Qi), respectively. Accepting that there is no denitrification in the secondary settling tank (which needs to be minimized anyway due to the problem of rising sludges), the aerobic reactor and system effluent nitrate concentrations (Nnar and

Table 14 Additional wastewater characteristics required for denitrification (and BEPR) design Wastewater Readily biodegradable soluble organics (RBSO) as.y (1)y.fraction of biodegradable organics (BO, Sbi) COD (fSb0 s) (2)y.fraction of total organics (Sti, COD) (fS0 bs) VFA fraction of biodegradable soluble organics (RBSO), (fSbs0 a)

Raw

Settled

0.25

0.385

0.194

0.324

0.10

0.10

Nne, respectively) are equal and given by

Nne ¼ Nnar ¼ Nc =ða þ s þ 1Þ

ð167Þ

Knowing Nne and Nnar and taking into account DO concentrations in the a and s recycles, that is, Oa and Os mgO l1 respectively, the equivalent nitrate load on the primary anoxic reactor (Nnlp) by the a and s recycles is

    Oa Os a þ Nne þ s Nnlp ¼ Nnar 2:86 2:86 The optimum denitrification (i.e., lowest effluent nitrate concentration) is obtained when the equivalent nitrate load on the anoxic reactor is equal to the denitrification potential of the anoxic reactor (i.e., Dp1 ¼ Nnlp), viz.,

 Dp1 ¼

   Nc Oa Nc Os þ þ aþ s ð168Þ ða þ s þ 1Þ 2:86 ða þ s þ 1Þ 2:86

Solving Equation (168) for a yields the a recycle ratio which exactly loads the primary anoxic reactor to its denitrification potential with nitrate and DO. This a value is the optimum because it results in the lowest Nne, that is,

aopt ¼ ½B þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi B 2 þ 4AC=ð2AÞ

ð169Þ

where

A ¼ Oa =2:86 B ¼ Nc  Dp1 þ fðs þ 1ÞOa þ sOs g=2:86 C ¼ ðs þ 1ÞðDpp  sOs =2:86Þ  sNc and

Nnemin ¼ Nneaopt ¼ Nc =ðaopt þ s þ 1Þ ðmgN l1 Þ

ð170Þ

For a ¼ aopt, Equation (170) for Nne is valid and will give the minimum Nne attainable. When a raopt Equation (170) is also valid because the assumption on which Equation (169) is based is valid, that is, NnlprDp1 or equivalently, zero nitrate concentration in the outflow of the anoxic reactor. For a 4aopt this assumption is no longer valid and Nne increases as the a recycle ratio increases due to increasing DO flux entering the anoxic reactor. For a4aopt, Nne is given by the difference between the equivalent nitrate load on the anoxic reactor (which is the sum of the nitrification capacity Nc and the nitrate

Biological Nutrient Removal MLE system (settled) Effluent nitrate and a recycle ratio

MLE system (raw) Effluent nitrate vs. a recycle

20

487

20

14 °C s = 0.5 s = 1.0 s = 2.0 22 °C s = 1.0

15

10

Effluent nitrate (mgN I−1)

Effluent nitrate (mgN I−1)

a-opt (14 °C)

a-opt (14 °C) a-opt (22 °C)

5

14 °C s = 0.5 s = 1.0 s = 2.0

15

22 °C s = 1.0

10

N ne min (14 °C)

5

N ne min (14 °C)

0 0

0 5

10 a Recycle ratio

15

0

5

10 a Recycle ratio

15

Figure 37 Effluent nitrate concentration vs. mixed liquor a recycle ratio for the example raw (a) and settled (b) wastewaters for underflow (s) recycle ratio of 1:1 at 14 1C (bold line) and 22 1C (thin line) and for s ¼ 0.5:1 and 2.0:1 at 14 1C (dashed lines).

equivalent of the oxygen concentration with respect to the influent flow) and the denitrification potential Dp1, viz.,

Nne ¼ Nc þ

aOa sOs þ  Dp1 2:86 2:86

ðmgN l1 Þ

ð171Þ

As Nc, Dp1, Os, and Oa are constants, the increase in Nne with increasing a above aopt is linear with slope Oa/2.86 mgN l1. At a ¼ aopt, Equations (170) and (171) give the same Nne concentrations. Accepting the design sludge age of 20 days, which allows a maximum unaerated sludge mass fraction fxm of 0.534, the denitrification behavior of the MLE system is demonstrated below for the example raw and settled wastewaters at 14 and 22 1C. In the calculations the DO concentrations in the a and s recycles, Oa and Os are 2 and 1 mgO l1, respectively, and the underflow recycle ratio s is 1:1. This s recycle ratio is usually fixed at a value such that satisfactory settling tank operation is obtained. Details of secondary settling tank theory, design, modeling, and operation are discussed by Ekama et al. (1997) and Ekama and Marais (2004). Substituting the values for the nitrification capacity Nc and denitrification potential Dp1 into Equations (169) and (170), the optimum mixed liquor recycle ratio aopt and minimum effluent nitrate concentration Nneaopt are obtained, for example, for the settled wastewater at 14 1C

A ¼ 2=2:86 ¼ 0:70 B ¼ 39:6  40:1 þ fð1 þ 1Þ2 þ 1  1g=2:86 ¼ þ1:52 C ¼ ð1 þ 1Þð40:1  1  1=2:86Þ  1  39:6 ¼ þ39:61 Hence, aopt ¼ 6.5 and Nnemin ¼ 4.7 mgN l1. The calculations for the example raw and settled wastewater at 14 and 22 1C show that for all four cases aopt exceeds 5. Although the calculations include the discharge of DO to the anoxic reactor, a recycle ratios above 5 to 6 are not cost effective. The small decreases in Nne which are obtained for even large increases in a recycle ratio above about 5:1 do not warrant the additional pumping costs.

This is illustrated in Figure 37 which shows Nne versus a recycle ratio for the example raw (Figure 37(a)) and settled (Figure 37(b)) wastewater at 14 and 22 1C plotted from Equations (170) and (171). For the settled wastewater (Figure 37(b)) at 14 1C and s ¼ 1:1, for aoaopt, the anoxic reactor is underloaded with nitrate and DO and as the a recycle increases up to aopt, the equivalent nitrate load increases toward the anoxic reactor’s denitrification potential. Initially, Nne decreases sharply for increases in a, but as a increases the decrease in Nne becomes smaller. At 14 1C with a ¼ aopt ¼ 6.5, the anoxic reactor is loaded to its denitrification potential by the a and s recycles and a Nnemin ¼ Nneaopt ¼ 4.7 mgN l1 is achieved. At a ¼ aopt ¼ 6.5, the greatest proportion of the anoxic reactor’s denitrification potential is used for denitrification and therefore yields the minimum effluent nitrate concentration (Nneaopt). This is shown in Figures 38(a) and 38(b) for the raw and settled wastewaters at 14 1C. For the settled wastewater at 14 1C (Figure 38(b)) at a ¼ aopt ¼ 6.5, 88% of the equivalent nitrate load (i.e. (a þ s)  Nnemin ¼ 35.2 mgN l1 out of a Dp1 ¼ 40.1 mgN l1) is nitrate and therefore 88% of the denitrification potential of the anoxic reactor is utilized for denitrification and 12% for DO removal. The higher the a recycle ratio, the greater the proportion of the denitrification potential is utilized for DO removal. At 14 1C, for a4aopt, the equivalent nitrate load exceeds the denitrification potential and as the a recycle increases so Nne increases due to the increased DO mass flow to the anoxic reactor. From Equation (171), at a ¼ 15, Nne ¼ 10.6 mgN l1 and 27% of the denitrification potential is required to remove DO, leaving only 73% for denitrification (Figures 37(b) and 38(b)). For 14 1C, the plots of Nne versus a at underflow s recycle ratios of 0.5:1 and 2.0:1 are also given in Figure 37 and show that aopt is not significantly different at different s recycle ratios. Also, at low a recycle ratios, changes in s have a significant influence on Nne, but at high a recycle ratios, even significant changes in s do not significantly change Nne. This is because at high a, most of the nitrate is recycled to the anoxic reactor by the a recycle, so that changes in s do not

488

Biological Nutrient Removal MLE system (raw) use of denitrification potential 100

Denitrification potential used for nitrate removal

20

% Denit. potential

% Denit. potential

Denitrification potential used for nitrate removal

0

Denitrification potential used for nitrate removal

Denitrification potential used for nitrate removal

40

a-opt (14 °C) a-prac

0 0

(a)

60

20

a-opt (14 °C)

a-prac

Used DO removal

Unused denitrification potential

80

60 40

100

Used DO removal

Unused denitrification potential

80

MLE system (settled) use of denitrification potential

5

10 a Recycle ratio

15

0 (b)

5

10 a Recycle ratio

15

Figure 38 % Denitrification potential unused, used by dissolved oxygen in the recycles and for denitrification vs. a recycle ratio for the example raw (a) and settled (b) wastewaters for underflow (s) recycle ratio of 1:1 at 14 1C.

significantly change the nitrate load on the anoxic reactor. Hence, for the MLE system, decreases in s can be compensated for by increases in a – it makes little difference which recycle brings the nitrate to the anoxic reactor as long as the anoxic reactor is loaded as closely as practically possible to its denitrification potential in order to minimize Nne. For the settled wastewater at 22 1C and s ¼ 1:1 (Figure 37(b)), Nne versus a is similar to that at 14 1C up to a ¼ 6.5. This is because Nc values at 14 and 22 1C for the example raw and settled wastewater are almost the same (i.e., 39.9 and 41.6 mgN l1 at 14 and 22 1C, respectively). However, at 22 1C, the denitrification potential is significantly higher than at 14 1C (40.1 mgN l1 at 14 1C and 52.4 mgN l1 at 22 1C) so that a higher aopt is required (e.g., 17.9) at 22 1C to load the anoxic reactor to its denitrification potential than at 14 1C. Therefore, at 22 1C, as the a recycle increases above 6.5, Nne continues to decrease until aopt ¼ 17.9 is reached. The increase in a from 6.5 to 17.9 reduces Nne from 4.9 to 2.1, that is, only 2.8 mgN l1. This small decrease in Nne is not worth the large increase in pumping costs from 6.5:1 to 17.9:1 required to produce it. Consequently, for economical reasons, the a recycle ratio is limited at a practical maximum (aprac) of say 5:1, which fixes the lowest practical effluent nitrate concentration (Nneprac) from the MLE system between 5 and 10 mgN l1 depending on the influent TKN/ COD ratio. From the design procedure demonstrated so far, it is clear that the procedure hinges around balancing the equivalent nitrate load with the denitrification potential by appropriate choice of the a recycle ratio: for selected system design parameters (sludge age, anoxic mass fraction, underflow recycle ratio, etc.) and wastewater characteristics (temperature, readily biodegradable COD fraction, TKN/COD ratio, etc.), the denitrification potential of the MLE system is fixed. With all the above fixed, the system denitrification performance is controlled by the a recycle ratio, and this performance is optimum when the a recycle ratio is set at the optimum aopt. For aoaopt, the performance will be below optimum because the equivalent nitrate load is less than the denitrification potential (Figure 38); for a ¼ aopt, the performance is optimal because the equivalent

nitrate load equals the denitrification potential; and for a4aopt, the performance is again suboptimal because now the equivalent nitrate load is greater than the denitrification potential and more than necessary DO is recycled to the anoxic reactor which reduces the denitrification (see Figures 37 and 38). If a practical limit on a is set at say aprac ¼ 5:1 and aopt is significantly higher, then a significant proportion of the anoxic reactor’s denitrification potential is not used (Figure 38). There are two options to deal with this unused denitrification potential: (1) change the design, that is, decrease the sludge age (Rs) and/or unaerated sludge mass fraction (fxm) or (2) leave the system as designed (i.e., Rs ¼ 20 days and fxm ¼ 0.534) and keep the unused denitrification potential in reserve as a factor of safety against changes in wastewater characteristics, such as (1) increased organic load, which will require a reduction in sludge age, (2) increased TKN/COD ratio, which will load the anoxic reactor with nitrate at lower a recycle ratios, or (3) decreased RBSO fraction, which decreases the anoxic reactors denitrification potential.

4.14.26.3.2 The balanced MLE system With option (1) the anoxic sludge mass fraction fx1 is decreased to eliminate the unused denitrification potential. The decrease in fx1 increases the aerobic mass fraction and therefore the factor of safety (Sf) on nitrification. To maintain the same Sf, the sludge age of the system can be reduced to that value at which the lower fx1 is equal to the maximum unaerated sludge mass fraction fxm allowed (i.e., fx1 ¼ fxm) for the selected mAm20 and Tmin. An MLE system with a sludge age (Rs) and influent TKN concentration (Nti) such that fx1 ¼ fxm and aopt ¼ aprac (say 5:1), so that this aprac loads the anoxic reactor exactly to its denitrification potential, is called a balanced MLE system. This approach to design of the MLE system was proposed by van Haandel et al. (1982) and gives the most economical AS reactor design, that is, the lowest sludge age, and therefore the smallest reactor volume, and the highest denitrification with the a recycle ratio fixed at some maximum practical limit. The influent TKN/COD ratio, fxm ¼ fx1, fx1 min, Nne, and %N removal (%Nrem) versus sludge age for balanced

Biological Nutrient Removal MLE system (settled, 14 °C) Design at fixed a -opt = 5:1

MLE system (raw, 14 °C) Design at fixed a-opt = 5:1 Balanced sludge age

0.8

0.12

f x1 = f xm

0.6 0.4

TKN/COD Raw WW

0.08

0.2

Influent TKN/COD ratio

0.14

0.16

1.0 Balanced sludge age

0.14 0.12

0.6

Settled WW f x1 = f xm

0.10

0.4

0.08

0.0 0

5

(a)

10 15 20 Sludge age (days)

25

0.06

30

0.0 0

100

20

25

30

100

75

Balanced sludge age

10

50

Effluent nitrate

5

25

0

0 0

5

10 15 20 Sludge age (days)

25

15

75

Balanced sludge age

10

50 Effluent nitrate

5

25

0

30

0 0

(d)

% Nitrogen removal

%N removal

15

Effluent nitrate (mgN I−1)

%N removal % Nitrogen removal

Effluent nitrate (mgN I−1)

10 15 20 Sludge age (days)

MLE system (settled, 14 °C) Design at fixed a -opt = 5:1

MLE system (raw, 14 °C) Design at fixed a-opt = 5:1

(c)

5

(b)

20

0.2

f x1min

f x1min 0.06

0.8

TKN/COD

Anoxic mass fraction

1.0 Anoxic mass fraction

Influent TKN/COD ratio

0.16

0.10

489

5

10 15 20 Sludge age (days)

25

30

Figure 39 Influent TKN/COD ratio (TKN/COD), maximum unaerated (fxm), primary anoxic (fx1), and minimum primary anoxic (fx1 min) sludge mass fractions (a,b) and effluent nitrate concentration and %N removal (c,d) for balanced MLE systems with a 5:1 practical upper limit to the a recycle ratio for the example raw (a,c) and settled (b,d) wastewaters at 14 1C.

MLE systems for the example raw and settled wastewaters at 14 and 22 1C are shown in Figures 39 and 40, respectively. The sludge age which balances the MLE system for given wastewater characteristics and aprac cannot be calculated directly. It is easiest to calculate the influent TKN concentration for a range of sludge ages and choose the sludge age which matches the wastewater TKN concentration (Nti). The procedure for calculating Nti for a balanced MLE system is as follows: from the design mAm20, Tmin, Sf, and a selected sludge age, fxm is calculated from Equation (136). Provided fxm4fx1 min (Equation (165)), fx1 is set equal to fxm. Knowing fx1 and the wastewater characteristics, Dp1 is calculated from Equation (163). This Dp1 and a selected value for aprac are then substituted into Equation (168), which sets the equivalent nitrate load on the anoxic reactor equal to the denitrification potential and hence aopt equals the selected aprac. With Dp1 and a known, Nc is calculated from Equation (168). Once Nc is known, Nti is calculated from Nti ¼ Nte þ Ns þ Nc (Equation (152)), where Nte ¼ Nouse þ Nae (Equation (150)) and Nae is given by Equation (132) because with Sf fixed the Rs  fxm relationship is fixed. With Nc and Nti known, the effluent nitrate concentration Nne and % nitrogen removal (%Nrem) are

found from Equation (170) and %Nrem ¼ 100[Nti  (Nne þ Nte)]/Nti, respectively. This calculation is repeated for different sludge ages. The shortest sludge age allowed is the one which gives fx1 ¼ fxm ¼ fx1min. In Figure 39, for 14 1C, for the raw wastewater (Figures 39(a) and 39(c)), it can be seen that fx1( ¼ fxm) increases from about 0.09 at 8 days sludge age, at which fxm is just greater than fx1 min, to 0.60 at 26 days sludge age, at which fxm is equal to the upper limit set for it. As fx1 increases so the influent TKN/COD ratio increases from 0.061 at 8 days sludge age to 0.115 at 26 days sludge age. With the increase in TKN/COD ratio, the nitrification capacity Nc increases and hence Nne increases from about 3.2 mgN l1 at 8 days sludge age to 9.3 mgN l1 at 26 days sludge age because the a and s recycle ratios remain at 5:1 and 1:1, respectively (see Equation (170)). The %N removal, which includes the N removed via sludge wastage Ns, decreases marginally from 85% to 82% as the influent TKN/COD ratio and sludge age increase for the balanced MLE system. For the settled wastewater at 14 1C (Figures 39(b) and 39(d)), the influent TKN/COD ratio, fx1 and fx1min results are similar to those for the raw wastewater, that is, for the same

Biological Nutrient Removal MLE system (raw, 22 °C) Design at fixed a-opt = 5:1 1.0

0.6

f x1 = f xm

0.4

0.10 Raw WW

0.2

0.14

Balanced sludge age

0.12

0.0

0.4

0.10

0.2

0.08

100

Effluent nitrate

50

25

Balanced sludge age

0 0

5

10 15 20 Sludge age (days)

25

10 15 20 Sludge age (days)

25

30

MLE system (settled, 22 °C) Design at fixed a-opt = 5:1

20

% Nitrogen removal

75

15

5

5

(b)

0 (c)

0

%N removal

10

0.0

0.06

30

MLE system (raw, 22 °C) Design at fixed a-opt = 5:1

20 Effluent nitrate (mgN I−1)

25

Effluent nitrate (mgN I−1)

(a)

10 15 20 Sludge age (days)

0.6

f x1mm

0.06 5

f x1 = f xm

Settled WW

f x1mm 0

0.8

Anoxic mass fraction

0.8

0.08

1.0 TKN/COD

0.14 0.12

0.16

TKN/COD Influent TKN/COD ratio

Balanced sludge age

Anoxic mass fraction

Influent TKN/COD ratio

0.16

MLE system (settled, 22 °C) Design at fixed a-opt = 5:1

100

%N removal 15

75

10

50 Effluent nitrate

5

25

Balanced sludge age

0

0

30

% Nitrogen removal

490

0 (d)

5

10 15 20 Sludge age (days)

25

30

Figure 40 Influent TKN/COD ratio (TKN/COD), maximum unaerated (fxm), primary anoxic (fx1), and minimum primary anoxic (fx1 min) sludge mass fractions (a,b) and effluent nitrate concentration and %N removal (c,d) for balanced MLE systems with a 5:1 practical upper limit to the a recycle ratio for the example raw (a,c) and settled (b,d) wastewaters at 22 1C.

sludge age approximately the same TKN/COD ratio is found for the balanced MLE system. For the settled wastewater, the Nne is slightly lower, increasing from about 3.2 to 6.7 mgN l1 from 8 to 26 days sludge age; also the %N removal is somewhat lower, around 78% mainly due to the lower N removal via sludge wastage Ns. However, it must be remembered that the TKN/COD ratio and RBSO fraction of a settled wastewater are higher than those of the raw wastewater from which it is produced, viz. TKN/COD ratio 0.113 and 0.080 mgN/mgCOD and RBSO fraction (fSb’s) 0.25 and 0.385 for the example settled and raw wastewaters, respectively. Therefore, at 14 1C, while the raw wastewater can be treated in a balanced MLE system at about 11 days sludge age (Figure 39(a)), the sludge age for the settled wastewater balanced MLE system is about 17 days (Figure 39(b)). A comparison of the balanced MLE systems for the example raw and settled wastewaters is given in Table 15. From Table 15 it can be seen that Nne is less than 1 mgN l1 higher for the settled wastewater but the reactor volume and total oxygen demand significantly lower compared with the

Table 15 Comparison of balanced MLE systems treating the example raw and settled wastewaters at 14 1C Parameter

Raw

Settled

Influent TKN/COD ratio Unaerated mass fraction(fxm) Anoxic mass fraction (fx1) Minimum anoxic fraction a Recycle ratio (aprac ¼ aopt) Sludge age (days) Effluent nitrate (Nne, mgN l1) Effluent TKN (Nte, mgN l1) Reactor vol. at 4.5 gTSS l1 (m3) Carb O2 demand (FOc, kgO d1) Nit O2 demand (FOn, kgO d1) O2 recovered (FOd, kgO d1) Tot. O2 demand (FOtd, kgO d1) %N removal Mass TSS wasted (FXt, kg d1) Active fraction wrt TSS (fatOHO)

0.08 0.306 0.306 0.08 5:1 11 5.1 4.3 9484 6156 2492 1327 7321 84.3 3880 0.316

0.113 0.485 0.485 0.108 5:1 17 5.7 4.1 5264 4251 2685 1437 5499 80.9 1394 0.414

Biological Nutrient Removal

raw wastewater. Therefore, from an AS system point of view, treating settled wastewater would be more economical than treating raw wastewater for a comparable effluent quality. Also, both systems require sludge treatment; for the raw wastewater because 11 days sludge age waste sludge is not stable (high active fraction, favOHO) and for the settled wastewater, the primary sludge needs to be stabilized. The 11 days sludge age waste sludge can be stabilized with anoxic aerobic digestion which allows the N released in digestion to be nitrified and denitrified (Warner et al. 1986; Mebrahtu et al., 2010) and primary sludge can be anaerobically digested to benefit from gas generation. The choice of treating raw or settled wastewater therefore does not depend so much on the effluent quality or the economics of the AS system itself, but on the economics of the whole WWTP, including sludge treatment. Because the minimum wastewater temperature (Tmin) governs the AS system (and sludge treatment) design, the balanced MLE system results for 22 1C are not particularly relevant to the temperate climate regions. However, in equatorial and tropical regions, where wastewater treatment is becoming a matter of increasing concern, high wastewater temperatures are encountered. For this reason and for illustrative purposes also, the balanced MLE results for the raw and settled wastewaters are shown in Figure 40. Compared with 14 1C, the upper limit to fxm ¼ 0.60 is reached already at 7 days sludge age and significantly higher influent TKN/COD ratios can be treated at equal sludge ages. These higher TKN/COD ratios result in higher Nne, which for the raw wastewater increases from 3 to 13 mgN l1 and for the settled wastewater from 3 to 9 mgN l1 for increases in sludge age from 4 to 30 days. If Tmin were 22 1C, the example raw and settled wastewaters could be treated at 3 and 4 days sludge age, respectively, yielding Nne of 5 and 6.5 mgN l1, respectively. This reinforces the conclusion in Section 4.14.24.1 that in equatorial and tropical climates it is highly likely that AS plants will nitrify even at very short sludge ages (1–2 days) and therefore to design for denitrification for operational reasons if not for effluent quality reasons.

4.14.26.3.3 Effect of influent TKN/COD ratio When the unused denitrification potential in the anoxic reactor is kept in reserve as a safety factor (option 2), the sludge age and unaerated (anoxic) mass fraction are not changed. For this situation, it is useful to have a sensitivity analysis to see the influence of changing influent TKN/COD ratio and RBSO fraction on the a recycle ratio and effluent nitrate concentration. Continuing with the design for the example raw and settled wastewaters for fixed sludge age at 20 days and unaerated (anoxic) mass fraction at 0.534, a plot of the optimum a recycle ratio aopt and minimum effluent nitrate concentration Nneaopt for underflow recycle ratios s of 0.5, 1.0, and 2.0 versus influent TKN/COD ratio from 0.06 to 0.16 is given in Figure 41 for the raw (a), (c) and settled (b), (d) wastewaters at 14 1C (a), (b) and 22 1C (c), (d). From Figure 41, it can be seen that as the influent TKN/ COD increases, aopt decreases and Nneaopt increases. The aopt–Nneaopt lines in Figure 41 give the system denitrification performance when the denitrification potential of the anoxic reactor is fully used, that is, the system denitrification

491

performance is equal to its denitrification potential and the nitrate concentration is the lowest possible. Also, large increases in the underflow recycle ratio s (i.e., from 0.50:1 to 1.0:1 or 1.0:1 to 2.0:1) decrease aopt but do not change Nneaopt because the DO in the a and s recycles does not differ much in their influence on the anoxic reactor. Therefore, it matters little which recycle flow brings the nitrate load to the anoxic reactor. As long as the anoxic reactor is closely loaded to its denitrification potential, the same minimum effluent nitrate concentration (Nneaopt) will be obtained at aopt. The aopt–Nneaopt lines therefore give the system denitrification performance when the denitrification potential of the anoxic reactor is fully used (Figure 38(b)), that is, the systems denitrification performance is equal to its potential. A better denitrification performance is not possible – the denitrification is kinetics limited and the biomass (and so also the system) does the best it can (for the given K2 denitrification rate). From Equation (170), the system denitrification performance with increasing influent TKN/COD ratio at a fixed practical operating a recycle ratio (aprac) of 5:1 is also shown in Figure 41 as the aprac and Nneaprac lines. It can be seen that Nneaprac increases linearly with increase in influent TKN/COD ratio. For low influent TKN/COD ratios, aprac is considerably lower than aopt and the system denitrification performance is lower than its denitrification potential. This is evident from Nneaprac being greater than Nneaopt. As the TKN/COD ratio increases, aopt decreases until aopt ¼ aprac ¼ 5.0:1. For the raw wastewater at 14 1C (Figure 41(a)), this happens at an influent TKN/COD ratio of 0.104. This is the influent TKN/COD ratio which balances the MLE system for the selected design conditions, namely, 20 days sludge age, fxm ¼ 0.534 and aprac ¼ 5:1 for the example raw wastewater at 14 1C. For influent TKN/ COD ratios 40.104, the a recycle ratio should be set at aopt, which fully uses the anoxic reactor’s denitrification potential and is now lower than aprac ¼ 5:1. Therefore for aprac set at 5:1, only when the influent TKN/COD ratio is 40.104, is the denitrification potential of the anoxic reactor fully used. This same conclusion can be made from Figure 39(a) at 20 days sludge age, that is, fxm ¼ 0.534 and TKN/COD ratio ¼ 0.104. Therefore for influent TKN/COD ratioso0.104, while aprac oaopt, the system denitrification performance is lower than its denitrification potential because not all the denitrification potential of the anoxic reactor is used. Once the TKN/COD ratio increases above that value which balances the MLE system, aoptoaprac and a should be set at aopt to achieve the lowest effluent nitrate concentration (Nneaopt). For these influent TKN/COD ratios, the denitrification potential of the anoxic reactor is fully used and the system denitrification performance is defined by the aopt–Nneaopt lines. Figure 41 is useful because it combines the system denitrification performance (aprac–Nneaprac lines) and the denitrification potential (aopt–Nneaopt lines) in the same diagram as influent TKN/COD ratio increases for a particular wastewater and system design (Rs ¼ 20 days and fxm ¼ 0.534). The intersection point of the straight Nneaprac line and the curved Nneaopt line (i.e., at aopt ¼ aprac ¼ 5:1) gives the influent TKN/ COD ratio for the balanced MLE system for the selected aprac ¼ 5:1. From Figure 41(a), for the raw wastewater at 14 1C, the MLE system (at 20 days sludge age and fxm ¼ 0.534) with a

Biological Nutrient Removal

10

20 15

a-prac

5

0.06

0.08

0.10

15

25 20 15 s=0.5 s=1.0 s=2.0

a-prac Nne-a-prac Nne-a-opt

a-opt

0 0.06

10 5

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0.12

0.14

s=0.5 s=1.0 s =2.0

a-opt Nne-a-opt

0 0.06

0.08

0.10

0.12

10 5 0

0.14

0.16

Influent TKN/COD ratio MLE system (settled, 22 °C) a Recycle ratio and effluent nitrate

15

30 a-opt 25

5

20

a-prac

s=0.5 s =1.0 s =2.0

a-opt

15 10

Nne-a-prac

5 Nne-a-opt

0

0.16

Influent TKN/COD ratio

Example Settled WW

10

0 0.08

a-prac Nne-a-prac

30

10

5

5

(b)

a-opt

20 15

0.16

MLE system (raw, 22 °C) a Recycle ratio and effluent nitrate Example Raw WW

a Recycle ratio

0.14

25

10

0 0.12

Influent TKN/COD ratio

(a)

(c)

5

a-opt

Nne-a-opt

0

10

s=0.5 s=1.0 s=2.0

Nne-a-prac

a Recycle ratio

a-opt

30

Example Settled WW

a-opt

a Recycle ratio

a Recycle ratio

25

15

Effluent nitrate (mgN I−1)

30

Example Raw WW

Effluent nitrate (mgN I−1)

15

MLE system (settled, 14 °C) a Recycle ratio and effluent nitrate

Effluent nitrate (mgN I−1)

MLE system (raw, 14 °C) a Recycle ratio and effluent nitrate

Effluent nitrate (mgN I−1)

492

0.06 (d)

0 0.08 0.10 0.12 0.14 Influent TKN/COD ratio

0.16

Figure 41 Optimum (aopt) and practical upper limit (aprac ¼ 5:1) a recycle ratios (bold lines) and effluent nitrate concentration at aopt (Nneaopt, bold line) and aprac (Nneaprac, dashed line) vs. influent TKN/COD ratio at underflow (s) recycle ratio of 1:1 for the example raw (a,c) and settled (b,d) wastewaters at 14 1C (a,b) and 22 1C (c,d). The optimum a recycle ratio (aopt) values at underflow recycle ratios of 0.5:1 and 2:1 are also shown (thin lines).

recycle ratio 45:1 can maintain effluent nitrate concentrations below 8.1 (total N 12.4) mgN l1 for influent TKN/COD ratios below 0.104 (78.0 mgN l1). With settled wastewater at 14 1C (Figure 41(b)), the MLE system with a 45:1 can maintain effluent nitrate concentrations below 11.3 (total N 14.9) mgN l1 for influent TKN/COD ratios up to 0.132 (59.4 mgN l1). Similarly, from Figures 41(c) and (d), with raw and settled wastewater at 22 1C, the MLE system with a 45:1 can maintain effluent nitrate concentrations below 6.0 and 8.1 mgN l1 (total N 9.9 and 11.1 mgN l1) for influent TKN/COD ratios up to 0.119 (89.3 mgN l1) and 0.148 (66.6 mgN l1). These results show that the MLE system treating settled wastewater delivers lower Nne (by 2–3 mgN l1) than when treating raw wastewater and at influent TKN/ COD ratios significantly higher. However, it should be noted that (1) the influent TKN concentrations (given above) for the raw wastewater are considerably higher than those for the settled wastewater and (2) a settled wastewater with a TKN/ COD ratio of 0.119 (14 1C) or 0.148 (22 1C) would be produced from a raw wastewater with considerably lower influent TKN/COD ratio than 0.104 (14 1C) and 0.132 (22 1C).

4.14.26.3.4 MLE sensivity diagram In Figure 41, the system denitrification performance at a selected aprac ¼ 5 is combined with the system denitrification potential at a ¼ aopt for varying influent TKN/COD ratio and a single influent RBSO fraction value. This influent TKN/COD ratio sensitivity diagram can be extended by adding the Nneaopt lines for other influent RBSO fractions. A sensitivity analysis of the system at the design stage is useful for evaluating the denitrification performance under varying influent TKN/COD ratio and RBSO fractions. These two wastewater characteristics can vary considerably during the life of the plant and have a major impact on the N removal performance of the system. The denitrification potential and system performance are combined for varying influent TKN/COD ratio and RBSO fraction in Figure 42. For the fixed system design parameters (i.e., Rs ¼ 20 days, fxdm ¼ fxm ¼ 0.534, s ¼ 1.0), the curved (bold) lines give Nneaopt when the anoxic reactor is loaded to its denitrification potential, that is, Nne for a ¼ aopt for varying TKN/COD ratio from 0.06 to 0.16 and RBSO fractions from 0.10 to 0.35 for the example raw and settled wastewaters at

Biological Nutrient Removal MLE system (settled, 14 °C) Effluent nitrate vs. TKN/COD ratio

MLE system (raw, 14 °C) Effluent nitrate vs. TKN/COD ratio 40

20 0.5 1

30 RBCOD fraction 20

2 3

0.10 0.15

4

0.20 0.25 0.30 0.35

10

5

6

Effluent nitrate (mgN I−1)

Effluent nitrate (mgN I−1)

0

15

a recycle ratio 0 0.5 0.10

10

1 2

0.35

4

5 6 8 10

10 a recycle ratio

0.30

0.25

3

RBCOD fraction

0 0.06

0.08

0.10

0.12

0.14

0.16

Influent TKN/COD ratio

(a)

0.06

40

0.20

0 0.5

20

1

0.25 0.30 0.35

2 10

3

Effluent nitrate (mgN I−1)

0.15

15

0.15

0.5

0.20

1 10

0.25 2 3 4

5

5

4

5 6 8 10

8 RBCOD fraction

0

0.16

0.10

a recycle ratio 0

0.10 a recycle ratio

0.10 0.12 0.14 Influent TKN/COD ratio

MLE system (settled, 22 °C) Effluent nitrate vs. TKN/COD ratio

20

30

0.08

(b)

MLE system (raw, 22 °C) Effluent nitrate vs. TKN/COD ratio

Effluent nitrate (mgN I−1)

0.20 0.15

5

8

0

0.30 0.35

6 10

RBCOD fraction

0 0.06

(c)

493

0.08

0.10 0.12 0.14 Influent TKN/COD ratio

0.06

0.16 (d)

0.08

0.10 0.12 0.14 Influent TKN/COD ratio

0.16

Figure 42 Effluent nitrate concentration vs. influent TKN/COD ratio for influent readily biodegradable (RBSO) fractions (fSb0 s) of 0.10, 0.15, 0.20, 0.25, 0.30, and 0.35 and mixed liquor a recycle ratio from 0 to 10 for the example raw (a,c) and settled (b,d) wastewaters at 14 1C (a,b) and 22 1C (c,d).

14 1C (Figures 42(a) and 42(b)) and 22 1C (Figures 42(c) and 42(d)). The same Nneaopt lines are given in Figures 41(a) and 41(c) for the raw wastewater RBSO fraction (fSb0 s) ¼ 0.25. These Nneaopt lines are calculated from Equations (169) and (170). The straight lines in Figure 42 give Nneaprac for fixed a recycle ratios at indicated values ranging from 0.0:1 to 10:1. These straight Nneaprac lines give the system performance for some selected a recycle ratio and are calculated with the aid of Equation (170) from the nitrification capacity value at the given TKN/COD ratio, fixed s recycle ratio at 1.0:1, and the selected a recycle ratio. The Nneaprac lines for a ¼ aprac ¼ 5:1 are

the same as the dotted lines in Figure 41. At the intersection points of the straight Nneaprac and curved Nneaopt lines, the system performance equals the denitrification potential and represents balanced MLE designs, that is, aopt ¼ aprac. For example, for the raw wastewater at 14 1C, at a ¼ 5.0:1 and fSb0 s ¼ 0.25, the TKN/COD ratio needs to be 0.104 to give an optimal design, that is, aopt ¼ 5:1 and at this TKN/COD ratio, Nne ¼ 8.1 mgN l1. This is the TKN/COD ratio that balances the MLE system at Rs ¼ 20 days and fxm ¼ 0.534 (see Figure 39). For TKN/COD ratioso0.104, aopt increases above 5:1, but if a is maintained at 5:1 (i.e., a ¼ aprac ¼ 5:1), then Nne

494

Biological Nutrient Removal

versus TKN/COD ratio is given by the a ¼ 5:1 straight Nneaprac line. For TKN/COD ratios 40.104, aopt decreases below 5:1, and Nne versus TKN/COD ratio is given by the curved Nneaopt (bold) line. The aopt value at a particular TKN/COD ratio is given by the a recycle ratio value of the intersection point between the vertical influent TKN/COD ratio line and the curved Nneaopt line, for example, for the example raw wastewater (fSb0 s ¼ 0.25) at 14 1C (Figure 42(a)) at a TKN/COD ratio of 0.12, aopt ¼ 2:1, and Nne is 16.0 mgN l1. The usefulness of Figure 42 is that it gives a performance evaluation of an MLE system at a specified sludge age and anoxic mass fraction for varying influent TKN/COD ratio and RBSO fraction taking due account of an upper a recycle ratio limit of aprac. For the example raw wastewater at 22 1C with a RBSO fraction (fSb0 s) of 0.10 (Figure 42(c)), the influent TKN/ COD ratio needs to be greater than 0.113 for a to beo6.0:1. If a is fixed at aprac ¼ 6.0:1 and the TKN/COD is o0.113, then the anoxic reactor is underloaded with nitrate and the denitrification potential is not achieved. The system performance for influent TKN/CODo0.113 is given by the straight Nne line for a ¼ 6:1. At influent TKN/COD ¼ 0.113, the straight Nne line for a ¼ 6:1 cuts the curved Nneaopt line, a ¼ aopt ¼ 6:1 and the system performance equals the denitrification potential. If a is maintained at 6:1 for TKN/COD 40.113, then the anoxic reactor is overloaded with nitrate and optimal denitrification is not achieved due to the unnecessarily high DO load on the anoxic reactor (similar to that shown in Figure 37(b) for a 46.7). The a recycle ratio therefore should be reduced to aopt for influent TKN/COD ratios 40.113, where aopt is given by the a value along the curved Nne line, which represents system performance equal to denitrification potential. For example, if the TKN/COD ratio ¼ 0.120, a ¼ aopt ¼ 4:1 and this a recycle ratio loads the anoxic reactor to its denitrification potential giving Nne of 12.0 mgN l1. Therefore, for TKN/COD ratio 40.113, the system performance and Nne is given by the curved Nne line provided the a recycle ratio is set to aopt, which is given by a recycle ratio line which passes through the intersection point of the vertical TKN/COD ratio line and the curved Nne line. From the above, it can be seen that only on the curved Nne line for the particular RBSO fraction is the system performance equal to the denitrification potential; also the aopt that produces this is given by the a recycle ratio line that passes through the intersection point of the vertical TKN/COD ratio line and the curved Nne line. This curved Nne line (for which a ¼ aopt) marks the boundary between underloaded and overloaded conditions in the anoxic reactor. In the domain above the curved Nne line, the anoxic reactor is underloaded (left of aopt in Figures 37 and 38) and the system performance (Nne) for a particular TKN/COD ratio is given by the intersection point of the vertical TKN/COD ratio line and the straight a recycle ratio line. In the domain below, curved Nne line, the anoxic reactor is overloaded (right of aopt in Figures 37 and 38). The Nne values obtained from this domain are not valid, but if the a recycle ratio is reduced to aopt (i.e., the a value of the intersection point of the vertical TKN/ COD ratio line and the curved Nne line), then the Nne value again is valid. Valid Nne system performance values are therefore given in Figure 42 only on or above the curved Nne boundary line.

From Figure 42, it can be seen that for MLE system at the design Rs ¼ 20 days and fxm ¼ 0.534 and a recycle ratio limited at say 5.0:1 for economical reasons, then the system is best suited to treating high TKN/COD ratios, depending on the RBSO fraction: 40.091 for fSb0 s ¼ 0.10 and 40.117 for fSb0 s ¼ 0.35. This is because with only a primary anoxic reactor, the MLE system cannot produce a low effluent nitrate concentration (o4–6 mgN l1) at a recycle ratio limited at 5.0:1. If obtaining low effluent nitrate concentrations is not required at low TKN/COD ratios, then a balanced MLE design can be selected by reducing the sludge age as demonstrated in Figures 39 and 40. If obtaining low effluent nitrate concentrations is important at low (o0.10) TKN/COD ratios, then this can be achieved at high a recyle ratios (aopt4aprac) in MLE systems or at low a recycle ratios by including a secondary anoxic reactor. Incorporation of a secondary anoxic reactor (and a re-aeration reactor for practical reasons – see Section 4.14.24.5) produces the four-stage Bardenpho system (Figure 34(c)). However, because the K3 denitrification rate is so low and needs to be reduced by at least 20% to account for the ammonia released during endogenous denitrification (which is re-nitrified in the re-aeration reactor), the net additional nitrate removal achieved in a secondary anoxic reactor is very low, too low for secondary anoxic reactors to be included in N removal systems, unless the influent TKN/COD ratio is unusually low.

4.14.27 System Volume and Oxygen Demand 4.14.27.1 System Volume Having determined the subdivision of the sludge mass into anoxic and aerobic fractions to achieve the required N removal, the actual sludge mass in the system needs to be calculated to determine the volumes of the different reactors. The mass of sludge, total (MLSS) or volatile (MLVSS), in the system for selected sludge age and wastewater characteristics for N removal system is the same as for fully aerobic (COD removal) systems. The equations given in Section 4.14.9 therefore apply to N removal systems also. For the example raw and settled wastewaters, the design parameters for the MLE system are listed in Table 16. The MLSS mass values in the system at 20 days sludge age and 14 1C are 68168 and 26 422 kgTSS, respectively. Selecting an MLSS concentration of 4500 mg l1 (4 kg m3) (see Section 4.14.11) means that the volume of the system treating raw wastewater is 15148 m3 and that treating settled wastewater is 5871 m3. Because the sludge mass in the N removal systems usually is uniformly distributed in the system, that is, each reactor of the system has the same MLSS concentration, the volume fraction of each reactor is equal to its sludge mass fraction. For the example raw and settled wastewaters at 14 1C, the volume of the anoxic reactors are 0.534 15148 ¼ 8089 m3 and 0.534  5871 ¼ 3135 m3, respectively. The nominal and actual hydraulic retention times of the anoxic and aerobic reactors are calculated from the reactor volumes divided by the nominal (influent) and total flows passing through them (Equation (59) and Table 16). Note that the reactor nominal retention time is a consequence of the mass of sludge generated from the influent COD flux, the selected MLSS concentration, and the sludge mass fraction – the retention time per se has no significance in

Biological Nutrient Removal Table 16 Design details of MLE systems treating the example raw and settled wastewaters at 14 1C at 20 days sludge and 0.534 unaerated sludge mass fraction Parameter

Raw

Settled

Influent TKN/COD ratio Influent RBCOD fraction (fSb0 s) Unaerated mass fraction(fxm) Anoxic mass fraction (fx1) Minimum anoxic fraction a Recycle ratio (apracoaopt) Sludge age (days) Effluent nitrate (Nne, mgN l1) Effluent TKN (Nte, mgN l1) Effluent total N (Nne þ Nte) System vol at 4.5 gTSS l1 (m3) Anoxic volume (m3) System ret time – nom (h) Aerobic ret time – nom (h) Aerobic ret time – actual (h) Anoxic ret time – nom (h) Anoxic ret time – actual (h) Carb. O2 demand (FOc, kgO d1) Nit O2 demand (FOn, kgO d1) O2 recovered (FOd, kgO d1) Tot. O2 demand (FOtd, kgO d1) %N removal Mass TSS wasted (FXt, kg d1) Active fraction wrt TSS (fatOHO)

0.08 0.25 0.534 0.534 0.07 5:1 20 5.6 3.8 9.4 15148 8089 24.2 11.2 1.6 12.9 1.85 6679 2685 1440 7924 84.4 3408 0.23

0.113 0.385 0.534 0.534 0.105 5:1 20 5.7 3.8 9.5 5871 3135 9.4 4.4 0.63 5 0.72 4311 2719 1458 5572 81.4 1321 0.383

kinetics of and design for nitrification and denitrification (see Section 4.14.9.3).

4.14.27.2 Daily Average Total Oxygen Demand The total oxygen demand in a nitrogen removal system is the sum of that required for organic material (COD) degradation and nitrification, less than recovered by denitrification. The daily average oxygen demand for (1) organic material removal (FOc) is given by Equations (111) and (2) nitrification is given by Equation (155). These oxygen demands in the MLE system at 20 days sludge age for the example raw and settled wastewaters at 14 and 22 1C are 9364 and 7030 kgO d1 (Table 16). The oxygen recovered by denitrification (FOd) is given by 2.86 times the nitrate flux denitrified (Section 4.14.24.2) where nitrate flux denitrified is the product of the daily average influent flow Qi and the nitrate concentration denitrified. The nitrate concentration denitrified is given by the difference in the nitrification capacity Nc and the effluent nitrate concentration. Hence,

FOd ¼ 2:86ðNc  Nne ÞQi

ðmgO d1 Þ

495

demand by incorporating ND is only 20% of that required for COD removal only, and (4) the effect of temperature on the total oxygen demand is marginal – less than 3% (see also Figure 32). For the settled wastewater, Table 16 shows that (1) the nitrification oxygen demand is about 63% of that required for COD removal; (2) about 54% of the nitrification oxygen demand can be recovered by denitrification; (3) the additional oxygen demand by incorporating nitrification and denitrification is about 30% of that required for COD removal only, and (4) the effect of temperature on the total oxygen demand is marginal – less than 3% more at the lower temperature. Comparing the total oxygen demand (FOtd) for the raw and settled wastewaters, the total oxygen demand for the latter is about 30% less than that of the former. This saving is possible because primary sedimentation removes 35–45% of the raw wastewater COD. Furthermore, for the settled wastewater, the nitrification oxygen demand is a greater proportion of the total; also, less of the nitrification oxygen demand can be recovered by denitrification compared to the raw wastewater. These effects are due to the higher TKN/COD ratio of the settled wastewater. Knowing the average daily total oxygen demand, (FOtd) the peak total oxygen demand can be roughly estimated by means of a simple design rule (Musvoto et al., 2002). From a large number of simulations with AS model no. 1 (ASM1), it was found that, provided the factor of safety on nitrification (Sf) is greater than 1.25–1.35, the relative amplitude (i.e., (peak  average)/average) of the total oxygen demand variation is a fraction 0.33 of the relative amplitude of the TOD of the influent COD and TKN load (i.e., Qi(Sti þ 4.57Nti)). For example, with the raw wastewater case, if the peak influent TOD flux is obtained at a time of day when the influent flow rate, COD and TKN concentrations are 25 M l d1, 1250 mgCOD l1 and 90 mgN l1, respectively – that is, 25(1250 þ 4.57  90) ¼ 41 532 kgTOD d1, and the average influent TOD flux is 15(750 þ 4.57  60) ¼ 15 363 kgTOD d1, the amplitude of the total influent TOD flux is (41 532 15 363)/15 363 ¼1.70; hence, the amplitude of the total oxygen demand is approximately 0.33 1.70 ¼ 0.56; from Table 16 the average daily total oxygen demand (FOtd) is 7924 kgO d1 and hence the peak oxygen demand is (1 þ0.56)  7924 ¼12 378 kgO d1. As with all simplified design rules, the above rule should be used with discretion and caution, and where possible, the peak total oxygen demand is best estimated by means of the AS simulations models.

4.14.28 Biological Excess Phosphorus Removal ð172Þ

From the denitrification performance of the MLE system in Table 16, the oxygen recovered by denitrification for the example raw and settled wastewaters at 14 1C are 1440 and 1458 kgO d1. For the raw wastewater, Table 16 shows that (1) the nitrification oxygen demand (FOn) is about 40% that required for COD removal (FOc), (2) about 55% of FOn can be recovered by incorporating denitrification, (3) the additional oxygen

4.14.28.1 Introduction Phosphorus is the key element in aquatic environments that limits the growth of aquatic plants and algae controls eutrophication. Unlike nitrogen that can be fixed from the atmosphere which contains about 80% nitrogen gas, phosphorus can only come from upstream of aquatic systems (neglecting atmospheric deposition). Diffuse sources of phosphorus, for example, from agricultural fields, are best controlled by proper fertilization plans, while point sources of

Biological Nutrient Removal

phosphorus, for example, from WWTPs, can be removed by chemical or biological processes. Considering the benefit to aquatic environments, strict regulations are being applied for phosphorus removal from wastewaters. Considering the potential benefits of removing phosphorus biologically rather than chemically, along with organic matter and nitrogen from wastewater, BEPR has stimulated much interest in the study of the biochemical mechanisms, the microbiology of the systems, the process engineering and optimization of plants, and in mathematical modeling. Reviews of the development of BEPR have been regularly published over the years (Marais et al., 1983; Arvin, 1985; Wentzel et al., 1991; Jenkins and Tandoi, 1991; van Loosdrecht et al., 1997; Mino et al., 1998; Blackall et al., 2002; Seviour et al., 2003; Oehmen et al., 2007). This section briefly reviews the mechanisms of BEPR, outlines the practical systems to achieve it, summarizes some of the experimental research that led to the development of BEPR models (both steady state and dynamic kinetic), discusses the impact of anoxic zones for denitrification on BEPR, and sets out guidelines for design of NDBEPR systems. In order not to unduly complicate this, the concepts are presented for strictly aerobic phosphorus accumulating organisms (aerobic PAOs) which can use only oxygen as the electron acceptor for energy production. Considering that some denitrifying PAOs (DPAOs) exist and may have a significant impact on the performance of the process, their influence is discussed where appropriate, but is not included in the models described.

4.14.28.2 Principles of BEPR BEPR is the biological uptake and removal of P by AS systems in excess of the amount that is removed by normal completely aerobic AS systems. This is in excess of the normal P requirements for growth of AS. In the completely aerobic AS system, the amount of P typically incorporated in the sludge mass is about 0.02 mgP/mgVSS (0.015 mgP/mgTSS). By the daily wastage of surplus sludge phosphorus is thus effectively removed. This can give a P removal of about 15–25% of the P in many municipal wastewaters. In an BEPR AS system, the amount of P incorporated in the sludge mass is increased from the normal value of 0.02 mgP/mgVSS to values around 0.06–0.15 mgP/ mgVSS (0.05–0.10 mgP/mgTSS). This is achieved by system design or operational modifications that stimulate, in addition to the OHOs present in AS, the growth of organisms that can take up large quantities of P and store them internally in long chains called polyphosphates (polyPs); generically, these organisms are called phosphate accumulating organisms (PAOs). PAOs can incorporate up to 0.38 mgP/mgVSS (0.17 mgP/ mgTSS). In the biological P removal system both the OHOs, which do not remove P in excess, and the PAOs coexist. The larger the proportion of PAOs that can be stimulated to grow in the system, the greater the P content of the AS and, accordingly, the larger the amount of P that can be removed from the influent. Thus, the challenge in design is to increase the amount of the PAOs relative to the OHOs present in the AS as this will increase the capacity for P-accumulation and thereby high phosphorus removal efficiency. The relative proportion of the two organism groups depends, to a large degree, on the fraction

15 Example settled WW % P of VSS (mgP/mgVSS as %)

496

Settled WW 10 Example Raw WW Raw WW 5

P removal =

%P × VSS mass Sludge age × Q i

0 0

10 20 30 % Bio COD obtained by PAOs

40

Figure 43 Percentage P (mgP/mgVSS  100) in VSS mass vs. the proportion of biodegradable COD mass (as %) obtained by PAOs.

of the influent wastewater biodegradable COD that each organism group obtains. The greater the fraction of PAOs in the mixed liquor, the greater the %P content of the AS and the greater the BEPR (Figure 43). Design and operational procedures are oriented toward maximizing the growth of PAOs. In an appropriately designed BEPR system, the PAOs can make up about 40% of the active organisms present (or 15% of VSS; 11% of TSS), and this system can usually remove about 10–12 mgP per 500 mg influent COD l1. From the first publications reporting enhanced P removal in some AS systems, there has been some controversy as to whether the mechanism is a precipitation of inorganic compounds, albeit perhaps biologically mediated, or biological through formation and accumulation of P compounds in the organisms. The objective here is not to discuss the evidence that supports the biological nature of enhanced P removal, but to briefly describe the theory of biological P removal and to demonstrate how this theory can be used as an aid for the design of biological P removal AS systems. This does not imply that precipitation of P due to chemical changes resulting from biological action (e.g. alkalinity and pH) does not take place. Although inorganic precipitation of P can certainly take place, it would appear that in the treatment of municipal wastewaters by an appropriately designed AS system, within the normal ranges of pH, alkalinity and calcium concentrations in the influent, enhanced P removal is principally mediated by a biological mechanism (Maurer et al., 1999; de Haas et al., 2000). These mechanisms are described below.

4.14.28.3 Mechanism of BEPR 4.14.28.3.1 Background Historically, several research groups have made a number of important contributions toward elucidating the mechanisms

Biological Nutrient Removal

4.14.28.3.2 Biological P removal microorganisms The basic requirement for BEPR is the presence in the AS system of microorganisms which can accumulate P in excess of normal metabolic requirements, in the form of polyP stored in granules called volutins. In the BEPR models, all organisms in the AS system accumulating polyP in this fashion and exhibiting the classical observed BEPR behavior – anaerobic P release, aerobic P uptake, and associated processes – are lumped together and represented by the generic PAO group. PolyPs can be accumulated by a wide range of bacteria. In general, they are accumulated as a phosphate reserve in relatively low amounts. Only very few types of bacteria seem to be able to harvest the energy that is stored in polyPs to take up VFAs and store them as PHAs under anaerobic conditions (in the absence of an external electron acceptor such as oxygen or nitrate). In the original research on BEPR microbiology conducted with cultivation studies, it was incorrectly considered that PAOs were of the genus Acinetobacter (Fuhs and Chen, 1975; Buchan, 1983; Wentzel et al., 1986), Microlunatus phosphovorus (Nakamura et al., 1995), Lampropedia (Stante et al., 1997), and Tetrasphaera (Maszenan et al., 2000). More recently, cultureindependent methods have shown that Accumulibacter phosphatis, a member of the genus Rhodocyclus (a beta proteobacterium), is a PAO which can be grown in enriched cultures (at up to 90% purity, as shown by fluorescence in situ hybridization (FISH) molecular probes) but not yet in axenic cultures (Wagner et al., 1994; Hesselmann et al., 1999; Crocetti et al., 2000; Martin et al., 2006; Meyer et al., 2006; Oehmen et al., 2007). From a modeling and design perspective, however, the identification of the exact organisms responsible for BEPR is of minor importance, although this may provide information that can be used to refine the models and design procedures; these are not based on the behavior of specific organisms, but rather on the observed behavior of groups of organisms identified by their function, in this case the PAOs.

4.14.28.3.3 Prerequisites To achieve BEPR in AS systems, the growth of organisms that accumulate polyP (PAOs) has to be stimulated. To accomplish this, two conditions are essential: (1) an anaerobic and aerobic (or anoxic) sequence of reactors/conditions and (2) the addition or formation of VFAs in the anaerobic reactor/period.

Glycogen

Concentration

of BEPR, including Shapiro et al. (1967), Fuhs and Chen (1975), Nicholls and Osborn (1979), Rensink et al. (1981), Marais et al. (1983), Lotter (1985), Comeau et al. (1986), Wentzel et al. (1986, 1991), Mino et al. (1987, 1994, 1998), Kuba et al (1993), Smolders et al. (1994a, 1994b, 1995), van Loosdrecht et al. (1997), Maurer et al. (1997), Seviour et al. (2003), Martin et al. (2006), and Oehmen et al. (2007). In this section, an explanation of the basic concepts underlying the more sophisticated mechanistic models for the biological P removal phenomenon is presented. For detailed description of the mechanisms, the reader is referred to the references mentioned earlier in this paragraph.

497

Poly P

PHA VFA Anaerobic

PO4 Aerobic

Figure 44 Schematic diagram showing the changes as a function of time in concentrations of volatile fatty acids (VFAs), P (PO4), polyphosphates (polyPs), polyhydroxyalkanoate (PHA), and glycogen through the anaerobic–aerobic sequence of reactors in a BEPR system.

4.14.28.3.4 Observations With the prerequisites for BEPR present, the following observations have been made at full, pilot, and laboratory scale (Figure 44). Under anaerobic conditions, bulk solution VFAs and intracellular polyP and glycogen decrease and soluble phosphate, Mg2þ, Kþ, and intracellular poly-b-hydroxyalcanoates (PHAs) increase (Randall et al., 1970; Rensink et al., 1981; Hart and Melmed, 1982; Fukase et al., 1982; Watanabe et al., 1984; Arvin, 1985; Hascoe¨t et al., 1985a; Wentzel et al., 1985; Comeau et al., 1986, 1987; Murphy and Lo¨tter, 1986; Gerber et al., 1987; Wentzel et al., 1988; Satoh et al., 1992; Smolders et al., 1994a; Maurer et al., 1997). Under aerobic conditions; intracellular polyP and glycogen increase; soluble phosphate, Mg2þ, Kþ, and intracellular PHA decrease (Fukase et al., 1982; Arvin, 1985; Hascoe¨t et al., 1985a; Comeau et al., 1986; Murphy and Lo¨tter, 1986; Gerber et al., 1987; Wentzel et al., 1988; Satoh et al., 1992; Smolders et al., 1994b; Maurer et al., 1997).

4.14.28.3.5 Biological P removal mechanism In describing the mechanisms of BEPR, a clear distinction is made between the PAOs and OHOs. In the anaerobic/aerobic sequence of reactors, it is considered that VFAs are present in the influent waste stream entering the anaerobic reactor or produced in the anaerobic reactor by fermenting organisms (accepted to be the OHOs in models). In the anaerobic reactor (zero nitrate and oxygen in or entering reactor), the OHOs cannot utilize the VFAs due to the absence of an external electron acceptor, oxygen or nitrate. The PAOs, however, can take up the VFAs from the bulk liquid and store them internally by linking the VFAs together to form complex long-chain carbon molecules of poly-b-hydroxyalkanoates (PHAs). The two common PHAs are poly-b-hydroxybutyrate (PHB: four-carbon compound synthesized from two acetate molecules) and polyhydroxyvalerate (PHV: five-carbon compound from one acetate and one propionate molecules) (Figure 45(a)). Forming PHAs from the VFAs requires energy for three functions: active transport of VFAs across the cell membrane, energization of VFAs into coenzyme A compounds (e.g.,

498

Biological Nutrient Removal Liquid Cell

PHA e−

Glycogen

VFA-CoA Energy

VFA

(PO4)n

VFA

(PO4)n−1 Pi

PAO

Pi

(a)

Liquid CO2

CO2

Catabolism PHA

Cell

e− Glycogen ETC

Anabolism

Energy H2O

New cells

O2 (PO4)n Pi

(b)

(PO4)n−1 PAO

Pi

Figure 45 (a) Simplified biochemical model for PAOs under anaerobic conditions. Anaerobic uptake of volatile fatty acids (VFAs), originating from the influent or from fermentation in the anaerobic reactor, and storage of polyhydroxyalkanoates (PHAs) by the PAOs with associated P release. (b) Simplified biochemical model for PAOs under aerobic conditions. Aerobic utilization of PHAs and growth of PAOs, with P uptake by existing and new PAOs.

acetyl-CoA) and reducing power (NADH) for PHA formation. PolyP degradation is associated with the formation of ADP from AMP, with the phosphokinase enzyme 2 ADP are converted to adenosine triphosphate (ATP) and adenosine monophosphate (AMP) (van Groenestijn et al., 1987). When ATP is used, orthophosphates are released and accumulate in the cell interior together with the counterions of polyP (potassium and magnesium). The efflux of these compounds might be related to building a proton motive force, which either can help in the uptake of acetate or in the generation of a small amount of extra ATP. It is observed (Smolders et al., 1994a) that the energy requirements for acetate uptake increase with increasing pH. This can be associated with the fact that the energy needed for acetate transport increases with pH. ATP is used, notably, for the energization of acetate and propionate into acetyl-CoA and propionyl-CoA. Glycogen degradation also results in ATP formation, NADH production, and intermediates that are

transformed into acetyl-CoA (or propionyl-CoA). Finally, acetyl-CoA and propionyl-CoA are stored as PHA (Comeau et al., 1986; Wentzel et al., 1986; Mino et al., 1998; Smolders et al., 1994b; Martin et al., 2006; Oehmen et al., 2007; Saunders et al., 2007). Thus, the PAOs in the anaerobic reactor have taken up for their exclusive use the VFAs under anaerobic conditions where the OHOs are unable to use these organics. To accomplish this, some of the stored polyP has been consumed and P released to the bulk solution. To stabilize the negative charges on the polyP, the cations Mg2þ, Kþ, and sometimes Ca2þare complexed, which add to the inorganic settleable solids (TSS) in the system (Ekama and Wentzel, 2004). When polyPs are consumed and P is released, mainly Mg2þ and Kþ cations are released in the approximate molar ratio P:Mg2þ:Kþ of 1:0.33:0.33 (Comeau et al., 1987; Brdjanovic et al., 1996; Pattarkine and Randall, 1999). In the subsequent aerobic reactor (presence of DO). In the presence of dissolved oxygen (or of nitrate under anoxic conditions) as an external electron acceptor, the PAOs utilize the stored PHA as a carbon and energy source for energy generation and growth of new cells as well as for regenerating the glycogen consumed in the anaerobic period. The stored PHA is also used as an energy source to take up P from the bulk solution to regenerate the polyP used in the anaerobic reactor, and to synthesize polyP in the new cells that are generated – P uptake (Figure 45(b)). The uptake of P to synthesize polyP in the new cells generated means that more P is taken up than is released in the anaerobic reactor, giving a net removal of P from the liquid phase in the AS system. Accompanying the P uptake, the cations Mg2þ and Kþ also are taken as countercharge for the negatively charged polyP polymer, in the approximate molar ratio P:Mg2þ:Kþ of 1:0.33:0.33. The PAOs, with stored polyP, are removed from the aerobic reactor of the system (where the internally stored polyP concentration in the PAOs is the highest in the system) via the waste sludge stream (wastage from the underflow recycle stream is possible, but not desirable for hydraulic control of sludge age; see Section 4.14.14). At steady state the mass of PAOs wasted per day (with stored polyP) equals the mass of new PAOs generated per day (with stored polyP). Thus, for a fixed sludge age, loading, and system operation, the mass of PAOs in the biological reactors remains constant, so that in the AS system at steady state there is neither a buildup nor a loss of PAOs, and the P/VSS ratio stays approximately constant. The mass of new PAOs formed depends on the mass of stored substrate (PHA) available to the PAOs. Accordingly, the enhanced P removal attained will depend on the mass of PHA stored in the anaerobic reactor.

4.14.28.3.6 Fermentable COD and slowly biodegradable COD As indicated above, under anaerobic conditions, PAOs can take up and store VFAs. However, some wastewaters contained very little VFAs, yet exhibited significant BEPR. This was ascribed to the influent RBOs, (Sbsi) which comprises both VFAs (Sbsai) and fermentable RBO (FBSO, Sbsfi) (Siebritz et al., 1983; Wentzel et al., 1985, 1990; Nicholls et al., 1985; Pitman et al., 1988; Randall et al., 1994). This influent FBSO is

Biological Nutrient Removal Liquid Cell F-RBCOD

F-RBCOD

Energy

VFA OHO

VFA

VFA

PAO

Figure 46 Simplified biochemical model for fermentation of RBSO to VFA by OHOs under anaerobic conditions – VFAs released by OHOs are taken up by PAOs.

fermented to VFAs by the OHOs in the anaerobic reactor, the VFAs becoming available for uptake and storage by the PAOs because the OHOs cannot utilize them due to the absence of an electron acceptor (NO3 or O) (Figure 46). Slowly biodegradable organics (SBO, XS), even though these can be hydrolyzed into RBO under anaerobic conditions, has been shown not to be linked to anaerobic phosphate release. This aspect is of crucial importance as it will influence both the design and operation of BNR systems, such as sizing and determining the number of anaerobic reactors, inclusion of primary sedimentation and maximum BEPR achievable. For the purpose of the BEPR models, the experimental evidence linking BEPR to the RBO is accepted, but a conversion of SBO to RBO is considered to be small enough to be negligible. Accordingly, where VFA production does occur, this will essentially be from the RBO. One exception to this consideration is when primary sludge is fermented in a separate fermentation reactor upstream of the anaerobic reactor – in these dedicated fermenters, some hydrolysis of SBO to RBO and VFAs takes place to augment the influent VFA and RBO concentrations (Lilley et al., 1992).

4.14.28.3.7 Functions of the anaerobic zone From the description of the mechanisms above, with normal domestic wastewater as influent, the anaerobic zone/reactor serves two functions: (1) it stimulates conversion of fermentable organics to VFAs by OHOs, that is, facultative acidogenic fermentation and (2) because it is not possible for the OHOs to metabolize the VFAs (no external electron acceptor), the PAOs take up the released VFAs and store them as PHA. Thereafter, the PAOs do not have to compete for substrate when an external electron acceptor becomes available in the aerobic (or anoxic) zone. Of the above two processes, the former is the slower and determines the size of the anaerobic reactor (Wentzel et al., 1985, 1990). Should primary sludge fermentation be implemented at the treatment plant, the first process would not be needed as much and the size of the anaerobic reactor could be decreased.

499

4.14.28.3.8 Influence of recycling oxygen and nitrate to the anaerobic reactor Numerous investigators (e.g., Barnard, 1976; Venter et al., 1978; Siebritz et al., 1980; Hascoe¨t and Florentz, 1985b) noted that the recycling of oxygen and/or nitrate to the anaerobic reactor causes a corresponding decrease in BEPR. In terms of the mechanisms described above, if oxygen and/or nitrate is recycled to the anaerobic reactor, the OHOs are able to utilize the fermentable COD for energy and growth using the oxygen or nitrate as external electron acceptor. For every 1 mgO recycled to the anaerobic reactor 3 mgCOD of fermentable RBO are consumed and for every 1 mgN of nitrate recycled 8.6 mgCOD of fermentable RBO are consumed by the OHOs. The ratio of 3 mgCOD/ mgO consumed comes from the catabolic oxygen requirement in organics utilization (i.e., 1/(1  fcvYH)E3) (Equation (46)). Similarly, considering that 1 mgNO3-N is equivalent to 2.86 mgO (Section 4.14.24.2), a ratio of 2.86/ (1  fcvYH)E8.6 mgCOD consumed by mgNO3-N reduced is obtained. The fermentable RBOs metabolized by the OHOs are not released to the bulk liquid as VFAs. Therefore, the amount of VFAs generated and released to the bulk liquid is reduced by the amount of RBO consumed by the OHOs. Consequently, the mass of VFAs available to the PAOs for storage is reduced, and correspondingly so is the P release, P uptake, and the net P removal. Should the influent RBO already consist of VFAs and oxygen and/or nitrate be recycled, the PAOs and OHOs will compete for the VFAs, the PAOs to take up the VFAs, and the OHOs to metabolize it. Accordingly, even in this situation recycling of oxygen and/or nitrate will reduce the BEPR. Thus, preventing the recycling of oxygen and nitrate to the anaerobic reactor is one of the primary considerations in the design and operation strategy for BEPR systems.

4.14.28.3.9 Denitrification by PAOs The extent of denitrification with associated anoxic P uptake by the PAOs appears to be highly variable (Ekama and Wentzel, 1999b), from near-zero anoxic P uptake (e.g., Wentzel et al., 1989a, Clayton et al., 1989, 1991) to anoxic P uptake dominant over aerobic P uptake (e.g. Sorm et al., 1996; Hu et al., 2000). Experimental evidence tends to suggest that magnitude of anoxic P uptake is influenced by the anoxic mass fraction and the mass of nitrate loaded on the anoxic reactor relative to its denitrification potential (Hu et al., 2002). For the purpose of design it will be considered that anoxic P uptake is not significant. Anoxic P uptake decreases the magnitude of P removal in the system (Ekama and Wentzel, 1999a, 1999b; Hu et al., 2002), and from a design point of view in which maximizing P removal is a priority, anoxic P uptake should be avoided in the system. Hence, in this chapter, anoxic P uptake will not be considered. It must be emphasized, however, that due to the anaerobic conversion of RBO to VFA which are taken up by PAOs, the kinetics of denitrification in the subsequent anoxic reactor change compared with that in the primary anoxic reactor of an MLE system.

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4.14.29 Principles of Maximizing BEPR The principles of maximizing BEPR can be grouped into seven categories. A number of configurations or systems that are based on these principles are identified by specific names (Figure 47). 1. Oxygen entrainment in the anaerobic reactor should be minimized. For this purpose, mixing vortexes, upstream cascades, and screw pumps or air lift pumps should be avoided.

2. Nitrate (and nitrite) entering in the anaerobic reactor should be minimized. A number of named configurations were developed precisely for this purpose (Section 4.14.34). Based on observations a number of laboratory-, pilot- and full-scale systems (Barnard, 1974, 1975a, 1975b; Nicholls, 1975b), to achieve BEPR in the simplest configuration, Barnard (1976) proposed the Phoredox system (Figure 47(a) also known as the A/O process). This system comprises only an anaerobic and aerobic reactor and is intended not to nitrify to avoid nitrate entering the

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Biological Nutrient Removal

anaerobic reactor. Because nitrification can take place even at short sludge ages, particularly in warm climates, one or more anoxic reactors for denitrification are included in the two-reactor anaerobic–aerobic system to protect the anaerobic reactor from nitrate entering it. The position of the anoxic reactor(s) has led to a number of different configurations: (1) one between the anaerobic and aerobic reactors with the return sludge discharged to the anaerobic reactor (three-stage Bardenpho or A2/O systems, Figure 47(b)), (2) anoxic reactors before and after the aerobic reactor with the return sludge discharged to the anaerobic reactor (five-stage Bardenpho, Figure 47(c)), (3) one or two anoxic reactors between the anaerobic and aerobic reactors with the sludge return discharged to the first or only anoxic reactor (UCT, Siebritz et al., 1980 or VIP, Daigger et al., 1987; Figure 47(d) and modified UCT systems; Figure 47(e)), and (4) an anoxic reactor between the anaerobic and aerobic reactors and another in the sludge return flow (JHB system; Figure 47(f)). 3. VFA uptake by PAOs in the anaerobic reactor should be maximized. Primary sludge fermentation is an efficient way to increase the VFA content of the influent even though it also contributes to an increased loading in organic matter and ammonia to the AS system. Sodium acetate or fermentable industrial wastes can be added directly to the anaerobic reactor or industries that produce fermentable organics (e.g., breweries or food processing factories) should not be penalized for discharging their high RBO containing wastewater to the sewer. The sludge mass fraction of the anaerobic reactor can be increased to favor in situ fermentation of the influent or added fermentable organic matter. 4. Effluent particulate phosphorus should be minimized by removing TSSs efficiently. The particulate phosphorus content can reach as high as 18% gP/gTSS for enriched cultures. With a more typically 5–10% P content for municipal wastewater (Figure 43), every 10 mgTSS l1 in the effluent will contribute 0.5 to 1 mgP l1. Thus, efficient secondary clarification, avoiding floating sludge from denitrification in the settling tank, sand filtration, or even ultrafiltration (in a membrane bioreactor) are means of reducing the effluent TSS concentration. 5. Effluent soluble phosphorus should be minimized. Besides optimizing the BEPR process, chemical coagulants such as iron (e.g., FeCl3), aluminum (e.g., alum), or calcium (e.g., lime) salts can be added in the mainstream for pre-, co-, or post-precipitation (in the primary settling tank, in the AS process, downstream of the secondary settling tank, respectively, de Haas et al., 2001). Extracting the supernatant from the anaerobic tank or taking some sludge from the return AS and coagulating them can also lead to lower effluent soluble phosphorus (Sehayek and Marais, 1981; van Loosdrecht et al., 1998; e.g., BCFS process; Figure 47(g)). Sidestream lime precipitation of phosphate released anaerobically from the return sludge can also be done. More efficient phosphate release can be achieved in this sidestream tank by diverting some influent containing readily biodegradable COD (e.g., PhoStrip process, Figure 47(h)). These systems support the biological

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process by phosphate stripping and potential P recovery in the main line, stabilizing the sludge settling properties and optimizing the control of nitrogen removal. In the BCFS system, a third recycle is added from the aerated reactor to the first anoxic reactor in order to maximize denitrification and to be able to aerate the second anoxic reactor during peak flows or cold temperatures. In this way both ammonium and nitrate can be better controlled to low effluent values (ammonium typical below 0.5 gN l1 and nitrate around 5–8 mg N l1). The recycle flows are controlled by a simple redox electrode-based controller (van Loosdrecht et al., 1998). Compartmentalizing the reactors and low effluent ammonia concentration contributes to a stable low SVI – around 120 ml g1 (Kruit et al., 2002; Tsai et al., 2003). Biological phosphorus removal can be supplemented by addition of precipitants to the anaerobic tank. Since phosphate concentrations are high in this tank, the precipitants are used effectively. Dosing chemicals, however, should be done carefully. Too much precipitation will make the phosphate unavailable for PAOs and deteriorate the BEPR efficiency (de Haas et al., 2001). A complicating factor is that the WWTP will respond rapidly to changes in addition of chemicals whereas the biological phosphorus removal process might have a response time of several days if not weeks. In the BCFS process, a small baffle is placed at the end of a plugflow anaerobic tank. The sludge will locally settle back into the anaerobic tank and a clear supernatant can be withdrawn for phosphate precipitation. The phosphorus can then be recovered (Barat and van Loosdrecht, 2006) or the chemical sludge produced can be prevented from accumulating in the AS which would limit the overall capacity of the plant by reducing the sludge age. Should anaerobic or aerobic digestion be performed with the wasted secondary sludge, essentially all of the polyPs will be hydrolyzed to ortho-P and the phosphate released in solution (Jardin and Po¨pel, 1994; Harding et al., 2009; Mebrahtu et al., 2010). Phosphorus recovery in the form of struvite (MgNH4PO4) or hydroxyapatite (Ca10(PO4)6OH2), which can be used as fertilizers, are also means of reducing the loading of soluble phosphate back to the AS process and, eventually, to the effluent. 6. Phosphorus uptake for cell synthesis should be maximized. Although more limited than the other maximization principles in its potential efficiency, maintaining the sludge age as short as possible will result in an increase in phosphorus removal by sludge production (cell synthesis). Although the endogenous respiration rate of the PAOs is low (0.04 d1), another small benefit of reducing the sludge age is that the PAOs degrade to a lower extent their polyP reserves for cell maintenance. 7. Because anoxic P uptake BEPR reduces the P content of the PAOs (Ekama and Wentzel, 1999a, b; Hu et al., 2002), growth of denitrifying PAOs should be avoided to maximize aerobic P uptake BEPR to maximize PAO P content – up to 0.38 mgP/mgPAOVSS (Wentzel et al., 1989b, 1990). For a review of how these developments took place, the reader is referred to Henze et al. (2008). In order to efficiently construct all the tanks in these complex BNR systems, it is possible

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Figure 48 Zandvliet nitrification–denitrification (ND)BEPR WWTP Cape Town, South Africa. By arranging interconnecting recycle flows between the anaerobic (centre), anoxic (inner ring), and aerobic (outer ring) reactors, the system has the flexibility to be operated as a UCT, threestage Bardenpho or JHB system. Photo: GA Ekama.

to shift the construction from rectangular tanks to one round tank with a sloping outside wall divided in rings for the different aerobic/anoxic/anaerobic zones. In this way, the amount of concrete needed is minimized as all the walls require much less strength (Figure 48).

4.14.30 Model Development for BEPR 4.14.30.1 Early Developments When the first mainstream NDBEPR system was proposed (the five-stage Bardenpho, Figure 47(a); Barnard, 1976), initial conceptualization of the phenomenon extended little beyond recognition of (1) the necessity of an anaerobic/aerobic sequence of reactors and (2) the adverse influence of nitrate recycled to the anaerobic zone. With the inclusion of the secondary anoxic reactor, it was believed that nearly complete denitrification of nitrate would be achieved, thereby discharging very low nitrate concentrations to the anaerobic reactor. Design procedures were based on empirically based estimates for sizing denitrification and anaerobic reactors in terms of nominal hydraulic retention time, and sizing of the anaerobic reactor appeared to be linked to depression of the redox potential below some critical value. No rational method for predicting N and P removal was available and for design, removals were estimated largely from experience gained in operating experimental systems similar to the proposed systems (McLaren and Wood, 1976; Simpkins and McLaren, 1978; Osborn and Nicholls, 1978).

4.14.30.2 RBO and Anaerobic Mass Fraction In seeking an explanation for the different P release and enhanced P removal behavioral patterns in lab-scale modified UCT (Figure 47(e)) and MLE (Figure 34(b)) systems, Siebritz et al. (1980, 1983) applied the concept of RBO developed in denitrification and aerobic studies (Dold et al., 1980; van Haandel et al., 1981) to BEPR systems. They noted that the only evident difference between the modified UCT and MLE

systems lay in the concentration of RBO surrounding the organisms in the anaerobic reactor. (They also observed that the UCT and MLE systems with the same anoxic mass fractions yielded approximately the same effluent nitrate concentrations and the ND kinetic models (such as ASM1, Henze et al., 1987 or UCTOLD, Dold et al., 1991) predicted the NDBEPR system response reasonably well even at full scale (Nicholls, 1982). This implied that the anaerobic reactor did not appear to have a detrimental effect on the denitrification (the questions this raises regarding denitrification in NDBEPR systems are discussed in Section 4.14.34.) In the modified UCT system the RBO concentration in the anaerobic reactor is the maximum possible as no nitrate is recycled to the anaerobic reactor; in contrast, in the MLE system sufficient nitrate is recycled to the anoxic reactor to utilize all the RBO. Therefore, the different behavioral patterns of the systems would be consistently described if it is assumed that the concentration of RBO from the influent in the anaerobic reactor surrounding the organisms is a key parameter determining whether or not P release and BEPR take place. (Later it became clear that the parameter influent RBO concentration in the anaerobic reactor surrounding the organisms represented the influent and produced VFAs taken up by the PAOs in the anaerobic reactor.) The validity of this RBO hypothesis was established by Siebritz et al. (1983) at laboratory scale and Nicholls et al. (1985) at full scale, who found that the magnitude of the P release was proportional to the influent RBO concentration. This opened the way for enquiry into other factors affecting the P release and the BEPR and quantifying BEPR. It was concluded that the BEPR depended on two main parameters, viz. (1) influent RBO concentration and (2) the anaerobic sludge mass fraction. Testing the concepts of the parametric model did, in general, demonstrate the utility of the model. At laboratory scale, the concepts were tested in the modified UCT system at different sludge ages, temperatures, anaerobic mass fractions, and influent COD concentrations in which the RBSO fraction of the influent (unsettled municipal sewage) was augmented by the addition of glucose or acetate. Based on the influent RBO concentration and anaerobic mass fraction parameters, the predicted P removal compared quite consistently with the measured P removal. At full scale, evaluation of the Goudkoppies and Northern Works WWTPs with the parametric model provided a consistent explanation when good or poor P removal was obtained (Nicholls et al., 1985; 1986; 1987). Thus, the parametric model allowed some quantitative approach to design of N and P removal plants and provided a basis for evaluating the performance of existing plants (Ekama et al., 1983). This parametric BEPR model, as well the organics removal, ND models presented earlier in this chapter, were published in the NDBEPR system design guide (WRC, 1984). At the time of its publication (1984), the NDBEPR system design approach was criticized and rightly so, primarily because the influent RBO was used twice, once by the PAOs for P removal (uptake in the anaerobic reactor) and again by the OHOs for denitrification in the primary anoxic reactor. This would be possible only if in NDBEPR systems the PAOs utilize all the RBO in the primary anoxic reactor with nitrate as electron acceptor for growth and polyP accumulation in the same fashion as the RBO is completely utilized by the OHOs

Biological Nutrient Removal

in the primary anoxic reactor of the ND system. In this event the major portion of the P uptake and polyP storage by the PAOs should take place in the primary anoxic reactor of the NDBEPR systems. However, P uptake was observed taking place principally in the aerobic zone. This indicated that the denitrification behavior in NDBEPR systems is not the same as that observed ND systems so that the good predictions that had been obtained by the ND models for the NDBEPR systems were fortuitous. Denitrification behavior in NDBEPR systems is discussed in Section 4.14.34 after presenting the BEPR model based on PAO behavior. Essentially up to this time, models of NDBEPR system behavior did not recognize the presence of any specific organism mediating BEPR, only the OHOs for COD removal, denitrification, and RBO fermentation, and the ANOs for nitrification (Table 1). The parametric model in fact considered the active biomass as one group (OHOs) to represent a BEPR sludge with a propensity for P removal; variation in BEPR between different systems was modeled as changes in the propensity for P removal of OHO biomass caused by changes in influent RBSO concentration, anaerobic mass fraction, and/ or nitrate discharge to the anaerobic reactor. However, parallel research in the natural sciences had identified specific organism groups that have the propensity to store large quantities of P in the form of polyP (e.g., Buchan, 1983). This led to a shift in the approach to modeling BEPR in NDBEPR systems, from a representative OHO biomass to a specific organism group mediating BEPR, like the ANOs, the specific organism groups that mediate nitrification. The BEPR organism group became generically termed polyP organisms (Wentzel et al., 1986), bio-P organisms (Comeau et al., 1986), or PAOs (ASM2, Henze et al., 1995).

4.14.30.2.1 NDBEPR system kinetics Wentzel et al. (1988) set out to develop a general model that describes NDBEPR system behavior. They assumed that in an NDBEPR system treating municipal wastewaters, a mixed culture would develop which could be categorized into three groups of organisms: (1) heterotrophic organisms able to accumulate polyP, termed PAO; (2) heterotrophic organisms unable to accumulate polyP, termed OHOs; and (3) autotrophic organisms mediating nitrification, termed ANOs (Table 1). With regard to OHOs and ANOs, they accepted the ND models described in this chapter, viz., the steady-state (WRC, 1984) and general kinetic model (Dold et al., 1980, 1991; van Haandel et al., 1981). These models were extended to incorporate PAO behavior. To achieve this, the kinetic and stoichiometric characteristics of the PAOs in the AS environment needed to be established. From attempts to obtain information on the characteristics of the PAOs using mixed liquor from NDBEPR systems treating municipal wastewaters, Wentzel et al. (1988) noted that the OHO behavior masked the PAO behavior except in its P release, P uptake, and P removal. Accordingly, to isolate the PAO biomass characteristics, they developed enhanced cultures of PAOs in open (nonsterile) AS systems. (Serendipitously, because the UCT laboratory did not have the equipment to develop pure cultures, this was never attempted – in hindsight, this would have been the wrong approach because even today, a pure culture of PAOs has not

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yet been established.) By enhanced culture is meant a culture in which (1) the growth of PAOs is selected into the system to the extent that they become the principal organism group so that their behavior dominates the system in all the measured parameters (OUR, VSS) and (2) growth of competing organisms is selected out but not positively excluded; neither are predation or other interaction effects.

4.14.30.3 Enhanced PAO Cultures 4.14.30.3.1 Enhanced culture development From the biochemical models, Wentzel et al. (1988) were able to identify conditions to be imposed in an NDBEPR AS system to produce an enhanced PAO culture – anaerobic/aerobic sequence with adequate anaerobic mass fraction; influent fed to the anaerobic reactor with acetate as substrate and with adequate macro- and micronutrients, in particular Mg2þ, Kþ, and to a lesser degree Ca2þ, and pH control in the aerobic reactor. Using the UCT and three-stage modified Bardenpho systems, with system sludge ages ranging from 7.5 to 20 days, they developed enhanced cultures of PAOs with greater than 90% of the organisms cultured aerobically being identified as Acinetobacter spp. using the analytical profile index (API) 20NE procedure. (The API 20NE procedure has subsequently been shown to overestimate Acinetobacter spp. numbers due to the testing technique (Lotter et al. 1986; Venter et al. 1989) and selection in culturing (e.g., Wagner et al. 1994). However, for the development of the design and simulation models exact identification of the PAOs in the enhanced cultures has been of minor consequence as the models are based on quantitative experimental observations.) The response of the enhanced culture systems indicated that significant concentrations of PAOs developed. For example, the UCT system (anaerobic mass fraction 15%, sludge age 10 days, and influent of acetate at 500 mgCOD l1) gave phosphate release of 253 mgP l1, phosphate uptake of 314 mgP l1, and phosphate removal of 61 mg l1, all as mgP l1 influent flow. This BEPR behavior was much higher than observed in a mixed culture NDBEPR systems with municipal wastewater influent of 500 mgCOD l1 giving a phosphate release of 45 mg l1, phosphate uptake of 57 mg l1, and phosphate removal of 12 mgP l1. In fact, the behavior of the enhanced culture systems corresponded closely to that of the mixed culture system in terms of the influent RBO/VFA fed – at 100% and 20% influent RBO/VFA respectively for 500 mgCOD l1 feed, the enhanced culture system removed 5 times more P (61 mgP l1) than the mixed culture system. The enhanced culture mixed liquor in the aerobic zone contained 0.25–0.20 mgP/mgVSS and had a VSS/ TSS ratio of 0.46–0.48 as sludge age increased from 7.5 to 20 days, much higher than for mixed culture systems at a P/VSS ratio of 0.1 and a VSS/TSS fraction of 0.78. The low VSS/TSS ratio for the enhanced culture systems is due to the high concentration of polyP with associated counterions in the PAOs, a phenomenon later included in the model by Ekama and Wentzel (2004).

4.14.30.3.2 Enhanced culture kinetic model From experimental observations on the enhanced culture steady-state systems and on a variety of batch tests (anaerobic, anoxic, and aerobic) on mixed liquor harvested from the

Biological Nutrient Removal

4.14.30.3.3 Simplified enhanced culture steady-state model Wentzel et al. (1990) simplified the enhanced culture kinetic model, to develop a steady-state model for the enhanced culture systems under constant flow and load conditions. From an examination of the kinetics of the processes under steady-state conditions, many of the processes were virtually complete so these kinetic relationships no longer serve an important function under steady-state conditions and could be replaced by stoichiometric relationships. The three examples are given as follows: (1) The anaerobic mass fractions provided in the enhanced culture systems were sufficient to ensure that all the acetate substrate was sequestered in the anaerobic zone, that is, the kinetics of acetate storage need not be incorporated. (2) Virtually, all the substrate taken up by the PAOs in the anaerobic zone was utilized in the subsequent aerobic zone, that is, the kinetics of PHA substrate utilization (and polyP storage) did not need to be incorporated. This implied that for the

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steady-state enhanced PAO systems, Wentzel et al. (1989a) elucidated the characteristics and kinetic response of the PAO biomass. Two characteristics of the PAOs in these enhanced cultures were of particular interest: (1) very little propensity to denitrify so that no provision for this process needed to be made in modeling PAO behavior – this has important implications in modeling denitrification in mixed culture NDBEPR systems (see Section 4.14.34) and (2) an extremely low endogenous mass loss rate, 0.04 mgPAOVSS/(mgPAOVSS d) which is much lower than that of OHOs in aerobic AS system at 0.24 mgOHOVSS/(mgOHOVSS d) (Marais and Ekama, 1976). A similar observation had been made by Wentzel et al. (1985) in studies on mixed culture NDBEPR systems treating municipal wastewaters; they noted from plots of phosphate uptake versus phosphate release for various sludge ages that, for a given phosphate release, the phosphate uptake was relatively insensitive to sludge age. In modeling PAO endogenous mass loss, Wentzel et al. (1989a) used the classical endogenous respiration approach (Equation (53)), as distinct from the death-regeneration approach used for the OHOs (Section 4.14.5.4.2), except that provision was made for the situations where no external electron acceptor is available. Taking note of the above, Wentzel et al. (1989a) developed a conceptual model for PAO behavior in the enhanced cultures incorporating the characteristics, processes, and compounds identified as important from the experimental investigation. Using the conceptual model as a basis, Wentzel et al. (1989b) formulated mathematically the process rates and their stoichiometric interactions with the compounds, to develop a kinetic model for the enhanced cultures of PAO. The kinetic and stoichiometric constants of the PAOs in the enhanced cultures were quantified by a variety of experimental procedures (Wentzel et al., 1989b). With these constants, application of the kinetic model to the various batch test responses observed with the enhanced cultures gave good correlation between observations and simulations (Figures 49 and 51). The model was then applied to simulate the steadystate behavior of the enhanced culture UCT and three-stage modified Bardenpho systems, for which good correlation was also obtained. (Wentzel et al., 1989b).

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Figure 49 Experimentally observed and simulated (a) oxygen utilization rate (OUR), (b) total soluble phosphorus (PO4) and nitrate (NO3) concentrations and (c) filtered COD concentrations with time in a batch aerobic digestion test of mixed liquor from an enhanced PAO culture system. Modified from Wentzel MC, Dold PL, Ekama GA, & Marais GR (1989b) Enhanced polyphosphate organism cultures in activatedsludge systems 3. Kinetic model. Water SA 15(2): 89–102.

PAOs, like for the OHOs, the growth process could be accepted as complete so that at steady state, for a given sludge age, a constant relationship exists between the flux of acetate fed to the system and the mass of PAOs formed with stored polyP. (3) P release for anaerobic maintenance energy requirements was small compared with P release for VFA uptake energy requirements, that is, the kinetics of phosphate release for anaerobic

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maintenance energy did not need to be incorporated. However, because the endogenous respiration process is never complete, it had to be retained in the steady-state model and, as for the OHOs, was accepted to take place in all the reactors of the system. Applying these simplifications and assumptions in the steady-state PAO model indicated that the P content of the PAOs was constant with sludge age at 0.38 gP/gPAOVSS, of which 0.03 was biomass P content and 0.35 was polyP content, to account for the observed P removal. What did vary was the relative proportion of PAOs (with stored polyP) in the VSS which accounted for the difference in P removal with sludge age. The resulting steady-state PAO model was identical to the OHO model (Section 4.14.31.1.5), including the value for the PAO yield coefficient (YG ¼ 0.45 mgPAOVSS/mgCOD), but the values for the PAO unbiodegradable residue fraction (fEG) and endogenous respiration rate (bG) were different to those of the OHOs (i.e., 0.25 and 0.04 d1, respectively). The PAO steady-state model provided the means for quantifying the PAO VSS mass and its endogenous residue in mixed culture NDBEPR systems receiving municipal wastewaters as influent.

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Soluble P concentration (mgP I−1)

Biological Nutrient Removal

Figure 51 Experimentally observed and simulated total soluble phosphorus (PO4) concentration and carbonaceous oxygen utilization rate (OUR)–time profiles on aeration following anaerobic acetate addition of (a) 0.207 mgCOD/mgVSS (low),(b) 0.363 mgCOD/mgVSS (moderate), and (c) 0.220 mgCOD/mgVSS (high) to mixed liquor drawn from a three-stage Bardenpho enhanced PAO culture system. Modified from Wentzel MC, Dold PL, Ekama GA, and Marais GR (1989a) Enhanced polyphosphate organism cultures in activated sludge systems 2. Experimental behaviour. Water SA 15(2): 71–88.

4.14.30.3.4 Steady-state mixed culture NDBEPR systems Mixed culture steady-state model. Having developed the steadystate model for enhanced culture systems, Wentzel et al. (1990) extended this model to incorporate mixed cultures of PAOs and OHOs present in NDBEPR systems receiving

506

Biological Nutrient Removal

domestic wastewater as influent, to give a steady-state mixed culture model. This extension proved to be possible because (1) enhanced cultures rather than pure cultures were used to establish the kinetic and stoichiometric characteristics of the PAOs. In the enhanced cultures, PAOs present in mixed culture AS were enriched and no single species was artificially selected (as in pure cultures); (2) competing organisms and predators were not artificially excluded (as in pure cultures) so that the PAOs were subjected to the same selective pressures in enhanced as in mixed cultures; (3) the PAOs were also subjected to the same conditions present in mixed culture AS systems (e.g., anaerobic/aerobic sequencing, long SRT 45 days, etc.); and (4) per influent RBO/VFA, the PAOs exhibited the same behavioral patterns in the enhanced cultures as they did in mixed culture AS systems (i.e., P release/uptake, PHA/ polyP accumulation, etc.) – in fact, the similar, though magnified behavior of the PAO enhanced culture compared to the mixed culture systems was one criterion used to establish that the correct enhanced cultures had been established. In extending the model one aspect that emerged was the difference in the endogenous mass loss rate between PAO enhanced culture sludges and the normal aerobic OHO AS. As noted earlier, the high specific endogenous mass loss rate with OHO systems had been attributed to a high rate of predation and regrowth, formulated as death regeneration in the ND kinetic model by Dold et al. (1980). The low specific endogenous mass loss rate with PAOs in the enhanced cultures systems led Wentzel et al. (1989a) to conclude that the PAOs were not predated to the same degree as OHOs, and to adopt an endogenous respiration approach in modeling PAO endogenous mass loss. (From subsequent simulations with the steady-state mixed culture model, it was found that if the PAOs were subjected to a high predation rate, then significant BEPR in the mixed culture NDBEPR system would not be possible – the rate of death of the PAOs would be so high that no significant mass of these organisms could accumulate in the system, and BEPR would be near zero.) The low predation rate on the PAOs, and the fact that the PAOs and OHOs essentially do not compete for the same substrate, implied that PAO and OHO populations act virtually independently of each other in normal mixed culture NDBEPR systems. This allowed modeling the two population groups as essentially separate, except for the fermentation F-RBO to VFA conversion process in the anaerobic reactor, which could be used to quantify the proportion of the biodegradable organics (BO) obtained by the PAOs. This rate of conversion is much slower than the rate of VFA uptake, so that the rate of conversion controls the rate of VFA uptake. Hence, the flux of VFAs that becomes available in the anaerobic reactor to the PAOs is governed by the kinetics of conversion mediated by the OHOs. The work of Me´ganck et al. (1985) and Brodisch (1985) supported this conversion approach, which is also included in the NDBEPR kinetic models (UCTPHO, Wentzel et al., 1992; ASM2, Henze et al., 1995) – they showed that anaerobic/aerobic systems developed organisms which

convert sugars and similar compounds into VFAs in the anaerobic reactor. If nitrate (or oxygen) is recycled to the anaerobic reactor, RBO is utilized preferentially by the OHOs with nitrate (or oxygen) as external electron acceptor, thereby reducing the flux of VFAs available for uptake by the PAOs. A schematic diagram showing the proportion of the influent RBO obtained by the PAOs is shown in Figure 52. OHOs obtain BO that is not obtained by PAOs. From the above, the RBSO is subdivided into two fractions, VFAs (e.g., acetate) and fermentable RBSO (FBSO, e.g., glucose). Both these fractions are measured as RBO in the conventional bioassay (e.g., Ekama et al., 1986; Wentzel et al., 1995, 1999, 2000) and filtration (e.g., Dold et al. 1986; Mamais et al., 1993; Mbewe et al., 1994) tests (see Section 4.14.4.2.2). The rate of VFA uptake by PAOs is so rapid that all influent VFAs will be taken by the PAOs even in very small anaerobic reactors (Figure 50). The F-RBO is converted to VFAs by the OHOs in the anaerobic reactor and the resultant VFAs is available for uptake by the PAOs (Figure 46). The model for this conversion is given by Wentzel et al. (1985) and will be described below. The above model provided Wentzel et al. (1990) with the means for calculating the flux of BO (influent VFA and

Unbiodegradable soluble (effluent)

Influent wastewater COD Biodegradable COD

Unbiodegradable particulate COD

RBCOD F-RBCOD

SBCOD External acid fermentation

Internal acidification

Inert VSS accumulation

Volatile fatty acids (VFAs) P accumulating organisms (PAOs)

Ordinary heterotrophic organisms (OHOs)

Enhanced culture steady-state equations PAO activemass 0.03 mgP/ mg-VSS

O2,NO3−

Usual activated sludge steady-state equations

Usual OHO activemass 0.03 mgP/mg-VSS

PAD endogenous mass 0.03 mgP/mg-VSS

OHO Endogenous mass 0.03 mgP/mg-VSS

Inert mass 0.03 mgP/ mg-VSS

Mixed VSS in system has variable P content (mass P/mass VSS %) Depending on proportion of biodegradable COD obtained by PAOs

Figure 52 Schematic diagram showing the fate of various influent COD fractions in relation to the various OHO and PAO active, endogenous, and inert masses of the sludge.

Biological Nutrient Removal

Predicted P release (mgP I−1 influent)

100 90 80 70 60 50

R S (days) 3 4 5 6 8 10 15 20 21 25 28

40 30 20 10 0

0 (a)

10 20 30 40 50 60 70 80 90 100 Measured P release (mgP I−1 influent)

30

Predicted P removal (mgP I−1)

converted of F-RBO) taken up by the PAOs in the anaerobic reactor. The remainder of the BO flux is obtained by the OHOs. In effect, the conversion model splits the influent BO (COD) into two fractions, one eventually utilized by the PAOs and the other to be utilized by the OHOs. Because of the independent action of these two organism groups, the masses of PAOs (MXBG) and their endogenous residue (MXEG) in the system could be calculated from the enhanced PAO culture steady-state model and the masses of OHOs (MXBH) and their endogenous residue (MXEH) could be calculated from the steady-state OHO model. The mass UPO in the reactor from the influent (XI) could be calculated from the unbiodegradable particulate COD fraction (fS’up) as before (Section 4.14.9.3.2). The five VSS components, each with their P content – 0.38 mgP/mgPAOVSS for the PAOs and 0.025 mgP/mgVSS for the other four components – give the average P content of the VSS. The P removal achieved by the NDBEPR system is the P in sludge mass wasted per day from the system. Wentzel et al. (1990) evaluated the predictive power of the steady-state mixed culture BEPR model against observations made on 30 laboratory-scale NDBEPR systems over a 6-year period. The system configurations were Phoredox, three-stage modified Bardenpho, UCT, MUCT, and JHB with system sludge ages ranging from 3 to 28 days. For the evaluation, the measured nitrate in the recycle to the anaerobic zone was used to estimate the fermentable COD removal in the anaerobic zone by the OHOs with nitrate as external electron acceptor. The fermentable COD remaining was available for conversion in the anaerobic reactor to VFAs and uptake and storage as PHA by the PAOs. Plots of the predicted versus measured P release, P removal, and VSS concentration (Figures 53(a)– 53(c)) show good correlation.

507

25 20 R S (days)

15

3 4 5 6 8 10 15 20 21 25 28

10 5 0 0

(b)

4.14.31 Mixed Culture Steady-State Model 4.14.31.1 Division of Biodegradable Organics between PAOs and OHOs

Sbsi ¼ Sbsai þ Sbsfi

ð173Þ

The VFA in the influent (Sbsai) is directly available to the PAOs for uptake in the anaerobic reactor.

30

4000

Predicted VSS (mg VSS I−1)

4.14.31.1.1 Subdivision of influent RBO From the mechanism for BEPR, only VFAs can be taken up directly by the PAOs in the anaerobic reactor. Accordingly, the influent RBO (Sbsi) is subdivided into two fractions: (1) VFA (Sbsai) and (2) fermentable RBO (FBSO, Sbsfi). Hence,

5 10 15 20 25 Measured P removal (mgP I−1)

3000

R S (days)

2000

3 4 5 6 8 10 15 20 21 25 28

1000

0

4.14.31.1.2 Conversion of FBSO Wentzel et al. (1985) show that the FBSO component (Sbsfi) is converted to VFA in the anaerobic reactor by the OHOs, thereby making additional VFA available to the PAOs for uptake. The rate of conversion is much slower than the rate of VFA uptake, so that the rate of conversion controls the rate of uptake of generated VFA. Wentzel et al. (1985) proposed a

0 (c)

1000

2000

3000

4000

Measured VSS (mgVSS I−1)

Figure 53 Predicted vs. measured P release (a), P removal (b) and VSS concentration (c) in a variety of BEPR systems with various configurations. From Wentzel et al. (1990).

508

Biological Nutrient Removal

first-order conversion rate, viz.,

dSbsf ¼  KCT XBHn Sbsfn dt ðmgCOD l1 h1 Þ

equations for the conversion of FBSO to VFA can be developed. This yields equations for the concentration of FBSO in exiting the nth anaerobic compartment and the mass of OHOs in the entire NDBEPR reactor, MXBH viz.,

ð174Þ

where KCT is the first-order rate constant at temperature T ¼ 0.06 l/(mgOHOVSS d) at 20 1C, and XBHn and Sbsfn the concentrations of OHOs (mgOHOVSS l1) and FRBO (mgCOD l1) exiting the nth anaerobic compartment of the anaerobic reactor.

4.14.31.1.3 Effect of recycling nitrate or oxygen When nitrate or oxygen enter the anaerobic reactor via recycle and influent flows, the OHOs utilize FBSO with these electron acceptors. Hence, the OHOs do not release the VFA generated but completely metabolize the FBSO until the oxygen or nitrate is depleted. In the conversion model this is accommodated by reducing the concentration of FBSO available for conversion, that is,

S0bsfi

¼ Sbsfi  2:86=ð1  f cv YH ÞðrNnr þ Nni Þ  1=ð1  f cv YH ÞðrOr þ Oi Þ

ð175Þ

where S0bsfi is the FBSO available for conversion to VFA (mgCOD l1 influent), Sbsfi the influent FBSO concentration (mgCOD l1), r the recycle ratio to anaerobic reactor relative to the influent flow, Nnr, Or the nitrate and oxygen concentration in the recycle to anaerobic reactor (mgNO3-N l1 and mgO l1, respectively), Nni, Oi the nitrate and oxygen concentrations in the influent to anaerobic reactor (mgNO3-N l1 and mgO l1, respectively), 2.86/(1  fcvYH) ¼ 8.6 the mass of COD utilized per unit nitrate denitrified (mgCOD/mgNO3N), and 1/(1  fcvYH) ¼ 3.0 the mass of COD utilized per unit oxygen utilized (mgCOD/mgO). Kinetics of conversion of FBSO to VFA. The conversion model proposed by Wentzel et al. (1985) assumes that: 1. Only FBSO can be converted to a form suitable for uptake by the PAOs (i.e., VFA); within the timescale of the mixed liquor in the anaerobic reactor, conversion of SBO to VFA is assumed to be negligible. 2. The conversion is mediated by the OHOs in the absence of oxygen and nitrate only. 3. All VFA generated by conversion is immediately taken up by the PAOs. 4. All FBSO not converted to VFA in the anaerobic reactor is utilized subsequently by OHOs. 5. The rate of conversion of FBSO is first order with respect to the FBSO and OHO concentrations in the anaerobic reactor and given by Equation (174). 6. All VFA present in the influent to the anaerobic reactor is immediately taken up by the PAOs.

4.14.31.1.4 Steady-state FBSO conversion equation Applying Equations (174) and (175) within mass balances over the nth anaerobic compartment in a series of N equal volume anaerobic compartments in the anaerobic reactor receiving in a continuous flow NDBEPR system, the steady-state

S0bsfi =ð1 þ rÞ  n Sbsfn ¼  f xa MXBH 1 1 þ KCT N Qi ð1 þ RÞ ðmgCOD l1 Þ

ð176Þ

where fxa is the anaerobic mass fraction of the NDBEPR system, N the total number of compartments of equal volume in the anaerobic reactor, n the nth compartment of the series, n ¼ 1,2,yy,N, Sbsfn the concentration of FBSO exiting the nth compartment, MXBH the mass of OHOs in the system (mgOHOVSS), and Qi the influent flow rate (l d1). Equation (176) provides the means to calculate the flux of FBSO converted to VFA in a series of N anaerobic compartments, that is,

FSbCON ¼ Qi ½S0bsfi  ð1 þ rÞSbsfN 

ðmgCOD d1 Þ

ð177Þ

However, to calculate SbsfN, MXBH/Qi needs to be known. This is calculated from the flux of BO not obtained by the PAOs. All the VFA generated by conversion and all the VFA in the influent are taken up by the PAOs, so the flux of COD taken up by the PAOs, FSbPAO, is given by

FSbPAO ¼ FSbCON þ Qi Sbsai ¼ Qi ½S0bsfi  ð1 þ rÞSbsfN  þ Qi Sbsai

ðmgCOD d1 Þ

ð178Þ

and the flux of biodegradable COD taken up by the OHOs is given by

FSbOHO ¼ Qi Sbi  FSbPAO

ðmgCOD d1 Þ

ð179Þ

Hence, from Equation (103), the mass of OHOs in the NDBEPR system is given by

MXBH ¼

FSbOHO YH Rs ð1 þ bHT Rs Þ

ðmgOHOVSSÞ

ð180Þ

Substituting Equations (179) and (178) into Equation (180) and dividing by Qi yields the MXBH/Qi required in Equation (176), viz.,

MXBH ðSbi  ½S0bsfi  ð1 þ rÞSbsN þ Sbsai ÞYH Rs ¼ Qi ð1 þ bHT Rs Þ ðmgOHOVSSÞ

ð181Þ

Equations (176) and (181) need to be solved simultaneously to calculate the concentration of FBSO (SbsfN) exiting the last anaerobic compartment (N); the following procedure converges in three to four iterations: (1) Assume SbsfN ¼ 0 mgCOD l1, (2) calculate MXBH/Qi with Equation (181), (3) with MXBH/Qi known, calculate SbsfN with Equation (176), (4) recalculate MXBH/Qi using the new value for SbsfN, (5) repeat steps (3)–(5) until SbsfN and MXBH/Qi are constant. This procedure splits the influent BO between the OHOs and PAOs. Because the growth processes of two organism

Biological Nutrient Removal

groups after the anaerobic reactor are noncompetitive and VFA uptake process and the growth processes on the available organics are complete for both groups, the stoichiometric equations relating the flux of COD utilized and the biomass produced derived earlier (Equation (103)) can be applied to calculate the PAO and OHO masses and their endogenous residue masses.

509

(Supi, XIi) (Equation (67)), viz.,

MXI ¼ FSti f S0 up =f cv Rs

ðmgIVSSÞ

Total VSS in the NDBEPR system is the sum of the five VSS components:

MXv ¼ MXBH þ MXBG þ MXEH þ MXEG þ MXI ðmgVSSÞ

4.14.31.1.5 Mass of VSS in the NDBEPR system

ð188Þ

PAO mass

MXBG ¼ FSbPAO

YG Rs 1 þ bGT Rs

ðmgPAOVSSÞ

ð182Þ

where, YG is the PAO yield coefficient (mgPAOVSS/mgCOD utilized), FSbPAO the flux BO taken up by PAOs in the anaerobic reactor (mgCODd1), and bGT the PAO specific endogenous mass loss rate constant at temperature T (d1).

4.14.31.1.6 PAO P release From the mechanisms of BEPR (Wentzel et al., 1985, 1990), for every mole of VFA taken up, 1 mol of P is released to provide energy to synthesize and store the VFA as PHA. Accordingly, the P release in the anaerobic reactor is given by

FPrel ¼ f prel FSbPAO

ðmgP d1 Þ

ð189aÞ

PAO endogenous mass or

MXEG ¼ f EG bGT MXBG Rs

ðmgVSSÞ

ð183Þ

where fEG is the fraction of PAOs that is unbiodegradable particulate endogenous residue. PAO oxygen demand

FOGc ¼ FOGs ðsynthesisÞ þ FOGe ðendogenous respirationÞ ¼ ð1  f cv YG ÞFSbPAO þ f cv ð1  f EG ÞbGT MXBG   YG Rs ¼ FSbPAO ð1  f cv YG Þ þ f cv ð1  f EG ÞbGT 1 þ bGT Rs ðmgO d1 Þ

ð184Þ

OHO mass

MXBH

Prel ¼ f prel SbPAO

ðmgP l1 influentÞ

where fprel is the ratio P release/VFA uptake E1.0 molP/mol COD E0.5 mgP/mgCOD and SbPAO the concentration COD taken up by the PAOs per liter influent ¼ FSbPAO/Qi.

4.14.31.1.7 P removal The P removal via the waste sludge is calculated from the individual P content of the five VSS components, viz: By PAOs

  MXBG MXEG 1 DPG ¼ f XBGP þ f XEGP Rs Rs Qi ðmgP l1 influentÞ

YH Rs ¼ FSbOHO 1 þ bHT Rs

ðmg OHOVSSÞ

ð185Þ

where YH is the OHO yield coefficient (mgOHOVSS/mgCOD utilized), FSbOHO the flux BO taken up by OHOs in the anaerobic reactor (mgCOD d1), and bHT the OHO specific endogenous mass loss rate constant at temperature T (d1).

ðmgVSSÞ

By OHOs

ð186Þ

FOHc ¼ FOHs ðsynthesisÞ þ FOHe ðendogenous respirationÞ ¼ ð1  f cv YH ÞFSbOHO þ f cv ð1  f EH ÞbGT MXBH   YH Rs ¼ FSbOHO ð1  f cv YH Þ þ f cv ð1  f EH ÞbHT bHT Rs ðmgO d Þ

where PG is the P removal by the PAOs (mgP l1influent), fXBGP the P content of PAOs ¼ 0.38 mgP/mgPAOVSS, and fXEGP the P content PAO endogenous mass ¼ 0.03 mgP/ mgEVSS.

 MXBH MXEH 1 DPH ¼ f XBHP þ f XEHP Rs Rs Qi

where fEH is the fraction of OHOs that is unbiodegradable particulate endogenous residue. OHO oxygen demand

1

ð190Þ



OHO endogenous mass

MXEH ¼ f EH bHT MXBH Rs

ð189bÞ

ðmgP l1 influentÞ

ð191Þ

where PH is the P removal by the OHOs (mgP l1influent), fXBHP the P content of OHOs ¼ 0.03 mgP/mgOHOVSS, and fXEHP the P content OHO endogenous mass ¼ 0.03 mgP/ mgEVSS. By inert mass

  MXI 1 DPI ¼ f XIP Rs Qi

ðmgP l1 influentÞ

ð192Þ

ð187Þ

The same equations derived earlier in Section 4.14.7.1.1 apply for the UPO that accumulate in the reactor from the influent

where PI is the P removal due to inert mass (mgP l1 influent) and fXIP the P content inert VSS mass (mgP/mgIVSS) ¼ 0.025– 0.03 mgP/mgIVSS.

510

Biological Nutrient Removal

The total P removal is given by the sum of the individual P removals, i.e. Total P removal

DPT ¼ DPG þ DPH þ DPI

ðmgP l1 influentÞ

ð193Þ

The effluent P concentration is given by the difference between the influent P and the P removal, i.e. Effluent P concentration

Pte ¼ Pti  PT

ðmgP l1 Þ

mgPAOVSS), and the polyP ISS, which is 3.286 mgISS/mgP times the PAO polyP content, which is its total P content (fXBGP) minus its biomass P content (Ekama and Wentzel, 2004). Hence,

ð194Þ

If the P removal is greater than the influent P concentration, then the expectation is that the effluent P concentration will be below 0.5 mgP l1. How far below 0.5 mgP l1 is uncertain because currently this appears to be plant specific. Research is being conducted to investigate what the limits of BEPR technology are and what conditions in the NDBEPR system cause them (Neethling et al., 2009). Revised PAO P content (fXBGP). If the P removal is greater than the influent P concentration, then there is insufficient P in the influent for the PAOs to take up P up to their maximum P content of 0.38 mgP/mgPAOVSS. Their P content (fXBGP) will therefore be limited by the available P. Under these conditions, the PAO P content needs to be revised to match the available P. If this is not done, the reactor ISS concentration, which is strongly influenced by the PAO P content, will be overestimated. In the calculation for the revised PAO P content, it is assumed that the effluent P concentration (Pte) is equal to the P concentration of the unbiodegradable soluble organics (USO; see Section 4.14.4.4.3) and that the P content of the non-PAO VSS components remains unchanged. Unless data are available to indicate a nonzero USO P concentration (Pousi40), it is reasonable to accept it as zero. Clearly, if the wastewater contains USO P, then this will impact achieving the very low effluent P standards that are being set for NDBEPR systems these days. However, it would appear that USO P in municipal wastewaters is effectively zero, or at least masked by the scatter of the difference between membrane filtered effluent TP and OP concentrations. The revised PAO P content (fXBGP) is found by making fXBGP the subject of Equation (193) and the P removal equal to the difference between the influent P and USO P concentrations (Equation (39)), viz.,

f XBGP ¼ ½ðPti  Pousi ÞQi Rs  f XEGP MXEG  f XBHP MXBH  f XEHP MXEH  f XIP MXI =MXBG ðmgP=mgPAOVSSÞ ð195Þ

4.14.31.2 VSS and TSS Sludge Masses in the Reactor (System) The VSS mass in the NDBEPR reactor is the sum of the five VSS component masses (Equation (188)). The ISS concentration is the sum of the ISS that accumulates in the reactor from the influent (Equation (97)), the OHO ISS, and the PAO ISS. The OHO ISS is 15% of its VSS mass, that is, fiOHO ¼ 0.15 mgISS/ mgPHOVSS (Equation (98)). The PAO ISS is the sum of its biomass ISS, which is the same at the OHO ISS (0.15 mgISS/

XIO ¼ FXIOi Rs þ MXBH þ 3:286ðf XBGP  f XBGPBM ÞMXBG ðmgISSÞ ð196Þ where fXBGPBM is the PAO biomass P content ¼ OHO biomass P content ¼ 0.025–0.03 mgP/mgPAOVSS. The TSS mass in the NDBEPR system is the sum of the VSS and ISS masses, that is,

MXt ¼ MXv þ MXIO

ðmgTSSÞ

ð197Þ

This TSS mass is distributed in the various reactors of the NDBEPR system, not necessary at the same TSS concentration in each reactor. The reactor configuration (Figure 47) influences the TSS concentration in the different reactors of the system. Calculating the reactor concentrations from the various mass fraction of the reactors is discussed below.

4.14.31.3 BEPR System Design Considerations 4.14.31.3.1 Process volume requirements An approximate reactor volume, that is, a nonconfigurationspecific volume, can be estimated from a selected average reactor TSS concentration required for the system, that is,

Vp ¼ MXt =Xt

ðm3 Þ

ð198Þ

where Xt is the zone/reactor volume weighed average TSS concentration in the NDBEPR system (mgTSS l1). For all NDBEPR system configurations with SSTs, or with membranes (MBR), at steady-state and average dry weather flow (ADWF) conditions, the concentrations of TSS in the preanoxic (Figure 47(f)) and anaerobic (if present) and anoxic and aerobic zones (Xtpax, Xtana, Xtanx, Xtaer), as fractions of the average system TSS concentration Xt are equal to the ratio of the sludge mass fraction and volume fraction of the zones, that is,

Xtana f mana Xtanx f manx ¼ ; ¼ ; Xt f vana Xt f vanx Xtpax f mpax Xtaer f maer ¼ ; ¼ Xt f vaer Xt f vpax

ð199Þ

where fm, fv are the zone sludge mass and volume fractions respectively, and subscripts ana, anx, aer, and pax are the anaerobic, anoxic, aerobic, and pre-anoxic zones, respectively. For BNR systems with SSTs in which the sludge mass is uniformily distributed, that is, the TSS concentrations are the same in the anaerobic, anoxic, and aerobic zones of the reactor, the sludge mass and volume fractions are equal, such as in the three- and five-stage Bardenpho systems (Figures 47(b) and 47(c)) for N and P removal and the pre- (modified Ludzack–Ettinger, MLE) and post-(Wuhrmann) denitrification and four-stage Bardenpho systems for N removal. For example, if an MLE ND system (Figure 34(b)) requires anoxic and aerobic mass fractions (fmanx, fmaer) of 0.45 and 0.55,

Biological Nutrient Removal

respectively, or a three-stage Bardenpho (Figure 47(b)) system requires anaerobic, anoxic, and aerobic mass fractions (fmana, fmanx, fmaer) of 0.15, 0.35, and 0.50 respectively, the corresponding volume fractions of these zones (fvana, fvanx, fvaer) with respect to the reactor volume (VR) will also be 0.45 and 0.55 for the MLE system and 0.15, 0.35, and 0.50 for the three-stage Bardenpho system. This is because the influent flow dilutes the SST return sludge concentration in the first zone by the same amount as the SST concentrates it after the last zone. This equality of sludge mass and volume fractions does not apply to any multizone BNR system with membrane solid–liquid separation in the aerobic zone, because the aerobic zone concentration is in effect the equivalent of the return sludge concentration from the SST (if there were SSTs). For BNR systems with SSTs, in which the TSS concentrations are not the same in the pre-anoxic, anaerobic, anoxic, or aerobic zones (e.g., in the UCT (Figure 47(d)) or in the JHB (Figure 47(f)) systems), the volume and mass fractions are not equal. For the UCT system, the volume fractions (with respect to Vp) of the anaerobic, anoxic, and aerobic zones (fvana, fvanx, fvaer), and the anaerobic, anoxic, and aerobic TSS concentrations (Xtana, Xtanx, Xtaer) at steady-state and ADWF conditions are related to the anaerobic and aerobic mass fractions (fmana, fmaer), recycle ratio (r) from the anoxic to the anaerobic reactor and system average TSS concentration Xt , as follows:

f mana ðr þ 1Þ rB

ð200aÞ

ð1  f mana  f maer Þ B

ð200bÞ

f maer B

ð200cÞ

rB ðr þ 1Þ

ð200dÞ

f vana ¼ f vanx ¼

f vaer ¼

Xtana ¼ Xt

Xtanx ¼ Xtaer ¼ Xt B

ð200eÞ

  f mana 1þ r

ð200f Þ

where

B¼

For the JHB system with SSTs, assuming the influent flow to the pre-anoxic zone, which is sometimes included to increase pre-denitrification, is zero, the volume fractions (with respect to Vp) of the pre-anoxic, anaerobic, anoxic, and aerobic zones (fvpax, fvana, fvanx, fvaer), and the pre-anoxic, anaerobic, anoxic, and aerobic TSS concentrations (Xtpax, Xtana, Xtanx, Xtaer) at steady-state and ADWF conditions are related to the pre-anoxic, anaerobic, and aerobic mass fractions (fmpax, fmana, fmaer), underflow recycle ratio (s) from the SST to the pre-anoxic reactor and average TSS concentration Xt , as follows:

f mana C

ð201aÞ

ð1  f mana  f maer  f mpax Þ C

ð201bÞ

f vana ¼ f vanx ¼

f maer C

ð201cÞ

f mpax s Cðs þ 1Þ

ð201dÞ

f vaer ¼ f vpax ¼

Xtana ¼ Xtanx ¼ Xtaer ¼ Xt C

C¼

ð201eÞ

Cs ð1 þ sÞ

ð201f Þ

  f mpax 1 1þs

ð201gÞ

Xtpax ¼ Xt where

511

In BNR systems with membrane solid–liquid separation in the aerobic zone, the sludge mass distributes itself differently in the different zones of the system compared with systems with SSTs. This is because the effluent is withdrawn via the membranes from the aerobic zone which concentrates the sludge in this zone relative to that in the other zones. However, in recycling this concentrated aerobic zone sludge to an upstream zone, it is diluted by the less concentrated incoming sludge stream from the upstream zones. The higher the recycles from downstream zones to upstream zones, the more uniformily the sludge mass is distributed around the system and the closer the sludge concentrations in the different zones. For the UCT system with membranes, the volume fractions (with respect to Vp) of the anaerobic, anoxic, and aerobic zones (fvana, fvanx, fvaer), and the anaerobic, anoxic, and aerobic TSS concentrations (Xtana, Xtanx, Xtaer) at steady-state and ADWF conditions are related to the anaerobic and aerobic mass fractions (fmana, fmaer), recycle ratio (r) from the anoxic to the anaerobic zone, recycle ratio (a) from the aerobic to the anoxic zones, and system average TSS concentration Xt , as follows:

f mana ðr þ 1Þ Dr

ð202aÞ

ð1  f mana  f maer Þ D

ð202bÞ

af maer ða þ 1ÞD

ð202cÞ

rD ðr þ 1Þ

ð202dÞ

f vana ¼

f vanx ¼

f vaer ¼

Xtana ¼ Xt

Xtanx ¼ Xt D Xtaer ¼ Xt where

D¼

ða þ 1ÞD a

  f mana f maer 1þ  r ða þ 1Þ

ð202eÞ ð202f Þ

ð202gÞ

For the JHB system with membranes, the volume fractions (with respect to Vp) of the pre-anoxic, anaerobic, anoxic, and aerobic zones (fvpax, fvana, fvanx, fvaer), and the pre-anoxic, anaerobic, anoxic, and aerobic TSS concentrations (Xtpax, Xtana,

512

Biological Nutrient Removal

Xtanx, Xtaer) at steady-state and ADWF conditions are related to the pre-anoxic, anaerobic, and aerobic mass fractions (fmpax, fmana, fmaer), recycle ratio (s) from the aerobic to the pre-anoxic zones, recycle ratio (a) from the aerobic to the anoxic zones, and average TSS concentration Xt , as follows:

f vana ¼ f vanx ¼

f mana ð1 þ sÞ sE

ð1  f mana  f maer  f mpax Þða þ s þ 1Þ ða þ sÞE

ð203aÞ

ð203bÞ

f vaer ¼

f maer E

ð203cÞ

f vpax ¼

f mpax E

ð203dÞ

sE ðs þ 1Þ

ð203eÞ

Xtana ¼ Xt

Eða þ sÞ Xtanx ¼ Xt ða þ s þ 1Þ Xtaer ¼ Xtpax ¼ Xt E

ð203f Þ ð203gÞ

 ð1 þ sÞ ða þ s þ 1Þ E ¼ f mana þ f manx s ða þ sÞ  þf maer þ f mpax

ð203hÞ

Equation (203) applies also to the MLE ND system and the three-stage Bardenpho system with membranes. In Equation (203h) for E, for the MLE system, the anaerobic and pre-anoxic mass fractions are both set to zero, the anoxic mass fraction is 1 minus the aerobic mass fraction (i.e., fmanx ¼ 1  fmaer), and the mixed liquor recycle ratio (a) is also set to zero – only one recycle (s) is required to return nitrate and sludge to the anoxic reactor. For the three-stage Bardenpho system, only the pre-anoxic sludge mass fraction (fmpax) is set to zero. From Equations (200)–(203), the volumes of, and the TSS concentrations in, the various zones of common BNR systems with SST or membrane solid–liquid separation can be calculated for selected anaerobic, aerobic, and pre-anoxic mass fractions (fmaer, fmana, fmpax), and interzone recycle ratios (a, r, and s). In the derivation of these equations, steady-state conditions were assumed and the sludge waste flow rate was ignored – the effect of this is negligible (o2%), especially if the sludge age is long. Generally, a uniform distribution of sludge mass in BNR MBR systems will not occur, even in systems with a single recycle flow from the aerobic to the zone receiving the influent flow. For example, changing an MLE ND system, or a three-stage Bardenpho system with SSTs to membrane solid– liquid separation systems, will change these systems from uniformly distributed sludge mass systems in which the sludge mass and volume fractions are equal to nonuniformly distributed sludge mass systems in which the sludge mass and volume fractions are different, the magnitude of difference depending on the magnitude of the recycle ratios. In multizone BNR systems with membranes in the aerobic reactor and fixed volumes for the anaerobic, anoxic, and

aerobic zones (i.e., fixed volume fractions), the mass fractions can be varied (within a range) by varying the inter-reactor recycle ratios. For example, in a UCT system with anaerobic, anoxic, and aerobic zone volume fractions of 0.25, 0.35, and 0.40 and an r recycle ratio from the anoxic to the anaerobic zones of 1:1, the anaerobic, anoxic, and aerobic zone mass fractions can be varied from 0 to 0.131, 0 to 0.366, and 1 to 0.503, respectively, by changing the a recycle ratio from 0:1 to 5:1. Increasing the a recycle ratio concomitantly increases the nitrate load on the anoxic reactor, thereby increasing the denitrification and N removal as the anoxic mass fraction increases. Increasing the r recycle ratio increases the anaerobic mass fraction (at the expense of the other two zone mass fractions) and increases (not proportionally) the P removal. This zone mass fraction flexibility is a significant advantage of membrane BNR systems over conventional BNR system with SSTs because it allows changing the mass fractions to optimize biological N and P removal in conformity with influent wastewater characteristics and the effluent N and P concentrations required. If required, the performance of membrane BNR systems can be simulated with current BNR AS models such as UCTOLD (for ND, Dold et al., 1991), UCTPHO (for NDBEPR with 490 aerobic P uptake BEPR, Wentzel et al., 1992; Hu et al., 2003), and IWA ASM Nos 1, 2 (ND and BEPR, Henze et al., 1987) by returning the SST underflow into the aerobic zone from which the SST feed flow exits (Parco et al., 2009). However, such simulations require a priori information on the reactor and zone volumes and recycle flows, which would need to be determined with the steady-state procedures set out in this chapter.

4.14.31.3.2 Nitrogen requirements for sludge production The form of the equation for calculating the nitrogen requirement for sludge production (Ns, mgN l1 influent) is the same as set out in Section 4.14.21, Equation (142), that is,

Qi Ns ¼ f n MXv =Rs

ðmgN d1 Þ

However, for the BEPR system the term MXv needs to take account of the changes in VSS components, that is, it must be calculated using Equation (188). Effect of this is to increase Ns because MXv is greater in the NDBEPR system than in the same sludge age ND system receiving the same wastewater. The increase in Ns decreases the nitrification capacity (Nc) (Equation (152)), and hence also the nitrification oxygen demand (FOn, mgO d1). For the rest, the nitrification model calculations remain the same.

4.14.31.3.3 Total oxygen demand The carbonaceous oxygen demand (FOc) is the sum of oxygen demands of PAOs (Equation (184)) and OHOs (Equation (187)):

FOc ¼ FOGc þ FOHc ¼ ð1  f cv YG ÞFSbPAO þ f cv ð1  f EG ÞbGT MXBG þ ð1  f cv YH ÞFSbOHO þ f cv ð1  f EH ÞbHT MXBH ðmgO d1 Þ

ð204Þ

Biological Nutrient Removal

4.14.32 Influence of BEPR on the System 4.14.32.1 Influence on VSS, TSS, and Carbonaceous Oxygen Demand The model for BEPR systems presented above enables the VSS and TSS of the mixed liquor (Equations (188) and (197), respectively) and the carbonaceous oxygen demand (Equation (204)) to be calculated. A comparison of the masses of VSS and TSS in the reactor and the carbonaceous oxygen demand per kg COD load on the bioreactor versus sludge age with and without BEPR are shown in Figures 54(a) and 54(b) for the example raw and settled wastewaters respectively, with influent RBO fractions with respect to the biodegradable COD (fSb’s) of 0.25 and 0.38, respectively (Table 14) and an VFA fraction of 25% of the RBO, that is, influent RBO and VFA concentration of 146 and 36 mgCOD l1 for both wastewaters. The features of the BEPR system are a UCT configuration operated at 20 1C with two equal-sized in-series anaerobic compartments with a total anaerobic mass fraction (fxana) of 0.15, an anoxic to anaerobic (r) recycle of 1:1, and no nitrate recycled to the anaerobic reactor. From Figures 54(a) and 54(b), BEPR in the AS system increases the VSS slightly, by about 5–12% and 15–25% for

Raw wastewater

0.8

8 MLTSS Oxygen demand

0.6

0.4

4 MLVSS

0.2

2

0.0

0 0

5

Although there is only a small difference in VSS production between a BEPR and a non-BEPR system, the constituent

10 15 20 25 Sludge age (days)

30

Settled wastewater

10 Sludge mass − kgVSS/(kgCOD/d) reactor

With BEPR No BEPR 20 °C

6

4.14.32.2 VSS Composition

0.1

Oxygen demand − (kgO/d)/(kgCOD/d)

Sludge mass − kgVSS/(kgCOD/d) reactor

10

raw and settled wastewaters respectively depending on sludge age – the longer the sludge age the greater the difference. This increase in VSS is due to the lower endogenous mass loss/ death rate of the PAOs (0.04 d1 at 20 1C) compared with the OHOs (0.24 d1 at 20 1C). However, the TSS is increased substantially, by about 20–25% and 45–55% for raw and settled wastewaters, respectively, depending on the sludge age. This higher TSS production is due to the large quantities of stored inorganic polyP and the associated inorganic cations necessary to stabilize the polyP chains – principally Mg2þ and Kþ (Fukase et al., 1982; Arvin, 1985; Comeau et al., 1986; Wentzel et al., 1988; Ekama and Wentzel, 2004). The high inorganic content of the PAOs causes the VSS/TSS to be much lower than that of the OHOs, 0.46 mgVSS/mgTSS compared with 0.87 mgVSS/mgTSS (excluding the influent ISS). Thus, the higher the PAO fraction of the mixed liquor, the higher the BEPR, but the lower the VSS/TSS ratio of the mixed liquor. The increase in TSS with the inclusion of BEPR needs to be taken into account in the design of the bioreactor volume (Equation (198)) and daily sludge production. Also, since the inorganic cations that stabilize the polyP are derived from the influent wastewater, there must be sufficient concentrations of these cations in the influent; otherwise, the BEPR may be adversely affected (Wentzel et al., 1989a; Lindrea et al., 1994). Further, because the VSS mass generated per kg COD load is greater with BEPR than without, the oxygen demand with BEPR is correspondingly reduced, by about 5–6% and 8–9% for raw and settled wastewaters, respectively (depending on sludge age, Figures 54(a) and 54(b)).

1.0

With BEPR No BEPR 20 °C 8

0.8 Oxygen demand 0.6

6 MLTSS 4

0.4

MLVSS

2

0.2

0

Oxygen demand − (kgO/d)/(kgCOD/d)

The total oxygen demand (FOt, mgO d1) is the sum of the carbonaceous and nitrification oxygen demands, taking due account of the change in nitrogen requirements for sludge production (Ns) and nitrification capacity (Nc). For a nonnitrifying BEPR systems, the total oxygen demand FOt is given by FOc. Including nitrification in the BEPR system necessarily means that denitrification must also be included; the effect of nitrification and denitrification on the total oxygen demand will be considered in Section 4.14.34.

513

0.0 0

5

15 20 25 10 Sludge age (days)

30

Figure 54 Predicted masses of volatile solids (MXV, MLVSS) and total solids (MXt MLTSS) and daily carbonaceous oxygen demand (FOC) per kg COD load on the biological reactor in ND (thin line) and BEPR (bold line) systems treating raw (a) raw and settled (b) wastewater.

Biological Nutrient Removal

Settled wastewater with BEPR

200

I−1

750 mg COD 25% RBCOD fraction % Of VSS mass (settled WW)

% Composition of VSS mass

Inert 60 OHO endogenous 40 OHO active PAO endog 20

90 80

Additional VSS mass in system treating raw WW, i.e., raw WW produces about 100% more activated sludge VSS mass

140 120

70 60

100

50 Inert

80

40

OHO endogenous

60

30

OHO active

PAO endog

40

20 10

PAO active

0

0 0

5

10

15

20

25

30

Sludge age (days)

(a)

0 0

5

% Of VSS mass (settled WW)

Inert mass 60 OHO endogenous mass

40

20

OHO active mass

20

25

30

100

450 mg COD I−1 38% RBCOD fraction

180

80

15

Settled wastewater no BEPR

200

−1

750 mg COD I 25% RBCOD fraction

10

Sludge age (days)

(b)

Raw wastewater no BEPR

100

% Composition of VSS mass

160

20

PAO active

90 80

160 Additional VSS mass in system treating raw WW. i.e., raw WW produces about 100% more activated sludge VSS mass

140 120

70 60 50

100 Inert

40

80 OHO endogenous

60

30 20

40 OHO active

20

10 0

0

0 0 (c)

450 mg COD I 38% RBCOD fraction

180

80

100

−1

5

10

15

20

25

0

30

Sludge age (days)

(d)

% Of VSS mass (raw WW)

Raw wastewater with BEPR

100

% Of VSS mass (raw WW)

514

5

10

15

20

25

30

Sludge age (days)

Figure 55 Percentage composition of VSS mass for BEPR systems (a, c) and ND systems (No BEPR, b, d) treating raw (a, b) and settled (c, d) wastewater.

sludge fractions for the two systems differ markedly. This can be readily demonstrated by comparing the percentage composition of the VSS mass generated in systems exhibiting BEPR with the ND system that does not. To illustrate, percentage composition of the VSS mass is shown in Figures 55(a) to 55(d) for systems at 20 1C with no BEPR (Figures 55(b)– 55(d)) and with BEPR (Figures 55(a) and 55(c)) respectively treating the example raw (Figures 55(a) and 55(b)) and settled (Figures 55(c) and 55(d)) wastewaters. Note that the BEPR system has a smaller OHO active mass than the no BEPR ND system, but the BEPR system has additionally a significant concentration of PAO biomass.

4.14.32.3 P/VSS ratio A parameter often used to evaluate the BEPR performance of an AS system is the P/VSS (or P/TSS) ratio of the mixed liquor. In Figures 56(a) and 56(b), the calculated P/VSS ratio for a BEPR system with a two-compartment anaerobic reactor and the example raw and settled wastewater characteristics are plotted versus sludge age. A zero discharge of nitrate to the anaerobic reactor is assumed. From Figures 56(a) and 56(b), as the system sludge age increases, the P/VSS ratio increases up to a sludge age of about 10 days. Further increases in sludge age cause a decrease in P/VSS ratio. The initial increase in

Biological Nutrient Removal 15

20

10

0.05 fxana

5

0.0

0

15 0.10 0.05

10

fxana 5 0.0

0 5

0 (a)

10 15 20 Sludge age (days)

25

30

0

5

(b)

10 15 20 Sludge age (days)

25

30

20

15

Settled WW

10

%P in TSS (mgP/mgVSS*100)

Raw WW %P in TSS (mgP/mgVSS*100)

0.25 0.20 0.15

Settled WW

0.25 0.20 0.15 0.10

%P in VSS (mgP/mgVSS*100)

%P in VSS (mgP/mgVSS*100)

Raw WW

0.25 0.20 0.15 0.10

5 fxana

0.05 0.0

15

0.25 0.20 0.15 0.10

10

0.05 fxana

5

0.0

0

0 0 (c)

515

5

10 15 20 Sludge age (days)

25

0

30 (d)

5

10 15 20 Sludge age (days)

25

30

Figure 56 Predicted percentage phosphorus to VSS (P/VSS  100; a, b) and TSS (P/TSS  100; c, d) ratios vs. sludge age for mixed liquor in a BEPR system with various anaerobic mass fractions (fxana) treating the example raw (a, c) and settled (b, d) wastewater.

P/VSS with sludge age is due increasing OHO mass with sludge age, which increase the fermentable RBO to VFAs conversion efficiency in the anaerobic reactor and accordingly yields an increased PAO mass (with associated P content of 0.38 mgP/mgVSS). The decrease in P/VSS can be ascribed to the endogenous respiration effect on PAOs. The P/VSS ratio is therefore a consequence of the selection of the system design parameters sludge age and anaerobic mass fraction and wastewater characteristics. Accordingly, the P/VSS ratio can neither fulfill a function in BEPR plant design nor in BEPR performance assessment between different BEPR plants.

4.14.33 Factors Influencing Magnitude of BEPR The influence of the main design parameters on the magnitude of P removal is demonstrated with the mixed culture steady-state BEPR model. These main parameters are: raw settled wastewater (Sti ¼ 750 and 450 mgCOD l1 respectively, Tables 7, 11, 14), sludge age (SRT ¼ 20 days), anaerobic sludge

mass fraction (fxana ¼ 0.15), influent RBO COD fraction (fSb’s ¼ 0.25 for raw and 0.385 for settled), discharge of nitrate and oxygen to the anaerobic reactor (0 for both) and subdivision of anaerobic reactor into compartments (N ¼ 2). The numbers in brackets are the default wastewater characteristics and system design parameter values.

4.14.33.1 Sludge Age and Anaerobic Mass Fraction Using the characteristics of the example raw and settled wastewater with a total influent COD of 750 and 450 mgCOD l1 respectively, assuming (1) no nitrate and DO enters the anaerobic reactor, (2) a recycle ratio to the anaerobic (r) of 1:1 and the anaerobic reactor is subdivided into two compartments, the P removal (normalized with respect to influent COD, mgPmg1 influent COD) versus sludge age is shown in Figures 57(a) (raw) and 57(b) (settled) for anaerobic mass fractions of 0.00 (no BEPR), 0.05, 0.10, 0.15, 0.20, and 0.25. In the same figures, the actual P removal in mgP l1 is shown on the right-hand axis.

Biological Nutrient Removal

0.02

0.25 0.20 0.15 0.10

22.5

15.0

Influent P 0.05

fxana

0.01

7.5

0.0

5

10 15 20 25 Sludge age (days)

0.04 0.03

Influent P

0.02

0.25 0.20 0.15 0.10

18.0 13.5 9.0

0.05

fxana 4.5

0.01 0.0

0.0

0.00

0.0 0

22.5 Settled WW

0

30 (b)

P removal (mgP I−1)

0.03

0.00 (a)

0.05

30.0 Raw WW

P removal (mgP/mg Infl COD)

P removal (mgP/mg Infl COD)

0.04

P removal (mgP I−1)

516

5

10 15 20 25 Sludge age (days)

30

Figure 57 Predicted P removal vs. sludge age for various anaerobic mass fractions (fxana) for a two-compartment anaerobic reactor BEPR system at 20 days sludge age, treating the example raw (a) and settled (b) wastewater.

The effect of sludge age on P removal is complex. For sludge age o3 days, the P removal increases with increase in sludge age and for sludge age 43 days, P removal decreases with increase in sludge age. The reason for this is that an increase in sludge age causes an increase in the system OHO mass, which in turn causes an increase in fermentable RBO conversion and, therefore, an increase in P release, P uptake, and P removal. However, the increased sludge age also causes a decrease in PAO biomass, its associated P content, due to endogenous respiration which decreases the P removal. At sludge age o3 days, the former effect dominates the P removal, while at sludge age 43 days the latter dominates. The decrease in both PAO biomass with increase in sludge age is crucially affected by the specific endogenous mass loss rate of the PAOs – should the endogenous mass loss rate of the PAOs (0.04 d1) have been the same as that of the OHOs (0.24 d1), virtually no BEPR would have been obtained. The effect of anaerobic mass fraction (fxana) on P removal also is shown in Figures 57(a) and 57(b). For a selected sludge age, an increase in fxana always gives rise to an increase in P removal. This is due to the increased conversion of fermentable RBO with larger anaerobic mass fractions. The improvement in P removal, however, diminishes with each step increase in fxana, due to the first-order nature of the RBO conversion kinetics. From Figures 57(a) and 57(b), it can be seen that for fxana 40.15 only small additional increases in P removal are obtained, which usually are not justified due to the decrease in unaerated sludge mass fraction this causes and the consequent impact on the minimum sludge age for nitrification.

4.14.33.2 Raw and Settled Influent The effect of primary settled wastewater on P removal can be seen by comparing Figures 57(a) and 57(b), which show the P removal for the raw wastewater of original COD of 750 mgCOD l1 and the settled wastewater produced from the raw wastewater with a 450 mgCOD l1. It can be seen that although the P removal per mg influent COD is higher for the settled WW, the P removal in mgP l1 is lower. This decrease is due to the decrease in the flux of biodegradable COD entering

the AS system which causes a reduction in OHO biomass and hence in the fermentable RBO converted and in the mass of PAOs generated. The P removal per influent COD entering the biological reactor is higher for the settled because the fraction of the biodegradable organics that is RBO (Sbsi/Sbi) is higher for settled than for unsettled wastewater because primary settling removes only the settleable organics (although not strictly true, RBO loss or gain in primary settling appears to be small; it is assumed that the RBO is not changed during primary settling).

4.14.33.3 Influence of Influent RBO Fraction Assuming zero discharge of nitrate to the two-compartment anaerobic reactor of mass fraction (fxana) of 0.15, the effect of the influent RBO fraction (fSb’s ¼ Sbsi/Sbi) is illustrated in Figures 58(a) and 58(b) for raw and settled wastewaters, respectively. At any anaerobic mass fraction, the higher the influent RBO fraction, the higher the P removal. In design, one option to improve the P removal is supplementation of influent RBO by, for example, acid fermentation of primary sludge (Pitman et al., 1983; Barnard, 1984; Osborn et al., 1989) or dosing other RBO or VFA into the anaerobic reactor.

4.14.33.4 Influence of Recycling Nitrate and Oxygen to the Anaerobic Reactor The influence of nitrate recycled to the anaerobic reactor is illustrated in Figures 59(a) and 59(b) which show P removal versus nitrate concentration recycled to the anaerobic reactor in a recycle ratio 1:1. Clearly, in agreement with numerous experimental and full-scale NDBEPR systems, recycling nitrate has a markedly deleterious influence on the magnitude of P removal. As the nitrate concentration recycled to the anaerobic reactor increases, the P removal decreases. The same applies to oxygen entering the anaerobic reactor, except that its effect is 1/2.86 times that of nitrate because the oxygen equivalent of nitrate is 2.86 mgO/mgNO3-N. If oxygen and/or nitrate are recycled to the anaerobic reactor, the OHOs no longer convert fermentable RBO to VFAs but instead themselves utilize it for energy and growth with the oxygen or nitrate as external electron acceptor. For every 1

Biological Nutrient Removal

0.02

13.5

9.0

0.05

fxana

0.01

4.5

0.0

0.00 0.1

0.2

0.3

0.4

0.5

22.5

Influent P

0.02

15.0 0.05

fxana 0.01

0.6

7.5

0.0

0.0 0

Influent RBO fraction (fSb’s)

(a)

0.03

0.00

0.0 0

30.0

0.25 0.20 0.15 0.10

Raw WW

P removal (mgP I−1)

Influent P

0.03

0.04

18.0

0.25 0.20 0.15 0.10

P removal (mgP/mg Infl COD)

Settled WW

P removal (mgP I−1)

P removal (mgP/mg Infl COD)

0.04

517

0.1

0.2

0.3

0.4

Influent RBO fraction (fSb’s)

(b)

Figure 58 Predicted P removal vs. readily biodegradable COD (RBCOD, Sbsi) as a fraction of the biodegradable COD (Sbi) fSb’s ¼ Sbsi/Sbi) for various anaerobic mass fractions (fxana) for a two-compartment anaerobic reactor BEPR system at 20 days sludge age, treating the example raw (a) and settled (b) wastewater.

0.04

0.25 0.20 0.15 0.10

0.02

22.5 Influent P

15.0

0.05

fxana

0.01

7.5

0.0

0.00 (a)

8 2 4 6 Nitrate in recycle (NO3−N I−1)

0.25 0.20 0.15 0.10

0.03

Influent P

13.5

0.02

9.0 0.05

fxana 0.01

4.5 0.0

0.00

0.0 0

18.0 Settled WW

10

0.0 0

(b)

P removal (mgP I−1)

0.03

P removal (mgP/mg Infl COD)

30.0 Raw WW P removal (mgP I−1)

P removal (mgP/mg Infl COD)

0.04

2 4 6 8 Nitrate in recycle (NO3−N I−1)

10

Figure 59 Predicted P removal vs. nitrate concentration in recycle to anaerobic (recycle 1:1) for various anaerobic mass fractions (fxana) for a twocompartment anaerobic reactor BEPR system at 20 days sludge age treating the example raw (a) and settled (b) wastewater.

mgO2 and 1 mgNO3-N recycled to the anaerobic reactor, 3.0 and 8.6 mgCOD, respectively, are utilized (Equation (175)). Consequently, allowing oxygen and/or nitrate to enter the anaerobic reactor reduces the flux of VFAs available to the PAOs for storage, and correspondingly reduces the P release, P uptake, and P removal. From Figures 59(a) and 59(b), when the nitrate concentration in the recycle exceeds about 12 mgN l1, the P removal decreases to 4 (raw) and 2.2 mgP l1 (settled) which is the same as that of an ND system (fxana ¼ 0) with zero BEPR. In this situation, all the influent RBO is denitrified by the OHOs with the result that no VFAs are released and no VFAs are available to the PAOs, and BEPR no longer takes place – the P removal obtained is due to wastage of sludge with normal metabolic P content (0.03 mgP/mgVSS). If the influent RBO concentration increases or decreases, the concentration of recycled nitrate that completely consumes the RBO will increase or decrease correspondingly below about 12 mgN l1 (provided the recycle ratio remains unchanged). Clearly, one of the principal orientations in any design for BEPR is to minimize oxygen entrainment and nitrate recycling to the anaerobic reactor. To achieve this in situations where nitrification is obligatory or unaviodable, a number of different system configurations have been developed (Figure 47).

4.14.33.5 Subdivision of the Anaerobic Reactor into Compartments The effect of subdividing the anaerobic reactor into compartments is shown in Figures 60(a) (raw) and 60(b) (settled). Increasing the anaerobic reactor from a single completely mixed one to two compartments in series significantly improves the P removal, but increasing the number of compartments to greater than 3 yields little additional increase. This increase is due to the increased fermentable RBO conversion with in-series anaerobic reactor operation as a result of the first-order nature of the conversion kinetics. For design, at least two equal-sized in-series anaerobic reactors should be used.

4.14.34 Denitrification in NDBEPR Systems 4.14.34.1 Introduction Because usually N removal is also a requirement for BNR systems, nitrification is included and hence also denitrification to benefit from its advantages (Section 4.14.24). In the steady mixed culture BEPR model, the nitrate recycled to the anaerobic reactor needs to be known considering the adverse

Biological Nutrient Removal

0.02

0.10 0.05

22.5

15.0

Influent P

fxana

0.01

7.5

0.0

2

4 3 No anaerobic compartments

18.0

0.25 0.20

0.03

13.5 0.15 0.10

0.02

Influent P

9.0

0.05

fxana

0.01

4.5

0.0

0.00

0.0 1

Settled WW

5

0.0 1

(b)

P removal (mgP I−1)

0.25 0.20 0.15

0.03

0.00 (a)

0.04

30.0 Raw WW

P removal (mgP/mg Infl COD)

P removal (mgP/mg Infl COD)

0.04

P removal (mgP I−1)

518

2

4 3 No anaerobic compartments

5

Figure 60 Predicted P removal vs. the number of compartments in the anaerobic reactor for various anaerobic mass fractions (fxana) in a BEPR system at 20 days sludge age treating the example raw (a) and seattled (b) wastewater.

influence of recycling nitrate to the anaerobic reactor on P removal. Indeed, one of the principal orientations in the design procedure for P removal is to prevent nitrate recycling. Where nitrification is not required, this can be suppressed in a simple configuration such as the Phoredox (or the A/O) system (Figure 47(a)) but this option may not viable in some countries because nitrification is either obligatory or unavoidable due to high wastewater temperature. Accordingly, reliable and accurate quantification of denitrification in NDBEPR systems is essential not only for security in P removal but also for estimating the N removal by the NDBEPR system. The early approach (1976–85) to quantify denitrification in NDBEPR systems was to use the theory and procedures for ND systems, as set out in Sections 4.14.17–4.14.27 (Nicholls, 1982; Ekama et al., 1983; WRC, 1984). Experimental data indicated that this approach appeared to predict the observed denitrification quite closely. However, from the mechanisms for BEPR, which emerged later, this approach was theoretically inconsistent. The influent RBO was apparently used twice – first in the anaerobic reactor where it is converted to VFAs which are taken up and stored as PHA by the PAOs, and again in the primary anoxic reactor for denitrification via the K1 rate (Section 4.14.25.1). This situation is possible only if the PAOs denitrified significantly using most of their internally stored PHA with nitrate as electron acceptor in the anoxic reactor. This implies that the most of the P uptake should take place in the primary anoxic reactor. However, this was not usually observed. Practically, all (490%) the P uptake took place in the aerobic reactor of the UCT NDBEPR systems operated during the 1980s (Wentzel et al., 1985, 1990).

4.14.34.2 Experimental Basis for Denitrification Kinetics in NDBEPR Systems Clayton et al. (1991) undertook an experimental investigation into the denitrification kinetic behavior in mixed culture NDBEPR systems. A laboratory-scale modified UCT system (Figure 47(e)) was set up and operated for a year and a half. For the first 6 months, the first primary anoxic reactor was a plugflow reactor, thereafter a completely mixed one. The response of the system was monitored daily and profiles on the

plugflow primary anoxic reactor measured periodically. In addition, a variety of anoxic batch tests that reproduced the conditions in primary and secondary anoxic reactors were conducted on mixed liquor harvested from the MUCT system. In the plugflow reactor and batch tests, all the important parameters were measured to delineate the behavior of the OHOs and PAOs. No differences in the concentration time profiles from the plugflow reactor and batch tests were noted. From these tests: (1) Under the steady-state conditions of the MUCT system, the general denitrification formulation for ND systems dNO3/ dt ¼  KXBH applied also to NDBEPR systems. (2) In the primary anoxic reactor, (1) the rapid rate of denitrification associated with RBO in ND systems (K0 1, Figure 35; the K0 rate here for NDBEPR systems is used to distinguish it from the K rate in ND systems) was usually absent or of very short duration, (2) the slower rate of denitrification associated with BPO (K0 2) continued over the entire duration of the plugflow retention time or batch test (as in ND systems) but its rate was approximately 2 1/2 times faster than in the primary anoxic reactor of ND systems, i.e., 0.224 mgNO3-N/(mgOHOVSS d), where the OHOVSS concentration was calculated from the experimental system data with the ND model, not the BEPR model, that is, ignoring the reduction in OHOVSS due to the presence of the PAOs (Clayton et al., 1991). Based on the BEPR model, which yields lower OHOVSS due to the presence of PAOs, the K0 2 rate would be even higher compared with ND systems (Table 17). (3) In the secondary anoxic reactor, the denitrification rate (K0 3) was approximately 1 1/2 times the rate measured in secondary anoxic reactors of ND systems (K3, Figure 35(b)), also based on the ND model (Table 17). Clayton et al. (1991) proposed three possible explanations for the increased denitrification rate constant K0 2 observed in the primary anoxic zone of NDBEPR systems: 1. PAOs can denitrify, thereby contributing to the denitrification rate by utilizing the intracellular PHB acquired in the anaerobic zone. 2. PAOs cannot denitrify and the influent BPO is modified in the anaerobic zone to a more readily hydrolyzable form, thereby inducing a faster denitrification rate by the OHO in the primary anoxic reactor.

Biological Nutrient Removal

519

Table 17 Specific denitrification rates (K) at 20 1C observed by Clayton et al. (1991) in the MUCT NDBEPR system based on the WRC (1984) and Wentzel et al. (1990) models compared with those in ND systems (WRC, 1984) System

NDBEPR systems based on ND model

NDBEPR systems based on BEPR model

ND systems based on ND model

Units

mgNO3–N/(mgVSS d)

mgNO3–N/(mgAHVSS d)

mgNO3-N/(mgAHVSS d)

Primary anoxic

K10 ¼ 0.61a K20 ¼ 0.224 K30 ¼ 0.100

K10 ¼ 0.70a K20 ¼ 0.255 K30 ¼ 0.114

K1 ¼ 0.720 K2 ¼ 0.101b K3 ¼ 0.072

Secondary anoxic a

Denitrification by this rate contributes negligibly to the N removal of the NDBEPR system. In single-reactor intermittent aeration ND systems Warburton et al. (1991) obtained K2 ¼ 0.128 at 20 1C.

b

Denitrification in N removal systems Primary anoxic reactor

Aerobic reactor

Denitrification in BioP removal systems

Secondary anoxic Reaeration reactor reactor

Mixed liquor recycle a Influent

Waste flow Settler Effluent Sludge recycle s

Influent RBCOD + SBCOD

K1 + K2 K2

Influent SBCOD only

K3

Endogenous SBCOD only

Anaerobic Anoxic reactor reactors

Aerobic reactor

Mixed liquor recycle Influent

Secondary anoxic reactor Waste flow Settler

a

r

Effluent RBCOD acidified and taken up by PAOs No initial rapid K1 rate

Sludge recycle s

K 2′

Influent SBCOD only

K 3′

Endogenous SBCOD only

Figure 61 Comparison of steady-state specific denitrification rates (K, mgNO3-N/(mgOHOVSS d)) in the primary and secondary anoxic reactors of ND (a) and NDBEPR (b) systems. The rates are compared in Table 15.

3. PAOs cannot denitrify and the BPO is not modified in the anaerobic zone but a higher rate of BPO hydrolysis/utilization is stimulated in the OHOs in NDBEPR systems by the anaerobic–anoxic–aerobic sequencing. The PHB concentrations measured in the (1) anaerobic, anoxic, and aerobic zones of the MUCT parent system; (2) anoxic batch tests on mixed liquor harvested from the MUCT system; and (3) anoxic batch tests on mixed liquor harvested from the enhanced PAO cultures of Wentzel et al. (1988, 1989a) demonstrated that PHB did not serve as a substrate source for denitrification (negligible decrease). Therefore, the PAOs did not contribute significantly to the K’2 denitrification rate in the primary anoxic reactor and so cause (1) had to be rejected. This conclusion was supported from the observation that in the mixed and enhanced culture systems and the batch tests, the P uptake was predominantly (490%) aerobic – negligible anoxic P uptake was observed. If the anaerobic reactor pretreats the influent BPO to a more readily utilizable form, then the K denitrification rates should be lower when the NDBEPR sludge is mixed with influent wastewater. Batch tests on sludge from the MUCT system fed the same wastewater as the parent system, yielded the same high K0 2 denitrification rates. Therefore, the anaerobic reactor did not modify the BPO to a more utilizable form. So cause (2) was rejected and default cause (3) had to be accepted. No experimental means was devised to test this third

cause. However, it did at least provide a consistent explanation also for the higher K0 3 in the secondary anoxic reactor – causes (1) and (2) explain only a higher K0 2 rate. A comparison between the denitrification rates in the ND and NDBEPR systems is shown in Figure 61. Because the PAOs did not significantly contribute to the denitrification, the K0 rates had to be recalculated so that the denitrification process in NDBEPR systems is correctly ascribed to the OHO group performing it. The proportion of OHOs in the VSS of NDBEPR systems is smaller than in ND systems (Figure 55) because the PAOs obtain most of the influent RBO. Ekama and Wentzel (1999a) calculated the OHO fraction (favOHO) for NDBEPR systems iteratively with the aid of the steady-state BEPR model (Section 4.14.31) using the measured value for the influent RBO fraction and varying the influent UPOs fraction (UPOCOD, fS’up) until the calculated system VSS mass, now comprising active and endogenous PAO and OHO components and unbiodegradable particulate VSS from the influent, matched that measured. When the correct fS’up had been found, the calculated P removal was matched to that measured by changing the PAO P content (fXBGP) from the enhanced PAO culture value of 0.38 mgP/mgPAOVSS. For MUCT system of Clayton et al. (1991), the recalculated average VSS fractionation results are fS’up ¼ 0.15, favOHO ¼ 0.21, and fXBGP ¼ 0.388 mgP/mgPAOVSS. Because the favOHO based on ND model (Section 4.14.9.4) was 0.24, on average the K0 rates were 0.24/0.21 ¼1.14 or 14%

520

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higher (Table 17). Following this calculation procedure, Ekama and Wentzel (1999a) also calculated the fS’up, favOHO, fXBGP, and K0 2 for four other UCT investigations, viz., Musvoto et al. (1994), Pilson et al. (1995), Sneyders et al. (1997), and Mellin et al. (1997). For the same wastewater source, reasonably consistent fS’up values are expected. For fully aerobic and ND systems, this has been the case in the UCT laboratory. For the Mitchells Plain unsettled wastewater usually fed to the experimental systems, this value was found to be around 0.12 for widely differing aerobic and ND systems, e.g., 0.10870.052 for aerobic systems (Mbewe et al., 1994), and 0.13570.060 (Warburton et al., 1991) and 0.1270.04 (Ubisi et al., 1997a, b) for anoxicaerobic systems. However, for NDBEPR systems, this was not the case. Not only was fS’up higher for NDBEPR systems fed the Mitchells Plain unsettled wastewater, it also varied widely in the different NDBEPR systems, from 0.0470.055 (Sneyders et al., 1997) to 0.29370.063 (Musvoto et al., 1994). Because of the method calculating the fS’up fraction, by reconciling the calculated VSS mass with the measured VSS mass, the variation in fS’up changes the favOHO. This, in turn, affects the K0 2 and K0 3, rates, which are higher for higher fS’up and lower for lower fS’up (Ekama and Wentzel, 1999b). Clearly, there are factors that affect the sludge production per unit COD load in the NDBEPR system that the models do not recognize. Two such factors appear to be the unaerated sludge mass fraction (fxt) and sludge settleability (measured as diluted sludge volume index, DSVI). The higher the fxt, the higher the fS’up, which could be due to an accumulation of undegraded BPO in the system. If this were the only factor, then the method of calculating fS’up and favOHO would be acceptable because undegraded BPO in effect is unbiodegradable particulate organics. However, this is not the only factor because systems with the same fxt yielded different fS’up and favOHO values depending on the DSVI (Musvoto et al., 1994; Casey et al., 1994a, 1994b). As the DSVI and hence AA (low F/M) filament abundance increased, so the system VSS mass decreased and vice versa. The calculated K0 rates varied accordingly, decreasing as the system VSS mass increased and vice versa. No explanation for this variation with DSVI can be advanced. The NDBEPR models, both steady-state (e.g., Wentzel et al., 1990) and dynamic simulation (e.g., ASM2, Henze et al., 1995), are extensions of their predecessors (WRC, 1984, ASM1; Henze et al., 1987) by including the kinetics of BEPR. Relatively few interactions between the ND and BEPR processes take place in these models, the main ones being that (1) the VFAs for the PAOs are generated by the OHOs in the anaerobic zone from the influent RBO, and more importantly, (2) the reduction factor for the BPO hydrolysis/utilization rate, Z, is increased from 0.33 in ASM1 (and UCTOLD, Dold et al., 1991) to 0.60 in ASM2 (and UCTPHO, Wentzel et al., 1992) to account for the increased K0 2 and K0 3 rates observed by Clayton et al. (1991). Insofar as the BEPR kinetics in the ASM2 and UCTPHO models are concerned, P release and uptake occur exclusively in the anaerobic and aerobic reactors respectively, in conformity with the observations of Siebritz et al. (1983), Wentzel et al. (1985, 1989b, 1990), Clayton et al. (1991), and Sneyders et al. (1997). Therefore, for the last two mentioned investigations, given the correct input fS’up and Z values, the NDBEPR models will satisfactorily predict the

performance of the M/UCT systems. However, in three other investigations, viz., Musvoto et al. (1994), Pilson et al. (1995), and Mellin et al. (1997), the P release, P uptake, and P removal behavior were significantly different to that observed on which the models are based. Not only was the excess P removal depressed at about 60% of that expected from the model of Wentzel et al. (1990), but also the P release to removal ratio was decreased. With the depressed P removal, significant P uptake took place in the (second) anoxic reactors of the MUCT systems. This was confirmed in the anoxic batch tests; whereas in the tests of Clayton et al. (1991) and Sneyders et al. (1997) negligible anoxic P uptake took place, in those of Mellin et al. (1997) significant (B40%) P uptake took place. The significant decrease in BEPR with anoxic P uptake BEPR was subsequently confirmed by Vermande et al. (2002) in parallel aerobic P uptake BEPR and anoxic P uptake BEPR systems. It is possible that different species of PAOs find a niche in the systems that can accomplish anoxic P uptake, but which have lower RBCOD to P release, P release to P removal, and fXBGP ratios. Biochemical assays have indicated that some PAOs can denitrify (Lo¨tter, 1985; Lo¨tter et al., 1986) and even anaerobic–anoxic (no aerobic) BEPR systems have been operated successfully (Kuba et al., 1993). Also, in several other studies significant anoxic P uptake has been observed (e.g., Vlekke et al., 1988; Kerrn-Jespersen and Henze, 1993; Bortone et al., 1996; Kuba et al., 1996; Kuba and van Loosdrecht, 1996; Hu et al., 2000, 2007a, 2007b). Denitrification by PAOs is included in the biochemical model of Wentzel et al. (1986, 1991) but is not included in ASM2 (Henze et al., 1995) and UCTPHO (Wentzel et al., 1992) kinetic models. Anoxic P uptake behavior of PAOs has been included in ASM2d (Henze et al., 1999) but it merely allows the P uptake to commence in the anoxic reactor without changing the P uptake, that is, anoxic P uptake is modeled with the same stoichiometry and kinetics as aerobic P uptake, which clearly is not observed experimentally. Realistic anoxic P uptake behavior of PAOs therefore cannot be simulated with current suite of IWA ASM models. Proposals to include PAO denitrification have been made (e.g., Mino et al., 1995; Barker and Dold, 1997; Hu et al., 2007a, 2007b), with varying success. One of the main problems with modeling anoxic P-uptake BEPR is that the triggers that stimulate it are not well understood. Hu et al. (2002) conclude that anoxic P-uptake BEPR is undesirable in NDBEPR systems due to the reduction in P removal per influent RBO it causes. For maximum BEPR with (usually) limited influent RBO, aerobic P-uptake BEPR is required. Large aerobic mass fractions (fxto0.5) and underloaded primary anoxic reactors with nitrate appear to favor aerobic P-uptake BEPR.

4.14.34.3 Denitrification Potential in NDBEPR Systems The denitrification potential is the maximum amount of nitrate per liter influent flow that can be removed by biological means in the anoxic reactors. As the experimental investigation into denitrification kinetics in NDBEPR systems indicated that the formulation

dNO3 =dt ¼ KXBH

Biological Nutrient Removal

developed for ND systems can also be applied to NDBEPR systems, the techniques set out in Section 4.14.25.2 to develop equations for denitrification potential in ND systems can be followed for NDBEPR systems also. For development of these equations, the experimental observation that the PAOs do not denitrify is accepted. Denitrification in the primary anoxic reactor is via utilization of any RBO leaking through the anaerobic reactor, and BPO. Procedures to determine the amount of RBO leaking through the anaerobic reactor to the primary anoxic reactor were set out in Section 4.14.31.1.4, where SbsfN is the concentration of FRBO exiting the last anaerobic compartment. Hence, SbsfN (1 þ recycle ratio) is the concentration FRBO per liter influent flow exiting the anaerobic reactor and available for denitrification in the primary anoxic reactor by OHOs. Accordingly, the denitrification potential in the primary anoxic reactor (Dp1) can be expressed as

Dp1 ¼ SbsfN ð1 þ rÞð1  f cv YH Þ=2:86 þ K02 XBH Rnp

ð205Þ

Following the procedures set out in Section 4.14.26.3, Equation (205) can be modified and simplified to give

Dp1 ¼ SbsfN ð1 þ rÞð1  f cv YH Þ=2:86 þ f x1 K02T ðSbOHO ÞYH Rs =ð1 þ bHT Rs Þ ðmg NO3 -N l1 influentÞ ¼ a0 þ f x1 K02T b0

ð206Þ

where fx1 is the primary anoxic sludge mass fraction and a0 ¼ SbsfN ð1 þ rÞð1  f cv YH Þ=2:86 and b0 ¼ ðSbOHO ÞY H Rs = ð1 þ bHT Rs Þ. In Equation (206), it is assumed that the initial rapid rate of denitrification (K0 1T) on FRBO leaking through the anaerobic reactor, SbsfN(1 þ r) is always complete, that is, the actual retention time in the primary anoxic reactor is longer than the time required to utilize this usually low concentration FRBO. As with ND systems, an equation can be developed to determine the minimum primary anoxic mass fraction f’x1min to deplete this RBO. This minimum will be a very low value (o0.05) which is much smaller than the primary anoxic reactors in NDBEPR systems, so generally Equation (206) is valid. However, Equation (206) is not without complication. To calculate the primary anoxic denitrification potential (Dp1), the concentration of RBO in the outflow from the anaerobic reactor (SbsfN) is required. To calculate SbsfN, the concentration of nitrate recycled to the anaerobic reactor is required which in turn requires Dp1 to be known. This problem is overcome by assuming initially that the nitrate concentration exiting the primary anoxic reactor is zero, as was done for the primary anoxic reactor of the MLE system in Section 4.14.25.2, which for the UCT systems means zero nitrate discharge to the anaerobic reactor. For brevity, other NDBEPR configurations are not considered in this chapter. However, if required, the denitrification potential of the secondary anoxic reactor is found using the principles set out in Section 4.14.25.2, viz.,

Dp3 ¼ f x3 K03T ðSbOHO Þ YH Rs =ð1 þ bHT Rs Þ ¼ f x3 K03T b0

ðmgNO3 -N l1 influentÞ

where fx3 is the secondary anoxic sludge mass fraction.

ð207Þ

521

Equation (207) applies to secondary anoxic reactors situated both in the mainstream (e.g., five-stage Bardenpho) and in the underflow recycle (e.g., JHB system). However, in applying Equation (207) to secondary anoxic reactors situated in the underflow recycle, care must be taken in evaluating fx3, because the mixed liquor concentration is increased by a factor (1 þ s)/s in the underflow anoxic reactor compared with the mainstream reactors.

4.14.34.4 Principles of Denitrification Design Procedures for NDBEPR Systems In NDBEPR systems design is oriented to achieve in a single sludge system COD removal, N removal (ND), and P removal (BEPR). Conflict between the last two objectives may arise, for example, the proportion of the total unaerated sludge mass assigned to the anoxic reactor(s) (N removal) and the anaerobic reactor (P removal). For each design, the priorities for treatment need to be assessed and a compromise reached to optimize the system for the particular effluent quality required. Because P is the element that is the main driver for eutrophication, for most designs of NDBEPR systems the focus is on BEPR with denitrification as a secondary design priority. Accordingly, the principle in denitrification design for NDBEPR systems is to ensure that the anaerobic reactor is protected from recycling of nitrate, which causes a disproportionate decrease in the magnitude of P removal (Figure 59). This principle guides selection of the system configuration (five-stage modified Bardenpho, JHB, and M/ UCT; Figure 47) and provides a starting point for sizing the anoxic reactors. When selecting a system configuration for BEPR, it is necessary to establish whether complete denitrification can be achieved. For the wastewater characteristics (i.e., influent TKN and COD concentrations (Nti and Sti)), maximum specific growth rate of the nitrifiers at 20 1C (mAm20), and the average minimum water temperature, the maximum unaerated sludge mass fraction (fxm) and the nitrification capacity (Nc) can be calculated for a selected sludge age (Rs) (Section 4.14.20.3). This fxm needs to be divided between anaerobic (for BEPR) and anoxic (for denitrification) mass fractions. Consequently, the maximum anoxic sludge mass fraction (fxdm) is the difference between the maximum unaerated mass fraction (fxm) and the selected anaerobic sludge mass fraction (fxa), that is,

f xdm ¼ f xm  f xa

ð208Þ

where fxm is given by Equation (136) for a selected Rs, mAm20, Sf, and Tmin. The fxdm then can be subdivided between primary and secondary anoxic sludge mass fractions (fx1 and fx3) and this division fixes the denitrification potential of these two reactors (Dp1 and Dp3) and hence also of the system. If the denitrification potential of the system exceeds the nitrification capacity (i.e., Dp1 þ Dp34Nc), then complete denitrification is possible and the secondary anoxic reactor can be situated in the mainstream, that is, a five-stage Bardenpho system can be selected. If complete denitrification is not possible, then depending on the magnitude of the effluent nitrate

522

Biological Nutrient Removal

concentration, the underflow (s) recycle cannot be discharged directly to the anaerobic reactor. If the nitrate concentration is low (o3 mgN l1), the secondary anoxic sludge mass fraction (fx3) can be combined with the primary anoxic sludge mass fraction to form a three-stage Bardenpho, which, with a higher a recycle ratio, may produce a lower nitrate concentration in the sludge underflow (and effluent) than the fivestage Bardenpho because the K0 2 rate is higher than the K0 3 (Table 17). If the nitrate concentration is high (43 mgN l1), the secondary anoxic reactor can be moved into underflow recycle to form the JHB system, in which event the denitrification potential of the secondary anoxic reactor (Dp3) must exceed the nitrate and oxygen loads via the underflow s recycle. If this requirement is not met, nitrate will leak through the underflow secondary anoxic reactor to the anaerobic reactor. In this event, since the denitrification potential of the primary anoxic reactor (Dp1) is greater than that of the secondary anoxic reactor (Dp3) for equal anoxic mass fractions, incorporation of a secondary anoxic reactor becomes an inefficient utilization of anoxic mass fraction, and the secondary anoxic mass fraction is added to the primary anoxic reactor, the underflow s recycle needs to be denitrified in the primary anoxic reactor to form the M/UCT system. With each change of configuration, more nitrogen removal is sacrificed (i.e., the effluent nitrate concentration increases) to protect maximum BEPR, that is, zero or very low nitrate discharge to the anaerobic reactor.

4.14.34.5 Analysis of Denitrification in NDBEPR Systems Analysis of the denitrification behavior in the NDBEPR system is essentially the same as for the ND system (Section 4.14.26.3) except: (1) The maximum anoxic mass fraction for denitrification (fxdm) for the NDBEPR system is given by Equation (208), whereas fxdm for the ND system is given by Equation (166). Hence, for the same maximum unaerated sludge mass fraction (fxm), the NDBEPR system has a lower fxdm than the ND system, by an amount equal to fxa. (2) The specific denitrification rates for ND systems (K2 and K3) are substituted with the rates for NDBEPR systems (K0 2 and K0 3, Table 17). (3) The denitrification potentials for the primary and secondary anoxic reactors are modified from Equations (163) and (164) for ND systems to Equations (206) and (207) for the NDBEPR system to take account of the uptake of COD by the PAOs in the anaerobic reactor, the zero denitrification by the PAOs, and the faster OHO denitrification rates in NDBEPR systems. Taking account of the above, denitrification equations can be developed for all the NDBEPR configurations (Figure 47). However, in the interests of brevity, only the UCT configuration will be considered.

4.14.35 Denitrification in the UCT System In the UCT system the denitrification behavior is very similar to that in the MLE system, because the a and s recycles discharge into the primary anoxic reactor, so that taking due account of the effect of incorporating the anaerobic reactor, the design equations and procedures developed for the MLE system can be applied to the UCT system. Since complete

denitrification is not possible in the UCT system (high effluent nitrate concentration), the entire anoxic mass fraction (fxdm) available is used as a primary anoxic reactor (fx1). As in the MLE system, the a and s recycle ratios determine the split of the nitrate generated in the aerobic reactor (nitrification capacity, Nc) between the primary anoxic reactor and the effluent. The a recycle ratio is selected so that the equivalent nitrate load on the primary anoxic reactor via the a and s recycles is equal to its denitrification potential (Dp1). For a selected s recycle ratio, the a recycle ratio that loads the primary anoxic reactor via to its denitrification potential is the optimum a recycle ratio (aopt). The denitrification potential of the (primary) anoxic reactor (Dp1) is found from Equation (206) with fx1 ¼ fxdm. Following the same reasoning as in Section 4.14.26.3, the optimum a recycle ratio (aopt) is given by Equation (169), with the proviso that the Nc and Dp1 are applicable to NDBEPR systems, that is, Nc is lower due to the higher sludge production (Section 4.14.31.5.2) and Dp1 is based on Equation (206) with K0 2. As for the MLE system, at a ¼ aopt, Equation (170) gives the minimum effluent nitrate concentration (Nne) achievable. Equation (170) is valid for all aoaopt because for all aoaopt the assumption on which Equation (169) is based is valid, that is, zero nitrate concentration exiting the primary anoxic reactor. If the system is operated with a4aopt, the equivalent nitrate load on the primary anoxic reactor via the a and s recycles exceeds the denitrification potential and nitrate will also be recycled via the r recycle to the anaerobic reactor, to the detriment of BEPR. Furthermore, if nitrate does leak through the primary anoxic reactor, then the nitrate concentration in the outflow from the primary anoxic reactor no longer is zero, and consequently, Equation (170) for the effluent nitrate concentration (Nne) is no longer valid. Equations for the effluent nitrate concentration for a4aopt can be derived by following the principles applied above for aoaopt, but are not considered because aoaopt is required for zero discharge of nitrate to the anaerobic reactor and maximum BEPR. If Equation (169) yields aopt ¼ 0, then the equivalent nitrate load via the s recycle is sufficient to match the denitrification potential of the primary anoxic reactor; if aopto0, the equivalent nitrate load via the s recycle exceeds Dp1 and nitrate will be recycled via the r recycle to the anaerobic reactor. The implication of this is that the Nc that gives aopt ¼ 0 represents the upper limit (equivalently the maximum influent TKN/ COD concentration ratio) that the UCT system is able to treat and still protect the anaerobic reactor against nitrate entry. All Nc (equivalently influent TKN/COD ratios) above this limit will result in nitrate recycle to the anaerobic reactor, which cannot be controlled in the UCT system (a ¼ 0) except by reducing the s recycle ratio. From the above, the minimum a recycle ratio is a ¼ 0. The maximum a recycle ratio (amax) is determined by some practical upper limit (aprac), usually in the range 5–6, beyond which the higher pumping costs outweigh the small gain in lower effluent nitrate concentration (see Section 4.14.26.3). However, for oxidation ditch type systems, or for systems with ‘‘through the wall’’ a recycles via low head high volume pumps, the a recycle ratio ( ¼ aopt) may be significantly higher than the aprac of 5–6. If aopt 4 aprac and aprac is selected, then the

Biological Nutrient Removal

primary anoxic reactor is oversized. This unused denitrification potential (Figure 38) can be kept (i.e., fx1 not decreased) as a factor of safety (for uncertainty if K0 rate) or the size of the fx1 of primary anoxic reactor reduced to match the its denitrification potential (Dp1) to the equivalent nitrate load, as was done for the balanced MLE system (Section 4.14.26.3.2), which will allow a reduction in sludge age. The procedure for the balance MLE sytem can be followed to determine the new sludge age. All the aspects discussed in Sections 4.14.11 and 4.14.14– 4.14.16 regarding reactor concentration selection, system design and control, selection of sludge age, and treatment of the primary and/or secondary sludge produced also apply to NDBEPR systems and should be referred to there.

4.14.36 Conclusion The ND and NDBEPR models such as IWA ASM1 and 2 (Henze et al., 1987, 1995), UCTOLD (Dold et al., 1991), and UCTPHO (Wentzel et al., 1992) are very helpful for biological nutrient removal process description and simulation. However, models always need to be used with great circumspection and experience of real systems. It would appear that the ND models such as ASM1 and UCTOLD give an acceptably reliable description of the ND AS systems – the model predictions compare favorably with observed results and the wastewater characteristic, stoichiometric, and kinetic constants in the models to achieve this are reasonably consistent. For these models some scientific maturity is apparent, where the default kinetic and stoichiometric constants predict the performance of an ND system with acceptable risk of deviation. For the NDBEPR models, this is not the case. The experiments described in the literature point to three important observations in real NDBEPR systems not recognized in NDBEPR models that model users need to be aware of for prudent and proper application: that is, (1) the large variation in the unbiodegradable particulate COD fraction (fS’up) and hence the OHO active fraction (favOHO) and denitrification rate (K0 2); (2) the large variation in biological P removal behavior and P content of PAOs (fXBGP) with anoxic P-uptake BEPR stimulated in some systems for reasons not well defined yet; and (3) the unaccounted loss of influent COD in NDBEPR systems, in that even in carefully controlled laboratory systems, only 75–85% of the influent COD can be recovered in a COD mass balance (Ekama et al., 1999a,b).

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Pitman AR, Vandalsen L, and Trim BC (1988) Operating experience with biological nutrient removal at the Johannesburg Bushkoppie works. Water Science and Technology 20(4–5): 51--62. Pitman AR, Venter SLV, and Nicholls HA (1983) Practical experience with biological phosphorus removal plants in Johannesburg. Water Science and Technology 15(3/4): 233--259. Poinapen J and Ekama GA (2010) Biological sulphate reduction using primary sewage sludge in a upflow anaerobic sludge bed reactor–Part 5: Development of a steady state model. Water SA 36(2): 193--202. Randall AA, Benefield LD, and Hill WE (1994) The effect of fermentation products on enhanced biological phosphorus removal, polyphosphate storage, and microbial population dynamics. Water Science and Technology 30(6): 213--219. Randall CW, Marshall DW, and King PH (1970) Phosphate release in activated sludge process. Journal of the Sanitary Engineering Division, ASCE 96(SA2): 395--408. Randall EW, Wilkinson A, and Ekama GA (1991) An instrument for the direct determination of oxygen utilization rate. Water SA 17(1): 11--18. Rensink JH, Donker HJGW, and Vries HPD (1981) Biological P-removal in domestic wastewater by the activated sludge process. In: Proceedings of the 5th Europe Sewage and Refuse Symposium, Munich. European Water Association, Hennep, D53773, Germany or International Solid Waste Association (ISWA), Vienna, A-1080, Austria. Richard MG, Jenkins D, Hao O, and Shimizu G (1982) The isolation and characterization of filamentous micro-organisms from activated sludge bulking. Progress Report No. 81-2. Berkeley: SERL, University of California. Riddell MDR, Lee JS, and Wilson TE (1983) Method for estimating the capacity of an activated sludge plant. Journal of the Water Pollution Control Federation 55(4): 360--368. Samson KA and Ekama GA (2000) An assessment of sewage sludge stability with a specific oxygen utilization rate (SOUR) test method. Water Science and Technology 42(9): 37--40. Satoh H, Mino T, and Matsuo T (1992) Uptake of organic substrates and accumulation of polyhydroxyalkanoates linked with glycolysis of intracellular carbohydrates under anaerobic conditions in the biological excess phosphate removal processes. Water Science and Technology 26(5–6): 933--942. Saunders AM, Mabbett AN, McEwan AG, and Blackall LL (2007) Proton motive force generation from stored polymers for the uptake of acetate under anaerobic conditions. FEMS Microbiology Letters 274(2): 245--251. Sehayek L and Marais GVR (1981) Supplementary phosphorus removal by side-line addition of lime in the activated sludge process. Research Report W40. Rondebosch: Department of Civil Engineering, University of Cape Town. Sen D, Mitta P, and Randall CW (1994) Performance of fixed film media integrated in activated sludge reactors to enhanced nitrogen removal. Water Science and Technology 30(11): 13--24. Setter LR, Carpenter WT, and Winslow GC (1945) Practical application of modified sewage aeration. Sewage Works Journal 17(4): 669. Seviour RJ, Mino T, and Onuki M (2003) The microbiology of biological phosphorus removal in activated sludge systems. FEMS Microbiology Reviews 27(1): 99--127. Shapiro J, Levin GV, and Humberto HZ (1967) Anoxically induced release of phosphate in wastewater treatment. Journal of the Water Pollution Control Federation 39: 1810--1818. Siebritz IP, Ekama GA, and Marais GVR (1980) Excess biological phosphorus removal in the activated sludge process at warm temperature climate. In: Proceedings of the Waste Treatment and Utilization 2, pp. 233–251, Toronto: Pergamon. Siebritz IP, Ekama GA, and Marais GVR (1983) A parametric model for biological excess phosphorus removal. Water Science and Technology 15(3/4): 127--152. Simpkins MJ and McLaren AR (1978) Consistent biological phosphate and nitrate removal in an activated sludge plant. Progress in Water Technology 10(5/6): 433--442. Smolders GJF, van der Meij J, van Loosdrecht MCM, and Heijnen JJ (1994a) Stoichiometric model of the aerobic metabolism of the biological phosphorus removal process. Biotechnology and Bioengineering 44(7): 837--848. Smolders GJF, van der Meij J, van Loosdrecht MCM, and Heijnen JJ (1994b) Model of the anaerobic metabolism of the biological phosphorus removal process: Stoichiometry and pH influence. Biotechnology and Bioengineering 43: 461--470. Smolders GJF, Meij Jvd, Loosdrecht MCMv, and Heijnen JJ (1995) A structured metabolic model for anaerobic and aerobic stoichiometry and kinetics of the biological phosphorus removal process. Biotechnology and Bioengineering 47: 277--287. Sneyders MJ, Wentzel MC, and Ekama GA (1997) The effect of unstabilized landfill leachate addition on biological nutrient removal performance in activated sludge

4.15 Biofilms in Water and Wastewater Treatment Z Lewandowski, Montana State University, Bozeman, MT, USA JP Boltz, CH2M HILL, Inc., Tampa, FL, USA & 2011 Elsevier B.V. All rights reserved.

4.15.1 4.15.2 4.15.2.1 4.15.2.2 4.15.2.2.1 4.15.2.2.2 4.15.2.2.3 4.15.2.2.4 4.15.2.2.5 4.15.2.2.6 4.15.2.2.7 4.15.2.2.8 4.15.3 4.15.3.1 4.15.3.1.1 4.15.3.1.2 4.15.3.2 4.15.3.3 4.15.3.4 4.15.3.4.1 4.15.3.4.2 4.15.3.4.3 4.15.3.4.4 4.15.3.4.5 4.15.3.5 4.15.3.5.1 4.15.3.5.2 4.15.3.5.3 4.15.3.5.4 4.15.3.5.5 4.15.3.5.6 4.15.4 4.15.4.1 4.15.4.1.1 4.15.4.1.2 4.15.4.2 4.15.4.3 4.15.4.4 References

Introduction Part I: Biofilm Fundamentals Biofilm Formation and Propagation The Concepts of Biofilms and Biofilm Processes Quantifying microbial activity, hydrodynamics, and mass transport in biofilms Biofilm heterogeneity and its effects Biofilm activity Quantifying local biofilm activity and mass transport in biofilms from microscale measurements Horizontal variability in diffusivity and microbial activity in biofilms Mechanism of mass transfer near biofilm surfaces Biofilm processes at the macroscale and at the microscale Biofilms in conduits Part II: Biofilm Reactors Application of Biofilm Reactors Techniques for evaluating biofilm reactors Graphical procedure Empirical and Semi-Empirical Models Mathematical Biofilm Models for Practice and Research Biofilm Model Features Attachment and detachment process kinetics and rate coefficients Concentration gradients external to the biofilm surface and the mass transfer boundary layer Diffusivity coefficient for the rate-limiting substrate inside the biofilm Parameters: estimation and variable coefficients Calibration protocol Biofilm Reactors in Wastewater Treatment Biofilm reactor compartments Moving bed biofilm reactors Biologically active filters Expanded and fluidized bed biofilm reactors Rotating biological contactors Trickling filters Part III. Undesirable Biofilms: Examples of Biofilm-Related Problems in the Water and Wastewater Industries Biofilms on Metal Surfaces and MIC Differential aeration cells on iron surfaces SRB corrosion Biofilms on Concrete Surfaces: Crown Corrosion of Sewers Biofilms on Filtration Membranes in Drinking Water Treatment Biofilms on Filtration Membranes in Wastewater Treatment

4.15.1 Introduction Fundamental principles describing biofilms exist as a result of focused research. The use of reactors for the treatment of municipal wastewater is a common application of biofilms. Applied research exists that provides a basis for the mechanistic understanding of biofilm reactors. The empirical information derived from such applied research has been used to develop design criteria for biofilm reactors and remains the basis for biofilm reactor design despite the emergence of

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mathematical models as reliable tools for research and practice. Unfortunately, little information exists to bridge the gap between our current understanding of biofilm fundamentals and reactor-scale empirical information. Therefore, there is a clear dichotomy in literature: micro- (biofilm) and macro(reactor) scales. This chapter highlights the division. Part I is dedicated to basic research and communicating the state of the art with respect to understanding biofilms. Part II is practice oriented and describes the use of biofilms for the sanitation of municipal wastewater. A basis for addressing this

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disconnection is presented by (1) describing the fundamental biofilm principles that can be uniformly applied to biofilms in several disciplines extending from medicine to environmental biotechnology and (2) describing a fundamentalbased approach in order to understand and apply biofilms in reactors. The use of mathematical biofilm models is common in both research and practice, but only a cursory presentation of their mathematical description is presented here. Finally, Part III gives examples of undesirable biofilms in water and wastewater industries and describes the attempts to mitigate their effects. Metabolic reactions mediated by microorganisms residing in biofilms promote the biodeterioration of materials, including metals, concrete, and plastics. It is estimated that microbially influenced corrosion (MIC) alone costs the US economy billions of dollars every year.

4.15.2 Part I: Biofilm Fundamentals 4.15.2.1 Biofilm Formation and Propagation Biofilm formation is a process that consists of a sequence of steps. It begins with the adsorption of macromolecules (e.g., proteins, polysaccharides, nucleic acids, and humic acids) and smaller molecules (e.g., fatty acids, lipids, and pollutants such as polyaromatic hydrocarbons and polychlorinated biphenyls) onto surfaces. These adsorbed molecules form conditioning films which may have multiple effects, such as altering the physicochemical characteristics of the surface, acting as a concentrated nutrient source for microorganisms, suppressing or enhancing the release of toxic metal ions from the surface, detoxifying the bulk solution through the adsorption of inhibitory substances, supplying the nutrients and trace elements required for a biofilm, and triggering biofilm sloughing. Once the surface is prepared, cells begin to attach. The initial stages of biofilm formation are well documented, mostly because acquiring images of microorganisms at this stage of biofilm formation is relatively easy. The adherence of bacteria to a surface is followed by the production of slimy adhesive substances, extracellular polymeric substances (EPS). These are predominantly made of polysaccharides and proteins. Although the association of EPS with attached bacteria has been well documented in the literature, there is little evidence to suggest that EPS participates in the initial stages of adhesion. However, EPS definitely assists the formation of mature biofilms by forming a slimy substance called the biofilm matrix. Figure 1 shows the steps in the formation of mature biofilms. The existence of these three phases of biofilm development, as depicted in Figure 1, is generally acknowledged, although the terminology may vary among authors. For example, Notermans et al. (1991) called these phases: (1) adsorption, (2) consolidation, and (3) colonization. Once a mature biofilm has been established on a surface, it actively propagates and eventually covers the entire surface. The mechanisms of propagation in mature biofilms are more complex than those of initial attachment, and several of these mechanisms of biofilm propagation are depicted in Figure 2. Although biofilms can be seen with an unaided eye, imaging their structure, microbial community structure, and

Biofilm formation Attachment

Colonization

Growth

Bulk fluid

Surface Figure 1 Steps in biofilm formation. & 1995 Center for Biofilm Engineering, MSU-BOZEMAN.

distribution of EPS requires the use of several types of microscopy combined with various probes, such as fluorescent in situ hybridization (FISH) probes and fluorescent proteins (FPs) used as reporter genes. The favorite types of microscopy among biofilm researchers are those that allow the examination of living and fully hydrated biofilms. In addition, sophisticated image acquisition devices are often needed that can selectively stimulate and image various probes when more than one type of multicolored probe is used simultaneously. Using these techniques in conjunction with a suitable microscopy, biofilm researchers can detect the presence of the selected physiological groups of microorganisms in the biofilm, their position in the biofilm with respect to other microorganisms and surface, and even their physiological state – dead, injured, or alive. The in vitro FISH techniques, popular in medical diagnostics, require that DNA or RNA be isolated from the sample and separated on a gel, and that the fluorescent probes then be added to the sample. The in situ variety of the hybridization technique, which is extensively used in biofilm research, does not require isolating DNA or RNA prior to the use of the probes; instead, the probes are hybridized to the respective nucleotide sequences inside the cells (Biesterfeld et al., 2001; Delong et al., 1999; Ito et al., 2002; Jang et al., 2005; Manz et al., 1999). In situ hybridization uses fluorescence-labeled complementary DNA or RNA probes, often derived from fragments of DNA that have been isolated, purified, and amplified. In microbial ecology, ribosomal RNA in bacterial cells is targeted by fluorescencelabeled oligonucleotide probes. Figure 3 shows an image of manganese-oxidizing bacteria (MOB) Leptotrix discophora stained with a FISH probe (green) and counterstained with propidium iodide (red). Propidium iodide is a general stain which is quite popular with biofilm researchers (GrayMerod et al., 2005; McNamara et al., 2003; Nancharaiah et al., 2005). In mature biofilms, microorganisms are imbedded in the layer of EPS. Figure 4 shows an image of a mature biofilm acquired using scanning electron microscopy (SEM). It shows microbes embedded in a matrix of EPS attached to a surface, although the EPS in this image were reduced to an entangled network of dry strands because the sample had to be dehydrated before the biofilm was imaged using electron microscopy.

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Streaming Detaching

Seeding dispersal

Rippling

Rolling

Figure 2 Mechanisms of biofilm propagation (MSU-CBE, P.Dirx).

Figure 4 SEM image of a biofilm of Desulfovibrio desulfuricans G20 embedded in EPS (Beyenal et al., 2004).

Figure 3 L. discophora stained with FISH probes and counterstained with propidium iodide. Red indicates cells that were stained with propidium iodide, and green indicates cells that react positively to the fluorescent FISH probe. Yellow indicates green and red overlay. The scale bar is 20 mm (Campbell, 2003).

4.15.2.2 The Concepts of Biofilms and Biofilm Processes It is difficult to offer precise definitions of biofilms and biofilm processes that will satisfy everyone who is interested in studying biofilms and biofilm-based technologies. Several currently used definitions have roots in historical approaches to biofilm studies. These approaches initially referred to biofilms as physical objects – microbial deposits on surfaces – but later expanded the concept to consider biofilms as a mode of

microbial growth, an alternative to microbial growth in suspension. Life scientists often emphasize the definitions that refer to biofilms as a mode of microbial growth. Engineers often find that the definitions that refer to aggregates of microorganisms which are embedded in a matrix composed of microbially excreted EPS and attached to a surface are useful for their applications. Here, we will refer to biofilms as microorganisms and microbial deposits attached to surfaces. We will use the term biofilm processes in reference to all physical, chemical, and biological processes in biofilm systems that affect, or are affected by, the rate of biofilm deposition or the microbial activity in biofilms. Biofilm processes are carried out in biofilm reactors. Colloquially, the terms biofilm reactors and biofilm systems are used interchangeably. However, biofilm systems exist with or without human intervention, while biofilm reactors are produced by our actions.

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When we promote or suppress a biofilm process in a biofilm system, or even when we quantify a biofilm process in a biofilm system without affecting its rate, the biofilm system becomes a biofilm reactor. For example, wetlands can be natural or constructed. However, even natural wetlands become biofilm reactors once we start monitoring biofilm processes in them. We will use the term biofilm system to refer to a group of compartments and their components determining biofilm structure and activity. Biofilm systems are composed of four compartments:

• • • •

the the the the

surface to which the microorganisms are attached; biofilm (the microorganisms and the matrix); solution of nutrients; and gas phase (if present).

Each compartment of a biofilm system can have a number of components. The exact number of components in each compartment may vary, depending on the needs of a particular description. For example, for some analyses it may be convenient to identify two components of the biofilm: (1) the EPS (matrix) and (2) the microorganisms. In another study, it may be convenient to identify three components of the biofilm: (1) the EPS, (2) the microorganisms, and (3) the particular matter trapped in the matrix. Similarly, in some studies it may be convenient to single out two components of the surface – (1) the bulk material and (2) the biomineralized deposits – or, if MIC is studied, it may be convenient to describe the surface by identifying three components: (1) the metal substratum, (2) the corrosion products, and (3) the biomineralized deposits on the surface. The needs of the specific study or analysis dictate the number of components identified in each compartment of the biofilm system. Biofilm studies can be characterized as studies of the relations among the compartments, the properties of one or more compartments, or one or more components of a compartment. Among many factors that are used to quantify biofilm processes, biofilm activity is most often used. Biofilm reactors are often designed and operated to optimize biofilm activity, as are the biofilm reactors used for wastewater treatment discussed later in the text. Typically, biofilm activity is identified with the rate of utilization of the growth-limiting nutrient. In some instances, however, rates other than the rate of substrate utilization or biofilm accumulation are better descriptors of the system dynamics. For example, in studies of MIC, the rate of anodic dissolution of the metal affected by the process may be a more useful descriptor of biofilm activity than the rate at which the growth-limiting substrate is utilized. The choice of the process for evaluating biofilm activity is dictated by the nature of the study, and sometimes by analytical convenience. Monitoring the rate of biofilm accumulation is important in many applications, whether we want to enhance or inhibit the growth of biofilms. The methods employed include optical microscopy (Bakke and Olsson, 1986; Bakke et al., 2001), measuring light intensity reflected from microbially colonized surfaces (Bremer and Geesey, 1991; Cloete and Maluleke, 2005), collecting and analyzing images of biofilm depositions (Milferstedt et al., 2006; Pons et al., 2009), surface sensors based on piezoelectric devices (Nivens et al., 1993; Pereira

et al., 2008), and electrochemical sensors in which stainless steel electrodes change their electrochemical behavior as a result of biofilm deposition (Licina et al., 1992; Borenstein and Licina, 1994).

4.15.2.2.1 Quantifying microbial activity, hydrodynamics, and mass transport in biofilms Microbial activity (biofilm activity), hydrodynamics, and mass transport in biofilms are difficult to discuss separately as they affect each other in many ways. Biofilm activity at the microscale is quantified as the flux, from the bulk solution to the biofilm surface, of the substance selected for evaluating biofilm activity. Since fluxes at the microscale are quantified locally, rather than averaged over the entire surface area as is done when biofilm activity is evaluated at the macroscale, the concentration profiles of the selected substance must be measured with microsensors to assure adequate spatial resolution. The idealized model of hydrodynamics and mass transfer in biofilms shown in Figure 5 is a good starting point for a discussion of biofilm activity at the microscale. In this model the overall flow velocity in the main stream is considered to be the average flow velocity, Cb. This decreases toward the surface of the biofilm, as required by hydrodynamics, and reaches concentration Cs at the biofilm surface. The layer of liquid just above the biofilm surface, where the flow velocity decreases as a result of proximity to the surface, is the hydrodynamic boundary layer, and it is denoted by j. As the flow velocity decreases toward the biofilm surface, the mechanism of mass transport changes from being dominated by convection at locations away from the biofilm, where the flow velocity is high, to being dominated by diffusion at locations near the biofilm surface, where the flow velocity is low. As the microorganisms in the biofilm consume nutrients at the rate at which they are delivered and the mass transport becomes less efficient near the biofilm surface, the nutrient concentration decreases near the surface, forming a nutrient concentration profile within the hydrodynamic boundary layer. The layer of liquid above the biofilm surface where the nutrient concentration decreases is the mass transport boundary layer, and it is denoted by LL and RL is the mass transfer resistance external to the biofilm.

Substrate concentration profile

Flow velocity profile

N = k(Cb − Cs) k =

vb

DW 1 = RL LL Cb C LF

LL

ϕ Biofilm Substratum

Figure 5 Profiles of flow velocity and growth-limiting nutrient concentration near the surface of an idealized biofilm.

Biofilms in Water and Wastewater Treatment 7 6 CO2 (mg l−1)

4.15.2.2.2 Biofilm heterogeneity and its effects The term biofilm heterogeneity refers to the extent of the nonuniform distribution of any selected constituent in any of the compartments of the biofilm system, such as the distribution of the biomass, selected nutrients, selected products of microbial metabolism, or selected groups of microorganisms. Since there are many choices for the constituents selected to evaluate biofilm heterogeneity, the term biofilm heterogeneity is usually combined with an adjective referring to the selected constituent, such as structural heterogeneity, chemical heterogeneity, or physiological heterogeneity. The term biofilm heterogeneity was initially used exclusively to refer to the nonuniform distribution of the biomass in a biofilm. As time has passed, more types of heterogeneity have been described, and the term biofilm heterogeneity is not self-explanatory anymore: the specific feature of the biofilm with respect to which the heterogeneity is quantified needs to be specified. Quantifying biofilm heterogeneity is equivalent to quantifying the extent of nonuniform distributions, such as the distribution of biomass in the biofilm. Several tools from the statistical toolbox are available for evaluating the extent of nonuniform distribution; the most popular is the standard deviation. The procedure for estimating the heterogeneity of a selected constituent of a biofilm is identical with the procedure for evaluating the standard deviation of a set of experimental data with one important difference: the deviations from the average are not due to errors in measurement but reflect a feature of the biofilm – heterogeneity. One of the most profound effects of biofilm heterogeneity is that microscale measurements in biofilms deliver different results at different locations. This is an obvious concern as most models referring to microbial growth and activity have been developed for well-mixed reactors, in which the result of a measurement does not depend on the location. Figure 6 shows this effect: three very different profiles of carbon dioxide concentration were measured at three locations in a biofilm. Because of the biofilm heterogeneity, it is impossible to determine a representative location to make the local measurements of biofilm activity that are used to validate models of biofilm processes. To include the effects of biofilm heterogeneity in mathematical models of biofilm processes, the extent of these effects – the spatial variability of the features measured in biofilms – needs to be evaluated experimentally using tools that can take measurements in biofilms to a high spatial resolution. Such tools are routinely used in biofilm research in the form of microelectrodes and various types of microscopy, often enhanced with fluorescent probes. These types of measurements deliver information about selected locations in the biofilm, and their results are referred to as local properties. The most common such measurements are local biofilm activity, local mass transfer coefficient, local diffusivity, and local flow velocity. The definition of the local mass transport coefficient is derived from the measurement procedure: the coefficient of the mass transport of an electroactive species to the tip of an electrically polarized microelectrode. The local mass transport coefficient is measured using an amperometric microelectrode without a membrane operated at the limiting current condition (masstransfer-limited). Local diffusivity is computed from these

533

A B C

5 4 3 2 1 0 0

100 200 300 400 Distance from the bottom (µm)

500

Figure 6 Carbon dioxide concentration profiles measured perpendicularly to the bottom (substratum) at three locations in a biofilm microcolony.

measurements by calibrating local mass transport microelectrodes in gels of known diffusivities (Beyenal et al., 1998).

4.15.2.2.3 Biofilm activity Biofilm activity in a biofilm reactor can be evaluated from the mass balance on the growth-limiting nutrient in the reactor:

Biofilm activity ¼

ðCInfluent  CEffluent Þ  Q A

ð1Þ

where C is the concentration of the growth-limiting nutrient (kg m3), Q the volumetric flow rate in the reactor (m3 s1), and A the surface area covered by the biofilm (m2). Therefore, biofilm activity at the scale of the reactor is the average flux of nutrients across the biofilm surface, which corresponds to the approach delineated in Equations (12) and (13) used in graphical procedure to evaluate pilot-plant observations. Average biofilm activity in a reactor is a useful descriptor of reactor performance. However, when the underlying biofilm processes are to be studied, an image of local biofilm activity is often required. This information can be extracted from growth-limiting substrate concentration profiles measured at

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surface on the oxygen profile coincides with the inflection point of the nutrient concentration profile. It is not easy to determine the exact position of the surface, though. We use a simplified procedure, explained later in Figure 16, to find the approximate position of biofilm surface on concentration profiles measured with microelectrodes. One use of such data is to estimate the local biofilm activity in terms of the flux of the growth-limiting nutrient at the location where the profile was measured. The flux of the nutrient across the biofilm surface, JLF at the location of the measurement is computed as the product of the slope of the concentration profile at the biofilm bulk solution interface by the diffusivity coefficient in water of the substance whose concentration was measured:

Oxygen concentration (mg l−1)

6 5 4 3 2 1

JLF ¼ Dw

0 0

300

600

900

1200

1500

Distance from the bottom (µm) Figure 7 Oxygen concentration profile. The vertical line marks the approximate position of the biofilm surface (Rasmussen and Lewandowski, 1998).

selected locations in the biofilm, as shown in Figure 7. The results from the two scales of observation – (1) the local biofilm activity evaluated from the concentration profiles and (2) the average biofilm activity evaluated from the mass balances around the reactor – provide different types of information. The measurements at the microscale deliver information that cannot be extracted from the measurements at the macroscale. For some biofilm processes, it is important to quantify the extreme values of biofilm activity because the locations in the biofilm where these extreme values occur exhibit extreme properties. For example, in studying MIC, which causes highly localized damage to metal surfaces, it is important to evaluate the extreme values of biofilm activity because the extreme, and highly localized, microbial activity in biofilms determines the extent of microbial corrosion. The average biofilm activity estimated from measurements at the macroscale cannot deliver this information.

4.15.2.2.4 Quantifying local biofilm activity and mass transport in biofilms from microscale measurements The profiles of flow velocity and growth-limiting substrate concentration shown in the conceptual image depicted in Figure 5 can be measured experimentally. Their interpretation leads to a better understanding of the processes occurring in biofilms. Figure 7 shows an oxygen concentration profile measured in a biofilm using an oxygen microelectrode. Nutrient concentration profiles, such as the one shown in Figure 7, are composed of two parts, the part above and the part below the biofilm surface. Different factors shape these parts of the profile: the shape of the profile above the surface is dominated by bulk liquid hydrodynamics, whereas the shape of the profile below the surface is dominated by microbial respiration in the biofilm. These two parts are described by different equations but are connected at the biofilm surface by the requirement of oxygen flux continuity. The position of the

  dC dx ðxxs Þ¼0

ð2Þ

where Dw is the diffusivity in water of the substance selected for the evaluation of biofilm activity, usually the growthlimiting nutrient (m2 s1). Diffusivity of this substance in the biofilm is not constant, but instead it varies with distance, as explained below. Early mathematical descriptions of biofilm activity and the shape of the concentration profile within the biofilm were based on the conceptual model of so-called uniform biofilms, depicting biomass uniformly distributed in the space occupied by the biofilm (Atkinson and Davies, 1974; Williamson and McCarty, 1976). Formally, these early mathematical models of microbial activity in biofilms imitated the models of microbial activity in suspension, with the addition of mass transport resistance. They quantified the equilibrium between the rate of utilization of the growth-limiting nutrient and the rate of mass transport in one dimension, toward the surface:

 2      qC q C mmax Xf C ¼ Df  ; qt f qx2 f Yx=s Ks þ C

0 r x r xs

ð3Þ

At steady state, this equation delivers

Df

d 2C mmax CXf ¼ 2 Yx=s ðKs þ CÞ dx

ð4Þ

Two boundary conditions were generally used to specify the concentrations of oxygen at the bottom and surface of the biofilm:



dC dx

 ¼ 0;

Cðx¼xs Þ ¼ Cs;

t0

ð5Þ

ðx¼0Þ

where Df is the averaged effective diffusivity of growth-limiting nutrient in the biofilm (m2 s1); x the distance from the bottom (m); xs the distance from the biofilm surface in the new system of coordinates (m); Xf the averaged biofilm density (kg m3); Yx/s the yield coefficient (kg microorganisms/kg nutrient); mmax the maximum specific growth rate (s1); Ks the Monod half-rate constant (kg m3); C the growth-limiting substrate concentration (kg m3); and Cs the growth-limiting substrate concentration at the biofilm surface (kg m3). These early models were subsequently refined by adding additional factors affecting biofilm processes, such as bacterial

Biofilms in Water and Wastewater Treatment

  d dC d 2 C dDfl dC mmax CXfl Dfl ¼ Dfx 2 þ ¼ dx dx Yx=s ðKs þ CÞ dx dx dx

ð6Þ

where Dfl is the local effective diffusivity of the growth-limiting nutrient (m2 s1) and Xfl the local biofilm density (kg m3). Accepting that diffusivity and biofilm density are variable introduces two new variables into the equation, and functions describing changes in effective diffusivity and biofilm density need to be quantified before the equations can be solved. Experimental data show that density changes are surprisingly regular in biofilms and can be described as a linear function of biofilm depth. Relative surface-averaged effective diffusivity

0.70 0.65

D*fz = 0.001z + 0.2968

0.60 0.55 D fz

growth and decay in a steady-state biofilm (Rittmann and McCarty, 1980a, 1980b) and then the model was extended to include unsteady states and dual nutrient limitations (Rittmann and Brunner, 1984; Rittmann and Dovantzis, 1983). One of the most popular biofilm models, initially marketed as a software called BIOSIM (Wanner and Gujer, 1986), was later improved to include irregular biofilm structure and renamed AQUASIM (Wanner et al., 1995; Wanner and Reichert, 1996). The growing popularity of the conceptual model of heterogeneous biofilms coincided with the growing popularity of cellular automata (CA) (Wolfram, 1986), and it is not surprising that the heterogeneous biofilm structures were modeled using CA procedures (Wimpenny and Colasanti, 1997a, 1997b). Soon after, Picioreanu et al. (1998a, 1998b) improved this model using more realistic assumptions and used differential equations to describe mass transport with the discrete model describing the structure (Picioreanu et al., 1998a, 1998b). Since its early applications, CA remains the most popular model used to generate biofilm structure. Further improvement of the biofilm model came from Kreft et al. (2001), who developed a two-dimensional (2-D) multinutrient, multi-species model of nitrifying biofilms to predict biofilm structures, that is, surface enlargement, roughness, and diffusion distance. These authors compared the predicted structure of the biofilm with the predictions of the biomass (cells and EPS)-based model developed by Picioreanu et al. (1998a, 1998b), and concluded that the two models had similar solutions. Meanwhile, biofilm researchers urgently needed mathematical description of the biofilm processes that could be used to describe recent progress in understanding biofilm processes. The main problems that needed to be addressed were horizontal and vertical profiles in mass transport and activity in biofilms. These were experimentally verified and the assumption that the effective diffusivity and biofilm density were constant across the biofilm had become difficult to defend. Biofilm diffusivity decreases toward the bottom of the biofilm and biofilm density increases. There have been attempts to include these results in the modeling of biofilm processes but they lead to more complicated mathematical expressions in which diffusivity and biofilm density are functions of distance. To simplify these expressions it is possible to model a biofilm as a stack of layers with constant diffusivity and density, which change from layer to layer rather than continuously. At steady state, this approach delivers the mass transport and activity related to the local properties of the biofilm:

535

*

0.50 0.45 0.40 0.35 0.30 0.25

50

100

150

200

250

300

Distance from the bottom, z (µm) Figure 8 The surface-averaged relative effective diffusivity (Dfz*) is multiplied by the diffusivity of the growth-limiting nutrient in the water to calculate the surface-averaged effective diffusivity (Dfz). Since, in the example, the growth-limiting nutrient is oxygen, to calculate the effective diffusivity of oxygen at various distances from the bottom, we must multiply the relative effective diffusivity at various distances from the bottom by the diffusivity of oxygen in water (2.1  105 cm2 s1) (Beyenal and Lewandowski, 2005).

profile, reproduced from Beyenal and Lewandowski (2005), is shown in Figure 8. Assuming that biofilm density varies with depth in a linear fashion, as shown in Figure 8, the diffusivity gradient (x) is constant:

dDfx ¼z dx

ð7Þ

At steady state, this simplifies Equation (5) to the form

Dfl

d 2C dC mmax CXfl ¼ þz 2 dx dx Yx=s ðKs þ CÞ

ð8Þ

Further, it has been demonstrated that in biological aggregates, including biofilms, density is related to effective diffusivity (Fan et al., 1990):

Dfl ¼ 1 

0:43X0:92 fl 11:19 þ 0:27X0:99 fl

ð9Þ

Using this equation, we can estimate biofilm density from the variation in local effective diffusivity (Figure 9).

4.15.2.2.5 Horizontal variability in diffusivity and microbial activity in biofilms Concentration profiles of growth-limiting nutrients, such as the one shown in Figure 7, are taken at a specific location in a biofilm. Based on the results, the biofilm activity at that location can be computed. However, when the next profile is taken at another location, even as close as several micrometers from the first location, the two profiles can be significantly different. This is not surprising, considering that biofilms are heterogeneous. However, it brings into question the practice of

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Biofilms in Water and Wastewater Treatment

evaluating biofilm activity based on a single measurement at an arbitrarily selected location. For microscale measurements in stratified biofilms, the selected variable, such as local effective diffusivity or local dissolved oxygen concentration, is measured at locations on a grid (Figure 10). Grids are positioned at various distances from the bottom. The results are then presented as maps of the distributions of the selected parameter at the specified distances from the bottom, as shown in Figure 11. One of the main advantages of this approach is that it allows us to average the concentrations of oxygen at the selected distances from the bottom and arrive at a representative profile of oxygen that illustrates its distribution across the biofilm and also shows the deviations from the average due to biofilm heterogeneity. The maps of oxygen distributions shown in Figure 11 served to construct the representative profile of oxygen across this biofilm shown in Figure 12.

100 Pseudomonas aeruginosa (v = 3.2 cm s−1) Mixed culture (v = 1.6 cm s−1) Mixed culture (v = 3.2 cm s−1)

Biofilm density (g l−1)

80

60

40

20

0

0

100 200 300 400 Distance from the bottom, z (μm)

500

Figure 9 Variation in biofilm density with distance from the bottom (Beyenal et al., 1998).

4.15.2.2.6 Mechanism of mass transfer near biofilm surfaces When the local nutrient concentrations measured across a biofilm are plotted versus distance, they form a nutrient concentration profile. It would be expected that the shape of the nutrient concentration profile will follow the shape of the local mass transport coefficient profile when they are measured at the same location. It would also be expected that, at locations where the local mass transport coefficient is high, the local nutrient concentration will be high as well, at least higher than at a location where the local mass transport coefficient is low. Figure 13 shows profiles of oxygen concentration and local mass transport coefficient measured at the same location in a biofilm (Rasmussen and Lewandowski, 1998). As can be seen in Figure 13, the mass transport coefficient profile does not correlate well with the oxygen concentration profile. Approaching the biofilm surface, for example, the oxygen concentration decreases rapidly and reaches quite low levels at the biofilm surface, while the local mass transport coefficient remains quite high at that location. This observation seems difficult to explain: since there is no oxygen consumption in the bulk, the oxygen concentration profile would be expected to follow the shape of the mass transport coefficient profile much closer than it does in Figure 13. However, although these two profiles do not match, each of them is consistent with our knowledge of the system’s behavior. We expect to measure a low concentration of oxygen at the biofilm surface: this result fits the concept of a mass transfer boundary layer of high mass transport resistance above the biofilm surface. Measuring a high mass transport coefficient near the biofilm surface is also not surprising because, as we have estimated, convection is the predominant mass transport mechanism in that zone. The two features cannot coexist: high mass transport resistance and convection. To explain this apparent discrepancy, we need to examine the procedure for measuring flow velocity in biofilms. All available flow velocity measurements in biofilms report only one component of the

Figure 10 Microscale measurements in stratified biofilms. The selected variable, such as the local effective diffusivity or local dissolved oxygen concentration, is measured at the locations where the gridlines intersect. Such grids are positioned at various distances from the bottom (MSU-CBE, P.Dirx).

Biofilms in Water and Wastewater Treatment

400

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Figure 11 Distribution of oxygen measured in a biofilm at the specified distances from the bottom (Veluchamy, 2006).

flow velocity vector, parallel to the bottom. Based on these results, we estimated that mass transport is controlled by convection near biofilms. However, the convective mass transport rate equals the nutrient concentration times the flow velocity component normal to the reactive surface. The component of the flow velocity parallel to the surface has nothing to do with the convective mass transport toward that surface. Consequently, the estimate of the mass

transport mechanism based on flow velocity holds only in the direction in which the flow velocity was measured. Indeed, when the flow near a surface is laminar, the laminas of liquid slide parallel to the surface, and there is little or no convection across these layers: the mass transport parallel to the surface is convective, while the mass transport perpendicular to the surface remains diffusive. This mechanism is visualized in Figure 14.

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Biofilms in Water and Wastewater Treatment 7

Bulk

Biofilm 6

CSA (mg l−1)

5 4 3 2 1 0 0

100

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7

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6

0.8 5

0.7 0.6

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1

k/kmax

Dissolved oxygen concentration (mg l−1)

Figure 12 Surface averaged oxygen concentrations (CSA) and standard deviations computed for each data set in Figure 11. The average oxygen concentrations form a representative profile of oxygen concentration, characterizing the area covered with the biofilm, and the envelope of the standard deviation is a measure of the heterogeneity of the measured variable, oxygen concentration in this case (Veluchamy, 2006).

0.2 0.1

k/kmax

0 0

0.0

200 400 600 800 1000 1200 1400 Distance from substratum (µm)

Figure 13 Profiles of oxygen and local mass transfer coefficient through a thin biofilm cluster (’, dissolved oxygen; , local mass transfer coefficient). The vertical line marks the observed thickness of the biofilm. At distances of less than 30 mm, the wall effect caused the local mass transport coefficient to decrease. The biofilm thickness was 70 mm in this location. The value of k/kmax was only slightly affected by the presence of the biofilm up to a distance of less than 30 mm from the substratum (Rasmussen and Lewandowski, 1998).

4.15.2.2.7 Biofilm processes at the macroscale and at the microscale Accurate mathematical models are necessary for advances in biofilm research. Biofilm researchers use mathematical models of biofilm processes not only to predict the outcome of these processes, but also to interpret the results of biofilm studies. In the absence of suitable models, the interpretation of biofilm studies is impaired. Biofilm science and technology are relatively young, and mathematical descriptions of biofilm

processes often lag behind the rapidly expanding knowledge of biofilm processes. On the other hand, most of the experience that was accumulated in modeling biofilm processes in water and wastewater treatment was based on the operating reactors with suspended biomass. Biofilm reactors are different, and some effects common in biofilm reactors are much less usual in reactors with suspended biomass. One effect that is particularly difficult to accommodate in biofilm models is the influence of biofilm heterogeneity on biofilm processes. Biofilm models that describe biofilm processes on the scale of the entire reactor assume that the biofilm is uniformly distributed and its effects do not depend on the location in the reactor. This assumption, which is justified in the case of well-mixed reactors, may or may not be justified in biofilm reactors. With the current sophistication in exploring biofilm processes at the microscale, it is not surprising to observe that the local conditions quantified in biofilms deviate widely from the average conditions described by the biofilm models. One hopes that these deviations from the idealized models cancel each other and that overall, at the macroscale, they do not matter much. One particularly troubling problem is the definition of and the existence of a steady state in biofilm reactors. Defining a steady state in a biofilm reactor may well be the most important question facing biofilm researchers, both those who focus on experiment and those who focus on modeling. The existence of a steady state is obvious in flow reactors, where microbial growth occurs in suspension. In such reactors, the interplay among the microbial growth rates, biomass concentration, and hydraulic and biomass retention times leads to a steady state in which process variables do not change for a long time. In contrast, the reasons for the existence of a steady state in a biofilm reactor are much less clear because an important condition for a steady state is not satisfied in a biofilm reactor: the concentration of biomass in a

Biofilms in Water and Wastewater Treatment

Convection Diffusion Convection and diffusion

539

Direction of mass transport Convection Diffusion Direction of measured flow velocity

Figure 14 Alternating zones of convective and diffusive mass transport in heterogeneous biofilms. This hypothetical model of mass transport is consistent with the results in Figure 13. Mass transport in the space occupied by the biofilm is convective, but the amount of nutrient delivered to this space is limited by the diffusive mass transport just above the biofilm surface (MSU-CBE, P.Dirx).

biofilm reactor is not a simple function of retention time and growth rate. Some biofilm technologies actually take advantage of this fact and grow biofilm microorganisms using retention times at which the microorganisms would be washed out from reactors operated with suspended microorganisms. Practically, this problem corresponds to the fact that we are uncertain what function describes detachment in biofilms, and what mechanisms are involved in biofilm detachment, except perhaps for shear stress. The mechanism of biofilm sloughing remains unknown. A steady state for the biomass concentration assumes that the same amount of biomass is generated as is removed by various processes, particularly biofilm detachment. One can argue that if the biofilm reactor is large enough, the microscale biofilm processes will average out on the scale of the reactor, and that this average may be stable even if the components of the average vary over time. This argument, even if it is true, however, does not settle the issue. A question follows: how large does the reactor have to be to ensure that the variations in the microscale biofilm processes average out and the reactor reaches a steady state at the macroscale? There are also difficulties at the microscale. Experimentally measured concentration profiles and flow velocity profiles corroborate the conceptual model shown in Figure 5. However, when it comes to interpreting experimental data, the idealized image of biofilms in Figure 5 is not adequate for many reasons. One reason is shown in Figure 15: the difficulty with locating the position of the biofilm surface. The position of the biofilm surface is important: one of the boundary conditions in the equation describing biofilm activity and mass transport specifies the conditions at the biofilm surface. As can be seen in Figure 15, however, locating it is not trivial. This problem has been addressed experimentally by judiciously locating the surface on a nutrient concentration profile at the location where the profile ends its curvature near the bottom. The rule of locating the biofilm surface at that location has been developed based on the results of studies in which an oxygen electrode and an optical sensor were used to measure the oxygen concentration profile and detect the biofilm surface, where optical density changed (Figure 16). The position of the biofilm surface coincides with the location where the oxygen profile becomes linear. The biofilm surface in Figure 7 was positioned using this principle.

Figure 15 Surface of a biofilm grown at a flow velocity of 0.81 m s1 (Groenenboom, 2000).

4.15.2.2.8 Biofilms in conduits Among the many possible effects that biofilms may have in water conduits, we will discuss two effects in more detail: (1) the effect on flow characteristics – pressure drop in conduits and (2) the effect on material performance – MIC. Flow velocity near the biofilm surface. It is well known that flow velocity affects biofilm processes. Figure 5 shows an example of the effect of flow velocity on mass transport dynamics near the biofilm surface. However, biofilm also affects flow velocity: flow velocity near a wall covered with biofilm is different from that near a wall with no biofilm. Figure 17 shows this effect. The effect of biofilm on flow velocity distribution most certainly influences the dynamics of mass transfer. However, this is not the only effect that biofilm has on hydrodynamics. For example, it is well known that biofilms increase the pressure drop in conduits, but it is not clear what the mechanism of this process is or how to quantify it. To predict pressure drop in pipes the Moody diagram is used, which correlates the Reynolds number and the relative roughness to provide the friction factor, f. This friction factor is then

Biofilms in Water and Wastewater Treatment 9.0

0.30

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0.00 2.40

Figure 16 Profiles of oxygen concentration and optical density in a biofilm. A combined microsensor – an oxygen microelectrode and an optical density microprobe – permitted locating the biofilm surface at 0.60 mm from the bottom. This distance, when marked on the oxygen concentration profile, indicates that the biofilm surface is at the beginning of the linear part of the oxygen profile within the mass transfer boundary layer; I is the local light intensity, and Io is the maximum light density (Lewandowski et al., 1991).

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1000

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250 Depth (µm) 3000

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500 5000

Depth (µm) Figure 17 The flow velocity profile near a wall covered with a biofilm is different from the flow velocity profile near the same wall without the biofilm (DeBeer et al., 1994).

plugged into the Darcy–Weisbach equation to calculate the pressure drop:

HL ¼ f

l V2 D 2g

ð10Þ

where HL is the head loss due to friction, l the pipe length, V the average fluid velocity, g the gravitation constant, D the pipe diameter, and f the friction factor provided by the Moody diagram. When the flow velocity increases, the thickness of the boundary layer decreases, and the roughness elements protrude through the boundary layer, further affecting the drag and the pressure drop.

Unfortunately, the Moody diagram is of little help in predicting the pressure drop in conduits covered with biofilms. The pressure drop in such conduits is caused by different factors than the pressure drop in conduits without biofilms because different mechanisms are responsible for the shape of the pressure drop in each of these conduits. These differences sometimes demonstrate themselves in the form of puzzling experimental results, such as decreasing pressure drop resulting from increasing flow velocity, which is a consequence of the elastic and viscoelastic properties of biofilms. Microcolonies are made of bacterial cells embedded in gelatinous EPS that can change shape under stress. At high flow velocities the hydrodynamic boundary layer separates from the

Biofilms in Water and Wastewater Treatment

3 2 1 m

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8 4

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m

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microcolonies, causing pressure drag downstream of the microcolony and pulling the material in this direction. The microcolonies slowly flow under the strain, forming elongated shapes that we call streamers. Streamers are often seen when biofilms grow at high flow velocities. The streamers contribute to pressure drop by moving rapidly and dissipating the kinetic energy of the flowing water. Another important consequence of a streamer’s oscillations is that they are transmitted to the underlying microcolonies, which also oscillate rhythmically. This system reacts with turbulent boundary layers much differently than the rigid surface roughness elements of clean pipes do. One way to gain experimental access to the interactions between flowing water and biofilm is to monitor flow velocity profiles. Imaging flow velocity profiles makes it possible to evaluate the effect of biofilm formation on the flow in conduits by quantifying its effect on the entry length in the conduit. The hydrodynamic entry length is defined as the distance needed to develop a steady flow, after the water has passed through the entrance to the reactor. If the presence of biofilm makes the entry length longer, then the biofilm contributes to flow instability, and vice versa. There is a simple relation between the Reynolds number and the entry length: the higher the Reynolds number, the longer the entry length. This effect was used as a base for quantifying the effects of biofilm on the flow in conduits. Flow velocity distribution was measured in a rectangular reactor when the flow velocity was increasing from one measurement to another. As the flow velocity and the Reynolds number increased, the flow stability was monitored in a rectangular conduit using nuclear magnetic resonance (NMR) imaging. The results, shown in Figure 18, demonstrate that the presence of biofilm actually made the flow more stable. The entry length was shorter and the flow reached stability closer to the entrance in the presence of biofilm than in its absence. It is difficult to interpret this result immediately because it is well known that the presence of biofilm increases pressure drop in conduits: traditionally, pressure drop in pipes is related to friction. As pressure drop is larger in biofilm-covered pipes, a natural conclusion was that biofilms must increase friction and therefore the presence of the biofilm should introduce flow instability rather than reduce it. The relation between flowing water and biofilms is determined by two facts: (1) biofilms are made of viscoelastic polymers which actively interact with the oscillations generated by the flow of water and (2) the flow of water affects the biofilm structure. Based on what we now understand, at low flow velocities biofilms can effectively smooth surfaces and stabilize the flow because the oscillating layer of elastic polymeric matrix can effectively damp the vibrations coming from the flowing water. This effect delays the onset of turbulence in conduits covered with biofilm and explains the results shown in Figure 18. However, as the flow velocity increases further, the elastic polymeric matrix must oscillate faster and faster and, eventually, the frequency of its oscillation cannot follow the frequency of the incoming eddies. At that point the biofilm oscillation is out of phase and the biofilm not only fails to damp the flow instabilities but also actively introduces instability by randomly oscillating at a different frequency than the incoming eddies. The pressure drop in the conduit

541

2.0 cm

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m

2.5 m

2.0 cm

Figure 18 Flow velocity profiles in a rectangular conduit whose walls were colonized with a biofilm. The increasing flow velocity did not affect the character of the velocity profiles in the reactor with biofilm. On the other hand, the same increase in velocity had a pronounced effect on the reactor without biofilm.

increases rapidly. This effect was, in early biofilm works, mistaken for a similar effect caused by rough surface elements. For example, Picologlou et al. (1980) observed a considerable increase in frictional resistance after the film thickness reached a value approximately equal to the calculated thickness of the hydrodynamic boundary layer for a clean surface. In clean pipes covered with surface roughness elements, when flow velocity increases the boundary layer becomes thinner and at some flow velocity the boundary layer thickness is smaller than the height of the roughness elements. When this happens, the roughness elements protrude through the boundary layer and cause an additional drag, which exhibits itself in a sudden increase of the pressure drop for flow velocities exceeding this critical flow velocity. This model was commonly accepted and was used to explain the pressure drop in conduits covered with biofilms, although even at that time some authors warned that this might not be the true mechanism of the process (Characklis, 1981). Currently, there are no models that can account accurately for pressure drop in conduits covered with biofilm.

4.15.3 Part II: Biofilm Reactors Biological systems treating municipal wastewater require (1) the accumulation of active microorganisms in a bioreactor and (2) the separation of the microorganisms from treated effluent. In suspended growth reactors, such as the activated

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Biofilms in Water and Wastewater Treatment

sludge process, microorganisms grow and bioflocculate; the resultant flocs are suspended freely in the bulk phase. Flocculated bacteria are then separated from the bulk liquid by sedimentation or membranes. Clarifier-coupled suspended growth reactors rely on return activated sludge, or underflow, from the coupled clarifier to provide the desired active biomass concentration in the bioreactor. Consequently, clarification unit processes may be limited by the hydraulic loading rate (HLR) or solids loading rate (SLR). Biofilm reactors retain bacterial cells in a biofilm that is attached to the fixed or free moving carriers. The biofilm matrix consists of water and a variety of soluble (C) and particulate (X) components that include soluble microbial products, inert material, and EPS. Without suspended biomass, the bioreactor is decoupled from the liquid–solids separation unit. Active biomass concentrations inside the biofilm are large at 10–60 g of volatile suspended solids (VSS) l1 of biofilm. This biomass range can be compared with the range of concentrations expected for suspended growth reactors, which is typically 3–8 g VSS l1 of reactor volume. The lower value in this range is associated with clarifier-coupled activated sludge processes, and the upper range with membrane bioreactors. In biofilm reactors, bacteria attached to carriers periodically detach from the biofilm matrix and exit the system in the effluent stream. Figure 19 provides a conceptual illustration of different biofilm reactor types. Biofilm reactors can be classified based on the number of phases involved – gas, liquid, solid – according to the biofilm being attached to a fixed or moving carrier within the reactor. They are also classified based on how electron donors or acceptors are applied to seven basic types as listed below (adapted from Harremo¨es and Wilderer (1993)): 1. Three-phase system – fixed biofilm-laden carrier, bulk water, and air. Water trickles over the biofilm surface and

2.

3.

4.

5.

6.

7.

air moves upward or downward in the third phase (e.g., trickling filter (TF)) (Figure 19(a)). Three-phase system – fixed (or semifixed) biofilm-laden carrier, bulk water, and air. Water flows through the biofilm reactor with gas bubbles (e.g., aerobic biologically active filter (BAF)). Gravel is a fixed media and polystyrene beads are semifixed (Figures 19(b) and 19(c)). Three-phase system – moving biofilm-laden carrier, bulk water, and air. Water flows through the biofilm reactor. Air is introduced with gas bubbles (e.g., aerobic moving bed biofilm reactor (MBBR)) (Figure 19(g)). Two-phase system – moving biofilm-laden carrier and bulk water. Water flows through the biofilm reactor with the electron donor and electron acceptor (e.g., denitrification fluidized bed biofilm reactor (FBBR)) (Figure 19(g)). Two-phase system – fixed biofilm-laden carrier material and bulk water. Water flows through the biofilm reactor with the electron donor and electron acceptor (e.g., denitrification filter) (Figures 19(b) and 19(c)). Three-phase membrane system – a microporous hollowfiber membrane with biofilm and water on one side and gas on the other, diffusing through the membrane to the biofilm (e.g., membrane biofilm reactor (MBfR)) (Figure 19(h)). Two-phase membrane system – a proton exchange membrane separating a compartmentalized biofilm-laden anode from a compartmentalized cathode with water on both sides, but with the electron donor on one side and electron acceptor on the other (e.g., biofilm-based microbial fuel cell (MFC)).

Biofilms are ubiquitous in nature and in engineered systems and can be used beneficially in municipal water and wastewater treatment. Biofilm and suspended growth reactors can meet similar treatment objectives for carbon oxidation,

Air

Air (a)

(b)

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(f)

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Air (e)

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Figure 19 Types of biofilm reactors: (a) trickling filter; (b) submerged fixed bed biofilm reactor operated as up flow or (c) down flow mode; (d) rotating biological contactor; (e) suspended biofilm reactor including airlift reactor; (f) fluidized bed reactor; (g) moving bed biofilm reactor; and (h) membrane attached biofilm reactors. From Morgenroth (2008) Modelling biofilm systems. In: Henze M, van Loosdrecht MCM, Ekama G, and Brdjanovic D (eds.) Biological Wastewater Treatment – Principles, Modelling, and Design, pp. 457–492. London: IWA Publishing.

Biofilms in Water and Wastewater Treatment

nitrification, denitrification, and desulfurization. Biofilm reactors have also been used for the treatment of a variety of oxidized contaminants including perchlorate and bromate. The same microorganisms are responsible for biochemical reactions in both activated sludge and biofilm systems, and respond in the same way to local environmental conditions (i.e., pH, temperature, electron donor, electron acceptor, and macronutrient availability) (Morgenroth, 2008). A key component to be considered by anyone who is evaluating a biofilm reactor is the effect of multiple substrates and biomass fractions and the manner in which the reactor is affected by mass-transport limitations. Substrates typically considered are: 1. soluble compounds, including electron donors (e.g., readily biodegradable chemical oxygen demand (rbCOD),   NHþ 4 , NO2 , and H2), electron acceptors (e.g., O2, NO3 ,  2 3 NO2 , and SO4 ), and nutrients and buffers (e.g., PO4 ,  NHþ 4 , and HCO3 ) and 2. particulate compounds, including electron donors (e.g., slowly biodegradable COD (sbCOD)), active biomass fractions (e.g., heterotrophic and autotrophic bacteria), inert biomass, and EPS.

4.15.3.1 Application of Biofilm Reactors This section exists to provide the reader with a general overview of biofilm reactor applications. While general biofilm reactor applicability is described here, several treatment scenarios exist that are not conveniently generalized yet warrant the use of biofilm reactor technology. Water-quality regulations exist to protect human health and the water environment. Organic matter and the nutrients such as nitrogen and phosphorus are major contributors to water-quality impairment. In municipal wastewater-treatment scenarios, biofilm reactors are generally applied for the removal of carbon-based organic matter and/or nitrogenous compounds. Specifically, these biofilm reactors may achieve carbon oxidation, combined carbon oxidation and nitrification, tertiary nitrification, or tertiary denitrification. Biofilm reactors are not commonly used for biological phosphorus removal. Biofilm reactors treating industrial wastewaters have been applied to meet treatment objectives similar to those in municipal wastewater treatment and industrial pretreatment. The objective of pretreatment is to process industrial waste streams until their characteristics are similar to raw sewage (see Metcalf and Eddy (2003) for a description). As a result the industry can then discharge their treated wastewater into municipal sewers where further processing is accomplished at a municipal wastewater-treatment plant. Biofilm reactors are common for industrial applications because the processes are reliable, robust, easy to operate, and resilient to toxic or shock loading.

4.15.3.1.1 Techniques for evaluating biofilm reactors Several approaches exist to evaluate biofilm reactors. The primary objective of a biofilm or biofilm reactor model is to predict soluble substrate flux (J) through the biofilm surface. This flux (M L2 T1) can be used to obtain an estimate of the (1) overall biofilm reactor performance, (2) required biofilm surface area, (3) electron acceptor (e.g., dissolved oxygen), (4)

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external electron donor (e.g., methanol or hydrogen), and (5) biosolids management requirements. This section discusses the relative benefits and limitations to some general methods of evaluating biofilm reactors. The use of mathematical biofilm models is common in both research and practice, but only a cursory presentation of their mathematical description is presented. Excellent resources exist describing aspects of mathematical modeling of biofilms and biofilm reactors (for additional information, see Wanner et al. (2006) and Morgenroth (2008)). The approaches discussed here include a graphical procedure, empirical models, semiempirical models, and mechanistic mathematical models.

4.15.3.1.2 Graphical procedure A graphical procedure can be used to determine the total hydraulic load (THL) required to decrease a substrate concentration, and by definition the biofilm surface area required to provide a desired substrate concentration remaining in the effluent stream. These items can be determined directly. The graphical procedure can be used to determine effluent substrate concentration from any series of continuous flow stirred tank reactors (CFSTRs). A stepwise procedure must be used when a series of CFSTRs will be used. Antoine (1976) and Grady et al. (1999) developed the graphical procedure described here and the approach is valid for any biofilm-based CFSTR. If multiple stages are expected to have different characteristics, then the graphical method requires different flux curves to describe system response in each of the CFSTRs. The procedure requires a graphical representation of substrate flux (J) as a function of bulk-liquid substrate concentration (CB). This relationship between flux and bulk-liquid substrate concentration can be obtained from numerical simulations, full-scale or pilot-plant observations. In practice, this graphical procedure is typically used to extend pilot-plant observations to full-scale biofilm reactor design criteria. The process designer should recognize that the relationship between flux and bulk-liquid substrate concentration is based on the system and location. Therefore, the flux curve required to implement the graphical procedure may not be obtained from or correlate well with values reported in the literature or from different systems. As a result, the process designer should consider carefully the conditions under which the flux curve was developed before applying results. A flux curve representing mass transfer and environmental conditions characteristic of a specific system and operating mode may not be the representative of different biofilm reactor types designed to meet the same treatment objectives. A flux curve generated for the same biofilm reactor type under similar operating conditions, however, may offer some direction in the absence of system-specific numerical simulation or pilot/full-scale observations. When using the graphical procedure to evaluate pilot-plant observations, fluxes should be compared to rates in full-scale systems. Any flux that deviates significantly from those reported for biofilm reactors in published studies should be used only after careful consideration. Pilot or experimental systems may promote a greater flux than expected. The basis for the graphical procedure is a material balance on a

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biofilm-based CFSTR:

0 = Q ⋅ Cin ,i − Q ⋅ C B ,i − J LF ,i ⋅ A − rB ,i ⋅ VB mass per time input

mass per time output

biofilm transformation rate

suspended growth transformation rate

ð11Þ

where Q is the flow rate through the system (m3 d1); Cin,i the influent concentration of soluble substrate i (g m3); CB,i the effluent, or bulk-liquid, concentration of soluble substrate i (g m3); JLF,i the flux of soluble substrate i across the biofilm surface equal to the average biofilm activity in the reactor, as shown in Equation (1) (g m2 d1); A the biofilm surface area (m2); rB,i the rate of substrate i conversion because of suspended biomass (g m2 d1); and VB the bulk-liquid volume (m3). Assuming that transformation occurring in the bulk liquid is negligible, the suspended growth transformation rate (Equation (11)) can be neglected. Rearranging Equation (11) provides the rationale for the graphical procedure:

JLF;i ¼

Q Q  Cin;i   CB;i A A |fflfflfflffl{zfflfflfflffl} |{z} const:

ð12Þ

slope

The slope, or (  (Q/A)), is referred to as the operating line and represents the total hydraulic load on each stage. Figure 20 illustrates the graphical method. The flux curves have been created based on observations in the first and second stage of a post-denitrification biofilm reactor. The ordinate represents nitrate–nitrogen flux and the abscissa nitrate–nitrogen concentration remaining in the effluent stream. The graphical solution indicates that the

first-stage denitrification biofilm reactor effluent nitrate– nitrogen concentration is approximately 3.9 mg l1. The secondstage effluent nitrate–nitrogen concentration is approximately 1.1 mg l1 with fluxes of approximately 1.6 and 1.1 g m2 d1 in the first and second stage, respectively. The graphical procedure depends on the substrate flux curve(s). The method requires development of multiple flux curves if the performance characteristics of respective stages vary significantly. When using pilot-plant data to generate a flux curve, appropriate scale considerations must be given when designing the pilot unit and experiments.

4.15.3.2 Empirical and Semi-Empirical Models Empirical models can be implemented easily by hand or using a spreadsheet, but they have limited applicability because of their black-box consideration of system parameters. Because environmental conditions and bioreactor configuration affect biofilm reactor performance, a system can respond differently from the description provided by an empirical model. The limited descriptive capacity of empirical models typically results from parameter values and model features based on data that were obtained from few system installations or operating conditions. Therefore, the engineer or scientist should be aware of conditions under which system-specific model parameters have been defined. Significant sources of variability in values include differences in biofilm carrier type and configuration, the extent of concentration gradients external to the biofilm surface, and biofilm composition. Despite their ease of implementation, empirical models can produce results that vary 50–100% of actual system performance.

3.5 Denitrification rate (g m−2 d−1 as NO3−N)

Stage 1 operating line 3.0 Stage 2 operating line

Stage 1 flux response curve

2.5 Stage 2 flux response curve

J LF1

2.0

1.5 −Q/A J LF2

1.0

0.5 CB -stage

0.0 0

1

2

3

CB -stage 1 4

C in 5

6

7

8

9

10

CNO3−N (mg-N l−1) Figure 20 Graphical procedure for describing the response of a denitrification moving bed biofilm reactor to defined conditions, including (1) firstand second-stage operating lines and (2) flux curves based on observations at a pilot-scale denitrification moving bed biofilm reactor (Boltz et al., 2010b).

Biofilms in Water and Wastewater Treatment

Coefficient values, and sometimes the empirical models, are typically created to describe system response for the removal of a specific material. The models can be used as an indicator of system viability to meet treatment objectives with respect to the specific process governing transformation. Empirical models are, however, inadequate for describing complex processes such as the explicit evaluation of two-step ammonium oxidation first to nitrite by ammonia-oxidizing bacteria and then to nitrate by nitrite-oxidizing bacteria. Therefore, empirical models have limited application in defining the conditions that either promote or deter complex processes in biological systems. Historically, biofilm reactors have been designed using empirical criteria and models, but this trend is changing. One should recognize that the coefficients in empirical models describing biofilm reactors include system, and many times, location-specific mass-transfer resistances (Grady et al., 1999). For this reason, the values typically differ from apparent or intrinsic values reported in the literature. Once a flux has been determined, Equation (11) can be rearranged, neglecting bulkphase conversion processes, to calculate the material concentration remaining in the effluent:

CB;i ¼ Cin 

JLF;i  A Q

ð13Þ

If sufficient data exist to allow for the development of parameter values and mathematical relationships capable of describing a complete range of conditions expected when treating municipal wastewater, then empirical models can be used. The addition of model components to account for specific phenomenon encroaches on the premise of mechanistic mathematical model development. For this reason, a distinction is made between empirical and semi-empirical models. Gujer and Boller (1986) and Sen and Randall (2008) provided an example of the latter describing nitrifying TFs, and MBBRs and IFAS systems, respectively.

C

LF

LL

4.15.3.3 Mathematical Biofilm Models for Practice and Research Mathematical modeling can be used to describe certain features of a biofilm or biofilm system (such as a bioreactor) by selecting and solving mathematical expressions. Biofilm reactor research and design commonly involve the use of mathematical biofilm models. These mathematical models are tools that allow the user to efficiently evaluate a variety of complex scenarios. Empirical models fail to provide information that is a concern for biofilm researchers and environmental protection such as biofilm composition and competition among bacteria for multiple substrates and space inside the biofilm, and the influence of individual processes on the interaction between several bacterial types. Mathematical biofilm models have been used as a research tool, but only recently modern biofilm reactors have encouraged the use of biofilm models in engineering practice. Submerged and completely mixed biofilm reactors allow for the application of modern biofilm knowledge, and are conducive to simulation with existing biofilm models (Boltz and Daigger, 2010). As a result, a majority of existing wastewater-treatment plant simulators have been improved to include a biofilm reactor module(s) that is based on the mathematical description of a 1-D biofilm. A user should understand the mathematical biofilm model basis, underlying assumptions, and limitations before applying the model to research or design. A biofilm schematic is shown in Figure 21. The schematic illustrates diffusion and reaction occurring inside a 1-D biofilm. In addition, concentration gradients external to the biofilm surface are illustrated in the manner that they are modeled, namely an external mass transfer resistance represented by a mass transfer boundary layer. The partial differential equation describing molecular diffusion, substrate utilization inside a biofilm, and dynamic accumulation has been presented as Equation (3). It should be emphasized that the basis for a mathematical description of the 1-D biofilm, as described by Equation (3), is simultaneously

C

LF

LL

CB

CB

C LF

C LF

Distance from growth medium

Distance from growth medium

Z Distance from surface, X

545

Z Distance from surface, X

Figure 21 Schematic of a 1-D biofilm of thickness LF having an assumed homogeneous (a) and heterogeneous, or layered, (b) biomass distribution. Soluble substrate concentration profile is illustrated with a bulk-liquid concentration (CB) decreasing through a mass transfer boundary layer of thickness LL until reaching the liquid–biofilm interfacial concentration (CLF), and then decreasing through the biofilm.

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occurring molecular diffusion and biochemical reaction. Molecular diffusion is based on Fick’s law. Monod-type kinetics is typically applied to describe the biochemical transformation rate. Analytical solutions to Equation (3) are available only for first- and zero-order rate expressions and assuming steady state. Zero-order kinetics are valid if the bulkliquid substrate concentration is well above the half-saturation concentration (i.e., CB,i 4 Ki), and first-order kinetics is applicable for low substrate concentrations (i.e., CB,ioKi). Solving the second-order differential equations requires constants that can be derived from two boundary conditions described by Equation (5). From the concentration profile (Cf,i(x)) the flux through the biofilm surface (JLF) is calculated as Equation (2). This substrate flux, JLF, is used in biofilm reactor material balances (see Equation (11)). The concentration gradient external to the biofilm surface is not explicitly modeled. Rather, it is modeled as a mass transfer resistance:

JMTBL ¼

1 ðCB;i  CLF;i Þ RL

ð14Þ

•

•

Here, JMTBL is the substrate flux in the stagnant liquid layer and RL the mass transfer resistance external to the biofilm. It is helpful to visualize RL by introducing the concept of a mass transfer boundary layer. Defining the thickness of this mass transfer boundary layer provides a more intuitive understanding compared to the mass transfer resistance. Resistance to mass transfer and the mass transfer boundary layer thickness are related according to Equation (15):

RL ¼

LL Dw

ð15Þ

Here, LL is the mass transfer boundary layer thickness and Dw the solute diffusion coefficient in the water phase. The substrate flux through the mass transfer boundary layer (Equation (15)) is linked to the substrate flux across the biofilm surface (Equation (2)). This provides an additional Equation (16) (boundary condition) that is required to calculate the additional unknown value of the substrate concentration at the liquid–biofilm interface (JLF):

JMTBL ¼ JLF

ð16Þ

One of the most difficult aspects of choosing an approach to model biofilms and biofilm reactors is to choose the appropriate level of complexity. An overview of the different model approaches is provided below (after Taka´cs et al., 2010):

•

•

0-D biofilm. One aspect of modeling biofilms is that bacteria are retained in the system and are not washed out with effluent water. The simplest approach for biofilm modeling would be to assume that all biomass in the reactor is exposed to bulk phase concentrations neglecting the effect of mass transport limitations (i.e., 0-D). In wastewater treatment biofilms are relatively thick and are usually masstransfer-limited. Thus, the 0-D modeling approach that neglects mass transfer limitations is not useful except in special cases. 1-D homogeneous biofilm (single limiting substrate). This approach takes into account mass transfer limitations into

•

the biofilm and the corresponding effects on concentration profiles and substrate flux into the biofilm. It is assumed that active bacteria are homogeneously distributed over the thickness of the biofilm. The approach is valid only if calculations are performed for the limiting substrate which has to be determined a priori by the user as described in Morgenroth (2008). The flux of the nonlimiting substrates can be calculated based on reaction stoichiometry. 1-D homogeneous biofilm (multiple substrates and multiple biomass components). One key aspect of modeling biofilms is to evaluate the competition and coexistence of different groups of bacteria and local environmental conditions. Local process conditions can be accurately determined by calculating penetration depths for different soluble substrates. Based on the fluxes the growth of individual groups of bacteria can be determined. To simplify calculations it can be assumed that all bacterial groups are homogeneously distributed over the thickness of the biofilm (Rauch et al., 1999; Boltz et al., 2009a). 1-D heterogeneous biofilm. Different groups of bacteria are competing in a biofilm not only for substrate but also for space where bacteria toward the surface are less influenced by mass transport limitations. Bacteria growing toward the base of the biofilm are often rate limited by substrate availability resulting from mass transfer limitations. On the other hand, these bacteria are better protected from detachment. These 1-D heterogeneous biofilm models must keep track of local growth and decay of the different bacterial groups and of detachment to calculate biomass distributions over the biofilm thickness. 2-D and 3-D biofilm models. Practically, biofilms are not as smooth and flat as is assumed in 1-D biofilm models. Mathematical models have been developed that predict the development of biofilms in two or three dimensions, the influence of the heterogeneous structure on fluid flow, and ultimately the combination of fluid flow and biofilm structure on substrate availability and removal inside the biofilm. For most questions related to practical biofilm reactor studies, such multi-dimensional models are not necessary. However, it is important for model users to recognize that biofilm structure influences local fluid dynamics and external mass transport, which are simultaneously affected by biofilm reactor appurtenances and mode of operation. Such interactions are not accounted for in existing 1-D biofilm models due to a rigid segregation of the bulk phase, mass transfer boundary layer, and biofilm (which is assumed to have a uniform thickness and smooth surface). Multi-dimensional biofilm models have been used to quantify the influence of biofilm structure on local fluid dynamics and external mass transport (Eberl et al., 2000).

Different scales of heterogeneity are relevant for biofilm reactors. The length scale of the biofilm thickness, which is on the order of 100–1000 mm, is taken into account in 1-D and multi-dimensional biofilm models. Substrate fluxes from these simulations can then be integrated into models describing overall reactor performance where the length scale is on the order of 1 m. However, heterogeneities can also be observed in biofilm reactors in between these scales where, in some cases, patchy biofilms are observed and where certain

Biofilms in Water and Wastewater Treatment

parts of the biofilm support medium is bare while at other areas dense biofilms develop (B1–10 cm). These heterogeneities in between the small and the large scale are typically not considered in biofilm models and it is not clear to what extent they are relevant (Taka´cs et al., 2010). No simple and general recommendations can be given on what approach is the most appropriate for describing biofilm reactors. Wanner et al. (2006) provided a detailed description of different modeling approaches and a discussion on how the modeling approaches compare for different modeling scenarios. Many commercially available wastewater-treatment plant simulators used for biofilm reactor design and evaluation takes into account multiple substrates and biomass fractions in either a heterogeneous or a homogeneous 1-D biofilm. Examples of software, and references to the biofilm model that constitutes the biofilm reactor module, that is applied to design, optimize, and evaluate, typically pilot- or full-scale biofilm reactors are summarized in Table 1.

4.15.3.4 Biofilm Model Features Excellent guides exist that describe the mathematical modeling of biofilms (see Wanner et al., 2006; Morgenroth, 2008). However, the state of biofilm modeling is subject to several uncertainties. In the context of this chapter, Boltz et al. (2010a) summarized the following items which cause uncertainty when using 1-D biofilm models to describe biofilm reactors: (1) the fate of particulate substrates, (2) biofilm distribution in the reactor and the effect biofilms have on reactor components, (3) dynamics and fate of biofilm detachment, (4) quantifying concentration gradients external to the biofilm surface, and (5) a lack of generally accepted biofilm reactor Table 1

model calibration protocol. Parameter estimation and model calibration are serious concerns for process engineers who apply biofilm models in engineering practice. Therefore, parameters that are critical components of biofilm reactor models (that use a 1-D mathematical biofilm model) are introduced, including: attachment (kat) and detachment (kdet) coefficients, the mass transfer boundary layer, rate-limiting substrate diffusivity coefficient inside the biofilm (Df,ratelimiting), and the biokinetic parameters maximum growth rate (m) and the ratelimiting substrate half-saturation coefficient (Ki,ratelimiting) (Boltz et al., 2010b).

4.15.3.4.1 Attachment and detachment process kinetics and rate coefficients An accurate mathematical description of particle attachment and detachment processes is a critical component of biofilm (reactor) models. Unfortunately, attachment/detachment process mechanics are poorly understood. Conceptually, particles suspended in the bulk liquid are hydrodynamically transported to the vicinity of the biofilm. From the bulk phase, particles are subjected to concentration gradients external to the biofilm surface. Particles enter the biofilm matrix through channels, crevasses, and other structural irregularities where they attach to the biofilm surface (see Reichert and Wanner (1997) for a description of particle transport within the biofilm matrix). Once entrapped, the particles can be hydrolyzed by extracellular polymeric enzymes resulting in soluble substrate that diffuses into the biofilm. Then, the soluble substrate is subject to well-known biochemical transformation processes that yield biomass. Alternatively, particles that have attached to the biofilm surface from the bulk phase remain unaltered and exit the system after detaching from the biofilm

Biofilm models used in practice (Boltz et al. 2010b)

Software

Company

Biofilm model type and biomass distribution

Reference

AQUASIMTM

EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Du¨bendorf, Switzerland (www.eawag.ch/index_EN) Aquaregen, Mountain View, California (www.aquifas.com) EnviroSim Associates Ltd., Flamborough, Canada (www.envirosim.com)

1-D, DY, N; heterogeneous

Wanner and Reichert (1996) (modified)

1-D, DY, SE and N, heterogeneous 1-D, DY, N, heterogeneous

Sen and Randall (2008)

Hydromantis Inc., Hamilton, Canada (www.hydromantis.com) CH2M HILL Inc., Englewood, Colorado (www.ch2m.com/corporate) ifak GmbH, Magdeburg, Germany (www.ifak-system.com) WRc, Wiltshire, England (www.wateronline.com/ storefronts/wrcgroup.html) MOSTforWATER, Kortrijk, Belgium (www.mostforwater.com)

1-D, DY, N, heterogeneous

AQUIFASTM BioWinTM

GPS-XTM Pro2DTM SimbaTM STOATTM WESTTM

547

1-D, SS, N(A), homogeneous (constant Lf) 1-D, DY, N, heterogeneous 1-D, DY, N, heterogeneous 1-D, DY, N(A)a, Nb, homogeneousa, heterogeneousb

Wanner and Reichert (1996) (modified), Taka´cs et al. (2007) Hydromantis (2006) Boltz et al. (2009a; 2009b) Wanner and Reichert (1996) (modified) Wanner and Reichert (1996) (modified) Rauch et al. (1999)a, Wanner and Reichert (1996) (modified)b

a

Rauch et al. (1999) is linked with the definition ’N(A)’ and ’homogeneous’. Wanner and Reichert (1996) (modified) is linked with the definition ’N’ and ’heterogeneous’.

b

1-D, one dimensional; DY, dynamic; N, numerical; N(A), numerical solution using analytical flux expressions; SE, semi-empirical; SS, steady-state. Hydromantis, Inc. (2006) Attached growth models. In: GPS-X Technical Reference, pp. 157–185 (unpublished).

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Biofilms in Water and Wastewater Treatment

matrix. Most of the heterogeneous 1-D biofilm models listed in Table 1 describe the rate of particle attachment (rat) as a first-order process ðrat ¼ kat  XTSS;bulk Þ depending on an attachment rate coefficient (kat) and the bulk-liquid particle concentration. Boltz and La Motta (2007) presented a model describing variability in this parameter with influent particle concentrations. The researchers postulated that increasing particle concentrations ultimately reduced the biofilm surface area available for particle attachment; thereby, the particle attachment coefficient decreases until reaching a plateau. The plateau was considered commensurate with a condition in which a minimum biofilm area was consistently available as a result of continuously detaching biofilm fragments (during steady operating conditions – variable hydrodynamics can influence biofilm structure). Given the current state of the science, experimental data are required to develop/validate or evaluate existing approaches for simulating the fate of particles in biofilm reactors. Steady-state biofilm models have assumed a constant biofilm thickness in which case biofilm growth is balanced by internal loss (e.g., decay and hydrolysis, or endogenous respiration) and/or detachment. This approach has been successfully applied to simulate biofilm reactors at steady state, but their dynamic simulation requires that a detachment model is included despite rather limited mechanistic understanding. The rate (Morgenroth and Wilderer, 2000; Boltz et al., 2010a) and category (i.e., abrasion, erosion, sloughing, and predator grazing) of detachment can have a significant influence on biofilm reactor simulation and performance (Morgenroth, 2003). Kissel et al. (1984) stated that problems inherent to biofilm detachment modeling include a poor understanding of fundamental (biofilm detachment) process mechanics and the inability to predict exactly at what location inside the biofilm that detachment will occur. Detachment location is important when taking into account a heterogeneous biofilm distribution throughout the reactor either by combining multiple 1-D simulations or by 2- or 3-D modeling (Morgenroth et al., 2000). Unlike attachment, Boltz et al. (2010a) described eight different biofilm detachment rate expressions (rdet) for the heterogeneous 1-D biofilm models listed in Table 1. Detachment rate equations can be categorized based on the aspect controlling detachment: biofilm thickness (LF), shear, or growth/activity. Mixed-culture biofilms, such as those growing in a combined carbon oxidation and nitrification MBBR, are subject to competition for substrate between fast-growing heterotrophic and slow-growing autotrophic organisms (primarily for dissolved oxygen). Morgenroth and Wilderer (2000) performed a modeling study that demonstrated ammonium flux was significantly influenced by the mode of simulated detachment. Essentially, biofilm (thickness) dynamics influenced competition for substrate between heterotrophic and autotrophic organisms; high variations in biofilm thickness dynamics favored the faster growing heterotrophic organisms.

4.15.3.4.2 Concentration gradients external to the biofilm surface and the mass transfer boundary layer Biofilms growing virtually in all full-scale biofilm reactors are subject to some degree of substrate concentration gradients

external to the biofilm surface. Concentration gradients external to the biofilm surface are not explicitly simulated in 1-D biofilm models. Rather, the reduction in concentration of any substrate is modeled as a mass-transfer resistance, RL ( ¼ LL/Dw). Based on the observation that the external masstransfer resistance, RL, is more dependent on biofilm reactor bulk-liquid hydrodynamics than biofilm thickness or surface heterogeneity, the impact of RL can be accounted for by empirical correlations (Boltz et al., 2010a). However, a realistic description of hydrodynamic effects ultimately depends on an accurate estimate of the mass-transfer boundary layer thickness LL. Therefore, the mass-transfer boundary layer thickness is an important facet of biofilm-reactor models that use a 1-D biofilm model. Despite the potential significant impact the mass-transfer boundary layer thickness may have on biofilmreactor model results and process design, factors influencing the interface between the biofilm model and reactor scale is one important feature of biofilm-reactor models that is not well understood.

4.15.3.4.3 Diffusivity coefficient for the rate-limiting substrate inside the biofilm Soluble substrates are primarily transported into biofilms by a combination of advection and molecular diffusion. Generally, the most important mechanism is molecular diffusion (Zhang and Bishop, 1994). The largest component of biofilm is water, but the diffusivity of a solute inside the biofilm is generally less than that in water because of the tortuosity of the pores and minimal biofilm permeability. Consequently, an effective diffusivity must be applied. Many biofilm reactor models treat this value as 80% of the diffusivity in water (i.e., Dw ¼ Df/0.80) (Stewart, 2003). However, it has been demonstrated that the effective diffusion coefficient (Df,i) for any soluble substrate i can vary with depth inside the biofilm (Beyenal and Lewandowski, 2000). The effective diffusivity decreases with depth because of increasing density and decreasing porosity and permeability of the biofilm with depth. Flow velocity past the biofilm is a major influencing factor determining biofilm density. Varying liquid velocity in the vicinity of the biofilm surface can influence a soluble substrate effective diffusivity inside a biofilm. Consequently, the varying flow rate can affect the rate of internal mass transfer and transformation rates (Bishop, 2003). Turbulent, high-sheer stress environments result in planar and denser biofilms while quiescent, low-sheer stress environments will result in rough and less dense biofilms (van Loosdrecht et al., 1995). Picioreanu (1999) defined a growth number ðG ¼ L2f  mmax  Xf =ðDf  CB ÞÞ that can be related to biofilm roughness. According to Picioreanu (1999), the biofilm may have a dense solid matrix and a flat surface when Go5. However, if G 4 10 the biofilm may develop complex structures such as mushroom clusters and streamers.

4.15.3.4.4 Parameters: estimation and variable coefficients A parameter is an arbitrary constant whose value characterizes a system member. Biokinetic parameter estimation is a serious concern for those who seek to use biofilm models for biofilm reactor process design and research because most parameter values cannot be measured directly in full-scale municipal

Biofilms in Water and Wastewater Treatment

wastewater-treatment plants (Brockmann et al., 2008). Parameters exist for every aspect of biofilm models, including stoichiometry, kinetics, mass transfer, and the biofilm itself. A majority of parameter values in modern process models (e.g., those described by Henze et al. (2000)) have a substantial database that serves to define a relatively narrow range of values that are applicable to a majority of municipal wastewater-treatment systems. Existing biofilm models are relatively insensitive to changes in a majority of the biokinetic parameter values, most of which are described by Henze et al. (2000), within a range of values reported in the literature except for, as an example, the autotrophic nitrifier maximum growth rate (m). However, the mathematical description of some processes includes variable, or lumped, parameters. These parameter values are often system specific and subject to significant uncertainty. The lumped parameters account for an incomplete mechanistic description of the simulated process. Lumped parameters in a majority of biofilm models, including those described in this chapter, are the oxygen affinity constant for autotrophic nitrifiers (KO2,A), endogenous respiration rate constants (bres), attachment rate coefficient (kat), detachment rate coefficient (kdet), mass-transfer boundary layer thickness (LL), ratio of diffusion in biofilm to diffusion in water (Df/Dw). Indentifying parameter subsets that require definition for biofilm model calibration has been the subject of several investigations by Smets et al. (1999), Van Hulle et al. (2004), and Brockmann et al. (2008).

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model for another period. Similarly, Bilyk et al. (2008) reported the calibration of a denitrification filter model by adjusting assumed biofilm thickness and incorporating the assimilative denitrification reaction. Both of these biofilm reactor model calibration efforts were based on bulk-phase measurements, but only Sin et al. (2008) utilized measured characteristics of the biofilm. Such adjustments to systemspecific biofilm and biokinetic parameters in order to match observed data may not produce a properly calibrated model that is capable of describing a variety of design conditions for a wastewater-treatment plant. As previously discussed, the attachment coefficient, for example, has been experimentally demonstrated (and described mathematically) to change as a function of particle (total suspended solids) load (Boltz and La Motta, 2007). Then, it may be argued that adjusting the attachment coefficient (during calibration) to match an observed dataset would naturally render the calibrated model incapable of describing another scenario with a different particle load. Suffice it to say that a reliable and transparent description of recommended approaches for the application and calibration of biofilm models are required for the models to gain general acceptance, understanding, and become effectively used in engineering practice. Protocol defining methodology for sampling, testing, evaluating and applying data to mathematical biofilm reactor models is required. It is likely that existing biofilm reactor models will require improvement for reliable dynamic simulation in practice.

4.15.3.5 Biofilm Reactors in Wastewater Treatment 4.15.3.4.5 Calibration protocol Application of a dynamic biofilm model to describe full-scale municipal wastewater-treatment processes requires a calibration of the selected model. Ad hoc expert-based trial and error and standardized systematic approaches have been used to calibrate process models. Sin et al. (2005) presented a critical comparison of systematic calibration protocols for activated sludge models. These protocols have many similarities that are applicable to biofilm reactor models including goal definition, data collection/testing/reconciliation, and validation. The major differences between protocols reported by Sin et al. (2005) are related to the measurement campaign, experimental methods for influent wastewater characterization, and parameter subset selection and calibration. The major differences speak to areas of systematic calibration protocols for activated sludge models that will almost certainly be exasperated when creating systematic protocol for calibrating a biofilm reactor model. Certainly, additional tests will be required to characterize the physical attributes of both suspended growth and biofilm compartments, and mathematical biofilm models have more parameters than activated sludge models. Furthermore, the biofilm compartment parameters must be estimated from bulk-phase measurements in order to have a timely and costeffective approach to calibrating biofilm reactor models. Sin et al. (2008) reported the calibration of a dynamic biologically active (continuously backwashing) filter model using traditional expert-based manual trial and error. The researchers manipulated system-specific parameters related to attachment, detachment, and biofilm thickness. After calibration, Sin et al. (2008) successfully tested the calibrated

Biofilm reactors play an important role in environmental biotechnology, but many aspects of their design and operation remain poorly understood. Biofilm reactors can be traced to origination of modern water sanitation. Corbett (1903) reported the use of continuously distributed sewage flow over a fixed bed, and Stoddart (1911) reported the use of a coarse biofilm-covered medium dosed with a continuous trickling flow. These accounts are acknowledged as the creation of the TF process. Approximately 100 years following these reports significant advances in the design, academic understanding, and mathematical modeling of biofilms have led to the development of new and emerging biofilm reactors conducive to fundamentally based design approaches and the application of fundamentally based design and operation procedures for traditional biofilm reactors. Two processes – mass transfer and biochemical conversion – are characteristics of all biofilm reactors and influence biofilm structure and function. Compartments that are common to every biofilm reactor exist to optimize mass-transfer and biochemical conversion.

4.15.3.5.1 Biofilm reactor compartments Biofilm reactors have five primary compartments: (1) influent wastewater (distribution) system; (2) containment structure; (3) biofilm carrier; (4) effluent water collection system; and (5) an aeration system (for aerobic processes and scour) or mixing system (for anoxic processes that require bulk-liquid agitation and biofilm carrier distribution). Five components influence local conditions inside the biofilm: (1) biofilm carrier surface (i.e., substratum); (2) biofilm (including both particulate and liquid fractions); (3) mass-transfer boundary

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Biofilms in Water and Wastewater Treatment

layer; (4) bulk liquid; and (5) gas phase (when significant). The components typical of biofilm reactors are described in context of some commercially available biofilm reactors. The five biofilm reactors described include the MBBR, BAF, FBBR, rotating biological contactor (RBC), and TF.

4.15.3.5.2 Moving bed biofilm reactors The MBBR is a two- (anoxic) or three- (aerobic) phase system with a buoyant free-moving plastic biofilm carrier that requires mechanical mixing or aeration to distribute carriers throughout the tank. The process includes a submerged, completely mixed biofilm reactor and liquid–solids separation unit (Ødegaard, 2006). A range of pollutant loading and bulkphase external carbon sources in denitrification MBBRs and dissolved oxygen concentrations in carbon-oxidation and/or nitrification MBBRs have been applied, and system response evaluated (Lazarova and Manem, 1994). It has been demonstrated that MBBRs are capable of processing wastewater to meet a variety of effluent water-quality standards ranging, for example, from the US Environmental Protection Agency definition of secondary treatment (30 mg TSS l1 and 30 mg BOD5/l monthly average) to more stringent enhanced nitrogen removal limits (e.g., total nitrogen less than 3–5 mg l1) under a variety of loading conditions. The MBBR process is capable of meeting similar treatment objectives as the activated sludge process for carbon oxidation, nitrification, and denitrification, but the MBBR makes use of a smaller tank volume than a clarifier-coupled activated sludge system. Biomass retention is clarifier independent; therefore, solids loading in liquid–solids separation unit are significantly reduced when compared with the activated sludge process. Because it is a continuously flowing process, the MBBR does not require a special operational cycle for biofilm thickness control (e.g., backwashing in a BAF or flushing in a TF). Hydraulic head loss and operational complexity is minimal. The MBBR offers much of the same flexibility to manipulate the process flow sheet (to meet specific treatment objectives) as the activated sludge process. Multiple reactors can be configured in series without the need for intermediate pumping or return activated sludge pumping (to accumulate mixed liquor). Liquid–solids separation may be achieved with a variety of processes including sedimentation basins, dissolved air flotation, cloth-disk and membrane filters. The MBBR is well suited for retrofit installation into existing municipal wastewater-treatment plants. An MBBR may be a single reactor or several reactors in a series. Typically, each MBBR has a length-to-width ratio (L:W) in the range of 0.5:1–1.5:1. Plans with an L:W greater than 1.5:1 can result in nonuniform distribution of the biofilm carriers. MBBRs contain a plastic biofilm carrier volume up to 67% of the liquid volume. Screens are typically installed with one MBBR wall and allow treated effluent to flow to the next treatment step while retaining the free-moving plastic biofilm carriers. Aerobic MBBRs use a diffused aeration system to evenly distribute the plastic biofilm carriers and meet process oxygen requirements. On the other hand, anoxic MBBRs use mechanical mixers to evenly distribute the plastic biofilm carriers because there is no process oxygen requirements. Each process mechanical component is submerged. Figure 22

depicts the Williams-Monaco WWTP, Commerce City, Colorado, a two-train bioreactor that consists of four MBBRs in series. The biofilm carriers are extruded or molded from either virgin or recycled high-density polyethylene (HDPE). Table 2 summarizes characteristics of several commercially available plastic biofilm carriers. The carriers are slightly buoyant and have a specific gravity between 0.94 and 0.96 g cm3. Both native and biofilm-covered plastic biofilm carriers have a propensity to float in quiescent water. Biofilms primarily develop on the protected surface inside the plastic biofilm carrier. For this reason, the specific surface areas of plastic biofilm carriers listed in the table exclude areas not inside the plastic carrier. The listed bulk-specific surface area, which is based on 100% carrier fill, is characteristic of a plastic biofilm carrier. The net specific surface area is characteristic of plastic biofilm carrier and fill percentage. For example, if a plastic biofilm carrier has a 500 m2 m3 bulk-specific surface area, then the net specific surface area at 50% carrier fill is 250 m2 m3. Similarly, the net liquid volume displacement at 50% carrier fill is 0.0725 for a plastic biofilm carrier having a characteristic 0.15-bulk-liquid volume displacement (at 100% carrier fill). Plastic biofilm carriers are retained in an MBBR by horizontally configured cylindrical screens or vertically configured flat screens as shown in Figure 23. Aerobic zones typically contain cylindrical screens; anoxic zones contain the flat wall screens. Cylindrical screens are desired. They extend horizontally into the upward-flowing air bubbles imparted by the diffuser grid which aids in scouring any accumulated debris. Energy imparted by the mechanical mixers is insufficient to dislodge debris accumulated on the flat wall screen. Therefore, scouring of flat screens is accomplished with a sparging air header in a denitrification MBBR. Removing the debris retained on a screen aids in maintaining hydraulic throughput. Hydraulically, an MBBR is commonly designed to process a maximum approach velocity (based on the tank cross-sectional area perpendicular to forward flow) in the range 30– 35 m h1. Screen area is defined by the maximum allowable head loss through the screens, which is typically in the range of 5–10 cm. The screen superficial hydraulic load is typically in the range of 50–55 m h1 for average design conditions. The screens and their supporting structural assemblies, if required, are typically constructed from stainless steel and may be from wedge-wire mesh or perforated plates. Low-pressure diffused air is applied to aerobic MBBRs. The airflow enters the reactor through a network of air piping and diffusers that are attached to the tank bottom. Airflow has the dual purpose of meeting process oxygen requirements and uniformly distributing plastic biofilm carriers. To promote uniform distribution of the plastic biofilm carriers, the diffuser grid layout and drop pipe arrangement provide a rolling water circulation pattern. Coarse-bubble diffusers are typically used in moving bed reactors (Figure 25). Coarse-bubble diffusers typically used in MBBRs are stainless steel pipes with circular orifices along the underside. These coarse-bubble diffusers are less affected by scaling and fouling because of the large dimension and turbulent airflow through the discharge orifice (Stenstrom and Rosso, 2008). As a result, coarse-bubble diffusers require less maintenance than fine-bubble diffusers. The coarse-bubble diffusers are designed with a structural end

Biofilms in Water and Wastewater Treatment

551

Aerated reactor #2

Aerated reactor #1

RECIR

Mixer Mixed bed reactor #2

Screen

Effluent overflow Effluent

Airflow distribution area

RECIR pump Effluent basin

RECIR

RECIR

Mixed bed reactor #1

Effluent

Influent Influent splitter box

Aerated reactor #3

RECIR

Aerated reactor #4

(a)

Mixed bed reactor #4

Mixed bed reactor #3

RECIR

Effluent

RECIR

(b)

Figure 22 (a) Moving bed biofilm reactor at the Williams-Monaco Wastewater Treatment Plant, Colorado, USA. (b) Schematic representation of the photographed system which illustrates the system consisting of two parallel trains each with four reactors in series.

support that enables them to withstand the weight of plastic biofilm carriers when the MBBR is out of service and drained. Denitrification MBBRs use mechanical mixers to agitate the bulk of the liquid and to distribute plastic biofilm carriers uniformly throughout the tank. The mechanical mixers are typically rail-mounted submersible (wet motor) units. Stateof-the-art submersible mechanical mixers typically have a maximum 120-rpm impeller speed and a minimum of three blades per impeller. The mixer uses a stainless steel backwardcurve propeller with a round bar welded along its leading edge to avoid damage to the plastic biofilm carriers and impeller wear. The mixer has a large diameter impeller with a fairly low rotational speed (90 rpm at 50 Hz and 105 rpm at 60 Hz). The plastic biofilm carriers float in quiescent water. As a result, the mixers need to be located near the water surface but not so close as to create an air-entraining vortex. A slight negative

inclination of mixer orientation helps maintain the rollingwater circulation pattern and uniformly distribute plastic biofilm carriers (see Figure 24). Rail-mounted units facilitate access to the mixer when maintenance is required. The mixers are typically sized to input 25 W m3 of reactor volume. Carbon-oxidizing MBBRs are classified as low-rate, normalrate, or high-rate bioreactors. Low-rate carbon-oxidizing MBBRs promote conditions for nitrification in downstream reactors. High- and normal-rate MBBRs are strictly carbon-oxidizing bioreactors. In the absence of site-specific pilot-scale observations or a calibrated mathematical model, high-rate MBBRs are typically designed to receive a filtered BOD5 load in the range of 15–20 g m2 d1 at 15 1C. This corresponds to total BOD5 loads as high as 45–60 g m2 d1 at 15 1C (Ødegaard, 2006). To reach secondary treatment effluent standards, a hydraulic residence time less than 30 min is not

552

Biofilms in Water and Wastewater Treatment

Table 2

Moving bed biofilm reactor plastic biofilm carrier characteristicsa

Manufacturer

Name

Bulk specific surface area, weight, gravity

Nominal carrier dimensions (depth; diameter)

Veolia Inc.

AnoxKaldnesTM K1

500 m2 m3 145 kg m3 0.96–0.98

7.2 mm; 9.1 mm

AnoxKaldnesTM K3

500 m2 m3 95 kg m3 0.96–0.98

10 mm; 25 mm

AnoxKaldnesTM Biofilm Chip (M)

1,200 m2 m3 234 kg m3 0.96–1.02

2.2 mm; 45 mm

AnoxKaldnesTM Biofilm Chip (P)

900 m2 m3 173 kg m3 0.96–1.02

3 mm; 45 mm

ActiveCellTM 450

450 m2 m3 134 kg m3 0.96

15 mm; 22 mm

ActiveCellTM 515

515 m2 m3 144 kg m3 0.96

15 mm; 22 mm

ABC4TM

600 m2 m3 150 kg m3 0.94–0.96

14 mm; 14 mm

ABC5TM

660 m2 m3 150 kg m3 0.94–0.96

12 mm; 12 mm

BioPortzTM

589 m2 m3

14 mm, 18 mm

Infilco Degremont Inc.

Aquise

Entex Technologies Inc.

Carrier photo

a

As reported by manufacturer. Modified from Boltz JP, Morgenroth E, deBarbadillo C, et al. (2010b) Biofilm reactor technology and design. In: Design of Municipal Wastewater Treatment Plants, WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, 5th edn, vol. 2, ch. 13, (ISBN P/N 978-0-07-166360-1 of set 978-0-07-166358-8; MHID P/N 0-07166360-6 of set 0-07-166358-4). New York: McGraw-Hill.

recommended. Medium-rate MBBRs designed for meeting basic secondary treatment standards are typically designed for a loading of 5–10 g BOD5 m2 d1 at 10 1C, depending on the choice of liquid–solids separation process. Values in the higher range are used when coagulation occurs before the separation unit; values in the lower range are used without coagulation. Studying a pilot-scale combined carbon oxidation and nitrification MBBR receiving primary effluent and a (tertiary) nitrification MBBR receiving secondary effluent while maintaining a 4–6 g m3 bulk-liquid dissolved-oxygen concentration in both units, Hem et al. (1994) observed that a total BOD5 load of 1–2 g m2 d1 resulted in nitrification rates

from 0.7 to 1.2 g m2 d1, a total BOD5 load of 2–3 g m2 d1 resulted in nitrification rates from 0.3 to 0.8 g m2 d1, and a total BOD5 load greater than 5 g m2 d1 resulted in virtually no nitrification.

4.15.3.5.3 Biologically active filters BAFs have natural mineral, structured or random plastic media that supports biofilm growth and serves as a filtration medium. Solids accumulated from filtration and biochemical transformation are removed by backwashing. Media density influences BAF configuration and backwash regimes. BAF

Biofilms in Water and Wastewater Treatment

553

(a)

(b)

Figure 23 (a) Horizontal cylindrical screens constructed of wedge wire. Stainless steel coarse-bubble diffusers typically used in aerobic MBBRs are also pictured on the tank floor. (b) Flat wall screen constructed of wedge wire. A single air-header is pictured. Air is periodically introduced to scour debris accumulated on the screen.

A

B

30°

D (a)

(b)

Figure 24 (a) Schematic and (b) picture of mechanical mixers that are specially designed for anoxic moving bed biofilm reactors.

influent requires preliminary and primary treatment. Historically, the acronym BAF has meant biological aerated filters which have been used to refer to aerated biofilters used for secondary treatment. However, Boltz et al. (2010b) revised the acronym BAF to cover all BAFs, including those that operate under anoxic conditions for denitrification. BAFs are characterized by their media configurations and flow regime.

Downflow BAFs with media heavier than water include the Biocarbones process, which was marketed during the 1980s for secondary and tertiary treatment, and packed-bed tertiary denitrification filters such as the Tetra Denites process. These BAFs are backwashed using an intermittent counter-current flow. Upflow BAFs with media heavier than water such as the Infilco Degremont Biofors process have been used for

554

Biofilms in Water and Wastewater Treatment

secondary and tertiary treatment. The systems make use of expanded clay or another mineral media. These BAFs are backwashed using an intermittent concurrent flow. BAFs with floating media such as the Veolia Biostyrs process have also been used for secondary and tertiary treatment, and uses polystyrene, polypropylene, or polyethylene media. These BAFs operate with an intermittent backwash counter-current flow. Continuous backwashing filters operate in an upflow mode and contains media that is heavier than water. The media continuously moves counter-current to the wastewater flow (i.e., downward), and is continuously channeled to a center air lift where it is scoured, rinsed, and returned to the top of the media bed. Nonbackwashing submerged filters consist of a submerged static media bed, and have been called submerged aerated filters (SAFs). Solids are not retained in these filters. Therefore, nonbackwashing submerged filters require a dedicated liquid–solids separation process. A downflow BAF with media heavier than water, such as the Tetra Denites filter, is illustrated in Figure 25. The

Denites process has been used since the late 1970s for meeting stringent total nitrogen limits while providing a filtered effluent. Methanol or another external carbon source is added to the influent wastewater stream to promote biological denitrification. A typical installation includes 1.8 m of 2–3 mm diameter sand media over 457 mm of graded support gravel. In a downflow denitrification BAF, the backwash cycle typically consists of a brief air scour followed by an air–water backwash and water rinse cycle. Backwash water and air scour flow rates are typically 15 and 90 m3 m2 h1, respectively. Backwash water usage is typically 2–3% of the average flow being treated. Nitrogen gas accumulates in the media. A releasing mechanism is pumping backwash water up through the media bed for a short duration. The denitrification capacity between nitrogen release cycles typically ranges from 0.25 to 0.5 kg NOX-N m2. An upflow BAF with media heavier than water, such as the Infilco Degremont Biofors, is illustrated in Figure 26. The Degremont Biofors operates such that solids are trapped Proces air

Raw water

Air

Backwash water extraction

Water Biofilter media Support layer

Air scour Backwash water Treated water Figure 25 Downflow BAF with media heavier than water (e.g., Biocarbones and Tetra Denites). From ATV (1997) Biologische und weitergehende Abwasserreinigung (German), 4th edn. Berlin: Ernst and Sohn as presented by Tschui (1994).

Water

Process air

Biofilter media

Backwash water extraction

Air

Support layer

Air scour Treated water Backwash water

Raw water Figure 26 Upflow BAF with media heavier than water (e.g., Infilco Degremont Biofors). From ATV (1997) Biologische und weitergehende Abwasserreinigung (German), 4th edn. Berlin: Ernst and Sohn as presented by Tschui (1994).

Biofilms in Water and Wastewater Treatment

mostly in the lower part of the filter medium during normal operation and are removed through backwashing and applying scour air. As the backwash consists of concurrent scour air and backwash water, accumulated solids travel up through the media bed before being released at the top. Three types of media can be used in the Biofors depending on the application; the media types include expanded clay, expanded shale (both in the form of spherical grains with an effective size of 3.5 or 4.5 mm), and angular grains (with an effective size of 2.7 mm). The media form a submerged, fixed bed in the bottom of the reactor. The media bed typically has a height of 3–4 m with approximately 1-m freeboard. The grains-specific surface area is approximately 1640 m2 m3. Influent water to the bed flows through a plenum and nozzle air/water distribution system. The nozzles are installed in a false floor located approximately 1 m above the filter floor. Nozzles in the false floor are subject to clogging. Therefore, backwash water and scour air flow through the same plenum/nozzle system. Process air is introduced through separate air diffusers located in the media bed above the inlet nozzles. A key issue with the backwash of sunken media systems is the potential for boils during backwashing. The flow will short-circuit through the line of least resistance. This will result in a boil, or violent eruption of the flow through the point of least resistance. Similar short circuits and boils can also occur if the nozzles are blocked. These boils can result in excessive media loss during backwashing. Therefore, to achieve even backwashing the water must be well distributed across the BAF plan area. Therefore, the headloss across the distribution system must be greater than the headloss through the bed. An upflow BAF with floating media, such as the Veolia Biostyrs, is illustrated in Figure 27. These processes use a floating bed of media to provide area for biofilm development and filtration. Coarse-bubble aeration diffusers exist at the bottom of the media to enhance the contact of air, water, and biomass (Rogalla and Bourbigot, 1990). The Biostyrs process uses light weight expanded polystyrene (specific gravity of 0.05). Alternatively, the Biobeads process uses

555

recycled polypropylene with a specific gravity slightly lower than 1. The Biostyrs reactor is partially filled with (2–6 mm) polystyrene beads. Process objectives determine selection of the bead size; larger beads can be more heavily loaded. The beads, which are lighter than water, form a floating bed in the upper portion of the reactor, typically a height of 3–4 m with approximately 1.5 m of freeboard. The top of the bed is restrained by a slab fitted with filtration nozzles to evenly collect treated wastewater. The clean specific surface area of spherical beads is 1000–1400 m2 m3. In the bottom of the reactor, influent is distributed by troughs formed in the base of the cells. Process air is distributed through diffusers located along the bottom of the reactor or within an aeration grid in the media bed. The latter is used if an anoxic zone is required for denitrification. Backwashing consists of counter-current air scour and backwash water flow. The Biobeads BAF process is similar to Biostyrs, except that the media is larger and heavier, using polypropylene or polyethylene with a density of approximately 0.95. To prevent media attrition, a metal grid is fixed near the top of the reactor. Upflow floating media BAFs may also require a certain number of mini-backwashes (typically 4–8 and, in extreme cases, more than 10) to bump the filter, remove some solids, and lower headloss to achieve a complete filtration cycle of 24 or 48 h (which is the time between normal backwashes). The requirement for minibackwashes plus normal backwashes can generate a significant backwash water volume. During demonstration testing in San Diego, California, USA, a single-stage carbon-oxidation BAF with floating media generated a backwash water volume in the range of 10.3–13.9% of influent flow, compared to a sunken media BAF which produced a backwash water volume in the range of 7.4–7.9% (Newman et al., 2005). An upflow continuous backwash BAF, such as the Parkson Dynsands, is illustrated in Figure 28. Moving bed, continuous backwash filters operate in an upflow mode and consist of media heavier than water. The media continuously moves downward, counter-current to the wastewater flow. These filters are used widely for tertiary suspended solids and turbidity Backwash water

Air scour Process air

Air Aerobic filter zone

Treated water

Anoxic filter zone Water

Recirculation pump Raw water

Backwash water extraction Figure 27 Upflow BAF with floating media (e.g., Veolia Biostyrs). Adapted from ATV (1997) Biologische und weitergehende Abwasserreinigung (German), 4th edn. Berlin: Ernst and Sohn as presented by Tschui (1994).

556

Biofilms in Water and Wastewater Treatment Central reject compartment (H)

Feed (influent) (A)

Rejects (L) Top of airlift pump (G) Filtrate weir (J)

Reject weir (K) Sand washer (L) Effluent (E)

Downward moving sand bed (D)

Downward feed (B) Feed radials (C)

Bottom of airlift pump (F) Figure 28 Parkson Dynasands process schematic, continuous backwash BAF.

removal but have also been applied to separate stage nitrification and denitrification. Two commercially available systems using this technology are the Parkson DynaSands and Paques Astrasands filters. The filter cells are supplied as 4.65-m2 modules with center airlift assembly. The effective media depth is typically 2 m, and sand media size typically ranges from approximately 1 to 1.6 mm. Influent wastewater enters the filter bed through radials located at the bottom of the filter. The flow moves up through the downward-moving sand bed and effluent flows over a weir at the top of the filter. The media, with the accumulated solids, is drawn downward to the bottom cone of the filter. Compressed air is introduced through an airlift tube extending to the conical bottom of the filter and rises upward with a velocity exceeding 3 m s1 creating an air pump that lifts the sand at the bottom of the filter through the center column. The turbulent upward flow in the airlift provides scrubbing action that effectively separates solids from the media before discharge to a wash box. There is a constant upward flow of liquid into the wash box (backwash water) controlled by the wash box discharge weir. Moving bed filter manufacturers typically set the reject weir to provide a wash water flow rate equivalent to approximately 10% of the forward flow at an average filter loading rate of 4.9 m h1. The backwash frequency is quantified by the bed turnover rate. To maintain sufficient biomass for denitrification, the bed turnover rate must be reduced to approximately 100–250 mm h1.

Several media types are available for use in BAFs. Media selection is integral to meeting treatment objectives, flow and backwashing regimes. Typically, media can be categorized as mineral media and plastic media. In most cases, mineral media is denser than water and plastic media is buoyant. The media needs to resist breakdown from abrasion during backwashing and chemical degradation by constituents in municipal wastewater. Commercially available BAF systems and their media are listed in Table 3. Backwashing BAFs maximizes solids capture and filter run time. Proper backwashing requires filter bed expansion and rigorous scouring followed by efficient rinsing. Accumulation of solids and media (mud balling) results in wastewater short-circuiting and can result in excessive media loss. Feed characteristics and type of treatment provided by the BAF affect solids production and frequency requirements for backwashing. Biomass yield in tertiary BAF systems is typically low, so backwashing is relatively infrequent (i.e., one backwash per 36–48 h). Reactor characteristics and media type influence backwash frequency. More openly structured media capture fewer solids which reduces backwash frequency. During backwashing the media bed is typically expanded or fluidized (depending on the system) to allow for grain separation and free movement in order to remove as much accumulated solids as possible. Table 4 compares typical BAF backwashing requirements. BAFs designed for carbon oxidation and suspended solids removal in secondary treatment typically have volumetric BOD loading rates in the range of 1.5–6 kg m3 d1. Average and peak HLRs for secondary and tertiary treatment systems are typically in the range of 4–8 and 10–20 m h1, respectively. As BAFs for secondary treatment are typically placed immediately downstream of primary clarifiers, the applied volumetric mass loading rate is almost always the limiting design parameter. Combined carbon oxidation and nitrification will proceed when the organic loading at lower temperatures is limited to 2.5 kg BOD m3 d1 (Rogalla et al., 1990). Under these conditions a total Kjeldahl nitrogen removal rate of 0.4 kg N m3 d1 may be achieved. Inversely, Rogalla et al. (1990) found that nitrification decreases when soluble COD loadings approach 4 kg m3 d1. Ammonium removal of 80– 90% can be achieved for ammonium loads in the range of 2.5–2.9 kg m3 d1 (Peladan et al., 1996).

4.15.3.5.4 Expanded and fluidized bed biofilm reactors Expanded bed biofilm reactors (EBBRs) and FBBRs use small media particles that are suspended in vertically flowing wastewater, so that the media becomes fluidized and the bed expands. Individual particles become suspended once the drag force of the relatively fast flowing wastewater (30–50 m h1) overcomes gravity and they are separated. In municipal applications, fluidized beds are typically used for tertiary denitrification. Design criteria for denitrifying FBBRs are listed in Table 5. When treating groundwater or industrial wastewater, FBBRs are used for the removal of oxidized contaminants such as nitrate and perchlorate. Suspension of the media maximizes the contact surface between microorganisms and wastewater. It also increases treatment efficiency by improving mass transfer because there

Biofilms in Water and Wastewater Treatment Table 3

557

Biologically active filter systems and commercially available media

Process

Supplier

Flow regime

Media

Specific gravity

Size (mm)

Astrasands Biobeads Biocarbones Biofors Biolest Biopur

Paques/Siemens Brightwater F.L.I. OTV/Veolia Degremont Stereau Sulzer/Aker Kvaerner Kruger/Veolia Severn Trent Severn Trent Parkson FB Leopold Severn Trent

Upflowa Upflow Downflow Upflow Upflow Downflow

Sand Polyethylene Expanded shale Expanded clay Pumice/pouzzolane Polyethylene

42.5 0.95 1.6 1.5–1.6 1.2

1–1.6

Upflow Upflow Downflow Upflowa Downflow Up/down

Polystyrene Sand Sand Sand Sand Slag

0.04–0.05 2.6 2.6 2.6 2.6 2–2.5

3.3–5 2–3 2–3 1–1.6 2 28–40

Washed gravel

2.6

19–38

Biostyrs ColoxTM Denites Dynasands Eliminites Submerged activated filter

2–6 2.7, 3.5, and 4.5

Specific surface area (m2 m3)

1400–1600

Structured 1000 656 656

240

a

Moving bed. From Boltz JP, Morgenroth E, deBarbadillo C, et al. (2010b) Biofilm reactor technology and design. In: Design of Municipal Wastewater Treatment Plants, WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, 5th edn, vol. 2, ch. 13 (ISBN P/N 978-0-07-166360-1 of set 978-0-07-166358-8; MHID P/N 0-07-166360-6 of set 0-07-166358-4). New York: McGraw-Hill.

Table 4

Summary of biologically active filter (BAF) backwashing (BW) requirements

Upflow, sunken media Normal BW Energetic BWa Upflow, floating media Normal BW Mini-BWb Downflow, sunken media Upflow, moving bedf

Backwash water rate, m h1

Air scour rate, m h1

Total duration minc

Total backwash water volume per cellc

Total backwash water volume per celld

20 (8.2) 30 (12.3)

97 (5.3) 97 (5.3)

50 25

9.2 m3 m2 9.2 m3 m2

12 m3 m2 10 m3 m2

55 (22.5) 55 (22.5) 15 (6) 0.5–0.6

12 (0.65) 12 (0.65) 90 (5) Continuous through air lift

16 5 20–25 Continuous

2.5 m3 m3 mediae 1.5 m3 m3 mediae 3.75–5 m3 m2 55–67 m3 d1

2.5 m3 m3 mediae 1.5 m3 m3 mediae 3.75–5 m3 m2 55–67 m3 d1

(0.2–0.24) a

Energetic backwash once every 1–2 months depending on trend in ‘‘clean bed’’ headloss following normal backwash. Mini-backwash applied as interim measure when pollutant load exceeds design load. c Backwash duration reflects total duration of the typical backwash cycle, which includes valve cycle time and pumping and nonpumping steps. The duration of each step is adjustable via programmable logic controller and supervisory control and data acquisition control systems. d The total backwash water volume includes drain and filter to waste steps, where applicable. e Backwash volume requirements for upflow floating media BAF typically are based on media volume rather than cell area because depths vary. f Continuous backwash filter BW is based on a standard 4.65 m2 cell and a typical weir setting for reject flow of approximately 2.3–2.8 m3 h1 cell1. From Boltz JP, Morgenroth E, deBarbadillo C, et al. (2010b) Biofilm reactor technology and design. In: Design of Municipal Wastewater Treatment Plants, WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, 5th edn, vol. 2, ch. 13 (ISBN P/N 978-0-07-166360-1 of set 978-0-07-166358-8; MHID P/N 0-07-166360-6 of set 0-07-166358-4). New York: McGraw-Hill. b

is significant relative motion between the biofilm and flowing wastewater. Because of the balance of forces involved in particle fluidization and bed expansion, the smallest particles are found at the top and the largest at the bottom of the fluid bed. Therefore, the media particles should be graded to a relatively tight size range. The degree of bed expansion determines whether a bed is deemed expanded or fluidized. The transition lies between 50% and 100% expansion over the static bed height. This discussion assumes the upper limit: beds less than double static bed height

(o100% expanded) are considered expanded; those more than double the static bed height (4100% expanded) are fluidized. A lower degree of bed expansion is advantageous, because it requires a lower flow velocity, less energy, and increases effective biomass concentration, which reduces the reactor footprint. In aerobic processes, however, it increases volumetric oxygen demand because of increased biomass concentration. The FBBR/EBBR is illustrated in Figure 29. The system consists of a column in which the particles are fluidized and a

558

Biofilms in Water and Wastewater Treatment

Table 5

Design criteria for denitrifying fluidized bed biofilm reactors

Parameter

Value

Packing Type Effective size Sphericity Uniformity coefficient Specific gravity Initial depth Bed expansion Empty-bed upflow velocity Hydraulic loading rate Recirculation ratio NO 3  N loading: 13 1C 20 1C Empty-bed contact time C:N (methanol) Specific surface areaa Biomass concentrationa

Unit

Range

Typical

mm Unitless Unitless Unitless m % m h1 m3effluent m2 Bioreactor Unitless

Sand 0.3–0.5 0.8–0.9 1.25–1.50 2.4–2.6 1.5–2.0 75–150 36–42 400–600 2:1–5:1

Sand 0.4 0.8–0.85 r1.4 2.6 2.0 100 36 500 3:1

2.0–4.0 3.0–6.0 10–20 3.0–3.5 1000–3000 15 000–40 000

3.0 5.0 15 3.2 2000 30 000

area

kg m3 d1 kg m3 d1 min Unitless m2 m3 mg l1

d1

a

Specific surface area range based on sand particles; alternate media used in fluidized bed reactors such as carbon or glassy coke may have a different specific surface area range. From Boltz JP, Morgenroth E, deBarbadillo C, et al. (2010b) Biofilm reactor technology and design. In: Design of Municipal Wastewater Treatment Plants, WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, 5th edn, vol. 2, ch. 13 (ISBN P/N 978-0-07-166360-1 of set 978-0-07-166358-8; MHID P/N 0-07-166360-6 of set 0-07-166358-4). New York: McGraw-Hill.

Figure 29 Fluidized bed biofilm reactor process flow diagram (Shieh and Keenan, 1986).

Process flow enters at the bottom of the reactor and flows through a distribution system to ensure even dispersion and uniform fluidization. Silica sand (0.3–0.7 mm diameter) and granular activated carbon (GAC; 0.6–1.4 mm) are typically used. Other materials, however, have been used at pilot scale, such as 0.7–1.0 mm glassy coke (McQuarrie et al., 2007). Small carrier particles (1 mm) provide a large specific surface area for biofilm growth (up to 2400 m2 m3 when expanded 50%), which is one of the key advantages of this process technology. In a study of tertiary nitrification of activated sludge-settled effluent using a pilot-scale EBBR, Dempsey et al. (2006) found that the process also removed up to 56% CBOD and 62% TSS from the influent stream. Removal of these materials was attributed to the activities of protozoa (free-living and stalked) and metazoa (rotifers, nematodes, and oligochaetes) as shown in Figure 30.

recycle line that is used to maintain a fixed, vertical hydraulic flow. In this way, bed expansion is kept constant and biofilm covered particles are retained independent of influent flow. Aeration typically is achieved during recycle, during which influent wastewater mixes with effluent recycled from the top of the bed. If aeration is conducted within the fluidized bed, then a significant volume of gas disturbs the fluidized state by causing turbulence and increased force of interparticle collisions. This can cleave biofilm from the substratum. Nevertheless, this approach has been used. The advantage of adding air to the recycle stream is that biomass is not stripped from the media by turbulence of rising gas bubbles; therefore, the treated effluent typically has a lower suspended concentration (Jeris et al., 1981).

The RBC process has been applied where average effluent water-quality standards are less than or equal to 30 mg l1 BOD5 and TSS. The RBC employs a cylindrical, synthetic media bundle that is mounted on a horizontal shaft. Figure 31 illustrates the shaft-mounted media. The bundled media is partially submerged (typically 40%) and slowly (1–1.6 rpm) rotates to expose the biofilm to substrate in the bulk of the liquid (when submerged), and to air (when not submerged). Detached biofilm fragments suspended in the RBC effluent stream are removed by liquid– solids separation units. The RBC process is typically configured with several stages operating in series. Each reactorin-series may have one or more shafts. Parallel trains are

Effluent

Excess biomass

Recycle Separator

Influent

Media Reactor Bioparticle

O2 Chemicals (optional) 1 2

1 Medium 2 Biofilm

4.15.3.5.5 Rotating biological contactors

Biofilms in Water and Wastewater Treatment

(a)

(b)

(c)

(d)

(e)

(f)

559

Figure 30 Particulate biofilms with associated protozoa and metazoan from expanded bed: (a) bioparticles in expanded bed; (b) bioparticles with surface attached; (c) closeup of rotifer attached to bioparticle; (d) stalked protozoa on surface of particulate biofilms; (e) testate amoeba grazing on biofilm; and (f) oligochaete worm grazing on bioparticles (Dempsey et al., 2006).

implemented to provide additional surface area for biofilm development. Media-supporting shafts typically are rotated by mechanical drives. Diffused air-drive systems and an array of airentraining cups that are fixed to the periphery of the media (to capture diffused air) have been used to rotate the shafts. RBCs have failed as a result of shaft, media, or media support system structural failure; poor treatment performance; accumulation of nuisance macrofauna; poor biofilm thickness control; and inadequate performance of air-drive systems for shaft rotation. Typically, the RBC tank is sized at 4.9 103 m3 m2 of media for low-density units. Disks typically have a 3.5-m diameter and are situated on a 7.5-m-long rotating shaft. The RBCs may contain low- or high-density media. Low-density media has a 118-m2 m3 biofilm active specific surface; high-density units have 180 m2 m3. Low-density media typically are used in the first stages of RBC systems which are designed for BOD5 removal to reduce potential media clogging and weight problems resulting from substantial biofilm accumulation. High-density media typically is used for nitrification. Mechanical shaft drives consist of an electric motor, speed reducer, and belt or chain drive. Typically, 3.7-kW mechanical drives have been provided for full-scale RBCs. Air-driven shafts require a remote blower for air delivery. Air headers are equipped with coarse-bubble diffusers. The air flow rate is typically in the range of 4.2–11.3 m3 min1 per shaft. Air quantity required by systems using air-driven shaft rotation, however, must be evaluated on a site-specific basis. Mechanical drive units have been designed for operation from 1.2 to 1.6 rpm. Air-drive units have been designed for 1.0–1.4 rpm. Ideally, shaft rotational speed is consistent. The development of an evenly distributed biofilm is desirable to avoid an uneven weight distribution, which may cause cyclical loadings in mechanical-drive systems and loping (uneven rotation) in air-driven shaft rotating systems. A loping condition often

accelerates rotational speed and, if not corrected, may lead to inadequate treatment and the inability to maintain shaft rotation. Air-drive systems should provide ample reserve air supply to maintain rotational speeds, restart stalled shafts, and provide short-term increased speeds (2–4 times normal operation) to control excessive or unbalanced biofilm thicknesses. Available data indicate that in excess of an 11.3-m3 min1 airflow rate per shaft may be required to maintain a 1.2-rpm shaft rotational speed during peak organic loading conditions (Brenner et al., 1984). Large-capacity air cups (150 mm diameter) typically are provided in the first stage of the process to exert a greater torque on the shaft and reduce loping. The RBC process is typically covered to avoid ultraviolet (UV) light-induced media deterioration and algae growth, to prevent excessive cooling, and to provide odor control. RBCs have been installed in buildings or under prefabricated fiberglass-reinforced plastic (FRP) covers (as pictured in Figure 31).

4.15.3.5.6 Trickling filters The TF is a three-phase biofilm reactor with fixed carriers. Wastewater enters the bioreactor through a distribution system, trickles downward over the biofilm surface, and air moves upward or downward in the third phase where it diffuses through the flowing liquid and into the biofilm. TF components generally include an influent water distribution system, containment structure, rock or plastic media, and underdrain and ventilation system. Wastewater treatment using the TF results in a net production of total suspended solids. Therefore, liquid–solids separation is required, and is typically achieved with circular or rectangular secondary clarifiers. The TF process generally includes an influent/ recirculation pump station, the TF(s), and liquid–solids separation unit(s).

560

Biofilms in Water and Wastewater Treatment

Figure 31 Photograph of the Envirexs rotating biological contactor cylindrical synthetic media bundle mounted on a horizontal shaft (a) and rotating biological contactor covers (b). Photographs courtesy Siemens Water Technologies.

Figure 32 (a) Hydraulically driven rotary distributors use variable frequency drive controlled gates that either open or close distributor orifices which adjust with varying pumped flow rates to maintain a constant preset rotational speed. (b) Electrically driven rotary distributor. Photographs courtesy WesTech, Inc.

Primary effluent or screened and degritted wastewater is either pumped or flows by gravity to the TF distribution system. Essentially, there are two types of TF distribution systems: fixed-nozzle and rotary distributors. Because their efficiency is poor, distribution with fixed nozzles should not be used (Harrison and Timpany, 1988). Rotary distributors may be hydraulically or electrically driven. A properly designed rotary distribution system allows for effective media wetting and the intermittent application of wastewater to biofilm carriers. The intermittent application of influent wastewater allows the biofilm to have periods of resting which primarily serves as a process aeration mechanism. Poor media wetting may lead to dry pockets, ineffective treatment zones, and odor. An electrically or modern hydraulically driven rotary distributor

(Figure 32) controls rotational speed independent of the influent wastewater flow rate, and may be used to maintain the desired hydraulic dosing rate. Ideal TF media provides a high specific surface area, low cost, high durability, and high enough porosity to avoid clogging and promote ventilation (Metcalf and Eddy, 2003). TF media types include rock (RO), random (RA) (synthetic), vertical flow (synthetic) (VF), and cross-flow (synthetic) (XF). Both VF and XF media are constructed with smooth and/or corrugated plastic sheets. Another commercially available synthetic media, although not commonly used, is vertically hanging plastic strips. Horizontal redwood or treated wooden slats have also been used, but are generally no longer considered viable because of high cost or limited supply. Modules

Biofilms in Water and Wastewater Treatment

of plastic sheets (i.e., self-supporting VF or XF modules) are used almost exclusively for new and improved TFs, but several TFs with rock media exist, and have proven capable of meeting treatment objectives when properly designed and operated. Table 6 compares the characteristics of some TF media. The higher specific surface area and void space in modular synthetic media allow for higher hydraulic loading, enhanced oxygen transfer, and biofilm thickness control in comparison to rock media. Rock media has, ideally, a 50-mm diameter, but may range in size. Due to structural requirements associated with the large unit weight of rock, rock-media TFs are shallow in comparison to synthetic-media TFs. Their large surface area makes them more susceptible to excessive cooling. Generally, rock media is considered to have a low specific surface area, void space, and high unit weight. Although recirculation is common, the low void ratio in rock-media TFs limits hydraulic application rates. Excessive hydraulic application can result in ponding, limited oxygen transfer, and poor bioreactor performance. Performance of existing rock-media TFs may sometimes be improved by providing mechanical ventilation, solids contact channels, and/or deepened secondary clarifiers that include energy dissipating inlets and flocculator-type feed wells. Grady

Table 6

et al. (1999) suggested that under low organic loading (i.e., o1 kg BOD5 d1 m3) rock- and synthetic-media TFs are capable of equivalent performance. However, as organic loading increases, synthetic-media TFs are less susceptible to operational problems and have reduced potential for plugging. Synthetic TF media has a higher specific surface area and void space, and lower unit weight than rock media. Modular synthetic media is generally manufactured with the following specific surface areas: 223 m2 m3 as high density, 138 m2 m3 as medium density, and 100 m2 m3 as low density. Both VF and XF media are reported to remove BOD5 and NH3–N (Harrison and Daigger, 1987), but sufficient scientific evidence exists to surmise that there is a difference in the treatment efficiency of TFs constructed with XF and VF media even when manufactured with virtually identical specific surface areas. Plastic modules with a specific surface area in the range of 89–102 m2 m3 are well suited for carbon oxidation and combined carbon oxidation and nitrification. Parker et al. (1989) recommended medium-density XF media against the use of high-density XF media in nitrifying TFs. This is supported by observations from a pilot-scale nitrifying TF application data and conclusions of Gujer and Boller (1983, 1984)

Properties of some trickling filter media Nominal size (m)

Bulk density (kg m3)

Specific surface area (m2 m3)

Void space (%)

0.024–0.076

1442

62

50

0.076–0.128

1600

46

60

0.61  0.61  1.22

24–45

100, 138, and 223

95

Vertical flow

0.61  0.61  1.22

24–45

102 and 131

95

Randomb

0.185 ø  0.051 H

27

98

95

Media type Rock River

Slag

Plastica Cross flow

a

561

Material

Manufacturers of modular plastic media: (formerly) BF Goodrich, American Surf-Pac, NSW, Munters, (currently) Brentwood Industries, Jaeger Environmental, and SPX Cooling. Manufacturers of random plastic media: (formerly) NSW Corp. and (currently) Jaeger Environmental.

b

562

Biofilms in Water and Wastewater Treatment

which show lower nitrification (flux) rates for lower-density modular synthetic media. The researchers claim that lower rates occur with high-density media due to the development of dry spots below the flow interruption points (i.e., higherdensity media has more flow interruptions and, therefore, less effective wetting). Using medium-density media also reduces plugging potential. Vertically oriented modular synthetic (VF) media is generally accepted as being ideally suited for highstrength wastewater (perhaps industrial) and high organic loadings such as with a roughing TF. In some cases, XF media has been placed in the top layer to enhance wastewater distribution and VF media comprises the remainder of the TF media. Typically, the top layer of a TF’s modular plastic media is covered with FRP or HDPE grating. The grating protects modular plastic media from deterioration by UV light and potential structural damage that may result from waterinduced load exerted during periods of high-intensity dosing. Figure 33 illustrates a typical TF column and a picture of the grating. Rock and random synthetic media are not self-supporting and require structural support to contain the media within the bioreactor. These containment structures are typically precast or panel-type concrete tanks. When self-supporting media such as plastic modules are used, other materials such as wood, fiberglass, and coated steel have been used as containment structures. The containment structure serves to avoid wastewater splashing, and to provide media support, wind protection, and flood containment. In some cases TF containment structures have been designed to allow flooding of the media, which increases operator flexibility in controlling macrofauna accumulation. The TF underdrain system is designed to meet two objectives: collect treated wastewater for conveyance to downstream unit processes and create a plenum that allows for the transfer of air throughout the TF media (Grady et al., 1999). Clay or concrete underdrain blocks are commonly used for rock-media TFs because of the required structural support. A variety of support systems including concrete piers and reinforced fiberglass plastic grating are used for other media types. The volume created between concrete and media bottom creates the underdrain.

TFs require oxygen to sustain aerobic biochemical transformation processes. The VF of air through the media can be induced mechanically or by natural draft. Natural air ventilation results from a difference in ambient air temperature outside and inside the TF. The temperature causes air to expand when warmed or contract when cooled. The net result is an air-density gradient throughout the TF, and an air front either rises or sinks depending on the differential condition. This rising or sinking action results in a continuous air flow through the bioreactor. Natural ventilation may become unreliable or inadequate in meeting process air requirements when neutral temperature gradients do not produce air movement. Currently, the provision of adequate underdrain and effluent channel sizing to permit free air flow is standard. Passive devices for ventilation include vent stacks on the TF periphery, extensions of underdrains through TFs side walls, ventilating manholes, louvers on the sidewall of the tower near the underdrain, and discharge of TF effluent to the subsequent settling basin in an open channel or partially filled pipes. Drains, channels, and pipes should be sufficiently sized to prevent submergence greater than 50% of their crosssectional area under design hydraulic loading. Ventilating access ports with open grating covers should be installed at both ends of the central collection channel. Large diameter TFs typically have branch channels (to collect the treated wastewater). These branches should also include ventilating manholes or vent stacks installed at the TF periphery. The open area of the slots in the top of the underdrain blocks should not be less than 15% of the TF area. One square meter gross area of open grating in ventilating manholes and vent stacks should be provided for each 23 m2 of TF area. Typically, 0.1 m2 of ventilating area is provided for every 3–4.6 m of TF periphery, and 1–2 m2 of ventilation area in the underdrain area per 1000 m3 of TF media. Another criterion for rockmedia TFs is the provision of a vent area at least equal to 15% of the TF cross-sectional area. Mechanical ventilation enhances and controls air flow with low-pressure fans that continuously circulate air throughout the TF. Therefore, a majority of new and improved TFs use low-pressure fans to mechanically promote air flow. The air flow resulting from natural draft will distribute itself. This will

Figure 33 Skid-resistant (polyethylene or fiberglass-reinforced plastic) grating placed on top of a typical modular plastic media trickling filter column.

Biofilms in Water and Wastewater Treatment

not occur with mechanical ventilation. Pressure loss through synthetic TF media is typically low, often less than 1-mm H2O/ m of TF depth (Grady et al., 1999). The low pressure drop typically results in low fan power requirements (B3–5 kW). The head on the fan is typically less than 1500-mm H2O. Unfortunately, the low pressure drop allows air to rise upward through the TF media without distributing itself across the bioreactor section. Therefore, fans are typically connected to distribution pipes. The air flow distribution piping has openings sized such that air flow through each is equal and air flow distribution is uniform. The pipes typically have a velocity in the range of 1100–2200 m h1 in order to further promote uniform air flow distribution. Air flow requirements are calculated based on process oxygen requirements and characteristic oxygen-transfer efficiency which is typically in the range of 2–10%. The mechanical air stream may flow upward to downward. Down-flow systems can be designed without covers. However, covers are required for systems that do not have air distribution through a network of pipes under the media. Covering TFs offers a wintertime benefit of limiting cold airflow and minimizing wastewater cooling. Mechanical ventilation and covered TFs may be used to destroy odorous compounds. A critical unit in the TF process is the pump station that lifts primary effluent (or screened raw sewage), and recirculates unsettled trickling effluent (here, referred to as underflow) to the influent stream. In general, TF underflow is recirculated to the distribution system to achieve the hydraulic load (influent þ recirculation) required for proper media wetting and biofilm thickness control, and decouple hydraulic and organic loading. TF influent is generally pumped to allow TF underflow to flow by gravity to the suspended growth reactor (or solids contact basin), secondary clarifier, or other downstream of the TF. When fit with weirs, a single pump station can be used to convey both influent and recirculation streams.

Table 7

563

TFs can be classified as roughing, carbon oxidation, carbon oxidation and nitrification, and nitrification. Table 7 summarizes characteristics of each TF. The performance ranges are associated with average design conditions. Single day or average week observations may significantly be greater.

4.15.4 Part III. Undesirable Biofilms: Examples of Biofilm-Related Problems in the Water and Wastewater Industries Biofilms are unavoidably associated with water environments, so biofilm control, a component of many industrial processes, is especially important in water and wastewater treatment. Depending on the particular setting, biofilms may cause process performance problems, material performance problems, health problems, and esthetic problems. The specific problems that biofilms cause in industrial settings are as diverse as the technological processes affected by the biofilms. In this section, we discuss four biofilm-related problems that have been reported in the water and wastewater industries: 1. biofilms on metal surfaces and MIC; 2. biofilms on concrete surfaces and crown corrosion of sewers; 3. biofilms on filtration membranes in drinking water treatment; and 4. biofilms on filtration membranes in wastewater treatment.

4.15.4.1 Biofilms on Metal Surfaces and MIC In the manufacturing of metals and metal alloys, raw materials – the ores – are chemically reduced and their internal chemical energy increases. These materials are used by microorganisms as sources of energy in a sequence of processes in which the chemical energy of the affected material decreases, bringing the energy levels of the products closer to

Trickling filter classification

Design parameter

Roughinga

Carbon oxidizing (cBOD5 removal)a

Carbon oxidation and nitrificationa

Nitrificationa

Media typically used

VF

RO, XF, or VF

RO, XF, or VF

XF

Wastewater source

Primary effluent

Primary effluent

Primary effluent

Secondary effluent

Hydraulic loading m3 d1 m2 BOD5 and NH3  N Load kg m3 d1 g m2 d1

52.8–178.2

14.7–88.0

14.7–88.0

35.2–88.0

1.6–3.52 NA

0.32–0.96 NA

0.08–0.24 0.2–1.0

NA 0.5–2.4

Conversion (%) or effluent concentration (mg l1) Macro fauna

50–75% filtered cBOD5 conversion No appreciable growth

20–30 mg l1 cBOD5 and TSSb Beneficial

0.5–3 mg l1 as NH3  Nb

Depth, m (ft)

0.91–6.10

r12.2

o10 mg l1 as cBOD5; o3 mg l1 as NH3  Nb Detrimental (nitrifying biofilm) r12.2

a

Detrimental r12.2

Applicable to shallow trickling filters. gpm ft2, gallons per minute per square foot of trickling filter plan area. Concentration remaining in the clarifier effluent stream. From Boltz JP, Morgenroth E, deBarbadillo C, et al. (2010b) Biofilm reactor technology and design. In: Design of Municipal Wastewater Treatment Plants, WEF Manual of Practice No. 8, ASCE Manuals and Reports on Engineering Practice No. 76, 5th edn, vol. 2, ch. 13, p. 238 (ISBN P/N 978-0-07-166360-1 of set 978-0-07-166358-8; MHID P/N 0-07166360-6 of set 0-07-166358-4). New York: McGraw-Hill. b

564

Biofilms in Water and Wastewater Treatment

the energy levels of the materials from which they were made. MIC can affect a variety of materials, both metallic and nonmetallic. If nonmetallic materials are affected, the term biodeterioration of materials is more often used than MIC, although this terminology is not very consistent and, for example, the term crown corrosion of sewers, which in fact refers to the biodeterioration of concrete, is quite popular among water professionals. Accelerated corrosion of metals in the presence of microorganisms stems from microbial modifications to the chemical environment near metal surfaces (Beech et al., 2005; Geiser et al., 2002; Lee and Newman, 2003; Lewandowski et al., 1997). Such modifications depend, of course, on the properties of the corroding metal and on the microbial community structure of the biofilm deposited on the metal surface (Beech and Sunner, 2004; Dickinson et al., 1996; Flemming, 1995; Olesen et al., 2000, 2001). Beech et al. (2005) described MIC as a consequence of coupled biological and abiotic electron transfer reactions, that is, redox reactions of metals enabled by microbial ecology (Beech et al., 2005). Hamilton (2003) attempted to generate a unified concept of MIC but found common features in only some of the possible mechanisms (Hamilton, 2003). It is unlikely that a unified concept of MIC can be generated at all. Rather, there are many mechanisms by which microorganisms may affect metal surfaces, and we demonstrate some of them here. These do not exhaust the possibilities, of course, but are rather used to exemplify the possible mechanisms. As we have restricted the discussion of MIC to metal surfaces only, it is convenient to define corrosion as anodic dissolution of a metal. In this way we can easily separate the corrosion reaction, the anodic dissolution of the metal, from many other anodic reactions that can occur at a metal surface covered with a biofilm. These other anodic reactions deliver electrons originating from substances metabolized near the metal surface, but only the reaction in which the metal itself is oxidized is defined as corrosion. The presence of the other anodic reactions causes confusion in MIC studies, as the current between the anode and the cathode is made up of electrons originating from many anodic reactions occurring at the surface, not only from the corrosion reaction. Microorganisms generate chemical environments that are conducive to corrosion reactions even if they do not take part in the process themselves. As in most industrial processes microorganisms are always present on metal surfaces, it is not immediately obvious whether the microorganisms attached to the surface accelerate the corrosion process or are just innocent bystanders. The only way to resolve this is by demonstrating that a specific mechanism of MIC is present because a product of microbial metabolism consistent with this mechanism can be detected. Many mechanisms of MIC have been proposed. Accelerated corrosion may result from the action of acid-producing bacteria, such as Thiobacillus thiooxidans and Clostridium aceticum; iron-oxidizing bacteria, such as Gallionella, Sphaerotilus, and Leptothrix; MOB, such as L. discophora; or hydrogenproducing bacteria. These mechanisms have been studied and the results described in numerous publications. We describe here representative examples of such mechanisms: the effects of differential aeration cells, sulfate-reducing bacteria (SRB corrosion), and MOB corrosion.

4.15.4.1.1 Differential aeration cells on iron surfaces MIC caused by differential aeration cells is an example of a nonspecific mechanism of MIC, because it depends on the presence of biofilm, and not on the type of microorganisms that reside in the biofilm. If the oxygen concentrations at two adjacent locations on an iron surface are different, then the half-cell potentials at these locations are different as well. The location where the oxygen concentration is higher will have a higher potential (more cathodic) than the location where the oxygen concentration is lower (more anodic). The difference in potential will give rise to a current flow from the anodic locations to the cathodic locations and to the establishment of a corrosion cell. This is the mechanism of differential aeration cells, and the prerequisite to this mechanism is that the concentration of oxygen varies among locations (Acuna et al., 2006; Dickinson and Lewandowski, 1996; Hossain and Das, 2005). Indeed, many measurements using oxygen microsensors have demonstrated that oxygen concentrations in biofilms can vary from one location to another (Lewandowski and Beyenal, 2007). If the anodic reaction is the oxidation of iron,

Fe-Fe 2þ þ 2e 

ð17Þ

and the cathodic reaction is the reduction of oxygen,

O2 þ 2H2 O þ 4e -4OH

ð18Þ

then the overall reaction describing the process is

2Fe þ O2 þ 2H2 O-2Fe2þ þ 4OH

ð19Þ

The Nernst equation quantifying the potential for this reaction is

E ¼ Eo 

0:059 ½Fe 2þ  2 ½OH   4 log 4 pðO2 Þ

ð20Þ

Figure 34 visualizes this mechanism.

4.15.4.1.2 SRB corrosion SRB causes corrosion of cast iron, carbon, and low alloy steels and stainless steels. SRB corrosion of potable water mains is a common (US EPA, 1984) and well-recognized problem (Seth and Edyvean, 2006; Tuovinen et al., 1980). MIC caused by SRB is an example of a mechanism that depends on the activity of a specific group of microorganisms in a biofilm. The corrosion of mild steel caused by SRB is the most notorious case of MIC, and it provides a direct and easy-to-understand link between microbial reactions and electrochemistry (Javaherdashti, 1999). According to the mechanism that was originally proposed by Von Wohlzogen Kuhr in 1934, SRB oxidizes cathodically generated hydrogen to reduce sulfate ions to H2S, thereby removing the product of the cathodic reaction and stimulating the progress of the reaction (Al Darbi et al., 2005). This mechanism was later found to be inadequate to explain the field observations. More involved mechanisms were implicated in this type of microbial corrosion, including the puzzling effect of oxygen, which can stimulate what is apparently an anaerobic process. It is now certain that the

Biofilms in Water and Wastewater Treatment

565

Aerated water Cathodic site; corrosion products

Biofilm

OH−

OH−

Cathode − e

Biof ilm

O2

e−

Anodic site

Biofilm

O2

O O2 Aerobic 2 O2 O2 O2 Anaerobic O2 O O2 2 O2 O2 Anaerobic O 2 O2 O2 O2 M+ M+ M+ Anode

O2 O2 OH−OH− e−

Cathode e−

1 mm

Metal (b)

(a)

Figure 34 Biofilm heterogeneity results in differential aeration cells. (a) This schematic shows pit initiation due to oxygen depletion under a biofilm (Borenstein, 1994). (b) An anodic site and pit under the biofilm and corrosion products deposited on mild steel.

possible pathways for cathodic reactions include sulfides and bisulfides as cathodic reactants (Videla, 2001; Videla and Herrera, 2005). The currently accepted mechanism of SRB corrosion is composed of a network of reactions that reflects the complexity of the environment near corroding metal surfaces covered with biofilms; the following paragraphs illustrate some of this complexity. The process starts with the microbial metabolism of SRB producing hydrogen sulfide by reducing sulfate ions. Hydrogen sulfide can serve as a cathodic reactant, thus affecting the rate of corrosion (Antony et al., 2007; Costello, 1974):

2H2 S þ 2e -H2 þ 2HS

ð21Þ

Ferrous iron generated from anodic corrosion sites precipitates with the metabolic product of microbial metabolism, hydrogen sulfide, forming iron sulfides, FeSx:

Fe 2þ þ HS  ¼ FeS þ H þ

ð22Þ

This reaction may provide protons for the cathodic reaction (Crolet, 1992). The precipitated iron sulfides form a galvanic couple with the base metal. For corrosion to occur, the iron sulfides must have electrical contact with the bare steel surface. Once contact is established, the mild steel behaves as an anode and electrons are conducted from the metal through the iron sulfide to the interface between the sulfide deposits and water, where they are used in a cathodic reaction. Surprisingly, the most notorious cases of SRB corrosion often occur in the presence of oxygen. As SRB is anaerobic microorganisms, this fact has been difficult to explain. This effect of oxygen can be explained based on a mechanism in which iron sulfides (resulting from the reaction between iron ions and sulfide and bisulfide ions) are oxidized by oxygen to elemental sulfur, which is known to be a strong corrosion agent (Lee et al., 1995). Biofilm heterogeneity plays an important role in this process, because the central parts of microcolonies are anaerobic while the outside edges remain aerobic

(Lewandowski and Beyenal, 2007). This arrangement makes this mechanism of microbial corrosion possible, because the oxidation of iron sulfides produces highly corrosive elemental sulfur, as illustrated by the following reaction:

2H2 O þ 4FeS þ 3O2 -4So þ 4FeOðOHÞ

ð23Þ

Hydrogen sulfide can also react with the oxidized iron to form ferrous sulfide and elemental sulfur (Schmitt, 1991), thereby aggravating the situation by producing even more elemental sulfur, and closing the loop through production of the reactant used in the first reaction, FeS:

3H2 S þ 2FeOðOHÞ-2FeS þ So þ 4H2 O

ð24Þ

The product of these reactions – elemental sulfur – increases the corrosion rate. Schmitt (1991) has shown that the corrosion rate caused by elemental sulfur can reach several hundred mpy (Schmitt, 1991). We have demonstrated experimentally that elemental sulfur is deposited in the biofilm during SRB corrosion (Nielsen et al., 1993), thereby detecting the component vital for this mechanism to occur. It is also well known that the sulfur disproportionation reaction that produces sulfuric acid and hydrogen sulfide is carried out by sulfur-disproportionating microorganisms (Finster et al., 1998). Also, several microbial species, such as T. thiooxidans, can oxidize elemental sulfur and sulfur compounds and produce sulfuric acid:

4S o þ 4H2 O-3H2 S þ H2 SO4

ð25Þ

In summary, the SRB corrosion of mild steel in the presence of oxygen is an acid corrosion: Anodic reaction:

Fe-Fe 2þ þ 2e

ð26Þ

2H þ þ 2e-H2

ð27Þ

Cathodic reaction:

566

Biofilms in Water and Wastewater Treatment 4.15.4.3 Biofilms on Filtration Membranes in Drinking Water Treatment

O2

2−

FeO(OH)

SO4

SO42−

SO42− H2S 0

S

FeS2 FeS

H+

O2

Fe2+ Metal

S0

HS− H2

O2

e

Figure 35 The SRB corrosion of mild steel in the presence of oxygen is an acid corrosion (Lewandowski et al., 1997).

The mechanism of SRB corrosion involves several loops, cycles in which reactants are consumed in one reaction and recycled in other reaction; the process is spontaneous at the expense of the energy released by the oxidation of the metal. This mechanism also demonstrates how the reactants and products of corrosion processes are included in the metabolic reactions of the microorganisms. For example, hydrogen, the product of the cathodic reaction above, is oxidized by some species of SRB to reduce sulfate and generate hydrogen sulfide, H2S (Cord-Ruwisch and Widdel, 1986), which is the reactant in the first reaction we referred to in this section. Hydrogen sulfide then dissociates to bisulfides:

H2 S ¼ Hþ þ HS

ð28Þ

which are then used in the reactions described above. Figure 35 shows the network of reactions described above.

4.15.4.2 Biofilms on Concrete Surfaces: Crown Corrosion of Sewers The mechanism of crown corrosion of sewers is very similar to the mechanism of MIC corrosion of metals caused by SRB. In sewers, SRB reduces sulfate ions to sulfides, which are oxidized by oxygen to elemental sulfur. Then the elemental sulfur is further oxidized, mainly by T. thiooxidans, but also by other Thiobacillus species, such as T. novellus/intermedius and T. neapolitanus, in a complex ecosystem on the sewer pipe (Vincke et al., 2001). As a result, sulfuric acid is produced, which dissolves the concrete and damages the sewers (Padival et al., 1995; Islander et al., 1991; Sand and Bock, 1984). The following reactions illustrate this action:

H2 SO4 þ CaCO3 -CaSO4 þ H2 CO3

ð29Þ

H2 SO4 þ CaðOHÞ2 -CaSO4 þ 2H2 O

ð30Þ

Crown corrosion of sewers depends on the presence of biofilm on the concrete surface and on the generation of sulfuric acid in immediate proximity to the concrete surface.

The common use of membranes in various technologies of water and wastewater treatment is probably the most visible mark of the changes that occurred in these applications in the last decade, and it is expected that filtration membranes will be even more popular in the future than they are now (Shannon et al., 2008). The traditional use of membranes in water treatment has been in the desalination of sea and brackish waters using the reverse osmosis (RO) process, and there is a large body of knowledge accumulated on this application. RO membrane filtration is becoming even more popular as the cost of desalination decreases because of various improvements in the technology that reduce the energy consumption and because of the use of new materials that produce less expensive and more robust membranes (Veerapaneni et al., 2007). Membrane processes have been introduced into other types of water treatment, besides desalination, such as water softening (Conlon et al., 1990). The main advantages of using membrane filtration in water treatment are that the process does not require using chemicals and that the membrane modules have a smaller footprint than the conventional treatment facilities. Membrane filtration can be used instead of other traditional processes in water treatment, such as coagulation, sand and activated carbon filtration, or ion exchange, without the necessity of adding chemicals to the water, which helps prevent the formation of disinfection byproducts, for example. Membrane filtration can be used alone in water treatment or in combination with other processes, in hybrid arrangements. For example, it can be used in combination with powdered activated carbon (PAC) to remove disinfection byproducts that exist in the raw water (Khan et al., 2009). Excessive biofouling of membranes is a problem in all membrane applications, but RO and nanofiltration (NF) processes are the most sensitive to biofouling (Vrouwenveldera et al., 2009). Much research has been done toward understanding the process of biofilm formation on these membranes and developing methods for cleaning the membranes. The removal of biofilm from RO membranes can be accomplished by mechanical or by chemical methods, or by a combination of mechanical and chemical methods. Mechanical methods include flushing with water or with water and air. Mechanical cleaning can be used alone or it can be followed by chemical cleaning. The simplest method of mechanical cleaning is the forward flush, in which the water flow rate above the membrane is increased to increase the shearing force and remove the deposits from the membrane. To increase the shearing force even further, air can be introduced into the conduit delivering the cleaning water. The air bubbles introduce additional instability into the flow field and increase the shearing force exerted on the surface. The backward flush is based on reversing the direction of filtration: cleaning water is filtered in the opposite direction and the particles trapped in membrane pores are removed. Depending on the contaminants deposited on the membranes, the surface can be cleaned chemically using various type of chemicals. If the deposits are predominantly inorganic scale, then the chemical cleaning can include agents that act mostly on scale, such as hydrochloric acid (HCl) or nitric acid (HNO3). If the

Biofilms in Water and Wastewater Treatment

biofilm is the main problem, then the cleaning substance may include antimicrobial agents to remove the biofilms. Two types of antimicrobial agents are in common use for this purpose: oxidizing and nonoxidizing biocides. The oxidizing biocides popular in membrane cleaning processes include chlorine, bromine, chloramine, chlorine dioxide, hydrogen peroxide, peroxyacetic acid, and ozone. Nonoxidizing biocides include formaldehyde, glutaraldehyde, and quaternary ammonium compounds. One recent study targeted cell–cell communications in biofilms to develop a novel approach in controlling membrane fouling (Yeon et al., 2009). Much effort has been directed toward the development of membranes with new or modified materials that can resist biofouling and toward modifying the surfaces of ultrafiltration (UF) and NF membranes by the graft polymerization of hydrophilic monomers that resist biofouling or allow more aggressive chemical treatment of the membranes (Hester et al., 2002; Wang et al., 2005; Asatekin et al., 2006, 2007). According to recent studies, in spiral-wound membrane modules, biofilm accumulation has a major impact on the spacer channel but the actual fouling of the membrane contributes to the overall pressure drop to a much smaller extent than previously assumed (Vrouwenveldera et al., 2009).

4.15.4.4 Biofilms on Filtration Membranes in Wastewater Treatment Membrane filtration is used in two types of wastewater technologies: (1) membrane bioreactors (MBRs) and (2) membrane biofilm reactors (MBfRs). This terminology is somewhat confusing: the names sound similar, and the fact that the obvious acronyms for the two technologies are the same does not help. It is therefore customary to call the MBRs and the MBfRs. From the biofouling point of view, microbial growth on membranes is undesirable (Le-Clech et al., 2006) while in MBfRs biofilm growth on the membrane is necessary for process performance. MBfRs are used to deliver dissolved gases, such as oxygen, hydrogen, and methane, to the microorganisms attached to the membrane (Brindle and Stephenson, 1996; Brindle et al., 1998; Suzuki et al., 2000; Lee and Rittmann, 2000; Pankhania et al., 1999; Modin et al., 2008). MBRs are used to replace gravity settling in the secondary sedimentation tanks used in traditional biological wastewater treatment; for example, the activated sludge process where membrane processes can be used to separate the biomass of suspended microorganisms from the effluent. The membranes used in MBRs are typically UF membranes. MBR technology is well established in wastewater treatment: it has been implemented on large scales (Melin et al., 2006), and textbooks have been published describing its application (Stephenson et al., 2000; Judd, 2006). Using membrane filtration to replace gravity settling has many advantages, and one of them is avoidance of the notorious problems with sludge bulking that plague many activated sludge treatment plants. Membranes in MBRs suffer from biofouling, which decreases the permeate flow (Howell et al., 2003; Young et al., 2006; Kimura et al., 2005) Large-scale operations suffer from this problem, particularly the irreversible fouling that cleaning does not remove (Wang et al., 2005). The most common solution to the excessive accumulation of biomass is bubbling air near the

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membrane’s surface, which creates high shear and removes the biomass (Hong et al., 2002). Basic studies on biofilm formation (Davies et al., 1998) indicate that bacteria regulate their group behaviors, such as biofilm formation, in response to population density using small signal molecules called autoinducers, or quorumsensing molecules. It is expected that interference with microbial communication systems in biofilms may lead to novel approaches to preventing biofouling in many areas. Three strategies for interfering with autoinducer molecules have been proposed: blockage of autoinducer production, interference with signal receptors, and inactivation of autoinducer molecules (Rassmusen and Givskov, 2006). In a recent study, Yeon et al. (2009) demonstrated that inactivating the autoinducer molecules in a batch-type MBR reactor decreased the amount of EPS deposited on the membrane and that interfering with cell–cell communication in biofilms can alleviate the fouling of filtration membranes.

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systems. Part I: Semi-empirical model development. Water Environment Research 80: 439--453. Seth AD and Edyvean RGJ (2006) The function of sulfate-reducing bacteria in corrosion of potable water mains. International Biodeterioration and Biodegradation 58: 108--111. Shannon MA, Bohn PW, Elimelech M, Georgiadis JG, Marinas BJ, and Mayes AM (2008) Science and technology for water purification in the coming decades. Nature 452: 301--310. Shieh WK and Keenan JD (1986) Fluidized bed biofilm reactor for wastewater treatment. Advances in Biochemical Engineering Biotechnology 33: 133--169. Sin G, Van Hulle SWH, De Pauw DJW, van Griensven A, and Van rolleghem PA (2005) A critical comparison of systematic calibration protocols for activated sludge models: A SWOT analysis. Water Research 39: 2459--2474. Sin G, Weijma J, Spanjers H, and Nopens I (2008) Dynamic model development and validation for a nitrifying moving bed biofilter: Effect of temperature and influent load on the performance. Process Biochemistry 43: 384--397. Smets BF, Riefler RG, Lendenmann U, and Spain JC (1999) Kinetic analysis of simultaneous 2,4-dinitrotoluene (DNT) and 2,6-DNT biodegradation in an aerobic fluidized-bed biofilm reactor. Biotechnology and Bioengineering 63(6): 642--653. Stenstrom MK and Rosso D (2008) Aeration and mixing. In: Henze M, van Loosdrecht MCM, Ekama G, and Brdjanovic D (eds.) Biological Wastewater Treatment – Principles, Modelling, and Design. London: IWA Publishing. Stephenson T, Judd S, Jefferson B, and Brindle K (2000) Membrane Bioreactors for Wastewater Treatment. London: IWA Publishing (Alliance House). Stewart PS (2003) Diffusion in biofilms. Guest commentaires. Journal of Bacteriology 185(5): 1485--1491. Stoddart FW (1911) Nitrification and the absorption theory. Journal of the Society of Chemical Industry 30: 236--237. Suzuki Y, Hatano N, Ito S, and Ikeda H (2000) Performance of nitrogen removal and biofilm structure of porous gas permeable membrane reactor. Water Science and Technology 41: 211--217. Taka´cs I, Bye CM, Chapman K, Dold PL, Fairlamb PM, and Jones RM (2007) A biofilm model for engineering design. Water Science and Technology 55(8–9): 329--336. Takacs I, Dold P, Belia L., et al. (2010) Structured process models for nutrient removal. In: Johnson BR (ed.) Nutrient Removal, WEF Manual of Practice No. 34, ch. 11. New York: McGraw-Hill. Tschui M (1994) Submerse Festbettreaktoren. Skriptum der EAWAG (Du¨bendorf) fu¨r den Studiengang Umwelt- und Kulturingenieure an der ETH Zu¨rich, Vertieferblock, WS 94/95 [unvero¨ffentlicht]. Tuovinen OH, Button KS, Vuorinen A, Carlson L, Mair DM, and Yut LY (1980) Bacterial, chemical, and mineralogical characteristics of tubercles in distribution pipelines. Journal of American Water Works Association 72: 626--635. US EPA (1984) Corrosion manual for internal corrosion of water distribution systems. EPA/570/9-84/001. Washington, DC: Office of Drinking Water. van Loosdrecht MCM, Eikelboom D, Gjaltema A, Mulder A, Tijhuis L, and Heijnen JJ (1995) Biofilm structures. Water Science and Technology 32(8): 35--43.

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4.16 Membrane Biological Reactors FI Hai, University of Wollongong, Wollongong, NSW, Australia K Yamamoto, University of Tokyo, Tokyo, Japan & 2011 Elsevier B.V. All rights reserved.

4.16.1 4.16.2 4.16.2.1 4.16.2.2 4.16.3 4.16.3.1 4.16.3.2 4.16.3.2.1 4.16.3.2.2 4.16.3.2.3 4.16.3.2.4 4.16.3.2.5 4.16.3.2.6 4.16.3.3 4.16.3.4 4.16.3.4.1 4.16.3.4.2 4.16.3.4.3 4.16.3.4.4 4.16.3.4.5 4.16.4 4.16.4.1 4.16.4.2 4.16.4.3 4.16.4.4 4.16.4.4.1 4.16.4.4.2 4.16.4.4.3 4.16.4.4.4 4.16.4.4.5 4.16.4.4.6 4.16.4.4.7 4.16.4.5 4.16.4.6 4.16.4.7 4.16.4.7.1 4.16.4.7.2 4.16.4.7.3 4.16.4.7.4 4.16.5 4.16.5.1 4.16.5.1.1 4.16.5.1.2 4.16.5.1.3 4.16.5.1.4 4.16.5.2 4.16.5.3 4.16.5.4 4.16.5.4.1 4.16.5.4.2 4.16.5.4.3 4.16.5.5 4.16.5.5.1

Introduction Aeration and Extractive Membrane Biological Reactors Aeration Membrane Biological Reactor Extractive Membrane Biological Reactor History and Fundamentals of Biosolid Separation MBR Historical Development Process Comparison with Conventional Activated Sludge Process Treatment efficiency/removal capacity Sludge properties and composition Sludge production and treatment Space requirements Wastewater treatment cost Comparative energy usage Relative Advantages of MBR Factors Influencing Performance/Design Considerations Pretreatment Membrane selection and applied flux Sludge retention time Mixed liquor suspended solids concentration Oxygen transfer Worldwide Research and Development Challenges Importance of Water Reuse and the Role of MBR Worldwide Research Trend Modeling Studies on MBR Innovative Modifications to MBR Design Inclined plate MBR Integrated anoxic–aerobic MBR Jet-loop-type MBR Biofilm MBR Nanofiltration MBR Forward osmosis MBR Membrane distillation bioreactor Technology Benefits: Operators’ Perspective Technology Bottlenecks Membrane Fouling – the Achilles’ Heel of MBR Technology Fouling development Types of membrane fouling Parameters influencing MBR fouling Fouling mitigation Worldwide Commercial Application Installations Worldwide Location-specific drivers for MBR applications Plant size Development trend and the current status in different regions Decentralized MBR plants: Where and why? Commercialized MBR Formats Case-Specific Suitability of Different Formats MBR Providers Market share of the providers Design considerations Performance comparison of different providers Standardization of Design and Performance-Evaluation Method Standardization of MBR filtration systems

571 572 572 574 574 574 576 576 576 577 577 578 579 580 581 581 581 581 581 581 582 582 583 583 584 585 585 585 585 585 586 586 586 587 588 588 588 589 593 596 596 596 596 596 598 600 600 601 601 601 602 604 604

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4.16.5.5.2 4.16.6 4.16.7 References

Standardization of MBR characterization methods Future Vision Conclusion

4.16.1 Introduction

Size

Membrane biological reactors combine the use of biological processes and membrane technology to treat wastewater. The use of biological treatment can be traced back to the late nineteenth century. It became a standard method of wastewater treatment by the 1930s (Rittmann, 1987). Both aerobic and anaerobic biological treatment methods have been extensively used to treat domestic and industrial wastewater (Visvanathan et al., 2000). After removal of the soluble biodegradable matter in the biological process, any biomass formed needs to be separated from the liquid stream to produce the required effluent quality. In the conventional process, a secondary settling tank is used for such solid/liquid Apprx. molecular weight 200 µm 0.001

20k

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separation and this clarification is often the limiting factor in effluent quality (Benefield and Randall, 1980). Membrane filtration, on the other hand, denotes the separation process in which a membrane acts as a barrier between two phases. In water treatment, the membrane consists of a finely porous medium facilitating the transport of water and solutes through it (Ho and Sirkar, 1992). The separation spectrum for membranes, illustrated in Figure 1, ranges from reverse osmosis (RO) and nanofiltration (NF) for the removal of solutes, to ultrafiltration (UF) and microfiltration (MF) for the removal of fine particulates. MF and UF membranes are predominantly used in conjunction with biological reactors (Pearce, 2007). UF can remove the finest particles found in water supply, with the removal rating dependent upon the

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Viruses Pesticide

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Human hair

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Reverse osmosis Nano filtration Ultrafiltration Microfiltration • Reverse osmosis (RO): salts, pesticides, herbicides, metal ions, endocrine disruptors, disinfection by-products • Nanofiltration (NF): divalents salts, pesticides, herbicides, divalent metal ions • Ultrafiltration (UF): virus, bacteria, endotoxin • Microfiltration (MF): bacteria, (virus) Figure 1 The separation spectrum for membranes.

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• •

Easily meets regulatory levels Suitable for discharge to pristine environment

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Meets standards for potable applications Increased value for industrial applications May be useful in obtaining development permit

Figure 2 Market drivers for membranes in wastewater. Information from Howell JA (2004) Future of membranes and membrane reactors in green technologies and for water reuse. Desalination 162: 1–11; and Pearce G (2007) Introduction to membranes: Filtration for water and wastewater treatment. Filtration and Separation 44(2): 24–27.

Membrane permeate

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Criteria

et al., 2000); for bubble-less aeration of the bioreactor (Brindle and Stephenson, 1996); and for extraction of priority organic pollutants from hostile industrial wastewaters (Stephenson et al., 2000). There are other forms of membrane biological reactors such as enzymatic membrane bioreactor (Charcosset, 2006) for production of drugs, vitamins, etc., or membrane biological reactors for waste-gas treatment (Reij et al., 1998), a discussion about which is beyond the scope of this chapter. Solid–liquid membrane-separation bioreactors employ UF or MF modules for the retention of biomass to be recycled into the bioreactor. Gas-permeable membranes are used to provide bubble-less oxygen mass transfer to degradative bacteria

Oxygen transfer

pore size of the active layer of the membrane. The complete pore-size range for UF is approximately 0.001–0.02 mm, with a typical removal capability of UF for water and wastewater treatment of 0.01–0.02 mm. MF typically operates at a particle size that is up to an order of magnitude coarser than this. In water treatment, the modern trend is to use a relatively tight MF with a pore size of approximately 0.04–0.1 mm, whereas wastewater normally uses a slightly more open MF with a pore size of 0.1–0.4 mm (though wastewater can be treated using UF membranes, or MF membranes used for water applications). The market drivers for membranes in wastewater are illustrated in Figure 2. However, as in any separation process, in membrane technology too, the management and disposal of concentrate is a significant issue. Environment-friendly management and disposal of the resulting concentrates at an affordable cost is a significant challenge to water and wastewater utilities and industry. To eradicate the respective disadvantages of the individual technologies, the biological process can be integrated with membrane technology. Although some recent studies have demonstrated case-specific feasibility of direct UF of raw sewage (Janssen et al., 2008), membranes by themselves are seldom used to filter untreated wastewater, since fouling prevents the establishment of steady-state conditions and because water recovery is very low (Schrader et al., 2005; Fuchs et al., 2005; Judd and Jefferson, 2003). However, membrane filtration can be efficiently used in combination with a biological process. The biological process converts dissolved organic matter into suspended biomass, reducing membrane fouling and allowing increase in recovery. On the other hand, in the membrane filtration process, the membranes introduced into the bioreactors not only replace the settling unit for solid–liquid separation but also form an absolute barrier to solids and bacteria and retain them in the process tank. As our understanding of membrane technology grows, we learn that membrane technology is now being applied to a wider range of industrial applications and is used in many new forms for wastewater treatment. Combining membrane technology with biological reactors for the treatment of municipal and industrial wastewaters has led to the development of three generic membrane processes within bioreactors (Figure 3): for separation and recycle of solids (Visvanathan

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Biofilm Nutrient biomedium (c) Figure 3 Simplified representation of membrane biological reactors: (a) biosolid separation, (b) aeration, and (c) extractive membrane biological reactors.

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present in the bioreactor. Additionally, the membrane can act as support for biofilm development, with direct oxygen transfer through the membrane wall in one direction and nutrient diffusion from the bulk liquid phase into the biofilm in the other direction. An extractive membrane process has been devised for the transfer of degradable organic pollutants from hostile industrial wastewaters, via a nonporous silicone membrane, to a nutrient medium for subsequent biodegradation. Biosolid separation is, however, the most widely studied process and has found full-scale applications in many countries. In a comprehensive review published in 2006, Yang et al. (2006) pointed out that the vast majority of research on membrane biological reactors since 1990 focused on biosolidseparation-type applications. There was no significant increase in the number of studies on gas diffusion and extractive membrane biological reactors over time. Publications on extractive and diffusive membrane biological reactors became available during 1994–95, after which a steady output of less than five publications a year was observed. This indicates that current research is predominantly in the water and wastewater-filtration area, in parallel with the commercial success in this field. In line with the current trend of research and commercial application, this chapter focuses on the biosolidseparation membrane biological reactors, which is more commonly known as membrane bioreactor (MBR). However, a brief outline of the other two types of membrane biological reactors is furnished in Section 4.16.2. The remainder of this chapter elaborates on the history, fundamentals, research and development challenges, as well as the commercial application of the biosolid-separation membrane biological reactors, which are henceforth referred to as MBRs.

4.16.2 Aeration and Extractive Membrane Biological Reactors 4.16.2.1 Aeration Membrane Biological Reactor Wastewater-treatment processes using high-purity oxygen have a greater volumetric degradation capacity compared to the conventional air-aeration process. However, conventional oxygenation devices have high power requirements associated with the need for high mixing rate, and cannot be used in conjunction with biofilm processes. In the membraneaeration biological reactors (MABRs), the capability of biofilm to retain high concentrations of active bacteria is coupled with the high oxygen transfer rate to the biofilm. The key characteristic advantages of MABRs are summarized as follows:

• •

High oxygen transfer rate, especially suitable for highoxygen-demanding wastewaters. In conventional aerobic biological wastewater treatment, volatile organic compounds (VOCs) can escape to the atmosphere without being biodegraded as a result of air bubbles stripping out the compounds from the bulk liquid. Since no oxygen bubbles are formed in MABRs, gas stripping of VOCs and foaming due to the presence of surfactants can be prevented (Rothemund et al., 1994; Semmens 1991; Wilderer et al., 1985) to a greater extent.

•

Membrane-attached biofilms are in intimate contact with the oxygen source, with direct interfacial transfer and utilization of oxygen within the biofilm. In contrast to conventional biofilm processes, in MABR biofilms, oxygen from the membrane and pollutant substrate(s) from the bulk liquid are transferred across the biofilm in countercurrent directions (Figure 4). Biofilm stratification in MABRs results from this distribution of the maximum oxygen and pollutant-substrate concentrations at different locations within the biofilm and the relative thickness of MABR biofilms; this enables the removal of more than one pollutant type. The high oxygen concentrations coupled with the low organic carbon concentrations near the membrane/biofilm interface encourage nitrification, an aerobic heterotrophic layer above this facilitates organic carbon oxidation, and an anoxic layer near the biofilm/ liquid interface supports denitrification (Stephenson et al., 2000).

MABRs have been used to treat a variety of wastewater types at the laboratory scale (Brindle and Stephenson, 1996). However, in line with the characteristics of MABRs discussed above, most investigations show that the process is particularly suitable for the treatment of high-oxygen-demanding wastewaters, biodegradation of VOCs, combined nitrification, denitrification, and/or organic carbon oxidation in a single biofilm. Bubble-less oxygen mass transfer can be accomplished using gas-permeable dense membranes or hydrophobic microporous membranes (Cote et al., 1988). Both plate and frame and hollow-fiber membrane configurations have been used to supply the oxygen. Oxygen diffusion through dense membrane material can be achieved at high gas pressures without bubble formation. In hydrophobic microporous membranes, the pores remain gas filled; and oxygen is transported to the shell side of the membrane through the pores by gaseous diffusion or Knudsen flow-transport mechanisms. The partial pressure of the gas is kept below the bubble point to ensure the bubble-less supply of oxygen (Ahmed and Semmens, 1992; Rothemund et al., 1994; Semmens, 1991; Semmens and Gantzer, 1993). Pressurized hollow fibers have been investigated in the dead-end and flow-through modes of operation. The evacuation of carbon dioxide from the bioreactor is a benefit of flow-through operation, though no quantitative work to determine removal rates has been undertaken (Cote et al., 1997; Kniebusch et al., 1990). Deadend operation has usually been avoided, due to significantly decreased performance and condensate formation in the lumen (Cote et al., 1997). The nonbiological fouling and loss of performance of dead-end porous hollow fibers due to iron oxidation, absorption of free oils and greases into pores, surfactants, and suspended solids, and fiber tangling have been reported (Semmens and Gantzer, 1993). Chemical treatment of the dead ends of these hollow fibers may provide a means for the condensate to escape. The liquid boundary layer normally has a greater impact upon the overall oxygen mass transfer than the membrane, with mixing of the liquid a key operational parameter (Cote et al., 1997; Kniebusch et al., 1990; Wilderer et al., 1985). However, wall thickness significantly affects the transport of

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Biofilm Figure 4 Simplified representation of the steady-state concentration profiles of oxygen, carbon substrate, and microbial activity in case of MABR biofilm and conventional biofilm.

oxygen through dense polymer membranes (Wilderer et al., 1985). Oxygen transport is also controlled by the presence of membrane-attached biofilm and its thickness; the partial pressure of oxygen and flow-velocity characteristics on the lumen side; and the wastewater flow-velocity characteristics on the shell side of the membrane (Kniebusch et al., 1990; Pankania et al., 1994). Oxygen partial pressure provides the means for controlling the depth of oxygen penetration into the wastewater, with an increase in partial pressure resulting in an increase in the metabolic activity of the membraneattached biofilm (Rothemund et al., 1994). In bioreactors, most membranes used for oxygen mass transfer operate with the biofilm attached to the membrane surface. These biofilms are in intimate contact with the oxygen source and are protected against abrasion and grazing (Kniebusch et al., 1990; Rothemund et al., 1994). Scanning electron micrographs show that some attached bacteria inhabit the membrane pores, with the location of the oxygen and wastewater interphase very close to the bacteria (Rothemund et al., 1994). Thus, oxygen-transfer resistance due to the thickness of the porous membrane and the liquid boundary layer are not necessarily decisive limiting factors (Kniebusch et al., 1990; Rothemund et al., 1994; Wilderer et al., 1985).

Excessive biofilm accumulation can result in the transport limitation of oxygen and nutrients, plugging of membrane fibers, a decline in biomass activity, metabolite accumulation deep within the biofilm, and the channeling of flow in the bioreactor such that steady-state conditions may not be maintained (Debus and Wanner, 1992; Pankania et al., 1994; Yeh and Jenkins, 1978). To operate at maximum efficiency, occasional membrane washing, air scouring, backwashes, and high recirculation rate of wastewater to achieve high shear velocities have all been employed to control biomass accumulation. In the MABR process, oxygen is transferred without forming bubbles and therefore cannot be utilized to mix the bulk liquid. In laboratory scale MABRs, liquid-phase mixing has been achieved using recirculation pumps, impellers, magnetic stirrers, nitrogen, or air sparging.

4.16.2.2 Extractive Membrane Biological Reactor The extractive membrane biological reactor (EMBR) process enables the transfer of degradable organic pollutants from hostile industrial wastewaters, via a dense silicone membrane,

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to a nutrient medium for subsequent degradation (Brindle and Stephenson, 1996). Membranes used for the extraction of pollutants into a bioreactor have been developed using pervaporation by exchanging the vacuum phase with a nutrient biomedium phase where biodegradation mechanisms maintain the concentration gradient needed to transfer organic pollutants present in hostile industrial wastewaters (Lipski and Cote, 1990; Nguyen and Nobe, 1987; Yun et al., 1992). The inorganic composition of the nutrient medium is unaffected by the industrial wastewater within the hydrophobic hollow-fiber membrane. Hence, the conditions within the bioreactor can be optimized to ensure high biodegradation rate (Brookes and Livingston, 1993; Livingston, 1993, 1994). The extraction and biodegradation of toxic volatile organic pollutants, such as chloroethanes, chlorobenzenes, chloroanilines, and toluene from hostile industrial wastewaters, with high salinity and extremes of pH, using EMBRs have been demonstrated at the laboratory scale (Stephenson et al., 2000). Further information on these two generic types of MBRs can be derived from the review papers by Brindle and Stephenson (1996) and McAdam and Judd (2006), and the book by Stephenson et al. (2000). Yang et al. (2006) argued that extractive or aeration MBRs present a significant opportunity for researchers as niche areas of application as these novel processes remain unexplored. Hazardous waste treatment and toxic waste cleanup present two potential areas for the EMBR (Brookes and Livingston, 1994; Dossantos and Livingston, 1995; Livingston et al., 1998), whereas hydrogenotrophic denitrification of groundwater (Clapp et al., 1999; Mo et al., 2005; Modin et al., 2008; Nuhoglu et al., 2002; Rezania et al., 2005) and gas-extractionassisted fermentation (Daubert et al., 2003; Lu et al., 1999) are potential research areas for the AMBR. It is also important to recognize the fact that these three membrane processes are not mutually exclusive and, if necessary, could be coupled into one bioreactor (Brindle and Stephenson, 1996). Once the research field has gained momentum, commercial interest might correspondingly follow.

4.16.3 History and Fundamentals of Biosolid Separation MBR 4.16.3.1 Historical Development Membranes have been finding wide application in water and wastewater treatment ever since the early 1960s when Loeb and Sourirajan invented an asymmetric cellulose acetate membrane for RO (Visvanathan et al., 2000). Many combinations of membrane solid/liquid separators in biological treatment processes have been studied since. The first descriptions of the MBR technology date from the late 1960s. The trends that led to the development of today’s MBR are depicted in Figure 5. When the need for wastewater reuse first arose, the conventional approach was to use advanced treatment processes. The progress of membrane manufacturing technology and its applications could lead to the eventual replacement of tertiary treatment steps by MF or UF (Figure 5(a)). Parallel to this development, MF or UF was used for solid/liquid separation in the biological treatment

process and thereby sedimentation step could be eliminated (Figure 5(b)). The original process was introduced by DorrOlivier Inc. and combined the use of an activated sludge bioreactor with a cross-flow membrane-filtration loop (Smith et al., 1969). By pumping the mixed liquor at a high pressure into the membrane unit, the permeate passes through the membrane and the concentrate is returned to the bioreactor (Hardt et al., 1970; Arika et al., 1966; Krauth and Staab, 1988; Muller et al., 1995). The flat-sheet membranes used in this process were polymeric and featured pore size ranging from 0.003 to 0.01 mm (Enegess et al., 2003). Although the idea of replacing the settling tank of the conventional activated sludge (CAS) process was attractive, it was difficult to justify the use of such a process because of the high cost of membranes, low economic value of the product (tertiary effluent), and the potential rapid loss of performance due to fouling. Due to the poor economics of the first-generation MBRs, apart from a few examples such as installations at the basement level of skyscrapers in Tokyo, Japan, for wastewater reuse in flushing toilets, they usually found applications only in niche areas with special needs such as isolated trailer parks or ski resorts. The breakthrough for the MBRs occurred in 1989, the process involved submerging the membranes in the reactor itself and withdrawing the treated water through the membranes (Yamamoto et al., 1989; Kayawake et al., 1991; Chiemchaisri et al., 1993; Visvanathan et al., 1997). In this development, membranes were suspended in the reactor above the air diffusers (Figure 5(c)). The diffusers provided the oxygen necessary for treatment to take place and scour the surface of the membrane to remove deposited solids. There have been other parallel attempts to save energy in membrane-coupled bioreactors. In this regard, the use of jet aeration in the bioreactor was investigated (Yamagiwa et al., 1991). The main feature of this process was that the membrane module was incorporated into the liquid recirculation line for the formation of the liquid jet such that aeration and filtration could be accomplished using only a single pump. Jet aeration works on the principle that a liquid jet, after passing through a gas layer, plunges into a liquid bath entraining a considerable amount of air. Using only one pump makes it mechanically simpler and therefore useful to small communities. The limited amount of oxygen transfer possible with this technique, however, restricts this process only to such small-scale applications. The invention of air-backwashing techniques for membrane declogging led to the development of using the membrane itself as both clarifier and air diffuser (Parameshwaran and Visvanathan, 1998). In this approach, two sets of membrane modules are submerged in the aeration tank. While the permeate was extracted through one of the sets, the other set was supplied with compressed air for backwashing. The cycle was repeated alternatively, and there was a continuous airflow into the aeration tank, which was sufficient to aerate the mixed liquor. Eventually, two broad trends have emerged in recent times, namely submerged MBRs and sidestream MBRs. Submerged technologies tend to be more cost effective for largerscale lower-strength applications, and sidestream technologies are favored for smaller-scale higher-strength applications. The sidestream MBR envelope has been extended in recent years by the development of the air-lift concept, which

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Prescreening Sludge withdrawal (a)

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Figure 5 Evolution of membrane use in conjunction with bioreactor.

bridges the gap between submerged and cross-flow sidestream MBR, and may have the potential to challenge submerged systems in larger-scale applications (Pearce, 2008b). The economic viability of the current generation of MBRs depends on the achievable permeate flux, mainly controlled by effective fouling control with modest energy input (typically r1 kW h1 m3 product). More efficient fouling-mitigation methods can be implemented only when the phenomena occurring at the membrane surface are fully understood. Detailed discussion on the technology bottlenecks and the design aspects are provided in Sections 4.16.4 and 4.16.5, respectively. It is worth noting that as the oxygen supply limits maximum mixed-liquor suspended solids (MLSSs) in aerobic MBR, anaerobic MBRs (AnMBRs) were also developed. The first test of the concept of using membrane filtration with anaerobic treatment of wastewater appears to have been reported by Grethlein (1978). The first commercially available AnMBR was developed by Dorr-Oliver in the early 1980s for high-strength whey-processing wastewater treatment. The process, however, was not applied at full scale, possibly due to high membrane costs (Sutton et al., 1983). The Ministry of International Trade and Industry (MITI), Japan, launched a 6-year research and development (R&D) project named Aqua-Renaissance ’90 in 1985 with the particular objective of developing energy-saving and smaller footprint water-

treatment processes utilizing sidestream AnMBR to produce reusable water from industrial wastewater and sewage. However, a high cross-flow velocity and frequent physicochemical cleaning was required to maintain the performance of such a high-rate MBR (Yamamoto, 2009). It was difficult to reduce the energy consumption significantly by adopting the sidestream operation using a big recirculation pump. On the other hand, commencing in 1987, a system known as anaerobic digestion ultrafiltration (ADUF) was developed in South Africa for industrial wastewater treatment (Ross et al., 1992). This process is currently in operation. Further details on AnMBRs can be derived from the comprehensive review by Liao et al. (2006). This chapter, however, focuses on aerobic MBRs.

4.16.3.2 Process Comparison with Conventional Activated Sludge Process Some important basic characteristics of CAS and MBR are compared in this section.

4.16.3.2.1 Treatment efficiency/removal capacity The MBR process involves a suspended growth-activated sludge system that utilizes microporous membranes for solid/ liquid separation in lieu of secondary clarifiers. The biological treatment in MBR is performed according to the principles

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known from activated sludge treatment. However, higher suspended solids, biological oxygen demand (BOD), and chemical oxygen demand (COD) removals in MBR have been reported throughout the literature. With CAS, the colloidal fraction (that represents about 20% of the organic content of wastewater) has a residence time (hydraulic residence time (HRT)) in the range of few hours while with MBR, due to total SS retention, the residence time of this fraction (sludge retention time (SRT)) is in the range of several days. Thus, the biodegradation for this fraction is higher in MBR than in CAS. Some soluble compounds too, after being adsorbed on SS, can be retained in MBR and can be biodegraded to a better extent. Thus, some studies have ascribed the better removal of soluble COD in MBR to the fact that the effluent is particle free (Cote et al., 1997; Engelhardt et al., 1998; De Wilde et al., 2003). MBR produces quality effluent suitable for reuse applications or as a high-quality feedwater source for RO treatment. Indicative output quality includes suspended solids o1 mg l1, turbidity o0.2 nephelometric turbidity unit (NTU), and up to 4 log removal of virus (depending on the membrane nominal pore size). In addition, it provides a barrier to certain chlorine-resistant pathogens such as Cryptosporidium and Giardia. In comparison to the CAS process, which typically achieves 95%, COD removal can be increased to 96–99% in MBRs (Stephenson et al., 2000). Nutrient removal is one of the main concerns in modern wastewater treatment especially in areas that are sensitive to eutrophication. As in the CAS, currently, the most widely applied technology for N removal from municipal wastewater is nitrification combined with denitrification. Total nitrogen removal through the inclusion of an anoxic zone is possible in MBR systems. Besides phosphorus precipitation, enhanced biological phosphorus removal (EBPR) can be implemented, which requires an additional anaerobic process step. Some characteristics of MBR technology render EBPR in combination with post-denitrification as an attractive alternative that achieves very low nutrient effluent concentrations (Drews et al., 2005b).

4.16.3.2.2 Sludge properties and composition The presence of a membrane for sludge separation has many consequences. This influences the rheological properties and composition of the sludge. Defrance et al. (2000) observed in a sidestream MBR with high cross-flow velocity that MBR sludge was less viscous than conventional sludge. The same was observed by Rosenberger et al. (2002). Furthermore, with increasing shear rate, viscosity of the sludge decreases (Rosenberger et al., 2002), although in some cases, the activated sludge behaves as a Newtonian fluid (Xing et al., 2001). Defrance and Jaffrin (1999) found out that filtering-activated sludge from an MBR resulted in fouling that could be totally, physically removed, whereas filtration of CAS led to physically irremovable fouling. It is quite difficult to generalize information about sludge composition from different installations, since each installation promotes different types of activated sludge. This has its effect on the microbial community that can be found in an activated sludge system. Nevertheless, it is obvious that the presence of the membrane in an MBR system influences the biomass composition. Since no suspended solids are washed

out with the effluent, the only sink is surplus sludge. From a secondary clarifier, lighter species are washed out, whereas in an MBR they are retained in the system by the membrane. Furthermore, changes in SRT and higher MLSS concentrations might lead to changes in the microbial community. Microbialcommunity analyses have revealed significant differences between CAS system and an MBR and a higher fraction of bacteria was found in the nongrowing state in the MBR (Witzig et al., 2002; Wagner and Rosenwinkel, 2000).

4.16.3.2.3 Sludge production and treatment Small-scale laboratory studies revealed a great advantage of MBRs, that is, lower or even zero excess sludge production, caused by low loading rates and high SRTs (Benitez et al., 1995). When longer SRTs are applied, sludge production, of course, decreases in the MBR (Wagner and Rosenwinkel, 2000). However, the amount of excess secondary sludge produced in larger MBR installations operated under the practical range of SRTs is somewhat lower than or even equal to that in conventional systems (Gu¨nder and Krauth, 2000). Table 1 provides a general comparison of the sludge-production rates from different treatment processes. It should be noted that the primary sludge production in the case of the MBR is lower. The suited pretreatment for the MBR is grids and/or sieves, and in an average, screened water was observed to contain 30% more solids than settled water (Jimenez et al., 2010). MBR sludge treatment is almost the same compared to CAS systems. The dewaterability of waste-activated sludge from the MBR seems to pose no additional problem, compared to aerobic stabilized waste sludge from CAS systems (Kraume and Bracklow, 2003).

4.16.3.2.4 Space requirements One of the advantages of the MBR is its compactness, because large sedimentation tanks are not needed. An interesting parameter in this respect is the surface-overflow rates for the two systems. The overflow rate of a secondary clarifier is defined as the ratio of its flow and footprint, that is, the volume of water that can be treated per square meter of tank. In practice, values around 22 m d1 are used. For an MBR filtration tank, an overflow rate can also be estimated from the permeate flux and the membrane-packing density within the

Table 1

Sludge production in case of different treatment processes

Treatment process

Sludge production kg (kg BOD)1

Submerged MBR Structured media biological aerated filter Trickling filter Conventional activated sludge Granular media BAF

0.0–0.3 0.15–0.25 0.3–0.5 0.6 0.63–1.06

Data from Stephenson T, Judd S, Jeferson B, and Brindle K (2000) Membrane Bioreactors for Wastewater Treatment. London: IWA. Gander MA, Jefferson B, and Judd SJ (2000) Membrane bioreactors for use in small wastewater treatment plants: Membrane materials and effluent quality. Water Science and Technology 41: 205–211, and Metcalf and Eddy, Inc. (2003) Wastewater Engineering – Treatment and Reuse, 4th edn. New York: McGraw-Hill.

Membrane Biological Reactors

tank. Following this method, Evenblij et al. (2005a) showed that with an average permeate flux of 15 l m2 h1, the overflow rates of the membrane tanks are in the range 25–62 m d1 which is up to 3 times higher than the overflow rate of a conventional secondary clarifier. Compared to an average overflow rate of 22 m d1 with a secondary clarifier, the space consumption for sludge-water separation in an MBR is 10–60% lower when flux is 15 l m2 h1 and 50–80% lower when flux is 25 l m2 h1. A further reduction in footprint is caused by the higher MLSS concentration that can be applied in an MBR. This estimate however did not take into account backflushing or relaxation periods, which reduce the overflow rate. Nevertheless, full-scale MBR plants also manifest these space-saving characteristics. For instance, Brescia WWTP, in Italy, which is the world’s largest MBR retrofit of an existing conventional plant, gives a full-scale example of a ratio of 2 when comparing area needed by CAS and MBR (Brepols et al., 2008).

4.16.3.2.5 Wastewater treatment cost

Relative cost (1994 cost equals 1)

The high cost connected with MBR is often mentioned in discussions about applicability of MBR. However, it is not easy to make a general economical comparison between MBR and CAS systems. First of all, the reference system should not simply be an activated sludge system, but a system that produces an effluent of the same quality. Moreover, an MBR is a modular system, that is, easily expandable, which is often mentioned as an advantage of the system. However, this makes the system less competitive with conventional systems, since these become relatively less expensive per population equivalent (p.e.) at larger scale. It should be noted that although the equipment and energy costs of an MBR are higher than systems used in conventional treatment, total water costs can be competitive due to the lower footprint and installation costs (Pearce, 2008b; Lesjean et al., 2004; Cote et al., 2004; De Wilde et al., 2003). MBR costs have declined sharply since the early 1990s, falling typically by a factor of 10 in 15 years. As MBR technology has become accepted, and the scale of installations has increased, there has been a steady downward trend in membrane prices (Figure 6), which is still continuing. This is particularly notable with the acceptance of the MBRs in the municipal sector. The uptake of membrane technology for municipal applications has had the affect of

1.0 Membrane cost (per unit flow rate) 0.8

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downward pressure on price. A detailed holistic cost comparison may reveal reasonably comparable results between the cost of the MBR option versus other advanced treatment options, especially if land value is considered. Studies show that depending on the design and site-specific factors the total water cost associated with MBR may be less or higher than the CAS-UF/MF option. For example, a cost comparison by the US consultant HDR in 2007 showed that MBR was 15% more expensive on a 15 million liters a day (MLD) case study, whereas a study by Zenon in 2003 gave MBR 5% lower costs (Pearce, 2008a). The differences were due to the design fluxes assumed and the capital charge rate for the project. Neither study allocated a cost advantage from the reduced footprint, which could typically translate to a treated water cost saving of up to 5%. It is interesting to evaluate the development in cost estimates over the past several years. Davies et al. (1998) made a cost comparison for two wastewater treatment plants (WWTPs), with capacities of 2350 and 37 500 p.e. With the assumptions they made (e.g., a membrane lifetime of 7 years) they conclude that depending on the design capacity (i.e., 2 times DWF to be treated) MBR is competitive with conventional treatment up to a treatment capacity of 12 000 m3 d1 (Table 2). Engelhardt et al. (1998) after carrying out pilot experiments also made a cost calculation for an MBR with a capacity of 3000 p.e., designed for nitrification/denitrification and treatment of 2*DWF. Investment costs were estimated at h3104 000 (including pretreatment) and operational cost at h194 000 yr1. Adham et al. (2001) made a cost comparison between MBR oxidation ditch followed by membrane filtration and CAS followed by membrane filtration. They concluded that MBR is competitive with the other treatment systems (Table 3). Chang et al. (2001) report experiments with low-cost membranes. The effect of membrane cost on the investment cost is considerable, but operational problems hinder further application of low-cost membranes. A drawback of the applied membranes is its limited disinfecting capacity. Van Der Roest et al. (2002a) described a cost comparison between an MBR installation and a CAS system with tertiary sand filtration. The calculations were carried out for two new WWTPs with the aim of producing effluent with low

Table 2 Capital and operating cost ratios of MBR and conventional activated sludge (CAS) process assuming a capacity of 2*(dry weather flow) Parameter

Cost ratio (MBR:ASP)

Capital cost 2350 p.e 37 500 p.e Operating costs per year

0.63 2.00

2350 p.e 37 500 p.e

1.34 2.27

0.6 0.4 0.2 0.0 (1994)

(1995)

(1997)

(1999)

(2000)

Figure 6 Sharp cost decline of membranes for MBR (cost of Zenon membranes as an example).

Data from Davies WJ, Le MS, and Heath CR (1998) Intensified activated sludge process with submerged membrane microfiltration. Water Science and Technology 38(5): 421–428.

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concentrations of nitrogen and phosphorus. Almost the same investment costs and 10–20% higher operating costs, depending on the capacity of the plant, for MBR were estimated (Table 4). Cost differences between an MBR and a traditional WWTP concerning manpower, chemical consumption, and sludge treatment were noted to be minimal. WERF (2001) summarized operating and water-quality data obtained over 1 year from two MBR pilot plants located at the Aqua 2000 Research Center at the City of San Diego (California) North City Plant. Preliminary cost estimates of the MBR technology were also developed. MBRs demonstrated that their effluent was suitable to be fed directly into an RO process from a particulate standpoint with silt density index (SDI) values averaging well below 3. The MBR effluent water quality was superior to the quality of a full-scale tertiary conventional WWTP. The preliminary cost estimate in this report was performed for a 1 million gallons a day (mgd) scalping facility (WWTP drawing a designated amount of flow from the sewer system; excess sewage flow is treated at another plant located at the end of the sewer line). This facility produced an effluent suitable as feedwater for an RO process. Based upon this estimate, the present value was estimated as $0.81 m3, $0.96 m3, and $1.16 m3 for the MBR process, oxidation ditch with MF, and oxidation ditch with conventional tertiary lime pre-treatment, respectively. Therefore, the MBR process was reported as the most cost-effective alternative for water reclamation where demineralization or indirect drinking water-production (RO) is required. McInnis (2005) reported a detailed comparative cost analysis of two membrane-based tertiary treatment options: (1)

Table 3 Capital and total cost ratios of MBR and tertiary MF following alternative biological processes Alternatives

Oxidation ditch-MF CAS-MF

Cost ratio (MBR:alternative) Capital

Total per year

0.91 0.85

0.89 0.9

Data from Adham S, Mirlo R, and Gagliardo P (2000) Membrane bioreactors for water reclamation – phase II. Desalination Research and Development Program Report No. 60, Project No. 98-FC-81-0031. Denver, CO: US Department of the Interior, Bureau of Reclamation, Denver Office.

MBR and, (2) CAS process followed by MF (CAS/MF). According to that study, irrespective of design flow rate, the MBR entails slightly higher unit capital costs as compared to CAS/ MF process, while, depending on the design flow rate, the operation and maintenance costs (O&M) of the former are higher than or comparable to that of the latter. Comparative O&M cost breakdown revealed that MBR entails less labor cost, considerably higher power and chemical consumptions and slightly higher membrane cost, other costs remaining virtually the same. In the CAS/MF process, labor cost induces the highest cost, while in case of the MBR process, labor and electrical power-consumption costs are almost similar. Overall, the MBR imposes slightly higher capital and operating/ maintenance cost over that of CAS/MF. Cote et al. (2004) explored two membrane-based options available to treat sewage for water reuse, tertiary filtration (TF) of the effluent from a CAS process, and an integrated MBR. These options were compared from the point of view of technical performance and cost using ZeeWeed immersed membranes. The analysis showed that an integrated MBR is less expensive than the CAS-TF option. The total life cycle costs for the treatment of sewage to a quality suitable for irrigation reuse or for feeding RO decreased from 0.40$ m3 to 0.20$ m3 as plant size increased to 75 000 m3 d1. It was also shown that the incremental life-cycle cost to treat sewage to indirect potable water-reuse standards (i.e., by UF and RO) was only 39% of the cost of seawater desalination. A recent market research report (BCC Research, 2008) estimated the capital cost of a 50 000 gallons per day (gpd) (190 m3 d1) plant at US$350 000, a 100 000 gpd plant at US$500 000, and a 500 000 gpd plant at US$2 million. For systems of 1 mgd (million gallons per day) and larger, capital costs start at US$3.5 million (Table 5). The largest percentage of new system installations, 93%, continue to fall into the 5000–500 000 gpd range (most of those, about 57% of them, have capacities of less than 25 000 gpd), 2% of installations range from 0.5–1 mgd, and 5% of them are larger than 1 mgd. Tables 2–5 list cost values reported during the period 1998–2008. Obviously, the data from the initial stage of the MBR development holds little relevance today. However, these are listed here to provide a general trend of cost-data evolution.

4.16.3.2.6 Comparative energy usage Table 4 Capital and total cost ratios of MBR and tertiary sand filtration following CAS Parameter

Cost ratio

Capital cost 10 000 p.e 50 000 p.e Operating costs per year

0.92 1.01

10 000 p.e 50 000 p.e

1.09 1.21

Data from Van Der Roest HF, Lawrence DP, and Van Bentem AG (2002a) Membrane Bioreactors for Municipal Wastewater Treatment (Water and Wastewater Practitioner Series: Stowa Report). London: IWA.

MBR provides an equivalent treatment level to CAS-UF/MF, but at the expense of higher energy cost since the efficiency of air usage in MBR is relatively low. The MBR process uses more Table 5

Capital cost of MBR depending on plant sizea

Plant size, gpd  103

Capital cost, US$  103

50 100 500 1000

350 500 2000 3500

a

1 m3 d1 ¼ 264.17 gpd. Data from BCC Research (2008) Membrane bioreactors: Global markets. Report Code MST047B, Report Category – Membranes & Separation Technology.

Membrane Biological Reactors

air, and hence higher energy than conventional treatment. This is because aeration is required for both the biological process and the membrane cleaning, and the type, volume, and location of air required for the two processes are not matched. Biotreatment utilizes fine air bubbles, since oxygen needs to be absorbed for the biological reaction step. In contrast, fouling control is best achieved by larger bubbles, since the air is required to scour the membrane surface or shake the membrane to remove the foulant. Accordingly, although the concept of MBR was first developed to exploit the fact that the biological wastewater-treatment process and the process of membrane-fouling control can both use aeration (Pearce, 2008b), the potential for dual-purpose aeration is strictly limited. Based on a survey of conventional wastewater-treatment facilities in the US, Metcalfe and Eddy, Inc. (2003) reported that the energy usage range was 0.32–0.66 kW h1 m3. Energy usage in wastewater treatment is somewhat lower in Europe, partly due to a greater consciousness for energy efficiency, and partly due to the fact that average BOD loading/ capita in the US is 20–25% greater than that in Europe (due to the use of kitchen disposal units). Long-term monitoring of wastewater-treatment systems has shown usages as low as 0.15 kW h1 m3 for activated sludge, increasing to 0.25 kW h1 m3 if a biological aerated filter (BAF) stage is included (Pearce, 2008a). Membrane filtration after conventional treatment is estimated to add 0.1–0.2 kW h1 m3 to the energy, equivalent to a total energy use for CAS-UF/MF of 0.35–0.5 kW h1 m3 in a new facility (Lesjean et al., 2004). Experience in large-scale commercial MBRs shows an energy usage of around 1.0 kW h1 m3, although smaller-scale facilities typically operate at 1.2–1.5 kW h1 m3 or higher (Judd, 2006). However, in comparison to these values, energy consumption of around 1.9 kW h1 m3 was reported in 2003 (Zhang et al., 2003) and up to 2.5 kW h1 m3 in 1999 (Ueda and Hata, 1999). This proves that there is a gradual improvement in MBR design (Figure 7). Further improvements in air efficiency and membrane-packing density are expected

Energy consumption, kW hr−1 m−3

3.0

2.0

1.0

to improve the current values in the future. Even so, it seems likely that MBR energy costs will continue to exceed those of CAS-UF/MF by 0.4 kW h1 m3 or more (Pearce, 2008a). However, the fact that membrane filtration after conventional treatment is estimated to add only 0.1–0.2 kW h1 m3 to the energy points out that the higher energy consumption of MBR over CAS-UF/MF is due to the difference in consumption in the respective biological processes. MBRs are generally operated at quite low F/M ratios (less than 0.2), or high MLSS concentrations, and this is one of the reasons for the excellent biodegradation efficiency, and high aeration cost as well. CAS plants, on the other hand, are operated at higher F/M ratios, implying lower oxygen need for biodegradation. Table 6 lists typical energy-use rates of different biologicalbased treatment combinations. Section 4.16.5 provides further information on energy comparison of the MBR formats.

4.16.3.3 Relative Advantages of MBR There are several advantages associated with the MBR technology, which make it a valuable alternative over other treatment techniques. The combination of activated sludge with membrane separation in the MBR results in efficiencies of footprint, effluent quality, and residual production that cannot be attained when these same processes are operated in sequence. The MBR system is particularly attractive when applied in situations where long biological solid-retention times are necessary and physical retention and subsequent hydrolysis are critical to achieving biological degradation of pollutants (Chen et al., 2003). The prime advantages of MBR are the treated water quality, the small footprint of the plant, less sludge production, and flexibility of operation (Visvanathan et al., 2000). First of all, the retention of all suspended matter and most of the soluble compounds within the bioreactor leads to excellent effluent quality capable of meeting stringent discharge requirements and paving the way for direct water reuse. The possibility of retaining all bacteria and viruses results in a sterile effluent, eliminating extensive disinfection and the corresponding hazards related to disinfection by-products. As the entire process equipment can be made airtight, odor dispersion can be prevented quite successfully. Since suspended solids are not lost in the clarification step, total separation and control of the SRT and hydraulic retention time (HRT) are possible enabling optimum control of the microbial population and flexibility in operation. The absence of a clarifier, which also acts as a natural selector for settling organisms, enables sensitive, slow-growing

0.0 1999

2003 Year

2006

Figure 7 Gradual reduction in reported values of energy consumption by MBR. Data from Ueda T and Hata K (1999) Domestic wastewater treatment by a submerged membrane bioreactor with gravitational filtration. Water Research 33: 2888–2892; Zhang SY, Van Houten R, Eikelboom DH, et al. (2003) Sewage treatment by a low energy membrane bioreactor. Bioresource Technology 90: 185–192; and Judd S (ed.) (2006) The MBR Book: Principles & Applications of MBRs in Water & Wastewater Treatment. Oxford: Elsevier.

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Table 6 Comparative typical energy consumption by different treatment options Treatment option

Energy use (kW h1 m3)

CAS CAS-BAF CAS-MF/UF MBR

0.15 0.25 0.35–0.5 0.75–1.5a

a

Power consumption range for large- to smaller-scale plants.

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species (nitrifying bacteria, bacteria capable of degrading complex compounds) to develop and persist in the system (Cicek et al., 2001; Rosenberger et al., 2002). The membrane not only retains the entire biomass but also prevents the escape of exocellular enzymes and soluble oxidants creating a more active biological mixture capable of degrading a wider range of carbon sources (Cicek et al., 1999b). MBRs eliminate process difficulties and problems associated with settling, which is usually the most troublesome part of wastewater treatment. The potential for operating the MBR at very high SRTs without the obstacle of settling allows high biomass concentrations in the bioreactor. Consequently, higher-strength wastewater can be treated and lower biomass yields are realized (Muller et al., 1995). This also results in more compact systems than conventional processes, significantly reducing plant footprint and making it useful in waterrecycling applications (Konopka et al., 1996). The low sludge load in terms of BOD forces the bacteria to mineralize poorly degradable organic compounds. The higher biomass loading also increases shock tolerance, which is particularly important where feed is highly variable (Xing et al., 2000). The increased endogenous (autolytic) metabolism of the biomass (Liu and Tay, 2001) under long SRT allows development of predatory and grazing communities, with the accompanying trophiclevel energy losses (Ghyoot and Verstraete, 1999). These factors, in addition to resulting in lower overall sludge production, lead to higher mineralization efficiency than those of a CAS process. High molecular weight soluble compounds, which are not readily biodegradable in conventional systems, are retained in the MBR (Cicek et al., 2002). Thus, their residence time is prolonged and the possibility of oxidation is improved. The system is also able to handle fluctuations in nutrient concentrations due to extensive biological acclimation and retention of decaying biomass (Cicek et al., 1999a).

4.16.3.4 Factors Influencing Performance/Design Considerations This section sheds light on some important design considerations of MBR. More detailed information on some of these parameters is provided in Section 4.16.4.7, in relation to membrane fouling.

4.16.3.4.1 Pretreatment All MBRs require pretreatment, for example, screening and grit removal, to protect the membranes. Screening has historically been limited to 3 mm; however, hair and fiber can still pass through this size of the screen and become embedded or wrapped around the hollow fibers. The MBR providers have standardized their screen selections to a 2-mm traveling band, punched screen. Conversely, the flat-sheet membranes experience less problems with hair and fiber, and are standardized to a 3-mm screen. Further discussion regarding mechanical pretreatment is provided in Section 4.16.4.6.

4.16.3.4.2 Membrane selection and applied flux An MBR membrane needs to be mechanically robust, chemically resistant to high Cl2 concentrations used in cleaning, and nonbiodegradable (Pearce, 2008a). Clean-water permeability

is not as important in an MBR as in membrane-filtration applications, since the membrane transport properties are strongly influenced by the accumulation of foulant particles at the membrane surface. However, process flux in treating a wastewater feed is important since it directly affects capital cost, due to its effect on membrane area and footprint, and operating costs due to the effect of membrane area on chemical and air use. Most MBRs operate at an average flux rate between 12.5 and 25 l m2 h1, with Mitsubishi’s unit operating in the lower range. The key flux rates that determine the number of membranes required are associated with the peak flow rates. For plants with peaking factors of less than two, an MBR can handle the plant flow variation without having a significantly impact on the average design flux rate. Otherwise, equalization needs to be provided with either a separate tank at the head of the facility or within the aeration basin, allowing sidewater depth variations during peak flow.

4.16.3.4.3 Sludge retention time In the past, most MBR systems were designed with extremely long SRTs, of the order of 30–70 days, and very few were operated at less than about 20 days. Two reasons prompted such practice: (1) the drive to minimize sludge production or eliminate it all together and (2) the concern over the reduced flux resulting from short SRT operation, presumably due to the fouling effect of extracellular excretions from younger sludge. Currently, the selection of SRT is based more on the treatment requirements, and SRTs as low as 8–10 days can now be contemplated.

4.16.3.4.4 Mixed liquor suspended solids concentration From the point of view of bioreactor volume reduction and minimization of excess sludge, submerged MBR systems have been typically operated with MLSS concentrations of more than 12 000 mg l1, and often in the range of 20 000 mg l1. Hence, they offer greater flexibility in the selection of the design SRT. However, excessively high MLSS may render the aeration system ineffective and reduce membrane flux. A trade-off, therefore, comes into play. Current design practice is to assume the MLSS to be closer to 10 000 mg l1 to ensure adequate oxygen transfer and to allow for higher membrane flux. With larger systems, it is more cost effective to reduce the design MLSS because of the high relative cost of membranes when compared to the cost of additional tank volume.

4.16.3.4.5 Oxygen transfer At high MLSS concentrations, the demand for oxygen can be significant. In some cases, the demand can exceed the volumetric capacity of typical oxygenation systems. The oxygentransfer capacity of the aeration system must also be carefully analyzed. Submerged membranes are typically provided with shallow coarse bubble air to agitate the membranes as a means to control fouling. Such aeration provides some oxygenation, but at low efficiency. In compact systems, fine bubble aeration may be placed at greater depth below the membrane aeration; however, the combined efficiency and the bubblecoalescing effects require further consideration during design (Visvanathan et al., 2000).

Membrane Biological Reactors

The lower operating cost obtained with the submerged configuration along with the steady decrease in the membrane cost encouraged an exponential increase in MBR plant installations from the mid-1990s onward. Since then, further improvements in the MBR design and operation have been introduced and incorporated into larger plants. The key steps in the recent MBR development are summarized below:

• •

•

The acceptance of modest fluxes (25% or less of those in the first generation), and the idea of using two-phase bubbly flow to control fouling. While early MBRs were operated at SRTs as high as 100 days with MLSS up to 30 g l1, the recent trend is to apply a lower SRT (around 10–20 days), resulting in more manageable MLSS levels (10–15 g l1). Thanks to these new operating conditions, the fouling propensity in the MBR has tended to decrease and overall maintenance has been simplified, as less-frequent membrane cleaning is necessary.

Further discussion on these aspects is provided in the following sections.

4.16.4 Worldwide Research and Development Challenges 4.16.4.1 Importance of Water Reuse and the Role of MBR The need for pure water is a problem of global proportions. In the Earth’s hydrologic cycle, freshwater supplies are fixed and constant, while global water demand is growing (Howell, 2004; Bixio et al., 2006). With each passing year, the quality of the planet’s water measurably deteriorates, presenting challenges for the major users: the municipal, industrial, and environmental sectors. Increasing demand for water, and drought and water scarcity are now common issues facing many urban and rural communities around the world (Howell, 2004; Tadkaew et al., 2007; Jimenez and Asano, 2008). Water treatment has, therfore, become an area of global concern as individuals, communities, industries, countries, and their national institutions strive for ways to keep this essential resource available and suitable for use. Water recycling is a pragmatic and sustainable approach for many countries to mitigate or solve the problems of water supply. There is a growing interest in using nontraditional water resources by means of water reclamation and water recycling for long-term sustainability. It can be divided into two categories, internal domestic or industrial recycling and external recycling, where high-quality reclaimed water from a sewage treatment plant is used for aquifer recharge or irrigation. With the current focus on water-reuse projects and the role they play in the water cycle, the search for cost-competitive advanced wastewater-treatment technologies has never before been so important. Treatment technology for water recycling encompasses a vast number of options. A general paucity of legislative and socioeconomic information has led to the development of a diverse range of technical solutions (Jefferson et al., 2000). Membrane processes are regarded as key elements of advanced wastewater reclamation and reuse schemes and are included in a number of prominent schemes worldwide, for example, for artificial groundwater recharge, indirect

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potable reuse, as well as for industrial-process water production (Melin et al., 2006; Bixio et al., 2008). Among the many treatment alternatives, MBRs, which combine membrane filtration and biological process for wastewater treatment, are seen to have an effective technology capable of transforming various types of wastewater into high-quality effluent exceeding most discharge requirements and suitable for a variety of nonpotable water-reuse applications such as flushing toilets and for irrigation (Tadkaew et al., 2007; Jimenez and Asano, 2008). In some cases, treated water can be applied to recharge groundwater to halt saltwater intrusion into coastal aquifers, abate subsidence in areas sinking due to overpumping groundwater, and support aquifer storage and recovery. Issues of water quality, water quantity, and aging/nonexistent infrastructure propel the market for MBRs. Escalating water costs due to dwindling supplies for communities and businesses also drive the growing acceptance of MBRs. Anticipated stricter environmental regulations are driving sales of MBRs to industry, municipalities, and are prompting maritime users to consider MBR technology (Jefferson et al., 2000; Jimenez and Asano, 2008). This is probably due to the effectively disinfected high-quality effluent and high performance in trace organic removal for safe and environmentally benign discharge that MBRs can offer. In practical terms, the process has many benefits, which make it suitable for the size of the systems applicable to recycling. The ability to run independently of load variation and produce no sludge are critical and highlight MBRs as possibly the most viable small-footprint, high-treatment option for water recycling (Jefferson et al., 2000; Melin et al., 2006; Tadkaew et al., 2007). Comparison with other technologies used for water recycling reveals that MBRs not only produce lower residual concentrations but do so more robustly than the alternatives (Jefferson et al., 2000; Melin et al., 2006). The favorable microbiological quality of the effluent of MBRs is a major factor in their frequent selection for water reuse, even if full disinfection cannot be expected, particularly considering the distribution and storage components of a full-scale system, which can be prone to regrowth of microorganism and contamination from various sources. However, the MBR effluent is adequate for many water-reuse applications with little residual chlorine disinfection for subsequent distribution. The MBR then does provide a dual layer of protection against pathogen breakthrough, greatly lowering the risk during operation. MBRS have the greatest efficacy toward water recycling, albeit contingent upon a loading rate constrained by the operable flux. Not only do they comply with all likely waterquality criteria for domestic recycling but they also produce a product that is visibly clear and pathogen free, both of which are likely to be key concerns in terms of public acceptability. There are some issues that still need to be addressed and these are highlighted throughout Sections 96.4.6 and 96.4.7 of this chapter.

4.16.4.2 Worldwide Research Trend Early development efforts in MBR technology were concentrated in UK, France, Japan, and South Korea, whereas extensive research in China and Germany began after 2000. Much

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of the research in the newcomer countries is building on pioneering work from the UK, France, Japan, and South Korea. Three stages may be identified in the worldwide MBR research: 1. An entry-level stage spanning from 1966 to 1980, during which lab-scale research was mainly conducted. Membranes of that period had low flux and short life span due to undeveloped membrane-manufacturing technology. 2. A slow-to-moderate growth period from 1980 to 1995, when MBR technology was well investigated especially in Japan, Canada, and the USA. During this stage, new membrane-material development, MBR configuration design, and MBR operation were critically studied. The submerged MBR concept was put forward by Japanese researchers in 1989. 3. The rapid development stage started in 1995 and continues even now, when MBR technology underwent a rapid development prompted by deep understanding of the technology in research communities and by the installation of full-scale MBRs. Much of the published information on MBRs to date has mainly focused on bench or pilot-scale studies, performance results of treating a specific type of wastewater, and short-term operations. Regardless of the source of wastewater, whether it is municipal or industrial, very few publications involved fullscale studies for long-term operational periods. In a comprehensive review, Yang et al. (2006) grouped the available worldwide publications regarding MBR into six main research areas: (1) literature and critical reviews; (2) fundamental aspect; (3) municipal and domestic wastewater treatment; (4) industrial wastewater and landfill leachate treatment; (5) drinking-water treatment; and (6) others, which include gas removal, sludge treatment, hydrogen production, and gas diffusion. The fundamental research category was based on studies that exclusively looked at membrane fouling, operation and design parameters, sludge properties, microbiological characteristics, cost, and modeling. Studies, which focused on applied research and general reactor performance, were categorized by influent (feed) type (groups 3–6). Membrane fouling, which has been widely considered as one of the major limitations to faster commercialization of MBRs, has been investigated from various perspectives including the causes, characteristics, mechanisms of fouling, and methods to prevent or reduce membrane fouling. More than one-third of studies in the fundamental aspects group were found to deal with issues related to membrane fouling.

4.16.4.3 Modeling Studies on MBR Models that can accurately describe the MBR process are important for the design, prediction, and control of MBR systems. Due to the intrinsic complexity and uncertainty of MBR processes, basic models that can provide a holistic understanding of the technology at a fundamental level are of great necessity. Complex models that are also practical for real applications can greatly assist in capitalizing on the benefits of MBR technology. However, compared to experimental R&D, followed by commercialization of the technology, modeling studies for system-design analysis and performance prediction

are at a relatively preliminary stage. In an attempt to identify the required research initiatives in this regard, this section looks briefly into the state-of-the-art MBR modeling efforts. Effluent quality and the investment and operating costs are the primary concerns for any given wastewater treatment system. Therefore, model development should center on components for which water-quality standards have been set and parameters which are strongly correlated to cost. Ng and Kim (2007) put forward a few key model components and parameters for MBR modeling:

•

•

•

•

•

The ability to quantify individual resistance (i.e., resistance from cake formation, biofilm formation, and adsorptive fouling) as a function of the various influencing parameters is important in determining which parameters have the greatest influence on fouling and for designing and optimizing the system to achieve an economical balance between production and applied pressure. Determining the relationship between biomass concentration and other parameters can aid in identifying an optimal biomass concentration for operation, which can lead to significant economical savings. Aeration accounts for a significant portion of energy costs in the operation of MBR systems. The factors that influence oxygen requirement (wastewater and biomass concentration/growth rates) and the oxygen-transfer rate (MLSS concentration, MBR configuration, type of bubbles used, and specific airflow rate) should receive due consideration in the model to optimize aeration. Carbon and nutrient (nitrogen and phosphorous components) concentrations and their influencing factors (e.g., respective concentrations and growth rates of the various types of organisms and concentration of oxygen) should be incorporated into the models. Soluble microbial products (SMPs), which comprise a major portion of the organic matter in effluents from biological treatment processes and are potentially associated with issues such as disinfection by-product formation, biological growth in distribution systems, and membrane fouling, should be given proper consideration in models.

MBR models available in the literature can be broadly classified into three categories: biomass kinetic models, membranefouling models, and integrated models to describe the complete MBR process (Ng and Kim, 2007; Zarragoitia-Gonza´lez et al., 2008). Models describing biomass kinetics in an MBR include the activated sludge model (ASM) family (Henze et al., 2000), the SMP model (Furumai and Rittmann, 1992; Urbain et al., 1998; de Silva et al., 1998), and the ASM–SMP hybrid model (Lu et al., 2001; Jiang et al., 2008). The ASMs were developed to model the activated sludge process. The MBR process is the activated sludge process with the secondary clarification step replaced by membrane filtration; therefore, it is reasonable to use ASMs to characterize the biomass dynamics in an MBR system. However, their ability to describe the MBR process accurately has not been verified by in-depth experiments. Research suggests that SMPs are important components in describing biomass kinetics due to high SRTs in MBR systems. Accordingly, the SMP model demonstrated the capability of

Membrane Biological Reactors

characterizing the biomass with a reasonable-to-high degree of accuracy. Lu et al. proposed that the modified versions of ASM1 (Lu et al., 2001) and ASM3 (Lu et al., 2002), which incorporate SMPs, demonstrated fairly reasonable accuracy in quantifying COD and soluble nitrogen concentrations. Jiang et al. (2008) extended the existing ASM No. 2d (ASM2d) to ASM2dSMP with introduction of only four additional SMPrelated parameters. In addition to minimizing model complexity and parameter correlations, the model parameter estimation resulted in reasonable confidence intervals. Models describing membrane fouling include the empirical hydrodynamic model (Liu et al., 2003), fractal permeation model (Meng et al., 2005), sectional resistance model (Li and Wang, 2006), subcritical fouling behavior model (Saroj et al., 2008), and the resistance-in-series models that were presented as a part of the integrated models. Some of them are simply based on solid–liquid separation and simulate filtration processes (Chaize and Huyard, 1991; Gori et al., 2004). Other models consider specific physical approaches: cross-flow filtration (Cheryan, 1998; Hong et al., 2002; Beltfort et al., 1994) and mass-transport models (Beltfort et al., 1994; Bacchin et al., 2002). Nevertheless, membrane fouling is generally evaluated by employing the resistance-in-series model (Wintgens et al., 2003; Wisniewski and Grasmick, 1998) or, rarely, using empirical models (Benitez et al., 1995; De Wilde et al., 2003). The integrated models, basically, couple the kinetic models with the fouling ones (such as the resistance-in-series model) and they often consider the formation and degradation of SMPs (Ng and Kim, 2007). The models reported to date are valuable preliminary attempts, but require further improvements. For instance, the empirical hydrodynamic model is too simple to describe the membrane-fouling phenomenon, and the sectional resistance model lacks accuracy. Both the fractal permeation model and resistance-in-series model by Lee et al. (2002) provide good scientific insight, but specific experimental verification is necessary for general use of the models. The resistance-in-series model developed by Wintgens et al. (2003) shows the most promise, as it is fairly accurate, accounts for cleaning cycles, and can predict permeability changes over time. Further tests are needed to determine whether the model requires calibration or if the model parameters are applicable to other MBR systems. Recently, Zarragoitia-Gonza´lez et al. (2008) included the biological kinetics and the dynamic effect of the sludge attachment and detachment from the membrane, in relation to the filtration and a strong intermittent aeration in a hybrid model. The model was established considering SMP formation–degradation kinetic based on previous published models (Cho et al., 2003; Lu et al., 2001). A modification of Li and Wang’s model (Li and Wang, 2006) allows to calculate the increase of the transmembrane pressure (TMP), evaluating, at the same time, the influence of an intermittent aeration of bubbles synchronized with the filtration cycles on fouling control, and to analyze the effects of shear intensity on sludge cake removal. On the other hand, in order to describe the biological system behavior, a modified ASM1 model was used. The final hybrid model was developed to calculate the evolution of sludge properties, its relation to sludge cake growth, and the influence of sludge properties on membrane fouling.

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A simple model for evaluating energy demand arising from aeration of an MBR was presented by Verrecht et al. (2008) based on a combination of empirical data for membrane aeration and biokinetic modeling for biological aeration. The model assumes that aeration of the membrane provides a portion of the dissolved oxygen needed for biotreatment. The model also assumes, based on literature information sources, a linear relationship between membrane permeability and membrane aeration up to a threshold value, beyond which permeability is unchanged with membrane aeration. An analysis reveals that significant reductions in energy demand are attained through operating at lower MLSS levels and membrane fluxes. The complete organic removal in MBR is due to all the inseries phenomena: biological degradation of biomass, biological filtration of cake layer, and final filtration of physical membrane. Di Bella et al. (2008) set up a mathematical model for the simulation of physical–biological wastewater organic removal for SMBR system. The model consists of two submodels: the first one for the simulation of the biological processes and a second one for the physical processes. In particular, regarding the biological aspects, it is based on the ASM concept. On the other hand, organic-matter removal due to filtration (the physical process) was described by simple models proposed in the literature (Kuberkar and Davis, 2000; Jang et al., 2006; Li and Wang, 2006). It is conceivable that several of the existing models, particularly the ASMs, require validation to determine their applicability for modeling the MBR process and to evaluate whether they can serve as a base for future MBR model development. Membrane fouling in MBRs is affected by the biotransformation processes in the system; therefore, a more effective integration of biomass kinetics and membrane fouling into the models is required. Moreover, examination of alternative empirical modeling approaches, such as the application of artificial neural networks, is worthwhile to establish a thorough link between inputs and outputs of MBR systems and to find phenomenological interrelationships among components and parameters (Ng and Kim, 2007).

4.16.4.4 Innovative Modifications to MBR Design Researchers have put forth different modifications to the conventional design of MBRs in order to enhance removal performance and/or mitigate membrane fouling. This section highlights some of such examples (Table 7). The commercialized MBR formats are discussed separately in Section 4.16.5.2.

4.16.4.4.1 Inclined plate MBR Theoretically, an infinite SRT provides a possibility of naturally achieving zero-excess sludge discharge from MBR under normal environment. It should, however, be noted that zeroexcess sludge production is just a theoretical concept which can only be obtained with a feed containing only solutes. In real life, sewage or industrial effluents contain nonbiodegradable suspended solids and colloids that accumulate in the reactor, continuously increasing the sludge concentration. Therefore, an immediate challenge encountered at infinite SRT is the extremely high sludge concentration

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Table 7

Examples of innovative modifications to MBR design

Modified design

Main purpose

Selected reference

Inclined plate MBR

Omit excess sludge production and thereby realize long-term stable membrane filtration Derive the simultaneous advantages of efficient nutrient removal and mitigate membrane fouling (Chae et al., 2006 a,b;). Treatment of high-strength wastewater without encountering severe fouling Enhanced removal of recalcitrant compound and/or membrane fouling mitigation Obtain in one step indirect potable reuse standard effluent Indirect potable reuse along with energy demand reduction Obtain in one step indirect potable reuse standard effluent

Xing et al. (2006)

Integrated anoxic–aerobic MBR

Jet-loop-type MBR Biofilm MBR Nanofiltration MBR Forward osmosis MBR Membrane distillation bioreactor (MDBR)

produced in the bioreactor (Wen et al., 1999). Consequently, the method to achieve zero-excess sludge discharge translates into how to realize long-term stable membrane filtration of high-concentration sludge beyond the guideline value of 10– 20 g l1 recommended for submerged MBRs when applied to domestic wastewater treatment. In order to omit excess sludge production, Xing et al. (2006) proposed an innovative MBR design comprising an anoxic tank equipped with settlingenhancer inclined plates and a subsequent aerobic tank containing the membrane. The inclined plates together with intermittent air blowing (to blow off gaseous content generated by denitrification, etc.) proved to be quite effective in confining high MLSS sludge within the anoxic tank leading to an MLSS difference of 0.1– 13.1 g l1 between the aerobic and anoxic sludge. Consequently, the capability of MBRs in handling the extremely high MLSS challenge encountered especially at zero-excess sludge could be extended. Results indicated that at an HRT of 6 h, average removals of COD, ammonia nitrogen, and turbidity were 92.1, 93, and 99.9%, resulting in daily averages of 12.6 mg COD l1, 1.3 mg NH3–N l1, and 0.03 NTU, respectively.

4.16.4.4.2 Integrated anoxic–aerobic MBR In contrast to separate anoxic tanks for denitrification or creation of alternating anoxic/oxic conditions within the same tank by intermittent aeration, an integrated anoxic/oxic MBR, containing anoxic/oxic compartments in one reactor, was developed to derive simultaneous advantages of efficient nutrient removal (Chae et al., 2006a, 2006b) and mitigated membrane fouling (Chae et al., 2006a, 2006b; Hai, 2007; Hai et al., 2007; Hai et al., 2006b; Hai et al., 2008a). Under the optimal volume ratio of anoxic and oxic zones of 0.6 and the desirable internal recycle rate and HRT of 400% and 8 h, respectively, the average removal efficiencies of total nitrogen (T-N) and total phosphorus (T-P) were 75% and 71%, respectively (Chae et al., 2006b). Furthermore, comparison with sequential anoxic/oxic MBR under the same conditions revealed the membrane-fouling reduction potential of this specific design (Chae et al., 2006a).

Chae et al. (2006a,b), Hai et al. (2006b, 2008a)

Park et al. (2005), Yeon et al. (2005) Lee et al. (2006), Leiknes and Odegaard (2007), Ngo et al. (2008), Hai et al. (2008) Choi et al. (2002) Achilli et al. (2009), Cornelissen et al. (2008) Phattaranawik et al. (2008, 2009)

Working with a high-strength industrial wastewater, Hai et al. (2006a, 2006b, 2008a) demonstrated minimization of excess sludge growth and maintenance of less MLSS concentration in contact with the membrane at the aerobic zone by exploring a similar reactor design along with a strategy of splitting the feed through the two zones.

4.16.4.4.3 Jet-loop-type MBR The so-called high-performance compact reactor (HCR) which is a jet-loop-type reactor with a draft tube and a two-phase nozzle was coupled with a submerged membrane by Park et al. (2005). The HCR is able to deal with very high organic loading rates due to the high efficiency of oxygen transfer, mixing, and turbulence achieved. The significant amount of bubbles and turbulence present in the HCR can be beneficial in retarding fouling of the submerged membrane. The developed MBR showed much greater membrane permeability than the conventional MBR, promising very high potential for the treatment of high-strength wastewater without encountering severe fouling (Park et al., 2005; Yeon et al., 2005).

4.16.4.4.4 Biofilm MBR Membrane-coupled moving-bed biofilm reactor system, wherein the membrane is submerged within the same tank (Lee et al., 2006) or in an additional tank (Leiknes and Odegaard, 2007), has been extensively studied in association with different kinds of biocarriers. Powdered activated carbon (PAC) which also acts as an adsorbent is commonly added into the bioreactor as the biocarrier (Ng et al., 2006; Hai, 2007; Hai et al., 2008b). However, carriers made of inert materials, such as plastic (Leiknes and Odegaard, 2007) and sponge (Lee et al., 2006; Ngo et al., 2008), have also been used. Biomass granulation with shell-support media coupled with membrane separation is also worth mentioning in this context (Thanh et al., 2008). The mechanisms of enhanced removal and/or membranefouling mitigation depend on the specific design and the utilized biocarrier type. For example, in an integrated membrane-coupled moving-bed biofilm reactor using sponge as the biocarrier, frictional force exerted by the circulating

Membrane Biological Reactors

carrier on the submerged membrane reduced the formation of cake layer on the membrane surface and thus enhanced the membrane permeability (Lee et al., 2006). On the other hand, Leiknes and Odegaard (2007) demonstrated that operation under high volumetric-loading rates of 2–8 kg COD m3 d1and HRTs up to 4 h and maintenance of membrane fluxes around 50 l m2 h1 were possible by placing the moving-bed biofilm reactor prior to the submerged MBR. The specific purpose of the biofilm reactor in this case was to reduce the organic loading on MBR. Ng et al. (2006) contend that the improved membrane performance of the MBR with added PAC could be due to a number of factors including, PAC providing sink for some of the fouling components and the scouring action of PAC. Hai et al. (2008b) reported that simultaneous PAC adsorption within a fungiMBR treating dye wastewater resulted in multiple advantages including co-adsorption of dye and fungal enzyme onto activated carbon and subsequent enzymatic dye degradation.

4.16.4.4.5 Nanofiltration MBR The potential for using NF technology in wastewater treatment and water reuse is noteworthy. A new concept with the addition of RO membrane after conventional MBR has been recently developed to reclaim municipal wastewater. The new MBR-RO process demonstrated the capability of producing the same or more consistent product quality (in terms of total organic carbon (TOC), NH4, and NO3) and sustained higher flux compared to the CAS-MF-RO process in reclamation of domestic sewage (Qin et al., 2006). Choi et al. (2002, 2007), on the other hand, demonstrated the technical feasibility of a submerged NF-MBR. For the initial 130 days, the NF-MBR achieved high permeate quality (DOC concentration ¼ 0.5–2.0 mg l1) and maintained reasonable water productivity. With low electrolyte rejection, operation under a low suction pressure was possible, and electrolyte accumulation in the bioreactor, which may hinder biological activity, did not occur. The permeate quality, however, deteriorated to some extent (DOC concentration ¼ 3.0 mg l1) due to the deterioration of the cellulose membrane.

4.16.4.4.6 Forward osmosis MBR The forward osmosis (FO)–MBR is an innovative technique for the reclamation of wastewater, which combines activated sludge treatment and FO membrane separation with an RO posttreatment. FO membranes, either submerged or external, are driven by an osmotic pressure difference over the membrane. Through osmosis, water is transported from the mixed liquor across the semipermeable membrane into a draw solution (DS) with a higher osmotic pressure. To produce potable water, the diluted DS is then treated in an RO unit, and the concentrated DS is reused in the FO process. The FO-MBR is expected to have the same advantages as conventional MBRs; however, it has to deal with the most important drawback, that is, a high energy demand. In this system, FO membranes with structures comparable with NF or RO membranes are used instead of MF/UF membranes for the separation of suspended solids, multivalent ions, natural organic matter, and biodegradable materials. Since fluxes are generally lower and no internal fouling occurs, fouling of NF

587

or RO membranes, compared to that of the MF or UF membranes in conventional MBR, may be dealt with easily. The RO system after FO-MBR can be operated with higher fluxes because all the bivalent ions are removed in the FO-MBR. Recent studies have demonstrated high sustainable flux and relatively low reverse transport of solutes from the DS into the mixed liquor, along with very high removal performance (Achilli et al., 2009; Cornelissen et al., 2008).

4.16.4.4.7 Membrane distillation bioreactor A novel wastewater-treatment process known as the membrane distillation bioreactor (MDBR) incorporating membrane distillation in an SMBR operated at an elevated temperature was developed and experimentally demonstrated by Phattaranawik et al. (2008, 2009). The ability of membrane distillation (MD) to transfer only volatiles means that very high quality treated water is obtainable, with TOC levels below 1 ppm and negligible quantity of salts. A unique feature is that the MDBR allows for organic retention times to be much greater than the HRT. The TOC in the permeate was consistently lower than 0.7 mg l1 for all experiments. Stable fluxes in the range 2–5 l m2 h1 have been sustained over extended periods. The MDBR was described to have the potential to achieve in a single step, the reclamation obtained by the combined MBR þ RO process. It was also suggested that for viable operation, it would be necessary to use low-grade (waste) heat and water cooling. Several other emerging approaches are also noticeable in contemporary literature. These include hybrid MBR-CAS concept (De Wilde et al., 2009), anaerobic baffled reactor-MBR combination (Pillay et al., 2008), etc.

4.16.4.5 Technology Benefits: Operators’ Perspective The relative advantages of MBR over the CAS process were outlined in Section 4.16.3.3. This section highlights the technical benefits of MBRs cited by the operators: 1. high-quality effluent, ideal for post membrane treatments (e.g., NF and UF); 2. space savings, enabling upgrading of plants without land expansion; 3. shorter start-up time compared to conventional treatment systems; 4. low operating and maintenance manpower requirement (average of 1.7 working hours per MLD); and 5. (5) automated control.

4.16.4.6 Technology Bottlenecks MBR technology is facing some research and development challenges. The technology bottlenecks as reported in the literature include (Howell, 2002, 2004; Lesjean et al., 2004; Le-Clech et al., 2005a; Yang et al., 2006; Melin et al., 2006) 1. Membrane fouling. Further understanding the mechanisms of membrane fouling and developing more effective and easier methods to control and minimize membrane fouling. 2. Pretreatment. Effective methods to limiting membrane clogging and operational failures.

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3. Membrane life span. Increasing membrane mechanical and chemical stability. 4. Cost. Further reduction of costs for maintenance and replacement of membranes, energy requirement, and labor requirements. 5. Plant capacity. Scaling up for large plants. 6. Exchangeability of modules. Module exchangeability between different brands (reduction of costs for replacement of membranes). Some other problems often encountered by the operators include (Leslie and Chapman, 2003; Adham et al., 2004; LeClech et al., 2005a; Yang et al., 2006)

• • • • • • • • • •

membrane fouling during permeate backpulsing, entrained air impacting suction-pump operation, bioreactor foaming, inefficient aeration due to partial clogging of aerator holes, no significant decrease of biosolid production, scale buildup on membrane and piping, corrosion of concrete, hand rails, and metallic components due to corrosive vapor produced during high temperature NaOCl cleaning, membrane delamination and breakage during cleanings, odor from screening, compaction, drying beds, and storage areas (although normally less than in CAS), and failure of control system.

Although the commercialization of MBRs has expanded substantially in the past 20 years, target markets have not been tapped to a large extent and new potential areas of applications are continually developing. The R&D challenges mentioned above, when tackled, will lead to a more competitive and mature market for MBR applications. Lesjean et al. (2004) contend that academic research is addressing only some of these issues. For instance, while many publications on fouling are being produced and some cost studies are conducted, no significant research efforts have addressed membrane life span, pretreatment, and scale-up issues. Academic researchers can expect interest from MBR companies and plant operators on these subjects, and should direct some of their research programs to address these needs. Among the challenges underscored by the experts, membrane fouling is one of the most serious problems that has retarded faster commercialization of MBR technology. The causes, characteristics, mechanisms of fouling, and methods to prevent or reduce membrane fouling are discussed elaborately in Section 4.16.4.7; Section 4.16.5.5 sheds light on the issue of exchangeability of modules. The remainder of the current section will be devoted to the issues closely related to membrane fouling and performance, that is, mechanical pretreatment and membrane integrity:

•

Pretreatment. Pretreatment is one of the most critical factors for ensuring a stable and continuous MBR operation. Due to membrane sensitivity to the presence of foreign bodies, fine prescreening of the feed (and sometimes of the mixed liquors) must occur. The type of sieve installed is very important with regard to the total screening of hair and fibers. Recent studies (Frechen et al., 2006; Schier et al., 2009) have shown sieves with smaller gap sizes and with

•

two-dimensional gap geometries to perform better. On the other hand, even intensive long-term pilot plant trials can fail to suggest the effective scale-up design of the sieve (Melin et al., 2006). If too many clogging problems occur, the original pre-screen systems are usually upgraded to finer screens. However, when both the influent and the mixed liquor are filtered with a fine prescreen, a large amount of trash is produced (up to 3.8 m3 per week for a 1.4 MLD plant) (Le-Clech et al., 2005a; Melin et al., 2006; Schier et al., 2009). It should be noted that the investment in pretreatment is of little use if the bioreactor is uncovered, in which case, different sorts of debris can easily enter the bioreactor. It is recommended to remove these items using a high-pressure water hose. However, many MBR users report that this type of manual cleaning causes membrane-fiber breakage. In order to keep the membrane effectively separated from the fibrous materials, Schier et al. (2009)proposed the following mechanical-treatment concept: conventional pretreatment including screen and grit chamber/grease trap to be placed before the biological tank, causing braid of hair and fibers formed therein to be removed by the sieve placed before the separate filtration chamber housing the membrane modules. Membrane integrity. A major problem facing MBR systems is the loss of membrane integrity, which leads to the permeate-quality deterioration and ineffective backwashing. When breakage occurs in a submerged hollow-fiber MBR system, continuous filtration may allow solids and particles to quickly clog the broken fiber. However, application of backwash would force the solids out of the fiber. Accordingly, once damaged, disinfection of the product water would be compromised and it would also cause the loss of the backwash efficiency; and the faulty membrane/module would need to be changed quickly.

Faulty installation is one obvious reason for membrane failure. Once under pressure, an incorrectly installed membrane module can be compressed. Other reasons associated with regular operation include frequent and/or extended contact between membrane and cleaning solution causing delamination of the membrane, scoring and cleaving of the membrane resulting from the presence of abrasive or sharpedged materials in the influent, and operating stress and strain occurring in the system due to fiber movement and membrane backwashing. A better understanding of the effect of membrane material, age, and fouling on membrane integrity may be gained from hollow-fiber-tensile test reported in the literature (Childress et al., 2005; Gijsbertsen-Abrahamse et al., 2006). Even flat-sheet membranes used in MBRs are not immune to occasional failure (Cornel and Krause, 2003). The construction of current flat-sheet MBR membrane panels is a labor-intensive, multistep operation. These are typically sandwich constructions with three separate layers. Two of them are pre-fabricated membrane layers, while the third one is a permeate drainage layer which is sandwiched between them. The three layers of the sandwich are held together by gluing or laminating techniques over their entire surface or just at their edges. Flat-sheet membranes have been found to be sensitive to breaking near the top

Membrane Biological Reactors

due to poor adhesion of the membrane to the support layer (Doyen et al., 2010).

4.16.4.7 Membrane Fouling – the Achilles’ Heel of MBR Technology Although MBR has become a reliable alternative to CAS processes and an option of choice for many domestic and industrial applications, membrane fouling and its consequences in terms of plant maintenance and operating costs limit the widespread application of MBRs (Le-Clech et al., 2006). Membrane fouling can be defined as the undesirable deposition and accumulation of microorganisms, colloids, solutes, and cell debris within pores or on membrane surface (Meng et al., 2009). It results from the interaction between the membrane material and the components of the activated sludge liquor, which include biological flocs formed by a large range of living microorganisms along with soluble and colloidal compounds. Thus, it is not surprising that the fouling behavior in MBRs is more complicated than that in most membrane applications. The suspended biomass has no fixed composition and varies with both feedwater composition and MBR operating conditions employed. Accordingly, although many investigations of membrane fouling have been published, the diverse range of operating conditions and feedwater matrices employed, and the limited information reported in most studies on the biomass composition in suspension or on the membrane, have made it difficult to establish any generic behavior pertaining to membrane fouling in MBRs. Three fouling phenomena need to be recognized and duly addressed:

• • •

Cake formation. This results from the balance of forces (shear stress at the membrane wall and filtration force) and is evidently linked to the biomass characteristics. Blockage of bundle of fibers. The bundle of fibers act as a deep bed filter (depending on biomass characteristics and structure of the bundle). Biofilm formation. This is not strictly dependent upon biomass characteristics as, very often, the microorganisms involved in the biofilm formation are not the dominant species in the biomass.

4.16.4.7.1 Fouling development Zhang et al. (2006a) proposed a three-stage history for membrane fouling in MBRs:

• • •

Stage 1. An initial short-term rise in TMP due to conditioning. Stage 2. Long-term rise in TMP, either linear or weakly exponential. Stage 3. A sudden rise in TMP, with a sharp increase in dTMP/dt, also known as the TMP jump.

When operating at fluxes well below the apparent critical flux of the MLSS, a slow steady rise in TMP (stage 2) is observed which eventually changes to a rapid rise in TMP (stage 3). For sustainable operation, the aim would be to limit the extent of stage 1, prolong stage 2, and avoid stage 3, since it could be difficult to restore.

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4.16.4.7.2 Types of membrane fouling Definitions based on ease of removal and a variety of confusing terminologies have been proposed in the literature to describe fouling. For example, based on the ease of removal, some authors prefer to use the term ‘irreversible fouling’ to the fouling that can be removed by chemical cleaning but not by physical cleaning. Recently, Meng et al. (2009) proposed a somewhat changed definition and used the terms ‘removable’ and ‘irremovable’ for the fouling which is easily eliminated and which requires chemical cleaning, respectively. This chapter, however, uses the more direct terms – physically removable fouling and chemically removable fouling. The formation of a cake layer which can be described as a porous media with a complex system of interconnected interparticle voids has been reported as the major contributor to membrane fouling in MBRs (Jeison and van Lier, 2007; Ramesh et al., 2007). Such fouling is usually physically removable. Recently, a large number of scientific investigations have been performed in order to gain a better understanding of cake-layer formation and cake-layer morphology employing techniques such as confocal laser-scanning microscopy (CLSM), multiphoton microscopy, etc. (Yang et al., 2007; Hughes et al., 2006, 2007). During initial filtration, colloids, solutes, and microbial cells pass through and deposit inside the membrane pores. However, during the long-term operation of MBRs, the deposited cells multiply and yield extracellular polymeric substance (EPS), which clog the pores and form a strongly attached fouling layer. Chemical cleaning is usually required to remove such fouling. Evaluation of physically removable and chemically removable fouling propensity of MBR mixed liquor has been the focus of many studies to date (Field et al., 1995; Ognier et al., 2004; Pollice et al., 2005; Bacchin et al., 2006; Guglielmi et al., 2007; Lebegue et al., 2008; Wang et al., 2008b). Some of the definitions are based on the fouling components. The fouling in MBRs can be classified into three major categories: biofouling, organic fouling, and inorganic fouling, although, in general, all of them take place simultaneously during membrane filtration of activated sludge. Biofouling refers to the deposition, growth, and metabolism of bacteria cells or flocs on the membranes. Biofouling may start with the deposition of individual cell or cell cluster on the membrane surface, after which the cells multiply and form a biocake (Liao et al., 2004; Pang et al., 2005; Wang et al., 2005; Ramesh et al., 2007). Techniques such as scanning electron microscopy (SEM), CLSM, atomic force microscopy (AFM), and direct observation through the membrane (DOTM) have been extensively used to derive valuable information regarding floc/cell-deposition process and the microstructure or architecture of the cake layer. Certain studies have also analyzed the microbial community structures and microbial colonization on the membranes in MBRs (Chen et al., 2004; Jin et al., 2006; Jinhua et al., 2006; Zhang et al., 2006b; Miura et al., 2007; Lee et al., 2009) employing molecular techniques. Such studies reported that the microbial communities on membrane surfaces were quite different from those in the suspended biomass and initially a specific phylogenetic group of bacteria may play the key role in development of the mature biofilm. However, a temporal change

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of microbial-community structure can take place due to the development of anoxic conditions in the cake layer. Organic fouling in MBRs refers to the deposition of biopolymers on the membranes (Meng et al., 2009). Due to the small size, the soluble biopolymers can be deposited onto the membranes more readily, but they have lower back-transport velocity in comparison to large particles (e.g., colloids and sludge flocs). Powerful analytical tools such as Fourier transform infrared (FTIR) spectroscopy, solid-state 13C-nuclear magnetic resonance (NMR) spectroscopy, and high-performance size-exclusion chromatography (HP-SEC) are usually utilized for identification of the deposited biopolymers (Kimura et al., 2005; Rosenberger et al., 2006; Zhou et al., 2007; Teychene et al., 2008) and studies have confirmed that SMP or EPS is the origin of organic fouling in MBR. Inorganic elements such as Mg, Al, Fe, Ca, Si, etc. and metals can enhance the formation of biofouling and organic fouling and can together form a recalcitrant cake layer (Lyko et al., 2007; Wang et al., 2008b). Inorganic fouling can form in two ways – due to concentration-polarization-led chemical precipitation and entrapment within biopolymer gel layer (Meng et al., 2009). Chemical cleaning agents such as ethylenediaminetetraacetic acid (EDTA) might efficiently remove inorganics on the membrane surface (Al-Amoudi and Lovitt, 2007); however, the fouling caused by inorganic scaling may not be easy to eliminate even by chemical cleaning (You et al., 2006).

Figure 8 lists the membrane-fouling parameters, while Figure 9 illustrates the interrelations and combined effect of those parameters. Some of the membrane characteristics and the parameters that influence the performance of the MBRs are discussed in the following: 1. Physical parameters.

•

Pore size and distribution. Studies revealed that the pore size alone could not predict hydraulic performances. The effects of pore size (and distribution of pore size) on membrane fouling are strongly related to the feedsolution characteristics and in particular the particlesize distribution. The complex and changing nature of

Membrane fouling

4.16.4.7.3 Parameters influencing MBR fouling All the parameters involved in the design and operation of MBR processes have an influence on membrane fouling (Le-Clech et al., 2006; Meng et al., 2009). While some of these parameters have a direct influence on MBR fouling, many others result in subsequent effects on phenomena exacerbating fouling propensity. However, three main categories of factors can be identified – membrane and module characteristics, feed and biomass parameters, and operating conditions.

Membrane characteristics • Physical parameters -Pore size and distribution -Porosity/roughness -Membrane configuration • Chemical parameters -Hydrophobicity -Materials

Mixed liquor characteristics

Feed

Biomass

Figure 9 Interrelations and combined effect of the membrane fouling parameters.

Feed–biomass characteristics

Operating conditions

• Nature of feed and concentration • Biomass fractionation • Biomass (bulk) parameters -MLSS concentration -Viscosity -Temperature -Dissolved oxygen (DO) • Floc characteristics -Floc size -Hydrophobicity/surface charge

• Aeration, cross-flow velocity • Sludge retention time (SRT) • Unsteady state operation

• Extracellular polymeric substance (EPS) • Soluble microbial products (SMP) Figure 8 Membrane fouling parameters at a glance.

Operating conditions

Membrane characteristics

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•

•

the biological suspension present in MBR systems and the large pore-size distribution of the membrane generally used in MBR systems are the main reasons for the undefined general dependency of the flux propensity on pore size (Chang et al., 2002a; Le-Clech et al., 2003b). It is generally expected that smaller-pore membranes would reject a wider range of materials, and the resulting cake layer would feature a higher resistance compared to large-pore membranes. However, this type of fouling is easily removed during the maintenance cleaning than fouling due to internal pore clogging obtained in larger-pore membrane systems. The chemically removable fouling, due to the deposition of organic and inorganic materials onto and into the membrane pores, is the main cause of the poor longterm performances of larger pore-size membranes (Chang et al., 2001; He et al., 2005). However, the opposite trend is sometimes reported (Gander et al., 2000). The duration of the experiment and other operating parameters such as cross-flow velocity and constant pressure or constant flux operation have a direct influence on the determination of the optimization of the membrane pore size and are responsible for contradictory reports in the literature. Porosity/roughness. Membrane roughness and porosity along with membrane microstructure, material, and pore-size distribution were suggested as potential reasons for the different fouling behaviors observed (Kang et al., 2006; Ho and Zydney, 2006). For instance, a track-etched membrane, with its dense structure and small but uniform cylindrical pores, featured the lowest resistance due to pore fouling in contrast to the other membranes having interwoven sponge-like highly porous network (Fang and Shi, 2005). Other studies have pointed out the importance of pore-aspect ratio (mean major-axis length/mean minor-axis length) (Kim et al., 2004) or roughness (He et al., 2005) on fouling in an MBR. Membrane configuration. In submerged MBR processes, the membrane can be configured as vertical flat plates, vertical or horizontal hollow fine fibers (filtration from out to in) or, more rarely as tubes (filtration from in to out). Each of hollow-fiber and flat-sheet membrane types has specific footprint and air scouring and chemical cleaning requirement, which may favor one process over another for a given application (Judd, 2002; Hai et al., 2005). Nevertheless, hollow-fiber modules are generally more economical to manufacture, provide high specific membrane area, and can tolerate vigorous backwashing (Stephenson et al., 2000). For low-flux operation, hollow fibers are attractive due to their high packing density. A higher fiber-packing density would increase productivity; however, increasing the packing density may lead to severe interstitial blockage due to the impeded propagation of air bubbles toward the core, limiting their effect on fouling limitation (Kiat et al., 1992; Yeo and Fane, 2005; Sridang et al., 2005). However, Hai et al. (2008a) developed a spacer-filled module in order to utilize high packing density without encountering

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severe fouling. Studies have also revealed the effects of other membrane characteristics including hollowfiber orientation, size, and flexibility ( Cui et al., 2003; Ognier et al., 2004; Chang and Fane, 2002; Lipnizki and Field, 2001; Zheng et al., 2003; Zhongwei et al., 2003). 2. Chemical parameters.

•

•

Hydrophobicity. The influence of the membrane hydrophobicity on the early stage of the fouling formation may be significant; however, this parameter is expected to play only a minor role during extended filtration periods in MBRs (Le-Clech et al., 2006). Once initially fouled, the membrane’s chemical characteristics would become secondary to those of the sludge materials covering the membrane surface. Nevertheless, because of the hydrophobic interactions occurring between solutes, microbial cells and membrane material, membrane fouling is expected to be more severe with hydrophobic rather than hydrophilic membranes (Madaeni et al., 1999; Chang et al., 1999; Yu et al., 2005a), although different results have also been reported (Fang and Shi, 2005). In many reported studies, change in membrane hydrophobicity often occurs with other membrane modifications such as pore size and morphology, which make the correlation between membrane hydrophobicity and fouling more difficult to assess. Materials. The large majority of the membranes used in MBRs are polymeric based. A direct comparison between polyethylene (PE) and polyvinylidene fluoride (PVDF) membranes clearly indicated that the latter leads to a better prevention of physically irremovable fouling and that PE membrane fouled more quickly (Yamato et al., 2006). Zhang et al. (2008b) studied the affinity between EPS and the three polymeric UF membranes, and observed that the affinity capability of the three membranes was of the order polyacrylonitrile (PAN)oPVDFopolyethersulfone (PES). Although featuring superior chemical, thermal, and hydraulic resistances, ceramic (Fan et al., 1996; Scott et al., 1998; Luonsi et al., 2002; Xu et al., 2003; Judd et al., 2004) and stainless steel (Zhang et al., 2005) membrane modules are not the preferred option for MBR applications due to their high cost (around an order of magnitude more expensive than the polymeric materials).

3. Feed–biomass characteristics.

•

Nature of feed and concentration. Fouling in the MBR is mostly affected by the interactions between the membrane and the biological suspension rather than wastewater itself (Choi et al., 2005). Nevertheless, the fouling propensity of the wastewater has to be indirectly taken into consideration during the characterization of the biomass, as the wastewater nature can significantly influence the physicochemical changes in the biological suspensions (Le-Clech, 2003b; Jefferson et al., 2004), which in turn may aggravate fouling.

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Biomass fractionation. The many studies (Bae and Tak, 2005; Li et al., 2005a; Itonga et al., 2004; Lee et al., (2003); Lee et al., 2001a; Wisniewski and Grasmick, 1998; Bouhabila et al., 2001) that are available on the contribution of different fractions of the biomass to fouling usually report contradictory results. Although the relatively low fouling role played by the suspended solids (biofloc and the attached EPS) compared to those of the soluble and colloids (generally defined as soluble microbial products or SMP) is usually reported, the reported relative contribution of the SMP to overall membrane fouling ranges from 17% (Bae and Tak, 2005) to 81% (Itonga et al., 2004). These wide discrepancies may be explained by the different operating conditions and biological states of the suspension used in the reported studies (Figure 10). Although an interesting approach for studying MBR fouling, the fractionation experiments neglect any coupling or synergistic effects which may occur among the different components of the biomass.

•

•

4. Biomass (bulk) parameters.

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MLSS concentration. Although the increase in MLSS concentration has often been reported to have a mostly negative impact on the MBR hydraulic performances (Cicek et al., 1999b; Chang and Kim, 2005), controversies exist (Defrance and Jaffrin, 1999; Hong et al., 2002; Le-Clech et al., 2003b; Lesjean et al., 2005; Brookes et al., 2006). The existence of threshold values above (Lubbecke et al., 1995) or below (Rosenberger et al., 2005) which the MLSS concentration has a negative influence was also reported. Figure 11 depicts the influence of shift in MLSS concentration on flux as reported in different studies. Nowadays, information on additional biomass characteristics (e.g., composition and concentration of EPS) is deemed necessary to furnish a comprehensive picture. On the other hand,

100

Variable: Membrane type

•

Hai et al. (2006a)showed that the extent of fouling was independent of MLSS concentration itself, and was rather more influenced by the efficiency of the foulingprevention strategies adopted. Viscosity. The importance of MLSS viscosity is that it modifies bubble size and can dampen the movement of hollow fibers in submerged bundles (Wicaksana et al., 2006). The net result of this phenomenon would be a greater rate of fouling. Increased viscosity also reduces the efficiency of mass transfer of oxygen and can therefore effect dissolved oxygen (DO) (Germain and Stephenson, 2005); fouling, as discussed later, tends to be worse at low DO. Critical MLSS concentrations have been reported in the literature (Itonga et al., 2004) above which, suspension viscosity tends to increase exponentially with the solid concentration. Temperature. Experiments conducted under moderate temperature usually report greater deposition of materials on the membrane surface at lower temperatures. Temperature may impact membrane filtration by increasing fluid viscosity, causing defloculation of biomass and higher EPS secretion, reducing biodegradation rate, etc. (Jiang et al., 2005; Rosenberger et al., 2006). Dissolved oxygen. The effects of DO on MBR fouling are multiple and may include changes in biofilm structure, SMP levels, and floc-size distribution (Lee et al., 2005). The average level of DO in the bioreactor is controlled by the aeration rate, which not only provides oxygen to the biomass but also tends to limit fouling formation on the membrane surface. Optimum aeration would result in lower specific cake resistance of the fouling layer featuring larger particle sizes and greater porosity (Kang et al., 2003; Kim et al., 2006). Therefore, in general, higher DO tends to lead to better filterability, and lower fouling rate.

Variable: Sludge type

Variable: SRT

80 60 40 20 0 (a)

(b)

Soluble

Colloids

Suspended solids

(c)

Colloid + soluble

Figure 10 Influence of different parameters (membrane type, sludge type, and SRT) on the relative contributions (in %) of the different biomass fractions to MBR fouling. Data from (a) Bae TH and Tak TM (2005) Interpretation of fouling characteristics of ultrafiltration membranes during the filtration of membrane bioreactor mixed liquor. Journal of Membrane Science 264: 151–160; (b) Meng F and Yang F (2007) Fouling mechanisms of deflocculated sludge, normal sludge, and bulking sludge in membrane bioreactor. Journal of Membrane Science 305: 48–56; and (c) Lee W, Kang S, and Shin H (2003) Sludge characteristics and their contribution to microfiltration in submerged membrane bioreactors. Journal of Membrane Science 216: 217–227.

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Limiting or critical or stabilized flux, (l m−2 h−1)

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irregular floc shape, and higher hydrophobicity (Meng et al., 2006).

(1)

100 80

(2)

(7)

60

(3) (4)

40 (5) 20

(6)

0 0

5

10

15

20

25

MLSS concentration, gl−1 Figure 11 Influence of shift in MLSS concentration on flux (fouling) as reported in different studies. Data from (1) Cicek N, Franco JP, Suidan MT, and Urbain V (1998) Using a membrane bioreactor to reclaim wastewater. Journal of American Water Works Association 90: 105–113; (2) Beaubien A, Baty M, Jeannot F, Francoeur E, and Manem J (1996) Design and operation of anaerobic membrane bioreactors: Development of a filtration testing strategy. Journal of Membrane Science 109: 173–184; (3) Madaeni SS, Fane AG, and Wiley D (1999) Factors influencing critical flux in membrane filtration of activated sludge. Journal of Chemical Technology and Biotechnology 74: 539–543; (4) Han SS, Bae TH, Jang GG, and Tak TM (2005) Influence of sludge retention time on membrane fouling and bioactivities in membrane bioreactor system. Process Biochemistry 40: 2393–2400; (5) Bouhabila EH, Ben Aim R, and Buisson H (1998) Microfiltration of activated sludge using submerged membrane with air bubbling (application to wastewater treatment). Desalination 118: 315–322; (6) Bin C, Xiaochang W, and Enrang W (2004) Effects of TMP, MLSS concentration and intermittent membrane permeation on a hybrid submerged MBR fouling. In: Proceedings of the IWA – Water Environment – Membrane Technology (WEMT) Conference. Seoul, Korea, 7–10 June; and (7) Defrance L and Jaffrin MY (1999) Reversibility of fouling formed in activated sludge filtration. Journal of Membrane Science 157: 73–84.

5. Floc characteristics.

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•

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Floc size. The floc-size distribution obtained with the MBR sludge is lower than the results generally obtained from CASP (Zhang et al., 1997; Wisniewski and Grasmick, 1998; Lee et al., 2003; Cabassud et al., 2004; Bae and Tak, 2005). Unlike in the CAS systems, the effective separation of suspended biomass from the treated water is not critically dependent on aggregation of the microorganisms, and the formation of large floc. However, independent of their size, biological floc play a major role in the secretion of EPS and formation of the fouling cake on the membrane surface. Hydrophobicity/surface charge. The direct effect of floc hydrophobicity on MBR fouling is difficult to assess. Conceptually, hydrophobic flocs would lead to high flocculation propensity, less secretion of EPS, and low interaction with the hydrophilic membrane (Jang et al., 2006). However, reports of highly hydrophobic flocs fouling MBR membranes can be found in the literature. For instance, the excess growth of filamentous bacteria, known to be responsible for severe MBR fouling, also resulted in higher EPS levels, lower zeta potential, more

6. Extracellular polymeric substances. The term EPS is used as a general and comprehensive concept for different classes of macromolecules such as polysaccharides, proteins, nucleic acids, (phosphor-)lipids, and other polymeric compounds which have been found at, or outside, the cell surface and in the intercellular space of microbial aggregates (Flemming and Wingender, 2001). EPS are the construction materials for microbial aggregates such as biofilms, flocs, and activated sludge liquors. The functions of EPS matrix are multiple and include aggregation of bacterial cells in flocs and biofilms, formation of a protective barrier around the bacteria, retention of water, and adhesion to surfaces (Laspidou and Rittmann, 2002). With its heterogeneous and changing nature, EPS can form a highly hydrated gel matrix in which microbial cells are embedded (Nielson and Jahn, 1999). Therefore, they can be responsible for the creation of a significant barrier to permeate flow in the membrane processes. Contemporary literature is replete with reports identifying EPS as a major fouling parameter (Chang and Lee, 1998; Cho and Fane, 2002; Nagaoka et al., 1996, 1998; Rosenberger and Kraume, 2002). On the other hand, since the EPS matrix plays a major role in the hydrophobic interactions among microbial cells and thus in the floc formation (Liu and Fang, 2003), it was proposed that a decrease in EPS levels may cause floc deterioration and may be detrimental for the MBR performances. This indicates the existence of an optimum EPS level for which floc structure is maintained without featuring high fouling propensity. Many parameters including gas sparging, substrate composition (Fawehinmi et al., 2004), and loading rate (Cha et al., 2004; Ng et al., 2005) affect EPS characteristics in the MBR, but SRT probably remains the most significant of them (Hernandez et al., 2005). A functional relationship between specific resistance, mixed liquor volatile suspended solids (MLVSS), TMP, and permeate viscosity, and EPS is believed to exist (Cho et al., 2005). 7. Soluble microbial products. SMPs are defined as soluble cellular components that are released during cell lysis, diffuse through the cell membrane, and are lost during synthesis or are excreted for some purpose (Laspidou and Rittmann, 2002; Li et al., 2005a). During filtration, SMPs adsorb on the membrane surface, block membrane pores, and/or form a gel structure on the membrane surface where they provide a possible nutrient source for biofilm formation and a hydraulic resistance to permeate flow (Rosenberger et al., 2005). Since direct relationships between the carbohydrate level in SMP (SMPc) solution with fouling rate (Lesjean et al., 2005), filtration index and capillary suction time (CST) (Greiler et al., 2005; Evenblij et al., 2005b; Tarnacki et al., 2005), critical flux tests (Le-Clech et al., 2005b), and specific flux (Rosenberger et al., 2005) have been clearly described, it reveals SMPc to be the major foulant indicator in MBR systems. However, controversy over the relative contribution of carbohydrate and protein portions of SMP to fouling exists (Evenblij and Van der Graaf, 2004; Drews et al., 2005a; Drews et al., 2006).

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The operating conditions of MBrs are discussed as follows:

•

•

•

Aeration, cross-flow velocity. Since the origin of the SMBR, bubbling has been defined as the strategy of choice to induce flow circulation and shear stress on the membrane surface. Aeration used in MBR systems has three major roles: providing oxygen to the biomass, maintaining the activated sludge in suspension, and mitigating fouling by constant scouring of the membrane surface (Dufresne et al., 1997). However, an optimum aeration rate, beyond which a further increase has no significant effect on fouling suppression, has been observed on many occasions (Ueda et al., 1997; Le-Clech et al., 2003a, 2003b; Liu et al., 2003; Psoch and Schiewer, 2005b). It is also important to note that too intense an aeration rate may damage the floc structure reducing their size, and release EPS into the bioreactor (Park et al., 2005; Ji and Zhou, 2006), and thereby aggravate fouling. Solid retention time. SRT (and thereby the F/M ratio), which greatly controls biomass characteristics, is regarded as the most important operating parameter influencing fouling propensity in MBRs. Considering the advantages of this process over the conventional activated sludge process (CASP), the early MBRs were typically run at very long SRTs to minimize excess sludge (Liu et al., 2005; Gao et al., 2004; Nuengjamnong et al., 2005). But unlike in bench-scale studies employing simpler synthetic feed, the progressive accumulation of nonbiodegradable materials (such as hair and lint) in an MBR fed with real sewage definitely leads to clogging of the membrane module (Le-Clech et al., 2005b). Operating an MBR at higher SRT leads inevitably to increase of MLSS concentration (Zhang et al., 2006c). The increase in aeration intensity to retain high MLSS levels in suspension and maintain proper oxygenation may not be a sustainable option for the treatment process. In this scenario, the increased shear provided to control fouling could cause biofloc deterioration as well as cell lysis and enhanced EPS secretion, and lead to fatal fouling. On the other hand, at infinite SRT, most of the substrate is consumed to ensure the maintenance needs and the synthesis of storage products. The very low apparent net biomass generation observed can explain the low fouling propensity observed for high SRT operation in certain studies (Orantes et al., 2004). It is likely that there is an optimal SRT, between the high fouling tendency of very low SRT operation and the high viscosity suspension prevalent for very long SRT. Unsteady state operation. In practical applications, unsteady state conditions such as variations in operating conditions (flow input/HRT and organic load) and shifts in oxygen supply could occur regularly (Drews et al., 2005a). The start-up phase can also be considered as unsteady operation and data collected before biomass stabilization (including the period necessary to reach acclimatization) may become relevant in the design of MBRs (Cho et al., 2005). Such unsteady state conditions have also been defined as additional factors leading to changes in MBR fouling propensity. For instance, the addition of a spike of acetate in the feedwater significantly decreased the filterability of the biomass in an MBR due to the rise in SMP levels resulting from the feed spike (Evenblij et al., 2005a).

4.16.4.7.4 Fouling mitigation The complex interactions between the fouling parameters complicate the perception of MBR fouling and it is therefore crucial to have a complete understanding of the biological, chemical, and physical phenomena occurring in MBRs to assess fouling propensity and mechanisms and thereby formulate mitigation strategies. As membrane fouling increases with increasing flux in all membrane separation processes, the operating flux should be lower than the critical flux. When the operating flux is below the critical flux, particle accumulation in the region of membranes can be effectively prevented. However, due to physicochemical solute–membrane material interactions, the membrane permeability decreases over time, even when MBRs are operated in subcritical (below critical flux) conditions. Other preventative methods need to be considered to maintain stable operation of MBR systems (Figure 12). Fouling can be removed by various methods and they are as discussed herein: 1. Physical cleaning. The following methods are usually used in combination to remove membrane fouling:

•

Permeate backwashing. Membrane backwashing or backflushing refers to pumping permeate in the reverse direction through the membrane. Backwashing has been found to successfully remove most of the reversible fouling due to pore blocking, transport it back into the bioreactor, and partially dislodge loosely attached sludge cake from the membrane surface (Bouhabila et al., 2001; Psoch and Schiewer, 2005a; Psoch and Schiewer, 2006). Frequency, duration, the ratio between those two parameters, and its intensity are the key parameters in the design of backwashing and different combinations of these parameters have proved to be more efficient in different studies (Jiang et al., 2005; Schoeberl et al., 2005). Between 5% and 30% of the produced permeate is used for backwashing. This also

Removal of fouling

Limitation of fouling

• Physical cleaning --Backwashing --Air backwashing --Intermittent operation --Sonification and other energy-intensive processes

• Optimization of membrane characteristics

• Chemical cleaning --Maintenance cleaning --Intensive cleaning

• Optimization of operating conditions --Aeration --Other operating conditions --Membrane module design • Modification of biomass characteristics -Aerobic granular sludge -Coagulant/flocculent -Adsorbent/flux enhancers

Figure 12 Reported membrane fouling mitigation strategies at a glance.

Membrane Biological Reactors

•

•

•

affects operating costs as, obviously, energy is required to achieve a pressure suitable for flow reversion. Certain studies are, therefore, devoted to optimization of backwashing (Smith et al., 2005). Air backwashing. Air, instead of permeate, can also be used as the backflushing medium (Visvanathan et al., 1997; Sun et al., 2004). The invention of air backwashing techniques for membrane declogging led to the development of using the membrane itself as both clarifier and air diffuser. In this approach, two sets of membrane modules are submerged in the aeration tank. While the permeate is extracted through one of the sets, the other is supplied with compressed air for backwashing. The cycle is repeated alternatively, and there is a continuous airflow into the aeration tank, which is sufficient to aerate the mixed liquor. However, air backwashing may also present potential issues of membrane breakage and rewetting (Le-Clech et al., 2006). Intermittent operation. Intermittent operation or membrane relaxation can significantly improve membrane productivity (Yamamoto et al., 1989). During relaxation, back transport of foulants is naturally enhanced as loosely attached foulants can diffuse away from the membrane surface (Ng et al., 2005). Although some studies found it more important than backwashing for fouling removal (Schoeberl et al., 2005), recent studies tend to combine intermittent operation with frequent backwashing for optimum results (Zhang et al., 2005; Vallero et al., 2005). The economic feasibility of intermittent operation for large-scale MBRs has been the focus of certain studies (Hong et al., 2002); however, it seems rather an established operation mode nowadays. Sonification and other energy-intensive processes. Although sonification would be difficult to apply at a large scale due to the focused nature of the sonic energy, laboratory-scale studies have explored sonification for breaking down cake layers in MBRs, especially in case of ceramic membranes. Certain studies have confirmed the efficiency of application of sonification alone or in combination with backwashing for removing the cake layer (Lim and Bai, 2003; Fang and Shi, 2005). However, other studies report that fouling may even worsen due to pore blocking (Hai et al., 2006a). Attempts have also been made to control fouling or modify sludge by using ozone and electric field (Chen et al., 2007; Huang and Wu, 2008; Sui et al., 2008; Wen et al., 2008).

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Maintenance cleaning with moderate chemical concentration (weekly) is applied to maintain design permeability and it helps to reduce the frequency of intense cleaning. This may be replaced by a more frequent

(e.g., on a daily basis) chemically enhanced backwash utilizing mild chemical concentration. Intensive (or recovery) chemical cleaning (once or twice a year) is generally carried out when further filtration is no longer sustainable because of an elevated TMP.

The MBR suppliers propose their own chemical cleaning recipes, which differ mainly in terms of concentration and methods, and often site-specific protocols are followed (Kox, 2004; Tao et al., 2005; Le-Clech et al., 2005b). Mainly, sodium hypochlorite (for organic foulants) and citric acid (for inorganics) are used as chemical agents. Some pitfalls of chemical cleaning are worth noting. The detrimental effect of cleaning chemicals on biological performance has been reported (Lim et al., 2005; Hai et al., 2007). It has also been mentioned that the level of pollutants (measured as TOC) in the permeate rises just after the chemical cleaning step (Tao et al., 2005). This raises concern especially in case of MBRs used in the reclamation process trains (i.e., e.g., upstream of RO) (Le-Clech et al., 2006). Chemical cleaning may also shorten the membrane lifetime and disposal of spent chemical agents causes environmental problems (Yamamura et al., 2007). The measures to limit fouling are discussed next. Recently, there have been a significant number of studies which focused on the ways to limit fouling. The proposed strategies include (1) improving the antifouling properties of the membrane, (2) operating the MBR under specific nonor-little-fouling conditions, and/or (3) pretreating the biomass suspension to limit its fouling propensity. They are discussed as follows: 1. Membrane modification.

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2. Chemical cleaning. The effectiveness of physical cleaning tends to decrease with operation time as more recalcitrant fouling accumulates on the membrane surface. Therefore, in addition to physical cleaning, different types/intensities of chemical cleaning are applied in practice. A combination of the following types of cleaning is usually applied (Le-Clech et al., 2006):

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Optimization of membrane characteristics. Many studies have shown that chemical modifications of the membrane surface can efficiently improve antifouling properties. Recent examples comprise (1) increasing membrane hydrophilicity by NH3 and CO2 plasma treatments (Yu et al., 2005a, 2005b) and ultraviolet (UV) irradiation (Yu et al., 2007), (2) TiO2 entrapped membrane (Bae and Tak, 2005), and (3) applying precoating of TiO2 (Bae et al., 2006), GAC (Hai, 2007), ferric hydroxide (Zhang et al., 2004), polyvinylidene fluoride-graft-polyoxyethylene methacrylated (PVDF-gPOEM) (Asatekin et al., 2006), polyvinyl alcohol (PVA) (Zhang et al., 2008a), etc. Improved performance in case of precoated membrane has been attributed to the adsorption of soluble organics on the precoat, limiting the direct contact between the organics and the membrane. Self-forming dynamic membrane-coupled bioreactors, utilizing coarse pore-sized substrates and allowing cake and gel layers to deposit on the surface, have been reported to obtain high flux and good removal in certain studies, although stable performance cannot be expected with such a filtration barrier (Wu et al., 2004). Membrane module design. The membrane module design by optimizing the packing density of hollow fibers or flat sheets, the location of aerators, the orientation of

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fibers, and diameters of fibers (Chang and Fane, 2001; Chang et al., 2002b; Fane et al., 2002) remains another important parameter in the optimization of the MBR operation. In a specially designed module in which air bubbles were confined in close proximity to the hollow fiber (rather than diffusing in the reactor), higher permeability was obtained (Ghosh, 2006). Two major design approaches are adopted in case of the commercially available hollow-fiber bundles. One of these approaches relies on partitioning of bundles of fibers, which are fixed at both ends, to secure flow path of air bubbles introduced from the center of the bundle at the base, thereby leading sludge out of the module. In another approach, bundle of one-end free fibers are allowed to float freely under the scouring action of air bubbles introduced from the core of the bundle to avoid accumulation of sludge. In order to utilize high packing density without encountering severe fouling, a new approach to hollow-fiber module design was explored by Hai et al. (2008a). Spacer was introduced within usual hollow-fiber bundles with the aim of minimizing the intrusion of sludge into the module. The little amount of intruded sludge was then backwashed through the bottom end, while the sludge deposited on the surface was effectively cleaned by air scouring. In this way, efficient utilization of cleaning solution and air for backwashing and surface cleaning, respectively, were possible. Recent approaches such as novel fiber sheet (FiSh) membrane (Heijnen et al., 2009), multimodule flat-sheet concept (Kreckel et al., 2009), and vacuum rotation membrane (Alnaizy and Sarin, 2009; Komesli et al., 2007) are also noticeable.

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3. Modification of biomass characteristics.

•

•

2. Optimization of operating conditions.

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Aeration. As mentioned earlier, bubbling is an established strategy to induce flow circulation and shear stress on the membrane surface. The aeration intensity (air/permeate ratio, m3/m3) applied by MBR suppliers may vary between 24 and 50, depending on the membrane configuration (flat sheet vs. hollow fiber) and the MBR tank design (whether the membrane and aerobic zone combined into a single tank or not) (Tao et al., 2005; Le-Clech et al., 2006). However, recent large-scale studies revealed these original ratios to be quite conservative (Tao et al., 2005). The specific design of bubble size, airflow rate and patterns, and location of aerators have been defined as crucial parameters in fouling mitigation. As the energy involved in providing aeration to the membrane remains a significant cost factor in MBR design, efforts have been focused on optimization of aeration both from the points of view of fouling mitigation and reducing energy requirement. Recent developments in aeration design include cyclic aeration systems (Rabie et al., 2003), intermittent aeration (Yeom et al., 1999; Nagaoka and Nemoto, 2005), air pulsing (Judd et al., 2006), air sparging (Ghosh, 2006), improved aerator systems (Miyashita et al., 2000; Cote, 2002; Hai et al., 2008), etc.

Other operating conditions. The overall performance of the MBR is closely related to the choice of SRT value. Further optimizations of operating conditions through reactor design have been studied and include the addition of a spiral flocculator (Guo et al., 2004), vibrating membranes (Genkin et al., 2005), helical baffles (Ghaffour et al., 2004), suction mode (Kim et al., 2004) and high-performance compact reactor (Yeon et al., 2005), novel types of air lift (Chang and Judd, 2002), porous and flexible suspended membrane carriers (Yang et al., 2006), and the sequencing batch MBR (Zhang et al., 2006d). A reasonable flux rate without significant fouling is ideally expected. The concept of sustainable flux in MBRs was introduced from this point of view (Ng et al., 2005).

•

Aerobic granular sludge. In order to obtain higher biological aggregates in the bioreactor, aerobic granular sludge has also been used in MBR systems (Li et al., 2005b). With an average size around 1 mm, granular sludge increased the membrane permeability by 50%, but lower cleaning recoveries were observed (88% of those obtained with a conventional MBR). Such granular sludge may also not be stable under long-term operation (Hai, 2007). Coagulant/flocculant. Due to back transport and shearinduced fouling control mechanisms, large microbial flocs are expected to have a lower impact on membrane fouling. Based on this expectation, studies have explored addition of coagulants such as alum (Holbrook et al., 2004), ferric chloride, zeolite (Lee et al., 2001b), chitosan (Ji et al., 2008), etc. and have shown permeability enhancement. Pretreatment of the effluent is also possible and studies based on the pre-coagulation/ sedimentation of effluent before its introduction in the bioreactor revealed the fouling limitation offered by this technique (Itonga and Watanabe, 2004; Le-Clech et al., 2006). Adsorbent/flux enhancers. Lower fouling propensity is observed in MBR processes when biomass is mixed with adsorbents in that addition of adsorbents into biological treatment systems decreases the level of pollutants, and more particularly organic compounds (Kim and Lee, 2003; Lesage et al., 2005; Li et al., 2005c; Ng et al., 2006). In view of saturation of PAC during longterm studies, researchers have suggested periodic addition of PAC (Ng et al., 2005; Fang et al., 2006). Certain studies have proposed pre-flocculation and PAC addition (Guo et al., 2004; Cao et al., 2005).

A cationic polymer-based membrane performance enhancer (MPE 50) has been commercialized by Nalco recently. The interaction between the polymer and the soluble organics was reported as the main mechanism responsible for performance enhancement (Yoon et al., 2005). The potential impacts of coagulants or adsorbents on biomass community or biomass metabolism need to be taken into account (Iversen et al., 2009), and the discharge of some chemicals that are used as coagulants or adsorbents might be a potential environmental

Membrane Biological Reactors

risk. Such flux enhancers are probably best suited for solving occasional upsets rather than their continuous addition. Emerging fouling monitoring/control techniques such as interference of microbial intercellular communication by enzymatic degradation of signal molecules (Kjelleberg et al., 2008; Yeon et al., 2009), proteins and polysaccharides sensor for online fouling control (Mehrez et al., 2007), application of two-dimensional fluorescence for monitoring MBR performance (Galinha et al., 2009), etc., are worth noting.

4.16.5 Worldwide Commercial Application 4.16.5.1 Installations Worldwide The MBR process is an emerging advanced wastewater-treatment technology that has been successfully applied at an everincreasing number of locations around the world. MBRs were first developed 40 years ago and have been used commercially in Japan for almost 30 years. Since 1990, MBR technology has been adopted in North America and Europe, and it is now experiencing rapid growth in a wide variety of applications. In Asia, the drive in Japan was followed by an enthusiastic uptake in South Korea in the 1990s, and more recently by China. The highest growth rates are found in areas of greatest water stress for reuse applications, such as the southwestern US, China, Singapore, and Australia. The low footprint of the MBR is a significant driver for developed economies.

4.16.5.1.1 Location-specific drivers for MBR applications Howell (2004) stipulated the location-specific global drivers for MBR technology as follows: 1. Asia. MBR technology is being considered at many locations all over Asia, the main driver being water reclamation. Examples of settings vary from small-scale applications in Japan, where MBR product water is reused as toilet-flushing water in apartment blocks, medium-sized industrial applications in various countries, and large-scale municipal WWTPs in China. 2. Middle East. Clean-water shortages are the obvious driver for MBR applications in the Middle East, in treatment of both municipal as well as industrial (petrochemical) wastewater. 3. Europe. In Western Europe, water reclamation is not the main driver. In the UK, an important driver is compactness and strict discharge limits due to bathing wastewater requirements. In Germany and the Netherlands, important push factors are strict discharge requirements due to ecologically sensitive surface waters and the innovative character of the technological developments related to MBR. In Southern Europe, water reclamation can be considered as the main driver. 4. Northern America. In the US and Canada, MBR initiatives are predominantly driven by strict discharge requirements due to ecologically sensitive surface waters. At some locations, water reclamation is another important driver. In the US, where wastewater-treatment infrastructure lags behind population growth, MBRs are being increasingly implemented to make up the shortfall. Where there is

597

limited space to locate treatment plants, MBRs offer the potential to meet the needs of communities. 5. Australia. Stringent effluent-quality targets and water-reuse potential are obvious drivers for drought-stricken Australia.

4.16.5.1.2 Plant size Earlier MBR technology was favored in difficult applications or those applications where compactness was important and reuse was the target; and it usually involved smaller plants. As the demand for MBR technology grows globally, both the number of installations and the capacity of the installed plants are increasing dramatically. The most optimistic industry estimates suggest that up to 1000 new MBR plants will be built annually during the survey period. The size of the constructed plants has grown from facilities treating hundreds to thousands of gallons of wastewater per day to those treating tens of millions of gallons per day in just a few years. However, the most common capacity for current worldwide MBR installations ranges from the 50 000 gpd (200 m3 d1) to 500 000 gpd systems. The largest MBR plant in the world is set to be operational in 2010/11 in King County, Washington State. When completed, the facility will have an initial peak flow capacity of 495 000 m3 d1 (average 136 000 m3 d1), rising to a daily 645 000 m3 (average 205 000 m3) by 2040.

4.16.5.1.3 Development trend and the current status in different regions Figure 13 shows the regional share of total MBR plants as of 2003. Next, we discusss the trend of MBR growth in the three continents, Asia, Europe, and North America. 1. Asia. In the 1970s sidestream technology first entered the Japanese market. By 1993, 39 of such facilities had been reported for use in sanitary and industrial applications (Aya, 1994). The application of MBR in Japan concerned Europe 11%

N. America 16%

Asia 73% Figure 13 Regional share of total MBR plants (2003). Data from Pearce G (2008a) Introduction to membranes: An introduction to membrane.

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small-scale installations for domestic wastewater treatment and reuse and some industrial applications, mainly in the food and beverage industries where highly concentrated flows are common. The domestic application often consists of so-called Johkaso or septic-tank treatment and inbuilding (office or domestic) wastewater-collection systems. In the early 1990s, the Japanese Government launched an ambitious 6-year research and development (R&D) project which led to a major technological and industrial breakthrough of the MBR process: the conception of submerged membrane modules, working with low negative pressure (out-to-in permeate suction), and membrane aeration to reduce fouling. This paved the way toward a significant reduction of capital and operation costs, due to the reduction and simplification of equipment and the abandonment of the energy-demanding sludge-recirculation loop. Since then, commercial MBRs proliferated in Japan, which had 66% of the world’s processes in 2000 (Stephenson et al., 2000). In Japan, although MBRs have long been used for industrial wastewater treatment or for reuse of wastewater in large buildings and so on, the introduction of municipal MBRs has lagged behind compared with other water-related fields. The first MBR for municipal wastewater treatment with an installed capacity of 2100 m3 d1 (total design capacity 12 500 m3 d1) in Japan started operation in March 2005, and this accelerated the introduction of MBRs in Japanese sewerage systems. Nine MBR plants, mostly small scale, for municipal wastewater treatment, are in operation at present (Table 8). In addition, there are several MBR plants currently in the design or planning stage. The number of MBRs for municipal wastewater is expected to increase in the near future and the technology will also play an important role in retrofitting and upgrading of existing treatment plants. The MBR technology saw an enthusiastic uptake in South Korea in the 1990s following its introduction in Japan. By 2005, the number of MBR plants rose up to more than 1300 (Namkung, 2008). The plants are mostly small, with more than 60% of the total plants having a capacity of less than 50 m3 d1. The plants were predominantly built on the submerged membrane technology (hollow fiber, 79%; flat sheet, 12%), while a meager 9% facilities utilized the tubular membranes in sidestream format. China has recently emerged as a strong MBR market. Hence, it would be interesting to cast light on the specific

Table 8

mode of development in that country. While the first paper on MBR was published in 1991, the emergence of a number of local and overseas companies developing MBR market in China accelerated with the funding of R&D projects by the Ministry of Science and Technology (MOST) in 1996 (Wang et al., 2008a). Since then, much progress has been achieved both in research and in practical applications of MBR in China. This is evident by the recent yearly publication rate of 35–40 English articles on MBR in China and the construction of a total of 254 plants for municipal (137) and industrial (117) wastewater treatment by 2008. The Chinese MBR market has the presence of a total of 33 companies or institutes, including famous overseas companies such as GE–Zenon Environmental Inc., Mitsubishi–Rayon (Japan), Toray (Japan), NOVO Environmental Technology (Singapore), and XFlow (Netherlands). Among these, only three companies provide flat-sheet MBR, and, interestingly, the worldwide renowned flat-sheet membrane provider, Kubota (Japan), was not found to be very active in the Chinese membrane market. Most of the plants in operation are medium scale or small scale in terms of treatment capacity, the number of plants with treatment capacity below 1000 m3 d1 totaling 225. The largest MBR plant with a capacity of 80 000 m3 d1 for municipal wastewater treatment and reuse is located in Beijing. Several other large MBR plants are also in the planning stage. Wang et al. (2008a) contend that the increasingly stringent discharge standards and the great need of water reclamation and reuse will further push forward the application of everlarger municipal MBR plants in China, especially in North China which has severe water shortage. 2. Europe. A market survey of the European MBR industry was performed by Lesjean and Huisjes (2008). They identified MBR plants constructed up to 2005, and about 300 references of industrial applications (420 m3 d1) and about 100 municipal WWTPs 4500 p.e. were listed. In Europe, the first full-scale MBR plant for treatment of municipal wastewater was constructed in Porlock (UK, commissioned in 1998, 3800 p.e.), soon followed by WWTPs in Bu¨chel and Ro¨dingen (Germany, 1999, 1000 and 3000 p.e., respectively), and in Perthes-en-Gaˆtinais (France, 1999, 4500 p.e.). In 2004, the largest MBR plant worldwide so far was commissioned to serve a population of 80 000 p.e. (in Kaarst, Germany). The installations thus grew from small WWTPs to very large WWTPs within a few

Municipal MBR plants in Japan

Name of plant

Total design capacity (m3d1)

Capacity at commissioning (m3d1)

Membrane format

Start of operation

Fukusaki Kobuhara Yusuhara Okutsu Daito Kaietsu Zyosai Heta Ooda

12 500 240 720 580 2000 230 1375 3200 8600

2100 240 360 580 1000 230 1375 2140 1075

Flat sheet Flat sheet Flat sheet Hollow fiber Flat sheet Hollow fiber – – –

2005 2005 2005 2006 2006 2007 2008 2008 2009

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years. Nevertheless, the favored range for MBR systems still appears to be only 100–500 m3 d1 and 1000–20 000 p.e. for industrial and municipal wastewaters, respectively. The design capacity of the industrial units is more than an order of magnitude smaller than for the municipal WWTPs. Lesjean and Huisjes (2008) opined that, although the construction of very large MBR plants (4100 000 p.e.) were recently announced with much publicity, this will remain the exception in Europe, because of the lower lifecycle costs (Lesjean et al., 2004) of WWTP plants equipped with tertiary-membrane filtration (Figure 14). Although not representative of the market, the very large plants will attract much attention and thereby may contribute to the market expansion. The industrial market was the pioneer in the early 1990s, whereas the municipal market took off only in 1999. In 2002, 154 MBR units could be counted, among which 85% were for industrial applications. However, taking the installed membrane surface as an indicator of market share, for the period 2003–05, the municipal sector represented 75% of the market volume. Both municipal and industrial sectors saw a sharp increase in the following years, due to the commercial success and much lower capital and operating costs. By 2005, the market growth rate was linear with at least 50 industrial units and 20 municipal plants constructed per year. This progression rate is expected to sustain in the next years or may even further accelerate owing to the evolution and implementation of European and national regulations (Lesjean et al., 2006). The survey by Lesjean and Huisjes (2008) also demonstrated the predominance of the suppliers Kubota (Japan) and GE–Zenon. Their technologies based on submerged filtration modules have been outstandingly successful since 2002. In recent years, the European market can therefore be seen as a quasi-duopoly of two nonEuropean suppliers. In contrast, the most successful MBR technologies in the 1990s, based on sidestream configurations supplied by Wehrle, Norit X-Flow, Berghof, Rodia Orelis, etc., did not experience any significant market

599

growth over the last 3 years. This could explain the recent move of companies such as Wehrle and Norit to develop and commercialize novel low-energy airlift MBR systems. They argued that the industrial market has become mature: the MBR is considered as the best available technology by many industries. On the other hand, the municipal market is expected to witness further growth over the next decade under the combined effects of the acceleration of plant construction and the capacity increase. 3. North America. Full-scale commercial applications of MBR technology in North America for treatment of industrial wastewaters dated back to 1991 (Sutton, 2003). In the early 1990s, MBR installations were mostly constructed in external configuration. After the mid-1990s, with the development of SMBR system, MBR applications in municipal wastewater extended widely. In the past 15 years, MBR technology has been of increased interest both for municipal and industrial wastewater treatment in North America. The hesitancy on the part of North American municipalities to consider alternative treatment systems to the well-established conventional treatment options delayed the introduction of MBRs into the municipal arena. Industrial applications, particularly for high-strength, difficult-to-treat waste streams, on the other hand, allowed for the considerations of alternative technologies, such as MBRs (Yang et al., 2006). Nevertheless, currently, commercial application in treating industrial wastewaters does not constitute a high percentage of total full-scale MBR plants. Zenon occupies the majority of the MBR market in North America. In 2006, the North American installations constituted about 11% of worldwide installations. As in other places, in North America too, although plant capacities of MBR systems for municipal wastewater treatment are becoming larger, most of the plants in operation are medium scale or small scale in terms of capacity. The largest capacity MBR plant in operation is in Traverse City, MI at 26 900 m3 d1, and the two largest capacity plants under construction are in Johns Creek, GA at 60 000 m3 d1 and King County, Washington State at 136 000 m3 d1.

Capacity, p.e × 104

8

4.16.5.1.4 Decentralized MBR plants: Where and why? 6

4

2

0 1996

1998

2000

2002

2004

2006

2008

Year of commissioning Figure 14 Plot of capacity of randomly selected European MBR plants showing predominance of medium size plants (similar trend prevails worldwide). Data from Schier W, Frechen FB, and Fischer S (2009) Efficiency of mechanical pre-treatment on European MBR plants. Desalination 236: 85–93.

MBR technology can also provide decentralized small-scale wastewater treatment for remote or isolated communities, campsites, tourist hotels, or industries not connected to municipal treatment plants. In small communities, houses are spread out, the population density is low, and hence the use of an on-site system for an individual home or for a cluster of homes could be a cost-effective option. For emerging nations with vast unsewered areas, the population has practically no access to water sanitation, whereby wastewater is directly discharged into water bodies or reused for irrigation without treatment, thus spreading waterborne diseases and causing eutrophication and pollution of water resources. MBR technology could provide a decentralized, robust, and cost-effective treatment for achieving high-quality effluent in such instances. MBRs also offer excellent retrofit capability for expanding or upgrading existing conventional WWTPs.

600

Membrane Biological Reactors

Even when appropriate infrastructure for large-scale water recycling facility exists, the decentralized option may be preferable in some cases. This is because the cost of large-scale water-recycling applications remains high and often uneconomical due to the need to overhaul the existing waterdistribution systems. Large-scale water-recycling applications are, hence, currently somewhat restricted. Furthermore, there is a significant risk of cross-connection associated with the dual-reticulation network, which can seriously dampen public support. While the implementation of the large-scale water recycling is expected to take many years, decentralized water recycling can be applied much more readily. It is expected that MBRs can contribute to a significant increase in decentralized water reclamation and reuse activities. The discussion now centers on the limitations of traditional onsite treatment systems. A gradual but permanent reduction in per-capita water use through socially acceptable means is widely recognized by all stakeholders in the water industry as the strategic longterm sustainable solution to address the ongoing water shortage currently experienced by many countries (Tadkaew et al., 2007). Decentralized wastewater management is not a new concept. Tchobanoglous et al. (2003) defined it as the collection, treatment, and disposal/reuse of wastewater from individual dwellings, clusters of homes or isolated communities, industries, or institution facilities. Traditional decentralized treatment systems such as septic tanks were, in the past, widely used to treat small quantities of wastewater. Due to the likely toughening of environmental legislation in the near future, many of the currently operating wastewater treatments will no longer be acceptable and there will be a need to increase their efficiency significantly. Stricter regulations are found for especially sensitive areas, drinking-waterabstraction areas, and bathing waters. The problem of meeting existing and forecasted more-stringent new regulations affects especially small communities, hotels, and campsites in relatively remote areas without access to sophisticated WWTPs. A major obstacle of decentralized water recycling remains the lack of a suitable technology that can meet the strict and unique effluent criteria required for small-scale water treatment. Some essential requirements are high and reliable treated effluent quality, robustness, tolerance to variable contaminant loading, small footprint, and ease of operation and maintenance. We now discuss the advantages of MBRs in decentralized treatment. As discussed in Section 4.16.5.1.2, historically, the largest number of MBR applications was for a capacity of less than 100 m3 d1. This suggests that the application of MBRs for on-site decentralized system is possible and can offer the most advanced wastewater-treatment options in low-density areas at a cost lower than that of conventional large-scale pipeand-plant systems. Jefferson et al. (2000) argued that smallscale WWTPs constitute a potential growth market for the next millennium and urban sustainability through domestic water recycling is a major identified source for this development. Key advantages of MBRs for decentralized wastewater treatment and reuse are:

•

High and reliable treated effluent quality, small footprint, and high tolerance to variable contaminant loading.

•

•

Due to the robustness and modular nature of MBRs, smallscale MBRs can retain the superiority over conventional treatment methods such as septic tanks with regard to effluent quality, which has been very well documented in the literature (Fane and Fane, 2005). MBRs can be easily combined with other complementary treatment technologies such as UV disinfection and prescreening, which can further enhance the robustness of the treatment system and hence make it particularly suitable for water-recycling applications.

The MBRs for decentralized treatment are not without limitations. Besides the obstacles against widespread application of MBR, in general, the high capital cost can be seen as the key limitation of small-scale MBRs although currently there is very little information to substantiate this premise. Friedler and Hadari (2006) analyzed the economic feasibility of on-site graywater-reuse systems in buildings based on MBR systems. They found that on-site MBR systems became feasible when they were used for the treatment of wastewater incorporating several buildings together because cost was sensitive to building size. Therefore, the on-site MBR system for single building might be unfeasible. This could be a limitation of decentralized MBR systems. However, the true cost of water supply, which takes into account the externalities of resource depletion, was not used in their analysis. It is expected that as the demand for decentralized MBRs increases and membrane technology continues to develop, the use of on-site MBRs even for individual dwellings can be cost competitive in the near future. Some of the examples of worldwide decentralized MBRs are discussed next. The successful introduction of MBR systems into small-scale and decentralized applications has led to the development of packaged treatment solutions from most of the main technology suppliers. Sports stadia, shopping complexes, and office blocks are becoming typical end users, especially in areas of water stress (Stephenson et al., 2000; Melin et al., 2006; Tadkaew et al., 2007). The application of MBRs in Japan to date has predominantly concerned small-scale installations for domestic wastewater treatment. One of the earliest reported case studies is on graywater recycling facilities in the Mori building, Tokyo (Stephenson et al., 2000). The plant consists of a sidestream Pleiade MBR (Ubis) to treat the building flow of 500 m3 d1. The selection of an MBR over a traditional treatment process saved an area equivalent to 25 car-parking places. The treated graywater contained less than 5.5 mg l1 BOD and belowdetection level of suspended solids, colon bacilli, and n-hexane extract, enabling reuse of the graywater. Today, the main Japanese MBR providers such as Kubota or Mitsubishi Rayon offer commercial MBR package plants for on-site domestic water treatment. In Australia, small-scale MBR systems for graywater recycling at a single household level have been marketed by several companies such as AquaCell in New South Wales and BushWater in Queensland (Tadkaew et al., 2007). Commercially available systems in Europe include the package treatment plant Clereflo MBR (Conder Products, UK), designed to service populations up to 5000 and the ZeeMOD (Zenon Environmental Inc.) which is available for flow rates

Membrane Biological Reactors of up to 7500 m3 d1. Most of the manufacturers offer similar systems which means that effluent qualities of 5:5:5 (mg l1) (BOD: NH4-N:SS) are now routinely available to end users as standard treatment options (Melin et al., 2006). Households/ community units (4–50 p.e.) is a concept pioneered by Busse (Germany) in 2000 (Lesjean and Huisjes, 2008). This has become a very competitive market (at least eight products available in Germany). The units are mostly covered by maintenance contracts. The number of sales is expected to increase to address wastewater schemes of small and remote communities, although the revenue may remain marginal in the overall European MBR market. An example in USA is in eastern San Diego County, California, where expansion of an existing casino and development of a shopping mall required extension to the existing treatment facilities. The existing extended aeration system was converted to a ZeeWeed MBR allowing almost triple the capacity of the infrastructure (Melin et al., 2006). The scheme has been operational since July 2000 with the water quality meeting the California tertiary effluent standards for waterreclamation plants.

4.16.5.2 Commercialized MBR Formats As mentioned in Section 4.16.3.1, the first-generation MBRs in wastewater treatment used a sidestream format, in which feed was pumped from the bioreactor through an external membrane system. This approach was suitable for the early stage, small-scale applications for difficult-to-treat feeds. An alternative format was developed in the 1990s using modules submerged in the bioreactor tank, or in an adjoining compartment. This was much more cost effective for treating larger-scale flows with more easily treatable wastewater. The submerged format is available with modules either in a flat-sheet configuration or as hollow fibers or capillary membranes. Originally, the favored concept was to submerge the modules directly into the bioreactor for simplicity. However, in order to gain better control of the balance between the biological and filtration-treatment capacity, it is now more common to use the membrane in an external membrane tank (Brow, 2007). The external arrangement allows the size and design of the membrane tank to be optimized independently, with practical advantages for operation and maintenance. The sidestream approaches are also divided into two formats – the long-established traditional method of crossflow, now used only for the most difficult feeds, and the newer concept of airlift, which uses air to recirculate the feed and thereby significantly reduces energy demand. Both sidestream formats use tubular membranes.

4.16.5.3 Case-Specific Suitability of Different Formats The competing MBR formats based on submerged and sidestream configurations each have their own pros and cons for different application types and plant size. The energy cost for the aeration to control membrane fouling in the MBR is of an order similar to the microbiology aeration for an easy-to-treat feed, and increases by 2.5–3.0 times for the more difficult feed (Cornel and Krause, 2006).

601

Crossflow is more energy intensive – very high cross-flow velocities (up to 5–6 m3 h1) may be necessary to control the fouling; but for the more difficult feeds, it may be the only option that works reliably. Airlift is a more cost-effective way of improving mass transfer through the creation of slug-flow conditions in the lumen of the membrane tubes (Laborie et al., 1997), but there is a limit to how much air flow can be used while retaining slug-flow conditions. Airlift technology has a power cost similar to that of the submerged technology. In general, submerged MBR formats based on hollow fibers have been found to provide the most cost-effective solution for large-scale, easy-to-treat applications. Technology has been developed with optimized packing density and aeration bubble size to achieve stable performance at minimum energy use (Fane et al., 2005). However, this format can experience operational difficulties due to fibers becoming matted close to the potted ends, and therefore pretreatment and removal of hairs and fibers is essential. Hollow-fiber technology hence requires more instrumentation and control. The submerged MBR formats based on flat sheets have been found to be cost effective for similar types of wastewater, but due to higher air use and lower compactness, tend to be selected for small- to medium-scale duties. The flat-sheet format has operational advantages in terms of plugging and cleaning, and has been used in somewhat more difficult feeds. Flat-sheet systems have the advantage of relatively low manufacturing cost compared to hollow-fiber systems. However, packing density tends to be significantly lower than a hollow-fiber system (e.g., by a factor of 2.5–3 times). Therefore, flat-sheet systems tend to have a cost advantage for smallto medium-scale systems, whereas hollow fiber becomes more attractive at large scale due to the footprint advantage (Pearce, 2008b). The comparison is made more complicated, however, since aeration costs for hollow-fiber systems are often lower. This means that the most cost-effective solution for total treatment costs at medium scale is closely contested, and both approaches are found across the size range due to site-specific circumstances, which could favor either solution. Lesjean et al. (2004), taking into account the current knowledge, anticipated a future market share as follows: for municipal applications, it is expected that the hollow-fiber submerged configuration would be competitive for mediumto large-size plants. For small to medium sizes, flat-sheet technologies would have an advantage. However, in case of larger plants, or a plant refurbishment, the alternative membrane scheme (secondary/tertiary treatment followed by an MF/UF membrane filtration) is very likely to be cost competitive, unless high-cost land has to be purchased for the construction. This multi-barrier scheme will also be easier to control and to optimize because of the disconnection of the treatment steps. It will also be associated with the lowest risk in relation to the membrane operation, as the membranes will be operated under smooth hydrodynamic conditions in terms of particle matter, turbulence, and backwash re´gime. In a recent paper, Lesjean and Huisjes (2008) reiterated this expectation despite the present trend of large MBR plant construction. The airlift format has been developed as a low-energy alternative to the energy-intensive cross-flow sidestream format,

602

Membrane Biological Reactors

which has been used historically for the most difficult feeds. As mentioned earlier, the energy cost of crossflow prohibits it as a treatment option for any application other than small scale or where there is no other treatment option. However, the airlift has very low energy use, and may even undercut the energy requirements of the submerged options, due to the advantage of containment of the feed inside the tubular membrane (Van ‘T Oever, 2005; Futselaar et al., 2007). Since airlift eliminates operator contact and has good operational characteristics, it may as well make a major impact on the MBR market in the long run. Pearce (2008a, 2008b) argued that the airlift format may find applications throughout a broader range than the submerged formats. Figure 15 depicts the concept of airlift MBR.

4.16.5.4 MBR Providers 4.16.5.4.1 Market share of the providers The global market value of MBR is expected to rise up to US$500 million by 2013 from around US$300 million in 2008 (BCC Research, 2008). The MBR market is dominated by three companies, namely GE–Zenon, Kubota, and Mitsubishi Rayon Engineering (MRE). Only GE–Zenon and Kubota have a strong presence in Europe and North America, while MRE have until now mainly focused on sales in Asia. All these companies use submerged formats, with GE–Zenon and MRE Air release

Return to bioreactor

Permeate

Permeate backwash

Air injection

Airlift Feed supply Figure 15 The concept of airlift MBR.

using hollow-fiber membranes, and Kubota, flat-sheet membranes. Another three companies too have an international presence, namely Siemens–Memcor, Norit, and Koch-Puron, but the sales for these three companies makes up a small portion of the worldwide market. Among the latter three, Norit promotes the airlift format. Figure 16(a) shows the worldwide relative market share (in terms of installations numbers) for the three large players (Yang et al., 2006; Pearce, 2008b). The MBR market has several dozen regional or application specialists, quite a few of who use flat-sheet formats as adopted by Kubota: for example, Japan’s Toray and A3 from Germany. In addition to these international companies, there are a further 30 companies in the European Union (EU) market that have either significant regional presence, or an application focus, or a low-level international presence (Lesjean and Huisjes, 2008). Many of these companies are significant in the local markets, but individually, they have a small share of the international market. It is interesting to note that the MBR market has characteristics different from that of the UF/MF market. In UF/MF, there are 10–12 significant players with worldwide presence, with four market leaders, none of who dominate the market. Besides these companies, other regional players are relatively insignificant (Pearce, 2008a, 2008b). Zenon is long established in the market and has been one of the major companies promoting the MBR concept, and the use of PVDF membranes. The North American market is dominated by Zenon (Yang et al., 2006) as shown by the revenue share illustrated in Figure 16(b) and has many more opportunities in the municipal sector than in industry. Zenon leads the European market as well (Figure 16(c)). Kubota was one of the early pioneers of the MBR concept, encouraged by a Japanese Government initiative in the 1980s. They achieved a very large number of installations in small- to medium-scale systems, initially focusing on the residential/ commercial market in Japan and have approached export markets through exclusive partnerships. Kubota has a significantly greater number of plants than Zenon, with a slightly higher proportion of industrial plants. Many of Kubota’s installations in Japan and Korea are for small-scale municipal and domestic applications. Figure 17 shows the market characteristics of the two market leaders, Kubota and Zenon, illustrating the significantly different market strategies with regard to the size of plant targeted. Kubota is the strongest market player for industrial and small-scale municipal applications. MRE is a long-established supplier of MBR, with a very strong position in the relatively mature MBR market in Japan and Korea. There are a large number of references for this technology in Asia, but relatively few installations elsewhere. MRE also has a very large number of installations, with a higher proportion of industrial users, mostly with small flowrates. Koch Membrane Systems (KMS) is a long-established membrane manufacturer and membrane-systems company. In 2004, KMS acquired the MBR start-up company Puron, which had been founded in 2001. They introduced an approach to fiber potting different from that of the other hollow-fiber module providers.

Membrane Biological Reactors

603

15

17

68

(a) Worldwide (relative installation numbers % in 2006)

2 3

6

10

20

33

65

61

(b) North America (revenue % in 2003)

GE−Zenon

Kubota

(c) Europe (installed membrane surface % in 2005)

Mitsubishi−Rayon

Siemens−Memcor

Koch−Puron

Others (N. America: Mitsubishi, Norit; Europe: Norit, Wehrle and other EU and non-EU suppliers) Figure 16 Market share of the suppliers. Data from (a) Yang Q, Chen J, and Zhang F (2006) Membrane fouling control in a submerged membrane; (b) Pearce G (2008 b) Introduction to membranes – MBRs: Manufacturers’ comparison: Part 1. Filtration and Separation 45(3): 28–31; and (c) calculated from Lesjean B and Huisjes EH (2008) Survey of the European MBR market: Trends and perspectives. Desalination 231: 71–81.

Memjet product is characterized by high permeability and packing density, providing a competitive position for capital and operating costs. However, worldwide market share for MemJet MBR is not very significant, since the company tends to focus on selected regional markets (Yang et al., 2006; Pearce, 2008b).

100 Plant capacity

80

60

No. of plants

% 40

20

0 Kubota

GE−Zenon

Figure 17 Relative market share (number of plants and capacity) showing distinct market strategies of the two market leaders.

Memcor have extensive experience in the use of their products in wastewater polishing. Their very fine polypropylene (PP) fibers developed in the 1980s were inexpensive and flexible, but unfortunately had low chlorine tolerance (Judd et al., 2004). In the late 1990s, Memcor developed a PVDF fiber, and now use the PVDF fiber for their MBR product range. The

4.16.5.4.2 Design considerations The design of the reactor (including membrane, baffle, and aerator locations) and the mode of operation of the membrane are key parameters in the optimization of the system. The leading MBR providers propose several MBR designs. In each case, the process proposed is very specific. Not only are the membrane material and configuration used different, but the operating conditions, cleaning protocols, and reactor designs also change from one company to another. For example, the flat-sheet membrane provided by Kubota does not require backwash operation, while hollow-fiber membranes have been especially designed to hydraulically backwash the membrane on a given frequency. The MBR industry first developed in Japan with the use of chlorinated polyethylene (PE) flat-sheet membrane by Kubota, and PE fibers by MRE (Stephenson et al., 2000). The modified PE is characterized by reasonable strength, flexibility, wettability, and resistance to chlorine. Although PE is normally made as an MF membrane, it has relatively low permeability, so process fluxes of PE membranes tend to be at the

Membrane Biological Reactors

Table 9

air-usage efficiency. In addition, the companies using hollow fiber use intermittent aeration, for example, based on a timer in the case of Zenon, or in proportion to flow in the case of Koch–Puron. Memcor introduced a novel cleaning method by using a mixture of air and mixed liquor, instead of using only air bubbles, to scour the membranes. The air bubbles effectively scour the membranes and the semi-crossflow of mixed liquor along the membranes continuously delivers the refresh mixed liquor to the membrane surface, minimizing the solidconcentration polarization at the membrane surface and therefore reducing filtration resistance. These enhancements have significantly reduced air usage and therefore power cost.

4.16.5.4.3 Performance comparison of different providers Few large-scale studies based on comparison of the commercially available MBR systems have been conducted. The city of San Diego, California, and the research consultant, Montgomery Watson Harza, have been evaluating the MBR process through various projects since 1997, including feasibility of using MBRs to produce reclaimed water (Adham and Gagliardo, 1998, 2000), optimization of MBR operation, and parallel comparison and cost estimations of the four leading MBR suppliers (Adham et al., 2004). MBRs were evaluated for their ability to produce high-quality effluent and to operate with minimum fouling. In terms of hydraulic performances, it (8.5−12)

500

400 (17−24) (50−60)

(17−24)

0

Toray

(29)

Norit

Siemens−Memcor

Koch−Puron

100

(30−34)

GE−Zenon

(17−24)

(14−26)

Mitsu. (PVDF)

200

Mitsubishi (PE module)

300

Kubota

low end of the range. Consequently, PE membranes are very cost effective at small scale, but struggle to compete in largerscale systems. In the 1990s, PVDF became established in MBRs through the reinforced capillary fiber in Zenon’s ZW 500 module (Yamato et al., 2006). PVDF has impressive performance in terms of strength and flexibility, but is significantly more expensive as a polymer. Nevertheless, PVDF membranes can achieve substantially higher flux, thereby overcoming price disadvantage. Recently, MRE also developed a PVDF-based membrane system. This membrane, designated as SADF, promises to be very competitive in both capital and operating costs, and despite it having a lower packing density than the PE product, it operates at much higher flux. With several companies now offering PVDF products in both capillary and flat-sheet formats, this is the dominant membrane polymer in the MBR market (Pearce, 2008c, 2008d). The third significantly used membrane polymer in MBR is a reinforced PES, used by Koch–Puron. Although PES is an important polymer in water treatment, in wastewater applications, its lack of flexibility limits the possibility of using air scour. Reinforcing the capillary does allow air scour, but at the expense of permeability. The Puron product uses reinforced PES rather than the PVDF, favored by its rivals. However, its main distinguishing feature is that the membrane fibers are potted at only one end. This overcomes the problem of fouling below the potting interface by hairs and fibers, which is a problem for the other hollow-fiber technologies (Vilim et al., 2009). Norit is the one major MBR company that offers a system based on a sidestream format with tubular membranes rather than a submerged format. Crossflow is only used for smallscale applications, with feeds that are difficult to treat, whereas airlift is cost effective for larger-scale municipal applications (Futselaar et al., 2007). Table 9 summarizes the specifications of the membranes used by different suppliers and Figure 18 compares the packing density and applicable flux of the membranes. Each of the suppliers makes regular improvements in air usage, since this has an important impact on total water cost. For example, the flat-sheet suppliers now use 1.5-m panels, which reduce air flow by up to 30% compared to the original 1 m panel (Pearce, 2008c, 2008d). In addition, they also use double-deck stacks wherever possible, which further improves

Membrane packing density, m2 m−3

604

Figure 18 Packing density (bar chart, m2 m3) and flux (values within parentheses, l m2 h1) of membranes from different suppliers.

MBR supplier specificationsa

Company

Membrane material

Pore size, mm

Membrane format

Fiber/tube dia (id,od),mm

pH tolerance

Kubota Mitsubishi Mitsubishi GE–Zenon Koch–Puron Siemens–Memcor Noritb Toray

Cl2 PE PE PVDF PVDF PES PVDF PVDF PVDF

0.4 0.4 0.4 0.04 0.05 0.04 0.03 0.08

FS HF HF HF HF HF TUB FS

– 0.37, 0.54 11, 2.8 0.8, 1.9 –, 2.6 –, 1.3 –, 5.2 or 8.0 –

1–13 1–13 1–10 2–10.5 2–12 2–10.5 1–11 1–11

a

All the membranes have moderate hydrophilicity and high chlorine resistance. All the companies except Norit use submerged format; Norit supplies airlift sidestream MBRs. FS, flat sheet; HF, hollow fiber; TUB, tubular. b

Membrane Biological Reactors

was shown that all four processes were able to cope with flux rates exceeding 33 l m2 h1 and HRTs as low as 2 h. A 6-year development program has also been initiated for the introduction of MBR technology in the Netherlands market. Started in 2000, a comparative study of four 750 m3 d1 MBRs carried out by DHV water has been reported (van der Roest et al., 2002b). Three MBR plants, treating a design flow of 300 m3 d1 each, have been operated in parallel during 2003 and 2004 in Singapore (Le-Clech et al., 2006). A 4-year study, started in 2001, comparing the performance of Mitsubishi, Kubota, and Zenon MBR was conducted by the Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Judd, 2006). The Zenon MBR exhibited the most stable performance in the study. Although these studies have been conducted with the MBR systems running in parallel (with the same influent water), the MBR maximum flux, operating conditions and general design applied were those recommended by the suppliers, and therefore somewhat different for each system. This makes it difficult to make a fair comparison. Therefore, it is not possible to classify the MBRs as a function of their relative hydraulic performances, which need to be considered along with the cleaning protocols applied to each system. Mansell et al. (2004) performed measurements in which MS2 coliphage were seeded to the influent of a Kubota MBR (characteristic pore size 0.4 mm) and a Zenon MBR (characteristic pore size 0.04 mm). Permeate concentrations showed a log removal range of 3.2–7.4 for the Kubota installation and 5.32–7.5 for the Zenon installation. All of the heavy metals detected in the influent were removed to levels below detection limit, as well as the VOCs that were measured.

4.16.5.5 Standardization of Design and Performance-Evaluation Method The MBR market is very fragmented and exhibits many MBR filtration products with diverse geometries, module capacities, and operational modes (De Wilde et al., 2008; Lesjean and Huisjes, 2008). Although this situation promotes a competitive market, it is detrimental for the acceptance of the technology as a state-of-the-art process, and raises concern with potential clients or end users. From the point of view of the MBR operators, the possibility of interchanging filtration modules of different companies/suppliers would facilitate the replacement of the modules at the end of their life, and would reduce the risk of a supplier withdrawing from the market or releasing a new series of the product. In addition, the stakeholders in the industry employ various methods of membrane characterization and performance evaluation. This creates confusion among the users and prohibits fair comparison. Based on an extensive survey of the MBR industry, De Wilde et al. (2008) provided an overview of the market interests/expectations and technical potential of going through a standardization process of the SMBR technology in Europe. Due to the predominance of submerged filtration systems in municipal applications, the study focused only on this configuration. Two different aspects of standardization were considered:

•

standardization of MBR filtration modules toward interchangeable modules in MBRs and

•

605

standardization of MBR acceptance and monitoring test methods toward uniform quality-assessment methods of MBR filtration systems.

4.16.5.5.1 Standardization of MBR filtration systems In relation to the market expectations, about 20 potential technological, financial, economical, or environmental benefits/opportunities and drawbacks/threats of MBR module standardization for suppliers and operators were identified and mapped. It appeared that the number of advantages and disadvantages was quite balanced for both sides of the market, the main advantage perceived by the industry being that standardization should contribute to the growth of the MBR market. Other main advantages/opportunities are avoidance of vendor lock-in, price decrease, and increased trust and acceptance. Main disadvantages/threats for the end users are overdimensioning of civil constructions and supplementary works and costs to the peripherals during replacement. Main disadvantages for the module suppliers seem to be the higher competition, lower profit margins, and a limitation for innovative module producers to enter the market. From the technical point of view, the analysis showed that a standardization process common for both flat-sheet and hollow-fiber membranes/modules would not be realistic. In order to achieve interchangeability of filtration modules, not only should the prospect of pure dimensional standards for the module be considered, but also the design and mode of operation of the peripheral components, such as the filtration tank, pumps, blowers, and pretreatment should be borne in mind. More than 30 technical factors hampering or interfering with a standardization process were identified and quantified, and their relative potential for affecting the possible outcome was evaluated. For instance, four factors were grouped as the extremely high hindering factors: module dimensions, filtration tank dimensions, specific permeate production capacity, and specific coarse-bubble aeration demand. These factors are mainly the result of a completely different geometry and design of the filtration module and discussions for the standardization of MBR filtration systems should in essence focus on these factors. For each category, more or less the same number of obstacles lies ahead. Nevertheless, the nature of some of these obstacles or points of attention can be different. Some factors are specifically important for FS modules (e.g., flushing of air-supply pipes and design of a permeatecollection tank), and others for HF modules (e.g., type of prescreening, whether gravity filtration or any other type).

4.16.5.5.2 Standardization of MBR characterization methods The survey conducted by De Wilde et al. (2008) also revealed the respondents’ consensus in general on the positive impact of harmonization of membrane-acceptance tests at module delivery and monitoring methods on municipal MBR market growth. Some important parameters, for which a common definition and measurement protocol could be helpful, are mentioned below:

•

clearly defined and harmonized parameters to monitor membrane fouling, integrity, and aging;

606

• • • • • • • • •

Membrane Biological Reactors

a common definition of membrane lifetime for the guarantee clause; determination/definition of flux (operation and nominal design); common definition for sustainable peak hydraulic load; harmonized tests to check membrane performances over a defined period and under specific conditions; characterization method for membrane acceptance at module delivery; minimum requirements and technical methods to check membrane performance at plant commissioning; monitoring methods of normalized permeability in clear water, permeability in sludge, transmembrane pressure, and fouling rate; monitoring methods of sustainable flux and maximum flux; and operating conditions (biology and filtration systems) for warranty clauses.

It is interesting to note that, most of the newcomers in the market are developing their systems so that they can easily replace the products of the two main suppliers (Zenon–GE and Kubota). A standardization process driven by the end users could accelerate this evolution and contribute to the market development (Lesjean and Huisjes, 2008). Pearce (2008a, 2008b, 2008c, 2008d) also pointed out that, although the dimensions of the relatively newer Puron products are not identical to Zenon’s ZW 500d or MRE’s SADF, the elements are similar, and cassettes made from the elements could be used interchangeably. This begins to introduce retrofit possibilities into what hasuntil now been a fragmented market with no standardization.

4.16.6 Future Vision In addition to the alleviation of the technology bottlenecks illustrated in this chapter, a radical shift from the conventional concept of advanced wastewater treatment is deemed

Urine separation is also worthwhile to be considered

4.16.7 Conclusion MBR is a physicobiological hybrid process. The membrane provides a physical barrier for hygienically safe and clean water with the help of microbial–ecological treatment that can achieve good public acceptance. It is also well recognized by the experts that the clear membrane permeate makes post treatment easy; then, a variety of hybrid systems having the MBR as the core can be considered depending on the specific quality requirements of the reclaimed water . These advantages

A large amount of diluted organic wastewater (graywater)

To co-generation system A small amount of highstrength organic waste kitchen waste disposer-wastewater and toilet flushing)

imperative. In the context of sustainable water system, the advanced treatment must couple technologies to produce water of the required quality and realize material conversion from waste as well. The required quality does not always mean high quality. The quality comes from necessity. Membrane technology has the potential to be an on-demand quality provider just by separation. The conversion mainly comes from the biological reaction in the MBR. Three aspects of a sustainable society, namely, the low carbon society, sound material cycle society, and ecological society, are notable. From the point of view of sustainable water system, the advanced wastewater-treatment processes can be classified into the categories of energy saving (or productive), material productive, and ecologically oriented. The MBR technology might match more with the first two. However, present MBR technologies are still large energy consumers. Next-generation MBRs need to be developed to reduce the significant aeration requirement (by compact module design and sludge-concentration control techniques) and recover energy (e.g., by adding other organic wastes and combining anaerobic digestion for methane recovery). In line with the proposed definition of advanced treatment, the notion needs to be changed from organic wastewater treatment to water/biomass production by developing next-generation MBRs where the membrane acts as a separator of water and biomass and biomass is utilized for energy production. The concept is illustrated in Figures 19 and 20.

Anaerobic pretreatment Pretreatment Methane production

Biomass production from liquid organic waste (*)

Aerobic MBR

(*)

(A very small amount of residue) • Renewable energy utilization • IT-based maintenance service system • User participation in monitoring

(*) N,P recovery option

Figure 19 Next-generation MBR system: anaerobic combination for on-site small-scale advanced treatment.

Safe effluent

Membrane Biological Reactors

W.W.

Solid−liquid Solid-liquid separation

Solid concentration/ concentration/ Anoxic anoxic reaction reaction

Biosorption/ membrane separation/ aerobic reaction

607

Safe reclaimed water

Energy and/or material recovery process Other than biogas production, physicochemical treatments are also candidates for energy recovery, for example, supercritical water gasification of sludge−water mixture where the biomass sludge is utilized as energy source to produce hydrogen from water molecules (coupling clean energy production). Figure 20 Next-generation MBR system: renovation of existing wastewater-treatment plants.

make MBR a good device in water reclamation and/or advanced wastewater treatment. The continued push toward stricter discharge standards, increased requirement for water reuse, and greater than before urbanization and land limitations fuel the use of MBRs. However, there is room for improvement to utilize the potential of the MBR fully. The challenges will center on energy saving, ease of operation, simplified membrane cleaning and replacement strategies, and peak-flow management. The international adventure on R&D of MBR technologies continues.

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4.17 Anaerobic Processes DJ Batstone and PD Jensen, The University of Queensland, Brisbane, QLD, Australia & 2011 Elsevier B.V. All rights reserved.

4.17.1 4.17.1.1 4.17.1.1.1 4.17.1.1.2 4.17.1.1.3 4.17.1.1.4 4.17.1.2 4.17.1.3 4.17.1.4 4.17.1.5 4.17.2 4.17.2.1 4.17.2.1.1 4.17.2.1.2 4.17.2.1.3 4.17.2.1.4 4.17.2.1.5 4.17.2.2 4.17.2.2.1 4.17.2.2.2 4.17.3 4.17.3.1 4.17.3.2 4.17.3.2.1 4.17.3.2.2 4.17.3.3 4.17.3.3.1 4.17.3.3.2 4.17.3.4 4.17.4 4.17.4.1 4.17.4.2 4.17.4.3 References

Anaerobic Process Fundamentals Anaerobic Conversion Processes Hydrolysis Fermentation/acidogenesis Acetogenesis and methanogenesis from hydrogen Aceticlastic methanogenesis Physicochemical Processes and pH Temperature Inhibition and Toxicity Rate-Limiting Steps Selection and Design of Anaerobic Technology Anaerobic Digester Technologies High-rate anaerobic digestion Anaerobic ponds Fully mixed liquid digester Plug-flow liquid digesters Solid phase (leach bed) Digester Selection and Design for Specific Applications Domestic and industrial wastewater Sewage solids and activated sludge biosolids Interpretation and Operation of Anaerobic Systems Evaluating and Determining Controlling Mechanisms Performance and Process Indicators High-rate anaerobic reactors Sludge digesters Evaluating Substrate and Microbial Properties Activity testing Biological methane potential testing Advanced Model-Based Analysis Future Applications of Anaerobic Digestion Sewage Treatment and Nutrient Removal Nutrient Recovery Future Applications in Energy Generation and Transport

4.17.1 Anaerobic Process Fundamentals Anaerobic digestion is the biological conversion by a complex microbial ecosystem of organic and occasionally inorganic substrates in the absence of an oxygen source. During the process, organic material is converted mainly to methane, carbon dioxide, and biomass. Nitrogen released from converted organics is in the form of ammonia. Anaerobic processes for wastewater treatment have advantages over aerobic treatment in that there are no power requirements for air supply, production of sludges requiring treatment and disposal is much lower, and the methane production can be used for energy production. Aerobic processes are catabolically more favorable, yielding approximately 10 times the energy, with a correspondingly higher microbial yield (Madigan et al., 2009). For this reason, yields used for mixed heterotrophic processes are of the order of

615 615 616 618 619 620 621 623 624 625 626 626 626 626 627 627 627 627 627 628 631 631 632 632 632 633 633 634 635 636 636 636 637 637

0.63 gCODX gCODS1 (Henze et al., 2000) as compared to 0.05–0.1 gCODX gCODS1 for anaerobic processes. COD is the chemical oxygen demand and is a measure of organics. In this case, gCODX represents the biomass generated (in grams COD), while gCODS represents the substrate consumed (Batstone et al., 2002). This lower microbial yield results in decreased operating costs. The lower yield generally implies that extended solid-retention times are required to avoid washout of active biomass. This can be done either in parallel with an increased liquid retention time, or by separation of liquid and solid-retention times. Operation, design, and interpretation of engineered anaerobic processes have greatly advanced over the last 20 years. This improvement is based on a very good understanding of underlying concepts, which has allowed implementation of technology such that it will stably and reliably operate without intervention. The process itself has (1) multiple microbial

615

616

Anaerobic Processes

steps, mediated by different organisms; (2) different steps that can be rate limiting under specific conditions; (3) interaction with the physicochemical system, particularly weak acid and base inhibition of microbial processes, and (4) highly nonlinear behavior, particularly with respect to pH regulation and inhibition. Therefore, application of anaerobic technology needs careful thought, especially to achieve an optimally engineered process for a specific application. Fortunately, understanding of the underlying microbial and chemical processes is very good, both in the scientific and in engineering sectors. Good understanding of fundamentals, as outlined in this section, has allowed the use of anaerobic technologies in a wide variety of applications, as outlined in Section 4.17.2.

The different microbial groups mediating each step have been well characterized, and are from phylogenetically defined regions. As examples, all methanogenic organisms discovered so far are archaea, while acidogens and acetogens are largely bacteria. Aceticlastic methanogens belong to one of the two specific genera: Methanosaeta or Methanosarcina. As shown in Figure 1, under different conditions, different steps can be rate limiting. Specifically, for particulate or slowly degradable materials, hydrolysis is rate limiting. Under conditions of stress, or where the primary substrate is rapidly degradable, aceticlastic methanogenesis is normally rate limiting. The first condition normally results in decreased performance as undegraded substrate is washed out, while the second condition results in elevated, effluent organic-acid concentrations.

4.17.1.1 Anaerobic Conversion Processes 4.17.1.1.1 Hydrolysis Anaerobic digestion proceeds through a series of parallel and sequential processes by a variety of consortia as represented in Figure 1 (Batstone et al., 2002; Pavlostathis and GiraldoGomez, 1991). In contrast to aerobic digestion, where oxygen is an external electron acceptor, gaseous and dissolved products (largely methane and carbon dioxide) have the same combined carbon-oxidation state as the primary substrates. Thus, anaerobic digestion is largely constrained by the need to find appropriate internal electron acceptors. When this is impossible, hydrogen ions or bicarbonate must be used as electron acceptors via anaerobic oxidation to produce hydrogen or formate. This introduces thermodynamic constraints that bring in obligate syntrophic relationships between the electron producer and the methanogenic electron consumer (Schink, 1997). It is conceptually correct and convenient to group complex organics into carbohydrates, proteins, and lipids, and their soluble analogs of sugars, amino acids, and long-chain fatty acids (LCFAs). Any mixed organic stream can be represented by these components, while preserving full information of mass, energy density (or COD), and nitrogen content (Nopens et al., 2009). Anaerobic digestion processes consist of four main steps:

• •

•

•

Hydrolysis is an enzyme-mediated extracellular step which solubilizes particulates and substrates that cannot be directly utilized by the anaerobic organisms. Acidogenesis or fermentation is the conversion of soluble substrates such as amino acids and sugars, which can be converted largely without an external electron acceptor. The products are largely organic acids and alcohols. Syntrophic acetogenesis is the degradation of fermentation products to acetate using hydrogen ions or bicarbonate as an external electron acceptor. This process is coupled with hydrogen or formate utilizing methanogenesis, which maintains a low hydrogen or formate concentration. Acetoclastic methanogenesis is the cleavage of acetate to methane and carbon dioxide.

Processes such as homoacetogenesis (conversion of hydrogen and carbon dioxide to acetate), and its reverse, acetate oxidation to hydrogen and carbon dioxide, have not been included in Figure 1, but can be important in specific circumstances as outlined further in this chapter.

While the formal definition of hydrolysis is much stricter, as a digestion component, hydrolysis is a term that is used to refer to solubilization of complex particulate materials. The material can be regarded either as a mixture of the basic components (carbohydrates, proteins, and fats), or as a composite compound (e.g., homogeneous material such as activated sludge and yeast). Separate classification and analysis of composite material as a separate input was proposed in the International Water Association (IWA) Anaerobic Digestion Model No. 1 (Batstone et al., 2002), but this was found to be cumbersome, especially when representing both composites and primary aggregates (e.g., waste-activated sludge (WAS) and primary sludges), and the current trend is to represent all feed materials as a combination of carbohydrates, proteins, and fats (Nopens et al., 2009). There are three main pathways for enzymatic hydrolysis. 1. The organisms excrete enzymes into the bulk liquid where it adsorbs onto a particle or reacts with a soluble substrate (Jain et al., 1992). 2. The organism attaches to the particle and secretes enzymes into the vicinity of the particle. The organism benefits from the soluble substrates being released (Vavilin et al., 1996). 3. The organism has an attached enzyme which may double up as a transport receptor to the interior of the cell (Tong and McCarty, 1991). This method requires the organism to adsorb onto the surface of the particle. The actual mechanism used depends heavily on the nature of the material, reactor hydraulics, and solid concentration, but forms 1 and 2 in the list are variations on the same mechanism, and are the principal forms considered here. Steps in extracellular enzymatic hydrolysis include (Figure 2): 1.

2.

4.

Production of enzyme – production rate can decrease when there is excessive soluble substrate available (Ramsay, 1997). Steps 2, 3, and 6 are transport processes, which can be limited due to large particles, or in solid-phase systems due to inadequate carrier liquid. Adsorption processes that are limited by surface area.

Anaerobic Processes 5. 7.

•

Reaction rates that are limited by surface area and enzyme concentrations. Deactivation can be excessive when away from optimal temperature and pH.

•

While there have been complex models that include all of these functions (e.g., Humphrey, 1979), in practice, it is very difficult to properly validate these models, and the most commonly used model is the first-order one. The use of first-order models has been justified as ‘‘an empirical expression that reflects the cumulative effect of all the microscopic processes occurringy’’ (Eastman and Ferguson, 1981). First order (or slightly more complex) has also been found to be just as effective as more complex models (Vavilin et al., 1996). Hydrolysis commonly becomes rate limiting when

•

In a continuous mixed digester, without retained solids, hydraulic-loading rate becomes too high (there is not enough time to hydrolyze the solids). Mass-loading rate is generally not an issue, and higher concentrations allow higher loading rates. Normally, a minimum of 9 days of hydraulic-retention time is required for any significant degradation (see Section 4.17.2.2.2). Mixed carbohydrate feeds are among the slowest to degrade.

In a batch system, there is insufficient batch time. Batch digesters have a higher volumetric efficiency, due to kinetic considerations. In a plug-flow system, there is insufficient reactor volume. Plug-flow digesters are highly efficient on a volumetric basis. Time of contact with the active biomass can also be an issue if the system is not effectively mixed at the inlet.

Particularly for mixed systems (the most common form of digester), where hydrolysis is rate limiting, the hydrolysis rate determines the size of the digester. We now discuss the hydrolysis of various feed materials: 1. Hydrolysis of WAS. There has been a large amount of work investigating the rate and extent of WAS digestion, but only limited analysis of the actual mechanisms of cell solubilization as specific to activated sludge. It is a complex process, involving lysis of the cell, and subsequent degradation of both soluble and particulate cellular components (Aquino et al., 2008; Madigan et al., 2009). This is further complicated by the issue that microbial cells are naturally resistant to cell lysis by other cells, and that the cells are in flocs, with varying sizes. Degradability and hydrolysis rate have been extensively analyzed. As mentioned earlier, activated-sludge hydrolysis

Particulate carbohydrates, proteins, and lipids

Acidogens produce enzymes

Hydrolysis

Sugars and amino acids Fermentation acidogenesis

CO2 Alcohols and Long-chain organic acids fatty acids

NH3

Acetogenesis CO2

CO2 Hydrogen

Acetic acid

Hydrogenotrophic methanogenesis

Aceticlastic methanogenesis

Methane Figure 1 Key steps in anaerobic digestion processes.

617

Methane

CO2

May be rate limiting

618

Anaerobic Processes

4. Adsorption of enzyme onto surface

6. Transport of product to bulk

5. Reaction

2. Transport to bulk or local environment 1. Production of enzyme

7. Deactivation of enzyme 3. Diffusion from bulk to particle

Figure 2 Steps in enzymatic hydrolysis.

is an extremely complicated physical and chemical process that is, of necessity, represented as a first-order process (Eastman and Ferguson, 1981). Practical batch testing indicates that this complex material is well represented by first-order kinetics (Dwyer et al., 2008), while primary sludge (for example) has a far more complex kinetic profile, due to the presence of multiple primary substrates (Yasui et al., 2008). Extensive analysis also indicates that for untreated activated sludge, hydrolysis rates are relatively constant at approximately 0.1 d1 (Batstone et al., 2002; Eastman and Ferguson, 1981; Ge et al., 2010; Pavlostathis and Giraldo-Gomez, 1991). The degradability of activated sludge can be entirely related back to upstream sludge age, and longer sludge-age material will be less degradable (i.e., have a higher inert fraction; Ekama et al., 2007; Gossett and Belser, 1982). It is now widely accepted that material that is undegradable under aerobic conditions, is also largely undegradable under anaerobic conditions (Park et al., 2006; Speece, 2008). Therefore, material that is degradable under anaerobic conditions can be numerically calculated from the degradable fraction of the active aerobic biomass in the WAS (Ekama et al., 2007; Nopens et al., 2009). There is a wide range of pretreatment methods to increase sludgedegradability extent and rate (Aquino et al., 2008), and these are discussed further in the Section 4.17.2 of this chapter. 2. Hydrolysis of carbohydrates. Carbohydrates mainly originate directly or indirectly from plants. Generally, plant material is a mixture of cellulose (25–60%), hemicellulose (15– 30%), and lignin (15–20%) (Tong and McCarty, 1991). Straw, a commonly used feed material, consists of 70% cellulose and hemicellulose, 8% lignins, 15% mineral solids, and 7% other organic compounds (Hashimoto, 1986). The remainder is tannins, soluble sugars, and ash. The first two components are very similar and are digested anaerobically via similar mechanisms. Tong and McCarty (1991) list typical chemical compositions of lignocellulosic materials. Cellulose is made up of linear chains of D-glucose units. Hemicellulose is a branched polymer comprising several natural minor sugars. Ease of degradation depends on the nature (crystalline or amorphous) and chain length. Hemicellulose is of a shorter length (200 units), while cellulose can have a chain length of up to 10 000 units. Lignin is a dense three-dimensional polymer of aromatic molecules. It is hydrophobic and is linked by carbon as well as ether bonds. Conversion of lignin by anaerobic bacteria is unknown,

H

R

O

H

R O

H R O

N

C

C

N

C

N

H

H

C

C

C

H

Figure 3 Protein chain with amino acids linked by amide groups.

and high lignin contents (together with the presence of crystalline cellulose) generally restrict or prevent hydrolysis of the underlying cellulosic material (Yang et al., 2009). 3. Hydrolysis of proteins. Proteins are natural polymers of different amino acids joined together by peptide (amide) bonds. The backbone of a protein is a repeating sequence of one nitrogen and two carbon atoms (Figure 3). There are 20 amino acids found in nature. These are differentiated by the R group, which defines the function of the amino acid. A protein has three structural components: • Amino-acid composition and sequence (primary structure). • The three-dimensional shape as set by bond angles and hydrogen bonds forms a helical shape in complex proteins. This is the secondary structure. • The tertiary structure defines the macromolecular shape as set by bonding between di-sulfide groups and to a lesser extent, other inter-R bonding. There are two major areas of importance for hydrolysis processes. Amino-acid composition (primary structure) affects the products. The tertiary structure defines the proteins as either fibrous or globular. Fibrous proteins are structural materials such as keratin, which is protective, and collagen, which is connective. Globular proteins are often chemically functional and act as enzymes, hormones, transport proteins, or storage proteins. Hydrolysis of proteins can be rate limiting in the overall process, depending on ease of structure degradation (Pavlostathis and Giraldo-Gomez, 1991). Protein structure is one of the main factors affecting the rate of hydrolysis. Globular proteins are rapidly hydrolyzable, while fibrous proteins are difficult to hydrolyze (McInerney, 1988). In general, all proteins apart from the most rigid type of keratin (such as the outer layer of hair and fingernails) are hydrolyzable (Figure 4). There are three main groups of proteases: serine, metallo, and acid proteases which have alkaline (8–11),

Anaerobic Processes

619

The 1,3-specific lipases can only act at the outside bonds of the triglycerides, yielding 1,2-diacylglycerols and 2-monoacylglycerols. These glyceride esters are unstable and undergo acyl migration to 1,3-diacylglycerol and 1-monoacylglycerol. Subsequently, these can be degraded further by the 1,3-specific lipase to glycerol and free fatty acids. Fatty-acid-specific lipases catalyze the removal of a specific fatty acid, preferentially removing cis-D9-monounsaturated fatty acids. Other fatty acids are degraded very slowly, especially those containing an additional double bond between D1 and D9. Figure 4 Cow hair from an anaerobic reactor showing intact keratin (A) compared with degradation of interior by anaerobic organisms (B). Photograph by Dr Damien Batstone.

CH2 OH CH

OH

CH2-O-fatty acid CH-O- fatty acid

CH2 OH

CH2-O-fatty acid

Glycerol

Triglyceride

Figure 5 Glycerol and triglycerides.

neutral (6–8), and acidic (4–6) pH optimums, respectively (Ramsay, 1997). Enzyme production may be suppressed when readably biodegradable substrates such as glucose or amino acids are supplied (Patterson-Curtis and Johnson, 1989; Ramsay, 1997). 4. Hydrolysis of lipids. Lipids are glycerol bonded to LCFAs, alcohols, and other groups by an ester or ether linkage (Madigan et al., 2009). Fats and oils have all the alcohol groups esterified with fatty acids as shown in Figure 5 and these form the bulk of glyceridic material in mixed oils and fat with other glyceridic compounds, usually a result of processing. Hydrolysis is catalyzed by LCFA ester hydrolases, called lipases. These act at the lipid–water interface in enzymatic hydrolysis to degrade the insoluble reactant to soluble products. There is little work on degradation of lipids in anaerobic environments when compared with that on carbohydrate and protein substrates. Most of this has been focused on the rumen, reviewed by (McInerney, 1988). One particular characteristic of lipases is increased activity with insoluble rather than soluble lipids (Martinelle and Hult, 1994), indicating that the activity of lipases increases greatly when the concentration of triglycerides reaches saturation and forms a second phase. The lipases are adsorbed at the interface. As there is an adsorption mechanism, combined reaction and adsorption rate may be dependent on the surface area of the insoluble triglycerides. Bacterial lipases can be divided into three main types: nonspecific lipases, 1,3-specific lipases, and fatty-acid-specific lipases (Finnerty, 1988). Nonspecific lipases can hydrolyze any fatty acid triglyceride regardless of structure, acting at any of the fatty acids. These can completely hydrolyze the ester bonds acting equally at all alkyl sites.

4.17.1.1.2 Fermentation/acidogenesis Fermentation and acidogenesis refer to the same process of conversion of sugars and amino acids to simpler compounds (mostly acids and alcohols). Fermentation is commonly applied in biotechnology processes where the focus is on the product. Acidogenesis is applied in wastewater processes. In our opinion, fermentation is a more precise and preferred term. Fermentation is defined as the conversion of organics without an obligate external electron acceptor to produce both reduced and oxidized products. The two major groups of compounds subject to fermentation under anaerobic conditions are sugars and amino acids, which are discussed next. Fermentation of sugars. Anaerobic fermentation from sugars is likely the most widely applied biotechnology process worldwide. It is used to produce food products, renewable fuels, pharmaceuticals, and industrial chemicals. It is currently in focus for production of biofuels (e.g., ethanol and butanol). Historically, fermentation has been carried out by pure or specialized microbial cultures, which are constrained to produce specific products from sugars, based on their physiology and genetic capabilities. In anaerobic digestion processes, fermentation is mediated by mixed culture, and a wide range of potential products can be formed. Sugars ferment via the Embden–Meyerhof–Parnas (EMP) pathway to pyruvate, and subsequently to C3 products (propionate or lactate), or C2–C6 products via acetyl-CoA (Madigan et al., 2009; Figure 6). The most common products are shown in Figure 6, as determined in practical mixedculture fermentation tests (Ren et al., 1997; Temudo et al., 2008). Smaller amounts of additional compounds, including metabolic intermediates, are also often detected. Actual product mixes are regulated by a number of environmental conditions, including pH, gas-phase hydrogen concentration, temperature, and biomass retention time. It is reasonable to assume that hydrogen-rich reactions (e.g., production of acetate) would be enhanced at low hydrogen concentrations and production of alcohols enhanced at low pH (Ren et al., 1997). Regulation of mixed-culture fermentation is exciting, as it offers the possibility of producing fuels and industrial chemicals directly from raw feedstocks such as crop residues and straw. While a number of models have been proposed (Costello et al., 1991; Mosey, 1983; Rodrı´guez et al., 2006), none of these can effectively describe the mixture of products under dynamic conditions. The most promising current approach evaluates the thermodynamic driving forces under varying conditions (Rodrı´guez et al., 2006).

620

Anaerobic Processes 1glucose

4e−

2pyruvate

4e−

4e−

8e−

2lactate 2propionate

2CO 2

2e−

2e− 2acetyl-CoA 2e−=H

2

6e−

2acetate

2CO 2 2e−

2e− 1butyrate

2ethanol

Figure 6 Major products from C6 monosaccharide fermentation. Excess electrons are removed as hydrogen as shown.

Fermentation of amino acids. There are 20 common amino acids, which can be divided based on the R group (Figure 3) into the following groups:

• • • • • • •

Alkyl R groups: glycine, alanine, valine, leucine, and isoleucine. Alcohol R groups: serine and threonine. Carboxyl R groups: Aspartic and glutamic acids. Nitrogen-containing R groups: lysine, arginine, and histidine. Sulfur-containing R groups: cysteine and methionine. Aromatic R groups: phenylalanine, tyrosine, and tryptophan. Proline, which forms an amide ring with the amide group.

Fermentation of amino acids can either be by direct oxidation, or by fermentation in pairs along a coupled pathway. The coupled pathway is termed ‘Stickland digestion’, and it has several properties: a. Amino acids are degraded as a pair. b. One of the pair of amino acids acts as an electron acceptor (i.e., it is reduced), and the other as the electron donor (i.e., it is oxidized). c. The donor amino acid is oxidized to NH3, CO2, and a carboxylic acid with a chain length one carbon atom shorter than the original donor amino acid. d. The acceptor amino acid is reduced to NH3, and a carboxylic acid with a chain length equal to the original amino acid. e. Amino acids can act as an electron acceptor, an electron donor, or as both, but there is no rule based on the R chain. f. In general, there is a 10% shortfall in electron-acceptor amino acids in commonly found proteins. Due to the properties of Stickland reactions, and because the amino-acid compositions of most commonly encountered proteins are known, it is possible to estimate the organic acids

produced from a given protein (Ramsay and Pullammanappallil, 2001). This, however, assumes that Stickland reactions are used. If the hydrogen concentration is low, uncoupled oxidation of amino acids can occur (Stams, 1994). Uncoupled degradation can also result in a higher energy yield and, as in Stickland reactions, energy is only produced from the oxidation reaction (during regeneration of carboxyl-CoA). Figure 7 shows coupled and oxidation reactions for alanine (which is always a donor acid), and glycine (which is always an acceptor). The degradation of alanine is the same in both cases, as it is oxidized during the coupled reaction. The only change is that electrons are wasted into hydrogen ions, rather than glycine.

4.17.1.1.3 Acetogenesis and methanogenesis from hydrogen Organic acids and alcohols are converted to acetate (oddchained organics to propionate also) by anaerobic oxidation. This process utilizes hydrogen ions or bicarbonate ions to produce hydrogen gas or formate, respectively. The thermodynamics of the oxidation reaction require that the electronacceptor end product (hydrogen or formate) be maintained at a very low concentration, and, hence acetogenesis is obligately linked to a hydrogen-utilizing reaction, such as methanogenesis (Batstone et al., 2006b; Boone et al., 1989). Hence, interspecies electron transfer (IET), in which hydrogen is the electron carrier, is vital to the growth of both microbes. Indeed, the only the syntrophic association is obligate, and other forms of electron carriers are possible – even direct electron transfer via microbial nanowires (Reguera et al., 2005). In anaerobic biofilms, the oxidizing organism is normally a bacteria, while the methanogen is an archaea, and can be directly observed in close relationship (Figure 8). Hydrogen (plus bicarbonate) and formate are functionally, and thermodynamically, very similar, with hydrogen having a higher diffusivity, and formate having a higher solubility. Advanced modeling has indicated that their microscopic characteristics will be similar in either of the electron carriers (Batstone et al., 2006b). In addition, the free energy of conversion between formate and hydrogen is relatively low (5.7 kJ mol1), and the two may exist in enzyme-assisted equilibrium (Thiele and Zeikus, 1988). Therefore, hydrogen can be regarded as the representative electron carrier. The thermodynamics of the reactions can be assessed by a free-energy calculation. For the reaction a A þ b B3c C þ d D (with stoichiometry a, b, c, and d), the adjusted free energy of reaction is (Madigan et al., 2009)

DG0 ¼ DG00 þ RT ln

½C c  ½D d ½A a  ½B b

ð1Þ

where DG0 is the adjusted free energy of reaction, DG0 is the standard free energy of reaction, and ½C c ½D d =½A a ½B b is the reaction quotient, or concentration of products divided by concentration of reactants. The adjusted free energy DG0 must be less than zero for the reaction to proceed. Based on standard concentrations in a digester (0.001 M organic acids and 0.1 M bicarbonate), the hydrogen thresholds for different acetogenic reactions can be calculated. These are shown in Table 1. These are thermodynamic thresholds, and the actual

Anaerobic Processes Coupled

Uncoupled

C

COO− Alanine (donor)

NH 2 H3C

C

Oxidation

Reduction

Oxidation H H2C

Glycine (acceptor) 2

H2C

COO−

Alanine (donor)

NH2

2e−

2e− COO−

O

Pyruvate, NH3

Pyruvate, NH3 CO2

Acetate H3C COO−

2e−

CO2

2e−

Acetyl CoA

Acetyl CoA

Acetyl phosphate

H3C Alanine

H2 2H+ H2

Energy Acetate

Acetate

Acetate + CO2 + NH3 + 4H

2H+

Acetyl phosphate

Acetate H3C COO− Energy

COO−

621

2glycine + 4H

2acetate + 2NH3

Alanine

Acetate + CO2 + NH3 + 2H2

Figure 7 Coupled and uncoupled conversion of alanine.

Figure 8 Syntrophic community of bacteria and archaea (anaerobic granule), engaged in acetogenesis and methanogenesis. Bar is 500 nm.

levels are higher. This indicates there is only a narrow region of hydrogen concentrations where these reactions may proceed. The mechanism of the reaction can be explained thus. Oxidation of butyrate and larger organic acids (C4þ) is by b-oxidation, a process in which larger organic acids are sequentially oxidized in a cyclic process. Two carbon atoms are removed as acetyl-CoA per cycle, and energy is recovered by substrate-level phosphorylation (Ratledge, 1994). The cycle continues until only acetyl, or propionyl-CoA, remains. This is converted directly to acetate or propionate. Unsaturated bonds are reduced directly with hydrogen (with the unsaturated bond as electron acceptor), in a favorable reaction (Ratledge, 1994). While common organisms oxidize a range of C4þ fatty

acids (McInerney et al., 1981; Roy et al., 1985), the kinetics, particularly of branched chain fatty acids, can vary substantially (Batstone et al., 2003). Propionate conversion is by a limited number of specialized organisms, with the carboxyl group being converted to carbonate, and the two methyl groups being randomly converted to either the methyl or the carboxyl group on the final acetate product (de Bok et al., 2004; Stams and Plugge, 1994). Ethanol was the first observed syntrophic methanogenic culture (Bryant et al., 1967), and due to favorable thermodynamics, it was found to accumulate substantial hydrogen before thermodynamic limitations set in. Degradation was found to be via acetyl-CoA. The major pathway of hydrogen or formate removal in mesophilic high-rate reactors is methanogenesis. This occurs by activation of a carbon dioxide molecule or formate molecule and successive hydrogenation of this complex. As a final step, methyl-CoM is formed and this is reduced to methane with a yield of 1 (adenosine triphosphate (ATP) mol1 methane formed. None of the methanogenic archaea can utilize energy from substrate-level phosphorylation and ATP is probably generated from a proton-motive force (Boone et al., 1993). While methanogenesis is the major sink for electrons in anaerobic systems, there are a number of alternative sinks, including nitrate reduction, sulfate reduction, iron reduction, and homoacetogenesis (formation of acetate from hydrogen). Alternative electron acceptors, such as nitrate, sulfate, and Fe3þ, are preferred substrates to hydrogen ions. Homoacetogenesis can occur whenever there is elevated hydrogen, but is commonly observed under lower temperatures, where the thermodynamics of this reaction are more favorable.

4.17.1.1.4 Aceticlastic methanogenesis This is the major methanogenic step, where acetate is cleaved to methane and carbon dioxide. Only a limited number

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Anaerobic Processes

Table 1

Acetogenic reactions and hydrogen thresholdsa

Reactant

Reaction

H2 threshold (Pa)

Propionate

CH3 CH2 COO þ 3H2 O-CH3 COO þ 3H2 þ HCO3  þ Hþ 



10

þ

Butyrate

CH3CH2CH2COO þ 2H2O-2CH3COO þ 2H2 þ H

100

Valerate

CH3CH2CH2CH2COO þ 2H2O -CH3CH2COO þ CH3COO þ 2H2 þ Hþ

100



þ

Ethanol

CH3CH2OH þ H2O-CH3COO þ 2H2 þ H

Palmitate

CH3(CH2)14COO þ 14H2O -8CH3COO þ 14H2 þ 7H

H2, HCO 3

4H2 þ HCO3 þ H -CH4 þ 3H2 O





1000



þ

10

þ

0.2

a

For acetogenic reactions, concentrations must be below the threshold levels. For the last reaction, concentration must be above the threshold.

of methanogens within the archaea have been identified that are capable of cleaving acetate:

• •

Members from the genus Methanosaeta within Methanosaetaceae – these are obligate acetate cleavers. Members from the genus Methanosarcina within Methanosarcinaceae. Members of this genus can also utilize hydrogen, CO2, and methylated C1 compounds (Ferry, 1993).

Methanosaeta is more pH, and ammonia, sensitive and dominates at below 103 M acetate (Zinder, 1993), while Methanosarcina is found outside these conditions, generally in high-ammonia conditions where there is also higher-effluent organic acids (Karakashev et al., 2005). Recent work has indicated that Methanosarcina may, instead of cleaving acetate to hydrogen and carbon dioxide, oxidize acetate to hydrogen, with subsequent reduction by a syntrophic methanogenic partner to methane (Karakashev et al., 2006) (Table 2). Therefore, under these conditions, Methanosarcina does not act as a methanogen, but simply provides electrons to another methanogen via hydrogen or an alternative electron carrier.

4.17.1.2 Physicochemical Processes and pH Physicochemical processes are those that are not biologically mediated, and hence occur spontaneously in water systems. This research field is generally referred to as aquatic chemistry (Stumm and Morgan, 1996). Some important physicochemical reactions that occur in anaerobic digesters are shown in Figure 9, and include 1. Association and dissociation of weak acids and bases such as water, organic acids, carbon dioxide, and ammonia – this is a rapid process. 2. Gas–liquid transfer of carbon dioxide, methane, hydrogen, and hydrogen sulfide – this is a medium-rate process. 3. Metal-ion precipitation to form solid precipitates – this is a medium-slow process. Unlike biochemical reactions, almost all physicochemical reactions are spontaneous and reversible. Therefore, equilibrium calculations are an important issue to assess physicochemical systems. The physicochemical state is most commonly expressed by the pH, or negative log of the hydrogen-ion concentration (–log10[Hþ]). It expresses the net balance of strong

Table 2

Acetoclastic methanogenesis

Substrate

Reactiona

Acetate (cleavage)

CH3COO þ H2O - CH4 þ HCO 3

þ Acetate (oxidation) CH3COO þ 4H2O - 2HCO 3 þ 4H2 þ H a

DG0  31 þ 105

Reactions for coupled acids are shown. DG0 was calculated for reaction at pH 7.

and weak acids present in the system, but not their individual concentrations or strength. The physicochemical and biochemical reaction system are strongly linked in anaerobic digesters, through the following mechanisms:

• • • •

•

Biochemical reactions produce weak acids and bases, including organic acids, LCFAs, ammonia, and carbon dioxide. Biochemical reactions produce gases. Low pH inhibits biological activity through disruption of homeostasis and denaturing of enzymes, though specialized organisms can operate at extremes. The free form of many weak acids and bases, particularly ammonia, organic acids, and hydrogen sulfide, is inhibitory to organisms (Batstone et al., 2002). This means that not only does the total concentration of the parent compounds (e.g., inorganic nitrogen, sulfides, etc.) have an impact, but the pH also has an influence by determining the concentration of the inhibitory form (e.g., ammonia and hydrogen sulfide). Weak acids and bases buffer around their characteristic acidity coefficient (pKa, see further). This means that bicarbonate, in particular, resists pH changes around 6.3, since that is its pKa.

Of the three key classes of physicochemical reactions – acid– base, liquid–gas, and metal-ion precipitation – only the first two have been extensively addressed in anaerobic digestion models (Batstone et al., 2002). This is a clear limitation in anaerobic-digestion modeling, since the behavior of more concentrated systems, and particularly the behavior of solids cannot be effectively described without describing metal-ion precipitation (Batstone, 2009). Acid–base reactions are characteristically rapid, and can hence be described by the equilibrium equation. For the

Anaerobic Processes

CO2

623

H2 Gas

H2O

CH4

Composites

Biochemical

Liquid Gas Inerts

Death/decay

Carbohydrates

Proteins

MS

AA +

Lipids

NH3

NH4

VFA−, HCO3−, NH4+

HVFA, CO2, NH3 HCO3−

Ca2+ Growth

H2

HAc

Microbes

−

CO2

CO32−

Gas

CH4

H2O

CaCO3

Physicochemical Figure 9 Biochemical (vertical) and physicochemical processes (horizontal) in an anaerobic digester. AA, amino acids; MS, monosaccharides; HVFA, associated organic acids; VFA, dissociated organic acids; HAc, acetic acid; Ac, acetate. Adapted from Batstone DJ, Keller J, Angelidaki I, et al. (2002) Anaerobic Digestion Model No. 1 (ADM1), IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes. London: IWA Publishing.

reaction acid 2 base þ Hþ, the equilibrium relationship is

½Base½H þ  ¼ Ka ½Acid

ð2Þ

where Ka is the acidity coefficient, and is often expressed as pKa ¼ –log10Ka, in a similar way to pH. Most analytical methods measure or report the total species concentration:

½Totalmeas ¼ ½Acid þ ½Base

ð3Þ

These two equations can be combined to give either the acid concentration, or base concentration, as a function of the measured concentration, the pH, and the acidity constant:

Ka ½Totalmeas Ka þ ½Hþ 

ð4Þ

½H þ ½Totalmeas Ka þ ½Hþ 

ð5Þ

½Base ¼

½Acid ¼

The equation system is complicated when there are three reactive species (e.g., the inorganic carbon system containing

CO2, HCO3  , and CO3 2 ), or more (e.g., the phosphorous system containing four reactive species). Equations (4) and (5) are commonly used to produce acid–base speciation diagrams. An example is shown for the inorganic nitrogen acid– base system in Figure 10. This demonstrates the relationship between pKa, pH, and fractionation. The reason why fractionation is practically important is that many acids and bases are mainly inhibitory in their free or uncharged form. Ammonia is free as the base (NH3), which is why ammonia inhibition increases at elevated pH levels (discussed later in the chapter). Other acid/base pairs of importance are the organic acids (most volatile fatty acids (VFAs) have pKa levels of 4.6– 4.8), CO2/HCO3  pair (pKa ¼ 6.35), H2S/HS (pKa ¼ 7.05), NH4 þ /NH3 (pKa ¼ 9.25), and HCO3  =CO3 2 , (pKa ¼ 10.3) (Batstone et al., 2002). Gas–liquid transfer is normally described by equilibriumdriven dynamic gas–liquid transfer. Hydrogen and methane are relatively insoluble, while carbon dioxide and hydrogen sulfide are relatively soluble. This means that the latter two compounds have a substantial impact on the liquid system. Metal-ion precipitation is also generally described by equilibrium-driven dynamic relationships. The actual mechanism of crystallization is complex and includes a number

624

Anaerobic Processes 1 Fraction as acid (NH4+)

0.9 0.8

Fraction as base (NH3)

Fraction

0.7 0.6 0.5 0.4 0.3 0.2 0.1

pKa = 9.3

0 8

8.5

9

9.5

10

pH Figure 10 Inorganic nitrogen acid–base speciation vs. pH. Note that the total inorganic nitrogen is equally split between ammonia and ammonium at a pH of 9.3.

of different factors, including the presence of seed, presence of confounding compounds, such as inhibitors and promoters, and solution activity. While simple first-order relationships have been used in complex systems, these are normally ineffective (Batstone, 2009). The basic solubility of a precipitant is described by equilibrium. For the reaction aAbþ þ bBa–2AaBb, the equilibrium relationship is

KSP ¼ ½Aa ½Bb

ð6Þ

where KSP is the equilibrium constant, and is normally referred to as the solubility product. For convenience, it is also often represented as pKSP ¼  log10KSP. The higher the pKSP, the less soluble the compound. Examples include CaCO3 (KSP ¼ 8.25), FeS (KSP ¼ 18), and CaOH2 (KSP ¼ 5.3). Note that it is the pH-adjusted anion concentration that is to be used in Equation (6), and, therefore, CaCO3 precipitation is driven by the concentration of CO3 2 . This means that pH has a strong impact on metal-ion precipitation. Until recently, physicochemical models within anaerobic digestion models have been relatively simple, mainly consisting of acid–base equilibrium equations, a charge balance to determine hydrogen-ion concentration, and gas–liquid transfer (Batstone et al., 2002). More complex models have represented limited nonideality, including ion activity (Musvoto et al., 2000a), and precipitation (van Langeraak and Hamelers, 1997). Simpler models work well with dilute, ideal systems without metal-ion precipitation, but have poor predictive power in concentrated systems, or where precipitationaffected compounds exist. This has led to implementation of a more robust but more complex physicochemical framework for anaerobic digester systems (Batstone, 2009).

include 1. Reaction rates increase with increased temperature according to the Arrhenius equation (Siegrist et al., 2002). As a rule, anaerobic digesters are relatively sensitive to temperature, with temperatures below 30 1C causing a substantial loss in activity. 2. A rapid decrease in activity with abrupt temperature increases above the maximum (Van Lier et al., 1996). Normally temperature rises are maintained below 2 1C d1. 3. Decrease in microbial yields, and an increase in apparent saturation concentration (KS), with increased temperature (Van Lier et al., 1996) related to an increase in cell maintenance. 4. Shifts in reaction pathways due to changes in the free energy of reaction with temperature. This is particularly relevant for oxidative reactions, and acetate oxidation becomes more competitive as compared to aceticlastic methanogenesis at higher temperatures (Zinder and Koch, 1984), while the reverse reaction (homoacetogenesis from hydrogen and carbon dioxide) is more favorable at lower temperatures (Rebac et al., 1995). 5. Pathogen deactivation increases with temperature. These impacts occur across the temperature range, but operating modes have been split based on reactor operability and dominant microbial population into the following three temperature ranges:

• • •

Psychrophilic  10–30 1C. Mesophilic  30–40 1C. Thermophilic  40–70 1C.

4.17.1.3 Temperature

Psychrophilic conditions are largely environmental, while mesophilic and thermophilic conditions are largely in engineered systems. There are also a number of physicochemical impacts:

Temperature has a number of impacts on outputs and internal processes in anaerobic digesters, including both biochemical and physicochemical impacts. Biochemical impacts

1. Increased temperature causes decreased gas solubility. 2. Volumetric gas production increases with increased temperature due to thermal expansion.

Anaerobic Processes

3. A change in temperature changes the solubility of solids. This may increase or decrease depending on solid enthalpy of precipitation. 4. Gas transfer rates increase, due to increases in diffusivity. 5. Increased temperature increases the water-vapor fraction in the gas phase. 6. The acid–base pKa values change with temperature (generally decreases). The variation in this is enormous. Organic acid pKa is relatively unaffected by temperature, while ammonia pKa changes dramatically. 7. Liquid viscosity increases with increased temperature. This changes the energy required to pump and mix reactor contents. Overall, as temperature increases from mesophilic to thermophilic conditions, the combination of all of these impacts can be observed as follows:

• • • • •

•

Rates increase due to increased activity. This can be especially important in hydraulic limited systems. Effluent organic-acid levels increase due to increased maintenance and substrate-saturation levels. Gas quality drops as the water and carbon dioxide fractions increase. Gas production increases because of increased activity and thermal expansion. pH is normally relatively stable. It drops due to increased organic-acid concentrations and lower pKa values, but rises due to decreased CO2 solubility. The net effect can be an increase or decrease depending on the feed type and reactor performance. The system is more susceptible to ammonia inhibition, due to a decrease in ammonia pKa, and hence, there is a higher concentration of free ammonia (see next section).

4.17.1.4 Inhibition and Toxicity Speece (2008) uses two definitions within the area of general restriction of biological processes: ‘‘inhibition: an impairment of bacterial function’’ (p. 432) and ‘‘toxicity: an adverse effect (not necessarily lethal) on bacterial metabolism.’’ Commonly, inhibition is reversible, while the effects of toxicants are irreversible. That is, if an inhibitor is removed, bacterial function will return to normal levels, while if a toxicant is removed, a portion of the population will have residual effects (e.g., be dead). Inhibition is measured by the IC50, or concentration at which bacterial catabolic rate is reduced by 50%, while toxicity is measured by a LD50 or median dose – dose which will kill half the population. While there are mechanisms or chemicals that particularly influence specific functional groups, methanogenic archaea are generally more vulnerable to inhibition, and toxicity than bacteria. The order of the least-to–most-impacted processes is as follows: acidogenesis-hydrolysis-acetogenesis/hydrogenotrophic methanogenesis-aceticlastic methanogenesis. While it has not been well documented in the literature, propionate is an exception, in that it responds after acetate to initial overloads, but can remain in the effluent for long periods (1–2 weeks) after the initial overload. Inhibition is the more commonly observed phenomena in anaerobic digesters. The IC50 measure is directly applicable for

625

use in noncompetitive functions for dynamic modeling (Batstone et al., 2002), while toxicity is not commonly modeled, largely because modeling has limited capacity to address the impacts of toxicants. The mechanism of toxicants is often specific, acting on a particular mechanism of cellular metabolism. LCFAs are a common toxicant, which are thought to adsorb to the cell surface and block substrate and membrane proton transfer (Hwu et al., 1996). Other examples of toxicants include detergents, aldehydes, nitro-compounds, cyanide, azides, antibiotics, and electrophiles (Batstone et al., 2002; Speece, 2008). Inhibition can follow a number of different mechanisms, most of which either decrease the energy available from catabolism, or increase the amount of energy needed for maintenance. Common forms of inhibition are pH inhibition, ionic inhibition, product inhibition, and weak acid and base inhibition. They are discussed in detail in the following. pH Inhibition. pH inhibition is a combination of weak acid or base inhibition, disruption of cellular homeostasis, and reversible and irreversible protein denaturation. Most anaerobic organisms have a relatively broad pH optimum, with activity steady through the optimum. Anaerobic digestion operates best at a pH below 8.0, with activity of most organisms dropping above that pH, due to either free-ammonia inhibition, or other mechanisms. Lower pH is a combination of free-acid inhibition and pH inhibition. Since anaerobic digestion is mostly an acid-producing process, low pH inhibition is the most relevant form. Optimal pH levels for the different anaerobic biochemical functional groups are:

• • • •

Hydrolysis. Normally optimal above pH of 6.0, feasible up to 5.0. Acidogens. Optimal between 5.5 and 8.0, feasible up to 4.0 (Batstone et al., 2002). Acetogens/hydrogen-utilizing methanogens. Optimal between 6.5 and 8.0, feasible up to 5.0 (Batstone et al., 2002; Ferry, 1993). Aceticlastic methanogens. Optimal between 7.0 and 8.0, feasible up to 6.0.

As shown above, acid-producing microbes (acidogens and acetogens) have a higher tolerance for lower pH values than acid-consuming microbes (aceticlastic methanogens). An increase in load to a methanogenic digester will generally cause a decrease in pH, due to an increase in most acids, as well as the weak acid bicarbonate – even in an ideally operated digester. Where wastewater is poorly buffered, that is, where a lack of weak acids or bases causes poor resistance to pH changes, the pH can dip below 7.0 in response to substantial load increase. This can cause aceticlastic methanogens to be inhibited, which causes a further pH decrease due to accumulation of acetic acid. The overload is therefore self-reinforcing, and causes an acid overload. This can be difficult to recover from. This is mainly an issue in high-rate systems, where there is little or no ammonia release, a lower level of bicarbonate buffering, and a lower operating pH. Most high-rate anaerobic digesters operating on carbohydrate wastewaters require active base dosing to maintain a suitable pH, and this can form a major portion of the cost in these plants, though it is possible to reduce this by effluent CO2

626

Anaerobic Processes

stripping and recirculation (Ramsay and Pullammanappallil, 2005). Ionic inhibition. The mechanism of ionic inhibition involves increasing maintenance requirements, due to an increase in basic osmotic pressure. Sodium is the most relevant ion, with IC50 values between 5 and 30 g l1 depending on the level of acclimatization, function, and antagonistic or protagonistic ions (Feijoo et al., 1995). Acclimatization is possible and common. Product inhibition. Product inhibition occurs when products build up to the point where the catabolic reaction becomes unfavorable, that is, where the adjusted free energy of reaction as shown in Equation (1) becomes positive. The most common case is inhibition of propionate acetogenesis, caused by

Active transport of H+ (requires energy) CH3COOH (acetic acid) Passive transport

H+ CH3COO−

Cell; pH = 7.3

4.17.1.5 Rate-Limiting Steps

CH3COO− (Acetate) Bulk; pH = 7.0 H+ Active transport of H+ (requires energy) NH3 (Ammonia) Passive transport

H+ NH4+

Cell; pH = 7.3 NH4+ (Ammonium) Bulk; pH = 7.8 Figure 11 Mechanism of weak acid (top), and base (inhibition) by passive diffusion of the free form of the acid or base into the cell, and disruption of homeostasis.

Table 3

accumulation of hydrogen levels above those shown in Table 1, or substantial accumulation of acetate. Weak acid and base inhibition. Weak acid and base inhibition are caused by passive transport of uncharged acids (e.g., organic acids) or bases (e.g., ammonia) into the cell. These acids or bases then dissociate or associate within the cell to disrupt homeostasis (Figure 11). This causes increased maintenance requirements. Some important compounds causing free acid or base inhibition are listed in Table 3. While adjusting pH is a normal method to address free acid or base inhibition, it is an expensive exercise, due to the inherent buffering in most anaerobic digesters. Free-ammonia inhibition is likely the most commonly encountered form of inhibition, particularly in manure digesters and where the feed is proteinaceous, as the ammonia causes a high pH and acts as an inhibitory agent as well. This not only causes poorer overall performance, but can also cause more fundamental shifts, and (Karakashev et al., 2006) found that high-ammonia systems were dominated by Methanosarcina, oxidizing instead of cleaving the acetate. As the free form of ammonia is most important, and because temperature has a strong impact on the pKa, the entire system is heavily impacted by both temperature and pH. This is demonstrated in Figure 12, which shows that in a system with 2000 mg N l1 a thermophilic system (55 1C) will have a pH threshold of approximately 7.5 before strong inhibition occurs, while a 37 1C system will have a threshold of approximately 8.0.

This chapter outlines the key processes that occur in an anaerobic digester. In most cases, for a given wastewater type or reactor design, there is a rate-limiting step that needs to be managed in order to achieve optimal design and operation. The most common controlling mechanisms are hydrolysis and methanogenesis. Hydrolysis is normally the rate-limiting step for solid digesters (41% solids), where there are no other inhibitory factors present. For most solids, a retention time of 410 days is required (see Section 4.17.2.2.2), and at lower retention times, undigested solids will go to the effluent. Performance, as assessed by solid destruction, will decrease. Aceticlastic methanogenesis is normally the rate limitingstep in high-rate anaerobic wastewater-treatment systems, or where there is a higher level of inhibitors. Aceticlastic methanogenesis generally controls treatment systems where there are biomass limitations, or where the system is heavily loaded. Occasionally, (e.g., manure digesters), both hydrolysis can limit, due to slow solid degradation, and simultaneously, methanogenesis can cause elevated organic acids, due to ammonia inhibition.

Compounds causing free acid or base inhibition

Compound

Inhibitory concentration (free acid or base)

pKa

Condition at which inhibition occurs

NH3 (ammonia) H2 S (hydrogen sulfide) HVFA (organic acids)

1–2 mM (14–30 mgN l1) 2–3 mM (32–40 mgS l1) 0.2 mM (13 mg l1)

9.25 7.05 4.8

High pH Neutral and low pH Low pH

Anaerobic Processes

627

Free ammonia at total NH3/NH4+ of 2000 mg l−1 0.04

55°C

37°C

20°C

Free ammonia (M)

0.035 0.03 0.025 Inhibition strong 0.02 0.015

Inhibition significant

0.01 0.005

Inhibition starts

0 6.5

7

7.5

8

8.5

9

pH Figure 12 Free ammonia levels and ammonia inhibition.

In rarer cases, acetogenesis/hydrogenotrophic methanogenesis can be the rate-limiting step (e.g., hydrogen overload in a highly loaded high-rate system fed with soluble sugars), but increases in the higher organic acids may also be in response to an increase in acetic acid. In the following sections, these controlling mechanisms are discussed in context with technology selection, design, and operation.

4.17.2 Selection and Design of Anaerobic Technology 4.17.2.1 Anaerobic Digester Technologies Implementation of anaerobic digestion needs to address the two key issues of (1) maintaining sufficient retention time to allow for hydrolysis of particulate substrates and (2) providing beneficial conditions for aceticlastic methanogenesis, including maintenance of pH above 7.0. Technologies are split between wastewater-treatment technologies, which need to focus on goal 2, with extended sludge-retention times, but limited liquid-retention times, and those which need to focus on goal 1, with extended solid-retention times (Figure 13). Technologies except for high-rate systems are largely hydrolysis limited. Treatment technologies are summarized in, and described further, in the following sections (Table 4).

4.17.2.1.1 High-rate anaerobic digestion High-rate anaerobic digesters normally operate with extended solid-retention time, and short hydraulic-retention times, by integrating solid retention within the main digester (Figures 14 and 15). The most common type is an upflow anaerobic sludge blanket (UASB) reactor, in which liquid percolates through a partially settled sludge blanket. This operates with a flocculant sludge blanket, but relies on formation of anaerobic granular sludge (particles 4200 mm) for higher loading systems, especially if high effluent quality is to be maintained. High-rate digesters require a low solid feed, with relatively high amounts of soluble feed material, and are most often used for industrial wastewaters as well as domestic sewage

treatment (van Lier, 2008). Hydraulic-retention times are normally short with o48 h, while solid-retention times can be very long (4200 days, years). UASB reactors have a gas– liquid–solid separation in the upper part of the digester, while variations may include packing (Figure 14) in hybrid reactors, or extended super-high-rate/low footprint systems such as expanded granular sludge bed (EGSB) and internal circulation (IC) reactors. Other alternatives for high-rate anaerobic systems include anaerobic baffled reactors (multi-compartment reactors), fluidized bed or attached-growth systems, fixed-media anaerobic filters, anaerobic membrane bioreactors, and sequencing anaerobic batch reactors. UASB type systems are currently the market leaders in high-rate systems by a large margin (van Lier, 2008).

4.17.2.1.2 Anaerobic ponds Anaerobic ponds are a low-capital cost option, but they tie up land and require desludging approximately every 10 years, which can be excessively expensive (US$150 per dry ton). Anaerobic ponds are typically operated with very limited external control (e.g., temperature) and are therefore largely impacted by the local climate. This limits the effectiveness of ponds in colder regions. Overall costs are heavily driven by solid loading. Methane capture is relatively poor, and this results in an increase in greenhouse-gas emissions, and, generally, odors from the pond. Due to the large volumes, correction under failure can be extremely expensive or impractical. Anaerobic ponds have a depth of 5 m, with surface area determined by loading rates.

4.17.2.1.3 Fully mixed liquid digester Fully mixed digesters are most often applied to sewage sludge, activated sludge, and manure digestion (Speece, 2008). They are the most commonly applied configuration for anaerobic digestion. They operate as fully mixed reactors, with either gas recirculation or mechanical/liquid mixing systems. Mixing configuration is critical, and is reviewed further in (Tchobanoglous et al., 2003), particularly with respect to sludge

628

Anaerobic Processes 100

Plug flow

Hydraulic retention time (d)

Anaerobic ponds Liquid mixed digesters

10

Solid-phase leach bed

1

High-rate AD

0.1 0.01

0.1

1

10

100

Feed solids concentration (%) Figure 13 Anaerobic treatment technologies ranked by hydraulic-retention time (vertical axis) and solid concentration (horizontal axis).

digestion. Their configurations include cylindrical (normally with recirculated gas or liquid mixing) and egg-shaped (normally with mechanical mixing) systems. Maximum loading rate is heavily dependent on achievable solid levels, and performance can often be enhanced by pre-concentrating solids. Due to viscosity and heat-exchange consideration, the maximum in-reactor solid concentration is approximately 4% (feed concentration of approximately 8%). Costs are relatively high due to their engineered nature.

4.17.2.1.4 Plug-flow liquid digesters Plug-flow liquid digesters operate as a semisolid liquid (10– 20%) in a long polyethylene tube, vaulted brick, or concreteshaped reactor. Material is loaded at the front of the digester, and passes through to product at the end. As it is not mixed, contact with biomass is poor. These reactors have high kinetic efficiency, due to the plug-flow configuration, but are susceptible to lack of inoculation and topical souring. They are most often applied to agricultural solid digestion.

material removed). The latter is considerably more expensive due to solid handling and feed requirements. An alternative to in-reactor methanogenesis is recirculated leachate leach bed reactors (Figure 15). In this configuration, leachate is continuously percolated through a loop that includes the main solid phase leach bed, as well as a high-rate system to remove organic acids produced by the leach bed. This system has the advantage that overload and souring of the leach bed is far less likely, and gas production is steadier. The main disadvantage is susceptibility of the UASB reactor to solids. This type of system has been applied to municipal solid waste, and poultry litter (Rao et al., 2008).

4.17.2.2 Digester Selection and Design for Specific Applications Common wastewater types are shown in Table 5. As demonstrated in Figure 13, wastewater technologies are classified by their solid concentration. Specific considerations for application of technologies are given in the following sections.

4.17.2.1.5 Solid phase (leach bed)

4.17.2.2.1 Domestic and industrial wastewater

Solid-phase digesters are similar to an engineered, high-rate landfill, where material is loaded in a reactor, tumbler, or baskets, and leachate liquid is circulated through the reactor. Liquid percolates through the solid matrix and liberates organic acids, which are subsequently degraded to produce methane. It can be produced either in batches (where the system is reacted until no more methane is produced), or continuously (where material is continually added, and spent

The main criteria for application of high-rate granular anaerobic treatment technology are higher strength (4500 mg COD  l1), low solids (2000 mg l1), and low oil and grease (o500 mg l1). Given these constraints, it is not surprising that 75% of applications of high-rate technology are on wastewater largely containing soluble carbohydrates and organic acids (e.g., cannery, brewery, confectionery, and distillery) (van Lier, 2008). High-rate anaerobic digestion has been

Anaerobic Processes Table 4

629

Anaerobic digestion technologiesa

Technology

Principle

Advantages

Disadvantages

Loading rate (kg COD m3d1)

High-rate digester/upflow anaerobic sludge blanket

Mainly liquid wastewater flows upward through a granular bed

Low footprint, low capital cost, very stable, produces good effluent

Intolerant to solids

10 (UASB) 20 (EGSB/IC)

Anaerobic pond

Large retention time mixed vessel

Low capital cost

Very high footprint Must be desludged Methane capture poor Can produce odors

0.1

Mixed tank

Dilution to 3–6%, and continuous feed in mixed tank. Retention of 20 days. Used across many industries

Established tech Easy to control Continuous gas production

Poor volumetric loading rate Expensive tanks Need dilution liquid Liquid (not solid) residue

1–3

Liquid plug flow

Dilution to 15%, and feed through a liquid plugflow reactor

Very high loading rates Continuous gas production

Need dilution liquid. Poor contact with active biomass. Liquid residue

5

Batch solid phase

Fill and react in a solidphase reactor. Can be an engineered landfill (but must be properly sealed). System is loaded, enclosed, and leachate/inoculum circulated intermittently

Can be very cheap Very high loading rates Good gas conversion due to retention of active biomass Easy to control via leachate No milling required

Non continuous system (gas–flow changes in quality and flow over time) Can be difficult to seal (gas seals) Needs loading and unloading

6–10

Continuous dry solid phase (plug flow)

Continuous feed of solid phase through a system. Recirculation of leachate around solid phase

Continuous gas and residue production Do not need dilution liquid Very good loading rates

Extremely high capital costs, and only really practical at very large scale. Very complicated mechanical system Potential solid handling issues

10

a

Note that the high loading of later options is achieved by high solids concentrations.

traditionally regarded as being less applicable to proteinaceous wastewaters, due to poor granule development (Fang et al., 1994). However, it is more likely that this is due to the particulate nature of these wastewaters (Batstone et al., 2004), and high-rate granular systems fed with soluble proteins (e.g., gelatine, casein) can be as effective as those fed with soluble carbohydrates (Moosbrugger et al., 1990). One of the main considerations associated with carbohydrate wastewater is buffering and pH. Carbohydrate wastewaters have no inherent buffering, which means that the acidity associated with carbon dioxide production needs to be offset by addition of a base. This can be a substantial cost consideration as outlined in the physicochemical section, although substantial savings can be achieved by effluent CO2 stripping and recycling. This is not as severe for protein-type wastewaters, as the weak base ammonia is produced during acidogenesis of proteins. Excessive ammonia release can cause free-ammonia inhibition. The flexibility of high-rate anaerobic digestion is illustrated by its applicability to domestic wastewater. Domestic

wastewater would normally be a poor feed source for high-rate anaerobic digestion, being low in strength (o1500 mg COD l1), relatively high in proteins, fats, and solids (often 4500 mg SS l1), and normally at lower temperatures. However, it has been successfully applied in both pilot and full-scale for removal of organics, and for sanitization (Seghezzo et al., 1998). This is further addressed in a later section.

4.17.2.2.2 Sewage solids and activated sludge biosolids Primary sewage solids (primary sludge) and activated sludge are the two main solid streams produced from activated sludge treatment plants. Primary sludge is material that can be settled out of raw sewage, and is relatively degradable, that is, 60– 100% can be anaerobically degraded, depending on the upstream catchment. Primary sludge has a relatively large lipid component (approximately 50% by COD; Siegrist et al., 2002; Speece, 2008). Activated sludge is a combination of microbial material produced during the activated sludge process

630

Anaerobic Processes Gas

Gas

Effluent

Effluent

Gas−liquid−solid Packing Granules

Inlet

Inlet

(a)

(b)

Figure 14 Upflow anaerobic sludge blanket (UASB) (a) and hybrid (b) systems.

Bleed stream

Gas Leach bed Makeup water

Overflow

Solid feed

High rate (UASB) Figure 15 Combined leach bed and high-rate system.

Table 5

Preferred technologies for different wastewater types

Application

Solids concentration (%)

Preferred technology

Design parameter

Nominal design parameter

Domestic or industrial wastewater

o0.2% (soluble solids may be up to 5) 2–7 2–7 10–30

High rate

Mass loading

10 kg COD m3d1

Mixed liquid phase Mixed liquid phase Solid phase

Retention time Retention time Retention time

10–15 days 20 days 30–50 days (batch)

Sewage solids, activated sludge Animal manure Organic solid wastes

(partially degradable), inert particulate material derived from influent material (not degradable), and undegradable cellular product (not degradable; Nopens et al., 2009). Activated sludge is a more homogeneous material than primary sludge, with a lower lipid content, and consequently higher protein and carbohydrate content. Overall, degradability is heavily dependent on sludge age (see further). The cost of biosolid handling and disposal can be a substantial fraction (30–50%) of overall wastewater-treatment costs, with cost being determined on a per wet ton basis. The

key considerations for sludge treatment are (1) volume and mass reduction, to reduce all costs associated with handling; (2) removal of unstable organics, to improve utility and storage options; and (3) pathogen removal, to increase utility and safety of the sludge product. The driving consideration is volume/mass reduction, since this determines eventual cost. Anaerobic digestion is effective in meeting all considerations, providing cost-effective solids and organics destruction, allowing essential pathogen destruction, and improving dewaterability of the final product.

Anaerobic Processes

70 60 50 40 30 20 10 0 5

10

15

20

25

30

0.9 0.8 0.7 0.6 0.5

Primary sludge khyd = 0.5 d−1

Activated sludge khyd = 0.3 d−1

Crop residues khyd = 0.1 d−1

10

15

40

Figure 17 Anaerobic degradability waste-activated sludge (WAS) vs. upstream sludge age.

0.4 5

35

Activated sludge age (d)

khyd = 1 d−1 Methanogenesis begins to fail

% of degradable fraction destroyed

1

temperatures, the degradability of activated sludge simply becomes too low for viable anaerobic digestion, as the biogas produced is insufficient to meet mixing requirements and provide sufficient energy for heating of the digester. In these cases, without a supplementary primary sludge stream, anaerobic digestion is no longer an option for sludge stabilization. For these poorly degradable streams, there are pretreatment options to improve both apparent hydrolysis coefficient and degradability. Lower-energy options, such as sonication, temperature-phased anaerobic digestion (TPAD), and enzymatic pretreatment, appear to largely act to increase apparent hydrolysis coefficient (Ge et al., 2010), and hence move the material upward as shown in Figure 16. High-energy options, such as thermal hydrolysis, increase the amount available and the hydrolysis coefficient, and hence can result in considerably enhanced performance (Batstone et al., 2009), though at higher capital and operating cost.

Activated sludge degradability (%)

Sizing of sludge digesters is driven by sludge hydrolysis rate coefficient (Speece, 2008; Tchobanoglous et al., 2003). Primary sludge is rapidly degradable, with first-order coefficients of the order of 0.3–0.5 d1 (Gujer and Zehnder, 1983; O’Rourke, 1968; Siegrist et al., 2002). Activated sludges are more slowly degradable, with hydrolysis rates of the order of 0.1–0.3 d1 (Ge et al., 2010). The impact that this has on digester sizing and performance is shown Figure 16, which demonstrates that as hydrolysis rate decreases, a longer retention time is required to achieve the equivalent efficiency. Apart from hydrolysis rate, the other major factor determining performance is degradability (fd). This represents the amount of material, either as COD, or as organic volatile solids (VS) that can be broken down to methane or biogas, respectively. For a perfect digester, it would be equivalent to the organic solids, or VS, destruction, but in most cases, the VS destruction is 70–90% of the degradable fraction. It has been extensively shown that the availability of material in both primary and activated sludge is the same across aerobic and anaerobic systems (Ekama et al., 2007; Gossett and Belser, 1982), and hence, degradability, or inert fraction can be directly translated between activated sludge and anaerobic models (Nopens et al., 2009). Primary sludge has a degradability of 60–100%, depending on the upstream catchment. A larger proportion of industrial input normally results in a lower net degradability. Activated sludge degradability depends heavily on the inert fraction remaining from the activated sludge process, and is hence heavily dependent on upstream activated sludge age. This was evaluated by Gossett and Belser (1982), and the results are summarized in Figure 17. Therefore, a WAS with an upstream sludge age of 15 days would be expected to have a degradability of 45%. With reference to Figure 16, a digester with a retention time of 20 days would be expected to have an efficiency of 85%. Therefore, feeding this digester with this sludge, an overall VS destruction of 45  85% ¼ 38% would be expected. As can be seen, WASs are generally poorer candidates for anaerobic digestion as compared to primary sludges. At higher sludge ages and/or higher activated sludge

631

20

25

30

Digester hydraulic retention time (d) Figure 16 Digester performance (% efficiency on degradable fraction) vs. hydraulic-retention time, and hydrolysis coefficient (khyd).

632

Anaerobic Processes

4.17.3 Interpretation and Operation of Anaerobic Systems Anaerobic reactor systems have been traditionally regarded as more difficult to control than aerobic wastewater systems. In practice, this is partially true, as commonly the only control handle is feed rate, which in the wastewater industry is largely determined by upstream considerations (Steyer et al., 2006). Aerobic wastewater treatment or digestion has a wider range of control handles, including aeration intensity and internal and sludge recycles and bypasses. Monitoring of anaerobic reactors and digesters is also more problematic, as the nonlinearity of the physicochemical process (see Section 4.17.1.2) means that simple sensors, such as pH and gas flow, have limited utility (Steyer et al., 2006). Conversely, anaerobic processes operate at higher loading rates and feed concentrations than aerobic systems, meaning that reactor sizes are smaller, and they can be readily overdesigned. In addition, anaerobic systems have large time constants (change relatively slowly) in the linear region, and it is suitable to choose a long-term optimal operating condition and aim for that set point.

4.17.3.1 Evaluating and Determining Controlling Mechanisms Optimal instrumentation, interpretation, and operation of digesters depend heavily on the controlling mechanism as discussed in Section 4.17.1.5, which is in turn dependent on configuration and feed type. Systems can be divided into 1. Methanogenesis controlled systems. Aceticlastic methanogenesis is the controlling mechanism in systems which are fed predominantly soluble wastewaters, or where pH buffering is poor. These are most often high-rate anaerobic digesters. Diminished performance is indicated by elevated acetate (and other organic acid concentrations), as well as liquidand gas-phase hydrogen concentrations (Pauss and Guiot, 1993), at mild overload conditions, and substantially decreased pH (o7.0) and gas flow during process failure. The kinetics are fast, and the process is highly nonlinear at the point between mild and severe overload. 2. Hydrolysis controlled systems. Hydrolysis is normally the controlling mechanism for systems fed with predominantly solids (41% solids), and performance is mainly determined by retention time of solids in the digester. The kinetics are relatively slow, and the overload mode is diminished performance in terms of gas flow, and unstabilized solids in the effluent. Poor performance is determined by analysis of these measures. Solid digesters are commonly well buffered due to release of ammonia from protein digesters, and pH is therefore a less-useful measure of process stability. Most of these systems are relatively linear and stable in response to changes in load. 3. Inhibited-hydrolysis controlled systems. These are relatively stable hydrolysis controlled systems (normally solid digesters), but which have a methanogenic inhibitor in the feed. The most common instance is manure digesters, where gas flow is largely determined by hydrolytic processes, and hence the retention time, while the presence of

ammonia causes substantially elevated organic-acid levels. In this case, the process itself is relatively stable, due to the ammonia buffering, but long-term performance may be poor both due to effluent organic acids (hence, lost biogas), and an inappropriate retention time for hydrolysis. Thus, the controlling mechanisms can be determined both from reactor and feed types (e.g., solid digesters vs. liquid-fed high-rate systems), and from direct analysis of solids destruction levels, and organic acid levels. This then leads to ongoing analysis of the most suitable performance indicators.

4.17.3.2 Performance and Process Indicators Suitable performance offline indicators or online sensors should provide an accurate reflection of process performance, related to process goals. If necessary, they should also offer potential for process correction, and in advanced cases, online process control. Again, selection of a suitable indicator or sensor depends heavily on the application or reactor type. As examples, the process goal for solid digesters is solid destruction (and hence gas production). A suitable indicator would be VS destruction, or gas yield. The process goal for high-rate anaerobic systems is good effluent quality, and process stability. Therefore, an indicator that provides early warning (prior to process failure) is desired. The process goal for fermentation systems is the extent of fermentation (or desired mix of organic acids). A good indicator would be product mix as measured by VFA concentration. Stability-state sensors for anaerobic digesters are difficult to apply online. As stated previously, there is a balance between the rate of hydrolysis and fermentation, and the rate of methanogenesis. When the production rate of organic acids exceeds the capacity of the digester to remove organic acids, the pH can drop, and the reactor sours. This is a hysteric process that can be extremely expensive, time consuming, and difficult to recover from. Start-up is a particularly hazardous period for this, as the biomass is nonacclimatized, and may vary in quality. The most simple sensors for anaerobic digesters are pH and gas flow. Due to system nonlinearity and stability characteristics, these measures are generally unsuitable (Steyer et al., 2006), only changing after the reactor sours. The best indicators are the intermediates, including volatile fatty acids (Pind et al., 2003), and measures such as bicarbonate alkalinity which indicate resistance to overload (Steyer et al., 2006). Overall, VFA concentration is by far the most widely applicable, direct, and meaningful measure of stability. However, the measurement of VFAs is generally an offline process involving measurement by gas chromatography-flame ionization detection (GC-FID), which is relatively slow and expensive, or titration, which is slow, and can be inaccurate in the presence of other buffers. While online methods for VFA measurement have been developed (Boe et al., 2007; Pind et al., 2003; Steyer et al., 2006), these are generally expensive, and/or require extensive sample preparation (e.g., online membrane filtration or gas-phase extraction), and there is still a need for a simple, relatively low cost online sensor to indicate anaerobic process stability state.

Anaerobic Processes 4.17.3.2.1 High-rate anaerobic reactors Most high-rate anaerobic digesters operate on mainly carbohydrate-based industrial wastewaters (van Lier, 2008), including agro-food, beverage, distillery, and pulp/paper. The objectives of the process are to remove organics, in order to reduce load to downstream treatment units, and potentially produce an effluent suitable for reuse or discharge to sewer. This requires good organic-acid removal, and process stability. The key performance measure is therefore VFA concentration, which indicates the level of acid-contributing compounds, as well as the bicarbonate, or partial alkalinity (PA – titration to pH 5.8). Titration to pH 4.2 indicates the total alkalinity (TA), which includes both bicarbonate, as well as organic acids. The contribution of the organic acids is termed intermediate alkalinity (IA ¼ TA  PA). The current guideline for reactor stability is IA/TA r0.3, that is, the ratio of VFA contributed versus TA should be less than 0.3 (Steyer et al., 2006). Speece (2008) critiques this in influent analysis, pointing out that a large proportion of the total alkalinity must be also allocated to neutralize the CO2 produced during the digestion process, and suggests that reserve alkalinity (after production of CO2) is a better measure. For direct digester analysis however, the IA/TA measure is a reasonable indicator, though absolute values (of PA and VFA) should also be assessed. The two terms in this measure are contributed by bicarbonates and organic acids, and the IA/TA terms can either be measured directly (by offline, or online titration), or indirectly, and calculated by alternative methods, including Fourier transform-infrared (FTIR) (Steyer et al., 2006) and GC-FID, which are the standard analytical methods in commercial laboratories. For smaller systems, offline titration is simple, low cost, and relatively informative.

4.17.3.2.2 Sludge digesters As stated in Section 4.17.2.2.2, sludge and higher solid digesters can never achieve a high-quality effluent, due to the presence of inert solids. The main cost associated with primary and activated sludge digesters is disposal of the product, and the value of gas produced is relatively low compared to this. Therefore, the primary performance-related measure is organic solid destruction (or VS destruction). This is naturally related to methane production, since any solid destroyed must be created as methane. Secondary performance measures include stability (as measured by remaining degradable solids) and pathogen levels (Speece, 2008), and both of these are normally regulated in sludges, on the basis of vector attraction, usability, and disease control grounds. Additional performance measures may include mineral and nutrient content, odor, dewaterability, and texture, which are largely related to primary and secondary measures. As an example, a wellstabilized anaerobic biosolid product will generally have good dewaterability and low odor. Given that the primary measure is solid destruction, there are three ways to calculate this (Ge et al., 2010): mass-balance VS destruction, which assesses the flow of organic solids out, compared to the flow of organic solids in; Van Kleeck VS destruction, which is a modification of the mass balance to use VS fraction only; and apparent VS destruction of gas flow, which relies on the principle that organics destroyed must be

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converted into gas. All measures assume that the system is at steady state, or values are averaged over longer terms. If the system is not at steady state, mass balance VS destruction and gas flow VS destruction need to be adjusted for flow, and van Kleeck VS destruction cannot be used. Mass-balance VS destruction is calculated as follows:

VSdestroyed ¼ ðVSconc;in  VSconc;out Þ=VSconc;in

ð7Þ

where VSconc is the concentration of organics as measured by the volatile solids method (g l1), and subscript in and out indicate concentrations in the inlet and outlet streams. Van Kleeck VS destruction is calculated as follows:

VSdestroyed ¼

VSfrac;in  VSfrac;out VSfrac;in  VSfrac;in VSfrac;out

ð8Þ

where VSfrac is the fraction of total solids that is volatile (VSconc/TSconc). Gas flow VS destruction is calculated as

VSdestroyed ¼

CODgas ðkg COD d21 Þ CODin ðkg COD d21 Þ

ð9Þ

where CODgas is the calculated gas flow COD in kg COD d1. It can normally be calculated as

CODgas ¼ 2:9Qgas pCH4

ð10Þ

where Qgas is the gas flow at standard temperature and pressure (N m3 d1), pCH4 is the partial pressure of methane (atm), and 2.9 is a conversion factor (kg COD N m3). CODin is the incoming COD, and can be either directly measured, and multiplied by flow for a kg COD d1, or calculated as

CODin ¼ 1:5VSconc;in Qin

ð11Þ

where 1.5 is the assumed COD:VS ratio for activated sludges (approximately 1.7–1.8 for primary sludges), VSconc,in is the influent organic solids (kg m3 or g l1), and Qin is the inflow/ reactor hydraulic flow (m3 d1). Each of these measures has specific advantages, and can be influenced by different systematic and random errors: 1. Mass balance VS destruction. It is sensitive to errors in flow measurement, and systematic sampling issues. For example, it is common to have differential settling around sample points, such that the solid concentration is not representative of the in-reactor, or the outlet concentration. 2. Van Kleeck VS destruction. It is sensitive to accumulation of minerals in the reactor (which will read as a false low destruction), or precipitation (which will read as a false high). It is not as susceptible to systematic sampling issues, as dilution of mineral and organic solids are normally consistent. It is not dependent on flow-rate measurement. 3. Gas flow VS destruction. It is the least reliable, and is dependent on correct flow measurement in both liquid and gas streams, as well as correct VS inlet measurement. It is also sensitive to the assumed COD:VS ratio, and this can vary significantly (e.g., longer sludge ages normally result

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in a higher COD:VS ratio). Finally, it assumes that the COD:VS ratio is the same across the digester. While it should not be used as a primary measure of VS destruction, it is a useful comparison to other terms. It is not sensitive at all on outlet flow measurement or outlet liquid-digester analysis. While VS destruction is the key indicator of performance, digester stability and health can be assessed as discussed in the previous section as either total organic-acid concentration, or a combination of organic-acid concentration and PA. Sludge and manure digesters normally have higher levels of inherent PA due to the pH rise produced by free-ammonia release during hydrolysis of proteins.

4.17.3.3 Evaluating Substrate and Microbial Properties

Methanogenic activity tests are common due to methanogenesis as a rate-limiting step, and because methane can be readily measured. In the case of methanogenic activity tests, the substrates (acetate, H2, and CO2) are direct precursors for methane, and activity may be determined from direct measurements of methane produced. This is not true for determinations of acetogenic, acidogenic, proteolytic, and hydrolytic activities where substrates are converted through several steps; thus, measurements of methane production are not sufficient to determine activity, as the methane-production rate will only reflect the slowest step of a more complex degradation process. Measurements of substrate depletion are more valuable in this type of test. Methanogenic activity testing is particularly important as methanogenesis is the final stage in any degradation process, and the slowest, and the most sensitive step. Methanogenic activity is estimated based on the initial rate of methane production during a controlled batch test. Only the initial

4.17.3.3.1 Activity testing Anaerobic activity tests are used to evaluate the performance of anaerobic sludge communities, and may be used to select an adapted sludge as inoculum, to estimate maximum applicable loading rates of certain processes or sludges, and to evaluate batch kinetic parameters. These tests can also be used to monitor possible changes in sludge activities over time due to the build up of toxic or inhibitory material or the accumulation of inert material. Activity testing cannot determine the presence or concentration of individual microbial species; however, relative activities indicate the balance of trophic groups within the community. The microbial activity of the different trophic groups determines the rate of each of the four main steps in anaerobic digestion and allows identification of the limiting step. The rate-limiting step will give information about the maximum organic load which can be applied to the system without causing a loss in its stability. Activity as identified by testing can be used together with other reactor indicators to promote stable operation. The quality of inoculum is important for prediction of degradation characteristics of novel waste materials. For example, in applications where the waste material is a complex organic solid, the hydrolysis step will limit the material available for fermentation. In applications where the waste is soluble or readily fermentable, the production of intermediates will be more rapid and a high methanogenic activity is required to balance this. For determination of activities of different trophic groups, model substrates should be used. The concentration of model substrate used in activity testing is a critical factor in the test set up. The initial concentration should be sufficiently high such that the biomass concentration is the limiting factor in the test, but sufficiently low to prevent inhibition of the microbial community as well. Model substrates used to determine the activity of the main trophic groups in anaerobic communities and recommended concentration ranges are shown in Table 5. A synthetic medium may be used in the assays to ensure that necessary nutrients/micronutrient/vitamins are available to allow optimal performance of anaerobic microorganisms. The composition of basic anaerobic (BA) medium recommended for anaerobic activity testing is given in Tables 6 and 7.

Table 6 Model substrates for determination of specific activities of trophic groups in anaerobic communities Trophic group

Substrate

Concentration range

Hydrolytic Proteolytic Acidogenic Acetogenic

Cellulose Casein Glucose Propionic acid n-butyric acid Acetic acid

1–10 g l1 1 g l1 1–2 g l1 0.5–1 g l1 0.5–1 g l1 1–2 g l1

H2/CO2 (80:20)

1 bar total

Methanogenic – acetoclastic Methanogenic – hydrogenotrophic

Table 7

Base anaerobic (BA) medium suggested for activity testing

Stock solution

Volume per l

Components (g l 1 of stock solution)

A

10 ml

B C D

2 ml 1 ml 1 ml

E

1 ml

NH4Cl, 100; NaCl, 10; MgCl2  6H2O, 10; CaCl2  2H2O, 5 K2PO4  3H2O, 200 Resazurin, 0.5 Trace metals: FeCl2  4H2O 2; H3BO3 0.05; ZnCl2 0.05; CuCl2.2H2O 0.038; MnCl2  4H2O 0.05; (NH4)6Mo7O24  4H2O, 0.05; AlCl3 0.05; CoCl2  6H2O 0.05; NiCl2  6H2O 0.092; EDTA, 0.5; conc. HCl, 1 ml; Na2SeO3  5H2O, 0.1 Vitamins: biotin, 2; folic acid, 2; pyridoxine acid, 10; riboflavin, 5; thiamine hydrochloride, 5; cyanocobalamine, 0.1; nicotinic acid, 5; P-aminobenzoic acid, 5; lipoic acid, 5; DL-pantothenic acid, 5

NaHCO3 Na2S  9H2O

2.6 g 0.5 g

From Angelidaki I and Sanders W (2004) Assessment of the anaerobic biodegradability of macropollutants. Reviews in Environmental Science and Bio/Technology 3: 117.

Anaerobic Processes

635

0.09 0.08

y = 0.2735 × −0.0191 R 2 = 0.9967

Methane (g COD)

0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Time (day) Figure 18 Methane generation during specific methanogenic activity assay.

linear methane-production rate is used to reduce the influence of biomass growth and adaptations or changes of the biomass characteristics. Environmental factors including substrate and nutrient concentrations and pH also vary during the tests. The methane-production curve for a specific methanogenic activity test is shown in Figure 18. This shows the slope of the curve, which in the linear region indicates the methanogenic activity. The specific methanogenic activity (SMA) is this slope divided by the VS present in the test vial. Methanogenic activity is generally higher than 0.2 g COD CH4 g VS1 d1 for digesters and industrial high-rate sludges, and may be far higher for laboratory-grown granules.

The ultimate methane yield represents the potential to recover energy during waste treatment; however, the value of energy produced is often only a small consideration in determining the feasibility of the anaerobic project. An example output from a methane potential test is shown in Figure 19. The key parameters used to indicate degradability of a complex feed are degradation extent (fd), the fraction of the substrate that may be converted to methane, and apparent first-order hydrolysis rate coefficient (khyd), an indicator of the rate at which conversion occurs. Determination of degradability parameters is critical in feasibility analysis, system design, troubleshooting, and competitive testing of inoculums. Hydrolysis is normally represented using a first-order model as discussed in Section 4.17.1 of this chapter:

4.17.3.3.2 Biological methane potential testing The biological methane potential (BMP) test is a simple batch assay used to determine the potential methane generated from anaerobic biodegradation of a mass of test substrate. In addition to potential methane (ml) generated per gram of substrate (wet, dry, and VS basis), the BMP assay is used in determination of parameters critical in process design, troubleshooting, and competitive testing of inoculums. The BMP test requires a test substrate to be mixed with a known good inoculum (containing a strong and balanced anaerobic community) in a controlled environment. BMP testing can be done at multiple scales ranging from several grams of test material up to tons (in pilot digesters). Extensive biodegradability testing of thousands of different materials in both aerobic and anaerobic conditions has been performed over the last 50 years; however, comparison of biodegradability data between studies in the literature has been limited by a lack of a common basis. Previously, factors including type of equipment, operating conditions, method of analysis, test compound, inoculum, and nutrient medium varied among studies and influenced the outcome of the batch assays (Rozzi and Remigi, 2004). However, this is improving with the publication of practical and standardized methods (e.g., activities of the IWA Anaerobic Biodegradation Activity and Inhibition (ABAI) Taskgroup; Angelidaki et al., 2009).

dS ¼ khyd S dt

ð12Þ

where S is the degradable portion of substrate, t is the incubation time, and khyd is the first-order hydrolysis rate constant. Determination of residual substrate, S, requires that the degradable fraction of the substrate is known. A simplified approach is achieved through the separation of variables and the integration of Equation (12):

ln

Pf  P ¼ khyd t Pf

ð13Þ

where residual substrate at time t, is represented as the difference between the methane yield at that time P, and the ultimate methane yield Pf. Equation (13) will produce a linear curve when the degradation kinetics are of the first order, and the hydrolysis rate constant is represented by the slope of the curve. Alternatively, the first-order hydrolysis coefficient and degradability parameters can be estimated using a dynamic firstorder (single step) model. The first-order hydrolysis rate is used to estimate process-retention time and thus digester size, while the degradability fraction can be used to calculate the expected VS destruction during the process. Replicate testing

636

Anaerobic Processes 250

Methane (ml CH4 .gVS−1)

200

150

100

50

0 0

2

4

6

8

10

12

14

16

18

Time (day) Figure 19 Example output from biological methane potential (BMP) test. Error bars indicate 95% confidence errors from triplicate batches. The line indicates the model used to return key parameters.

is essential to determine repeatability and variability in the test results. More advanced modeling methods, including nonlinear parameter estimation and parameter uncertainty evaluations, are now being used to determine a multidimensional parameter surface showing the confidence region of the parameter estimations (e.g., Figure 20; Batstone et al., 2009). Batch assays are used on the basis that they are appropriate for assessing the performance or potential of full-scale anaerobic processes. It is not possible to replicate and maintain equal environmental factors such as nutrient, buffer, pH, and gas-phase conditions between the full-scale reactors and batch tests. The mode of operation (batch or continuous, mixed or plug flow) also varies between full-scale reactors systems and the standard assay (batch, no mixing). Degradability is a characteristic of test material rather than the test conditions and, indeed, degradability estimated using BMP tests are similar to the degradability performance achieved in continuous full-scale anaerobic processes; however, the first-order hydrolysis rate is influenced by test conditions and therefore is not directly comparable (Batstone et al., 2009). However, results from batch tests represent a conservative estimate of parameters needed for system-feasibility analysis and design. Environmental conditions and substrate characteristics vary between the BMP test and reactor used as an inoculum source; as a result, the inoculum is rarely optimized for the test material and significant adaptation does not occur during the batch test. Inoculum should be collected from a reactor operating on a complex feed material to provide a diverse and balanced microbial population and ensure complete breakdown of the degradable portion of the test material. The issue of inoculum to substrate ratio has been evaluated in some detail (Fernandez et al., 2001; Neves et al., 2004; Raposo et al., 2006). The inoculum to substrate ratio must be sufficient to ensure that hydrolysis is limited by surface availability or substrate concentration, rather than microbial concentration. This would typically require that the inoculum volume is greater than 50% of the test volume.

4.17.3.4 Advanced Model-Based Analysis Dynamic modeling of anaerobic systems developed reasonably quickly from simple dynamic first-order models, largely reflecting only hydrolysis (Gossett and Belser, 1982; Pavlostathis and Giraldo-Gomez, 1991; Pavlostathis and Gossett, 1986) to more complex dynamic multi-step models that include all the steps shown in Figure 1 (Costello et al., 1991; Siegrist et al., 1993). These include complex interactions such as physicochemical models, ammonia inhibition, and the production of organic acids. The wide variety of multi-step models have been largely consolidated in the IWA Anaerobic Digestion Model No. 1 (Batstone et al., 2002), which was designed to be a broadly applicable generic model of anaerobic digestion processes. This has been adapted to a number of diverse applications (Batstone et al., 2006a), including high-rate, sulfate reducing, nitrate reducing, solid and manure digestion, fermentative, and solid-phase digestion. In particular, there has been substantial effort into including anaerobic digesters in whole-plant models that largely depend on the IWA activated-sludge models (Nopens et al., 2009). This has allowed relatively easy characterization of input streams to the anaerobic digestion model – something which has been classically challenging. Dynamic modeling has a number of very practical applications, as well as enables specific areas of research. Specific practical applications include

• • •

Scenario analysis prior to major process changes – particularly with respect to particular inhibitors (Batstone and Keller, 2003). Its use to determine degradability rate and extent properties of upstream materials in situ, rather than through BMP testing (Batstone et al., 2009). Dynamic and detailed assessment of caustic dosing requirements and optimization for alkalinity addition in comparison with static analysis.

Anaerobic Processes

637

Hydrolysis rate - khyd (d−1)

0.2

0.15

0.1 0.3

0.35

0.4

0.45

Degradability fraction (f d) Figure 20 Surface estimation of degradability parameters – for two-parameter estimates on BMP tests. The 95% two-parameter region is represented by the line, while confidence intervals represent uncorrelated, linear estimates of parameter confidence.

4.17.4 Future Applications of Anaerobic Digestion 4.17.4.1 Sewage Treatment and Nutrient Removal High-rate anaerobic digestion has now been evaluated extensively for treatment of low concentration and domestic sewage (Barber and Stuckey, 1999; Foresti et al., 2006; Seghezzo et al., 1998). It is generally suitable for removal of bulk organics, and to remove some pathogens (though not to standards). In comparison with conventional aerobic treatment, high-rate anaerobic treatment of domestic wastewater is relatively low in capital costs and distinctly lower in operating cost, does not require aeration energy (and can produce energy as methane), and is relatively low maintenance. The main disadvantages are (1) low removal of nutrients; (2) relatively poor removal of organics (60–90%), and (3) release of methane dissolved either in the liquid or directly from the digester surface. While it is a suitable alternative to no treatment, high-rate anaerobic treatment cannot produce an effluent suitable for direct discharge to inland watercourses, with minimal environmental impact. Phosphorus can be removed (and recovered by precipitation) and methane can be captured during treatment, or removed in aerobic or other post treatment, which can also be used to remove residual organics and pathogens (Barber and Stuckey, 1999; Chernicharo, 2006; Foresti et al., 2006; Seghezzo et al., 1998). However, the key issue is removal of nitrogen. Currently, the main method of nitrogen removal (nitrification–denitrification) requires carbon for denitrification, and anaerobic processes remove carbon. Partial nitration to nitrite reduces the carbon load. While there are processes that can remove ammonia, such as the biological process anammox (using nitrite as electron acceptor to remove ammonia), stripping, and adsorption (Foresti et al., 2006), most of these are applicable at higher ammonia concentration. The anammox process is probably the most promising for low-concentration ammonia removal, and ammonia concentration is possible, through both adsorption and

membrane processes. While there are challenges, anaerobic processes both at low concentration and in solids digesters offer sustainable, low-cost alternatives to conventional aerobic processes.

4.17.4.2 Nutrient Recovery Phosphorus and nitrogen are key components in many organic sources, including biosolids and manure. Phosphorus in particular is a key resource, since it is a nonrenewable resource. World reserves are substantial, and depletion of ready resources is not expected until later this century (Isherwood, 2000). However, demand is also increasing substantially, and this has led to dramatic price increases, particularly through 2008. Alternative, sustainable, and low-cost alternatives are therefore highly desirable, particularly where national reserves are low, or supply restricted. Anaerobic digestion is already used to stabilize organic biosolids and manure for agricultural applications. Anaerobically stabilized organic biosolid is an excellent fertilizer, generally with comparable impacts (per unit nitrogen) to mineral fertilizer (Warne, 2009), with the added benefits of carbon, water, and trace-compound addition. However, stabilized organic solids are bulky, with nitrogen content between 3 and 10% (dry basis), or 0.3 and 2% on a wet basis. Transport costs are normally in the same cost order of magnitude as the value of the nutrients, meaning that beneficial use is driven by disposal costs, rather than the value of the nutrients. However, there is substantial scope for recovery and concentration of both nitrogen and phosphorus (De-Bashan and Bashan, 2004). Aerobic microbes can be used to accumulate phosphorus via enhanced biological phosphorus removal (EBPR), which results in a high phosphorus WAS stream. Anaerobic digestion plays a key component in phosphorus recovery, as it can be used to re-mobilize ammonia and phosphorus, which can then be recovered as precipitated phosphorus. Struvite ((MgNH4PO4  6H2O) is probably the

638

Anaerobic Processes

best mineral, as magnesium is low cost, and struvite precipitation also allows for nitrogen recovery (Munch and Barr, 2001). At the moment, struvite precipitation is largely used as a phosphorus removal method, than as a recovery technique, but this is likely to change in the future, with anaerobic digestion the major component to mobilize and recover accumulated phosphorus and nitrogen.

4.17.4.3 Future Applications in Energy Generation and Transport Until now, anaerobic digestion has been mainly industrially applied in developed nations for large-scale organic solid stabilization and destruction. Economics are normally driven by solid destruction and stabilization rather than energy production. Energy is either utilized to produce low-quality heat, or in co-generation engines. Economies of scale and maintenance requirements mean that the optimal economic size of co-generation engines is approximately 500 kW. While newer technologies such as microturbines are making anaerobic digestion more attractive at smaller scale, for electricity production, anaerobic digestion is still a large-scale proposition. It is also widely applied at very small scales across Asia, South America, and Africa and the Middle East directly for methane generation and utilization. Methane is used directly and effectively as a natural-gas replacement. In fact, anaerobic digestion is one of the only renewable energy technologies which is fully mature, completely scalable, and generates an energy product that can be stored as produced. There is a particular application in intensive agriculture and food processing, where there is a need for water treatment and energy, and much of the organics are being emitted as methane, which is currently lost (with a consequent greenhouse-gas impact). It is very likely that anaerobic digestion will be implemented increasingly at smaller scale once the technology is standardized further as a partner to other scalable renewable options such as wind and photovoltaic solar cells. The applications of small-to-medium-scale anaerobic digestion are not only limited to methanogenesis. Fermentation can also be used to produce a number of alternative products, including organic acids, alcohols, and hydrogen. While we do not yet have the knowledge to fully direct mixed-culture fermentation to specific products (Rodrı´guez et al., 2006), this is an exciting research area that will likely challenge classical pure-culture fermentation on cost, conversion, and specificity. It has been further enhanced by the application of bioelectrochemical systems, which have the capacity to utilize electrical current to drive full conversion of low-value carbon feedstocks to valuable products such as hydrogen, organic acids, and alcohols.

References Angelidaki I, Alves M, Bolzonella D, et al. (2009) Defining the biomethane potential (BMP) of solid organic wastes and energy crops: A proposed protocol for batch assays. Water Science and Technology 59: 927--934. Aquino SF, Chernicharo CAL, Soares H, Takemoto SY, and Vazoller RF (2008) Methodologies for determining the bioavailability and biodegradability of sludges. Environmental Technology 29: 855--862.

Barber WP and Stuckey DC (1999) The use of the anaerobic baffled reactor (ABR) for wastewater treatment: A review. Water Research 33: 1559--1578. Batstone DJ (2009) Towards a generalised physicochemical modelling framework. Reviews in Environmental Science and Biotechnology 8: 113--114. Batstone DJ and Keller J (2003) Industrial applications of the IWA Anaerobic Digestion Model No. 1 (ADM1). Water Science and Technology 47: 199--206. Batstone DJ, Keller J, Angelidaki I, et al. (2002) Anaerobic Digestion Model No. 1 (ADM1), IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes. London: IWA Publishing. Batstone DJ, Keller J, and Blackall LL (2004) The influence of substrate kinetics on the microbial community structure in granular anaerobic biomass. Water Research 38: 1390--1404. Batstone DJ, Keller J, and Steyer JP (2006a) A review of ADM1 extensions, applications, and analysis: 2002–2005. Water Science and Technology 54: 1--10. Batstone DJ, Picioreanu C, and van Loosdrecht MCM (2006b) Multidimensional modelling to investigate interspecies hydrogen transfer in anaerobic biofilms. Water Research 40: 3099--3108. Batstone DJ, Pind PF, and Angelidaki I (2003) Kinetics of thermophilic, anaerobic oxidation of straight and branched chain butyrate and valerate. Biotechnology and Bioengineering 84: 195--204. Batstone DJ, Tait S, and Starrenburg D (2009) Estimation of hydrolysis parameters in full-scale anerobic digesters. Biotechnology and Bioengineering 102: 1513--1520. Boe K, Batstone DJ, and Angelidaki I (2007) An innovative online VFA monitoring system for the anerobic process, based on headspace gas chromatography. Biotechnology and Bioengineering 96: 712--721. Boone DR, Johnson RL, and Liu Y (1989) Diffusion of interspecies electron carriers H2 and formate in methanogenic ecosystems and its implications in the measurement of Km for H2 or formate uptake. Applied and Environmental Microbiology 55: 1735--1741. Boone DR, Whitman WB, and Rouviere P (1993) Diversity and taxonomy of methanogens. In: Ferry JG (ed.) Methanogenesis: Ecology, Physiology, Biochemistry and Genetics, pp. 35--80. New York: Chapman and Hall. Bryant M, Wolin E, Wolin M, and Wolfe R (1967) Methanobacillus omelianskii, a symbiotic association of two species of bacteria. Archives of Microbiology 59: 20--31. Chernicharo C (2006) Post-treatment options for the anaerobic treatment of domestic wastewater. Reviews in Environmental Science and Bio/Technology 5: 73--92. Costello DJ, Greenfield PF, and Lee PL (1991) Dynamic modelling of a single-stage high-rate anaerobic reactor – I. Model development. Water Research 25: 859--871. De-Bashan LE and Bashan Y (2004) Recent advances in removing phosphorus from wastewater and its future use as fertilizer (1997–2003). Water Research 38: 4222--4246. de Bok FAM, Plugge CM, and Stams AJM (2004) Interspecies electron transfer in methanogenic propionate degrading consortia. Water Research 38: 1368--1375. Dwyer J, Starrenburg D, Tait S, Barr K, Batstone DJ, and Lant P (2008) Decreasing activated sludge thermal hydrolysis temperature reduces product colour, without decreasing degradability. Water Research 42: 4699--4709. Eastman JA and Ferguson JF (1981) Solubilization of particulate organic carbon during the acid phase of anaerobic digestion. Journal of the Water Pollution Control Federation 53: 352--366. Ekama GA, So¨temann SW, and Wentzel MC (2007) Biodegradability of activated sludge organics under anaerobic conditions. Water Research 41: 244--252. Fang HHP, Chui HK, Li YY, and Chen T (1994) Performance and granule characteristics of UASB process treating wastewater with hydrolyzed proteins. Water Science and Technology 30: 55--63. Feijoo G, Soto M, Mendez R, and Lema JM (1995) Sodium inhibition in the anaerobicdigestion process – antagonism and adaptation phenomena. Enzyme and Microbial Technology 17: 180--188. Fernandez B, Porrier P, and Chamy R (2001) Effect of inoculum substrate ratio on the start-up of solid waste anaerobic digesters. Water Science and Technology 44: 103--108. Ferry JG (1993) Methanogenesis: Ecology, Physiology, Biochemistry and Genetics. New York: Chapman and Hall. Finnerty WR (1988) B-oxidation of fatty acids. In: Ratledge C and Wilkinson SG (eds.) Microbial Lipids, vol. 2, pp. 528--566. London: Academic Press. Foresti E, Zaiat M, and Vallero M (2006) Anaerobic processes as the core technology for sustainable domestic wastewater treatment: Consolidated applications, new trends, perspectives, and challenges. Reviews in Environmental Science and Bio/ Technology 5: 3--19. Ge H, Jensen PD, and Batstone DJ (2010) Pre-treatment mechanisms during thermophilic–mesophilic temperature phased anaerobic digestion of primary sludge. Water Research 44: 123--130.

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Gossett JM and Belser RL (1982) Anaerobic digestion of waste activated sludge. ASCE: Journal of Environmental Engineering 108: 1101--1120. Gujer W and Zehnder AJB (1983) Conversion processes in anaerobic digestion. Water Science and Technology 15: 127--167. Hashimoto AG (1986) Pretreatment of wheat straw for fermentation to methane. Biotechnology and Bioengineering 28: 1857--1866. Henze M, Gujer W, Mino T, and van Loosdrecht M (2000) Activated Sludge Models ASM1, ASM2, ASM2d, and ASM3. London: IWA Publishing. Humphrey AE (1979) The hydrolysis of cellulosic materials to useful products. In: Brown RD and Jurasek L (eds.) Hydrolysis of Cellulose: Mechanisms of Enzymatic and Acid Catalysis, vol. 181, ch. 2, pp. 25–53. Washington, DC: American Chemical Society Hwu CS, Donlon B, and Lettinga G (1996) Comparative toxicity of long chain fatty acid to anaerobic sludges from various origins. Water Science and Technology 34: 351--358. Isherwood KF (2000) Mineral Fertilizer Use and the Environment. Paris: International Fertilizer Industry Association/United Nations Environment Programme. Jain S, Lala AK, Bhatia SK, and Kudchadker AP (1992) Modelling of hydrolysis controlled anaerobic digestion. Journal of Chemical Technology and Biotechnology 53: 337--344. Karakashev D, Batstone DJ, and Angelidaki I (2005) Influence of environmental conditions on methanogenic compositions in anaerobic biogas reactors. Applied and Environmental Microbiology 71: 331--338. Karakashev D, Batstone DJ, Trably E, and Angelidaki I (2006) Acetate oxidation is the dominant methanogenic pathway from acetate in the absence of Methanosaetaceae. Applied and Environmental Microbiology 72: 5138--5141. Madigan MT, Martinko JM, and Brock TD (2009) Brock Biology of Microorganisms. San Francisco, CA: Pearson. Martinelle M and Hult K (1994) Kinetics of triglyceride lipases. In: Woolley P and Petersen S (eds.) Lipases: Their Structure, Biochemistry and Application, 363pp. Cambridge: Cambridge University Press. McInerney MJ (1988) Anaerobic hydrolysis and fermentation of fats and proteins. In: Zehnder JBA (ed.) Biology of Anaerobic Microorganisms, vol. 1, pp. 373--416. New York: Wiley. McInerney MJ, Bryant MP, Hespell RB, and Costerton JW (1981) Syntrophomonas wolfei gen. nov. sp. nov., an anaerobic, syntrophic, fatty acid oxidising bacterium. Applied and Environmental Microbiology 41: 1029--1039. Moosbrugger RE, Loewenthal RE, and Marais GvR (1990) Pelletisation in a UASB system with protein (casein) as substrate. Water SA 16: 171--178. Mosey FE (1983) Mathematical modelling of the anaerobic digestion process: Regulatory mechanisms for the formation of short-chain volatile acids from glucose. Water Science and Technology 15: 209--232. Munch EV and Barr K (2001) Controlled struvite crystallisation for removing phosphorus from anaerobic digester sidestreams. Water Research 35: 151--159. Musvoto E, Wentzel M, Loewenthal R, and Ekama G (2000a) Integrated chemical– physical processes modelling – I. Development of a kinetic-based model for mixed weak acid/base systems. Water Research 34: 1857--1867. Neves L, Oliveira R, and Alves MM (2004) Influence of inoculum activity on the biomethanization of a kitchen waste under different waste/inoculum ratios. Process Biochemistry 39: 2019--2024. Nopens I, Batstone DJ, Copp JB, et al. (2009) An ASM/ADM model interface for dynamic plant-wide simulation. Water Research 43: 1913--1923. O’Rourke JT (1968) Kinetics of Anaerobic Treatment at Reduced Temperatures. PhD Thesis, Stanford University, Stanford, CA, USA. Park C, Abu-Orf MM, and Novak JT (2006) The digestibility of waste activated sludges. Water Environment Research 78: 59--68. Patterson-Curtis SI and Johnson EA (1989) Regulation of neurotoxin and protease formation in Clostridium botulinum Okra-B and Hall-A by arginine. Applied and Environmental Microbiology 55: 1544--1548. Pauss A and Guiot SR (1993) Hydrogen monitoring in anaerobic sludge bed reactors at various hydraulic regimes and loading rates. Water Environment Research 65: 276--280. Pavlostathis SG and Giraldo-Gomez E (1991) Kinetics of anaerobic treatment: A critical review. Critical Reviews in Environmental Control 21: 411--490. Pavlostathis SG and Gossett JM (1986) A kinetic model for anaerobic digestion of biological sludge. Biotechnology and Bioengineering 28: 1519--1530. Pind PF, Angelidaki I, and Ahring BK (2003) A new VFA sensor technique for anaerobic reactor systems. Biotechnology and Bioengineering 82: 54--61. Ramsay IR (1997) Modelling and Control of High-Rate Anaerobic Wastewater Treatment Systems. PhD Thesis, University of Queensland, Brisbane, QLD, Australia. Ramsay IR and Pullammanappallil PC (2001) Protein degradation during anaerobic wastewater treatment: Derivation of stoichiometry. Biodegradation 12: 247--257.

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Ramsay IR and Pullammanappallil PC (2005) Full-scale application of a dynamic model for high-rate anaerobic wastewater treatment systems. Journal of Environmental Engineering 131: 1030--1036. Rao AG, Reddy TSK, Prakash SS, et al. (2008) Biomethanation of poultry litter leachate in UASB reactor coupled with ammonia stripper for enhancement of overall performance. Bioresource Technology 99: 8679--8684. Raposo F, Banks CJ, Siegert I, Heaven S, and Boria R (2006) Influence of inoculum to substrate ratio on the biochemical methane potential of maize in batch tests. Process Biochemistry 41: 1444--1450. Ratledge C (1994) Biodegradation of oils, fats and fatty acids. In: Ratledge C (ed.) Biochemistry of Microbial Degredation, vol. 1, 590pp. Dordrecht: Kluwer. Rebac S, Ruskova J, Gerbens S, vanLier JB, Stams AJM, and Lettinga G (1995) Highrate anaerobic treatment of wastewater under psychrophilic conditions. Journal of Fermentation and Bioengineering 80: 499--506. Reguera G, McCarthy KD, Mehta T, Nicoll JS, Tuominen MT, and Lovley DR (2005) Extracellular electron transfer via microbial nanowires. Nature 435: 1098--1101. Ren N, Wan B, and JuChang H (1997) Ethanol-type fermentation from carbohydrate in high rate acidogenic reactor. Biotechnology and Bioengineering 54: 428--433. Rodrı´guez J, Kleerebezem R, Lema JM, and van Loosdrecht MCM (2006) Modeling product formation in anaerobic mixed culture fermentations. Biotechnology and Bioengineering 93: 592--606. Roy F, Albagnac G, and Samain E (1985) Influence of calcium addition on growth of highly purified syntrophic cultures degrading long-chain fatty acids. Applied Environmental Microbiology 49: 702--705. Rozzi A and Remigi E (2004) Methods of assessing microbial activity and inhibition under anaerobic conditions: A literature review. Reviews in Environmental Science and Bio/Technology 3: 93--115. Schink B (1997) Energetics of syntrophic cooperation in methanogenic degradation. Microbiology and Molecular Biology Reviews 61: 262--280. Seghezzo L, Zeeman G, van Lier JB, Hamelers HVM, and Lettinga G (1998) A review: The anaerobic treatment of sewage in UASB and EGSB reactors. Bioresource Technology 65: 175--190. Siegrist H, Renggli D, and Gujer W (1993) Mathematical modelling of anaerobic mesophilic sewage sludge treatment. Water Science and Technology 27: 25--36. Siegrist H, Vogt D, Garcia-Heras J, and Gujer W (2002) Mathematical model for meso and thermophilic anaerobic sewage sludge digestion. Environmental Science and Technology 36: 1113--1123. Speece RE (2008) Anaerobic Biotechnology and Odor/Corrosion Control for Municipalities and Industries. Nashville, TN: Archae Press. Stams AJM (1994) Metabolic Interactions between anaerobic-bacteria in methanogenic environments. Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology 66: 271--294. Stams AJM and Plugge CM (1994) Occurrence and function of the acetyl-CoA cleavage pathway in a syntrophic propionate oxidising bacterium. In: Drake HL (ed.) Acetogenesis, pp. 557--630. New York: Chapman and Hall. Steyer JP, Bernard O, Batstone DJ, and Angelidaki I (2006) Lessons learnt from 15 years of ICA in anaerobic digesters. Water Science and Technology 53: 25--33. Stumm W and Morgan JJ (1996) Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters. New York: Wiley. Tchobanoglous G, Burton F, and Stensel H (2003) Metcalf and Eddy Inc. Wastewater Engineering, Treatment and Reuse. New York, NY: McGraw-Hill. Temudo MF, Muyzer G, Kleerebezem R, and van Loosdrecht MCM (2008) Diversity of microbial communities in open mixed culture fermentations: Impact of the pH and carbon source. Applied Microbiology and Biotechnology 80: 1121--1130. Thiele JH and Zeikus JG (1988) Interactions between hydrogen and formate producing bacteria and methanogens during anaerobic digestion. In: Erickson CE and DanielYee-Chak-Fung (eds.) Handbook on Anaerobic Fermentations, pp. 537--595. New York, NY: Dekker. Tong Z and McCarty P (1991) Microbial hydrolysis of lignocellulosic materials. In: Isaacson R (ed.) Methane from Community Wastes, pp. 61--100. London: Elsevier. Van Langerak E and Hamelers H (1997) Influent calcium removal by crystallization reusing anaerobic effluent alkalinity. Water Science Technology 36: 341--348. Van Lier JB (2008) High-rate anaerobic wastewater treatment: Diversifying from endof-the-pipe treatment to resource-oriented conversion techniques. Water Science and Technology 57: 1137--1148. Van Lier JB, Sanz Martin JL, and Lettinga G (1996) Effect of temperature on the anaerobic thermophilic conversion of volatile fatty acids by dispersed and granular sludge. Water Resources 30(1): 199--207. Vavilin VA, Rytov SV, and Lokshina LYa (1996) A description of hydrolysis kinetics in anaerobic degradation of particulate organic matter. Bioresource Technology 56: 229--237.

4.18 Microbial Fuel Cells B Virdis, S Freguia, RA Rozendal, K Rabaey, Z Yuan, and J Keller, The University of Queensland, Brisbane, QLD, Australia & 2011 Elsevier B.V. All rights reserved.

4.18.1 4.18.1.1 4.18.1.2 4.18.1.3 4.18.2 4.18.3 4.18.4 4.18.4.1 4.18.4.2 4.18.4.3 4.18.4.4 4.18.5 4.18.5.1 4.18.5.2 4.18.5.3 4.18.5.4 4.18.6 4.18.7 4.18.8 4.18.9 4.18.9.1 4.18.9.2 4.18.9.3 4.18.9.4 4.18.10 4.18.10.1 4.18.10.2 4.18.10.3 4.18.10.4 4.18.10.5 4.18.11 References

Resource Recovery from Wastewater Water Recovery Nutrient Recovery Energy Recovery Microbial Fuel Cells Thermodynamics of Microbial Fuel Cells Factors Determining the Decrease of Cell Voltage Losses due to Mass-Transfer Limitation Losses due to Bacterial Metabolic Kinetics Losses due to Electron Transfer to (and from) the Electrode Losses due to the Resistance of the Electrolytes (Including the Ion-Exchange Membrane) and of the Electrical Interconnection to the Charges Flow Materials and Architectures Design Compartment Separation Electrodes Cathodic Compartment Electrochemically Active Microorganisms and Extracellular Electron Transfer Oxidative Processes Reductive Processes Challenges toward Improving MFC Efficiency Minimizing Electrode-Potential Losses Respiration, Fermentation, and Methanogenesis Reducing pH Gradients Wastewater and Electrode Resistance Opportunities for Bioelectrochemical Systems Wastewater Treatment Nitrogen Removal Bioremediation H2 Production Bioelectrochemical Production of Value-Added Chemicals Outlook

4.18.1 Resource Recovery from Wastewater Fossil fuel exploitation has significantly affected the economic growth of developed countries within the past century. The world energy-consumption rate is projected to double from 13.5 TW (1 TW ¼ 1012 W) in 2001 to 27 TW by the year 2050 and to triple to 43 TW by 2100 (Lewis and Nocera, 2006). Although the rise in prices of liquid fuels (e.g., crude oil, natural gas plant liquid, biofuels, oil shale, and bitumen) and natural gas is expected to rationalize energy demand, world energy consumption is still projected to increase due to continuing rapid economic growth and expanding population, particularly in the developing countries. Fossil fuels are predicted to remain the dominant sources of primary energy, accounting for close to 83% of the overall increase in energy demand between 2004 and 2030 (Figure 1). Increasing awareness of the possible anthropogenic effects on climate change, in combination with the instability of the

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fossil fuel market, is motivating political consciousness to reduce greenhouse-gas emissions and to promote renewable energy. The greatest challenge in the future lies in catering to the world’s growing energy demands while simultaneously reducing emission of greenhouse gases. This is certainly predicted to provide serious challenges for fossil-fuel-based economies (Logan, 2008). Nuclear fission alone does not represent a feasible alternative, as known uranium reserves would be depleted within a few decades, not considering the environmental damage caused by the mining and disposal of radioactive material (Lewis and Nocera, 2006). Solar energy is an attractive energy source as it is both renewable and available in large amounts (Seboldt, 2004); however, a society completely dependent on solar energy is not realistic for the short term due to technological and economical difficulties. Other renewable energy technologies must be developed in conjunction with solar energy. About 200 TW of the 170 000 TW solar-radiation flux is continuously transformed

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Microbial Fuel Cells 250 History

Quadrillion kJ

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Projection

Oil Coal Natural gas Renewable Nuclear

150

100

50

0 1990

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Figure 1 World energy use detailed by fuel type, 1990–2030. Data from Energy Information Administration (EIA).

into wind power, whereas 100 TW is stored as biomass through photosynthesis, and 6 TW is transformed into hydropower through the water cycle (Niele, 2005). These indirect forms of solar energy are already exploited to some extent for electricity production through wind turbines, biomass gasification/combustion, and hydroelectric dams. Nevertheless, the extent of this exploitation must be further developed in the future in parallel to elevating societal energy demands. Securing water resources to match increasing demand is also becoming challenging. Global trends such as urbanization and migration, often combined with frequent drought periods, even in areas traditionally rich in water resources, have increased the demand for water, food, and energy, and put at risk the sustainability of current living standards. The pressure of water availability has also affected other waterconsuming sectors, such as public water supply, agriculture, industry, and, of course, power generation. In this global picture of fading resources, we can therefore no longer afford to waste any potential sources of all these three key resources. For example, domestic and industrial wastewaters are ubiquitous and represent a potential source of energy, water, and nutrients. The development of technologies capable of simultaneously recovering energy, water, and nutrients from wastewater is crucial to resource management in the future.

4.18.1.1 Water Recovery Wastewater represents a valuable recyclable water resource. Although containing compounds dangerous to public health and to the environment (e.g., pathogens, chemicals, organics, and nutrients), at least 99.9% of wastewater is in fact water and, as such, it should by no means be considered as waste. Engineered technologies for the reintroduction of treated wastewater to water-supply grids appear to be an essential

priority, given the increasingly limited water resources in both quantity and quality. Wastewater treatment plants are a crucial part of the overall water-recycle process, being an important pre-treatment step for the advanced treatment processes, which are currently almost nonexistent worldwide, but which can generate water qualities suitable for reuse even in potable water applications. Wastewater-treatment processes aim to reduce the relevant concentration of pollutants by means of separation, destruction, and disinfection (Tchobanoglous et al., 2003). The efforts into improving the quality of wastewater-treatmentplant effluents have achieved levels of pollutant elimination well beyond the standards of environmental protection. An activated sludge treatment can reduce the influent biological oxygen demand (BOD) concentration from 4300 mg l1 to o5 mg l1 when upgraded for biological nutrient removal, while reducing the influent total nitrogen (N) concentration from 460 mg l1 to o3 mg l1; and influent phosphorus (P) concentrations from 412 mg l1 to o1 mg l1. (BOD is a measure of the concentration of biodegradable material present in wastewater expressed as the amount of oxygen consumed by microorganisms in breaking down the organic matter during a certain period of time. It normally represents a fraction of the chemical oxygen demand (COD), which is the total oxygen consumption consumed during chemical breakdown of organic and inorganic matter.) Further disinfection treatment can achieve up to 99.9999% removal of bacteria where membrane filtration is used (Foley and Keller, 2008). Advanced water treatments (AWTs) can further improve the efficacy of the disinfection process, by removing recalcitrant organics that are not metabolized in the biological nutrientremoval process and by reducing the content of total dissolved solids. AWT consists of a multi-barrier system against various acute and chronic risk factors, such as micropollutants and pathogens, that remain even after regular wastewater

Microbial Fuel Cells

treatment, and prior to the addition to ground or surface water for reuse. The most common forms of AWT are microfiltration and reverse osmosis, which can be followed by advanced oxidation processes (ozone and H2O2/ultraviolet (UV)) to remove recalcitrant contaminants. If an energy-recovery process is also included, the wastewater can be, for example, initially treated through anaerobic digestion, which would produce biogas that can power gas turbines for electricity generation. The effluent would then require further aerobic polishing to remove the slower biodegradable material, while achieving drinking water standards would require AWT. The water can subsequently be collected in the environment (e.g., in a dam) where time and environmental buffers will ensure that the higher-quality standards are met, even prior to any treatment processes already in place to produce safe drinking water. In this scheme, a large fraction of the wastewater is thus recovered as clean water to be reused for domestic, agricultural, and industrial purposes.

4.18.1.2 Nutrient Recovery For many years, wastewater treatment methods have been improved to achieve environmental protection from nutrient overload in receiving water bodies. Removal of carbon, nitrogen, and phosphorus from wastewater requires large amounts of energy and produces potentially useful resources of minerals and water that are normally disposed of. The most important minerals for living organisms are considered to be nitrogen and phosphorus, although potassium and sulfur should also be included as essential. Nitrogen and phosphorus for agriculture are produced from natural resources. Phosphorus is currently entirely derived from highly geographically concentrated geological reserves (mainly in North Africa, USA, China, and Russia). Phosphate rocks are finite nonrenewable resources and are therefore limited, with an estimated 50–100 years until depletion is reached under current extraction rates (Larsen et al., 2007). Moreover, extraction and production of good-quality phosphorus require energy, and produces a waste (the production of 1 kg of phosphorus produces up to 2 kg of gypsum, inclusive of heavy metals and radioactive elements). It is thus essential that phosphate is recovered efficiently in the future. Recycling of phosphorus contained in sewage is currently very limited, even though several techniques are available to incorporate it into the excess activated sludge. Nitrogen in fertilizers is almost completely supplied by atmospheric N2, which makes the source virtually infinite. However, to be accessible to living organisms, atmospheric nitrogen has to be in the form of ammonia or nitrate. Industrial processes for the conversion of N2 gas to ammonia require a large energy investment using the Haber–Bosch process (10.3 kW h1 kg1 nitrogen produced; Maurer et al., 2003). Additional energy is invested to obtain the opposite process during wastewater-treatment processes to achieve low nitrogen levels in the treated effluent. With this in consideration, wastewater treatment for nitrogen removal can be regarded as an indirect and inefficient method for nitrogen recovery (recycling over the atmosphere) since it engineers biological nitrification and denitrification to N2 gas that is

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returned to the atmosphere. Therefore, any direct nitrogen recovery process will have to be more energy efficient than the indirect route via the atmosphere to be environmental favorable. As pointed out by Maurer et al. (2003), direct recovery of nitrogen-rich wastewater into reusable forms can be more sustainable than indirect recovery by biological nitrification/ denitrification and ammonia production through the Haber– Bosch process, as the total net amount of energy required is lower. For instance, the energy demand for nitrification and denitrification, together with ammonia production through the Haber–Bosch process, would be 42.8 kW h1 kg1 nitrogen using a denitrification process with methanol addition; 25 kW h1 kg1 nitrogen in case of a preanoxic denitrification system; and 17.8 kW h1 kg1 N for nitrogen removal through the Sharon–Anammox process (Maurer et al., 2003). This considerable energy requirement for indirect nitrogen recycling makes some direct recovery techniques such as thermal volume reduction of urine (requiring about 8.1–9.4 kW h1 kg1 N), or even struvite production, economically and environmentally interesting (Maurer et al., 2003). (Struvite is a phosphate mineral with formula NH4MgPO4  6H2O. Its production requires 28.3 kW h1 kg1 N (Maurer et al., 2003), which is higher than the energy demand for alternative N recovery processes like thermal volume reduction of urine. However, together with nitrogen, phosphorus is also recovered (struvite contains about 2.2 kg phosphorus per each kilogram of nitrogen).)

4.18.1.3 Energy Recovery Many recovery processes can provide bioenergy or valuable chemicals from relatively concentrated biomass streams, such as from wood and agricultural by-products (Hatti-Kaul et al., 2007; Petrus and Noordermeer, 2006; Ragauskas et al., 2006; van Wyk, 2001). Yet, not many conversion processes exist for energy and chemical production from diluted aqueous streams, such as industrial, agricultural, and municipal wastewater. Wastewater contains significant amounts of renewable energy in the form of chemical bonds. For example, domestic wastewater could potentially yield energy up to 2.2 kW h1 m3. (This is considering the energy content of glucose as 4.4 kW h1 kg1 COD, and a wastewater with 500 mg COD l1.) If properly recovered, the chemical energy daily wasted with sewage can potentially cover up to 7% of the energy consumption used for residential purposes in developed countries. (This is assuming an energy recovery of 1.2 kW h1 kg1 COD and considering a total residential energy consumption of 649.8 kg of oil equivalent capita1, equal to 7556 kW h1 capita1, and a total water withdrawal of 948 m3 yr1 capita1, assuming that it all ends up in sewage. Energy-consumption data include all energy used for activities by households except for transportation. Data on energy and water withdrawal are available at the World Research Institute.) This figure is expected to be much higher in developing countries, since the energy consumption tends to be far lower. Even though technologies such as anaerobic digestion have been long known and implemented for many years to recover energy from wastewater, the activated sludge process is by far the most widely applied process for wastewater treatment. The

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Microbial Fuel Cells

process relies upon aeration of the wastewater, which allows microorganisms to convert the available organics into carbon dioxide. The solids are then separated from the treated water through sedimentation. Although this process yields valuable purified water, the high energy requirements for aeration, typically 0.5 kW h1 m3 treated water (Rabaey and Verstraete, 2005), makes anaerobic processes far more energy efficient than aerobic treatment. Methanogenic anaerobic digestion of organics has been shown to be advantageous over aerobic processes due to its high organic removal rates, low sludge production, low energy inputs, and, ultimately, for its energy production (Angenent et al., 2004). The methane produced thus has traditionally been used as the on-site fuel source for heat/electrical applications, or used to power gas turbines, with net energy efficiencies up to 35–40%. More recently, methane has also been converted into other products by catalytic conversion to syngas, a mixture of hydrogen and carbon monoxide, or into methanol for use in production of biodiesel (Angenent et al., 2004). However, whether methane is used in a gas turbine or to produce syngas, it is necessary to purify the biogas from impurities such as hydrogen sulfide also produced during anaerobic digestion, which therefore equates to an additional treatment process. Dark fermentation represents an alternative to biological methane production, which shares with it much of the same process reactions involved, except that during dark fermentation, the hydrogen-metabolizing organisms (methanogens) are inhibited through heat treatment of the initial inoculum while retaining only spore-forming fermenting bacteria in which hydrogen-forming bacteria are included. However, due to the limitations imposed by the thermodynamics of hydrogen formation through the hydrogenase reaction, the conversion yields of the total electron equivalents present as carbohydrate in wastewater does not normally exceed B15% (Angenent et al., 2004). As such, the process appears less appealing in comparison with the more reliable and mature biological methane production. Microbial fuel cells (MFCs) have been gaining increasing attention in recent times as devices able to produce electric power while simultaneously treating industrial, agricultural, or municipal wastewater (Rozendal et al., 2008a). Compared to treatment technologies, MFCs have the advantage of being able to theoretically achieve efficiencies. The underlining rationale is that fuel cells do not use heat as an intermediate form of energy for electricity production. As such, the process efficiency is not limited by the Carnot cycle, according to which, for a reversible process, the theoretical maximum conversion efficiency of heat to work is determined by the absolute temperature Th (K) of the process and the absolute temperature Tc (K) of the cold sink (i.e., the environment):

Zideal ¼ 1 

Tc Th

ð1Þ

As heat-resisting properties of construction materials are limited to a certain maximal temperature, the theorem implies that the overall yield of combustion processes is usually no higher than 35–45% (Carnot, 1824). Since fuel cells do not operate on a thermal cycle, they are not constrained to thermodynamic limitations such as the Carnot’s theorem.

Therefore, they can theoretically convert the entire free energy of the fuel oxidation into electric energy (Schroder and Harnisch, 2009).

4.18.2 Microbial Fuel Cells Although the existence of a bioelectrical phenomenon was first observed by Italian physicist Luigi Galvani in 1790 (Piccolino, 1997), the principle that microorganisms could generate voltage and current was put forth by Michael Cresse Potter, a professor of botany at the University of Durham, UK, at the beginning of the twentieth century (Potter, 1911). This occurred a few years earlier than the discovery of the activated sludge process and shortly after the invention of the Imhoff tank, an early form of anaerobic digester. In 1931, Barnett Cohen confirmed Potter’s observations reporting a stacked biological fuel cell delivering 35 V at a current of 2 mA (Cohen, 1931). However, it was not until the US National Aeronautics and Space Administration (NASA) became interested in exploiting opportunities for recycling organic wastes into electricity during long space flights that MFCs regained popularity, and by the year 1963, were already commercially available as a power supply for small electrical devices (Shukla et al., 2004). Despite these early successes, the rapid advancement of alternative technologies, such as solar photovoltaic systems, and the fact that the complexity of the underlying biochemical processes became more evident, MFCs suffered an inevitable setback. However, the growing awareness to reduce society’s dependency on fossil fuel and the emerging environmental consequences of their usage has triggered the revival of MFC research in the last 10–15 years. In an MFC, the chemical energy contained in soluble organic molecules, such as carbohydrates and volatile fatty acids (VFAs), can be directly recovered as electric energy. MFCs are galvanic cells that couple the oxidation of an electron donor at an anode with the reduction of an electron acceptor at a higher redox potential at the cathode. Power output is generated as the overall reaction is exergonic. MFCs are the most extensively described bioelectrochemical system (BES) which, more generically, refers to a device where microorganisms interact electrically with electrodes (Rabaey et al., 2007). Microbial electrolysis cells (MECs) are another category of bioelectrochemical systems where the oxidation reaction at the anode is coupled to the reduction of an electron acceptor at a lower potential at the cathode (i.e., water to produce hydrogen). Since the process is endergonic, a certain voltage needs to be applied. In its standard configuration, an MFC consists of two chambers: the anode and the cathode compartments (Figure 2). Bacteria growing at the anode catalyze the electron transfer from an organic (or inorganic) molecule to the anodic electrode. The reduction of the terminal electron acceptor takes place at the cathode, generally separated from the anode by an ion-selective membrane and electrically connected to it via an external circuit containing a resistor or power user that harvests the energy liberated by the reactions. Several electron acceptors can be used, for example, oxygen (O2), potassium hexacyanoferrate (also known as ferricyanide, K3Fe(CN)6), and nitrate, (NO3  ). The cathodic reaction can be of an

Microbial Fuel Cells

e-

A

The maximal work that can be derived from such processes can be measured by means of the Gibbs free energy of the general redox reaction nA A þ nB B- nC C þ nD D:

e-

H2O

CO2

O2

COD

Figure 2 Schematic representation of a microbial fuel cell (MFC). The substances (organics represented as chemical oxygen demand (COD)) are oxidized to CO2 by microorganisms, which transfer the gained electrons to the anode. At the cathode, the electrons are used to reduce oxygen abiotically or biotically, producing water. To maintain electroneutrality within the system, positive charges have to migrate from the anode to the cathode through an ion-permeable separator (a cation exchange membrane (CEM) in this representation).

electrochemical or bioelectrochemical nature. In the latter case, bacteria are involved as catalysts at the cathode as well, promoting electron transfer from the electrode to the final electron acceptor. If glucose is taken as an example of electron donor and oxygen as electron acceptor, Equations (2) and (3) characterize the reactions occurring at the anode and cathode, respectively:

C6 H12 O6 þ 6H2 O- 6CO2 þ 24H þ 24e

ð2Þ

6O2 þ 24Hþ þ 24eþ - 12H2 O

ð3Þ

þ

645



The aqueous media in the anodic and cathodic compartments are called the anolyte and the catholyte, respectively. The role of the ion-exchange membrane (IEM) is to allow the transport of charges between the two compartments, thus maintaining the electroneutrality of the system, and also to physically separate the two redox processes, thus preventing the electron acceptor from reacting directly with the electron donor. (IEM is a type of membrane that allows the selective diffusion of certain ions. Different types of IEMs exist, depending on the species that is transported, including cationexchange membranes (CEMs), proton-exchange membranes (PEMs), and anion-exchange membranes (AEMs).)

4.18.3 Thermodynamics of Microbial Fuel Cells Chemotrophic organisms fulfill their energy requirements by transferring electrons from a low redox potential molecule (primary electron donor) to a high redox potential molecule (primary electron acceptor). MFC electrodes virtually interpose within the electron-transfer process that would naturally occur in bacteria between the electron donor and acceptor.

DGr ¼ DGr0 þ RTln



anCC anDD anAA  anBB

 ð4Þ

where DGr is the Gibbs free energy of a reaction at specific conditions, measured in Joules (J), DG0r (J) is the Gibbs free energy at standard conditions (usually defined as 298.15 K, 1 bar pressure, and 1 M concentration of the species), R is the universal gas constant (8.3145 J mol1 K1), T is the absolute temperature (K), and ai is the activity of reactant i, and ni the respective stoichiometric coefficient. (The Gibbs free energy represents the maximum amount of useful work that can be obtained from a reaction.) In diluted systems, the relation can be simplified by replacing the activities with the concentrations, and Equation (4) can be rewritten as

 nC  ½C ½D nD DGr ¼ DGr0 þ RTln ½A nA ½B nB

ð5Þ

In order to generate a current, the overall process in an MFC needs to be thermodynamically spontaneous. This requires the Gibbs free-energy change of the process to be negative. For a bioelectrochemical conversion, it is useful to evaluate the reaction in terms of electromotive force (Eemf), which is expressed in volts (V). The electromotive force and the Gibbs free energy are related according to

DGr ¼ QEemf ¼ nFEemf

ð6Þ

where Q is the charge transferred in the reaction in coulombs (C), which is also equal to the number n of electrons exchanged in the reaction (mol) multiplied per the Faraday’s constant F (9.64853 104 C mol1). Equation (6) can therefore be rearranged, yielding

Eemf ¼ 

DGr nF

ð7Þ

At standard conditions, DGr is equal to DG0r , and Equation (7) can be written as

E0emf ¼ 

DGr0 nF

ð8Þ

where E0emf represents the electromotive force at standard conditions. Equations (4) and (8) can be combined to calculate the total electromotive force for a given redox reaction occurring at certain conditions, yielding Equation (9), which is known as the Nernst law:

Eemf ¼ E0emf 

 nC  RT ½C ½D nD ln nF ½A nA ½B nB

ð9Þ

Positive values for Eemf refer to spontaneous processes, whereas negative values indicate a nonspontaneous reaction. MFC technology is a galvanic process characterized by positive

646

Microbial Fuel Cells

values for the electromotive force. Microbial electrolysis is instead an electrolytic process where the electromotive force assumes negative values. The Eemf in an MFC can be evaluated by considering the generic redox reaction occurring as the sum of an oxidation and of a reduction. The Eemf is the result of the difference between the reduction potential of the reactions occurring at the cathode and at the anode (according to Equation (6)), each of them evaluated through the Nernst equation applied to the half reaction. Half-cell potentials are reported under the International Union of Pure and Applied Chemistry (IUPAC) convention as reduction potentials in comparison with the standard hydrogen electrode (which has a reduction potential conventionally set to zero at pH2 ¼1 bar, [Hþ] ¼ 1 M); therefore, the reaction is always written as an electron-consuming reaction (reduction). Table 1 lists a series of half-reaction reduction potentials important in MFCs and biological systems in general (Thauer et al., 1977). For biological purposes, the redox potentials are

Table 1 systemse

Summary of redox reactions important in biological

EAn ¼ E0An 

ECat ¼

RT ½C6 H12 O6  ln 24F ½CO2 6  ½Hþ 24

E0Cat

RT 1  ln 4F pO2 ½Hþ 4

!

E0 0

6CO2 þ 24Hþ þ 24e-Glucose þ 6H2O 2Hþ þ 2e-H2 NADþ þ Hþ þ 2e-NADH 2CO2 þ 8Hþ 8e-Acetate þ 2H2O S þ 2Hþ þ 2e-H2S SO4 2 þ 10Hþ þ 8e - H2 S þ 4H2 O Pyruvate þ 2Hþ þ 2e-Lactate FADþ þ 2Hþ þ 2e-FADH2 Fumarate2 þ 2Hþ þ 2e-Succinate2 Cytochrome b (Fe3þ) þ e-Cytochrome b (Fe2þ) Ubiquinone þ 2Hþ þ 2e-Ubiquinone H2 Cytochrome c (Fe3þ) þ e-Cytochrome c (Fe2þ) NO2  þ 2Hþ þ e - NO þ H2 O FeðCNÞ6 3 þ e - FeðCNÞ6 4 Cytochrome a (Fe3þ) þ e-Cytochrome a (Fe2þ) NO3  þ 2Hþ þ 2e - NO2  þ H2 O NO2  þ 8Hþ þ 6e - NH4 þ þ 2H2 O NO3  þ 6Hþ þ 5e - 0:5N2 þ 3H2 O Fe3þ þ e-Fe2þ O2 þ 4Hþ þ 4e-2H2O NO þ Hþ þ e-0.5NO þ 0.5H2O 0.5N2O þ Hþ þ e-0.5N2 þ 0.5H2O

 0.43 Va  0.42 Va  0.32 Va  0.28 Va  0.28 Va  0.22 Va  0.19 Va  0.180 Vd þ 0.03 Va þ 0.035 Va þ 0.11 Va þ 0.25 Va þ 0.350 Vb þ 0.36 Vc þ 0.39 Va þ 0.433 Vb þ 0.440 Vd þ 0.74 Va þ 0.76 (pH ¼ 2)a þ 0.82 Va þ 1.175 Vb þ 1.355 Vb

From Madigan MT, Martinko J, and Parker J (2000) Brock Biology of Microorganisms. Upper Saddle River, NJ: Prentice Hall. b From Thauer RK, Jungermann K, and Decker K (1977) Energy-conservation in chemotropic anaerobic bacteria. Bacteriological Reviews 41: 100–180. c From He Z and Angenent LT (2006) Application of bacterial biocathodes in microbial fuel cells. Electroanalysis 18: 2009–2015. d From Rabaey K and Verstraete W (2005) Microbial fuel cells: Novel biotechnology for energy generation. Trends in Biotechnology 23(6): 291–298. e The standard redox potentials are measured at pH 7 and 25 1C. Redox couples are arranged from the strongest oxidant (more positive reduction potential) at the bottom, to the strongest reductants (most negative reduction potential) at the top. Electrons naturally flow from lower to higher redox potentials. The larger the difference in reduction potential between electron donor and electron acceptor, the larger is the energy released.

ð10Þ

! ð11Þ

The difference between ECat and EAn would then give

Eemf ¼ ECat  EAn

Redox reaction

a

generally referred to at pH 7 and 25 1C (in which case they are indicated with the symbol E0 0). These reactions include not only oxidations of organics and reductions of terminal electron acceptors, but also redox reactions of intermediate metabolites. Based on the values in Table 1, if glucose is the electron donor (–0.43 V) and oxygen is the electron acceptor ( þ 0.82 V), an electromotive force of 1.25 V would develop across the MFC at standard conditions. Under more general conditions, the application of the Nernst law on Equations (2) and (3) would yield the following potentials for the two half-cell reactions:

ð12Þ

4.18.4 Factors Determining the Decrease of Cell Voltage Although the electromotive force represents the upper limit for the total voltage that the MFC can generate under certain conditions, the actual voltage will always be lower under practical conditions, due to a number of losses of either purely electrochemical and/or of biological nature. An ideal MFC would deliver any amount of current while maintaining a constant voltage, as determined by thermodynamics. In practice, the actual voltage output would be lower due to irreversible losses. These potential losses increase with increasing currents and can have a dramatic effect on the performance of the MFC, as the loss of voltage would result in a lower power output, accordingly to Equation (13)

P ¼ Vi

ð13Þ

where P is the power density (W cm2), V is the voltage (V), and i is the current density (A cm2). (In order to permit the comparison between different systems, current and power are usually normalized to some characteristic of the reactor, such as the projected surface area of anode or cathode, or alternatively to the compartment total volume or liquid (net) volume.) Maintaining a high voltage under high current production is therefore critical for successful MFC operations. Polarization curves represent the cell’s voltage as a function of the current. They are regarded as a useful tool for the measurement of the MFC performance (Figure 3). They are performed by periodically modifying the applied load (external resistance) and recording the resulting voltage and current, the latter evaluated through Ohm’s law (V ¼ R  i). They can be performed manually or automatically by means

Microbial Fuel Cells

647

Theoretical Eemf Voltage at open circuit (OCV)

Cell voltage (V)

P-i curve (b) Activation region

Ohmic region

Mass transport region

Vopt

Current density (A.cm−2)

iopt

Power density (w.cm−2)

Pmax

V-i curve (a)

isc

Figure 3 Typical polarization (a) and power (b) curves for an MFC. The point of maximal power (Pmax) corresponds to the optimal voltage (Vopt) and the optimal current density (iopt.). The maximal current density at short circuit (iSC) is reached when the external resistance is zero.

of a potentiostat. In this case, an appropriate scan rate (e.g., 0.1 mV s1) should be chosen (Velasquez-Orta et al., 2009). Polarization curves should be recorded from high to low external load and vice versa. While the Eemf as defined earlier represents the thermodynamic potential difference achievable in an electrochemical system, its value is not normally reached in real systems. The open circuit voltage (OCV) is the maximal voltage that can in fact be measured under conditions at which there is infinite resistance (i.e., at open circuit). There is a series of limitations imposed by the specific bacterial communities catalyzing the anodic reaction (and cathodic, in case of biocathode) that reduce the overall potential difference attainable (Logan, 2008). Three zones defining as many different operating regimes can be identified in a polarization curve (Benziger et al., 2006): 1. At open circuit there is no flow of electric current. However, when the current starts flowing, the voltage drops rapidly as a result of the activation-energy barrier of the reactions occurring at the electrodes; this zone is referred to as ‘activation polarization region’. (The voltage at open circuit measures the activity of reactants at anode and cathode electrode surfaces.) 2. At medium currents, the voltage decreases almost linearly with the current; this is referred to as ‘ohmic polarization region’, as it is dominated by ohmic losses, which arise from the resistance opposed by electrolytes and the IEM to the transport of ions as well as by electrodes and interconnection circuit to the transport of electrons. 3. At higher currents, the voltage drastically drops as a result of the insufficient mass transport of reactants or reaction products to and from the electrode, which limits the

reaction. This is known as ‘concentration polarization region’. The ratio of the cell voltage (V) and the cell voltage at open circuit (OCV) gives the potential efficiency (PE) (Lee et al., 2008). It is essentially the portion of the total potential difference between electron donor and acceptor that is captured as useful electric energy. The coulombic efficiency, or charge transfer efficiency (CE, or eC), is defined as the ratio of the charge that is transferred to the anode and the maximal charge that would be yielded if all the converted substrate generate electricity (Logan et al., 2006). It represents therefore the fraction of electrons recovered as electricity from the substrate converted. The energy conversion efficiency (ECE, or eE) is obtained by multiplying the PE and the CE (Lee et al., 2008). It represents the ratio of the power delivered from the system and the power that would be delivered in the absence of internal resistances (Benziger et al., 2006). For an MFC, the objective is to maximize the power output (represented by the peak of the P vs. i curve in Figure 3) and the ECE. Maximal power is obtained when both current and voltage are maximized, whereas maximal ECE is obtained when the potential efficiency and the coulombic efficiency are both maximized. However, the potential efficiency is negatively affected by the current density, which is in turn needed to maximize the power. This aspect is very important in engineering MFC systems (and fuel cells systems in general) as it means in other words that ECE and power output cannot be simultaneously optimized. At maximal power output, the ECE is 50% (Benziger et al., 2006). Higher efficiencies are achievable but with lower power outputs. Understanding the nature of the losses is of fundamental importance for successful operations of MFCs. Electricity generation in MFC is in fact the result of several steps that

648

Microbial Fuel Cells

A

CO2

H2O Microorganism

Link

Link

Microorganism O2

Voltage (V)

COD

OCV 1

2 3 4 3 2

Cell voltage

1

Figure 4 Potential losses during electron transfer in an MFC including a bioanode and a biocathode. 1: losses due to mass transfer limitation. 2: losses due to bacterial metabolic kinetics. 3: losses due to electron transfer to/from the electrode. 4: losses due to the resistance of the electrolytes (including the ion exchange membrane) and of the electrical interconnection to the flow of charges. These losses result in the reduction of the cell’s voltage from its value at open circuit (i.e., at infinite external resistance).

necessarily need to proceed at the same rate, as they occur in series. These steps are: (1) mass transfer of reactants and products from the bulk liquid to the electrode attached biofilm, and vice versa, (2) losses due to bacterial metabolic kinetics, (3) electron transfer from microbial cells to the electrode (and vice versa), and (4) transfer of charges through the electrodes and through the electrolyte and IEM. Some of these steps may limit the overall rate of electron transfer thus causing a larger voltage loss than others (Figure 4). The majority of the investigations carried out in the field of MFCs have been aimed at the improvement of power outputs by acting on one of the limiting steps to electricity generation.

4.18.4.1 Losses due to Mass-Transfer Limitation Electricity production in MFC relies on the flux of the reaction reactants in and out the biofilm. The flux of substrates is controlled by the diffusion in the biofilm as well as by the rates of utilization or production. If the reactions involving the substrates occur at a rate that is faster than that at which the reactants or products diffuse in or out of the biofilm, accumulation or depletion of one of the components occurs within the biofilm. As a result, the electrode potential becomes modified as depicted by the Nernst equation (Equation (9)).

If we consider, for instance, the anodic oxidation of glucose (Equation (2)), per mole of glucose that diffuses and is consumed within the biofilm, 6 mol of CO2 and 24 mol Hþ are produced and have to diffuse out of the biofilm. Protons are particularly important as their accumulation may lead the acidification of the biofilm. Torres et al. (2008) have shown that current density is largely determined by proton transport out of the biofilm. Current densities higher by more than 4 times were achieved when the phosphate buffer was increased from 12.5 to 100 mM. The authors also concluded that only in systems in which low COD concentrations are required in the effluent, substrate mass transport limitation may be more important than proton transport. However, it is important that the MFC compartments receive a proper loading rate of substrate to support the biomass attached to the electrodes. In continuous systems, organic loading rates at the anode of MFCs can vary between 0.5 and 4 kg COD m3 of anode liquid volume per day, with an optimum close to 3 kg COD m3 d1 (Rabaey et al., 2003). The hydrodynamic patterns are also important in order to provide homogeneous conditions on the biofilm/liquid interface.

4.18.4.2 Losses due to Bacterial Metabolic Kinetics Bacterial metabolic losses result from the rates of substrate uptake and utilization during the microbial metabolic activity,

Microbial Fuel Cells

which depends on both the specific microbial consortium catalyzing the reactions and the biomass density on the electrode surface, which in turn depends on the specific surface area of the electrode accessible to bacteria. For instance, the main limitation in MFC anodes is often not the specific uptake rate by the bacteria, but the bacterial density at the anode. Measurements have revealed that biomass concentrations at MFC anodes are 30 times lower compared to anaerobic digesters (Aelterman et al., 2008). Improvement of current and power outputs in MFCs requires the achievement of denser microbial colonization of the electrodes, while maintaining thin biofilms and open structures to facilitate diffusion and reduce mass-transfer limitation. While bacteria attach well to graphite electrodes, plain graphite may not be satisfactory if high power outputs are desired. Extensive research has been done on anode materials to maximize surface affinity with microorganisms (Cheng and Logan, 2007, Liu et al., 2007) and to facilitate electron transfer by the immobilization of mediators (Park and Zeikus, 2003) or conductive polymers (Schroder et al., 2003) on the anode surface. However, regardless of the material or design adopted, the anode biology does not currently constitute the main bottleneck of MFCs, unless competing populations such as fermentative bacteria outgrow the anodophilic population, driving the process to a failure (Rabaey et al., 2003). In addition, microorganisms themselves require energy for growth and maintenance purposes. Therefore, an anodic biofilm, for example, would take part of the energy available from the organic substrate and release the electrons at a slightly lower energy level, thus reducing the total voltage. As the anode is virtually the final electron acceptor, its potential would affect the total energy available for the microbes. The higher the difference is between the redox potential of the substrate and the electrode, the higher is the theoretical energy gain for bacteria growing on its surface, per electron-mole transferred. To maximize the voltage, anodic and cathodic electrode potentials should be kept as negative and as positive as possible, respectively, accordingly to the limits imposed by the redox potentials of the substrates used. Nevertheless, when the anode potential becomes very low, competitive processes such as fermentation or even acetoclastic methanogenesis may be favored, as the energy gain would be comparable in that case, as was shown in some recent studies (Aelterman et al., 2008; Finkelstein et al., 2006; Freguia et al., 2007b; Virdis et al., 2009). Furthermore, while higher cathodic potentials would maximize the voltage, the lower driving force pushing electrons from the cathode to the final electron acceptor may lead to the accumulation of intermediates as has been shown in the case of cathodic denitrification (Virdis et al., 2009).

4.18.4.3 Losses due to Electron Transfer to (and from) the Electrode Voltage losses due to electron transfer to (and from) the electrode are caused by the finite rate of electron transfer between microorganisms and the solid phase of the electrode (and vice versa). At an anode, the result is an accumulation of positive charge on the electrode and negative charge in the form of anions in the adjacent liquid layer. This double layer thus established causes the development of a potential

649

difference across it, called activation overpotential, which results in a reduction of cell voltage equal to its value. Bioelectrochemical reactions in BESs differ significantly from conventional electrocatalytic reactions by the fact that while in the latter the electron-transfer step from the electron donor to the electrode proceeds only at one particular point (e.g., the catalyst particle), the oxidation of the substrate in the former occurs throughout a more complex series of enzymatic reactions, the last of which is the electron transfer to the electrode in the case of the anodic reaction. Only this last step influences the activation polarization (Schroder and Harnisch, 2009). Activation overpotentials are extensively described in the electrochemistry literature. A detailed explanation of the origin of overpotentials can be found in Rieger (1994). Activation overpotentials are mathematically described by the Butler–Volmer equation, which dictates the logarithmic increase of the overpotential with the current density:

 bFZ  ð1bÞFZ i ¼ i0 e RT  e RT

ð14Þ

where Z (V) is the overpotential at the electrode, R is the universal gas constant (8.3145 J mol1 K1), T is the absolute temperature (K), b is the symmetry factor (unitless), which is a constant that represents the dependence of the activation energy on the electrode potential, F is the Faraday’s constant (9.648 53 104 C mol1), i is the current density (mA m2), and i0 is exchange current density (mA m2), which depends on the activation energy of the reaction at equilibrium conditions, in such a way that higher activation energy results in lower exchange currents. At overpotentials that are sufficiently high (greater than 80–100 mV at 25 1C, according to Freguia et al. (2007c)), the second term between brackets becomes negligible and Equation (14) can be rewritten in its simplified version, best known as Tafel equation:

ln

  i bFZ ¼ i0 RT

ð15Þ

Equation (15) can be used to experimentally estimate the parameters i0 and b using the so-called Tafel plots (ln(i) vs. Z), generated from polarization-curve measurements (Freguia et al., 2007c). The parameters i0 and b (and thus the activation overpotential) strongly depend on the activation energy of the reaction at the electrode. Electrodes with high specific surface area can not only support increased biomass densities but also decrease activation losses by reducing the current densities at the electrode surface (Chaudhuri and Lovley, 2003; Freguia et al., 2007c).

4.18.4.4 Losses due to the Resistance of the Electrolytes (Including the Ion-Exchange Membrane) and of the Electrical Interconnection to the Charges Flow It was described earlier (Section 4.18.2) that an equimolar amount of positive and negative charge is produced during the oxidation reaction at the anode (Equation (2)). While the electrons need to travel along the electrodes and the electrical circuitry to reach the cathode where the reduction reaction

650

Microbial Fuel Cells

takes place, ions needs to travel through the electrolyte and the IEM to ensure electroneutrality (cations in case a CEM is used; anions in the case of an AEM). The resistance of the different conductors (e.g., electrodes, current collectors, wires, IEM, and electrolyte) toward this charge flow introduces a loss of voltage, which is referred to as ohmic loss. According to Ohm’s law, the voltage loss due to charges migrating through a medium is proportional to the current and to the ohmic resistance of the medium. In turn, the ohmic resistance depends on the medium resistivity r(mO cm) (or equivalently its inverse, the conductivity s, mS cm1), the average distance traveled by the ions (L, cm), and the cross-section area over which the charges move (A, cm2, often identical to the nominal surface area of the electrodes for two-dimensional configurations), according to the following equation:

L R¼r A

ð16Þ

The intrinsic resistance of the electrode could become an important limitation to the generation of electricity as the electric resistivity of the conductors can be very high (the resistivity of graphite and carbon is 1000 times higher than that of iron; Rozendal et al., 2008a). The resistance offered by the electrolytes as well as by the IEM toward this transfer of charge is often a major limitation in MFCs.

4.18.5 Materials and Architectures Although typical MFC architecture consists of an anodic chamber and a cathodic chamber separated by an IEM, as depicted in Figure 2, different materials and reactor configurations have been implemented for lab-scale studies, depending on the scope of the study itself. Designs may vary from two-compartments to single-chambered MFCs, from tubular to stacked configurations, and with or without a membrane. Sediment MFCs have also been constructed by placing one electrode into marine sediments and the other in the overlying oxic water (Reimers et al., 2001; Tender et al., 2002).

4.18.5.1 Design As explained above (Section 4.18.4), the performance of MFC is strongly affected by a number of factors, particularly the resistivity of material used for the electrodes, the resistance offered by the electrolytes toward the charge transport, and the nonperfect selectivity of the IEM, which creates pH gradients between the compartments. It is therefore not surprising that the system performances are dictated by the design and the materials used. The H-shape two-chambered design is an inexpensive and easy-to-handle laboratory design that has been widely adopted in early MFC research. It simply consists of two bottles connected by a tube that can interpose an IEM or a salt bridge between anode and cathode (Bond et al., 2002; Park and Zeikus, 1999; Min et al., 2005). H-shape systems typically produce low current densities due to the high internal resistance, which limit their use to basic parameter research, such as

examining new materials or studies of microbial communities (Logan et al., 2006). Better performance can be obtained by the two flatchamber designs first developed by Delaney et al. (1984), which offer lower internal resistance due to the proximity at which anode and cathode can be put over a generally larger IEM. This compact configuration resembles that of traditional chemical fuel cells. This strategy was adopted by Min and Logan (2004) while designing their flat-plate MFC that comprises of two polycarbonate plates bolted together and contains a carbon-cloth cathode hot-pressed to an IEM also in contact with a carbon paper that serves as an anode, obtaining up to 7271 mW m2 of power density. The flattened design MFCs can easily be stacked together and electrically connected in series or in parallel in order to increase the overall system voltage (Aelterman et al., 2006b). Tubular shapes have also been designed (Rabaey et al., 2005b), or upflow types with anode below and cathode above (He et al., 2005), with the liquid sequentially passing through the two compartments. More complex designs have also been implemented to allow more complex measurements such as gas production and consumption, pH, and dissolved oxygen (Freguia et al., 2007b). When oxygen is used as electron acceptor, the cathode can be placed directly in contact with air, thus circumventing the need for a second chamber. In the single-chamber configuration, the cathode consists either of a catalyzed electrode open to the air, or is assembled with the anode within the same unit. Park and Zeikus (2003) used an MFC made of one compartment consisting of an anode coupled with a porous air-cathode directly exposed to air. In Liu and Logan (2004), an anode and a cathode were placed on opposite sides of a Plexiglas cylindrical chamber of length 4 cm and diameter 3 cm. The anode was made of carbon paper without wet proofing, while the cathode was manufactured by bonding the IEM directly on a carbon cloth (with platinum as catalyst). Liu et al. (2004) implemented the tubular shape within a single-chamber configuration for the treatment of wastewater. The anode, consisting of several graphite rods, surrounded the cathode made of carbon/Pt/IEM layers bolted together to a plastic support through which air was blown. Rabaey et al. (2005b) manufactured a tubular MFC with an inner cylindrical anode consisting in granular packed-bed graphite and an outer cathode.

4.18.5.2 Compartment Separation In MFCs, the function of the IEM is not only to provide a physical barrier to prevent fuel crossover between the compartments, but also to create a way for the ions to selectively diffuse to ensure electroneutrality. For example, as shown earlier, for every negative charge that is transferred to the cathode through the electrical circuitry, an equal amount of positive charge needs to flow through the electrolyte to prevent charge build-up. Finally, it also prevents the electrolytes from large pH fluctuations due to proton production at the anode and proton consumption at the cathode. Nafion (DuPont Inc., USA) and Ultrex CMI-7000 (Membranes International Inc., USA) are largely applied CEMs.

Microbial Fuel Cells

Although Nafion CEMs have been widely used in fuel-cell research, they do not perform as well under typical conditions at which MFCs work, for example, neutral pH, and in the presence of other cations in concentrations that can be 105 times higher than the proton concentration. Rozendal et al. (2006a) showed that under these conditions, Nafion membranes mainly transfer other cations rather than protons, thus lacking specific selectivity for protons. Ultrex is a more general CEM with larger mechanic strength compared to Nafion (Harnisch et al., 2008). It is considered a more cost-effective alternative to Nafion. The reader can refer to the works of Rozendal et al. (2008c) and Harnisch et al. (2008) where alternative types of membranes are compared in MECs and MFCs. Several attempts have been made by researchers toward the development of membrane-less MFC, in which the IEM is absent (e.g., sediment MFCs), or is replaced by different types of separators. Liu and Logan (2004) studied how the performances are affected by the presence or the lack of a Nafion membrane. Their results showed that increased power densities were possible without the IEM. Nevertheless, the enhanced oxygen diffusion led to a decrease of Coulombic efficiency as a higher portion of the carbon source was oxidized without electrons transferring to the anode. In an attempt to increase the oxygen-diffusion resistance, Park and Zeikus replaced the IEM with a porcelain septum (100% kaolin) and despite obtaining higher power outputs, the Coulombic efficiency was fairly low (Park and Zeikus, 2002, 2003). The addition of successive layers of polytetrafluoroethylene to the cathodic air-side of a single-chamber MFC resulted in increased coulombic efficiency and increased maximal power density (Cheng et al., 2006a). Jang et al. (2004) designed a membrane-less MFC in which anode and cathode were physically assembled within the same reactor unit and separated by glass wool and glass bead layers. Anode and cathode (made of graphite felt) were placed at the bottom and the top of the reactor and an upflow was imposed through the cylinder. Oxygen was bubbled in the cathode and its back diffusion was avoided simply by the stream flow. The protons formed during the anodic reaction were transported to the cathode by the same liquid stream. The results showed that the internal resistance was excessively high (several kO) due to the large anode to cathode distance, which resulted in low power generation. Moreover, most of the COD was removed in the cathode compartment by direct reaction with oxygen rather than by bioelectrochemical oxidation at the anode.

4.18.5.3 Electrodes MFC anodes and cathodes are typically made of graphite, which can be in the form of rods, felt, carbon paper, or cloth. Reactions occurring at the electrodes are subject to activation energies that need to be reduced by the use of appropriate catalysts. In the anode compartment, bacteria normally accomplish the role of catalysts. The electrodes therefore need to provide a suitable surface for the bacterial growth. Rough surfaces may provide several opportunities for adhesion, as well as decrease the current densities and therefore the potential losses, as is further described later (see Section 4.18.9.).

651

Increasing the surface area of the anode is also the simplest way to increase the loading rates that can be processed in an MFC as higher quantities of biomass can grow in the same reactor volume. Granular graphite is considered a convenient material for MFCs due to its cost (approximately US$ 0.5 kg1) and its high surface area and roughness. The porosity of the graphite is also an important aspect that needs to be taken into account when calculating the specific active surface of an electrode, as bacteria can grow in pores with a size larger than the bacteria themselves, but the smallest pores cannot be colonized and therefore do not contribute to the active electrode surface area. In addition, graphite granules have a high internal volume, which takes up about half the total reactor volume. Thus, carbon fiber brushes are increasingly considered as promising for future applications (Logan et al., 2007). Metal electrodes made, for example, of stainless steel can also be used (Tanisho et al., 1989), but despite being suggested as a good cathodic material, it has been shown to be less effective when used at anodes (Dumas et al., 2007). Moreover, metal electrodes do not normally offer a high specific surface area and their higher cost when compared to graphite limits their application, especially with regard to larger-scale use (Rozendal et al., 2008a). Uncoated titanium was also proposed by ter Heijne et al. (2008), although, based on DCvoltammetry and on electrochemical impedance spectroscopy (EIS), it was concluded that uncoated titanium was not suitable as an anodic material. Kargi and Eker (2007) proposed the use of copper and copper–gold electrodes, obtaining current and power production largely comparable with other studies. Increased performances have been obtained by adopting chemical–physical strategies, like incorporating Mn(IV) and using neutral red covalently linked to mediate electron transfer to the anode (Park and Zeikus, 2003). The use of materials such as polyanilines was also shown to improve current generation (Niessen et al., 2004; Schroder et al., 2003).

4.18.5.4 Cathodic Compartment Oxygen is by far the most suitable electron acceptor for MFC operations, due to its high redox potential (see Table 1), low cost, availability, and the fact that it does not produce any unwanted reaction product. However, its slow reduction kinetic on plain graphite requires in most instances the use of an appropriate catalyst. Platinum has been largely used in chemical fuel cells as an abiotic catalyst of the cathodic reaction. Nevertheless, platinum is not likely to be suitable for most of MFC applications because of its poisoning sensitivity toward some components in the substrate solution, especially to H2 S. The above-mentioned ferricyanide commonly used at MFC cathodes, but it cannot be considered a mediator for oxygen reduction, as its oxidation rate is much slower than its reduction (Pham et al., 2004). It acts therefore as an electron acceptor on its own, thus needing periodical replenishment. Yet, ferricyanide has the advantage of having a very low overpotential on plain carbon electrodes and operates at a potential close to its open circuit value. In spite of its sensitivity, it has been adopted for dissolved oxygen or open-air cathodes (Liu et al., 2004; Reimers et al.,

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2001), with 0.5 mg cm2 Pt loading being used in several studies (Liu and Logan, 2004). Platinum content as low as 0.1 mg Pt cm2 was also shown as effective (Cheng et al., 2006b). In an attempt to reduce the cost of the catalyst, some researchers have tested alternative electrode compositions where redox mediators were bound to the surface of the electrode, avoiding at the same time the use of soluble mediators. Park and Zeikus (2003) described a technique to bind ferric sulfate to woven graphite surfaces for improved oxygen reduction in an air-cathode MFC. Fe(III) was reduced to Fe(II) by the electrons generated at the anode and Fe(II) was subsequently re-oxidized by oxygen. An inexpensive cobalt-based material, cobalt tetramethylphenylporphyrin, was tested by two research groups (Cheng et al., 2006b; Zhao et al., 2006), both concluding that the performances were comparable with that of platinum but using a material less susceptible to poisoning. Zhao et al. (2005) found that transition metals phthalocyanines and porphyrins exhibit catalytic activity comparable to platinum. Other compounds have been employed to enhance cathode catalysis on active carbon or titanium electrodes, including cobalt oxide and molybdenum/ vanadium (Habermann and Pommer, 1991). Recently, the possibility of biocatalyzing the cathodic reaction has opened up a number of new opportunities. Biocathodes have a number of advantages compared to conventional chemical catalysts, such as their low cost, self(re)generation capacity, and the fact that they are less sensitive to the components typically present in the wastewater. In most of the biocathode studies in which oxygen was the final electron acceptor, microorganisms were used to transfer electrons from a reduced form of the metal compounds to oxygen itself. Manganese and iron have been used to transfer electrons from the electrode to oxygen by means of biological processes (Bergel et al., 2005; Rabaey et al., 2008; Rhoads et al., 2005). Ter Heijne et al. (2007) developed an oxygen cathode mediated by the couple Fe3þ/Fe2þ at very low pH with biological reoxidation of ferrous ions with oxygen by a culture of Acidithiobacillus ferrooxidans. Compounds other than oxygen can be also used as terminal electron acceptors. Nitrate, sulfate, iron, manganese, uranium, selenate, arsenate, urinate, fumarate, and carbon dioxide are all possible candidates for MFC applications (He and Angenent, 2006). Examples also exist of the use of oxygenase enzymes such as the multi-copper oxygenase laccase, as catalysts for oxygen reduction (Schaetzle et al., 2009). Although, the high costs together with the limited lifetime and stability of the enzymes are important drawbacks of enzymatic electrodes that need to be addressed.

4.18.6 Electrochemically Active Microorganisms and Extracellular Electron Transfer The underlying working principle of an MFC is extracellular electron transfer (EET; It refers to a mechanism by means of which bacteria donate or accept electrons to and from an electrode; Chang et al, 2006). Microorganisms use EET in order to utilize insoluble electron acceptors (or donors) that cannot enter the cell (Rabaey et al., 2007). Bacterial interaction with an insoluble electron acceptor has been first studied for

microorganisms that respire on Fe(III) and Mn(IV) or oxidize large humic substances that cannot enter the bacterial cell (Lovley et al., 1996, 1987; Myers and Nealson, 1988). Two pathways of EET are currently assumed to be used by microorganisms (Figure 5):

• •

through electron through electron

mobile components (also referred to as mediated transfer pathway) or immobilized structures (also referred to as direct transfer pathway).

Redox mediators (or shuttles) are soluble compounds that can transfer electrons between the microbial cells and the electrode surface. Reactions involving redox mediators can in principle occur outside or inside the cells (Gralnick and Newmann, 2007). In the first MFC prototypes, soluble redox mediators were added to the media to aid EET from bacteria to an electrode. The characteristics of redox mediators are: (1) the ability to be reversibly oxidized and reduced, (2) the resistance to biological degradation, (3) fast kinetics of oxidation at an electrode, (4) ease of diffusion through bacterial membranes, and (5) nontoxicity toward microbial consortia. Substances such as neutral red, hexacyanoferrate, thionin, or quinones were used to promote EET (Kim et al., 2000; Park and Zeikus, 2000). Delaney et al. (1984) and Allen and Bennetto (1993) developed and improved MFCs using different combinations of microorganisms and mediators. They showed that the use of suitable mediators could enhance both the efficiency and the rate of electron transfer. More recently, live–dead staining and confocal microscopy analysis showed that even in systems were no exogenous mediators were added, microorganisms could anyhow contribute to electricity generation. This means that bacteria growing at a certain distance from the electrode can also demonstrate EET. It was reported by Rabaey et al. (2005a) and Hernandez et al. (2004) that redox active compounds such as pyocyanin and phenazine-1-carboxamide were self-produced by Pseudomonas species. In particular, these compounds were essential for electricity production by Pseudomonas aeruginosa. The production of endogenous mediators was thereby identified as an additional strategy enabling mediated EET. Although redox mediators were long thought to be essential to enable EET, and were therefore extensively used in MFCs, the finding that bacteria have the ability to reduce insoluble electron acceptors such as Fe(III) and Mn(IV) in oxide forms by Lovley and Phillips as early as 1988 (Lovley and Phillips, 1988) already suggested that mediated EET was not necessarily the only mechanism for EET. This discovery indeed represented a landmark in MFC research and opened the door to the development of mediator-less MFCs. If neither endogenous nor exogenous redox mediators are used, a direct contact between the outer membrane of the bacterial cell and the electrode surface must be established in order to promote electron transfer. Direct electron transfer requires that the microorganisms rely on a transport structure that enables electron crossover to the outside of the cell where they can be delivered to a solid electron acceptor (a metal oxide or, more pertinently, to an MFC anode). Unusually high content of c-type cytochrome in Shewanella putrefaciens outer membrane during anaerobic growth was

Microbial Fuel Cells

A Anode

Substrate

653

CO2

e−

Medox

B Medred

Bacterial cell C

Bulk

Figure 5 Possible microbial interactions with the anode. Microorganisms can transfer electrons through immobilized structures such as membrane-bound proteins (A) or electrically conductive pilus (B), or through mobile components (redox mediators or shuttles) that are alternatively oxidized and reduced at the electrode (C).

reported in as early as 1992 (Myers and Myers, 1992). Kim et al. (1999) were the first to measure the electrochemical activity of Shewanella putrefaciens when grown under anaerobic conditions without nitrate, as revealed by cyclic voltammetry. Cyclic voltammetry is an electrochemical technique where an electrode is immersed in a medium and its potential is changed cyclically by a potentiostat while the current is measured. The current versus potential diagram (called cyclic voltammogram) shows peaks in correspondence of the potential of each reversible redox couple in contact with the electrode. A double peak in the cyclic voltammogram revealed that a redox active compound (possibly a cytochrome) was responsible for the electrochemical activity of the cells of Shewanella putrefaciens. The study also showed that the cells lost their electrochemical activity when grown aerobically. In a further study, the electrochemical activity of Shewanella putrefaciens was demonstrated by current production in a mediatorless MFC (Kim et al., 2002). Bond et al. (2002) showed that some bacteria are capable of transferring electrons from anoxic marine sediments to an anode, connected to a cathode in the overlying aerobic zone. Community analysis of these bacteria showed that many of them belonged to the d-proteobacteria phylum. In particular, Geobacter metallireducens and Desulforomonas acetoxidans were identified. Another bacterium,

Geobacter sulfurreducens, was successfully tested by Bond and Lovley (2003) as a pure culture in a two-chamber fuel cell with acetate as substrate. Chaudhuri and Lovley (2003) demonstrated that the bacterium Rhodoferax ferriducens can perform electron transfer to an anode when fed with glucose. Recently, it has been reported that outer-membrane proteins are not always sufficient for the reduction of Fe(III) oxides. Geobacter and Shewanella species were shown to produce conductive appendages that were referred to as ‘nanowires’ (Gorby et al., 2006; Reguera et al., 2005). In Gorby et al. (2006) the conductivity of these pilus-like nanowires produced by Shewanella oneidensis was measured through conductive-scanning tunneling microscopy. A similar observation was done for nanowires produced by Geobacter sulfurreducens using conducting-probe atomic-force microscopy. Results show that while Shewanella species appear to produce rather thick bundles of conductive wires, Geobacter seems to produce more thin structures. Evidence also exists of interspecies electron transfer between bacteria in mixed communities, as suggested, for instance, by the presence of filaments connecting the propionate-fermenting Pelotomaculum thermopropionicum with the methanogen Methanothermobacter thermautotrophicus in Ishii et al. (2005). Whether interspecies electron transfer occurs

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through wired connections or the use of mediators, it may significantly impact BES engineering as it will broaden the array of organisms able to survive on an electrode. Although the nomenclature for microorganisms that perform EET is far from being uniform (Lovley, 2008), the term electrochemically active microorganisms has been introduced to refer to microorganisms that demonstrate the ability to perform EET on an electrode without the addition of exogenous mediators (Chang et al., 2006). More specifically, the term ‘electrode-reducing microorganisms’ refers to organisms that can use an electrode as electron acceptor (thus reducing the electrode), whereas ‘electrode-oxidizing microorganisms’ refers to the organisms that use the electrode as electron donor (and thus oxidize the electrode) (Lovley, 2008). Community analyses of mixed anodic cultures enriched in electrochemically active microorganisms were undertaken by several investigators (Holmes et al., 2004; Kim et al., 2007b; Rabaey et al., 2004a). The striking conclusion from these studies was that EET capacity is widespread in nature and it is found among most phyla of bacteria. The ubiquity of these microorganisms in nature is also confirmed by the diversity of inocula that can be used to start up lab-scale MFCs. Raw sewage (Liu et al., 2004), activated sludge (Lee et al., 2003), anaerobic and methanogenic sludge (Rabaey et al., 2003), river sediments (Gregory et al., 2004), and seawater (Bond et al., 2002) were all successfully used to develop bioelectrochemical activity. Pure or enriched mixed cultures have shown the ability to use anodes to oxidize a variety of organic substrates, including acetate (Bond and Lovley, 2003), propionate (Bond and Lovley, 2005), butyrate (Liu et al., 2005c), ethanol (Kim et al., 2007b), lactate (Kim et al., 1999), glucose (Chaudhuri and Lovley, 2003), domestic wastewater (Gil et al., 2003), and beer-processing wastewater (Wang et al., 2007). Whereas respiratory flexibility in mammalian mitochondria is rather poor, it can be extremely broad in Bacteria and Archaea, as a diverse range of electron acceptors can be used, including nitrogen oxyanions and nitrogen oxides, elemental sulfur and sulfur oxyanions, halogenated compounds, transition metals such as Fe(III) and Mn(IV), as well as radionuclides such as U(VI) (Richardson, 2000). It is widely agreed upon that the first respiratory processes to evolve on Earth over 3.5 billion years ago would have used Fe(III) or S(0) as electron acceptors. The fact that the ability to electrically interact with an insoluble electron acceptor is widespread in nature is not therefore particularly surprising.

4.18.7 Oxidative Processes As we have seen previously in Section 4.18.3, the difference in reduction potentials between primary electron donor and terminal electron acceptor determines the net energy change of the reaction, and thus the energy gain for chemotrophic microorganisms. Energy is conserved by production of adenosine triphosphate (ATP) molecules. Depending on the availability of electron acceptor, two possible pathways are possible: fermentation or respiration. In the case of fermentation, the electron acceptor is a compound internally generated from the initial substrate, whereas in the case of respiration, the electron acceptor is externally provided. When

oxygen is the electron acceptor, it is referred to as oxic respiration. When the electron acceptor is oxygen linked with other compounds, it is referred to as anoxic respiration. The basic respiratory process involves the transfer of electrons from a low redox potential electron donor such as nicotinamide adenine dinucleotide (abbreviated as NADþ in its oxidized form, and as NADH in its reduced form), to the terminal electron acceptor at a high redox potential. The transfer occurs through a chain of intermediate redox complexes. The case where an insoluble electron acceptor rather than a soluble compound is oxidized is still a form of respiration, with similar mechanisms for energy generation to other forms of respiration. Anodic oxidation relies on the tricarboxylic acid (TCA) cycle, which together with glycolysis and pyruvate oxidation before the TCA cycle and electron transfer chain after it, permits the chemical conversion of the organic substrates into carbon dioxide and water, and generates energy in the form of ATP (White, 1995). Many bacterial species have been shown to produce electricity in MFCs using compounds such as acetate, lactic acid, and ethanol which enter the TCA cycle through pyruvate or acetyl-CoA (Bond and Lovley, 2003; Kim et al., 1999, 2007b), whereas more complex carbohydrates such as glucose require glycolysis before entering the TCA cycle. NADH, nicotinamide adenine dinucleotide phosphate (NADPH), and flavin adenine dinucleotide (FADH2) are generated through the TCA cycle. These are reduced molecules that represent the primary electron donor for the electron transport chain, through a series of membrane-associated electron carriers, including flavoproteins, iron–sulfur proteins, quinone pool, and a series of cytochromes. The electron transport chain has two basic functions: (1) to accept electrons from an electron donor and to transfer these electrons to the next electron acceptor and (2) to conserve the energy released during electron transfer for the synthesis of ATP. The electron carriers are arranged in the membrane in such a way that the electrons are transferred from one complex to the following at a higher potential (Figure 6). Hydrogen atoms removed from carriers such as NADH are separated from the electrons. While the electrons are transferred to the following carrier, the protons are pumped outside the cell (or to the periplasm in Gram-negative bacteria). This generates a proton motive force across the membrane, which extrudes up to approximately 10 protons for each electron pair derived from 1 NADH. The proton motive force drives ATP generation through a process called phosphorylation, which involves a large membrane complex called protontranslocating ATP-synthase, an enzyme that exploits the electrochemical potential liberated by the protons as they return to the cytoplasm. It is assumed that about three protons are required to generate one molecule of ATP, although recent research suggested that this stoichiometry may vary from 3 to 5 protons per ATP (Nakanishi-Matsui and Futai, 2008). It is generally accepted that maximally three molecules of ATP are generated by prokaryotes per NADH molecule (White, 1995). Bacteria in MFCs establish a direct contact with the electrode through cytochromes or nanowires or via soluble redox mediators (see Section 4.18.6). In any case, the electrontransfer chain as explained earlier cannot be entirely exploited, as the potentials of the electron carrier used to transfer electrons to the electrode (soluble mediator or cytochrome) are

Microbial Fuel Cells

655

Periplasm 4H+

4H+

3H+

2H+

Complex I

Complex IV cyt c

Q

ATP synthase

Complex III NADH

NAD

+

Quinone pool

Cytoplasm

O2 + 4H+

2H2O ADP+P

ATP

Figure 6 Respiratory electron transport chain of an organism such as Paracoccus denitrificans, a model for studies of respiration. Electrons are sequentially transferred through a series of membrane-associated electron carriers, which are embedded in the lipid bilayer of membranes in such a manner that most have access to both the inside and the outside of the cell. Hþ atoms removed from carriers such as NADH are separated from electrons and pumped outside the cell or in the periplasm. The reduction of O2 to H2O plus the extrusions of Hþ during electron transport generate a pH gradient and an electrochemical potential across the membrane (proton motive force, expressed in volts). This potential energy is used to drive the formation of high-energy phosphate bonds in ATP. Complex I: membrane-spanning complex comprising of flavoproteins and Fe–S proteins. Quinone: lipid electron carrier. Complex III: comprises cytochrome b, Fe–S proteins, and cytochrome c1. Cyt c: cytochrome c. Complex IV: cytochrome aa3 oxidase. From Madigan MT, Martinko J, and Parker J (2000) Brock Biology of Microorganisms. Upper Saddle River, NJ: Prentice Hall.

normally not electronegative enough to receive the electrons from the next step in the electron transport chain. Therefore, the maximal ATP yield for electroactive microorganisms is limited by the redox level at which the transfer chain is interrupted. As bacterial growth depends on the availability of intracellular ATP, the growth yield for bacteria in MFC will ultimately depend on the mechanism of electron transfer. Furthermore, as the potential of the anode determines the last step of the electron transfer to the electrode, the microbial growth will ultimately depend upon the anodic potential (Aelterman et al., 2008; Finkelstein et al., 2006; Freguia et al., 2008b; Schroder, 2007). A broad range of biodegradable materials has been shown to serve as electron donors for electricity generation in MFCs. Volatile fatty acids (e.g., acetate, formate, and butyrate), alcohols (e.g., ethanol and methanol), as well as more complex carbohydrates (e.g., glucose, sucrose, cellulose, and even starch), and even amino acids and proteins were used as organic electron donors (Freguia et al., 2007b; He et al., 2005; Heilmann and Logan, 2006; Ishii et al., 2008; Liu et al., 2005b; Logan et al., 2005; Min and Logan, 2004; Rabaey et al., 2003). Inorganic compounds such as sulfide (Rabaey et al., 2006) and synthetic acid-mine drainage (Cheng et al., 2007) have also been reported. Thus far it is still not clear which role the different types of substrates play. Acetate was reported to be the preferred substrate when compared to buyrate (Liu et al., 2005b) or to wastewater (Rabaey et al., 2005b), suggesting that MFC organisms prefer rapidly biodegradable substances to more complex compounds. Coulombic efficiency of 100% (i.e., a stoichiometric conversion of the substrate into current) was

reported in Freguia et al. (2007b) in their acetate-fed anode, whereas much lower efficiencies were obtained when glucose was used instead. Acetate has therefore been the substrate of choice in a large number of studies. Nevertheless, even if acetate is considered inert to alternative biochemical conversions such as fermentation in MFCs (Aelterman, 2009; Freguia et al., 2007b, 2008b), acetoclastic methanogenesis has recently been reported as an important anodic electron sink when the operating conditions favor the establishment of a methanogen community alongside electrochemically active microorganisms (Virdis et al., 2009).

4.18.8 Reductive Processes Bioelectrochemical oxidation of organics at the anode of MFCs has to be coupled with a reduction reaction at a counter electrode (cathode). Several electron acceptors have been used, depending on the scope of the BES. When power generation is the goal, oxygen appears to be the preferred choice due to its high availability and its high redox potential (see Table 1). As seen in Section 4.18.5, hexacyanoferrate has been extensively used in laboratory studies focused on the anodic reaction, due to its ability to provide a constant potential. More recently, the demonstration that compounds such as nitrate (Clauwaert et al., 2007a), nitrite (Virdis et al., 2008), hexavalent uranium (Gregory and Lovley, 2005), perchlorate (Thrash et al., 2007), and trichloroethene (Aulenta et al., 2009) can be reduced at the cathode, has broadened the array of applications of MFCs on nutrient removal and bioremediation as well.

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While oxygen has been studied extensively for conventional chemical fuel cells, MFCs face specific limitations that substantially differentiate them from their hydrogen, methanol, or methane counterparts. In particular, they must operate at ambient pressure and moderate temperatures; moreover, the relatively low current outputs that are achieved in MFC do not justify the use of expensive chemicals as catalysts and, on the other hand, the nature of wastewater environments exposes most metal catalysts to irreversible poisoning. Three major bottlenecks can be identified for oxygen utilization at the cathode of MFCs: (1) the low solubility of oxygen in water limits its delivery to the electrode surface; (2) direct oxygen reduction at graphite electrodes exhibits large overpotentials; and (3) oxygen can diffuse (to some degree) through most membranes causing coulombic losses by direct oxidation of the organic electron donor. As oxygen reduction on plain carbon was found to happen at limited rates (Oh et al., 2004; Zhao et al., 2005), oxygen cathodes were developed with a platinum coating, based on the knowledge acquired from years of research in the chemical fuel cell area. It was found that the use of platinum enhanced oxygen-reduction rates when applied at a surface concentration of at least 0.1 mg Pt cm2 (Cheng et al., 2006b), with best performances at a load of 0.5 mg Pt cm2. Platinumcoated graphite electrodes have thereby set a benchmark for oxygen cathodes in MFCs, despite the fact that encouraging results had previously been obtained by immobilizing Fe(III) on graphite (Park and Zeikus, 2002, 2003). If a current of 1000 A m3 is expected to be delivered by the bioanode (Rozendal et al., 2008a), the aeration capacity that would have to be provided at the cathode can be estimated to be as high as about 0.0035 m3 O2 min1 per cubic meter of liquid, which is in the same order of magnitude as that provided by aeration systems normally applied for aeration tanks of activated sludge-treatment plants (Tchobanoglous et al., 2003). Oh et al. (2004) showed that aqueous oxygen cathodes (with Pt as catalyst) are the limiting step to electron transfer in two-chambered MFCs: a cathodic overpotential of B0.5 mV implied that about half of the total electromotive force expected was lost entirely due to oxygen reduction. The same investigators found that oxygen reduction behaves accordingly to Monod-type kinetics, with a half saturation constant of 1.74 mg O2 l1, which indicates that a further reduction of the electron-transfer rates is expected at low aeration rates. The reasons for the low performance of platinum cathodes in MFCs are not well understood. It can be speculated that the relatively mild conditions at which they operate, such as pH of around 7 in most cases and ambient temperatures, may affect the reaction rate. Conventional chemical PEM fuel cells normally sustain much higher current densities but they also typically operate at very low pH values (lower than 1). Given that protons are reactants in the cathodic reaction (Equation (3)), low pH values guarantee that protons are available in high concentration. In addition, chemical fuel cells operate at temperatures ranging from 50 to 100 1C in the case of PEM fuel cells, higher than that used for microbial fuel cells, to enhance the reaction rate. While the solubility of oxygen in water is a physical property and as such, cannot be increased, the thickness of the liquid film across which O2 has to diffuse can be minimized to reduce mass-transfer resistance. These

constraints have led to the development of open-air cathodes (Liu et al., 2004), where the cathodes have a two-dimensional structure and are open to the air, letting oxygen diffuse to the electrode surface directly from the air. This passive aeration process is more sustainable for scale-up applications as it does not entail large energy requirements for air pumping. In order to increase oxygen supply to the cathode surface, rotating cathodes have also been developed (He et al., 2007). Despite the widespread use of platinum as cathode catalyst, its high cost and energy-intensive production technology make this metal usually unsuitable as a catalyst for wastewater applications. Considering that the economic feasibility of MFCs is strongly dependent on the cathodic compartment (almost half of the capital costs of MFCs are associated with the cathodic compartment when platinum is used as catalyst; Rozendal et al., 2008a), platinum needs to be replaced by alternative catalytic materials. Three strategies have been explored thus far: (1) the use of a material with increased surface area; (ii) alternative chemical catalysts; and (iii) biocathodes. Freguia and co-workers (2007c) have shown that by using a noncatalyzed material with a high surface area it was possible to decrease the overpotential for cathodic oxygen reduction. As shown by Equation (15), the activation overpotential Z at the cathode increases with the current density i. The use of a better catalyst has the effect of reducing the overpotential as it increases the exchange current i0, which is a characteristic of the material used. The approach of the researchers instead was to reduce the current density by using a material with a higher surface area (coarse highly porous industrial-grade granular graphite) rather than modify the exchange current using a catalyst. The current generated with this configuration was able to sustain COD removals up to 1.46 kg COD m3 d1, which is similar to that of a conventional aerobic process based on activated sludge. Among other strategies for enhancing the cathodic reaction, the use of bacteria as catalyst has attracted particular interest in the MFC field. Similarly to bioanodes, biocathodes utilize the electrical interactions that can be established between the microbes and the cathodic electrode. Biological catalysis has been shown to enhance the rate of cathodic oxygen reduction at a stainless-steel cathode in seawater sediments (Bergel et al., 2005). The existence of such a consortium of bacteria was also later demonstrated for freshwater applications (Clauwaert et al., 2007b; Freguia et al., 2008a). The involvement of microorganisms in the catalysis was unequivocally established by a pure culture study (Rabaey et al., 2008). Freguia et al. (2008a) observed increased current production in an MFC system operated with recirculation of the anode effluent to the cathode, and correlated this result to the increased microbial activity at the cathode. Also, Rozendal et al. (2008b) reported of a cathodic microbial consortia catalyzing hydrogen production at a graphite cathode. The concept of using denitrifying bacteria to reduce nitrate in the cathode of an MFC was first proposed by Lewis more than 40 years ago (Lewis, 1966). However, it was only recently that the presence of reactions involving nitrogen at the cathode was confirmed. Catalytic reduction of nitrate and nitrite driven by electric current has been explored by Mellor et al. (1992). Nitrate-enriched water was pumped into the anode chamber of an electro-bioreactor and recirculated into the

Microbial Fuel Cells

cathode chamber where purified NADH:nitrate reductase, nitrite reductase, and N2O reductase enzymes were immobilized on the surface of the cathode. The applied electric current provided the reducing power needed to carry out the process. Hydrogen is an excellent electron donor and it can be easily produced by electrolysis of water. When a denitrifying biofilm is growing on the surface of a hydrogen-producing electrode, it has the advantage of having a continuous supply of an electron donor to carry out the process. Several studies attempted therefore to obtain nitrate reduction by applying a potential difference to form hydrogen at the cathode (Kuroda et al., 1997; Sakakibara et al., 1994; Sakakibara and Kuroda, 1993). Kuroda et al. (1997) extended the concept to obtain simultaneous COD removal and denitrification. However, hydrogen was still the actual electron donor for nitrate reduction. Although bacteria were considered as able to directly use the electrode as the sole electron donor, it was only very recently that this microbial capability was experimentally verified. Gregory et al. (2004) showed that a bacterial culture enriched in Geobacter species could reduce nitrate (NO3  ) to nitrite (NO2  ) using the cathode as the sole electron donor, without producing hydrogen as redox mediator. As nitrate reduction did not occur in the absence of bacteria, the researchers concluded that the process was biochemically activated by the biofilm, showing for the first time that bacteria were using the cathode as the sole electron donor. A later study confirmed their hypothesis and showed that complete denitrification to nitrogen gas could be achieved (Park et al., 2005). More recently, full denitrification with simultaneous carbon removal was reported in MFCs by Clauwaert et al. (2007a) and Virdis et al. (2008). Besides denitrification, other cathodic reactions have also been reported. Perchlorate, a compound extensively used in industry, was shown to be reduced with the help of 2,6anthraquinone disulfonate (Thrash et al., 2007). Shea et al. (2008) coupled a perchlorate-reducing biocathode with an acetate-oxidizing bioanode in an MFC configuration. Recently, an as yet unknown self-produced redox mediator appeared to be involved in the reduction of trichloroethene to more reduced compounds such as vinyl chloride and ethane (Aulenta et al., 2009). Finally, the recent demonstration of methane production from carbon dioxide reduction (Cheng et al., 2009) and biocathodic alcohol production from VFAs (Steinbusch et al., 2008, 2009), has opened up new possibilities for BES applications to biofuel production.

4.18.9 Challenges toward Improving MFC Efficiency Currently, several bottlenecks of both microbiological and technological nature limit the efficiencies of MFCS. It implies that despite the fact that laboratory MFCs already produce current densities suitable for practical applications, full-scale implementations are not necessarily straightforward (Rozendal et al., 2008a). The presence of alternative electron acceptors in the anode compartment (e.g., due to crossover of electron acceptors from the cathodic compartment to the anode), competitive processes such as fermentations and methanogenesis, and bacterial growth, are recognized as responsible for diverting a part

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of the total electrons provided by the electron donor from the electricity-generation process, thus reducing the coulombic efficiency. The coulombic efficiencies reported in literature range from 1% (Kim et al., 2002) to about 100% (Freguia et al., 2007b). Besides, the presence of overpotentials at the electrodes, ohmic losses due to wastewater and electrode conductivity, and pH gradients due to imperfect ion selectivity of IEMs, are responsible for reducing the ECE as they reduce the cell voltage, described previously in Section 4.18.4. Their practical implications are described in this section.

4.18.9.1 Minimizing Electrode-Potential Losses Overpotentials at the electrodes can significantly limit the performance of MFCs as they decrease the actual voltage attainable, and therefore the energy efficiency. These losses are due to the electron-transfer kinetics from the microbial cells to the electrode (and vice versa) and to the bacterial metabolic kinetics. Moreover, as all heterotrophic bacteria retain a portion of their carbonaceous substrate to produce more biomass, a coulombic (and energetic) loss from this activity has to be taken into account as well. Interestingly, the potential losses at the anode have been reported to be much lower than that at the cathode. If, for example, an MFC can theoretically produce up to 1.1 V, less than 0.1 V is typically lost at the anode and more than 0.5 V can be lost at the cathode under working conditions (Logan et al., 2006). This would leave 0.5 V for power generation, without taking into account other losses such as ohmic losses. It is therefore obvious that any strategy intended to reduce the MFC overpotentials would have to pay particular attention to the cathodic reaction. As discussed above, the reasons for the high cathodic overpotential are mainly due to the slow kinetics of oxygen reduction. The use of biocathodes may be a valuable alternative to improve this catalytic process.

4.18.9.2 Respiration, Fermentation, and Methanogenesis In a complex environment such as wastewater, a multitude of other processes may occur alongside the conversion of organic molecules to electrons, thus competing with electricity generation in MFCs. When electron acceptors other than the electrode are present in the anode chamber, the organic electron donor can be oxidized using alternative pathways, thus reducing the electron transfer efficiency. In particular, nitrate and sulfate are commonly found in wastewater and are likely to divert the substrate electrons from the anode, as their redox potential makes them often more favorable electron acceptors than the anode. The presence of oxygen in the anodic compartment depletes substrate electrons through aerobic oxidation. Oxygen may be present due to possible pre-treatment of the MFC influent or through diffusion from the cathodic compartment in the case of oxygen cathodes. The potential for oxygen crossover to the anode is considerable in membraneless configurations (Liu and Logan, 2004). Methane is the end product of most anaerobic processes and it is regarded as one of the major bottlenecks of anode operations in MFCs, because methanogens compete with electrochemically active microorganisms for the organic material in the wastewater. It was recently shown that notable

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amounts of methane were produced with glucose as substrate, consuming between 15% and 50% of the fermentable substrate electrons (Freguia et al., 2007a, 2007b, Lee et al., 2008). Methane was also observed in ethanol-fed MFCs (Torres et al., 2007). Fermentable substrates such as ethanol and glucose yield hydrogen when fermented. Hydrogen can be used by electrochemically active microorganisms for electricity production, or it can be further converted into methane through hydrogenotrophic methanogenesis. Observations suggest that electrochemically active microorganisms cannot completely outcompete methanogens for hydrogen (Freguia, 2008). One possible explanation for this behavior is that contrary to electroactive microbes, methanogens can grow at any distance from the electrode, as they do not need it as an electron acceptor. Therefore, they can grow on the top layer of the anodic biofilm, where they can scavenge the hydrogen that is formed by fermentation before it reaches the underlying electrochemically active biofilm. In the perspective of a real application, pre-fermentation would be required whenever fermentable substrates are present in the wastewater, in order to convert fermentable substrates into nonfermentable substrates such as acetate, thus providing electrode-reducing organisms with better chances to compete with methanogens. However, it is worth noting that when a bioelectrochemical system is operated at a controlled potential, or when a low anodic potential is the result of a low-current-producing process, the energy gain for electrochemically active organisms may become too low to successfully compete with low-energyyielding biological processes (e.g., acetoclastic methanogenesis). To conclude, controlling competitive processes requires a complex synergy of operational strategies in order to avoid the conditions at which methanogens are likely to scavenge the electrons away from electrochemically active microorganisms.

4.18.9.3 Reducing pH Gradients As extensively recalled throughout the text, anodic reactions produce protons while cathodic reactions (such as oxygen reduction) consume them. IEMs are typically used to provide physical separation between the two compartments while enabling the transport of charge at the same time (Section 4.18.2). The bottleneck that typically draws from the use of IEMs in MFCs derives from their lack of selectivity. Rozendal et al. (2006a) showed that Naþ, Kþ, and NHþ 4 normally account for most of the ionic-charge transfer across Nafion CEMs, due to their typically much higher concentrations in wastewaters compared to the Hþ concentration. Further research revealed that limited proton transfer occurs with most types of membranes, including AEMs and charge mosaic membranes (CMMs) (Rozendal et al., 2008c). The inefficient proton transport causes a pH gradient across the membrane, which results in an acidic anolyte and an alkaline catholyte, accordingly to the stoichiometry described in Equations (2) and (3). The consequence of the membrane gradient is a significant decrease in performance due to the reduction of the electromotive force. From the Nernst equation (Equation (9)), an increase in proton concentration at the anode results in a higher anodic potential and, similarly, a

reduction in the cathodic potential, causing an estimated loss of B0.06 V per pH unit (Rozendal et al., 2007). Both domestic and industrial wastewaters are characterized by limited alkalinity, which during MFC treatment has to approximately match the quantity of protons produced by the anodic reaction. According to Equation (2), 24 mol of Hþ are produced per mole of glucose, which translates to 4 mol Hþ per mole of COD. The alkalinity should therefore be about 4 times the influent COD molar concentration. This is normally not a problem in lab-scale MFCs that work on highly buffered synthetic media (in the range of 60–100 meq l1). Nevertheless, it would represent an important limitation when treating real wastewater. A domestic wastewater with 500 mg COD l1 would in fact require a 62.5 mM buffer, which is already much higher than the typical alkalinity reported (50–200 mg l1 as CaCO3 (Tchobanoglous et al., 2003), equivalent to 1–4 meq l1 buffer). Membrane-less designs would partially solve the issue. Yet, as mentioned previously, the lack of physical separation between anode and cathode would lead to the crossover of electron acceptor to the anode with significant reduction of the electron recovery. Bipolar membranes (BPMs, the twolayer combinations of a cation and an anion exchange membrane) can partially solve the problem by splitting water into Hþ and OH in the liquid space between the two membranes. However, they do so at the expense of a larger membraneinternal resistance, resulting again in a reduced power output. The sequential loop operation of anode and cathode (Freguia et al., 2007a, 2008a; Virdis et al., 2008) partly alleviates the problem by enabling convective transfer of protons from anode to cathode together with the liquid stream. Proton production by the anodic process may also negatively affect the performance of MFCs at the biofilm level, when the protons do not leave fast enough and accumulate within the biofilm. The Nernstian effect discussed earlier would occur at a smaller scale with the same effect of reducing the total electromotive force attainable. Increasing the specific surface area of the electrode may provide a significant benefit when it promotes an increased biofilm/liquid contact interface area as it would increase the proton flux (Torres et al., 2008).

4.18.9.4 Wastewater and Electrode Resistance Ohmic losses derive from the resistance of materials to the transfer of charged particles (see Section 4.18.4). Ohmic losses can be considerable in real wastewaters as they typically have low conductivity (of the order of only 1–4 mS cm1). As noted by Rozendal et al. (2008a), in full-scale MFC applications delivering 10 A m2 anode surface area, the ohmic loss that is encountered would be B1 V cm1 distance between anode and cathode for a wastewater with conductivity of 1 mS cm1, which is already B90% of the theoretical maximal voltage attainable. As increasing the ionic strength of wastewater by salt addition is not economically feasible in practice, the design and the operation of a full-scale MFC can significantly affect the extent of the ohmic losses. Researchers have attempted to reduce the ohmic voltage losses by testing different types of IEMs or even by completely removing them from the system. These investigations have revealed that the internal ohmic resistance is not controlled by

Microbial Fuel Cells

the membrane but by the electrolyte. Kim et al. (2007a) tested several membranes in identical MFCs. These included cation and anion exchange membranes as well as different kinds of ultrafiltration membranes. All MFCs tested exhibited similar internal resistances, confirming that the membranes did not control the overall resistance. Liu and Logan (2004) showed that the complete removal of the CEM did increase the power output, but in that case the increase was attributed to a higher cathodic potential and not to a reduction of the internal resistance. Moreover, the coulombic efficiency dropped to 12% as no barrier for oxygen diffusion was present in the system. Liu et al. (2005a) observed that the current output increased with the ionic strength, which was set by the salt concentration. This result further confirmed that the ionic conductivity of the medium plays a crucial role in MFC performance by determining its internal resistance. Keeping the electrodes in very close proximity is crucial for reducing the ohmic losses; flat compartment systems have therefore been used in research, either singly, or as multiple stack electrically connected in series or in parallel. Flat systems can minimize the ohmic losses as the compartments can be placed very close, thereby reducing the travel distance of ions through the electrolyte between the electrodes. However, the drawback of this configuration when expanded to larger scale is that in order to keep the same electrode distance with a rather larger volume, the electrons would need to travel longer distances to reach the cathode, thus significantly increasing the electrode ohmic loss if the material that is used is not sufficiently conductive. Highly conductive current collectors such as stainless-steel meshes can be used alongside the carbon/ graphite electrodes, although they can significantly increase the overall cost of the MFC. The use of bipolar plates can partially solve this problem as the distance that the electrons need to travel is reduced compared to the single-cell design (Shin et al., 2006). Bipolar plates (usually made of graphite) connect the anode side of one cell to the cathode of the next cell. The electrons generated at the anode only need to cross the bipolar plate to the cathode. However, this creates a stackin-series arrangement of the cells and one of the common problems encountered in such systems is the cell reversal, that is the reversal of the cell polarity, which turns some cells in the stack into electrolytic cells (Aelterman et al., 2006b).

4.18.10 Opportunities for Bioelectrochemical Systems In spite of the great need for improvement, MFCs are undoubtedly a promising technology that offers a vast range of potential applications. Bioelectrochemical wastewater treatment is a novel and promising approach to the production of renewable energy and thus has been the main focus of investigations. Nevertheless, the key characteristic of BESs highlights the fact that they decouple the oxidative and the reductive process, offering the unique opportunity of having clean electrons (reducing power) derived from renewable resources that can potentially be used to drive a multitude of biotechnological processes. Important examples for bioremediation processes include the reductive dechlorination of chlorinated compounds (Aulenta et al., 2007), the reduction

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of soluble metals like hexavalent uranium into more insoluble forms (Gregory and Lovley, 2005), and denitrification (Clauwaert et al., 2007a, Virdis et al., 2008). Other niche applications of MFCs include 1. Electricity production in remote areas. MFCs can produce power from waste biomass, which is ubiquitous and could supply those areas with small amounts of electricity while reducing the environmental impact on local waterways. 2. Bacterial batteries. Energy stored in the form of sugars or other organic substrates can produce environmental friendly power that could be used for the small appliances. 3. Online sensors. The production of electric current in the presence of biodegradable material could be exploited for the online detection and quantification of soluble organics in waterways or wastewater treatment plant (WWTP) effluents.

4.18.10.1 Wastewater Treatment The power densities generated by MFCs are much smaller than those of chlorofluorocarbons (CFCs). Therefore, MFCs cannot compete with CFCs as power producers, but they become much more attractive if the production of electricity is combined with wastewater treatment. The COD contained in wastewaters can be thoroughly removed while producing a CO2-neutral power, which could potentially cover at least the electricity requirements of the WWTP. Complex substrates have been successfully used to generate power in MFCs, including domestic wastewater (Liu et al., 2004), anaerobic digesters’ effluent (Aelterman et al., 2006a), brewery wastewater (Feng et al., 2008), and paper-recycling wastewater (Huang and Logan, 2008). Additionally, if anodic carbon oxidation is coupled with cathodic nitrogen removal, the use of MFCs opens up new perspectives for an integrated and sustainable wastewater treatment process. Wastewater treatment with MFCs would also offer the unique feature of online monitoring of the process through current and electrode-potential measurement that can rapidly advise of system failures. A drop in the current coupled with a rise of the anodic potential would indicate, for instance, a drop in the catalytic activity of the anodic biofilm or a failure in the feeding system. The removal of organics contained in wastewater is considered as energy efficient when no energy is consumed for aeration. If this is provided by means of passive aeration, for instance, it would save around 0.7–2 kW h1 kg1 COD removed (Logan et al., 2008) for conventional aeration, and energy can indeed be harvested from the substrate, with a theoretical upper limit of 4.4 kW h1 kg1 COD. Moreover, as the energy gain for bacteria growing at the MFC anode is generally lower than that for aerobic processes (Section 4.18.6), sludge production would also be reduced. The bacterial-growth yield in MFCs is in fact expected to be between that of high energy-yielding aerobic processes (0.4– 0.6 g COD biomass g1 COD substrate according to Heijnen (1999)) and that of low energy-yielding anaerobic treatment (0.01–0.14 g COD biomass g1 COD substrate, Heijnen (1999)). Rabaey et al. (2003) reported a measured yield of 0.07–0.22 g COD biomass g1 COD substrate for a glucose-fed

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MFC. Freguia et al. (2007b) reported growth yields at anodes ranging from 0 to 0.3 g COD biomass g1 COD substrate acetate. MFCs can achieve significant increased organic removal rates compared to aerobic processes. Laboratory reactors have in fact reached current densities of the order of B10 A m2 anode surface area (Fan et al., 2007; Torres et al., 2007). Rozendal et al. (2008a) evaluated that this would correspond to a volumetric wastewater treatment capacity of B7.1 kg COD m3 reactor d1, assuming a minimal compartment thickness of 1 cm. If the same performances could be obtained on a larger scale, wastewater treatment with MFC would even outcompete traditional aerobic treatments, which are able to process B0.5–2 kg COD m3 d1 (Logan et al., 2006), with few advantages:

•

•

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MFCs can produce power and treat wastewater in a single stage, whereas anaerobic processes require expensive power-generation facilities: an anaerobic digester for the production of methane and a power-production stage in a gas turbine/engine. MFCs can obtain good effluent quality while treating dilute influents at temperatures below 20 1C (Pham et al., 2006), while anaerobic digesters work best with high-strength wastewater at increased temperatures (30–37 1C) and require further polishing of the effluent. In a (bio)electrochemical system, it is possible to utilize streams containing sulfur (Dutta et al., 2008; Rabaey et al., 2006) with no need for gas treatment, whereas the methane produced during anaerobic digestion normally contains traces of H2S that need to be removed before using the gas in a turbine (due to corrosion concerns and environmental regulations) or fed to a chemical fuel cell (as sulfide poisons the Pt catalyst).

However, to achieve practical implementation at a reasonable scale, several challenges will have to be solved (Section 4.18.9). Moreover, the capital costs of MFCs have to be reduced drastically as the material costs are very high and there is a limited economy of scale benefit, primarily due to the close anode/cathode distance required. In conclusion, MFCs should be considered more as a complementary system rather than as in competition with anaerobic digestion. Anaerobic digestion can be applied to the treatment of high-strength waste streams. Industrial effluents represent perfect examples of these applications. MFCs may operate better at a smaller scale, when anaerobic digestion would suffer from the high costs of gas treatment and handling; and with a more dilute waste stream such as, for example, the effluent from an anaerobic process. Furthermore, the challenge of wastewater complexity is yet to be addressed for real-scale applications. More studies using real wastewater are required to improve the knowledge of the degradation pathways of complex substances. Laboratory MFCs fed with wastewater mainly convert readily biodegradable organics, whereas more complex materials generally pass straight through the system. This can be due to the generally short hydraulic-retention time (Rabaey et al., 2005b) and the slower conversion rate of the more recalcitrant material.

4.18.10.2 Nitrogen Removal Nitrogen removal represents a topic of particular interest for MFC application. Currently, nitrogen is removed from wastewater by means of two sequential processes, both promoted by microorganisms: nitrification and denitrification. Nitrification is an autotrophic process that converts ammonium into nitrate using oxygen and an inorganic carbon source. Denitrification is instead a heterotrophic anoxic process that utilizes nitrate as an electron acceptor during the oxidation of an organic carbon source. Due to the competition between aerobic and anoxic organisms for the available organics, supplementary carbon supply is often used (typically methanol) in addition to the carbon already present in the wastewater, to increase the efficiency of denitrification. Cathodic denitrification was recently demonstrated in MFCs (Clauwaert et al., 2007a). In Virdis et al. (2008), the MFC was integrated with an external aerated vessel for nitrification, and the system was able to simultaneously remove carbon and nitrogen. As in the MFC configuration, the oxidative and reductive biomasses are kept physically separate by the IEM, and the competition between organisms can be minimized to achieve highly efficient denitrification at lower C/N ratios than generally required by heterotrophic denitrification. This represents an important advantage of MFCs as denitrification is driven by electrons directly supplied by the anode with no need for the organics to be added directly into the denitrification stage.

4.18.10.3 Bioremediation The possibility of removing metals and chlorinated compounds by means of bioelectrochemical systems is of particular importance for applications of this technology in bioremediation. A study by Gregory and Lovley (2005) reported the microbial reduction of uranium using an electrode as an electron donor which caused the conversion of soluble U(VI) into the rather insoluble U(IV), which precipitated onto the electrodes. Cathodic reduction of perchlorate, an industrial by-product (i.e., from the production of pyrotechnic compounds and lubricant oils) found in the environment due to a historical lack of regulation in its manufacturing and discharge, was also recently described (Thrash et al., 2007). Hydrogen likely served as an electron shuttle for dissimilatory perchlorate-reducing bacteria, although an isolate from a perchlorate-reducing reactor could accept electrons from the cathode via an added redox mediator. Reductive dechlorination of chlorinated compounds, such as trichloroethene (TCE), is typically achieved through the oxidation of an organic electron donor. It was recently shown that TCEdechlorinating bacteria could directly use a cathode as an electron donor (electrode polarization:  450 mV vs. SHE) (Aulenta et al., 2009).

4.18.10.4 H2 Production Hydrogen gas can effectively be produced through MECs. These are electrolysis-type BESs that are capable of producing hydrogen at the cathode on applying a small voltage (40.2 V in practice), while oxidizing organic matter at the anode (Liu et al., 2005c; Rozendal et al., 2006b).

Microbial Fuel Cells

The architecture of MECs is nearly identical to that of MFCs, except for the fact that an MEC requires gas collection at the cathode. Cathodic hydrogen production on plain carbon electrode is very slow due to high overpotentials. Platinum has been the most commonly used catalyst (Rozendal et al., 2006b). However, it was recently discovered that bacteria could be effectively used as catalysts for hydrogen production (Rozendal et al., 2008b), thus overcoming the disadvantages connected to the use of platinum-based cathodes (Section 4.18.5). More recently, the use of nickel and stainless steel in the form of flat sheets or brushes was observed to outcompete platinum as cathodic catalyst (Call et al., 2009; Selembo et al., 2009). MECs are a promising technology for sustainable hydrogen production from wastewater. While MFCs recover energy from wastewater in the form of electricity, MECs recover energy in the form of hydrogen. Nevertheless, to function as wastewater treatment systems, MECs need to guarantee reasonable COD conversion rates. Based on current H2-production performances, COD-loading rates would need to be of the order of 6.5 kg COD m3 d1 (Logan et al., 2008), which is between the range of activated sludge systems and anaerobic digesters, thus making the MEC technology competitive when compared with traditional wastewater treatment. However, MECs need to be more cost effective than existing technologies and since electric energy is consumed during their operation, the higher costs must be compensated for by sufficient hydrogen production. It is estimated that full-scale MEC systems require 1 kW h1 m3 of H2 produced and can produce up to 10 m3 H2 m3 d1 (Rozendal et al., 2007), which is equivalent to an energy requirement of B1.5 kW h1 kg1 COD treated (Logan et al., 2008), and which is similar to the energy consumption for activated sludge treatment (Rozendal et al., 2008a). On the contrary, energy recovery through anaerobic digestion does not require significant energy inputs. However, compared to MECs, anaerobic digestion produces a gas (methane) that is less valuable than hydrogen. On the other hand, anaerobic digestion is a well-established technology, whereas microbial electrolysis requires great research efforts on both engineering and biochemical aspects.

4.18.10.5 Bioelectrochemical Production of Value-Added Chemicals Currently, it is expected that the capital costs for a full-scale BES will always remain several times higher than that of conventional wastewater treatment systems (Rozendal et al., 2008a). Therefore, bioelectrochemical wastewater treatment will become economically advantageous when the larger investments are compensated for by the larger value of the products obtainable. Electricity production using MFCs has the disadvantage of its low revenue, which puts electricity among the least valuable products (Rozendal et al., 2008a). As we have seen in Section 4.18.10.4 energy can be recovered in a BES not only as electricity but also as hydrogen. In addition, BESs can offer other interesting opportunities to improve their economical feasibility. For instance, the hydrogen produced in a BES can be used to create other products in situ. Several researchers have already reported methane production as a side product in membrane-less MECs, due to hydrogen

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scavenging (Call and Logan, 2008; Clauwaert and Verstraete, 2009). When the IEM is omitted, hydrogenotrophic methanogens can use the hydrogen produced at the cathode and combine it with the carbon dioxide produced at the anode, thus producing methane. Even though methane has a lower energy content compared to hydrogen per unit of mass, removing the IEM from the MECs would significantly lower its capital costs as well as reduce the system’s ohmic losses and pH gradients. MECs could thus be used in combination with anaerobic digestion facilities at the polishing stage by treating the residual organics present in the effluent (Clauwaert and Verstraete, 2009). In addition, direct methane production without intermediate hydrogen production was also recently observed in a biocathode dominated by Methanobacterium palustre (Cheng et al., 2009), demonstrating that BESs can be used to convert electricity into a biofuel while also capturing carbon dioxide. It is expected that, in future, BES innovations will proceed on these lines. A whole range of value-added chemicals requires the reduction of power for their production. When CO2 and O2 impurities are present together with H2, the production of biopolymers such as polyhydroxyalkanoates (PHA) by hydrogen-oxidizing bacteria can be foreseen in membrane-less or loop-based MECs (Ishizaki et al., 2001). Moreover, alcohols can also be produced from VFAs using hydrogen as an electron donor (Steinbusch et al., 2008), or a mediator (methyl viologen) (Steinbusch et al., 2009). Moreover, hydrogen peroxide production has been obtained by coupling organic oxidation at the anode with oxygen reduction at the cathode and adding a small voltage (Rozendal et al., 2009).

4.18.11 Outlook Current approaches to waste management will have to change in the future since waste will have to be considered as an alternative resource rather than an inconvenient burden to dispose of. Wastewater, in particular, represents an important resource of nutrient (primarily nitrogen and phosphorus), energy (as energy contained in chemical bonds of organic matter), and water itself. In a sustainable society, wastewater treatment will no longer be regarded as a treatment per se, its sole purpose being the removal of contaminants, often requiring a great deal of nonrenewable energy (e.g., from coal extraction), which may ultimately cause more environmental damage than the direct discharge of the untreated wastewater. In the future, we will no longer refer to wastewater treatment plants but rather to bioelectrochemical-resource-recovery plants, or biorefineries. With this new picture emerging, the raw wastewater would follow several sequential treatment stages, the first stage of which would be a pre-treatment to remove the solids, for instance, through dissolved air flotation. The solid fraction can then be sent to an anaerobic digester wherein some biogas is formed and solid liquid/separation creates a sludge that can be used for composting, while the supernatant can be sent back to the main flow. A pre-fermenter would be likely added after the pre-treatment to breakdown complex organics and produce an effluent richer in VFAs that are better metabolized in a BES anode for electron extraction. After the anodic

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passage, the effluent would be primarily rich in nitrogen as ammonium, which can be recovered through thermal volume reduction, or as struvite. Further specific treatments would depend on the final utilization of the effluent; tertiary treatments will produce water suitable for use in other processes (as cooling water, for instance), or even be able to reach drinking standards through advanced treatment processes. The electrons harvested during the anodic passage would be conveyed to the cathodic side of the BES where they can be used to drive a wide array of processes, from direct electricity production through to MFC, or perhaps to produce hydrogen through MECs, or moreover to produce other value-added chemicals such as methane, hydrogen peroxide, alcohols, biopolymers, or biofuels as seen previously.

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Relevant Website http://www.wri.org World Research Institute (WRI).

4.19 Water in the Pulp and Paper Industry H Jung and D Pauly, Papiertechnische Stiftung, Munich, Germany & 2011 Elsevier B.V. All rights reserved.

4.19.1 4.19.2 4.19.2.1 4.19.2.2 4.19.2.3 4.19.3 4.19.3.1 4.19.3.2 4.19.3.2.1 4.19.3.2.2 4.19.3.2.3 4.19.3.2.4 4.19.3.2.5 4.19.3.2.6 4.19.3.3 4.19.3.3.1 4.19.3.3.2 4.19.4 4.19.4.1 4.19.4.2 4.19.4.2.1 4.19.4.2.2 4.19.4.2.3 4.19.4.3 4.19.4.3.1 4.19.4.3.2 4.19.4.3.3 4.19.5 4.19.5.1 4.19.5.2 4.19.5.3 4.19.6 References

Overview of Pulp and Papermaking Water in the Pulp and Paper industry Functions of Water in Papermaking Historical Evolution of Water Systems Current Water Consumption Levels in the Pulp and Paper Industry Water Use Freshwater Process Water Circuitry Primary, secondary, and tertiary water circuits Detrimental substances General principles of circuitry Closed water circuits Assessment of freshwater use and circuitry Wastewater Characterization of wastewater from the pulp and paper industry Wastewater discharging Water Treatment Freshwater Treatment Circuit Water Treatment Objectives of circuit water treatment Mechanical circuit water treatment Advanced circuit water treatment Wastewater Treatment Preliminary mechanical treatment: Mechanical processes for removal of solids Biological treatment Advanced and tertiary treatment Potentials and Limits of Water Saving Limiting Effects of System Closure Heat Balance Economic Benefits Improving Water Efficiency in Paper Manufacturing Industries – 30 Years of Success

4.19.1 Overview of Pulp and Papermaking Paper is currently a commodity product. The worldwide consumption of paper is growing steadily and it is hard to imagine the world without paper. Papermaking is based on a principle that is roughly 2000 years old. Today, in principle, the same process steps, which were used in the past, are included. The papermaking process can be divided into four main process steps (Figure 1), which can be either integrated at one site or located at several different sites. These process steps include

• • • •

pulp production (chemical or mechanical pulp and recycled fiber pulp (RCF)), stock preparation, paper machine, and coating and finishing.

Papermaking starts with the provision of the stock components such as fibers, fillers, and chemical additives. Primary

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fibers (chemical and mechanical pulp) are obtained from wood and annual plants by chemical pulping or mechanical defibration. Secondary fibers are produced from recovered paper. All these components have to be properly prepared for optimum use in papermaking. Stock preparation is followed by the approach flow system, which links stock preparation to the paper machine. Paper or board is produced at the paper machine. In doing so, a sheet is formed from a highly diluted fiber suspension and dewatered by means of filtration, pressing, and thermal drying. Coating and calendaring improve the surface quality of the paper and board. The final steps include slitting, sheeting, and packaging of the final product for shipment.

4.19.2 Water in the Pulp and Paper industry 4.19.2.1 Functions of Water in Papermaking Water is one of the key components in pulp and papermaking. Without water, the production of paper would be unthinkable.

667

668

Water in the Pulp and Paper Industry Raw material Wood / recovered paper / annual plants

Pulp production Chemical / thermal / mechanical

Pulp

Stock preparation Suspending / screening and cleaning / refining

Machine stock

Papermaking process Approach flow / paper machine

Paper / board Coating / finishing

Final product Figure 1 Process steps in papermaking.

Water performs numerous functions in the papermaking process. It is used as medium for suspension, dwelling, and transfer processes, and it serves to separate as well as to restore the bonds between fibers. Other uses include showers in the wire and press sections, sealing of pumps, and cooling and cleaning purposes. Furthermore, water in the form of steam is used as an energy carrier.

4.19.2.3 Current Water Consumption Levels in the Pulp and Paper Industry At the onset of industrial papermaking, paper was produced with high specific water consumption. The pulp and paper industry has improved the processes in the last few decades for economical and ecological reasons and, as a result, was able to reduce water consumption significantly. This was only possible because of increasing closure of in-mill water circuits and consistent reuse of clarified process water by former freshwater consumers. A survey conducted by the Papiertechnische Stiftung (PTS) and the German Pulp and Paper Association (VDP) showed that the average specific effluent volumes of Germany’s pulp and paper industry decreased from 46 to approximately 10 m3 per metric ton of product produced between 1974 and 2007 (Figure 2). Nevertheless, the German pulp and paper industry remains one of the six biggest consumers of industrial water (Federal Statistical Office, 2008). The consumption level in the different pulp and paper mills can vary because of both general and process-related reasons such as raw materials used, paper grades produced, and plant structure. Furthermore, local boundary conditions, such as requirements on wastewater discharge, have an impact on the consumption level. High specific effluent volumes occur particularly in specialty paper grades. These mills are often faced with structural handicaps that cause increased specific effluent volumes: small and obsolete paper machines, low production rates, frequent grade changes, and often very high quality requirements on the final product. The lowest water requirements can be found in mills that produce packaging papers, such as corrugated base paper or board. Some of these mills have already managed to close their water circuits completely, resulting in a zero effluent production.

4.19.3 Water Use 4.19.3.1 Freshwater

4.19.2.2 Historical Evolution of Water Systems According to Zippel (2001), there are three phases in the historical evolution of paper-mill water systems. Phase 1 began in the 1920s. During this phase, the basics of water circuit design were established. Freshwater saving potentials were initially introduced predominantly for economic reasons. Phase 2 began in the 1960s. During this phase, the final effluent became more important for paper mills. This was caused as a means of reducing solid losses and thereby increasing the yield on the one hand and, on the other hand, as a result of the increasing ecological awareness of the general population. Subsequently, mechanical and biological wastewater treatment plants were installed. A few mills even managed to close their water circuits completely. Phase 3 (starting in the 1970s) was marked by initial attempts undertaken to deal with the consequences of system closure. Thus, the third phase was characterized by basic investigational work on the constituents of the process water and their impact on runnability of the paper machine and paper quality.

Depending on the availability and local conditions, either surface water or groundwater is used as freshwater. Drinking water is used for certain purposes, such as trim squirts. In the German pulp and paper industry, roughly 80% of the fresh water is taken from surface waters (Jung et al., 2009). In stateof-the-art mills, there are only few freshwater consumers. Typical freshwater consumers include

• • • •

high- and low-pressure showers for felt conditioning and wire cleaning, trim squirts, sealing water for liquid-ring vacuum pumps and packing glands, and additive preparation and dilution.

In view of the limited freshwater volume available, it must be used efficiently. Hence, freshwater used for cooling purposes (oil coolers and steam condensers) should be collected and reused as fresh warm water in the paper machine. Process water should be used for all other purposes, such as stock dilution, consistency control, or cleaning.

Water in the Pulp and Paper Industry

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Specific effluent volume (m3 per metric ton of product)

50

40

30

20

10

0 1972

1976

1980

1984

1988

1992

1996

2000

2004

2008

Year Figure 2 Averaged specific effluent volume in the German pulp and paper industry (Jung et al., 2009).

Typically, approximately 40% of the entire freshwater volume is used for the high- and low-pressure showers in the wire and press sections. Depending on the paper grade and the nozzles used, different flow rates are used for showers in the wire and press sections. The entire consumption in the European paper industry for both showers in the wire section and showers in the press section averages out to approximately 1.0–2.5 m3 per metric ton of paper, depending on the degree of water circuit closure (Kappen et al., 2004). The sealing water consumption in liquid-ring vacuum pumps is highly dependent on the installed system. If a sealing water circuit is installed with a cooling tower, the freshwater can be less than 0.5 m3 per metric ton of paper. Without a sealing water circuit, the consumption typically amounts to approximately 4–5 m3 per metric ton of paper (Kappen et al., 2004). Sealing water is needed in packing glands to lubricate the sealing faces and to remove solids. According to Kappen et al. (2004), freshwater requirements amount to 0.15 m3 h1 in pumps and agitators and 0.2 m3 h1 in refiners and deflakers.

4.19.3.2 Process Water 4.19.3.2.1 Circuitry In papermaking, it is quite important to provide both adequate water quality and the required volume of water for every single consumer. Using freshwater for all purposes would consume several 100 m3 per metric ton of paper. The objectives of the water circuit system are to provide the required amount and quality of water for every consumer paying attention to economical and, at the same time also, to ecological aspects. In meeting these requirements, most of the water used in the pulp and paper industry is process water that has been recycled in different loops. Hence, the installation and proper design of water circuits are of fundamental importance for pulp and papermaking, since it contributes to enhanced product quality and reduced effluent volume.

Process water is mainly used for pulping and consistency control in the individual process steps. It is also used to a greater extent for purposes for which freshwater was formerly used, such as (low-pressure) showers, foam destruction, sealing water of liquid-ring vacuum pumps, or additive preparation. Process water is produced in the thickening and dewatering stages of the papermaking process by separating liquid phase from solid phase. In the stock preparation loops, this is done by disk filters, screw and double wire presses, and drum thickeners. Wire section, press section, and savealls provide the required process water volumes at the paper machine. If water quality achieved is still inadequate, advanced treatment technologies such as membrane or ozone treatment can be employed. The possibilities for designing water circuits greatly vary and depend on a number of parameters. One important parameter is the grade of paper being produced and the corresponding raw material being used.

4.19.3.2.2 Primary, secondary, and tertiary water circuits Based on the connection to the core process (sheet formation on the wire), it is generally possible to differentiate between three categories of water circuits: primary, secondary, and tertiary water circuits. Figure 3 illustrates the primary and secondary water circuits. The primary circuit consists of white water 1 originating from the wire section. This circuit is the largest as far as the volumetric flow rate is concerned. The circulating flow rate depends on the retention in the wire section and the consistency in the headbox. Its objective is to dilute the main stock flow after the machine chest in the approach flow system to a consistency of approximately 0.7–1.5%. The excess flow rate is part of the secondary circuit. Besides the excess flow rate of white water 1, the secondary circuit originates from the forming section and from the press section (see Figure 3). Most of this water is preferably fed to a saveall, and the recovered fibers are sent to the blend or machine chest and stock preparation, respectively. The clarified

670

Water in the Pulp and Paper Industry Mixing chest

Machine chest

Cleaner

Screen

Wire section

Press section

From stock preparation (DIP, chemical pulp, mechanical pulp, fillers, etc.

Primary circuit

White water 1

Showers, etc.

To stock preparation

White water 2

Secondary circuit Saveall

Waste water

Figure 3 Schematic illustration of principal water and stock flows in a paper machine (Hutter, 2008).

water is sent to a buffer tank and from there it is supplied to the process. The possible fields of application of clarified water are manifold: pulping, consistency control, foam destruction, and showers (mainly in the wire section). Further treatment (e.g., membrane filtration) might be necessary in the case of sensitive applications such as sealing waters or high-pressure showers. As the papermaking process is typically supplied by freshwater, there is always an excess of process water. This excess water is part of discharged wastewater. A tertiary circuit is required when, at least, a part of the treated wastewater is recirculated. In zero effluent systems, all treated wastewater is recirculated. In order to eliminate detrimental substances from the papermaking process, the recirculated wastewater should undergo full biological treatment. Possible fields of application of the recirculated wastewater are manifold and depend on the water quality attained. Besides being used as pulping or cleaning water, it may also be used as sealing water or as spraying water in showers after adequate pretreatment. Attention must be drawn to the danger of scale formation as biologically treated water often has a high calcium concentration (Demel et al., 2004a, 2004b).

• • • • • • •

a reduction in additive efficiency, a reduction in optical and strength properties, negative effects on drainage and paper drying, negative impacts on sizing, odor formation, deposits, and/or foam generation.

The main sources of detrimental substances and contraries in paper-mill process water are fibrous raw materials, additives, and freshwater (Negro and Tijero, 1998). Table 1 provides an overview of the composition and origin of detrimental substances. The content of detrimental substances is typically measured using sum parameters, such as anionic trash, cationic demand, or chemical oxygen demand (COD). Inorganic dissolved substances are measured as increased conductivity (Stetter, 2006). The COD denotes the volume of oxidizable substances in a water sample. It is considered to be balanceable and is thus a suitable optimization parameter.

4.19.3.2.4 General principles of circuitry 4.19.3.2.3 Detrimental substances Due to the increasing use of recovered paper and the reduced freshwater consumption, constituents known as detrimental substances have accumulated in water circuits, leading to growing problems in the papermaking process. Detrimental substances are substances that have a negative impact on the papermaking process and on product properties. Auhorn defined them as follows: "Detrimental substances are dissolved or colloidally soluble anionic oligomers or polymers and nonionic hydrocolloids" (Auhorn, 1984). They can result in

Figure 4 schematically illustrates a simplified water and stock system in a paper mill. Both the stock preparation loop and the paper machine loop can use freshwater. Wastewater is discharged mainly from the paper machine loop. Moreover, water is exchanged between the paper machine and stock preparation loops depending on the transfer consistency of the pulp coming from stock preparation. Based on a specific effluent volume of 10 m3 per metric ton of paper and a COD input of 10 kg per metric ton of raw material, this results in a COD concentration of 1.7 g l1 in the stock preparation loop and 1.2 g l1 in the paper machine loop.

Water in the Pulp and Paper Industry

There are two general principles used in designing water circuits that are described on the basis of this simplified model mill:

• •

loop separation and countercurrent arrangement.

Water circuits can be subdivided into separate loops by installing thickening units such as screw presses or double wire presses. In many cases, these thickening units are also necessary for downstream units such as dispergers. At the same time, soluble detrimental substances will be retained in the stock preparation water system. An increase of up to 30% in the transfer consistency between stock preparation and the Table 1 contraries

Composition and origin of detrimental substances and

Chemical compounds

Origin

Sodium silicate

Peroxide bleaching, deinking, recovered paper Filler dispersing agent Filler dispersing agent Coated broke, recovered paper Freshwater Chemical and mechanical pulp

Polyphosphate Polyacrylate Starch Humic acids Lignin derivates, lignosulfonates, hemicelluloses Fatty acids Volatile fatty acids

Chloride Calcium Sulfides Exopolymer saccharides

Mechanical pulp, deinking Anaerobic processes (high hydraulic retention times, spoiled recovered paper) Chemical additives Recovered paper, fillers Anaerobic processes, sulfate High C/N ratio

671

paper machine results in a significant reduction in the exchanged water volume. As the wastewater is still being discharged from the paper machine loop, there is no sink for the detrimental substances in the first loop (stock preparation). Detrimental substances build up in the stock preparation loop, resulting in a COD concentration of 4.9 g l1. It is not possible to relieve the paper machine loop (Figure 5). A countercurrent arrangement (Figure 6) completes the above-described principle of loop separation. The highly concentrated filtrate from the thickening unit is discharged to the wastewater treatment plant. The water deficit in the stock preparation loop is compensated by adding water from the paper machine loop. Hence, the most contaminated water is being discharged, while the better-quality water is being used in the more sensitive paper machine loop. The water flows in a direction opposite to the stock flow. This leads to significant relief of the paper machine loop, resulting in a COD concentration of 0.5 g l1 or 58% of the initial situation described above. Strict separation of the stock preparation water loops from the paper machine loop combined with a well-designed countercurrent arrangement is essential to meet high runnability and quality requirements because this strategy keeps detrimental substances out of the paper machine. Unlike the countercurrent dewatering arrangement in paper mills, a countercurrent washing arrangement is typically installed in chemical pulp mills. Substances that are dissolved during digestion, delignification, and bleaching are carried along into the next process steps together with the fibers. To accumulate and recirculate these substances to the digester, the washing liquor passes through a countercurrent washing arrangement within the different process steps (Figure 7). This ensures that most of the organic load and the digesting chemicals are recirculated to the digester, which guarantees the efficiency of the bleaching chemicals. Furthermore, it helps to

5 COD SP loop (g l−1)

Raw materials 10 kg COD t−1 Freshwater Stock preparation

4 3 2 1

20 l kg−1 0 5

Paper machine 10 l kg−1 Wastewater treatment plant

Paper Figure 4 Simplified schematic illustration of the water and stock systems of a paper mill.

COD PM loop (g l−1)

5%

4 3 2 1 0

672

Water in the Pulp and Paper Industry 5 COD SP loop (g l−1)

Raw materials 10 kg COD t−1 Freshwater Stock preparation

4 3 2 1

20 l kg−1 0 5

Paper machine 10 l kg−1 Wastewater treatment plant

COD PM loop (g l−1)

30%

Paper

4 3 2 1 0

Figure 5 Loop separation.

5 COD SP loop (g l−1)

Raw materials 10 kg COD t−1 Freshwater Stock preparation

4 3 2 1

10 l kg−1

2 l kg−1 10 l kg−1 Wastewater treatment plant

Paper

COD PM loop (g l−1)

Paper machine

0 5

8 l kg−1

30%

4 3 2 1 0

Figure 6 Countercurrent arrangement.

relieve the pulp dewatering machine from detrimental substances as efficient as possible (Borschke, 2006).

4.19.3.2.5 Closed water circuits Complete closure of water circuits implies eliminating any sort of effluent discharge. For some mills, it is the last resort to be able to continue production at that particular location. Motivating factors include costs of discharging effluents, absence of receiving waters, or the necessary discharge rights if the mill is moved to a new location. The specific effluent volume in the case of closure is 0 m3 per metric ton of paper. Freshwater is

used only to compensate for the loss of water by evaporation and in the finished product, and for the water removed together with the rejects. This volume normally amounts to approximately 1.5 m3 per metric ton of paper. The process is thus subject to massive limitations. Only few consumers can continue to be supplied with freshwater, leading to extremely high concentrations of detrimental substances which will be bled out of the system only by transferring them into the paper. Hence, when preparing water circuits for mill closure, first, all options for optimization of the water circuits must be exhausted. A subsequent installation of internal circuit water

Water in the Pulp and Paper Industry Deknotting and screening

Washing

Washing

Oxygen Washing delignification

Pulp from digester

673

Liquor Subsequent process steps

O2

Filtrate

Digester Evaporation plant

FT

FT

FT

FT

Filtrate tank

Filtrate tank

Filtrate tank

Filtrate tank

COD in the water circuit

Figure 7 Countercurrent washing in chemical pulp manufacturing (Borschke, 2006).

2. Circuit water treatment

1. Optimization of water circuits

Closure of water circuits

most delicate part of the papermaking process, which is why the white water should contain as few disturbing substances as possible. By comparing the COD levels (filtered samples) in the wastewater prior to biological treatment and in white water 1, the K1 value makes it possible to determine the utilization of freshwater (Equation (1)). K1 value significantly less than 1 indicates freshwater which is discharged to the effluent treatment plant directly without relieving the paper machine loop:

K1 ¼ Specific effluent volume

CODEffluent CODWhite water

ð1Þ 1

Figure 8 A stage-by-stage approach for water circuit closure.

treatment units makes it possible to remove dissolved substances (kidney technology). Only then subsequent closure can be met successfully (Figure 8). Possible kidney technologies include integrated biological treatment, membrane filtration, or ozone treatment (see Section 4.19.4.2).

4.19.3.2.6 Assessment of freshwater use and circuitry In order to be able to optimize a water circuit, it must first be clarified whether or not the following conditions are fulfilled:

• • •

freshwater should be used effectively and not passed directly to the wastewater treatment plant, the contaminant load at the paper machine should be as low as possible, and contaminants should be discharged wherever possible using the smallest effluent volumes.

The K-values established by Kappen (Kappen and Wilderer, 2002) are capable of quantifying the most important goals in optimizing circuit design. They work through comparisons of the COD levels at different locations in the water circuit. K1 value. Sheet formation is brought about by dewatering fiber suspensions in the paper machine followed by the subsequent formation of hydrogen bonds between fibers. It is the process stage that is decisive for mechanical and optical characteristics of the paper. Sheet formation constitutes the

K2 value. The K2 value expresses the COD concentration ratio in water loops of the stock preparation and paper machine (Equation (2)). To achieve maximum relief of the paper machines, the COD level in white water 1 ought to be substantially lower than that of the stock preparation system. K2 41 means that detrimental substances that give rise to COD are retained in the stock preparation system, that is, the sheet formation section and white water 1 are relieved:

K2 ¼

CODStock preparation CODWhite water 1

ð2Þ

K1/K2 ratio. K1/K2 indicates whether the wastewater discharged from the papermaking system is the optimum solution in terms of paper machine relief. To obtain a maximum COD relief through a minimum effluent flow, the water highest in COD loading must be discharged to the wastewater treatment plant. This maximum loading is found in the stock preparation system where detrimental substances accumulate. The quotient of COD levels in the wastewater and stock preparation system is referred to as the K1/K2 ratio:

K1 CODEffluent ¼ K2 CODStock preparation

ð3Þ

K1/K2 close to 1 indicates that the COD loadings of the effluents and stock preparation are nearly equal, that is, the

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Water in the Pulp and Paper Industry

effluents are discharging detrimental substances mainly from the section with the highest COD loading. This, in turn, ensures maximum paper machine relief by the given effluent volume and corresponds to the countercurrent arrangement. The K1/K2 ratio helps to evaluate the circuitries of a paper mill. A countercurrent arrangement is only realized for K1/K2 values close to 1. The above definitions apply exclusively to simple systems with one stock preparation system and one paper machine. In more complex systems, characterized by more than one line or loop in stock preparation or more than one paper machine, the COD loading of the wastewater, stock preparation, and paper machine loop are calculated as the weighted average of the individual COD loadings determined. Graphic representation of K1 and K2. The relationship between these K-values may be visualized in a K1–K2 performance characteristic (Figure 9). The K1–K2 performance characteristic depicts the current situation in a paper mill (operating point). The two lines to the left and right of the diagonal define the target range of best performance achievable under practical conditions. An operating point within the target range indicates an optimized water loop. The circuitry status and the optimization potential that exists can be visualized as the K1–K2 section that has experienced a local shift out of the target range. The success of optimization measures can be documented without much difficulty using these key parameters. The K1 value, the K2 value, and the K1/K2 ratio are the principal characteristics that make assessment of the efficiency of freshwater use and circuitry possible. The characteristics K1 and K2 make it possible to quantify the primary objectives of circuit optimization, that is, effective freshwater use, maximum paper machine relief, and effective elimination of anionic trash (Kappen and Wilderer, 2002).

4 Target area with loop separation 3

K2

Operating point 2

1 Target area without loop separation 0 0

1

2 K1

Figure 9 K1–K2 performance characteristic.

3

4

4.19.3.3 Wastewater 4.19.3.3.1 Characterization of wastewater from the pulp and paper industry In general, wastewater in the pulp and paper industry is produced in the form of excess process water, which is displaced by the freshwater input. The wastewater is loaded primarily with organics that enter the production process together with raw materials and additives. Effluents from the pulp and paper industry are still not completely understood in terms of their chemical composition (Hynninen, 2000). In the majority of cases, however, wastewater of paper mills is nontoxic and easily degradable biologically. Higher concentrations of dissolved organic and inorganic compounds are observed in productions, enabling a particularly intensive utilization of water (Mo¨bius, 2002). As far as organic loads are concerned, COD, biochemical oxygen demand (BOD5), and adsorbable organic halogens (AOX) are the key parameters that characterize papermaking effluents. Today, however, the total organic carbon (TOC) parameter is becoming more important – a development which is reflected in a growing number of measuring methods such as the cuvette test or online measuring systems. Effluent concentrations vary widely depending on

• • • •

raw materials, paper grades, specific freshwater consumption, and available installations.

The assessment of the biodegradability of effluents is based on parameters such as BOD5, COD, and their ratio in wastewater. For a completely degradable compound such as glucose, which resembles the dissolved material in paper-mill effluents, the BOD5/COD quotient is typically approximately 0.6, suggesting a very good biodegradability, whereas a lower quotient is indicative of poorer degradability and a higher residual COD. The BOD5/COD quotient from different paper mills varies roughly between 0.35 and 0.5. Moreover, some important inorganic effluent parameters, such as salt loads, have to be taken into consideration. Calcium and sulfate concentrations play a special role in the operation of anaerobic treatment plants. When treating effluents with a high calcium content, poorly soluble calcium carbonate may be precipitated. In plants employing carrier material, such precipitation products may cause deposit formation. In mixed reactors, precipitation products tend to accumulate in the sludge, impeding thorough mixing of effluents and sludge and finally reducing the share of active biomass. When treated effluents are recirculated back into production, additional precipitation problems may arise in the consumers due to the pH shift of the decreased buffer capacity. The growing use of calcium carbonate as a filler and coating pigment and the ever-tighter-closed water circuits increase the calcium concentrations in circuit water and the wastewater. This applies in particular to mills that convert recycled paper. In the case of high sulfate concentrations, anoxic conditions may trigger sulfate reduction and lead to sulfide formation. This may disturb the degradation processes (methanogenesis) in anaerobic treatment plants, whereas in

Water in the Pulp and Paper Industry

aerobic biological treatment such high sulfate concentrations may foster the growth of undesirable filamentous microorganisms. As an additional drawback, the hydrogen sulfide that forms may give rise to bad odors and corrosion phenomena. Sulfate concentrations up to 600 mg l1 are to be expected in the wastewater of paper mills producing mechanical paper due to the aluminum sulfate used for resin sizing. The sulfate concentrations are substantially lower for woodfree papers. Even higher concentrations can occur in the production of recycled fiber-based papers. Sulfate originates from recovered papers and becomes increasingly concentrated as a result of tightly closed water circuits, typical for paper mills converting recovered paper. Depending on the treatment process used, other parameters such as pH, conductivity, and temperature are important for operational safety. Normally, in paper industry effluents, phosphorus and nitrogen compounds serving as nutrients for microorganisms are either absent or only available in insufficient quantities (Hamm, 2006). Therefore, it must be ensured that dosages of nutrients in treatment plants provide a sufficient supply for the microbiota. However, simultaneously, the permissible limit values in final effluents have to be met.

4.19.3.3.2 Wastewater discharging In the German pulp and paper industry, most effluents undergo full biological treatment. Ninety-five percent of the production volume is produced in mills with an integrated biological wastewater treatment plant or mills that discharge their wastewater to municipal wastewater treatment plants; 4% of the annual production volume is produced in mills with a closed water circuit; and only 1% of the production volume comes from mills that discharge their effluents without biological treatment (Jung et al., 2009).

4.19.4 Water Treatment 4.19.4.1 Freshwater Treatment As mentioned in Section 4.19.3.1, the source of freshwater in the pulp and paper industry is usually surface water. Typically, freshwater does not meet the required quality parameters of the manufacturing process and therefore has to be treated. Well water seldom needs treatment. Objectives of freshwater treatment in the pulp and paper industry include

• • • •

removal of solids, removal of color and organic substances, decrease in hardness and removal of other dissolved salts, and, in some cases, the disinfection of the water.

Water quality can be improved by a range of treatment measures. Factors influencing the choice of the treatment method and equipment include required water quality, water volume to be treated, space available for freshwater treatment plant, and, to some extent, how well the plant operation and supervision can be integrated with the other operations in the mill (Hynninen, 2000).

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Predominantly mechanical or chemical–mechanical treatment technologies are used for freshwater treatment in the pulp and paper industry. According to a survey conducted by PTS and VDP, more than 90% of the surface water used as freshwater for the papermaking process is treated by filtering. An additional 75% of this water is conditioned by chemical coagulation, flocculation, and subsequent sedimentation. The volume of freshwater treated with biocides has increased significantly, whereas the use of chlorine has decreased in the past few years (Jung et al., 2009). Freshwater is softened and desalinated for boiler house use and for the production of some specialty papers (e.g., photographic base paper or cigarette paper; Stetter, 2006).

4.19.4.2 Circuit Water Treatment 4.19.4.2.1 Objectives of circuit water treatment At the beginning, the objective of circuit water treatment was primarily to recover fiber furnish from papermaking effluents. Under the economic and ecological necessity of reducing effluent volumes and loads, the circulation water treatment process took on ever-greater importance and function: circuit water treatment must provide clarified water with a predefined quality and has to remove interfering substances from the system. In doing so, circuit water treatment became responsible for removing not only insoluble and colloidal components but also dissolved substances. Therewith, circuit water treatment helps to stabilize production processes and ensures product quality. The objectives of circulation water treatment include

• • •

recovery of raw materials, production of mill water with a low solid concentration available, and reduction of contaminants in the circulation water.

Depending on the required water quality, the requirements on circuit water treatment vary from reducing solid losses in the case of relatively coarse treatment to preparing shower and sealing water in the high-pressure range in the case of precision treatment.

4.19.4.2.2 Mechanical circuit water treatment Sedimentation, flotation, and filtration methods in particular are employed in mechanical circuit water treatment. These techniques can also be used in combination with one another. Methods for screening and classification are mainly used in stock preparation. The market share of the individual types of savealls moves in the direction of a two-part system, as sedimentation is steadily declining, whereas filtration and flotation are both expanding due to innovations and technical improvements (Zippel, 2001). Hydraulic surface load, solid surface load, and purification performance are the key parameters for the layout of the treatment units. Other parameters that have to be considered are the concentration of suspended solids in the feed, presence of colloidal and dissolved substances, additive demand, available space, and overall energy consumption. Two of the most important factors are the investment and operational costs. Cost effectiveness is obtained by reducing raw material losses,

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Water in the Pulp and Paper Industry

increasing process performance, and the benefits arising from stable and efficient paper production (Weise et al., 2000). The fiber recovery unit, or synonymously the saveall, is supplied with the excess from the primary circuit (wire pit overflow) and water removed from the wire and press sections by the vacuum system. In rare cases, water from the floor channels or from the wet broke, for example, is supplied to circuit water treatment. The process connected to the saveall has to be designed so that a constant feed flow is maintained. Sedimentation. Sedimentation is generally the simplest form of a saveall, and conventional sedimentation savealls have long been known to be reliable and safe to operate. Nowadays, sedimentation plays a minor role and is commonly used only in old plants for circuit water treatment. A general disadvantage of sedimentation plants is a low density of the sediment. Hydraulic retention times are very long in some cases and can also provoke anaerobic degradation accompanied by the correspondingly disadvantageous consequences (odor, microbial contamination, etc.) that affect the entire water circuit. Long hydraulic retention times also become a problem if rapidly changing production programs are to be run on the paper machine. Flotation. Flotation denotes the use of air bubbles to float undissolved substances to the surface of a suspension. Hydrophobic or hydrophobized particles adhere to the air bubbles, rise through the suspension, and are carried along to the surface and scooped off there by a suitable skimming device. Different flotation processes vary according to how bubbles are introduced into the suspension. Dissolved air flotation (DAF) has established itself in circuit water treatment. In this process, water is supersaturated with compressed air and then supplied to the flotation chamber (Figure 10). The resulting reduction in pressure causes very fine air bubbles to form that become attached to the suspended particles. Pressure saturation current can be the entire inflow, a partial flow, or recirculated clarified water (recycling process). A general problem associated with flotation units is a sharp fluctuation in inflow loadings. Fluctuations both in the volumetric flow rate and in the solid surface loading produce poor results.

Filtration. Filtration technologies are well suited for separating solid particles from suspension with assistance of a porous filter medium. Compared with other processes, good separation properties and high-quality clarified water that can be achieved are advantageous. Disadvantages are high investment and operating costs due to the considerable amount of maintenance work. In the pulp and paper industry, disk filters (Figure 11) are by far the most common type for mechanical circuit water treatment. A disk filter comprises several disks that consist of individual segments covered with a filter medium that rotate in a vat. The filtrate consistency declines during the filtration process and the filtrates are typically collected separately as cloudy filtrate and clear filtrate. In some cases, super-clear filtrate may also be produced. As an alternative to disk filters, drum filters can be used, which usually reduces the cost factor. However, for most applications, the hydraulic capacity of drum filters is too low and only one filtrate quality is produced. This normally makes them unsuitable for saveall application. Drum filters are often used as simple but reliable thickeners, for example, in the broke-handling system (Zippel, 2001; Weise et al., 2000).

4.19.4.2.3 Advanced circuit water treatment As a result of an increasing closure of water circuits, the use of freshwater for a steadily growing number of consumers in a system must be restricted. In order to replace the freshwater at these locations, the clarified water must be of high quality. In many cases, complete elimination of solids is required, especially for showers in the high-pressure range and sealing water. Large volumes of clarified water needed in a closed or virtually closed system that at the same time places high requirements on clarified water quality for only very few consumers have promoted the use of multistage treatment processes. Methods for fine cleaning of pretreated circuit water are based on filtration. The objectives include continuously improving the water quality and serving as a police filter, if any upstream treatment method fails. A wide variety of methods are employed, including drum filters with very fine filter media

Rotary contact Spiral scoop

Clarified water pipes

Inlet distribution Rotating carriage ADT distribution Floated sludge outlet Recycle suction Raw water inlet Inspection window Settled material outlet

Clarified water outlet

Dissolved air in water inlet

Figure 10 Dissolved air flotation unit for circuit water treatment. Adapted from Krofta Waters International (2010).

Water in the Pulp and Paper Industry

Cake removal

677

Cake removal

Filtrate

Suspension

outflow

inflow

Overflow

Suspension vat

Filter cake

Filter cake

Figure 11 Disk filter saveall system. Adapted from Wilichowski M (2009) Folien zur Vorlesung Mechanische Verfahrenstechnik I þ II. http:// www.mb.hs-wismar.de (accessed March 2010).

Process water

Freshwater

Paper and board production at 55−60 °C 100% recycled paper

Quality E

Quality D

Quality A

Quality C

Quality B

Biogas Acid

Buffer tank / sedimentation

pH

UASB/IC thermophil

Sedimentation

Aeration

Police filter

Solids

Solids Ozonation (opt.)

Heating

NF/ RO

Biogas Retentate Effluent

Figure 12 Kidney technology concept (Pauly, 2001).

(microfiltration), cartridge and backwash filters, sand filters, and membrane technology. The treatment units for elimination of dissolved substances accomplish important tasks in narrowing water circuits. They relieve the water circuits of detrimental substances and therewith avoid production restrictions due to limited freshwater capacities. The so-called kidney technologies, such as integrated biological treatment, softening, membrane technology, and ozonization, are promising approaches for obtaining effluent-free paper production by way of circuit closure. Combinations of treatment technologies make it possible to provide optimized solutions for different objectives. The decisions as to which concept and which treatment technology is to be used depend on specific boundary conditions. Figure 12 is a schematic view of a potential kidney concept for water circuit closure in a paper mill converting 100% recovered papers. Full circuit closure is not necessarily the solution of choice. Nevertheless, in many cases, advanced integrated treatment steps yield both economical and

ecological advantages (Pauly, 2002). Starting in 2008, the European Aquafit4Use research project focused on high waterreuse rates. The project also highlights maximum reduction in energy and chemicals, leading to more efficient use of limited resources by developing tailor-made treatment technologies and concepts (Pauly, 2008). Biokidney. Progressive system closure in a paper mill leads to increased concentrations of dissolved and colloidal compounds that in turn can give rise to increased microbial activity, slime formation, foaming, pitch disposition, corrosion, altered wet-end chemistry, and odor problems. Biological treatment of the circuit water can reduce or eliminate the buildup of troublesome compounds. Integrated biological treatment process is called the biokidney. Biological processes are state of the art in paper-mill wastewater treatment plants and are suitable for the elimination of biologically degradable substances, the reduction of sulfates, and the preliminary purification for nanofiltration and reverse osmosis.

678

Water in the Pulp and Paper Industry

Flow

Conc.

Exhaust gas p

Inflow UF

Aeration

T pH

Sedimentation

T

Fi

Retentate overflow

Storage vessel

LC

Storage tank

Permeate vessel

Red.

Pellets

Exhaust air

UASB outlet

Separator

Permeate Module

Compressed air

T Fi

p

Exhaust air

Insulated heated room

Outflow

Figure 13 Kidney concept – thermophilic anaerobic treatment and softening step – implemented at a Belgian paper mill in the frame of EU-project Paper Kidney; further downstream options: membrane treatment and ozonation (Pauly, 2002).

Biological treatment of process water has been carried out within a very wide range of operating conditions. Both anaerobic and aerobic process designs have been shown to be successful. Thermophilic treatment of process waters (Figure 13; Pauly, 2002) has a distinct advantage of eliminating the need for process water cooling and reheating for water recycling. A biokidney improves runnability due to a better overall quality of the process water caused by a decrease in soluble organic matter. Using biological treatment for COD reduction has allowed some packaging paper mills to operate with zero effluent systems (see, e.g., Stra¨tz, 2008; Herberz and Bahn, 2006). Practical trials conducted with aerobic and anaerobic laboratory-scale biokidneys also demonstrated that biokidneys have the potential to remove odorous compounds. (Jung et al., 2007). Membrane technology. This technology has experienced difficulties in trying to make headway into the papermaking sector. There are a variety of reasons for these difficulties, including scaling and fouling caused by high concentrations of salts and other detrimental substances, not fully developed concepts, and high investment costs due to high volumetric flow rates. A constant increase in the interest expressed by the European paper industry in membrane technology confirms that this method is due to evolve into a key technology for ¨ ller, continued water savings in the future (Simstich and O 2007). Membrane technology typically improves the quality of process waters substantially, since it removes suspended solids, microorganisms, and colloidal COD. Even salts can be separated out using reverse osmosis. Ultrafiltrated water is free of suspended solids and colloids. Bacteria, latex, and other micro-stickies are removed, for example. As anionic trash is cut to approximately half of its original level, the quality of the filtrate makes trouble-free recirculation back into the process possible (Sutela et al., 2006).

Fields of application for membrane technology in the pulp and paper industry are numerous and may offer many advantages, depending on treated partial flow such as

• • • • •

reduction in volumes of freshwater and effluent, increased product quality due to reduced system loading, recovery of raw materials from the effluents (e.g., coating color pigments), enhanced possibilities for the recirculation of biologically treated wastewater, and compliance with statutory limits on effluent concentrations.

In paper machine water circuits, ultrafiltration systems are mainly employed to provide high-quality water for use in high-demand consumers such as high-pressure showers or chemical dilutions. This leads to reduced freshwater consumption and a better machine runnability and paper quality. Ozone treatment. This treatment can be used as an internal treatment technology for decoloration of process waters. The brown color of paper-mill waters is mainly caused by derivatives similar to lignin and humic acid that are characterized by C¼C double bonds. These are the preferred sites of attack by ¨ ller, 2007). ozone, which then destroys them (Bierbaum and O As ozone is one of the strongest oxidants known, it is also possible to eliminate COD by oxidizing water components. ¨ ller and Offermanns (2002) have shown that recirculation of O ozonized waters into papermaking process is possible on the mill scale without any adverse effects on the process or product quality. Numerous other ozone applications – such as COD and AOX reduction, decoloration, microbial and odor control, and improvement of biosludge characteristics – in mill water systems (freshwaters, circuit waters, or effluents) are conceivable as well.

Water in the Pulp and Paper Industry 4.19.4.3 Wastewater Treatment 4.19.4.3.1 Preliminary mechanical treatment: Mechanical processes for removal of solids Effluents from pulp and paper mills contain solids and dissolved matter. The principal methods used to remove solids from pulp and paper mills effluents include screening, settling/clarification, and flotation. The choice of method depends on the characteristics of the solid matter to be removed and the requirements placed on the purity of the treated water. The separation of solids from the effluents is accomplished with the help of screens, grid chambers, and settling tanks. Screens are units which operate according to the sieving/filtration process. The function of the screens is to remove coarse, bulky, and fibrous components from the effluents. If necessary, fractionated particle separation can be achieved by graduating the gap width (bar screen, fine screen, inlet screen, and ultrafine screen). For reasons of operating reliability of wastewater treatment plants, it is also necessary to separate the grit transported with the effluents and other mineral materials from the degradable organic material. Grit separation from effluents can prevent operational troubles such as grit sedimentation, increased wear, and clogging. The grit separating systems currently in use are subdivided into longitudinal grit traps, circular grit traps, and vortex grit traps, depending on their design and process layout. Sedimentation technology is the simplest and most economical method of separating solid substances from the liquid phase. High efficiency is achieved in subsequent effluent treatment processes when the solid substances suspended in the effluents settle in a sedimentation tank as completely as possible, and settled sludge is removed from the sedimentation tank. Sedimentation tanks must be appropriately designed and operated. Alternative sedimentation equipment, with sets of lamella-shaped passages, is employed in the paper industry, especially for effluents with high fiber concentrations. Mechanical effluent treatment alone, however, is not sufficient to keep lakes and rivers clean, since it is incapable of removing colloidal, suspended, and dissolved substances.

4.19.4.3.2 Biological treatment Biological wastewater treatment is designed to degrade pollutants dissolved in effluents by the action of microorganisms. The microorganisms utilize these substances to live and reproduce. Pollutants are used as nutrients. A prerequisite for such degradation activity, however, is that the pollutants are soluble in water and nontoxic. Degradation process can take place either in the presence of oxygen (aerobic treatment) or in the absence of oxygen (anaerobic treatment). Both these naturally occurring principles of effluent treatment give rise to fundamental differences in the technical and economic processes involved (Table 2). The paper industry uses a variety of effluent treatment systems. The preferred process combination for each individual case depends on the grade-specific quality of the effluent that is to be treated. Experience shows that multistage processes based on an aerobic–aerobic or anaerobic–aerobic processing principle enable significantly more reliable

Table 2 treatment

679

Main characteristics of anaerobic and aerobic wastewater

Anaerobic treatment l

COD41000 mg l Low amount of excess sludge Energy generation by use of biogas Low energy demand Low required space Sensitive against high sulfate and calcium concentrations No fully biological degradation

Aerobic treatment High High High Fully

amount of excess sludge energy demand required space biological degradation

operation of the plant. The same effect can be achieved through a cascade system, which allows a graduation of the loading conditions. Among the German pulp and paper mills with onsite wastewater treatment plants, 60% have only aerobic treatment (operated as one- or two-stage processes) for their effluents, whereas 40% have an additional anaerobic stage (Jung et al., 2009). Anaerobic treatment. Anaerobic processes are employed for treatment of more highly polluted effluents such as effluents from recovered paper converting mills (Hamm, 2006). Anaerobic microorganisms conduct their metabolism only in the absence of oxygen. Anaerobic processes are characterized by a small amount of excess sludge produced and low energy requirements. As biogas is produced during the degradation process, anaerobic processes produce an excess of energy. Biogas is a mixture of its principal components, methane and carbon dioxide, with traces of hydrogen sulfide, nitrogen, and oxygen. Biogas is energetically utilized mainly in internal combustion engines or boilers. In its function as a regenerative energy carrier, biogas replaces fossil fuels in the generation of process steam, heat, and electricity. The composition and quality of biogas depend on both effluent properties and process conditions such as temperature, retention time, and volume load. Before discharge into surface waters, anaerobically treated effluents have to undergo aerobic posttreatment, because – according to the current state of the art – fully biological degradation of paper-mill effluents is not feasible (Mo¨bius, 2002). When introducing anaerobic technology into the pulp and paper industry, operational problems and their possible consequences, shown in Table 3, must be taken into account: Among different types of anaerobic reactors, ICs reactors (internal circulation) have achieved a share of more than onethird of the operating reactors and are currently the most frequently used reactors in the German pulp and paper industry. The rest of the market is shared by Biobeds and UASB reactors (UASB, Upflow Anaerobic Sludge Blanket) as well as reactors operating according to the contact sludge principle. Aerobic treatment. Aerobic microorganisms require oxygen to support their metabolic activity. In effluent treatment, oxygen is supplied to the effluent in the form of air by special aeration equipment. Bacteria use dissolved oxygen to convert organic components into carbon dioxide and biomass. In addition, aerobic microorganisms convert ammonified organic nitrogen compounds and oxidize ammonium and

680

Water in the Pulp and Paper Industry

nitrite to form nitrate (nitrification). The key factors for the success of an aerobic process are an adequate amount of nutrients in relation to the amount of biomass, a certain temperature and pH regime, and the absence of toxic substances (Hynninen, 2000). Aerobic processes are characterized by high volumes of excess sludge and higher energy demands compared to anaerobic processes. Furthermore, these reactors typically have large space requirements. Aerobic treatment allows fully biological degradation of paper-mill effluents. The BOD5 efficiency achievable with welloperated activated sludge processes is typically within the range of 90–98% (Hamm, 2006). The drawbacks of aerobic treatment technology include the relatively high operating costs due to the aeration of the effluent. On the other hand, aerobically operated plants exhibit higher plant stability and are less sensitive to fluctuations in effluent and plant parameters. Among different types of aerobic treatment technologies, activated sludge processes are currently the most frequently used treatment technologies in the German pulp and paper industry and have achieved a share of three-quarters of the operating reactors. Both moving-bed bioreactors (MBBRs) and biofilters represent another 10% of the reactors used (Jung et al., 2009). Secondary clarification. Secondary clarification is intended to separate the biomass (activated sludge) formed in biological reactors and is therefore a key element in all processes employed in the final stage of a treatment plant. The quality of the separation process is just as crucial for the final effluent quality as is for the biological treatment itself. As far as activated sludge process is concerned, secondary clarification determines the bioreactor performance. Separation and thickening of the recirculated sludge are crucial for

Table 3 Operational problems and possible consequences on anaerobic treatment in the pulp and paper industry

sludge volumes in biological treatment and for the potential sludge loading as well. Correct dimensioning of secondary clarification is therefore of great importance for overall plant performance.

4.19.4.3.3 Advanced and tertiary treatment Tertiary and/or advanced wastewater treatment is used to remove specific wastewater constituents that cannot be removed by secondary treatment. Different treatment processes are necessary to remove nitrogen, phosphorus, additional suspended solids, refractory organics, or dissolved solids. Sometimes it is referred to as tertiary treatment because advanced treatment usually follows high-rate secondary treatment. However, advanced treatment processes are sometimes combined with primary or secondary treatment (e.g., chemical addition to primary clarifiers or aeration basins to remove phosphorus) or used in place of secondary treatment (e.g., overland flow treatment of primary effluent). The reasons for advanced effluent treatment include

• • •

reduction in costs (discharge fee), compliance with limit values, and increase in production.

Advanced wastewater treatment in the pulp and paper industry is mainly focused on additional biological membrane reactors, membrane filtration techniques such as micro-, ultra-, or nanofiltration, and ozone treatment. Due to the relatively limited full-scale experience, relatively high costs, and greater complexity of water treatment, there have been only few fullscale applications of tertiary treatment of mill effluents up to now. The method that is ultimately chosen depends on the treatment aim and economic efficiency of the method in a given application. Table 4 shows the treatment aims that can be achieved by the different methods.

Operational problem

Possible consequences

4.19.5 Potentials and Limits of Water Saving

High concentrations of suspended solids in the feed flow High sulfate concentrations

 Displacement of biomass  Loss of pellets  Displacement of methane

4.19.5.1 Limiting Effects of System Closure

bacteria

 Inhibiting or toxic effects of

When reducing specific effluent volume within the framework of water circuit optimization, typical limits occur that usually require considerable investment to ensure that they will not be

sulfide High calcium concentrations Additives used in production (especially biocides and detergents)

 Performance losses  Precipitation of CaCO3  Displacement of biomass  Inhibiting/toxic influences  Poorer degradation 

Insufficient supply of nitrogen and phosphorus Temperature variations Fluctuating organics loads (e.g., shock loads)

      

performance Decomposition/washout of pellets Unstable operation Performance losses Loss of pellets Unstable operation Performance losses Excessive production of organic acids Methanation disturbed

Table 4

Treatment aims of different advanced treatment methods

Treatment method

Aim of treatment

Biofiltration

 Reduction in COD and BOD concentration

Ozone treatment Membrane treatment

Filtration processes Denitrification and phosphate precipitation

 Removal of suspended solids  Elimination of residual COD  Decoloration  Elimination of residual COD  Elimination of suspended solids  Demineralization  Decoloration  Removal of suspended solids  Nitrogen and phosphate elimination

Water in the Pulp and Paper Industry

exceeded (Figure 14). A limit in this sense is the freshwater volume that is taken into the system as process freshwater and is used for cooling prior to its final use (2). The second limit is water volume that accumulates together with the rejects and is discharged together with the effluents (3). The third limit is the maximum COD value that the respective product can tolerate in the white water (4). In a selected circuit, this value also corresponds to a minimum effluent volume for the respective system. The above-mentioned limits differ in every individual system. The factors that influence these limits include the existing plant technology, raw materials used, and paper grades produced. A limit encountered in narrowing water circuits that is similar to the cooling water requirements discussed above are the rejects that accumulate when discharged with the paper mill effluent. The proportion of effluents contained in the rejects compared to the total effluent volume may amount to 40–50% of the total effluent volume, especially in paper mills with an integrated deinking plant. If the effluent volume of such a plant is to be reduced drastically, water volume added to the effluents together with the rejects constitutes a lower limit. If a further reduction in the specific effluent volume is intended, then the rejects must be dewatered and part of the filtrate returned to the water circuit. Low specific effluent volumes result in growing system loads in process waters in terms of dissolved and colloidal material (Figure 15) that cause severe quality deterioration (slime spots, odor, color shifts, etc.) and a drop in productivity (machine failures due to scaling and corrosion, slime formation, web breaks, etc.). This situation is aggravated by the use of heavily loaded waste paper.

681

If specific effluent volume is to be reduced successfully, the impact of such measures on the papermaking process must also be taken into consideration. Only if we succeed in reconciling the goal of preventing effluent production with the goal of reliable production and satisfactory product quality, can the narrowing and ultimate closure of water circuits come about successfully.

4.19.5.2 Heat Balance Narrowing and closure of water circuits lead to increased temperatures in the stock and water systems of paper mills, taking a constant energy input into account. Nowadays, temperatures of 40–50 1C are achievable in the paper machine loop without additional steam heating (Zippel, 2001). Loop separation and the countercurrent arrangement enable the paper mills to reduce the transfer of detrimental substances coming from highly loaded loops (e.g., stock preparation) into the subsequent process steps, thus relieving paper machine loop. Regarding heat balance of the stock and water system, this is disadvantageous as the highly loaded loops are typically also the hottest (e.g., thermomechanical pulp plant). Heat with quite a high-temperature level is transferred to the effluent. However, at the paper machine, a higher-temperature level would be desirable to improve mechanical dewatering and in turn decrease the energy consumption for thermal drying. Besides the above-mentioned effects, there are other positive and negative impacts of higher process temperatures:

Specific effluent volume l kg−1 Cooling Cooling water water

1

In receiving waters

Fresh water (process water)

2

Waste water

3 4

Cooling water (process water)

Rejects (waste water)

5 COD 5' 5

Rising COD

4

1

To the wastewater treatment plant

} Fresh water

Production

Evaporation

Waste water

Figure 14 Limits in reducing the specific effluent volume. (1) Current situation; (2) cooling water limitation; (3) reject limitation; (4) maximum white water loading; (5) closed water circuit.

682

Water in the Pulp and Paper Industry

COD concentration in WW1 (g l−1)

30.000 25.000 20.000 15.000 10.000 5.000 0 0

2

4

6

8

10

12

Specific effluent volume (m3 per metric ton of paper) Figure 15 Chemical oxygen demand (COD) concentration in white water 1 of European paper mills producing corrugated base paper as a function of the specific effluent volume.

• • • • •

•

The solubility and activity of most functional chemicals increase with increasing water temperature. The consumption of certain additives such as wet strength agents may increase due to increased temperatures in the water circuit. Slime formation can be restricted by increasing the process temperature above a certain limit. There might be greater formation of anaerobic metabolic products such as hydrogen sulfide. High water temperatures in the papermaking process reduce the energy consumption for pulping and increase the cleaning efficiency of showers. On the other hand, high water temperatures have negative impacts on the energy efficiency of liquid-ring vacuum pumps and the hall climate. Finally, high process temperatures lead to high effluent temperatures. Without any countermeasures, this can cause problems in aerobic effluent treatment plants (poor oxygen solubility) and with the statutory temperature limits.

Integration of waste heat streams is one possibility for paper mills to reduce their energy consumption, but presents them with the conflicting challenges of ensuring both maximum waste heat utilization and safe compliance with statutory limits on effluent temperature. Heat integration measures help optimizing heat balance of paper mills and are a cost-effective way to reduce the specific energy demand of paper mills, thus achieving a productivity increase. Apparently, conflicting objectives, such as increased process and decreased effluent temperatures, may be achieved by appropriately selected measures for heat-balance optimization. Based on available heat sources and sinks and considering other boundary conditions, there are several potential scenarios for heat integration and utilization of waste heat by means of water–water heat exchange or air–water heat exchange. Pinch analysis and process simulation are useful tools for an evaluation of the individual scenarios and an optimization of heat balances. Studies have shown that the

replacement of steam used for process- or freshwater heating yields particularly profitable energy savings (Jung, 2008).

4.19.5.3 Economic Benefits There are many reasons to reduce the specific effluent volume. One important reason is the reduction in water-related costs. In the German pulp and paper industry, the costs of discharging effluents into receiving waters are high and average h0.40 m3 for direct dischargers. Discharging and treating effluents for indirect dischargers, however, are considerably more expensive. The latter involves average costs amounting to h1.12 m3. Reducing effluent volume is very attractive, especially for indirect dischargers. Additional costs arise due to a user fee for freshwater outtake and the operational costs for freshwater treatment (Jung et al., 2009). Despite the above-mentioned problems encountered in narrowing the water circuits, potentials for a reduction of the effluent volume have been discovered in many paper mills studied by PTS in the past few years (Figure 16). Besides water-related costs, another possibility is to reduce energy-related costs by reducing energy consumption due to increased process temperature. As a rule of thumb, every 101 increase in process temperature equals approximately 1% increase in dryness after mechanical dewatering in the wire and press sections. This allows energy consumption in the drying section to be reduced by up to 4%.

4.19.6 Improving Water Efficiency in Paper Manufacturing Industries – 30 Years of Success Water is one of the key components in papermaking. Using more than 1 billion m3 of water per year, the paper industry in Europe had been challenged to reduce the impact on regionally available water resources as one of the most important industrial water consumers. Legislation, stringent discharge

Water in the Pulp and Paper Industry

683

Specific effluent volume (m3 per metric ton of product)

25 Production rate proportional weighted mean 20

15

10

5

8 mills

6 mills

4 mills

4 mills

From recovered paper

Wood containing

Wood free

Specialty paper

0

Figure 16 Optimization potentials of paper-specific effluent volumes.

standards, as well as process and product demands force industry to ensure higher water quality corresponding to increasing costs. For the water-consuming industry, water is no longer regarded as a consumable or utility but as a highly valuable asset. Attention to water scarcity and pollution results in new legislative directives, forcing industries to reduce water use and pollution, and motivating them to implement innovations and carefully observe the impact of measures. The Water Framework Directive (WFD) is one of the main drivers for sustainable water use in Europe, which forced the member states to pay more attention to sustainable and efficient water use. Competent decision making at the top management and well-trained and motivated staff delivered substantial progress in reducing the water consumption in the pulp and paper industry: high competence in closing water circuits, substantially supported by process modeling and automation, and kidney technologies as internal process water treatment, lead to a significant decrease of the average specific effluent volume in the past 30 years. The European collaborative research project, AquaFit4Use, started in 2008, focuses on optimization of existing water circuits and development of new treatment concepts to support the European sustainability policy, such as reducing the use of scarce freshwater, improving the water quality (micropollutants, salts, etc.), and sharing corresponding experiences with other sectors.

References Auhorn W (1984) Das Sto¨rstoff-Problem bei der Verringerung der spezifischen Abwassermenge. Wochenblatt fu¨r Papierfabrikation 2: 37--48. Bierbaum S and O¨ller H-J (2007) Anlagenkonzepte zur Ozonbehandlung von Papierfabriksabwa¨ssern. Allgemeine Papier Rundschau 3: 38--40. Borschke D (2006) Zellstoff- und Papierfabrikation – Prozesswassersysteme im Vergleich. Wochenblatt fu¨r Papierfabrikation 17: 971--981.

Demel I, Dietz W, Bobek B, and Hamm U (2004a) Criteria for the recirculation of biologically treated water to the production. ipw – Das Papier 1: 37--40. Demel I, Dietz W, Bobek B, and Hamm U (2004b) Criteria for the recirculation of biologically treated water to the production (II). ipw – Das Papier 2: 33--35. Federal Statistical Office (2008) Statistical Yearbook 2008. Wiesbaden, Germany. http:// www.destatis.de (accessed March 2010). Hamm U (2006) Environmental aspects. In: Holik H (ed.) Handbook of Paper and Board, pp. 208--218. Weinheim: Wiley-VCH. Herberz J and Bahn W (2006) 10 Jahre Betriebserfahrungen mit einer integrierten biologischen Reinigung im geschlossenen Wasserkreislauf. In: Jung H and Simstich B (eds.) Proceedings Wasserkreisla¨ufe in der Papiererzeugung Verfahrenstechnik und Mikrobiologie, pp. 8/1–8/10. Munich, Germany, 05–06 December. Munich: PTS. Hutter A (2008) Wasserkreisla¨ufe und Wasserqualita¨t in der Papiererzeugung. In: Jung H and Simstich B (eds.) Proceedings Wasserkreisla¨ufe in der Papiererzeugung, pp. 1/1–1/20. Munich, Germany, 02–03 December. Munich: PTS. Hynninen P (ed.) (2000) Papermaking Science and Technology Book 19 Environmental Control. Helsinki, Finland: Fapet Oy. Jung H (2008) Optimisation of the heat balance of papermills. PTS News 1: 30--33. Jung H, Hentschke C, Pongratz J, and Go¨tz B (2009) Wasser- und Abwassersituation in der deutschen Papier- und Zellstoffindustrie – Ergebnisse der Wasserumfrage 2007. Wochenblatt fu¨r Papierfabrikation 6–7: 280–283. Jung H, Pauly D, Beimfohr C, et al. (2007) Odour control – eliminating odour problems in the paper industry. PTS-News 2: 25--29. Kappen J, Hutter A, Bobek B, and Hamm U (2004) Qualitative and quantitative requirements on the water supply of internal consumers. INFOR-Project No. 52R, Munich/Darmstadt. Kappen J and Wilderer PA (2002) Key parameter methodology for increased water recovery in the pulp and paper industry. In: Lens P, Hulshoff Pol L, Wilderer P, and Asano T (eds.) Water Recycling and Resource Recovery in Industries: Analysis, Technologies and Implementation, pp. 229--251. London: IWA Publishing. Mo¨bius CH (2002) Waste Water of the Pulp and Paper Industry, 3 rd edn., Revision December 2008. Augsburg, Germany. http://www.cm-consult.de (accessed March 2010). Negro C and Tijero J (1998) Water in the pulp and paper industry. In: Blanco MA, Negro C, and Tijero J (eds.) Paper Recycling: An Introduction to Problems and their Solutions, pp. 17--46. Luxembourg: European Communities. O¨ller H-J and Offermanns U (2002) Successful start-up of the world’s 1st ozone-based effluent re-circulation system in a paper mill. In: Graham NJD (ed.) Proceedings of the International Conference Advances in Ozone Science and Engineering: Environmental Processes and Technological Applications, pp. 365–372. Hong Kong, People’s Republic of China, 15–16 April. Hong Kong: The Hong Kong Polytechnic University and The International Ozone Association. Pauly D (2001) Kidney-technology opens up new opportunities of integrated white water treatment in recycling mills. In: Gopalaratnam N and Panda A (eds.)

4.20 Water in the Textile Industry J Volmajer Valh, A Majcen Le Marechal, S Vajnhandl, T Jericˇ, and E Sˇimon, University of Maribor, Maribor, Slovenia & 2011 Elsevier B.V. All rights reserved.

4.20.1 4.20.1.1 4.20.1.2 4.20.1.2.1 4.20.1.2.2 4.20.2 4.20.2.1 4.20.2.2 4.20.2.2.1 4.20.2.2.2 4.20.3 4.20.3.1 4.20.3.1.1 4.20.3.1.2 4.20.3.1.3 4.20.3.2 4.20.3.2.1 4.20.3.2.2 4.20.3.2.3 4.20.4 References

Textile Industry Textile and Clothing Industry in Europe Processes in Textile Industry Fibers Finishing processes Characteristic of Textile Water and Wastewater Supply Water Textile Wastewater Textile wastewater from different process steps General characteristics of textile wastewater Treatment and Reuse of Textile Wastewater Wastewater Treatment Technologies Physical methods Chemical processes Biological treatment processes Reuse Pollution-prevention techniques Chemicals and water reuse and recycle: Start-of-pipe approach Process-water reuse and recycle: End-of-pipe approach Conclusions

4.20.1 Textile Industry The textile industry is one of the longest and most complicated industrial chains in the manufacturing industry. It is a fragmented and heterogeneous sector dominated by smalland medium-sized enterprises (SMEs), with a demand mainly driven by three main end uses: clothing, home furnishing, and industrial use. The textile industry is composed of a wide number of subsectors, covering the entire production cycle from the production of raw materials (man-made fibers) to semiprocessed (yarn, and woven and knitted fabrics with their finishing processes), and final products (carpets, home textiles, clothing, and industrial-use textiles) (EURATEX, 2000). The textile industry is a very diverse and heterogeneous industry, with its products being used by virtually everybody – private households and businesses alike. Downstream parts of the textile industry – such as the clothing industry – consume the output of more upstream parts (such as fabrics of all types and colors). The textile industry is also intertwined with the agricultural sector when it needs inputs in the form of natural fibers (such as cotton or wool), and with the chemical industry when it comes to the wide range of man-made fibers (such as nylon or polyester). Hardly any other industrial sector could do without the so-called technical textiles, which include products which are as diverse as filters, optical fibers, packing textiles, ribbons and tapes, air bags, insulation, and roofing materials (Stengg, 2001).

685 685 686 686 686 687 687 689 689 692 695 695 696 697 699 701 702 702 702 703 703

The textile industry is a significant contributor to many national economies, encompassing both small- and large-scale operations worldwide. In terms of its output or production and employment, the textile industry is one of the largest industries in the world. The textile manufacturing process is characterized by high consumption of different resources: water, fuel, and a variety of chemicals in a lengthy process that generates a significant amount of waste. The main environmental problems associated with the textile industry are typically those associated with water pollution caused by the discharge of untreated effluents. Other environmental issues of equal importance are air emission, notably volatile organic compounds (VOCs), excessive noise or odor, as well as workspace safety (UNEP, 1994).

4.20.1.1 Textile and Clothing Industry in Europe The textile and clothing sector is an important part of the European manufacturing industry, giving employment to more than 2 million people. Its importance for social and economic cohesion is increased by the fact that it is dominated by a large number of SMEs, which are often concentrated in particular regions, thus contributing greatly to their wealth and cultural heritage (Stengg, 2001). Being one of the oldest sectors in the history of industrial development, the textile and clothing industry is often referred to as a ‘traditional industry’, as a sector belonging to the socalled ‘old economy’. These notions divert attention from the fact that the European textile and clothing industry has

685

686

Water in the Textile Industry

undergone significant restructuring and modernization efforts during the past 10–15 years, making redundant about onethird of the total work force, increasing productivity throughout the production chain, and reorienting production toward innovative, high-quality products. Like many other sectors, the textile and clothing industry has been greatly affected by the phenomenon of globalization. Europe and the United States are not only important producers of textile and clothing products, but also the most attractive outlets for the so-called exporting countries, many of which are situated in South-East Asia. It should be noted that many developing countries and, indeed, even least developed countries have become very competitive in textiles and clothing, as they combine low-wage costs with high-quality textile equipment and know-how imported from more industrialized countries (Stengg, 2001). The textile and clothing industry is one of the world’s most global industries, and constitutes an important source of income and employment for many European Union (EU) countries. It is important to be aware of how the European textile and clothing industry operates, as well as its many complex structures and processes. The textile industry is a multifaceted area requiring a deep understanding of design, management, and technology. It plays a crucial role in creating innovative and attractive products of multiple uses for various users. It accounts for 5.7% of the production value of world manufacturing output, 8.3% of the value of manufactured goods traded in the world, and over 14% of world employment (Perivoliotis, 2002). Research and innovation have been important tools for the European textile and clothing industry to assert its leading position in global markets. The importance of research and innovation for continued industrial competitiveness is on the increase. The importance of the textile (and clothing) industry in the European economy is shown in Table 1 (EURATEX, 2002). The figures in Table 1 cover only a part of the total number of manufacturing companies in 2000 (i.e., they cover only companies with more than 20 employees). This portion of the industry represents

• • •

• • •

textile finishing, industrial and other textiles (including carpets and wool scouring), and home textiles.

4.20.1.2 Processes in Textile Industry The textile chain begins with the production or harvest of raw fiber. The basic steps in this chain are schematically represented in Figure 1 (US EPA/625/R-96/004, 1996).

4.20.1.2.1 Fibers Two general categories of fibers are used in the textile industry: natural and man-made (comments made by UK to the First Draft of the BREF Textiles, UK, 2001). Man-made fibers encompass both purely synthetic materials of petrochemical origin and regenerative cellulosic materials manufactured from wood fibers. A more detailed classification of fibers is presented in Table 2.

4.20.1.2.2 Finishing processes Pretreatment. Pretreatment processes should ensure (UBA, 1994)

• • •

the removal of foreign materials from the fibers in order to improve their uniformity, hydrophilic characteristics, and affinity for dyestuffs, and finishing treatments; the improvement of the ability to absorb dyes uniformly; and the relaxation of tensions in synthetic fibers.

Pretreatment processes and techniques depend

3.4% of EU manufacturing, 3.8% of the added valued, and 6.9% of industrial employment.

•

The textile chain is composed of a wide range of industrial subsectors, using the entire range of fibers. European industry is still engaged in all production stages, ranging from raw materials (in particular, the production of man-made fibers), to semiprocessed products (in particular, spinning, weaving, knitting, Table 1

and finishing activities), to the final products (e.g., home textiles, carpets, technical textiles, and garments) (Stengg, 2001). The complexity of the sector is also reflected in the difficulty of finding a clear-cut classification system for the different activities involved. As for the scope of this chapter, it is confined to those activities in the textile industry that involve wet processes. This refers primarily to those activities falling within the following new Classification of Economic Activities in the European Community (NACE):

• •

on the kind of fiber to be treated (natural or synthetic fibers), on the form of the fiber (flock, yarn, woven, or knitted fabrics), and on the amount of material to be treated.

Pretreatment operations are often carried out in the same type of equipment used for dyeing (in batch processing, in

Share of the EU-15 textile–clothing industry in the manufacturing industry (companies with 20 employees or more)

2000

Turnover (EUR, billion)

Added value at factor costs (EUR, billion)

Employment (million)

Turnover (%)

Added value (%)

Employment (%)

Textile Clothing

100.5 61.5

31.2 18.2

0.89 0.73

2.1 1.3

2.4 1.4

3.8 3.1

Total textile and clothing

162.0

49.4

1.62

3.4

3.8

6.9

4756.8

1308.0

23.62

100.0

100.0

100.0

Total manufacturing

Water in the Textile Industry

Polymers

Fibers manufacturing

Man-made fibers

Natural fibers

Fibers preparation

Finishing processes Pretreatment Dyeing

Loose fibers /stock

Yarn manufacturing – Spinning

Printing

Yarn

Finishing Fabric production Coating and laminating Carpet back coating

– – – –

Weaving Knitting Tufting Needle felt

particular, the material is most often pretreated in the same machine in which it is subsequently dyed). Dyeing. It is a method for coloring a textile material in which a dye is applied to the substrate in a uniform manner to obtain an even shade with a performance and fastness appropriate to its final use (Bailey et al., 2000; EURATEX, 2000). From a molecular point of view, four different steps are involved: 1. The dye, previously dissolved or dispersed in the dye liquor, diffuses from the liquor to the substrate. 2. The dye accumulates on the surface of the textile material. 3. The dye diffuses/migrates into the interior of the fiber until this is uniformly dyed. 4. The dye must be anchored (fixation) to suitable places within the substrate. Textiles can be colored at any of several stages of the manufacturing process and therefore the following coloring processes are possible:

• • •

Washing Fabric Drying Manufacture of end products

687

• • •

flock or stock dyeing; top dyeing, wherein fibers are shaped in lightly twisted roving before dyeing; tow dyeing, which consists in dyeing the mono-filament material (called tow) produced during the manufacture of synthetic fibers; yarn dyeing; piece (e.g., woven, knitted, and tufted cloths) dyeing; and ready-made goods (finished garments, carpet rugs, bathroom sets, etc.).

Clothing, knitwear, carpet, etc.

Figure 1 Schematic presentation of textile production.

Table 2

Classification of fibers

Natural fibers Animal origin Raw wool Silk fiber Hair Vegetable origin Raw cotton fiber Flax Jute Chemical fibers Natural polymers fibers Viscose, cupro, lyocell Cellulose acetate Triacetate Synthetic polymer fibers Inorganic polymer Glass for fiber glass Metal for metal fiber Organic polymer Polyester Polyamide Polyacrylonitrile Polypropylene Elastane

Dyeing can be carried out in a batch or in continuous/semicontinuous mode. The choice between the two processes depends on the type of makeup, the chosen class of dye, the equipment available, and the cost involved. Both continuous and discontinuous dyeing involve the following steps:

• • • •

preparation of the dye, dyeing, fixation, and washing and drying.

Printing. This is a process for applying color to a substrate. Print color is applied only to defined areas to obtain the desired pattern. This involves different techniques and different machinery with respect to dyeing, but the physical and chemical processes that take place between the dye and the fiber are analogous to dyeing. A typical printing process involves the following steps:

• • • •

Color-paste preparation. When printing textiles, the dye or pigment is not in an aqueous liquor; instead, it is usually finely dispersed in a printing paste, in high concentration. Printing. The dye or pigment paste is applied to the substrate using different techniques: Fixation. Immediately after printing, the fabric is dried and then the prints are fixed mainly with steam or hot air. After-treatment. This final operation consists in washing and drying the fabric (it is not necessary when printing with pigments or with other particular techniques such as transfer printing).

688

Water in the Textile Industry

Finishing (functional finishing). The term finishing covers all those treatments that serve to impart to the textile the desired end-use properties. These can include properties relating to visual effect, handling, and special characteristics such as waterproofing and nonflammability. Finishing may involve mechanical/physical and chemical treatments. Washing. Washing with water is normally carried out in hot water (40–1001C) in the presence of wetting agent and detergent. The detergent emulsifies the mineral oils and disperses the undissolved pigments. Washing always involves a final rinsing step to remove the emulsified impurities. Dry cleaning is sometimes necessary, especially for delicate fabrics. In this case, the impurities are carried away by the solvent, which is usually tetrachloroethylene (perchloroethylene). In the same step, softening treatments may also be carried out. In this case, water and surfactant-based chemicals are added to the solvent. Drying. It is necessary to eliminate or reduce the water content of the fibers, yarns, and fabrics following wet processes. Drying, in particular, by water evaporation, is a highenergy-consuming step.

Synthetic

Cotton

Wool

Fiber preparation

Scouring

Spinning

Carbonizing

W W Texturing

Warping Knitting

W Yarn dyeing W

Knitting Sizing

Heat setting

Weaving

Carbonizing

Singeing W Desizing W

W Scouring / washing

Wool felting

W Bleaching Singeing W

W Dyeing

Mercerizing

W Printing

4.20.2 Characteristic of Textile Water and Wastewater

W Finishing

4.20.2.1 Supply Water Cutting / sewing

The textile industry is very water intensive. Water is used for cleaning the raw material and for many flushing steps during the whole production (Water Treatment Solutions, 2010). In Figure 2, a general flowchart for processes in textile manufacturing is shown, and the processes that need the input water (marked with rounded W) (Bisschops and Spanjers, 2003). Processes using water are desizing, scouring or kiering, bleaching, mercerizing, dyeing, washing, neutralization, and salt bath. Most of them are presented in Tables 3–5. Textile operations vary greatly in water consumption. Wool and felted fabrics processes are more water intensive than other processing subcategories such as wovens, knits, stock, and carpet. Water use can vary widely between similar operations as well (US EPA/625/R-96/004, 1996). The highest water use generally refers to natural fibers. Synthetic fibers require lower water volumes per unit of product, mainly due to the lower cleaning and scouring needs (Matioli et al., 2002). Water consumption varies greatly among unit processes. Certain dyeing processes and print after washing are among the more intensive unit processes. Within the dyeing category, certain unit processes are particularly low in water consumption (e.g., pad batch) (US EPA/625/R-96/004, 1996). An abundant supply of clean water is necessary in order to run a dyeing and finishing plant. Dye houses are usually located in areas where the natural water supply is sufficiently pure and plentiful. Rivers, lakes, and wells represent the major sources of freshwater available for use in wet processing (Tomasino, 1992). Almost all dyes, especially chemicals, and finishing additives are applied to textile substrates from water baths. In addition, most fabric-preparation steps, including desizing, scouring, bleaching, and mercerizing, use aqueous systems.

End product

Figure 2 General flowchart for processes in textile manufacturing.

Table 3 Average water supply for different textile wet processes (Correia et al., 1994) Material

Process

Water usage (l kg1)

Cotton

Desizing Scouring or kiering Bleaching Mercerizing Dyeing

3–9 26–43 3–124 232–308 8–300

Wool

Scouring Dyeing Washing Neutralization Bleaching

46–100 16–22 334–835 104–131 3–22

Nylon

Scouring Dyeing

50–67 17–33

Acrylic

Scouring Dyeing Final scour

50–67 17–33 67–83

Polyester

Scouring Dyeing Final scour

25–42 17–33 17–33

Viscose

Scouring and dyeing Salt bath

17–33 4–13

Acetate

Scouring and dyeing

33–50

Water in the Textile Industry Table 4

Water usage (l kg1) for different materials and processes (Correia et al., 1994)

Material

Process Desizing

Wool Cotton Synthetic Nonspecified

Scouring

Bleaching

Dyeing

Printing

4–77.5 2.5–43 17–67

40–150 38–143 38–143

280–520

30–50

12.5–35

20–300

Table 5 Average, minimum, and maximum water supply for different textile operations (US EPA/625/R-96/004, 1996) Subcategory

689

Table 6

Liquor ratio for various dyeing processes

Process

l kg1

Dyeing winches Hank machines Jet dyeing Package dyeing Pad batch ULLR dyeing

20–30 30 7–10 5–8 5 5

Water usage (l kg1) Minimum Average Maximum

Wool scouring 4.2 Wool finishing 110.9 Low water use processing 0.8 Woven fabric finishing Simple processing 12.5 Complex processing 10.8 Complex processing plus desizing 5.0 Knit fabric finishing Simple processing 8.3 Complex processing 20.0 Hosiery processing 5.6 Carpet finishing 8.3 Stock and yarn finishing 3.3 Nonwoven finishing 2.5 Felted fabric finishing 33.4

11.7 283.6 9.2

77.6 657.2 140.1

78.4 86.7 113.4

275.2 276.9 507.9

135.9 83.4 69.2 46.7 100.1 40.0 212.7

392.8 377.8 289.4 162.6 557.1 82.6 930.7

The amount of water used varies widely in the industry, depending on the specific processes operated at the mill, the equipment used, and the prevailing management philosophy concerning water use. Different types of processing machinery use different amounts of water, particularly in relation to the bath ratio in dyeing processes (the ratio of the mass of water in an exhaust dyebath to the mass of fabric). Washing fabric processes greater quantities of water than dyeing. Water consumption of a batch-processing machine depends on its bath ratio and also on mechanical factors, such as agitation, mixing, bath and fabric turnover rate (called contact), turbulence, and other mechanical considerations, as well as physical flow characteristics involved in washing operations. All these factors affect washing efficiency (US EPA/625/R-96/004, 1996). The influence of the equipment and process selected is presented in Table 6 (EPA Victoria, 1998). From Table 6 we can see that hank machines and dyeing winches are the biggest water consumers (20–30 l kg1). Pad batch and ultralow liquor ratio dyeing processes need only 5 l kg1. The quantity of water used for a particular process also depends on equipment modernization and development. As an example, batch dyeing machines for knitwear have gone from 30 l kg1 to only 6 l kg1 of treated material over the last four decades (Wenzel and Knudsen, 2005). In general, heating of dyebaths constitutes the major portion of energy consumed in dyeing. Therefore,

low-bath-ratio dyeing equipment not only conserves water but also saves energy, in addition to reducing steam use and air pollution from boilers. Low-bath-ratio dyeing machines conserve chemicals as well as water and also achieve higher fixation efficiency. However, the washing efficiency of some types of low-bath-ratio dyeing machines, such as jigs, is inherently poor; therefore, a correlation between bath ratio and total water use is not always exact (US EPA/625/R-96/004, 1996). Water quality for all processes should be of such quality as to avoid any process and final-product-quality problems. Mostly, fresh softened water is used for all processes, although sometimes water of lower quality can be used as well. Three types of water quality are suggested for use in textile industry (Lockerbie and Skelly, 2003; Vandevivere et al., 1998): 1. High-quality water. It can be used for all processes, such as dyebaths, print pastes, finishing baths, and final rinse bath (Table 7). Consumption of such water is 10–20% of the total water consumption. Four different sources are presented: fresh softened water, recycled effluent (proposed), mains drinking-water prescribed concentrations or values (PCVs), and Confederation of British Wool Textiles (CBWT) water specification. 2. Moderate-quality water. It is used for washing-off stages after scouring, bleaching, dyeing/printing, and finishing (Table 8). About 50–70% of total water consumption consists of such water needs. Final rinse bath in the washing processes should be always high-quality water to ensure that the material is free from traces of contamination. 3. Low-quality water. It can be used for washing-down equipment, screen washing in print works, and general washdown of print paste containers and floors (Table 9). Quantity presents only 10–20% of total water consumption, but it is wasteful to use high-quality water for such operations.

690

Water in the Textile Industry

Table 7

Water quality suitable for all processes

Colora (mg l1 Pt scale) COD (mg l1 O2) pH Total hardness (mg l1) Chloride (mg l1) Sulfate (mg l1 SO4) Fe (mg l1) Cu (mg l1) Cr (mg l1) Al (mg l1) Mn (mg l1) Zn (mg l1)

Fresh softened water

Recycled effluent

Main water PVCs

CBWT specification

None visible

None visible 20–50 6.5–7.5 90b 500

20 5.5–9.5

None visible 6.0–8.0

250 (Ca), 50 (Mg) 400 250 0.2 3 0.05 0.2 0.050 5

60–80b

6.5–7.5 50b 300 0.05 0.05

0.1 0.005 0.01 0.02

0.1 0.1

0.05 0.1

a

Suggested specification for water with no visible color absorbance in 10 mm cell: 450 nm, 0.020.04; 500 nm 0.020.05; 550 nm, 0.010.03; 600 nm, 0.010.02. Measured as ppm CaC03. COD, chemical oxygen demand; PVC, polyvinylchloride; CBWT, Confederation of British Wool Textiles. b

Table 8 Suggested water quality suitable for washing-off processesa

Table 9 only

Parameter

Parameter

b

Color COD (mg l1) pH Total hardness (ppm CaCO3) Chloride (mg l1) Fe (mg l1) Cu (mg l1) Cr (mg l1)

Maximum recommended level None visible 200 7.0–8.0 100 500–2000 0.1 0.05 0.1

a

Final rinse bath to use high-quality water. Suggested specification for water with no visible color absorbance in 10 mm cell: 450 nm, 0.020.04; 500 nm, 0.020.05; 550 nm, 0.010.03; 600 nm, 0.010.02.

a

Suggested water quality suitable for equipment washdown

Color COD (mg l1) pH Total hardness (ppm CaCO3) Chloride (mg l1) Fe (mg l1) Cu (mg l1) Cr (mg l1)

Maximum recommended level None visible 500–2000 6.5–8.0 100 3000–4000 0.1 0.05 0.1

a

b

Suggested specification for water with no visible color absorbance in 10 mm cell: 450 nm, 0.020.04; 500 nm, 0.020.05; 550 nm, 0.010.03; 600 nm, 0.010.02.

4.20.2.2 Textile Wastewater

In Europe, 108 million tons of wastewater is produced on a yearly basis and 36 million tons of chemicals and auxiliaries have to be removed from the textile wastewater. Textile wastewater typically contains a complex mixture of organic and inorganic chemicals, due to the wide variety of the process steps.

The textile industry is one of the most polluting industries. Many different processes are used and almost all of them generate wastewater. Wastewater from textile sector is composed of cleaning water, process water, noncontact cooling water, and storm water. The amount and the composition of wastewater vary and depend on different factors, including the nature of the processed fabric, applied dye, or special finishing; the type of the process; the equipment used; and the prevailing management philosophy regarding water use. Changes in machines, used chemicals, or any characteristic of the processes also change the nature of the generated wastewater. Scouring, dyeing, printing, finishing, and washing generate the majority of the textile wastewater. Large-volume wastes include wash water from preparation and continuous dyeing, alkaline wastewater from preparation, and batch dye wastewater containing large amounts of dye, salts, acids, or alkalis, and also other toxic additives in smaller amounts. Primary sources of biological oxygen demand (BOD) include waste chemicals or batch dumps, starch-sizing agents, knitting oils, and degradable surfactants.

4.20.2.2.1 Textile wastewater from different process steps The following processes in the textile industry produce wastewater containing different pollutants: 1. Desizing. It is the process for removing the size chemicals from the textile. Wastewater from the desizing process varies according to the used sizes and recipes and contains pollutants such as different additives, surfactants, enzymes, acids or alkalis, as well as the size themselves. The generated wastewater can be the largest contributor to the overall BOD and the total suspended solids (TSSs). When the natural sizes, based on starch or proteins, are used for sizing, the wastewater after desizing is characterized by high BOD and BOD/COD ratio. If sizing is carried out using synthetic materials, such as polyvinyl alcohol or carboxymethyl cellulose, the BOD reduction can be up to

Water in the Textile Industry

90%. Possible pollutants in wastewater after desizing process are shown in Table 10 (Correia et al., 1994). 2. Scouring. It is the process for removing different impurities from both natural and synthetic materials. The intensity of the scouring process depends on the type of material. Oils, fats, waxes, minerals, and plant matter can be present in natural fibers, whereas synthetic fibers can contain spin finishing and knitting oils. These impurities can be removed either with water or with organic solvents. Water scouring is usually preferred over solvent scouring, because water is nonflammable, nontoxic, plentiful, and cheaper. For cotton scouring, hot alkaline solutions, containing detergents or soaps, are used. Sourcing effluents can also contain herbicides, insecticides, defoliants, and desiccants, which are used in the growing of cotton, as well as fungicides such as pentachlorophenols used to prevent mildew during storage and transportation of cotton. Raw-wool scouring is the most polluting process in the textile industry. The pollution load results from impurities present in raw wool such as wax, suint, urine, feces, vegetable

691

matter, mineral dirt, and, on the other hand, the soap detergent and alkali used during the scouring and washing processes. Wool grease is the major problem in treating wool, because of its nonbiodegradability. It is a mixture of cholesterol esters, long-chain fatty acids, free fatty acid, free alcohol, and hydrocarbons. Synthetic souring requires less scouring than cotton or wool. Inorganic and organic substances which could be present in wastewater after scouring, for different fibers, are shown in Table 11 (Correia et al., 1994). 3. Bleaching. It is commonly used to remove natural coloring of cotton and other fibers. In this step, the most common agents are hydrogen peroxide, sodium hypochlorite, sodium chlorite, and sulfur dioxide gas. Auxiliary chemicals such as sulfuric acid, hydrochloric acid, sodium hydroxide (caustic soda), sodium hydrogen sulfite (sodium bisulfite), surfactants, and chelating agents are also used and released into the wastewater. Bleaching wastewater usually has high solid content with low-to-moderate BOD levels. Inorganic and organic substances which could be present in wastewater after bleaching, for different fibers, are shown in Table 12 (Correia et al., 1994). 4. Mercerizing. It improves strength, luster, and dye affinity of cotton fabrics. Cotton fabrics are treated with solutions of sodium hydroxide (caustic soda) followed by neutralization and several rinses. Wastewater generated by mercerizing has low BOD and total solid levels but high pH (Table 13) (Correia et al., 1994). 5. Dyeing. The dyeing operations of textiles may take part in the process chain at different stages of production (fibers, yarn, or piece dyeing). Stock dyeing is used to dye fibers. Top dyeing is used to dye combed wool silver. Yarn dyeing and piece dyeing are used after the yarn has been constructed into the fabric.

Table 10

Possible pollutants in desizing effluents

Fibers

Inorganic substances

Organic substances

Cotton Linen Viscose

Naþ, Ca2þ, NH4 þ , SO4 2 , CI

Silk Acetates Synthetics

Naþ, NHþ 4, CO3 2 , PO4 3

Carboxymethyl cellulose, enzymes, fats, hemicellulosses, modified starches, nonionic surfactants, oils, starch, waxes Carboxymethyl cellulose, enzymes, fats, gelatine, oils, polymeric sizes, polyvinyl alcohol, starch, waxes

Table 11

Possible pollutants and characteristics of effluents from scouring

Fibers

pH

BOD (mg l1)

TSS (mg l1)

Inorganic substances

Organic substances

Cotton

10–13

50–2900

7600–17 400

Naþ, CO3 2 , PO4 3

Anionic surfactants, cotton waxes, fats, glycerol, hemicelluloses, nonionic surfactants, peptic matter, sizes, soaps, starch

Viscose

8.5

2832

3334

Na þ , CO3 2 , PO4 3

Acetates

9.3

2000

1778

Anionic detergents, fats, nonionic detergents, oils, sizes, soaps, waxes

Naþ, CO3 2 , PO4 3

Anionic surfactants, antistatic agents, fats, nonionic surfactants, oils, petroleum spirit, sizes, soaps, waxes

Naþ, NH4 þ , CO3 2 , PO4 3

Anionic detergents, glycol, mineral oils, nonionic detergents, soaps

Naþ, NH4 þ , Kþ, Ca2 þ , CO3 2 , PO4 3

Acetate, anionic surfactants, formate, nitrogenous matter, soaps, suint, wool grease, wool wax

Synthetics

Wool (yarn and fabric)

Wool (loose fiber)

9–14

3000–40 000

1129–64 448

692

Water in the Textile Industry

Textiles are dyed using a wide range of dyestuffs, techniques, and equipment. Each dyeing process requires different amounts of dye per unit of fabric to be dyed. In the textile industry, synthetic dyes, derived from coal tar and petroleum-based intermediates, are used. Dyes can be present as powders, granules, pastes, and liquid dispersions, with concentrations of active ingredients ranging typically from 20% to 80%. Dyeing can be performed by using continuous or batch processes. Auxiliary chemicals and controlled dye-bath conditions accelerate and optimize the migration of the dye molecules from the solutions to the fiber. The dye is fixed on the fiber thermally and/or chemically. Table 12

Possible pollutants in bleaching effluents

Fibers

Inorganic substances

Organic substances

Cotton Linen Viscose Jute

Naþ, NH4 þ CIO, CI, O2 2 , F, SiO3 2

Formate

Synthetics Acetates

SiO3 2 , PO4 3 , F

Wool

Naþ, O2 2

Table 13

The water consumption in dyeing processes is very high (up to 300 l kg1). Water is used not only in the dyeing process itself, but also for rinsing operations of the dyed material. Dyes and different auxiliaries such as organic acid, fixing agents, defoamers, oxidizing/reducing agents, and diluents are typical pollutants generated in the dyeing step. Quite a large amount of the unfixed dye leaves the dyeing unit. Metals and almost all of the salts and dyes present in the overall textile wastewater originate from dyeing operations. The possible pollutants and characteristics of effluents from dyeing processes for different fibers are listed in Table 14 (Correia et al., 1994). 6. Printing. For fabric printing, many different colorants and patterns, including a variety of techniques and machines, are used. The most common printing techniques used are rotary screen, and other methods such as direct, discharge, resist, flat screen, and roller printing often used commercially. Pigments are used for about 75–85% of all printing operations. Pigments do not require washing steps and generate little waste. Compared to the dyes, pigments are typically insoluble and have high affinity for the fibers. An important component in textile printing is the print paste, which consists of water, thickeners, dyes, urea, and various other chemicals such as surfactants and organic solvents. The printing method determines the wastewater characteristics. Printing wastewaters are small in volume

Oxalate

Possible pollutants and characteristics of effluents from mercerizing

Fibers

pH

BOD (mg l1)

TSS (mg l1)

Inorganic substances

Organic substances

Cotton Linen

5.5–9.5

45–65

600–1900

Naþ, NH4 þ , CO3 2 , SO4 2

Alcohol sulfates, anionic surfactants, cyclohexanol

Table 14 Fibers Cotton Linen

Possible pollutants and characteristics of effluents from dyeing pH

BOD (mg l1)

TSS (mg l1)

Polyester

3þ

2þ

Organic substances

11–1800

500–14 100

Na , Cr , Cu , Sb3þ, Kþ, NH4 þ , CI CO3 2 , CO4 2 , F, NO2  , O2 2 , S2, S2 O3 2 , SO3 2 , SO4 2

Naphtol, acetate, amides of naphtoic acid, anionic dispersing agents, anionic surfactants, cationic fixing agents, chloro amines, formaldehyde, formate, nitro amines, nonionic surfactants, residual dyes, soaps, soluble oils, sulfated oils, tannic acid, tartrate, urea

4.8–8

380–2200

3855–8315

Naþ, Cr3þ, Cu2þ, Sb3þ, Kþ, NH4 þ , Al3þ CI, CO3 2 , S2 O4  , SO3 2 , SO4 2 Naþ CI, CO3 2

Acetate, dispersing agents, formate, lactate, residual dyes, sulfated oils, tartrate

Naþ, NH4 þ , Cu2þ, SO4 2

Acetate, aromatic amines, formate, leveling agents, phenolic compounds, residual dyes, retardants, surfactants, thiourea dioxide

Naþ, NH4 þ , Cl, S4 O6 2 , CIO, SO3 2 , NO3 

Acetate, anionic surfactants, antistatic agents, dispersing agents, dye carriers, EDTA, ethylene oxide condensates, formate, mineral oils, nonionic surfactants, residual dyes, soaps, solvents

Polyamide Acrylic

þ

5–10

Viscose

Wool

Inorganic substances

1.5–3.7

175–2000

480–27 000

833–1968

Acetate, formate, polyamide oligeines, residual dyes, sulfonated oils

Water in the Textile Industry

and contain urea, dyes or pigments, organic solvents, and metals. The concentration of the pollutants in printing wastewater is higher than that in dyeing wastewater. 7. Finishing. This can refer to the chemical or mechanical treatments performed on fiber, yarn, or fabric to improve appearance, texture, or performance. Mechanical finishes can involve brushing, ironing, or other physical treatments used to increase luster and feel of textiles, such as heat setting, napping, softening, optical finishing, shearing, and compacting. The application of chemical finishes to textile can impart a variety of properties ranging from decreasing static cling to increasing flame resistance. Chemical treatments are optical finishes, adsorbent and soil-release finishes, softeners and abrasion-resistant finishes, and physical stabilization and crease-resistant finishes. Wastewaters from the finishing units are extremely variable in composition and can contain resins, waxes, softeners, acetate, stearate, as well as toxic organic compounds (pentachlorophenols and ethylchlorophosphates).

4.20.2.2.2 General characteristics of textile wastewater Textile wastewater is characterized mainly by measuring BOD, chemical oxygen demand (COD), suspended solids, and dissolved solids. Typical characteristics of textile industry wastewater are presented in Table 15. Wastewaters from the textile industry are usually polluted with recalcitrant or hazardous organics, such as dyes, surfactants, metals, salts, and persistent organic pollutants (POPs) as well. They are discussed in the following: 1. Dyes. Most of the wastewater produced during the textile material processing is colored. The main sources of color in the textile effluents arise from dyes and pigments in the dyeing and printing operations. It is known that the presence of very small quantities of dyes in water (less than 1 ppm) is highly visible due to their brilliance. There are more than 10 000 commercially available dyes with a production of over 7 105 tons yr1 (Zollinger, 1987). The exact data on the quantity of dyes discharged into the environment are also not available. It is assumed that 2% of the dyes produced is discharged directly in aqueous effluent, and B10% is subsequently lost during the textile coloration process (Easton, 1995). Dyes cause a lot of problems in the environment. They can remain in the environment for an extended period of time, because of high thermal and photo stability (the half-life of hydrolyzed Reactive Blue 19 is about 46 years at pH 7 and 25 1C Table 15

Characteristics of textile wastewater

Parameters

Values

pH BOD (mg l1) COD (mg l1) TSS (mg l1) TDS Chloride (mg l1) Total Kjeldahl nitrogen (mg l1) Color (Pt–Co)

1.9–13 50–40 000 150–12 000 15–64 000 2900–3100 1000–1600 70–80 50–2500

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(Hao et al., 2000)). Depending on dye concentration and exposure time, dye can have acute and/or chronic effects on exposed organism. The greatest environmental concern with dissolved dyes is their absorption and reflection of sunlight entering the water (rivers, lakes, etc.). Light absorption diminishes photosynthetic activity of algae and seriously influences the food chain. Many dyes and their breakdown products are carcinogenic, mutagenic, and/or toxic to life. Mathur et al. (2005) studied the influence of textile dyes (known only by their trade name) on the health of textile-dyeing workers and the environment. The dyes were used in their crude form (without previous purification), because they wanted to test the potential danger that dyes represent in actual use. The results clearly indicated that most of the used dyes are highly mutagenic. Brown and DeVito (1993) studied how it is possible to predict the toxicity of new azo dyes. The systematic backtracking of the flows of wastewater from textile-finishing companies led to the identification of textile dyes as a cause for strongly mutagenic effects. Several textile dyes used in the textile-finishing companies in the European Union were examined for mutagenicity. According to the obtained results, the dyes which were considered to present a potential toxicity have been withdrawn from the market and have been replaced with less harmful and biodegradable substances (Ja¨ger et al., 2004; Schneider et al., 2004). Degradation of dye Direct Blue 14 led to the carcinogenic aromatic amine o-tolidine (Platzek et al., 1999). Dyes can cause allergies such as contact dermatitis (Pratt and Taraska, 2000) and respiratory diseases(Estlander, 1988; Wilkinson and McGechaen, 1996; Zuskin et al., 1998), allergic reaction in eyes, skin irritation, and irritation to mucous membrane and the upper respiratory tract. As it is known, reactive dyes form covalent bonds with cellulose, woolen, and polyacrylate fibers. It is assumed that in the same manner, reactive dyes can bond with –NH2 and –SH groups of proteins in living organisms. Many investigations have been made on respiratory diseases in workers dealing with reactive dyes. Certain reactive dyes have caused respiratory sensitization of workers occupationally exposed to them (Majcen Le Marechal et al., 1996). Organic dyes contain substituted aromatic and heteroaromatic groups. The color of dyes results from conjugated chains or rings that can absorb different regions of wavelength. The chromophores of organic dyes are usually composed of double carbon–carbon bonds, double nitrogen– nitrogen bonds, double carbon–nitrogen bonds, and aromatic and heterocyclic rings containing oxygen, nitrogen, or sulfur. Azo dyes, which contain one or more azo bonds, are the most widely used synthetic dyes and are present in 60–70% of all textile dyestuffs produced (Carliell et al., 1995). Azo dyes can be used on natural fibers (cotton, silk, and wool) and synthetic fibers (polyesters, polyacrylic, rayon, etc.). Azo dyes are mostly used for yellow, orange, and red colors. Biodegradation of more than 100 azo dyes have been tested and it was found that only a very few were degraded aerobically. The degree of stability of azo dyes under aerobic conditions depends on structure of the molecule. Dye C.I. Acid Orange 7 is one of the rare dyes which is aerobically biodegradable. Under anoxic conditions, azo dyes

694

Water in the Textile Industry

are cleaved to aromatic amines, which are not further metabolized under anaerobic conditions but are readily biodegraded in an anaerobic environment (Figure 3) (Vandevivere et al., 1998). Anthraquinone dyes constitute the second most important class of textile dyes. They have a wide range of colors in almost the whole visible spectrum, but they are most commonly used for violet, blue, and green colors. With regard to method and domain of usage, dyes are classified into acid, reactive, direct, basic, disperse, metal complex, vat, mordant, and sulfur dyes. Most commonly in use today are reactive and direct dyes for cotton and viscose-rayon dyeing and disperse dyes for polyester dyeing. Reactive dyes are termed chemically as colored compounds with a functional group capable of forming a covalent bond with a suitable substrate. Reactive dyes represent 20– 30% of the total dyes in the market. Reactive dyes are characterized by low fixation rate, and around 30% of the applied reactive dyes are wasted because of dye hydrolysis in the alkaline conditions of the dyebath. As a result, dyehouse effluents typically contain 0.6–0.8 g dye dm3 (Stenken-Richter and Kermer, 1992). Generally, dyes can be classified with regard to (1) their chemical structure, (2) the method and domain of usage, and (3) chromogen (Table 16).

HO

N HO3S

Ar — NH2

N

Figure 3 Chemical structure and degradation under anoxic condition of the azo dye with C.I. Acid Orange 7.

Table 16

In the textile-dyeing process, dyes are always used in combination with other chemicals (acids, alkali, salts, fixing agents, carriers, dispersing agents, and surfactants) which are partly or almost completely discharged in the wastewater together with the numerous additives and impurities present in the commercial dye products. Public perception of water quality is greatly influenced by the color. Therefore, the removal of color from wastewater is often more important than the removal of the soluble colorless organic substances. 2. Metals. Many textile mills have metals in their effluent, but their concentration decreased in the last decade, mainly because of the reduction of the metal contents in the dye. Metals include copper, cadmium, chromium, nickel, zinc, and lead. Metals enter the textile effluents in many ways: incoming supply water, metal parts (such as pumps, pipes, and valves), oxidizing and reducing agents, electrolyte, acid and alkali, dyes and pigments, certain finishes, herbicides, and pesticides. However, the main source of heavy metals is the dyeing process. Dyes may contain metals such as zinc, cobalt, and chromium. In some dyes, metals can form an integral part of the dye molecule; metals are functional, but in most dyes metals are just impurities generated during the dye manufacture. Mercury or other metals may be used as catalysts in the synthesis of dyes and may be present as by-products. Concentrations of metals in the dyeing effluents can be in the range 1–10 mg l1. For example, after dyeing of wool with basic dyes, the concentration of cadmium in wastewater is 7.5 mg l1. The concentration of chromium in dyeing effluents after dyeing cotton with direct dyes is 12.05 mg l1. Dyeing viscose with direct dyes revealed measurements of 2.7 mg of chromium l1, 8.52 mg of copper l11, and 1.95 mg of lead l1 in the wastewater. (EURATEX, 2000).

Classification of the dyes Classification

With regard to chemical structure (C.I.) With regard to method and domain of usage (C.I.) With regard to chromogen n-p* With regard to the nature of donor– acceptor couple With regard to the nature of polyenes Acyclic and cyclic

Cyanine

Subclass

Characteristic

Azo, anthraquinone, triphenylmethane, indigo, etc. Direct, acid, basic, reactive, reductive, sulfuric, chromic, metal-complex, disperse, pigment, etc. Absorptive, fluorescent and dyes with energy transfer, etc. 1-Aminoanthraquinone, p-nitroaniline, etc.

The classification of a dye by chemical structure into a specific group is determined by the chromophore Dyes used in the same technological process of dyeing and with similar fastness are classified into the same group This classification is based on the type of excitation of electrons, which takes place during light adsorption These chromogens contain a donor of electrons (unbound electron couple), which directly bonds to the system of conjugated p electrons

Polyolefins, annulenes, carotenoids, rhodopsin, etc.

Polyene chromogen contains sp2 (or sp) hybridized atoms. The molecules enclose single and double bonds that form open chains, circles, or a combination of both Cyanine chromogens have a system of conjugated p electrons, in which the number of electrons matches the number of p-orbitals

Cyanines, amino-substituted di- and tri-arylmethane, oxonols, hydroxyarylmethanes, etc.

Water in the Textile Industry

3. Salts. The presence of salts in textile wastewater has been identified as a potential problem by several authors. Salts in textile processes are used as raw materials or produced as by-products of neutralization, or in other reactions. Salt is used mostly to assist the exhaustion of ionic dyes, particularly anionic dyes, such as direct and reactive dyes on cotton. Typical cotton batch-dyeing operations use salts in the range 20–80% weight of dyed material. The concentration of salts in such wastewater is 2000–3000 ppm (Matioli et al., 2002). Sodium chloride (common salt) and sodium sulfate (Glaubers salt) constitute the majority of total salt use. Other salts used as raw materials or formed during the textile operations include magnesium chloride (Epson salt) and potassium chloride, and others in low concentrations. 4. Persistent organics or hazardous organics. The persistent molecules present in textile wastewater belong to very diverse chemical classes, each used in relatively small amounts. The persistent organics include surfactants or their byproducts, dyeing auxiliaries such as polyacrylates, phosphates, sequestering agents (ethylenediaminetetraacetic acid (EDTA)), deflocculating agents (lignin or naphtahalenesulfonates), antistatic agents for synthetic fibers, carriers in disperse dyeing of polyester, fixing agents in direct dyeing of cotton, preservatives (substituted phenol), and a large number of finishing auxiliaries used for fireproofing, mothproofing, and water proofing. The most toxic among POPs are the commonly named dioxins and dioxin-like compounds. Dioxin (Figure 4) is the term for a group of chemical compounds with 75 polychlorinated dibenzo-p-dioxins (PCDDs) and 135 polychlorinated dibenzofurans (PCDFs). The textile industry is a potential source of PCDD/Fs. They can arise from the various processes involved in the industry (Krizˇanec and Majcen Le Marechal, 2006):  Pesticide pentachlorophenol (PCP) is used as a biocide for cotton and other materials. Pesticides, such as pentachlorophenol, are known to be contaminated with PCDD/Fs.  Dyestuffs are contaminated by PCDD/Fs.  Textile processes may utilize chlorinated chemicals contaminated by PCDD/Fs.  Washing processes in alkaline media are part of the textile finishing processes.  Large volumes of effluent water are released into the environment. The main source of dioxins in the textile industry are dioxazine and antraquinone dyes and pigments, produced 9 Clx

O

1

8 7 6

O

9 2

ClY

3 4

Clx

1

8 7 6

O

2

ClY

3 4

X + Y = 1– 8 (75 congeners)

X + Y = 1– 8 (135 congeners)

Polychlorinated dibenzo-p-dioxins

Polychlorinated dibenzofurans

Figure 4 Molecular structure of the polychlorinated dibenzo-p-dioxins and dibenzofurans.

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from chloranil as intermediate product, and chloranil itself used as a catalyst in the production of dyes and pigments. Various dyes and pigments were analyzed for the presence of PCDD/Fs. Considerable levels of PCDD/Fs were determined in some dioxazine dyes and pigments, phatalocyanine dyes, and in printing inks. Concentrations of PCDD/Fs in Direct Blue 106 dye, Direct Blue 108 dye, and Violet 23 pigment were in the mg kg1 range with octachlorodibenzodioxin (OCDD) and octachlorodibenzofuran (OCDF) as dominant homologs. The concentration of OCDD in Direct Blue 106 was 41.9 mg kg1 and the concentration of OCDF was 12.4 mg kg1 (Williams et al., 1992). Hutzinger and Filder (US Environmental Protection Agency, 2000) found mg kg1 range levels of PCDD/Fs for higher chlorinated congress in sample of Ni-phthalocyanine dye. Results of the analyses of PCDD/Fs were reported for four printing inks obtained from a supplier in Germany. In the two inks used for rotogravure printing and two used for offset printing, the content of PCDD/Fs ranged from 17.7 to 87.2 ng TEQ kg1 (TEQ, toxicity equivalent; Santl et al., 1994). A high concentration of mixed polychlorinated and polybrominated dibenzo-p-dioxins and polychlorinated and polybrominated dibenzofurans (PBCDD/Fs) was detected after flame-retardant finishing-textile processes. A flame-retardant finish on upholstery material on the basis of PVC, Sb2O3, and hexabromocyclododecane results in the final product concentrations up to 19 mg kg1 of PBCDD/Fs. PCP and other chlorophenols can be the source of PCDD/Fs in wastewaters. A generation of dioxins was reported from the direct photolysis of pentachlorophenolcontaining water. Waddell et al. (1995) investigated the formation of dioxins by the ultraviolet (UV) photolysis of pentachlorophenol with or without addition of H2O2. Their study showed high levels of PCDD, especially OCDD. The presence of halogenated organic compounds (adsorbable organic halides (AOX)) in textile wastewater may derive from hypochlorite bleaching operations or from spent liquors following shrink-proofing finishing treatment by chlorine. The effluents after bleaching with hypochlorite may contain up to 100 mg dm3 AOX including considerable amounts of chloroform. Some reactive dyes also contain AOX. In the effluent from textiledyeing operation, an average of 0.75 mg dm3 was measured (Grutner et al., 1994). 5. Toxicity of wastewater. The toxicity of textile wastewater varies considerably among different processes in textile industry. Wastewater of some processes have high aquatic toxicity, while others show little or no toxicity. It is impossible to identify all toxic compounds used in textile production, because of the huge variety of chemicals used and the lack of data about their toxicities. Textile wastewater can contain thousands of different compounds, and identifying and testing all of them are practically impossible and too expensive. In general, the overall toxicity is determined by the toxicity test of the whole effluent stream on aquatic organisms, which is a cost-effective method. Table 17 summarizes the results for about

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Water in the Textile Industry

Table 17 textile mills

Results from aquatic toxicity testing of effluent from 75

Toxicity (%)

Number of tests

o9 10–19 20–29 30–39 40–49 50–59 60–69 70–79 80–89 90–100 4100 (no toxicity)

7 6 8 2 4 9 3 8 2 3 38

Table 18 Agent Salt Surfactants Metals Organics Biocides Toxic anions

Typical causes of aquatic toxicity Chemical example NaCI, Na2SO4 Ethoxylated phenols Copper, zinc Chlorinated solvents Pentachlorophenol Sulfide

Source Dyeing Multiple processes Dyes Scour, machine cleaning Wool fibers contaminant Sulfur dyeing

75 companies (Horning, 1977); toxicity in the table is LC50 in percent and the higher number represents the lower toxicity. The source of aquatic toxicity can be dyes, salt, surfactants, ionic metals, toxic organic chemicals, biocides, and toxic anions. Examples of compounds in each of these classes and their source are shown in Table 18 (EPA/625/R96/004, 1996).

4.20.3 Treatment and Reuse of Textile Wastewater 4.20.3.1 Wastewater Treatment Technologies Textile wastewater may be treated by physical, chemical, or biological methods. For decoloration and degradation of textile wastewaters, many treatment technologies have been developed, but every existing technology presents limitations – advantages and disadvantages. Textile wastewater is very complex, so the use of a universal wastewater treatment seems to be impossible. The wastewater-treatment technologies used will depend on the wastewater characteristic (type, dye concentration and auxiliaries, and pH). It is apparent that a single wastewater-treatment system is unable to overcome all problems by itself to provide an efficient treatment of effluents and be cost effective at the same time. In this section, an overview of treatment technologies used in textile effluents is presented. Dyes containing wastewater can be treated by chemical or physical methods of dye removal, which refer to the process called decoloration, and by means of biodegradation, which tells us more about the fate of dyes in the environment.

Physical methods include different precipitation methods (coagulation, flocculation, and sedimentation), adsorption (on a wide variety of inorganic and organic supports), filtration, reverse osmosis, ultrafiltration, and nanofiltration. Biological treatments differ according to the presence or absence of oxygen and are termed aerobic and anaerobic treatment, respectively. Since biological treatments simulate degradation processes that occur in the environment, they are also called biodegradation. Chemical treatment methods are those in which the removal or conversion of dyes and other contaminants is brought about by the addition of chemicals or by chemical reactions (reduction, oxidation, compleximetric methods, ion exchange, and neutralization). The treatment of colored wastewaters is therefore restricted not only to the reduction of ecological parameters (COD, BOD, total organic carbon (TOC), AOX, temperature, and pH), but also to reduction of dye concentrations in wastewaters.

4.20.3.1.1 Physical methods The physical methods of treating textile wastewater are as follows: Adsorption. It is the process of collecting soluble substances that are in solution on a suitable interface. Adsorption methods for decoloration are based on the high affinity of many dyes for adsorbent materials. Some physical and chemical factors have an influence on dye removal by adsorption. These factors are dye-adsorbent interactions, adsorbent surface area, particle size, temperature, pH, and contact time. The main criteria for selection of an adsorbent should be based on characteristics such as high affinity and capacity for target compounds and the possibility of adsorbent regeneration (Santos et al., 2007). Adsorption on sludge is the main abiotic mechanism of removing dyes from wastewater. The most important factors influencing the adsorption test are sludge quality, water hardness, duration of the test, and test-substance concentration. Pagga and Taeger (1994) have described the static and dynamic removal studies involving water-soluble dyes (acid and reactive) and poorly soluble dyes (disperse). Activated carbon is the most commonly used method of dye removal by adsorption. It is very effective in adsorbing cationic, mordant, and acid dyes, and to a slightly lesser extent, disperse, direct, vat, pigment, and reactive dyes (Nassar and El-Geundi, 1991; Raghavacharya, 1997). Its performance depends on the type of carbon used and the characteristic of the wastewater. It is, like many other dye-removal treatments, well suited for one particular waste system and ineffective for another. Activated carbon is relatively expensive and has to be regenerated offsite with losses of about 10% in the thermal regeneration process (Robinson et al., 2001). Biomass referring to the dead plant and animal matter is also a suitable adsorbent for wastewater treatment. The adsorption of organic material onto various types of waste biomass such as sawdust (Poots et al., 1976a), peat (Poots et al., 1976b), chitin (McKay et al., 1982), bagasse pith (Al-Duri et al., 1990), carbonized wool waste (Malmary et al., 1985), wood chips (Nigam et al., 2000), maize cob (El Geundi, 1991), banana pith (Namasivayam et al., 1993), rice husk,

Water in the Textile Industry

hair, cotton waste, and bark (McKay et al., 1987) has been studied. The capacities of these materials have been examined through their adsorption of synthetic dyes. Two mechanisms are presented on the decoloration occurring in the biomass – adsorption and ion exchange. Both of them are influenced by dye–sorbent interaction, sorbent surface area, particle size, temperature, pH, and contact time. Biomass of different origins has been used for decoloration of acid, direct, and reactive dyes. Of all the described adsorbents, only a few have characteristics necessary for commercial use. Considering the price and binding capacity, quarternized lignocellulose-based adsorbents are the most appropriate for treating wastewatercontaining acid dyes. After the adsorption processes, the adsorbent needs to be regenerated, which adds to the cost of the process, and is sometimes a very time-consuming procedure. Decoloration with alternative materials such as zeolites, polymeric resins, ion exchangers, and granulated ferric hydroxide has also been studied in order to decrease adsorbent losses during regeneration. Filtration methods. Ultrafiltration, nanofiltration, and reverse osmosis can be used in the textile industry. These methods can be used not only for both filtering and recycling pigment-rich streams, but also for mercerizing and bleaching wastewaters. The specific temperature and chemical composition of the wastewater determine the type and porosity of the filter to be applied. The main drawbacks of membrane technology are high investment costs, potential membrane fouling, and the production of a concentrated dyebath which needs to be treated (Mishra and Tripathy, 1993; Xu and Lebrun, 1999). Coagulation and flocculation processes. These are widely used in several wastewater treatments in Germany and France. Coagulant agents such as aluminum sulfate, ferrous and ferric sulfate, ferric chloride, calcium chloride, copper sulfate, as well as several copolymers such as pentaethylene, hexamine, and ethylediene dichloride are used to form flocks with the dye, which are then separated by filtration or sedimentation. Coagulation–flocculation methods were successfully applied for decoloration of sulfur and disperse dyes, whereas acid, direct, reactive, and vat dyes presented very low coagulation–flocculation capacity. Polyelectrolyte can also be dosed during the flocculation phase to improve the flock settleability (Lee, 2000; Anjaneyulu et al., 2005). The main advantage of these processes is decoloration of the waste stream due to the removal of dye molecules from the dyebath effluents, and not due to a partial decomposition of dyes, which can lead to an even more potentially harmful and toxic aromatic compound. The major disadvantage of coagulation–flocculation processes is the production of sludge.

4.20.3.1.2 Chemical processes Some of the chemical processes are described in the following: Oxidation. The simplicity of its application makes oxidation the most commonly used chemical decoloration process. With conventional oxidation treatments, it is difficult to oxidize dyes (mainly for removing color) and toxic organic compounds in textile effluents. The development of so-called advanced oxidation processes (AOPs) has overcome the chemical limitations of conventional chemical oxidation

697

techniques. The goal of AOPs is to generate free hydroxyl radicals (OHd) which may represent a rate increase of one to several orders of magnitude compared with normal oxidants in the absence of catalysts. Hydroxyl radicals oxidize the dyes and toxic organic compounds. In AOPs, oxidizing agents such as ozone and hydrogen peroxide are used with catalysts (Fe, Mn, and TiO2), either in the presence or in the absence of an irradiation source. Table 19 shows the oxidation potential of common species. Fenton’s reagent. Hydroxyl radicals are activated by Fe2þ (ferrous ions) in an acid solution (pH ¼ 3–4) (Table 20) from hydrogen peroxide. In this process, it is important to find the optimal concentration of hydrogen peroxide because excess of H2O2 acts as a scavenger of radicals, disturbs the COD measurements, and is toxic for microorganisms. This method is suitable for the oxidation of wastewaters, which inhibit biological treatment or are poisonous. Fenton’s reagent offers a cost-effective source of hydroxyl radicals and it is easy to operate and maintain. The advantages of this system are COD, color, and toxicity reduction and the disadvantage is sludge generation, through flocculation; impurities are transferred from the wastewater to the sludge, which contains the concentrated impurities and is still ecologically questionable. Conventionally, it has been incinerated to produce power, but such a disposal, according to some, is far from being environment friendly. To avoid this problem, Gnann et al. (1993) suggest the regeneration of Fe2þ from iron sludge at pHo1, with the so-called Fenton sludge recycling system (FSRS), in which Fe(III)-sludge deposition is eliminated. Fenton’s reagent as a decoloration agent has been studied by many authors and it is suitable for different dye classes: acid, reactive, direct, metal-complex, disperse, and vat dyes, as well as pigments. Low decoloration rates were observed when C.I. Vat red (50%) and C.I. Disperse Blue (0.5%) were treated (Slokar and Majcen Le Marechal, 1997). Studies on the decoloration and mineralization of commercial reactive dyes using solar Fenton and photo-Fenton reaction indicated good color removal. The use of solar light was proved to be clearly

Table 19

Oxidation potential of common oxidizing agents

Oxidizing agents

Oxidation potential (V)

Fluorine (F2) Hydroxyl radical (OHd) Atomic oxygen Ozone (O3) Hydrogen peroxide (H2O2) Potassium permanganate (KMnO4) Hypochlorous acid (HCIO) Chlorine (Cl2) Bromine (Br2) Molecular oxygen (O2)

3.06 2.80 2.42 2.07 1.78 1.67 1.49 1.36 1.09 1.23

Table 20 Degradation of hydrogen peroxide into hydroxyl radicals activated by Fe2þ Fe2þ þ H2O2-Fe3þ þ OHd þ HO

698

Water in the Textile Industry

beneficial for the removal of color, aromatic compounds, and TOC (Garcı´a-Montan˜o et al., 2006; Torrades et al., 2004). Ozone. Once dissolved in water, ozone reacts with a great number of organic compounds in two different ways, namely direct molecular and indirect free radical-type reactions. The direct reactions are often highly solute selective, slow, and are dominant in acidic solutions. They are suitable for opening aromatic rings by means of ozone cycloaddition. The indirect hydroxyl radical reactions are nonselective, fast, proceed more rapidly with increasing pH, and constitute a significant portion of ozonation at basic pH. Indirect attacks are suitable for mineralization of TOC (Zhao et al., 2004). Although the original purpose of oxidation with ozone is disinfection of potable water, it can also be used for removing many toxic chemicals from wastewater to facilitate the decomposition of detergents, chlorinated hydrocarbons, phenols, pesticides, and aromatic hydrocarbons (Science Applications International Crop., 1987). The advantages of ozonation include

• • • • • •

decoloration and degradation occur in a single step, danger to humans is minimal, no sludge remains, all residual ozone can be decomposed easily into oxygen and water, little space is required, and ozonation is easily performed (Oguz et al., 2005).

The disadvantage is its very short half-life in water – ozone decomposes in about 20 min. The time can be significantly shortened if compounds such as dyes are present (Rice et al., 1986). Ozone stability is affected by the presence of salts, pH, and temperature. If alkaline salts are present, the solubility of ozone is reduced, while neutral salts may increase its solubility (Mallevialle, 1982). Under alkaline conditions, ozone decomposes more rapidly than under acidic conditions. With increasing temperature, ozone solubility decreases (Perkins et al., 1980). Studies of decoloration presented by several authors revealed that ozone decolorizes all dyes, except nonsoluble disperse and vat dyes which react slowly and take longer time (Namboodri et al., 1994; Marmagne and Coste, 1996; Liakou et al., 1997). Color removal strongly depends on dye concentration. Ozonation alone has low TOC and COD removal. Species such as oxalic, glyoxalic, and acetic acids cannot be completely mineralized by ozone alone at least at neutral or acidic pH (Hoigne and Bader, 1983). To enhance the efficiency of ozonation, a combination of various advanced oxidation processes has been developed, such as ozon/ UV, ozon/H2O2, and catalytic ozonation. Ozone–UV. Combination of ozone with UV results in a net enhancement of organic-matter degradation due to direct and indirect production of hydroxyl radicals upon ozone decomposition and H2O2 formation (Table 21). UV radiation decomposes ozone in water and generates highly reactive hydroxyl radicals. Hydroxyl radicals oxidize organics more rapidly than ozone itself. The efficiency of ozone/UV treatment depends on operating temperature (at higher temperature the ozone solubility is lower), pH (degradation favors neutral or slightly alkaline medium), and ozone-flow rate. For comparison of both ozonation and

Table 21 Direct and indirect production of hydroxyl radicals in O3– UV process Direct O3 þ hn-O2 þ O O þ H2O-OHd þ OHd O þ H2O-H2O2 H2O2 þ hn-OHd þ OHd Indirect O3 þ H2O þ hn-O2 þ H2O2 H2O2 þ hn-OHd þ OHd

Table 22

Reactions between O3 and H2O2

Initiation HO2  þ O3 -HO2  þ O3  H þ þ O3  # HO3  -OH þ O2 H2 O2 þ O3 -H2 O þ 2O2

kr ¼ 2.2  106 l mol1 s1 kr ¼ 1.1  105 l mol1 s1 kro102 l mol1 s1

Promotion OH  þ O3 -O2 þ HO2  OH  þ H2 O2 -H2 O þ HO2  OH  þ HO2  -H2 O þ O2 

kr ¼ 1.1  108 l mol1 s1 kr ¼ 2.7  107 l mol1 s1 kr ¼ 7.5  109 l mol1 s1

ozone/UV process, the degradation of eight commercial azo dyes in water (Shu and Huang, 1995a) and a model dyehouse wastewater (Perkowski and Kos, 2003) has been studied. In both studies, the ozone/UV process did not significantly enhance the degradation rates; the dye competed with ozone for UV absorbance. However, ozone/UV treatment, in terms of COD removal, is more effective compared to that by ozone (Bes-Pia et al., 2003). Ozone/H2O2. Addition of hydrogen peroxide to ozone enhances the production of hydroxyl radicals. The aqueous reactions between ozone and hydrogen peroxide are rather complex. The mechanisms and the kinetics of the production of hydroxyl radicals from ozone and hydrogen peroxide are known. The reactions and reaction rate constants are shown in Table 22. In the initiation sequence, reactive OHd radicals are generated. During the promotion reactions, the hydroxyl radicals are converted into the peroxy radical. At acidic pH, H2O2 reacts only very slowly with ozone, whereas at pH values greater than 5, a strong acceleration of ozone decomposition by hydrogen peroxide has been observed. The ozone decomposition rate increases with increasing pH. Decoloration with O3/H2O2 process is applicable for direct, metal-complex, or blue disperse dyes. There are some problems with the decoloration of acid and red disperse dyes, though, as well as with mixtures of direct, metal-complex, disperse, and reactive-dye decoloration. The efficiency of the decoloration with O3/H2O2 for a few of the dyes is presented in Table 23. H2O2/UV. In H2O2/UV processes, hydroxyl radicals are formed when water-containing H2O2 is exposed to UV wavelengths of 200–280 nm. The most commonly used UV source is low-pressure mercury vapor lamps with a 254-nm peak emission.

Water in the Textile Industry Table 23

Decoloration of dyes with O3/H2O2

Table 25

Textile dye

Decoloration (%)

Time (min)

Red 219 Blue 186 Direct Yellow 44 Direct Yellow 50 Red 23 Red 26 Direct Red 5B Direct Blue 1 Direct Blue 25 Direct Blue 71 Disperse Yellow 3 Disperse Yellow 64 Red 13 Red 60 Red 279 Blue 60 Palanil Blue 3RT Sulfo/disperse dye Reactive Yellow 37 Reactive Yellow 125 Reactive Yellow 125 Remazol Yellow RNL Reactive Red 35 Reactive Red 195 Blue 27 Blue 221 Green 13 Reactive dyes Vat dyes Azoic dyes

100 85 100 100 100 100 99 100 100 90 95 100 100 100 99 100 90 98 93 98 100 93 99 100 94 100 98 100 80 87

5 1 0.5 0.5 0.5 0.5 45 0.5 0.5 7 1 4.5 0.7 1 98 0.7 31 30 4 2.5 7 4 4.5 6 0.9 9 4 1 30 30

Adapted from Slokar YM and Majcen Le Marechal A (1997) Methods of decoloration of textile wastewaters. Dyes and Pigments 37(4): 335–356.

Table 24

The main reactions that occur during the H2O2/UV process

H2O2 þ hn-OHd þ OHd RH þ OHd-H2O þ Rd-further-oxidation

Problems such as sludge formation and regeneration, and increased pollution of wastewater caused by ozone, can be avoided by oxidation with hydrogen peroxide activated with UV light. The only chemical used in the treatment is H2O2, which, due to its final decomposition into oxygen, is not problematic. The most direct method for generation of hydroxyl radicals is through the cleavage of H2O2. Photolysis of H2O2 yields hydroxyl radicals by direct process with a yield of two radicals formed per photon absorbed at 254 nm. Hydroxyl radicals can oxidize organic compounds (RH)-producing organic radicals (Rd), which are highly reactive and can be further oxidized (Table 24) (Tuhkanen, 2004). The maximum absorbance of H2O2 is needed to generate sufficient hydroxyl radicals because of low absorption coefficient. However, high concentration of H2O2 scavenges the radicals, making the process less effective, while low concentration of hydrogen peroxide does not generate enough hydroxyl radicals to be consumed by the dye and this leads to

699

Reactions of H2O2 as a radical scavenger

OH  þ H2 O2 -HO2  þ H2 O HO2  þ H2 O2 -HO2  þ H2 O þ O2 HO2  þ HO2  -H2 O2 þ O2

a slow rate of oxidation. Therefore, an optimum hydrogenperoxide dose needs to be verified experimentally (Table 25). The rate of dye removal is influenced by the intensity of UV radiation, pH, dye structure, and dyebath composition. In general, decoloration is most effective at neutral pH medium, at higher UV radiation intensity (1600 W rather than 800 W), with an optimal H2O2 concentration, which is different for different dye classes, and with a dyebath that does not contain oxidizing agents having an oxidizing potential higher than that of peroxide. According to Shu and Huang (1995b) acid dyes are the easiest to decompose, and with an increasing number of azo groups, the decoloration effectiveness decreases. Yellow and green reactive dyes need longer decoloration times, while other reactive dyes as well as direct, metal-complex, and disperse dyes are decolorized quickly. In the group of blue dyes examined, only blue vat dyes were not decolorized. For pigments, H2O2/UV treatment is not suitable, because they form a film-like coating on the UV lamp, which is difficult to remove. Several authors (Georgiou et al., 2002; Neamtu et al., 2002; Galindo and Kalt 1999; Colonna et al., 1999) reported complete decoloration of reactive and azo dyes in 30–90 min. The results indicated that H2O2/UV processes could be successfully used for the decoloration of acid, direct, basic, and reactive dyes but it proved to be inadequate for vat and disperse dyes (Yang et al., 1998). A comparative study between ozone and H2O2/UV was carried out on simulated reactive dyebath effluent containing a mixture of monochlorotriazinetype reactive dyes and various auxiliary chemicals. The H2O2/ UV process presented the decoloration rates close to those rates obtained with ozone but at a lower cost (Alaton et al., 2002). H2O2/UV systems may be set up in a batch or in a continuous column unit (Namboodri and Walsh, 1996). Decoloration of some dyes with H2O2/UV is presented in Table 26. Ultrasound. Sonolysis is a relatively innovative advanced oxidation process and was found to be a suitable method for the destruction of textile dyes. The ultrasonic irradiation of liquids generates cavitation (typically in the range 20–1000 kHz). Cavitation is a phenomenon of micro-bubble formation. Micro-bubbles grow during the compression/rarefaction cycles until they reach a critical size, and implode generating heat and highly reactive radical species. Inside the cavitation bubbles, the temperature and pressure rise to the order of 5000 K and 100 MPa, respectively. Under such conditions, water molecules degrade releasing hydroxyl radicals (OHd) and hydrogen radicals (Hd) as mentioned in Table 27. These radical species can either recombine or react with other gaseous molecules within the cavity, or in the surrounding liquid, after their migration. Pyrolitic and radical reactions inside, or near, the bubble and radical

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Water in the Textile Industry

reactions in the solution are two major pathways for sonochemical degradation. Hydrophilic and nonvolatile compounds mainly degrade through hydroxyl-radical-mediated reactions in the solution, while hydrophobic and volatile

Table 26

Decoloration of dyes with H2O2/UV

Textile dye

Decoloration (%)

Time (min)

Reactive Yellow 37a Reactive Yellow 125a Remazol Yellow RNLa Reactive Red 35a Reactive Red 195b Reactive Black 5c Acid Yellow 17d Orange 10d Blue 21b Blue 27a Green 13a Vat Bluea Red 1d Red 14d Red 18d Blue 186e Black 1d Direct Yellow 4d Direct Blue 71a Palanil Blue 3RTa

85 96 85 100 100 100 98.2 100 95 100 93 15.5 99.9 100 99.1 80 89.9 83.2 98.5 96

8 8 8 8 60 4 40 60 150 5 8 10 30 60 40 10 60 60 3 10

a

Hoigne and Bader (1983). Liakou et al. (1997). c Ince and Go¨rnec (1997). d Bes-Pia et al. (2003). e Pittroff and Greorg (1992). b

Table 27

Radical formation and depletion during water sonolysis d

H2O-))) OH þ Hd OHd þ Hd-H2O 2OHd-H2O þ Od 2OHd-H2O2

Table 28

species degrade thermally inside or in the vicinity of the bubble. Reactive azo dyes are nonvolatile, water-soluble compounds and their passage into the gas cavity is unlikely. Hence, oxidative radical reactions in the bulk solution are expected to be the major route for their destruction. According to several studies, it is difficult to obtain the total mineralization (degradation to carbon dioxide, short-chain organic acid, oxalate, formate, and inorganic ions such as sulfate and nitrate) of the complex textile dyes with ultrasound alone. For this reason, the combination of ultrasound with other advanced oxidation processes is a more convenient approach in the remediation of such pollutants. Sonochemical degradation of textile dyes has become quite an interesting research area confirmed by several reports over the last few years (Vajnhandl and Majcen Le Marechal, 2005). In Table 28, a comparison of individual AOP is given.

4.20.3.1.3 Biological treatment processes Biological degradation or breakdown by living organisms is the most important removal process of organics, which are transferred from industry processes into solid and aquatic ecosystems. The application of microorganisms for the biodegradation of synthetic dyes is an attractive method and offers considerable advantages. The process is relatively inexpensive, the running costs are low, and the end products of complete mineralization are not toxic. An extensive review of large numbers of different species of microorganisms tested for decoloration and mineralization of different dyes has been published by Forgacs et al. (2004). The efficiency of biological-treatment systems is greatly influenced by the operational parameters. To produce the maximum rate of dye reduction, the level of aeration, temperature, pH, and redox potential of the system must be optimized. The concentration of the electron donor and the redox mediator must be balanced with the amount of biomass in the system and the quantity of the dye present in the wastewater. The compounds present (sulfur compounds and salts) in the wastewater may have an inhibitory effect on the

Technical comparison of oxidative decoloration

Oxidation process

Advantages

Disadvantages

Fenton

Effective decoloration of both soluble and insoluble dyes. Simple equipment and easy implementation. Reduction of COD (except with reactive dyes). No alternation in volume. Simple equipment and implementation. Reduction of COD (except with reactive dyes). Applied in gaseous state. No alteration of volume. No sludge production. Effective for azo dye removal. No sludge formation. No salt formation. Short reaction times. Very short reaction times for reactive dyes. No sludge formation. No salt formation. Short reaction times. Reduction of COD. Simplicity in use. Very effective in integrated system.

Sludge formation. Long reaction times. Salt formation. Hazardous waste. Prohibitively expensive.

FSR (Fenton sludge recycling system) Ozone Ozone/H2O2

H2O2/UV Ultrasound

Salt formation. Formation of gasses (H2, O2 during electrolysis). Short half-life (20 min). Not suitable for disperse dyes. Releases of aromatic amines. Not applicable for all types. Toxicity, hazard, problematic handling. No COD reduction. Additional load of water with ozone. Not applicable to all types of dyes. Requires separation of suspended solid particles. Relatively new method and awaiting full scale application.

Water in the Textile Industry

dye-reduction process. For these reasons, it is important to study the effect of these factors on decoloration before the biological system can be used to treat industrial wastewater (Pearce et al., 2003). Biodegradation processes may be anaerobic, aerobic, or involve a combination of both. Anaerobic biodegradation. Under anaerobic conditions, a low redox potential (o 50 mV) can be achieved, which is necessary for the effective decoloration of dyes. Color removal under anaerobic conditions is also referred to as dye reduction. Many bacteria under anaerobic conditions reduce the highly electrophilic azo bond in the dye molecules and produce colorless aromatic amines. The anaerobic decoloration of azo dyes was first investigated using intestinal anaerobic bacteria (Allan and Roxon, 1974; Brown, 1981; Chung et al., 1992). Later, it was found that azo dyes can also be decolorized with various other anaerobical cultures (Brown and Laboureur, 1983; Beydilli et al., 1998; Donlon et al., 1997). The efficacy of various anaerobic-treatment applications for the degradation of a wide variety of synthetic dyes has been demonstrated in several experiments. The exact mechanism of azo dye reduction is not clearly understood yet. There may be different mechanisms involved, such as enzymatic (Haug et al., 1991; Rafii et al., 1990), nonenzymatic (Gingell and Walker, 1971), mediated (Kudlich et al., 1997), intracellular (Mechsner and Wuhrmann, 1982; Wuhrmann et al., 1980), extracellular (Carliell et al., 1995), and various combinations of these mechanisms. A complete anaerobic mineralization of the azo dye azodisalicylate was observed under methanogenic conditions (Razo-Flores et al., 1997). The reduction of azo dye under anaerobic conditions strongly depends on the presence and disponibility of the cosubstrate. It acts as an electron donor for the azo dye reduction. The decoloration of reactive water-soluble azo dyes was achieved under anaerobic conditions using glucose as a co-substrate (Carliell et al., 1996). Anaerobic decoloration of reactive dyebath effluents with tapioca as a co-substrate also enhances color-removal efficiency (Chinwetkitvanich et al., 2000). The other suitable co-substrates were hydrolyzed starch, yeast extract, and a mixture of acetate, butyrate, and propionate. Much effort has been devoted to the study of the influence of various modern technologies on the decomposition rate of the dyes and the effect of the presence of the other compounds in the media. It has been recently established that the development of high rate systems, in which the hydraulic-retention times are decoupled from the solid-retention times, facilitates the removal of dyes from textile-processing wastewater (Rice et al., 1986). The effect of nitrate and sulfate salts used in textile dyeing on the microbial decoloration of a reactive azo dye has been studied. The results indicated that nitrate delays the onset of decoloration while sulfate did not influence the biodegradation process (Carliell et al., 1998). The reduction of azo dyes proceeds better under anaerobic thermophilic conditions than under mesophilic conditions, although the thermophilic process seems to be less stable compared to the mesophilic process (Willetts et al., 2000).

701

Carliell et al. (1994) studied the biodegradation of reactive dyes and they decolorized 80% of a range of tested dyes. From a detailed study of a selected dye, it was proposed that this occurred via a reduction mechanism. The results were supported by tentative chemical identification of the dyedegradation products. Hu (1994) isolated Pseudomonas luteola bacteria; after a 6-month adaptation in colored wastewater, he obtained microorganisms capable of reductive cleavage of the azo group in the dye. Decoloration with these microorganisms was complete within 4 days. Van der Zee et al. (2001) studied the decoloration of 20 selected azo dyes by granular sludge from an upward-flow anaerobic sludge-bed reactor and for all the azo dyes tested, complete reduction was achieved. Aromatic amines, due to azo dye reduction, are not commonly degraded under anaerobic conditions. Many aromatic amines were tested, but only a few were degraded. Some aromatic amines, substituted with hydroxyl or carboxyl group were degraded under methanogenic and sulfate-reducing conditions (Kalyuzhnyi et al., 2000; Kuhn and Suflita, 1989; Razo-Flores et al., 1999). Aerobic biodegradation. It is a process that often takes place in the environment, for example, in natural ecosystems such as soil or surface waters, and it is often associated with technical systems such as wastewater-treatment plants. Although for long, it was considered that azo dyes cannot readily metabolize under aerobic conditions, some specific aerobic bacterial cultures were found to be able to reduce the azo linkage via an enzymatic reaction. The aerobic conversions of sulfonated azo dyes were studied by Heiss et al. (1992) and Shaul et al. (1991), and sometimes even a complete mineralization of sulfonated azo dyes was found. In some studies, aerobic color removal of certain azo dyes was achieved, but all these stains required an additional energy and carbon source for growth. Since the supply of this additional substrate could have easily led to the formation of anaerobic microniches, the occurrence of anaerobic azo dye reduction certainly cannot be excluded (Govindaswami et al., 1993; Horitsu et al., 1977; Wong and Yuen, 1996; Zissi et al., 1997). The aerobic biodegradation of different aromatic amines (aniline (Lyons et al., 1984), carboxylated aromatic amines (Stolz et al., 1992), chlorinated aromatic amines (Loidl et al., 1990), benzidines (Baird et al., 1977), and sulfonated aromatic amines) has been extensively studied and many of these compounds were found to be degraded. Sulfonated aromatic amines are difficult to degrade. Combination of anaerobic/aerobic biodegradation. Although the anaerobic reduction of azo dyes is generally more satisfactory than aerobic degradation, carcinogenic aromatic amines, as products of anaerobic degradation, have to be degraded by an aerobic process. Diverse technologies for the successive anaerobic/aerobic treatment of textile wastewater have been developed. Anaerobic/aerobic conditions can be implemented by spatial separation of the two sludges using a sequential anaerobic/aerobic reactor system (Zitomer and Speece, 1993). These conditions can also be imposed on a single reactor in the so-called integrated anaerobic/aerobic reactor system (Field et al., 1995).

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Water in the Textile Industry

4.20.3.2 Reuse New ecolabels for textile products and tighter restrictions on wastewater discharges are forcing textile wet processors to reuse process water and chemicals (Vandevivere et al., 1998). It is quite difficult to define a general quality standard for textilewater reuse because of the different requirements of each fiber (silk, cotton, polyester, etc.) of the textile process (e.g., scouring, desizing, dyeing, and washing) and because of the different quality required for the final fabric (Water Treatment Solutions, 2010). The actions aimed at the reduction of pollution and reuse of wastewater may normally be ranked in the following order according to their cost effectiveness: 1. prevention of pollution generation, 2. treatment of polluted streams close to the source of pollution (start-of-pipe approach), and 3. treatment of the final effluents (end-of-pipe approach). Pollution-prevention actions can normally succeed in all companies, while the start-of-pipe approach is mostly indicated in medium or big enterprises and the simplicity of the end-of-pipe approach makes it suitable in small as well as medium enterprises (Matioli et al., 2002).

4.20.3.2.1 Pollution-prevention techniques Pollution-prevention techniques have proved to be an effective means to improve process efficiency and to increase company profits, and at the same time, they minimize environmental impact. During the implementation of each of these techniques, the specific conditions must be carefully considered and every option and change must be examined, to understand how it could affect air, land, and water-pollutant releases (Matioli et al., 2002). Some of the pollution-prevention techniques that can be adopted are as follows: 1. Quality control for raw materials. Textile companies can reduce waste emissions by working with suppliers to find out less-polluting raw materials. Pre-screening raw materials is a useful practice to determine interactions among processes, substrates, and other chemicals with the aim to reduce waste production (Matioli et al., 2002). 2. Chemical substitution. Textile manufacturing is a chemically intensive process, and therefore a primary focus for pollution prevention should be on textile process chemicals. Opportunities for chemical substitution vary substantially among mills because of differences in: (US EPA/625/R-96/004, 1996) • environmental conditions, • process conditions, • product, and • raw materials. Possible actions are replacement of chemicals as desizing agents, dyes and auxiliaries with less-polluting ones, and replacement of chemical treatment in some processes with mechanical or other nonchemical treatment (Matioli et al., 2002). 3. Process modification. Optimization of the processes can be obtained by modifying some operations. Examples of possible modifications are (Matioli et al., 2002)

•

substitution of dyeing machines using low liquor ratio (equipment able to substantially reduce bath ratio and allow considerable savings of energy, water, dyes, and chemicals), • optimization of process conditions (temperature and time), and • combining operations to save energy and water (combining scouring and bleaching). 4. Equipment modification. An effective way to reduce waste is also by modifying, retrofitting, or replacing equipment and introducing automation (Matioli et al., 2002). 5. Good operating practices. A suitable way to prevent pollution without changing industrial processes is introduction of pollution-prevention procedures, including pollution-prevention objectives in research, new facility design, and ad hoc worker-training programs (Matioli et al., 2002).

4.20.3.2.2 Chemicals and water reuse and recycle: Start-of-pipe approach Recycling (reusing water and chemicals in the same process that produced the effluent) can save water, chemicals, and energy as well. An example is the reuse of exhausted hot dyebaths to dye further batches of material. In order to reuse the dyebath, it is necessary to determine the exact quantities of residual chemicals remaining in the dyebath. As a following step, to respect the characteristics demanded by the next dyeing cycle, the dyebath must be reconstituted by adding water, auxiliary chemicals, and dyestuffs (Matioli et al., 2002; EPA/310-R-97-00, 1997). Several examples of water reuse without treatment are based on the recovery of the water used in rinsing operation. Implementation of countercurrent washing (reusing the last contaminated water from the final wash for the next-to-last wash and so on) can significantly reduce the overall water consumption and is already applied in continuous textile operations. A systematic analysis of the water networks is required every time the overall use of water needs to be optimized and new options of water treatment and reuse have to be evaluated. Tools such as pinch analysis provide a formal procedure to determine near-optimal designs of energy and mass-transfer networks (Matioli et al., 2002; Majozi et al., 1998). When applied to water-use optimization, pinch analysis allows the identification of reuse, regeneration, and treatment opportunities. This approach normally generates start-of-pipe solutions implementing specific-process effluent treatment. The process-integrated wastewater treatment required by start-of-pipe solutions is based upon the possibility of efficient, reliable, cost-effective, and easy-to-operate treatment of single wastewater streams. These results can be obtained by proper applications of membrane technology (Matioli et al., 2002).

4.20.3.2.3 Process-water reuse and recycle: End-of-pipe approach In some cases, the classical end-of-pipe approach for reuse and recycling of industrial final effluents can also be efficient and cost effective. It fits very well in some typical European areas that can be defined as ‘textile districts’. A textile district refers to an area where many textile factories, mainly small and

Water in the Textile Industry

medium enterprises, are widespread and utilize the same water and wastewater facilities (Matioli et al., 2002). The end-of-pipe treatment was the first approach examined for cleaning up the total effluent flow in order to meet the standards for reuse. End-of-pipe treatment involves multistage-process combinations typically composed of biological and physicochemical techniques. Recently, the interest in membrane processes applied to textile-wastewater reuse is increasing, thanks to technological innovations that render them as reliable and feasible alternatives to other systems (Schoeberl et al., 2004). Membrane systems can successfully remove the large amount of suspended solids in wastewater (Chen et al., 2005). Centralized treatment plant for mixed industrial and municipal wastewater uses an aerobic biological stage. Some compounds are completely degraded, while others (dyes, surfactants, and their metabolites) are either absorbed on the sludge or discharged into the final effluent. Textile wastes contain poorly degradable organics (at least in aerobic conditions). Many contain toxicants, which are also often poorly biodegradable. Traditional aerobic biological process presents serious technical limitations for the purification of textile wastewaters (Matioli et al., 2002). The EU founded the Research and Technological Development (RTD) project Integrated Waste Recycling and Emission Abatement in the Textile Industry (EU, 1999) proposed several combined process modules to improve the actual wastewater-treatment plants, aimed at the reuse of final effluents: 1. A module for chemical precipitation of heavy metals and adsorption of dyes on anaerobic sludge (consisted in a pretreatment option) (Terras et al., 1999; O’Neill et al., 1999). 2. Enhancement of the biological treatment to a sensor-protected aerobic stage to remove biodegradable organics and to oxidize reduced nitrogen compounds while monitoring potential toxicity (Terras et al., 1999; Massone et al., 1998; Guwy et al., 1998). 3. The optimization of the final polishing involving various tertiary treatment lines to bring the water up to the standard required for use by the industries (Bergna et al., 1999; Bianchi et al., 1999; Rozzi et al., 1997, 2000). Posttreatment for mixed textile and domestic effluents has been successfully tested on the following unit process: ozonation, clariflocculation, multimedia filtration, granular activated carbon adsorption, ceramic crossflow and hollow-fiber microfiltration, nanofiltration, and low-pressure reverse osmosis. All the processes were investigated at medium and large pilot scale (Matioli et al., 2002). New advanced respirometric methodologies based on respirometry and titration may be used as wastewater-characterization techniques. They are particularly suited to evaluate the possible effects of a given wastewater on the final wastewater-treatment plant, due to their organic biodegradable and refractory load and inhibitory potential. The use of these characterization methods makes it possible to prevent treatment problems due to toxic discharges (Rozzi et al., 1999). The fee lever based on the treatability of the discharges can also be used to design the influent wastewater in a given

703

treatment plant, discouraging the discharge of refractory and inhibitory compounds. It can also lead to the introduction of cleaner technologies when an industry, billed with high fees for the presence of inhibitory compounds in its wastewater, is pushed toward the application of pollution-prevention techniques. The concept of waste design should not be limited to an offline procedure of characterization and of request to industries of qualitative or quantitative changes to their discharges. This concept should be extended to an online management system, based on a network of sensors, actuators, and facilities that can allow the plant manager to detect in the sewer (or before to discharge to it) the presence of excess hydraulic loading, organic or nutrient loading, or toxicants, and put in operation measures that can allow to maintain an optimal treatment result (Matioli et al., 2002; Bortone et al., 1997).

4.20.4 Conclusions Textile processing is one of the largest and oldest industries worldwide and it is responsible for substantial resource consumption and pollution. The wet processing, that is, pretreatment, dyeing, printing, and finishing, is especially polluting and resource consuming in terms of water, energy, and chemicals and like in most industries, freshwater is used in all processes with almost no exceptions. Textile industry has significant impact on the aquatic system, both by consuming a lot of water, freshwater sources, and also by discharging effluents into the environment. Water savings, reclamation, and reuse in industry are topics of increasing economic interest due to increasing water scarcity and costs. For this reason, research and development activities within this topic are increasing, methods and tools for analyzing water savings and reuse possibilities are being developed, and solutions are being implemented. The problem of water scarcity and the need for a rational water management has raised an interest in the use of recycled and reclaimed water as well as further water-loop closure. The typical textile SME today does not implement water reuse, while fresh high-quality water is used in all the production processes. Furthermore, the process effluents are mixed and discharged after onsite or centralized treatment in conventional wastewater-treatment plants. Despite the fact that during the last decades, new knowledge and technologies related to process-water production, wastewater treatment, and water-loop closure have been developed and implemented, current available technologies for textile wastewater treatment are often limited in efficiency and cost, and are not environmentally selective enough. On the other hand, it is not always clear which treatment lines are best suited to achieve the desired water quality at the lowest cost. Besides, textile companies are mainly SMEs and the small scale could represent a problem, because the water streams might have very different compositions. Over the last 30 years, drought and water scarcities have cost the European economy an estimated h100 bn. The most severe impacts of climate change that the world is facing are related to water. Climate change is intensifying the hydrological cycle. Risks from flood, drought, and coastal

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inundation, melting of glaciers and changes in the flow regimes of rivers are growing. Despite an understanding of the dangers to the economy, social stability, and the environment, not enough attention was given until recently to reduce the impacts of climate change on water and to increase adaptation efforts. In light of this, the vision of technological platforms (Water Supply and Sanitation Technology Platform (WSSTP), textile platform) and industrial associations (European Water Partnership (EWP) and European Apparel and Textile Organization (EURATEX)) is trying to follow some new ideas and approaches that would bring water to the forefront of a comprehensive strategy and promote adaptation measures across all water-related sectors.

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4.21 Water Availability and Its Use in Agriculture D Molden, International Water Management Institute, Battaramulla, Sri Lanka M Vithanage, Institute of Fundamental Studies, Kandy, Sri Lanka C de Fraiture, International Water Management Institute, Accra, Ghana JM Faures, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy L Gordon, Stockholm University, Stockholm, Sweden F Molle, Institut de Recherche pour le De´veloppement and International Water Management Institute, Colombo, Sri Lanka D Peden, International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia & 2011 Elsevier B.V. All rights reserved.

4.21.1 Water Availability and Its Use in Agriculture 4.21.1.1 Sources of Water for Agriculture, Their Distribution, Use, and Possible Climate Change Effects 4.21.1.1.1 Green water 4.21.1.1.2 Agricultural water use in river basins 4.21.1.1.3 Open, closing, and closed river basins 4.21.1.1.4 Groundwater 4.21.1.1.5 Wetlands 4.21.1.1.6 Water consumption 4.21.1.1.7 Water use 4.21.1.1.8 Climate change, agriculture, and water 4.21.1.1.9 Drivers of water use 4.21.1.2 Physical and Economic Water Scarcity 4.21.1.3 Future Demands for Water 4.21.1.4 Future Scenarios for Rainfed and Irrigated Agriculture 4.21.2 Productive Use of Agricultural Water 4.21.2.1 Water Productivity in Agriculture 4.21.2.2 Rainfed Agriculture Productivity 4.21.2.3 Irrigated Agriculture and Productivity 4.21.2.4 Livestock 4.21.2.5 Aquaculture and Fisheries 4.21.3 Environmental and Health Implications of Agricultural Water Use 4.21.3.1 Impact on Rivers, Wetlands, and Biodiversity 4.21.3.1.1 Aquatic ecosystems 4.21.3.1.2 Terrestrial ecosystems 4.21.3.2 Health Impacts 4.21.3.3 Environmental and Health Mitigation 4.21.4 Water Governance 4.21.4.1 Definition 4.21.4.2 Types of Governance for River Basin Management 4.21.4.3 Basin Governance Challenges Acknowledgments References

4.21.1 Water Availability and Its Use in Agriculture With growing populations, shifting geographies, and changing dietary patterns, agriculture and food production face formidable challenges in the near future. Understanding issues of water availability and its use is fundamental for assessing and responding to these challenges. The following section examines the topics of water availability and use as they relate to agricultural production. While 3% of Earth’s total water volume is fresh (most of it is found in the form of ice in polar regions), only 1% is easily accessible for human use and is found in the physical forms of lakes, rivers, and shallow

707 707 707 708 709 709 710 711 711 711 711 712 713 714 716 716 717 718 719 720 721 721 721 723 723 724 725 725 725 727 728 728

aquifers (UN, 1997). Water for food and agricultural production is the largest use of this finite resource.

4.21.1.1 Sources of Water for Agriculture, Their Distribution, Use, and Possible Climate Change Effects Conventionally, agricultural water resources have been thought of in terms of surface water and groundwater. This approach, however, can be limiting. Besides considering surface- and groundwater, accounting for rainfall and soil moisture, as they factor into hydrological and agricultural systems,

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allows for a systematic understanding of water. The concepts of blue water and green water are useful in thinking about water availability in relation to a broader range of agricultural practices and a variety of users (CA, 2007). Blue water refers to water found in rivers, lakes, reservoirs, and aquifers. In addition to its use in agriculture, blue water is the measured and managed freshwater resource needed to meet domestic, commercial, and hydroelectric power demands while also functioning to sustain ecosystems (UN, 2006). Of total renewable blue water resources, 9% is used annually. Cities and industries extract 1 200 km3 of blue water per year but return more than 90% of it. This return is often of degraded quality and much of the flow returns to the sea, where it supports coastal ecosystems (Figure 1). Green water refers to

soil moisture available to plants generated by infiltrating rainfall. Green water is the main source of water for rainfed agriculture, whereas blue water is the main source for irrigated agriculture. Rainfed agriculture strictly depends on green water only, whereas irrigated agriculture uses blue water to supplement soil moisture. By adding blue water to crops, farmers can maintain soil moisture in dry periods and allow their crops to fulfill yield potentials. Through the process of evapotranspiration both green and blue water are ‘‘consumed’’ by vegetation and not returned to the system like in the case of other sections. The implications of green and blue water use are quite different. Increased evapotranspiration of blue water reduces stream flow and groundwater levels. Agricultural

Global water use Rainfall (thousands of cubic kilometers per year) 110 100%

Green water Bioenergy Forest products Grazing lands Biodiversity Landscape 56%

Blue water Rivers Wetlands Lakes Groundwater

Soil moisture from rain Crops Livestock Rainfed agriculture 4.5%

Water storage Aquatic biodiversity Fisheries

Crops Livestock Aquaculture Irrigated agriculture 0.6% 1.4%

Open water evaporation 1.3%

Green water

Cities and industries 0.1%

Blue water

Ocean 36%

Landscape Dam and reservoir

Landscape Irrigated agriculture

Wetlands

Rainfed agriculture

Cities

Figure 1 Global water uses. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007). From Oki and Kanae (2006) Global hydrological cycles and world water resources. Science 313(5790): 1068–1072; UNESCO–UN World Water Assessment Programme (2006) Water: A Shared Responsibility, The United Nations World Water Development Report 2. New York: UNESCO and Berghahn Books.

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4.21.1.1.2 Agricultural water use in river basins

evapotranspiration is necessary for food production, and generally as food production increases, so does evapotranspiration. Increased evapotranspiration from green water sources is usually due to expansion of agricultural land area, a terrestrial impact, but has less impact on blue water flows. Still, any change in land use can affect river flows. In South Africa, recognition of the effects of stream flow-reducing activities has led to initiatives to control commercial forestry and to remove invasive tree species in order to reduce evapotranspiration and increase river flow (Hope, 2006).

The remaining 20% of crop evapotranspiration is from blue water drawn from surface- and groundwater sources. Blue water resources are, systematically, part of hydrological regions called river basins. River basins bounded by the area that catches water and directs it to common outlets. Basins and serve as important units of analysis because they connect various water in the basins uses. A change in use in one area often influences other uses of water. Efforts to control rivers go back many thousands of years, similarly, the practice of using these physical areas as regional units of organization for planning, developing, and managing water. More recently, in the latter half of the twentieth century, major dams were constructed which resulted in the multipurpose development and management of river basins. Hydroelectric power, flood control, water storage, and navigation became linked politically, economically, and ecologically in these river systems. Meanwhile, investment in irrigation accelerated rapidly in the 1960s and the 1970s, with irrigated area expansion in developing countries at 2.2% a year reaching 155 million hectares in 1982. During the same period, total global irrigated lands rose from 168 to 215 million hectares (Carruthers et al., 1997).

4.21.1.1.1 Green water Globally, about 80% of agricultural evapotranspiration comes directly from green water (Figure 2). This implies that the majority of the world’s agricultural production comes predominantly from rainfed lands despite major increases in large-scale irrigation infrastructure over the past half century. Some 55% of the world’s gross value of crop production is grown under rainfed agriculture on 72% of harvested land (Table 1). There are, however, large geographical differences in the percentages of rainfed and irrigated agricultural lands. For instance, over 95% of sub-Saharan Africa’s cultivated lands are strictly rainfed agriculture. Similarly, Latin America’s cultivated lands are 90% rainfed agriculture. In several countries of the Near East and North Africa, more than 40% of cultivated areas is irrigated. Meawhile investment in irrigation accelerated, in the 1960s and 1970s, from about 150 million hectares to a present total of over 270 million hectares (Faures et al., 2007). Hence, irrigated agriculture is relatively important in Asia and North Africa, while rainfed agriculture dominates in sub-Saharan Africa and Americas.

4.21.1.1.3 Open, closing, and closed river basins When a river basin can supply water to meet withdrawal demands and maintain its ecological functions, it is considerd an open basin (Seckler, 2006). A river basin is closing or closed when the volume of water use approaches or exceeds the volume of discharge. Often this is a problem of overcommitment, where water resources have been allocated beyond availability. As infrastructure develops around rivers,

More than half of production from rainfed areas

More than half of production from irrigated areas

More than 75% of production from rainfed areas

More than 75% of production from irrigated areas Global total: 7130 km3 (80% from green water, 20% from blue water) 780

220

650

235

1670 Blue water

Green water

905

1080

1480 110

Figure 2 Food crop evapotranspiration from rain and irrigation. Production refers to gross value of production. The pie charts show total crop water evapotranspiration in km3 by region. From International Water Management Institute analysis done for the comprehensive assessment for water management in agriculture using the Watersim model. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

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Water Availability and Its Use in Agriculture

Table 1

Global water and land statistics 3

Water (km ) Use

Land (millions of hectares) Statistics

Use

Total precipitation over continents 11 000 Vapor flow back to the Runoff to the oceans 40 000 atmosphere 70 000 Evapotranspiration Biomass consumed by grazing livestock Rainfed crops Irrigated crops Irrigation Rainfall Municipal use Industrial use Reservoirs

Total terrestrial land 13 000

Withdrawals

840 4910 2664 1570 650 53 88 208

Statistics

Grazing lands

3430

Rainfed harvested lands Irrigated cultivated lands

860 Harvested 340a

381 785

a

Of which 277 are equipped. From For water withdrawal statistics and equipped irrigation area, FAO (2006a); for evapotranspiration, International Water Management Institute analysis using the Watersim model; for harvested irrigated crop area, Chapter 3 on scenarios; for biomass consumed by grazing livestock, Stockholm Environment Institute calculations for the Comprehensive Assessment of Water Management in Agriculture; for municipal, industrial, and reservoir use, Shiklomanov (2000); for land statistics, FAO (2006b). Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

streams can become increasingly diverted, controlled, and utilized. In closed or closing basin scenarios, water users (especially those downstream) will not have full access to withdraw from the resource. Many rivers around the globe are closing or closed. Closing basins are sensitive to seasonal and inter annual variations of rainfall. Meanwhile, the ecological functions of a river will suffer, it is important for rivers to have adequate flow for fish and wildlife habitat, flushing sediments, diluting pollutants, preventing salinity intrusion, and sustaining estuarine and costal ecosystems. Increasing supply through inter-basin transfers is a common response to reopen closed basins, and desalinization to increase supply is a much discussed option (Falkenmark and Molden, 2008). Many closing basins are typically under stress for 1–6 months a year. China’s Yellow River dried up for the first time in 1972. In 1997, the dry-up lasted 226 days and reached 700 km upstream (Ren and Walker, 1998). The Colorado in the United States, the Indus flowing through India and Pakistan, the Murray-Darling in Australia, and most rivers in the Middle East and Central Asia are also severely overcommitted. Even basins in monsoon regions, such as Chao Phraya River in Thailand and the Cauvery River in India, experience months of closure, when salinity creeps inland as outflows of freshwater do not flush into the sea.

4.21.1.1.4 Groundwater The Earth’s fresh groundwater resources are estimated at approximately 10 000–12 000 km3, more than 200 times the volume of global annual rainfall. Only a tiny proportion, approximately 12 000 km3, of the total volume of groundwater reserves is recharged each year, compared to the large volume in stock (Doll and Fiedler, 2008). It is

estimated that, on average, 2091 m3 per capita are withdrawn from groundwater stores, with agriculture withdrawing the majority. About 2 billion people worldwide use groundwater, making it the single most utilized natural resource on the planet. The estimated annual use of groundwater is between 600 and 700 km3 (Struckmeier et al., 2005) and it keeps increasing. In the United States, for example, groundwater use in irrigation water has increased from 23% in 1950 to 42% in 2000 (Winter et al., 1998). This trend is reflected around the globe and particularly in Asia. There are many reasons why irrigation is a major user of groundwater. For farmers, the water is available when it is needed, is of reasonable quality, and very often can be abstracted without gaining permission or consulting with other users, a situation which is often much simpler than obtaining irrigation water from a canal system. Although agriculture is the largest user of groundwater, domestic dependence on groundwater use is increasing. Groundwater has historically supplied domestic water requirements in numerous urban and rural human settlements around the world. According to one estimate, more than half of the world’s population relies on groundwater for its drinking water supply (Coughanowr, 1994). In Spain, from 1960 to 2000, groundwater use increased from 2 to 6 km3 yr1 (Martinez-Cortina and Hernandez-Mora, 2003). In the Indian subcontinent, groundwater use soared from around 10–20 km3 yr1 before 1950 to 240–260 km3 yr1 by the year 2000 (Shah et al., 2003). In the United States, the volume of groundwater used as irrigation water increased from 23% in 1950 to 42% in 2000 (Winter et al., 1998). Chinese history records occasional cases of farmers lifting water from shallow wells by barrels to irrigate vegetables; however, North China had very little irrigation

Water Availability and Its Use in Agriculture

until 1950, and its tubewell irrigation revolution took off only after 1970. In total, then, the silent revolution in groundwater irrigation is essentially a story of the past 50 years (Llamas and Custodio, 2003). These can be considered as global pockets of intensive groundwater irrigation areas (Shah et al., 2007). Now there are pockets of intensive groundwater use, usually in food-producing areas of the world, such as the North China Plains, western and southern India, and parts of Mexico and the Ogallala aquifer of the USA.

4.21.1.1.5 Wetlands Wetlands act as sources of water for the majority of the global population. Agriculture’s impacts and dependencies upon wetlands are becoming increasingly significant. Wetlands are the key areas for managing extreme water flows after heavy rainfall and for providing water during droughts. Two recent global estimates have reported on the distribution of wetlands. Compiling national inventories, Finlayson et al. (1999) estimate global wetland area at 1280 million hectares. A more recent study by Lehner and Do¨ll (2004) used multiple geospatial data sets to estimate global wetland area at 917 million hectares. Accurate information on the distribution and extent of wetland ecosystems, both regionally and globally, is clearly an area requiring further work. Nevertheless, taking these data as the best-available estimates, a minimum of 131 million hectares of wetlands occur in Africa and 286 million hectares in Asia. The millennium ecosystem assessment (MEA, 2005a) identified agriculture as the major cause of wetland degradation and loss because it is the major economic activity in and around many wetlands, where crops such as rice, maize, and various vegetables and fruits are cultivated (Dries, 1989; Soerjani, 1992; Omari, 1993). However, agricultural development has considerably decreased the ecosystem services of wetlands (FAO, 2008). More recently, the comprehensive assessment of water management in agriculture (Falkenmark et al., 2007) concluded that pressures on wetlands would probably increase, with the prospect of serious loss of wetlands and ecosystem degradation. The needs of agriculture for flat, fertile land with a ready supply of water frequently make wetlands a valuable agricultural resource. In many arid and semi-arid regions of seasonal rainfall, where even major rivers can run dry for parts of the year, wetlands function to retain moisture. For this reason, they also make attractive resources for agriculture. Where people have to cope with both seasonal and interannual shortages of water, wetlands continue to be a vital resource for cultivators and pastoralists. In recent decades, agricultural use of wetlands has increased significantly in many developing countries, particularly in Africa, where they are perceived by some as the new frontier for agriculture (Wood and Dixon, 2009). This increase is driven partly by population growth, partly by the degradation of overexploited upland fields, and partly by market opportunities and the need to earn cash income (Wood and van Halsema, 2008). For poor rural households short of food, wetlands can offer good soils as well as water for irrigation, fisheries, and edible plants. In this way, wetlands can provide a safety net for poor households.

711

Some rural households increasingly use wetlands to supply local markets with irrigated vegetables and other products, which generate income. Seasonal wetlands also provide an important resource for livestock grazing. Sometimes these act as grazing land, but in some cases, they are used for hay production. This is prominent in many of African savannahs where the climate is semi-arid, rainfall is seasonal, and wetland grazing is widespread (FAO, 2008). For these households, wetlands represent a development opportunity that can lead them out of poverty.

4.21.1.1.6 Water consumption In an agricultural context, water consumption refers to water rendered immediately unusable by way of evaporation and transpiration from crops, soil, and open water bodies. Of total agricultural water consumption, the sources are estimated at 78% green water and 22% blue water. However, blue water withdrawal rates are greater than blue water consumption rates because not all water used for irrigation evaporates. The 22% of blue water consumed equals 1570 km3, whereas 2630 km3 are withdrawn for irrigation annually. In total, 60% of the water withdrawn for agriculture is consumed, while 40% returns to surface water or groundwater. The ratio of consumption to withdrawal is commonly referred to as the consumptive fraction or depleted fraction (Molden, 1996). Consumptive fractions tend to be low in water-abundant areas (where intensive water management is not cost effective), whereas they tend to be higher in water-scarce areas (where plants use shallow groundwater and farmers reuse drainage water). In the arid Middle East and North Africa, for example, the consumptive fraction is 77% with peak values close to 100%. In water-abundant areas, the consumptive fraction can be as low as 35%. Generally, it is not feasible or desirable to have a consumptive fraction higher than 70% at the basin scale, due to substantial infrastructure and environmental costs (Molden et al., 2000). However, in all scenarios the demand for freshwater increases to meet future food demands. Water consumption increases substantially in irrigated and rainfed areas).

4.21.1.1.7 Water use Annual global water withdrawals are estimated at 3830 km3, 70% of which is used for agriculture (i.e., 2664 km3) (FAO, 2006a). The net evapotranspiration from irrigation is 1570 km3 yr1, while the majority of total evapotranspiration is directly from rainfall. About 1000 km3 or 25–30% of the 3830 km3 of total water withdrawals originate from groundwater, and is mostly used for drinking and irrigation purposes. In the past century, industrial and municipal water demands, including those for energy generation, have grown in relative proportion to agricultural water demands. As competition between these sectors intensifies, agriculture can expect to receive decreasing shares of developed freshwater resources. Again, geographical differences are important to note. Approximately 70% of the world’s irrigated land is in Asia. Of Asia’s total cultivated land, however, only 34% is irrigated. Furthermore, China and India alone account for more than half of the irrigated land in Asia. By contrast, however, there is very little irrigation in sub-Saharan Africa.

712

Water Availability and Its Use in Agriculture

There has been relatively small investment in irrigation in Africa compared to massive investments in Asia, where irrigation fueled the green revolution. Demand for water for industrial and municipal uses, including for energy generation, is growing relative to demand for agriculture. As competition for water from these other sectors intensifies, agriculture can expect to receive a decreasing share of developed freshwater resources.

4.21.1.1.8 Climate change, agriculture, and water Predicted climate change scenarios show many potential risks to agriculture and agricultural water use:

• • • • •

increased precipitation intensity and variability could therefore the occurrence of flood and drought conditions as well as runoff patterns and the risk of crop failure; changing sea levels, water logging and after cause, could cause seawater intrusion and groundwater salinization in delta zones and other coastal areas; increasing temperatures will likely reduce crop productivity and increase water requirements in low latitude regions, thereby directly decreasing water-use efficiency; irrigation demands will likely increase; and water scarcity will increase in areas with growing populations and decreased precipitation.

At the watershed scale, changes in evaporation, precipitation, and water-storage cycles will alter the seasonal, annual, and interannual water availability for both terrestrial and aquatic agro-ecosystems (FAO, 2003). Several studies have also linked increased temperatures and evaporation, and decreased rainfall with greater needs for irrigation (Barnett et al., 2005; Bates et al., 2008; IPCC, 2001). Therefore, under these conditions, issues surrounding water demand and availability will increasingly affect agricultural activities, food security, forestry, and fisheries (Bates et al., 2008). In addition to these longterm climate issues, the severity of specific climate events will also influence agriculture around the globe. For example, more than 90% of simulations predict increased droughts in the subtropics by the end of the twenty-first century, while increased extremes in precipitation are projected in the major agricultural production areas of southern and eastern Asia, eastern Australia, and northern Europe (Bates et al., 2008).

4.21.1.1.9 Drivers of water use Population growth and changing diets are the two prominent drivers of increased food demand and, as it follows, increased water use (the following discussion is after Fraiture et al., 2007). From 6.1 billion people living on the planet in 2000, global population is projected to grow to 7.2 billion in 2015, 8.1 billion in 2030, and 8.9 billion in 2050 (UN, 2003). This growth curve is projected to level off after mid-century, except in sub-Saharan Africa where populations are projected to continue to grow. Furthermore, in regions where incomes increase, diets often change. In these scenarios, while the production of staple cereals goes up, greater numbers of people will also shift away from eating cereals as their primary food source and begin consuming greater quantities of livestock products, such as fish, and high-value crops. The world food supply increased from about 2400 kcal per person per day in

1970 to 2800 kcal per person per day in 2000, a 16.6% increase. However, geographical differences must be used in context here. In developed countries during the same period, food supply increased from 3050 to 3450 kcal per person per day, while in sub-Saharan Africa supply only increased from 2100 kcal per person per day to about 2200 kcal per person per day. The growth in per capita food consumption has been accompanied by significant changes in the commodities people choose to consume. Meat consumption has increased in all regions except sub-Saharan Africa, and industrial countries are by far the largest meat consumers, at 103 kg per person per year, a trend that is projected to continue for the next 50 years. The same patterns apply to dairy products as well. In total, wealthier populations consume more food per person and eat richer, more varied diets, while producing these foods means using more water. Increased urbanization and urban migration also drive food production and agricultural water demands. In the 1960s, two-thirds of the world’s population lived in rural areas, and 60% of the economically active population worked in agriculture. Today these ratios have changed. About half of the people alive today live in rural areas. Furthermore, a little more than 40% of the economically active population depends directly on agriculture as a means of well-being. In absolute terms, rural populations will begin declining in the next few years, and by 2050, two-thirds of the world’s people will live in cities and mega-cities. But, again, global averages do not express significant regional variations. In many poor countries in South Asia and sub-Saharan Africa, the rural population will continue to grow until about 2030, while the number of people depending on agriculture in these places will continue to rise (CA, 2007). Rapid rural-to-urban migration in developing countries also influences farming practices and water demand. In this process, more men are migrating to urban centers leaving women, older people, and children behind in rural areas. Consequently, in developing countries, women’s presence in agricultural economies is growing, rising from 39% in 1961 to 44% in 2004, whereas in developed countries these numbers are falling, dropping from 44% to 35% over the same period (FAO, 2006b). As this happens, issues of gender will be increasingly important to water management, productivity, equity, and governance. As cities expand in size, their demands on and claims to water resources increase, often at a loss to rural agricultural areas. In many cities today, poor or nonexistent urban planning and enforcement of land-use regulations compound water management problems. Urbanization also physically affects hydrological environments. Buildings, roads, and parking lots, among other human structures, create impermeable surfaces while sewage systems redirect large volumes of water. As a result, surface areas available for water infiltration are decreased, and increased runoff can become a significant problem. In river basins affected by urban footprints, peak discharge occurs quicker and reaches higher volumes. This can result in greater stream channel erosion, possible channel destruction, and habitat degradation, while it can also damage human life and property. Furthermore, these surfaces reduce groundwater recharge and can decrease long-term groundwater inflow to streams. Urban centers are cites of water

Water Availability and Its Use in Agriculture

pollution. The increased presence of sediments, nutrients, microbes, toxic metals, and organics is a major externality of urbanization. All of these factors can have significant effects on human health, downstream environments, and agricultural systems. Hydroelectric power generation is another significant driver influencing water availability and agricultural production. Dams worldwide produce 715 000 MW or 19% of the world’s electricity. The process of hydroelectric power generation requires water storage and stream flow regulation, both of which can influence water availability for agriculture and other users. Thus, significant volume of the world’s blue water resources is held in river basins where multipurpose water management is linked to energy production. Many factors relate to the nature of hydroelectric production condition the way these water resources are managed. Dams have the ability to store and release water at specific times and this means electricity can be generated on demand. In the same way, dams also have the ability to regulate water for irrigation, navigation, and recreation. As dams are not sources of CO2 emissions, provided vegetation is cleared before following up hydroelectric power may be an attractive energy option in the future. However, other issues, including habitat degradation and cost efficiency, are at stake and need to be considered in analyzing trade-offs. At a different scale, smallscale hydro or micro-hydro power has been increasingly popular, especially in remote areas where other power sources are not feasible. Small-scale hydropower systems are installed in small rivers or streams with little or no marked environmental effects. In poor areas, many remote communities have no electricity. Micro-hydro power, with a capacity of 100 kW or less, allows communities to generate electricity. Changing consumption patterns, more people moving into urban areas, and increasing demand for low-carbon energy mean that agricultural water use will see greater outside competitive pressures. Small holders and individual water users, with little political power in water governance processes, will face greater risks in these conditions. The following section examines concepts of water scarcity as a means of understanding how these risks affect people differently. Water scarcity is often viewed as a physical issue, where aggregated demand for water by all potential uses is lays than the available supply (FAO, in preparation). Such approach, however does not capture the knowledge.

4.21.1.2 Physical and Economic Water Scarcity Another tool for examining water availability is the concept of water scarcity (Seckler et al., 2000; Molden et al., 2007). Rather than analyzing water availability from a hydrological approach (using river basins as units of analysis), water scarcity focuses on social and political regions (using populations as units of analysis). Evaluating water scarcity begins at a micro-level, one can the water security of individuals. Individuals are water secure when they have consistent access to safe and affordable water to satisfy their needs for drinking, washing, food productions, and other livelihood endeavors; they are water insecure when these needs cannot be met. A region is water scarce when a large number of people

713

are water insecure (Rijsberman, 2006). In adapting such approaches, economic, financial, social political and institutional dimensions of the problem of access to water become as relevant as the physical availability of water. These multiple dimensions of the problem have been captured in the dual concept of physical and economic water scarcity. Access to water is difficult for millions of people for social, political, and economic reasons, in addition to physical resource constraints. About 2.8 billion people live in areas facing water scarcity, and more than 1.2 billion of them – onefifth of the world’s population – live in areas of physical water scarcity (Molden et al., 2007 – trends chapter). Another 1.6 billion people live in basins that face economic water scarcity, where human and institutional capacity or financial resources are likely to be insufficient to develop adequate water resources even though adequate water is available to meet human needs (Figure 3). Within these regions, poor people suffer disproportionately from the implications of scarcity. Lack of finance, lack of human capacity, poor management, and a lack of good governance all contribute to water scarcity. Physical water scarcity occurs when available water resources are insufficient to meet all demands, including minimum environmental flow requirements (Figure 3). Arid regions are most often associated with physical water scarcity; however, an alarming new trend of artificial physical water scarcity is affecting even regions where water is abundant. This problem is due to the over-allocation and over-development of water resources, leaving no scope for making water available to meet new demands except through interbasin transfers. In these scenarios, there is not enough water to meet both human demands and environmental flow needs. The implications of physical water scarcity include severe environmental degradation, such as river desiccation and pollution, declining groundwater tables, water allocation disputes, and failure to meet the needs of individuals and groups. Some 1.2 billion people live in river basins where human water use has surpassed sustainable limits. Meanwhile, another 500 million people live in river basins that are fast approaching physical water scarcity. While physical scarcity introduces complex problems, investments in good management can mitigate many of the issues. Economic water scarcity occurs when the investments needed to keep up with growing water demand are constrained by limited financial, human, or institutional capacities. Much of the scarcity felt by people is due to problems with institutions and politics, favoring one group over another, not listening to the voices of women and disadvantaged groups, for instance. Problems of economic water scarcity include: inadequate infrastructure development, where people have trouble getting enough water for agriculture and domestic purposes; high vulnerability to seasonal water fluctuations, including floods and long- and short-term droughts; and inequitable distribution of water even though infrastructure exists. Much of sub-Saharan Africa experiences economic water scarcity, and there are many areas across the globe where water resources are inequitably distributed. Further water development could ease problems of poverty and inequality.

714

Water Availability and Its Use in Agriculture

Little or no water scarcity

Approaching physical water scarcity

Physical water scarcity

Economic water scarcity

Not estimated

Figure 3 Areas of physical and economic water scarcity. Definitions and indicators: (1) Little or no water scarcity. Abundant water resources relative to use, with less than 25% of water from rivers withdrawn for human purposes. (2) Physical water scarcity (water resources development is approaching or has exceeded sustainable limits). More than 75% of river flows are withdrawn for agriculture, industry, and domestic purposes (accounting for recycling return flows). This definition – relating water availability to water demand – implies that dry areas are not necessarily water scarce. (3) Approaching physical water scarcity. More than 60% of river flows are withdrawn. These basins will experience physical water scarcity in the near future. (4) Economic water scarcity (human, institutional, and financial capital limit access to water even though water in nature is available locally to meet human demands). Water resources are abundant relative to water use, with less than 25% of water from rivers withdrawn for human purposes, but malnutrition exists. From International Water Management Institute analysis done for the comprehensive assessment for water management in agriculture using the Watersim model. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

Table 2

Withdrawals by nonagricultural sector will increase by a factor of 2.2 by 2050

Region

Sub-Saharan Africa East Asia South Asia Central Asia and Eastern Europe Latin America Middle East and North America OECD countries World

Agriculture

Domestic

Manufacturing

Thermo cooling

Total nonagricultural

2000

2000 2050

2000

2050

2000

2050

2000

2050

68 518 1095 244 175 173 233 2630

7 48 15 40 31 14 121 278

2 21 4 68 12 3 135 245

8 159 29 236 42 10 131 617

1 32 15 48 10 7 262 376

18 75 55 52 134 22 307 664

10 101 34 156 53 24 518 902

60 419 175 377 254 82 590 1963

35 185 90 88 78 51 152 681

Annual increase (%) 2000–50

3.7 2.9 3.3 1.8 3.2 2.5 0.3 1.6

Note: Units are in km3 unless otherwise indicated. From Comprehensive Assessment for Water Management in Agriculture (2007). Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

4.21.1.3 Future Demands for Water If improvements in land and water productivity or major shifts in production patterns do not occur in the near future, crop water consumption would increase 70–90% by 2050 depending upon actual population growth, changing income levels, and water requirements for livestock and fisheries. In this scenario, crop water consumption would go from a current rate of 7130 km3 yr1 to somewhere in the range of 12 050–13 500 km3 yr1. This estimated range accounts for crop water depletion for food and feed production,

plus losses through evaporation from soil and open water sites. Nevertheless, even with improvements in water productivity, agriculture will continue to consume a large portion of the world’s developed water supply. This topic is discussed further in the following section. Industrial and domestic demand for water will continue to increase with urbanization. Withdrawals for nonagricultural sectors are expected to more than double by 2050, and, as it follows, there will be increasing competition for water between sectors (Table 2). In most countries, urban water demands receive customary or legal priority over water for

Water Availability and Its Use in Agriculture

715

10

8 Projection (high)

mt ha–1

6

OECD countries FAO

IWMI Projection (low)

4 World IWMI

2 FAO

0

sub-Saharan Africa

1961

1970

1980

1990

2000

2010

2020

2030

2040

2050

Figure 4 Global water withdrawals increase. Points marked FAO (Food and Agriculture Organization) are based on projections in Bruinsma (2003); those marked IWMI (International Water Management Institute) are based on projections in Seckler and others (2000). From FAOSTAT (2006), for 1960–2003; International Water Management Institute analysis done for the comprehensive assessment for water management in agriculture using the Watersim model, for 2000–50. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

agriculture (Molle and Berkoff, 2006). Greater competition for water will leave less for agriculture, particularly near large cities in water-short areas. The regions of the Middle East, North Africa, Central Asia, India, Pakistan, Mexico, and northern China, among other areas, will see greater competition for water as urban centers continue to develop there. Estimates also show that while the proportion of water diverted for nonagricultural sectors increases, agriculture remains the largest water user among the productive sectors. Although major trade-offs will occur between all water-using sectors, they will be particularly pronounced between agriculture and the environment as the two largest water-demanding sectors (Figure 4) (Rijsberman and Molden, 2001). Unlike agricultural water consumption, only a small part of the water diverted for domestic and industrial purposes is consumed. In urban areas, 75–85% of water diverted flows back to rivers, lakes, and groundwater as return flow. In many urban areas, particularly in water-scarce developing countries, wastewater is used for high-value vegetable production, a livelihood activity for millions of city dwellers (Gupta and Gangopadhyay, 2006; Hussain et al., 2001, 2002; Raschid-Sally et al., 2005). The use of urban wastewater for irrigation will increase as water becomes scarcer in urbanizing areas. If by 2050 half of return flows from cities are reused, 200 km3 of wastewater could be used for irrigation. This would represent only 6–8% of future agricultural withdrawals, but the economic values generated could be substantial. Much of the wastewater would likely be used to produce highly valued vegetables, helping sustain the livelihoods of millions of small farmers (Hussain et al., 2001, 2002). As reuse of city wastewater for agriculture poses environmental and health risks, these can be minimized with proper management. Water demand for managing ecological functions has also created greater resource competition, as reflected in changing

policies for water allocation and pricing. In many countries, rising incomes are correlated with increasing demands for restoring and maintaining environmental services. The demand for environmental amenities adds pressure on scarce water resources. A first-cut estimate by Smakhtin et al. (2004) indicates that 20–45% of long-term annual flows must be preserved to maintain essential ecosystem services. UNESCO (2006) suggests that 100 km3 need to be added to estimates of future water demands to account for current overexploitation of groundwater and 30 km3 must be added to account for the mining of nonrenewable groundwater, or fossil groundwater.

4.21.1.4 Future Scenarios for Rainfed and Irrigated Agriculture As outlined above, there will be greater demands on agriculture and water by the year 2050. Considering agricultural productivity, in the context of different irrigation scenarios, is an effective way to understand how these demands can be met. The comprehensive assessment of water management in agriculture (CA, 2007) provided scenarios to allow us to explore various futures. Most importantly, if irrigation development were to remain static from now until 2050, the agricultural potential of rainfed agriculture would be sufficient for meeting the projected additional food requirements in 2050 such as cultural values. In an optimistic yield growth scenario, in which cereal yields grow by 72%, the demand for agricultural commodities is met by increasing rainfed-harvested area by 7% (this work follow Fraiture et al., 2007). The contribution of rainfed agriculture to the total gross value of food supply would increase from 52% in 2000 to 60% in 2050 (CA, 2007). In the optimistic yield scenario, sub-Saharan Africa, Asia, and Latin America can be largely self-sufficient in producing major food

716

Water Availability and Its Use in Agriculture

crops. East Asia, however, would need to import maize to meet the large increase in feed demand. In addition, the Middle East and North Africa would need to import food due to lack of suitable lands for rainfed production. Global food trade would increase from 14% to 17% of total production. The scenario analysis also demonstrates the risks inherent in a rainfed-based strategy. In the pessimistic scenario, with a low rate of adoption of water harvesting and only modest improvements in rainfed yields, the area of rainfed production must increase by 53% to meet future food demands (an additional 400 million hectares as compared with the optimistic yield scenario; see Figure 5). The Food and Agriculture Organization of the United Nations (FAO) estimates suggest ample capacity for increasing the area under cultivation, except in South Asia, the Middle East, and North Africa. In sub-Saharan Africa and Latin America, only one-fifth of the potential land area is already in use. Although there are significant amounts of land available for cultivation, more than half are now forested or protected areas (Alexandratos, 2005). Furthermore, some of these lands might be of marginal quality (Bruinsma, 2003) or not suitable for cereal crops. In the pessimistic yield scenario, countries without potential to expand rainfed areas – due to either

lack of suitable land or unreliable rainfall – must increase food importation. In this case, the Middle East and North Africa would import more than two-thirds of their agricultural needs. South and East Asia, due to land limitations, would become major importers of maize and other grains, importing 30–50% of their domestic needs. Latin America, developed countries, Central Asia, and Eastern Europe, having the potential to expand land in agriculture, would increase their exports. Globally, food trade would increase from 14% of total agricultural production to 22% in 2050. Large grain imports by East and South Asia would put upward pressure on food prices (the model results suggest an increase of 11%). There is a risk that poor countries may not be able to afford food imports, and household-level food insecurity and inequity might worsen. Climate change, which is expected to increase the variability and intensity of weather events, exacerbates the risks of rainfed production, particularly in semi-arid areas vulnerable to drought (Kurukulasuriya et al., 2006). Both the optimistic and pessimistic rainfed scenarios lead to substantial increases in soil water consumption. While the global average of rainfed cereal yield would improve by 72%, crop water productivity would improve by 35%. In the pessimistic yield scenario,

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Millions of hectares Figure 5 The optimistic and pessimistic scenarios. The comprehensive assessment scenario combines elements of the other approaches. The purple segments of the bars show the difference between optimistic and pessimistic assumptions for the two rainfed and two irrigated scenarios. The brown bar shows the worst-case scenario of no improvement in productivity. From International Water Management Institute analysis done for the comprehensive assessment for water management in agriculture using the Watersim model. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

Water Availability and Its Use in Agriculture

global rainfed cereal yields improve by 20% and water productivity by 10%, while total crop water consumption increases by 54% to 10 980 km3, an additional 3850 km3 after the year 2000. Increases agricultural evapotraspiration of that order of magnitude will have impacts on river flows and groundwater recharge, with implications for downstream water users and those relying on groundwater resources.

4.21.2 Productive Use of Agricultural Water The long-term sustainability of food production and agriculture depends on the efficient management of limited water resources. Moreover, as these resources come into greater demand, driven by a broadening range of applications and functions, the implications of agricultural water use will have increasing effect on other water users and the environment. In other words, the impact of agricultural water use is increasing. By analyzing agricultural water productivity, crop production can be assessed in terms of its social, economic, and ecological costs and benefits. The following section outlines several approaches for assessing water productivity and contextualizes the topic in relation to rainfed and irrigated agricultural systems. From here, future productivity scenarios are considered, and livestock and fisheries agriculture are discussed (this section draws from analysis presented in de Fraiture et al., 2007).

4.21.2.1 Water Productivity in Agriculture Water productivity is defined as the ratio of the net benefits from crop, forestry, fishery, livestock, and mixed agricultural systems to the amount of water required to produce these benefits (this section draws from Molden et al., 2007). In its broadest sense, water productivity reflects the objectives of producing more food, income, livelihoods, and ecological benefits at less social and environmental cost per unit of water used. Water productivity can be defined in several ways. Physical water productivity is the ratio of the mass of agricultural output to the amount of water used, while economic

water productivity is defined as the value derived per unit of water used (Figure 6). Other modes of analysis include crop water productivity, where specific crops are measured individually for comparative purposes, and livestock water productivity where the ratio of the net beneficial livestock-related products and services is calculated in relation to the volume of water depleted in production including the water to feed them (Peden et al., 2007). In areas of the world already exhibiting high physical water productivity, the scale for improvement is limited. Many rainfed, irrigated, livestock, and fisheries systems across the globe, however, do not exhibit high physical water productivity. Many farmers in developing countries could raise their water productivity by adopting better management practices. These include supplemental irrigation; soil fertility maintenance; deficit irrigation; small-scale water storage, delivery, and application; modern irrigation technologies (such as pressurized systems and drip irrigation); and soil water conservation through mulching zero or minimum tillage. Breeding technologies and biotechnology can also indirectly help agricultural systems become more efficient by reducing biomass losses through increased resistance to pests and diseases, enhancing the vigorous early growth for fast ground cover to reduce soil surface evaporation, and by reducing drought susceptibility for specific crops. However, water productivity gains are context dependent, and, in some cases, a gain for one group of people can mean a loss for the others. Water productivity, especially in physically water-scarce basins, can be properly assessed only by taking an integrated basin perspective where trade-offs between uses are considered. Employment opportunities, income generation, nutrition, and opportunities for women can all be linked to agricultural productivity; in this way, increasing values derived per unit of water is important to poverty reduction. However, carefully crafted programs are required to ensure that these gains reach the poor, especially rural women, and are not captured exclusively by wealthier or more powerful users. As described above, rising demand for livestock and fish products also leads to rising demand for water. In producing

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Figure 6 Agricultural water management: a continuum of practices. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

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these animal products, water productivity gains can be made by carefully considering feed sources and feeding strategies animal species produced (chicken use less water than cattle), improving the quality of produce, and integrating fisheries and livestock into farm production systems. Because freshwater fisheries are increasingly threatened by reductions in stream flows, basin water productivity analysis should consider the social and ecological values generated by fisheries before reducing river flows that support them. Several studies describe multiple uses of agricultural water and the ways in which these uses can improve productivity. Poor rural households use agricultural water in multiple ways (Laamrani et al., 2000; Moriarty et al., 2004; Jehangir et al., 2000). Agricultural water can be used for drinking, sanitation, home gardens, livestock, rural industries, and aquaculture. The increase of agricultural water productivity can result in many benefits such as:

• • •

helping to meet food demand; contributing to poverty reduction and economic growth; and helping to reduce pressures to reallocate water from agriculture and to ensure that water is available for environmental uses.

Integrated and multiple-use systems – in which water serves crops, fish, livestock, and domestic purposes – can increase the value derived per unit of water used. Gains in crop production have often come, for instance, at the expense of fisheries. Values generated by fisheries, including ecosystem sustenance values, are routinely underestimated. Recognizing these values helps us to understand where there are win–win situations and what trade-offs will have to be made. However, these values are poorly recognized today and rarely influence the decision-making processes.

4.21.2.2 Rainfed Agriculture Productivity Rainfed agriculture includes both permanent crops (such as rubber, tea, and coffee) as well as annual crops (such as wheat, maize, and rice). For example, tubers, a staple crop for subSaharan Africa, have been all but uninfluenced by the technological developments of the green revolution. Rainfed farming constitutes 80% of the world’s cropland and produces more than 60% of the world’s cereal grains, generating livelihoods in rural areas while producing food for cities. In temperate regions with relatively reliable rainfall and good soils, rainfed agriculture generates high yields. Supplemental irrigation practices boost yields even higher. With rising concerns over the high cost of expanding largescale irrigation and the environmental impacts of large dams, upgrading rainfed agriculture is gaining increased attention (Rockstro¨m et al., 2007). Many people dependent on rainfed agriculture are highly vulnerable to both short-term dry spells and long-term droughts. Exposure to these risks can contribute to a reluctance to invest in agricultural inputs that could increase crop yields. Moreover, changing precipitation patterns resulting from climate change will compound this issue for many small farmers. There are several compelling reasons to invest in agricultural water management technologies and institutions

connected to rainfed agriculture (Rockstro¨m et al., 2007). To start, there is high potential to improve productivity, especially where yields are low. A majority of the rural poor are small holders who depend on rainfed rather than irrigated agriculture. Improving productivity in rainfed areas is therefore a way of supporting the poor. Boosting the potential of existing rainfed areas reduces the need for new large-scale irrigation development, which can generate adverse environmental and social impacts. Furthermore, the cost of upgrading rainfed areas is generally lower than the cost of constructing new irrigation schemes, particularly in sub-Saharan Africa. Even with these incentives, the potential contributions of rainfed agriculture to world food production are debatable, and forecasts regarding the relative roles of irrigated and rainfed agriculture vary considerably. Relying on rainfed agriculture also involves considerable risk. Water-harvesting techniques are useful for bridging short-term dry spells. Investments in water management are thus a way to decrease risk in rainfed agriculture. However, adoption rates of waterharvesting techniques are low, and extending successful local techniques over larger areas has proved to be difficult in the past. As longer dry spells may lead to crop failure, rainfed agriculture generally entails more risk than fully irrigated agriculture. Farmers adopt risk management strategies in line with the level of risk. There is a range of ’soft’ and hard’ measures that are available to integrate the risk related to climate variability in agriculture. Better control of water, either through full-fledged irrigation or supplemented irrigation, in costs or systems of crop insurances, can also integrate risk and provide farmers with better incentives to invest in their crop (Faures, 2010).

4.21.2.3 Irrigated Agriculture and Productivity The last 50 years have seen major investments in large-scale public surface irrigation as part of a global effort to increase staple food production, ensure food self-sufficiency, and to avoid devastating famine (this section follows Faures et al., 2007). From 1961 to 2008, for example, the world’s cultivated land increased approximately 12% (i.e., from 1368 to 1526 million hectares). At the same time, irrigated land area increased by 120%. The percentage of cultivated land equipped with irrigation rose from 10% in 1961 to 20% in 2008 (i.e., from 139 to 306 million hectares). Paralleling these global trends, irrigation investments in developing countries also accelerated rapidly in the 1960s and 1970s. On average, irrigated land in these regions grew by 2.2% per year reaching 155 million hectares by 1982. Widespread use of newly developed, high yielding, and fertilizer-responsive crops partially constituted the increased demand for water during this period. To achieve the higher yields now possible from these new crops, agriculture simply needed more water. In the developing world, other factors were important to the increased use of irrigation. Private and community-based investments in these countries, particularly programs aimed at groundwater pumping, grew from the 1980s onward. These projects were propelled by cheap drilling technology, rural electrification, and the availability of inexpensive small water pumps. Approximately 70% of the world’s irrigated land is in Asia (Figure 7), where it accounts for 34% of cultivated land.

Water Availability and Its Use in Agriculture 300

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Figure 7 The area equipped for irrigation. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007).

China and India alone account for more than half of irrigated land in Asia. Over time, Asia, with its high population densities, has come to rely increasingly on irrigated agriculture to boost agricultural productivity and thus to ensure domestic food security. Sub-Saharan Africa is much different. Notwithstanding a few large commercial irrigation schemes developed during the colonial period and a relatively modest small-scale irrigation subsector, there is very little irrigation in sub-Saharan Africa where water application methods are largely surface irrigation based, and little has been done to improve water productivity. The 1990s, however, saw a substantial rise in private irrigated peri-urban agriculture in sub-Saharan Africa in response to higher demand from growing cities for fresh fruits and vegetables (FAO, 2005). Today, it is suggested that global harvested irrigated area, which includes double cropping (two crops are grown in the same year), is estimated at 340 million hectares, although new incomplete evidence suggests otherwise. According to some studies, global harvested irrigated area might actually be higher than previously calculated after adjusting for higher cropping intensity and unreported, often informal, groundwater, or private irrigation use (Thenkabail et al., 2006). By the mid-1990s, irrigation projects leveled off around the world. Before this, the rapid growth in irrigated area, along with the other technological advancements of the green revolution – such as improved crop varieties and substantial growth in fertilizer use – led to a steady increase in staple food production and a reduction of real-world food prices. Until very recently, food prices in developed countries have been kept low by agricultural subsidies (Rosegrant et al., 2002), and since the late 1970s, the annual growth rate of global irrigation development, particularly in large-scale public schemes, has decreased. Other factors also contributed to the post-green revolution slow down of irrigation development. Most areas best suited for dams and irrigation have been developed, and as a result, new dams for irrigation and the related infrastructure for water delivery will cost more to construct in less

ideal locations. As a result, these geographic and economic conditions have led to overall less economic incentives for the development of large-scale irrigation projects. Other recent factors have created disincentives for irrigation investments as well. Some research has shown that the underperformance of large-scale irrigation (Chambers, 1988) has reduced donor interest (Merrey, 1997). Attention to the negative social and environmental externalities of dams – particularly the displacement of residents in affected communities and the calls for increased in-stream flows for environmental purposes – has discouraged the lending markets for irrigation investment. More competition for water from other sectors (as mentioned above) has reduced the scope for further development of irrigation. Irrigation is particularly crucial in sustaining agriculture across the dry belt, a region that extends from North Africa, the Middle East, through Northern China to Central America and parts of the United States (Figure 8). The advent of affordable drilling and pumping technologies in India and Pakistan in the mid-1980s led to the rapid development of shallow tube wells and the combined or conjunctive use of surface water and groundwater (Shah, 1993; Palmer Jones and Mandal, 1987). These technologies enabled farmers to have direct, individual control over water resources. By harvesting water by way of groundwater pumping, drainage reuse, or direct pumping from ponds, canals, and rivers, small holders gained flexibility and reliability in water delivery. Large-scale surface distribution systems did not offer these advantages. Yet, these technologies also contributed new challenges to water management. The indirect subsidization of electricity enabled farmers to pump water at zero to little cost. As a result, water tables have fallen in many regions of the world. In 1995, 38% of cereals grown in developing countries were on irrigated land, accounting for just less than 60% of all cereal production (Ringler et al., 2003). Rainfed cereal yields averaged 1.5 Mt ha1 in the developing world in 1995, but irrigated yields were 3.3 Mt ha1 (Rosegrant et al., 2002). The

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Less than 5%

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Figure 8 Irrigated areas as a share of cultivated area by country, 2003. Source: Comprehensive assessment of water management in agriculture (2007), Water for Food, Water for Life (Earthscan, 2007). From FAO (2006a) AQUASTAT database. Rome.

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difference in productivity between irrigated and rainfed agriculture varies widely, depending on the climate, combination of crops, and technologies used. Typically, land productivity is 2–4 times higher in irrigated agriculture. Moreover, cropping intensity is typically higher under irrigation, with up to three rice crops per year in parts of Southeast Asia and two crops per year in most of the Asian subcontinent. Figure 9 shows the distribution of crops under irrigation worldwide.

4.21.2.4 Livestock Keeping livestock is one of the most important, complex, and diverse subsectors of world agriculture and for many people it

is a primary means of escaping poverty in rural areas. Modest amounts of meat in the diets of African children appear to improve mental, physical, and behavioral development (Sigman et al., 2005; Neumann et al., 2003). This suggests that meat production and water productivity must account for social and health values as well as produced food mass. However, current literature on livestock–water interactions does not address this important topic. Moreover, past research has underestimated the contributions of livestock to rural livelihoods in part because studies were predominantly concerned with food mass productivity. Limited consideration has been given to the nonmonetized products and services associated with livestock.

Water Availability and Its Use in Agriculture

Poor and subsistence households obtain multiple benefits from the use of livestock (Shackleton et al., 1999; Landefeld and Bettinger, 2005). Therefore, assessing the water resources used to support these animals must account for values beyond meat production. Livestock contribute to the livelihoods of at least 70% of the world’s rural poor and strengthen their capacity to cope with income shocks (Ashley et al., 1999). They provide milk, blood, manure, hides, and farm power essential for the cultivation and marketing of crops. Livestock assets are often an important source of wealth security. As mentioned above, livestock water productivity examines the net beneficial livestock-related products and services in relation to water use. As a systems concept, livestock water productivity attempts to account for the complex relationships among food production, livelihood, and water demands. The implications of livestock on water use have been generally overlooked by research. Animals ‘consume’ water directly for drinking purposes, but it is the food they eat that requires large quantities of water, as discussed earlier. The type, quality and origin of the feed used for animals, together with animal management practices can have major impact on livestock water productivity. Livestock water productivity differs from water or rainuse efficiency because it examines water depleted rather than applied or inflowing water. Four basic strategies help to increase livestock water productivity: improving water supply, feed sourcing, enhancing animal productivity water conservation, and spatially optimistic distributing of watering points, animal stocking rates, and pasture productivity (CA, 2007). Providing sufficient and adequate quality drinking water also improves livestock water productivity as it keeps the animal healthy and productive. However, it does not factor directly into the livestock water productivity equation because water that has been consumed remains inside the animal and thus within the production system, although subsequent evaporative depletion may follow. A balanced, site-specific approach that considers all four strategies will help increase the benefits derived from the use of agricultural water for the production of animal products and services. Children, women, and men often receive different benefits from animal keeping and have different roles in managing livestock–water interactions These are considerations that need to be taken into account in attempts to improve livestock water productivity. Livestock water productivity does not necessarily seek to maximize the number of livestock or the production of animal products and services. Rather it seeks to reach a higher level of animal products per unit of water consumed.

4.21.2.5 Aquaculture and Fisheries Inland fisheries and aquaculture contribute about 25% to the world’s fish production and are a fast growing sector (see Dugan et al., 2007). In addition, many important estuarine and coastal fisheries are closely linked to the ecological processes that occur in freshwater systems. Fisheries and aquaculture from lakes, reservoirs, rivers, ponds, and wetlands contributed about 25% (i.e., 34 million metric tons) of the reported world fisheries production in 2003 (FAO, 2004). However, catches in rivers and wetlands are easy to

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underestimate because the contributions of numerous fisheries on smaller tributaries and water bodies are generally overlooked (Coates, 2002). Reported harvests from river fisheries alone have been shown to account for only 30–50% of actual catch (Kolding and van Zwieten, forthcoming), and the contribution from inland fisheries is therefore believed to be underestimated. Aquaculture uses water in two ways. Blue water is needed for the fish ponds and the processes of aquaculture; blue and green water is also necessary for feed production. Water productivity in terms of aquaculture is defined as the mass or value of the aquaculture produce divided by the amount of water required for feed plus the amount of evaporation from the pond. On-farm water use in aquaculture can be as low as 500–700 l in super-intensive recirculation systems and as high as 45 000 l of water (evaporation plus seepage plus feed) per kg of produce in extensive ponds (Verdegem et al., 2006). Fish can often be integrated into water management systems with the addition of little or no water (Prein, 2002). Renwick (2001) found that the fisheries in irrigation reservoirs at Kirindi Oya, Sri Lanka, contributed income equal to 18% of the rice production in the system. Haylor (1994, 1997) assessed the potential for aquaculture in small- and large-scale irrigated farming systems in the Punjab, Pakistan. The study noted that aquaculture in the region was almost entirely focused on carp production using groundwater sourced from tube wells. It also concluded that there was economic justification for expanding such aquaculture using local shallow tube wells. The study also found that the revenue potential for cage aquaculture in irrigation canals was also attractive, but operational conflicts in the use of water for agriculture would need to be resolved. Murray et al. (2002) have pointed out that traditional power structures may undermine attempts to integrate aquaculture in irrigation systems and that changes in laws and regulations would be required from community to national levels. In coastal areas, aquaculture may severely degrade land and water quality and biodiversity, requiring special attention (Gowing et al., 2006). Fisheries in lakes, rivers, and wetlands present a special case for water productivity assessment because fish are only one of the many ecosystem services provided by aquatic ecosystems. The values and livelihood benefits of fisheries are high and often ignored or underestimated, but considering only the values of fish produced would grossly underestimate the value of water in these aquatic ecosystems. The water productivity of fisheries systems needs to be considered in terms of ecosystem services and livelihoods supported per unit of water. Thus, maintenance of wetlands and biodiversity should be considered as potential benefits for leaving water in these aquatic ecosystems.

4.21.3 Environmental and Health Implications of Agricultural Water Use Agricultural water management has both negative and positive impacts on environment and health (this section follows Falkenmark et al., 2007). On the one hand, agricultural water management can improve health status through better

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nutrition, increasing the availability of drinking water, and controlling diseases such as malaria. On the other hand, extensive reporting shows many cases where agricultural water management has encouraged the spread of some waterborne diseases (Ersado, 2005); impacted upstream and downstream ecological services (Gichuki, 2004); affected water logging and salinization (Khan et al., 2006); and increased agrochemical usage, pollution, and eutrophication (Hendry et al., 2006).

4.21.3.1 Impact on Rivers, Wetlands, and Biodiversity Water management affects the physical and chemical characteristics of inland and coastal aquatic ecosystems, the quality and quantity of water, as well as direct and indirect biological change (Finlayson and D’Cruz, 2005; Agardy and Alder, 2005; Vo¨ro¨smarty et al., 2005). It has also affected terrestrial ecosystems through the expansion of agricultural lands and changes in water balances (Foley et al., 2005). Regulation of the world’s rivers has altered water regimes, with substantial declines in discharges to the ocean (Meybeck and Ragu, 1997). A long-term trend analysis (i.e., more than 25 years) of 145 major world’s rivers indicates that discharge has declined in one-fifth of the basins (Walling and Fang, 2003). Worldwide, large artificial impoundments hold vast quantities of water and cause significant distortion of flow regimes (Vo¨ro¨smarty et al., 2003).

4.21.3.1.1 Aquatic ecosystems Water diversion and the construction of hydraulic infrastructure have had the following negative effects: loss of local livelihood options, fragmentation, destruction of aquatic habitats, changes in the composition of aquatic communities, species loss, and health problems resulting from stagnant water. Improved flood control – an important agricultural mechanism for reducing risk – has led to the reduction of sedimentation and the deposition of nutrients on floodplains, as well as reduced flows and nutrient deposition to parts of coastal zones (Finlayson and D’Cruz, 2005). Inter basin transfers of water, particularly large transfers between major river systems have been in consideration in India and China, for example, are expected to be particularly harmful to downstream ecosystems (Gupta and Deshpande, 2004; Alam and Kabir, 2004) and will likely exacerbate pressures from hydrological regulation (Snaddon et al., 1999). Junk (2002) has highlighted the similar adverse consequences on water regimes expected from the construction of industrial waterways (i.e., hydrovias) through large wetlands, such as the Pantanal of Mato Grosso, Brazil. Shrinking lakes and rivers. There are many instances where consumptive water use and water diversions have contributed to the severe degradation of downstream ecosystem services by shrinking lakes and drying rivers. The degradation of the Aral Sea in Central Asia is an extreme case. Similarly, Lake Chapala, the world’s largest shallow lake, situated in the Lerma-Chapala Basin in central Mexico, is an example of consumptive water use upstream affecting lake-size downstream. From 1979 to 2001, water volume in the lake dropped substantially to about 20% of volume capacity due to excessive water extraction for agricultural and municipal needs.

Stream flow depletion is a widespread phenomenon in tropical and subtropical regions in river basins with large-scale irrigation, including the Pangani (IUCN, 2003), Yellow (He et al., 2005), Aral Sea tributaries, Chao Phraya, Ganges, Incomati, Indus, Murray-Darling, Nile, and Rio Grande (Falkenmark and Lannerstad, 2005). Smakhtin et al. (2004) have suggested that environmental flow (i.e., the stream flow required for aquatic ecosystem health) has already been overappropriated in many river basins. For example, in the United States the construction of dams and water diversions for irrigation and other purposes in the Colorado Basin, together with large-scale inter-basin transfers, have greatly reduced the flow of the river to the delta. As a result, a considerable portion of the delta has been transformed into mudflats, salt flats, and exposed sand. With the loss of the delta habitats, wetlands now exist mainly in areas where agricultural drainage has occurred (Postel, 1996). The Ganges is among the major rivers of South Asia that no longer discharges year round to the sea. As a result, there is a rapid upstream advance of saline water, with consequent changes in mangrove communities, fish habitat, cropping, and human livelihoods (Postel, 1996; Mirza, 1998; Rahman et al., 2000). On the Zambezi River in Southern Africa, damming for electricity and agriculture has reduced flows to the coast and led to a decline in shrimp production that could have been worth as much as $10 million a year (Gammelsrod, 1992). The regulation of rivers has brought many benefits to people, but the adverse impacts, especially those related to reduced downstream flows, have often failed to receive adequate and transparent consideration (WCD, 2000; Revenga et al., 2000; MEA, 2005a). Effects on wetlands. Water regulation and drainage for agricultural development are the main causes of wetland habitat loss and degradation (Revenga et al., 2000; Finlayson and D’Cruz, 2005) as well as the consequent loss of ecosystem services. By 1985, drainage and conversion of wetlands, mainly for agriculture, had affected an estimated 56–65% of inland and coastal marshes in Europe and North America and 27% in Asia (OECD, 1996). Drainage of wetlands often reduces important regulating ecosystem services, with such outcomes as increased vulnerability to storms and flooding and further eutrophication of lakes and coastal waters. The loss of small wetlands (regionally referred to as potholes) on the prairies of Canada and the United States through drainage and infilling has led to the loss of habitat for large numbers of migratory water birds (North American Waterfowl Management Plan, 2004). The loss of forested riparian wetlands adjacent to the Mississippi River in the United States was seen as an important factor contributing to the severity and damage of the 1993 flood in the Mississippi Basin (Daily et al., 1997). Changes in water quality. The use of fertilizers has brought major benefits to agriculture, and has also led to widespread contamination of surface water and groundwater through runoff. Over the past four decades, excessive nutrient loading has emerged as one of the most important direct drivers of ecosystem change in inland and coastal wetlands, with reactive nitrogen entering oceans at an increased rate of nearly 80% from 1860 to 1990 (MEA, 2005b). Phosphorus applications have also increased, rising threefold since 1960, with a steady increase until 1990 followed by a leveling off at approximately the application rates of the 1980s (Bennett et al., 2001). These

Water Availability and Its Use in Agriculture

changes are mirrored by phosphorus accumulation in soils, with high levels of phosphorus runoff. Excessive nutrient loading causes algal blooms, decreased drinking water quality, eutrophication of freshwater ecosystems and coastal zones, and hypoxia in coastal waters. In Lake Chivero, Zimbabwe, agricultural runoff is responsible for algal blooms, infestations of water hyacinth, and fish declines as a result of high levels of ammonia and low oxygen levels (UNEP, 2002). In Australia, extensive algal blooms in coastal inlets and estuaries, inland lakes, and rivers have been attributed to increased nutrient runoff from agricultural fields (Lukatelich and McComb, 1986; Falconer, 2001). Diffuse runoff of nutrients from agricultural land is considered a major cause for increased eutrophication of coastal waters in the United States as well as for the periodic development, often varying from year to year, of anoxic conditions in coastal water in many parts of the world, such as the Baltic and Adriatic Seas and the Gulf of Mexico (Hall, 2002). Extensive evidence shows that up to 80% of the global incidents of nitrogen loading can be retained within wetlands (Green et al., 2004; Galloway et al., 2004). However, the ability of such ecosystems to cleanse nutrient-enriched water varies and is limited (Alexander et al., 2000; Wollheim et al., 2001). Verhoeven et al. (2006) pointed out that many wetlands in agricultural catchments receive excessively high loads, with detrimental effects on biodiversity. Bioaccumulation, as a consequence of the wide use of agrochemicals, has had dire outcomes for many species that reside in or feed predominantly in wetlands or lakes where residues from pesticides have accumulated. The declined breeding success of raptors was a turning point in developing awareness about the dangers of pesticide use (Carson, 1962). An increasing amount of analytical and eco-toxicological data has become available for aquatic communities, and more recent research has also focused on risk assessments and the development of diagnostic tests that can guide management decisions about the use of such chemicals (van den Brink et al., 2003). Taylor et al. (2002) have highlighted the high levels of pesticide use and low levels of environmental risk assessment in developing countries. Vo¨ro¨smarty et al. (2005) reported that water contamination by pesticides has increased rapidly since the 1970s despite increased regulations, especially in developed countries, of xenobiotic substances (i.e., those chemical compounds foreign to a living organism). However, bans on the use of these chemicals have generally been imposed only two to three decades after their first commercial use, as with dichlorodiphenyltrichloroethane (DDT) and the common herbicide atrazine. Many of these substances are highly persistent in the environment, but because of the generally poor monitoring of their long-term effects, global and long-term implications of their use cannot be fully assessed.

4.21.3.1.2 Terrestrial ecosystems In many parts of the world, extensive sheet wash and gully erosion, due to poor land management practices, have had significant environmental effects. Large tracts of land have been devastated resulting in reduced agricultural productivity. Erosion has also contributed to the rapid siltation of reservoirs

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and increased sediment loads in many rivers (CA, 2007). On a regional scale, some reservoirs in Southern Africa are at risk of losing more than a quarter of their storage capacity within 20–25 years (Magadza, 1995). While many Australian and Southern African waters are naturally silty, many have experienced increased silt loads as a result of agricultural practices (Davies and Day, 1998). Zimbabwe’s more than 8000 small- to medium-size dams, for example, are threatened by sedimentation from soil erosion, while the Save River, an international river shared with Mozambique, has been reduced from a perennial to a seasonal river system in large part due to increased siltation caused by soil erosion. While it is not always easy to differentiate natural erosion from human-induced erosion. The high sediment loads carried by Asian rivers are partly a consequence of land-use practices, particularly land-clearing practices for agriculture that lead to erosion, a situation likely to continue as a consequence of the expansion of agriculture in Africa, Asia, and Latin America (Hall, 2002). Changes in the water table. Water builds up in a soil profile when the rate of input exceeds the rate of throughput (e.g., when irrigation volumes are greater than crop water consumption by way of evapotranspiration). This can cause water logging and salinization, which are extensively described for irrigated agriculture (Postel, 1998). Excessive irrigation can result in soil salinization in areas where the water table rises close to the surface and evaporation leaves salts behind in the soil profile. Salt-affected soils in irrigation schemes are often related to poor soil and water management, in addition to the unsuitability of many soils for irrigation. Clearing woody vegetation for pastures and crops can also lead to dryland salinization. Tree-covered landscapes provide an important regulating service by consuming rainfall through high evapotranspiration, limiting groundwater recharge, and keeping the groundwater low enough to prevent salt from being carried upward through the soil. Australia has had major problems with soil salinization as native woody vegetation was cleared in the 1930s for pastures and agricultural expansion (Farrington and Salama, 1996). Consumptive water use has declined there, the water table has risen, and salt has moved into the surface soils so that large tracts of land have become less suitable or unusable for agriculture (Anderies et al., 2001; Briggs and Taws, 2003). The overall trend, however, in irrigation is one of increased pumping and reduced water levels, but good salt management increases a critical issue in particular in arid regions. Moisture recycling. Increased irrigation and land clearing for agriculture have modified green water flows across the globe, reducing them by 3000 km3 through forest clearing and increasing them by 1000–2600 km3 in irrigated areas (Do¨ll and Siebert, 2002; Gordon et al., 2005). Changes in land cover affect evapotranspiration and ultimately impact the hydrologic cycle. It has been suggested that large-scale deforestation can reduce moisture recycling, affect precipitation (Savenije, 1995, 1996; Trenberth, 1999), and alter regional climates, with indications of global impacts (Kabat et al., 2004; Nemani et al., 1996; Marland et al., 2003; Savenije, 1995). Pielke et al. (1998) concluded that the evidence is convincing that land cover changes can significantly influence weather and climate and are as important as other human-induced changes for

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the Earth’s climate. Regional studies in West Africa (Savenije, 1996; Zheng and Eltathir, 1998), the United States (Baron et al., 1998; Pielke et al., 1998), and East Asia (Fu, 2003) have illustrated the ways in which changes in land cover affect green water flows, with impacts on local and regional climates. There are also indications that increased vapor flows through irrigation can alter local and regional climates (Pielke et al., 1997; Chase et al., 1999). The conversion of dry lands to irrigated croplands in Colorado resulted in a 120% increase in evapotranspiration, contributing to higher precipitation, lower temperatures, and an increase in thunderstorm activity (Pielke et al., 1997).

4.21.3.2 Health Impacts Among the agrochemicals that pose the greatest threats to domestic use of groundwater are nitrate and biocide residues. In addition, arsenic contamination in groundwater has emerged as a major health issue in Asia recently. Other health aspects concern malnutrition and vector-borne diseases. Many of the rural poor in Asia obtain water for drinking and household use from shallow aquifers under agricultural land. Irrigated rice fields can serve as breeding sites for mosquitoes, snails, and other intermediate hosts capable of transmitting human parasites. In particular, before transplanting and after harvest, puddles in rice fields are attractive breeding grounds for the mosquito Anopheles gambiae, Africa’s most efficient malaria vector. The conditions for mosquito breeding in rice fields have been identified and management practices, such as alternate wetting and drying of fields, exist to mitigate the problem. Moreover, countries such as Sri Lanka have made great strides in controlling epidemics through broad-based public health campaigns. Japanese B-encephalitis is highly correlated with rice irrigation in Asia, especially where pigs are also reared, as in China and Vietnam. Again, alternate wetting and drying can help reduce the breeding of disease vectors (Keiser et al., 2005a). Nitrate leaching from flooded rice fields is normally negligible because of rapid denitrification under anaerobic conditions (the following section follows from Bouman et al., 2007). In the Philippines, for example, nitrate pollution of groundwater under rice-based cropping systems exceeded the 10 mg l1 limit for safe drinking water only when highly fertilized vegetables were included in the cropping system (Bouman et al., 2002). In the Indian Punjab, however, an increase in nitrate of almost 2 mg l1 was recorded between 1982 and 1988, with a simultaneous increase in nitrogen fertilizer use from 56 to 188 kg ha1, most of it on combined rice–wheat cultivation (Bijay-Singh et al., 1991). These may lead to the blood disorder methemoglobinemia in human populations, especially in babies. Mean biocide use in irrigated rice systems varies from some 0.4 kg active ingredients per hectare in Tamil Nadu, India, to 3.8 kg ha1 in Zhejiang Province, China (Bouman et al., 2002). In the warm and humid conditions of the tropics, volatilization is the major process of biocide loss, especially when biocides are applied on water surfaces or on wet soil. Relatively high temperatures favor rapid transformation of remaining biocides by photochemical and microbial degradation, but little is known about the toxicity of the residual components.

In case studies in the Philippines, mean biocide concentrations in groundwater under irrigated rice-based cropping systems were one to two orders of magnitude below the single and multiple biocide limits for safe drinking water (i.e., 0.1 and 0.5 mg l1), although temporary peak concentrations of 1.14–4.17 mg l1 were measured (Bouman et al., 2002). Biocides and their residues may be directly transferred to open water bodies through drainage water that flows overland from rice fields. The potential for water pollution from biocides is greatly affected by field water management. Different water regimes result in different pest and weed populations and densities, which farmers may combat with different amounts and types of biocides. Agricultural use of untreated wastewater can affect human health through exposure to pathogens, parasite infections, and heavy metals. Leafy vegetables, eaten raw, can transmit contaminations from farm fields to consumers. Hookworm infections are transmitted by direct exposure to contaminated water and soils. A survey along the Musi River in India revealed the transfer of metal ions from wastewater to cow’s milk through fodder irrigated with wastewater. About 4% of grass samples showed excessive amounts of cadmium and all samples showed excessive lead levels. Milk samples were contaminated with metal ions ranging from 1.2 to 40 times permissible levels (Minhas and Samra, 2004). Farmers and their families using untreated wastewater are exposed to health risks from parasitic worms, viruses, and bacteria. Many farmers cannot afford treatment for some of the health problems caused by exposure. Generally, farmers irrigating with wastewater have higher rates of parasite infections than farmers using freshwater do, but there are exceptions (Trang et al., 2006). In addition, skin and nail problems occur more frequently among farmers using wastewater (Van der Hoek et al., 2002).

4.21.3.3 Environmental and Health Mitigation Agricultural water use is closely linked with health and environmental impacts. For health, the negative impacts of irrigation development can be mitigated through better design and operation of new and existing systems, especially through the multiple uses of irrigation water. Integrated approaches have taken many forms, including integrated river basin management, integrated land and water management, ecosystem approaches, integrated coastal zone management, and integrated natural resources management. These management strategies often seek to do the following: address the integration of a broad range of benefits and costs associated with land-use and water decisions, including effects on ecosystem services, food production, and social equity; involve key stakeholders at cross-institutional levels; and address interconnectedness across subbasin, river basin, and other biophysical scales. The MEA (2003) has provided a major advancement in understanding the links between the provision of ecosystem services and human well-being. Increased awareness is still needed on several different levels. The scientific knowledge of how ecosystem services contribute to human well-being within and between different sectors of society, and the role of water in sustaining these services, needs to be improved.

Water Availability and Its Use in Agriculture

Dissemination of information on these issues and dialog with stakeholders should be enhanced. Civil society organizations can help to ensure that appropriate consideration is given to the voices of individuals and social groups and to nonutilitarian values in decision making. Minority groups and disadvantaged groups, such as indigenous people and women, in particular, need to be heard. Women play a critical and increasing role in agriculture in many parts of the developing world (Elder and Schmidt, 2004). Mitigation measures are modifications to the design or operation of agricultural water development projects to reduce negative environmental and health impacts. However, present levels of understanding mean that very often some negative impacts are not foreseen prior to project implementation. Consequently, there should be a constant reevaluation of the need for mitigation measures throughout the life of a project. This requires monitoring so that measures can be introduced retrospectively when necessary. Monitoring enables health authorities to target resources and treatment interventions at times and locations of greatest need. A wide range of technical mitigation measures to prevent environmental damage has been developed for formal irrigation schemes. Measures that promote high water-use efficiency also tend to mitigate negative environmental and health impacts. For example, good water management and drainage are prerequisites to preventing water logging, decreasing habitats for mosquitoes, and minimizing salt accumulation in soils. Over the last 20 years, considerable progress has been made in the development of methods to determine environmental flows downstream of dams and extractions for irrigation. Increasingly, these techniques are taking a systems approach that includes holistic ecosystem assessments and works to predict the impacts of different flow regimes on the livelihoods of water users (Dyson et al., 2003). For example, downstream response to imposed flow transformation (DRIFT) is a scenario-based environmental flow assessment process designed specifically for use in negotiations over water resources. It is designed to quantify the linkages between changing river conditions, and the social and economic impacts for riparian people who rely on rivers for their livelihoods (Brown and King, 2000). Hydrological environmental flow assessment methods are being developed for use in areas where insufficient ecological data exist for conclusive analyses (Smakhtin and Shilpakar, 2005). Such approaches help define environmental targets and thus facilitate the design of mitigation measures by specifying desired environmental conditions. Concerning disease vectors, the primary approach is to design irrigation or pastoral water systems that do not provide habitats for vectors, while also conducting health education. In these cases, good design and construction of canal and drainage systems, and proper leveling of fields, can ensure that water is fast flowing and stagnant pools do not occur. Other options include direct vector control using chemicals or biological methods. However, with chemical control, care is required in application to ensure that the vectors do not develop resistance. Physical removal of habitats, for example, through manual or mechanical cutting of weeds is also possible. Good cleaning and preventative maintenance of all infrastructures, including canals, cattle troughs, hydraulic

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structures, and drains reduce the breeding of vectors and intermediate hosts, as well as improve irrigation performance. In several sub-Saharan African countries, weed control in canals has been applied as an effective method of vector control. However, routine cleaning work can itself be a health hazard. In the Gezira scheme in Sudan, canal cleaning personnel became the group most infected with the disease schistosomiasis (Fenwick et al., 1982). In some places, attempts have been made to minimize risk exposure by adapting the time of cleaning activities to the cycle of the parasite or by providing alternative tools (Euroconsult, 1993). A recent review of 40 largely pre-DDT interventions suggests that environmental modification can be a very effective malariacontrol strategy (Keiser et al., 2005b). Adapting water management to modify the vector habitat is another approach that has often been proposed in biomedical studies as an easy and cheap measure for vector control. However, there are very few examples where this type of environmental manipulation has been applied in practice (Matsuno et al., 1999; Laamrani and Boelee, 2002; Boelee and Laamrani, 2004). This is because, in reality, it is neither simple nor cheap to change established water management patterns. Water management interacts not only with vector breeding or disease transmission, but also with the irrigation system itself. Changes to water distribution often require modifications in design, notably the sizing of canals and type of structures. For example, if continuous delivery is replaced by rotation of the water flow to disrupt breeding sites, the discharge in the canals alters from constant low flows to intermittent high flows, requiring larger canals. At the same time, the wider human environment is influenced. With water flowing in the canals continuously, farmers can irrigate their crops at any time. With rotation, the flow has to be divided over time between users, demanding a higher level of organization. Water scheduling to meet crop water requirements is complicated, especially when conflicting interests between higher water-use efficiencies and farmers demanding flexibility have to be accounted for. If disease-control measures have to be observed as well, scheduling and management become very complicated (Boelee, 1999). Adaptive water management in rice fields may result in reduced vector breeding and hence reduced transmission of Japanese encephalitis and malaria (van der Hoek et al., 2001; Keiser et al., 2005b). However, these studies mainly report from Asia. In an African context, with constraints on resources and capacity, it may be especially difficult to achieve the required water deliveries and level of water management (Mutero et al., 2000). In reality, effective health interventions require an integrated approach that simultaneously implements avoidance and mitigation measures in collaboration between the water and health authorities.

4.21.4 Water Governance Improving agricultural water productivity in the future will take careful attention to the ways in which water use is linked to economies, social well-being, and ecological systems. Implementing these changes will require more effective water governance, which means rethinking how water is managed. Beyond national-scale water legislation, water governance

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encompasses various bodies of formal and informal regulations and institutions and also the way decision-making and political power is exercised. In short, it refers to the social mobilization and other actions designed to promote ownership, co-investment, capacity building, incentives for participation, and willingness to pay for services at the community level (UNDP, 2004). The following section outlines various forms of water governance and points to future challenges water governance will face.

4.21.4.1 Definition Governance is the way authority is organized and executed in society, and often includes the normative notion of the necessity for good governance (Merrey et al., 2007). The Global Water Partnership defines water governance as ‘‘the range of political, social, economic, and administrative systems that are in place to develop and manage water resources, and the delivery of water services, at different level of society’’ (Rogers and Hall, 2003). Governance is therefore a broad term that includes institutions, organizations, and policies. Effective water governance builds institutional capacity from the local level upward and empowers stakeholders with knowledge and the ability to make decisions about matters that directly affect their lives. It promotes the equal participation of women and men in decision making. Water governance is critical for resource planning and allocation among riparian states (those sharing a water basin) and vital for conflict resolution to defuse upstream–downstream tensions and balance the needs of different groups sharing water resources. Good water governance determines the appropriate role for the government in service delivery (i.e., as a facilitator or as a service provider) and ensures that water and sanitation services provided by both public and private actors meet the needs of the people they serve and do not fall prey to corruption. Good water governance corrects market distortions, perverse incentives, and pricing that shuts out the poor (UNDP, 2004).

basin, essential functions are partly or completely carried out, with their sum constituting basin governance (Table 3). Much attention has been given to the ideal organizational model for river basin management, while much less emphasis has been placed on the process of developing, managing, and maintaining collaborative relationships for river basin governance. More fundamentally, the essential function in river basin management – allocating water between competing uses and users, including the environment – has not received sufficient attention, although it is at the heart of integrated water resources management. Moreover, agricultural water and land practice, such as rainfed agriculture, livestock and fisheries practices often do not feature strongly. There are two main trends in basin governance. One trend concerns watersheds, or sub-basins of a limited size (typically from 10–1000 km2), where local stakeholders and agencies attempt to solve their land- and water-related problems. A second trend concerns the management of wider river basins. This trend has three salient aspects (Svendsen and Wester, 2005). First is the consensus that integrated water resources management should be carried out at the river basin level. This, together with the desire to realize the promise of integration, has placed river basin management on the agenda of

Table 3

Functions for river basin management

Function

Description

Plan

Formulation of medium- to long-term plans for managing and developing water resources in the basin. Activities executed for the design and construction of hydraulic infrastructure. Activities executed to maintain the serviceability of the hydraulic infrastructure in the basin. Mechanisms and criteria by which water is apportioned among different use sectors, including the environment. Activities executed to ensure that allocated water reaches its point of use. Activities executed to monitor water pollution and salinity levels and ensure that they remain at or below accepted standards. Flood and drought warning, prevention of floods, and development of emergency works, drought preparedness, and coping mechanisms. Provision of space or mechanisms for negotiation and litigation. Priorities and actions to protect ecosystems, including awareness campaigns. Harmonization of policies and actions undertaken in the basin by state and nonstate actors relevant to land and water management.

Construct facilities Maintain facilities

Allocate water

Distribute water

4.21.4.2 Types of Governance for River Basin Management The growing pressure on water resources and the increasing hydrological, social, and ecological interdependencies in closing river basins have led to widespread recognition of the need for holistic approaches to water management. There is a renewed emphasis on river basins as the most appropriate spatial unit for water management. The decision to manage water on the basis of river basins is a political choice, and river basins thus become a scale of governance in which tensions arise among effectiveness, participation, and legitimacy (Barham, 2001; Schlager and Blomquist, 2000; Wester and Warner, 2002). Progress in establishing adaptive, multilevel, collaborative governance arrangements for river basin management has been weak, with undue emphasis on form (setting up river basin organizations) over process. Although there may not be a central basin manager, this does not mean that river basins are not managed (Schlager and Blomquist, 2000). This can be accomplished by identifying the roles of various actors engaged in river basin water management, asking who does what, where, to what end, and how well. In any river

Monitor and enforce water quality Preparedness against water disasters

Resolve conflicts Project ecosystems Coordinate

Note: The functions listed here subsume supporting functions such as data collection and resource mobilization, which are not ends in themselves, but rather facilitate the higher-level functions listed. From Svendsen M, Wester P, and Molle F (2005) Managing river basins: An institutional perspective. In: Svendsen M (ed.) Irrigation and River Basin Management: Options for Governance and Institutions. Wallingford: CABI Publishing.

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governments and international funding agencies and has led to many new river basin initiatives. Second, the number of public and private sector actors involved in, or concerned with basin planning and management, is increasing, from environmental agencies and civil society or interest groups to regulatory bodies and service providers for agricultural, municipal, and industrial water users. With rising standards of living, urbanization, and continuing environmental deterioration, more diverse stakeholders and worldviews need to be integrated. Third, organizations associated with basin planning and management have become more specialized and differentiated into regulators, resource managers, and service providers (Millington, 2000). Regulation and standard setting are carried out in the public interest and are necessarily functions of government, but other tasks may be fulfilled by commercial or hybrid public–private organizations. River basin organizations cover a wide gamut of organizations with quite varied roles and structures. At first this may seem a source of confusion, but it also suggests that both the nature of the problems faced (e.g., development or management) and the particular history and context of each basin reflect on each river basin organization. The following typology can be inferred from a broad-brush review of river basin organizations, keeping in mind that there are no clear-cut definitions and that there are large variations in roles and power, even within the same category. In other words, the generic terms may not correspond to particular bodies. Basin authorities are autonomous executive organizations with extensive mandates for their river basin, undertaking most water-related development and management functions. They serve as regulator, resource manager, and service provider all in one. The Damodar Valley Corporation in India, the Mahaweli Authority in Sri Lanka, the Companhia de Desenvolvimento dos Vales de Sa˜o Franciscoe do Parnaiba in Brazil, and the Confederaciones Hidrograficas in Spain are examples of such basin authorities. Authorities generally exhibit poor responsiveness to local demands and are often undermined by bureaucratic conflict because they infringe on the competence of other government agencies and line ministries. Some of these authorities receive basin-wide, multifunctional mandates covering various domains but are not endowed with the legal, political, or administrative power to achieve them. They generally end up focusing on construction projects and dam management (mostly for hydropower or flood control). Examples include the Damodar Valley Corporation in India (Saha, 1979), the River Basin Development Authorities in Nigeria (Adams, 1985), and the China River Commissions (Millington, 2000). Some authorities were designed to ensure regional infrastructure development (the early River Basin Commissions in Mexico), others endured as powerful manager/operators (Brantas basin in Indonesia, Tarim in China), while others shrank and were confined to one issue or degenerated into powerless parallel structures with narrow scope and erratic funding (A. Dourojeanni, personal communication). Basin commissions or committees focus on policy setting, basin-wide planning, water allocation, and information management, with varying degrees of stakeholder participation. They are usually endowed with authority to manage water resources (allocating permits, defining taxation,

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negotiating water allocations, and defining effluent standards), and sometimes to plan future developments, but are not involved in operation or construction. Examples include the Delaware Commission in the United States, the Murray–Darling Commission in Australia, the British water authorities, and the French Agences de l’eau. Coordinating councils are deliberative decision-making bodies incorporating public and private stakeholders and integrating policymaking across different policy areas. They are not organizations in the strict sense, but rather bring together stakeholders from various agencies and water-use sectors. Their role is coordination, conflict resolution, and review of water resources allocation or management. Examples include the river basin councils in Mexico (Wester et al., 2005), the proposed catchment management agencies in South Africa (Waalewijn et al., 2005), the Zimbabwean catchment councils (Jaspers, 2001), the river basin committees and users commissions in Brazil (Lemos and Oliveira, 2004), and several river commissions in the United States. International river commissions are unique because coordination is achieved between countries rather than among stakeholders and because political dimensions are pervasive. They were frequently established as part of a treaty signed among riparian countries or to manage dams on shared rivers (e.g., Senegal, Volta, or Zambezi rivers) (Barrows, 1998). They mediate water conflicts through consultation and cooperation and may also manage common databases, and their work may lead to concrete agreements. From a governance perspective, institutional arrangements for river basin management may be distributed along two axes, one that distinguishes between state-driven and stakeholder-driven functioning, and the other that contrasts centralized and decentralized modes. This yields four models for basin governance: unicentric (state-driven, centralized), deconcentrated (state-driven, decentralized), coordination (stakeholder-driven, centralized), and polycentric (stakeholder-driven, decentralized). Under the unicentric model, a basin authority or line ministry manages the river basin. In the polycentric model, the actions of existing organizations, layers of government, and stakeholder initiatives are coordinated to cover an entire river basin or sub-basin.

4.21.4.3 Basin Governance Challenges River basin governance is about the emergence of the appropriate blend of government, civil society, and markets in decision making and regulation. In addition to greater control, rigor, and openness for water resource planning and allocation, as just described, integrated river basin management demands adequate governance. This brings out two main challenges: ensuring that all stakeholders, including the environment, have a voice, and coordinating uses and policies within the basin. Although frequently advocated as a key to achieving effective water management (Rogers and Hall, 2003), stakeholder participation in river basin management is not straightforward, and achieving substantive stakeholder representation have proved to be elusive in practice (Wester et al., 2003). Emphasizing participation in river basin management may draw attention away from the very real social and economic differences among people and the need for

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redistributing resources, entitlements, and opportunities. This is unlikely to happen without challenges, and decision makers committed to social equity need to devise mechanisms that strengthen the representation of marginal groups in river basin management and empower them. Stakeholder platforms, whether river basin councils, catchment management agencies, or watershed councils, democratize river basin management by giving voice to multiple actors. However, much depends on the institutional arrangements from which these river basin management institutions emerge, as many roles, rights, and technologies and physical infrastructure for controlling water are already in place. Stakeholders have different levels and types of education, differ in access to resources and politics, hold different beliefs about how nature and society function, and often speak different languages (Edmunds and Wollenberg, 2001). If these differences are not taken into account when creating new rules, roles, and rights, the institutional outcome can easily privilege those who are literate and have access to the legal system and eventually institutionalize inequality and power differentials instead of giving voice to marginal groups (Wester and Warner, 2002). This review of basin governance patterns identified the various types of organizations and arrangements for basin management. A strong civil engineering body capable of planning, designing, and constructing infrastructure to tap available water is useful and effective when resources are plentiful and management is not a strong requirement. In the later phases of basin closure, however, experience shows that large civil engineering organizations (and agricultural or other line agencies) are not well suited to deal with the challenges of basin governance. They have limited experience in political negotiation or interaction with key stakeholders and lack the breadth of experience in dealing with complex, broad-based issues, and multiple values. Further, they often tend to adopt stances based on vested interest in continuing infrastructure development, a position antagonistic to that of stakeholders with ecosystem concerns. Countries that have strong civil engineering organizations reluctant to cede any power will face intense negotiations and struggles before an acceptable form of river basin coordination emerges that is capable of undertaking the key tasks required. However, wherever the scope for construction is reduced and societal values have changed, the trend is likely to follow that of countries such as Australia and the United States, where engineering bodies have contracted and evolved into environmental agencies. Decision makers should not infer from the integrated water resources management message that river basin management needs a strong centralized organization. Basins facing complex problems of conflicting societal values and pressure on resources will probably not be well managed by a single body. Nested or polycentric patterns of basin governance, in which user and community organizations, layers of government, and stakeholder initiatives are coordinated at the basin level, perform better and can be especially effective in settings where participation and democratic practices are well established. Moving toward sustainable river basin management requires much more emphasis on developing, managing, and maintaining collaborative relationships for river basin governance, building on existing organizations, customary practices, and administrative structures.

Acknowledgments The material from this chapter was drawn largely from the Comprehensive Assessment of Water Management in Agriculture which drew together hundreds of researchers and practitioners to find water solutions for tomorrow.

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Relevant Websites http://www.maweb.org Millennium Ecosystem Assessment.